Why LangGraph handles complex agent flows in LangChain - See It in Action

Why LangGraph handles complex agent flows

📖 Scenario: You are building a smart assistant that can handle multiple tasks by connecting different small helpers (agents) in a flow. LangGraph helps you organize these helpers so they work together smoothly, even when the tasks get complicated.

🎯 Goal: Build a simple LangGraph flow that connects two agents: one that greets the user and another that says goodbye. This shows how LangGraph manages multiple agents in a sequence.

📋 What You'll Learn

Create a LangGraph object called graph

Create two agents: greet_agent and farewell_agent

Connect greet_agent to farewell_agent in the graph

Run the graph to see the flow from greeting to farewell

💡 Why This Matters

🌍 Real World

LangGraph helps developers build complex workflows by connecting small task-specific agents, making it easier to manage and scale smart assistants or automation pipelines.

💼 Career

Understanding LangGraph is useful for roles in AI development, chatbot design, and automation engineering where managing multiple agents and their interactions is key.

1

Create two simple agents

Create two functions called greet_agent and farewell_agent. greet_agent should return the string "Hello! How can I help you today?" and farewell_agent should return "Goodbye! Have a great day!".

LangChain

# Define greet_agent function
# Define farewell_agent function

Need a hint?

Define two functions with the exact names greet_agent and farewell_agent. Each should return the exact string given.

2

Create a LangGraph object

Import LangGraph from langchain.graphs and create a variable called graph that is an instance of LangGraph.

LangChain

def greet_agent():
    return "Hello! How can I help you today?"

def farewell_agent():
    return "Goodbye! Have a great day!"

# Import LangGraph and create graph instance
# Your code here

Need a hint?

Use from langchain.graphs import LangGraph and then create graph = LangGraph().

3

Add agents to the graph and connect them

Add greet_agent and farewell_agent to graph using graph.add_node(). Then connect greet_agent to farewell_agent using graph.add_edge().

LangChain

from langchain.graphs import LangGraph

def greet_agent():
    return "Hello! How can I help you today?"

def farewell_agent():
    return "Goodbye! Have a great day!"

graph = LangGraph()

# Add greet_agent and farewell_agent to graph
# Connect greet_agent to farewell_agent
# Your code here

Need a hint?

Use graph.add_node() to add each agent, then graph.add_edge() to connect them in order.

4

Run the LangGraph flow

Run the graph flow by calling graph.run() and store the result in a variable called result.

LangChain

from langchain.graphs import LangGraph

def greet_agent():
    return "Hello! How can I help you today?"

def farewell_agent():
    return "Goodbye! Have a great day!"

graph = LangGraph()

graph.add_node(greet_agent)
graph.add_node(farewell_agent)

graph.add_edge(greet_agent, farewell_agent)

# Run the graph and save output to result
# Your code here

Need a hint?

Call graph.run() and assign it to result.