FlowBot: Inducing LLM Workflows with Bilevel Optimization and Textual Gradients

arXiv:2604.26258v1 Announce Type: new Abstract: LLM workflows, which coordinate structured calls to individual LLMs (each augmented with varying instructions and tools) to achieve a particular goal, offer a promising path towards extending the capabilities of LLMs and building powerful systems that can tackle diverse tasks. However, existing approaches for building such workflows generally rely on human-crafted pipelines and prompts, which presents a substantial bottleneck in real world deployment. How can automatically induce and optimize such workflows in a data-driven way? This paper describes a simple data-driven approach for automatically inducing LLM workflows. We formulate workflow induction as a bilevel optimization problem: an outer loop which optimizes a high-level sketch of the workflow (in particular how the LLM calls should be structured), and an inner loop which optimizes each individual LLM call one-by one. Both loops are optimized with textual gradients'' where for the inner loop we optimize each component in a modular way through backpropagating'' textual gradients layer-by-layer. We find that LLM workflows discovered through our extsc{FlowBot} (workextbf{flow} induction through extbf{b}ilevel extbf{o}ptimization and extbf{t}extual gradients) approach performs competitively against strong baselines that make use of human-crafted or automatically-generated workflows.

Source: arXiv cs.CL | 2026-04-30