by Davide
This Workflow simulates an AI-powered phone agent with two main functions: 📅 Appointment Booking – It can schedule appointments directly into Google Calendar. 🧠 RAG-based Information Retrieval – It provides answers using a Retrieval-Augmented Generation (RAG) system. For example, it can respond to questions such as store opening hours, return policies, or product details. The guide also explains how to purchase a dedicated phone number (with a +1 prefix) and link it to the AI agent. This setup is cost-effective, as it uses a FREE $10 credit to operate without additional charges in the beginning. ✨ Advantages 🕐 24/7 Availability** – The AI agent can answer calls and assist customers at any time. 🤖 Automation** – It reduces the workload on human staff by handling repetitive tasks like appointment scheduling and FAQ responses. 🔌 Easy Integration** – Built with n8n, it’s flexible and customizable for various platforms and tools. 💸 Low-cost Setup** – Using the free credit, businesses can get started without an upfront investment. 📦 Use Cases 🛍 E-commerce** – Answer common product questions or order inquiries. 🏬 Retail Stores** – Provide store hours, address info, and return policies. 🍽 Restaurants** – Take reservations or share menu information. 💼 Service Providers** – Book appointments or consultations. 📞 Any Local Business** – Offer phone support without needing a live operator. How It Works This Workflow simulates an AI-powered phone agent with two primary functions: Appointment Booking The workflow captures call events (e.g., call_ended or call_analyzed) and extracts key details (transcript, caller info, duration, etc.). Using OpenAI, it summarizes the conversation and parses structured data (e.g., names, contact info, dates). For scheduling, it converts user-provided dates into Google Calendar-compatible formats and creates events automatically. RAG-Based Information Retrieval When a query is received (e.g., store hours, product details), the workflow retrieves relevant information from a Qdrant vector store. An AI agent processes the query using the retrieved data and responds via a webhook, ensuring accurate, context-aware answers. Set Up Steps Prepare Qdrant Vector Store Create/refresh a Qdrant collection (via HTTP requests). Upload and vectorize documents (e.g., from Google Drive) using OpenAI embeddings. Configure RetellAI Agent Sign up for RetellAI, create an agent, and set the webhook URLs (n8n_call for call events, n8n_rag_function for RAG queries). Purchase a Twilio phone number and link it to the agent. n8n Workflow Setup Connect OpenAI, Qdrant, Google Calendar, and Telegram nodes with credentials. Customize prompts for summarization, date parsing, and RAG responses. Test the workflow to ensure data flows from call events → processing → actions (e.g., calendar bookings, Telegram alerts). Deploy Trigger the workflow via RetellAI webhooks during calls. Monitor outputs (e.g., call summaries in Telegram, calendar events). Note: Replace placeholders (e.g., QDRANTURL, COLLECTION, CHAT_ID) with actual values. Need help customizing? Contact me for consulting and support or add me on Linkedin.
by David Olusola
⚙️ 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
by Mark Shcherbakov
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.
by Lucas Peyrin
How it works This template is a powerful, reusable utility for managing stateful, long-running processes. It allows a main workflow to be paused indefinitely at "checkpoints" and then be resumed by external, asynchronous events. This pattern is essential for complex automations and I often call it the "Async Portal" or "Teleport" pattern. The template consists of two distinct parts: The Main Process (Top Flow): This represents your primary business logic. It starts, performs some actions, and then calls the Portal to register itself before pausing at a Wait node (a "Checkpoint"). The Async Portal (Bottom Flow): This is the state-management engine. It uses Workflow Static Data as a persistent memory to keep track of all paused processes. When an external event (like a new chat message or an approval webhook) comes in with a specific session_id, the Portal looks up the corresponding paused workflow and "teleports" the new data to it by calling its unique resume_url. This architecture allows you to build sophisticated systems where the state is managed centrally, and your main business logic remains clean and easy to follow. When to use this pattern This is an advanced utility ideal for: Chatbots:** Maintaining conversation history and context across multiple user messages. Human-in-the-Loop Processes:** Pausing a workflow to wait for a manager's approval from an email link or a form submission. Multi-Day Sequences:** Building user onboarding flows or drip campaigns that need to pause for hours or days between steps. Any process that needs to wait for an unpredictable external event** without timing out. Set up steps This template is a utility designed to be copied into your own projects. The workflow itself is a live demonstration of how to use it. Copy the Async Portal: In your own project, copy the entire Async Portal (the bottom flow, starting with the A. Entry: Receive Session Info trigger) into your workflow. This will be your state management engine. Register Your Main Process: At the beginning of your main workflow, use an Execute Workflow node to call the Portal's trigger. You must pass it a unique session_id for the process and the resume_url from a Wait node. Add Checkpoints: Place Wait nodes in your main workflow wherever you need the process to pause and wait for an external event. Trigger the Portal: Configure your external triggers (e.g., your chatbot's webhook) to call the Portal's entry trigger, not your main workflow's trigger. You must pass the same session_id so the Portal knows which paused process to resume. To see it in action, follow the detailed instructions in the "How to Test This Workflow" sticky note on the canvas.
by Cyril Nicko Gaspar
📌 AI Agent Template with Bright Data MCP Tool Integration This template obtains all the possible tools from Bright Data MCP, process this through chatbot, then run any tool based on the user's query ❓ Problem It Solves The problem that the MCP solves is the complexity and difficulty of traditional automation, where users need to have specific knowledge of APIs or interfaces to trigger backend processes. By allowing interaction through natural language, automatically classifying and routing queries, and managing context and memory effectively, MCP simplifies complex data operations, customer support, and workflow orchestration scenarios where inputs and responses change dynamically. 🧰 Pre-requisites Before deploying this template, ensure you have: An active n8n instance (self-hosted or cloud). A valid OpenAI API key (or any AI models) Access to Bright Data MCP API with credentials. Basic familiarity with n8n workflows and nodes. ⚙️ Setup Instructions **Install the MCP Community Node in N8N In your N8N self-hosted instance, go to Settings → Community Nodes. Search and install n8n-nodes-mcp. Configure Credentials: Add your OpenAI API key or any AI mdeols to the relevant nodes. If you want other AI model, please replace all associated nodes of OpenAI in the workflow Set up Bright Data MCP client credentials in the installed community node (STDIO) Obtain your API in Bright Data and put it in Environment field in the credentials window. It should be written as API_Key=<your api key from Bright Data> 🔄 Workflow Functionality (Summary) User message** triggers the workflow. AI Classifier** (OpenAI) interprets the intent and maps it to a tool from Bright Data MCP. If no match is found, the user is notified. If more information is needed, the AI requests it. Memory** preserves context for follow-up actions. The tool is executed, and results are returned contextually to the user. > 🧠 Optional memory buffer and chat memory manager nodes keep conversations context-aware across multiple messages. 🧩 Use Cases Data Scraping Automation**: Trigger scraping tasks via chat. Lead Generation Bots**: Use MCP tools to fetch, enrich, or validate data. Customer Support Agents**: Automatically classify and respond to queries with tool-backed answers. Internal Workflow Agents**: Let team members trigger backend jobs (e.g., reports, lookups) by chatting naturally. 🛠️ Customization Tool Matching Logic**: Modify the AI classifier prompt and schema to suit different APIs or services. Memory Size and Retention**: Adjust memory buffer size and filtering to fit your app’s complexity. Tool Execution**: Extend the "Execute the tool" sub-workflow to handle additional actions, fallback strategies, or logging. Frontend Integration**: Connect this with various platforms (e.g., WhatsApp, Slack, web chatbots) using the webhook. ✅ Summary This template delivers a powerful no-code/low-code agent that turns chat into automation, combining AI intelligence with real-world tool execution. With minimal setup, you can build contextual, dynamic assistants that drive backend operations using natural language.
by Yaron Been
Transform chaotic support requests into organized, actionable insights automatically. This intelligent workflow captures support tickets from forms, uses AI to categorize and analyze sentiment, stores everything in organized databases, and delivers comprehensive analytics reports to your team - eliminating manual sorting while providing valuable business intelligence. 🚀 What It Does Intelligent Ticket Processing: Automatically categorizes incoming support requests into Billing, Bug Reports, Feature Requests, How-To questions, and Complaints using advanced AI analysis. Sentiment Analysis: Analyzes customer emotion (Positive, Neutral, Negative) to prioritize responses and identify satisfaction trends. Real-Time Analytics: Generates instant reports showing ticket distribution, sentiment patterns, and team workload insights. Automated Data Storage: Organizes all ticket information in searchable Google Sheets with timestamps and customer details. Smart Reporting: Sends regular email summaries to stakeholders with actionable insights and trend analysis. 🎯 Key Benefits ✅ Save 10+ Hours Weekly: Eliminate manual ticket sorting and categorization ✅ Improve Response Times: Prioritize tickets based on category and sentiment ✅ Boost Customer Satisfaction: Never miss urgent issues or complaints ✅ Track Performance: Monitor support trends and team effectiveness ✅ Scale Operations: Handle increasing ticket volume without additional staff ✅ Data-Driven Decisions: Make informed improvements based on real patterns 🏢 Perfect For Customer Support Teams SaaS companies managing user inquiries and bug reports E-commerce stores handling order and product questions Service businesses organizing client communications Startups scaling support operations efficiently Business Applications Help Desk Management**: Organize and prioritize incoming support requests Customer Success**: Monitor satisfaction levels and identify improvement areas Product Development**: Track feature requests and bug report patterns Team Management**: Distribute workload based on ticket categories and urgency ⚙️ What's Included Complete Workflow Setup: Ready-to-use n8n workflow with all nodes configured AI Integration: Google Gemini-powered classification and sentiment analysis Form Integration: Works with Typeform (easily adaptable to other platforms) Data Management: Automated Google Sheets organization and storage Email Reporting: Professional summary reports sent to your team Documentation: Step-by-step setup and customization guide 🔧 Technical Requirements n8n Platform**: Cloud or self-hosted instance Google Gemini API**: For AI classification (free tier available) Typeform Account**: For support form creation (alternatives supported) Google Workspace**: For Sheets data storage and Gmail reporting SMTP Email**: For automated report delivery 📊 Sample Output Daily Support Summary Email: 📧 Support Ticket Summary - March 15, 2024 📊 TICKET BREAKDOWN: • Billing: 12 tickets (30%) • Bug Report: 8 tickets (20%) • Feature Request: 6 tickets (15%) • How-To: 10 tickets (25%) • Complaint: 4 tickets (10%) 😊 SENTIMENT ANALYSIS: • Positive: 8 tickets (20%) • Neutral: 22 tickets (55%) • Negative: 10 tickets (25%) ⚡ PRIORITY ACTIONS: • 4 complaints requiring immediate attention • 3 billing issues escalated to finance team • 6 feature requests for product backlog review 🎨 Customization Options Categories: Easily modify ticket categories for your specific business needs Form Platforms: Adapt to Google Forms, JotForm, Wufoo, or custom webhooks Reporting Frequency: Set daily, weekly, or real-time report delivery Team Notifications: Configure alerts for urgent tickets or negative sentiment Data Visualization: Create custom dashboards and charts in Google Sheets Integration Extensions: Connect to CRM, project management, or chat platforms 🔄 How It Works Customer submits support request via your form AI analyzes message content and assigns category + sentiment Data is automatically stored in organized Google Sheets System generates real-time analytics on all historical tickets Professional report is emailed to your support team Team can prioritize responses based on urgency and sentiment 💡 Use Case Examples SaaS Company: Automatically route billing questions to finance, bugs to development, and feature requests to product team E-commerce Store: Prioritize shipping complaints, categorize product questions, and track customer satisfaction trends Consulting Firm: Organize client requests by service type, monitor project-related issues, and ensure timely responses Healthcare Practice: Sort appointment requests, billing inquiries, and medical questions while maintaining HIPAA compliance 📈 Expected Results 80% reduction** in manual ticket sorting time 50% faster** initial response times through better prioritization 25% improvement** in customer satisfaction scores 100% visibility** into support trends and team performance Unlimited scalability** as your business grows 📞 Get Help & Learn More 🎥 Free Video Tutorials YouTube Channel: https://www.youtube.com/@YaronBeen/videos 💼 Professional Support LinkedIn: https://www.linkedin.com/in/yaronbeen/ Connect for implementation consulting Share your automation success stories Access exclusive templates and updates 📧 Direct Support Email: Yaron@nofluff.online Technical setup assistance Custom workflow modifications Integration with existing systems Response within 24 hours 🏆 Why Choose This Workflow Proven Results: Successfully deployed across 100+ businesses worldwide Expert Created: Built by automation specialist with 10+ years experience Continuously Updated: Regular improvements and new features added Money-Back Guarantee: Full refund if not satisfied within 30 days Lifetime Support: Ongoing help and updates included with purchase
by Ranjan Dailata
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.
by Oneclick AI Squad
Transform your meetings into actionable insights automatically! This workflow captures meeting audio, transcribes conversations, generates AI summaries, and emails the results to participants—all without manual intervention. What's the Goal? Auto-record meetings** when they start and stop when they end Transcribe audio** to text using Vexa Bot integration Generate intelligent summaries** with AI-powered analysis Email summaries** to meeting participants automatically Eliminate manual note-taking** and post-meeting admin work Never miss important discussions** or action items again Why Does It Matter? Save 90% of Post-Meeting Time**: No more manual transcription or summary writing Never Lose Key Information**: Automatic capture ensures nothing falls through cracks Improve Team Productivity**: Focus on discussions, not note-taking Perfect Meeting Records**: Searchable transcripts and summaries for future reference Instant Distribution**: Summaries reach all participants immediately after meetings How It Works Step 1: Meeting Detection & Recording Start Meeting Trigger**: Detects when meeting begins via Google Meet webhook Launch Vexa Bot**: Automatically joins meeting and starts recording End Meeting Trigger**: Detects meeting end and stops recording Step 2: Audio Processing & Transcription Stop Vexa Bot**: Ends recording and retrieves audio file Fetch Meeting Audio**: Downloads recorded audio from Vexa Bot Transcribe Audio**: Converts speech to text using AI transcription Step 3: AI Summary Generation Prepare Transcript**: Formats transcribed text for AI processing Generate Summary**: AI model creates concise meeting summary with: Key discussion points Decisions made Action items assigned Next steps identified Step 4: Distribution Send Email**: Automatically emails summary to all meeting participants Setup Requirements Google Meet Integration: Configure Google Meet webhook and API credentials Set up meeting detection triggers Test with sample meeting Vexa Bot Configuration: Add Vexa Bot API credentials for recording Configure audio file retrieval settings Set recording quality and format preferences AI Model Setup: Configure AI transcription service (e.g., OpenAI Whisper, Google Speech-to-Text) Set up AI summary generation with custom prompts Define summary format and length preferences Email Configuration: Set up SMTP credentials for email distribution Create email templates for meeting summaries Configure participant list extraction from meeting metadata Import Instructions Get Workflow JSON: Copy the workflow JSON code Open n8n Editor: Navigate to your n8n dashboard Import Workflow: Click menu (⋯) → "Import from Clipboard" → Paste JSON → Import Configure Credentials: Add API keys for Google Meet, Vexa Bot, AI services, and SMTP Test Workflow: Run a test meeting to verify end-to-end functionality Your meetings will now automatically transform into actionable summaries delivered to your inbox!
by Floyd Mahou
How it works • Transcribes a WhatsApp voice or text message from a prospect using Whisper or GPT • Extracts key information (name, need, context, urgency) via AI • Matches the most relevant service pack by comparing the prospect’s need with Airtable data • Dynamically fills a branded template via APITEMPLATE (HTML or PDF) • Generates a clean, personalized business proposal — including dynamic links (payment, calendar, etc.) • Sends the final PDF back instantly via WhatsApp or email Set up steps • ⏱ Estimated setup time: 45–60 minutes • ✅ You’ll need: ◦ WhatsApp Business Cloud API access (with webhook configured) ◦ OpenAI API key (Whisper + GPT) ◦ Airtable (to store service packs and client input) ◦ APITEMPLATE account (template with placeholders like {{nom}}, {{prix}}, {{lien_reservation}}, etc.) ◦ n8n instance (cloud or self-hosted) • 📦 Create your service packs in Airtable with associated links (Stripe, Calendly…) • 🔗 The proposal auto-includes these links dynamically inside the PDF • 🚀 Workflow orchestrates the end-to-end process: from WhatsApp input to PDF delivery
by inderjeet Bhambra
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. How it works? This workflow is an intelligent SEO analysis pipeline that ethically scrapes blog content and performs comprehensive SEO evaluation using AI. It receives blog URLs via webhook, validates permissions through robots.txt compliance, extracts content, and generates detailed SEO insights across four strategic dimensions: Content Optimization, Keyword Strategy, Technical SEO, and Backlink Building potential. The system prioritizes ethical web scraping by checking robots.txt permissions before proceeding, ensuring compliance with website policies. Upon successful analysis, it returns a structured JSON report with actionable SEO recommendations, performance scores, and optimization strategies. Technical Specifications Trigger: HTTP POST webhook Processing Time: 30-60 seconds depending on content size AI Model: GPT-4.1 minimum with specialized SEO analysis prompt. Output Format: Structured JSON Error Handling: Graceful failure with informative messages Compliance: Respects website robots.txt policies
by Ranjan Dailata
Who this is for The TrustPilot SaaS Product Review Tracker is designed for product managers, SaaS growth teams, customer experience analysts, and marketing teams who need to extract, summarize, and analyze customer feedback at scale from TrustPilot. This workflow is tailored for: Product Managers** - Monitoring feedback to drive feature improvements Customer Support & CX Teams** - Identifying sentiment trends or recurring issues Marketing & Growth Teams** - Leveraging testimonials and market perception Data Analysts** - Tracking competitor reviews and benchmarking Founders & Executives** - Wanting aggregated insights into customer satisfaction What problem is this workflow solving? Manually monitoring, extracting, and summarizing TrustPilot reviews is time-consuming, fragmented, and hard to scale across multiple SaaS products. This workflow automates that process from unlocking the data behind anti-bot layers to summarizing and storing customer insights enabling teams to respond faster, spot trends, and make data-backed product decisions. This workflow solves: The challenge of scraping protected review data (using Bright Data Web Unlocker) The need for structured insights from unstructured review content The lack of automated delivery to storage and alerting systems like Google Sheets or webhooks What this workflow does Extract TrustPilot Reviews: Uses Bright Data Web Unlocker to bypass anti-bot protections and pull markdown-based content from product review pages Convert Markdown to Text: Leverages a basic LLM chain to clean and convert scraped markdown into plain text Structured Information Extraction: Uses OpenAI GPT-4o via the Information Extractor node to extract fields like product name, review date, rating, and reviewer sentiment Summarization Chain: Generates concise summaries of overall review sentiment and themes using OpenAI Merge & Aggregate Output: Consolidates individual extracted records into a structured batch output Outbound Data Delivery: Google Sheets – Appends summary and structured review data Write to Disk – Persists raw and processed content locally Webhook Notification – Sends a real-time alert with summarized insights Pre-conditions You need to have a Bright Data account and do the necessary setup as mentioned in the "Setup" section below. You need to have an OpenAI Account. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, Configure the Google Sheet Credentials with your own account. Follow this documentation - Set Google Sheet Credential In n8n, configure the OpenAi account credentials. Ensure the URL and Bright Data zone name are correctly set in the Set URL, Filename and Bright Data Zone node. Set the desired local path in the Write a file to disk node to save the responses. How to customize this workflow to your needs Target Multiple Products : Configure the Bright Data input URL dynamically for different SaaS product TrustPilot URLs Loop through a product list and run parallel jobs for each Customize Extraction Fields : Update the prompt in the Information Extractor to include: Review title Response from company Specific feature mentions Competitor references Tune Summarization Style Change tone**: executive summary, customer pain-point focus, or marketing quote extract Enable sentiment aggregation** (e.g., 30% negative, 50% neutral, 20% positive) Expand Output Destinations Push to Notion, Airtable, or CRM tools using additional webhook nodes Generate and send PDF reports (via PDFKit or HTML-to-PDF nodes) Schedule summary digests via Gmail or Slack
by Ranjan Dailata
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 Scrape Web Data with Bright Data and MCP Automated AI Agent workflow is built for professionals who need to automate large-scale, intelligent data extraction by utilizing the Bright Data MCP Server and Google Gemini. This solution is ideal for: Data Analysts - Who require structured, enriched datasets for analysis and reporting. Marketing Researchers - Seeking fresh market intelligence from dynamic web sources. Product Managers - Who want competitive product and feature insights from various websites. AI Developers - Aiming to feed web data into downstream machine learning models. Growth Hackers - Looking for high-quality data to fuel campaigns, research, or strategic targeting. What problem is this workflow solving? Manually scraping websites, cleaning raw HTML data, and generating useful insights from it can be slow, error-prone, and non-scalable. This workflow solves these problems by: Automating complex web data extraction through Bright Data’s MCP Server. Reducing the human effort needed for cleaning, parsing, and analyzing unstructured web content. Allowing seamless integration into further automation processes. What this workflow does? This n8n workflow performs the following steps: Trigger: Start manually. Input URL(s): Specify the URL to perform the web scrapping. Web Scraping (Bright Data): Use Bright Data’s MCP Server tools to accomplish the web data scrapping with markdown and html format. Store / Output: Save results into disk and also performs a Webhook notification. 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>. Update the LinkedIn URL person and company workflow. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. Update the file name and path to persist on disk. How to customize this workflow to your needs Different Inputs: Instead of static URLs, accept URLs dynamically via webhook or form submissions. Outputs: Update the Webhook endpoints to send the response to Slack channels, Airtable, Notion, CRM systems, etc.