Constella: Supporting Storywriters' Interconnected Character Creation through LLM-based Multi-Agents
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
Creating a cast of characters by attending to their relational dynamics is a critical aspect of most long-form storywriting. However, our formative study (N=14) reveals that writers struggle to envision new characters that could influence existing ones, balance similarities and differences among characters, and intricately flesh out their relationships. Based on these observations, we designed Constella, an LLM-based multi-agent tool that supports storywriters' interconnected character creation process. Constella suggests related characters (FRIENDS DISCOVERY feature), reveals the inner mindscapes of several characters simultaneously (JOURNALS feature), and manifests relationships through inter-character responses (COMMENTS feature). Our 7-8 day deployment study with storywriters (N=11) shows that Constella enabled the creation of expansive communities composed of related characters, facilitated the comparison of characters' thoughts and emotions, and deepened writers' understanding of character relationships. We conclude by discussing how multi-agent interactions can help distribute writers' attention and effort across the character cast.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 44 of its 141 references that are in the map. First posted on arXiv 8 Jul 2025.
Builds on
44 of 141 references.
Closest ancestors · 2
Same direction, newest first
- 2024Agents' Room: Narrative Generation through Multi-step Collaboration3 Oct
- 2024IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script Generation1 Jul
Other references · 42
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Built on by
Citing papers per year: 2025: 1; 2026: 3.
4 papers.
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- 2025Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing16 Nov 2025
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