.living_spec/stories/archive/18_streaming_responses.md

# Story 18: Token-by-Token Streaming Responses

## User Story
As a user, I want to see the AI's response appear token-by-token in real-time (like ChatGPT), so that I get immediate feedback and know the system is working, rather than waiting for the entire response to appear at once.

## Acceptance Criteria
- [x] Tokens appear in the chat interface as Ollama generates them, not all at once
- [x] The streaming experience is smooth with no visible lag or stuttering
- [x] Auto-scroll keeps the latest token visible as content streams in
- [x] When streaming completes, the message is properly added to the message history
- [x] Tool calls work correctly: if Ollama decides to call a tool mid-stream, streaming stops gracefully and tool execution begins
- [ ] The Stop button (Story 13) works during streaming to cancel mid-response
- [x] If streaming is interrupted (network error, cancellation), partial content is preserved and an appropriate error state is shown
- [x] Multi-turn conversations continue to work: streaming doesn't break the message history or context

## Out of Scope
- Streaming for tool outputs (tools execute and return results as before, non-streaming)
- Throttling or rate-limiting token display (we stream all tokens as fast as Ollama sends them)
- Custom streaming animations or effects beyond simple text append
- Streaming from other LLM providers (Claude, GPT, etc.) - this story focuses on Ollama only

## Technical Notes
- Backend must enable `stream: true` in Ollama API requests
- Ollama returns newline-delimited JSON, one object per token
- Backend emits `chat:token` events (one per token) to frontend
- Frontend appends tokens to a streaming buffer and renders in real-time
- When streaming completes (`done: true`), backend emits `chat:update` with full message
- Tool calls are detected when Ollama sends `tool_calls` in the response, which triggers tool execution flow
Story 18: Token-by-token streaming responses - Backend: Added OllamaProvider::chat_stream() with newline-delimited JSON parsing - Backend: Emit chat:token events for each token received from Ollama - Backend: Added futures dependency and stream feature for reqwest - Frontend: Added streamingContent state and chat:token event listener - Frontend: Real-time token display with auto-scroll - Frontend: Markdown and syntax highlighting support for streaming content - Fixed all TypeScript errors (tsc --noEmit) - Fixed all Biome warnings and errors - Fixed all Clippy warnings - Added comprehensive code quality documentation - Added tsc --noEmit to verification checklist Tested and verified: - Tokens stream in real-time - Auto-scroll works during streaming - Tool calls interrupt streaming correctly - Multi-turn conversations work - Smooth performance with no lag 2025-12-27 16:50:18 +00:00			`# Story 18: Token-by-Token Streaming Responses`

			`## User Story`
			`As a user, I want to see the AI's response appear token-by-token in real-time (like ChatGPT), so that I get immediate feedback and know the system is working, rather than waiting for the entire response to appear at once.`

			`## Acceptance Criteria`
			`- [x] Tokens appear in the chat interface as Ollama generates them, not all at once`
			`- [x] The streaming experience is smooth with no visible lag or stuttering`
			`- [x] Auto-scroll keeps the latest token visible as content streams in`
			`- [x] When streaming completes, the message is properly added to the message history`
			`- [x] Tool calls work correctly: if Ollama decides to call a tool mid-stream, streaming stops gracefully and tool execution begins`
			`- [ ] The Stop button (Story 13) works during streaming to cancel mid-response`
			`- [x] If streaming is interrupted (network error, cancellation), partial content is preserved and an appropriate error state is shown`
			`- [x] Multi-turn conversations continue to work: streaming doesn't break the message history or context`

			`## Out of Scope`
			`- Streaming for tool outputs (tools execute and return results as before, non-streaming)`
			`- Throttling or rate-limiting token display (we stream all tokens as fast as Ollama sends them)`
			`- Custom streaming animations or effects beyond simple text append`
			`- Streaming from other LLM providers (Claude, GPT, etc.) - this story focuses on Ollama only`

			`## Technical Notes`
			- Backend must enable `stream: true` in Ollama API requests
			`- Ollama returns newline-delimited JSON, one object per token`
			- Backend emits `chat:token` events (one per token) to frontend
			`- Frontend appends tokens to a streaming buffer and renders in real-time`
			- When streaming completes (`done: true`), backend emits `chat:update` with full message
			- Tool calls are detected when Ollama sends `tool_calls` in the response, which triggers tool execution flow