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Lemma is an observability platform for AI agents. You send one trace per agent execution, and Lemma lets you search, debug, and monitor what your agents actually did in production - and automatically surfaces silent failures. You instrument with the Lemma SDK; Lemma reads the result and owns the analysis. Follow Lemma’s trace contract so every run is complete and easy to debug.
The one rule: one agent execution = one trace. LLM calls and tool calls are child spans of that trace, not separate traces.

Get started

Quickstart

Install the SDK, point it at Lemma, and send your first trace in a few minutes.

Lemma tracing skill

Let a coding agent install the SDK and add tracing from the public Lemma repo.

Building high-quality traces

The ideal instrumentation path, with bad → better → best examples.

How it works

  1. Instrument with the SDK. Wrap one agent execution in lemma.trace(), and record generation, tool, and app work inside it.
  2. The SDK sends to Lemma over HTTPS, using your Lemma credentials.
  3. Lemma reads the trace shape to show inputs and outputs, model calls, tool calls, threads, and to run automated issue detection.

What to instrument

Instrument every part of your agent. Each layer unlocks more of Lemma, and a complete trace is what powers full debugging and automated issue detection. Aim to record all of them. See Building high-quality traces for what a complete, well-instrumented trace looks like.

What you can do with Lemma

Trace agents - see the full execution tree for every trace: LLM calls, tools, retrieval, latency, and errors. Debug failures - inspect inputs, outputs, and errors across nested spans; filter by user, session, and custom attributes. Connect your IDE - use the Lemma MCP server to query traces from Cursor, Claude Desktop, or Claude Code.

Choose your path

Instrument with the SDK

Install the SDK, then add traces, generations, tool calls, and spans.

Building high-quality traces

The ideal instrumentation path, with bad → better → best examples.