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Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence

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arxiv 2407.07061 v2 pith:HXOXUEQE submitted 2024-07-09 cs.CL

Weize Chen , Ziming You , Ran Li , Yitong Guan , Chen Qian , Chenyang Zhao , Cheng Yang , Ruobing Xie
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Zhiyuan Liu Maosong Sun
This is my paper · ORCID
classification cs.CL
keywords agentsframeworksinternetagentcapablecollaborationdiversedynamic
verification ladder T0 review T1 audit T2 compute T3 formal
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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}.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    CPMobius uses iterative coach-player reinforcement learning to improve mathematical reasoning in LLMs without external training data, yielding +4.9 average accuracy gains on Qwen2.5-Math-7B-Instruct.

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  9. Agentic Web: Weaving the Next Web with AI Agents

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