Annotation Queues
LangSmith · LangSmith
LLM observability and evaluation — LangSmith
Evaluates LangSmith's Annotation Queues across 7 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's LLM observability and evaluation eval coverage.
About LangSmith
LangSmith is LangChain's LLM observability and evaluation platform: tracing, datasets, evaluators (LLM-as-judge, code, and human), experiments, prompt management, and online monitoring used by AI teams to measure and improve LLM apps in production.
Employees
~200
Industry
LLM Observability
Headquarters
San Francisco, CA
Website
www.langchain.com/langsmithSample tests· showing 3 of 7
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | Moderators need queue filtered to feedback.score<0.5 safety runs. | Create annotation queue in UI or SDK with project scope and filter; route flagged runs; document queue purpose and reviewer RBAC. | Pass / FailAi Platformhigh |
| 02 | list_runs returns candidates; script should add to queue without manual UI clicks. | Use annotation-queues-sdk patterns to add runs by id; idempotent enqueue; log queue id in job artifact. | Pass / FailAi Platformmedium |
| 03 | Reviewer assigns score 0/1 via feedback API post-annotation. | Use client.create_feedback with run_id, key, score, comment; follow feedback-data-format types. | Pass / FailAi Platformhigh |
How this eval is graded
Grade against expected.ideal_behavior and expected.rubric. Penalize failure_modes.
Rubric criteria
- Langsmith
- Ai Platform
- Annotation Queues
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ViewFrequently asked questions
What does the Annotation Queues eval for LangSmith LangSmith test?+
Evaluates LangSmith's Annotation Queues across 7 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's LLM observability and evaluation eval coverage.
How is the Annotation Queues eval scored?+
The judge rubric: Grade against expected.ideal_behavior and expected.rubric. Penalize failure_modes.
How many test cases does this eval pack include?+
The Annotation Queues pack for LangSmith LangSmith contains 7 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.
How do I run this eval?+
Sign up for Corsac, connect your model or agent endpoint, and run the Annotation Queues pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.
Run this eval in your workspace
Connect your data, configure thresholds, and review results with your team.