Workflow Templates

Who is this template for? Growth teams, SDRs, recruiters, or anyone who routinely hunts for hard‑to‑find business emails and would rather spend time reaching out than guessing formats. What problem does this workflow solve? Manually piecing together email patterns, cross‑checking them in a verifier, and updating a tracking sheet is slow and error‑prone. This template automates the entire loop—research, guess, verify, and log—so you hit Start and watch rows fill up with ready‑to‑send addresses. What this workflow does Pull fresh leads – Grabs only the rows in your Google Sheet where Status = FALSE. Find the company pattern – Queries Serper.dev for snippets and feeds them to Gemini Flash (via OpenRouter) to spot the dominant email format. Build the address – Constructs a likely email for every first/last name. Verify in real time – Pings Prospeo by default (API) or lets you bulk‑clean in Sparkle.io. Write it back – Updates the sheet with pattern, email, confidence, verification status, and flips Status to TRUE. Loop until done – Runs batch‑by‑batch so you never hit API limits. 🆓 Work free‑tier magic (up to \~2,500 contacts/month) | Service | Free allowance | How this template uses it | | -------------- | ----------------------------- | ------------------------------------------------------------------------------------ | | Serper.dev | 2,500 searches/mo | Scrapes three public email snippets per domain to learn the pattern | | Sparkle.io | 10,000 bulk verifications/day | Manual upload‑download option—perfect to clean your first 2.5k emails at zero cost | | Prospeo | 75 API calls/mo | Built‑in if you prefer fully automated verification | Quick Sparkle workflow: Let the template generate emails. Export the “Email” column to CSV → upload to Sparkle.io. Download the results and paste the "verification\_status" back into the sheet (or add a small n8n import sub‑flow). Setup (5 minutes) Copy the Google Sheet linked in the sticky note and paste its ID into the Get Rows and Update Rows nodes. Add credentials for Google Sheets, Serper (X‑API‑KEY), OpenRouter, and optionally Prospeo. Hit Execute Workflow—that’s it. How to customise Prefer Sparkle for volume:** Skip the Prospeo node, export emails in one click, bulk‑verify in Sparkle, and re‑import results. Swap the search source:* Replace the *Get Email Pattern HTTP node with Bing, Brave, etc. Extend enrichment:* Add phone look‑ups or LinkedIn scrapers before the *Update Rows node. Auto‑run:** Replace the Manual Trigger with a Cron node so the sheet cleans itself every morning. Additional resources | Tool | Purpose | Link | | --------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------- | -------------------------------------------------------- | | Prospeo – API‑ready email verificationSpecial offer: 20 % free credits for the first 3 months on any plan using this link! | Real‑time, single‑call mailbox validation | prospeo.io | | Sparkle.io – high‑volume bulk verifier (manual upload) | Free daily quota of 10 000 verifications | app.sparkle.io/sign‑up | | OpenRouter – API gateway for Gemini Flash & other LLMs | One key unlocks multiple frontier models | openrouter.ai | | Serper.dev – Google Search API | 2 500 searches/month on the free tier | serper.dev | Add the relevant keys or signup details from these links, drop them into the matching n8n credentials, and you’re all set to enrich your first 2 500 contacts at zero cost. Happy building!

Who is this for? This workflow is perfect for Gmail users who want a tidy inbox without manual effort. It’s especially great for those overwhelmed by SPAM, social media updates, or promotional emails and want them automatically removed regularly. What problem is this workflow solving? Unwanted emails like SPAM, social notifications, and promotions can clutter your Gmail inbox, making it hard to focus on what matters. Manually deleting them is repetitive and time-consuming. This workflow automates the cleanup, keeping your inbox streamlined. What this workflow does Every 3 days, this workflow deletes emails from Gmail’s SPAM, Social, and Promotions categories. It uses n8n’s Gmail node to fetch these emails, merges them into a single list, splits out individual email IDs, and deletes each one. The scheduled process ensures consistent inbox maintenance. Setup Set up valid Gmail OAuth2 credentials in n8n. Import the "Clean My Mail" workflow into your n8n instance. Confirm the Gmail nodes target SPAM, CATEGORY_SOCIAL, and CATEGORY_PROMOTIONS labels. Adjust the "Run Every 3 Days (Trigger)" node’s schedule if needed. Activate the workflow to begin automated cleaning. How to customize this workflow to your needs Change the Gmail node labels to target other categories or custom labels. Adjust the schedule frequency in the trigger node. Add filters to spare specific emails from deletion. Extend functionality with nodes for archiving or notifications. made by:* khmuhtadin Need a custom? contact me on LinkedIn or Web

Video Introduction Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin Transform your messy inbox into a calm, organized command center - in minutes - using this ready-to-use n8n automation! Tired of your Gmail looking like this? With this template, you can have this instead: What does this automation do? AI-powered categorization:** Every new email is analyzed with OpenRouter AI and sorted into categories you define (like Orders, Support, Invoices, Urgent, etc.). Instant color-coded labels:** The workflow creates and applies Gmail labels with custom colors, so you can spot important messages at a glance. Supports Gmail’s Multiple Inboxes:** Display different categories in their own sections—see what matters most right away. Flexible and customizable:** You control the categories and definitions using a simple Google Sheet. How it works – Step by Step See the full setup & demo: Copy the Template Open the n8n workflow template and click Use for free. Log in (or sign up) for n8n Cloud for the quickest start. Customize Your Categories in Google Sheets Use the provided Google Sheets template linked in the workflow notes. Go to File → Make a copy to your own Drive. Edit the categories and their definitions for your business. Example: Add categories like “Existing Order Questions,” define each one to guide the AI, and copy your Google Sheet’s URL into the workflow config node. Connect AI with OpenRouter Go to OpenRouter.ai, log in, and generate a new API key. Paste your API key into the workflow where prompted. Test and Activate the Workflow Connect your Gmail account to n8n. Hit “Test Workflow”—watch as the AI processes your latest emails and applies labels automatically. Labels will appear instantly in Gmail, and any missing ones are created by the automation. Schedule Automatic Runs Switch workflow status to Active in n8n. Set the scheduler trigger—most people use hourly, but you can use crontab.guru for custom times (like only business hours). Tips for Best Results Color Code Your Labels:** In Gmail, you can assign colors to labels—set high-priority categories (like “Customer Complaints”) to a bright color to stand out. Upgrade Your Gmail View:** Enable Multiple Inboxes in Gmail’s settings and set up sections for your key categories. Example search queries: in:inbox label:customer-complaints OR label:urgent-emails in:inbox label:existing-order-questions in:inbox label:support-requests Why Use This? Get rid of inbox chaos for good - no more lost emails or missed deadlines Fully customize the system to your business with just a Google Sheet Works with zero coding - set up in 10-15 minutes Flexible: add auto-replies, draft suggestions, and more as you grow

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: text similarity, measured character-by-character. The workflow takes images of hand-written codes, extracts the code and compares it with the expected answer from the dataset. The images look like this: The workflow works as follows: 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 download the image and use AI to extract the code If we’re evaluating (i.e. the execution started from the evaluation trigger), we calculate the string distance metric We pass this information back to n8n as a metric

README Automating Video File Download from Sample.cat with Airtop.ai Use Case Automating file downloads from web pages is useful for scenarios like bulk media retrieval, dataset access, or recurring content backups. This workflow ensures a hands-free, consistent process for retrieving downloadable content. What This Automation Does This automation performs a reliable download of a video file from a specified webpage using the following steps: Initiates an Airtop browser session. Opens a specified URL containing downloadable media. Interacts with the page to click the download button. Waits for the file to be processed and made available. Retrieves metadata to confirm availability. Downloads the file. Terminates the browser session to clean up resources. How It Works Manual Trigger: Activated by user test. Session: Starts an Airtop browser session. Window: Navigates to https://sample.cat/en/webm. Interaction: Simulates a click on the download button for the video titled “SD 640x360 (Seawater, drone view video, 30 FPS)”. Wait: Pauses for 10 seconds to allow the file to be ready for download. Get File Data: Checks for downloadable files in the session. Download File: Retrieves the file using its ID. Terminate: Ends the browser session to free up resources. Setup Requirements Airtop API Key — required to authenticate API calls. Next Steps Enhance with Retry Logic**: Loop file availability check until status = available for more robust automation. Customize File Targets**: Dynamically pass URLs and button descriptors for multi-source downloads. Connect to Storage**: Pipe downloaded files to cloud storage or databases for archiving. Read more about automating file downloads from the web

📰 This AI-powered agent performs real-time sentiment analysis on Tesla (TSLA) news to support trading decisions. It aggregates headlines from 5 trusted sources and uses DeepSeek Chat to classify sentiment and generate structured summaries. This tool is a critical sub-agent in the broader Tesla Quant Trading AI Agent system. ⚠️ Not standalone — this agent is designed to be executed by the Tesla Quant Trading AI Agent. ⚙️ Requires: DeepSeek Chat API Key 🔌 Workflow Role This tool processes Tesla-related news and produces output like: { "sentiment": "bullish", "summary": "Tesla stock rallied today after strong delivery numbers and Cybertruck updates. Analysts remain optimistic.", "topHeadlines": [ "Tesla beats Q2 delivery forecast – Yahoo Finance", "Cybertruck ramps up in Texas – Electrek", "Berlin Gigafactory expands battery production – CleanTechnica" ] } Its output feeds directly into the master trading agent’s final trade report. 📰 News Sources Used This agent collects real-time headlines from: Google News (filtered by “Tesla” or “TSLA”) Yahoo Finance (TSLA-specific feed) Electrek (Tesla archive) CleanTechnica (Tesla sustainability news) TeslaNorth (app/product release updates) These five tools are always queried together to ensure market-wide signal coverage. 🤖 What the Agent Does Pulls headlines from all 5 Tesla-specific RSS feeds Uses DeepSeek Chat to: Analyze narrative tone (bullish / bearish / neutral) Identify macro/financial drivers Generate a 2–3 sentence summary Return top 3–5 headlines Outputs structured JSON for downstream use 🛠️ Setup Instructions 1. Install & Name Import this file and name it: Tesla_News_and_Sentiment_Analyst_Tool 2. Add DeepSeek API Credentials Go to: Credentials → Add New → DeepSeek API Save as: DeepSeek account 3. Internet Access Required Ensure RSS feeds can fetch live headlines Works best with a cloud-hosted n8n instance or tunnel-enabled local install 4. Must Be Triggered by Parent Triggered via Execute Workflow by the Tesla Quant Trading AI Agent Requires these inputs: message: optional query context sessionId: passed to maintain short-term memory across executions 🧠 Agent Architecture | Node Name | Function | | ---------------------------------- | ------------------------------------------------ | | DeepSeek Chat Model | Performs AI-based sentiment analysis | | Tesla News and Sentiment Analyst | Combines results, formats output in strict JSON | | Simple Memory | Stores session-level context (short-term memory) | | 5x RSS nodes | Aggregate Tesla news from trusted media outlets | 📌 Sticky Notes Included 🟢 Trigger from Parent Workflow – Executed only by main TSLA agent 🟠 News Feeds Overview – Lists and explains each of the 5 feeds 🧠 DeepSeek Chat Notes – Describes LLM behavior and parsing role 🔵 Short-Term Memory – Buffers sentiment context during user session 📘 Sentiment Analyst Agent – Summarizes key responsibilities 📎 Licensing & Attribution © 2025 Treasurium Capital Limited Company This architecture, workflow structure, and prompt design are licensed for educational and operational use only. Commercial resale or rebranding prohibited without authorization. 🔗 Creator: Don Jayamaha 🔗 Templates: https://n8n.io/creators/don-the-gem-dealer/ 🚀 Power your TSLA trading with AI-driven sentiment—built with DeepSeek Chat and 5 trusted news sources. This tool is required by the Tesla Quant Trading AI Agent.

This n8n template demonstrates one approach to customer authentication via chat agents. Unlike approaches where you have to authenticate users prior to interacting with the agent, this approach allows guest users to authenticate at any time during the session or not at all. Note about Security: this template is for illustration purposes only and requires much more work to be ready for production! How it works A conversational agent is used for this demonstration. The key component is the Redis node just after the chat trigger which acts as the session context. For guests, the session item is blank. for customers, the session item is populated with their customer profile. The agent is instructed to generate a unique login URL only for guests when appropriate or upon request. This login URL redirects the guest user to a simple n8n form also hosted in this template. The login URL has the current sessionID as a query parameter as the way to pass this data to the form. Once login is successful, the matching session item by sessionId is populated with the customer profile. The user can now return to the chat window. Back to the agent, now when the user sends their next message, the Redis node will pick up the session item and the customer profile associated with it. The system prompt is updated with this data which let's the agent know the user is now a customer. How to use You'll need to update the "auth URL" tool to match the URL of your n8n instance. Better yet, copy the production URL of your form from the trigger. Activate the workflow to turn on production mode which is required for this workflow. Implement the authentication logic in step 3. This could be sending the user and pass to a postgreSQL data for validation. Requirements OpenAI for LLM (feel free to swap to any provider) Redis for Cache/Sessions (again, feel free to swap this out for postgresql or other database) Customising this workflow Consider not populating the session item with the user data as it can become stale. Instead, just add the userId and instruct the agent to query using tools. Extend the Login URL idea by experimenting with signup URLs or single-use Urls.

How it Works Disclaimer: This template is for self-hosted n8n instances only. This workflow is designed for developers, data analysts, and automation enthusiasts seeking to automate personalized news collection and delivery. It seamlessly combines n8n, OpenAI (e.g., GPT-4.1), and Bright Data’s Model Context Protocol (MCP) to collect, extract, and email the latest global news headlines. On a schedule or via a manual trigger, the workflow prompts an AI agent to gather fresh news. The agent leverages context-aware memory and integrated MCP tools to conduct both search engine queries and direct web page scraping in real time, delivering more than just meta search results—it extracts actual on-page headlines and trusted links. Results are formatted and delivered automatically by email via your SMTP provider, requiring zero manual effort once configured. Who is this for? Developers, data engineers, or automation pros wanting an AI-powered, fully automated newsfeed Teams needing up-to-date news digests from trusted global sources Anyone self-hosting n8n who wishes to combine advanced LLMs with real-time web data Setup Steps Setup time: Approx. 15–30 minutes (n8n install, API configuration, node setup) Requirements: Self-hosted n8n instance OpenAI API key Bright Data MCP account credentials SMTP/email provider details Install the community MCP node (n8n-nodes-mcp) for n8n and set up Bright Data MCP access. Configure these nodes: Schedule Trigger: For automated delivery at your chosen interval. Edit Fields: To inject your AI news collection prompt. AI Agent: Connects to OpenAI and MCP, enabled with memory for context. OpenAI Chat Model: Connects via your OpenAI credentials. MCP Clients: Configure at least two—one for search (e.g. search_engine) and one for scraping (e.g. scrape_as_markdown). Send Email: Set up with recipient and SMTP information. Credentials must be entered into their respective nodes for successful execution. Customization Guidance Prompt Tweaks:** Refine your AI news prompt to target specific genres, regions, or sources, or broaden/narrow the coverage as needed. Tool Configuration:** Carefully define tool descriptions and parameters in MCP client nodes so the agent can pick the best tool for each step (e.g., only scrape real news sites). Delivery Settings:** Adjust email recipient(s) and SMTP details as needed. Workflow Enhancements:** Use sticky notes in n8n for extended documentation, alternate prompts, or troubleshooting tips. Run Frequency:** Set schedule as needed—from hourly to daily updates. Once configured, this workflow will automatically gather, extract, and email curated news headlines and links—no manual curation required!

This n8n template demonstrates how to calculate the evaluation metric "Summarization" which in this scenario, measures the LLM's accuracy and faithfulness in producing summaries which are based on an incoming Youtube transcript. The scoring approach is adapted from https://cloud.google.com/vertex-ai/generative-ai/docs/models/metrics-templates#pointwise_summarization_quality How it works This evaluation works best for an AI summarization workflows. For our scoring, we simple compare the generated response to the original transcript. A key factor is to look out information in the response which is not mentioned in the documents. A high score indicates LLM adherence and alignment whereas a low score could signal inadequate prompt or model hallucination. Requirements n8n version 1.94+ Check out this Google Sheet for a sample data https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing

This workflow will archive your Spotify Discover Weekly playlist to an archive playlist named "Discover Weekly Archive" which you must create yourself. If you want to change the name of the archive playlist, you can edit value2 in the "Find Archive Playlist" node. It is configured to run at 8am on Mondays, a conservative value in case you forgot to set your GENERIC_TIMEZONE environment variable (see the docs here). Special thanks to erin2722 for creating the Spotify node and harshil1712 for help with the workflow logic. To use this workflow, you'll need to: Create then select your credentials in each Spotify node Create the archive playlist yourself Optionally, you may choose to: Edit the archive playlist name in the "Find Archive Playlist" node Adjust the Cron node with an earlier time if you know GENERIC_TIMEZONE is set Setup an error workflow like this one to be notified if anything goes wrong

🎉 Do you want to master AI automation, so you can save time and build cool stuff? I’ve created a welcoming Skool community for non-technical yet resourceful learners. 👉🏻 Join the AI Atelier 👈🏻 Monitor Zalando product pricing and get notified if a Zalando product price falls under a limit you have defined. This n8n workflow lets you follow the evolution of the price of products you select. For each product, you define a minimal price. The workflow automatically scrapes the price for you on a daily basis. If the price falls under your minimal price settings, you receive a notification. This workflow is very easy to use. From a simple form, just paste the URL of the Zalando product you want to monitor and fill in the minimal price. Features Monitor Zalando Product price: follow the price evolution of your favorite Zalando products. Email notification: set a minimal price, if the product price falls below this limit, you get notified by email. Visual price evolution: get a graphical overview of the product pricing evolutions. Automated Daily check-up: this workflow automatically checks the price of your selected Zalando products on a daily basis. Set up Copy this workflow to your n8n interface. Create a new Google Spreadsheet, copy this template Setup your workflow with your Google credential, your email, and your copy of the Spreadsheet. Activate the Workflow and start pasting Zalando product URLs. I hope you will enjoy this workflow that is probably one of the simplest ways to monitor the pricing evolution of your favorite Zalando products. Feel free to contact me should you have any questions or suggestions. Created by the n8n.inja ✨ follow on X 📺 follow on YT

What does this template help with? Save the data of activities recorded and stored in Strava to a Google Sheets document. How it works: We have a Google Sheets spreadsheet where each row represents a Strava activity with the date, reference, distance, time, and elevation. Periodically, the workflow checks the latest activities in our Strava account to see if any are missing from the spreadsheet and adds them to the list. All fields must be properly formatted according to how they are stored in the Google Sheets spreadsheet. Set up instructions Complete the Set up credentials step when you first open the workflow. You'll need a Google Sheets and Strava account. In the 'activities' node, you must enter the name of the file and the sheet where you want to save the imported data. In the 'Strava' node, you must select the corresponding credential. You can adjust the format of dates, times, and distances according to your needs in the 'strava_last' node. The rest of the information is available at sherblog.es Template was created in n8n v1.72.1