Comet: AI Developer Platform for LLM Evaluation and MLOps

Key Features

• LLM Tracing and Observability

  Logs and visualizes steps of an AI application's execution, including context retrieval and tool calls.

• Experiment Tracking

  Records and compares machine learning training runs, including hyperparameters and system metrics.

• Automated Agent Optimization

  Supports the use of optimization algorithms to generate and test prompts for agentic systems based on evaluation metrics.

• Model Registry

  Centralizes and versions machine learning models to support deployment workflows.

• Production Monitoring

  Tracks LLM applications in production to detect data drift and identify performance issues.

• Human Feedback Debugging

  Provides a UI for subject matter experts to annotate and review LLM responses.

Use Cases

• LLM Application Debugging

  Logging traces to identify where a GenAI workflow or agent may be failing.

• Prompt Engineering

  Testing and comparing different system prompts using the Prompt Playground and structured experiments.

• Benchmarking AI Performance

  Using LLM-as-a-judge metrics to score application outputs for hallucination and relevance against a test dataset.

• ML Model Versioning

  Tracking training datasets and model binaries to support reproducibility across team experiments.

Integrations

• OpenAI
• LangChain
• LlamaIndex
• LiteLLM
• OpenTelemetry
• Ragas
• Hugging Face Datasets

FAQ

What is the difference between Opik and Comet MLOps?

Opik is designed for GenAI observability and evaluating LLM applications and agents. The MLOps platform is for teams building and training machine learning models.

Does Comet offer a free version?

Yes, Comet provides a free open-source version available on GitHub, as well as a free cloud tier.

Who is the intended user of the Comet platform?

The platform is built for ML practitioners, data scientists, and AI engineers.