AI Agents using LangGraph

This repository contains a collection of Jupyter notebooks for learning and experimenting with AI agent workflows built with LangGraph.

What’s included

• Introductory notebooks for setting up the environment and testing the installation
• Example workflows for prompt chaining, LLM calls, and task-based agent patterns
• Sample notebooks for finance, research, and API-related agent use cases

Getting started

1. Create and activate a virtual environment
2. Install the required dependencies
3. Start Jupyter Notebook and open the examples

Example setup

python -m venv venv
source venv/bin/activate   # Linux / macOS
# or
venv\Scripts\activate      # Windows
pip install -r requirements.txt
jupyter notebook

Notes

• Keep secrets and API keys out of the repository
• Use the .env file locally for configuration
• Notebook outputs are not required for version control