📘 Introduction

Welcome to this hands-on LangChain + LangGraph tutorial! In this guide, we’ll walk you step-by-step through creating your first AI agent using LangChain, LangGraph, and OpenAI models — perfect for beginners who want to build intelligent, autonomous assistants with flexible workflows.

💡 What are LangChain and LangGraph?

  • LangChain is a powerful Python framework for building applications powered by large language models. It provides abstractions for prompts, tools, memory, and workflows so you can build much more than simple chatbots.
  • LangGraph extends LangChain by letting you build stateful, graph-based agents. Instead of only sequential chains, you can define nodes (steps) and edges (transitions) to control how an agent plans and executes tasks.

With LangChain + LangGraph, you can:
🤖 Create agents that maintain context across tasks.
🛠️ Equip them with tools (e.g., web search, calculators, or APIs).
🔗 Define custom workflows using a graph structure.
⚡ Scale from simple prototypes to complex AI assistants.

💡
With LangChain + LangGraph, you can create agents that maintain context, follow instructions, and reason through tasks. Together with OpenAI models, this stack gives you everything you need to turn an LLM into a proactive assistant that can reason, plan, and act.

✅ Prerequisites

Before starting, make sure you have the following:

🐍☑️ Python installed
📦☑️ Pip installed
🌐☑️ A virtual environment (venv) created and activated
🔑☑️ An OpenAI API key generated (get one from OpenAI)
🔐☑️ OpenAI API key set as environment variable OPENAI_API_KEY

📦1️⃣ Install Libraries

Install the following Python packages using pip:

pip install langchain-openai langgraph

📥2️⃣ Import Libraries

Start by importing the required Python modules:

from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

⚙️3️⃣ Initialize your Agent

We’ll create a ReAct-style agent using LangGraph. Define a system prompt to guide your agent’s behavior and set up the model:

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