Discover the best Hybrid Search open source repositories on GitHub. Explore trending tools, live star counts, source code, and developer projects updated daily.
AI search engine and MCP server for bots and agents. One Rust binary indexes code, docs, logs, JSONL/CSV and PDFs for BM25 full-text, vector and hybrid search, code search without grep, agent memory and RAG. Elasticsearch-compatible API; Corpus Hub serves hundreds of reference memory corpora.
Your Personal Librarian Agent. Ask anything on your computer, organize files, remember important info and files.
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
One Postgres for your application data, full-text search, vector retrieval, and aggregations. Home of the pg_search extension.
The open-source context layer for AI agents. PipesHub turns your company's knowledge (Slack, Drive, Jira, GitHub, Microsoft 365 and 40+ connectors) into a permission-aware workspace that agents can search, grep, navigate and cite. MCP, SDKs and built-in agents. Self-hosted.
Embedded retrieval library built on Parquet. Fast, efficient, and scalable.
SeekStorm: vector & lexical search - in-process library & multi-tenancy server, in Rust.
Search Claude Code, Codex, Pi, OpenCode, Github Copilot & Cursor transcripts. Resume sessions. Track tokens.
The retrieval layer for production AI systems. Lightning-fast (<10ms) search without vector databases. Built for browser, edge, on-device, and cloud.
valkey-search is a C++ module which extends valkey with vector search and secondary indexing capabilities. It enables users to index and query data stored in Valkey using complex queries with filters while maintaining high performance and scalability.
Agent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across Python, TypeScript, Rust, Go, .NET, Java
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.
The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text.
The one data index for your agentic applications