streaming-llm-responses

Real-time streaming responses display LLM output as it's generated. Use lifecycle hooks like onResponseStart and onResponseEnd to manage UI state, onEffect for fire-and-forget updates during streaming, and ProgressUpdateEvent to show progress indicators. Integrate with a ChatKit-like client to emit server-to-client streaming events and render live feedback.

Streaming chat requires onResponseStart to lock UI before generation begins, onResponseEnd to unlock after completion, onEffect for side effects during streaming, onThreadChange for state synchronization, and onClientTool for client-tool interactions. These hooks enable response lifecycle management and event-driven UI updates.

Yes. Use ProgressUpdateEvent to emit dynamic progress messages during streaming. Combined with onEffect lifecycle hooks, you can display real-time progress updates, task status, and intermediate feedback as the LLM generates responses.

Use onThreadChange hooks to detect thread updates and synchronize UI state in real-time. This ensures the chat interface reflects current thread context while responses stream, maintaining consistency across response lifecycle events and client interactions.

onResponseStart locks or unlocks UI at response boundaries, controlling when users can interact. onEffect fires fire-and-forget updates during streaming for intermediate feedback. Together they enable both lifecycle state management and continuous progress rendering.

Client tool interactions through onClientTool are optional but enable advanced patterns like tool calling, form handling, and client-side effects during streaming. They integrate tool contracts into the response lifecycle for richer chat experiences.