ratel

Ratel is a context engineering layer for AI agents that optimizes tool usage by selecting only relevant tools for each task. It aims to reduce token costs and improve accuracy for LLMs by preventing 'tool overload' without relying on vector databases or embeddings.

ratel sits in the Memory & Context category, so it is usually better judged by workflow fit, deployment style, and team needs than by isolated features alone. Based on the available metadata, it leans most clearly toward Context-aware Tool Selection for AI Agents. A representative use case is Developing AI agents in Python or TypeScript that efficiently manage external tools. The listed license is MIT. GitHub stars are currently 484. Relevant tags include AI Agent Tools, Context Management.