Workflow Templates

Settyan Flash V2.0.0 Beta.0 AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the settyan/flash-v2.0.0-beta.0 model. This powerful AI model can generate high-quality other content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters prompt** (string): Prompt for generated image. If you include the trigger_word used in the training process you are more likely to activate the trained object, style, or concept in the resulting image. Optional Parameters mask** (string, default: None): Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. seed** (integer, default: None): Random seed. Set for reproducible generation image** (string, default: None): Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. model** (string, default: dev): Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps. width** (integer, default: None): Width of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation height** (integer, default: None): Height of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation go_fast** (boolean, default: False): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 extra_lora** (string, default: None): Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars' lora_scale** (number, default: 1): Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora. megapixels** (string, default: 1): Approximate number of megapixels for generated image How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate other content Access the generated output from the final node API Reference Model: settyan/flash-v2.0.0-beta.0 API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

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.

This template quickly shows how to use RAG in n8n. 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. 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.

This workflow backup Squarespace website header and footer injections into Github How It Works The Squarespace injections are fetched when an URL is placed Setup Instructions First, edit HTTP Request's URL to put your Squarespace site URL there Next, to configure the Github, update the Globals node with the following values: repo.owner – Your GitHub username repo.name – The name of your GitHub repository storing the workflows repo.path – The folder path within the repository where workflows are stored For example, if your GitHub username is john-doe, your repository is named n8n-backups, and injections are stored in a squarespace-backup/ folder, you would set: repo.owner → john-doe repo.name → n8n-backups repo.path → squarespace-backup/ Each site's injections will be added into seperate folder Required Credentials GitHub API – Access to your repository Who Is This For? This template is made for Squarespace users who want to backup their header and footer injections at interval to or on demand Check out my other templates: 👉 My n8n Templates

This is a simple webpage scraper that specifically grabs today's newest 4K Bluray Preorders as listed on the Blu-ray.com website. This is a scheduled workflow that can run every day and will post a formatted summary message of links to a Discord channel of your choice. Minimal setup required: Just create a webhook for the channel you want posted to in Discord and provide that in the final step. The timezone format step is set to East Coast (NYC) by default, feel free to change. No API keys or any special configuration needed (beyond your Discord webhook) Feel free to customize the formatting of the message that gets posted 👍 How it works: First format todays date to match the formatting used on the website Grab the HTML for the preorders page at www.blu-ray.com Filter only the hyperlinks for each Bluray on the page Then further filter only those with an html header matching today's date Format how you want the message to be sent to your Discord channel (in this case a simple list of Hyperlinks for each Title) Send to Discord! Disclaimer: This should be only for personal use.** The links go back to the blu-ray.com website, which is a good thing! Don't abuse this by slamming their site with some crazy level of automation frequency. Support the blu-ray.com website by using their affiliate links whenever you do want to preorder a title ;) This is one of my first shared templates, so it may not be super optimal or perfect but it works for my needs and hopefully you'll find some use out of it! Discord currently has a 2000 character limit on webhook messages. Some of the messages may get truncated as a result.

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 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.

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!