chore(deps): bump tinyagents to land Langfuse observation-id collision fix (#32)#4601
Conversation
…n-id collision fix) Advances vendor/tinyagents 6bf67ac -> 3d9fcfb, landing tinyhumansai/tinyagents#32 which namespaces Langfuse observation ids by trace id (`{trace_id}:{call_id}`). Without it, model generations + tool spans took their Langfuse observation id from the run-scoped `call_id` (e.g. `agent_turn-model-1`), which repeats on every interactive turn. Langfuse upserts observations by id project-wide, so each new turn overwrote the prior model generation onto the newest trace — observed as 75 agent-turn traces retaining only 5 `model` generations, i.e. per-turn token usage / cost / prompt+reply vanishing from nearly every trace. Also pulls in already-merged tinyagents tinyhumansai#30 (orchestration tool coverage tests) and tinyhumansai#31 (config-driven rlm runtime module). Claude-Session: https://claude.ai/code/session_013fVjLxoGF9pEkdEZdHksHN
|
Warning Review limit reachedYou’ve reached a temporary PR review limit under our Fair Usage Limits Policy. Next review available in: 49 minutes Enable usage-based reviews in Billing to review now. Otherwise, wait until the next included review is available. How can I continue?After more reviews become available, a review can be triggered using the To avoid repeated limits, reduce automatic review volume by pausing incremental auto-reviews earlier, using label-based review opt-in, excluding WIP or generated PR titles, or requesting reviews manually when the PR is ready. If your team needs uninterrupted high-volume reviews, an organization admin can enable usage-based reviews. How do review limits work?CodeRabbit enforces per-developer PR review limits for each organization. Most developers receive the normal plan review availability. For paid Pro and Pro+ PR reviews, CodeRabbit uses adaptive limits for sustained high-volume activity. When a developer's recent PR review activity reaches the 95th percentile or higher among CodeRabbit users, additional reviews become available more gradually as earlier reviews age out of the rolling window. Please refer docs for additional details. Comment |
Summary
Advances the vendored
tinyagentssubmodule6bf67ac → 3d9fcfbto land tinyhumansai/tinyagents#32, which fixes a Langfuse observation-id collision that stripped per-turn metadata from agent-turn traces.Model generations and tool spans took their Langfuse observation
idfrom the run-scopedcall_id(e.g.agent_turn-model-1). The logical run id (agent_turn) repeats on every interactive turn, so the id is identical across all turns and all threads. Langfuse upserts observations byidproject-wide, so each new turn'smodelgeneration silently overwrote the previous one and reattached it to the newest trace — leaving every earlier trace with no model usage/cost/prompt+reply.Measured on staging Langfuse (
fuse.tinyhumans.ai): 75 agent-turn traces existed, but only 5modelgenerations survived — nearly every trace had lost its per-turn token usage, cost, and content. The fix namespaces the id as{trace_id}:{call_id}(globally unique per turn, stable for idempotent re-ingestion) and keeps the rawcall_idin observation metadata for in-run correlation.Submodule range
Verification
cargo check --manifest-path Cargo.toml(GGML_NATIVE=OFF) — passes against the bumped submodule.cargo test --lib observability::langfuse— 17 passed, incl. the newcall_scoped_observation_ids_are_unique_per_traceregression test.Follow-up (not in this PR)
Existing corrupted staging traces can be repaired by re-ingesting each run's local journal with the corrected ids; and the chat path never writes
run_telemetry(so therun.totalaggregate is skipped) — minor now that childmodelgenerations attach correctly and Langfuse derives per-trace usage/cost from them.https://claude.ai/code/session_013fVjLxoGF9pEkdEZdHksHN