Ratel

Ratel is a context engine for production AI agents. Its premise is that agents fail when their context fills with stale tools, drifting memory and dead history, so Ratel loads only the tools, skills and memory that matter at each turn, and learns from every interaction so new data improves the agent rather than bloating its prompt. The vendor publishes benchmark claims on its home page. In a test with a local qwen model and a catalog of 100 tools, single tool selection accuracy rose from 8.3 percent to 76.7 percent with Ratel, a gain of 69 percentage points. It also reports an 85 percent reduction in net tokens from loading only relevant context, and a 69 percent cut in token cost on frontier models with stable accuracy. A further feature shares context across a fleet of agents, so memory, skills, tools and history are unified instead of siloed. Ratel is described as managed infrastructure that supports any stack and any model, in the cloud or locally, and is meant to make agents leaner, more accurate and easier to debug. It is aimed at teams running agents in production who are managing token bills and reliability. The site offers a try now option and a contact route, and does not publish prices.