RAG-ready web scraping that cuts your LLM token costs
Feeding raw data directly into your AI agents eats up context windows and spikes your OpenAI and Anthropic costs. Earlier this year, we launched standard HTML, JSON, and Markdown extraction. Today, we are introducing outputs built entirely for AI: markdown-llm, text-llm, and html-llm. We automatically strip out navbars, footers, ads, and scripts, delivering only the context your models actually need. You can save up to 85% on tokens compared to raw HTML when using text-llm output format.
Earlier this year, we launched the Geekflare Scraping API with standard Markdown, JSON, and HTML support. We prioritized your feedback about feeding our scraping results directly into AI agents and RAG pipelines.
Today we are launching our new -llm endpoints (markdown-llm, text-llm, html-llm). We do the heavy lifting behind the scenes to clean the DOM, strip the boilerplate, and return optimized structured content ready for generation.
Refer to the API reference for all supported formats.
You save up to 85% on tokens, speed up your LLM response times, and get better AI accuracy because the noise is gone.
I will be hanging out in the comments all day. Please let me know what you think and what you are building!
About Geekflare Scraping API v2 on Product Hunt
“RAG-ready web scraping that cuts your LLM token costs”
Geekflare Scraping API v2 launched on Product Hunt on April 17th, 2026 and earned 86 upvotes and 7 comments, placing #22 on the daily leaderboard. Feeding raw data directly into your AI agents eats up context windows and spikes your OpenAI and Anthropic costs. Earlier this year, we launched standard HTML, JSON, and Markdown extraction. Today, we are introducing outputs built entirely for AI: markdown-llm, text-llm, and html-llm. We automatically strip out navbars, footers, ads, and scripts, delivering only the context your models actually need. You can save up to 85% on tokens compared to raw HTML when using text-llm output format.
On the analytics side, Geekflare Scraping API v2 competes within API, Artificial Intelligence and Development — topics that collectively have 570k followers on Product Hunt. The dashboard above tracks how Geekflare Scraping API v2 performed against the three products that launched closest to it on the same day.
Who hunted Geekflare Scraping API v2?
Geekflare Scraping API v2 was hunted by Chandan Kumar. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Geekflare Scraping API v2 including community comment highlights and product details, visit the product overview.
Chandan Kumar
Hello, everyone! 👋
Earlier this year, we launched the Geekflare Scraping API with standard Markdown, JSON, and HTML support. We prioritized your feedback about feeding our scraping results directly into AI agents and RAG pipelines.
Today we are launching our new -llm endpoints (markdown-llm, text-llm, html-llm). We do the heavy lifting behind the scenes to clean the DOM, strip the boilerplate, and return optimized structured content ready for generation.
Refer to the API reference for all supported formats.
You save up to 85% on tokens, speed up your LLM response times, and get better AI accuracy because the noise is gone.
I will be hanging out in the comments all day. Please let me know what you think and what you are building!