  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.
