This guide explains how to run JARVIS on your local machine.
- Python 3.11+ - Download from python.org
- Ollama - For local LLM inference
- Download from: https://ollama.com
- Must be running on
localhost:11434
- Node.js 18+ (optional) - For CLI npm package
git clone <repository-url>
cd JARVISE# Windows
python -m venv .venv
.venv\Scripts\activate
# Linux/Mac
python3 -m venv .venv
source .venv/bin/activatepip install -r requirements.txtCopy .env.example to .env and configure:
# Required: Ollama Configuration
OLLAMA_HOST=http://localhost:11434
OLLAMA_MODEL=llama3.2:latest
# Optional: UI Configuration
UI_HOST=127.0.0.1
UI_PORT=8000Ensure Ollama is running with your chosen model:
ollama serve
ollama pull llama3.2Starts both backend API and web UI:
python main.pyThen open http://localhost:8000 in your browser.
For API-only access:
python -m backend.mainThe API will be available at http://localhost:8000
Interactive terminal interface:
# Using installed package
jarvis shell
# Or using Python module
python -m cli shellCommands:
- Type normally to chat
:help- Show help:stats- System statistics:memory- View memories:clear- Clear conversation:quit- Exit
Send a single message:
jarvis chat "Hello, how are you?"The frontend is a React application. To run it separately:
# Navigate to UI directory
cd ui
# Install dependencies
npm install
# Build for production
npm run build
# The built files are served by the backend (main.py)# Check if Ollama is running
ollama list
# Start Ollama
ollama serveChange the port in .env:
UI_PORT=8001If you encounter VRAM issues, use a smaller model:
OLLAMA_MODEL=llama3.2:1b- See README.md for full feature documentation
- Check Docs/ folder for advanced topics
- Configure API key for remote access in
.env
For more information, see README.md