EvoFlow: Evolving Diverse Agentic Workflows On The Fly
Guibin Zhang, Kaijie Chen, Guancheng Wan, Heng Chang, Hong Cheng, Kun, Wang, Shuyue Hu, Lei Bai

TL;DR
EvoFlow is an evolutionary framework that automatically generates diverse, high-performing, and cost-effective heterogeneous agentic workflows for multi-agent systems, surpassing existing methods in performance and efficiency.
Contribution
EvoFlow introduces a niching evolutionary algorithm to automatically evolve diverse and adaptive agentic workflows, addressing limitations of homogeneity and single-objective optimization in prior systems.
Findings
EvoFlow produces a diverse set of workflows from simple to complex tasks.
EvoFlow outperforms previous workflows by up to 29.86% in performance.
EvoFlow reduces inference costs by 87.6% compared to powerful LLMs.
Abstract
The past two years have witnessed the evolution of large language model (LLM)-based multi-agent systems from labor-intensive manual design to partial automation (\textit{e.g.}, prompt engineering, communication topology) and eventually to fully automated design. However, existing agentic automation pipelines often lack LLM heterogeneity and focus on single-objective performance optimization, limiting their potential to combine weaker models for more customized and cost-effective solutions. To address this challenge, we propose EvoFlow, a niching evolutionary algorithm-based framework to automatically search a population of heterogeneous and complexity-adaptive agentic workflows, rather than a single homogeneous, complex workflow. Technically, EvoFlow performs \textit{(1) tag-based retrieval} to extract parent workflows from an agentic population, evolves new workflows through…
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Taxonomy
TopicsScientific Computing and Data Management · Multi-Agent Systems and Negotiation · Simulation Techniques and Applications
MethodsFocus
