GoKawiilRatel Your AI agent is paying for tools it never uses. Ratel fixes that. Docs • Skills • Discord
Introduction
The context engineering layer for AI agents. Selects only the tools and skills relevant to each turn, recovering accuracy lost to tool overload and cutting what you pay per call. No vector DB, no infra.
Why
Cost: Every tool schema, every skill, and a growing list of instructions in the system prompt are tokens you pay for on every call. Send them all up front and you pay for them all, every turn.
Every tool schema, every skill, and a growing list of instructions in the system prompt are tokens you pay for on every call. Send them all up front and you pay for them all, every turn. Accuracy: Models get worse as that context grows. Crowd it with tools, skills, and instructions a turn doesn't need and the model picks the wrong option and drifts off task.
Models get worse as that context grows. Crowd it with tools, skills, and instructions a turn doesn't need and the model picks the wrong option and drifts off task. Ratel fixes both: it indexes your tools and skills into a catalog the agent progressively discloses, searching for what each turn needs and injecting only the matching capabilities instead of loading everything up front.
Across local, open-source, and frontier model setups, Ratel cuts token usage and recovers accuracy lost to tool overload, with no vector DB required. Full results: benchmark.ratel.sh
Quickstart
Guides: Quickstart · TypeScript SDK · Python SDK
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