EvoAgentX: An Automated Framework for Evolving Agentic Workflows
Yingxu Wang, Siwei Liu, Jinyuan Fang, Zaiqiao Meng

TL;DR
EvoAgentX is an open-source platform that automates the creation, execution, and optimization of multi-agent workflows, significantly enhancing performance across various complex tasks through integrated evolutionary algorithms.
Contribution
It introduces a modular framework with integrated optimization algorithms for dynamic evolution and performance tuning of multi-agent workflows.
Findings
Achieved a 7.44% increase in HotPotQA F1 score.
Improved MBPP pass@1 by 10%.
Enhanced MATH solve accuracy by 10%.
Abstract
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…
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Taxonomy
TopicsScientific Computing and Data Management · Business Process Modeling and Analysis · Simulation Techniques and Applications
