Kuzu V0 136 ⚡

Kùzu v0.13.6 Released: Pushing the Boundaries of In-Process Graph Databases

Represent entities (e.g., User , Product ) where rows are unique instances.

As an embedded graph database, Kuzu is renowned for its ability to run directly within application processes, providing fast, localized access to large datasets without the overhead of client-server communication. Key Updates in Kuzu v0.136 kuzu v0 136

import kuzu # 1. Initialize the database and connect to it # Provide a directory path for persistent storage, or leave empty for an in-memory DB db = kuzu.Database("my_graph_db") conn = kuzu.Connection(db) # 2. Create Node Schemas conn.execute("CREATE NODE TABLE User(name STRING, age INT64, PRIMARY KEY (name))") conn.execute("CREATE NODE TABLE Software(name STRING, language STRING, PRIMARY KEY (name))") # 3. Create Relationship Schema conn.execute("CREATE REL TABLE WROTE(FROM User TO Software, year INT64)") # 4. Insert Data conn.execute("CREATE (:User name: 'Alice', age: 30)") conn.execute("CREATE (:User name: 'Bob', age: 25)") conn.execute("CREATE (:Software name: 'Kuzu', language: 'C++')") # Connect nodes with relationships conn.execute(""" MATCH (u:User name: 'Alice'), (s:Software name: 'Kuzu') CREATE (u)-[:WROTE year: 2026]->(s) """) # 5. Query the Graph using Cypher print("Querying graph relationships:") response = conn.execute(""" MATCH (u:User)-[w:WROTE]->(s:Software) RETURN u.name, w.year, s.name, s.language """) while response.has_next(): row = response.get_next() print(f"Developer: row[0] | Year: row[1] | Software: row[2] (row[3])") Use code with caution. Comparing Kùzu with Other Database Paradigms Kùzu v0.13.6 DuckDB / SQLite In-Process (Embedded) Client-Server In-Process (Embedded) Data Model Property Graph Property Graph Relational (Tables) Query Language Join Efficiency Extremely High (Factorized) High (Index-free adjacency) Low-Medium (Expensive Joins) Primary Focus Embedded Graph Analytics Enterprise Graph Platform Embedded Tabular Analytics When to Choose Kùzu over Neo4j

Because Kùzu is embedded and highly performant, it excels in domains where deploying a massive server-based graph database like Neo4j is overkill or structurally impossible. 1. Graph Retrieval-Augmented Generation (GraphRAG) Kùzu v0

Kùzu v0.13.6 reinforces the database's position as a premier choice for local, high-performance graph analytics. By doubling down on memory efficiency, ingestion speed, and Cypher fidelity, the development team continues to bridge the gap between heavy-duty graph analysis and dead-simple developer ergonomics.

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Data is stored column by column, allowing Kùzu to scan massive datasets rapidly and execute analytical queries efficiently.

Data is structured inside disk-backed columns, maximizing cache locality. When executing queries that look at specific node or relationship properties, the engine scans only the necessary columns rather than pulling whole records into memory. 3. Columnar Sparse Row (CSR) Adjacency Lists