Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Due to the impressive planning and reasoning abilities of LLMs, they have been used as autonomous agents to do many tasks automatically. Recently, based on the development of using one LLM as a single planning or decision-making agent, LLM-based multi-agent systems have achieved considerable progress in complex problem-solving and world simulation. To provide the community with an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects of multi-agent systems based on LLMs, as well as the challenges. Our goal is for readers to gain substantial insights on the following questions: What domains and environments do LLM-based multi-agents simulate? How are these agents profiled and how do they communicate? What mechanisms contribute to the growth of agents' capacities? For those interested in delving into this field of study, we also summarize the commonly used datasets or benchmarks for them to have convenient access. To keep researchers updated on the latest studies, we maintain an open-source GitHub repository, dedicated to outlining the research on LLM-based multi-agent systems.

翻译：大语言模型在广泛的任务中取得了显著成功。凭借其出色的规划与推理能力，大语言模型已被用作自主智能体，自动完成众多任务。近期，在将单个LLM作为规划或决策智能体的研究基础上，基于LLM的多智能体系统在复杂问题求解和世界模拟方面取得了重要进展。为了向学界提供这一活跃领域的全景概览，本综述深入探讨了基于LLM的多智能体系统的关键方面及其面临的挑战。我们旨在帮助读者深入理解以下问题：基于LLM的多智能体系统模拟了哪些领域与环境？这些智能体如何构建特征画像并进行通信？哪些机制促进了智能体能力的增长？为便于研究人员入门，我们还总结了该领域常用数据集与基准。我们维护了一个开源GitHub仓库，持续追踪基于LLM的多智能体系统的最新研究成果，供研究者及时获取前沿动态。