A TypeScript-based MCP server that implements a simple notes system, allowing users to create, access, and generate summaries of text notes through Claude Desktop.
Provides AI assistants with persistent memory across sessions using local SQLite and keyword search, allowing storage and retrieval of user preferences, project context, and decisions.
Enables AI assistants to store and retrieve persistent memories with a web management interface. Supports creating, searching, and managing memories through natural language commands or a visual web dashboard.
A lightweight MCP server that provides persistent knowledge graph storage for AI assistants, enabling memory across sessions through entity-relationship storage with JSON file persistence.
An MCP server that tracks AI prompts in Claude Code and automatically annotates git commits with the history of what was asked. It provides tools to query session summaries, retrieve uncommitted work, and manage AI provenance directly within Claude.
Self-hosted knowledge manager and MCP server that lets you organize snippets, questions, and documents via a web UI and exposes them to AI assistants for reading and writing.
A robust MCP server that transforms OneNote notebooks into an AI-accessible knowledge base for Gemini Spark, enabling natural language queries to list, read, and search notes via Microsoft Graph API.
An MCP server that extends AI agents' context window by providing tools to store, retrieve, and search memories, allowing agents to maintain history and context across long interactions.
Enables LLMs to store, search, and manage memories with hybrid semantic and keyword search using ChromaDB and Neo4j for persistent memory and knowledge graph capabilities.
A bridge between MCP Host applications and mem0 cloud service, specialized for project management with capabilities to store, retrieve, and search project information within a structured format.
Connects to Obsidian vaults via the Local REST API plugin, enabling AI-assisted Zettelkasten workflows including creating atomic notes, searching content, managing links and tags, and performing precise content editing operations.
Hebbian learning MCP server with neural memory graphs, eligibility traces, and three-factor
reward signals. Associative memory that strengthens through use.
Provides a scalable knowledge graph implementation for Model Context Protocol using Elasticsearch, enabling AI models to store and query information with advanced search capabilities, memory-like behavior, and multi-zone architecture.