How to Scrape Web Content and Save It in Markdown Format

Thinking about turning a website into Markdown? Markdown is a simple text-based format that uses basic characters to add structure, making it straightforward to write and understand. It is widely used by developers and platforms such as GitHub because it keeps content tidy and easy to move around. In this guide, you will learn how to collect information from a site and quickly save it in a clean, organized format.

What is Markdown

Markdown was introduced in 2004 by John Gruber as a simple way to write web content using plain text rather than complex HTML markup. The idea was to make online writing feel as easy as composing an email, while still allowing a smooth conversion to HTML when needed. Over time, it became a favorite format for programmers, writers, and platforms such as GitHub, Reddit, and Stack Overflow.

In simple terms, Markdown is a lightweight markup language that lets you build structured documents using plain text. Its rules are very easy to remember. You use the number sign for titles, stars for emphasis, dashes for lists, and similar symbols for clarity. This minimal approach makes it natural to write and equally pleasant to read. When preparing online content, Markdown is valued for its portability, ease of conversion, and suitability for creating neat, focused documentation or notes.

Why scrape a website to Markdown

Extracting a website directly into Markdown feels like receiving a finished meal without the effort of preparing it. For tasks like AI and LLM data work, Markdown provides clean, well-organized text without unnecessary clutter, speeding processing and improving accuracy. For documentation teams or internal knowledge bases, Markdown is excellent because it is readable by humans and machines and can be dropped into repositories, wikis, or static site builders. Instead of digging through complex code, you get the content you need immediately.

Unlike raw HTML, Markdown remains light and tidy. Many modern pages come packed with huge HTML structures full of nested blocks, trackers, script tags, and styling junk added by builders or large frameworks. Pulling out the meaningful text becomes slow and frustrating. Markdown avoids all this by giving you only the important parts, such as titles, lists, links, and body text, in a clean and compact form.

Challenges in scraping a website to Markdown

Scraping a website and converting it directly to Markdown has its own challenges. Some are technical, and others are related to strategy. Understanding these challenges helps you choose better tools and avoid messy results.

Handling pages that rely on JavaScript

Many sites today load essential content only after the first request, often through JavaScript. A simple scraper may miss most of the page, leaving you with incomplete data. To capture everything before converting to Markdown, you need tools that can render the page the same way a browser does, so you can pull accurate content.

Keeping the original structure

Scraping is not just copying text. You must preserve the structure. If your scraper cannot detect titles, understand lists, or maintain code formatting, the final Markdown will look confusing and untidy. You need a tool that understands HTML elements and can translate them correctly into Markdown rules.

Removing unnecessary elements

Pages often include sections you do not want, such as ads, sidebars, navigation, footers, and embedded widgets. These disrupt your Markdown output. A good scraper should let you exclude these parts and keep only the meaningful section of the page.

Facing anti-bot systems and request limits

Large-scale scraping can trigger a site’s protective systems, causing blocks, challenges, or a complete shutdown of access. To avoid these issues, you often need rotating, reliable proxies to help you remain undetected. Reliable residential proxies from Decodo allow you to bypass such restrictions and maintain stable scraping.

Tools and services for converting scraped pages into Markdown – Overview

Here are some tools that help you skip tedious HTML cleanup and get clean Markdown files quickly.

Simplescraper

Simplescraper is a user-friendly tool that allows you to extract data without coding and export it as Markdown. You can schedule crawls, save reusable patterns, or use their API. It works well for small or medium-sized tasks, but may not be ideal for heavy workloads.

ScrapingAnt

ScrapingAnt provides an API based Markdown conversion service that converts raw page content into MD files. Since it mostly works through the API, it integrates easily into automated setups. However, advanced filtering may require additional configuration.

Firecrawl

Firecrawl is built for developers who need flexibility. It supports multiple formats, including Markdown, HTML, and JSON. It also handles dynamic content and can scrape several links at once.

Apify Dynamic Markdown Scraper

This tool, part of the Apify system, is designed to capture content from JavaScript-based pages. It offers customizable crawling rules and options to filter out unnecessary parts while preserving the Markdown structure.

Decodo Web Scraping API

Decodo combines built-in scraping and automated proxy rotation to help you avoid blocks. It provides various output formats such as Markdown, JSON, HTML, and table outputs without extra parsing. It also includes a simple interface with ready-made examples in curl, Node JS, and Python, making it easy for beginners and equally convenient for developers who want to integrate it with their own systems.

Step-by-step guide to scraping a website into Markdown

Start by picking a tool. In this walkthrough, we will use the Decodo Web Scraping API because it offers a simple setup along with flexible options. With just a few steps, you can move from your first trial request to running large batches of scraping tasks.

Using the visual interface for beginners

If you want an easy start or prefer not to write code, the Decodo online dashboard lets you grab clean Markdown output with almost no effort.

• Create an account and start your trial from the Decodo dashboard.
• Go to the Scraping APIs area and select the Advanced Web Scraping API.
• Open the Web Scraping API interface.
• Under Choose target, keep the default selection named Web Scraper.
• Enter the page address you want to extract in the URL box.
• Add optional settings if needed, such as method, region, language, device type, extra headers, or cookies.
• Tick the Markdown option to request Markdown output.
• Press Send Request, and the API will fetch the page and convert everything into clean Markdown within a moment.

You can review the Markdown result right inside the preview window. When you are satisfied, click Export and Copy to clipboard, then save it as an MD file on your system.

Using the API with ready-made code samples

After you set up your request inside the dashboard, Decodo automatically prepares code samples for Node JS, Python, and curl. These snippets already include your chosen settings, such as page address, headers, cookies, and the Markdown format option.

This lets you check the dashboard output first and, once it looks correct, simply copy the sample code and paste it into your script or application.

For instance, if you selected Markdown as your output, the Python sample will already contain the field markdown set to true. There is no need to adjust anything manually.

Scraping many links in Python

If you want to extract several pages at once, you can create a list of page addresses and loop through them, sending each one to the Decodo API. This makes it simple to process multiple URLs back-to-back.

import requests

API_ADDRESS = “https://scraper” + chr(45) + “api.decodo.com/v2/scrape”
AUTH_HEADER_VALUE = “Basic [YOUR_BASE64_ENCODED_CREDENTIALS]”

targets = [
“https://ip.decodo.com”,
“https://example.com”,
“https://www.scrapethissite.com/pages/simple”
]

content_type_key = “content” + chr(45) + “type”

request_headers = {
“accept”: “application/json”,
content_type_key: “application/json”,
“authorization”: AUTH_HEADER_VALUE,
}

for index, page_address in enumerate(targets, start=1):
body = {
“url”: page_address,
“headless”: “html”,
“markdown”: True,
}

try:
reply = requests.post(API_ADDRESS, json=body, headers=request_headers)
reply.raise_for_status()

data = reply.json()

# Take only the markdown part from the json reply
content = data.get(“results”, [{}])[0].get(“content”, “”)

md_nam_

The script above sends each page address to the Web Scraping API, gathers the content, and stores the results as MD files. These files are ready to use right away and do not need any extra processing.

Review and tidy the Markdown output.

Saving scraped content as Markdown can sometimes introduce minor issues, such as incorrect links, leftover HTML, spacing errors, and similar inconsistencies. You can produce a cleaner Markdown file by using the suggestions below.

Check the Markdown layout.

The simplest method to confirm your Markdown is correct is to inspect it yourself. These are some frequent problem areas:

Look for broken links

Use regular expressions to search for the standard pattern text and quickly test each address with a requests.head call to confirm it is still reachable.

Watch for open code blocks.

Search for backticks and ensure each pair appears.

Confirm proper heading order.

Title levels must move in sequence. For instance, do not jump from a single number sign to three number signs without including the two number sign levels in between.

Correct spacing problems

Extra blank lines around elements may disrupt the final Markdown layout, so remove unnecessary gaps.

Clear out any remaining HTML

Sometimes scraped Markdown contains small HTML fragments. You can remove these using Beautiful Soup or by applying an appropriate regular expression that strips unwanted tags.

import re
content = re.sub(r”<[^>]+>”, “”, content)

Normalize spacing

Clear out unnecessary blank lines between sections and remove extra spaces at the end of each line:

content = re.sub(r”\n{3,}”, “\n\n”, content)
content = “\n”.join(line.rstrip() for line in content.splitlines())

Repair image paths and links

When a page uses relative paths such as /images/file.png, change them to full URLs using the base address of the page you scraped:

from urllib.parse import urljoin
absolute_url = urljoin(base_url, relative_url)

Use a Markdown linter

You can rely on ready-made tools to clean and polish your Markdown so you do not have to fix everything by hand. A good option is markdownlint, a static checking tool that helps keep your MD files consistent, tidy, and easy to read. It is available as a GitHub project and can also be installed as an extension from the Visual Studio Marketplace.

Advanced features and customization

Focus on the main content.

You can adjust your script to collect the full Markdown output first, then filter it on your local machine to keep only the important sections. This approach helps you maintain a clear, organized dataset without cluttering your workflow with unnecessary material.

Here is an example of an updated Python script that captures only the headings and the paragraphs directly beneath them:

import requests
import re

API_ADDRESS = “https://scraper” + chr(45) + “api.decodo.com/v2/scrape”
AUTH_VALUE = “Basic [YOUR_BASE64_ENCODED_CREDENTIALS]”

page_list = [
“https://ip.decodo.com”,
“https://example.com”,
“https://www.scrapethissite.com/pages/simple/”
]

content_type_key = “content” + chr(45) + “type”

request_headers = {
“accept”: “application/json”,
content_type_key: “application/json”,
“authorization”: AUTH_VALUE,
}

def pull_headings_with_text(markdown_text, max_headings=3):
“””
Take the first few headings from level one to level three
and the text that follows each one from the given markdown.
“””

lines = markdown_text.splitlines()
collected_lines = []
count_headings = 0
collect_mode = False

for index, current_line in enumerate(lines):
# Treat any line that starts with one to three number signs as a heading
if re.match(r”^#{1,3} “, current_line):
count_headings += 1
if count_headings > max_headings:
break
collect_mode = True
collected_lines.append(current_line)
continue

if collect_mode:
# Keep text, blank lines, lists, and code sections
collected_lines.append(current_line)

return “\n”.join(collected_lines).strip()

for position, address in enumerate(page_list, start=1):
payload = {
“url”: address,
“headless”: “html”,
“markdown”: True,
}

try:
reply = requests.post(API_ADDRESS, json=payload, headers=request_headers)
reply.raise_for_status()

data = reply.json()
# Pull the markdown body from the reply
body_text = data.get(“results”, [{}])[0].get(“content”, “”)

# Keep only a limited number of headings and the text attached to them
trimmed_text = pull_headings_with_text(body_text, max_headings=3)

file_name = f”filtered_result_{position}.md”
with open(file_name, “w”, encoding=”utf” + chr(45) + “8”) as file:
file.write(trimmed_text)

print(f”Filtered scrape {address} -> {file_name}”)

except requests.RequestException as error:
print(f”Could not scrape {address}: {error}”)

except ValueError:
print(f”Could not read reply from {address}”)

Using prompts for focused extraction

You can rely on AI models to generate clear prompts that tell the scraper exactly what to collect, such as product information, short summaries, or specific sections. With the Decodo MCP server, these prompts can be passed through the Web Scraping API so the system can pull only the key material you want. This method blends AI guidance with automated scraping to deliver clean, targeted Markdown without manual sorting.

Setting region and language choices

You can tune your scraping request to load content meant for a certain country or preferred language by adjusting the request body. For example, adding “geo”: “United States” will simulate a visitor from that area. You can also use the Decodo dashboard to select both region and language from simple dropdown menus, then copy the updated settings into your code sample.

For a complete list of settings and instructions, visit the official documentation.

Working with interactive pages

A large number of sites now use JavaScript to load parts of the page only after certain actions. A normal request will not capture this. By using Playwright, you can simulate real user behavior, such as tapping, scrolling, or waiting for elements to appear, before scraping. This ensures the full page loads and your Markdown includes every important detail.

Best tips and practices

When exporting scraped pages to Markdown, keep in mind the standard scraping guidelines:

Respect robots.txt and site rules

Always review robots.txt and the site’s terms, and follow any blocked paths or crawl-delay instructions.

Do not overload servers.

Limit how many times you request a single domain, add short random delays, and slow down as soon as you notice 429 or repeated faults to avoid being blocked.

Watch for anti-bot systems.

Look for signs such as CAPTCHA, strange redirects, or unusual responses. Change your scraping speed, rotate proxies, or update session handling to maintain access.

Review the Markdown structure and completeness.

Scan your Markdown and run it through a linter to confirm that headings, lists, code blocks, images, and links are correctly formed and that nothing important is missing.

Use cases

Scraping web pages directly into Markdown is more than a fun trick. It opens up valuable uses for developers, analysts, and content teams. Here are some of the most common:

Supplying content to LLM and RAG systems

Lightweight Markdown works well with vector stores and improves retrieval-based generation tasks.

Moving documentation or blogs

Quickly migrate content from legacy systems into Markdown-based static site generators like Hugo or Jekyll.

Running content analysis and summaries

Convert articles to Markdown so natural language processing tools, sentiment evaluators, or summary generators can easily read them.

Building static sites from scraped Markdown

Pair Markdown output with a static site builder to create simple, fast pages.

Saving web pages for storage

Keep information in Markdown for long-term archiving, offline reading, or placing under version control.

Preparing training data

Produce clean, labeled content for machine learning without navigating through messy HTML.

Final thoughts

In this guide, you learned how to scrape sites and convert the results into neat, ready-to-use Markdown. From learning the basics to handling interactive pages and batch jobs, you now have a clear path to capture the content you need. No more fighting with cluttered HTML. With Decodo Web Scraping API, you can make the process smooth and dependable and enjoy clean, structured output every time.