# yobix-ai/extractous

Fast and efficient unstructured data extraction. Written in Rust with bindings for many languages.

Repository: https://github.com/yobix-ai/extractous
Canonical: https://ross.abutalabs.com/products/extractous
Language: Rust
License: Apache-2.0
License Family: permissive
Topics: extraction, pdf, tika, unstructured, unstructured-data, data-pipelines, docx, etl, etl-pipelines, llm, machine-learning, natural-language-processing, nlp, ocr, pdf-parser, rag, rust
Last push: 2024-12-21T10:13:30+00:00

## Health v2 (maintenance only)
Score: 24/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 58
- inputs: {"age_days": 820, "days_push": 620, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1772, forks 95 (observed 2026-08-28T04:05:34.183186+00:00)

## What it is
Extractous is a fast Rust library for extracting content and metadata from unstructured documents like PDF, Word, Excel, HTML, CSV, and email, with bindings for multiple languages including Python. It uses a natively compiled Apache Tika internally for formats not natively supported and is positioned as a much faster alternative to unstructured-io.

## Use cases
- extract text from pdf files
- parse docx documents into plain text
- build rag document ingestion pipelines
- extract content from emails and html locally without external apis
- replace unstructured-io for faster document parsing
- preprocess documents for llm pipelines

## When to choose
- you need fast, in-process document extraction without calling external services
- performance and memory efficiency matter for large extraction workloads
- you want a Rust core with Python bindings for etl or rag pipelines

## When to avoid
- you need deep document structure analysis or chunking strategies rather than raw extraction
- you rely on unstructured-io's element classification features
- you need a managed extraction API service

## Facets
- artifact type: library
- maturity: active
- function: parser, ocr, pdf, etl, rag
- domain: pdf, files
- platform: rust, python, cross-platform
- tags: unstructured-data, document-extraction, apache-tika, text-extraction, docx, bindings, natural-language-processing, data-engineering, retrieval-augmented-generation

## Member repositories
- yobix-ai/extractous (main) score 24

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:34.183186+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:25:58.754166+00:00, confidence not recorded.
  - readme: https://github.com/yobix-ai/extractous (fetched 2026-08-28T04:05:34.183186+00:00, sha 2f387b4608a9)
  - registry_crates: https://crates.io/api/v1/crates/extractous (fetched 2026-08-29T11:04:06.947005+00:00, sha ae7d1a88e20f)
- Data as of 2026-08-30T08:39:29.467469+00:00.