QueryGraph: Reliable Multi-Tool Query Execution Planning via LLM-Based Graph Generation

Abstract:Many real-world queries over personal data span multiple applications and require structured planning, as individual tools expose only partial information. While LLMs show strong reasoning and tool use, reliably executing multi-step, cross-tool queries remains challenging. We introduce a system that converts natural language queries into structured graphs and executes them via a deterministic planner. Our approach uses depth-first search to resolve dependencies and combine results across tools, improving reliability and enabling queries beyond traditional keyword-based search. We demonstrate high accuracy even with smaller or locally hosted LLMs.

Machine Learning (cs.LG)

arXiv:2606.08300 [cs.LG]

(or arXiv:2606.08300v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.08300 Focus to learn more arXiv-issued DOI via DataCite

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