Building a State Graph with LangGraph: Understanding Nodes, Edges, and State

Graphs are a fundamental data structure used to model relationships between entities. In Python, LangGraph provides an intuitive way to build stateful computation graphs, making it a powerful tool for workflows, AI pipelines, and complex state transitions.

In this blog post, we’ll explore the core concepts of Nodes, Edges, and State by implementing a simple StateGraph with LangGraph. We’ll also visualize the graph to understand its structure.

- Understanding the Core Concepts
Before diving into the code, let's break down the essential components:

🟢 Nodes (Vertices)
A node represents a point in the graph where computation or a transition occurs. It can be thought of as a function that modifies or processes data.

🔵 Edges (Connections)
An edge is a connection between nodes, defining how data flows through the graph. It determines the sequence of execution.

🟡 State (Data Structure)
State represents the information passed between nodes. In our example, we use Python’s TypedDict to define structured data.

- Defining the Graph Structure
We start by defining a TypedDict that represents the state of our system:

from typing import TypedDict

class InputState(TypedDict):
    string_value: str
    numeric_value: int

Here, InputState ensures that our state always contains a string_value (a string) and a numeric_value (an integer).

- Creating Nodes to Modify State
Each node processes the state. In this case, we define a simple function that returns the input without modification (for demonstration purposes).

def modify_state(input: InputState):
    return input

Later, we can extend this function to modify the state dynamically.

- Constructing the Graph with LangGraph
Now, let's create a StateGraph that processes data through multiple nodes.

from langgraph.graph import END, START, StateGraph

graph = StateGraph(InputState)

graph.add_node("branch_a", modify_state)
graph.add_node("branch_b", modify_state)

graph.add_edge(START, "branch_a")  # Connect START to first node
graph.add_edge("branch_a", "branch_b")  # Connect first node to second node
graph.add_edge("branch_b", END)  # Connect second node to END



runnable = graph.compile()

🔗 Breaking Down the Graph Structure

1. We create the graph using StateGraph(InputState), ensuring that it adheres to the InputState structure.

2. Nodes (branch_a, branch_b) are added—each node will process the state.

3. Edges define transitions:

         `START → branch_a
   
         branch_a → branch_b
   
         branch_b → END`
   

4. Setting the entry point tells the graph where execution begins.

- Visualizing the Graph
To understand the structure better, we use Mermaid diagrams to generate a visual representation of the graph.

from IPython.display import Image, display
from langchain_core.runnables.graph import MermaidDrawMethod

display(
    Image(
        runnable.get_graph().draw_mermaid_png(
            draw_method=MermaidDrawMethod.API,
        )
    )
)

This will produce a flowchart-like diagram showing the nodes and how they connect.

- Why Use LangGraph?
LangGraph makes it easy to:

• Build workflows with structured state transitions.

• Ensure type safety using TypedDict.

• Visualize and debug complex processes.

• Integrate with LangChain for AI-based applications.