This is a fork of the original project to enable its use with GEAI
I have adapted the code to redirect LLM calls to GEAI endpoints. See the Getting Started section to start using it.
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!
🔸 🎉 Reached Hacker News Front Page (April 2025) with >900 up‑votes: Discussion »
🔸 🎊 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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NumPy Core - Master the engine behind data science that makes Python as fast as C!
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OpenManus - Build AI agents with digital brains that think, learn, and use tools just like humans do!
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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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Provide GEAI data
LLM_PROVIDER=GEAI
GEAI_MODEL=openai/gpt-4.1
GEAI_BASE_URL=https://api.agents.globant.com
GEAI_API_KEY=<FOR_CORP_USE_API_KEY_SENT_YOU_BY_EMAIL>-
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:
