Memory Accurate Response Mode v2.2.5 - The intelligent persistent memory system for AI agents, stop fighting your memory and control it. Experience long-term recall, session continuity, and reliable conversation history, so your LLMs never lose track of what matters.

Note: This is the official MARM repository. All official versions and releases are managed here.

Forks may experiment, but official updates will always come from this repo.

Why MARM MCP: The Problem & Solution

Your AI forgets everything. MARM MCP doesn't.

Modern LLMs lose context over time, repeat prior ideas, and drift off requirements. MARM MCP solves this with a unified, persistent, MCP‑native memory layer that sits beneath any AI client you use. It blends semantic search, structured session logs, reusable notebooks, and smart summaries so your agents can remember, reference, and build on prior work—consistently, across sessions, and across tools.

MCP in One Sentence: MARM MCP provides persistent memory and structured session context beneath any AI tool, so your agents learn, remember, and collaborate across all your workflows.

The Problem → The MARM Solution

• Problem: Conversations reset; decisions get lost; work scatters across multiple AI tools.

• Solution: A universal, persistent memory layer that captures and classifies the important bits (decisions, configs, code, rationale), then recalls them by meaning—not keywords.

Before vs After

• Without MARM: lost context, repeated suggestions, drifting scope, "start from scratch."

• With MARM: session memory, cross-session continuity, concrete recall of decisions, and faster, more accurate delivery.

What MARM MCP Delivers

Learn More

• Protocol walkthrough, commands, and reseeding patterns: MARM-HANDBOOK.md

• Join the community for updates and support: MARM Discord

What Users Are Saying

“MARM successfully handles our industrial automation workflows in production. We've validated session management, persistent logging, and smart recall across container restarts in our Windows 11 + Docker environment. The system reliably tracks complex technical decisions and maintains data integrity through deployment cycles.”
@Ophy21, GitHub user (Industrial Automation Engineer)

“MARM proved exceptionally valuable for DevOps and complex Docker projects. It maintained 100% memory accuracy, preserved context on 46 services and network configurations, and enabled standards-compliant Python/Terraform work. Semantic search and automated session logs made solving async and infrastructure issues far easier. Value Rating: 9.5/10 - indispensable for enterprise-grade memory, technical standards, and long-session code management.”
@joe_nyc, Discord user (DevOps/Infrastructure Engineer)

🚀 Quick Start for MCP

Docker (Fastest - 30 seconds):

Quick Local Install:

Key Information:

• Server Endpoint: http://localhost:8001/mcp

• API Documentation: http://localhost:8001/docs

• Supported Clients: Claude Code, Qwen CLI, Gemini CLI, and any MCP-compatible LLM client or LLM platform

All Installation Options:

• Docker (Fastest): One command, works everywhere

• Automated Setup: One command with dependency validation

• Manual Installation: Step-by-step with virtual environment

• Quick Test: Zero-configuration trial run

Choose your installation method:

🛠️ MARM MCP Server Guide

Now that you understand the ecosystem, here's info and how to use the MCP server with your AI agents

🛠️ Complete MCP Tool Suite (18 Tools)

💡 Pro Tip: You don't need to manually call these tools! Just tell your AI agent what you want in natural language:

• "Claude, log this session as 'Project Alpha' and add this conversation as 'database design discussion'"

• "Remember this code snippet in your notebook for later"

• "Search for what we discussed about authentication yesterday"

The AI agent will automatically use the appropriate tools. Manual tool access is available for power users who want direct control.

🏗️ Architecture Overview

Core Technology Stack

Database Schema (5 Tables)

memories - Core Memory Storage

sessions - Session Management

Plus: log_entries, notebook_entries, user_settings

📈 Performance & Scalability

Production Optimizations

• Custom SQLite Connection Pool: Thread-safe with configurable limits (default: 5)

• WAL Mode: Write-Ahead Logging for concurrent access performance

• Lazy Loading: Semantic models loaded only when needed (resource efficient)

• Intelligent Caching: Memory usage optimization with cleanup cycles

• Response Size Management: MCP 1MB compliance with smart truncation

Rate Limiting Tiers

• Default: 60 requests/minute, 5min cooldown

• Memory Heavy: 20 requests/minute, 10min cooldown (semantic search)

• Search Operations: 30 requests/minute, 5min cooldown

📚 Documentation for MCP

🆚 Competitive Advantage

vs. Basic MCP Implementations

🤝 Contributing

Aren't you sick of explaining every project you're working on to every LLM you work with?

MARM is building the solution to this. Support now to join a growing ecosystem - this is just Phase 1 of a 3-part roadmap and our next build will complement MARM like peanut butter and jelly.

Join the repo that's working to give YOU control over what is remembered and how it's remembered.

Why Contribute Now?

• Ground floor opportunity - Be part of the MCP memory revolution from the beginning

• Real impact - Your contributions directly solve problems you face daily with AI agents

• Growing ecosystem - Help build the infrastructure that will power tomorrow's AI workflows

• Phase 1 complete - Proven foundation ready for the next breakthrough features

Development Priorities

1. Load Testing: Validate deployment performance under real AI workloads

2. Documentation: Expand API documentation and LLM integration guides

3. Performance: AI model caching and memory optimization

4. Features: Additional MCP protocol tools and multi-tenant capabilities

Join the MARM Community

Help build the future of AI memory - no coding required!

Connect: MARM Discord | GitHub Discussions

Easy Ways to Get Involved

• Try the MCP server or Coming soon CLI and share your experience

• Star the repo if MARM solves a problem for you

• Share on social - help others discover memory-enhanced AI

• Open with bugs, feature requests, or use cases

• Join discussions about AI reliability and memory

For Developers

• Build integrations - MCP tools, browser extensions, API wrappers

• Enhance the memory system - improve semantic search and storage

• Expand platform support - new deployment targets and integrations

• Submit - Every PR helps MARM grow. Big or small, I review each with respect and openness to see how it can improve the project

⭐ Star the Project

If MARM helps with your AI memory needs, please star the repository to support development!

License & Usage Notice

This project is licensed under the MIT License. Forks and derivative works are permitted.

However, use of the MARM name and version numbering is reserved for releases from the official MARM repository.

Derivatives should clearly indicate they are unofficial or experimental.

📁 Project Documentation

Usage Guides

• MARM-HANDBOOK.md - Original MARM protocol handbook for chatbot usage

• MCP-HANDBOOK.md - Complete MCP server usage guide with commands, workflows, and examples

• PROTOCOL.md - Quick start commands and protocol reference

• FAQ.md - Answers to common questions about using MARM

MCP Server Installation

• INSTALL-DOCKER.md - Docker deployment (recommended)

• INSTALL-WINDOWS.md - Windows installation guide

• INSTALL-LINUX.md - Linux installation guide

• INSTALL-PLATFORMS.md - Platform installation guide

Chatbot Installation

• CHATBOT-SETUP.md - Web chatbot setup guide

Project Information

• README.md - This file - ecosystem overview and MCP server guide

• CONTRIBUTING.md - How to contribute to MARM

• DESCRIPTION.md - Protocol purpose and vision overview

• CHANGELOG.md - Version history and updates

• ROADMAP.md - Planned features and development roadmap

• LICENSE - MIT license terms

mcp-name: io.github.Lyellr88/marm-mcp-server

Built with ❤️ by MARM Systems - Universal MCP memory intelligence