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https://github.com/bytedance/deer-flow.git
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feat(persistence): add ORM models, repositories, DB/JSONL event stores, RunJournal, and API endpoints
Phase 2-B: run persistence + event storage + token tracking. - ORM models: RunRow (with token fields), ThreadMetaRow, RunEventRow - RunRepository implements RunStore ABC via SQLAlchemy ORM - ThreadMetaRepository with owner access control - DbRunEventStore with trace content truncation and cursor pagination - JsonlRunEventStore with per-run files and seq recovery from disk - RunJournal (BaseCallbackHandler) captures LLM/tool/lifecycle events, accumulates token usage by caller type, buffers and flushes to store - RunManager now accepts optional RunStore for persistent backing - Worker creates RunJournal, writes human_message, injects callbacks - Gateway deps use factory functions (RunRepository when DB available) - New endpoints: messages, run messages, run events, token-usage - ThreadCreateRequest gains assistant_id field - 92 tests pass (33 new), zero regressions Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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@@ -19,7 +19,7 @@ from fastapi import APIRouter, HTTPException, Query, Request
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from fastapi.responses import Response, StreamingResponse
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from pydantic import BaseModel, Field
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from app.gateway.deps import get_checkpointer, get_run_manager, get_stream_bridge
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from app.gateway.deps import get_checkpointer, get_run_event_store, get_run_manager, get_run_store, get_stream_bridge
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from app.gateway.services import sse_consumer, start_run
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from deerflow.runtime import RunRecord, serialize_channel_values
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@@ -263,3 +263,77 @@ async def stream_existing_run(
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"X-Accel-Buffering": "no",
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},
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)
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# ---------------------------------------------------------------------------
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# Messages / Events / Token usage endpoints
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# ---------------------------------------------------------------------------
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@router.get("/{thread_id}/messages")
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async def list_thread_messages(
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thread_id: str,
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request: Request,
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limit: int = Query(default=50, le=200),
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before_seq: int | None = Query(default=None),
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after_seq: int | None = Query(default=None),
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) -> list[dict]:
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"""Return displayable messages for a thread (across all runs)."""
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event_store = get_run_event_store(request)
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return await event_store.list_messages(thread_id, limit=limit, before_seq=before_seq, after_seq=after_seq)
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@router.get("/{thread_id}/runs/{run_id}/messages")
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async def list_run_messages(thread_id: str, run_id: str, request: Request) -> list[dict]:
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"""Return displayable messages for a specific run."""
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event_store = get_run_event_store(request)
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return await event_store.list_messages_by_run(thread_id, run_id)
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@router.get("/{thread_id}/runs/{run_id}/events")
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async def list_run_events(
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thread_id: str,
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run_id: str,
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request: Request,
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event_types: str | None = Query(default=None),
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limit: int = Query(default=500, le=2000),
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) -> list[dict]:
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"""Return the full event stream for a run (debug/audit)."""
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event_store = get_run_event_store(request)
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types = event_types.split(",") if event_types else None
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return await event_store.list_events(thread_id, run_id, event_types=types, limit=limit)
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@router.get("/{thread_id}/token-usage")
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async def thread_token_usage(thread_id: str, request: Request) -> dict:
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"""Thread-level token usage aggregation."""
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run_store = get_run_store(request)
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runs = await run_store.list_by_thread(thread_id, limit=10000)
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completed = [r for r in runs if r.get("status") in ("success", "error")]
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total_tokens = sum(r.get("total_tokens", 0) for r in completed)
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total_input = sum(r.get("total_input_tokens", 0) for r in completed)
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total_output = sum(r.get("total_output_tokens", 0) for r in completed)
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by_model: dict[str, dict] = {}
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for r in completed:
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model = r.get("model_name") or "unknown"
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entry = by_model.setdefault(model, {"tokens": 0, "runs": 0})
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entry["tokens"] += r.get("total_tokens", 0)
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entry["runs"] += 1
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by_caller = {
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"lead_agent": sum(r.get("lead_agent_tokens", 0) for r in completed),
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"subagent": sum(r.get("subagent_tokens", 0) for r in completed),
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"middleware": sum(r.get("middleware_tokens", 0) for r in completed),
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}
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return {
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"thread_id": thread_id,
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"total_tokens": total_tokens,
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"total_input_tokens": total_input,
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"total_output_tokens": total_output,
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"total_runs": len(completed),
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"by_model": by_model,
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"by_caller": by_caller,
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}
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@@ -63,6 +63,7 @@ class ThreadCreateRequest(BaseModel):
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"""Request body for creating a thread."""
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thread_id: str | None = Field(default=None, description="Optional thread ID (auto-generated if omitted)")
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assistant_id: str | None = Field(default=None, description="Associate thread with an assistant")
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metadata: dict[str, Any] = Field(default_factory=dict, description="Initial metadata")
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