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evidently

Evidently is \u200b\u200ban open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

Best fitUsers who want source-backed project understanding before installing it.

Check whether this project matches your task before installing it.

What it can doskill, recipe, host_instruction, eval, preflight

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot7.5k stars

847 forks · 97 contributors

Official first step Read manual preview Source repository

Preview status · 2026-05-16

What is evidently?

Related topics: Core Components, Data Management and Data Flow
Best fit: Users who want source-backed project understanding before installing it.
Capability added to an AI workflow: skill, recipe, host_instruction, eval, preflight
Evidence base: https://github.com/evidentlyai/evidently, https://github.com/evidentlyai/evidently, https://github.com/evidentlyai/evidently#readme
Preview pages are noindex until English quality, canonical, and citation gates pass.
evidently still needs sandbox verification before production use.

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forUsers who want source-backed project understanding before installing it.

Match the project to your task before installing it.

Capabilityskill, recipe, host_instruction, eval, preflight

Evidently is \u200b\u200ban open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

Repositoryevidentlyai/evidently

7.5k stars · 847 forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Architecture Overview

Related topics: Core Components, Data Management and Data Flow

Sources: [README.md:1-30]()

Core Components

Related topics: Architecture Overview, Reports and Test Suites, Custom Metrics and Extensibility

Source: https://github.com/evidentlyai/evidently / Human Manual

Data Management and Data Flow

Related topics: Architecture Overview, ML Model Evaluation, LLM Evaluation and Judging

Sources: [src/evidently/core/datasets.py:1-50]()

ML Model Evaluation

Related topics: LLM Evaluation and Judging, Descriptors and Features System, Presets and Metric Presets

Sources: [src/evidently/core/report.py:1-50]()

LLM Evaluation and Judging

Related topics: ML Model Evaluation, Descriptors and Features System

Sources: [src/evidently/descriptors/generated_descriptors.py:1-200]()

Sources: https://github.com/evidentlyai/evidently, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars7.5k stars

Forks847 forks

Contributors97 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using evidently with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
IndexError in infer_column_type when column contains only null values
github / github_issue
02
Protect this repo from AI-generated PRs
github / github_issue
03
Numpy 2.x support?
github / github_issue
04
PromptOptimizer throws OpenAIError when using Vertex AI judge
github / github_issue
05
Update scikit-learn version requirement to support v1.6.0
github / github_issue
06
Update evidently hashlib usage for FIPS-Compliant Systems and Security B
github / github_issue
07
v0.7.21
github / github_release
08
v0.7.20
github / github_release
09
v0.7.19
github / github_release
10
v0.7.18
github / github_release
11
v0.7.17
github / github_release
12
v0.7.16
github / github_release

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install evidently

Official start command · https://github.com/evidentlyai/evidently#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

evidently Manual

Related topics: Core Components, Data Management and Data Flow

Open the full manual

evidently Human Manual
Table of Contents
Architecture Overview
Related Pages
Introduction
High-Level Architecture
Core Module Structure
Key Imports for LLM Evals

Architecture Overview

Related topics: Core Components, Data Management and Data Flow

Sources: [README.md:1-30]()

Core Components

Related topics: Architecture Overview, Reports and Test Suites, Custom Metrics and Extensibility

Source: https://github.com/evidentlyai/evidently / Human Manual

Data Management and Data Flow

Related topics: Architecture Overview, ML Model Evaluation, LLM Evaluation and Judging

Sources: [src/evidently/core/datasets.py:1-50]()

ML Model Evaluation

Related topics: LLM Evaluation and Judging, Descriptors and Features System, Presets and Metric Presets

Sources: [src/evidently/core/report.py:1-50]()

LLM Evaluation and Judging

Related topics: ML Model Evaluation, Descriptors and Features System

Sources: [src/evidently/descriptors/generated_descriptors.py:1-200]()

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start ManifestPROJECT_PACK.json

Preflight checks

Treat this preview as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
The preview remains noindex until English quality and reciprocal indexing gates are explicitly opened.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.