Workflow Templates

Purpose of workflow: The purpose of this workflow is to automate scraping of a website, transforming it into a structured format, and loading it directly into a Google Sheets spreadsheet. How it works: Web Scraping: Uses the Jina AI service to scrape website data and convert it into LLM-friendly text. Information Extraction: Employs an AI node to extract specific book details (title, price, availability, image URL, product URL) from the scraped data. Data Splitting: Splits the extracted information into individual book entries. Google Sheets Integration: Automatically populates a Google Sheets spreadsheet with the structured book data. Step by step setup: Set up Jina AI service: Sign up for a Jina AI account and obtain an API key. Configure the HTTP Request node: Enter the Jina AI URL with the target website. Add the API key to the request headers for authentication. Set up the Information Extractor node: Use Claude AI to generate a JSON schema for data extraction. Upload a screenshot of the target website to Claude AI. Ask Claude AI to suggest a JSON schema for extracting required information. Copy the generated schema into the Information Extractor node. Configure the Split node: Set it up to separate the extracted data into individual book entries. Set up the Google Sheets node: Create a Google Sheets spreadsheet with columns for title, price, availability, image URL, and product URL. Configure the node to map the extracted data to the appropriate columns.

Automate Telegram Chat Responses Using Google Gemini By WeblineIndia* ⚡ TL;DR (Quick Steps) Create a Telegram bot using @BotFather and copy the API Token. Obtain Google Gemini API Key via Google Cloud. Set up the n8n workflow: Trigger: Telegram message received. AI Model: Google Gemini generates response. Output: AI reply sent back to user via Telegram. Customize the system prompt, model, or message handling to suit your use case. 🧠 Description This n8n workflow enables seamless automation of real-time chat replies in Telegram by integrating with Google Gemini's Chat Model. Every time a user sends a message to your Telegram bot, the workflow routes it through the Gemini AI, which analyzes and crafts a professional response. This reply is then automatically delivered back to the user. The setup acts as a lightweight but powerful chatbot system — ideal for businesses, customer service, or even personal productivity bots. You can easily modify its tone, intelligence level, or logging mechanisms to cater to specific domains such as sales, tech support, or general Q&A. 🎯 Purpose of the Workflow The primary goal of this workflow is to automate intelligent, context-aware chat responses in Telegram using a robust AI model. It eliminates manual reply handling, enhances user engagement, and ensures 24/7 interaction capabilities — all through a no-code or low-code setup using n8n. 🛠️ Steps to Configure and Use ✅ Pre-Conditions / Requirements Telegram Bot Token**: Get it from @BotFather. Google Gemini API Key**: Available via Google Cloud PaLM/Gemini API access. n8n Instance**: Hosted or local instance with required nodes installed (Telegram, Basic LLM Chain, and Google Gemini support). 🔧 Setup Instructions Step 1: Telegram Trigger – Listen for Incoming Messages Add Telegram Trigger node. Select Trigger On: Message. Authenticate using your Telegram Bot Token. This will capture incoming messages from any user interacting with your bot. Step 2: Google Gemini AI – Generate a Smart Reply Add the Basic LLM Chain node. Connect the input message ({{$json.message.text}}) from the Telegram Trigger. System Prompt: > "You are an AI assistant. Reply to the following user message professionally:" Choose Google Gemini Chat Model (models/gemini-1.5-pro). Connect this node to receive the text input and pass it to Gemini for processing. Step 3: Telegram Reply – Send the AI Response Add a Telegram node (Operation: Send Message). Set Chat ID dynamically from the Telegram Trigger node. Input the generated message from the Gemini output. Enable Parse Mode as HTML for rich formatting. Final Step: Link All Nodes Receive Telegram Message → Generate AI Response → Send Telegram Reply. > Tip: Test the workflow by sending a message to your Telegram bot and ensure you receive an AI-generated reply. 🧩 Customization Guidance ✏️ Modify the AI tone by updating the system prompt. 🤖 Use other AI models (e.g., OpenAI GPT-4o). 🔍 Add filters to respond differently based on specific keywords. 📊 Extend the workflow to store chats in Google Sheets, Airtable, or databases for audit or analytics. 🌐 Multi-language support: Add translation layers before and after AI processing. 🛠️ Troubleshooting Guide No message received?** Check if your Telegram bot is active and webhook is working. AI not responding?** Validate your Google Gemini API key and usage quota. Wrong replies?** Refine the system prompt or validate message routing. Formatting issues?** Ensure Parse Mode is correctly set to HTML. 💡 Use Case Examples Customer Service Chatbot** for product queries. Educational Bots** for answering user questions on a topic. Mental Health Companion** that gives supportive replies. Event-based Announcers** or automatic responders during off-hours. > And many more! This workflow can be easily extended to support advanced use cases with just a few additional nodes. 👨‍💻 About the Creator This workflow is developed by WeblineIndia, a trusted provider of AI development services and process automation solutions. If you're looking to build or customize intelligent workflows like this, we invite you to get in touch with our team. We also offer specialized Python development and AI developer hiring services to supercharge your automation needs.

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.

Description Turn your Telegram bot into a powerful OCR (Optical Character Recognition) tool. This workflow allows you to send any image (like a screenshot, a photo of a document, or a picture of a sign) to your bot, and it will instantly extract and send back the text from that image. Powered by Google's advanced Gemini AI, this automation is perfect for quickly digitizing notes, saving important snippets, or avoiding manual typing. How it works This workflow performs a few high-level steps: It triggers when a new image is sent to your Telegram bot. It sends the image to the Google Gemini Vision API to be analyzed. It extracts the text found in the image. It sends the extracted text back to you as a message in Telegram. Set up steps Estimated set up time: Less than 5 minutes. The setup is straightforward. You only need to configure two credentials: Telegram Bot Credentials: To connect your bot. Google Gemini API Credentials: To use the OCR feature. You can get a free API key from Google AI Studio.

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

What this workflow does This workflow helps HR teams screen CVs with AI, store compatibility ratings in Google Sheets, and send email notifications to candidates and HR. It simplifies the recruitment process. CV Submission Form: Candidates submit their details and CV (PDF) through a web form, triggering the workflow in n8n. PDF Extraction & AI Rating: The submitted CV is processed to extract text, and AI analyzes it to generate a compatibility rating. Results Storage & Notifications: Ratings are stored in a Google Sheet for easy access and organization. Confirmation emails are automatically sent to both HR and the candidate. Setup Use the provided template to configure your form and connect it to n8n. Ensure your Google Sheets and email service integrations are active. Customization Instructions: Modify the email template to match your organization’s branding. Adjust the AI compatibility rating thresholds based on your requirements. Ensure you have updated the prompt for cv screening.

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

A lightweight no-code workflow that captures student check-in data via a mobile app or webhook, stores it in a Google Sheet, and instantly notifies the class teacher via email. 🎯 What This Does Students check in using a mobile app or QR code Their data is formatted and saved to a Google Sheet A notification email is sent to the class teacher in real time 🔧 Workflow Steps | Step | Description | | ------------------------------ | ----------------------------------------------------------- | | Student Check-in (Webhook) | Triggered via POST request from mobile app or QR scanner | | Format Data | Cleans and prepares incoming JSON into structured format | | Append or Update Row | Saves student check-in data into Google Sheets | | Email Teacher | Sends formatted check-in email to the class teacher | | Success Response | Returns a confirmation response to the mobile app or system | 📱 Example Check-in Input (Webhook Body) { "student_name": "Aarav Mehta", "student_id": "STU025", "class_name": "Grade 6B" } 📊 Google Sheets Format | Student Name | Student ID | Class | Date | Time | | ------------ | ---------- | -------- | ---------- | ----- | | Aarav Mehta | STU025 | Grade 6B | 2025-08-06 | 08:35 | Date and time are added dynamically in the workflow. ⚙️ Setup Requirements n8n Instance** – Deployed with public webhook support Google Sheets** – Sheet with columns as shown above Email SMTP Settings** – For sending teacher notification ✅ Quick Setup Instructions Import the workflow into your n8n instance Replace the webhook URL in your mobile app Set your Google Sheet ID and range Enter the teacher’s email in the “Email Teacher” node Test with mock data Deploy and use live!

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" } } } }

Automate Video Creation with Luma AI Dream Machine and Airtable (Part 1) Description This workflow automates video creation using Luma AI Dream Machine and n8n. It generates dynamic videos based on custom prompts, random camera motion, and predefined settings, then stores the video and thumbnail URLs in Airtable for easy access and tracking. This automation makes it easy to create high-quality videos at scale with minimal effort. 👉 Airtable Base Template 🎥 Tutorial Video Setup 1. Luma AI Setup Create an account with Luma AI. Generate an API key from Luma AI for authentication. Ensure the API key has permission to create and manage video requests. 2. Airtable Setup Create an Airtable base with the following fields: Generation ID** – To match incoming webhook data. Status** – Workflow status (e.g., "Done"). Video URL** – Stores the generated video URL. Thumbnail URL** – Stores the thumbnail URL. Prompt** – The video prompt used in the request. Aspect Ratio** – Defines the video format (e.g., 9:16). Duration** – Length of the video. 👉 Use the Airtable template linked above to simplify setup. 3. n8n Setup Install n8n (local or cloud). Set up Luma AI and Airtable credentials in n8n. Import the workflow and customize the settings based on your needs. How It Works 1. Global Settings Configuration The Set node defines key settings such as: Prompt** – Example: "A crocheted parrot in a crocheted pirate outfit swinging on a crocheted perch." Aspect Ratio** – Example: "9:16" Loop** – Example: "true" Duration** – Example: "5 seconds" Cluster ID** – Used to group related videos for easy tracking. Callback URL** - Used for the Webhook workflow in Part 2 2. Random Camera Motion The Code node randomly selects a camera motion (e.g., Zoom In, Pan Left, Crane Up) to create dynamic and visually engaging videos. 3. API Request to Luma AI The HTTP Request node sends a POST request to Luma AI’s API with the following parameters: Prompt – Uses the defined global settings. Aspect Ratio – Matches the target platform (e.g., TikTok or YouTube). Duration – Length of the video. Loop – Determines if the video should loop. Callback URL – Sends a POST response when the video is complete. 4. Capture API Response Luma AI sends a POST response to the callback URL once video generation is complete. The response includes: Video URL – Direct link to the video. Thumbnail URL – Link to the video thumbnail. Generation ID – Used to match the record in Airtable. 5. Store in Airtable The Airtable node updates the record with the video and thumbnail URLs. Generation ID** is crucial for matching future webhook responses to the correct video record. Why This Workflow is Useful ✅ Automates high-quality video creation ✅ Reduces manual effort by handling prompt generation and API calls ✅ Random camera motion makes videos more dynamic ✅ Ensures organized tracking with Airtable ✅ Scalable – Ideal for automating large-scale content creation Next Steps Part 2** – Handling webhook responses and updating Airtable automatically. Future Enhancements** – Adding more camera motions, multi-platform support, and automated video editing.

This workflow processes emails received in Gmail and adds the sender's name and email address to a MySQL database. Use Cases: A sales or marketing agency can use this to automatically save client contact info to a database to build a list of leads Companies can use this to automatically save contacts to a database in case of Gmail data loss / losing access to their Gmail account Companies can build mailing lists to automatically send promotions to all of the clients who have contacted them in a given time period Before using, you need to have: Gmail credential MySQL database credential A Table in the MySQL database to store your contacts The table should have a "name" column, which allows NULL values The table should have an "email" column, which should be UNIQUE How it works: The Gmail Trigger will listen for a new email every minute For each email, the code node will extract the name and email address of the sender. If there is no name, it will return null The MySQL node will insert the new contact into a table in your database If the contact email already exists in your database, the MySQL node will update the contact name How to use: Please set up the MySQL node by selecting the correct table to store contacts in Please choose your "email" column to match on Please choose your "name" column to store names Customizing this Workflow: You can customize this workflow to save more data to MySQL. Here are some examples: In the MySQL node, click "Add Value", and choose one of the fields from the Gmail node to save in your database column. You can try saving the following items: Subject line MessageID ThreadID Snippet Recipient Info

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.