"Semantic search, RAG, and memory systems" matching MCP servers:

A local hybrid-search MCP server that enables coding agents to query files and folders using natural language, returning relevant code chunks with exact source paths. Everything runs on-device with no API keys or network calls.

Exposes local memory vaults as 6 MCP tools over stdio, enabling clients like Windsurf, Zed, and Claude Desktop to read and write memory.

MCP server that enables local hybrid semantic and keyword search over private PDF, DOCX, Markdown, and text documents without sending data to embedding APIs.

MCP server that exposes agent-memory-daemon to any MCP-compatible client — Kiro (CLI & IDE), Claude Desktop, Cursor, and others. The daemon does the thinking (consolidation + extraction); this server is a thin filesystem bridge so agents can read, append, and search memory through the Model Context Protocol.

Provides persistent memory for Claude with hierarchical categorization, cross-corpus recall, session journals, and customizable persona, enabling memory continuity across sessions.

Enables AI agents to find local files by meaning rather than exact filename, across Korean and English queries, using a fully local multilingual embedding index on top of voidtools Everything. It also diagnoses and repairs misconfigured Everything settings that cause hangs, with no es.exe or SDK DLL required.

A TypeScript MCP server that allows querying documents using LLMs with context from locally stored repositories and text files through a RAG (Retrieval-Augmented Generation) system.

A local MCP server that provides agents with tools to list, read, search, inspect history and diffs, and capture unstructured text in a user-owned Git repository of durable memory.

Provides local hybrid search over files by combining semantic vector and full-text BM25 retrieval, with MCP stdio/HTTP support, OAuth 2.1 authentication, and tools for reading and converting documents to Markdown.

Provides project memory management for Claude Desktop, storing memories as Markdown files with intelligent keyword-based retrieval and automatic creation/update decisions by LLM.

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.

Provides a local markdown-based memory system for coding agents through MCP, enabling search, add, register, inventory, sync, and ingest operations across user and project memory. It gives any agent tool a durable, provider-agnostic shared memory stored in folders you own.

Provides fully local, privacy-preserving semantic search over file names and paths via MCP, using BGE embeddings for fuzzy matching and supporting incremental updates, watch mode, and a loopback-only HTTP server.

Enables reading, writing, and searching a local markdown-based memory store using MCP tools, with safety checks and index consistency.

A memory, docs, and repo context layer for engineering agents, providing typed persistent memory, RAG document retrieval, and repository-aware tools to recall decisions, runbooks, and operational knowledge across sessions.

Enables advanced semantic search and document management by integrating the GroundX API with Claude Desktop and GitHub Copilot. It supports local file uploads, website crawling, and Google Drive synchronization for RAG workflows.

An MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.

Exposes local persistent memory as an MCP server with Markdown storage, PARA organization, Zettelkasten linking, and SQLite FTS5 search. Enables Claude and other MCP-compatible agents to store, organize, and retrieve knowledge with context-based recommendations through the Olima association engine.

A local-first MCP server that enables semantic search over PDF and DOCX documents using structure-aware parsing and vector storage. It allows users to query their local knowledge base through Claude Code without cloud dependencies or GPU requirements.

A local-first, Markdown-native AI agent layered memory system that provides MCP tools for storing, recalling, exporting, and importing memories with types like working, persona, and fact.

MCP server for full-text search across PDF document collections with offline indexing, ranked results, snippets, and page rendering.

An MCP server that gives AI models a persistent notebook backed by a folder of Markdown files, with tools to discover, read, write, edit, and delete notes.

Enables blazingly fast file and content searching in large codebases using ripgrep, with intelligent filtering, fuzzy finding, and directory tree visualization while respecting .gitignore and avoiding common bloat directories.

A local-first MCP server that gives AI coding assistants persistent, structured, human-readable memory for a software project by storing project knowledge as Markdown files in the project's .dev-context-memory/ folder.

为Kimi Code CLI提供持久化记忆系统，支持文件学习、智能检索和混合存储，让大模型拥有长效记忆能力。

A Model Context Protocol server that enables keyword search within files, returning matching lines with line numbers.

Enables AI clients to search and read a local folder of notes, fetch public web pages as markdown with SSRF and robots.txt safeguards, and convert times between IANA timezones. It exposes these read-only capabilities as MCP tools over stdio or streamable HTTP.

Integrates Everything Search with Claude Code for lightning-fast file searching across entire drives, up to 400x faster than traditional methods.

Enables AI assistants to search indexed Windows drives for files and folders by name, path, type, size, or date, browse directories, and retrieve counts or total sizes in batched queries. It returns paginated results with optional metadata through a read-only local connection.

Fast file search across Windows (Everything SDK), macOS (Spotlight/mdfind) and Linux (locate/plocate). Single search tool with platform-specific filters, sorting and an optional configurable tool name.