mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-08-04 20:08:40 +00:00
45865e9f3f
The goal evaluator (runtime/goal.py) runs from runtime/runs/worker.py after
the main graph run has already completed, so there is no graph root for it
to inherit tracing from. create_goal_evaluator_model was built with
attach_tracing=False, and evaluate_goal_completion invoked the model with a
bare config={"run_name": "goal_evaluator"} — no tracing callbacks, no
Langfuse session/user attribution. Every goal-evaluator LLM call went
untraced.
Same class of gap fixed by #2944 for the main agent graph and by #3902 for
memory_agent/suggest_agent: a standalone call site that invokes a model
directly instead of through a traced graph root must attach its own tracing
callbacks and inject Langfuse trace-attribute metadata itself.
- create_goal_evaluator_model: attach_tracing=False -> True, matching the
other standalone non-graph callers (oneshot_llm.run_oneshot_llm,
MemoryUpdater).
- evaluate_goal_completion: accept optional thread_id/user_id/
deerflow_trace_id and inject Langfuse trace metadata onto the ainvoke
config via the shared inject_langfuse_metadata() helper, mirroring
oneshot_llm.py's pattern.
- worker.py: thread user_id (resolve_runtime_user_id(runtime)) and
deerflow_trace_id through _prepare_goal_continuation_input into
evaluate_goal_completion so the evaluator's trace groups under the
triggering run's thread/session.
Updates the existing test that pinned attach_tracing=False as expected
behavior, and adds a regression test asserting the ainvoke config carries
Langfuse trace metadata when enabled.