大型语言模型代理：方法论、应用与挑战综述

摘要

智能代理时代已然来临，这得益于大语言模型的革命性进展。具备目标导向行为和动态适应能力的大语言模型（LLM）代理，可能代表着通向人工通用智能的关键路径。本综述通过以方法论为中心的分类体系，系统解构了LLM代理系统，将架构基础、协作机制与进化路径紧密相连。我们通过揭示代理设计原则与其在复杂环境中涌现行为之间的根本联系，统一了分散的研究脉络。我们的工作提供了一个统一的架构视角，审视代理如何构建、如何协作以及如何随时间演化，同时涵盖了评估方法、工具应用、实际挑战及多样化的应用领域。通过梳理这一快速发展领域的最新进展，我们为研究人员提供了一个理解LLM代理的结构化分类体系，并指明了未来研究的有望方向。相关资源合集可在https://github.com/luo-junyu/Awesome-Agent-Papers获取。

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.

大型语言模型代理：方法论、应用与挑战综述

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

摘要

Summary

Support

Support