Agents & Automations

🎥 YouTube Video AI Agent Workflow This n8n workflow template allows you to interact with an AI agent that extracts details and the transcript of a YouTube video using a provided video ID. Once the details and transcript are retrieved, you can chat with the AI agent to explore or analyze the video's content in a conversational and insightful manner. 🌟 How the Workflow Works 🔗 Input Video ID: The user provides a YouTube video ID as input to the workflow. 📄 Data Retrieval: The workflow fetches essential details about the video (e.g., title, description, upload date) and retrieves its transcript using YouTube's Data API and additional tools for transcript extraction. 🤖 AI Agent Interaction: The extracted details and transcript are processed by an AI-powered agent. Users can then ask questions or engage in a conversation with the agent about the video's content, such as: - Summarizing the transcript. - Analyzing key points. - Clarifying specific sections. 💬 Dynamic Responses: The AI agent uses natural language processing (NLP) to generate contextual and accurate responses based on the video data, ensuring a smooth and intuitive interaction. 🚀 Use Cases 📊 Content Analysis: Quickly analyze long YouTube videos by querying specific sections or extracting summaries. 📚 Research and Learning: Gain insights from educational videos or tutorials without watching them entirely. ✍️ Content Creation: Repurpose transcripts into blogs, social media posts, or other formats efficiently. ♿ Accessibility: Provide an alternative, text-based way to interact with video content for users who prefer reading over watching. 🛠️ Resources for Getting Started - Google Cloud Console (for API setup): Visit Google Cloud's Get Started Guide to configure your API access. - YouTube Data API Key Setup: Follow this guide to create and manage your YouTube Data API key. - Install n8n Locally: Refer to this installation guide for setting up n8n on your local machine. ✨ Sample Prompts - "Tell me about this YouTube video with id: JWfNLF_g_V0" - "Can you provide a list of key takeaways from this video with id: [youtube-video-id]?"

This workflow is designed to optimize prompts by enhancing user inputs for clarity and specificity using AI. The workflow takes a user-provided prompt as input and uses a Natural Language Processing (NLP) model to refine and improve the prompt. The optimized prompt is then sent back to the user, ready for use in further workflows or processes. This workflow is suitable for users who want to improve their prompts for better communication and understanding in their workflows. The workflow utilizes an AI Agent powered by an OpenAI Chat Model to enhance user prompts. Expected Outcomes: Users can provide vague or imprecise prompts as input to the workflow. The AI Agent will refine and optimize the prompt, adding clarity and specific details. The optimized prompt will be delivered back to the user via Telegram or can be input for the next nodes. Extra Information: A. A Telegram node is used to deliver the optimized prompt back to the user. B. Ensure you have the necessary credentials set up for Telegram and OpenAI accounts. C. Customize the workflow's settings, such as the AI model used for prompt optimization, to suit your requirements. D. Activate the workflow once all configurations are set to start optimizing prompts efficiently.

🧩 What This Workflow Does This workflow automates the process of identifying and enriching decision-maker contacts from a list of companies. By integrating with Apollo's APIs and Google Sheets, it streamlines lead generation, ensures data accuracy through human verification, and maintains an organized leads database. 📚 Use Case Ideal for sales and marketing teams aiming to: - Automate the discovery of key decision-makers (e.g., CEOs, CTOs). - Enrich contact information with LinkedIn profiles, emails, and phone numbers. - Maintain an up-to-date leads database with minimal manual intervention. - Receive weekly summaries of newly verified leads. 🧪 Setup 1. Google Sheets Preparation: Use the following pre-configured Google Sheet: Company Decision Maker Discovery Sheet. This spreadsheet includes the necessary tabs and columns: Companies, Contacts, and Contacts (Verified). It also contains a custom onEdit Apps Script function that automatically updates the Status column to Pending whenever the Domain field is modified. To review or modify the script, navigate to Extensions > Apps Script within the Google Sheet. 2. Credentials Setup: Configure the following credentials in your n8n instance: - Google Sheets: To read from and write to the spreadsheet. - Slack: To send verification prompts and weekly reports. - Apollo: To access the Organization Search, Organization Enrichment, People Search, and Bulk People Enrichment APIs. - LLM Service (e.g., OpenAI): To generate company summaries and determine departments based on job titles. 3. Workflow Configuration: Import the workflow into your n8n instance. Update the nodes to reference the correct Google Sheet and Slack channel. Ensure that the Apollo and LLM nodes have the appropriate API keys and configurations. 4. Testing the Workflow: Add a new company entry in the Companies tab of the Google Sheet. Verify that the workflow triggers automatically, processes the data, and updates the Contacts and Contacts (Verified) tabs accordingly. Check Slack for any verification prompts and confirm that weekly reports are sent as scheduled.

This n8n template demonstrates the beginnings of building your own n8n-powered WhatsApp chatbot! Under the hood, utilise n8n's powerful AI features to handle different message types and use an AI agent to respond to the user. A powerful tool for any use-case! How it works Incoming WhatsApp Trigger provides a way to get messages into the workflow. The message received is extracted and sent through 1 of 4 branches for processing. Each processing branch uses AI to analyse, summarize or transcribe the message so that the AI agent can understand it. The supported types are text, image, audio (voice notes) and video. The AI Agent is used to generate a response generally and uses a Wikipedia tool for more complex queries. Finally, the response message is sent back to the WhatsApp user using the WhatsApp node. How to use Once you have set up and configured your WhatsApp account, you'll need to activate your workflow to start processing messages. Good to know: Large media files may negatively impact workflow performance. Customising this workflow For performance reasons, consider detecting large audio and video before sending to the LLM. Pre-processing such files may allow your agent to perform better. Go beyond and create rich and engaging customer experiences by responding using images, audio, and video instead of just text!

This n8n workflow template automates the process of collecting and delivering the "Top Deals of the Day" from MediaMarkt, tailored to user preferences. By combining user-submitted forms, Bright Data web scraping, GPT-4o-mini deal generation, and email delivery, this workflow sends personalized product recommendations straight to a user’s inbox. ⚠️ Note: This workflow uses community nodes (Bright Data and Document Generator) which only work on self-hosted n8n instances. 🚀 What It Does - Collects user preferences via a form (categories + email) - Scrapes MediaMarkt’s deals page using Bright Data - Uses GPT-4o-mini (OpenAI) to recommend top deals - Generates a structured HTML email using a template - Sends the personalized deals directly via email 🧩 Community Node Integration We created and used the following community nodes: - Bright Data – To scrape MediaMarkt deals using proxy-based scraping - Document Generator – To generate a templated HTML document from deal data These nodes are not available in n8n Cloud and require self-hosted n8n. 🛠️ Step-by-Step Setup 1. Install Community Nodes Make sure you're on a self-hosted n8n instance. Install: - n8n-nodes-brightdata - n8n-nodes-document-generator 2. Configure Credentials - Bright Data API Key (Proxy + Scraping setup) - OpenAI API Key (GPT-4o-mini access) - SMTP Credentials for sending emails 3. Customize the Form Adapt the form node to collect desired categories and email addresses. Typical categories include appliances, phones, laptops, etc. 4. Design Your HTML Template In the Document Generator node, you can tweak the HTML/CSS to change how deals appear in the final email. 5. Test the Workflow Submit the form with test data and check that the entire flow—from scraping to email—executes as expected. 🧠 How It Works: Workflow Overview - User Interaction via Form Users select product categories and enter their email. This triggers the workflow. - Data Extraction via Bright Data Bright Data scrapes the MediaMarkt offers page and returns HTML content. - HTML Parsing Key elements like product names, prices, and links are extracted for processing. - GPT-4o-mini Recommendation Generation The extracted data is sent to OpenAI (GPT-4o-mini), which filters, ranks, and enhances deals based on the user’s preferences. - Data Structuring & Split The result is split into individual deal items to be formatted. - HTML Document Creation Document Generator populates a clean HTML template with the top recommended deals. - Email Delivery The final document is emailed via SMTP to the user with a friendly message. 📨 Final Output Users receive a custom HTML email featuring a curated list of top MediaMarkt deals based on their selected categories. 🔐 Credentials Used - Bright Data API – Web scraping with proxy support - OpenAI API – Generating personalized recommendations - SMTP – Sending personalized deal emails ✨ Customization Tips - Change the Data Source: You can adapt this to scrape other e-commerce sites. - Update the Email Template: Make it match your branding or include images. - Extend the Form: Add preferences like price range or specific brands. - Add Scheduling: Use Cron to run the workflow daily or weekly. ❓ Questions? Template and node created by Miquel Colomer and n8nhackers.com. Need help customizing or deploying? Contact us for consulting and support.

Master the creation of impactful press releases using Google Docs and ChatGPT, boosting your public relations strategy and enhancing your media visibility.

Instagram Auto-Comment Responder with AI Agent Integration Version: 1.1.0 ‧ n8n Version: 1.88.0+ ‧ License: MIT A fully automated workflow for managing and responding to Instagram comments using AI agents. Designed to improve engagement and save time, this system listens for new Instagram comments, verifies and filters them, fetches relevant post data, processes valid messages with a natural language AI, and posts context-aware replies directly on the original post. Key Features 💬 AI-Driven Engagement: Intelligent responses to comments via a GPT-powered agent. ✅ Webhook Verification: Handles Instagram webhook handshake to ensure secure integration. 📦 Data Extraction: Maps incoming payload fields (user ID, username, message text, media ID) for processing. 🚫 Self-Comment Filtering: Automatically skips comments made by the account owner to prevent loops. 📡 Post Data Retrieval: Fetches the media’s id and caption from the Graph API (v22.0) before generating a reply. 🧠 Natural Language Processing: Uses a custom system prompt to maintain brand tone and context. 🔁 Automated Replies: Posts the AI-generated message back to the comment thread using Instagram’s API. 🧩 Modular Architecture: Clear separation of steps via sticky notes and dedicated HTTP Request and Agent nodes. Use Cases - Social Media Automation: Keep followers engaged 24/7 with instant, relevant replies. - Community Building: Maintain a consistent voice and tone across all interactions. - Brand Reputation Management: Ensure no valid comment goes unanswered. - AI Customer Support: Triage simple questions and direct followers to resources or support. Technical ImplementationWebhook Verification Node: Webhook + Respond to Webhook Echoes hub.challenge to confirm subscription and secure incoming events. Data Extraction Node: Set Maps payload fields into structured variables: conta.id, usuario.id, usuario.name, usuario.message.id, usuario.message.text, usuario.media.id, endpoint. User Validation Node: Filter Skips processing if conta.id equals usuario.id (self-comments). Post Data Retrieval Node: HTTP Request (Get post data) GET https://graph.instagram.com/v22.0/{{ $json.usuario.media.id }}?fields=id,caption&access_token={{ credentials }} Captures the media’s caption for richer context in replies. AI Response Generation Nodes: AI Agent + OpenRouter Chat Model Uses a detailed system prompt with: - Profile persona (expert in AI & automations, friendly tone). - Input data (username, comment text, post caption). - Filtering logic (spam, praise, questions, vague comments). Returns either the reply text or [IGNORE] for irrelevant content. Posting the Reply Node: HTTP Request (Post comment) POST {{ $json.endpoint }}/{{ $json.usuario.message.id }}/replies with message={{ $json.output }} Sends the AI answer back under the original comment. Instructions for Setup 1. Import Workflow: In n8n > Workflows > Import from File, upload the provided .json template. 2. Configure Credentials: Instagram Graph API (Header Auth or FacebookGraphApi) with instagram_basic, instagram_manage_comments scopes. OpenRouter/OpenAI API key for AI agent. 3. Customize System Prompt: Edit the AI Agent’s prompt to adjust brand tone, language (Brazilian Portuguese), length, or emoji usage. 4. Test & Activate: Publish a test comment on an Instagram post. Verify each node’s execution, ensuring the webhook, filter, data extraction, HTTP requests, and AI Agent respond as expected. 5. Extend & Monitor: Add sentiment analysis or lead capture nodes as needed. Monitor execution logs for errors or rate-limit events.

This workflow automates document processing using LlamaParse to extract and analyze text from various file formats. It intelligently processes documents, extracts structured data, and delivers actionable insights through multiple channels. --- How It WorksDocument Ingestion & Processing 📄 - Monitors Gmail for incoming attachments or accepts documents via webhook - Validates file formats against supported LlamaParse extensions - Uploads documents to LlamaParse for advanced text extraction - Stores original documents in Google Drive for reference Intelligent Document Analysis 🧠 - Automatically classifies document types (invoices, reports, etc.) - Extracts structured data using customized AI prompts - Generates comprehensive document summaries with key insights - Converts unstructured text into organized JSON data Invoice Processing Automation 💼 - Extracts critical invoice details (dates, amounts, line items) - Organizes financial data into structured formats - Calculates tax breakdowns, subtotals, and payment information - Maintains detailed records for accounting purposes Multi-Channel Delivery 📱 - Saves extracted data to Google Sheets for tracking and analysis - Sends concise summaries via Telegram for immediate review - Creates searchable document archives in Google Drive - Updates spreadsheets with structured financial information --- Setup StepsConfigure API Credentials 🔑 - Set up LlamaParse API connection - Configure Gmail OAuth for email monitoring - Set up Google Drive and Sheets integrations - Add Telegram bot credentials for notifications Customize AI Processing ⚙️ - Adjust document classification parameters - Modify extraction templates for specific document types - Fine-tune summary generation prompts - Customize invoice data extraction schema Test and Deploy 🚀 - Test with sample documents of various formats - Verify data extraction accuracy - Confirm notification delivery - Monitor processing pipeline performance

This workflow generates CSV files containing a list of 10 random users with specific characteristics using OpenAI's GPT-4 model. It then splits this data into batches, converts it to CSV format, and saves it to disk for further use. The execution of the workflow begins from here when triggered manually. "OpenAI" Node. This uses the OpenAI API to generate random user data. The input to the OpenAI API is a fixed string, which asks for a list of 10 random users with some specific attributes. The attributes include a name and surname starting with the same letter, a subscription status, and a subscription date (if they are subscribed). There is also a short example of the JSON object structure. This technique is called one-shot prompting. "Split In Batches" Node. This node is used to handle the OpenAI responses one by one. "Parse JSON" Node. This node converts the content of the message received from the OpenAI node (which is in string format) into a JSON object. "Make JSON Table" Node. This node is used to convert the JSON data into a tabular format, which is easier to handle for further data processing. "Convert to CSV" Node. This node converts the table format data received from the "Make JSON Table" node into CSV format and assigns a file name. "Save to Disk" Node. This node is used to save the CSV generated in the previous node to disk in the ".n8n" directory. The workflow is designed in a circular manner. So, after saving the file to disk, it goes back to the "Split In Batches" node to process the OpenAI output, until all batches are processed.

Auto Source LinkedIn Candidates with GPT-4 Boolean Search & Google X-ray How It Works:User Input: The user pastes a job description or ideal candidate specifications into the workflow. Boolean Search String Generation: OpenAI processes the input and generates a precise LinkedIn Boolean search string formatted as:site:linkedin.com/in ("Job Title" AND "Skill1" AND "Skill2") This search string is optimized to find relevant LinkedIn profiles matching the provided criteria. Google Sheet Creation: A new Google Sheet is automatically created within a specified document to store extracted LinkedIn profile URLs. Google Search Execution: The workflow sends a search request to Google using an HTTP node with the generated Boolean string. Iterative Search & Data Extraction: The workflow retrieves the first 10 results from Google. If the desired number of LinkedIn profiles has not been reached, the workflow loops, fetching the next set of 10 results until the condition is met. Data Storage: The workflow extracts LinkedIn profile URLs from the search results and saves them to the newly created Google Sheet for further review. Setup Steps: 1. API Key Configuration Under "Credentials", add your OpenAI API key from your OpenAI account settings. This key is used to generate the LinkedIn Boolean search string. 2. Adjust Search Parameters Navigate to the "If" node and update the condition to define the desired number of LinkedIn profiles to extract. The default is 50, but you can set it to any number based on your needs. 3. Establish Google Sheets Connection Connect your Google Sheets account to the workflow. Create a document to store the sourced LinkedIn profiles. The workflow automatically creates a new sheet for each new search, so no manual setup is needed. 4. Authenticate Google Search Google search requires authentication for better results. Use the Cookie-Editor browser extension to export your header string and enable authenticated Google searches within the workflow. 5. Run the Workflow Execute the workflow and monitor the Google Sheet for newly added LinkedIn profiles. Benefits: ✅ Automates profile sourcing, reducing manual search time. ✅ Generates precise LinkedIn Boolean search strings tailored to job descriptions. ✅ Extracts and saves LinkedIn profiles efficiently for recruitment efforts. This solution leverages OpenAI and advanced search techniques to enhance your talent sourcing process, making it faster and more accurate! 🚀

What This Workflow Does This n8n workflow reads backlinks from a Google Sheet, sends each one to the DataForSEO On-Page API, and checks: - Whether the backlink is still live on the target page - Whether it's dofollow or nofollow - Whether it's missing (i.e., lost) The result is then written back to the same Google Sheet under a Status column. Your result will look like this: Step-by-Step Setup Instructions 1. Add your DataForSEO and Google Sheets credentials in n8n. 2. Make sure your Google Sheet has these columns: Backlink URL, Landing page, and Status. 3. Click the Test Workflow button to check a batch of backlinks. Workflow Breakdown - Trigger: Manual test start - Read Data: Pulls backlink URLs and target pages from Google Sheets - Format URLs: Extracts domain from URL - Send POST Request to DataForSEO: Triggers a crawl on the backlink URL - Wait 20 seconds: Allows crawl to finish - Fetch Link Results: Retrieves backlink data from DataForSEO - Validate Backlink: Checks if the backlink is live, and whether it’s dofollow - Update Google Sheets: Logs the status as Live, Lost, or Lost (Nofollow)

n8n Creators Leaderboard WorkflowWhy Use This Workflow? The n8n Creators Leaderboard Workflow is a powerful tool for analyzing and presenting detailed statistics about workflow creators and their contributions within the n8n community. It provides users with actionable insights into popular workflows, community trends, and top contributors, all while automating the process of data retrieval and report generation. Benefits - Discover Popular Workflows: Identify workflows with the most unique visitors and inserters (weekly and monthly). - Understand Community Trends: Gain insights into what workflows are resonating with the community. - Recognize Top Contributors: Highlight impactful creators to foster collaboration and inspiration. - Save Time with Automation: Automates data fetching, processing, and reporting for efficiency. Use Cases - For Workflow Creators: Track performance metrics of your workflows to optimize them for better engagement. - For Community Managers: Identify trends and recognize top contributors to improve community resources. - For New Users: Explore popular workflows as inspiration for building your own automations. How It Works This workflow aggregates data from GitHub repositories containing statistics about workflow creators and their templates. It processes this data, filters it based on user input, and generates a detailed Markdown report using an AI agent. Key Features - Data Aggregation: Fetches creator and workflow statistics from GitHub JSON files. - Custom Filtering: Focuses on specific creators based on a username provided via chat. - AI-Powered Reports: Generates comprehensive Markdown reports with summaries, tables, and insights. - Output Flexibility: Saves reports locally with timestamps for easy access. Data Retrieval & Processing - Creators Data: Retrieved via an HTTP Request node from a JSON file containing aggregated statistics about creators. - Workflows Data: Pulled from another JSON file with workflow metrics like visitor counts and inserter statistics. - Data Merging: Combines creator and workflow data by matching usernames to provide enriched statistics. Report Generation The AI agent generates a Markdown report that includes: - A summary of the creator’s contributions. - A table of workflows with key metrics (e.g., unique visitors, inserters). - Insights into trends or community feedback. The report is saved locally as a file with a timestamp for tracking purposes. Quick Start GuidePrerequisites - Ensure your n8n instance is running. - Verify that the GitHub base URL and file variables are correctly set in the Global Variables node. - Confirm that your OpenAI credentials are configured for the AI Agent node. How to Start 1. Activate the Workflow: Make sure the workflow is active in your n8n environment. 2. Trigger via Chat: Use the Chat Trigger node to initiate the workflow by sending a message like: show me stats for username [desired_username] Replace [desired_username] with the username you want to analyze. 3. Processing & Report Generation: The workflow fetches data, processes it, and generates a Markdown report. 4. View Output: The final report is saved locally as a file (with a timestamp), which you can review to explore leaderboard insights.

Trigger ChatGPT messages and update Airtable records via webhook.Automate data retrieval and updates for streamlined communication and record management.

This is the workflow powering the n8n demo shown at StrapiConf 2022. The workflow searches matching Tweets every 30 minutes using the Interval node and listens to Form submissions using the Webhook node. Sentiment analysis is handled by Google using the Google Cloud Natural Language node before the result is stored in Strapi using the Strapi node. (These were originally two separate workflows that have been combined into one to simplify sharing.)

This workflow uses OpenAI to generate tweets to be stored in Airtable for review. A JS snippet handles the topics to be tweeted about in the form of hashtags.

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. 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. For example, 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. Simply 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. 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. 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 template serves as a Chatbot that enables you to ask questions about the content of a PDF directly in Telegram. It checks incoming Telegram messages to see if they contain a document. If they do, it stores the PDF in a Pinecone Vector store. If there's no document, it will search the Vector Store for information and try to answer your question. Setup: 1. Open the Telegram app and search for the BotFather user (@BotFather). 2. Start a chat with the BotFather. 3. Type /newbot to create a new bot. 4. Follow the prompts to name your bot and get a unique API token. 5. Save your access token and username. Once you set up your bot, you can send the PDF and then ask questions about the content. How to adjust it to your needs: - You can exchange the Groq chat model with any model that you like. - Exchange Pinecone with any other vector store tool you like (e.g., Supabase, Postgres, or QDrant).

Streamline Airtable record management by triggering updates via a custom webhook and Perplexity AI. Automate data retrieval and modification with this powerful integration.

Retrieves the news from a news feed, identifies a gap using InfraNodus, then generates a question that bridges this gap and helps you generate new ideas in relation to the main trends. This template was created by a 3rd party and has not been tested by Make. For support and other questions, please reach out to [email protected] or https://support.noduslabs.com.

This n8n template uses existing emails from customers as context to customise and "finetune" outreach emails to them using AI. By now, it should be common knowledge that we can leverage AI to generate unique emails, but in a way, they can remain generic as the AI lacks the customer context to be truly personalised. One way to solve this is by pulling in a source of customer data - and what better way than by using existing email correspondence. How it works Customers to target are pulled from Hubspot, and each customer is then run in a loop. We're using a loop as the retrieved emails for each customer become separate items, and a loop helps with item reference. We connect to our Gmail account to pull all emails received from the customer. The contents of the email will be suitable to build a short persona of the customer. We use the Information Extractor to get our AI model to pull out the key attributes of this persona such as decision-making style and communication preferences. With this persona, we can now pass this to our AI model to generate a personalised outreach email specifically for our customer. Finally, a draft email is created for human review before sending. If you would rather send the email straight away, this is also possible. How to use Define the topic of the outreach email in the "variables" node. This directs the AI on what outreach email to generate. Ensure the emails are pulled from the right account. If emails may contain sensitive data, adjust the filters and text parsing to ensure these are not leaked to the AI (which might then leak into the generated email). Customising this workflow Not using Hubspot? Any CRM would work just as well or even a simple text CSV! If you have customer past deals or engagements in your CRM, consider using this as additional context for the AI to use.

🧠 Analyze and Diagnose n8n Workflow Errors Automatically via OpenAI and Email ⚠️ This template is available on ☁️ Cloud & 🖥️ self-hosted n8n instances with the OpenAI node enabled. 👤 Who is this for? This workflow is designed for n8n developers, automation engineers, and DevOps teams who want to automatically capture and analyze workflow errors, and receive professional HTML-styled diagnostics directly in their inbox. 💥 What problem does this solve? Manually troubleshooting failed workflows in n8n can be time-consuming. This template streamlines error detection by: - Capturing workflow failures using the Error Trigger node - Diagnosing root causes with the help of OpenAI - Sending a fully-formatted, human-readable HTML error report via email - Including practical resolutions and next-step suggestions This helps you or your team resolve issues faster and avoid repeated manual debugging. ⚙️ What this workflow does ⚡ Triggers on any n8n workflow error 📦 Extracts relevant error metadata including node, execution ID, and timestamps 🧠 Sends error content to OpenAI for analysis and recommendations 💌 Generates an HTML email report with inline styles and clear formatting 📥 Emails the result to a system administrator or support email 🛠️ Setup - Install the OpenAI node in your self-hosted n8n instance. - Add your OpenAI API Key securely in credentials. - Configure the SMTP Email node with your email credentials. - Adjust the Error Trigger to monitor specific workflows or all workflows. - Set your preferred admin or dev email address in the final node. 🔧 How to customize this workflow to your needs 🧩 Use a Set node to define your variables, such as: - Default admin email - Workflow filter (optional) ✍️ Customize the prompt sent to OpenAI if you want deeper or more specific analysis. 🎨 Modify the email HTML styles to match your brand or internal format. 💾 Add additional logging (e.g., to Airtable, Google Sheets, or Notion) for long-term error tracking. 📌 Sticky NoteTitle: Automated Error Reporter with AI-Powered DiagnosisDescription: Captures any n8n error, sends it to OpenAI, and emails a beautiful HTML report to the administrator with steps to resolve the issue. Requires OpenAI credentials and SMTP configured.