Evaluation & observability
comet-ml/opik
Opik provides tracing, evaluation and monitoring for LLM and agent workflows. It helps teams examine multi-step behaviour, compare experiments and measure production performance through a shared platform.
THE PRACTICAL EXPLANATION
Opik provides tracing, evaluation and monitoring for LLM and agent workflows. It helps teams examine multi-step behaviour, compare experiments and measure production performance through a shared platform.
WHERE TO USE IT
Use it for debugging agent chains, evaluating releases and monitoring model-powered applications where inputs, tool calls and outputs need to be connected.
WHO MAY USE IT
AI engineering and operations teams looking for an integrated open-source evaluation and observability layer.
HOW TO USE IT
Instrument a single critical path, decide which metadata is safe and useful, and create an evaluation dataset before collecting large trace volumes. Use experiments to compare changes and review production exceptions against the same criteria.
GETTING THE BEST RESULTS
WHY IT MAY BE USEFUL
Tracing, evaluation and monitoring for LLM and agent workflows.
Best considered for: Debugging and measuring agent behaviour in development and production.
READ BEFORE YOU ADOPT IT
Choose useful evaluation criteria before collecting large volumes of traces.
Confirm the current licence, maintenance status, dependency risk, data path, model access, tool permissions and human approval points at the source. A public repository is inspectable raw material—not proof that a system is secure, supported or suitable for your production environment.