# The Complete LangGraph Blueprint: Build 50+ AI Agents for Business Success

[![The Complete LangGraph Blueprint - Book Cover](https://m.media-amazon.com/images/I/81DpVdSAclL._SY466_.jpg)](https://www.amazon.com/Complete-LangGraph-Blueprint-Business-Success-ebook/dp/B0DP69QV7K)

Welcome to the official repository for **The Complete LangGraph Blueprint**, authored by James Karanja Maina. This book guides you through creating 50+ AI agents for real-world business applications using LangGraph and other cutting-edge AI tools.

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## About the Book 📚

This project is a comprehensive guide to building AI agents using LangGraph, an open-source framework for graph-based AI workflows. It covers fundamental programming concepts, LangGraph principles, and step-by-step tutorials to develop intelligent, autonomous systems. Whether you're a beginner or an experienced developer, this book provides everything you need to harness AI for innovation and success.

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## Why Buy This Book? 🎯

- **Exclusive Knowledge**: Master the techniques for building 50+ AI agents.
- **Hands-On Learning**: Follow detailed examples and exercises to build your AI agents step by step.
- **Real-World Applications**: Learn how to solve practical business problems with AI.
- **Expert Guidance**: Written by an industry expert with a wealth of experience in AI and automation.

👉 [Get your copy now on Amazon!](https://www.amazon.com/Complete-LangGraph-Blueprint-Business-Success-ebook/dp/B0DP69QV7K)

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## Key Features 🚀

- **50+ AI Agents**: Build and customize AI agents with dynamic decision-making and tool integration.
- **Graph-Based AI Workflows**: Learn how to create workflows using nodes, edges, states, and conditions.
- **LLM Integration**: Leverage Large Language Models like GPT-4 for natural language understanding.
- **Tool Nodes**: Integrate APIs and external systems into your AI agents.
- **Memory & Persistence**: Implement short-term and long-term memory for enhanced user experiences.
- **Use Cases**: Apply your skills across industries like customer service, healthcare, and finance.
- **Practical Exercises**: Reinforce learning with hands-on coding examples.

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## Project Structure 🗂

The repository is organized as follows:

```
LANGGRAPHPROJECTS/
├── chapter1/             # Code for Chapter 1
├── chapter2/             # Code for Chapter 2
├── chapter3/             # Code for Chapter 3
├── chapter4/             # Code for Chapter 4
├── chapter5/             # Code for Chapter 5
├── chapter6/             # Code for Chapter 6
├── chapter7/             # Code for Chapter 7
├── chapter8/             # Code for Chapter 8
├── chapter9/             # Code for Chapter 9
├── chapter10/            # Code for Chapter 10
├── chapter11/            # Code for Chapter 11
├── chapter12/            # Code for Chapter 12
├── chapter13/            # Code for Chapter 13
├── chapter15/            # Code for Chapter 15
├── chapter16/            # Code for Chapter 16
├── chapter17/            # Code for Chapter 17
├── chapter18/            # Code for Chapter 18
├── chapter19/            # Code for Chapter 19
├── chapter20/            # Code for Chapter 20
├── output/               # Generated outputs and artifacts
├── testing/              # Unit tests and experimental workflows
├── .env                  # Environment configuration file
├── .gitignore            # Git ignored files
├── data.json             # Sample data for workflows
├── display_graph.py      # Visualization script for LangGraph workflows
├── graph_24371.png       # Example graph visualization
├── graph_80385.png       # Example graph visualization
├── lesson4.py            # Lesson 4 code file
├── lesson5.py            # Lesson 5 code file
└── README.md             # Project documentation (this file)
```

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## Getting Started 🚦

### Prerequisites

- **Python 3.10+**: Install from [python.org](https://www.python.org/downloads/).
- **Dependencies**: Install required libraries like `langgraph`, `langchain`, and `openai`.

```bash
pip install langgraph langchain_openai python-dotenv
```

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### Setting Up Your Environment

1. Clone the repository:
   ```bash
   git clone https://github.com/jkmaina/LangGraphProjects.git
   cd langgraph-blueprint
   ```

2. Create a virtual environment:
   ```bash
   python -m venv langgraph_env
   source langgraph_env/bin/activate  # On Linux/macOS
   langgraph_env\Scripts\activate     # On Windows
   ```

3. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

4. Add your OpenAI API key:
    - Create a `.env` file in the root directory:
      ```
      OPENAI_API_KEY=your-api-key-here
      ```

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### Running Your First AI Agent

- Navigate to the `/chapter1/lesson1a.py` directory.
- Run the Hello World LangGraph workflow:
   ```bash
   python lesson1a.py
   ```

---

## Contributing 🤝

Contributions are welcome! Submit issues, feature requests, or pull requests to improve this project.

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## License 📝

This repository is licensed under the Apache 2.0 License. See the `LICENSE` file for details.

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## Support and Resources 📚

- **Book Updates and Code Samples**: Subscribe for updates and new content at [james.karanja@zavora.ai](mailto:james.karanja@zavora.ai).
- **Community Discussions**: Join the conversation on LangGraph workflows, agent designs, and AI tools.
- **LangGraph Documentation**: [Official LangGraph Docs](https://langgraph.docs.example)

Happy Coding! 🎉
