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DeepEval

Freemium

LLM evaluation framework offering pytest-style unit tests with research-backed metrics.

Visit websiteGitHub

Pricing

Freemium

Type

Automation

Languages

Python

// VERDICT

Reach for DeepEval when you want code-first LLM evals that feel like writing unit tests, with ready RAG and hallucination metrics, gating in CI. Skip it when you want a hosted platform (LangSmith/Braintrust) or no-code evaluation.

Best for

An open-source LLM evaluation framework with a pytest-like developer experience - score model outputs on metrics (relevancy, faithfulness, hallucination, RAG) and run evals as unit tests in CI.

Avoid when

You want a hosted eval+observability platform, no-code evaluation, or you're not testing LLM outputs.

CI/CD fit

pytest-style runner · GitHub Actions · GitLab CI · CI eval gates

Languages

Python

Team fit

Dev/QA teams testing LLM features · RAG app teams · Teams putting evals in CI

Setup

Easy

Maintenance

Low

Learning

Intermediate

Licence

Freemium

// BEST FOR

  • Scoring LLM outputs on metrics (relevancy, faithfulness, hallucination)
  • A pytest-like API that feels like writing unit tests
  • Ready-made RAG evaluation metrics
  • Running evals in CI and gating on thresholds
  • Building eval datasets from real cases
  • Open-source and self-hostable

// AVOID WHEN

  • You want a hosted eval+observability platform
  • No-code evaluation is required
  • You're not testing LLM/AI outputs
  • You want only manual human evaluation
  • A managed dataset/UI is essential
  • You need enterprise support out of the box

// QUICK START

pip install deepeval
# write eval cases asserting metric scores (answer_relevancy, faithfulness, ...)
deepeval test run test_llm.py   # gate in CI on thresholds

// ALTERNATIVES TO CONSIDER

ToolChoose it when
RagasYou specifically want RAG-focused evaluation metrics.
PromptFooYou want config-driven prompt testing and red-teaming.
LangSmithYou want a hosted eval + tracing platform.

// FEATURES

  • Pytest-compatible test syntax for LLM outputs
  • 14+ metrics including hallucination, toxicity, and answer relevance
  • Synthetic dataset generation utilities
  • Red-teaming probes for safety and bias
  • Integration with Confident AI for hosted dashboards

// PROS

  • Familiar pytest workflow lowers adoption friction
  • Wide metric coverage out of the box
  • Local-first runs — no cloud account required
  • Easy CI integration via standard pytest tooling

// CONS

  • Most metrics rely on an LLM judge with corresponding cost
  • Hosted tracing and analytics require Confident AI
  • Younger ecosystem than MLflow or W&B

// EXAMPLE QA WORKFLOW

  1. Install DeepEval (pip)

  2. Build an eval dataset of representative cases

  3. Define metrics (relevancy, faithfulness, hallucination)

  4. Write eval cases asserting on scores

  5. Run in CI and gate on thresholds

  6. Grow the dataset from real failures

// RELATED QA.CODES RESOURCES

Cheat sheets