opik: LLM Evaluation and Tracing for Better Models

opik is an open-source MCP Server designed by Comet for evaluating, testing, and monitoring LLM applications. It provides a comprehensive framework to build better, faster, and cheaper LLM systems, particularly focusing on RAG chatbots, code assistants, and agentic pipelines. With opik, developers gain visibility into their LLM interactions through tracing, and can systematically evaluate model performance.

Core Capabilities for LLM Development

opik offers a suite of tools that span the entire LLM application lifecycle, from initial development to production monitoring.

Development & Tracing

During development, opik allows you to:

Trace all LLM calls: Track every interaction your application makes with an LLM. This is crucial for debugging and understanding complex agentic behaviors.
Annotate LLM calls: Log feedback scores directly via the Python SDK or through the UI, providing immediate qualitative insights.
Prompt Playground: Experiment with different prompts and models in an interactive environment to fine-tune responses.

Evaluation & Experimentation

For systematic evaluation, opik includes:

Datasets and Experiments: Store test cases and run structured experiments against them.
LLM as a judge metrics: Utilize opik's built-in metrics for complex evaluations such as hallucination detection, moderation, and RAG evaluation. This moves beyond simple accuracy to assess nuanced LLM behaviors.
CI/CD integration: Integrate evaluations into your continuous integration/continuous deployment pipeline using its PyTest integration, ensuring consistent quality.

Production Monitoring

Once deployed, opik continues to provide value:

Log production traces: Designed to handle high volumes of data, opik can log all production traces for ongoing analysis.
Monitoring dashboards: Review feedback scores, trace counts, and token usage over time in the dedicated opik Dashboard, offering a clear view of your application's performance in the wild.
Online evaluation metrics: Continuously monitor and evaluate your LLM application's performance in a live environment.

Getting Started with opik

opik is available as a fully open-source local installation or through Comet.com as a hosted solution. The easiest way to begin is by creating a free Comet account.

For self-hosting, you can clone the GitHub repository and use Docker Compose to start the platform. On Linux or Mac, this typically involves:

# Clone the repository
# Start the platform using Docker Compose

The Python SDK can be installed via pip and configured:

pip install opik
opik configure

opik supports integrations with popular frameworks and models, including OpenAI and LangChain. The track decorator simplifies logging traces from your code.

opik MCP Server: LLM Evaluation for RAG & Agentic Pipelines