LangChain Academy

Introduction

Welcome to LangChain Academy! This is a growing set of modules focused on foundational concepts within the LangChain ecosystem. Module 0 is basic setup and Modules 1 - 4 focus on LangGraph, progressively adding more advanced themes. In each module folder, you'll see a set of notebooks. A video accompanies 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

Clone repo

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

Create an environment and install dependencies

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

Running notebooks

Notebooks for each module are in the module- folders.

$ jupyter notebook

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 for LangSmith

• Sign up here
• Set LANGCHAIN_API_KEY, LANGCHAIN_TRACING_V2=true in your environment

Tavily 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 generous free tier. Some lessons (in Module 4) will use Tavily. Set TAVILY_API_KEY in your environment.

Set up LangGraph Studio

• Currently Studio only has macOS support
• Download the latest .dmg file here
• Install Docker desktop for Mac here

Running Studio

Graphs for studio are in the module-x/studio/ folders.

• 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 4, as an example:

$ for i in {1..4}; 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