FastAPI + LangChain: Building Production-Ready AI APIs

FastAPI’s async support and automatic OpenAPI docs pair naturally with LangChain for production AI backends.

Project Structure

app/
  main.py
  routers/chat.py
  services/rag.py
  models/schemas.py

Async Endpoint

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class ChatRequest(BaseModel):
    message: str

@app.post("/chat")
async def chat(req: ChatRequest):
    result = await rag_chain.ainvoke({"input": req.message})
    return {"answer": result["answer"]}

Production Checklist

Rate limiting, API keys, structured logging, health checks, timeout on LLM calls, background tasks for long ingest jobs.

Conclusion

FastAPI + LangChain is a proven stack for Python teams building RAG and agent APIs. Add Redis for sessions and Celery for heavy indexing offline.