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🚀 OWL with Qwen3 MCP Integration

community_usecase/qwen3_mcp/README.md

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🚀 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:

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

  2. Install dependencies:

    bash
    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:

    bash
    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:

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:

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:
    bash
    npm install -D @playwright/mcp
    Then you can update the config to use the local installation:
    json
    "playwright": {
  "command": "npx",
  "args": [
    "@playwright/mcp"
  ]
}

🚀 Usage

Run the script with a task prompt:

bash
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