Agent Demos

A comprehensive collection of AI agent implementations using various agentic frameworks, with a focus on data analysis and Keboola MCP Server integration.

🚀 Overview

This repository demonstrates how to build powerful AI agents using different frameworks:

• CrewAI: Multi-agent collaboration for complex data analysis tasks
• LangChain: Flexible agent framework with extensive tool integration
• DSPy: Programming foundation models with optimized prompting

All demos integrate with the Keboola MCP Server for seamless data platform operations.

📁 Repository Structure

agent-demos/
├── crew-ai/           # CrewAI-based agents
├── langchain/         # LangChain agent implementations  
├── dspy/              # DSPy framework examples
├── .env.template      # Environment variables template
└── README.md          # This file

🛠️ Framework Comparisons

Framework	Strengths	Best For
CrewAI	Multi-agent collaboration, role-based agents	Complex workflows requiring specialized roles
LangChain	Extensive tool ecosystem, mature framework	General-purpose agents with many integrations
DSPy	Optimized prompting, systematic approach	Research and performance-critical applications

🔧 Setup

1. Clone the repository:

   git clone <repository-url>
   cd agent-demos

2. Set up environment variables:

   cp .env.template .env
   # Edit .env with your API keys and configuration

3. Install dependencies for each framework:

   # CrewAI
   cd crew-ai && pip install -r requirements.txt
   
   # LangChain  
   cd ../langchain && pip install -r requirements.txt
   
   # DSPy
   cd ../dspy && pip install -r requirements.txt

🎯 Quick Start

CrewAI Demo

cd crew-ai
python demo.py

LangChain Demo

cd langchain
python demo.py

DSPy Demo

cd dspy
python demo.py

🔌 Keboola MCP Integration

All frameworks integrate with Keboola MCP Server for:

• Data Access: Query tables and buckets
• Transformations: Create and manage data transformations
• Job Management: Monitor and execute data pipelines
• Metadata: Access component and configuration information

Required Environment Variables

KBC_STORAGE_API_URL=https://connection.keboola.com
KBC_STORAGE_TOKEN=your_keboola_token
OPENAI_API_KEY=your_openai_key

📊 Use Cases

Data Analysis

• Customer segmentation and behavior analysis
• Revenue forecasting and trend analysis
• Operational metrics and KPI tracking
• Churn prediction and retention analysis

Business Intelligence

• Automated report generation
• Cross-platform data correlation
• Real-time dashboard updates
• Performance monitoring

Data Engineering

• Pipeline orchestration
• Data quality monitoring
• Transformation optimization
• Schema management

🔍 Climbing Gym Analysis Example

This repository includes comprehensive examples for analyzing climbing gym data, including:

• Program Attendance Analysis: Track attendance by location and department
• Yoga Class Popularity: Identify the most popular yoga classes
• ROI Analysis: Calculate return on investment considering instructor pay rates and class revenue
• Youth Program Metrics: Analyze youth class attendance and coach-to-kid ratios
• Financial Performance: Join labor data with revenue data for comprehensive analysis

See the individual framework directories for specific implementations.

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Keboola for the MCP Server
• CrewAI team
• LangChain community
• DSPy researchers

📞 Support

For questions and support:

• Open an issue in this repository
• Check the individual framework documentation
• Review the Keboola MCP Server documentation