AI Frameworks · Unstructured-IO

unstructured

Unstructured is an open-source ETL library that converts complex documents (PDFs, Word, HTML, images) into structured data suitable for LLM pipelines. It provides modular partitioning, text extraction, and pre-processing functions available via PyPI, Docker, or local development.

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Source: GitHub — github.com/Unstructured-IO/unstructured

15.1k

GitHub stars

1.3k

Forks

HTML

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	Unstructured-IO/unstructured
Owner	Unstructured-IO
Primary language	HTML
License	Apache-2.0 — OSI-approved
Stars	15.1k
Forks	1.3k
Open issues	261
Latest release	0.24.0 (2026-07-06)
Last updated	2026-07-06
Source	https://github.com/Unstructured-IO/unstructured

What unstructured is

Python-based document processing library using OCR, image analysis, and NLP techniques to extract and structure content from diverse document types. Offers pluggable connectors, container deployment, and integrations with LangChain and other ML frameworks.

Quickstart

Get the unstructured source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/Unstructured-IO/unstructured.git2cd unstructured3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

LLM data preparation pipeline

Automate ingestion and cleaning of unstructured documents before feeding into language models for RAG, summarization, or fine-tuning workflows.

Document ETL at scale

Convert batches of PDFs, scanned documents, and mixed-format files into JSON or structured formats for downstream data warehousing and analytics.

Proof-of-concept document processing

Rapid experimentation with document parsing logic using open-source components before committing to enterprise solutions.

Implementation considerations

Supports multiple Python versions (see PyPI badge); verify compatibility with your environment before production rollout.
Docker images provided for x86_64 and Apple silicon; multi-platform support reduces architecture friction.
Modular design allows selective dependency installation; comment out unused parsers in Dockerfile to reduce image size and build time.
Integration with LangChain and other frameworks documented; test compatibility and data pipeline flow early.
261 open issues and active development (latest release 0.24.0 as of 2026-07-06) suggest ongoing refinement; plan for API changes or dependency updates.

When to avoid it — and what to weigh

Production workflows requiring SLAs — Open-source library has no guaranteed support, uptime, or performance SLAs. Company offers separate Platform product for production use.
Specialized document formats — If you need advanced OCR for low-quality scans, table extraction enrichment, or proprietary format support, the Platform product may be required.
Zero ML/DevOps infrastructure — Requires Python environment setup, Docker knowledge for containerization, and ML dependency management; not a managed SaaS.
High-volume inference with strict latency budgets — Open-source version performance and optimization are not detailed; production-grade throughput guarantees require Platform product.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution with liability and trademark disclaimers. No royalties or restrictions on derivative works.

Apache 2.0 permits commercial use in proprietary products without restriction. However, verify all transitive dependencies (especially OCR, image processing libraries) for compatible licenses. Company separately offers paid Platform product for production; clarify whether open-source version is intended for commercial production or development only by reviewing their support/SLA terms.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

No security audit, vulnerability disclosure policy, or penetration test results provided in data. Open-source code is auditable by community; review dependency versions (especially OCR/image libraries) for known CVEs. Handling of sensitive document content (PII extraction, retention) not documented. Run security scanning on Docker image before production use. Recommend reviewing transitive dependencies for supply-chain risks.

Alternatives to consider

Unstructured Platform (commercial)

Same company's managed solution with SLAs, advanced OCR/table enrichment, chunking, embedding, and low-code UI; for production workflows.

LlamaIndex (formerly GPT Index)

Open-source document ingestion and indexing framework with loader abstractions; positioned for LLM integration but less document-format-specific than Unstructured.

Lightweight, single-purpose libraries for PDF/Word parsing; suitable for simple use cases; lower complexity but limited multi-format and LLM-specific features.

Software development agency

Build on unstructured with DEV.co software developers

Start with the open-source library on GitHub or explore Unstructured Platform for production-ready workflows with enrichment and SLAs.

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unstructured FAQ

Can I use this in production?

The open-source library is usable in production applications under Apache 2.0, but has no SLAs, support, or guarantees. The company offers a separate Platform product (paid) for production-grade workflows with embedded enrichment and guarantees.

What document formats does it support?

Topics mention PDF, DOCX, HTML, images, and more. Full list at docs.unstructured.io/open-source/core-functionality/partitioning. Specifics of format support and fidelity not detailed in excerpt.

Does it require GPU or special hardware?

Not stated in provided data. Docker images support x86_64 and Apple silicon. GPU support and system requirements require review of full documentation or dependency specifications.

How do I report security issues?

Not documented in excerpt. No security.md or vulnerability disclosure policy visible. Recommend contacting maintainers directly or reviewing GitHub security advisories.

Work with a software development agency

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If unstructured is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.

Ready to process documents at scale?

Start with the open-source library on GitHub or explore Unstructured Platform for production-ready workflows with enrichment and SLAs.

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