LangGraph Agents Collection

This repository contains a collection of various AI and non-AI agents written as jupyter notebooks for learning innerworkings of LangGraph. The project is structured to use a single, shared Python virtual environment to manage dependencies for all agents.

Project Structure

The repository is organized as follows:

.
├── AI-agents/
│   ├── cli-agents/
│       └── ...
├── non-AI/
│   └── ...
├── venv/
├── .env
├── .gitignore
└── requirements.txt

• AI-agents/: Jupyter notebooks explaining and implementing LangGraph agents that use LLM's.
• cli-agents/: Unlike the rest of the files these are not jupyter notebooks but regular .py files, therefore they need to be run from the terminal. These are AI agents that I built as example projects.
• non-AI/: Contains simple LangGraph graphs without integration of LLM's, for the purpose of grasping LangGraph concepts clearly.
• venv/: The shared Python virtual environment for all projects in this repository. This folder is not tracked by Git.
• .env: A file to store secret keys and environment variables (e.g., OPENAI_API_KEY). This file is not tracked by Git.
• requirements.txt: A list of all Python packages required to run the projects in this repository.

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Prerequisites

Before you begin, ensure you have Python 3.8 or newer installed on your system. You can check your Python version by running:

python3 --version

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Setup and Installation

Follow these steps to set up the project environment on your local machine.

1. Clone the Repository

If you haven't already, clone the project to your local machine:

git clone <your-repository-url>
cd <repository-name>

2. Create and Activate the Virtual Environment

This project uses a single virtual environment named venv located in the root directory.

To create the environment (only needs to be done once):

python3 -m venv venv

To activate the environment:

• On macOS / Linux:

  source venv/bin/activate
  

• On Windows (Command Prompt):

  .\venv\Scripts\activate
  

• On Windows (PowerShell):

  .\venv\Scripts\Activate.ps1
  

Your terminal prompt should now be prefixed with (venv), indicating the environment is active. You must activate the environment every time you work on this project in a new terminal session.

3. Install Dependencies

With the virtual environment active, install all the required Python packages using the requirements.txt file:

pip install -r requirements.txt

4. Set Up Environment Variables

The AI agents require API keys to function. These are stored in a .env file.

1. Create a file named .env in the root of the project directory.

2. Open the .env file and add the necessary key-value pairs. For example:

   # .env file
   OPENAI_API_KEY="your_openai_api_key_here"
   # Add other keys as needed
   # TAVILY_API_KEY="your_tavily_api_key_here"
   

The Python scripts are configured to automatically load these variables from this file.

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How to Run Agents

Running agents in cli-agents folder

Each script or agent within the cli-agents are designed to be run independently.

1. Ensure your virtual environment is active. If you see (venv) in your terminal prompt, you are ready. If not, follow step 2 in the installation guide.

2. Navigate to the script's directory or run it from the root. For example, to run a chatbot script located at AI-agents/cli-agents/Chatbot.py, you would run:

   python AI-agents/cli-agents/Chatbot.py
   

Running Jupyter Notebooks (.ipynb files):

• Make sure you have Jupyter installed (pip install jupyter).
• Start the Jupyter server from the project root:

jupyter notebook

• This will open a new tab in your browser. Navigate to the notebook file you wish to run and open it. Ensure the notebook is using the project's venv kernel.