I’m working on a project where I have a Next.js application as my frontend and a FastAPI backend that runs a LangGraph-based AI agent. I need to implement real-time text streaming from the agent to my frontend interface.
The tricky part is that I also need to handle additional features like tool calls and interrupt functionality. I’ve been searching for tutorials or example code but haven’t found anything comprehensive yet.
Has anyone implemented something similar? I’m looking for any guidance, code examples, or documentation that could point me in the right direction. Even a basic implementation example would be super helpful to get me started.
I’m particularly interested in how to handle the streaming connection between FastAPI and Next.js while maintaining support for the advanced LangGraph features.
I dealt with this same issue a few months ago. Switched to Server-Sent Events instead of WebSockets and it solved everything. FastAPI’s StreamingResponse works great with SSE, and Next.js handles EventSource connections without any fuss. For LangGraph, you’ll need to yield JSON chunks from your FastAPI endpoint that include the text and metadata about tool calls or interrupt states. The tricky bit is keeping connection state during interrupts - I built a simple state tracker that holds conversation context so you can resume after interrupts. Watch out for connection drops though. Make sure you’ve got solid error handling and reconnection logic on the Next.js side. LangGraph’s docs have streaming examples, but they’re pretty basic compared to what you’re building.
Been there. Built exactly this for our internal chat platform last year.
Use FastAPI’s background tasks with asyncio queues. Have your LangGraph agent push updates to a queue, then your streaming endpoint consumes from it. Handles tool calls and interrupts without breaking the stream.
For interrupts, I used Redis to store agent state. When an interrupt hits, dump current state to Redis with a session ID. Your Next.js frontend sends a resume request with that ID and picks up where it left off.
One gotcha - tool calls can take forever. Don’t let them block your stream. Send a “tool_started” message right away, then stream results when ready. Frontend can show loading state.
LangGraph streaming’s straightforward once wrapped properly. Just yield proper JSON with message types so your frontend knows what to do with each chunk.