Workflow Templates

LinkedIn & Indeed Job Scraper with Bright Data & Google Sheets Export Overview This n8n workflow automates the process of scraping job listings from both LinkedIn and Indeed platforms simultaneously, combining results, and exporting data to Google Sheets for comprehensive job market analysis. It integrates with Bright Data for professional web scraping, Google Sheets for data storage, and provides intelligent status monitoring with retry mechanisms. Workflow Components 1. 📝 Trigger Input Form Type**: Form Trigger Purpose**: Initiates the workflow with user-defined job search criteria Input Fields**: City (required) Job Title (required) Country (required) Job Type (optional dropdown: Full-Time, Part-Time, Remote, WFH, Contract, Internship, Freelance) Function**: Captures user requirements to start the dual-platform job scraping process 2. 🧠 Format Input for APIs Type**: Code Node (JavaScript) Purpose**: Prepares and formats user input for both LinkedIn and Indeed APIs Processing**: Standardizes location and job title formats Creates API-specific input structures Generates custom output field configurations Function**: Ensures compatibility with both Bright Data datasets 3. 🚀 Start Indeed Scraping Type**: HTTP Request (POST) Purpose**: Initiates Indeed job scraping via Bright Data Endpoint**: https://api.brightdata.com/datasets/v3/trigger Parameters**: Dataset ID: gd_lpfll7v5hcqtkxl6l Include errors: true Type: discover_new Discover by: keyword Limit per input: 2 Custom Output Fields**: jobid, company_name, job_title, description_text location, salary_formatted, company_rating apply_link, url, date_posted, benefits 4. 🚀 Start LinkedIn Scraping Type**: HTTP Request (POST) Purpose**: Initiates LinkedIn job scraping via Bright Data (parallel execution) Endpoint**: https://api.brightdata.com/datasets/v3/trigger Parameters**: Dataset ID: gd_l4dx9j9sscpvs7no2 Include errors: true Type: discover_new Discover by: keyword Limit per input: 2 Custom Output Fields**: job_posting_id, job_title, company_name, job_location job_summary, job_employment_type, job_base_pay_range apply_link, url, job_posted_date, company_logo 5. 🔄 Check Indeed Status Type**: HTTP Request (GET) Purpose**: Monitors Indeed scraping job progress Endpoint**: https://api.brightdata.com/datasets/v3/progress/{snapshot_id} Function**: Checks if Indeed dataset scraping is complete 6. 🔄 Check LinkedIn Status Type**: HTTP Request (GET) Purpose**: Monitors LinkedIn scraping job progress Endpoint**: https://api.brightdata.com/datasets/v3/progress/{snapshot_id} Function**: Checks if LinkedIn dataset scraping is complete 7. ⏱️ Wait Nodes (60 seconds each) Type**: Wait Node Purpose**: Implements intelligent polling mechanism Duration**: 1 minute Function**: Pauses workflow before rechecking scraping status to prevent API overload 8. ✅ Verify Indeed Completion Type**: IF Condition Purpose**: Evaluates Indeed scraping completion status Condition**: status === "ready" Logic**: True: Proceeds to data validation False: Loops back to status check with wait 9. ✅ Verify LinkedIn Completion Type**: IF Condition Purpose**: Evaluates LinkedIn scraping completion status Condition**: status === "ready" Logic**: True: Proceeds to data validation False: Loops back to status check with wait 10. 📊 Validate Indeed Data Type**: IF Condition Purpose**: Ensures Indeed returned job records Condition**: records !== 0 Logic**: True: Proceeds to fetch Indeed data False: Skips Indeed data retrieval 11. 📊 Validate LinkedIn Data Type**: IF Condition Purpose**: Ensures LinkedIn returned job records Condition**: records !== 0 Logic**: True: Proceeds to fetch LinkedIn data False: Skips LinkedIn data retrieval 12. 📥 Fetch Indeed Data Type**: HTTP Request (GET) Purpose**: Retrieves final Indeed job listings Endpoint**: https://api.brightdata.com/datasets/v3/snapshot/{snapshot_id} Format**: JSON Function**: Downloads completed Indeed job data 13. 📥 Fetch LinkedIn Data Type**: HTTP Request (GET) Purpose**: Retrieves final LinkedIn job listings Endpoint**: https://api.brightdata.com/datasets/v3/snapshot/{snapshot_id} Format**: JSON Function**: Downloads completed LinkedIn job data 14. 🔗 Merge Results Type**: Merge Node Purpose**: Combines Indeed and LinkedIn job results Mode**: Merge all inputs Function**: Creates unified dataset from both platforms 15. 📊 Save to Google Sheet Type**: Google Sheets Node Purpose**: Exports combined job data for analysis Operation**: Append rows Target**: "Compare" sheet in specified Google Sheet document Data Mapping**: Job Title, Company Name, Location Job Detail (description), Apply Link Salary, Job Type, Discovery Input Workflow Flow Input Form → Format APIs → [Indeed Trigger] + [LinkedIn Trigger] ↓ ↓ Check Status Check Status ↓ ↓ Wait 60s Wait 60s ↓ ↓ Verify Ready Verify Ready ↓ ↓ Validate Data Validate Data ↓ ↓ Fetch Indeed Fetch LinkedIn ↓ ↓ └─── Merge Results ───┘ ↓ Save to Google Sheet Configuration Requirements API Keys & Credentials Bright Data API Key**: Required for both LinkedIn and Indeed scraping Google Sheets OAuth2**: For data storage and export access n8n Form Webhook**: For user input collection Setup Parameters Google Sheet ID**: Target spreadsheet identifier Sheet Name**: "Compare" tab for job data export Form Webhook ID**: User input form identifier Dataset IDs**: Indeed: gd_lpfll7v5hcqtkxl6l LinkedIn: gd_l4dx9j9sscpvs7no2 Key Features Dual Platform Scraping Simultaneous LinkedIn and Indeed job searches Parallel processing for faster results Comprehensive job market coverage Platform-specific field extraction Intelligent Status Monitoring Real-time scraping progress tracking Automatic retry mechanisms with 60-second intervals Data validation before processing Error handling and timeout management Smart Data Processing Unified data format from both platforms Intelligent field mapping and standardization Duplicate detection and removal Rich metadata extraction Google Sheets Integration Automatic data export and storage Organized comparison format Historical job search tracking Easy sharing and collaboration Form-Based Interface User-friendly job search form Flexible job type filtering Multi-country support Real-time workflow triggering Use Cases Personal Job Search Comprehensive multi-platform job hunting Automated daily job searches Organized opportunity comparison Application tracking and management Recruitment Services Client job search automation Market availability assessment Competitive salary analysis Bulk candidate sourcing Market Research Job market trend analysis Salary benchmarking studies Skills demand assessment Geographic opportunity mapping HR Analytics Competitor hiring intelligence Role requirement analysis Compensation benchmarking Talent market insights Technical Notes Polling Interval**: 60-second status checks for both platforms Result Limiting**: Maximum 2 jobs per input per platform Data Format**: JSON with structured field mapping Error Handling**: Comprehensive error tracking in all API requests Retry Logic**: Automatic status rechecking until completion Country Support**: Adaptable domain selection (indeed.com, fr.indeed.com) Form Validation**: Required fields with optional job type filtering Merge Strategy**: Combines all results from both platforms Export Format**: Standardized Google Sheets columns for easy analysis Sample Data Output | Field | Description | Example | |-------|-------------|---------| | Job Title | Position title | "Senior Software Engineer" | | Company Name | Hiring organization | "Tech Solutions Inc." | | Location | Job location | "San Francisco, CA" | | Job Detail | Full description | "We are seeking a senior developer..." | | Apply Link | Direct application URL | "https://company.com/careers/123" | | Salary | Compensation info | "$120,000 - $150,000" | | Job Type | Employment details | "Full-time, Remote" | Setup Instructions Import Workflow: Copy JSON configuration into n8n Configure Bright Data: Add API credentials for both datasets Setup Google Sheets: Create target spreadsheet and configure OAuth Update References: Replace placeholder IDs with your actual values Test Workflow: Submit test form and verify data export Activate: Enable workflow and share form URL with users For any questions or support, please contact: info@incrementors.com or fill out this form: https://www.incrementors.com/contact-us/

Notice Community nodes can only be installed on self-hosted instances of n8n. Who this is for This n8n-powered automation uses Bright Data's MCP Client to extract real-time data from a price drop site listing the amazon products, including price changes and related product details. The extracted data is enriched with structured data transformation, content summarization, and sentiment analysis using Google Gemini LLM. The Amazon Price Drop Intelligence Engine is designed for: Ecommerce Analysts** who need timely updates on competitor pricing trends Brand Managers** seeking to understand consumer sentiment around pricing Data Scientists** building pricing models or enrichment pipelines Affiliate Marketers** looking to optimize campaigns based on dynamic pricing AI Developers** automating product intelligence pipelines What problem is this workflow solving? This workflow solves several key pain points: Reliable Scraping: Uses Bright Data MCP, a managed crawling platform that handles proxies, captchas, and site structure changes automatically. Insight Generation: Transforms unstructured HTML into structured data and then into human-readable summaries using Google Gemini LLM. Sentiment Context: Goes beyond raw pricing data to reveal how customers feel about the price change, helping businesses and researchers measure consumer reaction. Automated Reporting: Aggregates and stores data for easy access and downstream automation (e.g., dashboards, notifications, pricing models). What this workflow does Scrape price drop site with Bright Data MCP The workflow begins by scraping targeted price drop site for Amazon listings using Bright Data's Model Context Protocol (MCP). You can configure this to target: Structured Data Extraction Once the HTML content is retrieved, Google Gemini is employed to: Parse and structure the product information (title, price, discount, brand, ratings) Summarization & Sentiment Analysis The extracted data is passed through an LLM chain to: Generate a concise summary of the product and its recent price movement Perform sentiment analysis on user reviews and public perception Store the Results Save to disk for archiving or bulk processing Updated in a Google Sheet, making it instantly shareable with your team or integrated into a BI dashboard 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. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). 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 Target different platforms**: Switch Amazon for Walmart, eBay, or any ecommerce source using Bright Data’s flexible scraping infrastructure. Enrich with more LLM tasks**: Add brand tone analysis, category classification, or competitive benchmarking using Gemini prompts. Visualize output**: Pipe the Google Sheet to Looker Studio, Tableau, or Power BI. Notification integrations**: Add Slack, Discord, or email notifications for price drop alerts.

Workflow: Snapshot Contabo How it Works This workflow automates daily backups (snapshots) of VPS instances hosted on Contabo. Each day at midnight, it checks for existing snapshots and ensures that only the latest backups are retained by removing older ones. It provides a seamless, hands-off backup process to keep your data secure. Setup Steps Setting up this workflow is quick, typically taking about 10-15 minutes. The essential part of the setup is providing the necessary credentials, which you can easily retrieve from your Contabo control panel. Import the Workflow: Download and upload the workflow JSON into n8n. Configure Credentials: Add CLIENT_ID, CLIENT_SECRET, API_USER, and API_PASSWORD in the credential node. Activate the Workflow: Enable it to run automatically at midnight every day. Flow Overview Schedule Trigger (00:00 daily):** Automatically initiates the workflow. Formatted Date:** Prepares a timestamp for naming the snapshot. List Snapshots:** Verifies if an existing snapshot is available for each VPS. Conditional Logic:** No Snapshot? Proceeds to create a new one. Snapshot Found? Deletes the old snapshot before creating a new one. Key Points Snapshot Retention:** Old snapshots are deleted to ensure only the latest backups are stored. Unique Identifiers:** UUIDs are used to track and guarantee unique operations.

🧠 What It Does This n8n workflow turns your Telegram bot into a smart, multi-modal AI assistant that accepts text, documents, images, and audio messages, interprets them using OpenAI models, and responds instantly with context-aware answers. It integrates a Supabase vector database to store document embeddings and retrieve relevant information before sending a prompt to OpenAI — enabling a full RAG experience 💡 Why This Workflow? Most support bots can only handle basic text input. This workflow: Supports multiple input formats (voice, documents, images, text) Dynamically extracts and processes data from uploaded files Implements RAG by combining user input with relevant memory or vector-based context Delivers more accurate, relevant, and human-like AI responses. 👤 Who It's For Businesses looking to automate support using Telegram Freelancers or solopreneurs offering AI Chatbots for businesses. Creators building AI-powered bots for real use cases as it's great for Customer support knowledge, Legal or Policy document, long FAQs, Project documentation, and Product information retrieval. Devs or analysts exploring AI + multi-format input + vector memory. ⚙️ How It Works 🗂️ Knowledge Base Setup Run the “Add to Supabase Vector DB” workflow manually to upload a document from your google drive and embed it into your vector database. This powers the Telegram chatbot’s ability to answer questions using your content. 🔁 Telegram Message Routing Telegram Trigger captures the user message (Text, Image, Voice, Document) Message Router routes input by type using a Switch node Each type is handled separately: Voice → Translate recording to text (.ogg, .mp3) Image → Analyze image to text. Text → Sent directly to AI Agent (.txt). Document → Parsed (e.g. .docx to .txt) accordingly. 📎 Document Type Routing Before routing documents by type, the Supported Document File Types node first checks if the file extension is allowed. If not supported, it exits early with an error message — preventing unnecessary processing. Supported documents are then routed using the Document Router node, and converted to text for further processing. Supported Document File Types .jpg .jpeg .png .webp .pdf .doc .docx .xls .xlsx .json .xml. The text content is combined with stored memory and embedded knowledge using a RAG approach, enabling the AI to respond based on real uploaded data. 🧠 RAG via Supabase Uploaded documents are vectorized using OpenAI Embeddings. Embeddings are stored in Supabase with metadata. On new questions, the chatbot: Extracts question intent Queries Supabase for semantically similar chunks Ranks retrieved chunks to find the most relevant match. Injects them into the prompt for OpenAI. OpenAI generates a grounded response based on actual document content. Response is sent to the Telegram user with content awareness. 🛠 How to Set It Up Open n8n or your local/self-hosted instance. Import the `.json ` workflow file. Set up these credentials: Google drive API Key Telegram API (Bot Token) Guide OpenAI API Supabase API Key + Environment ConvertAPI API Key Postgres API Key Cohere API Key Add a prompt suited to your business. Add a custom AI agent prompt that reflects your business domain, tone, and purpose. This is very important. Without it, your agent won't know how best to respond. Activate the workflow. Start testing by sending a message or document to your Telegram bot.

Healthcare Clinic Assistant with WhatsApp and Telegram Integration Version: 1.1.0 n8n Version: 1.88.0+ License: MIT 📋 Description A comprehensive and modular automation workflow designed for healthcare clinics. It manages patient communication, appointment scheduling, confirmations, rescheduling, internal tasks, and media processing by integrating WhatsApp, Telegram, Google Calendar, and Google Tasks, combined with AI-powered agents for maximum efficiency. This system guarantees proactive communication with patients, streamlined internal clinic management, and consistent data synchronization across platforms. 🌟 Key Features 🤖 AI-Powered Specialized Agents: Distinct agents handle WhatsApp patient support, appointment confirmations, and internal rescheduling tasks. 📱 Omnichannel Communication: Handles patient interactions via WhatsApp and staff commands via Telegram. 📅 Google Calendar Appointment Management: Full synchronization for creating, updating, canceling, and confirming appointments. 📋 Task Management with Google Tasks: Manages shopping lists and administrative tasks efficiently through staff Telegram requests. 🔔 Automated Appointment Reminders: Daily-triggered system proactively sends WhatsApp confirmations to patients for next-day appointments. 🖼️ Intelligent Media Processing: Transcribes audios, extracts text from images, and processes documents using OpenAI and OpenRouter AI models. 🛡️ Escalation to Human Support: Automatically detects sensitive or urgent cases and escalates them to a human agent when needed. 🏥 Use Cases Patient Communication:** Respond to inquiries, schedule, reschedule, and confirm appointments seamlessly via WhatsApp. Internal Clinic Operations:** Allow staff to modify appointments or add shopping list reminders directly from Telegram. Appointment Confirmation System:** Automatically contacts patients one day prior to appointments for confirmation or rescheduling. Task and Reminder Management:** Keeps clinic operations organized through automatic task management with Google Tasks. 🛠️ Technical Implementation WhatsApp Patient Interaction Flow Webhook Reception:** Incoming WhatsApp messages captured via Evolution API webhook. Message Classification:** Intelligent routing of messages based on content type (text, image, audio, document). Media Content Processing:** Audios: Download, convert, and transcribe via OpenAI Whisper. Images: Analyze and extract text/descriptions with OpenAI Vision model. Patient Request Handling:** Specialized WhatsApp assistant responds appropriately using AI prompts. Outbound Message Formatting:** Ensures messages comply with WhatsApp format standards. Message Delivery:** Sends responses back via Evolution API. Telegram Staff Management Flow Telegram Webhook Reception:** Captures messages from authorized staff accounts. Internal Assistant Processing:** Appointment Rescheduling: Identifies and updates appointments through MCP Google Calendar. Task Creation: Adds new entries to the clinic's shopping list using Google Tasks. Notifications and Confirmations:** Sends confirmations back to staff through Telegram. Appointment Reminder System Daily Trigger Activation:** Fires every weekday at 08:00 AM. Calendar Scraping:** Lists next day's appointments from Google Calendar. Patient Contact:** Sends WhatsApp confirmation messages for each appointment. Response Management:** Redirects confirmation or rescheduling replies to appropriate agents. ⚙️ Setup Instructions Import the Workflow n8n → Workflows → Import from File → Upload this JSON file. Configure Credentials Evolution API (WhatsApp Communication) Telegram Bot API (Staff Communication) Google Calendar OAuth2 (Appointment Management) Google Tasks OAuth2 (Task Management) OpenAI and OpenRouter APIs (AI Agents) PostgreSQL Database (Chat Memory) Set Sensitive Variables Replace placeholder values: {sua instância aqui} → Evolution API instance name {número_whatsapp} → WhatsApp numbers {url_do_servidor} → Server URLs {a sua apikey aqui} → API keys {seu_calendario} → Google Calendar ID Customize AI Prompts Adjust system prompts to fit your clinic’s tone, service style, and patient communication guidelines. Set clinic operating hours, escalation rules, and cancellation procedures in AI prompts. Activate and Test Simulate patient messages via WhatsApp. Test Telegram commands from staff members. Validate daily appointment reminders using the scheduled trigger. 🏷️ Tags Healthcare Clinic Management WhatsApp Integration Telegram Bot Appointment Scheduling Google Calendar Google Tasks AI Agents n8n Automation 📚 Technical Notes PostgreSQL is used for persistent chat memory across sessions. Multiple AI Models Used: OpenAI GPT-4.1-nano OpenAI GPT-4.1-mini Google Gemini 2.0 and 2.5 Full media content processing supported (audio, image, text). Compliant escalation workflows ensure patient safety and proper handoff to human staff when necessary. All sensitive patient data are securely stored inside calendar event descriptions for easy retrieval by agents. 📜 License This workflow is provided under the MIT License. Feel free to adapt and customize it for your clinic’s specific needs.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Uncover new business leads with this AI-Powered Prospect Discovery Agent! This n8n workflow acts as a specialized intelligent assistant that, given a business type and location, uses multiple search strategies to identify a list of potential prospect companies and their websites. Stop manually trawling through search results! This agent automates the initial phase of lead generation by: Understanding your target business profile (type, location, keywords). Strategically using web search tools (Brave Search, Google Gemini Search) to find relevant businesses. Performing quick validations to confirm relevance. Returning a clean, structured JSON list of prospect names and their website URLs. How it Works: The workflow is built around an AI agent powered by Google Gemini. This agent is equipped with tools like: Brave Web Search:** For broad initial sourcing of potential business candidates. Google Gemini Search:** For advanced, context-aware discovery and finding businesses mentioned in various online sources. Brave Local Search (Selective):** For quick verification of local presence or finding website URLs for identified names. Jina AI Web Page Scraper (Very Selective):** For extremely rapid relevance checks on uncertain websites by scanning page content for keywords. The agent's system prompt guides it to use these tools efficiently to build a list of prospects without getting bogged down in deep research on any single one at this discovery stage. Use Cases: Lead Generation:** Automatically generate lists of potential clients based on industry and location. Market Research:** Identify key players or types of businesses in a specific geographical area. Sales Development:** Provide SDRs with initial lists of companies to research further. Called as a Sub-Workflow:** Designed to be easily integrated as a "tool" into more complex orchestrating AI agents (e.g., a BNI Pitch Planner that first needs to identify who to target). Setup: Import the workflow. Configure Credentials: You'll need n8n credentials for: Google Gemini (for the Chat model and the Gemini Search/Vertex AI Search tool). Brave Search (e.g., via Smithery MCP, or adapt if you have direct API access). Jina AI (for the web scraper). Assign these to the respective nodes. Review System Prompt: The prospect_discovery_agent node contains a detailed system prompt. You can fine-tune this to adjust its search strategies or the strictness of its matching. Inputs: This workflow is triggered by an "Execute Workflow Trigger" node (prospect_discovery_workflow). It expects the following inputs: business_type (string): e.g., "artisan bakery" location_query (string): e.g., "Portland, Oregon" desired_num_prospects (number): e.g., 5 additional_keywords (string, optional): e.g., "organic, gluten-free" To Use (as a Sub-Workflow/Tool): This workflow is typically called by another n8n workflow (e.g., using a "Tool Workflow" node from the Langchain nodes). The calling workflow would provide the inputs listed above. The "Prospect Discovery" workflow will then execute and its final node (the prospect_discovery_agent) will output a JSON array of found prospects, like: [ { "business_name": "Rose Petal Bakery", "website_url": "https://rosepetalbakerypdx.com" }, { "business_name": "The Daily Bread Artisans", "website_url": "https://dailybreadpdx.com" } ] If no prospects are found, it returns an empty array []. This template provides a powerful and focused tool for automating the initial stages of prospect identification.

🔍 Email Lookup with Google Search from Postgres Database This N8N workflow is designed to enrich seller data stored in a Postgres database by performing automated Google search lookups. It uses Bright Data's Web Unlocker to bypass search result restrictions and the HTML Extract node to parse and extract relevant information from webpages. The main purpose of this workflow is to discover missing contact details, company domains, and secondary emails for businesses or sellers based on existing database entries. 🎯 Problem This Workflow Solves Manually searching for missing seller or business details—like secondary emails, websites, or domain names—can be time-consuming and inefficient, especially for large datasets. This workflow automates the search and data enrichment process, significantly reducing manual effort while improving the quality and completeness of your seller database. ✅ Prerequisites Before using this template, make sure the following requirements are met: ✔️ A Bright Data account with access to the Web Unlocker or Amazon Scraper API ✔️ A valid Bright Data API key ✔️ An active PostgreSQL database with seller data ✔️ N8N self-hosted instance (recommended for using community nodes like n8n-nodes-brightdata) ✔️ Installed n8n-nodes-brightdata package (custom node for Bright Data integration) ⚙️ Setup Instructions Step 1: Prepare Your Postgres Table Create a table in Postgres with the following structure (you can adjust field names if needed): CREATE TABLE sellers ( seller_id SERIAL PRIMARY KEY, seller_name TEXT, primary_email TEXT, company_info TEXT, trade_name TEXT, business_address TEXT, coc_number TEXT, vat_number TEXT, commercial_register TEXT, secondary_email TEXT, domain TEXT, seller_slug TEXT, source TEXT ); Step 2: Setup Web Unlocker on Bright Data Go to your Bright Data dashboard. Navigate to Proxies & Scraping → Web Unlocker. Create a new zone, selecting Web Unlocker API under Scraping Solutions. Whitelist your server IP if required. Step 3: Generate API Key In the Bright Data dashboard, go to the API section. Generate a new API key. In N8N, create HTTP Request Credentials using Bearer Authentication with the API key. Step 4: Install the Bright Data Node in N8N In your N8N self-hosted instance, go to Settings → Community Nodes. Search and install n8n-nodes-brightdata. 🔄 Workflow Functionality 🔁 Trigger: Can be set to run on a schedule (e.g., daily) or manually. 📥 Read: Fetches seller records from the Postgres table. 🌐 Search: Uses Bright Data to perform a Google search based on seller_name, company_info, or trade_name. 🧾 Extract: Parses the HTML content using the HTML Extract node to identify potential websites and email addresses. 📝 Update: Writes enriched data (like domain or secondary_email) back to the Postgres table. 💡 Use Cases Lead enrichment for e-commerce sellers Domain and contact info discovery for B2B databases Email and web domain verification for CRM systems Market research automation 🛠️ Customization Tips You can enhance the parsing logic in the HTML Extract node to look for phone numbers, LinkedIn profiles, or social media links. Modify the search query logic to include additional parameters like location or industry for more refined results. Integrate additional APIs (e.g., Hunter.io, Clearbit) for email validation or social profile enrichment. Add filtering to skip entries that already have domain or secondary_email.

Who is this for? 🧑🏻🫱🏻‍🫲🏻🤖 Humans and Robots alike. This workflow can be used as a Chat Trigger, as well as a Workflow Trigger. It will take a natural language request, and then generate a SQL query. The resulting query parameter will contain the query, and a sqloutput parameter will contain the results of executing such query. What's the use case? This template is most useful paired with other workflows that extract e-mail information and store it in a structured Postgres table, and use LLMs to understand inquiries about information contained in an e-mail inbox and formulate questions that needs answering. Plus, the prompt can be easily adapted to formulate SQL queries over any kind of structured database. Privacy and Economics As LLM provider I'm using Ollama locally, as I consider my e-mail extremely sensitive information. As model, phi4-mini does an excellent job balancing quality and efficiency. Setup Upon running for the first time, this workflow will automatically trigger a sub-section to read all tables and extract their schema into a local file. Then, either by chatting with the workflow in n8n's interface or by using it as a sub-workflow, you will get a query and a sqloutput response. Customizations If you want to work with just one particular table yet keep edits at bay, append a condition to the List all tables in a database step, like so: WHERE table_schema='public' AND table_name='my_emails_table_name' To repurpose this workflow to work with any other data corpus in a structured database, inspect the AI Agent user and system prompts and edit them accordingly.

This template attempts to replicate OpenAI's DeepResearch feature which, at time of writing, is only available to their pro subscribers. > An agent that uses reasoning to synthesize large amount of online information and complete multi-step research tasks for you. Source Though the inner workings of DeepResearch have not been made public, it is presumed the feature relies on the ability to deep search the web, scrape web content and invoking reasoning models to generate reports. All of which n8n is really good at! Using this workflow, n8n users can enjoy a variation of the Deep Research experience for themselves and their teams at a fraction of the cost. Better yet, learn and customise this Deep Research template for their businesses and/or organisations. Check out the generated reports here: https://jimleuk.notion.site/19486dd60c0c80da9cb7eb1468ea9afd?v=19486dd60c0c805c8e0c000ce8c87acf How it works A form is used to first capture the user's research query and how deep they'd like the researcher to go. Once submitted, a blank Notion page is created which will later hold the final report and the researcher gets to work. The user's query goes through a recursive series of web serches and web scraping to collect data on the research topic to generate partial learnings. Once complete, all learnings are combined and given to a reasoning LLM to generate the final report. The report is then written to the placeholder Notion page created earlier. How to use Duplicate this Notion database template and make sure all Notion related nodes point to it. Sign-up for APIFY.com API Key for web search and scraping services. Ensure you have access to OpenAI's o3-mini model. Alternatively, switch this out for o1 series. You must publish this workflow and ensure the form url is publically accessible. On depth & breadth configuration For more detailed reports, increase depth and breadth but be warned the workflow will take exponentially longer and cost more to complete. The recommended defaults are usually good enough. Depth=1 & Breadth=2 - will take about 5 - 10mins. Depth=1 & Breadth=3 - will take about 15 - 20mins. Dpeth=3 & Breadth=5 - will take about 2+ hours! Customising this workflow I deliberately chose not to use AI-powered scrapers like Firecrawl as I felt these were quite costly and quotas would be quickly exhausted. However, feel free to switch web search and scraping services which suit your environment. Maybe you don't decide to source the web and instead, data collection comes from internal documents instead. This template gives you freedom to change this. Experiment with different Reasoning/Thinking models such as Deepseek and Google's Gemini 2.0. Finally, the LLM prompts could definitely be improved. Refine them to fit your use-case. Credits This template is largely based off the work by David Zhang (dzhng) and his open source implementation of Deep Research: https://github.com/dzhng/deep-research

📌 AI Agent via GoHighLevel SMS with Website-Based Knowledgebase This n8n workflow enables an AI agent to interact with users through GoHighLevel SMS, leveraging a knowledgebase dynamically built by scraping the company's website. ❓ Problem It Solves Traditional customer support systems often require manual data entry and lack real-time updates from the company's website. This workflow automates the process by: Scraping the company's website at set intervals to update the knowledgebase. Integrating with GoHighLevel SMS to provide users with timely and accurate information. Utilizing AI to interpret user queries and fetch relevant information from the updated knowledgebase. 🧰 Pre-requisites Before deploying this workflow, ensure you have: An active n8n instance (self-hosted or cloud). A valid OpenAI API key (or any compatible AI model). A Bright Data account with Web Unlocker setup. A GoHighLevel SMS LeadConnector account. A GoHighLevel Marketplace App configured with the necessary scopes. Installed n8n-nodes-brightdata community node for Bright Data integration (if self-hosted). ⚙️ Setup Instructions 1. Install the Bright Data Community Node in n8n For self-hosted n8n instances: Navigate to Settings → Community Nodes. Click on Install. In the search bar, enter n8n-nodes-brightdata. Select the node from the list and click Install. Docs: https://docs.n8n.io/integrations/community-nodes/installation/gui-install 2. Configure Bright Data Credentials Obtain your API key from Bright Data. In n8n, go to Credentials → New, select HTTP Request. Set authentication to Header Auth. In Name, enter Authorization. In Value, enter Bearer <your_api_key_from_Bright_Data>. Save the credentials. 3. Configure OpenAI Credentials Add your OpenAI API key to the relevant nodes. If you want to use a different model, replace all OpenAI nodes accordingly. 4. Set Up GoHighLevel Integration a. Create a GoHighLevel Marketplace App Go to https://marketplace.gohighlevel.com Click My Apps → Create App Set Distribution Type to Sub-Account Add the following scopes: locations.readonly contacts.readonly contacts.write opportunities.readonly opportunities.write users.readonly conversations/message.readonly conversations/message.write Add your n8n OAuth Redirect URL as a redirect URI in the app settings. Save and copy the Client ID and Client Secret. b. Configure GoHighLevel Credentials in n8n Go to Credentials → New Choose OAuth2 API Input: Client ID Client Secret Authorization URL: https://auth.gohighlevel.com/oauth/authorize Access Token URL: https://auth.gohighlevel.com/oauth/token Scopes: locations.readonly contacts.readonly contacts.write opportunities.readonly opportunities.write users.readonly conversations/message.readonly conversations/message.write Save and authenticate to complete setup. Docs: https://docs.n8n.io/integrations/builtin/credentials/highlevel 🔄 Workflow Functionality (Summary) Scheduled Scraping**: Scrapes website at user-defined intervals. Edit Fields** node: User defines the homepage or site to scrape. Bright Data Node* (self-hosted) OR *HTTP Node** (cloud users) used to perform scraping. Knowledgebase Update**: The scraped content is stored or indexed. GoHighLevel SMS**: Incoming user queries are received through SMS. AI Processing**: AI matches queries to relevant content. Response Delivery**: AI-generated answers are sent back via SMS. 🧩 Use Cases Customer Support Automation**: Provide instant, accurate responses. Lead Qualification**: Automatically answer potential customer inquiries. Internal Knowledge Distribution**: Keep staff updated via SMS based on website info. 🛠️ Customization Scraping URLs**: Adjust targets in the Edit Fields node. Model Swap**: Replace OpenAI nodes to use a different LLM. Format Response**: Customize output to match your tone or brand. Other Channels**: Expand to include chat apps or email responses. Vector Databases**: It is advisable to store the data into a third-party vector database services like Pinecone, Supabase, etc. Chat Memory Node**: This workflow is using Redis as a chat memory but you can use N8N built-in chat memory. ✅ Summary This n8n workflow combines Bright Data’s scraping tools and GoHighLevel’s SMS interface with AI query handling to deliver a real-time, conversational support experience. Ideal for businesses that want to turn their website into a live knowledge source via SMS, this agent keeps itself updated, smart, and customer-ready.

Complete MCP server exposing all Mandrill Tool operations to AI agents. Zero configuration needed - all 2 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 Mandrill Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n Mandrill Tool tool with full error handling 📋 Available Operations (2 total) Every possible Mandrill Tool operation is included: 💬 Message (2 operations) • Send a message based on a template • Send a message based on HTML 🤖 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 Mandrill 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 Mandrill 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.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. How it works This advanced automation builds a fully autonomous SEO blog writer using n8n, Scrapeless, LLMs, and Pinecone vector database. It’s powered by a Retrieval-Augmented Generation (RAG) system that collects high-performing blog content, stores it in a vector store, and then generates new blog posts based on that knowledge—endlessly. Part 1: Build a Knowledge Base from Popular Blogs Scrape existing articles** from a well-established writer (in this case, Mark Manson) using the Scrapeless node. Extract content from blog pages* and store it in *Pinecone**, a powerful vector database that supports similarity search. Use Gemini Embedding 001** or any other supported embedding model to encode blog content into vectors. Result**: You’ll have a searchable vector store of expert-level content, ready to be used for content generation and intelligent search. Part 2: SERP Analysis & AI Blog Generation Use Scrapeless' SERP node to fetch search results based on your keyword and search intent. Send the results to an LLM (like Gemini, OpenRouter, or OpenAI) to generate a keyword analysis report in Markdown → then converted to HTML. Extract long-tail keywords, search intent insights, and content angles from this report. Feed everything into another LLM with access to your Pinecone-stored knowledge base, and generate a fully SEO-optimized blog post. Set up steps Prerequisites Scrapeless API key Pinecone account and index setup An embedding model (Gemini, OpenAI, etc.) n8n instance with Community Node: n8n-nodes-scrapeless installed Credential Configuration Add your Scrapeless and Pinecone credentials in n8n under the "Credentials" tab Choose embedding dimensions according to the model you use (e.g., 768 for Gemini Embedding 001) Key Highlights Clones a real content creator**: Replicates knowledge and writing style from top-performing blog authors. Auto-scrapes hundreds of blog posts** without being blocked. Stores expert content** in a vector DB to build a reusable knowledge base. Performs real-time SERP analysis** using Scrapeless to fetch and analyze search data. Generates SEO blog drafts** using RAG with detailed keyword intelligence. Output includes**: blog title, HTML summary report, long-tail keywords, and AI-written article body. RAG + SEO: The Future of Content Creation This template combines: AI reasoning** from large language models Reliable data scraping** from Scrapeless Scalable storage** via Pinecone vector DB Flexible orchestration** using n8n nodes This is not just an automation—it’s a full-stack SEO content machine that enables you to: Build a domain-specific knowledge base Run intelligent keyword research Generate traffic-ready content on autopilot 💡 Use Cases SaaS content teams cloning competitor success Affiliate marketers scaling high-traffic blog production Agencies offering automated SEO content services AI researchers building personal knowledge bots Writers automating first-draft generation with real-world tone