Workflow Templates

> Note: This template requires a self-hosted community edition of n8n. Does not work on cloud. Try It Out This n8n template shows how to validate API requests with Auth0 Authorization tokens. Auth0 doesn't work with the standard JWT auth option because: 1) Auth0 tokens use the RS256 algorithm. 2) RS256 JWT credentials in n8n require the user to use private and public keys and not secret phrase. 3) Auth0 does not give you access to your Auth0 instance private keys. The solution is to handle JWT validation after the webhook is received using the code node. How it works There are 2 approaches to validate Auth0 tokens: using your application's JWKS file or using your signing cert. Both solutions uses the code node to access nodeJS libraries to verify the token. JWKS**: the JWK-RSA library is used to validate the application's JWKS URI hosted on Auth0 Signing Cert**: the application's signing cert is imported into the workflow and used to verify token. In both cases, when the token is found to be invalid, an error is thrown. However, as we can use error outputs for the code node, the error does not stop the workflow and instead is redirected to a 401 unauthorized webhook response. When token is validated, the webhook response is forwarded on the success branch and the token decoded payload is attached. How to use Follow the instructions as stated in each scenario's sticky notes. Modify the Auth0 details with that of your application and Auth0 instance. Requirements Self-hosted community edition of n8n Ability to install npm packages Auth0 application and some way to get either the JWK url or signing cert.

This n8n template demonstrates how to build a simple but effective vintage image restoration service using an AI model with image editing capabilities. With Gemini now capable of multimodal output, it's a great time to explore this capability for image or graphics automation. Let's see how well it does for a task such as image restoration. Good to know At time of writing, each image generated will cost $0.039 USD. See Gemini Pricing for updated info. The model used in this workflow is geo-restricted! If it says model not found, it may not be available in your country or region. How it works Images are imported into our workflow via the HTTP node and converted to base64 strings using the Extract from file node. The image data is then pipelined to Gemini's Image Generation model. A prompt is provided to instruct Gemini to "restore" the image to near new condition - of course, feel free to experiment with this prompt to improve the results! Gemini's responds with the image as a base64 string and hence, a convert to file node is used to transform the data to binary. With the restored image as a binary, we can then use this with our Google Drive node to upload it to our desired folder. How to use This demonstration uses 3 random images sourced from the internet but any typical image file will work. Use a webhook node to allow integration from other applications. Use a telegram trigger for instant mobile service! Requirements Google Gemini for LLM/Image generation Google Drive for Upload Storage Customising this workflow AI image editing can be applied to many use-cases not just image restoration. Try using it to add watermarks, branding or modify an existing image for marketing purposes.

Automating File (Image) Upload to Postimages.org Use Case Manually uploading screenshots or other image files to hosting platforms like Postimages.org can be tedious and time-consuming. This automation simplifies the process by automatically uploading an image to Postimages.org and validating the result, which is especially useful for repetitive QA tasks, reporting, or archiving visual web data. What This Automation Does This automation uploads a screenshot to Postimages.org using the following steps: Creates a browser session using Airtop. Navigates to the Postimages.org upload page. Takes a screenshot using the browser session. Uploads the screenshot to the site via the "Choose images" button. Waits briefly to ensure upload processing. Captures a post-upload screenshot for validation. How It Works Session Initialization: Starts a browser session using the Airtop node. Navigation: Opens the URL https://postimages.org/ in a new window. Screenshot Capture: Takes a screenshot to use for upload. File Upload: Uploads the screenshot to the site using the file upload interaction. Validation: Waits 5 seconds and then captures a second screenshot to confirm the image was uploaded successfully. Setup Requirements Airtop API Key — required for session creation and browser interactions. Next Steps Customize for Other Sites**: Adapt this workflow to automate file uploads to different platforms. Integrate with Reporting Tools**: Combine this automation with workflows that require image reporting or archiving. Enhance Validation**: Add logic to parse the upload confirmation or retrieve the image URL programmatically for logging or sharing. Read more about how to automate file uploads to the web

This workflow demonstrates a simple way to run evals on a set of test cases stored in a Google Sheet. The example we are using comes from an info extraction task dataset, where we tested 6 different LLMs on 18 different test cases. This workflow extends the functionality of my simple eval for benchmarking legal tasks here. Rather than running executions sequentially (waiting for each one to respond before making another request), we use parallel processing to fire 2 requests every second. You can see our sample data in this spreadsheet here to get started. Once you have this working for our dataset, you can plug in your own test cases matching different LLMs to see how it works with your own data. How it works Pull our test cases from Google Sheets. For each case, fire off an HTTP request to a webhook. That webhook grabs the relevant source file from Google Drive and converts it to text. The text gets sent to an LLM via Open Router (so we can easily swap out models). Results come back and are logged in Google Sheets. Set up steps: Add your credentials for Google Sheets, Google Drive, and OpenRouter. Make a copy of the original data spreadsheet so that you can edit it yourself. You will need to plug your version in the Update Results node to see the spreadsheet update on each run of the loop.

🤖 Telegram Bot Webhook Configuration Tool This workflow creates a simple web form that helps you configure Telegram bot webhooks quickly. Instead of manually constructing the Telegram API URL, this tool does it for you automatically. How It Works The workflow consists of three main steps: Form Input: A web form collects your bot token and webhook URL URL Construction: Automatically builds the correct Telegram API URL Redirect: Takes you directly to the Telegram API to complete the configuration What You Need Bot Token**: Get this from @BotFather on Telegram (format: 123456789:ABCdefGHIjklMNOpqrsTUVwxyz) Webhook URL**: Your n8n webhook endpoint (must be HTTPS) Setup Instructions Import this workflow into your n8n instance Activate the workflow Access the generated form URL Fill in your bot details and submit Form Fields | Field | Description | Example | |-------|-------------|---------| | Bot API Token | Token from BotFather | 123456789:ABCdefGHIjklMNOpqrsTUVwxyz | | Webhook URL | Your n8n webhook endpoint | https://your-instance.app.n8n.cloud/webhook/telegram | What Happens You enter your bot token and webhook URL in the form The workflow constructs this URL: https://api.telegram.org/bot{TOKEN}/setWebhook?url={WEBHOOK_URL} You're redirected to that URL where Telegram configures your webhook Telegram shows you a success or error message Benefits No Manual URL Building**: Eliminates copy-paste errors Quick Setup**: Configure webhooks in seconds Privacy Focused**: No data is stored anywhere Team Friendly**: Share the form URL with team members Common Webhook URLs n8n Cloud: https://your-instance.app.n8n.cloud/webhook/telegram-bot Self-hosted: https://your-domain.com/webhook/telegram-bot Requirements n8n with form trigger support Valid Telegram bot token Publicly accessible webhook URL (HTTPS required) Troubleshooting Invalid Token Error: Make sure you copied the complete token from BotFather Webhook Error: Ensure your URL is publicly accessible and uses HTTPS SSL Error: Verify your webhook URL has a valid SSL certificate This tool simply automates the manual process of visiting the Telegram API URL to configure your bot's webhook. Perfect for developers who frequently set up or change Telegram bot configurations.

🧩 Jamf Patch Summary to Slack Stay on top of software patch compliance by automatically posting Jamf patch summaries to Slack. This helps IT and security teams quickly identify outdated installs and take action—without logging into Jamf. ✅ Prerequisites • A Jamf Pro API key with permissions to read software titles and patch summary • A Slack app or incoming webhook URL with permission to post messages to your desired channel 🔍 How it works • Manually trigger the flow or Add a webhook • Fetch a list of software titles from Jamf Pro • Filter to select the software you're tracking (e.g. Chrome, Edge) • Retrieve the patch summary for that software (latest version, up-to-date, out-of-date counts) • Format the summary into Slack Block Kit • Post the formatted summary into a Slack channel ⚙️ Set up steps • Takes ~5–10 minutes to configure • Set your server BaseURL variable in the Set Node • Add your Jamf Pro API credentials in the HTTP Request nodes (Get & Retrieve) • Set the target software ID in the Filter node • Add your Slack webhook URL or token in the final HTTP node • Optional: Adjust Slack formatting inside the Function node

This template shows how to use the Question and Answer tool to save costs in RAG use cases. Who is this for? This template is for everyone who wants to start giving knowledge to their Agents through RAG. Requirements Have a PDF with custom knowledge that you want to provide to your agent. Setup No setup required. Just hit Execute Workflow, upload your knowledge document and then start chatting. How to customize this to your needs Add custom instructions to your Agent by changing the prompts in it. Add a different way to load in knowledge to your vector store, e.g. by looking at some Google Drive files or loading knowledge from a table. Describe your data properly in the Q&A tool Exchange the Simple Vector Store nodes with your own vector store tools ready for production. Add a more sophisticated way to rank files found in the vector store. For more information read our docs on RAG in n8n.

Easily generate QR codes from any URL! This workflow lets users submit a URL via a simple form and instantly receive a downloadable QR code image—perfect for quick sharing or promotions. Setup is fast and user-friendly, so you’ll be up and running in minutes! 🚀 How it works The end user submits a URL through a simple online form. The workflow automatically sends the submitted URL to a QR code generation API. The user receives a downloadable QR code image corresponding to their URL. ⚙️ Setup instruction Import Workflow: Click "Import from JSON" in your n8n environment and paste the provided workflow JSON. Click "Save" and activate the workflow. Double click the "On form submission" node to obtain the production URL. You may now use this URL to do QR code generation.

This n8n template demonstrates how to obtain token usage from AI Agents and places the data into a spreadsheet that calculates the estimated cost of the execution. Obtaining the token usage from AI Agents is tricky, because it doesn't provide all the data from tool calls. This workflow taps into the workflow execution metadata to extract token usage information. Works well with OpenAI, Google and Anthropic. Other LLM providers might need small tweaks. How it works The AI Agent executes and then calls a subworkflow to calculate the token usage. The data is stored in Google Sheets The spreadsheet has formulas to calculate the estimated cost of the execution. How to use The AI Agent is used as an example. Feel free to replace this with other agents you have. Call the subworkflow AFTER all the other branches have finished executing. Requirements LLM account (OpenAI, Gemini...) for API usage. Google Drive and Sheets credentials n8n API key of your instance

How it Works This meta-workflow is designed to intelligently scan all your active workflows in n8n, identify those that contain Webhook nodes, and automatically generate a Swagger (OpenAPI) specification based on them. The output Swagger document reflects all accessible endpoints from your Webhook nodes, making it easier to: Visualize your API structure Share your endpoints Integrate with tools like Postman or Swagger UI Enhanced Parameter Support If you want the Swagger to reflect request parameters (e.g., query or body fields), you can annotate your Webhook nodes using the Note section. When configured properly, these annotations enrich your Swagger documentation with parameter names, types, and descriptions. Setup Steps Add the WebhookDocs to n8n Import the WebhookDocs JSON file into your n8n instance. Activate the WebhookDocs (you can also use the test-endpoint) Annotate Webhook Nodes (Optional but Recommended) To enable parameter documentation, open the Note section of each Webhook node and add annotations in the following format: //@body field_name string description //@query field_name string description Open the page https://n8n.youristance.com/webhook/swagger

This n8n template demonstrates how to use OpenAI's Responses API with existing LLM and AI Agent nodes. Though I would recommend just waiting for official support, if you're impatient and would like a round-about way to integrate OpenAI's responses API into your existing AI workflows then this template is sure to satisfy! This approach implements a simple API wrapper for the Responses API using n8n's builtin webhooks. When the base url is pointed to these webhooks using a custom OpenAI credential, it's possible to intercept the request and remap for compatibility. How it works An OpenAI subnode is attached to our agent but has a special custom credential where the base_url is changed to point at this template's webhooks. When executing a query, the agent's request is forwarded to our mini chat completion workflow. Here, we take the default request and remap the values to use with a HTTP node which is set to query the Responses API. Once a response is received, we'll need to remap the output for Langchain compatibility. This just means the LLM or Agent node can parse it and respond to the user. There are two response formats, one for streaming and one for non-streaming responses. How to use You must activate this workflow to be able to use the webhooks. Create the custom OpenAI credential as instructed. Go to your existing AI workflows and replace the LLM node with the custom OpenAI credential. You do not need to copy anything else over to the existing template. Requirements OpenAI account for Responses API Customising this workflow Feel free to experiment with other LLMs using this same technique! Keep up to date with the Responses API announcements and make modifications as required.

RAG over a PDF with Weaviate This workflow allows you to upload a PDF file and ask questions about it using the Question and Answer Chain and the Weaviate Vector Store nodes. Who it's for This workflow is the simplest possible implementation of RAG with Weaviate in n8n. It's intended to act as an extendable template for RAG over your own documents. Prerequisites An existing Weaviate cluster. You can view instructions for setting up a local cluster with Docker here or a Weaviate Cloud cluster here. API keys to generate embeddings and power chat models. We use OpenAI, but feel free to switch out the models as you like. Self-hosted n8n instance. See this video for how to get set up in just three minutes. How it works Part 1: Manually upload data In this example, we manually upload a 100+ page article from arXiv called "A Survey of Large Language Models". But you can replace this with your own more advanced data pipeline, if you wish. Part 2: Embed and load data into Weaviate collection Here, we generate embeddings for the full-text of the article and store them in Weaviate. Part 3: Perform RAG over PDF file with Weaviate In this part of the workflow, you can enter your query by running the Chat Node and get a RAG response grounded in context via the Question and Answer Chain node. How to run the workflow Go through the prerequisites, creating a Weaviate cluster (can be local or cloud), downloading self-hosted n8n, and adding your API keys and other credentials. Select the embedding and chat models you'd like to use. Upload a PDF file you want to ask questions about. Execute the rest of the workflow.