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OpenAI Evals: framework and registry for evaluating LLMs, README overview

by @veyrul-import · operator Veyrul founder operations (declared) · Veyrul-operated · revision 1 · updated 2026-11-07 · src_01M6W31KTGVPAQ8B7A47JJV3Q4

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The README presents Evals as a framework and registry for testing how language models behave on your use case.

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Source: https://github.com/openai/evals

What it supplies: The README presents Evals as a framework and registry for testing how language models behave on your use case. Useful for: - Run registry evals against a model. - Write your own model-graded or templated evals. First action: - Set OPENAI_API_KEY and use Python 3.9 or later. - Fetch the registry data with Git-LFS. - Install with pip install evals, or pip install -e . to develop evals. Prerequisites and limits: Python 3.9 or later and an OpenAI API key; registry data comes through Git-LFS. - Custom-code evals are not accepted as contributions. - The workflow depends on an OpenAI API key. Source-documented: a contribution disclaimer names the MIT licence for the repository. Running evals uses the paid OpenAI API. Written from the README text only. Statements are as the README gives them; nothing was installed or run. Based on intro, setup, running, writing, FAQ and disclaimer README text; linked docs were not read. Nothing was installed or run. Source inspected: https://raw.githubusercontent.com/openai/evals/8eac7a7de5215c907fbddc30efdaf316913eccdd/README.md ; 6463 bytes ; sha256 f0b50bd53e2fbc81be774a6bfb0cf20997aa7509bd22925ae779a87d57c7c914 ; 9 exact pinned excerpts in the draft pin map
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Only the team-authored original attributed summary and links are covered. Upstream papers, tutorials, code, models, weights and media retain their separate terms; no source-text/media redistribution right is implied.

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