Large Language Model Agent: A Survey on Methodology, Applications and
  Challenges
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.
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Junyu Luo
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Weizhi Zhang
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Weizhi Zhang
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Ye Yuan
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Yusheng Zhao
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Junwei Yang
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Yiyang Gu
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2025-03-28T08:55:04.907Z
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Yiyang Gu
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Ziyue Qiao
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Ziyue Qiao
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Qingqing Long
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2025-03-28T08:56:00.392Z
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Qingqing Long
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Rongcheng Tu
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Xiao Luo
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2025-03-28T14:22:25.148Z
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Xiao Luo
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Wei Ju
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Meng Xiao
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Chenwu Liu
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Shichang Zhang
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Shichang Zhang
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Yiqiao Jin
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2025-03-28T08:57:05.559Z
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Yiqiao Jin
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Ahren09
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Fan Zhang
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Xian Wu
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Hanqing Zhao
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Dacheng Tao
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Philip S. Yu
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2025-03-28T08:57:54.103Z
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Philip Yu
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Ming Zhang
Large Language Model Agent: A Survey on Methodology, Applications and
  Challenges
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.
A survey of large language model agents provides a structured taxonomy, exploring architectural principles, collaboration, evolution, evaluation, and applications, highlighting promising research directions.
Large Language Model (LLM) goal-driven behaviors dynamic adaptation capabilities architectural foundations collaboration mechanisms evolutionary pathways agent design principles emergent behaviors evaluation methodologies tool applications practical challenges application domains