Chroma vs latticedb
Open-source embedding database for building AI apps with retrieval. — 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.
Chroma is an embedding database: point RAG at it and get vector search. LatticeDB is one embedded file where vector search, BM25 and graph traversal compose in a single query. Pick Chroma for pure similarity at scale with an ecosystem behind it; pick LatticeDB when the edges between chunks are load-bearing.
| Chroma | latticedb | |
|---|---|---|
| Stars | 29k | 568 |
| Forks | 2.5k | 25 |
| Language | Python | Zig |
| License | Apache-2.0 | MIT |
| Last activity | yesterday | today |
| Topics | rag, memory | knowledge-graphs, rag, local |
| Curated connections | 14 | 3 |
Chroma — the curator's take
Open-source embedding database for building AI apps with retrieval.
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.