LangGraph Practice Examples

This repository contains practical examples of different workflow patterns implemented using LangGraph. Each example demonstrates a specific type of workflow that can be used for various data processing and automation tasks.

Table of Contents

• Overview
• Workflow Examples
• Installation
• Common Dependencies
• Usage
• Contributing
• License

Overview

LangGraph is a powerful library for creating and managing workflows in Python. This repository provides practical examples of different workflow patterns that can be used as a reference or starting point for your own projects.

Workflow Examples

Workflow Type	Description	Example
Sequential	Linear processing of data through a series of steps	BMI Calculator
Conditional	Branching logic based on conditions	Quadratic Equation Solver, Sentiment Analysis
Iterative	Repeated processing with refinement	AI-Powered Tweet Generator
Parallel	Simultaneous execution of independent tasks	Cricket Statistics, Essay Evaluation
State Persistence	Maintaining state across workflow executions	Joke Generator with Memory
Prompt Chaining	Chaining multiple LLM prompts together	Blog Post Generator
LLM Integration	Basic LLM interaction	Simple Q&A System
ChatBot	Interactive chat interface	Full-stack AI ChatBot

Installation

1. Clone this repository:

   git clone https://github.com/0x-Professor/LangGraph-Practice.git
   cd LangGraph-Practice

2. Navigate to the specific workflow directory you're interested in:

   cd workflow_directory_name

3. Install the required dependencies:

   pip install -r requirements.txt

Common Dependencies

Most examples require:

• Python 3.8+
• langgraph
• Jupyter Notebook (for .ipynb examples)

Additional dependencies for specific examples:

• langchain-google-genai (for LLM integration examples)
• pydantic (for data validation)
• fastapi, streamlit (for web interfaces)

Usage

1. Navigate to the specific workflow directory
2. Follow the instructions in the workflow's README.md
3. Run the example:
   • For Jupyter notebooks: jupyter notebook practice.ipynb
   • For Python scripts: python main.py

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.