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Decision brief
Unstructured is an open-source ETL solution designed specifically to convert complex documents into structured data suitable for language models.
Good fit when
- When you need to transform various types of unprocessed, complex documents (including PDFs, images with text) into formats that are easily usable by machine learning pipelines.
- If your project requires processing a wide variety of document formats and the ability to parse these quickly using Docker containers or locally installed packages.
Avoid when
- When your workflow is limited to only one type of data, as Unstructured might introduce unnecessary complexity due to its broad support for multiple data types.
- If you prefer a proprietary solution with dedicated enterprise-level support and features that are beyond the scope of an open-source project like Unstructured.
- Requirements:
- Requires Docker; Building Docker images independently may require customizing the `Dockerfile` to include only necessary packages/requirements based on specific data parsing use; The base image `wolfi-base`, a regularly updated image, might cause build failures due to upstream changes but can be managed by specifying requirements.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/Unstructured-IO/unstructuredHow it fits your stack(11)
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Unstructured is an open-source ETL solution for transforming complex documents into clean, structured formats suitable for language models.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 1, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 1, 2026
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 1, 2026
- Languages
- html, python
Source: github.language+pyproject.toml · Aug 1, 2026
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 1, 2026)
Once in the running container, you can try things directly in Python interpreter's interactive mode.Source link
Tags
README
:eight_pointed_black_star: Quick Start
There are several ways to use the unstructured library:
- Run the library in a container or
- Install the library
- For installation with
condaon Windows system, please refer to the documentation
this will drop you into a bash shell where the Docker image is running
docker exec -it unstructured bash
You can also build your own Docker image. Note that the base image is `wolfi-base`, which is
updated regularly. If you are building the image locally, it is possible `docker-build` could
fail due to upstream changes in `wolfi-base`.
If you only plan on parsing one type of data you can speed up building the image by commenting out some
of the packages/requirements necessary for other data types. See Dockerfile to know which lines are necessary
for your use case.
```bash
make docker-build
---
# this will drop you into a bash shell where the Docker image is running
make docker-start-bash
Once in the running container, you can try things directly in Python interpreter's interactive mode.
---
### Installation Instructions for Local Development
The following instructions are intended to help you get up and running with `unstructured`
locally if you are planning to contribute to the project.
This project uses [uv](https://docs.astral.sh/uv/) for dependency management. Install it first:
```bash
For agents
This page has a .md twin and JSON over the API.