Xberg
Xberg is an open-source content intelligence engine that extracts 107 document, web, code, image and audio formats into clean Markdown,
Pick Xberg when extraction has to run on CPUs you already pay for and you want the same engine callable from Python, Rust, Go or a Cloudflare Worker. The 371-language code intelligence, guarded recursive archive traversal and the ONNX memory bounds added in 1.3.3 are the parts competitors make you assemble yourself. Pass if nobody on the team writes code, or if you need a hosted endpoint with a support contract before your pilot ships.
Verified 4d ago · liveness 75/100 · cite: rightaichoice.com/tools/xberg
- Developers building high-throughput document extraction pipelines for RAG, search or data lakes on CPU-only infrastructure
- Engineering teams running polyglot microservices that need the same extraction engine in Python, TypeScript, Go, Rust and more
- Platform teams deploying extraction in Docker, Kubernetes, Cloudflare Workers or serverless WASM
- Researchers processing LaTeX, BibTeX and JATS academic formats into structured text and embeddings
- Non-technical users — every path (SDK, CLI, REST, MCP) assumes you write and deploy code
- Anyone wanting a GUI or low-code connector for a same-day prototype
- Live or streaming speech transcription — the Whisper path processes files, not real-time audio
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Skip Xberg if nobody on your team writes or deploys code, if you need live streaming transcription rather than file-based Whisper passes, or if every OCR job must run on a GPU-accelerated VLM backend.
Xberg's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
In short
Xberg — Xberg is an open-source content intelligence engine that extracts 107 document, web, code, image and audio formats into clean Markdown,. Best for Developers building high-throughput document extraction pipelines for RAG, search or data lakes on CPU-only infrastructure, Engineering teams running polyglot microservices that need the same extraction engine in Python, TypeScript, Go, Rust and more, Platform teams deploying extraction in Docker, Kubernetes, Cloudflare Workers or serverless WASM. Free to use.
What's new in Xberg
Checked 4 days agoAcross the latest 10 updates: 10 changelog entries.
Xberg 1.3.10
Xberg 1.3.10 released — bug-fix-only patch per the changelog.
Xberg 1.3.9
Xberg 1.3.9 adds new functionality plus fixes and changes.
Xberg 1.3.8
Xberg 1.3.8 ships additions, changes and fixes.
Xberg 1.3.7
Xberg 1.3.7 adds features alongside changes and fixes.
Xberg 1.3.6
Xberg 1.3.6 is a maintenance release with changes and fixes.
Xberg 1.3.5
Xberg 1.3.5 adds features plus changes and fixes.
Xberg 1.3.4
Xberg 1.3.4 is a bug-fix-only patch release.
Xberg 1.3.3
Xberg 1.3.3 adds new functionality and fixes.
Xberg 1.3.2
Xberg 1.3.2 adds features, changes and deprecations plus fixes.
Xberg 1.3.0
Xberg 1.3.0 minor release adds new functionality, changes and 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.
Average across the 2 sources that answered — each source counts once, not each post.
- +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: October 2026
How we score →Key Features
- Extract text, tables and metadata from 107 document, web and media formats to Markdown or five other output formats
- Multi-backend OCR via Tesseract, PaddleOCR, EasyOCR and VLM backends with confidence scores and language detection
- Whisper audio and video transcription with backend fallback for file-based media
- Code intelligence: extract functions, classes, imports and symbols across 371 languages
- Structured entity and schema-matched JSON extraction using a local or hosted LLM without prompt engineering
- Embedding generation for search and RAG pipelines, with bounded ONNX Runtime session memory
- Web fetching and crawling via crawlberg in Auto, Document and Crawl modes
- Recursive nested archive extraction from .zip, .tar, .gz and .7z with zip-bomb and nesting-depth guards
- Configurable inline data-media handling for HTML output, added in 1.3.3
- Native SDKs for Python, TypeScript, Rust, Go, Java, Kotlin, C#, Ruby, PHP, Elixir, Dart, Swift, Zig and C
- Pure WebAssembly package for browsers, Cloudflare Workers, Deno, Bun and serverless at 60–80% of native speed
- Deploy as REST API server, CLI, MCP server for AI agents, Docker container or WASM module
- Named entity recognition and redaction for sensitive content, plus summarization, translation and page classification
- Diagram recovery from vector SVG and PDF sources to Graphviz DOT
- Plugin system for custom extractors and OCR backends
About Xberg
Xberg is a native-Rust extraction engine you call from your own code to turn documents, URLs, source files and media into structured, RAG-ready data. It covers 107 formats — PDFs, Office files, images, email, e-books and academic papers — and returns clean Markdown or one of five other output formats with no per-format configuration. Scanned pages go through OCR; audio and video tracks are transcribed with Whisper. Both paths return confidence scores, language detection and backend fallback, so you can route low-confidence pages to a human or a heavier backend. The crawlberg engine handles the web side: point Xberg at an http(s) URL and it fetches and extracts the document, or crawls and follows links in Auto, Document and Crawl modes. Nested .zip, .tar, .gz and .7z archives are traversed recursively under zip-bomb, compression-ratio and nesting-depth limits. Code intelligence pulls functions, classes, imports and symbols across 371 languages, structured extraction returns entities and schema-matched JSON via a local or hosted LLM without prompt engineering, and embedding generation feeds search and RAG pipelines directly. The project benchmarks itself against Docling, MinerU and Unstructured on both quality and speed. You reach it through native SDKs in Python, TypeScript, Rust, Go, Java, Kotlin, C#, Ruby, PHP, Elixir, Dart, Swift, Zig and C, plus a CLI, REST API server, MCP server, Docker image and a pure-WASM package for browsers and serverless that runs at 60–80% of native speed. Two TypeScript packages split the difference: NAPI-RS bindings for Node servers and CLI tools, and @xberg-io/xberg-wasm for Cloudflare Workers, Deno, Bun and the browser. The Rust core keeps throughput high on plain CPUs — relevant when extraction runs in containers without GPU budget. This is built for engineers. Every path assumes you write and deploy code. It ships on an unusually fast cadence: 1.2.8 on September 23 through 1.3.3 on October 2, 2026, with the latest release adding
Behind the Verdict
The reason to look at Xberg is deployment shape. If your extraction runs in a Docker container, a Kubernetes job or a serverless function with no GPU attached, the Rust core plus SIMD is the whole pitch, and the WASM package means the same code path works in a browser or a Cloudflare Worker at roughly 60–80% of native speed. That combination is rare. Unstructured and Tika can both be containerised, but you are usually bolting on Python runtimes and per-format dependencies rather than calling one engine. Where it earns its keep day to day is the unglamorous middle of the pipeline. OCR with backend fallback, Whisper transcription with confidence scores and language detection, and entity extraction to a schema you supply instead of a prompt you tune. Teams building RAG ingestion spend most of their time on exactly those steps, and having them behind one API with consistent output formats removes a lot of glue code. We'd reach for this on a migration. The project ships a dedicated migration guide for moving off Unstructured, and the SDK list is long enough that polyglot shops won't need a Python sidecar just to call the thing. Code intelligence across 371 languages also opens use cases beyond documents — repo indexing, symbol extraction for search, embedding source files alongside PDFs in one index. In practice, the archive handling is where we've seen teams get bitten by other tools. An ingestion job that pulls a customer-supplied .zip is an attack surface. Xberg traverses .zip, .tar, .gz and .7z recursively with zip-bomb, compression-ratio and nesting-depth limits built in, which is a control you otherwise write and test yourself. What to watch. The release cadence is aggressive — 1.2.8 to 1.3.3 in nine days — and 1.3.2 explicitly warns you to read the changelog
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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 install the Python SDK with pip install xberg, point extraction at a folder of PDFs and Office files, and get back Markdown chunks plus embeddings. Metadata and table extraction come from the same call, so you skip a separate table-parsing library.
Outcome: One API replaces the PDF library, table parser and embedding generator you were stitching together, and the CPU-only pipeline runs in your existing containers without GPU nodes.
You pull the ghcr.io/xberg-io/xberg Docker image, deploy the REST API server as a pod, and call it from polyglot microservices using the Go and TypeScript SDKs. The crawlberg engine handles any http(s) URLs your pipeline needs fetched.
Outcome: Extraction becomes a shared internal service instead of a per-language dependency, and recursive archive handling with zip-bomb guards covers the .zip and .tar inputs your users upload.
You install @xberg-io/xberg-wasm and run extraction client-side or on a serverless runtime, accepting 60–80% of native speed in exchange for running where native binaries cannot.
Outcome: Document extraction lands in environments that would otherwise have no path to it, without a server round trip.
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 plus 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
- Extract functions, classes and imports across 371 languages to index a polyglot codebase
- Run extraction inside a browser or edge worker using the pure-WASM package
Models Under the Hood
as of 2026-10-17
Limitations
- Xberg is an open-source engine oriented toward developers: its documentation, SDK bindings, CLI, REST API, and MCP server all assume code rather than a GUI application.
- Structured entity and schema-matched JSON extraction relies on a local or hosted LLM you supply, and the evidence names no specific underlying model for that path.
- Transcription is Whisper-based and file-oriented, covering audio and video tracks rather than live streaming.
- Deployment, OCR backend selection, plugin wiring, and LLM endpoint configuration are the operator's responsibility rather than a managed vendor service.
as of 2026-10-04
Verification history
We have re-verified Xberg 10 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
Showing the 6 most recent of 10 verification passes.
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
Ideal for
Individual developers and engineering teams who can self-host and want the full extraction engine with no per-seat cost
What this tier adds
Starting tier — the complete open-source engine for 107 formats, OCR, Whisper transcription, code intelligence, crawling and all 17 SDKs
Xberg Pro
Custom
Xberg Enterprise
Custom
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 pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
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 or TypeScript developer with an existing pipeline: first extraction is typically minutes from install to printed output. Teams deploying the Docker image or REST API server into Kubernetes should budget setup around container configuration, OCR backend selection and LLM endpoint wiring. Browser or edge work needs additional time to package the WASM build.
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 Unstructured: the docs ship a dedicated migration guide covering how to swap in Xberg extraction while keeping your downstream chunking and embedding steps
- →From Kreuzberg v4: Xberg 1.0.0 was itself the migration off Kreuzberg v4, and the changelog documents the rename, removals and packaging changes
- →From Apache Tika: replace per-format Tika handlers with a single extract call across the 107 supported formats
- ↗To Unstructured: if you need its hosted platform or connector ecosystem rather than a self-deployed engine, the extraction concepts map closely enough to port pipelines
- ↗To Apache Tika: if you want a Java-native extraction stack and can give up the polyglot SDKs, WASM build and embedding generation
- ↗To a managed extraction API: if you would rather pay a vendor to run extraction than own deployment, tuning and OCR backend selection yourself
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Xberg”, and we withheld 6: 6 could not be judged, because “Xberg” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Xberg.
Official links
Tools that pair well with Xberg
Common stack mates teams adopt alongside Xberg, with the specific reason each pairing earns its keep.
DocLine.ai
DocLine.ai extracts data from invoices, receipts, contracts, and forms using pre-trained document AI models and no-code custom field
RAGFlow
Open-source RAG engine with built-in ETL, hybrid vector/BM25 search and visual agent orchestration for enterprise AI agents.
GraphRAG
Open-source Microsoft Research RAG that builds a knowledge graph and community summaries from your documents, then queries them in four modes.
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
View all
DocLine.ai
DocLine.ai extracts data from invoices, receipts, contracts, and forms using pre-trained document AI models and no-code custom field
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