streaming-llm-responses

To implement real-time streaming for AI chat responses, wire your interface to handle event-driven lifecycle hooks like onResponseStart and onResponseEnd, rendering partial tokens and progress indicators as they arrive across web and mobile clients.

Streaming client effects are UI updates triggered by onEffect events during an AI conversation. You need them to keep your interface in sync with progress updates, status banners, and live metrics while tokens actively stream.

You can keep user interactions controlled while tokens stream by coordinating UI state with the onResponseStart and onResponseEnd lifecycle handlers to lock interactions when the response starts and unlock them upon completion.

Yes, you can query current UI state to inform AI decisions by using the onClientTool handler, which queries client state during the conversation to provide context for the AI's next response.

You track thread changes during a streaming chat session by satisfying the onThreadChange handler, which monitors thread transitions and maintains a predictable, event-driven streaming workflow throughout the conversation.