Tutorial 7: Introduction to LangGraph

Welcome to the seventh tutorial in our series. LangGraph is an extension of LangChain for building stateful, multi-step workflows.

What you'll learn

1. LangGraph fundamentals

   • Graph-based workflows vs. LangChain chains
   • Nodes, edges, and state management
   • Graph compilation and execution

2. Building workflows with LangGraph

   • Creating nodes with custom functions
   • Defining edges and transitions
   • Managing conversation state
   • Visualizing graph flows

Prerequisites

• Completion of Tutorials 1-6
• Python 3.7+
• Groq API key (https://console.groq.com)

Getting Started

1. Ensure Virtual Environment is Activated

Linux/macOS:

cd langchain-langgraph-tutorial
source venv/bin/activate
cd Tutorial07

Windows:

cd langchain-langgraph-tutorial
.\venv\Scripts\activate
cd Tutorial07

2. Launch Jupyter Notebook

jupyter notebook Tutorial_7_introduction_to_langgraph.ipynb

What's Included

Core Components

• Tutorial notebook with practical examples
• Sample conversation workflow implementation
• Graph visualization tools
• State management examples

Key Concepts Covered

Graph Structure

• Node creation and configuration
• Edge definition and flow control
• State typing with TypedDict
• Graph compilation process

Practical Implementation

• Conversational agent workflow
• State management in conversations
• Interactive user input handling
• Graph visualization with Mermaid

Next Steps

After completing this tutorial:

1. Experiment with complex graph structures
2. Add conditional branching
3. Implement custom node types
4. Build multi-agent systems

Stay tuned for Tutorial 8 where we'll explore advanced LangGraph patterns.

Additional Resources

• LangGraph Documentation
• Groq API Documentation