NodeBox is a visual automation platform inspired by [n8n], built for people who want to automate anything with the power of Python.
At its core, NodeBox lets you design workflows on a canvas-like interface, where you can:
- Drag, drop, and connect nodes
- Each node is just Python code: it can receive inputs, process them, and return outputs
- Combine nodes to build simple scripts → advanced multi-step automations
Unlike traditional automation tools that lock you into pre-defined actions, NodeBox gives you the full flexibility of Python — limited only by your creativity.
If you can write Python, you can automate it with NodeBox.
One of the standout features of NodeBox is its deep integration with Ollama. Since much of modern automation relies on AI, NodeBox allows you to:
- Browse Ollama models
- Download & manage models locally
- Integrate LLMs directly into your workflows
This means you can build AI-powered automations without pricey API keys. Everything runs locally, offline, and under your control.
- Automate repetitive tasks
- Build custom AI-driven workflows
- Connect & process data from multiple sources
- Stay private with local execution
Follow these steps to set up the application from source:
Before installing, make sure you have the following installed on your system:
- Python 3.10+ click here
- Ollama click here
- PyQt6 (
pip install PyQt6)
git clone https://github.com/shreyansh-shankar/NodeBox.git
cd NodeBoxpython -m venv venvActivate it:
- Linux/Mac:
source venv/bin/activate- Windows:
venv\Scripts\activate- Install Ollama following the official guide
- Run the command in terminal to check installation:
ollama --version. - Run the command:
ollama serve. If it starts a server or returns something likeollama is already runniing, you are good to go.
python main.pyFollow these steps to build your very first automation inside the app:
- Open the Application – Start the app from your system.
- Browse Models – Head over to the Browse Models section.
- Pick a small model (recommended for first-time setup).
- Click Download.
- Verify Download – Go to View Local Models to ensure the model is installed.
- Create a New Automation – Click New Automation and give it a name.
- Open Automation Editor – Select your automation and click Edit.
- Add a Node – Right-click on the canvas and choose Add Node.
- Edit the Node – Click on the node and click on the open button.
- This will open the Node Editor, where you can write custom Python code.
- Run and test the node to ensure it works as expected.
- Build More Nodes – Add additional nodes (e.g., input, processing, output).
- Connect Nodes – Drag from one node's output port to another's input port to link them.
- Run the Automation – Once connected, click Run to test the complete workflow.
- Debug & Iterate – If something breaks, check node logs and update code accordingly.
- Save Your Work – Don't forget to save your automation for later use.
Tip: Start small (like a text-to-text pipeline) before experimenting with complex multi-node automations.
- Run a local LLM to summarize documents
- Watch a folder and auto-organize files
- Scrape data from websites and process it
- Send notifications when system events occur
- Chain together AI models + traditional scripts
We welcome contributions from the community! Whether it's bug fixes, new features, documentation improvements, or testing, your help is appreciated.
- Fork the repository on GitHub.
- Clone your fork to your local system.
- Create a new branch for your feature or fix.
- Make your changes with proper commits.
- Push your branch to your fork on GitHub.
- Open a Pull Request to the main repository.
- Keep your code clean and well-documented.
- Follow the existing coding style.
- Write meaningful commit messages.
- Test your changes before submitting.
For more details, visit our website: nodeboxlab.web.app
MIT License – free to use, modify, and distribute.