Ratel

Context engineering layer that selects only relevant tools and skills per turn, cutting AI agent token costs.

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Ratel is a context engineering layer for AI agents that selects only the tools and skills relevant to each turn instead of sending every available tool schema and instruction on every call, recovering accuracy lost to tool overload and cutting token costs by around 80%, using in-process BM25 and semantic retrieval rather than a vector database.

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Problem & pain-point mapping
Who we surface it to (audience fit)
What it's a strong alternative to
Trust & credibility signals

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