With the emergence of large language models (LLMs), LLM-powered multi-agent systems (LLM-MA systems) have been proposed to tackle real-world tasks. However, their agents mostly follow predefined Standard Operating Procedures (SOPs) that remain unchanged across the whole interaction, lacking autonomy and scalability. Additionally, current solutions often overlook the necessity for effective agent cooperation. To address the above limitations, we propose MegaAgent, a practical framework designed for autonomous cooperation in large-scale LLM Agent systems. MegaAgent leverages the autonomy of agents to dynamically generate agents based on task requirements, incorporating features such as automatically dividing tasks, systematic planning and monitoring of agent activities, and managing concurrent operations. In addition, MegaAgent is designed with a hierarchical structure and employs system-level parallelism to enhance performance and boost communication. We demonstrate the effectiveness of MegaAgent through Gobang game development, showing that it outperforms popular LLM-MA systems; and national policy simulation, demonstrating its high autonomy and potential to rapidly scale up to 590 agents while ensuring effective cooperation among them. Our results indicate that MegaAgent is the first autonomous large-scale LLM-MA system with no pre-defined SOPs, high effectiveness and scalability, paving the way for further research in this field. Our code is at https://anonymous.4open.science/r/MegaAgent-81F3.

翻译：随着大语言模型（LLM）的出现，基于LLM的多智能体系统（LLM-MA系统）已被提出以解决现实世界任务。然而，现有系统中的智能体大多遵循预定义的标准操作程序（SOP），这些程序在整个交互过程中保持不变，缺乏自主性和可扩展性。此外，当前解决方案往往忽视了有效智能体协同的必要性。为应对上述局限，我们提出了MegaAgent——一个专为大规模LLM智能体系统中自主协同设计的实用框架。MegaAgent利用智能体的自主性，根据任务需求动态生成智能体，并整合了自动任务分解、系统化的智能体活动规划与监控以及并发操作管理等特性。此外，MegaAgent采用分层架构设计，并利用系统级并行机制以提升性能、优化通信效率。我们通过五子棋游戏开发实验证明了MegaAgent的有效性，其性能优于主流LLM-MA系统；通过国家政策模拟实验，展示了该系统的高自主性及快速扩展至590个智能体同时确保有效协同的潜力。实验结果表明，MegaAgent是首个无需预定义SOP、具备高效性与高可扩展性的自主大规模LLM-MA系统，为该领域的进一步研究开辟了道路。代码开源地址：https://anonymous.4open.science/r/MegaAgent-81F3。