"A system for managing personal knowledge and organizing information" matching MCP servers:

A portable, AI-agnostic second brain that stores typed memories with semantic recall and self-learning re-ranking, exposed to any MCP-capable AI as a local server.

A personal knowledge MCP server with vector database for the Opencode ecosystem. It enables semantic search, tag organization, and session memory logging, all running locally without external API keys.

Indexes local and enterprise documents to provide a unified personal knowledge base for AI clients via the Model Context Protocol. It supports full-text search across various file formats and integrates with platforms like Feishu and WeChat Work.

A Personal Knowledge-Base MCP Server that provides semantic search over a student-owned document collection using MCP, Gemini embeddings, and Qdrant. It exposes tools for searching notes, retrieving full documents, and listing indexed sources.

Personal local AI memory/document MCP that manages short and long-term memories and Markdown documents, with tools for import, search, and retrieval.

A local-first RAG system that indexes personal documents and enables querying them via an MCP tool, keeping data private with local embeddings.

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.

Enables LLMs to access a user's personal writing context—voice, style, opinions, expertise, projects, and communication patterns—via curated markdown files, helping the LLM match the user's voice when generating written content.

Saves the user's exact words as immutable, SHA-256-hashed captures before anything else, then derives events, entities, context cards, and causal hypotheses — every fact traceable to its verbatim source. Local-first personal memory for Claude Code, Codex, and any MCP client.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

An MCP server that enables AI agents to query specialized, domain-specific knowledge bases built using the LightRAG framework for enhanced retrieval-augmented generation. It allows for managing and searching knowledge graphs and vector embeddings to provide accurate, context-aware information during an AI assistant's reasoning process.

MCP server for querying a personal knowledge base using semantic RAG, powered by DeepSeek and local embeddings from Ollama.

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.

Stores and retrieves personal information fields securely, allowing Claude to access your name, address, email, and professional details without re-typing them.

An improved implementation of persistent memory using a local knowledge graph with a customizable --memory-path. This lets Claude remember information about the user across chats.

A personal knowledge base MCP server that allows AI assistants to manage notes, tasks, and ideas through tools, resources, and prompts.

Provides centralized knowledge management for projects, allowing users to store, search, and maintain project-specific knowledge that persists across sessions.

A privacy-first memory layer for AI that securely bridges local knowledge with AI assistants through MCP tools, with intelligent redaction and local vector database.

Enables cross-session memory and recall for AI coding assistants through a git-synced knowledge base, hybrid semantic and TF-IDF search across tool transcripts, and automatic distillation of insights with secrets scrubbing.

Long-term memory for AI agents. Compiles conversations into a structured knowledge base with Claim/Evidence model, source provenance, append-only timeline, and contradiction detection. Multi-path retrieval (Exact + BM25 + Graph + weighted RRF + reranker) — 96.6% R@5 on LongMemEval-S, zero vector dependencies.