Observability and Evaluation · Public

deepeval

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 snapshot16k stars

1.5k forks · 299 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 deepeval?

deepeval 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/confident-ai/deepeval, https://github.com/confident-ai/deepeval#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.

Repositoryconfident-ai/deepeval

16k stars · 1.5k forks

What it can do

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

DeepEval Overview and Core Architecture

Related topics: Tracing, Observability and Framework Integrations, Evaluation Engine, Metrics and Synthetic Data

Source: https://github.com/confident-ai/deepeval / Human Manual

Tracing, Observability and Framework Integrations

Related topics: DeepEval Overview and Core Architecture, Evaluation Engine, Metrics and Synthetic Data

Source: https://github.com/confident-ai/deepeval / Human Manual

Evaluation Engine, Metrics and Synthetic Data

Related topics: DeepEval Overview and Core Architecture, CLI, Tooling, Extensibility and TypeScript

Source: https://github.com/confident-ai/deepeval / Human Manual

CLI, Tooling, Extensibility and TypeScript

Related topics: DeepEval Overview and Core Architecture, Evaluation Engine, Metrics and Synthetic Data

Source: https://github.com/confident-ai/deepeval / 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/confident-ai/deepeval, 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.

Stars16k stars

Forks1.5k forks

Contributors299 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

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

01
LLM tokens not displayed when using custom OpenTelemetry / OpenInference
github / github_issue
02
ConfidentInstrumentationSettings with pydantic-ai: tools_called, expecte
github / github_issue
03
Security: request for a submitting security vulnerabilities.
github / github_issue
04
Feature: support cached input tokens in LLM span cost tracking
github / github_issue
05
CLI improvement: option to display only failed tests
github / github_issue
06
Contextual Precision over-penalizes overlapping chunks in financial-docu
github / github_issue
07
DeepEval for Typescript
github / github_issue
08
Opus 4.8: Day 0 Support
github / github_release
09
🎉 New Decision Graph Logic for Granular Simulation Control
github / github_release
10
🔥 DeepEval 4.0: Eval Harness for Coding Agents, 1-line integrations, TUI
github / github_release
11
🎉 Metrics for AI agents, multi-turn synthetic data generation, and more!
github / github_release
12
🎉 New Interfaces, Reduce ETL Code < 50%!
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 -U deepeval

Official start command · https://github.com/confident-ai/deepeval#readme · verified: yes

Human Manual

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

8+ sections · Human Manual

deepeval Manual

The LLM Evaluation Framework

Open the full manual

https://github.com/confident-ai/deepeval Project Manual
Table of Contents
DeepEval Overview and Core Architecture
Related Pages
Purpose and Scope
High-Level Architecture
Model Gateway and Provider Coverage
CLI, Test Runs, and Synthetic Data

DeepEval Overview and Core Architecture

Related topics: Tracing, Observability and Framework Integrations, Evaluation Engine, Metrics and Synthetic Data

Source: https://github.com/confident-ai/deepeval / Human Manual

Tracing, Observability and Framework Integrations

Related topics: DeepEval Overview and Core Architecture, Evaluation Engine, Metrics and Synthetic Data

Source: https://github.com/confident-ai/deepeval / Human Manual

Evaluation Engine, Metrics and Synthetic Data

Related topics: DeepEval Overview and Core Architecture, CLI, Tooling, Extensibility and TypeScript

Source: https://github.com/confident-ai/deepeval / Human Manual

CLI, Tooling, Extensibility and TypeScript

Related topics: DeepEval Overview and Core Architecture, Evaluation Engine, Metrics and Synthetic Data

Source: https://github.com/confident-ai/deepeval / 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.

Pitfall Log and verification risks

Doramagic surfaces high-risk items before users treat a candidate capability as verified.

high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Security or permission risk requires verification

Developers may expose sensitive permissions or credentials: Security: request for a submitting security vulnerabilities.

high

Security or permission risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Configuration risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Configuration risk requires verification

Upgrade or migration may change expected behavior: 🎉 New Interfaces, Reduce ETL Code < 50%!

medium

Configuration risk requires verification

Upgrade or migration may change expected behavior: 🔥 DeepEval 4.0: Eval Harness for Coding Agents, 1-line integrations, TUI for trace inspection!

medium

Capability evidence risk requires verification

May increase setup, validation, or first-run risk for the user.