Workflow Templates

How it works: The workflow starts by sending a request to a website to retrieve its HTML content. It then parses the HTML extracting the relevant information The extracted data is storted and converted into a CSV file. The CSV file is attached to an email and sent to your specified address. The data is simultaneously saved to both Google Sheets and Microsoft Excel for further analysis or use. Set-up steps: Change the website to scrape in the "Fetch website content" node Configure Microsoft Azure credentials with Microsoft Graph permissions (required for the Save to Microsoft Excel 365 node) Configure Google Cloud credentials with access to Google Drive, Google Sheets and Gmail APIs (the latter is required for the Send CSV via e-mail node).

What this workflow does Runs automatically every Monday morning at 8 AM Collects your Meta Ads data from the last 7 days for a given account (date range is configurable) Formats the data, aggregating it at the campaign, ad set, and ad levels Generates AI-driven analysis and insights on your results, providing actionable recommendations Renders the report as a visually appealing PDF with charts and tables Sends the report via Slack (you can also add email or WhatsApp) A sample for the first page of the report: Setup Guide Create an account of pdforge and use the pre-made Meta Ads template. Connect Meta Ads, OpenAI and Slack to n8n Set your Ad Account Id and date range (choose from 'last_7d', 'last_14d', 'last30d') (opcional) Customize the scheduling date and time Requirements Meta Ads (via Facebook Graph API): Documentation pdforge access: Integration guide AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) Slack acces (via OAuth2): Documentation Feel free to contact me via Linkedin, if you have any questions! 👋🏻

This workflow automatically generates draft replies in Gmail. It's designed for anyone who manages a high volume of emails or often face writer's block when crafting responses. Since it doesn't send the generated message directly, you're still in charge of editing and approving emails before they go out. How It Works: Email Trigger: activates when new emails reach the Gmail inbox Assessment: uses OpenAI gpt-4o and a JSON parser to determine if a response is necessary. Reply Generation: crafts a reply with OpenAI GPT-4 Turbo Draft Integration: after converting the text to html, it places the draft into the Gmail thread as a reply to the first message Set Up Overview (~10 minutes): OAuth Configuration (follow n8n instructions here): Setup Google OAuth in Google Cloud console. Make sure to add Gmail API with the modify scope. Add Google OAuth credentials in n8n. Make sure to add the n8n redirect URI to the Google Cloud Console consent screen settings. OpenAI Configuration: add OpenAI API Key in the credentials Tweaking the prompt: edit the system prompt in the "Generate email reply" node to suit your needs Detailed Walkthrough Check out this blog post where I go into more details on how I built this workflow. Reach out to me here if you need help building automations for your business.

This workflow enables you to automate the daily monitoring of how an AI model (like ChatGPT) responds to specific queries relevant to your market. It identifies mentions of your brand and predefined competitors, logs detailed interactions in Google Sheets, and delivers a comprehensive email report. Main Use Cases Monitor how your brand is mentioned by AI in response to relevant user queries. Track mentions of key competitors to understand AI's comparative positioning. Gain insights into AI's current knowledge and portrayal of your brand and market landscape. Automate daily intelligence gathering on AI-driven brand perception. How it works The workflow operates as a scheduled process, organized into these stages: Configuration & Scheduling Triggers daily (or can be run manually). Key variables are defined within the workflow: your brand name (e.g., "YourBrandName"), a list of queries to ask the AI, and a list of competitor names to track in responses. AI Querying For each predefined query, the workflow sends a request to the OpenAI ChatGPT API (via an HTTP Request node). Response Analysis Each AI response is processed by a Code node to: Check if your brand name is mentioned (case-insensitive). Identify if any of the listed competitors are mentioned (case-insensitive). Extract the core AI response content (limited to 500 characters for brevity in logs/reports). Data Logging to Google Sheets Detailed results for each query—including timestamp, date, the query itself, query index, your brand name, the AI's response, whether your brand was mentioned, and any errors—are appended to a specified Google Sheet. Email Report Generation A comprehensive HTML email report is compiled. This report summarizes: Total queries processed, number of times your brand was mentioned, total competitor mentions, and any errors encountered. A summary of competitor mentions, listing each competitor and how many times they were mentioned. A detailed table listing each query, whether your brand was mentioned, and which competitors (if any) were mentioned in the AI's response. Automated Reporting The generated HTML email report is sent to specified recipients, providing a daily snapshot of AI interactions. Summary Flow: Schedule/Workflow Trigger → Initialize Brand, Queries, Competitors (in Code node) → For each Query: Query ChatGPT API → Process AI Response (Check for Brand & Competitor Mentions) → Log Results to Google Sheets → Generate Consolidated HTML Email Report → Send Email Notification Benefits: Fully automated daily monitoring of AI responses concerning your brand and competitors. Provides objective insights into how AI models are representing your brand in user interactions. Delivers actionable competitive intelligence by tracking competitor mentions. Centralized logging in Google Sheets for historical analysis and trend spotting. Easily customizable with your specific brand, queries, competitor list, and reporting recipients.

Hello there! This is a supporting workflow for an Airtable Base that handles Recurring Tasks. The objective of the workflow is to handle creating tasks on a recurring basis depending on the Airtable Setup You can access that Airtable Template here for complete context- Airtable Universe The functionality of the workflow can be easliy adapted to any data source. Feel free to contact us with any doubts or questions at http://sidetool.co ​ Use this as is, or adapted to your existing Airtable Base – embrace automated simplicity! 🚀🌟

For Who? Content Creators Youtube Automation Marketing Team How it works? 1 - Enter the ID of the YTB channel to trigger the workflow when a new video is posted 2 - Apify scrape the last YTB video of the channel 3 - Wait until the dataset is completed in Apify and get it 4 - Verify if Metadata are not already generated and generate them with LLM 5 - Format all the data created and update YTB Video 📺 YouTube Video Tutorial: SETUP Setup Input YTB Chanel : Go to the channel's page on YouTube, and look at the URL of the page. The channel ID is the value that comes after channel/ in the URL. Add it after "?channel_id=" You can also use free tools available to retrieve channel ID. Setup Output YTB Video Update : Connect your YTB account to your n8n instance thanks to the Google Cloud Console. You can find tutorials by typing "youtube api Oauth" on Google. APIs : For the following third-party integrations, replace ==[YOUR_API_TOKEN]== with your API Token or connect your account via Client ID / Secret to your n8n instance : Apify : https://docs.apify.com/api/v2/getting-started Youtube : https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.youtube/?utm_source=n8n_app&utm_medium=node_settings_modal-credential_link&utm_campaign=n8n-nodes-base.youTube#templates-and-examples 👨‍💻 More Workflows : https://n8n.io/creators/nasser/

How it works The workflow loads a list of test cases from a Google Sheet (previous results stored from an LLM) For each test case, we execute a call to an LLM judge in parallel (using HTTP Request + Webhook nodes) The judge uses the Input, Output, and Reference Answer fields from the spreadsheet to mark each LLM response as Pass/Fail The results are logged into a separate sheet in the same Sheets file. Set up steps: Add your credentials for Google Sheets and OpenRouter (or replace the OpenRouter node with your favourite chat model). Make a copy of the example Sheet to populate it with you own test data. Run the workflow with the Execute Workflow button next to the Manual Trigger node.

Who it's for This workflow is for anyone who wants to build an automated, AI-enhanced reading list. Ideal for: Knowledge workers and researchers who collect and organize articles Students managing study materials Productivity hackers who use Telegram and Notion for personal knowledge management Anyone using the AI-Enhanced Knowledge Base Tracker Notion Template How it works This workflow takes any article link sent to your Telegram bot and automatically: Parses the article into a clean title and body Uses OpenAI to generate a 1–2 sentence highlight and topic tag Saves it into your Notion database Sends a confirmation message with the highlight and Notion link back to Telegram Main steps: Telegram Trigger - Listens for incoming message containing an article link. Fetch Article Title & Content - Calls the article-parser-api deployed on Vercel to fetch and parse the article content into structured JSON (title and content). Generate Highlight + Tag (AI Agent) - Processes the parsed content to generate Highlight and Type tag values. Structured Metadata for Notion - Adjusts the extracted data before saving it to Notion. Save Article to Notion Database - Inserts the article and generated metadata into your Notion knowledge base. Confirm Save via Telegram - Sends a confirmation message and the Notion page link back to the Telegram bot chat after the entry is created. Setup Create and connect your API credentials: Telegram Bot OpenAI API Key Notion Integration Deploy the article parser: Use this repo: article-parser-api Deploy it to Vercel or any serverless environment Link your Notion database: Duplicate the AI‑Enhanced Knowledge Base Tracker Copy the database URL and connect it in the Notion node Test your workflow: Click Execute workflow Send an article link to your Telegram bot Once verified, activate the workflow so it runs automatically Requirements Telegram bot token OpenAI API key Notion integration and shared database A deployed article parser (e.g., article-parser-api) Optional customization Edit the AI Agent prompt to change tone or tagging style Add filtering or additional fields in the Edit Fields node Trigger from other sources (e.g., Slack or Email)

How It works It's very important to come prepared to Sales calls. This often means a lot of manual research about the person you're calling with. This workflow delivers the latest news about businesses you are about to interact with each day. Scans Your Calendar**: Each morning, it reviews your Google Calendar for any scheduled meetings or calls with companies. Fetches Latest News**: For each identified company, it searches the web for the most recent and relevant news articles using newsapi.org Delivers Insights**: You receive personalized emails via Gmail, each dedicated to a company you're meeting with that day, containing a curated list of news headlines, brief descriptions, and direct links to full articles. Setup steps The workflow requires you to have the following accounts set up in their respective nodes: Google Calendar GMail Besides those, there are a few parameters in the node called Setup that can be used to tweak the workflow:

What it is Chat with your event schedule from Google Sheets in Telegram: "When is the next meetup?" "How many events are there next month?" "Who presented most often?" "Which future meetups have no presenters yet?" This workflow lets you chat with a telegram bot about past, present and future events that are scheduled in a Google Spreadsheet. (Info: This proof-of-concept was created as a demo for a hackathon of an AI & Developer Meetup in Da Nang (Vietnam) that uses a telegram group to organize) Who it is for If you want an easy way for your audience to get information about your events, you can us this workflow for the same purpose, or easily adapt it to your needs and different use-cases where you want to query smaller amounts of tabular data in natural language. How it works Upon getting triggered by a chat message to a telegram bot, the schedule of meetups is retrieved from Google Spreadsheets, converted into a markdown table syntax and fed into the system prompt of an LLM (we're using OpenRouter in this example), whose output is posted back as answer into the same telegram chat. Setup steps TO REVIEWING IN ACTION As the reviewer of this workflow, you can temporarily use it via an existing telegram bot, simply point your telegram client to https://t.me/AiDaNangBot and start to ask questions like: "When is the next meetup?" "What future meetings do not have presenters?" "Who presented on Future of Human Relationships?" To build upon this workflow: Import the workflow Customize the Google Docs credentials for your individual access Create a telegram bot and connect it to the workflow by entering its API token into the credentials used in the telegram trigger node In the "Settings" node, replace the "scheduleURL" with the URL of your own copy of the Google Spreadsheet or a copy of the Event Schedule Template Sheet to spin off your own – whereby the structure of the spreadsheet doesn't matter, it's just important that you semantically structure your information in dedicated columns clearly labeled in the header row.

Use case When working with multiple teams, bugs must get in front of the right team as quickly as possible to be resolved. Normally this includes a manual grooming of new bugs that have arrived in your ticketing system (in our case Linear). We found this way too time-consuming. That's why we built this workflow. What this workflow does This workflow triggers every time a Linear issue is created or updated within a certain team. For us at n8n, we created one general team called Engineering where all bugs get added in the beginning. The workflow then checks if the issue meets the criteria to be auto-moved to a certain team. In our case, that means that the description is filled, that it has the bug label, and that it's in the Triage state. The workflow then classifies the bug using OpenAI's GPT-4 model before updating the team property of the Linear issue. If the AI fails to classify a team, the workflow sends an alert to Slack. Setup Add your Linear and OpenAi credentials Change the team in the Linear Trigger to match your needs Customize your teams and their areas of responsibility in the Set me up node. Please use the format Teamname. Also, make sure that the team names match the names in Linear exactly. Change the Slack channel in the Set me up node to your Slack channel of choice. How to adjust it to your needs Play around with the context that you're giving to OpenAI, to make sure the model has enough knowledge about your teams and their areas of responsibility Adjust the handling of AI failures to your needs 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're using an automation that enables everyone to add new bugs easily with the right data via a /bug command in Slack (check out this template if that's interesting to you) This workflow was built using n8n version 1.30.0

This n8n workflow demonstrates how to automate oftern time-consuming form filling tasks in the early stages of the tendering process; the Request for Proposal document or "RFP". It does this by utilising a company's knowledgebase to generating question-and-answer pairs using Large Language Models. How it works A buyer's RFP is submitted to the workflow as a digital document that can be parsed. Our first AI agent scans and extracts all questions from the document into list form. The supplier sets up an OpenAI assistant prior loaded with company brand, marketing and technical documents. The workflow loops through each of the buyer's questions and poses these to the OpenAI assistant. The assistant's answers are captured until all questions are satisified and are then exported into a new document for review. A sales team member is then able to use this document to respond quickly to the RFP before their competitors. Example Webhook Request curl --location 'https://<n8n_webhook_url>' \ --form 'id="RFP001"' \ --form 'title="BlueChip Travel and StarBus Web Services"' \ --form 'reply_to="jim@example.com"' \ --form 'data=@"k9pnbALxX/RFP Questionnaire.pdf"' Requirements An OpenAI account to use AI services. Customising the workflow OpenAI assistants is only one approach to hosting a company knowledgebase for AI to use. Exploring different solutions such as building your own RAG-powered database can sometimes yield better results in terms of control of how the data is managed and cost.