A Real and Proactive MCP Memory Tool

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A Real and Proactive MCP Memory Tool

I've built something that's been missing from AI assistants: a real, persistent, intelligent memory across all your conversations and projects, with proactive suggestions based on context. What Is It? MCP Memory is a Model Context Protocol (MCP) server that transforms any AI assistant into a learning companion that remembers everything, recognizes patterns, and proactively suggests relevant context from your history. Think of it as giving your AI assistant the equivalent of human episodic memory - but better. The MCP is written in go, and in order to implement, I've written the entire protocol as an SDK (also available, at https://github.com/fredcamaral/gomcp-sdk) Why Does This Matter? Ever had these frustrations with AI assistants? - "I already told you this last week..." - No conversation persistence - "You solved this same problem before!" - No pattern recognition across sessions - "Remember my coding preferences?" - Starts from scratch every time - "What was that solution we discussed?" - No searchable memory MCP Memory solves all of these. Add one config block to Claude Desktop/VS Code/Cursor, and suddenly your AI: - Remembers everything across sessions and projects - Finds similar problems you've solved before using semantic search - Recognizes patterns in your coding style, preferences, and decisions - Suggests relevant context proactively based on what you're working on - Learns across repositories with intelligent cross-referencing Technical Highlights - Smart chunking that understands conversation flow and context boundaries - Vector embeddings with ChromaDB for semantic similarity search - Pattern recognition engine that learns your preferences and workflows - Multi-repository intelligence for cross-project insights - Production-ready with Docker auto-updates, monitoring, and security - Memory decay that automatically summarizes and archives old memories - Relationship mapping that links related conversations and solutions - Serves through HTTP or SSE; for the HTTP, I've implemented a stdio <> HTTP proxy in JS (following the idea of Hashicorp's Terraform MCP) Demo After setup, try this: 1. Tell your AI: "Remember I prefer TypeScript over JavaScript for new projects" 2. Later: "What do you know about my coding preferences?" 3. It remembers and builds on that knowledge over time The memory persists across: - Different conversation sessions - Multiple projects and repositories - Weeks and months of usage - Various AI clients (Claude, VS Code, Cursor, etc.), customized for the capabilities of each one What Makes It Different Unlike simple conversation history or the current MCPs alike, MCP Memory: - Understands context - knows when you're debugging vs. implementing - Learns patterns - recognizes your decision-making style - Connects dots - links related problems across different projects - Evolves - gets smarter as you use it more Production Ready - Auto-updating Docker deployment with Watchtower - Comprehensive monitoring and health checks - Security with encryption and access controls - Backup and restoration capabilities - Scales from personal use to team deployments Source & Docs GitHub: https://github.com/fredcamaral/mcp-memory License: MIT Setup time: Under 5 minutes Compatible with Claude Desktop, VS Code (Continue), Cursor, and any MCP-supporting AI client. --- I've been using this for my own development work for weeks, and it's genuinely changed how productive I am with AI assistants. Instead of re-explaining context every session, my AI actually builds on our previous conversations. Would love feedback from the HN community - especially from those working with AI coding assistants daily. Try it out and let me know what you think!