AI Codebase Knowledge Builder

Generate beginner-friendly tutorials for any codebase using AI!

Features

• 🔍 Codebase Analysis: Automatically identify key abstractions and relationships
• 📊 Interactive Visualization: View relationships between components
• 📝 Tutorial Generation: Create a multi-chapter tutorial with code examples and diagrams
• 🧠 Multiple LLM Options: Use Google Gemini, OpenAI, Claude, or DeepSeek models
• 🌐 User-Friendly Interface: Easy-to-use Streamlit web interface for configuration and generation

🚀 Getting Started

1. Clone this repository:

   git clone https://github.com/danghung1202/Tutorial-Codebase-Knowledge.git
   cd Tutorial-Codebase-Knowledge

2. Install dependencies:

   pip install -r requirements.txt

3. Set up your environment variables in a .env file:

   GEMINI_API_KEY=your_gemini_api_key  # Required for default Gemini model
   OPENAI_API_KEY=your_openai_api_key  # Optional, for OpenAI models
   ANTHROPIC_API_KEY=your_anthropic_api_key  # Optional, for Claude models
   GITHUB_TOKEN=your_github_token  # Optional, for private repositories
   

4. Launch the web interface:

   python -m streamlit run Home.py

   or

   streamlit run Home.py

   This will open a browser window where you can:

   • Enter a GitHub repository URL or upload a local project
   • Choose your preferred LLM model
   • Configure analysis settings
   • Generate and view your tutorial in real-time

For advanced users who prefer the command line, you can also use:

# Analyze a GitHub repository
python main.py --repo https://github.com/username/repo --include "*.py" "*.js" --exclude "tests/*"

# Analyze a local directory
python main.py --dir /path/to/your/codebase --include "*.py" --exclude "*test*"

# Generate in different languages
python main.py --repo https://github.com/username/repo --language "Chinese"

Common CLI options:

• --repo or --dir - GitHub repo URL or local directory path
• -n, --name - Project name (optional)
• -t, --token - GitHub token for private repos
• -o, --output - Output directory (default: ./output)
• -i, --include - Files to include (e.g., ".py" ".js")
• -e, --exclude - Files to exclude (e.g., "tests/*")
• -s, --max-size - Maximum file size in bytes (default: 100KB)
• --language - Tutorial language (default: "english")

How It Works

1. Source Analysis: The tool processes your repository files, filtering by specified patterns
2. Abstraction Identification: Using LLMs, the key abstractions in your codebase are identified
3. Relationship Analysis: Connections between abstractions are determined
4. Chapter Ordering: The abstractions are ordered into a logical tutorial structure
5. Tutorial Generation: Detailed chapters are created with explanations, diagrams, and code examples

Customization

The web interface provides easy access to all customization options:

• LLM Provider: Choose between Google Gemini, Anthropic Claude, OpenAI, or DeepSeek
• Model Parameters: Select specific models and configure API settings
• File Filtering: Include/exclude specific file patterns with an intuitive interface
• Output Settings: Customize the output directory and format
• Language Selection: Generate tutorials in different languages
• Real-time Preview: View the generated content as it's being created

Output Examples

The generated tutorial includes:

• A markdown index file with project summary and relationship diagram
• Individual chapter markdown files for each abstraction
• Mermaid diagrams visualizing concepts and relationships

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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!

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

• Showcase Your AI-Generated Tutorials in Discussions!

💡 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: