Workflow Templates

This workflow demonstrates how to enrich data from a list of companies in a spreadsheet. While this workflow is production-ready if all steps are followed, adding error handling would enhance its robustness. Important notes Check legal regulations**: This workflow involves scraping, so make sure to check the legal regulations around scraping in your country before getting started. Better safe than sorry! Mind those tokens**: OpenAI tokens can add up fast, so keep an eye on usage unless you want a surprising bill that could knock your socks off! 💸 Main Workflow Node 1 - Webhook This node triggers the workflow via a webhook call. You can replace it with any other trigger of your choice, such as form submission, a new row added in Google Sheets, or a manual trigger. Node 2 - Get Rows from Google Sheet This node retrieves the list of companies from your spreadsheet. here is the Google Sheet Template you can use. The columns in this Google Sheet are: Company**: The name of the company Website**: The website URL of the company These two fields are required at this step. Business Area**: The business area deduced by OpenAI from the scraped data Offer**: The offer deduced by OpenAI from the scraped data Value Proposition**: The value proposition deduced by OpenAI from the scraped data Business Model**: The business model deduced by OpenAI from the scraped data ICP**: The Ideal Customer Profile deduced by OpenAI from the scraped data Additional Information**: Information related to the scraped data, including: Information Sufficiency: Description: Indicates if the information was sufficient to provide a full analysis. Options: "Sufficient" or "Insufficient" Insufficient Details: Description: If labeled "Insufficient," specifies what information was missing or needed to complete the analysis. Mismatched Content: Description: Indicates whether the page content aligns with that of a typical company page. Suggested Actions: Description: Provides recommendations if the page content is insufficient or mismatched, such as verifying the URL or searching for alternative sources. Node 3 - Loop Over Items This node ensures that, in subsequent steps, the website in "extra workflow input" corresponds to the row being processed. You can delete this node, but you'll need to ensure that the "query" sent to the scraping workflow corresponds to the website of the specific company being scraped (rather than just the first row). Node 4 - AI Agent This AI agent is configured with a prompt to extract data from the content it receives. The node has three sub-nodes: OpenAI Chat Model: The model used is currently gpt4-o-mini. Call n8n Workflow: This sub-node calls the workflow to use ScrapingBee and retrieves the scraped data. Structured Output Parser: This parser structures the output for clarity and ease of use, and then adds rows to the Google Sheet. Node 5 - Update Company Row in Google Sheet This node updates the specific company's row in Google Sheets with the enriched data. Scraper Agent Workflow Node 1 - Tool Called from Agent This is the trigger for when the AI Agent calls the Scraper. A query is sent with: Company name Website (the URL of the website) Node 2 - Set Company URL This node renames a field, which may seem trivial but is useful for performing transformations on data received from the AI Agent. Node 3 - ScrapingBee: Scrape Company's Website This node scrapes data from the URL provided using ScrapingBee. You can use any scraper of your choice, but ScrapingBee is recommended, as it allows you to configure scraper behavior directly. Once configured, copy the provided "curl" command and import it into n8n. Node 4 - HTML to Markdown This node converts the scraped HTML data to Markdown, which is then sent to OpenAI. The Markdown format generally uses fewer tokens than HTML. Improving the Workflow It's always a pleasure to share workflows, but creators sometimes want to keep some magic to themselves ✨. Here are some ways you can enhance this workflow: Handle potential errors Configure the scraper tool to scrape other pages on the website. Although this will cost more tokens, it can be useful (e.g., scraping "Pricing" or "About Us" pages in addition to the homepage). Instead of Google Sheets, connect directly to your CRM to enrich company data. Trigger the workflow from form submissions on your website and send the scraped data about the lead to a Slack or Teams channel.

Prunaai Flux Schnell Image Generator Description This is a 3x faster FLUX.1 [schnell] model from Black Forest Labs, optimised with pruna with minimal quality loss. Contact us for more at pruna.ai Overview This n8n workflow integrates with the Replicate API to use the prunaai/flux-schnell model. This powerful AI model can generate high-quality image content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters prompt** (string): Prompt for generated image Optional Parameters seed** (integer, default: None): Random seed. Set for reproducible generation megapixels** (string, default: 1): Approximate number of megapixels for generated image speed_mode** (string, default: Juiced 🔥 (default)): Run faster predictions with model optimized for speed num_outputs** (integer, default: 1): Number of outputs to generate aspect_ratio** (string, default: 1:1): Aspect ratio of the output image output_format** (string, default: jpg): Format of the output images output_quality** (integer, default: 80): Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs num_inference_steps** (integer, default: 4): Number of denoising steps. 4 is recommended, and lower number of steps produce lower quality outputs, faster. How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate image content Access the generated output from the final node API Reference Model: prunaai/flux-schnell API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters

The Sound Effects Generator is an automated workflow that allows users to create realistic sound effects using AI and save them directly to Google Drive. It generates high-quality sound effects (up to 30 seconds long) based on user prompts. How It Works: User Input via Web Form A form is presented to the user asking for: A prompt describing the sound (e.g. "waves crashing", "laser blast"). A duration in seconds (up to 30 seconds). API Request to Generate Audio The input is sent to CassetteAI via a POST request using API with proper authentication. Status Polling The workflow waits for 10 seconds and then checks the status of the request. Conditional Flow If the audio generation is complete (COMPLETED), it proceeds to fetch the audio file URL. If not, it waits and retries. Download & Save The audio file is downloaded from the URL. It is automatically uploaded to a specific folder in the user’s Google Drive, with a timestamped filename. Key Advantages Fast & Efficient**: Generates up to 30 seconds of audio in just 1 second of processing time. No Coding Required**: Entire flow can be triggered via a simple form interface. Automated Storage**: Files are automatically saved to a preconfigured Google Drive folder. Scalable**: Can be reused for multiple projects by simply changing the input prompts. Secure**: Uses secure API key-based authentication for interaction with Fal.run and Google Drive. Customizable**: Easy to adapt or extend—for example, sending download links via email or Telegram. How It Works Form Submission: The workflow starts with a form where users input a prompt and the desired duration (max 30 seconds) for the sound effect. Audio Creation: The submitted data is sent to the CassetteAI Sound Effects Generator API via an HTTP request, which initiates the sound effect generation process. Status Check: The workflow periodically checks the status of the request. If the status is "COMPLETED," it proceeds to fetch the audio file. Audio Retrieval: The generated audio file is downloaded from the provided URL and uploaded to a specified Google Drive folder, with a timestamped filename for organization. Set Up Steps API Key Configuration: Create an account on fal.ai and obtain an API key. In the "Create audio" node, set the "Header Auth" with: Name: Authorization Value: Key YOURAPIKEY (replace YOURAPIKEY with your actual API key). Google Drive Integration: Ensure the Google Drive node is configured with the correct OAuth2 credentials and folder ID. Adjust the folder ID in the "Upload Audio" node if a different destination is preferred. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Who is this for? This workflow is ideal for sales teams, marketers, and virtual assistants who manage outbound campaigns and want to improve their cold outreach personalization. It helps automate the research and writing process for each lead, saving time while improving quality. What problem is this workflow solving? Cold outreach often lacks personalization because manually reviewing each lead's website takes time. This workflow eliminates that bottleneck by using AI to auto-generate personalized icebreakers, summaries, and outreach emails based on a lead’s website—without human research. What this workflow does This n8n workflow runs on a schedule and pulls leads from Airtable who don't yet have an "Ice breaker" field filled out. For each lead, it does the following: Trigger: Scheduled daily via the Run Daily to Process New Leads node. Search Airtable: Finds leads in Airtable where the Ice breaker field is empty using the Search Cold Leads Without Icebreaker node. Split in Batches: Iterates through each lead one by one using Loop Through Each Lead. Rate Limiting: Waits briefly before each request using Wait Before Making Request to avoid rate limits. Scrape Website: Sends each lead’s website to Dumpling AI's /scrape endpoint via the Scrape Lead Website with Dumpling AI HTTP request. Generate AI Copy: Sends the scraped content to GPT-4o using the Generate Icebreaker, Summary & Email (GPT-4o) node. It asks the LLM to create: A short personalized icebreaker A 2–3 line website summary A short email body for cold outreach Save Results: Updates the original Airtable record with the generated content using the Save AI Output Back to Airtable node. Sticky Note: Provides an overview of the workflow and usage instructions for future editors or collaborators. This loop continues for all leads found, updating Airtable with fresh AI-generated outreach content. Integration Requirements Airtable (Personal Access Token) Dumpling AI API Key (Header Auth) OpenAI (GPT-4o)

🧾 Description: This automation uses GPT-4o to scan unread Gmail emails and intelligently classify them as: Action → Requires your attention (reply, review, schedule, or respond) No Action → Informational or promotional; no action needed The result? You eliminate inbox noise and gain a clear daily routine: only check what's in Action Required. ⚙️ How It Works: Trigger: Runs on a customizable schedule Fetch Emails: Pulls unread messages from Gmail Classify via GPT-4o: Determines if each email needs action or not Sort Emails: Labels actionable emails as Action Required Labels non-actionable ones as No Action Removes the Inbox label to clean your primary inbox view ✅ Emails stay in your account—just better organized 🚀 How to Use: Import the workflow into your n8n instance Set up Gmail and OpenAI credentials Create Gmail labels: Action Required No Action Activate the workflow Start your day by checking only the Action Required label 📦 Requirements: n8n (self-hosted or cloud) Gmail OAuth2 account OpenAI API key (GPT-4o or GPT-4o-mini) Gmail labels: Action Required, No Action 💡 Why It Matters: Stop manually filtering emails. This workflow helps you focus only on what matters while keeping everything else out of your way—without deleting or archiving anything.

Not sure what to eat tonight? Have recipes emailed to you daily based on your criterial. To run this workflow, you will need to have: A Recipe Search API key from Edamam An active email account with configured credentials To set up your credentials: Set your Edamam AppID and AppKey in the Search Criteria node Select (or create) your email credentials in the Send Recipes node (and set up the to: and from: email addresses while you are at it) To customize the recipes that you receive, open up the Search Criteria node and modify one or more of the following: RecipeCount** - the numner of recipes you would like to receive IngredientCount** - the maximum number of ingredients you would like each recipe to have CaloriesMin** - the minimum number of calories the recipe will have CaloriesMax** - the maximum number of calories the recipe will have TimeMin** - the minimum amount of time (in minutes) the recipe will take to prepare TimeMax** - the maximum amount of time (in minutes) the recipe will take to prepare Diet** - Select one of the following options: balanced - Protein/Fat/Carb values in 15/35/50 ratio high-fiber - More than 5g fiber per serving high-protein - More than 50% of total calories from proteins low-carb - Less than 20% of total calories from carbs low-fat - Less than 15% of total calories from fat low-sodium - Less than 140mg Na per serving random - selects a different random diet each day Health** - Select one of the following options: alcohol-free - No alcohol used or contained immuno-supportive - Recipes which fit a science-based approach to eating to strengthen the immune system celery-free - does not contain celery or derivatives crustacean-free - does not contain crustaceans (shrimp, lobster etc.) or derivatives dairy-free - No dairy; no lactose egg-free - No eggs or products containing eggs fish-free - No fish or fish derivatives fodmap-free - Does not contain FODMAP foods gluten-free - No ingredients containing gluten keto-friendly - Maximum 7 grams of net carbs per serving kidney-friendly - per serving – phosphorus less than 250 mg AND potassium less than 500 mg AND sodium: less than 500 mg kosher - contains only ingredients allowed by the kosher diet. However it does not guarantee kosher preparation of the ingredients themselves low-potassium - Less than 150mg per serving lupine-free - does not contain lupine or derivatives mustard-free - does not contain mustard or derivatives low-fat-abs - Less than 3g of fat per serving no-oil-added - No oil added except to what is contained in the basic ingredients low-sugar - No simple sugars – glucose, dextrose, galactose, fructose, sucrose, lactose, maltose paleo - Excludes what are perceived to be agricultural products; grains, legumes, dairy products, potatoes, refined salt, refined sugar, and processed oils peanut-free - No peanuts or products containing peanuts pecatarian - Does not contain meat or meat based products, can contain dairy and fish pork-free - does not contain pork or derivatives red-meat-free - does not contain beef, lamb, pork, duck, goose, game, horse, and other types of red meat or products containing red meat. sesame-free - does not contain sesame seed or derivatives shellfish-free - No shellfish or shellfish derivatives soy-free - No soy or products containing soy sugar-conscious - Less than 4g of sugar per serving tree-nut-free - No tree nuts or products containing tree nuts vegan - No meat, poultry, fish, dairy, eggs or honey vegetarian - No meat, poultry, or fish wheat-free - No wheat, can have gluten though random - selects a different random health option each day SearchItem* - the general term that you are looking for e.g. *chicken

Tags*: Sustainability, Supply Chain, AI Agent, CO2 Emissions, Carbon Interface API, Logistics, Automation Context Hi! I’m Samir — a Supply Chain Engineer and Data Scientist based in Paris, and founder of LogiGreen Consulting. I help logistics teams reduce their environmental footprint by combining AI automation and carbon estimation APIs. This workflow is part of our green logistics initiative, allowing businesses to track the CO₂ emissions of last-mile or regional shipments. > Automate carbon tracking for shipping operations with n8n! 📬 For business inquiries, feel free to connect with me on LinkedIn Who is this template for? This workflow is designed for logistics coordinators, transportation planners, or sustainability officers who want to estimate and record emissions for B2B shipments. Let’s imagine your carrier sends a shipment confirmation email after a pickup is scheduled: An AI Agent reads the email and extracts structured data: addresses, distance, cargo weight, and delivery time. The Carbon Interface API is then called to calculate CO₂ emissions based on weight and distance, and the results are stored in a Google Sheet. How does it work? This workflow automates the process of tracking CO₂ emissions for scheduled shipments: 📨 Gmail Trigger captures shipment confirmation emails 🧠 AI Agent parses the shipment info (pickup, delivery, weight, distance) 🚚 Carbon Interface API estimates CO₂ emissions 📊 Google Sheets is used to store shipment metadata and carbon results Steps: 💌 Trigger on new shipment confirmation email 🧠 Extract structured shipment info with AI Agent 📋 Store metadata in Google Sheets ⚙️ Call Carbon Interface API with weight and distance 📥 Append estimated CO₂ emissions to the shipment row What do I need to get started? You’ll need: A Gmail account to receive shipment confirmation emails A Google Sheet to track shipment data and CO₂ A free Carbon Interface API key OpenAI access for using the AI Agent parser A few sample emails from your logistics provider to test Next Steps 🗒️ Use the sticky notes in the n8n canvas to: Add your Gmail and Carbon Interface credentials Try with a sample shipment confirmation email Check your Google Sheet to verify emissions and timestamps This template was built using n8n v1.93.0 Submitted: June 7, 2025

This workflow automatically connects to a Cloudflare R2 bucket (via S3-compatible API), filters out files older than 14 days, deletes them, and then sends a Telegram notification for each deletion. It runs on a daily schedule. 🕘 Schedule Trigger Executes the workflow once a day at a specified hour (e.g., 9 AM). 📦 S3 Node – List Files Retrieves all objects from a specific folder (prefix) in a Cloudflare R2 bucket using the S3 API. 🔎 Code Node – Filter Files Older Than 2 Weeks Filters the retrieved files by comparing their LastModified timestamps to the current date. Files older than 14 days (2 weeks) are selected for deletion. 🗑️ S3 Node – Delete File Deletes each filtered file from the R2 bucket. 📨 Telegram Node – Notify Deletion Sends a Telegram message with the name of the deleted file to a specified chat ID. The message includes:

How to scrap emails from websites This workflow shows how to quickly build an Email scraping API using n8n. Email marketing is at the core of most marketing strategies, be it content marketing, sales, etc. As such, being able to find contacts in bulk for your business on a large scale is key. There are available tools available in the market that can do this, but most are premium; why not build a custom one with n8n? Usage The workflow gets the data from a website and performs an extraction based on the date around on the website Copy the webhook URL to your browser Add a query parameter eg ?Website=https://mailsafi.com . This should give you a URL like this {{$n8nhostingurl/webhook/ea568868-5770-4b2a-8893-700b344c995e?Website=https://mailsafi.com Click on the URL and wait for the extracted email to be displayed. This will return the email address on the website, or if there is no email, the response will be "workflow successfully executed." Make sure to use HTTP:// for your domains Otherwise, you may get an error.

AI-Powered Workflow for Auto-Responding to Positive Cold Email Replies This workflow is designed for agencies, freelancers, and sales teams who want to turn positive cold email replies into booked meetings automatically—without hiring VAs or spending hours on manual responses. ❓ The Problem Most teams waste time replying manually or pay for virtual assistants, leading to delays and missed opportunities. This template eliminates that bottleneck. ✅ What the Workflow Does Detects positive replies from Instantly.ai campaigns Uses AI to analyze intent and craft natural, human-like responses Adds personalization to keep replies authentic Includes Calendly links, product docs, or FAQs based on the lead’s intent Sends responses instantly—so you never miss a hot lead again No robotic AI text. Just smooth, human-style emails that get booked calls faster. 👥 Who is This For? Agencies** running Instantly.ai or similar outbound tools Founders** handling their own cold email outreach Sales teams** looking to automate follow-up and booking Anyone who gets 5–20 positive replies a week and wants to 2x–4x conversions ✅ Requirements n8n** (Cloud or self-hosted) Instantly.ai account** with API access OpenAI API key** (stored securely in n8n credentials) (Optional) Calendly or booking link, Notion or Google Docs for resources ⚙️ How to Set Up Import the workflow into n8n Add your Instantly.ai API credentials and OpenAI key using n8n’s credential manager Customize the AI prompt for your tone, CTA, and offer Insert your Calendly or booking link in the response template Test with one positive reply to confirm filtering and response quality Activate the workflow to auto-reply in real time 🔧 How to Customize Adjust the filtering logic for different keywords or intent signals Add branching for multiple booking links (e.g., based on region or service type) Push responses to a CRM for tracking Include extra resources like case studies or pricing docs

📦 Send Telegram Notifications for New WooCommerce Orders This workflow automatically sends a Telegram notification when an order status in WooCommerce changes to "Processing." Perfect for online store owners who want instant updates on order fulfillment. ⚙️ Set Up Telegram Alerts for WooCommerce Orders Configure WooCommerce Webhook to trigger on order updates. Create a Telegram Bot and obtain the API token. Set Up Telegram Credentials in n8n. Configure the Telegram Node with your chat ID. Activate and Test the workflow by placing a new order. ##💡 Notes You can customize the message format in the 🖋️ Design Message Template node to include additional order details. Contact me on [Telegram]: https://t.me/amir676080 Message structure includes the following details 🆔 Order Number: 11234 👦🏻 Customer Name: John Doe 💵 Amount: 299.99 USD 📅 Order Date: ➖ 25th November 2024 at 14:42 🏙 City: New York 📞 Phone: +1 555-1234 ✍🏻 Order Note: Fast delivery requested 📦 Ordered Products: 🔹 Wireless Earbuds (2 items) 📝 Type: Premium Sound Edition Contact me on [Telegram]: https://t.me/amir676080

Ndreca Hunyuan3d 2 Test AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the ndreca/hunyuan3d-2-test model. This powerful AI model can generate high-quality other content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters image** (string): Input image for generating 3D shape Optional Parameters seed** (integer, default: 1234): Random seed for generation steps** (integer, default: 50): Number of inference steps num_chunks** (integer, default: 200000): Number of chunks for mesh generation max_facenum** (integer, default: 40000): Maximum number of faces for mesh generation guidance_scale** (number, default: 5.5): Guidance scale for generation octree_resolution** (string, default: 512): Octree resolution for mesh generation remove_background** (boolean, default: True): Whether to remove background from input image How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate other content Access the generated output from the final node API Reference Model: ndreca/hunyuan3d-2-test API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters