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mdstill VS assertpy

Compare mdstill VS assertpy and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Reviews
  3. ยป Alternatives

mdstill logo 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

assertpy logo assertpy

A straightforward assertion library for Python.
  • 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

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.

  • assertpy Landing page
    Landing page //
    2022-11-06

mdstill

Website
mdstill.com
Pricing URL
Official mdstill Pricing
$ Details
freemium
Platforms
Web REST API
Release Date
2026 April
Edit details

assertpy

Website
github.com
Pricing URL
-
$ Details
-
Platforms
-
Release Date
-
Categories
#Testing #Python
Edit details

mdstill features and specs

  • Supported formats
    20+ (PDF, Word, Excel, PowerPoint, EPUB, HTML, CSV, JSON, Apple iWork)
  • Output format
    GitHub-flavored Markdown (GFM) with preserved tables, headers, structure
  • REST API
    Yes โ€” for RAG pipelines and batch automation
  • Token efficiency
    ~40% fewer tokens than raw text extraction
  • Privacy
    Files deleted immediately after processing โ€” no storage, no logging, no training
  • Pricing
    Free tier + Pro plans
  • LLM integrations
    ChatGPT, Claude, Gemini โ€” output tuned for AI context windows

assertpy features and specs

  • Fluent API
    Assertpy offers a fluent API that makes assertions more readable and expressive, enabling developers to write assertions in a natural language style that is easy to understand.
  • Chainable Assertions
    It allows for chainable assertions, enabling multiple checks to be performed in a single line of code, thereby reducing verbosity and enhancing clarity.
  • Comprehensive Assertion Methods
    The library provides a wide range of built-in assertion methods, catering to various types of data validations, such as checking for size, type, value, and more.
  • Extensibility
    Assertpy supports extending its functionality by defining custom assertions, allowing developers to tailor it to their specific needs.
  • Pythonic
    Designed with Pythonic principles in mind, Assertpy fits seamlessly into Python projects, enabling idiomatic and consistent code style.

Possible disadvantages of assertpy

  • Learning Curve
    Developers new to the library may encounter a learning curve due to the distinct approach of using fluent and chainable assertions as opposed to traditional methods.
  • Limited by Python Version
    The library may have limitations in terms of compatibility with older versions of Python, requiring users to ensure their environment is up-to-date.
  • Performance Overhead
    The additional abstraction layer introduced by a fluent interface might introduce some performance overhead, especially in performance-critical or resource-constrained environments.
  • Less Community Support
    Compared to more established testing libraries, Assertpy might have less community support and fewer resources available for resolving issues or getting help.
  • Dependency Management
    Using a third-party library introduces additional dependencies to manage, which could complicate project maintenance and compatibility.

Analysis of mdstill

Overall verdict

  • I don't have verified information about mdstill.com in my knowledge base, so I can't confirm its legitimacy, quality, or reputation. Before using this service, you should independently research it.

Why this product is good

  • No reliable data available on this specific domain to assess product quality or service reliability
  • Unable to verify company background, ownership, or business history
  • No access to user reviews, ratings, or third-party evaluations for this site
  • Cannot confirm security practices, payment safety, or customer support quality

Recommended for

  • Anyone considering this site should first check independent review platforms like Trustpilot or Better Business Bureau
  • Users should verify the site's SSL certificate, contact information, and return policy before purchasing
  • Consider checking domain age and registration details via WHOIS lookup tools
  • Look for verified customer reviews on social media or forums before making a decision

Analysis of assertpy

Overall verdict

  • assertpy is a well-regarded, lightweight assertion library for Python that provides a fluent, chainable API for writing readable and expressive test assertions, making it a solid choice for improving test clarity.

Why this product is good

  • Offers a fluent, chainable assertion syntax that makes tests more readable and self-documenting
  • Comprehensive built-in assertions for strings, numbers, lists, dicts, files, dates, and more
  • Produces clear, descriptive failure messages that speed up debugging
  • Lightweight with minimal dependencies and easy to integrate into existing test suites
  • Framework-agnostic, working seamlessly with pytest, unittest, and other test runners
  • Actively maintained open-source project with good documentation and community support

Recommended for

  • Python developers who want more readable and expressive test assertions
  • Teams using pytest or unittest looking to enhance assertion clarity
  • Projects that value descriptive failure messages for faster debugging
  • Developers coming from fluent assertion libraries in other languages (like AssertJ or Chai)
  • QA engineers and testers writing maintainable, self-documenting test code

Category Popularity

0-100% (relative to mdstill and assertpy)

mdstill

assertpy

Markdown Editor
100 100%
Markdown Editor
0% 0
Testing
0 0%
Testing
100% 100
PDF Conversion API
100 100%
PDF Conversion API
0% 0
Python
0 0%
Python
100% 100

Questions & Answers

As answered by people managing mdstill and assertpy.

What makes your product unique?

mdstill's answer

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.

Why should a person choose your product over its competitors?

mdstill's answer

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.

How would you describe the primary audience of your product?

mdstill's answer

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.

What's the story behind your product?

mdstill's answer

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.

Who are some of the biggest customers of your product?

mdstill's answer

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.

Which are the primary technologies used for building your product?

mdstill's answer

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

User comments

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What are some alternatives?

When comparing mdstill and assertpy, you can also consider the following products

Firecrawl - Turn any website into LLM-ready data.

grappa - grappa is an declarative, verbose, and expressive assertion library for Python.

Reducto - Reducto is the complete agentic document platform for leading AI teams needing performance at enterprise scale.

Mindee - Extract any data point, from any document, in a second

Parsio.io - No-code email & PDF parser

ConvertAPI - High-Performance File Conversion API

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