EvoFlow: Evolving Diverse Agentic Workflows On The Fly

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 (textite.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: (I) diverse, evolving a population of workflows ranging from simple I/O tasks to complex multi-turn interactions; (II) high-performing, outperforming previous handcrafted and automated workflows by 1.23%~29.86%; (III) economical, surpassing powerful llmnameo1-preview at 12.4% of its inference cost using weaker open-source models.