Workflow Templates

Goodreads Quote Extraction with Bright Data and Gemini This workflow demonstrates how to fetch data specifically from Goodreads web pages using Bright Data and then extract specific information (quotes) from that data using a Google Gemini AI model. How it works The workflow is triggered manually. It sends a request to a Bright Data collector to scrape data from a predefined list of Goodreads URLs. The collected text data from Goodreads is then passed to a Google Gemini AI node. The AI node processes the text and extracts quotes based on a specified JSON schema output format. Set up steps Setting up this workflow should take only a few minutes. You will need a Bright Data API key to configure the 'Header Auth' credential. You will need a Google Gemini API key to configure the 'Google Gemini(PaLM) Api account' credential. Ensure the correct Bright Data collector ID is set in the 'Perform Bright Data Web Request' node URL. Make sure the full list of target Goodreads URLs is correctly added to the 'Perform Bright Data Web Request' node's body. Link your created credentials to the respective nodes ('Perform Bright Data Web Request' and 'Quotes Extractor'). Keep detailed descriptions for specific node configurations in sticky notes inside your workflow canvas.

This n8n workflow template checks for new major releases (tagged with .0) of the n8n project using its official GitHub releases feed. It runs multiple times a day and sends notifications via email and Telegram if a new release is found. > ⚠️ Note: You must *activate the workflow* to start receiving release notifications. 🚀 What It Does Monitors the n8n GitHub releases feed Detects major versions (e.g., 1.0.0, 2.0.0) Sends alert messages via Telegram and email (SES) when a release is published ⏰ Scheduling Details The Cron node checks for new releases three times per day: 10:00, 14:00, and 18:00 server time. 🛠️ Step-by-Step Setup Configure Telegram Bot Connect your Telegram bot and specify the chat ID where you want to receive notifications. Set up AWS SES Credentials Use a verified sender email and set up AWS SES credentials in your n8n instance. Activate the Workflow Enable the workflow in your instance to start receiving notifications. Customize Notification Messages (Optional) You can modify the email subject, Telegram format, or filter logic. 🧠 How It Works: Workflow Overview Cron Trigger Runs the workflow at 10:00, 14:00, and 18:00 daily. Read RSS Feed Pulls data from https://github.com/n8n-io/n8n/releases.atom. Filter by Current Day Filters the feed to match: Releases published in the last 4 hours Titles starting with n8n@ and ending with .0 Condition Check Uses a regex to check if the filter result contains any release data. Notifications If a new major release is found, sends: Telegram message to a specified chat Email via AWS SES with release info 📨 Final Output You'll receive a Telegram message and email when a new major n8n version is released. 🔐 Credentials Used Telegram API** – For sending chat notifications AWS SES** – To send email alerts ✨ Customization Tips Change Notification Channels**: Add Slack, Discord, or other preferred channels. Adjust Cron Schedule**: Modify the Cron node to fit your check frequency. Modify Filters**: Detect patch or beta versions by changing the .0 condition. Send Release Notes**: Extend the feed parsing to include release content. ❓Questions? Template created by Miquel Colomer and n8nhackers.com. Need help customizing or deploying? Contact us for consulting and support.

Automating LinkedIn Company Data Extraction Use Case This automation extracts detailed company insights from a LinkedIn company page, including identity, scale, classification, and funding data. Ideal for investors, sales teams, and market researchers. What This Automation Does This automation accepts the following inputs: Company's LinkedIn URL**: The public LinkedIn page URL of the company. Airtop Profile (connected to LinkedIn)**: Your Airtop Profile authenticated on LinkedIn. It then extracts and returns structured data with: 1. Company Identity Full name Tagline Headquarters location (city, state, country) About section Website 2. Company Scale Current employee count Employee size bracket: [0-9], [10-150], [150+] 3. Business Classification Is the company an automation agency? (true/false) AI implementation level: Low / Medium / High Technical sophistication: Basic / Intermediate / Advanced / Expert 4. Funding Profile Most recent funding round Total amount raised Key investors Last funding update date How It Works Creates an Airtop session using the provided profile. Navigates to the company LinkedIn page. Executes an Airtop query to extract data. Outputs the result in a standardized JSON schema. Setup Requirements Airtop API Key A LinkedIn-authenticated Airtop Profile Next Steps Feed into CRM**: Enrich your accounts with detailed LinkedIn data. Prioritize Leads**: Use classification and funding data to prioritize outreach. Combine with People Data**: Integrate with individual-level enrichment for full context. Read more about how to extract company data from Linkedin with Airtop and n8n

Description Turn your Telegram bot into a powerful OCR (Optical Character Recognition) tool. This workflow allows you to send any image (like a screenshot, a photo of a document, or a picture of a sign) to your bot, and it will instantly extract and send back the text from that image. Powered by Google's advanced Gemini AI, this automation is perfect for quickly digitizing notes, saving important snippets, or avoiding manual typing. How it works This workflow performs a few high-level steps: It triggers when a new image is sent to your Telegram bot. It sends the image to the Google Gemini Vision API to be analyzed. It extracts the text found in the image. It sends the extracted text back to you as a message in Telegram. Set up steps Estimated set up time: Less than 5 minutes. The setup is straightforward. You only need to configure two credentials: Telegram Bot Credentials: To connect your bot. Google Gemini API Credentials: To use the OCR feature. You can get a free API key from Google AI Studio.

Ok google download "movie name" I develop this automation to improve my quality of life in handling torrents in my media-center. Goal Automate the search operations of a movie based on its name and trigger a download using your transmission-daemon. Setup Prerequisite Transmission daemon up and running and its authentication method N8N configured self-hosted or with the possibility to add npm package better with docker-compose.yaml Telegram bot credential [optional] Configuration Create a folder where your docker-compose.yaml belongs n8n_dir and proceed in installing the node package. cd ~/n8n_dir npm i torrent-search-api Configuring your docker-compose.yaml file this way. You must include all the dependencies of torrent-search-api. This will let you run the new torrent search node presented in this workflow. version: '3.3' services: n8n: container_name: n8n ports: '5678:5678' restart: always volumes: '~/n8n_dir/.n8n:/home/node/.n8n' '~/n8n_dir/node_modules/@tootallnate:/usr/local/lib/node_modules/@tootallnate' '~/n8n_dir/node_modules/accepts:/usr/local/lib/node_modules/accepts' '~/n8n_dir/node_modules/agent-base:/usr/local/lib/node_modules/agent-base' '~/n8n_dir/node_modules/ajv:/usr/local/lib/node_modules/ajv' '~/n8n_dir/node_modules/ansi-styles:/usr/local/lib/node_modules/ansi-styles' '~/n8n_dir/node_modules/asn1:/usr/local/lib/node_modules/asn1' '~/n8n_dir/node_modules/assert:/usr/local/lib/node_modules/assert' '~/n8n_dir/node_modules/assert-plus:/usr/local/lib/node_modules/assert-plus' '~/n8n_dir/node_modules/ast-types:/usr/local/lib/node_modules/ast-types' '~/n8n_dir/node_modules/asynckit:/usr/local/lib/node_modules/asynckit' '~/n8n_dir/node_modules/aws-sign2:/usr/local/lib/node_modules/aws-sign2' '~/n8n_dir/node_modules/aws4:/usr/local/lib/node_modules/aws4' '~/n8n_dir/node_modules/base64-js:/usr/local/lib/node_modules/base64-js' '~/n8n_dir/node_modules/batch:/usr/local/lib/node_modules/batch' '~/n8n_dir/node_modules/bcrypt-pbkdf:/usr/local/lib/node_modules/bcrypt-pbkdf' '~/n8n_dir/node_modules/bluebird:/usr/local/lib/node_modules/bluebird' '~/n8n_dir/node_modules/boolbase:/usr/local/lib/node_modules/boolbase' '~/n8n_dir/node_modules/brotli:/usr/local/lib/node_modules/brotli' '~/n8n_dir/node_modules/bytes:/usr/local/lib/node_modules/bytes' '~/n8n_dir/node_modules/caseless:/usr/local/lib/node_modules/caseless' '~/n8n_dir/node_modules/chalk:/usr/local/lib/node_modules/chalk' '~/n8n_dir/node_modules/cheerio:/usr/local/lib/node_modules/cheerio' '~/n8n_dir/node_modules/cloudscraper:/usr/local/lib/node_modules/cloudscraper' '~/n8n_dir/node_modules/co:/usr/local/lib/node_modules/co' '~/n8n_dir/node_modules/color-convert:/usr/local/lib/node_modules/color-convert' '~/n8n_dir/node_modules/color-name:/usr/local/lib/node_modules/color-name' '~/n8n_dir/node_modules/combined-stream:/usr/local/lib/node_modules/combined-stream' '~/n8n_dir/node_modules/component-emitter:/usr/local/lib/node_modules/component-emitter' '~/n8n_dir/node_modules/content-disposition:/usr/local/lib/node_modules/content-disposition' '~/n8n_dir/node_modules/content-type:/usr/local/lib/node_modules/content-type' '~/n8n_dir/node_modules/cookiejar:/usr/local/lib/node_modules/cookiejar' '~/n8n_dir/node_modules/core-util-is:/usr/local/lib/node_modules/core-util-is' '~/n8n_dir/node_modules/css-select:/usr/local/lib/node_modules/css-select' '~/n8n_dir/node_modules/css-what:/usr/local/lib/node_modules/css-what' '~/n8n_dir/node_modules/dashdash:/usr/local/lib/node_modules/dashdash' '~/n8n_dir/node_modules/data-uri-to-buffer:/usr/local/lib/node_modules/data-uri-to-buffer' '~/n8n_dir/node_modules/debug:/usr/local/lib/node_modules/debug' '~/n8n_dir/node_modules/deep-is:/usr/local/lib/node_modules/deep-is' '~/n8n_dir/node_modules/degenerator:/usr/local/lib/node_modules/degenerator' '~/n8n_dir/node_modules/delayed-stream:/usr/local/lib/node_modules/delayed-stream' '~/n8n_dir/node_modules/delegates:/usr/local/lib/node_modules/delegates' '~/n8n_dir/node_modules/depd:/usr/local/lib/node_modules/depd' '~/n8n_dir/node_modules/destroy:/usr/local/lib/node_modules/destroy' '~/n8n_dir/node_modules/dom-serializer:/usr/local/lib/node_modules/dom-serializer' '~/n8n_dir/node_modules/domelementtype:/usr/local/lib/node_modules/domelementtype' '~/n8n_dir/node_modules/domhandler:/usr/local/lib/node_modules/domhandler' '~/n8n_dir/node_modules/domutils:/usr/local/lib/node_modules/domutils' '~/n8n_dir/node_modules/ecc-jsbn:/usr/local/lib/node_modules/ecc-jsbn' '~/n8n_dir/node_modules/ee-first:/usr/local/lib/node_modules/ee-first' '~/n8n_dir/node_modules/emitter-component:/usr/local/lib/node_modules/emitter-component' '~/n8n_dir/node_modules/enqueue:/usr/local/lib/node_modules/enqueue' '~/n8n_dir/node_modules/enstore:/usr/local/lib/node_modules/enstore' '~/n8n_dir/node_modules/entities:/usr/local/lib/node_modules/entities' '~/n8n_dir/node_modules/error-inject:/usr/local/lib/node_modules/error-inject' '~/n8n_dir/node_modules/escape-html:/usr/local/lib/node_modules/escape-html' '~/n8n_dir/node_modules/escape-string-regexp:/usr/local/lib/node_modules/escape-string-regexp' '~/n8n_dir/node_modules/escodegen:/usr/local/lib/node_modules/escodegen' '~/n8n_dir/node_modules/esprima:/usr/local/lib/node_modules/esprima' '~/n8n_dir/node_modules/estraverse:/usr/local/lib/node_modules/estraverse' '~/n8n_dir/node_modules/esutils:/usr/local/lib/node_modules/esutils' '~/n8n_dir/node_modules/extend:/usr/local/lib/node_modules/extend' '~/n8n_dir/node_modules/extsprintf:/usr/local/lib/node_modules/extsprintf' '~/n8n_dir/node_modules/fast-deep-equal:/usr/local/lib/node_modules/fast-deep-equal' '~/n8n_dir/node_modules/fast-json-stable-stringify:/usr/local/lib/node_modules/fast-json-stable-stringify' '~/n8n_dir/node_modules/fast-levenshtein:/usr/local/lib/node_modules/fast-levenshtein' '~/n8n_dir/node_modules/file-uri-to-path:/usr/local/lib/node_modules/file-uri-to-path' '~/n8n_dir/node_modules/forever-agent:/usr/local/lib/node_modules/forever-agent' '~/n8n_dir/node_modules/form-data:/usr/local/lib/node_modules/form-data' '~/n8n_dir/node_modules/format-parser:/usr/local/lib/node_modules/format-parser' '~/n8n_dir/node_modules/formidable:/usr/local/lib/node_modules/formidable' '~/n8n_dir/node_modules/fs-extra:/usr/local/lib/node_modules/fs-extra' '~/n8n_dir/node_modules/ftp:/usr/local/lib/node_modules/ftp' '~/n8n_dir/node_modules/get-uri:/usr/local/lib/node_modules/get-uri' '~/n8n_dir/node_modules/getpass:/usr/local/lib/node_modules/getpass' '~/n8n_dir/node_modules/graceful-fs:/usr/local/lib/node_modules/graceful-fs' '~/n8n_dir/node_modules/har-schema:/usr/local/lib/node_modules/har-schema' '~/n8n_dir/node_modules/har-validator:/usr/local/lib/node_modules/har-validator' '~/n8n_dir/node_modules/has-flag:/usr/local/lib/node_modules/has-flag' '~/n8n_dir/node_modules/htmlparser2:/usr/local/lib/node_modules/htmlparser2' '~/n8n_dir/node_modules/http-context:/usr/local/lib/node_modules/http-context' '~/n8n_dir/node_modules/http-errors:/usr/local/lib/node_modules/http-errors' '~/n8n_dir/node_modules/http-incoming:/usr/local/lib/node_modules/http-incoming' '~/n8n_dir/node_modules/http-outgoing:/usr/local/lib/node_modules/http-outgoing' '~/n8n_dir/node_modules/http-proxy-agent:/usr/local/lib/node_modules/http-proxy-agent' '~/n8n_dir/node_modules/http-signature:/usr/local/lib/node_modules/http-signature' '~/n8n_dir/node_modules/https-proxy-agent:/usr/local/lib/node_modules/https-proxy-agent' '~/n8n_dir/node_modules/iconv-lite:/usr/local/lib/node_modules/iconv-lite' '~/n8n_dir/node_modules/inherits:/usr/local/lib/node_modules/inherits' '~/n8n_dir/node_modules/ip:/usr/local/lib/node_modules/ip' '~/n8n_dir/node_modules/is-browser:/usr/local/lib/node_modules/is-browser' '~/n8n_dir/node_modules/is-typedarray:/usr/local/lib/node_modules/is-typedarray' '~/n8n_dir/node_modules/is-url:/usr/local/lib/node_modules/is-url' '~/n8n_dir/node_modules/isarray:/usr/local/lib/node_modules/isarray' '~/n8n_dir/node_modules/isobject:/usr/local/lib/node_modules/isobject' '~/n8n_dir/node_modules/isstream:/usr/local/lib/node_modules/isstream' '~/n8n_dir/node_modules/jsbn:/usr/local/lib/node_modules/jsbn' '~/n8n_dir/node_modules/json-schema:/usr/local/lib/node_modules/json-schema' '~/n8n_dir/node_modules/json-schema-traverse:/usr/local/lib/node_modules/json-schema-traverse' '~/n8n_dir/node_modules/json-stringify-safe:/usr/local/lib/node_modules/json-stringify-safe' '~/n8n_dir/node_modules/jsonfile:/usr/local/lib/node_modules/jsonfile' '~/n8n_dir/node_modules/jsprim:/usr/local/lib/node_modules/jsprim' '~/n8n_dir/node_modules/koa-is-json:/usr/local/lib/node_modules/koa-is-json' '~/n8n_dir/node_modules/levn:/usr/local/lib/node_modules/levn' '~/n8n_dir/node_modules/lodash:/usr/local/lib/node_modules/lodash' '~/n8n_dir/node_modules/lodash.assignin:/usr/local/lib/node_modules/lodash.assignin' '~/n8n_dir/node_modules/lodash.bind:/usr/local/lib/node_modules/lodash.bind' '~/n8n_dir/node_modules/lodash.defaults:/usr/local/lib/node_modules/lodash.defaults' '~/n8n_dir/node_modules/lodash.filter:/usr/local/lib/node_modules/lodash.filter' '~/n8n_dir/node_modules/lodash.flatten:/usr/local/lib/node_modules/lodash.flatten' '~/n8n_dir/node_modules/lodash.foreach:/usr/local/lib/node_modules/lodash.foreach' '~/n8n_dir/node_modules/lodash.map:/usr/local/lib/node_modules/lodash.map' '~/n8n_dir/node_modules/lodash.merge:/usr/local/lib/node_modules/lodash.merge' '~/n8n_dir/node_modules/lodash.pick:/usr/local/lib/node_modules/lodash.pick' '~/n8n_dir/node_modules/lodash.reduce:/usr/local/lib/node_modules/lodash.reduce' '~/n8n_dir/node_modules/lodash.reject:/usr/local/lib/node_modules/lodash.reject' '~/n8n_dir/node_modules/lodash.some:/usr/local/lib/node_modules/lodash.some' '~/n8n_dir/node_modules/lru-cache:/usr/local/lib/node_modules/lru-cache' '~/n8n_dir/node_modules/media-typer:/usr/local/lib/node_modules/media-typer' '~/n8n_dir/node_modules/methods:/usr/local/lib/node_modules/methods' '~/n8n_dir/node_modules/mime:/usr/local/lib/node_modules/mime' '~/n8n_dir/node_modules/mime-db:/usr/local/lib/node_modules/mime-db' '~/n8n_dir/node_modules/mime-types:/usr/local/lib/node_modules/mime-types' '~/n8n_dir/node_modules/monotonic-timestamp:/usr/local/lib/node_modules/monotonic-timestamp' '~/n8n_dir/node_modules/ms:/usr/local/lib/node_modules/ms' '~/n8n_dir/node_modules/negotiator:/usr/local/lib/node_modules/negotiator' '~/n8n_dir/node_modules/netmask:/usr/local/lib/node_modules/netmask' '~/n8n_dir/node_modules/nth-check:/usr/local/lib/node_modules/nth-check' '~/n8n_dir/node_modules/oauth-sign:/usr/local/lib/node_modules/oauth-sign' '~/n8n_dir/node_modules/object-assign:/usr/local/lib/node_modules/object-assign' '~/n8n_dir/node_modules/on-finished:/usr/local/lib/node_modules/on-finished' '~/n8n_dir/node_modules/optionator:/usr/local/lib/node_modules/optionator' '~/n8n_dir/node_modules/pac-proxy-agent:/usr/local/lib/node_modules/pac-proxy-agent' '~/n8n_dir/node_modules/pac-resolver:/usr/local/lib/node_modules/pac-resolver' '~/n8n_dir/node_modules/parseurl:/usr/local/lib/node_modules/parseurl' '~/n8n_dir/node_modules/performance-now:/usr/local/lib/node_modules/performance-now' '~/n8n_dir/node_modules/prelude-ls:/usr/local/lib/node_modules/prelude-ls' '~/n8n_dir/node_modules/process-nextick-args:/usr/local/lib/node_modules/process-nextick-args' '~/n8n_dir/node_modules/promise-polyfill:/usr/local/lib/node_modules/promise-polyfill' '~/n8n_dir/node_modules/proxy-agent:/usr/local/lib/node_modules/proxy-agent' '~/n8n_dir/node_modules/proxy-from-env:/usr/local/lib/node_modules/proxy-from-env' '~/n8n_dir/node_modules/psl:/usr/local/lib/node_modules/psl' '~/n8n_dir/node_modules/punycode:/usr/local/lib/node_modules/punycode' '~/n8n_dir/node_modules/qs:/usr/local/lib/node_modules/qs' '~/n8n_dir/node_modules/querystring:/usr/local/lib/node_modules/querystring' '~/n8n_dir/node_modules/raw-body:/usr/local/lib/node_modules/raw-body' '~/n8n_dir/node_modules/readable-stream:/usr/local/lib/node_modules/readable-stream' '~/n8n_dir/node_modules/request:/usr/local/lib/node_modules/request' '~/n8n_dir/node_modules/request-promise:/usr/local/lib/node_modules/request-promise' '~/n8n_dir/node_modules/request-promise-core:/usr/local/lib/node_modules/request-promise-core' '~/n8n_dir/node_modules/request-x-ray:/usr/local/lib/node_modules/request-x-ray' '~/n8n_dir/node_modules/safe-buffer:/usr/local/lib/node_modules/safe-buffer' '~/n8n_dir/node_modules/safer-buffer:/usr/local/lib/node_modules/safer-buffer' '~/n8n_dir/node_modules/selectn:/usr/local/lib/node_modules/selectn' '~/n8n_dir/node_modules/setprototypeof:/usr/local/lib/node_modules/setprototypeof' '~/n8n_dir/node_modules/sliced:/usr/local/lib/node_modules/sliced' '~/n8n_dir/node_modules/smart-buffer:/usr/local/lib/node_modules/smart-buffer' '~/n8n_dir/node_modules/socks:/usr/local/lib/node_modules/socks' '~/n8n_dir/node_modules/socks-proxy-agent:/usr/local/lib/node_modules/socks-proxy-agent' '~/n8n_dir/node_modules/source-map:/usr/local/lib/node_modules/source-map' '~/n8n_dir/node_modules/sshpk:/usr/local/lib/node_modules/sshpk' '~/n8n_dir/node_modules/statuses:/usr/local/lib/node_modules/statuses' '~/n8n_dir/node_modules/stealthy-require:/usr/local/lib/node_modules/stealthy-require' '~/n8n_dir/node_modules/stream-to-string:/usr/local/lib/node_modules/stream-to-string' '~/n8n_dir/node_modules/string-format:/usr/local/lib/node_modules/string-format' '~/n8n_dir/node_modules/string_decoder:/usr/local/lib/node_modules/string_decoder' '~/n8n_dir/node_modules/superagent:/usr/local/lib/node_modules/superagent' '~/n8n_dir/node_modules/superagent-proxy:/usr/local/lib/node_modules/superagent-proxy' '~/n8n_dir/node_modules/supports-color:/usr/local/lib/node_modules/supports-color' '~/n8n_dir/node_modules/toidentifier:/usr/local/lib/node_modules/toidentifier' '~/n8n_dir/node_modules/torrent-search-api:/usr/local/lib/node_modules/torrent-search-api' '~/n8n_dir/node_modules/tough-cookie:/usr/local/lib/node_modules/tough-cookie' '~/n8n_dir/node_modules/tslib:/usr/local/lib/node_modules/tslib' '~/n8n_dir/node_modules/tunnel-agent:/usr/local/lib/node_modules/tunnel-agent' '~/n8n_dir/node_modules/tweetnacl:/usr/local/lib/node_modules/tweetnacl' '~/n8n_dir/node_modules/type-check:/usr/local/lib/node_modules/type-check' '~/n8n_dir/node_modules/type-is:/usr/local/lib/node_modules/type-is' '~/n8n_dir/node_modules/universalify:/usr/local/lib/node_modules/universalify' '~/n8n_dir/node_modules/unpipe:/usr/local/lib/node_modules/unpipe' '~/n8n_dir/node_modules/uri-js:/usr/local/lib/node_modules/uri-js' '~/n8n_dir/node_modules/util:/usr/local/lib/node_modules/util' '~/n8n_dir/node_modules/util-deprecate:/usr/local/lib/node_modules/util-deprecate' '~/n8n_dir/node_modules/uuid:/usr/local/lib/node_modules/uuid' '~/n8n_dir/node_modules/vary:/usr/local/lib/node_modules/vary' '~/n8n_dir/node_modules/verror:/usr/local/lib/node_modules/verror' '~/n8n_dir/node_modules/word-wrap:/usr/local/lib/node_modules/word-wrap' '~/n8n_dir/node_modules/wrap-fn:/usr/local/lib/node_modules/wrap-fn' '~/n8n_dir/node_modules/x-ray:/usr/local/lib/node_modules/x-ray' '~/n8n_dir/node_modules/x-ray-crawler:/usr/local/lib/node_modules/x-ray-crawler' '~/n8n_dir/node_modules/x-ray-parse:/usr/local/lib/node_modules/x-ray-parse' '~/n8n_dir/node_modules/x-ray-scraper:/usr/local/lib/node_modules/x-ray-scraper' '~/n8n_dir/node_modules/xregexp:/usr/local/lib/node_modules/xregexp' '~/n8n_dir/node_modules/yallist:/usr/local/lib/node_modules/yallist' '~/n8n_dir/node_modules/yieldly:/usr/local/lib/node_modules/yieldly' image: 'n8nio/n8n:latest-rpi' environment: N8N_BASIC_AUTH_ACTIVE=true N8N_BASIC_AUTH_USER=username N8N_BASIC_AUTH_PASSWORD=your_secret_n8n_password EXECUTIONS_DATA_PRUNE=true EXECUTIONS_DATA_MAX_AGE=120 EXECUTIONS_TIMEOUT=300 EXECUTIONS_TIMEOUT_MAX=500 GENERIC_TIMEZONE=Europe/Berlin NODE_FUNCTION_ALLOW_EXTERNAL=torrent-search-api Once configured this way run n8n and create a new workflow coping the one proposed. Configure workflow Transmission In order to send command to transmission you must validate the Basic Auth. To do so: open the Start download node and edit the Credentials. Perform the same operation choosing the new credentials also in node Start download new token. In this automation we call transmission twice due to a security protocol in transmission system that prevents single click commands to be triggered, performing the request twice bypasses this security mechanism. https://en.wikipedia.org/wiki/Cross-site_request_forgery We use the X-Transmission-Session-Id provided by the first request to authenticate the second request. Telegram In order to make the workflow work as expected you must create a telegram bot and configure the nodes (Torrent not found and Telegram1) to send your message once the workflow is complete. Here's an easy guide to follow https://docs.n8n.io/nodes/n8n-nodes-base.telegram/ In those nodes you also should configure the Chat ID, you may use your telegram username or use a bot to retrieve your id. You may chat with useridinfobot that sends you your id. Ok google automation Since right now we do not have a n8n client for mobile that can trigger automation using google assistant I decided to use an IFTTT automation to trigger the webhook. I connect my IFTTT account with google assistant and pick the trigger. Say a phrase with a text ingredient as in the picture below. And configure the trigger this way. scarica $ -> download $ or metti in download $ -> put in download $ or some other trigger you may want. Then configure your server to trigger the webhook of n8n. Conclusion In conclusion we provide a fully working automation that integrates in n8n a node library and provides an easy trigger to perform a complex operation. Security concern Giving the ability to trigger a download may be problematic for potential unwanted torrent malware download, so you may decide to authenticate the webhook request passing in the body another field with a shared token between the two endpoints. Moreover the torrent-search-api library and its dependencies have some vulnerability that you may want to avoid on your own media-center, this will hopefully be patched soon in a further release of the library. This is just an interesting proof of concept. Quality of the download You may want to introduce another block between torrent search and webhook trigger to search for a movie based on the words detected by google assistant, sometimes it misinterprets something and you may end up downloading potential copyrighted material. Please use this automation only for free and open source movies and music.

AI LinkedIn Outreach Automation with Apollo, OpenAI & PhantomBuster Categories:* Sales Automation Lead Generation AI Personalization This workflow creates a complete LinkedIn outreach automation system that generates targeted lead lists from Apollo using natural language, enriches profiles with AI-personalized icebreakers, and automatically sends connection requests through PhantomBuster. Built by someone who's made over $1 million with AI automation, this system demonstrates the real-world approach to building profitable automation workflows. Benefits* Natural Language Lead Targeting - Describe your ideal prospects in plain English and automatically generate Apollo search URLs AI-Powered Personalization - Creates custom icebreakers based on LinkedIn profile data, employment history, and professional background Complete Outreach Pipeline - From lead discovery to personalized connection requests, fully automated end-to-end Smart Data Management - Automatically tracks all prospects in Google Sheets with deduplication and status tracking Cost-Effective Scraping - Uses Apify to extract Apollo data without expensive subscription costs Scalable Architecture - Processes hundreds of leads while respecting LinkedIn's connection limits How It Works* Natural Language Lead Generation: Form input accepts audience descriptions in plain English AI converts descriptions into properly formatted Apollo search URLs Automatically includes location, company size, job titles, and keyword filters Apollo Data Extraction: Uses Apify actor to scrape targeted lead lists from Apollo Extracts LinkedIn URLs, email addresses, employment history, and profile data Processes 500+ leads per run with detailed professional information AI Personalization Engine: Analyzes LinkedIn profile data including job history and company information Generates personalized icebreakers using proven connection request templates Creates human-like messages that reference specific career details and achievements Google Sheets Integration: Automatically stores all lead data in organized spreadsheet format Tracks prospect information, contact details, and generated icebreakers Provides easy data management and campaign tracking PhantomBuster Automation: Connects to PhantomBuster API to trigger LinkedIn connection campaigns Sends personalized connection requests with custom icebreakers Respects LinkedIn's daily limits and mimics human behavior patterns Business Use Cases* Sales Teams - Automate prospecting for B2B outreach campaigns Agencies - Scale client acquisition through targeted LinkedIn outreach Recruiters - Find and connect with qualified candidates efficiently Entrepreneurs - Build professional networks in specific industries Business Development - Generate qualified leads for partnership opportunities Revenue Potential This system can replace expensive LinkedIn outreach tools that cost $200-500/month. Users typically see: 400% improvement in response rates through personalization 10x faster lead generation compared to manual prospecting Ability to process 500+ leads per hour vs. 10-20 manually Difficulty Level: Intermediate Estimated Build Time: 1-2 hours Monthly Operating Cost: ~$50 (Apollo + PhantomBuster + AI APIs) Watch My Complete 1-Hour Build* Want to see exactly how I built this system from scratch? I walk through the entire development process live, including all the debugging, API integrations, and real-world testing that goes into building profitable automation systems. 🎥 See My Live Build Process: "Build This Automated AI LinkedIn DM System in 1 Hour (N8N)" This comprehensive tutorial shows my actual development approach - including the detours, problem-solving, and iterative testing that real automation building involves. Required Google Sheets Setup* Create a Google Sheet with these exact column headers: Essential Lead Columns: id - Unique prospect identifier first_name - Contact's first name last_name - Contact's last name name - Full name linkedin_url - LinkedIn profile URL title - Current job title email_status - Email verification status photo_url - Profile photo URL icebreaker - AI-generated personalized message Setup Instructions: Create Google Sheet with these headers in row 1 Connect Google Sheets OAuth in n8n Update the document ID in the "Add to Google Sheet" node PhantomBuster will read from this sheet for automated outreach Set Up Steps* Apollo & Apify Configuration: Set up Apify account and obtain API credentials Configure Apollo scraper actor with proper parameters Test lead extraction with sample audience descriptions AI Personalization Setup: Configure OpenAI API for natural language processing and personalization Set up prompt templates for audience targeting and icebreaker generation Test personalization quality with sample LinkedIn profiles Google Sheets Integration: Create lead tracking spreadsheet with proper column structure Configure Google Sheets API credentials and permissions Set up data mapping for automatic lead storage PhantomBuster Connection: Set up PhantomBuster account and LinkedIn connection Configure LinkedIn auto-connect agent with custom message templates Connect API for automated campaign triggering Form and Workflow Setup: Configure form trigger for audience input collection Set up data flow between all components Add proper error handling and rate limiting Testing and Optimization: Start with small batches (5-10 connections daily) Monitor LinkedIn account health and response rates Optimize icebreaker templates based on performance data Important Compliance Notes* LinkedIn Limits: Respect 100 connection requests per week limit Account Safety: Use PhantomBuster's human-like behavior patterns Message Quality: Regularly update templates to avoid automation detection Response Management: Monitor and respond to replies within 24 hours Advanced Extensions* This system can be enhanced with: Multi-channel Outreach: Add email sequences for comprehensive campaigns A/B Testing: Test different icebreaker templates automatically CRM Integration: Connect to Salesforce, HubSpot, or other sales systems Response Tracking: Monitor reply rates and optimize messaging Explore My Channel* For more advanced automation systems that generate real business results, check out my YouTube channel where I share the exact strategies I've used to make over $1 million with AI automation.

This workflow automatically adds closed deals from Pipedrive as new customers into Stripe. Prerequisites Pipedrive account and Pipedrive credentials Stripe account and Stripe credentials How it works Pipedrive trigger node starts the workflow when a deal gets updated in Pipedrive. IF node checks that the current won time is not equal to the previuos one in the deal and continues the workflow if it's true. Pipedrive node extracts the organization's details to pass it further. HTTP Request node searches for the same organization's details within Stripe. If a customer doesn't exist within Stripe, Merge node passes a new customer details to Stripe. Stripe node creates a new customer.

AI Voice Calling Bot - OpenAI GPT-4o + ElevenLabs + Twilio Integration for Multilingual Appointment Booking & Service Orders Overview Transform your business with an intelligent voice calling bot that handles customer calls automatically in 25+ languages. This N8n workflow integrates OpenAI GPT-4o, ElevenLabs text-to-speech, and Twilio for seamless appointment scheduling, pizza orders, and service bookings. Key Features Multilingual Support**: Conversations in English, Spanish, French, German, Italian, Portuguese, Chinese, Japanese, Arabic, and 20+ more languages Natural AI Conversations**: GPT-4o powered responses with ElevenLabs realistic voice synthesis Multi-Service Handling**: Appointments, orders, and service requests with automatic logging Real-time Processing**: Instant speech-to-text and audio response generation Prerequisites N8n instance (self-hosted or cloud) Twilio account with phone number OpenAI API key (GPT-4o access) ElevenLabs API credentials Google Sheets access Cloud storage for audio files Setup Instructions Step 1: Configure Credentials Add API keys for OpenAI, ElevenLabs, Twilio, and Google Sheets in N8n credentials manager. Step 2: Prepare Data Storage Create Google Sheets for call logs and appointments with columns: timestamp, caller_id, speech_input, ai_response, language, call_sid. Step 3: Configure Twilio Set webhook URL to your N8n endpoint: https://your-n8n-instance.com/webhook/voice-webhook Step 4: Update Sheet IDs Replace placeholder Google Sheet IDs in workflow nodes with your actual sheet IDs. Customization Options Voice Settings**: Adjust ElevenLabs multilingual voice models and parameters AI Behavior**: Modify system prompts for specific business needs and languages Service Types**: Add custom service handling logic Business Hours**: Implement language-specific operating hours Monitoring Track call analytics, language preferences, conversion rates, and customer satisfaction across all supported languages through automated Google Sheets logging. Ready for production use with comprehensive error handling and scalability for global businesses.

This workflow automatically adds a note of the PR from GitHub to the Pipedrive contact if their GitHub email matches a Person in Pipedrive. Prerequisites Pipedrive account and Pipedrive credentials GitHub account and GitHub credentials How it works GitHub Trigger node activates the workflow when a GitHub user adds a PR. HTTP Request node gets the user's data and sends it further. Pipedrive node searches the same email that GitHub user has in Pipedrive. IF node checks whether a person with the same email exists in Pipedrive. In case there's such a person in Pipedrive, the Pipedrive node creates a note within the person's profile.

Streamline your accounting by automatically creating QuickBooks Online customers and sales receipts whenever a successful Stripe payment is processed. Ideal for businesses looking to reduce manual data entry and improve accounting efficiency. How it works Trigger: The workflow is triggered when a new successful payment intent event is received from Stripe. Retrieve Customer Data: Fetches customer details from Stripe associated with the payment. Check QuickBooks Customer: Searches QuickBooks Online to see if the customer already exists using their email address. Create or Use Existing Customer: If the customer doesn't exist in QuickBooks, they are created; otherwise, the existing customer is used. Generate Sales Receipt: A sales receipt is created in QuickBooks Online with payment details, including item descriptions, amounts, and currency. Set up steps Connect Accounts: Authenticate both your QuickBooks Online and Stripe accounts in n8n. Webhook Setup: Configure the Stripe webhook to send payment_intent.succeeded events to this workflow. Test the Workflow: Trigger a test payment in Stripe to validate the integration. Customize Details: Adjust item descriptions or other fields in the QuickBooks sales receipt JSON body as needed. This workflow requires basic familiarity with n8n, but setup can be completed in under 15 minutes for most users.

🎤 Audio-to-Insights: Auto Meeting Summarizer Transform your meeting recordings into actionable insights automatically. This powerful n8n workflow monitors your Google Drive for new audio files, transcribes them using OpenAI's Whisper, generates intelligent summaries with ChatGPT, and logs everything in Google Sheets - all without lifting a finger. 🔄 How It Works This workflow operates as a seamless 6-step automation pipeline: Step 1: Smart Detection The workflow continuously monitors a designated Google Drive folder (polls every minute) for newly uploaded audio files. Step 2: Secure Download When a new audio file is detected, the system automatically downloads it from Google Drive for processing. Step 3: AI Transcription OpenAI's Whisper technology converts your audio recording into accurate text transcription, supporting multiple audio formats. Step 4: Intelligent Summarization ChatGPT processes the transcript using a specialized prompt that extracts: Key discussion points and decisions Action items with assigned persons and deadlines Priority levels and follow-up tasks Clean, professional formatting Step 5: Timestamp Generation The system automatically adds the current date and formats it consistently for tracking purposes. Step 6: Automated Logging The final summary is appended to your Google Sheets document with the date, creating a searchable archive of all meeting insights. ⚙️ Setup Steps Prerequisites Before setting up the workflow, ensure you have: Active Google Drive account OpenAI API key with credits Google Sheets access n8n instance (cloud or self-hosted) Configuration Steps 1. Credential Setup Google Drive OAuth2**: Required for folder monitoring and file downloads OpenAI API Key**: Needed for both transcription (Whisper) and summarization (ChatGPT) Google Sheets OAuth2**: Essential for writing summaries to your spreadsheet 2. Google Drive Configuration Create a dedicated folder in Google Drive for meeting recordings Copy the folder ID from the URL (the long string after /folders/) Update the folderToWatch parameter in the workflow 3. Google Sheets Preparation Create a new Google Sheet or use an existing one Ensure it has columns: Date and Meeting Summary Copy the spreadsheet ID from the URL Update the documentId parameter in the workflow 4. Audio Requirements Supported Formats**: MP3, WAV, M4A, MP4 Recommended Size**: Under 100MB for optimal processing Language**: Optimized for English (customizable for other languages) Quality**: Clear audio produces better transcriptions 5. Workflow Activation Import the workflow JSON into your n8n instance Configure all credential connections Test with a sample audio file Activate the workflow trigger 🚀 Use Cases Project Management Team Standup Summaries**: Convert daily standups into actionable task lists Sprint Retrospectives**: Extract improvement points and action items Stakeholder Updates**: Generate concise reports for leadership Sales & Customer Success Discovery Call Notes**: Capture prospect pain points and requirements Demo Follow-ups**: Track questions, objections, and next steps Customer Check-ins**: Monitor satisfaction and expansion opportunities Consulting & Professional Services Client Strategy Sessions**: Document recommendations and implementation plans Requirements Gathering**: Organize complex project specifications Progress Reviews**: Track deliverables and milestone achievements HR & Training Interview Debriefs**: Standardize candidate evaluation notes Training Sessions**: Create searchable knowledge bases Performance Reviews**: Document development plans and goals Research & Development Brainstorming Sessions**: Capture innovative ideas and concepts Technical Reviews**: Log decisions and architectural choices User Research**: Organize feedback and insights systematically 💡 Advanced Customization Options Enhanced Summarization Modify the ChatGPT prompt to focus on specific elements: Add speaker identification for multi-person meetings Include sentiment analysis for customer calls Generate department-specific summaries (technical, sales, legal) Extract financial figures and metrics automatically Integration Expansions Slack Integration**: Auto-post summaries to relevant channels Email Notifications**: Send summaries to meeting participants CRM Updates**: Push action items directly to Salesforce/HubSpot Calendar Integration**: Schedule follow-up meetings based on action items Quality Improvements Audio Preprocessing**: Add noise reduction before transcription Multi-language Support**: Configure for international teams Custom Templates**: Create industry-specific summary formats Approval Workflows**: Add human review before final storage 🛠️ Troubleshooting & Best Practices Common Issues Large File Processing**: Split recordings over 100MB into smaller segments Poor Audio Quality**: Use noise reduction tools before uploading API Rate Limits**: Implement delay nodes for high-volume usage Formatting Issues**: Adjust ChatGPT prompts for consistent output Optimization Tips Upload files in supported formats only Ensure stable internet connection for cloud processing Monitor OpenAI API usage and costs Regularly backup your Google Sheets data Test workflow changes with sample files first 📊 Expected Outputs Sample Summary Format: Meeting Summary - March 15, 2024 Key Discussion Points: Q1 budget review and allocation decisions New product launch timeline and milestones Team restructuring and role assignments Action Items: John: Finalize budget proposal by March 20th (High Priority) Sarah: Schedule product demo sessions for March 25th Team: Submit org chart feedback by March 18th Decisions Made: Approved additional marketing budget of $50K Delayed product launch to April 15th for quality assurance Promoted Lisa to Senior Developer role 📞 Questions & Support For any questions, customizations, or technical support regarding this workflow: 📧 Email Support Primary Contact**: Yaron@nofluff.online Response Time**: Within 24 hours on business days Best For**: Setup questions, customization requests, troubleshooting 🎥 Learning Resources YouTube Channel**: https://www.youtube.com/@YaronBeen/videos Step-by-step setup tutorials Advanced customization guides Workflow optimization tips 🔗 Professional Network LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Connect for ongoing support Share your workflow success stories Get updates on new automation ideas 💡 What to Include in Your Support Request Describe your specific use case Share any error messages or logs Mention your n8n version and setup type Include sample audio file characteristics (if relevant) Ready to transform your meeting chaos into organized insights? Download the workflow and start automating your meeting summaries today!

Extracting Comments from an X Post Use Case Engaging with conversations on X (formerly Twitter) is critical for brands and individuals monitoring sentiment, leads, or emerging trends. Manually collecting comments is time-consuming—this automation enables scalable extraction of comment data to inform your outreach or analysis. What This Automation Does This automation extracts comments from a specified X post, with the following input parameters: airtop_profile**: The name of your Airtop Profile connected to X. x_post_url**: The URL of the X post to extract comments from. max_number_of_comments**: The maximum number of comments to retrieve. How It Works Takes input via a form or another workflow. Normalizes the input values. Creates a new browser session using Airtop. Navigates to the provided X post. Uses a prompt to extract up to the specified number of comments, returning: Author name Author profile URL Comment text Setup Requirements Airtop API Key — free to generate. An Airtop Profile connected to X (requires one-time login). Next Steps Pair with X Monitoring**: Use this with the X monitoring automation to detect relevant posts and extract discussion context automatically. Feed into Analytics**: Combine with summarization or sentiment analysis tools to understand audience response at scale. Export for CRM/BI**: Pipe the structured comment data into your CRM or business intelligence stack for lead tracking or reporting. Read more about Extracting Comments from X Posts