unstructured-inference
A library for performing inference using trained models.
Decision gist · record as of 2026-08-14
Yes, if you need to extract structured elements from documents and can meet the Python version and dependency requirements. The package is actively maintained, has no known vulnerabilities, and offers a clean API for layout-based document analysis. Install only if you accept the large dependency footprint (torch, transformers, onnxruntime) and can handle Detectron2's platform constraints—particularly on Windows, where it lacks official support.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.11, <3.14.
- Detectron2 (for layoutparser models) must be installed separately and is not officially supported on Windows.
- Low friction install with a pure-Python wheel, but requires Python >=3.11, <3.14.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and note any modifications.
last release 2026-06-11 (64 days) · last repo commit 2026-07-23 · 209 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,158,796 downloads/mo, #4,288 on PyPI
Alternatives
Verify before relying
pip install unstructured-inference
from unstructured_inference.inference.layout import DocumentLayout
layout = DocumentLayout.from_file("sample.pdf")
print(layout.pages[0].elements)- Whether Detectron2 installation difficulty on Windows is a practical blocker for typical use cases.
- Performance characteristics and model accuracy on different document types (scanned vs. digital PDFs, various layouts).
- Whether the 15 runtime dependencies (torch, transformers, onnxruntime, etc.) are all required or conditionally loaded.
What it is and what it does
unstructured-inference provides hosted model inference for document layout analysis and element extraction. It wraps detection models (Detectron2, YOLOX) and OCR pipelines to identify and extract text regions, tables, and structural elements from PDFs and images. The core workflow detects layout regions on a page, then extracts their contents via direct text extraction, OCR, or table-specific inference.
The package is designed as a backend for the broader unstructured ecosystem—it handles the model inference layer while the unstructured package orchestrates partitioning and higher-level document processing. It requires modern Python (3.11–3.13) and a substantial dependency stack including torch, transformers, and onnxruntime, reflecting its reliance on deep-learning models for detection and OCR.
Use it for
- Extract structured text and table regions from PDF documents for downstream NLP or data-pipeline processing.
- Detect and classify layout elements (headers, paragraphs, tables, images) in scanned or digital documents.
- Integrate custom detection models by wrapping them in the UnstructuredObjectDetectionModel interface.
- Preprocess documents for machine-learning pipelines that require clean, labeled element boundaries.
- Build document-parsing workflows that combine layout detection with OCR for mixed-format inputs.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to extract structured elements from documents and can meet the Python version and dependency requirements.
The package is actively maintained, has no known vulnerabilities, and offers a clean API for layout-based document analysis. Install only if you accept the large dependency footprint (torch, transformers, onnxruntime) and can handle Detectron2's platform constraints—particularly on Windows, where it lacks official support.
Install
unstructured-inference on PyPI
Before you install
Low friction install with a pure-Python wheel, but requires Python >=3.11, <3.14. Detectron2, needed for layoutparser models, must be installed separately and has platform constraints (not officially supported on Windows). Active maintenance with a recent release.
Requires Python >=3.11, <3.14. Detectron2 (for layoutparser models) must be installed separately and is not officially supported on Windows.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and note any modifications.
Quickstart
pip install unstructured-inference
from unstructured_inference.inference.layout import DocumentLayout
layout = DocumentLayout.from_file("sample.pdf")
print(layout.pages[0].elements)Verify before relying
- Whether Detectron2 installation difficulty on Windows is a practical blocker for typical use cases.
- Performance characteristics and model accuracy on different document types (scanned vs. digital PDFs, various layouts).
- Whether the 15 runtime dependencies (torch, transformers, onnxruntime, etc.) are all required or conditionally loaded.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.14,>=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 15 packagesacceleratehuggingface-hubmatplotlibnumpyonnxonnxruntimeopencv-pythonpandaspdfminer-sixpypdfium2rapidfuzzscipytimmtorchtransformers |
| Maintenance | Actively maintained 64 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,158,796 / month, #4,288 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: unstructured_inference-1.6.13-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “pdf element detection”
- unstructured-inferenceRuns inference on layout-parsing and document-analysis models to…
- layoutparserLayoutParser provides deep learning-based document layout detection…
- PyNiteFEAPyNiteFEA performs 3D elastic structural finite element analysis,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also layoutparser · unstructured · unstructured-client · paddleocr · surya-ocr · docling-ibm-models · opendataloader-pdf · marker-pdf · pymupdf-layout · img2table