Kuzu V0 120 Best ((new)) ✓

: Take advantage of the performance improvements for recursive relationship records to handle deep traversals and path-finding more efficiently.

Backing up or version-controlling your graph database is now as simple as copying a single file. 2. Native Filtered Vector Search for AI Agents

The rise of large language models (LLMs) requires databases that understand both structured relationships and high-dimensional vector embeddings. Kuzu v0.12.0 is highly optimized for GraphRAG (Graph-based Retrieval-Augmented Generation).

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If you are looking for the original research behind the system, it was formally introduced in the paper at the CIDR 2023 conference. kuzudb/kuzu: Embedded property graph database ... - GitHub

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Modeled on the architectural philosophy that made DuckDB the "SQLite for analytics," Kùzu is an built from the ground up for speed, scalability, and seamless integration into modern AI pipelines.

: Data is processed in tightly packed, fixed-size vectors rather than tuple-by-tuple, keeping the CPU cache fully saturated and slashing processing overhead.

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The v0.1.20 release focuses on three core pillars: query optimization, expanded Cypher support, and memory management.

import kuzu db = kuzu.Database("my_graph") conn = kuzu.Connection(db) conn.execute("CREATE NODE TABLE City(name STRING, PRIMARY KEY(name))") conn.execute("CREATE (:City name: 'Singapore')") result = conn.execute("MATCH (c:City) RETURN c.name") print(result.get_as_df())