标题： AgentMesh：一种用于软件开发自动化的协作多智能体生成式人工智能框架 显示英文标题

标题： AgentMesh: A Cooperative Multi-Agent Generative AI Framework for Software Development Automation

摘要： 软件开发是一个复杂的多阶段过程，传统上需要具有不同专业知识的个体之间的协作。 我们提出 AgentMesh，一个基于Python的框架，该框架使用多个协作的LLM驱动代理来自动化软件开发任务。 在AgentMesh中，专门的代理 - 一个规划者、编码者、调试器和审查者 - 协同工作，将高层次的需求转化为完整的代码。 规划者代理首先将用户请求分解为具体的子任务；编码者代理将每个子任务实现为代码；调试者代理测试并修复代码；而 审查者代理验证最终输出的正确性和质量。 我们描述了这些代理及其通信的架构和设计，并提供了包括提示策略和工作流编排在内的实现细节。 一个案例研究说明了AgentMesh通过顺序任务规划、代码生成、迭代调试和最终代码审查来处理一个非平凡的开发请求。 我们讨论了如何在协作代理之间分配责任，以利用大型语言模型的优势，同时减轻单个代理的局限性。 最后，我们检查了当前的局限性——例如错误传播和上下文扩展——并概述了未来的工作方向，以实现更稳健、可扩展的多代理AI系统，用于软件工程自动化。 显示英文摘要

摘要： Software development is a complex, multi-phase process traditionally requiring collaboration among individuals with diverse expertise. We propose AgentMesh, a Python-based framework that uses multiple cooperating LLM-powered agents to automate software development tasks. In AgentMesh, specialized agents - a Planner, Coder, Debugger, and Reviewer - work in concert to transform a high-level requirement into fully realized code. The Planner agent first decomposes user requests into concrete subtasks; the Coder agent implements each subtask in code; the Debugger agent tests and fixes the code; and the Reviewer agent validates the final output for correctness and quality. We describe the architecture and design of these agents and their communication, and provide implementation details including prompt strategies and workflow orchestration. A case study illustrates AgentMesh handling a non-trivial development request via sequential task planning, code generation, iterative debugging, and final code review. We discuss how dividing responsibilities among cooperative agents leverages the strengths of large language models while mitigating single-agent limitations. Finally, we examine current limitations - such as error propagation and context scaling - and outline future work toward more robust, scalable multi-agent AI systems for software engineering automation.