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This workflow uses AI to analyze customer sentiment from product feedback. If the sentiment is negative, AI will determine whether offering a coupon could improve the customer experience. Upon completing the sentiment analysis, the workflow creates a personalized email templates. This solution streamlines the process of engaging with customers post-purchase, particularly when addressing dissatisfaction, and ensures that outreach is both personalized and automated. This workflow won the 1st place in our last AI contest. Note that to use this template, you need to be on n8n version 1.19.4 or later.

This n8n workflow is designed for security monitoring and incident response when suspicious login events are detected. It can be initiated either manually from within the n8n UI for testing or automatically triggered by a webhook when a new login event occurs. The workflow first extracts relevant data from the incoming webhook payload, including the IP address, user agent, timestamp, URL, and user ID. It then splits into three parallel processing paths. In the first path, it queries GreyNoise's Community API to retrieve information about the investigated IP address. Depending on the classification and trust level received from GreyNoise, the alert is given a High, Medium, or Low priority. This priority is assigned based on the best practices documentation from GreyNoise on how to apply their data to analysis. Once a priority is assigned, a message is sent to a Slack channel to notify users about the alert. The second path involves fetching geolocation data about the IP address using IP-API's Geolocation API and merging it with data from the UserParser node. This data is then combined with the data obtained from GreyNoise. In the third path, the UserParser node queries the Userparser IP address and user agent lookup API to obtain information about the user's IP and user agent. This data is merged with the IP-API data and GreyNoise data. The workflow then checks if the IP address is considered an unknown threat by examining both the noise and riot fields from GreyNoise. If it is considered an unknown threat, the workflow proceeds to retrieve the last 10 login records for the same user from a Postgres database. If there are any discrepancies in the login information, indicating a new location or device/browser, the user is informed via email. Potential issues when setting up this workflow include ensuring that credentials are correctly entered for GreyNoise and UserParser nodes, and addressing any discrepancies in the data sources that could lead to false positives or negatives in threat detection. Additionally, the usage of hardcoded API keys should be replaced with credentials for security and flexibility. Thorough testing and validation with sample data are crucial to ensure the workflow performs as expected and aligns with security incident response procedures.

Intro This workflow needs a user to authenticate by using an openid connect provider in order to call the webhook. If the user is not authenticated, it starts a login process by using an Authorization Code with PKCE https://datatracker.ietf.org/doc/html/rfc7636, a standard way to authenticate users with openid connect. Then, after the user logs in, the webhook is refreshed and gets the user's token from a cookie. With this token, all details about the user are requested through the userinfo endpoint on the identity provider. How to set up with Keycloak Keycloak Keycloak is an open source identity and access management solution. Feel free to get a demo realm at https://please-open.it or get your own Keycloak server up and running. After creating a realm, go to "Realm Settings" and click on "OpenID Endpoint Configuration" Retrieve authorization_endpoint, token_endpoint and userinfo_endpoint values. Set those variables in the "Set variables" node. In Keycloak, create a new client (name it as you want) Disable the client authentication, check only "standard flow" : At the third step, put the webhook url in "valid redirect URIs", fill "Web origins" with a "+". You're done, open the webhook and it asks you to authenticate. Usage User informations The userinfo node returns this structure about the user has logged in : [ { "sub":"73a6543f-f420-4fa6-9811-209e903c348b", "email_verified":true, "preferred_username": "mathieu.passenaud@please-open.it", "email": "mathieu.passenaud@please-open.it" } ] I can use those infos in my workflow for custom operations. APIs calls the "code" node returns me a cookie named "n8n-custom-auth" which is the access_token returned by the identity provider. This access_token can be used to call APIs connected to this identity provider (for example, we call userinfo API with this token). Example : asks a user to log in with his Google account then call an API (Gmail, drive...) with his own token. How it works We published a blog post about this flow, how it works and how you can use it : https://blog.please-open.it/n8n-openid-client/

This workflow monitor G2 reviews URLS. When a new review is published, it will: trigger a Slack notification record the review in Google Sheets To install it, you'll need: access to Slack, Google Sheets and ScrapingBee Full guide here: https://lempire.notion.site/Scrape-G2-reviews-with-n8n-3f46e280e8f24a68b3797f98d2fba433?pvs=4

Usage This workflow gets all the posts from your WordPress site and sorts them into a clear format before publishing them to medium. Step 1. Set up the HTTP node and set the URL of the source destination. This will be the URL of the blog you want to use. We shall be using https://mailsafi.com/blog for this. Step 2. Extract the URLs of all the blogs on the page This gets all the blog titles and their URLs. Its an easy way to sort ou which blogs to share and which not to share. Step 3. Split the entries for easy sorting or a cleaner view. Step 4. Set a new https node with all the blog URLs that we got from the previous steps. Step 5. Extract the contents of the blog Step 6. Add the medium node and then set the contents that you want to be shared out. Execute your workflow and you are good to go

Overview This N8N workflow facilitates advanced URL parsing and shortening, incorporating metadata extraction, OpenGraph tag handling, and integration with Switchy API for link management. It employs various nodes for URL processing, metadata extraction, and creation or updating of shortened links with enriched metadata. Features URL Metadata Extraction:** Parses URLs to extract metadata such as titles, descriptions, images, and favicons. OpenGraph API Integration:** Utilizes OpenGraph API for detailed metadata retrieval. Switchy API Integration:** Manages shortened links via the Switchy API. GitHub API Integration:** Uses GitHub for hosting images related to the URLs. Screenshot Capabilities:** Captures screenshots of web pages as part of metadata. API Authorization and Configuration:** Manages API keys and configurations for external service integration. Workflow Structure Split In Batches: Processes URLs in batches. API Auth: Configures API authorization. URL Processing Nodes: Extract metadata using various nodes like Get Headers, OpenGraph API, and Meta tags Scraper. Conditional Nodes: Include IF OpenGraph Invalid and If - Enable ScreenShots for logic handling. Data Aggregation: Uses nodes like Method 1 - META for final metadata aggregation. Switchy API: Handles link creation and updating. GitHub Integration: Hosts screenshots and images on a personal GitHub repository. Final Output: Provides the shortened URL after processing. API Stack | API | Description | |---------------------------------|-------------------------------------------------| | switchy | For creating and updating shortened links. | | opengraph | To retrieve URL metadata using OpenGraph tags. | | dub.sh | Used for scraping meta tags from web pages. | | microlink | Captures screenshots of web pages. | | pxl.to | Alternative service for capturing screenshots. | | favicone | Retrieves favicons for given URLs. | | github | Hosts images and screenshots on GitHub repo. | | statically | Used for CDN services and image hosting. | | Other APIs | Additional APIs used for various purposes. | GitHub Repository Setup To use this workflow, ensure your GitHub API is linked for hosting images. Set up a repository where the workflow can upload screenshots and other related images. This repository will be referenced in the workflow nodes where images are handled. Configuration Before running the workflow, set up the necessary API keys and configurations in the API Auth node. Adjust batch size and other parameters as needed. Error Handling The workflow includes nodes like Stop and Error for robust error handling, post an issue and mention the creator using N8N community. Contributions This workflow is open for community contributions. Enhancements and improvements are welcome.

How it works It’s very important to come prepared to Sales calls. This often means a lot of manual research about the person you’re calling with. This workflow delivers the latest social media activity (LinkedIn + X) for businesses you are about to interact with each day. Scans Your Calendar**: Each morning, it reviews your Google Calendar for any scheduled meetings or calls with companies based on each attendee email address. Fetches Latest Posts**: For each identified company, it fetches recent LinkedIn and X posts Delivers Insight**s: You receive personalized emails via Gmail, each dedicated to a company you’re meeting with that day, containing a reminder of the meeting, list of posts categorized by the social media platform, and direct links to posts. Setup steps The workflow requires you to have the following accounts set up in their respective nodes: Google Calendar GMail Clearbit Besides those, you will need an account on the RapidAPI platform and subscribe to the following APIs: Fresh LinkedIn Profile Data Twitter Email example

How it works It’s very important to come prepared to Sales calls. This often means a lot of manual research about the person you’re calling with. This workflow delivers a summary of the latest social media activity (LinkedIn + X) for businesses you are about to interact with each day. Scans Your Calendar**: Each morning, it reviews your Google Calendar for any scheduled meetings or calls with companies based on each attendee email address. Fetches Latest Posts**: For each identified company, it fetches recent LinkedIn and X posts and summerizes them using AI to deliver a qucik overview for a busy sales rep. Delivers Insights**: You receive personalized emails via Gmail, each dedicated to a company you’re meeting with that day, containing a reminder of the meeting and a summary of company's recent social media activity. Setup steps The workflow requires you to have the following accounts set up in their respective nodes: Google Calendar GMail Clearbit OpenAI Besides those, you will need an account on the RapidAPI platform and subscribe to the following APIs: Fresh LinkedIn Profile Data Twitter Email example

The News Site from Colt, a telecom company, does not offer an RSS feed, therefore web scraping is the choice to extract and process the news. The goal is to get only the newest posts, a summary of each post and their respective (technical) keywords. Note that the news site offers the links to each news post, but not the individual news. We collect first the links and dates of each post before extracting the newest ones. The result is sent to a SQL database, in this case a NocoDB database. This process happens each week thru a cron job. Requirements: Basic understanding of CSS selectors and how to get them via browser (usually: right click → inspect) ChatGPT API account - normal account is not sufficient A NocoDB database - of course you may choose any type of output target Assumptions: CSS selectors work on the news site The post has a date with own CSS selector - meaning date is not part of the news content "Warnings" Not every site likes to be scraped, especially not in high frequency Each website is structured in different ways, the workflow may then need several adaptations.

Extract Title tag and meta description from url for SEO analysis. How it works The workflows takes records from Airtable, get the url in the records and extract from the related webpage the title tag (<title>) and meta description (<meta name="description" content="Some content">). If title tag and/or meta description tag isn't available on the webpage, the result will be empty. Setup Set a Base in Airtable with a table with the following structure: url (field type url), title tag (field type text string), meta desc (field type text field) Minimum suggested table structure is: url (https://example.com), title (Title example), meta desc* (This is the meta description of the example page) Connect Airtable to both Airtable nodes in the template and, with the following formula, get all the records that miss title tag and meta desc. Formula: AND(url != "", {title tag} = "", {meta desc} = "") Insert the url to be analyzed in the table in the field url and let the workflow do the rest. Extra You can also calculate the length for title tag and meta desc using formula field inside Airtable. This is the formula: LEN({title tag}) or LEN({meta desc}) You can automate the process calling a Webhook from Airtable. For this, you need an Airtable paid plan.

Using n8n a lot? Soar above the limitations of the default n8n dashboard! This template gives you an overview of your workflows, nodes, and tags – all in one place. 💪 Built using XML stylesheets and the Bootstrap 5 library, this workflow is self-contained and does not depend on any third-party software. 🙌 It generates a comprehensive overview JSON that can be easily integrated with other BI tools for further analysis and visualization. 📊 Reach out to Eduard if you need help adapting this workflow to your specific use-case! 🚀 Benefits: Workflow Summary** 📈: Instant overview of your workflows, active counts, and triggers. Left-Side Panel** 📋: Quick access to all your workflows, nodes, and tags for seamless navigation. Workflow Details** 🔬: Deep dive into each workflow's nodes, timestamps, and tags. Node Analysis** 🧩: Identify the most frequently used nodes across your workflows. Tag Organization** 🗂️: Workflows are grouped according to their tags. Visually Stunning** 🎨: Clean, intuitive, and easy-to-navigate dashboard design. XML & Bootstrap 5** 🛠️: Built using XML stylesheets and Bootstrap 5, ensuring a self-contained and responsive dashboard. No Dependencies** 🔒: The workflow does not rely on any third-party software. Bootstrap 5 files are loaded via CDN but can be delivered directly from your server. ⚠️ Important note for cloud users Since the cloud version doesn't support environmental variables, please make the following changes: get-nodes-via-jmespath node. Update the instance_url variable: enter your n8n URL instead of {{$env["N8N_PROTOCOL"]}}://{{$env["N8N_HOST"]}} Create HTML node. Please provide the n8n instance URL instead of {{ $env.WEBHOOK_URL }} 🌟Example: Check out our other workflows: n8n.io/creators/eduard n8n.io/creators/yulia

How it works: The workflow starts by sending a request to a website to retrieve its HTML content. It then parses the HTML extracting the relevant information The extracted data is storted and converted into a CSV file. The CSV file is attached to an email and sent to your specified address. The data is simultaneously saved to both Google Sheets and Microsoft Excel for further analysis or use. Set-up steps: Change the website to scrape in the "Fetch website content" node Configure Microsoft Azure credentials with Microsoft Graph permissions (required for the Save to Microsoft Excel 365 node) Configure Google Cloud credentials with access to Google Drive, Google Sheets and Gmail APIs (the latter is required for the Send CSV via e-mail node).

This workflow with AI agent is designed to navigate through the page to retrieve specific type of information (in this example: social media profile links). The agent is equipped with 2 tools: text tool:** to retrieve all the text from the page, URLs tool:** to extract all possible links from the page. 💡 You can edit prompt and JSON schema connected to the agent in order to return other data then social media profile links. 👉 This workflow uses Supabase as storage (input/output). Feel free to change it to any other database of your choice. 🎬 See this workflow in action in my YouTube video. How it works? The workflow uses the input URL (website) as a starting point to retrieve the data (e.g. example.com). Using the "URLs tool", the agent is able to retrieve all links from the page and navigate to them. For example, if you want to retrieve contact information, agent will try to find a subpage that might contain this information (e.g. example.com/contact) and extract the information using the text tool. Set up steps Connect database with input data (website addresses) or pin sample data to trigger node. Configure the crawling agent to retrieve the desired data (e.g. modify prompt and/or parsing schema). Set credentials for OpenAI. Optionally: split agent tools to separate workflows. If you like this workflow, please subscribe to my YouTube channel and/or my newsletter.

This n8n workflow demonstrates creating a recipe recommendation chatbot using the Qdrant vector store recommendation API. Use this example to build recommendation features in your AI Agents for your users. How it works For our recipes, we'll use HelloFresh's weekly course and recipes for data. We'll scrape the website for this data. Each recipe is split, vectorised and inserted into a Qdrant Collection using Mistral Embeddings Additionally the whole recipe is stored in a SQLite database for later retrieval. Our AI Agent is setup to recommend recipes from our Qdrant vector store. However, instead of the default similarity search, we'll use the Recommendation API instead. Qdrant's Recommendation API allows you to provide a negative prompt; in our case, the user can specify recipes or ingredients to avoid. The AI Agent is now able to suggest a recipe recommendation better suited for the user and increase customer satisfaction. Requirements Qdrant vector store instance to save the recipes Mistral.ai account for embeddings and LLM agent Customising the workflow This workflow can work for a variety of different audiences. Try different sets of data such as clothes, sports shoes, vehicles or even holidays.

This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: Survey Insights Customer Insights Community Insights This template demonstrates the Customer Insights scenario where Trustpilot reviews can be quickly grouped by similarity and an AI agent can generate insights on those groupings. With this workflow, marketers can save days and even weeks of work breaking down their own or competitor reviews and identify frequently mentioned positives and negatives. Sample Output: https://docs.google.com/spreadsheets/d/e/2PACX-1vQ6ipJnXWXgr5wlUJnhioNpeYrxaIpsRYZCwN3C-fFXumkbh9TAsA_JzE0kbv7DcGAVIP7az0L46_2P/pubhtml How it works Trustpilot reviews are scraped for a particular company using the HTTP request node. Reviews are then inserted into a Qdrant collection carefully tagged with the question and Trustpilot metadata. Reviews are fetched and put through a clustering algorithm using the Python Code node. The Qdrant points are returned in clustered groups. Each group is looped to fetch the payloads of the points and feed them to the AI agent to summarise and generate insights for. The resulting insights and raw responses are then saved to the Google Spreadsheet for further analysis by the marketer. Requirements Qdrant Vectorstore for storing embeddings. OpenAI account for embeddings and LLM. Customising the Template Adjust clustering parameters which make sense for your data. Consider expanding date range of reviews for insights over common intervals: 3mth, 6mth and YTD.

This workflow is a experiment to build HTML pages from a user input using the new Structured Output from OpenAI. How it works: Users add what they want to build as a query parameter The OpenAI node generate an interface following a structured output defined in the body The JSON output is then converted to HTML along with a title The HTML is encapsulated in an HTML node (where the Tailwind css script is added) The HTML is rendered to the user via the Webhook response. Set up steps Create an OpenAI API Key Create the OpenAI credentials Use the credentials for both nodes HTTP Request (as Predefined Credential type) and OpenAI Activate your workflow Once active, go to the production URL and add what you'd like to build as the parameter "query" Example: https://production_url.com?query=a%20signup%20form Example of generated page

Who this is for This template is for everyone that wants to download their n8n Cloud invoices automatically as a PDF instead of downloading them manually. How it works This workflow checks your Gmail inbox for new n8n invoice emails from n8n's payment provider Paddle. Once it finds something, it converts the URL into a PDF using pdflayer and saves it in Google Drive. Setup Setup your Gmail and Google Drive credentials Create a free account at https://pdflayer.com/ Insert your pdflayer API key into the Setup node Insert the URL to the wanted drive folder into the setup node (make sure to remove everything after the ?) How to adjust it to your need Instead of saving the PDF in Google drive, you could also save it in your local system, any other storage provider or send the PDF automatically to the right person in your company.

What this template does This workflow will collect order data as it is produced, then send a summary email of all orders at the end of every day, formatted in a table. It receives new orders via webhook and stores in Airtable. At 7PM every day, it sends a summary email with the day's orders in a HTML table Setup: Instructions Video Create a new table in Airtable and give it a field time with type date, orderID with type number, and orderPrice also with type number. Create a new access token if you haven't already at https://airtable.com/create/tokens/new. Make sure to give the token the scopes data.records:read, data.records:write, schema.bases:read and access to whichever table you choose to store the orders. A pop-up window appears with the token. Use this token to make Create New Credential > Access Token for Airtable in the Store Order and Airtable Get Today's Orders nodes. Create access credentials for your Gmail as described here: https://developers.google.com/workspace/guides/create-credentials. Use the credentials from your client_secret.json in the Send to Gmail node. In the Store Order node, change Base and Table to the database and table in your Airtable account you wish to use to store orders. Make sure to use these same values in the Airtable Get Today's Orders node. Every time an order is created in your system, send a POST request to Webhook from your order software. Each request must contain a single order containing fields 'orderID' and 'orderPrice' (or, edit Set Order Fields to select which incoming fields you wish to save) Change the schedule time for sending email from Everyday at 7PM to whichever time you choose. Test: Activate the workflow. From the node Webhook, copy Production URL Send the following CURL request to the URL given to you: curl -X POST -H "Content-Type: application/json" -d '{"orderID": 12345, "orderPrice": 99.99}' YOUR_URL_HERE It should say Node executed successfully. Now check your Airtable and confirm the order was stored in the right place.

Who might benfit from this workflow? Everyone organizing him/herself by using a notion database for tasks but losing track on some important tasks having a deadline. The weekly reminder helps you to not forget about your notion tasks. How it works: The workflow fetches all your notion tasks from a desired database but the closed ones It generates a html template for each tasks containing a headline and a short list of key data (prio, status deadline, tags) It creates two groups based on the deadline date if a task is already overdue or not It generates a complete html email containing both groups and some sugar around them It sends the email to your desired email It uses Pushover to send you a push notification to your phone It is scheduled by the beginning of each week How to set it up: Fill out the "Set Workflow vars" node with your data Connect your notion account and select the database your tasks are stored at define the status filters to the ones you are using for your tasks Setup your email server to enable the email node to deliver your html email Create a Pushover account and setup the authentication for the Pushover node Adjust the last html node to change email style for your desire How to customize this workflow for your needs? You might adjust the filtering of the notion fetch node to filter for other statuses than provided in the example. You apply your custom design to the html email You could remove the filter which is filtering for tasks having a deadline and just send yourself a reminder for all tasks You need help implementing this or any other n8n workflow? Feel free to contact me via LinkedIn or my business website. You want to start using n8n? Use this link to register for n8n (This is an affiliate link)

How it works Fetch transaction notification emails (including attachments) Clean up data Let AI (Basic LLM Chain node) generate bookkeeping item Send to Google sheet Details The example fetch email from Gmail lables, suggested using filters to automatically orgianize email into the labels Data will send to "raw data" sheet Example google sheet: https://docs.google.com/spreadsheets/d/1_IhdHj8bxtsfH2MRqKuU2LzJuzm4DaeKSw46eFcyYts/edit?gid=1617968863#gid=1617968863

What this template does The Ultimate Scraper for n8n uses Selenium and AI to retrieve any information displayed on a webpage. You can also use session cookies to log in to the targeted webpage for more advanced scraping needs. ⚠️ Important: This project requires specific setup instructions. Please follow the guidelines provided in the GitHub repository: n8n Ultimate Scraper Setup : https://github.com/Touxan/n8n-ultimate-scraper/tree/main. The workflow version on n8n and the GitHub project may differ; however, the most up-to-date version will always be the one available on the GitHub repository : https://github.com/Touxan/n8n-ultimate-scraper/tree/main. How to use Deploy the project with all the requirements and request your webhook. Example of request: curl -X POST http://localhost:5678/webhook-test/yourwebhookid \ -H "Content-Type: application/json" \ -d '{ "subject": "Hugging Face", "Url": "github.com", "Target data": [ { "DataName": "Followers", "description": "The number of followers of the GitHub page" }, { "DataName": "Total Stars", "description": "The total numbers of stars on the different repos" } ], "cookie": [] }' Or to just scrap a url : curl -X POST http://localhost:5678/webhook-test/67d77918-2d5b-48c1-ae73-2004b32125f0 \ -H "Content-Type: application/json" \ -d '{ "Target Url": "https://github.com", "Target data": [ { "DataName": "Followers", "description": "The number of followers of the GitHub page" }, { "DataName": "Total Stars", "description": "The total numbers of stars on the different repo" } ], "cookies": [] }' `

What this workflow does Downloads the daily top podcasts of a selected genre Summarizes the content of each podcast in a few paragraphs Sends the summaries and the direct link to each podcast in a formatted email Setup Create a free API key on Taddy here: https://taddy.org/signup/developers Input your user number and API key into the TaddyTopDaily node in the header parameters X-USER-ID and X-API-KEY respectively. Create access credentials for your Gmail as described here: https://developers.google.com/workspace/guides/create-credentials. Use the credentials from your client_secret.json in the Gmail node. In the Genre node, set the genre of podcasts you want a summary for. Valid values are: TECHNOLOGY, NEWS, ARTS, COMEDY, SPORTS, FICTION, etc. Look at api.taddy.org for the full list (they will be displayed in the help docs as PODCASTSERIES_TECHNOLOGY, PODCASTSERIES_NEWS, etc.) Enter your email address in the Gmail node. Change the schedule time for sending email from Schedule to whichever time you want to receive the email. Test: Hit Test Workflow. Check your email for the results. That's it! It should take less than 5 minutes total.

Template Description This n8n workflow is designed to manage Fastmail masked email addresses using the Fastmail API. The workflow provides the following functionalities: Retrieve all masked emails: Fetches all masked email addresses associated with the Fastmail account. Create masked email: Allows creating a new masked email with a specified state (pending, enabled, etc.). Update masked email state: Updates the state of a masked email such as enabling, disabling, or deleting it. Generate HTML template: Constructs an HTML table to display the masked emails in a user-friendly format. Steps to Make it Work Webhook Node: This node listens for incoming requests to manage masked emails. Needs Basic Authentication credentials to secure the endpoint. Session Node: Sends a request to obtain session information from Fastmail's API. Requires an HTTP Header Auth credential with your Fastmail API token. Switch Node: Routes the workflow based on the state of the incoming masked email request (pending, enabled, disabled, deleted). HTTP Request Nodes: These nodes handle various Fastmail API calls for masked emails (get, set, update, delete). All HTTP Request nodes require an HTTP Header Auth credential attached, using the Fastmail API token. Set Node: Gathers the retrieved masked email list into an array for further processing. HTML Node: Generates an HTML template to render the masked email addresses in a table format. Respond to Webhook Node: Sends back the HTML table to the client in response to the webhook request. Needed Credentials Fastmail Masked E-Mail Addresses: An API token from Fastmail's API. Each HTTP call to Fastmail requires this credential for authentication. Note Ensure that you correctly configure authentication for the API calls and webhook security. Use your actual Fastmail API credentials with the correct scope. The workflow assumes that the Fastmail API is correctly configured and accessible from your n8n instance. Update URLs and credentials IDs according to your n8n configuration.

This workflow automates the process of gathering LinkedIn advice articles, extracting their content, and generating unique contributions for each article using an AI model. The contributions are then posted to a Slack channel and a NocoDB database for record-keeping. The workflow is triggered weekly to ensure new articles are continuously collected and responded to. Who is this for? This workflow is designed for professionals, marketers, and content creators looking to boost their LinkedIn presence by regularly engaging with LinkedIn advice articles. It’s especially useful for those who want to be seen as a "thought leader" or "top voice" in their niche by contributing relevant and unique advice to trending topics. What problem is this workflow solving? Manually searching for relevant LinkedIn articles, reading through them, and crafting thoughtful contributions can be time-consuming. This workflow solves that by automating the process of finding new articles, extracting key content, and generating AI-powered contributions. It helps users stay consistently active on LinkedIn, contributing value to trending discussions. What this workflow does Triggers Weekly: The workflow is set to run every Monday at 8:00 AM. Search Google for LinkedIn Advice Articles: Uses a predefined Google search URL to find the latest LinkedIn advice articles based on the user's area of expertise. Extract LinkedIn Article Links: A code node extracts all LinkedIn advice article links from the search results. Retrieve Article Content: For each article link, the workflow retrieves the HTML content and extracts the article title, topics, and existing contributions. Generate AI-Powered Contributions: The workflow sends the extracted article content to an AI model, which generates unique, helpful advice for each topic within the article. Post to Slack & NocoDB: The AI-generated contributions, along with the article links, are posted to a designated Slack channel and stored in a NocoDB database for future reference. Setup Google Search URL: Update the Google search URL with the relevant LinkedIn advice query for your field (e.g., "site:linkedin.com/advice 'marketing automation'"). Slack Integration: Connect your Slack account and specify the Slack channel where you want the contributions to be posted. NocoDB Integration: Set up your NocoDB project to store the generated contributions along with the article titles and links. How to customize this workflow Change Search Terms**: Modify the Google search URL to focus on a different LinkedIn topic or expertise area. Adjust Trigger Frequency**: The workflow is set to run weekly, but you can adjust the frequency by changing the schedule trigger. Enhance Contribution Quality**: Customize the AI model's prompt to generate contributions that align with your brand voice or content strategy. Workflow Summary This workflow helps users maintain a consistent presence on LinkedIn by automating the discovery of new advice articles and generating unique contributions using AI. It is ideal for professionals who want to engage with LinkedIn content regularly without spending too much time manually searching and drafting responses.

📈 Daily Financial News - Description This workflow automates the process of collecting, organizing, and delivering a daily summary of financial news by following these key steps: Scheduled Activation The workflow starts at 7:00 AM each day, triggered by the Schedule Trigger node. News Retrieval The HTTP Request node fetches the latest financial news from FT.com. Content Extraction The Extract Specific Content node scrapes targeted sections of the HTML (headlines, editor's picks, top stories, etc.) using CSS selectors to locate and capture relevant content. News Aggregation The Set Node gathers and organizes the extracted news data, preparing it for summarization. Categories like "Headline #1," "Editor's Picks," and "Europe News" are all structured into a single data block. Summarization An AI Agent (Google Gemini) takes the aggregated news data and creates a concise, HTML-formatted summary tailored to give investors an insightful market snapshot. Email Delivery Finally, the Microsoft Outlook node sends the summary via email to the designated recipient with the subject "Financial news from today." This process ensures that financial news is efficiently curated, summarized, and delivered without manual intervention.

This n8n template combines an AI agent with n8n's multi-page forms to create a novel interaction which allows automated question-and-answer sessions. One of the more obvious use-cases of this interaction is what I'm calling the AI interviewer. You can read the full post here: https://community.n8n.io/t/build-your-own-ai-interview-agents-with-n8n-forms/62312 Live demo here: https://jimleuk.app.n8n.cloud/form/driving-lessons-survey How it works A form trigger is used to start the interview and a new session is created in redis to capture the transcript. An AI agent is then tasked to ask questions to the user regarding the topic of the interview. This is setup as a loop so the questions never stop unless the user wishes to end the interview. Each answer is recorded in our session set up earlier between questions. When the user requests to end the interview we break the loop and show the interview completion screen. Finally, the session is then saved in a Google Sheet which can then be shared with team members and for the purpose of data analysis. How to use You'll need to be on a n8n instance that is accessible to your target audience. Not technical enough to setup your own server? Try out n8n cloud and instantly deploy template! Remember to activate the workflow so the form trigger is published and available for users to use. Requirements Groq LLM for AI agent. Feel free to swap this out for any other LLM. Redis(-compatible) storage for capturing sessions Customising this workflow The next step would be adding tools! AI interviews with knowledge retrieval could definitely open up other possibilities. Eg. An onboarding wizard generating questions by pulling facts from internal knowledgebase.

This n8n template builds a meeting assistant that compiles timely reminders of upcoming meetings filled with email history and recent LinkedIn activity of other people on the invite. This is then discreetly sent via WhatsApp ensuring the user is always prepared, informed and ready to impress! How it works A scheduled trigger fires hourly to check for upcoming personal meetings. When found, the invite is analysed by an AI agent to pull email and LinkedIn details of the other invitees. 2 subworkflows are then triggered for each invitee to (1) search for last email correspondence with them and (2) scrape their LinkedIn profile + recent activity for social updates. Using both available sources, another AI agent is used to summarise this information and generate a short meeting prep message for the user. The notification is finally sent to the user's WhatsApp, allowing them ample time to review. How to use There are a lot of moving parts in this template so in it's current form, it's best to use this for personal rather than team calendars. The LinkedIn scraping method used in this workflow requires you to paste in your LinkedIn cookies from your browser which essentially let's n8n impersonate you. You can retrieve this from dev console or ask someone technical for help! Note: It may be wise to switch to other LinkedIn scraping approaches which do not impersonate your own account for production. Requirements OpenAI for LLM Gmail for Email Google Calendar for upcoming events WhatsApp Business account for notifications Customising this workflow Try adding information sources which are relevant to you and your invitees. Such as company search, other social media sites etc. Create an on-demand version which doesn't rely on the scheduled trigger. Sometimes you want to know prepare for meetings hours or days in advance where this could help immensely.

This n8n template scrapes a list of AI grants from grants.gov and qualifies them using AI; determining interest and eligibility for the business. It then sends an email alert of interesting items to team members in an email. The template also shows how you can use the "Remove Duplicates" node to simplify deduplication of external listings without the need to manage this yourself. Not particularly interested in AI Grants? This template works for other tender websites as long as you're able to scrape them. How it works A scheduled trigger is set to fetch a list of AI grants listed on the grants.gov website in the past day. A Remove Duplicates node is used to track Grant IDs to filter out those already processed by the workflow. New grants are summarized and analysed by AI nodes to determine eligibility and interest which is then saved to an Airtable database. Another scheduled trigger starts a little later than the first to collect and summarize the new grants The results are then compiled into an email template using the HTML node, in the form of a newsletter designed to alert and brief team members of new AI grants. This email is then sent to a list of subscribers using the gmail node. How to use Make a copy of sample Airtable here: https://airtable.com/appiNoPRvhJxz9crl/shrRdP6zstgsxjDKL The filters for fetching the grants is currently set to the "AI" category. Feel free to change this to include more categories. Not interested in grants, this template can works for other sources of leads just change the endpoint and how you're defining the item ID to track. Requirements Airtable for database OpenAI for LLM Note: These are not hard requirements and can be exchanged for services available to you. customising the workflow "Eligibility" criteria at this stage may be better served by identifying hard blockers instead ie. certifications, geographical considerations or certain legal checks. Be sure to mention any hard blockers into the Eligibility prompt. Not particularly interested in AI prompts? This template works for other tender websites as long as you're able to scrape them.

This n8n template demonstrates a simple approach to using AI to automate the generation of blog content which aligns to your organisation's brand voice and style by using examples of previously published articles. In a way, it's quick and dirty "training" which can get your automated content generation strategy up and running for very little effort and cost whilst you evaluate our AI content pipeline. How it works In this demonstration, the n8n.io blog is used as the source of existing published content and 5 of the latest articles are imported via the HTTP node. The HTML node is extract the article bodies which are then converted to markdown for our LLMs. We use LLM nodes to (1) understand the article structure and writing style and (2) identify the brand voice characteristics used in the posts. These are then used as guidelines in our final LLM node when generating new articles. Finally, a draft is saved to Wordpress for human editors to review or use as starting point for their own articles. How to use Update Step 1 to fetch data from your desired blog or change to fetch existing content in a different way. Update Step 5 to provide your new article instruction. For optimal output, theme topics relevant to your brand. Requirements A source of text-heavy content is required to accurately breakdown the brand voice and article style. Don't have your own? Maybe try your competitors? OpenAI for LLM - though I recommend exploring other models which may give subjectively better results. Wordpress for blog but feel free to use other preferred publishing platforms. Customising this workflow Ideally, you'd want to "train" your agent on material which is similar to your output ie. your social media post may not get the best results from your blog content due to differing formats. Typically, this brand voice extraction exercise should run once and then be cached somewhere for reuse later. This would save on generation time and overall cost of the workflow.

Instructions This automation enables you to just upload any Image (via Form) of a Logo Sheet, containing multiple Images of Product Logos (most likely) which brings them in some context to one another. After submitting an AI-Agent eats that Logo Sheet, turning it into an List of "Productname" and "Attributes", also checks if Tools are kind of similar to another, given the Context of the Image. We utilize AI Vision capabilities for that. NOTE: It might not be able to extract all informations. For a "upload and forget it" Workflow it works for me. You can even run it multiple times, to be sure. But if you need to make sure it extracts everything you might need to think about an Multi-Agent Setup with Validation-Agent Steps. Once the Agent finishes the extraction, it will traditionally and deterministicly add those Attributes to Airtable (Creates those, if not already existing.) and also Upserts the Tool Informations. It uses MD5 Hashes for turning Product Names into.. something fancy really, you could also use it without that, but I wanted to have something that looks atleast like an ID. Setup Set Up the Airtable like shown below. Update and set Credentials for all Airtable Nodes. Check or Adjust the Prompt of the Agent matching your use-case. Activate the Workflow. Open the Form (default: https://your-n8n.io/form/logo-sheet-feeder) Enjoy growing your Airtable. Enjoy the workflow! ❤️ let the work flow — Workflow Automation & Development

This workflow creates a documentation system for n8n instances using Docsify.js. It serves a dynamic documentation website that allows users to: View an overview of all workflows in a tabular format Filter workflows by tags Access automatically generated documentation for each workflow Edit documentation with a live Markdown preview Visualize workflow structures using Mermaid.js diagrams > 📺 Check out the short 2-min demonstration on LinkedIn. Don't forget to connect! 🔧 Key Components Main Documentation Portal Serves a Docsify-powered website Provides a navigation sidebar with workflow tags Displays workflow status, creation date, and documentation links Documentation Generator Uses GPT model to auto-generate workflow descriptions Creates Mermaid.js diagrams of workflow structures Maintains consistent documentation format Live Editor Split-screen Markdown editor with preview Real-time Mermaid diagram rendering Save/Cancel functionality ⚙️ Technical Details Environment Setup Requires write access to the specified project directory Uses environment variables for n8n instance URL configuration Implements webhook endpoints for serving documentation ⚠️ Security Considerations > Note: The current implementation doesn't include authentication for editing. Consider adding authentication for production use. Dependencies Docsify.js for documentation rendering Mermaid.js for workflow visualization OpenAI GPT for documentation generation 🔍 Part of the n8n Observability Series This workflow is part of a broader series focused on n8n instance observability. Check out these related workflows: Workflow Dashboard - Get comprehensive analytics of your n8n instance Visualize Your n8n Workflows with Mermaid.js - Create beautiful workflow visualizations Each workflow in this series helps you better understand and manage your n8n automation ecosystem!

This is a simple workflow that grabs HackerNews front-page headlines from today's date across every year since 2007 and uses a little AI magic (Google Gemini) to sort 'em into themes, sends a neat Markdown summary on Telegram. How it works Runs daily, grabs Hacker News front page for this day across every year since 2007. Pulls headlines & dates. Uses Google Gemini to sort headlines into topics & spot trends. Sends a Markdown summary to Telegram. Set up steps Clone the workflow. Add your Google Gemini API key. Add your Telegram bot token and chat ID. **Built on Day-01 as part of the #100DaysOfAgenticAi Fork it, tweak it, have fun!**

Purpose: This workflow exemplifies a sophisticated yet pragmatic mechanism for automating currency conversions by leveraging simple HTTP queries routed through a webhook. By intercepting user requests, sourcing real-time exchange rate data via Google search results, and formatting the data into actionable responses, it obviates the reliance on third-party APIs. This efficiency renders it an indispensable instrument for diverse applications, including dynamic pricing strategies for AI-driven systems, financial data automation, and real-time currency computation within complex workflows. The workflow's architectural simplicity ensures seamless integration across professional and academic domains, optimizing both scalability and reliability. Workflow Steps: Capture Conversion Query: The workflow initiates by intercepting user input delivered as a GET request through a configured webhook. Inputs should adhere to a structured syntax, such as 5usd to mxn, to ensure flawless processing. Testing Tip: Use tools like Postman or a browser to test GET requests and verify that the Webhook receives inputs correctly. Fetch Exchange Rate: Utilizing the HTTP Request node, a Google search query is executed to retrieve current exchange rate data. This method ensures the workflow remains economical and adaptable while circumventing API dependencies. Extract Conversion Data: By processing the returned HTML from Google's search results, this node extracts precise exchange rate figures and contextual information critical for accurate conversions. Error Handling: If extraction fails, verify that the input format is correct and update CSS selectors to reflect any changes in Google's HTML structure. Format Currency Response: The extracted data undergoes refinement and is formatted into a structured, user-friendly string that conveys the conversion results with clarity and precision. Send Conversion Response: The workflow culminates by dispatching the formatted response back to the request origin, completing the loop with efficiency and reliability. Required Configuration: Configure the Webhook node to accommodate GET requests. The query parameters should follow the format: https://your-webhook-url/currency-converter?q=5usd+to+mxn. Inputs must adhere strictly to the predefined syntax (e.g., 5usd to mxn). Deviations may induce processing errors or yield erroneous outputs. Customization Options: The Extract Conversion Data node’s CSS selectors can be fine-tuned to align with modifications in Google’s HTML structure, ensuring long-term operability. Adjustments to the Format Currency Response node enable bespoke output formatting, incorporating additional metadata or altering the response structure to meet specific project requisites. Advanced Features: This workflow’s modular design supports seamless integration into expansive systems. For instance, an e-commerce platform could employ it to dynamically localize product pricing based on user geolocation. Enhanced functionality can be achieved by appending nodes to log conversions, monitor performance metrics, or trigger auxiliary workflows conditioned on conversion outputs. Expected Results: For a query like 5usd to mxn, the workflow generates a response formatted as: 5 USD = 95 MXN. This output is optimized for readability and practical application. Use Case Examples: AI Integration:** Enables artificial intelligence agents to process real-time price conversions efficiently across diverse currencies, enhancing their computational capabilities. Financial Operations:** Automates precise currency conversions for corporate reports, international transactions, and market analytics. Personal Financial Planning:** Assists users in calculating currency conversions for investment decisions or travel budgeting with minimal manual effort. E-commerce Applications:** Facilitates dynamic price adjustments on online marketplaces, displaying localized prices to augment user experience and conversion rates. Workflow Integration:** Embeds seamlessly into larger systems, such as CRMs or ERPs, to streamline financial operations and enhance interoperability. Key Benefits: No API Dependency:** By leveraging publicly available data from Google, the workflow eliminates the complexities and costs associated with API integration, reducing overhead and enhancing accessibility. Precision and Currency:** Ensures accurate and real-time results by querying Google directly. Flexibility:** Designed to adapt to various operational contexts and input formats, making it a versatile asset in computational and commercial applications. Tags: currency conversion, automation, webhook, data extraction, AI integration, financial automation, e-commerce, real-time data, scalable workflows.

> Like this template? Connect with Eduard via LinkedIn. This workflow is a prototype of an AI-powered image editing interface, similar to Photoshop's Generative Fill feature, but running entirely in the browser. It provides a web-based editor that allows users to: Select areas in images using an adjustable brush tool Input text prompts to guide the AI generation Compare original and generated images side by side Iterate on edits with different prompts and settings Save or reuse generated images > 🎨 Perfect for product catalog management, seasonal content updates, and creative image editing tasks! 📋 Requirements FLUX API Access: You'll need API credentials from FLUX to use this workflow. Configure the HTTP Header Auth credential in n8n with your FLUX API key 🔧 Key Components FLUX Fill API for AI-powered image generation Konva.js for canvas manipulation img-comparison-slider for result visualization Custom CSS/JS for editor functionality Simple Editor Interface HTML page with an editor is served on the Webhook call Adjustable brush selection tool Provides several mock examples and allows uploading custom images Basic prompt and FLUX model parameter controls Image Processing Pipeline Handles image and mask separately Processes FLUX Fill API requests Delivers results back to the editor Result Viewer Split-screen comparison of original and generated images Interactive slider for before/after comparison Options to save or continue editing Support for multiple iteration cycles 🎯 Use Cases This prototype is particularly useful for: Testing AI-powered image editing concepts Quick product visualization experiments Exploring creative image variations Demonstrating inpainting capabilities > 💡 Pro Tip: Save masks for frequently edited areas to quickly generate variations with different prompts! The workflow can be extended to integrate with various data sources and can be customized for specific business needs.

How the Automated Workflow Works Scheduled Fetching from Hugging Face ⏰ The workflow triggers every weekday at 8 AM, automatically fetching the latest papers from Hugging Face for easy access. Duplication Check to Avoid Redundant Entries 🔍 It ensures the paper's summary is not already stored in your Notion workspace, preventing duplicate records and keeping your database organized. Content Analysis with OpenAI 🧠 Using OpenAI's powerful capabilities, the workflow analyzes the fetched paper summary, extracts key insights, and categorizes the content for easier understanding. Data Storage and Notification Integration 📥🔔 Once the summary is processed, it's automatically stored in your Notion workspace, and a notification containing the paper details is sent to your designated Slack channel for quick reference. Set Up Your Automated Workflow Create Your n8n Account 📝 Start by registering for an n8n account and logging into the n8n cloud service. Connect OpenAI, Notion, and Slack 🔗 Link your OpenAI, Notion, and Slack accounts by entering the appropriate tokens. This step will take approximately 10–15 minutes to complete. Import the Workflow Template 📥 Import the provided workflow template into your n8n instance to streamline the setup process. Activate the Workflow for Daily Summaries 🚀 After importing, simply enable the workflow, and you’re all set to receive daily paper summaries automatically. Setup Time ⏳: Approximately 15–20 minutes. Why Use This Automated Workflow? This automated workflow not only saves you time by fetching and categorizing the latest research papers but also helps streamline your Notion workspace and Slack notifications, allowing you to stay organized and efficient without manual intervention. Results Presentation

Purpose This solution enables you to manage all your Notion and Todoist tasks from different workspaces as well as your calendar events in a single place. All tasks can be managed in Todoist and additionally Fantastical can be used to manage scheduled tasks & events all together. Demo & Explanation How it works The realtime sync consists of two workflows, both triggered by a registered webhook from either Notion or Todoist To avoid overwrites by lately arriving webhook calls, every time the current task is retrieved from both sides. Redis is used to prevent from endless loops, since an update in one system triggers another webhook call again. Using the ID of the task, the trigger is being locked down for 15 seconds. Depending on the detected changes, the other side is updated accordingly. Generally Notion is treaded as the main source. Using an "Obsolete" Status, it is guaranteed, that tasks never get deleted entirely by accident. The Todoist ID is stored in the Notion task, so they stay linked together An additional full sync workflow daily fixes inconsistencies, if any of them occurred, since webhooks cannot be trusted entirely. Since Todoist requires a more complex setup, a tiny workflow helps with activating the webhook. Another tiny workflow helps generating a global config, which is used by all workflows for mapping purposes. Mapping (Notion >> Todoist) Name: Task Name Priority: Priority (1: do first, 2: urgent, 3: important, 4: unset) Due: Date Status: Section (Done: completed, Obsolete: deleted) <page_link>: Description (read-only) Todoist ID: <task_id> Current limitations Changes on the same task cannot be made simultaneously in both systems within a 15-20 second time frame Subtasks are not linked automatically to their parent yet Recurring tasks are not supported yet Tasks names do not support URL’s yet Prerequisites Notion A database must already exist (get a basic template here) with the following properties (case matters!): Text: "Name" Status: "Status", containing at least the options "Backlog", "In progress", "Done", "Obsolete" Select: "Priority", containing the options "do first", "urgent", "important" Date: "Due" Checkbox: "Focus" Text: "Todoist ID" Todoist A project must already exist with the same sections like defined as Status in Notion (except Done and Obsolete) Redis Create a Free Redis Cloud instance or self-host Setup The setup involves quite a lot of steps, yet many of them can be automated for business internal purposes. Just follow the video or do the following steps: Setup credentials for Notion (access token), Todoist (access token) and Redis - you can also create empty credentials and populate these later during further setup Clone this workflow by clicking the "Use workflow" button and then choosing your n8n instance - otherwise you need to map the credentials of many nodes. Follow the instructions described within the bundle of sticky notes on the top left of the workflow How to use You can apply changes (create, update, delete) to tasks both in Notion and Todoist which then get synced over within a couple of seconds (this is handled by the differential realtime sync) The daily running full sync, resolves possible discrepancies in Todoist and sends a summary via email, if anything needed to be updated. In case that contains an unintended change, you can jump to the Task from the email directly to fix it manually.

Workflow Overview This workflow automates the process of scraping Trustpilot reviews, extracting key details, analyzing sentiment, and saving the results to Google Sheets. It uses OpenAI for sentiment analysis and HTML parsing for review extraction. How It Works 1. Scrape Trustpilot Reviews HTTP Request**: Fetches review pages from Trustpilot (https://it.trustpilot.com/review/{{company_id}}). Paginates through pages (up to max_page limit). HTML Parsing**: Extracts review URLs using CSS selectors Splits the URLs into individual review links. 2. Extract Review Details Information Extractor**: Uses DeepSeek to extract structured data from the review: Author: Name of the reviewer. Rating: Numeric rating (1-5). Date: Review date in YYYY-MM-DD format. Title: Review title. Text: Full review text. Total Reviews: Number of reviews by the user. Country: Reviewer’s country (2-letter code). 3. Sentiment Analysis Sentiment Analysis Node**: Uses OpenAI to classify the review text as Positive, Neutral, or Negative. Example output: { "category": "Positive", "confidence": 0.95 } 4. Save to Google Sheets Google Sheets Node**: Appends or updates the extracted data to a Google Sheet Set Up Steps 1. Configure Trustpilot Scraping Edit Fields1 Node**: Set company_id to the Trustpilot company name Set max_page to limit the number of pages scraped. 2. Configure Google Sheets Google Sheets Node**: Update the documentId with your Google Sheet ID Ensure the sheet has the required columns (Id, Data, Nome, etc.). 3. Configure OpenAI OpenAI Chat Model Node**: Add your OpenAI API key. Sentiment Analysis Node**: Ensure the categories match your desired sentiment labels (Positive, Neutral, Negative). Key Components Nodes**: HTTP Request/HTML: Scrape and parse Trustpilot reviews. Information Extractor: Extract structured review data using DeepSeek. Sentiment Analysis: Classify review sentiment. Google Sheets: Save and update review data. Credentials**: OpenAI API key. DeepSeek API key. Google Sheets OAuth2.

How it works: This workflow empowers marketers and content creators to seamlessly include Getty Images in their posts, provided they adhere to Getty Images' guidelines: Enter a search phrase and select criteria for Getty Images' editorial collection. Retrieve the latest images that match your input parameters. Extract embeddable links for use in your content. Automate this previously manual process to save time and maintain compliance. Set up steps: Estimated time: ~10 minutes. Configure the input node with your desired search terms and criteria. Link the workflow to your preferred CMS or storage solution. Perform a test run to ensure proper retrieval and embedding of images. Review Getty Images' usage guidelines for compliance before publishing. This solution is ideal for independent news channels and small publications looking for a legal, automated way to use Getty Images on their websites without incurring costs.

This workflow automates the process of retrieving Hugging Face paper summaries, analyzing them with OpenAI, and storing the results in Notion. Here’s a breakdown of how it works: ⏰ Scheduled Trigger: The flow is set to run automatically at 8 AM on weekdays. 📄 Fetching Paper Data: It fetches Hugging Face paper summaries using their API. 🔍 Data Check: Before processing, the workflow checks if the paper already exists in Notion to avoid duplicates. 🤖 Content Analysis with OpenAI: If the paper is new, it extracts the summary and uses OpenAI to analyze the content. 📥 Store Results in Notion: After analysis, the summarized data is saved in Notion for easy reference. ⚙️ Set Up Steps for Automation Follow these steps to set up this automated workflow with Hugging Face, OpenAI, and Notion integration: 🔑 Obtain API Tokens: You’ll need the Notion and OpenAI API tokens to authenticate and connect these services with n8n. 🔗 Integration in n8n: Link Hugging Face, OpenAI, and Notion by configuring the appropriate nodes in n8n. 🔧 Configure Workflow Logic: Set up a cron trigger for automatic execution at 8 AM on weekdays. Use an HTTP request node to fetch Hugging Face paper data. Add logic to check if the data already exists in Notion. Set up the OpenAI integration to analyze the paper’s summary. Store the results in Notion for easy access and reference. Result:

How It Works: • Scrapes company review data from Glassdoor using ScrapingBee. • Extracts demographic-based ratings using AI-powered text analysis. • Calculates workplace disparities with statistical measures like z-scores, effect sizes, and p-values. • Generates visualizations (scatter plots, bar charts) to highlight patterns of discrimination or bias. Example Visualizations: Set Up Steps: Estimated time: ~20 minutes. • Replace ScrapingBee and OpenAI credentials with your own. • Input the company name you want to analyze (best results with large U.S.-based organizations). • Run the workflow and review the AI-generated insights and visual reports. This workflow empowers users to identify potential workplace discrimination trends, helping advocate for greater equity and accountability. Additional Credit: Wes Medford For algorithms and inspiration

Retrieve 20 Latest TechCrunch Articles Who is this for? This workflow is designed for developers, content creators, and data analysts who need to scrape recent articles from TechCrunch. It’s perfect for anyone looking to aggregate news articles or create custom feeds for analysis, reporting, or integration into other systems. What problem is this workflow solving? This workflow automates the process of scraping recent articles from TechCrunch. Manually collecting article data can be time-consuming and inefficient, but with this workflow, you can quickly gather up-to-date news articles with relevant metadata, saving time and effort. What this workflow does This workflow retrieves the latest 20 news articles from TechCrunch’s “Recent” page. It extracts the article URLs, metadata (such as titles and publication dates), and main content for each article, allowing you to access the information you need without any manual effort. Setup Clone or download the workflow template. Ensure you have a working n8n environment. Configure the HTTP Request nodes with your desired parameters to connect to the TechCrunch API. (Optional) Customize the workflow to target specific sections or topics of interest. Run the workflow to retrieve the latest 20 articles. How to customize this workflow to your needs Modify the HTTP request to pull articles from different pages or sections of TechCrunch. Adjust the number of articles to retrieve by changing the selection criteria. Add additional processing steps to further filter or analyze the article data. Workflow Steps Send an HTTP request to the TechCrunch "Recent" page. Parse a posts box that holds the list of articles. Parse all posts to extract all articles. spilt out posts for each article. Extract the URL and metadata from each article. Send an HTTP request for each article using its URL. Locate and parse the main content of each article. Note: Be sure to update the HTTP Request nodes with any necessary headers or authentication to work with TechCrunch’s website.

Scrape Latest 20 TechCrunch Articles Who is this for? This workflow is designed for developers, researchers, and data analysts who need to track the latest trending repositories on GitHub. It is useful for anyone who wants to stay updated on popular open-source projects without manually browsing GitHub’s trending page. What problem is this workflow solving? Manually checking GitHub’s trending repositories daily can be time-consuming and inefficient. This workflow automates the extraction of trending repositories, providing structured data including repository name, author, description, programming language, and direct repository links. What this workflow does This workflow scrapes the trending repositories from GitHub’s trending page and extracts essential metadata such as repository names, languages, descriptions, and URLs. It processes the extracted data and structures it into an easy-to-use format. Setup Ensure you have n8n installed and configured. Import this workflow into your n8n instance. Run the workflow manually or schedule it to execute at regular intervals. (Optional) Customize the extracted data or integrate it with other systems. How to customize this workflow to your needs Modify the HTTP request node to target different GitHub trending categories (e.g., specific programming languages). Add further processing steps such as filtering repositories by stars, forks, or specific keywords. Integrate this workflow with Slack, email, or a database to store or notify about trending repositories. Workflow Steps Trigger execution manually using the "When clicking ‘Test workflow’" node. Send an HTTP request to fetch GitHub’s trending page using "Request to Github Trend". Extract the trending repositories box from the HTML response using "Extract Box". Extract all repository data including names, authors, descriptions, and languages using "Extract all repositories". Convert extracted data into a structured list for easier processing using "Turn to a list". Extract detailed repository information using "Extract repository data". Format and set variables to ensure clean and structured data output using "Set Result Variables". Note: Since GitHub’s trending page updates dynamically, ensure you run this workflow periodically to capture the latest trends.

Webhook | Paper Summarization Who is this for? This workflow is designed for researchers, students, and professionals who frequently read academic papers and need concise summaries. It is useful for anyone who wants to quickly extract key information from research papers hosted on arXiv. What problem is this workflow solving? Academic papers are often lengthy and complex, making it time-consuming to extract essential insights. This workflow automates the process of retrieving, processing, and summarizing research papers, allowing users to focus on key findings without manually reading the entire paper. What this workflow does This workflow extracts the content of an arXiv research paper, processes its abstract and main sections, and generates a structured summary. It provides a well-organized output containing the Abstract Overview, Introduction, Results, and Conclusion, ensuring that users receive critical information in a concise format. Setup Ensure you have n8n installed and configured. Import this workflow into your n8n instance. Configure an external trigger using the Webhook node to accept paper IDs. Test the workflow by providing an arXiv paper ID. (Optional) Modify the summarization model or output format according to your preferences. How to customize this workflow to your needs Adjust the HTTPRequest node to fetch papers from other sources beyond arXiv. Modify the Summarization Chain node to refine the summary output. Enhance the Reorganize Paper Summary step by integrating additional language models. Add an email or Slack notification step to receive summaries directly. Workflow Steps Webhook receives a request with an arXiv paper ID. Send an HTTP request using "Request to Paper Page" to fetch the HTML content of the paper. Extract the abstract and sections using "Extract Contents". Split out all sections using "Split out All Sections" to process individual paragraphs. Clean up text using "Remove useless links" to remove unnecessary elements. Summarize extracted content using "Summarization Chain". Aggregate summarized content using "Aggregate summarized content". Reorganize the paper summary into structured sections using "Reorganize Paper Summary". Extract key information using "Content Extractor" to classify data into Abstract Overview, Introduction, Results, and Conclusion. Respond to the webhook with the structured summary. Note: This workflow is designed for use with arXiv research papers but can be adapted to process papers from other sources.

🎯 Who is this automation for? Perfect for content creators, marketers, and any X (Twitter) user looking to grow their account quickly without manual effort. ⚙️ How does this workflow work? ✔️ Auto-publishing: Whenever a new article appears in your RSS feed, a tweet is generated and posted on X. ✔️ Optimized content: A unique, engaging tweet with text and an image is created in under 280 characters. (Verified users can bypass this limit for longer tweets). ✔️ Free X API: No extra subscription costs for automation. ✔️ AI-powered: Use models like OpenAI, gemini and Perplexity, but you can configure your preferred model, whether free or paid. 🛠️ Easy Setup 📂 The downloaded file includes a step-by-step video tutorial. If you need help, I’m here for you! 💡 Save time and grow your X audience efficiently with this n8n automation.

Turn BBC News Articles into Podcasts using Hugging Face and Google Gemini Effortlessly transform BBC news articles into engaging podcasts with this automated n8n workflow. Who is this for? This template is perfect for: Content creators** who want to quickly produce podcasts from current events. Students** looking for an efficient way to create audio content for projects or assignments. Individuals** interested in generating their own podcasts without technical expertise. Setup Information Install n8n: If you haven't already, download and install n8n from n8n.io. Import the Workflow: Copy the JSON code for this workflow and import it into your n8n instance. Configure Credentials: Gemini API: Set up your Gemini API credentials in the workflow's LLM nodes. Hugging Face Token: Obtain an access token from Hugging Face and add it to the HTTP Request node for the text-to-speech model. Customize (Optional): Filtering Criteria: Adjust the News Classifier node to fine-tune the selection of news articles based on your preferences. Output Options: Modify the workflow to save the generated audio file to a cloud storage service or publish it to a podcast hosting platform. Prerequisites An active n8n instance. Basic understanding of n8n workflows (no coding required). API credentials for Gemini and a Hugging Face account with an access token. What problem does it solve? This workflow eliminates the manual effort involved in creating podcasts from news articles. It automates the entire process, from fetching and filtering news to generating the final audio file. What are the benefits? Time-saving:** Create podcasts in minutes, not hours. Easy to use:** No coding or technical skills required. Customizable:** Adapt the workflow to your specific needs and preferences. Cost-effective:** Leverage free or low-cost services like Gemini and Hugging Face. How does it work? The workflow fetches news articles from the BBC website. It filters articles based on their suitability for a podcast. It extracts the full content of the selected articles. It uses Gemini LLM to create a podcast script. It converts the script to speech using Hugging Face's text-to-speech model. The final podcast audio is ready for use. Nodes in the Workflow Fetch BBC News Page: Retrieves the main BBC News page. News Classifier: Categorizes news articles using Gemini LLM. Fetch BBC News Detail: Extracts detailed content from suitable articles. Basic Podcast LLM Chain: Generates a podcast script using Gemini LLM. HTTP Request: Converts the script to speech using Hugging Face. Add Story I'm excited to share this workflow with the n8n community and help content creators and students easily produce engaging podcasts! Additional Tips Explore the n8n documentation and community resources for more advanced customization options. Experiment with different filtering criteria and LLM prompts to achieve your desired podcast style.

Personal Portfolio CV Rag Chatbot - with Conversation Store and Email Summary Target Audience This template is perfect for: Individuals looking to create a working professional and interactive personal portfolio chatbot. Developers interested in integrating RAG Chatbot functionality with conversation storage. 1. Description Create a stunning Personal Portfolio CV with integrated RAG Chatbot capabilities, including conversation storage and daily email summaries. 2.Features: Training: Setup Ingestion stage Upload your CV to Google Drive and let the Drive trigger updates to read your resume cv and convert it into your vector database (RAG purpose). Modify any parts as needed. Chat & Track: Use any frontend/backend interface to call the chat API and chat history API. Reporting Daily Chat Conversations: Receive daily automatic summaries of chat conversations. Data stored via NocoDB. 3.Setup Guide: Step-by-Step Instructions: Ensure all credentials are ready. Follow the notes provided. Ingestion: Upload your CV to Google Drive. The Drive triggers RAG update in your vector database. You can change the folder name, files and indexname of the vector database accordingly. Chat: Use any frontend/backend interface to call the chat API (refer to the notes for details) . [optional] Use any frontend/backend interface to call the update chat history API (refer to the notes for details). 3.Tracking Chat: Get daily automatic summaries of chat conversations.Format email conversations report as you like. You are ready to go!

🔥 n8n Members Sale – n8n Community Members Get ideoGener8r for Just $10! (Reg. $15) Use Coupon Code: FeelinTheFlowgramming (Valid for n8n community members) 💪 How it works Seamlessly track, manage, and version-control your n8n workflows with an intuitive Git-style system. Whether you're making updates or creating new workflows, this tool ensures structured commits, detailed change tracking, and seamless GitHub integration—all within a Matrix-inspired UI. 🎯 Who is this for? Perfect for: n8n Developers & Automation Engineers** – Maintain workflow history and prevent accidental overwrites. Teams Collaborating on Automation** – Track and commit changes with structured version control. DevOps & IT Professionals** – Streamline deployment across multiple n8n environments. 🚀 Key Features Version Control & Workflow Management Commit changes when editing existing workflows Create & commit new workflows from scratch Detect modifications with deep node comparison Access repo with direct links from changes window Workflow JSON Browser Caching **to avoid large loads on n8n" Core Features Workflow Management Workflow Selection**: Easy selection of n8n workflows and GitHub workflows from dropdown menus Smart Caching**: Client-side caching of workflows for faster loading (1-week cache duration) Cache Control**: Manual cache clearing via hard refresh (Cmd+Shift+R) Matrix-Style UI**: Terminal-inspired interface with glowing effects and animations GitHub Integration Push to GitHub**: Direct pushing of n8n workflows to GitHub repositories Commit Control**: Automated commit message generation with workflow details Branch Management**: Automatic branch creation and management Pull from GitHub**: Future feature for pulling workflows from GitHub (Coming Soon) Workflow Comparison Side-by-Side Comparison**: Compare n8n workflows with their GitHub counterparts Change Detection**: Automatic detection and highlighting of workflow changes Diff Visualization**: Clear visualization of additions, deletions, and modifications JSON Validation**: Ensures workflow JSON integrity before operations User Experience System Notifications**: Matrix-themed toast notifications for important alerts 30-second duration with manual close option Context-aware messages (e.g., cache refresh hints) Glowing effects matching system console aesthetics Loading Animations**: Matrix-style loading indicators during operations Error Handling**: Clear error messages and recovery suggestions Responsive Design**: Adapts to different screen sizes Security & Performance Local Storage**: Efficient client-side storage for workflow data Header Authentication**: Secure API communication with authentication tokens Performance Optimization**: Smart caching to reduce server load Data Validation**: Input validation and sanitization Technical Features Code Organization Modular Structure**: Well-organized HTML, CSS, and JavaScript Component Separation**: Clear separation of concerns CSS Variables**: Consistent styling with CSS custom properties Event Handling**: Robust event listeners and handlers API Integration n8n Integration**: Direct communication with n8n workflow API GitHub API**: Seamless integration with GitHub repositories RESTful Endpoints**: Clean API design for workflow operations Error Handling**: Comprehensive error handling and user feedback Development Features Debug Mode**: Console logging for development Code Comments**: Well-documented code sections CSS Animations**: Smooth transitions and effects Browser Compatibility**: Works across modern browsers Future Enhancements Pull workflows from GitHub repositories Enhanced diff visualization Additional GitHub integration features Extended caching options More customization options for notifications Advanced workflow comparison tools GitHub Integration Push workflows directly to GitHub Commit new workflows directly to GitHub Auto-generate structured commit messages Pull workflows from GitHub into n8n Change Detection Identifies node additions, deletions, and property modifications Compares connections, settings, and configurations Real-time visualization** of detected changes Sleek UI for Effortless Control Matrix-style interface** for a futuristic feel Clear prompts to prevent accidental overwrites Interactive workflow commit & sync options 🔧 Setup & Usage Generate your n8n API key Set up GitHub repository & access tokens Create Generic Basic Auth credentials Create Generic Header Auth credentials Set the workflow variables Deploy & start tracking your workflow versions 📚 Use Cases 🔹 Workflow Development – Ensure clean versioning and commit history. 🔹 Team Collaboration – Review and track modifications before deployment. 🔹 Audit & Compliance – Maintain an organized change log. 🙌 Built for the n8n Community Created with ❤️ to bring reliable version control to the n8n community.

This error handling workflow emails detailed notifications on workflow execution and trigger errors. It extends Send email via Gmail on workflow error template by covering trigger-level errors. Features Get notifications on both main workflow trigger and execution time errors. Subject line will have failed workflow id, name, error source (execution or trigger), error message. Body will contain links to both failed and error handling workflows as well as execution or trigger-level error details. Body will also contain a machine readable and enriched JSON from Error Trigger describing the error. Use this error handling workflow for as many workflows as you need. Configiration Copy this workflow to your workspace and, optionally, move it under the project that contains your main workflow In this error handling workflow settings, set This workflow can be called by as appropriate In Config node, define your app url, notifications recipient email, and sender name (useful to build filters in your inbox) In Gmail node, create and select credentials In your main workflow settings, pick this error handling workflow in the Error Workflow field (How to...) Related resources n8n Error Trigger documentation. Author Reach out Olek on community.n8n.io Olek on n8n creators hub

Startup Funding Research Automation with Claude, Perplexity AI, and Airtable How it works This intelligent workflow automatically discovers and analyzes recently funded startups by: Monitoring multiple news sources (TechCrunch and VentureBeat) for funding announcements Using AI to extract key funding details (company name, amount raised, investors) Conducting automated deep research on each company through perplexity deep research or jina deep search. Organizing all findings into a structured Airtable database for easy access and analysis Set up steps (10-15 minutes) Connect your news feed sources (TechCrunch and VentureBeat). Could be extended. These were easy to scrape and this data can be expensive. Set up your AI service credentials (Claude and Perplexity or jina which has generous free tier) Connect your Airtable account and create a base with appropriate fields (can be imported from my base) or see structure below. Airtable Base Structure Funding Round Base | Field Name | Data Type | Description | |------------|-----------|-------------| | website_url | String | URL of the company website | | company_name | String | Name of the company | | funding_round | String | The funding stage or round (e.g., Series A, Seed, etc.) | | funding_amount | Number | The amount of funding received | | lead_investor | String | The primary investor leading the funding round | | market | String | The market or industry sector the company operates in | | participating_investors | String | List of other investors participating in the funding round | | press_release_url | String | URL to the press release about the funding | | evaluation | Number | The company's valuation | Structure Company Deep Research Base | Field Name | Data Type | Description | |------------|-----------|-------------| | website_url | String | URL of the company website | | company_name | String | Name of the company | | funding_round | String | The funding stage or round (e.g., Series A, Seed, etc.) | | funding_amount | Number | The amount of funding received | | currency | String | Currency of the funding amount | | announcement_date | String | Date when the funding was announced | | lead_investor | String | The primary investor leading the funding round | | participating_investors | String | List of other investors participating in the funding round | | industry | String | The industry sectors the company operates in | | company_description | String | Description of the company's business | | hq_location | String | Company headquarters location | | founding_year | Number | Year the company was founded | | founder_names | String | Names of the company founders | | ceo_name | String | Name of the company CEO | | employee_count | Number | Number of employees at the company | | total_funding | Number | Total funding amount received to date | | total_funding_currency | String | Currency of total funding | | funding_purpose | String | Purpose or use of the funding | | business_model | String | Company's business model | | valuation | Object | Company valuation information | | previous_rounds | Object | Information about previous funding rounds | | source_urls | String | Source URLs for the funding information | | original_report | String | Original report text about the funding | | market | String | The market the company operates in | | press_release_url | String | URL to the press release about the funding | | evaluation | Number | The company's valuation | Notes I found that by using perplexity via open router, we lose access to the sources, as they are not stored in the same location as the report itself so I opted to use perplexity API via HTTP node. For using perplexity and or jina you have to configure header auth as described in Header Auth - n8n Docs What you can learn How to scrape data using sitemaps How to extract strucutred data from unstructured text How to execute parts of the workflow as subworkflow How to use deep research in a practical scenario How to define more complex JSON schemas

Transcript Evalu8r – AI-Powered Transcribing and Transcript Analysis Workflow Overview Transcript Evalu8r is an AI-powered transcript analysis workflow that automates the processing, visualization, and evaluation of transcribed conversations. This n8n workflow template is designed to help users quickly analyze sentiment, extract topics and intents, generate key insights, and enhance transcript navigation. It is ideal for customer service teams, legal professionals, researchers, content creators, and businesses seeking actionable intelligence from recorded conversations. Key Features 🔹 Automated Transcript Processing Audio-to-Text Conversion** – Upload an audio file and generate transcripts. File Upload Management** – Track file uploads with real-time progress indicators. Transcript Selection** – Browse and select previously analyzed transcripts from a dropdown. Download Transcripts* – Export transcripts in various formats (Google Docs and JSON*). Error Handling** – Detects and alerts users about upload or processing failures. 🔹 Advanced UI Components Interactive Transcript Viewer** – Displays full transcripts with: Timestamps and speaker identification. Clickable words for easy navigation. Auto-scrolling to highlight the active portion of the transcript. Key Points & Action Items Summary** – AI extracts: Critical insights from conversations. Actionable follow-ups detected in the discussion. Speaker Labeling* – Dynamically assigns *real speaker names** instead of generic placeholders (e.g., "Speaker 1"). Collapsible Sections* – Enables users to *expand/collapse** transcript details, key insights, and analysis sections. 🔹 AI-Powered Conversation Analysis Sentiment Analysis & Visualization** Dynamic sentiment graph tracks emotional shifts. Color-coded speaker sentiment tracking: 🔵 Positive ⚫ Neutral 🔴 Negative Time-based sentiment tracking – Shows sentiment fluctuations across the conversation. Topic & Intent Recognition** Automated Topic Extraction – Identifies key themes discussed. Hierarchical Topic Organization – Groups related topics for structured insights. Intent Classification – Recognizes whether statements reflect: Questioning Commands Decision-making Information seeking Confidence Scoring – Displays confidence levels for topic and intent detection. Speaker Contribution Metrics** Talk-time percentages per speaker. Interruption detection – Highlights moments where speakers cut each other off. Word Frequency Analysis – Identifies commonly used terms and phrases. 🔹 Seamless Workflow Automation API Integration* – Connects with transcription engines like *OpenAI Whisper, AWS Transcribe, and Deepgram**. Webhook Support** – Automates workflows when new transcripts are available. Batch Processing* – Handles *multiple transcript analyses** simultaneously. Export & Reporting** – Sends insights to Google Sheets, Notion, Airtable, or Slack. How It Works Upload an audio file or select a pre-existing transcript. Transcript Processing: Speech-to-text conversion (if required). AI-driven sentiment, topic, and intent analysis. Visualizations & Insights: Review sentiment charts, speaker contributions, and key topics. Extract action items and critical takeaways. Download results or trigger automated follow-ups. Use Cases ✅ Customer Support Review – Detect customer concerns and analyze sentiment trends. ✅ Podcast & Video Content Analysis – Summarize episodes and extract key themes. ✅ Sales Call Insights – Identify common objections and customer pain points. ✅ Market Research – Extract trends from focus groups and discussions. ✅ Legal Compliance Monitoring – Track policy violations in recorded conversations. Setup Instructions Import the Transcript Evalu8r workflow into your n8n instance. Connect DeepGram Speech to Text API. Configure workflow parameters (sentiment thresholds, topic extraction settings). Run the workflow to analyze and visualize transcript data. Review insights and export as needed. Why Use Transcript Evalu8r? 🚀 Automate tedious transcript analysis 💡 Unlock hidden insights in conversations 📊 Visualize conversations with interactive sentiment tracking ⚡ Boost efficiency with AI-powered automation 🔗 Seamlessly integrate with your existing workflows Requirements n8n instance** (cloud or self-hosted) API key for DeegGram API

ideoGener8r – Self-Hosted Ideogram AI Interface in n8n 🔥 March Sale – n8n Community Members Get ideoGener8r for Just $27! (Reg. $47) Use Coupon Code: ILoven8n (Valid until 3/31/2025 for n8n community members) ideoGener8r is an n8n template that sets up a self-contained, front-end interface for Ideogram AI image generation. It offers a complete workflow to generate, upscale, remix, and store images—entirely on your self-hosted n8n instance. Key Benefits & Limited-Time Offer Fully Self-Hosted**: Avoid monthly fees and keep your data private. All-in-One**: Generate, remix, and upscale images without third-party tools. Streamlined Automation**: Integrates directly with Google Sheets & Drive. Sale: **Save $20 – Get ideoGener8r for just $27 with coupon code ILoven8n (valid until 3/31/2025). Requirements & Overview Ideogram AI API Key Sign up at Ideogram AI and create an API key. Google Sheets & Drive A Google Sheet for storing metadata. A Google Drive folder for saving generated images. n8n Auth Credentials Basic Auth for the login webhook. Header Auth (Bearer token) for the generation/remix webhooks. With ideoGener8r, you can instantly create a private user interface—within n8n—to produce AI images on demand, trigger image generation through webhooks, and automatically log data to Google Sheets. Required Google Sheet Columns Your Google Sheet must contain (at minimum) the following columns for proper logging: Cheeck Setup note in workflow for csv template Step-by-Step Setup Instructions Import the Template Download the JSON file you received upon purchase. In your n8n instance, go to Workflows → Import, then upload the JSON file. Configure Ideogram API In n8n, create an HTTP Header Auth credential. For the “Authorization” header, use Bearer YOUR_IDEOGRAM_API_KEY. Attach this credential to the Ideogram-related nodes (e.g., IDEOGRAM Image generator, ideogram Upscale). Google Sheets Setup Copy or create a Google Sheet with the columns listed above. In the template, locate the Google Sheets “Append” nodes. Select the correct Sheet, tab, and map each column accordingly. Google Drive Folder Create a Drive folder to store images (sharing permissions are up to you). In the Google Drive nodes, enter the folder ID to save new images there. Basic Auth for Login Open the Login Webhook-ideoGener8r node. Set it to use Basic Auth and create credentials for a username/password. This will secure the login route (e.g., /ideogener8r-login). Bearer Token for Generation & History Open the “Generation Webhook” (e.g., /ideogen) and “History Webhook” (e.g., /ideogener8r-history). Set them to Header Auth and use Authorization: Bearer <YOUR_SECRET_TOKEN>. Any requests to these endpoints must include that token in the header. Publish & Test Activate or publish your workflow. Go to /ideogener8r-login, enter your Basic Auth credentials, and test by generating images. Verify that images appear in your Google Drive folder, and the metadata is logged in the Google Sheet. Ideogram API Details Endpoint**: The template uses Ideogram’s /generate, /upscale, and /remix endpoints. Headers**: You must provide your API key as a Bearer token in the request header. Rate Limits**: Check your Ideogram AI account for any usage or rate-limiting policies. More info at ideoGener8r.com

The workflow performs tasks that would normally require human intervention on Google News links, transforming the RSS feeds into data that can be used by an automated system like n8n, thus creating a solid foundation for further applications Who is this for? This workflow is ideal for developers, journalists, and content aggregators who need to extract and clean Google News URLs from its RSS feed. What problem does this workflow solve? Google News RSS provides encoded URLs that contain additional tracking parameters. This workflow decodes those URLs and provides clean, direct links to news articles, making them easier to process, share, and analyze. What this workflow does Fetch Google News RSS – Retrieves news articles from Google News based on predefined parameters (language, country). Limit results – Reduces the number of requests to avoid excessive API usage. Extract encoded content – Retrieves the encoded news URLs. Decode the URLs – Uses a decoding mechanism to extract clean links. Remove unwanted characters – Cleans up the decoded URLs to ensure they are properly formatted. Aggregate results – Outputs a final list of clean, readable URLs. Setup Modify RSS parameters (hl, gl) to match your target region. Adjust the result limit to control the number of processed articles. How to customize this workflow To customize this workflow, you can add an HTTP Request node to retrieve the article's text, an HTML Extract node to process the text, an AI node to generate new content, and a WordPress node to publish it Another option is to use an AI Agent node to classify articles by category based on the title or through HTML Extract. You can then save the classified articles using a Google Sheets node, organizing them by category and creating an high quality editorial plan This workflow efficiently processes Google News RSS, removes unnecessary encoding, and delivers clean, shareable URLs. 🚀

Who Is This For? Web developers SEO specialists Digital marketers What Problem Is This Workflow Solving? Automates the extraction of internal links from a webpage Eliminates the manual and error-prone process of collecting links Facilitates analysis of website structure and optimization What This Workflow Does Uses HTTP request node to fetch HTML content from a specified webpage Parses the HTML to identify and extract internal links Compiles a list of URLs directing to pages within the same domain Setup Configure the Set Base URL node: Set the url field to the URL you want to analyze. How to Customize This Workflow to Your Needs Change the target URL in the Set Base URL node to analyze different webpages. Add nodes to: Filter or categorize the extracted links Export the list to a database or CSV Send links via email or integrate with other tools This workflow can be used as a base for workflows to manage the process of extracting internal links, aiding in website optimization and SEO efforts.

Ever wanted to build your own RAG search over Youtube videos? Well, now you can! This n8n template shows how you can build a very capable Youtube search engine powered by Apify, Qdrant and your LLM of choice to quickly and efficiently browse over many videos for research. I originally started to template to ask questions on the "n8n @ scale office-hours" livestream videos but then extended it to include the latest videos on the official channel. Check out a demo here: https://jimleuk.app.n8n.cloud/webhook/n8n_videos How it works Stage 1 is to collect the Youtube video transcripts and push them into a vector database. For this, I've used Apify to scrape Youtube and Qdrant to store the embeddings. Transcripts are broken down into smaller chunks and carefully tagged with metadata to assist in later search and filtering. Stage 2 is to build a web frontend for the user to query the vectorised transcripts. I'm using a webhook to serve a simple web app and API to dynamically fetch the results. When searching for a video, I've opted to use Qdrant's search groups API which in this use-case, performs better as it returns a wider range of videos results. In the web frontend, when the user clicks on the results, the matching Youtube video plays in an embedded video player. How to use Once credentials are all set, first run steps 1 - 3 to populate your vector store. Next, set the workflow to active to expose the web frontend. Visit the webhook URL in your browser to use it. If only for personal use, you may want to remove the rate limiting mechanism in step 4. Requirements Apify for Youtube Channel and Video Scraping Qdrant for Vector store OpenAI for LLM and Embeddings Customising the template Not interested in official n8n videos? Swap to a different channel - this template will work on many as long as videos are not private or set to prevent embeds. Technically any vector store should work but may not have the same grouping API. Use the simple vector store node and revert back to basic searching instead.

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