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

The Complete LangGraph Blueprint - Book Cover

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.

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.

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!

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.

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)

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

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

Running Your First AI Agent

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

Contributing 🤝

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

License 📝

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

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! 🎉