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

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!

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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.
• Dependencies: Install required libraries like langgraph, langchain, and openai.

pip install langgraph langchain_openai python-dotenv

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Setting Up Your Environment

1. Clone the repository:

   git clone https://github.com/jkmaina/LangGraphProjects.git
   cd langgraph-blueprint

2. Create a virtual environment:

   python -m venv langgraph_env
   source langgraph_env/bin/activate  # On Linux/macOS
   langgraph_env\Scripts\activate     # On Windows

3. Install dependencies:

   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:

  python lesson1a.py

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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.
• Community Discussions: Join the conversation on LangGraph workflows, agent designs, and AI tools.
• LangGraph Documentation: Official LangGraph Docs

Happy Coding! 🎉