LLM Agent Operating System
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). 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.
660245ac727c75cf509100bf
Kai Mei
660245ac727c75cf509100c0
Zelong Li
660245ac727c75cf509100c1
Shuyuan Xu
admin_assigned
2024-03-26T08:37:15.496Z
64f3aee61b9aa0ded19651dd
/avatars/f60d9ead11e3a30e0a328dd04f5637b3.svg
Shuyuan Xu
user
SeanSX
660245ac727c75cf509100c2
Ruosong Ye
660245ac727c75cf509100c3
Yingqiang Ge
admin_assigned
2024-03-26T08:37:45.032Z
631ce034a31a0a462ca26e3d
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/oTS8Md-DzMuExVnnZQmgg.png
Yingqiang Ge
user
gyq1101
660245ac727c75cf509100c4
Yongfeng Zhang
admin_assigned
2024-03-26T08:37:51.545Z
63b0deb7f2eb87a4d695055c
/avatars/85a87fa82f7579019a0c6212f03b6480.svg
Yongfeng Zhang
user
evison
LLM Agent Operating System
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). 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.
AIOS, an operating system embedding large language models, addresses resource allocation, context switching, and concurrency challenges for intelligent agents, demonstrating reliability and efficiency.
large language model intelligent agents AIOS resource allocation context switch concurrent execution access control