自动化优化多智能体工作流，显著提升复杂任务表现
EvoAgentX: An Automated Framework for Evolving Agentic Workflows
- 通过模块化架构自动演化智能体提示、工具配置和流程结构
- 在多个数据集上实现7.44%至20%的性能提升，最高达20%
- 适合需要高效协同推理与自动优化的AI系统开发者
多智能体系统（MAS）已成为协调大语言模型（LLM）与专用工具以协作解决复杂任务的强大范式。然而，现有框架常需手动配置工作流，缺乏动态演化与性能优化的原生支持，且多数优化算法未集成于统一平台。本文提出EvoAgentX，一个开源平台，可自动完成多智能体工作流的生成、执行与进化优化。该平台采用五层模块化架构：基础组件、智能体、工作流、演化与评估层。其中演化层集成TextGrad、AFlow和MIPRO三种MAS优化算法，迭代优化智能体提示、工具配置与工作流拓扑。我们在HotPotQA（多跳推理）、MBPP（代码生成）和MATH（数学求解）上评估，并在真实任务中使用GAIA进行测试。实验表明，EvoAgentX持续带来显著性能提升：HotPotQA F1提高7.44%，MBPP pass@1提升10.00%，MATH求解准确率提升10.00%，在GAIA上整体准确率最高提升20.00%。源代码已公开于https://github.com/EvoAgentX/EvoAgentX。
原文摘要 · Abstract (English)
Multi-agent systems (MAS) have emerged as a powerful paradigm for orchestrating large language models (LLMs) and specialized tools to collaboratively address complex tasks. However, existing MAS frameworks often require manual workflow configuration and lack native support for dynamic evolution and performance optimization. In addition, many MAS optimization algorithms are not integrated into a unified framework. In this paper, we present EvoAgentX, an open-source platform that automates the generation, execution, and evolutionary optimization of multi-agent workflows. EvoAgentX employs a modular architecture consisting of five core layers: the basic components, agent, workflow, evolving, and evaluation layers. Specifically, within the evolving layer, EvoAgentX integrates three MAS optimization algorithms, TextGrad, AFlow, and MIPRO, to iteratively refine agent prompts, tool configurations, and workflow topologies. We evaluate EvoAgentX on HotPotQA, MBPP, and MATH for multi-hop reasoning, code generation, and mathematical problem solving, respectively, and further assess it on real-world tasks using GAIA. Experimental results show that EvoAgentX consistently achieves significant performance improvements, including a 7.44% increase in HotPotQA F1, a 10.00% improvement in MBPP pass@1, a 10.00% gain in MATH solve accuracy, and an overall accuracy improvement of up to 20.00% on GAIA. The source code is available at: https://github.com/EvoAgentX/EvoAgentX
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。