README.md

Multi-Agent Data Collection and Summarization System

This project implements an agent-based system that leverages LlamaIndex and MCP servers to perform automated data collection, processing, and presentation tasks. The system supports multi-agent collaboration for research and summarization workflows.

Features

• Multi-agent architecture for distributed data collection and processing
• Integration with MCP servers for data management
• Utilization of LlamaIndex for intelligent data indexing and querying
• Automated summarization and visualization of data
• Text-to-speech capabilities for presentation

Requirements

This project requires the following Python libraries:

• llama-index
• mcp-server-client
• playwright
• networkx
• matplotlib
• pyttsx3
• openai
• requests

Setup

1. Clone the repository:

git clone <repository_url>
cd <repository_directory>

2. Install dependencies:

pip install -r requirements.txt

3. Install Playwright browsers (if needed):

python -m playwright install

Usage

To run the system, execute the main script:

python main.py

Ensure that any necessary configuration (e.g., API keys, server URLs) are set within the scripts or environment variables as appropriate.

Files

• main.py: Entry point for orchestrating the agents and workflows.
• tools.py: Utility functions for data processing, visualization, and speech synthesis.
• agent.py: Defines agent classes responsible for specific tasks such as data collection, processing, and summarization.
• requirements.txt: Lists all required dependencies.

Example

Below is a high-level example of how the system initializes and runs:

from main import run_system

if __name__ == "__main__":
    run_system()

License

This project is licensed under the MIT License.

Contact

For questions or contributions, please open an issue or contact [Your Name] at [your.email@example.com].

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Note: Replace placeholders such as <repository_url>, <repository_directory>, and contact info with actual details.