系统梳理大模型多智能体协同的关键技术与挑战。
LLMs Working in Harmony: A Survey on the Technological Aspects of Building Effective LLM-Based Multi Agent Systems
- 从架构、记忆、规划、工具四方面分析多智能体系统技术
- 指出可扩展性、实时响应和协作约束是主要瓶颈
- 适合研究多智能体系统或大模型应用的开发者参考
本综述探讨了构建高效基于大语言模型（LLM）的多智能体系统所需的基础技术。旨在回答如何在协作性、动态环境中优化这些系统，重点关注架构、记忆、规划及技术/框架四个关键领域。通过分析近期进展及其局限性——如可扩展性不足、实时响应困难以及智能体协调约束——提供了技术生态的详细图景。诸如Mixture of Agents架构和ReAct规划模型等框架展示了角色分配与决策能力的改进。本文综合了关键技术优势与持续挑战，提出提升系统可扩展性、智能体协作与适应性的实用建议。研究成果为未来研究提供路线图，助力构建稳健高效的多智能体系统，推动单个智能体性能与整体系统韧性双重提升。
原文摘要 · Abstract (English)
This survey investigates foundational technologies essential for developing effective Large Language Model (LLM)-based multi-agent systems. Aiming to answer how best to optimize these systems for collaborative, dynamic environments, we focus on four critical areas: Architecture, Memory, Planning, and Technologies/Frameworks. By analyzing recent advancements and their limitations - such as scalability, real-time response challenges, and agent coordination constraints, we provide a detailed view of the technological landscape. Frameworks like the Mixture of Agents architecture and the ReAct planning model exemplify current innovations, showcasing improvements in role assignment and decision-making. This review synthesizes key strengths and persistent challenges, offering practical recommendations to enhance system scalability, agent collaboration, and adaptability. Our findings provide a roadmap for future research, supporting the creation of robust, efficient multi-agent systems that advance both individual agent performance and collective system resilience.
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