sqliteai/sqlite-vector

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1100 stars · 49 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

SQLite-Vector is a cross-platform SQLite extension written in C that adds vector search capabilities to embedded SQLite databases, with SIMD-accelerated distance functions and TurboQuant 2/3/4-bit quantization. It stores vectors as BLOBs in ordinary tables without virtual tables or preindexing, runs on mobile, browser, edge, and server, and uses only ~30MB of memory by default.

Use cases

• add vector search to my sqlite database
• run semantic search offline on mobile devices
• store embeddings locally for a RAG app
• on-device similarity search with low memory footprint
• quantize vectors to 2/3/4-bit for edge AI
• build a local-first AI agent with embedding memory
• search vectors in the browser with sqlite wasm

When to choose

• you need vector search inside an existing SQLite app without a separate vector database
• you're building offline-first or on-device AI with tight memory constraints
• you want quantized vector scans (TurboQuant) with SIMD acceleration
• you need cross-platform support including mobile and browser (WASM)

When to avoid

• you need distributed, multi-node vector search at massive scale
• you require managed infrastructure with built-in auth and sync (consider SQLite Cloud or a dedicated vector DB)
• your workload needs ANN indexes over billions of vectors rather than exact scans

Facets

library · maturity active

vector-database database search-engine sdk databases machine-learning artificial-intelligence developer-tools embedded-systems windows cross-platform wasm browser sqlite-extension vector-search embeddings turboquant quantization simd edge-ai on-device-ai rag ios android linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="sqliteai/sqlite-vector")