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

2026-06-06 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

QueryWeaver is a novel system designed to enhance the reliability of multi-tool query execution planning by leveraging LLM-based graph generation. Addressing the challenge of natural language queries spanning multiple applications, where individual tools offer only partial information, QueryWeaver converts these queries into structured graphs. A deterministic planner then executes these graphs, employing depth-first search to resolve dependencies and seamlessly combine results across various tools. This approach significantly improves the reliability of complex, multi-step queries, extending capabilities beyond traditional keyword-based search. The system demonstrates high accuracy, even when utilizing smaller or locally hosted Large Language Models, making it a versatile solution for intricate data retrieval tasks.

Key takeaway

For Machine Learning Engineers developing multi-tool AI agents, QueryWeaver offers a robust approach to enhance query reliability. You should consider implementing graph-based planning and deterministic execution, especially when dealing with complex, cross-application data retrieval. This method allows your systems to resolve dependencies and integrate results more effectively, even with resource-constrained or locally hosted LLMs, improving overall agent performance and user satisfaction.

Key insights

QueryWeaver transforms natural language into structured graphs for reliable multi-tool query execution using a deterministic, dependency-resolving planner.

Principles

• Structured planning improves multi-tool query reliability.
• Graph generation enables complex query execution.
• Dependency resolution is key for cross-tool results.

Method

QueryWeaver converts natural language queries into structured graphs, then uses a deterministic planner with depth-first search to resolve dependencies and combine results across multiple tools.

In practice

• Execute complex queries over personal data.
• Combine information from disparate applications.
• Utilize smaller LLMs for multi-tool tasks.

Topics

• Multi-Tool AI
• Large Language Models
• Graph Generation
• Query Execution
• Depth-First Search
• Deterministic Planning

Best for: Research Scientist, AI Architect, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.