The openai/evals repository is a framework and registry for evaluating large language models and systems built with LLMs. It’s designed to let you define “evals” (evaluation tasks) in a structured way and run them against different models or agents, with the ability to score, compare, and analyze results. The framework supports templated YAML eval definitions, solver-based evaluations, custom metrics, and composition of multi-step evaluations. It includes utilities and APIs to plug in completion functions, manage prompts, wrap retries or error handling, and register new evaluation types. It also maintains a growing registry of standard benchmarks or “evals” that users can reuse (for example, tasks measuring reasoning, factual accuracy, or chain-of-thought capabilities). The design is modular so you can extend or compose new evals, integrate with your own model APIs, and capture rich metadata about each run (prompt, responses, metrics).

Features

  • Definition of evals via YAML plus helper classes and templated scaffolding
  • Support for “solver” style evaluations (models that solve tasks) as a subclass of evals
  • APIs and wrappers to plug in completion functions, prompt templates, and retry logic
  • A registry of benchmark tasks and reuse of shared evals across models
  • Modular, extensible architecture for adding new evaluation types
  • Metadata tracking (prompts, metrics, responses, diagnostics) for analysis

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License

MIT License

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Additional Project Details

Programming Language

Python

Related Categories

Python Large Language Models (LLM)

Registered

2025-10-02