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Tags: Vector database
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1–24 of 39 · Most stars

Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences

Open-source vector similarity search for Postgres

  • nearest-neighbor-search
  • approximate-nearest-neighbor-search

Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

  • c-plus-plus
  • python
  • nearest-neighbor-search
  • locality-sensitive-hashing

[MLsys2026 Best Paper]: https://arxiv.org/abs/2506.08276. RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.

  • approximate-nearest-neighbor-search
  • image-search
  • nearest-neighbor-search
  • recommender-system

🌌 A complete search engine and RAG pipeline in your browser, server or edge network with support for full-text, vector, and hybrid search in less than 2kb.

A distributed approximate nearest neighborhood search (ANN) library which provides a high quality vector index build, search and distributed online serving toolkits for large scale vector search scenario.

  • space-partition-tree
  • neighborhood-graph
  • vector-search
  • fresh-update

The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text.

RuVector provides High Performance, Real-Time decisions and agent memory , Self-Learning Ai, Vector GNN DB built in Rust.

  • ai
  • ai-ocr
  • gnn
  • graph

High-performance open-source in-memory graph database for GraphRAG, AI memory, agentic AI, and real-time graph analytics. Cypher-compatible, built in C++.

One proximity graph for vector search, sparse products & attention across RAM, VRAM & NVMe, over dense vectors, sequences, shapes & geospatial sets in 14 languages 🔍

The AI-Native Search Database. Best for agent storage, it unifies vector, text, structured, and semi-structured data into a single engine. This all-in-one database makes agents smarter, easier to run, and more stable.