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OpenAI Evals

Open Source

Framework for building and running evaluations of LLM outputs against custom datasets.

Visit websiteGitHub

Pricing

Free / Open source

Type

Automation

Languages

Python

// VERDICT

Reach for OpenAI Evals when you want a code-based, registry-backed way to define and run LLM benchmarks and behaviour checks. Skip it when you want a hosted eval+observability platform, RAG-focused metrics (Ragas), or no-code evaluation.

Best for

OpenAI's open-source framework and registry for evaluating LLMs - define evals as code/YAML, reuse community evals, and benchmark model behaviour systematically.

Avoid when

You want a hosted platform with a UI, RAG-specific metrics, or a no-code workflow.

CI/CD fit

Python framework · eval registry · CI runs

Languages

Python

Team fit

ML/LLM engineers · Teams benchmarking models · OpenAI-stack users

Setup

Medium

Maintenance

Low

Learning

Intermediate

Licence

Free / Open source

// BEST FOR

  • Defining LLM evals as code/YAML in a registry
  • Reusing community and built-in evals
  • Systematically benchmarking model behaviour
  • Comparing models on a consistent eval set
  • Open-source and extensible
  • Encoding behaviour checks as repeatable evals

// AVOID WHEN

  • You want a hosted platform with a UI
  • RAG-specific metrics are the need (Ragas)
  • A no-code workflow is required
  • You want a pytest-like DX (DeepEval)
  • Managed datasets/dashboards are essential
  • You need turnkey enterprise support

// QUICK START

pip install evals  # OpenAI Evals
# define an eval (samples + grader) in the registry format, then run it
oaieval <model> <eval>

// ALTERNATIVES TO CONSIDER

ToolChoose it when
PromptFooYou want config-driven prompt testing and comparison.
DeepEvalYou want a pytest-like eval framework with RAG metrics.
BraintrustYou want a hosted eval platform with UI and datasets.

// FEATURES

  • Registry of pre-built eval templates
  • Custom dataset and grader definitions
  • Model-graded evaluations using LLM-as-judge
  • YAML-driven eval configuration
  • CLI for running and comparing eval suites

// PROS

  • Standardised structure for benchmarking model outputs
  • Curated library of existing evals to fork
  • Pairs well with the OpenAI API and dashboard tooling
  • Simple pattern for capturing regression baselines

// CONS

  • Optimised for OpenAI models — other providers need adapters
  • Cost of LLM-graded evals grows quickly on large suites
  • Authoring complex graders requires Python and prompt fluency

// EXAMPLE QA WORKFLOW

  1. Install the OpenAI Evals framework

  2. Define an eval (samples + grading) in registry format

  3. Reuse community evals where useful

  4. Run against your model(s)

  5. Compare results and benchmark

  6. Run in CI and pin model versions