Introduction to LangGraph

Amazon processes over 1.6 million packages per day. Shopify handles $200+ billion in annual GMV across millions of stores. Behind these numbers are complex workflows: order validation, payment processing, inventory management, shipping coordination, and customer support - all happening simultaneously.

How do you automate these interconnected processes with AI? That's where LangGraph comes in.

What is LangGraph?

LangGraph is a low-level orchestration framework for building stateful, multi-agent applications. Built by the creators of LangChain, it lets you model workflows as directed graphs where:

• Nodes are agents or functions that process data
• Edges define the flow between nodes (including conditional routing)
• State is shared context that persists across the entire workflow

Why E-commerce Needs Multi-Agent AI

Consider what happens when a customer asks: "Where's my order? I ordered 3 days ago and it still hasn't shipped."

A single agent would need to:

• Authenticate the customer
• Look up their recent orders
• Check order status in the fulfillment system
• Check shipping carrier APIs
• Determine if there's a delay
• Decide on compensation if warranted
• Respond with empathy and accuracy

That's a lot for one agent. With LangGraph, you build specialized agents:

Customer Query
     │
     ▼
┌─────────────────┐
│  Router Agent   │ ─── Classifies: order, refund, product, or general
└────────┬────────┘
         │
    ┌────┴────┬──────────┬────────────┐
    ▼         ▼          ▼            ▼
┌───────┐ ┌───────┐ ┌─────────┐ ┌──────────┐
│ Order │ │Refund │ │ Product │ │ General  │
│ Agent │ │ Agent │ │  Agent  │ │  Agent   │
└───┬───┘ └───────┘ └─────────┘ └──────────┘
    │
    ▼
┌─────────────────┐
│ Response Agent  │ ─── Formats final response with brand voice
└─────────────────┘

Real-World LangGraph Use Cases

Your First LangGraph Concept

Here's a minimal example to show LangGraph's structure. We'll build a complete e-commerce system throughout this course.

from langgraph.graph import StateGraph, START, END
from typing import TypedDict

# 1. Define the state that flows through your graph
class OrderState(TypedDict):
    customer_id: str
    items: list
    total: float
    status: str

# 2. Define nodes (functions that process state)
def validate_order(state: OrderState) -> OrderState:
    # Check items exist and calculate total
    state["status"] = "validated"
    return state

def process_payment(state: OrderState) -> OrderState:
    # Charge the customer
    state["status"] = "paid"
    return state

# 3. Build the graph
workflow = StateGraph(OrderState)
workflow.add_node("validate", validate_order)
workflow.add_node("payment", process_payment)

# 4. Connect nodes with edges
workflow.add_edge(START, "validate")
workflow.add_edge("validate", "payment")
workflow.add_edge("payment", END)

# 5. Compile and run
app = workflow.compile()
result = app.invoke({
    "customer_id": "cust_123",
    "items": [{"sku": "SHIRT-M", "qty": 2}],
    "total": 0,
    "status": "pending"
})

Companies Using LangGraph

What You'll Build in This Course

By the end of this course, you'll have built a complete e-commerce AI backend with:

Key Takeaways

Next up: Graph Basics - Deep dive into nodes, edges, and state management.