PocketFlow-Tutorial-Codebase-Knowledge

What it is

A Python-based tutorial project that crawls GitHub repositories to build a knowledge base from code and generates beginner-friendly tutorials explaining how the code works.

How it works

The project analyzes an entire codebase to identify core abstractions and how they interact, then transforms complex code into tutorials with visualizations. It uses a 100-line Pocket Flow LLM framework to drive the process and generate tutorials entirely by AI.

Getting started

Getting started steps shown in the README:

• Clone this repository

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

• Install dependencies:

  pip install -r requirements.txt
  

• Set up LLM in utils/call_llm.py by providing credentials. To do so, you can put the values in a .env file. By default, you can use the AI Studio key with this client for Gemini Pro 2.5 by setting the GEMINI_API_KEY environment variable. If you want to use another LLM, you can set the LLM_PROVIDER environment variable (e.g. XAI), and then set the model, url, and API key (e.g. XAI_MODEL, XAI_URL,XAI_API_KEY). If using Ollama, the url is http://localhost:11434/ and the API key can be omitted. 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
  

• 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"
  

  • --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")
  • --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).

Getting started (Docker)

• Docker usage shows how to build and run with environment variables for API keys and optional GitHub token, mounting output to access tutorials.

Recent releases

• None

Traction

• Stars: 12595

Behind the repo

• The README references Pocket Flow and related project links, but no specific startup/company details beyond the project context.

Caveats

• License: MIT (as shown in README badge)
• Created: 2025-04-02
• Last push: 2026-05-31
• Language: Python
• Open issues: 77
• The repository has no releases listed in the latest releases section.