Workflow Templates

Notice Community nodes can only be installed on self-hosted instances of n8n. Who this is for This workflow automates the real-time extraction of Job Descriptions and Salary Information from job listing pages using Bright Data MCP and analyzes content using OpenAI GPT-4o mini. This workflow is ideal for: Recruiters & HR Tech Startups**: Automate job data collection from public listings Market Intelligence Teams**: Analyze compensation trends across companies or geographies Job Boards & Aggregators**: Power search results with structured, enriched listings AI Workflow Builders**: Extend to other career platforms or automate resume-job match analysis Analysts & Researchers**: Track hiring signals and salary benchmarks in real time What problem is this workflow solving? Traditional scraping of job portals can be challenging due to cluttered content, anti-scraping measures, and inconsistent formatting. Manually analyzing salary ranges and job descriptions is tedious and error-prone. This workflow solves the problem by: Simulating user behavior using Bright Data MCP Client to bypass anti-scraping systems Extracting structured, clean job data in Markdown format Using OpenAI GPT-4o mini to analyze and extract precise salary details and refined job descriptions Merging and formatting the result for easy consumption Delivering final output via webhook, Google Sheets, or file system What this workflow does Components & Flow Input Nodes job_search_url: The job listing or search result URL job_role: The title or role being searched for (used in logging/formatting) MCP Client Operations MCP Salary Data Extractor Simulates browser behavior and scrapes salary-related content (if available) MCP Job Description Extractor Extracts full job description as structured Markdown content OpenAI GPT-4o mini Nodes Salary Information Extractor Uses GPT-4o mini to detect, clean, and standardize salary range data (if any) Job Description Refiner Extracts role responsibilities, qualifications, and benefits from unstructured text Company Information Extractor Uses Bright Data MCP and GPT-4o mini to extract the company information Merge Node Combines the refined job description and extracted salary information into a unified JSON response object Aggregate node Aggregates the job description and salary information into a single JSON response object Final Output Handling The output is handled in three different formats depending on your downstream needs: Save to Disk** Output stored with filename including timestamp and job role Google Sheet Update** Adds a new row with job role, salary, summary, and link Webhook Notification** Pushes merged response to an external system Pre-conditions Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below. You need to have the Google Gemini API Key. Visit Google AI Studio You need to install the Bright Data MCP Server @brightdata/mcp You need to install the n8n-nodes-mcp Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. In n8n, configure the OpenAi account credentials. In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token> How to customize this workflow to your needs Modify Input Source Change the job_search_url to point to any job board or aggregator Customize job_role to reflect the type of jobs being analyzed Tweak LLM Prompts (Optional) Refine GPT-4o mini prompts to extract additional fields like benefits, tech stacks, remote eligibility Change Output Format Customize the merged object to output JSON, CSV, or Markdown based on downstream needs Add additional destinations (e.g., Slack, Airtable, Notion) via n8n nodes

Notice Community nodes can only be installed on self-hosted instances of n8n. Who this is for The DNB Company Search & Extract workflow is designed for professionals who need to gather structured business intelligence from Dun & Bradstreet (DNB). It is ideal for: Market Researchers B2B Sales & Lead Generation Experts Business Analysts Investment Analysts AI Developers Building Financial Knowledge Graphs What problem is this workflow solving? Gathering business information from the DNB website usually involves manual browsing, copying company details, and organizing them in spreadsheets. This workflow automates the entire data collection pipeline — from searching DNB via Google, scraping relevant pages, to structuring the data and saving it in usable formats. What this workflow does This workflow performs automated search, scraping, and structured extraction of DNB company profiles using Bright Data’s MCP search agents and OpenAI’s 4o mini model. Here's what it includes: Set Input Fields: Provide search_query and webhook_notification_url. Bright Data MCP Client (Search): Performs Google search for the DNB company URL. Markdown Scrape from DNB: Scrapes the company page using Bright Data and returns it as markdown. OpenAI LLM Extraction: Transforms markdown into clean structured data. Extracts business information (company name, size, address, industry, etc.) Webhook Notification: Sends structured response to your provided webhook. Save to Disk: Persists the structured data locally for logging or auditing. Pre-conditions Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below. You need to have the Google Gemini API Key. Visit Google AI Studio You need to install the Bright Data MCP Server @brightdata/mcp You need to install the n8n-nodes-mcp Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. In n8n, configure the OpenAi account credentials. In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token>. Update the Set input fields for search_query and webhook_notification_url. Update the file name and path to persist on disk. How to customize this workflow to your needs Search Engine**: Default is Google, but you can change the MCP client engine to Bing, or Yandex if needed. Company Scope**: Modify search query logic for niche filtering, e.g., "biotech startups site:dnb.com". Structured Fields**: Customize the LLM prompt to extract additional fields like CEO name, revenue, or ratings. Integrations**: Push output to Notion, Airtable, or CRMs like HubSpot using additional n8n nodes. Formatting**: Convert output to PDF or CSV using built-in File and Spreadsheet nodes.

🛒 Google Maps Business Phone Number Scraper Using Bright Data API & Google Sheets Integration This template requires a self-hosted n8n instance to run. An automated workflow that extracts business information including phone numbers from Google Maps using Bright Data's API and saves the data to Google Sheets for easy access and analysis. 📋 Overview This workflow provides an automated solution for extracting business contact information from Google Maps based on location and keyword searches. Perfect for lead generation, market research, competitor analysis, and business directory creation. ✨ Key Features 🎯 Form-Based Input: Easy-to-use form for location and keyword submission 🗺️ Google Maps Integration: Uses Bright Data's Google Maps dataset for accurate business data 📊 Comprehensive Data Extraction: Extracts business names, addresses, phone numbers, ratings, and more 📧 Automated Processing: Handles the entire scraping process automatically 📈 Google Sheets Storage: Automatically saves extracted data to organized spreadsheets 🔄 Smart Status Checking: Monitors scraping progress with automatic retry logic ⚡ Fast & Reliable: Professional scraping with built-in error handling 🎯 Customizable Output: Configurable data fields for specific business needs 🎯 What This Workflow Does Input Location:** Geographic area to search (city, state, country) Keywords:** Business type or industry keywords Processing Form Submission: User submits location and keywords through web form API Request: Sends scraping request to Bright Data's Google Maps dataset Status Monitoring: Continuously checks scraping progress Data Retrieval: Fetches completed business data when ready Data Storage: Saves extracted information to Google Sheets Error Handling: Implements retry logic for failed requests Output Data Points | Field | Description | Example | |-------|-------------|---------| | Business Name | Official business name from Google Maps | "Joe's Pizza Restaurant" | | Phone Number | Contact phone number | "+1-555-123-4567" | | Address | Complete business address | "123 Main St, New York, NY 10001" | | Rating | Google Maps rating score | 4.5 | | URL | Google Maps listing URL | "https://maps.google.com/..." | 🚀 Setup Instructions Prerequisites n8n instance (self-hosted or cloud) Google account with Sheets access Bright Data account with Google Maps 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: Credentials → + Add credential → HTTP Request Auth Enter your Bright Data API key Test the connection Configure dataset: Ensure you have access to Google Maps dataset (gd_m8ebnr0q2qlklc02fz) Verify dataset permissions in Bright Data dashboard Step 3: Configure Google Sheets Integration Create a Google Sheet: Go to Google Sheets Create a new spreadsheet named "Business Data" or similar Copy the Sheet ID from URL: https://docs.google.com/spreadsheets/d/SHEET_ID_HERE/edit Set up Google Sheets credentials: In n8n: Credentials → + Add credential → Google Sheets OAuth2 API Complete OAuth setup and test connection Prepare your data sheet with columns: Column A: Name Column B: Address Column C: Rating Column D: Phone Number Column E: URL Step 4: Update Workflow Settings Update Google Sheets node: Open "Save to Google Sheets" node Replace the document ID with your Sheet ID Select your Google Sheets credential Choose the correct sheet/tab name Update Bright Data nodes: Open HTTP Request nodes Replace BRIGHT_DATA_API_KEY with your actual API key Verify dataset ID matches your subscription Step 5: Test & Activate Test the workflow: Activate workflow (toggle switch) Submit test form with location: "New York" and keywords: "restaurants" Verify data appears in Google Sheet Check for proper phone number extraction 📖 Usage Guide Submitting Search Requests Access the form URL provided by n8n Enter the desired location (city, state, or country) Enter relevant keywords (business type, industry, etc.) Submit the form and wait for processing Understanding the Results Your Google Sheet will populate with business data including: Complete business contact information Verified phone numbers from Google Maps Accurate addresses and ratings Direct links to Google Maps listings 🔧 Customization Options Adding More Data Points Edit the "Bright Data API - Request Business Data" node to capture additional fields: Business descriptions Operating hours Reviews count Website URLs Photos and videos Modifying Search Parameters Customize the search behavior: Adjust "limit_per_input" for more or fewer results Modify search type and discovery method Add geographical coordinates for precise targeting 🚨 Troubleshooting Common Issues & Solutions 1. "Bright Data connection failed" Cause:** Invalid API credentials or dataset access Solution:** Verify credentials in Bright Data dashboard, check dataset permissions 2. "No business data extracted" Cause:** Invalid search parameters or no results found Solution:** Try broader keywords or different locations, verify dataset availability 3. "Google Sheets permission denied" Cause:** Incorrect credentials or sheet permissions Solution:** Re-authenticate Google Sheets, check sheet sharing settings 4. "Workflow execution timeout" Cause:** Large search results or slow API response Solution:** Reduce search scope, increase timeout settings, check internet connection 📊 Use Cases & Examples 1. Lead Generation Goal:** Find potential customers in specific areas Search for businesses by industry and location Extract contact information for outreach campaigns Build targeted prospect lists 2. Market Research Goal:** Analyze local business landscape Study competitor density in target markets Identify market gaps and opportunities Gather business intelligence for strategic planning 3. Directory Creation Goal:** Build comprehensive business directories Create industry-specific business listings Maintain updated contact databases Support local business communities 📈 Performance & Limits Expected Performance Processing time:** 1-5 minutes per search depending on results Data accuracy:** 95%+ for active Google Maps listings Success rate:** 90%+ for accessible businesses Concurrent requests:** Depends on Bright Data plan limits Resource Usage Memory:** ~50MB per execution Storage:** Minimal (data stored in Google Sheets) API calls:** 2-3 Bright Data calls + 1 Google Sheets call per search Bandwidth:** ~1-2MB per search request Execution time:** 2-5 minutes for typical searches Scaling Considerations Rate limiting:** Respect Bright Data API limits Error handling:** Implement retry logic for failed requests Data validation:** Add checks for incomplete business data Cost optimization:** Monitor API usage to control expenses Batch processing:** Group multiple searches for efficiency 🤝 Support & Community Getting Help n8n Community Forum:** community.n8n.io Documentation:** docs.n8n.io Bright Data Support:** Contact through your dashboard GitHub Issues:** Report bugs and feature requests Contributing Share improvements with the community Report issues and suggest enhancements Create variations for specific use cases Document best practices and lessons learned 🎯 Ready to Use! This workflow provides a solid foundation for automated Google Maps business data extraction. Customize it to fit your specific needs and use cases. Your workflow URL: https://your-n8n-instance.com/workflow/google-maps-scraper For any questions or support, please contact: info@incrementors.com or fill out this form: https://www.incrementors.com/contact-us/

Restore backed up workflows from GitHub to your n8n workspace. This workflow was inspired by this one that lets you back up your n8n workflows to GitHub. It will let you restore your backed up workflows in your workspace, without creating duplicates. In case of issue with your instance, it will save you a lot of time to restore them. How it works It retrieves the workflows saved in a GitHub repository. Then compares these saved workflows with the ones in your n8n workspace based on the name. It will only create them if they don't already exist. Set up steps Open the "Global" node and set your own information (see Configuration below) Click on "Test workflow" It will run through all the workflows in the GitHub repository, check if the name doesn't already exist in your workspace and, in this case, create it. Configuration repo.owner: your GitHub owner name repo.name: your GitHub repository name repo.path: the path within the GitHub repository

This low-code automation enables all eCommerce store visitors to upload a photo of themselves and virtually “try on” a garment in just a few clicks. With this workflow, WooCommerce, Prestashop, Shopify and more merchants can offer a cutting-edge “virtual try-on” feature with minimal development effort, enhancing customer engagement and reducing product returns. Key Advantages Zero-Coding, Visual Setup** Build end-to-end e-commerce features with drag-and-drop nodes instead of custom backend code. Asynchronous, Scalable Processing** Non-blocking “Wait” + “If” loop handles multi-second AI jobs gracefully, freeing up the workflow for other tasks. Dynamic Inputs & URLs** Query strings (e.g. ?Product=IMAGE_URL) allow you to embed the form on any product page and pass the garment image on the fly. Seamless User Experience** Instant pop-up within your storefront and automatic redirect to the generated mock-up keeps shoppers engaged without page reloads. Easy Credential Management** API keys, FTP credentials, and webhook IDs are all stored securely in n8n’s credential manager. How It Works Form Submission: A user submits a form with their name, an image of themselves ("Me"), and a hidden product image URL ("Product"). The form is triggered via the On form submission node, which collects the input data. Image Upload: The uploaded image ("Me") is sent to an FTP server for temporary storage using the FTP node. The filename includes a timestamp to ensure uniqueness. Virtual Try-on Request: The Create Image node sends a POST request to the Fal.run API, providing: The uploaded human image URL (from FTP). The product image URL (from the hidden form field). This generates a virtual try-on result. Result Processing: The workflow checks the status of the image generation (Get status node) in a loop (with a 10-second wait between checks) until it is marked as "COMPLETED." Once ready, the final image URL is fetched (Get Url image node) and displayed to the user via a redirect (Form node). User Experience: The user is redirected to the generated try-on image, completing the process. Set Up Steps API Key Setup: Create an account and obtain an API key. Configure the Create Image node with HTTP Header Authentication: Name: Authorization Value: Key YOURAPIKEY FTP/S3 Configuration: Set up an FTP server or S3 bucket to temporarily store uploaded user images. Configure the FTP node with your FTP credentials and storage path. Ecommerce Integration: On your WooCommerce site, add a "Try On" button that opens the form in a pop-up. Dynamically pass the product image URL as a query parameter: Example: https://URL_N8N/form/ca1c314d-46c6-4eeb-b6a5-359XXXXXX?Product=IMAGE_URL Testing: Verify the workflow by submitting a test form and ensuring the virtual try-on image is generated and displayed correctly. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Complete MCP server exposing all Cloudflare Tool operations to AI agents. Zero configuration needed - all 4 operations pre-built. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works • MCP Trigger: Serves as your server endpoint for AI agent requests • Tool Nodes: Pre-configured for every Cloudflare Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n Cloudflare Tool tool with full error handling 📋 Available Operations (4 total) Every possible Cloudflare Tool operation is included: 🔧 Zonecertificate (4 operations) • Delete a certificate • Get a certificate • Get many certificates • Upload a certificate 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Resource IDs and identifiers • Search queries and filters • Content and data payloads • Configuration options Response Format: Native Cloudflare Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • Other n8n Workflows: Call MCP tools from any workflow • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Complete Coverage: Every Cloudflare Tool operation available • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n error handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

Who Is This For? This workflow is designed for AI engineers, automation specialists, and content creators who need a scalable system to dynamically manage prompts stored in GitHub. It eliminates manual updates, enforces required variable checks, and ensures that AI interactions always receive fully processed prompts. 🚀 What Problem Does This Solve? Manually managing AI prompts can be inefficient and error-prone. This workflow: ✅ Fetches dynamic prompts from GitHub ✅ Auto-populates placeholders with values from the setVars node ✅ Ensures all required variables are present before execution ✅ Processes the formatted prompt through an AI agent 🛠 How This Workflow Works This workflow consists of three key branches, ensuring smooth prompt retrieval, variable validation, and AI processing. 1️⃣ Retrieve the Prompt from GitHub (HTTP Request → Extract from File → SetPrompt) The workflow starts manually or via an external trigger. It fetches a text-based prompt stored in a GitHub repository. The Extract from File Node retrieves the content from the GitHub file. The SetPrompt Node stores the prompt, making it accessible for processing. 📌 Note: The prompt must contain n8n expression format variables (e.g., {{ $json.company }}) so they can be dynamically replaced. 2️⃣ Extract & Auto-Populate Variables (Check All Prompt Vars → Replace Variables) A Code Node scans the prompt for placeholders in the n8n expression format ({{ $json.variableName }}). The workflow compares required variables against the setVars node: ✅ If all variables are present, it proceeds to variable replacement. ❌ If any variables are missing, the workflow stops and returns an error listing them. The Replace Variables Node replaces all placeholders with values from setVars. 📌 Example of a properly formatted GitHub prompt: Hello {{ $json.company }}, your product {{ $json.features }} launches on {{ $json.launch_date }}. This ensures seamless replacement when processed in n8n. 3️⃣ AI Processing & Output (AI Agent → Prompt Output) The Set Completed Prompt Node stores the final, processed prompt. The AI Agent Node (Ollama Chat Model) processes the prompt. The Prompt Output Node returns the fully formatted response. 📌 Optional: Modify this to use OpenAI, Claude, or other AI models. ⚠️ Error Handling: Missing Variables If a required variable is missing, the workflow stops execution and provides an error message: ⚠️ Missing Required Variables: ["launch_date"] This ensures no incomplete prompts are sent to AI agents. ✅ Example Use Case 📜 GitHub Prompt File (Using n8n Expressions) Hello {{ $json.company }}, your product {{ $json.features }} launches on {{ $json.launch_date }}. 🔹 Variables in setVars Node { "company": "PropTechPro", "features": "AI-powered Property Management", "launch_date": "March 15, 2025" } ✅ Successful Output Hello PropTechPro, your product AI-powered Property Management launches on March 15, 2025. 🚨 Error Output (If Missing launch_date) ⚠️ Missing Required Variables: ["launch_date"] 🔧 Setup Instructions 1️⃣ Connect Your GitHub Repository Store your prompt in a public or private GitHub repo. The workflow will fetch the raw file using the GitHub API. 2️⃣ Configure the SetVars Node Define the required variables in the SetVars Node. Make sure the variable names match those used in the prompt. 3️⃣ Test & Run Click Test Workflow to execute. If variables are missing, it will show an error. If everything is correct, it will output the fully formatted prompt. ⚡ How to Customize This Workflow 💡 Need CRM or Database Integration? Connect the setVars node to an Airtable, Google Sheets, or HubSpot API to pull variables dynamically. 💡 Want to Modify the AI Model? Replace the Ollama Chat Model with OpenAI, Claude, or a custom LLM endpoint. 📌 Why Use This Workflow? ✅ No Manual Updates Required – Fetches prompts dynamically from GitHub. ✅ Prevents Broken Prompts – Ensures required variables exist before execution. ✅ Works for Any Use Case – Handles AI chat prompts, marketing messages, and chatbot scripts. ✅ Compatible with All n8n Deployments – Works on Cloud, Self-Hosted, and Desktop versions.

Google Maps Email Scraper System Categories: Lead Generation, Web Scraping, Business Automation This workflow creates a completely free Google Maps email scraping system that extracts unlimited business emails without requiring expensive third-party APIs. Built entirely in N8N using simple HTTP requests and JavaScript, this system can generate thousands of targeted leads for any industry or location while operating at 99% free cost structure. Benefits Zero API Costs** - Operates entirely through free Google Maps scraping without expensive third-party services Unlimited Lead Generation** - Extract emails from thousands of Google Maps listings across any industry Geographic Targeting** - Search by specific cities, regions, or business types for precise lead targeting Complete Automation** - From search query to organized email list with minimal manual intervention Built-in Data Cleaning** - Automatic duplicate removal, filtering, and data validation Scalable Processing** - Handle hundreds of businesses per search with intelligent rate limiting How It Works Google Maps Search Integration: Uses strategic HTTP requests to Google Maps search URLs Processes search queries like "Calgary + dentist" to extract business listings Bypasses API restrictions through direct HTML scraping techniques Intelligent URL Extraction: Custom JavaScript regex patterns extract website URLs from Google Maps data Filters out irrelevant domains (Google, schema, static files) Returns clean list of actual business websites for processing Smart Website Processing: Loop-based architecture prevents IP blocking through intelligent batching Built-in delays and redirect handling for reliable scraping Processes each website individually with error handling Email Pattern Recognition: Advanced regex patterns identify email addresses within website HTML Extracts contact emails, info emails, and administrative addresses Handles multiple email formats and validation patterns Data Aggregation & Cleaning: Automatically removes duplicate emails across all processed websites Filters null entries and invalid email formats Exports clean, organized email lists to Google Sheets Required Google Sheets Setup Create a Google Sheet with these exact column headers: Search Tracking Sheet: searches - Contains your search queries (e.g., "Calgary dentist", "Miami lawyers") Email Results Sheet: emails - Contains extracted email addresses from all processed websites Setup Instructions: Create Google Sheet with two tabs: "searches" and "emails" Add your target search queries to the searches tab (one per row) Connect Google Sheets OAuth credentials in n8n Update the Google Sheets document ID in all sheet nodes The workflow reads search queries from the first sheet and exports results to the second sheet automatically. Business Use Cases Local Service Providers** - Find competitors and potential partners in specific geographic areas B2B Sales Teams** - Generate targeted prospect lists for cold outreach campaigns Marketing Agencies** - Build industry-specific lead databases for client campaigns Real Estate Professionals** - Identify businesses in target neighborhoods for commercial opportunities Franchise Development** - Research potential markets and existing competition Market Research** - Analyze business density and contact information across regions Revenue Potential This system transforms lead generation economics: $0 per lead vs. $2-5 per lead from paid databases Process 1,000+ leads daily without hitting API limits Sell as a service for $500-2,000 per industry/location Perfect for agencies offering lead generation to local businesses Difficulty Level: Intermediate Estimated Build Time: 1-2 hours Monthly Operating Cost: $0 (completely free) Watch My Complete Build Process Want to watch me build this entire system live from scratch? I walk through every single step - including the JavaScript code, regex patterns, error handling, and all the debugging that goes into creating a bulletproof scraping system. 🎥 Watch My Live Build: "Scrape Unlimited Leads WITHOUT Paying for APIs (99% FREE)" This comprehensive tutorial shows the real development process - including writing custom JavaScript, handling rate limits, and building systems that actually work at scale without getting blocked. Set Up Steps Basic Workflow Architecture: Set up manual trigger for testing and Google Sheets integration Configure initial HTTP request node for Google Maps searches Enable SSL ignore and response headers for reliable scraping URL Extraction Code Setup: Configure JavaScript code node with custom regex patterns Set up input data processing from Google Maps HTML responses Implement URL filtering logic to remove irrelevant domains Website Processing Pipeline: Add "Split in Batches" node for intelligent loop processing Configure HTTP request nodes with proper delays and redirect handling Set up error handling for websites that can't be scraped Email Extraction System: Implement JavaScript code node with email-specific regex patterns Configure email validation and format checking Set up data aggregation for multiple emails per website Data Cleaning & Export: Configure filtering nodes to remove null entries and duplicates Set up "Split Out" node to aggregate emails into single list Connect Google Sheets integration for organized data export Testing & Optimization: Use limit nodes during testing to prevent IP blocking Test with small batches before scaling to full searches Implement proxy integration for high-volume usage Advanced Optimizations Scale the system with: Multi-Page Scraping:** Extract URLs from homepages, then scrape contact pages for more emails Proxy Integration:** Add residential proxies for unlimited scraping without rate limits Industry Templates:** Create pre-configured searches for different business types Contact Information Expansion:** Extract phone numbers, addresses, and social media profiles CRM Integration:** Automatically add leads to sales pipelines and marketing sequences Important Considerations Rate Limiting:** Built-in delays prevent IP blocking during normal usage Scalability:** For high-volume usage, consider proxy services for unlimited requests Compliance:** Ensure proper usage rights for extracted contact information Data Quality:** System includes filtering but manual verification recommended for critical campaigns Check Out My Channel For more advanced automation systems and business-building strategies that generate real revenue, explore my YouTube channel where I share proven automation techniques used by successful agencies and entrepreneurs.

Purpose This workflow snippet allows for advanced error catching during retry attempts. There are cases, where you want to check if an item exists first, so you can determine the following actions. Some API’s do not support an endpoint (e.g. Todoist: completed tasks) to do so, which is why you would work with the error branch, only that this does not work well in combination with the retry functionality. How it works Instead of the builtin retry function of a Node a custom loop is used, to get more granular control in between the iterations If the main executed node fails, the error can be filtered for an expected error, which can trigger a separate action The retries only happen, if an unexpected error happened The workflow only stops, if the defined amount of retries exceeded Setup Copy the nodes into your existing workflow Replace the “Replace me” placeholder with the Node you want to apply the retry logic on Follow the sticky notes for more instructions and optional settings

Voice Cloning Workflow - Zyphra Zonos API Who is this for? This workflow is designed for developers, content creators, and businesses looking to automate high-quality voice synthesis using AI voice cloning technology. What problem does this solve? It automates the process of generating natural-sounding speech from text using a sample voice file, eliminating the need for manual voice recording and providing consistent voice output for applications like audiobooks, virtual assistants, or content localization. What this workflow does The workflow receives text and voice cloning parameters via webhook, reads a sample voice file from your storage, sends the data to Zyphra's Zonos API for voice synthesis, and saves the generated audio file to your specified output location. Prerequisites You'll need: API key from Zyphra (obtain from https://playground.zyphra.com/settings/api-keys) Account registration at https://playground.zyphra.com Sample voice file stored on accessible disk/cloud storage n8n instance running with webhook capabilities Setup Configure your Zyphra API key in the "Call Zyphra Clone API" node under Header Parameters (Name: X-API-Key, Value: your-api-key) Ensure your sample voice files are accessible at the paths you'll specify Test the webhook endpoint is accessible Supported Audio Formats The API supports multiple output formats through the mime_type parameter: WebM** (default) - audio/webm Ogg** - audio/ogg WAV** - audio/wav MP3** - audio/mp3 or audio/mpeg MP4/AAC** - audio/mp4 or audio/aac Usage Example Endpoint: POST http://localhost:5678/webhook-test/voice-clone Headers: Content-Type: application/json Request Body: { "text": "Hello there! This voice sounds just like the sample!", "speaking_rate": 18, "sample_voice_path": "/data/output/sampleVoice.wav", "output_path": "/data/output/", "language_iso_code": "en-us", "mime_type": "audio/wav", "model": "zonos-v0.1-transformer", "emotion": { "happiness": 0.8, "neutral": 0.3, "sadness": 0.05, "disgust": 0.05, "fear": 0.05, "surprise": 0.05, "anger": 0.05, "other": 0.5 } } Parameters Required Parameters text**: Text to synthesize into speech sample_voice_path**: Path to your voice sample file output_path**: Directory where generated audio will be saved Optional Parameters (with defaults) speaking_rate**: 15 - Speech speed language_iso_code**: "en-us" - Language code mime_type**: "audio/wav" - Output audio format model**: "zonos-v0.1-transformer" - AI model to use emotion**: Object with emotion levels (0-1 scale)

⚙️ How It Works: LocalRAG.AI ⚠️ Note: This system only works for self-hosted n8n instances. It will not function on n8n.cloud or other remote setups. LocalRAG.AI is a private, on-prem AI assistant that uses your own documents to answer questions intelligently. It combines LangChain, Ollama, Qdrant, and Postgres into a powerful AI pipeline — all running locally for maximum data privacy. 🔄 What It Does Monitors Your Google Drive Folders for new or updated files. Downloads the file, extracts the text, and prepares it. Generates Embeddings using your local Ollama model (e.g., LLaMA 3). Stores them in Qdrant, your local vector database. During a chat, it: Uses vector search to retrieve relevant chunks. Combines them with chat history stored in Postgres. Responds via a LangChain AI agent using your local model. 🛠️ Setup Steps (Self-hosted Only) Install and Self-host n8n (e.g., via Docker). Set up your Ollama instance locally and load your desired LLM (e.g., llama3). Deploy Qdrant locally for vector storage. Connect a Postgres DB to store chat history. Create and import the workflow in n8n. Authenticate Google Drive to monitor folders. Connect credentials for Ollama, Qdrant, Postgres in the n8n workflow. Start chatting through the Webhook Trigger or custom UI. 🧠 Perfect For: Research teams handling confidential data Internal documentation Q&A AI chatbots that don’t rely on OpenAI or cloud

Turn plain-language chat like “Tomorrow 9 AM: write blog post” into neatly organised Todoist tasks with GPT-4o and n8n—zero code. 🪄 Ultimate Personal Todoist Agent Turn natural-language requests into perfectly-organized Todoist tasks—all on autopilot inside n8n. > “Add Finish quarterly report by Friday afternoon” → the agent creates the task, sets the due date & priority, and even drops it into the right project. ✨ 🌟 Why this workflow rocks All-in-one Todoist super‑powers** – create, update, complete, move, archive… every major Todoist endpoint is wired up (tasks, projects, sections, labels, comments). LLM‑powered intent detection** – an OpenAI model interprets plain-English (or emoji‑filled!) messages so you don’t have to remember slash‑commands. Minimal setup** – just two credentials and you’re live. Battle‑tested building block** – use it as‑is, or plug the Todoist Agent node into your own agents & chatbots. 🛠️ What you’ll need | Credential | Where it’s used | How to set it up | | ------------------ | -------------------------------------- | --------------------------------------------------------------------------------------------- | | OpenAI API | Orchestrator & LLM nodes | Paste your OpenAI secret key into an OpenAI credential in n8n. | | Todoist OAuth2 | Todoist node and HTTP Request node | Log in Todoist from your browser to set up credential in n8n. | > That’s it—no webhooks, no extra secrets. > Tested with *gpt‑4o‑latest* – the fastest & most accurate model in our trials. ⚡ Quick‑start (5 minutes) Import the JSON template (hit ▶️ Try it out on the n8n template page or drag‑drop the file into your canvas). Select your credentials in the two credential dropdowns. Click Test workflow. In the sample Function node, tweak the message field (e.g. “Tomorrow at 9 am: write blog post”). Run → watch your new Todoist task appear. (Optional) Swap the Function node for your favourite chat trigger (Telegram, Slack, WhatsApp, Discord, you name it). Boom—your personal Todoist genie is alive! 🧞‍♂️ 🧩 How it works (under the hood) [Trigger / Chat message] │ ▼ [🗂️ Orchestrator Agent] ← OpenAI Chat Model + Short‑term Memory │ ↳ Parses intent & entities │ ▼ [🤖 Todoist Agent] ← 15+ Todoist endpoints │ ↳ Executes the right call (create, update, complete, etc.) ▼ [Done ✅ ] The Orchestrator is an example. In production you can drop it and simply expose the Todoist Agent as a tool for any other agent workflow. 🎛️ Customising & extending | Idea | How to do it | | ------------------------- | ---------------------------------------------------------------------------------------- | | Notion / Sheets sync | After the Todoist Agent node, add a Notion or Google Sheets node to log completed items. | | Voice commands | Swap the chat trigger for a Speech‑to‑Text node (e.g. Whisper). | 🤝 Need custom automations? Want me to build or tweak something for you? → Email maxemelyanenko@gmail.com and let’s make it happen! ⚠️ What’s not included (yet) Shared projects & other Todoist Pro/Business endpoints. File attachments in the comments. Editing comments. Pull requests welcome! 🙌