The Neo4j Graph Data Science (GDS) Library is a comprehensive framework that includes graph algorithms, the Neo4j Graph Database for persistence, and the Bloom data visualization tool. The library started with 42 graph algorithms in five categories and has since added nearly 60 new algorithms based on community feedback. New features include graph-native machine learning capabilities, such as three graph embedding algorithms and supervised machine learning workflows. The framework also includes transformation features to project, subset, and transform graphs, a new enterprise memory format that reduces the in-memory footprint by 75%, and support for persisting ML models in Neo4j. Additionally, GDS respects role-based access control and can execute graph projections and algorithms on each shard individually. Users can export an in-memory graph to a new database, and Neo4j Bloom has added categorical coloring and hierarchical layouts. The framework is available for download at the Neo4j website or GitHub repo.