OpenAI Evals is a framework and open-source registry designed for evaluating large language models and systems built with LLMs. The project provides both pre-built evaluation benchmarks and tools for creating custom evaluations tailored to specific use cases. Users can configure and run evaluations directly through the OpenAI Dashboard or locally using the command-line interface, with support for building private evaluations that test LLM patterns without exposing proprietary data.
The repository is written in Python and requires a minimum version of 3.9. It uses Git-LFS to store its evaluation registry, allowing users to download either the complete set of evals or select individual evaluations. The framework supports multiple integration options, including logging results to Snowflake databases for users who want to persist evaluation data. Users can also integrate with Weights & Biases for running and managing evaluations through that platform.
The project emphasizes accessibility for different user types. For those simply running existing evaluations, installation via pip provides a straightforward path. For contributors creating new evaluations, the repository offers cloning and development installation options with pre-commit hooks available for code quality enforcement. The documentation includes comprehensive guides covering the full evaluation lifecycle, from building initial evals to implementing custom evaluation logic and completion functions. The framework supports various evaluation approaches, including model-graded evaluations using YAML configuration files, which allow users to contribute evaluations without writing custom code.
According to GitGenius activity tracking, the repository shows a median issue and pull request response latency of 4.4 hours across 45 tracked items, indicating active maintenance. Bug reports represent the most common issue type with 20 tracked instances, followed by feature ideas for new evaluations. The most active contributors tracked include sahilrajput03 with 8 events, connerlambden with 4 events, and Jupiter813 with 3 events. The repository shares contributors with major projects including microsoft/vscode, microsoft/typescript, and rust-lang/rust, suggesting cross-pollination with the broader developer ecosystem.
The framework is classified across 27 distinct categories including task evaluation, model benchmarking, performance assessment, testing frameworks, and machine learning metrics. This broad classification reflects the repository's comprehensive approach to LLM evaluation across multiple dimensions. The project explicitly states it is not currently accepting evaluations with custom code, instead encouraging contributions through model-graded evaluations with YAML configuration. Contributors agree to license their evaluation logic and data under the MIT license, and OpenAI reserves the right to use contributed data for future service improvements. The repository includes example implementations and starter guides, with particular emphasis on the CoQA dataset implementation showing multiple evaluation template approaches.
