  Requirements Engineering (RE) is a critical phase in the software development
process that generates requirements specifications from stakeholders' needs.
Recently, deep learning techniques have been successful in several RE tasks.
However, obtaining high-quality requirements specifications requires
collaboration across multiple tasks and roles. In this paper, we propose an
innovative framework called MARE, which leverages collaboration among large
language models (LLMs) throughout the entire RE process. MARE divides the RE
process into four tasks: elicitation, modeling, verification, and
specification. Each task is conducted by engaging one or two specific agents
and each agent can conduct several actions. MARE has five agents and nine
actions. To facilitate collaboration between agents, MARE has designed a
workspace for agents to upload their generated intermediate requirements
artifacts and obtain the information they need. We conduct experiments on five
public cases, one dataset, and four new cases created by this work. We compared
MARE with three baselines using three widely used metrics for the generated
requirements models. Experimental results show that MARE can generate more
correct requirements models and outperform the state-of-the-art approaches by
15.4%. For the generated requirements specifications, we conduct a human
evaluation in three aspects and provide insights about the quality
