Keenable search engine for AI agents launches with custom ranking stack
A new search engine built specifically for AI agents launched this week with its own crawling and ranking stack, promising deeper results than mainstream engines.
Keenable, a search engine designed for AI agents, went live after six months of development with a fully custom index and ranking pipeline. The service integrates into Claude Code, Cursor, and similar coding assistants in under a minute without registration or API keys. Its technical stack includes proprietary crawlers and ranking models at every stage, surfacing primary sources, academic papers, and niche links that conventional search engines bury on later result pages.
The engine supports date filters and site: operators, optimized primarily for English-language queries and U.S. news coverage. Russian-language search quality remains limited due to English-trained ranking models. Rate limits are generous according to the official documentation, and the service is free to use. Most AI coding assistants today default to Google, Bing, or academic search APIs—systems built for consumer browsing, not agent workflows. By controlling the entire crawl-to-rank stack, Keenable's team can tune for recall on technical and academic content that commercial engines often deprioritize, though the tradeoff is narrower language coverage for now.
