Large Language Model Agent: A Survey on Methodology, Applications and
  Challenges
Authors: name: Junyu Luo, name: Weizhi Zhang, name: Ye Yuan, name: Yusheng Zhao, name: Junwei Yang, name: Yiyang Gu, name: Bohan Wu, name: Binqi Chen, name: Ziyue Qiao, name: Qingqing Long, name: Rongcheng Tu, name: Xiao Luo, name: Wei Ju, name: Zhiping Xiao, name: Yifan Wang, name: Meng Xiao, name: Chenwu Liu, name: Jingyang Yuan, name: Shichang Zhang, name: Yiqiao Jin, name: Fan Zhang, name: Xian Wu, name: Hanqing Zhao, name: Dacheng Tao, name: Philip S. Yu, name: Ming Zhang

The era of intelligent agents is upon us, driven by revolutionary
advancements in large language models. Large Language Model (LLM) agents, with
goal-driven behaviors and dynamic adaptation capabilities, potentially
represent a critical pathway toward artificial general intelligence. This
survey systematically deconstructs LLM agent systems through a
methodology-centered taxonomy, linking architectural foundations, collaboration
mechanisms, and evolutionary pathways. We unify fragmented research threads by
revealing fundamental connections between agent design principles and their
emergent behaviors in complex environments. Our work provides a unified
architectural perspective, examining how agents are constructed, how they
collaborate, and how they evolve over time, while also addressing evaluation
methodologies, tool applications, practical challenges, and diverse application
domains. By surveying the latest developments in this rapidly evolving field,
we offer researchers a structured taxonomy for understanding LLM agents and
identify promising directions for future research. The collection is available
at https://github.com/luo-junyu/Awesome-Agent-Papers.