README.md

Streaming (Graph API)

Streams a LangGraph run to an external client while the workflow is still running, using Temporal's durable, offset-addressed WorkflowStream. 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).

# 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