{"id":"openai-evals","name":"evals","af_score":42.2,"security_score":34.5,"reliability_score":37.5,"what_it_does":"OpenAI Evals is an open-source framework for evaluating LLMs and LLM systems. It provides an existing registry of benchmark/evaluation definitions (often data-driven), tooling to run local evaluations, and guidance for creating custom evals (including model-graded evals via YAML/templates).","best_when":"You want repeatable, versioned evaluation of LLM behavior (offline/local runs or CI), with the ability to extend evaluations using provided templates and data formats.","avoid_when":"You need a standalone REST/GraphQL service with hosted endpoints; or you require strict guarantees around cloud-managed uptime, idempotent job semantics, and standardized HTTP error codes.","last_evaluated":"2026-03-29T13:16:27.965279+00:00","has_mcp":false,"has_api":false,"auth_methods":["OPENAI_API_KEY environment variable (for OpenAI API access)"],"has_free_tier":false,"known_gotchas":["Evals are run locally via Python tooling/CLI; an agent must handle environment setup (OPENAI_API_KEY, optional Snowflake credentials) and the runtime behavior of evaluation jobs.","Git-LFS is required to fetch registry data pointers; agents should ensure LFS is installed and pulls are performed before running registry-based evals.","Some eval runs may 'hang at the very end' (known issue), so an agent should not assume completion strictly when the final report prints; interruption behavior is mentioned but retry guidance is not provided."],"error_quality":0.0}