Workflow Templates

This n8n workflow template automates the process of collecting and analyzing Twitter (X) posts for any public profile, then generates a clean, AI-powered summary including key metrics, interests, and activity trends. 🚀 What It Does Accepts a user's full name and date range through a public form. Automatically finds the person’s X (formerly Twitter) profile using a Google search. Uses Bright Data to retrieve full post data from the X.com profile. Extracts key post metrics like views, likes, reposts, hashtags, and mentions. Uses Google Gemini (PaLM) to generate a personalized summary: tone, themes, popularity, and sentiments. Stores both raw data and the AI summary into a connected Google Sheet for further review or team collaboration. 🛠️ Step-by-Step Setup Deploy the public form to collect full name and date range. Build a Google search query using the name to find their X profile. Scrape the search results via Bright Data (Web Unlocker zone). Parse the page content using the HTML node. Use Gemini AI to extract the correct X profile URL. Pull full post data via Bright Data dataset snapshot API. Transform post data into clean structured fields: date_posted, description, hashtags, likes, views, quoted_post.date_posted, quoted_post.description, replies, reposts, quotes, and tagged_users.profile_name. Analyze all posts using Google Gemini for interest detection and persona generation. Save results to a Google Sheet: structured post data + AI-written summary. Show success or fallback messages depending on profile detection or scraping status. 🧠 How It Works: Workflow Overview Trigger: When user submits form Search & Match: Google search → HTML parse → Gemini filters matching X profile Data Gathering: Bright Data → Poll for snapshot completion → Fetch post data Transformation: Extract and restructure key fields via Code node AI Summary: Use Gemini to analyze tone, interests, and trends Export: Save results to Google Sheet Fallback: Display custom error message if no X profile found 📨 Final Output A record in your Google Sheet with: Clean post-level data Profile-level engagement summary An AI-written overview including tone, common topics, and post popularity 🔐 Credentials Used Bright Data account** (for search & post scraping) Google Gemini (PaLM)** or Gemini Flash via - OpenAI/Google Vertex API Google Sheets (OAuth2) account** (for result storage) ⚠️Community Node Dependency This workflow uses a custom community node: n8n-nodes-brightdata Install it via UI (Settings → Community Nodes → Install).

🛠️ Demio Tool MCP Server Complete MCP server exposing all Demio Tool operations to AI agents. Zero configuration needed - all 4 operations pre-built. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works • MCP Trigger: Serves as your server endpoint for AI agent requests • Tool Nodes: Pre-configured for every Demio Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n Demio Tool tool with full error handling 📋 Available Operations (4 total) Every possible Demio Tool operation is included: 📅 Event (3 operations) • Get an event • Get many events • Register an event 🔧 Report (1 operations) • Get a report 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Resource IDs and identifiers • Search queries and filters • Content and data payloads • Configuration options Response Format: Native Demio Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • Other n8n Workflows: Call MCP tools from any workflow • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Complete Coverage: Every Demio Tool operation available • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n error handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

This n8n template shows you how to create an MCP server out of your existing n8n workflows. With this, any MCP client connected can get more done with powerful end-to-end workflows rather than just simple tools. Designing agent tools for outcome rather than utility has been a long recommended practice of mine and it applies well when it comes to building MCP servers; In gist, agents to be making the least amount of calls possible to complete a task. This is why n8n can be a great fit for MCP servers! This template connects your agent/MCP client (like Claude Desktop) to your existing workflows by allowing the AI to discover, manage and run these workflows indirectly. How it works An MCP trigger is used and attaches 4 custom workflow tools to discover and manage existing workflows to use and 1 custom workflow tool to execute them. We'll introduce an idea of "available" workflows which the agent is allowed to use. This will help limit and avoid some issues when trying to use every workflow such as clashes or non-production. The n8n node is a core node which taps into your n8n instance API and is able to retrieve all workflows or filter by tag. For our example, we've tagged the workflows we want to use with "mcp" and these are exposed through the tool "search workflows". Redis is used as our main memory for keeping track of which workflows are "available". The tools we have are "add Workflow", "remove workflow" and "list workflows". The agent should be able to manage this autonomously. Our approach to allow the agent to execute workflows is to use the Subworkflow trigger. The tricky part is figuring out the input schema for each but was eventually solved by pulling this information out of the workflow's template JSON and adding it as part of the "available" workflow's description. To pass parameters through the Subworkflow trigger, we can do so via the passthrough method - which is that incoming data is used when parameters are not explicitly set within the node. When running, the agent will not see the "available" workflows immediately but will need to discover them via "list" and "search". The human will need to make the agent aware that these workflows will be preferred when answering queries or completing tasks. How to use First, decide which workflows will be made visible to the MCP server. This example uses the tag of "mcp" but you can all workflows or filter in other ways. Next, ensure these workflows have Subworkflow triggers with input schema set. This is how the MCP server will run them. Set the MCP server to "active" which turns on production mode and makes available to production URL. Use this production URL in your MCP client. For Claude Desktop, see the instructions here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop. There is a small learning curve which will shape how you communicate with this MCP server so be patient and test. The MCP server will work better if there is a focused goal in mind ie. Research and report, rather than just a collection of unrelated tools. Requirements N8N API key to filter for selected workflows. N8N workflows with Subworkflow triggers! Redis for memory and tracking the "available" workflows. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow If your targeted workflows do not use the subworkflow trigger, it is possible to amend the executeTool to use HTTP requests for webhooks. Managing available workflows helps if you have many workflows where some may be too similar for the agent. If this isn't a problem for you however, feel free to remove the concept of "available" and let the agent discover and use all workflows!

Who is this workflow template for? This workflow template is perfect for freelancers, small business owners, accounting teams, or anyone responsible for managing and recording invoices regularly. If you deal with multiple invoices and spend considerable time manually entering invoice data into a database, this automation will significantly simplify your daily operations and reduce potential errors. What this workflow does The workflow automates the entire invoice logging process. It continuously monitors a designated Google Drive folder every minute for new PDF invoice uploads. Once a new invoice is detected, it is automatically converted from PDF to an image format using the ILovePDF API. After conversion, Google's Gemini AI analyzes the image, intelligently extracting essential details such as vendor name, item description, invoice amount, invoice date, payment date, and bank reference numbers. Finally, this structured data is automatically recorded in an Airtable database (or optionally in a Google Sheet), ensuring organized, accessible records. Detailed Workflow Explanation Step 1: Invoice Detection** Monitors Google Drive for newly uploaded PDF invoices. Step 2: PDF to Image Conversion** Converts PDFs into images using ILovePDF. Step 3: Data Extraction via Gemini AI** Uses Gemini AI to analyze the invoice image. Extracts data such as Vendor, Description, Amount, Invoice Date, Paid Date, and Bank Reference. Provides clear descriptions even when original invoice descriptions are vague or missing by analyzing vendor context. Step 4: Structured Data Storage** Automatically sends extracted data to Airtable or Google Sheets. Step 5: File Management** Moves processed PDF files into a separate "Done" folder to clearly differentiate between processed and unprocessed invoices. Step-by-Step Setup Instructions Set Up Google Drive: Log in to Google Drive and create two folders: One named Invoices (for incoming PDF files) One named Processed (for processed files) Obtain API Credentials: ILovePDF API: Sign up at ILovePDF Developers. Retrieve your API key from your account dashboard. Google Gemini AI API: Register at Google AI and generate an API key. Airtable Database Preparation: Create an Airtable base with the following columns: Vendor (Text) Description (Text) Amount (Number or Text) Invoice Date (Date) Paid Date (Date) Bank Reference (Text) Import and Configure Workflow in n8n: Import the provided workflow JSON file into your n8n instance. Connect your Google Drive, ILovePDF, Google Gemini AI, and Airtable accounts by entering your credentials in their respective nodes. Adjust Workflow Settings: In the Google Drive nodes, ensure your newly created Invoices and Processed folders are correctly selected. Update the ILovePDF public key in the appropriate HTTP Request node. Customize the Gemini AI prompt to refine or expand data extraction according to your specific needs. Testing Your Setup: Upload a sample PDF invoice into the Invoices folder. Execute the workflow by clicking Test Workflow in n8n and verify if data extraction and Airtable logging operate correctly. Airtable Column Specifications Ensure your Airtable includes the following structure: Vendor**: Single Line Text Description**: Single Line Text Amount**: Currency or Single Line Text Invoice Date**: Date (formatted as YYYY-MM-DD) Paid Date**: Date (formatted as YYYY-MM-DD) Bank Reference**: Single Line Text How to Customize the Workflow System Prompt:** Adjust the AI instructions by modifying the prompt text to focus on additional or fewer invoice details. Structured Output Parser:** Modify the JSON schema in the parser node to match the structure and data points your project specifically requires: By following these instructions, you’ll have a fully automated, reliable system for handling and logging invoice data, significantly enhancing your productivity.

Extract Local Business Contacts with Google Sheets, SerpAPI & GPT‑4o Status: Ready for Use ✅ Disclaimer: This workflow relies on community nodes that are not part of n8n’s core package. Install the following from n8n → Community Nodes before running: n8n-nodes-langchain** n8n-nodes-openai** (Structured Output Parser) n8n-nodes-apify** 📝 Description This n8n workflow automates discovery of local‑business contact details by search term and location, then enriches the results with publicly listed email addresses using GPT‑4o AI. 🔑 Key Features 🔗 Google Sheets Integration Reads search terms and locations from a Google Sheet. Processes only rows that are not marked Complete, preventing duplicates. 🗺️ Google Maps Search via SerpAPI Queries Google Maps through SerpAPI for every search‑term‑and‑location pair. Retrieves the following fields: business name, website, street address, and phone number. 🧠 Website Scraping & Email Extraction Scrapes the business homepage content with Apify’s Fast Website Content Crawler. Sends the scraped HTML to a GPT‑4o AI Agent. Extracts any publicly listed email address. Returns a clean, structured JSON object for downstream use. 💾 Data Storage & Tracking Writes every result to a Results tab in the same Google Sheet. Marks the corresponding row in the Searches tab as Complete once finished. 🧱 Extensible Design The workflow uses modular sub‑workflows and AI agents. You can easily extend it to add: Phone‑number verification with Twilio Social‑media enrichment with Clearbit Exports to HubSpot, Salesforce, Airtable, PostgreSQL, or CSV files 📄 Google Sheet Setup Create a Searches tab with these exact columns (one header row): Search | Area | Area Name | Complete Create a results tab with these columns title | website | address | phone | Search | Search Name | Area | email (Manual Entry) ⚙️ Prerequisites Google Cloud Project with Google Sheets API and Google Drive API enabled SerpAPI account (free trial or paid) – obtain an API key Apify account (free trial or paid) with the Fast Website Content Crawler actor installed OpenAI account with an API key that can access GPT‑4o models 🚀 Setup Instructions Copy the Google Sheet Make a personal copy of the template sheet. Ensure the tab names are Searches and Results. https://docs.google.com/spreadsheets/d/1QgcVMlXRlM_5ZFFUHr6bVK-93Tzia9XseTX03ZYnowI/edit?usp=sharing Configure Google Sheets nodes in n8n Open the workflow. Update the nodes Extract Search Terms and Save Emails to Sheet to point at your copied sheet. Authenticate using Google OAuth2 credentials that have access to the sheet. Add SerpAPI credentials Sign in at <https://serpapi.com>. Copy your API key. In the Search Google Maps node, create a new credential and paste the key. Set up Apify Sign up at <https://apify.com>. Add the Fast Website Content Crawler actor to your account. In the Scrape Web Page HTTP node, append ?token=YOUR_API_KEY to the actor URL. Add your OpenAI API key Go to <https://platform.openai.com>. Generate an API key. Add it to the AI Agent and OpenAI Chat Model node credentials. ✅ Running the Workflow Click Execute Workflow in n8n. For each unprocessed row in the Searches tab, the automation will: Retrieve business information from Google Maps via SerpAPI. Scrape the business website using Apify. Use GPT‑4o to extract a public email address. Write all collected data to the Results tab. Mark the original row as Complete. 🧩 Example Use Cases Build highly targeted lead lists for sales and marketing outreach. Compile local business directories for regional websites or apps. Automate contact‑information collection for lead‑generation campaigns and reduce manual data entry. 🤝 Connect with Me Description I’m Robert Breen, founder of Ynteractive — a consulting firm that helps businesses automate operations using n8n, AI agents, and custom workflows. I’ve helped clients build everything from intelligent chatbots to complex sales automations, and I’m always excited to collaborate or support new projects. If you found this workflow helpful or want to talk through an idea, I’d love to hear from you. Links 🌐 Website: https://www.ynteractive.com 📺 YouTube: @ynteractivetraining 💼 LinkedIn: https://www.linkedin.com/in/robert-breen 📬 Email: rbreen@ynteractive.com

Who this is for? The LinkedIn Company Story Generator is an automated workflow that extracts company profile data from LinkedIn using Bright Data's web scraping infrastructure, then transforms that data into a professionally written narrative or story using a language model (e.g., OpenAI, Gemini). The final output is sent via webhook notification, making it easy to publish, review, or further automate. This workflow is tailored for:​ Marketing Professionals**: Seeking to generate compelling company narratives for campaigns.​ Sales Teams**: Aiming to understand potential clients through summarized company insights.​ Content Creators**: Looking to craft stories or articles based on company data.​ Recruiters**: Interested in obtaining concise overviews of companies for talent acquisition strategies.​ What problem is this workflow solving? Manually gathering and summarizing company information from LinkedIn can be time-consuming and inconsistent. This workflow automates the process, ensuring:​ Efficiency**: Quick extraction and summarization of company data.​ Consistency**: Standardized summaries for uniformity across use cases.​ Scalability**: Ability to process multiple companies without additional manual effort. What this workflow does The workflow performs the following steps:​ Input Acquisition**: Receives a company's name or LinkedIn URL as input.​ Data Extraction**: Utilizes Bright Data to scrape the company's LinkedIn profile.​ Information Parsing**: Processes the extracted HTML content to retrieve relevant company details.​ Summarization**: Employs AI Google Gemini to generate a concise company story. Output Delivery**: Sends the summarized content to a specified webhook or email address. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). Update the LinkedIn URL by navigating to the Set LinkedIn URL node. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. How to customize this workflow to your needs Input Variations: Modify the **Set LinkedIn URL node to accept a different company LinkedIn URL. Data Points**: Adjust the HTML Data Extractor Node to retrieve additional details like employee count, industry, or headquarters location.​ Summarization Style**: Customize the AI prompt to generate summaries in different tones or formats (e.g., formal, casual, bullet points).​ Output Destinations**: Configure the output node to send summaries to various platforms, such as Slack, CRM systems, or databases.

📧 Email Validation Workflow Using APILayer API This n8n workflow enables users to validate email addresses in real time using the APILayer Email Verification API. It's particularly useful for preventing invalid email submissions during lead generation, user registration, or newsletter sign-ups, ultimately improving data quality and reducing bounce rates. ⚙️ Step-by-Step Setup Instructions Trigger the Workflow Manually: The workflow starts with the Manual Trigger node, allowing you to test it on demand from the n8n editor. Set Required Fields: The Set Email & Access Key node allows you to enter: email: The target email address to validate. access_key: Your personal API key from apilayer.net. Make the API Call: The HTTP Request node dynamically constructs the URL: https://apilayer.net/api/check?access_key={{ $json.access_key }}&email={{ $json.email }} It sends a GET request to the APILayer endpoint and returns a detailed response about the email's validity. (Optional): You can add additional nodes to filter, store, or react to the results depending on your needs. 🔧 How to Customize Replace the manual trigger with a webhook or schedule trigger to automate validations. Dynamically map the email and access_key values from previous nodes or external data sources. Add conditional logic to filter out invalid emails, log them into a database, or send alerts via Slack or Email. 💡 Use Case & Benefits Email validation is crucial in maintaining a clean and functional mailing list. This workflow is especially valuable in: Sign-up forms where real-time email checks prevent fake or disposable emails. CRM systems to ensure user-entered emails are valid before saving them. Marketing pipelines to minimize email bounce rates and increase campaign deliverability. Using APILayer’s trusted validation service, you can verify whether an email exists, check if it’s a role-based address (like info@ or support@), and identify disposable email services—all with a simple workflow. Keywords: email validation, n8n workflow, APILayer API, verify email, real-time email check, clean email list, reduce bounce rate, data accuracy, API integration, no-code automation

Fetches workflow definitions from within n8n, selecting only the ones that have one or more (configurable) assigned tags and then: Derives a suitable backup filename by reducing the workflow name to a string with alphanumeric characters and no-spaces Note: This isn't bulletproof, but works as long as workflow names aren't too crazy. Determines which workflows need to be backed up based on whether each one: has been modified. (Note: Even repositioning a node counts.) ...or... is new. (Note: Renaming counts as this.) Commits JSON copies of each workflow, as necessary, to a Gitlab repository with a generated, date-stamped commit message. Setup Credentials Create a Gitlab Credentials item and assign it to all Gitlab nodes. Create an n8n Credentials item and assign it to the n8n node Note: This was tested with http://localhost:5678/api/v1 but should work with any reachable n8n instance and API key. Modify these values in the "Globals" Node gitlab_owner - {{your gitlab account}} gitlab_project - {{ your gitlab project name }} gitlab_workflow_path - {{ subdirectory in the project where backup files should be saved/committed }} tags_to_match_for_backup - {{tag(s) to match for backup selection}} *ALERT: According to the n8n node's Filters -> tags field annotations, and API documentation, this supports a CSV list of multiple tags (e.g. tag1,tag2), but the API behavior requires workflows to have all-of the listed tags, not any-of them.* See: https://github.com/n8n-io/n8n/issues/10348 TL/DR - Don't expect a multiple tag list to be more inclusive. Possible workaround: To match more than one tag value, duplicate the n8n node into multiple single-tag matches, or split and iterate multiple values, and merge the results. Possible Enhancements Make the branch ("Reference") for all the gitlab nodes configurable. Fixed on all as "main" in the template. Add an n8n node to generate an audit and store the output in gitlab along with the backups. Extend the workflow at the end to create a Gitlab release/tag whenever any backup files are actually updated or created.

Who's this for? Anyone who wants to improve the SEO of their website Umami users who want insights on how to improve their site SEO managers who need to generate reports weekly Case study Watch youtube tutorial here Get my SEO A.I. agent system here You can read more about how this works here. How it works This workflow calls the Umami API to get data Then it sends the data to A.I. for analysis It saves the data and analysis to Baserow How to use this Input your Umami credentials Input your website property ID Input your Openrouter.ai credentials Input your baserow credentials You will need to create a baserow database with columns: Date, Summary, Top Pages, Blog (name of your blog). Future development Use this as a template. There's alot more Umami stats you can pull from the API. Change the A.I. prompt to give even more detailed analysis. Created by Rumjahn

Want to see it in action? Watch the full breakdown here: 📺 Video Link Template Description This n8n workflow empowers you to query structured financial data from Google Sheets or CSV files using AI-generated SQL. Unlike traditional vector database solutions that falter with numerical queries, this template leverages PostgreSQL for efficient data storage and an AI agent to dynamically create optimized SQL queries from natural language inputs. What It Does Retrieves data from Google Sheets or CSV files Infers the data schema and builds a PostgreSQL table Populates the table with your data Uses an AI agent to translate natural language questions into SQL queries Returns precise numerical results quickly and efficiently Why Use This? No SQL knowledge required—the AI generates queries for you Bypasses the inefficiencies and costs of vector database approaches Scales effortlessly without overwhelming the language model Fully free and open-source Setup Requirements Pre-Conditions PostgreSQL Database**: A running PostgreSQL instance (no specific extensions required beyond standard installation). Google Sheets Access**: A publicly accessible or shared Google Sheet URL with structured data (e.g., financial records). Need a starting point? Use this Sample Google Sheet Template. n8n Instance**: A working n8n setup with access to the Google Drive and PostgreSQL nodes. Step-by-Step Instructions Add Your Google Sheets URL Open the "Google Drive Trigger" node. Replace the placeholder URL with your Google Sheet’s link. Verify the sheet name matches your data source. Configure PostgreSQL Update the "PostgreSQL" nodes with your database credentials (host, database, user, password). The workflow automatically creates and populates the table based on your data schema. Run the Workflow Execute the workflow manually to set up the database. Once initialized, use the AI agent by asking questions like: "How much did I sell last week?" "What were the total sales for Product X in February?" (Optional) Automate Updates Add a "Schedule Trigger" node to sync your Google Sheets data with PostgreSQL on a regular basis. How It Works Schema Detection**: The workflow analyzes your Google Sheets or CSV data to infer its structure and create an appropriate PostgreSQL table. AI-Powered Queries**: An optimized AI agent converts your natural language questions into precise SQL queries, ensuring accurate results. Efficient Retrieval**: By using PostgreSQL instead of vector-based methods, this template avoids common pitfalls like slow performance or inaccurate numerical outputs. Tips for Success Ensure your Google Sheet or CSV has consistent column headers for smooth schema detection. Test with simple questions first to verify the AI agent’s query generation. Check out the n8n Template Submission Guidelines for more best practices.

This workflow makes it easier to keep track of the stocks market and get an email with a summary of the daily highlights on what happened, key insights and trends Setup Guide Define the schedule (days, times, intervals). Replace sample stock data with your desired stock list (ticker, name, etc.) in JSON format. Split Out the fields to have a clean list of the stocks to monitor set keyword node Extracts the stock ticker from each item and sets it to the keyword property. Financial times scraper Triggers the Bright Data Datasets API to scrape financial data. Set the node as below Method: POST URL: https://api.brightdata.com/datasets/v3/trigger Query Parameters: dataset_id: Replace with your Bright Data dataset ID. include_errors: true type: discover_new discover_by: keyword Headers: Authorization: Bearer YOUR_BRIGHTDATA_API_KEY Replace with your Bright Data API key. Body: JSON, ={{ $('set keyword').all().map(item => item.json)}} Execute Once: Checked. Get progress node Checks the status of the Bright Data scraping job if complete, or running Setup: URL: https://api.brightdata.com/datasets/v3/progress/{{ $json.snapshot_id }} Headers: Authorization: Bearer YOUR_BRIGHTDATA_API_KEY Replace with your Bright Data API key. Get snapshot + data retrieves the scraped data from the Bright Data API. Pass the request as URL: https://api.brightdata.com/datasets/v3/snapshot/{{ $json.snapshot_id }} Query Parameters: format: json Headers: Authorization: Bearer YOUR_BRIGHTDATA_API_KEY Replace with your Bright Data API key. Aggregate. Combines the data from each stock item into a single object Update to sheet and add all items to This sheet. Make a copy before you can map the data create summary node generates a summary of the scraped stock data using the Google Gemini AI model and notifies you via Gmail. Setup: Prompt Type: define Text: Customize the prompt to define the AI's role, input format, tasks, output format (HTML email), and constraints. Google Sheets. Appends the scraped data to a Google Sheet. This should be set to automap so as to adjust to the results found in the request Important Notes: Remember to replace placeholder values (API keys, dataset IDs, email addresses, Google Sheet IDs) with your actual values. Review and customize the AI prompt for the "create summary" node to achieve the desired email summary output. Consider adding error handling for a more robust workflow. Monitor API usage to avoid rate limits.

What problem does it solve? Manual candidate screening is time-consuming and inconsistent. This workflow automates initial interviews, providing 24/7 availability, consistent questioning, and objective assessments for every candidate. Who is it for? HR teams handling high-volume recruiting Small businesses without dedicated recruiters Companies scaling their hiring process Remote-first organizations needing asynchronous screening What this workflow does Creates AI interviewers from job descriptions that conduct natural conversations with candidates via BeyondPresence Agents. Automatically analyzes interviews and saves structured assessments to Google Sheets. Setup Copy template sheet: BeyondPresence HR Interview System Template Add credentials: BeyondPresence API Key OpenAI API Google Sheets Configure webhook in BeyondPresence dashboard: https://[your-n8n-instance]/webhook/beyondpresence-hr-interviews Paste job description and run setup Share generated link with candidates How it works Agent Creation: Converts job description into conversational AI interviewer Interview Conduct: Candidates chat naturally with AI via shared link Webhook Trigger: Completed interviews sent to n8n AI Analysis: OpenAI evaluates responses against job requirements Results Storage: Assessments saved to Google Sheets with scores and recommendations Resources Google Sheets Template BeyondPresence Documentation Webhook Setup Guide Example Use Case Tech startup screens 200 applicants for engineering role. Creates AI interviewer in 2 minutes, sends link to all candidates. Receives structured assessments within 24 hours, identifying top 20 candidates for human interviews. Reduces initial screening time from 2 weeks to 2 days.