Evidently AI
Open-source framework to evaluate, test, and monitor ML and LLM systems in production.
About
Evidently AI is an open-source Python framework (Apache 2.0) for evaluating, testing, and monitoring machine learning and LLM-powered systems. It ships with over 100 built-in metrics covering data drift, data quality, and model performance, plus LLM-as-judge evaluators for generative tasks like RAG, working across tabular and text data.
Developers generate interactive Reports for experiments and debugging, convert them into Test Suites with pass/fail conditions for CI/CD regression checks, and run a monitoring UI service to track metrics over time. A hosted Evidently Cloud tier adds dataset management, alerting, and no-code evals for teams that prefer not to self-host.
What it can do
- 100+ built-in metrics for drift, quality, and performance
- LLM-as-judge evaluators for generative AI tasks
- Reports for debugging and Test Suites for CI/CD gates
- Monitoring UI for tracking metrics over time
- Open architecture with exportable, integrable results
Setup
Install the Python package with pip, compute a Report or Test Suite on your eval dataset, and optionally self-host the monitoring UI or sign up for Evidently Cloud.
Prerequisites
Python environment and reference/production datasets to evaluate against.
Use cases
Works with
Trust score — 8/10
Scored 8/10 (verified tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 112; Trending score 80/100 from researcher assessment.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.