Evidently

Evidently is an open-source Python library to evaluate, test, and monitor machine learning and LLM systems.

Evidently is an open-source Python library and framework designed to evaluate, test, and monitor machine learning and large language model systems from experiments to production. It works with tabular and text data, supporting evaluations for predictive and generative tasks ranging from standard classification to RAG applications. Users can compute data quality, data drift, classification, regression, and ranking metrics using 100+ built-in evaluations or create custom metrics via the Python interface. The platform generates interactive reports and test suites with pass/fail conditions for CI/CD checks, as well as a monitoring dashboard to visualize metrics over time. It is built for data scientists, machine learning engineers, and developers who need offline evaluations or live production monitoring.

Evidently at a glance

Best for: Software engineer
Runs on: Python, self-hosted, open source, API
Pricing: Freemium
Released: November 2020
Category: AI coding tool
Website: github.com/evidentlyai/evidently

Key features

100+ built-in metrics for data drift, data quality, and model quality
Test suites with pass/fail conditions for regression testing and CI/CD
Row-level text descriptors for length, sentiment, toxicity, and regex
LLM-as-a-judge and model-based evaluations for generative tasks
Interactive reports exportable as JSON, HTML, or Python dictionaries
Monitoring dashboard to visualize metrics and test results over time
Python interface for creating custom metrics and evaluations

Use it for

Evaluate data drift and distribution shifts in machine learning models
Run automated regression tests and data validation in CI/CD pipelines
Assess LLM outputs like semantic similarity and retrieval relevance
Monitor live production machine learning and LLM systems over time
Analyze data quality, missing values, and duplicate rows

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