ListenBrainz

Best Self-hosted Alternatives to ListenBrainz

A curated collection of the 10 best self hosted alternatives to ListenBrainz.

ListenBrainz is an open music listening-tracking service that collects users' plays (scrobbles), provides listening statistics and charts, exposes scrobble and recommendation data via a public API, and supports data sharing and integration.

Alternatives List

#1
YourSpotify

YourSpotify

YourSpotify is a self-hosted app that tracks Spotify listening history and provides a web dashboard to explore your personal streaming statistics over time.

YourSpotify screenshot

YourSpotify is a self-hosted application that tracks your Spotify listening activity and presents it in a web dashboard. It includes a backend that periodically polls the Spotify Web API and a frontend for exploring your personal listening statistics.

Key Features

  • OAuth-based Spotify account connection using your own Spotify developer app keys
  • Periodic synchronization of recently played tracks via the Spotify API
  • Interactive dashboard to explore listening statistics and trends
  • Import of historical listening data from Spotify privacy exports (StreamingHistory and extended endsong files)
  • Multi-user support with optional control to disable new registrations
  • Configurable timezone handling for accurate, user-specific statistics

Use Cases

  • Personal analytics dashboard for Spotify listening habits (top tracks, artists, trends)
  • Self-hosted alternative to third-party music tracking services for improved data control
  • Import and visualize long-term listening history from Spotify GDPR/privacy exports

Limitations and Considerations

  • Without imports, initial synchronization is limited to roughly the last 24 hours due to Spotify API constraints
  • Requires a MongoDB instance and a Spotify developer application (client ID/secret and redirect URI)

YourSpotify is well-suited for users who want to self-host a private, dedicated dashboard for Spotify listening stats. With privacy-data imports and a simple Docker-based deployment, it can provide both recent and long-term insights into your streaming history.

4.3kstars
180forks
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#2
gonic

gonic

Gonic is a lightweight, self-hosted Subsonic API server for streaming your music library with transcoding, playlists, podcasts support, and multi-user access.

Gonic is a lightweight music streaming server that implements the Subsonic server API, allowing you to use many existing Subsonic-compatible clients. It scans your local music library, serves streams, and can transcode audio on the fly.

Key Features

  • Subsonic-compatible API for broad client support
  • Library browsing by folder structure and by tags
  • On-the-fly audio transcoding with caching (via FFmpeg)
  • Multi-user support with per-user preferences and playlists
  • Podcast support
  • Jukebox mode for server-side, gapless playback
  • Web UI for configuration, user management, and library scans
  • Scrobbling support (Last.fm and ListenBrainz)

Use Cases

  • Self-hosted personal or family music streaming with existing Subsonic clients
  • Lightweight music server for low-power devices (for example, Raspberry Pi)
  • Centralized library with transcoding for bandwidth- or device-limited playback

Limitations and Considerations

  • Transcoding features require FFmpeg to be available on the host
  • Client experience depends on the capabilities of the chosen Subsonic client

Gonic focuses on being small, fast, and compatible rather than providing an all-in-one media suite. It is a practical choice if you want a simple Subsonic API server with solid scanning, transcoding, and multi-user playback.

2.3kstars
144forks
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#3
Maloja

Maloja

Maloja is a self-hosted music scrobble database that tracks listens and generates personal charts and listening statistics via a web UI and API.

Maloja screenshot

Maloja is a self-hosted music scrobble database that stores your listening history and turns it into personal statistics, charts, and timelines. It focuses on private tracking and flexible tagging rather than social features.

Key Features

  • Web interface with charts for top artists, tracks, and albums across time ranges
  • Scrobble ingestion via a standard-compatible API (works with clients that support custom servers)
  • Manual scrobbling from the web UI for offline listening (vinyl, radio, etc.)
  • Support for associated artists and multi-artist tracks to improve chart accuracy
  • Custom artist/track images upload and local management
  • Proxy scrobbling to forward listens to other scrobble services
  • Import tools for historical data (e.g., Last.fm exports, Spotify exports, ListenBrainz exports, and other Maloja instances)

Use Cases

  • Run a private alternative to hosted scrobble platforms to track listening over time
  • Build a personal “listening dashboard” for home servers and music setups
  • Collect and analyze scrobbles from multiple players and devices via API keys

Limitations and Considerations

  • Artwork enrichment typically requires external API keys (e.g., Last.fm/Spotify)
  • The project recommends containerized deployment to avoid dependency/version mismatches

Maloja is well-suited for users who want ownership of their listening data and simple, accurate statistics without the social-networking features of larger scrobble platforms.

1.6kstars
82forks
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#4
SoulSync

SoulSync

SoulSync automates music discovery, playlist curation, downloads, and library organization, syncing results to media servers like Plex, Jellyfin, and Navidrome.

SoulSync is an automation platform that brings streaming-style music discovery to self-hosted music libraries. It monitors artists, generates curated playlists, downloads missing tracks from multiple sources, and keeps your media server library organized and up to date.

Key Features

  • Artist watchlists with automatic detection of new releases
  • Auto-generated playlists (e.g., Release Radar, Discovery Weekly, seasonal and genre/decade mixes)
  • Multi-source downloads via slskd/Soulseek and other supported sources, with quality profiles and fallback rules
  • Advanced matching and duplicate prevention against your existing library
  • Metadata enrichment including synchronized lyrics (LRC), album art, and improved tags
  • Template-based file organization and library management tools (quality scanning, duplicate cleaning, completion tracking)
  • Media server synchronization for platforms such as Plex, Jellyfin, and Navidrome

Use Cases

  • Replace streaming discovery with automated recommendations for a local music library
  • Maintain an always-updated collection by monitoring favorite artists and grabbing new releases automatically
  • Clean up and standardize large libraries with better metadata, naming, and duplicate control

Limitations and Considerations

  • Requires external services and credentials for some features (for example Spotify API) and a separate slskd setup for Soulseek downloads
  • Download sources and availability can vary, and correct file-sharing configuration is important when using Soulseek

SoulSync is best suited for music enthusiasts who want hands-off discovery, acquisition, and organization of a large local library. It combines playlist curation, automation, and library hygiene tools into a single self-hosted workflow.

1.1kstars
30forks
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#5
Multi-Scrobbler

Multi-Scrobbler

Self-hosted scrobbling hub that collects plays from many music sources and forwards them to services like Maloja, Last.fm, and ListenBrainz.

Multi-Scrobbler screenshot

Multi-Scrobbler is a web-enabled scrobbling hub that monitors what you listen to across many players and services, then forwards those plays to one or more scrobble destinations. It is designed to be platform-independent by integrating directly with source and client APIs, with support for single-user or multi-user setups.

Key Features

  • Scrobble from many sources, including Plex, Jellyfin, Spotify, Kodi, MPD/Mopidy, Subsonic-compatible servers, and more
  • Forward scrobbles to multiple clients such as Maloja, Last.fm, ListenBrainz, Libre.fm, and Koito
  • Optional “Now Playing” updates for supported scrobble clients
  • Web UI for status, basic control, statistics, and detailed logs
  • Data cleanup and enrichment via configurable transforms and MusicBrainz-based matching
  • Resilient delivery with queued scrobbles and automatic retries on network/client failures
  • Monitoring integrations via webhooks and a healthcheck endpoint
  • Configuration via environment variables or JSON, with credential handling and authorization flows

Use Cases

  • Centralize scrobbling for a mixed setup (home server, desktop players, streaming services) without per-device apps
  • Forward scrobbles from one profile/service to another (for example, from Last.fm to a self-hosted store)
  • Run multi-user scrobbling for family/friends while keeping sources and destinations separated

Limitations and Considerations

  • Some sources/clients require API authorization and may be subject to third-party rate limits or API changes
  • Match quality depends on available metadata and may require tuning transforms for edge cases

Multi-Scrobbler is a flexible way to consolidate listening history from many environments into the scrobble services you prefer. It fits well for homelabs and media-server users who want reliable scrobbling, better data hygiene, and optional multi-user support.

939stars
35forks
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#6
Koito

Koito

Self-hosted ListenBrainz-compatible scrobbler and listening-history visualizer with import and relay support for other scrobblers.

Koito screenshot

Koito is a modern, themeable scrobbler that implements the ListenBrainz-compatible scrobbling API and provides a web UI to explore listening history and trends. It is built for self-hosting with a focus on performance, theming, and interoperable relaying of scrobbles.

Key Features

  • ListenBrainz-compatible scrobbling endpoint compatible with clients that support custom ListenBrainz URLs
  • Scrobble relay to forward listens to other ListenBrainz-compatible servers
  • Import support for Maloja, ListenBrainz, LastFM, and Spotify export formats
  • Automatic metadata fetching from MusicBrainz and cover images from Deezer and Cover Art Archive
  • Themeable web UI with multiple built-in themes and support for custom themes
  • Docker-friendly deployment with example docker-compose and a PostgreSQL backend
  • Focus on performance and a sleek TypeScript-based frontend for responsive dashboards

Use Cases

  • Run a personal scrobbling server to store and visualize your listening history locally
  • Migrate or import historical scrobbles from LastFM, Spotify, or other ListenBrainz-compatible sources
  • Relay scrobbles to a public ListenBrainz instance or other compatible collectors while keeping a private copy

Limitations and Considerations

  • The project is under active development and marked as unstable; users may encounter bugs and breaking changes
  • Current deployments use PostgreSQL as the primary supported database, requiring a Postgres instance
  • Feature set and testing coverage are still growing; some advanced analytics or integrations may be missing

Koito is a practical option for users who want a fast, themeable, self-hosted scrobbler with import and relay capabilities. It emphasizes interoperability with ListenBrainz and provides a modern UI for exploring listening data.

634stars
27forks
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#7
Lidify

Lidify

Web app that recommends artists based on a Lidarr library, using Last.fm for discovery and optionally adding recommended artists to Lidarr.

Lidify is a music discovery tool that generates artist recommendations from a Lidarr-managed collection. It queries external music metadata services and can add recommended artists back into Lidarr, running as a self-hosted web application.

Key Features

  • Generates artist recommendations based on selected artists tracked in Lidarr
  • Integrates with Last.fm for discovery (Last.fm is the primary supported data source)
  • Can add recommended artists to Lidarr via the Lidarr API with configurable search and quality settings
  • Distributed as a Docker image and configurable via environment variables
  • Configurable options include API keys, Lidarr address and API key, quality/metadata profiles, auto-start and dry-run modes
  • Simple web UI for selecting artists, reviewing recommendations, and managing integration behavior

Use Cases

  • Expand a Lidarr-managed music library by discovering similar artists to add automatically
  • Seed a new music collection based on existing favorite artists tracked in Lidarr
  • Curate and validate recommended artists before importing into a Lidarr workflow

Limitations and Considerations

  • Spotify integration is no longer supported due to API changes; Last.fm is the supported discovery provider
  • Requires valid Last.fm API credentials and a reachable Lidarr instance with an API key
  • Matching accuracy depends on external metadata quality and may require manual review for ambiguous artist results

Lidify is a lightweight tool for automating music discovery around a Lidarr installation, focusing on Last.fm-based recommendations and easy Docker-based deployment.

520stars
9forks
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#8
Jellyfin Rewind

Jellyfin Rewind

Generate a yearly listening report from your Jellyfin music server with client-side processing, privacy-first data handling, and Docker or static hosting options.

Jellyfin Rewind screenshot

Jellyfin Rewind is a web-based tool that creates a Spotify Wrapped–style report from your Jellyfin music library. It connects to a Jellyfin server, pulls playback and library data, processes it in the browser, and produces aggregated listening statistics and a downloadable report.

Key Features

  • Client-side data processing: most aggregation and analytics run in the user's browser so no server-side data collection occurs
  • Aggregates listening stats such as top artists, tracks, albums and other summary metrics
  • Downloadable Rewind report for offline storage or future use
  • Deployable as a static site or via a provided Docker image for local network hosting
  • Designed to work with standard Jellyfin APIs and typical Jellyfin playback data

Use Cases

  • Personal annual listening recap for users who host music on Jellyfin
  • Self-hosted privacy-focused alternative to commercial "year in review" services
  • Generate shareable or archival reports of listening habits for personal record-keeping

Limitations and Considerations

  • Browser CORS and mixed-content restrictions may require self-hosting if the Jellyfin server is on a local/private address
  • Accuracy depends on the availability and completeness of Jellyfin playback data; data is more comprehensive if server-side playback reporting plugins are enabled
  • Large libraries can require noticeable client-side processing time and memory during report generation

Jellyfin Rewind is a lightweight, privacy-oriented way to visualize and export personal music listening statistics from a Jellyfin server. It is optimized for self-hosting and local network use and is focused on delivering an easy-to-read yearly recap.

348stars
7forks
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#9
Sonobarr

Sonobarr

Web app that uses your Lidarr library and Last.fm to surface similar artists, supports AI-driven prompts, real-time UI, and pushes additions back to Lidarr.

Sonobarr is a web-based music discovery tool that integrates directly with a Lidarr music library and Last.fm similarity data to surface artists a user is likely to enjoy. It provides a modern, real-time UI, administrative controls, and optional AI-assisted seeding for discovery sessions.

Key Features

  • Deep Lidarr integration: sync monitored artists, apply per-source monitor strategies, and add candidates back to Lidarr with chosen quality/metadata settings
  • Last.fm and ListenBrainz-powered discovery: batch similarity lookups streamed to the client as candidate artist cards with stats and genres
  • Real-time UI: Flask backend with Socket.IO keeps discovery progress, notifications, and actions in sync across connected clients
  • AI assistant: optional OpenAI-compatible prompt interface to seed discovery sessions with vibe/genre-based suggestions
  • Preview and enrichment: YouTube/iTunes previews, Last.fm biographies, and MusicBrainz matching for added context
  • Role-based access and admin tooling: user management, request/approval workflow for non-admin requests, and audit trails
  • Docker-first deployment and automatic migrations: official container image, config via .env, and SQLite-based persistent store

Use Cases

  • Allow a home music server to recommend new artists based on an existing Lidarr collection
  • Provide a shared discovery interface for friends or household members with admin-moderated requests
  • Seed curated discovery sessions using natural-language prompts to explore new artists and tastes

Limitations and Considerations

  • Discovery relies on external services (Lidarr, Last.fm, optional YouTube/ListenBrainz); those APIs and credentials are required for full functionality
  • Default persistent store is SQLite, which is suitable for single-instance/home use but may be limiting for large multi-tenant deployments
  • AI features require an OpenAI-compatible API endpoint and valid credentials; the assistant is disabled without them

Sonobarr is focused on bridging a managed Lidarr library with discovery sources to streamline artist discovery and additions. It is optimized for Docker-based home or small-group deployments and emphasizes a fast, real-time user experience.

330stars
5forks
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#10
Trackly

Trackly

Modern web app to browse Jellyfin music libraries, track artist releases via MusicBrainz, and send optional Discord webhook notifications.

Trackly is a web application that enhances a Jellyfin music library with a polished, responsive interface and automated release tracking. It scans artists in a single music library, looks up new releases via the MusicBrainz API, and can post notifications to Discord using webhooks.

Key Features

  • Modern React + Vite frontend with Tailwind CSS for a responsive music-focused UI
  • Integrates with Jellyfin music library folder structure to surface artists, albums, and artwork
  • Periodic automatic scans for new artist releases using the MusicBrainz API with configurable cron schedule
  • Optional Discord webhook notifications for new releases and scan completion, with role mention support
  • Docker and Docker Compose support with multi-architecture images (amd64 and arm64) for easy deployment
  • Simple persistent data directory for application state; one Trackly container is intended per music library

Use Cases

  • Maintain a browsable visual catalog of a Jellyfin music collection with artist pages and album artwork
  • Automatically detect and notify a community (via Discord) when tracked artists release new music
  • Run on small single-board computers (Raspberry Pi) or servers using Docker for lightweight library tracking

Limitations and Considerations

  • Each Trackly container tracks a single music library; multiple libraries require separate containers
  • Release detection depends on the MusicBrainz API and matching accuracy may vary; API rate limits apply
  • Requires music folders to follow the expected Jellyfin-oriented structure and include backdrop/cover images

Trackly is suited for Jellyfin users who want a focused release-tracking UI and Discord notifications. It is lightweight, container-friendly, and optimized for single-library deployments.

61stars
1forks
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Why choose an open source alternative?

  • •Data ownership: Keep your data on your own servers
  • •No vendor lock-in: Freedom to switch or modify at any time
  • •Cost savings: Reduce or eliminate subscription fees
  • •Transparency: Audit the code and know exactly what's running