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OpenAI Evals

OpenAI Evals is an MIT-licensed framework and open registry for evaluating LLMs and LLM systems, now complemented by evals in the OpenAI Dashboard.

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Introduction

OpenAI Evals is a framework for evaluating large language models or systems built with them, together with an open-source registry of benchmarks. The repository openai/evals carries an MIT licence (in LICENSE.md).

The README notes that you can now configure and run evals directly in the OpenAI Dashboard. The open framework lets you write custom evals for your use cases, including private evals on your own data that are not exposed publicly.

Features

  1. Eval registry: A public registry of evals across dimensions.
  2. Custom evals: Write evals for your use cases, including model-graded ones.
  3. Private evals: Use your data without publishing it.
  4. Templates: Templates for common eval patterns; see docs in the repo.
  5. W&B integration: Run and create evals with Weights & Biases, per the README.
  6. Dashboard option: Hosted evals in the OpenAI platform.

Pros

  1. MIT licence: Free to use.
  2. Large registry: Many example evals to learn from.
  3. Well known: About 19,700 GitHub stars at review.
  4. Simple concepts: Easy to read eval definitions.
  5. Hosted path: Dashboard evals for OpenAI users.

Cons

  1. Slower development: Last push in April 2026; the hosted Dashboard is where new work appears.
  2. OpenAI-centric: Setup assumes an OpenAI API key.
  3. API costs: Running evals costs tokens, as the README warns.
  4. Less agent tooling: Fewer agent and sandbox features than newer frameworks.
  5. Python setup: Requires a Python environment.

From the README: set OPENAI_API_KEY, install the package, and run an existing eval from the registry or write your own YAML and JSONL data. For hosted evals, use the Evals guide in the OpenAI platform docs.

Resources:

Pricing

The framework is free under MIT. Model calls are billed by OpenAI at its API prices.

  1. Teams on OpenAI models wanting a simple eval framework.
  2. Learners studying how evals are structured.
  3. Projects with private datasets needing custom evals.
  4. Not a fit for multi-provider agent evaluation at scale; consider Inspect or Promptfoo.

Conclusion

OpenAI Evals remains a useful, MIT-licensed reference framework and registry, though active development has shifted to OpenAI’s hosted evals.

Use it for simple evals on OpenAI models, and newer frameworks for agents.

Alternatives

Other tools on CheckOps that cover similar ground. Open a page, or compare one with OpenAI Evals side by side.