Workflow Templates

Who is this for? Content strategists analyzing web page semantic content SEO professionals conducting entity-based analysis Data analysts extracting structured data from web pages Marketers researching competitor content strategies Researchers organizing and categorizing web content Anyone needing to automatically extract entities from web pages What problem is this workflow solving? Manually identifying and categorizing entities (people, organizations, locations, etc.) on web pages is time-consuming and error-prone. This workflow solves this challenge by: Automating the extraction of named entities from any web page Leveraging Google's powerful Natural Language API for accurate entity recognition Processing web pages through a simple webhook interface Providing structured entity data that can be used for analysis or further processing Eliminating hours of manual content analysis and categorization What this workflow does This workflow creates an automated pipeline between a webhook and Google's Natural Language API to: Receive a URL through a webhook endpoint Fetch the HTML content from the specified URL Clean and prepare the HTML for processing Submit the HTML to Google's Natural Language API for entity analysis Return the structured entity data through the webhook response Extract entities including people, organizations, locations, and more with their salience scores Setup Prerequisites: An n8n instance (cloud or self-hosted) Google Cloud Platform account with Natural Language API enabled Google API key with access to the Natural Language API Google Cloud Setup: Create a project in Google Cloud Platform Enable the Natural Language API for your project Create an API key with access to the Natural Language API Copy your API key for use in the workflow n8n Setup: Import the workflow JSON into your n8n instance Replace "YOUR-GOOGLE-API-KEY" in the "Google Entities" node with your actual API key Activate the workflow to enable the webhook endpoint Copy the webhook URL from the "Webhook" node for later use Testing: Use a tool like Postman or cURL to send a POST request to your webhook URL Include a JSON body with the URL you want to analyze: {"url": "https://example.com"} Verify that you receive a response containing the entity analysis data How to customize this workflow to your needs Analyzing Specific Entity Modify the "Google Entities" node parameters to include entityType filters Add a "Function" node after "Google Entities" to filter specific entity types Create conditions to extract only entities of interest (people, organizations, etc.) Processing Multiple URLs in Batch: Replace the webhook with a different trigger (HTTP Request, Google Sheets, etc.) Add a "Split In Batches" node to process multiple URLs Use a "Merge" node to combine results before sending the response Enhancing Entity Data: Add additional API calls to enrich extracted entities with more information Implement sentiment analysis alongside entity extraction Create a data transformation node to format entities by type or relevance Additional Notes This workflow respects Google's API rate limits by processing one URL at a time The Natural Language API may not identify all entities on a page, particularly for highly technical content HTML content is trimmed to 100,000 characters if longer to avoid API limitations Consider legal and privacy implications when analyzing and storing entity data from web pages You may want to adjust the HTML cleaning process for specific website structures ❤️ Hueston SEO Team

Use Case In most companies, employees have a lot of great ideas. That was the same for us at n8n. We wanted to make it as easy as possible to allow everyone to add their ideas to some formatted database - it should be somewhere where everyone is all the time and could add a new idea without much extra effort. Since we're using Slack, this seemed to be the perfect place to easily add ideas and collect them in Notion. What this workflow does This workflow waits for a webhook call within Slack, that gets fired when users use the /idea command on a bot that you will create as part of this template. It then checks the command, adds the idea to Notion, and notifies the user about the newly added idea as you can see below: Creating your Slack bot Visit https://api.slack.com/apps, click on New App and choose a name and workspace. Click on OAuth & Permissions and scroll down to Scopes -> Bot token Scopes Add the chat:write scope Head over to Slash Commands and click on Create New Command Use /idea as the command Copy the test URL from the Webhook node into Request URL Add whatever feels best to the description and usage hint Go to Install app and click install Setup Add a Database in Notion with the columns Name and Creator Add your Notion credentials and add the integration to your Notion page. Fill the setup node below Create your Slack app (see other sticky) Click Test workflow and use the /idea comment in Slack Activate the workflow and exchange the Request URL with the production URL from the webhook How to adjust it to your needs You can adjust the table in Notion and for example, add different types of ideas or areas that they impact You might wanna add different templates in Notion to make it easier for users to fill their ideas with details Rename the Slack command as it works best for you How to enhance this workflow At n8n we use this workflow in combination with some others. E.g. we have the following things on top: We additionally have a /bug Slack command that adds a new bug to Linear. Here we're using AI to classify the bugs and move it to the right team. (see this template and this template) We also added other types, like /pain to be less solution-driven To make it easier for everyone to give input, we added a Votes column that allows everyone to vote on ideas/pain points in the list We're also running a workflow once a week that highlights the most popular new ideas and the most active voters (see here)

This n8n template demonstrates how to create a comprehensive voice-powered restaurant assistant that handles table reservations, food orders, and restaurant information requests through natural language processing. The system uses VAPI for voice interaction and PostgreSQL for data management, making it perfect for restaurants looking to automate customer service with voice AI technology. Good to know Voice processing requires active VAPI subscription with per-minute billing Database operations are handled in real-time with immediate confirmations The system can handle multiple simultaneous voice requests All customer data is stored securely in PostgreSQL with proper indexing How it works Table Booking & Order Handling Workflow Voice requests are captured through VAPI triggers when customers make booking or ordering requests The system processes natural language commands and extracts relevant details (party size, time, food items) Customer data is immediately saved to the bookings and orders tables in PostgreSQL Voice confirmations are sent back through VAPI with booking details and estimated wait times All transactions are logged with timestamps for restaurant management tracking Restaurant Info Provider Workflow Info requests trigger when customers ask about hours, menu, location, or services Restaurant details are retrieved from the restaurant_info table containing current information Wait nodes ensure proper data loading before voice response generation Structured restaurant information is delivered via VAPI in natural, conversational format Database Schema Bookings Table booking_id (PRIMARY KEY) - Unique identifier for each reservation customer_name - Customer's full name phone_number - Contact number for confirmation party_size - Number of guests booking_date - Requested reservation date booking_time - Requested time slot special_requests - Dietary restrictions or special occasions status - Booking status (confirmed, pending, cancelled) created_at - Timestamp of booking creation Orders Table order_id (PRIMARY KEY) - Unique order identifier customer_name - Customer's name phone_number - Contact for order updates order_items - JSON array of food items and quantities total_amount - Calculated order total order_type - Delivery, pickup, or dine-in special_instructions - Cooking preferences or allergies status - Order status (received, preparing, ready, delivered) created_at - Order timestamp Restaurant_Info Table info_id (PRIMARY KEY) - Information entry identifier category - Type of info (hours, menu, location, contact) title - Information title description - Detailed information content is_active - Whether info is currently valid updated_at - Last modification timestamp How to use The manual trigger can be replaced with webhook triggers for integration with existing restaurant systems Import the workflow into your n8n instance and configure VAPI credentials Set up PostgreSQL database with the required tables using the schema provided above Configure restaurant information in the restaurant_info table Test voice commands such as "Book a table for 4 people at 7 PM" or "What are your opening hours?" Customize voice responses in VAPI nodes to match your restaurant's tone and branding The system can handle multiple concurrent voice requests and scales with your restaurant's needs Requirements VAPI account for voice processing and natural language understanding PostgreSQL database for storing booking, order, and restaurant information n8n instance with database and VAPI integrations enabled Customising this workflow Voice AI automation can be adapted for various restaurant types - from quick service to fine dining establishments Try popular use-cases such as multi-location booking management, dietary restriction handling, or integration with existing POS systems The workflow can be extended to include payment processing, SMS notifications, and third-party delivery platform integration

Who is this tempate for? This workflow powers a simple yet effective customer and sales support chatbot for your webshop. It's perfect for solopreneurs who want to automate customer interactions without relying on expensive or complex support tools. How it works? The chatbot listens to user requests—such as checking product availability—and automatically handles the following Fetches product information from a Google Sheet Answers customer queries Places an order Updates the stock after a successful purchase Everything runs through a single Google Sheet used for both stock tracking and order management. Setup Instructions Before you begin, connect your Google Sheets credentials by following this guide: This will be used to connect all the tools to Google Sheets 👉 Setup Google sheets credentials Get Stock Open "Get Stock" tool node and select the Google sheet credentials you created. Choose the correct google sheet document and sheet name and you are done. Place order Go to your "Place Order" tool node and select the Google sheet credentials you have created. Choose the correct google sheet document and sheet name. Update Stock - Open your "Update Stock" tool node and select the Google sheet credentials you have created. Choose the correct google sheet document and sheet name. In "Mapping Column Mode" section select map each column manually. In "Column to match on" select the column with a unique identifier (e.g., Product ID) to match stock items. In values to update section, add only the column(s) that need to be updated—usually the stock count. AI Agent node Adjust the prompt according to your use case and customize what you need. Google Sheet Template Stock sheet |Case ID|Phone Model|Case Name|Case Type|Image URL|Quantity Avaialble|Initital Inventory|Sold| |-|-|-|-|-|-|-|-| |1023|Iphone 14 pro|Black Leather|Magsafe|https://example.com/url|90|100|10 Order sheet |Case ID|Phone Model|Case Name|Name|Phone Number|Address| |-|-|-|-|-|-| |1023|Black Leather |Iphone 14 pro|Fernando Torres|9998898888|Paris, France

What it is: In version 1.78, n8n introduced a dedicated node to use the OpenRouter service, which lets you to use a lot of different LLM models and providers and change models on the fly in an agentic workflow. For prior n8n versions, there's a workaround to make OpenRouter accessible, by using the OpenAI node with a OpenRouter-specific BaseURL. This trivial workflow demonstrates this for version before 1.78, so that you can use different LLM model dynamically with the available n8n nodes for OpenAI LLM and OpenAI credentials. What you can do: Use any of the OpenRouter models Have the model even dynamically configured or changing (by some external config, some rule, or some specific chat message) Setup steps: Import the workflow Ensure you have registered and account, purchased some credits and created and API key for OpenRouter.ai Configure the "OpenRouter" credentials with your own credentials, using an OpenAI type credential, but making sure in the credential's config form its "Base URL" is set to https://openrouter.ai/api/v1 so OpenRouter is used instead of OpenAI. Open the "Settings" node and change the model value to any valid model id from the OpenRouter models list or even have the model property set dynamically

This n8n template demonstrates how to create an intelligent food recipe assistant that accepts requests via Gmail and web forms, processes them using AI chat models (Ollama and Llama 3.2), and delivers personalized recipes back to users. The system combines multiple input methods with advanced AI processing to provide customized cooking instructions and ingredient lists. Good to know The system accepts recipe requests through both Gmail and web form submissions AI models understand dietary restrictions, cuisine preferences, and cooking skill levels Recipe responses include formatted ingredients, step-by-step instructions, and cooking tips All requests are processed automatically without manual intervention How it works Gmail Recipe Request Workflow Gmail triggers activate when users send emails with recipe requests to the designated email address The system extracts recipe requirements, dietary preferences, and cooking constraints from email content User queries are processed through the Ollama Recipe Generator for intelligent recipe creation AI-generated recipes are formatted with proper ingredients, instructions, and cooking times Formatted recipes are sent back to users via Gmail with a professional presentation Web Form Recipe Request Workflow Web form submissions trigger when users fill out structured recipe request forms Form data includes cuisine type, dietary restrictions, available ingredients, and cooking time preferences The Llama 3.2 Chef Model processes structured requests for optimized recipe generation Recipes are formatted with clear instructions, ingredient measurements, and cooking techniques Users receive formatted recipes via email with additional cooking tips and variations How to use Import the workflow into your n8n instance and configure Gmail integration for recipe requests Set up the web form with fields for cuisine preferences, dietary restrictions, and cooking skill level Configure Ollama and Llama 3.2 AI models with appropriate recipe generation prompts Test both Gmail and web form inputs with sample recipe requests Customize email templates to match your brand and include additional cooking resources The system scales automatically to handle multiple simultaneous recipe requests Requirements Gmail account for email-based recipe requests and responses Ollama installation with Recipe Generator model Llama 3.2 Chef Model access for advanced recipe processing n8n instance with Gmail and AI model integrations Customising this workflow Recipe automation can be adapted for different cuisines, dietary needs, and cooking skill levels Try popular use-cases such as meal planning assistance, ingredient substitution suggestions, or nutritional information inclusion The workflow can be extended to include recipe image generation, shopping list creation, and cooking video recommendations

📰 AI News Digest Agent: Auto News Summarizer & Email Newsletter Create an intelligent news curation system that automatically fetches breaking headlines, generates AI-powered summaries, and delivers personalized news digests to your subscriber list. Perfect for newsletter creators, team leaders, and content curators who want to keep their audience informed without the manual effort of news monitoring and summarization. 🔄 How It Works This streamlined 5-step automation delivers fresh news insights around the clock: Step 1: Automated News Collection The workflow runs on a configurable schedule (default: every 10 minutes) to fetch the latest headlines from NewsAPI, ensuring your content stays current with breaking developments. Step 2: Intelligent Content Curation The system pulls top headlines from reliable news sources, filtering by country, category, and relevance to deliver the most important stories of the day. Step 3: AI-Powered Summarization GPT-4 processes the collected headlines and creates: Concise 5-bullet point summaries Key insights and implications Easy-to-digest news overviews Professional formatting for email distribution Step 4: Subscriber Management The workflow accesses your Google Sheets subscriber list, retrieving names and email addresses for personalized delivery. Step 5: Automated Email Distribution Personalized news digests are automatically sent to each subscriber via Gmail, with custom greetings and professionally formatted content. ⚙️ Setup Steps Prerequisites NewsAPI account (free tier available) OpenAI API access for content summarization Google Sheets for subscriber management Gmail account for email distribution n8n instance (cloud or self-hosted) Required Google Sheets Structure Create a simple subscriber database: | Name | Email | |---------------|--------------------------| | John Smith | john@example.com | | Sarah Johnson | sarah@company.com | | Mike Chen | mike.chen@startup.co | Configuration Steps Credential Setup NewsAPI Key: Sign up at newsapi.org for free headline access OpenAI API Key: Required for AI-powered news summarization Google Sheets OAuth2: Access your subscriber spreadsheet Gmail OAuth2: Enable automated email sending News Source Configuration Country Selection: Choose target region (US, UK, CA, AU, etc.) Category Filters: Focus on specific topics (technology, business, health) Source Selection: Prefer certain news outlets or avoid others Language Settings: Configure for international audiences AI Summarization Customization Default prompt creates 5-bullet summaries, but can be tailored for: Industry Focus: Technology, finance, healthcare, politics Audience Type: General public, professionals, executives Content Depth: Brief overviews vs detailed analysis Tone & Style: Formal, conversational, or technical Email Template Personalization Subject Line Formatting: Include date, breaking news indicators Greeting Customization: Use subscriber names for personal touch Content Layout: Professional formatting with clear sections Branding Elements: Add your organization's signature or logo Delivery Schedule Optimization Frequency Settings: Every 10 minutes, hourly, or daily Time Zone Considerations: Optimize for subscriber locations Breaking News Alerts: Immediate delivery for urgent stories Digest Compilation: Collect multiple stories for periodic summaries 🚀 Use Cases Newsletter Publishers Content Automation: Generate newsletter content without manual curation Consistent Publishing: Maintain regular delivery schedules automatically Audience Growth: Provide value that encourages subscriptions and shares Time Savings: Eliminate hours of daily news monitoring and writing Corporate Communications Employee Updates: Keep teams informed about industry developments Executive Briefings: Deliver curated news summaries to leadership Client Communications: Share relevant industry insights with customers Stakeholder Relations: Maintain informed investor and partner networks Educational Institutions Student Resources: Provide current events for academic discussions Faculty Updates: Keep educators informed about relevant developments Research Support: Deliver news related to specific academic fields Parent Communications: Share educational policy and school-related news Professional Services Client Value Addition: Provide industry-specific news as a service benefit Thought Leadership: Position your firm as an informed industry expert Business Development: Share insights that demonstrate market knowledge Team Knowledge Sharing: Keep entire organization current on industry trends Community Organizations Member Engagement: Keep community members informed and engaged Local News Focus: Customize for regional or local news coverage Event Planning: Stay informed about developments affecting your community Advocacy Support: Monitor news relevant to your organization's mission 🔧 Advanced Customization Options Multi-Source News Aggregation Expand beyond NewsAPI with additional sources: RSS Feed Integration: Add specialized industry publications Social Media Monitoring: Include trending topics from Twitter/LinkedIn Government Sources: Official announcements and policy updates International Coverage: Global perspectives on major stories Intelligent Content Filtering Implement smart curation features: Sentiment Analysis: Filter positive, negative, or neutral news Relevance Scoring: Prioritize stories based on subscriber interests Duplicate Detection: Avoid sending repetitive story coverage Quality Assessment: Ensure content meets editorial standards Subscriber Segmentation Create targeted news experiences: Interest Categories: Technology, business, sports, entertainment Geographic Preferences: Local, national, or international focus Delivery Preferences: Frequency and format customization Engagement Tracking: Monitor opens, clicks, and subscriber behavior Enhanced Email Features Professional newsletter capabilities: HTML Templates: Rich formatting with images and links Call-to-Action Buttons: Drive engagement with your content or services Social Sharing: Enable easy sharing of newsletter content Analytics Integration: Track email performance and subscriber engagement 📊 Content Generation Examples Sample Email Output: Subject: 📰 Your Daily News Digest - March 15, 2024 Hi John, Please find today's top news headlines summarized below: 📈 BUSINESS & TECHNOLOGY Federal Reserve signals potential rate cuts following inflation data Major tech companies announce AI partnership for healthcare applications Renewable energy sector sees record investment levels in Q1 2024 Cryptocurrency markets stabilize after regulatory clarity announcement Supply chain disruptions ease as global shipping routes normalize 💡 These developments suggest growing economic optimism and continued technology sector innovation. The healthcare AI partnership particularly signals significant advances in medical technology accessibility. Stay informed and have a great day! Powered by AI News Digest Agent Unsubscribe | Update Preferences Breaking News Alert Format: Subject: 🚨 Breaking News Alert - Major Development Hi Sarah, BREAKING: [Headline] Key Details: [Critical point 1] [Critical point 2] [Impact analysis] Full coverage in your next scheduled digest. AI News Digest Agent 🛠️ Troubleshooting & Best Practices Common Issues & Solutions API Rate Limiting Monitor NewsAPI quota usage and upgrade plan if needed Implement intelligent caching to reduce redundant requests Stagger requests during high-traffic periods Set up alerts for approaching rate limits Email Delivery Challenges Monitor Gmail sending limits and implement delays if needed Use professional email authentication (SPF, DKIM) Maintain clean subscriber lists to avoid spam flags Implement unsubscribe functionality for compliance Content Quality Control Review AI summaries periodically for accuracy and bias Implement feedback loops for continuous prompt improvement Create editorial guidelines for consistent tone and style Monitor subscriber feedback and engagement metrics Optimization Strategies Performance Enhancement Use parallel processing for multiple news sources Implement intelligent caching for repeated content Optimize AI prompts for faster processing and better results Monitor workflow execution time and resource usage Subscriber Growth Create compelling value propositions for newsletter signups Implement referral systems for organic growth Share sample newsletters on social media and websites Collect feedback to continuously improve content quality Content Strategy A/B test different summary formats and lengths Analyze which news categories generate most engagement Experiment with sending times for optimal open rates Create themed newsletters for special events or topics 📈 Success Metrics Engagement Indicators Open Rates: Percentage of subscribers reading newsletters Click-Through Rates: Engagement with linked news sources Subscriber Growth: New signups and retention rates Forward/Share Rates: Viral coefficient of your content Content Quality Measurements Relevance Scores: Subscriber feedback on content usefulness Timeliness: How quickly breaking news reaches subscribers Accuracy: Verification of AI-summarized content Completeness: Coverage of important stories in your focus areas 📞 Questions & Support Need assistance with your AI News Digest Agent setup or optimization? 📧 Technical Support Email: Yaron@nofluff.online Response Time: Within 24 hours on business days Specialization: NewsAPI integration, AI content optimization, email deliverability 🎥 Educational Resources YouTube Channel: https://www.youtube.com/@YaronBeen/videos Complete setup and configuration tutorials Advanced customization techniques for different industries Email marketing best practices for automated newsletters Troubleshooting common integration issues Scaling strategies for growing subscriber lists 🤝 Professional Community LinkedIn: https://www.linkedin.com/in/yaronbeen/ Connect for ongoing newsletter automation support Share your news curation success stories Access exclusive templates and workflow variations Join discussions about content automation trends 💬 Support Request Best Practices Include in your support message: Your target audience and newsletter focus Current subscriber count and growth goals Specific news categories or geographic regions of interest Any technical errors or integration challenges Current content creation workflow and pain points

Use Case This workflow is a slight variation of a workflow we're using at n8n. In most companies, employees have a lot of great ideas. That was the same for us at n8n. We wanted to make it as easy as possible to allow everyone to add their ideas to some formatted database - it should be somewhere where everyone is all the time and could add a new idea without much extra effort. Since we're using Slack, this seemed to be the perfect place to easily add ideas. In this example, we're adding the ideas to Google Sheets instead of Notion, like we do. What this workflow does This workflow waits for a webhook call within Slack, that gets fired when users use the /idea command on a bot that you will create as part of this template. It then checks the command, adds the idea to Google Sheets and notifies the user about the newly added idea as you can see below: Creating your Slack bot Visit https://api.slack.com/apps, click on New App and choose a name and workspace. Click on OAuth & Permissions and scroll down to Scopes -> Bot token Scopes Add the chat:write scope Head over to Slash Commands and click on Create New Command Use /idea as the command Copy the test URL from the Webhook node into Request URL Add whatever feels best to the description and usage hint Go to Install app and click install Setup Create a Google Sheets document with the columns Name and Creator Add your Google credentials Fill the Set me up node. Create your Slack app (see other sticky) Click Test workflow and use the /idea comment in Slack Activate the workflow and exchange the Request URL with the production URL from the webhook How to adjust it to your needs You can adjust the table in Google Sheets and for example, add different types of ideas or areas that they impact Rename the Slack command as it works best for you How to enhance this workflow At n8n we use this workflow in combination with some others. E.g. we have the following things on top: We additionally have a /bug Slack command that adds a new bug to Linear. Here we're using AI to classify the bugs and move it to the right team. (Bug command workflow and Ai Classifier workflow) We also added other types, like /pain to be less solution-driven To make it easier for everyone to give input, we added a Votes column that allows everyone to vote on ideas/pain points in the list We're also running a workflow once a week that highlights the most popular new ideas and the most active voters

Overview This n8n workflow is specifically designed to monitor USDT TRC20 transactions within a specified wallet. It utilizes the public blockchain database of TronScan, requiring no API authentication, to periodically check and process transaction data. This workflow is ideal for users who need an automated solution to track their TRC20 wallet transactions. Features Automated Tracking**: Executes every 15 minutes to capture new transactions. Customizable Filters**: Tailors the tracking based on specific parameters like transaction time and wallet addresses. Data Aggregation**: Compiles transaction data into a single, structured list. Formatted Outputs**: Presents transaction data in an organized and comprehensible format. Requirements N8N (self-hosted or cloud version) setup and operational. Basic understanding of N8N workflows and nodes. Setup and Configuration Import Workflow: Load the provided JSON workflow into your N8N instance. Configure Edit Fields Node: Enter your TRC20 wallet address in the 'Your Wallet Address' field. Adjust 'Number of transactions to retrieve per request' if necessary. (Default one set to 20 which is recommanded) TronScan Data Access: The workflow accesses TronScan's public blockchain data, so no additional configuration is required for API access. Schedule Trigger Node: Defaulted to trigger every 15 minutes. Modify as per your requirements. Test the Workflow: Execute the workflow manually to ensure everything is operating correctly. How it Works Schedule Trigger: Initiates the workflow at predetermined intervals. Edit Fields: Sets up the wallet address and transaction retrieval count. TronScan Data Retrieval: Gathers transaction data from the TRC20 wallet using TronScan's public database. Split Out & Filter: Processes and filters the transaction data. Final Results: Organizes and formats the required transaction data for review. Aggregate: Consolidates all records (items) into a one comprehensive list (item). Customization Modify the filter conditions and fields to suit your tracking needs. (for example you can higher or lower the number of time to filter or IN / OUT transactions - Default is 15m/IN) Adjust the schedule trigger frequency according to your preference (default is 15m). Best Practices Regularly test the workflow to ensure consistent performance. Stay updated with any changes to the structure of TronScan's public data that might affect the workflow. Contributing Your feedback and contributions are greatly appreciated. Feel free to adapt, modify, and share enhancements with the n8n community.

What is this workflow doing? This simple workflow is pulling the latest Euro foreign exchange reference rates from the European Central Bank and responding expected values to an incoming HTTP request (GET) via a Webhook trigger node. Setup no authentication** needed the workflow is ready to use test** the workflow template by hitting the test workflow button and calling the URL in the webhook node optional: choose your own Webhook listening path in the Webhook trigger node Usage There are two possible usage scenarios: get all Euro exchange rates as an array of objects get only a specific currency exchange rate as a single object All available rates Using the HTTP query ?foreign=USD (where USD is one of the available currency symbols) will provide only that specificly asked rate. Response example: {"currency":"USD","rate":"1.0852"} Single exchange rate If no query is provided, all available rates are returned. Response example: [{"currency":"USD","rate":"1.0852"},{"currency":"JPY","rate":"163.38"},{"currency":"BGN","rate":"1.9558"},{"currency":"CZK","rate":"25.367"},{"currency":"DKK","rate":"7.4542"},{"currency":"GBP","rate":"0.85495"},{"currency":"HUF","rate":"389.53"},{"currency":"PLN","rate":"4.3053"},{"currency":"RON","rate":"4.9722"},{"currency":"SEK","rate":"11.1675"},{"currency":"CHF","rate":"0.9546"},{"currency":"ISK","rate":"149.30"},{"currency":"NOK","rate":"11.4285"},{"currency":"TRY","rate":"33.7742"},{"currency":"AUD","rate":"1.6560"},{"currency":"BRL","rate":"5.4111"},{"currency":"CAD","rate":"1.4674"},{"currency":"CNY","rate":"7.8100"},{"currency":"HKD","rate":"8.4898"},{"currency":"IDR","rate":"16962.54"},{"currency":"ILS","rate":"3.9603"},{"currency":"INR","rate":"89.9375"},{"currency":"KRW","rate":"1444.46"},{"currency":"MXN","rate":"18.5473"},{"currency":"MYR","rate":"5.1840"},{"currency":"NZD","rate":"1.7560"},{"currency":"PHP","rate":"60.874"},{"currency":"SGD","rate":"1.4582"},{"currency":"THB","rate":"38.915"},{"currency":"ZAR","rate":"20.9499"}] Further info Read more about Euro foreign exchange reference rates here.

🌐 Web Server Monitor & Alert System This automation pings web servers at regular intervals, logs their status, and sends email alerts if a server goes down. It’s perfect for maintaining visibility over server uptime — without complex monitoring tools. 🧠 How It Works This workflow performs minute-by-minute checks on all listed servers in a Google Sheet and: ✅ Logs all reachable servers in an “Alive” log. 🔻 Sends an email alert if a server is unreachable. 📄 Logs failed servers in a “Down” sheet with timestamps. 🧩 Key Components ⏰ 1. Schedule Trigger Runs the workflow every minute for real-time monitoring. 📄 2. Web Servers List (Google Sheets) Pulls server IPs or hostnames from a Google Sheet named Server_List. Each row = one server to monitor. This makes adding/removing servers effortless — just update the sheet. 🌐 3. Servers Alive Check (HTTP Request) Performs an HTTP GET request to each server (e.g., http://your-server.com). If the request fails, it automatically triggers the error path (handled via continueOnFail). ✅ 4. Web Server Alive Log (Google Sheets) Records successful pings in Server_Status_Alive with: Timestamp Server IP Status = Alive This log can be used for uptime reports or audits. 📧 5. Server Down Notification (Gmail) If a server fails, this node sends an email to the admin. It includes: Server address Timestamp Suggested action 📄 6. Web Server Down Log (Google Sheets) Logs failed pings in a separate sheet for historical tracking and debugging. ✅ Main Advantages Live Server Monitoring Stay informed about server health in near real-time. No-Code Configuration Add/remove servers from the Google Sheet — no need to touch the workflow. Email Alerts on Failure Proactively notifies you before users report the issue. Audit-Ready Logging Maintains logs for both healthy and failed checks for documentation or reporting. Flexible & Scalable Monitor 1 or 100 servers with the same template — just scale the list. ⚙️ Setup Steps 🔑 Prerequisites Google Sheet with server list (column name = “Server”) Gmail OAuth2 Connection for alerts n8n Instance running regularly 🛠 Configuration Google Sheets Sheet 1 (Server_List): Your list of servers. Sheet 2 (Server_Status_Alive): Log for reachable servers. Sheet 3 (Server_Status_Down): Log for unreachable servers. Gmail Integration Connect your Gmail account in the Server Down Notification node. Edit recipient email and message content as needed. HTTP Check Adjust the HTTP request URL template if using port numbers or paths (e.g., http://{{Server}}:8080/status). Schedule Default is every 1 minute. Change via Schedule Trigger if needed. 🧪 Testing Input a reachable server (e.g., example.com) and an unreachable IP. Run the workflow manually or wait for the next scheduled run. Check: Alive log updates correctly. Down log records failures. Email alert is received. 🚀 Deployment Activate the workflow, and it will quietly run in the background, notifying you of any server downtime instantly while keeping logs for future review.

This workflow template demonstrates how to create an AI-powered agent that provides users with current weather information and Wikipedia summaries. By integrating n8n with Ollama's local Large Language Models (LLMs), this template offers a seamless and privacy-conscious solution for real-time data retrieval and summarization. Who is this for? Developers and Enthusiasts: Individuals interested in building AI-driven workflows without relying on external APIs. Privacy-Conscious Users: Those who prefer processing data locally to maintain control over their information. Educators and Students: Learners seeking hands-on experience with AI integrations and workflow automation. What problem does this workflow solve? Accessing up-to-date weather information and concise Wikipedia summaries typically requires multiple API calls to external services, which can raise privacy concerns and incur costs. This workflow addresses these issues by utilizing Ollama's self-hosted LLMs within n8n, enabling users to retrieve and process information locally. What this workflow does: User Input Capture: Begins with a chat interface where users can input queries. AI Processing: The input is sent to an AI Agent node configured with Ollama's LLMs, which interprets the query and determines the required actions. Weather Retrieval: For weather-related queries, the workflow fetches current weather data from a specified source. Wikipedia Summarization: For queries seeking information, it retrieves relevant Wikipedia content and generates concise summaries. Setup: Install Required Tools: Ollama: Install and run Ollama to manage local LLMs. Configure n8n Workflow: Import the provided workflow template into your n8n instance. Set up the AI Agent node to connect with Ollama's API. Ensure nodes responsible for fetching weather data and Wikipedia content are correctly configured. Run the Workflow: Start the workflow and interact with the chat interface to test various queries. How to customize this workflow to your needs: Automate Triggers: Set up scheduled triggers to provide users with regular updates, such as daily weather forecasts or featured Wikipedia articles.