CodeBoarding/CodeBoarding

Interactive architecture diagrams for codebases

What it solves

CodeBoarding addresses the difficulty of understanding large, complex codebases and the risk of AI coding agents introducing "hidden debt" by making the system architecture visible. It provides a visual map of a codebase so developers and agents can see the high-level structure and component relationships before changes are implemented.

How it works

The tool combines static code analysis with LLM reasoning to generate architectural insights. It uses a static analyzer to extract code insights and an LLM agent core to deliver structured analysis. An incremental analysis engine ensures that only changed parts of the codebase are re-processed, reducing overhead. The output is generated as Mermaid diagrams, Markdown documentation, and a JSON analysis file that can be viewed in an IDE, a web platform, or integrated into CI/CD pipelines via GitHub Actions.

Who it’s for

  • Developers who need to onboard to large repositories faster or review AI-generated changes with system context.
  • Coding Agents that require a visual, high-level representation of a codebase to operate more accurately.
  • Teams looking to maintain up-to-date architecture diagrams and documentation automatically within their docs or pull requests.

Highlights

  • Multi-language support: Works with Python, TypeScript, JavaScript, Java, Go, PHP, Rust, and C#.
  • Flexible LLM integration: Compatible with OpenAI, Anthropic, Google, Ollama, and other providers via LiteLLM or OpenRouter.
  • Detailed outputs: Generates high-level system architecture diagrams, component-level breakdowns, and Markdown documentation.
  • Incremental updates: Only analyzes changed code segments to maintain efficiency.
  • Broad integration: Available as a CLI, VS Code extension, web platform, and GitHub Action.

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