Internet of Agents: Weaving a Web of Heterogeneous Agents for
  Collaborative Intelligence
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 https://github.com/OpenBMB/IoA.
668df9b93b77560291e55800
Weize Chen
admin_assigned
2024-07-10T10:30:45.468Z
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weize
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Ziming You
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2024-07-11T13:45:46.728Z
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Jasper YOU
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JasperYOU
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Ran Li
claimed_verified
2024-07-12T07:41:27.682Z
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Ran Li
user
Ran-Li
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Yitong Guan
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Chen Qian
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Chenyang Zhao
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2024-07-10T10:31:36.327Z
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Chenyang Zhao
user
cyzhao
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Cheng Yang
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Ruobing Xie
admin_assigned
2024-07-10T10:33:05.655Z
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Ruobing Xie
user
Ruobing-Xie
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Zhiyuan Liu
admin_assigned
2024-07-10T10:32:57.927Z
6472f1f7485a7c8e1bcf507b
/avatars/79dca137583875191ea883b193b630b7.svg
Zhiyuan Liu
user
zibuyu9
668df9b93b77560291e55809
Maosong Sun
Internet of Agents: Weaving a Web of Heterogeneous Agents for
  Collaborative Intelligence
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 https://github.com/OpenBMB/IoA.
Internet of Agents (IoA) framework facilitates seamless collaboration among heterogeneous large language models (LLMs) in distributed environments, outperforming existing baselines.
large language models autonomous agents multi-agent frameworks agent integration protocol instant-messaging-like architecture dynamic mechanisms agent teaming conversation flow control embodied AI tasks retrieval-augmented generation