MemoryBuddy 🧠
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 —
DELETEwipes 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?
🏗️ 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:
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?
📁 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
- MCP Server (Streamable HTTP)
- Multi-agent shared memory
- Semantic search
- Auto fact extraction
- Memory categories & filtering
- User authentication
- Batch memory import/export
- Multi-language support
- Hermes plugin (auto-inject memory at conversation start)
🤝 Contributing
- Fork → 2. Branch → 3. Commit → 4. Push → 5. PR
📄 License
MIT — see LICENSE
