四类协议统一LLM智能体通信标准，助力跨系统协作。
A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)
- 提出四种智能体通信协议，解决异构系统间交互难题。
- 支持安全调用工具、多模态消息与去中心化身份认证。
- 适合构建可扩展的智能体生态，开发者与研究者必读。
大语言模型驱动的自主智能体需要强大且标准化的协议，以在异构系统间集成工具、共享上下文数据并协调任务。临时集成难以规模化、保障安全且跨领域泛化。本文综述了四种新兴智能体通信协议：模型上下文协议（MCP）、智能体通信协议（ACP）、智能体对智能体协议（A2A）和智能体网络协议（ANP），分别应对不同部署场景下的互操作性问题。MCP通过JSON-RPC客户端-服务器接口实现安全工具调用与类型化数据交换。ACP基于RESTful HTTP定义通用通信协议，支持带有MIME类型的多部分消息及同步/异步交互；其轻量级、运行时无关的设计支持可扩展的智能体调用，具备会话管理、消息路由功能，并集成基于角色和去中心化标识符（DIDs）的身份体系。A2A利用基于能力的智能体卡片实现点对点任务委托，支持企业级工作流中的安全协同。ANP借助W3C去中心化标识符（DIDs）与JSON-LD图谱，支持开放网络中的智能体发现与安全协作。文中从交互模式、发现机制、通信模式与安全模型等维度对比各协议。基于分析，提出分阶段采纳路线图：先用MCP实现工具访问，再用ACP支持结构化、多模态、会话感知的交互与在线/离线发现，接着采用A2A执行协作任务，最终延伸至ANP构建去中心化智能体市场。本工作为设计安全、互操作且可扩展的大语言模型智能体生态系统提供了全面基础。
原文摘要 · Abstract (English)
Large language model powered autonomous agents demand robust, standardized protocols to integrate tools, share contextual data, and coordinate tasks across heterogeneous systems. Ad-hoc integrations are difficult to scale, secure, and generalize across domains. This survey examines four emerging agent communication protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP), each addressing interoperability in deployment contexts. MCP provides a JSON-RPC client-server interface for secure tool invocation and typed data exchange. ACP defines a general-purpose communication protocol over RESTful HTTP, supporting MIME-typed multipart messages and synchronous and asynchronous interactions. Its lightweight and runtime-independent design enables scalable agent invocation, while features like session management, message routing, and integration with role-based and decentralized identifiers (DIDs). A2A enables peer-to-peer task delegation using capability-based Agent Cards, supporting secure and scalable collaboration across enterprise agent workflows. ANP supports open network agent discovery and secure collaboration using W3C decentralized identifiers DIDs and JSON-LD graphs. The protocols are compared across multiple dimensions, including interaction modes, discovery mechanisms, communication patterns, and security models. Based on the comparative analysis, a phased adoption roadmap is proposed: beginning with MCP for tool access, followed by ACP for structured, multimodal messaging session-aware interaction and both online and offline agent discovery across scalable, HTTP-based deployments A2A for collaborative task execution, and extending to ANP for decentralized agent marketplaces. This work provides a comprehensive foundation for designing secure, interoperable, and scalable ecosystems of LLM-powered agents.
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