Produce repeatable drift and quality reports after data, model, or prompt changes so regressions are visible before rollout.
Scanned 6/8/2026
Install via CLI
openskills install agentskillexchange/skills---
name: "Generate drift and quality reports for ML and LLM pipelines with Evidently"
slug: "generate-drift-and-quality-reports-for-ml-and-llm-pipelines-with-evidently"
description: "Produce repeatable drift and quality reports after data, model, or prompt changes so regressions are visible before rollout."
github_stars: 7386
verification: "security_reviewed"
source: "https://github.com/evidentlyai/evidently"
author: "Evidently AI"
publisher_type: "organization"
category: "Monitoring & Alerts"
framework: "Multi-Framework"
tool_ecosystem:
github_repo: "evidentlyai/evidently"
github_stars: 7386
---
# Generate drift and quality reports for ML and LLM pipelines with Evidently
Produce repeatable drift and quality reports after data, model, or prompt changes so regressions are visible before rollout.
## Prerequisites
Python 3.9+, pip, datasets or eval outputs for comparison
## Installation
Use the upstream install or setup path that matches your environment:
- pip install evidently
- conda install -c conda-forge evidently
- uv run --with evidently evidently ui --demo-projects all
- pip install virtualenv
Requirements and caveats from upstream:
- Evidently is an open-source Python library to evaluate, test, and monitor ML and LLM systems—from experiments to production.
- 🛠️ Python interface for custom metrics.
- View interactive Reports in Python or export as JSON, Python dictionary, HTML, or view in monitoring UI.
Basic usage or getting-started notes:
- ||
- ||
- To install Evidently using the Conda installer, run:
- Source: https://github.com/evidentlyai/evidently
- Extracted from upstream docs: https://raw.githubusercontent.com/evidentlyai/evidently/HEAD/README.md
## Documentation
- https://docs.evidentlyai.com/
## Source
- [Agent Skill Exchange](https://agentskillexchange.com/skills/generate-drift-and-quality-reports-for-ml-and-llm-pipelines-with-evidently/)
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