OpenAI Evals
AIOpen SourceFreeOpen-source framework and registry for creating, running, and comparing evaluations of large language models and LLM systems.
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Free Available
Starting from Free
OpenAI Evals
AIOpen SourceFree
Open-source framework and registry for creating, running, and comparing evaluations of large language models and LLM systems.
-(0 Reviews)
Free Available
Starting from Free
About OpenAI Evals
OpenAI Evals is an open-source framework and benchmark registry for evaluating large language models and systems built with them. It provides a library, examples, and a registry of community and official evals that you can run locally or via the OpenAI platform, plus tools to author custom and private evals that reflect your workflows. Evals supports automated graders, rubric-based scoring, human-in-the-loop grading, dataset integration, and continuous evaluation to track model performance and regressions. Contributions to the public registry are MIT-licensed and contributors must ensure they have rights to any uploaded data; running evals typically incurs inference costs through the OpenAI API.
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Key Features
Registry of Benchmarks: A curated, open registry of existing evals and benchmarks for common LLM tasks, enabling quick comparison across models and tasks.
Custom & Private Evals: Author and run custom evals using your own datasets and grading logic; private evals let teams evaluate proprietary workflows without exposing data publicly.
Grader Framework: Build rubric-driven automated graders, model-based graders, or human-in-the-loop grading pipelines to produce consistent, repeatable scoring.
CLI/SDK & API Integration: Python-first SDK and CLI that integrate with the OpenAI API, support threaded execution, detailed logs, and programmatic control for batch runs.
Continuous Evaluation (CE): Integrate evals into development workflows to run on changes, detect regressions, and track performance over time across model versions.
Detailed Reporting & Metrics: Produces sample-level logs, aggregated counts and metrics, and final reports that summarize correctness, rubric scores, and other custom metrics.
Extensibility & Reproducibility: Templates and examples in the repository make it straightforward to extend eval types (e.g., classification, generation, instruction following) and reproduce results.
License & Contribution Controls: Public contributions are MIT-licensed with clear expectations about contributor rights and OpenAI’s reserved rights to use contributed data for product improvements.
Open-source registry of prebuilt evaluation suites (benchmarks) for LLMs
Author and run custom evals and private evals using your own data
Integration with OpenAI API and Evals API / dashboard for running and tracking evals
Support for structured outputs and JSON schema-based graders
Automated grader / LLM-as-judge capabilities to estimate human judgments
CLI and Python-based tooling; examples and Jupyter notebook demos
Threaded and batched execution for running large eval sets locally
Support for continuous evaluation (CE) workflows and comparison across runs
MIT-licensed contributions with requirement to have rights for uploaded data
Logging and reporting features with summary counts and final reports
Use Cases
Benchmarking Models: Run the registry or custom evals to compare multiple model families or model versions on shared task suites and metrics.
Prompt Optimization: Use dataset-driven evals to measure the effect of prompt edits and automatically iterate toward higher-quality prompts.
Continuous QA for Deployments: Integrate evals into CI/CD to run continuous evaluation that catches regressions when changing prompts, models, or system components.
Private Workflow Validation: Create private evals using internal data to validate an LLM’s behavior on organization-specific tasks without sharing sensitive data publicly.
Automated Grading & Labeling: Build automated graders and rubric pipelines to approximate expert judgments, triage outputs for human review, and scale label generation.
Research & Method Development: Use the open registry and tooling to prototype new evaluation methodologies, reproducible benchmarks, and shareable tasks with the community.
Comparative Performance Analysis: Track and report differences in accuracy, rubric scores, and failure modes across model releases for decision-making and model selection.
Benchmarking and comparing LLM models on task-specific datasets
Building private evaluation suites that reflect production workflows without exposing data
Automated grading and preference estimation to approximate human ratings
Continuous evaluation in CI to detect regressions and nondeterministic behavior
Measuring model performance on real-world occupation or task benchmarks (e.g., GDPval)
Developing and validating model improvements prior to deployment
