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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 ofPocket Flow, a 100-line LLM framework. It crawls GitHub repositories and builds 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.

• Check out theYouTube Development Tutorial for more!

• Check out theSubstack Post Tutorial for more!

  🔸 🎉 Reached Hacker News Front Page (April 2025) with >800 up‑votes:Discussion »

🤯 All these tutorials are generatedentirely 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 inutils/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:

   python utils/call_llm.py

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

   # Analyze a GitHub repositorypython main.py --repo https://github.com/username/repo --include"*.py""*.js" --exclude"tests/*" --max-size 50000# Or, analyze a local directorypython main.py --dir /path/to/your/codebase --include"*.py" --exclude"*test*"# Or, generate a tutorial in Chinesepython 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 usingAgentic Coding, the fastest development paradigm, where humans simplydesign and agentscode.

• The secret weapon isPocket 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: