AIOS is the AI Agent Operating System, which embeds large language model (LLM) into the operating system and facilitates the development and deployment of LLM-based AI Agents. AIOS is designed to address problems (e.g., scheduling, context switch, memory management, storage management, tool management, Agent SDK management, etc.) during the development and deployment of LLM-based agents, towards a better AIOS-Agent ecosystem for agent developers and agent users. AIOS includes the AIOS Kernel (this AIOS repository) and the AIOS SDK (the Cerebrum repository). AIOS supports both Web UI and Terminal UI.

The AIOS system is comprised of two key components: the AIOS kernel and the AIOS SDK. The AIOS kernel acts as an abstraction layer over the operating system kernel, managing various resources that agents require, such as LLM, memory, storage and tool. The AIOS SDK is designed for agent users and developers, enabling them to build and run agent applications by interacting with the AIOS kernel. AIOS kernel is the current repository and AIOS SDK can be found at here

Below shows how agents utilize AIOS SDK to interact with AIOS kernel and how AIOS kernel receives agent queries and leverage the chain of syscalls that are scheduled and dispatched to run in different modules.

• [2024-11-30] 🔥 AIOS v0.2.0.beta is released! Including the AIOS Kernel (this AIOS repository) and the AIOS SDK (The Cerebrum repository).
• [2024-09-01] 🔥 AIOS supports multiple agent creation frameworks (e.g., ReAct, Reflexion, OpenAGI, AutoGen, Open Interpreter, MetaGPT). Agents created by these frameworks can onboard AIOS. Onboarding guidelines can be found at the Doc.
• [2024-07-10] 📖 AIOS documentation is up, which can be found at Website.
• [2024-06-20] 🔥 Function calling for open-sourced LLMs (native huggingface, vLLM, ollama) is supported.
• [2024-05-20] 🚀 More agents with ChatGPT-based tool calling are added (i.e., MathAgent, RecAgent, TravelAgent, AcademicAgent and CreationAgent), their profiles and workflows can be found in OpenAGI.
• [2024-05-13] 🛠️ Local models (diffusion models) as tools from HuggingFace are integrated.
• [2024-05-01] 🛠️ The agent creation in AIOS is refactored, which can be found in our OpenAGI package.
• [2024-04-05] 🛠️ AIOS currently supports external tool callings (google search, wolframalpha, rapid API, etc).
• [2024-04-02] 🤝 AIOS Discord Community is up. Welcome to join the community for discussions, brainstorming, development, or just random chats! For how to contribute to AIOS, please see CONTRIBUTE.
• [2024-03-25] ✈️ Our paper AIOS: LLM Agent Operating System is released!
• [2023-12-06] 📋 After several months of working, our perspective paper LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem is officially released.

Here are some key notations that are required to know before introducing the different modes of AIOS.

• AHM (Agent Hub Machine): Central server that hosts the agent marketplace/repository where users can publish, download, and share agents. Acts as the distribution center for all agent-related resources.
• AUM (Agent UI Machine): Client machine that provides user interface for interacting with agents. Can be any device from mobile phones to desktops that supports agent visualization and control.
• ADM (Agent Development Machine): Development environment where agent developers write, debug and test their agents. Requires proper development tools and libraries.
• ARM (Agent Running Machine): Execution environment where agents actually run and perform tasks. Needs adequate computational resources for agent operations.

The following parts introduce different modes of deploying AIOS. Currently, AIOS already supports Mode 1 and Mode 2, other modes with new features are still ongoing.

• Features:
  • For agent users: They can download agents from agent hub from Machine B and run agents on Machine A.
  • For agent developers: They can develop and test agents in Machine A and can upload agents to agent hub on Machine B.

• Features:
  • Remote use of agents: Agent users / developers can use agents on Machine B, which is different from the development and running machine (Machine A).
  • Benefit users who would like to use agents on resource-restricted machine (e.g., mobile device or edge device)

• Features:
  • Remote development of agents: Agent developers can develop their agents on Machine B while running and testing their agents in Machine A. Benefit developers who would like to develop agents on resource-restricted machine (e.g., mobile device or edge device)
• Critical technique:
  • Packaging and agent transmission on different machines for distributed agent development and testing

• Ongoing Features:

  • Each user/developer can have their personal AIOS with long-term persistent data as long as they have registered account in the AIOS ecosystem
  • Their personal data can be synced to different machines with the same account

• Critical techniques:

  • User account registration and verification mechanism
  • Persistent personal data storage for each user's AIOS
  • Synchronization for different AIOS instances on different devices within the same account
  • Data privacy mechanism

• Ongoing Features:
  • Different user/developer’s personal AIOS kernels can co-exist in the same physical machine through virtualization
• Critical techniques:
  • Virtualization of different AIOS kernel instances in the same machine
  • Scheduling and resource allocation mechanism for different virtual machines located in the same machine

Please see our ongoing documentation for more information.

• Installation
• Quickstart
• WebUI Quickstart

• Supported versions: Python 3.10 - 3.11

AIOS supports several API integrations that require configuration. You can use the following commands:

• aios env list: Show current environment variables, or show available API keys if no variables are set
• aios env set: Show current environment variables, or show available API keys if no variables are set

When no environment variables are set, the following API keys will be shown:

• OPENAI_API_KEY: OpenAI API key for accessing OpenAI services
• GEMINI_API_KEY: Google Gemini API key for accessing Google's Gemini services
• GROQ_API_KEY: Groq API key for accessing Groq services
• HF_AUTH_TOKEN: HuggingFace authentication token for accessing models
• HF_HOME: Optional path to store HuggingFace models

To obtain these API keys:

1. OpenAI API: Visit https://platform.openai.com/api-keys
2. Google Gemini API: Visit https://makersuite.google.com/app/apikey
3. Groq API: Visit https://console.groq.com/keys
4. HuggingFace Token: Visit https://huggingface.co/settings/tokens

Git clone AIOS kernel

git clone https://github.com/agiresearch/AIOS.git
cd AIOS && git checkout v0.2.0.beta

Create venv environment (recommended)

python3.x -m venv venv # Only support for Python 3.10 and 3.11
source venv/bin/activate

or create conda environment

conda create -n venv python=3.x  # Only support for Python 3.10 and 3.11
conda activate venv

If you have GPU environments, you can install the dependencies using

pip install -r requirements-cuda.txt

or else you can install the dependencies using

pip install -r requirements.txt

Note: The machine where the AIOS kernel (AIOS) is installed must also have the AIOS SDK (Cerebrum) installed. Installing AIOS kernel will install the AIOS SDK automatically by default. If you are using the Local Kernel mode, i.e., you are running AIOS and agents on the same machine, then simply install both AIOS and Cerebrum on that machine. If you are using Remote Kernel mode, i.e., running AIOS on Machine 1 and running agents on Machine 2 and the agents remotely interact with the kernel, then you need to install both AIOS kernel and AIOS SDK on Machine 1, and install the AIOS SDK alone on Machine 2. Please follow the guidelines at Cerebrum regarding how to install the SDK.

For the config of LLM endpoints, multiple API keys may be required to set up. Here we provide the .env.example to for easier configuration of these API keys, you can just copy .env.example as .env and set up the required keys based on your needs.

You need to get your OpenAI API key from https://platform.openai.com/api-keys. Then set up your OpenAI API key as an environment variable

export OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>

You need to get your Anthropic API key from https://console.anthropic.com/settings/keys. Then set up your Anthropic API key as an environment variable

export ANTHROPIC_API_KEY=<YOUR_ANTHROPIC_API_KEY>

You need to get your Gemini API key from https://ai.google.dev/gemini-api

export GEMINI_API_KEY=<YOUR_GEMINI_API_KEY>

If you want to use open-sourced models provided by huggingface, here we provide three options:

• Use with ollama
• Use with native huggingface models
• Use with vLLM

You need to download ollama from from https://ollama.com/.

Then you need to start the ollama server either from ollama app

or using the following command in the terminal

ollama serve

To use models provided by ollama, you need to pull the available models from https://ollama.com/library

ollama pull llama3:8b # use llama3:8b for example

ollama can support both CPU-only and GPU environment, details of how to use ollama can be found at here

Some of the huggingface models require authentification, if you want to use all of the models you need to set up your authentification token in https://huggingface.co/settings/tokens and set up it as an environment variable using the following command

By default, huggingface will download the models in the ~/.cache directory. If you want to designate the download directory, you can set up it using the following command

export HF_HOME=<YOUR_HF_HOME>

If you want to speed up the inference of huggingface models, you can use vLLM as the backend.

Considering that vLLM itself does not support passing designated GPU ids, you need to either setup the environment variable,

export CUDA_VISIBLE_DEVICES="0" # replace with your designated gpu ids

or you can pass the CUDA_VISIBLE_DEVICES as the prefix

After you setup your keys or environment parameters, then you can follow the instructions below to start.

First, you need to start the AIOS kernel by running the following commands

bash runtime/launch_kernel.sh

Then you can start the client provided by the AIOS SDK either in the terminal or in the WebUI. The instructions can be found at here

• OpenAGI
• AutoGen
• Open-Interpreter
• MetaGPT

@article{mei2024aios,
  title={AIOS: LLM Agent Operating System},
  author={Mei, Kai and Li, Zelong and Xu, Shuyuan and Ye, Ruosong and Ge, Yingqiang and Zhang, Yongfeng}
  journal={arXiv:2403.16971},
  year={2024}
}
@article{ge2023llm,
  title={LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem},
  author={Ge, Yingqiang and Ren, Yujie and Hua, Wenyue and Xu, Shuyuan and Tan, Juntao and Zhang, Yongfeng},
  journal={arXiv:2312.03815},
  year={2023}
}

For how to contribute, see CONTRIBUTE. If you would like to contribute to the codebase, issues or pull requests are always welcome!

If you would like to join the community, ask questions, chat with fellows, learn about or propose new features, and participate in future developments, join our Discord Community!