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Evidently

Free

An open-source framework to evaluate, test and monitor ML and LLM-powered systems.

FreeFree tier
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Type
Open Source
Company
Evidently AI
Visit Website GitHub (38 stars)

About Evidently

Evidently is an open-source Python library designed to evaluate, test, and monitor ML and LLM-powered systems, from experiments to production. It works with both tabular and text data, supporting evaluations for predictive and generative tasks such as classification and RAG. With over 100 built-in metrics—ranging from data drift detection to LLM judges—and a Python interface for custom metrics, it offers both offline evaluations and live monitoring. Evidently features a modular architecture with Reports and Test Suites for one-off analyses or CI/CD checks, and a Monitoring Dashboard for visualizing metrics over time. It can be self-hosted or used with Evidently Cloud, which provides additional capabilities like user management and alerting.

Key Features

Works with tabular and text data
Supports evals for predictive and generative tasks (classification to RAG)
100+ built-in metrics including data drift detection and LLM judges
Python interface for custom metrics
Both offline evaluations and live monitoring
Open architecture: easily export data and integrate with existing tools
Reports and Test Suites with pass/fail conditions for CI/CD
Monitoring Dashboard for visualizing results over time
Auto-generation of test conditions from reference datasets
Exportable outputs: JSON, Python dictionary, HTML, and monitoring UI

Pros & Cons

Pros
  • Comprehensive open-source library with 100+ built-in metrics
  • Flexible: supports both offline and live monitoring
  • Easy integration with existing Python workflows and tools
  • Custom metric development via Python interface
  • Generous free tier available via Evidently Cloud with no-code evals
  • Active community and extensive documentation
Cons
  • Advanced features (user management, alerting) require Evidently Cloud
  • Primarily Python-based, requiring Python environment setup
  • Learning curve for building custom metrics and test suites

Best For

LLM evaluation and quality assessment (e.g., RAG pipelines)Data drift detection in production ML systemsRegression testing and CI/CD checks for data pipelinesExploratory analysis and debugging of ML/LLM modelsContinuous monitoring of model performance and data qualityValidation of tabular data in predictive models

FAQ

What is Evidently?
Evidently is an open-source Python framework for evaluating, testing, and monitoring ML and LLM systems, from experiments to production. It works with tabular and text data.
How do I install Evidently?
Install via pip: 'pip install evidently' or using Conda: 'conda install -c conda-forge evidently'.
Does Evidently support LLM evaluation?
Yes, Evidently supports evaluations for generative tasks, including LLM quality assessment and RAG pipeline evaluation.
Can I use Evidently for live monitoring?
Yes, Evidently offers both offline evaluations and live monitoring via a self-hosted Monitoring Dashboard or Evidently Cloud.