MCP-Mem0 is a template implementation that provides long-term memory functions for AI agents, integrating the Model Context Protocol (MCP) server and Mem0, supporting memory storage, retrieval, and semantic search.

CortexGraph is an MCP server that provides a human-like memory system for AI assistants. It realizes the natural decay and reinforcement use of memories by simulating the Ebbinghaus forgetting curve, including short-term memory storage, long-term memory archiving, and knowledge graph functions.

This project includes two servers based on the Model Context Protocol (MCP): an IoT device control server and a memory management server. The IoT server provides device control, status query, and real-time update functions, suitable for scenarios such as smart homes and industrial IoT; the memory management server provides long-term memory storage and semantic search functions, suitable for fields such as conversation history and knowledge management.

An LLM long-term memory storage system based on the MCP standard, supporting semantic search and multi-type memory management

DevContext is a powerful AI development context system that provides developers with project-centric continuous context awareness. It includes four memory types: short-term, long-term, situational, and semantic, supports code structure analysis and vector search, and significantly improves development efficiency.

The MCP Memory Server provides long-term memory functions for AI assistants and implements efficient vector similarity search based on PostgreSQL and pgvector.

A C# implementation tool for the MCP protocol based on Pieces' long-term memory function, which can query memory content from the past 7 days through the Claude desktop application.

The Memory MCP Server is a server that provides long-term memory functions for large language models (LLMs). By implementing the Model Context Protocol (MCP), it enhances the context awareness ability of LLMs in continuous interactions. The project provides a user-friendly API, supports storing and retrieving context information, is scalable, and is suitable for various LLM architectures.

An MCP server based on TinyDB that provides localized long-term memory storage for chatbots, supporting the storage, search, and validation of JSON records

Brain-MCP is an MCP service that provides a comprehensive memory management system for AI, simulating the human thinking process, supporting short-term memory caching, long-term memory associated storage, and thinking chain management, enabling continuous learning and experience accumulation of AI.

MCP-Mem0 is a Python-based server project focused on providing long-term memory management functions for intelligent agents. It offers efficient memory storage and retrieval capabilities and serves as a template to facilitate developers in building their own MCP servers. The project is lightweight and easy to deploy, suitable for scenarios requiring agent memory management.