AIOS: LLM Agent Operating System
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Cited by 61 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
LLM-based intelligent agents face significant deployment challenges, particularly related to resource management. Allowing unrestricted access to LLM or tool resources can lead to inefficient or even potentially harmful resource allocation and utilization for agents. Furthermore, the absence of proper scheduling and resource management mechanisms in current agent designs hinders concurrent processing and limits overall system efficiency. To address these challenges, this paper proposes the architecture of AIOS (LLM-based AI Agent Operating System) under the context of managing LLM-based agents. It introduces a novel architecture for serving LLM-based agents by isolating resources and LLM-specific services from agent applications into an AIOS kernel. This AIOS kernel provides fundamental services (e.g., scheduling, context management, memory management, storage management, access control) for runtime agents. To enhance usability, AIOS also includes an AIOS SDK, a comprehensive suite of APIs designed for utilizing functionalities provided by the AIOS kernel. Experimental results demonstrate that using AIOS can achieve up to 2.1x faster execution for serving agents built by various agent frameworks. The source code is available at https://github.com/agiresearch/AIOS.
Key facts. Cited by 94 papers in the map (data through 7 Oct 2026). Builds on 54 of its 94 references that are in the map. First posted on arXiv 25 Mar 2024.
Builds on
54 of 94 references.
References · 54
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50 most cited of 54 All 54 (JSON)
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Citing papers per year: 2024: 8; 2025: 33; 2026: 53.
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