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 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.
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Check out the YouTube Development Tutorial for more!
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Check out the Substack Post Tutorial for more!
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🔸 🎊 Online Service Now Live! (May 2025) Try our new online version at https://code2tutorial.com/ – just paste a GitHub link, no installation needed!
🤯 All these tutorials are generated entirely by AI by crawling the GitHub repo!
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Showcase Your AI-Generated Tutorials in Discussions!
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Clone this repository
git clone https://github.com/The-Pocket/PocketFlow-Tutorial-Codebase-Knowledge
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Install dependencies:
pip install -r requirements.txt
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Set up LLM in
utils/call_llm.pyby 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
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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"
--repoor--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")--max-abstractions- Maximum number of abstractions to identify (default: 10)--no-cache- Disable LLM response caching (default: caching enabled)
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).
🐳 Running with Docker
To run this project in a Docker container, you'll need to pass your API keys as environment variables.
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Build the Docker image
docker build -t pocketflow-app . -
Run the container
You'll need to provide your
GEMINI_API_KEYfor the LLM to function. If you're analyzing private GitHub repositories or want to avoid rate limits, also provide yourGITHUB_TOKEN.Mount a local directory to
/app/outputinside the container to access the generated tutorials on your host machine.Example for analyzing a public GitHub repository:
docker run -it --rm \ -e GEMINI_API_KEY="YOUR_GEMINI_API_KEY_HERE" \ -v "$(pwd)/output_tutorials":/app/output \ pocketflow-app --repo https://github.com/username/repo
Example for analyzing a local directory:
docker run -it --rm \ -e GEMINI_API_KEY="YOUR_GEMINI_API_KEY_HERE" \ -v "/path/to/your/local_codebase":/app/code_to_analyze \ -v "$(pwd)/output_tutorials":/app/output \ pocketflow-app --dir /app/code_to_analyze
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I built using Agentic Coding, the fastest development paradigm, where humans simply design and agents code.
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The secret weapon is Pocket Flow, a 100-line LLM framework that lets Agents (e.g., Cursor AI) build for you
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Check out the Step-by-step YouTube development tutorial:
