Workflow Templates

This workflow provides automated access to the Flux Kontext Apps Iconic Locations 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 Flux Kontext Apps Iconic Locations 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: Put yourself in an iconic location around the world from a single image 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 Flux Kontext Apps/iconic-locations AI model Flux Kontext Apps Iconic Locations**: 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 #machinelearning #artificialintelligence #aitools #automation #digitalart #contentcreation #productivity #innovation

Template Description This n8n workflow is designed to manage Fastmail masked email addresses using the Fastmail API. The workflow provides the following functionalities: Retrieve all masked emails: Fetches all masked email addresses associated with the Fastmail account. Create masked email: Allows creating a new masked email with a specified state (pending, enabled, etc.). Update masked email state: Updates the state of a masked email such as enabling, disabling, or deleting it. Generate HTML template: Constructs an HTML table to display the masked emails in a user-friendly format. Steps to Make it Work Webhook Node: This node listens for incoming requests to manage masked emails. Needs Basic Authentication credentials to secure the endpoint. Session Node: Sends a request to obtain session information from Fastmail's API. Requires an HTTP Header Auth credential with your Fastmail API token. Switch Node: Routes the workflow based on the state of the incoming masked email request (pending, enabled, disabled, deleted). HTTP Request Nodes: These nodes handle various Fastmail API calls for masked emails (get, set, update, delete). All HTTP Request nodes require an HTTP Header Auth credential attached, using the Fastmail API token. Set Node: Gathers the retrieved masked email list into an array for further processing. HTML Node: Generates an HTML template to render the masked email addresses in a table format. Respond to Webhook Node: Sends back the HTML table to the client in response to the webhook request. Needed Credentials Fastmail Masked E-Mail Addresses: An API token from Fastmail's API. Each HTTP call to Fastmail requires this credential for authentication. Note Ensure that you correctly configure authentication for the API calls and webhook security. Use your actual Fastmail API credentials with the correct scope. The workflow assumes that the Fastmail API is correctly configured and accessible from your n8n instance. Update URLs and credentials IDs according to your n8n configuration.

This workflow is designed to intelligently route user queries to the most suitable large language model (LLM) based on the type of request received in a chat environment. It uses structured classification and model selection to optimize both performance and cost-efficiency in AI-driven conversations. It dynamically routes requests to specialized AI models based on content type, optimizing response quality and efficiency. Benefits Smart Model Routing**: Reduces costs by using lighter models for general tasks and reserving heavier models for complex needs. Scalability**: Easily expandable by adding more request types or LLMs. Maintainability**: Clear logic separation between classification, model routing, and execution. Personalization**: Can be integrated with session IDs for per-user memory, enabling personalized conversations. Speed Optimization**: Fast models like GPT-4.1 mini or Gemini Flash are chosen for tasks where speed is a priority. How It Works Input Handling: The workflow starts with the "When chat message received" node, which triggers the process when a chat message is received. The input includes the chat message (chatInput) and a session ID (sessionId). Request Classification: The "Request Type" node uses an OpenAI model (gpt-4.1-mini) to classify the incoming request into one of four categories: general: For general queries. reasoning: For reasoning-based questions. coding: For code-related requests. google: For queries requiring Google tools. The classification is structured using the "Structured Output Parser" node, which enforces a consistent output format. Model Selection: The "Model Selector" node routes the request to one of four AI models based on the classification: Opus 4 (Claude 4 Sonnet): Used for coding requests. Gemini Thinking Pro: Used for reasoning requests. GPT 4.1 mini: Used for general requests. Perplexity: Used for search (Google-related) requests. AI Processing: The selected model processes the request via the "AI Agent" node, which includes intermediate steps for complex tasks. The "Simple Memory" node retains session context using the provided sessionId, enabling multi-turn conversations. Output: The final response is generated by the chosen model and returned to the user. Set Up Steps Configure Trigger: Ensure the "When chat message received" node is set up with the correct webhook ID to receive chat inputs. Define Classification Logic: Adjust the prompt in the "Request Type" node to refine classification accuracy. Verify the output schema in the "Structured Output Parser" node matches expected categories (general, reasoning, coding, google). Connect AI Models: Link each model node (Opus 4, Gemini Thinking Pro, GPT 4.1 mini, Perplexity) to the "Model Selector" node. Ensure credentials (API keys) for each model are correctly configured in their respective nodes. Set Up Memory: Configure the "Simple Memory" node to use the sessionId from the input for context retention. Test Workflow: Send test inputs to verify classification and model routing. Check intermediate outputs (e.g., request_type) to ensure correct model selection. Activate Workflow: Toggle the workflow to "Active" in n8n after testing. Need help customizing? Contact me for consulting and support or add me on Linkedin.

📱🤖 Create Stunning AI Images Directly from WhatsApp with Gemini This workflow transforms your WhatsApp into a personal AI image generation studio. Simply send a text message with your idea, and this bot will use the advanced prompt engineering capabilities of Gemini 2.5 Pro to craft a detailed, artistic prompt. It then uses Gemini 2.0 Flash to generate a high-quality image from that prompt and sends it right back to your chat. It's a powerful yet simple way to bring your creative ideas to life, all from the convenience of your favorite messaging app\! What this workflow does Listens for WhatsApp Messages: The workflow starts automatically when you send a message to your connected WhatsApp number. Enhances Your Idea with AI: It takes your basic text (e.g., "a knight on a horse") and uses Gemini 2.5 Pro to expand it into a rich, detailed prompt perfect for image generation (e.g., "A cinematic, full-body shot of a stoic knight in gleaming, ornate silver armor, riding a powerful black warhorse through a misty, ancient forest. The scene is lit by ethereal morning sunbeams piercing the canopy, creating dramatic volumetric lighting and long shadows. Photorealistic, 8K, ultra-detailed, award-winning fantasy concept art."). Generates a Unique Image: It sends this enhanced prompt to the Google Gemini 2.0 Flash image generation API. Prepares the Image: The API returns the image in Base64 format, and the workflow instantly converts it into a binary file. Sends it Back to You: The final, high-quality image is sent directly back to you in the same WhatsApp chat. Nodes Used 🟢 WhatsApp Trigger: The entry point that listens for incoming messages. 🧠 LangChain Chain (LLM): Uses Gemini 2.5 Pro for advanced prompt engineering. ➡️ HTTP Request: Calls the Google Gemini 2.0 Flash API to generate the image. 🔄 Convert to File: Converts the Base64 image data into a sendable file format. 💬 WhatsApp: Sends the final image back to the user. Prerequisites To use this workflow, you will need: An n8n instance. A WhatsApp Business Account connected to n8n. You can find instructions on how to set this up in the n8n docs. A Google Gemini API Key. You can get one for free from Google AI Studio. How to use this workflow Get your Google Gemini API Key: Visit the Google AI Studio and create a new API key. Configure the Gemini 2.5 Pro Node: In the n8n workflow, select the Gemini 2.5 Pro node. Under 'Connect your account', click 'Create New' to add a new credential. Paste your Gemini API key from the previous step and save. Configure the Generate Image (HTTP Request) Node: Select the Generate Image node. In the properties panel on the right, find the Query Parameters section. In the 'Value' field for the key parameter, replace "Your API Key" with your actual Google Gemini API Key. Connect WhatsApp: Select the WhatsApp Trigger node. Follow the instructions to connect your WhatsApp Business Account credential. If you haven't created one, the node will guide you through the process. Activate and Test: Save the workflow using the button at the top right. Activate the workflow using the toggle switch. Send a message to your connected WhatsApp number (e.g., "A futuristic city in the style of Van Gogh"). The bot will process your request and send a stunning AI-generated image right back to you\!

Try It Out! This n8n template helps you create SEO-optimized Blog Posts for your businesses website or for personal use. Whether you're managing a business or helping local restaurants improve their digital presence, this workflow helps you build SEO-Optimized Blog Posts in seconds using Google Autocomplete and People Also Ask (SerpAPI). Who Is It For? This is helpful for people looking to SEO Optimize either another person's website or their own. How It Works? You start with a list of blog inspirations in Google Sheets (e.g., “Best Photo Session Spots”). The workflow only processes rows where the “Status” column is not marked as “done”, though you can remove this condition if you’d like to process all rows each time. The workflow pulls Google Autocomplete suggestions and PAA questions using: A custom-built SEO API I deployed via Render (for Google Autocomplete + PAA), SerpAPI (for additional PAA data). These search insights are merged. For example, if your blog idea is “Photo Session Spots,” the workflow gathers related Google search phrases and questions users are asking. Then, GPT-4 is used to draft a full blog post based on this data. The finished post is saved back into your Google Sheet. How To Use Fill out the “Blog Inspiration” column in your Google Sheet with the topics you want to write about. Update the OpenAI prompt in the ChatGPT node to match your tone or writing style. (Tip: Add a system prompt with context about your business or audience.) You can trigger this manually, or replace it with a cron schedule, webhook, or other event. Requirements A SerpAPI account to get PAA An OpenAI account for ChatGPT Access to Google Sheets and n8n How To Set-Up? Your Google Sheet should have three columns: "Blog Inspiration", "Status" → set this to “done” when a post has been generated, "Blog Draft" → this is automatically filled by the workflow. To use the SerpAPI HTTP Request node: 1. Drag in an HTTP Request node, 2. Set the Method and URL depending on how you're using SerpAPI: Use POST to run the actor live on each request. Use GET to fetch from a static dataset (cheaper if reusing the same data). 3. Add query parameters for your SerpAPI key and input values. 4. Test the node. Refer to this n8n documentation for more help! https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolserpapi/. The “Autocomplete” node connects to a custom web service I built and deployed using Render. I wrote a Python script (hosted on GitHub) that pulls live Google Autocomplete suggestions and PAA questions based on any topic you send. This script was turned into an API and deployed as a public web service via Render. Anyone can use it by sending a POST request to: https://seo-api2.onrender.com/get-seo-data (the URL is already in the node). Since this is hosted on Render’s free tier, if the service hasn’t been used in the past ~15 minutes, it may “go to sleep.” When that happens, the first request can take 10–30 seconds to respond while it wakes up. Happy Automating!

This updated workflow will automatically archive your Spotify Discover Weekly tracks to another manually created playlist, without the nuisance of duplicate tracks. It utilizes the latest verisons of n8n's Schedule trigger, Spotify, Switch, Merge, and IF nodes. Special thanks to trey for their original version of the workflow, as well as ihortom for their help with navigating the Switch node's outputs. To use this workflow, you'll need to: Create a playlist for use as the archive playlist within your Spotify account Create and select your Spotify credentials within each Spotify node within the workflow See workflow README for additional information and optional setup steps.

This simple workflow will fetch a YouTube playlist every n minutes and send the new items s to a collection in Raindrop. You can connect any application at the end of the flow. Make sure to authenticate to YouTube using Google Auth, and to Raindrop using an API. Update the Playlist ID and the Raindrop collection.

Workflow Overview: Extract text from image using AI is worth because you need no code. It incorporates Google Gemini 2.0 Flash model for important text extraction from image. If you code without AI, you have to use multiple condition and may cause a lot of bug but with Google Gemini, you don't need any coding and if the Pay Slip is different, Gemini will extract it automatically. Workflow description: User uses Line Messaging API to send Pay Slip image or message to the chatbot, create Line Business ID from here: Line Business Classify the message which is image or text If the message is Pay Slip image, it will process using Gemini 2.0 Flash EXP and extract important information and response in JSON format without coding by using the following prompt: Analyze image and then return in JSON Response that has the only following value: Status, From, To, Date, Amount To get Google AI Studio API Key, you can find from the following link: Google AI Studio API Key Create Google Sheets which include the fileds (Status, From, To, Date, Amount) that we have created related to the AI prompt Google Sheets as the following example: If the message is text, it will process using Gemini 2.0 Flash EXP model as the AI Assistant else if the message is image, it will extract the important fields then reply to the User and insert into Google Sheets Key Features: Extract text from image with No Code** Without N8N, we have to write code to extract text from image, but with N8N and Google Gemini 2.0 Flash EXP together, we don't need to code and it will process all slip vendors or other document vendors. Multipurpose Chatbot** this chatbot accept both text and image so we don't have to create many chatbot accounts Reduce human error** this workflow let any officer to verify document status when the job ends Note: You can change the information by changing your prompt and also Google Sheets Column names relatively.

What this workflow does? This workflow converts orthographic three-view drawings into 360° rotation videos through PiAPI's GPT-4o-Image and Kling APIs (unofficial). The workflow could be set with our 3D Figurine Orthographic Views workflow for generation. Who is the workflow for? Designers**: Generate inspiration into 3D designs and make them spin to gain concrete details in a efficient way. Online shoppers**: Show protential products from all angles in videos and preview overall texture of models. Content Creators** (including toy bloggers): Make fun videos of collectible models. Step-by-step Instructions 1.Fill in basic params with your X-API-Key of your PiAPI account and 3-View image url. 2.Click test workflow. 3.Get the final video in the last node. Use Case Input Image Output Video

This n8n workflow demonstrates how to build an automated AI chat system using OpenRouter.ai. It includes a manual trigger, sets a model and user message, sends a POST request to the OpenRouter chat API, and summarizes the response. Workflow Steps: Manual Trigger – Starts the workflow when executed manually. Set Node – Defines: Model: mistralai/mistral-small-3.2-24b-instruct:free Message: What is the meaning of life? HTTP Request – Sends a POST request to https://openrouter.ai/api/v1/chat/completions using Bearer Token Authentication with the model and message as JSON. Summarize – Extracts and summarizes the AI’s response (choices[0].message.content). Use Cases: AI chatbot automation Content summarization Testing AI prompts in real-time Educational demos using OpenRouter.ai Lightweight conversational tools with no external server

This workflow dynamically chooses between two new powerful Anthropic models — Claude Opus 4 and Claude Sonnet 4 — to handle user queries, based on their complexity and nature, maintaining scalability and context awareness with Anthropic web search function and Think tool. Key Advantages 🔁 Dynamic Model Selection Automatically routes each user query to either Claude Sonnet 4 (for routine tasks) or Claude Opus 4 (for complex reasoning), ensuring optimal performance and cost-efficiency. 🧠 AI Agent with Tool Use The AI agent can utilize a web search tool to retrieve up-to-date information and a Think tool for complex reasoning processes — improving response quality. 📎 Memory Integration Uses session-based memory to maintain conversational context, making interactions more coherent and human-like. 🧮 Built-in Calculation Tool Handles numeric queries using an integrated calculator tool, reducing the need for external processing. 📤 Structured Output Parser Ensures outputs are always well-structured and formatted in JSON, which improves consistency and downstream integrations. 🌐 Web Search Capability Supports real-time information retrieval for current events, statistics, or details not available in the AI’s base knowledge. Components Overview Trigger**: Listens for new chat messages. Routing Agent**: Analyzes the message and returns the best model to use. AI Agent**: Handles the conversation, decides when to use tools. Tools**: web_search for internet queries Think for reasoning Calculator for math tasks Models Used**: claude-sonnet-4-20250514: Optimized for general and business logic tasks. claude-opus-4-20250514: Best for deep, strategic, and analytical queries. How It Works Dynamic Model Selection The workflow begins when a chat message is received. The Anthropic Routing Agent analyzes the user's query to determine the most suitable model (either Claude Sonnet 4 or Claude Opus 4) based on the query's complexity and requirements. The routing agent uses predefined criteria to decide: Claude Sonnet 4: Best for standard tasks like real-time workflow routing, data validation, and routine business logic. Claude Opus 4: Reserved for complex scenarios requiring deep reasoning, advanced analysis, or high-impact decisions. Query Processing and Response Generation The selected model processes the query, leveraging tools like web_search for real-time information retrieval, Think for internal reasoning, and Calculator for numerical tasks. The AI Agent coordinates these tools, ensuring the response is accurate and context-aware. A Simple Memory node retains session context for coherent multi-turn conversations. The final response is formatted and returned to the user without intermediate steps or metadata. Set Up Steps Node Configuration Trigger: Configure the "When chat message received" node to handle incoming user queries. Routing Agent: Set up the "Anthropic Routing Agent" with the system message defining model selection logic. Ensure it outputs a JSON object with prompt and model fields. AI Model Nodes: Link the "Sonnet 4 or Opus 4" node to dynamically use the selected model. The "Sonnet 3.7" node powers the routing agent itself. Tool Integration Attach the "web_search" HTTP tool to enable internet searches, ensuring the API endpoint and headers (e.g., anthropic-version) are correctly configured. Connect auxiliary tools (Think, Calculator) to the "AI Agent" for extended functionality. Add the "Simple Memory" node to maintain conversation history. Credentials Provide an Anthropic API key to all nodes requiring authentication (e.g., model nodes, web search). Testing Activate the workflow and test with sample queries to verify: Correct model selection (e.g., Sonnet for simple queries, Opus for complex ones). Proper tool usage (e.g., web searches trigger when needed). Memory retention across chat turns. Deployment Once validated, set the workflow to active for live interactions. Need help customizing? Contact me for consulting and support or add me on Linkedin.

How It Works – Data Deduplication in n8n This tutorial demonstrates how to remove duplicate records from a dataset using JavaScript logic inside n8n's Code nodes. It simulates real-world data cleaning by generating sample user data with intentional duplicates (based on email addresses) and walks you through the process of deduplication step-by-step. The process includes: Creating Sample Data with duplicates. Filtering Out Duplicates using filter() and findIndex() based on email. Displaying Cleaned Results with simple statistics for before-and-after comparison. This is ideal for scenarios like CRM imports, ETL processes, and general data hygiene. ⚙️ Set-Up Steps 🔹 Step 1: Manual Trigger Node: When clicking 'Test workflow' Purpose: Initiates the workflow manually for testing. 🔹 Step 2: Generate Sample Data Node: Create Sample Data (Code node) What it does: Creates 6 users, including 2 intentional duplicates (by email). Outputs data as usersJson with metadata (totalCount, message). Mimics real-world messy datasets. 🔹 Step 3: Deduplicate the Data Node: Deduplicate Users (Code node) What it does: Parses usersJson. Uses .filter() + .findIndex() to keep only the first instance of each email. Logs total, unique, and removed counts. Outputs clean user list as separate items. 🔹 Step 4: Display Results Node: Display Results (Code node) What it does: Outputs structured summary: Unique users Status Timestamp Prepares results for review or downstream use. 📈 Sample Output Original count: 6 users Deduplicated count: 4 users Duplicates removed: 2 users 🎯 Learning Objectives You'll learn how to: Use .filter() and .findIndex() in n8n Code nodes Clean JSON data within workflows Create simple, effective deduplication pipelines Output structured summaries for reporting or integration 🧠Best Practices Validate input format (e.g., JSON schema) Handle null or missing fields gracefully Use logging for visibility Add error handling for production use Use pagination/chunking for large datasets