comet-ml/opik
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
What it solves
Opik provides a comprehensive platform for the entire lifecycle of LLM applications and AI agents. It addresses the difficulty of monitoring complex, multi-step agent activities, evaluating the quality of LLM outputs (such as detecting hallucinations), and optimizing prompts and agents before and after they go into production.
How it works
Opik functions as an observability and evaluation layer that integrates into LLM applications via SDKs (Python, TypeScript) or direct framework integrations. It captures detailed "traces" of LLM calls, tool executions, and retrieval steps, organizing them into trace trees. It also provides a suite of evaluation tools, including datasets and "LLM-as-a-judge" metrics, to automate the assessment of RAG and agent performance. The platform can be self-hosted via Docker or Kubernetes or used as a managed cloud service.
Who it’s for
It is designed for ML engineers building LLM-powered agents, AI teams transitioning prototypes to production, and engineering teams that require an open-source, self-hostable observability platform to keep data within their own infrastructure.
Highlights
- Full-Stack Observability: Deep tracing for multi-step agents and tool calls with scalable production monitoring (up to 40M+ traces/day).
- Automated Evaluation: Built-in LLM-as-a-judge metrics for hallucination detection, moderation, and RAG assessment (e.g., Answer Relevance, Context Precision).
- Framework Agnostic: Works across various ecosystems with native integrations for Google ADK, Autogen, Flowise AI, and more.
- CI/CD Integration: PyTest integration allows teams to run LLM pipeline tests on every commit.
- Open Source: Apache-2.0 licensed, allowing the full platform (backend and UI) to be self-hosted for free.