Workflow Templates

Google Sheets to MailChimp Auto-Importer Overview This n8n workflow automatically imports contacts from Google Sheets into your MailChimp mailing list. Perfect for businesses collecting leads through Google Forms, event registrations, or maintaining contact lists in spreadsheets. Key Features 📊 Bulk Import: Process entire Google Sheets at once 🔄 Smart Name Parsing: Automatically splits full names into first and last names 📱 Phone Number Support: Includes phone numbers as merge fields ⚡ Error Resilience: Continues processing even if individual contacts fail 📝 Import Summary: Generates a summary of processed contacts Prerequisites Before using this workflow, ensure you have: An active n8n instance (self-hosted or cloud) A Google account with access to Google Sheets A MailChimp account with at least one audience/list created Basic understanding of n8n workflows Initial Setup Step 1: Import the Workflow Copy the workflow JSON In n8n, click "Import from File" or paste the JSON Save the workflow with a meaningful name Step 2: Configure Google Sheets Connection Click on the "Get Google Sheet Data" node Click on "Credential to connect with" Select "Create New" and choose "Google Sheets OAuth2" Follow the OAuth flow to authenticate your Google account Save the credentials Step 3: Configure MailChimp Connection Click on the "Add to MailChimp" node Click on "Credential to connect with" Select "Create New" and choose "MailChimp OAuth2" or "MailChimp API" For API method: Log into MailChimp Go to Account → Extras → API keys Generate a new API key Copy and paste it into n8n Save the credentials Step 4: Configure Your Specific Settings Google Sheets Settings: Open the "Get Google Sheet Data" node Replace YOUR_GOOGLE_SHEET_ID with your actual sheet ID Find this in your Google Sheets URL: https://docs.google.com/spreadsheets/d/[SHEET_ID]/edit Replace YOUR_SHEET_NAME with your worksheet name (e.g., "Sheet1" or "Form Responses 1") MailChimp Settings: Open the "Add to MailChimp" node Replace YOUR_MAILCHIMP_LIST_ID with your audience ID Find this in MailChimp: Audience → Settings → Audience name and defaults Verify the status is set to "subscribed" Google Sheets Format Requirements Your Google Sheet must have the following columns (exact names): Names**: Full name of the contact (e.g., "John Doe") Email address**: Valid email address Phone Number**: Contact phone number (optional) Example: | Names | Email address | Phone Number | |-------|--------------|--------------| | John Doe | john@example.com | +1234567890 | | Jane Smith | jane@example.com | +0987654321 | How to Use Manual Execution: Open the workflow in n8n Click "Execute Workflow" Monitor the execution progress Check the output of "Create Import Summary" for results Scheduling (Optional): To run this automatically: Replace the "Manual Trigger" node with a "Schedule Trigger" node Set your desired schedule (e.g., daily at 9 AM) Activate the workflow Customization Options Adding More Fields: To include additional fields like company name or address: Add columns to your Google Sheet Modify the "Edit Fields" node to include new fields Update the "Format Subscriber Data" code to map new fields Add corresponding merge fields in the MailChimp node Handling Duplicates: The workflow uses "continueRegularOutput" error handling, which means: Existing subscribers will be skipped New subscribers will be added The workflow continues processing Adding Email Notifications: To receive import summaries via email: Add a Gmail or Email node after "Create Import Summary" Configure with your email settings Use the import summary data in the email body Troubleshooting Common Issues: "Invalid API Key" (MailChimp) Verify your API key is correct Check that your MailChimp account is active "Sheet not found" (Google Sheets) Verify the sheet ID is correct Ensure the service account has access to the sheet "Email already exists" errors This is normal for existing subscribers The workflow will continue processing other contacts Missing data in MailChimp Check that column names match exactly (case-sensitive) Verify data exists in the Google Sheet Best Practices Test First: Always test with a small dataset first Backup Data: Export your MailChimp list before large imports Clean Data: Ensure email addresses are valid before importing Monitor Regularly: Check import summaries for any issues Respect Privacy: Only import contacts who have consented to receive emails Support For issues specific to: n8n platform: Visit n8n Community Forum Google Sheets API: Check Google Developers Documentation MailChimp API: See MailChimp API Documentation Need help customizing? Contact me for consulting and support or add me on LinkedIn - https://www.linkedin.com/in/femi-adedayo-h44/ License This workflow template is provided free for personal and commercial use. Feel free to modify and share!

🧩 Workflow: Process Tour PDF from Google Drive to Pinecone Vector DB with OpenAI Embeddings Overview This workflow automates the process of extracting tour information from PDF files stored in a Google Drive folder, processes and vectorizes the extracted data, and stores it in a Pinecone vector database for efficient querying. This is especially useful for building AI-powered search or recommendation systems for travel packages. Setup: Prerequisites A folder in Google Drive with PDF tour package brochures. Pinecone account + API key OpenAI API key n8n cloud or self-hosted instance Workflow Setup Steps Trigger Manual Trigger (When clicking 'Test workflow'): Used for manual testing and execution of the workflow. Google Drive Integration Step 1: Store Tour Packages in PDF Format Upload your curated tour packages containing the tours, activities and sight-seeings in PDF format into a designated Google Drive folder. Step 2: Search Folder Node: PDF Tour Package Folder (Google Drive) This node searches the designated folder for files (filter by MIME type = application/pdf if needed). Step 3: Download PDFs Node: Download Package Files (Google Drive) Downloads each matching PDF file found in the previous step. Process Each PDF File Step 4: Loop Through Files Node: Loop Over each PDF file Iterates through each downloaded PDF file to extract, clean, split, and embed. Data Preparation & Embedding Step 5: Data Loader Node: Data Loader Reads each PDF’s content using a compatible loader. It passes clean raw text to the next node. Often integrated with document loaders like pdf-loader, Unstructured, or pdfplumber. Step 6: Recursive Text Splitter Node: Recursive Character Text Splitter Splits large chunks of text into manageable segments using overlapping window logic (e.g., 500 tokens with 50 token overlap). This ensures contextual preservation for long documents during embedding. Step 7: Generate Embeddings Node: Embeddings OpenAI Uses text-embedding-3-small model to vectorize the split chunks. Outputs vector representations for each content chunk. Store in Pinecone Step 8: Pinecone Vector Store Node: Pinecone Vector Store - Store... Stores each embedding along with its metadata (source PDF name, chunk ID, etc.). This becomes the basis for fast, semantic search via RAG workflows or agents. 🛠️ Tools & Nodes Used Google Drive (Search & Download) Searches for all PDF files in a specified Google Drive folder. Downloads each file for processing. SplitInBatches (Loop Over Items) Loops through each file found in the folder, ensuring each is processed individually. Default Data Loader (LangChain) Reads and extracts text from the PDF files. Recursive Character Text Splitter (LangChain) Splits the extracted text into manageable chunks for embedding. OpenAI Embeddings (LangChain) Converts each text chunk into a vector using OpenAI’s embedding model. Pinecone Vector Store (LangChain) Stores the resulting vectors in a Pinecone index for fast similarity search and querying. 🔗 Workflow Steps Explained Trigger: The workflow starts manually for testing or can be scheduled. Google Drive Search: Finds all PDF files in the specified folder. Loop Over Files: Each file is processed one at a time using the SplitInBatches node. Download File: Downloads the current PDF file from Google Drive. Extract Text: The Default Data Loader node reads the PDF and extracts its text content. *Text Splitting: * The Recursive Character Text Splitter breaks the text into chunks (e.g., 1000 characters with 50 overlap) to optimize embedding quality. **Vectorization: **Each chunk is sent to the OpenAI Embeddings node to generate vector representations. Store in Pinecone: The vectors are inserted into a Pinecone index, making them available for semantic search and recommendations. 🚀 What Can Be Improved in the Next Version? *Error Handling: * Add error handling nodes to manage failed downloads or extraction issues gracefully. File Type Filtering: Ensure only PDF files are processed by adding a filter node. Metadata Storage: Store additional metadata (e.g., file name, tour ID) alongside vectors in Pinecone for richer search results. *Parallel Processing: * Optimize for large folders by processing multiple files in parallel (with care for API rate limits). Automated Triggers: Replace manual trigger with a time-based or webhook trigger for full automation. Data Validation: Add checks to ensure extracted text contains valid tour data before vectorization. User Feedback: Integrate notifications (e.g., email or Slack) to inform when processing is complete or if issues arise. 💡 Summary This workflow demonstrates how n8n can orchestrate a powerful AI data pipeline using Google Drive, LangChain, OpenAI, and Pinecone. It’s a great foundation for building intelligent search or recommendation features for travel and tour data. Feel free to ask for more details or share your improvements! Let me know if you want to see a specific part of the workflow or need help with a particular node!

Who is this template for? This template is for any Microsoft Outlook user who wants a trained AI agent to reason and reply on their behalf. Teach your agent tone and writing style to replicate your own, or develop a persona for a shared inbox. Requirements Outlook with authentication credentials OpenAI account with authentication credentials A few sample email replies of various lengths and topics How it works: Connect your Outlook account. Select (filter) which email sender(s) your trained AI agent will reply to. [Tip: pick a sender that has some repeatability either with a topic (ie. sales) or an individual (coworker@yourcompany.com)] Connect your OpenAI account. Choose your AI model (ie. gpt-4o-mini) Add Prompt (User Message) and select "system message" from the option below Update the instructions by filling in your name (or persona), response style, and add full email replies from the topic or individual you want the AI agent to emulate. [Tip: Add actual replies from your email sent folder, including your greeting and sign off. Paste each email sample between a set of <example> .... </example> tags] Configure the reply (or reply all) to remain within the original email string Test it! Send an email from the address to which your agent wants to respond. Check your sent (or draft) folder for the result. Enjoy all the free time you now have!! If you have questions or need assistance, email us at: support@teambisonandbird.com ++This template does not include retrieving email addresses out of the message or body of the email.++

🤖🥗 Telegram Nutrition AI Assistant (Alternative to Cal AI App) > AI-powered nutrition assistant for Telegram — log meals, set goals, and get personalized daily reports with Google Sheets integration. 📋 Description This n8n template creates a Telegram-based Nutrition AI Assistant 🥑🔥 designed as an open-source alternative to the Cal AI mobile app. It allows users to interact with an AI agent via text, voice, or images to track meals, calculate macros, and monitor nutrition goals directly from Telegram. The system integrates Google Sheets as the database, handling both user profiles and meal logs, while leveraging Gemini AI for natural conversation, food recognition, and daily progress reports. ✨ Key Features 💬 Multi-input support: Text, voice messages (transcribed), and food images (AI analysis). 📊 Macro calculation: Automatic estimation of calories, proteins, carbs, and fats. 📝 User-friendly registration: Simple onboarding without storing personal health data (no weight/height required). 🎯 Goal tracking: Users can set and update calorie and protein targets. 📈 Daily reports: Personalized progress messages with visual progress bars. 🗂 Google Sheets integration: Profile table for user targets. Meals table for food logs. 🔄 Advanced n8n nodes: Includes use of Merge, Subworkflow, and Code nodes for data processing and report generation. 💡 Acknowledgment Inspired by the Cal AI concept 💡 — this template demonstrates how to reproduce its main functionality with n8n, Telegram, and AI agents as a flexible, open-source automation workflow. 🏷 Tags telegram ai-assistant nutrition meal-tracking google-sheets food-logging voice-transcription image-analysis daily-reports n8n-template merge-node subworkflow-node code-node telegram-trigger google-gemini 💼 Use Case Use this template if you want to: 🥗 Log meals using text, images, or voice messages. 📊 Track nutrition goals (calories, proteins) with daily progress updates. 🤖 Provide a chat-based nutrition assistant without building a full app. 🗂 Store structured nutrition data in Google Sheets for easy access and analysis. 💬 Example User Interactions 📸 User sends a photo of a meal → AI analyzes the food and logs calories/macros. 🎤 User sends a voice message → AI transcribes and logs the meal. ⌨️ User types “report” → AI returns a daily nutrition summary with progress bars. 🥅 User says “update my protein goal” → AI updates profile in Google Sheets. 🔑 Required Credentials Telegram Bot API (Bot Token) Google Sheets API credentials AI Provider API (Google Gemini or compatible LLM) ⚙️ Setup Instructions 🗂 Create two Google Sheets tables: Profile: User_ID, Name, Calories_target, Protein_target Meals: User_ID, Date, Meal_description, Calories, Proteins, Carbs, Fats 🔌 Configure the Telegram Trigger with your bot token. 🤖 Connect your AI provider credentials (Gemini recommended). 📑 Connect Google Sheets with your credentials. ▶️ Deploy the workflow in n8n. 🎯 Start interacting with your nutrition assistant via Telegram. 📌 Extra Notes 🟩 Green section: Handles Telegram trigger and user check. 🟥 Red section: Registers new users and sets goals. 🟦 Blue section: Processes text, voice, and images. 🟨 Yellow section: Generates nutrition reports. 🟪 Purple section: Main AI agent controlling tools and logic. 💡 Need Assistance? If you’d like help customizing or extending this workflow, feel free to reach out: 📧 Email: johnsilva11031@gmail.com 🔗 LinkedIn: John Alejandro Silva Rodríguez

Extracting Comments from an X Post Use Case Engaging with conversations on X (formerly Twitter) is critical for brands and individuals monitoring sentiment, leads, or emerging trends. Manually collecting comments is time-consuming—this automation enables scalable extraction of comment data to inform your outreach or analysis. What This Automation Does This automation extracts comments from a specified X post, with the following input parameters: airtop_profile**: The name of your Airtop Profile connected to X. x_post_url**: The URL of the X post to extract comments from. max_number_of_comments**: The maximum number of comments to retrieve. How It Works Takes input via a form or another workflow. Normalizes the input values. Creates a new browser session using Airtop. Navigates to the provided X post. Uses a prompt to extract up to the specified number of comments, returning: Author name Author profile URL Comment text Setup Requirements Airtop API Key — free to generate. An Airtop Profile connected to X (requires one-time login). Next Steps Pair with X Monitoring**: Use this with the X monitoring automation to detect relevant posts and extract discussion context automatically. Feed into Analytics**: Combine with summarization or sentiment analysis tools to understand audience response at scale. Export for CRM/BI**: Pipe the structured comment data into your CRM or business intelligence stack for lead tracking or reporting. Read more about Extracting Comments from X Posts

What it does Captures token usage and cost from your AI Agent/LLM. Logs model, tokens, cost, tool use, and conversation I/O to Google Sheets for simple observability and billing. Perfect for Developers adding usage monitoring to AI agents. Teams needing cost transparency in prototypes. How it works Chat Trigger collects user input for the AI Agent. A Set node injects metadata like workflow, execution, and client IDs. LangChain Code node returns a configured Chat model with a callback that reads usage metadata. The callback computes input, output, and total costs based on per‑million token prices you define. It appends token metrics to a Google Sheet via the Google Sheets Tool. The Agent records intermediate tool calls. An If node checks whether a tool was used. When tools are used, the workflow logs input, output, tool name, and metadata to an Observability sheet. How to use SELF-HOSTED N8N ONLY - the Langchain Code node is only available in the self-hosted version of n8n. It is not available in n8n cloud. Requirements Self-hosted version of n8n If you have any questions in running the workflow, see the attached video: https://youtu.be/JSulRS128MA

Extracting LinkedIn Profile Information Use Case Manually copying data from LinkedIn profiles is time-consuming and error-prone. This automation helps you extract structured, detailed information from any public LinkedIn profile—enabling fast enrichment, hiring research, or lead scoring. What This Automation Does This automation extracts profile details from a LinkedIn URL using the following input parameters: airtop_profile**: The name of your Airtop Profile connected to LinkedIn. linkedin_url**: The URL of the LinkedIn profile you want to extract data from. How It Works Starts with a form trigger or via another workflow. Assigns the LinkedIn URL and Airtop profile variables. Opens the LinkedIn profile in a real browser session using Airtop. Uses an AI prompt to extract structured information, including: Name, headline, location Current company and position About section, experience, and education history Skills, certifications, languages, connections, and recommendations Returns structured JSON ready for further use or storage. Setup Requirements Airtop API Key — free to generate. An Airtop Profile connected to LinkedIn (requires one-time login). Next Steps Sync with CRM**: Push extracted data into HubSpot, Salesforce, or Airtable for lead enrichment. Combine with Search Automation**: Use with a LinkedIn search scraper to process profiles in bulk. Adapt to Other Platforms**: Customize the prompt to extract structured data from GitHub, Twitter, or company sites. Read more about the Extract Linkedin Profile Information automation.

This n8n template demonstrates how to use AI as a comprehensive personal assistant with multiple specialized agents. Use cases include email management, scheduling, web search, calculations, and more - all automated through AI coordination. Good to know This template integrates multiple AI services through OpenRouter Each agent specializes in different tasks (Gmail, Calendar, Search, etc.) Memory persistence maintains context across interactions How it works The workflow is triggered by Telegram messages (can be replaced with other triggers) A router node directs requests to the appropriate specialized agent Agents include: Gmail for email management Calculator for math operations Google Search for information retrieval Calendar for scheduling Contacts for CRM functions The OpenRouter Chat Model coordinates responses Final responses are sent back through Telegram How to use Connect your Telegram bot credentials Configure each service with appropriate API keys The system will automatically route requests to the right agent Requirements OpenRouter account for AI services Telegram bot token Google API credentials for relevant services Customising this workflow Add more specialized agents as needed Replace Telegram with other communication channels Adjust routing logic for different use cases

🚀 Automated Investor Intelligence: CrunchBase to Google Sheets Data Harvester! Workflow Overview This cutting-edge n8n automation is a sophisticated investor intelligence tool designed to transform market research into actionable insights. By intelligently connecting CrunchBase, data processing, and Google Sheets, this workflow: Discovers Investor Insights: Automatically retrieves latest investor data Tracks key investment organizations Eliminates manual market research efforts Intelligent Data Processing: Filters investor-specific organizations Extracts critical investment metrics Ensures comprehensive market intelligence Seamless Data Logging: Automatically updates Google Sheets Creates real-time investor database Enables rapid market trend analysis Scheduled Intelligence Gathering: Daily automated tracking Consistent investor insight updates Zero manual intervention required Key Benefits 🤖 Full Automation: Zero-touch investor research 💡 Smart Filtering: Targeted investment insights 📊 Comprehensive Tracking: Detailed investor intelligence 🌐 Multi-Source Synchronization: Seamless data flow Workflow Architecture 🔹 Stage 1: Investor Discovery Scheduled Trigger**: Daily market scanning CrunchBase API Integration** Intelligent Filtering**: Investor-specific organizations Key investment metrics Most recent data 🔹 Stage 2: Data Extraction Comprehensive Metadata Parsing** Key Information Retrieval** Structured Data Preparation** 🔹 Stage 3: Data Logging Google Sheets Integration** Automatic Row Appending** Real-Time Database Updates** Potential Use Cases Venture Capitalists**: Investment ecosystem mapping Startup Scouts**: Investor trend analysis Market Researchers**: Comprehensive investment insights Business Development**: Strategic partnership identification Investment Analysts**: Market intelligence gathering Setup Requirements CrunchBase API API credentials Configured access permissions Investor organization tracking setup Google Sheets Connected Google account Prepared tracking spreadsheet Appropriate sharing settings n8n Installation Cloud or self-hosted instance Workflow configuration API credential management Future Enhancement Suggestions 🤖 Advanced investment trend analysis 📊 Multi-source investor aggregation 🔔 Customizable alert mechanisms 🌐 Expanded investment stage tracking 🧠 Machine learning insights generation Technical Considerations Implement robust error handling Use secure API authentication Maintain flexible data processing Ensure compliance with API usage guidelines Ethical Guidelines Respect business privacy Use data for legitimate research Maintain transparent information gathering Provide proper attribution Hashtag Performance Boost 🚀 #InvestorIntelligence #VentureCapital #MarketResearch #AIWorkflow #DataAutomation #StartupEcosystem #InvestmentTracking #BusinessIntelligence #TechInnovation #StartupFunding Workflow Visualization [Daily Trigger] ⬇️ [Fetch Investor Data] ⬇️ [Extract Investor Fields] ⬇️ [Log to Google Sheets] Connect With Me Ready to revolutionize your investor research? 📧 Email: Yaron@nofluff.online 🎥 YouTube: @YaronBeen 💼 LinkedIn: Yaron Been Transform your market intelligence with intelligent, automated workflows!

What This Workflow Does: This n8n workflow automatically generates an AI-powered summary and relevant tags whenever a new row is added to your Notion database. Simply save any URL to your Notion database using the [Notion Web Clipper] Chrome extension or [Save to Notion]—on both desktop and mobile. This keeps all your saved content organized in one place instead of scattered across different platforms. How it works: The workflow is triggered when a new row is added to your Notion database (it checks for updates every minute). It retrieves the content from the saved URL. An AI agent analyzes the content to generate a summary and relevant tags. The AI output is then formatted properly. Finally, the formatted summary and tags are saved into the appropriate columns in your Notion database. Notes: Make sure your Notion database includes the following columns: URL – Stores the content URL you want to summarize. AI Summary – Where the AI-generated summary will be added. Tags – Where the AI-generated tags will be saved.

This workflow automates web scraping of Amazon search result pages by retrieving raw HTML, cleaning it to retain only the relevant product elements, and then using an LLM to extract structured product data (name, description, rating, reviews, and price), before saving the results back to Google Sheets. It integrates Google Sheets to supply and collect URLs, BrightData to fetch page HTML, a custom n8n Function node to sanitize the HTML, LangChain (OpenRouter GPT-4) to parse product details, and Google Sheets again to store the output. URL to scape . Result Who Needs Amazon Search Result Scraping? This scraping workflow is ideal for teams and businesses that need to monitor Amazon product listings at scale: E-commerce Analysts** – Track competitor pricing, ratings, and inventory trends. Market Researchers** – Collect data on product popularity and reviews for market analysis. Data Teams** – Automate ingestion of product metadata into BI pipelines or data lakes. Affiliate Marketers** – Keep affiliate catalogs up to date with latest product details and prices. If you need reliable, structured data from Amazon search results delivered directly into your spreadsheets, this workflow saves you hours of manual copy-and-paste. Why Use This Workflow? End-to-End Automation** – From URL list to clean JSON output in Sheets. Robust HTML Cleaning** – Strips scripts, styles, unwanted tags, and noise. Accurate Structured Parsing** – Leverages GPT-4 via LangChain for reliable extraction. Scalable & Repeatable** – Processes thousands of URLs in batches. Step-by-Step: How This Workflow Scrapes Amazon Get URLs from Google Sheets – Reads a list of search result URLs. Loop Over Items – Iterates through each URL in controlled batches. Fetch Raw HTML – Uses BrightData’s Web Unlocker proxy to retrieve the page. Clean HTML – A Function node removes doctype, scripts, styles, head, comments, classes, and non-whitelisted tags, collapsing extra whitespace. Extract with LLM – Passes cleaned HTML into LangChain → GPT-4 to output JSON for each product: name, description, rating, reviews, price Save Results – Appends the JSON fields as columns back into a “results” sheet in Google Sheets. Customization: Tailor to Your Needs Adaptable Sites** – This workflow can be adapted to any e-commerce or other website, for example Walmart or eBay. Whitelist Tags** – Modify the allowedTags array in the Code node to keep additional HTML elements. Schema Changes** – Update the Structured Output Parser schema to include more fields (e.g., availability, SKU). Alternate Data Sink** – Instead of Sheets, route output to a database, CSV file, or webhook. 🔑 Prerequisites Google Sheets Credentials** – OAuth credentials configured in n8n. BrightData API token** – Stored in n8n credentials as BRIGHTDATA_TOKEN. OpenRouter API Key** – Configured for the LangChain node to call GPT-4. n8n Instance** – Self-hosted or cloud with sufficient quota for HTTP requests and LLM calls. 🚀 Installation & Setup Configure Credentials** In n8n, set up Google Sheets OAuth under “Credentials.” Add BrightData token as a new HTTP Request credential. Create an OpenRouter API key credential for the LangChain node. Import the Workflow** Copy the JSON workflow into n8n’s “Import” dialog. Map your Google Sheet IDs and GIDs to the {{WEB_SHEET_ID}}, {{TRACK_SHEET_GID}}, and {{RESULTS_SHEET_GID}} placeholders. Ensure the BRIGHTDATA_TOKEN credential is selected on the HTTP Request node. Test & Run** Add a few Amazon search URLs to your “track” sheet. Execute the workflow and verify product data appears in your “results” sheet. Tweak batch size or parser schema as needed. ⚠ Important API Rate Limits** – Monitor your BrightData and OpenRouter usage to avoid throttling. Amazon’s Terms** – Ensure your scraping complies with Amazon’s policies and legal requirements. Summary This workflow delivers a fully automated, scalable solution to extract structured product data from Amazon search pages directly into Google Sheets—streamlining your competitive analysis and data collection. 🚀 Phil | Inforeole

Who is it for? This workflow is for marketers, sales teams, and local businesses who want to quickly collect leads (business name, phone, website, and email) from Google Maps and store them in Airtable. You can use it for real estate agents, restaurants, therapists, or any local niche. How it works Scrape Google Maps with Apify Google Maps Extractor. Clean and structure the data (name, address, phone, website). Visit each website and retrieve the raw HTML. Use GPT to extract the most relevant email from the site content. Save everything to Airtable for easy filtering and future outreach. It works for any location or keyword – just adapt the input in Apify. Requirements Before running this workflow, you’ll need: ✅ Apify account (to use the Google Maps Extractor) ✅ OpenAI API key (for GPT email extraction) ✅ Airtable account & base with the following fields: Business Name Address Website Phone Number Email Google Maps URL Airtable Structure Your Airtable base should contain these columns: Airtable Structure | Title | Street | Website | Phone Number | Email | URL | |-------------------------|-------------------------|--------------------|-----------------|------------------------|----------------------| | Paris Real Estate Agency| 10 Rue de Rivoli, Paris | https://agency.fr | +33 1 23 45 67 | contact@agency.fr | maps.google.com/... | | Example Business 2 | 25 Avenue de l’Opéra | https://example.fr | +33 1 98 76 54 | info@example.fr | maps.google.com/... | | Example Business 3 | 8 Boulevard Haussmann | https://demo.fr | +33 1 11 22 33 | contact@demo.fr | maps.google.com/... | Error Handling Missing websites:** If a business has no website, the flow skips the scraping step. No email found:** GPT returns Null if no email is detected. API rate limits:** Add a Wait node between requests to avoid Apify/OpenAI throttling. Now let’s take a detailed look at how to set up this automation, using real estate agencies in Paris as an example. Step 1 – Launch the Google Maps Scraper Start with a When clicking Execute workflow trigger to launch the flow manually. Then, add an HTTP Request node with the method set to POST. 👉 Head over to Apify: Google Maps Extractor On the page: https://apify.com/compass/google-maps-extractor Enter your business keyword (e.g., real estate agency, hairdresser, restaurant) Set the location you want to target (e.g., Paris, France) Choose how many results to fetch (e.g., 50) Optionally, use filters (only places with a website, by category, etc.) ⚠️ No matter your industry, this works — just adapt the keyword and location. Once everything is filled in: Click Run to test. Then, go to the top right → click on API. Select the API endpoints tab. Choose Run Actor synchronously and get dataset items. Copy the URL and paste it into your HTTP Request (in the URL field). Then enable: ✅ Body Content Type → JSON ✅ Specify Body Using JSON` Go back to Apify, click on the JSON tab, copy the entire code, and paste it into the JSON body field of your HTTP Request. At this point, if you run your workflow, you should see a structured output similar to this: title subTitle price categoryName address neighborhood street city postalCode ........ Step 2 – Clean and structure the data Once the raw data is fetched from Apify, we clean it up using the Edit Fields node. In this step, we manually select and rename the fields we want to keep: Title → {{ $json.title }} Address → {{ $json.address }} Website → {{ $json.website }} Phone → {{ $json.phone }} URL → {{ $json.url }}* This node lets us keep only the essentials in a clean format, ready for the next steps. On the right: a clear and usable table, easy to work with. Step 3 – Loop Over Items Now that our data is clean (see step 2), we’ll go through it item by item to handle each contact individually. The Loop Over Items node does exactly that: it takes each row from the table (each contact pulled from Apify) and runs the next steps on them, one by one. 👉 Just set a Batch Size of 20 (or more, depending on your needs). Nothing tricky here, but this step is essential to keep the flow dynamic and scalable. Step 4 – Edit Field (again) After looping through each contact one by one (thanks to Loop Over Items), we're refining the data a bit more. This time, we only want to keep the website. We use the Edit Fields node again, in Manual Mapping mode, with just: Website → {{ $json.website }} The result on the right? A clean list with only the URLs extracted from Google Maps. 🔧 This simple step helps isolate the websites so we can scrape them one by one in the next part of the flow. Step 5 – Scrape Each Website with an HTTP Request Let’s continue the flow: in the previous step, we isolated the websites into a clean list. Now, we’re going to send a request to each URL to fetch the content of the site. ➡️ To do this, we add an HTTP Request node, using the GET method, and set the URL as: {{ $json.website }} This value comes from the previous Edit Fields input This node will simply “visit” each website automatically and return the raw HTML code (as shown on the right). 📄 That’s the material we’ll use in the next step to extract email addresses (and any other useful info). We’re not reading this code manually — we’ll scan through it line by line to detect patterns that matter to us. This is a technical but crucial step: it’s how we turn a URL into real, usable data. Step 6 – Extract the Email with GPT Now that we've retrieved all the raw HTML from the websites using the HTTP Request node, it's time to analyze it. 💡 Goal: detect the most relevant email address on each site (ideally the main contact or owner). 👉 To do that, we’ll use an OpenAI node (Message a Model). Here’s how to configure it: ⚙️ Key Parameters: Model: GPT-4-1-MINI (or any GPT-4+ model available) Operation: Message a Model Resource: Text Simplify Output: ON Prompt (message you provide): Look at this website content and extract only the email I can contact this business. In your output, provide only the email and nothing else. Ideally, this email should be of the business owner, so if you have 2 or more options, try for most authoritative one. If you don't find any email, output 'Null'. Exemplary output of yours: name@examplewebsite.com {{ $json.data }} Step 7 – Save the Data in Airtable Once we’ve collected everything — the business name, address, phone number, website… and most importantly the email extracted via ChatGPT — we need to store all of this somewhere clean and organized. 👉 The best place in this workflow is Airtable. 📦 Why Airtable? Because it allows you to: Easily view and sort the leads you've scraped Filter, tag, or enrich them later And most importantly… reuse them in future automations ⚙️ What we're doing here We add an Airtable → Create Record node to insert each lead into our database. Inside this node, we manually map each field with the data collected in the previous steps: | Airtable Field | Description | Value from n8n | | -------------- | ------------------------ | ------------------------------------------ | | Title | Business name | {{ $('Edit Fields').item.json.Title }} | | Street | Full address | {{ $('Edit Fields').item.json.Address }} | | Website | Website URL | {{ $('Edit Fields').item.json.Website }} | | Phone Number | Business phone number | {{ $('Edit Fields').item.json.Phone }} | | Email | Email found by ChatGPT | {{ $json.message.content }} | | URL | Google Maps listing link | {{ $('Edit Fields').item.json.URL }} | 🧠 Reminder: we’re keeping only clean, usable data — ready to be exported, analyzed, or used in cold outreach campaigns (email, CRM, enrichment, etc.). ➡️ And the best part? You can rerun this workflow automatically every week or month to keep collecting fresh leads 🔁.