Laminar
Open-source agent observability with traces, failure detection and evals
Laminar captures LLM calls, tool calls, sub-agents, costs and tokens from agent runs and renders them as a readable transcript rather than raw spans.
Its Signals feature analyses every run to surface failure modes without them being defined in advance, then clusters similar failures into patterns. Those clusters can be turned into eval datasets, so a fix can be regression-tested after the next release.
Full SQL access over traces and signals, with a CLI and MCP server. Two-line integrations for the Claude Agent SDK, OpenAI Agents SDK, Mastra, Pydantic AI and LangChain.
Pricing: Monthly subscriptions
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