Matterflow is a visual tool for working with data from Matter-enabled IoT devices, simplifying the process of collecting, transforming, and integrating data from smart home systems. With Matterflow, you can Transform and Stream IoT Data to Cloud and AI Applications. Matterflow can be deployed as a Home Assistant addon or a standalone Docker container. It provides functionality to:
- Automatically receive and process IoT data.
- Transform and model data for use in various formats like CSV or JSON.
- Send processed data to the Cloud and AI applications or store it locally for further analysis.
Matterflow is ideal for developers, data scientists, and IoT enthusiasts looking to unlock the full potential of their smart devices.
A great place to start is to follow the "Getting Started Tutorial" on our documentation website: https://matterflow.cloud/docs/intro
cd api
- Install and activate python3.12
virtual environment
/usr/bin/python3.12 -m venv ./venv
source venv/bin/activate
On Mac OS Installing and activating python3.12 on Mac OS
Ensure you install python3.12
brew install [email protected]
Ensure you use the correct python binary path when activating the environment
/usr/local/bin/python3 -m venv ./venv
source venv/bin/activate
- Install dependencies
In the
apidirectory withrequirements.txt.
pip install -r requirements.txt
- Setup your local environment
- Create environment file with app secret
echo "SECRET_KEY='TEMPORARY SECRET KEY'" > mf/.environment
3.1. (Optionally) set location of db-sqlite3 file and tmp config files. Otherwise defaults to /tmp
- Create environment variable your specific db path
echo "DIR_PATH='/data'" >> mf/.environment
- Supervisor - This project requires supervisord to control unix based processes which run the flows in the background.
4.1. Start/Restart the supervisor
Cd to the api directory with supervisord.conf file
supervisord -c ./supervisord.conf
4.2. Check the status of the supervisor
supervisorctl status
Note: there should be one process running that is specified in the supervisor_confs folder and defined in the foo.conf file
- Start dev server from app root
cd mf
python manage.py migrate
python manage.py runserver
In a separate terminal window, perform the following steps to start the front-end.
- Install Prerequisites
cd web
npm install
- Start dev server
npm run dev
By default, your default browser should open on the main application page. If not, you can go to http://localhost:5173/ in your browser.
It is possible to locally test the react components in Storybook. The backend is mocked in that case. If running as Storybook, then the backend API will only provide static information and the functionality of adding, deleting, modifying, saving etc will not be saved to the backend
- Run as Storybook
cd web
npm run storybook
Note: it is not recommended to run as storybook as the lack of backend functionality will impact the functionality of the front end.
It is possible to run this application as a docker container on amd64 using but you will need to know the IP address of your main docker interface Find the docker0 ip address using ip a | grep docker0
docker run --rm --add-host="localhost:172.17.0.1" -p 4173:4173 -p 9001:9001 -v /data:/data:rw -v /config:/config oideibrett/image-amd64-matterflow:latest
Its essential that you have a Matter Server running and this can be run in Docker using
docker run -d \
--name matter-server \
--restart=unless-stopped \
--security-opt apparmor=unconfined \
-v /data:/data \
-v /run/dbus:/run/dbus:ro \
--network=host \
ghcr.io/home-assistant-libs/python-matter-server:stable --storage-path /data --paa-root-cert-dir /data/credentials --bluetooth-adapter 0
It is also possible to run this as a Home Assistant Addon
- Go to the Add-on store in Home Assistant.
- Click ⋮ → Repositories, and paste the following URL:
https://github.com/MatterCoder/addon-matterflow
Click Add → Close, or click the button below to add the repository directly:
The project builds on the work from the Visual Programming project and is based on React, React Bootstrap and react-diagrams