Workflow Templates

Who this is for? Extract Amazon Best Seller Electronic Info is an automated workflow that extracts best seller data from Amazon's Electronics section using Bright Data Web Unlocker, transform it into structured JSON using Google Gemini's LLM, and forwards a fully structured JSON response to a specified webhook for downstream use. This workflow is tailored for: eCommerce Analysts** Who need to monitor Amazon best-seller trends in the Electronics category and track changes in real-time or on a schedule. Product Intelligence Teams** Who want structured insights on competitor offerings, including rankings, prices, ratings, and promotions. AI-powered Chatbot Developers** Who are building assistants capable of answering product-related queries with fresh, structured data from Amazon. Growth Hackers & Marketers** Looking to automate competitive research and surface trending product data to inform pricing strategies. Data Aggregators and Price Trackers** Who need reliable and smart scraping of Amazon data enriched with AI-driven parsing. What problem is this workflow solving? Keeping up with Amazon's best sellers in Electronics is a time-consuming, error-prone task when done manually.This workflow automates the process, ensuring:​ Automating Data Extraction from Amazon Best Sellers using Bright Data, ensuring reliable access to real-time, structured data. Enhancing Raw Data with Google Gemini, turning product lists into structured JSON using the Google Gemini LLM. Sending Results to a Webhook, enabling seamless integration into dashboards, databases, or chatbots. What this workflow does The workflow performs the following steps:​ Extracts Amazon Best Seller Electronics page info using Bright Data's Web Unlocker API. Processes the unstructured content using Google Gemini's Flash Exp model to extract structured product data. Sends the structured information to a webhook endpoint. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). Update the Amazon URL with the Bright Data zone by navigating to the Amazon URL with the Bright Data Zone node. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. How to customize this workflow to your needs This workflow is built to be flexible - whether you're a market researcher, e-commerce entrepreneur, or data analyst. Here's how you can adapt it to fit your specific use case: Change the Amazon Category** Update the Amazon URL with the topic of your interest such as Computers & Accessories, Home Audio, etc. Customize the Gemini Prompt** Update the Gemini prompt to get different styles of output — comparison tables, summaries, feature highlights, etc. Send Output to Other Destinations** Replace the Webhook URL to forward output to: Google Sheets Airtable Slack or Discord Custom API endpoints

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Transform your expense tracking with automated AI receipt processing that extracts data and organizes it instantly. What this workflow does Monitors Google Drive for new receipt uploads (images/PDFs) Downloads and processes files automatically Extracts key data using VLM Run community node (merchant, amount, currency, date) Saves structured data to Google Sheets for easy tracking Setup Prerequisites: Google Drive/Sheets accounts, VLM Run API credentials, n8n instance. You need to install VLM Run community node. To install Community nodes you need to follow steps, Settings -> Community Nodes -> Install -> Search with name @vlm-run/n8n-nodes-vlmrun Quick Setup: Configure Google Drive OAuth2 and create receipt upload folder Add VLM Run API credentials Create Google Sheets with columns: Customer, Merchant, Amount, Currency, Date Update folder/sheet IDs in workflow nodes Test and activate How to customize this workflow to your needs Extend functionality by: Adding expense categories and approval workflows Connecting to accounting software (QuickBooks, Xero) Including Slack notifications for processed receipts Adding data validation and duplicate detection This workflow transforms manual receipt processing into an automated system that saves hours while improving accuracy.

How It Works This workflow automates the analysis of security alerts from Sophos Central, turning raw events into actionable intelligence. It uses the official Sophos SIEM integration tool to fetch data, enriches it with VirusTotal, and leverages Google Gemini to provide a real-time threat summary and mitigation plan via Telegram. Prerequisite (Important): This workflow is triggered by a webhook that receives data from an external Python script. You must first set up the Sophos-Central-SIEM-Integration script from the official Sophos GitHub. This script will fetch data and forward it to your n8n webhook URL. Tool Source Code: Sophos/Sophos-Central-SIEM-Integration The n8n Workflow Steps Webhook: Receives enriched event and alert data from the external Python script. IF (Filter): Immediately filters the incoming data to ensure only events with a high or critical severity are processed, reducing noise from low-priority alerts. Code (Prepare Indicator): Intelligently inspects the Sophos event data to extract the primary threat indicator. It prioritizes indicators in the following order: File Hash (SHA256), URL/Domain, or Source IP. HTTP Request (VirusTotal): The extracted indicator is sent to the VirusTotal API to get a detailed reputation report, including how many security vendors flagged it as malicious. Code (Prompt for Gemini): The raw JSON output from VirusTotal is processed into a clean, human-readable summary and a detailed list of flagging vendors. AI Agent (Google Gemini): All collected data—the original Sophos log, the full alert details, and the formatted VirusTotal reputation—is compiled into a detailed prompt for Gemini. The AI acts as a virtual SOC analyst to: Create a concise incident summary. Determine the risk level. Provide a list of concrete, actionable mitigation steps. Telegram: The complete analysis and mitigation plan from Gemini is formatted into a clean, easy-to-read message and sent to your specified Telegram chat. Setup Instructions Configure the external Python script to forward events to this workflow's Production URL. In n8n, create Credentials for Google Gemini, VirusTotal, and Telegram. Assign the newly created credentials to the corresponding nodes in the workflow.

How it works This workflow automatically scrapes real estate listings from Zillow and sends them to a Telegram channel. Key Steps Scheduled Trigger - Runs the workflow at specified intervals to find new listings. AI-Powered Scraping - Uses ScrapeGraphAI to extract property information from Zillow. Data Formatting - Processes and structures the scraped data for Telegram messages. Telegram Integration - Sends formatted listing details to your specified Telegram channel. Set up steps Setup time: 5-10 minutes Configure ScrapeGraphAI credentials - Add your ScrapeGraphAI API key. Set up Telegram connection - Connect your Telegram account and specify the target channel. Customize the Zillow URL - Update the URL to target specific locations or search criteria. Adjust schedule - Modify the trigger timing based on how frequently you want to check for new listings.

This n8n workflow demonstrates how we can use Multimodal LLMs to parse and extract from PDF documents in n8n. In this particular scenario, we're passing a candidate's CV/resume to an AI which filters out unqualified applications. However, this sneaky candidate has added in hidden prompt to bypass our bot! Whatever will we do? No fret, using AI Vision is one approach to solve this problem... read on! How it works Our candidate's CV/Resume is a PDF downloaded via Google Drive for this demonstration. The PDF is then converted into an image PNG using a tool called Stirling PDF. Since the hidden prompt has a white font color, it is is invisible in the converted image. The image is then forwarded to a Basic LLM node to process using our multimodal model - in this example, we'll use Google's Gemini 1.5 Pro. In the Basic LLM node, we'll need to set a User Message with the type of Binary. This allows us to directly send the image file in our request. The LLM is now immune to the hidden prompt and its response is has expected. The example CV/Resume with hidden prompt can be found here: https://drive.google.com/file/d/1MORAdeev6cMcTJBV2EYALAwll8gCDRav/view?usp=sharing Requirements Google Gemini API Key. Alternatively, GPT4 will also work for this use-case. Stirling PDF or another service which can convert PDFs into images. Note for data privacy, this example uses a public API and it is recommended that you self-host and use a private instance of Stirling PDF instead. Customising the workflow Swap out the manual trigger for another trigger such as a webhook to integrate into your existing services. This example demonstrates a validation use-case ie. "does the candidate look qualified?". You can try additionally extracting data points instead such as years of experiences, previous companies etc.

What this workflow does This workflow in n8n demonstrates how to send a message in Lark using a Lark bot. It begins with a manual trigger and then retrieves the necessary Lark token via a POST request. The token is used to authenticate and send a message to a specific chat using the Lark API. The input node provides the required app_id, app_secret, chat_id, and message content. After obtaining the token, the message is sent with the Lark API's message/v4/send/ endpoint. Who This Is For This n8n workflow is ideal for organizations, teams, and developers who need to automate message sending within Lark, especially those managing notifications, alerts, or team reminders. It can help users reduce manual messaging tasks by leveraging a Lark bot to deliver messages at specific intervals or based on particular conditions, enhancing team communication and responsiveness. Setup Fill the Input node with your values Exchange the bearer token in the Send Message node with your token Author: Hiroshi

Goodreads Quote Extraction with Bright Data and Gemini This workflow demonstrates how to fetch data specifically from Goodreads web pages using Bright Data and then extract specific information (quotes) from that data using a Google Gemini AI model. How it works The workflow is triggered manually. It sends a request to a Bright Data collector to scrape data from a predefined list of Goodreads URLs. The collected text data from Goodreads is then passed to a Google Gemini AI node. The AI node processes the text and extracts quotes based on a specified JSON schema output format. Set up steps Setting up this workflow should take only a few minutes. You will need a Bright Data API key to configure the 'Header Auth' credential. You will need a Google Gemini API key to configure the 'Google Gemini(PaLM) Api account' credential. Ensure the correct Bright Data collector ID is set in the 'Perform Bright Data Web Request' node URL. Make sure the full list of target Goodreads URLs is correctly added to the 'Perform Bright Data Web Request' node's body. Link your created credentials to the respective nodes ('Perform Bright Data Web Request' and 'Quotes Extractor'). Keep detailed descriptions for specific node configurations in sticky notes inside your workflow canvas.

What this does Show you how to us XMLRPC APIs via the generic HTTP-Request-node, by the example of posting to a wordpress blog This is also a feasible workaround if a specific n8n integration does not work or stops working (which happens e.g. with the Wordpress node) How it works First, the XML payload for the request is being prepared (in a code node, which also properly escapes special character in the values that you want to send to the XMLRPC endpoint) Then, the HTTP Request node sends the request using the HTTP post method Last, the returned XML response is converted to JSON which a conditional node uses to determine whether th operation was successful or not Setup steps: Import workflow Ensure you have a wordpress blog with a user that has an app-Password Edit the "Settings"-node and enter your individual values for url/user/app-pw

Automate Video Creation with Luma AI Dream Machine and Airtable (Part 1) Description This workflow automates video creation using Luma AI Dream Machine and n8n. It generates dynamic videos based on custom prompts, random camera motion, and predefined settings, then stores the video and thumbnail URLs in Airtable for easy access and tracking. This automation makes it easy to create high-quality videos at scale with minimal effort. 👉 Airtable Base Template 🎥 Tutorial Video Setup 1. Luma AI Setup Create an account with Luma AI. Generate an API key from Luma AI for authentication. Ensure the API key has permission to create and manage video requests. 2. Airtable Setup Create an Airtable base with the following fields: Generation ID** – To match incoming webhook data. Status** – Workflow status (e.g., "Done"). Video URL** – Stores the generated video URL. Thumbnail URL** – Stores the thumbnail URL. Prompt** – The video prompt used in the request. Aspect Ratio** – Defines the video format (e.g., 9:16). Duration** – Length of the video. 👉 Use the Airtable template linked above to simplify setup. 3. n8n Setup Install n8n (local or cloud). Set up Luma AI and Airtable credentials in n8n. Import the workflow and customize the settings based on your needs. How It Works 1. Global Settings Configuration The Set node defines key settings such as: Prompt** – Example: "A crocheted parrot in a crocheted pirate outfit swinging on a crocheted perch." Aspect Ratio** – Example: "9:16" Loop** – Example: "true" Duration** – Example: "5 seconds" Cluster ID** – Used to group related videos for easy tracking. Callback URL** - Used for the Webhook workflow in Part 2 2. Random Camera Motion The Code node randomly selects a camera motion (e.g., Zoom In, Pan Left, Crane Up) to create dynamic and visually engaging videos. 3. API Request to Luma AI The HTTP Request node sends a POST request to Luma AI’s API with the following parameters: Prompt – Uses the defined global settings. Aspect Ratio – Matches the target platform (e.g., TikTok or YouTube). Duration – Length of the video. Loop – Determines if the video should loop. Callback URL – Sends a POST response when the video is complete. 4. Capture API Response Luma AI sends a POST response to the callback URL once video generation is complete. The response includes: Video URL – Direct link to the video. Thumbnail URL – Link to the video thumbnail. Generation ID – Used to match the record in Airtable. 5. Store in Airtable The Airtable node updates the record with the video and thumbnail URLs. Generation ID** is crucial for matching future webhook responses to the correct video record. Why This Workflow is Useful ✅ Automates high-quality video creation ✅ Reduces manual effort by handling prompt generation and API calls ✅ Random camera motion makes videos more dynamic ✅ Ensures organized tracking with Airtable ✅ Scalable – Ideal for automating large-scale content creation Next Steps Part 2** – Handling webhook responses and updating Airtable automatically. Future Enhancements** – Adding more camera motions, multi-platform support, and automated video editing.

🔍 Glassdoor Job Finder: Bright Data Scraping + Keyword-Based Automation A comprehensive n8n automation that scrapes Glassdoor job listings using Bright Data's web scraping service based on user-defined keywords, location, and country parameters, then automatically stores the results in Google Sheets. 📋 Overview This workflow provides an automated job search solution that extracts job listings from Glassdoor using form-based inputs and stores organized results in Google Sheets. Perfect for recruiters, job seekers, market research, and competitive analysis. Workflow Description: Automates Glassdoor job searches using Bright Data's web scraping capabilities. Users submit keywords, location, and country via form trigger. The workflow scrapes job listings, extracts company details, ratings, and locations, then automatically stores organized results in Google Sheets for easy analysis and tracking. ✨ Key Features 🎯 Form-Based Input: Simple web form for job type, location, and country 🔍 Glassdoor Integration: Uses Bright Data's Glassdoor dataset for accurate job data 📊 Smart Data Processing: Automatically extracts key job information 📈 Google Sheets Storage: Organized data storage with automatic updates 🔄 Status Monitoring: Built-in progress tracking and retry logic ⚡ Fast & Reliable: Professional scraping with error handling 🎯 Keyword Flexibility: Search any job type with location filters 📝 Structured Output: Clean, organized job listing data 🎯 What This Workflow Does Input Job Keywords:** Job title or role (e.g., "Software Engineer", "Marketing Manager") Location:** City or region for job search Country:** Target country for job listings Processing Form Submission Data Scraping via Bright Data Status Monitoring Data Extraction Data Processing Sheet Update Output Data Points | Field | Description | Example | |-------|-------------|---------| | Job Title | Position title from listing | Senior Software Engineer | | Company Name | Employer name | Google Inc. | | Location | Job location | San Francisco, CA | | Rating | Company rating score | 4.5 | | Job Link | Direct URL to listing | https://glassdoor.com/job/... | 🚀 Setup Instructions Prerequisites n8n instance (self-hosted or cloud) Google account with Sheets access Bright Data account with Glassdoor scraping dataset access 5–10 minutes for setup Step 1: Import the Workflow Copy the JSON workflow code from the provided file In n8n: Workflows → + Add workflow → Import from JSON Paste JSON and click Import Step 2: Configure Bright Data Set up Bright Data credentials in n8n Ensure access to dataset: gd_lpfbbndm1xnopbrcr0 Update API tokens in: "Scrape Job Data" node "Check Delivery Status of Snap ID" node "Getting Job Lists" node Step 3: Configure Google Sheets Integration Create a new Google Sheet (e.g., "Glassdoor Job Tracker") Set up Google Sheets OAuth2 credentials in n8n Prepare columns: Column A: Job Title Column B: Company Name Column C: Location Column D: Rating Column E: Job Link Step 4: Update Workflow Settings Update "Update Job List" node with your Sheet ID and credentials Test the form trigger and webhook URL Step 5: Test & Activate Submit test data (e.g., "Software Engineer" in "New York") Activate the workflow Verify Google Sheet updates and field extraction 📖 Usage Guide Submitting Job Searches Navigate to your workflow's webhook URL Fill in: Search Job Type Location Country Submit the form Reading the Results Real-time job listing data Company ratings and reviews Direct job posting links Location-specific results Processing timestamps 🔧 Customization Options More Data Points:** Add job descriptions, salary, company size, etc. Search Parameters:** Add filters for salary, experience, remote work Data Processing:** Add validation, deduplication, formatting 🚨 Troubleshooting Bright Data connection failed:** Check API credentials and dataset access No job data extracted:** Validate search terms and location format Google Sheets permission denied:** Re-authenticate and check sharing Form submission failed:** Check webhook URL and form config Workflow execution failed:** Check logs, add retry logic Advanced Troubleshooting Check execution logs in n8n Test individual nodes Verify data formats Monitor rate limits Add error handling 📊 Use Cases & Examples Recruitment Pipeline:** Track job postings, build talent database Market Research:** Analyze job trends, hiring patterns Career Development:** Monitor opportunities, salary trends Competitive Intelligence:** Track competitor hiring activity ⚙️ Advanced Configuration Batch Processing:** Accept multiple keywords, loop logic, delays Search History:** Track trends, compare results over time External Tools:** Integrate with CRM, Slack, databases, BI tools 📈 Performance & Limits Single search:** 2–5 minutes Data accuracy:** 95%+ Success rate:** 90%+ Concurrent searches:** 1–3 (depends on plan) Daily capacity:** 50–200 searches Memory:** ~50MB per execution API calls:** 3 Bright Data + 1 Google Sheets per search 🤝 Support & Community n8n Community Forum:** community.n8n.io Documentation:** docs.n8n.io Bright Data Support:** Via your dashboard GitHub Issues:** Report bugs and features Contributing: Share improvements, report issues, create variations, document best practices. Need Help? Check the full documentation or visit the n8n Community for support and workflow examples.

Automate Video Creation with Luma AI Dream Machine and Airtable (Part 2) Description This is the second part of the Luma AI Dream Machine automation. It captures the webhook response from Luma AI after video generation is complete, processes the data, and automatically updates Airtable with the video and thumbnail URLs. This completes the end-to-end automation for video creation and tracking. 👉 Airtable Base Template 👉 Tutorial Video Setup 1. Luma AI Setup Ensure you’ve created an account with Luma AI and generated an API key. Confirm that the API key has permission to manage video requests. 2. Airtable Setup Make sure your Airtable base includes the following fields (set up in Part 1): Use the Airtable Base Template linked above to simplify setup. Generation ID** – To match incoming webhook data. Status** – Workflow status (e.g., "Done"). Video URL** – Stores the generated video URL. Thumbnail URL** – Stores the thumbnail URL. 3. n8n Setup Ensure that the n8n workflow from Part 1 is set up and configured. Import this workflow and connect it to the webhook callback from Luma AI. How It Works 1. Webhook Trigger The Webhook node listens for a POST response from Luma AI once video generation is finished. The response includes: Video URL – Direct link to the video. Thumbnail URL – Link to the video thumbnail. Generation ID – Used to match the record in Airtable. 2. Process Webhook Data The Set node extracts the video data from the webhook response. The If node checks if the video URL is valid before proceeding. 3. Store in Airtable The Airtable node updates the record with: Video URL – Direct link to the video. Thumbnail URL – Link to the video thumbnail. Status – Marked as "Done." Uses the Generation ID to match and update the correct record. Why This Workflow is Useful ✅ Automates the completion step for video creation ✅ Ensures accurate record-keeping by matching generation IDs ✅ Simplifies the process of managing and organizing video content ✅ Reduces manual effort by automating the update process Next Steps Future Enhancements** – Adding more complex post-processing, video trimming, and multi-platform publishing.

This template helps anyone track how often their website appears in Google’s AI Overview. a growing part of search results that can’t currently be tracked using traditional SEO tools. With this workflow, users can: Input a list of keywords (from Google Search Console or manual research). Use the SerpApi to pull Google search results. Extract AI Overview content and its list of sources. Map that information into a structured Google Sheet, including whether your site is listed in those sources. Setup is straightforward and fully automated, but you'll need: A SerpApi key A connected Google Sheets account Who is this for? This workflow is designed for SEO professionals, digital marketers, and site owners who want to track their website’s visibility in Google AI Overviews. What problem does it solve? AI Overviews are rapidly becoming more common in Google search results. However, there's no tool (yet) that tells you if your website is appearing in those answers. This is a blind spot for SEO. This workflow helps you check your site’s presence in AI Overviews manually, at scale. What does the workflow do? The workflow: Takes a list of target keywords (exported from GSC or elsewhere) Uses SerpApi to get search results from Google Extracts the AI Overview block and its sources Checks if your domain is among them Saves all results into a Google Sheet The final Google Sheet will contain: Keyword | AI Overview Exists | List of Sources | Is my domain listed Setup You’ll need: A SerpApi API key A Google Sheet with your list of keywords A connected Google Sheets account in n8n How to customize this workflow Change the list of keywords (pull from GSC or edit the sheet manually) Replace the placeholder domain with your own Adjust the Google Sheet column mapping as needed