helix-db vs latticedb
Rust OLTP graph database on object storage with native vector and BM25 search: property graph, traversal-prefiltered ANN and full-text in one transactional engine; Rust, TS, Go, Python SDKs. — versus — Embedded single-file graph database in Zig: graph traversal, HNSW vector search and BM25 full-text in one query language, plus a durable event log — built for Graph RAG and local agent memory.
Same bet: traversal, vector ANN and BM25 in one engine for Graph RAG and agent memory. latticedb is an embedded single-file Zig library; HelixDB runs as a server or embedded on object storage with four SDKs.
| helix-db | latticedb | |
|---|---|---|
| Stars | 6.1k | 694 |
| Forks | 371 | 30 |
| Language | Rust | Zig |
| License | Apache-2.0 | MIT |
| Last activity | today | 5 days ago |
| Topics | knowledge-graphs, rag | knowledge-graphs, rag, local |
| Curated connections | 3 | 6 |
helix-db — the curator's take
Pick HelixDB when your agent's retrieval is really a graph question with a similarity step inside it ('passages near this embedding, but only ones linked to this customer') and you would rather not glue Neo4j to a vector store and keep them consistent. Graph, vectors and BM25 live in one transactional engine on object storage, queried through typed SDK builders in four languages, and `helix chef` scaffolds an app through your coding agent. Local dev needs Docker or Podman unless you use embedded mode. Skip it if you need Cypher or Bolt compatibility with an existing Neo4j stack (hydradb speaks both), or if your data is flat documents: a plain vector store is simpler.
latticedb — the curator's take
SQLite's shape applied to the three retrieval modes agents actually need. One file, no server, one query layer where a Cypher-style MATCH can filter on vector distance and a full-text match in the same statement, and one WAL that graph writes and named event streams share — so your changefeed is transactional with the graph. Quoted 0.13µs node lookups and 0.83ms vector search at 1M vectors, with CLI, Python, TypeScript, Java and Go bindings. Reach for it when relationships are the point and you were about to bolt a vector DB onto a graph DB. Do not reach for it for multi-writer or multi-machine work: it is explicitly single-writer, one owning process, one machine. Also young — Zig, 567 stars, bundled native libs per platform, and the built-in `hash_embed` helper is a deterministic placeholder, not an embedding model.