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arXiv · 2606.08300

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

Aishwarya Chakravarthy·Vidhi Kulkarni·Duen Horng Chau
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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.

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Aishwarya Chakravarthy, Vidhi Kulkarni, Duen Horng Chau. 2026-08-29. QueryGraph: Reliable Multi-Tool Query Execution Planning via LLM-Based Graph Generation. https://arxiv.org/abs/2606.08300

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