大型語言模型代理：方法論、應用與挑戰綜述

Large Language Model Agent: A Survey on Methodology, Applications and Challenges

March 27, 2025

作者: Junyu Luo, Weizhi Zhang, Ye Yuan, Yusheng Zhao, Junwei Yang, Yiyang Gu, Bohan Wu, Binqi Chen, Ziyue Qiao, Qingqing Long, Rongcheng Tu, Xiao Luo, Wei Ju, Zhiping Xiao, Yifan Wang, Meng Xiao, Chenwu Liu, Jingyang Yuan, Shichang Zhang, Yiqiao Jin, Fan Zhang, Xian Wu, Hanqing Zhao, Dacheng Tao, Philip S. Yu, Ming Zhang

cs.AI

摘要

智能代理時代已然來臨，這是由大型語言模型的革命性進展所驅動的。具備目標導向行為與動態適應能力的大型語言模型（LLM）代理，可能代表了邁向人工通用智能的一條關鍵路徑。本綜述通過以方法論為中心的分類體系，系統地解構了LLM代理系統，將架構基礎、協作機制與演化路徑相互關聯。我們揭示了代理設計原則與其在複雜環境中湧現行為之間的基本聯繫，從而統一了分散的研究脈絡。我們的工作提供了一個統一的架構視角，考察了代理如何構建、如何協作以及如何隨時間演進，同時也探討了評估方法、工具應用、實際挑戰及多樣化的應用領域。通過綜述這一快速發展領域的最新進展，我們為研究者提供了一個理解LLM代理的結構化分類體系，並指明了未來研究的有望方向。相關文獻集可在https://github.com/luo-junyu/Awesome-Agent-Papers獲取。

English

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.