Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-world scenarios, cooperation among individuals is often required to enhance the efficiency and effectiveness of task accomplishment. Hence, inspired by human group dynamics, we propose a multi-agent framework \framework that can collaboratively and dynamically adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that \framework framework can effectively deploy multi-agent groups that outperform a single agent. Furthermore, we delve into the emergence of social behaviors among individual agents within a group during collaborative task accomplishment. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups. Our codes for \framework will soon be released at \url{https://github.com/OpenBMB/AgentVerse}.

翻译：借助大型语言模型（LLMs）赋能的自主智能体已取得显著改进，使其能够泛化到广泛的任务中。然而，在现实场景中，个体间的合作往往需要被调动以提升任务完成的效率与效果。因此，受人类群体动力学的启发，我们提出了一个多智能体框架\framework，该框架能够以“整体大于部分之和”的系统形式协作并动态调整其组成。我们的实验表明，\framework框架能有效部署优于单个智能体的多智能体群体。此外，我们深入探讨了在协作完成任务过程中，个体智能体在群体内部涌现出的社会行为。基于这些行为，我们讨论了一些可能的策略，旨在利用积极行为并缓解消极行为，以提升多智能体群体的协作潜力。\framework的代码将不久在\url{https://github.com/OpenBMB/AgentVerse}发布。