QueryGraph: Reliable Multi-Tool Query Execution Planning via LLM-Based Graph Generation
Aishwarya Chakravarthy, Vidhi Kulkarni, Duen Horng Chau
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.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.