Evidently
ActiveDescription
Evidently is an open-source ML and LLM observability framework with 100+ metrics for evaluating, testing, and monitoring any AI-powered system or data pipeline.
Key Features
- 100+ built-in evaluation metrics for data drift, ML models, and LLM outputs
- LLM-as-a-judge evaluators for generative AI quality assessment
- Reports and Test Suites with pass/fail conditions for CI/CD integration
- Self-hosted or cloud monitoring dashboard with real-time alerting
- Works with tabular data, text, classification, regression, ranking, and RAG
- Open architecture with custom metric support and data export
Use Cases
💡 Monitoring LLM-powered applications for quality degradation in production
💡 Running data drift detection on ML pipelines
💡 Evaluating RAG retrieval quality and relevance
💡 Automated regression testing of model performance in CI/CD
Strengths & Limitations
✅ Strengths
- • Actively maintained, recent updates
- • High community interest (7.9k stars)
- • Permissive open-source license (Apache-2.0)
- • Established track record (5 years in production)
Categories
Quick Start
Install: `pip install evidently`. Create a Report with built-in presets like `DataDriftPreset` or `TextEvals`, run it on a pandas DataFrame, and view results in Jupyter or export as HTML/JSON. Launch the monitoring UI with `evidently ui --demo-projects all` and visit localhost:8000.