OpenAI Evals
A framework and open-source registry for evaluating large language models (LLMs) and LLM systems.
What is OpenAI Evals?
OpenAI Evals is a framework and open-source registry for evaluating large language models (LLMs) and LLM-based systems. It provides tools to run existing benchmarks and create custom evaluations, helping developers assess model performance and understand how different versions affect their specific use cases. The framework supports both public benchmarks and private evaluations using proprietary data.
AI researchers, machine learning engineers, and developers building applications with LLMs who need to systematically evaluate model performance, compare versions, or create custom benchmarks for their workflows.
Developers choose OpenAI Evals because it offers a standardized, extensible framework from OpenAI itself, integrates directly with the OpenAI API, and provides a registry of vetted benchmarks alongside tools for creating private, data-secure evaluations without extensive coding.
Overview
Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
Use Cases
Best For
- Comparing performance between different LLM versions or models
- Creating custom benchmarks for specific LLM applications or domains
- Evaluating prompt engineering strategies and template effectiveness
- Running private evaluations with proprietary datasets
- Logging and analyzing eval results in a Snowflake database
- Assessing LLM systems with chain-of-thought or tool-using agents
Not Ideal For
- Teams evaluating non-OpenAI LLMs, such as Anthropic's Claude or open-source models like Llama
- Projects with strict budget constraints where frequent API calls for evaluations would be cost-prohibitive
- Developers seeking a no-code, web-based GUI for quick model testing without local Python setup
- Organizations without Snowflake integration needing to log results to alternative databases like PostgreSQL or BigQuery
Pros & Cons
Pros
Seamlessly connects to the OpenAI API with built-in key management and cost tracking, as specified in the setup with the OPENAI_API_KEY environment variable.
Provides a curated collection of benchmarks accessed via Git-LFS, offering vetted evaluations for various model dimensions without starting from scratch.
Enables evaluations using YAML templates without custom coding, making it accessible for prompt engineers, as highlighted in the FAQ and eval-templates.md.
Supports building evals with proprietary datasets without exposing them publicly, crucial for enterprises handling sensitive information in their workflows.
Cons
Primarily designed for OpenAI models, limiting utility for teams using other LLM providers or open-source alternatives, as evidenced by the API key dependency.
Requires Git-LFS for data fetching, Python 3.9+, and multiple environment variables, adding complexity to initial configuration, as noted in the download and setup sections.
Currently not accepting evals with custom code, which frustrates advanced users wanting to share complex evaluation logic, as stated in the writing evals section.
Incurs OpenAI API costs for running evals, and there are known issues like hanging at the end, impacting efficiency and budget, as mentioned in the FAQ.
Frequently Asked Questions
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