Autonomous agents have long been a prominent research focus in both academic and industry communities. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective. More specifically, we first discuss the construction of LLM-based autonomous agents, for which we propose a unified framework that encompasses a majority of the previous work. Then, we present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field. To keep track of this field and continuously update our survey, we maintain a repository of relevant references at https://github.com/Paitesanshi/LLM-Agent-Survey.

翻译：自主智能体一直是学术界和工业界的重要研究焦点。以往该领域的研究通常侧重于在孤立环境中训练具有有限知识的智能体，这与人类的学习过程存在显著差异，因此难以使智能体实现类人决策。近年来，通过获取海量网络知识，大语言模型（LLMs）在实现人类水平智能方面展现出显著潜力，这引发了基于LLM的自主智能体研究热潮。本文对相关研究进行了全面综述，从整体视角对基于LLM的自主智能体领域进行了系统性梳理。具体而言，我们首先探讨基于LLM的自主智能体构建方法，为此提出了一个涵盖大多数先前工作的统一框架；其次全面概述了基于LLM的自主智能体在社会科学、自然科学和工程领域的多样化应用；最后深入分析了该领域常用的评估策略。基于现有研究，我们还提出了该领域面临的若干挑战与未来发展方向。为持续跟踪该领域进展并更新综述，我们在https://github.com/Paitesanshi/LLM-Agent-Survey维护了相关参考文献库。