DataMagic: Authoring Data Videos through Declarative Multi-Agent Orchestration

Data videos communicate data insights through dynamic charts, voice narration, and synchronized animations, and have become a widely adopted form of data storytelling. However, their production requires multidisciplinary expertise spanning data analysis, narrative design, and video editing. Static visualization tools lack narrative and animation capabilities; authoring tools rely on pre-prepared charts rather than raw data; and pixel-level generation models, while capable of end-to-end synthesis, cannot guarantee data accuracy or provenance. End-to-end automatic generation faces two core challenges: how to uniformly represent charts, narration, and animations together with their temporal relationships, and how to efficiently search a vast design space for narrative-coherent compositions. We present DataMagic, a system that authors data videos from raw tabular data through declarative multi-agent orchestration, built on two core designs. First, the declarative specification DVSpec unifies charts, narration, and animations with data-bound references and declarative synchronization, ensuring data provenance and automatic audio-visual alignment. Second, a "Generate-then-Orchestrate" multi-agent strategy generates candidate scenes in parallel and then optimizes narrative coherence through global orchestration. DVSpec further serves as a shared state supporting three complementary interaction modes, bridging full automation with fine-grained human control. Evaluations on 109 real-world samples show that even the most advanced LLM (e.g., GPT-5) achieves only 2.13/5 with execution success rates between 48.62% and 86.24%; DataMagic improves quality to 3.89 (+83%) with success rates above 95%, with the most significant gains in animation and narrative dimensions. A user study further demonstrates that, compared to a conversational LLM workflow, DataMagic significantly improves creation efficiency (79.7% reduction in task time) and reduces perceived cognitive load. The project homepage is available at https://github.com/HKUSTDial/DataMagic.