Workflow Templates

🎬 Google Veo 3 Prompt and Video Generator via Leonardo.ai + Claude 4 Transform text descriptions into cinematic videos using Google's Veo 3 model through Leonardo.ai's platform! 🚀 What This Workflow Does This advanced automation pipeline takes your creative ideas and turns them into professional-quality videos using Google's powerful Veo 3 model (accessed via Leonardo.ai), enhanced by Claude 4's sophisticated prompt engineering. ✨ Key Features 🤖 AI-Powered Prompt Enhancement**: Uses Claude 4 Sonnet with Wikipedia integration to craft optimal Google Veo 3 prompts 🎥 Professional Video Generation**: Leverages Google's Veo 3 model through Leonardo.ai for high-quality text-to-video conversion ☁️ Automatic Cloud Storage**: Videos are automatically saved to your Google Drive 📋 Structured Prompting**: Follows Google Veo3 best practices with 8 essential elements (Subject, Context, Action, Style, Camera Motion, Composition, Ambiance, Audio) ⚡ Hands-Off Processing**: Set it and forget it - the workflow handles the entire pipeline 🔧 How It Works Input Your Concept - Describe your video idea in the "Video Context" node AI Enhancement - Claude 4 transforms your description into a cinematic Google Veo 3 prompt using advanced techniques Video Generation - Google's Veo 3 model (via Leonardo.ai) creates your video (720p resolution, ~8 seconds) Smart Waiting - 4-minute processing buffer ensures completion Auto-Download - Retrieves the finished video from Leonardo's servers Cloud Storage - Uploads directly to your Google Drive folder 💡 Perfect For Content Creators** looking to automate video production Marketing Teams** needing quick promotional videos Educators** creating engaging visual content Social Media Managers** generating scroll-stopping content Creative Professionals** exploring AI-assisted filmmaking 📋 Requirements Leonardo AI account with API access Anthropic API key (Claude 4 Sonnet) Google Drive integration N8N instance (cloud or self-hosted) 👨‍💻 About the Creator Created by: AlexK1919 - AI-Native Workflow Automation Architect, n8n Ambassador and Verified Partner, Co-Founder @ WotAI If you'd like to review more Google Veo 3 Prompts organized by business category, check out over 9,000+ free, pre-made prompts at: Google Veo 3 Prompts 📄 License This workflow is available under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You are free to use, adapt, and share this workflow for non-commercial purposes under the terms of this license. Full license details: https://creativecommons.org/licenses/by-nc-sa/4.0/ 🎯 Example Output Input: "Star Wars stormtrooper digging for uranium in desert, saying something funny" The AI generates a structured prompt with: Subject**: Detailed character description Context**: Desert environment specifics Action**: Dynamic digging movements Style**: Cinematic vlog aesthetic Camera**: Appropriate angles and movement Audio**: Dialogue, sound effects, and music ⚙️ Setup Notes Character Limit**: Prompts are optimized for Leonardo's 1,500 character API limit Processing Time**: Allow 4+ minutes for Google Veo3 video generation Quality**: 720p resolution with native audio generation Consistency**: Uses advanced Google Veo3 prompting for reliable results 🔄 Customization Options Modify the prompt engineering system message for different styles Adjust video resolution and model parameters Change storage destination (Google Drive folder) Add post-processing steps or notifications 📈 Why This Workflow Rocks Unlike simple text-to-video tools, this workflow: Intelligently enhances** your prompts using AI for Google Veo 3 Follows industry best practices** for Google Veo3 prompting Automates the entire pipeline** from idea to stored video Leverages multiple AI models** for superior results Handles technical details** like API limits and timing 🚨 Pro Tips Be specific in your initial context - detail creates better videos The workflow includes comprehensive Google Veo3 prompting guidelines Videos are typically 5-8 seconds - plan accordingly for longer content Experiment with different styles and camera movements optimized for Veo 3 The AI can access Wikipedia for factual enhancement Ready to revolutionize your video creation process? Import this workflow and start generating professional videos with just a text description! Perfect for anyone looking to harness the power of AI for content creation. Tags: #veo3 #GoogleVeo3 #AI #VideoGeneration #Leonardo #Claude #Automation #ContentCreation #GoogleAI

Set up a chat with your documents without the complex vector store setup. This templates helps you ingest** your PDF / text / MD documents into a knowledge graph use the graph as the knowledge base for your AI chatbots (and other workflows) visualize the main topics* and *gaps** in your documents (good for observability and research) The knowledge base is provided using the InfraNodus GraphRAG with the knowledge graphs offering high-quality responses without the need to set up complex RAG vector store workflows. The advantages of using GraphRAG instead of the standard vector stores for knowledge are: Easy and quick to set up and update** — no complex data import workflows needed A knowledge graph offers a holistic and interactive view of your knowledge base (accessible via our API or a web interface — also shareable) Better retrieval of relations** between the document chunks = higher quality responses How it works This template uses the InfraNodus knowledge graph as a knowledge base for your n8n AI agent node. The knowledge graph contains the documents you can upload using this template from your Google Drive. When the user asks a question via the chat interface, the agent forwards this question to the InfraNodus knowledge graph, retrieves a response, a summary, and a list of matching statements (based advanced Graph RAG), then delivers the final response back the user. Here's a description step by step: Step 1: Upload your documents Put the PDF / text / MD files you want to chat with into a folder on your Google drive Authorize access to that folder using the Google drive node in the template. Add the InfraNodus API key to the InfraNodus Save to Graph HTTP node Optional: change the name of the graph you want to save the data to in the InfraNodus HTTP node (in the name field of the HTTP post request). Run the workflow to ingest all the files and save them into the graph Optional: check the link provided in the Step 1 workflow description to see the visualization of your knowledge base. It will look something like that: Note:* you can replace the PDF to Text convertor node with a better quality *PDF convertor* from ConvertAPI which respects the original file layout and doesn't split text into small chunks Step 2: Chat with your documents Deactive the trigger in the Step 1 Activate the chat trigger in the Step 2 Add your InfraNodus API credentials to Knowledge Base GraphRAG InfraNodus node Optional: change the graph name in the Knowledge Base node to match the name you provided in the step 1 above Run the chat and ask the question Watch the magic How to use You need an InfraNodus GraphRAG API account and key to use this workflow. Create an InfraNodus account Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Requirements An InfraNodus account and API key An OpenAI (or any other LLM) API key A Google Drive OAuth access (follow the n8n instructions) Optional: ConvertAPI API key for better quality PDF conversion Customizing this workflow You can customize this workflow by adding several experts to your AI agent. Check out the complete guide at https://support.noduslabs.com/hc/en-us/articles/20174217658396-Using-InfraNodus-Knowledge-Graphs-as-Experts-for-AI-Chatbot-Agents-in-n8n Also check out the video tutorial with a demo: For support and feedback, please, contact us at https://support.noduslabs.com To learn more about InfraNodus: https://infranodus.com

For Who? Content Creators Youtube Automation Marketing Team How it works? 1 - Retrieve Base Image, Image Description and Situation from Airtable 2 - Generate Image Prompt 3 - Generate Image via Fal AI 4 - Verify if Image is generated 5 - Upload Image on Airtable 📺 YouTube Video Tutorial: SETUP Setup Input : The first part of the workflow can be replaced with anything else. You need as input a Prompt and the Base Image URL (publicly available). Setup Output : In this Workflow, the output is storing the image on Airtable but you can replace that with anything else but basically you have two options : Store the Generated Image somewhere : Keep everything like this and replace the last Airtable node with the Third Party you want to use. Use the Image directly in n8n : In HTTP Request "Generate Image" switch sync_mode to "true", remove all the following nodes and add "Extract form File" node (convert to Base64 String) 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: Fal AI (FLUX KONTEXT MAX) : https://fal.ai/models/fal-ai/flux-pro/kontext/max/api#schema-input Airtable : https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.airtable/?utm_source=n8n_app&utm_medium=node_settings_modal-credential_link&utm_campaign=n8n-nodes-base.airtable

This n8n workflow demonstrates how to automate image captioning tasks using Gemini 1.5 Pro - a multimodal LLM which can accept and analyse images. This is a really simple example of how easy it is to build and leverage powerful AI models in your repetitive tasks. How it works For this demo, we'll import a public image from a popular stock photography website, Pexel.com, into our workflow using the HTTP request node. With multimodal LLMs, there is little do preprocess other than ensuring the image dimensions fit within the LLMs accepted limits. Though not essential, we'll resize the image using the Edit image node to achieve fast processing. The image is used as an input to the basic LLM node by defining a "user message" entry with the binary (data) type. The LLM node has the Gemini 1.5 Pro language model attached and we'll prompt it to generate a caption title and text appropriate for the image it sees. Once generated, the generated caption text is positioning over the original image to complete the task. We can calculate the positioning relative to the amount of characters produced using the code node. An example of the combined image and caption can be found here: https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/l5xbb4ze4wyxwwefqmnc Requirements Google Gemini API Key. Access to Google Drive. Customising the workflow Not using Google Gemini? n8n's basic LLM node supports the standard syntax for image content for models that support it - try using GPT4o, Claude or LLava (via Ollama). Google Drive is only used for demonstration purposes. Feel free to swap this out for other triggers such as webhooks to fit your use case.

Use Case Manually extracting images from PDF files for analysis is often slow and inefficient. Many users resort to taking screenshots of each page, uploading them to an AI tool like OpenAI for image analysis, and then manually copying the insights into a document. This manual process is time-consuming and prone to errors. This workflow streamlines the entire process by automatically extracting images from a PDF, analyzing them using the GPT-4o model, and saving the results in seconds—eliminating the need for manual effort. What This Workflow Does Extracts all images from the uploaded PDF file automatically The workflow scans each page of the PDF and identifies embedded images without manual intervention. Uses the GPT-4o model to analyze each extracted image Each image is processed through GPT-4o to generate descriptive insights, summaries, or context-specific analysis depending on the use case. Saves the analysis results to a .txt file, including image URLs The final output is a plain text file containing both the image URLs (e.g., hosted on cloud storage) and the corresponding GPT-4o analysis, ready for further use or sharing. Setup 1.Set up your credentials when you first open the workflow. You’ll need accounts for OpenAI, Convert API, and Google Drive. 2.Convert API does not rate-limit your API, sometimes you may receive 503 service unavailable error. Nevertheless, it doesn’t mean that you cannot convert your file. It simply means that you should retry the conversion in a few seconds. 3.Upload a PDF with images to Google Drive. 4.Remove unnecessary parts and retrieve image-related information. 5.Integrate image and image analysis information together. 6.Analyze each image using the OPENAI GPT-4o model. 7.Retrieve all image analysis content and image URL 8.Integrate multiple image URLs and analysis content 9.Output content to a .txt file. Template was created in n8n v1.83.2 How to Customize Replace the manual trigger with a Google Drive trigger or other automation triggers Change the image analysis model (e.g., switch or fine-tune GPT-4o) Send the results to other platforms (e.g., Slack, Telegram, LINE, etc.) instead of saving to a .txt file

This n8n workflow demonstrates an approach to parsing bank statement PDFs with multimodal LLMs as an alternative to traditional OCR. This allows for much more accurate data extraction from the document especially when it comes to tables and complex layouts. Multimodal Parsing is better than traditiona OCR because: It reduces complexity and overhead by avoiding the need to preprocess the document into text format such as markdown before passing to the LLM. It handles non-standard PDF formats which may produce garbled output via traditional OCR text conversion. It's orders of magnitude cheaper than premium OCR models that still require post-processing cleanup and formatting. LLMs can format to any schema or language you desire! How it works You can use the example bank statement created specifically for this workflow here: https://drive.google.com/file/d/1wS9U7MQDthj57CvEcqG_Llkr-ek6RqGA/view?usp=sharing A PDF bank statement is imported via Google Drive. For this demo, I've created a mock bank statement which includes complex table layouts of 5 columns. Typically, OCR will be unable to align the columns correctly and mistake some deposits for withdrawals. Because multimodal LLMs do not accept PDFs directly, well have to convert the PDF to a series of images. We can achieve this by using a tool such as Stirling PDF. Stirling PDF is self-hostable which is handy for sensitive data such as bank statements. Stirling PDF will return our PDF as a series of JPGs (one for each page) in a zipped file. We can use n8n's decompress node to extract the images and ensure they are ordered by using the Sort node. Next, we'll resize each page using the Edit Image node to ensure the right balance between resolution limits and processing speed. Each resized page image is then passed into the Basic LLM node which will use our multimodal LLM of choice - Gemini 1.5 Pro. In the LLM node's options, we'll add a "user message" of type binary (data) which is how we add our image data as an input. Our prompt will instruct the multimodal LLM to transcribe each page to markdown. Note, you do not need to do this - you can just ask for data points to extract directly! Our goal for this template is to demonstrate the LLMs ability to accurately read the page. Finally, with our markdown version of all pages, we can pass this to another LLM node to extract required data such as deposit line items. Requirements Google Gemini API for Multimodal LLM. Google Drive access for document storage. Stirling PDF instance for PDF to Image conversion Customising the workflow At time of writing, Gemini 1.5 Pro is the most accurate in text document parsing with a relatively low cost. If you are not using Google Gemini however you can switch to other multimodal LLMs such as OpenAI GPT or Antrophic Claude. If you don't need the markdown, simply asking what to extract directly in the LLM's prompt is also acceptable and would save a few extra steps. Not parsing any bank statements any time soon? This template also works for Invoices, inventory lists, contracts, legal documents etc.

This workflow converts any n8n workflow outputs into Markdown notes that are accessible in your Obsidian Vault through Google Drive synchronization. Setup Requirements Create a designated folder in Google Drive (Desktop). Create a symbolic link between this folder and a new target folder in your Obsidian Vault. Configure Google Drive n8n node settings. Send the output of any workflow to the trigger, and the notes will appear in your Vault folder. Optional Features You can use AI agents to: Write notes in your preferred format (e.g., Zettelkasten). Compose YAML front matter. Suggest tags. Use Cases Convert RSS feed items to notes. Create notes from YouTube video transcripts. Transform tasks in Slack messages into Obsidian tasks. (Requires setting up a corresponding workflow, e.g., RSS trigger, YouTube transcriber, or Slack bot.)

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 is designed to automate the generation and updating of SEO meta titles and descriptions for WooCommerce products using n8n. It leverages Google Sheets for data input, a FREE language model (Gemini 2.0 Flash Exp. via OpenRouter) for generating SEO-optimized meta tags, and WooCommerce for updating product details. How It Works: Trigger: The workflow can be triggered manually or on a schedule. The manual trigger allows for testing, while the schedule trigger can be set to run at regular intervals (e.g., every few minutes) to process new products. Data Retrieval: The workflow starts by retrieving product IDs from a Google Sheets document. It looks for products that do not yet have meta titles or descriptions. Using the retrieved product ID, the workflow fetches the corresponding product details from WooCommerce, including the product name, description, short description, and categories. Meta Tag Generation: The product details are passed to a language model (Gemini 2.0 Flash Exp) via OpenRouter. The model generates SEO-optimized meta titles and descriptions based on the provided content. The generated meta tags are structured and validated to ensure they meet SEO best practices, such as character limits and keyword inclusion. Update WooCommerce: The generated meta title and description are then updated in the WooCommerce product metadata using the Yoast SEO fields. Update Google Sheets: Finally, the workflow updates the Google Sheets document with the newly generated meta tags, along with the product URL, title, and the timestamp of the update. Set Up Steps: Google Sheets Setup: Create a copy of the provided Google Sheets template and insert WooCommerce product IDs in column "B". Ensure the Google Sheets document has columns for METATITLE, METADESCRIPTION, URL, TITLE POST, and DATA (timestamp). n8n Workflow Configuration: Google Sheets Node: Configure the "Get product ID" node to connect to your Google Sheets document. Use OAuth2 for authentication. WooCommerce Node: Set up the WooCommerce nodes to connect to your WooCommerce store using the WooCommerce API credentials. OpenRouter Node: Configure the "Gemini 2.0 Flash Exp" node with your OpenRouter API credentials to access the language model. Structured Output Parser: Ensure the output parser is set to handle the structured data format for meta titles and descriptions. Workflow Execution: Trigger the workflow manually to test the process or set up a schedule trigger to automate the workflow at regular intervals. Monitor the workflow execution to ensure that meta tags are generated and updated correctly in both WooCommerce and Google Sheets. Validation: After the workflow runs, verify that the meta titles and descriptions in WooCommerce are correctly updated and that the Google Sheets document reflects the changes. This workflow streamlines the process of optimizing WooCommerce product pages for SEO, saving time and ensuring consistency in meta tag generation. Need help customizing? Contact me for consulting and support or add me on Linkedin.

This workflow shows how to use a self-hosted Large Language Model (LLM) with n8n's LangChain integration to extract personal information from user input. This is particularly useful for enterprise environments where data privacy is crucial, as it allows sensitive information to be processed locally. 📖 For a detailed explanation and more insights on using open-source LLMs with n8n, take a look at our comprehensive guide on open-source LLMs. 🔑 Key Features Local LLM Connect Ollama to run Mistral NeMo LLM locally Provide a foundation for compliant data processing, keeping sensitive information on-premises Data extraction Convert unstructured text to a consistent JSON format Adjust the JSON schema to meet your specific data extraction needs. Error handling Implement auto-fixing for LLM outputs Include error output for further processing ⚙️ Setup and сonfiguration Prerequisites n8n AI Starter Kit installed Configuration steps Add the Basic LLM Chain node with system prompts. Set up the Ollama Chat Model with optimized parameters. Define the JSON schema in the Structured Output Parser node. 🔍 Further resources Run LLMs locally with n8n Video tutorial on using local AI with n8n Apply the power of self-hosted LLMs in your n8n workflows while maintaining control over your data processing pipeline!

Use Case Transform and optimize images for web use: You need to host local images online You want to reduce image file sizes automatically You need image URLs for web content You want to generate and optimize AI-created images What this Workflow Does The workflow processes images through two services: Uploads images to ImgBB for hosting and URL generation (free but need API key) Optimizes images using ReSmush.it to reduce file size (free) Optional: Creates images using OpenAI's image generation Returns optimized image URLs ready for use Setup Create an ImgBB account and get your API key Add your ImgBB API key to the HTTP Request node (key parameter) Optional: Configure OpenAI credentials for image generation Connect your image input source How to Adjust it to Your Needs Skip OpenAI nodes if using your own image files Adjust image optimization parameters Customize image hosting settings Modify output format for your needs More templates and n8n workflows >>> @simonscrapes

Are you a cord-cutter? Do you find yourself looking through the many titles of videos uploaded to Youtube, just to find the ones you want to watch? Even when you subscribe to the channels you like, do you find that you want to watch the news now and my tech/n8n videos later? Well, now you can have n8n grab the last 8 videos, posted in the last 24 hours, and put them in a playlist for the day; and, each day the old playlist is deleted. Are you tired of a channel filling your subscriptions with tons of videos a day; this workflow can be used for any channel, whether you are subscribed to the channel or not. It's a YouTube playlist automation. How it works: Create your list of prefered Youtube Channels in a Google Sheet and it will create you a daily playlist; and, it will delete the playlist created yesterday. Instructions To set this up, you need to create a Google Sheet with the following headings in line 1: Channel User Name Channel Name Channel Link Channel ID Copy the 'Create your Channel List' into it's own workflow and link the Sheets links to your new sheet. To get the 'Create your Channel List' to work, you need to visit each channel's page that you want included in your playlist; you need to get the "@" name of the channel and add it to the 'Channel User Name' column of your Google Sheet. For example: if you wanted to include this channel: Recruit Training Videos - Corporal Stock, you would search for the name, to add to the next available row of the 'Channel User Name' column: @CorporalStock Once you add all Channel User Names, run the 'Create your Channel list workflow, and it will fill in the remaining details. Now the 'YT Playlist Creator' can be run. Note: The first time the workflow us run, disconnect the 'Delete Yesterday's Playlist' leg, or the workflow will error and stop (because there is no 'Yesterday's Playlist'. Note: this was made to create a playlist every day, delete yesterday's playlist, and only get the last 8 videos posted within the last 24 hours. I choose to put the date (YYMMDD format) in front of the playlist, to ensure that it doesn't conflict with another playlist. Also, I have it notifying me in Telegram, so I know that the new playlist is posted.