marm-mcp-server 2.2.4

MARM-Systems is a complete protocol and platform—combining an advanced memory backend, modular semantic search, and agent-to-agent coordination with a scientifically structured, community-vetted methodology for reasoning, session recall, and collaborative AI workflows. More then just a set of tools, it's a complete AI memory ecosystem

MARM: The AI That Remembers Your Conversations

Memory Accurate Response Mode v2.2.4 - The intelligent memory system for AI agents. Stop losing context. Stop hallucinations. Start controlling your LLM conversations.

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.

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⚡ INSTANT SETUP - Ready in 60 seconds

Already have MARM running? Connect instantly:

Claude Code users:

/mcp  # → Instant connection to your MARM server!

Claude Desktop users:

// Add to your MCP settings:
{
  "mcpServers": {
    "marm-memory": {
      "command": "docker",
      "args": ["exec", "marm-mcp-server", "python", "/app/server.py"]
    }
  }
}

✅ 19 memory tools loaded ✅ Persistent sessions ✅ Semantic search

Don't have MARM yet? Install + Connect:

# 1. Pull & Start (30 seconds)
docker run -d --name marm-mcp-server -p 8001:8001 lyellr88/marm-mcp-server:latest

# 2. Connect to Claude (5 seconds)
/mcp add marm-memory http://localhost:8001/mcp

# 3. Activate (instant)
/mcp

🎯 You now have AI with perfect memory across all conversations!

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🚀 Full Installation Guide

Docker (Fastest - 30 seconds):

docker pull lyellr88/marm-mcp-server:latest
docker run -d --name marm-mcp-server -p 8001:8001 lyellr88/marm-mcp-server:latest
claude mcp add marm-memory http://localhost:8001/mcp

Quick Local Install:

pip install marm-mcp-server==2.2.4
cd MARM-Systems/marm-mcp-server
# Cross-platform: pip install marm-mcp-server==2.2.4
claude mcp add marm-memory http://localhost:8001/mcp

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:

Installation Type	Guide	Best For
Docker	INSTALL-DOCKER.md	Cross-platform, production deployment
Windows	INSTALL-WINDOWS.md	Native Windows development
Linux	INSTALL-LINUX.md	Native Linux development
Platforms	INSTALL-PLATFORM.md	App & API integration

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🎯 Why MARM?

MARM (Memory Accurate Response Mode) is a comprehensive AI memory ecosystem I designed to solve the problem of context loss in large language models. What started as a simple protocol has evolved into a suite of tools that provide a persistent, intelligent, and cross-platform memory for any AI agent.

The MARM ecosystem consists of three main components:

• The MARM Protocol: A set of rules and commands for structured, reliable AI interaction.
• The MARM Universal MCP Server: A production-ready memory intelligence platform that provides a powerful, stateful backend for any MCP-compatible AI client.
• The MARM Chatbot: A web-based interface for interacting with the MARM protocol directly.

Whether you're a developer looking to build the next generation of AI agents, a researcher studying AI behavior, or simply a power user who wants to have more productive conversations with your AI, the MARM ecosystem provides the tools you need to unlock the full potential of large language models.

*Appears in Google AI Overview for AI memory protocol queries (as of Aug 2025)*

The newest addition to the ecosystem is MARM MCP it represents an emerging category of MCP server that integrates a complete protocol layer with intelligent memory systems. Built on FastAPI and SQLite, it combines the MARM protocol with semantic search, session management, and smart retrieval to bridge tool access with structured reasoning. This creates a more consistent, user-controlled LLM experience that goes beyond simple tool exposure.

Category	Feature	Description
🧠 Memory	Semantic Search	Find memories by meaning using AI embeddings, not keyword matching
	Auto-Classification	Content intelligently categorized (code, project, book, general)
	Cross-Session Memory	Memories survive across different AI agent conversations
	Smart Recall	Vector similarity search with context-aware intelligent fallbacks
🤝 Multi-AI	Unified Memory Layer	Accessible by any connected LLM (Claude, Qwen, Gemini, etc.)
	Cross-Platform Intelligence	Different AI agents learn from each other's interactions
	User-Controlled Memory	Granular control over memory sharing and "Bring Your Own History"
🏗️ Architecture	19 Complete MCP Tools	Full Model Context Protocol implementation
	Database Optimization	SQLite with WAL mode and connection pooling
	Rate Limiting	IP-based protection for sustainable free service
	MCP Compliance	Response size management for optimal performance
	Docker Ready	Containerized deployment with health monitoring
⚡ Advanced	Usage Analytics	Privacy-conscious insights for platform optimization
	Event-Driven System	Self-managing architecture with comprehensive error isolation
	Structured Logging	Development and debugging support with structlog
	Health Monitoring	Real-time system status and performance tracking

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Why I Built MARM

MARM started with my own frustrations: AI losing context, repeating itself, and drifting off track. But I didn’t stop there. I asked a simple question in a few AI subreddits:
“What’s the one thing you wish your LLM could do better?”

The replies echoed the same pain points:

• Keep memory accurate
• Give users more control
• Be transparent, not a black box

That feedback confirmed the gap I already saw. I took those shared frustrations, found the middle ground, and built MARM. Early contributors validated the idea and shaped features, but the core system grew out of both personal trial and community insight.

MARM is the result of combining individual persistence with collective needs, a protocol designed to solve what we all kept running into.

Discord

Join Discord for upcoming features and builds, plus a safe space to share your work and get constructive feedback.

MARM Discord

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Before MARM vs After MARM

Without MARM:

• "Wait, what were we discussing about the database schema?"
• AI repeats previous suggestions you already rejected
• Loses track of project requirements mid-conversation
• Starts from scratch every time you return

With MARM:

• AI references your logged project notes and decisions
• Maintains context across multiple sessions
• Builds on previous discussions instead of starting over
• Remembers what works and what doesn't for your project

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Why Use MARM?

Modern LLMs often lose context or fabricate information. MARM introduces a session memory kernel, structured logs, and a user-controlled knowledge library. Anchoring the AI to your logic and data. It’s more than a chatbot wrapper. It’s a methodology for accountable AI.

Command Overview

Category	Command	Function
Session	/start marm	Activate protocol
	/refresh marm	Reaffirm/reset context
Core	/log	Start structured session logging
	/notebook	Store key data
	/summary:	Summarize and reseed sessions
Advanced	/deep dive	Request context-aware response
	/show reasoning	Reveal logic trail of last answer

Need a walkthrough or troubleshooting help? The MARM-HANDBOOK.md covers all aspects of using MARM.

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🛠️ MARM MCP Server Guide

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

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🛠️ Complete MCP Tool Suite (19 Tools)

Category	Tool	Description
🧠 Memory Intelligence	marm_smart_recall	AI-powered semantic similarity search across all memories. Supports global search with search_all=True flag
	marm_contextual_log	Intelligent auto-classifying memory storage using vector embeddings
🚀 Session Management	marm_start	Activate MARM intelligent memory and response accuracy layers
	marm_refresh	Refresh AI agent session state and reaffirm protocol adherence
📚 Logging System	marm_log_session	Create or switch to named session container
	marm_log_entry	Add structured log entry with auto-date formatting
	marm_log_show	Display all entries and sessions (filterable)
	marm_log_delete	Delete specified session or individual entries
🔄 Reasoning & Workflow	marm_summary	Generate context-aware summaries with intelligent truncation for LLM conversations
	marm_context_bridge	Smart context bridging for seamless AI agent workflow transitions
📔 Notebook Management	marm_notebook_add	Add new notebook entry with semantic embeddings
	marm_notebook_use	Activate entries as instructions (comma-separated)
	marm_notebook_show	Display all saved keys and summaries
	marm_notebook_delete	Delete specific notebook entry
	marm_notebook_clear	Clear the active instruction list
	marm_notebook_status	Show current active instruction list
⚙️ System Utilities	marm_current_context	Get current date/time for accurate log entry timestamps
	marm_system_info	Comprehensive system information, health status, and loaded docs
	marm_reload_docs	Reload documentation into memory system

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🏗️ Architecture Overview

Core Technology Stack

FastAPI (0.115.4) + FastAPI-MCP (0.4.0) - v2.2.4
├── SQLite with WAL Mode + Custom Connection Pooling  
├── Sentence Transformers (all-MiniLM-L6-v2) + Semantic Search
├── Structured Logging (structlog) + Memory Monitoring (psutil)
├── IP-Based Rate Limiting + Usage Analytics
├── MCP Response Size Compliance (1MB limit)
├── Event-Driven Automation System
├── Docker Containerized Deployment + Health Monitoring
└── Advanced Memory Intelligence + Auto-Classification

Database Schema (5 Tables)

memories - Core Memory Storage

CREATE TABLE memories (
    id TEXT PRIMARY KEY,
    session_name TEXT NOT NULL,
    content TEXT NOT NULL,
    embedding BLOB,              -- AI vector embeddings for semantic search
    timestamp TEXT NOT NULL,
    context_type TEXT DEFAULT 'general',  -- Auto-classified content type
    metadata TEXT DEFAULT '{}',
    created_at TEXT DEFAULT CURRENT_TIMESTAMP
);

sessions - Session Management

CREATE TABLE sessions (
    session_name TEXT PRIMARY KEY,
    marm_active BOOLEAN DEFAULT FALSE,
    created_at TEXT DEFAULT CURRENT_TIMESTAMP,
    last_accessed TEXT DEFAULT CURRENT_TIMESTAMP,
    metadata TEXT DEFAULT '{}'
);

Plus: log_entries, notebook_entries, user_settings

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📈 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

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📚 Documentation for MCP

Guide Type	Document	Description
Docker Setup	INSTALL-DOCKER.md	Cross-platform, production deployment
Windows Setup	INSTALL-WINDOWS.md	Native Windows development
Linux Setup	INSTALL-LINUX.md	Native Linux development
Platform Integration	INSTALL-PLATFORM.md	App & API integration
MCP Handbook	MCP-HANDBOOK.md	Complete usage guide with all 19 MCP tools, cross-app memory strategies, pro tips, and FAQ

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🆚 Competitive Advantage

vs. Basic MCP Implementations

Feature	MARM v2.2.4	Basic MCP Servers
Memory Intelligence	AI-powered semantic search with auto-classification	Basic key-value storage
Tool Coverage	19 complete MCP protocol tools	3-5 basic wrappers
Scalability	Database optimization + connection pooling	Single connection
MCP Compliance	1MB response size management	No size controls
Deployment	Docker containerization + health monitoring	Local development only
Analytics	Usage tracking + business intelligence	No tracking
Codebase Maturity	2,500+ lines professional code	200-800 lines

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🤝 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

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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 Chatbot and share your experience
• Star the repo if MARM solves a problem for you
• Share on social - help others discover memory-enhanced AI
• Open issues 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 Pull Requests - 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!

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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.

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📁 Project Documentation

Usage Guides

• 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 - Platfrom installtion 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

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

Built with ❤️ by MARM Systems - Universal MCP memory intelligence