Track real estate market pain points with Apify, GPT-4o and Telegram alerts
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: AI Agent v1 (old agent modes). 1 breaking item(s) found. Static check, last run 2026-10-19.
What to replace before upgrading
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) Extract Pain Points (v1) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (7)
- Schedule Trigger
n8n-nodes-base.scheduleTrigger - HTTP Request
n8n-nodes-base.httpRequest - Code
n8n-nodes-base.code - AI Agent
@n8n/n8n-nodes-langchain.agent - Airtable
n8n-nodes-base.airtable - Telegram
n8n-nodes-base.telegram - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi
What it does
How it works This workflow runs on a daily schedule. It starts by scraping real estate-related queries from Google using Apify. The organic search results are parsed and summarized into a single text block. That text is then sent to an AI model (GPT-4o) which extracts the top 3 pain points faced by real estate agents based on current online sentiment. The workflow compares today's insights with yesterday's data stored in Airtable to detect recurring or new pain points. Finally, it sends a summary notification via Telegram and stores the current day's insights into Airtable for trend tracking. How to set up 1. Clone or import the workflow into your n8n instance. 2. Get an Apify API token and insert it into the HTTP Request node. 3. Create an Airtable base with a table containing two fields: "Date" (text) and "Summary" (long text). Copy the Base ID and Table ID into the Airtable nodes. 4. Connect your Telegram bot and replace the chat ID in the Telegram node. 5. Set up OpenAI credentials with GPT-4o or GPT-4o-mini for the LLM node. 6. Run once manually to test, then activate the schedule trigger to run daily. 7. (Optional) Extend the flow to generate cold outreach emails based on pain points, or sync to Notion/CRM.