  Large Language Models (LLMs) have revolutionized open-domain dialogue agents
but encounter challenges in multi-character role-playing (MCRP) scenarios. To
address the issue, we present Neeko, an innovative framework designed for
efficient multiple characters imitation. Unlike existing methods, Neeko employs
a dynamic low-rank adapter (LoRA) strategy, enabling it to adapt seamlessly to
diverse characters. Our framework breaks down the role-playing process into
agent pre-training, multiple characters playing, and character incremental
learning, effectively handling both seen and unseen roles. This dynamic
approach, coupled with distinct LoRA blocks for each character, enhances
Neeko's adaptability to unique attributes, personalities, and speaking
patterns. As a result, Neeko demonstrates superior performance in MCRP over
most existing methods, offering more engaging and versatile user interaction
experiences. Code and data are available at
https://github.com/weiyifan1023/Neeko.
