对比主流智能体框架，梳理其架构与通信协议设计。
Agentic AI Frameworks: Architectures, Protocols, and Design Challenges
- 系统分析7个智能体框架的架构与协作机制。
- 提出代理通信协议分类，增强系统互操作性。
- 适合研究自主AI系统与多智能体协同的开发者。
大型语言模型（LLMs）的兴起催生了智能体人工智能（Agentic AI）这一范式，使智能体具备目标导向的自主性、上下文推理能力及动态多智能体协作能力。本文对CrewAI、LangGraph、AutoGen、Semantic Kernel、Agno、Google ADK和MetaGPT等主流智能体框架进行系统性综述与对比分析，评估其架构原则、通信机制、记忆管理、安全防护以及与面向服务计算范式的契合度。针对智能体间通信问题，深入研究合同网协议（CNP）、Agent-to-Agent（A2A）、代理网络协议（ANP）和Agora等通信协议。研究结果建立了一个智能体系统的基础分类体系，并提出未来在可扩展性、鲁棒性和互操作性方面的研究方向。本工作为推动下一代自主人工智能系统的研究与实践提供全面参考。
原文摘要 · Abstract (English)
The emergence of Large Language Models (LLMs) has ushered in a transformative paradigm in artificial intelligence, Agentic AI, where intelligent agents exhibit goal-directed autonomy, contextual reasoning, and dynamic multi-agent coordination. This paper provides a systematic review and comparative analysis of leading Agentic AI frameworks, including CrewAI, LangGraph, AutoGen, Semantic Kernel, Agno, Google ADK, and MetaGPT, evaluating their architectural principles, communication mechanisms, memory management, safety guardrails, and alignment with service-oriented computing paradigms. Furthermore, we identify key limitations, emerging trends, and open challenges in the field. To address the issue of agent communication, we conduct an in-depth analysis of protocols such as the Contract Net Protocol (CNP), Agent-to-Agent (A2A), Agent Network Protocol (ANP), and Agora. Our findings not only establish a foundational taxonomy for Agentic AI systems but also propose future research directions to enhance scalability, robustness, and interoperability. This work serves as a comprehensive reference for researchers and practitioners working to advance the next generation of autonomous AI systems.
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