Workflow Templates

Who is it for Beauty & Fashion Consultants: To visualize trends for specific client profiles. Content Creators: To generate personalized variations of trending aesthetics. Trend Watchers: To automate the collection and adaptation of social media designs. How it works Configuration: You define the target hashtags (e.g., #trendnails) and the target skin tone (e.g., "Yellow Base Spring") in the set node. Scraping: The workflow uses Apify to scrape the latest top posts from Instagram based on your hashtags. Analysis & Prompting: GPT-4o (Vision) analyzes the original image to understand the pattern and atmosphere, then generates a new prompt optimized for the specified skin tone. Generation: DALL-E 3 creates a new image based on the generated prompt. Delivery: The final personalized image is uploaded directly to a Slack channel. How to set up Apify: Create an account and get your API Token. Ensure you have access to the instagram-hashtag-scraper actor. OpenAI: You need an API Key with access to GPT-4o and DALL-E 3. Slack: Connect your Slack account with OAuth2 in n8n. Workflow Configuration: Open the "Workflow Configuration" node and fill in your apifyApiToken, desired hashtags, and skinTone. Requirements Apify account OpenAI API key (GPT-4o & DALL-E model access) Slack account

Who's it for This template is built for real estate agencies and agents in Spain, Italy, and Portugal who receive property inquiries through Idealista and want to respond to leads faster using automation and AI. How it works The workflow monitors your inbox for new emails and filters for messages from Idealista. When a match is found, an AI agent extracts key lead details like name, phone number, and property reference from the unstructured email. A WhatsApp template message is then automatically sent to the lead via Superchat, initiating the qualification process within seconds of the inquiry. Set up steps Setup takes around 10–15 minutes. Connect your email account, link your WhatsApp Business through Superchat, and approve your WhatsApp template. Detailed step-by-step instructions are included in the sticky notes inside the workflow. Requirements An email account connected to n8n (Gmail, Outlook, or IMAP) A WhatsApp account connected to Superchat An approved WhatsApp template message An OpenAI API key or other LLM provider How to customize Create a WhatsApp template that matches your agency's tone and branding to personalize the lead's first impression.

Video Introduction Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin Workflow Overview This workflow creates an intelligent AI chat agent that allows you to query and retrieve data from your Airtable base using natural language conversations. Instead of manually searching through tables or writing complex formulas, simply ask questions like "List all Microsoft employees" or "Show me contacts from California," and the agent will intelligently search the appropriate tables and provide accurate answers. The agent is powered by OpenAI's GPT model and equipped with two specialized tools: one for searching your Contacts table and another for searching your Companies table. It maintains conversation context through built-in memory, allowing for follow-up questions and more natural interactions. Key Features Natural language queries**: Ask questions in plain English instead of writing formulas Multi-table search**: Automatically searches both Contacts and Companies tables Intelligent tool selection**: The AI decides which table to query based on your question Conversation memory**: Remembers context for follow-up questions Web-based chat interface**: Interact with your data through a simple chat UI Setup Requirements Airtable Connection: Create a Personal Access Token at airtable.com/create/tokens Add these scopes: data.records:read, data.records:write, and schema.bases:read Grant access to your bases and copy the token Add the token as an Airtable credential in n8n Update both Airtable tool nodes to point to your specific base and tables OpenAI Connection: Connect your OpenAI API credentials in the OpenAI Chat Model node. The workflow uses GPT-4.1-mini by default, but you can select any available model. Once published, the workflow provides a chat URL where you can start asking questions about your Airtable data immediately.

📌 What It Does This workflow connects a Telegram bot with your ClickUp workspace, allowing you to create, read, update, and delete tasks just by sending a message. The AI agent interprets natural language commands and takes the appropriate action — all without needing to open ClickUp. It’s like having a personal assistant inside Telegram that manages your task list for you. ✅ Prerequisites To use this workflow, you'll need the following credentials set up in n8n: Telegram Bot API Credential** (used in all Telegram nodes) ClickUp OAuth2 Credential** (for task operations) OpenAI Credential** (to power the AI agent that parses your commands) ⚠️ Before First Use Make sure to add your bot's user ID to the Ignore Bot Messages node. This prevents infinite loops caused by the bot responding to its own messages. If you're unsure of your bot's ID: Temporarily disable the two Telegram tool nodes connected to the AI Agent. Send a test message from the bot and capture its ID. Add that ID to the ignore filter, then re-enable the nodes. ⚙️ How It Works Trigger: The workflow starts when your Telegram bot receives a message. Ignore Self: If the message was sent by the bot itself, the workflow stops. AI Analysis: The message is passed to an AI agent (OpenAI) that determines what action to take. Decision Tree: 📌 Create a new task in ClickUp ✏️ Update an existing task 🔍 Find a task and return its details 🗑️ Delete a task ❓ Ask for more details if input is unclear ✅ Send confirmation or feedback to the user 💡 Example Use Cases “Add a task called ‘Follow up with supplier’ for tomorrow.” “What tasks are due this week?” “Update the task ‘Website Launch’ to ‘in progress’.” “Delete the task ‘Old client notes’.” This workflow is ideal for solo operators, remote teams, or anyone who wants to manage ClickUp while on the go — without switching apps. 🛠️ Setup Instructions Telegram Bot: Create a Telegram bot using BotFather Add your Telegram credential to all Telegram nodes in this workflow Bot ID Filter: Add your bot’s Telegram user ID to the Ignore Bot Messages node OpenAI Setup: Add your OpenAI credential to the AI Agent node ClickUp Integration: Connect your ClickUp credential Set your workspace, list, and folder IDs in the task creation and search nodes 🚀 How to Use Save the Telegram bot to your contacts Open the Telegram chat with your bot and send a message like: "Add a task to follow up with invoices every Friday" The bot will reply with confirmation or ask for clarification The task will appear in your ClickUp workspace within seconds 🔧 Customization Options Add new intents to the AI agent to support more actions (e.g., time tracking or comments) Customize the bot’s responses for branding or tone Add notifications or reminders using additional Telegram nodes ✨ Why It's Useful This workflow eliminates the friction of switching between Telegram and your task manager. It reduces manual data entry, saves time, and gives you a simple way to manage your to-do list using natural language — even on mobile. Perfect for freelancers, managers, or team leads who want a faster, more intuitive way to stay organized.

Turn a song into a ready-to-hang poster, by using Musixmatch and AI This n8n workflow turns a song title and artist name into a gallery-ready poster by combining Musixmatch lyric intelligence with a tightly controlled AI art direction pipeline. At its core, Musixmatch is used to identify the correct song and retrieve official lyrics, which then drive a highly curated image-generation prompt and final poster artwork. What this workflow does Trigger via chat The workflow starts when a chat message is received. The user provides a song title and artist name. Identify the correct track with Musixmatch The Musixmatch “Match track by metadata” node is used to: Resolve ambiguities in song titles Confirm the correct track Retrieve a stable commontrack_id This ensures all downstream steps reference the correct song. Retrieve full lyrics from Musixmatch Using the confirmed commontrack_id, the Musixmatch lyrics endpoint fetches the official song lyrics. Lyrics are treated as authoritative source material for creative interpretation. AI Agent selects and interprets a lyric A dedicated AI Agent: Selects one single-line lyric (strictly enforced constraints) Analyzes its emotional and thematic meaning Interprets mood, tension, and implied narrative The lyric becomes the conceptual anchor of the poster. AI Agent constructs a high-fidelity image prompt The agent outputs a fully structured, production-ready image-generation prompt including: Song metadata (reference only) Emotional interpretation Visual style choice Composition and layout rules Typography and legibility constraints Quality and design guardrails No drafts, explanations, or partial output are allowed. Generate the final poster image The completed prompt is passed directly to the image generation node. The result is a high-resolution, A4 portrait poster designed to feel intentional, modern, and gallery-ready. Output A single poster image (1024×1536, A4 portrait ratio) Features: One carefully chosen lyric line Clean, modern typography High-end contemporary poster design Subtle film grain Fully legible text and metadata Requirements Musixmatch API credentials** Track matching and lyric retrieval OpenAI API credentials** Language model for creative direction Image model for poster generation Use cases Creating lyric posters for personal collections Album or song promotion visuals Editorial or social media artwork Print-ready music-inspired posters Exploring visual interpretations of song lyrics

📊 Description Automate proactive brand reputation monitoring across public platforms using AI-driven risk analysis 🤖. This workflow continuously scans Reddit, Glassdoor, and review sites via SerpAPI to detect negative sentiment, public complaints, and early crisis signals 🔍. Each mention is analyzed with AI to assess risk level and urgency, ensuring no critical issue goes unnoticed. High-risk threats automatically trigger real-time Google Chat alerts and create priority Asana tasks 🚨, enabling fast, coordinated response without manual monitoring. 🔍 What This Template Does Runs on an hourly schedule ⏰ to continuously track brand mentions online. Searches public platforms like Reddit and review sites using SerpAPI 🔍. Parses and structures insights from AI-powered search results 📄. Analyzes sentiment and crisis risk using Azure OpenAI 🤖. Filters high-risk reputation threats automatically ⚠️. Sends instant Google Chat alerts for urgent issues 💬. Creates priority Asana tasks to ensure fast team action ✅. ✅ Key Benefits ✅ Detect brand crises before they escalate publicly ✅ Eliminate manual reputation monitoring across platforms ✅ Get real-time alerts only for high-risk issues ✅ Centralize crisis response with automatic task creation ✅ Improve brand trust and response time ✅ AI-powered, scalable, and always-on monitoring ⚙️ Features Hourly automated monitoring trigger SerpAPI Google AI Mode search integration AI-based sentiment and crisis risk classification Structured JSON output for reliability Google Chat alerts for instant visibility Asana task creation for crisis management Fail-safe handling for malformed AI responses 🔑 Requirements SerpAPI account (Google AI Mode search) Azure OpenAI credentials Google Chat OAuth2 credentials Asana OAuth2 credentials Brand name or keywords for monitoring 🎯 Target Audience SaaS founders and startup teams Brand, PR, and reputation managers Digital agencies managing multiple clients Customer support and crisis response teams

✍️ Ultimate AI Blog Content Creator with Slack + Pinecone + Perplexity Description Turn your marketing team’s blog ideas into full, research-backed, brand-aligned articles with one Slack mention. This workflow connects Slack, Pinecone, and Perplexity to deliver high-impact blog posts that match your company’s voice and leverage the latest research — all written directly into Google Docs for immediate publishing. 👉 Watch step-by-step build of this workflow on: www.youtube.com/@automatewithmarc How It Works Slack Trigger – Marketing team members @mention the bot with a blog idea. Perplexity Tool – Gathers the most up-to-date insights and research on the topic. Pinecone Vector DB – Injects your brand guidelines, tone, and style from stored vectors. AI Blogpost Agent – Powered by Anthropic/OpenAI, it blends research + style to create a polished, structured blog post. Simple Memory – Keeps context across requests for more consistent content. Google Docs – Creates and updates a document with the generated article, ready for review or publishing. Why Content Teams Will Love It ⚡ Faster turnaround — go from idea to publish-ready blog in minutes. 📝 On-brand every time — uses your Pinecone-stored brand guidelines. 🌍 Research-driven — Perplexity ensures content is current and credible. 🤝 Team-friendly — triggered right inside Slack for effortless collaboration. Requirements Slack App (with app_mentions:read, chat:write) Pinecone account with embedded brand guideline vectors Perplexity API key Anthropic/OpenAI API key Google Docs account

This workflow integrates Google Sheets with Supabase Vector Store for storing personal data as vectors. It utilizes OpenAI and Google Gemini AI models for enhanced data processing and querying. The workflow performs the following tasks: Extracts personal data** from Google Sheets. Processes the data using AI tools like OpenAI and Google Gemini for intelligent insights. Inserts the data* into *Supabase** as vectors, enabling efficient storage and fast querying. Includes seamless integration with Postgres for memory management. Supports data loading, embedding, and management. This template is ideal for: Personal data storage with AI-driven querying and analysis. Building intelligent agents that interact with your data. Efficient vector-based storage for personal information. Perfect for those looking to integrate AI into their personal data workflows.

AI Agent for GitHub AI Agent to learn directly from your GitHub repository. It automatically syncs source files, converts them into vectorized knowledge How It Works Provide your GitHub repository — the workflow will automatically pull your source files and update the knowledge base (vectorstore) for the AI Agent. This allows the AI Agent to answer questions directly based on your repository’s content. How to Use Commit your files to your GitHub repository. Trigger the Sync Data workflow. Ask questions to the AI Agent — it will respond using your repository knowledge. Requirements A valid GitHub account An existing repository with accessible content Customization Options Customize the prompt for specific or detailed tasks Replace or connect to your own vector database provider

AI Chat Bot workflow for WordPress & Webhook Live Chats This workflow powers a versatile AI chatbot that can be integrated into any live chat interface, such as our free Forerunner™ AI Chat Bot for WordPress. It's designed to automate customer support and lead generation by handling a variety of user queries independently. The setup process is straightforward and typically takes less than five minutes. This involves connecting your preferred Large Language Model (LLM) and a live chat platform to the workflow via webhooks. How the Workflow Works The core of this workflow is an AI Agent that acts as the brain of the chatbot. It processes user input and generates responses based on predefined rules and your chosen language model. User Input: When a user sends a message through your live chat, it's sent to the workflow via a webhook. This message is then passed to the AI Agent for processing. AI Response Generation: The AI Agent analyzes the message, retrieves relevant conversational history from the Simple Memory node to maintain context, and uses the selected Large Language Model (e.g., OpenAI, Gemini, or Claude) to formulate a response. Conditional Logic: After the response is generated, the workflow uses an If node to check if the conversation should end. If the response contains the specific tag [END_OF_CONVERSATION], the workflow prepares to end the chat. Otherwise, the conversation continues. Send to Client: The final response is then sent back to the live chat interface, where it is displayed to the user. This completes the loop, allowing the chatbot to engage in a continuous conversation until the task is complete.

📊 Token Usage Metrics Workflow Descripción: Este flujo de trabajo en n8n extrae y resume las métricas de uso de tokens (prompt, completion y total) y los modelos utilizados en una ejecución específica. Requiere el execution_id y un array con los nombres de los nodos de AI (por ejemplo: openai, gemini). Requisitos: execution_id: ID de la ejecución de n8n de la cual se extraerán los datos. model_names: Array con los nombres de los nodos AI a buscar (ejemplo: openai, gemini). Funcionamiento: Obtiene la ejecución con el ID proporcionado. Busca en los nodos indicados la información de token usage dentro de la ejecución. Suma los tokens usados y genera un listado de modelos utilizados. Devuelve métricas totales y un desglose detallado por modelo. Configuración recomendada: Este workflow debe configurarse para ejecutarse siempre al final del flujo de trabajo. Desactivar la opción “Esperar a que termine” para evitar bloqueos y asegurar que se obtenga la ejecución completa. 📊 Token Usage Metrics Workflow Description: This n8n workflow extracts and summarizes token usage metrics (prompt, completion, and total tokens) along with the models used in a specific execution. It requires the execution_id and an array of AI node names (e.g., openai, gemini). Requirements: execution_id: The n8n execution ID from which data will be extracted. model_names: An array of AI node names to search for (e.g., openai, gemini). How It Works: Fetches the execution using the provided ID. Searches the specified nodes for token usage information within the execution. Aggregates token counts and compiles a list of models used. Returns total metrics and a detailed breakdown per model. Recommended Configuration: Configure this workflow to run always at the end of your workflow. Disable the “Wait until finished” option to avoid blocking and ensure the complete execution data is available.

What this workflow does Provides the tools layer for the Parent agent to manage Google Calendar: Get (list events), Create, and Delete. Accepts text + sessionid from the Parent and uses an LLM with short-term memory to choose and run the correct tool. Pipeline: Execute Workflow Trigger → Sub-Agent → (Get / Create / Delete) → Google Calendar Category: Productivity / Calendar / Agentic\ Time to set up: ~10 minutes\ Difficulty: Intermediate\ Cost: Mostly free (n8n CE; OpenAI + Google Calendar usage as configured) * What you'll need OpenAI credentials. Google Calendar OAuth2 credentials. A calendar ID (use a placeholder like your.calendar@example.com in the node and select your actual calendar at runtime). * Set up steps Import this Sub-Agent workflow. Open the Google Calendar tool nodes (Get, Create, Delete) and select your OAuth2 credential and calendar. Ensure the Execute Workflow Trigger exposes two inputs: text and sessionid. Connect the Parent's toolWorkflow node to this workflow. * Testing (direct call example) From the Parent, send: "Schedule 'Team Sync' tomorrow 10:00--11:00" → Sub-Agent should call Create. "List events next week" → Get with timeMin/timeMax. "Delete event 'Team Sync'" → Delete with eventId once matched.