OpenAI Evals
Evaluation framework and registry for organizing model tasks and inspecting how examples become aggregate scores.
External resource. No execution or independent verification is claimed here.
Does a deterministic synthetic evaluation preserve example order, expected labels and aggregate denominators?
Use an independently defined fixture and expected score ledger; separately scope community evaluation data and actual model-provider behavior.
What you could produce
- A version-pinned protocol stating inputs, rights, expected behavior, tolerances and resource limits before execution.
- A retained per-case result and failure ledger with an independently controlled comparison, if qualified execution is later authorized.
Before you use it
- Python and evaluation-specific dependencies; live model credentials and any API usage costs require separate authorization.
- A separately qualified isolated runtime with a reviewed, pinned dependency and input closure.
Limits to keep in view
- No source program, example, build hook, package, dataset, model or generated research code has been executed or downloaded as a payload.
- The documented self-contained Python 3.13, 90-second pilot does not establish support for this package, its compiled dependencies, GPUs, services or agent sandboxes.
- Installation success, scientific outcomes, runtime compatibility, latency, memory use, API costs and security properties are unmeasured.
Source and permission context
Preserve the upstream project name, repository, exact source commit and applicable contributor/notices; resolve upstream citation guidance for any later formal use.
Catalog listing reviewed. This review covers the description and source links displayed here.
Approved for catalog metadata and links. The displayed entry identifies OpenAI Evals with the concise collection-authored summary “Evaluation framework and registry for organizing model tasks and inspecting how examples become aggregate scores.” and points to the public upstream repository https://github.com/openai/evals. The wording describes function and possible investigation without reproducing upstream source or documentation, claiming execution, or implying endorsement or rights beyond the recorded scopes.
Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.
code and repository content except the explicitly listed datasets · MIT
The root notice applies MIT to repository content except specified datasets; the exception list must be retained when selecting files. Interpretation is bound to the reviewed response digest at source commit 8eac7a7de5215c907fbddc30efdaf316913eccdd.
Inspect the license evidence ↗data exceptions listed in the root license; individual source grants unverified · No SPDX identifier
The source lists dataset-specific terms including attribution, share-alike, noncommercial and custom notices. These are upstream-reported descriptions, not verification of the original dataset or every incorporated third-party item. Interpretation is bound to the reviewed response digest at source commit 8eac7a7de5215c907fbddc30efdaf316913eccdd.
Inspect the license evidence ↗Before copying source material
- Only the cited license and README texts were observed; file exceptions, dependency closure, vendored components and submodules are not comprehensively audited.
- Dataset files, task prompts, generated outputs, model weights, tokenizer assets and hosted APIs are not cleared by a root code license.
- README/documentation reuse rights and version-specific citation guidance remain separately unresolved; only links and original descriptions are retained.
- The root notice explicitly carves out datasets and reports several CC-BY-NC and other obligations. Its README contribution language must not override the more specific dataset exception list; each original dataset source needs review.