A collection of small exercises providing a basic introduction to LangGraph, a powerful framework for building structured agents and workflows with LangChain.
This repository contains practical exercises designed to help you learn the fundamentals of LangGraph. LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful AI agents. It provides the flexibility to create fully customizable agent workflows with support for durable execution, human-in-the-loop interactions, and comprehensive memory management.
LangGraph is a Python framework inspired by Pregel and Apache Beam that allows you to:
- Build stateful, multi-actor applications with LLMs
- Create cyclical graph structures (unlike traditional DAGs)
- Implement explicit control flow between agent nodes
- Manage persistent state across conversations
- Support human oversight and intervention
- Stream results in real-time
LangGraph-Tutorial/
├── agents/ # Agent implementations and examples
├── .gitignore # Git ignore file
├── README.md # This file
└── requirements.txt # Python dependencies
- Python 3.8 or higher
- pip package manager
- Clone the repository:
git clone https://github.com/varrahan/LangGraph-Tutorial.git
cd LangGraph-Tutorial- Create a virtual environment (recommended):
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate- Install dependencies:
pip install -r requirements.txtThe core building block of LangGraph applications. Define nodes (functions) and edges (transitions) to create your agent workflow.
Individual functions that process the current state and return updates. Each node represents a step in your agent's reasoning process.
Define the flow between nodes. Can be:
- Normal edges: Direct transitions
- Conditional edges: Routing based on state
- Entry/Exit points: START and END nodes
- LangGraph Documentation
- LangChain Academy - Free structured LangGraph course
- LangGraph Templates - Pre-built reference applications
- LangSmith - Debugging and observability platform
Contributions are welcome! Feel free to:
- Submit bug reports or feature requests via issues
- Create pull requests with improvements or new exercises
- Share feedback on the learning materials
This project is available for educational purposes. Please check the repository for specific license information.
This tutorial is built on top of:
For questions or feedback, please open an issue in this repository.
Happy learning! 🎓 Start building powerful AI agents with LangGraph!