Opik

What it does

Opik connects traces, datasets, evaluation experiments, natural-language assertions, prebuilt metrics, custom scoring, optimization, and annotation. Production failures can be promoted into repeatable test cases.

• Natural-language assertion suites and more than 30 prebuilt metrics
• Dataset experiments, custom metrics, version comparison, and annotation queues
• Trace-to-test workflows that convert production failures into regression cases

Where it fits

Teams wanting hosted or self-hosted observability with failure-driven regression suites, experiments, metrics, and human review.

Use it after defining the behavior that matters, collecting representative cases, and deciding how each case will be graded. A tool can execute and organize an eval, but it cannot decide which failures are costly for your users.

Limitations and cautions

• The basic local self-hosted installation is not production-ready; Kubernetes is the production route.
• Self-hosted editions omit some user-management features and require server and SDK version coordination.

How to evaluate the evaluator

Before using any score as a release gate, create a small evaluator-validation set that humans have labeled. Compare false passes, false failures, disagreement by failure type, run-to-run variance, latency, and cost. Keep deterministic checks for verifiable facts, calibrate model judges against domain experts, and review raw traces when aggregate scores move.