Integrate Execute Workflow with 500+ apps and services

Purpose This ensures that executions of scheduled workflows do not overlap when they take longer than expected. How it works This is a separate workflow which monitors the execution of the main workflow Stores a flag in Redis (key dynamically named after workflow ID) which indicates if the main workflow is running or idle Only calls the main workflow if the last execution has finished Setup Update the credentials suitable for your Redis instance Replace the Schedule Trigger of your main workflow by an Execute Workflow Trigger Copy the workflow ID from the URL Paste the workflow ID in the Execute Workflow Node of this workflow Configure the Schedule Trigger Node

This workflow is a building block designed to be called from other workflows via an Execute workflow node. When called from another workflow, and given the JSON input of a "pulse" field with the ID to pull from monday, this workflow will return: The items name and ID All column data, indexable by the column name All column data, indexable by the column's ID string All board relation columns, with their data and column values All subitems, with their data and column values For example: ++Prerequisites++ A monday.com account and credential A workflow that needs to get detailed data from a monday.com row The pulse id of the monday.com row to retreive data from. ++Setup++ Import the workflow Configure all monday nodes with your credentials and save the workflow Copy the workflow ID from it's URL In a different workflow, add an Edit Fields node, to output the field "pulse", with the monday item you want to retrieve. Feed the Edit Fields node with your pulse into an Execute workflow node, and paste the workflow ID from above into it This "pulse" field will tell the workflow what pulse to retreive. This can be populated by an expression in your workflow There is an example of the Edit Fields and Execute Workflow nodes in the template

Are you a popular tech startup accelerator (named after a particular higher order function) overwhelmed with 1000s of pitch decks on a daily basis? Wish you could filter through them quickly using AI but the decks are unparseable through conventional means? Then you're in luck! This n8n template uses Multimodal LLMs to parse and extract valuable data from even the most overly designed pitch decks in quick fashion. Not only that, it'll also create the foundations of a RAG chatbot at the end so you or your colleagues can drill down into the details if needed. With this template, you'll scale your capacity to find interesting companies you'd otherwise miss! Requires n8n v1.62.1+ How It Works Airtable is used as the pitch deck database and PDF decks are downloaded from it. An AI Vision model is used to transcribe each page of the pitch deck into markdown. An Information Extractor is used to generate a report from the transcribed markdown and update required information back into pitch deck database. The transcribed markdown is also uploaded to a vector store to build an AI chatbot which can be used to ask questions on the pitch deck. Check out the sample Airtable here: https://airtable.com/appCkqc2jc3MoVqDO/shrS21vGqlnqzzNUc How To Use This template depends on the availability of the Airtable - make a duplicate of the airtable (link) and its columns before running the workflow. When a new pitchdeck is received, enter the company name into the Name column and upload the pdf into the File column. Leave all other columns blank. If you have the Airtable trigger active, the execution should start immediately once the file is uploaded. Otherwise, click the manual test trigger to start the workflow. When manually triggered, all "new" pitch decks will be handled by the workflow as separate executions. Requirements OpenAI for LLM Airtable For Database and Interface Qdrant for Vector Store Customising This Workflow Extend this starter template by adding more AI agents to validate claims made in the pitch deck eg. Linkedin Profiles, Page visits, Reviews etc.

This n8n workflow template lets you easily generate comprehensive FAQ (Frequently Asked Questions) content for multiple services (or any items or pages you need to add the FAQs to). Simply provide the Google Sheets document containing the items to scrape, and the workflow automatically creates detailed, AI-enhanced FAQ documents. How it works The workflow reads data from a Google Sheets document containing information about different services and categories (again, in your case - whatever objects you need). For each service and category, it generates a set of standard questions and answers covering setup, permissions, integrations, use cases, and pricing benefits. An AI model (OpenAI's GPT) is used to enhance or complete some of the answers, making the content more comprehensive and natural-sounding. The workflow formats the Q&A pairs, combining AI-generated content with predefined answers where applicable. It creates a text file (JSON) for each service or category, containing the formatted Q&A pairs. The generated files are saved to specific folders in Google Drive, organized by the type of integration (native, credential-only, non-native) or category. After processing each service or category, it updates the status in the original Google Sheets document to mark it as completed. Ideal for: Marketing teams: Rapidly create comprehensive FAQ documents for multiple products or services. Customer support: Generate consistent and detailed answers for common customer queries. Product managers: Easily maintain up-to-date documentation as products evolve. Content creators: Streamline the process of creating informative content about various offerings. Accounts required Google account (for Google Sheets and Google Drive) OpenAI API account (for AI-enhanced content generation) n8n.io account (for workflow execution) Set up instructions Set up the required credentials for Google Sheets, Google Drive, and OpenAI when you first open the workflow. Prepare your Google Sheets document with the service/category information. Here's an example of Google Sheet. Fill the "Define Sheets" node with your sheets Adjust the folder IDs in the "Prepare Job" node to match your Google Drive structure. Configure the OpenAI model settings in the "OpenAI Chat Model" node if needed. Test the workflow with a small subset of data before running it on your entire dataset. Adjust the questions asked in the "Create your Q&A templates" section After testing, activate your workflow for automated FAQ generation. 🙏 Big, big kudos to Jim Le for his ideas, input and support when building this workflow. Your approach to AI workflows is always super helpful!

Telegram Bot Starter template workflow provides a foundational setup for creating powerful Telegram bots with n8n. It handles incoming messages, photos, files, and voice notes, making it an excellent starting point for developers looking to create bots for customer engagement, support, or interactive services. Key Features: Dynamic Message Handling: Respond to text messages, photos, files, and more. Modular Design: Easily integrate additional workflows such as user registration, payment modules, or custom commands. Error Handling: Ensure the bot gracefully manages errors and user inputs. Who Can Use This Workflow? Developers looking for a quick way to build and customize Telegram bots. Businesses and service providers who need customer interaction automation. Setup Instructions: Replace Telegram credentials with your own API credentials. Customize responses for different message types (text, photo, file). If integrating with external services (like Google Sheets), update the necessary credentials and links. Extensibility: This workflow is the base for building any Telegram bot. Additional modules, such as a user registration module, payment integration, and user profile management, are available for easy connection to expand the bot’s functionality. ✍🏻Use the Telegram user registration workflow → 💵Use the Telegram Payment, Invoicing and Refund Workflow for Stars → UPDATES: 🔥 Get the most up-to-date and expanded version → Aug 24: Changed processing of system events: “new user” and ‘user who blocked bot’ events Sep 24: Improved message handler: Updated logic to handle various types of messages using Switch (text, photo, file, voice, and callback). Payment processing: Added new nodes for sending invoices and handling payments via Telegram Please reach out to Victor if you need further assistance with your n8n workflows and automations!

This n8n template is designed to assist and improve customer support team member capacity by automating the resolution of long-lived and forgotten JIRA issues. How it works Schedule Trigger runs daily to check for long-lived unresolved issues and imports them into the workflow. Each Issue is handled as a separate subworkflow by using an execute workflow node. This allows parallel processing. A report is generated from the issue using its comment history allowing the issue to be classified by AI - determining the state and progress of the issue. If determined to be resolved, sentiment analysis is performed to track customer satisfaction. If negative, a slack message is sent to escalate, otherwise the issue is closed automatically. If no response has been initiated, an AI agent will attempt to search and resolve the issue itself using similar resolved issues or from the notion database. If a solution is found, it is posted to the issue and closed. If the issue is blocked and waiting for responses, then a reminder message is added. How to use This template searches for JIRA issues which are older than 7 days which are not in the "Done" status. Ensure there are some issues that meet this criteria otherwise adjust the search query to suit. Works best if you frequently have long-lived issues that need resolving. Ensure the notion tool is configured as to not read documents you didn't intend it to ie. private and/or internal documentation. Requirements JIRA for issues management OpenAI for LLM Slack for notifications Customising this workflow Why not try classifying issues as they are created? One use-case may be for quality control such as ensuring reporting criteria is adhered to, summarising and rephrasing issue for easier reading or adjusting priority.

This workflow provides a complete solution for handling Telegram Stars payments, invoicing and refunds using n8n. It automates the process of sending invoices, managing pre-checkout approvals, recording transactions, and processing refunds for stars, making it ideal for businesses using Telegram Stars for digital payments. What are Telegram Stars? Learn more here. Key Features Payment Handling**: Automate invoice creation and sending via Telegram, with pre-checkout approval for smooth payment processing. Refund Management: Simplify the refund process using user IDs and payment charge IDs from successful **Telegram Stars transactions. Transaction Recording**: Record all payment details, such as user information and payment charge IDs, in Google Sheets for transparent financial tracking. Who Can Use This Workflow? Developers and Businesses: Looking to implement **Telegram Stars as a payment system within Telegram. Service Providers**: Managing subscriptions, donations, or digital sales through Telegram automation. Use Cases Subscription Sales Automation**: Use the workflow to issue invoices and automatically process payments for recurring subscriptions. Infopreneurs and Marketers: Use the workflow for delivering lead magnets, tripwires, and further automating sales via **Telegram Stars. Course Sales Automation**: Automate invoicing and refunds for educational platforms selling online courses. Developers and Businesses: Looking to implement **Telegram Stars as a payment system within Telegram. Service Providers**: Managing subscriptions, donations, or digital sales through Telegram automation. Online Educational Platforms**: Automate billing for courses and handle refunds easily. Setup Instructions Replace the Telegram API credentials with your bot API token from BotFather. Customize invoice details, including product name, description, and payment amount. Connect your Google Sheets for storing transaction logs. Configure refund steps for easy processing of star refunds when needed. Note: The setup is very simple—just follow the instructions provided on the yellow sticky notes within the workflow and insert your data. All other nodes are pre-configured and require no additional customization. The entire setup process takes just 1 minute. I provided a short Loom record with an explanation. Extensibility This workflow can be further customized to include user profile management, payment analytics, or integration with external services like CRMs or accounting tools. Additional modules can be easily connected to manage advanced features like Telegram User Registration. Available Templates Telegram Bot Starter Template: A foundational setup for creating custom Telegram bots. Telegram User Registration Workflow: Automate user registration and data collection via Telegram. Telegram Payment and Refund Workflow for Stars**: Streamline your Telegram payment processing with stars, invoices, and refunds. Support and Updates This workflow is supported and regularly updated to stay compatible with the latest Telegram features and n8n improvements. If you encounter any issues, technical support is available to ensure smooth integration and setup. Key terms: Telegram Stars payment workflow, Telegram refund automation, n8n payment template, Google Sheets transaction logging, Telegram bot for payments, automated refunds on Telegram, Telegram Stars invoice workflow. Please reach out to Victor if you need further assistance with your n8n workflows and automation!

Overview This workflow aims at providing data visualization to a native SQL Agent. Together, they can help with fostering data analysis and data visualization within a team. It uses the native SQL Agent that works well and adds some visualization capabilities thanks to OpenAI Structured Output and Quickchart.io. How it works The first part of the workflow is a regular SQL Agent: it connects to a Database and is able to query it and translate the response in a human format. Then, the Text Classifier is deciding if the user would benefit from a chart, supporting the SQL Agent's response. If it does, then it executes the subworkflow to dynamically generate a chart and append the chart to the response from the SQL Agent. If it doesn't, then the SQL Agent response is directly outputted. The sub-workflow calls OpenAI through the HTTP Request node to retrieve a chart definition. In the "set response" node, the chart definition is added at the end of a quickchart.io URL - the URL to the chart image. It is sent back to the AI Agent. How to use it Use an existing or create a new database. For example, I've used this Kaggle dataset and uploaded it to a Supabase DB. Add the PostgreSQL or MySQL credentials. Alternatively, you can use SQLite binary files (check this template). Activate the workflow. Start chatting with the AI SQL Agent. If the Text Classifier considers a chart would be useful, it will generate a chart in addition to the response from the SQL Agent. Notes The full Quickchart.io specifications have not been integrated, thus there are some possible glitches (e.g., due to the size of the graph, radar graphs are not displayed properly).

Enhance Security Operations with the Qualys Slack Shortcut Bot! Our Qualys Slack Shortcut Bot is strategically designed to facilitate immediate security operations directly from Slack. This powerful tool allows users to initiate vulnerability scans and generate detailed reports through simple Slack interactions, streamlining the process of managing security assessments. Workflow Highlights: Interactive Modals**: Utilizes Slack modals to gather user inputs for scan configurations and report generation, providing a user-friendly interface for complex operations. Dynamic Workflow Execution**: Integrates seamlessly with Qualys to execute vulnerability scans and create reports based on user-specified parameters. Real-Time Feedback**: Offers instant feedback within Slack, updating users about the status of their requests and delivering reports directly through Slack channels. Operational Flow: Parse Webhook Data**: Captures and parses incoming data from Slack to understand user commands accurately. Execute Actions**: Depending on the user's selection, the workflow triggers other sub-workflows like 'Qualys Start Vulnerability Scan' or 'Qualys Create Report' for detailed processing. Respond to Slack**: Ensures that every interaction is acknowledged, maintaining a smooth user experience by managing modal popups and sending appropriate responses. Setup Instructions: Verify that Slack and Qualys API integrations are correctly configured for seamless interaction. Customize the modal interfaces to align with your organization's operational protocols and security policies. Test the workflow to ensure that it responds accurately to Slack commands and that the integration with Qualys is functioning as expected. Need Assistance? Explore our Documentation or get help from the n8n Community for more detailed guidance on setup and customization. Deploy this bot within your Slack environment to significantly enhance the efficiency and responsiveness of your security operations, enabling proactive management of vulnerabilities and streamlined reporting. To handle the actual processing of requests, you will also need to deploy these two subworkflows: Qualys Start Vulnerability Scan Qualys Create Report To simplify deployment, use this Slack App manifest to quickly create an app with the correct permissions: { "display_information": { "name": "Qualys n8n Bot", "description": "n8n Integration for Qualys", "background_color": "#2a2b2e" }, "features": { "bot_user": { "display_name": "Qualys n8n Bot", "always_online": false }, "shortcuts": [ { "name": "Scan Report Generator", "type": "global", "callback_id": "qualys-scan-report", "description": "Generate a report from the latest scan to review vulnerabilities and compliance." }, { "name": "Launch Qualsys VM Scan", "type": "global", "callback_id": "trigger-qualys-vmscan", "description": "Start a Qualys Vulnerability scan from the comfort of your Slack Workspace" } ] }, "oauth_config": { "scopes": { "bot": [ "commands", "channels:join", "channels:history", "channels:read", "chat:write", "chat:write.customize", "files:read", "files:write" ] } }, "settings": { "interactivity": { "is_enabled": true, "request_url": "Replace everything inside the double quotes with your workflow webhook url, for example: https://n8n.domain.com/webhook/99db3e73-57d8-4107-ab02-5b7e713894ad"", "message_menu_options_url": "Replace everything inside the double quotes with your workflow message options webhook url, for example: https://n8n.domain.com/webhook/99db3e73-57d8-4107-ab02-5b7e713894ad"" }, "org_deploy_enabled": false, "socket_mode_enabled": false, "token_rotation_enabled": false } }

Use case Slackbots are super powerful. At n8n, we have been using them to get a lot done.. But it can become hard to manage and maintain many different operations that a workflow can do. This is the base workflow we use for our most powerful internal Slackbots. They handle a lot from running e2e tests for Github branch to deleting a user. By splitting the workflow into many subworkflows, we are able to handle each command seperately, making it easier to debug as well as support new usecases. In this template, you can find eveything to setup your own Slackbot (and I made it simple, there's only one node to configure 😉). After that, you need to build your commands directly. This bot can create a new thread on an alerts channel and respond there. Or reply directly to the user. It responds for help request to return a help page. It automatically handles unknown commands. It also supports flags and environment variables. For example /cloudbot-test info mutasem --full-info -e env=prod would give you the following info, when calling subworkflow. How to setup Add Slack command and point it up to the webhook. For example. Add the following to the Set config node alerts_channel with alerts channel to start threads on instance_url with this instance url to make it easy to debug slack_token with slack bot token to validate request slack_secret_signature with slack secret signature to validate request help_docs_url with help url to help users understand the commands Build other workflows to call and add them to commands in Set Config. Each command must be mapped to a workflow id with an Execute Workflow Trigger node Activate workflow 🚀 How to adjust Add your own commands. Depending on your need, you might need to lock down who can call this.

This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: Survey Insights Customer Insights Community Insights This template demonstrates the Customer Insights scenario where Trustpilot reviews can be quickly grouped by similarity and an AI agent can generate insights on those groupings. With this workflow, marketers can save days and even weeks of work breaking down their own or competitor reviews and identify frequently mentioned positives and negatives. Sample Output: https://docs.google.com/spreadsheets/d/e/2PACX-1vQ6ipJnXWXgr5wlUJnhioNpeYrxaIpsRYZCwN3C-fFXumkbh9TAsA_JzE0kbv7DcGAVIP7az0L46_2P/pubhtml How it works Trustpilot reviews are scraped for a particular company using the HTTP request node. Reviews are then inserted into a Qdrant collection carefully tagged with the question and Trustpilot metadata. Reviews are fetched and put through a clustering algorithm using the Python Code node. The Qdrant points are returned in clustered groups. Each group is looped to fetch the payloads of the points and feed them to the AI agent to summarise and generate insights for. The resulting insights and raw responses are then saved to the Google Spreadsheet for further analysis by the marketer. Requirements Qdrant Vectorstore for storing embeddings. OpenAI account for embeddings and LLM. Customising the Template Adjust clustering parameters which make sense for your data. Consider expanding date range of reviews for insights over common intervals: 3mth, 6mth and YTD.

Enhance Security Operations with the Venafi Slack CertBot! Venafi Presentation - Watch Video Our Venafi Slack CertBot is strategically designed to facilitate immediate security operations directly from Slack. This tool allows end users to request Certificate Signing Requests that are automatically approved or passed to the Secops team for manual approval depending on the Virustotal analysis of the requested domain. Not only does this help centralize requests, but it helps an organization maintain the security certifications by allowing automated processes to log and analyze requests in real time. Workflow Highlights: Interactive Modals**: Utilizes Slack modals to gather user inputs for scan configurations and report generation, providing a user-friendly interface for complex operations. Dynamic Workflow Execution**: Integrates seamlessly with Venafi to execute CSR generation and if any issues are found, AI can generate a custom report that is then passed to a slack teams channel for manual approval with the press of a single button. Operational Flow: Parse Webhook Data**: Captures and parses incoming data from Slack to understand user commands accurately. Execute Actions**: Depending on the user's selection, the workflow triggers other actions within the flow like automatic Virustotal Scanning. Respond to Slack**: Ensures that every interaction is acknowledged, maintaining a smooth user experience by managing modal popups and sending appropriate responses. Setup Instructions: Verify that Slack and Qualys API integrations are correctly configured for seamless interaction. Customize the modal interfaces to align with your organization's operational protocols and security policies. Test the workflow to ensure that it responds accurately to Slack commands and that the integration with Qualys is functioning as expected. Need Assistance? Explore Venafi's Documentation or get help from the n8n Community for more detailed guidance on setup and customization. Deploy this bot within your Slack environment to significantly enhance the efficiency and responsiveness of your security operations, enabling proactive management of CSR's.

This workflow will backup your workflows to Github. It uses the public api to export all of the workflow data using the n8n node. It then loops over the data checks in Github to see if a file exists that uses the workflow name. Once checked it will then update the file on Github if it exists, Create a new file if it doesn't exist and if it's the same it will ignore the file. Config Options repo_owner - Github owner repo_name - Github repository name repo_path - Path within the Github repository >This workflow has been updated to use the n8n node and the code node so requires at least version 0.198.0 of n8n

This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: Survey Insights Customer Insights Community Insights This template demonstrates the Survey Insights scenario where survey participant responses can be quickly grouped by similarity and an AI agent can generate insights on those groupings. With this workflow, researchers can save days and even weeks of work breaking down cohorts of participants and identify frequently mentioned positives and negatives. Sample Output: https://docs.google.com/spreadsheets/d/e/2PACX-1vT6m8XH8JWJTUAfwojc68NAUGC7q0lO7iV738J7aO5fuVjiVzdTRRPkMmT1C4N8TwejaiT0XrmF1Q48/pubhtml# How it works All survey questions and responses are imported from a Google Sheet. Responses are then inserted into a Qdrant collection carefully tagged with the question and survey metadata. For each question, all relevant response are put through a clustering algorithm using the Python Code node. The Qdrant points are returned in clustered groups. Each group is looped to fetch the payloads of the points and feed them to the AI agent to summarise and generate insights for. The resulting insights and raw responses are then saved to the Google Spreadsheet for further analysis by the researcher. Requirements Survey data and format as shown in the attached google sheet. Qdrant Vectorstore for storing embeddings. OpenAI account for embeddings and LLM. Customising the Template Adjust clustering parameters which make sense for your data. Add more clusters for open-ended questions and less clusters when responses are multiple choice.

This is the fourth workflow for the Mattermost Standup Bot. This workflow sends the team a message every morning to ask them three standup questions. What have you accomplished since your last report? What do you want to accomplish until your next report? Is anything blocking your progress? Once answered, the answers are sent to a Mattermost channel. The "Read Config" nodes will need to be updated to point to the ID of the "Standup Bot - Read Config" workflow and the "Override Config" node will need to point to "Standup Bot - Override Config"

This workflow allows you to send multi-step email campaigns using n8n, Gmail and Google Sheets. You define a sequence of emails, and a list of email addresses to send them to. The first email is sent to everyone, but the later emails in the sequence are only sent if no-one has replied to the thread yet. This means you only need to worry about replying to people who respond to your email, not chasing people who don’t. Requirements A list of emails in a Google sheet. You can define extra info that will be available to your email templates (e.g. name, company, etc.) A sequence of emails to send, plus how long to wait to send each one, e.g. On day 0:** “Hi, {name} — nice to meet you at the conference. Was wondering whether {company} would be interested in a quick call about X?” On day 3:** “Hi, {name}, just wanted to check in on this. Let me know if there’s any interest!” On day 7:** “{name}, just wanted to give this one last try” A Gmail account (although you could also swap out that part for any other email service) How it works When sending the emails, n8n inserts a hidden attribute in each one that tags it as being part of the email campaign. It then regularly looks through the email threads with that tag and checks whether: No-one has replied yet It’s time to send the next message The workflow doesn’t send emails on weekends. Notes This workflow is not intended for spam! Please use responsibly You can use this workflow for multiple different campaigns by making copies of the workflow and changing the sequence / Google Sheet that it uses