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

2026-06-06

AI Summary

Existing approaches struggle to reliably execute cross-application natural language queries due to their reliance on local data and lack of structured planning. This work proposes a novel framework that integrates large language models (LLMs) with deterministic execution: it first leverages an LLM to parse user queries into structured query graphs, then employs depth-first search for deterministic planning, explicitly modeling inter-tool dependencies and fusing results from multiple sources. The approach substantially enhances both the expressiveness and execution reliability of complex queries, achieving high accuracy even with small or locally deployed LLMs, thereby demonstrating its effectiveness and practical utility.

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.

Problem

• multi-tool query
• query execution planning
• cross-tool queries
• structured planning
• personal data

Innovation

• multi-tool query planning
• LLM-based graph generation
• deterministic execution planner
• cross-application querying
• structured query representation