LangGraph is a library created to make it easier to create stateful, multi-agent applications that make use of Large Language Models (LLMs). By expressing complex agent operations as cyclic graphs, it makes it possible to create more dynamic and adaptable behaviors than conventional linear execution models. Creating and managing chat state graphs can be challenging, but with tools like StateGraph from the langgraph library, you can seamlessly build and visualize complex workflows. This post walks you through a simple example to illustrate how to define custom nodes and edges in a state graph and visualize it using multiple formats. Prerequisites Before diving into the code, ensure you have the following Python libraries installed: pip install -U langgraph pip install grandalf Building the Graph Here’s the core code that constructs a simple state graph, including custom nodes and transitions: from IPython.display import Image from typing_extensions import TypedDict from langgraph...