GoKawiilLatticeDB
Embedded property-graph database with native vector and full-text indexing.
LatticeDB is a single-file local database for connected, semantic, and textual data. It lets you traverse relationships, run vector similarity search, and do BM25 full-text search over the same dataset in one engine and one query layer. It is designed for relationship-heavy workloads on a single machine, with zero-config operation and an embedded single-writer model.
LatticeDB is an embedded, single-file graph database that lets local applications query the same data by relationship, semantics, and text, then consume durable graph and application events from the same file. Workloads like Graph RAG, agent memory, and local knowledge tools are examples built on those primitives, not the definition of the engine.
One file. Your entire database is a single portable file. No server, no configuration.
Your entire database is a single portable file. No server, no configuration. One query layer. Graph traversal, HNSW vector similarity, and BM25 full-text — in the same query language.
Graph traversal, HNSW vector similarity, and BM25 full-text — in the same query language. One event log. Durable named streams and a built-in graph changefeed share the same transaction/WAL path as graph writes.
Durable named streams and a built-in graph changefeed share the same transaction/WAL path as graph writes. Local-first. Designed for one owning process on one machine, with WAL-backed durability.
Designed for one owning process on one machine, with WAL-backed durability. Fast. 0.13 μs node lookups. 0.83 ms vector search at 1M vectors with 100% recall.
-- Find chunks similar to a query , traverse to their document , then to the author MATCH ( chunk : Chunk ) - [ : PART_OF ] -> ( doc : Document ) - [ : AUTHORED_BY ] -> ( author : Person ) WHERE chunk . embedding <=> $ query_vector < 0.3 AND doc . content @@ "neural networks" RETURN doc . title , chunk . text , author . name ORDER BY chunk . embedding <=> $ query_vector LIMIT 10
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