Music discovery tool that provides recommendations through the ListenBrainz Labs similar-artists API, based on artists in Lidarr and feeding these recommendations back into Lidarr.
Forked from Lidify.
Listenarr can be run natively or through a container.
To run natively, install dependencies and run src/Listenarr.py, or run the app through Gunicorn (see Dockerfile for recommended Gunicorn flags).
To run as a container, use the image ghcr.io/andrewtwelch/listenarr:latest or use the docker-compose.yml file. The main tag is also available, following the main branch.
Once you have started Listenarr, click the settings cog in the top right corner.
Enter your Lidarr address and API key, then click Test to confirm connectivity and load options for Root Folder, Quality Profile and Metadata Profile.
Tick Search for Missing Albums if you want Lidarr to automatically search for releases when an artist is added.
Tick Auto Start and set a delay if you want Listenarr to automatically start a search with all artists when opened.
Light/Dark Mode can be toggled in the bottom right corner.
Click Save to save all settings.
Click the sidebar button in the top left to open the sidebar.
Click the Get Lidarr Artists button to pull artists from your Lidarr instance.
Select any number of artists, then click Start to have Listenarr give you a list of recommended artists to add.
Once recommended artists show up, you can click Add to Lidarr to add an artist, or View on ListenBrainz to see more info about the artist.
Happy to take pull requests to the dev branch, however any pull requests believed to be written using AI must be declared as such and may be declined at maintainer discretion.
Alternate sources for recommendations will only be considered if MusicBrainz IDs for artists can be used and returned (to guarantee correct artist matches).
No AI features will be added to Listenarr.