HyperAgent: Planning and Acting over Tool-Schema Hypergraphs for Tool-Use LLM Agents

2026-07-31

AI Summary

This work addresses the challenge that large language models struggle to efficiently plan tool usage in complex tasks due to implicit reasoning and dynamic environmental changes. It introduces a novel approach that models tool relationships at the schema level by constructing a tool–schema hypergraph, where each tool is represented as a hyperedge connecting input and output schema nodes. The method further incorporates a task-relevant context graph, a schema-aware task-directed acyclic graph (DAG), and a gap-driven expansion mechanism conditioned on system state to enable precise dynamic planning. Evaluated on the AppWorld benchmark, this framework significantly improves task completion rates while simultaneously reducing redundant API calls, LLM interactions, and token consumption.

Abstract

Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks. However, reliable tool-use planning remains challenging due to the limitations of implicit reasoning and the evolving nature of real-world execution environments. Existing tool-use agents typically rely on LLMs to infer tool compositions from textual descriptions, which can lead to inefficient exploration and unreliable execution in complex tasks. To address these challenges, we model tool relations at the schema level and construct a directed Tool--Schema Hypergraph, in which tools are represented as hyperedges from their required input-schema nodes to their output-schema nodes. Furthermore, we propose HyperAgent, a Tool--Schema Hypergraph-guided framework for dynamic planning and execution. Given a task, HyperAgent first extracts a task-relevant tool context graph and uses it to guide the construction of a schema-aware Task DAG. During execution, HyperAgent dynamically realizes each subtask by constructing a state-conditioned tool support graph through deficit-oriented expansion, which identifies unresolved requirements and retrieves supporting producer tools according to the current agent state. Experiments on AppWorld demonstrate that HyperAgent improves task completion performance while reducing redundant API calls, LLM interactions, and token consumption compared with existing agent baselines.

Problem

• tool-use
• LLM agents
• planning
• schema
• hypergraph

Innovation

• Tool-Schema Hypergraph
• Dynamic Planning
• Schema-aware Task DAG
• Deficit-oriented Expansion
• LLM Agents