Workflow Templates

Encode JSON to Base64 String in n8n This example workflow demonstrates how to convert a JSON object into a base64-encoded string using n8n’s built-in file processing capabilities. This is a common requirement when working with APIs, webhooks, or SaaS integrations that expect payloads to be base64-encoded. > Tip: The three green-highlighted nodes (Stringify → Convert to File → Extract from File) can be wrapped in a Subworkflow to create a reusable Base64 encoder in your own projects. 🔧 Requirements Any running n8n instance (local or cloud) No credentials or external services required What This Workflow Does Generates example JSON data Converts the JSON to a string Saves the string as a binary file Extracts the file’s contents as a base64 string Outputs the base64 string on the final node Step-by-Step Setup Manual Trigger Start the workflow using the Manual Execution node. This is useful for testing and development. Create JSON Data The Create Json Data node uses raw mode to construct a sample object with all major JSON types: strings, numbers, booleans, nulls, arrays, nested objects, etc. Convert to String The Convert to String node uses the expression ={{ JSON.stringify($json) }} to flatten the object into a single string field named json_text. Convert to File The Convert to File node takes the json_text value and saves it to a UTF-8 encoded binary file in the property encoded_text. Extract from File This node takes the binary file and extracts its contents as a base64-encoded string. The result is saved in the base64_text field. Customization Tips Replace the sample JSON in the Create Json Data node with your own payload structure. To make this reusable, extract the three core nodes into a Subworkflow or wrap them in a custom Function. Use the base64_text output field to post to APIs, store in databases, or include in webhook responses.

Automating LinkedIn Company URL Verification Use Case This automation verifies that a given LinkedIn URL actually belongs to a company by comparing the website listed on their LinkedIn page against the expected company domain. It is essential for ensuring data accuracy in lead qualification, enrichment, and CRM updates. What This Automation Does Input Parameters Company LinkedIn**: The LinkedIn URL to be verified. Company Domain**: The expected domain (e.g., example.com) for validation. Airtop Profile (connected to LinkedIn)**: Airtop Profile with LinkedIn authentication. Output Confirmation whether the LinkedIn page corresponds to the provided domain. Returns the verified LinkedIn URL if the match is confirmed. How It Works Extracts the website URL from the specified LinkedIn company profile. Compares the extracted URL with the provided company domain. If the domain is contained in the extracted website, the LinkedIn profile is confirmed as valid. Returns the original LinkedIn URL if the match is successful. Setup Requirements Airtop API Key LinkedIn-authenticated Airtop Profile Next Steps Use for LinkedIn Discovery Validation**: Ensure correctness after automated LinkedIn page discovery. Combine with CRM Updates**: Prevent incorrect LinkedIn links from being stored in CRM. Automate in Data Pipelines**: Use this as a validation gate before enrichment or scoring steps.

This workflow restores all n8n instance workflows from GitHub backups using the n8n API node. It complements the Backup Your Workflows to GitHub template by allowing users to seamlessly restore previously saved workflows. How It Works The workflow fetches workflows stored in a GitHub repository and imports them into your n8n instance. Setup Instructions To configure the workflow, update the Globals node with the following values: repo.owner** – Your GitHub username repo.name** – The name of your GitHub repository storing the workflows repo.path** – The folder path within the repository where workflows are stored For example, if your GitHub username is john-doe, your repository is named n8n-backups, and workflows are stored in a workflows/ folder, you would set: repo.owner → john-doe repo.name → n8n-backups repo.path → workflows/ Required Credentials GitHub API** – Access to your repository n8n API** – To import workflows into your n8n instance Who Is This For? This template is ideal for users who want to restore their workflows from GitHub backups, ensuring easy migration and recovery in case of data loss. Check out my other templates: 👉 My n8n Templates

This N8N workflow automates the process of sharing files from Google Drive. It includes OAuth2 authentication, batch processing, public link generation, and access status modification for efficient file handling. Suitable for users seeking to streamline their Google Drive file sharing process. sutiable for bulk actions, tested on 4.2K files folder working like charm. How It Works Initialize Workflow: The process begins with a Manual Trigger, allowing the user to start the workflow at their convenience. Folder ID Specification: A 'Set Folder ID' node where the user can enter the desired Google Drive Folder ID. List Files from Google Drive: The 'Google Drive' node lists all files within the specified folder using OAuth2 authentication. Batch Processing: The 'Loop Over Items' node processes the files in batches for efficiency. Generate Public Links: The 'Generate Download Links' node creates downloadable links for each file. Change File Access: The 'Change Status' node alters the file status to make them publicly accessible. Merge and Output: A 'Merge' node consolidates the data, preparing it for further actions or output. Set Up Steps Estimated Time**: The setup should take approximately 10-15 minutes. Initial Setup**: You'll need to provide OAuth2 credentials for Google Drive and specify a folder ID. Customization**: Adjust the batch size and file access permissions according to your needs. Detailed Descriptions**: For specific configuration details, refer to the sticky notes within the workflow. Example Item output { "link": "https://drive.google.com/u/3/uc?id=1hojqPfXchNTY8YRTNkxSo-8txK9re-V4&export=download&confirm=t&authuser=0", "name": "firefox_rNjA0ybKu7.png", "kind": "drive#permission", "id": "anyoneWithLink", "type": "anyone", "role": "reader", "allowFileDiscovery": false } You can store the output data with any data store node you want, for example save them into Excel Sheet or Airtable etc... Keywords: n8n workflow, Google Drive integration, file sharing automation, batch file processing, public link generation, OAuth2 authentication, workflow automation

Daily YouTrack In-Progress Tasks Summary to Discord by Assignee Keep your team in sync with a daily summary of tasks currently In Progress in YouTrack — automatically posted to your Discord channel. This workflow queries issues, filters them by status, groups them by assignee and priority, and sends a formatted message to Discord. It's perfect for teams that need a lightweight, automated stand-up report. > 📝 This workflow uses Discord as an example. You can easily replace the messaging integration with Slack, Mattermost, MS Teams, or any other platform that supports incoming webhooks. Use Case Remote development teams using YouTrack + Discord Replacing daily stand-up meetings with async updates Project managers needing quick visibility into active tasks Features Scheduled** daily execution (default: weekdays at 09:00) Status filter**: only issues marked as In Progress Grouping** by assignee and priority Custom mapping** for user mentions (YouTrack → Discord) Clean Markdown output** for Discord, with direct task links Setup Instructions YouTrack Configuration Get a permanent token: Go to your YouTrack profile → Account Security → Authentication Create a new permanent token with "Read Issue" permissions Copy the token value Set the base API URL: Format: https://yourdomain.youtrack.cloud/api/issues Replace yourdomain with your actual YouTrack instance Identify custom field IDs: Method 1: Go to YouTrack → Administration → Custom Fields → find your "Status" field and note its ID Method 2: Use API call GET /api/admin/customFieldSettings/customFields to list all field IDs Method 3: Inspect a task's API response and look for field IDs in the customFields array Example Status field ID: 105-0 or 142-1 Discord Configuration Create a webhook URL in your Discord server: Server Settings → Integrations → Webhooks → New Webhook Choose target channel and copy the webhook URL Extract webhook ID from URL (numbers after /webhooks/) Environment Variables & Placeholders | Placeholder | Description | |-------------|-------------| | {{API_URL}} | Your YouTrack API base URL | | {{TOKEN}} | YouTrack permanent token | | {{FIELD_ID}} | ID of the "Status" custom field | | {{QUERY_FIELDS}} | Fields to fetch (e.g., summary, id) | | {{PROJECT_LINK}} | Link to your YouTrack project | | {{USER_X}} | YouTrack usernames | | {{DISCORD_ID_X}} | Discord mentions or usernames | | {{NAME_X}} | Display names | | {{WEBHOOK_ID}} | Discord webhook ID | | {{DISCORD_CHANNEL}} | Discord channel name | | {{CREDENTIAL_ID}} | Your credential ID in n8n | Testing the Workflow Test YouTrack connection: Execute the "HTTP Request YT" node individually Verify that issues are returned from your YouTrack instance Check if the Status field ID is correctly filtering tasks Verify filtering: Run the "Filter fields" node Confirm only "In Progress" tasks pass through Check message formatting: Execute the "Discord message" node Review the generated message content and formatting Test Discord delivery: Run the complete workflow manually Verify the message appears in your Discord channel Schedule verification: Enable the workflow Test weekend skip functionality by temporarily changing dates Customization Tips Language**: All labels/messages are in English — customize if needed User mapping**: Adjust assignee → Discord mention logic in the message builder Priorities**: Update the priorityMap to reflect your own naming structure Schedule**: Modify the trigger time in the Schedule Trigger node Alternative platforms**: Swap out the Discord webhook for another messaging service if preferred

Supercharge Your Security Operations for Free Stop wasting time manually investigating suspicious IP addresses. This workflow template is your launchpad to automating real-time IP cybersecurity analysis using the NixGuard platform, which you can use for free. This is the first of a two-part system designed to integrate seamlessly into your existing security stack, especially with Wazuh. It calls our main workflow, Automate IP Reputation Checks and Get AI Risk Summaries from NixGuard, to do the heavy lifting. What This Workflow Unlocks for You Free AI-Powered Risk Summaries:** Don't just get data; get answers. NixGuard provides a clear, human-readable summary of why an IP is considered risky. Automated IP Reputation Checks:** Programmatically check any IP against a vast array of threat intelligence sources. A Foundation for Your SOC Automation:** Use the results to trigger your incident response process. The template includes a pre-built example of how to send a detailed alert to Slack, which you can easily adapt for Jira, TheHive, or any other tool. How the Two-Workflow System Works This "Dispatcher" workflow is designed for flexibility. It holds your API key and input, then calls the main analysis workflow. This allows you to easily create multiple triggers (e.g., one for Slack bots, one for webhooks) without duplicating the core logic. Critical Setup Instructions Get the Main Workflow: First, add the main analysis engine to your n8n instance from the community page: NixGuard Analysis Workflow. Add Your Free API Key: In this workflow, click the blue Set API Key & Initial Prompt node. Paste your free NixGuard API key into the apiKey value field. Connect The Workflows: Click the purple Execute NixGuard & Wazuh Workflow node. In the parameters, use the dropdown to select the main analysis workflow you added in Step 1. Ready to automate your threat intelligence? Get your free API key and learn more at; 🔗 Learn more about NixGuard: [thenex.world](thenex.world )🔗 Get started with a free security subscription: thenex.world/security/subscribe Tags: Free, IP Analysis, NixGuard, Wazuh, Security, Automation, AI, Cybersecurity, Threat Intelligence, SOC, Incident Response, IP Reputation, DevSecOps, API

Overview This workflow answers user requests sent via Mac Shortcuts Several Shortcuts call the same webhook, with a query and a type of query Types of query are: translate to english translate to spanish correct grammar (without changing the actual content) make content shorter make content longer How it works Select a text you are writing Launch the shortcut The text is sent to the webhook Depending on the type of request, a different prompt is used Each request is sent to an OpenAI node The workflow responds to the request with the response from GPT Shortcut replace the selected text with the new one For a demo and setup instructions: How to use it Activate the workflow Download this Shortcut template Install the shortcut In step 2 of the shortcut, change the url of the Webhook In Shortcut details, "add Keyboard Shortcut" with the key you want to use to launch the shortcut Go to settings, advanced, check "Allow running scripts" You are ready to use the shortcut. Select a text and hit the keyboard shortcut you just defined

Who is this for? Content strategists analyzing web page semantic content SEO professionals conducting entity-based analysis Data analysts extracting structured data from web pages Marketers researching competitor content strategies Researchers organizing and categorizing web content Anyone needing to automatically extract entities from web pages What problem is this workflow solving? Manually identifying and categorizing entities (people, organizations, locations, etc.) on web pages is time-consuming and error-prone. This workflow solves this challenge by: Automating the extraction of named entities from any web page Leveraging Google's powerful Natural Language API for accurate entity recognition Processing web pages through a simple webhook interface Providing structured entity data that can be used for analysis or further processing Eliminating hours of manual content analysis and categorization What this workflow does This workflow creates an automated pipeline between a webhook and Google's Natural Language API to: Receive a URL through a webhook endpoint Fetch the HTML content from the specified URL Clean and prepare the HTML for processing Submit the HTML to Google's Natural Language API for entity analysis Return the structured entity data through the webhook response Extract entities including people, organizations, locations, and more with their salience scores Setup Prerequisites: An n8n instance (cloud or self-hosted) Google Cloud Platform account with Natural Language API enabled Google API key with access to the Natural Language API Google Cloud Setup: Create a project in Google Cloud Platform Enable the Natural Language API for your project Create an API key with access to the Natural Language API Copy your API key for use in the workflow n8n Setup: Import the workflow JSON into your n8n instance Replace "YOUR-GOOGLE-API-KEY" in the "Google Entities" node with your actual API key Activate the workflow to enable the webhook endpoint Copy the webhook URL from the "Webhook" node for later use Testing: Use a tool like Postman or cURL to send a POST request to your webhook URL Include a JSON body with the URL you want to analyze: {"url": "https://example.com"} Verify that you receive a response containing the entity analysis data How to customize this workflow to your needs Analyzing Specific Entity Modify the "Google Entities" node parameters to include entityType filters Add a "Function" node after "Google Entities" to filter specific entity types Create conditions to extract only entities of interest (people, organizations, etc.) Processing Multiple URLs in Batch: Replace the webhook with a different trigger (HTTP Request, Google Sheets, etc.) Add a "Split In Batches" node to process multiple URLs Use a "Merge" node to combine results before sending the response Enhancing Entity Data: Add additional API calls to enrich extracted entities with more information Implement sentiment analysis alongside entity extraction Create a data transformation node to format entities by type or relevance Additional Notes This workflow respects Google's API rate limits by processing one URL at a time The Natural Language API may not identify all entities on a page, particularly for highly technical content HTML content is trimmed to 100,000 characters if longer to avoid API limitations Consider legal and privacy implications when analyzing and storing entity data from web pages You may want to adjust the HTML cleaning process for specific website structures ❤️ Hueston SEO Team

Use Case In most companies, employees have a lot of great ideas. That was the same for us at n8n. We wanted to make it as easy as possible to allow everyone to add their ideas to some formatted database - it should be somewhere where everyone is all the time and could add a new idea without much extra effort. Since we're using Slack, this seemed to be the perfect place to easily add ideas and collect them in Notion. What this workflow does This workflow waits for a webhook call within Slack, that gets fired when users use the /idea command on a bot that you will create as part of this template. It then checks the command, adds the idea to Notion, and notifies the user about the newly added idea as you can see below: Creating your Slack bot Visit https://api.slack.com/apps, click on New App and choose a name and workspace. Click on OAuth & Permissions and scroll down to Scopes -> Bot token Scopes Add the chat:write scope Head over to Slash Commands and click on Create New Command Use /idea as the command Copy the test URL from the Webhook node into Request URL Add whatever feels best to the description and usage hint Go to Install app and click install Setup Add a Database in Notion with the columns Name and Creator Add your Notion credentials and add the integration to your Notion page. Fill the setup node below Create your Slack app (see other sticky) Click Test workflow and use the /idea comment in Slack Activate the workflow and exchange the Request URL with the production URL from the webhook How to adjust it to your needs You can adjust the table in Notion and for example, add different types of ideas or areas that they impact You might wanna add different templates in Notion to make it easier for users to fill their ideas with details Rename the Slack command as it works best for you How to enhance this workflow At n8n we use this workflow in combination with some others. E.g. we have the following things on top: We additionally have a /bug Slack command that adds a new bug to Linear. Here we're using AI to classify the bugs and move it to the right team. (see this template and this template) We also added other types, like /pain to be less solution-driven To make it easier for everyone to give input, we added a Votes column that allows everyone to vote on ideas/pain points in the list We're also running a workflow once a week that highlights the most popular new ideas and the most active voters (see here)

This n8n template demonstrates how to create a comprehensive voice-powered restaurant assistant that handles table reservations, food orders, and restaurant information requests through natural language processing. The system uses VAPI for voice interaction and PostgreSQL for data management, making it perfect for restaurants looking to automate customer service with voice AI technology. Good to know Voice processing requires active VAPI subscription with per-minute billing Database operations are handled in real-time with immediate confirmations The system can handle multiple simultaneous voice requests All customer data is stored securely in PostgreSQL with proper indexing How it works Table Booking & Order Handling Workflow Voice requests are captured through VAPI triggers when customers make booking or ordering requests The system processes natural language commands and extracts relevant details (party size, time, food items) Customer data is immediately saved to the bookings and orders tables in PostgreSQL Voice confirmations are sent back through VAPI with booking details and estimated wait times All transactions are logged with timestamps for restaurant management tracking Restaurant Info Provider Workflow Info requests trigger when customers ask about hours, menu, location, or services Restaurant details are retrieved from the restaurant_info table containing current information Wait nodes ensure proper data loading before voice response generation Structured restaurant information is delivered via VAPI in natural, conversational format Database Schema Bookings Table booking_id (PRIMARY KEY) - Unique identifier for each reservation customer_name - Customer's full name phone_number - Contact number for confirmation party_size - Number of guests booking_date - Requested reservation date booking_time - Requested time slot special_requests - Dietary restrictions or special occasions status - Booking status (confirmed, pending, cancelled) created_at - Timestamp of booking creation Orders Table order_id (PRIMARY KEY) - Unique order identifier customer_name - Customer's name phone_number - Contact for order updates order_items - JSON array of food items and quantities total_amount - Calculated order total order_type - Delivery, pickup, or dine-in special_instructions - Cooking preferences or allergies status - Order status (received, preparing, ready, delivered) created_at - Order timestamp Restaurant_Info Table info_id (PRIMARY KEY) - Information entry identifier category - Type of info (hours, menu, location, contact) title - Information title description - Detailed information content is_active - Whether info is currently valid updated_at - Last modification timestamp How to use The manual trigger can be replaced with webhook triggers for integration with existing restaurant systems Import the workflow into your n8n instance and configure VAPI credentials Set up PostgreSQL database with the required tables using the schema provided above Configure restaurant information in the restaurant_info table Test voice commands such as "Book a table for 4 people at 7 PM" or "What are your opening hours?" Customize voice responses in VAPI nodes to match your restaurant's tone and branding The system can handle multiple concurrent voice requests and scales with your restaurant's needs Requirements VAPI account for voice processing and natural language understanding PostgreSQL database for storing booking, order, and restaurant information n8n instance with database and VAPI integrations enabled Customising this workflow Voice AI automation can be adapted for various restaurant types - from quick service to fine dining establishments Try popular use-cases such as multi-location booking management, dietary restriction handling, or integration with existing POS systems The workflow can be extended to include payment processing, SMS notifications, and third-party delivery platform integration

Who is this tempate for? This workflow powers a simple yet effective customer and sales support chatbot for your webshop. It's perfect for solopreneurs who want to automate customer interactions without relying on expensive or complex support tools. How it works? The chatbot listens to user requests—such as checking product availability—and automatically handles the following Fetches product information from a Google Sheet Answers customer queries Places an order Updates the stock after a successful purchase Everything runs through a single Google Sheet used for both stock tracking and order management. Setup Instructions Before you begin, connect your Google Sheets credentials by following this guide: This will be used to connect all the tools to Google Sheets 👉 Setup Google sheets credentials Get Stock Open "Get Stock" tool node and select the Google sheet credentials you created. Choose the correct google sheet document and sheet name and you are done. Place order Go to your "Place Order" tool node and select the Google sheet credentials you have created. Choose the correct google sheet document and sheet name. Update Stock - Open your "Update Stock" tool node and select the Google sheet credentials you have created. Choose the correct google sheet document and sheet name. In "Mapping Column Mode" section select map each column manually. In "Column to match on" select the column with a unique identifier (e.g., Product ID) to match stock items. In values to update section, add only the column(s) that need to be updated—usually the stock count. AI Agent node Adjust the prompt according to your use case and customize what you need. Google Sheet Template Stock sheet |Case ID|Phone Model|Case Name|Case Type|Image URL|Quantity Avaialble|Initital Inventory|Sold| |-|-|-|-|-|-|-|-| |1023|Iphone 14 pro|Black Leather|Magsafe|https://example.com/url|90|100|10 Order sheet |Case ID|Phone Model|Case Name|Name|Phone Number|Address| |-|-|-|-|-|-| |1023|Black Leather |Iphone 14 pro|Fernando Torres|9998898888|Paris, France

What it is: In version 1.78, n8n introduced a dedicated node to use the OpenRouter service, which lets you to use a lot of different LLM models and providers and change models on the fly in an agentic workflow. For prior n8n versions, there's a workaround to make OpenRouter accessible, by using the OpenAI node with a OpenRouter-specific BaseURL. This trivial workflow demonstrates this for version before 1.78, so that you can use different LLM model dynamically with the available n8n nodes for OpenAI LLM and OpenAI credentials. What you can do: Use any of the OpenRouter models Have the model even dynamically configured or changing (by some external config, some rule, or some specific chat message) Setup steps: Import the workflow Ensure you have registered and account, purchased some credits and created and API key for OpenRouter.ai Configure the "OpenRouter" credentials with your own credentials, using an OpenAI type credential, but making sure in the credential's config form its "Base URL" is set to https://openrouter.ai/api/v1 so OpenRouter is used instead of OpenAI. Open the "Settings" node and change the model value to any valid model id from the OpenRouter models list or even have the model property set dynamically