jdagdelen/hyperDB

Health v2 · maintenance only

Adoption not part of the score

1406 stars · 88 forks observed · 2026-08-28

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

HyperDB is a lightweight, local vector database library for Python with a C++/MKL-accelerated backend, designed to store and query document embeddings for LLM agents. It offers a simple API for indexing documents with metadata and performing fast similarity searches.

Use cases

• store and query document embeddings locally for LLM agents
• build retrieval-augmented generation pipelines without a server
• semantic search over JSONL documents with metadata
• quickly prototype vector search in Python
• persist and reload a vector index from disk

When to choose

• you need a fast, dependency-light local vector store with no server setup
• you're prototyping RAG or agent memory in Python
• your dataset fits in memory and you want hardware-accelerated similarity search

When to avoid

• you need distributed, production-grade vector search with replication and scaling
• you require advanced filtering, hybrid search, or managed cloud features
• you need long-term support guarantees from a mature project

Facets

library · maturity experimental

vector-database rag llm-inference search-engine databases large-language-models python cross-platform embeddings similarity-search local-vector-store llm-agents mkl-blas ai-agents retrieval-augmented-generation

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