Lemma: Observability and Evaluation for AI Agents

What

Lemma is an observability and evaluation platform for AI agents. It helps teams understand why agents fail, detect regressions, inspect traces, and use both automated evaluations and user feedback to improve behavior before issues reach more users.

The product appears aimed at teams building and operating production AI agents, especially those using common agent frameworks and SDKs. Its positioning is likely as a developer and ML-operations layer for agent quality: instrument the agent, collect traces and feedback, search and cluster failures, and compare strategies or configurations over time.

Features

• Agent observability with trace-level visibility: Lemma captures agent runs and spans so teams can inspect failures such as hallucinations, wrong tool use, repeated tool calls, or task-adherence issues.
• Online evaluations tied to real user signals: It combines automated evals with observed behaviors like thumbs-up feedback, checkout starts, or completed actions to measure performance beyond static benchmarks.
• Semantic search across traces and evaluations: Every trace is embedded and indexed, allowing teams to search run histories in plain English and quickly find recurring failure patterns without learning a query language.
• Automatic cluster discovery for emerging issues: Lemma continuously groups traces into behavior or input clusters, helping teams surface unknown failure modes without predefined labels or manual categorization.
• Experiment comparison for prompts, models, and architectures: The product describes a coming-soon experiments workflow for testing strategies against a fixed test set before production deployment.
• Lightweight instrumentation with OpenTelemetry support: Teams can wrap an agent function, generate a run ID, and link traces to feedback or experiment outcomes, with SDK support for TypeScript and Python and integrations with existing observability tools.

Helpful Tips

• Check whether your team needs runtime observability or offline eval tooling most: Lemma is strongest where production agent failures are hard to diagnose and user feedback needs to be tied back to exact runs.
• Prioritize instrumentation design early: The value of search, clustering, and evaluation depends on consistently capturing traces, tool calls, outputs, and feedback events with reliable run IDs.
• Use user signals carefully: Observed actions such as checkout progression can be useful outcome signals, but they should be paired with domain-specific quality metrics to avoid misleading conclusions.
• Validate framework coverage against your stack: The site lists native support for some frameworks and notes others as coming soon, so teams should confirm implementation fit before standardizing.
• Treat the experiments capability as roadmap, not current core functionality: The page labels experiments as coming soon, so buying decisions should focus on current observability, search, clustering, and evaluation capabilities.