Workflow Templates

This n8n workflow automates subdomain creation and deletion on GoDaddy using their API, triggered via email requests. This empowers developers to manage subdomains directly without involving DevOps for minor tasks. Good to know Ensure GoDaddy API credentials are securely configured to avoid unauthorized access. Email parsing accuracy depends on the consistency of request formats. How it works Detect new email requests using the Start Workflow (GET Request) node. Use the Extract Data from Email node to parse relevant details (e.g., subdomain name, action type). Validate the action type with the Validate Action Type node to proceed with create (true) or delete (false). If true, the Create Subdomain node sends a POST request to GoDaddy’s API to create the subdomain. If false, the Delete Subdomain node sends a DELETE request to remove the subdomain. The Send Email Response node notifies the requester of the action’s success or failure. How to use Import the workflow into n8n and configure the nodes with your GoDaddy API and email credentials. Test with sample email requests to ensure proper parsing and API calls. Requirements GoDaddy API credentials Email service (e.g., SMTP or API) for notifications Customising this workflow Adjust the Extract Data from Email node to match your email format or add additional validation steps for security.

Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. Until now, these pieces had to be custom-wired. But with the new native n8n nodes for MongoDB Atlas, we reduce that overhead dramatically. With just a few clicks: Store and recall long-term memory from MongoDB Query vector embeddings stored in Atlas Vector Search Use these results in your LLM chains and automation logic In this example we present an ingestion and AI Agent flows that focus around Travel Planning. The different interest points that we want the agent to know about can be ingested into the vector store. The AI Agent will use the vector store tool to get relevant context about those points of interest if it needs to. Prerequisites MongoDB Atlas project and Cluster OpenAI Valid API Key for embeddings (can be other provider) Gemini API Key for the LLM (can be other provider) How it works: There are 2 main flows. One is ingesting flow: Gets a document from a webhook and use MongoDB Vector Atlas to embed the document title and description into points_of_interest collection. Embeddings are stored in a field named embedding Embeddings used are OpenAI's but it can be any type of supported embedders. Second flow is an AI Agent node with Chat Memory Stored in MongoDB Atlas and a Vector Search node as a tool: Chat Message Trigger**: Chatting with the AI Agent will trigger the conversation store in the MongoDB Chat Memory node. When data is necessary like a location search or details it will go to the "Vector Search" tool. Vector Search Tool** - uses Atlas Vector Search index created on the points_of_interest collection: // index name : "vector_index" // If you change an embedding provider make sure the numDimensions correspond to the model. { "fields": [ { "type": "vector", "path": "embedding", "numDimensions": 1536, "similarity": "cosine" } ] } Additional Resources MongoDB Atlas Vector Search n8n Atlas Vector Search docs

What Does This Flow Do? This workflow demonstrates how to dynamically generate a line chart using the QuickChart node based on data provided in a JSON object and then upload the resulting chart image to Google Drive. Use Cases You can use it in presentations or requesting for chart generation from a software with HTTP requests. Automated report generation (e.g., daily sales charts). Visualizing data fetched from APIs or databases. Simple monitoring dashboards. Adding charts to internal tools or notifications. How it Works Trigger: The workflow starts manually when you click 'Test workflow'. Set Sample Data: A Set node (Edit Fields: Set JSON data to test) defines a sample JSON object named jsonData. This object contains: reportTitle: A title (not used in the chart generation in this example, but useful for context). labels: An array of strings representing the labels for the chart's X-axis (e.g., ["Q1", "Q2", "Q3", "Q4"]). salesData: An array of numbers representing the data points for the chart's Y-axis (e.g., [1250, 1800, 1550, 2100]). Generate Chart: The QuickChart node is configured to: Create a line chart. Dynamically read labels from the jsonData.labels array (Labels Mode: From Array). Use the jsonData.salesData array as the input data (Note: This configuration places data in the top-level 'Data' field. For more complex charts with multiple datasets or specific dataset options, configure datasets under 'Dataset Options' instead). The node outputs the generated chart image as binary data in a field named data. Upload to Google Drive: The Google Drive node (Google Drive: Upload File): Takes the binary data (data) from the QuickChart node. Uploads the image to your specified Google Drive folder. Dynamically names the file based on its extension (e.g., chart.png). Setup Steps Import: Import this template into your n8n instance. Configure Google Drive Credentials: Select the Google Drive: Upload File node. You MUST configure your own Google Drive credentials. Click on the 'Credentials' dropdown and either select existing credentials or create new ones by following the authentication prompts. (Optional) Customize Google Drive Folder: In the Google Drive: Upload File node, you can change the Drive ID and Folder ID to specify exactly where the chart should be uploaded. Activate: Activate the workflow if you want it to run automatically based on a different trigger. How to Use & Customize Change Input Data:** Modify the labels and salesData arrays within the Edit Fields: Set JSON data to test node to use your own data. Ensure the number of labels matches the number of data points. Use Real Data Sources:** Replace the Edit Fields: Set JSON data to test node with nodes that fetch data from real sources like: HTTP Request (APIs) Postgres / MongoDB nodes (Databases) Google Sheets node Ensure the output data from your source node is formatted similarly (providing labels and salesData arrays). You might need another Set node to structure the data correctly before the QuickChart node. Change Chart Type:** In the QuickChart node, modify the Chart Type parameter (e.g., change from line to bar, pie, doughnut, etc.). Customize Chart Appearance:** Explore the Chart Options parameter within the QuickChart node to add titles, change colors, modify axes, etc., using QuickChart's standard JSON configuration options. Use Datasets (Recommended for Complex Charts):** For multiple lines/bars or more control, configure datasets explicitly in the QuickChart node: Remove the expression from the top-level Data field. Go to Dataset Options -> Add option -> Add dataset. Set the Data field within the dataset using an expression like {{ $json.jsonData.salesData }}. You can add multiple datasets this way. Change Output Destination:** Replace the Google Drive: Upload File node with other nodes to handle the chart image differently: Write Binary File: Save the chart to the local filesystem where n8n is running. Slack / Discord / Telegram: Send the chart to messaging platforms. Move Binary Data: Convert the image to Base64 to embed in HTML or return via webhook response. Nodes Used Manual Trigger Set QuickChart Google Drive Tags: (Suggestions for tags field) QuickChart, Chart, Visualization, Line Chart, Google Drive, Reporting, Automation

Who is this for? Sales professionals looking to build lead lists from target company domains Business development teams conducting outreach campaigns Marketers building contact databases for account-based marketing Recruiters searching for potential candidates at specific companies Anyone needing to transform a list of company domains into actionable contact information What problem is this workflow solving? Finding business email addresses for outreach is a time-consuming process. The Apollo API doesn't provide a direct way to extract email contacts from domains in a single call. This workflow bridges that gap by: Automating the two-step process required by Apollo's API Processing multiple domains in batches without manual intervention Extracting, enriching, and storing contact information in a structured format Eliminating hours of manual data entry and API interaction What this workflow does This workflow creates an automated pipeline between Google Sheets and Apollo's API to: Pull a list of target domains from a Google Sheet Submit each domain to Apollo's search API to find associated people Loop through each person found and enrich their profile data Extract key information: name, title, email address, and LinkedIn URL Write the enriched contact information back to a results sheet Process the next domain automatically until all are complete Setup Prerequisites: An n8n instance (cloud or self-hosted) Apollo.io account with API access Google account with access to Google Sheets Google Sheets Setup: Create a new Google Sheet with two tabs: Tab 1: "Target Domains" with a column named "Domain To Enrich" Tab 2: "Results" with columns: Company, First Name, Last Name, Title, Email, LinkedIn n8n Setup: Import the workflow JSON into your n8n instance Set up Google Sheets credentials in n8n Update the Google Sheets document ID in both Google Sheets nodes Add your Apollo API key to both HTTP Request nodes Review and adjust API rate limits if needed Testing: Add a few test domains to your "Target Domains" sheet Run the workflow manually to verify it's working correctly Check the "Results" sheet to confirm data is being properly populated How to customize this workflow to your needs Adding More Contact Fields: Modify the "Clean Up" node to extract additional fields from the Apollo API response Add corresponding columns to your "Results" sheet Update the "Results To Results Sheet" node mapping to include the new fields Filtering Results: Add a Filter node after "Clean Up" to include only contacts with specific roles Create conditions based on title, seniority, or other fields returned by Apollo Automating Workflow Execution: Replace the manual trigger with a Schedule Trigger to run daily/weekly Add a Filter node to process only domains with "Not Processed" status Update the status field in Google Sheets after processing Additional Notes This workflow respects Apollo's API rate limits by processing one contact at a time The Apollo API may not return contact information for all domains or all employees Consider legal and privacy implications when collecting and storing contact information Made with ❤️ by Hueston

This workflow enriches new accounts in Pipedrive using Datagma API by adding data about ICP (ideal customer profile). Instead of Pipedrive, you can use any other CRM. In this example, ideal buyers are heads of sales/business development. Prerequisites Pipedrive account and Pipedrive credentials How it works Pipedrive trigger node starts the workflow when a new company is created. HTTP Request node queries data from Datagma. Pipedrive node updates Pipedrive contact with new data from Datagma. The Item Lists node simplifies returned data from Datagma that contain lists (arrays), enabling you to easily modify the structure for further processing without the need to use Function nodes and write custom JavaScript. IF node identifies if the lead corresponds ICP. HTTP Request node searches for emails in Datagma. Set node prepares data for further merging. Merge node combines data from multiple streams. Pipedrive node adds a new person in Pipedrive.

Receive realtime call-event data from telli Purpose and Problem Solved This template automates the process of receiving and acting upon real-time call event data from telli, an AI-powered voice agent platform. It solves the challenge of manually updating CRM records and initiating follow-up actions based on call outcomes. By leveraging webhooks and n8n's powerful workflow capabilities, this template enables businesses to instantly update their Airtable CRM and trigger appropriate follow-up actions, enhancing efficiency and responsiveness in customer interactions. Prerequisites An active telli account with API access and webhook capabilities An Airtable base set up as your CRM n8n instance (cloud or self-hosted) Airtable Specifications Create an Airtable base with the following table and fields: Table: Contacts Fields: Name (Single line text) Phone (Phone number) Email (Email) Appointment_Booked (Checkbox) Interest (Single select: High, Medium, Low) Last_Call_Date (Date) Notes (Long text) Step-by-Step Setup Instructions Webhook Configuration in telli: Log into your telli dashboard Navigate to the webhook settings Set the endpoint URL to your n8n Webhook node URL Select the "call_ended" event to trigger the webhook n8n Workflow Setup: Create a new workflow in n8n Add a Webhook node as the trigger Configure the Webhook node to receive POST requests Parse Webhook Data: Add a Set node to extract relevant information from the webhook payload Map fields such as call_outcome, appointment_booked, and interest Decision Logic: Add a Switch node to create different paths based on the call outcome Create branches for scenarios like "Appointment Booked", "Interested", and "Not Interested" Airtable Integration: Add Airtable nodes for each outcome to update the Contacts table Configure the nodes to update fields like Appointment_Booked, Interest, and Last_Call_Date Follow-up Actions: For "Interested" but not booked outcomes, add an Email node to trigger a follow-up email campaign For "Appointment Booked", add a node to create a calendar event or task Testing and Activation: Use the n8n testing feature to simulate webhook calls and verify each path Once satisfied, activate the workflow Example Workflow Webhook receives a "call_ended" event from telli Set node extracts call_outcome: appointment_booked = true, interest = true Switch node directs to the "Appointment Booked" path Airtable node updates the contact record: Set Appointment_Booked to true Set Interest to "High" Update Last_Call_Date Calendar node creates an appointment for the booked slot Example Payload Below is an example of the payload you might receive from telli when a call ends: { "event": "call_ended", "call": { "call_id": "b4a05730-2abc-4eb0-8066-2e4d23b53ba9", "attempt": 1, "from_number": "+17755719467", "to_number": "+16506794960", "external_contact_id": "external-123", "contact_id": "6bd1e7e0-6d00-4c0b-ad5b-daa72457a27d", "agent_id": "d8931604-92ad-45cf-9071-d9cd2afbad0c", "triggered_at": 1731956924302, "started_at": 1731956932264, "booked_slot_for": "2025-02-24T15:30:00Z", "ended_at": 1731957002078, "call_length_min": 2, "call_status": "COMPLETED", "transcript": "Agent: Hello...", "transcriptObject": [ { "role": "agent", "content": "Hello..." } ], "call_analysis": { "summary": { "value": true, "details": "A call between an agent and a customer talking about buying an ice cream machine" }, "appointment": { "value": true, "details": "2025-02-18T15:30:00Z" }, "interest": { "value": true, "details": "The customer is interested in buying an ice cream machine" } } } } In this example, you can see that the call resulted in a booked appointment and showed customer interest. Your n8n workflow would process this data, updating the Airtable CRM and triggering any necessary follow-up actions. By implementing this template, businesses can automate their post-call processes, ensuring timely follow-ups and accurate CRM updates. This real-time integration between telli's AI voice agents and your Airtable CRM streamlines operations, improves customer engagement, and increases the efficiency of your sales and support teams.

Who is this for? This workflow is for digital marketers, small business owners, lead generation agencies, and VAs who need a scalable way to find and store local business leads using AI. It’s especially useful for teams that want to enrich leads with real-time news insights and save the structured data to Airtable. What problem is this workflow solving? Manually researching local businesses and staying up to date with relevant news is time-consuming and inefficient. This automation eliminates that burden by using Dumpling AI chat agents to generate leads and context, GPT-4o to summarize, and Airtable to store everything in one place. What this workflow does This AI workflow listens for a manual trigger in n8n and executes the following steps: Extracts local business leads using a Local Business Agent from Dumpling AI. Pulls current news related to the business type or location using a News Agent from Dumpling AI. Uses GPT-4o to combine both responses into a human-readable summary. Extracts structured lead data like name, category, and city. Saves the summary and lead data into Airtable for easy follow-up. Setup 1. Create AI Agents in Dumpling AI Sign in at Dumpling AI Create two separate agents: Local Business Agent: Designed to respond with structured lists of businesses by location and category. News Agent: Designed to fetch relevant recent news and summaries about a specific industry or region. After setting up each agent, copy the Agent Key from Dumpling AI. These keys will be required in the headers of your HTTP Request nodes in n8n. 2. Manual Trigger This workflow begins with a manual trigger inside n8n, Which is the When chat message is recieved. This makes it easy to test and reuse, especially during setup. 3. Get Local Business Data from Dumpling AI The first HTTP Request node sends a prompt like List 5 top real estate companies in Atlanta with full address and services. Include your Local Business Agent Key in the x-agent-key header. The response will return a structured list of business leads. 4. Get News Context from Dumpling AI The second HTTP Request node sends a prompt such as Give me the latest news related to the real estate market in Atlanta. Use your News Agent Key in the header. This fetches a brief set of recent news summaries relevant to the businesses being researched. 5. Use GPT-4o to Merge and Summarize The GPT node combines the list of businesses and news into one coherent summary. You can modify the prompt to output in paragraph format, bullet points, or structured notes. 6. Save Lead to Airtable The Airtable node sends all structured fields into your selected base and table. Be sure to connect your Airtable account and confirm the columns match exactly. How to customize this workflow Replace the prompt inside the HTTP node to focus on different types of businesses or cities. Expand the GPT output to include additional lead info like websites, phone numbers, or emails if the agent includes them. Add a webhook trigger to allow this flow to be run via a chatbot, external app, or button. Link to HubSpot or another CRM to sync the leads automatically. Duplicate the process to run for multiple industries in parallel. Final Notes You must create and configure your Dumpling AI agents first before running this workflow. The Agent Keys from Dumpling AI are required in both HTTP Request nodes. This flow is modular and flexible, ready for deeper CRM integrations. The manual trigger is great for testing, but you can add a Webhook node to automate it. This workflow helps you launch an intelligent lead gen process that combines location-targeted business discovery, AI-generated insights, and structured CRM-friendly output, all powered by Dumpling AI and OpenAI.

🤖 AI Agent Web Search using SearchApi & LLM Who is this for? This workflow is ideal for anyone conducting online research, including students, researchers, content creators, and professionals looking for accurate, up-to-date, and verifiable information. It also serves as an excellent foundation for building more sophisticated AI-driven applications. What problem does this workflow solve? / Use case This workflow automates web searches by enabling an AI agent to efficiently retrieve and summarize external, verifiable information, ensuring accuracy through source citations. What this workflow does Connects an AI agent node to SearchApi.io as an integrated search tool. Empowers the AI agent to perform real-time web searches using various SearchApi engines (e.g., Google, Bing). Allows the AI agent to dynamically determine search parameters based on user interaction, delivering contextually relevant results. Ensures responses include clearly cited sources for validation and further exploration. Setup Install the SearchApi community node: Open Settings → Community Nodes inside your self‑hosted n8n instance. Fill npm Package Name with @searchapi/n8n-nodes-searchapi. Accept the risk prompt, and hit Install. It should now appear as a node when you search for it. API Configuration: Set up your SearchApi.io credentials in n8n. Add your preferred LLM provider credentials (e.g., OpenRouter API). Input Requirements: Provide the YouTube video ID (e.g., wBuULAoJxok). Connect LLM Integration: Configure the summarization chain with your chosen model and parameters for text splitting. How to customize this workflow to your needs Integrate additional nodes to structure or store search results (e.g., saving to databases, Notion, Google Sheets). Extend chatbot capabilities to integrate with messaging platforms (Slack, Discord) or email notifications. Adjust search parameters and filters within the AI agent node to tailor information retrieval. Example Usage Input**: User asks, "What are the latest developments in AI regulation?" Output**: AI retrieves, summarizes, and cites recent, authoritative articles and news sources from the web.

Edit an existing image with OpenAI ImageGen1 via API Request Transform your creative pipeline by letting n8n call OpenAI ImageGen1’s edit image endpoint, automatically replacing or augmenting parts of any image you supply and returning a brand-new version in seconds. Designers, marketers, and product teams can eliminate repetitive manual edits and test more variations, faster. Who is this for? Content creators who need quick, on-brand image tweaks Marketers running A/B visual tests at scale Developers exploring the new ImageGen1 API inside low-code automations Use case / problem solved Opening design software to mask, fill, or swap objects is slow and error-prone. This workflow feeds an input image plus a prompt to OpenAI ImageGen1, receives the edited output, and passes it on to any service you like—perfect for bulk-editing product shots, social visuals, or UI mocks. What this workflow does Read or receive the source image (Webhook → Binary Data). Call OpenAI ImageGen1 with an HTTP Request node, sending the image and edit prompt. Parse the JSON response to capture the returned image URL. Download & hand off the edited file (e.g., upload to S3, post to Slack, or store in Drive). Setup Add your OpenAI API key in the API KEY node. Follow the notes on the workflow for more information. (Optional) Point the final node to your preferred storage or chat tool. > 📝 A sticky note in the workflow summarizes these steps and links to the OpenAI documentation. How to customize this workflow Trigger alternatives**: Replace the Chat with Google Drive, Airtable, etc. Chained edits**: Loop the output back for successive prompts. Conditional flows**: Add an If node to branch actions by image size or category. With renamed nodes, color-coded sticky notes, and a concise setup guide, you’ll be editing images via OpenAI ImageGen1 in under five minutes—no code, maximum creativity.

This workflow implements the Gemini AI chat model to summarize your daily meetings and send the summary to a Slack channel daily at 9 AM (or any other time you choose). It automatically retrieves your Google Calendar events and feeds them to the model. The workflow uses Google’s Gemini AI for response generation. How it works The workflow uses a Scheduled Trigger Node as the main trigger. The AI Agent Node uses the Google Calendar action to retrieve relevant meeting data. The AI Agent sends the retrieved information to the Google Gemini Chat Model (gemini-flash). The Google Gemini Chat Model generates a summary and informative response based on today’s meetings. ++Setup Steps++ Google Cloud Project and Vertex AI API: Create a Google Cloud project. Enable the Vertex AI API for your project. Google AI API Key: Obtain a Google AI API key from Google AI Studio. Credentials in n8n: Configure credentials in your n8n environment for: Google Gemini (PaLM) API (using your Google AI API key). Import the Workflow: Import this workflow into your n8n instance. Configure the Workflow: Update both Slack and Gemini nodes with your credentials.

Community Node Disclaimer: This workflow uses KlickTipp community nodes. How It Works This workflow automates personalized WhatsApp message template delivery triggered by events in KlickTipp or by messages sent to the Whatsapp Business account. When a contact triggers an Outbound, the workflow uses a pre-approved WhatsApp message template to send dynamic, real-time messages through the WhatsApp Business Cloud API. When receiving messages it checks whether a cancellation should be processed or if a auto-response is sent. This setup is ideal for time-sensitive campaigns such as: Birthday greetings Discount or promo notifications Follow-ups on product or service interest Key Features KlickTipp Trigger Starts the workflow when a specific outbound is triggered Typical use case: subcriber receives activation Tag and triggers an Outbound which sends a webhook call to trigger WhatsApp messaging. WhatsApp Business Cloud - Message Trigger Listens to messages from the contact and processes answers with answering auto-responder or by tagging the contact in KlickTipp. WhatsApp Business Cloud - Sending Template Messages Sends WhatsApp message templates using a pre-approved template. Template placeholders are filled with data from KlickTipp custom fields. Setup Instructions Set up the KlickTipp and Whatsapp nodes in your n8n instance. Authenticate your WhatsApp and KlickTipp accounts. Create the necessary custom fields to match the data structure Verify and customize field assignments in the workflow to align with your specific form and subscriber list setup. | Field Label | Field Type | |--------------------------------|-------------| | Whatsapp_Produkt/Dienstleistung | Single line | | | Whatsapp_Name/Unternehmen | Single line | | Whatsapp_Link_Endung | Single line | Testing & Deployment Use a real test contact with all required fields filled. Trigger the Outbound in KlickTipp using the activation tag and answer with a message to the template. Run the scenario once in n8n to verify successful delivery of the whatsapp message template to your test contact as well as the reception of the auto-responder and the subscription and tagging in KlickTipp to stop further messages. Campaign Expansion Ideas Connect campaign to process keywords like "STOP" from WhatsApp messages Pair WhatsApp with welcome email series for onboarding. Use tags like product_interest_X for precise segmentation. A/B test templates with different CTA formats or timings. Monitor CTRs via dynamic URLs in WhatsApp templates. Benefits Multi-channel engagement:** Adds WhatsApp to your marketing toolkit. Dynamic content:** Personalizes messages using contact data. KlickTipp campaign control** Whatsapp contacts can for example signal with messages like "STOP" to receive the according Tag in KlickTipp in order to start/end automations. > 💡 Pro Tip: Customize the domain link ending per campaign or product line. This allows targeted redirects, e.g., meinshop.de/ProduktA or `mein Ressources: Send WhatsApp Templates with KlickTipp Use KlickTipp Community Node in n8n Automate Workflows: KlickTipp Integration in n8n

Use case This workflow uses Gmail to send outreach emails to new Hubspot contacts that have yet to be contacted (usually unknown contacts), and records the outreach in Hubspot. Setup Setup HubSpot Oauth2 creds (Be careful with scopes. They have to be exact, not less or more. Yes, it's not simple, but it's well documented in the n8n docs. Be smarter than me, read the docs) Setup Gmail creds. Change the from email and from name in the Record outreach in HubSpot node How to adjust this template to your needs Change the email message in the Set keys node Think about your criteria to reach out to new contacts. Here we simply filter for only contacts with unknown dates.