owl/community_usecase/qwen3_mcp

🚀 OWL with Qwen3 MCP Integration

This project demonstrates how to use the CAMEL-AI OWL framework with Qwen3 large language model through MCP (Model Context Protocol). The example showcases improved terminal output formatting, markdown log generation, and seamless integration with MCP servers.

✨ Features

• Enhanced Terminal Output: Colorful, well-formatted console output for better readability
• Automatic Log Generation: Creates detailed markdown logs of agent conversations with timestamps
• Qwen3 Integration: Seamlessly uses Qwen3-Plus for both user and assistant agents
• MCP Server Support: Connects to MCP servers including EdgeOne Pages and Playwright
• Robust Error Handling: Graceful cleanup and exception management

📋 Prerequisites

• Python 3.10+
• OWL framework installed
• Qwen API key
• Node.js (for Playwright MCP)

🛠️ Setup

1. Clone the OWL repository:

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

2. Install dependencies:

   pip install -r requirements.txt
   

3. Add your Qwen API key to the .env file:

   QWEN_API_KEY=your_api_key_here
   

4. Configure MCP servers in mcp_sse_config.json

5. (Optional) Install Playwright MCP manually:

   npm install -D @playwright/mcp
   

   Note: This step is optional as the config will auto-install Playwright MCP through npx.

🧩 MCP Servers Included

This example integrates with two powerful MCP servers:

1. EdgeOne Pages MCP (edgeone-pages-mcp)

EdgeOne Pages MCP is a specialized service that enables:

• Instant HTML Deployment: Deploy AI-generated HTML content to EdgeOne Pages with a single call
• Public Access URLs: Generate publicly accessible links for deployed content
• No Setup Required: Uses an SSE (Server-Sent Events) endpoint, so no local installation is needed

Configuration in mcp_sse_config.json:

"edgeone-pages-mcp": {
  "type": "sse",
  "url": "https://mcp.api-inference.modelscope.cn/sse/fcbc9ff4e9704d"
}

2. Playwright MCP (playwright)

Playwright MCP is a powerful browser automation tool that allows:

• Web Navigation: Browse websites and interact with web pages
• Screen Capture: Take screenshots and extract page content
• Element Interaction: Click, type, and interact with page elements
• Web Scraping: Extract structured data from web pages

Configuration in mcp_sse_config.json:

"playwright": {
  "command": "npx",
  "args": [
    "@playwright/mcp@latest"
  ]
}

Installation Options:

1. Auto-installation: The configuration above automatically installs and runs Playwright MCP through npx.
2. Manual installation: If you prefer to install it permanently in your project:

   npm install -D @playwright/mcp
   

   Then you can update the config to use the local installation:

   "playwright": {
     "command": "npx",
     "args": [
       "@playwright/mcp"
     ]
   }
   

🚀 Usage

Run the script with a task prompt:

python run_mcp_qwen3.py "Your task description here"

If no task is provided, a default task will be used.

📊 Output

1. Terminal Output: Colorful, formatted output showing:

   • System messages
   • Task specifications
   • Agent conversations
   • Tool calls
   • Task completion status

2. Markdown Logs: Detailed conversation logs saved to conversation_logs/ directory

🔧 Customization

• Modify mcp_sse_config.json to add or remove MCP servers
• Adjust model parameters in the construct_society function
• Change the maximum conversation rounds with the round_limit parameter

📝 Technical Details

This implementation extends the standard OWL run_mcp.py script with:

1. Colorized terminal output using Colorama
2. Structured markdown log generation
3. Improved error handling and graceful termination
4. Enhanced formatting for tool calls and agent messages

🤝 Acknowledgements

• CAMEL-AI Organization
• OWL Framework
• Model Context Protocol (MCP)
• Qwen API
• EdgeOne Pages
• Microsoft Playwright