AIOS Monorepo

This repository contains the AIOS (Artificial Intelligence Operating System) project, split into a frontend desktop application and a backend LangGraph server.

Project Structure

- /aios-desktop-app: Contains the Electron-based frontend application.
- /aios-langgraph-server: Contains the Python-based LangGraph backend server.

Prerequisites

Before you begin, ensure you have the following installed:

- Node.js (LTS version recommended) and npm: For the desktop application.
- Python (version 3.12.4 recommended) and pip: For the backend server.
- uv: A Python package manager.

Setup and Running

You'll need to set up and run each project in separate terminal sessions.

1. AIOS LangGraph Server (Backend)

The backend server provides the core AI functionalities and exposes them via an API, which the desktop application consumes.

Setup:

1. Navigate to the backend directory:

    cd aios-langgraph-server

    

2. Create your environment configuration file:
Copy .env.example to a new file named .env.

    cp .env.example .env

    

Then, open .env and fill in the necessary environment-specific variables (e.g., API keys, database credentials).
3. Create and activate a Python virtual environment using uv:

    uv venv

    # On macOS/Linux:

    source .venv/bin/activate

    # On Windows:

    .venv\Scripts\activate

    

4. Install the project dependencies using uv (this will use pyproject.toml):

    uv sync

    

Running the Server:

1. Ensure your uv virtual environment is activated.
2. Start the LangGraph server in development mode:

    langgraph dev --no-browser

    

This command starts the server, typically without opening a browser window. Check the terminal output for the server address (usually http://localhost:2024).

2. AIOS Desktop App (Frontend)

The frontend is an Electron application that provides the user interface for interacting with AIOS.

Setup:

1. Navigate to the frontend directory:

    cd aios-desktop-app

    

2. Create your environment configuration file:
Copy .env.example to a new file named .env.

    cp .env.example .env

    

Then, open .env and fill in any necessary environment-specific variables for the desktop application.
3. Install Node.js dependencies:

    npm install

    

The postinstall script will automatically run electron-builder install-app-deps to ensure all Electron-specific native dependencies are correctly set up.

Running the App (Development Mode):

1. Start the development server:

    npm run dev

    

This will launch the Electron application with hot-reloading enabled, allowing you to see changes live as you develop.

Development Workflow

1. Start the aios-langgraph-server first.
2. Then, start the aios-desktop-app.
3. The desktop app should connect to the locally running backend server.

Building for Production

aios-desktop-app:

Refer to the scripts in aios-desktop-app/package.json for building the Electron app for different platforms (Windows, macOS, Linux):

cd aios-desktop-app

npm run build:win

npm run build:mac

npm run build:linux

aios-langgraph-server:

The LangGraph server is typically deployed as a Python web application. Deployment strategies can vary (e.g., Docker, serverless functions, traditional ASGI server hosting). The langgraph export command can be used to package the LangGraph application. Further details on deployment would depend on the chosen hosting environment.

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This README provides the basic steps to get started. For more detailed information on specific components, refer to the documentation within each project's directory (if available) or consult their respective package.json and pyproject.toml files.