HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation
Hongyang Yang, Yanxin Zhang, Yang She, Yue Xiao, Hao Wu, Yiyang Zhang, Jiapeng Hou, and Rongshan Zhang

TL;DR
HabitatAgent is a novel multi-agent system powered by large language models designed to provide transparent, reliable, and end-to-end housing consultation by integrating specialized agents for memory, retrieval, generation, and validation.
Contribution
This work introduces HabitatAgent, the first multi-agent LLM-powered architecture for housing consultation, improving accuracy and transparency over existing recommendation systems.
Findings
HabitatAgent achieves 95% accuracy on real user scenarios.
Baseline Dense+Rerank achieves 75% accuracy.
HabitatAgent provides an auditable, end-to-end decision workflow.
Abstract
Housing selection is a high-stakes and largely irreversible decision problem. We study housing consultation as a decision-support interface for housing selection. Existing housing platforms and many LLM-based assistants often reduce this process to ranking or recommendation, resulting in opaque reasoning, brittle multi-constraint handling, and limited guarantees on factuality. We present HabitatAgent, the first LLM-powered multi-agent architecture for end-to-end housing consultation. HabitatAgent comprises four specialized agent roles: Memory, Retrieval, Generation, and Validation. The Memory Agent maintains multi-layer user memory through internal stages for constraint extraction, memory fusion, and verification-gated updates; the Retrieval Agent performs hybrid vector--graph retrieval (GraphRAG); the Generation Agent produces evidence-referenced recommendations and explanations; and…
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