  The integration and deployment of large language model (LLM)-based
intelligent agents have been fraught with challenges that compromise their
efficiency and efficacy. Among these issues are sub-optimal scheduling and
resource allocation of agent requests over the LLM, the difficulties in
maintaining context during interactions between agent and LLM, and the
complexities inherent in integrating heterogeneous agents with different
capabilities and specializations. The rapid increase of agent quantity and
complexity further exacerbates these issues, often leading to bottlenecks and
sub-optimal utilization of resources. Inspired by these challenges, this paper
presents AIOS, an LLM agent operating system, which embeds large language model
into operating systems (OS) as the brain of the OS, enabling an operating
system "with soul" -- an important step towards AGI. Specifically, AIOS is
designed to optimize resource allocation, facilitate context switch across
agents, enable concurrent execution of agents, provide tool service for agents,
and maintain access control for agents. We present the architecture of such an
operating system, outline the core challenges it aims to resolve, and provide
the basic design and implementation of the AIOS. Our experiments on concurrent
execution of multiple agents demonstrate the reliability and efficiency of our
AIOS modules. Through this, we aim to not only improve the performance and
efficiency of LLM agents but also to pioneer for better development and
deployment of the AIOS ecosystem in the future. The project is open-source at
https://github.com/agiresearch/AIOS.
