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Ground truth layer for humans and AI agents working together. Version control for knowledge.

local-first semantic code search engine

ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Model DBMS. ArcadeDB supports Vector Embeddings.

Epsilla is a high performance Vector Database Management System

A Python library powered by Language Models (LLMs) for conversational data discovery and analysis.

The open source Meme Search Engine and Finder. Free and built to self-host locally with Python, Ruby, and Docker.

RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connect external tools and data sources.

A Python vector database you just need - no more, no less.

A NodeJS RAG framework to easily work with LLMs and embeddings

A dead-simple API to build LLM-powered apps

🕵️‍♂️ Library designed for developers eager to explore the potential of Large Language Models (LLMs) and other generative AI through a clean, effective, and Go-idiomatic approach.

Mercury - Train your own custom GPT. Chat with any file, or website.

Quickly and easily build AI website or application by using embeddings!

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

Use local files or public GitHub repository as a source and ask questions through ChatGPT about it

Universal vector search wrapper for Postgres, MySQL, SQLite (pgvector, Vector Store, sqlite‑vss).

Turn your Readwise library into a blazing-fast, self-hosted semantic search engine – complete with nightly syncs, vector search API, Prometheus metrics, and a streaming MCP server for LLM clients.

Sophisticated AI chatbot with long-term memory capabilities, complete Notion workspace integration, and MCP (Model Context Protocol) implementation. Features semantic, episodic, and procedural memory systems.

Project-aware collection management based on Qdrant, including a Rust MCP, daemon and CLI: hybrid semantic, pattern and full-text (FTS5) search into single or cross-concerns collection. Dedicated collections for knowledge library, LLM behavioral rules, and an LLM scratchpad

Fast Python web crawler for RAG and AI ingestion. Extracts clean Markdown from any site for LLMs and vector stores.