Google researchers have unveiled DialogLab, an open-source framework for designing and testing dynamic human-AI group conversations. Presented at ACM UIST 2025, the prototype addresses the challenge of simulating natural multi-party interactions that go beyond simple one-on-one exchanges with large language models.

Traditional conversational AI tools force designers to choose between rigid scripted interactions and unpredictable generative models. DialogLab bridges this gap by combining structured scripting with real-time improvisation capabilities. The framework allows creators to configure group dynamics, define agent personas, manage turn-taking rules, and orchestrate transitions between scripted narratives and spontaneous dialogue.

Evaluated with 14 participants from game design, education, and social science research, DialogLab demonstrated its effectiveness across three testing modes: human control, autonomous, and reactive. Users particularly praised the human control mode for its engaging and realistic simulation of real-world conversations. The visual drag-and-drop interface and verification dashboard enable rapid iteration and detailed analysis of conversation dynamics without parsing lengthy transcripts. DialogLab represents a significant step toward more nuanced human-AI collaboration in applications ranging from virtual meetings to educational scenarios.