Blog
A large engineering org ran controlled experiments to measure how structured Mintlify docs affect agent performance on massive codebases. The results: 64% more precise, 39% more discoverable, half the tokens, 1.5x faster.
Han Wang
Co-Founder
We benchmarked four ways to serve documentation to AI agents (HTML, plain markdown, markdown linking to llms.txt, and markdown with llms.txt inlined) across 2,400 runs on 20 Mintlify docs sites, and found that a single link to llms.txt eliminates most agent 404s at no added cost.
Anthropic's Technical Content Engineer for Claude Code shares how she uses Mintlify and Claude to automate documentation improvements from user feedback.
Documentation is often the highest-traffic, highest-intent surface in your entire funnel. It's the first place a buyer visits before trying your product. It's where agents surface and push your product to prospects.
Mintlify might look like a Markdown hosting service from the outside. But the build versus buy question for content infrastructure goes much deeper than rendering Markdown.
Every company is now paying for AI tokens, but few can draw a clean line from that spend to business outcomes. Here's a framework that maps AI Credits to the P&L categories finance teams already understand.
A round-up of the UX improvements coming to Mintlify's web editor.