Turn Any Codebase Into a Beginner-Friendly Tutorial with AI

Ever feel lost staring at a complex codebase? This guide shows you how to leverage AI to dissect any GitHub repository and create easy-to-understand tutorials. Imagine transforming mountains of code into digestible insights—no more code-induced headaches! Learn how to build an AI agent that analyzes code, identifies core concepts, and generates clear, visualized explanations.

Unlock Codebase Secrets: Introducing the AI-Powered Tutorial Generator

This project uses Pocket Flow, a lean LLM framework, to crawl GitHub repositories and build a knowledge base. It's designed to transform complex code into simpler terms. The result? Beginner-friendly tutorials with visualizations that actually make sense.

• Crawl and Analyze: The AI agent digs deep, examining code structures and relationships.
• Identify Core Abstractions: It pinpoints crucial concepts, saving you hours of manual investigation.
• Visualize Complexity: Transforms code into visually appealing, easy-to-grasp tutorials.

Real-World Examples: AI Transforming Open-Source Projects

See the power of AI in action! Check out these AI-generated tutorials for popular GitHub repositories:

• AutoGen Core: Understand AI team collaboration.
• FastAPI: Learn to create APIs quickly.
• NumPy Core: Grasp the fundamentals of data science.

These examples showcase how accessible even complex codebases become when explained by AI. Learn how to create these yourself!

Jumpstart Your Learning: Getting Started is Easy

Ready to create your own AI powered tutorials? Follow these simple steps:

1. Clone the Repository: Get the project files onto your local machine.
2. Install Dependencies: Run pip install -r requirements.txt to install all necessary packages. This ensures you have the tools to run the AI tutorial generator.
3. Configure LLM: Inside utils/call_llm.py, provide your API credentials. The easiest is to use an AI Studio key with Gemini Pro 2.5. This connects the project to the AI brain that will analyze the code.

   client = genai.Client(
     api_key = os.getenv("GEMINI_API_KEY", "your-api_key"),
   )
   

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Generate Your First Tutorial: Command-Line Magic

Now for the exciting part! Use the main.py script to generate your tutorial:

# Analyze a GitHub repository
python main.py --repo https://github.com/username/repo --include "*.py" "*.js" --exclude "tests/*" --max-size 50000

# Or, analyze a local directory
python main.py --dir /path/to/your/codebase --include "*.py" --exclude "*test*"

# Or, generate a tutorial in Chinese
python main.py --repo https://github.com/username/repo --language "Chinese"

• --repo or --dir: Specify a GitHub URL or a local directory.
• -i, --include: Define file types to include (e.g., "*.py").
• -e, --exclude: Filter out unwanted directories (e.g., "tests/").

The AI agent will crawl the code, dissect its structure, and generate a tutorial in your chosen language, saving it to the ./output directory. Unleash the power of AI to understand even the most intimidating codebases!

Behind the Scenes: Agentic Coding and Pocket Flow

This project leverages Agentic Coding, where humans design and AI agents code. Pocket Flow, the 100-line LLM framework, empowers AI agents to build complex applications with ease. Check out the YouTube tutorial to dive deeper into the development process, and unlock the power of AI-driven code understanding.