TradingAgents/docs/agent/decisions/013-agentos-websocket-streaming.md

ADR 013: AgentOS WebSocket Streaming Architecture

Status

Accepted

Context

TradingAgents needed a visual observability layer to monitor agent execution in real-time. The CLI (Rich-based) works well for terminal users but doesn't provide graph visualization or persistent portfolio views. Key requirements:

1. Stream LangGraph events to a web UI in real-time
2. Visualize the agent workflow as a live graph
3. Show portfolio holdings, trades, and metrics
4. Support all 4 run types (scan, pipeline, portfolio, auto)

Decision

REST + WebSocket Split

REST endpoints (POST /api/run/{type}) only queue runs to an in-memory store. The WebSocket endpoint (WS /ws/stream/{run_id}) is the sole executor — it picks up queued runs, calls the appropriate LangGraph engine method, and streams events back to the frontend.

This avoids the complexity of background task coordination. The frontend triggers a REST call, gets a run_id, then connects via WebSocket to that run_id to receive all events.

Event Mapping

LangGraph v2's astream_events() produces raw events with varying structures per provider. LangGraphEngine._map_langgraph_event() normalizes these into 4 event types: thought, tool, tool_result, result. Each event includes:

• node_id, parent_node_id for graph construction
• metrics (model, tokens, latency)
• Optional prompt and response full-text fields

The mapper uses try/except per event type and a _safe_dict() helper to prevent crashes from non-dict metadata (e.g., some providers return strings or lists).

Field Mapping (Backend → Frontend)

Portfolio models use different field names than the frontend expects. The /latest endpoint maps: shares → quantity, portfolio_id → id, cash → cash_balance, trade_date → executed_at. Computed runtime fields (market_value, unrealized_pnl) are included from enriched Holding properties.

Pipeline Recursion Limit

run_pipeline() passes config={"recursion_limit": propagator.max_recur_limit} (default 100) to astream_events(). Without this, LangGraph defaults to 25, which is insufficient for the debate + risk cycles (up to ~10 iterations).

Consequences

• Pro: Real-time visibility into agent execution with zero CLI changes
• Pro: Crash-proof event mapping — one bad event doesn't kill the stream
• Pro: Clean separation — frontend can reconnect to ongoing runs
• Con: In-memory run store is not persistent (acceptable for V1)
• Con: Single-tenant auth (hardcoded user) — needs JWT for production

Source Files

• agent_os/backend/services/langgraph_engine.py
• agent_os/backend/routes/websocket.py
• agent_os/backend/routes/runs.py
• agent_os/backend/routes/portfolios.py
• agent_os/frontend/src/hooks/useAgentStream.ts
• agent_os/frontend/src/Dashboard.tsx