FlowBot: Inducing LLM Workflows with Bilevel Optimization and Textual Gradients

LLM workflows, which coordinate structured calls to individual LLMs/agents to achieve a particular goal, offer a promising path towards building powerful AI 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 we automatically induce LLM-based agents and workflows in a data-driven way? This paper describes a simple data-driven approach for automatically inducing agents and 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 FlowBot (work flow induction through b ilevel o ptimization and t extual gradients) approach performs competitively against strong baselines that make use of human-crafted or generated workflows.