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.

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