The-Pocket/Tutorial-Codebase-Knowledge: Turns Codebase into Easy Tutorial with AI

Ever stared at a new codebase written by others feeling completely lost? This tutorial shows you how to build an AI agent that analyzes GitHub repositories and creates beginner-friendly tutorials explaining exactly how the code works.

This is a tutorial project of Pocket Flow, a 100-line LLM framework. It crawls GitHub repositories and build a knowledge base from the code. It analyzes entire codebases to identify core abstractions and how they interact, and transforms complex code into beginner-friendly tutorials with clear visualizations.

🤯 All these tutorials are generated entirely by AI by crawling the GitHub repo!

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1. Clone this repository

2. Install dependencies:

   pip install -r requirements.txt

3. Set up LLM in utils/call_llm.py by providing credentials. By default, you can use the AI Studio key with this client for Gemini Pro 2.5:

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

   You can use your own models. We highly recommend the latest models with thinking capabilities (Claude 3.7 with thinking, O1). You can verify that it is correctly set up by running:

4. Generate a complete codebase tutorial by running the main script:

   # 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 either a GitHub repo URL or a local directory path (required, mutually exclusive)
   • -n, --name – Project name (optional, derived from URL/directory if omitted)
   • -t, --token – GitHub token (or set GITHUB_TOKEN environment variable)
   • -o, --output – Output directory (default: ./output)
   • -i, --include – Files to include (e.g., “.py” “.js”)
   • -e, --exclude – Files to exclude (e.g., “tests/” “docs/“)
   • -s, --max-size – Maximum file size in bytes (default: 100KB)
   • --language – Language for the generated tutorial (default: “english”)

The application will crawl the repository, analyze the codebase structure, generate tutorial content in the specified language, and save the output in the specified directory (default: ./output).

• I built using Agentic Coding, the fastest development paradigm, where humans simply design and agents code.

• The secret weapon is Pocket Flow, a 100-line LLM framework that lets Agents (e.g., Cursor AI) build for you

• Check out the Step-by-step YouTube development tutorial:

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