Open source
All primitives, in 14 languages. Embed in your app or self-host the extraction server. Use any subset of the pipeline.
Get the librariesDocument intelligence & RAG
Crawl, extract,enrich, retrieve.for AI agents
A self-hostable document intelligence and RAG platform — an integral component in AI meshes. Open-source primitives, one managed backend. 96 file formats, 306 programming languages, RAG built in.
96file formatsEXTRACT
EXTRACTPDFs, Office docs, images, email and more — one API returns clean, structured text and tables from 96 formats.
306programming languagesCODE INTELLIGENCE
CODE INTELLIGENCEParse and understand source across 306 languages for code-aware retrieval, search and analysis.
143LLM providersENRICH
ENRICHSummarize, classify and embed through 143 LLM providers — bring whichever model you already use.
14language bindingsOPEN SOURCE
OPEN SOURCENative SDKs for 14 languages, so you can call Xberg from whatever stack you build on.
96file formatsEXTRACT
EXTRACTPDFs, Office docs, images, email and more — one API returns clean, structured text and tables from 96 formats.
306programming languagesCODE INTELLIGENCE
CODE INTELLIGENCEParse and understand source across 306 languages for code-aware retrieval, search and analysis.
143LLM providersENRICH
ENRICHSummarize, classify and embed through 143 LLM providers — bring whichever model you already use.
14language bindingsOPEN SOURCE
OPEN SOURCENative SDKs for 14 languages, so you can call Xberg from whatever stack you build on.
96file formatsEXTRACT
EXTRACTPDFs, Office docs, images, email and more — one API returns clean, structured text and tables from 96 formats.
306programming languagesCODE INTELLIGENCE
CODE INTELLIGENCEParse and understand source across 306 languages for code-aware retrieval, search and analysis.
143LLM providersENRICH
ENRICHSummarize, classify and embed through 143 LLM providers — bring whichever model you already use.
14language bindingsOPEN SOURCE
OPEN SOURCENative SDKs for 14 languages, so you can call Xberg from whatever stack you build on.
96file formatsEXTRACT
EXTRACTPDFs, Office docs, images, email and more — one API returns clean, structured text and tables from 96 formats.
306programming languagesCODE INTELLIGENCE
CODE INTELLIGENCEParse and understand source across 306 languages for code-aware retrieval, search and analysis.
143LLM providersENRICH
ENRICHSummarize, classify and embed through 143 LLM providers — bring whichever model you already use.
14language bindingsOPEN SOURCE
OPEN SOURCENative SDKs for 14 languages, so you can call Xberg from whatever stack you build on.
The pipeline
Four stages. One backend. Open underneath.
02
Extract
96 formats. 306 code languages. Lossless HTML.
04
Embed & Retrieve
Collections. Hybrid retrieval. Reranking.
services/rag
Live playgroundTry every use case on
Try every use case on
a real document.
Everything teams build on the pipeline — run any of them and see structured output. No account, no setup.
RAG Pipeline Ingestion
Turn a pile of PDFs, Office docs, and HTML into clean, chunked, embedded data for your vector database — in one call.
RAGChunkingEmbeddings
Pipeline stages
AcquireExtractEnrichEmbed & Retrieve
Input · Documents → chunked, embedded data
10-K excerpt.pdfpdf
output.json
Your output will appear here
Choose a sample or upload a document, then run the extraction to inspect the result.
Run to extract a real document through the live API — no account, no setup.
IntegrationsFits into
Fits into
your workflow.
Drop-in integrations for the AI frameworks and tools you already build with.
langchain.py
from langchain_xberg import XbergLoader# Load any document as LangChain Documentsloader = XbergLoader("report.pdf")docs = loader.load()# Drop straight into your vector storedb.add_documents(docs)
pip install langchain-xberg
For developersOne API.
One API.
Every language.
Install Xberg in your stack and call the same extraction API — from Python to Rust to the browser.
12 languages
Python, TypeScript, Rust, Go, Java, Ruby, and more.
306 code formats
Functions, classes, imports, symbols — all parsed.
Docker & CLI
Run our images or the single-binary CLI.
ELv2 licensed
Free for personal, internal, and commercial use.
terminal — Python
$pip install xberg
Two doorsAdopt the
Adopt the
way that fits.
Design Partner program
Direct roadmap input, early access to new pipeline stages, 24-month locked pricing. We work closely with a small cohort across commercial, non-profit, and research teams. Free-tier access available at our discretion in exchange for attribution.
Apply for partnershipCapabilitiesWhy Xberg?
Why Xberg?
Built for AI pipelines.
CapabilitiesWhy Xberg?
Why Xberg?
Built for AI pipelines.
01
Speed That Unblocks Your Team
Process documents in milliseconds instead of seconds. Your RAG pipeline moves at the speed of API calls, not extraction bottlenecks. Index millions of documents without waiting weeks for processing to complete.
02
Batch-Processing at Scale
Effectively process large numbers of documents in bulk. Xberg is built for batch processing, and our cloud infrastructure is designed to scale.
03
Embeddings
Ultra-fast embeddings via a Rust-native ONNX engine. 4 presets out of the box, extensible to any model. No separate embedding pipeline needed.
04
Chunking and Metadata
Semantic chunking across code, markdown, and plain text. Token reduction, keyword extraction, and rich metadata — structured output ready for any AI pipeline.
05
Code Intelligence
Extract functions, classes, imports, and symbols from code files across 306 programming languages. Structured output, ready for semantic chunking and RAG pipelines.
06
LLM-Powered Intelligence
Go beyond extraction. Use vision language models as an OCR backend, extract structured JSON from documents using a schema, and generate embeddings — all via 143 LLM providers, including local models with zero API key configuration.
ComparisonBeyond the
Beyond the
extraction box.
Textract, Document AI and Azure DI stop at OCR — and send your files to their cloud. Xberg runs the whole pipeline, open source, on your own infrastructure.
| Capability | Xberg | Google Document AI | Amazon Textract | Azure Doc Intelligence |
|---|---|---|---|---|
| Deployment | Self-host or managed | Cloud only (GCP) | Cloud only (AWS) | Cloud only (Azure) |
| Source model | Open source | Proprietary | Proprietary | Proprietary |
| Pipeline scope | Acquire → retrieve | Extract / OCR | Extract / OCR | Extract / OCR |
| Data residency | Stays in your infra | Sent to vendor | Sent to vendor | Sent to vendor |
| Formats | 96 + 306 code langs | Docs / forms | PDF / images | Docs / forms |
| Code intelligence | Built in | — | — | — |
| Pricing | Usage or self-host | Per page | Per page | Per page |
Want the numbers — latency, throughput, accuracy? Every figure is reproducible from our open-source harness.
See the head-to-head benchmarksUse CasesBuilt for how teams
Built for how teams
actually work.
01
RAG Systems
Feed your vector database with semantically accurate document chunks. Preserve table structure so your AI understands relationships. Bulk-process your knowledge base in hours instead of weeks.
02
Document Classification & Routing
Extract content, metadata, and structure to power smart routing. Automatically categorize incoming documents. Reduce manual sorting and classification workflows.
03
Compliance & Document Review
Extract and structure compliance documents, contracts, and regulatory filings. Preserve table relationships and metadata for audit trails. Support for scanned documents with OCR means nothing falls through the cracks.
See how teams put document intelligence to work. Explore all use cases
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