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Comet is an AI developer platform that combines open-source LLM observability with enterprise-grade machine learning operations tools. The platform enables development teams to log, evaluate, and optimize AI applications through comprehensive tracing, annotation, and automated evaluation capabilities. Opik, the company's GenAI observability platform, provides full visibility into LLM application behavior with trace logging, agent execution graphs, session tracking, and multi-media support across 40+ AI frameworks and model providers.
The platform addresses the challenge of understanding and improving black-box LLM systems through systematic testing and evaluation workflows. Teams can capture application traces to visualize context retrieval, tool selection, and model responses, then collaborate with subject matter experts on human review and annotation directly within the platform. Automated LLM evaluation metrics score new versions against defined datasets, measuring hallucination, context precision, and relevance at scale. Production monitoring with online evaluation enables rapid detection and mitigation of issues while creating test datasets for the next iteration cycle.
Comet's MLOps platform provides experiment tracking, dataset management, model versioning, and production monitoring for traditional machine learning workflows. The platform integrates with PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, and other popular frameworks, automatically logging hyperparameters, metrics, and model predictions. Teams gain complete model lineage tracking, reproducibility through dataset versioning, and automated hyperparameter optimization capabilities. Production monitoring features include data drift detection, feature distribution analysis, and customizable alerts.
The platform offers flexible deployment options including a free open-source version, cloud-hosted plans, and custom enterprise deployments. Enterprise features include single sign-on, service accounts, view-only users, and compliance certifications for regulated industries. Comet has achieved 16,000+ GitHub stars and serves teams ranging from individual researchers to large organizations requiring dedicated support and SLAs.
- Log and trace every step of LLM application execution paths from context retrieval to model responses and tool calls
- Debug AI applications with human feedback and annotations from subject matter experts collaborating in the platform
- Scale testing and scoring with automated LLM evaluation metrics for hallucination detection and context precision
- Monitor AI applications in production with online evaluation that scores data as it's created
- Optimize agent performance automatically by generating and testing prompts for agentic system steps
- Track and compare machine learning training runs to accelerate development and improve model performance
- Manage and version training datasets with complete model lineage tracking for reproducibility
- Detect data drift automatically in production ML models to maintain performance over time
- Create custom visualizations using Python to tailor Comet dashboards for specific data insights
- Systematically test LLM applications over entire datasets using experiments for benchmarking and regression testing
- Build and evaluate prompts in the playground by comparing models side-by-side with no setup required
- Export traces, datasets, and experiment data to CSV or JSON through the UI or API for external analysis
