MCP 服务器

marm-mcp

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

README

<div align="center"> <picture> <img src="https://raw.githubusercontent.com/Lyellr88/MARM-Systems/MARM-main/media/marm-logo.svg" alt="MARM - The AI That Remembers Your Conversations" width="700" height="350"> </picture> <h1 align="center">MARM: The AI That Remembers Your Conversations</h1>

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.

</div>

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.

<br> <div align="center"> <picture> <img src="https://raw.githubusercontent.com/Lyellr88/MARM-Systems/MARM-main/media/google-overview.png" alt="MARM appears in Google AI Overview for AI memory protocol queries" width="900" height="550" /> </picture> </div> <p align="center"><i>Appears in Google AI Overview for AI memory protocol queries (as of Aug 2025)</i></p>

What MARM MCP Delivers

Memory	Multi-AI	Architecture
Semantic Search - Find by meaning using AI embeddings	Unified Memory Layer - Works with Claude, Qwen, Gemini, MCP clients	18 Complete MCP Tools - Full Model Context Protocol coverage
Auto-Classification - Content categorized (code, project, book, general)	Cross-Platform Intelligence - Different AIs learn from shared knowledge	Database Optimization - SQLite with WAL mode and connection pooling
Persistent Cross-Session Memory - Memories survive across agent conversations	User-Controlled Memory - "Bring Your Own History," granular control	Rate Limiting - IP-based tiers for stability
Smart Recall - Vector similarity search with context-aware fallbacks		MCP Compliance - Response size management for predictable performance
		Docker Ready - Containerized deployment with health/readiness checks

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):

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

Quick Local Install:

pip install marm-mcp-server==2.2.5
marm-mcp-server
claude mcp add --transport http 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

🛠️ 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.

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`	Background Tool - Automatically provides current date/time for log entries (AI agents use automatically)
	`marm_system_info`	Comprehensive system information, health status, and loaded docs
	`marm_reload_docs`	Reload documentation into memory system

🏗️ Architecture Overview

Core Technology Stack

FastAPI (0.115.4) + FastAPI-MCP (0.4.0)
├── 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`

📈 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

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 18 MCP tools, cross-app memory strategies, pro tips, and FAQ

🆚 Competitive Advantage

vs. Basic MCP Implementations

Feature	MARM v2.2.5	Basic MCP Servers
Memory Intelligence	AI-powered semantic search with auto-classification	Basic key-value storage
Tool Coverage	18 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

🤝 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

Load Testing: Validate deployment performance under real AI workloads
Documentation: Expand API documentation and LLM integration guides
Performance: AI model caching and memory optimization
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 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!