ContextGem

In plain words

ContextGem is a free, open-source LLM framework designed to extract structured data and insights from documents with minimal code. It targets developers and data engineers who need to process document content efficiently. The framework stands out for its simplicity, requiring only a small amount of code to implement complex extraction tasks powered by language models.

written from the facts on this page · September 2026

From the sources

ContextGem is a free, open-source LLM framework that makes it radically easier to extract structured data and insights from documents — with minimal code.

ContextGem: Effortless LLM extraction from documents - shcherbak-ai/contextgem

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  DeepTagger2025 · deeptagger.com · ▲386

  Hi HN, I built a tool for teaching LLMs how to extract structured data from documents by annotating, not prompt engineering. I’d love your feedback. How it works: - Upload a document (DOCX, PDF, image, etc.) - Select and tag parts of it (supports nesting, arrays, custom tag structures) - Upload another document → click "predict" → see editable annotations - Amend them and save as a new example - Call the API with a third document → get JSON back Use cases: - Identify "important clauses" in contracts - Extract "total value" from invoices - Anything subjective, like "healthy ingredients" on a…

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  l1m.io2025 · l1m.io · ▲135

  The simplest API to get structured data from any LLM

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  Min.js style compression of tech docs for LLM context2025 · github.com · ▲177

  Min.js Style Compression of Tech Docs for LLM Context - marv1nnnnn/llm-min.txt

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  I 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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  Context GatewayMar 2026 · github.com · ▲220

  We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…

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  Robust LLM extractor for websites in TypeScriptMar 2026 · github.com · ▲72

  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…

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