Workflow Templates

This n8n template demonstrates how to audit your brand’s visibility across multiple AI systems and automatically log the results to Google Sheets. It sends the same prompt to OpenAI, Perplexity, and (optionally) a ChatGPT web actor, then runs sentiment and brand-hierarchy analysis on the responses. Use cases are many: benchmark how often (and how positively) your brand appears in AI answers, compare responses across models, and build a repeatable “AI visibility” report for marketing and comms teams. 💡 Good to know You’ll bring your own API keys for OpenAI and Perplexity. Usage costs depend on your providers’ pricing. The optional APIfy actor automates the ChatGPT web UI and may violate terms of service. Use strictly at your own risk. ⁉ How it works A Manual Trigger starts the workflow (you can replace it with any trigger). Input prompts are read from a Google Sheet (or you can use the included “manual input” node). The prompt is sent to three tools: -- OpenAI (via API) to check baseline LLM knowledge. -- Perplexity (API) to retrieve an answer with citations. -- Optionally, an APIfy actor that scrapes a ChatGPT response (web interface). Responses are normalized and mapped (including citations where available). An LLM-powered sentiment pass classifies each response into: -- Basic Polarity: Positive, Neutral, or Negative -- Emotion Category: Joy, Sadness, Anger, Fear, Disgust, or Surprise -- Brand Hierarchy: ordered list such as Nike>Adidas>Puma The consolidated record (Prompt, LLM, Response, Brand mentioned flag, Brand Hierarchy, Basic Polarity, Emotion Category, Source 1–3/4) is appended to your “Output many models” Google Sheet. A simplified branch shows how to take a single response and push it to a separate sheet. 🗺️ How to use Connect your Google Sheets OAuth and create two tabs: -- Input: a single “Prompt” column -- Output: columns for Prompt, LLM, Response, Brand mentioned, Brand Hierarchy, Basic Polarity, Emotion Category, Source 1, Source 2, Source 3, Source 4 Add your OpenAI and Perplexity credentials. (Optional) Add an APIfy credential (Query Auth with token) if you want the ChatGPT web actor path. Run the Manual Trigger to process prompts in batches and write results to Sheets. Adjust the included “Limit for testing” node or remove it to process more rows. ⚒️ Requirements OpenAI API access (e.g., GPT-4.1-mini / GPT-5 as configured in the template) Perplexity API access (model: sonar) Google Sheets account with OAuth connected in n8n (Optional) APIfy account/token for the ChatGPT web actor 🎨 Customising this workflow Swap the Manual Trigger for a webhook or schedule to run audits automatically. Extend the sentiment analyzer instructions to include brand-specific rules or compliance checks. Track more sources (e.g., additional models or vertical search tools) by duplicating the request→map→append pattern. Add scoring (e.g., “visibility score” per prompt) and charts by pointing the output sheet into Looker Studio or a BI tool.

Automated SSL/TLS Certificate Expiry Report for AWS > Automatically generates a weekly report of all AWS ACM certificates, including status, expiry dates, and renewal eligibility. The workflow formats the data into both Markdown (for PDF export to Slack) and HTML (for email summary), helping teams stay on top of certificate compliance and expiration risks. Who’s it for This workflow is designed for DevOps engineers, cloud administrators, and compliance teams who manage AWS infrastructure and need automated weekly visibility into the status of their SSL/TLS certificates in AWS Certificate Manager (ACM). It's ideal for teams that want to reduce the risk of expired certs, track renewal eligibility, and maintain reporting for audit or operational purposes. How it works / What it does This n8n workflow performs the following actions on a weekly schedule: Trigger: Automatically runs once a week using the Weekly schedule trigger. Fetch Certificates: Uses Get many certificates action from AWS Certificate Manager to retrieve all certificate records. Parse Data: Processes and reformats certificate data (dates, booleans, SANs, etc.) into a clean JSON object. Generate Reports: 📄 Markdown Report: Uses the Certificate Summary Markdown Agent (OpenAI) to generate a Markdown report for PDF export. 🌐 HTML Report: Uses the Certificate Summary HTML Agent to generate a styled HTML report for email. Deliver Reports: Converts Markdown to PDF and sends it to Slack as a file. Sends HTML content as a formatted email. How to set up Configure AWS Credentials in n8n to allow access to AWS ACM. Create a new workflow and use the following nodes in sequence: Schedule Trigger: Weekly (e.g., every Monday at 08:00 UTC) AWS ACM → Get many certificates Function Node → Parse ACM Data: Converts and summarizes certificate metadata OpenAI Chat Node (Markdown Agent) with a system/user prompt to generate Markdown Configure Metadata → Define file name and MIME type (.md) Create document file → Converts Markdown to document stream Convert to PDF Slack Node → Upload the PDF to a channel (Optional) Add a second OpenAI Chat Node for generating HTML and sending it via email Connect Output: Markdown report → Slack file upload HTML report → Email node with embedded HTML Requirements 🟩 n8n instance (self-hosted or cloud) 🟦 AWS account with access to ACM 🟨 OpenAI API key (for ChatGPT Agent) 🟥 Slack webhook or OAuth credentials (for file upload) 📧 Email integration (e.g., SMTP or SendGrid) 📝 Permissions to write documents (Google Drive / file node) How to customize the workflow Change report frequency**: Adjust the Weekly schedule trigger to daily or monthly as needed. Filter certificates**: Modify the function node to only include EXPIRED, IN_USE, or INELIGIBLE certs. Add tags or domains to include/exclude. Add visuals**: Enhance the HTML version with colored rows, icons, or company branding. Change delivery channels**: Replace Slack with Microsoft Teams, Discord, or Telegram. Send Markdown as email attachment instead of PDF. Integrate ticketing**: Create a JIRA/GitHub issue for each certificate that is EXPIRED or INELIGIBLE.

This workflow checks if the task in Todoist has a specific label and based on that creates a new database page in Notion. Prerequisites Todoist account and Todoist credentials Notion account and Notion credentials How it works To start the workflow add a task to Todoist and mark it with a label, e.g. “send-to-n8n”. Wait a maximum of 30 seconds. Todoist node identifies the tasks marked as “send-to-n8n”. Notion node creates a new Notion database page. Notice Notion has a new task now with the same name as in Todoist.

⚠️ DISCLAIMER: This workflow uses the HDW LinkedIn community node, which is only available on self-hosted n8n instances. It will not work on n8n.cloud. Overview This workflow automates the entire LinkedIn lead generation process from finding prospects that match your Ideal Customer Profile (ICP) to sending personalized messages. It uses AI to analyze lead data, score potential clients, and prioritize your outreach efforts. Key Features AI-Driven Lead Generation**: Convert ICP descriptions into LinkedIn search parameters Comprehensive Data Enrichment**: Analyze company websites, LinkedIn posts, and news Intelligent Lead Scoring**: Prioritize leads based on AI analysis of intent signals Automated Outreach**: Connect with prospects and send personalized messages Requirements Self-hosted n8n instance with the HDW LinkedIn community node installed OpenAI API access (for GPT-4o) Google Sheets access HDW API key (available at app.horizondatawave.ai) LinkedIn account Setup Instructions 1. Install Required Nodes Ensure the HDW LinkedIn community node is installed on your n8n instance Command: npm install n8n-nodes-hdw (or use this instruction) 2. Configure Credentials OpenAI**: Add your OpenAI API key Google Sheets**: Set up Google account access HDW LinkedIn**: Configure your API key from horizondatawave.ai 3. Set Up Google Sheet Create a new Google Sheet with the following columns (or copy template): Name, URN, URL, Headline, Location, Current company, Industry, etc. The workflow will populate these columns automatically 4. Customize Your ICP Use chat to provide the AI Agent with your Ideal Customer Profile Example: "Target marketing directors at SaaS companies with 50-200 employees" 5. Adjust Scoring Criteria Modify the lead scoring prompt in the "Company Score Analysis" node to match your specific product/service Tune the evaluation criteria based on your unique business needs 6. Configure Message Templates Update the HDW LinkedIn Send Message node with your custom message How It Works ICP Translation: AI converts your ICP description into LinkedIn search parameters Lead Discovery: Workflow searches LinkedIn using these parameters Data Collection: Results are saved to Google Sheets Enrichment: System collects additional data about each lead: Company website analysis Lead's LinkedIn posts Company's LinkedIn posts Recent company news Intent Analysis: AI analyzes all data to identify buying signals Lead Scoring: Leads are scored on a 1-10 scale based on likelihood of interest Connection Requests: Top-scoring leads receive connection requests Follow-Up: When connections are accepted, automated messages are sent Customization Search Parameters**: Adjust the AI Agent prompt to refine your target audience Scoring Criteria**: Modify scoring prompts to highlight indicators relevant to your product Message Content**: Update message templates for personalized outreach Schedule**: Configure when connection requests and messages are sent Rate Limits & Best Practices LinkedIn has connection request limits (approximately 100-200 per week) The workflow includes safeguards to avoid exceeding these limits Consider spacing your outreach for better response rates Note: Always use automation tools responsibly and in accordance with LinkedIn's terms of service.

This n8n workflow assists property managers and surveyors by reducing the time and effort it takes to complete property inventory surveys. In such surveys, articles and goods within a property may need to be captured and reported as a matter of record. This can take a sizable amount of time if the property or number of items is big enough. Our solution is to delegate this task to a capable AI Agent who can identify and fill out the details of each item automatically. How it works An AirTable Base is used to capture just the image of an item within the property Our workflow monitoring this AirTable Base sends the photo to an AI image recognition model to describe the item for purpose of identification. Our AI agent uses this description and the help of Google's reverse image search in an attempt to find an online product page for the item. If found, the product page is scraped for the item's specifications which are then used to fill out the rest of the details of the item in our Airtable. Requirements Airtable for capturing photos and product information OpenAI account to for image recognition service and AI for agent SerpAPI account for google reverse image search. Firecrawl.dev account for webspacing. Customising this workflow Try building an internal inventory database to query and integrate into the workflow. This could save on costs by avoiding fetching new each time for common items.

How It Works This template is a complete, hands-on tutorial for building a RAG (Retrieval-Augmented Generation) pipeline. In simple terms, you'll teach an AI to become an expert on a specific topic—in this case, the official n8n documentation—and then build a chatbot to ask it questions. Think of it like this: instead of a general-knowledge AI, you're building an expert librarian. 🔧 Workflow Overview The workflow is split into two main parts: Part 1: Indexing the Knowledge (📚 Building the Library) This is a one-time process you run manually. The workflow will: Automatically scrape all pages of the n8n documentation. Break them down into small, digestible chunks. Use an AI model to create a numerical representation (an embedding) for each chunk. Store these embeddings in n8n's built-in Simple Vector Store. > This is like a librarian reading every book and creating a hyper-detailed index card for every paragraph. > ⚠️ Important: This in-memory knowledge base is temporary. It will be erased if you restart your n8n instance. You'll need to run the indexing process again in that case. Part 2: The AI Agent (🧠 The Expert Librarian) This is the chat interface. When you ask a question: The AI agent doesn't guess the answer. It searches the knowledge base to find the most relevant “index cards” (chunks). It feeds those chunks to a language model (Gemini) with strict instructions: > “Answer the user's question using ONLY this information.” This ensures answers are accurate, factual, and grounded in your documents. 🚀 Setup Steps > Total setup time: ~2 minutes > Indexing time: ~15–20 minutes This template uses n8n’s built-in tools, so no external database is needed. 1. Configure OpenAI Credentials You’ll need an OpenAI API key (for GPT models). In your n8n workflow: Go to any of the three OpenAI nodes (e.g., OpenAI Chat Model). Click the Credential dropdown → + Create New Credential. Enter your OpenAI API key and save. 2. Apply Credentials to All Nodes Your new credential is now saved. Go to the other two OpenAI nodes (e.g., OpenAI Embeddings) and select the newly created credential from the dropdown. 3. Build the Knowledge Base Find the Start Indexing manual trigger node (top-left of the workflow). Click the Execute Workflow button to start indexing. > ⚠️ Be patient: This takes 15–20 minutes to scrape and process the full documentation. > You only need to do this once per n8n session. 4. Chat With Your Expert Agent After indexing completes, activate the entire workflow (toggle at the top). Open the RAG Chatbot chat trigger node (bottom-left). Copy its Public URL. Open it in a new tab and ask questions about n8n! Example questions: "How does the IF node work?" "What is a sub-workflow?" 👤 Credits All credits go to Lucas Peyrin 🔗 lucaspeyrin on n8n.io

This workflow is a highly advanced multimodal AI assistant designed to operate through WhatsApp. It can understand and respond to text, images, voice messages, and PDF documents by combining OpenAI models with smart logic to adapt to the content received. 🎯 Core Features 📥 1. Automatic Message Type Detection Using the Input type node, the bot detects whether the user has sent: Text Voice messages Images Files (PDF) Other unsupported content 💬 2. Smart Text Message Handling Text messages are processed by an OpenAI GPT-4o-mini agent with a customized system prompt. Replies are concise, accurate, and formatted for mobile readability. 🖼️ 3. Image Analysis & Description Images are downloaded, converted to base64, and analyzed by an image-aware AI model. The output is a rich, structured description, designed for visually impaired users or visual content interpretation. 🎙️ 4. Voice Message Transcription & Reply Audio messages are downloaded and transcribed using OpenAI Whisper. The transcribed text is analyzed and answered by the AI. Optionally, the AI reply can be converted back to voice using OpenAI's text-to-speech, and sent as an audio message. 📄 5. PDF Document Extraction & Summary Only PDFs are allowed (filtered via MIME type). The document’s content is extracted and combined with the user's message. The AI then provides a relevant summary or answer. 🧠 6. Contextual Memory Each user has a personalized session ID with a memory window of 10 interactions. This ensures a more natural and contextual conversation flow. How It Works Thisworkflow is designed to handle incoming WhatsApp messages and process different types of inputs (text, audio, images, and PDF documents) using AI-powered analysis. Here’s how it functions: Trigger: The workflow starts with the **WhatsApp Trigger node, which listens for incoming messages (text, audio, images, or documents). Input Routing: The **Input type (Switch node) checks the message type and routes it to the appropriate processing branch: Text: Directly forwards the message to the AI agent for response generation. Audio: Downloads the audio file, transcribes it using OpenAI, and sends the transcription to the AI agent. Image: Downloads the image, analyzes it with OpenAI’s GPT-4 model, and generates a detailed description. PDF Document: Downloads the file, extracts text, and processes it with the AI agent. Unsupported Formats: Sends an error message if the input is not supported. AI Processing: The **AI Agent1 node, powered by OpenAI, processes the input (text, transcribed audio, image description, or PDF content) and generates a response. Response Handling**: For audio inputs, the AI’s response is converted back into speech (using OpenAI’s TTS) and sent as a voice message. For other inputs, the response is sent as a text message via WhatsApp. Memory: The **Simple Memory node maintains conversation context for follow-up interactions. Setup Steps To deploy this workflow in n8n, follow these steps: Configure WhatsApp API Credentials: Set up WhatsApp Business API credentials (Meta Developer Account). Add the credentials in the WhatsApp Trigger, Get Image/Audio/File URL, and Send Message nodes. Set Up OpenAI Integration: Provide an OpenAI API key in the Analyze Image, Transcribe Audio, Generate Audio Response, and AI Agent1 nodes. Adjust Input Handling (Optional): Modify the Switch node ("Input type") to handle additional message types if needed. Update the "Only PDF File" IF node to support other document formats. Test & Deploy: Activate the workflow and test with different message types (text, audio, image, PDF). Ensure responses are correctly generated and sent back via WhatsApp. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Purpose This workflow adds the capability to build a RAG on living data. In this case Notion is used as a Knowledge Base. Whenever a page is updated, the embeddings get upserted in a Supabase Vector Store. It can also be fairly easily adapted to PGVector, Pinecone, or Qdrant by using a custom HTTP request for the latter two. Demo How it works A trigger checks every minute for changes in the Notion Database. The manual polling approach improves accuracy and prevents changes from being lost between cached polling intervals. Afterwards every updated page is processed sequentially The Vector Database is searched using the Notion Page ID stored in the metadata of each embedding. If old entries exist, they are deleted. All blocks of the Notion Database Page are retrieved and combined into a single string The content is embedded and split into chunks if necessary. Metadata, including the Notion Page ID, is added during storage for future reference. A simple Question and Answer Chain enables users to ask questions about the embedded content through the integrated chat function Prerequisites To setup a new Vector Store in Supabase, follow this guide Prepare a simple Database in Notion with each Database Page containing at least a title and some content in the blocks section. You can of course also connect this to an existing Database of your choice. Setup Select your credentials in the nodes which require those If you are on an n8n cloud plan, switch to the native Notion Trigger by activating it and deactivating the Schedule Trigger along with its subsequent Notion Node Choose your Notion Database in the first Node related to Notion Adjust the chunk size and overlap in the Token Splitter to your preference Activate the workflow How to use Populate your Notion Database with useful information and use the chat mode of this workflow to ask questions about it. Updates to a Notion Page should quickly reflect in future conversations.

Generate AI video clips to promote products, services or events on social media. Use gotoHuman as an interface to control and supervise each step of the workflow to create content that's actually worth posting. How it works gotoHuman will show the workflow steps that need approval or input in its' inbox and notify you via email or Slack. We choose from different topics for our post suggested by AI We select the image style, a product to show, and review an AI-generated tag line We use Fal.ai to generate an image that serves as a reference image for our video clip. And we use Cloudinary to add an overlay for the tag line. We review the image in gotoHuman and can iterate on it by retrying or even changing the prompt. We review the video clip that's generated with Fal.ai based on the approved image and can, again, retry or reprompt. How to set up Most importantly, install the gotoHuman node before importing this template! (Just add the node to a blank canvas before importing) Follow the instructions shown along the workflow and in the incl. video guide. You mainly need to set up your credentials for gotoHuman, OpenAI, Fal.ai and Cloudinary import the review templates with these IDs in gotoHuman: Z7V1jyImY1pho9eY039R,0GBaOCWd27tqV562kkCL,E2wlCVPWmk2UnLHVt4uu,DitPdbIapS4rBxBTIYGt,Z2T7nFwkXVFQlD6z50eV select these templates in the gotoHuman nodes do a quick setup for Cloudinary Requirements You need accounts for gotoHuman (human supervision) OpenAI (ideation) Fal.ai (image/video generation) Cloudinary (text overlay) How to customize Adjust/Replace the workflow triggers as needed Change the prompt in the topics generation node Replace the product image URLs used in the "gotoHuman - Content" node Adjust the available styles for image generation in the gotoHuman review template and the prompts they link to in the "Set Initial Image Prompt" node Adjust the prompt used for video generation in the "Set Initial Video Prompt" node If you want to use a different service/model for image and video generation, replace the nodes related to Fal.ai. Also, if you do not need a text overlay, remove the Cloudinary nodes.

🎥 YouTube Video AI Agent Workflow This n8n workflow template allows you to interact with an AI agent that extracts details and the transcript of a YouTube video using a provided video ID. Once the details and transcript are retrieved, you can chat with the AI agent to explore or analyze the video's content in a conversational and insightful manner. 🌟 How the Workflow Works 🔗 Input Video ID: The user provides a YouTube video ID as input to the workflow. 📄 Data Retrieval: The workflow fetches essential details about the video (e.g., title, description, upload date) and retrieves its transcript using YouTube's Data API and additional tools for transcript extraction. 🤖 AI Agent Interaction: The extracted details and transcript are processed by an AI-powered agent. Users can then ask questions or engage in a conversation with the agent about the video's content, such as: Summarizing the transcript. Analyzing key points. Clarifying specific sections. 💬 Dynamic Responses: The AI agent uses natural language processing (NLP) to generate contextual and accurate responses based on the video data, ensuring a smooth and intuitive interaction. 🚀 Use Cases 📊 Content Analysis**: Quickly analyze long YouTube videos by querying specific sections or extracting summaries. 📚 Research and Learning**: Gain insights from educational videos or tutorials without watching them entirely. ✍️ Content Creation**: Repurpose transcripts into blogs, social media posts, or other formats efficiently. ♿ Accessibility**: Provide an alternative, text-based way to interact with video content for users who prefer reading over watching. 🛠️ Resources for Getting Started Google Cloud Console** (for API setup): Visit Google Cloud's Get Started Guide to configure your API access. YouTube Data API Key Setup**: Follow this guide to create and manage your YouTube Data API key. Install n8n Locally**: Refer to this installation guide for setting up n8n on your local machine. ✨ Sample Prompts "Tell me about this YouTube video with id: JWfNLF_g_V0" "Can you provide a list of key takeaways from this video with id: [youtube-video-id]?"

Effortlessly track your expenses with MoneyMate, an n8n workflow that transforms receipts into organized financial insights. Upload a photo or text via Telegram, and let MoneyMate extract key details—store info, transaction dates, items, and totals—using Google Vision OCR and AI-powered parsing via OpenRouter. It categorizes expenses (e.g., Food & Beverages, Transport, Household) and delivers a clean, emoji-rich summary back to your Telegram chat. Handles zero-total errors with a friendly nudge to double-check inputs. Perfect for freelancers, small business owners, or anyone seeking hassle-free expense management. No database required, ensuring privacy and simplicity. Deploy MoneyMate and take control of your finances today! Key Features 📱 Telegram Integration: Input via photo or text, receive summaries instantly. 📸 Receipt Scanning: Converts receipt images to text using Google Vision API. 🤖 AI Parsing: Categorizes transactions with OpenRouter’s AI analysis. 🛡️ Privacy-First: Processes data on-the-fly without storage. ⚠️ Smart Error Handling: Catches zero totals with user-friendly prompts. 📊 Flexible Categories: Supports Income/Expense and custom expense types. Ideal For Budget-conscious individuals** managing personal finances. Entrepreneurs** tracking business expenses. Teams** needing quick, automated expense reporting. Pre-Requirements n8n Instance:** A running n8n instance (cloud or self-hosted). Credentials:** Telegram: A bot token and webhook setup (obtained via BotFather). For more information, please refer to Telegram bots creation Google Cloud: A service account with Google Vision API enabled and API key. For more informations, please refer to Google cloud Vision OpenRouter: An account with API access for AI language model usage. Telegram Bot:* A configured *Telegram** bot to receive inputs and send summaries. Setup Instructions Import Workflow:* Copy the *MoneyMate** workflow JSON and import it into your n8n instance using the "Import Workflow" option. Set Up Telegram Bot:* Create a bot via BotFather on *Telegram** to get a token and set up a webhook. For detailed steps, refer to n8n’s Telegram setup guide. Configure Credentials:** In the Telegram Trigger, Send Error Message, and Send Expense Summary nodes, add Telegram API credentials with your bot token. In the Get Telegram File and Download Image nodes, ensure Telegram API credentials are linked. In the Google Vision OCR node, add Google Cloud credentials with Google Vision API access. In the OpenRouter AI Model node, set up OpenRouter API credentials. Test the Workflow:* Send a test receipt photo or text (e.g., "Lunch 50,000 IDR") via *Telegram** and verify the summary in your chat. Activate:** Enable the workflow in n8n to run automatically for each input. Customization Options Add Categories:* Modify the *AI Categorizer* node to include new expense types (e.g., *Entertainment**). Change Output Format:* Adjust the *Format Summary Message** node to include more details like taxes or payment methods. Switch AI Model:* In the *OpenRouter AI Model* node, select a different *OpenRouter** model for better parsing. Store Data:* Add a *Google Sheets* node after *Parse Receipt Data** to save expense records. Enhance Errors:* Include an email notification node after *Check Invalid Input** for failed inputs. Why Choose MoneyMate? Save time, reduce manual entry, and gain clarity on your spending with MoneyMate’s AI-driven workflow. Ready to streamline your finances? Get MoneyMate now! Made by: khmuhtadin Need a custom? contact me on LinkedIn or Web

How it works This template is an interactive playground designed to help you master the most useful keyboard shortcuts in n8n and supercharge your building speed. Forget boring lists—this workflow gives you hands-on tasks to complete, turning learning into a practical exercise. The workflow is structured into four chapters, each focusing on a different aspect of workflow development: Node Basics: Learn the fundamentals of interacting with a single node, such as renaming, editing, duplicating, and deactivating. Canvas Navigation & Selection: Master the art of moving around the canvas and selecting multiple nodes efficiently. Advanced Actions: Discover powerful moves like tidying up messy connections and creating sub-workflows. Execution & Debugging: Uncover essential shortcuts for testing your workflows, like pinning data and navigating the executions panel. Each step provides a clear task in a sticky note, guiding you to perform the action yourself. Set up steps Setup time: 0 minutes! This workflow is a self-contained tutorial and requires no setup, credentials, or configuration. Open the workflow. Follow the instructions in the sticky notes, starting from the top. Perform the actions as described to build muscle memory for each shortcut. That's it! Get ready to become an n8n power user.