注意,这个仓库已经不再维护了,作者把它完全重写成了 Harness Anything 项目,新功能、修复和文档都搬到那边去了。Harness Anything 能把智能体的决策、任务、事实和审查变成持久化的项目记忆,用基于证据的门槛来验证“完成”不是嘴上说说。老版本本身是个开源、文档原生的工具,用来让 Codex、Claude Code、Gemini CLI 这些编程智能体在长期开发中保持清晰、透明、可审查。如果你是新项目,直接去用 Harness Anything 吧,它不是简单的升级,得按新项目的指南来。
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Coding Agent Harness [!IMPORTANT] This repository is no longer maintained. Coding Agent Harness has been completely rewritten as Harness Anything . New development, fixes, and documentation now live there. Harness Anything turns agent decisions, tasks, facts, and reviews into durable project memory, and uses evidence backed gates so “done” is more than a claim. For new projects, use Harness Anything. It is a new system rather than a drop in upgrade; please follow its start guide. The content below is preserved for existing users and historical reference. English 简体中文 日本語 한국어 Français Español Deutsch An open source, document native, ready to use Agent Harness for keeping Codex, Claude Code, Gemini CLI, and other coding agents clear, transparent, and reviewable during long running software work. At A Glance Coding Agent Harness is not another collection of chat prompts. It turns the durable facts that coding agents need into repository files: entry agreements, task plans, execution evidence, regression results, dashboards, and closeout records. Requires Node.js 24 or newer. The smallest loop is: A human states the goal, and the agent reads the repository Harness first. The agent follows Diagnose → Decide → Scaffold → Configure → Verify → Deliver. The CLI and Dashboard expose status, risk, migration plans, and review evidence. The next agent resumes from repository facts instead of previous chat memory. What It Is Coding Agent Harness is a project engineering framework for AI coding agents. It adds working agreements, document structure, task lifecycle, regression evidence, and review loops directly into your repository so agents can read, execute, update, and verify the project from durable local facts. Why It Exists Generating a few thousand lines of code with AI is not the hard part. The hard part is keeping the agent oriented after days of work, preventing parallel agents from overwriting each other, and letting a new agent continue from repository facts instead of