# openai/evals

**Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/openai-evals).**

18,702 stars · 2,990 forks · Python · NOASSERTION

## Links

- GitHub: https://github.com/openai/evals
- awesome-repositories: https://awesome-repositories.com/repository/openai-evals.md

## Description

Evals is a framework designed for automating, managing, and executing repeatable benchmarking suites to analyze the quality and performance of language models. It provides a platform for running standardized tests to measure model accuracy and track behavioral changes over time.

The system distinguishes itself through a modular architecture that uses a standardized adapter layer to normalize inputs and outputs, allowing different models to be swapped and tested interchangeably. It supports the creation of custom benchmarks using proprietary data, enabling quality assurance on sensitive tasks without exposing information to public datasets.

The framework covers a broad range of evaluation capabilities, including the use of declarative templates to instantiate testing patterns and a registry-based system for discovering and executing specific evaluation logic. It incorporates event-driven logging to capture granular performance metrics and interaction data, facilitating detailed analysis of model behavior across both public and private testing environments.

## Tags

### Artificial Intelligence & ML

- [Model Performance Benchmarking](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-analysis/model-analysis/model-performance-benchmarking.md) — Measures the accuracy and behavior of language models using standardized tests to identify performance changes.
- [AI Evaluation Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-analysis/ai-evaluation-frameworks.md) — Enables the definition of bespoke evaluation logic and datasets to assess unique model behaviors.
- [Model Benchmarking Suites](https://awesome-repositories.com/f/artificial-intelligence-ml/model-benchmarking-suites.md) — Provides a collection of testing patterns and custom logic for assessing specific model behaviors.
- [Model Benchmarking](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-and-validation/model-benchmarking.md) — Enables private evaluation benchmarking by using proprietary data to assess model performance on sensitive tasks. ([source](https://cdn.jsdelivr.net/gh/openai/evals@main/README.md))
- [Model Abstractions](https://awesome-repositories.com/f/artificial-intelligence-ml/model-abstractions.md) — Normalizes diverse model inputs and outputs into a uniform format for interchangeable performance testing.
- [Model Benchmarking Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/model-benchmarking-frameworks.md) — Supports the definition of bespoke evaluation logic and datasets to measure specific model behaviors. ([source](https://cdn.jsdelivr.net/gh/openai/evals@main/README.md))
- [Model Benchmarking Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/model-benchmarking-interfaces.md) — Enables model interface standardization to ensure different language models can be swapped and tested interchangeably. ([source](https://github.com/openai/evals/tree/main/docs/))
- [Model Evaluation Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/model-evaluation-frameworks.md) — Executes standardized or custom test suites against language models to generate performance reports. ([source](https://github.com/openai/evals/tree/main/docs/))

### Testing & Quality Assurance

- [Model Testing](https://awesome-repositories.com/f/testing-quality-assurance/model-testing.md) — Provides a platform for executing repeatable evaluations against language models to analyze output quality.
- [LLM Evaluation](https://awesome-repositories.com/f/testing-quality-assurance/model-testing/llm-evaluation.md) — Serves as a toolkit for building, running, and managing standardized benchmarks for large language models.
- [Private Benchmarking](https://awesome-repositories.com/f/testing-quality-assurance/software-testing/testing-frameworks/quality-assurance-frameworks/private-benchmarking.md) — Constructs internal benchmarks using proprietary data to assess model performance on sensitive tasks.
- [Model Interface Protocols](https://awesome-repositories.com/f/testing-quality-assurance/model-testing/model-interface-protocols.md) — Implements a uniform communication protocol to ensure different language models can be swapped and tested interchangeably.
- [Test Registries](https://awesome-repositories.com/f/testing-quality-assurance/testing-infrastructure-management/test-execution-management/test-registries.md) — Uses a centralized lookup system to map unique identifiers to specific evaluation logic and datasets.

### Software Engineering & Architecture

- [Evaluation Templates](https://awesome-repositories.com/f/software-engineering-architecture/declarative-configuration-schemas/evaluation-templates.md) — Provides declarative templates to instantiate and structure complex evaluation tasks for language models.
- [Evaluation Completion Logic](https://awesome-repositories.com/f/software-engineering-architecture/modular-extension-architectures/evaluation-completion-logic.md) — Allows developers to inject custom scoring algorithms and specialized prompting strategies into the evaluation pipeline.
- [Evaluation Interaction Logs](https://awesome-repositories.com/f/software-engineering-architecture/event-logging/evaluation-interaction-logs.md) — Captures granular interaction logs and performance metrics during test execution to facilitate post-hoc analysis.