Build Your First AI Agent with LangGraph

Introduction

This workshop will walk you through building your very first AI agent using LangGraph, a framework built on top of LangChain for creating agentic applications.

We’ll cover:

• Setting up your environment
• Running Jupyter notebooks for experimentation
• Using LangGraph Studio and LangSmith to visualize and debug your agents
• Exploring APIs and integrations like GroqCloud, Google AI Studio and Tavily

By the end of this workshop, you’ll have a working understanding of LangGraph, as well as a few custom-built agents you can extend further.

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Setup

Python Version

To get the most out of this course, please ensure you're using Python 3.11 or later. This version is required for optimal compatibility with LangGraph. If you're on an older version, upgrading will ensure everything runs smoothly.

python3 --version

Clone repo

git clone https://github.com/nazakun021/build-your-first-ai-agent-langgraph.git
cd build-your-first-ai-agent-langgraph

Create an environment and install dependencies

Mac/Linux/WSL

python3 -m venv langgraph-workshop-env
source langgraph-workshop-env/bin/activate
pip install -r requirements.txt

Windows PowerShell

python3 -m venv langgraph-workshop-env
Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope Process
langgraph-workshop-env\Scripts\Activate.ps1
pip install -r requirements.txt

Running notebooks

jupyter notebook

Setting up API Keys

You’ll need an API key to connect to the service. Make sure to store your API keys somewhere safe—for example, in a password manager or a .env file (if you’re using something like the python-dotenv library).

Never hardcode your API keys directly into your code or share them publicly.

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Set GroqCloud API Key

• If you don't have a GroqCloud API key, you can sign up here.
• Set GROQ_API_KEY in your .env file.

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Set Google AI Studio API Key

• If you don't have a Google AI Studio API key, you can sign up here.
• Set GOOGLE_API_KEY in your .env file.

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Set LangSmith API Key

• Sign up for LangSmith here.
• Learn more about LangSmith here and check the docs.
• Store the following in your .env file:

LANGCHAIN_API_KEY=your-key-here
LANGCHAIN_TRACING_V2=true

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Set up Tavily API Key (for Web Search)

• Tavily Search API is a search engine optimized for LLMs and RAG, aimed at efficient, quick, and persistent search results.
• You can sign up for an API key here. It's easy to sign up and offers a very generous free tier. Some notebooks later will use Tavily.
• Set TAVILY_API_KEY in your .env file.

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Example .env File

Create a .env file in your project root directory and add all your API keys there. Example:

GROQ_API_KEY=your-groqcloud-key
GOOGLE_API_KEY=your-google-key
LANGCHAIN_API_KEY=your-langsmith-key
LANGCHAIN_TRACING_V2=true
TAVILY_API_KEY=your-tavily-key

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Set up LangGraph Studio

• LangGraph Studio is a custom IDE for viewing and testing agents.
• Studio can be run locally and opened in your browser on Mac, Windows, and Linux.
• See documentation here on the local Studio development server and here.
• Graphs for LangGraph Studio are in the x-topic/studio/ folders.
• To start the local development server, run the following command in your terminal in the /studio directory:

langgraph dev

You should see the following output:

- 🚀 API: http://127.0.0.1:2024
- 🎨 Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
- 📚 API Docs: http://127.0.0.1:2024/docs

Open your browser and navigate to the Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024.

• To use Studio, you will need to create a .env file with the relevant API keys

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Set up ADC for a local development environment

To configure Application Default Credentials (ADC) with a Google Account, you can use the Google Cloud CLI:

1. Install the Google Cloud CLI
   Download and install it from Google Cloud CLI installation docs.
2. Authenticate your account and set up local ADC
   Run the following in your terminal or PowerShell:

gcloud auth application-default login