Cooperating Agent Team for Property Listing Analysis and Matching

Choosing a property means reconciling conflicting perspectives: what the listing claims, what market data says about the price, what the buyer actually needs, and what a negotiator would counter. A single AI prompt collapses all of that into one undifferentiated answer. Real estate is therefore an ideal domain for multi-agent design, where distinct agents argue their briefs and a better recommendation emerges from their interaction.

The intern builds a real estate application demonstrating exactly this collaboration. Using LangGraph to define the workflow and CrewAI-style role specialization within it, the intern creates a team of agents in Python: a listing analyst that extracts and evaluates property details, a market agent that assesses pricing against comparable data, a buyer advocate that scores listings against stated requirements, and a negotiation agent that simulates offers and counteroffers. LangChain provides the tooling layer and OpenAI models power each role. The agents exchange findings through the graph's shared state, and the system produces ranked property recommendations with the reasoning of each agent visible.

The project demonstrates orchestration of genuinely interacting agents, graph-based state design, role prompt engineering, and the ability to turn an abstract architecture pattern into a consumer-comprehensible product.