openai-evals

Overview

Per oa-gh, a registry of YAML eval-specs lives under evals/registry/evals/, each pointing to a JSONL data file under evals/registry/data/ (Git-LFS managed). The oaieval CLI runs an eval against any completion-function-protocol model - either one of OpenAI's curated evals or a custom one registered by the team.

When to use

• The team contributes evals upstream to OpenAI's registry.
• The user needs the broad set of OpenAI-curated evals as a baseline.
• A custom Python eval class is required (e.g., complex grading logic that doesn't fit YAML templates).
• The team standardized on OpenAI Evals before alternatives like Promptfoo / DeepEval emerged.

For new projects without a registry-contribution motive, evaluate promptfoo-evaluation or deepeval-evaluation first - both have lower friction for non-OpenAI workflows.

Step 1 - Install

For running existing evals (per oa-gh):

pip install evals

For contributing new evals (clone first, then editable install):

git clone https://github.com/openai/evals.git
cd evals
pip install -e .

The editable install is required to register new evals and access the full registry source.

Step 2 - Run an eval

Per github.com/openai/evals/blob/main/docs/run-evals.md:

oaieval gpt-3.5-turbo test-match

Pattern: oaieval <model> <eval-name>. Per oa-run:

"Any implementation of the CompletionFn protocol can be run against oaieval."

Eval names are "specified in the YAML files under evals/registry/evals" (oa-run); implementations live in evals/elsuite.

Step 3 - Logging

Per oa-run:

"logging locally or to Snowflake will write to tmp/evallogs"

Override with --record_path /custom/path/. Logs are JSONL events "which can be inspected using a text editor or analyzed programmatically" (oa-run).

Common flags (oa-run):

• --no-local-run - Snowflake DB logging
• --record_path <dir> - output directory
• oaieval --help - full CLI options

Step 4 - Eval templates (YAML-defined)

Eval templates avoid Python authoring for common evaluation patterns. The four built-in templates per oa-gh (referenced in eval-templates.md):

• Match - exact-match scoring: completion must equal an entry in ideal (single string or list)
• Includes - substring scoring: completion must contain ideal text
• FuzzyMatch - relaxed-match scoring: token-level overlap between completion and ideal
• ModelBasedClassify - judge-model evaluates a completion (used for open-ended outputs where exact-match doesn't apply)

A registered eval YAML lives at evals/registry/evals/<name>.yaml and references a JSONL file at evals/registry/data/<name>/samples.jsonl. Each JSONL row contains the input prompt + the ideal field used by the template.

Step 5 - Custom Python evals

For grading logic beyond templates, subclass the Eval interface. The full pattern lives in docs/custom-eval.md and docs/build-eval.md in the oa-gh repository - author per the doc when authoring, then register in the YAML registry.

Step 6 - CI integration

OpenAI Evals does not ship a first-party CI action. Pattern:

oaieval gpt-4 my-eval --record_path ./evallogs
# parse JSONL evallog for pass-rate; fail CI if below threshold
jq -s '[.[] | select(.spec) | .]' ./evallogs/<run>.jsonl  # extract spec + outcomes

For PR-comment integration, parse the events JSONL into a summary and post via gh CLI (no built-in action).

Anti-patterns

Limitations

• OpenAI-first design - non-OpenAI providers via the CompletionFn protocol work but require shim code; for multi-provider evals start with promptfoo-evaluation.
• The registry is large but dated - many evals target older OpenAI models; check eval-spec freshness before contributing.
• No first-party CI integration - assemble pass-rate gates manually.
• JSONL log inspection requires familiarity with the events schema (per-event types: sampling, match, metrics).

References

• oa-gh - main repo + framework overview
• oa-run - oaieval CLI reference
• docs/build-eval.md, docs/custom-eval.md, docs/eval-templates.md in oa-gh - authoring details (load from repo when building a new eval)
• promptfoo-evaluation, deepeval-evaluation - lower-friction alternatives for new projects
• prompt-eval-reviewer - adversarial reviewer