Workflow Templates

Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. Until now, these pieces had to be custom-wired. But with the new native n8n nodes for MongoDB Atlas, we reduce that overhead dramatically. With just a few clicks: Store and recall long-term memory from MongoDB Query vector embeddings stored in Atlas Vector Search Use these results in your LLM chains and automation logic In this example we present an ingestion and AI Agent flows that focus around Travel Planning. The different interest points that we want the agent to know about can be ingested into the vector store. The AI Agent will use the vector store tool to get relevant context about those points of interest if it needs to. Prerequisites MongoDB Atlas project and Cluster OpenAI Valid API Key for embeddings (can be other provider) Gemini API Key for the LLM (can be other provider) How it works: There are 2 main flows. One is ingesting flow: Gets a document from a webhook and use MongoDB Vector Atlas to embed the document title and description into points_of_interest collection. Embeddings are stored in a field named embedding Embeddings used are OpenAI's but it can be any type of supported embedders. Second flow is an AI Agent node with Chat Memory Stored in MongoDB Atlas and a Vector Search node as a tool: Chat Message Trigger**: Chatting with the AI Agent will trigger the conversation store in the MongoDB Chat Memory node. When data is necessary like a location search or details it will go to the "Vector Search" tool. Vector Search Tool** - uses Atlas Vector Search index created on the points_of_interest collection: // index name : "vector_index" // If you change an embedding provider make sure the numDimensions correspond to the model. { "fields": [ { "type": "vector", "path": "embedding", "numDimensions": 1536, "similarity": "cosine" } ] } Additional Resources MongoDB Atlas Vector Search n8n Atlas Vector Search docs

What Does This Flow Do? This workflow demonstrates how to dynamically generate a line chart using the QuickChart node based on data provided in a JSON object and then upload the resulting chart image to Google Drive. Use Cases You can use it in presentations or requesting for chart generation from a software with HTTP requests. Automated report generation (e.g., daily sales charts). Visualizing data fetched from APIs or databases. Simple monitoring dashboards. Adding charts to internal tools or notifications. How it Works Trigger: The workflow starts manually when you click 'Test workflow'. Set Sample Data: A Set node (Edit Fields: Set JSON data to test) defines a sample JSON object named jsonData. This object contains: reportTitle: A title (not used in the chart generation in this example, but useful for context). labels: An array of strings representing the labels for the chart's X-axis (e.g., ["Q1", "Q2", "Q3", "Q4"]). salesData: An array of numbers representing the data points for the chart's Y-axis (e.g., [1250, 1800, 1550, 2100]). Generate Chart: The QuickChart node is configured to: Create a line chart. Dynamically read labels from the jsonData.labels array (Labels Mode: From Array). Use the jsonData.salesData array as the input data (Note: This configuration places data in the top-level 'Data' field. For more complex charts with multiple datasets or specific dataset options, configure datasets under 'Dataset Options' instead). The node outputs the generated chart image as binary data in a field named data. Upload to Google Drive: The Google Drive node (Google Drive: Upload File): Takes the binary data (data) from the QuickChart node. Uploads the image to your specified Google Drive folder. Dynamically names the file based on its extension (e.g., chart.png). Setup Steps Import: Import this template into your n8n instance. Configure Google Drive Credentials: Select the Google Drive: Upload File node. You MUST configure your own Google Drive credentials. Click on the 'Credentials' dropdown and either select existing credentials or create new ones by following the authentication prompts. (Optional) Customize Google Drive Folder: In the Google Drive: Upload File node, you can change the Drive ID and Folder ID to specify exactly where the chart should be uploaded. Activate: Activate the workflow if you want it to run automatically based on a different trigger. How to Use & Customize Change Input Data:** Modify the labels and salesData arrays within the Edit Fields: Set JSON data to test node to use your own data. Ensure the number of labels matches the number of data points. Use Real Data Sources:** Replace the Edit Fields: Set JSON data to test node with nodes that fetch data from real sources like: HTTP Request (APIs) Postgres / MongoDB nodes (Databases) Google Sheets node Ensure the output data from your source node is formatted similarly (providing labels and salesData arrays). You might need another Set node to structure the data correctly before the QuickChart node. Change Chart Type:** In the QuickChart node, modify the Chart Type parameter (e.g., change from line to bar, pie, doughnut, etc.). Customize Chart Appearance:** Explore the Chart Options parameter within the QuickChart node to add titles, change colors, modify axes, etc., using QuickChart's standard JSON configuration options. Use Datasets (Recommended for Complex Charts):** For multiple lines/bars or more control, configure datasets explicitly in the QuickChart node: Remove the expression from the top-level Data field. Go to Dataset Options -> Add option -> Add dataset. Set the Data field within the dataset using an expression like {{ $json.jsonData.salesData }}. You can add multiple datasets this way. Change Output Destination:** Replace the Google Drive: Upload File node with other nodes to handle the chart image differently: Write Binary File: Save the chart to the local filesystem where n8n is running. Slack / Discord / Telegram: Send the chart to messaging platforms. Move Binary Data: Convert the image to Base64 to embed in HTML or return via webhook response. Nodes Used Manual Trigger Set QuickChart Google Drive Tags: (Suggestions for tags field) QuickChart, Chart, Visualization, Line Chart, Google Drive, Reporting, Automation

This workflow creates an Asana task when a new ticket is created in Zendesk. Subsequent comments on the ticket in Zendesk are added as comments to the task in Asana. Prerequisites Zendesk account and Zendesk credentials. Asana account and Asana credentials. Asana workspace to create tasks in. How it works The workflow listens for new tickets in Zendesk. When a new ticket is created, the workflow creates a new task in Asana. The Asana GID is then saved in one of the ticket's fields (in setup we call this "Asana GID"). The next time a comment is added to the ticket, the workflow retrieves the Asana GID from the ticket's field and adds the comment to the task in Asana. Setup This workflow requires that you set up a webhook in Zendesk. To do so, follow the steps below: In the workflow, open the On new Zendesk ticket node and copy the webhook URL. In Zendesk, navigate to Admin Center > Apps and integrations > Webhooks > Actions > Create Webhook. Add all the required details which can be retrieved from the On new Zendesk ticket node. The webhook URL gets added to the “Endpoint URL” field, and the “Request method” should match what is shown in n8n. Save the webhook. In Zendesk, navigate to Admin Center > Objects and rules > Business rules > Triggers > Add trigger. Give trigger a name such as “New tickets”. Under “Conditions” in “Meet ALL of the following conditions”, add “Status is New”. Under “Actions”, select “Notify active webhook” and select the webhook you created previously. In the JSON body, add the following: { "id": "{{ticket.id}}", "comment": "{{ticket.latest_comment_html}}" } Save the Zendesk trigger. You will also need to set up a field in Zendesk to store the Asana GID. To do so, follow the steps below: In Zendesk, navigate to Admin Center > Objects and rules > Tickets > Fields > Add field. Use the number field option and give the field a name such as “Asana GID”. Save the field. In n8n, open the Update ticket node and select the field you created in Zendesk.

Receive realtime call-event data from telli Purpose and Problem Solved This template automates the process of receiving and acting upon real-time call event data from telli, an AI-powered voice agent platform. It solves the challenge of manually updating CRM records and initiating follow-up actions based on call outcomes. By leveraging webhooks and n8n's powerful workflow capabilities, this template enables businesses to instantly update their Airtable CRM and trigger appropriate follow-up actions, enhancing efficiency and responsiveness in customer interactions. Prerequisites An active telli account with API access and webhook capabilities An Airtable base set up as your CRM n8n instance (cloud or self-hosted) Airtable Specifications Create an Airtable base with the following table and fields: Table: Contacts Fields: Name (Single line text) Phone (Phone number) Email (Email) Appointment_Booked (Checkbox) Interest (Single select: High, Medium, Low) Last_Call_Date (Date) Notes (Long text) Step-by-Step Setup Instructions Webhook Configuration in telli: Log into your telli dashboard Navigate to the webhook settings Set the endpoint URL to your n8n Webhook node URL Select the "call_ended" event to trigger the webhook n8n Workflow Setup: Create a new workflow in n8n Add a Webhook node as the trigger Configure the Webhook node to receive POST requests Parse Webhook Data: Add a Set node to extract relevant information from the webhook payload Map fields such as call_outcome, appointment_booked, and interest Decision Logic: Add a Switch node to create different paths based on the call outcome Create branches for scenarios like "Appointment Booked", "Interested", and "Not Interested" Airtable Integration: Add Airtable nodes for each outcome to update the Contacts table Configure the nodes to update fields like Appointment_Booked, Interest, and Last_Call_Date Follow-up Actions: For "Interested" but not booked outcomes, add an Email node to trigger a follow-up email campaign For "Appointment Booked", add a node to create a calendar event or task Testing and Activation: Use the n8n testing feature to simulate webhook calls and verify each path Once satisfied, activate the workflow Example Workflow Webhook receives a "call_ended" event from telli Set node extracts call_outcome: appointment_booked = true, interest = true Switch node directs to the "Appointment Booked" path Airtable node updates the contact record: Set Appointment_Booked to true Set Interest to "High" Update Last_Call_Date Calendar node creates an appointment for the booked slot Example Payload Below is an example of the payload you might receive from telli when a call ends: { "event": "call_ended", "call": { "call_id": "b4a05730-2abc-4eb0-8066-2e4d23b53ba9", "attempt": 1, "from_number": "+17755719467", "to_number": "+16506794960", "external_contact_id": "external-123", "contact_id": "6bd1e7e0-6d00-4c0b-ad5b-daa72457a27d", "agent_id": "d8931604-92ad-45cf-9071-d9cd2afbad0c", "triggered_at": 1731956924302, "started_at": 1731956932264, "booked_slot_for": "2025-02-24T15:30:00Z", "ended_at": 1731957002078, "call_length_min": 2, "call_status": "COMPLETED", "transcript": "Agent: Hello...", "transcriptObject": [ { "role": "agent", "content": "Hello..." } ], "call_analysis": { "summary": { "value": true, "details": "A call between an agent and a customer talking about buying an ice cream machine" }, "appointment": { "value": true, "details": "2025-02-18T15:30:00Z" }, "interest": { "value": true, "details": "The customer is interested in buying an ice cream machine" } } } } In this example, you can see that the call resulted in a booked appointment and showed customer interest. Your n8n workflow would process this data, updating the Airtable CRM and triggering any necessary follow-up actions. By implementing this template, businesses can automate their post-call processes, ensuring timely follow-ups and accurate CRM updates. This real-time integration between telli's AI voice agents and your Airtable CRM streamlines operations, improves customer engagement, and increases the efficiency of your sales and support teams.

Edit an existing image with OpenAI ImageGen1 via API Request Transform your creative pipeline by letting n8n call OpenAI ImageGen1’s edit image endpoint, automatically replacing or augmenting parts of any image you supply and returning a brand-new version in seconds. Designers, marketers, and product teams can eliminate repetitive manual edits and test more variations, faster. Who is this for? Content creators who need quick, on-brand image tweaks Marketers running A/B visual tests at scale Developers exploring the new ImageGen1 API inside low-code automations Use case / problem solved Opening design software to mask, fill, or swap objects is slow and error-prone. This workflow feeds an input image plus a prompt to OpenAI ImageGen1, receives the edited output, and passes it on to any service you like—perfect for bulk-editing product shots, social visuals, or UI mocks. What this workflow does Read or receive the source image (Webhook → Binary Data). Call OpenAI ImageGen1 with an HTTP Request node, sending the image and edit prompt. Parse the JSON response to capture the returned image URL. Download & hand off the edited file (e.g., upload to S3, post to Slack, or store in Drive). Setup Add your OpenAI API key in the API KEY node. Follow the notes on the workflow for more information. (Optional) Point the final node to your preferred storage or chat tool. > 📝 A sticky note in the workflow summarizes these steps and links to the OpenAI documentation. How to customize this workflow Trigger alternatives**: Replace the Chat with Google Drive, Airtable, etc. Chained edits**: Loop the output back for successive prompts. Conditional flows**: Add an If node to branch actions by image size or category. With renamed nodes, color-coded sticky notes, and a concise setup guide, you’ll be editing images via OpenAI ImageGen1 in under five minutes—no code, maximum creativity.

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

This workflow provides a robust solution for automatically backing up all your n8n workflows to a designated GitHub repository on a daily basis. By leveraging the n8n API and GitHub API, it ensures your workflows are version-controlled and securely stored, safeguarding against data loss and facilitating disaster recovery. How it works The automation follows these key steps: Scheduled trigger: The workflow is initiated automatically every day at a pre-configured time. List existing backups: It first connects to your GitHub repository to retrieve a list of already backed-up workflow files. This helps in determining whether a workflow's backup file needs to be created or updated. Retrieve n8n workflows: The workflow then fetches all current workflows directly from your n8n instance using the n8n REST API. Process and prepare: Each retrieved workflow is individually processed. Its data is converted into JSON format. This JSON content is then encoded to base64, a format suitable for GitHub API file operations. Commit to GitHub: For each n8n workflow: A standardized filename is generated (e.g., workflow-name-tag.json). The workflow checks if a file with this name already exists in the GitHub repository (based on the list fetched in step 2). If the file exists: It updates the existing file with the latest version of the workflow. If it's a new workflow (file doesn't exist): A new file is created in the repository. Each commit is timestamped for clarity. This process ensures that you always have an up-to-date version of all your n8n workflows stored securely in your GitHub version control system, providing peace of mind and a reliable backup history. Pre-requisites Before you can use this template, please ensure you have the following: An active n8n instance (self-hosted or cloud). A GitHub account. A GitHub repository created where you want to store the workflow backups. A GitHub Personal Access Token with repo scope (or fine-grained token with read/write access to the specific backup repository). This token will be used for GitHub API authentication. n8n API credentials (API key) for your n8n instance. Set up steps Setting up this workflow should take approximately 10-15 minutes if you have your credentials ready. Import the template: Import this workflow into your n8n instance. Configure n8n API credentials: Locate the "Retrieve workflows" node. In the "Credentials" section for "n8n API", create new credentials (or select existing ones). Enter your n8n instance URL and your n8n API Key (you can create your n8n api key in the settings of your n8n instance) Configure GitHub credentials: Locate the "List files from repo" node (and subsequently "Update file" / "Upload file" nodes which will use the same credential). In the "Credentials" section for "GitHub API", create new credentials. Select OAuth2/Personal Access Token authentication method. Enter the GitHub Personal Access Token you generated as per the pre-requisites. Specify repository details: In the "List files from repo", "Update file", and "Upload file" GitHub nodes: Set the Owner: Your GitHub username or organization name. Set the Repository: The name of your GitHub repository dedicated to backups. Set the Branch (e.g., main or master) where backups should be stored. (Optional) Specify a Path within the repository if you want backups in a specific folder (e.g., n8n_backups/). Leave blank to store in the root. Adjust schedule (Optional): Select the "Schedule Trigger" node. Modify the trigger interval (e.g., change the time of day or frequency) as needed. By default, it's set for a daily run. Activate the workflow: Save and activate the workflow. Explanation of nodes Here's a detailed breakdown of each node used in this workflow: Schedule trigger** Type: n8n-nodes-base.scheduleTrigger Purpose: This node automatically starts the workflow based on a defined schedule (e.g., daily at midnight). List files from repo** Type: n8n-nodes-base.github Purpose: Connects to your specified GitHub repository and lists all files, primarily to check for existing workflow backups. Aggregate** Type: n8n-nodes-base.aggregate Purpose: Consolidates the list of file names obtained from the "List files from repo" node into a single item for easier lookup later in the "Check if file exists" node. Retrieve workflows** Type: n8n-nodes-base.n8n Purpose: Uses the n8n API to fetch a list of all workflows currently present in your n8n instance. Json file** Type: n8n-nodes-base.convertToFile Purpose: Takes the data of each workflow (retrieved by the "Retrieve workflows" node) and converts it into a structured JSON file format. To base64** Type: n8n-nodes-base.extractFromFile Purpose: Converts the binary content of the JSON file (from the "Json file" node) into a base64 encoded string. This encoding is required by the GitHub API for file content. Commit date & file name** Type: n8n-nodes-base.set Purpose: Prepares metadata for the GitHub commit. It generates: commitDate: The current date and time for the commit message. fileName: A standardized file name for the workflow backup (e.g., my-workflow-vps-backups.json), typically using the workflow's name and its first tag. Check if file exists** Type: n8n-nodes-base.if Purpose: A conditional node. It checks if the fileName (generated by "Commit date & file name") is present in the list of files aggregated by the "Aggregate" node. This determines if the workflow backup already exists in GitHub. Update file** Type: n8n-nodes-base.github Purpose: If the "Check if file exists" node determines the file does exist, this node updates that existing file in your GitHub repository with the latest workflow content (base64 encoded) and a commit message. Upload file** Type: n8n-nodes-base.github Purpose: If the "Check if file exists" node determines the file does not exist, this node creates and uploads a new file to your GitHub repository with the workflow content and a commit message. Customization Here are a few ways you can customize this template to better fit your needs: Backup path**: In the GitHub nodes ("List files from repo", "Update file", "Upload file"), you can specify a Path parameter to store backups in a specific folder within your repository (e.g., workflows/ or daily_backups/). Filename convention**: Modify the "Commit date & file name" node (specifically the expression for fileName) to change how backup files are named. For example, you might want to include the workflow ID or a different date format. Commit messages**: Customize the commit messages in the "Update file" and "Upload file" GitHub nodes to include more specific information if needed. Error handling**: Consider adding error handling branches (e.g., using the "Error Trigger" node or checking for node execution failures) to notify you if a backup fails for any reason. Filtering workflows**: If you only want to back up specific workflows (e.g., those with a particular tag or name pattern), you can add a "Filter" node after "Retrieve workflows" to include only the desired workflows in the backup process. Backup frequency**: Adjust the "Schedule Trigger" node to change how often the backup runs (e.g., hourly, weekly, or on specific days). Template was created in n8n v1.92.2

Do you consistently forget to set a Default Error Workflow when creating new workflows? Then this helper workflow is for you! When activated, this helper workflow will: Scan ALL other workflows every 4 hours Make sure ALL workflows have a default error workflow set (based on what Workflow ID you provide) This helper will SKIP OVER any workflows that have the default_error:false tag set (make sure your default error workflow has the default_error:false tag set, so that you don't end up with recursive loops during errors) Setup Nodes: Once imported, edit the Set Vars node with your default_error_workflow_id value. If you want to change the default_error:false tag to some other tag name, you can do so here as well. You need to update the Set Default Error Workflow node with your PostgreSQL credentials to access the n8n database.

Who is this for? This workflow is perfect for: Companies that manage invoices through Google Drive Business owners who want to minimize manual data entry and maximize accuracy Accounting teams and finance departments seeking to automate invoice processing What problem is this workflow solving? Processing invoices manually is time-consuming, error-prone, and inconsistent. This workflow solves those issues by: Automating invoice processing** from detection to data extraction to storage Improving accuracy** by using AI to extract key invoice data fields reliably Reducing human workload** while maintaining compliance and consistency What this workflow does This workflow creates a fully automated invoice processing system by: Monitoring a Google Drive folder for new PDF invoices in real time Downloading the PDF files and extracting their content using OCR technology Using AI (OpenAI) to parse and extract key invoice fields such as invoice number, date, total amount, vendor name, itemized details, tax, and category Validating the extracted data to ensure compliance with a structured JSON schema Storing structured data in Google Sheets for easy access, review, and reporting Key Features: AI-powered extraction handles both text-based and scanned PDF invoices Provides a structured, searchable invoice database in Google Sheets Configured to run as frequently as the user needs, ensuring timely processing. Setup Copy the Google Sheet template here: 👉 PDF Invoice Parser – Google Sheet Template Connect your Google Drive account to the Drive Trigger and File Download nodes Add your OpenAI API key in the AI Parser node Link the Google Sheet in the final storage node Drop a test invoice PDF into the monitored Drive folder Required Credentials: OpenAI API Key** Google Drive Credentials** Google Sheets Credentials** How to customize this workflow to your needs Modify the polling interval** (default: every minute) for higher/lower frequency. Integrate with your accounting software** by adding nodes (e.g., QuickBooks, Xero). Use alternative LLM** such as Gemini, Claude.

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.

This cutting-edge n8n automation is a powerful market research tool designed to continuously monitor and capture User-Generated Content (UGC) opportunities on Fiverr. By intelligently scraping, parsing, and logging gig data, this workflow provides: Automated Market Scanning: Daily scrapes of Fiverr UGC gigs Real-time market intelligence Consistent, hands-off data collection Intelligent Data Extraction: Parses complex HTML structures Captures key gig details Transforms unstructured web data into actionable insights Seamless Data Logging: Automatic Google Sheets integration Comprehensive gig marketplace tracking Historical data preservation Key Benefits 🤖 Full Automation: Continuous market research 💡 Smart Filtering: Detailed UGC gig insights 📊 Instant Reporting: Real-time market trends ⏱️ Time-Saving: Eliminate manual research Workflow Architecture 🔍 Stage 1: Automated Triggering Scheduled Scraping**: Daily gig discovery Precise Timing**: Configurable run intervals Consistent Monitoring**: Always-on market intelligence 🌐 Stage 2: Web Scraping HTTP Request**: Fetch Fiverr search results Dynamic Headers**: Bypass potential scraping restrictions Targeted Search**: UGC-specific gig discovery 🧩 Stage 3: Data Extraction HTML Parsing**: Extract critical gig information Structured Data Collection**: Gig Prices Seller Names Gig Titles Direct Gig URLs 📋 Stage 4: Data Logging Google Sheets Integration**: Automatic data storage Historical Tracking**: Build comprehensive gig databases Easy Analysis**: Spreadsheet-ready format Potential Use Cases Content Creators**: Market rate research Freelance Platforms**: Competitive intelligence Marketing Agencies**: UGC trend analysis Recruitment Specialists**: Talent pool mapping Business Strategists**: Market opportunity identification Setup Requirements Fiverr Search Configuration Targeted search keywords Specific UGC categories Web Scraping Preparation User-agent rotation strategy Potential proxy configuration Robust error handling Google Sheets Setup Connected Google account Prepared spreadsheet Appropriate sharing permissions n8n Installation Cloud or self-hosted instance Import workflow configuration Configure API credentials Future Enhancement Suggestions 🤖 AI-powered gig trend analysis 📊 Advanced data visualization 🔔 Real-time price change alerts 🧠 Machine learning market predictions 🌐 Multi-platform gig tracking Ethical Considerations Respect Fiverr's Terms of Service Implement responsible scraping practices Avoid overwhelming target websites Use data for legitimate research purposes Technical Recommendations Implement exponential backoff for requests Use randomized delays between scrapes Maintain flexible CSS selector strategies Consider rate limiting and IP rotation Connect With Me Ready to unlock market insights? 📧 Email: Yaron@nofluff.online 🎥 YouTube: @YaronBeen 💼 LinkedIn: Yaron Been Transform your market research with intelligent, automated workflows!

How it works This template uses the n8n AI agent node as an orchestrating agent that decides which tool (knowledge graph) to use based on the user's prompt. How to use Create an account and apply for an API key on https://ai.nero.com/ai-api?utm_source=n8n-base-workflow. Fill your key into the Create task and Query task status nodes. Select an AI service and modify Create task node parameters, the API doc: https://ai.nero.com/ai-api/docs. Execute the workflow so that the webhook starts listening. Make a test request by postman or other tools, the test URL from the Webhook node. You will receive the output in the webhook response. Our API doc Please create an account to access our API docs. https://ai.nero.com/ai-api/docs. Use cases Large Scale Printing Upscale images into ultra-sharp, billboard-ready masterpieces with 300+ DPI and billions of pixels. Game Assets Compression Improve your game performance with AI-Image Compression: Faster, Better & Lossless. E-commerce Image Editing Remove & replace your product image backgrounds, create virtual showrooms. Photo Retouching Remove & reduce grains & noises from images. Face Animation Transform static images into dynamic facial expression videos or GIFs with our cutting-edge Face Animation API Photo Restoration Our Al-driven Photo Restoration API offers advanced scratch removal, face enhancement, and image upscaling. Colorize Photo Transform black & white images into vivid colors. Avatar Generator Turn your selfie into custom avatars with different styles and backgrounds Website Compression Speed up your website, compress your images in bulk.