Ratel

Ratel is a context engine designed to support production AI agents by improving how context is managed and delivered during agent operation. It addresses common issues where agents become less reliable and less accurate due to outdated or irrelevant context, which can lead to increased token usage and operational costs. The platform aims to make agents leaner, more accurate, and easier to debug by ensuring that only the most relevant context is loaded at each step, maximizing token efficiency and reducing unnecessary context bloat.

Key features of Ratel include the ability to inject the right context as needed, rather than loading all available context, which helps prevent context windows from filling with stale tools, drifting memory, or dead history. The tool supports both cloud and local models and is designed to integrate with any agent stack. Ratel also unifies context across multiple agents, allowing shared access to memory, skills, tools, and history, so that knowledge gained by one agent can benefit others, rather than having each agent start from scratch. The platform provides detailed traceability, showing every step taken by an agent and explaining the reasoning behind each action, as it serves as the layer routing agent decisions.

Ratel offers an SDK and open-source products to facilitate integration and use by developers working with AI agents. The service is positioned for teams and organizations running agents in production environments, where maintaining agent reliability, accuracy, and cost-effectiveness is critical.

The tool is developed by Agentified SRL, based in Milan, Italy. Ratel classifies itself as a context management infrastructure for AI agents, focusing on optimizing context loading, improving agent behavior, and providing transparency into agent decision-making processes.