Workflow Templates

Overview & Setup This n8n template demonstrates how to automatically generate authentic User-Generated Content (UGC) style marketing videos for eCommerce products using AI. Simply upload a product image, and the workflow creates multiple realistic influencer-style video ads complete with scripts, personas, and video generation. Use cases Generate multiple UGC video variations for A/B testing Create authentic-looking product demonstrations for social media Produce influencer-style content without hiring creators Quickly test different marketing angles for new products Scale video content creation for eCommerce catalogs Good to know Sora 2 video generation takes approximately 2-3 minutes per 12-second video Each video generation costs approximately $0.50-1.00 USD (check OpenAI pricing for current rates) The workflow generates multiple video variations from a single product image Videos are automatically uploaded to Google Drive upon completion Generated videos are in 720x1280 (9:16) format optimized for social media How it works Product Analysis: OpenAI's vision API analyzes the uploaded product image to understand its features, benefits, and target audience Persona Creation: The system generates a detailed profile of the ideal influencer/creator who would authentically promote this product Script Generation: Gemini 2.5 Pro creates multiple authentic UGC video scripts (12 seconds each) with frame-by-frame breakdowns, natural dialogue, and camera movements Frame Generation: For each script, Gemini generates a custom first frame that adapts the product image to match UGC aesthetic and aspect ratio Video Production: Sora 2 API generates the actual video using the script and custom first frame as reference Status Monitoring: The workflow polls the video generation status every 15 seconds until completion Upload & Storage: Completed videos are automatically uploaded to Google Drive with organized naming How to use Click the form trigger URL to access the submission form Upload your product image (works best with clean product shots on white/neutral backgrounds) Enter the product name Submit the form and wait for the workflow to complete Find your generated UGC videos in the specified Google Drive folder Each run produces multiple video variations you can test Requirements OpenAI API** account with Sora 2 access for video generation and GPT-4 Vision Google Gemini API** account for script generation and image adaptation Google Drive** account for video storage Sufficient API credits for video generation (budget accordingly) Customizing this workflow Adjust the video duration in the generate_video node (currently set to 12 seconds) Modify the persona prompt in analyze_product node to target different audience demographics Change the script style in set_build_video_prompts node for different UGC aesthetics (excited discovery, casual recommendation, etc.) Update the Google Drive folder in upload_video node to organize videos by campaign Add additional processing nodes for video editing, subtitle generation, or thumbnail creation Modify the aspect ratio in resize_image node for different platforms (1:1 for Instagram feed, 16:9 for YouTube, etc.)

Use Case Generate keywords for your Amazon product. You want to find more keywords for advertising your product on Amazon. What this Workflow Does The workflow connects to Amazon's completion API to retrieve keywords for your advertising campaigns Use Airtable to send the workflow your keywords. Download airtable here. It sends the keyword to Amazon and returns keywords related to your keyword. It stores the results back into Airtable. How to Adjust it to Your Needs You can modify the Amazon Completion API URL to get more data back from Amazon. Made by Rumjahn

Telegram Voice Message → Automatic Email Imagine: What if you could turn a simple Telegram voice message into a professional email—without typing, copying, pasting, or even opening Gmail? This workflow does it all for you: just record a voice note, and it will transcribe, format, and write a clean HTML email, then send it to the right person—all by itself. Prerequisites Create a Telegram bot** (via BotFather) and get the token. Have an OpenAI account** (API key for Whisper and GPT-4). Set up a Gmail account with OAuth2.** Import the JSON template** into your automation platform. 🧩 Detailed Flow Architecture 1. Telegram Trigger Node: Telegram Trigger This node listens to all Message events received by the specified bot (e.g., “BOT OFFICIEL BAPTISTE”). Whenever a user sends a voice message, the trigger fires automatically. > ⚠️ Only one Telegram trigger per bot is possible (API limitation). Key parameter: Trigger On: Message 2. Wait Node: Wait Used to buffer or smooth out calls to avoid collisions if you receive several voice messages in a row. 3. Retrieve the Audio File Node: Get a file Type:** Telegram (resource: file) Parameter:** fileId = {{$json"message"["file_id"]}} This node fetches the voice file from Telegram received in step 1 4. Automatic Transcription (Whisper) Node: Transcribe a recording Resource:** audio Operation:** transcribe API Key:** Your OpenAI account The audio file is sent to OpenAI Whisper: the output is clean, accurate text ready to be processed. 5. Optional Wait (Wait1) Node: Wait1 Same purpose as step 2: useful if you want to buffer or add a delay to absorb processing time. 6. Structured Email Generation (GPT-4 + Output Parser) Node: AI Agent This is the core of the flow: The transcribed text is sent to GPT-4 (or GPT-4.1-mini here, via OpenAI Chat Model) Prompt used:** You are an assistant specialized in writing professional emails. Based on the text below, you must: {{ $json.text }} Detect if there is a recipient's email address in the text (or something similar like "send to fort.baptiste.pro") If it’s not a complete address, complete it by assuming it ends with @gmail.com. Understand the user's intent (resignation, refusal, application, excuse, request, etc.) Generate a relevant and concise email subject, faithful to the content Write a professional message, structured in HTML: With a polite tone, adapted to the situation Formatted with HTML tags (`, `, etc.) No spelling mistakes in French My first name is jeremy and if the text says he is not happy, specify the wish to resign ⚠️ You must always return your answer in the following strict JSON format, with no extra text: { "email": "adresse@gmail.com", "subject": "Objet de l’email", "body": "Contenu HTML de l’email" } Everything is strictly validated and formatted with the Structured Output Parser node. 7. Automatic Email Sending (Gmail) Node: Send a message To:** {{$json.output.email}} Subject:** {{$json.output.subject}} HTML Body:** {{$json.output.body}} This node takes the JSON structure returned by the AI and sends the email via Gmail, to the right recipient, with the correct subject and full HTML formatting.

This workflow template shows how to load JSON data into a workflow and push that data into an App or convert it into a Spreadsheet file. Specifically, this workflow shows how to make a generic API request that returns JSON. It then shows how to load that into a Google Sheets spreadsheet, or convert it to .CSV file format. However, you can use the general pattern to load data into any app or convert to any spreadsheet file format (such as .xlsx).

Make your n8n instance faster, cleaner, and more efficient by deleting old workflow executions — while keeping only the most recent ones you actually need. Whether you're using n8n Cloud or self-hosted, this lightweight workflow helps reduce database/storage usage and improves UI responsiveness, using only official n8n nodes. 🔍 Description Automatically clean up old executions in your n8n instance using only official nodes — no external database queries required. Whether you're on the Cloud version or running self-hosted, this workflow helps you optimize performance and keep your instance tidy by maintaining only the most recent executions per workflow. Ideal for users managing dozens or hundreds of workflows, this solution reduces storage usage and improves the responsiveness of the n8n UI, especially in environments where execution logs can accumulate quickly. ✅ What It Does Retrieves up to 250 recent executions across all workflows Groups executions by workflow Keeps only the most recent N executions per workflow (value is configurable) Deletes all older executions (regardless of their status: success, error, etc.) Works entirely with native n8n nodes — no external database access required Optionally: set the number of executions to keep as 0 to delete all past executions from your instance in a single run 🛠️ How to Set Up 🔑 Create a Personal API Key in your n8n instance: Go to Settings → API Keys → Create a new key 🔧 Create a new n8n API Credential (used by both nodes): In your n8n credentials panel: Name: anything you like (e.g., “Internal API Access”) API Key: paste the Personal API Key you just created Base URL: your full n8n instance URL with the /api/v1 path, e.g. https://your-n8n-instance.com/api/v1 ✅ Use this credential in both: The Get Many Executions node (to fetch recent executions) The Delete Many Executions node (to remove outdated executions) 🧩 In the “Set Executions to Keep” node: Edit the variable executionsToKeep and set the number of most recent executions to retain per workflow (e.g. 10) Tip: Set it to 0 to delete all executions 📦 Note: The “Get Many Executions” node will retrieve up to 250 executions per run — this is the maximum allowed by the n8n API. 🧠 No further setup is required — the filtering and grouping logic is handled inside the Code Node automatically. 🧪 Included Nodes Overview 🕒 Schedule Trigger → Set to run daily, weekly, etc. 📥 Get Many Executions → Fetches past executions via n8n API 🛠️ Set Executions to Keep → Set how many recent ones to keep 🧠 Code Node → Filters out executions to delete per workflow 🗑️ Delete Executions → Deletes outdated executions 💡 Why Use This? Reduce clutter and improve performance in your n8n instance Maintain execution logs only when they’re useful Avoid bloating your storage or database with obsolete data Compatible with both n8n Cloud and self-hosted setups Uses only official, supported n8n nodes — no SQL, no extra setup 🔒 This workflow modifies and deletes execution data. Always review and test it first on a staging instance or on a limited set of workflows before using it in production.

This n8n workflow automatically creates and sends regular performance summaries to parents using data from a Learning Management System (LMS). It pulls student grades and attendance, formats them into easy-to-read reports, and emails them without any manual work. Good to Know Fully Automated**: Generates reports and sends emails using LMS data. Regular Updates**: Sends summaries on a set schedule (e.g., every Monday at 9 AM). Clear Reports**: Includes student grades, attendance, and progress notes. Error Alerts**: Notifies admins via email if something goes wrong. Scalable**: Works for multiple students across different classes. How It Works Report Generation Flow Weekly Trigger: Starts the process every Monday at 9 AM. Fetch LMS Data: Pulls grades, attendance, and progress from the LMS. Process Data: Organizes the data into a clear report format. Generate HTML Report: Creates a readable report with student details. Send Email to Parents: Emails the report to parents’ addresses. Log Report Delivery: Records the sent reports in a log. Example Sheet Columns Student ID**: Unique identifier for each student. Name**: Full name of the student. Grade**: Current academic grade or score. Attendance**: Percentage of classes attended. Progress Notes**: Brief comments on performance. Report Date**: Date the report was generated. How to Use Import Workflow: Add the workflow to n8n using the “Import Workflow” option. Set Up LMS Access: Configure n8n with LMS credentials to fetch data. Configure Email: Add parent email addresses and set up an email service (e.g., Gmail). Activate Workflow: Save and turn on the workflow in n8n. Check Logs: Verify reports are sent and logs are updated. Requirements n8n Instance**: Self-hosted or cloud-based n8n setup. LMS Access**: API or credentials to connect to the LMS. Email Service**: SMTP setup (e.g., Gmail) for sending reports. Admin Oversight**: Someone to monitor and fix any errors. Customizing This Workflow Change Schedule**: Adjust the trigger to send reports weekly or monthly. Add More Data**: Include extra LMS fields like behavior notes. Custom Email**: Change the email template for a personalized touch.

Who is this for? For developers and organizations that need to combine PDF files. What problem is this workflow solving? PDF file merging problem. What this workflow does Downloads two PDF files from the web. Merges two PDF files into one. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the PDF merge HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.

Great — here’s a complete Workflow Description for your n8n Creator submission based on the JSON you shared: 🔒 SSL Expiry Notifier (No Paid APIs) 🧩 How it Works This workflow automatically checks SSL certificate expiry dates for a list of websites and sends timely email alerts if any certificate is close to expiring. It’s ideal for DevOps, IT Operations, or Web Admin teams to stay ahead of certificate issues — without relying on any paid API services. Here's the high-level logic: Trigger: Runs daily at 8 AM using the Schedule Trigger node. Fetch URLs: Reads URLs to monitor from a Google Sheet. Check SSL: Calls the free ssl-checker.io API to retrieve SSL certificate details (valid from, valid till, days left). Conditional Check: Filters any certificates expiring in 7 days or less. Send Email: Sends a styled HTML email alert to predefined recipients. Update Sheet: Updates the same Google Sheet with the latest SSL info for each domain. ⚙️ Set Up Steps (Quick Overview) 🕐 Estimated setup time: \~10–15 minutes Connect Google Sheets – OAuth2 credentials required. Sheet must include a column titled URL with domains to monitor. Set Up SMTP – Add your email credentials under the SMTP node to enable notifications. Optional – Customize the threshold days in the "Expiry Alert" IF node if you want alerts for a different timeframe. Deploy and Activate – That’s it. Once active, the flow runs automatically every day. 💡 Detailed field mappings and logic notes are included inside the workflow as sticky notes. 🚀 Ready to Get Started? Let’s start your journey on n8n by creating your free account here.

This workflow provides automated access to the Openai Clip AI model through the Replicate API. It saves you time by eliminating the need to manually interact with AI models and provides a seamless integration for image generation tasks within your n8n automation workflows. Overview This workflow automatically handles the complete image generation process using the Openai Clip model. It manages API authentication, parameter configuration, request processing, and result retrieval with built-in error handling and retry logic for reliable automation. Model Description: Official CLIP models, generate CLIP (clip-vit-large-patch14) text & image embeddings Key Capabilities High-quality image generation from text prompts** Advanced AI-powered visual content creation** Customizable image parameters and styles** Tools Used n8n**: The automation platform that orchestrates the workflow Replicate API**: Access to the Openai/clip AI model Openai Clip**: The core AI model for image generation Built-in Error Handling**: Automatic retry logic and comprehensive error management How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Replicate API: Add your Replicate API token to the 'Set API Token' node Customize Parameters: Adjust the model parameters in the 'Set Image Parameters' node Test the Workflow: Run the workflow with your desired inputs Integrate: Connect this workflow to your existing automation pipelines Use Cases Content Creation**: Generate unique images for blogs, social media, and marketing materials Design Prototyping**: Create visual concepts and mockups for design projects Art & Creativity**: Produce artistic images for personal or commercial use Marketing Materials**: Generate eye-catching visuals for campaigns and advertisements Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Replicate API**: https://replicate.com (Sign up to access powerful AI models) #n8n #automation #ai #replicate #aiautomation #workflow #nocode #imagegeneration #aiart #texttoimage #visualcontent #aiimages #generativeart #openai #machinelearning #artificialintelligence #aitools #automation #digitalart #contentcreation #productivity #innovation

This workflow provides automated access to the Creativeathive Lemaar Door Wm AI model through the Replicate API. It saves you time by eliminating the need to manually interact with AI models and provides a seamless integration for other generation tasks within your n8n automation workflows. Overview This workflow automatically handles the complete other generation process using the Creativeathive Lemaar Door Wm model. It manages API authentication, parameter configuration, request processing, and result retrieval with built-in error handling and retry logic for reliable automation. Model Description: Advanced AI model for automated processing and generation tasks. Key Capabilities Specialized AI model with unique capabilities** Advanced processing and generation features** Custom AI-powered automation tools** Tools Used n8n**: The automation platform that orchestrates the workflow Replicate API**: Access to the Creativeathive/lemaar-door-wm AI model Creativeathive Lemaar Door Wm**: The core AI model for other generation Built-in Error Handling**: Automatic retry logic and comprehensive error management How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Replicate API: Add your Replicate API token to the 'Set API Token' node Customize Parameters: Adjust the model parameters in the 'Set Other Parameters' node Test the Workflow: Run the workflow with your desired inputs Integrate: Connect this workflow to your existing automation pipelines Use Cases Specialized Processing**: Handle specific AI tasks and workflows Custom Automation**: Implement unique business logic and processing Data Processing**: Transform and analyze various types of data AI Integration**: Add AI capabilities to existing systems and workflows Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Replicate API**: https://replicate.com (Sign up to access powerful AI models) #n8n #automation #ai #replicate #aiautomation #workflow #nocode #aiprocessing #dataprocessing #machinelearning #artificialintelligence #aitools #automation #digitalart #contentcreation #productivity #innovation

This workflow uses a Mautic Trigger to check for new contacts. It then validates the contacts' email using OneSimpleAPI. If there are any a message will be sent to Slack. To configure this workflow you will need to set the credentials for the Hubspot, OneSimpleAPI and Slack Nodes. You will also need to select the Slack channel to use for sending the message.

Title RAG AI Agent for Documents in Google Drive → Pinecone → OpenAI Chat (n8n workflow) Short Description This n8n workflow implements a Retrieval-Augmented Generation (RAG) pipeline + AI agent, allowing users to drop documents into a Google Drive folder and then ask questions about them via a chatbot. New files are indexed automatically to a Pinecone vector store using OpenAI embeddings; the AI agent loads relevant chunks at query time and answers using context plus memory. Why this workflow matters / what problem it solves Large language models (LLMs) are powerful, but they lack up-to-date, domain-specific knowledge. RAG augments the LLM with relevant external documents, reducing hallucination and enabling precise answers. (Pinecone) This workflow automates the ingestion, embedding, storage, retrieval, and chat logic — with minimal manual work. It’s modular: you can swap data sources, vector DBs, or LLMs (with some adjustments). It leverages the built-in AI Agent node in n8n to tie all the parts together. (n8n) How to get the required credentials | Service | Purpose in Workflow | Setup Link | What you need / steps | | ------------------------- | ------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | | Google Drive (OAuth2) | Trigger new file events & download the file | https://docs.n8n.io/integrations/builtin/credentials/google/oauth-generic/ | Create a Google Cloud OAuth app, grant it Drive scopes, get client ID & secret, configure redirect URI, paste into n8n credentials. | | Pinecone | Vector database for embeddings | https://docs.n8n.io/integrations/builtin/credentials/pinecone/ | Sign up at Pinecone, in dashboard create an index, get API key + environment, paste into n8n credential. | | OpenAI | Embeddings + chat model | https://docs.n8n.io/integrations/builtin/credentials/openai/ | Log in to OpenAI, generate a secret API key, paste into n8n credentials. | You’ll configure these under n8n → Credentials → New Credential, matching credential names referenced in your workflow nodes. Detailed Walkthrough: How the Workflow Works Here’s a step-by-step of what happens inside your workflow (matching your JSON): 1. Google Drive Trigger Watches a specified folder in Google Drive. Whenever a new file appears (fileCreated event), the workflow is triggered (polling every minute). You must set the folder ID (in “folderToWatch”) to the Drive folder you want to monitor. 2. Download File Takes the file ID from the trigger and downloads the file content (binary). 3. Indexing Path: Embeddings + Storage (This path only runs when new files arrive) The file is sent to the Default Data Loader node (via the Recursive Character Text Splitter) to break it into chunks with overlap (so context is preserved). Each chunk is fed into Embeddings OpenAI to convert text into embedding vectors. Then Pinecone Vector Store (insert mode) ingests the vector + text metadata into your Pinecone index. This ensures your vector store stays up-to-date with files you drop into Drive. 4. Chat / Query Path (Triggered by user chat via webhook) When a chat message arrives via When Chat Message Received, it gets passed into the AI Agent node. Before generation, the AI Agent calls the Pinecone Vector Store1 set in “retrieve-as-tool” mode, which runs a vector-based retrieval using the user query embedding. The relevant text chunks are pulled as tools/context. The OpenAI Chat Model node is linked as the language model for the agent. Simple Memory** node provides conversational memory (keeping history across messages). The agent combines retrieved context + memory + user input and instructs the model to produce a response. 5. Connections / Flow Logic The Embeddings OpenAI node’s output is wired into Pinecone Vector Store (insert) and also into Pinecone Vector Store1 (so the same embeddings can be used for retrieval). The AI Agent has tool access to Pinecone retrieval and memory. The Download File node triggers the insert path. The When chat message triggers the agent path. Similar Workflows / Inspirations & Comparisons To help understand how your workflow fits into what’s already out there, here are a few analogues: n8n Blog: “Build a custom knowledge RAG chatbot”** — they show a workflow that ingests documents from external sources, indexes them in Pinecone, and responds to queries via n8n + LLM. (n8n Blog) Index Documents from Google Drive to Pinecone** — this is nearly identical for the ingestion part: trigger on Drive, split, embed, upload. (n8n) Build & Query RAG System with Google Drive, OpenAI, Pinecone** — shows the full RAG + chat logic, same pattern. (n8n) Chat with GitHub API Documentation (RAG)** — demonstrates converting API spec into chunks, embedding, retrieving, and chatting. (n8n) Community tutorials & forums** talk about using the AI Agent node with tools like Pinecone, and how the RAG part is often built as a sub-workflow feeding an agent. (n8n Community) What sets your workflow apart is your explicit combination: Google Drive → automatic ingestion → chat agent with tool integration + memory. Many templates show either ingestion or chat, but fewer show them combined cleanly with n8n’s AI Agent. Suggested Published Description (you can paste/adjust) > RAG AI Agent for Google Drive Documents (n8n workflow) > > This workflow turns a Google Drive folder into a live, queryable knowledge base. Drop PDF, docx, or text files into the folder → new documents are automatically indexed into a Pinecone vector store using OpenAI embeddings → you can ask questions via a webhook chat interface and the AI agent will retrieve relevant text, combine it with memory, and answer in context. > > Credentials needed > > * Google Drive OAuth2 (see: https://docs.n8n.io/integrations/builtin/credentials/google/oauth-generic/) > * Pinecone (see: https://docs.n8n.io/integrations/builtin/credentials/pinecone/) > * OpenAI (see: https://docs.n8n.io/integrations/builtin/credentials/openai/) > > How it works > > 1. Drive trigger picks up new files > 2. Download, split, embed, insert into Pinecone > 3. Chat webhook triggers AI Agent > 4. Agent retrieves relevant chunks + memory > 5. Agent uses OpenAI model to craft answer > > This is built on the core RAG pattern (ingest → retrieve → generate) and enhanced by n8n’s AI Agent node for clean tool integration. > > Inspiration & context > This approach follows best practices from existing n8n RAG tutorials and templates, such as the “Index Documents from Google Drive to Pinecone” ingestion workflow and “Build & Query RAG System” templates. (n8n) > > You're free to swap out the data source (e.g. Dropbox, S3) or vector DB (e.g. Qdrant) as long as you adjust the relevant nodes. If you like, I can generate a polished Markdown README for you (with badges, diagrams, instructions) ready for GitHub/n8n community publishing. Do you want me to build that? [1]: https://www.pinecone.io/learn/retrieval-augmented-generation/?utm_source=chatgpt.com "Retrieval-Augmented Generation (RAG) - Pinecone" [2]: https://n8n.io/integrations/agent/?utm_source=chatgpt.com "AI Agent integrations | Workflow automation with n8n" [3]: https://blog.n8n.io/rag-chatbot/?utm_source=chatgpt.com "Build a Custom Knowledge RAG Chatbot using n8n" [4]: https://n8n.io/workflows/4552-index-documents-from-google-drive-to-pinecone-with-openai-embeddings-for-rag/?utm_source=chatgpt.com "Index Documents from Google Drive to Pinecone with OpenAI ... - N8N" [5]: https://n8n.io/workflows/4501-build-and-query-rag-system-with-google-drive-openai-gpt-4o-mini-and-pinecone/?utm_source=chatgpt.com "Build & Query RAG System with Google Drive, OpenAI GPT-4o-mini ..." [6]: https://n8n.io/workflows/2705-chat-with-github-api-documentation-rag-powered-chatbot-with-pinecone-and-openai/?utm_source=chatgpt.com "Chat with GitHub API Documentation: RAG-Powered Chatbot ... - N8N"