Workflow Templates

Submit a research topic through a form and receive a professionally styled executive report in your inbox — fully automated, with built-in scraping resilience. The workflow searches Google via SerpApi, scrapes each result with Jina.ai (free, no key needed), and uses Claude to extract key findings. If a page is blocked by a CAPTCHA or login wall, it automatically retries with Firecrawl. Blocked sources are gracefully skipped after two attempts. Once all sources are processed, Claude synthesises a structured executive report and delivers it as a styled HTML email via Gmail. How it works A web form collects the research topic, number of sources (5–7), and recipient email SerpApi searches Google and returns a buffer of results (2× requested + 3 to survive domain filtering) Junk domains are filtered out automatically (Reddit, YouTube, Twitter, PDFs, etc.) Each URL is processed one at a time in a serial loop: Round 1 — Jina.ai: free Markdown scraper, no API key required Claude checks the content — if it's a CAPTCHA or wall, it returns RETRY_NEEDED Round 2 — Firecrawl: paid fallback scraper retries the blocked URL If still blocked, the source is marked as unavailable and the loop continues All extracted findings are aggregated and Claude writes a structured executive report (Executive Summary, Key Findings, Detailed Analysis, Data & Evidence, Conclusions, Sources) The report is converted to styled HTML (with tables, headings, and lists) and emailed to the recipient Setup steps Required credentials | Service | Where to get it | Where to paste it | |---|---|---| | SerpApi | serpapi.com — free tier: 100 searches/month | SerpApi Search node → query param api_key | | Firecrawl | firecrawl.dev — free tier: 500 pages/month | Firecrawl (Fallback) node → Authorization header | | Anthropic | n8n credentials → Anthropic API | Connect to: Claude Extractor, Claude Re-Analyzer, Claude Synthesizer | | Gmail | n8n credentials → Gmail OAuth2 | Connect to: Send Gmail | Error handler (optional) The workflow includes a built-in error handler that captures the failed node name, error message, and execution URL. To activate it: Workflow Settings → Error Workflow → select this workflow. Add a Slack or Gmail node after Format Error to receive failure alerts. Nodes used n8n Form Trigger** — collects topic, source count, and recipient email HTTP Request** × 3 — SerpApi (Google Search), Jina.ai (primary scraper), Firecrawl (fallback scraper) Code** × 6 — URL filtering, response normalisation, prompt assembly, HTML rendering Split In Batches** — serial loop (one URL at a time, prevents rate limit collisions) IF** × 2 — CAPTCHA/block detection after each scrape attempt Wait** — 3-second pause before Firecrawl retry Basic LLM Chain** × 3 — page analysis (×2) and report synthesis (×1), all powered by Claude Aggregate** — collects all per-URL findings before synthesis Gmail** — sends the final HTML report Error Trigger + Set** — error handler sub-flow Notes Jina.ai is free and works without an API key for most public pages Firecrawl is only called when Jina is blocked — most runs won't consume Firecrawl credits SerpApi fetches numSources × 2 + 3 results to ensure enough survive domain filtering Claude model is set to claude-sonnet-4-5 — swap to any Anthropic model in the three Claude nodes The HTML email renders markdown tables, headings, lists, and bold correctly in Gmail

Product Price Monitor with Slack and Jira ⚠️ 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 multiple e-commerce sites, analyses weekly seasonal price trends, and notifies your team in Slack while opening Jira tickets for items that require price adjustments. It helps retailers plan inventory and pricing by surfacing actionable insights every week. Pre-conditions/Requirements Prerequisites n8n instance (self-hosted or n8n cloud) ScrapeGraphAI community node installed Slack workspace & channel for notifications Jira Software project (cloud or server) Basic JavaScript knowledge for optional custom code edits Required Credentials ScrapeGraphAI API Key** – Enables web scraping Slack OAuth Access Token** – Required by the Slack node Jira Credentials** – Email & API token (cloud) or username & password (server) (Optional) Proxy credentials – If target websites block direct scraping Specific Setup Requirements | Resource | Purpose | Example | |----------|---------|---------| | Product URL list | Seed URLs to monitor | https://example.com/products-winter-sale | | Slack Channel | Receives trend alerts | #pricing-alerts | | Jira Project Key | Tickets are created here | ECOM | How it works This workflow automatically scrapes multiple e-commerce sites, analyses weekly seasonal price trends, and notifies your team in Slack while opening Jira tickets for items that require price adjustments. It helps retailers plan inventory and pricing by surfacing actionable insights every week. Key Steps: Webhook Trigger**: Kicks off the workflow via a weekly schedule or manual call. Set Product URLs**: Prepares the list of product pages to analyse. SplitInBatches**: Processes URLs in manageable batches to avoid rate limits. ScrapeGraphAI**: Extracts current prices, stock, and seasonality hints from each URL. Code (Trend Logic)**: Compares scraped prices against historical averages. If (Threshold Check)**: Determines if price deviations exceed ±10%. Slack Node**: Sends a formatted message to the pricing channel for each deviation. Jira Node**: Creates/updates a ticket linking to the product for further action. Merge**: Collects all batch results for summary reporting. Set up steps Setup Time: 15-20 minutes Install Community Nodes: In n8n, go to Settings → Community Nodes, search for “ScrapeGraphAI”, and install. Add Credentials: a. Slack → Credentials → New, paste your Bot/User OAuth token. b. Jira → Credentials → New, enter your domain, email/username, API token/password. c. ScrapeGraphAI → Credentials → New, paste your API key. Import Workflow: Upload or paste the JSON template into n8n. Edit the “Set Product URLs” Node: Replace placeholder URLs with your real product pages. Configure Schedule: Replace the Webhook Trigger with a Cron node (e.g., every Monday 09:00) or keep as webhook for manual runs. Map Jira Fields: In the Jira node, ensure Project Key, Issue Type (e.g., Task), and Summary fields match your instance. Test Run: Execute the workflow. Confirm Slack message appears and a Jira issue is created. Activate: Toggle the workflow to Active so it runs automatically. Node Descriptions Core Workflow Nodes: Webhook** – Default trigger, can be swapped with Cron for weekly automation. Set (Product URLs)** – Stores an array of product links for scraping. SplitInBatches** – Limits each ScrapeGraphAI call to five URLs to reduce load. ScrapeGraphAI** – Crawls and parses HTML, returning JSON with title, price, availability. Code (Trend Logic)** – Calculates percentage change vs. historical data (stored externally or hard-coded for demo). If (Threshold Check)** – Routes items above/below the set variance. Slack** – Posts a rich-format message containing product title, old vs. new price, and link. Jira* – Creates or updates a ticket with priority set to *Medium and assigns to the Pricing team lead. Merge** – Recombines batch streams for optional reporting or storage. Data Flow: Webhook → Set (Product URLs) → SplitInBatches → ScrapeGraphAI → Code (Trend Logic) → If → Slack / Jira → Merge Customization Examples Change Price Deviation Threshold // Code (Trend Logic) node const threshold = 0.05; // 5% instead of default 10% Alter Slack Message Template { "text": ${item.name} price changed from $${item.old} to $${item.new} (${item.diff}%)., "attachments": [ { "title": "Product Link", "title_link": item.url, "color": "#4E79A7" } ] } Data Output Format The workflow outputs structured JSON data: { "product": "Winter Jacket", "url": "https://example.com/winter-jacket", "oldPrice": 129.99, "newPrice": 99.99, "change": -23.06, "scrapedAt": "2023-11-04T09:00:00Z", "status": "Below Threshold", "slackMsgId": "A1B2C3", "jiraIssueKey": "ECOM-101" } Troubleshooting Common Issues ScrapeGraphAI returns empty data – Verify selectors; many sites use dynamic rendering, require a headless browser flag. Slack message not delivered – Check that the OAuth token scopes include chat:write; also confirm channel ID. Jira ticket creation fails – Field mapping mismatch; ensure Issue Type is valid and required custom fields are supplied. Performance Tips Batch fewer URLs (e.g., 3 instead of 5) to reduce timeout risk. Cache historical prices in an external DB (Postgres, Airtable) instead of reading large CSVs in the Code node. Pro Tips: Rotate proxies/IPs within ScrapeGraphAI to bypass aggressive e-commerce anti-bot measures. Add a Notion or Sheets node after Merge for historical logging. Use the Error Trigger workflow in n8n to alert when ScrapeGraphAI fails more than X times per run.

Transform property searches into personalized experiences! This powerful automation delivers dream home matches straight to clients' inboxes with professional CSV reports - all from a simple web form. 🚀 What this workflow does Create a complete real estate search experience that works 24/7: ✨ Smart Web Form - Beautiful property search form captures client preferences 🧠 Dynamic SQL Builder - Intelligently creates optimized queries from user input ⚡ Lightning Database Search - Scans 1000+ properties in milliseconds 📊 Professional CSV Export - Excel-ready reports with complete property details 📧 Automated Email Delivery - Personalized emails with property previews and attachments 🎯 Perfect for: Real Estate Agents** - Generate leads and impress clients with instant service Property Managers** - Automate tenant matching and recommendations Brokerages** - Provide 24/7 self-service property discovery Developers** - Showcase available properties with professional automation 💡 Why this workflow is a game-changer > "From property search to professional report delivery in under 30 seconds!" ⚡ Instant Results: Zero wait time for property matches 🎨 Professional Output: Beautiful emails that showcase your expertise 📱 Mobile Optimized: Works flawlessly on all devices 🧠 Smart Filtering: Only searches criteria clients actually specify 📈 Infinitely Scalable: Handles unlimited searches simultaneously 📊 Real Estate Data Source Built on authentic US market data from the Github: 🏘️ 1000+ Real Properties across all US states 💰 Actual Market Prices from legitimate listings 🏠 Complete Property Details (bedrooms, bathrooms, square footage, lot size) 📍 Verified Locations with accurate cities, states, and ZIP codes 🏢 Broker Information for authentic real estate context 🛠️ Quick Setup Guide Prerequisites Checklist ✅ [ ] SQL Server database (MySQL/PostgreSQL also supported) [ ] Gmail account for automated emails [ ] n8n instance (cloud or self-hosted) [ ] 20 minutes setup time Step 1: Import Real Estate Data 📥 🌟 Download the data 💾 Download CSV file (1000+ properties included) 🗄️ Create SQL Server table with this exact schema: CREATE TABLE [REALTOR].[dbo].[realtor_usa_price] ( brokered_by BIGINT, status NVARCHAR(50), price DECIMAL(12,2), bed INT, bath DECIMAL(3,1), acre_lot DECIMAL(10,8), street BIGINT, city NVARCHAR(100), state NVARCHAR(50), zip_code INT, house_size INT, prev_sold_date NVARCHAR(50) ); 📊 Import your CSV data into this table Step 2: Configure Database Connection 🔗 🔐 Set up Microsoft SQL Server credentials in n8n ✅ Test connection to ensure everything works 🎯 Workflow is pre-configured for the table structure above Step 3: Gmail Setup (The Magic Touch) 📧 🌐 Visit Google Cloud Console 🆕 Create new project (or use existing) 🔓 Enable Gmail API in API Library 🔑 Create OAuth2 credentials (Web Application) ⚙️ Add your n8n callback URL to authorized redirects 🔗 Configure Gmail OAuth2 credentials in n8n ✨ Authorize your Google account Step 4: Launch Your Property Search Portal 🚀 📋 Import this workflow template (form is pre-configured) 🌍 Copy your webhook URL from the Property Search Form node 🔍 Test with a sample property search 📨 Check email delivery with CSV attachment 🎉 Go live and start impressing clients! 🎨 Customization Playground 🏷️ Personalize Your Brand // Customize email subjects in the Gmail node "🏠 Exclusive Properties Curated Just for You - ${results.length} Perfect Matches!" "✨ Your Dream Home Portfolio - Handpicked by Our Experts" "🎯 Hot Market Alert - ${results.length} Premium Properties Inside!" 🔧 Advanced Enhancements 🎨 HTML Email Templates**: Create stunning visual emails with property images 📊 Analytics Dashboard**: Track popular searches and user engagement 🔔 Smart Alerts**: Set up automated price drop notifications 📱 Mobile Integration**: Connect to React Native or Flutter apps 🤖 AI Descriptions**: Add ChatGPT for compelling property descriptions 🌍 Multi-Database Flexibility // Easy database switching // MySQL: Replace Microsoft SQL node → MySQL node // PostgreSQL: Swap for PostgreSQL node // MongoDB: Use MongoDB node with JSON queries // Even CSV files: Use CSV reading nodes for smaller datasets 🚀 Advanced Features & Extensions 🔥 Pro Tips for Power Users 🔄 Bulk Processing**: Handle multiple searches simultaneously 💾 Smart Caching**: Store popular searches for lightning-fast results 📈 Lead Scoring**: Track which properties generate most interest 📅 Follow-up Automation**: Schedule nurturing email sequences 🎯 Integration Possibilities 🏢 CRM Connection**: Auto-add qualified leads to your CRM 📅 Calendar Integration**: Add property viewing scheduling 📊 Price Monitoring**: Track market trends and price changes 📱 Social Media**: Auto-share featured properties to social platforms 💬 Chat Integration**: Connect to WhatsApp or SMS for instant alerts 🔗 Expand Your Real Estate Automation 🌟 Related Workflow Ideas 🤖 AI Property Valuation - Add machine learning for price predictions 📊 Market Analysis Reports - Generate comprehensive market insights 📱 SMS Property Alerts - Instant text notifications for hot properties 🏢 Commercial Property Search - Adapt for office and retail spaces 💹 Investment ROI Calculator - Add financial analysis for investors 🏘️ Neighborhood Analytics - Include school ratings and demographics 🛠️ Technical Extensions 📷 Image Processing: Auto-resize and optimize property photos 🗺️ Map Integration: Add interactive property location maps 📱 Progressive Web App: Create mobile app experience 🔔 Push Notifications: Real-time alerts for saved searches 🚀 Get Started Now Import this workflow template Configure your database and Gmail Customize branding and messaging Launch your professional property search portal Watch client satisfaction soar!

What this workflow does This workflow automatically audits web pages for SEO issues and generates an executive-friendly SEO report using AI. It is designed for marketers, founders, and SEO teams who want fast, actionable insights without manually reviewing HTML, meta tags, or SERP data. The workflow: Reads URLs from Google Sheets Scrapes page content using Decodo (reliable scraping, even on protected sites) Extracts key SEO elements (title, meta description, canonical, H1/H2, visible text) Uses an AI Agent to analyze the page and generate: Overall SEO status Top issues Quick wins Title & meta description recommendations Saves results to Google Sheets Sends a formatted HTML executive report by email (Gmail) Who this workflow is for This template is ideal for: SEO consultants and agencies SaaS marketing teams Founders monitoring their landing pages Content teams doing SEO quality control It focuses on on-page SEO fundamentals, not backlink analysis or technical crawling. Setup (step by step) 1. Google Sheets Create an input sheet with one URL per row Create an output sheet to store SEO results 2. Decodo Add your Decodo API credentials The URL is automatically taken from the input sheet 👉 Decodo – Web Scraper for n8n 3. AI Agent Connect your LLM credentials (OpenAI, Gemini, etc.) The prompt is already optimized for non-technical SEO summaries 4. Gmail Connect your Gmail account Set the recipient email address Emails are sent in clean HTML format Notes & disclaimer This is a community template Results depend on page accessibility and content structure It focuses on on-page SEO, not backlinks or rankings

Property Listing Aggregator with Mailchimp and Notion ⚠️ 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 scrapes multiple commercial-real-estate websites, consolidates new property listings into Notion, and emails weekly availability updates or immediate space alerts to a Mailchimp audience. It automates the end-to-end process so business owners can stay on top of the latest spaces without manual searching. Pre-conditions/Requirements Prerequisites n8n instance (self-hosted or n8n.cloud) ScrapeGraphAI community node installed Active Notion workspace with permission to create/read databases Mailchimp account with at least one Audience list Basic understanding of JSON; ability to add API credentials in n8n Required Credentials ScrapeGraphAI API Key** – Enables web scraping functionality Notion OAuth2 / Integration Token** – Writes data into Notion database Mailchimp API Key** – Sends campaigns and individual emails (Optional) Proxy credentials – If target real-estate sites block your IP Specific Setup Requirements | Resource | Requirement | Example | |----------|-------------|---------| | Notion | Database with property fields (Address, Price, SqFt, URL, Availability) | Database ID: abcd1234efgh | | Mailchimp | Audience list where alerts are sent | Audience ID: f3a2b6c7d8 | | ScrapeGraphAI | YAML/JSON config per site | Stored inside the ScrapeGraphAI node | How it works This workflow scrapes multiple commercial-real-estate websites, consolidates new property listings into Notion, and emails weekly availability updates or immediate space alerts to a Mailchimp audience. It automates the end-to-end process so business owners can stay on top of the latest spaces without manual searching. Key Steps: Manual Trigger / CRON**: Starts the workflow weekly or on-demand. Code (Site List Builder)**: Generates an array of target URLs for ScrapeGraphAI. Split In Batches**: Processes URLs in manageable groups to avoid rate limits. ScrapeGraphAI**: Extracts property details from each site. IF (New vs Existing)**: Checks whether the listing already exists in Notion. Notion**: Inserts new listings or updates existing records. Set**: Formats email content (HTML & plaintext). Mailchimp**: Sends a campaign or automated alert to subscribers. Sticky Notes**: Provide documentation and future-enhancement pointers. Set up steps Setup Time: 15-25 minutes Install Community Node Navigate to Settings → Community Nodes and install “ScrapeGraphAI”. Create Notion Integration Go to Notion Settings → Integrations → Develop your own integration. Copy the integration token and share your target database with the integration. Add Mailchimp API Key In Mailchimp: Account → Extras → API keys. Copy an existing key or create a new one, then add it to n8n credentials. Build Scrape Config In the ScrapeGraphAI node, paste a YAML/JSON selector config for each website (address, price, sqft, url, availability). Configure the URL List Open the first Code node. Replace the placeholder array with your target listing URLs. Map Notion Fields Open the Notion node and map scraped fields to your database properties. Save. Design Email Template In the Set node, tweak the HTML and plaintext blocks to match your brand. Test the Workflow Trigger manually, check that Notion rows are created and Mailchimp sends the message. Schedule Add a CRON node (weekly) or leave the Manual Trigger for ad-hoc runs. Node Descriptions Core Workflow Nodes: Manual Trigger / CRON** – Kicks off the workflow either on demand or on a schedule. Code (Site List Builder)** – Holds an array of commercial real-estate URLs and outputs one item per URL. Split In Batches** – Prevents hitting anti-bot limits by processing URLs in groups (default: 5). ScrapeGraphAI** – Crawls each URL, parses DOM with CSS/XPath selectors, returns structured JSON. IF (New Listing?)** – Compares scraped listing IDs against existing Notion database rows. Notion** – Creates or updates pages representing property listings. Set (Email Composer)** – Builds dynamic email subject, body, and merge tags for Mailchimp. Mailchimp* – Uses the *Send Campaign endpoint to email your audience. Sticky Note** – Contains inline documentation and customization reminders. Data Flow: Manual Trigger/CRON → Code (URLs) → Split In Batches → ScrapeGraphAI → IF (New?) True path → Notion (Create) → Set (Email) → Mailchimp False path → (skip) Customization Examples Filter Listings by Maximum Budget // Inside the IF node (custom expression) {{$json["price"] <= 3500}} Change Email Frequency to Daily Digests { "nodes": [ { "name": "Daily CRON", "type": "n8n-nodes-base.cron", "parameters": { "triggerTimes": [ { "hour": 8, "minute": 0 } ] } } ] } Data Output Format The workflow outputs structured JSON data: { "address": "123 Market St, Suite 400", "price": 3200, "sqft": 950, "url": "https://examplebroker.com/listing/123", "availability": "Immediate", "new": true } Troubleshooting Common Issues Scraper returns empty objects – Verify selectors in ScrapeGraphAI config; inspect the site’s HTML for changes. Duplicate entries in Notion – Ensure the “IF” node checks a unique ID (e.g., listing URL) before creating a page. Performance Tips Reduce batch size or add delays in ScrapeGraphAI to avoid site blocking. Cache previously scraped URLs in an external file or database for faster runs. Pro Tips: Rotate proxies in ScrapeGraphAI for heavily protected sites. Use Notion rollups to calculate total available square footage automatically. Leverage Mailchimp merge tags (|FNAME|) in the Set node for personalized alerts.

Automatically discover and extract article URLs from any website using AI to identify valid content links while filtering out navigation, category pages, and irrelevant content—perfect for building content pipelines, news aggregators, and research databases. What Makes This Different: AI-Powered Intelligence** - Uses GPT-5-mini to understand webpage context and identify actual articles vs navigation pages, eliminating false positives Browser Spoofing** - Includes realistic User-Agent headers and request patterns to avoid bot detection on publisher sites Smart URL Normalization* - Automatically strips tracking parameters (utm_, fbclid, etc.), removes duplicates, and standardizes URLs Source Categorization** - AI assigns logical source names based on domain and content type for easy filtering Rate Limiting Built-In** - Configurable delays between requests prevent IP blocking and respect website resources Deduplication on Save** - Google Sheets append-or-update pattern ensures no duplicate URLs in your database Key Benefits of AI-Powered Content Discovery: Save 10+ Hours Weekly** - Automate manual link hunting across dozens of publisher sites Higher Quality Results** - AI filters out 95%+ of junk links (nav pages, categories, footers) that rule-based scrapers miss Scale Effortlessly** - Add new seed URLs to your sheet and the same workflow handles any website structure Industry Agnostic** - Works for news, blogs, research papers, product pages—any content type Always Up-to-Date** - Schedule daily runs to catch new content as it's published Full Audit Trail** - Track discovered URLs with timestamps and sources in Google Sheets Who's it for This template is designed for content marketers, SEO professionals, researchers, media monitors, and anyone who needs to aggregate content from multiple sources. It's perfect for organizations that need to track competitor blogs, curate industry news, build research databases, monitor brand mentions, or aggregate content for newsletters without manually checking dozens of websites daily or writing complex scraping rules for each source. How it works / What it does This workflow creates an intelligent content discovery pipeline that automatically finds and extracts article URLs from any webpage. The system: Reads Seed URLs - Pulls a list of webpages to crawl from your Google Sheets (blog indexes, news feeds, publication homepages) Fetches with Stealth - Downloads each webpage's HTML using browser-like headers to avoid bot detection Converts for AI - Transforms messy HTML into clean Markdown that the AI can easily process AI Extraction - GPT-5-mini analyzes the content and identifies valid article URLs while filtering out navigation, categories, and junk links Normalizes & Saves - Cleans URLs (removes tracking params), deduplicates, and saves to Google Sheets with source tracking Key Innovation: Context-Aware Link Filtering - Unlike traditional scrapers that rely on CSS selectors or URL patterns (which break when sites update), the AI understands the semantic difference between an article link and a navigation link. It reads the page like a human would, identifying content worth following regardless of the website's structure. How to set up 1. Create Your Google Sheets Database Create a new Google Spreadsheet with two sheets: "Seed URLs" - Add column URL with webpages to crawl (blog homepages, news feeds, etc.) "Discovered URLs" - Add columns: URL, Source, Status, Discovered At Add 3-5 seed URLs to start (e.g., https://abc.com/, https://news.xyz.com/) 2. Connect Your Credentials Google Sheets**: Click the "Read Seed URLs" and "Save Discovered URLs" nodes → Select your Google Sheets account OpenAI**: Click the "OpenAI GPT-5-mini" node → Add your OpenAI API key Select your spreadsheet and sheet names in both Google Sheets nodes 3. Customize the AI Prompt (Optional) Open the "AI URL Extractor" node Modify the system message to add industry-specific rules: // Example: Add to system message for tech blogs For tech sites, also extract: Tutorial and guide URLs Product announcement pages Changelog and release notes Adjust source naming conventions to match your taxonomy 4. Test Your Configuration Click "Test Workflow" or use the Manual Trigger Check the execution to verify: Seed URLs are being read correctly HTML is fetched successfully (check for 200 status) AI returns valid JSON array of URLs URLs are saved to your output sheet Review the "Discovered URLs" sheet for results 5. Schedule and Monitor Adjust the Schedule Trigger (default: daily at 6 AM) Enable the workflow to run automatically Monitor execution logs for errors: Rate limiting: Increase wait time if sites block you Empty results: Check if seed URLs have changed structure AI errors: Review AI output in execution data Set up error notifications via email or Slack (add nodes after Completion Summary) Requirements Google Sheets Account** - OAuth2 connection for reading seed URLs and saving results OpenAI API Key** - For GPT-5-mini (or swap for any LangChain-compatible LLM)

Automated Failed Login Detection with Jira Security Tasks, Slack Notifications Webhook: Failed Login Attempts → Jira Security Case → Slack Warnings This n8n workflow monitors failed login attempts from any application, normalizes incoming data, detects repeated attempts within a configurable time window and automatically: Sends detailed alerts to Slack, Creates Jira security tasks (single or grouped based on repetition), Logs all failed login attempts into a Notion database. It ensures fast, structured and automated responses to potential account compromise or brute-force attempts while maintaining persistent records. Quick Implementation Steps Import this JSON workflow into n8n. Connect your application to the failed-login webhook endpoint. Add Jira Cloud API credentials. Add Slack API credentials. Add Notion API credentials and configure the database for storing login attempts. Enable the workflow — done! What It Does Receives Failed Login Data Accepts POST requests containing failed login information. Normalizes the data, ensuring consistent fields: username, ip, timestamp and error. Validates Input Checks for missing username or IP. Sends a Slack alert if any required field is missing. Detects Multiple Attempts Uses a sliding time window (default: 5 minutes) to detect multiple failed login attempts from the same username + IP. Single attempts → standard Jira task + Slack notification. Multiple attempts → grouped Jira task + detailed Slack notification. Logs Attempts in Notion Records all failed login events into a Notion database with fields: Username, IP, Total Attempts, Attempt List, Attempt Type. Formats Slack Alerts Single attempt → lightweight notification. Multiple attempts → summary including timestamps, errors, total attempts, and Jira ticket link. Who’s It For This workflow is ideal for: Security teams monitoring authentication logs. DevOps/SRE teams maintaining infrastructure access logs. SaaS platform teams with high login traffic. Organizations aiming to automate breach detection. Teams using Jira + Slack + Notion + n8n for incident workflows. Requirements n8n (Self-Hosted or Cloud). Your application must POST failed login data to the webhook. Jira Software Cloud credentials (Email, API Token, Domain). Slack Bot Token with message-posting permissions. Notion API credentials with access to a database. Basic understanding of your login event sources. How It Works Webhook Trigger: Workflow starts when a failed-login event is sent to the failed-login webhook. Normalization: Converts single objects or arrays into a uniform format. Ensures username, IP, timestamp and error are present. Prepares a logMessage for Slack and Jira nodes. Validation: IF node checks whether username and IP exist. If missing → Slack alert for missing information. Multiple Attempt Detection: Function node detects repeated login attempts within a 5-minute sliding window. Flags attempts as multiple: true or false. Branching: Multiple attempts → build summary, create Jira ticket, format Slack message, store in Notion. Single attempts → create Jira ticket, format Slack message, store in Notion. Slack Alerts: Single attempt → concise message Multiple attempts → detailed summary with timestamps and Jira ticket link Notion Logging: Stores username, IP, total attempts, attempt list, attempt type in a dedicated database for recordkeeping. How To Set Up Import Workflow → Workflows → Import from File in n8n. Webhook Setup → copy the URL from Faield Login Trigger node and integrate with your application. Jira Credentials → connect your Jira account to both Jira nodes and configure project/issue type. Slack Credentials → connect your Slack Bot and select the alert channel. Notion Credentials → connect your Notion account and select the database for storing login attempts. Test the Workflow → send sample events: missing fields, single attempts, multiple attempts. Enable Workflow → turn on workflow once testing passes. Logic Overview | Step Node | Description | |---------------------------------|-----------------------------------------------| | Normalize input | Normalize Login Event — Ensures each event has required fields and prepares a logMessage. | | Validate fields | Check Username & IP present — IF node → alerts Slack if data is incomplete. | | Detect repeats | Detect Multiple Attempts — Finds multiple attempts within a 5-minute window; sets multiple flag. | | Multiple attempts | IF - Multiple Attempts + Build Multi-Attempt Summary — Prepares grouped summary for Slack & Jira. | | Single attempt | Create Ticket - Single Attempt — Creates Jira task & Slack alert for one-off events. | | Multiple attempt ticket | Create Ticket - Multiple Attempts — Creates detailed Jira task. | | Slack alert formatting | Format Fields For Single/Multiple Attempt — Prepares structured message for Slack. | | Slack alert delivery | Slack Alert - Single/Multiple Attempts — Posts alert in selected Slack channel. | | Notion logging | Login Attempts Data Store in DB — Stores structured attempt data in Notion database. | Customization Options Webhook Node** → adjust endpoint path for your application. Normalization Function** → add fields such as device, OS, location or user-agent. Multiple Attempt Logic** → change the sliding window duration or repetition threshold. Jira Nodes** → modify issue type, labels or project. Slack Nodes** → adjust markdown formatting, channel routing or severity-based channels. Notion Node** → add or modify database fields to store additional context. Optional Enhancements: Geo-IP lookup for country/city info. Automatic IP blocking via firewall or WAF. User notification for suspicious login attempts. Database logging in MySQL/Postgres/MongoDB. Threat intelligence enrichment (e.g., AbuseIPDB). Use Case Examples Detect brute-force attacks targeting user accounts. Identify credential stuffing across multiple users. Monitor admin portal access failures with Jira task creation. Alert security teams instantly when login attempts originate from unusual locations. Centralize failed login monitoring across multiple applications with Notion logging. Troubleshooting Guide | Issue | Possible Cause | Solution | |-------------------------------|---------------------------------------------------|-------------------------------------------------------------| | Workflow not receiving data | Webhook misconfigured | Verify webhook URL & POST payload format | | Jira ticket creation fails | Invalid credentials or insufficient permissions | Update Jira API token and project access | | Slack alert not sent | Incorrect channel ID or missing bot scopes | Fix Slack credentials and permissions | | Multiple attempts not detected| Sliding window logic misaligned | Adjust Detect Multiple Attempts node code | | Notion logging fails | Incorrect database ID or missing credentials | Update Notion node credentials and database configuration | | Errors in normalization | Payload format mismatch | Update Normalize Login Event function code | Need Help? If you need help setting up, customizing or extending this workflow, WeblineIndia can assist with full n8n development, workflow automation, security event processing and custom integrations.

Automate your B2B lead generation by scraping Google Maps, generating AI business summaries, and extracting hidden contact emails from websites, all triggered via Telegram. Why Use This Workflow? Time Savings: Reduces lead research time from 4 hours of manual searching to 5 minutes of automated execution per 50 leads. Cost Reduction: Replaces expensive monthly lead database subscriptions with a pay-as-you-go model using Apify and OpenAI. Error Prevention: Uses AI to deduplicate results and ensure company summaries are professional and consistent for your CRM. Scalability: Allows you to trigger massive scraping tasks from your mobile phone via Telegram while the backend handles the heavy lifting. Ideal For Sales Development Reps (SDRs):** Rapidly building targeted lists of local businesses for cold outreach or door-knocking campaigns. Marketing Agencies:** Identifying new businesses in specific sectors (e.g., "Dentists in Paris") to offer SEO or advertising services. Real Estate Investors:** Finding specific commercial properties or business types in a geographic area to identify investment opportunities. How It Works Trigger: The workflow starts when you send a message to your Telegram bot in the format: Sector; Limit; MapsURL. Data Collection: n8n parses these parameters and triggers an Apify Actor to scrape Google Maps for business details. Processing: The workflow retrieves the results, removes duplicate entries, and begins a loop to process each business. Intelligence Layer (Summary): OpenAI analyzes the business data to create a natural, human-like summary of the company. Enrichment (Email Hunting): If the business has a website, Jina AI fetches the page content, and OpenAI extracts the most authoritative contact email. Output & Delivery: Core data is upserted into Google Sheets, and the email is updated once found. A "DONE" notification is sent to Telegram upon completion. Setup Guide Prerequisites | Requirement | Type | Purpose | |-------------|------|---------| | n8n instance | Essential | Workflow execution and logic | | Apify Account | Essential | Scrapes Google Maps data | | OpenAI API Key | Essential | Generates summaries and extracts emails | | Google Sheets | Essential | Data storage and lead management | | Telegram Bot | Essential | User interface for triggering searches | | Jina AI Key | Essential | Converts websites to LLM-friendly Markdown | Installation Steps Import the JSON file into your n8n instance. Configure credentials: Telegram: Create a bot via @BotFather and add your token to the Telegram nodes. Apify: Provide your API token to the "Run Maps Scraper" and "Fetch Dataset" nodes. OpenAI: Add your API key to the enrichment nodes. Google Sheets: Connect your Google account and select your target Spreadsheet ID. Set up the Sheet: Ensure your Google Sheet has headers matching the "Upsert" node (Title, Email, Category, Website, etc.). Test execution: Send a message like Coffee Shops; 5; https://www.google.com/maps/search/coffee+shops+london to your bot. Technical Details Core Nodes | Node | Purpose | Key Configuration | |------|---------|-------------------| | Telegram Trigger | Captures user input | Listens for /message updates | | Apify Node | Executes Maps Scraper | Uses nwua9Gu5YrADL7ZDj actor ID | | OpenAI Node | AI Analysis | Configured for JSON output (Summary) and extraction (Email) | | Jina AI | Web Scraping | Converts HTML to Markdown for AI readability | | Google Sheets | Database | Uses Append or Update based on the business Title | Workflow Logic The workflow utilizes a Split In Batches loop to ensure stability. It first performs a "shallow" save of business details (name, phone, address) and then attempts a "deep" enrichment only if a website URL is detected. This two-stage approach ensures you don't lose data if a website crawl fails. Customization Options Basic Adjustments: Rate Limit:** Adjust the "Wait" node duration (default 2s) to comply with Google Sheets or API limits. Language:** Modify the OpenAI prompt to generate summaries in your preferred language (e.g., English, French, Spanish). Advanced Enhancements: CRM Integration:** Replace Google Sheets with HubSpot or Pipedrive for direct lead injection. Slack Notifications:** Send the final lead list or business summary directly to a sales channel. Auto-Emailer:** Add a Gmail/Outlook node to automatically send an introductory email once a lead is found. Troubleshooting Common Issues: | Problem | Cause | Solution | |---------|-------|----------| | Empty Email column | Website protected by bot-blockers | Try a different scraper or use a proxy in Jina AI | | Apify Timeout | Scraping limit set too high | Lower the "limit" parameter in your Telegram message | | 429 Errors | Google Sheets rate limits | Increase the duration in the "Wait Rate Limit" node | Use Case Examples Scenario 1: Local SEO Agency Challenge: Finding local contractors with poor reviews but high revenue potential. Solution: Use the workflow to scrape "Plumbers" in a city, use AI to summarize their online presence, and collect emails. Result: A curated list of 100 leads with contact info and a "Pitch" summary generated in minutes. Scenario 2: SaaS Cold Outreach Challenge: Getting the direct email of a business owner for a new booking software. Solution: Trigger the scraper via Telegram while in the field. The workflow extracts the "authoritative" email (manager/owner) from their site. Result: Accurate, high-intent lead data delivered directly to a master spreadsheet for the sales team. Created by: Khaisa Studio Category: Marketing | Tags: Lead Gen, AI, Google Maps, Telegram Need custom workflows? Contact us Connect with the creator: Portfolio • Workflows • LinkedIn • Medium • Threads

Who’s it for This workflow is designed for Japanese-speaking individuals who want to efficiently stay up to date with practical, experience-based AI and engineering insights shared by developers on platforms like Qiita and note. It specifically targets users who prefer real-world knowledge such as implementation examples, troubleshooting solutions, and hands-on AI use cases written in Japanese, rather than generalized global IT news or curated media content. The workflow is optimized for those who want to quickly consume high-quality Japanese technical content on a daily basis. What it does This workflow collects, processes, and summarizes Japanese AI and engineering-related articles published within the last 24 hours from Qiita and note RSS feeds. It merges multiple RSS sources, filters only recent articles (last 24 hours), and prepares structured data for AI processing. Then, it uses AI to evaluate and rank the articles, selects the most valuable ones, retrieves each article page, extracts readable content, and generates structured summaries in Japanese, including: Summary Target audience Use cases Merits Demerits Finally, it formats the results and sends a daily digest to Slack in Japanese. Users can also customize RSS sources to match their preferred content. How it works A scheduled trigger starts the workflow automatically. RSS feeds from Qiita and note are fetched and merged. Articles are filtered to only include those published within the last 24 hours. Articles are normalized into a structured format for AI processing. Gemini evaluates and ranks articles based on usefulness and selects the top 10. Article links are prepared and each page is fetched. HTML is cleaned and converted into readable text. OpenAI generates structured summaries in Japanese. The final digest is formatted and posted to Slack in Japanese. Requirements Google Gemini API credentials OpenAI API credentials Slack OAuth2 credentials A Slack channel for notifications How to set up Add your API credentials in n8n, set the Slack destination channel, review and adjust the AI prompts if needed, and activate the workflow. You can also customize RSS sources depending on your preferred Japanese content (e.g., specific hashtags, niche blogs, or categories). How to customize the workflow You can customize this workflow by: Adding or replacing RSS sources (e.g., Japanese niche engineering blogs or communities) Adjusting filtering conditions (e.g., time range beyond 24 hours or keyword-based filtering) Refining AI scoring criteria to better match your interests Modifying summary structure or output format (Japanese-focused customization) Customizing Slack message layout for better readability Changing the output language (default is Japanese)

📊 Description This workflow helps travel, booking, and customer support businesses create an AI chatbot that answers customer questions from their own knowledge base. Instead of manually replying to repeated questions about bookings, cancellations, refunds, pricing, policies, or service details, the business can upload a knowledge document and let the chatbot respond through a webhook. Built for travel agencies, booking platforms, hotels, shortlets, service businesses, and automation builders who want to create a simple RAG chatbot using Google Drive, OpenAI, Pinecone, and n8n. What This Workflow Does 📄 Downloads a knowledge document from Google Drive ✂️ Splits the document into smaller chunks for better retrieval 🧠 Creates embeddings using OpenAI 📦 Stores the knowledge base inside a Pinecone vector index 💬 Receives customer questions through a webhook 🔎 Searches Pinecone for the most relevant information 🤖 Uses OpenAI to generate a short, helpful answer 📤 Returns the chatbot response back to the connected frontend, chatbot, or app Key Benefits ✅ Turns a simple document into a searchable AI knowledge base ✅ Helps answer repeated customer questions faster ✅ Uses RAG so responses are based on the uploaded business information ✅ Works with any frontend, chatbot, or app that can send a POST request ✅ Easy to customize for different travel, booking, or support businesses ✅ Separates knowledge indexing from live chatbot responses ✅ Includes a simple response formatting step before returning the answer How It Works SW1 — Knowledge Base Indexing The workflow starts with a manual trigger. It downloads a knowledge document from Google Drive, loads the document content, splits it into smaller text chunks, creates embeddings with OpenAI, and stores those chunks inside a Pinecone vector index. This section should be run first because the chatbot can only answer from information that has already been indexed into Pinecone. SW2 — Chatbot Question Answering The webhook receives a customer question from a frontend, chatbot, or app. The workflow sends the question into a retrieval QA chain, searches the Pinecone vector store for relevant context, and uses OpenAI to generate a short answer based on the retrieved information. The response is then cleaned by a Code node and returned through Respond to Webhook. Features Google Drive knowledge document download OpenAI embeddings Pinecone vector storage Recursive text splitting Webhook-based chatbot input Retrieval QA chain for context-aware answers Short customer-support style responses Response cleanup before returning to the frontend Reusable structure for travel, booking, hotel, shortlet, and service businesses Requirements Google Drive account OpenAI API key Pinecone account and index n8n account or self-hosted n8n instance A prepared knowledge document such as FAQs, policies, service details, or booking information A frontend, chatbot, or app that can send questions to the webhook Setup Steps Connect your Google Drive, OpenAI, and Pinecone accounts. Add your own knowledge document to Google Drive. Create or select a Pinecone index for the vector database. Replace the Google Drive file placeholder with your own file. Replace the Pinecone index placeholder with your own index name. Run the indexing section once to store your document in Pinecone. Connect your frontend, chatbot, or app to the webhook URL. Send a test question using the chatInput field. Customize the system prompt for your business or client use case. Target Audience ✈️ Travel agencies answering booking and policy questions 🏨 Hotels and shortlet businesses handling repeated guest enquiries 🧳 Booking platforms that need a lightweight support chatbot 🤖 Automation builders creating RAG chatbot demos for clients 📞 Customer support teams that want answers based on internal documents

This n8n template demonstrates how to build O'Carla, an advanced all-in-one Discord AI assistant. It intelligently handles natural conversations, professional image generation, and visual file analysis within a single server integration. Use cases are many: Deploy a smart community manager that remembers past interactions, an on-demand artistic tool for your members, or an AI that can "read" and explain uploaded documents and images! Good to know API Costs:** Each interaction costs vary depending on the model used (Gemini vs. OpenRouter). Check your provider's dashboard for updated pricing. Infrastructure:* This workflow requires a separate Discord bot script (e.g., Node.js) to forward events to the n8n Webhook. It is recommended to host the bot using *PM2** for 24/7 uptime. How it works Webhook Trigger: Receives incoming data (text and attachments) from your Discord bot. Intent Routing: The workflow uses conditional logic to detect if the user wants an image (via keyword gambar:), a vision analysis (via attachments), or a standard chat. Multi-Model Intelligence: Gemini 2.5: Powers rapid and high-quality general chat reasoning. Llama 3.2 Vision (via OpenRouter): Specifically used to describe and analyze images or text-based files. Flux (via Pollinations): Uses a specialized AI Agent to refine prompts and generate professional-grade images. Contextual Memory: A 50-message buffer window ensures O'Carla maintains the context of your conversation based on your Discord User ID. Clean UI Output: Generated image links are automatically shortened via TinyURL to keep the Discord chat interface tidy. How to use Connect your Google Gemini and OpenRouter API keys in the respective nodes. Replace the Webhook URL in your bot script with this workflow's Production Webhook URL. Type gambar: [your prompt] in Discord to generate images. Upload an image or file to Discord to trigger the AI Vision analysis. Requirements n8n instance (Self-hosted or Cloud). Google Gemini API Key. OpenRouter API Key. Discord Bot Token and hosting environment. Customising this workflow O'Carla is highly flexible. You can change her personality by modifying the System Message in the Agent nodes, adjust the memory window length, or swap the LLM models to specialized ones like Claude 3.5 or GPT-4o.

Intelligent chatbot with custom knowledge base Who's it for Businesses, developers, and organizations who need a customizable AI chatbot for internal documentation access, customer support, e-commerce assistance, or any use case requiring intelligent conversation with access to specific knowledge bases. What it does This workflow creates a fully customizable AI chatbot that can be deployed on any platform supporting webhook triggers (websites, Slack, Teams, etc.). The chatbot accesses a personalized knowledge base stored in Supabase and can perform advanced actions like sending emails, scheduling appointments, or updating databases beyond simple conversation. How it works The workflow combines several powerful components: Webhook Trigger: Accepts messages from any platform that supports webhooks AI Agent: Processes user queries with customizable personality and instructions Vector Database: Searches relevant information from your Supabase knowledge base Memory System: Maintains conversation history for context and traceability Action Tools: Performs additional tasks like email sending or calendar booking Technical architecture Chat trigger connects directly to AI Agent Language model, memory, and vector store all connect as tools/components to the AI Agent Embeddings connect specifically to the Supabase Vector Store for similarity search Requirements Supabase account and project AI model API key (any LLM provider of your choice) OpenAI API key (for embeddings - this is covered in Cole Medin's tutorial) n8n built-in PostgreSQL access (for conversation memory) Platform-specific webhook configuration (optional) How to set up Step 1: Configure your trigger The template uses n8n's default chat trigger For external platforms: Replace with webhook trigger and configure your platform's webhook URL Supported platforms: Any service with webhook capabilities (websites, Slack, Teams, Discord, etc.) Step 2: Set up your knowledge base For creating and managing your vector database, follow this comprehensive guide: Watch Cole Medin's tutorial on document vectorization This video shows how to build a complete knowledge base on Supabase The tutorial covers document processing, embedding creation, and database optimization Important: The video explains the OpenAI embeddings configuration required for vector search Step 3: Configure the AI agent Define your prompt: Customize the agent's personality and role Example: "You are the virtual assistant for example.com. Help users by answering their questions about our products and services." Select your language model: Choose any AI provider you prefer (OpenAI, Anthropic, Google, etc.) Set behavior parameters: Define response style, tone, and limitations Step 4: Connect Supabase Vector Store Add the "Supabase Vector Store" tool to your agent Configure your Supabase project credentials Mode: Set to "retrieve-as-tool" for automatic agent integration Tool Description: Customize description (default: "Database") to describe your knowledge base Table configuration: Specify the table containing your knowledge base (example shows "growth_ai_documents") Ensure your table name matches your actual knowledge base structure Multiple tables: You can connect several tables for organized data structure The agent will automatically decide when to search the knowledge base based on user queries Step 5: Set up conversation memory (recommended) Use "Postgres Chat Memory" with n8n's built-in PostgreSQL credentials Configure table name: Choose a name for your chat history table (will be auto-created) Context Window Length: Set to 20 messages by default (adjustable based on your needs) Benefits: Conversation traceability and analytics Context retention across messages Unique conversation IDs for user sessions Stored in n8n's database, not Supabase How to customize the workflow Basic conversation features Response style: Modify prompts to change personality and tone Knowledge scope: Update Supabase tables to expand or focus the knowledge base Language support: Configure for multiple languages Response length: Set limits for concise or detailed answers Memory retention: Adjust context window length for longer or shorter conversation memory Advanced action capabilities The chatbot can be extended with additional tools for: Email automation: Send support emails when users request assistance Calendar integration: Book appointments directly in Google Calendar Database updates: Modify Airtable or other databases based on user interactions API integrations: Connect to external services and systems File handling: Process and analyze uploaded documents Platform-specific deployments Website integration Replace chat trigger with webhook trigger Configure your website's chat widget to send messages to the n8n webhook URL Handle response formatting for your specific chat interface Slack/Teams deployment Set up webhook trigger with Slack/Teams webhook URL Configure response formatting for platform-specific message structures Add platform-specific features (mentions, channels, etc.) E-commerce integration Connect to product databases Add order tracking capabilities Integrate with payment systems Configure support ticket creation Results interpretation Conversation management Chat history: All conversations stored in n8n's PostgreSQL database with unique IDs Context tracking: Agent maintains conversation flow and references previous messages Analytics potential: Historical data available for analysis and improvement Knowledge retrieval Semantic search: Vector database returns most relevant information based on meaning, not just keywords Automatic decision: Agent automatically determines when to search the knowledge base Source tracking: Ability to trace answers back to source documents Accuracy improvement: Continuously refine knowledge base based on user queries Use cases Internal applications Developer documentation: Quick access to technical guides and APIs HR support: Employee handbook and policy questions IT helpdesk: Troubleshooting guides and system information Training assistant: Learning materials and procedure guidance External customer service E-commerce support: Product information and order assistance Technical support: User manuals and troubleshooting Sales assistance: Product recommendations and pricing FAQ automation: Common questions and instant responses Specialized implementations Lead qualification: Gather customer information and schedule sales calls Appointment booking: Healthcare, consulting, or service appointments Order processing: Take orders and update inventory systems Multi-language support: Global customer service with language detection Workflow limitations Knowledge base dependency: Quality depends on source documentation and embedding setup Memory storage: Requires active n8n PostgreSQL connection for conversation history Platform restrictions: Some platforms may have webhook limitations Response time: Vector search may add slight delay to responses Token limits: Large context windows may increase API costs Embedding costs: OpenAI embeddings required for vector search functionality