How do I install Memory Buddy?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Memory Buddy need an API key?

No required environment variable is listed on this record.

What transport does Memory Buddy use?

Memory Buddy runs locally over stdio.

When was Memory Buddy last checked?

Last checked 2026-10-28. Shared memory layer for AI agents. Deploy once, connect Hermes/Trae/Cursor/Claude Desktop via MCP - they all share one memory. $0/month on Cloudflare free tier.

Memory Buddy MCP 서버

에이전트 강화

이 서버 추가

Install from the publisher. Not a PlugMCP endpoint.

PlugMCP에서 실행

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

PlugMCP에서 실행

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

실행

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-28

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

MemoryBuddy 🧠

中文版

Give your AI agents a shared memory that lasts. Deploy once, connect any MCP-compatible AI tool — Hermes, Trae, Cursor, Claude Desktop — they all share the same memory.

🌟 What is this?

Most AI tools suffer from "goldfish memory" — refresh the page, start a new session, switch to another app, and everything's gone. You keep reintroducing yourself, re-explaining your preferences, re-stating context.

MemoryBuddy fixes this with a shared memory layer that any AI tool can read from and write to:

🧠 Long-term memory — facts, preferences, decisions persist across sessions
🔍 Semantic search — find relevant memories by meaning, not just keywords
🤖 Auto fact extraction — LLM automatically distills what's worth remembering
📝 Smart summarization — long conversations get compressed, key points retained
🗑️ One-click forget — DELETE wipes everything, GDPR compliant
🔌 MCP protocol — any MCP-compatible client can connect, zero integration code
💸 $0/month — runs entirely on Cloudflare's free tier

💡 What problem does it solve?

😣 Without MemoryBuddy	✅ With MemoryBuddy
Every AI tool starts fresh — you re-explain yourself constantly	All your AI tools share one memory — tell one, they all know
Switching from Hermes to Trae means losing all context	Switch freely — memory lives in the cloud, not in the tool
AI forgets your preferences between sessions	Preferences persist forever, across all sessions and all tools
Long conversations hit context limits	Auto-summarization keeps things compact
Privacy concerns — can't delete what it remembers	One API call wipes everything, fully GDPR compliant

🏗️ Architecture

┌─────────┐   ┌─────────┐   ┌─────────┐   ┌─────────┐
│ Hermes  │   │  Trae   │   │ Cursor  │   │ Claude  │
└────┬────┘   └────┬────┘   └────┬────┘   └────┬────┘
     │ MCP         │ MCP         │ MCP         │ MCP
     ▼             ▼             ▼             ▼
┌──────────────────────────────────────────────────┐
│           MemoryBuddy Worker (Cloudflare)         │
│                                                  │
│   /mcp  → MCP Server (5 tools, Streamable HTTP)  │
│   /chat → HTTP API (SSE streaming + auto-extract)│
│   /memory/:userId → REST API                     │
└──────────┬──────────────────┬────────────────────┘
           │                  │
     ┌─────▼─────┐    ┌──────▼──────┐
     │ D1 (facts)│    │ Vectorize   │
     │ SQLite DB │    │ (embeddings)│
     └───────────┘    └─────────────┘

Three-tier memory:

Short-term (Durable Object) — current conversation context
Long-term (D1 database) — structured facts: name, preferences, key entities
Semantic (Vectorize) — vector embeddings for meaning-based recall

🚀 Quick Start (3 steps, ~5 minutes)

Prerequisites

Cloudflare account (free is fine)
Node.js 18+

1. Clone & Install

git clone https://github.com/Trainspotting31/memory-buddy.git
cd memory-buddy
npm install

2. Create Cloudflare Resources

npx wrangler login

# Create D1 database
npx wrangler d1 create memory-buddy-db

# Create Vectorize index
npx wrangler vectorize create memory-buddy-index --dimensions 768 --metric cosine

# Initialize database schema
npx wrangler d1 execute memory-buddy-db --remote --file=schema.sql

Copy the generated database_id into wrangler.toml (rename from wrangler.toml.example).

3. Deploy

npx wrangler deploy

Done! Your memory server is live at https://memory-buddy.<your-subdomain>.workers.dev 🎉

🔌 Connect Your AI Tools

MemoryBuddy speaks MCP (Model Context Protocol). Any MCP-compatible tool can connect — they all share the same memory.

Hermes Agent

hermes mcp add memory-buddy --url https://memory-buddy.<your-subdomain>.workers.dev/mcp

Trae IDE

Settings → MCP → Add Manually
Type: Streamable HTTP
URL: https://memory-buddy.<your-subdomain>.workers.dev/mcp

Or create .trae/mcp.json in your project:

{
  "mcpServers": {
    "memory-buddy": {
      "type": "streamable-http",
      "url": "https://memory-buddy.<your-subdomain>.workers.dev/mcp"
    }
  }
}

Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "memory-buddy": {
      "url": "https://memory-buddy.<your-subdomain>.workers.dev/mcp"
    }
  }
}

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "memory-buddy": {
      "type": "streamable-http",
      "url": "https://memory-buddy.<your-subdomain>.workers.dev/mcp"
    }
  }
}

Any MCP Client (raw config)

Endpoint: https://memory-buddy.<your-subdomain>.workers.dev/mcp
Transport: Streamable HTTP
Auth: None (or add your own)

🛠️ MCP Tools

Once connected, the AI gets 5 tools:

Tool	What it does	When AI calls it
`recall_memory`	Load all memory for a user	Start of conversation
`search_memory`	Semantic search by meaning	"What did I say about X?"
`store_memory`	Save a new fact	User shares preferences, decisions
`forget_memory`	Delete all memory	User says "forget everything"
`list_memory_users`	List all memory spaces	Checking what exists

Shared memory: All tools default to userId: "hermes-shared". Use different userIds to isolate memory per project/persona.

📡 HTTP API (no MCP needed)

`POST /chat` — Chat with memory

curl -N -X POST https://your-worker.workers.dev/chat \
  -H "Content-Type: application/json" \
  -d '{"userId":"user123","message":"Hi! I'm John and I love espresso."}'

`GET /memory/:userId` — Get all memory

curl https://your-worker.workers.dev/memory/user123

`DELETE /memory/:userId` — Wipe memory

curl -X DELETE https://your-worker.workers.dev/memory/user123

`GET /health` — Health check

curl https://your-worker.workers.dev/health

⚙️ Configuration

Edit wrangler.toml:

[vars]
LLM_MODEL = "@cf/meta/llama-3.2-3b-instruct"  # Default: Workers AI (free)

# Optional: use external LLM instead of Workers AI
LLM_API_KEY = "sk-your-key"
LLM_API_BASE = "https://api.openai.com/v1"
LLM_MODEL = "gpt-4o-mini"

💸 Why Cloudflare Free Tier?

Component	Free Tier	Self-Hosted Equivalent
Compute (Workers)	100K req/day	$5–$50/mo (VPS)
Database (D1)	1GB storage	$10–$100/mo (Postgres)
Vector DB (Vectorize)	256K vectors	$70+/mo (Pinecone)
LLM (Workers AI)	10K neurons/day	$10+/mo (API)
Total	$0	~$100+/mo

📁 Project Structure

memory-buddy/
├── src/
│   ├── index.ts          # Hono router: /mcp + /chat + /memory + /health
│   ├── mcp.ts            # MCP Server factory (5 tools, stateless)
│   ├── agent-do.ts       # Durable Object: chat session + memory orchestration
│   ├── llm.ts            # LLM abstraction (Workers AI / OpenAI-compatible)
│   └── memory/
│       ├── extract.ts    # LLM-powered fact extraction
│       ├── retrieve.ts   # Hybrid retrieval (D1 + Vectorize)
│       └── summarize.ts  # Conversation summarization
├── public/index.html     # Built-in demo chat UI
├── schema.sql            # D1 database schema
├── wrangler.toml.example # Cloudflare config template
└── package.json

🎮 Try the Demo

Open your Worker URL in a browser — you'll see a built-in chat interface.

Tell the agent your name and a preference ("I'm Sarah, I'm allergic to peanuts")
Refresh the page
Ask: "What do you know about me?"

It remembers everything. That's MemoryBuddy.

🗺️ Roadmap

🤝 Contributing

Fork → 2. Branch → 3. Commit → 4. Push → 5. PR

📄 License

MIT — see LICENSE

이 서버 추가

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Memory Buddy?

Does Memory Buddy need an API key?

What transport does Memory Buddy use?

When was Memory Buddy last checked?

Documentation

MemoryBuddy 🧠

🌟 What is this?

💡 What problem does it solve?

🏗️ Architecture

🚀 Quick Start (3 steps, ~5 minutes)

Prerequisites

1. Clone & Install

2. Create Cloudflare Resources

3. Deploy

🔌 Connect Your AI Tools

Hermes Agent

Trae IDE

Cursor

Claude Desktop

Any MCP Client (raw config)

🛠️ MCP Tools

📡 HTTP API (no MCP needed)

POST /chat — Chat with memory

GET /memory/:userId — Get all memory

DELETE /memory/:userId — Wipe memory

GET /health — Health check

⚙️ Configuration

💸 Why Cloudflare Free Tier?

📁 Project Structure

🎮 Try the Demo

🗺️ Roadmap

🤝 Contributing

📄 License

`POST /chat` — Chat with memory

`GET /memory/:userId` — Get all memory

`DELETE /memory/:userId` — Wipe memory

`GET /health` — Health check