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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]
Official sources
- [1]Evidently 0.7.23 README(2026-10-04)
- [2]Evidently 0.7.23 project metadata(2026-10-04)
- [3]Evidently 0.7.23 release(2026-10-04)
- [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
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
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
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
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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