by David Roberts
AI evaluation in n8n This is a template for n8n's evaluation feature. Evaluation is a technique for getting confidence that your AI workflow performs reliably, by running a test dataset containing different inputs through the workflow. By calculating a metric (score) for each input, you can see where the workflow is performing well and where it isn't. How it works This template shows how to calculate a workflow evaluation metric: whether a specific tool was called by an agent. We use an evaluation trigger to read in our dataset It is wired up in parallel with the regular trigger so that the workflow can be started from either one. More info We make sure that the agent outputs the list of tools that it used We then check whether the expected tool (from the dataset) is in that list Finally we pass this information back to n8n as a metric
by Max Tkacz
Who is this for This workflow is perfect for teams and individuals who manage extensive data in Notion and need a quick, AI-powered way to interact with their databases. If you're looking to streamline your knowledge management, automate searches, and get faster insights from your Notion databases, this workflow is for you. It’s ideal for support teams, project managers, or anyone who needs to query specific data across multiple records or within individual pages of their Notion setup. Check out the Notion template this Assistant is set up to use: https://www.notion.so/templates/knowledge-base-ai-assistant-with-n8n How it works The Notion Database Assistant uses an AI Agent built with Retrieval-Augmented Generation (RAG) to query this Knowledge Base style Notion database. The assistant can search across multiple properties like tags or question and retrieves content from inside individual Notion pages for additional context. Key features include: Querying the database with flexible filters. Searching within individual Notion pages and extracting relevant blocks. Providing a reference link to the exact Notion pages used to inform its responses, ensuring transparency and easy verification. This assistant uses two HTTP request tools—one for querying the Notion database and another for pulling data from within specific pages. It streamlines knowledge retrieval, offering a conversational, AI-driven way to interact with large datasets. Set up Find basic set up instructions inside the workflow itself or watch a quickstart video 👇
by Bela
In this automation we first make a screenshot with a screenshot API called URLbox and then send this screenshot into the OpenAI API and analyze it. You can extend this automation by the way you want to ingest the website url's & names into this workflow. Options as data source: Postgres Google Sheets Your CRM ... Setup: Replace Website & URL in Setup Node Put in your URLbox API Key Put in your OpenAI credentials Click here for a blog article with more information on the automation.
by Aadarsh Jain
Who is this for? This workflow is designed for DevOps engineers, platform engineers, and Kubernetes administrators who want to interact with their Kubernetes clusters through natural language queries in n8n. It's perfect for teams who need quick cluster insights without memorizing complex kubectl commands or switching between multiple cluster contexts manually. How it works? The workflow operates in three intelligent stages: Cluster Discovery & Context Switching - Automatically lists available clusters from your kubeconfig and switches to the appropriate cluster based on your natural language query Command Generation - Uses GPT-4o to analyze your request and generate the correct kubectl command with proper flags, selectors, and output formatting Command Execution - Executes the generated kubectl command against your selected cluster and returns the results The workflow supports multi-cluster environments and can handle queries like: "Show me all pods in production cluster" "List failing deployments in production" "Get pod details in kube-system namespace" Setup Clone the MCP Server git clone https://github.com/aadarshjain/kubectl-mcp-server cd kubectl-mcp-server Configure your kubeconfig - Ensure your ~/.kube/config contains all the clusters you want to access Set up MCP STDIO credentials in n8n Command: /full/path/to/python-package Arguments: /full/path/to/kubectl-mcp-server/server.py Import the workflow into your n8n instance Configure OpenAI credentials for the GPT-4o models Test the workflow using the chat interface with queries like "show pods in [cluster-name]"
by Henry
Who is this for? This workflow is ideal for SEO specialists, web designers, and digital marketers who want to quickly draft effective landing page layouts by referencing established competitors. It suits users who need a fast, structured starting point for web design while ensuring competitive relevance. What problem is this workflow solving? / Use case Designing a high-converting landing page from scratch can be time-consuming. This workflow automates the process of analyzing a competitor’s website, identifying essential sections, and producing a tailored layout—helping users save time and improve their website’s effectiveness. What this workflow does The workflow fetches and analyzes your chosen competitor’s landing page, using web scraping and structure-detection nodes in n8n. It identifies primary sections like hero banners, service highlights, testimonials, and contact forms, and then generates a simplified, customizable layout suitable for wireframing or initial design. Setup Prepare your unique services and target audience profile for customization later. Gather the competitor’s landing page URL you wish to analyze. Run the workflow, inputting your competitor’s URL when prompted. How to customize this workflow to your needs After generating the initial layout, adapt section names and content blocks to highlight your services and brand messaging. Add or remove sections based on your objectives and audience insights. Integrate additional nodes for richer analysis, such as keyword extraction or design pattern detection, to tailor the output further.
by Floyd Mahou
How it works • Allows users to manage their Google Calendar via WhatsApp using natural language • Handles event creation, updates, deletions, availability checks, and agenda overviews • AI agent interprets the user’s message and triggers the appropriate calendar action • Responses are sent back to the user via WhatsApp, with confirmation or schedule info Set up steps • Set up a WhatsApp Business Cloud account and configure your webhook • Connect your Google Calendar using n8n credentials • Deploy OpenAI API key for natural language understanding • Link each calendar action (create, update, delete, search) to the TimePilot agent • Customize confirmation messages and automate reply formatting Note: More detailed configuration and custom logic are described inside sticky notes within the workflow.
by Batu Öztürk
Extract the main idea and key takeaways from YouTube videos and turn them into Airtable content ideas 📝 Description Automatically turn YouTube videos into clear, structured content ideas stored in Airtable. This workflow pulls new video links from Airtable, extracts transcripts using a RapidAPI service, summarizes them with your favourite LLM, and logs the main idea and key takeaways—keeping your content pipeline fresh with minimal effort. ⚙️ What It Does Scans Airtable for new YouTube video links every 5 minutes. Extracts the transcript of the video using a third-party API via RapidAPI. Summarizes the content to generate a main idea and takeaways. Updates the original Airtable entry with the insights and marks it as completed. 🛠 Prerequisites Before using this template, make sure you have: ✅ A RapidAPI account with access to the youtube-video-summarizer-gpt-ai API. ✅ A valid RapidAPI key. ✅ An OpenAI, Claude or Gemini account connected to n8n. ✅ An Airtable account with a base and table ready. 🧰 Setup Instructions Clone this template into your n8n workspace. Open the Get YouTube Sources node and configure your Airtable credentials. In the Get video transcript node: Enter your X-RapidAPI-Key under headers. The API endpoint is pre-configured. Connect your LLM credentials to the Extract detailed summary node. (Optional) Adjust the summarization prompt in the LangChain node to better suit your tone. Set your preferred schedule in the Trigger node. 📋 Airtable Setup Create a base (e.g., Content Hub) with a table named Ideas and the following columns: | Column Name | Type | Required | Notes | |-------------|------------|----------|----------------------------| | Type | Single select | ✅ | Must be set to Youtube Video | | Source | URL | ✅ | The YouTube video URL | | Status | Checkbox | ✅ | Leave empty initially; updated after processing | | MainIdea | Single line text | ✅ | Summary generated by OpenAI | | Key Takeaways | Long text | ✅ | List of takeaways extracted from the transcript Activate the workflow—and you're done!
by Vadym Nahornyi
This workflow automatically transcribes audio files, translates the content between languages, and generates natural-sounding speech from the translated text - all in one seamless process. Who's it for Content creators, educators, and businesses needing to make their audio content accessible across language barriers. Perfect for translating podcasts, voice messages, lectures, or any audio content while preserving the spoken format. How it works The workflow receives an audio file through a webhook, transcribes it using OpenAI's Whisper, translates and structures the text with GPT-4, generates new audio in the target language, and stores it in S3 for easy access. The entire process takes seconds and returns both the transcribed/translated text and a URL to the translated audio file. How to set up Configure OpenAI credentials - Add your OpenAI API key for Whisper transcription and GPT-4 translation Set up AWS S3 - Create a bucket with public read permissions for audio storage Update configuration - Replace 'YOUR-BUCKET-NAME' with your actual S3 bucket name Activate webhook - Deploy and copy your webhook URL for receiving audio files Send a POST request with: Binary audio file (as 'audiofile') Languages parameter (e.g., "English, Spanish") Requirements OpenAI API account with access to Whisper and GPT-4 AWS account with S3 bucket configured Basic understanding of webhooks and API requests How to customize Add language detection** - Automatically detect source language if not specified Customize voice settings** - Adjust speech speed, pitch, or select different voices Add file validation** - Implement size limits and format checks Enhance security** - Add webhook authentication and rate limiting Extend functionality** - Add subtitle generation or multiple output formats
by Nathan Lee
How it works Automates the retrieval of Calvin and Hobbes daily comics. Extracts the comic image URL from the website. Translates comic dialogues to English and Korean. Posts the comic and translations to Discord daily. Set up steps Estimated setup time: ~10-15 minutes. Use a Schedule Trigger to automate the workflow at 9 AM daily. Add nodes for parameter setup, HTTP request, data extraction, and integration with Discord. Add detailed notes to each node in the workflow for easy understanding.
by Yosua Surojo
Who it's for This workflow is for anyone who wants to build an automated, AI-enhanced reading list. Ideal for: Knowledge workers and researchers who collect and organize articles Students managing study materials Productivity hackers who use Telegram and Notion for personal knowledge management Anyone using the AI-Enhanced Knowledge Base Tracker Notion Template How it works This workflow takes any article link sent to your Telegram bot and automatically: Parses the article into a clean title and body Uses OpenAI to generate a 1–2 sentence highlight and topic tag Saves it into your Notion database Sends a confirmation message with the highlight and Notion link back to Telegram Main steps: Telegram Trigger - Listens for incoming message containing an article link. Fetch Article Title & Content - Calls the article-parser-api deployed on Vercel to fetch and parse the article content into structured JSON (title and content). Generate Highlight + Tag (AI Agent) - Processes the parsed content to generate Highlight and Type tag values. Structured Metadata for Notion - Adjusts the extracted data before saving it to Notion. Save Article to Notion Database - Inserts the article and generated metadata into your Notion knowledge base. Confirm Save via Telegram - Sends a confirmation message and the Notion page link back to the Telegram bot chat after the entry is created. Setup Create and connect your API credentials: Telegram Bot OpenAI API Key Notion Integration Deploy the article parser: Use this repo: article-parser-api Deploy it to Vercel or any serverless environment Link your Notion database: Duplicate the AI‑Enhanced Knowledge Base Tracker Copy the database URL and connect it in the Notion node Test your workflow: Click Execute workflow Send an article link to your Telegram bot Once verified, activate the workflow so it runs automatically Requirements Telegram bot token OpenAI API key Notion integration and shared database A deployed article parser (e.g., article-parser-api) Optional customization Edit the AI Agent prompt to change tone or tagging style Add filtering or additional fields in the Edit Fields node Trigger from other sources (e.g., Slack or Email)
by David Roberts
Overview This workflow takes some French text, and translates it into spoken audio. It then transcribes that audio back into text, translates it into English and generates an audio file of the English text. To do so, it uses ElevenLabs (which has a free tier) and OpenAI. Setup These steps should only take a few minutes: In ElevenLabs, add a voice to your voice lab and copy its ID. Add it to the 'Set voice ID' node Get your ElevenLabs API key (click your name in the bottom-left of ElevenLabs and choose ‘profile’) In the 'Generate French audio' node, create a new header auth cred. Set the name to xi-api-key and the value to your API key In the 'credential' field of the 'Transcribe audio' node, create a new OpenAI cred with your OpenAI API key Run the workflow by clicking the orange button at the bottom of the canvas
by Adam Janes
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. 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: It loads test cases from Google Sheets. For each row in our Google Sheet, it grabs the source document, converting it to text. Our "LLM judge" passes the input/output of each LLM to GPT-4.1 to evaluate each test case (Pass/Fail + Reason). It logs the outcome to a Google Sheet. A 0.5s pause between each request gets around OpenAI's API rate limits. 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.