Workflow Templates

🏖️ AI-Based Tour Itineraries via Email Using OpenAI & Pinecone Vector Search Overview This workflow automates the process of handling new tour package requests received via email, analyzes the request, and provides personalized tour package recommendations using AI and a vector database. It’s designed to streamline customer interactions and deliver quick, relevant responses. Precondition Create a Embedded Tour Package Database (refer to the link below): Pinecone Database setup Register and create API Keys for OpenAI, Pinecone Database. Copy Mail Credentials to access Email Inbox from n8n node This workflow automates the process of extracting tour information from PDF files stored in a Google Drive folder, processes and vectorizes the extracted data, and stores it in a Pinecone vector database for efficient querying. This is especially useful for building AI-powered search or recommendation systems for travel packages. 🛠️ Tools & Nodes Used Email Trigger (IMAP): Monitors the inbox for new tour package requests. Text Classifier: Categorizes incoming emails (e.g., New Request, Follow-up, Other). Code Node: Extracts and structures relevant data from the email (subject, sender, content, etc.). Tour Recommendation AI Agent: An AI agent that interprets the request and formulates a prompt for package recommendations. OpenAI & OpenRouter Chat Models: Used for natural language understanding and generating responses. Simple Memory: Maintains context for ongoing conversations. Pinecone Vector Store: Stores and retrieves tour packages using semantic search. Embeddings (OpenAI): Converts text data into vector embeddings for similarity search. Answer Questions with a Vector Store: Retrieves the most relevant packages from Pinecone. Send Email: Sends the AI-generated recommendations back to the customer. 🔄 Process & Flow Email Reception: The workflow starts with the Email Trigger (IMAP) node, which listens for new emails in the inbox. Classification: The Text Classifier node determines if the email is a new tour package request. Data Extraction: The Code node parses the email, extracting key details like sender, subject, and content. AI Agent Processing: The Tour Recommendation AI Agent receives the structured request and crafts a prompt for package recommendations. Vector Search: The agent queries the Pinecone Vector Store, which holds previously created tour packages, using OpenAI embeddings for semantic matching. Recommendation Generation: The AI agent selects the top 3 most relevant packages and generates a friendly, personalized response. Response Delivery: The Send Email node sends the recommendations back to the customer. 🚀 Recommendations & Improvements for Next Version Error Handling: Add error handling nodes to manage failed email parsing or AI response issues. Logging & Analytics: Integrate logging to track requests, recommendations, and customer responses for continuous improvement. Personalization: Enhance the AI agent to consider customer history or preferences for even more tailored recommendations. Multi-language Support: Add language detection and translation for international customers. Feedback Loop: Include a mechanism for customers to rate recommendations, feeding this data back into the system for improved future suggestions. Attachment Handling: Enable the workflow to process attachments (e.g., customer itineraries or preferences). Scalability: Consider batching or queueing requests if email volume increases. 💡 Conclusion This workflow demonstrates how n8n, combined with AI and vector databases, can automate and personalize customer service in the travel industry. With a few enhancements, it can become even more robust and customer-centric!

What it does This is a simplistic demo workflow showing how to extract a license plate number from an image of a car submitted via a form – or in more general terms showcasing how you can: use a form trigger to upload files and feed it into an LLM use a changeable LLM model for image-to-text analysis Set up steps Import the workflow Ensure you have registered and account, purchased some credits and created and API key for OpenRouter.ai Create/adapt the OpenRouter credential with your indivial API key for OpenRouter "Test workflow" and submit an image of a car with license plate to extract its number How to adapt By changing the "prompt" in th "Settings" node you can quickly adapt this exemplatory workflow to other image-to-text use cases, such as: summarization: "summarize what's seen in the image" location finding: "identify the location where the image was taken" text extraction: "extract all text from the image and return it as markdown" Thanks to using OpenRouter, you also can quickly experiment with finding good model choices by simply changing the "model" in the "Settings" node. The following models gave good results for this demo use-case: google/gemini-2.0-flash-001 meta-llama/llama-3.2-90b-vision-instruct openai/gpt-4o The llama-3.2-11b and even claude-3.5-sonnet didn't recognize all characters in all test images. Using a generic LLM-model offers a quick way of prototyping an image-to-text application. For specific use cases in serious and scalable production deployments, consider using an API based service specifically made to that purpose, such as: Google Cloud Vision API Microsoft Azure Computer Vision Azure AI Document Intelligence Amazon Textract

Overview: Automate Your Google OAuth2 Credential Creation This workflow template streamlines the process of creating and naming individual Google OAuth2 credentials for multiple Google services within n8n. By automating tasks typically done manually, it saves significant time and reduces the risk of errors. Who Is This For? This template is perfect for: Marketers who frequently use Google Docs, Google Sheets, Google Slides, and Google Drive for document automation. Developers or technical users who manage numerous integrations and need to minimize credential creation overhead. Teams or solo users with multiple Google accounts who want to eliminate repetitive Google OAuth2 credential creation tasks. Prerequisite Knowledge • Basic familiarity with n8n, including how to configure nodes and connect services. • A Google OAuth JSON file (client ID, client secret, etc.) already set up in your Google Cloud Console. • An understanding of Google services (Docs, Sheets, Slides, Drive, Gmail, Calendar, Contacts and others) and which you may need for your use cases What Problem Does It Solve? Manually creating and naming separate OAuth2 credentials for each Google service can be time-consuming and prone to errors. For instance, if you manage 16 Google accounts, you might need to create 112 credentials manually. This workflow automates that process by using your Google OAuth JSON file and email address, generating uniquely named OAuth2 credentials for each Google service in just a single run per account. Key Features Manual Trigger – Start the workflow on demand to generate new credentials. Set Node for Google OAuth Details – Store your client ID, client secret, and other JSON info securely in one place. Automated Naming Convention – Use your Google email address to create distinct names for each credential, simplifying organization. Array of Google Services – Customize which services to create credentials for, such as Docs, Sheets, Slides, Drive, Gmail, Calendar, or Contacts. Batch Credential Creation – Split the array of Google services and automatically create separate OAuth2 credentials for each. Seamless OAuth2 Integration – Effortlessly authenticate all generated credentials within n8n. How to Set Up Insert Your JSON Details – Paste your Google OAuth 2.0 JSON data into the “Google JSON” node. Add Your Google Email Address – Specify your email address in the “Google Email” node to personalize the naming convention. Configure n8n Account Credentials – In the “n8n Create Credentials” node, select or add proper credentials (e.g., n8n API credentials). Execute the Workflow – Run the workflow to automatically generate individual OAuth2 credentials. Finalize in n8n – Visit your n8n Credentials dashboard to complete the OAuth sign-in for each newly created credential. Customization Options Modify the Services Array** – Easily add or remove Google products based on your workspace needs by editing the array in the “Services” node. Adjust Naming Logic** – Tailor the credential naming convention in the “n8n Create Credentials” node to match your organization’s style or best practices. Set Role-Based Access** – In n8n, control who can view and use these automatically generated credentials by adjusting user permissions. Time-Saving Example I initially had 16 Google accounts, requiring 112 credentials if done manually. With this workflow, I needed just one run per account—saving around two hours of repetitive data entry. Yes, I spent three hours building and documenting this, but it pays off quickly for anyone managing multiple accounts or large teams.

Travel Agent that Auto Response on Mail In this guide, we’ll break down how to set up an AI-powered auto-reply system that works while you sleep. Ready to 10X your efficiency? Let’s dive in! What’s the Goal? AI-driven auto-responses for Email. Instant replies to FAQs, order confirmations, and support queries. 24/7 availability - no more “We’ll get back to you soon” delays. Seamless integration with existing business tools. By the end, you’ll have a self-running communication assistant that never takes a coffee break. Why Does It Matter? Why automate replies? Because time = money and manual typing is so 2010. Here’s why this workflow is a game changer: Zero Human Error: AI doesn’t get tired or make typos. Lightning-Fast Replies: Customers get instant answers, improving satisfaction. 24/7 Availability: No more “Out of Office” replies. Focus on High-Value Work: Free your team from mundane tasks. Think of it as hiring a super efficient virtual assistant - minus the salary. How It Works Here’s the step by step magic behind the automation Step 1: Trigger the Workflow Detect new messages from WhatsApp, Email, or Slack. Use n8n’s webhook or API integration to capture incoming queries. Step 2: Process the Message with AI Send the message to an AI model (like OpenAI GPT-4 or Gemini). Generate a context-aware, human-like response. Step 3: Send the Automated Reply Route the AI-generated response back to the original platform. Ensure personalization (e.g., “Hi [Name], thanks for reaching out!”). Step 4: Log & Optimize Store interactions in a database (Airtable, Google Sheets). Continuously improve AI responses based on past data. How to use the workflow? Importing a workflow in n8n is a straightforward process that allows you to use pre-built or shared workflows to save time. Below is a step-by-step guide to importing a workflow in n8n, based on the official documentation and community resources. Steps to Import a Workflow in n8n 1. Obtain the Workflow JSON Source the Workflow:** Workflows are typically shared as JSON files or code snippets. You might receive them from: The n8n community (e.g., n8n.io workflows page). A colleague or tutorial (e.g., a .json file or copied JSON code). Exported from another n8n instance (see export instructions below if needed). Format:** Ensure you have the workflow in JSON format, either as a file (e.g., workflow.json) or as text copied to your clipboard. 2. Access the n8n Workflow Editor Log in to n8n:** Open your n8n instance (via n8n Cloud or your self-hosted instance). Navigate to the Workflows tab in the n8n dashboard. Open a New Workflow:** Click Add Workflow to create a blank workflow, or open an existing workflow if you want to merge the imported workflow. 3. Import the Workflow Option 1: Import via JSON Code (Clipboard): In the n8n editor, click the three dots (⋯) in the top-right corner to open the menu. Select Import from Clipboard. Paste the JSON code of the workflow into the provided text box. Click Import to load the workflow into the editor. Option 2: Import via JSON File: In the n8n editor, click the three dots (⋯) in the top-right corner. Select Import from File. Choose the .json file from your computer. Click Open to import the workflow.

This workflow automates the Mark as Fulfilled action in Shopify for each order, ensuring a seamless fulfillment process without manual intervention. How It Works This workflow retrieves all unfulfilled orders and processes their fulfillment automatically. Since Shopify requires a Fulfillment Order ID (not Order ID) to trigger fulfillment, the workflow follows these steps: 1️⃣ Get all unfulfilled orders using the Shopify node. 2️⃣ Retrieve the Fulfillment Order ID using the "List Fulfillment Orders" action. 3️⃣ Create a fulfillment request using "Mark fulfillment order as fulfilled." 4️⃣ Handle edge cases, such as partially fulfilled orders or API errors. This ensures that every valid order is marked as fulfilled efficiently. 🔗 Ongoing discussions on this topic: Relevant Shopify API Discussion Step-by-step The workflow can be run as requested or on schedule You can adjust these parameters within the Shopify and filter nodes: Shopify Admin URL – A Global node to customize the Shopify store URL. To find your Shopify store ID, login into your Shopify admin, then look at the URL in your browser's address bar, the subdomain portion (e.g., example_store_id.myshopify.com) is your store ID (in this case: example_store_id) Order Filtering – Ensures only valid orders are fulfilled, particularly useful if: You sell digital downloads or gift cards exclusively. You use third-party fulfillment services for all products. Credentials To run this workflow, you'll need: Shopify API Key – Required for authentication. Who Is This For? Shopify store owners looking to automate their fulfillment process. Merchants selling digital or personalized products who need instant fulfillment. Explore More Templates 👉 Check out my other n8n templates

Monitor & Archive Keyword Tweets to Airtable 📌 Overview Automatically searches Twitter for any keyword/topic (person, brand, hashtag), filters duplicates, and stores new tweets in Airtable with rich metadata - all on a daily schedule. 🛠️ Workflow Steps ⏰ Schedule Trigger Runs daily at 8 AM (customizable) 🐦 Twitter Search Fetches 100 latest tweets matching your keyword 📦 Reformat Data Structures: Text | Likes | ID | URL Author | Timestamp 🗂️ Fetch Airtable Records Retrieves existing tweet IDs 🔍 Deduplicate Compares IDs to exclude duplicates ➕ Append New Tweets Saves only fresh entries to Airtable 💡 Key Benefits 🤖 Automated Monitoring**: Near real-time tracking 🧹 Clean Data**: Zero duplicate entries 📁 Structured Archive**: Organized metadata for analysis ⚙️ Fully Customizable**: Adapt keywords/schedule/output 🧩 Perfect For Social media analysts tracking brand mentions Journalists monitoring public figures Researchers archiving topic conversations Marketing teams measuring campaign reach 🚀 Getting Started Import into n8n Connect Credentials: Twitter API keys Airtable base + table ID Configure: Search keyword (e.g., "Narendra Modi") Schedule timing Run & Forget: Initial run to backfill Daily auto-archiving ✅ Enhancement Ideas Add Filters: Exclude retweets Filter by language Geolocation targeting Notifications: Slack alerts for new tweets Email digests AI Extensions: Sentiment analysis Auto-categorization Trend reporting

Description This n8n workflow template allows users to check if a Tron wallet address is blacklisted on the USDT contract via a Telegram bot. When a user sends the command {walletAddress} through the Telegram bot, the workflow queries the Tronscan API to determine if the provided wallet address is blacklisted. The result is then sent back to the user via the Telegram bot. Detailed Description Workflow Overview This workflow is designed to interact with users through a Telegram bot and check if a given Tron wallet address is blacklisted on the USDT contract. The workflow consists of four main nodes: Telegram Trigger Node: Listens for messages from the Telegram bot. HTTP Request Node: Sends a GET request to the Tronscan API to check the blacklist status of the provided wallet address. Function Node: Processes the API response and formats the message to be sent back to the user. Telegram Send Message Node: Sends the formatted message back to the user via the Telegram bot. Nodes Configuration 1.Telegram Trigger Node Event: Message Update Types: Message Command: /sorgu Description: This node listens for the {walletAddress} command followed by a wallet address from the user. 2.HTTP Request Node Method: GET URL: https://apilist.tronscanapi.com/api/stableCoin/blackList?blackAddress={{ $json.message.text }} Response Format: JSON Description: This node sends a GET request to the Tronscan API using the wallet address provided by the user. 3.Code Node Check Api Response: let message; if (response.total && response.total > 0) { message = 🚨🛑 This Wallet is Blacklisted! 🛑🚨: ${response.data[0].blackAddress}; } else { message = ✅💚 This Wallet is NOT Blacklisted! 💚✅.; } return [ { json: { text: message, }, }, ]; Description:** This node processes the API response to determine if the wallet address is blacklisted and formats the message to be sent back to the user. 4.Telegram Send Message Node Resource: Message Operation: Send Chat ID: ={{$json["chat_id"]}} Text: ={{$json["text"]}} Description: This node sends the formatted message back to the user via the Telegram bot. How to Use Set Up Telegram Bot: Create a Telegram bot and obtain the API token. Configure the bot to listen for the {walletAddress} command. Import Workflow: Import this workflow into your n8n instance. Configure Credentials: Add your Telegram API credentials to the Telegram Trigger and Telegram Send Message nodes. Run Workflow: Start the workflow. Users can now send the {walletAddress} command to the Telegram bot to check if a Tron wallet address is blacklisted. Example Usage User Telegram Command: {TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t} API Request: https://apilist.tronscanapi.com/api/stableCoin/blackList?blackAddress=TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t API Response: "total": 1, "data": [ { "blackAddress": "TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t", "tokenName": "USDT", "num": "367583344429", "time": 1593184959, "transHash": "af4bc4d793f82ca5ba500cf13cf93ca3e7a56fccc2aabf8b09e55fc756500ea8", "contractAddress": "TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t" } ] } Bot Response: 🚨🛑 This Wallet is Blacklisted! 🛑🚨: TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t > This workflow provides a simple and efficient way to check the blacklist status of Tron wallet addresses via a Telegram bot, making it easy for users to stay informed about the status of their wallets.

This workflow builds a conversational AI chatbot agent using Claude 3.7 Sonnet model with the new . It enhances standard LLM capabilities with Anthropic’s features: Web Search and Think: Real-time web search**, to answer up-to-date factual queries. A “Think” function, to support internal reasoning and memory-like behavior by Anthropic. A memory buffer, allowing the agent to maintain conversation history. A system prompt defining clear ethical, functional, and formatting rules for interaction. When a user sends a message (trigger), the chatbot evaluates the query, optionally performs a web search if needed, processes the result using Claude, and responds accordingly. ✅ Advantages 🧠 Enhanced Reasoning Abilities** The Think tool allows the agent to simulate deep thought processes or contextual memory storage, improving conversational intelligence. 🌐 Real-Time Knowledge via Web Search** The integrated web_search tool enables the agent to fetch the latest information from the internet, making it ideal for dynamic or news-driven use cases. 🧾 Contextual Responses with Memory Buffer** The inclusion of a memory buffer allows the agent to maintain state across messages, improving dialogue flow and continuity. 🛡️ Built-in Ethical Guidelines** The system prompt enforces privacy, factual integrity, neutrality, and ethical response generation, making the agent safe for public or enterprise use. How It Works Chat Trigger: The workflow begins when a chat message is received via a webhook. This triggers the AI Agent to process the user's query. AI Agent Processing: The AI Agent analyzes the query to determine if it requires information from the website or external sources. It follows a structured approach: For website-related queries, it uses the provided context. For external information, it employs the web_search tool to fetch up-to-date data from the internet. The Think tool is used for internal reasoning or caching thoughts without altering data. Language Model: The Anthropic Chat Model (Claude 3.7 Sonnet) generates responses based on the analyzed query, incorporating website context or web search results. Memory: A simple memory buffer retains context from previous interactions to maintain continuity in conversations. Output: The final response is delivered to the user, excluding internal processes like web searches or reasoning steps. Set Up Steps Configure Nodes: Chat Trigger: Set up the webhook to receive user messages. AI Agent: Define the system message and rules for handling queries. Anthropic Chat Model: Select the Claude 3.7 Sonnet model and configure parameters like maxTokensToSample. Memory: Initialize the memory buffer to store conversation context. Tools: web_search: Configure the HTTP request to the Anthropic API for web searches, including headers and authentication. Think: Set up the tool for internal reasoning. Connect Nodes: Link the Chat Trigger to the AI Agent. Connect the Anthropic Chat Model, Memory, and Tools (web_search and Think) to the AI Agent. Credentials: Ensure the Anthropic API credentials are correctly configured for both the chat model and the web_search tool. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Gmail Daily Summary Scheduler - Template Description Never miss important emails again! This intelligent automation runs every morning at 8 AM to analyze your Gmail inbox from the past 24 hours and delivers a comprehensive summary powered by Google Gemini AI. 🚀 What it does: Automatically triggers daily at 8 AM (customizable) Fetches Gmail emails from the last 24 hours AI analyzes all emails to identify themes and key updates Highlights priority emails with reasons why they're important Delivers structured output in consistent JSON format 💡 Perfect for: Busy professionals managing high email volumes Team leads who need quick inbox overviews Anyone wanting to prioritize email responses efficiently Remote workers staying on top of communications 🎯 Key Features: Smart filtering - Only processes emails from the last 24 hours AI-powered analysis - Uses Google Gemini for intelligent summaries Priority detection - Identifies urgent, time-sensitive emails Consistent output - Structured JSON with summary and highlights Fully automated - Set it and forget it 📋 Sample Output: Summary: "Today's emails covered 3 project updates, 2 meeting requests, and 1 urgent client issue requiring immediate attention." Important Emails: Subject: "Project Deadline Extension Request" | Sender: client@company.com | Reason: Client requesting timeline change for major deliverable due this week Subject: "Tomorrow's Board Meeting Moved" | Sender: assistant@office.com | Reason: Last-minute schedule change requiring immediate calendar adjustment 🛠 Prerequisites: Gmail account with API access Google Gemini API credentials Basic n8n knowledge for credential setup ⚙️ Easy to Customize: Change schedule time (default: 8 AM daily) Adjust time window (default: 24 hours) Modify AI analysis focus Add email filters by sender, subject, etc. Extend with notifications (Slack, email, etc.) 🔒 Privacy Note: This workflow processes email content through Google Gemini API. Ensure compliance with your organization's data policies. Save hours of manual email sorting every week! Import this template and start getting intelligent email summaries delivered automatically every morning.

Description This workflow automatically monitors and tracks trending topics across multiple platforms and websites. It helps content creators and marketers stay ahead of the curve by identifying emerging trends before they go mainstream. Overview This workflow automatically monitors and tracks trending topics across multiple platforms and websites. It uses Bright Data to scrape trend data from social media, news sites, and other sources, then compiles the information into a structured format. Tools Used n8n:** The automation platform that orchestrates the workflow. Bright Data:** For scraping trend data from various websites without getting blocked. Spreadsheets/Databases:** For storing and analyzing trend information. How to Install Import the Workflow: Download the .json file and import it into your n8n instance. Configure Bright Data: Add your Bright Data credentials to the Bright Data node. Set Up Data Storage: Configure where you want to store the trend data. Customize: Specify which platforms to monitor and what topics to focus on. Use Cases Content Creators:** Stay on top of trending topics for content ideas. Marketers:** Identify emerging trends for timely campaigns. Researchers:** Track the evolution of topics and conversations over time. Connect with Me Website:** https://www.nofluff.online YouTube:** https://www.youtube.com/@YaronBeen/videos LinkedIn:** https://www.linkedin.com/in/yaronbeen/ Get Bright Data:** https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #trends #trendtracking #brightdata #contentmarketing #trendanalysis #trendalerts #markettrends #trendmonitoring #n8nworkflow #workflow #nocode #trendresearch #emergingtrends #socialmediatrends #trendscraping #trenddata #contentideas #digitalmarketing #marketresearch #trendforecasting #trendspotting #dataanalysis #marketintelligence #trendautomation

Workflow: Publish to Contentful with Rich Text Formatting ⚡ About the Creators This workflow was created by Varritech Technologies, an innovative agency that leverages AI to engineer, design, and deliver software development projects 500% faster than traditional agencies. Based in New York City, we specialize in custom software development, web applications, and digital transformation solutions. If you need assistance implementing this workflow or have questions about content management solutions, please reach out to our team. 🏗️ Architecture Overview This workflow takes a JSON article payload, splits its markdown content into logical chunks, converts each chunk into Contentful Rich Text JSON via an AI agent, merges the resulting rich text nodes back into a single document, formats the entire entry according to Contentful's field schema, and finally publishes it to Contentful. Trigger → Executes when called by another workflow Split by Headings → Breaks markdown into ##-delimited chunks Markdown → Rich Text → AI agent converts each chunk to Contentful Rich Text JSON Combine Rich Text Objects → Aggregates all chunk outputs into one document Format Entry → Wraps metadata and rich-text content into Contentful schema Publish Entry → HTTP POST to Contentful API 📦 Node-by-Node Breakdown flowchart LR A[When Executed by Another Workflow] --> B[Split by Headings] B --> C[Markdown to Contentful format] C --> D[Combine Rich Text Objects] D --> E[Merge1] E --> F[Format1] F --> G[Create newly formatted Contentful Entry] 1. When Executed by Another Workflow Type: Execute Workflow Trigger Input Example: title, slug, category.id, description, keywords, content, metaTitle, metaDescription, readingTime, difficulty Purpose: Receives the JSON payload from the upstream workflow. 2. Split by Headings Type: Code Logic: Splits input.content into an array of markdown chunks at each second-level heading (##). Emits one item per chunk with index, slug, title, and contentChunk. 3. Markdown to Contentful format Type: LangChain Agent (+ OpenAI Chat model) System Prompt: Defines rules for generating valid Contentful Rich Text JSON (must include nodeType, data:{}, content:[], etc.). Provides examples for paragraphs, headings, lists, links, and images. User Prompt: Here is the markdown content to convert: Purpose: Converts each markdown chunk into an array of rich-text nodes. 4. Combine Rich Text Objects Type: Code Logic: Parses and merges all content arrays returned by the AI agent into one combined content array under a document root. 5. Merge1 Type: Merge Purpose: Joins the original item (with metadata) and the combined rich-text document into a single data stream. 6. Format1 Type: Code Logic: Maps workflow data into the Contentful entry schema by setting each field (title, slug, category link, description, keywords, rich-text content, metaTitle, metaDescription, readingTime, difficulty) under the appropriate locale and structure required by Contentful. 7. Create newly formatted Contentful Entry Type: HTTP Request Method: POST URL: https://api.contentful.com/spaces Headers: Authorization: Bearer token for Contentful Management API Content-Type: application/vnd.contentful.management.v1+json X-Contentful-Version: entry version number X-Contentful-Content-Type: content type ID Body: The formatted fields object produced by the previous node Purpose: Publishes the new entry with rich-text content to Contentful. 🔍 Design Rationale & Best Practices Chunked Conversion Splitting by headings prevents AI context limits and keeps conversions modular. Strict Rich Text Schema Enforcing nodeType, data, and content structure avoids validation errors on Contentful. Two-Phase Merge Separating "combine AI outputs" and "format entry" keeps transformations clear and testable. Idempotent Publish Uses explicit versioning and content type headers to ensure correct entry creation.

🧾 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.