MeKB-Sim: Personal Knowledge Base-Powered Multi-Agent Simulation

Abstract

Language agents have demonstrated remarkable emergent social behaviors within simulated sandbox environments. However, the characterization of these agents has been constrained by static prompts that outline their profiles, highlighting a gap in achieving simulations that closely mimic real-life interactions. To close this gap, we introduce MeKB-Sim, a multi-agent simulation platform based on a dynamic personal knowledge base, termed MeKB. Each agent’s MeKB contains both fixed and variable attributes—such as linguistic style, personality, and memory—crucial for theory-of-mind modeling. These attributes are updated when necessary, in response to events that the agent experiences. Comparisons with human annotators show that the LLM-based attribute updates are reliable. Based on the dynamic nature of MeKB, experiments and case study show that MeKB-Sim enables agents to adapt their planned activities and interactions with other agents effectively. Our platform includes a Unity WebGL game interface for visualization and an interactive monitoring panel that presents the agents’ planning, actions, and evolving MeKBs over time. For more information, including open-source code, a live demo website, and videos, please visit our project page at https://mekb-sim.github.io/.