langchain-ai/langchain-academy

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Introduction

Welcome to LangChain Academy, Introduction to LangGraph! This is a growing set of modules focused on foundational concepts within the LangChain ecosystem. Module 0 is basic setup and Modules 1 - 5 focus on building in LangGraph, progressively adding more advanced themes. Module 6 addresses deploying your agents. In each module folder, you'll see a set of notebooks. A link to the LangChain Academy lesson is at the top of each notebook to guide you through the topic. Each module also has a studio subdirectory, with a set of relevant graphs that we will explore using the LangGraph API and Studio.

Setup

Python version

Make sure you're using Python version 3.11, 3.12, or 3.13.

python3 --version

Clone repo

git clone https://github.com/langchain-ai/langchain-academy.git
$ cd langchain-academy

Or, if you prefer, you can download a zip file here.

Create an environment and install dependencies

Mac/Linux/WSL

$ python3 -m venv lc-academy-env
$ source lc-academy-env/bin/activate
$ pip install -r requirements.txt

Windows Powershell

PS> python3 -m venv lc-academy-env
PS> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope Process
PS> .\lc-academy-env\Scripts\Activate.ps1
PS> pip install -r requirements.txt

Running notebooks

If you don't have Jupyter set up, follow the installation instructions here.

$ jupyter notebook

Setting up env variables

Briefly going over how to set up environment variables.

Mac/Linux/WSL

$ export API_ENV_VAR="your-api-key-here"

Windows Powershell

PS> $env:API_ENV_VAR = "your-api-key-here"

Set OpenAI API key

• If you don't have an OpenAI API key, you can sign up here.
• Set OPENAI_API_KEY in your environment

Sign up and Set LangSmith API

• Sign up for LangSmith here, find out more about LangSmith and how to use it within your workflow here.
• Set LANGSMITH_API_KEY, LANGSMITH_TRACING_V2="true" LANGSMITH_PROJECT="langchain-academy"in your environment
• If you are on the EU instance also set LANGSMITH_ENDPOINT="https://eu.api.smith.langchain.com" as well.

Set up Tavily API 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 lessons (in Module 4) will use Tavily.

• Set TAVILY_API_KEY in your environment.

Set up Studio

• 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.
• Graphs for LangGraph Studio are in the module-x/studio/ folders for module 1-5.
• To start the local development server, make sure your virtual environment is active and run the following command in your terminal in the /studio directory in each module:

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
• Run this from the command line to create these files for module 1 to 5, as an example:

for i in {1..5}; do
  cp module-$i/studio/.env.example module-$i/studio/.env
  echo "OPENAI_API_KEY=\"$OPENAI_API_KEY\"" > module-$i/studio/.env
done
echo "TAVILY_API_KEY=\"$TAVILY_API_KEY\"" >> module-4/studio/.env