AutoMem MCP: Give Your AI Perfect Memory

One command. Infinite memory. Perfect recall across all your AI tools.

npx @verygoodplugins/mcp-automem setup

Your AI assistant now remembers everything. Forever. Across every conversation.

Works with Claude Desktop, Cursor IDE, Claude Code, GitHub Copilot (coding agent), ChatGPT, ElevenLabs, OpenAI Codex - any MCP-compatible AI platform.

# The Problem We Solve

Every AI conversation starts from zero. Claude forgets your coding style. Cursor can't learn your patterns. Your assistant doesn't remember yesterday's decisions.

Until now.

AutoMem MCP connects your AI to persistent memory powered by AutoMem - a graph-vector memory service.

# What You Get

# # 🧠 Persistent Memory Across Sessions

• AI remembers decisions, patterns, and context forever
• Works across all MCP platforms - Claude Desktop, Cursor, Claude Code
• Cross-device sync - same memory on Mac, Windows, Linux

# # 🏆 Graph-Vector Architecture

• 11 relationship types between memories (not just similarity)
• Research-validated approach (HippoRAG 2: 7% better associative memory)
• Sub-second retrieval even with millions of memories

# # 🚀 Works Everywhere You Code

| Platform | Support | Setup Time |
| - -- -- -- -- -- -- -- -- - | - -- -- -- | - -- -- -- -- - |
| Claude Desktop | ✅ Full | 30 seconds |
| Cursor IDE | ✅ Full | 30 seconds |
| Claude Code | ✅ Full | 30 seconds |
| GitHub Copilot | ✅ Full | 2 minutes |
| OpenAI Codex | ✅ Full | 30 seconds |
| Any MCP client | ✅ Full | 30 seconds |

# See It In Action

# # Claude Desktop with Custom Instructions

Claude automatically recalls memories at conversation start using custom instructions

# # Cursor IDE with Memory Rules

Cursor uses automem.mdc rule to automatically recall and store memories

# # Claude Code with Session Memory

Git commits, builds, and deployments automatically stored to memory

# # OpenAI Codex with Memory Rules

OpenAI Codex uses config.toml to automatically recall and store memories

# # Your AI Learns Your Code Style

// After 1 week, your AI writes EXACTLY like you
// ✅ It knows you prefer early returns
// ✅ It uses your specific variable naming
// ✅ It matches your comment style
// ✅ It follows YOUR patterns, not generic best practices

# # Decisions That Feel Like Yours

User: "Should we use Redis for this?"

Without AutoMem:
"Consider RabbitMQ, Kafka, or AWS SQS based on your needs..."

With AutoMem:
"Based on your pattern of preferring boring technology that works,
and your positive experience with Redis in Project X (March 2024),
yes. You specifically value operational simplicity over feature
richness - Redis fits perfectly."

# Quick Start

# # 1. Set Up AutoMem Service

You need a running AutoMem service (the memory backend). Choose one:

Option A: Local Development (fastest, free)

git clone https://github.com/verygoodplugins/automem.git
cd automem
make dev

Service runs at http://localhost:8001 - perfect for single-machine use.

Option B: Railway Cloud (recommended for production)

One-click deploy with $5 free credits. Typical cost: ~$0.50-1/month after trial.

👉 AutoMem Service Installation Guide - Complete setup instructions for local, Railway, Docker, and production deployments.

---

# # 2. Install MCP Client

# # # Claude Desktop - One-Click Install

Download and double-click to install AutoMem in Claude Desktop:

⬇️ Download AutoMem for Claude Desktop (.mcpb)

After installing:

1. Claude Desktop will prompt you for your AutoMem Endpoint (http://127.0.0.1:8001 for local)
2. Optionally enter your API Key (required for Railway, skip for local)
3. Click Enable

That's it! Claude now has persistent memory.

# # # Other Platforms

Connect your AI tools to the AutoMem service you just started.

#  Guided setup - creates .env and prints config for your AI platform
npx @verygoodplugins/mcp-automem setup

When prompted:

• AutoMem Endpoint: http://localhost:8001 (or your Railway URL if deployed)
• API Key: Leave blank for local development (or paste your token for Railway)

The wizard will:

• ✅ Save your endpoint and API key to .env
• ✅ Generate config snippets for Claude Desktop/Cursor/Code
• ✅ Validate connection to your AutoMem service

# # 3. Platform-Specific Setup

For Claude Desktop:

#  Setup prints config snippet - just paste into claude_desktop_config.json
npx @verygoodplugins/mcp-automem setup

For Cursor IDE:

#  Or use CLI to install automem.mdc rule file
npx @verygoodplugins/mcp-automem cursor

Note: After one-click install, configure your AUTOMEM_ENDPOINT in ~/.cursor/mcp.json or Claude Desktop config

For Claude Code:

# # # Option A: Plugin (Recommended)

#  In Claude Code, install the plugin:
/plugin marketplace add verygoodplugins/mcp-automem
/plugin install automem@verygoodplugins-mcp-automem

Only one Claude Code plugin ships in this repo: plugins/automem with the marketplace catalog at .claude-plugin/marketplace.json.

# # # Option B: CLI Setup

#  Installs SessionStart hook and MCP permissions
npx @verygoodplugins/mcp-automem claude-code

For OpenAI Codex:

#  Add to your Codex MCP configuration
npx @verygoodplugins/mcp-automem config - -format=json

#  Optional: add memory-first rules to this repo
npx @verygoodplugins/mcp-automem codex

👉 Full Installation Guide for detailed MCP client and platform-specific setup

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# New: Remote MCP via HTTP

You can now connect AutoMem to platforms that support remote MCP via Streamable HTTP (recommended) or SSE transport via an optional sidecar service (deployable to Railway or any Docker host).

• ChatGPT (Developer Mode custom connectors)
• Claude.ai (web) and Claude Mobile (iOS/Android)
• ElevenLabs Agents Platform

Quick connect URLs (after deploying the sidecar):

• Streamable HTTP (recommended): https://<your-mcp-domain>/mcp?api_token=<AUTOMEM_API_TOKEN>
• SSE (legacy): https://<your-mcp-domain>/mcp/sse?api_token=<AUTOMEM_API_TOKEN>
• ElevenLabs: https://<your-mcp-domain>/mcp with header Authorization: Bearer <AUTOMEM_API_TOKEN>

See the Installation Guide for complete steps and deployment options.

# # Remote MCP Platforms in Action

ChatGPT Developer Mode: Add your MCP endpoint as a custom connector

ChatGPT using AutoMem memories via remote MCP

Claude.ai website connected to AutoMem via remote MCP

Claude Mobile (iOS) connected to AutoMem via remote MCP

# What Happens Next

| Timeline | What Your AI Learns |
| - -- -- -- -- - | - -- -- -- -- -- -- -- -- -- -- -- -- -- -- - |
| Hour 1 | Starts capturing your patterns |
| Day 1 | Learns your decision factors |
| Day 3 | Recognizes your coding style |
| Week 1 | Writes in your voice |
| Week 2 | Makes decisions like you would |

# Architecture

┌─────────────────────────────────────────────┐
│         Your AI Platforms                   │
│  Claude Desktop │ Cursor │ Claude Code      │
└──────────────┬──────────────────────────────┘
               │ MCP Protocol
               ▼
┌──────────────────────────────────────────────┐
│   @verygoodplugins/mcp-automem (this repo)  │
│   • Translates MCP calls → AutoMem API      │
│   • Platform integrations & rules           │
│   • Handles authentication                   │
└──────────────┬───────────────────────────────┘
               │ HTTP API
               ▼
┌──────────────────────────────────────────────┐
│        AutoMem Service (separate repo)       │
│        github.com/verygoodplugins/automem    │
│   ┌────────────┐      ┌────────────┐        │
│   │  FalkorDB  │      │   Qdrant   │        │
│   │  (Graph)   │      │ (Vectors)  │        │
│   └────────────┘      └────────────┘        │
└──────────────────────────────────────────────┘

This repo (mcp-automem):

• MCP client that connects AI platforms to AutoMem
• Platform-specific integrations (Cursor rules, Claude Code hooks, etc.)
• Setup wizards and configuration tools

AutoMem service:

• Backend memory service with graph + vector storage
• Deployment guides (local, Railway, Docker, production)
• API server with FalkorDB + Qdrant

# Features

# # Core Memory Operations

• store_memory - Save memories with content, tags, importance, metadata
• recall_memory - Hybrid search with graph expansion and context awareness:
  • Basic search: query, multi-query, tags, time filters
  • Graph expansion: entity expansion (multi-hop reasoning), relation following
  • Expansion filtering: expand_min_importance and expand_min_strength to reduce noise in expanded results
  • Context hints: language, active file, priority types/tags
• associate_memories - Create relationships (11 types: RELATES_TO, LEADS_TO, etc.)
• update_memory - Modify existing memories
• delete_memory - Remove memories
• check_database_health - Monitor service status

# # Advanced Recall (v0.8.0+)

Multi-hop Reasoning - Answer complex questions like "What is Amanda's sister's career?"

mcp__memory__recall_memory({
  query: "What is Amanda's sister's career?",
  expand_entities: true, // Finds "Amanda's sister is Rachel" → memories about Rachel
});

Context-Aware Coding - Recall prioritizes language and style preferences

mcp__memory__recall_memory({
  query: "error handling patterns",
  language: "typescript",
  context_types: ["Style", "Pattern"],
});

# # Platform Integrations

# # # Cursor IDE

• ✅ Memory-first rule file (automem.mdc in .cursor/rules/)
• ✅ Automatic memory recall at conversation start
• ✅ Auto-detects project context (package.json, git remote)
• ✅ Global user rules option for all projects
• ✅ Simple setup via CLI or one-click install

# # # Claude Code

• ✅ MCP permissions for memory tools
• ✅ Memory rules in CLAUDE.md guide Claude's memory usage
• ✅ Simple setup - just permissions, Claude decides what to store

# # # Claude Desktop

• ✅ Direct MCP integration
• ✅ Manual and automated workflows
• ✅ Full memory API access

# Why AutoMem MCP?

# # vs. Building Your Own

• ✅ 2 years of R&D already done
• ✅ Research-validated architecture (HippoRAG 2, MELODI, A-MEM)
• ✅ Working integrations across all MCP platforms
• ✅ Active development and community

# # vs. Other Memory Solutions

• ✅ True graph relationships (not just vector similarity)
• ✅ Universal MCP compatibility (works with any MCP client)
• ✅ 7 memory types (Decision/Pattern/Preference/Style/Habit/Insight/Context)
• ✅ Self-hostable ($5/month vs $150+ for alternatives)

# # vs. Native AI Memory

• ✅ Persistent across sessions (not just context window)
• ✅ Cross-platform (same memory in Claude, Cursor, Code)
• ✅ Structured relationships (not just RAG)
• ✅ Infinite scale (no context window limits)

# Real-World Results

# # Code Review That Knows Your Standards

Before AutoMem:
"Consider adding error handling here."

After AutoMem:
"Missing your standard try/except pattern. Based on your PR# 127
review comments, you always wrap database calls with specific
logging for timeouts. Apply the same pattern here?"

# # Decisions With Context

Before AutoMem:
"Both approaches have tradeoffs..."

After AutoMem:
"You chose PostgreSQL over MongoDB for similar use case in Q1 2024.
Your decision memo cited team expertise and operational simplicity.
Same factors apply here - go with Postgres."

# Documentation

# # MCP Client & Integrations (this repo)

• 📦 Installation Guide - MCP client setup for all platforms
• 🌐 [Remote MCP via SSE](INSTALLATION.md# remote-mcp-via-sse-sidecar) - Connect ChatGPT, Claude Web/Mobile, ElevenLabs
• 🎯 [Cursor Setup](INSTALLATION.md# cursor-ide) - IDE integration with rules
• 🤖 Claude Code Setup - Memory rules integration
• 🚀 [OpenAI Codex Setup](INSTALLATION.md# openai-codex) - Codex CLI/IDE/Cloud integration
• 📖 [MCP Tools Reference](INSTALLATION.md# mcp-tools) - All memory operations

# # AutoMem Service (separate repo)

• 🏗️ AutoMem Service - Backend repository
• 🚀 Service Installation - Local, Railway, Docker deployment
• ⚙️ [API Documentation](https://github.com/verygoodplugins/automem# api-reference) - REST API reference

# The Science Behind AutoMem

The AutoMem service implements cutting-edge 2025 research:

• HippoRAG 2 (OSU, June 2025): Graph-vector approach achieves 7% better associative memory
• A-MEM (July 2025): Dynamic memory organization with Zettelkasten principles
• MELODI (DeepMind, 2025): 8x memory compression without quality loss
• ReadAgent (DeepMind, 2024): 20x context extension through gist memories

This MCP package provides the bridge between your AI and that research-validated memory system.

# Community & Support

• 💬 Discord - Join the community, get help, share feedback
• 🐦 X Community - Discussion and updates
• 📣 @automem_ai - Official announcements
• 📦 NPM Package - This MCP client
• 🔬 AutoMem Service - Backend repo with deployment guides
• 🐛 GitHub Issues - Bug reports and feature requests

# Quick Links

# # MCP Client Setup

• Installation Guide - MCP client setup for all platforms
• [Cursor Integration](INSTALLATION.md# cursor-ide) - IDE rules and configuration
• Claude Code Setup - Memory rules integration
• [OpenAI Codex](INSTALLATION.md# openai-codex) - Codex integration
• Changelog - Release history

# # AutoMem Service

• Service Repository - Backend source code
• Service Installation - Local, Railway, Docker deployment

# Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes with tests
4. Submit a pull request

# License

MIT - Because great memory should be free.

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Ready to give your AI perfect memory?

npx @verygoodplugins/mcp-automem setup

Built with obsession. Validated by neuroscience. Powered by graph theory. Works with every MCP-enabled AI.

Designed by Jack Arturo at Very Good Plugins 🧡

Transform your AI from a tool into a teammate. Start now.