Multi-agent systems (MAS) based on large language models (LLMs) have demonstrated significant potential in collaborative problem-solving. However, they still face substantial challenges of low communication efficiency and suboptimal task performance, making the careful design of the agents' communication topologies particularly important. Inspired by the management theory that roles in an efficient team are often dynamically adjusted, we propose AgentDropout, which identifies redundant agents and communication across different communication rounds by optimizing the adjacency matrices of the communication graphs and eliminates them to enhance both token efficiency and task performance. Compared to state-of-the-art methods, AgentDropout achieves an average reduction of 21.6% in prompt token consumption and 18.4% in completion token consumption, along with a performance improvement of 1.14 on the tasks. Furthermore, the extended experiments demonstrate that AgentDropout achieves notable domain transferability and structure robustness, revealing its reliability and effectiveness. We release our code at https://github.com/wangzx1219/AgentDropout.

本研究针对基于大语言模型的多智能体系统在通信效率低和任务性能不佳等方面的挑战，提出了AgentDropout方法，通过优化通信图的邻接矩阵识别并消除冗余代理及通信，从而增强令牌效率和任务性能。实验结果显示，相较于现有方法，AgentDropout在提示令牌消耗上平均降低了21.6%，在任务表现上提升了1.14，显示出其可靠性和有效性。

AgentDropout：用于令牌高效与高性能的动态代理消除