Genkit is an open-source framework for building AI-driven applications, providing Node.js and Go libraries, supporting the integration of multiple AI models and vector databases, and including development tools and a plugin ecosystem.

Claude Context is an MCP plugin that provides in - depth context of the entire codebase for AI programming assistants through semantic code search. It supports multiple embedding models and vector databases to achieve efficient code retrieval.

A memory system that enhances AI programming capabilities through multi-type database integration, including vector databases, SQLite, and knowledge graphs, for storing and retrieving code patterns, algorithms, and relationships between programming concepts.

A code search MCP server based on semantic understanding, using local embedding models and vector databases to achieve intelligent code retrieval, replacing traditional text search tools

The Florentine.ai MCP Server allows AI agents to query MongoDB and MySQL databases through natural language, supporting functions such as multi - tenant data isolation, automatic schema exploration, and semantic vector search.

A multi - agent AI infrastructure based on the Model Context Protocol (MCP), providing a standardized communication layer, supporting vector databases, tool integration, and multi - agent collaboration, including core components such as an inspector dashboard, Qdrant vector database, and Docker network.

A RAG system built with open-source embedding models, vector databases, and the Gemini large language model, supporting local document processing and dynamic index update.

A Python server project based on the MCP protocol, which uses OpenAI's vector storage function to build a RAG system, supporting the creation of vector databases from multiple file formats and semantic search.