Workflow Templates

Complete MCP server exposing all Oura Tool operations to AI agents. Zero configuration needed - all 4 operations pre-built. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works • MCP Trigger: Serves as your server endpoint for AI agent requests • Tool Nodes: Pre-configured for every Oura Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n Oura Tool tool with full error handling 📋 Available Operations (4 total) Every possible Oura Tool operation is included: 🔧 Profile (1 operations) • Get a profile 🔧 Summary (3 operations) • Get activity summary • Get readiness summary • Get sleep summary 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Resource IDs and identifiers • Search queries and filters • Content and data payloads • Configuration options Response Format: Native Oura Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • Other n8n Workflows: Call MCP tools from any workflow • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Complete Coverage: Every Oura Tool operation available • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n error handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

Overview This workflow automates the backup of all workflows from your system to a Git repository hosted on Gitea. It runs on a scheduled trigger, fetching, encoding, and committing workflow data, ensuring seamless version control and disaster recovery. 📌 Quick Setup: Just update three global variables and configure authentication—no manual exports needed! How It Works (Quick Glance) 1️⃣ Scheduled Execution → Runs automatically at defined intervals. 2️⃣ Fetch Workflows → Uses the API to retrieve all workflows. 3️⃣ Process Workflows → Converts workflow data into a Git-friendly format. 4️⃣ Commit & Push to Git → Saves workflows in a Gitea repository. Setup Steps (⚡ Takes ~5 min) 1️⃣ Set Global Variables Go to the Globals section in the workflow and update: repo.url* → https://your-gitea-instance.com *(Replace with your actual Gitea URL) repo.name* → workflows *(Repository name where backups will be stored) repo.owner* → octoleo *(Gitea account that owns the repository) 📌 These three variables define where the workflows are stored. 2️⃣ Configure Gitea Authentication Go to your Gitea account* → Generate a *Personal Access Token** In the credential manager, create a new Gitea Token with: Name:** Authorization Value:** Bearer YOUR_PERSONAL_ACCESS_TOKEN 📌 Ensure there is a space after Bearer before the token! 3️⃣ Link Credentials to Git Nodes Attach the Gitea credentials to these three Git nodes: GetGitea** → Retrieves existing repository data PutGitea** → Updates workflows PostGitea** → Adds new workflows 4️⃣ Link Credentials for API Requests Add API authentication** in the node that fetches all workflows. 5️⃣ Test & Activate Run the workflow manually** to confirm backups work. Enable the schedule trigger for automation. 📌 The workflow automatically checks for changes before committing updates. Why Use This Workflow? ✅ Automated Backups → No manual exports needed. ✅ Version Control → Easily track workflow changes. ✅ Simple Setup → Just configure globals & credentials. ✅ Secure → Uses token-based authentication. Next Steps 💬 Have questions? Reach out on the forum! 🚀

This n8n workflow demonstrates creating a recipe recommendation chatbot using the Qdrant vector store recommendation API. Use this example to build recommendation features in your AI Agents for your users. How it works For our recipes, we'll use HelloFresh's weekly course and recipes for data. We'll scrape the website for this data. Each recipe is split, vectorised and inserted into a Qdrant Collection using Mistral Embeddings Additionally the whole recipe is stored in a SQLite database for later retrieval. Our AI Agent is setup to recommend recipes from our Qdrant vector store. However, instead of the default similarity search, we'll use the Recommendation API instead. Qdrant's Recommendation API allows you to provide a negative prompt; in our case, the user can specify recipes or ingredients to avoid. The AI Agent is now able to suggest a recipe recommendation better suited for the user and increase customer satisfaction. Requirements Qdrant vector store instance to save the recipes Mistral.ai account for embeddings and LLM agent Customising the workflow This workflow can work for a variety of different audiences. Try different sets of data such as clothes, sports shoes, vehicles or even holidays.

👤 Who is this for? This workflow is great for n8n users who want to prevent duplicate or overlapping workflow runs. If you're a developer, DevOps engineer, or automation enthusiast managing tasks like database updates, syncing tools, or hitting rate-limited APIs, this one’s for you. 🧩 What problem does this solve? In the real world, automations can get triggered at the same time—whether that’s because of multiple webhook calls, overlapping schedules, or retries. And when two workflows try to do the same thing at once (like updating a record or syncing data), it can cause conflicts, data corruption, or wasted API calls. This workflow helps avoid that problem by using Redis as a lock system, so only one instance runs at a time. Think of it like putting up a “🚧 Workflow in Progress” sign while your logic is running. ⚙️ What this workflow does When the workflow starts, it tries to set a Redis key as a lock with a short expiry. If the lock is free: Your main business logic runs. Once it's done, the lock is cleared. If the lock is already taken (i.e., another run is in progress): The workflow will wait and retry a few times. If a duplicate request shows up while one is already being processed: It skips that duplicate to avoid unnecessary work. You can customize both the timeout and retry logic to match your needs. 🛠️ Setup guide To use this template: You’ll need access to a Redis instance (either self-hosted or managed like Upstash, Redis Cloud, etc). Set up your Redis credentials in the n8n Redis node. Swap out the webhook node with your actual trigger or logic. Adjust the lock timeout to match how long your task typically takes. > 💡 Bonus Tip: Use this pattern wherever you need idempotency or want to avoid duplicate processing. 🧪 Example use case Let’s say you have a workflow that syncs ClickUp tickets to Google Sheets. It runs daily at 9 AM and updates tickets, adds notes, and makes sure nothing is missed. But what if two runs start at the same time? Or someone triggers a manual sync while the scheduled one is still working? By wrapping that whole sync inside this Redis locking template, you can make sure it only runs one at a time, saving your APIs (and your sanity).

Complete MCP server exposing 3 Background Removal API operations to AI agents. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Credentials Add Background Removal API credentials Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works This workflow converts the Background Removal API into an MCP-compatible interface for AI agents. • MCP Trigger: Serves as your server endpoint for AI agent requests • HTTP Request Nodes: Handle API calls to https://api.remove.bg/v1.0 • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (3 total) 🔧 Account (1 endpoints) • GET /account: Fetch Account Balance 🔧 Improve (1 endpoints) • POST /improve: Submit Image for Improvement 🔧 Removebg (1 endpoints) • POST /removebg: Remove Image Background 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Path parameters and identifiers • Query parameters and filters • Request body data • Headers and authentication Response Format: Native Background Removal API responses with full data structure Error Handling: Built-in n8n HTTP request error management 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Cursor: Add MCP server SSE URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n HTTP request handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

Complete MCP server exposing 2 BIN Lookup API operations to AI agents. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Credentials Add BIN Lookup API credentials Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works This workflow converts the BIN Lookup API into an MCP-compatible interface for AI agents. • MCP Trigger: Serves as your server endpoint for AI agent requests • HTTP Request Nodes: Handle API calls to https://api.bintable.com/v1 • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (2 total) 🔧 Balance (1 endpoints) • GET /balance: Check Balance 🔧 {Bin} (1 endpoints) • GET /{bin}: Lookup for bin 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Path parameters and identifiers • Query parameters and filters • Request body data • Headers and authentication Response Format: Native BIN Lookup API responses with full data structure Error Handling: Built-in n8n HTTP request error management 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Cursor: Add MCP server SSE URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n HTTP request handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

This n8n template showcases a cool feature of n8n Forms where the form itself can be defined dynamically using the form fields schema. It may be debateable how useful this template actually is since both Airtable and Baserow provide form interfaces already but still a great exercise and demonstration if ever the use-case comes around. How it works A form trigger is used to dynamically select a database/table from which to build the n8n form from. the table's schema is imported into the workflow and using the code node, is converted into the n8n form fields schema. This let's us dynamically build the fields in our n8n form when we choose to define the form using the JSON option. Once the n8n form submits, we convert the values back into our table's API schema so that we can create a new row. Note any files/attachments fields are removed as they need to be handled separately. Files are processed separately as they may first need to be stored. Once complete, the reference is saved into the newly created row. Check out the example Airtable here - https://airtable.com/appfP15Xd0aVZR9xV/shrGFgXLyQ4Jg58SU How to use The n8n form is autogenerated which means you only need provide access to the table. Using this approach, this template can be reused for any number of Airtable and/or Baserow tables. Requirements You'll need either an Airtable account or a Baserow account to use this template. Accessible n8n instance to your users Customising this workflow Not using either Airtable or Baserow? Theoretically any datastore which provides a fields schema can be used with this template. If you're feeling creative, split the table into multiple forms for a better user experience.

Use Case Extend Bitrix24 tasks with custom widgets that display relevant task information and enable seamless interaction through a custom tab interface. What This Workflow Does Processes incoming webhook requests from Bitrix24 task interfaces Handles authentication and secure token validation Manages application installation and placement registration Displays task data in a custom formatted view Stores and retrieves configuration settings persistently Provides user-friendly HTML interfaces for task information Setup Instructions Configure Bitrix24 webhook endpoints for the task widget Set up authentication credentials in your Bitrix24 account Install the application and register the task view tab placement Customize the task data display format as needed Deploy and test the application functionality within Bitrix24 tasks

Revit to HTML Quantity Takeoff Generator Automates extraction of wall quantities from Revit models and creates a professional interactive HTML report. Key Features Automated wall quantity analysis Calculates volumes by wall type ("Type Name") Generates interactive HTML QTO report Includes summary statistics: total elements, total and average volumes Provides detailed breakdown by element type How it works Upload a Revit file as input Workflow extracts wall quantities and types Creates and saves a ready-to-share HTML dashboard with QTO data No API keys required Runs offline Output is a professional, ready-to-use HTML report

Who this is for The Async Structured Bulk Data Extract with Bright Data Web Scraper workflow is designed for data engineers, market researchers, competitive intelligence teams, and automation developers who need to programmatically collect and structure high-volume data from the web using Bright Data's dataset and snapshot capabilities. This workflow is built for: Data Engineers - Building large-scale ETL pipelines from web sources Market Researchers - Collecting bulk data for analysis across competitors or products Growth Hackers & Analysts - Mining structured datasets for insights Automation Developers - Needing reliable snapshot-triggered scrapers Product Managers - Overseeing data-backed decision-making using live web information What problem is this workflow solving? Web scraping at scale often requires asynchronous operations, including waiting for data preparation and snapshots to complete. Manual handling of this process can lead to timeouts, errors, or inconsistencies in results. This workflow automates the entire process of submitting a scraping request, waiting for the snapshot, retrieving the data, and notifying downstream systems all in a structured, repeatable fashion. It solves: Asynchronous snapshot completion handling Reliable retrieval of large datasets using Bright Data Automated delivery of scraped results via webhook Disk persistence for traceability or historical analysis What this workflow does Set Bright Data Dataset ID & Request URL: Takes in the Dataset ID and Bright Data API endpoint used to trigger the scrape job HTTP Request: Sends an authenticated request to the Bright Data API to start a scraping snapshot job Wait Until Snapshot is Ready: Implements a loop or wait mechanism that checks snapshot status (e.g., polling every 30 seconds) until completion i.e ready state Download Snapshot: Downloads the structured dataset snapshot once ready Persist Response to Disk: Saves the dataset to disk for archival, review, or local processing Webhook Notification: Sends the final result or a summary of it to an external webhook Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. Update the Set Dataset Id, Request URL for setting the brand content URL. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. How to customize this workflow to your needs Polling Strategy : Adjust polling interval (e.g., every 15–60 seconds) based on snapshot complexity Input Flexibility : Accept datasetId and request URL dynamically from a webhook trigger or input form Webhook Output : Send notifications to - Internal APIs – for use in dashboards Zapier/Make – for multi-step automation Persistence Save output to: Remote FTP or SFTP storage Amazon S3, Google Cloud Storage etc.

Optimize Your Top Performing Website Content with Google Analytics, Firecrawl, and InfraNodus This templates helps you extract** the top performing pages from your website using Google Analytics scrape** the content of the pages using Firecrawl API (HTTP node provided) build a knowledge graph* for all these pages with the *topics* and *gaps** identified using InfraNodus understand the main concepts and topical clusters in your top-performing content, so you can create more of it, while also identifying the content gaps — structural holes between the topics that you can use to generate new content ideas have access to a knowledge graph visualization of your top performing content to explore it using the interactive network interface How it works This template uses the InfraNodus to visualize and analyze your top performing content. It will extract the top pages from the Google Analytics data for the website you choose and scrape their text content using the high-quality Firecrawl API. Then it will ingest every page into an InfraNodus graph you specify. The graph can be used to explore the content visually. The insights from the graph, such as the main topics and gaps between them will be shown to you in the end of the workflow. You can use these insights to understand what kind of content you should focus on creating to get the highest number of views* and to establish *topical authority* in your area, which is good for *SEO* and *LLM optimization** — focusing on the topics identified in the top content discover the content gaps — which topics are not connected yet that you could link with new content ideas and publish — this caters to your audience's interests, but connects your existing ideas in a new way. So you deliver the content that's relevant but also novel. Here's a description step by step: Note:* you can replace the PDF to Text convertor node with a better quality *PDF convertor* from ConvertAPI which respects the original file layout and doesn't split text into small chunks Trigger the workflow Extract a list of top (25, 50) pages from your Google Analytics account (you'll need to connect it via the Google Cloud API) Fix the extracted data and add a correct URL prefix to each page (if your Analytics has relative paths only Loop through each page extracted Extract the text content of every page using the high-quality Firecrawl API Ingest the text content into the InfraNodus graph that you specify Once all the pages are ingested into the InfraNodus graph, access the AI insights endpoint in InfraNodus and get the information about the main topics and gaps Display this information to the user How to use You need an InfraNodus API account and key to use this workflow. Create an InfraNodus account Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Requirements An InfraNodus account and API key Optional: A Google Analytics account for your property (alternatively, you can modify this workflow to provide a list of the most popular pages) Optional: A Google Cloud API access (to access the data from Google Analytic saccount — follow the n8n instructions) Optional: A Firecrawl API key API key for better quality web page scraping (otherwise, use the standard HTTP to Text node from n8n) Customizing this workflow You can customize this workflow by using a list of the URL pages you want to analyze from a Google sheet. Alternatively, you can use the Google SERP node to extract top search results for a query and get the main topics for them. For support and feedback, please, contact us at https://support.noduslabs.com To learn more about InfraNodus: https://infranodus.com

How it works: This system functions by receiving expenses via webhook POST. It validates the data, stores it in Google Sheets, and, daily at 8 PM, generates and sends financial summaries. Automatic categorization simplifies the organization of expenses. Set up steps: Setup involves creating the Google Sheet, configuring the webhook, and defining the categorization rules. The process is quick and intuitive, taking about 10-15 minutes for the system to be ready to receive your expenses.