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 is currently grouped under Memory & Context, which makes it easier to evaluate through workflow fit instead of isolated features alone. Based on the available data, it leans most heavily toward Context-aware Tool Selection for AI Agents and Developing AI agents in Python or TypeScript that efficiently manage external tools. The listed license is MIT, which is useful when adoption constraints matter. It also shows measurable community traction with 219 GitHub stars.