Xberg

The fast, precise document-intelligence engine — for every language.

Point Xberg at anything — a PDF, a scanned image, a spreadsheet, an audio file, a URL, a whole archive, or a source tree — and get back clean text, tables, metadata, and structured data. One engine handles format detection, reading, OCR, and extraction, so you never stitch a pipeline together from a dozen libraries.

107 formats · 141 file extensions · 371 code languages · 15 language bindings · 6 output formats · OCR · transcription · embeddings

The fastest, most precise open-source document and PDF-to-Markdown engine — see the benchmarks.

Install · What you get · Capabilities · CLI · Docs

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

What you get

Point Xberg at anything — a PDF, a spreadsheet, a scanned image, an audio file, a URL, an archive, 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.

Capabilities marked requires a feature are Cargo feature flags on the core crate (url-ingestion, transcription, reranker, layout/ORT). Prebuilt language packages and the Docker image bundle the common set; a from-source build enables only what you select.

Installation

Language Packages

pip install xberg

For archived versions and the PyPI storage fallback:

pip install xberg --extra-index-url https://xberg-io.github.io/python-wheels/simple/

PyPI keeps all releases in the current minor and the latest stable patch of each older minor. See the permanent archive and retention policy.

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/packages/go@latest

⚠️ The repository root is not a Go module — go get github.com/xberg-io/xberg will fail. Always target the /packages/go subdirectory as shown above.

See Go README for full documentation.

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

dotnet add package XbergIo.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

Windows users can install the same binary through Scoop:

scoop bucket add xberg https://github.com/xberg-io/scoop-bucket
scoop install xberg

14 commands: extract, batch, detect, formats, version, cache, tree-sitter, doctor, serve, mcp, api, embed, chunk, and 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/xberg. Ships extraction APIs, OCR backends, configuration, and language conventions.

The bundle targets Claude Code, Codex, Cursor, Gemini CLI, Kimi, OpenCode, Factory Droid, Hermes, Pi, Agent Plugins and GitHub Copilot CLI. Read the plugin guide for runtime resources, CLI prerequisites and configuration. MCP-capable adapters expose the product's tools; Hermes uses a local stdio bridge and explicitly selected skills. Pi loads skills and prompt templates and requires the product CLI for executable operations.

Install only the public Agent Skills, without a plugin or MCP registration, using the skills CLI:

npx skills add xberg-io/xberg --list
npx skills add xberg-io/xberg

Choose the skills and target agents interactively. Installing instructions does not install the product CLI or configure provider credentials.

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

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

Search for xberg and select Install Plugin.

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

From a checkout of this repository:

gemini extensions install ./plugin

The manifest is inside plugin/. See the Gemini extension reference for local installation and updates.

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

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

Install from a local checkout now:

pi install ./plugin

After the next plugin release, install the same package used by OpenCode:

pi install npm:@xberg-io/opencode-xberg

Pi loads the bundled skills and prompt templates. Use /xberg-extract and /xberg-ocr to start a task. Install the CLI separately as shown in the plugin README. This package does not register MCP servers, hooks, or executable extensions in Pi.

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 (107 formats · 141 file extensions · 56 MIME aliases)

107 formats across 140 unique file extensions, with 56 compatibility MIME aliases, intelligent format detection, and comprehensive metadata extraction.

Office Documents

Images (OCR-Enabled)

Audio & Video

Web & Data

Email & Archives

Academic & Scientific

Code Intelligence (371 Languages)

Extract structure from 371 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 165 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 165 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 165 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 (Rust core) — every binding has its own page under /reference/
• Format Reference
• Live Demo (browser, WASM)

Built with Xberg

Projects that declare Xberg as a dependency. Xberg was previously published as kreuzberg, and most of these projects declare the package under that name.

Project	What it is	Stars
basemind	AI context and content layer for coding agents over one MCP server: code map, document RAG, shared memory and web crawl
delulu	A suite of MCP servers and CLI tools that give your LLM better search and fewer hallucinations
docs-mcp-server	Grounded documentation MCP server, an open-source alternative to Context7, Nia and Ref.Tools
erato	The open-source AI platform
fastmail-cli	CLI and MCP server for Fastmail: email, contacts, masked email, attachments and text extraction
ghfdb-portal	Web portal for the Global Heat Flow Database
hawki-toolkit-file-converter	Prepares and converts PDF files for the HAWKI toolkit
haystack-core-integrations	Integrations that extend Haystack with extra components and document stores
kreuzakt	A search engine for humans and computers, aimed at your most boring documents
lilbee	The whole local AI stack in one executable, with conversational search and cited answers over your files, code and the web
llm-workflow-engine	Power CLI and workflow manager for LLMs
MANSPIDER	Spiders entire networks for files sitting on SMB shares, searching filenames or contents with regex
otoroshi-llm-extension	Connect, secure and manage LLM models behind one OpenAI-compatible API
sift-kg	Turns a collection of documents into a knowledge graph, extracting entities and relationships with an LLM
sirchmunk	Turns raw data into a self-evolving, real-time search and intelligence layer
support-chatbot	Level-1 support chatbot for the Netherlands Red Cross 510 team

Using Xberg in your project? Open a PR adding it to this list.

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

Join our Discord community for questions and discussion.

Part of Xberg.io

• Xberg — the open-source content-intelligence engine: text, tables, and metadata from 107 formats (141 file extensions), with OCR, transcription, and code intelligence. MIT.
• Xberg Pro — a complete self-hosted content-intelligence backend in a single container. Commercial.
• Xberg Enterprise — the distributed, governed content-intelligence platform, scaled on Kubernetes with team governance and support. Commercial.
• 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 165 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.