opik: Trace, Evaluate, and Monitor LLM Applications

Overview

Opik is Comet’s open-source platform for LLM observability, evaluation, and AI agent tracing. It helps teams follow an application from development traces and evaluations to production monitoring, making it easier to inspect multi-step workflows and assess model outputs. The platform includes a Python and TypeScript SDK, a web application, and a backend, and can be used through Comet Cloud or self-hosted.

It is aimed at teams building LLM applications, RAG systems, and agents who need to inspect traces, compare experiments, and monitor behavior beyond individual model calls. Opik is framework-agnostic, with integrations for many agent and LLM frameworks and OpenTelemetry support.

Key Features

• Record nested traces for LLM calls, tool use, retrieval, and agent workflows through SDKs and framework integrations.
• Organize evaluation datasets and experiments to compare application outputs.
• Score outputs with built-in LLM-as-a-judge and heuristic metrics, or create custom metrics.
• Track production traces and review usage, feedback, and online evaluation results in dashboards.
• Manage prompts and test them in a playground.
• Run evaluations in CI/CD with the PyTest integration.
• Self-host the full platform or use Comet Cloud.
• Connect coding agents to Opik through its Model Context Protocol server.

Use Cases

• An ML engineer debugging a multi-step agent can inspect its model calls, tools, and retrieval steps as a trace.
• An evaluation team can build a dataset and compare prompt or application changes before a release.
• A team operating a RAG system can assess answer relevance, context precision, or hallucination signals.
• A platform team can monitor production traces and apply online evaluation rules to surface quality issues.
• A team with data-residency needs can deploy the platform in its own environment rather than send traces to a hosted service.

Getting Started

For the Python SDK:

pip install opik
opik configure

Then use the @track decorator to log function calls. See the README and documentation for tracing, evaluation, and self-hosting instructions.

Alternatives

• langsmith-sdk: LangSmith SDKs send traces to LangSmith, while Opik offers a broader platform for tracing, evaluation, datasets, experiments, and monitoring.
• weave: Weave focuses on tracing and comparing AI application runs, while Opik also provides datasets, experiments, prompt tools, and production dashboards.
• phoenix: Phoenix is a self-hosted platform for tracing and troubleshooting LLM apps, while Opik adds broader experiment, dataset, and prompt-management workflows.
• langwatch: LangWatch combines observability with simulation testing, an AI gateway, and governance, while Opik emphasizes tracing, evaluations, datasets, and experiments.

Considerations

• The repository is active, with regular commits and releases; its contributor base is broad rather than concentrated around one person. Recent issue handling is less evident than its merge and release activity, so teams should check open issues relevant to their deployment needs.
• Opik spans a server backend, web application, SDKs, and integrations. Self-hosting therefore involves operating a platform, with Docker Compose documented for local use and Kubernetes with Helm for larger deployments.
• The README describes a large and growing integration set. Confirm that the specific framework and workflow you use are covered by the current integration documentation.
• The project is Apache-2.0 licensed. Review the license and dependencies against your organization’s distribution and deployment requirements.