OpenAI Evals Tool — HeyClaude

Safety notes

Eval scores are regression and quality signals, not proof that a model or agent is safe, fair, or production-ready.
Run adversarial, prompt-injection, or tool-use evals against isolated environments and reviewed credentials.
Large eval runs can issue many model calls; set budgets, rate limits, and stop conditions before running them.

Privacy notes

Prompts, model outputs, labels, traces, retrieved documents, and grader notes can contain user, customer, or proprietary data.
Completion functions may send eval payloads to the configured model provider unless a reviewed local model path is used.
Store eval datasets and results according to the same retention and redaction rules used for production AI data.

Prerequisites

Python environment suitable for installing and running eval tooling.
Representative prompts, expected outputs, graders, and datasets for the behavior being tested.
Model-provider credentials only when the selected completion function requires them.

Schema details

Install type: cli
Troubleshooting: No

Source repository stats

Scope: Source repo

Tool listing metadata

Website: https://github.com/openai/evals
Pricing: open-source
Disclosure: editorial
Application category: DeveloperApplication
Operating system: macOS, Windows, Linux

Full copyable content

oaieval <completion-fn> <eval-name>

About this resource

Editorial notes

OpenAI Evals gives teams a code-first way to define and run repeatable language model evaluations. It is useful when a Claude or AI-agent workflow needs a regression suite instead of one-off prompt testing: expected outputs, graders, fixtures, completion functions, and run results can be kept close to the code that depends on them.

Why it belongs

Canonical open-source repository maintained by OpenAI.
Useful for prompt regression tests, model comparison, and agent behavior checks.
Fits Claude and AI workflow teams that need repeatable evaluation before release.
Can be combined with human review and trace tools when automated graders are not enough.

Review guidance

Treat eval results as evidence, not authority. A passing eval suite can still miss distribution shift, tool misuse, privacy leakage, security issues, or unfair behavior. Keep holdout cases, redaction rules, and human escalation paths separate from the framework itself.