xberg: what it is, what problem it solves & why it's gaining traction

What it solves

Xberg is a content-intelligence engine designed to solve the problem of extracting structured, clean text and metadata from a vast array of fragmented file formats. It eliminates the need for multiple disparate tools to handle PDFs, Office documents, images, audio/video, and source code, providing a unified interface for document processing.

How it works

Built on a Rust core, Xberg provides a single engine that supports 96 file formats and 306 programming languages. It uses intelligent MIME detection and streaming for large files. For images, it offers pluggable OCR backends (Tesseract, PaddleOCR, Candle, or VLMs). For audio and video, it uses Whisper ONNX for transcription. It can be deployed as a library, CLI tool, REST API, or an MCP server for AI agents.

Who it’s for

It is intended for developers building RAG pipelines, AI agents, or data extraction workflows who need to convert diverse documents into machine-readable formats (like Markdown or JSON) without requiring a GPU.

Highlights

• Massive Format Support: Extracts from 96 formats including Office, PDF, eBooks, Email, and Scientific publications.
• Code Intelligence: Extracts functions, classes, and symbols from 306 languages with syntax-aware chunking for RAG.
• Multi-Runtime Deployment: Native bindings for 16 languages (Python, Node.js, Rust, Go, Java, etc.) and WASM support.
• Crawl & Recurse: Ability to follow URLs and extract documents nested within archives or other documents.
• AI Integration: Built-in support for LLM-powered structured extraction, embeddings, and an MCP server for agentic workflows.