Comet ML

Comet provides machine learning experiment management alongside Opik, its agent observability and evaluation platform, and the current emphasis is firmly on the latter. The vendor reports over 150,000 developers, thousands of companies, 21,000 GitHub stars and 10,000 teams.

Opik is built around a specific loop: log every step an agent takes, detect problems, fix them and validate the fix, then monitor. The distinguishing claim is that it connects observability to action, automatically turning trace data and evaluation results into code fixes so the agent does not repeat the same mistake.

That closing of the loop is what separates useful agent tooling from dashboards. Observing that an agent failed is not the difficult part; determining which step in a multi-step trace caused the failure, and changing the prompt or code accordingly, is where the work sits. A system that proposes the fix from the trace is attempting the expensive half rather than the cheap half.

Published capabilities include tracing, evaluations, test suites and assertions, an agent playground, cost intelligence and an agent optimizer. Test suites and assertions are the capability worth prioritising in evaluation, because agent behaviour regresses silently as prompts and models change, and without automated tests a team discovers regressions from user complaints.

Cost intelligence addresses the other recurring problem, since agent runs consume tokens unpredictably and an agent that loops costs real money before anyone notices.

Pricing starts at zero. Opik is available open source to download, install and run yourself, and a free cloud tier covers analysis, test suites, assertions and the agent playground. Pro Cloud is $19 with expanded usage. The open-source option matters for teams that cannot send trace data containing customer information to a third party.