Vistoryteller: Designing Data Stories with LLM Agent-Based Generation and Interactive User Control

Data stories that combine data, visualizations, and prose are widely used for communication, decision making, and persuasion, but producing them typically requires coordinated effort across specialized roles such as analysts, scripters, and designers, which is time consuming and difficult to manage. Existing AI-assisted methods generally treat storytelling as a single-agent task and offer only coarse, global controls, limiting an author’s ability to preserve and shape their communication intention over the course of a narrative. In this work, we present Vistoryteller, a multi-agent authoring system that models the division of labor found in human teams by assigning specialized large language model agents to complementary roles and orchestrating their interactions to generate cohesive, intention-aligned data stories. Vistoryteller supports fine-grained authorial control through two complementary mechanisms: a sketch-based tension-flow control for specifying how thematic emphasis and narrative tension should evolve, and a conversational interface for issuing localized directives to individual agents or to the team. We evaluate Vistoryteller with two controlled experiments and a qualitative user study. Results show that Vistoryteller generates narratives that align more closely with user intentions, preserve coherence across agent contributions, and surface diverse and expressive insights.