Lemma
Observability and evaluation platform that helps AI agents learn from production failures and user feedback.
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Product Overview
What is Lemma?
Lemma is an observability and evaluation platform purpose-built for AI agents rather than traditional software. It captures full execution traces of agent runs—covering LLM calls, tool usage, retrieval steps, latency, and errors—so teams can pinpoint exactly where and why an agent went wrong. Beyond passive monitoring, Lemma closes the feedback loop by turning real user signals and detected failure patterns into concrete prompt and behavior improvements, aiming to cut manual debugging time significantly.
Key Features
Agent Trace Visualization
Displays the full execution tree of every agent run, including LLM calls, tool invocations, retrieval steps, timing, and error points for deep debugging.
Semantic Trace Search
Every trace is embedded and searchable using natural language, letting teams find failure patterns like hallucinations or misrouted intents without a query language.
Automated Cluster Discovery
Continuously groups similar traces to surface emerging failure clusters and behavioral anomalies before teams know to look for them.
Production Monitoring & Alerts
Tracks metrics such as latency, error rate, and tool call success, automatically opening incidents with root cause analysis when thresholds break.
Evaluation Framework
Combines automated online evaluations with observed user signals like feedback clicks or conversions to measure agent performance beyond synthetic metrics.
OpenTelemetry-Native Integrations
Plugs into existing stacks via OTLP export, with native support for Vercel AI SDK, OpenAI Agents, Langfuse, Arize Phoenix, and Azure Monitor.
Use Cases
- AI Agent Debugging : Engineering teams inspect nested spans, inputs, and outputs to diagnose why an agent selected the wrong action or tool during a live session.
- Regression Detection : Product teams catch silent performance drifts after prompt or model updates before they escalate into user-facing complaints.
- Customer Support Agent Optimization : Support teams use trace clustering and feedback signals to identify recurring misrouted requests and refine agent logic.
- IDE-Connected Troubleshooting : Developers query traces directly from Cursor, Claude Desktop, or Claude Code via the Lemma MCP server without leaving their coding environment.
- Compliance-Sensitive Deployments : Organizations handling sensitive data rely on Lemma's SOC 2 Type II certification and data isolation for secure agent monitoring.
FAQs
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