LangGraph plugin samples

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.

langgraph_plugin/README.md

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+# LangGraph Plugin Samples
+
+These samples demonstrate the [Temporal LangGraph plugin](https://github.com/temporalio/sdk-python/pull/1448), 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
+
+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 langgraph_plugin/<api>/<sample>/run_worker.py
+
+# Terminal 2: start the workflow
+uv run langgraph_plugin/<api>/<sample>/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
+```
+
+## 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** -- `get_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
+
+- [SDK PR: LangGraph plugin](https://github.com/temporalio/sdk-python/pull/1448)

langgraph_plugin/__init__.py

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+"""Temporal LangGraph plugin samples."""

langgraph_plugin/functional_api/__init__.py

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+"""LangGraph Functional API samples using @task and @entrypoint."""

langgraph_plugin/functional_api/continue_as_new/README.md

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+# Continue-as-New with Caching (Functional API)
+
+Same pattern as the Graph API version, using `@task` and `@entrypoint` decorators.
+
+## What This Sample Demonstrates
+
+- Task result caching across continue-as-new boundaries with `get_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: `extract` (x2) -> `transform` (+50) -> `load` (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.
+
+```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 |

langgraph_plugin/functional_api/continue_as_new/__init__.py

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+"""Continue-as-new pipeline with task result caching."""

langgraph_plugin/functional_api/continue_as_new/run_worker.py

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+"""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())

langgraph_plugin/functional_api/continue_as_new/run_workflow.py

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+"""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())

langgraph_plugin/functional_api/continue_as_new/workflow.py

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+"""Continue-as-new with caching using the LangGraph Functional API with Temporal.
+
+Same pattern as the Graph API version, but using @task and @entrypoint decorators.
+"""
+
+from dataclasses import dataclass
+from datetime import timedelta
+from typing import Any
+
+from langgraph.func import entrypoint as lg_entrypoint
+from langgraph.func import task
+from temporalio import workflow
+from temporalio.contrib.langgraph import entrypoint, get_cache
+
+
+@task
+def extract(data: int) -> int:
+    """Stage 1: Extract -- simulate data extraction by doubling the input."""
+    return data * 2
+
+
+@task
+def transform(data: int) -> int:
+    """Stage 2: Transform -- simulate transformation by adding 50."""
+    return data + 50
+
+
+@task
+def load(data: int) -> int:
+    """Stage 3: Load -- simulate loading by tripling the result."""
+    return data * 3
+
+
+@lg_entrypoint()
+async def pipeline_entrypoint(data: int) -> dict:
+    """Run the 3-stage pipeline: extract -> transform -> load."""
+    extracted = await extract(data)
+    transformed = await transform(extracted)
+    loaded = await load(transformed)
+    return {"result": loaded}
+
+
+all_tasks = [extract, transform, load]
+
+activity_options = {
+    t.func.__name__: {"start_to_close_timeout": timedelta(seconds=30)}
+    for t in all_tasks
+}
+
+
+@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]:
+        result = await entrypoint("pipeline", cache=input_data.cache).ainvoke(
+            input_data.data
+        )
+
+        if input_data.phase < 3:
+            workflow.continue_as_new(
+                PipelineInput(
+                    data=input_data.data,
+                    cache=get_cache(),
+                    phase=input_data.phase + 1,
+                )
+            )
+
+        return result

langgraph_plugin/functional_api/control_flow/README.md

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+# 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.
+
+```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 |

langgraph_plugin/functional_api/control_flow/__init__.py

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+"""Control flow: parallel execution, for loops, and if/else branching."""

langgraph_plugin/functional_api/control_flow/run_worker.py

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+"""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())

langgraph_plugin/functional_api/control_flow/run_workflow.py

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+"""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())