Xberg
Open-source content intelligence engine for CPU-only document extraction
Xberg is the open-source pick for CPU-efficient document extraction at scale, with polyglot SDK reach you won't find in Unstructured. The 1.0 and 1.1 releases signal maturity, but it's still developer-only—skip it if you need a no-code GUI or GPU-native OCR from a managed service.
Verified 1d ago · liveness 75/100 · cite: rightaichoice.com/tools/xberg
- Developers building high-throughput document extraction pipelines for RAG or data lakes
- Teams needing polyglot SDK support (Python, TypeScript, Rust, Go, etc.) in microservices
- DevOps deploying CPU-only extraction in containers or serverless environments
- Researchers processing academic formats like LaTeX, BibTeX, and JATS
- Non-technical users: requires programming knowledge or CLI usage
- GPU-accelerated OCR at scale: designed for CPU efficiency; some VLM OCR may need GPU
- Real-time speech transcription: Whisper is file-based, not streaming
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Skip Xberg if you're a non-technical user needing a hosted, no-code document extraction service, or if you require GPU-native OCR at massive scale and real-time transcription—Xberg is developer-focused, CPU-optimized, and Whisper is file-based only.
Enterprise tier requires a custom quote with Kubernetes deployment, governance, and support—costs are not published, so budget for enterprise sales.
Xberg's Community tier is free and open-source, making it ideal for small teams and individual developers on a budget—you get the full engine with no per-document fees. Peers like Unstructured charge per-call or per-volume, so Xberg's free tier can be a major cost saver for high-volume CPU-only extraction. The Enterprise tier (custom pricing) adds Kubernetes, governance, and support, comparable to Unstructured's enterprise pricing but with the open-source flexibility.
In short
Xberg — Open-source content intelligence engine for CPU-only document extraction. Best for Developers building high-throughput document extraction pipelines for RAG or data lakes, Teams needing polyglot SDK support (Python, TypeScript, Rust, Go, etc.) in microservices, DevOps deploying CPU-only extraction in containers or serverless environments. Free to use.
What's new in Xberg
Checked yesterdayAcross the latest 5 updates: 5 changelog entries.
Xberg 1.0.14 released
1.0.14 includes fixes and changes, likely addressing bugs in extraction and API behavior.
Xberg 1.0.13 released
1.0.13 includes fixes, maintaining stability after the 1.0.0 release.
Xberg 1.0.12 released
1.0.12 includes fixes, continuing the rapid patch cycle.
Xberg 1.0.11 released
1.0.11 includes fixes.
Xberg 1.0.9 and 1.0.10 released
1.0.9 includes additions, changes, and fixes; 1.0.10 includes fixes.
What people actually say about Xberg — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
6 mentions across 2 sources (GitHub, Lemmy) · researched Jul 3, 2026.
- +Supports extraction from 96+ file formats out of the box.
- +Rust core provides high performance and minimal external dependencies.
- +Completely free and open-source with permissive license.
- +Multi-backend OCR pipeline enables flexible text extraction.
- +Available as library, CLI, API, Docker, and MCP server.
- −Community feedback beyond GitHub stars is very limited.
- −Support relies on open-source community; no official helpdesk.
- −Integration guides for AI frameworks may be incomplete.
- −Advanced features like VLM OCR still experimental?
- −Setup for multi-language SDKs can be non-trivial.
- • Infrastructure cost for running OCR pipelines at scale
Viability Score
How well maintained and how widely used is Xberg? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Extract text, tables, and metadata from 107 formats to Markdown or five other output formats
- OCR via Tesseract, PaddleOCR, EasyOCR, and VLM (GLM-OCR, DeepSeek-OCR, PaddleOCR-VL 1.5, VLM-OCR)
- Whisper ONNX audio/video transcription with confidence scores and language detection
- Code intelligence: extract functions, classes, imports, symbols across 371 languages
- Named entity recognition and redaction
- Document summarization and translation
- Page classification and VLM image captions
- Web crawling via crawlberg (Auto, Document, Crawl modes)
- Nested archive extraction (.zip, .tar, .gz, .7z) with zip-bomb and nesting-depth guards
- Structured entity extraction via local or hosted LLMs, no prompt engineering
- Plugin system for custom extractors and OCR backends
- REST API, CLI, MCP server, Docker, and WebAssembly deployment
- Native SDKs in 17 languages including Python, TypeScript, Rust, Go, Java, C#, Ruby, PHP, Elixir, Dart, Swift, Zig, C
- SIMD-optimized CPU pipeline; no GPU required
- Diagram recovery from vector SVG/PDF to Graphviz DOT (1.1.0)
About Xberg
Xberg is an open-source content intelligence engine that turns documents, URLs, code files, and audio into clean, structured data—text, tables, entities, and embeddings—ready for RAG, data extraction, and archiving. Built on a Rust core with SIMD optimizations, it delivers high CPU-only throughput without requiring a GPU. The engine supports over 100 formats including PDFs, Office files, images, email, e-books, and academic papers, outputting clean Markdown or one of five other formats with no per-format setup. It also handles web crawling (via the crawlberg engine with Auto, Document, and Crawl modes), nested archive extraction (.zip, .tar, .gz, .7z) with security guards, and code intelligence for 371 programming languages. Recent releases (July–August 2026) brought stable 1.0, cancellable MCP tasks, typed cache results, and PDF/table fixes, following the migration from Kreuzberg v4. The 1.1.0 update adds diagram recovery from vector SVG/PDF sources, producing deterministic Graphviz DOT output without a detection model. For developers, Xberg offers native SDKs in 17 languages—Python, TypeScript, Rust, Go, Java, C#, Ruby, PHP, Elixir, Dart, Swift, Zig, C, and more—plus a CLI, REST API, MCP server, Docker, and WASM deployment. The TypeScript ecosystem provides NAPI-RS bindings for Node.js and a pure WebAssembly package for browsers and serverless environments (at 60–80% of native speed). Multi-engine OCR (Tesseract, PaddleOCR, EasyOCR, VLM via Candle) and Whisper ONNX transcription add confidence scores and language detection. A plugin system lets you build custom extractors and OCR backends. ML features include named entity recognition, redaction, summarization, translation, page classification, and VLM image captions. Structured entity extraction works with local or hosted LLMs without prompt engineering. This is a strong alternative to Unstructured or Apache Tika for teams that need CPU-efficient, polyglot document extraction without GPU overhead.
Behind the Verdict
Xberg is the open-source pick for CPU-efficient document extraction at scale, with polyglot SDK reach you won't find in Unstructured. The 1.0 and 1.1 releases signal maturity, but it's still developer-only—skip it if you need a no-code GUI or GPU-native OCR from a managed service. Xberg stands out for its polyglot SDK coverage—17 languages including Python, TypeScript, Rust, Go, Java, C#, Ruby, PHP, Elixir, Dart, Swift, Zig, C—and its CPU-first design. It handles 107 formats, OCR via multiple engines, Whisper transcription, code intelligence for 371 languages, and web crawling. The 1.1.0 update adds diagram recovery from vector SVG/PDF, producing deterministic Graphviz DOT output—a niche but useful feature. The migration path from Kreuzberg v4 is documented, and the project is part of the Xberg.io ecosystem (including crawlberg, html-to-markdown, liter-llm, and alef). Weaknesses: It's developer-only; there's no hosted GUI. While it's CPU-efficient, GPU-native OCR at scale may be faster (though it does support VLM OCR via Candle). Real-time speech transcription isn't supported (Whisper is file-based). Low-code integration isn't available. The structured extraction relies on LLMs, but the specific underlying LLMs aren't named in the docs. Where it fits: teams building RAG pipelines, data lakes, or archiving systems that need to handle diverse document types without GPU infrastructure. Where it doesn't: non-technical users, or teams needing GPU-accelerated OCR at massive scale.
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Real-world workflow fit
Concrete scenarios for the personas Xberg actually fits — and what changes day-one when you adopt it.
You need to ingest a mixed set of PDFs, Word docs, and emails, extract clean text and tables, and feed them into a vector store. With Xberg's CLI, you can batch-process files to Markdown and then use the embeddings API to generate chunks.
Outcome: Reduced preprocessing time by ~40% compared to your previous solution, and you avoided GPU costs because everything runs on CPU.
You have a folder of PDF contracts and need to extract party names, dates, and clauses into JSON. You configure the structured extractor with a schema and use a local LLM, no prompt engineering needed.
Outcome: Extracted structured JSON from thousands of contracts overnight, with high accuracy and no manual prompt tuning.
You want to run extraction in a containerized microservice. You pull the Docker image, configure the REST API server, and expose an endpoint to your internal tools.
Outcome: Deployed a scalable extraction service in under an hour, with Prometheus metrics for monitoring (added in 1.1.0).
Use Cases
- Extract full text and tables from PDF bank statements for automated data entry
- Parse academic papers in LaTeX, BibTeX, or JATS formats for research indexing
- Process employee onboarding documents (Word, Excel, ID scans) through a unified pipeline
- Transcribe meeting audio files and extract action items via Whisper + OCR
- Classify incoming email attachments and route them based on content type
- Build a RAG pipeline by chunking and extracting metadata from 91+ file types
Models Under the Hood
as of 2026-09-01
Limitations
- Xberg is an open-source document intelligence engine supporting 107 formats, with OCR via Candle-accelerated engines (GLM-OCR, DeepSeek-OCR, PaddleOCR-VL 1.5, VLM-OCR) and Whisper for audio transcription.
- It emphasizes CPU-only operation with no GPU required.
- Structured extraction relies on local or hosted LLMs, but the specific underlying LLM models are not named.
- Real-time speech transcription isn't supported.
- Requires programming knowledge; no GUI or hosted API.
as of 2026-09-01
Verification history
We have re-verified Xberg 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Xberg tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Community (Open Source)
$0/mo
Ideal for
Individual developers and small teams who want full-featured document extraction without cost and are comfortable with self-hosting via CLI, SDKs, or Docker.
What this tier adds
Free entry point: includes the complete engine, all 17 SDKs, OCR, transcription, and code intelligence, with community support.
Enterprise (xberg.io)
Custom
Ideal for
Large organizations needing managed Kubernetes deployment, governance, and dedicated support for production workloads.
What this tier adds
Adds Kubernetes orchestration, governance controls, and professional support, with custom pricing.
Where the pricing makes sense
The company stage and team size where Xberg's pricing actually pencils out — and where peers do it cheaper.
Xberg's Community tier is free and open-source, making it ideal for small teams and individual developers on a budget—you get the full engine with no per-document fees. Peers like Unstructured charge per-call or per-volume, so Xberg's free tier can be a major cost saver for high-volume CPU-only extraction. The Enterprise tier (custom pricing) adds Kubernetes, governance, and support, comparable to Unstructured's enterprise pricing but with the open-source flexibility.
Setup time & first value
How long it actually takes to get something useful out of Xberg — broken out by persona, not the marketing-page minute.
Python: install via pip and run your first extraction in <5 minutes. TypeScript/Node: npm install and code in ~10 minutes. Rust: cargo add and a basic extraction in ~10 minutes. Full production setup (Docker, REST API, MCP server) may take a few hours to configure and tune.
Switching to or from Xberg
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Kreuzberg v4: Follow the documented migration guide (docs.xberg.io) to adapt your code.
- →From Unstructured: Xberg offers a migration guide covering API and SDK changes.
- ↗To Unstructured: If you need a hosted API or GPU-native OCR, you may migrate to Unstructured's platform.
- ↗To Apache Tika: For Java-centric teams, Tika remains an option, but you'll lose Xberg's polyglot SDK breadth.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Xberg
Common stack mates teams adopt alongside Xberg, with the specific reason each pairing earns its keep.
GraphRAG
Open-source knowledge-graph RAG that maps entities and communities to answer complex, cross-document questions.
RAGFlow
Open-source RAG engine with advanced ETL, hybrid search, and visual agent orchestration for enterprise AI.
LanceDB
Open-source multimodal lakehouse for AI data curation, feature engineering, search, and training.
Featured Head-to-Head Comparisons
Xberg vs Spider Cloud
Spider Cloud is the choice for developers needing real-time web data for AI agents, with a generous free tier and cloud scalability. Xberg is ideal for offline, CPU-efficient document extraction from diverse file formats, but requires self-hosting. Choose based on your primary data source: live web vs. static documents.
Xberg vs Screenplayiq
ScreenplayIQ and Xberg serve completely different use cases: ScreenplayIQ is a specialized AI tool for screenwriting analysis and box office prediction, while Xberg is a developer-first open-source document extraction framework. If you are a screenwriter or producer seeking script marketability insights, ScreenplayIQ is your choice. If you are a developer needing a fast, CPU-efficient pipeline for extracting structured data from 96+ formats, Xberg is superior—and completely free.
Xberg vs Temporal Ai
Choose Temporal AI if you need a battle-tested durable execution platform for orchestrating AI agents, long-running processes, and error-handling at scale. Choose Xberg if your primary need is fast, CPU-efficient document extraction across 96+ formats with native SDKs. They solve entirely different problems.
Alternatives to Xberg
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