import kuzu # 1. Initialize an on-disk database deployment db = kuzu.Database("./my_graph_db") connection = kuzu.Connection(db) # 2. Define the schema for nodes and relationships connection.execute("CREATE NODE TABLE User(id INT64, name STRING, PRIMARY KEY (id))") connection.execute("CREATE REL TABLE Follows(FROM User TO User)") # 3. Insert data using Cypher connection.execute("CREATE (:User id: 1, name: 'Alice')") connection.execute("CREATE (:User id: 2, name: 'Bob')") connection.execute("CREATE (:User id: 3, name: 'Charlie')") # Create relationships connection.execute("MATCH (a:User id: 1), (b:User id: 2) CREATE (a)-[:Follows]->(b)") connection.execute("MATCH (b:User id: 2), (c:User id: 3) CREATE (b)-[:Follows]->(c)") # 4. Perform a multi-hop graph traversal query result = connection.execute( "MATCH (a:User)-[:Follows]->(b:User)-[:Follows]->(c:User) " "RETURN a.name AS UserA, c.name AS FriendOfFriend" ) while result.has_next(): print(result.get_next()) Use code with caution. 4. Kùzu v0.12.0 Performance Benchmarks
: The Future of Embedded Analytical Graph Databases
The abandonment of Kùzu was a blow to its community, but the quick emergence of the "kuzu v0 120" moniker for the LadybugDB 0.12.0 release shows that the technology has a future beyond its original creators. Projects like hakuzu are already appearing, aiming to add high-availability clustering features to LadybugDB. kuzu v0 120
As graph data continues to grow in importance—especially in the era of AI—tools like Kùzu v0.1.2.0 are no longer just "nice to have"; they are essential infrastructure.
conn.execute("CREATE NODE TABLE Product(id STRING, name STRING, price FLOAT, PRIMARY KEY (id))") conn.execute("CREATE NODE TABLE Category(name STRING, PRIMARY KEY (name))") conn.execute("CREATE REL TABLE BELONGS_TO(FROM Product TO Category)") import kuzu # 1
“For Kuzu V0.120 with 120V input, recommended load is 10–80W. Using 95W may trigger thermal throttling.”
As of April 2026, (often stylized as Kuzu) is a high-performance, embedded graph database designed for complex analytical workloads on very large datasets. The project has recently transitioned toward its Insert data using Cypher connection
CREATE NODE TABLE Document ( id STRING, content STRING, embedding FLOAT[1536], PRIMARY KEY (id) );
Kùzu is an embeddable graph database management system (GDBMS) designed for graph data science, analytical workloads, and Retrieval-Augmented Generation (RAG) pipelines. Unlike server-based graph databases like Neo4j, Kùzu runs directly inside your application process, similar to how SQLite operates for relational data or DuckDB operates for analytical tabular data. Key Architectural Pillars
Unlocking High-Performance Graph Analytics: A Deep Dive into Kuzu v0.120
: Optimized for fast analytical queries (OLAP) over large-scale graph structures.