Toollery: Scaling LLM Agents to Thousands of Skills and Tools
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 present \textbf{Toollery}, a training-free candidate-compression framework for scalable LLM skill/tool selection. Following established document-side query expansion, Toollery generates user-intent queries from each skill/tool specification and builds a retrieval index that maps real user requests to compact candidate sets before final LLM decision-making. By treating high-level skills and atomic t...
| Authors | Xiangxi Tian, Ran Guan |
|---|---|
| Primary category | cs.LG |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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