Workflow Templates

This n8n template demonstrates how to create a comprehensive marketing automation and booking system that combines Excel-based lead management with voice-powered customer interactions. The system utilizes VAPI for voice communication and Excel/Google Sheets for data management, making it ideal for restaurants seeking to automate marketing campaigns and streamline booking processes through intelligent voice AI technology. Good to know Voice processing requires active VAPI subscription with per-minute billing Excel operations are handled in real-time with immediate data synchronization The system can handle multiple simultaneous voice calls and lead processing All customer data is stored securely in Excel with proper formatting and validation Marketing campaigns can be scheduled and automated based on lead data How it works Lead Management & Marketing Automation Workflow New Lead Trigger: Excel triggers capture new leads when customers are added to the lead management spreadsheet Lead Preparation: The system processes and formats lead data, extracting relevant details (name, phone, preferences, booking history) Campaign Loop: Automated loop processes through multiple leads for batch marketing campaigns Voice Marketing Call: VAPI initiates personalized voice calls to leads with tailored restaurant offers and booking invitations Response Tracking: All call results and lead responses are logged back to Excel for campaign analysis Booking & Order Processing Workflow Voice Response Capture: VAPI webhook triggers when customers respond to marketing calls or make direct booking requests Response Storage: Customer responses and booking preferences are immediately saved to Excel sheets Information Extraction: System processes natural language responses to extract booking details (party size, preferred times, special requests) Calendar Integration: Booking information is automatically scheduled in restaurant management systems Confirmation Loop: Automated follow-up voice messages confirm bookings and provide additional restaurant information Excel Sheet Structure Lead Management Sheet | Column | Description | |--------|-------------| | lead_id | Unique identifier for each lead | | customer_name | Customer's full name | | phone_number | Primary contact number | | email | Customer email address | | last_visit_date | Date of last restaurant visit | | preferred_cuisine | Customer's food preferences | | party_size_typical | Usual number of guests | | preferred_time_slot | Preferred dining times | | marketing_consent | Permission for marketing calls | | lead_source | How customer was acquired | | lead_status | Current status (new, contacted, converted, inactive) | | last_contact_date | Date of last marketing contact | | notes | Additional customer information | | created_at | Lead creation timestamp | Booking Responses Sheet | Column | Description | |--------|-------------| | response_id | Unique response identifier | | customer_name | Customer's name from call | | phone_number | Contact number used for call | | booking_requested | Whether customer wants to book | | party_size | Number of guests requested | | preferred_date | Requested booking date | | preferred_time | Requested time slot | | special_requests | Dietary restrictions or special occasions | | call_duration | Length of VAPI call | | call_outcome | Result of marketing call | | follow_up_needed | Whether additional contact is required | | booking_confirmed | Final booking confirmation status | | created_at | Response timestamp | Campaign Tracking Sheet | Column | Description | |--------|-------------| | campaign_id | Unique campaign identifier | | campaign_name | Descriptive campaign title | | target_audience | Lead segments targeted | | total_leads | Number of leads contacted | | successful_calls | Calls that connected | | bookings_generated | Number of bookings from campaign | | conversion_rate | Percentage of leads converted | | campaign_cost | Total VAPI usage cost | | roi | Return on investment | | start_date | Campaign launch date | | end_date | Campaign completion date | | status | Campaign status (active, completed, paused) | How to use Setup: Import the workflow into your n8n instance and configure VAPI credentials Excel Configuration: Set up Excel/Google Sheets with the required sheet structure provided above Lead Import: Populate the Lead Management sheet with customer data from various sources Campaign Setup: Configure marketing message templates in VAPI nodes to match your restaurant's branding Testing: Test voice commands such as "I'd like to book a table for tonight" or "What are your specials?" Automation: Enable triggers to automatically process new leads and schedule marketing campaigns Monitoring: Track campaign performance through the Campaign Tracking sheet and adjust strategies accordingly The system can handle multiple concurrent voice calls and scales with your restaurant's marketing needs. Requirements VAPI account** for voice processing and natural language understanding Excel/Google Sheets** for storing lead, booking, and campaign data n8n instance** with Excel/Sheets and VAPI integrations enabled Valid phone numbers** for lead contact and compliance with local calling regulations Customising this workflow Multi-location Support**: Adapt voice AI automation for restaurant chains with location-specific offers Seasonal Campaigns**: Try popular use-cases such as holiday promotions, special event marketing, or loyalty program outreach Integration Options**: The workflow can be extended to include CRM integration, SMS follow-ups, and social media campaign coordination Advanced Analytics**: Add nodes for detailed campaign performance analysis and customer segmentation

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

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.

An intelligent WhatsApp-based chatbot designed for restaurants to automate customer interactions related to table bookings, menu inquiries, opening hours, services, and offers. Built using the n8n automation platform and powered by an AI language model, this solution streamlines communication, boosts efficiency, and improves customer satisfaction. Objectives Automate replies to common customer queries on WhatsApp Handle table booking requests with confirmation Provide menu item details, pricing, and dietary information Share restaurant timing, location, and service availability Promote offers and handle promotional queries Operate 24/7 without manual intervention Store bookings and conversations for reporting and analytics Workflow Summary Step 1: Message Reception Node: WhatsApp Trigger (Webhook or API-based) Function: Listens for incoming customer messages. Step 2: Intent Recognition Node: AI Query Processor (e.g., OpenAI API) Function: Detects customer intent (e.g., booking, menu, timing). Step 3: Conditional Routing Node: Switch or IF Node Function: Routes flow based on detected intent: General information (menu, timing, services) Table booking Step 4A: Respond to General Info Queries Node: AI Response or Static Reply Node Function: Returns relevant information (menu, timing, address, etc.). Step 4B: Process Booking Requests Nodes: Collect Booking Details** (via chatbot interactions) Store Booking Info** (to DB or Google Sheets) Send Booking Confirmation** (to customer) Step 5: Context Management Node: Set/Update Customer Data Function: Maintains conversation state and tracks follow-up messages. Database or Google Sheet Columns for Table Booking | Column Name | Description | | ----------------- | ----------------------------------------------- | | reservation\_id | Unique reservation identifier | | guest\_name | Full name of the guest | | contact\_number | Customer’s WhatsApp or mobile number | | email | (Optional) Email address | | booking\_date | Reservation date (YYYY-MM-DD format) | | booking\_time | Reservation time (HH\:MM format) | | party\_size | Number of guests | | table\_id | (Optional) Table number or identifier | | special\_requests | Allergies, seating preferences, etc. | | status | Booking status: Confirmed / Cancelled / Pending | | created\_at | Timestamp when booking was made | | updated\_at | Timestamp when booking was last modified | Prerequisites Verified WhatsApp Business Account with API access n8n instance (Cloud or self-hosted) Access to an AI service (e.g., OpenAI, Claude) Google Sheets, Airtable, MySQL, or other DB integration Setup Instructions Connect WhatsApp API using webhook or third-party WhatsApp provider (e.g., 360Dialog, Twilio). Integrate AI using HTTP Request or OpenAI node for response generation. Create Data Store (Google Sheet, Airtable, or MySQL) with defined booking columns. Design Workflow in n8n with intent detection, conditional logic, and response nodes. Test End-to-End by sending different WhatsApp queries and checking logs and stored data. Example Conversation Customer: “Can I book a table for 2 people tomorrow at 8 PM?” Bot: “Sure. Please provide your name and contact number to confirm the reservation for 2 people at 8:00 PM tomorrow.” \[Booking details are saved, and a confirmation is sent.] Benefits Fully automated customer interaction Supports real-time table reservations Accurate and quick responses Scales without increasing staff effort Operates 24/7 Centralized booking data for analytics Analytics and Reporting Track key performance metrics such as: Number of bookings per day/week Average response time Customer satisfaction scores (via feedback node) Popular menu items or query types Booking conversion rates Security and Compliance End-to-end encrypted WhatsApp messages Role-based access to sensitive data Compliance with data protection regulations (e.g., GDPR) Secure API integrations and storage solutions Conclusion This WhatsApp chatbot serves as a reliable, AI-powered digital front desk for restaurants. Built using n8n and scalable components, it automates customer support, manages bookings, and enhances operational efficiency while offering a seamless customer experience.

Build an AI Agent which accesses two MCP Servers: a RAG MCP Server and a Search Engine API MCP Server. This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Tutorial Click here to watch the full tutorial on YouTube! How it works We build an AI Agent which has access to two MCP servers: An MCP Server with a RAG database (click here for the RAG MCP Server An MCP Server which can access a Search Engine, so the AI Agent also has access to data about more current events Installation In order to use the MCP Client, you also have to use MCP Server Template. Open the MCP Client "MCP Client: RAG" node and update the SSE Endpoint to the MCP Server workflow Install the "n8n-nodes-mcp" community node via settings > community nodes ONLY FOR SELF-HOSTING: In Docker, click on your n8n container. Navigate to "Exec" and execute the below command to allow community nodes: N8N_COMMUNITY_PACKAGES_ALLOW_TOOL_USAGE=true Navigate to Bright Data and create a new "Web Unlocker API" with the name "mcp_unlocker". Open the "MCP Client" and add the following credentials: How to use it Run the Chat node and start asking questions More detailed instructions Missed a step? Find more detailed instructions here: Personal Newsfeed With Bright Data and n8n What is Retrievel Augmented Generation (RAG)? Large Language Models (LLM's) are trained on data until a specific cutoff date. Imagine a model is trained in December 2023 based data until September 2023. This means the model doesn't have any knowledge about events which happened in 2024. So if you ask the LLM who was the Formula 1 World Champion of 2024, it doesn't know the answer. The solution? Retrieval Augmented Generation. When using Retrieval Augmented Generation, a user's question is being sent to a semantic database. The LLM will use the information retrieved from the semantic database to answer the user's question. What is Model Context Protocol (MCP)? MCP is a communication protocol which is used by AI agents to call tools hosted on external servers. When an MCP client communicates with an MCP server, the server will provide an overview of all its tools, prompts and resources. The MCP server can then choose which tools to execute (based on the user's request) and execute the tools. An MCP client can communicate with multiple MCP servers, which can all host multiple tools.

Who is this for? This workflow is perfect for customer support teams, sales departments, or solopreneurs who receive frequent email enquiries and want to automate the initial response process using AI. If you spend too much time answering similar questions, this system helps respond faster and more intelligently—without writing a single line of code. What problem is this workflow solving? Manually responding to repeated customer enquiries slows productivity and increases delay. This workflow classifies if an incoming email is a real enquiry, analyzes the content with a LangChain-powered agent, fetches helpful context using Dumpling AI, and sends a personalized reply using Gmail—all within minutes. What this workflow does Listens for new incoming Gmail messages using the Gmail Trigger node. Classifies whether the email is an enquiry using a GPT-4o classification prompt. Uses a Filter node to continue only if the email was classified as an enquiry. Passes the email content to a LangChain Agent, enhanced with memory, AI tools, and Dumpling AI to search for relevant information. The agent constructs a smart, relevant response, then sends it to the original sender via Gmail. Setup Connect Gmail Use the Gmail Trigger node to connect to the Gmail account that receives enquiries. Make sure Gmail OAuth2 credentials are authenticated. Configure Dumpling AI Agent Sign up at Dumpling AI. Create an agent trained to search your help docs, site content, or FAQs. Copy your Dumpling agent ID and API key. Paste it in the Dumpling AI Agent – Search for Relevant Info HTTP Request node. Set Up LangChain Agent No extra setup needed beyond connecting OpenAI credentials. GPT-4o is used for classification and reply generation. Enable Gmail Reply Node The final Send Email Response via Gmail node will send the AI-generated reply back to the same thread. How to customize this workflow to your needs Change the classification prompt to include other email types like “support”, “complaint”, or “sales”. Add additional logic if you want to CC someone or forward certain types of enquiries. Add a Notion or Google Sheets node to log the conversation for analytics. Replace Gmail with Outlook or another email provider by switching the nodes. Improve context by adding more AI tools like database queries or preloaded FAQs.

Who is this for? This workflow is built for newsletter writers, marketers, content creators, or anyone who curates and summarizes web articles. It’s especially helpful for virtual assistants and founders who need to quickly turn web content into digestible, branded newsletters using AI. What problem is this workflow solving? Manually reading, summarizing, and formatting multiple articles into a newsletter takes time and focus. This workflow automates the process using Dumpling AI for crawling, GPT-4o for summarization, and Gmail for delivery—so you can go from raw URLs to a polished email in minutes. What this workflow does Starts manually (can also be scheduled) Reads a list of article URLs from Google Sheets Sends URLs to Dumpling AI to crawl and extract content Splits each article into a single item for processing Uses a Code node to clean and structure article data Uses an Edit Fields node to merge articles into one JSON block GPT-4o summarizes and generates HTML content for the newsletter Sends the formatted newsletter via Gmail Setup Google Sheets Create a sheet with a column (A) for article URLs Update the Read URLs from Google Sheet node to use your Sheet ID and tab name Connect your Google account in the credentials Dumpling AI Sign up at https://app.dumplingai.com Create an agent for web crawling under /crawl Add your Dumpling API key in the HTTP headers of the Crawl Content with Dumpling AI node Split Node Breaks apart the array of articles from Dumpling AI so each article is processed individually Code Node Structures each article as JSON with title, url, and cleaned text content Edit Fields Node Gathers all structured articles back into a single JSON array to prepare for AI summarization OpenAI (GPT-4o) Processes the article list and returns a formatted subject line and HTML newsletter content Gmail Connect your Gmail account to send the AI-generated newsletter to your inbox or team Update the recipient field in the Send HTML Email via Gmail node How to customize this workflow to your needs Replace the manual trigger with a Schedule node to send newsletters weekly Modify the GPT-4o prompt to change tone (e.g., more professional, funny, casual) Add filtering logic to skip low-value articles Connect Slack, Airtable, or Notion for internal team usage Change Gmail to SendGrid or Outlook if preferred Final Notes This workflow uses: Dumpling AI** /crawl endpoint to extract article content Split, **Code, and Edit Fields nodes to format multi-article input GPT-4o** for summarization and HTML formatting Gmail** for delivery This setup eliminates manual steps and delivers fast, consistent newsletters powered by AI.

Who is this for? This template is designed for anyone who wants to integrate MCP with their AI Agents using Airtable. Whether you're a developer, a data analyst, or an automation enthusiast, if you're looking to leverage the power of MCP and Airtable in your n8n workflows, this template is for you. What problem is this workflow solving? This template caters to MCP beginners seeking a hands-on example and developers looking to integrate Airtable MCP service. When integrating MCP with Airtable, manually updating AI Agents after changes to Airtable data on the MCP Server is time-consuming and error-prone. This template automates the process, enabling the AI Agent to instantly recognize changes made to Airtable on the MCP Server. In data management, for example, it ensures that record updates or additions in Airtable are automatically detected by the AI Agent. With detailed steps, it simplifies the integration process for all users. What this workflow does This workflow focuses on integrating MCP with Airtable within n8n. Specifically, it allows you to build an MCP Server and Client using Airtable nodes in n8n. Any changes made to the Airtable Base/Table on the MCP Server are automatically recognized by the MCP Client in the workflow. This means that you can make changes to your Airtable (such as adding, deleting, or modifying records) on the MCP Server, and the MCP Client in the n8n workflow will immediately detect these changes without any manual intervention. Setup Requirements An active n8n account. Access to Airtable API. A sample base and rows in Airtable that you can use to test. An API key from your preferred LLM to power the AI agent. Step-by-step guide Create a new workflow in n8n: Log in to your n8n account and create a new workflow. Add Airtable nodes: Search for and add the Airtable nodes to your workflow that you wish the MCP client to have access to. Set up the MCP Server and Client: Use the appropriate nodes in n8n to set up the MCP Server and Client. Connect the Airtable nodes to the MCP nodes as required. Activate and test the workflow: Talk to the chat trigger once all credentials have been updated and table data synced and try adding some rows, deleting or finding and updating cells. How to customize this workflow to your needs If you want to customize this workflow, you can: Modify the triggers:** You can change the conditions under which the MCP Client detects changes. For example, you can set it to detect changes only in specific fields or based on certain record values in Airtable. Integrate with other services:** You can add more nodes to the workflow to integrate with other services, such as sending notifications to Slack or triggering further actions based on the detected Airtable changes. Need help? Feel free to contact us at 1 Node. Get instant access to a library of free resources we created.

AI-Agent Code Review for GitHub Pull Requests Description: This n8n workflow automates the process of reviewing code changes in GitHub pull requests using an OpenAI-powered agent. It connects your GitHub repo, extracts modified files, analyzes diffs, and uses an AI agent to generate a code review based on your internal code best practices (fed from a Google Sheet). It ends by posting the review as a comment on the PR and tagging it with a visual label like ✅ Reviewed by AI. 🔧 What It Does Triggered on PR creation Extracts code diffs from the PR Formats and feeds them into an OpenAI prompt Enriches the prompt using a Google Sheet of Swift best practices Posts an AI-generated review as a comment on the PR Applies a PR label to visually mark reviewed PRs ✅ Prerequisites Before deploying this workflow, ensure you have the following: n8n Instance (Self-hosted or Cloud) GitHub Repository with PR activity OpenAI API Key** for GPT-4o, GPT-4-turbo, or GPT-3.5 GitHub OAuth App** (or PAT) connected to n8n to post comments and access PR diffs (Optional) Google Sheets API credentials if using the code best practices lookup node. ⚙️ Setup Instructions 1. Import the Workflow in n8n, click on Workflows → Import from file or JSON Paste or upload the JSON code of this template 2. Configure Triggers and Connections 🔁 GitHub Trigger Node**: PR Trigger Repository**: Select the GitHub repo(s) to monitor Events**: Set to pull_request Auth**: Use GitHub OAuth2 credentials 📥 HTTP Request Node: Get file's Diffs from PR No authentication needed; it uses dynamic path from trigger 🧠 OpenAI Model Node**: OpenAI Chat Model Model**: Select gpt-4o, gpt-4-turbo, or gpt-3.5-turbo Credential**: Provide your OpenAI API Key 🧑‍💻 Code Review Agent Node : Code Review Agent Connected to OpenAI and optionally to tools like Google Sheets 💬 GitHub Comment Poster Uses GitHub API to post review comments back on PR Node: GitHub Robot Credential: Use the agent Github account (OAuth or PAT) Repo : Pick your owen Github Repository 🏷️ PR Labeler (optional) Adds label ReviewedByAI after successful comment Node: Add Label to PR Label : you ca customize the label text of your owen tag. 📊 Google Sheet Best Practices config (optional) Connects to a Google Sheet for coding guideline lookups, we can replace Google sheet by another tool or data base First prepare your best practices list with the clear description and the code bad/good examples Add al the best practices in your Google Sheet Configure* the Code *Best Practices node** in the template : Credential : Use your Google Sheet account by OAuth2 URL : Add your Google Sheet document URL Sheet : Add the name of the best practices sheet

A fully automated content engine that researches, writes, scores, and visualizes LinkedIn posts — built with n8n, OpenAI, Perplexity, and Replicate. What it does: ✅ Researches any topic using real-time data ✅ Writes a personalized post in your voice ✅ Refines tone and structure ✅ Generates abstract, high-quality visual assets ✅ Scores the output and saves it to Google Sheets How it works: Triggered when you change a row status in Google Sheets Uses Perplexity to research GPT-4o (OpenAI) to create and polish content Replicate (FLUX Pro) to generate images Scores the post using heuristics Appends everything back to your sheet

Template for Kids' Story in Arabic The n8n template for creating kids' stories in Arabic offers a versatile platform for storytellers to captivate young audiences with educational and interactive tales. It allows for customization to suit various use cases and can be set up effortlessly. Check this example: https://t.me/st0ries95 Use Cases Educational Platforms: Educational platforms can automate the creation and distribution of educational stories in Arabic for children using this template. By incorporating visual and auditory elements into the storytelling process, educational platforms can enhance learning experiences and engage young learners effectively. Children's Libraries: Children's libraries can utilize this template to curate and share a diverse collection of Arabic stories with young readers. The automated generation of visual content and audio files enhances the storytelling experience, encouraging children to immerse themselves in new worlds and characters through captivating narratives. Language Learning Apps: Language learning apps focused on Arabic can integrate this template to offer culturally rich storytelling experiences for children learning the language. By translating stories into Arabic and supplementing them with visual and auditory components, these apps can facilitate language acquisition in an enjoyable and interactive manner. Configuration Guide for Nodes OpenAI Chat Model Nodes: Functionality**: Allows interaction with the OpenAI GPT-4 Turbo model. Purpose**: Enables communication with advanced chat capabilities. Create a Prompt for DALL-E Node: Customization**: Tailor prompts for generating relevant visual content. Summarization**: Define prompts for visual content generation without text. Generate an Image for the Story Node: Resource Type**: Specifies image as the resource. Prompt Setup**: Configures prompt for textless image creation within the visual content. Generate Audio for the Story Node: Resource Type**: Chooses audio as the resource. Input Definition**: Sets input text for audio file generation. Translate the Story to Arabic Node: Chunking Mode Selection**: Allows advanced chunking mode choice. Summarization Configuration**: Sets method and prompts for story translation into Arabic. Send the Story To Channel Node: Channel ID**: Specifies the channel ID for sending the story text. Text Configuration**: Sets up the text to be sent to the channel. By following these node descriptions, users can effectively configure the n8n template for kids' stories in Arabic, tailoring it to specific use cases for a seamless and engaging storytelling experience for young audiences.

Description This intelligent n8n automation streamlines the process of collecting, extracting, and scoring resumes sent to a Gmail inbox—making it an ideal solution for recruiters who regularly receive hundreds of applications. The workflow scans incoming emails with attachments, extracts relevant candidate information from resumes using AI, evaluates each candidate based on customizable criteria, and logs their scores alongside contact details in a connected Google Sheet. Who Is This For? Recruiters & Hiring Managers**: Automate the resume screening process and save hours of manual work. HR Teams at Startups & SMBs**: Quickly evaluate talent without needing large HR ops infrastructure. Agencies & Talent Acquisition Firms**: Screen large volumes of resumes efficiently and with consistent criteria. Solo Founders Hiring for Roles**: Use AI to help score and shortlist top candidates from email applications. What Problem Does This Workflow Solve? Manually reviewing resumes is time-consuming, error-prone, and inconsistent. This workflow solves these challenges by: Automatically detecting and extracting resumes from Gmail attachments. Using OpenAI to intelligently extract candidate info from unstructured PDFs. Scoring resumes using customizable evaluation criteria (e.g., relevant experience, skills, education). Logging all candidate data (Name, Email, LinkedIn, Score) in a centralized, filterable Google Sheet. Enabling faster, fairer, and more efficient candidate screening. How It Works 1. Gmail Trigger Runs on a scheduled interval (e.g., every 6 or 24 hours). Scans a connected Gmail inbox (using OAuth credentials) for unread emails that contain PDF attachments. 2. Extract Attachments Downloads the attached resumes from matching emails. 3. Parse Resume Text Sends the PDF file to OpenAI's API (via GPT-4 or GPT-3.5 with file support or via base64 + PDF-to-text tool). Prompts GPT with a structured format to extract fields like Name, Email, LinkedIn, Skills, and Education. 4. Score Resume Evaluates the resume on predefined scoring logic using AI or logic inside the workflow (e.g., "Has X skill = +10 points"). 5. Log to Google Sheets Appends a new row in a connected Google Sheet, including: Candidate Name Email Address LinkedIn URL Resume Score Setup Accounts & API Keys You’ll need accounts and credentials for: n8n** (hosted or self-hosted) Google Cloud Platform** (for Gmail, Drive, and Sheets APIs) OpenAI** (for GPT model access) Google Sheet Make a Google Sheet and connect it via Google Sheets node in n8n. Columns should include: Name Email LinkedIn Score Configuration Google Cloud: Enable Gmail API and Google Sheets API. Set up OAuth 2.0 Credentials in Google Console. Connect n8n Gmail, Drive, and Sheets nodes to these credentials. OpenAI: Generate an API Key. Use the HTTP Request node or official OpenAI node to send prompt requests. n8n Workflow: Add Gmail Trigger. Add extraction logic (e.g., filter PDFs). Add OpenAI prompt for resume parsing and scoring. Connect structured output to a Google Sheets node. Requirements Accounts: n8n** Google** (Gmail, Sheets, Drive, Cloud Console) OpenAI** API Keys & Credentials: OpenAI API Key Google Cloud OAuth Credentials Gmail Access Scopes (for reading attachments) Configured Google Sheet OpenAI usage (after free tier) Google Cloud API usage (if exceeding free quota)