"Ray" matching MCP servers:

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

Enables coding agents to retrieve project context from an AnythingLLM RAG at session start and write back human-approved durable facts via five workflow-shaped tools.

Intelligent knowledge base system that enables users to process documents in 25+ formats, perform semantic search and Q\&A through vector retrieval. Supports multiple AI models including OpenAI and DouBao with local processing capabilities.

Local repo-intelligence MCP for coding agents: indexes source, symbols, call graphs, git/GitHub history, and source-bound repo memories into local database.

A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.

Enables chat assistants to remember past conversations by saving each turn and retrieving semantically relevant memories via similarity search. Also allows fetching all memories from a specific date.

Enables personal AI agents to combine Meta Ray-Ban audio/vision sensory grounding with long-term episodic life memory graphs and empathetic emotional resonance. Integrates with MCP-compliant clients via JSON-RPC stdio for ambient, context-aware interactions.

A local-first Graph-RAG system that combines ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q\&A over your Obsidian vault.

A lightweight server that provides persistent memory and context management for AI assistants using local vector storage and database, enabling efficient storage and retrieval of contextual information through semantic search and indexed retrieval.

Enables semantic search over an Obsidian vault using natural language, retrieving relevant notes and extracted conclusions.

Enables advanced RAG with knowledge graphs, supporting document ingestion, multimodal extraction, and multiple query modes (naive, local, global, hybrid) via the Model Context Protocol.

Enables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.

MCP server providing RAG context and failure capture for Claude Code, enabling semantic search across project knowledge and storing/analyzing failures.

Converts family notices (hospital, government, insurance, delivery) into action cards with confirmed facts and pending verifications, enabling collaborative family confirmation and completion.

Enables to search, draft, and profile from your X/Twitter archive locally via MCP tools.

A headless local knowledge library and RAG substrate that enables LLM clients to search, retrieve chunks, and list documentation packs through read-only MCP tools.

RAG Knowledge - MCP server providing AI-powered tools and automation by MEOK AI Labs

Enables LLMs to perform conceptual search over local PDF/EPUB documents using a RAG pipeline with corpus-driven concept extraction and WordNet enrichment.

Enables querying private knowledge bases through a modular RAG pipeline with features like hybrid retrieval, reranking, and observability, exposed via the Model Context Protocol.

Enables hybrid retrieval over interpretable Gemini documentation knowledge graphs through eight MCP tools, combining vector, keyword, and graph-boosted search with deprecation filtering.