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Kai Mei, Xi Zhu, Wujiang Xu, Wenyue Hua, Mingyu Jin, Zelong Li, Shuyuan Xu, Ruosong Ye, Yingqiang Ge, Yongfeng Zhang
Outline
This paper proposes the AIOS (AIOS) architecture to address the resource management challenges of intelligent agents based on large-scale language models (LLMs). AIOS introduces a kernel that isolates LLM-specific services and resources from agent applications, addressing the inefficient resource allocation and utilization issues caused by unrestricted access to LLM and tool resources. The kernel provides core services such as scheduling, context management, memory management, storage management, and access control, and the AIOS SDK is provided for ease of use. Experimental results demonstrate that AIOS can improve the execution speed of agents built with various agent frameworks by up to 2.1 times. The source code is available on GitHub.
Takeaways, Limitations
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Takeaways:
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Proposing an efficient resource management and utilization method for LLM-based agents.
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AIOS architecture improves agent execution speed (up to 2.1x)
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Provides compatibility with various agent frameworks (AIOS SDK)
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Activating research and development through open source disclosure
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Limitations:
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Further verification of AIOS's scalability and stability is needed.
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Further research is needed on compatibility with various LLMs and tools.
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Performance evaluation in real complex environments is required.
