Laminar Review 2026

An open-source observability and evaluation platform that finds, explains, and helps fix agent failures.

Laminar is an open-source platform for understanding and improving AI agents. It automatically captures model calls, tool calls, sub-agent activity, tokens, and cost, then presents the run as a readable transcript. Signals analyze runs to surface failure patterns that were not anticipated during development, and clustering helps teams see how common each behavior is. Evaluations and replay tools support verifying changes against real traces rather than relying only on synthetic examples. Laminar also provides a CLI and MCP access so a coding agent can query traces, evaluations, and Signals with SQL, replay from a checkpoint, and help verify a fix.

Key capabilities include Automatic agent tracing, Readable transcripts, Failure detection with Signals, Failure clustering, Evaluations and checkpoint replay, SQL, CLI, and coding-agent access. These are most useful for Diagnose a failed production agent run; Cluster recurring agent failure modes; Replay a trace after a code change; Let a coding agent inspect observability data. A practical evaluation should start with one bounded workflow, representative production data, and clear success criteria. Teams should check output quality, failure recovery, permission boundaries, operational visibility, and the effort required to fit the tool into existing systems. For workloads that take actions or handle company data, reviewers should also validate identity controls, auditability, retention, and deployment requirements before broad rollout.