comet-ml / opik
ai-evaluationllm-observabilityprompt-engineeringtracing
A tracing, evaluation, and monitoring platform for LLM applications, RAG systems, and agentic workflows.
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description README.md
Opik is the open-source LLM observability and evaluation platform for AI agent tracing, LLM evaluation, prompt management, and production monitoring. Built by Comet. Apache-2.0 licensed, free to self-host the full platform, with 20,000+ GitHub stars.
Website • Slack Community • Twitter • Changelog • Documentation
Last updated: 2026-07-17
🚀 What is Opik? • ⚡ Quick Start • 📊 How Does Opik Compare? • ❓ FAQ • 🛠️ Opik Server Installation • 💻 Opik Client SDK • 📝 Logging Traces
🧑⚖️ LLM as a Judge • 🔍 Evaluating your Application • ⭐ Star Us • 🤝 Contributing
🧑⚖️ LLM as a Judge • 🔍 Evaluating your Application • ⭐ Star Us • 🤝 Contributing
🚀 What is Opik?
Opik covers the full LLM application lifecycle, from the first trace in development to production monitoring, for teams building LLM apps and AI agents. Key offerings include:
- AI Agent Tracing & Observability: Deep tracing of LLM calls, conversation logging, and agent activity, with full trace trees for multi-step agents and tool calls.
- LLM Evaluation: Datasets, experiments, and LLM-as-a-judge metrics for hallucination detection, moderation, and RAG assessment.
- Prompt & Agent Optimization: The Opik Agent Optimizer SDK to improve prompts and agents.
- Production-Ready Monitoring: Scalable dashboards and online evaluation rules.
- Opik Guardrails: Features to help you implement safe and responsible AI practices.
- CI/CD Evaluation: A PyTest integration to test LLM pipelines on every commit.
Key capabilities include:
-
Development & Tracing:
- Track all LLM calls and traces with detailed context during development and in production (Quickstart).
- Extensive 3rd-party integrations for easy observability: Seamlessly integrate with a growing list of frameworks, supporting many of the largest and most popular ones natively (including recent additions like Google ADK, Autogen, and Flowise AI). (Integrations)
- Annotate traces and spans with feedback scores via the Python SDK or the UI.
- Experiment with prompts and models in the Prompt Playground.
-
Evaluation & Testing:
- Automate your LLM application evaluation with Datasets and Experiments.
- Leverage powerful LLM-as-a-judge metrics for complex tasks like hallucination detection, moderation, and RAG assessment (Answer Relevance, Context Precision).
- Integrate evaluations into your CI/CD pipeline with our PyTest integration.
-
Production Monitoring & Optimization:
- Log high volumes of production traces: Opik is designed for scale (40M+ traces/day).
- Monitor feedback scores, trace counts, and token usage over time in the Opik Dashboard.
- Utilize Online Evaluation Rules with LLM-as-a-Judge metrics to identify production issues.
- Leverage Opik Agent Optimizer and Opik Guardrails to continuously improv
