Workflow Templates

This n8n template watches a Gmail inbox for support messages and creates an equivalent issue item in Linear. How it works A scheduled trigger fetches recent Gmail messages from the inbox which collects support requests. These support requests are filtered to ensure they are only processed once and their HTML body is converted to markdown for easier parsing. Each support request is then triaged via an AI Agent which adds appropriate labels, assesses priority and summarises a title and description of the original request. Finally, the AI generated values are used to create an issue in Linear to be actioned. How to use Ensure the messages fetched are solely support requests otherwise you'll need to classify messages before processing them. Specify the labels and priorities to use in the system prompt of the AI agent. Requirements Gmail for incoming support messages OpenAI for LLM Linear for issue management Customising this workflow Consider automating more steps after the issue is created such as attempting issue resolution or capacity planning.

This no-code n8n workflow finds recent Instagram posts by hashtag, scrapes profile data, and uses an AI agent to evaluate whether each account is a good collaboration lead. The workflow filters based on the number of followers and the content of their bio, and outputs structured reasoning for outreach decisions. Perfect for creators, marketers, or business developers looking to automate influencer or community partnership prospecting—especially in niche ecosystems like n8n. ✅ Key Features 🔍 Hashtag Discovery**: Finds recent Instagram posts from a specified hashtag (e.g., #n8n) 👤 Account Scraping**: Retrieves profile details such as follower count and biography 🧠 AI Evaluation**: Uses OpenAI and LangChain to determine if the profile is a good fit for outreach 📦 Structured Output**: Returns a JSON object with "Yes/No" lead status and reasoning 🛠️ Manual Execution**: Run on demand using the manual trigger 🧰 What You'll Need | Tool / API | Purpose | Setup Steps | |-------------------------|------------------------------------------|-------------| | Apify Account | To access Instagram scraping actors | Create account → Generate API Token → Use in httpQueryAuth credential in n8n | | OpenAI API Key | To power the AI decision-making agent | Sign up at OpenAI → Create API key → Paste into OpenAI credential in n8n | | LangChain Plugin for n8n | AI Orchestration with System Message | Install LangChain nodes from Community Nodes (already installed in this workflow) | 🔧 Step-by-Step Setup 1️⃣ Manual Trigger Node**: When clicking ‘Execute workflow’ Use**: Allows you to run the workflow manually while testing. 2️⃣ Define Hashtag Node**: Create Search Term Value**: Sets "n8n" as the default Instagram hashtag to scan. You can edit this to any other hashtag you'd like. 3️⃣ Find Recent Posts Node**: Find Recent Posts API**: Apify Instagram Hashtag Scraper Auth Setup**: Go to your Apify Console Click “Create new token” In n8n, create a new HTTP Query Auth credential Set token in the token query param (e.g., ?token=yourTokenHere) Choose the credential in this node 4️⃣ Scrape Each Profile Node**: Scrape Accounts API**: Apify Instagram Profile Scraper Body**: JSON with usernames from the hashtag search Note**: Uses the same httpQueryAuth credential as the previous node. 5️⃣ Extract Fields Node**: Set bio and follower count What it does**: Extracts biography and followersCount from the profile JSON and stores them in clean variables for AI input. 6️⃣ AI Lead Scoring Node**: AI Agent Purpose**: Uses GPT-4o-mini to analyze the bio and follower count Prompt Details**: 7️⃣ AI Model Node**: OpenAI Chat Model Model**: gpt-4o-mini Credential**: Connect your OpenAI account via API Key. Go to OpenAI API Keys Copy your key and create a new OpenAI API credential in n8n. 8️⃣ Output Parser Node**: Structured Output Parser What it does**: Parses the response from the AI into structured JSON for further use (e.g., storing leads, sending to Airtable, etc.) 🧪 Sample Output { "lead status": "Yes", "Reasoning": "The user has 3.5k followers and their bio shows they build automations with n8n." } 📬 Need More Help? If you'd like assistance setting this up, customizing it to your niche, or expanding it to score and store leads automatically — I can help! 👤 Robert Breen Automation Consultant | AI Workflow Designer | n8n Expert 📧 robert@ynteractive.com 🌐 ynteractive.com 🔗 LinkedIn

👤 Who’s it for This workflow is for content creators, marketers, educators, or anyone who wants to instantly summarize YouTube videos and repurpose them into different formats (LinkedIn post, tweet, etc.) via a simple Telegram chatbot. ⚙️ How it works This n8n automation listens for messages in Telegram. If the message contains a YouTube link, it: Extracts the video ID Fetches the video transcript using RapidAPI Cleans the transcript of any special characters Sends it to OpenAI to generate a summary If the message is not a link, it simply acts as an AI chatbot using OpenAI with memory support. ✅ Supports follow-up prompts like: “Make it shorter” “Turn this into a LinkedIn post” “Create a tweet thread” 🧑‍🤝‍🧑 Multi-User Support This Telegram bot supports multiple users simultaneously. It tracks memory and context separately for each user using Telegram's unique chat_id. ✅ Each user gets personalized AI replies ✅ Follow-up commands work per user ✅ No interference between users 🛠️ Requirements A Telegram bot token (get via @BotFather) An OpenAI API Key (from https://platform.openai.com/account/api-keys) A RapidAPI Key and Host (typically youtube-transcript3.p.rapidapi.com) > 🚨 API keys must be added manually — they are not included in the template. 🧩 How to Set It Up Configure the Telegram Trigger node with your bot token. In the HTTP Request node, set: X-RapidAPI-Key: your RapidAPI key X-RapidAPI-Host: your RapidAPI host URL Add your OpenAI API credentials to the AI Agent node. Use the provided sticky notes for guidance inside the workflow itself. 🎛️ How to Customize Modify AI prompt behavior in the AI Agent node Change the text formatting in the Code node Use a different transcript API if preferred Add commands like make it into a blog post, summarize in bullet points, etc. 📌 Notes All nodes are renamed to reflect their function API credentials are removed for security Includes colored boxes and sticky notes to guide the user Compatible with n8n cloud and self-hosted setups

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.

Who's it for This workflow is designed for users who want to implement iterative AI-powered content improvement processes. It's ideal for content creators, marketers, product managers, and anyone who needs to refine ideas through multiple rounds of critique and enhancement until they meet quality standards. How it works The workflow creates a sophisticated feedback loop using three specialized AI agents that work together to continuously improve content. Starting with an initial input (like a product description), the system generates ideas and then enters a reasoning loop where: A Critic Agent analyzes the current output and identifies flaws or areas for improvement A Refiner Agent takes the original input plus the critic's feedback to create enhanced versions An Evaluator Agent assesses the refined output and determines if it meets the quality threshold The loop continues until either the evaluator determines the output is satisfactory or a maximum number of iterations is reached (configurable, default is 5 turns). How to set up Configure the initial AI agent to generate your starting content Set up the loop structure with "Reset Loop" enabled in the loop node options Configure three AI agents within the loop: Critic: Provide detailed analysis prompts for identifying improvements Refiner: Create prompts that incorporate feedback to enhance content Evaluator: Define quality criteria and decision-making logic Add Edit Fields nodes at the beginning and end of the loop to maintain data structure Include a Code node to track iteration count and loop control Set up the IF node to check exit conditions (max turns or completion status) Requirements n8n workflow environment Access to AI/LLM nodes (OpenAI, Anthropic, etc.) Basic understanding of JSON data structures Configured AI model credentials How to customize the workflow Customize the system prompts for each agent based on your specific use case. The critic should focus on your quality criteria, the refiner should understand your improvement goals, and the evaluator should have clear success metrics. Adjust the maximum iteration count in the code node and IF condition based on your complexity needs and token budget considerations.

Trump-o-meter: Extract and Evaluate Truth Social Posts Use Case Automatically extracting posts from Donald Trump's Truth Social account and estimating their potential impact on the U.S. stock market enables teams to monitor high-profile communications that may influence financial markets. This automation streamlines intelligence gathering for analysts, traders, and policy observers. What This Automation Does This automation retrieves up to 3 posts from Donald Trump's Truth Social profile and outputs structured information including: Author name Image URL Post text Post URL Estimated stock market impact: Direction: positive, negative, or neutral Magnitude: None, Small, Medium, Large How It Works Creates a browser session on Truth Social using an Airtop profile. Navigates to https://truthsocial.com/@realDonaldTrump. Uses a natural language prompt with a defined JSON schema to extract structured data for up to 3 posts. Splits the results into individual post items. Filters posts that contain actual content and have a non-zero estimated market impact. Sends selected posts and impact summaries to a Slack channel. Terminates the browser session to clean up. Setup Requirements Airtop API Key — free to generate. An Airtop Profile that is connected and logged into Truth Social. A Slack workspace and authorized app with write permissions to a target channel. Next Steps Integrate with Trading Signals**: Link output to financial alert systems or dashboards for timely insights. Expand Monitoring**: Extend to other high-impact accounts (e.g., politicians, CEOs). Enhance Analysis**: Add sentiment scoring or topic classification for deeper context. Legal Disclaimer This tool is intended solely for informational and analytical purposes. The market impact estimations provided are speculative and should not be construed as financial advice. Do not make investment decisions based on this automation. Always consult with a licensed financial advisor before making any trades. Read more about Trump-o-meter automation

Who is this for? This workflow template is designed for AI enthusiasts, developers, and privacy-conscious users who want to leverage the power of local large language models (LLMs) without sending data to external services. It's particularly valuable for those running Ollama locally who want intelligent routing between different specialized models. What problem is this workflow solving? When working with multiple local LLMs, each with different strengths and capabilities, it can be challenging to manually select the right model for each specific task. This workflow automatically analyzes user prompts and routes them to the most appropriate specialized Ollama model, ensuring optimal performance without requiring technical knowledge from the end user. What this workflow does This intelligent router: Analyzes incoming user prompts to determine the nature of the request Automatically selects the optimal Ollama model from your local collection based on task requirements Routes requests between specialized models for different tasks: Text-only models (qwq, llama3.2, phi4) for various reasoning and conversation tasks Code-specific models (qwen2.5-coder) for programming assistance Vision-capable models (granite3.2-vision, llama3.2-vision) for image analysis Maintains conversation memory for consistent interactions Processes everything locally for complete privacy and data security Setup Ensure you have Ollama installed and running locally Pull the required models mentioned in the workflow using Ollama CLI (e.g., ollama pull phi4) Configure the Ollama API credentials in n8n (default: http://127.0.0.1:11434) Activate the workflow and start interacting through the chat interface How to customize this workflow to your needs Add or remove models from the router's decision framework based on your specific Ollama collection Adjust the system prompts in the LLM Router to prioritize different model selection criteria Modify the decision tree logic to better suit your specific use cases Add additional preprocessing steps for specialized inputs This workflow demonstrates how n8n can be used to create sophisticated AI orchestration systems that respect user privacy by keeping everything local while still providing intelligent model selection capabilities.

Who this template is for This template is for every engineer who wants to automate their code reviews or just get a 2nd opinion on their PR. How it works This workflow will automatically review your changes in a Gitlab PR using the power of AI. It will trigger whenever you comment with +0 to a Gitlab PR, get the code changes, analyze them with GPT, and reply to the PR discussion. Set up Steps Set up webhook of note_events in Gitlab repository (see here on how to do it) Configure ChatGPT credentials Note "+0" in MergeRequest to trigger automatic review by ChatGPT

How it works Triggers on submitting an n8n form Uses the form details to prepare a message Sends the message to Slack Set up Steps Add in your team name Add in message tone Set up Open AI Set up Slack

Find OAuth URIs with AI Llama Overview: The AI agent identifies: Authorization URI Token URI Audience Methodology: Confidence scoring is utilized to assess the trustworthiness of extracted data: Score Range: 0 < x ≤ 1 Score Granularity: 0.01 increments Model Details: Leveraging the Wayfarer Large 70b Llama 3.3 model. How it works: This template is designed to assist users in obtaining OAuth2 settings using AI-powered insights. It is ideal for developers, IT professionals, or anyone working with APIs that require OAuth2 authentication. By leveraging the AI agent, users can simplify the process of extracting and validating key details such as the authorization_url, token_url, and audience. Set up instructions: 1. Configuration Nodes Structured Output Node**: Parses the AI model's output using a predefined JSON schema. This ensures the data is structured for downstream processing. Code Node**: If the AI model’s output does not match the required format, use the Code node to re-arrange and transform the data. Example code snippets are provided below for common scenarios. 2. AI Model Prompt The prompt for the AI model includes: A detailed structure and objectives of the query. Flexibility for the model to improvise when accurate results cannot be determined. 3. Confidence Scoring The AI model assigns a confidence score (0 < x ≤ 1) to indicate the reliability of the extracted data. Scores are provided in increments of 0.01 for granularity. Adaptability Customize this template: Update the AI model prompt with details specific to your API or OAuth2 setup. Adjust the JSON schema in the Structured Output node to match the data format. Modify the Code logic to suit the application's requirements.

In this guide, we’ll walk you through setting up an AI-driven workflow that automatically processes highly-rated food photos from a Google Sheet, generates AI-powered captions, shares them to Pinterest, and updates the sheet to reflect the posts. Ready to automate your food photo sharing? Let’s dive in! What’s the Goal? Automatically detect and process highly-rated food photos (4 stars or above) from a Google Sheet. Use AI to generate engaging and relevant captions. Share the photos with captions to Pinterest via the Pinterest API. Update the Google Sheet to mark photos as posted. Enable scheduled automation for consistent posting. By the end, you’ll have a self-running system that shares your best food photos effortlessly. Why Does It Matter? Manual photo sharing is time-consuming and inconsistent. Here’s why this workflow is a game changer: Zero Human Error**: AI ensures consistent captions and posting accuracy. Time-Saving Automation**: Automatically handle photo sharing, boosting efficiency. Scheduled Posting**: Maintain a regular presence on Pinterest without manual effort. Focus on Creativity**: Free your team from repetitive posting tasks. Think of it as your tireless social media assistant that keeps your Pinterest feed vibrant. How It Works Here’s the step-by-step magic behind the automation: Step 1: Trigger the Workflow Detect new photos to post using the Daily Post Scheduler node (e.g., once daily). Initiate the workflow at a scheduled time to check for new food photos. Step 2: Fetch Food Photos from Sheet Retrieve rows from the Google Sheet that contain food photo metadata like image URLs, ratings, and status. Step 3: Filter 4+ Star Dishes Filter only those food entries with high ratings (4 stars or above) and unposted status. Step 4: AI Caption Generator Use AI (e.g., GPT/OpenAI) to create engaging and relevant captions for selected food photos. Step 5: Upload to Pinterest Automatically post the food photo with the generated caption to Pinterest via the Pinterest API. Step 6: Mark as Posted in Sheet Update the Google Sheet to reflect that the photo has been successfully shared. How to Use the Workflow? Importing a workflow in n8n is a straightforward process that allows you to use pre-built workflows to save time. Below is a step-by-step guide to importing the Automated Food Photo Sharing workflow in n8n. Steps to Import a Workflow in n8n Obtain the Workflow JSON Source the Workflow: Workflows are shared as JSON files or code snippets, e.g., from the n8n community, a colleague, or exported from another n8n instance. Format: Ensure you have the workflow in JSON format, either as a file (e.g., workflow.json) or copied text. Access the n8n Workflow Editor Log in to n8n (via n8n Cloud or self-hosted instance). Navigate to the Workflows tab in the n8n dashboard. Click Add Workflow to create a blank workflow. Import the Workflow Option 1: Import via JSON Code (Clipboard): Click the three dots (⋯) in the top-right corner to open the menu. Select Import from Clipboard. Paste the JSON code into the text box. Click Import to load the workflow. Option 2: Import via JSON File: Click the three dots (⋯) in the top-right corner. Select Import from File. Choose the .json file from your computer. Click Open to import. Setup Notes Google Sheet Columns**: Ensure your Google Sheet includes the following columns: Image URL, Rating (numeric, e.g., 1-5), Feedback (text), Pin Title, Pin Description, Destination URL, Board ID, and Status (e.g., "Pending" or "Posted"). Google Sheets Credentials**: Configure OAuth2 settings in the Fetch Food Photos node with your Google Sheet ID and credentials. AI Model**: Set up the AI Caption Generator node with OpenAI credentials (e.g., API key). Pinterest API**: Authorize the Upload to Pinterest node with Pinterest API credentials (e.g., Bearer Token) and obtain the Board ID. Scheduling**: Adjust the Daily Post Scheduler node to your preferred posting time (e.g., daily at 9 AM).

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.