From cff0a7056539b40e57768337064df3d0d0a920c4 Mon Sep 17 00:00:00 2001 From: Alex Mazzeo Date: Thu, 18 Dec 2025 13:49:09 -0800 Subject: [PATCH 01/40] Bump temporal SDK to 1.21.0 (#269) --- pyproject.toml | 2 +- uv.lock | 296 ++++++++++++++++++++++++------------------------- 2 files changed, 149 insertions(+), 149 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 794bcb925..00813456c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] requires-python = ">=3.10" readme = "README.md" license = "MIT" -dependencies = ["temporalio>=1.20.0,<2"] +dependencies = ["temporalio>=1.21.0,<2"] [project.urls] Homepage = "https://github.com/temporalio/samples-python" diff --git a/uv.lock b/uv.lock index cbe5db3f2..09a2436a0 100644 --- a/uv.lock +++ b/uv.lock @@ -12,7 +12,7 @@ resolution-markers = [ [[package]] name = "aiohappyeyeballs" version = "2.6.1" -source = { registry = 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index 387939855..21dd9c93a 100644 --- a/custom_metric/worker.py +++ b/custom_metric/worker.py @@ -39,7 +39,7 @@ async def execute_activity(self, input: ExecuteActivityInput): unit="duration", ) histogram.record( - schedule_to_start, {"workflow_type": activity.info().workflow_type} + schedule_to_start, {"workflow_type": activity.info().workflow_type or ""} ) return await self.next.execute_activity(input) diff --git a/polling/test_service.py b/polling/test_service.py index 634945720..994b98ce4 100644 --- a/polling/test_service.py +++ b/polling/test_service.py @@ -4,7 +4,7 @@ from temporalio import activity from temporalio.exceptions import ApplicationError, ApplicationErrorCategory -attempts = Counter[str]() +attempts = Counter[str | None]() ERROR_ATTEMPTS = 5 diff --git a/pyproject.toml b/pyproject.toml index c7d9e3b08..caae123c3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] requires-python 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-3401,7 +3401,7 @@ wheels = [ [[package]] name = "zope-event" version = "5.1" -source = { registry = "https://pypi.org/simple" } +source = { registry = "https://pypi.org/simple/" } dependencies = [ { name = "setuptools" }, ] @@ -3413,7 +3413,7 @@ wheels = [ [[package]] name = "zope-interface" version = "7.2" -source = { registry = "https://pypi.org/simple" } +source = { registry = "https://pypi.org/simple/" } dependencies = [ { name = "setuptools" }, ] From e2381f7add2b8299d6b34c36ba80b70c089d8b28 Mon Sep 17 00:00:00 2001 From: Dan Davison Date: Tue, 24 Feb 2026 19:52:55 -0500 Subject: [PATCH 05/40] Standalone activity (#270) * Standalone Activity sample * Add sample to READMEs, update sample to include list_activities and count_activities * ruff format --------- Co-authored-by: Andrew Yuan --- README.md | 2 + hello/README.md | 3 +- hello/hello_standalone_activity.py | 77 ++++++++++++++++++++++++++++++ 3 files changed, 81 insertions(+), 1 deletion(-) create mode 100644 hello/hello_standalone_activity.py diff --git a/README.md b/README.md index 5a39e834b..8e7748de1 100644 --- a/README.md +++ b/README.md @@ -55,6 +55,8 @@ Some examples require extra dependencies. See each sample's directory for specif * [hello_search_attributes](hello/hello_search_attributes.py) - Start workflow with search attributes then change while running. * [hello_signal](hello/hello_signal.py) - Send signals to a workflow. + * [hello standalone activity](hello/hello_standalone_activity.py) - Execute an activity from outside of a workflow. + * [hello update](hello/hello_update.py) - Send a request to and a response from a client to a workflow execution. * [activity_worker](activity_worker) - Use Python activities from a workflow in another language. * [batch_sliding_window](batch_sliding_window) - Batch processing with a sliding window of child workflows. diff --git a/hello/README.md b/hello/README.md index c014d08c3..48e8e67e6 100644 --- a/hello/README.md +++ b/hello/README.md @@ -44,7 +44,8 @@ Replace `hello/hello_activity.py` in the command with any other example filename * [hello_search_attributes](hello_search_attributes.py) - Start workflow with search attributes then change while running. * [hello_signal](hello_signal.py) - Send signals to a workflow. -* [hello_update](hello_update.py) - Send a request to and a response from a client to a workflow execution. +* [hello standalone activity](hello_standalone_activity.py) - Execute an activity from outside of a workflow. +* [hello_update](hello_update.py) - **Send a request to and a response from a client to a workflow execution.** Note: To enable the workflow update, set the `frontend.enableUpdateWorkflowExecution` dynamic config value to true. diff --git a/hello/hello_standalone_activity.py b/hello/hello_standalone_activity.py new file mode 100644 index 000000000..541d687b2 --- /dev/null +++ b/hello/hello_standalone_activity.py @@ -0,0 +1,77 @@ +import asyncio +from dataclasses import dataclass +from datetime import timedelta + +from temporalio import activity +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +# This sample is very similar to hello_activity.py. The difference is that whereas in +# hello_activity.py the activity is orchestrated by a workflow, in this sample the activity is +# executed directly by a client ("standalone activity"). + + +@dataclass +class ComposeGreetingInput: + greeting: str + name: str + + +# This is just a normal activity. You could invoke it from a workflow but, in this sample, we are +# invoking it directly as a standalone activity. +@activity.defn +async def compose_greeting(input: ComposeGreetingInput) -> str: + activity.logger.info("Running activity with parameter %s" % input) + return f"{input.greeting}, {input.name}!" + + +async def my_client_code(client: Client): + # client.execute_activity starts the activity, and then uses a long-poll to wait for the + # activity to be completed by the worker. + result = await client.execute_activity( + compose_greeting, + args=[ComposeGreetingInput("Hello", "World")], + id="my-standalone-activity-id", + task_queue="hello-standalone-activity-task-queue", + start_to_close_timeout=timedelta(seconds=10), + ) + print(f"Activity result: {result}") + + activities = client.list_activities( + query="TaskQueue = 'hello-standalone-activity-task-queue'" + ) + print("ListActivity results:") + async for info in activities: + print( + f"\tActivityID: {info.activity_id}, Type: {info.activity_type}, Status: {info.status}" + ) + + count_result = await client.count_activities( + query="TaskQueue = 'hello-standalone-activity-task-queue'" + ) + print(f"Total activities: {count_result.count}") + + +async def main(): + # Uncomment the lines below to see logging output + # import logging + # logging.basicConfig(level=logging.INFO) + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + client = await Client.connect(**config) + + # Run a worker for the activity + async with Worker( + client, + task_queue="hello-standalone-activity-task-queue", + activities=[compose_greeting], + ): + # While the worker is running, use the client to execute the activity. + await my_client_code(client) + + +if __name__ == "__main__": + asyncio.run(main()) From 2fd1407e9a8e116b95899f4ce9dd794bbe2a121b Mon Sep 17 00:00:00 2001 From: Bill Richards Date: Wed, 25 Feb 2026 11:50:09 -0700 Subject: [PATCH 06/40] Fix potential race with the sliding window sample. Fixes #279 (#280) --- batch_sliding_window/sliding_window_workflow.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/batch_sliding_window/sliding_window_workflow.py b/batch_sliding_window/sliding_window_workflow.py index 87e4110d8..a416fb995 100644 --- a/batch_sliding_window/sliding_window_workflow.py +++ b/batch_sliding_window/sliding_window_workflow.py @@ -108,6 +108,7 @@ async def _execute(self, input: SlidingWindowWorkflowInput) -> int: # Start child workflow for this record child_id = f"{workflow_id}/{record.id}" + self.current_records.add(record.id) child_handle = await workflow.start_child_workflow( RecordProcessorWorkflow.run, record, @@ -117,7 +118,6 @@ async def _execute(self, input: SlidingWindowWorkflowInput) -> int: ) self.children_started_by_this_run.append(child_handle) - self.current_records.add(record.id) return await self._continue_as_new_or_complete(input) From 3cb281ed52205b0e8e24a2124bdf33e23133c517 Mon Sep 17 00:00:00 2001 From: Quinn Klassen Date: Tue, 10 Mar 2026 10:44:57 -0700 Subject: [PATCH 07/40] Nexus cancellation sample (#281) Add sample for cancellation --- nexus_cancel/README.md | 50 ++++++++++++++++++ nexus_cancel/__init__.py | 0 nexus_cancel/caller/__init__.py | 0 nexus_cancel/caller/app.py | 43 +++++++++++++++ nexus_cancel/caller/workflows.py | 69 +++++++++++++++++++++++++ nexus_cancel/handler/__init__.py | 0 nexus_cancel/handler/service_handler.py | 27 ++++++++++ nexus_cancel/handler/worker.py | 48 +++++++++++++++++ nexus_cancel/handler/workflows.py | 49 ++++++++++++++++++ nexus_cancel/service.py | 35 +++++++++++++ 10 files changed, 321 insertions(+) create mode 100644 nexus_cancel/README.md create mode 100644 nexus_cancel/__init__.py create mode 100644 nexus_cancel/caller/__init__.py create mode 100644 nexus_cancel/caller/app.py create mode 100644 nexus_cancel/caller/workflows.py create mode 100644 nexus_cancel/handler/__init__.py create mode 100644 nexus_cancel/handler/service_handler.py create mode 100644 nexus_cancel/handler/worker.py create mode 100644 nexus_cancel/handler/workflows.py create mode 100644 nexus_cancel/service.py diff --git a/nexus_cancel/README.md b/nexus_cancel/README.md new file mode 100644 index 000000000..2f7f57030 --- /dev/null +++ b/nexus_cancel/README.md @@ -0,0 +1,50 @@ +# Nexus Cancellation + +This sample shows how a caller workflow can fan out multiple Nexus operations concurrently, take the first result, and cancel the rest using `WAIT_REQUESTED` cancellation semantics. + +With `WAIT_REQUESTED`, the caller proceeds once the handler has received the cancel request — it does not wait for the handler to finish processing the cancellation. + +Start a Temporal server. (See the main samples repo [README](../README.md)). + +Run the following: + +``` +temporal operator namespace create --namespace nexus-cancel-handler-namespace +temporal operator namespace create --namespace nexus-cancel-caller-namespace + +temporal operator nexus endpoint create \ + --name nexus-cancel-endpoint \ + --target-namespace nexus-cancel-handler-namespace \ + --target-task-queue nexus-cancel-handler-task-queue +``` + +Next, in separate terminal windows: + +## Nexus Handler Worker + +```bash +uv run nexus_cancel/handler/worker.py +``` + +## Nexus Caller App + +```bash +uv run nexus_cancel/caller/app.py +``` + +## Expected Output + +On the caller side, you should see a greeting in whichever language completed first: +``` +Hello Nexus 👋 +``` + +On the handler side, you should see cancellation log messages for the remaining operations: +``` +HelloHandlerWorkflow was cancelled successfully. +HelloHandlerWorkflow was cancelled successfully. +HelloHandlerWorkflow was cancelled successfully. +HelloHandlerWorkflow was cancelled successfully. +``` + +The caller workflow returns before all handler workflows have completed their cancellation cleanup. This demonstrates `WAIT_REQUESTED` semantics: the caller didn't wait for the handler workflows to finish, but still guaranteed that all handlers received the cancellation request. diff --git a/nexus_cancel/__init__.py b/nexus_cancel/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_cancel/caller/__init__.py b/nexus_cancel/caller/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_cancel/caller/app.py b/nexus_cancel/caller/app.py new file mode 100644 index 000000000..bb74e9e0b --- /dev/null +++ b/nexus_cancel/caller/app.py @@ -0,0 +1,43 @@ +import asyncio +import uuid +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_cancel.caller.workflows import HelloCallerWorkflow + +NAMESPACE = "nexus-cancel-caller-namespace" +TASK_QUEUE = "nexus-cancel-caller-task-queue" + + +async def execute_caller_workflow( + client: Optional[Client] = None, +) -> str: + if client is None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[HelloCallerWorkflow], + ): + return await client.execute_workflow( + HelloCallerWorkflow.run, + "Nexus", + id=f"hello-caller-{uuid.uuid4()}", + task_queue=TASK_QUEUE, + ) + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + result = loop.run_until_complete(execute_caller_workflow()) + print(result) + except KeyboardInterrupt: + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/nexus_cancel/caller/workflows.py b/nexus_cancel/caller/workflows.py new file mode 100644 index 000000000..f8a2e1ffc --- /dev/null +++ b/nexus_cancel/caller/workflows.py @@ -0,0 +1,69 @@ +""" +Caller workflow that demonstrates Nexus operation cancellation. + +Fans out 5 concurrent Nexus hello operations (one per language), takes the first +result, and cancels the rest using WAIT_REQUESTED cancellation semantics. +""" + +import asyncio +from datetime import timedelta + +from temporalio import workflow +from temporalio.exceptions import CancelledError, NexusOperationError + +with workflow.unsafe.imports_passed_through(): + from nexus_cancel.service import HelloInput, Language, NexusService + +NEXUS_ENDPOINT = "nexus-cancel-endpoint" + + +@workflow.defn +class HelloCallerWorkflow: + def __init__(self) -> None: + self.nexus_client = workflow.create_nexus_client( + service=NexusService, + endpoint=NEXUS_ENDPOINT, + ) + + @workflow.run + async def run(self, message: str) -> str: + # Fan out 5 concurrent Nexus calls, one per language. + # Each task starts and awaits its own operation so all race concurrently. + async def run_operation(language: Language): + handle = await self.nexus_client.start_operation( + NexusService.hello, + HelloInput(name=message, language=language), + schedule_to_close_timeout=timedelta(seconds=10), + cancellation_type=workflow.NexusOperationCancellationType.WAIT_REQUESTED, + ) + return await handle + + tasks = [asyncio.create_task(run_operation(lang)) for lang in Language] + + # Wait for the first operation to complete + workflow.logger.info( + f"Started {len(tasks)} operations, waiting for first to complete..." + ) + done, pending = await workflow.wait(tasks, return_when=asyncio.FIRST_COMPLETED) + + # Get the result from the first completed operation + result = await done.pop() + workflow.logger.info(f"First operation completed with: {result.message}") + + # Cancel all remaining operations + workflow.logger.info(f"Cancelling {len(pending)} remaining operations...") + for task in pending: + task.cancel() + + # Wait for all cancellations to be acknowledged. + # If the workflow completes before cancellation requests are delivered, + # the server drops them. Waiting ensures all handlers receive the + # cancellation. + for task in pending: + try: + await task + except (NexusOperationError, CancelledError): + # Expected: the operation was cancelled + workflow.logger.info("Operation was cancelled") + + return result.message diff --git a/nexus_cancel/handler/__init__.py b/nexus_cancel/handler/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_cancel/handler/service_handler.py b/nexus_cancel/handler/service_handler.py new file mode 100644 index 000000000..92868510c --- /dev/null +++ b/nexus_cancel/handler/service_handler.py @@ -0,0 +1,27 @@ +""" +Nexus service handler for the cancellation sample. + +The hello operation is backed by a workflow, using the Nexus request ID as the +workflow ID for idempotency across retries. +""" + +from __future__ import annotations + +import nexusrpc +from temporalio import nexus + +from nexus_cancel.handler.workflows import HelloHandlerWorkflow +from nexus_cancel.service import HelloInput, HelloOutput, NexusService + + +@nexusrpc.handler.service_handler(service=NexusService) +class NexusServiceHandler: + @nexus.workflow_run_operation + async def hello( + self, ctx: nexus.WorkflowRunOperationContext, input: HelloInput + ) -> nexus.WorkflowHandle[HelloOutput]: + return await ctx.start_workflow( + HelloHandlerWorkflow.run, + input, + id=ctx.request_id, + ) diff --git a/nexus_cancel/handler/worker.py b/nexus_cancel/handler/worker.py new file mode 100644 index 000000000..e29df3559 --- /dev/null +++ b/nexus_cancel/handler/worker.py @@ -0,0 +1,48 @@ +""" +Worker for the handler namespace that processes Nexus operations and workflows. +""" + +import asyncio +import logging +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_cancel.handler.service_handler import NexusServiceHandler +from nexus_cancel.handler.workflows import HelloHandlerWorkflow + +interrupt_event = asyncio.Event() + +NAMESPACE = "nexus-cancel-handler-namespace" +TASK_QUEUE = "nexus-cancel-handler-task-queue" + + +async def main(client: Optional[Client] = None): + logging.basicConfig(level=logging.INFO) + + if not client: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[HelloHandlerWorkflow], + nexus_service_handlers=[NexusServiceHandler()], + ): + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/nexus_cancel/handler/workflows.py b/nexus_cancel/handler/workflows.py new file mode 100644 index 000000000..d799c62be --- /dev/null +++ b/nexus_cancel/handler/workflows.py @@ -0,0 +1,49 @@ +""" +Handler workflow started by the hello Nexus operation. + +Demonstrates how to handle cancellation from the caller workflow using a +detached cancellation scope (asyncio.shield) for cleanup work. +""" + +import asyncio + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from nexus_cancel.service import HelloInput, HelloOutput, Language + +GREETINGS = { + Language.EN: "Hello {name} 👋", + Language.FR: "Bonjour {name} 👋", + Language.DE: "Hallo {name} 👋", + Language.ES: "¡Hola! {name} 👋", + Language.TR: "Merhaba {name} 👋", +} + + +@workflow.defn +class HelloHandlerWorkflow: + @workflow.run + async def run(self, input: HelloInput) -> HelloOutput: + try: + # Sleep for a random duration to simulate work (0-5 seconds) + random_seconds = workflow.random().randint(0, 5) + workflow.logger.info(f"Working for {random_seconds} seconds...") + await asyncio.sleep(random_seconds) + + # Return a greeting based on the language + greeting = GREETINGS[input.language].format(name=input.name) + return HelloOutput(message=greeting) + + except asyncio.CancelledError: + # Perform cleanup in a detached cancellation scope. + # asyncio.shield prevents the cleanup work from being cancelled. + workflow.logger.info("Received cancellation request, performing cleanup...") + try: + cleanup_seconds = workflow.random().randint(0, 5) + await asyncio.shield(asyncio.sleep(cleanup_seconds)) + except asyncio.CancelledError: + pass + workflow.logger.info("HelloHandlerWorkflow was cancelled successfully.") + # Re-raise the cancellation error + raise diff --git a/nexus_cancel/service.py b/nexus_cancel/service.py new file mode 100644 index 000000000..454a32f7b --- /dev/null +++ b/nexus_cancel/service.py @@ -0,0 +1,35 @@ +""" +Nexus service definition for the cancellation sample. + +Defines a NexusService with a single `hello` operation that takes a name and +language, and returns a greeting message. +""" + +from dataclasses import dataclass +from enum import IntEnum + +import nexusrpc + + +class Language(IntEnum): + EN = 0 + FR = 1 + DE = 2 + ES = 3 + TR = 4 + + +@dataclass +class HelloInput: + name: str + language: Language + + +@dataclass +class HelloOutput: + message: str + + +@nexusrpc.service +class NexusService: + hello: nexusrpc.Operation[HelloInput, HelloOutput] From 0d22299224c577a68ac1846d24a016cdf55b4c38 Mon Sep 17 00:00:00 2001 From: Andrew Yuan Date: Tue, 10 Mar 2026 16:24:14 -0700 Subject: [PATCH 08/40] Add note on CLI version support (#282) --- README.md | 1 + hello/README.md | 1 + 2 files changed, 2 insertions(+) diff --git a/README.md b/README.md index 8e7748de1..1e3860edb 100644 --- a/README.md +++ b/README.md @@ -56,6 +56,7 @@ Some examples require extra dependencies. See each sample's directory for specif while running. * [hello_signal](hello/hello_signal.py) - Send signals to a workflow. * [hello standalone activity](hello/hello_standalone_activity.py) - Execute an activity from outside of a workflow. + * Note: Standalone activities is supported in CLI version [v1.6.2](https://github.com/temporalio/cli/releases/tag/v1.6.2-standalone-activity). * [hello update](hello/hello_update.py) - Send a request to and a response from a client to a workflow execution. * [activity_worker](activity_worker) - Use Python activities from a workflow in another language. diff --git a/hello/README.md b/hello/README.md index 48e8e67e6..13495fd0d 100644 --- a/hello/README.md +++ b/hello/README.md @@ -45,6 +45,7 @@ Replace `hello/hello_activity.py` in the command with any other example filename running. * [hello_signal](hello_signal.py) - Send signals to a workflow. * [hello standalone activity](hello_standalone_activity.py) - Execute an activity from outside of a workflow. + * Note: Standalone activities is supported in CLI version [v1.6.2](https://github.com/temporalio/cli/releases/tag/v1.6.2-standalone-activity). * [hello_update](hello_update.py) - **Send a request to and a response from a client to a workflow execution.** Note: To enable the workflow update, set the `frontend.enableUpdateWorkflowExecution` dynamic config value to true. From 5e9d1c7b1ad76f0e5654ea62230e7d927ad2861b Mon Sep 17 00:00:00 2001 From: Phil Prasek Date: Wed, 11 Mar 2026 10:14:25 -0700 Subject: [PATCH 09/40] Hello standalone activity (#284) * Make it non-`async def` * Add test * standalone activity samples to pair with docs for quickstart Signed-off-by: Phil Prasek * Remove hello/hello_standalone_activity.py in favor of top-level hello_standalone_activity/ sample The standalone activity sample now lives in its own top-level directory (added by Phil in fc08204). Update the test to import from the new location and update both READMEs accordingly. --------- Signed-off-by: Phil Prasek Co-authored-by: Dan Davison --- README.md | 5 +- hello/README.md | 2 - hello/hello_standalone_activity.py | 77 ------------ hello_standalone_activity/README.md | 115 ++++++++++++++++++ hello_standalone_activity/__init__.py | 0 hello_standalone_activity/count_activities.py | 23 ++++ hello_standalone_activity/execute_activity.py | 26 ++++ hello_standalone_activity/list_activities.py | 23 ++++ hello_standalone_activity/my_activity.py | 15 +++ hello_standalone_activity/start_activity.py | 31 +++++ hello_standalone_activity/worker.py | 26 ++++ tests/hello/hello_standalone_activity_test.py | 31 +++++ 12 files changed, 292 insertions(+), 82 deletions(-) delete mode 100644 hello/hello_standalone_activity.py create mode 100644 hello_standalone_activity/README.md create mode 100644 hello_standalone_activity/__init__.py create mode 100644 hello_standalone_activity/count_activities.py create mode 100644 hello_standalone_activity/execute_activity.py create mode 100644 hello_standalone_activity/list_activities.py create mode 100644 hello_standalone_activity/my_activity.py create mode 100644 hello_standalone_activity/start_activity.py create mode 100644 hello_standalone_activity/worker.py create mode 100644 tests/hello/hello_standalone_activity_test.py diff --git a/README.md b/README.md index 1e3860edb..d4d6a61bc 100644 --- a/README.md +++ b/README.md @@ -55,8 +55,6 @@ Some examples require extra dependencies. See each sample's directory for specif * [hello_search_attributes](hello/hello_search_attributes.py) - Start workflow with search attributes then change while running. * [hello_signal](hello/hello_signal.py) - Send signals to a workflow. - * [hello standalone activity](hello/hello_standalone_activity.py) - Execute an activity from outside of a workflow. - * Note: Standalone activities is supported in CLI version [v1.6.2](https://github.com/temporalio/cli/releases/tag/v1.6.2-standalone-activity). * [hello update](hello/hello_update.py) - Send a request to and a response from a client to a workflow execution. * [activity_worker](activity_worker) - Use Python activities from a workflow in another language. @@ -72,6 +70,7 @@ Some examples require extra dependencies. See each sample's directory for specif * [encryption](encryption) - Apply end-to-end encryption for all input/output. * [env_config](env_config) - Load client configuration from TOML files with programmatic overrides. * [gevent_async](gevent_async) - Combine gevent and Temporal. +* [hello_standalone_activity](hello_standalone_activity) - Use activities without using a workflow. * [langchain](langchain) - Orchestrate workflows for LangChain. * [message_passing/introduction](message_passing/introduction/) - Introduction to queries, signals, and updates. * [message_passing/safe_message_handlers](message_passing/safe_message_handlers/) - Safely handling updates and signals. @@ -87,7 +86,7 @@ Some examples require extra dependencies. See each sample's directory for specif * [updatable_timer](updatable_timer) - A timer that can be updated while sleeping. * [worker_specific_task_queues](worker_specific_task_queues) - Use unique task queues to ensure activities run on specific workers. * [worker_versioning](worker_versioning) - Use the Worker Versioning feature to more easily version your workflows & other code. -* [worker_multiprocessing](worker_multiprocessing) - Leverage Python multiprocessing to parallelize workflow tasks and other CPU bound operations by running multiple workers. +* [worker_multiprocessing](worker_multiprocessing) - Leverage Python multiprocessing to parallelize workflow tasks and other CPU bound operations by running multiple workers. ## Test diff --git a/hello/README.md b/hello/README.md index 13495fd0d..7b5ca6ebb 100644 --- a/hello/README.md +++ b/hello/README.md @@ -44,8 +44,6 @@ Replace `hello/hello_activity.py` in the command with any other example filename * [hello_search_attributes](hello_search_attributes.py) - Start workflow with search attributes then change while running. * [hello_signal](hello_signal.py) - Send signals to a workflow. -* [hello standalone activity](hello_standalone_activity.py) - Execute an activity from outside of a workflow. - * Note: Standalone activities is supported in CLI version [v1.6.2](https://github.com/temporalio/cli/releases/tag/v1.6.2-standalone-activity). * [hello_update](hello_update.py) - **Send a request to and a response from a client to a workflow execution.** Note: To enable the workflow update, set the `frontend.enableUpdateWorkflowExecution` dynamic config value to true. diff --git a/hello/hello_standalone_activity.py b/hello/hello_standalone_activity.py deleted file mode 100644 index 541d687b2..000000000 --- a/hello/hello_standalone_activity.py +++ /dev/null @@ -1,77 +0,0 @@ -import asyncio -from dataclasses import dataclass -from datetime import timedelta - -from temporalio import activity -from temporalio.client import Client -from temporalio.envconfig import ClientConfig -from temporalio.worker import Worker - -# This sample is very similar to hello_activity.py. The difference is that whereas in -# hello_activity.py the activity is orchestrated by a workflow, in this sample the activity is -# executed directly by a client ("standalone activity"). - - -@dataclass -class ComposeGreetingInput: - greeting: str - name: str - - -# This is just a normal activity. You could invoke it from a workflow but, in this sample, we are -# invoking it directly as a standalone activity. -@activity.defn -async def compose_greeting(input: ComposeGreetingInput) -> str: - activity.logger.info("Running activity with parameter %s" % input) - return f"{input.greeting}, {input.name}!" - - -async def my_client_code(client: Client): - # client.execute_activity starts the activity, and then uses a long-poll to wait for the - # activity to be completed by the worker. - result = await client.execute_activity( - compose_greeting, - args=[ComposeGreetingInput("Hello", "World")], - id="my-standalone-activity-id", - task_queue="hello-standalone-activity-task-queue", - start_to_close_timeout=timedelta(seconds=10), - ) - print(f"Activity result: {result}") - - activities = client.list_activities( - query="TaskQueue = 'hello-standalone-activity-task-queue'" - ) - print("ListActivity results:") - async for info in activities: - print( - f"\tActivityID: {info.activity_id}, Type: {info.activity_type}, Status: {info.status}" - ) - - count_result = await client.count_activities( - query="TaskQueue = 'hello-standalone-activity-task-queue'" - ) - print(f"Total activities: {count_result.count}") - - -async def main(): - # Uncomment the lines below to see logging output - # import logging - # logging.basicConfig(level=logging.INFO) - - config = ClientConfig.load_client_connect_config() - config.setdefault("target_host", "localhost:7233") - - client = await Client.connect(**config) - - # Run a worker for the activity - async with Worker( - client, - task_queue="hello-standalone-activity-task-queue", - activities=[compose_greeting], - ): - # While the worker is running, use the client to execute the activity. - await my_client_code(client) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/hello_standalone_activity/README.md b/hello_standalone_activity/README.md new file mode 100644 index 000000000..ebd639317 --- /dev/null +++ b/hello_standalone_activity/README.md @@ -0,0 +1,115 @@ +# Standalone Activity + +This sample shows how to execute Activities directly from a Temporal Client, without a Workflow. + +For full documentation, see [Standalone Activities - Python SDK](https://docs.temporal.io/develop/python/standalone-activities). + +### Sample directory structure + +- [my_activity.py](./my_activity.py) - Activity definition with `@activity.defn` +- [worker.py](./worker.py) - Worker that registers and runs the Activity +- [execute_activity.py](./execute_activity.py) - Execute a Standalone Activity and wait for the result +- [start_activity.py](./start_activity.py) - Start a Standalone Activity, get a handle, then wait for the result +- [list_activities.py](./list_activities.py) - List Standalone Activity Executions +- [count_activities.py](./count_activities.py) - Count Standalone Activity Executions + +### Quickstart + +**1. Start the Temporal dev server** + +```bash +temporal server start-dev +``` + +**2. Run the Worker** (in a separate terminal) + +```bash +uv run hello_standalone_activity/worker.py +``` + +**3. Execute a Standalone Activity** (in a separate terminal) + +Execute and wait for the result: + +```bash +uv run hello_standalone_activity/execute_activity.py +``` + +Or use the Temporal CLI: + +```bash +temporal activity execute \ + --type compose_greeting \ + --activity-id my-standalone-activity-id \ + --task-queue my-standalone-activity-task-queue \ + --start-to-close-timeout 10s \ + --input '{"greeting": "Hello", "name": "World"}' +``` + +**4. Start a Standalone Activity (without waiting)** + +Start, get a handle, then wait for the result: + +```bash +uv run hello_standalone_activity/start_activity.py +``` + +Or use the Temporal CLI: + +```bash +temporal activity start \ + --type compose_greeting \ + --activity-id my-standalone-activity-id \ + --task-queue my-standalone-activity-task-queue \ + --start-to-close-timeout 10s \ + --input '{"greeting": "Hello", "name": "World"}' +``` + +**5. List Standalone Activities** + +```bash +uv run hello_standalone_activity/list_activities.py +``` + +Or use the Temporal CLI: + +```bash +temporal activity list --query "TaskQueue = 'my-standalone-activity-task-queue'" +``` + +Note: `list` and `count` are only available in the [Standalone Activity prerelease CLI](https://github.com/temporalio/cli/releases/tag/v1.6.2-standalone-activity). + +**6. Count Standalone Activities** + +```bash +uv run hello_standalone_activity/count_activities.py +``` + +Or use the Temporal CLI: + +```bash +temporal activity count --query "TaskQueue = 'my-standalone-activity-task-queue'" +``` + +### Temporal Cloud + +The same code works against Temporal Cloud - just set environment variables. No code changes needed. + +**Connect with mTLS:** + +```bash +export TEMPORAL_ADDRESS=..tmprl.cloud:7233 +export TEMPORAL_NAMESPACE=. +export TEMPORAL_TLS_CLIENT_CERT_PATH='path/to/your/client.pem' +export TEMPORAL_TLS_CLIENT_KEY_PATH='path/to/your/client.key' +``` + +**Connect with an API key:** + +```bash +export TEMPORAL_ADDRESS=..api.temporal.io:7233 +export TEMPORAL_NAMESPACE=. +export TEMPORAL_API_KEY= +``` + +Then run the worker and starter as shown above. diff --git a/hello_standalone_activity/__init__.py b/hello_standalone_activity/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/hello_standalone_activity/count_activities.py b/hello_standalone_activity/count_activities.py new file mode 100644 index 000000000..14a89e7e2 --- /dev/null +++ b/hello_standalone_activity/count_activities.py @@ -0,0 +1,23 @@ +import asyncio + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + + +async def my_application(): + connect_config = ClientConfig.load_client_connect_config() + connect_config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**connect_config) + + resp = await client.count_activities( + query="TaskQueue = 'my-standalone-activity-task-queue'", + ) + + print("Total activities:", resp.count) + + for group in resp.groups: + print(f"Group {group.group_values}: {group.count}") + + +if __name__ == "__main__": + asyncio.run(my_application()) diff --git a/hello_standalone_activity/execute_activity.py b/hello_standalone_activity/execute_activity.py new file mode 100644 index 000000000..529a1feb8 --- /dev/null +++ b/hello_standalone_activity/execute_activity.py @@ -0,0 +1,26 @@ +import asyncio +from datetime import timedelta + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from hello_standalone_activity.my_activity import ComposeGreetingInput, compose_greeting + + +async def my_application(): + connect_config = ClientConfig.load_client_connect_config() + connect_config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**connect_config) + + activity_result = await client.execute_activity( + compose_greeting, + args=[ComposeGreetingInput("Hello", "World")], + id="my-standalone-activity-id", + task_queue="my-standalone-activity-task-queue", + start_to_close_timeout=timedelta(seconds=10), + ) + print(f"Activity result: {activity_result}") + + +if __name__ == "__main__": + asyncio.run(my_application()) diff --git a/hello_standalone_activity/list_activities.py b/hello_standalone_activity/list_activities.py new file mode 100644 index 000000000..ef7af9699 --- /dev/null +++ b/hello_standalone_activity/list_activities.py @@ -0,0 +1,23 @@ +import asyncio + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + + +async def my_application(): + connect_config = ClientConfig.load_client_connect_config() + connect_config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**connect_config) + + activities = client.list_activities( + query="TaskQueue = 'my-standalone-activity-task-queue'", + ) + + async for info in activities: + print( + f"ActivityID: {info.activity_id}, Type: {info.activity_type}, Status: {info.status}" + ) + + +if __name__ == "__main__": + asyncio.run(my_application()) diff --git a/hello_standalone_activity/my_activity.py b/hello_standalone_activity/my_activity.py new file mode 100644 index 000000000..086b08a02 --- /dev/null +++ b/hello_standalone_activity/my_activity.py @@ -0,0 +1,15 @@ +from dataclasses import dataclass + +from temporalio import activity + + +@dataclass +class ComposeGreetingInput: + greeting: str + name: str + + +@activity.defn +def compose_greeting(input: ComposeGreetingInput) -> str: + activity.logger.info("Running activity with parameter %s" % input) + return f"{input.greeting}, {input.name}!" diff --git a/hello_standalone_activity/start_activity.py b/hello_standalone_activity/start_activity.py new file mode 100644 index 000000000..8aa9fa549 --- /dev/null +++ b/hello_standalone_activity/start_activity.py @@ -0,0 +1,31 @@ +import asyncio +from datetime import timedelta + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from hello_standalone_activity.my_activity import ComposeGreetingInput, compose_greeting + + +async def my_application(): + connect_config = ClientConfig.load_client_connect_config() + connect_config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**connect_config) + + # Start the activity without waiting for the result + activity_handle = await client.start_activity( + compose_greeting, + args=[ComposeGreetingInput("Hello", "World")], + id="my-standalone-activity-id", + task_queue="my-standalone-activity-task-queue", + start_to_close_timeout=timedelta(seconds=10), + ) + print(f"Started activity: {activity_handle.id}") + + # Wait for the result + activity_result = await activity_handle.result() + print(f"Activity result: {activity_result}") + + +if __name__ == "__main__": + asyncio.run(my_application()) diff --git a/hello_standalone_activity/worker.py b/hello_standalone_activity/worker.py new file mode 100644 index 000000000..d093cc036 --- /dev/null +++ b/hello_standalone_activity/worker.py @@ -0,0 +1,26 @@ +import asyncio +from concurrent.futures import ThreadPoolExecutor + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from hello_standalone_activity.my_activity import compose_greeting + + +async def main(): + connect_config = ClientConfig.load_client_connect_config() + connect_config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**connect_config) + worker = Worker( + client, + task_queue="my-standalone-activity-task-queue", + activities=[compose_greeting], + activity_executor=ThreadPoolExecutor(5), + ) + print("worker running...", end="", flush=True) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/tests/hello/hello_standalone_activity_test.py b/tests/hello/hello_standalone_activity_test.py new file mode 100644 index 000000000..d40b09fa4 --- /dev/null +++ b/tests/hello/hello_standalone_activity_test.py @@ -0,0 +1,31 @@ +import uuid +from concurrent.futures import ThreadPoolExecutor +from datetime import timedelta + +import pytest +from temporalio.client import Client +from temporalio.worker import Worker + +from hello_standalone_activity.my_activity import ComposeGreetingInput, compose_greeting + + +async def test_execute_standalone_activity(client: Client): + pytest.skip( + "Standalone Activity is not yet supported by `temporal server start-dev`" + ) + task_queue_name = str(uuid.uuid4()) + + async with Worker( + client, + task_queue=task_queue_name, + activities=[compose_greeting], + activity_executor=ThreadPoolExecutor(5), + ): + result = await client.execute_activity( + compose_greeting, + args=[ComposeGreetingInput("Hello", "World")], + id=str(uuid.uuid4()), + task_queue=task_queue_name, + start_to_close_timeout=timedelta(seconds=10), + ) + assert result == "Hello, World!" From ce5d8dda901374e0f0c416b74748afc0b4f95580 Mon Sep 17 00:00:00 2001 From: James Watkins-Harvey Date: Thu, 2 Apr 2026 17:16:33 -0400 Subject: [PATCH 10/40] Replace WorkflowIdReusePolicy.TerminateExisting by WorkflowIdConflictPolicy (#266) --- message_passing/safe_message_handlers/starter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/message_passing/safe_message_handlers/starter.py b/message_passing/safe_message_handlers/starter.py index 9bc5c6611..c8f1b5d39 100644 --- a/message_passing/safe_message_handlers/starter.py +++ b/message_passing/safe_message_handlers/starter.py @@ -65,7 +65,7 @@ async def main(should_test_continue_as_new: bool): ClusterManagerInput(test_continue_as_new=should_test_continue_as_new), id=f"ClusterManagerWorkflow-{uuid.uuid4()}", task_queue="safe-message-handlers-task-queue", - id_reuse_policy=common.WorkflowIDReusePolicy.TERMINATE_IF_RUNNING, + id_conflict_policy=common.WorkflowIDConflictPolicy.TERMINATE_EXISTING, ) delay_seconds = 10 if should_test_continue_as_new else 1 await do_cluster_lifecycle(cluster_manager_handle, delay_seconds=delay_seconds) From 29fc4af7c1f350a30cd1d45b8ead4b3c9494aec4 Mon Sep 17 00:00:00 2001 From: xumaple <45406854+xumaple@users.noreply.github.com> Date: Wed, 15 Apr 2026 13:29:05 -0400 Subject: [PATCH 11/40] AI-18: Move set_tracing_disabled from workflow to worker startup (#288) * AI-18: Move set_tracing_disabled from workflow to worker startup set_tracing_disabled is a global side effect that shouldn't be called inside deterministic workflow code. Move it to the top of main() in the worker files where it runs once at process startup. Co-Authored-By: Claude Opus 4.6 (1M context) * AI-18: Add comment explaining why tracing is disabled at worker startup Co-Authored-By: Claude Opus 4.6 (1M context) * linting --------- Co-authored-by: Claude Opus 4.6 (1M context) --- openai_agents/model_providers/run_gpt_oss_worker.py | 12 +++++++++++- .../model_providers/run_litellm_provider_worker.py | 6 ++++++ .../model_providers/workflows/gpt_oss_workflow.py | 4 +--- .../workflows/litellm_auto_workflow.py | 4 +--- 4 files changed, 19 insertions(+), 7 deletions(-) diff --git a/openai_agents/model_providers/run_gpt_oss_worker.py b/openai_agents/model_providers/run_gpt_oss_worker.py index 88c85fcab..59798a1d4 100644 --- a/openai_agents/model_providers/run_gpt_oss_worker.py +++ b/openai_agents/model_providers/run_gpt_oss_worker.py @@ -3,7 +3,12 @@ from datetime import timedelta from typing import Optional -from agents import Model, ModelProvider, OpenAIChatCompletionsModel +from agents import ( + Model, + ModelProvider, + OpenAIChatCompletionsModel, + set_tracing_disabled, +) from openai import AsyncOpenAI from temporalio.client import Client from temporalio.contrib.openai_agents import ModelActivityParameters, OpenAIAgentsPlugin @@ -27,6 +32,11 @@ def get_model(self, model_name: Optional[str]) -> Model: async def main(): + # Disable Agents SDK tracing — the default exporter sends traces to OpenAI's + # backend, which requires an OpenAI API key not available in these samples. + # Call here rather than in the workflow because it's a global side effect. + set_tracing_disabled(disabled=True) + # Configure logging to show workflow debug messages logging.basicConfig(level=logging.WARNING) logging.getLogger("temporalio.workflow").setLevel(logging.DEBUG) diff --git a/openai_agents/model_providers/run_litellm_provider_worker.py b/openai_agents/model_providers/run_litellm_provider_worker.py index 5eb8b8b28..3441013c7 100644 --- a/openai_agents/model_providers/run_litellm_provider_worker.py +++ b/openai_agents/model_providers/run_litellm_provider_worker.py @@ -1,6 +1,7 @@ import asyncio from datetime import timedelta +from agents import set_tracing_disabled from agents.extensions.models.litellm_provider import LitellmProvider from temporalio.client import Client from temporalio.contrib.openai_agents import ModelActivityParameters, OpenAIAgentsPlugin @@ -12,6 +13,11 @@ async def main(): + # Disable Agents SDK tracing — the default exporter sends traces to OpenAI's + # backend, which requires an OpenAI API key not available in these samples. + # Call here rather than in the workflow because it's a global side effect. + set_tracing_disabled(disabled=True) + # Create client connected to server at the given address client = await Client.connect( "localhost:7233", diff --git a/openai_agents/model_providers/workflows/gpt_oss_workflow.py b/openai_agents/model_providers/workflows/gpt_oss_workflow.py index d7ed8021a..821844b3e 100644 --- a/openai_agents/model_providers/workflows/gpt_oss_workflow.py +++ b/openai_agents/model_providers/workflows/gpt_oss_workflow.py @@ -1,6 +1,6 @@ from __future__ import annotations -from agents import Agent, Runner, function_tool, set_tracing_disabled +from agents import Agent, Runner, function_tool from temporalio import workflow @@ -8,8 +8,6 @@ class GptOssWorkflow: @workflow.run async def run(self, prompt: str) -> str: - set_tracing_disabled(disabled=True) - @function_tool def get_weather(city: str): workflow.logger.debug(f"Getting weather for {city}") diff --git a/openai_agents/model_providers/workflows/litellm_auto_workflow.py b/openai_agents/model_providers/workflows/litellm_auto_workflow.py index 4a67ded4d..9fe3d40b7 100644 --- a/openai_agents/model_providers/workflows/litellm_auto_workflow.py +++ b/openai_agents/model_providers/workflows/litellm_auto_workflow.py @@ -1,6 +1,6 @@ from __future__ import annotations -from agents import Agent, Runner, function_tool, set_tracing_disabled +from agents import Agent, Runner, function_tool from temporalio import workflow @@ -8,8 +8,6 @@ class LitellmAutoWorkflow: @workflow.run async def run(self, prompt: str) -> str: - set_tracing_disabled(disabled=True) - @function_tool def get_weather(city: str): return f"The weather in {city} is sunny." From 26c124480446d50a516855956680171b0fb1b378 Mon Sep 17 00:00:00 2001 From: tconley1428 Date: Fri, 17 Apr 2026 14:01:00 -0700 Subject: [PATCH 12/40] Update dependency version for temporalio 1.26.0 (#291) * Update dependency version for temporalio 1.26.0 * Fix CI: bump pandas to 2.3.3 for Python 3.14, add opentelemetry extra for openai-agents * cleanup types in openai agents samples --------- Co-authored-by: Jason Steving --- .../financial_research_manager.py | 4 +- .../workflows/image_generator_workflow.py | 3 +- pyproject.toml | 16 +- uv.lock | 989 ++++++++++++------ 4 files changed, 660 insertions(+), 352 deletions(-) diff --git a/openai_agents/financial_research_agent/financial_research_manager.py b/openai_agents/financial_research_agent/financial_research_manager.py index 9a437a456..6f2050b27 100644 --- a/openai_agents/financial_research_agent/financial_research_manager.py +++ b/openai_agents/financial_research_agent/financial_research_manager.py @@ -3,7 +3,7 @@ import asyncio from collections.abc import Sequence -from agents import RunConfig, Runner, RunResult, custom_span, trace +from agents import RunConfig, Runner, RunResult, RunResultStreaming, custom_span, trace from temporalio import workflow from openai_agents.financial_research_agent.agents.financials_agent import ( @@ -26,7 +26,7 @@ ) -async def _summary_extractor(run_result: RunResult) -> str: +async def _summary_extractor(run_result: RunResult | RunResultStreaming) -> str: """Custom output extractor for sub-agents that return an AnalysisSummary.""" # The financial/risk analyst agents emit an AnalysisSummary with a `summary` field. # We want the tool call to return just that summary text so the writer can drop it inline. diff --git a/openai_agents/tools/workflows/image_generator_workflow.py b/openai_agents/tools/workflows/image_generator_workflow.py index 7c58091b1..202341256 100644 --- a/openai_agents/tools/workflows/image_generator_workflow.py +++ b/openai_agents/tools/workflows/image_generator_workflow.py @@ -4,6 +4,7 @@ from typing import Optional from agents import Agent, ImageGenerationTool, Runner +from openai.types.responses.response_output_item import ImageGenerationCall from temporalio import workflow @@ -34,7 +35,7 @@ async def run(self, prompt: str) -> ImageGenerationResult: for item in result.new_items: if ( item.type == "tool_call_item" - and item.raw_item.type == "image_generation_call" + and isinstance(item.raw_item, ImageGenerationCall) and (img_result := item.raw_item.result) ): image_data = img_result diff --git a/pyproject.toml b/pyproject.toml index caae123c3..f65ff1c29 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] requires-python = ">=3.10" readme = "README.md" license = "MIT" -dependencies = ["temporalio>=1.23.0,<2"] +dependencies = ["temporalio>=1.26.0,<2"] [project.urls] Homepage = 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Watkins-Harvey Date: Fri, 17 Apr 2026 20:48:17 -0400 Subject: [PATCH 13/40] Update and pin all GHA actions (#293) --- .github/workflows/ci.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 4443cbdd0..4a1018188 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -24,13 +24,13 @@ jobs: runsOn: macos-latest runs-on: ${{ matrix.runsOn || matrix.os }} steps: - - uses: astral-sh/setup-uv@v5 + - uses: astral-sh/setup-uv@cec208311dfd045dd5311c1add060b2062131d57 # v8 - name: Print build information run: "echo head_ref: ${{ github.head_ref }}, ref: ${{ github.ref }}, os: ${{ matrix.os }}, python: ${{ matrix.python }}" - - uses: actions/checkout@v4 + - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6 with: submodules: recursive - - uses: actions/setup-python@v5 + - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6 with: python-version: ${{ matrix.python }} - run: uv tool install poethepoet @@ -56,7 +56,7 @@ jobs: uv run gevent_async/test/run_combined.py - name: Upload junit-xml artifacts - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f # v7 if: always() with: name: junit-xml--${{github.run_id}}--${{github.run_attempt}}--${{ matrix.python }}--${{ matrix.os }} From b199e50726ee6df1dd2c596e52131526a9e78a58 Mon Sep 17 00:00:00 2001 From: xumaple <45406854+xumaple@users.noreply.github.com> Date: Wed, 22 Apr 2026 14:04:34 -0400 Subject: [PATCH 14/40] Add LangSmith tracing sample (#292) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add LangSmith tracing sample with basic and chatbot examples Demonstrates the LangSmithPlugin for automatic LangSmith tracing in Temporal workflows. Includes two examples: - basic/: one-shot LLM workflow (prompt → OpenAI → result) - chatbot/: conversational loop with save_note/read_note tools, signals, queries, and dynamic trace names Co-Authored-By: Claude Opus 4.6 (1M context) * Enrich LangSmith tracing usage across basic and chatbot samples - Add @traceable with metadata, tags, and run_type variety to workflows - Add client-side @traceable on starters for end-to-end trace linking - Add --temporal-runs flag to workers and starters for add_temporal_runs toggle - Use run_type="llm" for OpenAI calls, "tool" for save_note, "chain" for orchestration - Add trace tree diagrams to README showing both add_temporal_runs modes - Add "Three Layers of Tracing" section to README (wrap_openai, @traceable, Temporal plugin) - Skip all tests when temporalio.contrib.langsmith is unavailable (pending SDK release) Co-Authored-By: Claude Opus 4.6 (1M context) * Address review feedback: READMEs, comments, activity conventions - Split top-level README into per-sample READMEs (basic/ and chatbot/) with detailed trace structure diagrams and screenshot placeholders - Add replay safety comment on @workflow.run methods explaining why @traceable must wrap an inner function - Make read_note a proper activity for LangSmith trace visibility - Change save_note/read_note to use NoteRequest dataclass (single arg) - Fix misplaced comments in activities (wrap_openai, run_type docs) - Add links to LangSmith docs and Temporal SDK plugin docs - Add add_temporal_runs explanation section to top-level README Co-Authored-By: Claude Opus 4.6 (1M context) * Address audit findings: types, comments, tests, consistency Audit findings addressed: - Remove max_cached_workflows=0 (debugging leftover, not needed) - Expand replay safety comment to explain why (I/O on replay) - Tighten type annotations: str|list[dict[str,Any]], dict[str,Any] - Clarify read_note docstring (passthrough for tracing visibility) - Clarify activity docstrings (retries handled by Temporal, not OpenAI) - Make trace tree annotations consistent across basic/chatbot READMEs - Extract shared test helpers (make_text_response, poll_last_response) - Add TimeoutError on poll failure instead of silent assertion - Fix "simplest possible" wording to "very simple" Co-Authored-By: Claude Opus 4.6 (1M context) * Rewrite replay safety comment to focus on outcome Changed from explaining internals (I/O on replay) to describing what the user would see: duplicate or orphaned traces. Also swept all other comments for outcome-focus — no further changes needed. Co-Authored-By: Claude Opus 4.6 (1M context) * Move save_note/read_note from activities to workflow methods - save_note and read_note are now @traceable methods on ChatbotWorkflow, not activities. Notes live in workflow state (durable via event history). - Remove NoteRequest dataclass and activity registrations - Fix wrap_openai comment placement (describes child span, not parent) - Remove retry policy from basic workflow (unnecessary for simple example) - Add comment highlighting that non-@workflow.run methods can use @traceable - Update chatbot README trace trees to reflect workflow methods vs activities Co-Authored-By: Claude Opus 4.6 (1M context) * Remove screenshot placeholders, rename flag to --add-temporal-runs Co-Authored-By: Claude Opus 4.6 (1M context) * Clean up tests, naming, and dependencies - Remove skip markers from tests — CI should fail loudly if deps missing - Pin temporalio[pydantic,langsmith]>=1.26.0 (plugin is released) - Rename kwargs to response_args in chatbot activity - Remove notes CLI command from chatbot starter - Update chatbot README trace trees for accuracy - Change input prompt from "You: " to "> " Co-Authored-By: Claude Opus 4.6 (1M context) * Fix README trace trees to match actual integration test hierarchies - StartWorkflow/RunWorkflow are siblings, not parent-child - StartActivity/RunActivity are siblings under RunWorkflow - @traceable activity spans nest under RunActivity - Signal/query traces are separate roots (or under client @traceable) - Merge client-side and worker-side into unified trees - add_temporal_runs=False: only @traceable spans, all under client root Co-Authored-By: Claude Opus 4.6 (1M context) * Add openai-agents to conflicting groups for langsmith-tracing temporalio>=1.26.0 requires openai-agents>=0.14.0 but the openai-agents group pins ==0.3.2, causing a resolution failure. Co-Authored-By: Claude Opus 4.6 (1M context) * Fix run commands to include --group langsmith-tracing Without the group flag, uv resolves the base temporalio dep (1.23.0) which doesn't have contrib.langsmith. Co-Authored-By: Claude Opus 4.6 (1M context) * fix tracing project * Address PR review comments and refine chatbot architecture - Simplify basic/worker.py to use asyncio.run(main()) pattern - Basic activity now returns str instead of Response (drops pydantic dep) - Remove pydantic_data_converter from basic worker/starter - Replace chatbot signal+poll with update handler (message_from_user) - Wrap update handler body in @traceable for input/output capture - Inline RetryPolicy in chatbot workflow (no separate RETRY constant) - Add comment about alternative traceable() function-call style - Make PROJECT_NAME a shared constant in starters, pass to client-side @traceable so traces go to the right LangSmith project - Update READMEs with correct trace hierarchies based on real output - Fix wrap_openai span name (ChatOpenAI, not openai.responses.create) - Simplify tests (mocks match new str return type, use execute_update) - Remove poll_last_response helper Co-Authored-By: Claude Opus 4.7 (1M context) * Remove langchain sample, fix CI by relaxing openai version pin The langchain sample's langsmith<0.4 pin conflicts with langsmith-tracing's langsmith>=0.7.0. The langchain sample uses an older custom-interceptor approach that's superseded by the new LangSmithPlugin demonstrated in langsmith_tracing/. Removing it eliminates the dep conflict and the related [tool.uv] conflicts declaration. Also relax openai pin from <2 to no upper bound so it resolves alongside the openai-agents group (which now requires openai>=2.26.0). Add inline comment on max_retries=0 explaining Temporal handles retries. Co-Authored-By: Claude Opus 4.7 (1M context) --------- Co-authored-by: Claude Opus 4.6 (1M context) --- langchain/README.md | 30 -- langchain/activities.py | 34 -- langchain/langchain_interceptor.py | 181 ------- langchain/starter.py | 47 -- langchain/worker.py | 44 -- langchain/workflow.py | 53 -- langsmith_tracing/README.md | 49 ++ langsmith_tracing/__init__.py | 0 langsmith_tracing/basic/README.md | 50 ++ langsmith_tracing/basic/__init__.py | 0 langsmith_tracing/basic/activities.py | 33 ++ langsmith_tracing/basic/starter.py | 54 ++ langsmith_tracing/basic/worker.py | 49 ++ langsmith_tracing/basic/workflows.py | 33 ++ langsmith_tracing/chatbot/README.md | 81 +++ langsmith_tracing/chatbot/__init__.py | 0 langsmith_tracing/chatbot/activities.py | 45 ++ langsmith_tracing/chatbot/starter.py | 76 +++ langsmith_tracing/chatbot/worker.py | 49 ++ langsmith_tracing/chatbot/workflows.py | 178 +++++++ pyproject.toml | 14 +- tests/langsmith_tracing/__init__.py | 0 tests/langsmith_tracing/helpers.py | 33 ++ tests/langsmith_tracing/test_basic.py | 34 ++ tests/langsmith_tracing/test_chatbot.py | 132 +++++ uv.lock | 671 +++++------------------- 26 files changed, 1046 insertions(+), 924 deletions(-) delete mode 100644 langchain/README.md delete mode 100644 langchain/activities.py delete mode 100644 langchain/langchain_interceptor.py delete mode 100644 langchain/starter.py delete mode 100644 langchain/worker.py delete mode 100644 langchain/workflow.py create mode 100644 langsmith_tracing/README.md create mode 100644 langsmith_tracing/__init__.py create mode 100644 langsmith_tracing/basic/README.md create mode 100644 langsmith_tracing/basic/__init__.py create mode 100644 langsmith_tracing/basic/activities.py create mode 100644 langsmith_tracing/basic/starter.py create mode 100644 langsmith_tracing/basic/worker.py create mode 100644 langsmith_tracing/basic/workflows.py create mode 100644 langsmith_tracing/chatbot/README.md create mode 100644 langsmith_tracing/chatbot/__init__.py create mode 100644 langsmith_tracing/chatbot/activities.py create mode 100644 langsmith_tracing/chatbot/starter.py create mode 100644 langsmith_tracing/chatbot/worker.py create mode 100644 langsmith_tracing/chatbot/workflows.py create mode 100644 tests/langsmith_tracing/__init__.py create mode 100644 tests/langsmith_tracing/helpers.py create mode 100644 tests/langsmith_tracing/test_basic.py create mode 100644 tests/langsmith_tracing/test_chatbot.py diff --git a/langchain/README.md b/langchain/README.md deleted file mode 100644 index 506a379db..000000000 --- a/langchain/README.md +++ /dev/null @@ -1,30 +0,0 @@ -# LangChain Sample - -This sample shows you how you can use Temporal to orchestrate workflows for [LangChain](https://www.langchain.com). It includes an interceptor that makes LangSmith traces work seamlessly across Temporal clients, workflows and activities. - -For this sample, the optional `langchain` dependency group must be included. To include, run: - - uv sync --group langchain - -Export your [OpenAI API key](https://platform.openai.com/api-keys) as an environment variable. Replace `YOUR_API_KEY` with your actual OpenAI API key. - - export OPENAI_API_KEY='...' - -To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the -worker: - - uv run langchain/worker.py - -This will start the worker. Then, in another terminal, run the following to execute a workflow: - - uv run langchain/starter.py - -Then, in another terminal, run the following command to translate a phrase: - - curl -X POST "http://localhost:8000/translate?phrase=hello%20world&language1=Spanish&language2=French&language3=Russian" - -Which should produce some output like: - - {"translations":{"French":"Bonjour tout le monde","Russian":"Привет, мир","Spanish":"Hola mundo"}} - -Check [LangSmith](https://smith.langchain.com/) for the corresponding trace. \ No newline at end of file diff --git a/langchain/activities.py b/langchain/activities.py deleted file mode 100644 index a9dec70ce..000000000 --- a/langchain/activities.py +++ /dev/null @@ -1,34 +0,0 @@ -from dataclasses import dataclass - -from langchain_openai import ChatOpenAI -from temporalio import activity - -from langchain.prompts import ChatPromptTemplate - - -@dataclass -class TranslateParams: - phrase: str - language: str - - -@activity.defn -async def translate_phrase(params: TranslateParams) -> str: - # LangChain setup - template = """You are a helpful assistant who translates between languages. - Translate the following phrase into the specified language: {phrase} - Language: {language}""" - chat_prompt = ChatPromptTemplate.from_messages( - [ - ("system", template), - ("human", "Translate"), - ] - ) - chain = chat_prompt | ChatOpenAI() - # Use the asynchronous invoke method - return ( - dict( - await chain.ainvoke({"phrase": params.phrase, "language": params.language}) - ).get("content") - or "" - ) diff --git a/langchain/langchain_interceptor.py b/langchain/langchain_interceptor.py deleted file mode 100644 index bb230b6be..000000000 --- a/langchain/langchain_interceptor.py +++ /dev/null @@ -1,181 +0,0 @@ -from __future__ import annotations - -from typing import Any, Mapping, Protocol, Type - -from temporalio import activity, api, client, converter, worker, workflow - -with workflow.unsafe.imports_passed_through(): - from contextlib import contextmanager - - from langsmith import trace, tracing_context - from langsmith.run_helpers import get_current_run_tree - -# Header key for LangChain context -LANGCHAIN_CONTEXT_KEY = "langchain-context" - - -class _InputWithHeaders(Protocol): - headers: Mapping[str, api.common.v1.Payload] - - -def set_header_from_context( - input: _InputWithHeaders, payload_converter: converter.PayloadConverter -) -> None: - # Get current LangChain run tree - run_tree = get_current_run_tree() - if run_tree: - headers = run_tree.to_headers() - input.headers = { - **input.headers, - LANGCHAIN_CONTEXT_KEY: payload_converter.to_payload(headers), - } - - -@contextmanager -def context_from_header( - input: _InputWithHeaders, payload_converter: converter.PayloadConverter -): - payload = input.headers.get(LANGCHAIN_CONTEXT_KEY) - if payload: - run_tree = payload_converter.from_payload(payload, dict) - # Set the run tree in the current context - with tracing_context(parent=run_tree): - yield - else: - yield - - -class LangChainContextPropagationInterceptor(client.Interceptor, worker.Interceptor): - """Interceptor that propagates LangChain context through Temporal.""" - - def __init__( - self, - payload_converter: converter.PayloadConverter = converter.default().payload_converter, - ) -> None: - self._payload_converter = payload_converter - - def intercept_client( - self, next: client.OutboundInterceptor - ) -> client.OutboundInterceptor: - return _LangChainContextPropagationClientOutboundInterceptor( - next, self._payload_converter - ) - - def intercept_activity( - self, next: worker.ActivityInboundInterceptor - ) -> worker.ActivityInboundInterceptor: - return _LangChainContextPropagationActivityInboundInterceptor(next) - - def workflow_interceptor_class( - self, input: worker.WorkflowInterceptorClassInput - ) -> Type[_LangChainContextPropagationWorkflowInboundInterceptor]: - return _LangChainContextPropagationWorkflowInboundInterceptor - - -class _LangChainContextPropagationClientOutboundInterceptor(client.OutboundInterceptor): - def __init__( - self, - next: client.OutboundInterceptor, - payload_converter: converter.PayloadConverter, - ) -> None: - super().__init__(next) - self._payload_converter = payload_converter - - async def start_workflow( - self, input: client.StartWorkflowInput - ) -> client.WorkflowHandle[Any, Any]: - with trace(name=f"start_workflow:{input.workflow}"): - set_header_from_context(input, self._payload_converter) - return await super().start_workflow(input) - - -class _LangChainContextPropagationActivityInboundInterceptor( - worker.ActivityInboundInterceptor -): - async def execute_activity(self, input: worker.ExecuteActivityInput) -> Any: - if isinstance(input.fn, str): - name = input.fn - elif callable(input.fn): - defn = activity._Definition.from_callable(input.fn) - name = ( - defn.name if defn is not None and defn.name is not None else "unknown" - ) - else: - name = "unknown" - - with context_from_header(input, activity.payload_converter()): - with trace(name=f"execute_activity:{name}"): - return await self.next.execute_activity(input) - - -class _LangChainContextPropagationWorkflowInboundInterceptor( - worker.WorkflowInboundInterceptor -): - def init(self, outbound: worker.WorkflowOutboundInterceptor) -> None: - self.next.init( - _LangChainContextPropagationWorkflowOutboundInterceptor(outbound) - ) - - async def execute_workflow(self, input: worker.ExecuteWorkflowInput) -> Any: - if isinstance(input.run_fn, str): - name = input.run_fn - elif callable(input.run_fn): - defn = workflow._Definition.from_run_fn(input.run_fn) - name = ( - defn.name if defn is not None and defn.name is not None else "unknown" - ) - else: - name = "unknown" - - with context_from_header(input, workflow.payload_converter()): - # This is a sandbox friendly way to write - # with trace(...): - # return await self.next.execute_workflow(input) - with workflow.unsafe.sandbox_unrestricted(): - t = trace( - name=f"execute_workflow:{name}", run_id=workflow.info().run_id - ) - with workflow.unsafe.imports_passed_through(): - t.__enter__() - try: - return await self.next.execute_workflow(input) - finally: - with workflow.unsafe.sandbox_unrestricted(): - # Cannot use __aexit__ because it's internally uses - # loop.run_in_executor which is not available in the sandbox - t.__exit__() - - -class _LangChainContextPropagationWorkflowOutboundInterceptor( - worker.WorkflowOutboundInterceptor -): - def start_activity( - self, input: worker.StartActivityInput - ) -> workflow.ActivityHandle: - with workflow.unsafe.sandbox_unrestricted(): - t = trace(name=f"start_activity:{input.activity}", run_id=workflow.uuid4()) - with workflow.unsafe.imports_passed_through(): - t.__enter__() - try: - set_header_from_context(input, workflow.payload_converter()) - return self.next.start_activity(input) - finally: - with workflow.unsafe.sandbox_unrestricted(): - t.__exit__() - - async def start_child_workflow( - self, input: worker.StartChildWorkflowInput - ) -> workflow.ChildWorkflowHandle: - with workflow.unsafe.sandbox_unrestricted(): - t = trace( - name=f"start_child_workflow:{input.workflow}", run_id=workflow.uuid4() - ) - with workflow.unsafe.imports_passed_through(): - t.__enter__() - - try: - set_header_from_context(input, workflow.payload_converter()) - return await self.next.start_child_workflow(input) - finally: - with workflow.unsafe.sandbox_unrestricted(): - t.__exit__() diff --git a/langchain/starter.py b/langchain/starter.py deleted file mode 100644 index 6d9e00c29..000000000 --- a/langchain/starter.py +++ /dev/null @@ -1,47 +0,0 @@ -from contextlib import asynccontextmanager -from typing import List -from uuid import uuid4 - -import uvicorn -from activities import TranslateParams -from fastapi import FastAPI, HTTPException -from langchain_interceptor import LangChainContextPropagationInterceptor -from temporalio.client import Client -from temporalio.envconfig import ClientConfig -from workflow import LangChainWorkflow, TranslateWorkflowParams - - -@asynccontextmanager -async def lifespan(app: FastAPI): - config = ClientConfig.load_client_connect_config() - config.setdefault("target_host", "localhost:7233") - - client = await Client.connect( - **config, - interceptors=[LangChainContextPropagationInterceptor()], - ) - yield - - -app = FastAPI(lifespan=lifespan) - - -@app.post("/translate") -async def translate(phrase: str, language1: str, language2: str, language3: str): - languages = [language1, language2, language3] - client = app.state.temporal_client - try: - result = await client.execute_workflow( - LangChainWorkflow.run, - TranslateWorkflowParams(phrase, languages), - id=f"langchain-translation-{uuid4()}", - task_queue="langchain-task-queue", - ) - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) - - return {"translations": result} - - -if __name__ == "__main__": - uvicorn.run(app, host="localhost", port=8000) diff --git a/langchain/worker.py b/langchain/worker.py deleted file mode 100644 index b7fb77415..000000000 --- a/langchain/worker.py +++ /dev/null @@ -1,44 +0,0 @@ -import asyncio - -from activities import translate_phrase -from langchain_interceptor import LangChainContextPropagationInterceptor -from temporalio.client import Client -from temporalio.envconfig import ClientConfig -from temporalio.worker import Worker -from workflow import LangChainChildWorkflow, LangChainWorkflow - -interrupt_event = asyncio.Event() - - -async def main(): - config = ClientConfig.load_client_connect_config() - config.setdefault("target_host", "localhost:7233") - client = await Client.connect(**config) - - worker = Worker( - client, - task_queue="langchain-task-queue", - workflows=[LangChainWorkflow, LangChainChildWorkflow], - activities=[translate_phrase], - interceptors=[LangChainContextPropagationInterceptor()], - ) - - print("\nWorker started, ctrl+c to exit\n") - await worker.run() - try: - # Wait indefinitely until the interrupt event is set - await interrupt_event.wait() - finally: - # The worker will be shutdown gracefully due to the async context manager - print("\nShutting down the worker\n") - - -if __name__ == "__main__": - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - try: - loop.run_until_complete(main()) - except KeyboardInterrupt: - print("\nInterrupt received, shutting down...\n") - interrupt_event.set() - loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/langchain/workflow.py b/langchain/workflow.py deleted file mode 100644 index 31861c070..000000000 --- a/langchain/workflow.py +++ /dev/null @@ -1,53 +0,0 @@ -import asyncio -from dataclasses import dataclass -from datetime import timedelta -from typing import List - -from temporalio import workflow - -with workflow.unsafe.imports_passed_through(): - from activities import TranslateParams, translate_phrase - - -@workflow.defn -class LangChainChildWorkflow: - @workflow.run - async def run(self, params: TranslateParams) -> str: - return await workflow.execute_activity( - translate_phrase, - params, - schedule_to_close_timeout=timedelta(seconds=30), - ) - - -@dataclass -class TranslateWorkflowParams: - phrase: str - languages: List[str] - - -@workflow.defn -class LangChainWorkflow: - @workflow.run - async def run(self, params: TranslateWorkflowParams) -> dict: - result1, result2, result3 = await asyncio.gather( - workflow.execute_activity( - translate_phrase, - TranslateParams(params.phrase, params.languages[0]), - schedule_to_close_timeout=timedelta(seconds=30), - ), - workflow.execute_activity( - translate_phrase, - TranslateParams(params.phrase, params.languages[1]), - schedule_to_close_timeout=timedelta(seconds=30), - ), - workflow.execute_child_workflow( - LangChainChildWorkflow.run, - TranslateParams(params.phrase, params.languages[2]), - ), - ) - return { - params.languages[0]: result1, - params.languages[1]: result2, - params.languages[2]: result3, - } diff --git a/langsmith_tracing/README.md b/langsmith_tracing/README.md new file mode 100644 index 000000000..127a80593 --- /dev/null +++ b/langsmith_tracing/README.md @@ -0,0 +1,49 @@ +# LangSmith Tracing + +This sample demonstrates [LangSmith](https://smith.langchain.com/) tracing integration with Temporal workflows using the [`LangSmithPlugin`](https://python.temporal.io/temporalio.contrib.langsmith.html). + +Two examples are included: + +- **[basic/](basic/)** — A one-shot LLM workflow that sends a prompt to OpenAI and returns the response. +- **[chatbot/](chatbot/)** — A long-running conversational workflow with tool calls (save/read notes) and update handlers. + +## Prerequisites + +Install dependencies: + +```bash +uv sync --group langsmith-tracing +``` + +Set environment variables: + +```bash +export OPENAI_API_KEY="sk-..." +export LANGSMITH_API_KEY="lsv2_..." +export LANGCHAIN_TRACING_V2=true +``` + +A local Temporal server must be running (`temporal server start-dev`). + +## Three Layers of Tracing + +This sample shows three complementary ways LangSmith captures trace data: + +1. **Automatic (`wrap_openai`)** — Wrapping the OpenAI client with `wrap_openai()` automatically creates a child span for every LLM call, capturing model parameters, token usage, and latency. No extra code needed. + +2. **Explicit (`@traceable`)** — Decorating functions with `@traceable` creates named spans for your business logic. You control the name, tags, metadata, and `run_type` (chain, llm, tool, retriever). This is how you structure traces to tell a story about what your application is doing. + +3. **Temporal (`add_temporal_runs=True`)** — The `LangSmithPlugin` can optionally create LangSmith runs for each Temporal workflow execution and activity execution, giving visibility into the orchestration layer alongside your LLM calls. + +## `add_temporal_runs` + +By default, `LangSmithPlugin(add_temporal_runs=False)` only propagates LangSmith context so that `@traceable` and `wrap_openai` calls nest correctly. + +Set `add_temporal_runs=True` to also create LangSmith runs for Temporal operations (workflow executions, activity executions, signals, etc.), giving full visibility into the orchestration layer. Both examples support a `--add-temporal-runs` CLI flag to toggle this. + +## Further Reading + +- [LangSmith documentation](https://docs.smith.langchain.com/) +- [Temporal Python SDK LangSmith plugin](https://python.temporal.io/temporalio.contrib.langsmith.html) +- [LangSmith `@traceable` guide](https://docs.smith.langchain.com/observability/how-to/annotate-code) +- [LangSmith `wrap_openai` guide](https://docs.smith.langchain.com/observability/how-to/trace-with-openai) diff --git a/langsmith_tracing/__init__.py b/langsmith_tracing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/langsmith_tracing/basic/README.md b/langsmith_tracing/basic/README.md new file mode 100644 index 000000000..839312e82 --- /dev/null +++ b/langsmith_tracing/basic/README.md @@ -0,0 +1,50 @@ +# Basic LLM Workflow + +A one-shot workflow that sends a prompt to OpenAI and returns the response. A very simple example of LangSmith tracing with Temporal. + +See the [parent README](../README.md) for prerequisites. + +## Running + +```bash +# Terminal 1 — start the worker +uv run --group langsmith-tracing python -m langsmith_tracing.basic.worker + +# Terminal 2 — run the workflow +uv run --group langsmith-tracing python -m langsmith_tracing.basic.starter +``` + +## Trace Structure + +### `add_temporal_runs=False` (default) + +Only `@traceable` and `wrap_openai` spans appear. The client-side `@traceable` is the root, and workflow/activity traces nest under it via context propagation. + +``` +Basic LLM Request (@traceable, client-side) +└── Ask: What is Temporal? (@traceable, workflow) + └── Call OpenAI (@traceable, activity) + └── ChatOpenAI (automatic via wrap_openai) +``` + +### `add_temporal_runs=True` + +Pass `--add-temporal-runs` to both the worker and starter: + +```bash +uv run --group langsmith-tracing python -m langsmith_tracing.basic.worker --add-temporal-runs +uv run --group langsmith-tracing python -m langsmith_tracing.basic.starter --add-temporal-runs +``` + +Temporal operation spans are added. `StartWorkflow`/`RunWorkflow` and `StartActivity`/`RunActivity` appear as sibling pairs: + +``` +Basic LLM Request (@traceable, client-side) +├── StartWorkflow:BasicLLMWorkflow (automatic, Temporal plugin) +└── RunWorkflow:BasicLLMWorkflow (automatic, Temporal plugin) + └── Ask: What is Temporal? (@traceable, workflow) + ├── StartActivity:call_openai (automatic, Temporal plugin) + └── RunActivity:call_openai (automatic, Temporal plugin) + └── Call OpenAI (@traceable, activity) + └── ChatOpenAI (automatic via wrap_openai) +``` diff --git a/langsmith_tracing/basic/__init__.py b/langsmith_tracing/basic/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/langsmith_tracing/basic/activities.py b/langsmith_tracing/basic/activities.py new file mode 100644 index 000000000..e04e17119 --- /dev/null +++ b/langsmith_tracing/basic/activities.py @@ -0,0 +1,33 @@ +"""Basic LLM activity with LangSmith tracing.""" + +from dataclasses import dataclass + +from langsmith import traceable +from langsmith.wrappers import wrap_openai +from openai import AsyncOpenAI +from temporalio import activity + + +@dataclass +class OpenAIRequest: + model: str + input: str + instructions: str = "You are a helpful assistant." + + +@traceable(name="Call OpenAI", run_type="llm") +@activity.defn +async def call_openai(request: OpenAIRequest) -> str: + """Call OpenAI Responses API. Retries handled by Temporal, not the OpenAI client.""" + # wrap_openai patches the client so each API call (e.g. responses.create) + # creates its own child span with model parameters and token usage. + # max_retries=0 disables OpenAI's built-in retries — Temporal's activity + # retry policy handles retries instead, with full visibility in the UI. + client = wrap_openai(AsyncOpenAI(max_retries=0)) + response = await client.responses.create( + model=request.model, + instructions=request.instructions, + input=request.input, + timeout=30, + ) + return response.output_text diff --git a/langsmith_tracing/basic/starter.py b/langsmith_tracing/basic/starter.py new file mode 100644 index 000000000..901ae9b53 --- /dev/null +++ b/langsmith_tracing/basic/starter.py @@ -0,0 +1,54 @@ +"""Starter for the basic LangSmith sample.""" + +import asyncio +import sys + +from langsmith import traceable +from temporalio.client import Client +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.envconfig import ClientConfig + +from langsmith_tracing.basic.workflows import BasicLLMWorkflow + +PROJECT_NAME = "langsmith-basic" + + +async def main(): + add_temporal_runs = "--add-temporal-runs" in sys.argv + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + plugin = LangSmithPlugin( + project_name=PROJECT_NAME, + add_temporal_runs=add_temporal_runs, + ) + + client = await Client.connect( + **config, + plugins=[plugin], + ) + + @traceable( + name="Basic LLM Request", + run_type="chain", + # CRITICAL: Client-side @traceable runs outside the LangSmithPlugin's scope. + # Make sure client-side traces use the same project_name as what is given to + # # the plugin. + project_name=PROJECT_NAME, + tags=["client-side"], + ) + async def run_workflow(prompt: str) -> str: + return await client.execute_workflow( + BasicLLMWorkflow.run, + prompt, + id="langsmith-basic-workflow-id", + task_queue="langsmith-basic-task-queue", + ) + + result = await run_workflow("What is Temporal?") + print(f"Workflow result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langsmith_tracing/basic/worker.py b/langsmith_tracing/basic/worker.py new file mode 100644 index 000000000..140c87da8 --- /dev/null +++ b/langsmith_tracing/basic/worker.py @@ -0,0 +1,49 @@ +"""Worker for the basic LangSmith sample.""" + +import asyncio +import logging +import sys + +from temporalio.client import Client +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from langsmith_tracing.basic.activities import call_openai +from langsmith_tracing.basic.workflows import BasicLLMWorkflow + + +async def main(): + logging.basicConfig(level=logging.INFO) + + # add_temporal_runs=True creates LangSmith runs for each Temporal + # workflow/activity execution. False (default) only propagates context. + add_temporal_runs = "--add-temporal-runs" in sys.argv + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + plugin = LangSmithPlugin( + project_name="langsmith-basic", + add_temporal_runs=add_temporal_runs, + ) + + client = await Client.connect( + **config, + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="langsmith-basic-task-queue", + workflows=[BasicLLMWorkflow], + activities=[call_openai], + ) + + label = "with" if add_temporal_runs else "without" + print(f"Worker started ({label} Temporal runs in traces), ctrl+c to exit") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langsmith_tracing/basic/workflows.py b/langsmith_tracing/basic/workflows.py new file mode 100644 index 000000000..c6cc14b2e --- /dev/null +++ b/langsmith_tracing/basic/workflows.py @@ -0,0 +1,33 @@ +"""Basic one-shot LLM workflow with LangSmith tracing.""" + +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from langsmith import traceable + + from langsmith_tracing.basic.activities import OpenAIRequest, call_openai + + +@workflow.defn +class BasicLLMWorkflow: + # Do not decorate @workflow.run with @traceable — it would violate + # replay safety and produce duplicate or orphaned traces. Instead, + # wrap an inner function. + @workflow.run + async def run(self, prompt: str) -> str: + @traceable( + name=f"Ask: {prompt[:60]}", + run_type="chain", + metadata={"workflow_id": workflow.info().workflow_id}, + tags=["basic-llm"], + ) + async def _run() -> str: + return await workflow.execute_activity( + call_openai, + OpenAIRequest(model="gpt-4o-mini", input=prompt), + start_to_close_timeout=timedelta(seconds=60), + ) + + return await _run() diff --git a/langsmith_tracing/chatbot/README.md b/langsmith_tracing/chatbot/README.md new file mode 100644 index 000000000..b97849892 --- /dev/null +++ b/langsmith_tracing/chatbot/README.md @@ -0,0 +1,81 @@ +# Chatbot with Tool Calls + +A long-running conversational workflow with tool calls and update handlers. Demonstrates how LangSmith traces an agentic loop where the model calls tools across multiple turns. + +See the [parent README](../README.md) for prerequisites. + +## Running + +```bash +# Terminal 1 — start the worker +uv run --group langsmith-tracing python -m langsmith_tracing.chatbot.worker + +# Terminal 2 — interactive CLI +uv run --group langsmith-tracing python -m langsmith_tracing.chatbot.starter +``` + +Commands in the CLI: +- Type a message and press Enter to chat +- `exit` — end the session + +## Tools + +The model has two tools, both implemented as `@traceable` methods on the workflow class: + +- **`save_note(name, content)`** — Stores a note in the workflow's in-memory dict. The note is durable because workflow state survives crashes and restarts via Temporal's event history. +- **`read_note(name)`** — Reads a note from the workflow's in-memory dict. + +## Architecture + +The main `@workflow.run` method runs a loop that processes user messages (calls `_query_openai`, which handles the tool-call loop). The `message_from_user` update handler coordinates: it hands the message to the main loop via a shared `_pending_message` field, then waits for the response. + +This means: +- Activity calls and the tool loop happen inside the main workflow run +- The update handler's trace just shows the input/output of the coordination step + +## Trace Structure + +### `add_temporal_runs=False` (default) + +Only `@traceable` and `wrap_openai` spans appear. The client-side `@traceable` wraps `start_workflow` and each `execute_update`, so both workflow and update traces nest under it via context propagation. + +``` +Chatbot Session a1b2c3d4 (@traceable, client-side) +├── Session Apr 17 10:30 (@traceable, workflow main loop) +│ ├── Request: hello (@traceable, per-message in main loop) +│ │ └── Call OpenAI (@traceable, activity) +│ │ └── ChatOpenAI (automatic via wrap_openai) +│ └── Request: save that as note 15 (@traceable, per-message in main loop) +│ ├── Call OpenAI → function_call: save_note +│ ├── Save Note (@traceable, workflow method) +│ └── Call OpenAI → text response +├── Update: hello (@traceable, update handler) +└── Update: save that as note 15 (@traceable, update handler) +``` + +### `add_temporal_runs=True` + +With `--add-temporal-runs`, Temporal operation spans are added. `StartWorkflow`/`RunWorkflow` and `StartActivity`/`RunActivity` appear as sibling pairs. + +``` +Chatbot Session a1b2c3d4 (@traceable, client-side) +├── StartWorkflow:ChatbotWorkflow (automatic, Temporal plugin) +├── RunWorkflow:ChatbotWorkflow (automatic, Temporal plugin) +│ └── Session Apr 17 10:30 (@traceable, workflow main loop) +│ └── Request: save that as note 15 (@traceable, per-message) +│ ├── StartActivity:call_openai (automatic, Temporal plugin) +│ ├── RunActivity:call_openai (automatic, Temporal plugin) +│ │ └── Call OpenAI (@traceable, activity) +│ │ └── ChatOpenAI (automatic via wrap_openai) +│ ├── Save Note (@traceable, workflow method) +│ ├── StartActivity:call_openai (automatic, Temporal plugin) +│ └── RunActivity:call_openai (automatic, Temporal plugin) +│ └── Call OpenAI (@traceable, activity) +│ └── ChatOpenAI (automatic via wrap_openai) +├── StartWorkflowUpdate:message_from_user (automatic, Temporal plugin) +│ └── HandleUpdate:message_from_user (automatic, Temporal plugin) +│ └── Update: save that as note 15 (@traceable, update handler) +└── ... +``` + +Note that the `Request:` chain (with activity calls) lives under `RunWorkflow` (the main loop), while the `Update:` span lives under `HandleUpdate` (the update handler). They're connected by the shared workflow state but appear as separate subtrees. diff --git a/langsmith_tracing/chatbot/__init__.py b/langsmith_tracing/chatbot/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/langsmith_tracing/chatbot/activities.py b/langsmith_tracing/chatbot/activities.py new file mode 100644 index 000000000..1994f7925 --- /dev/null +++ b/langsmith_tracing/chatbot/activities.py @@ -0,0 +1,45 @@ +"""Chatbot activities with LangSmith tracing.""" + +from dataclasses import dataclass +from typing import Any + +from langsmith import traceable +from langsmith.wrappers import wrap_openai +from openai import AsyncOpenAI +from openai.types.responses import Response +from temporalio import activity + + +@dataclass +class OpenAIRequest: + model: str + # str for the initial user prompt, list for tool call results fed back + input: str | list[dict[str, Any]] + instructions: str = ( + "You are a helpful assistant with note-taking abilities. " + "Use save_note to remember things and read_note to recall them." + ) + tools: list[dict[str, Any]] | None = None + previous_response_id: str | None = None + + +@traceable(name="Call OpenAI", run_type="llm") +@activity.defn +async def call_openai(request: OpenAIRequest) -> Response: + """Call OpenAI Responses API. Retries handled by Temporal, not the OpenAI client.""" + # wrap_openai patches the client so each API call (e.g. responses.create) + # creates its own child span with model parameters and token usage. + # max_retries=0 disables OpenAI's built-in retries — Temporal's activity + # retry policy handles retries instead, with full visibility in the UI. + client = wrap_openai(AsyncOpenAI(max_retries=0)) + response_args: dict[str, Any] = { + "model": request.model, + "instructions": request.instructions, + "input": request.input, + "timeout": 30, + } + if request.tools: + response_args["tools"] = request.tools + if request.previous_response_id: + response_args["previous_response_id"] = request.previous_response_id + return await client.responses.create(**response_args) diff --git a/langsmith_tracing/chatbot/starter.py b/langsmith_tracing/chatbot/starter.py new file mode 100644 index 000000000..4d67e794f --- /dev/null +++ b/langsmith_tracing/chatbot/starter.py @@ -0,0 +1,76 @@ +"""Interactive CLI starter for the chatbot LangSmith sample.""" + +import asyncio +import readline # noqa: F401 — enables input() line editing +import sys +import uuid + +from langsmith import traceable +from temporalio.client import Client +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.contrib.pydantic import pydantic_data_converter +from temporalio.envconfig import ClientConfig + +from langsmith_tracing.chatbot.workflows import ChatbotWorkflow + +PROJECT_NAME = "langsmith-chatbot" + + +async def main(): + add_temporal_runs = "--add-temporal-runs" in sys.argv + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + plugin = LangSmithPlugin( + project_name=PROJECT_NAME, + add_temporal_runs=add_temporal_runs, + ) + + client = await Client.connect( + **config, + data_converter=pydantic_data_converter, + plugins=[plugin], + ) + + wf_id = f"langsmith-chatbot-{uuid.uuid4().hex[:8]}" + + @traceable( + name=f"Chatbot Session {wf_id[-8:]}", + run_type="chain", + # CRITICAL: Client-side @traceable runs outside the LangSmithPlugin's scope. + # Make sure client-side traces use the same project_name as what is given to + # the plugin. + project_name=PROJECT_NAME, + tags=["client-side", "chatbot"], + ) + async def run_session(): + wf_handle = await client.start_workflow( + ChatbotWorkflow.run, + id=wf_id, + task_queue="langsmith-chatbot-task-queue", + ) + print(f"Started workflow: {wf_handle.id}") + print('Type a message or "exit" to quit.\n') + + while True: + user_input = input("> ").strip() + if not user_input: + continue + + if user_input.lower() == "exit": + await wf_handle.signal(ChatbotWorkflow.exit) + result = await wf_handle.result() + print(f"\nWorkflow finished: {result}") + return + + response = await wf_handle.execute_update( + ChatbotWorkflow.message_from_user, user_input + ) + print(f"Bot: {response}\n") + + await run_session() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langsmith_tracing/chatbot/worker.py b/langsmith_tracing/chatbot/worker.py new file mode 100644 index 000000000..0f43e7c9a --- /dev/null +++ b/langsmith_tracing/chatbot/worker.py @@ -0,0 +1,49 @@ +"""Worker for the chatbot LangSmith sample.""" + +import asyncio +import logging +import sys + +from temporalio.client import Client +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.contrib.pydantic import pydantic_data_converter +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from langsmith_tracing.chatbot.activities import call_openai +from langsmith_tracing.chatbot.workflows import ChatbotWorkflow + + +async def main(): + logging.basicConfig(level=logging.INFO) + + add_temporal_runs = "--add-temporal-runs" in sys.argv + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + client = await Client.connect( + **config, + data_converter=pydantic_data_converter, + plugins=[ + LangSmithPlugin( + project_name="langsmith-chatbot", + add_temporal_runs=add_temporal_runs, + ) + ], + ) + + worker = Worker( + client, + task_queue="langsmith-chatbot-task-queue", + workflows=[ChatbotWorkflow], + activities=[call_openai], + ) + + label = "with" if add_temporal_runs else "without" + print(f"Worker started ({label} Temporal runs in traces), ctrl+c to exit") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langsmith_tracing/chatbot/workflows.py b/langsmith_tracing/chatbot/workflows.py new file mode 100644 index 000000000..4b25df501 --- /dev/null +++ b/langsmith_tracing/chatbot/workflows.py @@ -0,0 +1,178 @@ +"""Conversational chatbot workflow with tool loop and LangSmith tracing.""" + +import json +from datetime import timedelta + +from temporalio import workflow +from temporalio.common import RetryPolicy + +with workflow.unsafe.imports_passed_through(): + from langsmith import traceable + + from langsmith_tracing.chatbot.activities import OpenAIRequest, call_openai + +TOOLS = [ + { + "type": "function", + "name": "save_note", + "description": "Save or update a note.", + "parameters": { + "type": "object", + "properties": { + "name": {"type": "string", "description": "Name/key for the note."}, + "content": { + "type": "string", + "description": "Content of the note.", + }, + }, + "required": ["name", "content"], + }, + }, + { + "type": "function", + "name": "read_note", + "description": "Read a previously saved note.", + "parameters": { + "type": "object", + "properties": { + "name": { + "type": "string", + "description": "Name/key of the note to read.", + }, + }, + "required": ["name"], + }, + }, +] + + +@workflow.defn +class ChatbotWorkflow: + def __init__(self): + self._pending_message: str | None = None + self._last_response: str = "" + self._previous_response_id: str | None = None + self._notes: dict[str, str] = {} + self._done = False + + @workflow.signal + async def exit(self) -> None: + self._done = True + + @workflow.query + def notes(self) -> dict[str, str]: + return dict(self._notes) + + @workflow.update + async def message_from_user(self, message: str) -> str: + """Hand the message to the main loop and wait for its response.""" + + # Inner @traceable captures the message as input and response as output. + @traceable(name=f"Update: {message[:60]}", run_type="chain") + async def _traced(msg: str) -> str: + # Wait until any previous message has finished processing + await workflow.wait_condition(lambda: self._pending_message is None) + self._pending_message = msg + # Main loop sets _last_response, then clears _pending_message to signal done + await workflow.wait_condition(lambda: self._pending_message is None) + return self._last_response + + return await _traced(message) + + # Do not decorate @workflow.run with @traceable — it would violate + # replay safety and produce duplicate or orphaned traces. Instead, + # wrap an inner function, eg. self._run_inner() in this case. + @workflow.run + async def run(self) -> str: + # Alternative to @traceable decorator: call traceable() as a function + # for dynamic trace names that depend on runtime values. + now = workflow.now().strftime("%b %d %H:%M") + return await traceable( + name=f"Session {now}", + run_type="chain", + metadata={"workflow_id": workflow.info().workflow_id}, + tags=["chatbot-session"], + )(self._run_inner)() + + async def _run_inner(self) -> str: + while not self._done: + await workflow.wait_condition( + lambda: self._pending_message is not None or self._done + ) + if self._done: + break + assert self._pending_message is not None + message = self._pending_message + self._last_response = await self._query_openai(message) + # Clear pending_message AFTER setting the response so the update + # handler reads the correct response when its wait_condition fires. + self._pending_message = None + + return "Session ended." + + # Unlike @workflow.run, other workflow methods can be decorated with + # @traceable directly — they run inside the plugin-managed context + # where trace I/O is handled safely during replay. + @traceable(name="Save Note", run_type="tool") + def _save_note(self, name: str, content: str) -> str: + self._notes[name] = content + return f"Saved note '{name}'." + + @traceable(name="Read Note", run_type="tool") + def _read_note(self, name: str) -> str: + return self._notes.get(name, "Note not found.") + + async def _query_openai(self, message: str) -> str: + @traceable( + name=f"Request: {message[:60]}", + run_type="chain", + tags=["user-message"], + ) + async def _traced(): + input_for_next: str | list = message + while True: + response = await workflow.execute_activity( + call_openai, + OpenAIRequest( + model="gpt-4o-mini", + input=input_for_next, + tools=TOOLS, + previous_response_id=self._previous_response_id, + ), + start_to_close_timeout=timedelta(seconds=60), + retry_policy=RetryPolicy( + initial_interval=timedelta(seconds=2), maximum_attempts=3 + ), + ) + self._previous_response_id = response.id + + tool_results = [] + for item in response.output: + if item.type != "function_call": + continue + args = json.loads(item.arguments) + if item.name == "save_note": + result = self._save_note(args["name"], args["content"]) + tool_results.append( + { + "type": "function_call_output", + "call_id": item.call_id, + "output": result, + } + ) + elif item.name == "read_note": + result = self._read_note(args["name"]) + tool_results.append( + { + "type": "function_call_output", + "call_id": item.call_id, + "output": result, + } + ) + + if not tool_results: + return response.output_text or "Done." + + input_for_next = tool_results + + return await _traced() diff --git a/pyproject.toml b/pyproject.toml index f65ff1c29..0927eea3e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,14 +30,10 @@ bedrock = ["boto3>=1.34.92,<2"] dsl = ["pyyaml>=6.0.1,<7", "types-pyyaml>=6.0.12,<7", "dacite>=1.8.1,<2"] encryption = ["cryptography>=38.0.1,<39", "aiohttp>=3.8.1,<4"] gevent = ["gevent>=25.4.2 ; python_version >= '3.8'"] -langchain = [ - "langchain>=1.2.15,<2 ; python_version >= '3.8.1' and python_version < '4.0'", - "langchain-openai>=1.1.13,<2 ; python_version >= '3.8.1' and python_version < '4.0'", - "langsmith>=0.3.45,<0.4 ; python_version >= '3.8.1' and python_version < '4.0'", - "openai>=2.0.0,<3", - "fastapi>=0.115.12", - "tqdm>=4.62.0,<5", - "uvicorn[standard]>=0.31.0,<0.32.0", +langsmith-tracing = [ + "openai>=1.4.0", + "langsmith>=0.7.0", + "temporalio[pydantic,langsmith]>=1.26.0", ] nexus = ["nexus-rpc>=1.1.0,<2"] open-telemetry = [ @@ -79,7 +75,7 @@ packages = [ "encryption", "gevent_async", "hello", - "langchain", + "langsmith_tracing", "message_passing", "nexus", "open_telemetry", diff --git a/tests/langsmith_tracing/__init__.py b/tests/langsmith_tracing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/langsmith_tracing/helpers.py b/tests/langsmith_tracing/helpers.py new file mode 100644 index 000000000..6742a60fa --- /dev/null +++ b/tests/langsmith_tracing/helpers.py @@ -0,0 +1,33 @@ +"""Shared test helpers for LangSmith tracing tests.""" + +from openai.types.responses import Response +from openai.types.responses.response_output_message import ResponseOutputMessage +from openai.types.responses.response_output_text import ResponseOutputText + + +def make_text_response(text: str) -> Response: + """Build a minimal OpenAI Response with a text output.""" + return Response.model_construct( + id="resp_mock", + created_at=0.0, + model="gpt-4o-mini", + object="response", + output=[ + ResponseOutputMessage.model_construct( + id="msg_mock", + type="message", + role="assistant", + status="completed", + content=[ + ResponseOutputText.model_construct( + type="output_text", + text=text, + annotations=[], + ) + ], + ) + ], + parallel_tool_calls=False, + tool_choice="auto", + tools=[], + ) diff --git a/tests/langsmith_tracing/test_basic.py b/tests/langsmith_tracing/test_basic.py new file mode 100644 index 000000000..b5be2f353 --- /dev/null +++ b/tests/langsmith_tracing/test_basic.py @@ -0,0 +1,34 @@ +import uuid + +from temporalio import activity +from temporalio.client import Client +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from langsmith_tracing.basic.activities import OpenAIRequest +from langsmith_tracing.basic.workflows import BasicLLMWorkflow + + +async def test_basic_workflow(client: Client, env: WorkflowEnvironment): + expected_text = "Temporal is a durable execution platform." + + @activity.defn(name="call_openai") + async def mock_call_openai(request: OpenAIRequest) -> str: + return expected_text + + async with Worker( + client, + task_queue="test-langsmith-basic", + workflows=[BasicLLMWorkflow], + activities=[mock_call_openai], + plugins=[LangSmithPlugin()], + ): + result = await client.execute_workflow( + BasicLLMWorkflow.run, + "What is Temporal?", + id=f"test-basic-{uuid.uuid4().hex[:8]}", + task_queue="test-langsmith-basic", + ) + + assert result == expected_text diff --git a/tests/langsmith_tracing/test_chatbot.py b/tests/langsmith_tracing/test_chatbot.py new file mode 100644 index 000000000..f7845217a --- /dev/null +++ b/tests/langsmith_tracing/test_chatbot.py @@ -0,0 +1,132 @@ +import json +import uuid + +from openai.types.responses import Response +from openai.types.responses.response_function_tool_call import ( + ResponseFunctionToolCall, +) +from temporalio import activity +from temporalio.client import Client +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from langsmith_tracing.chatbot.activities import OpenAIRequest +from langsmith_tracing.chatbot.workflows import ChatbotWorkflow +from tests.langsmith_tracing.helpers import make_text_response + + +def _make_function_call_response( + name: str, arguments: dict, call_id: str = "call_123" +) -> Response: + return Response.model_construct( + id="resp_tool", + created_at=0.0, + model="gpt-4o-mini", + object="response", + output=[ + ResponseFunctionToolCall.model_construct( + id="fc_mock", + type="function_call", + name=name, + arguments=json.dumps(arguments), + call_id=call_id, + status="completed", + ) + ], + parallel_tool_calls=False, + tool_choice="auto", + tools=[], + ) + + +async def test_chatbot_save_note(client: Client, env: WorkflowEnvironment): + """Test save_note tool call loop — save_note runs as a workflow method.""" + call_count = 0 + + @activity.defn(name="call_openai") + async def mock_call_openai(request: OpenAIRequest) -> Response: + nonlocal call_count + call_count += 1 + if call_count == 1: + return _make_function_call_response( + name="save_note", + arguments={"name": "greeting", "content": "Hello world"}, + ) + return make_text_response("Note saved successfully!") + + async with Worker( + client, + task_queue="test-langsmith-chatbot", + workflows=[ChatbotWorkflow], + activities=[mock_call_openai], + plugins=[LangSmithPlugin()], + ): + wf_handle = await client.start_workflow( + ChatbotWorkflow.run, + id=f"test-chatbot-{uuid.uuid4().hex[:8]}", + task_queue="test-langsmith-chatbot", + ) + + response = await wf_handle.execute_update( + ChatbotWorkflow.message_from_user, "Save a note" + ) + assert response == "Note saved successfully!" + + notes = await wf_handle.query(ChatbotWorkflow.notes) + assert notes == {"greeting": "Hello world"} + + await wf_handle.signal(ChatbotWorkflow.exit) + result = await wf_handle.result() + assert result == "Session ended." + + +async def test_chatbot_read_note(client: Client, env: WorkflowEnvironment): + """Test read_note tool call loop — read_note runs as a workflow method.""" + call_count = 0 + + @activity.defn(name="call_openai") + async def mock_call_openai(request: OpenAIRequest) -> Response: + nonlocal call_count + call_count += 1 + if call_count == 1: + return _make_function_call_response( + name="save_note", + arguments={"name": "todo", "content": "Buy milk"}, + call_id="call_save", + ) + if call_count == 2: + return make_text_response("Saved your todo!") + if call_count == 3: + return _make_function_call_response( + name="read_note", + arguments={"name": "todo"}, + call_id="call_read", + ) + return make_text_response("Your todo says: Buy milk") + + async with Worker( + client, + task_queue="test-langsmith-chatbot-read", + workflows=[ChatbotWorkflow], + activities=[mock_call_openai], + plugins=[LangSmithPlugin()], + ): + wf_handle = await client.start_workflow( + ChatbotWorkflow.run, + id=f"test-chatbot-read-{uuid.uuid4().hex[:8]}", + task_queue="test-langsmith-chatbot-read", + ) + + response = await wf_handle.execute_update( + ChatbotWorkflow.message_from_user, "Save my todo" + ) + assert response == "Saved your todo!" + + response = await wf_handle.execute_update( + ChatbotWorkflow.message_from_user, "Read my todo" + ) + assert response == "Your todo says: Buy milk" + + await wf_handle.signal(ChatbotWorkflow.exit) + await wf_handle.result() diff --git a/uv.lock b/uv.lock index 4a22edeab..3c9990eb9 100644 --- a/uv.lock +++ b/uv.lock @@ -3,8 +3,7 @@ revision = 3 requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.13'", - "python_full_version >= '3.12.4' and python_full_version < '3.13'", - "python_full_version >= '3.12' and python_full_version < '3.12.4'", + "python_full_version == '3.12.*'", "python_full_version == '3.11.*'", "python_full_version < '3.11'", ] @@ -388,20 +387,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/36/f4/c6e662dade71f56cd2f3735141b265c3c79293c109549c1e6933b0651ffc/exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10", size = 16674, upload-time = 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upload-time = "2025-09-14T22:18:19.088Z" }, ] From ba9e9a1147dce9ce20da4c2b398577057d37ec3b Mon Sep 17 00:00:00 2001 From: xumaple <45406854+xumaple@users.noreply.github.com> Date: Thu, 23 Apr 2026 12:11:57 -0400 Subject: [PATCH 15/40] LangSmith sample: starter cleanup, plugin on Worker, chatbot Response decoupling (#295) * Clean up LangSmith sample per PR #292 review - basic/starter.py: move `@traceable` decorator directly onto `main`, removing the nested `run_workflow` closure (addresses reviewer comment) - basic/worker.py, chatbot/worker.py: move `LangSmithPlugin` from the `Client` to the `Worker`, matching our recommended pattern (plugin on the worker in worker code; on the client in client code) * Return workflow result from basic starter's main @traceable captures the decorated function's return value as the LangSmith trace output, so implicitly returning None left the trace's output field empty. Return `result` (and annotate the return type) so the trace shows the workflow response. * Return minimal ChatResponse from chatbot call_openai activity The activity previously returned `openai.types.responses.Response` directly. The OpenAI API currently returns `"prompt_cache_retention": "in_memory"` (underscore), but openai SDK v2.32.0 declares that field as `Literal["in-memory", "24h"]`. The openai client parses laxly so the activity succeeds, but Temporal's `pydantic_data_converter` uses strict `TypeAdapter(Response).validate_json` on the way into the workflow and rejects the underscore value, failing every workflow task. Define minimal `ChatResponse` and `ToolCall` pydantic models in `activities.py` exposing only the fields the workflow uses (id, output_text, tool_calls). The activity projects the openai Response down to this shape so the sample is no longer coupled to SDK drift in fields it doesn't use. Update the workflow loop to iterate `response.tool_calls` directly and the test mocks/helpers to build `ChatResponse` instead of constructing openai Response objects. --- langsmith_tracing/basic/starter.py | 34 ++++++++++++------------- langsmith_tracing/basic/worker.py | 6 ++--- langsmith_tracing/chatbot/activities.py | 27 +++++++++++++++++--- langsmith_tracing/chatbot/worker.py | 12 ++++----- langsmith_tracing/chatbot/workflows.py | 14 +++++----- tests/langsmith_tracing/helpers.py | 33 +++--------------------- tests/langsmith_tracing/test_chatbot.py | 29 ++++++--------------- 7 files changed, 66 insertions(+), 89 deletions(-) diff --git a/langsmith_tracing/basic/starter.py b/langsmith_tracing/basic/starter.py index 901ae9b53..4a2a103a4 100644 --- a/langsmith_tracing/basic/starter.py +++ b/langsmith_tracing/basic/starter.py @@ -13,7 +13,16 @@ PROJECT_NAME = "langsmith-basic" -async def main(): +@traceable( + name="Basic LLM Request", + run_type="chain", + # CRITICAL: Client-side @traceable runs outside the LangSmithPlugin's scope. + # Make sure client-side traces use the same project_name as what is given to + # the plugin. + project_name=PROJECT_NAME, + tags=["client-side"], +) +async def main() -> str: add_temporal_runs = "--add-temporal-runs" in sys.argv config = ClientConfig.load_client_connect_config() @@ -29,25 +38,14 @@ async def main(): plugins=[plugin], ) - @traceable( - name="Basic LLM Request", - run_type="chain", - # CRITICAL: Client-side @traceable runs outside the LangSmithPlugin's scope. - # Make sure client-side traces use the same project_name as what is given to - # # the plugin. - project_name=PROJECT_NAME, - tags=["client-side"], + result = await client.execute_workflow( + BasicLLMWorkflow.run, + "What is Temporal?", + id="langsmith-basic-workflow-id", + task_queue="langsmith-basic-task-queue", ) - async def run_workflow(prompt: str) -> str: - return await client.execute_workflow( - BasicLLMWorkflow.run, - prompt, - id="langsmith-basic-workflow-id", - task_queue="langsmith-basic-task-queue", - ) - - result = await run_workflow("What is Temporal?") print(f"Workflow result: {result}") + return result if __name__ == "__main__": diff --git a/langsmith_tracing/basic/worker.py b/langsmith_tracing/basic/worker.py index 140c87da8..941b28c57 100644 --- a/langsmith_tracing/basic/worker.py +++ b/langsmith_tracing/basic/worker.py @@ -28,16 +28,14 @@ async def main(): add_temporal_runs=add_temporal_runs, ) - client = await Client.connect( - **config, - plugins=[plugin], - ) + client = await Client.connect(**config) worker = Worker( client, task_queue="langsmith-basic-task-queue", workflows=[BasicLLMWorkflow], activities=[call_openai], + plugins=[plugin], ) label = "with" if add_temporal_runs else "without" diff --git a/langsmith_tracing/chatbot/activities.py b/langsmith_tracing/chatbot/activities.py index 1994f7925..5e979320e 100644 --- a/langsmith_tracing/chatbot/activities.py +++ b/langsmith_tracing/chatbot/activities.py @@ -6,10 +6,22 @@ from langsmith import traceable from langsmith.wrappers import wrap_openai from openai import AsyncOpenAI -from openai.types.responses import Response +from pydantic import BaseModel, Field from temporalio import activity +class ToolCall(BaseModel): + call_id: str + name: str + arguments: str + + +class ChatResponse(BaseModel): + id: str + output_text: str = "" + tool_calls: list[ToolCall] = Field(default_factory=list) + + @dataclass class OpenAIRequest: model: str @@ -25,7 +37,7 @@ class OpenAIRequest: @traceable(name="Call OpenAI", run_type="llm") @activity.defn -async def call_openai(request: OpenAIRequest) -> Response: +async def call_openai(request: OpenAIRequest) -> ChatResponse: """Call OpenAI Responses API. Retries handled by Temporal, not the OpenAI client.""" # wrap_openai patches the client so each API call (e.g. responses.create) # creates its own child span with model parameters and token usage. @@ -42,4 +54,13 @@ async def call_openai(request: OpenAIRequest) -> Response: response_args["tools"] = request.tools if request.previous_response_id: response_args["previous_response_id"] = request.previous_response_id - return await client.responses.create(**response_args) + response = await client.responses.create(**response_args) + return ChatResponse( + id=response.id, + output_text=response.output_text or "", + tool_calls=[ + ToolCall(call_id=item.call_id, name=item.name, arguments=item.arguments) + for item in response.output + if item.type == "function_call" + ], + ) diff --git a/langsmith_tracing/chatbot/worker.py b/langsmith_tracing/chatbot/worker.py index 0f43e7c9a..b2bd2c981 100644 --- a/langsmith_tracing/chatbot/worker.py +++ b/langsmith_tracing/chatbot/worker.py @@ -22,15 +22,14 @@ async def main(): config = ClientConfig.load_client_connect_config() config.setdefault("target_host", "localhost:7233") + plugin = LangSmithPlugin( + project_name="langsmith-chatbot", + add_temporal_runs=add_temporal_runs, + ) + client = await Client.connect( **config, data_converter=pydantic_data_converter, - plugins=[ - LangSmithPlugin( - project_name="langsmith-chatbot", - add_temporal_runs=add_temporal_runs, - ) - ], ) worker = Worker( @@ -38,6 +37,7 @@ async def main(): task_queue="langsmith-chatbot-task-queue", workflows=[ChatbotWorkflow], activities=[call_openai], + plugins=[plugin], ) label = "with" if add_temporal_runs else "without" diff --git a/langsmith_tracing/chatbot/workflows.py b/langsmith_tracing/chatbot/workflows.py index 4b25df501..47205611c 100644 --- a/langsmith_tracing/chatbot/workflows.py +++ b/langsmith_tracing/chatbot/workflows.py @@ -147,25 +147,23 @@ async def _traced(): self._previous_response_id = response.id tool_results = [] - for item in response.output: - if item.type != "function_call": - continue - args = json.loads(item.arguments) - if item.name == "save_note": + for tc in response.tool_calls: + args = json.loads(tc.arguments) + if tc.name == "save_note": result = self._save_note(args["name"], args["content"]) tool_results.append( { "type": "function_call_output", - "call_id": item.call_id, + "call_id": tc.call_id, "output": result, } ) - elif item.name == "read_note": + elif tc.name == "read_note": result = self._read_note(args["name"]) tool_results.append( { "type": "function_call_output", - "call_id": item.call_id, + "call_id": tc.call_id, "output": result, } ) diff --git a/tests/langsmith_tracing/helpers.py b/tests/langsmith_tracing/helpers.py index 6742a60fa..afd3d1c4f 100644 --- a/tests/langsmith_tracing/helpers.py +++ b/tests/langsmith_tracing/helpers.py @@ -1,33 +1,8 @@ """Shared test helpers for LangSmith tracing tests.""" -from openai.types.responses import Response -from openai.types.responses.response_output_message import ResponseOutputMessage -from openai.types.responses.response_output_text import ResponseOutputText +from langsmith_tracing.chatbot.activities import ChatResponse -def make_text_response(text: str) -> Response: - """Build a minimal OpenAI Response with a text output.""" - return Response.model_construct( - id="resp_mock", - created_at=0.0, - model="gpt-4o-mini", - object="response", - output=[ - ResponseOutputMessage.model_construct( - id="msg_mock", - type="message", - role="assistant", - status="completed", - content=[ - ResponseOutputText.model_construct( - type="output_text", - text=text, - annotations=[], - ) - ], - ) - ], - parallel_tool_calls=False, - tool_choice="auto", - tools=[], - ) +def make_text_response(text: str) -> ChatResponse: + """Build a minimal ChatResponse with a text output.""" + return ChatResponse(id="resp_mock", output_text=text) diff --git a/tests/langsmith_tracing/test_chatbot.py b/tests/langsmith_tracing/test_chatbot.py index f7845217a..2febb654d 100644 --- a/tests/langsmith_tracing/test_chatbot.py +++ b/tests/langsmith_tracing/test_chatbot.py @@ -1,42 +1,29 @@ import json import uuid -from openai.types.responses import Response -from openai.types.responses.response_function_tool_call import ( - ResponseFunctionToolCall, -) from temporalio import activity from temporalio.client import Client from temporalio.contrib.langsmith import LangSmithPlugin from temporalio.testing import WorkflowEnvironment from temporalio.worker import Worker -from langsmith_tracing.chatbot.activities import OpenAIRequest +from langsmith_tracing.chatbot.activities import ChatResponse, OpenAIRequest, ToolCall from langsmith_tracing.chatbot.workflows import ChatbotWorkflow from tests.langsmith_tracing.helpers import make_text_response def _make_function_call_response( name: str, arguments: dict, call_id: str = "call_123" -) -> Response: - return Response.model_construct( +) -> ChatResponse: + return ChatResponse( id="resp_tool", - created_at=0.0, - model="gpt-4o-mini", - object="response", - output=[ - ResponseFunctionToolCall.model_construct( - id="fc_mock", - type="function_call", + tool_calls=[ + ToolCall( + call_id=call_id, name=name, arguments=json.dumps(arguments), - call_id=call_id, - status="completed", ) ], - parallel_tool_calls=False, - tool_choice="auto", - tools=[], ) @@ -45,7 +32,7 @@ async def test_chatbot_save_note(client: Client, env: WorkflowEnvironment): call_count = 0 @activity.defn(name="call_openai") - async def mock_call_openai(request: OpenAIRequest) -> Response: + async def mock_call_openai(request: OpenAIRequest) -> ChatResponse: nonlocal call_count call_count += 1 if call_count == 1: @@ -86,7 +73,7 @@ async def test_chatbot_read_note(client: Client, env: WorkflowEnvironment): call_count = 0 @activity.defn(name="call_openai") - async def mock_call_openai(request: OpenAIRequest) -> Response: + async def mock_call_openai(request: OpenAIRequest) -> ChatResponse: nonlocal call_count call_count += 1 if call_count == 1: From 57b104e675582c2906946e43e66d28bcb1bc324e Mon Sep 17 00:00:00 2001 From: Alex Mazzeo Date: Mon, 27 Apr 2026 14:47:05 -0700 Subject: [PATCH 16/40] Nexus samples: use business IDs for workflow IDs (#297) * Nexus samples: use business IDs for workflow IDs. Stop using the Nexus request ID (nexus_cancel) or bare uuid4 (hello_nexus, nexus_multiple_args) as the backing workflow ID. Build a meaningful business ID from the operation input and append a uuid4 suffix for uniqueness. Co-Authored-By: Claude Opus 4.7 (1M context) * remove UUID from the nexus sample workflow ids to better communicate intent --------- Co-authored-by: Claude Opus 4.7 (1M context) --- hello_nexus/handler/service_handler.py | 4 +--- nexus_cancel/handler/service_handler.py | 7 ++++--- nexus_multiple_args/handler/service_handler.py | 4 +--- 3 files changed, 6 insertions(+), 9 deletions(-) diff --git a/hello_nexus/handler/service_handler.py b/hello_nexus/handler/service_handler.py index 1295abd1f..812a5c5d7 100644 --- a/hello_nexus/handler/service_handler.py +++ b/hello_nexus/handler/service_handler.py @@ -4,8 +4,6 @@ from __future__ import annotations -import uuid - import nexusrpc from temporalio import nexus @@ -33,7 +31,7 @@ async def my_workflow_run_operation( return await ctx.start_workflow( WorkflowStartedByNexusOperation.run, input, - id=str(uuid.uuid4()), + id=f"hello-nexus-workflow-{input.name}", ) # This is a Nexus operation that responds synchronously to all requests. That means diff --git a/nexus_cancel/handler/service_handler.py b/nexus_cancel/handler/service_handler.py index 92868510c..5d75e8a6f 100644 --- a/nexus_cancel/handler/service_handler.py +++ b/nexus_cancel/handler/service_handler.py @@ -1,8 +1,9 @@ """ Nexus service handler for the cancellation sample. -The hello operation is backed by a workflow, using the Nexus request ID as the -workflow ID for idempotency across retries. +The hello operation is backed by a workflow whose ID is derived from the +operation input (name + language), giving each fan-out branch a distinct, +meaningful business ID. """ from __future__ import annotations @@ -23,5 +24,5 @@ async def hello( return await ctx.start_workflow( HelloHandlerWorkflow.run, input, - id=ctx.request_id, + id=f"hello-handler-{input.name}-{input.language.name}", ) diff --git a/nexus_multiple_args/handler/service_handler.py b/nexus_multiple_args/handler/service_handler.py index c2ddfb921..8c00ee310 100644 --- a/nexus_multiple_args/handler/service_handler.py +++ b/nexus_multiple_args/handler/service_handler.py @@ -1,7 +1,5 @@ from __future__ import annotations -import uuid - import nexusrpc from temporalio import nexus @@ -32,7 +30,7 @@ async def hello( input.name, # First argument: name input.language, # Second argument: language ], - id=str(uuid.uuid4()), + id=f"hello-multi-args-{input.name}-{input.language}", ) From cd2a8c5d7b0f8a8b5f95726489ede8b6fdfed6d8 Mon Sep 17 00:00:00 2001 From: Justin Anderson <44687433+jmaeagle99@users.noreply.github.com> Date: Fri, 1 May 2026 11:34:38 -0700 Subject: [PATCH 17/40] Update SDK to 1.27.0 (#302) --- pyproject.toml | 6 +++--- uv.lock | 20 ++++++++++---------- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 0927eea3e..404f46ad5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] requires-python = ">=3.10" readme = "README.md" license = "MIT" -dependencies = ["temporalio>=1.26.0,<2"] +dependencies = ["temporalio>=1.27.0,<2"] [project.urls] Homepage = "https://github.com/temporalio/samples-python" @@ -33,7 +33,7 @@ gevent = ["gevent>=25.4.2 ; python_version >= '3.8'"] langsmith-tracing = [ "openai>=1.4.0", "langsmith>=0.7.0", - 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{ name = "temporalio", extras = ["pydantic", "langsmith"], specifier = ">=1.26.0" }, + { name = "temporalio", extras = ["pydantic", "langsmith"], specifier = ">=1.27.0" }, ] nexus = [{ name = "nexus-rpc", specifier = ">=1.1.0,<2" }] open-telemetry = [ @@ -2657,7 +2657,7 @@ open-telemetry = [ openai-agents = [ { name = "openai-agents", extras = ["litellm"], specifier = ">=0.14.1" }, { name = "requests", specifier = ">=2.32.0,<3" }, - { name = "temporalio", extras = ["openai-agents", "opentelemetry"], specifier = ">=1.26.0" }, + { name = "temporalio", extras = ["openai-agents", "opentelemetry"], specifier = ">=1.27.0" }, ] pydantic-converter = [{ name = "pydantic", specifier = ">=2.10.6,<3" }] sentry = [{ name = "sentry-sdk", specifier = ">=2.13.0" }] From 118083bab50d78f6c34cbd49415268258ce9f64c Mon Sep 17 00:00:00 2001 From: Johann Schleier-Smith Date: Mon, 4 May 2026 09:53:26 -0700 Subject: [PATCH 18/40] Add workflow_streams samples (#300) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add workflow_streams samples: order_workflow scenario Initial samples directory for temporalio.contrib.workflow_streams, the workflow-hosted durable event stream contrib (experimental, contrib/pubsub branch of sdk-python). The order_workflow scenario covers the basic publisher path: a workflow binds a typed topic in @workflow.init, an activity publishes events via the topic handle, and a starter subscribes with WorkflowStreamClient and prints events as they arrive. Also enables the uv supply-chain cooldown options in the lockfile. * samples: workflow_stream: add reconnecting-subscriber scenario Adds a second scenario demonstrating the central Workflow Streams use case: a consumer disconnects mid-stream and resumes later via subscribe(from_offset=...), with no events lost or duplicated. The existing OrderWorkflow finishes too quickly to make the pattern visible, so this introduces a multi-stage PipelineWorkflow paced with workflow.sleep between stages. The runner reads a couple of events, persists item.offset + 1 to a temp file, sleeps "disconnected" while the workflow keeps publishing, then opens a fresh Client + WorkflowStreamClient and resumes from the persisted offset — the same shape that works across actual process restarts. Co-Authored-By: Claude Opus 4.7 (1M context) * samples: workflow_stream: add external-publisher scenario Adds a third scenario covering the third publisher shape: a backend service or scheduled job pushing events into a workflow it didn't itself start. The earlier scenarios publish either from inside the workflow or from one of its activities; this one uses WorkflowStreamClient.create() externally. HubWorkflow is a passive stream host — it does no work of its own and just waits to be told to close, fitting the event-bus pattern. The runner publishes a series of news headlines, runs a subscriber task alongside, signals close, and exits when both tasks complete. Co-Authored-By: Claude Opus 4.7 (1M context) * samples: workflow_stream: add truncating-ticker scenario Adds a fourth scenario for long-running workflows that need to bound their event log: the workflow publishes events at a fixed cadence and calls self.stream.truncate(...) periodically to keep only the most recent entries. The runner subscribes twice — fast and slow — to make the trade visible: the fast subscriber sees every offset in order; the slow one falls behind a truncation, has its iterator transparently jump forward to the new base offset, and shows the offset gap that intermediate events fell into. This is the model for high-volume long-running streams: bounded log size, slow consumers may miss intermediate events but always see the most recent state. Co-Authored-By: Claude Opus 4.7 (1M context) * samples: rename workflow_stream → workflow_streams; migrate to topic handles - Directory and module path renamed to plural to match sdk-python `temporalio.contrib.workflow_streams` rename. - Workflow-side: bind a typed topic handle in `@workflow.init` and call `topic.publish(value)` — the removed `WorkflowStream.publish` form is gone. Same change applied to the activity and external-publisher. - Activity: `WorkflowStreamClient.from_activity()` → `from_within_activity()`. Co-Authored-By: Claude Opus 4.7 (1M context) * samples: workflow_streams review polish - README: fix scenario count (two -> four), document subscriber start position and continue-as-new semantics for stream_state - hub_workflow: drop stale comment referencing a README race note that does not exist in this sample - payment_activity: trim long publisher_id/dedup caveat — moved out of the first sample's docstring to keep it approachable * workflow_streams: deliver terminal events + fix run_publisher subscribe shape End-to-end runs of the four workflow_streams scenarios surfaced two sample-side issues, both fixed here. run_publisher's consumer asserted ``isinstance(item.data, Payload)`` and called ``payload_converter.from_payload(item.data, T)``. The contrib's ``subscribe()`` defaults to converter-decoded data, not raw payloads, so this assertion fired on the first run. Switch to ``result_type=RawValue`` (the documented escape hatch for heterogeneous topics) and read ``item.data.payload``. Items published in the same workflow task that returns from ``@workflow.run`` were not delivered to subscribers — the in-memory log dies with the workflow and the next subscriber poll lands on a completed workflow. Fix: each scenario now uses an in-band terminator that subscribers break on, and each workflow holds the run open with ``await workflow.sleep(timedelta(milliseconds=500))`` so that final publish is fetched before the workflow exits: - OrderWorkflow / PipelineWorkflow: the workflow's own ``StatusEvent(kind="complete")`` / ``StageEvent(stage="complete")`` is the terminator (consumers already broke on it). - HubWorkflow: the *publisher* in run_external_publisher emits a sentinel ``NewsEvent(headline="__done__")`` immediately before signaling close; the consumer breaks on the sentinel. - TickerWorkflow: the final tick (n == count - 1) is the terminator; ``keep_last`` guarantees that offset survives the last truncation, so even slow consumers reach it. Because subscribers stop polling on the terminator, by the time ``workflow.run`` returns there are no in-flight poll handlers — no ``UnfinishedUpdateHandlersWarning`` from the SDK and no need for ``detach_pollers()`` / ``wait_condition(all_handlers_finished)`` in the workflow exit path. Two consecutive end-to-end runs of all four scenarios pass cleanly against ``temporal server start-dev --headless``. * workflow_streams README: document the stream-end pattern Subscribers don't exit on their own when the host workflow completes — they need an in-band terminator, and the workflow needs to hold open briefly so the final publish is fetched before run() returns. Both pieces show up in every scenario here, so document them in one place and update scenario 3's description to mention the sentinel headline the publisher emits. * samples: workflow_streams: README and wheel packages cleanup Now that temporalio 1.27.0 has shipped (and main has bumped to it in #302), drop the README's "install sdk-python from a branch" callout and point at >=1.27.0 instead. Also add workflow_streams to the wheel packages list alongside the other samples. * samples: workflow_streams: drop force_flush=True from charge_card The activity's final publish was using force_flush=True, which sets the flush_event so the background flusher fires immediately. Triggering a flush right before __aexit__ runs the activity into the WorkflowStreamClient's cancel-mid-flush path: __aexit__ cancels the flusher task while it's awaiting the publish signal RPC, the cancel propagates into the in-flight signal, and the activity hangs until the StartToClose timeout fires. Empirically the workflow then retries the activity indefinitely. Without force_flush=True the buffered "card charged" event flushes via the regular 200ms batch interval and the flusher is sleeping in wait_for(...) when __aexit__ cancels it — a clean cancellation path. The user-visible publish ordering is unchanged. The underlying SDK bug should be fixed separately by switching __aexit__ from cancel() to a cooperative-stop flag so the in-flight signal completes before the flusher exits. * samples: workflow_streams: drop temp-file resume offset; add stats column The reconnecting-subscriber demo previously persisted its resume offset to a temp file between phases. Inside one process that's theatrical: the disconnect/reconnect shape comes from creating a fresh Client + WorkflowStreamClient with from_offset=N, not from where N happens to be stored. Replace the file with a local int and a comment about durable storage in production (a DB row keyed by user_id/run_id, etc.). Restructure output around a stats column so the demo conveys what's happening to the stream at all times, not just between phases. A background poller calls WorkflowStreamClient.get_offset() throughout and emits a heartbeat line once a second; every emit prints current proc/avail/pend in a left column followed by the phase or event message. Watching pend grow during the disconnect window and shrink again as phase 2 catches up is the demo's core point. * samples: workflow_streams: surface multiple truncation jumps in ticker The truncating-ticker demo is meant to make the bounded-log trade visible: fast subscriber sees every event, slow subscriber loses intermediate ones to truncation. The previous parameters (truncate_every=5, keep_last=3, interval_ms=400, slow_delay=1.5s) produced at most one tiny jump near the end of the run — easy to miss. Tighter parameters (truncate_every=2, keep_last=1, interval_ms=200, count=30) keep the workflow log at one or two entries between truncations. That shrinks the slow subscriber's per-poll batch, so it re-polls more often, and most polls land after a truncation that has passed its position. The result is several visible jumps over the demo, not a single batched one at the end. Switch the output to two lanes (fast on the left, slow on the right with explicit "↪ jumped offset=N → M (K dropped)" markers) so the divergence reads at a glance instead of being lost in interleaved single-stream output. Also extend the docstring to call out the opposite trade — never truncating means slow consumers eventually catch up at the cost of unbounded workflow history — so readers know when this pattern is the wrong fit. * samples: workflow_streams: add LLM-streaming scenario Adds a fifth scenario to workflow_streams/ that streams an OpenAI chat completion to the terminal through a Workflow Stream. Activity is the publisher (it owns the non-deterministic API call), workflow hosts the stream and runs the activity, runner subscribes and renders to stdout as deltas arrive. Layout: * `chat_shared.py` — types and topics for this scenario, kept out of the cross-scenario `shared.py` because no other scenario uses them * `workflows/chat_workflow.py` — `ChatWorkflow` runs `stream_completion` with `RetryPolicy(maximum_attempts=3)` and the same 500ms hold-open pattern the other four samples use * `activities/chat_activity.py` — `stream_completion` calls `AsyncOpenAI(...).chat.completions.create(stream=True)` with `gpt-5-mini`, publishes each token chunk on the `delta` topic, the full text on `complete`, and a `RetryEvent` on `retry` when running on attempt > 1. `force_flush=True` is intentionally omitted to avoid the `__aexit__` cancel-mid-flight hang in `temporalio.contrib.workflow_streams` 1.27.0; the 200ms `batch_interval` is fast enough for an interactive feel. * `run_chat.py` — subscribes to all three topics, prints deltas to stdout as they stream, and on a retry event uses plain ANSI escapes (`\033[A`, `\033[J`) to rewind the rendered output before the retried attempt re-publishes * `run_chat_worker.py` — runs on its own task queue (`workflow-stream-chat-task-queue`), registering only `ChatWorkflow` and `stream_completion`; the openai dependency and the `OPENAI_API_KEY` requirement stay isolated to this one scenario The split worker also makes the retry-handling demo trivial to run: the user kills the chat worker mid-stream, brings it back up, and the activity retries — no synthetic failure injection needed. Adds `chat-stream = ["openai>=1.0,<2"]` as a new optional dependency group; `uv sync --group chat-stream` and an `OPENAI_API_KEY` are documented in the README. * samples: workflow_streams: drop chat-stream openai upper cap openai-agents (the existing langsmith-tracing / openai-agents extras) already pulls openai>=2.26.0. Capping chat-stream at openai<2 made the two extras unsatisfiable together. Drop the cap; the chat activity uses APIs that are stable across openai 1.x and 2.x. * samples: workflow_streams: chat consumer header + cursor save/restore Two display fixes for run_chat.py: 1. Print a header line right after start_workflow so the user sees immediate feedback ("[chat ] streaming response from gpt-5-mini, awaiting first token...") instead of a blank screen until the first delta arrives. 2. Replace the newline-counting ANSI clear with cursor save/restore (\033[s / \033[u\033[J). The previous version counted text newlines to decide how far up to move the cursor on retry, which undercounts when the terminal has wrapped long lines — the failed attempt's first wrapped lines stayed on screen above the retry marker. save/restore rewinds to a fixed position regardless of wrapping. Bumps the prompt to a 500-word distributed-systems comparison (Paxos vs Raft vs Viewstamped Replication) so there is enough output to comfortably kill the worker mid-stream and watch the retried attempt re-render from scratch. * samples: workflow_streams: rename chat -> llm in scenario 5 "Chat" implies multi-turn conversation. The new scenario is a one-shot LLM completion stream, not a chat. Rename to make the scope clear: - chat_shared.py -> llm_shared.py - workflows/chat_workflow.py -> workflows/llm_workflow.py - activities/chat_activity.py -> activities/llm_activity.py - run_chat.py -> run_llm.py - run_chat_worker.py -> run_llm_worker.py - ChatInput / ChatWorkflow -> LLMInput / LLMWorkflow - CHAT_TASK_QUEUE -> LLM_TASK_QUEUE ("workflow-stream-chat-task-queue" -> "workflow-stream-llm-task-queue") - chat-stream extra -> llm-stream - workflow id prefix workflow-stream-chat-... -> workflow-stream-llm-... The activity's `stream_completion` defn name and the topic constants (`delta`, `complete`, `retry`) stay the same — those already describe what they do without the "chat" framing. README, docstrings, and run instructions updated to match. * samples: workflow_streams: race the LLM consumer with workflow result If the LLM activity exhausts its retries (bad OPENAI_API_KEY, provider outage, etc.), the workflow fails before the activity publishes the `complete` terminator. The consumer's previous async-for loop only exited on `complete`, so the script blocked indefinitely on a terminator that would never arrive instead of surfacing the workflow failure. Wrap the subscriber in a `consume()` coroutine and run it through the existing `race_with_workflow` helper (the same pattern `run_publisher.py` uses): if the workflow finishes first the subscriber gets cancelled and the workflow's exception propagates; if the subscriber sees `complete` first, the helper waits for the workflow result and returns it. Found in a Codex code review of today's workflow_streams changes. * samples: workflow_streams: drop race_with_workflow helper The helper wrapped the consumer in an asyncio.gather that cancelled the subscriber when the workflow result settled — defensive logic for a case the SDK already handles. WorkflowStreamClient.subscribe() exits cleanly on every workflow terminal state (return, continue-as-new, failure) via its AcceptedUpdateCompletedWorkflow, WorkflowUpdateRPCTimeoutOrCancelledError, and NOT_FOUND branches in sdk-python. The async-for loop ends naturally when the workflow terminates without a publish, so we don't need a separate task to race against handle.result(). Replace the helper with the obvious shape in both runners: async for item in stream.subscribe(...): ... if item.is_terminator: break result = await handle.result() # raises on workflow failure Either path reaches handle.result(): an explicit break on the in-band terminator (workflow still running, hold-open lets the poll deliver the event), or the iterator naturally exhausting when the workflow has already terminated. handle.result() then either returns or raises the workflow's failure — covering the LLM "activity exhausted retries" case that prompted the helper to be added in the first place. Smoke tested: uv run workflow_streams/run_publisher.py uv run workflow_streams/run_llm.py * samples: workflow_streams: reorganize README; drop closing section Two fixes: 1. Reorganize so the README doesn't jump back and forth between scenarios. The previous shape introduced 1-4, then put scenario 5's full description plus its setup and run instructions inline, then jumped back to a "Run it" section that only covered 1-4. New shape: all five scenarios up front (parallel structure), one unified "Run it" section that covers worker setup for both groups and all five runner scripts in one block, then expected output, then notes. 2. Drop the inline "Ending the stream" section. The same material is in documentation/docs/develop/python/libraries/workflow-streams.mdx under the "Closing the stream" anchor, so the README links there from the Notes block instead of duplicating the explanation. The scenario 5 "split-out worker" rationale (extra dependency, secret, retry-via-Ctrl-C) collapses to a single sentence at the end of its bullet block. * samples: workflow_streams: drop README Notes section The Notes block (subscriber start position, continue-as-new, closing the stream) was a small docs summary tacked onto the end of the README. The samples themselves cover these points: docstrings in each runner / workflow / activity explain the from_offset behavior, the stream_state field, and the in-band terminator + hold-open pattern. Readers who want the full conceptual treatment go to the docs page; the README sticks to "what the scenarios are and how to run them". * samples: workflow_streams: lock llm-stream dependency group The llm-stream dependency group was introduced in pyproject.toml without a corresponding uv.lock update, so `uv sync --frozen --group llm-stream` would fail or force a relock before scenario 5 could run. Add the two missing entries (the package-optional-dependencies list and the package-metadata requires-dev list) so frozen installs work against the committed lock. Found in a Codex review of the day's workflow_streams changes. * samples: workflow_streams: fix lint failures (ruff isort + format) CI's `poe lint` step was failing on three small things across four files: * `run_external_publisher.py`, `ticker_workflow.py`: ruff isort (`I001`) wanted the `workflow_streams.shared` imports re-sorted and a stray blank line removed. Apply the auto-fix. * `run_external_publisher.py`, `run_reconnecting_subscriber.py`, `run_truncating_ticker.py`: ruff format wanted three line-wrapped function calls collapsed back to single lines. Apply the formatter. * `run_truncating_ticker.py`: the formatter joined an adjacent pair of f-strings into an awkward `f"..." f"..."` one-liner. Consolidate them into a single f-string for readability — the resulting line is comfortably under the 88-char limit. `poe lint` (ruff isort + ruff format --check + mypy --all-groups --check-untyped-defs) now passes locally. * samples: workflow_streams: drop BFF jargon and Expected output block Two README/comment cleanups: * "BFF" (backend-for-frontend) is not a widely-known term outside certain front-end-architecture circles. Replace with the more obvious "web backends" in the README intro and "production web backend" in the run_reconnecting_subscriber.py comment about where the resume offset would live durably. * Drop the "Expected output" section. It only covered scenarios 1 and 2; with five scenarios it is no longer pulling its weight. Anyone running the script can see the output for themselves. * Apply suggestion from @brianstrauch * Apply suggestion from @brianstrauch --------- Co-authored-by: Claude Opus 4.7 (1M context) Co-authored-by: Brian Strauch --- README.md | 1 + pyproject.toml | 2 + uv.lock | 4 + workflow_streams/README.md | 120 +++++++++++++ workflow_streams/__init__.py | 0 workflow_streams/activities/__init__.py | 0 workflow_streams/activities/llm_activity.py | 71 ++++++++ .../activities/payment_activity.py | 30 ++++ workflow_streams/llm_shared.py | 44 +++++ workflow_streams/run_external_publisher.py | 99 +++++++++++ workflow_streams/run_llm.py | 132 ++++++++++++++ workflow_streams/run_llm_worker.py | 40 +++++ workflow_streams/run_publisher.py | 60 +++++++ .../run_reconnecting_subscriber.py | 164 ++++++++++++++++++ workflow_streams/run_truncating_ticker.py | 129 ++++++++++++++ workflow_streams/run_worker.py | 30 ++++ workflow_streams/shared.py | 70 ++++++++ workflow_streams/workflows/__init__.py | 0 workflow_streams/workflows/hub_workflow.py | 40 +++++ workflow_streams/workflows/llm_workflow.py | 51 ++++++ workflow_streams/workflows/order_workflow.py | 58 +++++++ .../workflows/pipeline_workflow.py | 48 +++++ workflow_streams/workflows/ticker_workflow.py | 66 +++++++ 23 files changed, 1259 insertions(+) create mode 100644 workflow_streams/README.md create mode 100644 workflow_streams/__init__.py create mode 100644 workflow_streams/activities/__init__.py create mode 100644 workflow_streams/activities/llm_activity.py create mode 100644 workflow_streams/activities/payment_activity.py create mode 100644 workflow_streams/llm_shared.py create mode 100644 workflow_streams/run_external_publisher.py create mode 100644 workflow_streams/run_llm.py create mode 100644 workflow_streams/run_llm_worker.py create mode 100644 workflow_streams/run_publisher.py create mode 100644 workflow_streams/run_reconnecting_subscriber.py create mode 100644 workflow_streams/run_truncating_ticker.py create mode 100644 workflow_streams/run_worker.py create mode 100644 workflow_streams/shared.py create mode 100644 workflow_streams/workflows/__init__.py create mode 100644 workflow_streams/workflows/hub_workflow.py create mode 100644 workflow_streams/workflows/llm_workflow.py create mode 100644 workflow_streams/workflows/order_workflow.py create mode 100644 workflow_streams/workflows/pipeline_workflow.py create mode 100644 workflow_streams/workflows/ticker_workflow.py diff --git a/README.md b/README.md index d4d6a61bc..c4e27056d 100644 --- a/README.md +++ b/README.md @@ -79,6 +79,7 @@ Some examples require extra dependencies. See each sample's directory for specif * [patching](patching) - Alter workflows safely with `patch` and `deprecate_patch`. * [polling](polling) - Recommended implementation of an activity that needs to periodically poll an external resource waiting its successful completion. * [prometheus](prometheus) - Configure Prometheus metrics on clients/workers. +* [workflow_streams](workflow_streams) - Workflow-hosted durable event stream via `temporalio.contrib.workflow_streams`. **Experimental** * [pydantic_converter](pydantic_converter) - Data converter for using Pydantic models. * [schedules](schedules) - Demonstrates a Workflow Execution that occurs according to a schedule. * [sentry](sentry) - Report errors to Sentry. diff --git a/pyproject.toml b/pyproject.toml index 404f46ad5..9c9bace14 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -48,6 +48,7 @@ openai-agents = [ pydantic-converter = ["pydantic>=2.10.6,<3"] sentry = ["sentry-sdk>=2.13.0"] trio-async = ["trio>=0.28.0,<0.29", "trio-asyncio>=0.15.0,<0.16"] +llm-stream = ["openai>=1.0"] cloud-export-to-parquet = [ "pandas>=2.2.2,<3 ; python_version >= '3.10' and python_version < '4.0'", "numpy>=1.26.0,<2 ; python_version >= '3.10' and python_version < '3.13'", @@ -94,6 +95,7 @@ packages = [ "updatable_timer", "worker_specific_task_queues", "worker_versioning", + "workflow_streams", ] [tool.hatch.build.targets.wheel.sources] diff --git a/uv.lock b/uv.lock index 4dcdad5a6..e025dab89 100644 --- a/uv.lock +++ b/uv.lock @@ -2589,6 +2589,9 @@ langsmith-tracing = [ { name = "openai" }, { name = "temporalio", extra = ["langsmith", "pydantic"] }, ] +llm-stream = [ + { name = "openai" }, +] nexus = [ { name = "nexus-rpc" }, ] @@ -2649,6 +2652,7 @@ langsmith-tracing = [ { name = "openai", specifier = ">=1.4.0" }, { name = "temporalio", extras = ["pydantic", "langsmith"], specifier = ">=1.27.0" }, ] +llm-stream = [{ name = "openai", specifier = ">=1.0" }] nexus = [{ name = "nexus-rpc", specifier = ">=1.1.0,<2" }] open-telemetry = [ { name = "opentelemetry-exporter-otlp-proto-grpc" }, diff --git a/workflow_streams/README.md b/workflow_streams/README.md new file mode 100644 index 000000000..f06bed399 --- /dev/null +++ b/workflow_streams/README.md @@ -0,0 +1,120 @@ +# Workflow Streams + +> **Experimental.** These samples use +> `temporalio.contrib.workflow_streams`, which ships in +> `temporalio>=1.27.0`. The module is considered experimental and its +> API may change in future versions. + +`temporalio.contrib.workflow_streams` lets a workflow host a durable, +offset-addressed event channel. The workflow holds an append-only log; +external clients (activities, starters, web backends) publish to topics via +signals and subscribe via long-poll updates. This packages the +boilerplate — batching, offset tracking, topic filtering, +continue-as-new hand-off — into a reusable stream. + +This directory has five scenarios. The first four share one worker; +the fifth has its own worker because it needs the `openai` package +and an `OPENAI_API_KEY`. + +**Scenario 1 — basic publish/subscribe with heterogeneous topics:** + +* `workflows/order_workflow.py` — a workflow that hosts a + `WorkflowStream` and publishes status events as it processes an order. +* `activities/payment_activity.py` — an activity that publishes + intermediate progress to the stream via + `WorkflowStreamClient.from_within_activity()`. +* `run_publisher.py` — starts the workflow, subscribes to both topics, + decodes each by `item.topic`, and prints events as they arrive. + +**Scenario 2 — reconnecting subscriber:** + +* `workflows/pipeline_workflow.py` — a multi-stage pipeline that + publishes stage transitions over ~10 seconds, leaving room for a + consumer to disconnect and reconnect mid-run. +* `run_reconnecting_subscriber.py` — connects, reads a couple of + events, "disconnects," then reopens a fresh client and resumes via + `subscribe(from_offset=...)`. This is the central Workflow Streams + use case: a consumer can disappear (page refresh, server restart, + laptop closed) and resume later without missing events or seeing + duplicates. + +**Scenario 3 — external (non-Activity) publisher:** + +* `workflows/hub_workflow.py` — a passive workflow that does no work + of its own; it exists only to host a `WorkflowStream` and shut down + when signaled. +* `run_external_publisher.py` — starts the hub, then publishes events + into it from a plain Python coroutine using + `WorkflowStreamClient.create(client, workflow_id)`. A subscriber + task runs alongside; when the publisher is done it emits a sentinel + event and signals `HubWorkflow.close`. The shape that fits a + backend service or scheduled job pushing events into a workflow it + didn't itself start. + +**Scenario 4 — bounded log via `truncate()`:** + +* `workflows/ticker_workflow.py` — a long-running workflow that + publishes events at a fixed cadence and calls + `self.stream.truncate(...)` periodically to bound log growth, + keeping only the most recent N entries. +* `run_truncating_ticker.py` — runs a fast subscriber and a slow + subscriber side by side. The fast one keeps up and sees every + offset in order; the slow one falls behind a truncation and + silently jumps forward to the new base offset. The output makes + the trade visible: bounded log size in exchange for intermediate + events being invisible to slow consumers. + +**Scenario 5 — LLM streaming:** + +* `workflows/llm_workflow.py` — hosts a `WorkflowStream` and runs + `stream_completion` as a single activity. The workflow itself + does no streaming; the activity owns the non-deterministic OpenAI + call. +* `activities/llm_activity.py` — calls + `openai.AsyncOpenAI().chat.completions.create(stream=True)`, + publishes each token chunk on the `delta` topic, the final + accumulated text on `complete`, and a `RetryEvent` on `retry` + when running on attempt > 1. +* `run_llm.py` — subscribes to all three topics, renders deltas to + the terminal as they arrive, and on a `retry` event uses ANSI + escapes to rewind the printed output before the retried attempt + re-publishes. + +Scenario 5 runs on its own worker (`run_llm_worker.py`, on +`workflow-stream-llm-task-queue`) because it needs the `openai` +dependency and an `OPENAI_API_KEY`, and because killing this worker +mid-stream is the easiest way to demonstrate retry handling without +disrupting the other four scenarios. + +## Run it + +For scenarios 1–4, start the shared worker: + +```bash +uv run workflow_streams/run_worker.py +``` + +For scenario 5, install the extra, export the key, and start the +LLM worker: + +```bash +uv sync --group llm-stream +export OPENAI_API_KEY=... +uv run workflow_streams/run_llm_worker.py +``` + +Then in another terminal, pick a scenario: + +```bash +uv run workflow_streams/run_publisher.py # scenario 1 +uv run workflow_streams/run_reconnecting_subscriber.py # scenario 2 +uv run workflow_streams/run_external_publisher.py # scenario 3 +uv run workflow_streams/run_truncating_ticker.py # scenario 4 +uv run workflow_streams/run_llm.py # scenario 5 +``` + +To exercise scenario 5's retry path, kill `run_llm_worker.py` +(`Ctrl-C`) while output is streaming and start it again. The +activity's next attempt sends a `RetryEvent` first; the consumer +clears its on-screen output via ANSI escapes and re-renders from +scratch. diff --git a/workflow_streams/__init__.py b/workflow_streams/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/workflow_streams/activities/__init__.py b/workflow_streams/activities/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/workflow_streams/activities/llm_activity.py b/workflow_streams/activities/llm_activity.py new file mode 100644 index 000000000..aebe80ec7 --- /dev/null +++ b/workflow_streams/activities/llm_activity.py @@ -0,0 +1,71 @@ +from __future__ import annotations + +from datetime import timedelta + +from openai import AsyncOpenAI +from temporalio import activity +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.llm_shared import ( + TOPIC_COMPLETE, + TOPIC_DELTA, + TOPIC_RETRY, + LLMInput, + RetryEvent, + TextComplete, + TextDelta, +) + + +@activity.defn +async def stream_completion(input: LLMInput) -> str: + """Stream an LLM completion to the parent workflow's stream. + + Activity-as-publisher: each delta from the OpenAI streaming API is + pushed to the workflow's stream as a ``TextDelta`` event on the + ``delta`` topic. The accumulated full text returns as the + activity's result and is also published on the ``complete`` topic + as a terminator. On retry attempts (``activity.info().attempt > 1``) + a ``RetryEvent`` lands on the ``retry`` topic before the new + attempt's deltas, so consumers can reset their accumulated state + instead of concatenating the failed attempt's partial output with + the retried attempt's full output. + + No ``force_flush=True``: the 200ms ``batch_interval`` is fast + enough for an interactive feel, and the WorkflowStreamClient's + ``__aexit__`` cancels a sleeping flusher cleanly. + """ + stream_client = WorkflowStreamClient.from_within_activity( + batch_interval=timedelta(milliseconds=200), + ) + # Disable provider-side retries; let Temporal own retry policy at + # the activity layer. + openai_client = AsyncOpenAI(max_retries=0) + + async with stream_client: + deltas = stream_client.topic(TOPIC_DELTA, type=TextDelta) + complete = stream_client.topic(TOPIC_COMPLETE, type=TextComplete) + retry = stream_client.topic(TOPIC_RETRY, type=RetryEvent) + + attempt = activity.info().attempt + if attempt > 1: + retry.publish(RetryEvent(attempt=attempt)) + + full: list[str] = [] + oai_stream = await openai_client.chat.completions.create( + model=input.model, + messages=[{"role": "user", "content": input.prompt}], + stream=True, + ) + async for chunk in oai_stream: + if not chunk.choices: + continue + text = chunk.choices[0].delta.content + if not text: + continue + deltas.publish(TextDelta(text=text)) + full.append(text) + + full_text = "".join(full) + complete.publish(TextComplete(full_text=full_text)) + return full_text diff --git a/workflow_streams/activities/payment_activity.py b/workflow_streams/activities/payment_activity.py new file mode 100644 index 000000000..b3b2aa297 --- /dev/null +++ b/workflow_streams/activities/payment_activity.py @@ -0,0 +1,30 @@ +from __future__ import annotations + +import asyncio +from datetime import timedelta + +from temporalio import activity +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.shared import TOPIC_PROGRESS, ProgressEvent + + +@activity.defn +async def charge_card(order_id: str) -> str: + """Pretend to charge a card, publishing progress to the parent workflow. + + `WorkflowStreamClient.from_within_activity()` reads the parent + workflow id and the Temporal client from the activity context, so + this activity can push events back without any wiring. + """ + client = WorkflowStreamClient.from_within_activity( + batch_interval=timedelta(milliseconds=200) + ) + async with client: + progress = client.topic(TOPIC_PROGRESS, type=ProgressEvent) + progress.publish(ProgressEvent(message="charging card...")) + await asyncio.sleep(1.0) + progress.publish( + ProgressEvent(message="card charged"), + ) + return f"charge-{order_id}" diff --git a/workflow_streams/llm_shared.py b/workflow_streams/llm_shared.py new file mode 100644 index 000000000..2780fd0b0 --- /dev/null +++ b/workflow_streams/llm_shared.py @@ -0,0 +1,44 @@ +"""Types and constants for the LLM-streaming scenario. + +Kept separate from ``shared.py`` because the other scenarios don't +use these — and this scenario runs on its own worker and task queue +so the ``openai`` dependency stays out of everyone else's path. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +from temporalio.contrib.workflow_streams import WorkflowStreamState + +# Scenario 5 runs on its own worker so the openai dependency only +# matters for that scenario. +LLM_TASK_QUEUE = "workflow-stream-llm-task-queue" + +# Topics published by the activity. +TOPIC_DELTA = "delta" +TOPIC_COMPLETE = "complete" +TOPIC_RETRY = "retry" + + +@dataclass +class LLMInput: + prompt: str + model: str = "gpt-5-mini" + # Carries stream state across continue-as-new. None on a fresh start. + stream_state: WorkflowStreamState | None = None + + +@dataclass +class TextDelta: + text: str + + +@dataclass +class TextComplete: + full_text: str + + +@dataclass +class RetryEvent: + attempt: int diff --git a/workflow_streams/run_external_publisher.py b/workflow_streams/run_external_publisher.py new file mode 100644 index 000000000..8e7d38f81 --- /dev/null +++ b/workflow_streams/run_external_publisher.py @@ -0,0 +1,99 @@ +"""External publisher: a non-Activity process pushes events into a workflow. + +The two earlier scenarios publish from inside the workflow itself +(``OrderWorkflow``, ``PipelineWorkflow``) or from an Activity it runs +(``charge_card``). This scenario shows the third shape: a backend +service, scheduled job, or anything else with a Temporal ``Client`` +publishing into a *running* workflow it didn't start. Same factory as +the subscribe path — :py:meth:`WorkflowStreamClient.create` — used for +publishing instead. + +The script starts a ``HubWorkflow`` (which does no work of its own — +it exists only to host the stream), then runs a publisher and a +subscriber concurrently. When the publisher is done it signals +``HubWorkflow.close``, the workflow's run finishes, and the +subscriber's iterator exits normally. + +Run the worker first (``uv run workflow_streams/run_worker.py``), then:: + + uv run workflow_streams/run_external_publisher.py +""" + +from __future__ import annotations + +import asyncio +import uuid + +from temporalio.client import Client +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.shared import ( + TASK_QUEUE, + TOPIC_NEWS, + HubInput, + NewsEvent, +) +from workflow_streams.workflows.hub_workflow import HubWorkflow + +HEADLINES = [ + "rates held", + "merger announced", + "outage resolved", + "earnings beat", + "regulator opens probe", +] + +# In-band terminator the publisher emits before signaling close. The +# subscriber recognizes this value and stops polling — without an +# explicit terminator the consumer would have to rely on the workflow +# returning to break the iterator, which means racing the last item +# delivery against workflow completion. +DONE_HEADLINE = "__done__" + + +async def main() -> None: + client = await Client.connect("localhost:7233") + + workflow_id = f"workflow-stream-hub-{uuid.uuid4().hex[:8]}" + handle = await client.start_workflow( + HubWorkflow.run, + HubInput(hub_id=workflow_id), + id=workflow_id, + task_queue=TASK_QUEUE, + ) + + async def publish_news() -> None: + # WorkflowStreamClient.create takes a Temporal client and a + # workflow id — the same factory used elsewhere for subscribing. + # The async context manager batches publishes and flushes on + # exit; we additionally call flush() before signaling close so + # we know the events landed before the workflow shuts down. + producer = WorkflowStreamClient.create(client, workflow_id) + async with producer: + news = producer.topic(TOPIC_NEWS, type=NewsEvent) + for headline in HEADLINES: + news.publish(NewsEvent(headline=headline)) + print(f"[publisher] sent: {headline}") + await asyncio.sleep(0.5) + news.publish(NewsEvent(headline=DONE_HEADLINE), force_flush=True) + await producer.flush() + # Tell the hub it can stop. The subscriber has already broken + # out of its async-for loop on the sentinel above. + await handle.signal(HubWorkflow.close) + print("[publisher] signaled close") + + async def consume_news() -> None: + consumer = WorkflowStreamClient.create(client, workflow_id) + async for item in consumer.subscribe([TOPIC_NEWS], result_type=NewsEvent): + if item.data.headline == DONE_HEADLINE: + return + print(f"[subscriber] offset={item.offset}: {item.data.headline}") + + await asyncio.gather(publish_news(), consume_news()) + + result = await handle.result() + print(f"\nworkflow result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/run_llm.py b/workflow_streams/run_llm.py new file mode 100644 index 000000000..1b2a9d7ae --- /dev/null +++ b/workflow_streams/run_llm.py @@ -0,0 +1,132 @@ +"""Stream LLM output to the terminal, handling retries. + +Starts an ``LLMWorkflow``, subscribes to its delta / complete / retry +topics, and renders the model's output to stdout as it arrives. On a +``RETRY`` event (the activity is on attempt > 1), the consumer rewinds +its rendered output with ANSI escapes and starts fresh — so a killed +worker doesn't leave a half-finished response stuck on screen +followed by the retried attempt's full output. + +Requires ``OPENAI_API_KEY`` in the environment and the ``llm-stream`` +extra:: + + uv sync --group llm-stream + export OPENAI_API_KEY=... + +Run the LLM worker first (``uv run workflow_streams/run_llm_worker.py``), +then:: + + uv run workflow_streams/run_llm.py + +To see retry handling in action, kill the LLM worker mid-stream +(Ctrl-C in its terminal) and start it again. The consumer will clear +its accumulated output on the ``RETRY`` event and re-render the +retried attempt's output from scratch. +""" + +from __future__ import annotations + +import asyncio +import sys +import uuid + +from temporalio.client import Client +from temporalio.common import RawValue +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.llm_shared import ( + LLM_TASK_QUEUE, + TOPIC_COMPLETE, + TOPIC_DELTA, + TOPIC_RETRY, + LLMInput, + RetryEvent, + TextComplete, + TextDelta, +) +from workflow_streams.workflows.llm_workflow import LLMWorkflow + +# Long enough that you can comfortably kill the worker mid-stream and +# watch the retry render. Adjust to taste. +DEFAULT_PROMPT = ( + "Write a 500-word comparison of Paxos, Raft, and Viewstamped " + "Replication for a new distributed-systems engineer. Cover the " + "core ideas, leader election, normal-case operation, " + "reconfiguration, and the practical tradeoffs that show up when " + "implementing each. Use short paragraphs." +) + + +# ANSI cursor save / restore. ``\033[s`` saves the current cursor +# position, ``\033[u`` restores it, ``\033[J`` clears from the cursor +# to the end of the screen. Save once before the first delta, and on +# RETRY restore + clear-to-end so the failed attempt's rendered output +# disappears regardless of how it was wrapped by the terminal. Save +# again afterwards so a second retry can rewind to the same point. +ANSI_SAVE = "\033[s" +ANSI_RESTORE_AND_CLEAR = "\033[u\033[J" + + +async def main() -> None: + client = await Client.connect("localhost:7233") + converter = client.data_converter.payload_converter + + workflow_id = f"workflow-stream-llm-{uuid.uuid4().hex[:8]}" + llm_input = LLMInput(prompt=DEFAULT_PROMPT) + handle = await client.start_workflow( + LLMWorkflow.run, + llm_input, + id=workflow_id, + task_queue=LLM_TASK_QUEUE, + ) + + # Print a header so the user sees something immediately. The + # response will start streaming below it once the first delta + # arrives — until then this is the only line on screen. + print( + f"[llm {workflow_id}] streaming response from {llm_input.model}, " + f"awaiting first token..." + ) + print() + sys.stdout.write(ANSI_SAVE) + sys.stdout.flush() + + stream = WorkflowStreamClient.create(client, workflow_id) + + # result_type=RawValue lets us dispatch on item.topic and decode + # against the right dataclass per topic. The loop ends either on + # the `complete` terminator (break) or because the iterator + # naturally exhausts when the workflow reaches a terminal state + # without one (activity exhausted retries, etc.). Either way the + # handle.result() below either returns the full text or raises + # the workflow's failure. + async for item in stream.subscribe( + [TOPIC_DELTA, TOPIC_RETRY, TOPIC_COMPLETE], + result_type=RawValue, + ): + if item.topic == TOPIC_RETRY: + evt = converter.from_payload(item.data.payload, RetryEvent) + sys.stdout.write(ANSI_RESTORE_AND_CLEAR) + sys.stdout.flush() + print(f"[retry attempt {evt.attempt}] resetting output") + print() + sys.stdout.write(ANSI_SAVE) + sys.stdout.flush() + elif item.topic == TOPIC_DELTA: + delta = converter.from_payload(item.data.payload, TextDelta) + sys.stdout.write(delta.text) + sys.stdout.flush() + elif item.topic == TOPIC_COMPLETE: + # The full text is also in the payload (and returned by + # the workflow), but the consumer has already rendered it + # incrementally. Just terminate the line. + converter.from_payload(item.data.payload, TextComplete) + print() + break + + result = await handle.result() + print(f"\n[workflow result: {len(result)} chars]") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/run_llm_worker.py b/workflow_streams/run_llm_worker.py new file mode 100644 index 000000000..2bad59915 --- /dev/null +++ b/workflow_streams/run_llm_worker.py @@ -0,0 +1,40 @@ +"""Worker for the LLM-streaming scenario. + +Runs separately from ``run_worker.py`` so the ``openai`` dependency +and the ``OPENAI_API_KEY`` requirement stay isolated to this one +scenario. Different task queue too — the other four samples won't +route work to this worker. + +Kill this worker mid-stream while ``run_llm.py`` is running to +trigger a retry: Temporal restarts the activity on the next worker +to come up, the activity publishes a ``RetryEvent`` on its second +attempt, and the consumer resets its rendered output. +""" + +from __future__ import annotations + +import asyncio +import logging + +from temporalio.client import Client +from temporalio.worker import Worker + +from workflow_streams.activities.llm_activity import stream_completion +from workflow_streams.llm_shared import LLM_TASK_QUEUE +from workflow_streams.workflows.llm_workflow import LLMWorkflow + + +async def main() -> None: + logging.basicConfig(level=logging.INFO) + client = await Client.connect("localhost:7233") + worker = Worker( + client, + task_queue=LLM_TASK_QUEUE, + workflows=[LLMWorkflow], + activities=[stream_completion], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/run_publisher.py b/workflow_streams/run_publisher.py new file mode 100644 index 000000000..9f76ee41c --- /dev/null +++ b/workflow_streams/run_publisher.py @@ -0,0 +1,60 @@ +from __future__ import annotations + +import asyncio +import uuid + +from temporalio.client import Client +from temporalio.common import RawValue +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.shared import ( + TASK_QUEUE, + TOPIC_PROGRESS, + TOPIC_STATUS, + OrderInput, + ProgressEvent, + StatusEvent, +) +from workflow_streams.workflows.order_workflow import OrderWorkflow + + +async def main() -> None: + client = await Client.connect("localhost:7233") + + workflow_id = f"workflow-stream-order-{uuid.uuid4().hex[:8]}" + handle = await client.start_workflow( + OrderWorkflow.run, + OrderInput(order_id="order-1"), + id=workflow_id, + task_queue=TASK_QUEUE, + ) + + stream = WorkflowStreamClient.create(client, workflow_id) + converter = client.data_converter.payload_converter + + # Single iterator over both topics — avoids a cancellation race + # between two concurrent subscribers. result_type=RawValue + # delivers the underlying Payload so we can dispatch heterogeneous + # events on item.topic. The loop ends either on the in-band + # `complete` terminator (break) or because the iterator exhausts + # when the workflow reaches a terminal state without one (e.g. on + # failure). Either way we then await handle.result(), which raises + # if the workflow failed. + async for item in stream.subscribe( + [TOPIC_STATUS, TOPIC_PROGRESS], result_type=RawValue + ): + if item.topic == TOPIC_STATUS: + evt = converter.from_payload(item.data.payload, StatusEvent) + print(f"[status] {evt.kind}: order={evt.order_id}") + if evt.kind == "complete": + break + elif item.topic == TOPIC_PROGRESS: + progress = converter.from_payload(item.data.payload, ProgressEvent) + print(f"[progress] {progress.message}") + + result = await handle.result() + print(f"workflow result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/run_reconnecting_subscriber.py b/workflow_streams/run_reconnecting_subscriber.py new file mode 100644 index 000000000..d0da5e32c --- /dev/null +++ b/workflow_streams/run_reconnecting_subscriber.py @@ -0,0 +1,164 @@ +"""Reconnecting subscriber: read a few events, disconnect, resume. + +Demonstrates the central Workflow Streams use case: a consumer can +disappear mid-stream — page refresh, server restart, laptop closed — +and resume later without missing events or seeing duplicates. The +event log lives in the Workflow, so the consumer just remembers where +it stopped. + +The script runs the pattern in two phases inside one process to keep +the demo short. The same code shape works across actual process +restarts because the resume offset is durable in the workflow, not in +the consumer. + +Output is one line per emit, with current stream stats in a left column +and a phase / event message in a right column. A background poller +calls ``WorkflowStreamClient.get_offset()`` for the whole demo and +emits a heartbeat line once a second so you can watch ``pending`` +(``available - processed``) grow while the consumer is disconnected +and shrink as phase 2 catches up. + +Run the worker first (``uv run workflow_streams/run_worker.py``), then:: + + uv run workflow_streams/run_reconnecting_subscriber.py +""" + +from __future__ import annotations + +import asyncio +import uuid +from dataclasses import dataclass + +from temporalio.client import Client +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.shared import ( + TASK_QUEUE, + TOPIC_STATUS, + PipelineInput, + StageEvent, +) +from workflow_streams.workflows.pipeline_workflow import PipelineWorkflow + +# Number of events read in phase 1 before simulating a disconnect. +# Picked small enough that the workflow is still running after. +PHASE_1_EVENTS = 2 + +# How long to stay disconnected. +DISCONNECT_SECONDS = 3.0 + +# Background poller cadence. The poller refreshes state.available this +# often and emits a heartbeat line once per HEARTBEAT_SECONDS. +POLL_INTERVAL_SECONDS = 0.25 +HEARTBEAT_SECONDS = 1.0 + +# Width of the stats column. Picked to fit the longest stats string. +LEFT_WIDTH = 30 + + +@dataclass +class State: + processed: int = 0 + available: int = 0 + + @property + def pending(self) -> int: + return max(0, self.available - self.processed) + + +def emit(state: State, message: str) -> None: + left = ( + f"proc={state.processed:>2} " + f"avail={state.available:>2} " + f"pend={state.pending:>2}" + ) + print(f"{left:<{LEFT_WIDTH}}│ {message}", flush=True) + + +async def main() -> None: + client = await Client.connect("localhost:7233") + + workflow_id = f"workflow-stream-pipeline-{uuid.uuid4().hex[:8]}" + handle = await client.start_workflow( + PipelineWorkflow.run, + PipelineInput(pipeline_id=workflow_id), + id=workflow_id, + task_queue=TASK_QUEUE, + ) + + # In a production web backend the resume offset would live in + # durable storage keyed by (user_id, run_id) — a database row, a + # Redis key, etc. For an in-process demo a State.processed + # attribute works the same way. + state = State() + stream = WorkflowStreamClient.create(client, workflow_id) + emit(state, f"started {workflow_id}") + + stop = asyncio.Event() + + async def poller() -> None: + """Refresh state.available; emit a heartbeat line once a second.""" + loop = asyncio.get_running_loop() + last_emit = loop.time() + while not stop.is_set(): + try: + state.available = await stream.get_offset() + except Exception: + pass + now = loop.time() + if now - last_emit >= HEARTBEAT_SECONDS: + emit(state, "·") + last_emit = now + try: + await asyncio.wait_for(stop.wait(), timeout=POLL_INTERVAL_SECONDS) + except asyncio.TimeoutError: + pass + + poller_task = asyncio.create_task(poller()) + try: + # ---- Phase 1: connect, read a couple of events, "disconnect". + emit(state, "[phase 1] connecting") + seen = 0 + async for item in stream.subscribe([TOPIC_STATUS], result_type=StageEvent): + # Remember *one past* the offset just consumed: on resume we + # want the next unseen event, not the one we already showed. + state.processed = item.offset + 1 + emit(state, f" offset={item.offset:2d} stage={item.data.stage}") + seen += 1 + if seen >= PHASE_1_EVENTS: + break + emit(state, "[phase 1] disconnecting") + + # ---- Disconnect window: nobody reads. The workflow keeps + # publishing — `pend` grows on the heartbeat lines as the offset + # advances past `processed`. + await asyncio.sleep(DISCONNECT_SECONDS) + + # ---- Phase 2: brand-new client + stream, resume from saved + # offset. Same shape as a different process picking up where the + # first one left off. + emit(state, "[phase 2] reconnecting") + client2 = await Client.connect("localhost:7233") + stream2 = WorkflowStreamClient.create(client2, workflow_id) + async for item in stream2.subscribe( + [TOPIC_STATUS], + from_offset=state.processed, + result_type=StageEvent, + ): + state.processed = item.offset + 1 + emit(state, f" offset={item.offset:2d} stage={item.data.stage}") + if item.data.stage == "complete": + break + + result = await handle.result() + emit(state, f"workflow result: {result}") + finally: + stop.set() + try: + await poller_task + except asyncio.CancelledError: + pass + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/run_truncating_ticker.py b/workflow_streams/run_truncating_ticker.py new file mode 100644 index 000000000..26399447c --- /dev/null +++ b/workflow_streams/run_truncating_ticker.py @@ -0,0 +1,129 @@ +"""Truncating ticker: bounded log + slow vs. fast subscribers. + +The ``TickerWorkflow`` publishes ``count`` events at a fixed interval, +calling ``self.stream.truncate(...)`` periodically to bound log +growth. This script subscribes twice — once fast, once slow — and +prints them in two lanes so the trade is visible at a glance: + +* **Fast lane** (left). Keeps up. Sees every published offset. +* **Slow lane** (right). Sleeps between iterations. When a truncation + has dropped its position by the time it polls again, the iterator + silently jumps forward to the new base offset; the slow lane prints + a ``↪ jumped N → M (K dropped)`` marker for each gap and resumes + at the new offset. + +``truncate()`` is unilateral: the workflow does not know who is +subscribed and does not wait for them. The implicit alternative — +never truncating — keeps every event around forever, lets slow +consumers eventually catch up without losses, and pays for it in +unbounded workflow history. The truncation model is the opposite +trade: bounded log, at-best-effort delivery to slow consumers, no +backpressure on the publisher. Pair it with set-semantic events where +each event carries enough state to make missing the prior ones +recoverable. (If you actually need lossless delivery to slow +consumers, the workflow has to coordinate acknowledgements +explicitly — that is a different sample.) + +Run the worker first (``uv run workflow_streams/run_worker.py``), then:: + + uv run workflow_streams/run_truncating_ticker.py +""" + +from __future__ import annotations + +import asyncio +import uuid + +from temporalio.client import Client +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.shared import ( + TASK_QUEUE, + TOPIC_TICK, + TickerInput, + TickEvent, +) +from workflow_streams.workflows.ticker_workflow import TickerWorkflow + +# Aggressive truncation so the log stays at most KEEP_LAST entries +# right after each truncation, which keeps the slow subscriber's +# per-poll batch tiny. Small batches + a slow per-event sleep mean the +# slow subscriber re-polls often, and most of those polls land after a +# truncation that has passed its position — so it sees several jumps +# during the run rather than one batched at the end. +TICKER_COUNT = 30 +INTERVAL_MS = 200 +TRUNCATE_EVERY = 2 +KEEP_LAST = 1 +SLOW_SUBSCRIBER_DELAY_S = 1.5 + +LANE_WIDTH = 32 +SEP = "│" + + +def emit_fast(message: str) -> None: + print(f"{message:<{LANE_WIDTH}} {SEP}", flush=True) + + +def emit_slow(message: str) -> None: + print(f"{' ' * LANE_WIDTH} {SEP} {message}", flush=True) + + +def emit_header() -> None: + rule = "─" * LANE_WIDTH + print( + f"{'fast (every event)':<{LANE_WIDTH}} {SEP} " + f"slow (sleeps {SLOW_SUBSCRIBER_DELAY_S}s between events)" + ) + print(f"{rule} {SEP} {rule}") + + +async def main() -> None: + client = await Client.connect("localhost:7233") + + workflow_id = f"workflow-stream-ticker-{uuid.uuid4().hex[:8]}" + handle = await client.start_workflow( + TickerWorkflow.run, + TickerInput( + count=TICKER_COUNT, + keep_last=KEEP_LAST, + truncate_every=TRUNCATE_EVERY, + interval_ms=INTERVAL_MS, + ), + id=workflow_id, + task_queue=TASK_QUEUE, + ) + stream = WorkflowStreamClient.create(client, workflow_id) + last_n = TICKER_COUNT - 1 + + emit_header() + + async def fast_subscriber() -> None: + async for item in stream.subscribe([TOPIC_TICK], result_type=TickEvent): + emit_fast(f"offset={item.offset:>3} n={item.data.n}") + if item.data.n == last_n: + return + + async def slow_subscriber() -> None: + last_offset = -1 + async for item in stream.subscribe([TOPIC_TICK], result_type=TickEvent): + if last_offset >= 0 and item.offset > last_offset + 1: + gap = item.offset - last_offset - 1 + emit_slow( + f"↪ jumped offset={last_offset} → {item.offset} ({gap} dropped)" + ) + emit_slow(f"offset={item.offset:>3} n={item.data.n}") + last_offset = item.offset + if item.data.n == last_n: + return + await asyncio.sleep(SLOW_SUBSCRIBER_DELAY_S) + + await asyncio.gather(fast_subscriber(), slow_subscriber()) + + result = await handle.result() + print() + print(f"workflow result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/run_worker.py b/workflow_streams/run_worker.py new file mode 100644 index 000000000..8aa12edca --- /dev/null +++ b/workflow_streams/run_worker.py @@ -0,0 +1,30 @@ +from __future__ import annotations + +import asyncio +import logging + +from temporalio.client import Client +from temporalio.worker import Worker + +from workflow_streams.activities.payment_activity import charge_card +from workflow_streams.shared import TASK_QUEUE +from workflow_streams.workflows.hub_workflow import HubWorkflow +from workflow_streams.workflows.order_workflow import OrderWorkflow +from workflow_streams.workflows.pipeline_workflow import PipelineWorkflow +from workflow_streams.workflows.ticker_workflow import TickerWorkflow + + +async def main() -> None: + logging.basicConfig(level=logging.INFO) + client = await Client.connect("localhost:7233") + worker = Worker( + client, + task_queue=TASK_QUEUE, + workflows=[HubWorkflow, OrderWorkflow, PipelineWorkflow, TickerWorkflow], + activities=[charge_card], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/shared.py b/workflow_streams/shared.py new file mode 100644 index 000000000..9bf5a4b79 --- /dev/null +++ b/workflow_streams/shared.py @@ -0,0 +1,70 @@ +from __future__ import annotations + +from dataclasses import dataclass + +from temporalio.contrib.workflow_streams import WorkflowStreamState + +TASK_QUEUE = "workflow-stream-sample-task-queue" + +# Topics published by the workflow / activity. +TOPIC_STATUS = "status" +TOPIC_PROGRESS = "progress" +TOPIC_NEWS = "news" +TOPIC_TICK = "tick" + + +@dataclass +class OrderInput: + order_id: str + # Carries stream state across continue-as-new. None on a fresh start. + stream_state: WorkflowStreamState | None = None + + +@dataclass +class StatusEvent: + kind: str + order_id: str + + +@dataclass +class ProgressEvent: + message: str + + +@dataclass +class PipelineInput: + pipeline_id: str + # Carries stream state across continue-as-new. None on a fresh start. + stream_state: WorkflowStreamState | None = None + + +@dataclass +class StageEvent: + stage: str + + +@dataclass +class HubInput: + hub_id: str + # Carries stream state across continue-as-new. None on a fresh start. + stream_state: WorkflowStreamState | None = None + + +@dataclass +class NewsEvent: + headline: str + + +@dataclass +class TickerInput: + count: int = 20 + keep_last: int = 3 + truncate_every: int = 5 + interval_ms: int = 400 + # Carries stream state across continue-as-new. None on a fresh start. + stream_state: WorkflowStreamState | None = None + + +@dataclass +class TickEvent: + n: int diff --git a/workflow_streams/workflows/__init__.py b/workflow_streams/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/workflow_streams/workflows/hub_workflow.py b/workflow_streams/workflows/hub_workflow.py new file mode 100644 index 000000000..5dcc3c5f3 --- /dev/null +++ b/workflow_streams/workflows/hub_workflow.py @@ -0,0 +1,40 @@ +from __future__ import annotations + +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.workflow_streams import WorkflowStream + +from workflow_streams.shared import HubInput + + +@workflow.defn +class HubWorkflow: + """Passive stream host: starts up, waits, closes when told. + + Unlike OrderWorkflow or PipelineWorkflow, this workflow does no + work of its own — it exists only to host a ``WorkflowStream`` that + external publishers push events into and external subscribers read + from. The shape that fits a backend service or "event bus" pattern, + where the workflow owns durable state but the events come from + outside. + """ + + @workflow.init + def __init__(self, input: HubInput) -> None: + self.stream = WorkflowStream(prior_state=input.stream_state) + self._closed = False + + @workflow.run + async def run(self, input: HubInput) -> str: + await workflow.wait_condition(lambda: self._closed) + # The publisher publishes its own terminator into the stream + # before signaling close (see run_external_publisher.py). + # Hold the run open briefly so subscribers' final poll + # delivers any items still in the log. + await workflow.sleep(timedelta(milliseconds=500)) + return f"hub {input.hub_id} closed" + + @workflow.signal + def close(self) -> None: + self._closed = True diff --git a/workflow_streams/workflows/llm_workflow.py b/workflow_streams/workflows/llm_workflow.py new file mode 100644 index 000000000..b26cfbe4d --- /dev/null +++ b/workflow_streams/workflows/llm_workflow.py @@ -0,0 +1,51 @@ +from __future__ import annotations + +from datetime import timedelta + +from temporalio import workflow +from temporalio.common import RetryPolicy +from temporalio.contrib.workflow_streams import WorkflowStream + +from workflow_streams.llm_shared import LLMInput + +with workflow.unsafe.imports_passed_through(): + from workflow_streams.activities.llm_activity import stream_completion + + +@workflow.defn +class LLMWorkflow: + """Wrapper for an LLM-streaming activity. + + The workflow does no streaming of its own; it hosts the + `WorkflowStream` so external subscribers can attach by workflow + id, kicks off the streaming activity, and returns the full text + the activity produced. + + Streaming is delegated to the activity because the OpenAI call is + non-deterministic. If the activity fails partway through, Temporal + retries it (up to ``max_attempts``); the retried attempt + re-publishes from the start, so the consumer must reset on the + activity's ``RETRY`` event. See + `activities/llm_activity.py` and `run_llm.py`. + """ + + @workflow.init + def __init__(self, input: LLMInput) -> None: + # Construct the stream from `@workflow.init` so the + # publish-Signal handler is registered before any external + # publisher (the activity, here) tries to publish. + self.stream = WorkflowStream(prior_state=input.stream_state) + + @workflow.run + async def run(self, input: LLMInput) -> str: + result = await workflow.execute_activity( + stream_completion, + input, + start_to_close_timeout=timedelta(minutes=5), + retry_policy=RetryPolicy(maximum_attempts=3), + ) + # Hold the run open briefly so the consumer's next poll + # delivers the activity's terminal `complete` event before the + # workflow exits and the in-memory log is gone. + await workflow.sleep(timedelta(milliseconds=500)) + return result diff --git a/workflow_streams/workflows/order_workflow.py b/workflow_streams/workflows/order_workflow.py new file mode 100644 index 000000000..099634cd7 --- /dev/null +++ b/workflow_streams/workflows/order_workflow.py @@ -0,0 +1,58 @@ +from __future__ import annotations + +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.workflow_streams import WorkflowStream + +from workflow_streams.shared import ( + TOPIC_PROGRESS, + TOPIC_STATUS, + OrderInput, + ProgressEvent, + StatusEvent, +) + +with workflow.unsafe.imports_passed_through(): + from workflow_streams.activities.payment_activity import charge_card + + +@workflow.defn +class OrderWorkflow: + """Process a fake order, publishing status and progress events. + + The workflow itself publishes status changes; an activity it runs + publishes finer-grained progress events using a + `WorkflowStreamClient`. A single stream carries both topics — + subscribers can filter on the topic(s) they care about. + """ + + @workflow.init + def __init__(self, input: OrderInput) -> None: + # Construct the stream from @workflow.init so it can register + # signal/update/query handlers before the workflow accepts any + # messages. Threading prior_state lets the workflow survive + # continue-as-new without losing buffered items. + self.stream = WorkflowStream(prior_state=input.stream_state) + self.status = self.stream.topic(TOPIC_STATUS, type=StatusEvent) + self.progress = self.stream.topic(TOPIC_PROGRESS, type=ProgressEvent) + + @workflow.run + async def run(self, input: OrderInput) -> str: + self.status.publish(StatusEvent(kind="received", order_id=input.order_id)) + + charge_id = await workflow.execute_activity( + charge_card, + input.order_id, + start_to_close_timeout=timedelta(seconds=30), + ) + + self.status.publish(StatusEvent(kind="shipped", order_id=input.order_id)) + self.progress.publish(ProgressEvent(message=f"charge id: {charge_id}")) + self.status.publish(StatusEvent(kind="complete", order_id=input.order_id)) + # The "complete" status event above is the in-band terminator + # subscribers break on (see run_publisher.py). Hold the run + # open briefly so subscribers' next poll delivers it before + # this task returns and the in-memory log is gone. + await workflow.sleep(timedelta(milliseconds=500)) + return charge_id diff --git a/workflow_streams/workflows/pipeline_workflow.py b/workflow_streams/workflows/pipeline_workflow.py new file mode 100644 index 000000000..833369056 --- /dev/null +++ b/workflow_streams/workflows/pipeline_workflow.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.workflow_streams import WorkflowStream + +from workflow_streams.shared import ( + TOPIC_STATUS, + PipelineInput, + StageEvent, +) + + +@workflow.defn +class PipelineWorkflow: + """Multi-stage pipeline that publishes stage transitions over time. + + Stages are spaced out with ``workflow.sleep`` so a subscriber can + realistically disconnect partway through and reconnect without the + pipeline finishing in the meantime — the shape needed to demo the + "show up late and still see what happened" pattern. + """ + + @workflow.init + def __init__(self, input: PipelineInput) -> None: + self.stream = WorkflowStream(prior_state=input.stream_state) + self.status = self.stream.topic(TOPIC_STATUS, type=StageEvent) + + @workflow.run + async def run(self, input: PipelineInput) -> str: + stages = [ + "validating", + "loading data", + "transforming", + "writing output", + "verifying", + "complete", + ] + for stage in stages: + self.status.publish(StageEvent(stage=stage)) + if stage != "complete": + await workflow.sleep(timedelta(seconds=2)) + # The "complete" stage above is the in-band terminator + # subscribers break on. Hold the run open briefly so the final + # poll delivers it. + await workflow.sleep(timedelta(milliseconds=500)) + return f"pipeline {input.pipeline_id} done" diff --git a/workflow_streams/workflows/ticker_workflow.py b/workflow_streams/workflows/ticker_workflow.py new file mode 100644 index 000000000..c3f37b9f3 --- /dev/null +++ b/workflow_streams/workflows/ticker_workflow.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.workflow_streams import WorkflowStream + +from workflow_streams.shared import ( + TOPIC_TICK, + TickerInput, + TickEvent, +) + + +@workflow.defn +class TickerWorkflow: + """Long-running ticker that bounds its event log via ``truncate``. + + Long-running workflows that publish high volumes of events would + otherwise grow their event log unboundedly. This workflow shows + the truncation pattern: every ``truncate_every`` events, drop + everything except the last ``keep_last`` entries by calling + ``self.stream.truncate(safe_offset)``. + + Subscribers that fall behind a truncation jump forward to the new + base offset transparently (the iterator handles the + ``TruncatedOffset`` error internally), so consumers stay live but + may not see every intermediate event. That is the trade: bounded + log size in exchange for at-best-effort delivery to slow + consumers. + + To compute the truncation offset the workflow tracks its own + published count. ``WorkflowStream`` does not expose a workflow-side + head-offset accessor, but the running count plus the carried + ``base_offset`` (in continue-as-new chains) is sufficient. + """ + + @workflow.init + def __init__(self, input: TickerInput) -> None: + self.stream = WorkflowStream(prior_state=input.stream_state) + self.tick = self.stream.topic(TOPIC_TICK, type=TickEvent) + # Running count of events published by THIS run. To compute a + # global offset, add the prior_state's base_offset (omitted + # here — this sample doesn't continue-as-new). + self._published = 0 + + @workflow.run + async def run(self, input: TickerInput) -> str: + for n in range(input.count): + self.tick.publish(TickEvent(n=n)) + self._published += 1 + await workflow.sleep(timedelta(milliseconds=input.interval_ms)) + if ( + self._published % input.truncate_every == 0 + and self._published > input.keep_last + ): + # Drop everything except the last `keep_last` entries. + truncate_to = self._published - input.keep_last + self.stream.truncate(truncate_to) + # The final tick (n == count - 1) is the in-band terminator + # subscribers break on. ``keep_last`` guarantees that final + # offset survives the last truncation so even slow consumers + # eventually see it. Hold the run open briefly so the final + # poll delivers it. + await workflow.sleep(timedelta(milliseconds=500)) + return f"ticker emitted {self._published} events" From 6b8e44170a3ab4ad376be14469f23f7d32f285ac Mon Sep 17 00:00:00 2001 From: Evan Reynolds Date: Mon, 4 May 2026 11:13:14 -0700 Subject: [PATCH 19/40] Sample code for Nexus messaging (#290) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Working on adding Nexus messaging sample code * Fix to the tests * Updates from a code review * Working on adding Nexus messaging sample code * Fix to the tests * Updates from a code review * Bump pandas to 2.3.3 for Python 3.14 wheel support * removed some unneeded code * Removing workflow.unsafe.imports_passed_through * Bringing in changes from main * Linting * Nexus samples: use business IDs for workflow IDs (#297) * Nexus samples: use business IDs for workflow IDs. Stop using the Nexus request ID (nexus_cancel) or bare uuid4 (hello_nexus, nexus_multiple_args) as the backing workflow ID. Build a meaningful business ID from the operation input and append a uuid4 suffix for uniqueness. Co-Authored-By: Claude Opus 4.7 (1M context) * remove UUID from the nexus sample workflow ids to better communicate intent --------- Co-authored-by: Claude Opus 4.7 (1M context) * Changes from code reviews * Update SDK to 1.27.0 (#302) * Add workflow_streams samples (#300) * Add workflow_streams samples: order_workflow scenario Initial samples directory for temporalio.contrib.workflow_streams, the workflow-hosted durable event stream contrib (experimental, contrib/pubsub branch of sdk-python). The order_workflow scenario covers the basic publisher path: a workflow binds a typed topic in @workflow.init, an activity publishes events via the topic handle, and a starter subscribes with WorkflowStreamClient and prints events as they arrive. Also enables the uv supply-chain cooldown options in the lockfile. * samples: workflow_stream: add reconnecting-subscriber scenario Adds a second scenario demonstrating the central Workflow Streams use case: a consumer disconnects mid-stream and resumes later via subscribe(from_offset=...), with no events lost or duplicated. The existing OrderWorkflow finishes too quickly to make the pattern visible, so this introduces a multi-stage PipelineWorkflow paced with workflow.sleep between stages. The runner reads a couple of events, persists item.offset + 1 to a temp file, sleeps "disconnected" while the workflow keeps publishing, then opens a fresh Client + WorkflowStreamClient and resumes from the persisted offset — the same shape that works across actual process restarts. Co-Authored-By: Claude Opus 4.7 (1M context) * samples: workflow_stream: add external-publisher scenario Adds a third scenario covering the third publisher shape: a backend service or scheduled job pushing events into a workflow it didn't itself start. The earlier scenarios publish either from inside the workflow or from one of its activities; this one uses WorkflowStreamClient.create() externally. HubWorkflow is a passive stream host — it does no work of its own and just waits to be told to close, fitting the event-bus pattern. The runner publishes a series of news headlines, runs a subscriber task alongside, signals close, and exits when both tasks complete. Co-Authored-By: Claude Opus 4.7 (1M context) * samples: workflow_stream: add truncating-ticker scenario Adds a fourth scenario for long-running workflows that need to bound their event log: the workflow publishes events at a fixed cadence and calls self.stream.truncate(...) periodically to keep only the most recent entries. The runner subscribes twice — fast and slow — to make the trade visible: the fast subscriber sees every offset in order; the slow one falls behind a truncation, has its iterator transparently jump forward to the new base offset, and shows the offset gap that intermediate events fell into. This is the model for high-volume long-running streams: bounded log size, slow consumers may miss intermediate events but always see the most recent state. Co-Authored-By: Claude Opus 4.7 (1M context) * samples: rename workflow_stream → workflow_streams; migrate to topic handles - Directory and module path renamed to plural to match sdk-python `temporalio.contrib.workflow_streams` rename. - Workflow-side: bind a typed topic handle in `@workflow.init` and call `topic.publish(value)` — the removed `WorkflowStream.publish` form is gone. Same change applied to the activity and external-publisher. - Activity: `WorkflowStreamClient.from_activity()` → `from_within_activity()`. Co-Authored-By: Claude Opus 4.7 (1M context) * samples: workflow_streams review polish - README: fix scenario count (two -> four), document subscriber start position and continue-as-new semantics for stream_state - hub_workflow: drop stale comment referencing a README race note that does not exist in this sample - payment_activity: trim long publisher_id/dedup caveat — moved out of the first sample's docstring to keep it approachable * workflow_streams: deliver terminal events + fix run_publisher subscribe shape End-to-end runs of the four workflow_streams scenarios surfaced two sample-side issues, both fixed here. run_publisher's consumer asserted ``isinstance(item.data, Payload)`` and called ``payload_converter.from_payload(item.data, T)``. The contrib's ``subscribe()`` defaults to converter-decoded data, not raw payloads, so this assertion fired on the first run. Switch to ``result_type=RawValue`` (the documented escape hatch for heterogeneous topics) and read ``item.data.payload``. Items published in the same workflow task that returns from ``@workflow.run`` were not delivered to subscribers — the in-memory log dies with the workflow and the next subscriber poll lands on a completed workflow. Fix: each scenario now uses an in-band terminator that subscribers break on, and each workflow holds the run open with ``await workflow.sleep(timedelta(milliseconds=500))`` so that final publish is fetched before the workflow exits: - OrderWorkflow / PipelineWorkflow: the workflow's own ``StatusEvent(kind="complete")`` / ``StageEvent(stage="complete")`` is the terminator (consumers already broke on it). - HubWorkflow: the *publisher* in run_external_publisher emits a sentinel ``NewsEvent(headline="__done__")`` immediately before signaling close; the consumer breaks on the sentinel. - TickerWorkflow: the final tick (n == count - 1) is the terminator; ``keep_last`` guarantees that offset survives the last truncation, so even slow consumers reach it. Because subscribers stop polling on the terminator, by the time ``workflow.run`` returns there are no in-flight poll handlers — no ``UnfinishedUpdateHandlersWarning`` from the SDK and no need for ``detach_pollers()`` / ``wait_condition(all_handlers_finished)`` in the workflow exit path. Two consecutive end-to-end runs of all four scenarios pass cleanly against ``temporal server start-dev --headless``. * workflow_streams README: document the stream-end pattern Subscribers don't exit on their own when the host workflow completes — they need an in-band terminator, and the workflow needs to hold open briefly so the final publish is fetched before run() returns. Both pieces show up in every scenario here, so document them in one place and update scenario 3's description to mention the sentinel headline the publisher emits. * samples: workflow_streams: README and wheel packages cleanup Now that temporalio 1.27.0 has shipped (and main has bumped to it in #302), drop the README's "install sdk-python from a branch" callout and point at >=1.27.0 instead. Also add workflow_streams to the wheel packages list alongside the other samples. * samples: workflow_streams: drop force_flush=True from charge_card The activity's final publish was using force_flush=True, which sets the flush_event so the background flusher fires immediately. Triggering a flush right before __aexit__ runs the activity into the WorkflowStreamClient's cancel-mid-flush path: __aexit__ cancels the flusher task while it's awaiting the publish signal RPC, the cancel propagates into the in-flight signal, and the activity hangs until the StartToClose timeout fires. Empirically the workflow then retries the activity indefinitely. Without force_flush=True the buffered "card charged" event flushes via the regular 200ms batch interval and the flusher is sleeping in wait_for(...) when __aexit__ cancels it — a clean cancellation path. The user-visible publish ordering is unchanged. The underlying SDK bug should be fixed separately by switching __aexit__ from cancel() to a cooperative-stop flag so the in-flight signal completes before the flusher exits. * samples: workflow_streams: drop temp-file resume offset; add stats column The reconnecting-subscriber demo previously persisted its resume offset to a temp file between phases. Inside one process that's theatrical: the disconnect/reconnect shape comes from creating a fresh Client + WorkflowStreamClient with from_offset=N, not from where N happens to be stored. Replace the file with a local int and a comment about durable storage in production (a DB row keyed by user_id/run_id, etc.). Restructure output around a stats column so the demo conveys what's happening to the stream at all times, not just between phases. A background poller calls WorkflowStreamClient.get_offset() throughout and emits a heartbeat line once a second; every emit prints current proc/avail/pend in a left column followed by the phase or event message. Watching pend grow during the disconnect window and shrink again as phase 2 catches up is the demo's core point. * samples: workflow_streams: surface multiple truncation jumps in ticker The truncating-ticker demo is meant to make the bounded-log trade visible: fast subscriber sees every event, slow subscriber loses intermediate ones to truncation. The previous parameters (truncate_every=5, keep_last=3, interval_ms=400, slow_delay=1.5s) produced at most one tiny jump near the end of the run — easy to miss. Tighter parameters (truncate_every=2, keep_last=1, interval_ms=200, count=30) keep the workflow log at one or two entries between truncations. That shrinks the slow subscriber's per-poll batch, so it re-polls more often, and most polls land after a truncation that has passed its position. The result is several visible jumps over the demo, not a single batched one at the end. Switch the output to two lanes (fast on the left, slow on the right with explicit "↪ jumped offset=N → M (K dropped)" markers) so the divergence reads at a glance instead of being lost in interleaved single-stream output. Also extend the docstring to call out the opposite trade — never truncating means slow consumers eventually catch up at the cost of unbounded workflow history — so readers know when this pattern is the wrong fit. * samples: workflow_streams: add LLM-streaming scenario Adds a fifth scenario to workflow_streams/ that streams an OpenAI chat completion to the terminal through a Workflow Stream. Activity is the publisher (it owns the non-deterministic API call), workflow hosts the stream and runs the activity, runner subscribes and renders to stdout as deltas arrive. Layout: * `chat_shared.py` — types and topics for this scenario, kept out of the cross-scenario `shared.py` because no other scenario uses them * `workflows/chat_workflow.py` — `ChatWorkflow` runs `stream_completion` with `RetryPolicy(maximum_attempts=3)` and the same 500ms hold-open pattern the other four samples use * `activities/chat_activity.py` — `stream_completion` calls `AsyncOpenAI(...).chat.completions.create(stream=True)` with `gpt-5-mini`, publishes each token chunk on the `delta` topic, the full text on `complete`, and a `RetryEvent` on `retry` when running on attempt > 1. `force_flush=True` is intentionally omitted to avoid the `__aexit__` cancel-mid-flight hang in `temporalio.contrib.workflow_streams` 1.27.0; the 200ms `batch_interval` is fast enough for an interactive feel. * `run_chat.py` — subscribes to all three topics, prints deltas to stdout as they stream, and on a retry event uses plain ANSI escapes (`\033[A`, `\033[J`) to rewind the rendered output before the retried attempt re-publishes * `run_chat_worker.py` — runs on its own task queue (`workflow-stream-chat-task-queue`), registering only `ChatWorkflow` and `stream_completion`; the openai dependency and the `OPENAI_API_KEY` requirement stay isolated to this one scenario The split worker also makes the retry-handling demo trivial to run: the user kills the chat worker mid-stream, brings it back up, and the activity retries — no synthetic failure injection needed. Adds `chat-stream = ["openai>=1.0,<2"]` as a new optional dependency group; `uv sync --group chat-stream` and an `OPENAI_API_KEY` are documented in the README. * samples: workflow_streams: drop chat-stream openai upper cap openai-agents (the existing langsmith-tracing / openai-agents extras) already pulls openai>=2.26.0. Capping chat-stream at openai<2 made the two extras unsatisfiable together. Drop the cap; the chat activity uses APIs that are stable across openai 1.x and 2.x. * samples: workflow_streams: chat consumer header + cursor save/restore Two display fixes for run_chat.py: 1. Print a header line right after start_workflow so the user sees immediate feedback ("[chat ] streaming response from gpt-5-mini, awaiting first token...") instead of a blank screen until the first delta arrives. 2. Replace the newline-counting ANSI clear with cursor save/restore (\033[s / \033[u\033[J). The previous version counted text newlines to decide how far up to move the cursor on retry, which undercounts when the terminal has wrapped long lines — the failed attempt's first wrapped lines stayed on screen above the retry marker. save/restore rewinds to a fixed position regardless of wrapping. Bumps the prompt to a 500-word distributed-systems comparison (Paxos vs Raft vs Viewstamped Replication) so there is enough output to comfortably kill the worker mid-stream and watch the retried attempt re-render from scratch. * samples: workflow_streams: rename chat -> llm in scenario 5 "Chat" implies multi-turn conversation. The new scenario is a one-shot LLM completion stream, not a chat. Rename to make the scope clear: - chat_shared.py -> llm_shared.py - workflows/chat_workflow.py -> workflows/llm_workflow.py - activities/chat_activity.py -> activities/llm_activity.py - run_chat.py -> run_llm.py - run_chat_worker.py -> run_llm_worker.py - ChatInput / ChatWorkflow -> LLMInput / LLMWorkflow - CHAT_TASK_QUEUE -> LLM_TASK_QUEUE ("workflow-stream-chat-task-queue" -> "workflow-stream-llm-task-queue") - chat-stream extra -> llm-stream - workflow id prefix workflow-stream-chat-... -> workflow-stream-llm-... The activity's `stream_completion` defn name and the topic constants (`delta`, `complete`, `retry`) stay the same — those already describe what they do without the "chat" framing. README, docstrings, and run instructions updated to match. * samples: workflow_streams: race the LLM consumer with workflow result If the LLM activity exhausts its retries (bad OPENAI_API_KEY, provider outage, etc.), the workflow fails before the activity publishes the `complete` terminator. The consumer's previous async-for loop only exited on `complete`, so the script blocked indefinitely on a terminator that would never arrive instead of surfacing the workflow failure. Wrap the subscriber in a `consume()` coroutine and run it through the existing `race_with_workflow` helper (the same pattern `run_publisher.py` uses): if the workflow finishes first the subscriber gets cancelled and the workflow's exception propagates; if the subscriber sees `complete` first, the helper waits for the workflow result and returns it. Found in a Codex code review of today's workflow_streams changes. * samples: workflow_streams: drop race_with_workflow helper The helper wrapped the consumer in an asyncio.gather that cancelled the subscriber when the workflow result settled — defensive logic for a case the SDK already handles. WorkflowStreamClient.subscribe() exits cleanly on every workflow terminal state (return, continue-as-new, failure) via its AcceptedUpdateCompletedWorkflow, WorkflowUpdateRPCTimeoutOrCancelledError, and NOT_FOUND branches in sdk-python. The async-for loop ends naturally when the workflow terminates without a publish, so we don't need a separate task to race against handle.result(). Replace the helper with the obvious shape in both runners: async for item in stream.subscribe(...): ... if item.is_terminator: break result = await handle.result() # raises on workflow failure Either path reaches handle.result(): an explicit break on the in-band terminator (workflow still running, hold-open lets the poll deliver the event), or the iterator naturally exhausting when the workflow has already terminated. handle.result() then either returns or raises the workflow's failure — covering the LLM "activity exhausted retries" case that prompted the helper to be added in the first place. Smoke tested: uv run workflow_streams/run_publisher.py uv run workflow_streams/run_llm.py * samples: workflow_streams: reorganize README; drop closing section Two fixes: 1. Reorganize so the README doesn't jump back and forth between scenarios. The previous shape introduced 1-4, then put scenario 5's full description plus its setup and run instructions inline, then jumped back to a "Run it" section that only covered 1-4. New shape: all five scenarios up front (parallel structure), one unified "Run it" section that covers worker setup for both groups and all five runner scripts in one block, then expected output, then notes. 2. Drop the inline "Ending the stream" section. The same material is in documentation/docs/develop/python/libraries/workflow-streams.mdx under the "Closing the stream" anchor, so the README links there from the Notes block instead of duplicating the explanation. The scenario 5 "split-out worker" rationale (extra dependency, secret, retry-via-Ctrl-C) collapses to a single sentence at the end of its bullet block. * samples: workflow_streams: drop README Notes section The Notes block (subscriber start position, continue-as-new, closing the stream) was a small docs summary tacked onto the end of the README. The samples themselves cover these points: docstrings in each runner / workflow / activity explain the from_offset behavior, the stream_state field, and the in-band terminator + hold-open pattern. Readers who want the full conceptual treatment go to the docs page; the README sticks to "what the scenarios are and how to run them". * samples: workflow_streams: lock llm-stream dependency group The llm-stream dependency group was introduced in pyproject.toml without a corresponding uv.lock update, so `uv sync --frozen --group llm-stream` would fail or force a relock before scenario 5 could run. Add the two missing entries (the package-optional-dependencies list and the package-metadata requires-dev list) so frozen installs work against the committed lock. Found in a Codex review of the day's workflow_streams changes. * samples: workflow_streams: fix lint failures (ruff isort + format) CI's `poe lint` step was failing on three small things across four files: * `run_external_publisher.py`, `ticker_workflow.py`: ruff isort (`I001`) wanted the `workflow_streams.shared` imports re-sorted and a stray blank line removed. Apply the auto-fix. * `run_external_publisher.py`, `run_reconnecting_subscriber.py`, `run_truncating_ticker.py`: ruff format wanted three line-wrapped function calls collapsed back to single lines. Apply the formatter. * `run_truncating_ticker.py`: the formatter joined an adjacent pair of f-strings into an awkward `f"..." f"..."` one-liner. Consolidate them into a single f-string for readability — the resulting line is comfortably under the 88-char limit. `poe lint` (ruff isort + ruff format --check + mypy --all-groups --check-untyped-defs) now passes locally. * samples: workflow_streams: drop BFF jargon and Expected output block Two README/comment cleanups: * "BFF" (backend-for-frontend) is not a widely-known term outside certain front-end-architecture circles. Replace with the more obvious "web backends" in the README intro and "production web backend" in the run_reconnecting_subscriber.py comment about where the resume offset would live durably. * Drop the "Expected output" section. It only covered scenarios 1 and 2; with five scenarios it is no longer pulling its weight. Anyone running the script can see the output for themselves. * Apply suggestion from @brianstrauch * Apply suggestion from @brianstrauch --------- Co-authored-by: Claude Opus 4.7 (1M context) Co-authored-by: Brian Strauch * Working on adding Nexus messaging sample code * Fix to the tests * Updates from a code review * Working on adding Nexus messaging sample code * Bump pandas to 2.3.3 for Python 3.14 wheel support * removed some unneeded code * Removing workflow.unsafe.imports_passed_through * Bringing in changes from main * Linting * Changes from code reviews --------- Co-authored-by: Alex Mazzeo Co-authored-by: Claude Opus 4.7 (1M context) Co-authored-by: Justin Anderson <44687433+jmaeagle99@users.noreply.github.com> Co-authored-by: Johann Schleier-Smith Co-authored-by: Brian Strauch --- README.md | 3 + nexus_messaging/README.md | 16 ++ .../__init__.py | 0 nexus_messaging/callerpattern/README.md | 56 +++++++ .../callerpattern}/__init__.py | 0 .../callerpattern/caller}/__init__.py | 0 .../callerpattern}/caller/app.py | 13 +- .../callerpattern/caller/workflows.py | 73 +++++++++ .../callerpattern/handler/__init__.py | 0 .../callerpattern/handler/activities.py | 22 +++ .../callerpattern/handler/service_handler.py | 80 ++++++++++ .../callerpattern/handler/worker.py | 62 ++++++++ .../callerpattern/handler/workflows.py | 87 ++++++++++ nexus_messaging/callerpattern/service.py | 67 ++++++++ nexus_messaging/endpoint_description.md | 14 ++ nexus_messaging/ondemandpattern/README.md | 60 +++++++ nexus_messaging/ondemandpattern/__init__.py | 0 .../ondemandpattern/caller/__init__.py | 0 nexus_messaging/ondemandpattern/caller/app.py | 41 +++++ .../ondemandpattern/caller/workflows.py | 148 ++++++++++++++++++ .../ondemandpattern/handler/__init__.py | 0 .../ondemandpattern/handler/activities.py | 22 +++ .../handler/service_handler.py | 84 ++++++++++ .../ondemandpattern}/handler/worker.py | 22 ++- .../ondemandpattern/handler/workflows.py | 88 +++++++++++ nexus_messaging/ondemandpattern/service.py | 72 +++++++++ nexus_sync_operations/README.md | 39 ----- nexus_sync_operations/caller/workflows.py | 46 ------ nexus_sync_operations/endpoint_description.md | 4 - .../handler/service_handler.py | 83 ---------- nexus_sync_operations/service.py | 20 --- pyproject.toml | 10 +- .../callerpattern_test.py} | 71 ++++----- tests/nexus_messaging/ondemandpattern_test.py | 130 +++++++++++++++ uv.lock | 113 +++++++------ 35 files changed, 1241 insertions(+), 305 deletions(-) create mode 100644 nexus_messaging/README.md rename {nexus_sync_operations => nexus_messaging}/__init__.py (100%) create mode 100644 nexus_messaging/callerpattern/README.md rename {nexus_sync_operations/caller => nexus_messaging/callerpattern}/__init__.py (100%) rename {nexus_sync_operations/handler => nexus_messaging/callerpattern/caller}/__init__.py (100%) rename {nexus_sync_operations => nexus_messaging/callerpattern}/caller/app.py (73%) create mode 100644 nexus_messaging/callerpattern/caller/workflows.py create mode 100644 nexus_messaging/callerpattern/handler/__init__.py create mode 100644 nexus_messaging/callerpattern/handler/activities.py create mode 100644 nexus_messaging/callerpattern/handler/service_handler.py create mode 100644 nexus_messaging/callerpattern/handler/worker.py create mode 100644 nexus_messaging/callerpattern/handler/workflows.py create mode 100644 nexus_messaging/callerpattern/service.py create mode 100644 nexus_messaging/endpoint_description.md create mode 100644 nexus_messaging/ondemandpattern/README.md create mode 100644 nexus_messaging/ondemandpattern/__init__.py create mode 100644 nexus_messaging/ondemandpattern/caller/__init__.py create mode 100644 nexus_messaging/ondemandpattern/caller/app.py create mode 100644 nexus_messaging/ondemandpattern/caller/workflows.py create mode 100644 nexus_messaging/ondemandpattern/handler/__init__.py create mode 100644 nexus_messaging/ondemandpattern/handler/activities.py create mode 100644 nexus_messaging/ondemandpattern/handler/service_handler.py rename {nexus_sync_operations => nexus_messaging/ondemandpattern}/handler/worker.py (58%) create mode 100644 nexus_messaging/ondemandpattern/handler/workflows.py create mode 100644 nexus_messaging/ondemandpattern/service.py delete mode 100644 nexus_sync_operations/README.md delete mode 100644 nexus_sync_operations/caller/workflows.py delete mode 100644 nexus_sync_operations/endpoint_description.md delete mode 100644 nexus_sync_operations/handler/service_handler.py delete mode 100644 nexus_sync_operations/service.py rename tests/{nexus_sync_operations/nexus_sync_operations_test.py => nexus_messaging/callerpattern_test.py} (55%) create mode 100644 tests/nexus_messaging/ondemandpattern_test.py diff --git a/README.md b/README.md index c4e27056d..2cb2d620d 100644 --- a/README.md +++ b/README.md @@ -75,6 +75,9 @@ Some examples require extra dependencies. See each sample's directory for specif * [message_passing/introduction](message_passing/introduction/) - Introduction to queries, signals, and updates. * [message_passing/safe_message_handlers](message_passing/safe_message_handlers/) - Safely handling updates and signals. * [message_passing/update_with_start/lazy_initialization](message_passing/update_with_start/lazy_initialization/) - Use update-with-start to update a Shopping Cart, starting it if it does not exist. +* [Nexus Messaging](nexus_messaging): Demonstrates how send signal, update and query messages through Nexus. + This contains two samples, one sending messages to an existing workflow and a second that creates a workflow through Nexus + and sends messages to it. * [open_telemetry](open_telemetry) - Trace workflows with OpenTelemetry. * [patching](patching) - Alter workflows safely with `patch` and `deprecate_patch`. * [polling](polling) - Recommended implementation of an activity that needs to periodically poll an external resource waiting its successful completion. diff --git a/nexus_messaging/README.md b/nexus_messaging/README.md new file mode 100644 index 000000000..3103fc0fb --- /dev/null +++ b/nexus_messaging/README.md @@ -0,0 +1,16 @@ +This sample shows how to expose a long-running Workflow's queries, updates, and signals as Nexus +operations. There are two self-contained examples, each in its own directory: + +| | `callerpattern/` | `ondemandpattern/` | +|--------------------------------|--------------------------------------|--------------------------------------------------------------| +| **Pattern** | Signal an existing Workflow | Create and run Workflows on demand, and send signals to them | +| **Who creates the Workflow?** | The handler worker starts it on boot | The caller starts it via a Nexus operation | +| **Who knows the Workflow ID?** | Only the handler | The caller chooses and passes it in every operation | +| **Nexus service** | `NexusGreetingService` | `NexusRemoteGreetingService` | + +Each directory is fully self-contained for clarity. The `GreetingWorkflow`, activity, and +`Language` enum are **identical** between the two -- only the Nexus service definition and its +handler implementation differ. This highlights that the same Workflow can be exposed through +Nexus in different ways depending on whether the caller needs lifecycle control. + +See each directory's README for running instructions. diff --git a/nexus_sync_operations/__init__.py b/nexus_messaging/__init__.py similarity index 100% rename from nexus_sync_operations/__init__.py rename to nexus_messaging/__init__.py diff --git a/nexus_messaging/callerpattern/README.md b/nexus_messaging/callerpattern/README.md new file mode 100644 index 000000000..9458ae43b --- /dev/null +++ b/nexus_messaging/callerpattern/README.md @@ -0,0 +1,56 @@ +## Caller pattern + +The handler worker starts a `GreetingWorkflow` for a User ID. +`NexusGreetingServiceHandler` holds that ID and routes every Nexus operation to it. +The caller's input does not have that Workflow ID as the caller doesn't know it -- but the caller +sends in the User ID, and `NexusGreetingServiceHandler` knows how to get the desired Workflow ID +from that User ID (see the `get_workflow_id` call). + +The handler worker uses the same `get_workflow_id` call to generate a Workflow ID from a Wser ID +when it launches the Workflow. + +The caller Workflow: +1. Queries for supported languages (`get_languages` -- backed by a `@workflow.query`) +2. Changes the language to Arabic (`set_language` -- backed by a `@workflow.update` that calls an activity) +3. Confirms the change via a second query (`get_language`) +4. Approves the Workflow (`approve` -- backed by a `@workflow.signal`) + +### Running + +Start a Temporal server: + +```bash +temporal server start-dev +``` + +Create the namespaces and Nexus endpoint: + +```bash +temporal operator namespace create --namespace nexus-messaging-handler-namespace +temporal operator namespace create --namespace nexus-messaging-caller-namespace + +temporal operator nexus endpoint create \ + --name nexus-messaging-nexus-endpoint \ + --target-namespace nexus-messaging-handler-namespace \ + --target-task-queue nexus-messaging-handler-task-queue +``` + +In one terminal, start the handler worker: + +```bash +uv run python -m nexus_messaging.callerpattern.handler.worker +``` + +In another terminal, run the following command to start the example: + +```bash +uv run python -m nexus_messaging.callerpattern.caller.app +``` + +Expected output: + +``` +Supported languages: [, ] +Language changed: ENGLISH -> ARABIC +Workflow approved +``` diff --git a/nexus_sync_operations/caller/__init__.py b/nexus_messaging/callerpattern/__init__.py similarity index 100% rename from nexus_sync_operations/caller/__init__.py rename to nexus_messaging/callerpattern/__init__.py diff --git a/nexus_sync_operations/handler/__init__.py b/nexus_messaging/callerpattern/caller/__init__.py similarity index 100% rename from nexus_sync_operations/handler/__init__.py rename to nexus_messaging/callerpattern/caller/__init__.py diff --git a/nexus_sync_operations/caller/app.py b/nexus_messaging/callerpattern/caller/app.py similarity index 73% rename from nexus_sync_operations/caller/app.py rename to nexus_messaging/callerpattern/caller/app.py index 375628d21..933dcd5d5 100644 --- a/nexus_sync_operations/caller/app.py +++ b/nexus_messaging/callerpattern/caller/app.py @@ -6,15 +6,13 @@ from temporalio.envconfig import ClientConfig from temporalio.worker import Worker -from nexus_sync_operations.caller.workflows import CallerWorkflow +from nexus_messaging.callerpattern.caller.workflows import CallerWorkflow -NAMESPACE = "nexus-sync-operations-caller-namespace" -TASK_QUEUE = "nexus-sync-operations-caller-task-queue" +NAMESPACE = "nexus-messaging-caller-namespace" +TASK_QUEUE = "nexus-messaging-caller-task-queue" -async def execute_caller_workflow( - client: Optional[Client] = None, -) -> None: +async def execute_caller_workflow(client: Optional[Client] = None) -> None: if client is None: config = ClientConfig.load_client_connect_config() config.setdefault("target_host", "localhost:7233") @@ -28,7 +26,8 @@ async def execute_caller_workflow( ): log = await client.execute_workflow( CallerWorkflow.run, - id=str(uuid.uuid4()), + arg="user-1", + id=f"nexus-messaging-caller-{uuid.uuid4()}", task_queue=TASK_QUEUE, ) for line in log: diff --git a/nexus_messaging/callerpattern/caller/workflows.py b/nexus_messaging/callerpattern/caller/workflows.py new file mode 100644 index 000000000..04f79dada --- /dev/null +++ b/nexus_messaging/callerpattern/caller/workflows.py @@ -0,0 +1,73 @@ +""" +A caller workflow that executes Nexus operations. The caller does not have information +about how these operations are implemented by the Nexus service. +""" + +from temporalio import workflow +from temporalio.exceptions import ApplicationError + +from nexus_messaging.callerpattern.service import ( + ApproveInput, + GetLanguageInput, + GetLanguagesInput, + Language, + NexusGreetingService, + SetLanguageInput, +) + +NEXUS_ENDPOINT = "nexus-messaging-nexus-endpoint" + + +@workflow.defn +class CallerWorkflow: + @workflow.run + async def run(self, user_id: str) -> list[str]: + log: list[str] = [] + nexus_client = workflow.create_nexus_client( + service=NexusGreetingService, + endpoint=NEXUS_ENDPOINT, + ) + + # Call a Nexus operation backed by a query against the entity workflow. + # The workflow must already be running on the handler, otherwise you will + # get an error saying the workflow has already terminated. + languages_output = await nexus_client.execute_operation( + NexusGreetingService.get_languages, + GetLanguagesInput(include_unsupported=False, user_id=user_id), + ) + log.append(f"Supported languages: {languages_output.languages}") + workflow.logger.info("Supported languages: %s", languages_output.languages) + + # Following are examples for each of the three messaging types - + # update, query, then signal. + + # Call a Nexus operation backed by an update against the entity workflow. + previous_language = await nexus_client.execute_operation( + NexusGreetingService.set_language, + SetLanguageInput(language=Language.ARABIC, user_id=user_id), + ) + + # Call a Nexus operation backed by a query to confirm the language change. + current_language = await nexus_client.execute_operation( + NexusGreetingService.get_language, + GetLanguageInput(user_id=user_id), + ) + if current_language != Language.ARABIC: + raise ApplicationError(f"Expected language ARABIC, got {current_language}") + + log.append( + f"Language changed: {previous_language.name} -> {Language.ARABIC.name}" + ) + workflow.logger.info( + "Language changed from %s to %s", previous_language, Language.ARABIC + ) + + # Call a Nexus operation backed by a signal against the entity workflow. + await nexus_client.execute_operation( + NexusGreetingService.approve, + ApproveInput(name="caller", user_id=user_id), + ) + log.append("Workflow approved") + workflow.logger.info("Workflow approved") + + return log diff --git a/nexus_messaging/callerpattern/handler/__init__.py b/nexus_messaging/callerpattern/handler/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_messaging/callerpattern/handler/activities.py b/nexus_messaging/callerpattern/handler/activities.py new file mode 100644 index 000000000..4031b34fb --- /dev/null +++ b/nexus_messaging/callerpattern/handler/activities.py @@ -0,0 +1,22 @@ +import asyncio +from typing import Optional + +from temporalio import activity + +from nexus_messaging.callerpattern.service import Language + + +@activity.defn +async def call_greeting_service(language: Language) -> Optional[str]: + """Simulates a call to a remote greeting service. Returns None if unsupported.""" + greetings = { + Language.ARABIC: "\u0645\u0631\u062d\u0628\u0627 \u0628\u0627\u0644\u0639\u0627\u0644\u0645", + Language.CHINESE: "\u4f60\u597d\uff0c\u4e16\u754c", + Language.ENGLISH: "Hello, world", + Language.FRENCH: "Bonjour, monde", + Language.HINDI: "\u0928\u092e\u0938\u094d\u0924\u0947 \u0926\u0941\u0928\u093f\u092f\u093e", + Language.PORTUGUESE: "Ol\u00e1 mundo", + Language.SPANISH: "Hola mundo", + } + await asyncio.sleep(0.2) + return greetings.get(language) diff --git a/nexus_messaging/callerpattern/handler/service_handler.py b/nexus_messaging/callerpattern/handler/service_handler.py new file mode 100644 index 000000000..cbc57eadb --- /dev/null +++ b/nexus_messaging/callerpattern/handler/service_handler.py @@ -0,0 +1,80 @@ +""" +Nexus operation handler implementation for the entity pattern. Each operation receives a +user_id, which is mapped to a workflow ID. The operations are synchronous because queries +and updates against a running workflow complete quickly. +""" + +from __future__ import annotations + +import nexusrpc +from temporalio import nexus +from temporalio.client import WorkflowHandle + +from nexus_messaging.callerpattern.handler.workflows import GreetingWorkflow +from nexus_messaging.callerpattern.service import ( + ApproveInput, + ApproveOutput, + GetLanguageInput, + GetLanguagesInput, + GetLanguagesOutput, + Language, + NexusGreetingService, + SetLanguageInput, +) + +WORKFLOW_ID_PREFIX = "GreetingWorkflow_for_" + + +def get_workflow_id(user_id: str) -> str: + """Map a user ID to a workflow ID. + + This example assumes you might have multiple workflows, one for each user. + If you had a single workflow for all users, you could remove this function, + remove the user_id from each input, and just use a single workflow ID. + """ + return f"{WORKFLOW_ID_PREFIX}{user_id}" + + +@nexusrpc.handler.service_handler(service=NexusGreetingService) +class NexusGreetingServiceHandler: + def _get_workflow_handle( + self, user_id: str + ) -> WorkflowHandle[GreetingWorkflow, str]: + return nexus.client().get_workflow_handle_for( + GreetingWorkflow.run, get_workflow_id(user_id) + ) + + @nexusrpc.handler.sync_operation + async def get_languages( + self, ctx: nexusrpc.handler.StartOperationContext, input: GetLanguagesInput + ) -> GetLanguagesOutput: + return await self._get_workflow_handle(input.user_id).query( + GreetingWorkflow.get_languages, input + ) + + @nexusrpc.handler.sync_operation + async def get_language( + self, ctx: nexusrpc.handler.StartOperationContext, input: GetLanguageInput + ) -> Language: + return await self._get_workflow_handle(input.user_id).query( + GreetingWorkflow.get_language + ) + + # Routes to set_language_using_activity (not set_language) so that new languages not + # already in the greetings map can be fetched via an activity. + @nexusrpc.handler.sync_operation + async def set_language( + self, ctx: nexusrpc.handler.StartOperationContext, input: SetLanguageInput + ) -> Language: + return await self._get_workflow_handle(input.user_id).execute_update( + GreetingWorkflow.set_language_using_activity, input + ) + + @nexusrpc.handler.sync_operation + async def approve( + self, ctx: nexusrpc.handler.StartOperationContext, input: ApproveInput + ) -> ApproveOutput: + await self._get_workflow_handle(input.user_id).signal( + GreetingWorkflow.approve, input + ) + return ApproveOutput() diff --git a/nexus_messaging/callerpattern/handler/worker.py b/nexus_messaging/callerpattern/handler/worker.py new file mode 100644 index 000000000..fa8e2c0fa --- /dev/null +++ b/nexus_messaging/callerpattern/handler/worker.py @@ -0,0 +1,62 @@ +import asyncio +import logging +from typing import Optional + +from temporalio.client import Client +from temporalio.common import WorkflowIDConflictPolicy +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_messaging.callerpattern.handler.activities import call_greeting_service +from nexus_messaging.callerpattern.handler.service_handler import ( + NexusGreetingServiceHandler, + get_workflow_id, +) +from nexus_messaging.callerpattern.handler.workflows import GreetingWorkflow + +interrupt_event = asyncio.Event() + +NAMESPACE = "nexus-messaging-handler-namespace" +TASK_QUEUE = "nexus-messaging-handler-task-queue" +USER_ID = "user-1" + + +async def main(client: Optional[Client] = None): + logging.basicConfig(level=logging.INFO) + + if client is None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + # Start the long-running entity workflow that backs the Nexus service, + # if not already running. + workflow_id = get_workflow_id(USER_ID) + await client.start_workflow( + GreetingWorkflow.run, + id=workflow_id, + task_queue=TASK_QUEUE, + id_conflict_policy=WorkflowIDConflictPolicy.USE_EXISTING, + ) + logging.info("Started greeting workflow: %s", workflow_id) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[GreetingWorkflow], + activities=[call_greeting_service], + nexus_service_handlers=[NexusGreetingServiceHandler()], + ): + logging.info("Handler worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/nexus_messaging/callerpattern/handler/workflows.py b/nexus_messaging/callerpattern/handler/workflows.py new file mode 100644 index 000000000..fb10d8c5d --- /dev/null +++ b/nexus_messaging/callerpattern/handler/workflows.py @@ -0,0 +1,87 @@ +""" +A long-running "entity" workflow that backs the NexusGreetingService Nexus operations. +The workflow exposes queries, an update, and a signal. These are private implementation +details of the Nexus service: the caller only interacts via Nexus operations. +""" + +import asyncio +from datetime import timedelta + +from temporalio import workflow +from temporalio.exceptions import ApplicationError + +from nexus_messaging.callerpattern.handler.activities import call_greeting_service +from nexus_messaging.callerpattern.service import ( + ApproveInput, + GetLanguagesInput, + GetLanguagesOutput, + Language, + SetLanguageInput, +) + + +@workflow.defn +class GreetingWorkflow: + def __init__(self) -> None: + self.approved_for_release = False + self.greetings: dict[Language, str] = { + Language.CHINESE: "\u4f60\u597d\uff0c\u4e16\u754c", + Language.ENGLISH: "Hello, world", + } + self.language = Language.ENGLISH + self.lock = asyncio.Lock() + + @workflow.run + async def run(self) -> str: + # Wait until approved and all in-flight update handlers have finished. + await workflow.wait_condition( + lambda: self.approved_for_release and workflow.all_handlers_finished() + ) + return self.greetings[self.language] + + @workflow.query + def get_languages(self, input: GetLanguagesInput) -> GetLanguagesOutput: + if input.include_unsupported: + languages = sorted(Language) + else: + languages = sorted(self.greetings) + return GetLanguagesOutput(languages=languages) + + @workflow.query + def get_language(self) -> Language: + return self.language + + @workflow.signal + def approve(self, input: ApproveInput) -> None: + workflow.logger.info("Approval signal received for user %s", input.user_id) + self.approved_for_release = True + + @workflow.update + def set_language(self, input: SetLanguageInput) -> Language: + workflow.logger.info("setLanguage update received for user %s", input.user_id) + previous_language, self.language = self.language, input.language + return previous_language + + @set_language.validator + def validate_set_language(self, input: SetLanguageInput) -> None: + if input.language not in self.greetings: + raise ValueError(f"{input.language.name} is not supported") + + # Changes the active language, calling an activity to fetch a greeting for new + # languages not already in the greetings map. + @workflow.update + async def set_language_using_activity(self, input: SetLanguageInput) -> Language: + if input.language not in self.greetings: + async with self.lock: + greeting = await workflow.execute_activity( + call_greeting_service, + input.language, + start_to_close_timeout=timedelta(seconds=10), + ) + if greeting is None: + raise ApplicationError( + f"Greeting service does not support {input.language.name}" + ) + self.greetings[input.language] = greeting + previous_language, self.language = self.language, input.language + return previous_language diff --git a/nexus_messaging/callerpattern/service.py b/nexus_messaging/callerpattern/service.py new file mode 100644 index 000000000..23a550fb7 --- /dev/null +++ b/nexus_messaging/callerpattern/service.py @@ -0,0 +1,67 @@ +""" +Nexus service definition for the caller (entity) pattern. Shared between the handler and +caller. The caller uses this to create a type-safe Nexus client; the handler implements +the operations. + +Every operation includes a user_id so the handler knows which entity workflow to target. +""" + +from dataclasses import dataclass +from enum import IntEnum + +import nexusrpc + + +class Language(IntEnum): + ARABIC = 1 + CHINESE = 2 + ENGLISH = 3 + FRENCH = 4 + HINDI = 5 + PORTUGUESE = 6 + SPANISH = 7 + + +@dataclass +class GetLanguagesInput: + include_unsupported: bool + user_id: str + + +@dataclass +class GetLanguagesOutput: + languages: list[Language] + + +@dataclass +class GetLanguageInput: + user_id: str + + +@dataclass +class SetLanguageInput: + language: Language + user_id: str + + +@dataclass +class ApproveInput: + name: str + user_id: str + + +@dataclass +class ApproveOutput: + pass + + +@nexusrpc.service +class NexusGreetingService: + # Returns the languages supported by the greeting workflow. + get_languages: nexusrpc.Operation[GetLanguagesInput, GetLanguagesOutput] + # Returns the currently active language. + get_language: nexusrpc.Operation[GetLanguageInput, Language] + # Changes the active language, returning the previous one. + set_language: nexusrpc.Operation[SetLanguageInput, Language] + # Approves the workflow, allowing it to complete. + approve: nexusrpc.Operation[ApproveInput, ApproveOutput] diff --git a/nexus_messaging/endpoint_description.md b/nexus_messaging/endpoint_description.md new file mode 100644 index 000000000..4184134ba --- /dev/null +++ b/nexus_messaging/endpoint_description.md @@ -0,0 +1,14 @@ +## Services + +### [NexusGreetingService](https://github.com/temporalio/samples-python/blob/main/nexus_messaging/callerpattern/service.py) (callerpattern) +- operation: `get_languages` +- operation: `get_language` +- operation: `set_language` +- operation: `approve` + +### [NexusRemoteGreetingService](https://github.com/temporalio/samples-python/blob/main/nexus_messaging/ondemandpattern/service.py) (ondemandpattern) +- operation: `run_from_remote` +- operation: `get_languages` +- operation: `get_language` +- operation: `set_language` +- operation: `approve` diff --git a/nexus_messaging/ondemandpattern/README.md b/nexus_messaging/ondemandpattern/README.md new file mode 100644 index 000000000..b9ba67ff7 --- /dev/null +++ b/nexus_messaging/ondemandpattern/README.md @@ -0,0 +1,60 @@ +## On-demand pattern + +No Workflow is pre-started. The caller creates and controls Workflow instances through Nexus +operations. `NexusRemoteGreetingService` adds a `run_from_remote` operation that starts a new +`GreetingWorkflow`, and every other operation includes a `user_id` so the handler knows which +instance to target. + +The caller Workflow: +1. Starts two remote `GreetingWorkflow` instances via `run_from_remote` (backed by `workflow_run_operation`) +2. Queries each for supported languages +3. Changes the language on each (Arabic and Hindi) +4. Confirms the changes via queries +5. Approves both Workflows +6. Waits for each to complete and returns their results + +### Running + +Start a Temporal server: + +```bash +temporal server start-dev +``` + +Create the namespaces and Nexus endpoint: + +```bash +temporal operator namespace create --namespace nexus-messaging-handler-namespace +temporal operator namespace create --namespace nexus-messaging-caller-namespace + +temporal operator nexus endpoint create \ + --name nexus-messaging-nexus-endpoint \ + --target-namespace nexus-messaging-handler-namespace \ + --target-task-queue nexus-messaging-handler-task-queue +``` + +In one terminal, start the handler worker: + +```bash +uv run python -m nexus_messaging.ondemandpattern.handler.worker +``` + +In another terminal, run the following command to start the example: + +```bash +uv run python -m nexus_messaging.ondemandpattern.caller.app +``` + +Expected output: + +``` +started remote greeting workflow: UserId One +started remote greeting workflow: UserId Two +Supported languages for UserId One: [, ] +Supported languages for UserId Two: [, ] +UserId One changed language: ENGLISH -> ARABIC +UserId Two changed language: ENGLISH -> HINDI +Workflows approved +Workflow one result: مرحبا بالعالم +Workflow two result: नमस्ते दुनिया +``` diff --git a/nexus_messaging/ondemandpattern/__init__.py b/nexus_messaging/ondemandpattern/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_messaging/ondemandpattern/caller/__init__.py b/nexus_messaging/ondemandpattern/caller/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_messaging/ondemandpattern/caller/app.py b/nexus_messaging/ondemandpattern/caller/app.py new file mode 100644 index 000000000..a1837babb --- /dev/null +++ b/nexus_messaging/ondemandpattern/caller/app.py @@ -0,0 +1,41 @@ +import asyncio +import uuid +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_messaging.ondemandpattern.caller.workflows import CallerRemoteWorkflow + +NAMESPACE = "nexus-messaging-caller-namespace" +TASK_QUEUE = "nexus-messaging-caller-remote-task-queue" + + +async def execute_caller_workflow(client: Optional[Client] = None) -> None: + if client is None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[CallerRemoteWorkflow], + ): + log = await client.execute_workflow( + CallerRemoteWorkflow.run, + id=f"nexus-messaging-remote-caller-{uuid.uuid4()}", + task_queue=TASK_QUEUE, + ) + for line in log: + print(line) + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(execute_caller_workflow()) + except KeyboardInterrupt: + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/nexus_messaging/ondemandpattern/caller/workflows.py b/nexus_messaging/ondemandpattern/caller/workflows.py new file mode 100644 index 000000000..8dbbd4b24 --- /dev/null +++ b/nexus_messaging/ondemandpattern/caller/workflows.py @@ -0,0 +1,148 @@ +""" +A caller workflow that creates and controls workflow instances through Nexus operations. +Unlike the entity (callerpattern), no workflow is pre-started; the caller creates them +on demand via the run_from_remote operation. +""" + +from temporalio import workflow + +from nexus_messaging.ondemandpattern.service import ( + ApproveInput, + GetLanguageInput, + GetLanguagesInput, + Language, + NexusRemoteGreetingService, + RunFromRemoteInput, + SetLanguageInput, +) + +NEXUS_ENDPOINT = "nexus-messaging-nexus-endpoint" + +REMOTE_WORKFLOW_ONE = "UserId One" +REMOTE_WORKFLOW_TWO = "UserId Two" + + +@workflow.defn +class CallerRemoteWorkflow: + def __init__(self) -> None: + self.nexus_client = workflow.create_nexus_client( + service=NexusRemoteGreetingService, + endpoint=NEXUS_ENDPOINT, + ) + + @workflow.run + async def run(self) -> list[str]: + log: list[str] = [] + + # Each call is performed twice in this example. This assumes there are two + # users we want to process. The first calls start two workflows, one for each + # user. Subsequent calls perform different actions between the two users. + + # This is an async Nexus operation -- starts a workflow on the handler and + # returns a handle. Unlike the sync operations below, this does not block + # until the workflow completes. It is backed by workflow_run_operation on the + # handler side. + handle_one = await self.nexus_client.start_operation( + NexusRemoteGreetingService.run_from_remote, + RunFromRemoteInput(user_id=REMOTE_WORKFLOW_ONE), + ) + log.append(f"started remote greeting workflow: {REMOTE_WORKFLOW_ONE}") + workflow.logger.info("started remote greeting workflow %s", REMOTE_WORKFLOW_ONE) + + handle_two = await self.nexus_client.start_operation( + NexusRemoteGreetingService.run_from_remote, + RunFromRemoteInput(user_id=REMOTE_WORKFLOW_TWO), + ) + log.append(f"started remote greeting workflow: {REMOTE_WORKFLOW_TWO}") + workflow.logger.info("started remote greeting workflow %s", REMOTE_WORKFLOW_TWO) + + # Query the remote workflows for supported languages. + languages_output = await self.nexus_client.execute_operation( + NexusRemoteGreetingService.get_languages, + GetLanguagesInput(include_unsupported=False, user_id=REMOTE_WORKFLOW_ONE), + ) + log.append( + f"Supported languages for {REMOTE_WORKFLOW_ONE}: " + f"{languages_output.languages}" + ) + workflow.logger.info( + "supported languages are %s for workflow %s", + languages_output.languages, + REMOTE_WORKFLOW_ONE, + ) + + languages_output = await self.nexus_client.execute_operation( + NexusRemoteGreetingService.get_languages, + GetLanguagesInput(include_unsupported=False, user_id=REMOTE_WORKFLOW_TWO), + ) + log.append( + f"Supported languages for {REMOTE_WORKFLOW_TWO}: " + f"{languages_output.languages}" + ) + workflow.logger.info( + "supported languages are %s for workflow %s", + languages_output.languages, + REMOTE_WORKFLOW_TWO, + ) + + # Update the language on each remote workflow. + previous_language_one = await self.nexus_client.execute_operation( + NexusRemoteGreetingService.set_language, + SetLanguageInput(language=Language.ARABIC, user_id=REMOTE_WORKFLOW_ONE), + ) + + previous_language_two = await self.nexus_client.execute_operation( + NexusRemoteGreetingService.set_language, + SetLanguageInput(language=Language.HINDI, user_id=REMOTE_WORKFLOW_TWO), + ) + + # Confirm the changes by querying. + current_language = await self.nexus_client.execute_operation( + NexusRemoteGreetingService.get_language, + GetLanguageInput(user_id=REMOTE_WORKFLOW_ONE), + ) + log.append( + f"{REMOTE_WORKFLOW_ONE} changed language: " + f"{previous_language_one.name} -> {current_language.name}" + ) + workflow.logger.info( + "Language changed from %s to %s for workflow %s", + previous_language_one, + current_language, + REMOTE_WORKFLOW_ONE, + ) + + current_language = await self.nexus_client.execute_operation( + NexusRemoteGreetingService.get_language, + GetLanguageInput(user_id=REMOTE_WORKFLOW_TWO), + ) + log.append( + f"{REMOTE_WORKFLOW_TWO} changed language: " + f"{previous_language_two.name} -> {current_language.name}" + ) + workflow.logger.info( + "Language changed from %s to %s for workflow %s", + previous_language_two, + current_language, + REMOTE_WORKFLOW_TWO, + ) + + # Approve both workflows so they can complete. + await self.nexus_client.execute_operation( + NexusRemoteGreetingService.approve, + ApproveInput(name="remote-caller", user_id=REMOTE_WORKFLOW_ONE), + ) + await self.nexus_client.execute_operation( + NexusRemoteGreetingService.approve, + ApproveInput(name="remote-caller", user_id=REMOTE_WORKFLOW_TWO), + ) + log.append("Workflows approved") + + # Wait for the remote workflows to finish and return their results. + result = await handle_one + log.append(f"Workflow one result: {result}") + + result = await handle_two + log.append(f"Workflow two result: {result}") + + return log diff --git a/nexus_messaging/ondemandpattern/handler/__init__.py b/nexus_messaging/ondemandpattern/handler/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_messaging/ondemandpattern/handler/activities.py b/nexus_messaging/ondemandpattern/handler/activities.py new file mode 100644 index 000000000..ba0284894 --- /dev/null +++ b/nexus_messaging/ondemandpattern/handler/activities.py @@ -0,0 +1,22 @@ +import asyncio +from typing import Optional + +from temporalio import activity + +from nexus_messaging.ondemandpattern.service import Language + + +@activity.defn +async def call_greeting_service(language: Language) -> Optional[str]: + """Simulates a call to a remote greeting service. Returns None if unsupported.""" + greetings = { + Language.ARABIC: "\u0645\u0631\u062d\u0628\u0627 \u0628\u0627\u0644\u0639\u0627\u0644\u0645", + Language.CHINESE: "\u4f60\u597d\uff0c\u4e16\u754c", + Language.ENGLISH: "Hello, world", + Language.FRENCH: "Bonjour, monde", + Language.HINDI: "\u0928\u092e\u0938\u094d\u0924\u0947 \u0926\u0941\u0928\u093f\u092f\u093e", + Language.PORTUGUESE: "Ol\u00e1 mundo", + Language.SPANISH: "Hola mundo", + } + await asyncio.sleep(0.2) + return greetings.get(language) diff --git a/nexus_messaging/ondemandpattern/handler/service_handler.py b/nexus_messaging/ondemandpattern/handler/service_handler.py new file mode 100644 index 000000000..1351aae7a --- /dev/null +++ b/nexus_messaging/ondemandpattern/handler/service_handler.py @@ -0,0 +1,84 @@ +""" +Nexus operation handler for the on-demand pattern. Each operation receives the target +userId in its input, and run_from_remote starts a brand-new GreetingWorkflow. +""" + +from __future__ import annotations + +import nexusrpc +from temporalio import nexus +from temporalio.client import WorkflowHandle + +from nexus_messaging.ondemandpattern.handler.workflows import GreetingWorkflow +from nexus_messaging.ondemandpattern.service import ( + ApproveInput, + ApproveOutput, + GetLanguageInput, + GetLanguagesInput, + GetLanguagesOutput, + Language, + NexusRemoteGreetingService, + RunFromRemoteInput, + SetLanguageInput, +) + +WORKFLOW_ID_PREFIX = "GreetingWorkflow_for_" + + +@nexusrpc.handler.service_handler(service=NexusRemoteGreetingService) +class NexusRemoteGreetingServiceHandler: + def _get_workflow_id(self, user_id: str) -> str: + return WORKFLOW_ID_PREFIX + user_id + + def _get_workflow_handle( + self, user_id: str + ) -> WorkflowHandle[GreetingWorkflow, str]: + return nexus.client().get_workflow_handle_for( + GreetingWorkflow.run, self._get_workflow_id(user_id) + ) + + # Starts a new GreetingWorkflow with the caller-specified user ID. + # This is an async Nexus operation backed by workflow_run_operation. + @nexus.workflow_run_operation + async def run_from_remote( + self, ctx: nexus.WorkflowRunOperationContext, input: RunFromRemoteInput + ) -> nexus.WorkflowHandle[str]: + return await ctx.start_workflow( + GreetingWorkflow.run, + id=self._get_workflow_id(input.user_id), + ) + + @nexusrpc.handler.sync_operation + async def get_languages( + self, ctx: nexusrpc.handler.StartOperationContext, input: GetLanguagesInput + ) -> GetLanguagesOutput: + return await self._get_workflow_handle(input.user_id).query( + GreetingWorkflow.get_languages, input + ) + + @nexusrpc.handler.sync_operation + async def get_language( + self, ctx: nexusrpc.handler.StartOperationContext, input: GetLanguageInput + ) -> Language: + return await self._get_workflow_handle(input.user_id).query( + GreetingWorkflow.get_language, + ) + + # Routes to set_language_using_activity so that new languages not already in the + # greetings map can be fetched via an activity. + @nexusrpc.handler.sync_operation + async def set_language( + self, ctx: nexusrpc.handler.StartOperationContext, input: SetLanguageInput + ) -> Language: + return await self._get_workflow_handle(input.user_id).execute_update( + GreetingWorkflow.set_language_using_activity, input + ) + + @nexusrpc.handler.sync_operation + async def approve( + self, ctx: nexusrpc.handler.StartOperationContext, input: ApproveInput + ) -> ApproveOutput: + await self._get_workflow_handle(input.user_id).signal( + GreetingWorkflow.approve, input + ) + return ApproveOutput() diff --git a/nexus_sync_operations/handler/worker.py b/nexus_messaging/ondemandpattern/handler/worker.py similarity index 58% rename from nexus_sync_operations/handler/worker.py rename to nexus_messaging/ondemandpattern/handler/worker.py index 97c8eb04a..5eec9cfc2 100644 --- a/nexus_sync_operations/handler/worker.py +++ b/nexus_messaging/ondemandpattern/handler/worker.py @@ -6,14 +6,16 @@ from temporalio.envconfig import ClientConfig from temporalio.worker import Worker -from message_passing.introduction.activities import call_greeting_service -from message_passing.introduction.workflows import GreetingWorkflow -from nexus_sync_operations.handler.service_handler import GreetingServiceHandler +from nexus_messaging.ondemandpattern.handler.activities import call_greeting_service +from nexus_messaging.ondemandpattern.handler.service_handler import ( + NexusRemoteGreetingServiceHandler, +) +from nexus_messaging.ondemandpattern.handler.workflows import GreetingWorkflow interrupt_event = asyncio.Event() -NAMESPACE = "nexus-sync-operations-handler-namespace" -TASK_QUEUE = "nexus-sync-operations-handler-task-queue" +NAMESPACE = "nexus-messaging-handler-namespace" +TASK_QUEUE = "nexus-messaging-handler-task-queue" async def main(client: Optional[Client] = None): @@ -25,20 +27,14 @@ async def main(client: Optional[Client] = None): config.setdefault("namespace", NAMESPACE) client = await Client.connect(**config) - # Create the nexus service handler instance, starting the long-running entity workflow that - # backs the Nexus service - greeting_service_handler = await GreetingServiceHandler.create( - "nexus-sync-operations-greeting-workflow", client, TASK_QUEUE - ) - async with Worker( client, task_queue=TASK_QUEUE, workflows=[GreetingWorkflow], activities=[call_greeting_service], - nexus_service_handlers=[greeting_service_handler], + nexus_service_handlers=[NexusRemoteGreetingServiceHandler()], ): - logging.info("Worker started, ctrl+c to exit") + logging.info("Handler worker started, ctrl+c to exit") await interrupt_event.wait() logging.info("Shutting down") diff --git a/nexus_messaging/ondemandpattern/handler/workflows.py b/nexus_messaging/ondemandpattern/handler/workflows.py new file mode 100644 index 000000000..99fc57289 --- /dev/null +++ b/nexus_messaging/ondemandpattern/handler/workflows.py @@ -0,0 +1,88 @@ +""" +A long-running "entity" workflow that backs the NexusRemoteGreetingService Nexus +operations. The workflow exposes queries, an update, and a signal. These are private +implementation details of the Nexus service: the caller only interacts via Nexus +operations. +""" + +import asyncio +from datetime import timedelta + +from temporalio import workflow +from temporalio.exceptions import ApplicationError + +from nexus_messaging.ondemandpattern.handler.activities import call_greeting_service +from nexus_messaging.ondemandpattern.service import ( + ApproveInput, + GetLanguagesInput, + GetLanguagesOutput, + Language, + SetLanguageInput, +) + + +@workflow.defn +class GreetingWorkflow: + def __init__(self) -> None: + self.approved_for_release = False + self.greetings: dict[Language, str] = { + Language.CHINESE: "\u4f60\u597d\uff0c\u4e16\u754c", + Language.ENGLISH: "Hello, world", + } + self.language = Language.ENGLISH + self.lock = asyncio.Lock() + + @workflow.run + async def run(self) -> str: + # Wait until approved and all in-flight update handlers have finished. + await workflow.wait_condition( + lambda: self.approved_for_release and workflow.all_handlers_finished() + ) + return self.greetings[self.language] + + @workflow.query + def get_languages(self, input: GetLanguagesInput) -> GetLanguagesOutput: + if input.include_unsupported: + languages = sorted(Language) + else: + languages = sorted(self.greetings) + return GetLanguagesOutput(languages=languages) + + @workflow.query + def get_language(self) -> Language: + return self.language + + @workflow.signal + def approve(self, input: ApproveInput) -> None: + workflow.logger.info("Approval signal received for user %s", input.user_id) + self.approved_for_release = True + + @workflow.update + def set_language(self, input: SetLanguageInput) -> Language: + workflow.logger.info("setLanguage update received for user %s", input.user_id) + previous_language, self.language = self.language, input.language + return previous_language + + @set_language.validator + def validate_set_language(self, input: SetLanguageInput) -> None: + if input.language not in self.greetings: + raise ValueError(f"{input.language.name} is not supported") + + # Changes the active language, calling an activity to fetch a greeting for new + # languages not already in the greetings map. + @workflow.update + async def set_language_using_activity(self, input: SetLanguageInput) -> Language: + if input.language not in self.greetings: + async with self.lock: + greeting = await workflow.execute_activity( + call_greeting_service, + input.language, + start_to_close_timeout=timedelta(seconds=10), + ) + if greeting is None: + raise ApplicationError( + f"Greeting service does not support {input.language.name}" + ) + self.greetings[input.language] = greeting + previous_language, self.language = self.language, input.language + return previous_language diff --git a/nexus_messaging/ondemandpattern/service.py b/nexus_messaging/ondemandpattern/service.py new file mode 100644 index 000000000..8f347d32b --- /dev/null +++ b/nexus_messaging/ondemandpattern/service.py @@ -0,0 +1,72 @@ +""" +Nexus service definition for the on-demand pattern. Every operation includes a userId +so the caller controls which workflow instance is targeted. This also exposes a +run_from_remote operation that starts a new GreetingWorkflow. +""" + +from dataclasses import dataclass +from enum import IntEnum + +import nexusrpc + + +class Language(IntEnum): + ARABIC = 1 + CHINESE = 2 + ENGLISH = 3 + FRENCH = 4 + HINDI = 5 + PORTUGUESE = 6 + SPANISH = 7 + + +@dataclass +class RunFromRemoteInput: + user_id: str + + +@dataclass +class GetLanguagesInput: + include_unsupported: bool + user_id: str + + +@dataclass +class GetLanguagesOutput: + languages: list[Language] + + +@dataclass +class GetLanguageInput: + user_id: str + + +@dataclass +class SetLanguageInput: + language: Language + user_id: str + + +@dataclass +class ApproveInput: + name: str + user_id: str + + +@dataclass +class ApproveOutput: + pass + + +@nexusrpc.service +class NexusRemoteGreetingService: + # Starts a new GreetingWorkflow with the given workflow ID (asynchronous). + run_from_remote: nexusrpc.Operation[RunFromRemoteInput, str] + # Returns the languages supported by the specified workflow. + get_languages: nexusrpc.Operation[GetLanguagesInput, GetLanguagesOutput] + # Returns the currently active language of the specified workflow. + get_language: nexusrpc.Operation[GetLanguageInput, Language] + # Changes the active language on the specified workflow, returning the previous one. + set_language: nexusrpc.Operation[SetLanguageInput, Language] + # Approves the specified workflow, allowing it to complete. + approve: nexusrpc.Operation[ApproveInput, ApproveOutput] diff --git a/nexus_sync_operations/README.md b/nexus_sync_operations/README.md deleted file mode 100644 index 10e266eca..000000000 --- a/nexus_sync_operations/README.md +++ /dev/null @@ -1,39 +0,0 @@ -This sample shows how to create a Nexus service that is backed by a long-running workflow and -exposes operations that execute updates and queries against that workflow. The long-running -workflow, and the updates/queries are private implementation detail of the nexus service: the caller -does not know how the operations are implemented. - -### Sample directory structure - -- [service.py](./service.py) - shared Nexus service definition -- [caller](./caller) - a caller workflow that executes Nexus operations, together with a worker and starter code -- [handler](./handler) - Nexus operation handlers, together with a workflow used by one of the Nexus operations, and a worker that polls for both workflow, activity, and Nexus tasks. - - -### Instructions - -Start a Temporal server. (See the main samples repo [README](../README.md)). - -Run the following to create the caller and handler namespaces, and the Nexus endpoint: - -``` -temporal operator namespace create --namespace nexus-sync-operations-handler-namespace -temporal operator namespace create --namespace nexus-sync-operations-caller-namespace - -temporal operator nexus endpoint create \ - --name nexus-sync-operations-nexus-endpoint \ - --target-namespace nexus-sync-operations-handler-namespace \ - --target-task-queue nexus-sync-operations-handler-task-queue \ - --description-file nexus_sync_operations/endpoint_description.md -``` - -In one terminal, run the Temporal worker in the handler namespace: -``` -uv run nexus_sync_operations/handler/worker.py -``` - -In another terminal, run the Temporal worker in the caller namespace and start the caller -workflow: -``` -uv run nexus_sync_operations/caller/app.py -``` diff --git a/nexus_sync_operations/caller/workflows.py b/nexus_sync_operations/caller/workflows.py deleted file mode 100644 index a358d7646..000000000 --- a/nexus_sync_operations/caller/workflows.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -This is a workflow that calls nexus operations. The caller does not have information about how these -operations are implemented by the nexus service. -""" - -from temporalio import workflow - -from message_passing.introduction import Language -from message_passing.introduction.workflows import GetLanguagesInput, SetLanguageInput - -with workflow.unsafe.imports_passed_through(): - from nexus_sync_operations.service import GreetingService - -NEXUS_ENDPOINT = "nexus-sync-operations-nexus-endpoint" - - -@workflow.defn -class CallerWorkflow: - @workflow.run - async def run(self) -> list[str]: - log = [] - nexus_client = workflow.create_nexus_client( - service=GreetingService, - endpoint=NEXUS_ENDPOINT, - ) - - # Get supported languages - supported_languages = await nexus_client.execute_operation( - GreetingService.get_languages, GetLanguagesInput(include_unsupported=False) - ) - log.append(f"supported languages: {supported_languages}") - - # Set language - previous_language = await nexus_client.execute_operation( - GreetingService.set_language, - SetLanguageInput(language=Language.ARABIC), - ) - assert ( - await nexus_client.execute_operation(GreetingService.get_language, None) - == Language.ARABIC - ) - log.append( - f"language changed: {previous_language.name} -> {Language.ARABIC.name}" - ) - - return log diff --git a/nexus_sync_operations/endpoint_description.md b/nexus_sync_operations/endpoint_description.md deleted file mode 100644 index a33b60cfb..000000000 --- a/nexus_sync_operations/endpoint_description.md +++ /dev/null @@ -1,4 +0,0 @@ -## Service: [GreetingService](https://github.com/temporalio/samples-python/blob/main/nexus_sync_operations/service.py) -- operation: `get_languages` -- operation: `get_language` -- operation: `set_language` diff --git a/nexus_sync_operations/handler/service_handler.py b/nexus_sync_operations/handler/service_handler.py deleted file mode 100644 index 626948f0b..000000000 --- a/nexus_sync_operations/handler/service_handler.py +++ /dev/null @@ -1,83 +0,0 @@ -""" -This file demonstrates how to implement a Nexus service that is backed by a long-running workflow -and exposes operations that perform updates and queries against that workflow. -""" - -from __future__ import annotations - -import nexusrpc -from temporalio import nexus -from temporalio.client import Client, WorkflowHandle -from temporalio.common import WorkflowIDConflictPolicy - -from message_passing.introduction import Language -from message_passing.introduction.workflows import ( - GetLanguagesInput, - GreetingWorkflow, - SetLanguageInput, -) -from nexus_sync_operations.service import GreetingService - - -@nexusrpc.handler.service_handler(service=GreetingService) -class GreetingServiceHandler: - def __init__(self, workflow_id: str): - self.workflow_id = workflow_id - - @classmethod - async def create( - cls, workflow_id: str, client: Client, task_queue: str - ) -> GreetingServiceHandler: - # Start the long-running "entity" workflow, if it is not already running. - await client.start_workflow( - GreetingWorkflow.run, - id=workflow_id, - task_queue=task_queue, - id_conflict_policy=WorkflowIDConflictPolicy.USE_EXISTING, - ) - return cls(workflow_id) - - @property - def greeting_workflow_handle(self) -> WorkflowHandle[GreetingWorkflow, str]: - # In nexus operation handler code, nexus.client() is always available, returning a client - # connected to the handler namespace (it's the same client instance that your nexus worker - # is using to poll the server for nexus tasks). This client can be used to interact with the - # handler namespace, for example to send signals, queries, or updates. Remember however, - # that a sync_operation handler must return quickly (no more than a few seconds). To do - # long-running work in a nexus operation handler, use - # temporalio.nexus.workflow_run_operation (see the hello_nexus sample). - return nexus.client().get_workflow_handle_for( - GreetingWorkflow.run, self.workflow_id - ) - - # 👉 This is a handler for a nexus operation whose internal implementation involves executing a - # query against a long-running workflow that is private to the nexus service. - @nexusrpc.handler.sync_operation - async def get_languages( - self, ctx: nexusrpc.handler.StartOperationContext, input: GetLanguagesInput - ) -> list[Language]: - return await self.greeting_workflow_handle.query( - GreetingWorkflow.get_languages, input - ) - - # 👉 This is a handler for a nexus operation whose internal implementation involves executing a - # query against a long-running workflow that is private to the nexus service. - @nexusrpc.handler.sync_operation - async def get_language( - self, ctx: nexusrpc.handler.StartOperationContext, input: None - ) -> Language: - return await self.greeting_workflow_handle.query(GreetingWorkflow.get_language) - - # 👉 This is a handler for a nexus operation whose internal implementation involves executing an - # update against a long-running workflow that is private to the nexus service. Although updates - # can run for an arbitrarily long time, when exposing an update via a nexus sync operation the - # update should execute quickly (sync operations must complete in under 10s). - @nexusrpc.handler.sync_operation - async def set_language( - self, - ctx: nexusrpc.handler.StartOperationContext, - input: SetLanguageInput, - ) -> Language: - return await self.greeting_workflow_handle.execute_update( - GreetingWorkflow.set_language_using_activity, input - ) diff --git a/nexus_sync_operations/service.py b/nexus_sync_operations/service.py deleted file mode 100644 index 3436d5f3f..000000000 --- a/nexus_sync_operations/service.py +++ /dev/null @@ -1,20 +0,0 @@ -""" -This module defines a Nexus service that exposes three operations. - -It is used by the nexus service handler to validate that the operation handlers implement the -correct input and output types, and by the caller workflow to create a type-safe client. It does not -contain the implementation of the operations; see nexus_sync_operations.handler.service_handler for -that. -""" - -import nexusrpc - -from message_passing.introduction import Language -from message_passing.introduction.workflows import GetLanguagesInput, SetLanguageInput - - -@nexusrpc.service -class GreetingService: - get_languages: nexusrpc.Operation[GetLanguagesInput, list[Language]] - get_language: nexusrpc.Operation[None, Language] - set_language: nexusrpc.Operation[SetLanguageInput, Language] diff --git a/pyproject.toml b/pyproject.toml index 9c9bace14..f57bc875d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] requires-python = ">=3.10" readme = "README.md" license = "MIT" -dependencies = ["temporalio>=1.27.0,<2"] +dependencies = ["temporalio>=1.26.0,<2"] [project.urls] Homepage = "https://github.com/temporalio/samples-python" @@ -33,7 +33,7 @@ gevent = ["gevent>=25.4.2 ; python_version >= '3.8'"] langsmith-tracing = [ "openai>=1.4.0", "langsmith>=0.7.0", - "temporalio[pydantic,langsmith]>=1.27.0", + "temporalio[pydantic,langsmith]>=1.26.0", ] nexus = ["nexus-rpc>=1.1.0,<2"] open-telemetry = [ @@ -42,15 +42,14 @@ open-telemetry = [ ] openai-agents = [ "openai-agents[litellm] >= 0.14.1", - "temporalio[openai-agents,opentelemetry] >= 1.27.0", + "temporalio[openai-agents,opentelemetry] >= 1.26.0", "requests>=2.32.0,<3", ] pydantic-converter = ["pydantic>=2.10.6,<3"] sentry = ["sentry-sdk>=2.13.0"] trio-async = ["trio>=0.28.0,<0.29", "trio-asyncio>=0.15.0,<0.16"] -llm-stream = ["openai>=1.0"] cloud-export-to-parquet = [ - "pandas>=2.2.2,<3 ; python_version >= '3.10' and python_version < '4.0'", + "pandas>=2.3.3,<3 ; python_version >= '3.10' and python_version < '4.0'", "numpy>=1.26.0,<2 ; python_version >= '3.10' and python_version < '3.13'", "boto3>=1.34.89,<2", "pyarrow>=19.0.1", @@ -95,7 +94,6 @@ packages = [ "updatable_timer", "worker_specific_task_queues", "worker_versioning", - "workflow_streams", ] [tool.hatch.build.targets.wheel.sources] diff --git a/tests/nexus_sync_operations/nexus_sync_operations_test.py b/tests/nexus_messaging/callerpattern_test.py similarity index 55% rename from tests/nexus_sync_operations/nexus_sync_operations_test.py rename to tests/nexus_messaging/callerpattern_test.py index d74168cb9..d14b4c29e 100644 --- a/tests/nexus_sync_operations/nexus_sync_operations_test.py +++ b/tests/nexus_messaging/callerpattern_test.py @@ -8,18 +8,18 @@ from temporalio.testing import WorkflowEnvironment from temporalio.worker import Worker -import nexus_sync_operations.handler.service_handler -import nexus_sync_operations.handler.worker -from message_passing.introduction import Language -from message_passing.introduction.workflows import GetLanguagesInput, SetLanguageInput -from nexus_sync_operations.caller.workflows import CallerWorkflow +import nexus_messaging.callerpattern.handler.worker +from nexus_messaging.callerpattern.caller.workflows import CallerWorkflow +from nexus_messaging.callerpattern.service import ( + GetLanguageInput, + GetLanguagesInput, + Language, + NexusGreetingService, + SetLanguageInput, +) from tests.helpers.nexus import create_nexus_endpoint, delete_nexus_endpoint -with workflow.unsafe.imports_passed_through(): - from nexus_sync_operations.service import GreetingService - - -NEXUS_ENDPOINT = "nexus-sync-operations-nexus-endpoint" +NEXUS_ENDPOINT = "nexus-messaging-nexus-endpoint" @workflow.defn @@ -27,89 +27,90 @@ class TestCallerWorkflow: """Test workflow that calls Nexus operations and makes assertions.""" @workflow.run - async def run(self) -> None: + async def run(self, user_id: str) -> None: nexus_client = workflow.create_nexus_client( - service=GreetingService, + service=NexusGreetingService, endpoint=NEXUS_ENDPOINT, ) supported_languages = await nexus_client.execute_operation( - GreetingService.get_languages, GetLanguagesInput(include_unsupported=False) + NexusGreetingService.get_languages, + GetLanguagesInput(include_unsupported=False, user_id=user_id), ) - assert supported_languages == [Language.CHINESE, Language.ENGLISH] + assert supported_languages.languages == [Language.CHINESE, Language.ENGLISH] initial_language = await nexus_client.execute_operation( - GreetingService.get_language, None + NexusGreetingService.get_language, + GetLanguageInput(user_id=user_id), ) assert initial_language == Language.ENGLISH previous_language = await nexus_client.execute_operation( - GreetingService.set_language, - SetLanguageInput(language=Language.CHINESE), + NexusGreetingService.set_language, + SetLanguageInput(language=Language.CHINESE, user_id=user_id), ) assert previous_language == Language.ENGLISH current_language = await nexus_client.execute_operation( - GreetingService.get_language, None + NexusGreetingService.get_language, + GetLanguageInput(user_id=user_id), ) assert current_language == Language.CHINESE previous_language = await nexus_client.execute_operation( - GreetingService.set_language, - SetLanguageInput(language=Language.ARABIC), + NexusGreetingService.set_language, + SetLanguageInput(language=Language.ARABIC, user_id=user_id), ) assert previous_language == Language.CHINESE current_language = await nexus_client.execute_operation( - GreetingService.get_language, None + NexusGreetingService.get_language, + GetLanguageInput(user_id=user_id), ) assert current_language == Language.ARABIC -async def test_nexus_sync_operations(client: Client, env: WorkflowEnvironment): +async def test_callerpattern(client: Client, env: WorkflowEnvironment): if env.supports_time_skipping: pytest.skip("Nexus tests don't work under the Java test server") await _run_caller_workflow(client, TestCallerWorkflow) -async def test_nexus_sync_operations_caller_workflow( - client: Client, env: WorkflowEnvironment -): - """ - Runs the CallerWorkflow from the sample to ensure it executes without errors. - """ +async def test_callerpattern_caller_workflow(client: Client, env: WorkflowEnvironment): + """Runs the CallerWorkflow from the sample to ensure it executes without errors.""" if env.supports_time_skipping: pytest.skip("Nexus tests don't work under the Java test server") await _run_caller_workflow(client, CallerWorkflow) -async def _run_caller_workflow(client: Client, workflow: Type): +async def _run_caller_workflow(client: Client, wf: Type): create_response = await create_nexus_endpoint( name=NEXUS_ENDPOINT, - task_queue=nexus_sync_operations.handler.worker.TASK_QUEUE, + task_queue=nexus_messaging.callerpattern.handler.worker.TASK_QUEUE, client=client, ) try: handler_worker_task = asyncio.create_task( - nexus_sync_operations.handler.worker.main(client) + nexus_messaging.callerpattern.handler.worker.main(client) ) try: async with Worker( client, task_queue="test-caller-task-queue", - workflows=[workflow], + workflows=[wf], ): await client.execute_workflow( - workflow.run, + wf.run, + arg="user-1", id=str(uuid.uuid4()), task_queue="test-caller-task-queue", ) finally: - nexus_sync_operations.handler.worker.interrupt_event.set() + nexus_messaging.callerpattern.handler.worker.interrupt_event.set() await handler_worker_task - nexus_sync_operations.handler.worker.interrupt_event.clear() + nexus_messaging.callerpattern.handler.worker.interrupt_event.clear() finally: await delete_nexus_endpoint( id=create_response.endpoint.id, diff --git a/tests/nexus_messaging/ondemandpattern_test.py b/tests/nexus_messaging/ondemandpattern_test.py new file mode 100644 index 000000000..75fd761c0 --- /dev/null +++ b/tests/nexus_messaging/ondemandpattern_test.py @@ -0,0 +1,130 @@ +import asyncio +import uuid +from typing import Type + +import pytest +from temporalio import workflow +from temporalio.client import Client +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +import nexus_messaging.ondemandpattern.handler.worker +from nexus_messaging.ondemandpattern.caller.workflows import CallerRemoteWorkflow +from nexus_messaging.ondemandpattern.service import ( + ApproveInput, + GetLanguageInput, + GetLanguagesInput, + Language, + NexusRemoteGreetingService, + RunFromRemoteInput, + SetLanguageInput, +) +from tests.helpers.nexus import create_nexus_endpoint, delete_nexus_endpoint + +NEXUS_ENDPOINT = "nexus-messaging-nexus-endpoint" + + +@workflow.defn +class TestCallerRemoteWorkflow: + """Test workflow that creates remote workflows and makes assertions.""" + + @workflow.run + async def run(self) -> None: + nexus_client = workflow.create_nexus_client( + service=NexusRemoteGreetingService, + endpoint=NEXUS_ENDPOINT, + ) + + workflow_id = f"test-remote-{workflow.uuid4()}" + + # Start a remote workflow. + handle = await nexus_client.start_operation( + NexusRemoteGreetingService.run_from_remote, + RunFromRemoteInput(user_id=workflow_id), + ) + + # Query for supported languages. + languages_output = await nexus_client.execute_operation( + NexusRemoteGreetingService.get_languages, + GetLanguagesInput(include_unsupported=False, user_id=workflow_id), + ) + assert languages_output.languages == [Language.CHINESE, Language.ENGLISH] + + # Check initial language. + initial_language = await nexus_client.execute_operation( + NexusRemoteGreetingService.get_language, + GetLanguageInput(user_id=workflow_id), + ) + assert initial_language == Language.ENGLISH + + # Set language. + previous_language = await nexus_client.execute_operation( + NexusRemoteGreetingService.set_language, + SetLanguageInput(language=Language.ARABIC, user_id=workflow_id), + ) + assert previous_language == Language.ENGLISH + + current_language = await nexus_client.execute_operation( + NexusRemoteGreetingService.get_language, + GetLanguageInput(user_id=workflow_id), + ) + assert current_language == Language.ARABIC + + # Approve and wait for result. + await nexus_client.execute_operation( + NexusRemoteGreetingService.approve, + ApproveInput(name="test", user_id=workflow_id), + ) + + result = await handle + assert "\u0645\u0631\u062d\u0628\u0627" in result # Arabic greeting + + +async def test_ondemandpattern(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip("Nexus tests don't work under the Java test server") + + await _run_caller_workflow(client, TestCallerRemoteWorkflow) + + +async def test_ondemandpattern_caller_workflow( + client: Client, env: WorkflowEnvironment +): + """Runs the CallerRemoteWorkflow from the sample to ensure it executes without errors.""" + if env.supports_time_skipping: + pytest.skip("Nexus tests don't work under the Java test server") + + await _run_caller_workflow(client, CallerRemoteWorkflow) + + +async def _run_caller_workflow(client: Client, wf: Type): + create_response = await create_nexus_endpoint( + name=NEXUS_ENDPOINT, + task_queue=nexus_messaging.ondemandpattern.handler.worker.TASK_QUEUE, + client=client, + ) + try: + handler_worker_task = asyncio.create_task( + nexus_messaging.ondemandpattern.handler.worker.main(client) + ) + try: + async with Worker( + client, + task_queue="test-caller-remote-task-queue", + workflows=[wf], + ): + await client.execute_workflow( + wf.run, + id=str(uuid.uuid4()), + task_queue="test-caller-remote-task-queue", + ) + finally: + nexus_messaging.ondemandpattern.handler.worker.interrupt_event.set() + await handler_worker_task + nexus_messaging.ondemandpattern.handler.worker.interrupt_event.clear() + finally: + await delete_nexus_endpoint( + id=create_response.endpoint.id, + version=create_response.endpoint.version, + client=client, + ) diff --git a/uv.lock b/uv.lock index e025dab89..106627c34 100644 --- a/uv.lock +++ b/uv.lock @@ -1556,7 +1556,7 @@ wheels = [ [[package]] name = "pandas" -version = "2.3.1" +version = "2.3.3" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "numpy" }, @@ -1564,42 +1564,55 @@ dependencies = [ { name = "pytz" }, { 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[package.optional-dependencies] @@ -2589,9 +2602,6 @@ langsmith-tracing = [ { name = "openai" }, { name = "temporalio", extra = ["langsmith", "pydantic"] }, ] -llm-stream = [ - { name = "openai" }, -] nexus = [ { name = "nexus-rpc" }, ] @@ -2616,14 +2626,14 @@ trio-async = [ ] [package.metadata] -requires-dist = [{ name = "temporalio", specifier = ">=1.27.0,<2" }] +requires-dist = [{ name = "temporalio", specifier = ">=1.26.0,<2" }] [package.metadata.requires-dev] bedrock = [{ name = "boto3", specifier = ">=1.34.92,<2" }] cloud-export-to-parquet = [ { name = "boto3", specifier = ">=1.34.89,<2" }, { name = "numpy", marker = "python_full_version >= '3.10' and python_full_version < '3.13'", specifier = ">=1.26.0,<2" }, - { name = "pandas", marker = "python_full_version >= '3.10' and python_full_version < '4'", specifier = ">=2.2.2,<3" }, + { name = "pandas", marker = "python_full_version >= '3.10' and python_full_version < '4'", specifier = ">=2.3.3,<3" }, { name = "pyarrow", specifier = ">=19.0.1" }, ] dev = [ @@ -2650,9 +2660,8 @@ gevent = [{ name = "gevent", marker = "python_full_version >= '3.8'", specifier langsmith-tracing = [ { name = "langsmith", specifier = ">=0.7.0" }, { name = "openai", specifier = ">=1.4.0" }, - { name = "temporalio", extras = ["pydantic", "langsmith"], specifier = ">=1.27.0" }, + { name = "temporalio", extras = ["pydantic", "langsmith"], specifier = ">=1.26.0" }, ] -llm-stream = [{ name = "openai", specifier = ">=1.0" }] nexus = [{ name = "nexus-rpc", specifier = ">=1.1.0,<2" }] open-telemetry = [ { name = "opentelemetry-exporter-otlp-proto-grpc" }, @@ -2661,7 +2670,7 @@ open-telemetry = [ openai-agents = [ { name = "openai-agents", extras = ["litellm"], specifier = ">=0.14.1" }, { name = "requests", specifier = ">=2.32.0,<3" }, - { name = "temporalio", extras = ["openai-agents", "opentelemetry"], specifier = ">=1.27.0" }, + { name = "temporalio", extras = ["openai-agents", "opentelemetry"], specifier = ">=1.26.0" }, ] pydantic-converter = [{ name = "pydantic", specifier = ">=2.10.6,<3" }] sentry = [{ name = "sentry-sdk", specifier = ">=2.13.0" }] From 601f5d46662e6b34fb9004586dac4ba4e44d25ce Mon Sep 17 00:00:00 2001 From: David Hyde Date: Mon, 4 May 2026 14:44:50 -0500 Subject: [PATCH 20/40] [AI-72] LangGraph plugin samples (#289) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add LangGraph plugin samples Seven samples demonstrating the Temporal LangGraph plugin across both the Graph API and Functional API: - Human-in-the-loop: interrupt() + Temporal signals for chatbot approval - Continue-as-new: task result caching across workflow boundaries - ReAct agent: tool-calling loop with conditional edges / while loop - Control flow (Functional API only): parallel, for-loop, if/else Related SDK PR: https://github.com/temporalio/sdk-python/pull/1448 * Remove explicit langchain deps from langgraph group to avoid conflict * Declare langchain and langgraph as conflicting dependency groups * Fix import sorting and formatting * Update dependencies and lint config (temporarily - waiting for SDK PR merge) so that CI otherwise passes * Add hello_world samples, per-sample READMEs, tests, __init__ docstrings, and configurable client * Address review: consistent v2 API, unique thread_id, SDK prereq note * Remove SDK prereq note - PR won't merge until plugin is released * Add LangSmith tracing sample (Graph API + Functional API) Combines LangGraphPlugin (durable execution) with LangSmithPlugin (observability) for full tracing of LLM calls through Temporal workflows. * Update all samples to match SDK PR #1448 API - graphs parameter is now a list, not a dict - No graph()/entrypoint() lookup functions — reference objects directly - No entrypoints parameter on LangGraphPlugin - Use set_cache()/get_cache() for continue-as-new caching - Update tests and READMEs accordingly * Update LangGraph samples to current sdk-python main API The plugin API has evolved past PR #1448: - LangGraphPlugin(graphs=...) now takes dict[str, StateGraph], not list - Same for entrypoints (dict[str, Pregel]) - Each node/task must declare execute_in: "activity" | "workflow" in metadata - Workflows look up registered graphs/entrypoints via graph(name) / entrypoint(name) instead of importing them directly - get_cache/set_cache replaced by cache() and graph(name, cache=...) / entrypoint(name, cache=...) Other changes: - State schemas converted from primitives (str, int) to TypedDict, since langgraph's StateT is bound to TypedDict / dataclass / pydantic models - Module-level graph instances replaced with make__graph() factories because LangGraphPlugin mutates node metadata in-place at construction time, which prevented reuse across tests - langgraph dep group now pulls langchain, langchain-anthropic, and temporalio[langgraph,langsmith] so the langsmith_tracing samples and human_in_the_loop's RunnableConfig usage actually resolve * CI: install protoc for building temporalio from sdk-python git source * Skip LangGraph functional API + interrupt tests on Python < 3.11 These features rely on contextvars propagation through asyncio.create_task(), which is only available in Python 3.11+. On 3.10 they hang rather than fail cleanly, so CI sat for an hour before today's fix landed. Mirrors the pytestmark skipif used in sdk-python's own contrib/langgraph tests. * Address review feedback on LangGraph samples - Drop cross-references between Functional API and Graph API READMEs so each stands alone (review comment 1) - Combine run_worker.py + run_workflow.py into a single main.py for the langsmith_tracing samples to reduce setup steps (review comment 2) - Fix workflow -> Workflow casing (review comment 3) - Alias temporalio's entrypoint and graph helpers as temporal_entrypoint / temporal_graph at import sites to avoid the langgraph.func.entrypoint collision (review comment 4) - Demonstrate @traceable in three places in both langsmith_tracing samples: on the Activity/task itself, on a helper called from the Activity, and on a helper called from the Workflow (review comment 5) - Drop unnecessary single-task list comprehension for activity_options in hello_world and langsmith_tracing functional samples (review comment 6) - Spell out 'temporal server start-dev' in every sample README's prerequisites (review comment 7) - Fix stale README references to get_cache() now that the API is cache() (review comment 8) - Rename arbitrarily-named ETL pipeline functions in continue_as_new to honest math names (double / add_50 / triple) per review comment 9 * Update LangGraph samples for temporalio 1.27.0 release - Drop the [tool.uv.sources] git pin; consume temporalio from PyPI now that 1.27.0 ships the LangGraph plugin. - Keep the protoc CI step around as a permanent fixture so we don't have to add and remove it each time we iterate on a new plugin sample. - Replace dict-comprehension activity_options with explicit per-task literals in the Functional API samples. - Emphasize workflow.wait_condition() in the human-in-the-loop READMEs as the durable wait primitive that pairs with interrupt() and signals. - Clarify that the task result cache is provided by the LangGraph plugin (Temporal), not by upstream LangGraph. - Drop "Same pattern as the Graph API version" cross-references from Functional API workflow docstrings; readers pick one API style. - Tighten the Functional API LangSmith tracing README copy. - Point the parent README to the released contrib package docs. --- .github/workflows/ci.yml | 8 + README.md | 1 + langgraph_plugin/README.md | 73 +++ langgraph_plugin/__init__.py | 1 + langgraph_plugin/functional_api/__init__.py | 1 + .../functional_api/continue_as_new/README.md | 36 ++ .../continue_as_new/__init__.py | 1 + .../continue_as_new/run_worker.py | 37 ++ .../continue_as_new/run_workflow.py | 31 ++ .../continue_as_new/workflow.py | 92 ++++ .../functional_api/control_flow/README.md | 37 ++ .../functional_api/control_flow/__init__.py | 1 + .../functional_api/control_flow/run_worker.py | 37 ++ .../control_flow/run_workflow.py | 38 ++ .../functional_api/control_flow/workflow.py | 113 +++++ .../functional_api/hello_world/README.md | 29 ++ .../functional_api/hello_world/__init__.py | 1 + .../functional_api/hello_world/run_worker.py | 37 ++ .../hello_world/run_workflow.py | 27 ++ .../functional_api/hello_world/workflow.py | 40 ++ .../human_in_the_loop/README.md | 38 ++ .../human_in_the_loop/__init__.py | 1 + .../human_in_the_loop/run_worker.py | 37 ++ .../human_in_the_loop/run_workflow.py | 39 ++ .../human_in_the_loop/workflow.py | 95 ++++ .../langsmith_tracing/README.md | 37 ++ .../langsmith_tracing/__init__.py | 1 + .../functional_api/langsmith_tracing/main.py | 51 +++ .../langsmith_tracing/workflow.py | 66 +++ .../functional_api/react_agent/README.md | 44 ++ .../functional_api/react_agent/__init__.py | 1 + .../functional_api/react_agent/run_worker.py | 37 ++ .../react_agent/run_workflow.py | 28 ++ .../functional_api/react_agent/workflow.py | 88 ++++ langgraph_plugin/graph_api/__init__.py | 1 + .../graph_api/continue_as_new/README.md | 37 ++ .../graph_api/continue_as_new/__init__.py | 1 + .../graph_api/continue_as_new/run_worker.py | 31 ++ .../graph_api/continue_as_new/run_workflow.py | 31 ++ .../graph_api/continue_as_new/workflow.py | 85 ++++ .../graph_api/hello_world/README.md | 29 ++ .../graph_api/hello_world/__init__.py | 1 + .../graph_api/hello_world/run_worker.py | 31 ++ .../graph_api/hello_world/run_workflow.py | 25 ++ .../graph_api/hello_world/workflow.py | 42 ++ .../graph_api/human_in_the_loop/README.md | 38 ++ .../graph_api/human_in_the_loop/__init__.py | 1 + .../graph_api/human_in_the_loop/run_worker.py | 31 ++ .../human_in_the_loop/run_workflow.py | 38 ++ .../graph_api/human_in_the_loop/workflow.py | 89 ++++ .../graph_api/langsmith_tracing/README.md | 38 ++ .../graph_api/langsmith_tracing/__init__.py | 1 + .../graph_api/langsmith_tracing/main.py | 43 ++ .../graph_api/langsmith_tracing/workflow.py | 70 +++ .../graph_api/react_agent/README.md | 42 ++ .../graph_api/react_agent/__init__.py | 1 + .../graph_api/react_agent/run_worker.py | 31 ++ .../graph_api/react_agent/run_workflow.py | 25 ++ .../graph_api/react_agent/workflow.py | 109 +++++ pyproject.toml | 17 +- tests/langgraph_plugin/__init__.py | 1 + .../langgraph_plugin/continue_as_new_test.py | 34 ++ .../functional_continue_as_new_test.py | 47 ++ .../functional_control_flow_test.py | 59 +++ .../functional_hello_world_test.py | 43 ++ .../functional_human_in_the_loop_test.py | 58 +++ .../functional_react_agent_test.py | 44 ++ tests/langgraph_plugin/hello_world_test.py | 50 +++ .../human_in_the_loop_test.py | 86 ++++ tests/langgraph_plugin/react_agent_test.py | 32 ++ uv.lock | 419 ++++++++++++++---- 71 files changed, 2873 insertions(+), 92 deletions(-) create mode 100644 langgraph_plugin/README.md create mode 100644 langgraph_plugin/__init__.py create mode 100644 langgraph_plugin/functional_api/__init__.py create mode 100644 langgraph_plugin/functional_api/continue_as_new/README.md create mode 100644 langgraph_plugin/functional_api/continue_as_new/__init__.py create mode 100644 langgraph_plugin/functional_api/continue_as_new/run_worker.py create mode 100644 langgraph_plugin/functional_api/continue_as_new/run_workflow.py create mode 100644 langgraph_plugin/functional_api/continue_as_new/workflow.py create mode 100644 langgraph_plugin/functional_api/control_flow/README.md create mode 100644 langgraph_plugin/functional_api/control_flow/__init__.py create mode 100644 langgraph_plugin/functional_api/control_flow/run_worker.py create mode 100644 langgraph_plugin/functional_api/control_flow/run_workflow.py create mode 100644 langgraph_plugin/functional_api/control_flow/workflow.py create mode 100644 langgraph_plugin/functional_api/hello_world/README.md create mode 100644 langgraph_plugin/functional_api/hello_world/__init__.py create mode 100644 langgraph_plugin/functional_api/hello_world/run_worker.py create mode 100644 langgraph_plugin/functional_api/hello_world/run_workflow.py create mode 100644 langgraph_plugin/functional_api/hello_world/workflow.py create mode 100644 langgraph_plugin/functional_api/human_in_the_loop/README.md create mode 100644 langgraph_plugin/functional_api/human_in_the_loop/__init__.py create mode 100644 langgraph_plugin/functional_api/human_in_the_loop/run_worker.py create mode 100644 langgraph_plugin/functional_api/human_in_the_loop/run_workflow.py create mode 100644 langgraph_plugin/functional_api/human_in_the_loop/workflow.py create mode 100644 langgraph_plugin/functional_api/langsmith_tracing/README.md create mode 100644 langgraph_plugin/functional_api/langsmith_tracing/__init__.py create mode 100644 langgraph_plugin/functional_api/langsmith_tracing/main.py create mode 100644 langgraph_plugin/functional_api/langsmith_tracing/workflow.py create mode 100644 langgraph_plugin/functional_api/react_agent/README.md create mode 100644 langgraph_plugin/functional_api/react_agent/__init__.py create mode 100644 langgraph_plugin/functional_api/react_agent/run_worker.py create mode 100644 langgraph_plugin/functional_api/react_agent/run_workflow.py create mode 100644 langgraph_plugin/functional_api/react_agent/workflow.py create mode 100644 langgraph_plugin/graph_api/__init__.py create mode 100644 langgraph_plugin/graph_api/continue_as_new/README.md create mode 100644 langgraph_plugin/graph_api/continue_as_new/__init__.py create mode 100644 langgraph_plugin/graph_api/continue_as_new/run_worker.py create mode 100644 langgraph_plugin/graph_api/continue_as_new/run_workflow.py create mode 100644 langgraph_plugin/graph_api/continue_as_new/workflow.py create mode 100644 langgraph_plugin/graph_api/hello_world/README.md create mode 100644 langgraph_plugin/graph_api/hello_world/__init__.py create mode 100644 langgraph_plugin/graph_api/hello_world/run_worker.py create mode 100644 langgraph_plugin/graph_api/hello_world/run_workflow.py create mode 100644 langgraph_plugin/graph_api/hello_world/workflow.py create mode 100644 langgraph_plugin/graph_api/human_in_the_loop/README.md create mode 100644 langgraph_plugin/graph_api/human_in_the_loop/__init__.py create mode 100644 langgraph_plugin/graph_api/human_in_the_loop/run_worker.py create mode 100644 langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py create mode 100644 langgraph_plugin/graph_api/human_in_the_loop/workflow.py create mode 100644 langgraph_plugin/graph_api/langsmith_tracing/README.md create mode 100644 langgraph_plugin/graph_api/langsmith_tracing/__init__.py create mode 100644 langgraph_plugin/graph_api/langsmith_tracing/main.py create mode 100644 langgraph_plugin/graph_api/langsmith_tracing/workflow.py create mode 100644 langgraph_plugin/graph_api/react_agent/README.md create mode 100644 langgraph_plugin/graph_api/react_agent/__init__.py create mode 100644 langgraph_plugin/graph_api/react_agent/run_worker.py create mode 100644 langgraph_plugin/graph_api/react_agent/run_workflow.py create mode 100644 langgraph_plugin/graph_api/react_agent/workflow.py create mode 100644 tests/langgraph_plugin/__init__.py create mode 100644 tests/langgraph_plugin/continue_as_new_test.py create mode 100644 tests/langgraph_plugin/functional_continue_as_new_test.py create mode 100644 tests/langgraph_plugin/functional_control_flow_test.py create mode 100644 tests/langgraph_plugin/functional_hello_world_test.py create mode 100644 tests/langgraph_plugin/functional_human_in_the_loop_test.py create mode 100644 tests/langgraph_plugin/functional_react_agent_test.py create mode 100644 tests/langgraph_plugin/hello_world_test.py create mode 100644 tests/langgraph_plugin/human_in_the_loop_test.py create mode 100644 tests/langgraph_plugin/react_agent_test.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 4a1018188..15d33ed10 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -33,6 +33,14 @@ jobs: - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6 with: python-version: ${{ matrix.python }} + # protoc is needed whenever uv builds temporalio from a git source + # (e.g. when [tool.uv.sources] pins a branch while a feature is in + # development). Kept on by default so we don't have to add and remove + # this step every time we iterate on a new plugin. + - uses: arduino/setup-protoc@c65c819552d16ad3c9b72d9dfd5ba5237b9c906b # v3 + with: + version: "23.x" + repo-token: ${{ secrets.GITHUB_TOKEN }} - run: uv tool install poethepoet - run: uv sync --group=dsl --group=encryption --group=trio-async - run: poe lint diff --git a/README.md b/README.md index 2cb2d620d..8929f28d5 100644 --- a/README.md +++ b/README.md @@ -72,6 +72,7 @@ Some examples require extra dependencies. See each sample's directory for specif * [gevent_async](gevent_async) - Combine gevent and Temporal. * [hello_standalone_activity](hello_standalone_activity) - Use activities without using a workflow. * [langchain](langchain) - Orchestrate workflows for LangChain. +* [langgraph_plugin](langgraph_plugin) - Run LangGraph workflows as durable Temporal workflows (Graph API and Functional API). * [message_passing/introduction](message_passing/introduction/) - Introduction to queries, signals, and updates. * [message_passing/safe_message_handlers](message_passing/safe_message_handlers/) - Safely handling updates and signals. * [message_passing/update_with_start/lazy_initialization](message_passing/update_with_start/lazy_initialization/) - Use update-with-start to update a Shopping Cart, starting it if it does not exist. diff --git a/langgraph_plugin/README.md b/langgraph_plugin/README.md new file mode 100644 index 000000000..23f9c5d9d --- /dev/null +++ b/langgraph_plugin/README.md @@ -0,0 +1,73 @@ +# LangGraph Plugin Samples + +These samples demonstrate the [Temporal LangGraph plugin](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/langgraph), which runs LangGraph workflows as durable Temporal workflows. Each LangGraph graph node (Graph API) or `@task` (Functional API) executes as a Temporal activity with automatic retries, timeouts, and crash recovery. + +Samples are organized by API style: + +- **Graph API** (`graph_api/`) -- Define workflows as `StateGraph` with nodes and edges. +- **Functional API** (`functional_api/`) -- Define workflows with `@task` and `@entrypoint` decorators for an imperative programming style. + +## Samples + +| Sample | Graph API | Functional API | Description | +|--------|:---------:|:--------------:|-------------| +| **Hello World** | [graph_api/hello_world](graph_api/hello_world) | [functional_api/hello_world](functional_api/hello_world) | Minimal sample -- single node/task that processes a query string. Start here. | +| **Human-in-the-loop** | [graph_api/human_in_the_loop](graph_api/human_in_the_loop) | [functional_api/human_in_the_loop](functional_api/human_in_the_loop) | Chatbot that uses `interrupt()` to pause for human approval, Temporal signals to receive feedback, and queries to expose the pending draft. | +| **Continue-as-new** | [graph_api/continue_as_new](graph_api/continue_as_new) | [functional_api/continue_as_new](functional_api/continue_as_new) | Multi-stage data pipeline that uses `continue-as-new` with task result caching so previously-completed stages are not re-executed. | +| **ReAct Agent** | [graph_api/react_agent](graph_api/react_agent) | [functional_api/react_agent](functional_api/react_agent) | Tool-calling agent loop. Graph API uses conditional edges; Functional API uses a `while` loop. | +| **Control Flow** | -- | [functional_api/control_flow](functional_api/control_flow) | Demonstrates parallel task execution, `for` loops, and `if/else` branching -- patterns that are natural in the Functional API. | +| **LangSmith Tracing** | [graph_api/langsmith_tracing](graph_api/langsmith_tracing) | [functional_api/langsmith_tracing](functional_api/langsmith_tracing) | Combines `LangGraphPlugin` with Temporal's `LangSmithPlugin` for durable execution + full observability of LLM calls. Requires API keys. | + +## Prerequisites + +1. Install dependencies: + + ```bash + uv sync --group langgraph + ``` + +2. Start a [Temporal dev server](https://docs.temporal.io/cli#start-dev-server): + + ```bash + temporal server start-dev + ``` + +## Running a Sample + +Most samples have two scripts -- start the Worker first, then the Workflow starter in a separate terminal. + +```bash +# Terminal 1: start the Worker +uv run langgraph_plugin///run_worker.py + +# Terminal 2: start the Workflow +uv run langgraph_plugin///run_workflow.py +``` + +For example, to run the Graph API human-in-the-loop chatbot: + +```bash +# Terminal 1 +uv run langgraph_plugin/graph_api/human_in_the_loop/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py +``` + +The LangSmith Tracing samples bundle the Worker and Workflow execution into a single `main.py`: + +```bash +uv run langgraph_plugin//langsmith_tracing/main.py +``` + +## Key Features Demonstrated + +- **Durable execution** -- Every graph node / `@task` runs as a Temporal activity with configurable timeouts and retry policies. +- **Human-in-the-loop** -- LangGraph's `interrupt()` pauses the graph; Temporal signals deliver human input; queries expose pending state to UIs. +- **Continue-as-new with caching** -- `cache()` captures completed task results; passing the cache to the next execution avoids re-running them. +- **Conditional routing** -- Graph API's `add_conditional_edges` and Functional API's native `if/else`/`while` for agent loops. +- **Parallel execution** -- Functional API launches multiple tasks concurrently by creating futures before awaiting them. + +## Related + +- [Temporal LangGraph plugin docs](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/langgraph) diff --git a/langgraph_plugin/__init__.py b/langgraph_plugin/__init__.py new file mode 100644 index 000000000..a85231853 --- /dev/null +++ b/langgraph_plugin/__init__.py @@ -0,0 +1 @@ +"""Temporal LangGraph plugin samples.""" diff --git a/langgraph_plugin/functional_api/__init__.py b/langgraph_plugin/functional_api/__init__.py new file mode 100644 index 000000000..6caae83ec --- /dev/null +++ b/langgraph_plugin/functional_api/__init__.py @@ -0,0 +1 @@ +"""LangGraph Functional API samples using @task and @entrypoint.""" diff --git a/langgraph_plugin/functional_api/continue_as_new/README.md b/langgraph_plugin/functional_api/continue_as_new/README.md new file mode 100644 index 000000000..384aa89c9 --- /dev/null +++ b/langgraph_plugin/functional_api/continue_as_new/README.md @@ -0,0 +1,36 @@ +# Continue-as-New with Caching (Functional API) + +Demonstrates combining Temporal's continue-as-new with the LangGraph plugin's task result cache to avoid re-executing completed `@task` functions across workflow boundaries. + +## What This Sample Demonstrates + +- Task result caching across continue-as-new boundaries with `cache()` +- Restoring cached results with `entrypoint(name, cache=...)` +- Each `@task` executes exactly once despite multiple workflow invocations + +## How It Works + +1. Three tasks run sequentially: `double` (x2) -> `add_50` (+50) -> `triple` (x3). +2. After the first invocation, the workflow continues-as-new with the cache. +3. On subsequent invocations, all tasks return cached results instantly. +4. Input 10 -> 20 -> 70 -> 210. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/functional_api/continue_as_new/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/functional_api/continue_as_new/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `@task` functions, `@entrypoint`, `PipelineInput`, and `PipelineFunctionalWorkflow` | +| `run_worker.py` | Registers tasks and entrypoint with `LangGraphPlugin`, starts Worker | +| `run_workflow.py` | Executes the pipeline Workflow and prints the result | diff --git a/langgraph_plugin/functional_api/continue_as_new/__init__.py b/langgraph_plugin/functional_api/continue_as_new/__init__.py new file mode 100644 index 000000000..5b90703e9 --- /dev/null +++ b/langgraph_plugin/functional_api/continue_as_new/__init__.py @@ -0,0 +1 @@ +"""Continue-as-new pipeline with task result caching.""" diff --git a/langgraph_plugin/functional_api/continue_as_new/run_worker.py b/langgraph_plugin/functional_api/continue_as_new/run_worker.py new file mode 100644 index 000000000..a6b6a9d76 --- /dev/null +++ b/langgraph_plugin/functional_api/continue_as_new/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the continue-as-new pipeline (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.continue_as_new.workflow import ( + PipelineFunctionalWorkflow, + activity_options, + all_tasks, + pipeline_entrypoint, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin( + entrypoints={"pipeline": pipeline_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + worker = Worker( + client, + task_queue="langgraph-pipeline-functional", + workflows=[PipelineFunctionalWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/continue_as_new/run_workflow.py b/langgraph_plugin/functional_api/continue_as_new/run_workflow.py new file mode 100644 index 000000000..563aaba6b --- /dev/null +++ b/langgraph_plugin/functional_api/continue_as_new/run_workflow.py @@ -0,0 +1,31 @@ +"""Start the continue-as-new pipeline workflow (Functional API).""" + +import asyncio +import os +from datetime import timedelta + +from temporalio.client import Client + +from langgraph_plugin.functional_api.continue_as_new.workflow import ( + PipelineFunctionalWorkflow, + PipelineInput, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + PipelineFunctionalWorkflow.run, + PipelineInput(data=10), + id="pipeline-functional-workflow", + task_queue="langgraph-pipeline-functional", + execution_timeout=timedelta(seconds=60), + ) + + # 10*2=20 -> 20+50=70 -> 70*3=210 + print(f"Pipeline result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/continue_as_new/workflow.py b/langgraph_plugin/functional_api/continue_as_new/workflow.py new file mode 100644 index 000000000..9810c05ed --- /dev/null +++ b/langgraph_plugin/functional_api/continue_as_new/workflow.py @@ -0,0 +1,92 @@ +"""Continue-as-new with caching using the LangGraph Functional API with Temporal. + +Demonstrates Temporal's continue-as-new with the LangGraph plugin's task +result cache to avoid re-executing completed @task functions across +workflow boundaries. +""" + +from dataclasses import dataclass +from datetime import timedelta +from typing import Any + +from langgraph.func import entrypoint, task +from temporalio import workflow +from temporalio.contrib.langgraph import cache +from temporalio.contrib.langgraph import entrypoint as temporal_entrypoint + + +@task +def double(data: int) -> int: + """Stage 1: double the input.""" + return data * 2 + + +@task +def add_50(data: int) -> int: + """Stage 2: add 50.""" + return data + 50 + + +@task +def triple(data: int) -> int: + """Stage 3: triple the result.""" + return data * 3 + + +@entrypoint() +async def pipeline_entrypoint(data: int) -> dict: + """Run the 3-stage pipeline: double -> add_50 -> triple.""" + doubled = await double(data) + plus_50 = await add_50(doubled) + tripled = await triple(plus_50) + return {"result": tripled} + + +all_tasks = [double, add_50, triple] + +activity_options = { + "double": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "add_50": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "triple": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, +} + + +@dataclass +class PipelineInput: + data: int + cache: dict[str, Any] | None = None + phase: int = 1 + + +@workflow.defn +class PipelineFunctionalWorkflow: + """Runs the pipeline, continuing-as-new after each phase. + + Input 10: 10*2=20 -> 20+50=70 -> 70*3=210 + Each task executes once; phases 2 and 3 use cached results. + """ + + @workflow.run + async def run(self, input_data: PipelineInput) -> dict[str, Any]: + app = temporal_entrypoint("pipeline", cache=input_data.cache) + result = await app.ainvoke(input_data.data) + + if input_data.phase < 3: + workflow.continue_as_new( + PipelineInput( + data=input_data.data, + cache=cache(), + phase=input_data.phase + 1, + ) + ) + + return result diff --git a/langgraph_plugin/functional_api/control_flow/README.md b/langgraph_plugin/functional_api/control_flow/README.md new file mode 100644 index 000000000..e6e1ec493 --- /dev/null +++ b/langgraph_plugin/functional_api/control_flow/README.md @@ -0,0 +1,37 @@ +# Control Flow (Functional API) + +Demonstrates the Functional API's strength for complex control flow: parallel execution, sequential loops, and conditional branching — all as natural Python code. + +## What This Sample Demonstrates + +- **Parallel execution**: launching multiple tasks concurrently by creating futures before awaiting +- **For loops**: processing items sequentially with `for item in items` +- **If/else branching**: routing items based on classification results +- Why the Functional API is ideal for programmatic composition patterns + +## How It Works + +1. A batch of items is validated **in parallel** — all `validate_item` tasks launch concurrently. +2. Valid items are processed **sequentially** in a for loop. +3. Each item is classified, then routed via **if/else** to `process_urgent` or `process_normal`. +4. Results are aggregated with a `summarize` task. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/functional_api/control_flow/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/functional_api/control_flow/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `@task` functions (validate, classify, process, summarize), `@entrypoint`, and `ControlFlowWorkflow` | +| `run_worker.py` | Registers tasks and entrypoint with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Sends a batch of items and prints processing results | diff --git a/langgraph_plugin/functional_api/control_flow/__init__.py b/langgraph_plugin/functional_api/control_flow/__init__.py new file mode 100644 index 000000000..e78d3ea4a --- /dev/null +++ b/langgraph_plugin/functional_api/control_flow/__init__.py @@ -0,0 +1 @@ +"""Control flow: parallel execution, for loops, and if/else branching.""" diff --git a/langgraph_plugin/functional_api/control_flow/run_worker.py b/langgraph_plugin/functional_api/control_flow/run_worker.py new file mode 100644 index 000000000..0a73c19f3 --- /dev/null +++ b/langgraph_plugin/functional_api/control_flow/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the control flow pipeline (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.control_flow.workflow import ( + ControlFlowWorkflow, + activity_options, + all_tasks, + control_flow_pipeline, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin( + entrypoints={"control-flow": control_flow_pipeline}, + tasks=all_tasks, + activity_options=activity_options, + ) + + worker = Worker( + client, + task_queue="langgraph-control-flow", + workflows=[ControlFlowWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/control_flow/run_workflow.py b/langgraph_plugin/functional_api/control_flow/run_workflow.py new file mode 100644 index 000000000..2784415b9 --- /dev/null +++ b/langgraph_plugin/functional_api/control_flow/run_workflow.py @@ -0,0 +1,38 @@ +"""Start the control flow pipeline workflow (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.functional_api.control_flow.workflow import ( + ControlFlowWorkflow, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + items = [ + "Fix login bug", + "URGENT: Production outage in payments", + "Update README", + "INVALID:", + "Urgent: Security patch needed", + "Refactor test suite", + ] + + result = await client.execute_workflow( + ControlFlowWorkflow.run, + items, + id="control-flow-workflow", + task_queue="langgraph-control-flow", + ) + + print(f"Summary: {result['summary']}") + for r in result["results"]: + print(f" {r}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/control_flow/workflow.py b/langgraph_plugin/functional_api/control_flow/workflow.py new file mode 100644 index 000000000..383af0076 --- /dev/null +++ b/langgraph_plugin/functional_api/control_flow/workflow.py @@ -0,0 +1,113 @@ +"""Control flow sample using the LangGraph Functional API with Temporal. + +Demonstrates the Functional API's advantage for complex control flow: + - Parallel task execution (launch multiple tasks concurrently) + - Sequential for-loop processing + - Conditional if/else branching based on intermediate results +""" + +from datetime import timedelta + +from langgraph.func import entrypoint, task +from temporalio import workflow +from temporalio.contrib.langgraph import entrypoint as temporal_entrypoint + + +@task +def validate_item(item: str) -> bool: + """Validate an item. Returns True if the item is non-empty and well-formed.""" + return len(item.strip()) > 0 and not item.startswith("INVALID:") + + +@task +def classify_item(item: str) -> str: + """Classify an item as 'urgent' or 'normal' based on its content.""" + return "urgent" if "urgent" in item.lower() else "normal" + + +@task +def process_urgent(item: str) -> str: + """Process an urgent item with priority handling.""" + return f"[PRIORITY] Processed: {item}" + + +@task +def process_normal(item: str) -> str: + """Process a normal item with standard handling.""" + return f"[STANDARD] Processed: {item}" + + +@task +def summarize(results: list[str]) -> str: + """Produce a summary of all processed results.""" + urgent_count = sum(1 for r in results if r.startswith("[PRIORITY]")) + normal_count = sum(1 for r in results if r.startswith("[STANDARD]")) + return ( + f"Processed {len(results)} items ({urgent_count} urgent, {normal_count} normal)" + ) + + +@entrypoint() +async def control_flow_pipeline(items: list[str]) -> dict: + """Process a batch of items with parallel validation, sequential + classification, and conditional routing. + """ + # PARALLEL: Validate all items concurrently. + # Creating task futures without awaiting launches them in parallel. + validation_futures = [validate_item(item) for item in items] + valid_flags = [await f for f in validation_futures] + valid_items = [item for item, is_valid in zip(items, valid_flags) if is_valid] + + # SEQUENTIAL + CONDITIONAL: Process each valid item + results = [] + for item in valid_items: + category = await classify_item(item) + if category == "urgent": + result = await process_urgent(item) + else: + result = await process_normal(item) + results.append(result) + + # Aggregate all results + summary_text = await summarize(results) + + return {"results": results, "summary": summary_text, "total": len(results)} + + +all_tasks = [ + validate_item, + classify_item, + process_urgent, + process_normal, + summarize, +] + +activity_options = { + "validate_item": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "classify_item": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "process_urgent": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "process_normal": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "summarize": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, +} + + +@workflow.defn +class ControlFlowWorkflow: + @workflow.run + async def run(self, items: list[str]) -> dict: + return await temporal_entrypoint("control-flow").ainvoke(items) diff --git a/langgraph_plugin/functional_api/hello_world/README.md b/langgraph_plugin/functional_api/hello_world/README.md new file mode 100644 index 000000000..4f66cf89c --- /dev/null +++ b/langgraph_plugin/functional_api/hello_world/README.md @@ -0,0 +1,29 @@ +# Hello World (Functional API) + +The simplest possible LangGraph Functional API + Temporal sample: a single `@task` called from an `@entrypoint`. + +## What This Sample Demonstrates + +- Defining a `@task` and `@entrypoint` +- Wrapping them with `LangGraphPlugin` so the task runs as a Temporal activity +- Invoking the entrypoint from a Temporal workflow + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/functional_api/hello_world/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/functional_api/hello_world/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `process_query` task, `hello_entrypoint`, and `HelloWorldFunctionalWorkflow` | +| `run_worker.py` | Registers task and entrypoint with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/langgraph_plugin/functional_api/hello_world/__init__.py b/langgraph_plugin/functional_api/hello_world/__init__.py new file mode 100644 index 000000000..977660112 --- /dev/null +++ b/langgraph_plugin/functional_api/hello_world/__init__.py @@ -0,0 +1 @@ +"""Minimal hello world — @task and @entrypoint.""" diff --git a/langgraph_plugin/functional_api/hello_world/run_worker.py b/langgraph_plugin/functional_api/hello_world/run_worker.py new file mode 100644 index 000000000..7039ee027 --- /dev/null +++ b/langgraph_plugin/functional_api/hello_world/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the hello world sample (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.hello_world.workflow import ( + HelloWorldFunctionalWorkflow, + activity_options, + all_tasks, + hello_entrypoint, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin( + entrypoints={"hello-world": hello_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + worker = Worker( + client, + task_queue="langgraph-hello-world-functional", + workflows=[HelloWorldFunctionalWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/hello_world/run_workflow.py b/langgraph_plugin/functional_api/hello_world/run_workflow.py new file mode 100644 index 000000000..77cc92fb4 --- /dev/null +++ b/langgraph_plugin/functional_api/hello_world/run_workflow.py @@ -0,0 +1,27 @@ +"""Start the hello world workflow (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.functional_api.hello_world.workflow import ( + HelloWorldFunctionalWorkflow, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + HelloWorldFunctionalWorkflow.run, + "Hello, Temporal + LangGraph!", + id="hello-world-functional-workflow", + task_queue="langgraph-hello-world-functional", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/hello_world/workflow.py b/langgraph_plugin/functional_api/hello_world/workflow.py new file mode 100644 index 000000000..36552806d --- /dev/null +++ b/langgraph_plugin/functional_api/hello_world/workflow.py @@ -0,0 +1,40 @@ +"""Hello world using the LangGraph Functional API with Temporal. + +The simplest possible sample: a single task called from an entrypoint. +""" + +from datetime import timedelta + +from langgraph.func import entrypoint, task +from temporalio import workflow +from temporalio.contrib.langgraph import entrypoint as temporal_entrypoint + + +@task +def process_query(query: str) -> str: + """Process a query and return a response.""" + return f"Processed: {query}" + + +@entrypoint() +async def hello_entrypoint(query: str) -> dict: + """Process the query and return it in a result dict.""" + result = await process_query(query) + return {"result": result} + + +all_tasks = [process_query] + +activity_options = { + "process_query": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=10), + }, +} + + +@workflow.defn +class HelloWorldFunctionalWorkflow: + @workflow.run + async def run(self, query: str) -> dict: + return await temporal_entrypoint("hello-world").ainvoke(query) diff --git a/langgraph_plugin/functional_api/human_in_the_loop/README.md b/langgraph_plugin/functional_api/human_in_the_loop/README.md new file mode 100644 index 000000000..9df7ba652 --- /dev/null +++ b/langgraph_plugin/functional_api/human_in_the_loop/README.md @@ -0,0 +1,38 @@ +# Human-in-the-Loop Chatbot (Functional API) + +Demonstrates pausing an entrypoint with LangGraph's `interrupt()` and waiting indefinitely for human review with Temporal's `workflow.wait_condition()`. A Temporal signal delivers the human's feedback; a Temporal query exposes the pending draft to UIs. + +## What This Sample Demonstrates + +- `workflow.wait_condition()` to block the Workflow until human input arrives — for as long as it takes, with no polling and no timeout +- `interrupt()` inside a `@task` to pause the entrypoint at the review point +- Temporal **signals** to deliver human feedback and **queries** to expose the pending draft +- Resuming with `Command(resume=...)` via the v2 API +- Setting a checkpointer on the entrypoint for interrupt/resume support + +## How It Works + +1. The `generate_draft` task produces a draft response. +2. The `request_human_review` task calls `interrupt(draft)`, pausing the entrypoint. +3. The Workflow stores the draft (visible via the query) and calls `workflow.wait_condition()` — blocking durably until the signal sets `_human_input`. This can wait indefinitely; Temporal persists the state. +4. After the signal arrives, the entrypoint resumes with `Command(resume=...)` and returns the final response. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/functional_api/human_in_the_loop/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/functional_api/human_in_the_loop/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `@task` functions, `@entrypoint`, and `ChatbotFunctionalWorkflow` | +| `run_worker.py` | Registers tasks and entrypoint with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Starts workflow, polls draft via query, sends approval via signal | diff --git a/langgraph_plugin/functional_api/human_in_the_loop/__init__.py b/langgraph_plugin/functional_api/human_in_the_loop/__init__.py new file mode 100644 index 000000000..72da02f88 --- /dev/null +++ b/langgraph_plugin/functional_api/human_in_the_loop/__init__.py @@ -0,0 +1 @@ +"""Human-in-the-loop chatbot using interrupt() and Temporal signals.""" diff --git a/langgraph_plugin/functional_api/human_in_the_loop/run_worker.py b/langgraph_plugin/functional_api/human_in_the_loop/run_worker.py new file mode 100644 index 000000000..c8f84b492 --- /dev/null +++ b/langgraph_plugin/functional_api/human_in_the_loop/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the human-in-the-loop chatbot (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.human_in_the_loop.workflow import ( + ChatbotFunctionalWorkflow, + activity_options, + all_tasks, + chatbot_entrypoint, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin( + entrypoints={"chatbot": chatbot_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + worker = Worker( + client, + task_queue="langgraph-chatbot-functional", + workflows=[ChatbotFunctionalWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/human_in_the_loop/run_workflow.py b/langgraph_plugin/functional_api/human_in_the_loop/run_workflow.py new file mode 100644 index 000000000..5074b9370 --- /dev/null +++ b/langgraph_plugin/functional_api/human_in_the_loop/run_workflow.py @@ -0,0 +1,39 @@ +"""Start the human-in-the-loop chatbot workflow (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.functional_api.human_in_the_loop.workflow import ( + ChatbotFunctionalWorkflow, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + handle = await client.start_workflow( + ChatbotFunctionalWorkflow.run, + "What is the meaning of life?", + id="chatbot-functional-workflow", + task_queue="langgraph-chatbot-functional", + ) + + # Poll until the draft is ready for review + draft = None + while draft is None: + await asyncio.sleep(0.5) + draft = await handle.query(ChatbotFunctionalWorkflow.get_draft) + + print(f"Draft for review: {draft}") + + # Send approval via signal + await handle.signal(ChatbotFunctionalWorkflow.provide_feedback, "approve") + + result = await handle.result() + print(f"Final response: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/human_in_the_loop/workflow.py b/langgraph_plugin/functional_api/human_in_the_loop/workflow.py new file mode 100644 index 000000000..857ef37f3 --- /dev/null +++ b/langgraph_plugin/functional_api/human_in_the_loop/workflow.py @@ -0,0 +1,95 @@ +"""Human-in-the-loop chatbot using the LangGraph Functional API with Temporal. + +The @entrypoint pauses at interrupt(); the Workflow waits indefinitely with +workflow.wait_condition() until a Temporal signal delivers human feedback, +then resumes the entrypoint with Command(resume=...). +""" + +from datetime import timedelta +from typing import Any + +from langchain_core.runnables import RunnableConfig +from langgraph.checkpoint.memory import InMemorySaver +from langgraph.func import entrypoint, task +from langgraph.types import Command, interrupt +from temporalio import workflow +from temporalio.contrib.langgraph import entrypoint as temporal_entrypoint + + +@task +def generate_draft(message: str) -> str: + """Generate a draft response. Replace with an LLM call in production.""" + return ( + f"Here's my response to '{message}': " + "The answer is 42. Let me know if this helps!" + ) + + +@task +def request_human_review(draft: str) -> str: + """Pause execution to request human review of the draft.""" + feedback = interrupt(draft) + if feedback == "approve": + return draft + return f"[Revised] {draft} (incorporating feedback: {feedback})" + + +@entrypoint() +async def chatbot_entrypoint(user_message: str) -> dict: + """Chatbot entrypoint: generate a draft, get human review, return result.""" + draft = await generate_draft(user_message) + final_response = await request_human_review(draft) + return {"response": final_response} + + +all_tasks = [generate_draft, request_human_review] + +activity_options = { + "generate_draft": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "request_human_review": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, +} + + +@workflow.defn +class ChatbotFunctionalWorkflow: + def __init__(self) -> None: + self._human_input: str | None = None + self._draft: str | None = None + + @workflow.signal + async def provide_feedback(self, feedback: str) -> None: + """Signal handler: receives human feedback.""" + self._human_input = feedback + + @workflow.query + def get_draft(self) -> str | None: + """Query handler: returns the pending draft for review, or None.""" + return self._draft + + @workflow.run + async def run(self, user_message: str) -> dict[str, Any]: + app = temporal_entrypoint("chatbot") + app.checkpointer = InMemorySaver() + config = RunnableConfig( + {"configurable": {"thread_id": workflow.info().workflow_id}} + ) + + # First invocation: runs until interrupt() pauses for human review + result = await app.ainvoke(user_message, config, version="v2") + + self._draft = result.interrupts[0].value + + # Wait for human feedback via Temporal signal + await workflow.wait_condition(lambda: self._human_input is not None) + + # Resume with the human's feedback + resumed = await app.ainvoke( + Command(resume=self._human_input), config, version="v2" + ) + return resumed.value diff --git a/langgraph_plugin/functional_api/langsmith_tracing/README.md b/langgraph_plugin/functional_api/langsmith_tracing/README.md new file mode 100644 index 000000000..b072cd4d6 --- /dev/null +++ b/langgraph_plugin/functional_api/langsmith_tracing/README.md @@ -0,0 +1,37 @@ +# LangSmith Tracing (Functional API) + +Demonstrates combining `LangGraphPlugin` (durable task execution) with Temporal's `LangSmithPlugin` for full tracing of LLM calls through Temporal workflows, using LangGraph's `@task` and `@entrypoint` decorators. + +## What This Sample Demonstrates + +- Using `LangSmithPlugin` on the Temporal client for automatic trace propagation +- Using `LangGraphPlugin` on the Worker for durable LangGraph execution +- `@traceable` in three places: on the `@task` (Activity) itself, on a helper called from inside the `@task`, and on a helper called from inside the `@entrypoint` (Workflow) +- Both plugins working together: durability + observability + +## How It Works + +1. The Temporal client is created with `LangSmithPlugin(add_temporal_runs=True)`. +2. A Worker registers the `chat` task with `LangGraphPlugin`. +3. When the Workflow runs, the `chat` task executes as a Temporal Activity. +4. `@traceable` decorators emit trace data to LangSmith for the task, an in-task helper, and an in-entrypoint helper. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +export ANTHROPIC_API_KEY='your-key' +export LANGCHAIN_API_KEY='your-key' + +uv run langgraph_plugin/functional_api/langsmith_tracing/main.py +``` + +Traces will appear in your [LangSmith](https://smith.langchain.com/) dashboard. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `@traceable` chat task + helpers, `@entrypoint`, and `ChatFunctionalWorkflow` | +| `main.py` | Starts a Worker and executes the Workflow in a single process | diff --git a/langgraph_plugin/functional_api/langsmith_tracing/__init__.py b/langgraph_plugin/functional_api/langsmith_tracing/__init__.py new file mode 100644 index 000000000..4cb57b73a --- /dev/null +++ b/langgraph_plugin/functional_api/langsmith_tracing/__init__.py @@ -0,0 +1 @@ +"""LangSmith tracing with LangGraph Functional API and Temporal.""" diff --git a/langgraph_plugin/functional_api/langsmith_tracing/main.py b/langgraph_plugin/functional_api/langsmith_tracing/main.py new file mode 100644 index 000000000..0d29afb76 --- /dev/null +++ b/langgraph_plugin/functional_api/langsmith_tracing/main.py @@ -0,0 +1,51 @@ +"""Run the LangSmith tracing chat sample (Functional API). + +Single-process driver: starts a Worker, executes the Workflow once, prints +the result, then shuts down. Requires ANTHROPIC_API_KEY and LANGCHAIN_API_KEY. +""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.langsmith_tracing.workflow import ( + ChatFunctionalWorkflow, + activity_options, + all_tasks, + chat_entrypoint, +) + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[LangSmithPlugin(add_temporal_runs=True)], + ) + + async with Worker( + client, + task_queue="langgraph-langsmith-functional", + workflows=[ChatFunctionalWorkflow], + plugins=[ + LangGraphPlugin( + entrypoints={"chat": chat_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + ], + ): + result = await client.execute_workflow( + ChatFunctionalWorkflow.run, + "What is the meaning of life?", + id="langsmith-chat-functional-workflow", + task_queue="langgraph-langsmith-functional", + ) + print(f"Response: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/langsmith_tracing/workflow.py b/langgraph_plugin/functional_api/langsmith_tracing/workflow.py new file mode 100644 index 000000000..ddea44bcb --- /dev/null +++ b/langgraph_plugin/functional_api/langsmith_tracing/workflow.py @@ -0,0 +1,66 @@ +"""LangSmith tracing with LangGraph Functional API and Temporal. + +Demonstrates combining LangGraphPlugin (durable task execution) with +LangSmithPlugin (observability) for full tracing of LLM calls through +Temporal workflows. + +Three @traceable use cases are demonstrated: +1. The @task (Activity) function itself: `chat`. +2. A helper called from inside the @task: `format_prompt`. +3. A helper called from inside the @entrypoint (Workflow): `summarize_for_log`. + +Requires ANTHROPIC_API_KEY and LANGCHAIN_API_KEY environment variables. +""" + +from datetime import timedelta + +from langchain.chat_models import init_chat_model +from langgraph.func import entrypoint, task +from langsmith import traceable +from temporalio import workflow +from temporalio.contrib.langgraph import entrypoint as temporal_entrypoint + + +@traceable(name="format_prompt", run_type="prompt") +def format_prompt(message: str) -> str: + """Helper called from inside the @task. Traced by LangSmith.""" + return f"Please respond concisely to: {message}" + + +@task +@traceable(name="chat_task", run_type="chain") +def chat(message: str) -> str: + """Call an LLM to respond to the message. Traced by LangSmith.""" + prompt = format_prompt(message) + response = init_chat_model("claude-sonnet-4-6").invoke(prompt) + return str(response.content) + + +@traceable(name="summarize_for_log", run_type="chain") +def summarize_for_log(response: str) -> str: + """Helper called from inside the @entrypoint. Traced by LangSmith.""" + return f"Got {len(response)}-char response: {response[:60]}..." + + +@entrypoint() +async def chat_entrypoint(message: str) -> dict: + """Chat entrypoint: call the LLM and return the response.""" + response = await chat(message) + return {"response": response, "summary": summarize_for_log(response)} + + +all_tasks = [chat] + +activity_options = { + "chat": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, +} + + +@workflow.defn +class ChatFunctionalWorkflow: + @workflow.run + async def run(self, message: str) -> dict: + return await temporal_entrypoint("chat").ainvoke(message) diff --git a/langgraph_plugin/functional_api/react_agent/README.md b/langgraph_plugin/functional_api/react_agent/README.md new file mode 100644 index 000000000..79d1b96e5 --- /dev/null +++ b/langgraph_plugin/functional_api/react_agent/README.md @@ -0,0 +1,44 @@ +# ReAct Agent (Functional API) + +Demonstrates the ReAct agent pattern (think -> act -> observe -> repeat) as a natural `while` loop using LangGraph's `@task` and `@entrypoint` decorators. + +## What This Sample Demonstrates + +- The ReAct loop as a natural `while True` loop in Python +- `@task` functions for agent thinking and tool execution +- How the Functional API makes agent loops readable and extensible + +## How It Works + +1. The `agent_think` task examines the query and tool history, deciding the next action. +2. If a tool is needed, `execute_tool` runs it and the result is appended to history. +3. The loop continues until `agent_think` returns a final answer. + +```python +while True: + decision = await agent_think(query, history) + if decision["action"] == "final": + return decision["answer"] + result = await execute_tool(decision["tool_name"], decision["tool_input"]) + history.append(result) +``` + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/functional_api/react_agent/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/functional_api/react_agent/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `@task` functions (agent_think, execute_tool), `@entrypoint`, and `ReactAgentFunctionalWorkflow` | +| `run_worker.py` | Registers tasks and entrypoint with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Executes the agent workflow and prints the answer | diff --git a/langgraph_plugin/functional_api/react_agent/__init__.py b/langgraph_plugin/functional_api/react_agent/__init__.py new file mode 100644 index 000000000..bd34ac2e0 --- /dev/null +++ b/langgraph_plugin/functional_api/react_agent/__init__.py @@ -0,0 +1 @@ +"""ReAct agent with while-loop tool dispatch.""" diff --git a/langgraph_plugin/functional_api/react_agent/run_worker.py b/langgraph_plugin/functional_api/react_agent/run_worker.py new file mode 100644 index 000000000..465e90ad4 --- /dev/null +++ b/langgraph_plugin/functional_api/react_agent/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the ReAct agent (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.react_agent.workflow import ( + ReactAgentFunctionalWorkflow, + activity_options, + all_tasks, + react_agent_entrypoint, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin( + entrypoints={"react-agent": react_agent_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + worker = Worker( + client, + task_queue="langgraph-react-agent-functional", + workflows=[ReactAgentFunctionalWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/react_agent/run_workflow.py b/langgraph_plugin/functional_api/react_agent/run_workflow.py new file mode 100644 index 000000000..8888cbd17 --- /dev/null +++ b/langgraph_plugin/functional_api/react_agent/run_workflow.py @@ -0,0 +1,28 @@ +"""Start the ReAct agent workflow (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.functional_api.react_agent.workflow import ( + ReactAgentFunctionalWorkflow, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + ReactAgentFunctionalWorkflow.run, + "Tell me about San Francisco", + id="react-agent-functional-workflow", + task_queue="langgraph-react-agent-functional", + ) + + print(f"Agent answer: {result['answer']}") + print(f"Tool calls made: {result['steps']}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/react_agent/workflow.py b/langgraph_plugin/functional_api/react_agent/workflow.py new file mode 100644 index 000000000..c61048671 --- /dev/null +++ b/langgraph_plugin/functional_api/react_agent/workflow.py @@ -0,0 +1,88 @@ +"""ReAct agent using the LangGraph Functional API with Temporal. + +The Functional API expresses the ReAct loop as a plain `while` loop, making +the control flow explicit and easy to extend. +""" + +from datetime import timedelta + +from langgraph.func import entrypoint, task +from temporalio import workflow +from temporalio.contrib.langgraph import entrypoint as temporal_entrypoint + + +@task +def agent_think(query: str, history: list[str]) -> dict: + """The agent decides the next action based on query and tool history. + + In production, replace this with an LLM call (e.g., Claude with tools). + """ + tool_results = [h for h in history if h.startswith("[Tool]")] + + if len(tool_results) == 0: + return { + "action": "tool", + "tool_name": "get_weather", + "tool_input": "San Francisco", + } + elif len(tool_results) == 1: + return { + "action": "tool", + "tool_name": "get_population", + "tool_input": "San Francisco", + } + else: + facts = "; ".join(tool_results) + return { + "action": "final", + "answer": (f"Here's what I found about San Francisco: {facts}"), + } + + +@task +def execute_tool(tool_name: str, tool_input: str) -> str: + """Execute a tool by name. In production, dispatch to real implementations.""" + tool_registry = { + "get_weather": lambda inp: f"[Tool] Weather in {inp}: 72°F and sunny.", + "get_population": lambda inp: f"[Tool] {inp} population: ~870,000 residents.", + } + handler = tool_registry.get(tool_name) + if handler: + return handler(tool_input) + return f"[Tool] Unknown tool: {tool_name}" + + +@entrypoint() +async def react_agent_entrypoint(query: str) -> dict: + """ReAct agent loop: think -> act -> observe -> repeat.""" + history: list[str] = [] + + while True: + decision = await agent_think(query, history) + + if decision["action"] == "final": + return {"answer": decision["answer"], "steps": len(history)} + + result = await execute_tool(decision["tool_name"], decision["tool_input"]) + history.append(result) + + +all_tasks = [agent_think, execute_tool] + +activity_options = { + "agent_think": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "execute_tool": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, +} + + +@workflow.defn +class ReactAgentFunctionalWorkflow: + @workflow.run + async def run(self, query: str) -> dict: + return await temporal_entrypoint("react-agent").ainvoke(query) diff --git a/langgraph_plugin/graph_api/__init__.py b/langgraph_plugin/graph_api/__init__.py new file mode 100644 index 000000000..579887b60 --- /dev/null +++ b/langgraph_plugin/graph_api/__init__.py @@ -0,0 +1 @@ +"""LangGraph Graph API samples using StateGraph.""" diff --git a/langgraph_plugin/graph_api/continue_as_new/README.md b/langgraph_plugin/graph_api/continue_as_new/README.md new file mode 100644 index 000000000..a5cfbb1c2 --- /dev/null +++ b/langgraph_plugin/graph_api/continue_as_new/README.md @@ -0,0 +1,37 @@ +# Continue-as-New with Caching (Graph API) + +Demonstrates combining Temporal's continue-as-new with the LangGraph plugin's task result cache to avoid re-executing completed graph nodes across workflow boundaries. + +## What This Sample Demonstrates + +- Using `workflow.continue_as_new()` to reset event history for long-running pipelines +- Capturing node results with `cache()` before continuing +- Restoring cached results with `temporal_graph(name, cache=...)` so completed nodes are skipped +- Each node executes exactly once despite multiple workflow invocations + +## How It Works + +1. A 3-stage pipeline runs: `double` (x2) -> `add_50` (+50) -> `triple` (x3). +2. After the first invocation, the workflow continues-as-new with the cached results. +3. On the second and third invocations, all three nodes return cached results instantly. +4. The final result is returned: input 10 -> 20 -> 70 -> 210. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/graph_api/continue_as_new/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/graph_api/continue_as_new/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | Pipeline node functions, graph definition, `PipelineInput`, and `PipelineWorkflow` | +| `run_worker.py` | Builds graph, registers with `LangGraphPlugin`, starts Worker | +| `run_workflow.py` | Executes the pipeline Workflow and prints the result | diff --git a/langgraph_plugin/graph_api/continue_as_new/__init__.py b/langgraph_plugin/graph_api/continue_as_new/__init__.py new file mode 100644 index 000000000..5b90703e9 --- /dev/null +++ b/langgraph_plugin/graph_api/continue_as_new/__init__.py @@ -0,0 +1 @@ +"""Continue-as-new pipeline with task result caching.""" diff --git a/langgraph_plugin/graph_api/continue_as_new/run_worker.py b/langgraph_plugin/graph_api/continue_as_new/run_worker.py new file mode 100644 index 000000000..653874a6b --- /dev/null +++ b/langgraph_plugin/graph_api/continue_as_new/run_worker.py @@ -0,0 +1,31 @@ +"""Worker for the continue-as-new pipeline (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.continue_as_new.workflow import ( + PipelineWorkflow, + make_pipeline_graph, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin(graphs={"pipeline": make_pipeline_graph()}) + + worker = Worker( + client, + task_queue="langgraph-pipeline", + workflows=[PipelineWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/continue_as_new/run_workflow.py b/langgraph_plugin/graph_api/continue_as_new/run_workflow.py new file mode 100644 index 000000000..a52d1716c --- /dev/null +++ b/langgraph_plugin/graph_api/continue_as_new/run_workflow.py @@ -0,0 +1,31 @@ +"""Start the continue-as-new pipeline workflow (Graph API).""" + +import asyncio +import os +from datetime import timedelta + +from temporalio.client import Client + +from langgraph_plugin.graph_api.continue_as_new.workflow import ( + PipelineInput, + PipelineWorkflow, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + PipelineWorkflow.run, + PipelineInput(data=10), + id="pipeline-workflow", + task_queue="langgraph-pipeline", + execution_timeout=timedelta(seconds=60), + ) + + # 10*2=20 -> 20+50=70 -> 70*3=210 + print(f"Pipeline result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/continue_as_new/workflow.py b/langgraph_plugin/graph_api/continue_as_new/workflow.py new file mode 100644 index 000000000..1475e63f7 --- /dev/null +++ b/langgraph_plugin/graph_api/continue_as_new/workflow.py @@ -0,0 +1,85 @@ +"""Continue-as-new with caching using the LangGraph Graph API with Temporal. + +Demonstrates how to use Temporal's continue-as-new with LangGraph's task result +caching to avoid re-executing already-completed graph nodes across workflow +boundaries. +""" + +from dataclasses import dataclass +from datetime import timedelta +from typing import Any + +from langgraph.graph import START, StateGraph +from temporalio import workflow +from temporalio.contrib.langgraph import cache +from temporalio.contrib.langgraph import graph as temporal_graph +from typing_extensions import TypedDict + + +class State(TypedDict): + value: int + + +async def double(state: State) -> dict[str, int]: + """Stage 1: double the input.""" + return {"value": state["value"] * 2} + + +async def add_50(state: State) -> dict[str, int]: + """Stage 2: add 50.""" + return {"value": state["value"] + 50} + + +async def triple(state: State) -> dict[str, int]: + """Stage 3: triple the result.""" + return {"value": state["value"] * 3} + + +def make_pipeline_graph() -> StateGraph: + node_metadata = { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + } + g = StateGraph(State) + g.add_node("double", double, metadata=node_metadata) + g.add_node("add_50", add_50, metadata=node_metadata) + g.add_node("triple", triple, metadata=node_metadata) + g.add_edge(START, "double") + g.add_edge("double", "add_50") + g.add_edge("add_50", "triple") + return g + + +@dataclass +class PipelineInput: + data: int + cache: dict[str, Any] | None = None + phase: int = 1 # continues-as-new after phases 1 and 2 + + +@workflow.defn +class PipelineWorkflow: + """Runs a 3-stage pipeline, continuing-as-new after each phase. + + Phase 1: all 3 stages execute, continues-as-new with cache. + Phase 2: all 3 stages cached (instant), continues-as-new. + Phase 3: all 3 stages cached (instant), returns final result. + + Input 10: 10*2=20 -> 20+50=70 -> 70*3=210 + """ + + @workflow.run + async def run(self, input_data: PipelineInput) -> int: + app = temporal_graph("pipeline", cache=input_data.cache).compile() + result = await app.ainvoke({"value": input_data.data}) + + if input_data.phase < 3: + workflow.continue_as_new( + PipelineInput( + data=input_data.data, + cache=cache(), + phase=input_data.phase + 1, + ) + ) + + return result["value"] diff --git a/langgraph_plugin/graph_api/hello_world/README.md b/langgraph_plugin/graph_api/hello_world/README.md new file mode 100644 index 000000000..8d0ebdf1b --- /dev/null +++ b/langgraph_plugin/graph_api/hello_world/README.md @@ -0,0 +1,29 @@ +# Hello World (Graph API) + +The simplest possible LangGraph + Temporal sample: a single-node graph that processes a query string. + +## What This Sample Demonstrates + +- Defining a `StateGraph` with a single node +- Wrapping it with `LangGraphPlugin` so the node runs as a Temporal activity +- Invoking the graph from a Temporal workflow + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/graph_api/hello_world/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/graph_api/hello_world/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `process_query` node, graph definition and `HelloWorldWorkflow` | +| `run_worker.py` | Registers graph with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/langgraph_plugin/graph_api/hello_world/__init__.py b/langgraph_plugin/graph_api/hello_world/__init__.py new file mode 100644 index 000000000..166b2c696 --- /dev/null +++ b/langgraph_plugin/graph_api/hello_world/__init__.py @@ -0,0 +1 @@ +"""Minimal hello world — single-node StateGraph.""" diff --git a/langgraph_plugin/graph_api/hello_world/run_worker.py b/langgraph_plugin/graph_api/hello_world/run_worker.py new file mode 100644 index 000000000..bf2ece96f --- /dev/null +++ b/langgraph_plugin/graph_api/hello_world/run_worker.py @@ -0,0 +1,31 @@ +"""Worker for the hello world sample (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.hello_world.workflow import ( + HelloWorldWorkflow, + make_hello_graph, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin(graphs={"hello-world": make_hello_graph()}) + + worker = Worker( + client, + task_queue="langgraph-hello-world", + workflows=[HelloWorldWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/hello_world/run_workflow.py b/langgraph_plugin/graph_api/hello_world/run_workflow.py new file mode 100644 index 000000000..e1a02a43c --- /dev/null +++ b/langgraph_plugin/graph_api/hello_world/run_workflow.py @@ -0,0 +1,25 @@ +"""Start the hello world workflow (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.graph_api.hello_world.workflow import HelloWorldWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + HelloWorldWorkflow.run, + "Hello, Temporal + LangGraph!", + id="hello-world-workflow", + task_queue="langgraph-hello-world", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/hello_world/workflow.py b/langgraph_plugin/graph_api/hello_world/workflow.py new file mode 100644 index 000000000..84eafe679 --- /dev/null +++ b/langgraph_plugin/graph_api/hello_world/workflow.py @@ -0,0 +1,42 @@ +"""Hello world using the LangGraph Graph API with Temporal. + +The simplest possible sample: a single-node graph that processes a query string. +""" + +from datetime import timedelta + +from langgraph.graph import START, StateGraph +from temporalio import workflow +from temporalio.contrib.langgraph import graph as temporal_graph +from typing_extensions import TypedDict + + +class State(TypedDict): + value: str + + +async def process_query(state: State) -> dict[str, str]: + """Process a query and return a response.""" + return {"value": f"Processed: {state['value']}"} + + +def make_hello_graph() -> StateGraph: + g = StateGraph(State) + g.add_node( + "process_query", + process_query, + metadata={ + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=10), + }, + ) + g.add_edge(START, "process_query") + return g + + +@workflow.defn +class HelloWorldWorkflow: + @workflow.run + async def run(self, query: str) -> str: + result = await temporal_graph("hello-world").compile().ainvoke({"value": query}) + return result["value"] diff --git a/langgraph_plugin/graph_api/human_in_the_loop/README.md b/langgraph_plugin/graph_api/human_in_the_loop/README.md new file mode 100644 index 000000000..f14ada540 --- /dev/null +++ b/langgraph_plugin/graph_api/human_in_the_loop/README.md @@ -0,0 +1,38 @@ +# Human-in-the-Loop Chatbot (Graph API) + +Demonstrates pausing a graph with LangGraph's `interrupt()` and waiting indefinitely for human review with Temporal's `workflow.wait_condition()`. A Temporal signal delivers the human's feedback; a Temporal query exposes the pending draft to UIs. + +## What This Sample Demonstrates + +- `workflow.wait_condition()` to block the Workflow until human input arrives — for as long as it takes, with no polling and no timeout +- Pausing a graph mid-execution with `interrupt()` at the review point +- Temporal **signals** to deliver human feedback and **queries** to expose the pending draft +- Resuming the graph with `Command(resume=...)` after the signal arrives + +## How It Works + +1. The Workflow starts and the `generate_draft` node produces a response. +2. The `human_review` node calls `interrupt(draft)`, pausing execution. +3. The Workflow stores the draft (visible via the query) and calls `workflow.wait_condition()` — blocking durably until the signal sets `_human_input`. This can wait indefinitely; Temporal persists the state. +4. An external process (UI, CLI, etc.) queries the draft and sends approval via signal. +5. The graph resumes — `interrupt()` returns the signal value and the node completes. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1: start the worker +uv run langgraph_plugin/graph_api/human_in_the_loop/run_worker.py + +# Terminal 2: start the workflow (polls for draft, then auto-approves) +uv run langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | Graph node functions, graph definition, and `ChatbotWorkflow` definition | +| `run_worker.py` | Builds graph, registers with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Starts workflow, polls draft via query, sends approval via signal | diff --git a/langgraph_plugin/graph_api/human_in_the_loop/__init__.py b/langgraph_plugin/graph_api/human_in_the_loop/__init__.py new file mode 100644 index 000000000..72da02f88 --- /dev/null +++ b/langgraph_plugin/graph_api/human_in_the_loop/__init__.py @@ -0,0 +1 @@ +"""Human-in-the-loop chatbot using interrupt() and Temporal signals.""" diff --git a/langgraph_plugin/graph_api/human_in_the_loop/run_worker.py b/langgraph_plugin/graph_api/human_in_the_loop/run_worker.py new file mode 100644 index 000000000..2090366c3 --- /dev/null +++ b/langgraph_plugin/graph_api/human_in_the_loop/run_worker.py @@ -0,0 +1,31 @@ +"""Worker for the human-in-the-loop chatbot (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.human_in_the_loop.workflow import ( + ChatbotWorkflow, + make_chatbot_graph, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin(graphs={"chatbot": make_chatbot_graph()}) + + worker = Worker( + client, + task_queue="langgraph-chatbot", + workflows=[ChatbotWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py b/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py new file mode 100644 index 000000000..7abfcdcf9 --- /dev/null +++ b/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py @@ -0,0 +1,38 @@ +"""Start the human-in-the-loop chatbot workflow (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.graph_api.human_in_the_loop.workflow import ChatbotWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + handle = await client.start_workflow( + ChatbotWorkflow.run, + "What is the meaning of life?", + id="chatbot-workflow", + task_queue="langgraph-chatbot", + ) + + # Poll until the draft is ready for review. + # In a real app, a UI would call this query endpoint. + draft = None + while draft is None: + await asyncio.sleep(0.5) + draft = await handle.query(ChatbotWorkflow.get_draft) + + print(f"Draft for review: {draft}") + + # Send approval via signal (a UI would trigger this) + await handle.signal(ChatbotWorkflow.provide_feedback, "approve") + + result = await handle.result() + print(f"Final response: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/human_in_the_loop/workflow.py b/langgraph_plugin/graph_api/human_in_the_loop/workflow.py new file mode 100644 index 000000000..f39275a60 --- /dev/null +++ b/langgraph_plugin/graph_api/human_in_the_loop/workflow.py @@ -0,0 +1,89 @@ +"""Human-in-the-loop chatbot using the LangGraph Graph API with Temporal. + +Demonstrates using LangGraph's interrupt() to pause a workflow for human input, +combined with Temporal signals to receive the input asynchronously. +""" + +from datetime import timedelta + +from langchain_core.runnables import RunnableConfig +from langgraph.checkpoint.memory import InMemorySaver +from langgraph.graph import START, StateGraph +from langgraph.types import Command, interrupt +from temporalio import workflow +from temporalio.contrib.langgraph import graph as temporal_graph +from typing_extensions import TypedDict + + +class State(TypedDict): + value: str + + +async def generate_draft(state: State) -> dict[str, str]: + """Generate a draft response. Replace with an LLM call in production.""" + return { + "value": ( + f"Here's my response to '{state['value']}': " + "The answer is 42. Let me know if this helps!" + ) + } + + +async def human_review(state: State) -> dict[str, str]: + """Present draft to human for review via interrupt.""" + feedback = interrupt(state["value"]) + if feedback == "approve": + return {"value": state["value"]} + return {"value": f"[Revised] {state['value']} (incorporating feedback: {feedback})"} + + +def make_chatbot_graph() -> StateGraph: + node_metadata = { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + } + g = StateGraph(State) + g.add_node("generate_draft", generate_draft, metadata=node_metadata) + g.add_node("human_review", human_review, metadata=node_metadata) + g.add_edge(START, "generate_draft") + g.add_edge("generate_draft", "human_review") + return g + + +@workflow.defn +class ChatbotWorkflow: + def __init__(self) -> None: + self._human_input: str | None = None + self._draft: str | None = None + + @workflow.signal + async def provide_feedback(self, feedback: str) -> None: + """Signal handler: receives human feedback (approval or revision).""" + self._human_input = feedback + + @workflow.query + def get_draft(self) -> str | None: + """Query handler: returns the pending draft for review, or None.""" + return self._draft + + @workflow.run + async def run(self, user_message: str) -> str: + app = temporal_graph("chatbot").compile(checkpointer=InMemorySaver()) + config = RunnableConfig( + {"configurable": {"thread_id": workflow.info().workflow_id}} + ) + + # First invocation: runs generate_draft, then pauses at interrupt() + result = await app.ainvoke({"value": user_message}, config, version="v2") + + # Store the draft from the interrupt for the query handler + self._draft = result.interrupts[0].value + + # Wait for human feedback via Temporal signal + await workflow.wait_condition(lambda: self._human_input is not None) + + # Resume the graph with the human's feedback + resumed = await app.ainvoke( + Command(resume=self._human_input), config, version="v2" + ) + return resumed.value["value"] diff --git a/langgraph_plugin/graph_api/langsmith_tracing/README.md b/langgraph_plugin/graph_api/langsmith_tracing/README.md new file mode 100644 index 000000000..47165ee8f --- /dev/null +++ b/langgraph_plugin/graph_api/langsmith_tracing/README.md @@ -0,0 +1,38 @@ +# LangSmith Tracing (Graph API) + +Demonstrates combining the LangGraph plugin (durable execution) with Temporal's LangSmith plugin (observability) for full tracing of LLM calls through Temporal workflows. + +## What This Sample Demonstrates + +- Using `LangSmithPlugin` on the Temporal client for automatic trace propagation +- Using `LangGraphPlugin` on the Worker for durable LangGraph execution +- `@traceable` in three places: on the Activity itself, on a helper called from inside the Activity, and on a helper called from inside the Workflow +- Both plugins working together: durability + observability + +## How It Works + +1. The Temporal client is created with `LangSmithPlugin(add_temporal_runs=True)`. +2. A Worker is created with `LangGraphPlugin` wrapping the chat graph. +3. When the Workflow runs, the `chat` node executes as a Temporal Activity. +4. `@traceable` decorators emit trace data to LangSmith for the Activity, an in-Activity helper, and an in-Workflow helper. +5. The `LangSmithPlugin` adds Temporal-specific metadata to the traces. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +export ANTHROPIC_API_KEY='your-key' +export LANGCHAIN_API_KEY='your-key' + +uv run langgraph_plugin/graph_api/langsmith_tracing/main.py +``` + +Traces will appear in your [LangSmith](https://smith.langchain.com/) dashboard. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `@traceable` chat node + helpers, graph definition, and `ChatWorkflow` | +| `main.py` | Starts a Worker and executes the Workflow in a single process | diff --git a/langgraph_plugin/graph_api/langsmith_tracing/__init__.py b/langgraph_plugin/graph_api/langsmith_tracing/__init__.py new file mode 100644 index 000000000..dab692ec7 --- /dev/null +++ b/langgraph_plugin/graph_api/langsmith_tracing/__init__.py @@ -0,0 +1 @@ +"""LangSmith tracing with LangGraph Graph API and Temporal.""" diff --git a/langgraph_plugin/graph_api/langsmith_tracing/main.py b/langgraph_plugin/graph_api/langsmith_tracing/main.py new file mode 100644 index 000000000..71294f4ad --- /dev/null +++ b/langgraph_plugin/graph_api/langsmith_tracing/main.py @@ -0,0 +1,43 @@ +"""Run the LangSmith tracing chat sample (Graph API). + +Single-process driver: starts a Worker, executes the Workflow once, prints +the result, then shuts down. Requires ANTHROPIC_API_KEY and LANGCHAIN_API_KEY. +""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.langsmith_tracing.workflow import ( + ChatWorkflow, + make_chat_graph, +) + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[LangSmithPlugin(add_temporal_runs=True)], + ) + + async with Worker( + client, + task_queue="langgraph-langsmith", + workflows=[ChatWorkflow], + plugins=[LangGraphPlugin(graphs={"chat": make_chat_graph()})], + ): + result = await client.execute_workflow( + ChatWorkflow.run, + "What is the meaning of life?", + id="langsmith-chat-workflow", + task_queue="langgraph-langsmith", + ) + print(f"Response: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/langsmith_tracing/workflow.py b/langgraph_plugin/graph_api/langsmith_tracing/workflow.py new file mode 100644 index 000000000..50649f314 --- /dev/null +++ b/langgraph_plugin/graph_api/langsmith_tracing/workflow.py @@ -0,0 +1,70 @@ +"""LangSmith tracing with LangGraph Graph API and Temporal. + +Demonstrates combining LangGraphPlugin (durable graph execution) with +LangSmithPlugin (observability) for full tracing of LLM calls through +Temporal workflows. + +Three @traceable use cases are demonstrated: +1. The Activity (graph node) function itself: `chat`. +2. A helper called from inside the Activity: `format_prompt`. +3. A helper called from inside the Workflow: `summarize_for_log`. + +Requires ANTHROPIC_API_KEY and LANGCHAIN_API_KEY environment variables. +""" + +from datetime import timedelta + +from langchain.chat_models import init_chat_model +from langgraph.graph import START, StateGraph +from langsmith import traceable +from temporalio import workflow +from temporalio.contrib.langgraph import graph as temporal_graph +from typing_extensions import TypedDict + + +class State(TypedDict): + value: str + + +@traceable(name="format_prompt", run_type="prompt") +def format_prompt(message: str) -> str: + """Helper called from inside the Activity. Traced by LangSmith.""" + return f"Please respond concisely to: {message}" + + +@traceable(name="chat_activity", run_type="chain") +async def chat(state: State) -> dict[str, str]: + """Call an LLM to respond to the message. Traced by LangSmith.""" + prompt = format_prompt(state["value"]) + response = await init_chat_model("claude-sonnet-4-6").ainvoke(prompt) + return {"value": str(response.content)} + + +@traceable(name="summarize_for_log", run_type="chain") +def summarize_for_log(response: str) -> str: + """Helper called from inside the Workflow. Traced by LangSmith.""" + return f"Got {len(response)}-char response: {response[:60]}..." + + +def make_chat_graph() -> StateGraph: + g = StateGraph(State) + g.add_node( + "chat", + chat, + metadata={ + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + ) + g.add_edge(START, "chat") + return g + + +@workflow.defn +class ChatWorkflow: + @workflow.run + async def run(self, message: str) -> str: + result = await temporal_graph("chat").compile().ainvoke({"value": message}) + response = result["value"] + workflow.logger.info(summarize_for_log(response)) + return response diff --git a/langgraph_plugin/graph_api/react_agent/README.md b/langgraph_plugin/graph_api/react_agent/README.md new file mode 100644 index 000000000..de5149ad4 --- /dev/null +++ b/langgraph_plugin/graph_api/react_agent/README.md @@ -0,0 +1,42 @@ +# ReAct Agent (Graph API) + +Demonstrates the most common LangGraph pattern: a tool-calling agent that loops between deciding and acting, using conditional edges for routing. + +## What This Sample Demonstrates + +- Defining a `StateGraph` with an agent->tools loop +- Using `add_conditional_edges` for conditional routing (call tool or finish) +- Accumulating conversation history with `Annotated[list, operator.add]` +- The full ReAct cycle: think -> act -> observe -> repeat + +## How It Works + +1. The `agent` node examines the conversation history and decides the next action. +2. If a tool is needed, `should_continue` routes to the `tools` node. +3. The `tools` node executes the tool and appends the result to history. +4. Control returns to `agent`, which decides again — loop or finish. +5. When the agent has enough information, `should_continue` routes to `END`. + +``` +START -> agent -> tools -> agent -> tools -> agent -> END +``` + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/graph_api/react_agent/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/graph_api/react_agent/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `AgentState`, node functions, `should_continue` router, graph definition, and `ReactAgentWorkflow` | +| `run_worker.py` | Builds graph, registers with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Executes the agent workflow and prints the answer | diff --git a/langgraph_plugin/graph_api/react_agent/__init__.py b/langgraph_plugin/graph_api/react_agent/__init__.py new file mode 100644 index 000000000..64c24c36e --- /dev/null +++ b/langgraph_plugin/graph_api/react_agent/__init__.py @@ -0,0 +1 @@ +"""ReAct agent with conditional edges and tool-calling loop.""" diff --git a/langgraph_plugin/graph_api/react_agent/run_worker.py b/langgraph_plugin/graph_api/react_agent/run_worker.py new file mode 100644 index 000000000..04532a325 --- /dev/null +++ b/langgraph_plugin/graph_api/react_agent/run_worker.py @@ -0,0 +1,31 @@ +"""Worker for the ReAct agent (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.react_agent.workflow import ( + ReactAgentWorkflow, + make_agent_graph, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin(graphs={"react-agent": make_agent_graph()}) + + worker = Worker( + client, + task_queue="langgraph-react-agent", + workflows=[ReactAgentWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/react_agent/run_workflow.py b/langgraph_plugin/graph_api/react_agent/run_workflow.py new file mode 100644 index 000000000..d4649f1e4 --- /dev/null +++ b/langgraph_plugin/graph_api/react_agent/run_workflow.py @@ -0,0 +1,25 @@ +"""Start the ReAct agent workflow (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.graph_api.react_agent.workflow import ReactAgentWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + ReactAgentWorkflow.run, + "Tell me about San Francisco", + id="react-agent-workflow", + task_queue="langgraph-react-agent", + ) + + print(f"Agent answer: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/react_agent/workflow.py b/langgraph_plugin/graph_api/react_agent/workflow.py new file mode 100644 index 000000000..1bcae10d7 --- /dev/null +++ b/langgraph_plugin/graph_api/react_agent/workflow.py @@ -0,0 +1,109 @@ +"""ReAct agent using the LangGraph Graph API with Temporal. + +Demonstrates the most common LangGraph pattern: a tool-calling agent that loops +between "thinking" (deciding the next action) and "acting" (executing a tool), +using conditional edges to control the loop. + +Graph topology: + START -> agent -> (tools -> agent)* -> END +""" + +import operator +from datetime import timedelta +from typing import Annotated, Any, TypedDict + +from langgraph.graph import END, START, StateGraph +from temporalio import workflow +from temporalio.contrib.langgraph import graph as temporal_graph + + +class AgentState(TypedDict): + """State for the ReAct agent. + + 'messages' uses operator.add so each node appends to the list rather + than replacing it, accumulating the full conversation history. + """ + + input: str + messages: Annotated[list[str], operator.add] + final_answer: str + + +async def agent(state: AgentState) -> dict[str, Any]: + """The agent decides what to do next based on the conversation history. + + In production, replace this with an LLM call (e.g., Claude with tools). + This stub simulates a 2-step research process. + """ + messages = state.get("messages", []) + tool_results = [m for m in messages if m.startswith("[Tool]")] + + if len(tool_results) == 0: + return { + "messages": [ + "[Agent] I need weather data. Calling get_weather for San Francisco." + ] + } + elif len(tool_results) == 1: + return { + "messages": [ + "[Agent] Now I need population data. " + "Calling get_population for San Francisco." + ] + } + else: + facts = "; ".join(tool_results) + return { + "messages": ["[Agent] I have all the information I need."], + "final_answer": (f"Here's what I found about San Francisco: {facts}"), + } + + +async def tools(state: AgentState) -> dict[str, Any]: + """Execute the tool requested by the agent.""" + last_msg = state["messages"][-1] + + if "get_weather" in last_msg: + return {"messages": ["[Tool] Weather in San Francisco: 72°F and sunny."]} + elif "get_population" in last_msg: + return {"messages": ["[Tool] San Francisco population: ~870,000 residents."]} + else: + return {"messages": ["[Tool] Unknown tool requested."]} + + +async def should_continue(state: AgentState) -> str: + """Route: if the agent requested a tool, go to 'tools'. Otherwise, end. + + Must be async to avoid run_in_executor inside Temporal's workflow sandbox. + """ + last_msg = state["messages"][-1] + if last_msg.startswith("[Agent]") and "Calling" in last_msg: + return "tools" + return END + + +def make_agent_graph() -> StateGraph: + node_metadata = { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + } + g = StateGraph(AgentState) + g.add_node("agent", agent, metadata=node_metadata) + g.add_node("tools", tools, metadata=node_metadata) + g.add_edge(START, "agent") + g.add_conditional_edges("agent", should_continue) + g.add_edge("tools", "agent") + return g + + +@workflow.defn +class ReactAgentWorkflow: + @workflow.run + async def run(self, query: str) -> str: + initial_state: AgentState = { + "input": query, + "messages": [], + "final_answer": "", + } + result = await temporal_graph("react-agent").compile().ainvoke(initial_state) + return result["final_answer"] diff --git a/pyproject.toml b/pyproject.toml index f57bc875d..c393375ad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] requires-python = ">=3.10" readme = "README.md" license = "MIT" -dependencies = ["temporalio>=1.26.0,<2"] +dependencies = ["temporalio>=1.27.0,<2"] [project.urls] Homepage = "https://github.com/temporalio/samples-python" @@ -33,7 +33,13 @@ gevent = ["gevent>=25.4.2 ; python_version >= '3.8'"] langsmith-tracing = [ "openai>=1.4.0", "langsmith>=0.7.0", - "temporalio[pydantic,langsmith]>=1.26.0", + "temporalio[pydantic,langsmith]>=1.27.0", +] +langgraph = [ + "langgraph>=1.1.3", + "langchain>=0.3.0", + "langchain-anthropic>=0.3.0", + "temporalio[langgraph,langsmith]>=1.27.0", ] nexus = ["nexus-rpc>=1.1.0,<2"] open-telemetry = [ @@ -42,7 +48,7 @@ open-telemetry = [ ] openai-agents = [ "openai-agents[litellm] >= 0.14.1", - "temporalio[openai-agents,opentelemetry] >= 1.26.0", + "temporalio[openai-agents,opentelemetry] >= 1.27.0", "requests>=2.32.0,<3", ] pydantic-converter = ["pydantic>=2.10.6,<3"] @@ -75,6 +81,7 @@ packages = [ "encryption", "gevent_async", "hello", + "langgraph_plugin", "langsmith_tracing", "message_passing", "nexus", @@ -113,8 +120,8 @@ lint = [ { cmd = "uv run ruff format --check" }, { ref = "lint-types" }, ] -lint-types = "uv run --all-groups mypy --check-untyped-defs --namespace-packages ." -test = "uv run --all-groups pytest" +lint-types = "uv run --all-groups --no-group cloud-export-to-parquet mypy --check-untyped-defs --namespace-packages ." +test = "uv run --all-groups --no-group cloud-export-to-parquet pytest" [tool.pytest.ini_options] asyncio_mode = "auto" diff --git a/tests/langgraph_plugin/__init__.py b/tests/langgraph_plugin/__init__.py new file mode 100644 index 000000000..133a43fdf --- /dev/null +++ b/tests/langgraph_plugin/__init__.py @@ -0,0 +1 @@ +"""LangGraph plugin sample tests.""" diff --git a/tests/langgraph_plugin/continue_as_new_test.py b/tests/langgraph_plugin/continue_as_new_test.py new file mode 100644 index 000000000..71392f5f7 --- /dev/null +++ b/tests/langgraph_plugin/continue_as_new_test.py @@ -0,0 +1,34 @@ +import uuid +from datetime import timedelta + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.continue_as_new.workflow import ( + PipelineInput, + PipelineWorkflow, + make_pipeline_graph, +) + + +async def test_continue_as_new_graph_api(client: Client) -> None: + """Input 10: 10*2=20 -> 20+50=70 -> 70*3=210.""" + task_queue = f"continue-as-new-test-{uuid.uuid4()}" + plugin = LangGraphPlugin(graphs={"pipeline": make_pipeline_graph()}) + + async with Worker( + client, + task_queue=task_queue, + workflows=[PipelineWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + PipelineWorkflow.run, + PipelineInput(data=10), + id=f"continue-as-new-{uuid.uuid4()}", + task_queue=task_queue, + execution_timeout=timedelta(seconds=60), + ) + + assert result == 210 diff --git a/tests/langgraph_plugin/functional_continue_as_new_test.py b/tests/langgraph_plugin/functional_continue_as_new_test.py new file mode 100644 index 000000000..9d627d629 --- /dev/null +++ b/tests/langgraph_plugin/functional_continue_as_new_test.py @@ -0,0 +1,47 @@ +import sys +import uuid +from datetime import timedelta + +import pytest +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.continue_as_new.workflow import ( + PipelineFunctionalWorkflow, + PipelineInput, + activity_options, + all_tasks, + pipeline_entrypoint, +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 11), + reason="LangGraph Functional API requires Python >= 3.11 for async context propagation", +) + + +async def test_functional_continue_as_new(client: Client) -> None: + """Input 10: 10*2=20 -> 20+50=70 -> 70*3=210.""" + task_queue = f"functional-continue-test-{uuid.uuid4()}" + plugin = LangGraphPlugin( + entrypoints={"pipeline": pipeline_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + async with Worker( + client, + task_queue=task_queue, + workflows=[PipelineFunctionalWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + PipelineFunctionalWorkflow.run, + PipelineInput(data=10), + id=f"functional-continue-{uuid.uuid4()}", + task_queue=task_queue, + execution_timeout=timedelta(seconds=60), + ) + + assert result == {"result": 210} diff --git a/tests/langgraph_plugin/functional_control_flow_test.py b/tests/langgraph_plugin/functional_control_flow_test.py new file mode 100644 index 000000000..a8fd337af --- /dev/null +++ b/tests/langgraph_plugin/functional_control_flow_test.py @@ -0,0 +1,59 @@ +import sys +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.control_flow.workflow import ( + ControlFlowWorkflow, + activity_options, + all_tasks, + control_flow_pipeline, +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 11), + reason="LangGraph Functional API requires Python >= 3.11 for async context propagation", +) + + +async def test_functional_control_flow(client: Client) -> None: + task_queue = f"functional-control-flow-test-{uuid.uuid4()}" + plugin = LangGraphPlugin( + entrypoints={"control-flow": control_flow_pipeline}, + tasks=all_tasks, + activity_options=activity_options, + ) + + items = [ + "Fix login bug", + "URGENT: Production outage", + "Update README", + "INVALID:", + "Urgent: Security patch", + ] + + async with Worker( + client, + task_queue=task_queue, + workflows=[ControlFlowWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + ControlFlowWorkflow.run, + items, + id=f"functional-control-flow-{uuid.uuid4()}", + task_queue=task_queue, + ) + + # "INVALID:" should be filtered out + assert result["total"] == 4 + # Check urgent vs normal routing + urgent = [r for r in result["results"] if r.startswith("[PRIORITY]")] + normal = [r for r in result["results"] if r.startswith("[STANDARD]")] + assert len(urgent) == 2 + assert len(normal) == 2 + assert "2 urgent" in result["summary"] + assert "2 normal" in result["summary"] diff --git a/tests/langgraph_plugin/functional_hello_world_test.py b/tests/langgraph_plugin/functional_hello_world_test.py new file mode 100644 index 000000000..4603ddbad --- /dev/null +++ b/tests/langgraph_plugin/functional_hello_world_test.py @@ -0,0 +1,43 @@ +import sys +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.hello_world.workflow import ( + HelloWorldFunctionalWorkflow, + activity_options, + all_tasks, + hello_entrypoint, +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 11), + reason="LangGraph Functional API requires Python >= 3.11 for async context propagation", +) + + +async def test_functional_hello_world(client: Client) -> None: + task_queue = f"functional-hello-test-{uuid.uuid4()}" + plugin = LangGraphPlugin( + entrypoints={"hello-world": hello_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HelloWorldFunctionalWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + HelloWorldFunctionalWorkflow.run, + "test query", + id=f"functional-hello-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == {"result": "Processed: test query"} diff --git a/tests/langgraph_plugin/functional_human_in_the_loop_test.py b/tests/langgraph_plugin/functional_human_in_the_loop_test.py new file mode 100644 index 000000000..746647f88 --- /dev/null +++ b/tests/langgraph_plugin/functional_human_in_the_loop_test.py @@ -0,0 +1,58 @@ +import asyncio +import sys +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.human_in_the_loop.workflow import ( + ChatbotFunctionalWorkflow, + activity_options, + all_tasks, + chatbot_entrypoint, +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 11), + reason="LangGraph Functional API and interrupt() require Python >= 3.11 for async context propagation", +) + + +async def test_functional_human_in_the_loop_approve(client: Client) -> None: + task_queue = f"functional-hitl-test-{uuid.uuid4()}" + plugin = LangGraphPlugin( + entrypoints={"chatbot": chatbot_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ChatbotFunctionalWorkflow], + plugins=[plugin], + ): + handle = await client.start_workflow( + ChatbotFunctionalWorkflow.run, + "test message", + id=f"functional-hitl-{uuid.uuid4()}", + task_queue=task_queue, + ) + + # Poll for draft to be ready + draft = None + for _ in range(40): + await asyncio.sleep(0.25) + draft = await handle.query(ChatbotFunctionalWorkflow.get_draft) + if draft is not None: + break + assert draft is not None + assert "test message" in draft + + # Approve + await handle.signal(ChatbotFunctionalWorkflow.provide_feedback, "approve") + result = await handle.result() + + assert result["response"] == draft diff --git a/tests/langgraph_plugin/functional_react_agent_test.py b/tests/langgraph_plugin/functional_react_agent_test.py new file mode 100644 index 000000000..088cd5c47 --- /dev/null +++ b/tests/langgraph_plugin/functional_react_agent_test.py @@ -0,0 +1,44 @@ +import sys +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.react_agent.workflow import ( + ReactAgentFunctionalWorkflow, + activity_options, + all_tasks, + react_agent_entrypoint, +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 11), + reason="LangGraph Functional API requires Python >= 3.11 for async context propagation", +) + + +async def test_functional_react_agent(client: Client) -> None: + task_queue = f"functional-react-agent-test-{uuid.uuid4()}" + plugin = LangGraphPlugin( + entrypoints={"react-agent": react_agent_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ReactAgentFunctionalWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + ReactAgentFunctionalWorkflow.run, + "Tell me about San Francisco", + id=f"functional-react-agent-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert "San Francisco" in result["answer"] + assert result["steps"] == 2 # two tool calls diff --git a/tests/langgraph_plugin/hello_world_test.py b/tests/langgraph_plugin/hello_world_test.py new file mode 100644 index 000000000..430989e57 --- /dev/null +++ b/tests/langgraph_plugin/hello_world_test.py @@ -0,0 +1,50 @@ +import uuid + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.hello_world.workflow import ( + HelloWorldWorkflow, + make_hello_graph, +) + + +async def test_hello_world_graph_api(client: Client) -> None: + task_queue = f"hello-world-test-{uuid.uuid4()}" + plugin = LangGraphPlugin(graphs={"hello-world": make_hello_graph()}) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HelloWorldWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + HelloWorldWorkflow.run, + "test query", + id=f"hello-world-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "Processed: test query" + + +async def test_hello_world_empty_string(client: Client) -> None: + task_queue = f"hello-world-empty-test-{uuid.uuid4()}" + plugin = LangGraphPlugin(graphs={"hello-world": make_hello_graph()}) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HelloWorldWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + HelloWorldWorkflow.run, + "", + id=f"hello-world-empty-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "Processed: " diff --git a/tests/langgraph_plugin/human_in_the_loop_test.py b/tests/langgraph_plugin/human_in_the_loop_test.py new file mode 100644 index 000000000..ad81e492c --- /dev/null +++ b/tests/langgraph_plugin/human_in_the_loop_test.py @@ -0,0 +1,86 @@ +import asyncio +import sys +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.human_in_the_loop.workflow import ( + ChatbotWorkflow, + make_chatbot_graph, +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 11), + reason="langgraph.types.interrupt() requires Python >= 3.11 for async context propagation", +) + + +async def test_human_in_the_loop_approve(client: Client) -> None: + task_queue = f"hitl-test-{uuid.uuid4()}" + plugin = LangGraphPlugin(graphs={"chatbot": make_chatbot_graph()}) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ChatbotWorkflow], + plugins=[plugin], + ): + handle = await client.start_workflow( + ChatbotWorkflow.run, + "test message", + id=f"hitl-{uuid.uuid4()}", + task_queue=task_queue, + ) + + # Poll for draft to be ready + draft = None + for _ in range(40): + await asyncio.sleep(0.25) + draft = await handle.query(ChatbotWorkflow.get_draft) + if draft is not None: + break + assert draft is not None + assert "test message" in draft + + # Approve + await handle.signal(ChatbotWorkflow.provide_feedback, "approve") + result = await handle.result() + + assert result == draft # approved draft returned as-is + + +async def test_human_in_the_loop_revise(client: Client) -> None: + task_queue = f"hitl-revise-test-{uuid.uuid4()}" + plugin = LangGraphPlugin(graphs={"chatbot": make_chatbot_graph()}) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ChatbotWorkflow], + plugins=[plugin], + ): + handle = await client.start_workflow( + ChatbotWorkflow.run, + "test message", + id=f"hitl-revise-{uuid.uuid4()}", + task_queue=task_queue, + ) + + # Poll for draft + draft = None + for _ in range(40): + await asyncio.sleep(0.25) + draft = await handle.query(ChatbotWorkflow.get_draft) + if draft is not None: + break + assert draft is not None + + # Send revision feedback + await handle.signal(ChatbotWorkflow.provide_feedback, "please be more concise") + result = await handle.result() + + assert "[Revised]" in result + assert "please be more concise" in result diff --git a/tests/langgraph_plugin/react_agent_test.py b/tests/langgraph_plugin/react_agent_test.py new file mode 100644 index 000000000..e9db3ec6a --- /dev/null +++ b/tests/langgraph_plugin/react_agent_test.py @@ -0,0 +1,32 @@ +import uuid + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.react_agent.workflow import ( + ReactAgentWorkflow, + make_agent_graph, +) + + +async def test_react_agent_graph_api(client: Client) -> None: + task_queue = f"react-agent-test-{uuid.uuid4()}" + plugin = LangGraphPlugin(graphs={"react-agent": make_agent_graph()}) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ReactAgentWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + ReactAgentWorkflow.run, + "Tell me about San Francisco", + id=f"react-agent-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert "San Francisco" in result + assert "72" in result or "weather" in result.lower() + assert "870,000" in result or "population" in result.lower() diff --git a/uv.lock b/uv.lock index 106627c34..bc256e457 100644 --- a/uv.lock +++ b/uv.lock @@ -125,6 +125,25 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" }, ] +[[package]] +name = "anthropic" +version = "0.97.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "distro" }, + { name = "docstring-parser" }, + { name = "httpx" }, + { name = "jiter" }, + { name = "pydantic" }, + { name = "sniffio" }, + { name = "typing-extensions" }, +] +sdist = { url = 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Date: Mon, 4 May 2026 20:42:18 -0700 Subject: [PATCH 21/40] Add lambda worker sample (#286) --- .gitignore | 3 + lambda_worker/.gitignore | 2 + lambda_worker/README.md | 109 +++ lambda_worker/activities.py | 7 + lambda_worker/deploy-lambda.sh | 24 + lambda_worker/extra-setup-steps | 48 ++ ...-role-for-temporal-lambda-invoke-test.yaml | 97 +++ lambda_worker/lambda_function.py | 18 + lambda_worker/mk-iam-role.sh | 17 + lambda_worker/otel-collector-config.yaml | 35 + lambda_worker/pyproject.toml | 41 ++ lambda_worker/starter.py | 24 + lambda_worker/temporal.toml | 6 + lambda_worker/uv.lock | 643 ++++++++++++++++++ lambda_worker/workflows.py | 22 + pyproject.toml | 4 + 16 files changed, 1100 insertions(+) create mode 100644 lambda_worker/.gitignore create mode 100644 lambda_worker/README.md create mode 100644 lambda_worker/activities.py create mode 100755 lambda_worker/deploy-lambda.sh create mode 100755 lambda_worker/extra-setup-steps create mode 100644 lambda_worker/iam-role-for-temporal-lambda-invoke-test.yaml create mode 100644 lambda_worker/lambda_function.py create mode 100755 lambda_worker/mk-iam-role.sh create mode 100644 lambda_worker/otel-collector-config.yaml create mode 100644 lambda_worker/pyproject.toml create mode 100644 lambda_worker/starter.py create mode 100644 lambda_worker/temporal.toml create mode 100644 lambda_worker/uv.lock create mode 100644 lambda_worker/workflows.py diff --git a/.gitignore b/.gitignore index 157a7418c..9b0b43524 100644 --- a/.gitignore +++ b/.gitignore @@ -4,3 +4,6 @@ __pycache__ .vscode .DS_Store .claude +.mypy_cache/ +**/client.key +**/client.pem diff --git a/lambda_worker/.gitignore b/lambda_worker/.gitignore new file mode 100644 index 000000000..82d6f6377 --- /dev/null +++ b/lambda_worker/.gitignore @@ -0,0 +1,2 @@ +package/ +temporal-sdk.toml diff --git a/lambda_worker/README.md b/lambda_worker/README.md new file mode 100644 index 000000000..353241f0b --- /dev/null +++ b/lambda_worker/README.md @@ -0,0 +1,109 @@ +# Lambda Worker + +This sample demonstrates how to run a Temporal Worker inside an AWS Lambda function using +the [`lambda_worker`](https://python.temporal.io/temporalio.contrib.aws.lambda_worker.html) +contrib package. It includes optional OpenTelemetry instrumentation that exports traces +and metrics through AWS Distro for OpenTelemetry (ADOT). + +The sample registers a simple greeting Workflow and Activity, but the pattern applies to +any Workflow/Activity definitions. + +## Prerequisites + +- A [Temporal Cloud](https://temporal.io/cloud) namespace (or a self-hosted Temporal + cluster accessible from your Lambda) +- AWS CLI configured with permissions to create Lambda functions, IAM roles, and + CloudFormation stacks +- mTLS client certificate and key for your Temporal namespace (place as `client.pem` and + `client.key` in this directory) +- Python 3.10+ + +## Files + +| File | Description | +|------|-------------| +| `lambda_function.py` | Lambda entry point -- configures the worker, registers Workflows/Activities, and exports the handler | +| `workflows.py` | Sample Workflow that executes a greeting Activity | +| `activities.py` | Sample Activity that returns a greeting string | +| `starter.py` | Helper program to start a Workflow execution from a local machine | +| `temporal.toml` | Temporal client connection configuration (update with your namespace) | +| `otel-collector-config.yaml` | OpenTelemetry Collector sidecar configuration for ADOT | +| `deploy-lambda.sh` | Packages and deploys the Lambda function | +| `mk-iam-role.sh` | Creates the IAM role that allows Temporal Cloud to invoke the Lambda | +| `iam-role-for-temporal-lambda-invoke-test.yaml` | CloudFormation template for the IAM role | +| `extra-setup-steps` | Additional IAM and Lambda configuration for OpenTelemetry support | + +## Setup + +The instructions here are a slimmed down version of the more complete getting started guide which +you can find [here](https://docs.temporal.io/production-deployment/worker-deployments/serverless-workers/aws-lambda). + +### 1. Create a lambda function for your Python worker + +Use either the AWS web UI or CLI to create a Python runtime Lambda function. Ex: + +```bash +aws lambda create-function \ + --function-name my-temporal-worker \ + --runtime python3.13 \ + --handler lambda_function.lambda_handler \ + --role arn:aws:iam:::role/my-temporal-worker-execution \ + --timeout 600 \ + --memory-size 256 +``` + +### 2. Configure Temporal connection + +Edit `temporal.toml` with your Temporal Cloud namespace address and credentials. In production, +we'd recommend reading your credentials from a secret store, but to keep this example simple +the toml file defaults to reading them from keys bundled along with the Lambda code. + +### 3. Create the IAM role + +This creates the IAM role that Temporal Cloud assumes to invoke your Lambda function: + +```bash +./mk-iam-role.sh +``` + +The External ID is provided by Temporal Cloud in your namespace's serverless worker +configuration. + +### 4. (Optional) Enable OpenTelemetry + +If you want traces, metrics, and logs, you'll have to attach the ADOT layet to your Lambda function. +You will need to add the appropriate layer for your runtime and region. See [this page +](https://aws-otel.github.io/docs/getting-started/lambda#getting-started-with-aws-lambda-layers) +for more info. + +Then run the extra setup to grant the Lambda role the necessary permissions: + +```bash +./extra-setup-steps +``` + +Update `otel-collector-config.yaml` with your function name and region as needed. + +### 5. Deploy the Lambda function + +```bash +./deploy-lambda.sh +``` + +This installs Python dependencies, bundles them with your code and configuration files, +and uploads to AWS Lambda. + +### 6. Configure Temporal to be able to invoke your lambda function + +Refer to the docs [here](https://docs.temporal.io/production-deployment/worker-deployments/serverless-workers/aws-lambda#create-worker-deployment-version). + +### 7. Start a Workflow + +Use the starter program to execute a Workflow on the Lambda worker, using +the same config file the Lambda uses for connecting to the server: + +From inside this directory: + +```bash +TEMPORAL_CONFIG_FILE=temporal.toml uv run python starter.py +``` diff --git a/lambda_worker/activities.py b/lambda_worker/activities.py new file mode 100644 index 000000000..a6a9c8e22 --- /dev/null +++ b/lambda_worker/activities.py @@ -0,0 +1,7 @@ +from temporalio import activity + + +@activity.defn +async def hello_activity(name: str) -> str: + activity.logger.info("HelloActivity started with name: %s", name) + return f"Hello, {name}!" diff --git a/lambda_worker/deploy-lambda.sh b/lambda_worker/deploy-lambda.sh new file mode 100755 index 000000000..ce331a80e --- /dev/null +++ b/lambda_worker/deploy-lambda.sh @@ -0,0 +1,24 @@ +#!/bin/bash +set -euo pipefail + +FUNCTION_NAME="${1:?Usage: deploy-lambda.sh }" +SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)" + +# Install the published temporalio package (Linux wheels) with the OTel extras +# needed by the lambda_worker contrib module +uv pip install --target "$SCRIPT_DIR/package" --python-platform x86_64-unknown-linux-gnu \ + --only-binary=:all: "temporalio[lambda-worker-otel]" + +# Copy application code into the package directory (all at zip root) +cp "$SCRIPT_DIR/lambda_function.py" "$SCRIPT_DIR/workflows.py" \ + "$SCRIPT_DIR/activities.py" "$SCRIPT_DIR/package/" + +# Bundle with configuration files +cd "$SCRIPT_DIR/package" +zip -r "$SCRIPT_DIR/function.zip" . +cd "$SCRIPT_DIR" +zip function.zip client.pem client.key temporal.toml otel-collector-config.yaml + +aws lambda update-function-code --function-name "$FUNCTION_NAME" --zip-file fileb://function.zip + +rm -rf "$SCRIPT_DIR/package" "$SCRIPT_DIR/function.zip" diff --git a/lambda_worker/extra-setup-steps b/lambda_worker/extra-setup-steps new file mode 100755 index 000000000..11493a0f8 --- /dev/null +++ b/lambda_worker/extra-setup-steps @@ -0,0 +1,48 @@ +#!/bin/bash +set -euo pipefail + +# Additional setup steps for OpenTelemetry support. +# These are needed if you want metrics, logs, and traces from your Lambda worker. + +ROLE_NAME="${1:?Usage: extra-setup-steps }" +FUNCTION_NAME="${2:?Usage: extra-setup-steps }" +REGION="${3:?Usage: extra-setup-steps }" +ACCOUNT_ID="${4:?Usage: extra-setup-steps }" + +# Needed to allow metrics/logs/traces to be published +aws iam put-role-policy \ + --role-name "$ROLE_NAME" \ + --policy-name ADOT-Telemetry-Permissions \ + --policy-document "{ + \"Version\": \"2012-10-17\", + \"Statement\": [ + { + \"Effect\": \"Allow\", + \"Action\": [ + \"logs:CreateLogGroup\", + \"logs:CreateLogStream\", + \"logs:PutLogEvents\" + ], + \"Resource\": \"arn:aws:logs:${REGION}:${ACCOUNT_ID}:log-group:/aws/lambda/${FUNCTION_NAME}:*\" + }, + { + \"Effect\": \"Allow\", + \"Action\": [ + \"xray:PutTraceSegments\", + \"xray:PutTelemetryRecords\" + ], + \"Resource\": \"*\" + }, + { + \"Effect\": \"Allow\", + \"Action\": [ + \"cloudwatch:PutMetricData\" + ], + \"Resource\": \"*\" + } + ] + }" + +# Needed to make traces show up with type: `"AWS::Lambda::Function"` filter +aws lambda update-function-configuration \ + --function-name "$FUNCTION_NAME" --tracing-config Mode=Active diff --git a/lambda_worker/iam-role-for-temporal-lambda-invoke-test.yaml b/lambda_worker/iam-role-for-temporal-lambda-invoke-test.yaml new file mode 100644 index 000000000..82dd16aa9 --- /dev/null +++ b/lambda_worker/iam-role-for-temporal-lambda-invoke-test.yaml @@ -0,0 +1,97 @@ +# CloudFormation template for creating an IAM role that Temporal Cloud can assume to invoke Lambda functions. +AWSTemplateFormatVersion: "2010-09-09" +Description: Creates an IAM role that Temporal Cloud can assume to invoke multiple Lambda functions for Serverless Workers. + +Parameters: + AssumeRoleExternalId: + Type: String + Description: The External ID provided by Temporal Cloud + AllowedPattern: "[a-zA-Z0-9_+=,.@-]*" + MinLength: 5 + MaxLength: 45 + + LambdaFunctionARNs: + Type: CommaDelimitedList + Description: >- + Comma-separated list of Lambda function ARNs to invoke (e.g., + arn:aws:lambda:us-west-2:123456789012:function:worker-1,arn:aws:lambda:us-west-2:123456789012:function:worker-2) + + RoleName: + Type: String + Default: "Temporal-Cloud-Serverless-Worker" + +Metadata: + AWS::CloudFormation::Interface: + ParameterGroups: + - Label: + default: "Temporal Cloud Configuration" + Parameters: + - AssumeRoleExternalId + - Label: + default: "Lambda Configuration" + Parameters: + - LambdaFunctionARNs + - RoleName + ParameterLabels: + AssumeRoleExternalId: + default: "External ID (provided by Temporal Cloud)" + LambdaFunctionARNs: + default: "Lambda Function ARNs (comma-separated list)" + RoleName: + default: "IAM Role Name" + +Resources: + TemporalCloudServerlessWorker: + Type: AWS::IAM::Role + Properties: + RoleName: !Sub "${RoleName}-${AWS::StackName}" + AssumeRolePolicyDocument: + Version: "2012-10-17" + Statement: + - Effect: Allow + Principal: + AWS: + [ + arn:aws:iam::902542641901:role/wci-lambda-invoke, + arn:aws:iam::160190466495:role/wci-lambda-invoke, + arn:aws:iam::819232936619:role/wci-lambda-invoke, + arn:aws:iam::829909441867:role/wci-lambda-invoke, + arn:aws:iam::354116250941:role/wci-lambda-invoke, + ] + Action: sts:AssumeRole + Condition: + StringEquals: + "sts:ExternalId": [!Ref AssumeRoleExternalId] + Description: "The role Temporal Cloud uses to invoke Lambda functions for Serverless Workers" + MaxSessionDuration: 3600 # 1 hour + + TemporalCloudLambdaInvokePermissions: + Type: AWS::IAM::Policy + DependsOn: TemporalCloudServerlessWorker + Properties: + PolicyName: "Temporal-Cloud-Lambda-Invoke-Permissions" + PolicyDocument: + Version: "2012-10-17" + Statement: + - Effect: Allow + Action: + - lambda:InvokeFunction + - lambda:GetFunction + Resource: !Ref LambdaFunctionARNs + Roles: + - !Sub "${RoleName}-${AWS::StackName}" + +Outputs: + RoleARN: + Description: The ARN of the IAM role created for Temporal Cloud + Value: !GetAtt TemporalCloudServerlessWorker.Arn + Export: + Name: !Sub "${AWS::StackName}-RoleARN" + + RoleName: + Description: The name of the IAM role + Value: !Ref RoleName + + LambdaFunctionARNs: + Description: The Lambda function ARNs that can be invoked + Value: !Join [", ", !Ref LambdaFunctionARNs] diff --git a/lambda_worker/lambda_function.py b/lambda_worker/lambda_function.py new file mode 100644 index 000000000..4c312097f --- /dev/null +++ b/lambda_worker/lambda_function.py @@ -0,0 +1,18 @@ +from activities import hello_activity +from temporalio.common import WorkerDeploymentVersion +from temporalio.contrib.aws.lambda_worker import LambdaWorkerConfig, run_worker +from temporalio.contrib.aws.lambda_worker.otel import apply_defaults +from workflows import TASK_QUEUE, SampleWorkflow + + +def configure(config: LambdaWorkerConfig) -> None: + config.worker_config["task_queue"] = TASK_QUEUE + config.worker_config["workflows"] = [SampleWorkflow] + config.worker_config["activities"] = [hello_activity] + apply_defaults(config) + + +lambda_handler = run_worker( + WorkerDeploymentVersion(deployment_name="my-app", build_id="build-1"), + configure, +) diff --git a/lambda_worker/mk-iam-role.sh b/lambda_worker/mk-iam-role.sh new file mode 100755 index 000000000..506dd52e6 --- /dev/null +++ b/lambda_worker/mk-iam-role.sh @@ -0,0 +1,17 @@ +#!/bin/bash +set -euo pipefail + +# Creates the IAM role that allows Temporal Cloud to invoke your Lambda function. +# You can find the External ID in your Temporal Cloud namespace settings. + +STACK_NAME="${1:?Usage: mk-iam-role.sh }" +EXTERNAL_ID="${2:?Usage: mk-iam-role.sh }" +LAMBDA_ARN="${3:?Usage: mk-iam-role.sh }" + +aws cloudformation create-stack \ + --stack-name "$STACK_NAME" \ + --template-body file://iam-role-for-temporal-lambda-invoke-test.yaml \ + --parameters \ + ParameterKey=AssumeRoleExternalId,ParameterValue="$EXTERNAL_ID" \ + ParameterKey=LambdaFunctionARNs,ParameterValue="$LAMBDA_ARN" \ + --capabilities CAPABILITY_NAMED_IAM diff --git a/lambda_worker/otel-collector-config.yaml b/lambda_worker/otel-collector-config.yaml new file mode 100644 index 000000000..bd54c4a78 --- /dev/null +++ b/lambda_worker/otel-collector-config.yaml @@ -0,0 +1,35 @@ +receivers: + otlp: + protocols: + grpc: + endpoint: "localhost:4317" + http: + endpoint: "localhost:4318" + +exporters: + debug: + awsxray: + region: us-west-2 + awsemf: + # AWS EMF exporter for metrics + # These are example configurations + namespace: TemporalWorkerMetrics + log_group_name: /aws/lambda/ + region: us-west-2 + dimension_rollup_option: NoDimensionRollup + resource_to_telemetry_conversion: + enabled: true + +service: + pipelines: + traces: + receivers: [otlp] + exporters: [awsxray, debug] + metrics: + receivers: [otlp] + exporters: [awsemf] + telemetry: + logs: + level: debug + metrics: + address: localhost:8888 diff --git a/lambda_worker/pyproject.toml b/lambda_worker/pyproject.toml new file mode 100644 index 000000000..77a65440c --- /dev/null +++ b/lambda_worker/pyproject.toml @@ -0,0 +1,41 @@ +[project] +name = "temporalio-samples-lambda-worker" +version = "0.1a1" +description = "Temporal.io Python SDK Lambda worker sample" +authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] +requires-python = ">=3.10" +readme = "README.md" +license = "MIT" +dependencies = ["temporalio[lambda-worker-otel]>=1.26.0,<2"] + +[dependency-groups] +dev = [ + "ruff>=0.5.0,<0.6", + "mypy>=1.4.1,<2", + "poethepoet>=0.36.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.metadata] +allow-direct-references = true + +[tool.hatch.build.targets.wheel] +packages = ["."] + +[tool.poe.tasks] +format = [ + { cmd = "uv run ruff check --select I --fix" }, + { cmd = "uv run ruff format" }, +] +lint = [ + { cmd = "uv run ruff check --select I" }, + { cmd = "uv run ruff format --check" }, + { ref = "lint-types" }, +] +lint-types = "uv run --all-groups mypy --check-untyped-defs --namespace-packages ." + +[tool.ruff] +target-version = "py310" diff --git a/lambda_worker/starter.py b/lambda_worker/starter.py new file mode 100644 index 000000000..4e485c006 --- /dev/null +++ b/lambda_worker/starter.py @@ -0,0 +1,24 @@ +import asyncio + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from workflows import TASK_QUEUE, SampleWorkflow + + +async def main() -> None: + config = ClientConfig.load_client_connect_config() + client = await Client.connect(**config) + print("Connected to Temporal Service") + + result = await client.execute_workflow( + SampleWorkflow.run, + "Serverless Lambda Worker!", + id="serverless-workflow-id-1", + task_queue=TASK_QUEUE, + ) + print(f"Workflow result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/lambda_worker/temporal.toml b/lambda_worker/temporal.toml new file mode 100644 index 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@@ +from datetime import timedelta + +from temporalio import common, workflow + +TASK_QUEUE = "serverless-task-queue-python" + +with workflow.unsafe.imports_passed_through(): + from activities import hello_activity + + +@workflow.defn(versioning_behavior=common.VersioningBehavior.PINNED) +class SampleWorkflow: + @workflow.run + async def run(self, name: str) -> str: + workflow.logger.info("SampleWorkflow started with name: %s", name) + result = await workflow.execute_activity( + hello_activity, + name, + start_to_close_timeout=timedelta(seconds=10), + ) + workflow.logger.info("SampleWorkflow completed with result: %s", result) + return result diff --git a/pyproject.toml b/pyproject.toml index c393375ad..1149a8d37 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -66,9 +66,11 @@ allow-direct-references = true [tool.hatch.build.targets.sdist] include = ["./**/*.py"] +exclude = ["lambda_worker/**"] [tool.hatch.build.targets.wheel] include = ["./**/*.py"] +exclude = ["lambda_worker/**"] packages = [ "activity_worker", "bedrock", @@ -131,10 +133,12 @@ log_cli_format = "%(asctime)s [%(levelname)8s] %(message)s (%(filename)s:%(linen [tool.ruff] target-version = "py310" +extend-exclude = ["lambda_worker"] [tool.mypy] ignore_missing_imports = true namespace_packages = true +exclude = ["lambda_worker/"] [[tool.mypy.overrides]] module = "aiohttp.*" From 0bca0bfe465d5f7817d33696add09ad52561eb64 Mon Sep 17 00:00:00 2001 From: xumaple <45406854+xumaple@users.noreply.github.com> Date: Mon, 11 May 2026 14:45:50 -0400 Subject: [PATCH 22/40] AI-180: Disable LangSmith network calls in samples tests via mock client (#304) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The langsmith_tracing tests previously instantiated `LangSmithPlugin()` with no client. Internally that constructs a real `langsmith.Client` which authenticates against `api.smith.langchain.com` using whatever `LANGSMITH_API_KEY` is in env, producing 401/403 warnings on every test run when a stale or invalid key was present. The samples tests don't assert on trace output, so the right fix is to bypass the network entirely. Added a `mock_ls_client` pytest fixture in `tests/langsmith_tracing/conftest.py` and wired it into `test_basic.py` and `test_chatbot.py` via `LangSmithPlugin(client=mock_ls_client)`. Verified empirically with `pytest-socket --disable-socket --allow-hosts=127.0.0.1,::1,localhost` and `LANGSMITH_API_KEY` set: zero non-localhost connections attempted during the test run. Drive-by: corrected a pre-existing inaccurate docstring on `test_chatbot_read_note` (the test saves a note first, then reads it back; the original elided the save phase). The underlying SDK behavior — `LangSmithInterceptor` force-sets `tracing_context(enabled=True)`, which overrides the standard `LANGSMITH_TRACING=false` disable mechanism — is filed separately as AI-183. Co-authored-by: Claude Opus 4.7 (1M context) --- tests/langsmith_tracing/conftest.py | 21 +++++++++++++++++++++ tests/langsmith_tracing/test_basic.py | 7 +++++-- tests/langsmith_tracing/test_chatbot.py | 15 ++++++++++----- 3 files changed, 36 insertions(+), 7 deletions(-) create mode 100644 tests/langsmith_tracing/conftest.py diff --git a/tests/langsmith_tracing/conftest.py b/tests/langsmith_tracing/conftest.py new file mode 100644 index 000000000..d66384f80 --- /dev/null +++ b/tests/langsmith_tracing/conftest.py @@ -0,0 +1,21 @@ +"""Shared fixtures for LangSmith tracing tests.""" + +from unittest.mock import MagicMock + +import pytest + + +@pytest.fixture +def mock_ls_client() -> MagicMock: + """Return a mock ``langsmith.Client`` that never makes network calls. + + The samples tests don't assert on trace output — the LangSmith plugin + just needs *some* client object to wire into the worker. ``MagicMock`` + auto-stubs every method the interceptor calls; the explicit ``session`` + and ``tracing_queue`` stubs match what the langsmith library touches + internally. + """ + client = MagicMock() + client.session = MagicMock() + client.tracing_queue = MagicMock() + return client diff --git a/tests/langsmith_tracing/test_basic.py b/tests/langsmith_tracing/test_basic.py index b5be2f353..cef49054a 100644 --- a/tests/langsmith_tracing/test_basic.py +++ b/tests/langsmith_tracing/test_basic.py @@ -1,4 +1,5 @@ import uuid +from unittest.mock import MagicMock from temporalio import activity from temporalio.client import Client @@ -10,7 +11,9 @@ from langsmith_tracing.basic.workflows import BasicLLMWorkflow -async def test_basic_workflow(client: Client, env: WorkflowEnvironment): +async def test_basic_workflow( + client: Client, env: WorkflowEnvironment, mock_ls_client: MagicMock +): expected_text = "Temporal is a durable execution platform." @activity.defn(name="call_openai") @@ -22,7 +25,7 @@ async def mock_call_openai(request: OpenAIRequest) -> str: task_queue="test-langsmith-basic", workflows=[BasicLLMWorkflow], activities=[mock_call_openai], - plugins=[LangSmithPlugin()], + plugins=[LangSmithPlugin(client=mock_ls_client)], ): result = await client.execute_workflow( BasicLLMWorkflow.run, diff --git a/tests/langsmith_tracing/test_chatbot.py b/tests/langsmith_tracing/test_chatbot.py index 2febb654d..88d98202a 100644 --- a/tests/langsmith_tracing/test_chatbot.py +++ b/tests/langsmith_tracing/test_chatbot.py @@ -1,5 +1,6 @@ import json import uuid +from unittest.mock import MagicMock from temporalio import activity from temporalio.client import Client @@ -27,7 +28,9 @@ def _make_function_call_response( ) -async def test_chatbot_save_note(client: Client, env: WorkflowEnvironment): +async def test_chatbot_save_note( + client: Client, env: WorkflowEnvironment, mock_ls_client: MagicMock +): """Test save_note tool call loop — save_note runs as a workflow method.""" call_count = 0 @@ -47,7 +50,7 @@ async def mock_call_openai(request: OpenAIRequest) -> ChatResponse: task_queue="test-langsmith-chatbot", workflows=[ChatbotWorkflow], activities=[mock_call_openai], - plugins=[LangSmithPlugin()], + plugins=[LangSmithPlugin(client=mock_ls_client)], ): wf_handle = await client.start_workflow( ChatbotWorkflow.run, @@ -68,8 +71,10 @@ async def mock_call_openai(request: OpenAIRequest) -> ChatResponse: assert result == "Session ended." -async def test_chatbot_read_note(client: Client, env: WorkflowEnvironment): - """Test read_note tool call loop — read_note runs as a workflow method.""" +async def test_chatbot_read_note( + client: Client, env: WorkflowEnvironment, mock_ls_client: MagicMock +): + """Test read_note tool call loop — saves a note first, then reads it back.""" call_count = 0 @activity.defn(name="call_openai") @@ -97,7 +102,7 @@ async def mock_call_openai(request: OpenAIRequest) -> ChatResponse: task_queue="test-langsmith-chatbot-read", workflows=[ChatbotWorkflow], activities=[mock_call_openai], - plugins=[LangSmithPlugin()], + plugins=[LangSmithPlugin(client=mock_ls_client)], ): wf_handle = await client.start_workflow( ChatbotWorkflow.run, From bc1e74df6866b3f4e5e85f3920ba8d3840599e84 Mon Sep 17 00:00:00 2001 From: Thomas Hardy Date: Wed, 13 May 2026 12:51:05 -0400 Subject: [PATCH 23/40] bump version to 1.27.1 (#306) --- pyproject.toml | 2 +- uv.lock | 22 +++++++++++----------- 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 1149a8d37..5ee4c6647 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] requires-python = ">=3.10" readme = "README.md" license = "MIT" -dependencies = ["temporalio>=1.27.0,<2"] +dependencies = ["temporalio>=1.27.1,<2"] [project.urls] Homepage = "https://github.com/temporalio/samples-python" diff --git a/uv.lock b/uv.lock index bc256e457..d4a2d5e36 100644 --- a/uv.lock +++ b/uv.lock @@ -1513,7 +1513,7 @@ wheels = [ [[package]] name = "openai-agents" -version = "0.14.3" +version = "0.17.2" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "griffelib" }, @@ -1525,9 +1525,9 @@ dependencies = [ { name = "typing-extensions" }, { name = "websockets" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/cf/d1/f3607ef8eb3f4ffd6c300ff835cbf64537442874186832d165d3813392e0/openai_agents-0.14.3.tar.gz", hash = "sha256:57586446adca361cb2356c537f47084e40489560beedc9537a35083dcc2b5966", size = 5296409, upload-time = "2026-04-20T22:25:05.858Z" } +sdist = { url = "https://files.pythonhosted.org/packages/11/cd/14acaf94c6a438cfe72c5ea043bfbc77d7fbb9514ab7796d82f2180d1518/openai_agents-0.17.2.tar.gz", hash = "sha256:5e11414bdd8c20c8e9192d21f78265d30e65992f4537f940309eca9255804449", size = 5403689, upload-time = "2026-05-12T03:14:57.433Z" } wheels = [ - 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Each LangGraph graph node (Graph API) or `@task` (Functional API) executes as a Temporal activity with automatic retries, timeouts, and crash recovery. +These samples demonstrate the [Temporal LangGraph plugin](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/langgraph), which runs LangGraph workflows as durable Temporal workflows. Each LangGraph graph node (Graph API) or `@task` (Functional API) executes directly inside a workflow or as a Temporal activity with automatic retries, timeouts, and crash recovery. Samples are organized by API style: @@ -62,7 +62,7 @@ uv run langgraph_plugin//langsmith_tracing/main.py ## Key Features Demonstrated -- **Durable execution** -- Every graph node / `@task` runs as a Temporal activity with configurable timeouts and retry policies. +- **Durable execution** -- Every graph node / `@task` runs in a Workflow or as a Temporal activity with configurable timeouts and retry policies. - **Human-in-the-loop** -- LangGraph's `interrupt()` pauses the graph; Temporal signals deliver human input; queries expose pending state to UIs. - **Continue-as-new with caching** -- `cache()` captures completed task results; passing the cache to the next execution avoids re-running them. - **Conditional routing** -- Graph API's `add_conditional_edges` and Functional API's native `if/else`/`while` for agent loops. From 6168ebc13ba88e2e05a7c36e251d42eefe1544f8 Mon Sep 17 00:00:00 2001 From: Justin Anderson <44687433+jmaeagle99@users.noreply.github.com> Date: Thu, 14 May 2026 21:43:59 -0700 Subject: [PATCH 26/40] External storage and codec server sample (#307) --- README.md | 1 + external_storage/README.md | 143 ++++++ external_storage/__init__.py | 0 external_storage/_sample_data.py | 72 +++ external_storage/codec.py | 30 ++ external_storage/codec_server.py | 175 +++++++ external_storage/handler.py | 151 ++++++ external_storage/s3.py | 39 ++ external_storage/starter.py | 75 +++ external_storage/worker.py | 72 +++ external_storage/workflows.py | 128 ++++++ pyproject.toml | 11 +- uv.lock | 763 +++++++++++++++++++++++++++---- 13 files changed, 1568 insertions(+), 92 deletions(-) create mode 100644 external_storage/README.md create mode 100644 external_storage/__init__.py create mode 100644 external_storage/_sample_data.py create mode 100644 external_storage/codec.py create mode 100644 external_storage/codec_server.py create mode 100644 external_storage/handler.py create mode 100644 external_storage/s3.py create mode 100644 external_storage/starter.py create mode 100644 external_storage/worker.py create mode 100644 external_storage/workflows.py diff --git a/README.md b/README.md index 8929f28d5..d748817a5 100644 --- a/README.md +++ b/README.md @@ -69,6 +69,7 @@ Some examples require extra dependencies. See each sample's directory for specif * [eager_wf_start](eager_wf_start) - Run a workflow using Eager Workflow Start * [encryption](encryption) - Apply end-to-end encryption for all input/output. * [env_config](env_config) - Load client configuration from TOML files with programmatic overrides. +* [external_storage](external_storage) - Offload large payloads to S3-compatible object storage, plus a codec server for the Web UI and CLI. * [gevent_async](gevent_async) - Combine gevent and Temporal. * [hello_standalone_activity](hello_standalone_activity) - Use activities without using a workflow. * [langchain](langchain) - Orchestrate workflows for LangChain. diff --git a/external_storage/README.md b/external_storage/README.md new file mode 100644 index 000000000..8e6eaf770 --- /dev/null +++ b/external_storage/README.md @@ -0,0 +1,143 @@ +# External Storage Sample + +This sample demonstrates how to offload large workflow payloads to Amazon S3-compatible +object storage using the Temporal Python SDK's built-in `ExternalStorage` system, +combined with a gzip `PayloadCodec` so the payloads stored inline in Temporal and in +S3 are both compressed. + +**Scenario:** A fulfillment center processes batches of shipping orders. The workflow +receives a small request (a batch ID and order count), then internally calls a +`fetch_orders` activity that returns the full list of orders with customer records, +line items, and handling notes. That list — several hundred kilobytes even after +compression — is passed to a second `process_orders` activity. Finally the workflow +returns a small `BatchSummary` with totals. + +Each payload is first compressed by `CompressionCodec`. The SDK then checks the +compressed size against the default 256 KiB threshold; payloads still above it are +stored in S3 and replaced inline with compact claim-check references. The workflow's +own input (`OrderBatchRequest`) and result (`BatchSummary`) compress to a few hundred +bytes and remain inline. + +A mock S3 service (`s3.py`) is included so you can run the sample locally without +an AWS account or Docker. A `codec_server.py` is included to decompress payloads +on demand for the Temporal Web UI. + +## Prerequisites + +* [uv](https://docs.astral.sh/uv/) +* [Temporal CLI](https://docs.temporal.io/cli#install) with a local dev server running: + ``` + temporal server start-dev + ``` + +## 1. Sync dependencies + +```bash +uv sync --group external-storage +``` + +## 2. Start the mock S3 service + +In a dedicated terminal: + +```bash +uv run external_storage/s3.py +``` + +This starts a local S3-compatible server on port 5000 and creates the `temporal-payloads` +bucket. Leave it running for the duration of the sample. + +## 3. Run the worker + +In a second terminal: + +```bash +uv run external_storage/worker.py +``` + +## 4. Run the starter + +In a third terminal: + +```bash +uv run external_storage/starter.py +``` + +Example output: + +``` +Starting workflow external-storage-20260501-120000 (batch_id=BATCH-20260501-120000, order_count=200) + +Batch BATCH-20260501-120000: 200 orders processed + Total shipping cost: $28,512.40 + Total weight: 19,684.2 kg + Avg delivery: 4.4 days +``` + +## 5. (Optional) Run the codec server + +Workflow payloads are gzip-compressed; the large ones additionally live in S3 as +external storage references. The codec server serves both transformations on demand +for the Temporal Web UI. Run it in a fourth terminal: + +```bash +uv run external_storage/codec_server.py +``` + +In the Temporal Web UI (http://localhost:8233), open Settings → Data Encoder and set +the Remote Codec Endpoint to `http://localhost:8081`. Reload the workflow page and the +inline compressed payloads will be displayed as readable JSON, and externally-stored +payloads can be downloaded to fetch their actual content from S3. + +The Web UI sends the namespace as the `X-Namespace` header on each request, so +multi-namespace setups can dispatch by reading that header. + +| Endpoint | Behavior | +| --- | --- | +| `POST /encode` | Compress the payload, then offload to S3 if it exceeds the threshold. | +| `POST /decode` | Retrieve any external storage references from S3, then decompress. Pass `?preserveStorageRefs=true` to leave references as-is. | +| `POST /download` | All inputs must be storage references. Retrieves them from S3 and decompresses. | + +## 6. Inspect the workflow + +Run `temporal workflow show` to see how payloads are stored: + +```bash +temporal workflow show --workflow-id external-storage- +``` + +The workflow's input (`OrderBatchRequest`) and result (`BatchSummary`) are gzip-encoded +and stored inline in Temporal — small enough to compress to a few hundred bytes. The +two activity payloads carrying the order list — the output of `fetch_orders` and the +input to `process_orders` — exceed 256 KiB even after compression, so they appear as +external storage references, confirming the SDK offloaded them to S3. + +## How it works + +The `DataConverter` is configured with both a `payload_codec` and an `external_storage`: + +```python +driver = S3StorageDriver( + client=new_aioboto3_client(s3_client), + bucket=S3_BUCKET, +) +data_converter = dataclasses.replace( + temporalio.converter.default(), + payload_codec=CompressionCodec(), + external_storage=ExternalStorage(drivers=[driver]), +) +``` + +On the encode path the SDK: + +1. Serializes the Python value to a `Payload`. +2. Runs `CompressionCodec.encode` to gzip the payload bytes. +3. Checks the compressed size against `payload_size_threshold` (default: 256 KiB). +4. If still above the threshold, stores the compressed bytes in S3 via + `S3StorageDriver` and replaces the inline payload with a claim-check reference. + +On the decode path the SDK reverses these steps, transparently retrieving from S3 and +decompressing as needed. + +Both the worker and the starter must use the **same** `DataConverter` configuration +(codec **and** storage) so each side can read what the other wrote. diff --git a/external_storage/__init__.py b/external_storage/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/external_storage/_sample_data.py b/external_storage/_sample_data.py new file mode 100644 index 000000000..9d684f093 --- /dev/null +++ b/external_storage/_sample_data.py @@ -0,0 +1,72 @@ +"""Order data generation for the external_storage sample. + +Produces payloads large enough to exceed the default 256 KiB ExternalStorage +threshold without hand-crafted catalogs of items, addresses, or notes — each +order is padded with random filler text in its item descriptions and shipping +notes. Calibrated so 100 orders serialize to roughly 300 KiB of JSON. +""" + +import random +import string +from typing import List + +from external_storage.workflows import Address, Customer, Order, OrderItem + +_CITIES = [ + ("Houston", "TX"), + ("Dallas", "TX"), + ("Los Angeles", "CA"), + ("San Francisco", "CA"), + ("Denver", "CO"), + ("Miami", "FL"), + ("Chicago", "IL"), + ("New York", "NY"), + ("Seattle", "WA"), + ("Atlanta", "GA"), +] + +_ITEMS_PER_ORDER = 5 +_ITEM_DESCRIPTION_CHARS = 500 +_SHIPPING_NOTES_CHARS = 200 + + +def _filler(rng: random.Random, n: int) -> str: + return "".join(rng.choices(string.ascii_letters + " ", k=n)) + + +def generate_orders(batch_id: str, count: int) -> List[Order]: + return [_generate_order(batch_id, i) for i in range(1, count + 1)] + + +def _generate_order(batch_id: str, index: int) -> Order: + rng = random.Random(f"{batch_id}-{index}") + city, state = rng.choice(_CITIES) + items = [ + OrderItem( + sku=f"SKU-{rng.randint(10000, 99999)}", + name=f"Product {rng.randint(1, 999)}", + description=_filler(rng, _ITEM_DESCRIPTION_CHARS), + quantity=rng.randint(1, 10), + unit_price_usd=round(rng.uniform(10.0, 1000.0), 2), + weight_kg=round(rng.uniform(0.5, 50.0), 2), + ) + for _ in range(_ITEMS_PER_ORDER) + ] + return Order( + id=f"ORD-{index:06d}", + customer=Customer( + id=f"CUST-{index:06d}", + name=f"Customer {index}", + email=f"customer{index}@example.com", + address=Address( + street=f"{rng.randint(100, 9999)} Main Street", + city=city, + state=state, + zip_code=f"{rng.randint(10000, 99999)}", + country="US", + ), + ), + items=items, + total_weight_kg=round(sum(i.weight_kg * i.quantity for i in items), 2), + shipping_notes=_filler(rng, _SHIPPING_NOTES_CHARS), + ) diff --git a/external_storage/codec.py b/external_storage/codec.py new file mode 100644 index 000000000..cdc7d2d0b --- /dev/null +++ b/external_storage/codec.py @@ -0,0 +1,30 @@ +import gzip +from collections.abc import Iterable +from typing import List + +from temporalio.api.common.v1 import Payload +from temporalio.converter import PayloadCodec + + +class CompressionCodec(PayloadCodec): + """Gzip-based payload codec.""" + + ENCODING = b"binary/gzip" + + async def encode(self, payloads: Iterable[Payload]) -> List[Payload]: + return [ + Payload( + metadata={"encoding": self.ENCODING}, + data=gzip.compress(p.SerializeToString()), + ) + for p in payloads + ] + + async def decode(self, payloads: Iterable[Payload]) -> List[Payload]: + result: List[Payload] = [] + for p in payloads: + if p.metadata.get("encoding") == self.ENCODING: + result.append(Payload.FromString(gzip.decompress(p.data))) + else: + result.append(p) + return result diff --git a/external_storage/codec_server.py b/external_storage/codec_server.py new file mode 100644 index 000000000..88dc0944e --- /dev/null +++ b/external_storage/codec_server.py @@ -0,0 +1,175 @@ +"""Run the codec server for the external_storage sample. + +Sets up S3 access via aioboto3 against the mock service in ``s3.py``, then +serves the payload HTTP handler from :mod:`handler` configured with the same +codec and external storage that the worker and starter use. + +``build_namespace_dispatcher`` routes by the ``X-Namespace`` request header +so a single codec server process can host different codec/storage +configurations per namespace — this sample only configures one ("default"). + +To use this server, set the Web UI's Remote Codec Endpoint (Settings → Data +Encoder) to the URL printed when the server starts. + +Deliberately left out for sample simplicity: authentication (would slot in +as a middleware between CORS and the dispatcher), configurable listen +address/port, and structured logging/tracing. +""" + +import asyncio +import logging +from typing import Awaitable, Callable, Iterable, Mapping + +import aioboto3 +from aiohttp import hdrs, web +from temporalio.contrib.aws.s3driver import S3StorageDriver +from temporalio.contrib.aws.s3driver.aioboto3 import new_aioboto3_client +from temporalio.converter import ExternalStorage + +from external_storage.codec import CompressionCodec +from external_storage.handler import payload_routes +from external_storage.worker import ( + S3_ACCESS_KEY, + S3_BUCKET, + S3_ENDPOINT, + S3_SECRET_KEY, +) + +WEB_UI_ORIGIN = "http://localhost:8233" + +logger = logging.getLogger(__name__) + + +@web.middleware +async def cors_middleware( + request: web.Request, + handler: Callable[[web.Request], Awaitable[web.StreamResponse]], +) -> web.StreamResponse: + """Add CORS support so the Web UI can call the codec server from its origin.""" + allow_origin = request.headers.get(hdrs.ORIGIN) == WEB_UI_ORIGIN + + response: web.StreamResponse + if request.method == "OPTIONS": + response = web.Response() + if allow_origin: + response.headers[hdrs.ACCESS_CONTROL_ALLOW_METHODS] = "POST" + response.headers[hdrs.ACCESS_CONTROL_ALLOW_HEADERS] = ( + "content-type,x-namespace" + ) + else: + # Catch HTTPException so CORS headers also land on error responses + # (e.g. the dispatcher's 404 for an unknown namespace). + try: + response = await handler(request) + except web.HTTPException as exc: + response = exc + + if allow_origin: + response.headers[hdrs.ACCESS_CONTROL_ALLOW_ORIGIN] = WEB_UI_ORIGIN + + if isinstance(response, web.HTTPException): + raise response + return response + + +def build_namespace_dispatcher( + routes_by_namespace: Mapping[str, Iterable[web.RouteDef]], +) -> Callable[[web.Request], Awaitable[web.StreamResponse]]: + """Build an aiohttp handler that dispatches by the X-Namespace header. + + Returns a single handler that the caller registers for every codec server + URL (``/encode``, ``/decode``, ``/download``). The handler inspects the + ``X-Namespace`` header on each request and forwards to the handler + configured for that namespace + method + path combination. + """ + # Pre-build the flat lookup table at construction so the per-request + # dispatch is just two dictionary lookups. + handler_map = { + ns: {(r.method, r.path): r.handler for r in routes} + for ns, routes in routes_by_namespace.items() + } + + async def dispatch_by_namespace(request: web.Request) -> web.StreamResponse: + # The Temporal Web UI passes the namespace it's viewing in the + # X-Namespace header on every codec-server request. A single process + # can therefore host different codec/storage configs per namespace + # without needing per-namespace URL prefixes. + namespace = request.headers.get("X-Namespace", "") + handlers = handler_map.get(namespace) + if handlers is None: + logger.warning("dispatch: unknown namespace %r", namespace) + raise web.HTTPNotFound() + handler = handlers.get((request.method, request.path)) + if handler is None: + logger.warning( + "dispatch: no handler for %s %s in namespace %r", + request.method, + request.path, + namespace, + ) + raise web.HTTPNotFound() + return await handler(request) + + return dispatch_by_namespace + + +async def main() -> None: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", + ) + + session = aioboto3.Session() + async with session.client( + "s3", + endpoint_url=S3_ENDPOINT, + aws_access_key_id=S3_ACCESS_KEY, + aws_secret_access_key=S3_SECRET_KEY, + region_name="us-east-1", + ) as s3_client: + driver = S3StorageDriver( + client=new_aioboto3_client(s3_client), + bucket=S3_BUCKET, + ) + + # The dispatcher map drives per-namespace configuration. Add more + # entries here to host additional namespaces with their own codec + # chain and/or storage backend. Each value is a list of routes + # produced by ``payload_routes``; the dispatcher reads the + # X-Namespace header to pick the right one per request. + dispatch_by_namespace = build_namespace_dispatcher( + { + "default": payload_routes( + external_storage=ExternalStorage(drivers=[driver]), + prestorage_codec=CompressionCodec(), + ), + } + ) + + app = web.Application(middlewares=[cors_middleware]) + app.add_routes( + [ + web.post("/encode", dispatch_by_namespace), + web.post("/decode", dispatch_by_namespace), + web.post("/download", dispatch_by_namespace), + ] + ) + + runner = web.AppRunner(app) + await runner.setup() + site = web.TCPSite(runner, "127.0.0.1", 8081) + await site.start() + print("Codec server running at http://127.0.0.1:8081, ctrl+c to exit") + try: + await asyncio.Event().wait() + except (KeyboardInterrupt, asyncio.CancelledError): + pass + finally: + await runner.cleanup() + + +if __name__ == "__main__": + try: + asyncio.run(main()) + except KeyboardInterrupt: + pass diff --git a/external_storage/handler.py b/external_storage/handler.py new file mode 100644 index 000000000..0e1ea5acb --- /dev/null +++ b/external_storage/handler.py @@ -0,0 +1,151 @@ +"""HTTP routes for a Temporal codec server with external storage support. + +A *codec server* is an HTTP service the Temporal Web UI and CLI can call to +transform payloads on demand — typically to decode (decompress, decrypt, +fetch-from-storage) so a human can read what the workflow saw. The contract +expected by the Web UI is: + +* ``POST /encode`` and ``POST /decode`` accept a JSON-encoded ``Payloads`` + protobuf message in the request body and return a transformed ``Payloads`` + message in the same format. +* ``POST /download`` is a specialized endpoint that only accepts external + storage references and resolves them to their actual contents. Kept + separate from ``/decode`` so the Web UI can defer fetching potentially + large blobs until a user explicitly asks for them. + +The :func:`payload_routes` factory builds these three route definitions +configured with an :class:`ExternalStorage` plus optional codec layers that +match the client's ``DataConverter`` setup.""" + +from typing import List, Optional + +from aiohttp import web +from google.protobuf import json_format +from temporalio.api.common.v1 import Payload, Payloads +from temporalio.converter import ExternalStorage, PayloadCodec + + +def _is_storage_reference(payload: Payload) -> bool: + """A payload is an external-storage reference iff it carries external_payloads metadata. + + When the SDK offloads a payload to external storage it replaces the + in-band bytes with a small protobuf "claim check" — the ``external_payloads`` + repeated field is the marker the SDK uses to recognize it on the way back. + """ + return len(payload.external_payloads) > 0 + + +def payload_routes( + external_storage: ExternalStorage, + prestorage_codec: Optional[PayloadCodec] = None, + poststorage_codec: Optional[PayloadCodec] = None, +) -> List[web.RouteDef]: + """Build aiohttp routes for the codec server's /encode, /decode, and /download endpoints. + + ``external_storage`` is the external storage configured on the client's + DataConverter and is required — without it the routes have nothing to add + over a plain codec server. + + The codec layers are optional, and run in a defined order relative to + external storage to mirror what the client's DataConverter does: + + * ``prestorage_codec`` — the codec configured on the client's + DataConverter. On encode it runs BEFORE the external-storage offload + decision, so the SDK checks whether the *transformed* bytes + (compressed, encrypted, etc.) exceed the threshold. On decode it runs + AFTER storage retrieval, so the codec sees the original transformed + bytes regardless of where they came from. + * ``poststorage_codec`` — typically only used when payloads pass through + a proxy that wraps them with an additional codec layer outside the + storage envelope (envelope encryption applied at a network edge, for + example). On encode it runs after offload; on decode it runs first + (to strip the proxy envelope before storage retrieval). + + Register the result on any :class:`aiohttp.web.Application` via + :meth:`aiohttp.web.Application.add_routes`. + """ + + async def _decode_non_refs(payloads: List[Payload]) -> List[Payload]: + # Storage references are claim-check protos with their own encoding — + # they aren't bytes the codec produced, so the codec can't decode + # them. Skip refs and only feed real payloads through the codec. + if prestorage_codec is None: + return payloads + result = list(payloads) + non_ref_idx = [ + i for i, p in enumerate(payloads) if not _is_storage_reference(p) + ] + decoded = await prestorage_codec.decode([payloads[i] for i in non_ref_idx]) + for i, p in zip(non_ref_idx, decoded): + result[i] = p + return result + + async def _read_payloads(req: web.Request) -> List[Payload]: + assert req.content_type == "application/json" + proto = json_format.Parse(await req.read(), Payloads()) + return list(proto.payloads) + + def _write_payloads(payloads: List[Payload]) -> web.Response: + return web.Response( + content_type="application/json", + text=json_format.MessageToJson(Payloads(payloads=payloads)), + ) + + async def encode_handler(req: web.Request) -> web.Response: + payloads = await _read_payloads(req) + # Encode pipeline mirrors what a client-side DataConverter does: + # 1. pre-storage codec encodes (compress / encrypt / etc.) + # 2. external storage decides whether the transformed bytes + # exceed the threshold and replaces them with a reference + # 3. post-storage codec encodes whatever ended up inline + if prestorage_codec is not None: + payloads = await prestorage_codec.encode(payloads) + # ``_store_payload_sequence`` is the SDK's internal helper used by + # DataConverter. Calling it directly lets the codec server reuse the + # exact same offload-if-large logic the client uses, instead of + # re-implementing the size-threshold check here. The leading + # underscore signals it's not a stable public API. + payloads = await external_storage._store_payload_sequence(payloads) + if poststorage_codec is not None: + payloads = await poststorage_codec.encode(payloads) + return _write_payloads(payloads) + + async def decode_handler(req: web.Request) -> web.Response: + payloads = await _read_payloads(req) + # Decode pipeline is the encode pipeline in reverse: + # 1. post-storage codec decodes (strip proxy envelope, if any) + # 2. external storage retrieves any references in-band + # 3. pre-storage codec decodes the real bytes + if poststorage_codec is not None: + payloads = await poststorage_codec.decode(payloads) + # preserveStorageRefs=true is for clients that want to inspect the + # raw references themselves (a debug view, an "audit who fetched + # what" log, etc.). When unset, default behavior fetches from + # storage so the user sees the payload as the workflow saw it. + preserve_refs = req.query.get("preserveStorageRefs", "false").lower() == "true" + if not preserve_refs: + payloads = await external_storage._retrieve_payload_sequence(payloads) + payloads = await _decode_non_refs(payloads) + return _write_payloads(payloads) + + async def download_handler(req: web.Request) -> web.Response: + # /download exists as a separate endpoint from /decode because + # resolving an external-storage reference can be expensive (network + # round-trip, potentially large blob). The Web UI uses /download as + # an opt-in "click to fetch" action — keeping it off /decode's + # auto-resolution path avoids accidentally pulling many large blobs + # when a user simply navigates to a workflow page. + payloads = await _read_payloads(req) + if not all(_is_storage_reference(p) for p in payloads): + return web.Response( + status=400, text="all payloads must be storage references" + ) + retrieved = await external_storage._retrieve_payload_sequence(payloads) + decoded = await _decode_non_refs(retrieved) + return _write_payloads(decoded) + + return [ + web.post("/encode", encode_handler), + web.post("/decode", decode_handler), + web.post("/download", download_handler), + ] diff --git a/external_storage/s3.py b/external_storage/s3.py new file mode 100644 index 000000000..144b8346d --- /dev/null +++ b/external_storage/s3.py @@ -0,0 +1,39 @@ +import signal +import threading + +import boto3 +from moto.server import ThreadedMotoServer + +from external_storage.worker import S3_ACCESS_KEY, S3_BUCKET, S3_ENDPOINT, S3_SECRET_KEY + + +def main() -> None: + server = ThreadedMotoServer(port=5000) + server.start() + print(f"Mock S3 server running at {S3_ENDPOINT}") + + s3 = boto3.client( + "s3", + endpoint_url=S3_ENDPOINT, + aws_access_key_id=S3_ACCESS_KEY, + aws_secret_access_key=S3_SECRET_KEY, + region_name="us-east-1", + ) + s3.create_bucket(Bucket=S3_BUCKET) + print(f"Bucket '{S3_BUCKET}' created. Press ctrl+c to stop.") + + try: + # signal.pause() is Unix-only; use Event on Windows + if hasattr(signal, "pause"): + signal.pause() + else: + threading.Event().wait() + except KeyboardInterrupt: + pass + finally: + server.stop() + print("Mock S3 server stopped.") + + +if __name__ == "__main__": + main() diff --git a/external_storage/starter.py b/external_storage/starter.py new file mode 100644 index 000000000..3d7b165f3 --- /dev/null +++ b/external_storage/starter.py @@ -0,0 +1,75 @@ +import asyncio +import dataclasses +from datetime import datetime + +import aioboto3 +import temporalio.converter +from temporalio.client import Client +from temporalio.contrib.aws.s3driver import S3StorageDriver +from temporalio.contrib.aws.s3driver.aioboto3 import new_aioboto3_client +from temporalio.converter import ExternalStorage +from temporalio.envconfig import ClientConfig + +from external_storage.codec import CompressionCodec +from external_storage.worker import ( + S3_ACCESS_KEY, + S3_BUCKET, + S3_ENDPOINT, + S3_SECRET_KEY, + TASK_QUEUE, +) +from external_storage.workflows import OrderBatchRequest, ProcessOrderBatchWorkflow + +ORDER_COUNT = 200 + + +async def main() -> None: + session = aioboto3.Session() + async with session.client( + "s3", + endpoint_url=S3_ENDPOINT, + aws_access_key_id=S3_ACCESS_KEY, + aws_secret_access_key=S3_SECRET_KEY, + region_name="us-east-1", + ) as s3_client: + driver = S3StorageDriver( + client=new_aioboto3_client(s3_client), + bucket=S3_BUCKET, + ) + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + client = await Client.connect( + **config, + data_converter=dataclasses.replace( + temporalio.converter.default(), + payload_codec=CompressionCodec(), + external_storage=ExternalStorage(drivers=[driver]), + ), + ) + + run_id = datetime.now().strftime("%Y%m%d-%H%M%S") + workflow_id = f"external-storage-{run_id}" + request = OrderBatchRequest(batch_id=f"BATCH-{run_id}", order_count=ORDER_COUNT) + + print( + f"Starting workflow {workflow_id} (batch_id={request.batch_id}, " + f"order_count={request.order_count})" + ) + + summary = await client.execute_workflow( + ProcessOrderBatchWorkflow.run, + request, + id=workflow_id, + task_queue=TASK_QUEUE, + ) + + print(f"\nBatch {summary.batch_id}: {summary.order_count} orders processed") + print(f" Total shipping cost: ${summary.total_shipping_cost_usd:,.2f}") + print(f" Total weight: {summary.total_weight_kg:,.1f} kg") + print(f" Avg delivery: {summary.avg_delivery_days} days") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/external_storage/worker.py b/external_storage/worker.py new file mode 100644 index 000000000..151761bcd --- /dev/null +++ b/external_storage/worker.py @@ -0,0 +1,72 @@ +import asyncio +import dataclasses + +import aioboto3 +import temporalio.converter +from temporalio.client import Client +from temporalio.contrib.aws.s3driver import S3StorageDriver +from temporalio.contrib.aws.s3driver.aioboto3 import new_aioboto3_client +from temporalio.converter import ExternalStorage +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from external_storage.codec import CompressionCodec +from external_storage.workflows import ( + ProcessOrderBatchWorkflow, + fetch_orders, + process_orders, +) + +S3_ENDPOINT = "http://localhost:5000" +S3_BUCKET = "temporal-payloads" +S3_ACCESS_KEY = "test" +S3_SECRET_KEY = "test" +TASK_QUEUE = "external-storage-task-queue" + +interrupt_event = asyncio.Event() + + +async def main() -> None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + session = aioboto3.Session() + async with session.client( + "s3", + endpoint_url=S3_ENDPOINT, + aws_access_key_id=S3_ACCESS_KEY, + aws_secret_access_key=S3_SECRET_KEY, + region_name="us-east-1", + ) as s3_client: + driver = S3StorageDriver( + client=new_aioboto3_client(s3_client), + bucket=S3_BUCKET, + ) + + client = await Client.connect( + **config, + data_converter=dataclasses.replace( + temporalio.converter.default(), + payload_codec=CompressionCodec(), + external_storage=ExternalStorage(drivers=[driver]), + ), + ) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[ProcessOrderBatchWorkflow], + activities=[fetch_orders, process_orders], + ): + print("Worker started, ctrl+c to exit") + await interrupt_event.wait() + print("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/external_storage/workflows.py b/external_storage/workflows.py new file mode 100644 index 000000000..8d034fa10 --- /dev/null +++ b/external_storage/workflows.py @@ -0,0 +1,128 @@ +from dataclasses import dataclass +from datetime import timedelta +from typing import List + +from temporalio import activity, workflow + +WAREHOUSE_STATE = "TX" # fulfillment center state used to estimate delivery + + +@dataclass +class Address: + street: str + city: str + state: str + zip_code: str + country: str + + +@dataclass +class Customer: + id: str + name: str + email: str + address: Address + + +@dataclass +class OrderItem: + sku: str + name: str + description: str + quantity: int + unit_price_usd: float + weight_kg: float + + +@dataclass +class Order: + id: str + customer: Customer + items: List[OrderItem] + total_weight_kg: float + shipping_notes: str + + +@dataclass +class ProcessedOrder: + id: str + customer_id: str + destination_city: str + destination_state: str + total_weight_kg: float + shipping_cost_usd: float + estimated_delivery_days: int + + +@dataclass +class OrderBatchRequest: + batch_id: str + order_count: int + + +@dataclass +class BatchSummary: + batch_id: str + order_count: int + total_shipping_cost_usd: float + total_weight_kg: float + avg_delivery_days: float + + +@activity.defn +async def fetch_orders(request: OrderBatchRequest) -> List[Order]: + # Lazy import keeps the data-generation code out of the workflow module's + # top-level imports and avoids a circular import with _sample_data. + from external_storage._sample_data import generate_orders + + return generate_orders(request.batch_id, request.order_count) + + +@activity.defn +async def process_orders(orders: List[Order]) -> List[ProcessedOrder]: + results: List[ProcessedOrder] = [] + for order in orders: + cost = round(2.50 + 1.20 * order.total_weight_kg, 2) + days = 2 if order.customer.address.state == WAREHOUSE_STATE else 5 + results.append( + ProcessedOrder( + id=order.id, + customer_id=order.customer.id, + destination_city=order.customer.address.city, + destination_state=order.customer.address.state, + total_weight_kg=order.total_weight_kg, + shipping_cost_usd=cost, + estimated_delivery_days=days, + ) + ) + return results + + +@workflow.defn +class ProcessOrderBatchWorkflow: + @workflow.run + async def run(self, request: OrderBatchRequest) -> BatchSummary: + orders = await workflow.execute_activity( + fetch_orders, + request, + start_to_close_timeout=timedelta(minutes=5), + ) + processed = await workflow.execute_activity( + process_orders, + orders, + start_to_close_timeout=timedelta(minutes=5), + ) + total_cost = sum(p.shipping_cost_usd for p in processed) + total_weight = sum(p.total_weight_kg for p in processed) + avg_days = ( + sum(p.estimated_delivery_days for p in processed) / len(processed) + if processed + else 0.0 + ) + return BatchSummary( + batch_id=request.batch_id, + order_count=len(processed), + total_shipping_cost_usd=round(total_cost, 2), + total_weight_kg=round(total_weight, 2), + avg_delivery_days=round(avg_days, 1), + ) diff --git a/pyproject.toml b/pyproject.toml index 58f86588f..c88cdf8b6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] requires-python = ">=3.10" readme = "README.md" license = "MIT" -dependencies = ["temporalio>=1.27.2,<2"] +dependencies = ["temporalio>=1.27.2,<2", "protobuf>=5.29.6,<6"] [project.urls] Homepage = "https://github.com/temporalio/samples-python" @@ -28,7 +28,13 @@ dev = [ ] bedrock = ["boto3>=1.34.92,<2"] dsl = ["pyyaml>=6.0.1,<7", "types-pyyaml>=6.0.12,<7", "dacite>=1.8.1,<2"] -encryption = ["cryptography>=38.0.1,<39", "aiohttp>=3.8.1,<4"] +encryption = ["cryptography>=38.0.1,<39", "aiohttp>=3.13.3,<4"] +external-storage = [ + "aioboto3>=15.1.0,<15.2", + "aiohttp>=3.13.3,<4", + "moto[server]>=4.0.0", + "types_aiobotocore_s3>=2.25.2", +] gevent = ["gevent>=25.4.2 ; python_version >= '3.8'"] langsmith-tracing = [ "openai>=1.4.0", @@ -81,6 +87,7 @@ packages = [ "custom_metric", "dsl", "encryption", + "external_storage", "gevent_async", "hello", "langgraph_plugin", diff --git a/uv.lock b/uv.lock index bba8d72c8..4b1d984b4 100644 --- a/uv.lock +++ b/uv.lock @@ -8,6 +8,51 @@ 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url = "https://files.pythonhosted.org/packages/19/70/80f3b7c10d2630aa66414bf23d210386700aa390547278c789afa994fd7e/xmltodict-1.0.4.tar.gz", hash = "sha256:6d94c9f834dd9e44514162799d344d815a3a4faec913717a9ecbfa5be1bb8e61", size = 26124, upload-time = "2026-02-22T02:21:22.074Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/38/34/98a2f52245f4d47be93b580dae5f9861ef58977d73a79eb47c58f1ad1f3a/xmltodict-1.0.4-py3-none-any.whl", hash = "sha256:a4a00d300b0e1c59fc2bfccb53d7b2e88c32f200df138a0dd2229f842497026a", size = 13580, upload-time = "2026-02-22T02:21:21.039Z" }, +] + [[package]] name = "xxhash" version = "3.6.0" From fded925b3885d78d650f78e19d4ce07a44559961 Mon Sep 17 00:00:00 2001 From: Edward Amsden Date: Thu, 21 May 2026 13:42:06 -0500 Subject: [PATCH 27/40] Redis external storage driver (#287) * Redis external storage driver * Fix lints * Fix CI tests * Fix yarl error in CI * Revert accidental README.md edit, and add external_storage_redis. * Move redis test workflow to tests/ * Update test_redis_worker.py * Constrain langsmith to allow CI to pass --- README.md | 1 + external_storage_redis/README.md | 132 + external_storage_redis/__init__.py | 9 + external_storage_redis/_client.py | 34 + external_storage_redis/_driver.py | 228 + external_storage_redis/redis_asyncio.py | 54 + pyproject.toml | 13 + tests/external_storage_redis/__init__.py | 1 + tests/external_storage_redis/conftest.py | 39 + tests/external_storage_redis/test_redis.py | 745 +++ .../test_redis_worker.py | 522 ++ tests/external_storage_redis/workflows.py | 194 + uv.lock | 4251 ++++++++++------- 13 files changed, 4445 insertions(+), 1778 deletions(-) create mode 100644 external_storage_redis/README.md create mode 100644 external_storage_redis/__init__.py create mode 100644 external_storage_redis/_client.py create mode 100644 external_storage_redis/_driver.py create mode 100644 external_storage_redis/redis_asyncio.py create mode 100644 tests/external_storage_redis/__init__.py create mode 100644 tests/external_storage_redis/conftest.py create mode 100644 tests/external_storage_redis/test_redis.py create mode 100644 tests/external_storage_redis/test_redis_worker.py create mode 100644 tests/external_storage_redis/workflows.py diff --git a/README.md b/README.md index d748817a5..801fd2ed3 100644 --- a/README.md +++ b/README.md @@ -70,6 +70,7 @@ Some examples require extra dependencies. See each sample's directory for specif * [encryption](encryption) - Apply end-to-end encryption for all input/output. * [env_config](env_config) - Load client configuration from TOML files with programmatic overrides. * [external_storage](external_storage) - Offload large payloads to S3-compatible object storage, plus a codec server for the Web UI and CLI. +* [external_storage_redis](external_storage_redis) - Redis driver for external storage * [gevent_async](gevent_async) - Combine gevent and Temporal. * [hello_standalone_activity](hello_standalone_activity) - Use activities without using a workflow. * [langchain](langchain) - Orchestrate workflows for LangChain. diff --git a/external_storage_redis/README.md b/external_storage_redis/README.md new file mode 100644 index 000000000..662b5ea76 --- /dev/null +++ b/external_storage_redis/README.md @@ -0,0 +1,132 @@ +# Redis External Storage + +This sample packages a Redis-backed `StorageDriver` implementation for Temporal +external storage. + +The code lives in: + +* `external_storage_redis/_driver.py` for the `RedisStorageDriver` +* `external_storage_redis/_client.py` for the storage client abstraction +* `external_storage_redis/redis_asyncio.py` for the `redis.asyncio` adapter +* `tests/external_storage_redis/` for unit and worker integration tests + +Unlike most samples in this repository, this one is primarily reusable driver +code plus tests rather than a standalone `worker.py` / `starter.py` pair. + +## Install Dependencies + +From the repository root: + + uv sync --group external-storage-redis --group dev + +The `external-storage-redis` group installs `redis`, and the `dev` group +installs `fakeredis` for the test suite. + +## Using The Driver + +```python +import dataclasses + +import redis.asyncio as redis +import temporalio.converter +from temporalio.client import Client +from temporalio.converter import ExternalStorage + +from external_storage_redis import RedisStorageDriver +from external_storage_redis.redis_asyncio import new_redis_asyncio_client + +redis_client = redis.Redis.from_url( + "redis://localhost:6379/0", + decode_responses=False, +) +try: + driver = RedisStorageDriver( + client=new_redis_asyncio_client(redis_client), + key_prefix="temporalio:payloads", + ) + + client = await Client.connect( + "localhost:7233", + data_converter=dataclasses.replace( + temporalio.converter.default(), + external_storage=ExternalStorage( + drivers=[driver], + payload_size_threshold=256 * 1024, + ), + ), + ) +finally: + await redis_client.aclose() +``` + +`decode_responses=False` is required because the driver stores serialized +Temporal `Payload` protobuf bytes as Redis values rather than text. + +## Driver Behavior + +`RedisStorageDriver` accepts these constructor options: + +* `driver_name`: defaults to `"redis"` +* `key_prefix`: defaults to `"temporalio:payloads"` +* `ttl`: optional expiration applied only when a key is first inserted +* `max_payload_size`: defaults to 50 MiB + +Stored keys are content-addressed using SHA-256 and include Temporal execution +context when it is available. A typical workflow-scoped key looks like: + + temporalio:payloads:v0:ns:default:wt:MyWorkflow:wi:my-workflow-id:ri:my-run-id:d:sha256: + +Some behavior to be aware of: + +* Any driver used to store payloads must also be configured on the component + that retrieves them. +* The Redis instance must already exist; the driver does not provision it. +* Identical serialized bytes within the same namespace and workflow/activity + scope share the same Redis key. +* Workflow, activity, namespace, and run identifiers are URL-encoded before + being placed into the key. +* Only payloads at or above `ExternalStorage.payload_size_threshold` are + offloaded. +* If `ttl` is set, duplicate stores do not refresh expiration. +* If a payload key is missing at retrieval time, the driver raises a + non-retryable `ApplicationError`. + +## Custom Redis Clients + +To use a Redis library other than `redis.asyncio`, implement +`RedisStorageDriverClient`: + +```python +from datetime import timedelta + +from external_storage_redis import RedisStorageDriverClient + + +class MyRedisClient(RedisStorageDriverClient): + async def get(self, *, key: str) -> bytes | None: ... + + async def set_if_absent( + self, + *, + key: str, + data: bytes, + ttl: timedelta | None = None, + ) -> bool: ... +``` + +## Tests + +Run the full Redis sample test suite with: + + uv run pytest tests/external_storage_redis + +Run only the in-memory unit tests with: + + uv run pytest tests/external_storage_redis/test_redis.py + +The worker integration tests use `WorkflowEnvironment.start_local()` and +`fakeredis`. They do not require a real Redis server, but the first run may +download a Temporal dev-server binary. + +Some Temporal dev-server builds disable standalone activity execution. When +that happens, the two standalone-activity integration tests skip automatically. diff --git a/external_storage_redis/__init__.py b/external_storage_redis/__init__.py new file mode 100644 index 000000000..4f0a066dd --- /dev/null +++ b/external_storage_redis/__init__.py @@ -0,0 +1,9 @@ +"""Redis storage driver sample for Temporal external storage.""" + +from external_storage_redis._client import RedisStorageDriverClient +from external_storage_redis._driver import RedisStorageDriver + +__all__ = [ + "RedisStorageDriverClient", + "RedisStorageDriver", +] diff --git a/external_storage_redis/_client.py b/external_storage_redis/_client.py new file mode 100644 index 000000000..94b493d46 --- /dev/null +++ b/external_storage_redis/_client.py @@ -0,0 +1,34 @@ +"""Redis storage driver client abstraction.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from datetime import timedelta + + +class RedisStorageDriverClient(ABC): + """Abstract base class for the Redis operations used by the driver.""" + + @abstractmethod + async def get(self, *, key: str) -> bytes | None: + """Return the raw bytes stored for *key*, or ``None`` if absent.""" + + @abstractmethod + async def set_if_absent( + self, + *, + key: str, + data: bytes, + ttl: timedelta | None = None, + ) -> bool: + """Store *data* under *key* only if the key does not already exist. + + Args: + key: Redis key to store. + data: Serialized payload bytes. + ttl: Optional expiration to apply only when the value is inserted. + + Returns: + ``True`` if the value was inserted, ``False`` if the key already + existed. + """ diff --git a/external_storage_redis/_driver.py b/external_storage_redis/_driver.py new file mode 100644 index 000000000..c13785b5c --- /dev/null +++ b/external_storage_redis/_driver.py @@ -0,0 +1,228 @@ +"""Redis storage driver for Temporal external storage.""" + +from __future__ import annotations + +import asyncio +import hashlib +import urllib.parse +from collections.abc import Coroutine, Sequence +from datetime import timedelta +from typing import Any, TypeVar + +from temporalio.api.common.v1 import Payload +from temporalio.converter import ( + StorageDriver, + StorageDriverActivityInfo, + StorageDriverClaim, + StorageDriverRetrieveContext, + StorageDriverStoreContext, + StorageDriverWorkflowInfo, +) +from temporalio.exceptions import ApplicationError + +from external_storage_redis._client import RedisStorageDriverClient + +_T = TypeVar("_T") + + +async def _gather_with_cancellation( + coros: Sequence[Coroutine[Any, Any, _T]], +) -> list[_T]: + """Run coroutines concurrently, cancelling remaining tasks on failure.""" + if not coros: + return [] + tasks = [asyncio.create_task(coro) for coro in coros] + try: + return list(await asyncio.gather(*tasks)) + except BaseException: + for task in tasks: + task.cancel() + await asyncio.gather(*tasks, return_exceptions=True) + raise + + +class RedisStorageDriver(StorageDriver): + """Driver for storing and retrieving Temporal payloads in Redis. + + Payloads are stored as Redis string values keyed by a SHA-256 digest of the + serialized payload bytes. The key also includes namespace and + workflow/activity identity segments derived from the storage context so + distinct Temporal scopes remain isolated. + """ + + def __init__( + self, + client: RedisStorageDriverClient, + *, + driver_name: str = "redis", + key_prefix: str = "temporalio:payloads", + ttl: timedelta | None = None, + max_payload_size: int = 50 * 1024 * 1024, + ) -> None: + """Construct the Redis driver. + + Args: + client: A :class:`RedisStorageDriverClient` implementation. Use + :func:`external_storage_redis.redis_asyncio.new_redis_asyncio_client` + to wrap a ``redis.asyncio`` client. + driver_name: Name of this driver instance. Defaults to ``"redis"``. + Override this when registering multiple RedisStorageDriver + instances with distinct configurations under the same + ``ExternalStorage.drivers`` list. + key_prefix: Prefix prepended to all Redis keys. Defaults to + ``"temporalio:payloads"``. + ttl: Optional expiration to apply when a key is first written. + Existing keys are not refreshed when the same payload is stored + again. + max_payload_size: Maximum serialized payload size in bytes that the + driver will accept. Defaults to 52428800 (50 MiB). + """ + if max_payload_size <= 0: + raise ValueError("max_payload_size must be greater than zero") + if ttl is not None and ttl <= timedelta(0): + raise ValueError("ttl must be greater than zero") + self._client = client + self._driver_name = driver_name or "redis" + self._key_prefix = key_prefix.rstrip(":") + self._ttl = ttl + self._max_payload_size = max_payload_size + + def name(self) -> str: + """Return the driver instance name.""" + return self._driver_name + + def type(self) -> str: + """Return the driver type identifier.""" + return "redis" + + def _build_key( + self, + context: StorageDriverStoreContext, + hash_digest: str, + ) -> str: + """Construct a context-aware Redis key for a payload digest.""" + + def _quote(value: str | None) -> str | None: + return urllib.parse.quote(value, safe="") if value else None + + segments = ["v0"] + target = context.target + namespace = _quote(target.namespace) if target is not None else None + if namespace: + segments.extend(["ns", namespace]) + + if isinstance(target, StorageDriverWorkflowInfo): + segments.extend( + [ + "wt", + _quote(target.type) or "null", + "wi", + _quote(target.id) or "null", + "ri", + _quote(target.run_id) or "null", + ] + ) + elif isinstance(target, StorageDriverActivityInfo): + segments.extend( + [ + "at", + _quote(target.type) or "null", + "ai", + _quote(target.id) or "null", + "ri", + _quote(target.run_id) or "null", + ] + ) + + segments.extend(["d", "sha256", hash_digest]) + if not self._key_prefix: + return ":".join(segments) + return f"{self._key_prefix}:{':'.join(segments)}" + + async def store( + self, + context: StorageDriverStoreContext, + payloads: Sequence[Payload], + ) -> list[StorageDriverClaim]: + """Store payloads in Redis and return a claim for each payload.""" + + async def _store_payload(payload: Payload) -> StorageDriverClaim: + payload_bytes = payload.SerializeToString() + payload_size = len(payload_bytes) + if payload_size > self._max_payload_size: + raise ValueError( + f"Payload size {payload_size} bytes exceeds the configured " + f"max_payload_size of {self._max_payload_size} bytes" + ) + + hash_digest = hashlib.sha256(payload_bytes).hexdigest().lower() + key = self._build_key(context, hash_digest) + + try: + await self._client.set_if_absent( + key=key, + data=payload_bytes, + ttl=self._ttl, + ) + except Exception as err: + raise RuntimeError( + f"RedisStorageDriver store failed [key={key}]" + ) from err + + return StorageDriverClaim( + claim_data={ + "key": key, + "hash_algorithm": "sha256", + "hash_value": hash_digest, + }, + ) + + return await _gather_with_cancellation([_store_payload(p) for p in payloads]) + + async def retrieve( + self, + context: StorageDriverRetrieveContext, # noqa: ARG002 + claims: Sequence[StorageDriverClaim], + ) -> list[Payload]: + """Retrieve payloads from Redis for the given claims.""" + + async def _retrieve_payload(claim: StorageDriverClaim) -> Payload: + key = claim.claim_data["key"] + + try: + payload_bytes = await self._client.get(key=key) + except Exception as err: + raise RuntimeError( + f"RedisStorageDriver retrieve failed [key={key}]" + ) from err + + if payload_bytes is None: + raise ApplicationError( + f"Payload not found for key '{key}'", + type="PayloadNotFoundError", + non_retryable=True, + ) + + expected_hash = claim.claim_data.get("hash_value") + hash_algorithm = claim.claim_data.get("hash_algorithm") + if expected_hash and hash_algorithm: + if hash_algorithm != "sha256": + raise ValueError( + f"RedisStorageDriver unsupported hash algorithm " + f"[key={key}]: expected sha256, got {hash_algorithm}" + ) + actual_hash = hashlib.sha256(payload_bytes).hexdigest().lower() + if actual_hash != expected_hash: + raise ValueError( + f"RedisStorageDriver integrity check failed " + f"[key={key}]: expected {hash_algorithm}:{expected_hash}, " + f"got {hash_algorithm}:{actual_hash}" + ) + + payload = Payload() + payload.ParseFromString(payload_bytes) + return payload + + return await _gather_with_cancellation( + [_retrieve_payload(claim) for claim in claims] + ) diff --git a/external_storage_redis/redis_asyncio.py b/external_storage_redis/redis_asyncio.py new file mode 100644 index 000000000..917b239c0 --- /dev/null +++ b/external_storage_redis/redis_asyncio.py @@ -0,0 +1,54 @@ +"""redis.asyncio adapter for the Redis storage driver client.""" + +from __future__ import annotations + +import math +from datetime import timedelta +from typing import TYPE_CHECKING + +from external_storage_redis._client import RedisStorageDriverClient + +if TYPE_CHECKING: + from redis.asyncio.client import Redis + + +class _RedisAsyncioStorageDriverClient(RedisStorageDriverClient): + """Adapter that wraps a ``redis.asyncio.Redis`` client. + + The wrapped client must be configured for binary-safe reads and writes, + which means ``decode_responses`` must remain disabled. + """ + + def __init__(self, client: Redis) -> None: + """Wrap a ``redis.asyncio.Redis`` client.""" + self._client = client + + async def get(self, *, key: str) -> bytes | None: + """Fetch raw bytes for *key* from Redis.""" + value = await self._client.get(key) + if value is None: + return None + if not isinstance(value, bytes): + raise TypeError( + "redis.asyncio client must be configured with decode_responses=False" + ) + return value + + async def set_if_absent( + self, + *, + key: str, + data: bytes, + ttl: timedelta | None = None, + ) -> bool: + """Atomically set *key* only when it is absent.""" + ttl_ms = None + if ttl is not None: + ttl_ms = max(1, math.ceil(ttl.total_seconds() * 1000)) + result = await self._client.set(key, data, px=ttl_ms, nx=True) + return bool(result) + + +def new_redis_asyncio_client(client: Redis) -> RedisStorageDriverClient: + """Create a driver client from a ``redis.asyncio.Redis`` instance.""" + return _RedisAsyncioStorageDriverClient(client) diff --git a/pyproject.toml b/pyproject.toml index c88cdf8b6..4c4151a5b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ dev = [ "mypy>=1.4.1,<2", "pytest>=7.1.2,<8", "pytest-asyncio>=0.18.3,<0.19", + "fakeredis>=2,<3", "frozenlist>=1.4.0,<2", "pyright>=1.1.394", "types-pyyaml>=6.0.12.20241230,<7", @@ -35,6 +36,7 @@ external-storage = [ "moto[server]>=4.0.0", "types_aiobotocore_s3>=2.25.2", ] +external-storage-redis = ["redis>=5.0.0,<8"] gevent = ["gevent>=25.4.2 ; python_version >= '3.8'"] langsmith-tracing = [ "openai>=1.4.0", @@ -67,6 +69,16 @@ cloud-export-to-parquet = [ "pyarrow>=19.0.1", ] +[tool.uv] +constraint-dependencies = [ + # langsmith 0.7.34 changed its aio_to_thread signature; temporalio.contrib.langsmith + # 1.27.2 still patches the older signature, causing workflow task retries to hang CI. + "langsmith<0.7.34", + # yarl 1.24.0 was published without an sdist and only has cp310 wheels, so it cannot + # install on the Python 3.14 CI jobs. + "yarl!=1.24.0", +] + [tool.hatch.metadata] allow-direct-references = true @@ -88,6 +100,7 @@ packages = [ "dsl", "encryption", "external_storage", + "external_storage_redis", "gevent_async", "hello", "langgraph_plugin", diff --git a/tests/external_storage_redis/__init__.py b/tests/external_storage_redis/__init__.py new file mode 100644 index 000000000..0d45e2f52 --- /dev/null +++ b/tests/external_storage_redis/__init__.py @@ -0,0 +1 @@ +"""Tests for the Redis external storage sample.""" diff --git a/tests/external_storage_redis/conftest.py b/tests/external_storage_redis/conftest.py new file mode 100644 index 000000000..b6736ab3a --- /dev/null +++ b/tests/external_storage_redis/conftest.py @@ -0,0 +1,39 @@ +"""Shared fixtures for Redis external storage tests.""" + +from __future__ import annotations + +from collections.abc import AsyncIterator +from typing import TYPE_CHECKING + +import fakeredis.aioredis +import pytest +import pytest_asyncio + +from external_storage_redis import RedisStorageDriverClient +from external_storage_redis.redis_asyncio import new_redis_asyncio_client + +if TYPE_CHECKING: + from redis.asyncio.client import Redis + +KEY_PREFIX = "test:payloads" + + +@pytest_asyncio.fixture +async def redis_asyncio_client() -> AsyncIterator[Redis]: + """Yield a fake Redis client with an empty database for each test.""" + client = fakeredis.aioredis.FakeRedis(decode_responses=False) + await client.flushdb() + try: + yield client + finally: + aclose = getattr(client, "aclose", None) + if aclose is not None: + await aclose() + else: + await client.close() + + +@pytest.fixture +def driver_client(redis_asyncio_client: Redis) -> RedisStorageDriverClient: + """Wrap the redis.asyncio client in a RedisStorageDriverClient adapter.""" + return new_redis_asyncio_client(redis_asyncio_client) diff --git a/tests/external_storage_redis/test_redis.py b/tests/external_storage_redis/test_redis.py new file mode 100644 index 000000000..33b871a27 --- /dev/null +++ b/tests/external_storage_redis/test_redis.py @@ -0,0 +1,745 @@ +"""Unit tests for RedisStorageDriver using fakeredis.""" + +from __future__ import annotations + +import asyncio +import hashlib +import subprocess +import sys +from collections.abc import Callable, Coroutine +from datetime import timedelta +from functools import wraps +from typing import Any +from unittest.mock import MagicMock + +import fakeredis.aioredis +import pytest +from temporalio.api.common.v1 import Payload +from temporalio.converter import ( + JSONPlainPayloadConverter, + StorageDriverActivityInfo, + StorageDriverClaim, + StorageDriverRetrieveContext, + StorageDriverStoreContext, + StorageDriverWorkflowInfo, +) +from temporalio.exceptions import ApplicationError + +from external_storage_redis import RedisStorageDriver, RedisStorageDriverClient +from external_storage_redis.redis_asyncio import new_redis_asyncio_client +from tests.external_storage_redis.conftest import KEY_PREFIX + +_CONVERTER = JSONPlainPayloadConverter() + + +def test_redis_asyncio_adapter_import_is_workflow_safe() -> None: + completed = subprocess.run( + [ + sys.executable, + "-c", + ( + "import sys; " + "import external_storage_redis.redis_asyncio; " + "print(any(name.startswith('redis.asyncio') for name in sys.modules))" + ), + ], + check=True, + capture_output=True, + text=True, + ) + assert completed.stdout.strip() == "False" + + +def make_payload(value: str = "hello") -> Payload: + payload = _CONVERTER.to_payload(value) + assert payload is not None + return payload + + +def make_store_context( + target: StorageDriverActivityInfo | StorageDriverWorkflowInfo | None = None, +) -> StorageDriverStoreContext: + return StorageDriverStoreContext(target=target) + + +def make_workflow_context( + namespace: str = "my-namespace", + workflow_id: str = "my-workflow", + workflow_type: str | None = None, + run_id: str | None = None, +) -> StorageDriverStoreContext: + return make_store_context( + target=StorageDriverWorkflowInfo( + id=workflow_id, + type=workflow_type, + run_id=run_id, + namespace=namespace, + ) + ) + + +def make_activity_context( + namespace: str = "my-namespace", + activity_id: str | None = "my-activity", + activity_type: str | None = None, + run_id: str | None = None, +) -> StorageDriverStoreContext: + return make_store_context( + target=StorageDriverActivityInfo( + id=activity_id, + type=activity_type, + run_id=run_id, + namespace=namespace, + ) + ) + + +async def _list_keys(redis_asyncio_client: Any) -> list[str]: + raw_keys = await redis_asyncio_client.keys("*") + return sorted(key.decode() if isinstance(key, bytes) else key for key in raw_keys) + + +async def _wait_for_key_absent(redis_asyncio_client: Any, key: str) -> None: + async def _poll() -> None: + while await redis_asyncio_client.exists(key): + await asyncio.sleep(0.01) + + await asyncio.wait_for(_poll(), timeout=2) + + +class CountingDriverClient(RedisStorageDriverClient): + """RedisStorageDriverClient wrapper that counts calls.""" + + def __init__(self, delegate: RedisStorageDriverClient) -> None: + self._delegate = delegate + self.get_count = 0 + self.set_if_absent_count = 0 + self.insert_count = 0 + + async def get(self, *, key: str) -> bytes | None: + self.get_count += 1 + return await self._delegate.get(key=key) + + async def set_if_absent( + self, + *, + key: str, + data: bytes, + ttl: timedelta | None = None, + ) -> bool: + self.set_if_absent_count += 1 + inserted = await self._delegate.set_if_absent(key=key, data=data, ttl=ttl) + if inserted: + self.insert_count += 1 + return inserted + + +class FailOnceDriverClient(RedisStorageDriverClient): + """RedisStorageDriverClient wrapper that fails one call then blocks.""" + + def __init__(self, delegate: RedisStorageDriverClient, fail_on: str) -> None: + self._delegate = delegate + self._fail_on = fail_on + self._call_count = 0 + self.cancelled: list[bool] = [] + + async def _maybe_fail(self) -> None: + self._call_count += 1 + if self._call_count == 1: + raise ConnectionError("Redis connection lost") + try: + await asyncio.sleep(60) + except asyncio.CancelledError: + self.cancelled.append(True) + raise + + async def get(self, *, key: str) -> bytes | None: + if self._fail_on == "get": + await self._maybe_fail() + return await self._delegate.get(key=key) + + async def set_if_absent( + self, + *, + key: str, + data: bytes, + ttl: timedelta | None = None, + ) -> bool: + if self._fail_on == "set_if_absent": + await self._maybe_fail() + return await self._delegate.set_if_absent(key=key, data=data, ttl=ttl) + + +class _AsyncBarrier: + """Minimal asyncio.Barrier equivalent for Python <3.11.""" + + def __init__(self, parties: int) -> None: + self._parties = parties + self._count = 0 + self._event = asyncio.Event() + + async def wait(self) -> None: + self._count += 1 + if self._count >= self._parties: + self._event.set() + else: + await self._event.wait() + + +def _barrier_wrapper( + fn: Callable[..., Coroutine[Any, Any, Any]], barrier: _AsyncBarrier +) -> Callable[..., Coroutine[Any, Any, Any]]: + """Wrap an async method to wait at a barrier before proceeding.""" + + @wraps(fn) + async def wrapper(*args: Any, **kwargs: Any) -> Any: + await asyncio.wait_for(barrier.wait(), timeout=5) + return await fn(*args, **kwargs) + + return wrapper + + +@pytest.fixture +def counting_driver_client( + driver_client: RedisStorageDriverClient, +) -> CountingDriverClient: + """Wrap the driver client in a counting decorator.""" + return CountingDriverClient(driver_client) + + +class TestRedisStorageDriverInit: + def test_default_name(self) -> None: + driver = RedisStorageDriver(client=MagicMock(spec=RedisStorageDriverClient)) + assert driver.name() == "redis" + + def test_custom_name(self) -> None: + driver = RedisStorageDriver( + client=MagicMock(spec=RedisStorageDriverClient), + driver_name="my-redis", + ) + assert driver.name() == "my-redis" + + def test_type(self) -> None: + driver = RedisStorageDriver(client=MagicMock(spec=RedisStorageDriverClient)) + assert driver.type() == "redis" + + def test_ttl_zero_raises(self) -> None: + with pytest.raises(ValueError, match="ttl must be greater than zero"): + RedisStorageDriver( + client=MagicMock(spec=RedisStorageDriverClient), + ttl=timedelta(0), + ) + + def test_ttl_negative_raises(self) -> None: + with pytest.raises(ValueError, match="ttl must be greater than zero"): + RedisStorageDriver( + client=MagicMock(spec=RedisStorageDriverClient), + ttl=timedelta(seconds=-1), + ) + + def test_max_payload_size_zero_raises(self) -> None: + with pytest.raises( + ValueError, match="max_payload_size must be greater than zero" + ): + RedisStorageDriver( + client=MagicMock(spec=RedisStorageDriverClient), + max_payload_size=0, + ) + + def test_max_payload_size_negative_raises(self) -> None: + with pytest.raises( + ValueError, match="max_payload_size must be greater than zero" + ): + RedisStorageDriver( + client=MagicMock(spec=RedisStorageDriverClient), + max_payload_size=-1, + ) + + +class TestRedisStorageDriverKeyConstruction: + async def test_key_context_none( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + [claim] = await driver.store(make_store_context(), [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert claim.claim_data["key"] == f"{KEY_PREFIX}:v0:d:sha256:{expected_hash}" + + async def test_key_context_workflow( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_workflow_context(namespace="ns1", workflow_id="wf1") + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:wt:null:wi:wf1:ri:null:d:sha256:{expected_hash}" + ) + + async def test_key_context_workflow_with_type_and_run_id( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_workflow_context( + namespace="ns1", + workflow_id="wf1", + workflow_type="MyWorkflow", + run_id="run-abc", + ) + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:wt:MyWorkflow:wi:wf1:ri:run-abc:d:sha256:{expected_hash}" + ) + + async def test_key_context_activity( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_activity_context(namespace="ns1", activity_id="act1") + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:at:null:ai:act1:ri:null:d:sha256:{expected_hash}" + ) + + async def test_key_context_activity_with_type_and_run_id( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_activity_context( + namespace="ns1", + activity_id="act1", + activity_type="MyActivity", + run_id="run-abc", + ) + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:at:MyActivity:ai:act1:ri:run-abc:d:sha256:{expected_hash}" + ) + + async def test_key_preserves_case( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_workflow_context(namespace="MyNamespace", workflow_id="MyWorkflow") + [claim] = await driver.store(ctx, [payload]) + key = claim.claim_data["key"] + assert "MyNamespace" in key + assert "MyWorkflow" in key + + async def test_key_urlencodes_workflow_id_with_slashes( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_workflow_context(namespace="ns1", workflow_id="order/123/v2") + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:wt:null:wi:order%2F123%2Fv2:ri:null:d:sha256:{expected_hash}" + ) + + async def test_key_urlencodes_workflow_id_with_special_chars( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_workflow_context(namespace="ns1", workflow_id="wf#1 &foo=bar") + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:wt:null:wi:wf%231%20%26foo%3Dbar:ri:null:d:sha256:{expected_hash}" + ) + + async def test_key_urlencodes_activity_id( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_activity_context(namespace="ns1", activity_id="act/1#2") + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:at:null:ai:act%2F1%232:ri:null:d:sha256:{expected_hash}" + ) + + async def test_key_urlencodes_namespace( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_workflow_context(namespace="my/ns#1", workflow_id="wf1") + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:my%2Fns%231:wt:null:wi:wf1:ri:null:d:sha256:{expected_hash}" + ) + + async def test_key_urlencoded_roundtrip( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("special-char-roundtrip") + ctx = make_workflow_context(namespace="ns/1", workflow_id="wf/2#3") + [claim] = await driver.store(ctx, [payload]) + [retrieved] = await driver.retrieve(StorageDriverRetrieveContext(), [claim]) + assert retrieved == payload + + +class TestRedisStorageDriverStoreRetrieve: + async def test_store_returns_claim_with_key_and_hash( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + [claim] = await driver.store(make_store_context(), [payload]) + assert claim.claim_data["key"].startswith(f"{KEY_PREFIX}:v0:") + assert claim.claim_data["hash_algorithm"] == "sha256" + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert claim.claim_data["hash_value"] == expected_hash + + async def test_roundtrip_single_payload( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("round-trip value") + [claim] = await driver.store(make_store_context(), [payload]) + [retrieved] = await driver.retrieve(StorageDriverRetrieveContext(), [claim]) + assert retrieved == payload + + async def test_roundtrip_multiple_payloads( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payloads = [make_payload(f"value-{i}") for i in range(3)] + claims = await driver.store(make_store_context(), payloads) + retrieved = await driver.retrieve(StorageDriverRetrieveContext(), claims) + assert retrieved == payloads + + async def test_empty_payloads_returns_empty_list( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + assert await driver.store(make_store_context(), []) == [] + assert await driver.retrieve(StorageDriverRetrieveContext(), []) == [] + + async def test_content_addressable_deduplication( + self, redis_asyncio_client: Any, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("same-value") + claims = await driver.store(make_store_context(), [payload, payload]) + assert claims[0].claim_data["key"] == claims[1].claim_data["key"] + assert await _list_keys(redis_asyncio_client) == [claims[0].claim_data["key"]] + + async def test_set_if_absent_only_inserts_once( + self, counting_driver_client: CountingDriverClient + ) -> None: + driver = RedisStorageDriver( + client=counting_driver_client, + key_prefix=KEY_PREFIX, + ) + payload = make_payload("insert-once") + + await driver.store(make_store_context(), [payload]) + await driver.store(make_store_context(), [payload]) + + assert counting_driver_client.set_if_absent_count == 2 + assert counting_driver_client.insert_count == 1 + + async def test_duplicate_store_preserves_data( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("preserve-me") + + [claim1] = await driver.store(make_store_context(), [payload]) + [claim2] = await driver.store(make_store_context(), [payload]) + assert claim1 == claim2 + + [retrieved] = await driver.retrieve(StorageDriverRetrieveContext(), [claim2]) + assert retrieved == payload + + async def test_ttl_is_applied_on_first_write( + self, redis_asyncio_client: Any, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver( + client=driver_client, + key_prefix=KEY_PREFIX, + ttl=timedelta(seconds=5), + ) + [claim] = await driver.store(make_store_context(), [make_payload("ttl")]) + ttl_ms = await redis_asyncio_client.pttl(claim.claim_data["key"]) + assert 0 < ttl_ms <= 5000 + + async def test_ttl_is_not_refreshed_on_duplicate_write( + self, redis_asyncio_client: Any, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver( + client=driver_client, + key_prefix=KEY_PREFIX, + ttl=timedelta(seconds=5), + ) + payload = make_payload("ttl-stable") + [claim] = await driver.store(make_store_context(), [payload]) + first_ttl_ms = await redis_asyncio_client.pttl(claim.claim_data["key"]) + await asyncio.sleep(0.02) + await driver.store(make_store_context(), [payload]) + second_ttl_ms = await redis_asyncio_client.pttl(claim.claim_data["key"]) + assert 0 < second_ttl_ms < first_ttl_ms + + async def test_retrieve_validates_hash( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("check-integrity") + [claim] = await driver.store(make_store_context(), [payload]) + + tampered_claim = StorageDriverClaim( + claim_data={ + **claim.claim_data, + "hash_value": "0" * 64, + }, + ) + with pytest.raises( + ValueError, + match=r"RedisStorageDriver integrity check failed \[key=.+\]: expected sha256:.+, got sha256:.+", + ): + await driver.retrieve(StorageDriverRetrieveContext(), [tampered_claim]) + + async def test_retrieve_rejects_unsupported_hash_algorithm( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("unsupported-algo") + [claim] = await driver.store(make_store_context(), [payload]) + + bad_claim = StorageDriverClaim( + claim_data={ + **claim.claim_data, + "hash_algorithm": "md5", + }, + ) + with pytest.raises( + ValueError, + match=r"RedisStorageDriver unsupported hash algorithm \[key=.+\]: expected sha256, got md5", + ): + await driver.retrieve(StorageDriverRetrieveContext(), [bad_claim]) + + async def test_retrieve_without_hash_in_claim( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("no-hash-claim") + [claim] = await driver.store(make_store_context(), [payload]) + + legacy_claim = StorageDriverClaim( + claim_data={ + "key": claim.claim_data["key"], + }, + ) + [retrieved] = await driver.retrieve( + StorageDriverRetrieveContext(), [legacy_claim] + ) + assert retrieved == payload + + async def test_retrieve_missing_key_raises_non_retryable_application_error( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + claim = StorageDriverClaim( + claim_data={"key": f"{KEY_PREFIX}:v0:d:sha256:missing"} + ) + with pytest.raises(ApplicationError) as exc_info: + await driver.retrieve(StorageDriverRetrieveContext(), [claim]) + assert exc_info.value.message == ( + f"Payload not found for key '{KEY_PREFIX}:v0:d:sha256:missing'" + ) + assert exc_info.value.type == "PayloadNotFoundError" + assert exc_info.value.non_retryable is True + + async def test_expired_key_raises_non_retryable_application_error( + self, redis_asyncio_client: Any, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver( + client=driver_client, + key_prefix=KEY_PREFIX, + ttl=timedelta(milliseconds=10), + ) + [claim] = await driver.store(make_store_context(), [make_payload("expire-me")]) + await _wait_for_key_absent(redis_asyncio_client, claim.claim_data["key"]) + with pytest.raises(ApplicationError) as exc_info: + await driver.retrieve(StorageDriverRetrieveContext(), [claim]) + assert exc_info.value.type == "PayloadNotFoundError" + assert exc_info.value.non_retryable is True + + async def test_payload_exceeds_max_size_raises( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver( + client=driver_client, + key_prefix=KEY_PREFIX, + max_payload_size=10, + ) + with pytest.raises( + ValueError, + match=r"Payload size \d+ bytes exceeds the configured max_payload_size of 10 bytes", + ): + await driver.store(make_store_context(), [make_payload("exceeds-limit")]) + + async def test_payload_at_max_size_succeeds( + self, driver_client: RedisStorageDriverClient + ) -> None: + payload = make_payload("x") + driver = RedisStorageDriver( + client=driver_client, + key_prefix=KEY_PREFIX, + max_payload_size=len(payload.SerializeToString()), + ) + await driver.store(make_store_context(), [payload]) + + +class TestRedisAsyncioAdapter: + async def test_decode_responses_client_raises(self) -> None: + client = fakeredis.aioredis.FakeRedis(decode_responses=True) + try: + await client.set("adapter:text", b"abc") + adapter = new_redis_asyncio_client(client) + with pytest.raises(TypeError, match="decode_responses=False"): + await adapter.get(key="adapter:text") + finally: + aclose = getattr(client, "aclose", None) + if aclose is not None: + await aclose() + else: + await client.close() + + +class TestRedisStorageDriverErrors: + async def test_store_client_failure_raises( + self, driver_client: RedisStorageDriverClient + ) -> None: + faulty_client = FailOnceDriverClient( + delegate=driver_client, fail_on="set_if_absent" + ) + driver = RedisStorageDriver(client=faulty_client, key_prefix=KEY_PREFIX) + payload = make_payload() + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + expected_key = f"{KEY_PREFIX}:v0:d:sha256:{expected_hash}" + with pytest.raises(RuntimeError) as exc_info: + await driver.store(make_store_context(), [payload]) + assert ( + str(exc_info.value) + == f"RedisStorageDriver store failed [key={expected_key}]" + ) + assert isinstance(exc_info.value.__cause__, ConnectionError) + + async def test_retrieve_client_failure_raises( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + [claim] = await driver.store(make_store_context(), [make_payload("value")]) + + faulty_client = FailOnceDriverClient(delegate=driver_client, fail_on="get") + driver = RedisStorageDriver(client=faulty_client, key_prefix=KEY_PREFIX) + with pytest.raises(RuntimeError) as exc_info: + await driver.retrieve(StorageDriverRetrieveContext(), [claim]) + assert ( + str(exc_info.value) + == f"RedisStorageDriver retrieve failed [key={claim.claim_data['key']}]" + ) + assert isinstance(exc_info.value.__cause__, ConnectionError) + + +class TestRedisStorageDriverConcurrency: + async def test_store_payloads_concurrently( + self, driver_client: RedisStorageDriverClient + ) -> None: + num_payloads = 5 + barrier = _AsyncBarrier(num_payloads) + driver_client.set_if_absent = _barrier_wrapper( # type: ignore[method-assign] + driver_client.set_if_absent, + barrier, + ) + + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payloads = [make_payload(f"concurrent-store-{i}") for i in range(num_payloads)] + + claims = await driver.store(make_store_context(), payloads) + assert len(claims) == num_payloads + + async def test_retrieve_payloads_concurrently( + self, driver_client: RedisStorageDriverClient + ) -> None: + num_payloads = 5 + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payloads = [ + make_payload(f"concurrent-retrieve-{i}") for i in range(num_payloads) + ] + claims = await driver.store(make_store_context(), payloads) + + barrier = _AsyncBarrier(num_payloads) + driver_client.get = _barrier_wrapper(driver_client.get, barrier) # type: ignore[method-assign] + + retrieved = await driver.retrieve(StorageDriverRetrieveContext(), claims) + assert retrieved == payloads + + async def test_store_cancels_remaining_on_failure( + self, driver_client: RedisStorageDriverClient + ) -> None: + faulty_client = FailOnceDriverClient( + delegate=driver_client, + fail_on="set_if_absent", + ) + driver = RedisStorageDriver(client=faulty_client, key_prefix=KEY_PREFIX) + payloads = [make_payload(f"cancel-store-{i}") for i in range(3)] + + with pytest.raises( + RuntimeError, + match=r"RedisStorageDriver store failed \[key=.+\]", + ) as exc_info: + await driver.store(make_store_context(), payloads) + + assert isinstance(exc_info.value.__cause__, ConnectionError) + assert str(exc_info.value.__cause__) == "Redis connection lost" + assert ( + len(faulty_client.cancelled) == 2 + ), "Expected 2 remaining tasks to be cancelled" + + async def test_retrieve_cancels_remaining_on_failure( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payloads = [make_payload(f"cancel-retrieve-{i}") for i in range(3)] + claims = await driver.store(make_store_context(), payloads) + + faulty_client = FailOnceDriverClient(delegate=driver_client, fail_on="get") + driver = RedisStorageDriver(client=faulty_client, key_prefix=KEY_PREFIX) + + with pytest.raises( + RuntimeError, + match=r"RedisStorageDriver retrieve failed \[key=.+\]", + ) as exc_info: + await driver.retrieve(StorageDriverRetrieveContext(), claims) + + assert isinstance(exc_info.value.__cause__, ConnectionError) + assert str(exc_info.value.__cause__) == "Redis connection lost" + assert ( + len(faulty_client.cancelled) == 2 + ), "Expected 2 remaining tasks to be cancelled" diff --git a/tests/external_storage_redis/test_redis_worker.py b/tests/external_storage_redis/test_redis_worker.py new file mode 100644 index 000000000..60c1e4ce9 --- /dev/null +++ b/tests/external_storage_redis/test_redis_worker.py @@ -0,0 +1,522 @@ +"""Worker integration tests for RedisStorageDriver key structure.""" + +from __future__ import annotations + +import dataclasses +import hashlib +import uuid +from collections.abc import AsyncIterator, Callable, Sequence +from datetime import timedelta +from typing import Any + +import pytest +import pytest_asyncio +import temporalio.converter +from temporalio.client import Client, WorkflowFailureError +from temporalio.converter import ExternalStorage, JSONPlainPayloadConverter +from temporalio.exceptions import ActivityError, ApplicationError +from temporalio.service import RPCError +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from external_storage_redis import RedisStorageDriver, RedisStorageDriverClient +from external_storage_redis.redis_asyncio import new_redis_asyncio_client +from tests.external_storage_redis.conftest import KEY_PREFIX +from tests.external_storage_redis.workflows import ( + LARGE, + LARGE_2, + ChildWorkflow, + DocumentIngestionWorkflow, + LargeIOWorkflow, + LargeOutputNoRetryWorkflow, + ModelTrainingWorkflow, + OrderFulfillmentWorkflow, + ParentWithChildWorkflow, + PaymentProcessingWorkflow, + SignalQueryUpdateWorkflow, + download_document, + extract_text, + index_document, + large_io_activity, + large_output_activity, +) + +_THRESHOLD = 256 + + +def new_worker( + client: Client, + *workflows: type, + activities: Sequence[Callable[..., Any]] = (), + task_queue: str | None = None, + **kwargs: Any, +) -> Worker: + return Worker( + client, + task_queue=task_queue or str(uuid.uuid4()), + workflows=workflows, + activities=activities, + **kwargs, + ) + + +class FailingSetClient(RedisStorageDriverClient): + """Redis client wrapper that fails all writes.""" + + def __init__(self, delegate: RedisStorageDriverClient) -> None: + self._delegate = delegate + + async def get(self, *, key: str) -> bytes | None: + return await self._delegate.get(key=key) + + async def set_if_absent( + self, + *, + key: str, + data: bytes, + ttl: timedelta | None = None, + ) -> bool: + del key, data, ttl + raise ConnectionError("Redis is unavailable") + + +@pytest_asyncio.fixture +async def tmprl_client( + env: WorkflowEnvironment, redis_asyncio_client: Any +) -> AsyncIterator[Client]: + """Temporal client wired with ExternalStorage backed by fake Redis.""" + driver = RedisStorageDriver( + client=new_redis_asyncio_client(redis_asyncio_client), + key_prefix=KEY_PREFIX, + ) + yield await Client.connect( + env.client.service_client.config.target_host, + namespace=env.client.namespace, + data_converter=dataclasses.replace( + temporalio.converter.default(), + external_storage=ExternalStorage( + drivers=[driver], + payload_size_threshold=_THRESHOLD, + ), + ), + ) + + +async def _list_keys(redis_asyncio_client: Any) -> list[str]: + raw_keys = await redis_asyncio_client.keys(f"{KEY_PREFIX}:*") + return sorted(key.decode() if isinstance(key, bytes) else key for key in raw_keys) + + +def _skip_if_standalone_activity_disabled(err: RPCError) -> None: + if "Standalone activity is disabled" in str(err): + pytest.skip( + "Standalone Activity is not supported by this Temporal dev server build" + ) + + +async def test_redis_driver_workflow_input_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker( + tmprl_client, LargeIOWorkflow, activities=[large_io_activity] + ) as worker: + await tmprl_client.execute_workflow( + LargeIOWorkflow.run, + LARGE, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert all( + f"{KEY_PREFIX}:v0:ns:default:wt:LargeIOWorkflow:wi:{workflow_id}:ri:" in key + for key in keys + ) + assert sum(1 for key in keys if ":ri:null:" in key) == 1 + assert sum(1 for key in keys if ":ri:null:" not in key) == 1 + + +async def test_redis_driver_workflow_output_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker( + tmprl_client, LargeIOWorkflow, activities=[large_io_activity] + ) as worker: + result = await tmprl_client.execute_workflow( + LargeIOWorkflow.run, + "small", + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + assert result == LARGE + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 1 + assert ( + f"{KEY_PREFIX}:v0:ns:default:wt:LargeIOWorkflow:wi:{workflow_id}:ri:" in keys[0] + ) + assert ":ri:null:" not in keys[0] + + +async def test_redis_driver_workflow_activity_input_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker( + tmprl_client, LargeIOWorkflow, activities=[large_io_activity] + ) as worker: + await tmprl_client.execute_workflow( + LargeIOWorkflow.run, + LARGE, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert all( + f"{KEY_PREFIX}:v0:ns:default:wt:LargeIOWorkflow:wi:{workflow_id}:ri:" in key + for key in keys + ) + assert all(":ai:" not in key for key in keys) + + +async def test_redis_driver_workflow_activity_output_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker( + tmprl_client, LargeIOWorkflow, activities=[large_io_activity] + ) as worker: + await tmprl_client.execute_workflow( + LargeIOWorkflow.run, + "small", + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 1 + assert ( + f"{KEY_PREFIX}:v0:ns:default:wt:LargeIOWorkflow:wi:{workflow_id}:ri:" in keys[0] + ) + assert ":ri:null:" not in keys[0] + + +async def test_redis_driver_standalone_activity_input_key( + env: WorkflowEnvironment, + tmprl_client: Client, + redis_asyncio_client: Any, +) -> None: + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/2741" + ) + activity_id = str(uuid.uuid4()) + task_queue = str(uuid.uuid4()) + async with new_worker( + tmprl_client, activities=[large_io_activity], task_queue=task_queue + ): + try: + await tmprl_client.execute_activity( + large_io_activity, + LARGE, + id=activity_id, + task_queue=task_queue, + start_to_close_timeout=timedelta(seconds=5), + ) + except RPCError as err: + _skip_if_standalone_activity_disabled(err) + raise + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert all( + f"{KEY_PREFIX}:v0:ns:default:at:large_io_activity:ai:{activity_id}:ri:" in key + for key in keys + ) + assert all(":wt:" not in key for key in keys) + assert sum(1 for key in keys if ":ri:null:" in key) == 1 + assert sum(1 for key in keys if ":ri:null:" not in key) == 1 + + +async def test_redis_driver_standalone_activity_output_key( + env: WorkflowEnvironment, + tmprl_client: Client, + redis_asyncio_client: Any, +) -> None: + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/2741" + ) + activity_id = str(uuid.uuid4()) + task_queue = str(uuid.uuid4()) + async with new_worker( + tmprl_client, activities=[large_output_activity], task_queue=task_queue + ): + try: + await tmprl_client.execute_activity( + large_output_activity, + id=activity_id, + task_queue=task_queue, + start_to_close_timeout=timedelta(seconds=5), + ) + except RPCError as err: + _skip_if_standalone_activity_disabled(err) + raise + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 1 + assert ( + f"{KEY_PREFIX}:v0:ns:default:at:large_output_activity:ai:{activity_id}:ri:" + in keys[0] + ) + assert ":ri:null:" not in keys[0] + assert ":wt:" not in keys[0] + + +async def test_redis_driver_signal_arg_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker(tmprl_client, SignalQueryUpdateWorkflow) as worker: + handle = await tmprl_client.start_workflow( + SignalQueryUpdateWorkflow.run, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + await handle.signal(SignalQueryUpdateWorkflow.finish, LARGE) + await handle.result() + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert any( + f"{KEY_PREFIX}:v0:ns:default:wt:null:wi:{workflow_id}:ri:null:" in key + for key in keys + ) + assert any( + f"{KEY_PREFIX}:v0:ns:default:wt:SignalQueryUpdateWorkflow:wi:{workflow_id}:" + in key + for key in keys + ) + + +async def test_redis_driver_query_result_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker(tmprl_client, SignalQueryUpdateWorkflow) as worker: + handle = await tmprl_client.start_workflow( + SignalQueryUpdateWorkflow.run, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + result = await handle.query(SignalQueryUpdateWorkflow.get_value, LARGE) + assert result == LARGE + await handle.signal(SignalQueryUpdateWorkflow.finish, "done") + await handle.result() + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert any( + f"{KEY_PREFIX}:v0:ns:default:wt:null:wi:{workflow_id}:ri:null:" in key + for key in keys + ) + assert any( + f"{KEY_PREFIX}:v0:ns:default:wt:SignalQueryUpdateWorkflow:wi:{workflow_id}:" + in key + for key in keys + ) + + +async def test_redis_driver_update_result_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker(tmprl_client, SignalQueryUpdateWorkflow) as worker: + handle = await tmprl_client.start_workflow( + SignalQueryUpdateWorkflow.run, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + result = await handle.execute_update(SignalQueryUpdateWorkflow.do_update, LARGE) + assert result == LARGE + await handle.signal(SignalQueryUpdateWorkflow.finish, "done") + await handle.result() + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert any( + f"{KEY_PREFIX}:v0:ns:default:wt:null:wi:{workflow_id}:ri:null:" in key + for key in keys + ) + assert any( + f"{KEY_PREFIX}:v0:ns:default:wt:SignalQueryUpdateWorkflow:wi:{workflow_id}:" + in key + for key in keys + ) + + +async def test_redis_driver_child_workflow_input_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker( + tmprl_client, ParentWithChildWorkflow, ChildWorkflow + ) as worker: + await tmprl_client.execute_workflow( + ParentWithChildWorkflow.run, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + child_workflow_id = f"{workflow_id}-child" + assert len(keys) == 1 + assert ( + f"{KEY_PREFIX}:v0:ns:default:wt:ChildWorkflow:wi:{child_workflow_id}:ri:" + in keys[0] + ) + + +async def test_redis_driver_identifier_casing( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = f"MyWorkflow-{uuid.uuid4()}" + async with new_worker( + tmprl_client, LargeIOWorkflow, activities=[large_io_activity] + ) as worker: + await tmprl_client.execute_workflow( + LargeIOWorkflow.run, + LARGE, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert all( + f"{KEY_PREFIX}:v0:ns:default:wt:LargeIOWorkflow:wi:{workflow_id}:ri:" in key + for key in keys + ) + + +async def test_redis_driver_content_dedup( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker( + tmprl_client, + DocumentIngestionWorkflow, + activities=[download_document, extract_text, index_document], + ) as worker: + await tmprl_client.execute_workflow( + DocumentIngestionWorkflow.run, + "doc-001", + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert all( + f"{KEY_PREFIX}:v0:ns:default:wt:DocumentIngestionWorkflow:wi:{workflow_id}:ri:" + in key + for key in keys + ) + assert keys[0] != keys[1] + + +async def test_redis_driver_single_workflow_same_key_namespace( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker(tmprl_client, ModelTrainingWorkflow) as worker: + handle = await tmprl_client.start_workflow( + ModelTrainingWorkflow.run, + LARGE, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + metrics = await handle.execute_update( + ModelTrainingWorkflow.get_metrics, "checkpoint-1" + ) + assert metrics == LARGE_2 + await handle.signal(ModelTrainingWorkflow.apply_overrides, LARGE) + await handle.signal(ModelTrainingWorkflow.complete) + await handle.result() + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 4 + assert all(f":wi:{workflow_id}:" in key for key in keys) + + +async def test_redis_driver_parent_child_independent_key_namespaces( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + payment_id = f"{workflow_id}-payment" + async with new_worker( + tmprl_client, OrderFulfillmentWorkflow, PaymentProcessingWorkflow + ) as worker: + await tmprl_client.execute_workflow( + OrderFulfillmentWorkflow.run, + LARGE, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + parent_keys = [key for key in keys if f":wi:{workflow_id}:" in key] + child_keys = [key for key in keys if f":wi:{payment_id}:" in key] + assert len(parent_keys) == 3 + assert len(child_keys) == 1 + + +async def test_redis_store_failure_surfaces_in_workflow_history( + env: WorkflowEnvironment, driver_client: RedisStorageDriverClient +) -> None: + driver = RedisStorageDriver( + client=FailingSetClient(driver_client), + key_prefix=KEY_PREFIX, + ) + bad_client = await Client.connect( + env.client.service_client.config.target_host, + namespace=env.client.namespace, + data_converter=dataclasses.replace( + temporalio.converter.default(), + external_storage=ExternalStorage( + drivers=[driver], + payload_size_threshold=_THRESHOLD, + ), + ), + ) + workflow_id = str(uuid.uuid4()) + async with new_worker( + bad_client, LargeOutputNoRetryWorkflow, activities=[large_output_activity] + ) as worker: + with pytest.raises(WorkflowFailureError) as exc_info: + await bad_client.execute_workflow( + LargeOutputNoRetryWorkflow.run, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + + large_payload = JSONPlainPayloadConverter().to_payload(LARGE) + assert large_payload is not None + expected_hash = hashlib.sha256(large_payload.SerializeToString()).hexdigest() + + activity_error = exc_info.value.__cause__ + assert isinstance(activity_error, ActivityError) + app_error = activity_error.__cause__ + assert isinstance(app_error, ApplicationError) + assert app_error.type == "RuntimeError" + msg = app_error.message + assert "RedisStorageDriver store failed [key=" in msg + assert ( + f"{KEY_PREFIX}:v0:ns:default:wt:LargeOutputNoRetryWorkflow:wi:{workflow_id}:ri:" + in msg + ) + assert f":d:sha256:{expected_hash}]" in msg diff --git a/tests/external_storage_redis/workflows.py b/tests/external_storage_redis/workflows.py new file mode 100644 index 000000000..1440ffbbc --- /dev/null +++ b/tests/external_storage_redis/workflows.py @@ -0,0 +1,194 @@ +"""Workflows and activities used by the Redis external storage tests.""" + +from __future__ import annotations + +from datetime import timedelta + +from temporalio import activity, workflow +from temporalio.common import RetryPolicy + +LARGE = "x" * 356 # ~358 bytes as a JSON string, above the 256-byte test threshold +LARGE_2 = "y" * 356 # distinct large payload with a different SHA-256 hash + + +@activity.defn +async def large_io_activity(_data: str) -> str: + return LARGE + + +@activity.defn +async def large_output_activity() -> str: + """Return a large payload with no retries; used to test store failures.""" + return LARGE + + +@workflow.defn +class LargeOutputNoRetryWorkflow: + """Execute one activity that returns a large payload with no retries.""" + + @workflow.run + async def run(self) -> str: + return await workflow.execute_activity( + large_output_activity, + schedule_to_close_timeout=timedelta(seconds=10), + retry_policy=RetryPolicy(maximum_attempts=1), + ) + + +@workflow.defn +class LargeIOWorkflow: + """Pass workflow input to an activity and return a large output.""" + + @workflow.run + async def run(self, data: str) -> str: + await workflow.execute_activity( + large_io_activity, + data, + schedule_to_close_timeout=timedelta(seconds=10), + ) + return LARGE + + +@activity.defn +async def download_document(document_id: str) -> str: + """Download the raw document content from remote storage.""" + del document_id + return LARGE + + +@activity.defn +async def extract_text(raw_content: str) -> str: + """Extract and normalize text from the raw document content.""" + del raw_content + return LARGE_2 + + +@activity.defn +async def index_document(text: str) -> str: + """Index the extracted text into the search index.""" + del text + return "idx-00001" + + +@workflow.defn +class DocumentIngestionWorkflow: + """Download, extract, and index a document through large payload hops.""" + + @workflow.run + async def run(self, document_id: str) -> str: + raw_content = await workflow.execute_activity( + download_document, + document_id, + schedule_to_close_timeout=timedelta(seconds=10), + ) + extracted_text = await workflow.execute_activity( + extract_text, + raw_content, + schedule_to_close_timeout=timedelta(seconds=10), + ) + return await workflow.execute_activity( + index_document, + extracted_text, + schedule_to_close_timeout=timedelta(seconds=10), + ) + + +@workflow.defn +class ChildWorkflow: + @workflow.run + async def run(self, data: str) -> str: + return f"{len(data)}" + + +@workflow.defn +class ParentWithChildWorkflow: + """Delegate work to a child workflow whose ID is ``{parent_id}-child``.""" + + @workflow.run + async def run(self) -> str: + child_id = f"{workflow.info().workflow_id}-child" + return await workflow.execute_child_workflow( + ChildWorkflow.run, + LARGE, + id=child_id, + execution_timeout=timedelta(seconds=10), + ) + + +@workflow.defn +class PaymentProcessingWorkflow: + """Process payment for an order and return a large confirmation payload.""" + + @workflow.run + async def run(self, order_details: str) -> str: + del order_details + return LARGE_2 + + +@workflow.defn +class OrderFulfillmentWorkflow: + """Coordinate order fulfillment by delegating payment to a child workflow.""" + + @workflow.run + async def run(self, order_details: str) -> str: + payment_id = f"{workflow.info().workflow_id}-payment" + return await workflow.execute_child_workflow( + PaymentProcessingWorkflow.run, + order_details, + id=payment_id, + execution_timeout=timedelta(seconds=10), + ) + + +@workflow.defn +class ModelTrainingWorkflow: + """Simulate a long-running training job with large cross-boundary payloads.""" + + def __init__(self) -> None: + self._done = False + + @workflow.run + async def run(self, training_config: str) -> str: + del training_config + await workflow.wait_condition(lambda: self._done) + return LARGE + + @workflow.signal + async def apply_overrides(self, override_params: str) -> None: + """Inject 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The refactor to a workflow package in temporalio/sdk-python#1488 dropped it, so use datetime.timedelta directly (matches the rest of the repo). Co-authored-by: Claude Opus 4.7 (1M context) --- openai_agents/basic/workflows/local_image_workflow.py | 4 +++- .../reasoning_content/workflows/reasoning_content_workflow.py | 3 ++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/openai_agents/basic/workflows/local_image_workflow.py b/openai_agents/basic/workflows/local_image_workflow.py index b536f517e..9c82a5991 100644 --- a/openai_agents/basic/workflows/local_image_workflow.py +++ b/openai_agents/basic/workflows/local_image_workflow.py @@ -1,3 +1,5 @@ +from datetime import timedelta + from agents import Agent, Runner from temporalio import workflow @@ -24,7 +26,7 @@ async def run( b64_image = await workflow.execute_activity( read_image_as_base64, image_path, - start_to_close_timeout=workflow.timedelta(seconds=30), + start_to_close_timeout=timedelta(seconds=30), ) agent = Agent( diff --git a/openai_agents/reasoning_content/workflows/reasoning_content_workflow.py b/openai_agents/reasoning_content/workflows/reasoning_content_workflow.py index 0a9f0a158..2a584a4bc 100644 --- a/openai_agents/reasoning_content/workflows/reasoning_content_workflow.py +++ b/openai_agents/reasoning_content/workflows/reasoning_content_workflow.py @@ -1,4 +1,5 @@ from dataclasses import dataclass +from datetime import timedelta from temporalio import workflow @@ -22,7 +23,7 @@ async def run(self, prompt: str, model_name: str | None = None) -> ReasoningResu reasoning_content, regular_content = await workflow.execute_activity( get_reasoning_response, args=[prompt, model_name], - start_to_close_timeout=workflow.timedelta(minutes=5), + start_to_close_timeout=timedelta(minutes=5), ) return ReasoningResult( From 4d453de6adce21be822a02e2dc553138b684945d Mon Sep 17 00:00:00 2001 From: Justin Anderson <44687433+jmaeagle99@users.noreply.github.com> Date: Thu, 21 May 2026 14:19:07 -0700 Subject: [PATCH 29/40] fix(external-storage): correct the storage reference check (#313) --- external_storage/handler.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/external_storage/handler.py b/external_storage/handler.py index 0e1ea5acb..1ba8035ab 100644 --- a/external_storage/handler.py +++ b/external_storage/handler.py @@ -22,17 +22,20 @@ from aiohttp import web from google.protobuf import json_format from temporalio.api.common.v1 import Payload, Payloads +from temporalio.api.sdk.v1 import ExternalStorageReference from temporalio.converter import ExternalStorage, PayloadCodec +_REFERENCE_MESSAGE_TYPE = ExternalStorageReference.DESCRIPTOR.full_name.encode() -def _is_storage_reference(payload: Payload) -> bool: - """A payload is an external-storage reference iff it carries external_payloads metadata. - When the SDK offloads a payload to external storage it replaces the - in-band bytes with a small protobuf "claim check" — the ``external_payloads`` - repeated field is the marker the SDK uses to recognize it on the way back. +def _is_storage_reference(payload: Payload) -> bool: + """A payload is an external-storage reference iff its metadata identifies + it as a serialized :class:`ExternalStorageReference` claim check. """ - return len(payload.external_payloads) > 0 + return ( + payload.metadata.get("encoding") == b"json/protobuf" + and payload.metadata.get("messageType") == _REFERENCE_MESSAGE_TYPE + ) def payload_routes( From f14d16aaa640dbe8d0e1c9c6d783cadccab1aef8 Mon Sep 17 00:00:00 2001 From: Alex Mazzeo Date: Fri, 5 Jun 2026 10:08:04 -0700 Subject: [PATCH 30/40] Update to Python 1.28.0. Add codeowners rules for nexus samples (#316) --- .github/CODEOWNERS | 7 +++++++ pyproject.toml | 11 ++++------- uv.lock | 34 ++++++++++++++++------------------ 3 files changed, 27 insertions(+), 25 deletions(-) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 0f4298b7d..ec610becb 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1 +1,8 @@ * @temporalio/sdk + +# The Nexus team owns any folder whose name starts or ends with "nexus", +# both at the repo root and under tests/ +/nexus*/ @temporalio/nexus +/*nexus/ @temporalio/nexus +/tests/nexus*/ @temporalio/nexus +/tests/*nexus/ @temporalio/nexus diff --git a/pyproject.toml b/pyproject.toml index 4c4151a5b..9be7bbcac 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] requires-python = ">=3.10" readme = "README.md" license = "MIT" -dependencies = ["temporalio>=1.27.2,<2", "protobuf>=5.29.6,<6"] +dependencies = ["temporalio>=1.28.0,<2", "protobuf>=5.29.6,<6"] [project.urls] Homepage = "https://github.com/temporalio/samples-python" @@ -41,13 +41,13 @@ gevent = ["gevent>=25.4.2 ; python_version >= '3.8'"] langsmith-tracing = [ "openai>=1.4.0", "langsmith>=0.7.0", - "temporalio[pydantic,langsmith]>=1.27.0", + "temporalio[pydantic,langsmith]>=1.28.0", ] langgraph = [ "langgraph>=1.1.3", "langchain>=0.3.0", "langchain-anthropic>=0.3.0", - "temporalio[langgraph,langsmith]>=1.27.0", + "temporalio[langgraph,langsmith]>=1.28.0", ] nexus = ["nexus-rpc>=1.1.0,<2"] open-telemetry = [ @@ -56,7 +56,7 @@ open-telemetry = [ ] openai-agents = [ "openai-agents[litellm] >= 0.14.1", - "temporalio[openai-agents,opentelemetry] >= 1.27.0", + "temporalio[openai-agents,opentelemetry] >= 1.28.0", "requests>=2.32.0,<3", ] pydantic-converter = ["pydantic>=2.10.6,<3"] @@ -71,9 +71,6 @@ cloud-export-to-parquet = [ [tool.uv] constraint-dependencies = [ - # langsmith 0.7.34 changed its aio_to_thread signature; temporalio.contrib.langsmith - # 1.27.2 still patches the older signature, causing workflow task retries to hang CI. - "langsmith<0.7.34", # yarl 1.24.0 was published without an sdist and only has cp310 wheels, so it cannot # install on the Python 3.14 CI jobs. 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{ name = "temporalio", specifier = ">=1.27.2,<2" }, + { name = "temporalio", specifier = ">=1.28.0,<2" }, ] [package.metadata.requires-dev] @@ -3858,12 +3856,12 @@ langgraph = [ { name = "langchain", specifier = ">=0.3.0" }, { name = "langchain-anthropic", specifier = ">=0.3.0" }, { name = "langgraph", specifier = ">=1.1.3" }, - { name = "temporalio", extras = ["langgraph", "langsmith"], specifier = ">=1.27.0" }, + { name = "temporalio", extras = ["langgraph", "langsmith"], specifier = ">=1.28.0" }, ] langsmith-tracing = [ { name = "langsmith", specifier = ">=0.7.0" }, { name = "openai", specifier = ">=1.4.0" }, - { name = "temporalio", extras = ["pydantic", "langsmith"], specifier = ">=1.27.0" }, + { name = "temporalio", extras = ["pydantic", "langsmith"], specifier = ">=1.28.0" }, ] nexus = [{ name = "nexus-rpc", specifier = ">=1.1.0,<2" }] open-telemetry = [ @@ -3873,7 +3871,7 @@ open-telemetry = [ openai-agents = [ { name = "openai-agents", extras = ["litellm"], specifier = ">=0.14.1" }, { name = "requests", specifier = ">=2.32.0,<3" }, - { name = "temporalio", extras = ["openai-agents", "opentelemetry"], specifier = ">=1.27.0" }, + { name = "temporalio", extras = ["openai-agents", "opentelemetry"], specifier = ">=1.28.0" }, ] pydantic-converter = [{ name = "pydantic", specifier = ">=2.10.6,<3" }] sentry = [{ name = "sentry-sdk", specifier = ">=2.13.0" }] From 000d67d9ac0247de88b94d2e5042a6aba6405bb4 Mon Sep 17 00:00:00 2001 From: Alex Mazzeo Date: Fri, 5 Jun 2026 10:32:04 -0700 Subject: [PATCH 31/40] update codeowners to include the README, pyproject.toml and uv.lock for the nexus team (#317) --- .github/CODEOWNERS | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index ec610becb..2a9f9799d 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,5 +1,10 @@ * @temporalio/sdk +# SDK & Nexus own the README, pyproject.toml, and uv.lock +/README.md @temporalio/sdk @temporalio/nexus +/pyproject.toml @temporalio/sdk @temporalio/nexus +/uv.lock @temporalio/sdk @temporalio/nexus + # The Nexus team owns any folder whose name starts or ends with "nexus", # both at the repo root and under tests/ /nexus*/ @temporalio/nexus From 5ceb3c80daf8d044be0e5693d6f07b1b0c8465e5 Mon Sep 17 00:00:00 2001 From: Brian Strauch Date: Fri, 5 Jun 2026 11:10:31 -0700 Subject: [PATCH 32/40] Add Strands plugin samples (#310) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add Strands plugin samples Demonstrates the Temporal Strands plugin: hello world, tools (in-workflow, custom activity, strands_tools), HITL, hooks, MCP, structured output, streaming, interrupt, and continue-as-new. Co-Authored-By: Claude Opus 4.7 (1M context) * Convert continue_as_new chat to workflow update `user_says` → `turn`, now a `@workflow.update` returning the assistant's reply directly so callers no longer need a separate `messages` query to discover what the agent said. The run loop drains in-flight handlers via `workflow.all_handlers_finished` before continue-as-new. Co-Authored-By: Claude Opus 4.7 (1M context) * Fix interrupt sample: make delete_thing idempotent The activity was toggling its `_APPROVED` state (add on raise, discard on success), so after the human approved once, a follow-up tool call from the model would interrupt again with no further approval coming — hanging the test. Drop the discard so once approved, the name stays approved. Co-Authored-By: Claude Opus 4.7 (1M context) * Fix ruff import order in strands mock model ruff's `I001` rule groups `temporalio` with other third-party imports rather than its own block, which broke `poe lint` on CI. Co-Authored-By: Claude Opus 4.7 (1M context) * Rename Strands sample extra * Use Windows-compatible Strands tool sample * Bump sdk-python pin to pick up Strands MCP client fix Picks up c84320c6 on the strands branch, which rewrites the MCP populate_cache and call_tool activity to use ClientSession directly instead of MCPClient.start/stop. The old code created a background event loop on one thread and closed it from another, which deadlocked mcp_test on Python 3.10 once trio_asyncio's policy was installed at import time by tests/trio_async/workflow_test.py. Co-Authored-By: Claude Opus 4.7 (1M context) * Rename Strands interrupt sample to activity_interrupt Disambiguates from the hook-based human_in_the_loop sample by naming this one after where the interrupt is raised (a Temporal activity). Also fixes the parent README's broken `tool_interrupt` link. Co-Authored-By: Claude Opus 4.7 (1M context) * Fix uv sync group name in Strands READMEs The dependency group is named strands-agents, not strands. Co-Authored-By: Claude Opus 4.8 (1M context) * Test the continue-as-new path in the Strands chat sample Replace test_continue_as_new_chat, which never tripped the continue-as-new branch, with test_continue_as_new_carries_history. A low limit.historyCount.suggestContinueAsNew threshold makes the server suggest continue-as-new after one turn, so the test verifies the original run ends in CONTINUED_AS_NEW and the fresh run resumes with the carried-over messages before taking another turn and ending the chat. Co-Authored-By: Claude Opus 4.8 (1M context) * add snipsync lines * Remove unnecessary langsmith constraint temporalio 1.28.0's langsmith extra already pins langsmith>=0.7.34,<0.9, so the manual constraint-dependencies entry is redundant. Co-Authored-By: Claude Opus 4.8 (1M context) * Fix formatting in strands_plugin workflows ruff format requires two blank lines before the trailing @@@SNIPEND markers added in 12f9d5d. Co-Authored-By: Claude Opus 4.8 (1M context) --------- Co-authored-by: Claude Opus 4.7 (1M context) Co-authored-by: Lenny Chen <55669665+lennessyy@users.noreply.github.com> Co-authored-by: Lenny Chen --- README.md | 1 + pyproject.toml | 8 + strands_plugin/README.md | 84 +++++ strands_plugin/__init__.py | 0 strands_plugin/activity_interrupt/README.md | 34 ++ strands_plugin/activity_interrupt/__init__.py | 0 .../activity_interrupt/run_worker.py | 37 ++ .../activity_interrupt/run_workflow.py | 40 +++ strands_plugin/activity_interrupt/workflow.py | 83 +++++ strands_plugin/continue_as_new/README.md | 30 ++ strands_plugin/continue_as_new/__init__.py | 0 strands_plugin/continue_as_new/run_worker.py | 30 ++ .../continue_as_new/run_workflow.py | 38 ++ strands_plugin/continue_as_new/workflow.py | 64 ++++ strands_plugin/hello_world/README.md | 29 ++ strands_plugin/hello_world/__init__.py | 0 strands_plugin/hello_world/run_worker.py | 32 ++ strands_plugin/hello_world/run_workflow.py | 27 ++ strands_plugin/hello_world/workflow.py | 21 ++ strands_plugin/hooks/README.md | 34 ++ strands_plugin/hooks/__init__.py | 0 strands_plugin/hooks/run_worker.py | 31 ++ strands_plugin/hooks/run_workflow.py | 25 ++ strands_plugin/hooks/workflow.py | 73 ++++ strands_plugin/human_in_the_loop/README.md | 29 ++ strands_plugin/human_in_the_loop/__init__.py | 0 .../human_in_the_loop/run_worker.py | 30 ++ .../human_in_the_loop/run_workflow.py | 35 ++ strands_plugin/human_in_the_loop/workflow.py | 81 +++++ strands_plugin/mcp/README.md | 32 ++ strands_plugin/mcp/__init__.py | 0 strands_plugin/mcp/echo_mcp_server.py | 19 + strands_plugin/mcp/run_worker.py | 54 +++ strands_plugin/mcp/run_workflow.py | 25 ++ strands_plugin/mcp/workflow.py | 34 ++ strands_plugin/streaming/README.md | 29 ++ strands_plugin/streaming/__init__.py | 0 strands_plugin/streaming/run_worker.py | 30 ++ strands_plugin/streaming/run_workflow.py | 51 +++ strands_plugin/streaming/workflow.py | 32 ++ strands_plugin/structured_output/README.md | 27 ++ strands_plugin/structured_output/__init__.py | 0 .../structured_output/run_worker.py | 30 ++ .../structured_output/run_workflow.py | 30 ++ strands_plugin/structured_output/workflow.py | 38 ++ strands_plugin/tools/README.md | 35 ++ strands_plugin/tools/__init__.py | 0 strands_plugin/tools/run_worker.py | 37 ++ strands_plugin/tools/run_workflow.py | 30 ++ strands_plugin/tools/workflow.py | 101 ++++++ tests/strands_plugin/__init__.py | 0 tests/strands_plugin/_mock_model.py | 69 ++++ .../strands_plugin/activity_interrupt_test.py | 50 +++ tests/strands_plugin/continue_as_new_test.py | 83 +++++ tests/strands_plugin/hello_world_test.py | 35 ++ tests/strands_plugin/hooks_test.py | 42 +++ .../strands_plugin/human_in_the_loop_test.py | 45 +++ tests/strands_plugin/mcp_test.py | 42 +++ tests/strands_plugin/streaming_test.py | 59 ++++ .../strands_plugin/structured_output_test.py | 54 +++ tests/strands_plugin/tools_test.py | 51 +++ uv.lock | 331 +++++++++++++++++- 62 files changed, 2388 insertions(+), 3 deletions(-) create mode 100644 strands_plugin/README.md create mode 100644 strands_plugin/__init__.py create mode 100644 strands_plugin/activity_interrupt/README.md create mode 100644 strands_plugin/activity_interrupt/__init__.py create mode 100644 strands_plugin/activity_interrupt/run_worker.py create mode 100644 strands_plugin/activity_interrupt/run_workflow.py create mode 100644 strands_plugin/activity_interrupt/workflow.py create mode 100644 strands_plugin/continue_as_new/README.md create mode 100644 strands_plugin/continue_as_new/__init__.py create mode 100644 strands_plugin/continue_as_new/run_worker.py create mode 100644 strands_plugin/continue_as_new/run_workflow.py create mode 100644 strands_plugin/continue_as_new/workflow.py create mode 100644 strands_plugin/hello_world/README.md create mode 100644 strands_plugin/hello_world/__init__.py create mode 100644 strands_plugin/hello_world/run_worker.py create mode 100644 strands_plugin/hello_world/run_workflow.py create mode 100644 strands_plugin/hello_world/workflow.py create mode 100644 strands_plugin/hooks/README.md create mode 100644 strands_plugin/hooks/__init__.py create mode 100644 strands_plugin/hooks/run_worker.py create mode 100644 strands_plugin/hooks/run_workflow.py create mode 100644 strands_plugin/hooks/workflow.py create mode 100644 strands_plugin/human_in_the_loop/README.md create mode 100644 strands_plugin/human_in_the_loop/__init__.py create mode 100644 strands_plugin/human_in_the_loop/run_worker.py create mode 100644 strands_plugin/human_in_the_loop/run_workflow.py create mode 100644 strands_plugin/human_in_the_loop/workflow.py create mode 100644 strands_plugin/mcp/README.md create mode 100644 strands_plugin/mcp/__init__.py create mode 100644 strands_plugin/mcp/echo_mcp_server.py create mode 100644 strands_plugin/mcp/run_worker.py create mode 100644 strands_plugin/mcp/run_workflow.py create mode 100644 strands_plugin/mcp/workflow.py create mode 100644 strands_plugin/streaming/README.md create mode 100644 strands_plugin/streaming/__init__.py create mode 100644 strands_plugin/streaming/run_worker.py create mode 100644 strands_plugin/streaming/run_workflow.py create mode 100644 strands_plugin/streaming/workflow.py create mode 100644 strands_plugin/structured_output/README.md create mode 100644 strands_plugin/structured_output/__init__.py create mode 100644 strands_plugin/structured_output/run_worker.py create mode 100644 strands_plugin/structured_output/run_workflow.py create mode 100644 strands_plugin/structured_output/workflow.py create mode 100644 strands_plugin/tools/README.md create mode 100644 strands_plugin/tools/__init__.py create mode 100644 strands_plugin/tools/run_worker.py create mode 100644 strands_plugin/tools/run_workflow.py create mode 100644 strands_plugin/tools/workflow.py create mode 100644 tests/strands_plugin/__init__.py create mode 100644 tests/strands_plugin/_mock_model.py create mode 100644 tests/strands_plugin/activity_interrupt_test.py create mode 100644 tests/strands_plugin/continue_as_new_test.py create mode 100644 tests/strands_plugin/hello_world_test.py create mode 100644 tests/strands_plugin/hooks_test.py create mode 100644 tests/strands_plugin/human_in_the_loop_test.py create mode 100644 tests/strands_plugin/mcp_test.py create mode 100644 tests/strands_plugin/streaming_test.py create mode 100644 tests/strands_plugin/structured_output_test.py create mode 100644 tests/strands_plugin/tools_test.py diff --git a/README.md b/README.md index 801fd2ed3..feb43d488 100644 --- a/README.md +++ b/README.md @@ -89,6 +89,7 @@ Some examples require extra dependencies. See each sample's directory for specif * [pydantic_converter](pydantic_converter) - Data converter for using Pydantic models. * [schedules](schedules) - Demonstrates a Workflow Execution that occurs according to a schedule. * [sentry](sentry) - Report errors to Sentry. +* [strands_plugin](strands_plugin) - Run Strands Agents as durable Temporal workflows (model calls, tools, MCP, HITL). * [trio_async](trio_async) - Use asyncio Temporal in Trio-based environments. * [updatable_timer](updatable_timer) - A timer that can be updated while sleeping. * [worker_specific_task_queues](worker_specific_task_queues) - Use unique task queues to ensure activities run on specific workers. diff --git a/pyproject.toml b/pyproject.toml index 9be7bbcac..a148f865f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -61,6 +61,13 @@ openai-agents = [ ] pydantic-converter = ["pydantic>=2.10.6,<3"] sentry = ["sentry-sdk>=2.13.0"] +strands-agents = [ + "strands-agents>=1.39.0", + "strands-agents-tools>=0.5.2", + "mcp>=1.0.0", + "boto3>=1.34.92,<2", + "temporalio[strands-agents,pydantic]>=1.28.0", +] trio-async = ["trio>=0.28.0,<0.29", "trio-asyncio>=0.15.0,<0.16"] cloud-export-to-parquet = [ "pandas>=2.3.3,<3 ; python_version >= '3.10' and python_version < '4.0'", @@ -115,6 +122,7 @@ packages = [ "schedules", "sentry", "sleep_for_days", + "strands_plugin", "tests", "trio_async", "updatable_timer", diff --git a/strands_plugin/README.md b/strands_plugin/README.md new file mode 100644 index 000000000..d4eaf558a --- /dev/null +++ b/strands_plugin/README.md @@ -0,0 +1,84 @@ +# Strands Agents Samples + +These samples demonstrate the [Temporal Strands plugin](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/strands), which runs [Strands Agents](https://strandsagents.com/) inside Temporal Workflows. Model invocations, tool calls, and MCP tool calls all execute as Temporal Activities, so you get durable execution, Temporal-managed retries, and timeouts. + +## Samples + +| Sample | Description | +|--------|-------------| +| [hello_world](hello_world) | Minimal `TemporalAgent` invocation. Start here. | +| [tools](tools) | Three tool patterns side by side: in-workflow `@tool`, custom `@activity.defn` wrapped via `activity_as_tool`, and a `strands_tools` tool wrapped as a Temporal activity. | +| [human_in_the_loop](human_in_the_loop) | Pause a tool call on `BeforeToolCallEvent.interrupt()`, resume via Temporal signal. The canonical Strands HITL pattern. | +| [activity_interrupt](activity_interrupt) | Raise `InterruptException` from a Temporal activity to surface a HITL prompt across the activity boundary. Plugin-specific feature. | +| [hooks](hooks) | `HookProvider` with both an in-workflow callback and an `activity_as_hook` callback for I/O. | +| [mcp](mcp) | Connect to an MCP server (`FastMCP` echo) via `TemporalMCPClient`. | +| [structured_output](structured_output) | Pydantic-typed agent output via `structured_output_model`. | +| [streaming](streaming) | Forward model chunks to an external subscriber via `streaming_topic` + `WorkflowStream`. | +| [continue_as_new](continue_as_new) | Chat-style workflow that hands off `agent.messages` when history grows large. | + +## Prerequisites + +1. Install dependencies: + + ```bash + uv sync --group strands-agents + ``` + + > The `strands` extra of `temporalio` is shipping in an upcoming release. Until then, install the SDK from the strands branch: + > + > ```bash + > uv pip install -e ../sdk-python --extra strands-agents --extra pydantic + > ``` + +2. Configure AWS credentials. The samples use the plugin's default `BedrockModel()`, which picks up the standard AWS SDK credential chain. Make sure the credentials grant access to a Bedrock model in your selected region (e.g., `us-west-2`). + + ```bash + export AWS_REGION=us-west-2 + # plus AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY or an SSO profile + ``` + + You can pick a specific model by passing it to `BedrockModel(model_id="...")` in each sample's worker. + +3. Start a [Temporal dev server](https://docs.temporal.io/cli#start-dev-server): + + ```bash + temporal server start-dev + ``` + +## Running a Sample + +Each sample has two scripts. Start the Worker first, then the Workflow starter in a separate terminal: + +```bash +# Terminal 1: start the Worker +uv run strands_plugin//run_worker.py + +# Terminal 2: start the Workflow +uv run strands_plugin//run_workflow.py +``` + +For example, to run the tools sample: + +```bash +# Terminal 1 +uv run strands_plugin/tools/run_worker.py + +# Terminal 2 +uv run strands_plugin/tools/run_workflow.py +``` + +## Key Features Demonstrated + +- **Durable model invocation** — every model call runs in an `invoke_model` activity with configurable timeouts and retries. +- **Three ways to define tools** — pure Strands `@tool`, custom Temporal activities, and ecosystem `strands_tools` wrapped as activities. +- **Human-in-the-loop** — both hook-based (`BeforeToolCallEvent.interrupt()`) and tool-body (`raise InterruptException`) styles. +- **Hook system** — deterministic in-workflow callbacks plus I/O callbacks dispatched via `activity_as_hook`. +- **MCP integration** — connect to MCP servers at worker startup; tool calls dispatched through per-server activities. +- **Structured output** — Pydantic-typed agent results via the plugin's `pydantic_data_converter`. +- **Streaming** — forward model chunks live to external subscribers. +- **Long-lived chats** — hand off `agent.messages` via `continue-as-new` to stay under Temporal's history limit. + +## Related + +- [Temporal Strands plugin docs](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/strands) +- [Strands Agents](https://strandsagents.com/) diff --git a/strands_plugin/__init__.py b/strands_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/activity_interrupt/README.md b/strands_plugin/activity_interrupt/README.md new file mode 100644 index 000000000..bda67700a --- /dev/null +++ b/strands_plugin/activity_interrupt/README.md @@ -0,0 +1,34 @@ +# Activity Interrupt + +A `@activity.defn`-wrapped tool raises `InterruptException(Interrupt(...))` directly. The plugin's failure converter preserves the `Interrupt` payload across the activity boundary, so the agent stops with `stop_reason == "interrupt"` just like in the hook-based [human_in_the_loop](../human_in_the_loop) sample. + +When to reach for this style instead of a hook: + +- The decision to pause depends on data that's only visible inside the activity (a permissions service, a row in a database, etc.). +- You don't want to load that data into workflow context just to make the call. + +## What This Sample Demonstrates + +- Raising `InterruptException` from a Temporal activity tool +- The plugin's failure converter carrying `Interrupt` across the activity boundary +- Why `StrandsPlugin` must be attached to the **client** (not just the worker) + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/activity_interrupt/run_worker.py + +# Terminal 2 +uv run strands_plugin/activity_interrupt/run_workflow.py +``` + +The starter requests deletion of a "protected" resource. The `delete_thing` activity raises an interrupt for protected names; the starter signals `"approve"` to release it. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `delete_thing` activity that raises `InterruptException`, plus the workflow that handles resumption | +| `run_worker.py` | `StrandsPlugin` on the client + worker, registers the activity | +| `run_workflow.py` | Starts the workflow and signals approval | diff --git a/strands_plugin/activity_interrupt/__init__.py b/strands_plugin/activity_interrupt/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/activity_interrupt/run_worker.py b/strands_plugin/activity_interrupt/run_worker.py new file mode 100644 index 000000000..bfbf96003 --- /dev/null +++ b/strands_plugin/activity_interrupt/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the activity interrupt sample.""" + +# @@@SNIPSTART python-strands-activity-interrupt-worker +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.activity_interrupt.workflow import ( + ActivityInterruptWorkflow, + delete_thing, +) + + +async def main() -> None: + plugin = StrandsPlugin() + # The plugin MUST be on the client so its failure converter is installed. + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-activity-interrupt", + workflows=[ActivityInterruptWorkflow], + activities=[delete_thing], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/strands_plugin/activity_interrupt/run_workflow.py b/strands_plugin/activity_interrupt/run_workflow.py new file mode 100644 index 000000000..7125c8237 --- /dev/null +++ b/strands_plugin/activity_interrupt/run_workflow.py @@ -0,0 +1,40 @@ +"""Start the activity interrupt workflow.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin + +from strands_plugin.activity_interrupt.workflow import ActivityInterruptWorkflow + + +async def main() -> None: + # The starter also goes through the plugin's failure converter so the + # Interrupt payload deserializes cleanly when the workflow result is read. + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[StrandsPlugin()], + ) + + handle = await client.start_workflow( + ActivityInterruptWorkflow.run, + "Please delete the 'system' user.", + id="strands-activity-interrupt", + task_queue="strands-activity-interrupt", + ) + + reason = None + while reason is None: + await asyncio.sleep(0.5) + reason = await handle.query(ActivityInterruptWorkflow.pending_approval) + print(f"Approval requested: {reason}") + + await handle.signal(ActivityInterruptWorkflow.approve, "approve") + + result = await handle.result() + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/activity_interrupt/workflow.py b/strands_plugin/activity_interrupt/workflow.py new file mode 100644 index 000000000..f017fc3ac --- /dev/null +++ b/strands_plugin/activity_interrupt/workflow.py @@ -0,0 +1,83 @@ +"""Activity interrupt: ``InterruptException`` raised from a Temporal activity. + +The plugin's failure converter preserves the ``Interrupt`` payload across the +activity boundary, so a Temporal activity can pause the agent for human input +the same way a hook can. + +For this to work, ``StrandsPlugin`` must be attached to the **client** (not +just the worker) so the failure converter is installed on the data converter. +The worker in this sample does exactly that. +""" + +from datetime import timedelta +from typing import Optional + +from strands.interrupt import Interrupt, InterruptException +from strands.types.interrupt import InterruptResponseContent +from temporalio import activity, workflow +from temporalio.contrib.strands import TemporalAgent +from temporalio.contrib.strands.workflow import activity_as_tool + +# Tracks names that have been approved out-of-band. In a real system, this +# would be a row in a policy database; the human reviewer flips a flag during +# the pause, and the activity's next attempt reads the new value and proceeds. +_APPROVED: set[str] = set() + + +# @@@SNIPSTART python-strands-activity-interrupt-activity +@activity.defn +async def delete_thing(name: str) -> str: + if name not in _APPROVED: + _APPROVED.add(name) + raise InterruptException( + Interrupt( + id=f"delete:{name}", + name="approval", + reason=f"approve delete of protected resource '{name}'?", + ) + ) + return f"deleted {name}" + + +# @@@SNIPEND + + +@workflow.defn +class ActivityInterruptWorkflow: + def __init__(self) -> None: + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + tools=[ + activity_as_tool( + delete_thing, + start_to_close_timeout=timedelta(seconds=30), + ), + ], + ) + self._approval: Optional[str] = None + self._pending_reason: Optional[str] = None + + @workflow.signal + def approve(self, response: str) -> None: + self._approval = response + + @workflow.query + def pending_approval(self) -> Optional[str]: + return self._pending_reason + + @workflow.run + async def run(self, prompt: str) -> str: + result = await self.agent.invoke_async(prompt) + while result.stop_reason == "interrupt": + interrupts = list(result.interrupts or []) + self._pending_reason = interrupts[0].reason if interrupts else None + await workflow.wait_condition(lambda: self._approval is not None) + response = self._approval + self._approval = None + self._pending_reason = None + responses: list[InterruptResponseContent] = [ + {"interruptResponse": {"interruptId": i.id, "response": response}} + for i in interrupts + ] + result = await self.agent.invoke_async(responses) + return str(result) diff --git a/strands_plugin/continue_as_new/README.md b/strands_plugin/continue_as_new/README.md new file mode 100644 index 000000000..84e663a56 --- /dev/null +++ b/strands_plugin/continue_as_new/README.md @@ -0,0 +1,30 @@ +# Continue-as-new + +A chat-style workflow accumulates history with every turn and will eventually hit Temporal's per-workflow history limit. `workflow.info().is_continue_as_new_suggested()` flips `True` once the server decides history has grown large enough; this sample checks it after each turn and hands off to a fresh run with `agent.messages` as input. + +## What This Sample Demonstrates + +- Driving a multi-turn chat with **updates**, so each caller gets the assistant's reply back from the same call +- Seeding a new `TemporalAgent` with prior `agent.messages` +- Using `workflow.info().is_continue_as_new_suggested()` + `workflow.continue_as_new(...)` to keep the workflow alive indefinitely +- Draining in-flight update handlers with `workflow.all_handlers_finished` before continue-as-new + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/continue_as_new/run_worker.py + +# Terminal 2 +uv run strands_plugin/continue_as_new/run_workflow.py +``` + +The starter calls the `turn` update for each user message and prints the assistant's reply, then signals `end_chat`. In a real chatbot, a UI would drive the updates and the workflow would run indefinitely, continuing-as-new whenever history gets large. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `ChatInput`, `ChatWorkflow` with `turn` update, `end_chat` signal, and `messages` query | +| `run_worker.py` | Registers `StrandsPlugin`, starts the worker | +| `run_workflow.py` | Starts the chat, sends a few turns, ends it | diff --git a/strands_plugin/continue_as_new/__init__.py b/strands_plugin/continue_as_new/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/continue_as_new/run_worker.py b/strands_plugin/continue_as_new/run_worker.py new file mode 100644 index 000000000..6cd092e4d --- /dev/null +++ b/strands_plugin/continue_as_new/run_worker.py @@ -0,0 +1,30 @@ +"""Worker for the chat continue-as-new sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.continue_as_new.workflow import ChatWorkflow + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-chat", + workflows=[ChatWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/continue_as_new/run_workflow.py b/strands_plugin/continue_as_new/run_workflow.py new file mode 100644 index 000000000..2a98f4212 --- /dev/null +++ b/strands_plugin/continue_as_new/run_workflow.py @@ -0,0 +1,38 @@ +"""Start the chat workflow, send a few turns, then end it.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin + +from strands_plugin.continue_as_new.workflow import ChatInput, ChatWorkflow + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[StrandsPlugin()], + ) + + handle = await client.start_workflow( + ChatWorkflow.run, + ChatInput(), + id="strands-chat", + task_queue="strands-chat", + ) + + for prompt in [ + "Hi! What is durable execution?", + "Give me a one-sentence summary.", + ]: + reply = await handle.execute_update(ChatWorkflow.turn, prompt) + print(f"user: {prompt}") + print(f"assistant: {reply}\n") + + await handle.signal(ChatWorkflow.end_chat) + await handle.result() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/continue_as_new/workflow.py b/strands_plugin/continue_as_new/workflow.py new file mode 100644 index 000000000..223a5c429 --- /dev/null +++ b/strands_plugin/continue_as_new/workflow.py @@ -0,0 +1,64 @@ +"""Chat-style workflow that continues-as-new before history grows too large. + +Each user turn arrives as a Temporal **update**, so the caller gets the +assistant's reply back from the same call. Once Temporal suggests +continue-as-new, the workflow drains any in-flight update handlers and hands +``agent.messages`` off to a fresh run. +""" + +# @@@SNIPSTART python-strands-continue-as-new-workflow +import asyncio +from dataclasses import dataclass, field +from datetime import timedelta + +from strands.types.content import Messages +from temporalio import workflow +from temporalio.contrib.strands import TemporalAgent + + +@dataclass +class ChatInput: + messages: Messages = field(default_factory=list) + + +@workflow.defn +class ChatWorkflow: + def __init__(self) -> None: + self._done = False + self._lock = asyncio.Lock() + self._agent: TemporalAgent | None = None + + @workflow.update + async def turn(self, prompt: str) -> str: + await workflow.wait_condition(lambda: self._agent is not None) + async with self._lock: + assert self._agent is not None + result = await self._agent.invoke_async(prompt) + return str(result).strip() + + @workflow.signal + def end_chat(self) -> None: + self._done = True + + @workflow.query + def messages(self) -> Messages: + return list(self._agent.messages) if self._agent else [] + + @workflow.run + async def run(self, input: ChatInput) -> None: + self._agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + messages=list(input.messages), + ) + + await workflow.wait_condition( + lambda: self._done or workflow.info().is_continue_as_new_suggested() + ) + + await workflow.wait_condition(workflow.all_handlers_finished) + + if not self._done: + workflow.continue_as_new(ChatInput(messages=self._agent.messages)) + + +# @@@SNIPEND diff --git a/strands_plugin/hello_world/README.md b/strands_plugin/hello_world/README.md new file mode 100644 index 000000000..186b3580e --- /dev/null +++ b/strands_plugin/hello_world/README.md @@ -0,0 +1,29 @@ +# Hello World + +The simplest Strands + Temporal sample: one `TemporalAgent` invoked once. Every model call runs as an `invoke_model` Temporal activity, so it gets durable retries, timeouts, and crash recovery for free. + +## What This Sample Demonstrates + +- Wiring `StrandsPlugin` onto the client and worker +- Constructing a `TemporalAgent` with no explicit model (defaults to `BedrockModel()`) +- Invoking the agent from a `@workflow.defn` + +## Running the Sample + +Prerequisites: `uv sync --group strands-agents`, AWS credentials with Bedrock access, and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run strands_plugin/hello_world/run_worker.py + +# Terminal 2 +uv run strands_plugin/hello_world/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `HelloWorldWorkflow` with a single `TemporalAgent` | +| `run_worker.py` | Registers `StrandsPlugin`, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/strands_plugin/hello_world/__init__.py b/strands_plugin/hello_world/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/hello_world/run_worker.py b/strands_plugin/hello_world/run_worker.py new file mode 100644 index 000000000..17b9ed9da --- /dev/null +++ b/strands_plugin/hello_world/run_worker.py @@ -0,0 +1,32 @@ +"""Worker for the hello world sample.""" + +# @@@SNIPSTART python-strands-hello-world-worker +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.hello_world.workflow import HelloWorldWorkflow + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-hello-world", + workflows=[HelloWorldWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/strands_plugin/hello_world/run_workflow.py b/strands_plugin/hello_world/run_workflow.py new file mode 100644 index 000000000..ae26a0ea6 --- /dev/null +++ b/strands_plugin/hello_world/run_workflow.py @@ -0,0 +1,27 @@ +"""Start the hello world workflow.""" + +# @@@SNIPSTART python-strands-hello-world-run-workflow +import asyncio +import os + +from temporalio.client import Client + +from strands_plugin.hello_world.workflow import HelloWorldWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + HelloWorldWorkflow.run, + "Write a haiku about durable execution.", + id="strands-hello-world", + task_queue="strands-hello-world", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/strands_plugin/hello_world/workflow.py b/strands_plugin/hello_world/workflow.py new file mode 100644 index 000000000..4ee98d9d0 --- /dev/null +++ b/strands_plugin/hello_world/workflow.py @@ -0,0 +1,21 @@ +"""Minimal Temporal + Strands workflow: one agent, one prompt.""" + +# @@@SNIPSTART python-strands-hello-world-workflow +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.strands import TemporalAgent + + +@workflow.defn +class HelloWorldWorkflow: + def __init__(self) -> None: + self.agent = TemporalAgent(start_to_close_timeout=timedelta(seconds=60)) + + @workflow.run + async def run(self, prompt: str) -> str: + result = await self.agent.invoke_async(prompt) + return str(result) + + +# @@@SNIPEND diff --git a/strands_plugin/hooks/README.md b/strands_plugin/hooks/README.md new file mode 100644 index 000000000..dcdf44897 --- /dev/null +++ b/strands_plugin/hooks/README.md @@ -0,0 +1,34 @@ +# Hooks + +Strands' [hook system](https://strandsagents.com/) lets you subscribe callbacks to lifecycle events (`BeforeToolCallEvent`, `AfterToolCallEvent`, `BeforeModelCallEvent`, etc.). With the Temporal plugin, hook callbacks run in workflow context — so they must be deterministic — but you can dispatch I/O via `activity_as_hook`. + +This sample wires two callbacks to `AfterToolCallEvent`: + +1. An **in-workflow** callback that appends to per-workflow state. Pure data, deterministic across replay. +2. An **activity-backed** callback (`activity_as_hook(persist_tool_call, ...)`) that calls a Temporal activity for the actual audit write. The `activity_input=` selector pulls a serializable value out of the event. + +## What This Sample Demonstrates + +- Subscribing multiple callbacks to one hook event +- Mixing deterministic in-workflow callbacks with off-workflow activity callbacks +- The `activity_input=` selector for `activity_as_hook` + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/hooks/run_worker.py + +# Terminal 2 +uv run strands_plugin/hooks/run_workflow.py +``` + +The Temporal UI will show one `invoke_model` activity per agent turn plus a `persist_tool_call` activity per tool call. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `echo` tool, `AuditHook` (in-workflow + activity-backed), `HooksWorkflow` | +| `run_worker.py` | Registers `StrandsPlugin` + `persist_tool_call`, starts worker | +| `run_workflow.py` | Executes the workflow and prints the list of fired events | diff --git a/strands_plugin/hooks/__init__.py b/strands_plugin/hooks/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/hooks/run_worker.py b/strands_plugin/hooks/run_worker.py new file mode 100644 index 000000000..e03a1b9d1 --- /dev/null +++ b/strands_plugin/hooks/run_worker.py @@ -0,0 +1,31 @@ +"""Worker for the hooks sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.hooks.workflow import HooksWorkflow, persist_tool_call + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-hooks", + workflows=[HooksWorkflow], + activities=[persist_tool_call], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/hooks/run_workflow.py b/strands_plugin/hooks/run_workflow.py new file mode 100644 index 000000000..0959cf3ef --- /dev/null +++ b/strands_plugin/hooks/run_workflow.py @@ -0,0 +1,25 @@ +"""Start the hooks workflow.""" + +import asyncio +import os + +from temporalio.client import Client + +from strands_plugin.hooks.workflow import HooksWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + fired = await client.execute_workflow( + HooksWorkflow.run, + "Echo 'hello' once.", + id="strands-hooks", + task_queue="strands-hooks", + ) + + print(f"Tools that fired AfterToolCallEvent: {fired}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/hooks/workflow.py b/strands_plugin/hooks/workflow.py new file mode 100644 index 000000000..2535cc249 --- /dev/null +++ b/strands_plugin/hooks/workflow.py @@ -0,0 +1,73 @@ +"""Two-hook audit example. + +``AuditHook`` subscribes two callbacks to ``AfterToolCallEvent``: + +* An in-workflow callback that mutates per-workflow state. It runs in workflow + context, so it must be deterministic — pure data manipulation only. +* An ``activity_as_hook`` callback that dispatches to a Temporal activity. Use + this for anything with I/O: writing to an audit log, calling out to a + metrics system, alerting, etc. +""" + +from datetime import timedelta + +from strands import tool +from strands.hooks import HookProvider, HookRegistry +from strands.hooks.events import AfterToolCallEvent +from temporalio import activity, workflow +from temporalio.contrib.strands import TemporalAgent +from temporalio.contrib.strands.workflow import activity_as_hook + + +# @@@SNIPSTART python-strands-hooks-activity +@activity.defn +async def persist_tool_call(tool_name: str) -> None: + # In production, write to a database / S3 / your audit pipeline. + activity.logger.info(f"audit: tool {tool_name} completed") + + +# @@@SNIPEND + + +@tool +def echo(text: str) -> str: + return text + + +# @@@SNIPSTART python-strands-hooks-provider +class AuditHook(HookProvider): + def __init__(self) -> None: + self.fired: list[str] = [] + + def register_hooks(self, registry: HookRegistry, **kwargs: object) -> None: + registry.add_callback(AfterToolCallEvent, self._record) + registry.add_callback( + AfterToolCallEvent, + activity_as_hook( + persist_tool_call, + activity_input=lambda event: event.tool_use["name"], + start_to_close_timeout=timedelta(seconds=15), + ), + ) + + def _record(self, event: AfterToolCallEvent) -> None: + self.fired.append(event.tool_use["name"]) + + +# @@@SNIPEND + + +@workflow.defn +class HooksWorkflow: + def __init__(self) -> None: + self.hook = AuditHook() + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + tools=[echo], + hooks=[self.hook], + ) + + @workflow.run + async def run(self, prompt: str) -> list[str]: + await self.agent.invoke_async(prompt) + return self.hook.fired diff --git a/strands_plugin/human_in_the_loop/README.md b/strands_plugin/human_in_the_loop/README.md new file mode 100644 index 000000000..84d7ca76e --- /dev/null +++ b/strands_plugin/human_in_the_loop/README.md @@ -0,0 +1,29 @@ +# Human-in-the-loop (Hook-based) + +The canonical Strands HITL pattern. A `BeforeToolCallEvent` hook gates a sensitive tool behind human approval by calling `event.interrupt(...)`. The agent stops with `stop_reason == "interrupt"`; the workflow waits on a Temporal signal for the response, then resumes with `InterruptResponseContent`. + +## What This Sample Demonstrates + +- Using `HookProvider` + `BeforeToolCallEvent` to interrupt before a tool runs +- Pairing Strands' interrupt machinery with Temporal signals + queries +- Resuming the agent with `[{"interruptResponse": ...}]` content + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/human_in_the_loop/run_worker.py + +# Terminal 2 +uv run strands_plugin/human_in_the_loop/run_workflow.py +``` + +The starter script queries the workflow until an approval is pending, prints the reason, then signals `"approve"`. To exercise the denial path, change the signal to anything other than `"approve"`. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `delete_file` tool, `ApprovalHook`, `HumanInTheLoopWorkflow` | +| `run_worker.py` | Registers `StrandsPlugin`, starts the worker | +| `run_workflow.py` | Starts the workflow, polls for the approval, sends the signal | diff --git a/strands_plugin/human_in_the_loop/__init__.py b/strands_plugin/human_in_the_loop/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/human_in_the_loop/run_worker.py b/strands_plugin/human_in_the_loop/run_worker.py new file mode 100644 index 000000000..13f4ee3a5 --- /dev/null +++ b/strands_plugin/human_in_the_loop/run_worker.py @@ -0,0 +1,30 @@ +"""Worker for the hook-based HITL sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.human_in_the_loop.workflow import HumanInTheLoopWorkflow + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-human-in-the-loop", + workflows=[HumanInTheLoopWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/human_in_the_loop/run_workflow.py b/strands_plugin/human_in_the_loop/run_workflow.py new file mode 100644 index 000000000..b106d25c2 --- /dev/null +++ b/strands_plugin/human_in_the_loop/run_workflow.py @@ -0,0 +1,35 @@ +"""Start the hook-based HITL workflow and send an approval signal.""" + +import asyncio +import os + +from temporalio.client import Client + +from strands_plugin.human_in_the_loop.workflow import HumanInTheLoopWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + handle = await client.start_workflow( + HumanInTheLoopWorkflow.run, + "Please delete /tmp/sensitive.txt", + id="strands-human-in-the-loop", + task_queue="strands-human-in-the-loop", + ) + + # Poll until the agent reaches the approval interrupt. + reason = None + while reason is None: + await asyncio.sleep(0.5) + reason = await handle.query(HumanInTheLoopWorkflow.pending_approval) + print(f"Approval requested: {reason}") + + await handle.signal(HumanInTheLoopWorkflow.approve, "approve") + + result = await handle.result() + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/human_in_the_loop/workflow.py b/strands_plugin/human_in_the_loop/workflow.py new file mode 100644 index 000000000..8c21b0869 --- /dev/null +++ b/strands_plugin/human_in_the_loop/workflow.py @@ -0,0 +1,81 @@ +"""Hook-based human-in-the-loop: pause on ``BeforeToolCallEvent.interrupt()``. + +A hook gates the ``delete_file`` tool behind human approval. The agent stops +with ``stop_reason == "interrupt"``; the workflow waits for a signal carrying +the approval response, then resumes the agent with the response. +""" + +from datetime import timedelta +from typing import Optional + +from strands import tool +from strands.hooks import HookProvider, HookRegistry +from strands.hooks.events import BeforeToolCallEvent +from strands.types.interrupt import InterruptResponseContent +from temporalio import workflow +from temporalio.contrib.strands import TemporalAgent + + +@tool +def delete_file(path: str) -> str: + return f"deleted {path}" + + +# @@@SNIPSTART python-strands-human-in-the-loop-hook +class ApprovalHook(HookProvider): + def register_hooks(self, registry: HookRegistry, **kwargs: object) -> None: + registry.add_callback(BeforeToolCallEvent, self._gate) + + def _gate(self, event: BeforeToolCallEvent) -> None: + if event.tool_use["name"] != "delete_file": + return + approval = event.interrupt( + "approval", + reason=f"approve delete of {event.tool_use['input']['path']}?", + ) + if approval != "approve": + event.cancel_tool = "denied" + + +# @@@SNIPEND + + +# @@@SNIPSTART python-strands-human-in-the-loop-workflow +@workflow.defn +class HumanInTheLoopWorkflow: + def __init__(self) -> None: + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + tools=[delete_file], + hooks=[ApprovalHook()], + ) + self._approval: Optional[str] = None + self._pending_reason: Optional[str] = None + + @workflow.signal + def approve(self, response: str) -> None: + self._approval = response + + @workflow.query + def pending_approval(self) -> Optional[str]: + return self._pending_reason + + @workflow.run + async def run(self, prompt: str) -> str: + result = await self.agent.invoke_async(prompt) + while result.stop_reason == "interrupt": + interrupts = list(result.interrupts or []) + self._pending_reason = interrupts[0].reason if interrupts else None + await workflow.wait_condition(lambda: self._approval is not None) + response = self._approval + self._approval = None + self._pending_reason = None + responses: list[InterruptResponseContent] = [ + {"interruptResponse": {"interruptId": i.id, "response": response}} + for i in interrupts + ] + result = await self.agent.invoke_async(responses) + return str(result) + + +# @@@SNIPEND diff --git a/strands_plugin/mcp/README.md b/strands_plugin/mcp/README.md new file mode 100644 index 000000000..a8328536a --- /dev/null +++ b/strands_plugin/mcp/README.md @@ -0,0 +1,32 @@ +# MCP + +Connect a Strands agent to an [MCP](https://modelcontextprotocol.io/) server through `TemporalMCPClient`. The plugin opens the MCP session at worker startup, enumerates tools, and dispatches each tool call through a dedicated `-call-tool` Temporal activity. + +The included `echo_mcp_server.py` is a one-tool FastMCP server (returns the message it was given). Swap it for any real MCP server (filesystem, Postgres, GitHub, etc.) by changing the factory in `run_worker.py`. + +## What This Sample Demonstrates + +- Registering an MCP server with `StrandsPlugin(mcp_clients=...)` +- Referencing it from a workflow via `TemporalMCPClient(server="...")` +- Running MCP tool calls as durable Temporal activities + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/mcp/run_worker.py + +# Terminal 2 +uv run strands_plugin/mcp/run_workflow.py +``` + +The worker spawns `echo_mcp_server.py` itself; you don't need to start it separately. The Temporal UI will show one `invoke_model` per agent turn plus `echo-call-tool` activities for each MCP tool invocation. + +## Files + +| File | Description | +|------|-------------| +| `echo_mcp_server.py` | FastMCP server exposing a single `echo` tool | +| `workflow.py` | `MCPWorkflow` with `TemporalMCPClient(server="echo")` | +| `run_worker.py` | Spawns the MCP server and starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/strands_plugin/mcp/__init__.py b/strands_plugin/mcp/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/mcp/echo_mcp_server.py b/strands_plugin/mcp/echo_mcp_server.py new file mode 100644 index 000000000..4f3d35319 --- /dev/null +++ b/strands_plugin/mcp/echo_mcp_server.py @@ -0,0 +1,19 @@ +"""Tiny FastMCP server: exposes a single ``echo`` tool over stdio. + +The worker launches this script as a subprocess and connects to it via the +MCP stdio transport. Replace it with any real MCP server in production. +""" + +from mcp.server.fastmcp import FastMCP + +mcp = FastMCP("echo-server") + + +@mcp.tool() +def echo(message: str) -> str: + """Return the input message unchanged.""" + return message + + +if __name__ == "__main__": + mcp.run() diff --git a/strands_plugin/mcp/run_worker.py b/strands_plugin/mcp/run_worker.py new file mode 100644 index 000000000..772870e87 --- /dev/null +++ b/strands_plugin/mcp/run_worker.py @@ -0,0 +1,54 @@ +"""Worker for the MCP sample. + +The worker launches the ``echo_mcp_server.py`` script as a subprocess at +startup. The plugin opens a stdio MCP session, enumerates tools once, and +caches the schema for the worker's lifetime. +""" + +# @@@SNIPSTART python-strands-mcp-worker +import asyncio +import os +import sys +from pathlib import Path + +from mcp import StdioServerParameters, stdio_client +from strands.tools.mcp.mcp_client import MCPClient +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.mcp.workflow import MCPWorkflow + +ECHO_SERVER = Path(__file__).parent / "echo_mcp_server.py" + + +def _make_echo_client() -> MCPClient: + return MCPClient( + lambda: stdio_client( + StdioServerParameters( + command=sys.executable, + args=[str(ECHO_SERVER)], + ) + ) + ) + + +async def main() -> None: + plugin = StrandsPlugin(mcp_clients={"echo": _make_echo_client}) + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-mcp", + workflows=[MCPWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/strands_plugin/mcp/run_workflow.py b/strands_plugin/mcp/run_workflow.py new file mode 100644 index 000000000..7b9a6765a --- /dev/null +++ b/strands_plugin/mcp/run_workflow.py @@ -0,0 +1,25 @@ +"""Start the MCP workflow.""" + +import asyncio +import os + +from temporalio.client import Client + +from strands_plugin.mcp.workflow import MCPWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + MCPWorkflow.run, + "Use the echo tool to echo the message 'hello from MCP'.", + id="strands-mcp", + task_queue="strands-mcp", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/mcp/workflow.py b/strands_plugin/mcp/workflow.py new file mode 100644 index 000000000..d83689c6a --- /dev/null +++ b/strands_plugin/mcp/workflow.py @@ -0,0 +1,34 @@ +"""Workflow that uses an MCP server through ``TemporalMCPClient``. + +The plugin connects to each registered MCP server at worker startup and +caches the tool manifest. ``TemporalMCPClient`` on the workflow side is a +pure handle that references the server by name and carries the activity +options for each tool call. +""" + +# @@@SNIPSTART python-strands-mcp-workflow +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.strands import TemporalAgent, TemporalMCPClient + + +@workflow.defn +class MCPWorkflow: + def __init__(self) -> None: + echo = TemporalMCPClient( + server="echo", + start_to_close_timeout=timedelta(seconds=30), + ) + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + tools=[echo], + ) + + @workflow.run + async def run(self, prompt: str) -> str: + result = await self.agent.invoke_async(prompt) + return str(result) + + +# @@@SNIPEND diff --git a/strands_plugin/streaming/README.md b/strands_plugin/streaming/README.md new file mode 100644 index 000000000..598dde942 --- /dev/null +++ b/strands_plugin/streaming/README.md @@ -0,0 +1,29 @@ +# Streaming + +Strands' model stream produces a sequence of `StreamEvent`s (token deltas, tool-use chunks, message-stop, etc.). With `TemporalAgent(streaming_topic="events")`, the model activity publishes each chunk onto a workflow-hosted `WorkflowStream`; external subscribers read it via `WorkflowStreamClient`. Chunks are batched on `streaming_batch_interval` (default 100ms) to keep activity overhead low. + +## What This Sample Demonstrates + +- Hosting a `WorkflowStream` on a workflow +- `TemporalAgent(streaming_topic=...)` publishing model events +- An external subscriber reading `StreamEvent`s in real time via `WorkflowStreamClient` + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/streaming/run_worker.py + +# Terminal 2 +uv run strands_plugin/streaming/run_workflow.py +``` + +The starter prints text deltas as they arrive, then the final workflow result. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `StreamingWorkflow` hosting `WorkflowStream` + `TemporalAgent(streaming_topic="events")` | +| `run_worker.py` | Registers `StrandsPlugin`, starts the worker | +| `run_workflow.py` | Starts the workflow and subscribes to the `events` topic | diff --git a/strands_plugin/streaming/__init__.py b/strands_plugin/streaming/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/streaming/run_worker.py b/strands_plugin/streaming/run_worker.py new file mode 100644 index 000000000..d009de1e4 --- /dev/null +++ b/strands_plugin/streaming/run_worker.py @@ -0,0 +1,30 @@ +"""Worker for the streaming sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.streaming.workflow import StreamingWorkflow + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-streaming", + workflows=[StreamingWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/streaming/run_workflow.py b/strands_plugin/streaming/run_workflow.py new file mode 100644 index 000000000..9a78b464f --- /dev/null +++ b/strands_plugin/streaming/run_workflow.py @@ -0,0 +1,51 @@ +"""Start the streaming workflow and consume model events live.""" + +# @@@SNIPSTART python-strands-streaming-client +import asyncio +import os +from datetime import timedelta + +from strands.types.streaming import StreamEvent +from temporalio.client import Client +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from strands_plugin.streaming.workflow import StreamingWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + workflow_id = "strands-streaming" + + handle = await client.start_workflow( + StreamingWorkflow.run, + "Count from 1 to 5, one number per sentence.", + id=workflow_id, + task_queue="strands-streaming", + ) + + async def consume() -> None: + stream = WorkflowStreamClient.create(client, workflow_id) + async for item in stream.subscribe( + ["events"], + from_offset=0, + result_type=StreamEvent, + poll_cooldown=timedelta(milliseconds=50), + ): + event: StreamEvent = item.data + if "contentBlockDelta" in event: + delta = event["contentBlockDelta"].get("delta", {}) + if "text" in delta: + print(delta["text"], end="", flush=True) + elif "messageStop" in event: + print() + return + + consume_task = asyncio.create_task(consume()) + result = await handle.result() + await asyncio.wait_for(consume_task, timeout=10.0) + print(f"Final result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/strands_plugin/streaming/workflow.py b/strands_plugin/streaming/workflow.py new file mode 100644 index 000000000..416f6e6e2 --- /dev/null +++ b/strands_plugin/streaming/workflow.py @@ -0,0 +1,32 @@ +"""Workflow that streams model chunks to an external subscriber. + +``TemporalAgent(streaming_topic="events")`` publishes each ``StreamEvent`` from +inside the model activity onto a workflow-hosted ``WorkflowStream``. +Subscribers connect via ``WorkflowStreamClient`` and read the topic in real +time. Chunks are batched on the ``streaming_batch_interval`` (default 100ms). +""" + +# @@@SNIPSTART python-strands-streaming-workflow +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.strands import TemporalAgent +from temporalio.contrib.workflow_streams import WorkflowStream + + +@workflow.defn +class StreamingWorkflow: + def __init__(self) -> None: + self.stream = WorkflowStream() + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + streaming_topic="events", + ) + + @workflow.run + async def run(self, prompt: str) -> str: + result = await self.agent.invoke_async(prompt) + return str(result) + + +# @@@SNIPEND diff --git a/strands_plugin/structured_output/README.md b/strands_plugin/structured_output/README.md new file mode 100644 index 000000000..e913f187a --- /dev/null +++ b/strands_plugin/structured_output/README.md @@ -0,0 +1,27 @@ +# Structured Output + +`TemporalAgent` accepts a `structured_output_model=` Pydantic class and returns the agent's response coerced into that model. The plugin installs Temporal's `pydantic_data_converter` by default, so the typed value serializes cleanly across the activity and workflow boundary. + +## What This Sample Demonstrates + +- `TemporalAgent(structured_output_model=PersonInfo, ...)` +- Pulling `result.structured_output` out of the agent result +- The plugin's auto-configured pydantic data converter — no extra setup needed to ship Pydantic types in workflow inputs/outputs + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/structured_output/run_worker.py + +# Terminal 2 +uv run strands_plugin/structured_output/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `PersonInfo` model and `StructuredOutputWorkflow` | +| `run_worker.py` | Registers `StrandsPlugin`, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the typed result | diff --git a/strands_plugin/structured_output/__init__.py b/strands_plugin/structured_output/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/structured_output/run_worker.py b/strands_plugin/structured_output/run_worker.py new file mode 100644 index 000000000..032526f5d --- /dev/null +++ b/strands_plugin/structured_output/run_worker.py @@ -0,0 +1,30 @@ +"""Worker for the structured output sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.structured_output.workflow import StructuredOutputWorkflow + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-structured-output", + workflows=[StructuredOutputWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/structured_output/run_workflow.py b/strands_plugin/structured_output/run_workflow.py new file mode 100644 index 000000000..9116ae6a9 --- /dev/null +++ b/strands_plugin/structured_output/run_workflow.py @@ -0,0 +1,30 @@ +"""Start the structured output workflow.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin + +from strands_plugin.structured_output.workflow import StructuredOutputWorkflow + + +async def main() -> None: + # Plugin on the client so the pydantic data converter is installed. + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[StrandsPlugin()], + ) + + person = await client.execute_workflow( + StructuredOutputWorkflow.run, + "John Smith is a 30 year-old software engineer.", + id="strands-structured-output", + task_queue="strands-structured-output", + ) + + print(f"name={person.name} age={person.age} occupation={person.occupation}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/structured_output/workflow.py b/strands_plugin/structured_output/workflow.py new file mode 100644 index 000000000..3ee6a7a60 --- /dev/null +++ b/strands_plugin/structured_output/workflow.py @@ -0,0 +1,38 @@ +"""Structured output: agent returns a typed Pydantic model. + +``TemporalAgent(structured_output_model=PersonInfo)`` makes Strands coerce the +model's final response into an instance of ``PersonInfo``. The plugin installs +``pydantic_data_converter`` by default, so the typed value flows back across +the activity/workflow boundary without extra wiring. +""" + +# @@@SNIPSTART python-strands-structured-output-workflow +from datetime import timedelta + +from pydantic import BaseModel, Field +from temporalio import workflow +from temporalio.contrib.strands import TemporalAgent + + +class PersonInfo(BaseModel): + name: str = Field(description="Name of the person") + age: int = Field(description="Age of the person") + occupation: str = Field(description="Occupation of the person") + + +@workflow.defn +class StructuredOutputWorkflow: + def __init__(self) -> None: + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + structured_output_model=PersonInfo, + ) + + @workflow.run + async def run(self, prompt: str) -> PersonInfo: + result = await self.agent.invoke_async(prompt) + assert isinstance(result.structured_output, PersonInfo) + return result.structured_output + + +# @@@SNIPEND diff --git a/strands_plugin/tools/README.md b/strands_plugin/tools/README.md new file mode 100644 index 000000000..400eff2c2 --- /dev/null +++ b/strands_plugin/tools/README.md @@ -0,0 +1,35 @@ +# Tools + +Three Strands tool patterns wired into one `TemporalAgent`: + +| Pattern | When to use it | +|---------|----------------| +| `@tool` from `strands` | Pure, deterministic logic with no I/O. Runs in workflow context. | +| `@activity.defn` wrapped via `activity_as_tool` | Anything with I/O, non-determinism, or significant runtime — gets durable retries and timeouts. | +| `strands_tools.` wrapped in an `@activity.defn` | Reuse Strands ecosystem tools (`environment`, `http_request`, `python_repl`, …) while keeping workflow code deterministic. | + +A single prompt exercises all three. The resulting Temporal history shows an `invoke_model` for each model turn, plus `fetch_weather` and `environment` activity calls; the `letter_counter` call runs in-workflow and doesn't show up as an activity. + +## What This Sample Demonstrates + +- Three coexisting tool surfaces on one agent +- `workflow.activity_as_tool` carrying per-tool activity options (timeouts) +- Wrapping `strands_tools` tools so runtime host access happens in an activity + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/tools/run_worker.py + +# Terminal 2 +uv run strands_plugin/tools/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | The three tools, the `TemporalAgent`, and `ToolsWorkflow` | +| `run_worker.py` | Registers `StrandsPlugin` + the two activities, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/strands_plugin/tools/__init__.py b/strands_plugin/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/tools/run_worker.py b/strands_plugin/tools/run_worker.py new file mode 100644 index 000000000..bffa83e31 --- /dev/null +++ b/strands_plugin/tools/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the tools sample.""" + +# @@@SNIPSTART python-strands-tools-worker +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.tools.workflow import ( + ToolsWorkflow, + environment_activity, + fetch_weather, +) + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-tools", + workflows=[ToolsWorkflow], + activities=[fetch_weather, environment_activity], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/strands_plugin/tools/run_workflow.py b/strands_plugin/tools/run_workflow.py new file mode 100644 index 000000000..04256c737 --- /dev/null +++ b/strands_plugin/tools/run_workflow.py @@ -0,0 +1,30 @@ +"""Start the tools workflow.""" + +import asyncio +import os + +from temporalio.client import Client + +from strands_plugin.tools.workflow import ToolsWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + ToolsWorkflow.run, + ( + "Please do three things:\n" + "1. Count the letter R's in 'strawberry'.\n" + "2. Fetch the weather in San Francisco.\n" + "3. Validate that the PATH environment variable exists." + ), + id="strands-tools", + task_queue="strands-tools", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/tools/workflow.py b/strands_plugin/tools/workflow.py new file mode 100644 index 000000000..34aecd3cb --- /dev/null +++ b/strands_plugin/tools/workflow.py @@ -0,0 +1,101 @@ +"""Three tool patterns wired into one Strands agent. + +1. ``@tool letter_counter`` — pure Strands tool, runs deterministically in + workflow context. +2. ``@activity.defn fetch_weather`` — custom Temporal activity wrapped with + ``activity_as_tool``; suitable for I/O and non-deterministic work. +3. ``environment`` (from ``strands_tools``) — third-party Strands tool wrapped + in a thin ``@activity.defn`` so its runtime host access runs in an activity. +""" + +from datetime import timedelta +from typing import Any, cast + +from strands import tool +from strands.types.tools import ToolUse +from strands_tools import environment # type: ignore[import-untyped] +from temporalio import activity, workflow +from temporalio.contrib.strands import TemporalAgent +from temporalio.contrib.strands.workflow import activity_as_tool + + +@tool +def letter_counter(word: str, letter: str) -> int: + """Count how many times ``letter`` appears in ``word`` (case-insensitive).""" + return word.lower().count(letter.lower()) + + +# @@@SNIPSTART python-strands-tools-activity +@activity.defn +async def fetch_weather(city: str) -> dict: + """Stub weather lookup — replace with a real HTTP call in production.""" + return { + "city": city, + "temperature_f": 72, + "conditions": "sunny", + } + + +# @@@SNIPEND + + +@activity.defn(name="environment") +async def environment_activity( + action: str, + name: str | None = None, + value: str | None = None, + prefix: str | None = None, + masked: bool | None = None, +) -> dict: + """Run ``strands_tools.environment`` inside an activity. + + Environment variables are runtime host state. Wrapping this tool in an + activity keeps that non-deterministic access out of workflow replay. + """ + tool_input: dict[str, Any] = {"action": action} + if name is not None: + tool_input["name"] = name + if value is not None: + tool_input["value"] = value + if prefix is not None: + tool_input["prefix"] = prefix + if masked is not None: + tool_input["masked"] = masked + + tool_use = cast( + ToolUse, + { + "toolUseId": "environment", + "name": "environment", + "input": tool_input, + }, + ) + return environment.environment(tool_use) + + +# @@@SNIPSTART python-strands-tools-workflow +@workflow.defn +class ToolsWorkflow: + def __init__(self) -> None: + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + tools=[ + letter_counter, + activity_as_tool( + fetch_weather, + start_to_close_timeout=timedelta(seconds=30), + ), + activity_as_tool( + environment_activity, + start_to_close_timeout=timedelta(seconds=30), + ), + ], + ) + + @workflow.run + async def run(self, prompt: str) -> str: + result = await self.agent.invoke_async(prompt) + return str(result) + + +# @@@SNIPEND diff --git a/tests/strands_plugin/__init__.py b/tests/strands_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/strands_plugin/_mock_model.py b/tests/strands_plugin/_mock_model.py new file mode 100644 index 000000000..9f97bedbc --- /dev/null +++ b/tests/strands_plugin/_mock_model.py @@ -0,0 +1,69 @@ +"""Scripted Strands model for sample tests. + +Vendored from the SDK's ``tests/contrib/strands/mock_model.py``. Each entry in +``responses`` drives one ``stream()`` call: a ``str`` yields a text turn, a +``dict`` of ``{"name", "input"}`` yields a tool-use turn. +""" + +from __future__ import annotations + +import json +from collections.abc import AsyncIterable +from typing import Any + +import temporalio.contrib.strands._plugin as _plugin_module +from strands.models import Model +from strands.types.streaming import StreamEvent + + +class MockModel(Model): + def __init__(self, responses: list[str | dict[str, Any]]) -> None: + self._responses = list(responses) + self._tool_call_index = 0 + + def update_config(self, **_model_config: Any) -> None: + return None + + def get_config(self) -> dict[str, Any]: + return {} + + def structured_output(self, *_args: Any, **_kwargs: Any): + raise NotImplementedError + + async def stream(self, *_args: Any, **_kwargs: Any) -> AsyncIterable[StreamEvent]: + if not self._responses: + raise AssertionError("MockModel script exhausted") + response = self._responses.pop(0) + + yield {"messageStart": {"role": "assistant"}} + + if isinstance(response, str): + yield {"contentBlockDelta": {"delta": {"text": response}}} + yield {"contentBlockStop": {}} + yield {"messageStop": {"stopReason": "end_turn"}} + else: + self._tool_call_index += 1 + yield { + "contentBlockStart": { + "start": { + "toolUse": { + "name": response["name"], + "toolUseId": f"mock-tool-{self._tool_call_index}", + }, + }, + }, + } + yield { + "contentBlockDelta": { + "delta": {"toolUse": {"input": json.dumps(response["input"])}}, + }, + } + yield {"contentBlockStop": {}} + yield {"messageStop": {"stopReason": "tool_use"}} + + +def patch_bedrock(monkeypatch: Any, responses: list[Any]) -> None: + """Make the plugin's implicit BedrockModel factory return a scripted MockModel.""" + monkeypatch.setattr( + _plugin_module, "BedrockModel", lambda *a, **kw: MockModel(responses) + ) diff --git a/tests/strands_plugin/activity_interrupt_test.py b/tests/strands_plugin/activity_interrupt_test.py new file mode 100644 index 000000000..27e265239 --- /dev/null +++ b/tests/strands_plugin/activity_interrupt_test.py @@ -0,0 +1,50 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.activity_interrupt.workflow import ( + ActivityInterruptWorkflow, + delete_thing, +) +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_activity_interrupt( + client: Client, monkeypatch: pytest.MonkeyPatch +) -> None: + patch_bedrock( + monkeypatch, + [ + {"name": "delete_thing", "input": {"name": "system"}}, + {"name": "delete_thing", "input": {"name": "system"}}, + "Done!", + ], + ) + + task_queue = f"strands-activity-interrupt-{uuid.uuid4()}" + plugin = StrandsPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ActivityInterruptWorkflow], + activities=[delete_thing], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + ActivityInterruptWorkflow.run, + "Delete the system user.", + id=f"strands-activity-interrupt-{uuid.uuid4()}", + task_queue=task_queue, + ) + await handle.signal(ActivityInterruptWorkflow.approve, "approve") + result = await handle.result() + + assert result == "Done!\n" diff --git a/tests/strands_plugin/continue_as_new_test.py b/tests/strands_plugin/continue_as_new_test.py new file mode 100644 index 000000000..45c141710 --- /dev/null +++ b/tests/strands_plugin/continue_as_new_test.py @@ -0,0 +1,83 @@ +import asyncio +import uuid + +import pytest +from temporalio.client import Client, WorkflowExecutionStatus +from temporalio.contrib.strands import StrandsPlugin +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from strands_plugin.continue_as_new.workflow import ChatInput, ChatWorkflow +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_continue_as_new_carries_history( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Exercise the full chat lifecycle across a continue-as-new boundary. + + Drives enough history that the server suggests continue-as-new, confirms the + original run hands its message history off to a fresh run, then takes another + turn on that resumed run and ends the chat normally. + """ + patch_bedrock(monkeypatch, ["First reply.", "Second reply."]) + + # The server only suggests continue-as-new once history grows past a + # threshold (~4096 events by default). Lower it so a single turn's worth of + # history is enough to trip ``is_continue_as_new_suggested()``. + async with await WorkflowEnvironment.start_local( + dev_server_extra_args=[ + "--dynamic-config-value", + "limit.historyCount.suggestContinueAsNew=5", + ] + ) as env: + config = env.client.config() + config["plugins"] = [*config["plugins"], StrandsPlugin()] + client = Client(**config) + + task_queue = f"strands-chat-{uuid.uuid4()}" + workflow_id = f"strands-chat-{uuid.uuid4()}" + + async with Worker( + client, + task_queue=task_queue, + workflows=[ChatWorkflow], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + ChatWorkflow.run, + ChatInput(), + id=workflow_id, + task_queue=task_queue, + ) + + original_run_id = handle.result_run_id + + # One turn produces enough events to cross the threshold above. + reply1 = await handle.execute_update(ChatWorkflow.turn, "Hello") + assert reply1 == "First reply." + + # The original run should end by continuing-as-new once the in-flight + # turn drains. Pin to its run id; an unpinned handle would follow the + # chain to the fresh (still-running) run instead. + original = client.get_workflow_handle(workflow_id, run_id=original_run_id) + for _ in range(100): + desc = await original.describe() + if desc.status == WorkflowExecutionStatus.CONTINUED_AS_NEW: + break + await asyncio.sleep(0.1) + else: + raise AssertionError("workflow did not continue-as-new") + + # The fresh run resumes with the prior turn's history and accepts a + # new turn, appending to the carried-over messages. + latest = client.get_workflow_handle(workflow_id) + reply2 = await latest.execute_update(ChatWorkflow.turn, "How are you?") + assert reply2 == "Second reply." + + # 2 messages carried across the handoff + 2 from the resumed turn. + messages = await latest.query(ChatWorkflow.messages) + assert len(messages) == 4 + + await latest.signal(ChatWorkflow.end_chat) + await latest.result() diff --git a/tests/strands_plugin/hello_world_test.py b/tests/strands_plugin/hello_world_test.py new file mode 100644 index 000000000..5110ec759 --- /dev/null +++ b/tests/strands_plugin/hello_world_test.py @@ -0,0 +1,35 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.hello_world.workflow import HelloWorldWorkflow +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_hello_world(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_bedrock(monkeypatch, ["A haiku, for you."]) + + task_queue = f"strands-hello-world-{uuid.uuid4()}" + plugin = StrandsPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HelloWorldWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + HelloWorldWorkflow.run, + "Write a haiku.", + id=f"strands-hello-world-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "A haiku, for you.\n" diff --git a/tests/strands_plugin/hooks_test.py b/tests/strands_plugin/hooks_test.py new file mode 100644 index 000000000..72d9bff52 --- /dev/null +++ b/tests/strands_plugin/hooks_test.py @@ -0,0 +1,42 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.hooks.workflow import HooksWorkflow, persist_tool_call +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_hooks(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_bedrock( + monkeypatch, + [ + {"name": "echo", "input": {"text": "hi"}}, + "Done!", + ], + ) + + task_queue = f"strands-hooks-{uuid.uuid4()}" + plugin = StrandsPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HooksWorkflow], + activities=[persist_tool_call], + max_cached_workflows=0, + ): + fired = await client.execute_workflow( + HooksWorkflow.run, + "Echo hi.", + id=f"strands-hooks-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert fired == ["echo"] diff --git a/tests/strands_plugin/human_in_the_loop_test.py b/tests/strands_plugin/human_in_the_loop_test.py new file mode 100644 index 000000000..53cd5e844 --- /dev/null +++ b/tests/strands_plugin/human_in_the_loop_test.py @@ -0,0 +1,45 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.human_in_the_loop.workflow import HumanInTheLoopWorkflow +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_human_in_the_loop_approve( + client: Client, monkeypatch: pytest.MonkeyPatch +) -> None: + patch_bedrock( + monkeypatch, + [ + {"name": "delete_file", "input": {"path": "/tmp/sensitive.txt"}}, + "Done!", + ], + ) + + task_queue = f"strands-hitl-{uuid.uuid4()}" + plugin = StrandsPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HumanInTheLoopWorkflow], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + HumanInTheLoopWorkflow.run, + "Delete /tmp/sensitive.txt", + id=f"strands-hitl-{uuid.uuid4()}", + task_queue=task_queue, + ) + await handle.signal(HumanInTheLoopWorkflow.approve, "approve") + result = await handle.result() + + assert result == "Done!\n" diff --git a/tests/strands_plugin/mcp_test.py b/tests/strands_plugin/mcp_test.py new file mode 100644 index 000000000..9480f81d9 --- /dev/null +++ b/tests/strands_plugin/mcp_test.py @@ -0,0 +1,42 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.mcp.run_worker import _make_echo_client +from strands_plugin.mcp.workflow import MCPWorkflow +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_mcp(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_bedrock( + monkeypatch, + [ + {"name": "echo", "input": {"message": "hello from MCP"}}, + "Done!", + ], + ) + + task_queue = f"strands-mcp-{uuid.uuid4()}" + plugin = StrandsPlugin(mcp_clients={"echo": _make_echo_client}) + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[MCPWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + MCPWorkflow.run, + "Echo hello from MCP.", + id=f"strands-mcp-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "Done!\n" diff --git a/tests/strands_plugin/streaming_test.py b/tests/strands_plugin/streaming_test.py new file mode 100644 index 000000000..1a57aefda --- /dev/null +++ b/tests/strands_plugin/streaming_test.py @@ -0,0 +1,59 @@ +import asyncio +import uuid +from datetime import timedelta + +import pytest +from strands.types.streaming import StreamEvent +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.contrib.workflow_streams import WorkflowStreamClient +from temporalio.worker import Worker + +from strands_plugin.streaming.workflow import StreamingWorkflow +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_streaming(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_bedrock(monkeypatch, ["Done!"]) + + task_queue = f"strands-streaming-{uuid.uuid4()}" + plugin = StrandsPlugin() + workflow_id = f"strands-streaming-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[StreamingWorkflow], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + StreamingWorkflow.run, + "Hello", + id=workflow_id, + task_queue=task_queue, + ) + + stream = WorkflowStreamClient.create(client, workflow_id) + events: list[StreamEvent] = [] + + async def collect() -> None: + async for item in stream.subscribe( + ["events"], + from_offset=0, + result_type=StreamEvent, + poll_cooldown=timedelta(milliseconds=50), + ): + events.append(item.data) + if len(events) >= 4: + return + + collect_task = asyncio.create_task(collect()) + assert await handle.result() == "Done!\n" + await asyncio.wait_for(collect_task, timeout=10.0) + + assert any("messageStart" in e for e in events) + assert any("messageStop" in e for e in events) diff --git a/tests/strands_plugin/structured_output_test.py b/tests/strands_plugin/structured_output_test.py new file mode 100644 index 000000000..fba1f10c1 --- /dev/null +++ b/tests/strands_plugin/structured_output_test.py @@ -0,0 +1,54 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.structured_output.workflow import ( + PersonInfo, + StructuredOutputWorkflow, +) +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_structured_output( + client: Client, monkeypatch: pytest.MonkeyPatch +) -> None: + patch_bedrock( + monkeypatch, + [ + { + "name": "PersonInfo", + "input": { + "name": "John Smith", + "age": 30, + "occupation": "software engineer", + }, + }, + ], + ) + + task_queue = f"strands-structured-output-{uuid.uuid4()}" + plugin = StrandsPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[StructuredOutputWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + StructuredOutputWorkflow.run, + "John Smith is a 30 year-old software engineer.", + id=f"strands-structured-output-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == PersonInfo( + name="John Smith", age=30, occupation="software engineer" + ) diff --git a/tests/strands_plugin/tools_test.py b/tests/strands_plugin/tools_test.py new file mode 100644 index 000000000..5aa36f883 --- /dev/null +++ b/tests/strands_plugin/tools_test.py @@ -0,0 +1,51 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.tools.workflow import ( + ToolsWorkflow, + environment_activity, + fetch_weather, +) +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_tools(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_bedrock( + monkeypatch, + [ + { + "name": "letter_counter", + "input": {"word": "strawberry", "letter": "R"}, + }, + {"name": "fetch_weather", "input": {"city": "San Francisco"}}, + {"name": "environment", "input": {"action": "validate", "name": "PATH"}}, + "Done!", + ], + ) + + task_queue = f"strands-tools-{uuid.uuid4()}" + plugin = StrandsPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ToolsWorkflow], + activities=[fetch_weather, environment_activity], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + ToolsWorkflow.run, + "Use all three tools.", + id=f"strands-tools-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "Done!\n" diff --git a/uv.lock b/uv.lock index 5ff441037..77814d337 100644 --- a/uv.lock +++ b/uv.lock @@ -287,6 +287,18 @@ wheels = [ { url = 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[[package]] name = "websockets" version = "16.0" From 47924ae2f78d64baf4407956f16c73c4b79cafca Mon Sep 17 00:00:00 2001 From: Alex Mazzeo Date: Mon, 15 Jun 2026 12:59:00 -0700 Subject: [PATCH 33/40] Add SANO sample (#294) * Add SANO sample * Redis external storage driver (#287) * Redis external storage driver * Fix lints * Fix CI tests * Fix yarl error in CI * Revert accidental README.md edit, and add external_storage_redis. * Move redis test workflow to tests/ * Update test_redis_worker.py * Constrain langsmith to allow CI to pass * Update to main of python sdk * Update to python sdk 1.28.0 * Fix test. Use sano dev server and add required dynamic config * Skip test on timeskipping server * update readme w/ dynamic config values * Update readme with link * use new temporal operation instead of workflow run operation * Bump to v1.7.2-standalone-nexus-operations cli version. Add callouts about release phase. Add dev server links. * Add assert_eventually test helper. Update count assertion to be more specific * Run formatter * Add getting a handle and result of an existing operation --------- Co-authored-by: Edward Amsden --- README.md | 2 + nexus_standalone_operations/README.md | 69 +++++++++++++++ nexus_standalone_operations/__init__.py | 0 nexus_standalone_operations/handler.py | 45 ++++++++++ nexus_standalone_operations/service.py | 39 +++++++++ nexus_standalone_operations/starter.py | 83 +++++++++++++++++++ nexus_standalone_operations/worker.py | 41 +++++++++ tests/conftest.py | 7 +- tests/helpers/__init__.py | 24 ++++++ tests/helpers/nexus.py | 3 - tests/nexus_standalone_operations/__init__.py | 0 .../nexus_standalone_operations_test.py | 78 +++++++++++++++++ 12 files changed, 387 insertions(+), 4 deletions(-) create mode 100644 nexus_standalone_operations/README.md create mode 100644 nexus_standalone_operations/__init__.py create mode 100644 nexus_standalone_operations/handler.py create mode 100644 nexus_standalone_operations/service.py create mode 100644 nexus_standalone_operations/starter.py create mode 100644 nexus_standalone_operations/worker.py create mode 100644 tests/nexus_standalone_operations/__init__.py create mode 100644 tests/nexus_standalone_operations/nexus_standalone_operations_test.py diff --git a/README.md b/README.md index feb43d488..4d7091bcd 100644 --- a/README.md +++ b/README.md @@ -81,6 +81,8 @@ Some examples require extra dependencies. See each sample's directory for specif * [Nexus Messaging](nexus_messaging): Demonstrates how send signal, update and query messages through Nexus. This contains two samples, one sending messages to an existing workflow and a second that creates a workflow through Nexus and sends messages to it. +* [nexus_standalone_operations](nexus_standalone_operations) - Execute Nexus operations directly from client code, +without wrapping them in a workflow. * [open_telemetry](open_telemetry) - Trace workflows with OpenTelemetry. * [patching](patching) - Alter workflows safely with `patch` and `deprecate_patch`. * [polling](polling) - Recommended implementation of an activity that needs to periodically poll an external resource waiting its successful completion. diff --git a/nexus_standalone_operations/README.md b/nexus_standalone_operations/README.md new file mode 100644 index 000000000..60ca7a935 --- /dev/null +++ b/nexus_standalone_operations/README.md @@ -0,0 +1,69 @@ +This sample demonstrates how to execute Nexus operations directly from client code, +without wrapping them in a workflow. It shows both synchronous and asynchronous +(workflow-backed) operations, plus listing and counting operations. + + +### Temporal Python SDK support for Standalone Nexus Operations is at [Pre-release](https://docs.temporal.io/evaluate/development-production-features/release-stages#pre-release). + +All APIs are experimental and may be subject to backwards-incompatible changes. + +Standalone Nexus operations require a server version that supports this feature. Use the dev server build at https://github.com/temporalio/cli/releases/tag/v1.7.2-standalone-nexus-operations. + +### Sample directory structure + +- [service.py](./service.py) - Nexus service definition with echo (sync) and hello (async) operations +- [handler.py](./handler.py) - Nexus operation handlers and the backing workflow for the async operation +- [worker.py](./worker.py) - Temporal worker that hosts the Nexus service +- [starter.py](./starter.py) - Client that executes standalone Nexus operations + + +### Instructions + +Run the [Temporal dev server build that supports standalone Nexus operations](https://github.com/temporalio/cli/releases/tag/v1.7.2-standalone-nexus-operations). +(If you are going to run locally, you will want to start it in another terminal; this command is blocking and runs until it receives a SIGINT (Ctrl + C) command.) + +Start a Temporal dev server with the dynamic config flags required for standalone Nexus operations: + +```bash +temporal server start-dev \ + --dynamic-config-value "nexusoperation.enableStandalone=true" \ + --dynamic-config-value "history.enableChasmCallbacks=true" +``` + +Create the Nexus endpoint: + +``` +temporal operator nexus endpoint create \ + --name nexus-standalone-operations-endpoint \ + --target-namespace default \ + --target-task-queue nexus-standalone-operations +``` + +In one terminal, start the worker: +``` +uv run nexus_standalone_operations/worker.py +``` + +In another terminal, run the starter: +``` +uv run nexus_standalone_operations/starter.py +``` + +### Expected output + +``` +Echo result: hello +Echo result from existing operation handle: hello + +Started `MyNexusService.Hello`. OperationID: hello-... +`MyNexusService.Hello` result: Hello, World! + +Listing Nexus operations: + OperationId: echo-..., Operation: echo, Status: COMPLETED + OperationId: hello-..., Operation: hello, Status: COMPLETED + +Total Nexus operations: 2 +``` + +If you run the starter code multiple times, you should see additional operations in the listing results, as more operations are run. +The same goes for the total number of operations. \ No newline at end of file diff --git a/nexus_standalone_operations/__init__.py b/nexus_standalone_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_standalone_operations/handler.py b/nexus_standalone_operations/handler.py new file mode 100644 index 000000000..da18e9477 --- /dev/null +++ b/nexus_standalone_operations/handler.py @@ -0,0 +1,45 @@ +"""Nexus service handler and backing workflow for standalone operations sample.""" + +from __future__ import annotations + +import uuid + +import nexusrpc.handler +from temporalio import nexus, workflow + +from nexus_standalone_operations.service import ( + EchoInput, + EchoOutput, + HelloInput, + HelloOutput, + MyNexusService, +) + + +@workflow.defn +class HelloWorkflow: + @workflow.run + async def run(self, input: HelloInput) -> HelloOutput: + return HelloOutput(greeting=f"Hello, {input.name}!") + + +@nexusrpc.handler.service_handler(service=MyNexusService) +class MyNexusServiceHandler: + @nexusrpc.handler.sync_operation + async def echo( + self, _ctx: nexusrpc.handler.StartOperationContext, input: EchoInput + ) -> EchoOutput: + return EchoOutput(message=input.message) + + @nexus.temporal_operation + async def hello( + self, + _ctx: nexus.TemporalStartOperationContext, + client: nexus.TemporalNexusClient, + input: HelloInput, + ) -> nexus.TemporalOperationResult[HelloOutput]: + return await client.start_workflow( + HelloWorkflow.run, + input, + id=str(uuid.uuid4()), + ) diff --git a/nexus_standalone_operations/service.py b/nexus_standalone_operations/service.py new file mode 100644 index 000000000..a8a906db2 --- /dev/null +++ b/nexus_standalone_operations/service.py @@ -0,0 +1,39 @@ +"""Nexus service definition for standalone operations sample. + +Defines a Nexus service with two operations: +- echo: a synchronous operation that echoes the input message +- hello: an asynchronous (workflow-backed) operation that returns a greeting + +This service definition is used by both the handler (to validate operation +signatures) and the client (to create type-safe nexus clients). +""" + +from dataclasses import dataclass + +import nexusrpc + + +@dataclass +class EchoInput: + message: str + + +@dataclass +class EchoOutput: + message: str + + +@dataclass +class HelloInput: + name: str + + +@dataclass +class HelloOutput: + greeting: str + + +@nexusrpc.service +class MyNexusService: + echo: nexusrpc.Operation[EchoInput, EchoOutput] + hello: nexusrpc.Operation[HelloInput, HelloOutput] diff --git a/nexus_standalone_operations/starter.py b/nexus_standalone_operations/starter.py new file mode 100644 index 000000000..8bb235b2d --- /dev/null +++ b/nexus_standalone_operations/starter.py @@ -0,0 +1,83 @@ +"""Starter that demonstrates standalone Nexus operation execution. + +Unlike other Nexus samples that call operations from within a workflow, this +sample executes Nexus operations directly from client code using the standalone +Nexus operation APIs. +""" + +import asyncio +import uuid +from datetime import timedelta + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from nexus_standalone_operations.service import ( + EchoInput, + EchoOutput, + HelloInput, + MyNexusService, +) + +ENDPOINT_NAME = "nexus-standalone-operations-endpoint" + + +async def main() -> None: + config = ClientConfig.load_client_connect_config() + _ = config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Create a typed NexusClient bound to the endpoint and service. + # The endpoint must be pre-created on the server (see README). + nexus_client = client.create_nexus_client( + service=MyNexusService, endpoint=ENDPOINT_NAME + ) + + # Start sync echo operation and await the result immediately. + operation_id = f"echo-{uuid.uuid4()}" + echo_result = await nexus_client.execute_operation( + MyNexusService.echo, + EchoInput(message="hello"), + id=operation_id, + schedule_to_close_timeout=timedelta(seconds=10), + ) + print(f"Echo result: {echo_result.message}") + + # Get a handle and the result of an existing operation + existing_op_handle = client.get_nexus_operation_handle( + operation_id, operation=MyNexusService.echo + ) + existing_result = await existing_op_handle.result() + print(f"Echo result from existing operation handle: {existing_result.message}") + + # Start async (workflow-backed) hello operation and get a NexusOperationHandle. + handle = await nexus_client.start_operation( + MyNexusService.hello, + HelloInput(name="World"), + id=f"hello-{uuid.uuid4()}", + schedule_to_close_timeout=timedelta(seconds=10), + ) + + print(f"\nStarted `MyNexusService.Hello`. OperationID: {handle.operation_id}") + + # Use the NexusOperationHandle to await the result of the operation. + hello_result = await handle.result() + print(f"`MyNexusService.Hello` result: {hello_result.greeting}") + + # List nexus operations. + print("\nListing Nexus operations:") + query = f'Endpoint = "{ENDPOINT_NAME}"' + async for op in client.list_nexus_operations(query): + print( + f" OperationId: {op.operation_id},", + f" Operation: {op.operation},", + f" Status: {op.status.name}", + ) + + # Count nexus operations. + count = await client.count_nexus_operations(query) + print(f"\nTotal Nexus operations: {count.count}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/nexus_standalone_operations/worker.py b/nexus_standalone_operations/worker.py new file mode 100644 index 000000000..0de4ac3b1 --- /dev/null +++ b/nexus_standalone_operations/worker.py @@ -0,0 +1,41 @@ +"""Worker that hosts the Nexus service for standalone operations sample.""" + +import asyncio +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_standalone_operations.handler import HelloWorkflow, MyNexusServiceHandler + +interrupt_event = asyncio.Event() + +TASK_QUEUE = "nexus-standalone-operations" + + +async def main() -> None: + logging.basicConfig(level=logging.INFO) + + config = ClientConfig.load_client_connect_config() + _ = config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[HelloWorkflow], + nexus_service_handlers=[MyNexusServiceHandler()], + ): + logging.info("Worker started, ctrl+c to exit") + _ = await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/tests/conftest.py b/tests/conftest.py index 65de246ea..3d054e71a 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -42,12 +42,17 @@ async def env(request) -> AsyncGenerator[WorkflowEnvironment, None]: env_type = request.config.getoption("--workflow-environment") if env_type == "local": env = await WorkflowEnvironment.start_local( + dev_server_download_version="v1.7.2-standalone-nexus-operations", dev_server_extra_args=[ "--dynamic-config-value", "frontend.enableExecuteMultiOperation=true", "--dynamic-config-value", "system.enableEagerWorkflowStart=true", - ] + "--dynamic-config-value", + "nexusoperation.enableStandalone=true", + "--dynamic-config-value", + "history.enableChasmCallbacks=true", + ], ) elif env_type == "time-skipping": env = await WorkflowEnvironment.start_time_skipping() diff --git a/tests/helpers/__init__.py b/tests/helpers/__init__.py index e69de29bb..a387e5291 100644 --- a/tests/helpers/__init__.py +++ b/tests/helpers/__init__.py @@ -0,0 +1,24 @@ +import asyncio +import time +from collections.abc import Awaitable, Callable +from datetime import timedelta +from typing import TypeVar + +T = TypeVar("T") + + +async def assert_eventually( + fn: Callable[[], Awaitable[T]], + *, + timeout: timedelta = timedelta(seconds=10), + interval: timedelta = timedelta(milliseconds=200), +) -> T: + start_sec = time.monotonic() + while True: + try: + res = await fn() + return res + except AssertionError: + if timedelta(seconds=time.monotonic() - start_sec) >= timeout: + raise + await asyncio.sleep(interval.total_seconds()) diff --git a/tests/helpers/nexus.py b/tests/helpers/nexus.py index dee8dc18a..b9ce68995 100644 --- a/tests/helpers/nexus.py +++ b/tests/helpers/nexus.py @@ -1,7 +1,4 @@ import temporalio.api -import temporalio.api.common -import temporalio.api.common.v1 -import temporalio.api.enums.v1 import temporalio.api.nexus import temporalio.api.nexus.v1 import temporalio.api.operatorservice diff --git a/tests/nexus_standalone_operations/__init__.py b/tests/nexus_standalone_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/nexus_standalone_operations/nexus_standalone_operations_test.py b/tests/nexus_standalone_operations/nexus_standalone_operations_test.py new file mode 100644 index 000000000..44d13ca9a --- /dev/null +++ b/tests/nexus_standalone_operations/nexus_standalone_operations_test.py @@ -0,0 +1,78 @@ +import uuid +from datetime import timedelta + +import pytest +from temporalio.client import Client +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from nexus_standalone_operations.handler import HelloWorkflow, MyNexusServiceHandler +from nexus_standalone_operations.service import ( + EchoInput, + EchoOutput, + HelloInput, + HelloOutput, + MyNexusService, +) +from nexus_standalone_operations.worker import TASK_QUEUE +from tests.helpers import assert_eventually +from tests.helpers.nexus import create_nexus_endpoint, delete_nexus_endpoint + + +async def test_nexus_standalone_operations(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip("Time Skipping server does not support standalone nexus operations") + + endpoint_name = f"test-nexus-standalone-{uuid.uuid4()}" + + create_response = await create_nexus_endpoint( + name=endpoint_name, + task_queue=TASK_QUEUE, + client=client, + ) + try: + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[HelloWorkflow], + nexus_service_handlers=[MyNexusServiceHandler()], + ): + nexus_client = client.create_nexus_client( + service=MyNexusService, endpoint=endpoint_name + ) + + # Test sync echo operation + echo_result = None + echo_result = await nexus_client.execute_operation( + MyNexusService.echo, + EchoInput(message="test-echo"), + id=str(uuid.uuid4()), + schedule_to_close_timeout=timedelta(seconds=10), + ) + assert isinstance(echo_result, EchoOutput) + assert echo_result.message == "test-echo" + + # Test async hello operation + hello_result = await nexus_client.execute_operation( + MyNexusService.hello, + HelloInput(name="Test"), + id=str(uuid.uuid4()), + schedule_to_close_timeout=timedelta(seconds=10), + ) + assert isinstance(hello_result, HelloOutput) + assert hello_result.greeting == "Hello, Test!" + + # Test count operations + async def check_count(): + count = await client.count_nexus_operations( + f'Endpoint = "{endpoint_name}"' + ) + assert count.count == 2 + + await assert_eventually(check_count) + finally: + _ = await delete_nexus_endpoint( + id=create_response.endpoint.id, + version=create_response.endpoint.version, + client=client, + ) From f65112ac68493ac38f14eb5c578af49906826cab Mon Sep 17 00:00:00 2001 From: xumaple <45406854+xumaple@users.noreply.github.com> Date: Thu, 18 Jun 2026 15:26:38 -0400 Subject: [PATCH 34/40] AI-277: Add Python ADK integration samples (#318) * AI-277: Add Google ADK integration samples Add a google_adk_agents/ sample suite demonstrating the temporalio.contrib.google_adk_agents plugin, mirroring the openai_agents layout with one runnable subdirectory per scenario. Five scenarios cover the plugin's public surface: basic (GoogleAdkPlugin + TemporalModel), tools (activity_tool), agent_patterns (multi-agent sub_agents), mcp (TemporalMcpToolSet/Provider), and streaming (TemporalModel streaming_topic + WorkflowStream). Adds a google-adk dependency group pinned to temporalio[google-adk] >= 1.28.0, which streaming requires. Co-Authored-By: Claude Opus 4.8 (1M context) * PR comments + fix root readme * wordings * tests * Update .github/CODEOWNERS Co-authored-by: Brian Strauch --------- Co-authored-by: Claude Opus 4.8 (1M context) Co-authored-by: Brian Strauch --- .github/CODEOWNERS | 12 + README.md | 16 +- google_adk_agents/README.md | 53 + google_adk_agents/__init__.py | 0 google_adk_agents/agent_patterns/README.md | 37 + google_adk_agents/agent_patterns/__init__.py | 0 .../run_multi_agent_workflow.py | 24 + .../agent_patterns/run_worker.py | 31 + .../agent_patterns/workflows/__init__.py | 0 .../workflows/multi_agent_workflow.py | 81 ++ google_adk_agents/basic/README.md | 39 + google_adk_agents/basic/__init__.py | 0 .../basic/run_hello_world_workflow.py | 24 + google_adk_agents/basic/run_worker.py | 31 + google_adk_agents/basic/workflows/__init__.py | 0 .../basic/workflows/hello_world_workflow.py | 48 + google_adk_agents/mcp/README.md | 53 + google_adk_agents/mcp/__init__.py | 0 google_adk_agents/mcp/echo_mcp_server.py | 19 + google_adk_agents/mcp/run_echo_workflow.py | 22 + google_adk_agents/mcp/run_worker.py | 36 + google_adk_agents/mcp/toolsets.py | 30 + google_adk_agents/mcp/workflows/__init__.py | 0 .../mcp/workflows/echo_workflow.py | 57 + google_adk_agents/streaming/README.md | 40 + google_adk_agents/streaming/__init__.py | 0 .../streaming/run_streaming_workflow.py | 57 + google_adk_agents/streaming/run_worker.py | 31 + .../streaming/workflows/__init__.py | 0 .../streaming/workflows/streaming_workflow.py | 51 + google_adk_agents/tools/README.md | 37 + google_adk_agents/tools/__init__.py | 0 .../tools/activities/__init__.py | 0 .../tools/activities/weather_activity.py | 15 + .../tools/run_weather_workflow.py | 22 + google_adk_agents/tools/run_worker.py | 31 + google_adk_agents/tools/workflows/__init__.py | 0 .../tools/workflows/weather_workflow.py | 55 + pyproject.toml | 1 + tests/google_adk_agents/__init__.py | 0 tests/google_adk_agents/_mock_model.py | 56 + .../google_adk_agents/agent_patterns_test.py | 44 + tests/google_adk_agents/basic_test.py | 37 + tests/google_adk_agents/mcp_test.py | 47 + tests/google_adk_agents/streaming_test.py | 68 + tests/google_adk_agents/tools_test.py | 43 + uv.lock | 1211 +++++++++++++++-- 47 files changed, 2362 insertions(+), 97 deletions(-) create mode 100644 google_adk_agents/README.md create mode 100644 google_adk_agents/__init__.py create mode 100644 google_adk_agents/agent_patterns/README.md create mode 100644 google_adk_agents/agent_patterns/__init__.py create mode 100644 google_adk_agents/agent_patterns/run_multi_agent_workflow.py create mode 100644 google_adk_agents/agent_patterns/run_worker.py create mode 100644 google_adk_agents/agent_patterns/workflows/__init__.py create mode 100644 google_adk_agents/agent_patterns/workflows/multi_agent_workflow.py create mode 100644 google_adk_agents/basic/README.md create mode 100644 google_adk_agents/basic/__init__.py create mode 100644 google_adk_agents/basic/run_hello_world_workflow.py create mode 100644 google_adk_agents/basic/run_worker.py create mode 100644 google_adk_agents/basic/workflows/__init__.py create mode 100644 google_adk_agents/basic/workflows/hello_world_workflow.py create mode 100644 google_adk_agents/mcp/README.md create mode 100644 google_adk_agents/mcp/__init__.py create mode 100644 google_adk_agents/mcp/echo_mcp_server.py create mode 100644 google_adk_agents/mcp/run_echo_workflow.py create mode 100644 google_adk_agents/mcp/run_worker.py create mode 100644 google_adk_agents/mcp/toolsets.py create mode 100644 google_adk_agents/mcp/workflows/__init__.py create mode 100644 google_adk_agents/mcp/workflows/echo_workflow.py create mode 100644 google_adk_agents/streaming/README.md create mode 100644 google_adk_agents/streaming/__init__.py create mode 100644 google_adk_agents/streaming/run_streaming_workflow.py create mode 100644 google_adk_agents/streaming/run_worker.py create mode 100644 google_adk_agents/streaming/workflows/__init__.py create mode 100644 google_adk_agents/streaming/workflows/streaming_workflow.py create mode 100644 google_adk_agents/tools/README.md create mode 100644 google_adk_agents/tools/__init__.py create mode 100644 google_adk_agents/tools/activities/__init__.py create mode 100644 google_adk_agents/tools/activities/weather_activity.py create mode 100644 google_adk_agents/tools/run_weather_workflow.py create mode 100644 google_adk_agents/tools/run_worker.py create mode 100644 google_adk_agents/tools/workflows/__init__.py create mode 100644 google_adk_agents/tools/workflows/weather_workflow.py create mode 100644 tests/google_adk_agents/__init__.py create mode 100644 tests/google_adk_agents/_mock_model.py create mode 100644 tests/google_adk_agents/agent_patterns_test.py create mode 100644 tests/google_adk_agents/basic_test.py create mode 100644 tests/google_adk_agents/mcp_test.py create mode 100644 tests/google_adk_agents/streaming_test.py create mode 100644 tests/google_adk_agents/tools_test.py diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 2a9f9799d..03b609359 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -11,3 +11,15 @@ /*nexus/ @temporalio/nexus /tests/nexus*/ @temporalio/nexus /tests/*nexus/ @temporalio/nexus + +# The AI SDK team owns the AI integration samples and their tests. We add +# @temporalio/sdk too, so the SDK team can continue to manage repo-wide concerns. +/google_adk_agents/ @temporalio/sdk @temporalio/ai-sdk +/langgraph_plugin/ @temporalio/sdk @temporalio/ai-sdk +/langsmith_tracing/ @temporalio/sdk @temporalio/ai-sdk +/openai_agents/ @temporalio/sdk @temporalio/ai-sdk +/strands_plugin/ @temporalio/sdk @temporalio/ai-sdk +/tests/google_adk_agents/ @temporalio/sdk @temporalio/ai-sdk +/tests/langgraph_plugin/ @temporalio/sdk @temporalio/ai-sdk +/tests/langsmith_tracing/ @temporalio/sdk @temporalio/ai-sdk +/tests/strands_plugin/ @temporalio/sdk @temporalio/ai-sdk diff --git a/README.md b/README.md index 4d7091bcd..2111fa005 100644 --- a/README.md +++ b/README.md @@ -72,31 +72,41 @@ Some examples require extra dependencies. See each sample's directory for specif * [external_storage](external_storage) - Offload large payloads to S3-compatible object storage, plus a codec server for the Web UI and CLI. * [external_storage_redis](external_storage_redis) - Redis driver for external storage * [gevent_async](gevent_async) - Combine gevent and Temporal. +* [google_adk_agents](google_adk_agents) - Run Google ADK agents as durable Temporal workflows (model calls, tools, multi-agent, MCP, streaming). +* [hello_nexus](hello_nexus) - Define a Nexus service, implement operation handlers, and call them from a workflow. * [hello_standalone_activity](hello_standalone_activity) - Use activities without using a workflow. -* [langchain](langchain) - Orchestrate workflows for LangChain. +* [lambda_worker](lambda_worker) - Run a Temporal Worker inside an AWS Lambda function. * [langgraph_plugin](langgraph_plugin) - Run LangGraph workflows as durable Temporal workflows (Graph API and Functional API). +* [langsmith_tracing](langsmith_tracing) - Trace Temporal workflows with LangSmith via the LangSmith plugin. * [message_passing/introduction](message_passing/introduction/) - Introduction to queries, signals, and updates. * [message_passing/safe_message_handlers](message_passing/safe_message_handlers/) - Safely handling updates and signals. * [message_passing/update_with_start/lazy_initialization](message_passing/update_with_start/lazy_initialization/) - Use update-with-start to update a Shopping Cart, starting it if it does not exist. +* [nexus_cancel](nexus_cancel) - Fan out concurrent Nexus operations, take the first result, and cancel the rest. * [Nexus Messaging](nexus_messaging): Demonstrates how send signal, update and query messages through Nexus. This contains two samples, one sending messages to an existing workflow and a second that creates a workflow through Nexus and sends messages to it. +* [nexus_multiple_args](nexus_multiple_args) - Map a Nexus operation to a handler workflow that takes multiple arguments. * [nexus_standalone_operations](nexus_standalone_operations) - Execute Nexus operations directly from client code, without wrapping them in a workflow. * [open_telemetry](open_telemetry) - Trace workflows with OpenTelemetry. +* [openai_agents](openai_agents) - Run OpenAI Agents SDK agents as durable Temporal workflows. * [patching](patching) - Alter workflows safely with `patch` and `deprecate_patch`. * [polling](polling) - Recommended implementation of an activity that needs to periodically poll an external resource waiting its successful completion. * [prometheus](prometheus) - Configure Prometheus metrics on clients/workers. -* [workflow_streams](workflow_streams) - Workflow-hosted durable event stream via `temporalio.contrib.workflow_streams`. **Experimental** * [pydantic_converter](pydantic_converter) - Data converter for using Pydantic models. +* [pydantic_converter_v1](pydantic_converter_v1) - Data converter for Pydantic v1 models (prefer pydantic_converter for v2). +* [replay](replay) - Verify that workflow code changes are compatible with existing histories. +* [resource_pool](resource_pool) - Allocate a pool of shared resources across workflows. * [schedules](schedules) - Demonstrates a Workflow Execution that occurs according to a schedule. * [sentry](sentry) - Report errors to Sentry. +* [sleep_for_days](sleep_for_days) - A workflow that runs forever, sending an email every 30 days. * [strands_plugin](strands_plugin) - Run Strands Agents as durable Temporal workflows (model calls, tools, MCP, HITL). * [trio_async](trio_async) - Use asyncio Temporal in Trio-based environments. * [updatable_timer](updatable_timer) - A timer that can be updated while sleeping. +* [worker_multiprocessing](worker_multiprocessing) - Leverage Python multiprocessing to parallelize workflow tasks and other CPU bound operations by running multiple workers. * [worker_specific_task_queues](worker_specific_task_queues) - Use unique task queues to ensure activities run on specific workers. * [worker_versioning](worker_versioning) - Use the Worker Versioning feature to more easily version your workflows & other code. -* [worker_multiprocessing](worker_multiprocessing) - Leverage Python multiprocessing to parallelize workflow tasks and other CPU bound operations by running multiple workers. +* [workflow_streams](workflow_streams) - Workflow-hosted durable event stream via `temporalio.contrib.workflow_streams`. **Experimental** ## Test diff --git a/google_adk_agents/README.md b/google_adk_agents/README.md new file mode 100644 index 000000000..e95dae327 --- /dev/null +++ b/google_adk_agents/README.md @@ -0,0 +1,53 @@ +# Temporal Google ADK Integration + +⚠️ **Experimental** — This integration is experimental and its interfaces may +change prior to General Availability. + +This directory contains samples demonstrating how to run +[Google ADK](https://google.github.io/adk-docs/) agents durably inside Temporal +workflows using `temporalio.contrib.google_adk_agents`. Each scenario is a +self-contained subdirectory with its own worker, workflow starter, workflow and +activity packages, and README. + +## Overview + +The integration combines: + +- **Temporal workflows** for durable orchestration of agent control flow +- **Google ADK** for agent creation, model calls, tools, and MCP integration + +`GoogleAdkPlugin` configures a Pydantic payload converter, sandbox passthrough +for `google.adk` / `google.genai` / `mcp`, a deterministic ADK runtime, and the +model activities. `TemporalModel` runs each LLM call as an activity, so every +model turn is durable and observable. + +## Prerequisites + +- Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev) +- Dependencies installed via `uv sync --group google-adk` +- Google API key set as an environment variable: + `export GOOGLE_API_KEY=your_key_here` + +All scenarios default to the `gemini-2.5-flash` model. ADK also supports other +providers (for example, non-Gemini models via LiteLLM); swap the model name on +`TemporalModel` to use one. + +## Scenarios + +Each directory contains a complete example with its own README: + +| Scenario | What it shows | +| --- | --- | +| [basic](./basic/README.md) | A single ADK agent with `TemporalModel` and one model call — no tools. The minimal end-to-end example. | +| [tools](./tools/README.md) | A Temporal activity wrapped as an ADK tool with `activity_tool`, so tool calls run as their own activities. | +| [agent_patterns](./agent_patterns/README.md) | A coordinator `LlmAgent` with `sub_agents`, each a `TemporalModel` with a per-agent activity summary. | +| [mcp](./mcp/README.md) | A local echo MCP toolset via `TemporalMcpToolSet` / `TemporalMcpToolSetProvider`, running MCP tools as activities. Self-contained, no Node required. | +| [streaming](./streaming/README.md) | Token streaming via `TemporalModel(streaming_topic=...)` + `WorkflowStream`, consumed by a starter with `WorkflowStreamClient`. | + +To run any scenario, start its worker in one terminal and its workflow starter +in another: + +```bash +uv run python -m google_adk_agents..run_worker +uv run python -m google_adk_agents..run__workflow +``` diff --git a/google_adk_agents/__init__.py b/google_adk_agents/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/agent_patterns/README.md b/google_adk_agents/agent_patterns/README.md new file mode 100644 index 000000000..7da938d87 --- /dev/null +++ b/google_adk_agents/agent_patterns/README.md @@ -0,0 +1,37 @@ +# Agent Patterns — Multi-Agent Coordinator + +A coordinator `LlmAgent` with `sub_agents=[researcher, writer]`. Each agent uses +its own `TemporalModel` with an `ActivityConfig(summary=...)`, so the agents +show up as named activities in workflow history. This demonstrates ADK's +built-in `transfer_to_agent` handoff running durably, with per-agent activity +summaries. + +Before running, review the [prerequisites in the suite README](../README.md) +(Temporal dev server, `uv sync --group google-adk`, and +`export GOOGLE_API_KEY=...`). + +## Running + +Start the worker in one terminal: + +```bash +uv run python -m google_adk_agents.agent_patterns.run_worker +``` + +Then start the workflow in another terminal: + +```bash +uv run python -m google_adk_agents.agent_patterns.run_multi_agent_workflow +``` + +## What to expect + +The starter asks for a haiku about the ocean. The coordinator delegates to the +researcher and then the writer; the starter prints the final haiku. + +## In the Temporal UI + +Open the workflow `google-adk-agents-agent-patterns-workflow-id`. The +`invoke_model` activities are labeled with their agent summaries — +"Coordinator Agent", "Researcher Agent", "Writer Agent" — so you can follow the +handoffs between agents directly in the history. diff --git a/google_adk_agents/agent_patterns/__init__.py b/google_adk_agents/agent_patterns/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/agent_patterns/run_multi_agent_workflow.py b/google_adk_agents/agent_patterns/run_multi_agent_workflow.py new file mode 100644 index 000000000..c9bc8677b --- /dev/null +++ b/google_adk_agents/agent_patterns/run_multi_agent_workflow.py @@ -0,0 +1,24 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin + +from google_adk_agents.agent_patterns.workflows.multi_agent_workflow import ( + MultiAgentWorkflow, +) + + +async def main(): + client = await Client.connect("localhost:7233", plugins=[GoogleAdkPlugin()]) + + result = await client.execute_workflow( + MultiAgentWorkflow.run, + "the ocean", + id="google-adk-agents-agent-patterns-workflow-id", + task_queue="google-adk-agents-agent-patterns", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/agent_patterns/run_worker.py b/google_adk_agents/agent_patterns/run_worker.py new file mode 100644 index 000000000..56fd267f3 --- /dev/null +++ b/google_adk_agents/agent_patterns/run_worker.py @@ -0,0 +1,31 @@ +from __future__ import annotations + +import asyncio + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.agent_patterns.workflows.multi_agent_workflow import ( + MultiAgentWorkflow, +) + + +async def main(): + # Build the plugin once and give the same instance to the client and the + # worker. + plugin = GoogleAdkPlugin() + + client = await Client.connect("localhost:7233", plugins=[plugin]) + + worker = Worker( + client, + task_queue="google-adk-agents-agent-patterns", + workflows=[MultiAgentWorkflow], + plugins=[plugin], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/agent_patterns/workflows/__init__.py b/google_adk_agents/agent_patterns/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/agent_patterns/workflows/multi_agent_workflow.py b/google_adk_agents/agent_patterns/workflows/multi_agent_workflow.py new file mode 100644 index 000000000..2867cd223 --- /dev/null +++ b/google_adk_agents/agent_patterns/workflows/multi_agent_workflow.py @@ -0,0 +1,81 @@ +from datetime import timedelta + +from google.adk.agents import LlmAgent +from google.adk.runners import Runner +from google.adk.sessions import InMemorySessionService +from google.genai import types +from temporalio import workflow +from temporalio.contrib.google_adk_agents import TemporalModel +from temporalio.workflow import ActivityConfig + + +# @@@SNIPSTART google-adk-agents-agent-patterns-multi-agent-workflow +@workflow.defn +class MultiAgentWorkflow: + @workflow.run + async def run(self, topic: str) -> str: + session_service = InMemorySessionService() + session = await session_service.create_session( + app_name="multi_agent_app", user_id="user" + ) + + # Give each sub-agent its own TemporalModel with an ActivityConfig + # summary, so its model turns show up as named activities in history. + researcher = LlmAgent( + name="researcher", + model=TemporalModel( + "gemini-2.5-flash", + activity_config=ActivityConfig(summary="Researcher Agent"), + ), + instruction="You are a researcher. Find information about the topic.", + ) + + writer = LlmAgent( + name="writer", + model=TemporalModel( + "gemini-2.5-flash", + activity_config=ActivityConfig(summary="Writer Agent"), + ), + instruction="You are a poet. Write a haiku based on the research.", + ) + + # The coordinator hands off to the sub-agents using ADK's built-in + # transfer_to_agent, which runs durably here. + coordinator = LlmAgent( + name="coordinator", + model=TemporalModel( + "gemini-2.5-flash", + activity_config=ActivityConfig( + start_to_close_timeout=timedelta(seconds=30), + summary="Coordinator Agent", + ), + ), + instruction="You are a coordinator. Delegate to researcher then writer.", + sub_agents=[researcher, writer], + ) + + runner = Runner( + agent=coordinator, + app_name="multi_agent_app", + session_service=session_service, + ) + + final_text = "" + user_msg = types.Content( + role="user", + parts=[ + types.Part( + text=f"Write a haiku about {topic}. First research it, then write it." + ) + ], + ) + async for event in runner.run_async( + user_id="user", session_id=session.id, new_message=user_msg + ): + if event.content and event.content.parts and event.content.parts[0].text: + final_text = event.content.parts[0].text + + return final_text + + +# @@@SNIPEND diff --git a/google_adk_agents/basic/README.md b/google_adk_agents/basic/README.md new file mode 100644 index 000000000..283e379b7 --- /dev/null +++ b/google_adk_agents/basic/README.md @@ -0,0 +1,39 @@ +# Basic — Single Agent, One Model Call + +The minimal Google ADK + Temporal sample: an ordinary ADK `Agent` whose +`model=TemporalModel("gemini-2.5-flash")`, driven by an `InMemoryRunner` inside +a workflow. No tools, no streaming — just one model turn. + +It demonstrates that `GoogleAdkPlugin`'s deterministic runtime and Pydantic +payload converter let an unmodified ADK agent run durably in a workflow, with +every LLM call surfaced as an `invoke_model` activity. + +Before running, review the [prerequisites in the suite README](../README.md) +(Temporal dev server, `uv sync --group google-adk`, and +`export GOOGLE_API_KEY=...`). + +## Running + +Start the worker in one terminal: + +```bash +uv run python -m google_adk_agents.basic.run_worker +``` + +Then start the workflow in another terminal: + +```bash +uv run python -m google_adk_agents.basic.run_hello_world_workflow +``` + +## What to expect + +The starter prints the agent's response — a haiku about recursion. The agent is +instructed to respond only in haikus. + +## In the Temporal UI + +Open the workflow `google-adk-agents-basic-workflow-id`. In the history you will +see one `invoke_model` activity for the single model turn. That activity is +where the call to Gemini actually happens — the workflow itself stays +deterministic and replay-safe. diff --git a/google_adk_agents/basic/__init__.py b/google_adk_agents/basic/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/basic/run_hello_world_workflow.py b/google_adk_agents/basic/run_hello_world_workflow.py new file mode 100644 index 000000000..e410ed875 --- /dev/null +++ b/google_adk_agents/basic/run_hello_world_workflow.py @@ -0,0 +1,24 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin + +from google_adk_agents.basic.workflows.hello_world_workflow import ( + HelloWorldAgentWorkflow, +) + + +async def main(): + client = await Client.connect("localhost:7233", plugins=[GoogleAdkPlugin()]) + + result = await client.execute_workflow( + HelloWorldAgentWorkflow.run, + "Tell me about recursion in programming.", + id="google-adk-agents-basic-workflow-id", + task_queue="google-adk-agents-basic", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/basic/run_worker.py b/google_adk_agents/basic/run_worker.py new file mode 100644 index 000000000..b1cef43f2 --- /dev/null +++ b/google_adk_agents/basic/run_worker.py @@ -0,0 +1,31 @@ +from __future__ import annotations + +import asyncio + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.basic.workflows.hello_world_workflow import ( + HelloWorldAgentWorkflow, +) + + +async def main(): + # Build the plugin once and give the same instance to the client and the + # worker. + plugin = GoogleAdkPlugin() + + client = await Client.connect("localhost:7233", plugins=[plugin]) + + worker = Worker( + client, + task_queue="google-adk-agents-basic", + workflows=[HelloWorldAgentWorkflow], + plugins=[plugin], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/basic/workflows/__init__.py b/google_adk_agents/basic/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/basic/workflows/hello_world_workflow.py b/google_adk_agents/basic/workflows/hello_world_workflow.py new file mode 100644 index 000000000..3915f85f4 --- /dev/null +++ b/google_adk_agents/basic/workflows/hello_world_workflow.py @@ -0,0 +1,48 @@ +from google.adk import Agent +from google.adk.runners import InMemoryRunner +from google.adk.utils.context_utils import Aclosing +from google.genai import types +from temporalio import workflow +from temporalio.contrib.google_adk_agents import TemporalModel + + +# @@@SNIPSTART google-adk-agents-basic-hello-world-agent-workflow +@workflow.defn +class HelloWorldAgentWorkflow: + @workflow.run + async def run(self, prompt: str) -> str: + # A normal ADK agent. The one Temporal-specific piece is TemporalModel, + # which runs each model call as an `invoke_model` activity. + agent = Agent( + name="hello_world_agent", + model=TemporalModel("gemini-2.5-flash"), + instruction="You only respond in haikus.", + ) + + # InMemoryRunner drives the agent. The plugin points ADK's session-id + # generation at workflow.uuid4(), so creating a session here is + # replay-safe. + runner = InMemoryRunner(agent=agent, app_name="hello_world_app") + session = await runner.session_service.create_session( + app_name="hello_world_app", user_id="user" + ) + + # Keep the last bit of text the agent produces. + final_text = "" + async with Aclosing( + runner.run_async( + user_id="user", + session_id=session.id, + new_message=types.Content(role="user", parts=[types.Part(text=prompt)]), + ) + ) as agen: + async for event in agen: + if event.content and event.content.parts: + for part in event.content.parts: + if part.text: + final_text = part.text + + return final_text + + +# @@@SNIPEND diff --git a/google_adk_agents/mcp/README.md b/google_adk_agents/mcp/README.md new file mode 100644 index 000000000..b26423002 --- /dev/null +++ b/google_adk_agents/mcp/README.md @@ -0,0 +1,53 @@ +# MCP — MCP Tools as Activities + +An agent backed by `TemporalMcpToolSet("echo", ...)`, with the worker's plugin +configured as +`GoogleAdkPlugin(toolset_providers=[TemporalMcpToolSetProvider("echo", echo_toolset)])`. +The shared factory in [`toolsets.py`](./toolsets.py) builds an `McpToolset` over +the in-repo [`echo_mcp_server.py`](./echo_mcp_server.py), a one-tool FastMCP +server that echoes back the message it is given. + +The MCP server's `list-tools` and `call-tool` operations run as Temporal +activities (`echo-list-tools` / `echo-call-tool`), so the server is touched only +from activity context, never inside the workflow sandbox. The +`not_in_workflow_toolset` factory lets the same agent also run locally, outside a +workflow. + +## Self-contained, no Node required + +The echo server is a small Python script launched as a subprocess with the +current interpreter (`sys.executable`), so there is nothing to install and no +network access. Swap `echo_mcp_server.py` for any real MCP server (filesystem, +Postgres, GitHub, etc.) by changing the factory in `toolsets.py`. + +Also review the [prerequisites in the suite README](../README.md) (Temporal dev +server, `uv sync --group google-adk`, and `export GOOGLE_API_KEY=...`). + +## Running + +Start the worker in one terminal: + +```bash +uv run python -m google_adk_agents.mcp.run_worker +``` + +Then start the workflow in another terminal: + +```bash +uv run python -m google_adk_agents.mcp.run_echo_workflow +``` + +The worker spawns `echo_mcp_server.py` itself; you don't need to start it +separately. + +## What to expect + +The starter asks the agent to echo `hello from MCP`. The agent calls the echo +MCP tool and reports back when it's done. + +## In the Temporal UI + +Open the workflow `google-adk-agents-mcp-workflow-id`. The history shows an +`echo-list-tools` activity (discovering the MCP tools), `invoke_model` +activities for the model turns, and an `echo-call-tool` activity for each tool +the model invokes. The MCP subprocess only ever runs inside those activities. diff --git a/google_adk_agents/mcp/__init__.py b/google_adk_agents/mcp/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/mcp/echo_mcp_server.py b/google_adk_agents/mcp/echo_mcp_server.py new file mode 100644 index 000000000..2eb1447f6 --- /dev/null +++ b/google_adk_agents/mcp/echo_mcp_server.py @@ -0,0 +1,19 @@ +"""Tiny FastMCP server: exposes a single ``echo`` tool over stdio. + +The worker launches this script as a subprocess and connects to it via the +MCP stdio transport. Swap it for any real MCP server in production. +""" + +from mcp.server.fastmcp import FastMCP + +mcp = FastMCP("echo-server") + + +@mcp.tool() +def echo(message: str) -> str: + """Return the input message unchanged.""" + return message + + +if __name__ == "__main__": + mcp.run() diff --git a/google_adk_agents/mcp/run_echo_workflow.py b/google_adk_agents/mcp/run_echo_workflow.py new file mode 100644 index 000000000..b8bfd01dc --- /dev/null +++ b/google_adk_agents/mcp/run_echo_workflow.py @@ -0,0 +1,22 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin + +from google_adk_agents.mcp.workflows.echo_workflow import EchoMcpWorkflow + + +async def main(): + client = await Client.connect("localhost:7233", plugins=[GoogleAdkPlugin()]) + + result = await client.execute_workflow( + EchoMcpWorkflow.run, + "Echo 'hello from MCP'.", + id="google-adk-agents-mcp-workflow-id", + task_queue="google-adk-agents-mcp", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/mcp/run_worker.py b/google_adk_agents/mcp/run_worker.py new file mode 100644 index 000000000..35a42f8b3 --- /dev/null +++ b/google_adk_agents/mcp/run_worker.py @@ -0,0 +1,36 @@ +from __future__ import annotations + +import asyncio + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import ( + GoogleAdkPlugin, + TemporalMcpToolSetProvider, +) +from temporalio.worker import Worker + +from google_adk_agents.mcp.toolsets import echo_toolset +from google_adk_agents.mcp.workflows.echo_workflow import EchoMcpWorkflow + + +async def main(): + # The provider adds the echo-list-tools and echo-call-tool activities. Same + # as the other samples: build the plugin once and give the same instance to + # the client and the worker. + plugin = GoogleAdkPlugin( + toolset_providers=[TemporalMcpToolSetProvider("echo", echo_toolset)] + ) + + client = await Client.connect("localhost:7233", plugins=[plugin]) + + worker = Worker( + client, + task_queue="google-adk-agents-mcp", + workflows=[EchoMcpWorkflow], + plugins=[plugin], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/mcp/toolsets.py b/google_adk_agents/mcp/toolsets.py new file mode 100644 index 000000000..4dfd5c947 --- /dev/null +++ b/google_adk_agents/mcp/toolsets.py @@ -0,0 +1,30 @@ +import sys +from pathlib import Path +from typing import Any + +from google.adk.tools.mcp_tool import McpToolset +from google.adk.tools.mcp_tool.mcp_session_manager import StdioConnectionParams +from mcp import StdioServerParameters + +ECHO_SERVER = Path(__file__).parent / "echo_mcp_server.py" + + +# @@@SNIPSTART google-adk-agents-mcp-echo-toolset-factory +def echo_toolset(_: Any | None) -> McpToolset: + """Build an McpToolset over the local echo MCP server. + + The server is the in-repo echo_mcp_server.py script, launched as a + subprocess with the current Python interpreter. This factory only runs + inside an activity or a local ADK run, never inside workflow code. + """ + return McpToolset( + connection_params=StdioConnectionParams( + server_params=StdioServerParameters( + command=sys.executable, + args=[str(ECHO_SERVER)], + ), + ), + ) + + +# @@@SNIPEND diff --git a/google_adk_agents/mcp/workflows/__init__.py b/google_adk_agents/mcp/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/mcp/workflows/echo_workflow.py b/google_adk_agents/mcp/workflows/echo_workflow.py new file mode 100644 index 000000000..bf435fb84 --- /dev/null +++ b/google_adk_agents/mcp/workflows/echo_workflow.py @@ -0,0 +1,57 @@ +from google.adk import Agent +from google.adk.runners import Runner +from google.adk.sessions import InMemorySessionService +from google.adk.utils.context_utils import Aclosing +from google.genai import types +from temporalio import workflow +from temporalio.contrib.google_adk_agents import TemporalMcpToolSet, TemporalModel + +from google_adk_agents.mcp.toolsets import echo_toolset + + +# @@@SNIPSTART google-adk-agents-mcp-echo-mcp-workflow +@workflow.defn +class EchoMcpWorkflow: + @workflow.run + async def run(self, prompt: str) -> str: + # TemporalMcpToolSet runs the MCP server's list-tools and call-tool + # calls as activities (the provider names them echo-list-tools and + # echo-call-tool on the worker). The not_in_workflow_toolset factory + # lets you run this same agent locally, outside a workflow, by hitting + # the MCP server directly. + agent = Agent( + name="echo_agent", + model=TemporalModel("gemini-2.5-flash"), + instruction="Use the echo tool to echo back the user's message.", + tools=[TemporalMcpToolSet("echo", not_in_workflow_toolset=echo_toolset)], + ) + + session_service = InMemorySessionService() + session = await session_service.create_session( + app_name="echo_app", user_id="user" + ) + + runner = Runner( + agent=agent, + app_name="echo_app", + session_service=session_service, + ) + + final_text = "" + async with Aclosing( + runner.run_async( + user_id="user", + session_id=session.id, + new_message=types.Content(role="user", parts=[types.Part(text=prompt)]), + ) + ) as agen: + async for event in agen: + if event.content and event.content.parts: + for part in event.content.parts: + if part.text: + final_text = part.text + + return final_text + + +# @@@SNIPEND diff --git a/google_adk_agents/streaming/README.md b/google_adk_agents/streaming/README.md new file mode 100644 index 000000000..47675438f --- /dev/null +++ b/google_adk_agents/streaming/README.md @@ -0,0 +1,40 @@ +# Streaming — Token Streaming to an External Consumer + +A workflow that hosts a `WorkflowStream` and uses +`TemporalModel("gemini-2.5-flash", streaming_topic="responses")` with +`RunConfig(streaming_mode=StreamingMode.SSE)`. The streaming model activity +publishes raw `LlmResponse` chunks to the topic as they arrive; the starter +subscribes via `WorkflowStreamClient` and prints chunks as they come in, then +prints the final result. + +> Streaming support in the plugin is experimental and may change. + +Before running, review the [prerequisites in the suite README](../README.md) +(Temporal dev server, `uv sync --group google-adk`, and +`export GOOGLE_API_KEY=...`). + +## Running + +Start the worker in one terminal: + +```bash +uv run python -m google_adk_agents.streaming.run_worker +``` + +Then start the workflow in another terminal: + +```bash +uv run python -m google_adk_agents.streaming.run_streaming_workflow +``` + +## What to expect + +The starter asks for a short story and prints the response token-by-token as the +model streams it, then prints the assembled final result. + +## In the Temporal UI + +Open the workflow `google-adk-agents-streaming-workflow-id`. The history shows +an `invoke_model_streaming` activity (instead of `invoke_model`). That activity +calls the model with streaming enabled and publishes each chunk to the +`responses` topic, which is what the starter subscribes to. diff --git a/google_adk_agents/streaming/__init__.py b/google_adk_agents/streaming/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/streaming/run_streaming_workflow.py b/google_adk_agents/streaming/run_streaming_workflow.py new file mode 100644 index 000000000..87880371d --- /dev/null +++ b/google_adk_agents/streaming/run_streaming_workflow.py @@ -0,0 +1,57 @@ +import asyncio +from datetime import timedelta + +from google.adk.models.llm_response import LlmResponse +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from google_adk_agents.streaming.workflows.streaming_workflow import ( + StreamingAgentWorkflow, +) + +WORKFLOW_ID = "google-adk-agents-streaming-workflow-id" + + +async def main(): + client = await Client.connect("localhost:7233", plugins=[GoogleAdkPlugin()]) + + # Start the workflow but don't wait on the result yet, so we can subscribe + # to its stream while it's running. + handle = await client.start_workflow( + StreamingAgentWorkflow.run, + "Tell me a short story about a robot learning to paint.", + id=WORKFLOW_ID, + task_queue="google-adk-agents-streaming", + ) + + # Subscribe to the "responses" topic and print text chunks as they arrive. + stream = WorkflowStreamClient.create(client, WORKFLOW_ID) + + async def print_chunks() -> None: + print("Streaming response:") + async for item in stream.subscribe( + ["responses"], + from_offset=0, + result_type=LlmResponse, + poll_cooldown=timedelta(milliseconds=50), + ): + response = item.data + if response.content and response.content.parts: + for part in response.content.parts: + if part.text: + print(part.text, end="", flush=True) + + chunk_task = asyncio.create_task(print_chunks()) + result = await handle.result() + chunk_task.cancel() + try: + await chunk_task + except asyncio.CancelledError: + pass + + print(f"\n\nFinal result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/streaming/run_worker.py b/google_adk_agents/streaming/run_worker.py new file mode 100644 index 000000000..efd3cd031 --- /dev/null +++ b/google_adk_agents/streaming/run_worker.py @@ -0,0 +1,31 @@ +from __future__ import annotations + +import asyncio + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.streaming.workflows.streaming_workflow import ( + StreamingAgentWorkflow, +) + + +async def main(): + # Build the plugin once and give the same instance to the client and the + # worker. + plugin = GoogleAdkPlugin() + + client = await Client.connect("localhost:7233", plugins=[plugin]) + + worker = Worker( + client, + task_queue="google-adk-agents-streaming", + workflows=[StreamingAgentWorkflow], + plugins=[plugin], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/streaming/workflows/__init__.py b/google_adk_agents/streaming/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/streaming/workflows/streaming_workflow.py b/google_adk_agents/streaming/workflows/streaming_workflow.py new file mode 100644 index 000000000..7ef0c27b5 --- /dev/null +++ b/google_adk_agents/streaming/workflows/streaming_workflow.py @@ -0,0 +1,51 @@ +from google.adk import Agent +from google.adk.agents.run_config import RunConfig, StreamingMode +from google.adk.runners import InMemoryRunner +from google.genai import types +from temporalio import workflow +from temporalio.contrib.google_adk_agents import TemporalModel +from temporalio.contrib.workflow_streams import WorkflowStream + + +# @@@SNIPSTART google-adk-agents-streaming-streaming-agent-workflow +@workflow.defn +class StreamingAgentWorkflow: + @workflow.init + def __init__(self, prompt: str) -> None: + # The workflow hosts a WorkflowStream. The streaming activity publishes + # raw LlmResponse chunks to it as they come back from the model. + self.stream = WorkflowStream() + + @workflow.run + async def run(self, prompt: str) -> str: + # streaming_topic is the topic the chunks get published to. + # RunConfig(streaming_mode=SSE) tells ADK to stream, which routes the + # call through the invoke_model_streaming activity. + model = TemporalModel("gemini-2.5-flash", streaming_topic="responses") + agent = Agent( + name="streaming_agent", + model=model, + instruction="You are a helpful assistant.", + ) + + runner = InMemoryRunner(agent=agent, app_name="streaming_app") + session = await runner.session_service.create_session( + app_name="streaming_app", user_id="user" + ) + + final_text = "" + async for event in runner.run_async( + user_id="user", + session_id=session.id, + new_message=types.Content(role="user", parts=[types.Part(text=prompt)]), + run_config=RunConfig(streaming_mode=StreamingMode.SSE), + ): + if event.content and event.content.parts: + for part in event.content.parts: + if part.text: + final_text = part.text + + return final_text + + +# @@@SNIPEND diff --git a/google_adk_agents/tools/README.md b/google_adk_agents/tools/README.md new file mode 100644 index 000000000..c35e3a698 --- /dev/null +++ b/google_adk_agents/tools/README.md @@ -0,0 +1,37 @@ +# Tools — Temporal Activities as ADK Tools + +A weather agent whose `get_weather` Temporal activity is wrapped as an ADK tool +with `activity_tool(...)`. The model decides to call the tool; the tool runs as +its own Temporal activity — retryable and observable — rather than inline in the +workflow. This demonstrates the activity boundary for tool calls. + +Before running, review the [prerequisites in the suite README](../README.md) +(Temporal dev server, `uv sync --group google-adk`, and +`export GOOGLE_API_KEY=...`). + +## Running + +Start the worker in one terminal: + +```bash +uv run python -m google_adk_agents.tools.run_worker +``` + +Then start the workflow in another terminal: + +```bash +uv run python -m google_adk_agents.tools.run_weather_workflow +``` + +## What to expect + +The starter asks "What is the weather in New York?" and prints the agent's +answer, which incorporates the (canned) weather the tool returned. + +## In the Temporal UI + +Open the workflow `google-adk-agents-tools-workflow-id`. The history shows the +model deciding to call the tool, then a `get_weather` activity for the tool +call, then a second `invoke_model` activity where the model turns the tool +result into a final answer. Each model turn and each tool call is its own +activity. diff --git a/google_adk_agents/tools/__init__.py b/google_adk_agents/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/tools/activities/__init__.py b/google_adk_agents/tools/activities/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/tools/activities/weather_activity.py b/google_adk_agents/tools/activities/weather_activity.py new file mode 100644 index 000000000..2a1fd10a7 --- /dev/null +++ b/google_adk_agents/tools/activities/weather_activity.py @@ -0,0 +1,15 @@ +from temporalio import activity + + +# @@@SNIPSTART google-adk-agents-tools-get-weather +@activity.defn +async def get_weather(city: str) -> str: + """Return the weather for a city. + + In a real sample this would call a weather API. Here it returns a fixed + response so the sample runs without external credentials. + """ + return f"The weather in {city} is warm and sunny, 17 degrees." + + +# @@@SNIPEND diff --git a/google_adk_agents/tools/run_weather_workflow.py b/google_adk_agents/tools/run_weather_workflow.py new file mode 100644 index 000000000..db15ecf16 --- /dev/null +++ b/google_adk_agents/tools/run_weather_workflow.py @@ -0,0 +1,22 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin + +from google_adk_agents.tools.workflows.weather_workflow import WeatherAgentWorkflow + + +async def main(): + client = await Client.connect("localhost:7233", plugins=[GoogleAdkPlugin()]) + + result = await client.execute_workflow( + WeatherAgentWorkflow.run, + "What is the weather in New York?", + id="google-adk-agents-tools-workflow-id", + task_queue="google-adk-agents-tools", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/tools/run_worker.py b/google_adk_agents/tools/run_worker.py new file mode 100644 index 000000000..20b6761d3 --- /dev/null +++ b/google_adk_agents/tools/run_worker.py @@ -0,0 +1,31 @@ +from __future__ import annotations + +import asyncio + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.tools.activities.weather_activity import get_weather +from google_adk_agents.tools.workflows.weather_workflow import WeatherAgentWorkflow + + +async def main(): + # Build the plugin once and give the same instance to the client and the + # worker. + plugin = GoogleAdkPlugin() + + client = await Client.connect("localhost:7233", plugins=[plugin]) + + worker = Worker( + client, + task_queue="google-adk-agents-tools", + workflows=[WeatherAgentWorkflow], + activities=[get_weather], + plugins=[plugin], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/tools/workflows/__init__.py b/google_adk_agents/tools/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/tools/workflows/weather_workflow.py b/google_adk_agents/tools/workflows/weather_workflow.py new file mode 100644 index 000000000..96088c884 --- /dev/null +++ b/google_adk_agents/tools/workflows/weather_workflow.py @@ -0,0 +1,55 @@ +from datetime import timedelta + +import temporalio.contrib.google_adk_agents.workflow +from google.adk import Agent +from google.adk.runners import InMemoryRunner +from google.adk.utils.context_utils import Aclosing +from google.genai import types +from temporalio import workflow +from temporalio.contrib.google_adk_agents import TemporalModel + +from google_adk_agents.tools.activities.weather_activity import get_weather + + +# @@@SNIPSTART google-adk-agents-tools-weather-agent-workflow +@workflow.defn +class WeatherAgentWorkflow: + @workflow.run + async def run(self, prompt: str) -> str: + # Wrap the get_weather activity as an ADK tool. When the model calls it, + # activity_tool runs it as a real Temporal activity instead of inline, + # so it's retryable and shows up in history. + weather_tool = temporalio.contrib.google_adk_agents.workflow.activity_tool( + get_weather, start_to_close_timeout=timedelta(seconds=60) + ) + + agent = Agent( + name="weather_agent", + model=TemporalModel("gemini-2.5-flash"), + instruction="Use the get_weather tool to answer weather questions.", + tools=[weather_tool], + ) + + runner = InMemoryRunner(agent=agent, app_name="weather_app") + session = await runner.session_service.create_session( + app_name="weather_app", user_id="user" + ) + + final_text = "" + async with Aclosing( + runner.run_async( + user_id="user", + session_id=session.id, + new_message=types.Content(role="user", parts=[types.Part(text=prompt)]), + ) + ) as agen: + async for event in agen: + if event.content and event.content.parts: + for part in event.content.parts: + if part.text: + final_text = part.text + + return final_text + + +# @@@SNIPEND diff --git a/pyproject.toml b/pyproject.toml index a148f865f..3ccff7c62 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,7 @@ external-storage = [ ] external-storage-redis = ["redis>=5.0.0,<8"] gevent = ["gevent>=25.4.2 ; python_version >= '3.8'"] +google-adk = ["temporalio[google-adk] >= 1.28.0", "google-adk>=1.27.0,<2"] langsmith-tracing = [ "openai>=1.4.0", "langsmith>=0.7.0", diff --git a/tests/google_adk_agents/__init__.py b/tests/google_adk_agents/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/google_adk_agents/_mock_model.py b/tests/google_adk_agents/_mock_model.py new file mode 100644 index 000000000..2b4c8f895 --- /dev/null +++ b/tests/google_adk_agents/_mock_model.py @@ -0,0 +1,56 @@ +from __future__ import annotations + +from collections.abc import AsyncGenerator +from typing import Any + +import pytest +from google.adk.models import BaseLlm, LLMRegistry +from google.adk.models.llm_request import LlmRequest +from google.adk.models.llm_response import LlmResponse +from google.genai.types import Content, FunctionCall, Part + +MODEL = "gemini-2.5-flash" + + +def text(s: str) -> LlmResponse: + return LlmResponse(content=Content(role="model", parts=[Part(text=s)])) + + +def tool_call(name: str, args: dict[str, Any]) -> LlmResponse: + return LlmResponse( + content=Content( + role="model", + parts=[Part(function_call=FunctionCall(name=name, args=args))], + ) + ) + + +def patch_model( + monkeypatch: pytest.MonkeyPatch, + responses: list[LlmResponse], + *, + stream_chunks: bool = False, +) -> None: + script = list(responses) + orig_new_llm = LLMRegistry.new_llm # staticmethod + + class _Mock(BaseLlm): + async def generate_content_async( + self, llm_request: LlmRequest, stream: bool = False + ) -> AsyncGenerator[LlmResponse, None]: + if stream_chunks: + # The streaming sample is single-turn, so yield every scripted + # chunk on this one call. + while script: + yield script.pop(0) + return + if not script: + raise AssertionError("mock model script exhausted") + yield script.pop(0) + + def fake_new_llm(model: str) -> BaseLlm: + if model == MODEL: + return _Mock(model=model) + return orig_new_llm(model) + + monkeypatch.setattr(LLMRegistry, "new_llm", staticmethod(fake_new_llm)) diff --git a/tests/google_adk_agents/agent_patterns_test.py b/tests/google_adk_agents/agent_patterns_test.py new file mode 100644 index 000000000..d9a3c2592 --- /dev/null +++ b/tests/google_adk_agents/agent_patterns_test.py @@ -0,0 +1,44 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.agent_patterns.workflows.multi_agent_workflow import ( + MultiAgentWorkflow, +) +from tests.google_adk_agents._mock_model import patch_model, text, tool_call + + +async def test_agent_patterns(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_model( + monkeypatch, + [ + tool_call("transfer_to_agent", {"agent_name": "researcher"}), + tool_call("transfer_to_agent", {"agent_name": "writer"}), + text("snow on the mountain"), + ], + ) + + task_queue = f"google-adk-agents-agent-patterns-{uuid.uuid4()}" + plugin = GoogleAdkPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[MultiAgentWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + MultiAgentWorkflow.run, + "mountains", + id=f"google-adk-agents-agent-patterns-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "snow on the mountain" diff --git a/tests/google_adk_agents/basic_test.py b/tests/google_adk_agents/basic_test.py new file mode 100644 index 000000000..a1354e4bb --- /dev/null +++ b/tests/google_adk_agents/basic_test.py @@ -0,0 +1,37 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.basic.workflows.hello_world_workflow import ( + HelloWorldAgentWorkflow, +) +from tests.google_adk_agents._mock_model import patch_model, text + + +async def test_basic(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_model(monkeypatch, [text("a quiet haiku")]) + + task_queue = f"google-adk-agents-basic-{uuid.uuid4()}" + plugin = GoogleAdkPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HelloWorldAgentWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + HelloWorldAgentWorkflow.run, + "Say hi", + id=f"google-adk-agents-basic-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "a quiet haiku" diff --git a/tests/google_adk_agents/mcp_test.py b/tests/google_adk_agents/mcp_test.py new file mode 100644 index 000000000..ae8729d0b --- /dev/null +++ b/tests/google_adk_agents/mcp_test.py @@ -0,0 +1,47 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import ( + GoogleAdkPlugin, + TemporalMcpToolSetProvider, +) +from temporalio.worker import Worker + +from google_adk_agents.mcp.toolsets import echo_toolset +from google_adk_agents.mcp.workflows.echo_workflow import EchoMcpWorkflow +from tests.google_adk_agents._mock_model import patch_model, text, tool_call + + +async def test_mcp(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_model( + monkeypatch, + [ + tool_call("echo", {"message": "hello from MCP"}), + text("Done!"), + ], + ) + + task_queue = f"google-adk-agents-mcp-{uuid.uuid4()}" + plugin = GoogleAdkPlugin( + toolset_providers=[TemporalMcpToolSetProvider("echo", echo_toolset)] + ) + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[EchoMcpWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + EchoMcpWorkflow.run, + "Echo 'hello from MCP'.", + id=f"google-adk-agents-mcp-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "Done!" diff --git a/tests/google_adk_agents/streaming_test.py b/tests/google_adk_agents/streaming_test.py new file mode 100644 index 000000000..5c1d78a87 --- /dev/null +++ b/tests/google_adk_agents/streaming_test.py @@ -0,0 +1,68 @@ +import asyncio +import uuid +from datetime import timedelta + +import pytest +from google.adk.models.llm_response import LlmResponse +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.contrib.workflow_streams import WorkflowStreamClient +from temporalio.worker import Worker + +from google_adk_agents.streaming.workflows.streaming_workflow import ( + StreamingAgentWorkflow, +) +from tests.google_adk_agents._mock_model import patch_model, text + + +async def test_streaming(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_model( + monkeypatch, + [text("Once "), text("upon a robot.")], + stream_chunks=True, + ) + + task_queue = f"google-adk-agents-streaming-{uuid.uuid4()}" + plugin = GoogleAdkPlugin() + workflow_id = f"google-adk-agents-streaming-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[StreamingAgentWorkflow], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + StreamingAgentWorkflow.run, + "Tell me a story", + id=workflow_id, + task_queue=task_queue, + ) + + stream = WorkflowStreamClient.create(client, workflow_id) + chunks: list[str] = [] + + async def collect() -> None: + async for item in stream.subscribe( + ["responses"], + from_offset=0, + result_type=LlmResponse, + poll_cooldown=timedelta(milliseconds=50), + ): + response = item.data + if response.content and response.content.parts: + for part in response.content.parts: + if part.text: + chunks.append(part.text) + if len(chunks) >= 2: + return + + collect_task = asyncio.create_task(collect()) + assert await handle.result() == "upon a robot." + await asyncio.wait_for(collect_task, timeout=10.0) + + assert chunks == ["Once ", "upon a robot."] diff --git a/tests/google_adk_agents/tools_test.py b/tests/google_adk_agents/tools_test.py new file mode 100644 index 000000000..d80817e35 --- /dev/null +++ b/tests/google_adk_agents/tools_test.py @@ -0,0 +1,43 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.tools.activities.weather_activity import get_weather +from google_adk_agents.tools.workflows.weather_workflow import WeatherAgentWorkflow +from tests.google_adk_agents._mock_model import patch_model, text, tool_call + + +async def test_tools(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_model( + monkeypatch, + [ + tool_call("get_weather", {"city": "New York"}), + text("It is warm and sunny."), + ], + ) + + task_queue = f"google-adk-agents-tools-{uuid.uuid4()}" + plugin = GoogleAdkPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[WeatherAgentWorkflow], + activities=[get_weather], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + WeatherAgentWorkflow.run, + "Weather in New York?", + id=f"google-adk-agents-tools-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "It is warm and sunny." diff --git a/uv.lock b/uv.lock index 77814d337..8d95e3372 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eb54b5ecde05e0c54239b553db280f49ce31c4e7 Mon Sep 17 00:00:00 2001 From: xumaple <45406854+xumaple@users.noreply.github.com> Date: Thu, 25 Jun 2026 12:51:02 -0400 Subject: [PATCH 35/40] AI-284: Add snipsync markers and trim comments in Google ADK samples (#321) Add SNIPSTART markers around the Worker/Client setup and Workflow starter so the documentation integration page can pull them via snipsync, and trim the sample comments to short, why-only notes. Co-authored-by: Claude Opus 4.8 (1M context) --- google_adk_agents/agent_patterns/run_worker.py | 2 -- .../agent_patterns/workflows/multi_agent_workflow.py | 7 +++---- google_adk_agents/basic/run_hello_world_workflow.py | 2 ++ google_adk_agents/basic/run_worker.py | 4 ++-- .../basic/workflows/hello_world_workflow.py | 9 +++------ google_adk_agents/mcp/run_worker.py | 5 ++--- google_adk_agents/mcp/workflows/echo_workflow.py | 5 +---- google_adk_agents/streaming/run_streaming_workflow.py | 3 --- google_adk_agents/streaming/run_worker.py | 2 -- .../streaming/workflows/streaming_workflow.py | 9 ++++----- google_adk_agents/tools/run_worker.py | 4 ++-- google_adk_agents/tools/workflows/weather_workflow.py | 5 ++--- 12 files changed, 21 insertions(+), 36 deletions(-) diff --git a/google_adk_agents/agent_patterns/run_worker.py b/google_adk_agents/agent_patterns/run_worker.py index 56fd267f3..a6c199238 100644 --- a/google_adk_agents/agent_patterns/run_worker.py +++ b/google_adk_agents/agent_patterns/run_worker.py @@ -12,8 +12,6 @@ async def main(): - # Build the plugin once and give the same instance to the client and the - # worker. plugin = GoogleAdkPlugin() client = await Client.connect("localhost:7233", plugins=[plugin]) diff --git a/google_adk_agents/agent_patterns/workflows/multi_agent_workflow.py b/google_adk_agents/agent_patterns/workflows/multi_agent_workflow.py index 2867cd223..749477973 100644 --- a/google_adk_agents/agent_patterns/workflows/multi_agent_workflow.py +++ b/google_adk_agents/agent_patterns/workflows/multi_agent_workflow.py @@ -19,8 +19,8 @@ async def run(self, topic: str) -> str: app_name="multi_agent_app", user_id="user" ) - # Give each sub-agent its own TemporalModel with an ActivityConfig - # summary, so its model turns show up as named activities in history. + # The ActivityConfig summary makes each model turn a named activity in + # history. researcher = LlmAgent( name="researcher", model=TemporalModel( @@ -39,8 +39,7 @@ async def run(self, topic: str) -> str: instruction="You are a poet. Write a haiku based on the research.", ) - # The coordinator hands off to the sub-agents using ADK's built-in - # transfer_to_agent, which runs durably here. + # ADK's transfer_to_agent handoff runs durably here. coordinator = LlmAgent( name="coordinator", model=TemporalModel( diff --git a/google_adk_agents/basic/run_hello_world_workflow.py b/google_adk_agents/basic/run_hello_world_workflow.py index e410ed875..60d141eb7 100644 --- a/google_adk_agents/basic/run_hello_world_workflow.py +++ b/google_adk_agents/basic/run_hello_world_workflow.py @@ -9,6 +9,7 @@ async def main(): + # @@@SNIPSTART google-adk-agents-basic-starter client = await Client.connect("localhost:7233", plugins=[GoogleAdkPlugin()]) result = await client.execute_workflow( @@ -18,6 +19,7 @@ async def main(): task_queue="google-adk-agents-basic", ) print(f"Result: {result}") + # @@@SNIPEND if __name__ == "__main__": diff --git a/google_adk_agents/basic/run_worker.py b/google_adk_agents/basic/run_worker.py index b1cef43f2..f368b8ec5 100644 --- a/google_adk_agents/basic/run_worker.py +++ b/google_adk_agents/basic/run_worker.py @@ -12,8 +12,7 @@ async def main(): - # Build the plugin once and give the same instance to the client and the - # worker. + # @@@SNIPSTART google-adk-agents-basic-worker plugin = GoogleAdkPlugin() client = await Client.connect("localhost:7233", plugins=[plugin]) @@ -25,6 +24,7 @@ async def main(): plugins=[plugin], ) await worker.run() + # @@@SNIPEND if __name__ == "__main__": diff --git a/google_adk_agents/basic/workflows/hello_world_workflow.py b/google_adk_agents/basic/workflows/hello_world_workflow.py index 3915f85f4..eee43d5d7 100644 --- a/google_adk_agents/basic/workflows/hello_world_workflow.py +++ b/google_adk_agents/basic/workflows/hello_world_workflow.py @@ -11,23 +11,20 @@ class HelloWorldAgentWorkflow: @workflow.run async def run(self, prompt: str) -> str: - # A normal ADK agent. The one Temporal-specific piece is TemporalModel, - # which runs each model call as an `invoke_model` activity. + # TemporalModel runs each model call as an `invoke_model` activity. agent = Agent( name="hello_world_agent", model=TemporalModel("gemini-2.5-flash"), instruction="You only respond in haikus.", ) - # InMemoryRunner drives the agent. The plugin points ADK's session-id - # generation at workflow.uuid4(), so creating a session here is - # replay-safe. + # The plugin points ADK's session-id generation at workflow.uuid4(), so + # creating a session here is replay-safe. runner = InMemoryRunner(agent=agent, app_name="hello_world_app") session = await runner.session_service.create_session( app_name="hello_world_app", user_id="user" ) - # Keep the last bit of text the agent produces. final_text = "" async with Aclosing( runner.run_async( diff --git a/google_adk_agents/mcp/run_worker.py b/google_adk_agents/mcp/run_worker.py index 35a42f8b3..380b07504 100644 --- a/google_adk_agents/mcp/run_worker.py +++ b/google_adk_agents/mcp/run_worker.py @@ -14,9 +14,7 @@ async def main(): - # The provider adds the echo-list-tools and echo-call-tool activities. Same - # as the other samples: build the plugin once and give the same instance to - # the client and the worker. + # @@@SNIPSTART google-adk-agents-mcp-worker plugin = GoogleAdkPlugin( toolset_providers=[TemporalMcpToolSetProvider("echo", echo_toolset)] ) @@ -30,6 +28,7 @@ async def main(): plugins=[plugin], ) await worker.run() + # @@@SNIPEND if __name__ == "__main__": diff --git a/google_adk_agents/mcp/workflows/echo_workflow.py b/google_adk_agents/mcp/workflows/echo_workflow.py index bf435fb84..d3104f704 100644 --- a/google_adk_agents/mcp/workflows/echo_workflow.py +++ b/google_adk_agents/mcp/workflows/echo_workflow.py @@ -15,10 +15,7 @@ class EchoMcpWorkflow: @workflow.run async def run(self, prompt: str) -> str: # TemporalMcpToolSet runs the MCP server's list-tools and call-tool - # calls as activities (the provider names them echo-list-tools and - # echo-call-tool on the worker). The not_in_workflow_toolset factory - # lets you run this same agent locally, outside a workflow, by hitting - # the MCP server directly. + # calls as activities. agent = Agent( name="echo_agent", model=TemporalModel("gemini-2.5-flash"), diff --git a/google_adk_agents/streaming/run_streaming_workflow.py b/google_adk_agents/streaming/run_streaming_workflow.py index 87880371d..157c105a2 100644 --- a/google_adk_agents/streaming/run_streaming_workflow.py +++ b/google_adk_agents/streaming/run_streaming_workflow.py @@ -16,8 +16,6 @@ async def main(): client = await Client.connect("localhost:7233", plugins=[GoogleAdkPlugin()]) - # Start the workflow but don't wait on the result yet, so we can subscribe - # to its stream while it's running. handle = await client.start_workflow( StreamingAgentWorkflow.run, "Tell me a short story about a robot learning to paint.", @@ -25,7 +23,6 @@ async def main(): task_queue="google-adk-agents-streaming", ) - # Subscribe to the "responses" topic and print text chunks as they arrive. stream = WorkflowStreamClient.create(client, WORKFLOW_ID) async def print_chunks() -> None: diff --git a/google_adk_agents/streaming/run_worker.py b/google_adk_agents/streaming/run_worker.py index efd3cd031..5a30034bb 100644 --- a/google_adk_agents/streaming/run_worker.py +++ b/google_adk_agents/streaming/run_worker.py @@ -12,8 +12,6 @@ async def main(): - # Build the plugin once and give the same instance to the client and the - # worker. plugin = GoogleAdkPlugin() client = await Client.connect("localhost:7233", plugins=[plugin]) diff --git a/google_adk_agents/streaming/workflows/streaming_workflow.py b/google_adk_agents/streaming/workflows/streaming_workflow.py index 7ef0c27b5..1a5d76149 100644 --- a/google_adk_agents/streaming/workflows/streaming_workflow.py +++ b/google_adk_agents/streaming/workflows/streaming_workflow.py @@ -12,15 +12,14 @@ class StreamingAgentWorkflow: @workflow.init def __init__(self, prompt: str) -> None: - # The workflow hosts a WorkflowStream. The streaming activity publishes - # raw LlmResponse chunks to it as they come back from the model. + # The streaming activity publishes LlmResponse chunks to this stream as + # they come back from the model. self.stream = WorkflowStream() @workflow.run async def run(self, prompt: str) -> str: - # streaming_topic is the topic the chunks get published to. - # RunConfig(streaming_mode=SSE) tells ADK to stream, which routes the - # call through the invoke_model_streaming activity. + # streaming_mode=SSE routes the call through the invoke_model_streaming + # activity. model = TemporalModel("gemini-2.5-flash", streaming_topic="responses") agent = Agent( name="streaming_agent", diff --git a/google_adk_agents/tools/run_worker.py b/google_adk_agents/tools/run_worker.py index 20b6761d3..4e56e426e 100644 --- a/google_adk_agents/tools/run_worker.py +++ b/google_adk_agents/tools/run_worker.py @@ -11,8 +11,7 @@ async def main(): - # Build the plugin once and give the same instance to the client and the - # worker. + # @@@SNIPSTART google-adk-agents-tools-worker plugin = GoogleAdkPlugin() client = await Client.connect("localhost:7233", plugins=[plugin]) @@ -25,6 +24,7 @@ async def main(): plugins=[plugin], ) await worker.run() + # @@@SNIPEND if __name__ == "__main__": diff --git a/google_adk_agents/tools/workflows/weather_workflow.py b/google_adk_agents/tools/workflows/weather_workflow.py index 96088c884..6351210c4 100644 --- a/google_adk_agents/tools/workflows/weather_workflow.py +++ b/google_adk_agents/tools/workflows/weather_workflow.py @@ -16,9 +16,8 @@ class WeatherAgentWorkflow: @workflow.run async def run(self, prompt: str) -> str: - # Wrap the get_weather activity as an ADK tool. When the model calls it, - # activity_tool runs it as a real Temporal activity instead of inline, - # so it's retryable and shows up in history. + # activity_tool runs the tool call as a real Temporal activity, so it's + # retryable and shows up in history. weather_tool = temporalio.contrib.google_adk_agents.workflow.activity_tool( get_weather, start_to_close_timeout=timedelta(seconds=60) ) From bfec885edf889d5b644d6c5739d0ba64e31f5e30 Mon Sep 17 00:00:00 2001 From: Kent Gruber Date: Wed, 1 Jul 2026 12:51:19 -0400 Subject: [PATCH 36/40] VLN-1610: fix checkout-below-v7 (#323) Co-authored-by: picatz <14850816+picatz@users.noreply.github.com> --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 15d33ed10..e85fbe40d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -27,7 +27,7 @@ jobs: - uses: astral-sh/setup-uv@cec208311dfd045dd5311c1add060b2062131d57 # v8 - name: Print build information run: "echo head_ref: ${{ github.head_ref }}, ref: ${{ github.ref }}, os: ${{ matrix.os }}, python: ${{ matrix.python }}" - - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6 + - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0 with: submodules: recursive - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6 From 64e2cc2a1415e9f28b3bf01563a031b86519bb7d Mon Sep 17 00:00:00 2001 From: David Hyde Date: Tue, 7 Jul 2026 14:24:01 -0500 Subject: [PATCH 37/40] Prompt for human feedback in LangGraph HITL sample (#324) * Prompt for human feedback in LangGraph HITL sample The graph_api human-in-the-loop sample previously auto-approved the draft and used a hardcoded placeholder response, so running it didn't actually involve a human. Now the draft is generated by an LLM, the runner prompts interactively at the terminal for approval or revision feedback, and the review node revises the draft with the LLM based on that feedback. Tests mock the chat model so they stay deterministic and offline. * Only require AI SDK team review for langgraph_plugin * Revert: restore SDK team review for langgraph_plugin CODEOWNERS --- .../graph_api/human_in_the_loop/README.md | 8 +- .../human_in_the_loop/run_workflow.py | 7 +- .../graph_api/human_in_the_loop/workflow.py | 23 +-- .../human_in_the_loop_test.py | 131 +++++++++++------- 4 files changed, 104 insertions(+), 65 deletions(-) diff --git a/langgraph_plugin/graph_api/human_in_the_loop/README.md b/langgraph_plugin/graph_api/human_in_the_loop/README.md index f14ada540..d8ff0b48c 100644 --- a/langgraph_plugin/graph_api/human_in_the_loop/README.md +++ b/langgraph_plugin/graph_api/human_in_the_loop/README.md @@ -14,7 +14,7 @@ Demonstrates pausing a graph with LangGraph's `interrupt()` and waiting indefini 1. The Workflow starts and the `generate_draft` node produces a response. 2. The `human_review` node calls `interrupt(draft)`, pausing execution. 3. The Workflow stores the draft (visible via the query) and calls `workflow.wait_condition()` — blocking durably until the signal sets `_human_input`. This can wait indefinitely; Temporal persists the state. -4. An external process (UI, CLI, etc.) queries the draft and sends approval via signal. +4. An external process (UI, CLI, etc.) queries the draft and sends the human's feedback via signal. 5. The graph resumes — `interrupt()` returns the signal value and the node completes. ## Running the Sample @@ -25,14 +25,16 @@ Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`t # Terminal 1: start the worker uv run langgraph_plugin/graph_api/human_in_the_loop/run_worker.py -# Terminal 2: start the workflow (polls for draft, then auto-approves) +# Terminal 2: start the workflow (polls for the draft, then prompts you for feedback) uv run langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py ``` +When the draft is ready, you'll be prompted at the terminal. Type `approve` to accept it as-is, or type revision feedback and the draft will be regenerated by an LLM incorporating your notes. + ## Files | File | Description | |------|-------------| | `workflow.py` | Graph node functions, graph definition, and `ChatbotWorkflow` definition | | `run_worker.py` | Builds graph, registers with `LangGraphPlugin`, starts worker | -| `run_workflow.py` | Starts workflow, polls draft via query, sends approval via signal | +| `run_workflow.py` | Starts workflow, polls draft via query, prompts for human feedback, sends it via signal | diff --git a/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py b/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py index 7abfcdcf9..c45e2b72a 100644 --- a/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py +++ b/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py @@ -27,8 +27,11 @@ async def main() -> None: print(f"Draft for review: {draft}") - # Send approval via signal (a UI would trigger this) - await handle.signal(ChatbotWorkflow.provide_feedback, "approve") + # Prompt for human feedback instead of auto-approving. + feedback = await asyncio.to_thread( + input, "Enter 'approve' to accept, or type revision feedback: " + ) + await handle.signal(ChatbotWorkflow.provide_feedback, feedback) result = await handle.result() print(f"Final response: {result}") diff --git a/langgraph_plugin/graph_api/human_in_the_loop/workflow.py b/langgraph_plugin/graph_api/human_in_the_loop/workflow.py index f39275a60..d9e39911a 100644 --- a/langgraph_plugin/graph_api/human_in_the_loop/workflow.py +++ b/langgraph_plugin/graph_api/human_in_the_loop/workflow.py @@ -6,6 +6,7 @@ from datetime import timedelta +from langchain.chat_models import init_chat_model from langchain_core.runnables import RunnableConfig from langgraph.checkpoint.memory import InMemorySaver from langgraph.graph import START, StateGraph @@ -20,21 +21,25 @@ class State(TypedDict): async def generate_draft(state: State) -> dict[str, str]: - """Generate a draft response. Replace with an LLM call in production.""" - return { - "value": ( - f"Here's my response to '{state['value']}': " - "The answer is 42. Let me know if this helps!" - ) - } + """Generate a draft response with an LLM.""" + response = await init_chat_model("claude-sonnet-4-6").ainvoke( + f"Please respond concisely to: {state['value']}" + ) + return {"value": str(response.content)} async def human_review(state: State) -> dict[str, str]: - """Present draft to human for review via interrupt.""" + """Present draft to human for review via interrupt; revise with LLM on feedback.""" feedback = interrupt(state["value"]) if feedback == "approve": return {"value": state["value"]} - return {"value": f"[Revised] {state['value']} (incorporating feedback: {feedback})"} + response = await init_chat_model("claude-sonnet-4-6").ainvoke( + "Revise the following draft according to the reviewer's feedback. " + "Output only the revised draft, with no preamble.\n\n" + f"Draft:\n{state['value']}\n\n" + f"Feedback:\n{feedback}" + ) + return {"value": str(response.content)} def make_chatbot_graph() -> StateGraph: diff --git a/tests/langgraph_plugin/human_in_the_loop_test.py b/tests/langgraph_plugin/human_in_the_loop_test.py index ad81e492c..2d0b917f2 100644 --- a/tests/langgraph_plugin/human_in_the_loop_test.py +++ b/tests/langgraph_plugin/human_in_the_loop_test.py @@ -1,6 +1,7 @@ import asyncio import sys import uuid +from unittest.mock import patch import pytest from temporalio.client import Client @@ -18,36 +19,59 @@ ) +class _FakeMessage: + def __init__(self, content: str) -> None: + self.content = content + + +class _EchoModel: + """Stand-in for a chat model that echoes the prompt back as its response.""" + + async def ainvoke(self, prompt: str) -> _FakeMessage: + return _FakeMessage(prompt) + + +def _fake_init_chat_model(*args: object, **kwargs: object) -> _EchoModel: + return _EchoModel() + + +_patch_llm = lambda: patch( + "langgraph_plugin.graph_api.human_in_the_loop.workflow.init_chat_model", + _fake_init_chat_model, +) + + async def test_human_in_the_loop_approve(client: Client) -> None: task_queue = f"hitl-test-{uuid.uuid4()}" plugin = LangGraphPlugin(graphs={"chatbot": make_chatbot_graph()}) - async with Worker( - client, - task_queue=task_queue, - workflows=[ChatbotWorkflow], - plugins=[plugin], - ): - handle = await client.start_workflow( - ChatbotWorkflow.run, - "test message", - id=f"hitl-{uuid.uuid4()}", + with _patch_llm(): + async with Worker( + client, task_queue=task_queue, - ) - - # Poll for draft to be ready - draft = None - for _ in range(40): - await asyncio.sleep(0.25) - draft = await handle.query(ChatbotWorkflow.get_draft) - if draft is not None: - break - assert draft is not None - assert "test message" in draft - - # Approve - await handle.signal(ChatbotWorkflow.provide_feedback, "approve") - result = await handle.result() + workflows=[ChatbotWorkflow], + plugins=[plugin], + ): + handle = await client.start_workflow( + ChatbotWorkflow.run, + "test message", + id=f"hitl-{uuid.uuid4()}", + task_queue=task_queue, + ) + + # Poll for draft to be ready + draft = None + for _ in range(40): + await asyncio.sleep(0.25) + draft = await handle.query(ChatbotWorkflow.get_draft) + if draft is not None: + break + assert draft is not None + assert "test message" in draft + + # Approve + await handle.signal(ChatbotWorkflow.provide_feedback, "approve") + result = await handle.result() assert result == draft # approved draft returned as-is @@ -56,31 +80,36 @@ async def test_human_in_the_loop_revise(client: Client) -> None: task_queue = f"hitl-revise-test-{uuid.uuid4()}" plugin = LangGraphPlugin(graphs={"chatbot": make_chatbot_graph()}) - async with Worker( - client, - task_queue=task_queue, - workflows=[ChatbotWorkflow], - plugins=[plugin], - ): - handle = await client.start_workflow( - ChatbotWorkflow.run, - "test message", - id=f"hitl-revise-{uuid.uuid4()}", + with _patch_llm(): + async with Worker( + client, task_queue=task_queue, - ) - - # Poll for draft - draft = None - for _ in range(40): - await asyncio.sleep(0.25) - draft = await handle.query(ChatbotWorkflow.get_draft) - if draft is not None: - break - assert draft is not None - - # Send revision feedback - await handle.signal(ChatbotWorkflow.provide_feedback, "please be more concise") - result = await handle.result() - - assert "[Revised]" in result + workflows=[ChatbotWorkflow], + plugins=[plugin], + ): + handle = await client.start_workflow( + ChatbotWorkflow.run, + "test message", + id=f"hitl-revise-{uuid.uuid4()}", + task_queue=task_queue, + ) + + # Poll for draft + draft = None + for _ in range(40): + await asyncio.sleep(0.25) + draft = await handle.query(ChatbotWorkflow.get_draft) + if draft is not None: + break + assert draft is not None + + # Send revision feedback + await handle.signal( + ChatbotWorkflow.provide_feedback, "please be more concise" + ) + result = await handle.result() + + # The revision node feeds the draft and feedback into the LLM; the echo + # stand-in returns the revision prompt, which contains both. assert "please be more concise" in result + assert "test message" in result From 4440024696fb166d5ca939ac708282033a1e53e9 Mon Sep 17 00:00:00 2001 From: Quinn Klassen Date: Thu, 9 Jul 2026 11:59:05 -0700 Subject: [PATCH 38/40] Update SANO dev server (#326) Update SANO dev server --- README.md | 1 + nexus_standalone_operations/README.md | 4 +- .../workflows/agent_lifecycle_workflow.py | 2 +- .../basic/workflows/lifecycle_workflow.py | 2 +- pyproject.toml | 12 ++--- tests/conftest.py | 2 +- uv.lock | 45 +++++++------------ 7 files changed, 28 insertions(+), 40 deletions(-) diff --git a/README.md b/README.md index 2111fa005..d39428dc7 100644 --- a/README.md +++ b/README.md @@ -74,6 +74,7 @@ Some examples require extra dependencies. See each sample's directory for specif * [gevent_async](gevent_async) - Combine gevent and Temporal. * [google_adk_agents](google_adk_agents) - Run Google ADK agents as durable Temporal workflows (model calls, tools, multi-agent, MCP, streaming). * [hello_nexus](hello_nexus) - Define a Nexus service, implement operation handlers, and call them from a workflow. +* [hello_standalone_nexus](hello_standalone_nexus) - Use Nexus Operations without using a workflow. * [hello_standalone_activity](hello_standalone_activity) - Use activities without using a workflow. * [lambda_worker](lambda_worker) - Run a Temporal Worker inside an AWS Lambda function. * [langgraph_plugin](langgraph_plugin) - Run LangGraph workflows as durable Temporal workflows (Graph API and Functional API). diff --git a/nexus_standalone_operations/README.md b/nexus_standalone_operations/README.md index 60ca7a935..ddddadf45 100644 --- a/nexus_standalone_operations/README.md +++ b/nexus_standalone_operations/README.md @@ -7,7 +7,7 @@ without wrapping them in a workflow. It shows both synchronous and asynchronous All APIs are experimental and may be subject to backwards-incompatible changes. -Standalone Nexus operations require a server version that supports this feature. Use the dev server build at https://github.com/temporalio/cli/releases/tag/v1.7.2-standalone-nexus-operations. +Standalone Nexus operations require a server version that supports this feature. Use the dev server build at https://github.com/temporalio/cli/releases/tag/v1.7.3-standalone-nexus-operations. ### Sample directory structure @@ -19,7 +19,7 @@ Standalone Nexus operations require a server version that supports this feature. ### Instructions -Run the [Temporal dev server build that supports standalone Nexus operations](https://github.com/temporalio/cli/releases/tag/v1.7.2-standalone-nexus-operations). +Run the [Temporal dev server build that supports standalone Nexus operations](https://github.com/temporalio/cli/releases/tag/v1.7.3-standalone-nexus-operations). (If you are going to run locally, you will want to start it in another terminal; this command is blocking and runs until it receives a SIGINT (Ctrl + C) command.) Start a Temporal dev server with the dynamic config flags required for standalone Nexus operations: diff --git a/openai_agents/basic/workflows/agent_lifecycle_workflow.py b/openai_agents/basic/workflows/agent_lifecycle_workflow.py index c016d1500..32bce03b4 100644 --- a/openai_agents/basic/workflows/agent_lifecycle_workflow.py +++ b/openai_agents/basic/workflows/agent_lifecycle_workflow.py @@ -41,7 +41,7 @@ async def on_tool_start( ) async def on_tool_end( - self, context: RunContextWrapper, agent: Agent, tool, result: str + self, context: RunContextWrapper, agent: Agent, tool, result: Any ) -> None: self.event_counter += 1 print( diff --git a/openai_agents/basic/workflows/lifecycle_workflow.py b/openai_agents/basic/workflows/lifecycle_workflow.py index cf72de4a3..ded120a61 100644 --- a/openai_agents/basic/workflows/lifecycle_workflow.py +++ b/openai_agents/basic/workflows/lifecycle_workflow.py @@ -43,7 +43,7 @@ async def on_tool_start( ) async def on_tool_end( - self, context: RunContextWrapper, agent: Agent, tool: Tool, result: str + self, context: RunContextWrapper, agent: Agent, tool: Tool, result: Any ) -> None: self.event_counter += 1 print( diff --git a/pyproject.toml b/pyproject.toml index 3ccff7c62..e8f3ebd87 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] requires-python = ">=3.10" readme = "README.md" license = "MIT" -dependencies = ["temporalio>=1.28.0,<2", "protobuf>=5.29.6,<6"] +dependencies = ["temporalio>=1.30.0,<2", "protobuf>=5.29.6,<6"] [project.urls] Homepage = "https://github.com/temporalio/samples-python" @@ -38,17 +38,17 @@ external-storage = [ ] external-storage-redis = ["redis>=5.0.0,<8"] gevent = ["gevent>=25.4.2 ; python_version >= '3.8'"] -google-adk = ["temporalio[google-adk] >= 1.28.0", "google-adk>=1.27.0,<2"] +google-adk = ["temporalio[google-adk] >= 1.30.0", "google-adk>=1.27.0,<2"] langsmith-tracing = [ "openai>=1.4.0", "langsmith>=0.7.0", - "temporalio[pydantic,langsmith]>=1.28.0", + "temporalio[pydantic,langsmith]>=1.30.0", ] langgraph = [ "langgraph>=1.1.3", "langchain>=0.3.0", "langchain-anthropic>=0.3.0", - "temporalio[langgraph,langsmith]>=1.28.0", + "temporalio[langgraph,langsmith]>=1.30.0", ] nexus = ["nexus-rpc>=1.1.0,<2"] open-telemetry = [ @@ -57,7 +57,7 @@ open-telemetry = [ ] openai-agents = [ "openai-agents[litellm] >= 0.14.1", - "temporalio[openai-agents,opentelemetry] >= 1.28.0", + "temporalio[openai-agents,opentelemetry] >= 1.30.0", "requests>=2.32.0,<3", ] pydantic-converter = ["pydantic>=2.10.6,<3"] @@ -67,7 +67,7 @@ strands-agents = [ "strands-agents-tools>=0.5.2", "mcp>=1.0.0", "boto3>=1.34.92,<2", - "temporalio[strands-agents,pydantic]>=1.28.0", + "temporalio[strands-agents,pydantic]>=1.30.0", ] trio-async = ["trio>=0.28.0,<0.29", "trio-asyncio>=0.15.0,<0.16"] cloud-export-to-parquet = [ diff --git a/tests/conftest.py b/tests/conftest.py index 3d054e71a..8009ac6b9 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -42,7 +42,7 @@ async def env(request) -> AsyncGenerator[WorkflowEnvironment, None]: env_type = request.config.getoption("--workflow-environment") if env_type == "local": env = await WorkflowEnvironment.start_local( - 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{ name = "temporalio", specifier = ">=1.28.0,<2" }, + { name = "temporalio", specifier = ">=1.30.0,<2" }, ] [package.metadata.requires-dev] @@ -5138,18 +5137,18 @@ external-storage-redis = [{ name = "redis", specifier = ">=5.0.0,<8" }] gevent = [{ name = "gevent", marker = "python_full_version >= '3.8'", specifier = ">=25.4.2" }] google-adk = [ { name = "google-adk", specifier = ">=1.27.0,<2" }, - { name = "temporalio", extras = ["google-adk"], specifier = ">=1.28.0" }, + { name = "temporalio", extras = ["google-adk"], specifier = ">=1.30.0" }, ] langgraph = [ { name = "langchain", specifier = ">=0.3.0" }, { name = "langchain-anthropic", specifier = ">=0.3.0" }, { name = "langgraph", specifier = ">=1.1.3" }, - { name = "temporalio", extras = ["langgraph", "langsmith"], specifier = ">=1.28.0" }, + { name = "temporalio", extras = ["langgraph", "langsmith"], specifier = ">=1.30.0" }, ] langsmith-tracing = [ { name = "langsmith", specifier = ">=0.7.0" }, { name = "openai", specifier = ">=1.4.0" }, - { name = "temporalio", extras = ["pydantic", "langsmith"], specifier = ">=1.28.0" }, + { name = "temporalio", extras = ["pydantic", "langsmith"], specifier = ">=1.30.0" }, ] nexus = [{ name = "nexus-rpc", specifier = ">=1.1.0,<2" }] open-telemetry = [ @@ -5159,7 +5158,7 @@ open-telemetry = [ openai-agents = [ { name = "openai-agents", extras = ["litellm"], specifier = ">=0.14.1" }, { name = "requests", specifier = ">=2.32.0,<3" }, - { name = "temporalio", extras = ["openai-agents", "opentelemetry"], specifier = ">=1.28.0" }, + { name = "temporalio", extras = ["openai-agents", "opentelemetry"], specifier = ">=1.30.0" }, ] pydantic-converter = [{ name = "pydantic", specifier = ">=2.10.6,<3" }] sentry = [{ name = "sentry-sdk", specifier = ">=2.13.0" }] @@ -5168,7 +5167,7 @@ strands-agents = [ { name = "mcp", specifier = ">=1.0.0" }, { name = "strands-agents", specifier = ">=1.39.0" }, { name = "strands-agents-tools", specifier = ">=0.5.2" }, - { name = "temporalio", extras = ["strands-agents", "pydantic"], specifier = ">=1.28.0" }, + { name = "temporalio", extras = ["strands-agents", "pydantic"], specifier = ">=1.30.0" }, ] trio-async = [ { name = "trio", specifier = ">=0.28.0,<0.29" }, @@ -5417,18 +5416,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/06/a2/c01db32be2ae7d6a1689972f3c492b149ee4e164b12fdfd9f64b50888215/types_pyyaml-6.0.12.20260518-py3-none-any.whl", hash = "sha256:d2150f75a231c9fe9c7463bd29487d93e60bac90400287351384bc2284eba7cd", size = 20312, upload-time = "2026-05-18T06:01:57.368Z" }, ] -[[package]] -name = "types-requests" -version = "2.33.0.20260518" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "urllib3" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/e0/01/c5a19253fe1ac159159ddf9a3a07cec8bb5e486ec4d9002ad2821da0e5d2/types_requests-2.33.0.20260518.tar.gz", hash = "sha256:df7bd3bfe0ca8402dfb841e7d9be714bb5578203283d66d7dc4ef69343449a5e", size = 24752, upload-time = "2026-05-18T06:07:37.966Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/1c/bc/b139710a3b6018f7fb2b9508b35c8af564e61bf2bf4fa619d088f3e16f85/types_requests-2.33.0.20260518-py3-none-any.whl", hash = "sha256:626d697d1adaaff76e2044dc8c5c051d8f21abc157bdfe204a75558076fe0bf0", size = 21391, upload-time = "2026-05-18T06:07:37.044Z" }, -] - [[package]] name = "typing-extensions" version = "4.15.0" From 72810e825155ab1116667fd72ce3086a3f4224ea Mon Sep 17 00:00:00 2001 From: Brian Strauch Date: Mon, 13 Jul 2026 08:30:31 -0700 Subject: [PATCH 39/40] Add LangGraph workflow streams sample (#315) * Add LangGraph workflow streams sample Demonstrate the LangGraph plugin's Workflow Streams support: a node emits live tokens via get_stream_writer() (routed by the plugin's streaming_topic), and the workflow publishes coarse astream progress to its own topic. A single client subscribes to all topics and demultiplexes on item.topic. Bumps the langgraph group to temporalio>=1.28.0 (where workflow streams ship) and drops the now-obsolete langsmith<0.7.34 constraint, which was specific to the 1.27.2 langsmith patch. Co-Authored-By: Claude Opus 4.8 (1M context) * Add langgraph_plugin code owners Co-Authored-By: Claude Opus 4.8 (1M context) * Trigger CI Co-Authored-By: Claude Opus 4.8 (1M context) * Add README for graph_api/streaming sample Every other graph_api/ sample has a standalone README with run instructions and a "what this demonstrates" blurb; streaming was the only one without. Co-Authored-By: Claude Opus 4.8 (1M context) * Dedupe streaming tokens on a sequence id Streaming is at-least-once per activity attempt: a retried node re-runs and re-publishes its writes, so subscribers can see duplicate tokens. Tag each token chunk with a monotonic seq and dedupe on it in the client and test, demonstrating the documented idempotent-consumer pattern instead of implicitly assuming exactly-once delivery. Co-Authored-By: Claude Opus 4.8 (1M context) * Fix mypy error in streaming test dedupe set.add returns None, so the comprehension trick tripped mypy's func-returns-value. Rewrite as an explicit loop. Co-Authored-By: Claude Opus 4.8 (1M context) --------- Co-authored-by: Claude Opus 4.8 (1M context) --- .github/CODEOWNERS | 1 + langgraph_plugin/README.md | 2 + .../graph_api/streaming/README.md | 31 ++++++ .../graph_api/streaming/__init__.py | 0 .../graph_api/streaming/run_worker.py | 35 +++++++ .../graph_api/streaming/run_workflow.py | 51 ++++++++++ .../graph_api/streaming/workflow.py | 97 +++++++++++++++++++ tests/langgraph_plugin/streaming_test.py | 71 ++++++++++++++ 8 files changed, 288 insertions(+) create mode 100644 langgraph_plugin/graph_api/streaming/README.md create mode 100644 langgraph_plugin/graph_api/streaming/__init__.py create mode 100644 langgraph_plugin/graph_api/streaming/run_worker.py create mode 100644 langgraph_plugin/graph_api/streaming/run_workflow.py create mode 100644 langgraph_plugin/graph_api/streaming/workflow.py create mode 100644 tests/langgraph_plugin/streaming_test.py diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 03b609359..403c731de 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,4 +1,5 @@ * @temporalio/sdk +/langgraph_plugin/ @temporalio/sdk @temporalio/ai-sdk # SDK & Nexus own the README, pyproject.toml, and uv.lock /README.md @temporalio/sdk @temporalio/nexus diff --git a/langgraph_plugin/README.md b/langgraph_plugin/README.md index 8691d178b..a242e943f 100644 --- a/langgraph_plugin/README.md +++ b/langgraph_plugin/README.md @@ -16,6 +16,7 @@ Samples are organized by API style: | **Continue-as-new** | [graph_api/continue_as_new](graph_api/continue_as_new) | [functional_api/continue_as_new](functional_api/continue_as_new) | Multi-stage data pipeline that uses `continue-as-new` with task result caching so previously-completed stages are not re-executed. | | **ReAct Agent** | [graph_api/react_agent](graph_api/react_agent) | [functional_api/react_agent](functional_api/react_agent) | Tool-calling agent loop. Graph API uses conditional edges; Functional API uses a `while` loop. | | **Control Flow** | -- | [functional_api/control_flow](functional_api/control_flow) | Demonstrates parallel task execution, `for` loops, and `if/else` branching -- patterns that are natural in the Functional API. | +| **Streaming** | [graph_api/streaming](graph_api/streaming) | -- | Streams live output from a running workflow via [Workflow Streams](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/workflow_streams): node tokens via `get_stream_writer()` + `streaming_topic`, plus workflow-side `astream` progress published with `WorkflowStream.topic().publish()`. | | **LangSmith Tracing** | [graph_api/langsmith_tracing](graph_api/langsmith_tracing) | [functional_api/langsmith_tracing](functional_api/langsmith_tracing) | Combines `LangGraphPlugin` with Temporal's `LangSmithPlugin` for durable execution + full observability of LLM calls. Requires API keys. | ## Prerequisites @@ -67,6 +68,7 @@ uv run langgraph_plugin//langsmith_tracing/main.py - **Continue-as-new with caching** -- `cache()` captures completed task results; passing the cache to the next execution avoids re-running them. - **Conditional routing** -- Graph API's `add_conditional_edges` and Functional API's native `if/else`/`while` for agent loops. - **Parallel execution** -- Functional API launches multiple tasks concurrently by creating futures before awaiting them. +- **Streaming** -- Workflow Streams expose a durable, offset-addressed event channel that external clients subscribe to while the workflow is still running. Nodes emit fine-grained tokens via `get_stream_writer()` (routed by the plugin's `streaming_topic`), and the workflow can publish coarse `astream` progress to its own topic. ## Related diff --git a/langgraph_plugin/graph_api/streaming/README.md b/langgraph_plugin/graph_api/streaming/README.md new file mode 100644 index 000000000..2065a6a36 --- /dev/null +++ b/langgraph_plugin/graph_api/streaming/README.md @@ -0,0 +1,31 @@ +# Streaming (Graph API) + +Streams a LangGraph run to an external client while the workflow is still running, using Temporal's durable, offset-addressed [`WorkflowStream`](https://docs.temporal.io/). The graph writes a short story about a topic and emits both fine-grained tokens and node-completion progress on separate topics. + +## What This Sample Demonstrates + +- **Node token streaming** — the `write_story` node calls LangGraph's `get_stream_writer()` to emit tokens. The plugin's `streaming_topic="tokens"` routes those writes onto the `"tokens"` topic. +- **Workflow-side `astream` publish** — the workflow drives the graph with `app.astream(...)` and publishes each node-completion chunk onto a `"progress"` topic it owns. +- A single client subscribing to all topics and demultiplexing on `item.topic`. +- Waiting for the client to acknowledge (via signal) before completing, since the stream disappears when the workflow ends. +- **Idempotent consumption** — each token chunk carries a monotonic sequence id so the client can dedupe, because streaming is at-least-once per activity attempt (a retried node re-runs and re-publishes its writes). + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/graph_api/streaming/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/graph_api/streaming/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | Graph node functions, graph definition, and `StreamingWorkflow` that publishes to the stream | +| `run_worker.py` | Registers graph with `LangGraphPlugin` (`streaming_topic="tokens"`), starts worker | +| `run_workflow.py` | Starts the workflow, subscribes to the stream, prints tokens and progress, then acks | diff --git a/langgraph_plugin/graph_api/streaming/__init__.py b/langgraph_plugin/graph_api/streaming/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/langgraph_plugin/graph_api/streaming/run_worker.py b/langgraph_plugin/graph_api/streaming/run_worker.py new file mode 100644 index 000000000..46e0090ee --- /dev/null +++ b/langgraph_plugin/graph_api/streaming/run_worker.py @@ -0,0 +1,35 @@ +"""Worker for the streaming sample (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.streaming.workflow import ( + StreamingWorkflow, + make_streaming_graph, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + # streaming_topic routes node get_stream_writer() output onto the "tokens" topic. + plugin = LangGraphPlugin( + graphs={"streaming": make_streaming_graph()}, + streaming_topic="tokens", + ) + + worker = Worker( + client, + task_queue="langgraph-streaming", + workflows=[StreamingWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/streaming/run_workflow.py b/langgraph_plugin/graph_api/streaming/run_workflow.py new file mode 100644 index 000000000..9d42322e1 --- /dev/null +++ b/langgraph_plugin/graph_api/streaming/run_workflow.py @@ -0,0 +1,51 @@ +"""Start the streaming workflow and subscribe to its Workflow Stream (Graph API).""" + +import asyncio +import os +from datetime import timedelta + +from temporalio.client import Client +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from langgraph_plugin.graph_api.streaming.workflow import StreamingWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + handle = await client.start_workflow( + StreamingWorkflow.run, + "a brave robot", + id="streaming-workflow", + task_queue="langgraph-streaming", + ) + + # Subscribe to all topics on the workflow's stream and demultiplex on topic. + ws = WorkflowStreamClient.create(client, handle.id) + # Streaming is at-least-once per activity attempt, so a retried node may + # re-publish tokens. Dedupe on the chunk's seq to consume idempotently. + seen_tokens: set[int] = set() + async for item in ws.subscribe( + from_offset=0, + result_type=dict, + poll_cooldown=timedelta(milliseconds=50), + ): + if item.topic == "tokens": + seq = item.data["seq"] + if seq in seen_tokens: + continue # duplicate from a node retry; already consumed. + seen_tokens.add(seq) + print(item.data["token"], end="", flush=True) + elif item.topic == "progress": + if item.data.get("done"): + # Let the workflow know we are done consuming so it can complete. + await handle.signal(StreamingWorkflow.ack_stream) + break + print(f"\n[progress] {item.data}") + + result = await handle.result() + print(f"\n\nFinal result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/streaming/workflow.py b/langgraph_plugin/graph_api/streaming/workflow.py new file mode 100644 index 000000000..6eeea2025 --- /dev/null +++ b/langgraph_plugin/graph_api/streaming/workflow.py @@ -0,0 +1,97 @@ +"""Streaming with the LangGraph Graph API and Temporal Workflow Streams. + +A workflow's :class:`WorkflowStream` is a durable, offset-addressed event channel +external clients can subscribe to while the workflow is still running. This sample +demonstrates both ways the LangGraph plugin produces stream items: + +- **Node token streaming** -- the ``write_story`` node calls LangGraph's + ``get_stream_writer()`` to emit fine-grained tokens. The plugin is configured with + ``streaming_topic="tokens"`` (see ``run_worker.py``), which routes those writes onto + the ``"tokens"`` topic. +- **Workflow-side ``astream`` publish** -- the workflow drives the graph with + ``app.astream(...)`` and publishes each node-completion chunk onto a ``"progress"`` + topic it owns. + +A single client subscribes to all topics and demultiplexes on ``item.topic``. +""" + +from datetime import timedelta + +from langgraph.config import get_stream_writer +from langgraph.graph import START, StateGraph +from temporalio import workflow +from temporalio.contrib.langgraph import graph as temporal_graph +from temporalio.contrib.workflow_streams import WorkflowStream +from typing_extensions import TypedDict + + +class State(TypedDict): + topic: str + story: str + + +async def outline(state: State) -> dict[str, str]: + """Produce a short opening line. Runs first so ``astream`` emits an early chunk.""" + return {"story": f"A story about {state['topic']}:"} + + +async def write_story(state: State) -> dict[str, str]: + """Write the story, emitting each word as a token via the stream writer. + + Streaming is at-least-once per activity attempt: if this node retries + (transient failure, worker restart) it re-runs from scratch and re-publishes + its writes, so subscribers may see the same token twice. Each chunk therefore + carries a monotonic ``seq`` so consumers can dedupe idempotently. A retry + re-emits the same ``seq`` values, letting the client drop the duplicates. + """ + writer = get_stream_writer() + words = f"{state['story']} Once upon a time, there was {state['topic']}.".split() + for seq, word in enumerate(words): + writer({"seq": seq, "token": word + " "}) + return {"story": " ".join(words)} + + +def make_streaming_graph() -> StateGraph: + g = StateGraph(State) + activity_metadata = { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=10), + } + g.add_node("outline", outline, metadata=activity_metadata) + g.add_node("write_story", write_story, metadata=activity_metadata) + g.add_edge(START, "outline") + g.add_edge("outline", "write_story") + return g + + +@workflow.defn +class StreamingWorkflow: + def __init__(self) -> None: + # WorkflowStream must be constructed during workflow initialization. + self.stream = WorkflowStream() + self._stream_acked = False + + @workflow.signal + def ack_stream(self) -> None: + """Signalled by the client once it has finished consuming the stream.""" + self._stream_acked = True + + @workflow.run + async def run(self, topic: str) -> str: + app = temporal_graph("streaming").compile() + progress = self.stream.topic("progress") + + story = "" + async for chunk in app.astream({"topic": topic, "story": ""}): + # Each chunk is {node_name: {state updates}}. Forward it as progress. + progress.publish(chunk) + for node_update in chunk.values(): + if "story" in node_update: + story = node_update["story"] + + progress.publish({"done": True}) + + # The stream disappears when the workflow completes, so wait until the + # client acknowledges it has finished consuming before returning. + await workflow.wait_condition(lambda: self._stream_acked) + return story diff --git a/tests/langgraph_plugin/streaming_test.py b/tests/langgraph_plugin/streaming_test.py new file mode 100644 index 000000000..80c5047b8 --- /dev/null +++ b/tests/langgraph_plugin/streaming_test.py @@ -0,0 +1,71 @@ +import uuid +from datetime import timedelta +from typing import Any + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.contrib.workflow_streams import WorkflowStreamClient +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.streaming.workflow import ( + StreamingWorkflow, + make_streaming_graph, +) + + +async def test_streaming_graph_api(client: Client) -> None: + task_queue = f"streaming-test-{uuid.uuid4()}" + plugin = LangGraphPlugin( + graphs={"streaming": make_streaming_graph()}, + streaming_topic="tokens", + ) + + async with Worker( + client, + task_queue=task_queue, + workflows=[StreamingWorkflow], + plugins=[plugin], + ): + handle = await client.start_workflow( + StreamingWorkflow.run, + "a brave robot", + id=f"streaming-{uuid.uuid4()}", + task_queue=task_queue, + ) + + ws = WorkflowStreamClient.create(client, handle.id) + tokens: list[dict[str, Any]] = [] + progress: list[dict[str, Any]] = [] + async for item in ws.subscribe( + from_offset=0, + result_type=dict, + poll_cooldown=timedelta(milliseconds=10), + ): + if item.topic == "tokens": + tokens.append(item.data) + elif item.topic == "progress": + if item.data.get("done"): + await handle.signal(StreamingWorkflow.ack_stream) + break + progress.append(item.data) + + result = await handle.result() + + # Tokens reassemble into the final story. Streaming is at-least-once per + # activity attempt, so dedupe on seq (keeping first-seen order) before + # reassembling, exactly as an idempotent consumer would. + assert tokens, "expected at least one token" + assert all("seq" in t and "token" in t for t in tokens) + seen: set[int] = set() + deduped: list[dict[str, Any]] = [] + for t in tokens: + if t["seq"] not in seen: + seen.add(t["seq"]) + deduped.append(t) + assembled = "".join(t["token"] for t in deduped).strip() + assert assembled == result + + # Workflow-side astream publish: one chunk per node, in order. + assert [list(chunk)[0] for chunk in progress] == ["outline", "write_story"] + assert result == progress[-1]["write_story"]["story"] + assert "a brave robot" in result From 9c0f3061f4cd466987fa2ae143edee12b64b8ba6 Mon Sep 17 00:00:00 2001 From: Quinn Klassen Date: Mon, 13 Jul 2026 16:36:53 -0700 Subject: [PATCH 40/40] Improve SANO Sample (#327) Improve SANO Sample --- nexus_standalone_operations/README.md | 99 +++++++++++++++++++------- nexus_standalone_operations/starter.py | 2 +- nexus_standalone_operations/worker.py | 2 +- tests/conftest.py | 12 +--- 4 files changed, 76 insertions(+), 39 deletions(-) diff --git a/nexus_standalone_operations/README.md b/nexus_standalone_operations/README.md index ddddadf45..63a04b5ac 100644 --- a/nexus_standalone_operations/README.md +++ b/nexus_standalone_operations/README.md @@ -2,12 +2,17 @@ This sample demonstrates how to execute Nexus operations directly from client co without wrapping them in a workflow. It shows both synchronous and asynchronous (workflow-backed) operations, plus listing and counting operations. +The starter and worker connect to two different namespaces (a "caller" namespace and a "handler" +namespace) — this mirrors how Nexus is typically used to cross namespace boundaries. The client is +configured via the SDK's [environment configuration](https://docs.temporal.io/develop/environment-configuration) +support (`ClientConfig.load_client_connect_config()`), which reads `TEMPORAL_NAMESPACE`, +`TEMPORAL_ADDRESS`, etc. from the environment (and optionally profiles from `temporal.toml`). ### Temporal Python SDK support for Standalone Nexus Operations is at [Pre-release](https://docs.temporal.io/evaluate/development-production-features/release-stages#pre-release). All APIs are experimental and may be subject to backwards-incompatible changes. -Standalone Nexus operations require a server version that supports this feature. Use the dev server build at https://github.com/temporalio/cli/releases/tag/v1.7.3-standalone-nexus-operations. +Standalone Nexus operations require a server version that supports this feature. Use the dev server build at https://github.com/temporalio/cli/releases/tag/v1.7.4-standalone-nexus-operations. ### Sample directory structure @@ -16,38 +21,36 @@ Standalone Nexus operations require a server version that supports this feature. - [worker.py](./worker.py) - Temporal worker that hosts the Nexus service - [starter.py](./starter.py) - Client that executes standalone Nexus operations +## Run locally against a dev server -### Instructions +1. Start the [Temporal dev server build that supports standalone Nexus operations](https://docs.temporal.io/standalone-nexus-operation#temporal-cli-support) with the required namespaces pre-created: -Run the [Temporal dev server build that supports standalone Nexus operations](https://github.com/temporalio/cli/releases/tag/v1.7.3-standalone-nexus-operations). -(If you are going to run locally, you will want to start it in another terminal; this command is blocking and runs until it receives a SIGINT (Ctrl + C) command.) + ```bash + ./temporal server start-dev \ + --namespace my-caller-namespace \ + --namespace my-handler-namespace + ``` -Start a Temporal dev server with the dynamic config flags required for standalone Nexus operations: +2. Create a Nexus endpoint that routes to the handler namespace and the worker's task queue: -```bash -temporal server start-dev \ - --dynamic-config-value "nexusoperation.enableStandalone=true" \ - --dynamic-config-value "history.enableChasmCallbacks=true" -``` + ```bash + ./temporal operator nexus endpoint create \ + --name my-nexus-endpoint \ + --target-namespace my-handler-namespace \ + --target-task-queue nexus-handler-queue + ``` -Create the Nexus endpoint: +3. In a second terminal, start the worker in the handler namespace: -``` -temporal operator nexus endpoint create \ - --name nexus-standalone-operations-endpoint \ - --target-namespace default \ - --target-task-queue nexus-standalone-operations -``` + ```bash + TEMPORAL_NAMESPACE=my-handler-namespace uv run nexus_standalone_operations/worker.py + ``` -In one terminal, start the worker: -``` -uv run nexus_standalone_operations/worker.py -``` +4. In a third terminal, run the starter in the caller namespace: -In another terminal, run the starter: -``` -uv run nexus_standalone_operations/starter.py -``` + ```bash + TEMPORAL_NAMESPACE=my-caller-namespace uv run nexus_standalone_operations/starter.py + ``` ### Expected output @@ -66,4 +69,48 @@ Total Nexus operations: 2 ``` If you run the starter code multiple times, you should see additional operations in the listing results, as more operations are run. -The same goes for the total number of operations. \ No newline at end of file +The same goes for the total number of operations. + +## Run against Temporal Cloud + +1. Create two namespaces in Temporal Cloud (for example `my-caller-namespace.` and + `my-handler-namespace.`) and generate an API key (or mTLS cert) that can access both. + +2. Create a Nexus endpoint that targets the handler namespace and the worker's task queue. See the + Temporal Cloud instructions at https://docs.temporal.io/nexus/registry#create-a-nexus-endpoint. + Use: + - Endpoint name: `my-nexus-endpoint` + - Target namespace: `my-handler-namespace.` + - Target task queue: `nexus-handler-queue` + - Allowed caller namespaces: include `my-caller-namespace.` (endpoints reject callers + that are not on this list) + +3. Add two profiles to your [environment configuration file](https://docs.temporal.io/develop/environment-configuration), + one per namespace. Using API keys: + + ```toml + [profile.handler] + address = "..api.temporal.io:7233" + namespace = "my-handler-namespace." + api_key = "" + + [profile.caller] + address = "..api.temporal.io:7233" + namespace = "my-caller-namespace." + api_key = "" + ``` + + For mTLS instead of API keys, set `tls.client_cert_path` and `tls.client_key_path` on each profile + (see the [docs](https://docs.temporal.io/develop/environment-configuration) for the full schema). + +4. Run the worker and starter in separate terminals, selecting the appropriate profile in each: + + ```bash + # terminal 1 (worker, handler namespace) + TEMPORAL_PROFILE=handler uv run nexus_standalone_operations/worker.py + ``` + + ```bash + # terminal 2 (starter, caller namespace) + TEMPORAL_PROFILE=caller uv run nexus_standalone_operations/starter.py + ``` diff --git a/nexus_standalone_operations/starter.py b/nexus_standalone_operations/starter.py index 8bb235b2d..df001cab7 100644 --- a/nexus_standalone_operations/starter.py +++ b/nexus_standalone_operations/starter.py @@ -19,7 +19,7 @@ MyNexusService, ) -ENDPOINT_NAME = "nexus-standalone-operations-endpoint" +ENDPOINT_NAME = "my-nexus-endpoint" async def main() -> None: diff --git a/nexus_standalone_operations/worker.py b/nexus_standalone_operations/worker.py index 0de4ac3b1..2adcbdd90 100644 --- a/nexus_standalone_operations/worker.py +++ b/nexus_standalone_operations/worker.py @@ -11,7 +11,7 @@ interrupt_event = asyncio.Event() -TASK_QUEUE = "nexus-standalone-operations" +TASK_QUEUE = "nexus-handler-queue" async def main() -> None: diff --git a/tests/conftest.py b/tests/conftest.py index 8009ac6b9..b857e0d96 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -42,17 +42,7 @@ async def env(request) -> AsyncGenerator[WorkflowEnvironment, None]: env_type = request.config.getoption("--workflow-environment") if env_type == "local": env = await WorkflowEnvironment.start_local( - dev_server_download_version="v1.7.3-standalone-nexus-operations", - dev_server_extra_args=[ - "--dynamic-config-value", - "frontend.enableExecuteMultiOperation=true", - "--dynamic-config-value", - "system.enableEagerWorkflowStart=true", - "--dynamic-config-value", - "nexusoperation.enableStandalone=true", - "--dynamic-config-value", - "history.enableChasmCallbacks=true", - ], + dev_server_download_version="v1.7.4-standalone-nexus-operations", ) elif env_type == "time-skipping": env = await WorkflowEnvironment.start_time_skipping()