An implementation of persistent memory for Claude using a local knowledge graph, allowing the AI to remember information about users across conversations with customizable storage location.

A flexible memory system for AI applications that supports multiple LLM providers and can be used either as an MCP server or as a direct library integration, enabling autonomous memory management without explicit commands.

Basic Memory is a knowledge management system that allows you to build a persistent semantic graph from conversations with AI assistants. All knowledge is stored in standard Markdown files on your computer, giving you full control and ownership of your data. Integrates directly with Obsidan.md

Memory Bank Server provides a set of tools and resources for AI assistants to interact with Memory Banks. Memory Banks are structured repositories of information that help maintain context and track progress across multiple sessions.

This project is based on the Knowledge Graph Memory Server from the MCP servers repository and retains its core functionality.

An AI-powered goal management system that transforms traditional goal tracking into storytelling, helping users focus on one goal at a time with personalized narratives and insights that increase motivation and achievement.

A Model Context Protocol server that enables AI assistants to search and access information stored in Kibela, supporting note search, retrieval, creation and updating.

A Python package that enables LLM models to interact with Memos server through the Model Context Protocol interface, allowing search, creation, retrieval, and management of memos.

A nostalgic virtual pet experience for the AI age that lets you adopt, nurture, and play with your own digital companion that evolves based on your care.

A simple note-taking MCP server that stores notes and can generate summaries of stored content.

An MCP tool that synchronizes user preferences, personal details, and code standards across multiple Claude interfaces, allowing users to maintain consistent personalized AI interactions without repeating themselves.

This project implements a Model Context Protocol (MCP) server for connecting AI models with Obsidian knowledge bases. Through this server, AI models can directly access and manipulate Obsidian notes, including reading, creating, updating, and deleting notes, as well as managing folder structures.

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 very simple vector store that provides capability to watch a list of directories, and automatically index all the markdown, html and text files in the directory to a vector store to enhance context.

A customized MCP memory server that enables creation and management of a knowledge graph with features like custom memory paths and timestamping for capturing interactions via language models.

A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.

A TypeScript-based MCP server help you write notes to Flomo.

This MCP server provides persistent memory integration for chat applications by utilizing a local knowledge graph to remember user information across interactions.

A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.

Enhances user interaction through a persistent memory system that remembers information across chats and learns from past errors by utilizing a local knowledge graph and lesson management.

Memory manager for AI apps and Agents using various graph and vector stores and allowing ingestion from 30+ data sources

An official AI Model Context Protocol server that enables AI assistants to interact with Dart project management by creating/managing tasks and documents through prompts and tools.

Enhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.

A Model Context Protocol server that allows Claude to create flashcards for Rember, helping users study and remember information through spaced repetition reviews.

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service.

Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.