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 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 the YouTube Development Tutorial for more!

• Check out the Substack Post Tutorial for more!

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

⭐ Example Results for Popular GitHub Repositories!

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

• AutoGen Core - Build AI teams that talk, think, and solve problems together like coworkers!

• Browser Use - Let AI surf the web for you, clicking buttons and filling forms like a digital assistant!

• Celery - Supercharge your app with background tasks that run while you sleep!

• Click - Turn Python functions into slick command-line tools with just a decorator!

• Codex - Turn plain English into working code with this AI terminal wizard!

• Crawl4AI - Train your AI to extract exactly what matters from any website!

• CrewAI - Assemble a dream team of AI specialists to tackle impossible problems!

• DSPy - Build LLM apps like Lego blocks that optimize themselves!

• FastAPI - Create APIs at lightning speed with automatic docs that clients will love!

• Flask - Craft web apps with minimal code that scales from prototype to production!

• Google A2A - The universal language that lets AI agents collaborate across borders!

• LangGraph - Design AI agents as flowcharts where each step remembers what happened before!

• LevelDB - Store data at warp speed with Google's engine that powers blockchains!

• MCP Python SDK - Build powerful apps that communicate through an elegant protocol without sweating the details!

• NumPy Core - Master the engine behind data science that makes Python as fast as C!

• OpenManus - Build AI agents with digital brains that think, learn, and use tools just like humans do!

• Pydantic Core - Validate data at rocket speed with just Python type hints!

• Requests - Talk to the internet in Python with code so simple it feels like cheating!

• SmolaAgents - Build tiny AI agents that punch way above their weight class!

• Showcase Your AI-Generated Tutorials in Discussions!

🚀 Getting Started

1. Clone this repository

   git clone https://github.com/The-Pocket/Tutorial-Codebase-Knowledge.git
   cd Tutorial-Codebase-Knowledge

2. Set up a virtual environment (recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Configure LLM access

   The tool supports multiple LLM providers. Configure at least one:

   • Google Gemini (default):

     # For Vertex AI:
     export GEMINI_PROJECT_ID="your-project-id"
     export GEMINI_LOCATION="us-central1"
     # OR for AI Studio:
     export GEMINI_API_KEY="your-api-key"

   • Anthropic Claude:

     export ANTHROPIC_API_KEY="your-api-key"
     # Uncomment Claude function in utils/call_llm.py

   • OpenAI:

     export OPENAI_API_KEY="your-api-key"
     # Uncomment OpenAI function in utils/call_llm.py

5. Set up GitHub token (recommended)

   export GITHUB_TOKEN="your-github-token"

6. Verify your setup

   python utils/call_llm.py

7. Generate a tutorial

   # From a GitHub repository
   python main.py --repo https://github.com/username/repo --include "*.py" "*.js"
   
   # Or from a local directory
   python main.py --dir /path/to/your/codebase --include "*.py"

For detailed setup instructions, see SETUP.md.

🚀 How to Run This Project

1. Set up environment variables (choose one option):

   Option 1: For Google Gemini (default):

   export GEMINI_PROJECT_ID="your-project-id"
   export GEMINI_LOCATION="us-central1"
   # OR for AI Studio instead of Vertex AI:
   export GEMINI_API_KEY="your-api-key"

   Option 2: For Anthropic Claude (uncomment in call_llm.py):

   export ANTHROPIC_API_KEY="your-api-key"

   Option 3: For OpenAI O1 (uncomment in call_llm.py):

   export OPENAI_API_KEY="your-api-key"

2. Test LLM connection:

   python utils/call_llm.py

3. Generate a tutorial from a GitHub repository:

   python main.py --repo https://github.com/username/repo --include "*.py"

4. Or analyze a local codebase:

   python main.py --dir /path/to/your/code --include "*.py" "*.js"

5. Check the generated output:

   cd output
   # View the generated tutorial files

💡 Development Tutorial

• 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: