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Autonomous agents have long been a prominent research focus in both academicand industry communities. Previous research in this field often focuses ontraining agents with limited knowledge within isolated environments, whichdiverges significantly from human learning processes, and thus makes the agentshard to achieve human-like decisions. Recently, through the acquisition of vastamounts of web knowledge, large language models (LLMs) have demonstratedremarkable potential in achieving human-level intelligence. This has sparked anupsurge in studies investigating LLM-based autonomous agents. In this paper, wepresent a comprehensive survey of these studies, delivering a systematic reviewof the field of LLM-based autonomous agents from a holistic perspective. Morespecifically, we first discuss the construction of LLM-based autonomous agents,for which we propose a unified framework that encompasses a majority of theprevious work. Then, we present a comprehensive overview of the diverseapplications of LLM-based autonomous agents in the fields of social science,natural science, and engineering. Finally, we delve into the evaluationstrategies commonly used for LLM-based autonomous agents. Based on the previousstudies, we also present several challenges and future directions in thisfield. To keep track of this field and continuously update our survey, wemaintain a repository of relevant references athttps://github.com/Paitesanshi/LLM-Agent-Survey.

A Survey on Large Language Model based Autonomous Agents