Opik

Opik: Open-Source LLM Observability, Evaluation & AI Agent Tracing
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.
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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
🚀 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 improve and secure your LLM applications in production.
**Who it's for:** ML engineers building LLM-powered agents, AI teams moving from prototype to production, and engineering teams that need open-source, self-hostable observability they can run in their own environment.
> **Why open source matters here:** Opik is Apache-2.0 licensed and free to self-host: the full platform, backend included, not just a client SDK. The repository includes the server backend, web application, tracing, datasets, experiments, evaluations, prompt management, online evaluation, and agent optimization components, all under Apache-2.0. You can run LLM observability inside your own infrastructure with no data leaving your environment and no Enterprise sales conversation required.
> [!TIP]
> If you are looking for features that Opik doesn't have today, please raise a new Feature request 🚀
⚡ Quick Start
Install the Python SDK and configure it:
Wrap any function with the @track decorator to start logging traces:
Every call to my_function is now logged to Opik, including nested calls, so this works for full agent and pipeline traces, not just single LLM calls. See the Quickstart guide for the TypeScript SDK and other setup options.
Connect your coding agent
Let Claude Code, Cursor, VS Code Copilot, Codex or opencode read your traces, score outputs and run evaluations from chat. One command sets it up. It needs uv and no SDK: