HelixDB – A Graph Database built on Object-storage
Graph-vector-FTS in one database, but Weaviate and Neo4j already offer hybrid search.
SQLite-backed embedded database for records, objects, and vectors in one file.
SQLite plus content-addressed objects and exact vector search in one file.
Developers building local-first applications needing unified storage
sqlite-vec · LiteFS · RxDB
Graph-vector-FTS in one database, but Weaviate and Neo4j already offer hybrid search.
Turbopuffer-inspired vector DB with RaBitQ quantization running on plain S3.
Using a single-file .pardus format with CREATE/INSERT/SELECT + SIMILARITY queries gives a very familiar developer UX for embedding storage. The combination of graph-based ANN, full transactions, thread-safety, and zero external dependencies is an uncommon and useful engineering combo for local-first AI work; it would win more attention with benchmark comparisons and richer ecosystem integrations (connectors/clients).
Sparse matrix graph operations with MCP server integration for AI agents.
Zero-config single-file persistence plus two query styles (Mongo-like dicts or a Pythonic Q builder) makes everyday prototyping painless. The built-in Studio UI and auto-index hints are thoughtful extras for inspecting data and nudging performance, though this competes with established tiny DBs and SQLite/JSON patterns — great for side projects, but check concurrency and durability needs first.
Single-file mmap storage plus an HNSW vector index and explicit graph edges is an elegant, practical combo — think "SQLite for agent memory" with CRC-32 crash recovery and zero-server convenience. The C++20 core + nanobind gives zero-copy NumPy views and GIL-free searches, and the claimed FAISS-like throughput makes this genuinely interesting for local setups; main caveat is build/toolchain friction and how rich the surrounding ecosystem becomes.