The rise and potential of large language model based agents: a survey

Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, Rui Zheng, Xiaoran Fan, Xiao Wang, Limao Xiong, Yuhao Zhou, Weiran Wang, Changhao Jiang, Yicheng Zou, Xiangyang Liu, Zhangyue Yin et al. (8 additional authors not shown)

Abstract: For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds human intelligence. AI agents, which are artificial entities capable of sensing the environment, making decisions, and taking actions, are seen as a means to achieve this goal. Extensive efforts have been made to develop AI agents, with a primary focus on refining algorithms or training strategies to enhance specific skills or particular task performance. The field, however, lacks a sufficiently general and powerful model to serve as a foundation for building general agents adaptable to diverse scenarios. With their versatile capabilities, large language models (LLMs) pave a promising path for the development of general AI agents, and substantial progress has been made in the realm of LLM-based agents. In this article, we conduct a comprehensive survey on LLM-based agents, covering their construction frameworks, application scenarios, and the exploration of societies built upon LLM-based agents. We also conclude some potential future directions and open problems in this flourishing field.