EvoFlow: Evolving Diverse Agentic Workflows On The Fly

• URL: http://arxiv.org/abs/2502.07373v1
• Date: Tue, 11 Feb 2025 08:48:46 GMT
• Title: EvoFlow: Evolving Diverse Agentic Workflows On The Fly
• Authors: Guibin Zhang, Kaijie Chen, Guancheng Wan, Heng Chang, Hong Cheng, Kun Wang, Shuyue Hu, Lei Bai,
• Abstract summary: EvoFlow is a niching evolutionary algorithm-based framework to automatically search a population of complexity and heterogeneous agentic. We show that EvoFlow can evolve a population ranging from simple I/O tasks to complex multi-turn interactions.
• Score: 21.82515160298748
• License:
• 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 \textit{(2) crossover} and \textit{(3) mutation}, and employs \textit{(4) niching-based selection} to maintain population diversity and quality. Extensive evaluations across seven benchmarks demonstrate that EvoFlow is: \textbf{(I) diverse}, evolving a population of workflows ranging from simple I/O tasks to complex multi-turn interactions; \textbf{(II) high-performing}, outperforming previous handcrafted and automated workflows by $1.23\%\sim29.86\%$; \textbf{(III) economical}, surpassing powerful \llmname{o1-preview} at $12.4\%$ of its inference cost using weaker open-source models.

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