Prepare Web Content and Documents for LLM Ingestion Using MarkItDown Microsoft's open-source MarkItDown Python library, with over 165k GitHub stars and 12 million monthly downloads, converts web pages, PDFs, Word documents, Excel spreadsheets, and other formats into Markdown for efficient LLM ingestion. The library supports dozens of input formats, including images, audio, YouTube videos, and structured data like JSON, XML, and CSV, reducing token consumption and inference costs in AI pipelines. Most LLMs understand Markdown exceptionally well. Consequently, when building AI-powered data pipelines, it makes sense to convert all input data into Markdown first. This way, scraped web pages, documents, and other content can then be processed in a consistent format. That’s where the MarkItDown Python library comes into play Here, I’ll show you everything you need to know about MarkItDown, from installation to practical examples, so you can convert HTML pages, PDFs, Word documents, Excel spreadsheets, and many other data sources into LLM-ready Markdown. Why the Markdown Data Format Is More Relevant Than Ever As I covered in a recent post https://substack.thewebscraping.club/p/why-scraping-return-markdown-llm-ai , AI-ready web scrapers, RAG pipelines, and agent frameworks all have one thing in common: they rely heavily on Markdown That’s no coincidence... Mainstream LLMs including OpenAI’s latest models natively “speak” Markdown and often generate Markdown-formatted responses without being prompted. Why? Because Markdown strikes the perfect balance between plain text and structured documents. It preserves headings, lists, tables, links, code blocks, and other semantic elements without the overhead and noise of HTML. As a result, LLMs can understand and process Markdown efficiently while consuming far fewer tokens. This translates directly into lower inference costs and leaves more of the context window available for meaningful content. In my experience and as I demonstrated in a benchmark I conducted for Bright Data https://www.kaggle.com/code/brightdataml/benchmarking-ai-on-different-data-formats , Markdown has become the natural format for feeding documents, especially content extracted from the web, into LLMs. Give your AI a web data layer – turns any site into clean, structured data your models can actually use. Decodo’s Web Scraping API Why Markdown Conversion Matters in Web Scraping Pipelines Web data is rarely the final destination. Quite the opposite, scraped content generally represents the starting point of a larger data analysis pipeline, where the goal is to extract insights, enrich datasets, or power downstream applications. In these workflows, the information you need tends to go beyond traditional web pages. PDFs, Word documents, videos, presentations, and other files can provide additional context required for accurate analysis and processing. These resources may come from the web and be collected during scraping, or they may be added later. Regardless of the original format, data is increasingly processed by AI systems. Since LLMs naturally work well with Markdown, converting different sources into a clean, structured Markdown representation can greatly simplify your data pipelines. That’s why you need a multi-format Markdown conversion library in your toolkit, such as MarkItDown The new anti-bot solution- Toughest walls, lowest price. Claim your free 10 000 requests with coupon codeWSCLUB MarkItDown: A Complete Overview Let me now introduce you to MarkItDown, one of the most popular libraries for converting multiple input formats into Markdown. What Is MarkItDown? MarkItDown https://github.com/microsoft/markitdown is an open-source Python library for converting a wide range of documents and other data sources into Markdown. The project is developed and maintained by Microsoft. It’s quickly become one of the most popular tools in its category more on this later in the post , with over 165k GitHub stars, over 12 million monthly downloads https://pypistats.org/packages/markitdown , and a thriving community. MarkItDown supports dozens of input formats, ranging from Office documents and PDFs to images, audio files, web pages, YouTube videos, and structured data formats such as JSON, XML, and CSV. Key Features The main capabilities supported by MarkItDown are: Broad format support : Converts dozens of input formats, including PDFs, Office documents, HTML, images, audio, EPUBs, ZIP archives, YouTube URLs, and structured data files. Python API : Offers a clean, developer-friendly API for integrating document conversion into Python applications. Native CLI : Includes a rich CLI for converting files directly from the terminal, making it ideal for scripting, batch processing, and DevOps workflows. Plugin architecture : Supports third-party plugins, allowing the community to extend the library with custom converters and additional processing features. AI-powered processing : Integrates with LLMs to generate image descriptions, perform OCR, and transcribe audio, enriching the resulting Markdown. Flexible dependency management : Lets you install only the converters you need, reducing dependencies and keeping deployments lightweight. MCP server support: Provides a lightweight Model Context Protocol server that allows AI agents to call MarkItDown as a tool. Azure AI integration : Works withand Azure Document Intelligence https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/overview for higher-quality OCR, layout analysis, and structured field extraction. Azure Content Understanding https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/ Supported Input Formats Installation and Setup Follow the steps below to add MarkItDown to your system. Prerequisites MarkItDown requires Python 3.10 or higher. It’s also recommended to install it inside a Python virtual environment https://docs.python.org/3/library/venv.html . Installation via pip The easiest way to install MarkItDown is through the PyPI https://pypi.org/project/markitdown/ markitdown https://pypi.org/project/markitdown/ package https://pypi.org/project/markitdown/ with: pip install 'markitdown all ' Note that all option adds all optional dependencies required to support every available input format. If you only need support for specific file types, you can install the specific dependencies individually for more control. For example: pip install 'markitdown pdf, docx ' This adds only the extra dependencies required for PDF and Word file conversion. Available optional dependencies include: all : Installs all optional dependencies. pptx : Adds support for PowerPoint files. docx : Adds support for Word documents. xlsx : Adds support for Excel files. xls : Adds support for older Excel files. pdf : Adds support for PDF documents. outlook : Adds support for Outlook messages. audio-transcription : Adds support for WAV and MP3 audio transcription. youtube-transcription : Adds support for YouTube video transcription. az-doc-intel : Enables integration with Azure Document Intelligence . az-content-understanding : Enables integration with Azure Content Understanding . Note : For installation from source or Docker-based usage, refer to the official MarkItDown docs https://github.com/microsoft/markitdown . MarkItDown in Action: Full Examples Time to explore some practical examples of converting different input data formats into Markdown. HTML Document to Markdown Suppose you have an HTML page stored locally e.g., as part of an offline web scraping procedure https://substack.thewebscraping.club/p/offline-web-scraping . You can convert it to Markdown using the MarkItDown CLI: markitdown example.html -o example.md Alternatively, you can achieve the same result via the Python API: python from markitdown import MarkItDown md = MarkItDown result = md.convert "example.html" with open "example.md", "w", encoding="utf-8" as f: f.write result.text content Now, assume the input is the following HTML page i.e., the homepage from example.com : The resulting example.md file will contain: Notice how MarkItDown focuses on the meaningful HTML content, ignoring tags like