# HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation

arXiv: https://arxiv.org/abs/2604.00556 · HTML: https://mlsanity.com/paper/2604.00556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 16 of 16)

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- [FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets](https://mlsanity.com/paper/2310.04793/) (2023)
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- [AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting](https://mlsanity.com/paper/2502.16810/) (2025)
- [FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs](https://mlsanity.com/paper/2412.10823/) (2024)
- [RE-RecSys: An End-to-End system for recommending properties in Real-Estate domain](https://mlsanity.com/paper/2404.16553/) (2024)
- [REAL: Benchmarking Abilities of Large Language Models for Housing Transactions and Services](https://mlsanity.com/paper/2507.03477/) (2025)
- [FairHome: A Fair Housing and Fair Lending Dataset](https://mlsanity.com/paper/2409.05990/) (2024)

## Abstract (arXiv)

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 the Validation Agent applies multi-tier verification and targeted remediation. Together, these agents provide an auditable and reliable workflow for end-to-end housing consultation. We evaluate HabitatAgent on 100 real user consultation scenarios (300 multi-turn question--answer pairs) under an end-to-end correctness protocol. A strong single-stage baseline (Dense+Rerank) achieves 75% accuracy, while HabitatAgent reaches 95%.