Implements a formal evaluation framework for Claude Code sessions using Eval-Driven Development (EDD) principles to ensure AI reliability.
Eval Harness is a specialized framework designed to bring engineering rigor to AI-assisted development. By implementing Eval-Driven Development (EDD), it allows developers to define success criteria before implementation, track regressions with precision, and measure agent performance using advanced metrics like pass@k. It supports multiple grading methods—including deterministic code checks, LLM-based qualitative assessments, and manual human reviews—ensuring that Claude's output remains stable and high-quality across model versions and complex task iterations.
主要功能
01Eval-Driven Development (EDD) workflow integration
02Support for pass@k and pass^k reliability metrics
03Multi-modal grading including code-based and model-based scoring
04Automated regression testing and capability benchmarking
05Standardized evaluation reporting and versioned artifact storage
06112,913 GitHub stars
使用场景
01Benchmarking Claude's performance across different model versions or prompt changes
02Measuring the success rate of non-deterministic AI coding tasks using pass@k metrics
03Setting up rigorous 'unit tests' for AI tasks to prevent regressions in complex codebases
