This repository provides @mermaid-js/mermaid-cli, a command-line interface (CLI) and Node.js library for rendering Mermaid diagram definitions into SVG, PNG, or PDF files. It processes individual .mmd files or Markdown documents containing Mermaid code blocks, replacing them with embedded images. The tool is a critical part of the Mermaid ecosystem, enabling automated diagram generation in CI/CD pipelines, documentation builds, and static site generators. The architecture is centered around Puppeteer, which launches a headless Chromium browser to render the diagrams. The src/index.js module orchestrates the entire workflow: parsing CLI arguments via commander, loading Mermaid configurations, setting up the browser page with custom CSS and icon packs, invoking the Mermaid rendering engine, and post-processing the output (e.g., font embedding, SVG serialization). A standout component is the custom Puppeteer request interceptor (src/puppeteerIntercept.js), which cleverly serves local ESM modules to the browser without requiring a local HTTP server, solving a common pain point in browser-based tooling. The codebase exhibits high craftsmanship with clean abstractions, comprehensive JSDoc documentation, and a strong test suite. Key modules like the font embedder (src/browser/fontEmbedder.js) and CSS tokenizer (src/browser/tokenizeCSS.js) are self-contained, well-tested, and demonstrate non-obvious skill in handling complex web standards. The renderMermaid function in src/index.js provides a clean, reusable API for programmatic use. Overall, this is a mature, actively maintained, and exceptionally well-engineered tool.
The dot package is a Go library for generating Graphviz DOT language graphs. It provides a fluent API to construct graphs, nodes, edges, and set attributes. The library supports features like subgraphs, clusters, record-shaped nodes, bidirectional edges, and Mermaid diagram output. It includes an extension package (dotx) for creating hierarchical graph composites. The code is well-structured, thoroughly tested, and has zero external dependencies beyond the Go standard library.
Madopic is a client-side web application that converts Markdown content into images (PNG, PDF, HTML) with support for mathematical formulas, diagrams, charts, and custom styling. It operates as a single-page application with a split-screen interface: users write Markdown in the left editor and see a live preview on the right. The application processes Markdown through a pipeline that includes syntax highlighting, formula rendering (via KaTeX/mhchem), diagram rendering (via Mermaid), chart rendering (via ECharts), and custom card syntax, all before updating the preview. The application emphasizes a seamless user experience with real-time previews, customizable themes (background, font size, layout), and multiple export options. It implements sophisticated features like offline image persistence using IndexedDB with an in-memory cache, a protected token system to prevent interference between different Markdown extensions (such as preventing math formula replacements inside code blocks), and exact export cloning to ensure that the exported image matches the preview precisely, including handling of specific aspect ratios for platforms like Xiaohongshu and WeChat Moments. Built with vanilla HTML, CSS, and JavaScript, Madopic relies on several CDN-hosted libraries for its core functionality but maintains a lightweight, dependency-conscious architecture. The application is designed as a progressive web app (PWA) and includes features like local draft autosave, keyboard shortcuts, and responsive design to cater to a wide range of use cases from academic to social media content creation.
MarkText is a cross-platform desktop markdown editor (Electron + Vue 3) powered by the Muya WYSIWYG engine. It provides real-time rendering of CommonMark, GFM, and extended syntax including KaTeX math, mermaid/plantuml diagrams, footnotes, tables, task lists, and 30+ themes. The monorepo contains four packages: `desktop` (Electron shell with IPC, preferences, spellcheck, export), `muya` (TypeScript core editor with block-based state, snabbdom rendering, undo/redo via ot-json1, clipboard, and floating UI components), `muyajs` (legacy JavaScript engine, still present for partial feature parity), and `website` (Next.js documentation site). The architecture follows a block tree pattern where each block type (paragraph, heading, list, table, etc.) is a subclass with consistent parent/content interfaces. Communication between the Electron main and renderer processes uses typed IPC channels. The codebase demonstrates strong software engineering practices with extensive unit and e2e test coverage (Playwright, Vitest), reproducible builds (pnpm monorepo), and a focus on regression testing for edge cases. However, the ongoing migration from the legacy `muyajs` engine to the new `muya` engine introduces some code duplication and technical drift, and some modules retain prototype-style JavaScript alongside modern TypeScript classes.
DeepV-Ki is an AI-powered wiki generator for code repositories that combines a Next.js frontend with a FastAPI backend. The system allows users to input a repository URL and automatically generates documentation, architecture diagrams (Mermaid), and provides a RAG-based Q&A interface. It features multi-LLM provider support (OpenAI, Google Gemini, Azure, AWS Bedrock, Ollama), GitLab/GitHub/Bitbucket integration, asynchronous task processing with Celery, and multi-layer caching. The architecture follows a monorepo structure with clear separation between frontend (React/Next.js) and backend (Python/FastAPI) components, communicating via REST APIs. The system includes sophisticated authentication (SSO, session management), progress tracking, and real-time WebSocket communication for streaming AI responses.