Unstract(AGPL) – Launch LLM-powered APIs to structure unstructured docs
Hello HN! One of the most common uses of LLMs is to go beyond what traditional RPA or IDP can do when it comes to structuring unstructured documents. However, there are a lot of challenges in getting this done right from extraction of text data from PDFs, scanned images or other formats, prompt engineering, evaluation and integration with existing systems. This very specific use case is where Unstract can help teams move really fast, leveraging LLMs. By doing the heavy-lifting in this fast-changing ecosystem it lets engineers concentrate on implementing core business workflow automations.…
Unstract(AGPL) – Launch LLM-powered APIs to structure unstructured docs launched on March 27, 2024 with 25 votes, #1,103 of 4,270 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
Unstract is an open-source platform for extracting and structuring data from unstructured documents like PDFs and scanned images using large language models. It handles text extraction, prompt engineering, and system integration, allowing developers to build LLM-powered APIs without managing low-level technical details. The tool is released under AGPL and can be deployed quickly using Docker Compose, enabling teams to automate document processing workflows faster than traditional RPA or intelligent document processing solutions.
written from the facts on this page · September 2026
From the sources
LLM-Driven Extraction of Unstructured Data — Built for API Deployments & ETL Pipeline Workflows - Zipstack/unstract
In the maker’s words, at launch
Hello HN! One of the most common uses of LLMs is to go beyond what traditional RPA or IDP can do when it comes to structuring unstructured documents. However, there are a lot of challenges in getting this done right from extraction of text data from PDFs, scanned images or other formats, prompt engineering…
Does a similar job
all alternatives →
Unstract – AGPL, LLM-powered Fivetran/Twilio for unstructured docs2024 · unstract.com · ▲5Hello HN! One of the most common uses of LLMs is to go beyond what traditional RPA or IDP can do when it comes to structuring unstructured documents. However, there are a lot of challenges in getting this done right from extraction of text data from PDFs, scanned images or other formats, prompt engineering, evaluation and integration with existing systems. This very specific use case is where Unstract can help teams move really fast, leveraging LLMs. By doing the heavy-lifting in this fast-changing ecosystem it lets engineers concentrate on implementing core business workflow automations.…
I just open sourced my document/website extractor for Vision-LLMs2024 · github.com · ▲37Hi HackerNews, Lately, I have seen an explosion in posts offering paid APIs/services to get unstructured data into LLMs (i.e. langchain extract, ragflow, unstructured, unstract, just to name a few) and I have been largely disappointed by them, either because they fail to implement multimodal support, fail to give good context for "really tricky" PDFs / Word docs / Powerpoints, or are just plain difficult to use. In light of all these posts I figured I'd share my solution that has been working smoothly for me and my clients. I put it up on GitHub for free so you can check it…

Structured output from LLMs without reprompting2023 · automorphic.ai · ▲174Built a tool for transforming unstructured data into structured outputs using language models (with 100% adherence). If you're facing problems getting GPT to adhere to a schema (JSON, XML, etc.) or regex, need to bulk process some unstructured data, or generate synthetic data, check it out. We run our own tuned model (you can self-host if you want), so, we're able to have incredibly fine grained control over text generation. Repository: https://github.com/automorphic-ai/trex Playground: https://automorphic.ai/playground

Beyond text splitting – improved file parsing for LLMs2024 · github.com · ▲206Improved file parsing for LLM’s. Contribute to Filimoa/open-parse development by creating an account on GitHub.
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