# Evidently

> Open-source Python framework to evaluate, test and monitor ML and LLM systems, with 100+ built-in metrics, LLM judges, test suites for CI and a monitoring UI.

- id: `evidently`
- category: Evals & Observability (`evals-observability`)
- url: https://www.evidentlyai.com/
- repo: https://github.com/evidentlyai/evidently
- docs: https://docs.evidentlyai.com/
- status: active
- pricing: freemium
- license: Apache-2.0
- tags: evals, llm-evaluation, monitoring, testing, python, open-source
- llms.txt: https://docs.evidentlyai.com/llms.txt
- related: DeepEval (https://indexagentica.com/entries/deepeval/); Promptfoo (https://indexagentica.com/entries/promptfoo/); Arize Phoenix (https://indexagentica.com/entries/arize-phoenix/); Langfuse (https://indexagentica.com/entries/langfuse/); TruLens (https://indexagentica.com/entries/trulens/)
- sources: https://github.com/evidentlyai/evidently , https://www.evidentlyai.com/ , https://pypi.org/project/evidently/ , https://docs.evidentlyai.com/llms.txt
- added: 2026-10-19
- updated: 2026-10-19
- last verified: 2026-10-19
- featured in: Data analysis agent stack (https://indexagentica.com/stacks/data-analysis-agent/)
- maintainer: Evidently AI
- submitted by: agentica-curator
- page: https://indexagentica.com/entries/evidently/
- json: https://indexagentica.com/api/entries/evidently.json

Reports compute data, ML and LLM quality evals (classification to RAG, text descriptors such as sentiment, length or denial detection, and LLM-as-a-judge); any Report becomes a Test Suite by adding pass/fail conditions, which suits regression testing and CI/CD checks. The monitoring UI can be self-hosted or used through Evidently Cloud, which Evidently says has a free tier plus extra features such as dataset and user management, alerting and no-code evals. Install with `pip install evidently`.