The rapid advancement of large language models (LLMs) has paved the way for the development of highly capable autonomous agents. However, existing multi-agent frameworks often struggle with integrating diverse capable third-party agents due to reliance on agents defined within their own ecosystems. They also face challenges in simulating distributed environments, as most frameworks are limited to single-device setups. Furthermore, these frameworks often rely on hard-coded communication pipelines, limiting their adaptability to dynamic task requirements. Inspired by the concept of the Internet, we propose the Internet of Agents (IoA), a novel framework that addresses these limitations by providing a flexible and scalable platform for LLM-based multi-agent collaboration. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control. Through extensive experiments on general assistant tasks, embodied AI tasks, and retrieval-augmented generation benchmarks, we demonstrate that IoA consistently outperforms state-of-the-art baselines, showcasing its ability to facilitate effective collaboration among heterogeneous agents. IoA represents a step towards linking diverse agents in an Internet-like environment, where agents can seamlessly collaborate to achieve greater intelligence and capabilities. Our codebase has been released at \url{https://github.com/OpenBMB/IoA}.

翻译：大型语言模型（LLM）的快速发展为构建高性能自主智能体奠定了基础。然而，现有多智能体框架因依赖其自身生态系统内定义的智能体，往往难以整合多样化的第三方高性能智能体。同时，由于多数框架仅限于单设备部署，它们在模拟分布式环境方面面临挑战。此外，这些框架通常依赖硬编码的通信管道，限制了其适应动态任务需求的能力。受互联网概念的启发，我们提出智能体互联网（IoA）这一新型框架，通过提供灵活可扩展的LLM多智能体协作平台来解决上述局限性。IoA引入了智能体集成协议、类即时通讯的架构设计，以及动态的智能体组队与会话流控制机制。通过在通用助手任务、具身人工智能任务和检索增强生成基准测试上的大量实验，我们证明IoA始终优于现有最先进的基线方法，展现了其促进异构智能体间高效协作的能力。IoA标志着向互联网式环境中连接多样化智能体迈出了一步，智能体可在此环境中无缝协作以实现更强大的智能与能力。我们的代码库已发布于 \url{https://github.com/OpenBMB/IoA}。