Workflow Templates

How it works This workflow automatically scrapes commercial real estate listings from LoopNet and sends opportunity alerts to Telegram while logging data to Google Sheets. Key Steps Scheduled Trigger - Runs every 24 hours to collect fresh CRE market data AI-Powered Scraping - Uses ScrapeGraphAI to extract property information from LoopNet Market Analysis - Analyzes listings for opportunities and generates market insights Smart Notifications - Sends Telegram alerts only when investment opportunities are found Data Logging - Stores daily market metrics in Google Sheets for trend analysis Set up steps Setup time: 10-15 minutes Configure ScrapeGraphAI credentials - Add your ScrapeGraphAI API key for web scraping Set up Telegram connection - Connect your Telegram bot and specify the target channel Configure Google Sheets - Set up Google Sheets integration for data logging Customize the LoopNet URL - Update the URL to target specific CRE markets or property types Adjust schedule - Modify the trigger timing based on your market monitoring needs Keep detailed configuration notes in sticky notes inside your workflow

Overview This n8n template automates the process of researching niche topics. It searches for a topic on Wikipedia, scrapes the relevant page using ScrapeOps, extracts the history or background section, and uses AI to generate a concise summary and timeline. The results are automatically saved to Google Sheets for easy content planning. Who is this for? Content Creators**: Quickly gather background info for videos or articles. Marketers**: Research niche markets and product histories. Educators/Students**: Generate timelines and summaries for study topics. Researchers**: Automate the initial data gathering phase. What problems it solves Time Consumption**: Manually reading and summarizing Wikipedia pages takes time. Blocking**: Scraping Wikipedia directly can sometimes lead to IP blocks; ScrapeOps handles this. Unstructured Data**: Raw HTML is hard to use; this workflow converts it into a clean, structured format (JSON/CSV). How it works Define Topic: You set a keyword in the workflow. Locate Page: The workflow queries the Wikipedia API to find the correct page URL. Smart Scraping: It uses the ScrapeOps Proxy API to fetch the page content reliably. Extraction: A code node intelligently parses the HTML to find "History", "Origins", or "Background" sections. AI Processing: GPT-4o-mini summarizes the text and extracts key dates for a timeline. Storage: The structured data is appended to a Google Sheet. Setup steps (~ 5-10 minutes) ScrapeOps Account: Register for a free API key at ScrapeOps. Configure the ScrapeOps Scraper node with your API key. OpenAI Account: Add your OpenAI credentials to the Message a model node. Google Sheets: Create a Google Sheet. You can duplicate this Template Sheet (copy the headers). Connect your Google account to the Append row in sheet node and select your new sheet. Pre-conditions An active ScrapeOps account. An OpenAI API key (or another LLM credential). A Google account for Sheets access. Disclaimer This template uses ScrapeOps as a community node. You are responsible for complying with Wikipedia's Terms of Use, robots directives, and applicable laws in your jurisdiction. Scraping targets may change at any time; adjust render/scroll/wait settings and parsers as needed. Use responsibly for legitimate business purposes.

What this workflow does This template extracts high-intent SEO keywords from any web page and turns them into a ranked keyword list you can use for content planning, landing pages, and SEO strategy. It runs in 3 phases: Scrape the target URL* with Decodo Decodo – Web Scraper for n8n Use AI to extract seed keywords* and understand the page topic Enrich each seed keyword with real Google SERP data via SerpApi* (related searches + questions + competitors), then apply a JavaScript scoring system to rank the best opportunities The final output is saved to Google Sheets as a clean table of ranked keywords. Who this workflow is for SEO consultants and agencies SaaS marketers and growth teams Founders validating positioning and messaging Content teams looking for “what people actually search for” This workflow is especially useful when you want keywords with commercial / solution intent, not generic single-word terms. Workflow overview Phase 1 — Scrape & clean page content Reads the URL from Google Sheets Scrapes the page via Decodo Cleans HTML into plain text (token-friendly) Phase 2 — AI keyword extraction AI returns a structured JSON with: brand / topic 5–10 mid-tail seed keywords intent + audience hints Phase 3 — SERP enrichment + scoring Uses SerpApi to fetch: related searches People Also Ask questions competitor domains Scores and ranks keywords based on: -- source type (related searches / PAA / organic) -- frequency across seeds -- modifiers (pricing, best, free, docs, etc.) -- mid-tail length preference Setup (step by step) 1) Google Sheets (input) Create a sheet with: Column name: urls One URL per row 2) Google Sheets (output) Create an output sheet with columns like: keyword score intent_hint source_type Tip: Clear the output sheet before each run if you want a clean export. 3) Decodo Add your Decodo credentials The URL is taken automatically from Google Sheets Decodo – Web Scraper for n8n 4) SerpApi Add your SerpApi key in the SerpApi node 5) AI Model Connect your preferred AI model (Gemini / OpenAI) The prompt is optimized to output valid JSON only Self-hosted disclaimer This is a community template. You must configure your own credentials (Google Sheets, Decodo, SerpApi, AI). Results depend on page accessibility and page content quality.

Overview This workflow, "Mood Graph Studio," offers a comprehensive solution to track and visualize your emotional well-being. By simply inputting a single sentence about your mood, this template uses AI to perform a sentiment analysis, generates a visual graph via Wolfram Alpha, provides personalized feedback, and logs everything to Google Sheets. It is designed for anyone interested in mindfulness, self-reflection, or quantified self-tracking. How It Works The workflow is divided into two main API functionalities and a manual trigger for easy testing. Analyze a Single Mood (/mood endpoint) An AI Agent (OpenAI) quantifies your mood text into valence (positivity) and energy (activity). A query is sent to Wolfram Alpha to generate a simple linear graph based on the score. A second AI Agent provides short, encouraging advice in Japanese. The complete entry is logged as a new row in Google Sheets. Returns a JSON response containing the full analysis and the graph image. Generate Mood History Graph (/history endpoint) Retrieves historical mood data for a specified user from Google Sheets. A Code node formats the data into a time-series plot query. Wolfram Alpha** generates a line graph visualizing the mood trend over time. The resulting graph is automatically posted to Slack. How to Set Up 1. Credentials You must add your own credentials for the following services in the respective nodes: OpenAI**: Used in both Chat Model nodes. Google Sheets**: Used in the "Log Mood" and "Get History" nodes. Slack**: Used in the "Send History" node. 2. Wolfram Alpha App ID This workflow uses the HTTP Request node to call the Wolfram Alpha API. Get a free App ID from the Wolfram|Alpha Developer Portal. Paste your App ID into the appid parameter value in both Generate...Graph nodes. 3. Google Sheet Configuration Create a new Google Sheet. Paste the Sheet ID into the Document ID field in both Google Sheets nodes. Crucially**, ensure the first row has the following headers exactly (case-sensitive): userId, moodText, valence, energy, createdAt, wolframQuery, feedback How to Use For Testing: Use the Manual Trigger. Modify the sample text in the "Set Test Data" node and click "Execute Workflow" on the canvas. For Production: Activate the workflow. Send POST requests to the Production URL of the Webhook nodes.

This workflow automates the process of scraping real estate property listings from websites using ScrapeGraph AI, extracting structured data, and saving it to a Google Sheet. It is designed to handle paginated listing pages and can be adapted to any real estate site that uses URL parameters for pagination. NOTE: This workflow has been tested with Immobiliare.it, the #1 real estate website in Italy. However, it is designed to be adaptable by modifying the pagination parameter and the listing URL pattern, you can use it with any real estate website that structures its listings with URL-based pagination. Business Use Cases: Real estate market intelligence Lead generation for agencies Price trend analysis Property comparison dashboards CRM enrichment Competitor monitoring Key Advantages 1. ✅ Fully Automated Lead Collection Automatically collects real estate listings without manual browsing. 2. ✅ AI-Powered Extraction Uses AI instead of rigid selectors: More resilient to website layout changes Handles dynamic content better Reduces maintenance effort 3. ✅ Structured Data Output The defined JSON schema ensures: Clean database-ready data Standardized fields Easy integration with CRM or analytics tools 4. ✅ Pagination Scalability Can easily scale: Increase number of pages Change city Adapt to different portals 5. ✅ Duplicate Prevention Google Sheets uses URL matching to: Avoid duplicates Update existing records 6. ✅ Modular Architecture The workflow is modular and reusable: URL generation logic is independent Extraction schema is customizable Storage layer can be replaced (CRM, database, Airtable, etc.) 7. ✅ Cost & Time Efficiency Eliminates manual data entry Saves research time Enables automated market monitoring How it works The workflow is structured in two main phases: Listing URL Discovery The user provides a base URL, the maximum number of pages to scrape, and the pagination parameter name (e.g., pag for Immobiliare.it). A Code node generates a list of page URLs by appending the pagination parameter. Each page URL is processed through the ScrapegraphAI node, which extracts all individual listing URLs. An Information Extractor node (powered by Google Gemini) filters and validates the extracted URLs based on a defined structure. A Wait node introduces a delay between requests to avoid rate limiting. A Loop Over Items node ensures all generated page URLs are processed. Data Extraction & Storage All collected listing URLs are aggregated and split into individual items. A second loop processes each listing URL through another ScrapegraphAI node, which extracts detailed property data (title, description, price, area, bedrooms, bathrooms, floor, rooms, balcony, terrace, cellar, heating, air conditioning, image URLs) based on a JSON schema. The extracted data is then written to a Google Sheet using the Google Sheets node, with each listing stored in a new row and deduplicated based on the listing URL. The workflow is fully automated and can scale to handle multiple listing pages and hundreds of individual property URLs. Set up steps To use this workflow, follow these steps: Import the workflow into your n8n instance. Configure credentials: ScrapegraphAI: Add your API key for ScrapegraphAI. Google Gemini (PaLM): Add your Google Gemini API credentials. Google Sheets OAuth2: Authenticate with the Google account where you want to store the data. Prepare your target Google Sheet: Create a new Google Sheet (or clone this template). Note the Sheet ID (from the URL) and the sheet name (tab name) where data should be written. Customize the input parameters: In the Set params node, define: url: The base URL of the listing page (without pagination parameters). max_pages: The number of pages to scrape. page_format_value: The query parameter used for pagination (e.g., pag for Immobiliare.it). Adjust the listing URL structure (if needed): In the Extract individual URL node, update the system prompt to match the URL pattern of the target website (e.g., https://www.xxx.it/xxx/xxxx). Review the output schema: In the Extract data node, you can modify the JSON schema to match the fields you want to extract from each listing. Update the Google Sheet node: Set the correct Document ID and Sheet Name in the Update real estate listings node. Ensure the column mapping matches your sheet structure. Activate the workflow and click Execute Workflow to start scraping. 👉 Subscribe to my new YouTube channel. Here I’ll share videos and Shorts with practical tutorials and FREE templates for n8n. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Certification Requirement Tracker with Rocket.Chat and GitLab ⚠️ COMMUNITY TEMPLATE DISCLAIMER: This is a community-contributed template that uses ScrapeGraphAI (a community node). Please ensure you have the ScrapeGraphAI community node installed in your n8n instance before using this template. This workflow automatically scrapes certification-issuing bodies once a year, detects any changes in certification or renewal requirements, creates a GitLab issue for the responsible team, and notifies the relevant channel in Rocket.Chat. It helps professionals and compliance teams stay ahead of changing industry requirements and never miss a renewal. Pre-conditions/Requirements Prerequisites An n8n instance (self-hosted or n8n.cloud) ScrapeGraphAI community node installed and activated Rocket.Chat workspace with Incoming Webhook or user credentials GitLab account with at least one repository and a Personal Access Token (PAT) Access URLs for all certification bodies or industry associations you want to monitor Required Credentials ScrapeGraphAI API Key** – Enables web scraping services Rocket.Chat Credentials** – Either: Webhook URL, or Username & Password / Personal Access Token GitLab Personal Access Token** – To create issues and comments via API Specific Setup Requirements | Service | Requirement | Example/Notes | | ------------- | ---------------------------------------------- | ---------------------------------------------------- | | Rocket.Chat | Incoming Webhook URL OR user credentials | https://chat.example.com/hooks/abc123… | | GitLab | Personal Access Token with api scope | Generate at Settings → Access Tokens | | ScrapeGraphAI | Domain whitelist (if running behind firewall) | Allow outbound HTTPS traffic to target sites | | Cron Schedule | Annual (default) or custom interval | 0 0 1 1 * for 1-Jan every year | How it works This workflow automatically scrapes certification-issuing bodies once a year, detects any changes in certification or renewal requirements, creates a GitLab issue for the responsible team, and notifies the relevant channel in Rocket.Chat. It helps professionals and compliance teams stay ahead of changing industry requirements and never miss a renewal. Key Steps: Scheduled Trigger**: Fires annually (or any chosen interval) to start the check. Set Node – URL List**: Stores an array of certification-body URLs to scrape. Split in Batches**: Iterates over each URL for parallel scraping. ScrapeGraphAI**: Extracts requirement text, effective dates, and renewal info. Code Node – Diff Checker**: Compares the newly scraped data with last year’s GitLab issue (if any) to detect changes. IF Node – Requirements Changed?**: Routes the flow based on change detection. GitLab – Create/Update Issue**: Opens a new issue or comments on an existing one with details of the change. Rocket.Chat – Notify Channel**: Sends a message summarizing any changes and linking to the GitLab issue. Merge Node**: Collects all branch results for a final summary report. Set up steps Setup Time: 15-25 minutes Install Community Node: In n8n, navigate to Settings → Community Nodes and install “ScrapeGraphAI”. Add Credentials: a. In Credentials, create “ScrapeGraphAI API”. b. Add your Rocket.Chat Webhook or PAT. c. Add your GitLab PAT with api scope. Import Workflow: Copy the JSON template into n8n (Workflows → Import). Configure URL List: Open the Set – URL List node and replace the sample array with real certification URLs. Adjust Cron Expression: Double-click the Schedule Trigger node and set your desired frequency. Customize Rocket.Chat Channel: In the Rocket.Chat – Notify node, set the channel or use an incoming webhook. Run Once for Testing: Execute the workflow manually to ensure issues and notifications are created as expected. Activate Workflow: Toggle Activate so the schedule starts running automatically. Node Descriptions Core Workflow Nodes: stickyNote – Workflow Notes**: Contains a high-level diagram and documentation inside the editor. Schedule Trigger** – Initiates the yearly check. Set (URL List)** – Holds certification body URLs and meta info. SplitInBatches** – Iterates through each URL in manageable chunks. ScrapeGraphAI** – Scrapes each certification page and returns structured JSON. Code (Diff Checker)** – Compares the current scrape with historical data. If – Requirements Changed?** – Switches path based on diff result. GitLab** – Creates or updates issues, attaches JSON diff, sets labels (certification, renewal). Rocket.Chat** – Posts a summary message with links to the GitLab issue(s). Merge** – Consolidates batch results for final logging. Set (Success)** – Formats a concise success payload. Data Flow: Schedule Trigger → Set (URL List) → SplitInBatches → ScrapeGraphAI → Code (Diff Checker) → If → GitLab / Rocket.Chat → Merge Customization Examples Add Additional Metadata to GitLab Issue // Inside the GitLab "Create Issue" node ↗️ { "title": Certification Update: ${$json.domain}, "description": What's Changed?\n${$json.diff}\n\n_Last checked: {{$now}}_, "labels": "certification,compliance," + $json.industry } Customize Rocket.Chat Message Formatting // Rocket.Chat node → JSON parameters { "text": :bell: Certification Update Detected\n>${$json.domain}\n>See the GitLab issue: ${$json.issueUrl} } Data Output Format The workflow outputs structured JSON data: { "domain": "example-cert-body.org", "scrapeDate": "2024-01-01T00:00:00Z", "oldRequirements": "Original text …", "newRequirements": "Updated text …", "diff": "- Continuous education hours increased from 20 to 24\n- Fee changed to $200", "issueUrl": "https://gitlab.com/org/compliance/-/issues/42", "notification": "sent" } Troubleshooting Common Issues No data returned from ScrapeGraphAI – Confirm the target site is publicly accessible and not blocking bots. Whitelist the domain or add proper headers via ScrapeGraphAI options. GitLab issue not created – Check that the PAT has api scope and the project ID is correct in the GitLab node. Rocket.Chat message fails – Verify webhook URL or credentials and ensure the channel exists. Performance Tips Limit the batch size in SplitInBatches to avoid API rate limits. Schedule the workflow during off-peak hours to minimize load. Pro Tips: Store last-year scrapes in a dedicated GitLab repository to create a complete change log history. Use n8n’s built-in Execution History Pruning to keep the database slim. Add an Error Trigger workflow to notify you if any step fails.

AI Website Analyzer to Product Ideas with FireCrawl and GPT-4.1 This n8n template demonstrates how to use AI to analyze any website and generate product ideas or summaries based on the website's content and purpose. Use cases are many: Try analyzing competitor websites, discovering product opportunities, understanding business models, or generating insights from landing pages! Good to know At time of writing, Firecrawl offers up to 500 free API calls. See Firecrawl Pricing for updated info. OpenAI API costs vary by model. GPT-3.5 is cheaper while GPT-4 and above offer deeper analysis but cost more per request. How it works We'll collect a website URL via a manual form trigger. The URL is sent to the Firecrawl API, which deeply crawls and analyzes the website content. Firecrawl returns the scraped data, including page structure, content, and metadata. The scraped data is then sent to OpenAI's API with a custom prompt. OpenAI generates an AI-powered summary analyzing what the website is doing, its purpose, and potential product ideas. The final output is displayed or can be stored for further use. How to use The manual trigger node is used as an example, but feel free to replace this with other triggers such as webhook or even a form. You can analyze multiple URLs by looping through a list, but of course, the processing will take longer and cost more. Requirements Firecrawl API key (get free 500 calls at https://firecrawl.dev) OpenAI API key for AI analysis Valid website URLs to analyze Customizing this workflow Change the output format from HTML to JSON, Markdown, or plain text by editing the Firecrawl parameters. Modify the AI prompt to focus on specific aspects like pricing strategy, target audience, or UX analysis. Upgrade to GPT-4.1, GPT-5.1, or GPT-5.2 for more advanced and detailed analysis. Add a webhook trigger to analyze websites automatically from other apps or services. Store results in a database like Supabase or Google Sheets for tracking competitor analysis over time.

Turn unstructured pitch decks and investment memos into polished Due Diligence PDF reports automatically. This n8n workflow handles everything from document ingestion to final delivery, combining internal document analysis with live web research to produce analyst-grade output in minutes. The Problem It Solves Reviewing a single deal manually reading the deck, cross-checking claims online, formatting the summary easily takes half a day. Multiply that by 10–20 inbound deals per week, and your team is buried in low-leverage work before any real analysis begins. This workflow compresses that cycle into a single automated pipeline. How It Works Upload: Send a PDF, DOCX, or PPTX to the webhook endpoint. Parse: LlamaParse extracts clean Markdown from complex layouts, preserving tables and financial data. Enrich: The workflow identifies the target company, then pulls supplementary data from the open web (corporate pages, risk signals) using Decodo's search and scraping APIs to verify and contextualize claims made in the source documents. Analyze: An AI Agent runs six targeted retrieval queries against the combined dataset: revenue history, key risks, business model, competitive landscape, management profile, and deal terms. Deliver: Results render into a branded HTML template, convert to PDF via Puppeteer, upload to Cloudflare R2, and return a download link. Each deal gets a unique namespace in Pinecone, so documents are isolated and repeat uploads skip redundant parsing. What You Need | Service | Role | | --- | --- | | n8n | Workflow orchestration | | LlamaIndex Cloud | Document parsing (LlamaParse) | | Pinecone | Vector storage & retrieval | | OpenAI API | Embeddings (text-embedding-3-small) & LLM analysis (GPT-5.4) | | Decodo API | Web search & page scraping | | Cloudflare R2 | Report file storage (S3-compatible) | Quick Start Import the workflow JSON into your n8n instance. Add credentials for OpenAI, Pinecone, LlamaIndex (Header Auth), Decodo, and Cloudflare R2 (S3-compatible). Update the R2 base URL in the "Build Public Report URL" node. Fire a test POST with a sample deck to the webhook. Customization Ideas Swap the HTML template to match your firm's branding and report structure. Extend the AI Agent prompt to cover additional dimensions like ESG scoring or technical debt. Route the finished PDF to Slack, email, or your CRM instead of (or alongside) R2. Troubleshooting | Symptom | Likely Fix | | --- | --- | | Parsing times out | Increase the Wait node duration; check file size against LlamaParse limits | | Thin or generic analysis | Verify the source PDF is text-based, not a scanned image, enable OCR if needed | | Broken PDF layout | Simplify CSS in the HTML render node; older Puppeteer builds handle basic layouts better | Created by: Khmuhtadin Category: Business Intelligence | Tags: AI, RAG, Due Diligence, Decodo Portfolio • Store • LinkedIn • Medium • Threads

How it works This workflow automates SEO analysis by comparing your website with a competitor’s site. It reads input URLs from Google Sheets, scrapes structured SEO data from both sites, and expands into important internal pages for deeper insights. The collected data is processed and merged before being analyzed using Google Gemini AI. Finally, it generates a structured SEO gap report and saves it back into Google Sheets while updating the workflow status. Step-by-step Trigger and filter input data** Manual Trigger – Starts the workflow execution manually. Google Sheets (Get row(s)) – Fetches website data from the input sheet. If – Filters only rows where status is NEW. Prepare and fetch website data** Set (Edit Fields) – Maps your website and competitor URLs. HTTP Request (My Website HTTP) – Fetches your website HTML. HTTP Request (Competitor) – Fetches competitor website HTML. HTML (Extract Data) – Extracts SEO elements like title, H1, H2, links, and content. Extract and process internal pages** Code (All Links) – Filters important internal URLs from both websites. Google Sheets (Insert Links) – Stores extracted links. Split In Batches – Iterates through each page URL. HTTP Request + HTML + Code – Scrapes and formats SEO data for each page. Store and update structured SEO data** Google Sheets (Append & Update) – Saves page-level SEO data. Wait nodes – Controls execution timing and prevents rate limits. Analyze SEO gaps using AI** Merge – Combines your site and competitor data. Code – Structures merged dataset. Google Gemini (Message a model) – Generates SEO gap analysis. Code (Parse JSON) – Cleans and validates AI output. Save report and finalize workflow** Google Sheets (Append Report) – Stores SEO gap report. Google Sheets (Update Row) – Marks input row as DONE. Why use this? Automates complete SEO competitor analysis without manual effort Identifies keyword, content, and technical SEO gaps instantly Scales across multiple websites and competitors efficiently Provides AI-driven insights and actionable SEO improvement plans Centralizes all SEO data and reports inside Google Sheets

How it Works This workflow automates competitive price intelligence using Bright Data's enterprise web scraping API. On a scheduled basis (default: daily at 9 AM), the system loops through configured competitor product URLs, triggers Bright Data's web scraper to extract real-time pricing data from each site, and intelligently compares competitor prices against your current pricing. The workflow handles the full scraping lifecycle: it sends scraping requests to Bright Data, waits for completion, fetches the scraped product data, and parses prices from various formats and website structures. All pricing data is automatically logged to Google Sheets for historical tracking and trend analysis. When a competitor's price drops below yours by more than the configured threshold (e.g., 10% cheaper), the system immediately sends detailed alerts via Slack and email to your pricing team with actionable intelligence. At the end of each monitoring run, the workflow generates a comprehensive daily summary report that aggregates all competitor data, calculates average price differences, identifies the lowest and highest competitors, and provides a complete competitive landscape view. This eliminates hours of manual competitor research and enables data-driven pricing decisions in real-time. Who is this for? E-commerce businesses and online retailers needing automated competitive price monitoring Product managers and pricing strategists requiring real-time competitive intelligence Revenue operations teams managing dynamic pricing strategies across multiple products Marketplaces competing in price-sensitive categories where margins matter Any business that needs to track competitor pricing without manual daily checks Setup Steps Setup time: Approx. 30-40 minutes (Bright Data configuration, credential setup, competitor URL configuration) Requirements: Bright Data account with Web Scraper API access Bright Data API token (from dashboard) Google account with a spreadsheet for price tracking Slack workspace with pricing channels SMTP email provider for alerts Sign up for Bright Data and create a web scraping dataset (use e-commerce template for product data) Obtain your Bright Data API token and dataset ID from the dashboard Configure these nodes: Schedule Daily Check: Set monitoring frequency using cron expression (default: 9 AM daily) Load Competitor URLs: Add competitor product URLs array, configure your current price, set alert threshold percentage Loop Through Competitors: Automatically handles multiple URLs (no configuration needed) Scrape with Bright Data: Add Bright Data

📖 Overview This template automates the process of researching a keyword, scraping top-ranking articles, cleaning their content, and generating a high-quality SEO-optimized blog post. It uses Google Search via RapidAPI, Ollama with Mistral AI, and Google Drive to deliver an end-to-end automated content workflow. Ideal for content creators, SEO specialists, bloggers, and marketers who need to quickly gather and summarize insights from multiple sources to create superior content. ⚙️ Prerequisites Before using this workflow, make sure you have: n8n installed (Desktop, Docker, or Cloud). Ollama installed with the mistral:7b model: ollama pull mistral:7b RapidAPI account (for Google Search API). Google Drive account (with a target folder where articles will be saved). 🔑 Credentials Required RapidAPI (Google Search API) Header authentication with your API key. Example headers: x-rapidapi-key: YOUR_API_KEY x-rapidapi-host: google-search74.p.rapidapi.com Google Drive OAuth2 Allow read/write permissions. Update the folderId with your Drive folder where articles should be stored. Ollama API Base URL: http://localhost:11434 (local n8n) http://host.docker.internal:11434 (inside Docker) Ensure the mistral:7b model is available. 🚀 Setup Instructions Configure RapidAPI Sign up at RapidAPI . Subscribe to the Google Search API. Create an HTTP Header Auth credential in n8n with your API key. Configure Google Drive In n8n, add a Google Drive OAuth2 credential. Select the Drive folder ID where output files should be saved. Configure Ollama Install Ollama locally. Pull the required model (mistral:7b). Create an Ollama API credential in n8n. Run the Workflow Trigger by sending a chat message with your target keyword. The workflow searches Google, extracts the top 3 results, scrapes the articles, cleans the content, and generates a structured blog post. Final output is stored in Google Drive as a .docx file. 🎨 Customization Options Search Engine → Swap out RapidAPI with Bing or SerpAPI. Number of Articles → Change limit: 3 in the Google Search node. Content Cleaning → Modify the regex in the “Clean Body Text” node to capture or tags. AI Model → Replace mistral:7b with llama3, mixtral, or any other Ollama-supported model. Storage → Save output to a different Google Drive folder or export to Notion/Slack. 📌 Workflow Highlights Google Search (RapidAPI) → Fetch top 3 results for your keyword. HTTP Request + Code Nodes → Extract and clean article body text. Mistral AI via Ollama → Summarize, optimize, and refine the content. Google Drive → Save the final blog-ready article automatically.

⚡ Quick Setup Import this workflow into your n8n instance. Add your Apify, Google Sheets, and Firecrawl credentials. Activate the workflow to start your automated lead enrichment system. Copy the webhook URL from the MCP trigger node. Connect AI agents using the MCP URL. 🔧 How it Works This solution combines two powerful workflows to deliver fully enriched, AI-ready business leads from Google Maps: Apify Google Maps Scraper Node: Collects business data and, if enabled, enriches each lead with contact details and social profiles. Leads Missing Enrichment: Any leads without contact or social info are automatically saved to a Google Sheet. Firecrawl & Code Node Workflow: A second workflow monitors the Google Sheet, crawls each business’s website using Firecrawl, and extracts additional social media profiles or contact info using a Code node. Personalization Logic: AI-powered nodes generate tailored outreach content for each enriched lead. Native Integration: The entire process is exposed as an MCP-compatible interface, returning enriched and personalized lead data directly to the AI agent. 📋 Available Operations Business Search: Find businesses on Google Maps by location, category, or keyword. Lead Enrichment: Automatically append contact details, social profiles, and other business info using Apify and Firecrawl. Personalized Outreach Generation: Create custom messages or emails for each lead. Batch Processing: Handle multiple leads in a single request. Status & Error Reporting: Get real-time feedback on processing, enrichment, and crawling. 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: Search queries (location, keywords, categories) Enrichment options (contact, social, etc.) Personalization variables (name, business type, etc.) Response Format: Returns fully enriched lead data and personalized outreach content in a structured format.