Observability and Evaluation · Public

evidently

Observability and evaluation project for turning logs, quality metrics, drift, or experiment results into reviewable signals.

ObservabilityEvaluationQuality metricsData driftExperiment tracking

Best fitDevelopers who need reviewable observability or evaluation workflows for AI apps, data pipelines, or experiments.

Check whether this project matches your task before installing it.

What it can doObservability setup paths, metric boundaries, sample-data redaction, evaluation checks, and failure triage

Review the portable capability path.

Before continuingVerify in a sandbox

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

GitHub snapshot7.6k stars

865 forks · 97 contributors

Doramagic.ai Last verification date: 2026-06-29 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-06-29

What is evidently?

evidently helps developers observe, evaluate, or monitor AI/data application behavior and quality.
Best fit: Developers who need reviewable observability or evaluation workflows for AI apps, data pipelines, or experiments.
Not for: Not for users without logs/sample data, privacy boundaries, or those who only need a chat UI.
Capability added to an AI workflow: Observability setup paths, metric boundaries, sample-data redaction, evaluation checks, and failure triage
First safe verification step: Verify collection, metric interpretation, export, and deletion paths with redacted sample data first.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: May increase setup, validation, or first-run risk for the user.
Evidence base: https://github.com/evidentlyai/evidently, https://github.com/evidentlyai/evidently#readme, Human Manual, Pitfall Log

Quick decision

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

Best forDevelopers who need reviewable observability or evaluation workflows for AI apps, data pipelines, or experiments.

Match the project to your task before installing it.

CapabilityObservability setup paths, metric boundaries, sample-data redaction, evaluation checks, and failure triage

Observability and evaluation project for turning logs, quality metrics, drift, or experiment results into reviewable signals.

Repositoryevidentlyai/evidently

7.6k stars · 865 forks

What it can do

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

Overview and System Architecture

Related topics: Core Evaluation Engine: Reports, Metrics, Presets, and Datasets, LLM Evaluation, Descriptors, Prompts, RAG, and Guardrails, UI Service, Storage Backends, and Deployment

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

Core Evaluation Engine: Reports, Metrics, Presets, and Datasets

Related topics: Overview and System Architecture, LLM Evaluation, Descriptors, Prompts, RAG, and Guardrails

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

LLM Evaluation, Descriptors, Prompts, RAG, and Guardrails

Related topics: Core Evaluation Engine: Reports, Metrics, Presets, and Datasets, UI Service, Storage Backends, and Deployment

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

UI Service, Storage Backends, and Deployment

Related topics: Overview and System Architecture, Core Evaluation Engine: Reports, Metrics, Presets, and Datasets, LLM Evaluation, Descriptors, Prompts, RAG, and Guardrails

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

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

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.

Stars7.6k stars

Forks865 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
SemanticSimilarity fails with sentence-transformers > 5.3.0
github / github_issue
02
Make `LLMEval` descriptors plottable from Tests
github / github_issue
03
Legacy metrics to new Report API
github / github_issue
04
Unauthenticated path traversal arbitrary file read in Evidently UI datas
github / github_issue
05
Plotly Graph Objects - Deprecated module is in use.
github / github_issue
06
Protect this repo from AI-generated PRs
github / github_issue
07
Fix semantic similarity in LLM eval tutorial
github / github_issue
08
The fixed value for feel_zeroes in get_binned_data may lead to deviation
github / github_issue
09
Error when trying to create collector config in self-hosted environment
github / github_issue
10
python 3.13 support
github / github_issue
11
Modify scales of plots generated in report
github / github_issue
12
Installation risk requires verification
GitHub / issue

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

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.

Open the full manual

https://github.com/evidentlyai/evidently Project Manual
Table of Contents
Overview and System Architecture
Related Pages
Purpose and Scope
Repository Layout
Core Subsystems
Frontend Stack and Data Contracts

Overview and System Architecture

Related topics: Core Evaluation Engine: Reports, Metrics, Presets, and Datasets, LLM Evaluation, Descriptors, Prompts, RAG, and Guardrails, UI Service, Storage Backends, and Deployment

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

Core Evaluation Engine: Reports, Metrics, Presets, and Datasets

Related topics: Overview and System Architecture, LLM Evaluation, Descriptors, Prompts, RAG, and Guardrails

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

LLM Evaluation, Descriptors, Prompts, RAG, and Guardrails

Related topics: Core Evaluation Engine: Reports, Metrics, Presets, and Datasets, UI Service, Storage Backends, and Deployment

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

UI Service, Storage Backends, and Deployment

Related topics: Overview and System Architecture, Core Evaluation Engine: Reports, Metrics, Presets, and Datasets, LLM Evaluation, Descriptors, Prompts, RAG, and Guardrails

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

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

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 EvidenceREPO_INSPECTION.json

Preflight checks

Treat this page 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.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.