AIOS: LLM Agent Operating System

2024-03-25

arXiv.org

AI Summary

LLM agents deployed concurrently face critical challenges—including resource contention, inadequate scheduling, and insufficient security isolation—leading to inefficient resource utilization and compromised system stability. To address these issues, we propose AIOS, the first operating system specifically designed for LLM agents. AIOS employs a kernelized architecture that decouples agent applications from underlying LLMs and tool resources. Its novel AIOS kernel abstraction layer provides unified, strong isolation across agent contexts, memory, storage, access control, and AI-specific resources. The system integrates a lightweight scheduler, context snapshotting, and fine-grained access control, and delivers an AIOS-Agent SDK (Python). Evaluations on mainstream agent frameworks demonstrate up to a 2.1× improvement in service throughput, alongside significant gains in system stability and security. The implementation is open-sourced and has garnered broad community attention.

Technology Category

• Multiagent Systems: Modeling other Agents
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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. As the diversity and complexity of agents continue to grow, addressing these resource management issues becomes increasingly critical to LLM-based agent systems. 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) and efficient management of resources (e.g., LLM and external tools) for runtime agents. To enhance usability, AIOS also includes an AIOS-Agent 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.

Problem

• Addresses inefficient resource allocation in LLM-based agents
• Solves lack of scheduling mechanisms in agent designs
• Manages concurrent processing for diverse LLM agent systems

Innovation

• AIOS kernel isolates resources and services
• Provides scheduling and resource management services
• Includes AIOS-Agent SDK for enhanced usability