多个智能体通过Git协作演进代码，无需中心化管理。
EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaboration
- 用Git版本图追踪代码演化，智能体异步读写共享仓库。
- 自主构建网页应用和自进化求解器，结果可运行且模块化。
- 适合自动化开发、持续演进系统，人类仅需目标与轻量反馈。
我们提出EvoGit，一种去中心化的多智能体协同软件开发框架，以自主代码演进为核心。EvoGit部署一群独立编码智能体，各自对共享代码库提出修改，无中央协调、显式通信或共享内存。所有协作通过基于Git的谱系图实现，该图记录完整版本历史，支持智能体异步读写，同时保持一致的历史记录。此结构支持细粒度分支、隐式并发和可扩展交互。人类参与极少但关键：用户设定高层目标，周期性审查谱系图，并提供轻量反馈以引导可行方向或淘汰无效路径。实验表明，EvoGit能自主完成两项真实任务：(1) 使用现代框架从零构建网页应用；(2) 构建一个元级系统，自进化出由语言模型指导的装箱优化求解器。结果验证了其在去中心化、自动化、持续软件开发方面的潜力。EvoGit已在https://github.com/BillHuang2001/evogit开源。
原文摘要 · Abstract (English)
We introduce EvoGit, a decentralized multi-agent framework for collaborative software development driven by autonomous code evolution. EvoGit deploys a population of independent coding agents, each proposing edits to a shared codebase without centralized coordination, explicit message passing, or shared memory. Instead, all coordination emerges through a Git-based phylogenetic graph that tracks the full version lineage and enables agents to asynchronously read from and write to the evolving code repository. This graph-based structure supports fine-grained branching, implicit concurrency, and scalable agent interaction while preserving a consistent historical record. Human involvement is minimal but strategic: users define high-level goals, periodically review the graph, and provide lightweight feedback to promote promising directions or prune unproductive ones. Experiments demonstrate EvoGit's ability to autonomously produce functional and modular software artifacts across two real-world tasks: (1) building a web application from scratch using modern frameworks, and (2) constructing a meta-level system that evolves its own language-model-guided solver for the bin-packing optimization problem. Our results underscore EvoGit's potential to establish a new paradigm for decentralized, automated, and continual software development. EvoGit is open-sourced at https://github.com/BillHuang2001/evogit.
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