New Alibaba AI framework skips loading every tool, cutting agent token use 99%
Alibaba researchers developed SkillWeaver, an AI framework that orchestrates multi-tool workflows by decomposing tasks, retrieving relevant tools, and composing them into an execution plan. SkillWeaver significantly reduces token consumption by over 99% compared to traditional methods, improving accuracy and efficiency. The framework introduces Skill-Aware Decomposition (SAD), a feedback loop that aligns task decomposition with specific tool vocabulary. Experiments show that SAD boosts accuracy, especially for complex tasks requiring multiple skills. SkillWeaver outperforms brute-force and vanilla LLM methods, offering developers a reproducible solution for efficient tool routing in enterprise AI systems.
Alibaba researchers developed SkillWeaver, an AI framework that orchestrates multi-tool workflows by decomposing tasks, retrieving relevant tools, and composing them into an execution plan. SkillWeaver significantly reduces token consumption by over 99% compared to traditional methods, improving accuracy and efficiency. The framework introduces Skill-Aware Decomposition (SAD), a feedback loop that aligns task decomposition with specific tool vocabulary. Experiments show that SAD boosts accuracy, especially for complex tasks requiring multiple skills. SkillWeaver outperforms brute-force and vanilla LLM methods, offering developers a reproducible solution for efficient tool routing in enterprise AI systems.
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