AI Real Estate Assistant

Ask in natural language. Get matched listings.

An open-source conversational assistant for property search, financial analysis and market insight. Ask for "2-bedroom apartment in Kraków under 500k" and it answers from a semantic index over your own catalogue — with 16 LLM providers, a toolbelt behind the chat (mortgage, comparison, ROI, valuation) and nine languages.

MIT licensed, no signup to look around, and it runs on your own server as happily as it runs on a managed platform.

Table of Contents

• Live demo
• Deploy it your way
• What's new
• What it does
• Why developers pick this
• Tech stack
• Architecture
• Quick start
• The hosted version, and what comes next
• Questions people actually ask
• Documentation
• Testing
• Contributing
• License
• Support

Live demo

Try the live demo →

No login required — explore in demo mode

Everything is live in demo mode — no signup, no keys, no setup:

• Natural-language search — ask "2-bedroom apartment in Kraków under 500k" and get matched listings
• A real toolbelt behind the chat — mortgage calculator, rent-vs-buy, ROI, TCO, valuation with a multi-year price forecast
• Interactive maps — clustered markers with area analytics
• Nine languages — English, Polish, Russian, German, Spanish, Italian, Portuguese, Turkish, Ukrainian
• Honest about its limits — the demo answers with a local model so it costs nothing to explore; a real deployment uses the provider key you bring

Go straight to a part of it:

Deploy it your way

There is a live deployment of this project — the demo above runs the release images, in demo mode, on a server its maintainer controls. You do not have to stop there: the same images are published on every release, and three ways to run them are supported today.

On a hosted platform, one click

The blueprint in render.yaml declares the web service and the API, the build steps and the health check. The platform builds the images, serves them and restarts them on every release.

On your own VPS server

deploy/docker-compose.selfhosted.yml is the whole stack: a FastAPI backend and a Next.js frontend, one persistent volume, one published port. It needs no paid model key to answer.

curl -O https://raw.githubusercontent.com/AleksNeStu/ai-real-estate-assistant/main/deploy/docker-compose.selfhosted.yml
cat > .env <<'ENV'
API_ACCESS_KEY=change-me
JWT_SECRET_KEY=change-me
SECURITY_PEPPER=change-me
ENV
docker compose -f docker-compose.selfhosted.yml up -d

Put a TLS terminator in front of it — a reverse proxy, a load balancer, a tunnel — and never expose the port raw.

With CI instead of a terminal

This repository also ships a self-host deploy workflow. Give it a deploy key and the address of your server as repository secrets, and every release tag rolls your machine onto the new images by itself. Nothing about your host is stored in this repository — the host, the user, the port and the path are all secrets you set yourself.

Or just pull the images

Every release publishes both images to GitHub Container Registry, multi-arch (amd64 and arm64):

docker pull ghcr.io/aleksnestu/ai-real-estate-assistant/backend:5.1.10
docker pull ghcr.io/aleksnestu/ai-real-estate-assistant/frontend:5.1.10

The full guide — the memory footprint per setup, the published tags and the Kubernetes variant — is in the deployment guide.

What's new

v5.1.10 — the demo lives on a domain of its own.

• A public address that can be printed on a business card — the demo moved from a platform subdomain to https://realty.kittray.com, on a domain delegated to Cloudflare. The Worker that fronts the deployment keeps its custom domain, so the front door did not change — only the name on it.
• One address in the tree — the README badge, the live-demo link, the package.json homepage, robots.txt, the sitemap line, llms.txt, the launch copy and the canonical URL all follow the same value. The public address of a deployment is a build argument (SITE_URL), so changing it is one setting and one release.
• A mark, a card and half the README (v5.1.9) — the icon is now a roof over a point, in the monochrome the design system asks for; the social preview says what the project is; the README is the short version.
• A second deployment target (v5.1.8) — the release pipeline now rolls a server you own, not only the managed platform. One compose file, one workflow, no credentials in the tree.
• AI property valuation — paste a property id, get an estimate of the current value plus a projection for 1, 3, 5 and 10 years, with a confidence band and the key drivers. Try it at /valuation.
• An AI neighbourhood summary on every property page — what the district feels like, what is around it, how easy it is to move around. It falls back silently when the model is unavailable.
• The monthly payment on the card — every listing shows its estimated monthly cost next to the title (20 % down, 30 years, 6.5 % APR by default; not a lending offer).
• A first-visit tour — five steps from the landing page to the features that matter, shown once and remembered locally.
• A citation layer for AI search — an expanded llms.txt, a robots.txt that names the AI crawlers it wants, FAQPage JSON-LD next to the existing schemas, a social preview image, and complete metadata in nine locales.
• 335 dependency alerts closed — every critical and high advisory resolved, the floors pinned in both the lock file and the project file.

Full notes: v5.1.10 release · changelog.

What it does

Why developers pick this

1. Multi-provider LLM routing — 16 providers with an automatic fallback chain; a per-request provider selection by header or parameter, no code change to switch models.
2. **Hybrid RAG + v