LangGraph Course

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Video: https://www.youtube.com/watch?v=jGg_1h0qzaM

Repository for all of the code written for the FreeCodeCamp LangGraph Course, including solutions for all exercises. This repo provides practical examples of using LangGraph for building agent-based applications through Python scripts and interactive Jupyter notebooks.

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Table of Contents

• Overview
• Repository Structure
• Getting Started (zsh/Mac)
  • Using pyenv and uv
• Usage
• Exercises
• Requirements

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Overview

LangGraph is a Python framework for designing and managing the flow of tasks in your application using graph structures. This course demonstrates LangGraph concepts through step-by-step exercises, agent implementations, and Jupyter notebooks.

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Repository Structure

LangGraph-Course/
├── Agents/            # Python agents for various tasks (e.g., RAG_Agent, Drafter)
├── Exercises/         # Jupyter notebooks with exercise solutions
├── Graphs/            # Jupyter notebooks illustrating LangGraph concepts
├── requirements.txt   # Python dependencies
└── README.md          # This file

Notable Directories:

• Agents/: Python scripts for agents such as Retrieval-Augmented Generation (RAG) and document drafting.
• Exercises/: Jupyter notebooks for each exercise (e.g. Exercise_Graph1.ipynb).
• Graphs/: Notebooks demonstrating LangGraph patterns (e.g., Hello World, Looping).

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Getting Started (zsh/Mac)

Using pyenv and uv

1. Clone the Repository

git clone https://github.com/rdtiv/LangGraph-Course.git
cd LangGraph-Course

2. Install pyenv (if not already installed)

brew update
brew install pyenv

Add the following to your ~/.zshrc if it's not already there:

export PYENV_ROOT="$HOME/.pyenv"
export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init --path)"
eval "$(pyenv init -)"

Restart your terminal or source your ~/.zshrc:

source ~/.zshrc

3. Install Python Version

pyenv install 3.12.6
pyenv local 3.12.6

4. Install uv

pipx install uv           # Recommended, or:
pip install --user uv

If you don't have pipx, install it with:

brew install pipx
pipx ensurepath

5. Set Up Virtual Environment with uv

uv venv .venv
source .venv/bin/activate

6. Install Dependencies

uv pip install -r requirements.txt

7. (Optional) Set up Environment Variables

If you need API keys (such as for OpenAI), create a .env file in the root directory:

echo "OPENAI_API_KEY=your_openai_key" > .env
# Add other variables as needed

8. Start JupyterLab

uv pip install jupyterlab  # Only if not already installed
jupyter lab

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Usage

• Open and run Jupyter notebooks in Graphs/ and Exercises/ for hands-on practice and exploration.
• Run agent scripts in Agents/ for more advanced experiments.
• All code is designed to work in a local, isolated Python environment managed by pyenv and uv.

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Exercises

• Explore the Exercises/ directory for self-contained solutions to LangGraph problems.
• Example notebooks:
  • Exercise_Graph1.ipynb: Agent state and basic graph usage.
  • Exercise_Graph2.ipynb: User input and graph visualization.
  • Exercise_Graph3.ipynb: Personalization and skills-based responses.
  • Exercise_Graph4.ipynb, Exercise_Graph5.ipynb: Advanced graph operations.

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Requirements

Core dependencies (see requirements.txt for full list):

• langgraph
• langchain
• ipython
• langchain_openai
• langchain_community
• dotenv
• typing
• chromadb
• langchain_chroma

Install all dependencies with:

uv pip install -r requirements.txt

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