Bee2Bee

Bee2Bee: Decentralized AI Network

Bee2Bee is a peer-to-peer network that allows you to easily deploy, route, and access AI models across any infrastructure (Local, Cloud, Colab) without complex networking configuration.

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🏗 System Architecture

The ecosystem consists of four main components:

1. Main Point (Tracker/API): The central supervisor that tracks active peers and exposes the HTTP API.
2. Worker Nodes (Providers): Machines that run deploy-hf to host AI models and serve requests.
3. Desktop App (Frontend): An Electron+React UI for managing the network and chatting with models.
4. Bee2Bee Cloud (Colab): A Google Colab notebook acting as a Cloud Node using Hybrid Tunneling.

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🚀 Quick Start Guide

1. Main End Point (The Supervisor)

This runs the core API server. Every network needs at least one Main Point.

Run Locally:

# Starts the API on Port 4002 and P2P Server on Port 4003
python -m bee2bee api

Output:

• API: http://127.0.0.1:4002 (Docs: /docs)
• P2P: ws://127.0.0.1:4003

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2. Desktop App (The Dashboard)

A modern UI to visualize the network and chat with models.

Prerequisites: Node.js 20+

Run Locally:

cd electron-app
npm install      # First time only
npm run dev

Usage:

• Open the App.
• It connects to http://localhost:4002 by default.
• Go to "Chat" to talk to available providers.
• See MANUAL_TESTING.md for detailed testing steps.

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3. Worker Node (The AI Provider)

Run this on any machine (or the same machine) to share an AI model.

Step A: Configure (Tell the node where the Main Point is)

# If running on the SAME machine as Main Point:
python -m bee2bee config bootstrap_url ws://127.0.0.1:4003

# If running on a DIFFERENT machine (LAN/WAN):
python -m bee2bee config bootstrap_url ws://<MAIN_POINT_IP>:4003

Step B: Deploy Model

Option 1: Hugging Face (Default) Uses transformers to run models like GPT-2, Llama, etc. on CPU/GPU.

# Deploys distilgpt2 (CPU friendly)
python -m bee2bee deploy-hf --model distilgpt2

Option 2: Ollama (Universal) Uses your local Ollama instance to serve models like Llama3, Mistral, Gemma, etc. Prerequisite: Install and run Ollama

# Serve a model (e.g., llama3)
python -m bee2bee serve-ollama --model llama3

Note: This creates a separate peer node on your machine.

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4. Bee2Bee Cloud (Google Colab)

Run a powerful node on Google's free GPU infrastructure using our Hybrid Tunneling setup.

Notebook Location: notebook/ConnectIT_Cloud_Node.ipynb

How it Works (Hybrid Tunneling): To bypass Colab's network restrictions, we use two tunnels:

1. API Tunnel (Cloudflare): Provides a stable HTTPS URL (trycloudflare.com) for the Desktop App to connect to.
2. P2P Tunnel (Bore): Provides a raw WebSocket URL (bore.pub) for other Worker Nodes to connect to.

Instructions:

1. Open the Notebook in Google Colab.
2. Run "Install Dependencies".
3. Run "Configure Hybrid Tunnels" (Installs cloudflared & bore).
   • Wait for it to output the URLs.
4. Run "Run Bee2Bee Node".
   • It automatically configures itself to announce the Bore address.

Connecting your Desktop App to Colab:

1. Copy the Cloudflare URL (e.g., https://funny-remote-check.trycloudflare.com).
2. Open Desktop App -> Settings.
3. Paste into "Main Point URL".

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🛠 Advanced Configuration

Environment Variables

You can override settings using ENV vars:

Variable	Description	Default
BEE2BEE_PORT	Port for P2P Server	4003 (Worker) / 4003 (API)
BEE2BEE_HOST	Bind Interface	0.0.0.0
BEE2BEE_ANNOUNCE_HOST	Public Hostname (for NAT/Tunnel)	Auto-detected
BEE2BEE_ANNOUNCE_PORT	Public Port (for NAT/Tunnel)	Auto-detected
BEE2BEE_BOOTSTRAP	URL of Main Point	None

Troubleshooting

• "Connection Refused": Ensure the bootstrap_url is correct and reachable (try ping).
• "0 Nodes Connected": Check if the Worker Node can reach the Main Point's P2P address (WSS).
• Colab Disconnects: Ensure the Colab tab stays open. Tunnels change if you restart the notebook.

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🤝 Contributing

Contributions are welcome! Please open an issue or PR on GitHub.