iuliandita

iuliandita / digarr

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Music discovery for Lidarr powered by AI. Analyzes your ListenBrainz/Last.fm history, finds similar artists via MusicBrainz and AI, and adds approved recommendations straight to Lidarr.

github.comiulianditadigarr#readme ai docker lastfm lidarr listenbrainz
11
0
100% credibility
Found Mar 19, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

Digarr helps music enthusiasts discover and add new artists to their Lidarr library by analyzing listening history from services like ListenBrainz or Last.fm, using AI for personalized recommendations.

How It Works

1
🔍 Discover Digarr

You find Digarr, a helpful tool that suggests new music artists based on what you love listening to, perfect for growing your music collection.

2
🚀 Set it up easily

With a simple copy-paste command, you get it running alongside your music setup in minutes, no hassle.

3
🔗 Connect your favorites

Follow the friendly guide to link your music manager, listening tracker, and a smart helper for personalized ideas.

4
✨ Run your first scan

Hit the button to analyze your tastes and uncover exciting new artists waiting just for you.

5
❤️ Browse recommendations

Swipe through beautiful cards of similar artists with previews, scores, and why-they-fit explanations.

🎉 Grow your collection

Approve your favorites and watch them automatically join your library, ready to enjoy fresh tunes anytime.

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AI-Generated Review

What is digarr?

Digarr automates music discovery for Lidarr users by pulling your ListenBrainz or Last.fm scrobble history, matching similar artists via MusicBrainz and AI providers like Anthropic or Ollama, then queuing approvals for one-click addition to your library. Built in TypeScript with Bun runtime, React frontend, and PostgreSQL backend, it runs as a self-hosted Docker container alongside your *arr stack—think Jellyseerr but for expanding music collections. Searchers for github music ai, music discovery project 2024, or variants like digar, digarra fdup hit gold here.

Why is it gaining traction?

Unlike basic scrapers, digarr scores recommendations with genre overlap, AI confidence, and feedback learning, plus features like Tinder-style swipes, genre subscriptions, library health checks, and SkyHook pre-caching to dodge Lidarr 503s. Pluggable sources and webhook alerts (Discord/Slack) make it extensible without code changes. Early adopters praise the polished UI, 590 passing tests, and easy Docker Compose/Helm deploys.

Who should use this?

Lidarr power users tired of manual artist hunts based on scrobbles. Self-hosters with ListenBrainz/Last.fm accounts wanting AI-boosted discovery without Spotify playlists. Genre explorers or multi-user households needing scheduled scans and bulk actions.

Verdict

Grab it if you're deep in the *arr ecosystem—solid Docker setup and UI make it production-ready despite 11 stars and 1.0% credibility score. Low maturity means watch for edge cases, but MIT license and thorough tests lower risks for music discovery project 2025 experiments.

(187 words)

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