Pricing

from $0.70 / 1,000 item scrapeds

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Universal Webpage to Clean Markdown LLM Converter

Try for free

Convert any webpage or URL into clean, token-efficient GitHub-Flavored Markdown optimized for LLM context windows, RAG pipelines, and prompt injection.

Pricing

from $0.70 / 1,000 item scrapeds

Rating

0.0

(0)

Developer

LiteZero

Actor stats

Bookmarked

Total users

Monthly active users

10 days ago

Last modified

📝 Universal Webpage to Clean Markdown LLM Converter

Convert any messy webpage, documentation, or blog post into clean, structured Markdown optimized for LLM prompts, RAG vector embeddings, and AI knowledge bases.

🥊 Why This Actor Beats the Competition

Feature	Firecrawl	Raw HTML to Text	This Actor
Pricing	💸 $20 – $100 / month	Free	💰 $1.50 per 1,000 pages (PPR)
Noise Filtering	Removes some navbars	❌ Retains cookie banners, ads, footers	🧹 Smart boilerplate & navigation strip
Table & Code Blocks	Yes	❌ Destroys formatting	✅ Full GitHub Flavored Markdown (GFM)
LLM Token Efficiency	Good	❌ Wasteful token consumption	🎯 60-80% fewer tokens than raw HTML

🛠️ Step-by-Step: How to Use

Method 1: Apify Console

Input target URLs into Webpage URLs.
Click Start to extract pristine Markdown text.

Method 2: Python SDK

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("litezero/universal-web-to-markdown").call(run_input={
    "urls": ["https://news.ycombinator.com"]
})

for doc in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"Title: {doc['title']}")
    print(f"Markdown Content:
{doc['markdown'][:500]}...")

📥 Input Parameters

Field	Type	Required	Default	Description
`urls`	Array	Yes	`["https://news.ycombinator.com"]`	Webpage URLs to convert to Markdown

📤 Output Data Dictionary

Field	Type	Description
`url`	String	Original webpage URL
`title`	String	Extracted page title
`description`	String	Meta description summary
`markdown`	String	Clean, formatted Markdown content
`wordCount`	Integer	Total word count
`scrapedAt`	String	ISO 8601 extraction timestamp

💡 Top Use Cases

RAG & Vector Search Ingestion: Feed LangChain, LlamaIndex, Pinecone, and Weaviate with pristine markdown.
LLM Fine-Tuning Datasets: Clean public web knowledge without advertising junk.

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