GitDiagram is an open-source tool that transforms GitHub repositories into interactive system architecture diagrams using AI. It’s designed for developers, open-source contributors, and system designers who need to rapidly comprehend complex codebases without manually navigating files. By analyzing repository structure, READMEs, and file trees, it generates visual diagrams with direct links to source code.
Built with Next.js, TypeScript, and Tailwind CSS on the frontend, and FastAPI or Next.js Route Handlers on the backend, GitDiagram uses Cloudflare R2 for diagram storage and Upstash Redis for quota and failure tracking. It leverages OpenAI’s gpt-5.4-mini or OpenRouter for AI-powered diagram generation, with full support for private repos via GitHub tokens and self-hosting options.
What You Get
- Instant Repository Visualization - Converts any GitHub repository’s file structure and README into an interactive Mermaid diagram in seconds, eliminating manual codebase exploration.
- Click-to-Navigate Source Files - Every node in the diagram links directly to its corresponding file or directory on GitHub, enabling seamless navigation from visualization to code.
- AI-Powered Architecture Generation - Uses GPT-5.4-mini or OpenRouter models to analyze code structure and generate accurate system diagrams with nodes, edges, and contextual explanations.
- Export as Mermaid or PNG - Copy the generated Mermaid.js code for integration into docs or download the diagram as a PNG for presentations and reports.
- Private Repository Support - Generate diagrams for private repos using a GitHub personal access token with repo scope, stored securely in Cloudflare R2 private buckets.
- Self-Hostable Architecture - Fully configurable backend with options for FastAPI (via Docker) or Next.js Route Handlers, with environment-based AI provider switching (OpenAI/OpenRouter).
Common Use Cases
- Onboarding to large open-source projects - A new contributor uses GitDiagram to visualize the architecture of FastAPI or Streamlit before diving into code, reducing onboarding time from hours to minutes.
- System design interviews - A candidate generates a diagram of a GitHub-hosted project like Monkeytype to explain its architecture during technical interviews, demonstrating analytical skills.
- Documentation generation for legacy code - A team uses GitDiagram to auto-generate architecture diagrams for undocumented repositories, then exports Mermaid code to embed in Confluence or Notion.
- Open-source contribution planning - A developer analyzes a repo’s structure via GitDiagram to identify high-impact areas to contribute to, based on component visibility and interconnectivity.
Under The Hood
Architecture
- Clear separation between frontend and backend with well-defined API boundaries, enabling independent development and scaling
- Layered service architecture decoupling storage, generation, and validation logic for testability and maintainability
- Server components and caching primitives in Next.js optimize data fetching and reduce redundant computations
- Dependency injection via environment configuration enforces usage policies without hardcoding business rules
- Modular UI components built on shadcn/ui primitives ensure consistency and reusability across the application
Tech Stack
- Full-stack React 19 and Next.js 16 with Server Components and TypeScript for type-safe frontend development
- FastAPI backend served via Uvicorn with Pydantic for data validation and uv for dependency management
- Monorepo structure with dedicated frontend and backend directories, coordinated through Docker Compose for consistent development environments
- Vitest for comprehensive testing, ESLint and Prettier for code quality, and Tailwind CSS with shadcn/ui for styling
- Infrastructure optimized for Vercel deployment with S3 integration, Mermaid.js for client-side rendering, and Bun as the package manager
Code Quality
- Extensive test coverage across all layers including unit, integration, and end-to-end scenarios
- Strong type safety enforced through TypeScript interfaces and strict prop validation in React components
- Clean, descriptive naming conventions that clearly communicate component and function intent
- Robust error handling with custom exceptions, structured responses, and graceful degradation for user-facing failures
- Comprehensive linting, mocking, and environment isolation ensure reliability and maintainability
What Makes It Unique
- Seamlessly bridges raw code repositories with AI-generated visual architectures, eliminating manual diagramming
- Implements a sustainable complimentary usage model with token-based quotas and provider-aware restrictions
- Enables secure private repo access via client-side PAT storage with transparent data handling
- Delivers dynamic, lazy-loaded diagram previews with interactive zoom controls for large codebases
- Pre-calculates and displays AI usage costs before generation, empowering users with transparent pricing