Integrate Split Out with 500+ apps and services

Use Case This workflow is beneficial when you're automatically adding new leads to your Pipedrive CRM. Usually, you'd have to manually review each lead to determine if they're a good fit. This process is time-consuming and increases the chances of missing important leads. This workflow ensures every new lead is promptly evaluated upon addition. What this workflow does The workflow runs every 5 minutes. On every run, it checks your new Pipedrive leads and enriches them with Clearbit. It then marks items as enriched and checks if the company of the new lead matches certain criteria (in this case if they are B2B and have more than 100 employees) and sends a Slack alert to a channel for every match. Pre Conditions You must have Pipedrive, Clearbit, and Slack accounts. You also need to set up the custom fields Domain and Enriched at in Pipedrive. Setup Go to Company Settings -> Data fields -> Organization and add Domain as a custom field Go to Company Settings -> Data fields -> Leads and add Enriched at as a custom date field Add your Pipedrive, Clearbit and Slack credentials. Fill the setup node below. To get the ID of your custom domain fields, simply run the Show only custom organization fields and Show only custom lead fields nodes below and copy the keys of your domain, and enriched at fields. How to adjust this workflow to your needs Modify the criteria to suit your definition of an interesting lead. If you only want to focus on interesting leads in Pipedrive, add a node that archives all others. This workflow was built using n8n version 1.29.1

Who is this template for? This workflow template is designed for everyone with a Gmail address, who wants to forward all Netflix emails, including temporary login codes, to friends and family effortlessly. How it works Scans your Gmail inbox every minute for new e-mails from Netflix Forwards all Netflix e-mails to all desired e-mail addresses via the e-mail provider Mailjet Setup Steps Connect your Google Mail Account to n8n following the official n8n instructions Add all recipients you want to the recipients array at the "Set all recipients" node. Create and connect your Mailjet Account to n8n following the official n8n instructions. Note: You cannot use an Gmail e-mail address as the sender address, as mailjet does not support this. I recommend using your own email address from a custom domain. This works perfectly.

Sync your Google Sheets Data with your Postgres database table, requiring minimal adjustments. Follow these steps: Retrieve Data: Pull data from Google Sheets and PostgreSQL. Compare Datasets: Identify differences, focusing on new or updated entries. Update PostgreSQL: Apply changes to ensure both platforms mirror each other. Automate this process to regularly synchronize data. Before starting, grant necessary access to both Google Sheets and PostgreSQL, and specify the data details for synchronization. This streamlined workflow enhances data consistency across platforms. This example is a one-way synchronization from Google Sheets into your Postgres. With small adjustments, you can make it the other way around, or 2-way.

This n8n workflows builds another example of creating a knowledgebase assistant but demonstrates how a more deliberate and targeted approach to ingesting the data can produce much better results for your chatbot. In this example, a government tax code policy document is used. Whilst we could split the document into chunks by content length, we often lose the context of chapters and sections which may be required by the user. Our approach then is to first split the document into chapters and sections before importing into our vector store. Additionally, using metadata correctly is key to allow filtering and scoped queries. Example Human: "Tell me about what the tax code says about cargo for intentional commerce?" AI: "Section 11.25 of the Texas Property Tax Code pertains to "MARINE CARGO CONTAINERS USED EXCLUSIVELY IN INTERNATIONAL COMMERCE." In this section, a person who is a citizen of a foreign country or an en..." How it works The tax code policy document is downloaded as a zip file from the government website and its pages are extracted as separate chapters. Each chapter is then parsed and split into its sections using data manipulation expressions. Each section is then inserted into our Qdrant vector store tagged with its source, chapter and section numbers as metadata. When our AI Agent needs to retrieve data from our vector store, we use a custom workflow tool to perform the query to Qdrant. Because we're relying on Qdrant's advanced filtering capabilities, we perform the search using the Qdrant API rather than the Qdrant node. When the AI Agent, needs to pull full wording or extracts, we can use Qdrant's scroll API and metadata filtering to do so. This makes Qdrant behave like a key-value store for our document. Requirements A Qdrant instance is required for the vector store and specifically for it's filtering functionality. Mistral.ai account for Embeddings and AI models. Customising this workflow Depending on your use-case, consider returning actual PDF pages (or links) to the user for the extra confirmation and to build trust. Not using Mistral? You are able to replace but note to match the distance and dimension size of Qdrant collection to your chosen embedding model.

Overview This template integrates an IOT multi-button switch (meant for controlling a dimmable light) with Spotify playback functions, via MQTT messages. This isn't likely to work without some tinkering, but should be a good head start on receiving/routing IOT/MQTT messages and hooking up to a Spotify-like API. Requirements An IOT device capable of generating events that can be delivered as MQTT messages through an MQTT Broker e.g. Ikea Strybar remote An MQTT Broker to which n8n can connect and consume messages e.g. Zigbee2MQTT in HomeAssistant A Spotify developer-account (which provides access to API functions via OAuth2 authorization) A Spotify user-account (which provides access to Spotify streamed content, user settings, etc.) Setup Create an MQTT Credential item in n8n and assign it to the MQTT Trigger node Modify the MQTT trigger node to match the topic for your IOT device messages Modify the switch/router nodes to map to the message text from your IOT button (e.g. arrow_left_click, brightness_up_click, etc.) Create a Spotify developer-account (or use the login for a user-account) Create an "App" in the developer-account to represent the n8n workflow Chicken/Egg ALERT: The n8n Spotify Credentials dialog box will display the "OAuth Redirect URL" required to create the App in Spotify, but the n8n Credential item itself cannot be created until AFTER the App has been created. Create a Spotify Credentials item in n8n Open the Settings on the Spotify App to find the required Client ID and Client Secret information. ALERT: Save this before proceeding to the Connect step. Connect the n8n Spotify Credential item to the Spotify user-account ALERT: Expect n8n to open a separate OAuth2 window on authorization.spotify.com here, which may require a login to the Spotify user-account Open each of the HTTP and Spotify nodes, one by one, and re-assign to your Spotify Credential (try not to miss any). (Then, probably, upvote this feature request: https://community.n8n.io/t/select-credentials-via-expression/5150 Modify the variable values in the Globals node to match your own environment. target_spotify_playback_device_name - The name of a playback device available to the Spotify user-account favorite_playlist_name - The name of a playlist to start when one of the button actions is indicated in the MQTT message. Used in example "Custom Function 2" sequence. Notes You're on your own for getting the multi-button remote switch talking to MQTT, figuring out what the exact MQTT topic name is, and mapping the message parts to the workflow (actions, etc.). The next / previous actions are wired up to not transfer control to the target device. This alternative routing just illustrates a different behavior than the remaining actions/functions, which include activation of the target device when required. Some of the Spotify API interactions use the Spotify node in n8n, but many of the available Spotify API functions are limited or not implemented at all in the Spotify node. So, in other cases, a regular HTTP node is used with the Spotify OAuth2 API credential instead. By modifying one of the examples included in the template, it should be possible to call nearly anything the Spotify API has to offer. Spotify+n8n OAuth Mini-Tutorial Definitions The developer-account is the Spotify login for creating a spotify-app which will be associated with a client id and client secret. The user-account is the Spotify login that has permission to stream songs, set up playback devices, etc. ++A spotify-login allows access to a Spotify user-account, or a Spotify developer-account, OR BOTH++ The spotify-app, which has a client id and client secret, is an object created in the developer-account. The app-implementation (in this case, an ++n8n workflow++) uses the spotify-app's credentials (client id / client secret) to call Spotify API endpoints on behalf of a user-account. Using One Spotify Login as Both User and Developer When an n8n Spotify-node or HTTP-node (i.e. an app-implementation) calls a Spotify API endpoint, the Credentials item may be using the client id and client secret from a spotify-app, which was created in a developer-account that is ++one and the same spotify-login as the user-account++. However, it helps to remind yourself that from the Spotify API server's perspective, the developer-account + spotify-app, and the user-account, are ++two independent entities++. n8n Spotify-OAuth2-API Credential Authorization Process The 2 layers/steps, in the process of authorizing an n8n Spotify-OAuth2-API credential to make API calls, are: n8n must identify itself to Spotify as the app-implementation associated with the developer-account/spotify-app by sending the app's credentials (client id and client secret) to Spotify. The Client ID and Client Secret are supplied in the n8n Spotify OAuth2 Credentials UI/dialog-box Separately, n8n must obtain an authorization token from Spotify to represent the permissions granted by the user to execute actions (call API endpoints) on behalf of the user (i.e. access things that belong to the user-account). This authorization for the user-account access is obtained when the "Connect" or "Reconnect" button is clicked in the n8n Spotify Credentials UI/dialog-box (which pops up a separate authorization UI/browser-window managed by Spotify). The Authorization for a given spotify-app stays "registered" in the user-account until revoked. See: https://support.spotify.com/us/article/spotify-on-other-apps/ Direct Link: https://www.spotify.com/account/apps/ More than one user-account can be authorized for a given spotify-app. A particular n8n Spotify-OAuth2-API credential item appears to cache an authorization token for the user-account that was most recently authorized. Up to 25 users can be allowed access to a spotify-app in Developer-Mode, but any user-account other than the one associated with the developer-account must be added by email address at https://developer.spotify.com/dashboard/{{app-credential-id}}/users ALERT: IF the browser running the n8n UI is ALSO logged into a Spotify account, and the spotify-app is already authorized for that Spotify account, the "reconnect" button in the Spotify-OAuth2-API credential dialog may automatically grab a token for that logged in user-account, offering no opportunity to select a different user-account. This can be managed somewhat by using "incognito" browser windows for n8n, Spotify, or both. References n8n Spotify Credentials Docs Spotify Authorization Docs

The template allows to make Dropcontact batch requests up to 250 requests every 10 minutes (1500/hour). Valuable if high volume email enrichment is expected. Dropcontact will look for email & basic email qualification if first_name, last_name, company_name is provided. +++++++++++++++++++++++++++++++++++++++++ Step 1: Node "Profiles Query" Connect your own source (Airtable, Google Sheets, Supabase,...) the template is using Postgres by default. Note I: Be careful your source is only returning a maximum of 250 items. Note II: The next node uses the next variables, make sure you can map these from your source file: first_name last_name website (company_name would work too) full_name (see note) Note III: This template is using the Dropcontact Batch API, which works in a POST & GET setup. Not a GET request only to retrieve data, as Dropcontact needs to process the batch data load properly. +++++++++++++++++++++++++++++++++++++++++ Step 2: Node "Data Transformation" Will transform the input variables in the proper json format. This json format is expected from the Dropcontact API to make a batch request. "full_name" is being used as a custom identifier to update the returned email to the proper contact in your source database. To make things easy, use a unique identiefer in the full_name variable. +++++++++++++++++++++++++++++++++++++++++ Step3: Node: "Bulk Dropcontact Requests". Enter your Dropcontact credentials in the node: Bulk Dropcontact Requests. +++++++++++++++++++++++++++++++++++++++++ Step4: Connect your output source by mapping the data you like to use. +++++++++++++++++++++++++++++++++++++++++ Step5: Node: "Slack" (OPTIONAL) Connect your slack account, if an error occur, you will be notified. TIP: Try to run the workflow with a batch of 10 (not 250) as it might need to run initially before you will be able to map the data to your final destination. Once the data fields are properly mapped, adjust back to 250.

This n8n workflow demonstrates how you can summarise and automate post-meeting actions from video transcripts fed into an AI Agent. Save time between meetings by allowing AI handle the chores of organising follow-up meetings and invites. How it works This workflow scans for the calendar for client or team meetings which were held online. * Attempts will be made to fetch any recorded transcripts which are then sent to the AI agent. The AI agent summarises and identifies if any follow-on meetings are required. If found, the Agent will use its Calendar Tool to to create the event for the time, date and place for the next meeting as well as add known attendees. Requirements Google Calendar and the ability to fetch Meeting Transcripts (There is a special OAuth permission for this action!) OpenAI account for access to the LLM. Customising the workflow This example only books follow-on meetings but could be extended to generate reports or send emails.

This n8n workflow automates the process of parsing and extracting data from PDF invoices. With this workflow, accounts and finance people can realise huge time and cost savings in their busy schedules. Read the Blog: https://blog.n8n.io/how-to-extract-data-from-pdf-to-excel-spreadsheet-advance-parsing-with-n8n-io-and-llamaparse/ How it works This workflow will watch an email inbox for incoming invoices from suppliers It will download the attached PDFs and processing them through a third party service called LlamaParse. LlamaParse is specifically designed to handle and convert complex PDF data structures such as tables to markdown. Markdown is easily to process for LLM models and so the data extraction by our AI agent is more accurate and reliable. The workflow exports the extracted data from the AI agent to Google Sheets once the job complete. Requirements The criteria of the email trigger must be configured to capture emails with attachments. The gmail label "invoice synced" must be created before using this workflow. A LlamaIndex.ai account to use the LlamaParse service. An OpenAI account to use GPT for AI work. Google Sheets to save the output of the data extraction process although this can be replaced for whatever your needs. Customizing this workflow This workflow uses Gmail and Google Sheets but these can easily be swapped out for equivalent services such as Outlook and Excel. Not using Excel? Simple redirect the output of the AI agent to your accounting software of choice.

What is this workflow doing? This simple workflow is pulling the latest Euro foreign exchange reference rates from the European Central Bank and responding expected values to an incoming HTTP request (GET) via a Webhook trigger node. Setup no authentication** needed the workflow is ready to use test** the workflow template by hitting the test workflow button and calling the URL in the webhook node optional: choose your own Webhook listening path in the Webhook trigger node Usage There are two possible usage scenarios: get all Euro exchange rates as an array of objects get only a specific currency exchange rate as a single object All available rates Using the HTTP query ?foreign=USD (where USD is one of the available currency symbols) will provide only that specificly asked rate. Response example: {"currency":"USD","rate":"1.0852"} Single exchange rate If no query is provided, all available rates are returned. Response example: [{"currency":"USD","rate":"1.0852"},{"currency":"JPY","rate":"163.38"},{"currency":"BGN","rate":"1.9558"},{"currency":"CZK","rate":"25.367"},{"currency":"DKK","rate":"7.4542"},{"currency":"GBP","rate":"0.85495"},{"currency":"HUF","rate":"389.53"},{"currency":"PLN","rate":"4.3053"},{"currency":"RON","rate":"4.9722"},{"currency":"SEK","rate":"11.1675"},{"currency":"CHF","rate":"0.9546"},{"currency":"ISK","rate":"149.30"},{"currency":"NOK","rate":"11.4285"},{"currency":"TRY","rate":"33.7742"},{"currency":"AUD","rate":"1.6560"},{"currency":"BRL","rate":"5.4111"},{"currency":"CAD","rate":"1.4674"},{"currency":"CNY","rate":"7.8100"},{"currency":"HKD","rate":"8.4898"},{"currency":"IDR","rate":"16962.54"},{"currency":"ILS","rate":"3.9603"},{"currency":"INR","rate":"89.9375"},{"currency":"KRW","rate":"1444.46"},{"currency":"MXN","rate":"18.5473"},{"currency":"MYR","rate":"5.1840"},{"currency":"NZD","rate":"1.7560"},{"currency":"PHP","rate":"60.874"},{"currency":"SGD","rate":"1.4582"},{"currency":"THB","rate":"38.915"},{"currency":"ZAR","rate":"20.9499"}] Further info Read more about Euro foreign exchange reference rates here.

Purpose This workflow enables you to listen to your recent favorites in very hight quality offline without sacrificing all of your storage. How it works This workflow automatically creates a playlist in Spotify named "Downloads" which periodically gets updated so it always contains only a defined amount of the latest liked songs. This enables only the Downloads playlist to set for automatic downloading and thus free up space on the device. Setup The workflow is ready to go. Just select your Spotify credentials and activate the workflow. In Spotify just enable automatic downloads on the automatically created Downloads folder after the first workflow run. Current limitations This setup currently supports a maximum of 50 songs in the Downloads Playlist. This is due to the paylod limits defined by Spotify encountered in the Get liked songs node. Implementing batching would solve the issue.

What it does The workflow is a simple yet efficient way to automate the process of indexing your website on Google using the Google Indexing API. How it works It works by extracting information from your sitemap, converting it into a JSON file, and looping through each URL to submit it for indexing. Here's a brief rundown of the workflow: The workflow can be triggered manually via the "Execute Workflow" button or scheduled to run at a specific time using the "Schedule Trigger" node. The sitemap of your website is fetched using the "sitemap_set" node with a HTTP Request to the sitemap URL. This XML sitemap is then converted into a JSON file using the "sitemap_convert" node. The "sitemap_parse" node splits the JSON file into individual URLs. The "url_set" node then prepares each URL to be sent to the Google Indexing API. A loop is created using the "loop" node to process each URL individually and make a POST request to Google Indexing API indicating that the URL has been updated. If the POST request is successful and the URL has been updated, the workflow waits for 2 seconds before moving to the next URL. In case the daily limit for the Google Indexing API is reached (200/day by default), an error message is triggered using the "Stop and Error" node. Before you use the workflow Activate the indexing API Create an account with Google Cloud Platform > Console and then create a new project Search for the Indexing API in the Library Activate the API Create a Service Account and get credentials Open the Service accounts page. If prompted, select a project. Click add Create Service Account, enter a name and description for the service account. You can use the default service account ID, or choose a different, unique one. When done click Create. On the Grant users access to this service account screen, scroll down to the Create key section. Click add Create key. In the side panel that appears, select the JSON format Click Create. Your new public/private key pair is generated and downloaded to your machine. Open the file and copy the private key. Add the credentials in the url_index node Add the user as owner of the site Beware, for each site you need to add the user as a owner like this: Set your sitemap Open the sitemap_set node and add the url to your sitemap. Now you should be able to ensure that Google is always up-to-date with the latest content on your website, improving your website's visibility and SEO rankings, have fun!

Use case To guarantee an effective sales process deals must be distributed between sales reps in the best way. Normally, this involves manually assigning new deals that have come in. This workflow automates it for you! What this workflow does This workflow runs once a day and checks for unassigned deals in your Hubspot CRM. Once it finds one, it enriches the deal with information about the assigned contact and their company. It then checks the region of the assigned company before looking at the company's employee size. Based on this, it assigns the deal to the right sales rep within your company. Requirements New deals in Hubspot need to be unassigned in the beginning New deals have to have an attached contact that has an attached company in Hubspot The company needs to have values for region and employee count in Hubspot Setup The setup is quite straight forward and will probably take a few minutes only. Add your Hubspot credentials Customize your criterias for assigning deals in the Assign by Region and the following Assign nodes Make sure deals are assigned to the right salesrep in the Hubspot nodes at the end Activate the workflow Customizing this to your needs Adjust the trigger interval to your needs. Currently, it defaults to once a day Adjust your region settings by adding/updating/removing options in the respective node Adjust your employee size settings by adding/updating/removing options in the respective node Ideas to enhance this flow Wrap each region's assigned criteria into different sub-workflows for easier maintainability. This will not consume additional execution counts. Add more logic on what happens once a deal does not match any criteria you've set

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

Who is it for If you are a postmaster or you manage email server, you can set up DKIM and SPF records to ensure that spoofing your email address is hard. On your domain you can also set up DMARC record to receive XML reports from email providers (rua tag). Those reports contain data if email they received passed DKIM and SPF verifications. Since DMARC email is public, you will receive a lot of emails from email providers, not only if DKIM/SPF fail. There is no need for it - you probably only need to know if SPF/DKIM failed. So this script is intended to automatically parse all DMARC reports that come from email providers, but ONLY send you notification if SPF or DKIM failed - meaning that either someone tries to spoof your email or your DKIM/SPF is improperly set up. How it works script monitors postmaster email for DMARC reprots (rua) unpacks report and parses XML into JSON maps JSON and formats fields for MySQL/MariaDB input inputs into database sends notification on DKIM or SPF failure Remember to set up email input mailbox notification channels for slack for email

This n8n workflow template allows you to write WordPress posts by just providing a few keywords. It uses AI technology to write the text and to create the post's featured image. The text includes an introduction, chapters, and conclusions. Each chapter is written independently and this allows you to create also very long articles. The workflow uses technologies provided by Open AI: Chat GPT for the text and Dall-E for the image. I suggest reviewing the created posts before publishing them on your WordPress website. The article generation might take some minutes as each chapter is created independently. Features Easy to use:** Easy web interface to start the generation of the WordPress post AI-powered:** Text and image generation is done by artificial intelligence Long-text ready:** Possibility to create very long articles Configurable:** Possibility to provide as many keywords as you want, to choose the number of chapters and the length of the article Plugs into your WordPress:** Easily integrates with your WordPress website Tweak it as you want:** Fine-tune the Open AI prompts and the workflow as you want Workflow Steps User form:** An n8n form is used to trigger the post creation Settings:** This node is used to set your WordPress URL (which is used later in the workflow) Article structure:** First AI action that writes the introduction, the conclusions, and the chapter structure. Data check:** Check that the data provided by the AI is valid Chapters split/Chapters text:** Splits the data for each chapter in a separate item and generates each chapter's text with AI Content preparation:** Prepares the text for posting merging the introduction, the chapters, and the conclusions. Adds some basic HTML formatting Draft on WordPress:** Creates the draft post on WordPress Featured image:** Creates a featured image and adds it to the post on WordPress User feedback:** Sends a feedback to the user on the n8n form Getting Started To deploy and use this template: Import the workflow into your n8n workspace Set your WordPress URL in the wordpress_url field in the "Settings" node. Include the slash (/) at the end of the URL Set up your Open AI n8n credentials by following this guide. The Open AI credentials are used by the Open AI nodes ("Create post title and structure", "Create chapters text", and "Generate featured image") Set up your WordPress n8n credentials by following this guide. The WordPress credentials are used by the WordPress and HTTP Request nodes ("Post on Wordpress", "Upload media", and "Set image ID for the post"). Pay attention that the "Password" in the WordPress credentials is not the user's password by the Application Password How use the workflow to create a WordPress post Activate the workflow Open the "Form" node and copy the "Production URL". This is the public URL of the form to AI-write the post Open the URL in a browser and fill in the form Wait a few minutes till you get the feedback in the form that the post was created Go to WordPress and check the newly created draft post. Review and publish your post!

This n8n workflow demonstrates how to automate indexing of images to build a object-based image search. By utilising a Detr-Resnet-50 Object Classification model, we can identify objects within an image and store these associations in Elasticsearch along with a reference to the image. How it works An image is imported into the workflow via HTTP request node. The image is then sent to Cloudflare's Worker AI API where the service runs the image through the Detr-Resnet-50 object classification model. The API returns the object associations with their positions in the image, labels and confidence score of the classification. Confidence scores of less the 0.9 are discarded for brevity. The image's URL and its associations are then index in an ElasticSearch server ready for searching. Requirements A Cloudflare account with Workers AI enabled to access the object classification model. An ElasticSearch instance to store the image url and related associations. Extending this workflow Further enrich your indexed data with additional attributes or metrics relevant to your users. Use a vectorstore to provide similarity search over the images.

How it works it will return workflows that have buil-in nodes not of latest version with information of node name, type, current version and latest version for that type Set up steps: You need to have n8n credentials set, you can get n8n API key under settings set your instance base URL in "instance base url" node Disclaimar: Only check build-in nodes, community nodes are not supported

How it works This template takes a YouTube video ID and identifies potentially engaging moments based on the intensity of specific timestamps 👇 Ideal for vloggers and YouTube content creators, it serves as a foundation for various automations to streamline content calendars or highlight popular moments in your videos. You can leverage it for: Automatic processes to analyze YouTube videos and create sizzle reels or clips for social media, particularly effective for microcontent strategies like those endorsed by Gary Vee. Instant alerts via Slack, Telegram, Email, or WhatsApp when significant moments occur in your videos. Utilizing transcripts of these moments with AI to refine content ideas or brainstorm chatbots in your editorial workflow. Example response from the Workflow-as-an-API A GET request to {your instance URL}/webhook/youtube-engaging-moments-extractor?ytID=IZsQqarWXtYy returns 👇 The workflow generates multiple moments; the screenshot above shows a truncated version. Not all videos contain timestamp intensity data, the workflow handles this case as well 👇 How to use Import the template into your n8n workspace or self-hosted instance, then activate the workflow. Open the Webhook trigger node and copy the Production URL. In a web browser or any tool capable of consuming HTTP Requests (e.g., native code, Bubble app, n8n workflow, another automation tool, Postman, etc.), pass along the URL parameter ?ytID={youtube video ID} when invoking the API endpoint. Your URL should resemble something like https://acme.app.n8n.cloud/webhook/youtube-engaging-moments-extractor?ytID=IZsQqarWXtYy. Keep in mind This workflow relies on an unofficial YouTube API graciously hosted for free by the folks at lemnoslife.com. It's not recommended for high-volume production usecases.

How it works This workflow reads a list of URLs every 15 minutes, and sends an HTTP request to every URL on the list. Set up steps Schedule the workflow to run at your desired frequency (default is every 15 minutes). Add your desired URLs to the list. The list should be in the same format as the image below (Don't forget to have single quotes around every URL in the list, and separate each one with a comma!): Turn the workflow ON. Ideas to customize the workflow for your own use cases: Change the HTTP method Add headers Add a request body

With this workflow you get a fully automated AI powered Support-Agent for your WooCommerce webshop. It allows customers to request information about things like: the status of their order the ordered products shipping and billing address current DHL shipping status How it works The workflow receives chat messages from an in a website integrated chat. For security and data-privacy reasons, does the website transmit the email address of the user encrypted with the requests. That ensures that user can just request the information about their own orders. An AI agent with a custom tool supplies the needed information. The tool calls a sub-workflow (in this case, in the same workflow for convenience) to retrieve the required information. This includes the full information of past orders plus the shipping information from DHL. If otherr shipping providers are used it should be simple to adjust the workflow to query information from other APIs like UPS, Fedex or others.

Workflow Information 📌 Purpose 🎯 The intention of this workflow is to integrate New Shopify Orders into MS Dynamics Business Central: Point-of-Sale (POS):** POS orders will be created in Business Central as Sales Invoices given no fulfillment is expected. Web Orders:** This type of orders will be created as Business Central Sales Orders. How to use it 🚀 Edit the "D365 BC Environment Settings" node with your own account values (Company Id, Tenanant Id, Tax & Discount Items). Go to the "Shopify" node and edit the connection with your environment. More help here. Go to the "Lookup Customers" node to edit the Business Central connection details with your environment settings. Set the required filters on the "Shopify Order Filter" node. Edit the "Schedule Trigger" node with the required frequency. Useful Workflow Links 📚 Step-by-step Guide/ Integro Cloud Solutions Business Central REST API Documentation Video Demo Need Help? Contact me at: ✉️[email protected] 📥 https://www.linkedin.com/in/greg-lopez-08b5071b/

🚀 Supercharge Your Website Indexing with This Powerful n8n Workflow! 🌐 Google page indexing too slow? Tired of manually clicking through each page in the Google Search Console? 😴 Say goodbye to that tedious process and hello to automation with this n8n workflow! 🎉 **NB: this workflow was tested with sitemap.xml generated by Ghost CMS and WordPress. Reach out to Eduard if you need help adapting this workflow to your specific use-case!** ⚙️ How this automation works 📅 The workflow runs on a schedule or when you click "Test workflow". 🌐 It fetches the website's primary sitemap.xml and extracts all the content-specific sitemaps (this is a typical structure of the sitemap). 📑 Each content-specific sitemap is then parsed to retrieve the individual page data. 🔄 The extracted page data is converted to JSON format for easy manipulation. 🗃️ The lastmod (last modified date) and loc (page URL) fields are assigned to each page entry to ensure compliance with the Sitemap protocol. 🔀 The page entries are sorted by the lastmod field in descending order (newest to oldest). 🔁 The workflow then loops over each page entry and performs the following steps: 🔍 Checks the URL metadata in the Google Indexing API. ✅ If the page is new or has been updated since the last indexing request, it sends a request to the Google Indexing API to update the URL. ⏳ Wait a sec and move on with the next page. 🌟 Benefits ⏰ Save time by automating the indexing process. 🎯 Ensure all your website pages are consistently indexed by Google. 🚀 Improve your website's visibility and search engine rankings. 🛠️ Customize the workflow to fit your specific CMS and requirements. 🔧 Getting started To start using this powerful n8n workflow, follow these steps: ☑️ Make sure to verify the website ownership in the Google Search Console. 👨‍💻 Import the workflow JSON into your n8n instance. Edit the Get sitemap.xml node and update the URL with your website's valid sitemap.xml 🔑 Set up the necessary credentials for the Google Indexing API. 🎚️ Adjust the schedule trigger to run the workflow at your desired frequency. 🎉 Sit back and let the workflow handle the indexing process for you! Ready to take your website indexing to the next level? 🚀 Try this workflow now and see the difference it makes! 😊 ⚠️ IMPORTANT NOTE 1 Need help with connecting Google Cloud Platform to n8n? Check out our article on connecting Google Sheets to n8n. The process is mainly the same. When activating Google APIs, make sure to add Web Search Indexing API. Also, in the credential page of n8n, add the https://www.googleapis.com/auth/indexing scope: Check out Yulia's page for more n8n workflows! ⚠️ IMPORTANT NOTE 2 Free Google Cloud Platform account allows (re)indexing only 200 pages per day. If your website has more, then the workflow will automatically fail on quota limit ⛔. Next day it will skip the previously added items and continue with remaining pages. Example:* Assuming you have a free Google account, 500 pages on your website and they don't change for 3 days: On the first day 200 pages will be added for indexing and the workflow will fail due to quota limits. On the second day, the workflow will check 200 pages again and skip them (because the date of re-indexing is later then the page last modified date). The next 200 pages will be added to indexing. Workflow will fail again due to quota limits. On the third day 400 pages will be checked and skipped, the last 100 pages will be added for indexing and the workflow finishes successfully.

How to scrap emails from websites This workflow shows how to quickly build an Email scraping API using n8n. Email marketing is at the core of most marketing strategies, be it content marketing, sales, etc. As such, being able to find contacts in bulk for your business on a large scale is key. There are available tools available in the market that can do this, but most are premium; why not build a custom one with n8n? Usage The workflow gets the data from a website and performs an extraction based on the date around on the website Copy the webhook URL to your browser Add a query parameter eg ?Website=https://mailsafi.com . This should give you a URL like this {{$n8nhostingurl/webhook/ea568868-5770-4b2a-8893-700b344c995e?Website=https://mailsafi.com Click on the URL and wait for the extracted email to be displayed. This will return the email address on the website, or if there is no email, the response will be "workflow successfully executed." Make sure to use HTTP:// for your domains Otherwise, you may get an error.

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 is a building block designed to be called from other workflows via an Execute workflow node. When called from another workflow, and given the JSON input of a "pulse" field with the ID to pull from monday, this workflow will return: The items name and ID All column data, indexable by the column name All column data, indexable by the column's ID string All board relation columns, with their data and column values All subitems, with their data and column values For example: ++Prerequisites++ A monday.com account and credential A workflow that needs to get detailed data from a monday.com row The pulse id of the monday.com row to retreive data from. ++Setup++ Import the workflow Configure all monday nodes with your credentials and save the workflow Copy the workflow ID from it's URL In a different workflow, add an Edit Fields node, to output the field "pulse", with the monday item you want to retrieve. Feed the Edit Fields node with your pulse into an Execute workflow node, and paste the workflow ID from above into it This "pulse" field will tell the workflow what pulse to retreive. This can be populated by an expression in your workflow There is an example of the Edit Fields and Execute Workflow nodes in the template

This workflow syncs multiple Notion databases to your Google Calendar. And it works both ways. What events are supported? Everything except recurring events. All day events, multiple day events, start and end date… these are all supported. You set them in Notion and they stay in sync with Google. And vice versa. Why doesn’t it support recurring events? Notion doesn’t support recurring events yet. So when you create a recurring event in Google, it will only consider the first date, ignoring future occurrences of the event. Can I connect more than one Notion database? Yes. You can have many Notion databases synced to one Google Calendar account. You can see how to do it in the workflow instructions. It is recommended that you create more calendars in your account, so that you can link each calendar to a different database in Notion. But that’s a choice. What happens if I delete an event or page? Notion page deleted → Deletes event in Google Notion date property cleared → Deletes event in Google Google event deleted → Clears the date property in Notion, but keeps the page, so you don’t lose your work. Does it update the events? Yes. When you update the event in Google or in Notion it syncs both ways. How can I know what Notion item was linked to an event? Either by the name or by clicking the hyperlink in the event description that says: 👉 View in Notion. When I create a new event in Google, does it add an item to Notion? Yes. When you create an event inside one of your calendars, the item is synced to the corresponding Notion database. Does it sync event descriptions? No. The event description will always be “View in Notion”. Even if you change it in Google Calendar it will be overwritten when you make a change to the Notion page. 🎉 When you buy this template you receive step-by-step instructions on how to set it up.

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 Survey Insights scenario where survey participant responses can be quickly grouped by similarity and an AI agent can generate insights on those groupings. With this workflow, researchers can save days and even weeks of work breaking down cohorts of participants and identify frequently mentioned positives and negatives. Sample Output: https://docs.google.com/spreadsheets/d/e/2PACX-1vT6m8XH8JWJTUAfwojc68NAUGC7q0lO7iV738J7aO5fuVjiVzdTRRPkMmT1C4N8TwejaiT0XrmF1Q48/pubhtml# How it works All survey questions and responses are imported from a Google Sheet. Responses are then inserted into a Qdrant collection carefully tagged with the question and survey metadata. For each question, all relevant response are 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 researcher. Requirements Survey data and format as shown in the attached google sheet. Qdrant Vectorstore for storing embeddings. OpenAI account for embeddings and LLM. Customising the Template Adjust clustering parameters which make sense for your data. Add more clusters for open-ended questions and less clusters when responses are multiple choice.

This is a very simple workflow that lets you subscribe to any github repository for the latest release (using n8n as example). How it works: daily poll to Github repository for release for latest (stable) version of n8n parses the content to HTML sends a gmail Setup steps: add your gmail credentials (or use other email node of choice) change the url to the right Github repository you want to check regularly change the To email address to the email that you want to receive the updates for Feedback & Questions If you have any questions or feedback about this workflow - Feel free to get in touch at [email protected]

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.

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

This n8n workflow takes in a document such as a research paper, marketing or sales deck or company filings, and breaks them down into 3 templates: study guide, briefing doc and timeline. These templates are designed to help a student, associate or clerk quickly summarise, learn and understand the contents to be more productive. Study guide - a short quiz of questions and answered generated by the AI Agent using the contents of the document. Briefing Doc - key information and insights are extracted by the AI into a digestable form. Timeline - key events, durations and people are identified and listed into a simple to understand timeline by the AI How it works A local file trigger watches a local network directory for new documents. New documents are imported into the workflow, its contents extracted and vectorised into a Qdrant vector store to build a mini-knowledgebase. The document then passes through a series of template generating prompts where the AI will perform "research" on the knowledgebase to generate the template contents. Generated study guide, briefing and timeline documents are exported to a designated folder for the user. Requirements Self-hosted version of n8n. Qdrant instance for knowledgebase. Mistral.ai account for embeddings and AI model. Customising your workflow Try adding your own templates or adjusting the existing templates to suit your unique use-case. Anything is quite possible and limited only by your imagination! Want to go fully local? A version of this workflow is available which uses Ollama instead. You can download this template here: https://drive.google.com/file/d/1VV5R2nW-IhVcFP_k8uEks4LsLRZrHSNG/view?usp=sharing

The Stripe API does not provide custom fields in invoice or charge data. So you have to get it from the Checkout Sessions endpoint. But that endpoint is not easy for begginners. It has dictionary parameters and pagination settings. This workflows solves that problem by having a preconfigured GET request that gets all the checkout sessions from the last 7 days. It then transforms the data to make it easier to work with and allows you to filter by the custom_fields you want to get.

Use case This workflow automatically turns any RSS feed into a newsletter. It send an email every time a new post is published to an RSS feed in the last hour. I have been using this personally to follow Derek Sivers and Anil Dash, who don't have newsletters. Check them out 😉 How to setup Add your email and email creds Add the RSS feed URLs you want to follow How to adjust this template Add the feeds you want to follow. It does not have to be just email. Why not get a telegram message? Or Slack message? Ah the power of n8n 🚀

Who this template is for This template is for every engineer who wants to automate their code reviews or just get a 2nd opinion on their PR. How it works This workflow will automatically review your changes in a Gitlab PR using the power of AI. It will trigger whenever you comment with +0 to a Gitlab PR, get the code changes, analyze them with GPT, and reply to the PR discussion. Set up Steps Set up webhook of note_events in Gitlab repository (see here on how to do it) Configure ChatGPT credentials Note "+0" in MergeRequest to trigger automatic review by ChatGPT

Pull data using the Facebook Graph API and get your Meta Ads insights into your Google Sheets for analysis. Get metrics like impressions, spend, reach, conversions, CTR, and CPC. Automate your data collection. Who is this template for? Marketing professionals looking to analyze Meta Ads data. You can also replace the Google Sheets node for a database node like MySQL or Postgres, and then use your BI tool (Power BI, Looker Studio, Tableau) to analyze the data. How it works Every day at 3am, the flow gets Meta Ads insights from the previous day and saves them to Google Sheets. How to set up All information you need to set up this workflow is already inside the template.

Campaign tracking is pivotal; it enables marketers to evaluate the efficacy of various strategies and channels. UTM parameters are particularly essential as they provide granular details about the source, medium, and campaign effectiveness. However, when this data is not automatically integrated into a centralized system, it can become a tedious and error-prone process to manually collate and analyze it. Retrieving UTM data from Shopify and storing it in Baserow enables oy to do more with this data. For example you could build a campaign database in Baserow and automatically add campaign revenue to it using this workflow template. This template will help you: Automatically retrieve UTM parameters from Shopify orders using the Shopify Admin API Process marketing data through n8n Store this data into Baserow, providing you with a dynamic, responsive base for campaign tracking and decision-making This template will demonstrate the follwing concepts in n8n: use the Schedule trigger node use the GraphQL node to call the Shopify Admin API split larger incoming datasets into n8n items with the Split node transform the data structure with the Set node control flow with the If node store data in Baserow with the Baserow node How to get started? Create a custom app in Shopify get the credentials needed to connect n8n to Shopify This is needed for the Shopify Trigger Create Shopify Acces Token API credentials n n8n for the Shopify trigger node Create Header Auth credentials: Use X-Shopify-Access-Token as the name and the Acces-Token from the Shopify App you created as the value. The Header Auth is neccessary for the GraphQL nodes. You will need a running Baserow instance for this. You can also sign up for a free account at https://baserow.io/ Please make sure to read the notes in the template. For a detailed explanation please check the corresponding video: https://youtu.be/VBeN-3129RM

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 n8n workflow is designed to work on the local network and assists with reconciling downloaded bank statements with internal tenant records to quickly highlight any issues with payments such as missed or late payments or those of incorrect amounts. This assistant can then generate a report to quick flag attention to ensure remedial action is taken. How it works The workflow monitors a local network drive to watch for new bank statements that are added. This bank statement is then imported into the n8n workflow, its contents extracted and sent to the AI Agent. The AI Agent analyses the line items to identify the dates and any incoming payments from tenants. The AI agent then uses an locally-hosted Excel ("XLSX") spreadsheet to get both tenant records and property records. From this data, it can determine for each active tenant when payment is due, the amount and the tenancy duration. Comparing to the bank statement, the AI Agent can now report on where tenants have missed their payments, made late payments or are paying the incorrect amounts. The final report is generated and logged in the same XLSX for a human to check and action. Requirements A self-hosted version of n8n is required. OpenAI account for the AI model Customising this workflow If you organisation has a Slack or Teams account, consider sending reports to a channel for increased productivity. Email may be a good choice too. Want to go fully local? A version of this workflow is available which uses Ollama instead. You can download this template here: https://drive.google.com/file/d/1YRKjfakpInm23F_g8AHupKPBN-fphWgK/view?usp=sharing

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 workflow uses AI to analyze the content of every new message in Gmail and then assigns specific labels, according to the context of the email. Default configuration of the workflow includes 3 labels: „Partnership” - email about sponsored content or cooperation, „Inquiry” - email about products, services, „Notification” - email that doesn't require response. You can add or edit labels and descriptions according to your use case. 🎬 See this workflow in action in my YouTube video about automating Gmail. How it works? Gmail trigger performs polling every minute for new messages (you can change the trigger interval according to your needs). The email content is then downloaded and forwarded to an AI chain. 💡 The prompt in the AI chain node includes instructions for applying labels according to the email content - change label names and instructions to fit your use case. Next, the workflow retrieves all labels from the Gmail account and compares them with the label names returned from the AI chain. Label IDs are aggregated and applied to processed email messages. ⚠️ Label names in the Gmail account and workflow (prompt, JSON schema) must be the same. Set up steps Set credentials for Gmail and OpenAI. Add labels to your Gmail account (e.g. „Partnership”, „Inquiry” and „Notification”). Change prompt in AI chain node (update list of label names and instructions). Change list of available labels in JSON schema in parser node. Optionally: change polling interval in Gmail trigger (by default interval is 1 minute). If you like this workflow, please subscribe to my YouTube channel and/or my newsletter.

This workflow integrates both web scraping and NLP functionalities. It uses HTML parsing to extract links, HTTP requests to fetch essay content, and AI-based summarization using GPT-4o. It's an excellent example of an end-to-end automated task that is not only efficient but also provides real value by summarizing valuable content. Note that to use this template, you need to be on n8n version 1.50.0 or later.

Use Case Track all Linear tickets in Google sheets. Useful if you want to do some custom analysis but don't want to pay for Linear's Plus features (Linear Insights) or that it does not cover. Setup Add Linear API header key Add Google sheets creds Update which teams to get tickets from in Graphql Nodes Update which Google Sheets page to write all the tickets to You only need to add one column, id, in the sheet. Google Sheets node in automatic mapping mode will handle adding the rest of the columns. Set any custom data on each ticket Activate workflow 🚀 How to adjust this template Set any custom fields you want to get out of this, that you can quickly do in n8n.

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 Community Insights scenario where HN commments can be quickly grouped by similarity and an AI agent can generate insights on those groupings. With this workflow, Researchers or HN users can quickly breakdown community consensus on a particular topic and identify frequently mentioned positives and negatives. Sample Output: https://docs.google.com/spreadsheets/d/e/2PACX-1vQXaQU9XxsxnUIIeqmmf1PuYRuYtwviVXTv6Mz9Vo6_a4ty-XaJHSeZsptjWXS3wGGDG8Z4u16rvE7l/pubhtml How it works HN comments are imported via the Hacknews API node. Comments are then inserted into a Qdrant collection carefully tagged with the Hackernews API metadata. Comments are then fetched and are 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 researcher or the HN user. Requirements Works best with lots of comments! Qdrant Vectorstore for storing embeddings. OpenAI account for embeddings and LLM. Customising the Template Adjust clustering parameters which make sense for your data. Adjust sentimentality setting if comments are overwhelmingly negative at times.

If you have a shared or personal drive location with a high frequency of files created by humans, it can become difficult to organise. This may not matter... until you need to search for something! This n8n workflow works with the local filesystem to target the messy folder and categorise as well as organise its files into sub directories automatically. Disclaimer Unfortunately due to the intended use-case, this workflow will not work on n8n Cloud and a self-hosted version of n8n is required. How it works Uses the local file trigger to activate once a new file is introduced to the directory The new file's filename and filetype are analysed using AI to determine the best location to move this file. The AI assess the current subdirectories as to not create duplicates. If a relevant subdirectory is not found, a new subdirectory is suggested. Finally, an Execute Command node uses the AI's suggestions to move the new file into the correct location. Requirements Self-hosted version of n8n. The nodes used in this workflow only work in the self-hosted version. If you are using docker, you must create a bind mount to a host directory. Mistral.ai account for LLM model Customise this workflow If the frequency of files created is high enough, you may not want the trigger to active on every new file created event. Switch to a timer to avoid concurrency issues. Want to go fully local? A version of this workflow is available which uses Ollama instead. You can download this template here: https://drive.google.com/file/d/1iqJ_zCGussXpfaUBYGrN5opziEFAEQMu/view?usp=sharing