#memory

Multi-project memory MCP - A Model Context Protocol server for managing project memory and context

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MCP server enabling persistent memory for Claude through a local knowledge graph - fork focused on local development

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This is a personal project to see if Claude 3.5 Sonnet can write a moderately complex MCP Server in Python.

Knowledge graph-based persistent memory system

Enhanced MCP memory system with dynamic compression, context management and thinking process integration. Based on the original MCP memory server.

This is a personal project to determine whether or not Claude 3.5 Sonnet can write moderately complex MCP Server code (Python).

MCP server for enabling memory for Claude through a knowledge graph

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MCP Memory Server with Neo4j backend for AI knowledge graph storage

MCP server to cache codebase as graph

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A long-term memory storage system for LLMs using the Model Context Protocol (MCP) standard. This system helps LLMs remember the context of work done over the entire history of a project, even across multiple sessions. It uses semantic search with embeddings to provide relevant context from past interactions and development decisions.

Standalone MCP server for enabling memory for Claude through a knowledge graph

Memory Control Protocol (MCP) server for the Agentic Memory (A-MEM) system - a flexible, dynamic memory system for LLM agents

Memory Control Protocol (MCP) server for the Agentic Memory (A-MEM) system - a flexible, dynamic memory system for LLM agents

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One simply siphons the excess thoughts from one's mind, pours them into the basin, and examines them at one's leisure. It becomes easier to spot patterns and links, you understand, when they are in this form.

An intelligent MCP server for optimizing VS Code Memory Bank usage

MCP Server with Remote SSH support

An MCP server to interact with Pieces LTM

I gave Claude a mass grave of 200 markdown files and now it remembers my entire project between sessions. No cloud, no API keys, one 10mb Go binary, and private. Stateless Agent Memory Engine is your synapse to the CLI. SAME (Stateless Agent Memory Engine) is persistent memory for AI coding agents. It indexes your markdown notes locally — Obsidian vaults, Logseq graphs, or plain folders — and surfaces relevant context automatically through a 6-gate relevance chain. ~80% of prompts are correctly skipped so your agent isn't drowning in context it doesn't need. 12 MCP tools (9 read, 3 write): semantic search across your knowledge base, filtered search by domain/tag, federated search across multiple vaults, session context with pinned notes and handoffs, save decisions, create handoffs for the next session. SQLite + Ollama embeddings on localhost. Falls back to keyword search without Ollama. No outbound network calls, no telemetry, no accounts. Your notes never leave your machine. Works with Claude Code (hooks + MCP), Cursor, Windsurf, and any MCP client via stdio transport.

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A Model Context Protocol (MCP) server implementation for remote memory bank management, inspired by Cline Memory Bank.

A Model Context Protocol (MCP) server that integrates Volatility 3 memory forensics framework with Claude

Your personal layer on top of any AI. Memory, tasks, goals, and daily planning — everywhere you chat. Your memory belongs to you!

A MCP (Model Context Protocol) server providing long-term memory for LLMs

A flexible system for managing various types of sources (papers, books, webpages, etc.) and integrating them with knowledge graphs.

A lightweight MCP server that integrates with Apple Notes to create a personal memory system for AI. Easily recall and save information from your Mac using simple AppleScript commands. Compatible with all macOS versions with minimal setup requirements.

MCP server for long term agent memory with Mem0. Also useful as a template to get you started building your own MCP server with Python!

DevContext is a cutting-edge Model Context Protocol (MCP) server designed to provide developers with continuous, project-centric context awareness. Unlike traditional context systems, DevContext continuously learns from and adapts to your development patterns and delivers highly relevant context providing a deeper understanding of your codebase.

Penfield gives AI agents long-term memory that compounds over time. Instead of starting every session from zero, your agent remembers conversations, learns preferences, connects ideas, and picks up exactly where it left off. Core capabilities: - 16 tools covering memory CRUD, knowledge graph, context management, artifacts, and reflection - Hybrid search with tunable weights — BM25 (keyword), vector (semantic), and graph traversal - Knowledge graph with 24 relationship types across 8 categories (evolution, evidence, hierarchy, causation, implementation, conversation, sequence, dependencies) - Context checkpoints for session handoff between agents or channels - Artifact storage for files, diagrams, and reference docs - 11 memory types intelligent filtering - Personality system with configurable personas via the portal Also available as a Claude Connector, Claude Code integration, native OpenClaw plugin (4-5x faster), and direct API.

🔥🖥️ MCP Memory is a MCP Server that gives MCP Clients (Cursor, Claude, Windsurf and more) the ability to remember information about users (preferences, behaviors) across conversations.

MCP server that execute applescript giving you full control of your Mac

Basic Memory MCP server integration, for Zed Editor's Assistant.

Persistent memory MCP server for Claude. Store decisions, code snippets, and knowledge that survives across sessions. Features semantic search, Git integration (auto-sync commits/PRs), task tracking, memory snapshots, and team collaboration.

Memory-enhanced MCP server with local RAG database and expiring memory capabilities

DevContext is a cutting-edge Model Context Protocol (MCP) server designed to provide developers with continuous, project-centric context awareness. Unlike traditional context systems, DevContext continuously learns from and adapts to your development patterns. DevContext leverages sophisticated retrieval methods, focusing on keyword analysis, relationship graphs, and structured metadata to deliver highly relevant context during development, understanding both your conversations and your codebase at a deeper level. The server operates with a database instance dedicated to a single project, eliminating cross-project complexity and ensuring performance with minimal resource requirements. DevContext builds a comprehensive understanding of your codebase - from repository structure down to individual functions - while continuously learning from and adapting to your development patterns.