Streaming LLM Applications with FastAPI
Stream LLM responses in real-time and master prompt engineering fundamentals.
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Streaming is the difference between an LLM app that feels fast and one that feels broken. Users do not wait eight seconds for a paragraph any more. They want tokens appearing as the model thinks, progress visible on long-running agents, and backends that survive a thousand concurrent SSE connections.
Curated by Param Harrison
These courses cover streaming across the whole pipeline. FastAPI and Uvicorn for the server side, SSE and Vercel AI SDK on the wire, React hooks for the UI, and the LangGraph events you forward when an agent is mid-run. Each course stays on the engineering side: how you handle backpressure, errors, reconnects, and the weird bugs that only show up with real traffic.
Showing 11 of 11 courses
Common questions
SSE for nearly every LLM use case. It is one-way, text-based, cache-friendly, and survives HTTP proxies that block WebSockets. WebSockets make sense when you need bidirectional real-time (voice, collaborative editing), not for token streaming.
Return a StreamingResponse with an async generator, yield SSE-formatted chunks, and set the right headers. The SSE streaming APIs course walks through the exact pattern plus the gotchas (buffering in proxies, keepalives, flushing).
Vercel AI SDK saves real time on the client. useChat and useCompletion handle reconnects, parsing, and tool-call events out of the box. Roll your own only if you need a specific protocol or are not using Next.js.
Forward the framework’s event stream. LangGraph emits node-start, node-end, and tool-call events you can turn into SSE chunks. The fullstack agents course shows this from graph to browser.
Connection limits on your reverse proxy, buffering in nginx, and cost blowouts when a client disconnects but your backend keeps generating. The streaming course covers the specific configs and cancellation patterns that fix all three.