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Unstructured: Parse Documents Into LLM-Ready JSON
Open-source ETL that converts PDFs, images, and tables into structured data. Skip the regex hell. Get clean JSON for your language model pipeline.
Unstructured-IO/unstructured ↗You have a pile of PDFs, scans, or Word docs. You need to feed them into an LLM, but first you have to extract the actual text, tables, and metadata without breaking your brain. Unstructured does that conversion work for you. It ingests messy documents and outputs clean JSON you can pipe straight into your model. No more writing fragile parsers or losing data in the noise. It handles images and tables as first-class citizens, not afterthoughts. For solopreneurs building document workflows or teams shipping LLM products, this saves weeks of parsing logic and debugging.
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Parse documents into JSON without writing regex
Unstructured is open-source ETL that converts PDFs, images, and tables into structured JSON for LLM pipelines. Skip the weeks of parsing logic. For builders ingesting documents into language models, this is concrete ROI.
Document parsing that doesn't make you write regex. Unstructured converts PDFs, images, tables into structured JSON for your LLM pipeline. Open source, LLM-first, saves weeks of ETL work. https://github.com/Unstructured-IO/unstructured
Building with documents and language models. Unstructured (open source) converts PDFs, scans, and tables into clean, structured JSON for your pipeline. No regex nightmares. No lost data. Just usable output for your LLM workflow.
just built a document parser that actually works with LLMs. Unstructured takes PDFs, images, tables, messy Word docs and converts them into clean JSON you can actually feed to Claude or GPT without losing your mind to regex. The problem: document ingestion kills most LLM pipelines. You spend weeks writing brittle parsing logic, handling edge cases, dealing with layout shifts. It's not interesting work. Unstructured handles the boring part. Arrow down: supports PDFs, images, tables, and multiple formats. Arrow down: partitions documents intelligently (knows tables from text). Arrow down: outputs structured JSON ready for embeddings or context windows. Arrow down: open source, so you can run it locally or self-host. If you're building anything that touches documents and language models, this saves you the parsing tax. Github link in the first comment.
built a doc parser that doesn't suck. Unstructured converts PDFs, images, tables into structured JSON for LLM pipelines. no regex hell. just works. open source. run it locally. feed the output straight to Claude or GPT. solves the document ingestion tax that kills most RAG systems. github: https://github.com/Unstructured-IO/unstructured