Workflow Templates

This n8n demonstrates how to build your own Qdrant MCP server to extend its functionality beyond that of the official implementation. This n8n implementation exposes other cool API features from Qdrant such as facet search, grouped search and recommendations APIs. With this, we can build an easily customisable and maintainable Qdrant MCP server for business intelligence. This MCP example is based off an official MCP reference implementation which can be found here - https://github.com/qdrant/mcp-server-qdrant How it works A MCP server trigger is used and connected to 5 custom workflow tools. We're using custom workflow tools as there is quite a few nodes required for each task. We use a mix of n8n supported Qdrant nodes for simple operations such as insert documents and similarity search, and HTTP node to hit the Qdrant API directly for Facet search, group search and recommendations. We use "Edit Field" and "Aggregate" nodes to return suitable responses to the MCP client. How to use This Qdrant MCP server allows any compatible MCP client to manage a Qdrant Collection by supporting select and create operations. You will need to have a collection available before you can use this server. Use the Prerequisite manual steps to get started! 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 help me list the available companies in the collection?" "What do customers say about product deliveries from company X?" "What do customers of company X and company Y say about product ease of use?" Requirements Qdrant for vector store. This can be an a cloud-hosted instance or one you can self-host internally. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow Depending on what queries you'll receive, adjust the tool inputs to make it easier for the agent to set the right parameters. Not interested in Reviews? The techniques shared in this template can be used for other types of collections. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

Extract, Transform LinkedIn Data with Bright Data MCP Server & Google Gemini

by Ranjan Dailata

Disclaimer This template is only available on n8n self-hosted as it's making use of the community node for MCP Client. Who this is for? The Extract, Transform LinkedIn Data with Bright Data MCP Server & Google Gemini workflow is an automated solution that scrapes LinkedIn content via Bright Data MCP Server then transforms the response using a Gemini LLM. The final output is sent via webhook notification and also persisted on disk. This workflow is tailored for: Data Analysts : Who require structured LinkedIn datasets for analytics and reporting. Marketing and Sales Teams : Looking to enrich lead databases, track company updates, and identify market trends. Recruiters and Talent Acquisition Specialists : Who want to automate candidate sourcing and company research. AI Developers : Integrating real-time professional data into intelligent applications. Business Intelligence Teams : Needing current and comprehensive LinkedIn data to drive strategic decisions. What problem is this workflow solving? Gathering structured and meaningful information from the web is traditionally slow, manual, and error-prone. This workflow solves: Reliable web scraping using Bright Data MCP Server LinkedIn tools. LinkedIn person and company web scrapping with AI Agents setup with the Bright Data MCP Server tools. Data extraction and transformation with Google Gemini LLM. Persists the LinkedIn person and company info to disk. Performs a Webhook notification with the LinkedIn person and company info. What this workflow does? This n8n workflow performs the following steps: Trigger: Start manually. Input URL(s): Specify the LinkedIn person and company URL. Web Scraping (Bright Data): Use Bright Data's MCP Server, LinkedIn tools for the person and company data extract. Data Transformation & Aggregation: Uses the Google LLM for handling the data transformation. Store / Output: Save results into disk and also performs a Webhook notification. Pre-conditions Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - model-context-protocol You need to have the Bright Data account and do the necessary setup as mentioned in the Setup section below. You need to have the Google Gemini API Key. Visit Google AI Studio You need to install the Bright Data MCP Server @brightdata/mcp You need to install the n8n-nodes-mcp Setup Please make sure to setup n8n locally with MCP Servers by navigating to n8n-nodes-mcp Please make sure to install the Bright Data MCP Server @brightdata/mcp on your local machine. Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). In n8n, configure the credentials to connect with MCP Client (STDIO) account with the Bright Data MCP Server as shown below. Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token>. Update the LinkedIn URL person and company workflow. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. Update the file name and path to persist on disk. How to customize this workflow to your needs Different Inputs: Instead of static URLs, accept URLs dynamically via webhook or form submissions. Data Extraction: Modify the LinkedIn Data Extractor node with the suitable prompt to format the data as you wish. Outputs: Update the Webhook endpoints to send the response to Slack channels, Airtable, Notion, CRM systems, etc.

Generate AI News LinkedIn Posts with GPT-4o-mini, NewsAPI, and Qdrant

by Lucien

Overview Automated LinkedIn content generator that: Fetches trending AI news using NewsAPI Enhances content with Qdrant vector store context Generates professional LinkedIn posts using GPT-4o-mini Tracks email interactions in Google Sheets 🛠️ Prerequisites API Keys : NewsAPI, OpenAI (GPT-4o-mini), Qdrant Accounts : Gmail Oauth, Google Sheets, LinkedIn developer API Environment Variables : OPENAI_API_KEY NEWSAPI_KEY QDRANT_URL/QDRANT_API_KEY 📁 Google Sheets Setup Create a spreadsheet with these columns: ISO date Email address Unique ID "Approve" or "Reject" ⚙️ Setup Instructions Pre-populate Qdrant : Create collection "posts" with LinkedIn post examples Add 10+ example posts for style reference Node Configuration : Update Gmail credentials (OAuth2) Set fromEmail/toEmail in email nodes Configure Google Sheets document IDs Test Workflow : Run Schedule Trigger manually first Verify email notifications work Check Qdrant vector store connectivity 🎨 Customization Options Tone Adjustment : Modify system message in "AI Agent" Post Style : Update prompt in "Generate LinkedIn Post" Filter Criteria : Edit NewsAPI URL parameters Scheduling : Change interval in Schedule Trigger

intermediate

9 nodes197 views

Upload & Rename Videos to Google Drive via Apps Script from URL

by Joseph

📄 Google Script Workflow: Upload File from URL to Google Drive (via n8n) 🔧 Purpose: This lightweight Google Apps Script acts as a server endpoint that receives a file URL (from n8n), downloads the file, uploads it to your specified Google Drive folder, and responds with the file’s metadata (like Drive file ID and URL). This is useful for large video/audio files that n8n cannot handle directly via HTTP Download nodes. 🚀 Setup Steps: 1. Create a New Script Project Go to https://script.google.com Click “New Project” Rename the project to something like: DriveUploader 2. Paste the Script Code Replace the default Code.gs content with the following (your custom script): function doPost(e) { const SECRET_KEY = 'your-strong-secret-here'; // Set your secret key here try { const data = JSON.parse(e.postData.contents); // 🔒 Check for correct secret key if (!data.secret || data.secret !== SECRET_KEY) { return ContentService.createTextOutput("Unauthorized") .setMimeType(ContentService.MimeType.TEXT); } const videoUrl = data.videoUrl; const folderId = 'YOUR_FOLDER_ID_HERE'; // Replace with your target folder ID const folder = DriveApp.getFolderById(folderId); const response = UrlFetchApp.fetch(videoUrl); const blob = response.getBlob(); const file = folder.createFile(blob); file.setName('uploaded_video.mp4'); // You can customize the name return ContentService.createTextOutput(file.getUrl()) .setMimeType(ContentService.MimeType.TEXT); } catch (err) { return ContentService.createTextOutput("Error: " + err.message) .setMimeType(ContentService.MimeType.TEXT); } } 3. Generate & Set Up Secret Key To allow authorized post requests to your script only, we need to generate a secret key from aany reliable key generator. You can head over to acte, click generate and copy the "Encryption key 256". Paste it in the 'your-strong-secret-here' placeholder in your script then click save const SECRET_KEY = 'your-strong-secret-here'; // Set your secret key here; 4. Replace Folder ID in Code Open the target Drive folder in your browser The folder ID is the part of the URL after /folders/ Example: https://drive.google.com/drive/u/0/folders/1Xabc12345678defGHIJklmn Paste that ID in the script: var folderId = "1Xabc12345678defGHIJklmn"; 5. Set Up Deployment as Web App Click “Deploy” > “Manage Deployments” > “New Deployment” Under Select type, choose Web app Description: Upload from URL to Drive Execute as: Me Who has access: Anyone Click Deploy Authorize the script when prompted Copy the Web App URL 📤 How to Use in n8n 1. HTTP Request Node Method: POST URL: (your web app URL) Secret Key: (Secret Key set in script) Body Content Type: JSON Paste code: { "videoUrl": "https://example.com/path/to/your.mp4", "secret": "your-strong-secret-here" } videoUrl: The file download URL secret: The generated and set up secret key 2. Rename Node A simple drive update node to rename the file using the file drive url returned from the script.

Personalise Outreach Emails using Customer data and AI

by Jimleuk

This n8n template uses existing emails from customers as context to customise and "finetune" outreach emails to them using AI. By now, it should be common knowledge that we can leverage AI to generate unique emails but in a way, they can remain generic as the AI lacks the customer context to be truly personalised. One way to solve this is by pulling in a source of customer data - and what better way then by using existing email correspondence. How it works Customers to target are pulled from Hubspot and each customer is then run in a loop. We're using a loop as the retrieved emails for each customer become separate items and a loop helps with item reference. We connect to our Gmail account to pull all emails recieved from the customer. The contents of the email will be suitable to build a short persona of the customer. We use the Information Extractor to get our AI model to pull out the key attributes of this persona such as decision making style and communication preferences. With this persona, we can now pass this to our AI model to generate a personalised outreach email specifically for our customer. Finally, a draft email is created for human review before sending. If you would rather send the email straight away, this is also possible. How to use Define the topic of the outreach email in the "variables" node. This directs the AI on what outreach email to generate. Ensure the emails are pulled from the right account. If emails may contain sensitive data, adjust the filters and text parsing to ensure these are not leaked to the AI (which might then leak into the generated email). Requirements Hubspot for Contacts List OpenAI for LLM Gmail for Existing Emails and Sending Emails Customising this workflow Not using Hubspot? Any CRM would work just as well or even a simple text csv! If you have customer past deals or engagements in your CRM, consider using this as additional context for the AI to use.

intermediate

4 nodes1792 views

Automate WordPress Contact Form (CF7) Responses and Classification with Gemini

by Davide

This workflow optimizes the management of inquiries received through a contact form (Contact Form 7 - CF7 Plugin) on a WordPress site, automating the process of classification, response drafting, and data storage. This workflow is particularly useful for businesses that receive multiple daily inquiries and want to improve their efficiency in managing customer communications. Benefits: ✅ Automation & Speed – Reduces the time needed to handle inquiries manually. ✅ Better Email Management – Ensures every message receives a timely and accurate response. ✅ Customization – The generated draft can be edited before sending, maintaining a personal touch. ✅ Inquiry History – Storing data in Google Sheets allows for easy tracking of customer interactions. ✅ Easy Integration – Works seamlessly with Contact Form 7 without complex configurations. How It Works Form Submission Handling: The workflow starts with a WordPress form submission captured via a webhook. The form data (first name, last name, email, phone, and message) is extracted and structured using the "Set Fields" node. Message Classification: The submitted message is classified into predefined categories (e.g., "Product Info," "Order Info," or "Other") using the "Message Classifier" node, powered by Google Gemini. Automated Email Drafting: Based on the classification, the workflow generates a professional email draft using one of three "Email Writer" nodes (for Product, Order, or Other requests). Each node uses Google Gemini to craft a personalized response with a structured format (subject and body). Email Draft Creation: The drafted email is sent as a Gmail draft to the appropriate department, including the original form data for context. Data Logging: All submissions, along with their classifications and email drafts, are logged in a Google Sheets spreadsheet for record-keeping and further action. Set Up Steps Install WordPress Plugin: Install the "CF7 to Webhook" plugin on WordPress and configure it to send form submissions to the n8n webhook URL. Configure Webhook in n8n: Set up the "From Wordpress" webhook node in n8n to receive POST requests from the WordPress form. Google Gemini Integration: Ensure the Google Gemini nodes are properly authenticated with the correct API credentials. Gmail and Google Sheets Setup: Authenticate the Gmail and Google Sheets nodes with the appropriate OAuth2 credentials and specify the target spreadsheet and sheet name. Customize Classification Categories: Adjust the categories in the "Message Classifier" node to match your business needs. Test the Workflow: Trigger a test form submission to verify the workflow processes data correctly, classifies the message, generates an email draft, and logs the data. Need help customizing? Contact me for consulting and support or add me on Linkedin.

intermediate

6 nodes1535 views

Book Ghostwriting & Research AI Agent

by Agniva Mahata

How it Works: Trigger: The workflow is triggered by a webhook, initiated by an Airtable automation. This automation sends the Book or Chapter record ID and the desired action (e.g., "Generate Book Details," "Generate Chapters," "Generate Chapter Research," "Generate Chapter Content"). Action Routing: A "Switch" node directs the workflow based on the action query parameter received from the webhook. This determines which part of the book creation process will be executed. Data Retrieval: The workflow fetches the relevant book or chapter data from Airtable using the provided recordId. AI Processing: Book Details Generation: If the action is "Generate Book Details," an AI Agent (powered by a Large Language Model (LLM) like Google Gemini and the Perplexity search tool) researches the book idea. It focuses on crafting a compelling book description, identifying the target audience, and conducting general book research to maximize bestseller potential. The research brief is then saved back to Airtable. Chapter Generation: If the action is "Generate Chapters," an LLM generates 7-10 chapter titles and descriptions based on the book idea and previous research. A structured output parser ensures the chapter data is in the correct format. The chapters are then split into individual items and saved as separate records in the "Chapter" table in Airtable, linked to the main book record. Chapter Research Generation: If the action is "Generate Chapter Research," another AI Agent conducts in-depth research on a specific chapter, using the Perplexity search tool multiple times. It focuses on finding stories, case studies, historical events, and expert perspectives to make the chapter engaging and credible. The research is saved back to the "Chapter" record in Airtable. Chapter Content Generation: If the action is "Generate Chapter Content," an LLM writes the full content of the chapter, using the research gathered in the previous step, the overall book research, and the chapter description. The generated content is saved back to the "Chapter" record in Airtable. Airtable Updates: In each of the AI processing steps, the workflow updates the corresponding Airtable record (either "Book" or "Chapter") with the generated results (research, chapter details, or content) and sets the "Action" field back to "Idle." Set Up Steps: Airtable Setup (Estimated time: 10-15 minutes): Copy the Airtable base blueprint: https://airtable.com/appfkz4KUlKvOjtbp/shra78TlDfqLRdSfT. This will create the "Book" and "Chapter" tables with the necessary fields. In the "Book" table, create three Airtable Automations: Trigger: When a record matches conditions -> Action is Generate Book Details Action: Run a script. Use the following script: let autoRoute = input.config(); await fetch(autoRoute.webhookUrl + "?recordId=" + autoRoute.recordId + "&action=" + autoRoute.action); In the script action's configuration, add three "Input variables": webhookUrl (map it to your n8n webhook URL, obtained in the next step) recordId (map it to the Airtable record ID) action (map it to Action) Repeat this process to create two more automations in the "Book" table, identical except triggered when Action is Generate Chapters, respectively. In the "Chapter" table, create two Airtable Automations: Trigger: When a record matches conditions -> Action is Generate Chapter Research Action: Run a script (use the same script as above, with the same input variables). Create a second automation, identical except triggered when Action is Generate Chapter Content. n8n Setup (Estimated time: 15-20 minutes): Import the provided JSON workflow into n8n. Webhook Node: Copy the "Test URL" from the Webhook node. This is the webhookUrl you'll use in the Airtable automations. Important: Once you've tested and are ready to go live, switch to the "Production URL." Airtable Nodes: Configure all Airtable nodes (there are eight). You'll need to connect your Airtable account using OAuth 2. Select the correct Base ("Book Agency \[v1] Cobuild" or whatever you named it) and Table ("Book" or "Chapter") for each node. The field mappings are already defined in the template, but double-check them. LLM Nodes (Google Gemini & OpenAI): Connect your Google Gemini and OpenAI accounts to the respective LLM nodes. You'll need API keys for both. You may also configure different LLM Models. Perplexity Nodes Connect your Perplexity AI API to the Perplexity nodes. You'll need API keys for that. Activate the workflow. Testing (Estimated Time: 5-10 minutes): Go to your Airtable "Book" table. Create a New Record. Fill in the "Idea" field with a book concept. Change the "Action" field to "Generate Book Details". The Airtable automation should trigger, sending a request to your n8n webhook. Monitor the n8n execution log to see the workflow in action. Check the Airtable record to see if the "Research" field is populated. Repeat the testing for Generate Chapters, Generate Chapter Research and Generate Chapter Content.

INSEE Company Data Enrichment for Agile CRM (For French companies only)

by Johan Denoyer

How it works 1) Extracts all company entries in Agile CRM 2) Search for company name in French INSEE OpenData database to extract address and government ID (SIREN) 3) Updates entries with data extracted from French Insee OpenData dabase Workflow also has a readonly feature to make sure entry is not overwritten. Setup steps Add your AgileCRM credentials Add your INSEE OpenData credentials Add two company custom fields in your Agile CRM (for SIREN data and ReadOnly support)

Sync Google Calendar Events to Outlook

by Alexander Bentlund

What this workflow does This workflow is used as a bridge between your private Google Calendar to your Work Outlook Calendar. The same mentality can be used with other calendar types. Description Send a copy of a Google Calendar event to your Outlook work account as a reminder to yourself or co-workers that you are booked for private matters like "Dentist appointment", "Taking kids to Disney Land" etc. How it works Create event -- You create a Google Calendar event. -- A trigger in n8n reacts and collects the event info. -- An Outlook event is created with the same information in your Outlook Calendar. Cancel -- You cancel an event in Google Calendar -- A trigger in n8n reacts and collects the canceled event info. -- Using the Outlook node to getAll events searches for the event in your Outlook Calendar. -- If the event is found it is then deleted. -- An email with the details of the cancelation is then sent to your Outook e-mail address. The n8n Merge node is used to combine results from two different nodes that are necessary to create the cancelled event e-mail notification. Important notice Make sure you use a dedicated Google Calendar for private events that will be displayed in your work Outlook calendar in order to avoid displaying unwanted calendar events that you do not wish to share with your co-workers. Requirements Active workflow* Google Calendar OAuth2 API Microsoft Outlook OAuth2 API .*The Google Calendar trigger is activated only if this workflow is active. You can however TEST the workflow in the editor by clicking “Test step”. You will then receive a response from Google Calendar that you can use in order to view what data Google Sends.

beginner

1 nodes4689 views

Error Handler send Telegram: Real-Time Workflow Failure Alerts

by L Hùng

This workflow acts as an error handler, sending real-time notifications to Telegram when another workflow fails. It provides detailed error information, including workflow name, timestamp, execution URL, last executed node, and error message. Pre-Conditions A Telegram bot created via BotFather. The bot token and Telegram group/channel chatId. An active n8n instance with the Telegram and Error Trigger nodes installed. Setup Workflow Configuration: Import the workflow into n8n. Update the Telegram chatId in the Config node. Add your Telegram bot token in the Telegram node credentials. Error Workflow Setup: Set this workflow as the Error Workflow in other workflows. Testing: Trigger an error in another workflow to verify Telegram notifications. Who the Workflow is For Developers:** Monitoring workflow failures in real-time. Teams:** Managing multiple n8n workflows and needing instant error alerts. n8n Users:** Looking for a simple way to handle workflow errors via Telegram. Primary Use Automates error notifications for failed workflows. Sends detailed error reports to Telegram for quick troubleshooting. Easily customizable to fit specific monitoring needs.

Pattern for Parallel Sub-Workflow Execution Followed by Wait-For-All Loop

by Hubschrauber