"Local RAG system for providing documentation to a large language model (LLM)" matching MCP servers:

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.

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

A Docker-based local RAG backend that provides advanced document search capabilities using vector, graph, and full-text retrieval via the Model Context Protocol. It supports over 28 file formats and tracks evolving relationships between concepts using a Neo4j-backed graphiti implementation.

Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.

Lets AI assistants act as a personal language teacher by storing a learner's vocabulary, writing stories around those words, judging translations, labeling mistakes by rule, and scheduling spaced-repetition reviews with FSRS. It also serves the agreed tutoring method to any connected assistant so teaching stays consistent across Claude, ChatGPT and Codex.

A fully offline local RAG server that utilizes ChromaDB and Ollama to index and query PDF, text, and Markdown documents. It allows users to manage local knowledge bases and perform semantic searches with AI-generated responses.

Semantic search and retrieval system for local documents using vector embeddings, enabling AI-powered search across your document collections with support for multiple embedding providers.

Exposes the local GraphRAG knowledge base over MCP, letting clients such as Claude Desktop query the markdown vault with cited answers and check graph or ingestion status.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.

A local-first memory cartridge for MCP-compatible AI clients, providing durable, inspectable, and auditable memory storage with a read-only-by-default MCP server.

Enables Claude Code (or any MCP client) to run hybrid vector and keyword search over a locally indexed Confluence Data Center / Server instance, retrieve full page text, check index status, and trigger background refreshes.

MCP server for searching, reading, listing, writing, and appending Markdown wiki content using RAG with ChromaDB.

Enables indexing local documents (PDF, Markdown, text, code) into a knowledge base and querying them via semantic search using local embeddings, all running privately on your machine.

Serves an LLM-maintained markdown wiki to agents over MCP, providing tools for querying and navigating wiki pages without direct filesystem access.

Persistent 3D/2D spatial world model MCP server for AI agents. Entity tracking, object permanence, AABB collision simulation & view frustum projection via local SQLite.

A lightweight knowledge base MCP server that enables full-text search and retrieval of markdown documents from indexed sites using Orama BM25.

An MCP server for a plain-markdown LLM wiki vault with YAML frontmatter and Obsidian-style wiki-links, enabling headless full-text search, note reading, backlink discovery, and safe append/create-only writing with path traversal protection and an immutable raw/ directory.

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.

Enables an LLM to autonomously maintain a Markdown-based wiki with YAML frontmatter, using hybrid BM25 and local CPU vector search for fully offline retrieval without external APIs or databases.