Xberg

Extract clean text, tables, and structured data from documents and code — no format detection, no OCR setup, no stitched-together libraries. One engine, 15 language bindings, runs anywhere.

Xberg is the next iteration of Kreuzberg. Same document-intelligence engine, rebuilt and rebranded under a fresh v1 line.

Feed documents → get clean text, tables, metadata, transcripts, code intelligence · Run it library, CLI, REST API, or MCP server · No GPU needed · Stream multi-GB files · Cache results.

Documents · Images · Spreadsheets · Email · Archives · Code · Audio · Video

Quick start · What you get · Capabilities · CLI · Docs

What you get

Point Xberg at anything — a PDF, a spreadsheet, a scanned image, an audio file, a source tree — and get back clean, structured content you can use right away. One core does the format detection, reading, and extraction, so you don't assemble a pipeline yourself. Call it from Rust, Python, Node.js, Go, Java, C#, Ruby, PHP, Elixir, Dart, Swift, Zig, WASM, Kotlin, or C FFI, and run it as a library, CLI tool, REST API, or MCP server.

Demos

Installation

Language Packages

pip install xberg

See Python README for full documentation.

npm install @xberg-io/xberg

See Node.js README for full documentation.

cargo add xberg

See Rust README for full documentation.

go get github.com/xberg-io/xberg

See Go README for full documentation.

Available on Maven Central as io.xberg:xberg. See Java README for the dependency snippet.

dotnet add package Xberg

See C# README for full documentation.

gem install xberg

See Ruby README for full documentation.

composer require xberg-io/xberg

See PHP README for full documentation.

Add {:xberg, "~> 1.0"} to your mix.exs dependencies. See Elixir README for full documentation.

npm install @xberg-io/xberg-wasm

See WebAssembly README for full documentation.

Available on Maven Central as io.xberg:xberg-android. See Kotlin README for the dependency snippet.

Add via Swift Package Manager. See Swift README for full documentation.

dart pub add xberg

See Dart README for full documentation.

Add via zig fetch. See Zig README for full documentation.

Build from source as part of this workspace. See C (FFI) README for full documentation.

CLI & Deployment

brew install xberg-io/tap/xberg

12 commands: extract, batch, detect, formats, version, cache (stats/clear/manifest/warm), serve, mcp, api, embed, chunk, completions.

See CLI usage guide for detailed documentation.

docker pull ghcr.io/xberg-io/xberg:latest

Run in API, CLI, or MCP modes. See Docker guide for examples.

xberg serve --host 0.0.0.0 --port 8000

One POST endpoint handles all formats. Returns JSON or Markdown. Stream large files. See API server guide.

xberg mcp --transport stdio

9 tools (extract, extract_batch, detect_mime_type, cache_stats, list_formats, cache_clear, get_version, cache_manifest, cache_warm). 3 prompts (extract_document, extract_with_ocr, semantic_search). 4 resources (formats, models, OCR languages, embedding presets).

Add to Claude Desktop or Cursor:

{
  "mcpServers": {
    "xberg": { "command": "xberg", "args": ["mcp"] }
  }
}

See MCP integration guide.

AI Coding Assistants

Install the Xberg plugin from xberg-io/plugins. Ships extraction APIs, OCR backends, configuration, and language conventions.

/plugin marketplace add xberg-io/plugins
/plugin install xberg@xberg

/plugins add https://github.com/xberg-io/plugins

Search for xberg and select Install Plugin.

Settings → Plugins → Add from URL → https://github.com/xberg-io/plugins, then select xberg.

gemini extensions install https://github.com/xberg-io/plugins

droid plugin marketplace add https://github.com/xberg-io/plugins
droid plugin install xberg@xberg

copilot plugin marketplace add https://github.com/xberg-io/plugins
copilot plugin install xberg@xberg

Add to opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "plugin": ["@xberg-io/opencode-xberg"]
}

Quick Start

Extract text from a document:

use xberg::{extract, ExtractInput, ExtractionConfig};

#[tokio::main]
async fn main() -> xberg::Result<()> {
    let config = ExtractionConfig::default();
    let output = extract(
        ExtractInput::from_uri("document.pdf"),
        &config
    ).await?;

    println!("{}", output.results[0].content);
    Ok(())
}

Common use cases — see Quick start guide for language-specific examples, OCR, batch processing, and API configuration.

Capabilities

Supported File Formats (96)

96 file formats across 8 major categories with intelligent format detection and comprehensive metadata extraction.

Office Documents

Images (OCR-Enabled)

Audio & Video

Web & Data

Email & Archives

Academic & Scientific

Code Intelligence (306 Languages)

Extract structure from 306 programming languages via tree-sitter:

Powered by tree-sitter-language-pack.

Output Formats (6)

Deployment Modes

OCR Backends

• Tesseract — Native C FFI (Linux/macOS/Windows) and WASM (browser)
• PaddleOCR — ONNX Runtime, mobile-optimized models
• Candle — Pure Rust, CPU-only, lightweight
• VLM — GPT-4 Vision, Claude Vision, Gemini Vision, or 143 providers via liter-llm

Fallback chains. Extensible via plugin system.

Embeddings

Local (ONNX Runtime):

• Preset models: fast, balanced (default), quality, multilingual
• Dimensions: 384, 768, 1024

Provider-hosted:

• OpenAI, Anthropic, Google, Hugging Face, Mistral, Cohere, and 143 providers total
• Via liter-llm integration

Reranking:

• Local ONNX rerankers (cross-encoder models)
• Provider-hosted: Cohere Rerank, others

Structured LLM Extraction

Local engines: Ollama, LM Studio, vLLM

Remote: OpenAI, Anthropic, Google, Mistral, Cohere, and 143 providers via liter-llm

Schema validation. Temperature, top-p, frequency penalty tuning.

Enrichment

• NER — GLiNER or LLM-based entity recognition
• Redaction — Mask PII (phone, email, SSN, credit card, addresses)
• Summarization — Document and section summaries via LLM
• Translation — Multi-language via LLM
• Page Classification — Tag document pages (cover, toc, content, etc.)
• QR Code Detection — Extract and decode QR codes from images
• Keyword Extraction — YAKE or RAKE algorithms
• Language Detection — Detect document language
• Layout Detection — RT-DETR + TATR models for document structure
• Table Extraction — Cell-level structure and content
• Token Reduction — TOON wire format (~30–50% fewer tokens than JSON)

CLI Reference

Run xberg --help or xberg <command> --help for detailed options.

Documentation

Full guides, API references for every binding, format reference, and configuration docs live at xberg.io.

• Getting Started
• Quick Start
• Guides
• API Reference
• Format Reference
• Live Demo (browser, WASM)

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

Join our Discord community for questions and discussion.

Part of Xberg.dev

Xberg is one of six open-source projects from Kreuzberg, Inc.:

• Xberg — document intelligence: text, tables, metadata from 91+ formats with optional OCR.
• Xberg Enterprise — managed extraction API with SDKs, dashboards, and observability.
• crawlberg — web crawling and scraping with HTML→Markdown and headless-Chrome fallback.
• html-to-markdown — fast, lossless HTML→Markdown engine.
• liter-llm — universal LLM API client with native bindings for 14 languages and 143 providers.
• tree-sitter-language-pack — tree-sitter grammars and code-intelligence primitives.
• alef — the polyglot binding generator that produces every per-language binding across the 5 polyglot repos.

License

MIT License (MIT) — see LICENSE for details.