Workflow Templates

This workflow creates a Jira issue when a new ticket is created in Zendesk. Subsequent comments on the ticket in Zendesk are added as comments to the issue in Jira. Prerequisites Zendesk account and Zendesk credentials. Jira account and Jira credentials. Jira project to create issues in. How it works The workflow listens for new tickets in Zendesk. When a new ticket is created, the workflow creates a new issue in Jira. The Jira issue key is then saved in one of the ticket's fields (in setup we call this "Jira Issue Key"). The next time a comment is added to the ticket, the workflow retrieves the Jira issue key from the ticket's field and adds the comment to the issue in Jira. Setup This workflow requires that you set up a webhook in Zendesk. To do so, follow the steps below: In the workflow, open the On new Zendesk ticket node and copy the webhook URL. In Zendesk, navigate to Admin Center > Apps and integrations > Webhooks > Actions > Create Webhook. Add all the required details which can be retrieved from the On new Zendesk ticket node. The webhook URL gets added to the “Endpoint URL” field, and the “Request method” should match what is shown in n8n. Save the webhook. In Zendesk, navigate to Admin Center > Objects and rules > Business rules > Triggers > Add trigger. Give the trigger a name such as “New tickets”. Under “Conditions” in “Meet ALL of the following conditions”, add “Status is New”. Under “Actions”, select “Notify active webhook” and select the webhook you created previously. In the JSON body, add the following: { "id": "{{ticket.id}}", "comment": "{{ticket.latest_comment_html}}" } Save the Zendesk trigger. You will also need to set up a field in Zendesk to store the Jira issue key. To do so, follow the steps below: In Zendesk, navigate to Admin Center > Objects and rules > Tickets > Fields > Add field. Use the text field option and give the field a name such as “Jira Issue Key". Save the field. In n8n, open the Update ticket node and select the field you created in Zendesk.

What this workflow does This workflow in n8n demonstrates how to send a message in Lark using a Lark bot. It begins with a manual trigger and then retrieves the necessary Lark token via a POST request. The token is used to authenticate and send a message to a specific chat using the Lark API. The input node provides the required app_id, app_secret, chat_id, and message content. After obtaining the token, the message is sent with the Lark API's message/v4/send/ endpoint. Who This Is For This n8n workflow is ideal for organizations, teams, and developers who need to automate message sending within Lark, especially those managing notifications, alerts, or team reminders. It can help users reduce manual messaging tasks by leveraging a Lark bot to deliver messages at specific intervals or based on particular conditions, enhancing team communication and responsiveness. Setup Fill the Input node with your values Exchange the bearer token in the Send Message node with your token Author: Hiroshi

This template helps anyone track how often their website appears in Google’s AI Overview. a growing part of search results that can’t currently be tracked using traditional SEO tools. With this workflow, users can: Input a list of keywords (from Google Search Console or manual research). Use the SerpApi to pull Google search results. Extract AI Overview content and its list of sources. Map that information into a structured Google Sheet, including whether your site is listed in those sources. Setup is straightforward and fully automated, but you'll need: A SerpApi key A connected Google Sheets account Who is this for? This workflow is designed for SEO professionals, digital marketers, and site owners who want to track their website’s visibility in Google AI Overviews. What problem does it solve? AI Overviews are rapidly becoming more common in Google search results. However, there's no tool (yet) that tells you if your website is appearing in those answers. This is a blind spot for SEO. This workflow helps you check your site’s presence in AI Overviews manually, at scale. What does the workflow do? The workflow: Takes a list of target keywords (exported from GSC or elsewhere) Uses SerpApi to get search results from Google Extracts the AI Overview block and its sources Checks if your domain is among them Saves all results into a Google Sheet The final Google Sheet will contain: Keyword | AI Overview Exists | List of Sources | Is my domain listed Setup You’ll need: A SerpApi API key A Google Sheet with your list of keywords A connected Google Sheets account in n8n How to customize this workflow Change the list of keywords (pull from GSC or edit the sheet manually) Replace the placeholder domain with your own Adjust the Google Sheet column mapping as needed

FTP to Google Drive Transfer Template What This Template Does This workflow automatically transfers files from an FTP server to Google Drive. It's perfect for: Backing up files from remote servers Migrating data from FTP to cloud storage Automating file synchronization tasks Creating scheduled backups of server content How It Works The workflow follows these steps: Manual Trigger - You start the process by clicking "Execute" Lists FTP Directory - Scans the specified FTP folder for all items Filters Files Only - Separates actual files from directories (folders) Downloads Files - Retrieves each file as binary data from the FTP server Uploads to Google Drive - Stores all downloaded files in your specified Google Drive folder Requirements Before using this template, you'll need: FTP Server Access**: Server address, username, and password Google Drive Account**: With OAuth2 authentication set up in n8n n8n Instance**: Self-hosted or cloud version Setup Instructions Step 1: Configure FTP Credentials In n8n, go to Settings → Credentials Create a new FTP credential Enter your FTP server details: Host: Your FTP server address Port: Usually 21 for FTP Username: Your FTP username Password: Your FTP password Test the connection and save Step 2: Set Up Google Drive Authentication Create a new Google Drive OAuth2 credential Follow n8n's Google Drive setup guide: Create a Google Cloud project Enable Google Drive API Create OAuth2 credentials Add your n8n callback URL Authorize the connection in n8n Step 3: Configure the Workflow Update FTP Path: Open the "List FTP Directory" node Change the path parameter from /_instalki to your desired FTP folder Set Google Drive Folder: Open the "Upload to Google Drive" node Replace the folderId with your target Google Drive folder ID To find folder ID: Open the folder in Google Drive and copy the ID from the URL Assign Credentials: Ensure both FTP nodes use your FTP credential Assign your Google Drive credential to the upload node How to Use Test First: Run the workflow manually with a few test files Monitor Execution: Check the execution log for any errors Verify Upload: Confirm files appear in your Google Drive folder Schedule (Optional): Add a schedule trigger if you want automatic runs Customization Options Filter Specific File Types Add a condition after "Filter Files Only" to process only certain file extensions: {{ $json.name.endsWith('.pdf') || $json.name.endsWith('.jpg') }} Add Error Handling Insert error-handling nodes to manage failed downloads or uploads gracefully. Organize by Date Modify the Google Drive upload to create date-based folders automatically. File Size Limits Add checks for file size before attempting upload (Google Drive has limits). Troubleshooting Common Issues: FTP Connection Failed**: Check server address, port, and credentials Google Drive Upload Error**: Verify OAuth2 setup and folder permissions Files Not Found**: Ensure the FTP path exists and contains files Large Files**: Consider Google Drive's file size limitations (15GB for free accounts) Tips: Test with small files first Check n8n execution logs for detailed error messages Ensure your Google Drive has sufficient storage space Verify FTP server allows multiple concurrent connections Security Notes Never hardcode credentials in the workflow Use n8n's credential system for all authentication Consider using SFTP instead of FTP for better security Regularly rotate your FTP passwords Review Google Drive sharing permissions Next Steps Once you have this basic transfer working, you might want to: Add email notifications for successful/failed transfers Implement file deduplication checks Create logs of transferred files Set up automatic cleanup of old files Add file compression before upload

This n8n demonstrates how to build a simple PostgreSQL MCP server to manage your PostgreSQL database such as HR, Payroll, Sale, Inventory and More! This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/postgres How it works A MCP server trigger is used and connected to 5 tools: 2 postgreSQL and 3 custom workflow. The 2 postgreSQL tools are simple read-only queries and as such, the postgreSQL tool can be simply used. The 3 custom workflow tools are used for select, insert and update queries as these are operations which require a bit more discretion. Whilst it may be easier to allow the agent to use raw SQL queries, we may find it a little safer to just allow for the parameters instead. The custom workflow tool allows us to define this restricted schema for tool input which we'll use to construct the SQL statement ourselves. All 3 custom workflow tools trigger the same "Execute workflow" trigger in this very template which has a switch to route the operation to the correct handler. Finally, we use our standard PostgreSQL node to handle select, insert and update operations. The responses are then sent back to the the MCP client. How to use This PostgreSQL MCP server allows any compatible MCP client to manage a PostgreSQL database by supporting select, create and update operations. You will need to have a database available before you can use this server. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Please help me check if Alex has an entry in the users table. If not, please help me create a record for her." "What was the top selling product in the last week?" "How many high priority support tickets are still open this morning?" Requirements PostgreSQL for database. This can be an external database such as Supabase or one you can host internally. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow If the scope of schemas or tables is too open, try restrict it so the MCP serves a specific purpose for business operations. eg. Confine the querying and editing to HR only tables before providing access to people in that department. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

This n8n workflow demonstrates multiple ways to harness DeepSeek's AI models in your automation pipeline! 🌟 Core Features Multiple Integration Methods 🔌 Local deployment using Ollama for DeepSeek-R1 Direct API integration with DeepSeek Chat V3 Conversational agent with memory buffer HTTP request implementation with both raw and JSON formats Model Options 🧠 DeepSeek Chat V3 for general conversation DeepSeek-R1 for advanced reasoning Memory-enabled agent for persistent context Quick Setup 🛠️ API Configuration Base URL: https://api.deepseek.com Get your API key from platform.deepseek.com/api_keys Local Setup 💻 Install Ollama for local deployment Set up DeepSeek-R1 via Ollama Configure local credentials in n8n Implementation Details 🔧 Conversational Agent Window Buffer Memory for context Customizable system messages Built-in error handling with retries API Endpoints 🌐 Chat completions for V3 and R1 models OpenAI API format compatibles

📝 Overview This workflow leverages Google Gemini 2.0 Flash multimodal AI to automatically generate detailed descriptions of video content from any public URL. It streamlines video understanding, making it ideal for content cataloging, accessibility, and content moderation. 💡 Use Cases ♿ Accessibility: Automatically generate detailed video descriptions for visually impaired users. 🛡️ Content Moderation: Detect inappropriate or off-brand material without manual watching. 🗂️ Media Cataloging: Enrich your media library with automatically extracted metadata. 📈 Marketing & Branding: Gain fast insights into key elements, tone, and branding in video content. ⚙️ Setup Instructions 🔑 Get a Gemini API Key Register at ai.google.dev and create an API key. Before running the workflow, set your Gemini API key as an environment variable named GeminiKey for secure access within the workflow. In the Set Input node, reference this environment variable instead of hardcoding the key. 🌐 Configure Video URL Replace the sample URL in the Set Input node with your desired public video URL. Ensure the video is directly accessible (no login or special permissions required). 📝 Optional: Customize the Analysis Edit the prompt in the Analyze video Gemini node to focus on the most relevant video details for your use case (e.g., branding, key actions, visual elements). 🔒 Security Tip Use n8n's credentials manager or environment variables (like GeminiKey) to store your API key securely. Avoid hardcoding API keys directly in workflow nodes, especially in production environments. 🔄 How It Works 📥 Download the video from the provided URL. ☁️ Upload the video to Gemini’s server for processing. ⏳ Wait for Gemini to complete processing. 🤖 Analyze the video with Gemini AI using your customized prompt. 📄 Output a comprehensive description of the video as videoDescription. ⚡ Technical Details Uses HTTP Request nodes to interact with Gemini API endpoints. Handles file download, upload, status checking, and result retrieval. Customizable Gemini AI parameters for fine-tuned response. Main output: videoDescription (detailed text describing video content). 🚀 Quickstart Set your Gemini API key as the GeminiKey environment variable and configure your video URL in the workflow. Execute the workflow. Retrieve your rich, AI-generated video description for downstream use such as automation, tagging, or reporting.

📝 Sync MySQL Rows to Google Sheet Description: This n8n template automates the process of syncing new records from a MySQL database table into a Google Sheet, ideal for reporting, backup, or lightweight dashboards. It is designed for teams or individuals who need to periodically export new data rows from a custom database (e.g., CRM, registrations, surveys) into a structured Google Sheet for further analysis, sharing, or archiving—without duplicates. 🛠️ What This Workflow Does: Runs every 15 minutes** via a schedule trigger. Selects unsynced rows** (sync = 0) from a MySQL table (fifa25_customers). Checks if records exist** to prevent unnecessary writes. Appends records to a Google Sheet**, mapping fields like name, email, phone, gender, and more. Updates the MySQL table** to mark those rows as synced (sync = 1) to avoid reprocessing. Fully annotated using sticky notes for easier understanding and onboarding. 📋 Setup Instructions: Create or select a Google Sheet and make sure the columns match the following: id, name, phone, birthdate, email, region, gender, datatime Ensure your MySQL table (fifa25_customers) has a sync column (default = 0 for new rows). Connect your MySQL and Google Sheets credentials inside n8n. (Optional): Add custom filtering or column transformations as needed. 👤 Who Is It For? Marketers syncing leads to a spreadsheet Ops teams pulling user data from internal tools Analysts logging form submissions or customer data Anyone needing lightweight scheduled ETL from MySQL to Sheets 🔐 Credentials Required: MySQL** Google Sheets OAuth2** ✅ Best Practices Followed: Uses IF node to prevent unnecessary processing Updates source database to avoid duplicates Includes sticky notes for clarity All columns are explicitly mapped Works out-of-the-box on any n8n instance with proper creds

How It Works This workflow automates the analysis of security alerts from Sophos Central, turning raw events into actionable intelligence. It uses the official Sophos SIEM integration tool to fetch data, enriches it with VirusTotal, and leverages Google Gemini to provide a real-time threat summary and mitigation plan via Telegram. Prerequisite (Important): This workflow is triggered by a webhook that receives data from an external Python script. You must first set up the Sophos-Central-SIEM-Integration script from the official Sophos GitHub. This script will fetch data and forward it to your n8n webhook URL. Tool Source Code: Sophos/Sophos-Central-SIEM-Integration The n8n Workflow Steps Webhook: Receives enriched event and alert data from the external Python script. IF (Filter): Immediately filters the incoming data to ensure only events with a high or critical severity are processed, reducing noise from low-priority alerts. Code (Prepare Indicator): Intelligently inspects the Sophos event data to extract the primary threat indicator. It prioritizes indicators in the following order: File Hash (SHA256), URL/Domain, or Source IP. HTTP Request (VirusTotal): The extracted indicator is sent to the VirusTotal API to get a detailed reputation report, including how many security vendors flagged it as malicious. Code (Prompt for Gemini): The raw JSON output from VirusTotal is processed into a clean, human-readable summary and a detailed list of flagging vendors. AI Agent (Google Gemini): All collected data—the original Sophos log, the full alert details, and the formatted VirusTotal reputation—is compiled into a detailed prompt for Gemini. The AI acts as a virtual SOC analyst to: Create a concise incident summary. Determine the risk level. Provide a list of concrete, actionable mitigation steps. Telegram: The complete analysis and mitigation plan from Gemini is formatted into a clean, easy-to-read message and sent to your specified Telegram chat. Setup Instructions Configure the external Python script to forward events to this workflow's Production URL. In n8n, create Credentials for Google Gemini, VirusTotal, and Telegram. Assign the newly created credentials to the corresponding nodes in the workflow.

A streamlined AI-powered tool that extracts actionable technical insights from HackerOne security reports for advanced bug bounty hunters. How It Works Send any HackerOne report URL (e.g., https://hackerone.com/reports/123456) to the chat interface. The AI agent will: Fetch the report JSON automatically Analyze for unique techniques, payloads, and root causes Extract reusable insights in a structured format Summarize with practical pentesting value Setup Requirements Google Gemini API credentials configured Chat interface deployed and accessible HackerOne report URLs Output Format Summary: One-liner impact statement Techniques: Payloads, code snippets, exploitation steps Pro Tips: Reusable insights for future hunts Perfect for rapid triage and building your personal exploit knowledge base.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Transform your expense tracking with automated AI receipt processing that extracts data and organizes it instantly. What this workflow does Monitors Google Drive for new receipt uploads (images/PDFs) Downloads and processes files automatically Extracts key data using VLM Run community node (merchant, amount, currency, date) Saves structured data to Google Sheets for easy tracking Setup Prerequisites: Google Drive/Sheets accounts, VLM Run API credentials, n8n instance. You need to install VLM Run community node. To install Community nodes you need to follow steps, Settings -> Community Nodes -> Install -> Search with name @vlm-run/n8n-nodes-vlmrun Quick Setup: Configure Google Drive OAuth2 and create receipt upload folder Add VLM Run API credentials Create Google Sheets with columns: Customer, Merchant, Amount, Currency, Date Update folder/sheet IDs in workflow nodes Test and activate How to customize this workflow to your needs Extend functionality by: Adding expense categories and approval workflows Connecting to accounting software (QuickBooks, Xero) Including Slack notifications for processed receipts Adding data validation and duplicate detection This workflow transforms manual receipt processing into an automated system that saves hours while improving accuracy.

Using the knowledge graphs instead of RAG vector stores This workflow creates an AI chatbot agent that has access to several knowledge bases at the same time (used as "experts"). These knowledge bases are provided using the InfraNodus GraphRAG using the knowledge graphs and providing high-quality responses without the need to set up complex RAG vector store workflows. The advantages of using GraphRAG instead of the standard vector stores for knowledge are: Easy and quick to set up (no complex data import workflows needed) A knowledge graph has a holistic view of your knowledge base Better retrieval of relations between the document chunks = higher quality responses How it works This template uses the n8n AI agent node as an orchestrating agent that decides which tool (knowledge graph) to use based on the user's prompt. Here's a description step by step: The user submits a question using the AI chatbot (n8n interface, in this case, which can be accessed via a URL or embedded to any website) The AI agent node checks a list of tools it has access to. Each tool has a description of the knowledge it has auto-generated by InfraNodus. The AI agent decides which tool should be used to generate a response. It may reformulate user's query to be more suitable for the expert. The query is then sent to the InfraNodus HTTP node endpoint, which will query the graph that corresponds to that expert. Each InfraNodus GraphRAG expert provides a rich response that takes the whole context into account and provides a response from each expert (graph) along with a list of relevant statements retrieved using a combination or RAG and GraphRAG. The n8n AI Agent node integrates the responses received from the experts to produce the final answer. The final answer is sent back to the user's chat (or a webhook endpoint) How to use You need an InfraNodus GraphRAG API account and key to use this workflow. Create an InfraNodus account Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Create a separate knowledge graph for each expert (using PDF / content import options) in InfraNodus For each graph, go to the workflow, paste the name of the graph into the body name field. Keep other settings intact or learn more about them at the InfraNodus access points page. Once you add one or more graphs as experts to your flow, add the LLM key to the OpenAI node and launch the workflow Requirements An InfraNodus account and API key An OpenAI (or any other LLM) API key Customizing this workflow You can use this same workflow with a Telegram bot, so you can interact with it using Telegram. There are many more customizations available. Check out the complete guide at https://support.noduslabs.com/hc/en-us/articles/20174217658396-Using-InfraNodus-Knowledge-Graphs-as-Experts-for-AI-Chatbot-Agents-in-n8n Also check out the video tutorial with a demo: