HyperAgent: Planning and Acting over Tool-Schema Hypergraphs for Tool-Use LLM Agents
Zian Zhai, Xingyu Tan, Gaowang Zou, Xiaoyang Wang, Wenjie Zhang
Why It Matters
What makes this one worth your time
This work addresses critical challenges in tool-use planning for LLM agents, potentially improving their efficiency and reliability in real-world applications.
HyperAgent enhances tool-use planning for LLM agents through dynamic schema-aware graph construction.
Summary
The paper introduces HyperAgent, a framework that utilizes a Tool-Schema Hypergraph to improve planning and execution of tool-use tasks by dynamically constructing task graphs and identifying necessary tools based on the agent's state.
Key contributions
- Introduction of the Tool-Schema Hypergraph model for representing tool relations.
- Development of the HyperAgent framework for dynamic planning and execution of tasks.
- Experimental validation demonstrating improved task completion and reduced resource consumption compared to existing baselines.
Notable insights
- The use of a directed Tool-Schema Hypergraph allows for a more structured representation of tool relations, which could lead to more efficient planning.
- Deficit-oriented expansion for dynamic task execution is a clever approach to adaptively resolve tool requirements.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2608.02650v1 Announce Type: new 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.