Workflow Templates

This n8n workflow demonstrates how we can use Multimodal LLMs to parse and extract from PDF documents in n8n. In this particular scenario, we're passing a candidate's CV/resume to an AI which filters out unqualified applications. However, this sneaky candidate has added in hidden prompt to bypass our bot! Whatever will we do? No fret, using AI Vision is one approach to solve this problem... read on! How it works Our candidate's CV/Resume is a PDF downloaded via Google Drive for this demonstration. The PDF is then converted into an image PNG using a tool called Stirling PDF. Since the hidden prompt has a white font color, it is is invisible in the converted image. The image is then forwarded to a Basic LLM node to process using our multimodal model - in this example, we'll use Google's Gemini 1.5 Pro. In the Basic LLM node, we'll need to set a User Message with the type of Binary. This allows us to directly send the image file in our request. The LLM is now immune to the hidden prompt and its response is has expected. The example CV/Resume with hidden prompt can be found here: https://drive.google.com/file/d/1MORAdeev6cMcTJBV2EYALAwll8gCDRav/view?usp=sharing Requirements Google Gemini API Key. Alternatively, GPT4 will also work for this use-case. Stirling PDF or another service which can convert PDFs into images. Note for data privacy, this example uses a public API and it is recommended that you self-host and use a private instance of Stirling PDF instead. Customising the workflow Swap out the manual trigger for another trigger such as a webhook to integrate into your existing services. This example demonstrates a validation use-case ie. "does the candidate look qualified?". You can try additionally extracting data points instead such as years of experiences, previous companies etc.

This n8n workflow automatically creates friendly, personalized travel itineraries based on messages received via email or WhatsApp. When a user says "I want to go to Dubai with friends for 5 days" or something similar, the AI agent understands the request, generates a detailed daily plan with suggested activities, transport tips, and hotel ideas — all in a warm, human tone. It saves time, adds value for travelers, and delivers ready-to-send itineraries without any manual effort. Good to know The AI agent uses advanced language processing to understand natural travel requests in multiple formats. Itineraries are generated with personalized recommendations based on travel preferences, group size, and duration. The workflow supports both email and WhatsApp communication channels for maximum accessibility. All responses maintain a warm, friendly tone to enhance user experience. How it works The Get Query from Email node captures travel requests sent via email, parsing the message content for trip details. The Get Query from WhatsApp node simultaneously monitors WhatsApp messages for travel planning requests. Both inputs feed into the Itinerary Creator Agent node, which uses AI to analyze the request and generate comprehensive travel plans including activities, accommodations, and transportation suggestions. The Check Proper Data node validates the generated itinerary to ensure all essential information is included and properly formatted. The Check where to send Answer node determines the appropriate response channel (email or WhatsApp) based on the original request source. If the request came via email, the Sending Itinerary from Email node sends the personalized itinerary back to the user's email address. If the request came via WhatsApp, the Send Itinerary from message node delivers the travel plan through WhatsApp messaging. How to use Import the workflow into n8n and configure the nodes with your email service credentials and WhatsApp API access. Set up the AI agent with your preferred travel data sources and recommendation algorithms. Test the workflow by sending sample travel requests through both email and WhatsApp channels. Monitor the generated itineraries to ensure quality and adjust the AI agent parameters as needed. Requirements Email service API credentials (SMTP or email provider API) WhatsApp Business API access or WhatsApp integration service AI/LLM service for the Itinerary Creator Agent (OpenAI, Anthropic, or similar) Access to travel data sources for recommendations (optional but recommended) Customising this workflow Modify the Itinerary Creator Agent node to include specific travel preferences, local recommendations, or branded content. Adjust the data validation rules in the Check Proper Data node to match your quality standards. Customize response templates in both sending nodes to align with your brand voice and style. Add additional input channels or integrate with other messaging platforms as needed.

This n8n template demonstrates how to use AI as a comprehensive personal assistant with multiple specialized agents. Use cases include email management, scheduling, web search, calculations, and more - all automated through AI coordination. Good to know This template integrates multiple AI services through OpenRouter Each agent specializes in different tasks (Gmail, Calendar, Search, etc.) Memory persistence maintains context across interactions How it works The workflow is triggered by Telegram messages (can be replaced with other triggers) A router node directs requests to the appropriate specialized agent Agents include: Gmail for email management Calculator for math operations Google Search for information retrieval Calendar for scheduling Contacts for CRM functions The OpenRouter Chat Model coordinates responses Final responses are sent back through Telegram How to use Connect your Telegram bot credentials Configure each service with appropriate API keys The system will automatically route requests to the right agent Requirements OpenRouter account for AI services Telegram bot token Google API credentials for relevant services Customising this workflow Add more specialized agents as needed Replace Telegram with other communication channels Adjust routing logic for different use cases

This n8n workflow shows how using multimodal LLMs with AI vision can tackle tricky image validation tasks which are near impossible to achieve with code and often impractical to be done by humans at scale. You may need image validation when users submitted photos or images are required to meet certain criteria before being accepted. A wine review website may require users only submit photos of wine with labels, a bank may require account holders to submit scanned documents for verification etc. In this demonstration, our scenario will be to analyse a set of portraits to verify if they meet the criteria for valid passport photos according to the UK government website (https://www.gov.uk/photos-for-passports). How it works Our set of portaits are jpg files downloaded from our Google Drive using the Google Drive node. Each image is resized using the Edit Image node to ensure a balance between resolution and processing speed. Using the Basic LLM node, we'll define a "user message" option with the type of binary (data). This will allow us to pass our portrait to the LLM as an input. With our prompt containing the criteria pulled off the passport photo requirements webpage, the LLM is able to validate the photo does or doesn't meet its criteria. A structured output parser is used to structure the LLM's response to a JSON object which has the "is_valid" boolean property. This can be useful to further extend the workflow. Requirements Google Gemini API key Google Drive account Customising this workflow Not using Gemini? n8n's LLM node works with any compatible multimodal LLM so feel free to swap Gemini out for OpenAI's GPT4o or Antrophic's Claude Sonnet. Don't need to validate portraits? Try other use cases such as document classification, security footage analysis, people tagging in photos and more.

Use case This automation is for teams working in Notion. When you have a lot of back and forth in the comment section, it’s easy to lose track of what is going on in the conversation. This automation relies on AI to generate a summary of the comment section. How it works Every hour (the trigger can be adapted to your need and usecase), the automation checks if new comments have been added to the pages of your Notion database. If there are new comments, the comments are sent to an AI model to write a summary. The summary is then added to a predefined page property. The automation also updates a “Last execution” property. This prevents to re-generate the AI summary when no new comments have been received. Setup Define your Notion variables: Notion database, property that will hold the AI summary, property that will hold the last execution date of the automation. Set up your Notion credentials. Set up your AI model credentials (API key). How to adjust it to your needs Use the LLM model of your choice. In this template, I used Gemini but you can easily replace it by ChatGPT, Claude, etc. Adapt the prompt to your use case to get better summaries: specify the maximum number of characters, give an example, etc. Adapt the trigger to your needs. You could use Notion webhooks as trigger in order to run the automation only when a new comment is added (this setup is advised if you’re on n8n cloud version).

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. ❓ What Problem Does It Solve? Manual transcription and action planning from meeting notes is often error-prone, time-consuming, and inconsistent. Important tasks, decisions, or deadlines can be overlooked or delayed. This workflow solves these pain points by automatically analyzing notes using AI and turning them into actionable, structured data. It drastically reduces follow-up delays, miscommunications, and administrative effort, letting teams focus on execution instead. 💡 Why Use Google Meet Automation? Save Hours of Manual Work:** Automatically transform raw meeting notes into structured tasks and emails without lifting a finger. Ensure Accurate Follow-up:** Never miss important action items or decisions buried in text; everything is extracted and assigned clearly. Improve Team Collaboration:** Instantly distribute meeting summaries and next steps to attendees, keeping everyone aligned. Leverage Advanced AI:** Utilize Google Gemini’s powerful natural language processing tailored specifically for meetings. Fully End-to-End Automated:** From receiving notes to task creation and email dispatch — your post-meeting workflow is completely hands-free. ⚡ Who Is This For? Project Managers:** Streamline task delegation and keep project timelines on track. Team Leads:** Quickly communicate key takeaways and follow-ups to team members. Sales and Account Teams:** Document client meetings efficiently and automate follow-up outreach. Remote Teams:** Ensure clarity and continuity after virtual meetings. Executives:** Get concise summaries and important decision logs automatically. 🔧 What This Workflow Does ⏱ Trigger: Activated via a POST webhook receiving meeting notes, title, attendees, date, and duration. 📎 Step 2: Validates inputs; if missing required fields, sends an error response. 🔍 Step 3: Extracts and formats meeting data into structured variables for processing. 🤖 Step 4: Sends meeting notes to Google Gemini AI for advanced analysis to identify action items, decisions, summaries, follow-ups, and dates. 💌 Step 5: Splits AI responses to create Google Tasks from action items and send personalized follow-up emails via Gmail. 🗂 Step 6: Generates a Google Docs meeting summary document and finally returns a success response with all processed results. 🔐 Setup Instructions Import the provided Google Meet Automation.json file into your n8n instance. use Payload example Set up credentials for: Google OAuth2 API (Google Tasks, Google Docs) Gmail OAuth2 API for sending emails Google Palm API (for Google Gemini AI access) Customize workflow parameters: Webhook URL and access permissions Google Tasks project or folders if applicable Email templates if desired (subject line, branding) Update any API endpoints or credential references to match your account setup. Thoroughly test with sample meeting note payloads to ensure smooth execution. 🧩 Pre-Requirements Active n8n instance (Cloud or Self-hosted) Google Cloud Platform project with: Google Tasks API enabled Google Docs API enabled Gmail API enabled Google Palm API access (Google Gemini AI) Valid OAuth2 credentials configured in n8n for above services API quota and permissions for sending emails, creating docs, and tasks 🛠️ Customize It Further Integrate with calendar apps (Google Calendar, Outlook) to auto-schedule next meetings. Add Slack or Microsoft Teams notifications for real-time alerts. Extend AI prompt for deeper insights like sentiment analysis or risk flags. Customize email templates with branding, signatures, or attachments. Connect task outputs with project management tools like Asana, Trello, or Jira. 📞 Support Made by: khaisa Studio Tag: automation, google meet, meeting notes, AI, google tasks, gmail, google docs Category: Productivity Need a custom? Contact Us

This workflow auto-fetches top financial headlines, cleans the content, and uses AI to summarize it into a short investor-friendly email. Good to know The workflow runs daily and relies on stable webpage access; check the URL (e.g., https://www.ft.com/) for availability. AI costs may apply depending on the LLM model used (e.g., GPT-4 or Gemini); refer to provider pricing. How it works Trigger the workflow daily with the Schedule Daily Trigger node. Fetch financial news from a webpage using the Fetch Webpage News node. Add a Delay to Ensure Page Load node to ensure content is fully loaded. Extract and clean headlines with the Extract News Headlines & Clean Extracted Data node. Process the data with the LLM Chat Model node to generate a summary. Send the summarized report via email using the Email Daily Financial Summary node. How to use Import the workflow into n8n and configure the nodes with your webpage URL and email credentials. Test the workflow to verify content fetching and email delivery. Requirements Webpage access (e.g., financial news site API or RSS) Email service (e.g., SMTP or API) LLM model credentials (e.g., GPT-4 or Gemini) Customising this workflow Adjust the Fetch Webpage News node to target different news sources or modify the LLM Chat Model prompt for a different summary style.

Company Website Chatbot Agent Overview This workflow implements a modular Website AI Chatbot Assistant capable of handling multiple types of customer interactions autonomously. Instead of relying on a single large agent to handle all logic and tools, this system routes user queries to specialized sub-agents—each dedicated to a specific function. By using a manager-style orchestration layer, this approach prevents overloading a single AI model with excessive context, leading to cleaner routing, faster execution, and easier scaling as your automation needs grow. How It Works 1. Chat Trigger The flow is initiated when a chat message is received via the website widget. 2. Manager Agent (Ultimate Website AI Assistant) The central LLM-based agent is responsible for parsing the message and deciding which specialized sub-agent to route it to. It uses an OpenAI GPT model for natural language understanding and a lightweight memory system to preserve recent context. 3. Sub-Agent Routing calendarAgent: Handles availability checks and books meetings on connected calendars. RAGAgent: Searches company documentation or FAQs to provide accurate responses from your internal knowledge base. ticketAgent: Forwards requests to human support by generating and sending support tickets to a designated email. Setup Instructions Embed the Chatbot Use a custom HTML widget or script to embed the chatbot interface on your website. Connect the frontend to the webhook that triggers the When chat message received node. Configure Your OpenAI Key Insert your API key in the OpenAI Chat Model node. Adjust the model parameters for temperature, max tokens, etc., based on how formal or creative you want the bot to be. Customize Sub-Agents calendarAgent: Connect to your Google or Outlook calendar. RAGAgent: Link to a vector store or document database via API or native integration. ticketAgent: Set the destination email and format for ticket generation (e.g. via SendGrid or SMTP). Deploy in Production Host on n8n Cloud or your self-hosted instance. Monitor usage through the Executions tab and refine prompts based on user behavior. Benefits Modular system with dedicated logic per function Reduces token bloat by offloading complexity to sub-agents Easy to scale by adding more tools (e.g. CRM, analytics) Fast and responsive user experience for customers on your site Cleaner code structure and easier debugging

Transform raw customer feedback into powerful testimonial quotes automatically. This intelligent workflow monitors feedback forms, uses AI to identify and extract the most emotionally engaging testimonial content, and organizes everything into a searchable database for your marketing campaigns - turning every piece of customer feedback into potential marketing assets. 🚀 What It Does Smart Feedback Monitoring: Automatically detects new customer feedback submissions from Google Forms and triggers testimonial extraction within minutes. AI-Powered Quote Extraction: Uses Google Gemini to analyze feedback and extract short, emotionally engaging testimonial quotes while filtering out neutral or irrelevant content. Marketing-Ready Output: Focuses on impactful phrases and statements that work perfectly for websites, social media, ads, and sales materials. Automated Database Building: Creates and maintains a searchable testimonial library in Google Sheets with customer details and extracted quotes. Instant Team Notifications: Sends immediate email alerts to your marketing team with new testimonials, ensuring no valuable social proof goes unused. 🎯 Key Benefits ✅ Never Miss Marketing Gold: Automatically extract value from every feedback submission ✅ Save 8+ Hours Weekly: Eliminate manual review of feedback for testimonials ✅ Build Social Proof Library: Create searchable database of customer quotes ✅ Boost Conversion Rates: Use authentic testimonials across marketing campaigns ✅ Identify Happy Customers: Spot satisfied clients for case studies and referrals ✅ Scale Content Creation: Generate testimonials faster than customers submit feedback 🏢 Perfect For Businesses Needing Social Proof E-commerce stores showcasing product satisfaction SaaS companies highlighting user success stories Service businesses building trust and credibility Coaches and consultants demonstrating client results Marketing Applications Website Content**: Populate testimonial sections automatically Social Media**: Create quote posts and success story content Sales Materials**: Include powerful customer quotes in proposals Email Marketing**: Add authentic testimonials to campaigns ⚙️ What's Included Complete Workflow Setup: Ready-to-deploy n8n workflow with all integrations configured Google Forms Integration: Automatically processes new feedback submissions AI Quote Extraction: Google Gemini identifies most impactful testimonial content Database Management: Organized Google Sheets storage with customer information Team Notifications: Instant email alerts to marketing team members Setup Documentation: Complete configuration and customization guide 🔧 Technical Requirements n8n Platform**: Cloud or self-hosted instance Google Workspace**: For Forms, Sheets, and Gmail integration Google Gemini API**: For AI-powered testimonial extraction (free tier available) Customer Feedback**: Existing or new feedback collection process 📊 Before & After Examples Before (Raw Customer Feedback): "I was really struggling with managing my team's projects and keeping track of all the deadlines. Everything was scattered across different tools and I was spending way too much time just trying to figure out what everyone was working on. Since we started using your project management software about 6 months ago, it's been a complete game changer. Now I can see everything at a glance, our team communication has improved dramatically, and we're actually finishing projects ahead of schedule. The reporting features are amazing too - I can finally show my boss concrete data about our team's productivity. I honestly don't know how we managed without it. The customer support team has been fantastic as well, always quick to help when we had questions during setup." After (AI Extracted Testimonial): "Complete game changer - now I can see everything at a glance, our team communication has improved dramatically, and we're actually finishing projects ahead of schedule." Healthcare Example: Raw Feedback: "I had been dealing with chronic back pain for over 3 years and had tried everything - physical therapy, medication, different doctors. Nothing seemed to help long-term. When I found Dr. Martinez, I was honestly pretty skeptical because I'd been disappointed so many times before. But after our first consultation, I felt hopeful for the first time in years. She really listened to me and explained everything clearly. The treatment plan she developed was comprehensive but manageable. Within just 2 months, I was experiencing significant pain reduction, and now after 6 months, I'm practically pain-free. I can play with my kids again, sleep through the night, and even started hiking on weekends. Dr. Martinez didn't just treat my symptoms - she helped me get my life back." Extracted Testimonial: "Within just 2 months, I was experiencing significant pain reduction, and now I'm practically pain-free. Dr. Martinez didn't just treat my symptoms - she helped me get my life back." 🎨 Customization Options Industry-Specific Extraction: Tailor AI prompts for healthcare, technology, finance, retail terminology Quote Length Control: Adjust extraction for short punchy quotes vs longer detailed testimonials Sentiment Targeting: Focus on specific emotions like excitement, relief, satisfaction, transformation Multi-Channel Forms: Connect multiple feedback sources to one testimonial database Approval Workflows: Add human review step before testimonials go live CRM Integration: Connect extracted testimonials to customer records 🔄 How It Works Customer submits feedback via your Google Form Workflow detects new submission within 1 minute automatically AI analyzes feedback content to identify most impactful statements Testimonial quote is extracted and formatted for marketing use Quote is saved to database with customer details and timestamp Marketing team receives email with new testimonial content 💡 Use Case Examples SaaS Company: Automatically extract user success quotes from feature feedback surveys for website testimonials E-commerce Store: Turn product review submissions into powerful testimonial quotes for product pages and ads Healthcare Practice: Extract patient satisfaction quotes from feedback forms for website and marketing materials Consulting Firm: Convert client project feedback into testimonials highlighting business transformation results 📈 Expected Results 300% increase** in testimonial collection vs manual methods 90% time savings** on testimonial creation and organization 50% improvement** in marketing content authenticity 25% boost** in conversion rates using extracted testimonials Unlimited scalability** as feedback volume grows 🛠️ Setup & Support Quick Deployment: Complete setup in 20 minutes with included guide Pre-Built Prompts: AI extraction prompts optimized for different industries Template Library: Ready-to-use feedback forms and testimonial layouts Video Tutorial: Complete walkthrough from setup to first extracted testimonial 📞 Get Help & Learn More 🎥 Free Video Tutorials YouTube Channel: https://www.youtube.com/@YaronBeen/videos Complete setup and configuration guide 💼 Professional Support LinkedIn: https://www.linkedin.com/in/yaronbeen/ Connect for testimonial marketing strategy consulting Share your social proof automation success stories Access exclusive templates for different business types 📧 Direct Support Email: Yaron@nofluff.online Technical setup assistance and customization help AI prompt optimization for your specific business Integration with existing marketing and CRM systems Response within 24 hours

Who is this for? This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven, retrieval-augmented question answering. What problem is this workflow solving? Support agents often spend too much time manually searching through lengthy documentation, leading to inconsistent or delayed answers. This solution automates importing, chunking, and indexing product manuals, then uses retrieval-augmented generation (RAG) to answer user queries accurately and quickly with AI. What these workflows do Workflow 1: Document Ingestion & Indexing Manually triggered to import product documentation from Google Docs. Automatically splits large documents into chunks for efficient searching. Generates vector embeddings for each chunk using OpenAI embeddings. Inserts the embedded chunks and metadata into a MongoDB Atlas vector store, enabling fast semantic search. Workflow 2: AI-Powered Query & Response Listens for incoming user questions (can be extended to webhook). Converts questions to vector embeddings and performs similarity search on MongoDB vector store. Uses OpenAI’s GPT-4o-mini model with retrieval-augmented generation to produce direct, context-aware answers. Maintains short-term conversation context using a memory buffer node. Setup Setting up vector embeddings Authenticate Google Docs and connect your Google Docs URL containing the product documentation you want to index. Authenticate MongoDB Atlas and connect the collection where you want to store the vector embeddings. Create a search index on this collection to support vector similarity queries. Ensure the index name matches the one configured in n8n (data_index). See the example MongoDB search index template below for reference. Setting up chat Configure the AI system prompt in the “Knowledge Base Agent” node to reflect your company’s tone, answering style, and any business rules. Update the workflow description and instructions to help users understand the chat’s purpose and capabilities. Connect the MongoDB collection used for vector search in the chat workflow and update the vector search index if needed to match your setup. Make sure Both MongoDB nodes (in ingestion and chat workflows) are connected to the same collection, with: An embedding field storing vector data, Relevant metadata fields (e.g., document ID, source), and The same vector index name configured (e.g., data_index). Search Index Example: { "mappings": { "dynamic": false, "fields": { "_id": { "type": "string" }, "text": { "type": "string" }, "embedding": { "type": "knnVector", "dimensions": 1536, "similarity": "cosine" }, "source": { "type": "string" }, "doc_id": { "type": "string" } } } }

How it works Trigger: Listens for an incoming chat message Copy Assistant: Feeds the message (plus memory) into an OpenAI Chat Model and exposes two “tools” Cold Email Writer Tool Sales Letter Tool• Tool execution: Depending on the user’s intent, the appropriate tool generates the copy • Save output: Writes the generated email or sales letter into your target document via the Update a document node Set up steps • Configure your OpenAI Chat Model credentials in n8n (no hard-coded keys!) • Add and authenticate the Simple Memory credential (to keep context across messages) • Create Google Docs (or MS Word) credentials for the Update a document node • Ensure your Chat trigger is pointing at your incoming-message endpoint • Mandatory: Drop sticky-note annotations on each tool node explaining where to enter API keys and how to tweak prompts Once everything’s wired up, send a test chat message like “Write me a cold email for a fintech startup” and watch the workflow spin up a polished draft in your document. How to use Import the workflow JSON into n8n. Configure your Chat trigger (webhook or form) to receive incoming messages. Send a chat prompt like: “Write me a cold email for a B2B SaaS offering.” The “Copy Assistant” custom GPT picks the right tool (Cold Email or Sales Letter). Generated copy is written directly into your linked Google Doc or Word document. Requirements OpenAI API Key (with Chat Completions & Custom GPTs enabled) Custom Assistant created in your ChatGPT dashboard (Assistant ID pasted into the Chat Model node) n8n instance (Cloud or self-hosted) with credentials set up for: Simple Memory (to persist context) Google Docs or Microsoft Word (for document output) Customising this workflow Tweak system and user prompts inside the Copy Assistant node to fit your brand voice. Swap in Slack, Teams or email nodes instead of a document writer to deliver copy where you need it. Add or remove tools (e.g., “Follow-up Email Writer”) by duplicating the existing tool pattern. Use sticky-note annotations on every node to explain where to enter API keys, Assistant IDs, or prompt tweaks.

Workflow Documentation Description: This workflow is designed to optimize prompts by enhancing user inputs for clarity and specificity using AI. The workflow takes a user-provided prompt as input and uses a Natural Language Processing (NLP) model to refine and improve the prompt. The optimized prompt is then sent back to the user, ready for use in further workflows or processes. Setup: This workflow is suitable for users who want to improve their prompts for better communication and understanding in their workflows. The workflow utilizes an AI Agent powered by an OpenAI Chat Model to enhance user prompts. Expected Outcomes: Users can provide vague or imprecise prompts as input to the workflow. The AI Agent will refine and optimize the prompt, adding clarity and specific details. The optimized prompt will be delivered back to the user via Telegram or can be input for the next nodes. Extra Information: A. A Telegram node is used to deliver the optimized prompt back to the user. B. Ensure you have the necessary credentials set up for Telegram and OpenAI accounts. C. Customize the workflow's settings, such as the AI model used for prompt optimization, to suit your requirements. D. Activate the workflow once all configurations are set to start optimizing prompts efficiently.