系统梳理大模型智能体的方法、应用与挑战，助力通向通用人工智能
Large Language Model Agent: A Survey on Methodology, Applications and Challenges
- 以方法为中心构建分类体系，解析智能体架构与演化路径
- 揭示设计原则与复杂环境行为间的内在关联，统一碎片化研究
- 适合关注AGI、智能体协作与实际应用的研究者参考
智能体时代已至，由大语言模型的革命性进展推动。大语言模型（LLM）智能体具备目标驱动行为和动态适应能力，可能成为通往通用人工智能的关键路径。本综述以方法为中心，系统解构LLM智能体系统，建立架构基础、协作机制与演进路径的分类体系。通过揭示智能体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。本文提供统一的架构视角，探讨智能体如何构建、协作与演化，并讨论评估方法、工具应用、实际挑战及多样化应用场景。通过梳理该快速发展的领域最新进展，为研究者提供理解LLM智能体的结构化分类框架，并指明未来研究的潜在方向。相关论文集合可访问 https://github.com/luo-junyu/Awesome-Agent-Papers。
原文摘要 · Abstract (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.
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