xberg
Xberg is a polyglot document intelligence framework with a Rust core that extracts text, metadata, images, and structured data from 97+ file formats (PDFs, Office docs, images, archives, code, audio, video). It runs as a library, CLI, REST API, or MCP server across 15+ programming languages with no GPU required.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | xberg-io/xberg |
| Owner | xberg-io |
| Primary language | Rust |
| License | MIT — OSI-approved |
| Stars | 8.6k |
| Forks | 510 |
| Open issues | 10 |
| Latest release | v5.0.0-rc.13 (2026-06-14) |
| Last updated | 2026-07-08 |
| Source | https://github.com/xberg-io/xberg |
What xberg is
MIT-licensed Rust engine providing format detection, streaming extraction, OCR backends (Tesseract, PaddleOCR, Candle, VLM), transcription (Whisper ONNX), embeddings, structured LLM outputs, and enrichment (NER, redaction, classification). Supports parallel batch processing, content-hash caching, and deployment via library bindings, REST, MCP, or Docker.
Get the xberg source
Clone the repository and explore it locally.
git clone https://github.com/xberg-io/xberg.gitcd xberg# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Language binding maturity varies; Rust and Python are primary, others (Dart, Zig, Swift) are newer. Test bindings in your target language before production commitment.
- OCR backends (Tesseract, PaddleOCR, Candle) and transcription (Whisper ONNX) require external dependencies or model downloads; factor into deployment and storage sizing.
- Batch concurrency and per-file timeouts must be tuned per workload; default parallelism may not suit memory-constrained or latency-sensitive environments.
- Content-hash caching assumes stable file content and config; stale cache invalidation policy must be managed explicitly in long-running systems.
- LLM-powered extraction requires external API credentials (OpenAI, Anthropic, Google) or local inference setup (Ollama, vLLM); costs and latency scale with document volume.
When to avoid it — and what to weigh
- Simple PDF-only Extraction — If you only need basic PDF text extraction, Xberg's breadth introduces unnecessary complexity. Lighter libraries (pdfplumber, pypdf) may suffice.
- Real-time, Sub-100ms Latency Strict Requirements — Multi-format detection, OCR fallback chains, and optional LLM enrichment incur variable latency. Timing-critical systems need benchmarking and may require custom tuning.
- Proprietary/Closed-source Constraints — MIT license permits commercial use, but audit, liability, and support models are not specified. Organizations requiring indemnification or SLA-backed support should clarify before adoption.
- Offline Air-gapped Environments with Licensing Audits — While MIT is permissive, the framework's dependency tree (Tesseract, PaddleOCR, ONNX models, external embeddings APIs) may introduce licensing or supply-chain audit complexity.
License & commercial use
MIT License. Permissive OSI-approved license allowing commercial use, modification, and distribution with attribution. No copyleft obligations.
MIT License clearly permits commercial use without restriction. However, the LICENSE file itself makes no warranty, liability, or indemnification claims. Organizations requiring enterprise support, SLA guarantees, or formal indemnification should contact the maintainers directly or conduct legal review. Dependency chain (Tesseract, PaddleOCR, ONNX, external LLM APIs) may have separate license requirements; audit before deployment.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security audit or threat model stated. Considerations: (1) Rust core mitigates memory-safety issues common in C/C++ document parsers. (2) OCR, transcription, and LLM enrichment involve external models/APIs; validate model sources and API endpoint security. (3) Caching uses content-hash keys; ensure cache storage is access-controlled. (4) File extraction from archives/nested documents may be vulnerable to zip bombs or path traversal; verify input validation. (5) REST API and MCP server expose extraction endpoints; implement authentication, rate limiting, and input validation. (6) External LLM provider credentials must be managed securely. Recommend security audit before handling sensitive documents.
Alternatives to consider
Unstructured.io (Unstructured SDK)
Polyglot document intelligence (Python-first, REST API, LLM integration). Strengths: mature API, commercial support available. Weaknesses: less language coverage than Xberg, different OCR/enrichment stacks.
Apache Tika
Established Java-based parser for 1400+ formats with REST API. Strengths: long history, heavy adoption. Weaknesses: older maintenance cycle, less AI-native, fewer language bindings.
LlamaIndex/LangChain + Custom Loaders
Framework-agnostic document ingestion for RAG pipelines. Strengths: flexible, integrates with LLMs natively. Weaknesses: requires assembly of parsers, OCR, embeddings; less opinionated than Xberg.
Build on xberg with DEV.co software developers
Xberg unifies multi-format document extraction, OCR, and AI enrichment in one polyglot framework. Get clean text, structured data, and code intelligence from PDFs, Office docs, images, and 97+ formats. Try the live demo or integrate via REST API, library, or MCP server.
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xberg FAQ
Does Xberg require GPU or external services?
What is the license and can I use it commercially?
How mature is the project and which language bindings are most stable?
Can I deploy Xberg as a service?
Software development & web development with DEV.co
Adopting xberg is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate rag frameworks software in production.
Extract, Parse, and Enrich Documents at Scale
Xberg unifies multi-format document extraction, OCR, and AI enrichment in one polyglot framework. Get clean text, structured data, and code intelligence from PDFs, Office docs, images, and 97+ formats. Try the live demo or integrate via REST API, library, or MCP server.