Struktur
All-in-one CLI/SDK for structured data extraction with LLMs
What it does
Everything structured data extraction needs in one tool. Parse any document with automatic image extraction, agentic exploration that reads and understands autonomously — or pick from 7 fixed strategies when you need predictable costs. Works with any LLM provider you choose. CLI for shell pipelines that compose with jq and curl, plus a TypeScript SDK for direct integration. Built-in schema validation retries until output matches your spec. Runs locally so documents never leave your machine.
Does the same job
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- DODocumind – Open-source AI tool to turn documents into structured data2024 · github.com · ▲169
Documind is an open-source tool that turns documents into structured data using AI. What it does: - Extracts specific data from PDFs based on your custom schema - Returns clean, structured JSON that's ready to use - Works with just a PDF link + your schema definition Just run npm install documind to get started.
Tabstack Structured ExtractionJun 2026 · tabstack.ai · ▲199Extract web data into structured JSON, no scraper required.
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We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
- ANA new benchmark for testing LLMs for deterministic outputsApr 2026 · interfaze.ai · ▲60
When building workflows that rely on LLMs, we commonly use structured output for programmatic use cases like converting an invoice into rows or meeting transcripts into tickets or even complex PDFs into database entries. The model may return the schema you want, but with hallucinated values like `invoice_date` being off by 2 months or the transcript array ordered wrongly. The JSON is valid, but the values are not. Structured output today is a big part of using LLMs, especially when building deterministic workflows. Current structured output benchmarks (e.g., JSONSchemaBench) only validate…
- IJI just open sourced my document/website extractor for Vision-LLMs2024 · github.com · ▲37
Hi 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…
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