streaming-llm-responses

Real-time streaming for LLM responses sends tokens to the UI as they arrive, providing immediate feedback and reducing perceived latency during AI chat generation. It improves user experience by showing live progress.

To implement streaming lifecycle handlers, wire onResponseStart and onResponseEnd events to lock and unlock the UI respectively. This synchronizes the chat interface state during token generation and ensures stable multi-turn conversations.

Client effects and progress updates work through backend streaming primitives yielding ClientEffectEvent and ProgressUpdateEvent. Frontend handlers like onEffect process these events to drive dynamic UI updates and display status indicators during generation.

Thread-state synchronization supports multi-turn conversations by maintaining context stability as tokens stream. By tying lifecycle events and client effects to thread state, the UI remains consistent across dynamic conversational turns.

Yes, you need backend streaming primitives that yield ProgressUpdateEvent and ClientEffectEvent. These backend events must be paired with frontend handlers like onLog and onClientTool to fully support rich UI patterns in dynamic conversations.