Workflow Templates

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. **This n8n template automates candidate outreach, call transcription, and structured feedback capture for HR teams and recruiters. It triggers on a new candidate row added in a Google Sheet, initiates a call using Vapi.ai, processes the transcript using Google Gemini, extracts key information like CTC, experience, and notice period, and then updates the same Google Sheet with parsed insights. This is ideal for recruiters or HR teams conducting high-volume candidate outreach and wanting to scale initial data collection using automated voice bots and AI transcription analysis.** How it works Trigger: Listens for new rows added to a Google Sheet (e.g., a new candidate lead). Call Initiation: Uses Vapi.ai to make a phone call to the candidate using an assistant bot. Transcript Retrieval: After the call, fetches the conversation transcript from the Vapi API. AI Transcript Analysis: Google Gemini parses the transcript and extracts structured fields like: Work experience Current & expected CTC Notice period & negotiability Work preferences and location Data Mapping: Extracted insights are mapped to structured JSON fields. Google Sheet Update: The same row in the source Sheet is updated with the collected information. Use Cases Pre-screening calls for job applicants Collecting missing candidate information asynchronously Replacing manual HR data entry with AI-powered automation Smart CRM updates from voice interactions Requirements Before you run this workflow, ensure the following: ✅ Google account with access to Google Sheets API ✅ Vapi.ai account with: Assistant ID Phone number ID Active API key ✅ Google Gemini API (via PaLM) enabled ✅ n8n version 1.40.0 or later with relevant credentials configured How to use Import the workflow into n8n. Set up your credentials for: Google Sheets Trigger Google Sheets Vapi.ai (add Bearer token) Google Gemini Replace the placeholder values in: Assistant ID Phone number ID Google Sheet ID and tab Start the workflow and add a row to the Google Sheet. Wait for the automated call and let the AI extract and populate the data. Customising this workflow Replace Google Gemini with OpenAI or Claude if preferred. Add sentiment analysis on the transcript using an LLM. Modify the Sheet column structure to add additional fields. Add a filter node to skip candidates with incomplete phone numbers. Use a Webhook trigger instead of Google Sheets to integrate with job portals or ATS.

This template extracts all customers from shopify using GraphQL and the shopify admin API and sync them into a Baserow table. Setup Notes Update the Endpoint in GraphQL node to reflect your Shopify store. In Baserow create a shopify database with a customer table in Baserow. Create columns in the Baserow customer table for first_name, last_name, and email. It takes about 1 second per row to insert.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 🎯 How it works This workflow captures new lead information from a web form, enriches it with Apollo.io data, qualifies the lead using AI, and—if the lead is strong—automatically sends a personalized outreach email via Gmail and logs the result in Google Sheets. 🛠️ Key Features 📩 Lead form capture with validation 🔍 Enrichment via Apollo API 🤖 Lead scoring using AI (LangChain + Groq) 📧 Dynamic email generation & sending via Gmail 📊 Logging leads with job title & org into Google Sheets ✅ Conditional email sending (score ≥ 6 only) 🧪 Set up steps Estimated time: 15–20 minutes Add your Apollo API Key to the HTTP Header credential (never hardcode!) Connect your Gmail account for sending emails Connect your Google Sheets account and set up the correct spreadsheet & sheet name Enable LangChain/Groq credentials for lead scoring and AI-generated emails Update the form endpoint to your live webhook if needed 📌 Sticky Notes Add the following mandatory sticky notes inside your workflow: FormTrigger Node: "Collects lead info via form. Ensure your form is connected to this endpoint." HTTP Request Node: "Enrich lead using Apollo.io API. Add your API key via header-based authentication." AI Agent (Lead Score): "Scores lead from 1-10 based on job title and industry match. Only leads with score ≥ 6 proceed." AI Agent (Email Composer): "Generates a concise, polite email using lead’s job title & company. Modify tone if needed." Google Sheets Append: "Logs enriched lead with job title, org, and LinkedIn URL. Customize sheet structure if needed." Gmail Node: "Sends personalized outreach email if lead passes score threshold. Uses AI-generated content." 💸 Free or Paid? Free – No paid API services are required (Apollo has a free tier).

This template is for Self-Hosted N8N Instances only. This n8n demonstrates how to build a simple SQLite MCP server to perform local database operations as well as use it for Business Intelligence. This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/sqlite How it works A MCP server trigger is used and connected to 5 tools: 2 Code Node and 3 Custom Workflow. The 2 Code Node tools use the SQLLite3 library and are simple read-only queries and as such, the Code Node tool can be simply used. The 3 custom workflow tools are used for select, insert and update queries as these are operations which require a bit more discretion. Whilst it may be easier to allow the agent to use raw SQL queries, we may find it a little safer to just allow for the parameters instead. The custom workflow tool allows us to define this restricted schema for tool input which we'll use to construct the SQL statement ourselves. All 3 custom workflow tools trigger the same "Execute workflow" trigger in this very template which has a switch to route the operation to the correct handler. Finally, we use our Code nodes to handle select, insert and update operations. The responses are then sent back to the the MCP client. How to use This SQLite MCP server allows any compatible MCP client to manage a SQLite database by supporting select, create and update operations. You will need to have a SQLite database available before you can use this server. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Please create a table to store business insights and add the following..." "what business insights do we have on current retail trends?" "Who has contributed the most business insights in the past week?" Requirements SQLite for database. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow If the scope of schemas or tables is too open, try restrict it so the MCP serves a specific purpose for business operations. eg. Confine the querying and editing to HR only tables before providing access to people in that department. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

This n8n demonstrates how to build your own Github MCP server to personalise it to your organisation's repositories, issues and pull requests. This n8n implementation, though not as fully featured as the official MCP server offered by Github, allows you to control precisely what access and/or functionality is granted to users which can make MCP use simpler and in some cases, more secure. The use-case in this template is to simply view and comment on issues within a specific repository but can be extended to meet the needs of your team. This MCP example is based off an official MCP reference implementation which can be found here https://github.com/modelcontextprotocol/servers/tree/main/src/github How it works A MCP server trigger is used and connected to 3 custom workflow tools. We're using custom workflow tools as there is quite a few nodes required for each task. Behind these tools are regular Github nodes although preconfigured with credentials and targeted repository. The "Get Issue Comments" and "Create Issue Comment" tools depend on obtaining an Issue Number first. The agent should call the "Get Latest Issues" tool for this. How to use This Github MCP server allows any compatible MCP client to view and comment on Github Issues. You will need to have a Github account and repository access available before you can use this server. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Can you get me the latest issues about MCP?" "What is the current progress on Issue 12345?" "Please can you add a comment to Issue 12345 that they should try installing the latest version and see if that works?" Requirements Github for account and repository access. The repository need not be your own but you'll still need to ensure you have the correct permissions. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow Extend this template to interactive with pull requests or workflows within your own company's Github repositories. Alternatively, pull in metrics and generate reports for programme managers. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. What is DIGIPIN? DIGIPIN (Digital Pincode) is a 10-character alphanumeric code introduced by India Post. It maps any 3x3 meter square in India to a unique digital address. This helps precisely locate homes, shops, or landmarks, especially in areas where physical addresses are inconsistent or missing. What this workflow does This workflow creates a fully offline DIGIPIN microservice using only JavaScript - no external APIs are used. You get two HTTP endpoints: GET /generate-digipin?lat={latitude}&lon={longitude} → returns a DIGIPIN GET /decode-digipin?digipin={code} → returns the latitude and longitude You can plug this into any system to: Convert GPS coordinates to a DIGIPIN Convert a DIGIPIN back to coordinates How it works An HTTP Webhook node receives the request A JS Function node either encodes or decodes based on input The result is returned as a JSON response All the logic is handled inside the workflow - no API keys, no external calls. Why use this Fast and lightweight Easily extendable: you can connect this to forms, CRMs, apps, or spreadsheets Ideal for field agents, address validation, logistics, or rural operations

This template provides a fully automated system for monitoring news on any topic you choose. It leverages Linkup's AI-powered web search to find recent, relevant articles, extracts key information like the title, date, and summary, and then neatly organizes everything in an Airtable base. Stop manually searching for updates and let this workflow deliver a curated news digest directly to your own database, complete with a Slack notification to let you know when it's done. This is the perfect solution for staying informed without the repetitive work. Who is this for? Marketing & PR professionals:** Keep a close eye on industry trends, competitor mentions, and brand sentiment. Analysts & researchers:** Effortlessly gather source material and data points on specific research topics. Business owners & entrepreneurs:** Stay updated on market shifts, new technologies, and potential opportunities without dedicating hours to reading. Anyone with a passion project:** Easily follow developments in your favorite hobby, field of study, or area of interest. What problem does this solve? Eliminates manual searching:** Frees you from the daily or weekly grind of searching multiple news sites for relevant articles. Centralizes information:** Consolidates all relevant news into a single, organized, and easily accessible Airtable database. Provides structured data:** Instead of just a list of links, it extracts and formats key information (title, summary, URL, date) for each article, ready for review or analysis. Keeps you proactively informed:** The automated Slack notification ensures you know exactly when new information is ready, closing the loop on your monitoring process. How it works Schedule: The workflow runs automatically based on a schedule you set (the default is weekly). Define topics: In the Set news parameters node, you specify the topics you want to monitor and the time frame (e.g., news from the last 7 days). AI web search: The Query Linkup for news node sends your topics to Linkup's API. Linkup's AI searches the web for relevant news articles and returns a structured list containing each article's title, URL, summary, and publication date. Store in Airtable: The workflow loops through each article found and creates a new record for it in your Airtable base. Notify on Slack: Once all the news has been stored, a final notification is sent to a Slack channel of your choice, letting you know the process is complete and how many articles were found. Setup Configure the trigger: Adjust the Schedule Trigger node to set the frequency and time you want the workflow to run. Set your topics: In the Set news parameters node, replace the example topics with your own keywords and define the news freshness that you'd like to set. Connect your accounts: Linkup: Add your Linkup API key in the Query Linkup for news node. Linkup's free plan includes €5 of credits monthly, enough for about 1,000 runs of this workflow. Airtable: In the Store one news node, select your Airtable account, then choose the Base and Table where you want to save the news. Slack: In the Notify in Slack node, select your Slack account and the channel where you want to receive notifications. Activate the workflow: Toggle the workflow to "Active", and your automated news monitoring system is live! Taking it further Change your database:* Don't use Airtable? Easily swap the *Airtable* node for a *Notion, **Google Sheets, or any other database node to store your news. Customize notifications:* Replace the *Slack* node with a *Discord, **Telegram, or Email node to get alerts on your preferred platform. Add AI analysis:** Insert an AI node after the Linkup search to perform sentiment analysis on the news summaries, categorize articles, or generate a high-level overview before saving them.

Meeting Prep: Automated Meeting Attendee Enrichment Overview This workflow automates the process of gathering critical information about your meeting attendees right after they book a meeting. Whether they book through Calendly or Cal.com, this workflow extracts key details, uses Apollo.io to enrich their profiles with company and contact data, and logs everything into a Google Sheet for easy access. This ensures you're always prepared with relevant insights before every meeting. Use Case This workflow is perfect for: Sales Professionals**: Get instant insights into prospects' companies, roles, and social presence before calls. Customer Success Teams**: Understand your clients' business context to provide more tailored support. Recruiters**: Gather comprehensive candidate information ahead of interviews. Consultants**: Prepare for client meetings with a deeper understanding of their organization and industry. Anyone who takes meetings**: Save time on manual research and ensure you always have the data you need to make a great impression. How It Works Meeting Booking Trigger: The workflow springs into action the moment a new meeting is booked. It supports two popular scheduling platforms: Calendly: Triggers when an invitee.created event occurs. Cal.com: Triggers on a BOOKING_CREATED event. Extract Initial Data: From the booking event, the workflow extracts essential information like the attendee's name, email, company, and any notes provided during scheduling. Log Initial Entry: It immediately logs these initial details into your designated Google Sheet ("Meeting Prep" spreadsheet). This ensures a record exists, even if further enrichment isn't possible. Generate Apollo Query: Using the extracted name and company, the workflow dynamically builds a search URL for Apollo.io. This query is designed to find the most relevant person and company profiles on Apollo. Enrich with Apollo.io: The generated Apollo URL is then used to scrape Apollo.io via an Apify Scraper. This step attempts to pull extensive data, including job title, location, phone numbers, company size, industry, website URL, and social media profiles (LinkedIn, Twitter, Facebook, Github) for both the person and their company. A conditional check verifies if data was successfully retrieved from Apollo. Update Google Sheet: If data is available from Apollo: The Google Sheet entry is updated with all the rich, newly found information, changing the status to "Enriched". If data is not available: The Google Sheet entry's status is updated to "Info Not Available," clearly indicating that manual research might be needed. How to Set It Up To set up this powerful meeting prep workflow, follow these steps: Get Your API Keys: Calendly: Obtain your Calendly API key for the "Calendly Trigger" node. Cal.com: Get your Cal.com API key for the "Cal.com Trigger1" node. Apify: You'll need an Apify API token. Replace <YOURAPIKEY> in the "Scrape Apollo" node's URL with your actual Apify token. Google Sheet Setup: Copy the Template: Make a copy of the provided Google Sheet Template ("Meeting Prep") into your own Google Drive. This template has the necessary columns for enriched data. Connect Google Sheets: Ensure your Google Sheets OAuth2 API credentials are set up in n8n and linked to the "Google Sheets1" and "Google Sheets2" nodes. Update Sheet IDs: In both "Google Sheets1" and "Google Sheets2" nodes, update the documentId with the ID of your copied "Meeting Prep" Google Sheet. Import the Workflow: Import the provided workflow JSON into your n8n instance. Activate and Test: Once all credentials and sheet IDs are configured, activate the workflow. Test it by booking a new meeting through your connected Calendly or Cal.com account. Watch as your Google Sheet automatically populates with detailed attendee information! This workflow will dramatically cut down on your meeting preparation time, allowing you to focus on more strategic conversations.

Who is this for? This workflow is for content creators, social media managers, marketing teams, and virtual assistants who want to automatically repurpose YouTube videos into ready-to-post social media content. If you need to quickly turn long-form videos into short posts for platforms like Instagram, Facebook, or LinkedIn, this workflow saves you hours of manual work. What problem is this workflow solving? Manually extracting ideas from YouTube videos, writing captions, creating images, and preparing social media posts takes a lot of time and effort. This workflow automates the entire process: it reads the video, generates posts with captions and AI images, and organizes everything into Airtable. It lets you focus more on growing your audience instead of spending hours repurposing content. What this workflow does Watches a YouTube channel RSS feed for new videos. Extracts the video transcript automatically using Dumpling AI. Summarizes and transforms the transcript into 3 social media captions (Instagram, Facebook, LinkedIn) using OpenAI. Generates 3 unique AI image prompts. Sends the prompts to Dumpling AI to create realistic social media images. Saves the captions and attaches the AI images into Airtable, ready for posting. Setup RSS Feed Setup Get your YouTube channel’s RSS feed URL. Insert the URL into the RSS Trigger node. This will monitor for new YouTube uploads automatically. Dumpling AI Setup for Transcript Extraction Sign up at Dumpling AI. Get your Dumpling AI API Key. In the first HTTP Request node after the RSS trigger, insert your API Key (use HTTP Header Authentication). This sends the YouTube URL to Dumpling AI’s /extract-transcript endpoint. OpenAI Setup for Caption and Prompt Generation Get your OpenAI API Key. In the OpenAI node, connect your account. The AI will: Generate 3 platform-specific captions. Generate 3 creative prompts to design images related to the video. Edit Fields Node This node organizes the generated captions and prompts into separate fields for easy Airtable mapping. Captions are split for Instagram, Facebook, and LinkedIn. Dumpling AI Setup for AI Image Generation After the Edit Fields node, the second HTTP Request node sends the image prompt to Dumpling AI’s /generate-image endpoint. This returns a realistic AI-generated image. Airtable Setup for Saving Posts (Without Image First) Create a new base in Airtable with the following fields: Platform (Single select: Instagram, Facebook, LinkedIn) Content (Long text field) Image (Attachment field) Connect your Airtable Personal Access Token to the Airtable node. The Airtable node saves the generated captions into separate records, initially without images. Upload Generated Images Back to Airtable The third HTTP Request node PATCHES the Airtable record. It updates the Image field with the generated AI image from Dumpling AI. Credentials Required Dumpling AI API Key (for transcript extraction and AI image generation) OpenAI API Key (for caption and prompt creation) Airtable Personal Access Token (for inserting and updating records) How to customize this workflow to your needs Change the OpenAI prompt to generate captions in your brand tone (e.g., friendly, professional, witty). Modify the image prompts to match your design style better. Adjust the Airtable base fields if you want to add more platforms or content formats. Add scheduling tools like Buffer or Metricool to automatically post from Airtable. ⚡ Quick Tips Make sure Dumpling AI credits are active to allow transcript and image generation. Set Airtable permissions properly so PATCH requests can update attachments.

How the sequence works: A "Schedule Trigger" node activates the automation at a defined schedule. An "Airtable" node will search for previously posted questions in your question database. Airtable Base Template: here An "Aggregate" node will take all the questions from Airtable and compress them to a single output. ChatGPT, or a model of your choice, will generate a discussion question based on the options in the system prompt. The discussion question will be posted to the subreddit of your choice by the "Reddit" node. You can choose between a text, image, or link post! The recently-posted discussion question will then be uploaded to your Airtable base using the "Airtable" node. This will be used to prevent ChatGPT from creating duplicate questions. Setup Steps The setup process will take about 5 minutes. An Airtable base template is also pre-made for you here: https://airtable.com/app6wzQqegKIJOiOg/shrzy7L9yv8BFRQdY Set the recurrence in the "Schedule" node Create an Airtable account, you can use the link here. Get an Airtable personal access token here. Configure the "Get Previous Discussions" Airtable node. Configure the options in brackets in the "Generate New Discussion" node. Set the desired subreddit to post to and the post type(text, image, or link) in the "Post Discussion" node. Configure the "Create Archived Discussion" node to map to the Airtable base(required) and specific subreddit(optional).

How it works Fetch all workflows from your n8n instance. Filter workflows that contain nodes with a modelId setting. Extract the node names, model IDs, model names, workflow names, and workflow URLs. Save the extracted information into a connected Google Sheet. Set up steps Connect your n8n API credentials. Connect your Google Sheets account. Replace "Your n8n domain" with your actual domain URL. Use this Google Sheet template to create a new sheet for results. Setup typically takes 5 minutes. Be cautious: if you have over 100 workflows, performance may be impacted. Notes Sticky notes inside the workflow provide extra guidance. This workflow clears old sheet data before writing new results. Make sure your n8n instance allows API access. Result Example Update: It didn't detect the AI model in tool originally. Now it's fixed! Update 20250429: Support 1.91.0 with open node directly! Optimize the url with node id.

This template shows how to use the Question and Answer tool to save costs in RAG use cases. Who is this for? This template is for everyone who wants to start giving knowledge to their Agents through RAG. Requirements Have a PDF with custom knowledge that you want to provide to your agent. Setup No setup required. Just hit Execute Workflow, upload your knowledge document and then start chatting. How to customize this to your needs Add custom instructions to your Agent by changing the prompts in it. Add a different way to load in knowledge to your vector store, e.g. by looking at some Google Drive files or loading knowledge from a table. Describe your data properly in the Q&A tool Exchange the Simple Vector Store nodes with your own vector store tools ready for production. Add a more sophisticated way to rank files found in the vector store. For more information read our docs on RAG in n8n.