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evaluate and monitor ML, LLM, and data pipelines with 100+ metrics, tests, reports, and an observability UI

About these scores

OSS scale score is an unbounded metric that log-compresses and weights Stars, Watchers, Forks, and Contributors. Discovery score is the current OSS scale score minus the score at discovery. Update pace is commits in the last 30 days, growth momentum is the OSS scale score difference within the recent observation window, and OSS health is a 0–100 rating based on available recency, Community Health, and release data.

Stars
7,967
Primary language
Jupyter Notebook
License
Apache-2.0
Repository last updated
Sep 29, 2026
On this page

Overview

Evidently is an open-source Python framework for evaluating, testing, and monitoring ML and LLM-powered systems and data pipelines. It covers data drift, data quality, predictive models, text, and LLM outputs through 100+ built-in metrics, Reports, Test Suites, and a monitoring UI.

Features and best fit

Based on official documentation; not hands-on tested · Content checked:

Key features

Compute data, ML, and LLM metrics through Reports

Reports combine presets or individual metrics and can be viewed interactively or exported as JSON, dictionaries, or HTML for experimentation and debugging.

Sources: [1]

Turn evaluations into pass/fail checks for CI

Conditions can convert report metrics into regression tests or data-validation checks, including reference-based condition generation.

Sources: [1]

Grow from offline evaluation into a monitoring UI

Evidently can start as a one-off local evaluator and later store metrics and test results in a self-hosted monitoring UI.

Sources: [1]

Best fit

Fits teams tracking model, data, and LLM quality through one evaluation layer

It is useful when experiments, CI, and production monitoring should share a common set of quality metrics across tabular and generative AI workloads.

Sources: [1]

Before adoption

Requires Python 3.10+ and scientific Python dependencies

Version 0.7.23 requires Python 3.10+ and includes pandas, scikit-learn, SciPy, statsmodels, and related packages in the core dependency set.

Sources: [2]

Separate LLM evaluators from offline metric dependencies

The optional llm extra includes OpenAI, LiteLLM, Transformers, and related packages. External judges require separate management of privacy, cost, and model/version drift.

Sources: [2][1]

Official sources

  1. [1]Evidently 0.7.23 README(2026-10-04)
  2. [2]Evidently 0.7.23 project metadata(2026-10-04)
  3. [3]Evidently 0.7.23 release(2026-10-04)
  4. [4]Evidently Apache-2.0 license(2026-10-04)
Supplemental curator note

The core package requires Python 3.10+. LLM-as-a-judge and related features may require optional llm dependencies and external models or APIs. Start with offline metrics and tests, and manage cost, privacy, and reproducibility separately when external evaluators are introduced.

Try it in 3 steps

  1. 1

    Install Evidently 0.7.23 in a Python 3.10+ virtual environment

    Pin the core package without enabling any external LLM API integrations.

    python3 -m venv evidently-demo && . evidently-demo/bin/activate && pip install 'evidently==0.7.23'
  2. 2

    Create a local Data Drift Report

    Use only local DataFrames to exercise the Report API.

    printf 'import pandas as pd\nfrom evidently import Report\nfrom evidently.presets import DataDriftPreset\nref=pd.DataFrame({"x":[1,2,3,4,5]})\ncur=pd.DataFrame({"x":[1,2,3,5,8]})\nr=Report([DataDriftPreset()]).run(cur, ref)\nprint(type(r).__name__)\n' > evidently-demo/demo.py
  3. 3

    Run the offline evaluation

    Confirm that a report result is produced without calling an external service or model.

    . evidently-demo/bin/activate && python evidently-demo/demo.py
Check the official README

Growth

Growth trends · Last 30 days

7,967 Stars

Trend data is still being collected.

Development activity

Last 90 days · weekly

Commits (last 30 days)
3
Open PRs
77

Development activity is still being collected.

Built with

Categories and tags

GitHub data

GitHub dataView detailed GitHub data

GitHub Topics

  • data-drift
  • jupyter-notebook
  • pandas-dataframe
  • machine-learning
  • model-monitoring
  • html-report
  • mlops
  • data-science
  • hacktoberfest
  • data-quality
  • data-validation
  • generative-ai
Stars
7,967
Forks
943
Watchers
56
Open issues
246
Contributors
88
Owner type
Organization
Primary language
Jupyter Notebook
License
Apache-2.0
Repository last updated
Sep 29, 2026
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