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Toollery: Scaling LLM Agents to Thousands of Skills and Tools

Tian, Xiangxi et al.
arXiv (All) · Papers
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computation-and-language
As LLM agents are exposed to hundreds to tens of thousands of skills, tools, and API functions, full-library prompting becomes costly, slow, and less reliable: each added candidate increases prompt tokens and latency, while longer candidate lists introduce more distractors for LLM selection. We pres
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