🤖 Puppeteer Task Runner (Streamlit + CAMEL-AI + MCP)

A Streamlit app powered by the CAMEL-AI OWL framework and MCP (Model Context Protocol) that connects to a Puppeteer-based MCP server. It allows natural language task execution via autonomous agents, combining local tool access with browser automation.

✨ Features

Text-to-action UI: Enter a task and let the agent figure out how to solve it.
OwlRolePlaying Agents: Multi-agent system using CAMEL-AI to simulate human–AI collaboration.
MCP Integration: Connects to Puppeteer MCP servers for real-world browser-based task execution.
Error handling & logs: Gracefully handles connection issues and provides debug logs.

📋 Prerequisites

Python >=3.10,<3.13
Node.js & npm (for the MCP Puppeteer server plugin)
A valid OpenAI API key set in your environment:
bash
```
export OPENAI_API_KEY="your_api_key_here"
```

🛠️ Setup

Clone the repository

bash

git clone https://github.com/camel-ai/owl.git
cd owl/community_usecase/Puppeteer MCP

Create a virtual environment

bash

python -m venv venv
source venv/bin/activate      # macOS/Linux
venv\\Scripts\\activate     # Windows

Install Python dependencies
bash
```
pip install -r requirements.txt
```

⚙️ Configuration

Environment Variables
Create a .env file in the root directory with:
ini
```
OPENAI_API_KEY=your_openai_key_here
```

MCP Server Config
Ensure mcp_servers_config.json is present and contains:

json

{
  "mcpServers": {
    "puppeteer": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/mcp-server-puppeteer"]
    }
  }
}

🚀 Running the App

Run the Streamlit app:

bash

streamlit run demo.py

This will open the UI in your browser. Enter a natural language task (e.g., “Search for the weather in Paris”) and click Run Task.

🔧 Customization

Model config: Change model types in the construct_society function.
Prompt behavior: Adjust task wording, agent roles, or tool combinations as needed.
Error handling: You can improve the exception output area for better UI display.

📂 Project Structure

Puppeteer-MCP/
├── demo.py                   # Streamlit frontend
├── mcp_servers_config.json   # MCP config     
└── .env                      # Secrets and keys

📚 References

Let your agents browse and automate the web for you!