LangChain Deep Dive: From Chain to Agent, Build an AI Customer Service System

LangChain is the most popular AI agent framework. This tutorial walks through building an e-commerce customer service bot, from basic Chain to full Agent with Tools and Memory.

💡 What You Will Learn

LangChain is the most popular AI agent framework. This tutorial walks through building an e-commerce customer service bot, from basic Chain to full Agent with Tools and Memory.

📜 Table of Contents

• Start with a Simple Chain
• Layer 2: Add Tools
• Summary

Start with a Simple Chain

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model='gpt-4o-mini')
prompt = ChatPromptTemplate.from_template('User asks: {question}')
chain = prompt | llm
print(chain.invoke({'question': 'When will my order ship?'}))

This runs immediately. LangChain's real power is in the next three layers.

Layer 2: Add Tools

from langchain.tools import tool

@tool
def check_order(order_id: str) -> str:
    """Check order status"""
    orders = {'ORD001': 'Shipped, arriving in 3 days', 'ORD002': 'Delivered'}
    return orders.get(order_id, 'Order not found')

tools = [check_order]

Summary

LangChain: Chain is foundation, Tool is capability, Agent is brain, Memory is context. Combine all four layers for a complete AI Agent.