Complete-Agentic-AI-Bootcamp

# Complete Agentic AI Bootcamp A comprehensive learning resource covering the fundamentals and advanced concepts of Agentic AI, from Pydantic data validation to production-ready multi-agent systems with LangGraph. ## 📚 Overview This bootcamp provides a structured, hands-on journey through modern Agentic AI development. Whether you're a beginner looking to understand AI agents or an advanced developer building complex autonomous systems, this course has something for you. **Key Focus Areas:** - Data validation and schema design with Pydantic - AI agent architecture and agentic AI paradigms - LangGraph framework for building stateful AI workflows - Advanced patterns including RAG, human-in-the-loop, and guardrails ## 🎯 Course Structure ### 01 - Pydantic in Python Master data validation and serialization with Pydantic. Learn how to create robust, type-safe data models that form the foundation of production AI systems. **Topics:** - Data validation basics - Complex type hints and nested models - Custom validators and transformations - Serialization and deserialization ### 02 - AI Agents VS Agentic AI Understand the distinction between traditional AI agents and the modern agentic AI paradigm. **Topics:** - Architecture of AI agents - Agentic AI principles and patterns - Comparison of approaches - Real-world use cases ### 03 - Introduction To LangGraph Get started with LangGraph, a framework for building stateful, multi-step AI workflows. **Topics:** - LangGraph fundamentals - Graph-based workflow design - State management - Basic workflow execution ### 04 - LangGraph Components Deep dive into the core building blocks of LangGraph. **Topics:** - Nodes and edges - State transitions - Tools and tool calling - Message handling ### 05 - Debugging LangGraph Learn best practices for troubleshooting and optimizing LangGraph workflows. **Topics:** - Debugging techniques - Logging and monitoring - Performance optimization - Common pitfalls and solutions ### 06 - LangGraph Workflows Explore practical workflow patterns and advanced LangGraph features. **Topics:** - Complex workflow patterns - Branching and conditional logic - Error handling and recovery - Workflow composition ### 07 - Human In The Loop Integrate human feedback into AI workflows for enhanced decision-making. **Topics:** - Human-in-the-loop architecture - Interruption and resumption patterns - Feedback collection and integration - Interactive AI systems ### 08 - RAG With LangGraph Build Retrieval-Augmented Generation systems using LangGraph. **Topics:** - RAG fundamentals - Document retrieval and indexing - Integration with LangGraph - Question-answering systems ### 09 - VectorLess RAG Explore modern RAG approaches that don't rely on vector databases. **Topics:** - Alternative retrieval methods - Keyword-based and semantic search - Hybrid retrieval strategies - Scalability and efficiency ### 10 - Guardrails Implement safety mechanisms and validation in AI systems. **Topics:** - Input/output validation - Prompt injection prevention - Content filtering - Compliance and safety patterns ## 🛠️ Prerequisites - **Python 3.8+** - Core programming language - **Jupyter Notebook** - Interactive learning environment - **pip** - Python package manager - **Basic Python knowledge** - Variables, functions, classes - **Understanding of LLMs** - Familiarity with large language models ## 📦 Installation 1. **Clone the repository:** ```bash git clone https://github.com/mdzaheerjk/Complete-Agentic-AI-Bootcamp-2026.git cd Complete-Agentic-AI-Bootcamp-2026 ``` 2. **Create a virtual environment:** ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` 3. **Install dependencies:** ```bash pip install -r requirements.txt ``` 4. **Launch Jupyter:** ```bash jupyter notebook ``` ## 🚀 Getting Started 1. Start with **01 Pydantic in Python** to understand data validation 2. Move through modules sequentially as they build on each other 3. Experiment with the code examples in each notebook 4. Try modifying examples and running variations ## 💻 Tech Stack - **Pydantic** - Data validation and settings management - **LangGraph** - Agentic workflow orchestration - **LangChain** - LLM and tool integrations - **Python** - Core language - **Jupyter** - Interactive notebooks ## 📖 Learning Format Each module includes: - 📓 **Jupyter Notebooks** - Interactive code examples and explanations - 💡 **Practical Examples** - Real-world use cases - 🧪 **Hands-on Exercises** - Reinforce learning through practice - 📝 **Notes and Comments** - Detailed explanations in code ## 🎓 Who Is This For? - **AI/ML Engineers** - Building production AI systems - **Python Developers** - Transitioning to AI development - **Data Scientists** - Implementing autonomous systems - **Researchers** - Exploring agent-based architectures - **Students** - Learning modern AI development ## 🤝 Contributing Contributions are welcome! If you find issues or have suggestions: 1. Open an issue to discuss your idea 2. Fork the repository 3. Create a feature branch 4. Submit a pull request ## 📄 License This project is licensed under the GNU General Public License v3.0 - see the [LICENSE](LICENSE) file for details. ## 📞 Support - Check existing notebooks for examples - Review code comments for clarifications - Open an issue for bugs or questions - Refer to official documentation links in notebooks ## 🔗 Resources - [LangGraph Documentation](https://langchain-ai.github.io/langgraph/) - [Pydantic Documentation](https://docs.pydantic.dev/) - [LangChain Documentation](https://python.langchain.com/) - [Python Official Docs](https://docs.python.org/3/) ## 📅 Updates & Roadmap This bootcamp is actively maintained and updated to reflect the latest developments in Agentic AI. Check back regularly for new content and improvements. ## 🌟 Acknowledgments Built with passion for the Agentic AI community. Special thanks to all contributors and learners making this resource better. --- **Happy Learning! 🚀** *Last Updated: June 2026*