Deploying LangGraph Agents to Production

Deploying a LangGraph agent to production means wrapping it in a web server so other services and UIs can call it via HTTP. The most common pattern is to expose the agent as a FastAPI endpoint - receive a request, run the graph with app.invoke() or app.astream(), and return the result. For ShopMax India, the customer support agent becomes a REST service that the mobile app and website call whenever a user opens a chat.

For persistence across requests, attach a SqliteSaver or PostgresSaver checkpointer so conversation threads survive server restarts. Pass a unique thread_id per user session in the config to maintain separate conversation histories. Use AsyncSqliteSaver with async FastAPI routes to avoid blocking the event loop under concurrent load.

The following example wraps a ShopMax India LangGraph agent in a FastAPI application with persistent SQLite checkpointing, ready to run with uvicorn.

It gives the following output,

# POST /chat
# Request: {"thread_id": "user-C001", "message": "I have a question about my order"}
# Response:
{
  "thread_id": "user-C001",
  "response": "I can help with your ShopMax India order. Please share your order ID."
}

For high-traffic production deployments at ShopMax India, replace SQLite with PostgresSaver for concurrent writes and use a process manager like gunicorn with multiple uvicorn workers. Add a Redis layer for caching frequent responses. Monitor agent latency and error rates using standard FastAPI middleware, and set recursion_limit in the graph config to guard against runaway loops.