Kuzu V0 136 Full 'link' ★ Quick & Best

result = conn.execute(query).fetchall() for row in result: print(row)

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If you are looking for a graph database that prioritizes , Kuzu v0.13.6 is the most stable and feature-complete version to date. If you'd like, I can: Write a Python code snippet for a specific graph task. Compare Kuzu's performance to DuckDB or Neo4j . Help you design a schema for your specific data. Let me know how you'd like to implement Kuzu ! Share public link

The technical features of Kuzu translate into powerful real-world applications. Many developers and organizations are exploring Kuzu's potential in areas where understanding relationships is critical. kuzu v0 136 full

CREATE REL TABLE KNOWS ( FROM Person, TO Person, since INT ); """ conn.execute(schema)

Adjacency lists are organized using CSR structures. This permits instantaneous multi-hop traversals across billions of edges without paying the computational cost of lookups.

| Dataset | #Vertices | #Edges | Query | Avg latency (ms) | Speed‑up vs Neo4j | |---------|-----------|--------|-------|------------------|-------------------| | | 1 B | 5 B | 2‑hop friend‑recommendation | 3.2 | 5.8× | | Citation‑Graph‑500M | 500 M | 2 B | PageRank (10 iterations) | 12.5 | 4.1× | | Road‑Network‑200M | 200 M | 300 M | Shortest‑path (Dijkstra) | 1.1 | 3.9× | result = conn

Kùzu distinguishes itself from traditional databases like Neo4j by adopting a highly specialized, read-optimized pipeline. It applies principles from modern analytical databases directly to graph structures.

The query planner now makes better decisions regarding the order of operations, drastically reducing the time required for complex multi-hop queries.

The release marks a significant step forward for the project, delivering better optimization, faster performance, and a more stable environment for developers [1]. Whether you are building a recommendation engine, social network analysis tool, or knowledge graph, Kuzu provides a robust, lightweight, and high-performance solution. Compare Kuzu's performance to DuckDB or Neo4j

# Create an in‑memory database instance db = kuzu.Database() # no path => in‑memory only conn = kuzu.Connection(db)

Use Kuzu as a feature store to feed graph neural networks (GNNs) in PyTorch Geometric or DGL.