让大模型网络自动通信协作，无需人工干预。
A Scalable Communication Protocol for Networks of Large Language Models
- 分层通信：高频用标准流程，低频用自然语言，中间用大模型自动生成代码。
- 在大型网络中实现全自动协议涌现，复杂任务无需人工介入。
- 支持去中心化扩展，能动态适应成员与接口变化，适合大规模智能体系统。
协作依赖通信。在扩展大模型驱动的智能体网络时，通信必须兼具灵活性、高效性和可移植性，我们称之为智能体通信三难困境。本文提出Agora——一种元协议，利用现有通信标准，使大模型智能体高效解决复杂问题。在Agora中，智能体通常使用标准化流程进行频繁通信，以自然语言处理稀有交互，并用大模型生成的代码处理中间场景。该协议有效规避了通信三难困境，可稳健应对接口和成员变动，实现前所未有的可扩展性与完全去中心化，人类参与极少。在大规模Agora网络中，我们观察到自组织、全自动协议的涌现，能够独立达成复杂目标。
原文摘要 · Abstract (English)
Communication is a prerequisite for collaboration. When scaling networks of AI-powered agents, communication must be versatile, efficient, and portable. These requisites, which we refer to as the Agent Communication Trilemma, are hard to achieve in large networks of agents. We introduce Agora, a meta protocol that leverages existing communication standards to make LLM-powered agents solve complex problems efficiently. In Agora, agents typically use standardised routines for frequent communications, natural language for rare communications, and LLM-written routines for everything in between. Agora sidesteps the Agent Communication Trilemma and robustly handles changes in interfaces and members, allowing unprecedented scalability with full decentralisation and minimal involvement of human beings. On large Agora networks, we observe the emergence of self-organising, fully automated protocols that achieve complex goals without human intervention.
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