Beets is a music library manager and metadata tagger that catalogs music collections and automatically improves their metadata using MusicBrainz and other sources.
The tool solves the problem of maintaining accurate, consistent metadata across a music collection. It works by importing music files, matching them against MusicBrainz to identify correct album and track information, and automatically correcting tags where confidence is high. When matches are ambiguous, it presents options for manual correction. The system learns from corrections and improves its matching over time, handling the tedious work of tag cleanup that other music players leave behind.
Beets suits music enthusiasts with large collections who want authoritative metadata without manual tagging. It works well for people who want to organize music once and have it stay organized, and for those comfortable with command-line tools. The tool's plugin architecture means it can fetch album art, lyrics, genres, acoustic fingerprints, and ReplayGain levels; transcode audio; detect duplicates; and browse collections through a web interface. If you need functionality beyond what exists, writing a plugin in Python is straightforward. The project is designed as a library first, making it extensible for custom workflows.
Development is steady and methodical, with automated testing covering the codebase. The project maintains active CI pipelines and tracks code coverage. Releases are published to standard package repositories, making installation straightforward for end users. The maintainers respond to community contributions through the standard pull request workflow.
