A comprehensive seminar and learning resource on Agent Oriented Programming (AOP), covering concepts, frameworks, and practical implementations.
This repository contains educational materials and practical examples for learning Agent Oriented Programming - a paradigm shift from traditional software development to autonomous, intelligent software entities that can perceive, reason, and act independently.
- Core Concepts: Understanding agents, autonomy, and multi-agent systems
- Communication Technologies: MCP (Model Context Protocol) and A2A (Agent-to-Agent) communication
- Development Frameworks: LangChain, LangGraph, AutoGen, CrewAI, and more
- Practical Applications: Real-world examples in customer service, content management, and trading
📦 Agent Oriented Programming/
├── 📄 seminar-slides.md # Complete seminar content in markdown
├── 📁 slides/ # Interactive web presentation
│ ├── index.html # Main presentation file
│ ├── styles.css # Presentation styling
│ └── script.js # Presentation functionality
└── 📁 projects/ # Practical implementation examples
└── personal_assistant/ # 🚧 Work in Progress
├── agents/ # Agent implementations
└── mcp_servers/ # MCP server examples
Option 1: Web Presentation (Recommended)
- Navigate to the
slides/
directory - Open
index.html
in your web browser - Use arrow keys or navigation buttons to browse slides
Option 2: Markdown Format
- Open
seminar-slides.md
in any markdown viewer or text editor
The practical examples are designed to demonstrate real-world agent implementations:
# Navigate to project directory
cd "projects/personal_assistant"
# Follow individual project README files for setup instructions
-
Introduction to Agents
- What are software agents?
- Key characteristics and types
- Agent vs. traditional programming
-
Agent Oriented Programming (AOP)
- Core principles and mental models
- Architecture patterns
- Paradigm evolution
-
Traditional vs. Agentic Programming
- System architecture comparison
- Control flow differences
- Benefits of the agentic approach
-
Communication Technologies
- Model Context Protocol (MCP)
- Agent-to-Agent (A2A) communication
- Protocol selection guidelines
-
Development Frameworks
- LangChain: Rapid LLM application development
- LangGraph: Graph-based agent workflows
- AutoGen: Conversational multi-agent systems
- CrewAI: Role-based agent coordination
-
Practical Examples
- Intelligent customer service system
- Smart content management system
- Real-time trading system
-
Best Practices & Next Steps
- Design principles
- Testing strategies
- Monitoring and observability
A comprehensive multi-agent personal assistant system demonstrating:
- Agent Architecture: Modular agent design with specialized capabilities
- MCP Integration: Server implementations for external tool access
- A2A Communication: Inter-agent coordination and collaboration
- Real-world Applications: Calendar management, contact handling, and more
Status: 🔄 Active Development
Components:
agents/
: Core agent implementationsmcp_servers/
: MCP server examples (contacter service)subagents/
: Specialized agent modules
Note: All projects in this repository are currently in development. They serve as educational examples and learning resources. Check individual project README files for current status and setup instructions.
- LangChain Documentation
- LangGraph Tutorials
- MCP Specification
- AutoGen Documentation
- CrewAI Documentation
- LangChain GitHub Repository
- LangGraph Examples
- AutoGen Examples
- MCP Servers
- Agent Development Patterns
- Start with the seminar slides (
slides/index.html
) - Read through the markdown content (
seminar-slides.md
) - Explore the basic concepts and terminology
- Dive into the practical examples in
projects/
- Study the MCP server implementations
- Experiment with different communication patterns
- Implement your own multi-agent system
- Contribute to the existing projects
- Explore advanced topics like agent learning and formal verification
We welcome contributions to improve the educational content and project examples!
- Documentation: Improve explanations, fix typos, add examples
- Code Examples: Add new agent implementations or improve existing ones
- Projects: Help complete the work-in-progress projects
- Resources: Suggest additional learning materials and references
- Fork this repository
- Create a feature branch (
git checkout -b feature/amazing-improvement
) - Make your changes
- Submit a pull request
This project is intended for educational purposes. Please check individual project directories for specific licensing information.
Have questions or suggestions? Feel free to:
- Open an issue in this repository
- Contribute improvements via pull requests
- Share your own agent implementations
"The future of software development is not just about writing code—it's about creating intelligent entities that can think, learn, and collaborate."
Happy Agent Building! 🤖✨