Workflow Templates

Who’s it for This template is designed for bakeries, event planners, and e-commerce platforms that want to automatically generate custom cake designs. It’s also ideal for marketers or digital creators who need personalized celebratory visuals for social media or email campaigns. How it works This workflow converts simple user input (e.g., “Sarah’s Birthday”) into a creative cake design: Webhook: Captures user input from the Bolt frontend form. OpenAI GPT: Generates a detailed and creative cake design prompt. Replicate Flux Schnell: Produces a unique cake image using the AI-generated prompt. HTTP Response: Sends the final cake image back to the frontend. How to set up Import this template into n8n. Add your OpenAI API Key under n8n Credentials for the OpenAI Chat Model node. Add your Replicate API Token as an HTTP Header Auth credential (do not hardcode it). Update the Webhook node URL in the Bolt frontend form to send a POST request to n8n. (Optional) Customize the OpenAI prompt in the Prompt Generator node to adjust cake style, colors, or decorations. Requirements n8n account (cloud or self-hosted). OpenAI API Key** for prompt generation. Replicate API Token** for AI image generation. A Bolt frontend or any form that can call the webhook endpoint. How to customize the workflow Replace "cake" with any product type (e.g., mugs, greeting cards, or T-shirts). Add a database node (Google Sheets or Supabase) to log user requests and images. Implement input moderation by adding an OpenAI moderation node before the prompt generation. Frontend

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. What is DIGIPIN? DIGIPIN (Digital Pincode) is a 10-character alphanumeric code introduced by India Post. It maps any 3x3 meter square in India to a unique digital address. This helps precisely locate homes, shops, or landmarks, especially in areas where physical addresses are inconsistent or missing. What this workflow does This workflow creates a fully offline DIGIPIN microservice using only JavaScript - no external APIs are used. You get two HTTP endpoints: GET /generate-digipin?lat={latitude}&lon={longitude} → returns a DIGIPIN GET /decode-digipin?digipin={code} → returns the latitude and longitude You can plug this into any system to: Convert GPS coordinates to a DIGIPIN Convert a DIGIPIN back to coordinates How it works An HTTP Webhook node receives the request A JS Function node either encodes or decodes based on input The result is returned as a JSON response All the logic is handled inside the workflow - no API keys, no external calls. Why use this Fast and lightweight Easily extendable: you can connect this to forms, CRMs, apps, or spreadsheets Ideal for field agents, address validation, logistics, or rural operations

This n8n demonstrates how to build a simple Google Drive MCP server to search and get contents of files from Google Drive. This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/gdrive How it works A MCP server trigger is used and connected to 1x Google Drive tool and 1x Custom Workflow tool. The Google Drive tool is set to perform a search on files within our Google Drive folder. The Custom Workflow tool downloads target files found in our drive and converts the binaries to their text representation. Eg. PDFs have only their text contents extracted and returned to the MCP client. How to use This Google Drive MCP server allows any compatible MCP client to manage a person or shared Google Drive. Simple select a drive or for better control, specify a folder within the drive to scope the operations to. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Please help me search for last month's expense reports." "What does the company policy document say about cancellations and refunds?" Requirements Google Drive for documents. OpenAI for image and audio understanding. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow Add additional capabilities such as renaming, moving and/or deleting files. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

This n8n template shows you how to connect Github's Free Models to your existing n8n AI workflows. Whilst it is possible to use HTTP nodes to access Github Models, The aim of this template is to use it with existing n8n LLM nodes - saves the trouble of refactoring! Please note, Github states their model APIs are not intended for production usage! If you need higher rate limits, you'll need to use a paid service. How it works The approach builds a custom OpenAI compatible API around the Github Models API - all done in n8n! First, we attach an OpenAI subnode to our LLM node and configure a new OpenAI credential. Within this new OpenAI credential, we change the "Base URL" to point at a n8n webhook we've prepared as part of this template. Next, we create 2 webhooks which the LLM node will now attempt to connect with: "models" and "chat completion". The "models" webhook simply calls the Github Model's "list all models" endpoint and remaps the response to be compatible with our LLM node. The "Chat Completion" webhook does a similar task with Github's Chat Completion endpoint. How to use Once connected, just open chat and ask away! Any LLM or AI agent node connected with this custom LLM subnode will send requests to the Github Models API. Allowing your to try out a range of SOTA models for free. Requirements Github account and credentials for access to Models. If you've used the Github node previously, you can reuse this credential for this template. Customising this workflow This template is just an example. Use the custom OpenAI credential for your other workflows to test Github models. References https://docs.github.com/en/github-models/prototyping-with-ai-models https://docs.github.com/en/github-models

Mistral OCR is a super convenient way to parse and extract data from multi-page PDFs or single images using AI. What makes it special and differs it from the competition is that Mistral OCR also performs document page splitting and markdown conversion. This helps reduce dependencies required for document parsing workflows where tools like StirlingPDF. Read the official documentation on Mistral OCR API here: https://docs.mistral.ai/capabilities/document/#tag/ocr/operation/ocr_v1_ocr_post How it works To access Mistral-OCR, you'll need to use Mistral Cloud API via the HTTP request node Mistral OCR can only accept 2 file types: PDF and Image. Here, we use 2 different request to the Mistral-OCR API to parse a bank statement PDF and an screenshot of a bank statement to extract the tables. Next, we explore a more secure method of uploading documents to the Mistral OCR API by using Mistral's cloud storage. In example 2, we first store a copy of our documents to Mistral cloud and then generate a signed URL to retreive the file before sending it to Mistral OCR. This ensures the file is not accessible publicly and protects it from unauthorised access. Finally, another way to use Mistral-OCR is via document understanding. This allows you to ask questions about the document rather than extract contents from it. In example 3, I demonstrate this use-case asking Mistral-small to tell me how many deposits are shown in the bank statement. How to use Ensure your documents are either publicly accessible for Mistral-OCR or upload them to Mistral Cloud. Alternatively, signed urls from AWS S3 or Cloudflare R2 should also work. Requirements Mistral Cloud account and API Key. You'll also need credit on your account to use Mistral-OCR. Customising the workflow Mistral-OCR also works for images such as charts and diagrams so try using it on Financial Reports. Mistral-OCR is even cheaper with batching enabled. This returns your results within 24hrs but is half the price per page.

Overview This workflow allows you to trigger custom logic in n8n directly from Retell's Voice Agent using Custom Functions. It captures a POST webhook from Retell every time a Voice Agent reaches a Custom Function node. You can plug in any logic—call an external API, book a meeting, update a CRM, or even return a dynamic response back to the agent. Who is it for For builders using Retell who want to extend Voice Agent functionality with real-time custom workflows or AI-generated responses. Prerequisites Have a Retell AI Account A Retell agent with a Custom Function node in its conversation flow (see template below) Set your n8n webhook URL in the Custom Function configuration (see "How to use it" below) (Optional) Familiarity with Retell's Custom Function docs Start a conversation with the agent (text or voice) Retell Agent Example To get you started, we've prepared a Retell Agent ready to be imported, that includes the call to this template. Import the agent to your Retell workspace (top-right button on your agent's page) You will need to modify the function URL in order to call your own instance. This template is a simple hotel agent that calls the custom function to confirm a booking, passing basic formatted data. How it works Retell sends a webhook to n8n whenever a Custom Function is triggered during a call (or test chat). The webhook includes: Full call context (transcript, call ID, etc.) Parameters defined in the Retell function node You can process this data and return a response string back to the Voice Agent in real-time. How to use it Copy the webhook URL (e.g. https://your-instance.app.n8n.cloud/webhook/hotel-retell-template) Modify the Retell Custom Function webhook URL (see template description for screenshots) Edit the function Modify the URL Modify the logic in the Set node or replace it with your own custom flow Deploy and test: Retell will hit your n8n workflow during the conversation Extension Ideas Call a third-party API to fetch data (e.g. hotel availability, CRM records) Use an LLM node to generate dynamic responses Trigger a parallel automation (Slack message, calendar invite, etc.) 👉 Reach out to us if you're interested in analyzing your Retell Agent conversations.

Use Case When building a product it's important to discover and eliminate bugs as quickly as possible. Since we're using our product at n8n a lot, we wanted to make it as easy as possible for everyone to add bugs with the needed level of detail. That's why we built this workflow that allows everyone to add bugs to our Linear account easily directly from Slack What this workflow does This workflow waits for a webhook call within Slack, that gets fired when users use the /bug command on a bot that you will create as part of this template. It then adds the bug to Linear using a pre-defined description and a defined label. It then notifies the user about the newly added bug as you can see below: How to create 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 /bug 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 Configure your Slack bot using the sticky to the left Fill the Set me up node. You can find the IDs easily using the Helper nodes section Make sure to exchange the Request URL in your Slack with the Prod URL of the Webhook node before activating this workflow How to adjust it to your needs Add zero, one, two or many labels when creating the new ticket Change the Slack message according to your needs Change the default description for a new bug ticket 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're using AI to classify the bugs and move them to the right team as soon as they get added to Linear (see this template) We also added other commands like /pain and /idea that allow us to submit other things to Notion. You can see the workflow for that here.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Clean Web Content Extraction with Anti-Bot Fallback Extract clean and structured text from any webpage with optional fallback to an anti-bot scraping service. Ideal for AI tools and content workflows. 🧠 How it Works This sub-workflow enables reliable and clean scraping of any public webpage by simply passing a url parameter. It is designed to be embedded into other workflows or used as a tool for AI agents. It supports two output modes: fulltext:* true — returns *{ title, text } with full page content fulltext:* false — returns *{ title, url, content } with a short excerpt 💡 If the site is protected by anti-bot systems (like Cloudflare), it will automatically fallback to Scrape.do, a scraping API with a generous free plan. 🧩 This template requires the n8n-nodes-webpage-content-extractor community node, so it only works in self-hosted n8n environments. 🚀 Use Cases As a reusable sub-workflow, via Execute Sub-workflow node. As a tool for an AI Agent, compatible with Call n8n Workflow Tool. Perfect for chatbots, summarization workflows, or RSS/feed enrichment. Empowers your AI Agent with the ability to browse and extract readable content from websites automatically. 🔖 Parameters url (string): the webpage URL to scrape fulltext (boolean): set true for full page content, false for summarized output ⚙️ Setup Install the community node n8n-nodes-webpage-content-extractor in your self-hosted n8n instance. Create a free account at Scrape.do and obtain your API Token. In the workflow, locate the Scrape.do HTTP Request node and configure the credentials using your API Token. Detailed step-by-step instructions are available in the workflow notes. The Scrape.do API is only used as a fallback when conventional scraping fails, helping you preserve your API credits.

Video Guide I prepared a detailed guide that showed the whole process of building a resume analyzer. Who is this for? This workflow is ideal for developers, data analysts, and business owners who want to enable conversational interactions with their database. It’s particularly useful for cases where users need to extract, analyze, or aggregate data without writing SQL queries manually. What problem does this workflow solve? Accessing and analyzing database data often requires SQL expertise or dedicated reports, which can be time-consuming. This workflow empowers users to interact with a database conversationally through an AI-powered agent. It dynamically generates SQL queries based on user requests, streamlining data retrieval and analysis. What this workflow does This workflow integrates OpenAI with a Supabase database, enabling users to interact with their data via an AI agent. The agent can: Retrieve records from the database. Extract and analyze JSON data stored in tables. Provide summaries, aggregations, or specific data points based on user queries. Dynamic SQL Querying: The agent uses user prompts to create and execute SQL queries on the database. Understand JSON Structure: The workflow identifies JSON schema from sample records, enabling the agent to parse and analyze JSON fields effectively. Database Schema Exploration: It provides the agent with tools to retrieve table structures, column details, and relationships for precise query generation. Setup Preparation Create Accounts: N8N: For workflow automation. Supabase: For database hosting and management. OpenAI: For building the conversational AI agent. Configure Database Connection: Set up a PostgreSQL database in Supabase. Use appropriate credentials (username, password, host, and database name) in your workflow. N8N Workflow AI agent with tools: Code Tool: Execute SQL queries based on user input. Database Schema Tool: Retrieve a list of all tables in the database. Use a predefined SQL query to fetch table definitions, including column names, types, and references. Table Definition: Retrieve a list of columns with types for one table.

This n8n template leverages n8n's multi-form feature to build a 2 part job application submission journey which aims to eliminate the need for applicants to re-enter data found on their CVs/Resumes. How it works The application submission process starts with an n8n form trigger to accept CV files in the form of PDFs. The PDF is validated using the text classifier node to determine if it is a valid CV else the applicant is asked to reupload. A basic LLM node is used to extract relevant information from the CV as data capture. A copy of the original job post is included to ensure relevancy. Applicant's data is then sent to an ATS for processing. For our demo, we used airtable because we could attach PDFs to rows. Finally, a second form trigger is used for the actual application form. However, it is prefilled to save the applicant's time and allow them to amend any of the generated application fields. How to use Ensure to change the redirect URL in the form ending node to use the host domain of your n8n instance. Requirements OpenAI for LLM Airtable to capture applicant data Customising the workflow Application form is pretty basic for this demonstration but could be extended to ask more in-depth questions. If it fits the job, why not ask applicants to upload portfolio works and have AI describe/caption them.

Who’s it for: Sales and business development professionals who want to monitor company news, hiring trends, and business signals for their leads. How it works / What it does: Add a company to the form, and the workflow will automatically search for the latest news, recent hires, company stage, and LinkedIn activity. The results are sent straight to your Google Sheet, helping you stay up to date with your leads and prospects. How to set up: Register for Extruct at www.extruct.ai/. Open the Extruct table template, copy the table ID from the browser’s address bar. Make a copy of the Google Sheets template to your Drive. Enter the table ID into the variables node in your n8n flow. Set up Bearer authentication in all HTTP Request nodes using your Extruct API token. In the Google Sheets node, paste your template link and connect your Google account. Run the flow once to load the mapping fields, then match each output to the correct column. Activate the flow and start adding companies through the form. Requirements: Extruct account and API token Extruct table template Google account with Google Sheets How to customize the workflow: To track more business development signals, add new columns in both the Extruct table and your Google Sheet, then map them in the Google Sheets node.

Who is this for? This template is ideal for developers, DevOps engineers, and automation managers who manage their n8n workflows using GitHub. It helps teams streamline their CI/CD automation by syncing changes from GitHub directly into n8n after a pull request (PR) is merged. What problem is this workflow solving? Manually restoring workflows after reviewing and merging code in GitHub can be tedious and error-prone. This workflow solves that by automating the restore process, ensuring that any new or updated workflow committed to your GitHub repo is automatically imported into your n8n environment. What this workflow does Triggers when a GitHub pull request is closed and merged. Fetches the details of the merge commit. Retrieves the list of added and modified workflow files. Downloads and decodes each workflow file. Creates or updates** the corresponding workflow in your n8n instance automatically. Setup Connect GitHub: Use the GitHub Trigger node and configure GitHub API credentials. Note: I'd recommended to use GitHub PAT (Personal Access Token) classic with repo and admin:repo_hook permission scopes enabled. Connect n8n API: Provide your n8n API credentials in the n8n nodes. - Check this doc Set repository variables: Update github_owner and repo_name in the Define Local Variables node. Enable webhook: Make sure your GitHub repository has a webhook for pull_request events pointing to this workflow. How to customize this workflow to your needs Modify filters to handle only certain branches or file paths. Add Slack or email notifications to confirm successful imports. Insert logging or version tagging for better traceability. Extend with conditional logic for workflow testing before applying changes. This automated flow provides a seamless CI/CD loop between GitHub and n8n, empowering teams to manage workflow versioning efficiently and securely.