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Clarify execution context for LangGraph tasks/nodes #305

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4 changes: 2 additions & 2 deletions langgraph_plugin/README.md
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# 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.
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:

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## 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.
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