DeepEval website screenshot

DeepEval

DeepEval is an open-source LLM evaluation framework — built and maintained by Confident AI — for testing and benchmarking large language model applications. It is structured like Pytest but specialized for LLM systems, providing 40+ research-backed metrics (G-Eval, DAG, RAG metrics, agent metrics, multi-turn conversation metrics, multimodal metrics, MCP metrics, hallucination, bias, toxicity, summarization, JSON correctness) that run locally against any LLM provider (OpenAI, Anthropic, Gemini, Bedrock, Vertex AI, Ollama, OpenRouter, vLLM, LM Studio, LiteLLM, Azure OpenAI, DeepSeek, Grok, Moonshot, Portkey). DeepEval supports end-to-end and component-level evaluation via the `@observe()` decorator, synthetic dataset generation, multi-turn conversation simulation, CI/CD integration, automatic prompt optimization, and one-line LLM benchmarking (MMLU, HellaSwag, DROP, BIG-Bench Hard, TruthfulQA, HumanEval, GSM8K). DeepEval ships as the `deepeval` Python package on PyPI together with a `deepeval` command-line tool. The framework integrates natively with pytest, LangChain, LangGraph, LlamaIndex, OpenAI Agents, CrewAI, Pydantic AI, AWS AgentCore, Google ADK, and Strands. DeepEval is open source under Apache 2.0 and is the engine that powers Confident AI's commercial LLM evaluation, observability, and red-teaming platform; `deepeval login` connects local test runs to the Confident AI cloud for shared regression reports, dataset management, production tracing, and prompt versioning. A sibling open-source framework, DeepTeam (`deepteam`), targets adversarial / red-team testing of LLM apps.

DeepEval is profiled on the APIs.io network. Tagged areas include LLM Evaluation, LLM Testing, Evaluation Framework, Evaluation Metrics, and LLM Observability.

DeepEval’s developer surface includes developer portal, documentation, getting-started guide, release notes, changelog, engineering blog, pricing, and 28 more developer resources.

27.1/100 emerging ▬ flat Agent 7/100 human only Full breakdown ↓
scored 2026-07-27 · rubric v0.5
0 APIs
LLM EvaluationLLM TestingEvaluation FrameworkEvaluation MetricsLLM ObservabilityLLM as a JudgeG-EvalRAG EvaluationAgent EvaluationHallucination DetectionBias DetectionToxicity DetectionRed TeamingBenchmarksMMLUSynthetic Data GenerationPrompt OptimizationCI CDPytestPythonOpen SourceApache 2.0MCP

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5
Composite quality — 27.1/100 · emerging
Contract Quality 0.0 / 25
Developer Ergonomics 8.7 / 20
Commercial Clarity 8.9 / 20
Operational Transparency 2.7 / 13
Governance 0.0 / 12
Discoverability 6.8 / 10
Agent readiness — 7/100 · human only
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 15
MCP Server 0 / 12
Machine-Readable Auth 0 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 7 / 7
Rate-Limit Signaling 0 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/deepeval: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Deepeval Domain Security

TLSv1.3 · HSTS · DNSSEC · DMARC

SECURITY

Resources

Get Started 4

Portal, sign-up, and the first successful call

Documentation 2

Reference material describing how the API behaves

Build 8

SDKs, sample code, and the tooling you integrate with

Scroll for all 8

Access & Security 1

Authentication, authorization, and security posture

Learn 1

Tutorials, courses, talks, and written guidance

Operate 4

Status, limits, changes, and where to get help

Commercial 4

Pricing, plans, and the legal terms of use

Company 5

The organization behind the API

Other 6

Properties that don't map to a standard resource type

Source (apis.yml)

apis.yml Raw ↑
aid: deepeval
name: DeepEval
description: DeepEval is an open-source LLM evaluation framework — built and maintained by Confident AI — for testing and
  benchmarking large language model applications. It is structured like Pytest but specialized for LLM systems, providing
  40+ research-backed metrics (G-Eval, DAG, RAG metrics, agent metrics, multi-turn conversation metrics, multimodal metrics,
  MCP metrics, hallucination, bias, toxicity, summarization, JSON correctness) that run locally against any LLM provider (OpenAI,
  Anthropic, Gemini, Bedrock, Vertex AI, Ollama, OpenRouter, vLLM, LM Studio, LiteLLM, Azure OpenAI, DeepSeek, Grok, Moonshot,
  Portkey). DeepEval supports end-to-end and component-level evaluation via the `@observe()` decorator, synthetic dataset
  generation, multi-turn conversation simulation, CI/CD integration, automatic prompt optimization, and one-line LLM benchmarking
  (MMLU, HellaSwag, DROP, BIG-Bench Hard, TruthfulQA, HumanEval, GSM8K). DeepEval ships as the `deepeval` Python package on
  PyPI together with a `deepeval` command-line tool. The framework integrates natively with pytest, LangChain, LangGraph,
  LlamaIndex, OpenAI Agents, CrewAI, Pydantic AI, AWS AgentCore, Google ADK, and Strands. DeepEval is open source under Apache
  2.0 and is the engine that powers Confident AI's commercial LLM evaluation, observability, and red-teaming platform; `deepeval
  login` connects local test runs to the Confident AI cloud for shared regression reports, dataset management, production
  tracing, and prompt versioning. A sibling open-source framework, DeepTeam (`deepteam`), targets adversarial / red-team testing
  of LLM apps.
type: Index
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
position: Provider
access: Open-Source
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/deepeval.png
tags:
- LLM Evaluation
- LLM Testing
- Evaluation Framework
- Evaluation Metrics
- LLM Observability
- LLM as a Judge
- G-Eval
- RAG Evaluation
- Agent Evaluation
- Hallucination Detection
- Bias Detection
- Toxicity Detection
- Red Teaming
- Benchmarks
- MMLU
- Synthetic Data Generation
- Prompt Optimization
- CI CD
- Pytest
- Python
- Open Source
- Apache 2.0
- MCP
url: https://raw.githubusercontent.com/api-evangelist/deepeval/refs/heads/main/apis.yml
created: '2026-05-25'
modified: '2026-05-25'
specificationVersion: '0.20'
apis: []
common:
- type: DomainSecurity
  url: security/deepeval-domain-security.yml
- type: Website
  url: https://www.confident-ai.com
- type: Portal
  url: https://deepeval.com
- type: Documentation
  url: https://deepeval.com/docs/getting-started
- type: GettingStarted
  url: https://deepeval.com/docs/getting-started
- type: Repository
  url: https://github.com/confident-ai/deepeval
- type: GitHubOrganization
  url: https://github.com/confident-ai
- type: SourceCode
  url: https://github.com/confident-ai/deepeval
- type: Package
  url: https://pypi.org/project/deepeval/
- type: License
  url: https://github.com/confident-ai/deepeval/blob/main/LICENSE.md
- type: Issues
  url: https://github.com/confident-ai/deepeval/issues
- type: ReleaseNotes
  url: https://github.com/confident-ai/deepeval/releases
- type: ChangeLog
  url: https://github.com/confident-ai/deepeval/releases
- type: Contributing
  url: https://github.com/confident-ai/deepeval/blob/main/CONTRIBUTING.md
- type: Blog
  url: https://www.confident-ai.com/blog
- type: Forums
  url: https://discord.com/invite/3SEyvpgu2f
- type: Pricing
  url: https://www.confident-ai.com/pricing
- type: Signup
  url: https://app.confident-ai.com/auth/signup
- type: Login
  url: https://app.confident-ai.com/auth/login
- type: Application
  url: https://app.confident-ai.com
- type: Careers
  url: https://www.confident-ai.com/careers
- type: TermsOfService
  url: https://www.confident-ai.com/terms
- type: PrivacyPolicy
  url: https://www.confident-ai.com/privacy
- type: Twitter
  url: https://twitter.com/confident_ai
- type: LinkedIn
  url: https://www.linkedin.com/company/confident-ai
- type: YouTube
  url: https://www.youtube.com/@confident-ai
- type: SDKs
  url: https://github.com/confident-ai/deepeval
  name: deepeval (Python)
- type: CLI
  url: https://deepeval.com/docs/getting-started
  name: deepeval CLI
- type: Tools
  url: https://github.com/confident-ai/deepteam
  name: DeepTeam — LLM red teaming framework
- type: Tools
  url: https://github.com/confident-ai/confident-mcp-server
  name: Confident MCP Server
- type: Product
  url: https://www.confident-ai.com/products/llm-evaluation
  name: Confident AI — LLM Evaluation
- type: Product
  url: https://www.confident-ai.com/products/llm-observability
  name: Confident AI — LLM Observability
- type: Product
  url: https://www.confident-ai.com/products/ai-red-teaming
  name: Confident AI — AI Red Teaming
- type: Documentation
  url: https://trydeepteam.com
  name: DeepTeam Documentation
- type: CodeExamples
  url: https://github.com/confident-ai/blog-examples
  name: Confident AI blog examples
- type: Integrations
  url: https://deepeval.com/integrations/models/openai
maintainers:
- FN: Kin Lane
  email: info@apievangelist.com
  X: apievangelist
  url: https://apievangelist.com