Workflow Templates

This template shows how to sync data from one service to another. Specifically, in this example we're saving a new qualified lead from a Postgres database to a Google Sheets file. Setup instructions are located inside the workflow template.

Who is this for? This workflow is perfect for marketers, SEO specialists, product teams, and competitive analysts who want to monitor and summarize public reviews of their competitors. It’s especially helpful for small teams who want fast insights from Google reviews without spending hours manually reading and sorting them. What problem is this workflow solving? Manually going through competitor reviews is time-consuming and repetitive. You risk missing patterns or insights, and it’s hard to share summaries with your team quickly. This workflow automatically scrapes reviews from Google and generates a structured summary of pain points and positive feedback. That way, you can focus on strategy instead of sorting through dozens of reviews. What this workflow does This automation watches for new competitor entries in a Google Sheet, then: Uses Dumpling AI to scrape the latest Google reviews (up to 20) for each business. Splits and cleans the reviews for analysis. Sends them to GPT-4o, which summarizes the most common complaints and praises. Saves the structured result back to the same Google Sheet. You’ll instantly get an overview of what people are saying about any competitor. Setup Google Sheet Setup Create a Google Sheet with at least one column: Business Add names or search queries for the competitors you want to analyze Optional: Add columns for Summary of Reviews and Pain Points Connect Dumpling AI Sign up at Dumpling AI Create an agent using the get-google-reviews endpoint Copy your agent key Use it in the HTTP Request node in this workflow OpenAI Setup Use your API key with GPT-4o access The prompt is already structured to generate grouped summaries from reviews Run the Workflow Trigger it manually or schedule it Make sure your Google Sheets, OpenAI, and Dumpling AI connections are active How to customize this workflow to your needs You can expand the number of reviews retrieved by changing the Dumpling AI agent config Replace Google Sheets with Airtable if you want more robust data views Add more fields like star ratings or review dates in your agent for richer analysis Change the GPT prompt to highlight emotional tone, urgency, or feature mentions 🧠 Node Details Google Sheets Trigger**: Watches for new competitor names HTTP Request (Dumpling AI)**: Scrapes 20 recent reviews from Google SplitOut Node**: Breaks review array into individual items Code Node**: Extracts and combines review text Edit Fields Node**: Structures the review content before GPT GPT-4o Node**: Analyzes and summarizes top pain points and praise Google Sheets Output**: Saves the summary back to the same sheet Dependencies Dumpling AI account and review scraping agent setup OpenAI API key with GPT-4o access Google Sheets OAuth2 credentials

Example: @SubAlertMe_Bot Summary: The automated image analysis and response workflow using n8n is a sophisticated solution designed to streamline the process of analyzing images sent via Telegram and delivering insightful responses based on the analysis outcomes. This cutting-edge workflow employs a series of meticulously orchestrated nodes to ensure seamless automation and efficiency in image processing tasks. Use Cases: This advanced workflow caters to a myriad of scenarios where real-time image analysis and response mechanisms are paramount. The use cases include: Providing immediate feedback on images shared within Telegram groups. Enabling automated content moderation based on the analysis of image content. Facilitating rapid categorization and tagging of images based on the results of the analysis. Detailed Workflow Setup: To effectively implement this workflow, users must adhere to a meticulous setup process, which includes: Access to the versatile n8n platform, ensuring seamless workflow orchestration. Integration of a Telegram account to facilitate image reception and communication. Utilization of an OpenAI account for sophisticated image analysis capabilities. Configuration of Telegram and OpenAI credentials within the n8n environment for seamless integration. Proficiency in creating and interconnecting nodes within the n8n workflow for optimal functionality. Detailed Node Description: Get the Image (Telegram Trigger): Actively triggers upon receipt of an image via Telegram, ensuring prompt processing. Extracts essential information from the received image message to initiate further actions. Merge all fields To get data from trigger: Seamlessly amalgamates all relevant data fields extracted from the trigger node for comprehensive data consolidation. Analyze Image (OpenAI): Harnesses the powerful capabilities of OpenAI services to conduct in-depth analysis of the received image. Processes the image data in base64 format to derive meaningful insights from the visual content. Aggregate all fields: Compiles and consolidates all data items for subsequent processing and analysis, ensuring comprehensive data aggregation. Send Content for the Analyzed Image (Telegram): Transmits the analyzed content back to the Telegram chat interface for seamless communication. Delivers the analyzed information in textual format, enhancing user understanding and interaction. Switch Node: The Switch node is pivotal for decision-making based on predefined conditions within the workflow. It evaluates incoming data to determine the existence or absence of specific elements, such as images in this context. Utilizes a set of rules to assess the presence of image data in the message payload and distinguishes between cases where images are detected and when they are not. This crucial node plays a pivotal role in directing the flow of the workflow based on the outcomes of its evaluations. Conclusion: The automation of image analysis processes through this sophisticated workflow not only enhances operational efficiency but also revolutionizes communication dynamics within Telegram interactions. By incorporating this advanced workflow solution, users can optimize their image analysis workflows, bolster communication efficacy, and unlock new levels of automation in image processing tasks.

Intro This workflow needs a user to authenticate by using an openid connect provider in order to call the webhook. If the user is not authenticated, it starts a login process by using an Authorization Code with PKCE https://datatracker.ietf.org/doc/html/rfc7636, a standard way to authenticate users with openid connect. Then, after the user logs in, the webhook is refreshed and gets the user's token from a cookie. With this token, all details about the user are requested through the userinfo endpoint on the identity provider. How to set up with Keycloak Keycloak Keycloak is an open source identity and access management solution. Feel free to get a demo realm at https://please-open.it or get your own Keycloak server up and running. After creating a realm, go to "Realm Settings" and click on "OpenID Endpoint Configuration" Retrieve authorization_endpoint, token_endpoint and userinfo_endpoint values. Set those variables in the "Set variables" node. In Keycloak, create a new client (name it as you want) Disable the client authentication, check only "standard flow" : At the third step, put the webhook url in "valid redirect URIs", fill "Web origins" with a "+". You're done, open the webhook and it asks you to authenticate. Usage User informations The userinfo node returns this structure about the user has logged in : [ { "sub":"73a6543f-f420-4fa6-9811-209e903c348b", "email_verified":true, "preferred_username": "mathieu.passenaud@please-open.it", "email": "mathieu.passenaud@please-open.it" } ] I can use those infos in my workflow for custom operations. APIs calls the "code" node returns me a cookie named "n8n-custom-auth" which is the access_token returned by the identity provider. This access_token can be used to call APIs connected to this identity provider (for example, we call userinfo API with this token). Example : asks a user to log in with his Google account then call an API (Gmail, drive...) with his own token. How it works We published a blog post about this flow, how it works and how you can use it : https://blog.please-open.it/n8n-openid-client/

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.

This workflow syncs Outlook Calendar events to a Notion database. The Outlook Calendar event must be within a specific time frame (default of within next year) for the workflow to pick up the event. The event subject will be the title of the Notion page, and the event link will be added to the Notion page as a property. Prerequisites Notion account and Notion credentials. Microsoft account and Microsoft credentials. How it works On scheduled intervals, find all Outlook Calendar events within a specific time frame. For each event, check if the event already exists in the Notion database. If it does not exist, create a new page in the Notion database, otherwise update the existing page. Setup This workflow requires that you set up a Notion database or use an existing one with at least the following fields: Title (title) Date (date) Event ID (text) Link (URL)

n8n Creators Template: Creator Profile Stats Updater This n8n workflow template is designed to automate the process of updating a creator's profile statistics, including total workflows, complex workflows, approved workflows, pending workflows, total nodes, and total views. It utilizes various nodes to fetch data, process it, and update a SVG file hosted on GitHub to reflect the latest stats. Workflow Overview Schedule Trigger: Triggers the workflow execution at specified intervals. Config: Sets up configuration details like creator username, colors for text, icons, border, and card. Get Workflows: Fetches workflows associated with the creator from the n8n API. Workflows Data: Processes the fetched data to calculate various statistics. Get User: Fetches user details from the n8n API. Download Image: Downloads the creator's profile image. Extract From File: Extracts binary data from the downloaded image file. SVG: Generates an SVG file with updated stats and visual representation. GitHub: Commits the updated SVG file to the specified GitHub repository. Final: Prepares the final data set for further processing or output. Sticky Note: Provides a visual note or reminder within the workflow editor. Embed & Live Preview Since it's a .SVG format you can host it anywhere. treat it like normal image so you can embed it with any site, forum, page that support posting images. here's example code for markdown: Here's the result Or served through CDN & Cache Setup Instructions GitHub Credentials: Ensure you have GitHub credentials set up in your n8n instance to allow the workflow to commit changes to your repository. Configure Trigger: Adjust the Schedule Trigger node to set the desired execution intervals for the workflow. Set Configuration: Customize the Config node with your GitHub username and preferred aesthetic options for the SVG. Deploy Workflow: Import the workflow into your n8n instance and deploy it. Customization Options Text and Icon Colors**: Customize the colors used in the SVG by modifying the respective fields in the Config node. Profile Image Size**: Adjust the image size in the Download Image node URL if needed. Commit Messages**: Modify the commit messages in the GitHub nodes to suit your version control conventions [I've used $now funaction to include current time in message which will gives allways a diffrent commit value]. Requirements n8n (Self-hosted or Cloud version compatible with 2024 releases and up) GitHub account and repository Basic understanding of n8n workflow configuration Support and Contributions For support, please refer to the n8n community forum or the official n8n documentation. Contributions to the template can be made you're allowed to reuse this workflow and reshare with edit (like new design/colors etc..) under your name.

. IOT device control with MQTT and webhook This workflow is for users wanting a practical example of how to control IOT systems using the MQTT protocol in an an n8n environment. The template provides typical n8n MQTT and Webhook node implementation and configuration settings necessary to set IOT device inputs and outputs. How it works A webpage with IOT control 'on and 'off' buttons is presented to the user. When a button is selected on the webpage the value is sent via a webhook to trigger the active workflow. The workflow set node then prepares the received value into a message payload. It then passes the message to the MQTT node for publishing the topic with the payload to a cloud based MQTT broker. A remote ESP32 micro-controller subscribes to the broker and reads the payload contained in the topic. The ESP32 will then toggle the GPIO pin depending on the topic payload value. The IOT control webpage The webpage is a simple HTML page containing the clickable 'on' and 'off' buttons. It also has the get webhook URL that sends the selected value to the n8n workflow in this case running locally. The URL webhook format is http://localhost:5678/webhook/pin-control?value=action The webpage code IOT-control.html IOT device The IOT device is an ESP32 micro-controller running on a remote network. To keep it simple GPIO2 is selected as the control output. In this case when the received value is "on" GPIO2 goes high a led will turn on in the ESP32. It will go off when the received value is "off". The program for the ESP32 IOT control is 'main.py' . You will require a micropython interpreter to be uploaded to the ESP32 for the program to run automatically. The code can be easily edited and modified to accommodate any further attached IOT devices. The ESP32 main.py code main.py How to customise this workflow to your needs ESP32 You will need a working ESP32 installed with a micro-python interpreter. The code main.py is provided. The main.py program can loaded and edited with a python IDE. I used Thonny for this example. Use a free MQTT broker to get started. I used "broker.emqx.io" in the code. IOT Control Webpage The webpage contains HTML and can be easily edit to enhance functionality. The embedded webhook is configured for n8n production mode. http://localhost:5678/webhook/pin-control?value=action If you want to run the page in test mode you will use the following URL. http://localhost:5678/webhook-test/pin-control?value=action n8n workflow. The workflow is a good demonstration of how to control IOT devices using n8n. Following these steps will give a good insight for microcontroller automation.

Automated pipeline to collect and analyze investor data from Crunchbase, tracking investment patterns, funding history, and portfolio companies for market analysis and lead generation. 🚀 What It Does Investor Profiling**: Collects comprehensive data on investors and VC firms Investment Pattern Analysis**: Tracks funding history and investment preferences Portfolio Monitoring**: Keeps tabs on investor portfolios and new investments Data Enrichment**: Enhances raw data with additional context and metrics 🎯 Perfect For Startup founders seeking investors Market research analysts Investment professionals Business development teams Competitive intelligence ⚙️ Key Benefits ✅ Comprehensive investor profiles ✅ Real-time investment tracking ✅ Market trend analysis ✅ Data-driven investment decisions ✅ Time-saving automation 🔧 What You Need Crunchbase API access n8n instance Storage solution (database or spreadsheet) 📊 Data Points Collected Investor/Firm details Investment history Portfolio companies Funding rounds participated in Investment focus areas Contact information (when available) 🛠️ Setup & Support Quick Setup Deploy in 30 minutes with our step-by-step configuration guide 📺 Watch Tutorial 💼 Get Expert Support 📧 Direct Help Transform your investor research with automated data collection and analysis. Spend less time gathering data and more time making strategic decisions.

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

Template Information Who is this template for? This template is for users looking to retrieve email information from LinkedIn profiles and update Google Sheets with the collected data. 🎥 quick set up video How it works** The template utilizes a series of nodes to fetch email information from LinkedIn profiles. It starts with a Schedule Trigger node that sets the interval for the workflow. The Conditional Check node verifies if certain fields like Name, Gender, Job Title, Summary, and LinkedIn URL are not empty. The HTTP Request node sends a POST request to the specified URL with API key and profile information. The Data Merge node merges the data collected. The Field Editing node modifies the fields as needed. Finally, the Google Sheets Update node updates the Google Sheets with the gathered information. Set Up Instructions Make sure to have the necessary credentials and permissions for accessing LinkedIn and Google Sheets. Set up the API key required for the HTTP Request node. Configure the Google Sheets Update node with the appropriate document ID and sheet name. Check and adjust field mappings in the Field Editing node according to your needs. Run the workflow and monitor the updates in your Google Sheets document. Overview: The workflow is designed to find contact information for LinkedIn profile URLs stored in a Google Sheet. It involves various nodes for different operations such as making HTTP requests, scheduling triggers, reading from and updating Google Sheets, field editing, data merging, and conditional checks. A video demonstrating the workflow process can be accessed here. Copy this template to get started : Google Sheets Using Prospeo.io LinkedIn Email Finder API with cURL To use the API endpoint "https://api.prospeo.io/linkedin-email-finder" with cURL, follow these steps: Use the cURL command with the following parameters: curl -X POST \ -H "Content-Type: application/json" \ -H "X-KEY: your_api_key" \ -d '{ "url": "https://www.linkedin.com/in/john-doe/" }' \ "https://api.prospeo.io/linkedin-email-finder" Replace "your_api_key" with your actual API key. Update the "url" field in the JSON data with the LinkedIn profile URL for which you want to find the email address. To get access to this API and obtain your API key, you need to sign up on the Prospeo platform and subscribe to their LinkedIn email finder service. Once you have subscribed, you will receive an API key that you can use to authenticate your requests to the API endpoint. Description: Schedule Trigger:** Triggers the workflow based on a defined schedule interval, in this case, based on minutes. Schedule Trigger Node Documentation Google Sheets Read:** Reads data from a Google Sheets document and sheet based on the provided document ID and sheet name. Google Sheets Node Documentation Conditional Check:** Checks multiple conditions based on the input data and performs actions accordingly. Conditional Node Documentation HTTP Request:** Sends an HTTP POST request to a specified URL with headers and body parameters. HTTP Request Node Documentation No Operation, do nothing:** Placeholder node that does not perform any operation. Data Merge:** Merges data based on specified mode and combination settings. Merge Node Documentation Field Editing:** Edits fields by setting specific values for each field based on input data. Set Node Documentation Google Sheets Update:** Updates data in a Google Sheets document and sheet based on specified columns and values. Google Sheets Node Documentation

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.