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An open-source music encyclopaedia API providing structured data on artists, albums, recordings, labels, and relationships. Used in data engineering for building music catalogues, constructing artist graph datasets, enriching streaming data with metadata, and practising complex JSON ingestion in Python.
The `musicbrainzngs` Python library implements the MusicBrainz XML API with proper rate limiting (1 req/sec per their policy). Engineers look up recordings by ISRC, artists by MBID, and releases by catalog number to build master reference datasets for music applications.
MusicBrainz provides the authoritative knowledge graph for music AI systems. Use it to build a RAG pipeline that answers 'Who produced Daft Punk's Random Access Memories?' with verified metadata, or train entity-linking models that map informal artist mentions to canonical MusicBrainz IDs.
# pip install musicbrainzngs
import musicbrainzngs
musicbrainzngs.set_useragent("my-app", "1.0", "my@email.com")
result = musicbrainzngs.search_artists(artist="Daft Punk", limit=3)
for artist in result["artist-list"]:
print(artist["name"], "-", artist.get("country", "unknown"))Official dataset source
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