AgentOrchestra: A Hierarchical Multi-Agent Framework for General-Purpose Task Solving

• URL: http://arxiv.org/abs/2506.12508v2
• Date: Tue, 17 Jun 2025 07:08:27 GMT
• Title: AgentOrchestra: A Hierarchical Multi-Agent Framework for General-Purpose Task Solving
• Authors: Wentao Zhang, Ce Cui, Yilei Zhao, Rui Hu, Yang Liu, Yahui Zhou, Bo An,
• Abstract summary: projectname is a hierarchical multi-agent framework for general-purpose task solving.<n>projectname features a central planning agent that decomposes complex objectives and delegates sub-tasks to a team of specialized agents.<n>Each sub-agent is equipped with general programming and analytical tools, as well as abilities to tackle a wide range of real-world specific tasks.
• Score: 30.50203052125566
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in agent systems based on large language models (LLMs) have demonstrated strong capabilities in solving complex tasks. However, most current methods lack mechanisms for coordinating specialized agents and have limited ability to generalize to new or diverse domains. We introduce \projectname, a hierarchical multi-agent framework for general-purpose task solving that integrates high-level planning with modular agent collaboration. Inspired by the way a conductor orchestrates a symphony and guided by the principles of \textit{extensibility}, \textit{multimodality}, \textit{modularity}, and \textit{coordination}, \projectname features a central planning agent that decomposes complex objectives and delegates sub-tasks to a team of specialized agents. Each sub-agent is equipped with general programming and analytical tools, as well as abilities to tackle a wide range of real-world specific tasks, including data analysis, file operations, web navigation, and interactive reasoning in dynamic multimodal environments. \projectname supports flexible orchestration through explicit sub-goal formulation, inter-agent communication, and adaptive role allocation. We evaluate the framework on three widely used benchmark datasets covering various real-world tasks, searching web pages, reasoning over heterogeneous modalities, etc. Experimental results demonstrate that \projectname consistently outperforms flat-agent and monolithic baselines in task success rate and adaptability. These findings highlight the effectiveness of hierarchical organization and role specialization in building scalable and general-purpose LLM-based agent systems.

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