Streaming in LangChain: A Complete FastAPI + SSE Implementation

The streaming example in most tutorials works in a terminal. Putting it into production behind a real endpoint requires a few more pieces.

The base pattern: astream() inside an async endpoint

Integrating LangChain with FastAPI means defining async endpoints that call `chain.ainvoke()` for complete responses or `chain.astream()` for token streaming. You can create an endpoint that streams events using Server-Sent Events (SSE) over a RAG pipeline — the typical pattern uses a FastAPI endpoint with `StreamingResponse`, iterating asynchronously through `chain.astream()` and emitting data in SSE format.

Reference code

astream_events for fine control over what gets streamed

LangChain's `astream_log` method uses JSON Patch to stream events, giving an efficient way to incrementally update parts of a JSON document. Callbacks intercept lifecycle events across every LangChain component, allowing granular control over streaming behavior — with `astream_events` you can stream agents token by token and wire it up to FastAPI's SSE for an experience with no loading spinners.

The 4 pieces of a production-ready setup

A production-ready configuration needs: an async FastAPI endpoint calling `chain.ainvoke()` or `chain.astream()`; structured error handling that catches `openai.RateLimitError` and `LangChainException` separately (not a generic catch-all); CORS middleware with explicitly allowed origins; and a Gunicorn process manager running 4 or more Uvicorn workers.

The most common mistake: mixing sync and async

Use `chain.ainvoke()` for single responses and `chain.astream()` for token streaming — never call synchronous methods inside async handlers. This is the most common production bug in LangChain + FastAPI integrations: a blocking synchronous method inside an async handler blocks the entire event loop, degrading the latency of every other concurrent request, not just the one that called it.