PocketFlow Tutorial Codebase Knowledge

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Basic Information

This repository is a tutorial project that demonstrates how to build an AI agent which crawls GitHub repositories or local directories, analyzes codebases, and automatically generates beginner-friendly tutorials and visualizations that explain how the code works. It is built as an example project for the Pocket Flow 100-line LLM framework and showcases an end-to-end pipeline that builds a code knowledge base, identifies core abstractions and interactions within a project, and transforms those findings into organized tutorial content. The README documents command-line usage, configuration options, LLM setup, Docker instructions, and links to example generated tutorials so users can reproduce the analysis locally or via the provided online service.

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App Details

Features
Crawls a target GitHub repository or local directory and builds a searchable code knowledge base. Identifies core abstractions and relationships inside a codebase and generates structured, beginner-oriented tutorial content with visualizations. Provides a CLI script with options for repo or dir, include/exclude patterns, output directory, max file size, language selection, max abstractions, and caching control. Includes an LLM integration utility for configuring model credentials and supports multiple model providers. Offers Dockerfiles and examples for containerized runs. Ships example outputs for many popular repositories and a development tutorial that explains the agentic coding workflow used to create the project.
Use Cases
Automates the task of understanding unfamiliar or large codebases by producing readable tutorials and diagrams that explain architecture and key components. Lowers onboarding time for new contributors, helps maintainers generate documentation, and aids educators who want demonstrable examples of code structure. The CLI and Docker support make it straightforward to analyze public or local projects, and the LLM configuration file lets users substitute different models. Example outputs and a development walkthrough make it easier to reproduce results or adapt the pipeline to other repositories. The project also serves as a hands-on demo of Pocket Flow and agentic coding practices.

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