Detect underpriced MLS properties with GPT and alert via Gmail and Slack
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 14 nodes and runs on a schedule (every day). Along the way it connects Gmail, Slack and OpenRouter Chat Model.
How it runs
Trigger: On a schedule (every day).
- Workflow Configuration (Edit Fields (Set)): sets or maps fields
- Fetch MLS Data (HTTP Request): calls an HTTP API
- Fetch Recent Sales Data (HTTP Request): calls an HTTP API
- Combine All Market Data (Merge): merges the data from its input branches
- Pricing Analysis Agent (AI Agent): runs an AI agent, with model: OpenRouter Chat Model; 1 tool (Market Research Agent Tool); output parser: Pricing Output Parser
- Check for Underpriced Properties (If): checks a condition and branches into true / false
- Send Underpriced Alert Email (Gmail): uses Gmail
- Send Slack Alert (Slack): uses Slack
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (11)
- Schedule Trigger
n8n-nodes-base.scheduleTrigger - Edit Fields (Set)
n8n-nodes-base.set - HTTP Request
n8n-nodes-base.httpRequest - Merge
n8n-nodes-base.merge - AI Agent
@n8n/n8n-nodes-langchain.agent - Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured - AI Agent Tool
@n8n/n8n-nodes-langchain.agentTool - If
n8n-nodes-base.if - Gmail
n8n-nodes-base.gmail - Slack
n8n-nodes-base.slack - OpenRouter Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenRouter
From the author
How It Works This workflow automates competitive real estate pricing analysis by combining multiple MLS data sources with AI-powered market intelligence. Designed for real estate professionals, property managers, and investment analysts, it solves the…