Open-Source LLM Observability Platform for Debugging, Evaluation, and Monitoring
Opik is an open-source platform developed by Comet, designed to provide comprehensive observability, evaluation, and optimization for large language model (LLM) applications, retrieval-augmented generation (RAG) systems, and AI agents. Released in 2023 under the Apache 2.0 license, Opik is framework-agnostic and supports Python, enabling teams to monitor and debug their AI systems from development through production. Its permissive licensing allows full self-hosting, including the backend, ensuring data remains within the user’s infrastructure without vendor lock-in or enterprise licensing requirements.
The platform addresses critical pain points in AI development by offering end-to-end tracing, automated evaluation metrics, prompt management, and production-grade dashboards. Unlike proprietary alternatives, Opik provides a fully open-source solution, allowing teams to deploy and customize the platform according to their specific needs while maintaining control over their data and workflows.
How It Works
Opik operates through a combination of client-side SDKs, server infrastructure, and a web-based dashboard, enabling seamless integration into existing AI pipelines. The platform captures detailed traces of LLM interactions, including multi-step agent workflows, tool invocations, and retrieval steps, providing a complete audit log of application behavior. These traces are stored with contextual metadata, such as input prompts, outputs, and associated feedback scores, facilitating post-hoc analysis and debugging.
The core functionality of Opik is built around four primary components: tracing, evaluation, optimization, and monitoring. During development, the Opik Python SDK can be integrated into applications using decorators or OpenTelemetry instrumentation, automatically logging traces for LLM calls, agent activities, and tool executions.
For evaluation, Opik supports automated metrics such as LLM-as-a-judge assessments for hallucination detection, moderation, and RAG relevance, as well as heuristic-based metrics like BLEU, METEOR, and ChrF. Users can define custom datasets and experiments to benchmark model performance against specific criteria, with results integrated into CI/CD pipelines via PyTest. The platform also includes an Agent Optimizer SDK, which systematically tests prompt variations and model configurations to identify optimal configurations without manual trial and error.
In production environments, Opik provides scalable monitoring through dashboards that track feedback scores, trace volumes, token usage, and latency. Online evaluation rules can be configured to automatically flag anomalies or deviations from expected performance, while guardrails ensure compliance with safety and responsibility standards.
Opik’s architecture is modular, allowing teams to deploy the platform either via Comet’s cloud service or self-hosted using Docker or Kubernetes. The self-hosting option includes a full suite of components—backend services, web application, tracing infrastructure, and evaluation tools—all under the Apache 2.0 license.
Use Cases
Opik is primarily targeted at machine learning engineers, AI research teams, and development organizations building LLM-powered applications, RAG pipelines, or autonomous agents. Its capabilities address key stages in the AI development lifecycle, from prototyping to production deployment.
For development teams, Opik provides real-time observability into LLM interactions, enabling rapid debugging of failures, prompt tuning, and model selection. The tracing system captures the entire execution flow of multi-step agents, including tool calls and retrieval steps, allowing engineers to reconstruct and analyze complex workflows. This is particularly valuable for teams using frameworks like LangChain, Autogen, or Flowise AI, as Opik offers native integrations with these and over 50 other providers, ensuring broad compatibility.
In evaluation and testing, Opik automates the assessment of LLM outputs against predefined criteria, such as factual accuracy, context relevance, and adherence to moderation policies. The LLM-as-a-judge feature evaluates responses using a secondary model, providing nuanced feedback on hallucinations, bias, or logical consistency. Additionally, the platform supports the creation of synthetic test cases from production traces, enabling teams to build comprehensive test suites derived from real-world failures.
For production monitoring, Opik offers scalable dashboards that aggregate trace data, feedback scores, and operational metrics. Teams can set up alerts for anomalies, such as sudden increases in hallucination rates or latency spikes, and use online evaluation rules to dynamically adjust thresholds based on evolving application behavior.
Opik also supports prompt optimization through its Agent Optimizer SDK, which systematically explores variations in prompt structure, model parameters, and retrieval strategies to maximize performance against user-defined metrics. This capability is particularly useful for teams fine-tuning RAG systems or conversational agents, where prompt engineering directly impacts output quality. The Prompt Playground allows users to experiment with different prompts interactively, compare results, and version-control optimal configurations.
The platform integrates with tools like Claude Code, Cursor, VS Code Copilot, and GitHub Copilot via the MCP (Model Coding Partner) server, enabling developers to analyze traces, score outputs, and run experiments directly from their IDE.
Key Features and Capabilities
- AI Agent Tracing and Observability: Opik captures detailed traces of LLM calls, tool invocations, and agent activities, including multi-step workflows and nested function calls. The platform supports over 50 frameworks and providers, including OpenAI, Anthropic, LangChain, and Google ADK, ensuring broad compatibility with existing AI stacks.
- LLM Evaluation and Metrics: The platform provides automated evaluation capabilities, including LLM-as-a-judge assessments for tasks like hallucination detection, moderation, and RAG relevance. Users can define custom datasets, experiments, and metrics, with results integrated into CI/CD pipelines via PyTest. Supported metrics include heuristic-based scores (BLEU, METEOR) and context-aware evaluations (Answer Relevance, Context Precision). Recent updates have improved the accuracy of G-Eval scores and fixed issues with tokenization and delimiter handling in judge prompts.
- Prompt and Agent Optimization: Opik’s Agent Optimizer SDK automates the process of testing prompt variations and model configurations against user-defined metrics. The Prompt Playground allows interactive experimentation with prompts, with results version-controlled and comparable across runs. Optimization algorithms systematically explore the prompt space to identify configurations that maximize performance, reducing the need for manual trial and error.
- Production Monitoring and Alerts: The Opik Dashboard provides real-time visibility into production traces, feedback scores, token usage, and latency metrics. Users can configure online evaluation rules to dynamically score incoming traces and set up alerts for anomalies, such as spikes in error rates or deviations from expected performance. Recent improvements include threshold validation for alerts, webhook destination checks, and bulk-deletion permissions for enhanced security and reliability.
- Self-Hosting and Data Sovereignty: Opik is fully open-source under the Apache 2.0 license, allowing teams to deploy the entire platform—backend, web application, and tracing infrastructure—within their own infrastructure. Self-hosting options include Docker for local development and Kubernetes for scalable deployments.
- Integration with AI Coding Assistants: Opik supports integration with AI-powered coding tools like Claude Code, Cursor, and VS Code Copilot via the MCP server. Developers can analyze traces, score outputs, and run evaluations directly from their IDE, streamlining debugging and reducing context-switching. The MCP server also supports self-hosted deployments, ensuring compatibility with on-premises environments.
- Recent performance optimizations include faster dataset and experiment uploads (up to 5x speed improvements via orjson encoding), reduced memory usage in streaming integrations, and improved ClickHouse query performance. These enhancements ensure that the platform remains responsive even as trace volumes scale.
- Security and Guardrails: The platform includes features to enforce responsible AI practices, such as content moderation, bias detection, and hallucination prevention. Recent updates have addressed vulnerabilities in authentication handling, such as support for OAuth2 client credentials in custom provider configurations, and improved validation for webhook destinations.
- Teams can generate test cases from production traces using tools like Ollie, which automatically identifies failures and creates reproducible test scenarios.
- Multi-Language and Framework Support: While the primary SDK is Python-based, Opik also supports TypeScript and OpenTelemetry instrumentation, broadening its compatibility with different programming languages and observability standards. The platform’s framework-agnostic design ensures that it can be adopted regardless of the underlying AI stack, from LangChain to custom agent architectures.