Workflow Templates

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 🎯 Purpose This workflow helps you automatically monitor stock related news, extract the main content, summarize it using a LLM (via OpenRouter), and send real time alerts to Telegram and store them in Google Sheets. ⚙️ How It Works Trigger A Cron node triggers the workflow every 15 minutes (adjustable). RSS Feed node checks latest articles from Google Alerts RSS. The workflow filters duplicates using Google Sheets as a log. The article URL is sent to Jina AI Readability API to extract the main body text. The content is summarized using a model from OpenRouter (e.g., Gemini, Claude, GPT-4). You can customize the prompt to suit your tone and analysis needs. The result is appended to a Google Sheets file. Sends the title, summary, and reccomendation to Telegram chat. 🧾 Google Sheets Template Create a Google Sheet using this template: Stock Alert 🧰 Requirements Telegram Bot + your Chat ID OpenRouter account and API key Jina AI account for content extraction Google Account with access to Google Sheets Google Alerts RSS feed 🛠 Setup Instructions Install required credentials: Add OpenRouter API key to n8n credentials. Add Telegram Bot Token and Chat ID. Add Google Sheets credentials. Add Jina AI credentials. Create or copy the Google Sheet using the link above. Go to Google Alerts, create alerts, and copy the RSS feed URL. Replace placeholder API keys and URLs. Adjust Telegram Chat ID. 🔐 Security Note All sensitive credentials (e.g., API keys, personal chat IDs) have been removed from this template. Please replace them using the n8n credentials manager before activating the workflow.

The blogblizt: polylang workflow streamlines the creation and publication of high-quality blog content using powerful automation with n8n, OpenAI’s GPT and the WordPress API. It enables effortlessly generate SEO-friendly articles complete with metadata and optimized featured images, improving content freshness and search engine visibility. 💡 Why Use blogblizt? Automate content creation** to keep your blog fresh and engaging Generate SEO-optimized posts** with expert-crafted titles, meta descriptions, and focus keyphrases Save hours** of manual writing, image sourcing, and SEO configuration Leverage AI** for topic ideation and high-quality writing tailored to international student audiences Seamlessly publish and manage drafts** directly on your WordPress site via API Produce captivating, relevant featured images** without external tools Support multilingual content creation** with randomized language selection for diversity ⚡ Who Is This For? Content strategists managing WordPress blogs needing efficient topic generation SEO specialists wanting automated post creation with optimized metadata Website owners aiming to maintain active, multilingual content Marketers who want to leverage AI for high-quality, consistent article production ❓ What Problem Does It Solve? This workflow automates the entire editorial cycle—from generating engaging topics with AI, drafting full-length articles, producing featured images automatically, to posting drafts configured for SEO on WordPress—dramatically reducing editor workload and improving content output. 🔧 What This Workflow Does ⏱ Trigger Runs on manual trigger or a weekly schedule to ensure consistent content flow 📎 Fetch Site Context Retrieves recent posts, taxonomies, and WordPress API schema to understand site structure 🔍 Generate Topic Uses OpenAI GPT-4.1-mini to roll a random language and craft a targeted blog post topic + SEO metadata 🤖 Draft Article Composes a comprehensive, SEO-friendly article tailored to the generated topic 💌 Create Draft Posts the draft on WordPress with Yoast SEO fields populated 🖼 Generate Image Creates a high-quality, cinematic featured image via AI 📤 Upload & Attach Uploads the image to the WordPress media library and sets it as the post’s featured image 🔐 Setup Instructions Import the workflow file into n8n: Add credentials: WordPress API (with create-post & media permissions) OpenAI API key (for GPT and image models) Customize categories, languages, and schedule in the relevant nodes Adjust the Schedule Trigger timing as desired (e.g. every Monday at 9 AM) Test end-to-end on a staging WordPress site to verify drafts and images publish correctly 🧩 Pre-Requirements An operational n8n instance (Cloud or self-hosted) (self-hosted or n8n cloud) WordPress site with REST API access & proper authentication OpenAI account with API access for both language and image models (Optional) Yoast SEO plugin installed for metadata recognition 🛠️ Customize It Further Tweak OpenAI prompts for niche topics or additional languages Add social-media nodes to auto-share new posts Insert an editorial review step before publishing Refine image prompts for different visual styles (e.g., “modern infographic” vs. “cinematic portrait”) 🧠 Nodes Used Manual Trigger** Schedule Trigger** (weekly) HTTP Request** (fetch posts, taxonomies, schema; upload media) Code** (JavaScript analyzers for API schema & taxonomy parsing) OpenAI Chat** (GPT-4.1-mini for topics & articles) OpenAI Image Generation** (for featured images) WordPress** (create draft post) Sticky Notes** (in-flow documentation) 📞 Support Built by: Khaisa Studio Tags: wordpress, marketing, polylang Category: Content Creation Need a custom? contact me on LinkedIn or Web

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. How it works This template is your personal launchpad into the world of AI-powered automation. It provides a fully functional, interactive AI chatbot that you can set up in minutes, designed specifically for those new to AI Agents. What is an AI Agent? Think of it as a smart assistant that doesn't just talk—it acts. You give it a set of "tools" (like other n8n tool nodes), and it intelligently decides which tool to use to answer your questions or complete your tasks. This starter kit comes with a pre-built "toolbox" of superpowers, allowing your agent to: Get the Weather:** Ask for the forecast anywhere in the world. Get the News:** Fetch the latest headlines from n8n, CNN, and others. The workflow is designed to be a hands-on learning experience, with detailed sticky notes explaining every component, from the chat interface to the agent's "brain" and "memory." Set up steps Setup time: ~2-3 minutes This workflow is designed to be incredibly easy to start. You only need one free API key to get it working. Add Your AI Key: The workflow uses Google's Gemini model by default. You will need a free Gemini API key. Find the Gemini node on the canvas. The sticky note right below it (How to Get Google Gemini Credentials) provides a link and simple instructions to get your key. In the Gemini node, click the Credential dropdown and select + Create New Credential to add your key. Activate the Workflow: At the top-right of the screen, click the "Inactive" toggle switch. It will turn green and say "Active". Your agent is now live! Start Chatting: Open the Example Chat Window node (it has a 💬 icon). In its parameter panel, you will see a Chat URL. Click the link to copy it. Paste the URL into a new browser tab and start asking your agent questions! Optional: The template also includes disabled OpenAI chat model node and tools for Google Calendar, and Gmail. You can enable and configure these later to change the underlying AI model or give your agent even more superpowers!

🎙️ AI Audio Assistant with Voice-to-Voice Response Who is this for? Businesses, customer service teams, content creators, and organizations who want to provide intelligent voice-based interactions through Telegram. Perfect for accessibility-focused services, multilingual support, or hands-free customer assistance. What problem does this solve? Enables natural voice conversations with AI Breaks down language and accessibility barriers Provides instant voice responses to customer queries Reduces typing requirements for users Offers 24/7 voice-based customer support Maintains conversation context across voice interactions What this workflow does: Receives voice messages via Telegram bot Transcribes audio using Deepgram's advanced speech-to-text Processes transcribed text through AI agent with knowledge base access Generates intelligent responses based on conversation context Converts AI response to natural-sounding speech using Deepgram TTS Sends audio response back to user via Telegram Maintains conversation memory for contextual interactions 🔧 Technical Architecture Core Components: Telegram Bot**: Receives and sends voice messages Deepgram STT**: Transcribes voice to text with high accuracy OpenAI GPT**: Processes queries and generates responses Supabase Knowledge Base**: Stores and retrieves business information Memory Management**: Maintains conversation context Deepgram TTS**: Converts text responses to natural speech Data Flow: Voice Message → Telegram API → File Download Audio File → Deepgram STT → Transcript Transcript → AI Agent → Response Generation Response → Deepgram TTS → Audio File Audio Response → Telegram → User 🛠️ Setup Instructions Prerequisites Telegram Bot Token Create bot via @BotFather Get bot token and configure webhook Deepgram API Key Sign up at deepgram.com Get API key for STT and TTS services Note: Currently hardcoded in workflow OpenAI API Key OpenAI account with API access Configure in OpenAI Chat Model node Supabase Database Create Supabase project Set up knowledge_base table Configure API credentials Step-by-Step Setup Configure Telegram Bot Update telegramToken in "Prepare Voice Message Data" node Set correct bot token in Telegram nodes Test bot connectivity Set Up Deepgram Integration Replace API key in "Transcribe with Deepgram" node Update TTS endpoint in "HTTP Request" node Test voice transcription accuracy Configure Knowledge Base -- Create knowledge_base table in Supabase CREATE TABLE knowledge_base ( id UUID DEFAULT gen_random_uuid() PRIMARY KEY, question TEXT NOT NULL, answer TEXT NOT NULL, category VARCHAR(100), keywords TEXT[], created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW() ); Customize AI Prompts Update system message in "Telegram AI Agent" node Adjust temperature and max tokens in OpenAI model Configure memory session keys Test End-to-End Flow Send test voice message to bot Verify transcription accuracy Check AI response quality Validate audio output clarity 🎛️ Configuration Options Voice Recognition Settings Model**: nova-2 (Deepgram's latest model) Language**: English (en) - can be changed Smart Format**: Enabled for better punctuation AI Response Settings Temperature**: 0.3 (conservative responses) Max Tokens**: 100 (adjust based on needs) Memory**: Session-based conversation context Text-to-Speech Settings Model**: aura-2-thalia-en (natural female voice) Alternative voices**: Available in Deepgram TTS API Audio Format**: Optimized for Telegram 🔒 Security Considerations API Key Management // Current implementation has hardcoded tokens // Recommended: Use environment variables const telegramToken = process.env.TELEGRAM_BOT_TOKEN; const deepgramKey = process.env.DEEPGRAM_API_KEY; Data Privacy Voice messages are processed by external APIs Consider data retention policies Implement user consent mechanisms Ensure GDPR compliance if applicable 📊 Monitoring & Analytics Key Metrics to Track Voice message processing time Transcription accuracy rates AI response quality scores User engagement metrics Error rates and failure points Recommended Logging // Add to workflow for monitoring console.log({ timestamp: new Date().toISOString(), user_id: userData.user_id, transcript_confidence: transcriptData.confidence, response_length: aiResponse.length, processing_time: processingTime }); 🚀 Customization Ideas Enhanced Features Multi-language Support Add language detection Support multiple TTS voices Translate responses Voice Commands Implement wake words Add voice shortcuts Create voice menus Advanced AI Features Sentiment analysis Intent classification Escalation triggers Integration Expansions Connect to CRM systems Add calendar scheduling Integrate with help desk tools Performance Optimizations Implement audio preprocessing Add response caching Optimize API call sequences Implement retry mechanisms 🐛 Troubleshooting Common Issues Voice Not Transcribing Check Deepgram API key validity Verify audio format compatibility Test with shorter voice messages Poor Audio Quality Adjust TTS model settings Check network connectivity Verify Telegram audio limits AI Responses Too Generic Improve knowledge base content Adjust system prompts Increase context window Memory Not Working Check session key configuration Verify user ID extraction Test conversation continuity 💡 Best Practices Voice Interface Design Keep responses concise and clear Use natural speech patterns Avoid technical jargon Provide clear next steps Knowledge Base Management Regular content updates Clear categorization Keyword optimization Quality assurance testing User Experience Fast response times (<5 seconds) Consistent voice personality Graceful error handling Clear capability communication 📈 Success Metrics Technical KPIs Response time: <3 seconds average Transcription accuracy: >95% User satisfaction: >4.5/5 Uptime: >99.5% Business KPIs Customer query resolution rate Support ticket reduction User engagement increase Cost per interaction decrease 🔄 Maintenance Schedule Daily Monitor error logs Check API rate limits Verify service uptime Weekly Review conversation quality Update knowledge base Analyze usage patterns Monthly Performance optimization Security audit Feature updates User feedback review 📚 Additional Resources Documentation Links Deepgram STT API Deepgram TTS API Telegram Bot API OpenAI API Supabase Documentation Community Support n8n Community Forum Telegram Bot Developers Group Deepgram Developer Discord OpenAI Developer Community Note: This template requires active API subscriptions for Deepgram and OpenAI services. Costs may apply based on usage volume.

How it works: Using a Crew of AI agents (Senior Researcher, Visionary, and Senior Editor), this crew will automatically determine the right questions to ask to produce a detailed fundamental stock analysis. This application has two components: a front-end and a Stock Q&A engine. The front end is the team of agents automatically figuring out the questions to ask, and the back-end part is the ability to answer those questions with the SEC 10K data. This template implements the Stock Q&A engine. For the front-end of the application, you can choose one of two options: using CrewAI with the Replit environment (code approach) fully visual approach with n8n template (AI-powered automated stock analysis) Setup steps: Use first workflow in template to upsert a company annual report PDF (such as from SEC 10K filling) Get URL for Webhook in second workflow template CrewAI front-end: Youtube overview video Fork this AI Agent environment Crew Agent Environment Set the webhook URL into N8N_WEBHOOK_URL variable Set OpenAI_API_KEY variable

This n8n template monitors active support issues in Linear.app to track the mood of their ongoing conversation between reporter and assignee using Sentiment Analysis. When sentiment dips into the negative, a notification is sent via Slack to alert the team. How it works A scheduled trigger is used to fetch recently updated issues in Linear using the GraphQL node. Each issue's comments thread is passed into a simple Information Extractor node to identify the overall sentiment. The resulting sentiment analysis combined with the some issue details are uploaded to Airtable for review. When the template is re-run at a later date, each issue is re-analysed for sentiment Each issue's new sentiment state is saved to the airtable whilst its previous state is moved to the "previous sentiment" column. An Airtable trigger is used to watch for recently updated rows Each matching Airtable row is filtered to check if it has a previous non-negative state but now has a negative state in its current sentiment. The results are sent via notification to a team slack channel for priority. Check out the sample Airtable here: https://airtable.com/appViDaeaFw4qv9La/shrq6HgeYzpW6uwXL How to use Modify the GraphQL filter to fetch issues to a relevant issue type, team or person. Update the Slack channel to ensure messages are sent to the correct location or persons. The Airtable also serves to give a snapshot of Sentiment across support tickets for a given period. It's possible to use this to assess the daily operations. Requirements Linear for issue tracking (but feel free to use another system if preferred) Airtable for Database OpenAI for LLM and Sentiment Analysis Customising the workflow Add more granular levels of sentiment to reduce the number of alerts. Explore different types of sentiment based on issue types and customer types. This may help prioritise alerts and response. Run across teams or categories of issues to get an overview of sentiment across the support organisation.

This automated workflow takes a static image and a textual prompt and transforms them into an animated video using the MiniMax Hailuo 02 model. It then uploads the generated video to YouTube and TikTok, and updates a Google Sheet with relevant links and metadata. Benefits of This Workflow Fully Automated Pipeline**: From prompt to video to social media publication — all without manual intervention. Scalable Content Creation**: Generate and distribute dozens of videos per hour with minimal human input. Cross-Platform Posting: Automatically pushes content to **YouTube and TikTok simultaneously. SEO Optimization**: Uses AI to generate catchy, keyword-rich video titles that improve visibility. Easy Integration**: Based on Google Sheets for input/output, making it accessible to non-technical users. Time-Efficient**: Batch-processing enabled with scheduled runs every few minutes. Customizable Duration**: Video duration can be adjusted (default is 6 seconds). How It Works Trigger & Data Fetching: The workflow starts either manually or via a scheduled trigger (e.g., every 5 minutes). It checks a Google Sheet for new entries where the "VIDEO" column is empty, indicating pending video generation tasks. Video Creation: For each entry, the workflow extracts the image URL and prompt from the Google Sheet. It sends these inputs to the MiniMax Hailuo 02 to generate a video. The API processes the image and prompt, optimizes the prompt, and creates a short video (default: 6 seconds). Status Monitoring: The workflow polls the API every 60 seconds to check if the video is COMPLETED. Once ready, it retrieves the video URL and uploads the file to Google Drive. YouTube & TikTok Upload: The video is sent to YouTube and TikTok via the Upload-Post.com API (The free plan allows uploads to all platforms except TikTok. To enable, upgrade to a paid plan.). A GPT-generated SEO-optimized title is created for the video. The Google Sheet is updated with the video URL and YouTube link. Set Up Steps Google Sheet Setup: Create a Google Sheet with columns: IMAGE (input image URL), PROMPT (video description), VIDEO (auto-filled), and YOUTUBE_URL (auto-filled). Link the sheet to the workflow using the Google Sheets node. API Keys: Obtain a fal.run API key (for MiniMax Hailuo) and configure the "Authorization" header in the "Create video" node. Get an Upload-Post.com API key (10 free uploads/month) and set it in the "Upload on YouTube/TikTok" nodes. Workflow Configuration: Replace YOUR_USERNAME in the Upload-Post nodes with your profile name (e.g., "test1"). Adjust the video duration (6 or 10 seconds) in the "Create video" node. Set the Schedule Trigger interval (e.g., 5 minutes) to automate checks for new tasks. Execution: Run the workflow manually or let the scheduler process new rows automatically. The system handles video generation, uploads, and Google Sheet updates end-to-end. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Video Guide I prepared a detailed guide explaining how to build an AI-powered meeting assistant that provides real-time transcription and insights during virtual meetings. Youtube Link Who is this for? This workflow is ideal for business professionals, project managers, and team leaders who require effective transcription of meetings for improved documentation and note-taking. It's particularly beneficial for those who conduct frequent virtual meetings across various platforms like Zoom and Google Meet. What problem does this workflow solve? Transcribing meetings manually can be tedious and prone to error. This workflow automates the transcription process in real-time, ensuring that key discussions and decisions are accurately captured and easily accessible for later review, thus enhancing productivity and clarity in communications. What this workflow does The workflow employs an AI-powered assistant to join virtual meetings and capture discussions through real-time transcription. Key functionalities include: Automatic joining of meetings on platforms like Zoom, Google Meet, and others with the ability to provide real-time transcription. Integration with transcription APIs (e.g., AssemblyAI) to deliver seamless and accurate capture of dialogue. Structuring and storing transcriptions efficiently in a database for easy retrieval and analysis. Real-Time Transcription: The assistant captures audio during meetings and transcribes it in real-time, allowing participants to focus on discussions. Keyword Recognition: Key phrases can trigger specific actions, such as noting important points or making prompts to the assistant. Structured Data Management: The assistant maintains a database of transcriptions linked to meeting details for organized storage and quick access later. Setup Preparation Create Recall.ai API key Setup Supabase account and table create table public.data ( id uuid not null default gen_random_uuid (), date_created timestamp with time zone not null default (now() at time zone 'utc'::text), input jsonb null, output jsonb null, constraint data_pkey primary key (id), ) tablespace pg_default; Create OpenAI API key Development Bot Creation: Use a node to create the bot that will join meetings. Provide the meeting URL and set transcription options within the API request. Authentication: Configure authentication settings via a Bearer token for interacting with your transcription service. Webhook Setup: Create a webhook to receive real-time transcription updates, ensuring timely data capture during meetings. Join Meeting: Set the bot to join the specified meeting and actively listen to capture conversations. Transcription Handling: Combine transcription fragments into cohesive sentences and manage dialog arrays for coherence. Trigger Actions on Keywords: Set up keyword recognition that can initiate requests to the OpenAI API for additional interactions based on captured dialogue. Output and Summary Generation: Produce insights and summary notes from the transcriptions that can be stored back into the database for future reference.

Description What Problem Does This Solve? 🛠️ This workflow automates the process of extracting key information from resumes received as email attachments and storing that data in a structured format within a Supabase database. It eliminates the manual effort of reviewing each resume, identifying relevant details, and entering them into a database. This streamlines the hiring process, making it faster and more efficient for recruiters and HR professionals. Target audience: Recruiters, HR departments, and talent acquisition teams. What Does It Do? 🌟 Monitors a designated email inbox for new messages with resume attachments. Extracts key information such as name, contact details, education, work experience, and skills from the attached resumes. Cleans and formats the extracted data. Stores the processed data securely in a Supabase database. Key Features 📋 Automatic email monitoring for resume attachments. Intelligent data extraction from various resume formats (e.g., PDF, DOC, DOCX). Customizable data fields to capture specific information. Seamless integration with Supabase for data storage. Uses OpenRouter to streamline API key management for services such as AI-powered parsing. Setup Instructions Prerequisites ⚙️ n8n Instance**: Self-hosted or cloud instance of n8n. Email Account**: Gmail account with Gmail API access for receiving resumes. Supabase Account**: A Supabase project with a database/table ready to store extracted resume data. You'll need the Supabase URL and API key. OpenRouter Account**: For managing AI model API keys centrally when using LLM-based resume parsing. Installation Steps 📦 1. Import the Workflow: Copy the exported workflow JSON. Import it into your n8n instance via “Import from File” or “Import from URL”. 2. Configure Credentials: In n8n > Credentials, add credentials for: Email account (Gmail API): Provide Client ID and Client Secret from the Google Cloud Platform. Supabase: Provide the Supabase URL and the anon public API key. OpenRouter (Optional): Add your OpenRouter API key for use with any AI-powered resume parsing nodes. Assign these credentials to their respective nodes: Gmail Trigger → Email credentials. Supabase Insert → Supabase credentials. AI Parsing Node → OpenRouter credentials. 3. Set Up Supabase Table: Create a table in Supabase with columns such as: name, email, phone, education, experience, skills, received_date, etc. Make sure the field names align with the structure used in your workflow. 4. Customize Nodes: Parsing Node(s):* Modify the workflow to use an *OpenAI model* directly for field extraction, replacing the *Basic LLM Chain** node that utilizes OpenRouter. 5. Test the Workflow: Send a test email with a resume attachment. Check n8n's execution log to confirm the workflow triggered, parsed the data, and inserted it into Supabase. Verify data integrity in your Supabase table. How It Works High-Level Workflow 🔍 Email Monitoring: Triggered when a new email with an attachment is received (via Gmail API). Attachment Check: Verifies the email contains at least one attachment. Prepare Data: Extracts the attachment and prepares it for analysis. Data Extraction: Uses OpenRouter-powered LLM (if configured) to extract structured information from the resume. Data Storage: The structured information is saved into the Supabase database. Node Names and Actions (Example) Gmail Trigger:** Triggers when a new email is received. IF**: Checks whether the received email includes any attachments. Get Attachments:** Retrieves attachments from the triggering email. Prepare Data:** Prepares the attachment content for processing. Basic LLM Chain:** Uses an AI model via OpenRouter to extract relevant resume data and returns it as structured fields. Supabase-Insert:** Inserts the structured resume data into your Supabase database.

Disclaimer This template is only available on n8n self-hosted as it's making use of the community node for MCP Client. Who this is for? The Chat Conversations with Bright Data MCP Search Engines & Google Gemini workflow is designed for users who need real-time, AI-enhanced conversations powered by live search engine results. This workflow is tailored for:​ Data Analysts - Who want live, search-based data fused with AI reasoning. Marketing Researchers - Seeking up-to-the-minute market or competitor insights via conversational AI. Product Managers - Exploring user needs, market trends, and competitor analysis in real time. AI Developers - Building dynamic applications that combine live search data with intelligent conversation agents. Growth Hackers - Who need fast, conversational research tools for campaign ideation, outreach, or content creation. What problem is this workflow solving? Traditional chatbots and AI systems often rely on static, outdated data. This workflow enables AI agents to fetch live search engine data and converse intelligently about it, making interactions dynamic, accurate, and highly contextual. This workflow solves the major gaps of: Outdated Knowledge: Regular chatbots lack up-to-date information from live web searches. Manual Search Fatigue: Manually searching for information and interpreting it is time-consuming. Context Bridging: Connecting search results into meaningful, conversational replies requires human-level reasoning. What this workflow does? Accepts a user's conversational query input. Triggers a search request to Bright Data’s MCP Search Engines API (Google, Bing, etc.) based on the query. Waits for the search task to complete. Retrieves real-time search results. Feeds the search results and original question into Google Gemini. Generates a human-like, contextually accurate AI response combining live information and conversational flow. Outputs the response back into a chat app. 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. Also, do "Account Setup" as mentioned in the @brightdata/mcp URL. Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the 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 Web Unlocker API Token within the Environments textbox above as API_TOKEN=<your-token>. Update the HTTP Request for Webhook Notification node for sending the Webhook notification for chat responses. How to customize this workflow to your needs Change Search Engine: Add or Remove the Search Engine MCP tools based upon the Bright Data MCP Server updates. Expand Outputs: Send AI chat responses to Slack, Discord, custom chat UIs, WhatsApp, or CRM systems. Store conversation logs in a database (PostgreSQL, MongoDB, etc.) for future audits or training.

Who is this for? Everyone! Did you dream of asking an AI "what hotel did I stay in for holidays last summer?" or "what were my marks last semester like?". Dream no more, as vector similarity searches and this workflow are the foundations to make it possible (as long as the information appears in your e-mails 😅). 100% Local and Open Source! This workflow is designed to use locally-hosted open source. Ollama as LLM provider, nomic-embed-text as the embeddings model, and pgvector as the vector database engine, on top of Postgres. Structured AND Vectorized This workflow combines structured and semantic search on your e-mail. No need for enterprise setups! Leverage the convenience of n8n and open source to get a bleeding edge solution. Setup You will need a PGVector database with embeddings for all your email. Use my other template Gmail to Vector Embeddings with PGVector and Ollama to set it up in a breeze! Make a copy of my Email Assistant: Convert Natural Language to SQL Queries with Phi4-mini and PostgreSQL, you will need it for structured searches. Install this template and modify the Call the SQL composer Workflow step, to point at your copy of the SQL workflow. Adjust the rest of necessary steps: Telegram Trigger, AI Chat model, AI Embeddings... Activate the workflow and chat around!

🔍 Competitor Review Scraper & Ad Copy Generator (Trustpilot + Bright Data + GPT-4o-mini) 📌 Who It's For Marketers, business owners, and agencies looking to: Analyze competitor pain points Generate high-impact Facebook ad copy Automate manual data processing 🧩 How It Works This n8n-based workflow combines Bright Data, Google Sheets, and OpenAI to scrape, process, and transform Trustpilot reviews into ready-to-use ad copy. 🔹 Step-by-Step Breakdown Trigger (Manual Form Submission) Input required: Competitor’s Trustpilot URL Review timeframe (30d, 3m, 6m, 12m) Fetch Reviews Calls Bright Data’s Dataset API with URL & timeframe Polls until snapshot is ready Retrieve & Store Extracts all reviews Saves them into a structured Google Sheet Filter & Aggregate Filters to only 1–2 star reviews Summarizes common negative feedback Generate Ad Copy Sends the summary to OpenAI GPT-4o-mini Produces 3 variations of ad copy targeting pain points Distribute Insights Sends ad copy + summary via email to the marketing team ✅ Requirements -LLM Account -Google Sheets - Copy this sheet: https://docs.google.com/spreadsheets/d/1Zi758ds2_aWzvbDYqwuGiQNaurLgs-leS9wjLWWlbUU/edit?gid=0#gid=0 -Bright Data account ⚙️ Setup Instructions **Step 1: Google Sheets ** Copy this Google Sheets template Do not change column headers **Step 2: n8n Credential Setup ** Google Sheets: OAuth2 Bright Data: Authorization Header OpenAI: API Key for GPT-4o-mini **Step 3: Import Workflow ** Import the .json file into n8n Configure your sheet + dataset ID Adjust GPT prompts as needed **Step 4: Run the Workflow ** Trigger via form Receive ad copy + review insights via email 🧠 Tips & Best Practices Bright Data snapshots may take time — polling is handled Focusing on 1–2 star reviews yields the most actionable pain points You can customize GPT-4o-mini prompts for tone or vertical 💬 Support & Feedback Need help or customization? 📧 Email: Yaron@nofluff.online 📺 YouTube: @YaronBeen 🔗 LinkedIn: linkedin.com/in/yaronbeen 📚 Bright Data Docs: docs.brightdata.com/introduction