让多个AI智能体协同工作，高效完成复杂任务。
OmniNova:A General Multimodal Agent Framework
- 分层架构设计，各司其职的协调与执行机制
- 任务完成率87%，比基线提升25个百分点
- 适合需要多步骤、跨工具协作的研究与自动化场景
将大语言模型（LLMs）与专用工具结合，为智能自动化系统带来新机遇。然而，协调多个基于LLM的智能体完成复杂任务仍面临协作困难、资源利用低效和信息流不一致等问题。我们提出OmniNova，一个模块化多智能体自动化框架，融合语言模型与网络搜索、爬取、代码执行等专用能力。OmniNova引入三项关键创新：(1) 分层多智能体架构，包含协调者、规划者、监督者和专家智能体；(2) 动态任务路由机制，根据任务复杂度优化智能体部署；(3) 多层级LLM集成系统，按认知需求分配合适模型。在科研、数据分析和网络交互领域共50个复杂任务上的评估显示，OmniNova在任务完成率（87% vs. 基线62%）、效率（令牌消耗减少41%）和结果质量（人工评分4.2/5 vs. 基线3.1/5）上均优于现有框架。我们贡献了多智能体系统设计的理论框架与开源实现，推动了基于LLM自动化系统的前沿发展。
原文摘要 · Abstract (English)
The integration of Large Language Models (LLMs) with specialized tools presents new opportunities for intelligent automation systems. However, orchestrating multiple LLM-driven agents to tackle complex tasks remains challenging due to coordination difficulties, inefficient resource utilization, and inconsistent information flow. We present OmniNova, a modular multi-agent automation framework that combines language models with specialized tools such as web search, crawling, and code execution capabilities. OmniNova introduces three key innovations: (1) a hierarchical multi-agent architecture with distinct coordinator, planner, supervisor, and specialist agents; (2) a dynamic task routing mechanism that optimizes agent deployment based on task complexity; and (3) a multi-layered LLM integration system that allocates appropriate models to different cognitive requirements. Our evaluations across 50 complex tasks in research, data analysis, and web interaction domains demonstrate that OmniNova outperforms existing frameworks in task completion rate (87\% vs. baseline 62\%), efficiency (41\% reduced token usage), and result quality (human evaluation score of 4.2/5 vs. baseline 3.1/5). We contribute both a theoretical framework for multi-agent system design and an open-source implementation that advances the state-of-the-art in LLM-based automation systems.
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