Understand Anything is a developer tool that analyzes codebases to create interactive knowledge graphs for code comprehension and onboarding. It combines deterministic static analysis (using tree-sitter) with LLM-powered semantic understanding to build explorable graphs of files, functions, classes, dependencies, and business domains. The system features a multi-agent pipeline for analysis, incremental updates via fingerprinting, and a React/Vite dashboard for visualization with guided tours, semantic search, and diff impact analysis. The architecture follows a monorepo structure with pnpm workspaces separating core analysis engine, dashboard UI, and plugin interface. Key capabilities include language-agnostic structural analysis across 30+ programming languages, business domain extraction, knowledge base analysis (Obsidian/Logseq-style wikis), and multi-platform support for AI coding assistants (Claude Code, Codex, Cursor, Copilot). The system operates offline for structural analysis while optionally leveraging LLMs for enrichment, with persistence to a .ua/ directory for team collaboration via version control.
Marker is a high-accuracy document-to-markdown converter that transforms PDFs, images, DOCX, PPTX, XLSX, HTML, and EPUB files into clean markdown, JSON, HTML, or chunked output. It uses a pipeline architecture: a Provider extracts raw text and layout from the input, Builders (layout, line, OCR) construct a structured document model, Processors apply domain-specific transformations (table reconstruction, equation extraction, footnote handling, LLM-based correction), and Renderers produce the final output format. The system is driven by the Surya VLM for layout analysis and OCR, with an optional LLM enhancement layer (Gemini, Claude, OpenAI, etc.) for complex tasks like table merging, inline math, and image descriptions. Key entry points include CLI tools (`marker`, `marker_single`), a FastAPI server (`marker_server`), a Streamlit GUI, and a Python API (`PdfConverter`, `TableConverter`, `OCRConverter`). External dependencies include surya-ocr, pdftext, pypdfium2, torch, transformers, and various LLM SDKs. The codebase is well-structured with a clear separation of concerns, extensive test coverage, and a benchmark suite (olmocr-bench) that validates quality scores (~76% overall balanced mode).