MoltBrain is a long-term memory layer plugin designed for OpenClaw, MoltBook, and Claude Code, capable of automatically learning and recalling project context, providing intelligent search, observation recording, analysis statistics, and persistent storage functions.

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.

Chainguard is an MCP server that provides task tracking, syntax validation, long-term memory, and intelligent context management for Claude Code, including code semantic search, hallucination prevention, and a kanban system.

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.

MCP-KG-Memory is an MCP server with a long-term memory layer based on a knowledge graph, aiming to solve the context forgetting problem of AI programming assistants. It persistently stores project goals, constraints, strategies, and user preferences through Neo4j, enabling semantic retrieval and active learning, and endowing the AI assistant with continuous memory and context awareness capabilities.

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

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

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 Vector Memory MCP Server provides long-term memory functions for AI assistants, supports saving and retrieving files or free-form notes, and enables natural language information queries through semantic search.

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.

The MemData MCP server provides long-term memory storage and retrieval functions for AI assistants across sessions, supporting usage through API keys or wallet payments, and achieving semantic memory management.

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.

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

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.