# Drama Llama: An LLM-Powered Storylets Framework for Authorable Responsiveness in Interactive Narrative

arXiv: https://arxiv.org/abs/2501.09099 · HTML: https://mlsanity.com/paper/2501.09099/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2025.

## Built on by (most cited first, 7 of 7)

- [A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications](https://mlsanity.com/paper/2503.17003/) (2025)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [Can LLM Agents Stick to the Script? A Benchmark for Long-Horizon Consistency in Interactive Narratives](https://mlsanity.com/paper/2608.08160/) (2026)
- [Designing and Evaluating Malinowski's Lens: An AI-Native Educational Game for Ethnographic Learning](https://mlsanity.com/paper/2511.07682/) (2025)
- [Elsewise: Authoring Open-ended Interactive Narrative with Possibility Space Visualization](https://mlsanity.com/paper/2601.15295/) (2025)
- [Design Techniques for LLM-Powered Interactive Storytelling: A Case Study of the Dramamancer System](https://mlsanity.com/paper/2601.18785/) (2026)
- [EvoSpark: Endogenous Interactive Agent Societies for Unified Long-Horizon Narrative Evolution](https://mlsanity.com/paper/2604.12776/) (2026)

## Abstract (arXiv)

In this paper, we present Drama Llama, an LLM-powered storylets framework that supports the authoring of responsive, open-ended interactive stories. DL combines the structural benefits of storylet-based systems with the generative capabilities of large language models, enabling authors to create responsive interactive narratives while maintaining narrative control. Rather than crafting complex logical preconditions in a general-purpose or domain-specific programming language, authors define triggers in natural language that fire at appropriate moments in the story. Through a preliminary authoring study with six content authors, we present initial evidence that DL can generate coherent and meaningful narratives with believable character interactions. This work suggests directions for hybrid approaches that enhance authorial control while supporting emergent narrative generation through LLMs.