Graphiti Cloud via Cloudflare Workers

A Cloudflare Container Worker that serves as a proxy for the Graphiti MCP (Model Context Protocol) server, providing scalable, serverless access to AI agent memory capabilities through Neo4j-backed knowledge graphs.

Overview

Graphiti Cloud bridges the gap between AI applications and persistent memory by leveraging Cloudflare's new container service to host and proxy requests to a Graphiti MCP server. This enables AI agents to maintain context and memory across interactions using a powerful knowledge graph backend.

Features

• Serverless Architecture: Built on Cloudflare Workers with container support
• AI Memory Service: Persistent memory for AI agents via knowledge graphs
• Neo4j Integration: Robust graph database for complex relationship storage
• Auto-scaling: Up to 5 container instances with intelligent sleep management
• Global Edge Network: Deployed across Cloudflare's global infrastructure
• Secure: Environment-based configuration for sensitive credentials

Architecture

---
config:
  theme: neutral
  look: handDrawn
  layout: dagre
---
flowchart TB
 subgraph subGraph0["MCP-Enabled Clients"]
        Cursor["Cursor IDE"]
        Claude["Claude Desktop"]
        MCPClient["Other MCP Clients"]
  end
 subgraph subGraph1["Cloudflare Edge"]
        Worker["Graphiti Cloud Worker"]
        Container["Graphiti MCP Container"]
        CF["Cloudflare Infrastructure"]
  end
 subgraph subGraph2["External Services"]
        Neo4j[("Neo4j Knowledge Graph")]
        OpenAI["OpenAI API"]
  end
    Cursor --> Worker
    Claude --> Worker
    MCPClient --> Worker
    Worker --> Container
    Container --> Neo4j & OpenAI
    Worker -.-> CF
    Container -.-> CF
     Cursor:::Aqua
     Cursor:::Ash
     Claude:::Pine
     Claude:::Peach
     Claude:::Ash
     MCPClient:::Rose
     MCPClient:::Ash
     Worker:::Sky
     Container:::Sky
     CF:::Peach
     Neo4j:::Sky
     OpenAI:::Aqua
    classDef Pine stroke-width:1px, stroke-dasharray:none, stroke:#254336, fill:#27654A, color:#FFFFFF
    classDef Ash stroke-width:1px, stroke-dasharray:none, stroke:#999999, fill:#EEEEEE, color:#000000
    classDef Rose stroke-width:1px, stroke-dasharray:none, stroke:#FF5978, fill:#FFDFE5, color:#8E2236
    classDef Peach stroke-width:1px, stroke-dasharray:none, stroke:#FBB35A, fill:#FFEFDB, color:#8F632D
    classDef Sky stroke-width:1px, stroke-dasharray:none, stroke:#374D7C, fill:#E2EBFF, color:#374D7C
    classDef Aqua stroke-width:1px, stroke-dasharray:none, stroke:#46EDC8, fill:#DEFFF8, color:#378E7A

Quick Start

Prerequisites

• Node.js (v18 or later)
• pnpm package manager
• Wrangler CLI
• Neo4j database instance
• OpenAI API key

Neo4j Setup

You'll need a Neo4j database to store the knowledge graph. Here are your options:

1. Neo4j AuraDB (Recommended) - Fully managed cloud service

   • Sign up at Neo4j AuraDB
   • Create a free instance (up to 200k nodes and 400k relationships)
   • Get your connection URI, username, and password

2. Self-hosted Neo4j

   • Download Neo4j Community Edition
   • Follow the installation guide

3. Neo4j Desktop

   • Download Neo4j Desktop
   • Create a local database for development

Learn more about Neo4j: Neo4j is a graph database that stores data as nodes and relationships, making it perfect for knowledge graphs. Check out the Neo4j Graph Database Concepts guide.

Installation

1. Clone the repository:

git clone https://github.com/adam-paterson/graphiti-cloud.git
cd graphiti-cloud

2. Install dependencies:

pnpm install

3. For Development: Configure environment variables in .dev.vars:

NEO4J_URI=neo4j://your-neo4j-instance:7687
NEO4J_USER=your-username
NEO4J_PASSWORD=your-password
OPENAI_API_KEY=your-openai-api-key
BEARER_TOKEN=your-secure-bearer-token

4. For Production: Add secrets to your Cloudflare Worker:

# Add each secret using wrangler
wrangler secret put NEO4J_URI
wrangler secret put NEO4J_USER
wrangler secret put NEO4J_PASSWORD
wrangler secret put OPENAI_API_KEY
wrangler secret put BEARER_TOKEN

Learn more: See the Cloudflare Workers Secrets documentation for detailed instructions on managing secrets.

Development

Start the development server:

pnpm dev

The worker will be available at http://localhost:8787

Debugging with MCP Inspector

For debugging the MCP protocol communication, you can use the official MCP Inspector:

# Install the MCP Inspector globally
npm install -g @modelcontextprotocol/inspector

# Start the inspector pointing to your local worker
mcp-inspector http://localhost:8787

This will open a web interface where you can:

• Send MCP protocol requests
• View request/response payloads
• Debug the communication between your client and the Graphiti MCP server

Learn more: Check out the MCP Inspector documentation for advanced debugging techniques.

Deployment

Deploy to Cloudflare:

wrangler deploy

Configuration

Container Settings

The GraphitiMCPContainer class extends Cloudflare's Container with these configurations:

• Default Port: 8000
• Sleep Timeout: 1 hour of inactivity
• Internet Access: Enabled for external API calls
• Max Instances: 5 containers
• Image: knowledge-graph-mcp:0.4.0

Environment Variables

| Variable | Description | Required | Example | |----------|-------------|----------|---------| | NEO4J_URI | Neo4j database connection string | | neo4j://localhost:7687 or neo4j+s://xxx.databases.neo4j.io | | NEO4J_USER | Neo4j username | | neo4j | | NEO4J_PASSWORD | Neo4j password | | your-secure-password | | OPENAI_API_KEY | OpenAI API key for AI functionality | | sk-... | | BEARER_TOKEN | Bearer token for authentication | | your-secure-bearer-token |

Usage

Basic Proxy Request

All requests are proxied to the Graphiti MCP container:

// Example client request
const response = await fetch('https://your-worker.your-subdomain.workers.dev/', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    // MCP protocol request
  })
})

Integration with AI Applications

Graphiti Cloud is designed to work with AI applications that support the MCP protocol:

import { MCPClient } from '@modelcontextprotocol/client'

const client = new MCPClient({
  serverUrl: 'https://your-worker.your-subdomain.workers.dev/'
})

// Use the client to interact with the knowledge graph
await client.addMemory({
  content: 'User prefers dark mode',
  context: 'user-preferences'
})

Development

Project Structure

graphiti-cloud/
├── src/
│   └── index.ts          # Main worker and container logic
├── test/
│   ├── index.spec.ts     # Test specifications
│   └── env.d.ts          # Environment type definitions
├── wrangler.jsonc        # Cloudflare Worker configuration
├── package.json          # Project dependencies
└── README.md            # This file

Scripts

• pnpm dev - Start development server
• pnpm lint - Run ESLint
• pnpm types - Generate Wrangler types

Testing

Currently, Cloudflare's test suite doesn't fully support containers. Integration testing is recommended using the deployed worker.

Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

Related Projects

• Graphiti - The underlying MCP server for knowledge graphs
• Model Context Protocol - The protocol specification
• Cloudflare Containers - Cloudflare's container service
• Neo4j - The graph database powering the knowledge storage

License

MIT License Adam Paterson

Support

• Email: [hello@adampaterson.co.uk]
• Issues: GitHub Issues
• Discussions: GitHub Discussions

_{Built with using Cloudflare Workers and the power of knowledge graphs}