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mdstill

Document-to-markdown preprocessor built for LLM and RAG workflows. Turn any document (PDF, Word, Excel, EPUB +20 formats) into clean, structure-preserving markdown ready for ChatGPT, Claude, Gemini, or your RAG pipeline.Includes REST API.Free to use.

mdstill is a document-ingestion tool purpose-built for LLM and RAG workflows. Where generic converters dump messy text, mdstill outputs clean, semantic markdown that preserves tables, headings, and document structure โ€” the things LLMs actually need to understand context.

What you can do with it:

Prepare documents for RAG pipelines (chunk-ready, semantic boundaries preserved) Feed PDFs, Word files, or spreadsheets into ChatGPT, Claude, or Gemini without losing tables Build knowledge bases in Obsidian, Notion, or Logseq from existing document archives Extract structured context for AI agents and embeddings How it's different: Deep-conversion mode runs layout-aware parsing (tables, OCR, multi-column PDFs) โ€” not just text dumping. Markdown output is ~40% more token-efficient than raw text, so your LLM costs drop. REST API available for pipeline automation.

Free tier, no signup required for basic use. Competes with markitdown, Unstructured.io, and LlamaParse โ€” but with a zero-friction web UI.

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Pricing:
Platforms:
  • Web
  • REST API
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mdstill Reviews and Details

This page is designed to help you find out whether mdstill is good and if it is the right choice for you.

#Developer Tools #Document Converter #Markdown Editor #Markdown Converter #PDF Conversion API

Screenshots and images

  • mdstill
    Image date //
    2026-04-21
  • mdstill
    Image date //
    2026-04-21
  • mdstill
    Image date //
    2026-04-21
  • mdstill
    Image date //
    2026-04-21
  • mdstill
    Image date //
    2026-04-21

Features & Specs

  1. Supported formats

    20+ (PDF, Word, Excel, PowerPoint, EPUB, HTML, CSV, JSON, Apple iWork)

  2. Output format

    GitHub-flavored Markdown (GFM) with preserved tables, headers, structure

  3. REST API

    Yes โ€” for RAG pipelines and batch automation

  4. Token efficiency

    ~40% fewer tokens than raw text extraction

  5. Privacy

    Files deleted immediately after processing โ€” no storage, no logging, no training

  6. Pricing

    Free tier + Pro plans

  7. LLM integrations

    ChatGPT, Claude, Gemini โ€” output tuned for AI context windows

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Questions & Answers

As answered by people managing mdstill.

  1. What makes mdstill unique?

    mdstill is built specifically for LLM and RAG workflows, not generic file conversion. Drop any of 20+ document formats (PDF, Word, Excel, PowerPoint, EPUB, and more) and get back clean, structure-preserving Markdown that's tuned for ChatGPT, Claude, Gemini context windows and vector-database ingestion. Tables stay intact, headers become linkable anchors, output is ~40% more token-efficient than raw text extraction. Free web tool + REST API โ€” humans and pipelines use the same engine.

  2. Why should a person choose mdstill over its competitors?

    Alternatives fall into two camps: developer libraries that require setup, or enterprise SDKs that require a sales call. mdstill fills the middle: open a browser, drop a file, get Markdown in seconds โ€” and when you need to scale, the same conversion runs through a REST API. 20+ formats in one tool instead of picking a different parser per format. Tables survive the trip (most tools mangle them). Files are deleted immediately after processing. Free tier, no credit card, no signup for basic use.

  3. How would you describe the primary audience of mdstill?

    Two overlapping groups. Developers building AI features โ€” engineers feeding documents into ChatGPT, Claude, or Gemini APIs; teams building RAG pipelines and AI agents who need reliable document ingestion. Knowledge workers and researchers โ€” Obsidian and Notion users importing legacy PDFs, students preparing papers for AI analysis, analysts converting spreadsheets for LLM review. Common thread: anyone who's discovered that pasting raw PDF text into an LLM loses tables and wastes tokens.

  4. What's the story behind mdstill?

    mdstill started from a personal frustration: feeding documents into ChatGPT and Claude meant pasting messy PDF text with broken tables and lost structure, or paying for heavyweight enterprise tools just to preprocess a few files. The fix seemed obvious โ€” Markdown is what LLMs understand best, so the conversion should be a utility anyone can use, not a product you buy. mdstill was built to make high-quality document-to-Markdown preprocessing free and instant for everyone, with an API for teams who need to scale.

  5. Who are some of the biggest customers of mdstill?

    mdstill launched publicly in April 2026 and is in the early-adopter phase. Currently used by individual developers, indie AI-tool builders, and small research teams โ€” customer logos will be added as early adopters opt in to share them.

  6. Which are the primary technologies used for building mdstill?

    Python + FastAPI on the backend, Next.js + TypeScript on the frontend.

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Is mdstill good?

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Is mdstill good? This is an informative page that will help you find out. Moreover, you can review and discuss mdstill here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.

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