VectLiteOpen source
An embedded vector database, in a single file.
A single-file vector database written in Rust. Dense and sparse hybrid search, HNSW indexing, MongoDB-style filters, transactions and crash-safe persistence. All in a portable .vdb file: no server, no Docker, no network calls.
Inside this edition
Vector search, without a server.
Hybrid search
Dense vectors with HNSW and a BM25 keyword index, fused with linear combination or RRF.
Single-file storage
Everything in one portable .vdb file. Crash-safe WAL, file locking, snapshots and backup.
Rich metadata filters
MongoDB-style operators ($eq, $gt, $in, $contains, $exists) with nested field access.
Multi-language
Native Rust core with Python (PyO3) and Node.js (napi-rs) bindings. One install.
Why VectLite?
Modern vector databases are built as distributed servers: Pinecone, Weaviate, Qdrant. That is the right answer at scale, but unnecessary overhead for a local app, a demo, an edge tool or an agent running on the user's machine.
VectLite borrows the SQLite model and applies it to vectors: one library, one file, zero servers. Written in Rust for performance and safety, with hybrid search, ACID transactions, crash-safe persistence and Python and Node.js bindings. The vector database that fits inside your app.
In brief
What is VectLite?
VectLite is an embedded, single-file vector database written in Rust. It runs in-process inside a Python or Node.js application, with no server and no Docker. It combines dense HNSW search and sparse BM25 search, MongoDB-style filters, ACID transactions and crash-safe persistence, all in a portable .vdb file. MIT licensed.
Read the guide: Vector database comparison for RAG (2026)Frequently asked
Everything people ask us.
Is VectLite an alternative to Pinecone, Weaviate or Chroma?
Yes. VectLite is a single-file embedded vector database on the SQLite model, written in Rust. Unlike Pinecone, Weaviate or Qdrant, which are distributed servers, VectLite runs in-process inside your Python or Node.js app, perfect for local-first and edge use cases.
Does VectLite support hybrid search?
Yes. VectLite combines a dense HNSW index and a BM25 sparse index for hybrid search, with MongoDB-style metadata filters.
What is the difference between VectLite and FAISS?
FAISS is a pure indexing library, with no persistence or transactions. VectLite adds crash-safe persistence, ACID transactions, MongoDB filters and ready-to-use Python and Node.js bindings, in a single install.
Discover next
Other editions from the catalogue.
Embed vector search.
Open source, MIT licensed. Available on PyPI and npm, a single install.