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e1624f5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | # ADR-005: LangGraph Streaming Migration & ChatGPT-style UI
**Date:** 2026-05-08
**Status:** Accepted
**Deciders:** Máximo López Chenlo
## Context
The OncoAgent UI was a static, one-shot dashboard using `agent_graph.invoke()` which blocked the Gradio event loop for the entire multi-agent pipeline duration (Router → RAG → Specialist ↔ Critic → Formatter). This caused:
1. **UI freezing** during inference (10-60s depending on model tier and RAG complexity)
2. **No conversational iteration** — oncologists could not ask follow-up questions
3. **Dropdown transparency bug** in Gradio's dark theme making model selection unreadable
4. **Monolithic CSS** embedded in `app.py` hurting maintainability
## Decision
1. **Replace `invoke()` with `stream(stream_mode="updates")`**: LangGraph's streaming API emits `{node_name: output_dict}` events as each node completes, enabling real-time progress display.
2. **ChatGPT-style layout**: Sidebar (session controls, KPIs, evidence tabs) + main chat area, following modern AI assistant conventions.
3. **CSS module extraction**: Moved all styling to `ui/styles.py` for separation of concerns.
4. **Dropdown fix**: Added 30+ explicit CSS selectors targeting Gradio's internal dropdown classes with solid backgrounds.
## Consequences
### Positive
- Zero perceived latency — UI updates node-by-node
- Conversational memory enables iterative clinical dialogue
- Cleaner code architecture (styles separated from logic)
- Fixed visual accessibility bug in model selector
### Negative
- Streaming requires generator functions, slightly more complex handler logic
- More CSS selectors to maintain for Gradio version upgrades
## Alternatives Considered
- **WebSocket-based streaming**: Too complex for Gradio's architecture
- **Polling-based progress**: Higher latency, more server load
- **Streamlit migration**: Would require full rewrite of all UI logic
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