Workflow Templates

Your Landing Page is Leaking Sales—Here’s How to Fix It in Seconds Visitors land on your page. But instead of converting, they bounce. Why? Something’s broken. Something’s missing. But what? ❌ Is your CTA too weak? ❌ Is your messaging unclear? ❌ Is your design creating friction? You know something is off, but don’t know what. What if you could get an instant, expert-level report on exactly what to fix? This workflow will do an AI Analysis of your landing page, provide a CRO Audit, so you can optimize your landing page. Who is This For? SaaS Founders & Startups**: Stop leaving money on the table. Make every visitor count. Marketers & Growth Experts**: Turn landing pages into high-converting assets. E-commerce & Lead Gen Businesses**: More conversions = more revenue. How It Works Paste your URL Get an instant roast + fix list Implement changes & watch conversions jump The workflow scrapes the url you input, gets the htlm source code of the landing page, and sends it to OpenAI AI Agent. The Agent makes a deep analysis, roasts the landing page, and provides 10 Conversion Rate Optimization Tips to improve your landing page. Setup Guide You will need OpenAI Credentials with an API Key to run the workflow. The workflow is using the OpenAI-o1 model to deliver the best results. It costs between $0.20/0.30 per run. You can adjust the prompt to your wish in the AI Agent parameters. Once the workflow has been completed, select Logs to get a readable version. Below is an example.

This workflow demonstrates a simple way to run evals on a set of test cases stored in a Google Sheet. The example we are using comes from an info extraction task dataset, where we tested 6 different LLMs on 18 different test cases. You can see our sample data in this spreadsheet here to get started. Once you have this working for our dataset, you can plug in your own test cases matching different LLMs to see how it works with your own data. How it works: It loads test cases from Google Sheets. For each row in our Google Sheet, it grabs the source document, converting it to text. Our "LLM judge" passes the input/output of each LLM to GPT-4.1 to evaluate each test case (Pass/Fail + Reason). It logs the outcome to a Google Sheet. A 0.5s pause between each request gets around OpenAI's API rate limits. Set up steps: Add your credentials for Google Sheets, Google Drive, and OpenRouter. Make a copy of the original data spreadsheet so that you can edit it yourself. You will need to plug your version in the Update Results node to see the spreadsheet update on each run of the loop.

Automate LinkedIn engagement without sounding like a bot. This workflow: 🌍 Detects language & tone (German / English) 👍 Chooses the right reaction (like / celebrate / support …) 🗣 Generates a personalised comment in your voice and mentions the author 📲 Optional Telegram review – approve ✅ or regenerate ❌ before posting 💸 Runs on cost-efficient GPT-4o mini or Claude 3.5 Haiku ☁️ Publishes comment + reaction via the Unipile API Setup (≈ 15-30 min) Unipile – connect LinkedIn → copy account_id, dsn, then create an Access-Token (X-API-KEY). Telegram (optional) – create a bot, add a credential named YOUR TELEGRAM ACCOUNT. OpenAI / Anthropic – add your API key and keep one LLM node (delete the other). Open the “Defining guardrails” node and replace the credential placeholders. (Optional) Tweak role, comment_length and openers_example_1-3 for your brand voice. Security: no live keys included – all secrets are placeholders. Best for: solopreneurs, marketing teams, personal-branding consultants.

AI evaluation in n8n This is a template for n8n's evaluation feature. Evaluation is a technique for getting confidence that your AI workflow performs reliably, by running a test dataset containing different inputs through the workflow. By calculating a metric (score) for each input, you can see where the workflow is performing well and where it isn't. How it works This template shows how to calculate a workflow evaluation metric: whether a specific tool was called by an agent. We use an evaluation trigger to read in our dataset It is wired up in parallel with the regular trigger so that the workflow can be started from either one. More info We make sure that the agent outputs the list of tools that it used We then check whether the expected tool (from the dataset) is in that list Finally we pass this information back to n8n as a metric

This workflow template is designed for dental assistants and anyone looking to automate appointment scheduling. It integrates Google Calendar for booking appointments and Google Sheets as a database to store patient information. How It Works The user interacts with the chatbot to schedule an appointment. The chatbot collects necessary details and checks availability via Google Calendar. If the requested time is available, the AI books the appointment. If unavailable, the AI suggests alternative time slots. Once booked, the AI logs the appointment details into Google Sheets for record-keeping. Setup Instructions 📌 Watch this 🎥 Setup Video for detailed instructions on running and customizing this workflow. Step 1: Set Up Credentials OpenAI API Key (for chatbot functionality). Google Account (for Google Sheets & Google Calendar integration). Step 2: Choose the Right Tools Select the correct Google Calendar in the Google Calendar tool. Choose the appropriate Google Sheets file in the Google Sheets tool. Step 3: Test Run a test to ensure everything works correctly. Once tested. Example Templates Below are sample Google Sheets template to help you get started.

How the sequence works: A "Schedule Trigger" node activates the automation at a defined schedule. An "Airtable" node will search for previously posted questions in your question database. Airtable Base Template: here An "Aggregate" node will take all the questions from Airtable and compress them to a single output. ChatGPT, or a model of your choice, will generate a discussion question based on the options in the system prompt. The discussion question will be posted to the subreddit of your choice by the "Reddit" node. You can choose between a text, image, or link post! The recently-posted discussion question will then be uploaded to your Airtable base using the "Airtable" node. This will be used to prevent ChatGPT from creating duplicate questions. Setup Steps The setup process will take about 5 minutes. An Airtable base template is also pre-made for you here: https://airtable.com/app6wzQqegKIJOiOg/shrzy7L9yv8BFRQdY Set the recurrence in the "Schedule" node Create an Airtable account, you can use the link here. Get an Airtable personal access token here. Configure the "Get Previous Discussions" Airtable node. Configure the options in brackets in the "Generate New Discussion" node. Set the desired subreddit to post to and the post type(text, image, or link) in the "Post Discussion" node. Configure the "Create Archived Discussion" node to map to the Airtable base(required) and specific subreddit(optional).

This n8n workflow demonstrates how to create a really simple yet effective customer support channel and pipeline by combining Slack, Linear and AI tools. Built on n8n's ability to integrate anything, this workflow is intended for small support teams who want to maximise re-use of the tools they already have with an interface which is doesn't require any onboarding. Read the blog post here: https://blog.n8n.io/automated-customer-support-tickets-with-n8n-slack-linear-and-ai/ How it works The workflow is connected to a slack channel setup with the customer to capture support issues. Only messages which are tagged with a "✅" reaction are captured by the workflow. Messages are tagged by the support team in the channel. Each captured support issue is sent to the AI model to classify, prioritise and rewrite into a support ticket. The generated support ticket is uploaded to Linear for the support team to investigate and track. Support team is able to report back to the user via the channel when issue is fixed. Requirements Slack channel to be monitored Linear account and project Customising this workflow Don't have Linear? This workflow can work just as well with traditional ticketing systems like JIRA.

Personalized Tour Package Recommendations via n8n + Pinecone + Lovable UI I've created an intelligent Travel Itinerary Planner that connects a Lovable front-end UI with a smart backend powered by n8n, Pinecone, and OpenAI to deliver personalized tour packages based on natural language queries. What It Does Users type in their travel destination and duration (e.g., "Paris 5 days trip" or "Bali Trip for 7 Days, would love water sports, adventures and trekking included, also some historical monuments") through a Lovable UI. This triggers a webhook in n8n, which processes the request, searches vectorized tour data in Pinecone, and generates a personalized itinerary using OpenAI’s GPT. The results are then structured and sent back to the frontend UI for display in an interactive, reorderable format. Workflow Architecture Lovable UI ➝ Webhook ➝ Tour Recommendation Agent ➝ Vector Search ➝ OpenAI Response ➝ Structured Output ➝ Response to Lovable Tools & Components Used Webhook Acts as the entry point between the Lovable frontend and n8n. Captures the user query (destination, duration) and forwards it into the workflow. OpenAI Chat Model To interpret the user query. To generate a user-friendly, structured tour package from the matched results. Simple Memory Keeps chat state and context for follow-up queries (extendable for future features like multi-step planning or saved itineraries). Question Answering with Vector Store Searches vector embeddings of pre-loaded tour data. Finds the most relevant tour packages by comparing query embeddings. Pinecone Vector Store Stores tour packages and activity data in vectorized format. Enables fast and scalable semantic search across destinations, themes (e.g., "adventure", "cultural"), and duration. OpenAI Embeddings Embeds all tour and activity documents stored in Pinecone. Converts input user queries into embedding vectors for semantic search. Structured Output Parser Parses the final OpenAI-generated response into a consistent, frontend-consumable JSON format. Frontend (Lovable UI) User types in destination or their travel package needs in the Tour Search. Lovable queries the n8n workflow. Displays beautifully structured, editable itineraries. How to Set It Up Webhook Setup in n8n Create a POST webhook node. Set Webhook URL and connect it with Lovable frontend. Pinecone & Embeddings Convert your static tour package documents (PDFs, JSON, CSV, etc.) into embeddings using OpenAI. Store the embeddings in a Pinecone namespace (e.g., kuala-lumpur-3-days). Configure “Answer with Vector Store” Tool Connect the tool to your Pinecone instance and pass query embedding for matching. Connect to OpenAI Chat Use the GPT model to process query + context from Pinecone to generate an engaging itinerary description. Optionally chain a second model to format it into UI-consumable output. Output Parser & Return Use Structured Output Parser to parse the response and pass it to Respond to Webhook node for UI display. Ideal Use Cases Smart itinerary planning for OTAs or DMCs Personalized travel recommendations in chatbots or apps Travel advisors and agents automating package generation Benefits Highly relevant, contextual travel suggestions Natural query understanding via OpenAI Seamless frontend-backend integration via Webhook If you’re building personalized experiences for travelers using AI, give this approach a try! Let me know if you’d like the JSON for this workflow or help setting up the Pinecone data pipeline.

Automate Instagram DMs with OpenAI GPT and ManyChat How It Works: Once connected, GPT will automatically initiate conversations with messages from new recipients in Intagram. Who Is This For? This workflow is ideal for marketers, business owners content creators who want to automatically respond to Instagram direct messages using OpenAI GPT. By integrating ManyChat, you can manage conversations, nurture leads, and provide instant replies at scale. What This Workflow Does Captures** incoming Instagram DMs through ManyChat’s integration. Processes** messages with GPT to generate a relevant response. Delivers** instant replies back to Instagram users, creating efficient, AI-driven communication. Setup Import the Template: Copy the n8n workflow into your workspace. OpenAI Credentials: Add your OpenAI API key in n8n so GPT can generate responses. ManyChat Account: Create (or log in to) your ManyChat account. Connect Instagram: Link your Instagram profile as a channel in ManyChat. ManyChat Custom Field: Create a custom field for storing user input or conversation context. Configure Default Reply: In ManyChat, set up the default Instagram reply flow to point to your n8n webhook. Add External Request: Create an external request step in ManyChat to send messages to n8n. Test the Flow: Send yourself a DM on Instagram to confirm the workflow triggers and GPT responds correctly. Instructions and links: Notion instruction Register in ManyChat

Medical Q&A Chatbot for Urology using RAG with Pinecone and GPT-4o This template provides an AI-powered Q&A assistant for the Urology domain using Retrieval-Augmented Generation (RAG). It uses Pinecone for vector search and GPT-4o for conversational responses. 🧠 Use Case This chatbot is designed for clinics or medical pages that want to automate question answering for Urology-related conditions. It uses a vector store of domain knowledge to return verified responses. 🔧 Requirements ✅ OpenAI API key (GPT-4o or GPT-4o-mini) ✅ Pinecone account with an active index ✅ Verified Urology documents embedded into Pinecone ⚙️ Setup Instructions Create a Pinecone vector index and connect it using the Pinecone credentials node. Upload Urology-related documents to embed using the Create Embeddings for Urology Docs node. Customize the chatbot system message to reflect your medical specialty. Deploy this chatbot on your website or link it with Telegram via the chat trigger node. 🛠️ Components chatTrigger: Listens for user messages and starts the workflow. Medical AI Agent: GPT-based agent guided by domain-specific instructions. RAG Tool Vector Store: Fetches relevant documents from Pinecone using vector search. Memory Buffer: Maintains conversation context. Create Embeddings for Urology Docs: Encodes documents into vector format. 📝 Customization You can replace the knowledge base with any other medical domain by: Updating the documents stored in Pinecone. Modifying the system prompt in the AI Agent node. 📣 CTA This chatbot is ideal for clinics, medical consultants, or educational websites wanting a reliable AI assistant in Urology.

Document-Aware WhatsApp AI Bot for Customer Support Google Docs-Powered WhatsApp Support Agent 24/7 WhatsApp AI Assistant with Live Knowledge from Google Docs 📝Description Template Smart WhatsApp AI Assistant Using Google Docs Help customers instantly on WhatsApp using a smart AI assistant that reads your company’s internal knowledge from a Google Doc in real time. Built for clubs, restaurants, agencies, or any business where clients ask questions based on a policy, FAQ, or services document. ⚙️ How it works Users send free-form questions to your WhatsApp Business number (e.g. “What are the gym rules?” or “Are you open today?”) The bot automatically reads your company’s internal Google Doc (policy, schedule, etc.) It merges the document content with today’s date and the user’s question to craft a custom AI prompt The AI (Gemini or ChatGPT) then replies back on WhatsApp using natural, helpful language All conversations are logged to Google Sheets for reporting or audit > 💡Bonus: The AI even understands dates inside the document and compares them to today’s date — e.g. if your document says “Closed May 25 for 30 days,” it will say “We're currently closed until June 24. 🧰 Set up steps Connect your WhatsApp Cloud API account (Meta) Add your Google account and grant access to the Doc containing your company info Choose your AI model (ChatGPT/OpenAI or Gemini) Paste your document ID into the Google Docs node Connect your WhatsApp webhook to Meta (only takes 5 minutes) Done — start receiving and answering customer questions! > 📄 Works best with free-tier OpenAI/Gemini, Google Docs, and Meta's Cloud API (no phone required). Everything is modular, extensible, and low-code. 🔄 Customization Tips Change the Google Doc anytime to update answers — no retraining needed Add your logo and business name in the AI agent’s “System Prompt” Add fallback routes like “Escalate to human” if the bot can't help Clone for multiple brands by duplicating the workflow and swapping in new docs 🤝 Need Help Setting It Up? If you'd like help connecting your WhatsApp Business API, setting up Google Docs access, or customizing this AI assistant for your business or clients… 📩 I offer setup, branding, and customization services: WhatsApp Cloud API setup & verification Google OAuth & Doc structure guidance AI model configuration (OpenAI / Gemini) Branding & prompt tone customization Logging, reporting, and escalation logic Just send a message via: Email: tharwat.elsayed2000@gmail.com WhatsApp: +20 106 180 3236

AI evaluation in n8n This is a template for n8n's evaluation feature. Evaluation is a technique for getting confidence that your AI workflow performs reliably, by running a test dataset containing different inputs through the workflow. By calculating a metric (score) for each input, you can see where the workflow is performing well and where it isn't. How it works This template shows how to calculate a workflow evaluation metric: whether an output matches an expected output (i.e. has the same meaning). The workflow takes questions about the causes of historical events and compares them with the reference answers in the dataset. We use an evaluation trigger to read in our dataset It is wired up in parallel with the regular chat trigger so that the workflow can be started from either one. More info If we're evaluating (i.e. the execution started from the evaluation trigger), we calculate the correctness metric using AI We pass this information back to n8n as a metric If we're not evaluating we avoid calculating the metric, to reduce cost