Workflow Templates

Stop guessing currency rates! Get a quick and clean exchange rate summary sent right to your phone. 📲 This workflow automatically checks the latest rates and builds a simple report for you. What it does: 🤑 Fetches the very latest exchange rates from an API. 🌍 Shows you what major currencies (like USD & EUR) are worth in your chosen local currency. ✍️ Creates a simple, easy-to-read report. 🚀 Delivers it straight to your Telegram! Setup is easy: All you need to do is set your base currency (e.g., 'EGP') and your Telegram Chat ID. Done! ✅

This n8n workflow automates personalized travel assistance via WhatsApp through a friendly virtual agent named Alex. It helps users plan trips, explore destinations, get visa/weather/hotel information, and book packages—all through a conversational interface. The system ensures quick, human-like support 24/7, improving customer experience and reducing manual handling by travel agents. Key Features The Travel Assistant agent provides contextual responses based on conversation history stored in memory. Alex maintains a friendly, professional tone throughout all interactions to enhance user experience. The workflow includes intelligent waiting mechanisms to ensure proper response processing. Memory functionality allows for seamless continuation of conversations across multiple interactions. Workflow Process The Get WhatsApp Message node captures incoming messages from users on WhatsApp, initiating the travel assistance process. The Travel Assistant node processes user queries using AI to understand travel needs and generate appropriate responses for trip planning, destination information, visa requirements, weather updates, and booking assistance. The Travel Plan Creator agent works in conjunction with the main assistant to generate detailed itineraries and travel recommendations based on user preferences. The Memory node stores conversation context and user preferences, enabling personalized responses and seamless conversation flow across multiple interactions. The Wait For Response node introduces intelligent delays to ensure proper message processing and natural conversation pacing. The Send Reply On WhatsApp node delivers the AI-generated travel assistance back to the user through WhatsApp messaging. Setup Instructions Import the workflow into n8n and configure WhatsApp Business API credentials for message handling. Set up the AI service for the Travel Assistant and Travel Plan Creator agents with your preferred language model. Configure the Memory node with appropriate storage settings for conversation persistence. Test the workflow by sending various travel-related queries through WhatsApp to ensure proper responses. Monitor conversation quality and adjust AI parameters as needed for optimal user experience. Prerequisites WhatsApp Business API access or WhatsApp integration service AI/LLM service for travel assistance (OpenAI, Anthropic, or similar) Database or storage service for conversation memory Access to travel data APIs for real-time information (weather, visa requirements, hotel availability) Modification Options Modify the Travel Assistant node to include specific travel databases, local recommendations, or branded responses. Adjust the conversation memory settings to control how much context is retained across interactions. Customize the Travel Plan Creator to include preferred booking platforms, hotel chains, or travel partners. Add additional specialized agents for specific travel services like flight booking, car rentals, or activity reservations. Configure response timing in the Wait For Response node to match your desired conversation flow.

Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. Until now, these pieces had to be custom-wired. But with the new native n8n nodes for MongoDB Atlas, we reduce that overhead dramatically. With just a few clicks: Store and recall long-term memory from MongoDB Query vector embeddings stored in Atlas Vector Search Use these results in your LLM chains and automation logic In this example we present an ingestion and AI Agent flows that focus around Travel Planning. The different interest points that we want the agent to know about can be ingested into the vector store. The AI Agent will use the vector store tool to get relevant context about those points of interest if it needs to. Prerequisites MongoDB Atlas project and Cluster OpenAI Valid API Key for embeddings (can be other provider) Gemini API Key for the LLM (can be other provider) How it works: There are 2 main flows. One is ingesting flow: Gets a document from a webhook and use MongoDB Vector Atlas to embed the document title and description into points_of_interest collection. Embeddings are stored in a field named embedding Embeddings used are OpenAI's but it can be any type of supported embedders. Second flow is an AI Agent node with Chat Memory Stored in MongoDB Atlas and a Vector Search node as a tool: Chat Message Trigger**: Chatting with the AI Agent will trigger the conversation store in the MongoDB Chat Memory node. When data is necessary like a location search or details it will go to the "Vector Search" tool. Vector Search Tool** - uses Atlas Vector Search index created on the points_of_interest collection: // index name : "vector_index" // If you change an embedding provider make sure the numDimensions correspond to the model. { "fields": [ { "type": "vector", "path": "embedding", "numDimensions": 1536, "similarity": "cosine" } ] } Additional Resources MongoDB Atlas Vector Search n8n Atlas Vector Search docs

Enhance Query Resolution with the Knowledge Base Tool! Our KB Tool - Confluence KB is crafted to seamlessly integrate into the IT Ops AI SlackBot Workflow, enhancing the IT support process by enabling sophisticated search and response capabilities via Slack. Workflow Functionality: Receive Queries**: Directly accepts user queries from the main workflow, initiating a dynamic search process. AI-Powered Query Transformation**: Utilizes OpenAI's models or local ai to refine user queries into searchable keywords that are most likely to retrieve relevant information from the Knowledge Base. Confluence Integration**: Executes searches within Confluence using the refined keywords to find the most applicable articles and information. Deliver Accurate Responses**: Gathers essential details from the Confluence results, including article titles, links, and summaries, preparing them to be sent back to the parent workflow for final user response. To view a demo video of this workflow in action, click here. Quick Setup Guide: Ensure correct configurations are set for OpenAI and Confluence API integrations. Customize query transformation logic as per your specific Knowledge Base structure to improve search accuracy. Need Help? Dive into our Documentation or get support from the Community Forum! Deploy this tool to provide precise and informative responses, significantly boosting the efficiency and reliability of your IT support workflow.

This workflow adds a new product in Stripe whenever a new product has been added to Pipedrive. Prerequisites Stripe account and Stripe credentials Pipedrive account and Pipedrive credentials How it works Pipedrive trigger node starts the workflow when a new product is added. HTTP Request node creates a new product in Stripe using previuos input. Merge node combines data of both Pipedrive and Stripe inputs. The output will contain the data of Pipedrive input merged with the data of Stripe input. The merge occurs based on the index of the items. The Item Lists node splits prices to separate items. HTTP Request node creates price records in Stripe.

How it Works: Capture Contact Requests:** This template efficiently handles contact requests coming through a WordPress website using the Contact Form 7 (CF7) plugin with a webhook extension. Contact Management:** It automatically creates or updates contacts in Pipedrive upon receiving a new request. Lead Management:** Each contact request is securely stored in the lead inbox of Pipedrive, ensuring no opportunity is missed. Task Creation:** For each new contact or update, the workflow triggers the creation of a related task, streamlining follow-up actions. Note Attachment:** A comprehensive note containing all details from the contact request is attached to the corresponding lead, ensuring that all information is readily accessible. Step-by-Step Guide: Estimated Setup Time: The setup process is straightforward and can be completed quickly. Specific time may vary depending on your familiarity with n8n and the systems involved. Detailed setup instructions are provided within the workflow via sticky notes. These notes offer in-depth guidance for configuring each component of the template to suit your specific needs.

WordPress Post Auto-Categorization Workflow 💡 Click here to read detailed case study 📺 Click here to watch youtube tutorial 🎯 Purpose Automatically categorize WordPress blog posts using AI, saving hours of manual work. This workflow analyzes your post titles and assigns them to predefined categories using artificial intelligence. 🔄 What This Workflow Does • Connects to your WordPress site • Retrieves all uncategorized posts • Uses AI to analyze post titles • Automatically assigns appropriate category IDs • Updates posts with new categories • Processes dozens of posts in minutes ⚙️ Setup Requirements WordPress site with admin access Predefined categories in WordPress OpenAI API credentials (or your preferred AI provider) n8n with WordPress credentials 🛠️ Configuration Steps Add your WordPress categories (manually in WordPress) Note down category IDs Update the AI prompt with your category IDs Configure WordPress credentials in n8n Set up AI API connection 🔧 Customization Options • Modify AI prompts for different categorization criteria • Adjust for multiple category assignments • Add tag generation functionality • Customize for different content types • Add additional metadata updates ⚠️ Important Notes • Backup your WordPress database before running • Test with a few posts first • Review AI categorization results initially • Categories must be created manually first 🎁 Bonus Features • Can be modified for tag generation • Works with scheduled posts • Handles bulk processing • Maintains categorization consistency Perfect for content managers, bloggers, and website administrators looking to organize their WordPress content efficiently. #n8n #WordPress #ContentManagement #Automation #AI Created by rumjahn

This n8n workflow automatically creates friendly, personalized travel itineraries based on messages received via email or WhatsApp. When a user says "I want to go to Dubai with friends for 5 days" or something similar, the AI agent understands the request, generates a detailed daily plan with suggested activities, transport tips, and hotel ideas — all in a warm, human tone. It saves time, adds value for travelers, and delivers ready-to-send itineraries without any manual effort. Good to know The AI agent uses advanced language processing to understand natural travel requests in multiple formats. Itineraries are generated with personalized recommendations based on travel preferences, group size, and duration. The workflow supports both email and WhatsApp communication channels for maximum accessibility. All responses maintain a warm, friendly tone to enhance user experience. How it works The Get Query from Email node captures travel requests sent via email, parsing the message content for trip details. The Get Query from WhatsApp node simultaneously monitors WhatsApp messages for travel planning requests. Both inputs feed into the Itinerary Creator Agent node, which uses AI to analyze the request and generate comprehensive travel plans including activities, accommodations, and transportation suggestions. The Check Proper Data node validates the generated itinerary to ensure all essential information is included and properly formatted. The Check where to send Answer node determines the appropriate response channel (email or WhatsApp) based on the original request source. If the request came via email, the Sending Itinerary from Email node sends the personalized itinerary back to the user's email address. If the request came via WhatsApp, the Send Itinerary from message node delivers the travel plan through WhatsApp messaging. How to use Import the workflow into n8n and configure the nodes with your email service credentials and WhatsApp API access. Set up the AI agent with your preferred travel data sources and recommendation algorithms. Test the workflow by sending sample travel requests through both email and WhatsApp channels. Monitor the generated itineraries to ensure quality and adjust the AI agent parameters as needed. Requirements Email service API credentials (SMTP or email provider API) WhatsApp Business API access or WhatsApp integration service AI/LLM service for the Itinerary Creator Agent (OpenAI, Anthropic, or similar) Access to travel data sources for recommendations (optional but recommended) Customising this workflow Modify the Itinerary Creator Agent node to include specific travel preferences, local recommendations, or branded content. Adjust the data validation rules in the Check Proper Data node to match your quality standards. Customize response templates in both sending nodes to align with your brand voice and style. Add additional input channels or integrate with other messaging platforms as needed.

AI-Powered Email Draft Automation Workflow In this guide, we’ll walk you through setting up an AI-driven workflow that automatically processes incoming emails using a custom AI model (e.g., Llama), prepares email content, and saves it as a Gmail draft. Ready to automate your email drafting process? Let’s dive in! What’s the Goal? Automatically detect and process new emails via IMAP. Use a custom AI model to analyze and generate email content. Prepare structured and relevant email responses. Save the generated content as a Gmail draft for review or sending. Enable 24/7 email automation with seamless integration. By the end, you’ll have a self-running email assistant that drafts responses effortlessly. Why Does It Matter? Manual email drafting is time-consuming and prone to delays. Here’s why this workflow is a game changer: Zero Human Error:** AI ensures consistent and accurate drafts. Time-Saving Automation:** Instantly process and draft emails, boosting efficiency. 24/7 Availability:** Handle emails anytime without manual intervention. Focus on Strategy:** Free your team from repetitive drafting tasks. Think of it as your tireless email drafting assistant that never misses a beat. How It Works Here’s the step-by-step magic behind the automation: Step 1: Trigger the Workflow Detect new emails using IMAP via the Check New Email (IMAP) node. Capture incoming email content for processing. Step 2: Process Email with AI Send the email text to a custom AI model (e.g., Llama) for analysis. Use the Custom AI Model node to generate a context-aware response or draft content. Step 3: Prepare Email Content Format the AI-generated content into a polished email structure using the Prepare Email Content node. Ensure the content is ready for drafting with proper salutations and structure. Step 4: Save as Gmail Draft Route the prepared email content to the Save as Gmail Draft node. Save the draft in Gmail for review or manual sending. Step 5: Log & Optimize Log all processed emails and drafts in a database (e.g., Airtable, Google Sheets). Continuously improve the AI model based on feedback or new email patterns. 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 the Smart Email Draft Generator 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. 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. Setup Notes: IMAP Credentials:** Configure IMAP settings in the Check New Email (IMAP) node with your email account credentials (e.g., Gmail IMAP settings). Custom AI Model:** Set up the Custom AI Model node with your AI model credentials (e.g., Llama API key or endpoint). Gmail Integration:** Authorize the Save as Gmail Draft node with Gmail API credentials to save drafts. Content Customization:** Adjust the Prepare Email Content node to tailor the email structure or tone as needed.

This n8n workflow shows how using multimodal LLMs with AI vision can tackle tricky image validation tasks which are near impossible to achieve with code and often impractical to be done by humans at scale. You may need image validation when users submitted photos or images are required to meet certain criteria before being accepted. A wine review website may require users only submit photos of wine with labels, a bank may require account holders to submit scanned documents for verification etc. In this demonstration, our scenario will be to analyse a set of portraits to verify if they meet the criteria for valid passport photos according to the UK government website (https://www.gov.uk/photos-for-passports). How it works Our set of portaits are jpg files downloaded from our Google Drive using the Google Drive node. Each image is resized using the Edit Image node to ensure a balance between resolution and processing speed. Using the Basic LLM node, we'll define a "user message" option with the type of binary (data). This will allow us to pass our portrait to the LLM as an input. With our prompt containing the criteria pulled off the passport photo requirements webpage, the LLM is able to validate the photo does or doesn't meet its criteria. A structured output parser is used to structure the LLM's response to a JSON object which has the "is_valid" boolean property. This can be useful to further extend the workflow. Requirements Google Gemini API key Google Drive account Customising this workflow Not using Gemini? n8n's LLM node works with any compatible multimodal LLM so feel free to swap Gemini out for OpenAI's GPT4o or Antrophic's Claude Sonnet. Don't need to validate portraits? Try other use cases such as document classification, security footage analysis, people tagging in photos and more.

Automating Company ICP Scoring via LinkedIn Use Case This automation scores companies based on their LinkedIn profile using custom Ideal Customer Profile (ICP) criteria. It’s ideal for qualifying B2B leads and prioritizing outreach based on fit. What This Automation Does Inputs required: Company LinkedIn URL**: Public LinkedIn profile of the company. Airtop Profile (connected to LinkedIn)**: Airtop Profile authenticated to access and extract profile data. The automation analyzes the LinkedIn page and calculates a score based on: Scoring Criteria | Category | Classification | Points | |--------------------|---------------------------|------------| | AI Focus | Low | 5 | | | Medium | 10 | | | High | 25 | | Technical Level | Basic | 5 | | | Intermediate | 15 | | | Advanced | 25 | | | Expert | 35 | | Employee Count | 0–9 | 5 | | | 10–150 | 25 | | | 150+ | 30 | | Agency Status | Not Automation Agency | 0 | | | Automation Agency | 20 | | Geography | Outside US/Europe | 0 | | | US/Europe Based | 10 | The result includes: Total ICP score Detailed justifications for each score component How It Works Opens the company’s LinkedIn page using Airtop. Analyzes metadata including employee count, headquarters, services, and keywords. Applies the scoring rubric and returns structured JSON with scores and reasons. Optionally flattens the result for storage or CRM integration. Setup Requirements Airtop API Key LinkedIn-authenticated Airtop Profile Next Steps Combine with Lead Lists**: Score companies from outreach lists. Push to CRM**: Add scores to HubSpot or Salesforce records. Adjust Scoring Weights**: Modify rubric to reflect your ICP strategy. Read more about company ICP scoring automation with Airtop and n8n

What this does Show you how to us XMLRPC APIs via the generic HTTP-Request-node, by the example of posting to a wordpress blog This is also a feasible workaround if a specific n8n integration does not work or stops working (which happens e.g. with the Wordpress node) How it works First, the XML payload for the request is being prepared (in a code node, which also properly escapes special character in the values that you want to send to the XMLRPC endpoint) Then, the HTTP Request node sends the request using the HTTP post method Last, the returned XML response is converted to JSON which a conditional node uses to determine whether th operation was successful or not Setup steps: Import workflow Ensure you have a wordpress blog with a user that has an app-Password Edit the "Settings"-node and enter your individual values for url/user/app-pw