Litho (deepwiki-rs) is an AI-powered open-source tool designed to automate the generation of professional architecture documentation from source code. It addresses the common problem of outdated and manually maintained documentation by transforming codebases into C4 model-compliant architecture diagrams and detailed explanations, benefiting development teams, open-source projects, and enterprise developers by ensuring documentation stays synchronized with code changes and improving onboarding.

How It Works

Litho employs a sophisticated four-stage processing pipeline: Preprocessing (code scanning, parsing, dependency extraction), Intelligent Research & Analysis (AI-driven inference of architectural roles, component boundaries, and relationships using ML models and ReAct reasoning), Documentation Generation (assembly of C4 diagrams and narrative content), and Verification & Enhancement (syntax checking, integrity checks, and diagram auto-repair). This AI-driven approach allows for automatic generation of context-aware documentation, keeping it consistently up-to-date with codebase evolution.

Quick Start & Requirements

• Primary Install: cargo install deepwiki-rs
• Prerequisites: Rust (version 1.70 or later), Cargo.
• Usage: deepwiki-rs -p ./my-project -o ./docs
• Links: Project repository: https://github.com/sopaco/deepwiki-rs

Highlighted Details

• Automated generation of C4 model diagrams (Context, Container, Component, Code).
• Supports multiple programming languages including Rust, Python, Java, Go, C#, and JavaScript.
• Integrates with CI/CD pipelines for automated documentation updates.
• Part of an ecosystem including Litho Book (documentation reader) and Mermaid Fixer (diagram error correction).
• Analyzes Git history to track architectural evolution.

Maintenance & Community

Contributions are welcomed via GitHub Issues, focusing on language support, template creation, diagram enhancements, performance optimization, test coverage, and bug fixes. Sponsorship opportunities are available on GitHub.

Licensing & Compatibility

The project is licensed under the MIT license, which is permissive for commercial use and integration into closed-source projects.

Limitations & Caveats

Full functionality, particularly advanced AI features, may depend on configuring LLM API keys and base URLs. Areas for improvement and contribution include expanding language support, enhancing diagram generation, and optimizing performance.