StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework
This addresses the problem of autobiography writing for individuals by providing a user-driven, structured approach, though it appears incremental as it builds on existing conversational assistants with a novel multi-agent framework.
The paper tackles the challenge of organizing scattered personal memories into coherent autobiographies by introducing StorySage, a multi-agent conversational system that iteratively collects user memories and updates narratives, resulting in improved conversational flow, narrative completeness, and higher user satisfaction compared to a baseline in user studies with 28 participants.
Every individual carries a unique and personal life story shaped by their memories and experiences. However, these memories are often scattered and difficult to organize into a coherent narrative, a challenge that defines the task of autobiography writing. Existing conversational writing assistants tend to rely on generic user interactions and pre-defined guidelines, making it difficult for these systems to capture personal memories and develop a complete biography over time. We introduce StorySage, a user-driven software system designed to meet the needs of a diverse group of users that supports a flexible conversation and a structured approach to autobiography writing. Powered by a multi-agent framework composed of an Interviewer, Session Scribe, Planner, Section Writer, and Session Coordinator, our system iteratively collects user memories, updates their autobiography, and plans for future conversations. In experimental simulations, StorySage demonstrates its ability to navigate multiple sessions and capture user memories across many conversations. User studies (N=28) highlight how StorySage maintains improved conversational flow, narrative completeness, and higher user satisfaction when compared to a baseline. In summary, StorySage contributes both a novel architecture for autobiography writing and insights into how multi-agent systems can enhance human-AI creative partnerships.