MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs
Qian Wang, Tianyu Wang, Zhenheng Tang, Qinbin Li, Nuo Chen, Jingsheng Liang, Bingsheng He
Abstract
LLM-based multi-agent systems (MAS) have shown promise in tackling complex tasks. However, existing solutions often suffer from limited agent coordination and heavy reliance on predefined Standard Operating Procedures (SOPs), which demand extensive human input. To address these limitations, we propose MegaAgent, a large-scale autonomous LLM-based multi-agent system. MegaAgent generates agents based on task complexity and enables dynamic task decomposition, parallel execution, efficient communication, and comprehensive system monitoring of agents. In evaluations, MegaAgent demonstrates exceptional performance, successfully developing a Gobang game within 800 seconds and scaling up to 590 agents in a national policy simulation to generate multi-domain policies. It significantly outperforms existing systems, such as MetaGPT, in both task completion efficiency and scalability. By eliminating the need for predefined SOPs, MegaAgent demonstrates exceptional scalability and autonomy, setting a foundation for advancing true autonomy in MAS.
BibTeX
@inproceedings{wang-etal-2025-megaagent,
title = "{M}ega{A}gent: A Large-Scale Autonomous {LLM}-based Multi-Agent System Without Predefined {SOP}s",
author = "Wang, Qian and
Wang, Tianyu and
Tang, Zhenheng and
Li, Qinbin and
Chen, Nuo and
Liang, Jingsheng and
He, Bingsheng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.259/",
doi = "10.18653/v1/2025.findings-acl.259",
pages = "4998--5036",
ISBN = "979-8-89176-256-5"
}