LangGraph Basics

Duration: 5 min

This module introduces LangGraph, a framework for building complex workflows with language models. Understanding LangGraph is crucial for developing advanced AI agents that can perform multi-step tasks, utilize tools, and maintain state. This module will cover the basics of LangGraph, including its components, how to create workflows, and integrate external tools.

Understanding LangGraph Components

LangGraph is built on top of LangChain and provides a way to define directed acyclic graphs (DAGs) of operations. Each node in the graph represents a step in the workflow, which can be a language model call, a tool invocation, or a conditional branch. Nodes are connected by edges that define the flow of data and control between steps.

from langgraph import Graph

# Create a LangGraph instance
workflow = Graph()

# Define nodes
workflow.add_node('start', lambda: 'Hello, LangGraph!')
workflow.add_node('end', lambda x: f'Received: {x}')

# Define edges
workflow.add_edge('start', 'end')

# Run the workflow
result = workflow.run()
print(result)

Try it in Google Colab:

Received: Hello, LangGraph!

Creating Conditional Workflows

LangGraph allows you to create conditional workflows where the next step depends on the output of the previous step. This is achieved using conditional nodes that evaluate the output and direct the flow accordingly. This feature is essential for building dynamic and adaptive AI agents.

from langgraph import Graph

# Create a LangGraph instance
workflow = Graph()

# Define nodes
workflow.add_node('start', lambda: 'Check condition')
workflow.add_node('condition', lambda x: x == 'Check condition')
workflow.add_node('true_branch', lambda: 'Condition is true')
workflow.add_node('false_branch', lambda: 'Condition is false')
workflow.add_node('end', lambda x: f'Final output: {x}')

# Define edges
workflow.add_edge('start', 'condition')
workflow.add_edge('condition', 'true_branch', condition=True)
workflow.add_edge('condition', 'false_branch', condition=False)
workflow.add_edge('true_branch', 'end')
workflow.add_edge('false_branch', 'end')

# Run the workflow
result = workflow.run()
print(result)

💡 Tip: When defining conditional workflows, ensure that all possible branches are accounted for to avoid deadlocks or unexpected behavior.

Key Concepts

Check Your Understanding