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Want to learn the basics of n8n? Our comprehensive quick quickstart tutorial is here to guide you through the basics of n8n, step by step. Designed with beginners in mind, this tutorial provides a hands-on approach to learning n8n's basic functionalities.

Disclaimer: This template contains a community node and therefore only works for n8n self-hosted users== This is Miquel from Aprende n8n and Automate with n8n. We have created a new community node Document Generator that generates dynamic content using templates. You can compose complex content with no SETs or FUNCTION ITEMs nodes using this node, like: Send one email with a list of items in the body (i.e., one email with the last entries of an RSS feed). Send one email per item (i.e., one invoice per email). Emails are just a sample. You can create complex dynamic content to: Send messages to Telegram/Slack. Create WordPress entries. Create HTML pages for your website. Create tickets. And more! The sky is your limit ;) If you want to use this workflow, install the community node n8n-nodes-document-generator from Settings > Community nodes. Type "n8n-nodes-document-generator", check "I understand the risks..." and click on "Install": Later, copy and paste this workflow into your n8n. You will get this workflow: This workflow uses the Customer Datastore node to generate sample input items. You can render one template with all items (enable "Render All Items with One Template"): or one template per input item: Visit the oficial NPM page to see more samples. Learning n8n by yourself is nice, but a bit tricky :) We offer n8n training video courses at Aprende n8n. If you need custom trainings, let us know. Additionally, you can contact us at Automate with n8n if you need the next services: Custom installations. Custom nodes. Monitor and alarms. Delegate 12/5 or 24/7 workflow issue resolutions. Automated backups of your workflows. HTTP integrations of non-supported APIs. Complex workflows. I hope you will enjoy this new node and this workflow. Automate your life! Automate it with n8n!

This workflow will help guide you through obtaining a spreadsheet file, reading it, making a change then saving it to local or cloud storage.

This workflow demonstrates how CSV file can be automatically imported into existing MySQL database. Before running the workflow please make sure you have a file on the server: /home/node/.n8n/concerts-2023.csv And the content of the file is the following: Date,Band,ConcertName,Country,City,Location,LocationAddress, 2023-05-28,Ozzy Osbourne,No More Tours 2 - Special Guest: Judas Priest,Germany,Berlin,Mercedes-Benz Arena Berlin,"Mercedes-Platz 1, 10243 Berlin-Friedrichshain", 2023-05-08,Elton John,Farewell Yellow Brick Road Tour 2023,Germany,Berlin,Mercedes-Benz Arena Berlin,"Mercedes-Platz 1, 10243 Berlin-Friedrichshain", 2023-05-26,Hans Zimmer Live,Europe Tour 2023,Germany,Berlin,Mercedes-Benz Arena Berlin,"Mercedes-Platz 1, 10243 Berlin-Friedrichshain", 2023-07-07,Depeche Mode,Memento Mori World Tour 2023,Germany,Berlin,Olympiastadion Berlin,"Olympischer Platz 3, 14053 Berlin-Charlottenburg", The detailed process is explained in the tutorial https://blog.n8n.io/import-csv-into-mysql

Enrich your company lists with OpenAI GPT-3 ↓ You’ll get valuable information such as: Market (B2B or B2C) Industry Target Audience Value Proposition This will help you to: add more personalization to your outreach make informed decisions about which accounts to target I've made the process easy with an n8n workflow. Here is what it does: Retrieve website URLs from Google Sheets Extract the content for each website Analyze it with GPT-3 Update Google Sheets with GPT-3 data

This workflow demonstrates how easy it is to export SQL query to Excel automatically! Before running the workflow please make sure you have access to a remote SQL server (MS SQL, MySQL, PostgreSQL etc.) with a sample table: Date,Band,ConcertName,Country,City,Location,LocationAddress, 2023-05-28,Ozzy Osbourne,No More Tours 2 - Special Guest: Judas Priest,Germany,Berlin,Mercedes-Benz Arena Berlin,"Mercedes-Platz 1, 10243 Berlin-Friedrichshain", 2023-05-08,Elton John,Farewell Yellow Brick Road Tour 2023,Germany,Berlin,Mercedes-Benz Arena Berlin,"Mercedes-Platz 1, 10243 Berlin-Friedrichshain", 2023-05-26,Hans Zimmer Live,Europe Tour 2023,Germany,Berlin,Mercedes-Benz Arena Berlin,"Mercedes-Platz 1, 10243 Berlin-Friedrichshain", 2023-07-07,Depeche Mode,Memento Mori World Tour 2023,Germany,Berlin,Olympiastadion Berlin,"Olympischer Platz 3, 14053 Berlin-Charlottenburg", The detailed process is explained in the tutorial https://blog.n8n.io/export-sql-to-excel

This workflow digests mentions of n8n on Reddit that can be sent as an single email or Slack summary each week. We use OpenAI to classify if a specific Reddit post is really about n8n or not, and then the summarise it into a bullet point sentence. How it works Get posts from Reddit that might be about n8n; Filter for the most relevant posts (posted in last 7 days and more than 5 upvotes and is original content); Check if the post is actually about n8n; If it is, categorise with OpenAI. Bear in mind: Workflow only considers first 500 characters of each reddit post. So if n8n is mentioned after this amount, it won't register as being a post about n8n.io. Next steps Improve OpenAI Summary node prompt to return cleaner summaries; Extend to more platforms/sources - e.g. it would be really cool to monitor larger Slack communities in this way; Do some classification on type of user to highlight users likely to be in our ICP; Separate a list of data sources (reddit, twitter, slack, discord etc.), extract messages from there and have them go to a sub workflow for classification and summarisation.

Primer workflow for OpenAI models: ChatGPT, DALLE-2, Whisper This workflow contains 5 examples on how to work with OpenAI API. Transcribe voice into text via Whisper model (disabled, please put your own mp3 file with voice) The old way of using OpenAI conversational model via text-davinci-003 Examples 1.x. Simple ChatGPT calls. Text completion and text edit Example 2. Provide system and user content into ChatGPT Examples 3.x. Create system / user / assistanc content via Code Node. Promtp chaining technique example Example 4. Generate code via ChatGPT Example 5. Return multiple answers. Useful for providing picking the most relevant reply IMPORTANT! Do not run the whole workflow, it's rather slow Better execute the last node of each branch or simply disconnect branches that are not needed

Convert PostgreSQL table to CSV CSV is a super useful and universal way to transfer data between different tools. This workflow gives an example of how to take data from PostgreSQL and convert it easily into a CSV. What you need Before running the workflow, please make sure you have access to a remote PostgreSQL server and have table data: book_title,book_author,read_date Demons,Fyodor Dostoyevsky,2022-09-08 Ulysses,James Joyce,2022-05-06 Catch-22,Joseph Heller,2023-01-04 The Bell Jar,Sylvia Plath,2023-01-21 Frankenstein,Mary Shelley,2023-02-14 How it works Trigger the workflow on click Declare the name of the Excel file and sheet names Remotely connect to the PostgreSQL database and specify query execution Write the query data to CSV The detailed process is explained further in the tutorial: https://blog.n8n.io/postgres-export-to-csv/

This workflow provides a simple approach to counting the items returned by a node. It uses a Set node with the Execute Once option: The expression uses $input.all() (documented here) to fetch all incoming items at once, and .length (documented for example here) to count them.

This workflow demonstrates how easy it is to export SQL query to CSV automatically! Before running the workflow please make sure you have access to a local or remote MSSQL server with a sample AdventureWorks database. The detailed process is explained in the tutorial https://blog.n8n.io/sql-export-to-csv/

This workflow is the opposite of this one. It transforms multiple different items with one binary object named data into a single item with multiple binary objects: This can be useful when creating a single .zip archive for example. It uses the updated Code node instead of the older Function node.

This workflow automatically downloads a CSV from the web, and parses it in a format that n8n can access. It then ensures that the data from the CSV is matched to the names of the columns in the database, and inserts this data as new rows in Snowflake. Prerequisites: A CSV with data A Snowflake account and credentials A Snowflake database to upload your CSV to Nodes: A HTTP Request node to download the CSV file A Spreadsheet File node to access the data from the CSV A Set node to ensure the data from the CSV is mapped to the column names of the Snowflake database A Snowflake node to insert these new rows into the database.

v1 Helper ℹ️ This workflow is to be run after upgrading to n8n v1. This workflow returns all locations where a node in an active workflow contains a parameter using an expression extension affected by v1 changes. For every location, please check that the workflow still behaves as intended.

This template shows how to sync data from one service to another. Specifically, in this example we're saving a new qualified lead from a Postgres database to a Google Sheets file. Setup instructions are located inside the workflow template.

This template shows how you can take any event from any service, transform its data and send an alert to your desired app. Specifically, this example monitors a Linear project for new bug submissions. Then it only sends a Slack notification to a channel if a new bug is urgent. You can swap the Linear trigger for another Task Management app such as Jira or Asana; or an entirely different usecase. Setup instructions are located inside the workflow template.

This workflow template shows how to load JSON data into a workflow and push that data into an App or convert it into a Spreadsheet file. Specifically, this workflow shows how to make a generic API request that returns JSON. It then shows how to load that into a Google Sheets spreadsheet, or convert it to .CSV file format. However, you can use the general pattern to load data into any app or convert to any spreadsheet file format (such as .xlsx).

This workflow is a template example for making Discord GPT-powered bots. Incoming user requests are analysed and categorised via OpenAI node with the help of a hand-crafted prompt. The response message is then routed to a different Discord channel.

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.

This workflow has multiple functionalities. It starts with a manual trigger, "When clicking 'Execute Workflow'", that activates two separate paths. The first path takes a preset string "Tell me a joke" and processes it through a custom Language Learning Model (LLM) chain node. This node interacts with an OpenAI node for query processing. The second path takes another preset string "What year was Einstein born?" and passes it to an "Agent" node. This agent further interacts with a Chat OpenAI node and a custom Wikipedia node to produce the required information. The workflow uses both built-in and custom nodes, and integrates with OpenAI for both paths. It's built for experimenting with language models, specifically in the context of conversational agents and information retrieval. Note that to use this template, you need to be on n8n version 1.19.4 or later.

This workflow performs several data integration and synchronization tasks between Google Sheets and a MySQL database. Here is a step-by-step description of what this workflow does: Manual Trigger: The workflow starts when the user clicks "Execute Workflow." Schedule Trigger: This node schedules the workflow to run at specific intervals on weekdays (Monday to Friday) between 6 AM and 10 PM. It ensures regular data synchronization. Google Sheet Data: This node connects to a specific Google Sheets document and retrieves data from the "Form Responses 1" sheet, filtering by the "DB Status" column. SQL Get inquiries from Google: This node retrieves data from a MySQL database table named "ConcertInquiries" where the "source_name" is "GoogleForm." Rename GSheet variables: This node renames the columns retrieved from Google Sheets and transforms the data into a format suitable for MySQL, assigning a value for "source_name" as "GoogleForm." Compare Datasets: This node compares the data retrieved from Google Sheets and the MySQL database based on timestamp and source_name fields. It identifies changes and updates. No reply too long?: This node checks if there has been no reply within the last four hours, using the "timestamp" field from the Google Sheets data. DB Status assigned?: This node checks if the "DB Status" field is not empty in the compared dataset. Update GSheet status: If conditions are met in the previous nodes, this node updates the "DB Status" field in Google Sheets with the corresponding value from the MySQL dataset. DB Status in sync?: This node checks if the "source_name" field in Google Sheets is not empty. Sync MySQL data: If conditions are met in the previous nodes, this node updates the "source_name" field in the MySQL database to "GoogleFormSync." Send Notifications: If conditions are met in the "No reply too long?" node, this node sends notifications or performs actions as needed. Sticky Notes: These nodes provide additional information and documentation links for users.

This workflow provides a simple example of how to use itemMatching(itemIndex: Number) in the Code node to retrieve linked items from earlier in the workflow.

Description: This template facilitates the transfer of a folder, along with all its files and subfolders, within a Nextcloud instance. The Nextcloud user must have access to both the source and destination folders. While Nextcloud allows folder movement, complications may arise when dealing with external storage that has rate limits. This workflow ensures the individual transfer of each file to avoid exceeding rate limits, particularly useful for setups involving external storage with rate limitations. How it works: Identify all files and subfolders within the specified source folder. Recursive search within subfolders for additional files. Replicate the folder structure in the target folder. Individually move each identified file to the corresponding location in the target folder. Set up steps: Set Nextcloud credentials for all Nextcloud nodes involved in the process. -Edit the trigger settings. Detailed instructions can be found within the respective trigger configuration. Initiate the workflow to commence the folder transfer process. Help If you need assistance with applying this template, feel free to reach out to me. You can find additional information about me and my services here. => https://nicokowalczyk.de/links I have also produced a video where I explain the workflow and provide an example. You can find this video over here. https://youtu.be/K1kmG_Q_jRk Cheers. Nico Kowalczyk

This template can be used to verify an email address with Icypeas. Be sure to have an active account to use this template. How it works This workflow can be divided into three steps : The workflow initiates with a manual trigger (On clicking 'execute'). It connects to your Icypeas account. It performs an HTTP request to verify an email address. Set up steps You will need a working icypeas account to run the workflow and get your API Key, API Secret and User ID. You will need an email address to perform the verification.

This template can be used to scan a domain/company with Icypeas. Be sure to have an active account to use this template. How it works This workflow can be divided into three steps : The workflow initiates with a manual trigger (On clicking 'execute'). It connects to your Icypeas account. It performs an HTTP request to scan a domain/company name. Set up steps You will need a working icypeas account to run the workflow and get your API Key, API Secret and User ID. You will need a domain/company name to perform the search.

This template can be used to search for an email address with Icypeas. Be sure to have an active account to use this template. How it works This workflow can be divided into three steps : The workflow initiates with a manual trigger (On clicking 'execute'). It connects to your Icypeas account. It performs an HTTP request to search for an email address. Set up steps You will need a working icypeas account to run the workflow and get your API Key, API Secret and User ID. You will need a personn firstname, lastname and domain/company name to perform the search.

This template can be used to verify email addresses with Icypeas. Be sure to have an active account to use this template. How it works This workflow can be divided into four steps : The workflow initiates with a manual trigger (On clicking ‘execute’). It reads your Google sheet file. It connects to your Icypeas account. It performs an HTTP request to search for the email addresses. Set up steps You will need a formated Google Sheet file with firstnames,lastnames and company/domain names. You will need a working icypeas account to run the workflow and get your API Key, API Secret and User ID. You will need a personn firstname, lastname and domain/company name to perform the search.

This template can be used to verify email addresses with Icypeas. Be sure to have an active account to use this template. How it works This workflow can be divided into four steps : The workflow initiates with a manual trigger (On clicking ‘execute’). It reads your Google Sheet file. It connects to your Icypeas account. It performs an HTTP request to scan the domains/companies. Set up steps You will need a formated Google sheet file with company/domain names. You will need a working icypeas account to run the workflow and get your API Key, API Secret and User ID. You will need domain/companies names to scan them.

This template can be used to verify email addresses with Icypeas. Be sure to have an active account to use this template. How it works This workflow can be divided into four steps : The workflow initiates with a manual trigger (On clicking ‘execute’). It reads your Google Sheet file. It converts your file to an array. It connects to your Icypeas account. It performs an HTTP request to verify the emails. Set up steps You will need a formated Google sheet file with email addresses. You will need a working icypeas account to run the workflow and get your API Key, API Secret and User ID. You will need email addresses to verify them.

n8n does not currently offer a way to retrieve emails from arbritrary providers via a regular node. Unless you're using Gmail or Outlook, you can only use the email trigger to start a workflow when a new email arrives. This currently limits the possible use cases you can cover in your n8n workflows, as you cannot (for example) get an idea of how many unread messages there are in an inbox, or search for specific messages when an event occurs. But fear not, there's a new sheriff in town! The JMAP standard allows you to interact with your mailboxes, calendars and contacts through single HTTP requests whenever needed. This n8n workflow demonstrates how to retrieve the total number of unread messages from a JMAP server and also retrieve details for the first 3 messages. It can easily be adapted to search for messages other than unread, or to return details for more than the first 3 messages. Screenshots FAQ Which n8n version do I need? The workflow was built using n8n 1.20 and should work here out of the box. HTTP requests are also supported on older n8n versions, so the workflow can be backported as an alternative. Which credentials do I need? The JMAP standard does not limit the available authentication options. Fastmail (the sponsor of the standard) supports Bearer authentication as well as OAuth2. In n8n you can implement the Fastmail Bearer authentication by creating Header Auth credentials with a name of Authorization and a value of Bearer $apiToken (replacing $apiToken with your actual API token from Fastmail). For other services you'd need to check the respective API documentation for more details on the support authentication methods. What even is JMAP? It's an official Internet Engineering Task Force (IETF) standard, sponsored by Fastmail, that will hopefully replace the legacy standards CalDAV, CardDAV, and IMAP soon. The full specs are available here. How can I use JMAP? If you're a Fastmail customer or if you're hosting your own Stalwart mail server you can use JMAP today. If your email provider doesn't yet support JMAP, you might want to contact them and let them know you're interested in this functionality.

Super simple workflow to convert image URLs to an uploaded attachment in Airtable. You'll need to adjust the field names to match your specific data, including in the filter formula where it says "Cover image URL". Just replace that with the field name where you are storing the image URL.

This workflow demonstrates the conversion of a CSV file to Excel format. First, an example CSV file is downloaded via a direct link. The source file is taken from the European Open Data Portal: https://data.europa.eu/data/datasets/veranstaltungsplaetze-potsdam-potsdam?locale=en The binary data is then imported via the Spreadsheet File node and converted to Excel format. N.B. Note that as of version 1.23.0 n8n, the Spreadsheet File node has been redesigned and is now called Convert to File node. Learn more on the release notes page: https://docs.n8n.io/release-notes/#n8n1230

This N8N workflow automates the process of sharing files from Google Drive. It includes OAuth2 authentication, batch processing, public link generation, and access status modification for efficient file handling. Suitable for users seeking to streamline their Google Drive file sharing process. sutiable for bulk actions, tested on 4.2K files folder working like charm. How It Works Initialize Workflow: The process begins with a Manual Trigger, allowing the user to start the workflow at their convenience. Folder ID Specification: A 'Set Folder ID' node where the user can enter the desired Google Drive Folder ID. List Files from Google Drive: The 'Google Drive' node lists all files within the specified folder using OAuth2 authentication. Batch Processing: The 'Loop Over Items' node processes the files in batches for efficiency. Generate Public Links: The 'Generate Download Links' node creates downloadable links for each file. Change File Access: The 'Change Status' node alters the file status to make them publicly accessible. Merge and Output: A 'Merge' node consolidates the data, preparing it for further actions or output. Set Up Steps Estimated Time**: The setup should take approximately 10-15 minutes. Initial Setup**: You'll need to provide OAuth2 credentials for Google Drive and specify a folder ID. Customization**: Adjust the batch size and file access permissions according to your needs. Detailed Descriptions**: For specific configuration details, refer to the sticky notes within the workflow. Example Item output { "link": "https://drive.google.com/u/3/uc?id=1hojqPfXchNTY8YRTNkxSo-8txK9re-V4&export=download&confirm=t&authuser=0", "name": "firefox_rNjA0ybKu7.png", "kind": "drive#permission", "id": "anyoneWithLink", "type": "anyone", "role": "reader", "allowFileDiscovery": false } You can store the output data with any data store node you want, for example save them into Excel Sheet or Airtable etc... Keywords: n8n workflow, Google Drive integration, file sharing automation, batch file processing, public link generation, OAuth2 authentication, workflow automation

This n8n template provides a comprehensive solution for managing Key-Value (KV) pairs using Cloudflare's KV storage. It's designed to simplify the interaction with Cloudflare's KV storage APIs, enabling users to perform a range of actions like creating, reading, updating, and deleting namespaces and KV pairs. Features Efficient Management**: Handle multiple KV operations seamlessly. User-Friendly**: Easy to use with pre-configured Cloudflare API credentials within n8n. Customizable**: Flexible for integration into larger workflows (Copy / paste your prefered part). Prerequisites n8n workflow automation tool (version 1.19.0 or later). A Cloudflare account with access to KV storage. Pre-configured Cloudflare API credentials in n8n. Workflow Overview This workflow is divided into three main sections for ease of use: Single Actions: Perform individual operations on KV pairs. Bulk Actions: Handle multiple KV pairs simultaneously. Specific Actions: Execute specific tasks like renaming namespaces. Key Components Manual Trigger**: Initiates the workflow. Account Path Node**: Sets the path for account details, a prerequisite for all actions. HTTP Request Nodes**: Facilitate interaction with Cloudflare's API for various operations. Sticky Notes**: Provide quick documentation links and brief descriptions of each node's function. Usage Setup Account Path: Input your Cloudflare account details in the 'Account Path' node. you can get your account path by your cloudflare URL Choose an Action: Select the desired operation from the workflow. Configure Nodes: Adjust parameters in the HTTP request nodes as needed. (each node contain sticky note with direct link to it own document page) Execute Workflow: Trigger the workflow manually to perform the selected operations. Detailed Node Descriptions I covered in this Workflow the full api calls of Cloudflare KV product. API NODE: Delete KV Type**: HTTP Request Function**: Deletes a specified KV pair within a namespace. Configuration**: This node requires the namespace ID and KV pair name. It automatically fetches these details from preceding nodes, specifically from the "List KV-NMs" and "Set KV-NM Name" nodes. Documentation**: Delete KV Pair API API NODE: Create KV-NM Type**: HTTP Request Function**: Creates a new Key-Value Namespace. Configuration**: Users need to input the title for the new namespace. This node uses the account information provided by the "Account Path" node. Documentation**: Create Namespace API API NODE: Delete KV1 Type**: HTTP Request Function**: Renames an existing Key-Value Namespace. Configuration**: Requires the old namespace name and the new desired name. It retrieves these details from the "KV to Rename" and "List KV-NMs" nodes. Documentation**: Rename Namespace API API NODE: Write KVs inside NM Type**: HTTP Request Function**: Writes multiple Key-Value pairs inside a specified namespace. Configuration**: This node needs a JSON array of key-value pairs along with their namespace identifier. It fetches the namespace ID from the "List KV-NMs" node. Documentation**: Write Multiple KV Pairs API API NODE: Read Value Of KV In NM Type**: HTTP Request Function**: Reads the value of a specific Key-Value pair in a namespace. Configuration**: Requires the Key's name and Namespace ID, which are obtained from the "Set KV-NM Name" and "List KV-NMs" nodes. Documentation**: Read KV Pair API API NODE: Read MD from Key Type**: HTTP Request Function**: Reads the metadata of a specific Key in a namespace. Configuration**: Similar to the "Read Value Of KV In NM" node, it needs the Key's name and Namespace ID, which are obtained from the "Set KV-NM Name" and "List KV-NMs" nodes. Documentation**: Read Metadata API > The rest can be found inside the workflow with sticky/onflow note explain what to do. Best Practices Modular Use**: Extract specific parts of the workflow for isolated tasks. Validation**: Ensure correct namespace and KV pair names before execution. Security**: Regularly update your Cloudflare API credentials for secure access, and make sure to give your API only access to the KV. Keywords: Cloudflare KV, n8n workflow automation, API integration, key-value storage management.

This Template gives the ability to monitor all uplinks for your Meraki Dashboard and then alert your team in a method you prefer. This example is a Teams notification to our Dispatch Channel Setup will probably take around 30 minutes to 1h provided with the Template. Most time intensive steps are getting a Meraki API key which I go over and setting up the Teams node which n8n has good documentation for. Tutorial & explanation https://www.youtube.com/watch?v=JvaN0dNwRNU

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

This workflow allows to scrape Google Maps data in an efficient way using SerpAPI. You'll get all data from Gmaps at a cheaper cost than Google Maps API. Add as input, your Google Maps search URL and you'll get a list of places with many data points such as: phone number website rating reviews address And much more. Full guide to implement the workflow is here: https://lempire.notion.site/Scrape-Google-Maps-places-with-n8n-b7f1785c3d474e858b7ee61ad4c21136?pvs=4

A n8n workflow designed to shorten URLs using Dub.co API. How it works: It shortens a url using Dub.co API, with the ability to use custom domains and projects. It updates the current shortened url if the slug has been already used. Estimated Time: Around 15 minutes. Requirements: A Dub.co account. Configuration: Configure the "API Auth" node to add your Dub.co API key, project slug, and the long URL. There some extras that you're able to configure too. You will be able to do that by clicking the "API Auth" node and filling the fields. Detailed Instructions: Sticky notes within the workflow provide extensive setup information and guidance. Keywords: n8n workflow, dub.co, dub.sh, url shortener, short urls, short links

This template is a set of building blocks to access Monday.com in ways not supported by the official Monday node. Prerequisites Monday account and Monday credentials. Included are setups to: Find a column value by the column's name (instead of a numerical index which can change when board structure is changed) Find a column value by the column's ID (again, instead of using a numerical index) Pull a board relation column, and get all the related pulses Pull an items subitems and split them out Upload a file to an item's files field Setup Create a Monday.com credential Update the nodes in the template to use your credential Copy/Paste the nodes you need from this template into any other workflow To retreive a column by name: Route a Monday.com node that gets an item to the COLUMN BY NAME node Edit the COLUMN BY NAME node, and enter the name in the first line of code. To retreive a column by its ID: Follow Monday.com's instructions to locate the column's ID Route a Monday.com node that gets an item to the COLUMN BY ID node -Edit the COLUMN BY ID node, and enter the ID in the first line of code. To retreive all linked pulses from a Board Relation column: Route a Monday.com node that gets an item to the GET BOARD RELATION node Edit the GET BOARD RELATION node to specify the column name. All linked pulses will be retrieved by the subsequent PULL LINKEDPULSE node To pull all subitems from an item: Route a Monday.com node that gets an item to the PULL SUBITEMS node All subitems will be retrieved by the subsequent GET EACH SUBITEM node To upload a File: Repalce the Convert to File node with whatever node you are using to output your binary file data Enable the MONDAY UPLOAD node If the destination column is named anything other then the default of "file" - edit the MONDAY UPLOAD node and change column_id:"file" in the first Value field to match the name of your file column

LangChain is a framework for building AI functionality that users large language models. By leveraging the functionality of LangChain, you can write even more powerful workflows. This workflow shows how you can write LangChain code within n8n, including importing LangChain modules. The workflow itself produces a summary of a YouTube video, when given the video's ID. Note that to use this template, you need to be on n8n version 1.19.4 or later.

Overview This workflow takes some French text, and translates it into spoken audio. It then transcribes that audio back into text, translates it into English and generates an audio file of the English text. To do so, it uses ElevenLabs (which has a free tier) and OpenAI. Setup These steps should only take a few minutes: In ElevenLabs, add a voice to your voice lab and copy its ID. Add it to the 'Set voice ID' node Get your ElevenLabs API key (click your name in the bottom-left of ElevenLabs and choose ‘profile’) In the 'Generate French audio' node, create a new header auth cred. Set the name to xi-api-key and the value to your API key In the 'credential' field of the 'Transcribe audio' node, create a new OpenAI cred with your OpenAI API key Run the workflow by clicking the orange button at the bottom of the canvas

How It Works This workflow sends an HTTP request to OpenAI's Text-to-Speech (TTS) model, returning an .mp3 audio recording of the provided text. This template is meant to be adapted for your individual use case, and requires a valid OpenAI credential. Gotchas Per OpenAI's Usage Policies, you must provide a clear disclosure to end users that the TTS voice they are hearing is AI-generated and not a human voice, if you are using this workflow to provide audio output to users.

Effortlessly optimize your workflow by automatically importing hundreds of manufacturers from a Google Sheet into your Shopware online store, saving countless hours of manual work. How it works Retrieve all manufactures from a Google Sheet Add each manufacture via Shopware sync API Endpoint to Shopware Upload a logo for each manufacture from a provided public URL to Shopware Set Up Steps Add your Shopware url to first node called Settings Create a Google Sheet in your Google account with the following columns (Demo Sheet) name (the name of the manufacturer which has to be unique and is required) website (url to the manufacturer website) description logo_url (public manufcaturer logo url. Have to be a png, jpg or svg file) translation_language_code_1 (optional. Language Code of your language. For example 'es-ES' for spanish. You have to make sure a language with this code exists in your Shopware shop.) translation_name_1 (optional. Manufacturer name translated to the language you defined at translation_language_code_1) translation_description_1 (optional. Manufacturer description translated to the language you defined at translation_language_code_1) translation_language_code_2 (optional. Same as translation_language_code_1 for another language) translation_name_2 (optional. Same as translation_name_1 for another language) translation_description_2 (optional. Same as translation_description_1 for another language) translation_language_code_3 (optional. Same as translation_language_code_1 for another language) translation_name_3 (optional. Same as translation_name_1 for another language) translation_description_3 (optional. Same as translation_description_1 for another language) Connect to your Google account Connect to your Shopware account Create a Shopware Integration Connect to Shopware at the nodes "Import Manufacturer" and "Upload Manufacturer Logo" using a Generic OAuth2 API Authentication with Grant Type "Client Credentials". The Access Token URL is https://your-shopware-domain.com/api/oauth/token. Run the workflow

Use Case This workflow retrieves all members of a Discord server or guild who have a specific role. Due to limitations in the Discord API, it only returns a limited number of users per call. To overcome this, the workflow uses Google Sheets to track which user we received last to return all Members (of a certain role) from a Discord server in batches of 100 members. Setup Add your Google Sheets and Discord credentials. Create a Google Sheets document that contains ID as a column. We're using this to remember which member we received last. Edit the fields in the setup node Setup: Edit this to get started. You can read up on how to get the Discord IDs via this link. Link to your Discord server in the Discord nodes Activate the workflow Call the production webhook URL in your browser Requirements Admin rights in the Discord server and access to the developer portal of discord Google Sheets Minimum n8n version 1.28.0 Potential Use cases Writing a direct message to all members of a certain role Analysing user growth on Discord regularly Analysing role distributions on Discord regularly Saving new members in a Discord ... Keywords Discord API, Getting all members from Discord via API, Google Sheets and Discord automation, How to get all Discord members via API

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!

Twitter Virtual AI Influencer Workflow Template This n8n workflow template empowers creators to launch a virtual AI influencer that tweets regularly, engaging audiences with a unique niche, writing style, and inspiration. By automating content creation and posting, it ensures a consistent and natural online presence, tailored to your specific influencer profile. Features Scheduled Posting**: Automates tweet posting every 6 hours, with randomized posting minutes to mimic natural activity. On-Demand Posting**: Offers flexibility with manual trigger options for immediate content sharing. Influencer Profile Configuration**: Customize your virtual influencer by defining a target niche, personal writing style, and sources of inspiration. Content Generation**: Leverages advanced AI to craft tweets that resonate with your audience, aiming for viral engagement. Tweet Validation**: Ensures all generated content adheres to Twitter's character limit, maintaining quality and relevance. Workflow Steps Schedule Posting: Configured to post every 6 hours, this step introduces randomness in posting time to simulate human behavior. Trigger Posting Manually: Provides an option to manually initiate a tweet, offering control over the timing of your content. Configure Influencer Profile: Set up your influencer's niche, style, and inspiration to guide the AI in generating targeted content. Generate Tweet Content: Utilizes a sophisticated AI model to produce engaging tweets based on the configured profile. Validate Tweet: Checks if the generated tweet meets Twitter's length constraints, ensuring all content is ready for posting. Post Tweet: Finalizes the process by sharing the AI-generated tweet to your designated Twitter account. Configuration Notes Niche**: Define a specific area of interest, such as "Modern Stoicism," to focus your influencer's content. Writing Style**: Customize the tone and style of the tweets to reflect a personal touch, enhancing relatability. Inspiration**: Input sources of inspiration, including books and philosophies, to steer the content generation process. Getting Started To deploy this template: Import the workflow into your n8n workspace. Customize the influencer profile settings to match your desired niche, style, and inspiration. Connect your Twitter account through the provided OAuth2 credentials setup. Activate the workflow to start building your virtual influencer's presence on Twitter. Embrace the power of AI to create a distinctive and engaging virtual influencer, captivating your audience with minimal effort.

This n8n workflow was developed to collect and summarize feedback from an event that was collected via a Google Form and saved in a Google Sheets document. The workflow is triggered manually by clicking on the "Test workflow" button. The Google Sheets node retrieves the responses from the feedback form. The Aggregate node then combines all responses for each question into arrays and prepares the data for analysis. The OpenAI node processes the aggregated feedback data. System Prompt instructs the model to analyze the responses and generate a summary report that includes the overall sentiment regarding the event and constructive suggestions for improvement. The Markdown node converts the summary report, which is in Markdown format, into HTML. Finally, the Gmail node sends an HTML-formatted email to the specified email address.

This workflow allows you to ask questions about a PDF document. The answers are provided by an AI model of your choice, and the answer includes a citation pointing to the information it used. You can use n8n’s built-in chat interface to ask the questions, or you could customise this workflow to use another one (e.g. Slack, Teams, etc.) Example The workflow is set up with the Bitcoin whitepaper. So you could ask things like: Question: “Which email provider does the creator of Bitcoin use?“ Answer: “GMX [Bitcoin whitepaper.pdf, lines 1-35]” Requirements A Pinecone account (they have a free tier at the time of writing that is easily enough for this workflow) Access to a large language model (e.g. an OpenAI account) Customizing this workflow The workflow only reads in one document, but you could customise it to read in all the documents in a folder (or more). The workflow is set up to use GPT 3.5, but you could swap that out for any other model (including self-hosted ones).

If you have multiple users managing workflows, there may come a time where a user “accidentally” turns off a workflow. Or, if you have workflows that automatically turn off other workflows, that code might “accidentally” turn off the wrong one. In either case, here’s a workflow that can attempt to “auto-start” accidentally disabled workflows: How it works: When activated, then every 4 hours, the workflow will search all other workflows that have the auto_resume:true tag present. If any other workflow has auto_resume:true set but is currently turned off, then this workflow will turn it back on. Of course, this watchdog won’t work if the watchdog workflow is turned off. That said, we’ve found this useful in recovering from accidental actions that cause production workflows to be turned off.

Do you consistently forget to set a Default Error Workflow when creating new workflows? Then this helper workflow is for you! When activated, this helper workflow will: Scan ALL other workflows every 4 hours Make sure ALL workflows have a default error workflow set (based on what Workflow ID you provide) This helper will SKIP OVER any workflows that have the default_error:false tag set (make sure your default error workflow has the default_error:false tag set, so that you don't end up with recursive loops during errors) Setup Nodes: Once imported, edit the Set Vars node with your default_error_workflow_id value. If you want to change the default_error:false tag to some other tag name, you can do so here as well. You need to update the Set Default Error Workflow node with your PostgreSQL credentials to access the n8n database.

This workflow will translate all your PDF documents from specified Google Drive folder to the desired language. Translated files will be automatically uploaded to the original folder. Required accounts 1️⃣ Google Drive account 2️⃣ DeepL developer account and API key How to setup? 1️⃣ Setup your google drive folder url, target and source language in the configuration node 2️⃣ Connect your Google Drive account with all Google Drive nodes 3️⃣ Setup HTTP header credentials that should be used for HTTP nodes in the template (replace yourAuthKey with your DeepL API key) 4️⃣ Set your DeepL header credentials in all HTTP nodes

How it works: Using a Crew of AI agents (Senior Researcher, Visionary, and Senior Editor), this crew will automatically determine the right questions to ask to produce a detailed fundamental stock analysis. This application has two components: a front-end and a Stock Q&A engine. The front end is the team of agents automatically figuring out the questions to ask, and the back-end part is the ability to answer those questions with the SEC 10K data. This template implements the Stock Q&A engine. For the front-end of the application, you can choose one of two options: using CrewAI with the Replit environment (code approach) fully visual approach with n8n template (AI-powered automated stock analysis) Setup steps: Use first workflow in template to upsert a company annual report PDF (such as from SEC 10K filling) Get URL for Webhook in second workflow template CrewAI front-end: Youtube overview video Fork this AI Agent environment Crew Agent Environment Set the webhook URL into N8N_WEBHOOK_URL variable Set OpenAI_API_KEY variable

This is an end-to-end workflow for creating a simple OpenAI Assistant. The whole process is done with n8n nodes and do not require any programming experience. The workflow is divided into three main steps: Step 1: Get a Google Drive File and Upload to OpenAI The workflow starts by retrieving a file from Google Drive using the "Get File" node. The example file used is a Music Festival document. The retrieved file is then uploaded to OpenAI using the "Upload File to OpenAI" node. Run this section only once. The file is stored persistently on the OpenAI side. Step 2: Set Up a New Assistant In this step, a new assistant is created using the "Create new Assistant" node. The assistant is given a name, description, and system prompt. The uploaded file from Step 1 is attached as a knowledge source for the assistant. Same as for Step 1, run this section only once. Step 3: Chat with the Assistant The "Chat Trigger" node initiates the conversation with the assistant. The "OpenAI Assistant" node handles the conversation, using the assistant created in Step 2. Step 4: Expand the Assistant This step provides resources for ideas on how to expand the Assistant's capabilities: Create a WhatsApp bot Create a simple Telegram bot Create a Telegram AI bot (YouTube video) By following this workflow, users can create their own AI-powered assistants using OpenAI's API and integrate them with various platforms like WhatsApp and Telegram.

In this automation we first make a screenshot with a screenshot API called URLbox and then send this screenshot into the OpenAI API and analyze it. You can extend this automation by the way you want to ingest the website url's & names into this workflow. Options as data source: Postgres Google Sheets Your CRM ... Setup: Replace Website & URL in Setup Node Put in your URLbox API Key Put in your OpenAI credentials Click here for a blog article with more information on the automation.

Introduction: Streamline your fundamental stock analysis process with AI-powered automation. By harnessing the power of SEC 10K reports - comprehensive documents required by the SEC containing vital company information - this template automates the analysis workflow. From planning by a Senior Research Analyst to execution by five Research Analysts and final review by a Senior Editor, this template takes an AI persona approach to compose the report that includes an overview of the business, strategy, SWOT (Strengths, Weaknesses, Opportunities, and Threads) analysis, near term catalysts, and top risks. Additionally, this template allows you to control the length and detail of the report generated. How it works There are three personas in the workflow: Senior Research Analyst** is the first part of the workflow. They are responsible for planning the work for the rest of the team. Squad of Research Analysts** is the second part of the workflow. They execute the plan created. Senior Editor** is the third part of the workflow. They polish the draft report and send it to publish This template uses a custom tool that is able to answer the SEC 10K questions from the Research team. Setup steps Setup the Stock Q&A Workflow for the company you want to analyze using this template Customize the setup node by specifying the company to analyze Customize the publish step by specifying the file name in the Google docs node Credit Inspired by Guilio's template

🚀 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.

🎉 Do you want to master AI automation, so you can save time and build cool stuff? I’ve created a welcoming Skool community for non-technical yet resourceful learners. 👉🏻 Join the AI Atelier 👈🏻 Keeping your YouTube video descriptions updated and consistent across your channel can be a daunting task. Manually editing each video is not only time-consuming but also prone to errors. 📋 Blog post 📺 Youtube Video This workflow streamlines this process, allowing you to maintain a shared section in all your video descriptions and effortlessly update them all at once. By incorporating a unique identifier, you can automate updates across your entire channel, keeping your content fresh and relevant with minimal effort. How it Works Define Your Unique Delimiter:** Choose your unique delimiter (e.g., "---n8ninja---"). It will be visible, so select something appropriate for your audience. Automate Updates:** Anything below the delimiter can be automatically updated by this workflow. Configure Text Updates:** Set the text you wish to add to every video description in the configuration node. Getting Started Integrate Google (YouTube) Credentials:** Securely add your credentials to enable API access. Set Up the Configuration Node:** Define your delimiter and the text for the shared section you wish to append to your video descriptions. Prepare Your Videos:** Add the chosen delimiter to all videos you want to update automatically. Execute the Workflow:** Run the workflow whenever you wish to batch update the descriptions of your videos. Created by the n8ninja ✨ follow on X 📺 follow on YT

What problem is this workflow solving? This workflow is aimed for email marketing enthusiasts looking for an easy way to either extract the domain from an email ad also check if the syntax is correct without having to use the code node. How this works For this to work, replace the debugger node with your actual data source. Map your data at match the above layout Run your workflow and check for all the emails that are either valid or not Once done, you will have a list of all your emails, domains, and whether they are valid or not.

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

Purpose This ensures that executions of scheduled workflows do not overlap when they take longer than expected. How it works This is a separate workflow which monitors the execution of the main workflow Stores a flag in Redis (key dynamically named after workflow ID) which indicates if the main workflow is running or idle Only calls the main workflow if the last execution has finished Setup Update the credentials suitable for your Redis instance Replace the Schedule Trigger of your main workflow by an Execute Workflow Trigger Copy the workflow ID from the URL Paste the workflow ID in the Execute Workflow Node of this workflow Configure the Schedule Trigger Node

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).

The built-in Gmail node doesn't yet support embedding images within the body of the email, but you can pull this off using the HTTP node, and this template shows you how. Requirements A Gmail account How it works The workflow downloads an image, converts it into the format that the Gmail API expects (base64), packages it into a multipart MIME email and uses the HTTP node to send it.

Restore backed up workflows from GitHub to your n8n workspace. This workflow was inspired by this one that lets you back up your n8n workflows to GitHub. It will let you restore your backed up workflows in your workspace, without creating duplicates. In case of issue with your instance, it will save you a lot of time to restore them. How it works It retrieves the workflows saved in a GitHub repository. Then compares these saved workflows with the ones in your n8n workspace based on the name. It will only create them if they don't already exist. Set up steps Open the "Global" node and set your own information (see Configuration below) Click on "Test workflow" It will run through all the workflows in the GitHub repository, check if the name doesn't already exist in your workspace and, in this case, create it. Configuration repo.owner: your GitHub owner name repo.name: your GitHub repository name repo.path: the path within the GitHub repository

This n8n workflow demonstrates how to create an agent using LangChain and SQLite. The agent can understand natural language queries and interact with a SQLite database to provide accurate answers. 💪 🚀 Setup Run the top part of the workflow once. It downloads the example SQLite database, extracts from a ZIP file and saves locally (chinook.db). 🗣️ Chatting with Your Data Send a message in a chat window. Locally saved SQLite database loads automatically. User's chat input is combined with the binary data. The LangChain Agend node gets both data and begins to work. The AI Agent will process the user's message, perform necessary SQL queries, and generate a response based on the database information. 🗄️ 🌟 Example Queries Try these sample queries to see the AI Agent in action: "Please describe the database" - Get a high-level overview of the database structure, only one or two queries are needed. "What are the revenues by genre?" - Retrieve revenue information grouped by genre, LangChain agent iterates several time before producing the answer. The AI Agent will store the final answer in its memory, allowing for context-aware conversations. 💬 Read the full article: 👉 https://blog.n8n.io/ai-agents/

Who is this for? For developers and organizations that need to convert DOCX files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Downloads the DOCX file from the web. Converts the DOCX file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.

Overview This template helps n8n cloud plan users execute all executions to a CSV for easy data analysis. Identify what workflows are generating the most executions or could be optimized. How this workflow works Click "Test Workflow" to manually execute the workflow Open the "Convert to CSV" node to access the binary data of the CSV file Download the CSV file Nodes included: n8n node Convert to File No Operation, do nothing - replace with another Set up steps Import the workflow to your workspace Add your n8n API credential Benefits of Exporting n8n Cloud Executions to CSV Exporting n8n Cloud executions to CSV offers significant advantages for enhancing workflow management and data analysis capabilities. Here are three key benefits: Enhanced Data Analysis: Comprehensive Insights: Exporting execution data allows for in-depth analysis of workflow performance, helping identify bottlenecks and optimize processes. Custom Reporting: CSV files can be easily imported into various data analysis tools (e.g., Excel, Google Sheets, or BI software) to create custom reports and visualizations tailored to specific business needs. Improved Workflow Monitoring: Historical Data Review: Accessing historical execution data enables users to track workflow changes and their impacts over time, facilitating better decision-making. Error Tracking and Debugging: By reviewing execution logs, users can quickly identify and address errors or failures, ensuring smoother and more reliable workflow operations. Regulatory Compliance and Auditing: Audit Trails: Keeping a record of all executions provides a clear audit trail, essential for regulatory compliance and internal audits. Data Retention: Exported data ensures that execution records are preserved according to organizational data retention policies, safeguarding against data loss. By leveraging the capabilities of CSV exports, users can gain valuable insights, streamline workflow management, and ensure robust data handling practices, ultimately driving better performance and efficiency in their n8n Cloud operations.

Who is this for? For developers and organizations that need to convert DOCX files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Downloads the DOCX file from the web. Converts the DOCX file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Adjust url_to_file in the Config node to URL pointing to your file. Optionally, additional Body Parameters can be added for the converter.

Who is this for? For developers and organizations that need to combine PDF files. What problem is this workflow solving? PDF file merging problem. What this workflow does Downloads two PDF files from the web. Merges two PDF files into one. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the PDF merge HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.

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

Who is this for? For developers and organizations that need to convert XLSX files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Downloads the XLSX file from the web. Converts the XLSX file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.

Who is this for? For developers and organizations that need to convert PPTX files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Downloads the PPTX file from the web. Converts the PPTX file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.

Who is this for? For developers and organizations that need to protect PDF files with the password. What problem is this workflow solving? PDF file protection problem. What this workflow does Downloads the PDF file from the web. Protects PDF file with the password. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Change the password in the parameter UserPassword Optionally, additional Body Parameters can be added for the converter.

Based on Jonathan's work. Check out his templates. This workflow will backup your credentials to GitHub. It uses a CLI command to export all credentials. It then loops over the data, checks in GitHub to see if a file exists that uses the credential's ID. Once checked it will: update the file on GitHub if it exists; create a new file if it doesn't exist; ignore if it's the same. Config Options repo.owner - Github owner repo.name - Github repository name repo.path - Path within the Github repository ⚠ The credentials are all decrypted. Make sure you save them safely or tweak the CLI command to store them encrypted.== Check out my other templates 👉 https://n8n.io/creators/solomon/

Who is this for? For developers and organizations that need to convert web page to PDF. What problem is this workflow solving? The web page conversion to PDF problem. What this workflow does Converts web page to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Change the parameter url to the webpage you want to convert to pdf Optionally, additional Body Parameters can be added for the converter.

Who is this for? For developers and organizations that need to convert HTML files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Converts HTML to file. Converts the HTML file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.

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.

Who is this for? For developers and organizations that need to convert image files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Downloads the JPG file from the web. Converts the JPG file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.

Who is this for? For developers and organizations that need to convert PDF files to PDFA for long term archiving. What problem is this workflow solving? The file format conversion problem. What this workflow does Downloads the PDF file from the web. Converts the PDF file to PDFA. Stores the PDFA file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.

Who is this for? This workflow is for all sales reps and lead generation manager who need to prepare their prospecting activities, and find relevant information to personalize their outreach. Use Case This workflow allows you to do account research with the web using AI. It has the potential to replace manual work done by sales rep when preparing their prospecting activities by searching complex information available online. What this workflow does The advanced AI module has 2 capabilities: Research Google using SerpAPI Visit and get website content using a sub-workflow From an unstructured input like a domain or a company name. It will return the following properties: domain company Linkedin Url cheapest plan has free trial has entreprise plan has API market (B2B or B2C) The strength of n8n here is that you can adapt this workflow to research whatever information you need. You just have to precise it in the prompt and to precise the output format in the "Strutured Output Parser" module. Detailed instructions + video guide can be found by following this link.

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 assists property managers and surveyors by reducing the time and effort it takes to complete property inventory surveys. In such surveys, articles and goods within a property may need to be captured and reported as a matter of record. This can take a sizable amount of time if the property or number of items is big enough. Our solution is to delegate this task to a capable AI Agent who can identify and fill out the details of each item automatically. How it works An AirTable Base is used to capture just the image of an item within the property Our workflow monitoring this AirTable Base sends the photo to an AI image recognition model to describe the item for purpose of identification. Our AI agent uses this description and the help of Google's reverse image search in an attempt to find an online product page for the item. If found, the product page is scraped for the item's specifications which are then used to fill out the rest of the details of the item in our Airtable. Requirements Airtable for capturing photos and product information OpenAI account to for image recognition service and AI for agent SerpAPI account for google reverse image search. Firecrawl.dev account for webspacing. Customising this workflow Try building an internal inventory database to query and integrate into the workflow. This could save on costs by avoiding fetching new each time for common items.

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.

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 workflow demonstrates how to manage your Qdrant vector store when there is a need to keep it in sync with local files. It covers creating, updating and deleting vector store records ensuring our chatbot assistant is never outdated or misleading. Disclaimer This workflow depends on local files accessed through the local filesystem and so will only work on a self-hosted version of n8n at this time. It is possible to amend this workflow to work on n8n cloud by replacing the local file trigger and read file nodes. How it works A local directory where bank statements are downloaded to is monitored via a local file trigger. The trigger watches for the file create, file changed and file deleted events. When a file is created, its contents are uploaded to the vector store. When a file is updated, its previous records are replaced. When the file is deleted, the corresponding records are also removed from the vector store. A simple Question and Answer Chatbot is setup to answer any questions about the bank statements in the system. Requirements A self-hosted version of n8n. Some of the nodes used in this workflow only work with the local filesystem. Qdrant instance to store the records. Customising the workflow This workflow can also work with remote data. Try integrating accounting or CRM software to build a managed system for payroll, invoices and more. 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/189F1fNOiw6naNSlSwnyLVEm_Ho_IFfdM/view?usp=sharing

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.

This n8n template showcases the new HTTP tool released in version 1.47.0. Overall, the tool helps simplify AI Agent workflows where custom sub-workflows were performing the same simple http requests. Comparisons 1. AI agent that can scrape webpages Remake of https://n8n.io/workflows/2006-ai-agent-that-can-scrape-webpages/ Changes: Replaces Execute Workflow Tool and Subworkflow Replaces Response Formatting 2. Allow your AI to call an API to fetch data Remake of https://n8n.io/workflows/2094-allow-your-ai-to-call-an-api-to-fetch-data/ Changes: Replaces Execute Workflow Tool and Subworkflow Replaces Manual Query Params Definitions Replaces Response Formatting

This n8n template demonstrates an approach to image embeddings for purpose of building a quick image contextual search. Use-cases could for a personal photo library, product recommendations or searching through video footage. How it works A photo is imported into the workflow via Google Drive. The photo is processed by the edit image node to extract colour information. This information forms part of our semantic metadata used to identify the image. The photo is also processed by a vision-capable model which analyses the image and returns a short description with semantic keywords. Both pieces of information about the image are combined with the metadata of the image to form a document describing the image. This document is then inserted into our vector store as a text embedding which is associated with our image. From here, the user can query the vector store as they would any document and the relevant image references and/or links should be returned. Requirements Google account to download image files from Google Drive. OpenAI account for the Vision-capable AI and Embedding models. Customise this workflow Text summarisation is just one of many techniques to generate image embeddings. If the results are unsatisfactory, there are dedicated image embedding models such as Google's vertex AI multimodal embeddings.

This n8n workflow is a fun way to query and search over your credentials on your n8n instance. Good to know Your credentials should remain safe as this workflow does not decrypt or use any decrypted data. Example Usage "Which workflows are using Slack and Google Calendar?" "Which workflows have AI in their name but are not using openAI?" How it works Using the n8n API, it fetches all workflow data on the instance. Workflow data contains references to credentials used so this will be extracted. With some necessary reformatting, the workflows and their credentials metadata are stored to a SQLite database. Next, an AI agent is used with a custom SQL tool that reads the SQLite database created in the previous step. The AI agent is instructed to perform SQL queries against our workflow credential table when asked about credentials by the user. Requirements You'll need an n8n API key. Please note that only workflows will be scoped to your API key. Customising the workflow Add extra table fields to the SQLite database to answer even more complex queries such as: workflow status to differentiate between active and inactive workflows.

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. Check out my other templates 👉 https://n8n.io/creators/solomon/

This n8n workflow demonstrates a simple multi-agent setup to perform the task of competitor research. It showcases how using the HTTP request tool could reduce the number of nodes needed to achieve a workflow like this. How it works For this template, a source company is defined by the user which is sent to Exa.ai to find competitors. Each competitor is then funnelled through 3 AI agents that will go out onto the internet and retrieve specific datapoints about the competitor; company overview, product offering and customer reviews. Once the agents are finished, the results are compiled into a report which is then inserted in a notion database. Check out an example output here: https://jimleuk.notion.site/2d1c3c726e8e42f3aecec6338fd24333?v=de020fa196f34cdeb676daaeae44e110&pvs=4 Requirements An OpenAI account for the LLM. Exa.ai account for access to their AI search engine. SerpAPI account for Google search. Firecrawl.dev account for webscraping. Notion.com account for database to save final reports. Customising the workflow Add additional agents to gather more datapoints such as SEO keywords and metrics. Not using notion? Feel free to swap this out for your own database.

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 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 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.

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

Purpose This workflow automatically creates Tasks from forwarded Emails, similar to Asana, but better. Emails are processed by AI and converted to rather actionable task. In addition this workflow is build in a way, that multiple users can share this single process by setting up their individual configuration through a user friendly portal (internal tool) instead of the need to manage their own workflows. Demo How it works One Gmail account is used to process inbound mails from different users. A custom web portal enables users to define “routes”. Thats where the mapping between an automatically generated Gmail Alias and a Notion Database URL, including the personal API Token, happens. Using a Gmail Trigger, new entries are split by the Email Alias, so the corresponding route can be retrieved from the Database connected to the portal. Every Email then gets processed by AI to get generate an actionable task and get a short summary of the original Email as well as some metadata. Based on a predefined structure a new Page is created in the corresponding Notion Database. Finally the Email is marked as “processed” in Gmail. If an error happens, the route gets paused for a possible overflow and the user gets notified by Email. Setup Create a new Google account (alternatively you can use an existing one and set up rules to keep your inbox organized) Create two Labels in Gmail: “Processed” and “Error” Clone this Softr template including the Airtable dataset and publish the application Clone this workflow and choose credentials (Gmail, Airtable) Follow the additional instructions provided within the workflow notes Enable the workflow, so it runs automatically in the background How to use Open published Softr application Register as a new user Create a new route containing the Notion API key and the Notion Database URL Expand the new entry to copy the Email address Save the address as a new contact in your Email provider of choice Forward an Email to it and watch how it gets converted to an actionable task Disclamer Airtable was chosen, so you can setup this template fairly quickly. It is advised to replace the persistence by something you own, like a self hosted SQL server, since we are dealing with sensitive information of multiple users This solution is only meant for building internal tools, unless you own an embed license for n8n.

Are you a visual thinker working with n8n? 🎨 View and understand workflow structures at a glance with this template! Built with mermaid.js, Bootstrap 5 and AXAJ to create an interactive web page displaying n8n workflows as flowcharts. 🌟 Perfect for documentation, presentations, or just getting a clearer picture of your automation processes. Need customization help? Reach out to Eduard! Benefits 📊 Instant workflow visualization 📱 Responsive design 🔗 Direct links to n8n workflows 🧩 Special shapes for different node types 🚫 Disabled node indication 🔒 No external dependencies – just paste the workflow and call the webhook 🛠️ Easily customizable – enhance the JS script or add custom styling ⚠️ Important note for cloud users ⚠️ Since the cloud version doesn't support environmental variables, please make the following changes in the CONFIG node: Update the instance_url variable: Enter your n8n URL instead of {{$env["N8N_PROTOCOL"]}}://{{$env["N8N_HOST"]}} Change the webhook_path to simply "webhook" instead of {{$env["N8N_ENDPOINT_WEBHOOK"] || "webhook"}} 🌟 Examples Multiple flowcharts on a single page: Several shapes for different nodes: Langchain nodes with special connections styling:

TOTP Validation with Function Node This template allows you to verify if a 6-digit TOTP code is valid using the corresponding TOTP secret. It can be used in an authentication system. The inputs need to be: a base32 totp secret (String) a 6 digits code (String) ++Important:++ The 6-digit code must be in text format. If the code starts with zeros and is treated as a number, it could cause validation issues. The function node will generate a 6-digit code from the TOTP secret, then compare it with the provided code. If they match, it will return 1 otherwise, it will return 0. Example usage: You retrieve the user's TOTP secret from a database, then you want to verify if the 2FA code provided by the user is valid. Setup Guidelines You only need the ==TOTP VALIDATION== node. You will need to modify lines 39 and 40== of the node with the correct values for your specific context. Testing the Template You can define a sample secret and code in the EXAMPLE FIELDS node of the template, then click "Test Workflow". If the code is valid for the provided secret, the flow will proceed to the true branch of the IF CODE IS VALID node. Otherwise, it will go to the false branch.

This template is inspired by Save your workflows into a GitHub repository by hikerspath and Back Up Your n8n Workflows To Github by jon-n8n. Basic Retrieve all workflows from an n8n instance and save it on a gitlab project. If the workflow exist, il will only save the changes. Flow What the workflow does : Sets custom parameters Gets workflows Iterates through each workflow one by one Get the file from Gitlab if exists Compare the files as objects (not as strings) Return a status on the workflow Create, Edit or ignore the file depending on the status Return a list of status for each workflow Configuration Select a credential in each Gitlab nodes. Edit the data in node "Globals" : repo.owner : slug of the user or team owning the repo repo.name : slug of the repository repo.branch : branch to commit on repo.path : from root of the repository. Should end with / Comments Error on gitlab nodes will not stop the run but will list the current workflow as error in the results Some fields are ignored to determined if there is changes : updatedAt : should be ignored if only ignores fields are changed globals : it's running information, no need to follow the changes

This workflow allows you to recognize a folder with receipts or invoices (make sure your files are in .pdf, .png, or .jpg format). The workflow can be triggered via the "Test workflow" button, and it also monitors the folder for new files, automatically recognizing them. Video Demo https://youtu.be/mGPt7fqGQD8 1. n8n import glitch After import, the trigger node "When clicking 'Test workflow'" might be disconnected. You need to connect it via 2 arrows to "Google Sheets1" and "Google Drive" nodes. So, the workflow has 2 triggers - via button, and via Google Sheets "new file" event - both of these triggers should be connected to 2 nodes. Here is how it should look like: https://ocr.oakpdf.com/n8n_fix.png 2. Set up RapidAPI HTTP auth key Create new "HTTP header" n8n credential and paste your RapidAPI key from https://rapidapi.com/restyler/api/receipt-and-invoice-ocr-api into it. https://ocr.oakpdf.com/n8n_api_key.png Make sure "HTTP Request" node uses this credential. 3. Set up your Google Auth You need a Google connection to work with your Google Sheets and Google Drive accounts: https://docs.n8n.io/integrations/builtin/credentials/google/oauth-generic/#finish-your-n8n-credential 4. Set up Google Sheets Copy this Google Sheets document: https://docs.google.com/spreadsheets/d/1G0w-OMdFRrtvzOLPpfFJpsBVNqJ9cfRLMKCVWfrTQBg/edit?usp=sharing Custom document formats and advanced usage Email: contact@scrapeninja.net Linkedin: https://www.linkedin.com/in/anthony-sidashin/

This n8n workflow automates the process of researching companies by gathering relevant data such as traffic volume, foundation details, funding information, founders, and more. The workflow leverages the ProspectLens API, which is particularly useful for researching companies commonly found on Crunchbase and LinkedIn. ProspectLens is an API that provides very detailed company data. All you need to do is supply the company's domain name. You can obtain your ProspectLens API key here: https://apiroad.net/marketplace/apis/prospectlens In n8n, create a new "HTTP Header" credential. Set x-apiroad-key as the "Name" and enter your APIRoad API key as the "Value". Use this credential in the HTTP Request node of the workflow.

Workflow: Snapshot Contabo How it Works This workflow automates daily backups (snapshots) of VPS instances hosted on Contabo. Each day at midnight, it checks for existing snapshots and ensures that only the latest backups are retained by removing older ones. It provides a seamless, hands-off backup process to keep your data secure. Setup Steps Setting up this workflow is quick, typically taking about 10-15 minutes. The essential part of the setup is providing the necessary credentials, which you can easily retrieve from your Contabo control panel. Import the Workflow: Download and upload the workflow JSON into n8n. Configure Credentials: Add CLIENT_ID, CLIENT_SECRET, API_USER, and API_PASSWORD in the credential node. Activate the Workflow: Enable it to run automatically at midnight every day. Flow Overview Schedule Trigger (00:00 daily):** Automatically initiates the workflow. Formatted Date:** Prepares a timestamp for naming the snapshot. List Snapshots:** Verifies if an existing snapshot is available for each VPS. Conditional Logic:** No Snapshot? Proceeds to create a new one. Snapshot Found? Deletes the old snapshot before creating a new one. Key Points Snapshot Retention:** Old snapshots are deleted to ensure only the latest backups are stored. Unique Identifiers:** UUIDs are used to track and guarantee unique operations.

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)

This n8n workflow demonstrates how we can use Multimodal LLMs to parse and extract from PDF documents in n8n. In this particular scenario, we're passing a candidate's CV/resume to an AI which filters out unqualified applications. However, this sneaky candidate has added in hidden prompt to bypass our bot! Whatever will we do? No fret, using AI Vision is one approach to solve this problem... read on! How it works Our candidate's CV/Resume is a PDF downloaded via Google Drive for this demonstration. The PDF is then converted into an image PNG using a tool called Stirling PDF. Since the hidden prompt has a white font color, it is is invisible in the converted image. The image is then forwarded to a Basic LLM node to process using our multimodal model - in this example, we'll use Google's Gemini 1.5 Pro. In the Basic LLM node, we'll need to set a User Message with the type of Binary. This allows us to directly send the image file in our request. The LLM is now immune to the hidden prompt and its response is has expected. The example CV/Resume with hidden prompt can be found here: https://drive.google.com/file/d/1MORAdeev6cMcTJBV2EYALAwll8gCDRav/view?usp=sharing Requirements Google Gemini API Key. Alternatively, GPT4 will also work for this use-case. Stirling PDF or another service which can convert PDFs into images. Note for data privacy, this example uses a public API and it is recommended that you self-host and use a private instance of Stirling PDF instead. Customising the workflow Swap out the manual trigger for another trigger such as a webhook to integrate into your existing services. This example demonstrates a validation use-case ie. "does the candidate look qualified?". You can try additionally extracting data points instead such as years of experiences, previous companies etc.

This n8n workflow demonstrates how to automate image captioning tasks using Gemini 1.5 Pro - a multimodal LLM which can accept and analyse images. This is a really simple example of how easy it is to build and leverage powerful AI models in your repetitive tasks. How it works For this demo, we'll import a public image from a popular stock photography website, Pexel.com, into our workflow using the HTTP request node. With multimodal LLMs, there is little do preprocess other than ensuring the image dimensions fit within the LLMs accepted limits. Though not essential, we'll resize the image using the Edit image node to achieve fast processing. The image is used as an input to the basic LLM node by defining a "user message" entry with the binary (data) type. The LLM node has the Gemini 1.5 Pro language model attached and we'll prompt it to generate a caption title and text appropriate for the image it sees. Once generated, the generated caption text is positioning over the original image to complete the task. We can calculate the positioning relative to the amount of characters produced using the code node. An example of the combined image and caption can be found here: https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/l5xbb4ze4wyxwwefqmnc Requirements Google Gemini API Key. Access to Google Drive. Customising the workflow Not using Google Gemini? n8n's basic LLM node supports the standard syntax for image content for models that support it - try using GPT4o, Claude or LLava (via Ollama). Google Drive is only used for demonstration purposes. Feel free to swap this out for other triggers such as webhooks to fit your use case.

This n8n workflow is a proof-of-concept template exploring how we might work with multimodal LLMs and their multi-image analysis capabilities. In this demo, we compare 2 screenshots of a webpage taken at different timestamps and pass both to our multimodal LLM for a visual comparison of differences. Handling multiple binary inputs (ie. images) in an AI request is supported by n8n's basic LLM node. How it works This template is intended to run as 2 parts: first to generate the base screenshots and next to run the visual regression test which captures fresh screenshots. Starting with a list of webpages captured in a Google sheet, base screenshots are captured for each using a external web scraping service called Apify.com (I prefer Apify but feel free to use whichever web scraping service available to you) These base screenshots are uploaded to Google Drive and will be referenced later when we run our testing. Phase 2 of the workflow, we'll use a scheduled trigger to fire sometime in the future which will reuse our web scraping service to generate fresh screenshots of our desired webpages. Next, re-download our base screenshots in parallel and with both old and new captures, we'll pass these to our LLM node. In the LLM node's options, we'll define 2 "user message" inputs with the type of binary (data) for our images. Finally, we'll prompt our LLM with our testing criteria and capture the regressions detected. Note, results will vary depending on which LLM you use. A final report can be generated using the LLM's output and is uploaded to Linear. Requirements Apify.com API key for web screenshotting service Google Drive and Sheets access to store list of webpages and captures Customising this workflow Have your own preferred web screenshotting service? Feel free to swap out Apify with your service of choice. If the web screenshot is too large, it may prove difficult for the LLM to spot differences with precision. Try splitting up captures into smaller images instead.

This n8n workflow shows how using multimodal LLMs with AI vision can tackle tricky image validation tasks which are near impossible to achieve with code and often impractical to be done by humans at scale. You may need image validation when users submitted photos or images are required to meet certain criteria before being accepted. A wine review website may require users only submit photos of wine with labels, a bank may require account holders to submit scanned documents for verification etc. In this demonstration, our scenario will be to analyse a set of portraits to verify if they meet the criteria for valid passport photos according to the UK government website (https://www.gov.uk/photos-for-passports). How it works Our set of portaits are jpg files downloaded from our Google Drive using the Google Drive node. Each image is resized using the Edit Image node to ensure a balance between resolution and processing speed. Using the Basic LLM node, we'll define a "user message" option with the type of binary (data). This will allow us to pass our portrait to the LLM as an input. With our prompt containing the criteria pulled off the passport photo requirements webpage, the LLM is able to validate the photo does or doesn't meet its criteria. A structured output parser is used to structure the LLM's response to a JSON object which has the "is_valid" boolean property. This can be useful to further extend the workflow. Requirements Google Gemini API key Google Drive account Customising this workflow Not using Gemini? n8n's LLM node works with any compatible multimodal LLM so feel free to swap Gemini out for OpenAI's GPT4o or Antrophic's Claude Sonnet. Don't need to validate portraits? Try other use cases such as document classification, security footage analysis, people tagging in photos and more.

This n8n workflow demonstrates an approach to parsing bank statement PDFs with multimodal LLMs as an alternative to traditional OCR. This allows for much more accurate data extraction from the document especially when it comes to tables and complex layouts. Multimodal Parsing is better than traditiona OCR because: It reduces complexity and overhead by avoiding the need to preprocess the document into text format such as markdown before passing to the LLM. It handles non-standard PDF formats which may produce garbled output via traditional OCR text conversion. It's orders of magnitude cheaper than premium OCR models that still require post-processing cleanup and formatting. LLMs can format to any schema or language you desire! How it works You can use the example bank statement created specifically for this workflow here: https://drive.google.com/file/d/1wS9U7MQDthj57CvEcqG_Llkr-ek6RqGA/view?usp=sharing A PDF bank statement is imported via Google Drive. For this demo, I've created a mock bank statement which includes complex table layouts of 5 columns. Typically, OCR will be unable to align the columns correctly and mistake some deposits for withdrawals. Because multimodal LLMs do not accept PDFs directly, well have to convert the PDF to a series of images. We can achieve this by using a tool such as Stirling PDF. Stirling PDF is self-hostable which is handy for sensitive data such as bank statements. Stirling PDF will return our PDF as a series of JPGs (one for each page) in a zipped file. We can use n8n's decompress node to extract the images and ensure they are ordered by using the Sort node. Next, we'll resize each page using the Edit Image node to ensure the right balance between resolution limits and processing speed. Each resized page image is then passed into the Basic LLM node which will use our multimodal LLM of choice - Gemini 1.5 Pro. In the LLM node's options, we'll add a "user message" of type binary (data) which is how we add our image data as an input. Our prompt will instruct the multimodal LLM to transcribe each page to markdown. Note, you do not need to do this - you can just ask for data points to extract directly! Our goal for this template is to demonstrate the LLMs ability to accurately read the page. Finally, with our markdown version of all pages, we can pass this to another LLM node to extract required data such as deposit line items. Requirements Google Gemini API for Multimodal LLM. Google Drive access for document storage. Stirling PDF instance for PDF to Image conversion Customising the workflow At time of writing, Gemini 1.5 Pro is the most accurate in text document parsing with a relatively low cost. If you are not using Google Gemini however you can switch to other multimodal LLMs such as OpenAI GPT or Antrophic Claude. If you don't need the markdown, simply asking what to extract directly in the LLM's prompt is also acceptable and would save a few extra steps. Not parsing any bank statements any time soon? This template also works for Invoices, inventory lists, contracts, legal documents etc.

Adobe developer API Did you know that Adobe provides an API to perform all sort of manipulation on PDF files : Split PDF, Combine PDF OCR Insert page, delete page, replace page, reorder page Content extraction (text content, tables, pictures) ... The free tier allows up to 500 PDF operation / month. As it comes directly from Adobe, it works often better than other alternatives. Adobe documentation: https://developer.adobe.com/document-services/docs/overview/pdf-services-api/howtos/ https://developer.adobe.com/document-services/docs/overview/pdf-extract-api/gettingstarted/ What does this workflow do The API is a bit painful to use. To perform a transformation on a PDF it requires to Authenticate and get a temporal token Register a new asset (file) Upload you PDF to the registered asset Perform a query according to the transformation requested Wait for the query to be proccessed by Adobe backend Download the result This workflow is a generic wrapper to perform all these steps for any transformation endpoint. I usually use it from other workflow with an Execute Workflow node. Examples are given in the workflow. Example use case This service is useful for example to clean PDF data for an AI / RAG system. My favorite use-case is to extract table as images and forward images to an AI for image recognition / description which is often more accuarate than feedind raw tabular data to a LLM.

This n8n workflow template lets you easily generate comprehensive FAQ (Frequently Asked Questions) content for multiple services (or any items or pages you need to add the FAQs to). Simply provide the Google Sheets document containing the items to scrape, and the workflow automatically creates detailed, AI-enhanced FAQ documents. How it works The workflow reads data from a Google Sheets document containing information about different services and categories (again, in your case - whatever objects you need). For each service and category, it generates a set of standard questions and answers covering setup, permissions, integrations, use cases, and pricing benefits. An AI model (OpenAI's GPT) is used to enhance or complete some of the answers, making the content more comprehensive and natural-sounding. The workflow formats the Q&A pairs, combining AI-generated content with predefined answers where applicable. It creates a text file (JSON) for each service or category, containing the formatted Q&A pairs. The generated files are saved to specific folders in Google Drive, organized by the type of integration (native, credential-only, non-native) or category. After processing each service or category, it updates the status in the original Google Sheets document to mark it as completed. Ideal for: Marketing teams: Rapidly create comprehensive FAQ documents for multiple products or services. Customer support: Generate consistent and detailed answers for common customer queries. Product managers: Easily maintain up-to-date documentation as products evolve. Content creators: Streamline the process of creating informative content about various offerings. Accounts required Google account (for Google Sheets and Google Drive) OpenAI API account (for AI-enhanced content generation) n8n.io account (for workflow execution) Set up instructions Set up the required credentials for Google Sheets, Google Drive, and OpenAI when you first open the workflow. Prepare your Google Sheets document with the service/category information. Here's an example of Google Sheet. Fill the "Define Sheets" node with your sheets Adjust the folder IDs in the "Prepare Job" node to match your Google Drive structure. Configure the OpenAI model settings in the "OpenAI Chat Model" node if needed. Test the workflow with a small subset of data before running it on your entire dataset. Adjust the questions asked in the "Create your Q&A templates" section After testing, activate your workflow for automated FAQ generation. 🙏 Big, big kudos to Jim Le for his ideas, input and support when building this workflow. Your approach to AI workflows is always super helpful!

What this workflow does This workflow allows you to monitor multiple Github repos simultaneously without polling due to use of Webhooks. It programmatically allows for adding and deleting of repos to your watchlist to make management convenient. Description Can monitor multiple repos simultaneously. Programmatically register or unregister repos from a list. No need for manual work. Webhook notification means no constant polling necessary. Setup 1. Creating Credentials on Github Generate a personal access token on github by following these esteps; Right hand side of page -> Settings -> scroll to bottom -> Developer Settings > Personal Access Token > Tokens (classic) > Generate New Token Give scopes: admin:repo_hook repo (if you want to use it for your own private repo) if you need more help, see here: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens 2. Setting Credentials in n8n In Register Github Webhook Authenticaion > Generic Credential Type Generic Auth Type > Header Auth Header Auth > Create New Credential with Name set to 'Authorization' and Value set to 'Bearer '. (You can reuse this for Delete Github Webhook and Get Existing Webhooks). Now in Register Github Webhook, scroll down to Send Body > JSON and inside the JSON, change the value of "url" to the webhook address given as Production URL in the node Webhook Trigger. 3. Notification settings In the third row, link up the Webhook Trigger to any API of your choice. Slack and Telegram are given as examples. You can also format the notification message as you wish. Setup time: roughly 10 minutes. Instructions Video: https://vimeo.com/1013473758 Test 1. Register Webhooks In Repos to Monitor, add any repo you want to monitor changes for. Disable Webhook Trigger, Click Test Workflow and if your Github credentials were set correctly, it will automatically register the webhooks. - You can test this by running the single node Get Existing Webhook and confirming it outputs the repo addresses. 2. Handle Github Events Now that you have registered the webhooks, re-enable Webhook Trigger and activate the workflow. Make a commit to any of the registered repos. Confirm that the notification went through. That's it!

What this workflow does Linkedin tracks which Chrome extensions are installed in your browser. This workflow uses a huge raw JSON of chrome extension ids, extracted from Linkedin pages, and builds a pretty Google Sheet with the list of these extensions. This workflow web scrapes Google to search for chrome extension id - and extracts the first search result. Setup Clone this Google Sheet template: https://docs.google.com/spreadsheets/d/1nVtoqx-wxRl6ckP9rBHSL3xiCURZ8pbyywvEor0VwOY/edit?gid=0#gid=0 Get API key for Google SERP API access here: https://rapidapi.com/restyler/api/serp-api1 Create n8n header auth for Google SERP API Some context and discussion https://www.linkedin.com/feed/update/urn:li:activity:7245006911807393792/ Follow the author and get the final Google Sheet with 1300+ Chrome extensions: https://www.linkedin.com/in/anthony-sidashin/

Create a recommendation tool without hallucinations based on RAG with the Qdrant Vector database. This example is based on movie recommendations on the IMDB-top1000 dataset. You can provide your wishes and your "big no's" to the chatbot, for example: "A movie about wizards but not Harry Potter", and get top-3 recommendations. How it works a video with the full design process Upload IMDB-1000 dataset to Qdrant Vector Store, embedding movie descriptions with OpenAI; Set up an AI agent with a chat. This agent will call a workflow tool to get movie recommendations based on a request written in the chat; Create a workflow which calls Qdrant's Recommendation API to retrieve top-3 recommendations of movies based on your positive and negative examples. Set Up Steps You'll need to create a free tier Qdrant Cluster (Qdrant can also be used locally; it's open-sourced) and set up API credentials You'll OpenAI credentials You'll need GitHub credentials & to upload the IMDB Kaggle dataset to your GitHub.

What this template does This workflow will read your Zotero Library and extract Meta Data from the articles of one collection in your bibliography. You can personalize the output for optimized results. How it works Mainly, follow the instructions in the Post it notes: Go to https://www.zotero.org/settings/security and find your USER ID (It's right under the APPLICATIONS Section. On the same website, create a New Private Key. In the "Collections" Node, select Generic Credential Type > Header Auth > Create New Credential using: NAME: Zotero-API-Key VALUE: [Your Private Key] Run your Flow to check if it works and open the "Select Collection" node. See the Results of the previous node as TABLE and copy the "KEY" of the collection you want to use. After that you should have a working flow that reads your bibliography. You can edit or delete the last 2 nodes to personalize your results (Filter and Edit Fields)

This workflows sends events to your Facebook Events Manager using the Meta Conversions API. Why should I use this? Enhance your Meta Ads tracking by taking control of the events you send. This n8n template allows you to: Improve Tracking Quality**: Even if your Facebook Pixel fails, events are still captured through the Meta Conversions API. Send Offline Events**: Record events that happen offline, like personal sales or in-store purchases. Integrate with Your CRM**: Automatically send events to Facebook when a lead converts after a sales call, updating customer details so Meta knows about the conversion. Use Advanced Tracking**: Tracking has become harder in recent years, making campaigns more expensive. By using Advanced Tracking, you help Facebook track events more easily, reducing campaign costs and improving the results. What events can I send? All of them! Purchase InitiateCheckout Lead Subscribe ViewContent … And many others. You can even send your own custom events. Who is this template for? Marketing professionals looking to improve the quality of the Meta Ads tracking. How it works You can send any event you want, as long as you have the necessary data (customer email, name, value of the purchase etc.) How to set up All information you need to set up this workflow is already inside the template. Check out my other templates 👉 https://n8n.io/creators/solomon/

This automation syncs your Invoice PDFs from Stripe to a (AWS) S3 Bucket each month, in a folder of your choice, with the following subPath: yourFolder/invoiceYear/invoiceMonth/fileName Fill in your Credentials and Settings in the Nodes marked with "*". You can adjust this Workflow to your needs. You can also override the yearand month in the ENV* Node for manual syncs. It will sync every Invoice PDF which created-date is greater then the provided year and month. It will automatically set the day to the first day of the desired month. Enjoy the Workflow! ❤️ https://let-the-work-flow.com Workflow Automation & Development

This workflow downloads all files from a specific folder in a S3 Bucket and compresses them so you can download it via n8n or do further processings. Fill in your Credentials and Settings in the Nodes marked with "*". Might serve well as Blueprint or as manual Download for S3 Folders. Since I found it rather tricky to compress all binary files into one zip file I figured might it be an interesting Template. Hint: This is the expression to get every binary key to compress them dynamically. (used in the "Compress"-Node) Enjoy the Workflow! ❤️ https://let-the-work-flow.com Workflow Automation & Development

This n8n workflow demonstrates how to automate a large-scale personalized promotional email campaign, leveraging artificial intelligence to generate unique content for each recipient. Save time and increase the effectiveness of your marketing campaigns by allowing AI to handle the creation of personalized content and email sending. How it works: The workflow fetches recipient data from a Google Sheets document. It validates email addresses and checks if recipients haven't been contacted before. For each valid recipient, the AI agent (using GPT-3.5) generates a personalized email including the recipient's name, product introduction, exclusive offer, and a unique promotional link. The personalized email is sent via SMTP to the recipient. The workflow updates the Google Sheet to mark the recipient as contacted. A random delay is introduced between emails to mimic natural sending patterns and avoid triggering spam filters. Requirements: Google Sheets account for storing and managing recipient data. OpenAI account for access to the GPT-3.5 model. SMTP server for sending emails. Customizing the workflow: This example focuses on sending personalized promotional emails, but it could be extended to include follow-up sequences, tracking email opens and clicks, or integrating with a CRM system for more comprehensive customer management.

Automate your email management with this workflow, designed for freelancers and business professionals who receive high volumes of emails. By leveraging AI-powered categorisation and dynamic email processing, this template helps you organise your inbox and streamline communication for better efficiency and productivity. Check out the YouTube video for step-by-step set up instructions! How it works: Fetch & Filter Emails: The workflow retrieves emails from your Microsoft Outlook account, filtering out flagged emails and those already categorised. Content Preparation: Each email is cleaned up and converted to a structured format using Markdown, making it easier for AI processing. AI Categorization: The content is analysed using an AI model, which categorises the emails into predefined categories (e.g., Action, Junk, Business, SaaS) based on the context and content. Email Categorization & Folder Management: The categorised emails are updated in Microsoft Outlook and moved to respective folders such as "Junk Email" or "Receipts" based on the AI's classification. Conditional Processing & Final Checks: Additional checks and conditions ensure that only unread emails are processed, and errors are gracefully managed to maintain workflow stability. Set up steps: Connect Microsoft Outlook: Link your Microsoft Outlook account using the built-in credentials node to enable email fetching, updating, and folder management. Configure AI Model (Ollama API): Set up the AI model by connecting to the Ollama API and choosing your desired language model for categorisation. Modify Email Categories (Optional): Customize the categories and subcategories within the workflow to suit your unique email management needs. Set Up Error Handling: Review the error handling node settings to ensure smooth workflow execution. This template offers a robust solution for managing and organising your inbox, helping you save time and keep your focus on important emails.

Instructions This automation overlays a background image with another image, making it easy to add watermarks or logos. You can use this automation to watermark your images by overlaying them with a transparent version of your logo. If you'd like to place your logo in a specific corner, feel free to adjust the position of the overlay image in the code node. How it Works Both images are downloaded, so we can process binary files (you can modify the source, tho.) We extract metadata, focusing on the dimensions of each image. The position of the overlay image is calculated (default: dead center of the background image). The two images are composited together. Limitations and Optimisation Opportunities The overlay image must be the same size or smaller than the background image for proper alignment. The overlay image does not automatically scale to match the proportions of the background image. Enjoy the workflow! ❤️ let the work flow — Workflow Automation & Development

This workflow provides a complete solution for handling Telegram Stars payments, invoicing and refunds using n8n. It automates the process of sending invoices, managing pre-checkout approvals, recording transactions, and processing refunds for stars, making it ideal for businesses using Telegram Stars for digital payments. What are Telegram Stars? Learn more here. Key Features Payment Handling**: Automate invoice creation and sending via Telegram, with pre-checkout approval for smooth payment processing. Refund Management: Simplify the refund process using user IDs and payment charge IDs from successful **Telegram Stars transactions. Transaction Recording**: Record all payment details, such as user information and payment charge IDs, in Google Sheets for transparent financial tracking. Who Can Use This Workflow? Developers and Businesses: Looking to implement **Telegram Stars as a payment system within Telegram. Service Providers**: Managing subscriptions, donations, or digital sales through Telegram automation. Use Cases Subscription Sales Automation**: Use the workflow to issue invoices and automatically process payments for recurring subscriptions. Infopreneurs and Marketers: Use the workflow for delivering lead magnets, tripwires, and further automating sales via **Telegram Stars. Course Sales Automation**: Automate invoicing and refunds for educational platforms selling online courses. Developers and Businesses: Looking to implement **Telegram Stars as a payment system within Telegram. Service Providers**: Managing subscriptions, donations, or digital sales through Telegram automation. Online Educational Platforms**: Automate billing for courses and handle refunds easily. Setup Instructions Replace the Telegram API credentials with your bot API token from BotFather. Customize invoice details, including product name, description, and payment amount. Connect your Google Sheets for storing transaction logs. Configure refund steps for easy processing of star refunds when needed. Note: The setup is very simple—just follow the instructions provided on the yellow sticky notes within the workflow and insert your data. All other nodes are pre-configured and require no additional customization. The entire setup process takes just 1 minute. I provided a short Loom record with an explanation. Extensibility This workflow can be further customized to include user profile management, payment analytics, or integration with external services like CRMs or accounting tools. Additional modules can be easily connected to manage advanced features like Telegram User Registration. Available Templates Telegram Bot Starter Template: A foundational setup for creating custom Telegram bots. Telegram User Registration Workflow: Automate user registration and data collection via Telegram. Telegram Payment and Refund Workflow for Stars**: Streamline your Telegram payment processing with stars, invoices, and refunds. Support and Updates This workflow is supported and regularly updated to stay compatible with the latest Telegram features and n8n improvements. If you encounter any issues, technical support is available to ensure smooth integration and setup. Key terms: Telegram Stars payment workflow, Telegram refund automation, n8n payment template, Google Sheets transaction logging, Telegram bot for payments, automated refunds on Telegram, Telegram Stars invoice workflow. Please reach out to Victor if you need further assistance with your n8n workflows and automation!

Are you a popular tech startup accelerator (named after a particular higher order function) overwhelmed with 1000s of pitch decks on a daily basis? Wish you could filter through them quickly using AI but the decks are unparseable through conventional means? Then you're in luck! This n8n template uses Multimodal LLMs to parse and extract valuable data from even the most overly designed pitch decks in quick fashion. Not only that, it'll also create the foundations of a RAG chatbot at the end so you or your colleagues can drill down into the details if needed. With this template, you'll scale your capacity to find interesting companies you'd otherwise miss! Requires n8n v1.62.1+ How It Works Airtable is used as the pitch deck database and PDF decks are downloaded from it. An AI Vision model is used to transcribe each page of the pitch deck into markdown. An Information Extractor is used to generate a report from the transcribed markdown and update required information back into pitch deck database. The transcribed markdown is also uploaded to a vector store to build an AI chatbot which can be used to ask questions on the pitch deck. Check out the sample Airtable here: https://airtable.com/appCkqc2jc3MoVqDO/shrS21vGqlnqzzNUc How To Use This template depends on the availability of the Airtable - make a duplicate of the airtable (link) and its columns before running the workflow. When a new pitchdeck is received, enter the company name into the Name column and upload the pdf into the File column. Leave all other columns blank. If you have the Airtable trigger active, the execution should start immediately once the file is uploaded. Otherwise, click the manual test trigger to start the workflow. When manually triggered, all "new" pitch decks will be handled by the workflow as separate executions. Requirements OpenAI for LLM Airtable For Database and Interface Qdrant for Vector Store Customising This Workflow Extend this starter template by adding more AI agents to validate claims made in the pitch deck eg. Linkedin Profiles, Page visits, Reviews etc.

This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions. This template is intended to help introduce n8n users interested in building with WhatsApp. How it works This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot. A product brochure is imported via HTTP request node and its text contents extracted. The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot. A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out. The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool. The Agent's response is sent back to the user via the WhatsApp node. How to use Once you've setup and configured your WhatsApp account and credentials First, populate the vector store by clicking the "Test Workflow" button. Next, activate the workflow to enable the WhatsApp chatbot. Message your designated WhatsApp number and you should receive a message from the AI sales agent. Tweak datasource and behaviour as required. Requirements WhatsApp Business Account OpenAI for LLM Customising this workflow Upgrade the vector store to Qdrant for persistance and production use-cases. Handle different WhatsApp message types for a more rich and engaging experience for customers.

This n8n template takes a video and extracts frames from it which are used with a multimodal LLM to generate a script. The script is then passed to the same multimodal LLM to generate a voiceover clip. This template was inspired by Processing and narrating a video with GPT's visual capabilities and the TTS API How it works Video is downloaded using the HTTP node. Python code node is used to extract the frames using OpenCV. Loop node is used o batch the frames for the LLM to generate partial scripts. All partial scripts are combined to form the full script which is then sent to OpenAI to generate audio from it. The finished voiceover clip is uploaded to Google Drive. Sample the finished product here: https://drive.google.com/file/d/1-XCoii0leGB2MffBMPpCZoxboVyeyeIX/view?usp=sharing Requirements OpenAI for LLM Ideally, a mid-range (16GB RAM) machine for acceptable performance! Customising this workflow For larger videos, consider splitting into smaller clips for better performance Use a multimodal LLM which supports fully video such as Google's Gemini.

What this workflow does: This flow uses an AI node to generate Seed Keywords to focus SEO efforts on based on your ideal customer profile. You can use these keywords to form part of your SEO strategy. Outputs: List of 20 Seed Keywords Setup Fill the Set Ideal Customer Profile (ICP) Connect with your credentials Replace the Connect to your own database with your own database Pre-requisites / Dependencies You know your ideal customer profile (ICP) An AI API account (either OpenAI or Anthropic recommended) More templates and n8n workflows >>> @simonscrapes

Use Case Research search engine rankings for SEO analysis: You need to track keyword rankings for your website You want to analyze competitor positions in search results You need data for SEO competition analysis You want to monitor SERP changes over time What this Workflow Does The workflow uses ScrapingRobot API to fetch Google search results: Retrieves SERP data for your target keywords Captures URL rankings and page titles Processes up to 5000 searches with free account Organizes results for SEO analysis Setup Create a ScrapingRobot account and get your API key Add your ScrapingRobot API key to the HTTP Request node's GET SERP token parameter Either connect your keyword database (column name "Keyword") or use the "Set Keywords" node Configure your preferred output database connection How to Adjust it to Your Needs Modify keyword source to pull from different databases Adjust the number of SERP results to capture Customize output format for your reporting needs More templates and n8n workflows >>> @simonscrapes

Use Case Transform web pages into AI-friendly markdown format: You need to process webpage content for LLM analysis You want to extract both content and links from web pages You need clean, formatted text without HTML markup You want to respect API rate limits while crawling pages What this Workflow Does The workflow uses Firecrawl.dev API to process webpages: Converts HTML content to markdown format Extracts all links from each webpage Handles API rate limiting automatically Processes URLs in batches from your database Setup Create a Firecrawl.dev account and get your API key Add your Firecrawl API key to the HTTP Request node's Authorization header Connect your URL database to the input node (column name must be "Page") or edit the array in Example fields from data source Configure your preferred output database connection How to Adjust it to Your Needs Modify input source to pull URLs from different databases Adjust rate limiting parameters if needed Customize output format for your specific use case More templates and n8n workflows >>> @simonscrapes

What this workflow does This workflow in n8n demonstrates how to send a message in Lark using a Lark bot. It begins with a manual trigger and then retrieves the necessary Lark token via a POST request. The token is used to authenticate and send a message to a specific chat using the Lark API. The input node provides the required app_id, app_secret, chat_id, and message content. After obtaining the token, the message is sent with the Lark API's message/v4/send/ endpoint. Who This Is For This n8n workflow is ideal for organizations, teams, and developers who need to automate message sending within Lark, especially those managing notifications, alerts, or team reminders. It can help users reduce manual messaging tasks by leveraging a Lark bot to deliver messages at specific intervals or based on particular conditions, enhancing team communication and responsiveness. Setup Fill the Input node with your values Exchange the bearer token in the Send Message node with your token Author: Hiroshi

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.

Use Case Generate accurate search volume data for SEO keyword research: You have a list of potential keywords to target for your website SEO but don't know their actual search volume You need historical data to identify seasonal trends in keyword popularity You want to assess keyword difficulty to prioritize your content strategy You need data-driven insights for planning your SEO campaigns What this Workflow Does The workflow connects to Google's Keyword Planner API to retrieve keyword metrics for your SEO research: Fetches monthly search volume for each keyword Provides historical trends data for the past 12 months Calculates keyword difficulty scores Delivers competition metrics from Google Ads Setup Fill the Set 20 Keywords with up to 20 Keywords of your choosing in an array e.g. ["keyword 1", "keyword 2",...] Create a Google Ads API account and add credentials to Get Search Data node Replace the Connect to your own database with your own database for the output How to Adjust it to Your Needs Change the Set 20 Keywords node input to a source of your choosing e.g. Airtable database with 20 keywords Connect to output source of your choosing More templates and n8n workflows >>> @simonscrapes

Use Case Automate image replacement in Google Docs: You need to update document images dynamically You want to create multiple versions of a template with different images You need to batch process document images from a URL database You want to generate shareable documents with custom images What this Workflow Does The workflow automates image replacement in Google Docs: Accepts image URLs from your database Finds and replaces images in template documents Creates new document copies with updated images Optionally converts to PDF and makes documents shareable Setup Connect your image URL database (column name must be "url") Set up Google Docs OAuth 2 API credentials Optional: Create a template document in Google Drive with placeholder images Optional: Configure Google Drive authentication for additional features How to Adjust it to Your Needs Remove template copying for single document processing Adjust image ID selection for documents with multiple images Configure sharing settings and download formats Customize file naming and storage location More templates and n8n workflows >>> @simonscrapes

This is the first version of a template for a RAG/GenAI App using WordPress content. As creating, sharing, and improving templates brings me joy 😄, feel free to reach out on LinkedIn if you have any ideas to enhance this template! How It Works This template includes three workflows: Workflow 1**: Generate embeddings for your WordPress posts and pages, then store them in the Supabase vector store. Workflow 2**: Handle upserts for WordPress content when edits are made. Workflow 3**: Enable chat functionality by performing Retrieval-Augmented Generation (RAG) on the embedded documents. Why use this template? This template can be applied to various use cases: Build a GenAI application that requires embedded documents from your website's content. Embed or create a chatbot page on your website to enhance user experience as visitors search for information. Gain insights into the types of questions visitors are asking on your website. Simplify content management by asking the AI for related content ideas or checking if similar content already exists. Useful for internal linking. Prerequisites Access to Supabase for storing embeddings. Basic knowledge of Postgres and pgvector. A WordPress website with content to be embedded. An OpenAI API key Ensure that your n8n workflow, Supabase instance, and WordPress website are set to the same timezone (or use GMT) for consistency. Workflow 1 : Initial Embedding This workflow retrieves your WordPress pages and posts, generates embeddings from the content, and stores them in Supabase using pgvector. Step 0 : Create Supabase tables Nodes : Postgres - Create Documents Table: This table is structured to support OpenAI embedding models with 1536 dimensions Postgres - Create Workflow Execution History Table These two nodes create tables in Supabase: The documents table, which stores embeddings of your website content. The n8n_website_embedding_histories table, which logs workflow executions for efficient management of upserts. This table tracks the workflow execution ID and execution timestamp. Step 1 : Retrieve and Merge WordPress Pages and Posts Nodes : WordPress - Get All Posts WordPress - Get All Pages Merge WordPress Posts and Pages These three nodes retrieve all content and metadata from your posts and pages and merge them. Important: ** **Apply filters to avoid generating embeddings for all site content. Step 2 : Set Fields, Apply Filter, and Transform HTML to Markdown Nodes : Set Fields Filter - Only Published & Unprotected Content HTML to Markdown These three nodes prepare the content for embedding by: Setting up the necessary fields for content embeddings and document metadata. Filtering to include only published and unprotected content (protected=false), ensuring private or unpublished content is excluded from your GenAI application. Converting HTML to Markdown, which enhances performance and relevance in Retrieval-Augmented Generation (RAG) by optimizing document embeddings. Step 3: Generate Embeddings, Store Documents in Supabase, and Log Workflow Execution Nodes: Supabase Vector Store Sub-nodes: Embeddings OpenAI Default Data Loader Token Splitter Aggregate Supabase - Store Workflow Execution This step involves generating embeddings for the content and storing it in Supabase, followed by logging the workflow execution details. Generate Embeddings: The Embeddings OpenAI node generates vector embeddings for the content. Load Data: The Default Data Loader prepares the content for embedding storage. The metadata stored includes the content title, publication date, modification date, URL, and ID, which is essential for managing upserts. ⚠️ Important Note : Be cautious not to store any sensitive information in metadata fields, as this information will be accessible to the AI and may appear in user-facing answers. Token Management: The Token Splitter ensures that content is segmented into manageable sizes to comply with token limits. Aggregate: Ensure the last node is run only for 1 item. Store Execution Details: The Supabase - Store Workflow Execution node saves the workflow execution ID and timestamp, enabling tracking of when each content update was processed. This setup ensures that content embeddings are stored in Supabase for use in downstream applications, while workflow execution details are logged for consistency and version tracking. This workflow should be executed only once for the initial embedding. Workflow 2, described below, will handle all future upserts, ensuring that new or updated content is embedded as needed. Workflow 2: Handle document upserts Content on a website follows a lifecycle—it may be updated, new content might be added, or, at times, content may be deleted. In this first version of the template, the upsert workflow manages: Newly added content** Updated content** Step 1: Retrieve WordPress Content with Regular CRON Nodes: CRON - Every 30 Seconds Postgres - Get Last Workflow Execution WordPress - Get Posts Modified After Last Workflow Execution WordPress - Get Pages Modified After Last Workflow Execution Merge Retrieved WordPress Posts and Pages A CRON job (set to run every 30 seconds in this template, but you can adjust it as needed) initiates the workflow. A Postgres SQL query on the n8n_website_embedding_histories table retrieves the timestamp of the latest workflow execution. Next, the HTTP nodes use the WordPress API (update the example URL in the template with your own website’s URL and add your WordPress credentials) to request all posts and pages modified after the last workflow execution date. This process captures both newly added and recently updated content. The retrieved content is then merged for further processing. Step 2 : Set fields, use filter Nodes : Set fields2 Filter - Only published and unprotected content The same that Step 2 in Workflow 1, except that HTML To Makrdown is used in further Step. Step 3: Loop Over Items to Identify and Route Updated vs. Newly Added Content Here, I initially aimed to use 'update documents' instead of the delete + insert approach, but encountered challenges, especially with updating both content and metadata columns together. Any help or suggestions are welcome! :) Nodes: Loop Over Items Postgres - Filter on Existing Documents Switch Route existing_documents (if documents with matching IDs are found in metadata): Supabase - Delete Row if Document Exists: Removes any existing entry for the document, preparing for an update. Aggregate2: Used to aggregate documents on Supabase with ID to ensure that Set Fields3 is executed only once for each WordPress content to avoid duplicate execution. Set Fields3: Sets fields required for embedding updates. Route new_documents (if no matching documents are found with IDs in metadata): Set Fields4: Configures fields for embedding newly added content. In this step, a loop processes each item, directing it based on whether the document already exists. The Aggregate2 node acts as a control to ensure Set Fields3 runs only once per WordPress content, effectively avoiding duplicate execution and optimizing the update process. Step 4 : HTML to Markdown, Supabase Vector Store, Update Workflow Execution Table The HTML to Markdown node mirrors Workflow 1 - Step 2. Refer to that section for a detailed explanation on how HTML content is converted to Markdown for improved embedding performance and relevance. Following this, the content is stored in the Supabase vector store to manage embeddings efficiently. Lastly, the workflow execution table is updated. These nodes mirros the **Workflow 1 - Step 3 nodes. Workflow 3 : An example of GenAI App with Wordpress Content : Chatbot to be embed on your website Step 1: Retrieve Supabase Documents, Aggregate, and Set Fields After a Chat Input Nodes: When Chat Message Received Supabase - Retrieve Documents from Chat Input Embeddings OpenAI1 Aggregate Documents Set Fields When a user sends a message to the chat, the prompt (user question) is sent to the Supabase vector store retriever. The RPC function match_documents (created in Workflow 1 - Step 0) retrieves documents relevant to the user’s question, enabling a more accurate and relevant response. In this step: The Supabase vector store retriever fetches documents that match the user’s question, including metadata. The Aggregate Documents node consolidates the retrieved data. Finally, Set Fields organizes the data to create a more readable input for the AI agent. Directly using the AI agent without these nodes would prevent metadata from being sent to the language model (LLM), but metadata is essential for enhancing the context and accuracy of the AI’s response. By including metadata, the AI’s answers can reference relevant document details, making the interaction more informative. Step 2: Call AI Agent, Respond to User, and Store Chat Conversation History Nodes: AI Agent** Sub-nodes: OpenAI Chat Model Postgres Chat Memories Respond to Webhook** This step involves calling the AI agent to generate an answer, responding to the user, and storing the conversation history. The model used is gpt4-o-mini, chosen for its cost-efficiency.

This workflow is a modification of the previous template on how to create an SQL agent with LangChain and SQLite. The key difference – the agent has access only to the database schema, not to the actual data. To achieve this, SQL queries are made outside the AI Agent node, and the results are never passed back to the agent. This approach allows the agent to generate SQL queries based on the structure of tables and their relationships, without having to access the actual data. This makes the process more secure and efficient, especially in cases where data confidentiality is crucial. 🚀 Setup To get started with this workflow, you’ll need to set up a free MySQL server and import your database (check Step 1 and 2 in this tutorial). Of course, you can switch MySQL to another SQL database such as PostgreSQL, the principle remains the same. The key is to download the schema once and save it locally to avoid repeated remote connections. Run the top part of the workflow once to download and store the MySQL chinook database schema file on the server. With this approach, we avoid the need to repeatedly connect to a remote db4free database and fetch the schema every time. As a result, we reach greater processing speed and efficiency. 🗣️ Chat with your data Start a chat: send a message in the chat window. The workflow loads the locally saved MySQL database schema, without having the ability to touch the actual data. The file contains the full structure of your MySQL database for analysis. The Langchain AI Agent receives the schema, your input and begins to work. The AI Agent generates SQL queries and brief comments based solely on the schema and the user’s message. An IF node checks whether the AI Agent has generated a query. When: Yes: the AI Agent passes the SQL query to the next MySQL node for execution. No: You get a direct answer from the Agent without further action. The workflow formats the results of the SQL query, ensuring they are convenient to read and easy to understand. Once formatted, you get both the Agent answer and the query result in the chat window. 🌟 Example queries Try these sample queries to see the schema-driven AI Agent in action: Would you please list me all customers from Germany? What are the music genres in the database? What tables are available in the database? Please describe the relationships between tables. - In this example, the AI Agent does not need to create the SQL query. And if you prefer to keep the data private, you can manually execute the generated SQL query in your own environment using any database client or tool you trust 🗄️ 💭 The AI Agent memory node does not store the actual data as we run SQL-queries outside the agent. It contains the database schema, user questions and the initial Agent reply. Actual SQL query results are passed to the chat window, but the values are not stored in the Agent memory.

This workflow is triggered by a parent workflow initiated via a Slack shortcut. Upon activation, it collects input from a modal window in Slack and initiates a vulnerability scan using the Qualys API. Key Features Trigger:** Launched by a parent workflow through a Slack shortcut with modal input. API Integration:** Utilizes the Qualys API for vulnerability scanning. Data Conversion:** Converts XML scan results to JSON for further processing. Loop Mechanism:** Continuously checks the scan status until completion. Slack Notifications:** Posts scan summary and detailed results to a specified Slack channel. Workflow Nodes Start VM Scan in Qualys: Initiates the scan with specified parameters. Convert XML to JSON: Converts the scan results from XML format to JSON. Fetch Scan Results: Retrieves scan results from Qualys. Check if Scan Finished: Verifies whether the scan is complete. Loop Mechanism: Handles the repetitive checking of the scan status. Slack Notifications: Posts updates and results to Slack. Relevant Links Qualys API Documentation Qualys Platform Documentation Parent workflow link Link to Report Generator Subworkflow

Who's this for? If you own a website and need to analyze your Google analytics data If you need to create an SEO report on which pages are getting most traffic or how your google search terms are performing If you want to grow your site based on suggestions from data Use case Instead of hiring an SEO expert, I run this report weekly. It checks compares the data from this week to the week before: Views based on countries The top performing pages Google search console performance Watch youtube tutorial here Get my SEO A.I. agent system here Read my detailed case study here How it works The workflow gathers google analytics for the past 7 days then it gathers the data for the week before for comparison. It does this 3 times to get: views per country, engagement per page and google search console results for organic search results. The google analytics nodes has already chosen the correct dimensions and metrics. At the end, it passes the data to openrouter.ai for A.I. analyse. Finally it saves to baserow. How to use this Input your Google analytics credentials Input your property ID Input your Openrouter.ai credentials Input your baserow credentials You will need to create a baserow database with columns: Name, Country Views, Page Views, Search Report, Blog (name of your blog). Created by Rumjahn

Workflow Overview This workflow automates the process of updating a Spotify playlist with tracks from a YouTube playlist, ensuring no duplicates are added. Key Components Manual Trigger: Starts the workflow when you click ‘Test workflow’. Spotify Integration: Retrieves tracks from a specified Spotify playlist. YouTube Integration: Fetches tracks from a designated YouTube playlist. Batch Processing: Processes tracks in batches to handle multiple items efficiently. Track Search: Searches for YouTube tracks on Spotify to find corresponding IDs. Comparison: Compares existing Spotify tracks with YouTube tracks to identify which ones to add. Track Addition: Adds new Spotify tracks to the playlist that are not already included. If you have any questions or need clarification, feel free to ask!

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

Automate Report Generation with n8n & Qualys Introducing the Save Qualys Reports to TheHive Workflow—a robust solution designed to automate the retrieval and storage of Qualys reports in TheHive. This workflow fetches reports from Qualys, filters out already processed reports, and creates cases in TheHive for the new reports. It runs every hour to ensure continuous monitoring and up-to-date vulnerability management, making it ideal for Security Operations Centers (SOCs). How It Works: Set Global Variables:** Initializes necessary global variables like base_url and newtimestamp. This step ensures that the workflow operates with the correct configuration and up-to-date timestamps. Ensure to change the Global Variables to match your environment. Fetch Reports from Qualys:** Sends a GET request to the Qualys API to retrieve finished reports. Automating this step ensures timely updates and consistent data retrieval. Convert XML to JSON:** Converts the XML response to JSON format for easier data manipulation. This transformation simplifies further processing and integration into TheHive. Filter Reports:** Checks if the reports have already been processed using their creation timestamps. This filtering ensures that only new reports are handled, avoiding duplicates. Process Each Report:** Loops through the list of new reports, ensuring each is processed individually. This step-by-step handling prevents issues related to bulk processing and improves reliability. Create Case in TheHive:** Generates a new case in TheHive for each report, serving as a container for the report data. Automating case creation improves efficiency and ensures that all relevant data is captured. Download and Attach Report:** Downloads the report from Qualys and attaches it to the respective case in TheHive. This automation ensures that all data is properly archived and easily accessible for review. Get Started: Ensure your Qualys and TheHive integrations are properly set up. Customize the workflow to fit your specific vulnerability management needs. Need Help? Join the discussion on our Forum or check out resources on Discord! Deploy this workflow to streamline your vulnerability management process, improve response times, and enhance the efficiency of your security operations.

This workflow automates the collection of comments from posts on a Facebook Page. Providing clean, structured data for analysis or further automation. What this workflow does Fetches recent posts from a Facebook Page. Retrieves comments for each post. Outputs structured data of Comments and Posts for further use. Setup Facebook Graph API: Connect your Access Token with the required permissions (pages_read_engagement, pages_read_user_content). Workflow: Set the Page ID and the number of posts to fetch in the "Set Number of Latest Posts to Fetch" node.

Facebook access tokens expire quickly, requiring regular updates for continued API access. This workflow simplifies the process of exchanging short-lived tokens for long-lived ones, saving time and reducing manual effort. What this workflow does Exchanges a short-lived Facebook User Access Token for a long-lived token using the Facebook Graph API. Optionally retrieves a long-lived Page Access Token associated with the user. Outputs both the user and page tokens for further use in automation or integrations. Setup Prerequisites: A valid Facebook App ID and App Secret. A short-lived User Access Token from the Facebook platform. (Optional) The App-Scoped User ID for fetching associated page tokens. Workflow Configuration: Replace placeholder values in the "Set Parameter" node with your Facebook credentials and token. Run the workflow manually to generate long-lived tokens. Documentation Reference: Follow the official Facebook guide for more details: https://developers.facebook.com/docs/facebook-login/guides/access-tokens/get-long-lived/

What this workflow does This (set of) workflow(s) shows how to start multiple sub-workflows, asynchronously, in parallel, and then wait for all of them to complete. Normally sub-workflows would need to be run synchronously, in series, or, if they are executed asynchronously (to run concurrently, in parallel), there is no easy way to merge/wait for an arbitrary number of them to complete. This is a "design pattern" template to show one approach for running multiple, data-driven instances of a sub-workflow "asynchronously," in parallel (instead of running them one at a time in series), but still prevent the later steps in the workflow from continuing until all of the sub-workflows have reported back that they are finished, via callback URL. There are other techniques involving messaging services, database tables, or other external "flow manager" helpers, but this technique accomplishes the goal fully within n8n. Setup To implement this pattern, examine the nodes in the template and modify the incoming data leading to: A split-out loop to acynchronously execute a sub-workflow multiple times, in parallel. For instance, each sub-workflow might process one of a list of incoming documents. The resumeUrl for the main/parent workflow is provided to all of the sub-workflow executions, along with a unique identifier that can be counted later (e.g. a document file-name). A "wait-for-all" loop that checks whether all sub-workflows have reported back (if node) and builds a unique list of identifiers from the callbacks received from each execution of the sub-workflow. The sub-workflow should be designed to respond immediately (async) and later send a callback request when it has finished processing. The callback request should include the unique identifier value received when the sub-workflow it was started. This is meant to be a possible answer to questions like this one about running things in parallel, maybe this one about waiting for things to finish, this one about managing sub-batches of things by waiting for each batch, or this one about running things in parallel. The topic of how to do this comes up A LOT, and this is one of the only techniques that (so far) seems to work.

Simple Social: Instagram Single Image Post with Facebook API Who is this workflow for? This workflow is designed for businesses, social media managers, content creators, and developers who need to automate the process of posting single images to Instagram using the Facebook API. It is ideal for anyone looking to streamline their social media posting process, saving time and ensuring consistent content delivery. Use Case / Problem Solved Manually posting images and captions on Instagram can be time-consuming, especially for businesses and content creators managing multiple accounts. This workflow automates the process from image preparation to publishing, reducing manual effort and increasing efficiency. What this workflow does Trigger Initialization: The workflow starts with a manual trigger that can be adapted to other triggers (e.g., HTTP webhook or schedule). Set Parameters: The workflow includes a node that sets essential parameters, such as the image URL, Instagram business account ID, and caption. Prepare Instagram Media: A node prepares the media for upload using the Facebook API, sending the image and caption for pre-publication processing. Check Media Upload Status: The workflow verifies if the media preparation is complete. Conditional Check: If the media preparation is successful, the workflow proceeds to publish; otherwise, it triggers an error-handling path. Publish Media: The media is published on Instagram if the conditions are met. Post-Publish Check: The workflow checks the status after publication. Conditional Check for Publication: If the publication status is "PUBLISHED," it triggers a success path; otherwise, it triggers a failure handling. Email Notifications: The workflow sends email notifications to indicate successful or unsuccessful outcomes. Setup Here is a quick video in italian language with sub eng(https://youtu.be/obWJFJvg_6g) Add API Credentials: Ensure that valid Facebook API credentials are added and configured for use. Permissions Required: Ensure your app has the necessary permissions (ads_management, business_management, instagram_basic, instagram_content_publish, pages_read_engagement). App review may be required for external user access. Node Configuration: Customize the Set Instagram Parameters node to specify the image URL, caption, and Instagram business account ID. Trigger Adaptation: Adapt the initial trigger if needed to fit your workflow's requirements (e.g., schedule, webhook). How to customize this workflow Change the Image URL and Caption**: Modify the Set Instagram Parameters node to change the image and caption. Trigger Customization**: Replace the manual trigger with other triggers like a webhook to automate posting based on external events. Notifications**: Adjust the email nodes to send customized messages or trigger other workflows based on the outcome. Limitations Image Format**: Only JPEG images are supported. Extended JPEG formats such as MPO and JPS are not compatible. Unsupported Tags**: Shopping tags, branded content tags, and filters are not supported. Instagram TV**: Publishing to Instagram TV is not supported. Rate Limit**: Instagram accounts are limited to 50 API-published posts within a rolling 24-hour period. Carousels count as a single post. Check usage with GET /{ig-user-id}/content_publishing_limit. Example Usage Imagine managing a business account that needs consistent posts. You can schedule this workflow or trigger it manually to automatically post images with captions at the right time, ensuring that your audience stays engaged without manual posting efforts.

Video Guide I prepared a detailed guide that showed the whole process of building a call analyzer. .png) Who is this for? This workflow is ideal for sales teams, customer support managers, and online education services that conduct follow-up calls with clients. It’s designed for those who want to leverage AI to gain deeper insights into client needs and upsell opportunities from recorded calls. What problem does this workflow solve? Many follow-up sales calls lack structured analysis, making it challenging to identify client needs, gauge interest levels, or uncover upsell opportunities. This workflow enables automated call transcription and AI-driven analysis to generate actionable insights, helping teams improve sales performance, refine client communication, and streamline upselling strategies. What this workflow does This workflow transcribes and analyzes sales calls using AssemblyAI, OpenAI, and Supabase to store structured data. The workflow processes recorded calls as follows: Transcribe Call with AssemblyAI: Converts audio into text with speaker labels for clarity. Analyze Transcription with OpenAI: Using a predefined JSON schema, OpenAI analyzes the transcription to extract metrics like client intent, interest score, upsell opportunities, and more. Store and Access Results in Supabase: Stores both transcription and analysis data in a Supabase database for further use and display in interfaces. Setup Preparation Create Accounts: Set up accounts for N8N, Supabase, AssemblyAI, and OpenAI. Get Call Link: Upload audio files to public Supabase storage or Dropbox to generate a direct link for transcription. Prepare Artifacts for OpenAI: Define Metrics: Identify business metrics you want to track from call analysis, such as client needs, interest score, and upsell potential. Generate JSON Schema: Use GPT to design a JSON schema for structuring OpenAI’s responses, enabling efficient storage, analysis, and display. Create Analysis Prompt: Write a detailed prompt for GPT to analyze calls based on your metrics and JSON schema. Scenario 1: Transcribe Call with AssemblyAI Set Up Request: Header Authentication: Set Authorization with AssemblyAI API key. URL: POST to https://api.assemblyai.com/v2/transcript/. Parameters: audio_url: Direct URL of the audio file. webhook_url: URL for an N8N webhook to receive the transcription result. Additional Settings: speaker_labels (true/false): Enables speaker diarization. speakers_expected: Specify expected number of speakers. language_code: Set language (default: en_us). Scenario 2: Process Transcription with OpenAI Webhook Configuration: Set up a POST webhook to receive AssemblyAI’s transcription data. Get Transcription: Header Authentication: Set Authorization with AssemblyAI API key. URL: GET https://api.assemblyai.com/v2/transcript/<transcript_id>. Send to OpenAI: URL: POST to https://api.openai.com/v1/chat/completions. Header Authentication: Set Authorization with OpenAI API key. Body Parameters: Model: Use gpt-4o-2024-08-06 for JSON Schema support, or gpt-4o-mini for a less costly option. Messages: system: Contains the main analysis prompt. user: Combined speakers’ utterances to analyze in text format. Response Format: type: json_schema. json_schema: JSON schema for structured responses. Save Results in Supabase: Operation: Create a new record. Table Name: demo_calls. Fields: Input: Transcription text, audio URL, and transcription ID. Output: Parsed JSON response from OpenAI’s analysis.

Automate Google Analytics Reporting with n8n This n8n workflow collects, processes, and formats Google Analytics data into a comprehensive HTML report. The report is segmented into three primary categories: Engagement Stats, Search Results, and Country Views. The formatted report can be emailed or saved as a document, and the workflow includes error handling and logging for better debugging. Overview Purpose To automate the extraction, processing, and presentation of Google Analytics data in a visually appealing and structured format for easier insights and decision-making. Features Data Parsing**: Individual parsers process raw Google Analytics data for different time periods and categories. Data Aggregation**: Combines parsed data into a single structured JSON object. HTML Report Generation**: Formats the aggregated data into an HTML table with color-coded segments for better readability. Email or Document Output**: The formatted report can be emailed or saved as a Google Doc (will need additional setup). Error Handling**: Includes checks for missing data and detailed error messages for debugging. Workflow Steps Data Fetching: Six separate Google Analytics data pulls: Page Engagement Stats (This Week and Prior Week) Google Search Results (This Week and Prior Week) Country Views (This Week and Prior Week) Data Parsing: Each data pull is processed using a dedicated parser node to generate a URL-safe string. Example nodes: Parse - Get Page Engagement This Week Parse - Country Views Prior Week Data Aggregation: Aggregates parsed data into a structured JSON object using the Aggregate Data node. Ensures consistency and handles missing or malformed data. HTML Report Generation: Creates a formatted HTML report with color-coded tables for each segment: Engagement Stats: Green Search Results: Blue Country Views: Orange Includes headers and neatly formatted tables for each data set. Output: The report can be sent via email using the Gmail API or saved to Google Docs. Example nodes: Gmail node for email delivery. Google Docs node for saving the report as a document. Requirements Prerequisites Google Cloud Setup**: Enable Google Analytics API. Enable Gmail API (if using email output). Generate OAuth credentials for API access. n8n Installation**: Self-hosted n8n instance with required nodes (Gmail, Google Docs, etc.). Free Cloud-based n8n account. Environment Variables Ensure API credentials and tokens are set up in the n8n environment. Update the respective nodes with client ID, client secret, and access tokens. Configuration Google Analytics Configure the Get Report nodes with the appropriate property ID and metrics. Ensure correct date ranges are selected for each node. Formatting Node The Format Data node processes aggregated data and generates the HTML content. Customize the HTML styling and segment colors as needed. Email Node Configure the Gmail node with OAuth credentials. Set the recipient email address and subject line dynamically. Error Handling Common Issues Authentication Errors: Ensure OAuth credentials are correct. Verify that the APIs are enabled in the Google Cloud Console. Empty Data: Check the raw data from Google Analytics. Validate the property ID and query parameters in the Get Report nodes. Parsing Errors: Ensure the parser nodes are correctly configured and match the expected input format. Debugging Use debug logs in each node to identify data flow issues. Add error-handling nodes to capture and log issues during execution. Example Usage Run the Workflow Trigger the workflow to fetch, process, and format Google Analytics data. Verify Output Check the formatted HTML output in the debug logs. Ensure the email or Google Doc contains the correctly formatted report. Future Enhancements Add support for additional metrics or dimensions. Integrate with Slack for notifications. Enable scheduling for automated reports. Add a visual dashboard for real-time analytics.

Purpose of workflow: The purpose of this workflow is to automate scraping of a website, transforming it into a structured format, and loading it directly into a Google Sheets spreadsheet. How it works: Web Scraping: Uses the Jina AI service to scrape website data and convert it into LLM-friendly text. Information Extraction: Employs an AI node to extract specific book details (title, price, availability, image URL, product URL) from the scraped data. Data Splitting: Splits the extracted information into individual book entries. Google Sheets Integration: Automatically populates a Google Sheets spreadsheet with the structured book data. Step by step setup: Set up Jina AI service: Sign up for a Jina AI account and obtain an API key. Configure the HTTP Request node: Enter the Jina AI URL with the target website. Add the API key to the request headers for authentication. Set up the Information Extractor node: Use Claude AI to generate a JSON schema for data extraction. Upload a screenshot of the target website to Claude AI. Ask Claude AI to suggest a JSON schema for extracting required information. Copy the generated schema into the Information Extractor node. Configure the Split node: Set it up to separate the extracted data into individual book entries. Set up the Google Sheets node: Create a Google Sheets spreadsheet with columns for title, price, availability, image URL, and product URL. Configure the node to map the extracted data to the appropriate columns.

n8n Workflow: Exponential Backoff for Google APIs Overview This n8n workflow implements an Exponential Backoff mechanism to handle retries when interacting with Google APIs. It ensures that failed API requests are retried with increasing delays, up to a specified maximum retry count. This approach helps mitigate transient errors (e.g., rate limits or temporary network issues) while maintaining workflow efficiency. Key Features: Exponential Backoff Logic**: Dynamically increases wait time between retries based on the retry count. Error Handling**: Stops the workflow and raises an error after a specified number of retries. Dynamic Waiting**: Waits for a calculated duration before each retry. Scalable Design**: Modular nodes for easy debugging and customization. Workflow Details Nodes in the Workflow: Trigger (When clicking "Test Workflow"): Manually starts the workflow for testing. Loop Over Items: Iterates over multiple input items to process Google API requests row by row. Google API Node (Example: Update Sheet): Sends a request to a Google API endpoint (e.g., updating a row in Google Sheets). On success: Moves to the next item in the loop. On error: Passes the error to the Exponential Backoff node. Exponential Backoff: Calculates the delay for the next retry based on the retry count. Logic: const retryCount = $json["retryCount"] || 0; const maxRetries = 5; const initialDelay = 1; // in seconds if (retryCount < maxRetries) { const currentDelayInSeconds = initialDelay * Math.pow(2, retryCount); return { json: { retryCount: retryCount + 1, waitTimeInSeconds: currentDelayInSeconds, status: 'retrying', } }; } else { return { json: { error: 'Max retries exceeded', retryCount: retryCount, status: 'failed' } }; } Wait: Dynamically waits for the waitTimeInSeconds value calculated in the Exponential Backoff node. Configuration: Resume: After Time Interval Wait Amount: {{ $json["waitTimeInSeconds"] }} Unit: Seconds Check Max Retries: Evaluates whether the retry count has exceeded the maximum limit. Routes the workflow: True: Passes to the Stop and Error node. False: Loops back to the Google API node for retry. Stop and Error: Stops the workflow and logs the error when the maximum retry count is reached. Parameters Configurable Settings: Max Retries: Defined in the Exponential Backoff node (const maxRetries = 5). Adjust this value based on your requirements. Initial Delay: The starting wait time for retries, defined as 1 second. Google API Configuration: Ensure your Google API node is properly authenticated and configured with the desired endpoint and parameters. How to Use Import the Workflow: Copy the workflow JSON and import it into your n8n instance. Configure Google API Node: Set up the Google API node with your credentials and target API endpoint (e.g., Google Sheets, Gmail, etc.). Test the Workflow: Manually trigger the workflow and observe the retry behavior in case of errors. Monitor Logs: Use the console logs in the Exponential Backoff node to debug retry timings and status. Example Scenarios Scenario 1: Successful Execution The Google API processes all requests without errors. Workflow completes without triggering the retry logic. Scenario 2: Transient API Errors The Google API returns an error (e.g., 429 Too Many Requests). The workflow retries the request with increasing wait times. Scenario 3: Maximum Retries Exceeded The workflow reaches the maximum retry count (e.g., 5 retries). An error is raised, and the workflow stops. Considerations Jitter: This workflow does not implement jitter (randomized delay) since it's not required for low-volume use cases. If needed, jitter can be added to the exponential backoff calculation. Retry Storms: If multiple workflows run simultaneously, ensure your API quotas can handle potential retries. Error Handling Beyond Max Retries: Customize the Stop and Error node to notify stakeholders or log errors in a centralized system. Customization Options Adjust the maximum retry limit and delay calculation to suit your use case. Add additional logic to handle specific error codes differently. Extend the workflow to notify stakeholders when an error occurs (e.g., via Slack or email). Troubleshooting Retry Not Triggering**: Ensure the retryCount variable is passed correctly between nodes. Confirm that the error output from the Google API node flows to the Exponential Backoff node. Incorrect Wait Time**: Verify the Wait node is referencing the correct field for waitTimeInSeconds. Request for Feedback We are always looking to improve this workflow. If you have suggestions, improvements, or ideas for additional features, please feel free to share them. Your feedback helps us refine and enhance this solution!

Hacker News to Video Content Overview This workflow converts trending articles from Hacker News into engaging video content. It integrates AI-based tools to analyze, summarize, and generate multimedia content, making it ideal for content creators, educators, and marketers. Features Article Retrieval: Pulls trending articles from Hacker News. Limits the number of articles to process (configurable). Content Analysis: Uses OpenAI's GPT model to: Summarize articles. Assess their relevance to specific topics like automation or AI. Extract key image URLs. Image and Video Generation: Leonardo.ai: Creates stunning AI-generated images based on extracted prompts. RunwayML: Converts images into high-quality videos. Structured Content Creation: Parses content into structured formats for easy reuse. Generates newsletter-friendly blurbs and social media-ready captions. Cloud Integration: Uploads generated assets to: Dropbox Google Drive Microsoft OneDrive MinIO Social Media Posting (Optional): Supports posting to YouTube, X (Twitter), LinkedIn, and Instagram. Workflow Steps 1. Trigger Initiated manually via the "Test Workflow" button. 2. Fetch Articles Retrieves articles from Hacker News. Limits the results to avoid processing overload. 3. Content Filtering Evaluates if articles are related to AI/Automation using OpenAI's language model. 4. Image and Video Generation Generates: AI-driven image prompts via Leonardo.ai. Videos using RunwayML. 5. Asset Management Saves the output to cloud storage services or uploads directly to social media platforms. Prerequisites API Keys**: Hacker News OpenAI Leonardo.ai RunwayML Creatomate n8n Installation**: Ensure n8n is installed and configured locally or on a server. Credentials**: Set up credentials in n8n for all external services used in the workflow. Customization Replace Hacker News with any other data source node if needed. Configure the "Article Analysis" node for different topics. Adjust the cloud storage services or add custom storage options. Usage Import this workflow into your n8n instance. Configure your API credentials. Trigger the workflow manually or schedule it as needed. Check the outputs in your preferred cloud storage or social media platform. Notes Extend this workflow further by automating social media posting or newsletter integration. For any questions, refer to the official documentation or reach out to the creator. About the Creator This workflow was built by AlexK1919, an AI-native workflow automation architect. Check out the overview video for a quick demo. Tools Used Leonardo.ai** RunwayML** Creatomate** Hacker News API** OpenAI GPT** Feel free to adapt and extend this workflow to meet your specific needs! 🎉

Who's this for? If you own a website and need to analyze your Matomo analytics data so you can increse the number of frequent visitors If you need to create an SEO report on what are the common trends amongst your most frequent visitors If you want to grow your site based on suggestions from data Matomo is an analytics tool that can give you details of each individual visitor. Much more powerful than Google analytics. Watch youtube tutorial here Get my SEO A.I. agent system here Read more -> How to create an A.I. Agent to analyze Matomo analytics using n8n for free Here's the A.I. output: Keywords showing the most improvement: Openrouter N8N. Keywords needing attention: Ai Generated Reference Letter Obsidian Second Brain Suggested actions for improvement: Optimize for "best Docker Synology" despite stable ranking, an improvement to top 10 is an achievable goal. Since "2nd brain app for developer" is of interest to a developer. Consider writing a blog post on how the app addresses the specific pain points of developers. Use case Instead of hiring an SEO expert, I run this report weekly. It looks at the data for the past week and looks for visitors with more than 3 visits and recommends ideas to convert more visitors into frequent visitors. How it works The workflow gathers matomo analytics for the past 7 days. We then parse the data The data is sent to Openrouter and using a FREE LLM, it analyses the data. It stores the results in baserow How to use this Input your Matomo analytics credentials Input your Matomo site ID Input your Openrouter.ai credentials Input your baserow credentials You will need to create a baserow database with columns: Dates, Notes, Blog. Created by Rumjahn

Who is this workflow for? This workflow is the baseline workflow for anyone who needs to automate the process of posting 1-4 images to Bluesky using the Bluesky API. It is ideal for anyone looking to streamline their social media posting process, saving time and ensuring consistent content delivery. Use Case / Problem Solved Manually posting images and captions on Instagram can be time-consuming, especially for businesses and content creators managing multiple accounts. This workflow automates the process from image preparation to publishing, reducing manual effort and increasing efficiency. What this workflow does Trigger Initialization: The workflow starts with a manual trigger that can be adapted to other triggers (e.g., HTTP webhook or schedule). Set Parameters: The workflow includes a node that sets essential parameters, such as the Bluesky account ID, image URLs, and caption. Prepare Bluesky Session: A node creates the authenticated session data used by the upload and post operations later in the workflow. Publish Media: Nodes retrieve image files from the specified URLS and uploads them as blobs to Bluesky, a necessary pre-requisite for a Bluesky post to have images attached. Post text caption + images: A node does the final call to the Bluesky API, including the text caption and relevant image references. Setup Sign-in to Bluesky and create an App Password Add your username and App Password to the Define Credentials node. Set the caption (text content) of your post in the Set Caption node. Set 1-4 image URLs in the Set Images node Adapt the initial trigger as needed to fit your workflow's requirements (e.g., schedule, webhook). Adapt the caption and images nodes to accept dynamic parameters. Limitations This workflow assumes a minimum of 1 image URL to function. If you want a text-only post, remove the whole embed section from the JSON in last Post to Bluesky node, as well as relevant image attachment nodes. The 300-character limit in Bluesky includes the caption + hashtags + image alt text. Going over 300 will return a Record/description must not be longer than 300 graphemes error.

Important Notes: Check Legal Regulations: This workflow involves scraping, so ensure you comply with the legal regulations in your country before getting started. Better safe than sorry! Workflow Description: 😮‍💨 Tired of struggling with XPath, CSS selectors, or DOM specificity when scraping ? This AI-powered solution is here to simplify your workflow! With a vision-based AI Agent, you can extract data effortlessly without worrying about how the DOM is structured. This workflow leverages a vision-based AI Agent, integrated with Google Sheets, ScrapingBee, and the Gemini-1.5-Pro model, to extract structured data from webpages. The AI Agent primarily uses screenshots for data extraction but switches to HTML scraping when necessary, ensuring high accuracy. Key Features: Google Sheets Integration**: Manage URLs to scrape and store structured results. ScrapingBee**: Capture full-page screenshots and retrieve HTML data for fallback extraction. AI-Powered Data Parsing**: Use Gemini-1.5-Pro for vision-based scraping and a Structured Output Parser to format extracted data into JSON. Token Efficiency**: HTML is converted to Markdown to optimize processing costs. This template is designed for e-commerce scraping but can be customized for various use cases.

YouTube to X Post Overview This n8n workflow automates the process of promoting your latest YouTube videos on X (formerly Twitter). It ensures that posts are engaging, concise, and follow platform-specific requirements. Additionally, the workflow logs social posts into a Google Sheet for record-keeping. Features Fetch Latest YouTube Videos:** Pulls recently uploaded videos from a specified channel. Generate Engaging X Posts:** Uses OpenAI's GPT-4 to create tailored, witty posts about your videos. Character Limit Validation:** Ensures posts comply with X's character limit by rewriting if necessary. Post Scheduling:** Automates post publishing to X. Google Sheets Integration:** Logs posts and their status for tracking and management. Optional Notifications:** Sends updates via Discord, Slack, or Gmail (disabled by default). How It Works Trigger: Manually trigger the workflow or set a scheduled check for new videos (disabled by default). Fetch YouTube Videos: Retrieves the latest videos from your YouTube channel, filtering duplicates. Content Creation: OpenAI generates a witty and humanized post under 220 characters, using video title and description. If the post exceeds the character limit, it is rewritten automatically. Google Sheets Logging: Logs the generated post to a Google Sheet with details like time, date, and post status. Post to X: Publishes the generated post on X. Updates the Google Sheet with the post URL. Optional Notifications: Sends notifications about the published post via Discord, Slack, or Gmail. Setup Instructions Credentials: Add credentials for: YouTube API OpenAI API Google Sheets API X (formerly Twitter) API Use the accounts associated with "AlexK1919." YouTube Node: Specify your YouTube Channel ID in the Fetch Latest Videos node. Find your Channel ID here. Google Sheets Node: Configure the node with your Google Sheet for logging posts. Use the linked sheet: AlexK1919 Social Posts. OpenAI Node: Set up your OpenAI API key for generating posts. X Post Node: Connect your X account to the Post to X node. Optional Notifications: Configure Discord, Slack, or Gmail nodes for custom notifications. Customization Schedule:** Enable and modify the Check Every 2 Hours node to automatically check for new videos. Post Format:** Edit the OpenAI prompt in the Generate X Post node to tweak the tone or style of the generated posts. Additional Platforms:** Expand this workflow to post on other social platforms by adding respective nodes (e.g., Facebook, LinkedIn). Notes Posts are validated for character limits but are restricted to 220 characters to ensure compliance with X's guidelines and leave space for links. Posts are logged in the Google Sheet with details, including post status and timestamps. Current integrations are focused on promoting YouTube content; additional types can be supported with minor adjustments. Contact For support or inquiries: Alex Kim** About Me

Who's this for? If you own a website and need to analyze your keyword rankings If you need to create a keyword report on your rankings If you want to grow your keyword positions SerpBear is an opensourced SEO tool specifically for keyword analytics. Click here to read details of how I use it Example output of A.I. Key Observations about Ranking Performance: The top-performing keyword is “Openrouter N8N” with a current position of 7 and an improving trend. Two keywords, “Best Docker Synology” and “Bitwarden Synology”, are not ranking in the top 100 and have a stable trend. Three keywords, “Obsidian Second Brain”, “AI Generated Reference Letter”, and “Actual Budget Synology”, and “N8N Workflow Generator” are not ranking well and have a declining trend. Keywords showing the most improvement: “Openrouter N8N” has an improving trend and a relatively high ranking of 7. Keywords needing attention: “Obsidian Second Brain” has a declining trend and a low ranking of 69. “AI Generated Reference Letter” has a declining trend and a low ranking of 84. “Actual Budget Synology”, “N8N Workflow Generator”, “Best Docker Synology”, and “Bitwarden Synology” are not ranking in the top 100. Use case Instead of hiring an SEO expert, I run this report weekly. It checks the keyword rankings of the past week and gives me recommendations on what to improve. How it works The workflow gathers SerpBear analytics for the past 7 days. It passes the data to openrouter.ai for A.I. analysis. Finally it saves to baserow. How to use this Input your SerpBearcredentials Enter your domain name Input your Openrouter.ai credentials Input your baserow credentials You will need to create a baserow database with columns: Date, Note, Blog Created by Rumjahn

Who is this template for? This workflow template is designed for sales, marketing, and business development professionals who want a cost-effective and efficient way to generate leads. By leveraging n8n core nodes, it scrapes business emails from Google Maps without relying on third-party APIs or paid services, ensuring there are no additional costs involved. Ideal for small business owners, freelancers, and agencies, this template automates the process of collecting contact information for targeted outreach, making it a powerful tool for anyone looking to scale their lead generation efforts without incurring extra expenses. You can watch the video tutorial here: https://youtu.be/HaiO-UeiKBA How it works This template streamlines email scraping from Google Maps using only n8n core nodes, ensuring a completely free and self-contained solution. Here’s how it operates: Input Queries You provide a list of queries, each consisting of keywords related to the type of business you want to target and the specific region or subregion you’re interested in. Iterates through Queries The workflow processes each query one at a time. For each query, it triggers a sub-workflow dedicated to handling the scraping tasks. Scrapes Google Maps for URLs Using these queries, the workflow scrapes Google Maps to collect URLs of business listings matching the provided criteria. Fetches HTML Content The workflow then fetches the HTML pages of the collected URLs for further processing. Extracts Emails Using a Code Node with custom JavaScript, the workflow runs regular expressions on the HTML content to extract business email addresses. Setup Add Queries: Open the first node, "Run Workflow" and input a list of queries, each containing the business keywords and the target region. Configure the Google Sheets Node: Open the Google Sheets node and select a document and specific sheet where the scraped results will be saved. Run the workflow: Click on "Test workflow" and watch your Google Sheets document gradually receive business email addresses. Customize as Needed: You can adjust the regular expressions in the Code Node to refine the email extraction logic or add logic to extract other kinds of information.

Video Guide I prepared a detailed guide that showed the whole process of building a resume analyzer. Who is this for? This workflow is ideal for recruitment agencies, HR professionals, and hiring managers looking to automate the initial screening of CVs. It is especially useful for organizations handling large volumes of applications and seeking to streamline their recruitment process. What problem does this workflow solve? Manually screening resumes is time-consuming and prone to human error. This workflow automates the process, providing consistent and objective analysis of CVs against job descriptions. It helps filter out unsuitable candidates early, reducing workload and improving the overall efficiency of the recruitment process. What this workflow does This workflow automates the resume screening process using OpenAI for analysis. It provides a matching score, a summary of candidate suitability, and key insights into why the candidate fits (or doesn’t fit) the job. Retrieve Resume: The workflow downloads CVs from a direct link (e.g., Supabase storage or Dropbox). Extract Data: Extracts text data from PDF or DOC files for analysis. Analyze with OpenAI: Sends the extracted data and job description to OpenAI to: Generate a matching score. Summarize candidate strengths and weaknesses. Provide actionable insights into their suitability for the job. Setup Preparation Create Accounts: N8N: For workflow automation. OpenAI: For AI-powered CV analysis. Get CV Link: Upload CV files to Supabase storage or Dropbox to generate a direct link for processing. Prepare Artifacts for OpenAI: Define Metrics: Identify the metrics you want from the analysis (e.g., matching percentage, strengths, weaknesses). Generate JSON Schema: Use OpenAI to structure responses, ensuring compatibility with your database. Write a Prompt: Provide OpenAI with a clear and detailed prompt to ensure accurate analysis. N8N Scenario Download File: Fetch the CV using its direct URL. Extract Data: Use N8N’s PDF or text extraction nodes to retrieve text from the CV. Send to OpenAI: URL: POST to OpenAI’s API for analysis. Parameters: Include the extracted CV data and job description. Use JSON Schema to structure the response. Summary This workflow provides a seamless, automated solution for CV screening, helping recruitment agencies and HR teams save time while maintaining consistency in candidate evaluation. It enables organizations to focus on the most suitable candidates, improving the overall hiring process.

Printify Automation - Update Title and Description Workflow This n8n workflow automates the process of retrieving products from Printify, generating optimized product titles and descriptions, and updating them back to the platform. It leverages OpenAI for content generation and integrates with Google Sheets for tracking and managing updates. Features Integration with Printify**: Fetch shops and products through Printify's API. AI-Powered Optimization**: Generate engaging product titles and descriptions using OpenAI's GPT model. Google Sheets Tracking**: Log and manage updates in Google Sheets. Custom Brand Guidelines**: Ensure consistent tone by incorporating brand-specific instructions. Loop Processing**: Iteratively process each product in batches. Workflow Structure Nodes Overview Manual Trigger: Manually start the workflow for testing purposes. Printify - Get Shops: Retrieves the list of shops from Printify. Printify - Get Products: Fetches product details for each shop. Split Out: Breaks down the product list into individual items for processing. Loop Over Items: Iteratively processes products in manageable batches. Generate Title and Desc: Uses OpenAI GPT to create optimized product titles and descriptions. Google Sheets Integration: Trigger: Monitors Google Sheets for changes. Log Updates: Records product updates, including old and new titles/descriptions. Conditional Logic: If Nodes: Ensure products are ready for updates and stop processing once completed. Printify - Update Product: Sends updated titles and descriptions back to Printify. Brand Guidelines + Custom Instructions: Sets brand tone and seasonal instructions. Setup Instructions Prerequisites n8n Instance: Ensure n8n is installed and configured. Printify API Key: Obtain an API key from your Printify account. Add it to n8n under HTTP Header Auth. OpenAI API Key: Obtain an API key from OpenAI. Add it to n8n under OpenAI API. Google Sheets Integration: Share your Google Sheets with the Google API service account. Configure Google Sheets credentials in n8n. Workflow Configuration Set Brand Guidelines: Update the Brand Guidelines + Custom Instructions node with your brand name, tone, and seasonal instructions. Batch Size: Configure the Loop Over Items node for optimal batch sizes. Google Sheets Configuration: Set the correct Google Sheets document and sheet names in the integration nodes. Run the Workflow: Start manually or configure the workflow to trigger automatically. Key Notes Customization**: Modify API calls to support other platforms like Printful or Vistaprint. Scalability**: Use batch processing for efficient handling of large product catalogs. Error Handling**: Configure retries or logging for any failed nodes. Output Examples Optimized Content Example Input Title**: "Classic White T-Shirt" Generated Title**: "Stylish Classic White Tee for Everyday Wear" Input Description**: "Plain white T-shirt made of cotton." Generated Description**: "Discover comfort and style with our classic white tee, crafted from premium cotton for all-day wear. Perfect for casual outings or layering." Next Steps Monitor Updates: Use Google Sheets to review logs of updated products. Expand Integration: Add support for more Printify shops or integrate with other platforms. Enhance AI Prompts: Customize prompts for different product categories or seasonal needs. Feel free to reach out for additional guidance or troubleshooting!

This workflow randomly select recipes from a Mealie instance (can use a specific category) and then creates a meal plan in Mealie with those recipes. How it works: Workflow has a scheduled trigger (set to run weekly on a Friday) Config node sets a few properties to configure the workflow A call to the Mealie API to get the list of recipes The code node holds most of the logic, this will loop through the number of recipes defined in the config node and randomly select a recipe from the list (making sure not to double up any recipes) Once all the recipes are selected it will call the Mealie API to set up the meal plan on the days Setup Add your Mealie API token as a credential and set it on the Http Request nodes Set the relevant schedule trigger to run when you like Update the Config node with the config you want numberOfRecipes - Number of recipes to populate for the meal plan offsetPlanDays - Number of days in the future to start the plan (0 will start it today, 1 tomorrow, etc.) mealieCategoryId - A category id of the category you want to pull in recipes from (default to select from all recipes) mealieBaseUrl - The base url of your Mealie instance

This workflow facilitates seamless synchronization between Entra (Microsoft Azure AD) and Zammad. It automates the following processes: Fetch Entra Group Members: Retrieves users from a designated Entra group. These users are candidates for synchronization. Create Universal User Object: Extracts key user information, such as email, phone, and name, and formats it for Zammad compatibility. Synchronize with Zammad: Identifies users in Zammad who need updates based on Entra data. Adds new users from Entra to Zammad. Deactivates users in Zammad if they are no longer in the Entra group. Key Features Dynamic Matching**: Compares users from Entra with existing Zammad users based on email and updates records accordingly. Efficient Management**: Automatically creates, updates, or deactivates Zammad users based on their status in Entra. Custom Fields**: Supports custom field mapping, ensuring enriched user profiles in Zammad. Setup Instructions Microsoft Entra Integration: Ensure proper API permissions for accessing Entra groups and members. Configure Microsoft OAuth2 credentials in n8n. Zammad Integration: Set up Zammad API credentials with appropriate access rights. Customize the workflow to include additional fields or map existing fields as needed. Run Workflow: Trigger the workflow manually or set up an automation schedule (e.g., daily sync). Review created/updated/deactivated users in Zammad. Use Cases IT Administration**: Keep your support system in sync with the organization’s Entra data. User Onboarding**: Automatically onboard new hires into Zammad based on Entra groups. Access Management**: Ensure accurate and up-to-date user records in Zammad. Prerequisites Access to an Entra (Azure AD) environment with group data. A Zammad instance with API credentials for user management. A custom field in Zammad User Object (entra_key) of type String. A custom field in Zammad User Object (entra_object_type) of type `Single selection field with two key value pairs user = User contact = Contact` This workflow is fully customizable and can be adapted to your organization’s specific needs. Save time and reduce manual errors by automating your user sync process with this template! If you have found an error or have any suggestions, please report them here on Github.

This n8n workflow automates the assignment of existing users to organizations in Zammad based on their email domains, utilizing Zammad’s "domain-based assignment" feature. Use Case Automate the post-hoc assignment of existing users to organizations in Zammad by leveraging their email domains. This ensures efficient management of user-organization relationships using Zammad’s "domain-based assignment" feature. Requirements An active Zammad account with API access. Existing users and organizations in Zammad with proper email and domain configurations. Zammad organizations must have domain-based assignment enabled. Credentials Set up your Zammad API credentials in n8n to allow the workflow to interact with your Zammad instance securely. If you have found an error or have any suggestions, please report them here on Github.

This n8n workflow enables you to export data from Zammad, including Users, Roles, Groups, and Organizations, into individual Excel files. It simplifies data handling and reporting by creating structured outputs for further processing or sharing. Features Export Users with associated details such as email, firstname, lastname, role_ids, and group_ids. Export Roles and Organizations with their respective identifiers and names. Convert all data into separate Excel files for easy access and use. Usage Import this workflow into your n8n instance. Configure the required Zammad API credentials (zammad_base_url and zammad_api_key) in the Basic Variables node. Run the workflow to generate Excel files containing Zammad data. Issues and Suggestions If you encounter any issues or have suggestions for improvement, please report them on the GitHub repository. We appreciate your feedback to help enhance this workflow!

This n8n workflow allows you to reset all user roles in Zammad to specified default roles. It ensures consistency in role management across your Zammad instance. Features Retrieve all active users from Zammad. Update each user's roles to predefined default role IDs. Exclude specific users by their IDs from the update process. Simple configuration for default roles and excluded users. Usage Import the Workflow: Upload the provided .json file into your n8n instance. Configure Variables: zammad_base_url: Your Zammad instance URL. zammad_api_key: Your Zammad API key. default_roles: List of default role IDs to apply to all users. exclude_zammad_users_by_id: List of user IDs to exclude from the update. Run the Workflow: Execute the workflow to update roles automatically. Issues and Suggestions For issues or suggestions, visit the GitHub Repository.

This n8n workflow allows you to update user roles in Zammad based on data from an Excel file. The workflow automates role assignments, ensuring efficient and consistent updates. Features Excel Integration**: Import user data from an Excel file containing emails and role assignments. Dynamic Updates**: Match Zammad users by email and update their roles. Error Handling**: Continue workflow execution even if some updates fail. Customizable Variables**: Configure Zammad API URL, API key, and Excel file URL. Usage Import the Workflow: Upload the provided .json file into your n8n instance. Set Variables: zammad_base_url: Your Zammad instance URL. excel_source_url: URL of the Excel file containing user data. Authentication for Zammad Create in the Node "Find Zammad User by email" and "Update User Roles" a Header Auth Authentication Name**: Authorization Value**: Bearer <put here your zammad api token> Run the Workflow: Execute the workflow to update user roles based on the Excel data. Issues and Suggestions For issues or suggestions, visit the GitHub Repository.

This workflow facilitates seamless synchronization between Entra (Microsoft Azure AD) and Zammad. It automates the following processes: Fetch Entra Contacts: Create Universal User Object: Extracts key user information, such as email, phone, and name, and formats it for Zammad compatibility. Synchronize with Zammad: Identifies users in Zammad who need updates based on Entra data. Adds new users from Entra to Zammad. Deactivates users in Zammad if they are no longer in Entra. Key Features Dynamic Matching**: Compares contacts from Entra with existing Zammad users based on email and updates records accordingly. Efficient Management**: Automatically creates, updates, or deactivates Zammad users based on their status in Entra. Custom Fields**: Supports custom field mapping, ensuring enriched user profiles in Zammad. Setup Instructions Microsoft Entra Integration: Ensure proper API permissions for accessing Entra contacts. Configure Microsoft OAuth2 credentials in n8n. Zammad Integration: Set up Zammad API credentials with appropriate access rights. Customize the workflow to include additional fields or map existing fields as needed. Run Workflow: Trigger the workflow manually or set up an automation schedule (e.g., daily sync). Review created/updated/deactivated users in Zammad. Use Cases IT Administration**: Keep your support system in sync with the organization’s Entra data. Customer Management**: Ensure accurate and up-to-date user records in Zammad. Prerequisites Access to an Entra (Azure AD) environment with contacts data. A Zammad instance with API credentials for user management. A custom field in Zammad User Object (entra_key) of type String. A custom field in Zammad User Object (entra_object_type) of type `Single selection field with two key value pairs user = User contact = Contact` This workflow is fully customizable and can be adapted to your organization’s specific needs. Save time and reduce manual errors by automating your user sync process with this template! If you have found an error or have any suggestions, please report them here on Github.

This workflow leverages n8n to perform automated Google Maps API queries and manage data efficiently in Google Sheets. It's designed to extract specific location data based on a given list of ZIP codes and categories. Features Queries the Google Maps API for location data using predefined ZIP codes and subcategories. Filters, de-duplicates, and organizes data into structured rows in Google Sheets. Implements exponential backoff retries to handle API rate limits. Logs and updates statuses directly in Google Sheets for easy tracking. Prerequisites Google OAuth Credentials: A configured Google Cloud project for Google Maps API and Sheets API access. Google Sheets: A sheet with ZIP codes and categories defined (e.g., "AZ Zips"). n8n Setup: A running instance of n8n with credentials configured for Google OAuth. Setup Instructions 1. Prepare Google Sheets Add the ZIP codes to the "AZ Zips" sheet. Define subcategories in another sheet (e.g., "Google Maps Categories"). Provide the sheet's URL in the Settings node of the workflow. 2. Configure API Access Set up Google OAuth credentials for Maps and Sheets APIs in n8n. Ensure your API key has access to the places.searchText endpoint. 3. Workflow Customization Modify textQuery parameters in the GMaps API node to match your query needs. Adjust trigger intervals as required (e.g., manual or scheduled execution). 4. Run the Workflow Execute the workflow manually or schedule periodic runs to keep your data updated. Notes This workflow includes robust error handling to retry failed API calls with exponential backoff. All data is organized and logged directly in Google Sheets for easy reference and updates. For more information or issues, feel free to reach out!

Video Guide I prepared a detailed guide explaining how to set up and implement this scenario, enabling you to chat with your documents stored in Supabase using n8n. Youtube Link Who is this for? This workflow is ideal for researchers, analysts, business owners, or anyone managing a large collection of documents. It's particularly beneficial for those who need quick contextual information retrieval from text-heavy files stored in Supabase, without needing additional services like Google Drive. What problem does this workflow solve? Manually retrieving and analyzing specific information from large document repositories is time-consuming and inefficient. This workflow automates the process by vectorizing documents and enabling AI-powered interactions, making it easy to query and retrieve context-based information from uploaded files. What this workflow does The workflow integrates Supabase with an AI-powered chatbot to process, store, and query text and PDF files. The steps include: Fetching and comparing files to avoid duplicate processing. Handling file downloads and extracting content based on the file type. Converting documents into vectorized data for contextual information retrieval. Storing and querying vectorized data from a Supabase vector store. File Extraction and Processing: Automates handling of multiple file formats (e.g., PDFs, text files), and extracts document content. Vectorized Embeddings Creation: Generates embeddings for processed data to enable AI-driven interactions. Dynamic Data Querying: Allows users to query their document repository conversationally using a chatbot. Setup N8N Workflow Fetch File List from Supabase: Use Supabase to retrieve the stored file list from a specified bucket. Add logic to manage empty folder placeholders returned by Supabase, avoiding incorrect processing. Compare and Filter Files: Aggregate the files retrieved from storage and compare them to the existing list in the Supabase files table. Exclude duplicates and skip placeholder files to ensure only unprocessed files are handled. Handle File Downloads: Download new files using detailed storage configurations for public/private access. Adjust the storage settings and GET requests to match your Supabase setup. File Type Processing: Use a Switch node to target specific file types (e.g., PDFs or text files). Employ relevant tools to process the content: For PDFs, extract embedded content. For text files, directly process the text data. Content Chunking: Break large text data into smaller chunks using the Text Splitter node. Define chunk size (default: 500 tokens) and overlap to retain necessary context across chunks. Vector Embedding Creation: Generate vectorized embeddings for the processed content using OpenAI's embedding tools. Ensure metadata, such as file ID, is included for easy data retrieval. Store Vectorized Data: Save the vectorized information into a dedicated Supabase vector store. Use the default schema and table provided by Supabase for seamless setup. AI Chatbot Integration: Add a chatbot node to handle user input and retrieve relevant document chunks. Use metadata like file ID for targeted queries, especially when multiple documents are involved. Testing Upload sample files to your Supabase bucket. Verify if files are processed and stored successfully in the vector store. Ask simple conversational questions about your documents using the chatbot (e.g., "What does Chapter 1 say about the Roman Empire?"). Test for accuracy and contextual relevance of retrieved results.

How it works 1) Extracts all company entries in Agile CRM 2) Search for company name in French INSEE OpenData database to extract address and government ID (SIREN) 3) Updates entries with data extracted from French Insee OpenData dabase Workflow also has a readonly feature to make sure entry is not overwritten. Setup steps Add your AgileCRM credentials Add your INSEE OpenData credentials Add two company custom fields in your Agile CRM (for SIREN data and ReadOnly support)

Short an simple: This Workflow will sync (add and delete) your Liked Songs to an custom playlist that can be shared. Setup: Create an app on the Spotify Developer Dashboard. Create Spotify Credentials - Just click on one of the Spotify Nodes in the Workflow an click on "create new credentials" and follow the guide. Create the Spotify Playlist that you want to sync to. Copy the exact name of you playlist, go into Node "Edit set Vars" and replace the value "CHANGE MEEEE" with your playlist name. Set your Spotify Credentiels on every Spotify Node. (Should be marekd with Yellow and Red Notes) Do you use Gotify? - No: Delete the Gotify Nodes (all the way to the right end of the Workflow) - Yes: Customize the Gotify Nodes to your needs.

This workflow automates AI-based image generation using the Fal.ai Flux API. Define custom prompts, image parameters, and effortlessly generate, monitor, and save the output directly to Google Drive. Streamline your creative automation with ease and precision. Who is this for? This template is for content creators, developers, automation experts, and creative professionals looking to integrate AI-based image generation into their workflows. It’s ideal for generating custom visuals with the Fal.ai Flux API and automating storage in Google Drive. What problem is this workflow solving? Manually generating AI-based images, checking their status, and saving results can be tedious. This workflow automates the entire process — from requesting image generation, monitoring its progress, downloading the result, and saving it directly to a Google Drive folder. What this workflow does Sets Custom Image Parameters: Allows you to define the prompt, resolution, guidance scale, and steps for AI image generation. Sends a Request to Fal.ai: Initiates the image generation process using the Fal.ai Flux API. Monitors Image Status: Checks for completion and waits if needed. Downloads the Generated Image: Fetches the completed image once ready. Saves to Google Drive: Automatically uploads the generated image to a specified Google Drive folder. Setup 1. Prerequisites: • Fal.ai API Key: Obtain it from the Fal.ai platform and set it as the Authorization header in HTTP Header Auth credentials. • Google Drive OAuth Credentials: Connect your Google Drive account in n8n. 2. Configuration: • Update the “Edit Fields” node with your desired image parameters: • Prompt: Describe the image (e.g., “Thai young woman net idol 25 yrs old, walking on the street”). • Width/Height: Define image resolution (default: 1024x768). • Steps: Number of inference steps (e.g., 30). • Guidance Scale: Controls image adherence to the prompt (e.g., 3.5). • Set your Google Drive folder ID in the “Google Drive” node to save the image. 3. Run the Workflow: • Trigger the workflow manually to generate the image. • The workflow waits, checks status, and saves the final output seamlessly. Customization • Modify Image Parameters: Adjust the prompt, resolution, steps, and guidance scale in the “Edit Fields” node. • Change Storage Location: Update the Google Drive node with a different folder ID. • Add Notifications: Integrate an email or messaging node to alert you when the image is ready. • Additional Outputs: Expand the workflow to send the generated image to Slack, Dropbox, or other platforms. This workflow streamlines AI-based image generation and storage, offering flexibility and customization for creative automation.

Automated Execution Pruning This workflow is designed to help you manage and optimize your n8n instance by automatically pruning old workflow executions, ensuring a cleaner environment and improved performance. You can customize the retention period to suit your needs. Key Features: Configurable Retention Period: The workflow is preconfigured to delete workflow executions older than 10 days. You can easily adjust this duration by modifying the condition in the If node. Daily Automation: Using the Schedule Trigger, the workflow runs daily at the specified time (default: 4:44 AM), retrieving all workflow executions and identifying those that are older than the defined retention period. On-Demand Testing: The Manual Trigger allows you to test the workflow at any time, ensuring everything is working as expected. Decision Making: The If node evaluates each execution based on its start date and determines whether it should be deleted or retained. Execution Pruning: Delete Action: Executions meeting the criteria are removed via the Delete Execution node. No-Operation: Executions that don't meet the criteria remain untouched. Workflow Nodes: Manual Trigger: Enables on-demand testing of the workflow. Schedule Trigger: Runs the workflow daily at the configured time. n8n List Execution: Fetches all executions in your n8n instance. If Node: Compares the execution's start date with the configured retention period. Delete Execution: Deletes executions older than the specified retention period. No Operation: Serves as a placeholder for executions that don't meet the pruning criteria. How to Customize: Retention Period**: Update the If node's condition to modify the retention period. For instance, change 10 * 24 * 60 * 60 * 1000 to the desired number of days in milliseconds. Schedule**: Adjust the timing of the Schedule Trigger to match your preferred automation schedule. This workflow ensures your instance remains efficient by keeping only the relevant execution logs. Use it to maintain a streamlined and clutter-free environment effortlessly.

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.

This n8n template demonstrates how to get started with Gemini 2.0's new Bounding Box detection capabilities in your workflows. The key difference being this enables prompt-based object detection for images which is pretty powerful for things like contextual search over an image. eg. "Put a bounding box around all adults with children in this image" or "Put a bounding box around cars parked out of bounds of a parking space". How it works An image is downloaded via the HTTP node and an "Edit Image" node is used to extract the file's width and height. The image is then given to the Gemini 2.0 API to parse and return coordinates of the bounding box of the requested subjects. In this demo, we've asked for the AI to identify all bunnies. The coordinates are then rescaled with the original image's width and height to correctl align them. Finally to measure the accuracy of the object detection, we use the "Edit Image" node to draw the bounding boxes onto the original image. How to use Really up to the imagination! Perhaps a form of grounding for evidence based workflows or a higher form of image search can be built. Requirements Google Gemini for LLM Customising the workflow This template is just a demonstration of an experimental version of Gemini 2.0. It is recommended to wait for Gemini 2.0 to come out of this stage before using in production.

Based on Jonathan's work. Check out his templates. How it works This workflow will backup your workflows to GitHub. It uses the n8n API node to export all workflows. It then loops over the data, checks in GitHub to see if a file exists that uses the credential's ID. Once checked it will: update the file on GitHub if it exists; create a new file if it doesn't exist; ignore if it's the same. Who is this for? People wanting to backup their workflows outside the server for safety purposes or to migrate to another server. Check out my other templates 👉 https://n8n.io/creators/solomon/

Vector Database as a Big Data Analysis Tool for AI Agents Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n". This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant Set up meta-variables for anomaly detection in Qdrant Anomaly detection tool KNN classifier tool For anomaly detection 1. This is the first pipeline to upload an image dataset to Qdrant. The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification 1. This is the first pipeline to upload an image dataset to Qdrant. The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. [This workflow] Batch Uploading Images Dataset to Qdrant This template imports dataset images from Google Could Storage, creates Voyage AI embeddings for them in batches, and uploads them to Qdrant, also in batches. In this particular template, we work with crops dataset. However, it's analogous to uploading lands dataset, and in general, it's adaptable to any dataset consisting of image URLs (as the following pipelines are). First, check for an existing Qdrant collection to use; otherwise, create it here. Additionally, when creating the collection, we'll create a payload index, which is required for a particular type of Qdrant requests we will use later. Next, import all (dataset) images from Google Cloud Storage but keep only non-tomato-related ones (for anomaly detection testing). Create (per batch) embeddings for all imported images using the Voyage AI multimodal embeddings API. Finally, upload the resulting embeddings and image descriptors to Qdrant via batch upload.

Vector Database as a Big Data Analysis Tool for AI Agents Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n". This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant Set up meta-variables for anomaly detection in Qdrant Anomaly detection tool KNN classifier tool For anomaly detection The first pipeline to upload an image dataset to Qdrant. 2. This is the second pipeline to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification The first pipeline to upload an image dataset to Qdrant. The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. [This workflow] Setting Up Cluster (Class) Centres & Cluster (Class) Threshold Scores for Anomaly Detection Preparatory workflow to set cluster centres and cluster threshold scores so anomalies can be detected based on these thresholds. Here, we're using two approaches to set up these centres: the "distance matrix approach" and the "multimodal embedding model approach".

This workflow automates the process of discovering and extracting APIs from various services, followed by generating custom schemas. It works in three distinct stages: research, extraction, and schema generation, with each stage tracking progress in a Google Sheet. 🙏 Jim Le deserves major kudos for helping to build this sophisticated three-stage workflow that cleverly automates API documentation processing using a smart combination of web scraping, vector search, and LLM technologies. How it works Stage 1 - Research: Fetches pending services from a Google Sheet Uses Google search to find API documentation Employs Apify for web scraping to filter relevant pages Stores webpage contents and metadata in Qdrant (vector database) Updates progress status in Google Sheet (pending, ok, or error) Stage 2 - Extraction: Processes services that completed research successfully Queries vector store to identify products and offerings Further queries for relevant API documentation Uses Gemini (LLM) to extract API operations Records extracted operations in Google Sheet Updates progress status (pending, ok, or error) Stage 3 - Generation: Takes services with successful extraction Retrieves all API operations from the database Combines and groups operations into a custom schema Uploads final schema to Google Drive Updates final status in sheet with file location Ideal for: Development teams needing to catalog multiple APIs API documentation initiatives Creating standardized API schema collections Automating API discovery and documentation Accounts required: Google account (for Sheets and Drive access) Apify account (for web scraping) Qdrant database Gemini API access Set up instructions: Prepare your Google Sheets document with the services information. Here's an example of a Google Sheet – you can copy it and change or remove the values under the columns. Also, make sure to update Google Sheets nodes with the correct Google Sheet ID. Configure Google Sheets OAuth2 credentials, required third-party services (Apify, Qdrant) and Gemini. Ensure proper permissions for Google Drive access.

Overview This n8n workflow automates the creation of 9:16 aspect ratio images optimized for short-form video content and thumbnails. It integrates multiple tools to retrieve content, generate scripts, and create AI-generated imagery. Key Features Trigger Workflow Manually The workflow starts when triggered manually in n8n. Retrieve Brand Guidelines Fetch brand elements like style, tone, and guidelines from Airtable. SEO Keywords and Blog Post Retrieval Retrieves blog posts and associated SEO keywords from Airtable to form the basis of image content. Content Preparation Uses GPT-4 to prepare a 4-scene script and thumbnail prompts for short-form videos. AI Image Generation Uses Leonardo.ai API to generate: Thumbnail Images Scene-specific Images (9:16 Aspect Ratio) Airtable Asset Management Generated assets (images) are saved back into Airtable with metadata like URLs and file sizes. Tools and Integrations n8n**: Workflow automation platform. OpenAI**: Generates scripts and prompts (GPT-4O-MINI). Leonardo.ai**: AI tool for improving prompts and generating high-quality images. Airtable**: Used as a data source for brand guidelines, blog posts, and to store generated assets. Workflow Steps Manual Trigger Initiate the workflow. Retrieve Brand and SEO Guidelines Fetch essential brand elements like tone, style, and keywords. Filter and Fetch Blog Content Search for blog posts relevant to selected SEO keywords. Script Preparation Use GPT-4 to generate a script with image prompts for scenes and thumbnails. Image Generation Call Leonardo.ai to create: Scene Images in 9:16 Aspect Ratio. A Thumbnail Image with an improved prompt. Store Assets Save generated assets (images) to Airtable for future use. Workflow Structure Nodes Breakdown: Manual Trigger**: Start the workflow. Get Brand Guidelines**: Pull brand-related information (style, tagline, tone, etc.) from Airtable. Set Guidelines**: Prepare fetched data. Get SEO Keywords**: Retrieve keywords to filter relevant content. Keyword Filter*: Filter results for specified keywords (e.g., *"AI Automation"). Script Prep**: Generate 4-scene scripts and prompts with GPT-4. Leo - Improve Prompt**: Improve image prompts for clarity and detail. Leo - Generate Image**: Create AI-generated images for scenes and thumbnails. Wait Nodes**: Ensures Leonardo image generation is complete. Add Asset Info**: Store the generated images back into Airtable with metadata. API Credentials Required Ensure the following credentials are configured in n8n: OpenAI API Key Leonardo.ai API Key Airtable API Token Output Generated Images**: High-quality AI-generated images with a 9:16 aspect ratio. Saved Metadata**: Asset details (URLs, sizes, types) stored in Airtable. Usage Import this workflow into n8n. Set up your Airtable API, Leonardo.ai API, and OpenAI API credentials. Run the workflow manually. Monitor image generation and check the Airtable output for results. Tags OpenAI** RunwayML** Leonardo** Airtable** Video Automation** Author AlexK1919 AI-Native Workflow Architect More Workflow Templates YouTube Channel Connect with Alex

Check Online Version ! [https://n8n-tools.streamlit.app/](https://n8n-tools.streamlit.app/ ) Who is it for? This workflow is perfect for n8n users who want to maintain clean and organized workflows without manually repositioning nodes. Whether you're building complex workflows or sharing them with a team, maintaining visual clarity is essential for efficiency and collaboration. This template automates the positioning process, saving time and ensuring consistent layout standards. How does it work? The template is divided into two parts: Positioning Engine: A webhook node kicks off the process by receiving a workflow ID. Using the provided workflow ID, an n8n API node fetches the workflow details. The fetched workflow is sent to a processing webhook that calculates optimized positions for the nodes. Finally, an n8n API node updates the workflow with the newly positioned nodes, ensuring a clean and professional layout. Reusable Positioning Block: This is an HTTP Request node that can be seamlessly integrated into any workflow you create. When triggered, it sends the current workflow for automatic positioning via the first part of this template. How to set it up? Enable n8n API Access: Ensure that your n8n instance has API access enabled with the appropriate credentials. Input Your n8n API URL and Credentials: Open the template, locate the n8n API nodes, and update them with your instance API key. Update the URL of the 'Magic Positioning' Http Request node to point to your n8n instance webhook URL. Embed the Reusable Block: Add the provided HTTP Request node to any of your workflows to instantly connect to the auto-positioning engine.

Generating Stripe invoices through the API can be tricky since it requires four steps to generate and send it via email to the customer. With this workflow you can create Stripe invoices automatically and make Stripe send the invoices to the customer email. How it works To generate a Stripe invoice, you need to create a customer, specify the invoice items, create the invoice, and finalize it. What should be a simple task involves multiple steps. This workflow simplifies the process by providing everything pre-built for you. Who is this for? Anyone who wants to generate invoices automatically and send them to the customer’s email. Stripe will only send invoices to customers if you generate the invoice correctly through the API. . Check out my other templates 👉 https://n8n.io/creators/solomon/

Introduction: This workflow template helps you determine if a Twitch user's stream is currently live or offline. Setup Instructions: The Document node holds the sample Twitch username you wish to check, you may adapt it in your workflow by replacing this with a chain that contains the Twitch username you want to check. This value is passed to the GraphQL node query as $('Document').item.json.twitch so make sure to change this based on your workflow. How it Works: The important nodes here are the GrapQL and IF nodes. The GrapQL queries the Twitch API, and then the output returns a document with the stream property. The IF node then checks if this property has a value, if null means the user is offline, otherwise the user is online or live. Common Use Cases: You can use this with other workflow templates to post live stream alerts to Twitter/X, Bluesky, and Discord via webhooks, etc to notify your community to join youR stream. You may also use an LLM node to write a custom alert based on the value of property title How to adjust this template If you want to check a list of Twitch channels, you can simply exchange the Document set node in the beginning with your list of channels. For more information on the GraphQL output please see the official Twitch API documentation: Get Streams

This workflow automates the process of fetching agricultural transaction data from the Taiwan Agricultural Products Open Data Platform and storing it in a Google Sheets document for further analysis. Key Features Manual Trigger: Allows manual execution of the workflow to control when data is fetched. HTTP Request: Sends a request to the Open Data Platform's API to retrieve detailed transaction data, including: Pricing (Upper, Middle, Lower, Average) Transaction quantities Crop and market details Split Out Node: Processes each record individually, ensuring accurate handling of every data entry. Google Sheets Integration: Appends the data into a structured Google Sheets document for easy access and analysis. Node Configurations 1. Manual Trigger Purpose**: Start the workflow manually. Configuration**: No setup needed. 2. HTTP Request Purpose**: Fetch agricultural data. Configuration**: URL: https://data.moa.gov.tw/api/v1/SheepQuotation Query Parameters: Start_time: 2024/12/01 End_time: 2024/12/31 MarketName: 台北二 api_key: <your_api_key> Headers: accept: application/json 3. Split Out Purpose**: Split the API response data array into individual items. Configuration**: Field to Split Out: Data 4. Google Sheets Purpose**: Append the data to Google Sheets. Configuration**: Operation: Append Document ID: <your_document_id> Sheet Name: Sheet1 Mapped Fields: TransDate, TcType, CropCode, CropName, MarketCode, MarketName Upper_Price, Middle_Price, Lower_Price, Avg_Price, Trans_Quantity 此 Workflow 從 台灣農業產品開放資料平臺 獲取農產品交易數據，並將其儲存到 Google Sheets 文件 中進行進一步分析。 主要功能 Manual Trigger：允許手動執行工作流程，以控制數據獲取的時間。 HTTP Request：向開放資料平臺的 API 發送請求，獲取詳細的交易數據，包括： 價格 (Upper, Middle, Lower, Average) 交易數量 作物和市場詳細資料 Split Out Node：逐筆處理每一筆記錄，確保數據準確無誤。 Google Sheets Integration：將數據追加到結構化的 Google Sheets 文件中，方便存取和分析。 節點設定 1. Manual Trigger 用途**：手動啟動工作流程。 設定**：無需額外設定。 2. HTTP Request 用途**：抓取農產品數據。 設定**： URL: https://data.moa.gov.tw/api/v1/SheepQuotation 查詢參數 (Query Parameters)： Start_time: 2024/12/01 End_time: 2024/12/31 MarketName: 台北二 api_key: <your_api_key> 標頭 (Headers)： accept: application/json 3. Split Out 用途**：將 API 回應的數據陣列分解為個別項目。 設定**： Field to Split Out: Data 4. Google Sheets 用途**：將數據追加至 Google Sheets。 設定**： Operation：Append Document ID：<your_document_id> Sheet Name：Sheet1 映射欄位 (Mapped Fields)： TransDate, TcType, CropCode, CropName, MarketCode, MarketName Upper_Price, Middle_Price, Lower_Price, Avg_Price, Trans_Quantity 請多利用 Curl Import 功能 例如 curl -X GET "https://data.moa.gov.tw/api/v1/AgriProductsTransType/?Start_time=114.01.01&End_time=114.01.01&MarketName=%E5%8F%B0%E5%8C%97%E4%BA%8C" -H "accept: application/json" 農業資料開放平台 文件

Setting Up and Generating TOTP Step 1: Receive QR Code and Extract the Link 1. Receive the QR Code from the 2FA service After enabling two-factor authentication (2FA) on services like OpenAI, Google, GitHub, etc., a QR Code will be given to you, which you need to scan. This QR Code contains the TOTP link used to generate one-time passcodes. 2. Extract the link from the received QR Code To extract the link from the QR Code, use online tools. These tools will help you extract the corresponding link. After using an online tool, the extracted link will appear in the following format: otpauth://totp/ServiceName:username?secret=secret_key&issuer=ServiceName For example: otpauth://totp/OpenAI:amir676080@gmail.com?secret=test-test-test&issuer=OpenAI Step 2: Create TOTP Credential in n8n Create a new Credential To use TOTP in n8n, you need to create a new TOTP Credential. Enter the details in the Credential In the Secret field:* Enter the *secret key** (extracted from the QR Code link). For example: test-test-test In the Label field:* Enter *ServiceName:username** For example: OpenAI:amir676080@gmail.com Save the Credential After entering the information, click Save to save the Credential. Step 3: Get the TOTP Code Click on Test Workflow After setting up the credentials in n8n, click on Test, and the corresponding code will be delivered to you. Output: [ { "token": "720769", "secondsRemaining": 18 } ] ==This code is exactly the same as the one generated by apps and services like Google Authenticator or Authy. 🔐== Contact me on [Telegram]: https://t.me/amir676080

Who is this for? This workflow is ideal for IT operations teams or system administrators who use ServiceNow to track incidents and Slack for team communication. It provides real-time updates on new ServiceNow incidents directly in a designated Slack channel, ensuring timely response and collaboration. What problem is this workflow solving? / Use case Manually monitoring ServiceNow for new incidents can be time-consuming and prone to delays. This workflow automates the process, ensuring that team members are instantly notified of new incidents, complete with all relevant details, in a Slack channel. It enhances operational efficiency and incident response time. What this workflow does Schedule or Manual Trigger: The workflow can be triggered manually or set to run automatically every 5 minutes. Retrieve New Incidents: Fetches incidents created in ServiceNow within the last 5 minutes. Error Handling: Posts an error message in Slack if there are issues connecting to ServiceNow. Incident Processing: If new incidents are found, they are sorted in ascending order by their number. Detailed incident information is formatted and sent to a specified Slack channel. No Incidents: If no new incidents are found, the workflow does nothing. Setup ServiceNow API Credentials: Configure ServiceNow Basic Authentication in the workflow to connect to your ServiceNow instance. Slack API Credentials: Add your Slack API credentials to enable message posting. Slack Channel Configuration: Define the Slack channel where notifications should be sent. Ensure the channel ID is correctly set in the Slack node. Adjust the Schedule: Modify the schedule in the Schedule Trigger node to suit your requirements. How to customize this workflow to your needs Notification Format: Customize the Slack message format to include additional or fewer details. Update the Blocks section in the Slack node for personalized messages. Incident Query Parameters: Adjust the sysparm_query parameter in the ServiceNow node to filter incidents based on specific criteria. Error Handling: Modify the error message in the Slack node for more detailed troubleshooting information. Features Real-Time Notifications**: Immediate updates on new ServiceNow incidents. Error Handling**: Alerts in Slack if the workflow encounters issues connecting to ServiceNow. Customizable Notifications**: Flexibility to modify incident details sent to Slack. This workflow streamlines incident management and fosters collaboration by delivering actionable updates directly to your team.

This workflow demonstrates a Retrieval Augmented Generation (RAG) chatbot that lets you chat with the GitHub API Specification (documentation) using natural language. Built with n8n, OpenAI's LLMs and the Pinecone vector database, it provides accurate and context-aware responses to your questions about how to use the GitHub API. You could adapt this to any OpenAPI specification for any public or private API, thus creating a documentation chatbout that anyone in your company can use. How it works: Data Ingestion: The workflow fetches the complete GitHub API OpenAPI 3 specification directly from the GitHub repository. Chunking and Embeddings: It splits the large API spec into smaller, manageable chunks. OpenAI's embedding models then generate vector embeddings for each chunk, capturing their semantic meaning. Vector Database Storage: These embeddings, along with the corresponding text chunks, are stored in a Pinecone vector database. Chat Interface and Query Processing: The workflow provides a simple chat interface. When you ask a question, it generates an embedding for your query using the same OpenAI model. Semantic Search and Retrieval: Pinecone is queried to find the most relevant text chunks from the API spec based on the query embedding. Response Generation: The retrieved chunks and your original question are fed to OpenAI's gpt-4o-mini LLM, which generates a concise, informative, and contextually relevant answer, including code snippets when applicable. Set up steps: Create accounts: You'll need accounts with OpenAI and Pinecone. API keys: Obtain API keys for both services. Configure credentials: In your n8n environment, configure credentials for OpenAI and Pinecone using your API keys. Import the workflow: Import this workflow into your n8n instance. Pinecone Index: Ensure you have a Pinecone index named "n8n-demo" or adjust the workflow accordingly. The workflow is set up to work with this index out of the box. Setup Time: Approximately 15-20 minutes. Why use this workflow? Learn RAG in Action: This is a practical, hands-on example of how to build a RAG-powered chatbot. Adaptable Template: Easily modify this workflow to create chatbots for other APIs or knowledge bases. n8n Made Easy: See how n8n simplifies complex integrations between data sources, vector databases, and LLMs.

WordPress Post Auto-Categorization Workflow 💡 Click here to read detailed case study 📺 Click here to watch youtube tutorial 🎯 Purpose Automatically categorize WordPress blog posts using AI, saving hours of manual work. This workflow analyzes your post titles and assigns them to predefined categories using artificial intelligence. 🔄 What This Workflow Does • Connects to your WordPress site • Retrieves all uncategorized posts • Uses AI to analyze post titles • Automatically assigns appropriate category IDs • Updates posts with new categories • Processes dozens of posts in minutes ⚙️ Setup Requirements WordPress site with admin access Predefined categories in WordPress OpenAI API credentials (or your preferred AI provider) n8n with WordPress credentials 🛠️ Configuration Steps Add your WordPress categories (manually in WordPress) Note down category IDs Update the AI prompt with your category IDs Configure WordPress credentials in n8n Set up AI API connection 🔧 Customization Options • Modify AI prompts for different categorization criteria • Adjust for multiple category assignments • Add tag generation functionality • Customize for different content types • Add additional metadata updates ⚠️ Important Notes • Backup your WordPress database before running • Test with a few posts first • Review AI categorization results initially • Categories must be created manually first 🎁 Bonus Features • Can be modified for tag generation • Works with scheduled posts • Handles bulk processing • Maintains categorization consistency Perfect for content managers, bloggers, and website administrators looking to organize their WordPress content efficiently. #n8n #WordPress #ContentManagement #Automation #AI Created by rumjahn

Workflow Description This workflow automates the process of retrieving emails from a food delivery platform, extracting key order details, and sending notifications to a Slack channel. Additionally, the Slack message includes a Moze accounting app URL scheme link for quick expense tracking. Key Features Manual Trigger: Allows the workflow to be executed manually for immediate testing. Gmail Integration: Retrieves emails containing specific keywords in the subject line (e.g., "透過 Uber Eats 系統送出的訂單"). (You can adjust the keywords to fit your language.) Data Extraction: Parses the email content to extract key details such as: Order price Shop name Order date and time Slack Notification: Sends a notification to a specified Slack channel using a structured block format, including a link to record the expense in the Moze accounting app. Node Configurations 1. Manual Trigger Purpose**: Starts the workflow manually for testing or immediate execution. Configuration**: No setup needed. 2. Gmail Trigger Purpose**: Automatically polls Gmail for new emails matching specific subject keywords. Configuration**: Filters: q: subject:透過 Uber Eats 系統送出的訂單 (You can adjust the keywords to fit your language.) Polling Frequency: Every hour at 30 minutes past the hour. Credentials: Linked Gmail account. 3. Extract Price, Shop, Date, Time Purpose**: Extracts key information from the email content using regular expressions. Extracted Data**: price: Order price (e.g., $200). shop: Shop name (e.g., "店名"). date: Order date (e.g., 2024.01.01). time: Order time converted to 24-hour format (e.g., 14:30). 4. Slack Notification Purpose**: Sends a formatted message to a Slack channel with extracted order details. Message Content**: Text: Ubereat 訂餐資訊: 商家: {{ shop }} 金額: {{ price }} 日期: {{ date }} Moze App Link: Includes a clickable button in the Slack message with a pre-filled Moze app URL scheme: moze3://expense?amount={{ price }}&account=信用卡&subcategory=外送&store={{ shop }}&date={{ date }}&time={{ time }}&project=生活開銷 Channel: Slack channel ID associated with food delivery notifications. Additional Notes Customization**: Adjust the email subject filter (subject) to match other types of food delivery platforms or services. Error Handling**: Ensure regular expressions for data extraction match the email format. Test with sample emails before deployment. Moze URL Scheme Reference**: Learn more about Moze app URL schemes for customization by visiting the Moze Documentation. This workflow is ideal for automating expense tracking and centralizing notifications for food delivery orders, streamlining personal or team expense management. Image: UberEat Gmail with order information Slack text with button Click the button will call moze url scheme 工作流程描述 此工作流程自動化從外送平台獲取郵件，提取關鍵訂單詳細資訊，並將通知發送到指定的 Slack 頻道。此外，Slack 消息中包含一個 Moze 記帳 App URL Scheme 的連結，方便快速記帳。 主要功能 Manual Trigger：允許手動執行工作流程，方便測試。 Gmail Integration：從 Gmail 中提取包含特定關鍵字（例如：「透過 Uber Eats 系統送出的訂單」）的郵件。 資料提取：解析郵件內容，提取以下關鍵資訊： 訂單金額 商家名稱 訂單日期與時間 Slack 通知：將結構化的通知發送到指定的 Slack 頻道，並包含一個連結供用戶快速記帳。 節點設定 1. Manual Trigger 用途**：手動啟動工作流程以進行測試或即時執行。 設定**：無需額外設定。 2. Gmail Trigger 用途**：自動檢查 Gmail 中是否有符合特定主題關鍵字的新郵件。 設定**： 篩選條件： q: subject:透過 Uber Eats 系統送出的訂單 檢查頻率：每小時的 30 分。 憑證：已連結的 Gmail 帳號。 3. Extract Price, Shop, Date, Time 用途**：使用正則表達式從郵件內容中提取關鍵資訊。 提取的資料**： price：訂單金額（例如：$200）。 shop：商家名稱（例如：「店名」）。 date：訂單日期（例如：2024.01.01）。 time：訂單時間（24 小時制，例如：14:30）。 4. Slack 通知 用途**：將訂單詳細資訊以格式化消息發送到 Slack。 消息內容**： 文字： Ubereat 訂餐資訊: 商家: {{ shop }} 金額: {{ price }} 日期: {{ date }} Moze App 連結：Slack 消息中包含一個可點擊按鈕，預填 Moze App URL Scheme： moze3://expense?amount={{ price }}&account=信用卡&subcategory=外送&store={{ shop }}&date={{ date }}&time={{ time }}&project=生活開銷 頻道：與外送通知相關的 Slack 頻道。 補充說明 自訂化**：可調整郵件主題篩選條件（subject），以匹配其他外送平台或服務。 錯誤處理**：確保正則表達式匹配郵件格式。在部署前使用樣本郵件進行測試。 Moze URL Scheme 參考**：了解更多關於 Moze App URL Scheme 的客製化資訊，請參閱 Moze 官方文件。 此工作流程適合自動化費用記帳以及集中管理外送訂單通知，提升個人或團隊的費用管理效率。

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 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. Check out my other templates 👉 https://n8n.io/creators/solomon/

Purpose This workflow snippet allows for advanced error catching during retry attempts. There are cases, where you want to check if an item exists first, so you can determine the following actions. Some API’s do not support an endpoint (e.g. Todoist: completed tasks) to do so, which is why you would work with the error branch, only that this does not work well in combination with the retry functionality. How it works Instead of the builtin retry function of a Node a custom loop is used, to get more granular control in between the iterations If the main executed node fails, the error can be filtered for an expected error, which can trigger a separate action The retries only happen, if an unexpected error happened The workflow only stops, if the defined amount of retries exceeded Setup Copy the nodes into your existing workflow Replace the “Replace me” placeholder with the Node you want to apply the retry logic on Follow the sticky notes for more instructions and optional settings

Workflow Description This workflow demonstrates how to use the LINE Messaging API to handle two scenarios: Replying to a user's message using a reply token. Sending a push message to a specific LINE user using their user ID. Key Features Webhook Integration: Receives and processes incoming messages from LINE using a webhook. Conditional Logic: Checks if the received event type is a message and handles it accordingly. Reply Message: Automatically responds to the user's message using the LINE reply token. Push Message: Sends a test message to a specific LINE user using their unique user ID. Pre-Configuration To simplify the setup process, create a Header Auth credential in n8n: Name**: Authorization Value**: Bearer {line token} This will authenticate all API requests to the LINE Messaging API. Node Configurations 1.1. Webhook from LINE Message Purpose**: Captures incoming events from the LINE Messaging API. Configuration**: HTTP Method: POST Path: {n8n-webhook-page} 1.2. If Condition Purpose**: Checks if the received event type is message. Configuration**: Condition: {{ $json.body.events[0].type }} equals "message" 1.3. Line: Reply with Token Purpose**: Replies to the user's message using the LINE reply token. Configuration**: Method: POST URL: https://api.line.me/v2/bot/message/reply JSON Body: { "replyToken": "{{ $('Webhook from Line Message').item.json.body.events[0].replyToken }}", "messages": [ { "type": "text", "text": "收到您的訊息 : {{ $('Webhook from Line Message').item.json.body.events[0].message.text }}" } ] } 2.1. Manual Trigger: Test Workflow Purpose**: Triggers the workflow for testing the push message functionality. Configuration**: No additional setup required. 2.2. Edit Fields Purpose**: Prepares the unique LINE user ID for the push message. Configuration**: Field: line_uid: Uxxxxxxxxxxxx 2.3. Line: Push Message Purpose**: Sends a test message to a specific LINE user. Configuration**: Method: POST URL: https://api.line.me/v2/bot/message/push JSON Body: { "to": "{{ $json.line_uid }}", "messages": [ { "type": "text", "text": "推播測試" } ] } 工作流程描述 此工作流程展示如何使用 LINE Messaging API 處理兩種情境： 使用 reply token 回應使用者的訊息。 使用使用者的 user ID 發送 推播訊息。 主要功能 Webhook 整合：透過 Webhook 接收並處理來自 LINE 的訊息。 條件邏輯：檢查接收到的事件類型是否為訊息並進行處理。 回應訊息：使用 LINE 的 reply token 自動回覆使用者的訊息。 推播訊息：使用 LINE User ID 向指定用戶發送測試訊息。 預先設定 為簡化設定流程，請在 n8n 中建立 Header Auth 憑證： 名稱**：Authorization 值**：Bearer {line token} 此設定將用於認證所有 LINE Messaging API 的請求。 節點設定 1.1. Webhook from LINE Message 用途**：接收來自 LINE Messaging API 的事件。 設定**： HTTP 方法：POST 路徑：{n8n-webhook-page} 1.2. If 條件判斷 用途**：檢查接收到的事件類型是否為 message。 設定**： 條件： {{ $json.body.events[0].type }} 等於 "message" 1.3. Line: Reply with Token 用途**：使用 LINE reply token 回應使用者訊息。 設定**： 方法：POST URL：https://api.line.me/v2/bot/message/reply JSON 主體： { "replyToken": "{{ $('Webhook from Line Message').item.json.body.events[0].replyToken }}", "messages": [ { "type": "text", "text": "收到您的訊息 : {{ $('Webhook from Line Message').item.json.body.events[0].message.text }}" } ] } 2.1. 手動觸發：測試工作流程 用途**：測試推播訊息功能。 設定**：無需額外設定。 2.2. Edit Fields 用途**：準備推播訊息所需的 LINE 使用者 ID。 設定**： 欄位： line_uid：Uxxxxxxxxxxxx 2.3. Line: 推播訊息 用途**：向特定 LINE 使用者發送測試訊息。 設定**： 方法：POST URL：https://api.line.me/v2/bot/message/push JSON 主體： { "to": "{{ $json.line_uid }}", "messages": [ { "type": "text", "text": "推播測試" } ] } 完成示意圖 (Storyboard Example):

Overview This workflow automates the end-to-end process of creating, optimizing, and publishing content on WordPress. It integrates AI-powered tools, Airtable, and WordPress plugins to generate high-quality, on-brand posts effortlessly. Perfect for content creators, marketers, and business owners looking to save time and scale their content strategy. Features Content Creation: AI-Powered Content:** Generates SEO-friendly blog posts with structured headings, relevant keywords, and meta descriptions. Custom Prompts:** Tailor the AI-generated content to match your brand’s tone and voice. SEO Optimization: RankMath Plugin Integration:** Updates RankMath SEO with focus keywords and meta descriptions, ensuring your content is search-engine optimized. Content Management: Airtable Integration:** Organizes content ideas, drafts, and publishing schedules in one place. Easily scalable for teams or solo creators. Visuals: Branded Featured Images:** Automatically generates on-brand images for every post. Publishing: Effortless Formatting:** Adapts content to fit your WordPress theme and schedules it for publication. Workflow Steps Trigger: Initiated manually or on a schedule. Content Management: Retrieves and organizes ideas from Airtable. Content Generation: Generates AI-driven blog content tailored to your audience. SEO Optimization: Automatically updates RankMath with SEO details. Featured Image Creation: Produces on-brand images for the post. Publishing: Formats and schedules the post on WordPress. Prerequisites API Keys: OpenAI Airtable WordPress REST API RankMath SEO Plugin Custom Code: Add a small update to your WordPress theme’s functions.php file to enable seamless automation. Customization Replace Airtable with another content management system if preferred. Adjust AI prompts to reflect different tones, styles, or industries. Add integrations for additional plugins, analytics, or storage services. Usage Import the workflow into your n8n instance. Configure API credentials for WordPress, Airtable, OpenAI, and RankMath. Update your functions.php file with the provided code snippet. Customize prompts and Airtable structure for your content needs. Trigger the workflow manually or set it on a schedule. Notes Experiment with Airtable views or add filters for more granular control over your content pipeline. Extend the workflow to include social media posting or analytics tracking. For questions, refer to n8n documentation or reach out to the creator. Tools Used Airtable OpenAI GPT WordPress REST API RankMath SEO Plugin Feel free to adapt and extend this workflow to meet your specific needs! 🎉

Template Detail This template automates the process of converting a list of addresses into their latitude and longitude (LatLong) coordinates using Google Sheets and the Google Maps API. It's designed for businesses, developers, and analysts who need accurate geolocation data for use cases like delivery routing, event planning, or market analysis. What the Template Does Fetch Address Data: Retrieves addresses from a Google Sheet. Google Maps API Integration: Sends each address to the Google Maps API and retrieves the corresponding LatLong coordinates. Update Google Sheets: Automatically updates the same Google Sheet with the LatLong data for each address. Enhancements Google Sheets Template: Provide a pre-configured Google Sheets template that users can copy. Example link: Google Sheets Template. Columns required: Address: Column to input addresses. LatLong: Column for the latitude and longitude results. Updated Workflow Structure Trigger: A manual trigger node starts the workflow. Retrieve Data from Google Sheets: Fetch addresses from a Google Sheet. Send to Google Maps API: For each address, retrieve the LatLong coordinates directly via the Google Maps API. Update Google Sheets: Write the LatLong results back into the Google Sheet. Steps to Use Prepare Google Sheet: Copy the provided Google Sheets template and add your addresses to the Address column. Configure Google Cloud API: Enable the Maps API for your Google Cloud project. Generate an API key with the required permissions. Run the Workflow: Start the workflow in n8n; it will process the addresses automatically. Updated LatLong data will appear in the corresponding Google Sheet. Review the Results: Use the enriched LatLong data for mapping or analysis.

This n8n workflow creates a financial analysis tool that generates reports on a company's quarterly earnings using the capabilities of OpenAI GPT-4o-mini, Google's Gemini AI and Pinecone's vector search. By analyzing PDFs of any company's earnings reports from their Investor Relations page, this workflow can answer complex financial questions and automatically compile findings into a structured Google Doc. How it works: Data loading and indexing Fetches links to PDF earnings document from a Google Sheet containing a list of file links. Downloads the PDFs from Google Drive. Parses the PDFs, splits the text into chunks, and generates embeddings using the Embeddings Google AI node (text-embedding-004 model). Stores the embeddings and corresponding text chunks in a Pinecone vector database for semantic search. Report generation with AI agent Utilizes an AI Agent node with a specifically crafted system prompt. The agent orchestrates the entire process. The agent uses a Vector Store Tool to access and retrieve information from the Pinecone database. Report delivery Saves the generated report as a Google Doc in a specified Google Drive location. Set up steps Google Cloud Project & Vertex AI API: Create a Google Cloud project. Enable the Vertex AI API for your project. Google AI API key: Obtain a Google AI API key from Google AI Studio. Pinecone account and API key: Create a free account on the Pinecone website. Obtain your API key from your Pinecone dashboard. Create an index named company-earnings in your Pinecone project. Google Drive - download and save financial documents: Go to a company you want to analize and download their quarterly earnings PDFs Save the PDFs in Google Drive Create a Google Sheet that stores a list of file URLs pointing to the PDFs you downloaded and saved to Google Drive Configure credentials in your n8n environment for: Google Sheets OAuth2 Google Drive OAuth2 Google Docs OAuth2 Google Gemini(PaLM) Api (using your Google AI API key) Pinecone API (using your Pinecone API key) Import and configure the workflow: Import this workflow into your n8n instance. Update the List Of Files To Load (Google Sheets) node to point to your Google Sheet. Update the Download File From Google Drive to point to the column where the file URLs are Update the Save Report to Google Docs node to point to your Google Doc where you want the report saved.

An intelligent chatbot that assists employees by answering common HR or IT questions, supporting both text and audio messages. This unique feature ensures employees can conveniently ask questions via voice messages, which are transcribed and processed just like text queries. How It Works Message Capture: When an employee sends a message to the chatbot in WhatsApp or Telegram (text or audio), the chatbot captures the input. Audio Transcription: For audio messages, the chatbot transcribes the content into text using an AI-powered transcription service (e.g., Whisper, Google Cloud Speech-to-Text). Query Processing: The transcribed text (or directly entered text) is sent to an AI service (e.g., OpenAI) to generate embeddings. These embeddings are used to search a vector database (e.g., Supabase or Qdrant) containing the company’s internal HR and IT documentation. The most relevant data is retrieved and sent back to the AI service to compose a concise and helpful response. Response Delivery: The chatbot sends the final response back to the employee, whether the input was text or audio. Set Up Steps Estimated Time**: 20–25 minutes Prerequisites**: Create an account with an AI provider (e.g., OpenAI). Connect WhatsApp or Telegram credentials in n8n. Set up a transcription service (e.g., Whisper or Google Cloud Speech-to-Text). Configure a vector database (e.g., Supabase or Qdrant) and add your internal HR and IT documentation. Import the workflow template into n8n and update environment variables for your credentials.

Overview This workflow helps you compare Claude 3.5 Sonnet and Gemini 2.0 Flash when extracting data from a PDF This workflow extracts and processes the data within a PDF in one single step, instead of calling an OCR and then an LLM” How it works The initial 2 steps download the PDF and convert it to base64. This base64 string is then sent to both Claude 3.5 Sonnet and Gemini 2.0 Flash to extract information. This workflow is made to let you compare results, latency, and cost (in their dedicated dashboard). How to use it Set up your Google Drive if not already done Select a document on your Google Drive Modify the prompt in "Define Prompt" to extract the information you need and transform it as wanted. Get a Claude API key and/or Gemini API key Note that you can deactivate one of the 2 API calls if you don't want to try both Test the Workflow

This n8n template introduces the Dynamic Prompts Ai workflow pattern which are incredible for certain types of data extraction tasks where attributes are unknown or need to remain flexible. The general idea behind this pattern is that the prompts for requested attributes to be extracted live outside the template and so can be changed at any time - without needing to edit the template. This seriously cuts down on maintainance requirements and is reusable for any number of tables at little cost. Check out the video demo I did for n8n Studio here: https://www.youtube.com/watch?v=_fNAD1u8BZw Check out the example Airtable here: https://airtable.com/appAyH3GCBJ56cfXl/shrXzR1Tj99kuQbyL Looking for the Baserow Version? https://n8n.io/workflows/2780-ai-data-extraction-with-dynamic-prompts-and-baserow/ How it works Given we have an "input" field for context and a number of fields for the data we want to extract, this template will run in the background to react to any changes to either the "input" or fields and automatically update the rows accordingly. The key is that Airtable fields have a special property called the "field description". In this pattern, we use this property to allow the user to store a simple prompt describing the data that should exist in the column. Our n8n template reads these column descriptions aka "prompts" to use as instructions to perform tasks on the "input". In this template, the "input" is a PDF of a resume/CV and the columns are attributes a HR person would want to extract from it - such as full name, address, last position, years of experience etc. How to use First publish this template and ensure it's accessible via webhook URL. You then have to run the "create airtable webhooks" mini-flow to configure your Airtable to send change events to the n8n template. This mini-flow exists in the template but you'll have to update the IDs. Check the template for more instructions. Requirements Airtable for Tables/Database OpenAI for LLM and extraction. Feel free to choose another LLM if preferred. Customising this workflow If you're not using files, you can replace the "input" field with anything you like. For example, the "input" could be single line text.

1. How it Works This n8n workflow automates fine-tuning OpenAI models through these key steps: Manual Trigger**: Starts with the "When clicking ‘Test workflow’" event to initiate the process. Downloads a .jsonl file from Google Drive Upload to OpenAI**: Uploads the .jsonl file to OpenAI via the "Upload File" node (with purpose "fine-tune"). Create Fine-tuning Job**: Sends a POST request to the endpoint https://api.openai.com/v1/fine_tuning/jobs with: { "training_file": "{{ $json.id }}", "model": "gpt-4o-mini-2024-07-18" } OpenAI automatically starts training the model based on the provided file. Interaction with the Trained Model**: An "AI Agent" uses the custom model (e.g., ft:gpt-4o-mini-2024-07-18:n3w-italia::XXXX7B) to respond to chat messages. 2. Set up Steps To configure the workflow: Prepare the Training File: Create a .jsonl file following the specified syntax (e.g., travel assistant Q/A examples). Upload it to Google Drive and update the ID in the "Google Drive" node. Configure Credentials: Google Drive: Connect an account via OAuth2 (googleDriveOAuth2Api). OpenAI: Add your API key in the "OpenAI Chat Model" and "Upload File" nodes. Customize the Model: In the "OpenAI Chat Model" node, specify the name of your fine-tuned model (e.g., ft:gpt-4o-mini-...). Update the HTTP request body (Create Fine-tuning Job) if needed (e.g., a different base model). Start the Workflow: Use the manual trigger ("Test workflow") to begin the upload and training process. Test the model via the "Chat Trigger" (chat messages). Integrated Documentation: Follow the instructions in the Sticky Notes to: Properly format the .jsonl (Step 1). Monitor progress on OpenAI (Step 2, link: https://platform.openai.com/finetune/). Note: Ensure the .jsonl file adheres to OpenAI’s required structure and that credentials are valid.

Workflow Overview This workflow automates the creation and management of a custom OpenAI Assistant for a travel agency ("Travel with us"), leveraging Google Drive for document storage. How It Works 1. Create the OpenAI Assistant Node**: OpenAI Action: Creates a custom assistant named "Travel with us" Assistant using the gpt-4o-mini model. Instructions: Respond only using the provided document (e.g., agency-specific info). Stay friendly, brief, and focused on travel-related queries. Ignore irrelevant questions politely. Credentials: Requires OpenAI API key. 2. Upload Agency Document Google Drive Node**: Action: Downloads a Google Doc as a PDF. OpenAI2 Node**: Action: Uploads the PDF to OpenAI with purpose: "assistants". Output: Generates a file_id. 3. Update the Assistant with the Document OpenAI Node**: Action: Updates the assistant to include the uploaded file. 4. Chat Interaction Chat Trigger**: Activates when a message is received ("When chat message received"). OpenAI Assistant Node**: Action: Uses the updated assistant to respond to user queries. Memory: Window Buffer Memory retains chat context for coherent conversations. Set Up Steps Prepare the Document: Store your travel agency guide in Google Drive (e.g., as a Google Doc). Update the Google Drive node with your document’s ID. Configure Credentials: Google Drive: Connect via OAuth2 (googleDriveOAuth2Api). OpenAI: Add your API key to all OpenAI nodes. Customize the Assistant: Modify the instructions in the OpenAI node to reflect your agency’s needs. Ensure the document includes FAQs, policies, and travel info. Test the Workflow: Trigger manually ("Test workflow") to create the assistant and upload the file. Send a chat message (e.g., "What are your travel packages?") to test responses. Dependencies Google Drive Account**: To store and retrieve the agency document. OpenAI API Access**: For assistant creation and file uploads.

This workflow combines OpenAI, Retrieval-Augmented Generation (RAG), and WooCommerce to create an intelligent personal shopping assistant. It handles two scenarios: Product Search: Extracts user intent (keywords, price ranges, SKUs) and fetches matching products from WooCommerce. General Inquiries: Answers store-related questions (e.g., opening hours, policies) using RAG and documents stored in Google Drive. How It Works 1. Chat Interaction & Intent Detection Chat Trigger**: Starts when a user sends a message ("When chat message received"). Information Extractor**: Uses OpenAI to analyze the message and determine if the user is searching for a product or asking a general question. Extracts: search (true/false). keyword, priceRange, SKU, category (if product-related). Example: { "search": true, "keyword": "red handbags", "priceRange": { "min": 50, "max": 100 }, "SKU": "BAG123", "category": "women's accessories" } 2. Product Search (WooCommerce Integration) AI Agent**: If search: true, routes the request to the personal_shopper tool. WooCommerce Node: Queries the WooCommerce store using extracted parameters (keyword, priceRange, SKU). Filters products in stock (stockStatus: "instock"). Returns matching products (e.g., "red handbags under €100"). 3. General Inquiries (RAG System) RAG Tool**: If search: false, uses the Qdrant Vector Store to retrieve store information from documents. Google Drive Integration: Documents (e.g., store policies, FAQs) are stored in Google Drive. Downloaded, split into chunks, and embedded into Qdrant for semantic search. OpenAI Chat Model: Generates answers based on retrieved documents (e.g., "Our store opens at 9 AM"). Set Up Steps 1. Configure the RAG System Google Drive Setup**: Upload store documents . Update the Google Drive2 node with your folder ID. Qdrant Vector Database**: Clean the collection (update Qdrant Vector Store node with your URL). Use Embeddings OpenAI to convert documents into vectors. 2. Configure OpenAI & WooCommerce OpenAI Credentials**: Add your API key to all OpenAI nodes (OpenAI Chat Model, Embeddings OpenAI, etc.). WooCommerce Integration**: Connect your WooCommerce store (credentials in the personal_shopper node). Ensure product data is synced and accessible. 3. Customize the AI Agent Intent Detection**: Modify the Information Extractor’s system prompt to align with your store’s terminology. RAG Responses**: Update the tool description to reflect your store’s documents. Notes This template is ideal for e-commerce businesses needing a hybrid assistant for product discovery and customer support. Need help customizing? Contact me for consulting and support or add me on Linkedin.

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.

What this workflow does This workflow retrieves the Zoom meeting data from the last 24 hours. The transcript of the last meeting is then retrieved, processed, a summary is created using AI and sent to all participants by email. AI is then used to create tasks and follow-up appointments based on the content of the meeting. Important: You need a Zoom Workspace Pro account and must have activated Cloud Recording/Transcripts! This workflow has the following sequence: manual trigger (Can be replaced by a scheduled trigger or a webhook) retrieval of of Zoom meeting data filter the events of the last 24 hours retrieval of transcripts and extract of the text creating a meeting summary, format to html and send per mail create tasks and follow-up call (if discussed in the meeting) in ClickUp/Outlook (can be replaced by Gmail, Airtable, and so forth) via sub workflow Requirements: Zoom Workspace (via API and HTTP Request): Documentation Microsoft Outlook: Documentation ClickUp: Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) SMTP access data (for sending the mail) You must set up the individual sub-workflows as separate workflows. Then set the “Execute workflow trigger” here. Then select the corresponding sub-workflow in the AI Agent Tools. You can select the number of domains yourself. If the data queries are not required, simply delete the corresponding tool (e.g. “Analytics_Domain_5). Feel free to contact me via LinkedIn, if you have any questions!

This workflow template helps Todoist users get a weekly overview of their completed tasks via email, making it easier to review their past week. Why use this workflow? Todoist doesn’t provide completed task reports or filters in its built-in reports or n8n app. This workflow solves that by using Todoist’s public API to fetch your completed tasks. How it works Runs every Friday afternoon (or manually). Uses the Todoist public API to retrieve completed tasks. Excludes specific projects you set (e.g., a grocery list). Sends an email summary, grouping tasks by the day they were completed. Set up steps Copy your Todoist API token (found here). Create a Todoist API credential in n8n. Create an SMTP credential in n8n. Alternatively, use a preferred email service like Brevo, Mailjet, etc. Import this workflow template. In the Get completed tasks via Todoist API step, select your Todoist API credential. In the Send Email step: Select your SMTP credential. Set the sender and recipient email addresses. Run the workflow manually and check your inbox! Ignoring specific projects If you do not want your grocery list, workouts, or other tasks from specific Todoist projects showing up in your weekly summary, modify the step called Optional: Ignore specific projects and change this line: const ignoredProjects = ['2335544024']; This should be an array with the id of each project you'd like to ignore. You can find a list of your projects (inc. their Ids) by visiting this link: https://api.todoist.com/rest/v2/projects

LinkedIn Profile Enrichment Workflow Who is this for? This workflow is ideal for recruiters, sales professionals, and marketing teams who need to enrich LinkedIn profiles with additional data for lead generation, talent sourcing, or market research. What problem is this workflow solving? Manually gathering detailed LinkedIn profile information can be time-consuming and prone to errors. This workflow automates the process of enriching profile data from LinkedIn, saving time and ensuring accuracy. What this workflow does Input: Reads LinkedIn profile URLs from a Google Sheet. Validation: Filters out already enriched profiles to avoid redundant processing. Data Enrichment: Uses RapidAPI's Fresh LinkedIn Profile Data API to retrieve detailed profile information. Output: Updates the Google Sheet with enriched profile data, appending new information efficiently. Setup Google Sheet: Create a sheet with a column named linkedin_url and populate it with the profile URLs to enrich. RapidAPI Account: Sign up at RapidAPI and subscribe to the Fresh LinkedIn Profile Data API. API Integration: Replace the x-rapidapi-key and x-rapidapi-host values with your credentials from RapidAPI. Run the Workflow: Trigger the workflow and monitor the updates to your Google Sheet. How to customize this workflow Filter Criteria**: Modify the filter step to include additional conditions for processing profiles. API Configuration**: Adjust API parameters to retrieve specific fields or extend usage. Output Format**: Customize how the enriched data is appended to the Google Sheet (e.g., format, column mappings). Error Handling**: Add steps to handle API rate limits or missing data for smoother automation. This workflow streamlines LinkedIn profile enrichment, making it faster and more effective for data-driven decision-making.

Microsoft Outlook AI Email Assistant Prerequisites 1. Microsoft 365 Login Credentials Provide your Office 365 credentials to connect Outlook. 2. Monday.com Generate an API token and have a board with your contact details. 3. Airtable Obtain an API key (or personal access token) and set up a base to store: Contacts (populated by the Monday.com sync). Rules & Categories (used by the AI Email Assistant). Use this Airtable base as the template: Airtable AI Email Assistant Template. Define your own rules, categories, and delete rules. 4. OpenAI API Key Sign up for OpenAI if you don’t already have an account. Generate a new API key at OpenAI API Keys. What the System Does 1. Daily Contact Sync (Monday.com → Airtable) Runs once a day to pull the latest contacts from Monday.com and store or update them in Airtable. 2. AI Email Categorisation & Prioritisation Fetches Outlook emails with filters. Cleans and processes email content. Matches emails with known contacts from Airtable. Uses an AI agent to classify, categorise, and prioritise emails. Updates Outlook categories and importance based on AI results. Runs in parallel with Airtable rules & categories retrieval for real-time decision-making. Workflow 1: Daily Contact Sync (Monday.com → Airtable) Purpose Keep Airtable’s Contacts table up to date by pulling new or updated contact data from Monday.com daily. Steps Schedule Trigger Runs at a set interval (daily) to initiate contact syncing. Monday.com: Get Contacts Reads the specified board/columns from Monday.com where you store contact details. Airtable - Contacts Upserts (adds or updates) the fetched Monday.com data into Airtable’s Contacts table. Ensures daily updates reflect changes from Monday.com. Result A consolidated contact list in Airtable, ready for AI email categorisation. Workflow 2: Categorise & Prioritise Outlook Emails Purpose Fetches Outlook emails, cleans and processes their content, matches senders with known contacts, and uses AI to categorise and prioritise them. Steps 1. Get Outlook Emails with Filters Trigger**: Either scheduled (Check Mail Schedule Trigger) or manual (Test Workflow). Outlook Filters**: Not flagged (flag/flagStatus == 'notFlagged'). Not categorised (not categories/any()). 🔹 Result: A batch of fresh, unprocessed emails ready for processing. 2. Sanitise Email Convert to Markdown: Strips **HTML tags and normalises formatting. Email Messages Processing: Allows manual removal of **signatures, disclaimers, or extra content. 🔹 Result: A clean, AI-friendly email for categorisation. 3. Match Contact Loop Over Emails**: Iterates over each email. Contact Lookup: Checks Airtable’s **Contacts table (updated daily). Merge Data: Enriches emails with known **client, supplier, or internal team info. 🔹 Result: Enhanced email context for AI processing. 4. AI Agent to Categorise & Prioritise Retrieve Rules & Categories** Reads Rules, Categories, and Delete Rules from Airtable in parallel. AI: Analyse Email (Tools Agent)** Uses email text, sender info, and rules to build a structured AI prompt. OpenAI Chat Model** Processes the AI prompt and outputs: Category Subcategory (optional) Priority level Short rationale Structured Output Parser** Ensures AI response is valid JSON format. 🔹 Result: Each email is labelled, categorised, and prioritised with AI-driven logic. 5. Set Outlook Category & Importance Set Category: Updates Outlook with the assigned **category. Check Priority Conditions** (If Node): If Action Required or from a VIP, mark as High Priority. Set Importance: Updates the email's **importance flag in Outlook. 🔹 Result: Outlook is updated with categories & importance based on AI recommendations. Parallel Processing: Retrieve Rules & Categories Runs alongside the email categorisation workflow. Ensures Airtable-based rules are available before AI processing. Steps Airtable: Get Rules & Categories Fetches Rules, Categories, and Delete Rules from Airtable. Delete Rules (Optional) If a delete rule matches, the email is removed. 🔹 Result: A dynamic, updatable rule system ensuring emails are handled properly. Final Outcome Daily Contact Sync** keeps contacts up to date. AI-driven email workflow** ensures smart categorisation. Outlook automatically updated** with AI-generated categories and importance. This automated system saves time, ensures efficient inbox management, and allows for customisable rules via Airtable.

Disclaimer: This template only works on self-hosted for now, as it uses a community node. Use Case Web scrapers often break due to web page layout changes. This workflow attempts to mitigate this problem by auto-generating web scraping data extractor code via LLM. How It Works This workflow leverages ScrapeNinja n8n community node to: scrape webpage HTML, feed it into LLM (Google Gemini) and ask to write a JS extractor function code, then it executes the written JS extractor against scraped HTML to extract useful data from webpage (the code is safely executed in a sandbox) Installation To install ScrapeNinja n8n node, in your self-hosted instance, go to Settings -> Community nodes, enter "n8n-nodes-scrapeninja", and install. Make sure you are using at least v0.3.0. See this in action: https://www.linkedin.com/feed/update/urn:li:activity:7289659870935490560/

This workflow is designed to generate SEO-friendly content with DeepSeek R1 (or V3), publish it on WordPress, and update a Google Sheets document with the details of the created post. Below is a detailed analysis of what each node in the workflow does: How It Works Triggering the Workflow: The workflow starts with a Manual Trigger node, which is activated when the user clicks "Test workflow" in the n8n interface. Fetching Data: The Get Ideas node retrieves data from a Google Sheets document. It reads a specific sheet and filters the data based on the "ID POST" column, returning the first matching row. Setting the Prompt: The Set your prompt node extracts the PROMPT field from the Google Sheets data and assigns it to a variable for use in subsequent steps. Generating Content: The Generate article node uses an AI model (DeepSeek) to create an SEO-friendly article based on the prompt. The article includes an introduction, 2-3 chapters, and a conclusion, formatted in HTML. The Generate title node uses the same AI model to generate a concise, SEO-optimized title for the article. Publishing on WordPress: The Create post on WordPress node creates a new draft post on WordPress using the generated title and article content. Generating and Uploading an Image: The Generate Image node creates a photorealistic image based on the article title using an AI model (OpenAI). The Upload image node uploads the generated image to WordPress as a media file. The Set Image node assigns the uploaded image as the featured image for the WordPress post. Updating Google Sheets: The Update Sheet node updates the original Google Sheets document with the post details, including the title, post ID, creation date, and row number. Set Up Steps Configure Google Sheets Integration: Set up the Google Sheets node to connect to your Google account and specify the document ID and sheet name to read from and update. Set Up AI Models: Configure the OpenAI nodes (for generating the article, title, and image) with the appropriate API credentials and model settings (e.g., deepseek-reasoner for text generation). Configure WordPress Integration: Set up the WordPress node with your WordPress site's API credentials to allow creating posts and uploading media. Define the Prompt and Content Structure: In the Set your prompt node, ensure the prompt variable is correctly mapped to the data from Google Sheets. In the Generate article and Generate title nodes, define the instructions for the AI model to generate the desired content. Set Up Image Generation: Configure the Generate Image node with the appropriate prompt and image settings (e.g., size, quality, style). Configure HTTP Requests for Media Upload: Set up the Upload image and Set Image nodes to use the WordPress REST API for uploading and assigning the featured image. Map Data for Google Sheets Update: In the Update Sheet node, map the relevant fields (e.g., title, post ID, date) to the appropriate columns in the Google Sheets document. Test and Activate the Workflow: Run the workflow manually to ensure all steps execute correctly. Once verified, activate the workflow for automated execution. Overall purpose of the workflow This workflow automates the creation of SEO-friendly content for a WordPress blog. Starting from a prompt extracted from a Google Sheets document, it generates an article, a title, and an image, publishes the post on WordPress, and updates the Google Sheets document with the details of the created post. This process is useful for blog managers who want to automate content creation and publishing.

Sync your Google Contacts with your Notion database. You can filter contacts by label or sync all contacts. All contact information is kept in sync between Google and Notion. What happens if I delete a contact? It gets deleted on the other end. Google Contact deleted → Notion page deleted Notion page deleted → Google contact deleted But you can easily change this behavior by disabling the respective deletion nodes. Who is this template for? Service providers looking to keep track of client information Organized individuals who like to centralize their lives in Notion Automation nerds who enjoy creating interesting projects How it works First, you perform an initial import to bring your contacts from Google to Notion. After that, the workflow listens for changes in both Notion and Google and keeps everything in sync. How to set up Instructions are included within the workflow itself. Check out my other templates 👉 https://n8n.io/creators/solomon/

Domain Outbound Machine: Automate Lead Generation, Email Extraction, and Targeted Outreach with n8n Description Domain Outbound Machine is an n8n workflow designed to fully automate the domain sales process: lead generation, email extraction, personalized outreach, and automated email sending. It also stores extracted email addresses and sent emails in an Excel file for tracking and follow-up. How It Works? 1. Domain Input You provide the domain name you want to sell. The workflow takes this domain as the starting point. 2. Google Maps Query Generation The workflow automatically generates 100 Google Maps queries based on the domain's niche or industry. This ensures you target businesses or individuals who might be interested in acquiring the domain. 3. Web Scraping for Emails Using the generated queries, the workflow scrapes relevant websites to extract email addresses. This step ensures you have a targeted list of potential buyers. 4. Email Enrichment For each extracted email, the workflow gathers publicly available information (e.g., LinkedIn, company websites) to enhance personalization. 5. Personalized Email Creation Based on the enriched data, the workflow crafts a custom email for each lead, emphasizing the domain’s value, its relevance to their business, and a compelling call-to-action. 6. Email Sending via Gmail The workflow sends the personalized emails directly through your Gmail account, ensuring timely and professional outreach. 7. Email & Lead Tracking in Excel All extracted email addresses and sent messages are automatically stored in an Excel file, allowing you to track responses and manage follow-ups efficiently. Key Features ✅ Automated Lead Generation – Saves time by automatically finding and extracting potential leads. ✅ Personalized Outreach – Boosts engagement by tailoring each email to the recipient. ✅ Seamless Integration – Uses n8n’s automation to connect Google Maps, web scraping tools, and Gmail in one workflow. ✅ Scalable & Trackable – Adjust the number of queries or emails as needed, while keeping track of everything in Excel. Domain Outbound Machine is perfect for domain investors, digital marketers, and anyone looking to sell domains efficiently. By automating the tedious tasks, you can focus on closing deals and growing your business.

What is this workflow? This n8n template automates the process of adding an AI-generated summary at the top of your WordPress posts. It retrieves, processes, and updates your posts dynamically, ensuring efficiency and flexibility without relying on a heavy WordPress plugin. Example of AI Summary Section How It Works Triggers → Runs on a scheduled interval or via a webhook when a new post is published. Retrieves posts → Fetches content from WordPress and converts HTML to Markdown for AI processing. AI Summary Generation → Uses OpenAI to create a concise summary. Post Update → Inserts the summary at the top of the post while keeping the original excerpt intact. Data Logging & Notifications → Saves processed posts to Google Sheets and notifies a Slack channel. Why use this workflow? ✅ No need for a WordPress plugin → Keeps your site lightweight. ✅ Highly flexible → Easily connect with Google Sheets, Slack, or other services. ✅ Customizable → Adapt AI prompts, formatting, and integrations to your needs. ✅ Smart filtering → Ensures posts are not reprocessed unnecessarily. 💡 Check the detailed sticky notes for setup instructions and customization options!

This workflow illustrates how to use Perplexity AI in your n8n workflow. Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question. Credentials Setup 1/ Go to the perplexity dashboard, purchase some credits and create an API Key https://www.perplexity.ai/settings/api 2/ In the perplexity Request node, use Generic Credentials, Header Auth. For the name, use the value "Authorization" And for the value "Bearer pplx-e4...59ea" (Your Perplexity Api Key) AI Model Sonar Pro is the current top model used by perplexity. If you want to use a different one, check this page: https://docs.perplexity.ai/guides/model-cards

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.

Automated Email Outreach Workflow This intelligent email automation workflow helps you maximize engagement through domain-based outreach. It utilizes AI-powered personalization and strategic follow-ups to increase response rates. The workflow includes a customizable three-step email sequence (initial email + two follow-ups) sent at optimized 3-day intervals. 🔥 Key Features ✅ Smart Domain Extraction – Automatically extracts domains from email addresses for precise targeting. ✅ Website Intelligence – Uses Jina.ai to analyze recipient websites and craft ultra-relevant messaging. ✅ AI-Powered Personalization – Leverages GPT-3.5 to generate compelling and highly customized email content. ✅ Adjustable AI Prompts – Easily customize AI-generated messages to align with your product, service, or campaign. ✅ Automated Follow-Up Sequence – Sends a structured series of three emails over time to increase engagement. ✅ Human-Like Sending Patterns – Randomized delays help bypass spam filters and improve deliverability. ✅ Error-Resilient Execution – Built-in error handling ensures a smooth, uninterrupted workflow. 🎯 Ideal Use Cases ✔️ Domain Sales & Lead Generation – Engage potential buyers with hyper-personalized emails. ✔️ B2B Partnerships & Outreach – Automate connections with potential business partners. ✔️ Cold Emailing at Scale – Improve engagement with structured, AI-optimized emails. ⚙️ How It Works 1️⃣ Setup & Configuration Integrate Gmail via OAuth for secure email sending. Connect your Google Sheets with leads for automated processing. Customize AI-generated messages by adjusting prompt inputs to fit your strategy. 2️⃣ Workflow Execution Domain Extraction – Automatically extracts recipient domains from email addresses. Website Analysis – Uses Jina.ai to gather insights for ultra-personalized messaging. AI-Powered Email Generation – GPT-3.5 crafts highly engaging outreach emails. Automated Email Sending – Initial email is sent, followed by two optimized follow-ups if no response is received. Engagement Tracking & Adaptation – Ensures delivery success and prevents emails from being flagged as spam. ⚡ Setup Instructions ✅ Preconditions You need a Gmail account with OAuth authentication enabled. Your Google Sheets must contain lead emails formatted correctly. Ensure your n8n instance is running and connected to your workflow. 🛠 Implementation Steps Install and open n8n. Import the workflow JSON file into your n8n instance. Configure your Gmail credentials in the workflow settings. Link your Google Sheets containing the leads' email addresses. Customize AI prompts in the GPT-3.5 node to match your campaign strategy. Test the workflow manually to ensure proper execution. Activate the workflow and let automation take over! 📈 Expected Results 🚀 Higher Response Rates – Personalization improves recipient engagement. 📊 Increased Conversions – Optimized email sequences boost lead-to-client conversions. 💡 Time-Saving Automation – Hands-free execution streamlines your outreach process. 🔧 Ready to supercharge your outreach? Set up the workflow and start automating today! 🚀🔥

This workflow automates the process of updating important Rank Math SEO fields (SEO Title, Description, and Canonical URL) directly via n8n. By leveraging a custom WordPress plugin that extends the WordPress REST API, this workflow ensures that you can programmatically manage SEO metadata for your posts and WooCommerce products efficiently. How it works: Sends a POST request to a custom API endpoint exposed by the Rank Math plugin. Updates SEO Title, Description, and Canonical URL fields for a specified post or product. Setup steps: Install and activate the Rank Math API Manager Extended plugin on WordPress. Provide the post or product ID you want to update in the workflow. Run the workflow to update the metadata automatically. Benefits: Full automation of SEO optimizations. Works for both standard posts and WooCommerce products. Simplifies large-scale SEO management tasks. Rank Math API Manager Extended plugin on WordPress <?php /** Plugin Name: Rank Math API Manager Extended v1.3 Description: Manages the update of Rank Math metadata (SEO Title, SEO Description, Canonical URL) via the REST API for WordPress posts and WooCommerce products. Version: 1.3 Author: Phil - https://inforeole.fr */ if ( ! defined( 'ABSPATH' ) ) { exit; // Exit if accessed directly. } class Rank_Math_API_Manager_Extended { public function __construct() { add_action('rest_api_init', [$this, 'register_meta_fields']); add_action('rest_api_init', [$this, 'register_api_routes']); } /** Registers the Rank Math meta fields in the REST API for posts and products (if WooCommerce is active). */ public function register_meta_fields() { $meta_fields = [ 'rank_math_title' => 'SEO Title', 'rank_math_description' => 'SEO Description', 'rank_math_canonical_url' => 'Canonical URL' ]; // Register meta for posts by default. $post_types = ['post']; // If WooCommerce is active, add the 'product' post type. if ( class_exists('WooCommerce') ) { $post_types[] = 'product'; } foreach ( $post_types as $post_type ) { foreach ( $meta_fields as $key => $description ) { register_post_meta( $post_type, $key, [ 'show_in_rest' => true, 'single' => true, 'type' => 'string', 'auth_callback' => [$this, 'check_update_permission'], 'description' => $description, ] ); } } } /** Registers the REST API route to update Rank Math meta fields. */ public function register_api_routes() { register_rest_route( 'rank-math-api/v1', '/update-meta', [ 'methods' => 'POST', 'callback' => [$this, 'update_rank_math_meta'], 'permission_callback' => [$this, 'check_update_permission'], 'args' => [ 'post_id' => [ 'required' => true, 'validate_callback' => function( $param ) { return is_numeric( $param ) && get_post( $param ); } ], 'rank_math_title' => [ 'type' => 'string', 'sanitize_callback' => 'sanitize_text_field', ], 'rank_math_description' => [ 'type' => 'string', 'sanitize_callback' => 'sanitize_text_field', ], 'rank_math_canonical_url' => [ 'type' => 'string', 'sanitize_callback' => 'esc_url_raw', ], ], ] ); } /** Updates the Rank Math meta fields via the REST API. */ public function update_rank_math_meta( WP_REST_Request $request ) { $post_id = $request->get_param( 'post_id' ); $fields = ['rank_math_title', 'rank_math_description', 'rank_math_canonical_url']; $result = []; foreach ( $fields as $field ) { $value = $request->get_param( $field ); if ( $value !== null ) { $update_result = update_post_meta( $post_id, $field, $value ); $result[ $field ] = $update_result ? 'updated' : 'failed'; } } if ( empty( $result ) ) { return new WP_Error( 'no_update', 'No metadata was updated', ['status' => 400] ); } return new WP_REST_Response( $result, 200 ); } /** Checks if the current user has permission to update the meta fields. */ public function check_update_permission() { return current_user_can( 'edit_posts' ); } } new Rank_Math_API_Manager_Extended(); . Phil | Inforeole

n8n Workflow: Automate SIEM Alert Enrichment with MITRE ATT&CK & Qdrant Who is this for? This workflow is ideal for: Cybersecurity teams & SOC analysts* who want to automate *SIEM alert enrichment**. IT security professionals* looking to integrate *MITRE ATT&CK intelligence** into their ticketing system. Organizations using Zendesk for security incidents* who need enhanced *contextual threat data**. Anyone using n8n and Qdrant* to build *AI-powered security workflows**. What problem does this workflow solve? Security teams receive large volumes of raw SIEM alerts that lack actionable context. Investigating every alert manually is time-consuming and can lead to delayed response times. This workflow solves this problem by: ✔ Automatically enriching SIEM alerts with MITRE ATT&CK TTPs. ✔ Tagging & classifying alerts based on known attack techniques. ✔ Providing remediation steps to guide the response team. ✔ Enhancing security tickets in Zendesk with relevant threat intelligence. What this workflow does 1️⃣ Ingests SIEM alerts (via chatbot or ticketing system like Zendesk). 2️⃣ Queries a Qdrant vector store containing MITRE ATT&CK techniques. 3️⃣ Extracts relevant TTPs (Tactics, Techniques, & Procedures) from the alert. 4️⃣ Generates remediation steps using AI-powered enrichment. 5️⃣ Updates Zendesk tickets with threat intelligence & recommended actions. 6️⃣ Provides structured alert data for further automation or reporting. Setup Guide Prerequisites n8n instance** (Cloud or Self-hosted). Qdrant vector store** with MITRE ATT&CK data embedded. OpenAI API key** (for AI-based threat processing). Zendesk account** (for ticket enrichment, if applicable). Clean Mitre Data Python Script Cleaned Mitre Data Full Mitre Data Steps to Set Up 1️⃣ Embed MITRE ATT&CK data into Qdrant This workflow pulls MITRE ATT&CK data from Google Drive and loads it into Qdrant. The data is vectorized using OpenAI embeddings for fast retrieval. 2️⃣ Deploy the n8n Chatbot The chatbot listens for SIEM alerts and sends them to the AI processing pipeline. Alerts are analyzed using an AI agent trained on MITRE ATT&CK. 3️⃣ Enrich Zendesk Tickets The workflow extracts MITRE ATT&CK techniques from alerts. It updates Zendesk tickets with contextual threat intelligence. The remediation steps are included as internal notes for SOC teams. How to Customize This Workflow 🔧 Modify the chatbot trigger: Adapt the chatbot node to receive alerts from Slack, Microsoft Teams, or any other tool. 🔧 Change the SIEM input source: Connect your workflow to Splunk, Elastic SIEM, or Chronicle Security. 🔧 Customize remediation steps: Use a custom AI model to tailor remediation responses based on organization-specific security policies. 🔧 Extend ticketing integration: Modify the Zendesk node to also work with Jira, ServiceNow, or another ITSM platform. Why This Workflow is Powerful ✅ Saves time: Automates alert triage & classification. ✅ Improves security posture: Helps SOC teams act faster on threats. ✅ Leverages AI & vector search: Uses LLM-powered enrichment for real-time context. ✅ Works across platforms: Supports n8n Cloud, Self-hosted, and Qdrant. 🚀 Get Started Now! 📖 Watch the Setup Video 💬 Have Questions? Join the Discussion in the YouTube Comments!

The provided workflow in n8n is designed to create a Business WhatsApp AI RAG (Retrieval-Augmented Generation) Chatbot. How it works: Webhook Setup: The workflow begins by setting up webhooks for verification and response. The Verify webhook receives GET requests and sends back a verification code, while the Respond webhook handles incoming POST requests from Meta regarding WhatsApp messages. Message Handling: Once a message is received, the workflow checks if the incoming JSON contains a user message. If it does, the message is processed further; otherwise, a generic response is sent. AI Agent Interaction: The user's message is passed to the AI Agent node, which uses a conversational agent with a predefined system message tailored for an electronics store. This ensures that the AI provides accurate and professional responses based on the knowledge base. Knowledge Base Utilization: The AI Agent references a knowledge base stored in Qdrant, a vector database. Documents from Google Drive are downloaded, vectorized using OpenAI embeddings, and stored in Qdrant for retrieval during conversations. Response Generation: The AI Agent generates a response using the OpenAI chat model (gpt-4o-mini) and sends it back to the user via WhatsApp. Set up steps: Create Qdrant Collection: Update the QDRANTURL and COLLECTION variables in the workflow. Use the Create collection HTTP request node to initialize the collection in Qdrant. Vectorize Documents: Configure the Get folder and Download Files nodes to fetch documents from a specified Google Drive folder. Use the Embeddings OpenAI node to generate embeddings for the downloaded files. Store the vectorized documents in Qdrant using the Qdrant Vector Store node. Configure Webhooks: Ensure both Verify and Respond webhooks have the same URL. Set the Verify webhook to use the GET HTTP method and the Respond webhook to use the POST HTTP method. Set Up AI Agent: Define the system prompt for the AI Agent, specifying guidelines for product information, technical support, customer service, and knowledge base usage. Link the AI Agent to the OpenAI chat model and configure any additional tools as needed. Test Workflow: Trigger the workflow manually using the When clicking ‘Test workflow’ node to ensure all components are functioning correctly. Monitor the flow of data through the nodes and verify that responses are being generated and sent accurately. By following these steps, the workflow will be fully operational, enabling a robust AI-powered chatbot capable of handling customer inquiries via WhatsApp. Need help customizing? Contact me for consulting and support or add me on Linkedin.

The "Voice RAG Chatbot with ElevenLabs and OpenAI" workflow in n8n is designed to create an interactive voice-based chatbot system that leverages both text and voice inputs for providing information. Ideal for shops, commercial activities and restaurants How it works: Here's how it operates: Webhook Activation: The process begins when a user interacts with the voice agent set up on ElevenLabs, triggering a webhook in n8n. This webhook sends a question from the user to the AI Agent node. AI Agent Processing: Upon receiving the query, the AI Agent node processes the input using predefined prompts and tools. It extracts relevant information from the knowledge base stored within the Qdrant vector database. Knowledge Base Retrieval: The Vector Store Tool node interfaces with the Qdrant Vector Store to retrieve pertinent documents or data segments matching the user’s query. Text Generation: Using the retrieved information, the OpenAI Chat Model generates a coherent response tailored to the user’s question. Response Delivery: The generated response is sent back through another webhook to ElevenLabs, where it is converted into speech and delivered audibly to the user. Continuous Interaction: For ongoing conversations, the Window Buffer Memory ensures context retention by maintaining a history of interactions, enhancing the conversational flow. Set up steps: To configure this workflow effectively, follow these detailed setup instructions: ElevenLabs Agent Creation: Begin by creating an agent on ElevenLabs (e.g., named 'test_n8n'). Customize the first message and define the system prompt specific to your use case, such as portraying a character like a waiter at "Pizzeria da Michele". Add a Webhook tool labeled 'test_chatbot_elevenlabs' configured to receive questions via POST requests. Qdrant Collection Initialization: Utilize the HTTP Request nodes ('Create collection' and 'Refresh collection') to initialize and clear existing collections in Qdrant. Ensure you update placeholders QDRANTURL and COLLECTION accordingly. Document Vectorization: Use Google Drive integration to fetch documents from a designated folder. These documents are then downloaded and processed for embedding. Employ the Embeddings OpenAI node to generate embeddings for the downloaded files before storing them into Qdrant via the Qdrant Vector Store node. AI Agent Configuration: Define the system prompt for the AI Agent node which guides its behavior and responses based on the nature of queries expected (e.g., product details, troubleshooting tips). Link necessary models and tools including OpenAI language models and memory buffers to enhance interaction quality. Testing Workflow: Execute test runs of the entire workflow by clicking 'Test workflow' in n8n alongside initiating tests on the ElevenLabs side to confirm all components interact seamlessly. Monitor logs and outputs closely during testing phases to ensure accurate data flow between systems. Integration with Website: Finally, integrate the chatbot widget onto your business website replacing placeholder AGENT_ID with the actual identifier created earlier on ElevenLabs. By adhering to these comprehensive guidelines, users can successfully deploy a sophisticated voice-driven chatbot capable of delivering precise answers utilizing advanced retrieval-augmented generation techniques powered by OpenAI and ElevenLabs technologies.

UPDATE This is an updated version of the previous Ultimate Content Generator for WordPress v2. https://n8n.io/workflows/2737-ultimate-content-generator-for-wordpress/ It includes an additional workflow to generate content using Anthropic Claude in addition to OpenAI 4o. I also mofidied it to use the AI Agent Tool node with structured output for more consistent outputs. Lastly, the workflow updates a new table in AirTable with the published blog post url and featured image url. Overview- This workflow automates the end-to-end process of creating, optimizing, and publishing content on WordPress. It integrates AI-powered tools, Airtable, and WordPress plugins to generate high-quality, on-brand posts effortlessly. Perfect for content creators, marketers, and business owners looking to save time and scale their content strategy. Features Content Creation: AI-Powered Content: Generates SEO-friendly blog posts with structured headings, relevant keywords, and meta descriptions. Custom Prompts: Tailor the AI-generated content to match your brand’s tone and voice. SEO Optimization: RankMath Plugin Integration: Updates RankMath SEO with focus keywords and meta descriptions, ensuring your content is search-engine optimized. Content Management: Airtable Integration: Organizes content ideas, drafts, and publishing schedules in one place. Easily scalable for teams or solo creators. Visuals: Branded Featured Images: Automatically generates on-brand images for every post. Publishing: Effortless Formatting: Adapts content to fit your WordPress theme and schedules it for publication. Workflow Steps Trigger: Initiated manually or on a schedule. Content Management: Retrieves and organizes ideas from Airtable. Content Generation: Generates AI-driven blog content tailored to your audience. SEO Optimization: Automatically updates RankMath with SEO details. Featured Image Creation: Produces on-brand images for the post. Publishing: Formats and schedules the post on WordPress. Prerequisites API Keys: OpenAI Claude Airtable WordPress REST API Custom Code: Add a small update to your WordPress theme’s functions.php file to enable seamless automation. Customization Replace Airtable with another content management system if preferred. Adjust AI prompts to reflect different tones, styles, or industries. Add integrations for additional plugins, analytics, or storage services. Usage Import the workflow into your n8n instance. Configure API credentials for WordPress, Airtable, OpenAI, Claude and Slack. Update your functions.php file with the provided code snippet. Customize prompts and Airtable structure for your content needs. Trigger the workflow manually or set it on a schedule. Notes Experiment with Airtable views or add filters for more granular control over your content pipeline. Extend the workflow to include social media posting or analytics tracking. Feel free to adapt and extend this workflow to meet your specific needs! 🎉

How it works This workflow enables companies to provide instant HR support by automating responses to employee queries about policies and benefits: Retrieves company policies, benefits, and HR documents from BambooHR. Uses AI to analyze and answer employee questions based on company records. Identifies the most relevant contact person for escalations. Seamlessly integrates with company systems to provide real-time HR assistance. Set up steps: Estimated time: ~20 minutes Connect your BambooHR account to allow policy retrieval. Configure AI parameters and access control settings. (Optional) Set up the employee lookup tool for personalized responses. Test the chatbot to ensure accurate responses and seamless integration. Benefits This workflow is perfect for HR teams looking to enhance employee support while reducing manual inquiries. Outperform BambooHR's "Ask BambooHR" Chatbot #1. Superior specificity of replies to general inquiries #2. More appropriate escalations when responding to sensitive employee concerns

This workflow is ideal for businesses looking to automate their email responses, especially for handling inquiries about company information. It leverages AI to ensure accurate and professional communication. How It Works Email Trigger: The workflow starts with the Email Trigger (IMAP) node, which monitors an email inbox for new messages. When a new email arrives, it triggers the workflow. Email Preprocessing: The Markdown node converts the email's HTML content into plain text for easier processing by the AI models. Email Summarization: The Email Summarization Chain node uses an AI model (DeepSeek R1) to generate a concise summary of the email. The summary is limited to 100 words and is written in Italian. Email Classification: The Email Classifier node categorizes the email into predefined categories (e.g., "Company info request"). If the email does not fit any category, it is classified as "other". Email Response Generation: The Write email node uses an AI model (OpenAI) to draft a professional response to the email. The response is based on the email content and is limited to 100 words. The Review email node uses another AI model (DeepSeek) to review and format the drafted response. It ensures the response is professional and formatted in HTML (e.g., using `, , , ` tags where necessary). Email Sending: The Send Email node sends the reviewed and formatted response back to the original sender. Vector Database Integration: The Qdrant Vector Store node retrieves relevant information from a vector database (Qdrant) to assist in generating accurate responses. This is particularly useful for emails classified as "Company info request". The Embeddings OpenAI node generates embeddings for the email content, which are used to query the vector database. Document Vectorization: The workflow includes steps to create and refresh a Qdrant collection (Create collection and Refresh collection nodes). Documents from Google Drive are downloaded (Get folder and Download Files nodes), processed into embeddings (Embeddings OpenAI1 node), and stored in the Qdrant vector store (Qdrant Vector Store1 node). Set Up Steps Configure Email Trigger: Set up the Email Trigger (IMAP) node with the appropriate IMAP credentials to monitor the email inbox. Set Up AI Models: Configure the DeepSeek R1, OpenAI, and DeepSeek nodes with the appropriate API credentials for text summarization, response generation, and review. Set Up Email Classification: Define the categories in the Email Classifier node (e.g., "Company info request", "Other"). Ensure the OpenAI 4-o-mini node is configured to assist in classification. Set Up Vector Database: Configure the Qdrant Vector Store and Qdrant Vector Store1 nodes with the appropriate Qdrant API credentials and collection details. Set up the Embeddings OpenAI and Embeddings OpenAI1 nodes to generate embeddings for the email content and documents. Set Up Document Processing: Configure the Get folder and Download Files nodes to access and download documents from Google Drive. Use the Token Splitter and Default Data Loader nodes to process and split the documents into manageable chunks for vectorization. Set Up Email Sending: Configure the Send Email node with the appropriate SMTP credentials to send responses. Test the Workflow: Trigger the workflow manually using the When clicking ‘Test workflow’ node to ensure all steps execute correctly. Verify that emails are summarized, classified, and responded to accurately. Activate the Workflow: Once tested, activate the workflow to automate the process of handling incoming emails. Key Features Automated Email Handling**: Automatically processes incoming emails, summarizes them, and generates professional responses. AI-Powered Classification**: Uses AI to classify emails into relevant categories for targeted responses. Vector Database Integration**: Retrieves relevant information from a vector database to enhance response accuracy. Document Vectorization**: Processes and stores documents from Google Drive in a vector database for quick retrieval. Professional Email Formatting**: Ensures responses are professionally formatted and concise. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Automated YouTube Shorts Creator from Google News Trends Transform trending Google News articles into engaging YouTube Shorts with this fully automated workflow. Save time and effort while creating dynamic, eye-catching videos that are perfect for content creators, journalists, and digital marketers. Key Features Trend Analysis: Automatically fetch Google News articles and entertainment trends every 24 hours. AI-Powered Video Creation: Generate accurate transcripts from articles using Deepseek AI. Create visually appealing backgrounds with AI image generation tools. Add voiceovers with ElevenLabs TTS or similar services. Dynamic Video Compilation: Combine images, audio, and background music to craft short, dynamic videos. Fully customizable video length and music selection. YouTube Integration: Upload videos directly to YouTube, complete with autogenerated titles and descriptions. Organized Management: Titles and descriptions are saved to a Google Sheet for easy tracking and editing. How It Works Fetch trending topics from Google News using Deepseek. Convert the article's content into a readable transcript. Automatically create a voiceover, generate visuals, and combine them into a complete video. Upload the video to YouTube with a dynamic caption and description. Who Is It For? Content creators looking to expand their reach on YouTube Shorts. News agencies aiming to deliver content faster. Digital marketers needing quick, engaging videos. Setup Requirements API keys for Google News, ElevenLabs, OpenAI, and Deepseek. Basic knowledge of API integration and video content creation. Start creating trending, eye-catching YouTube Shorts in minutes. Automate your workflow and stay ahead in the content game!

Overview This template describes a possible approach to handle a pseudo-callback/trigger from an independent, external process (initiated from a workflow) and combine the received input with the workflow execution that is already in progress. This requires the external system to pass through some context information (resumeUrl), but allows the "primary" workflow execution to continue with BOTH its own (previous-node) context, AND the input received in the "secondary" trigger/process. Primary Workflow Trigger/Execution The workflow path from the primary trigger initiates some external, independent process and provides "context" which includes the value of $execution.resumeUrl. This execution then reaches a Wait node configured with Resume - On Webhook Call and stops until a call to resumeUrl is received. External, Independent Process The external, independent process could be anything like a Telegram conversation, or a web-service as long as: it results in a single execution of the Secondary Workflow Trigger, and it can pass through the value of resumeUrl associated with the Primary Workflow Execution Secondary Workflow Trigger/Execution The secondary workflow execution can start with any kind of trigger as long as part of the input can include the resumeUrl. To combine / rejoin the primary workflow execution, this execution passes along whatever it receives from its trigger input to the resume-webhook endpoint on the Wait node. Notes IMPORTANT: The workflow ids in the Set nodes marked **Update Me have embedded references to the workflow IDs in the original system. They will need to be CHANGED to make this demo work. Note: The Resume Other Workflow Execution node in the template uses the $env.WEBHOOK_URL configuration to convert to an internal "localhost" call in a Docker environment. This can be done differently. ALERT:** This pattern is NOT suitable for a workflow that handles multiple items because the first workflow execution will only be waiting for one callback. The second workflow (not the second trigger in the first workflow) is just to demonstrate how the Independent, External Process needs to work.

How it Works This workflow automates the handling of incoming emails, summarizes their content, generates appropriate responses using a retrieval-augmented generation (RAG) approach, and obtains approval or suggestions before sending replies. Below is an explanation of its functionality divided into two main sections: Email Handling and Summarization: The process begins with the Email Trigger (IMAP) node which listens for new emails in a specified inbox. Once an email is received, the Markdown node converts its HTML content into plain text if necessary, followed by the Email Summarization Chain that uses AI to create a concise summary of up to 100 words. Response Generation and Approval: A Write email node generates a professional response based on the summarized content, ensuring brevity and professionalism while keeping within the word limit. Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through the Gmail node configured with free-text response options. If approved, the finalized email is sent back to the original sender using the Send Email node; otherwise, it loops back for further edits or manual intervention. Additionally, there's a Text Classifier node designed to categorize feedback from humans as either "Approved" or "Declined", guiding whether the email should proceed directly to being sent or require additional editing. Set Up Steps To replicate this workflow within your own n8n environment, follow these essential configuration steps: Configuration: Begin by setting up an n8n instance either locally or via cloud services offered directly from their official site. Import the provided JSON configuration file into your workspace, making sure all required credentials such as IMAP, SMTP, OpenAI API keys, etc., are properly set up under Credentials since multiple nodes rely heavily on external integrations for functionalities like reading emails, generating summaries, crafting replies, and managing approvals. Customization: Adjust parameters according to specific business needs, including but not limited to adjusting the conditions used during conditional checks performed by nodes like Approve?. Modify the template messages given to AI models so they align closely with organizational tone & style preferences while maintaining professionalism expected in business communications. Ensure correct mappings between fields when appending data to external systems where records might need tracking post-interaction completion, such as Google Sheets or similar platforms.

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.

Workflow Description This workflow automates the processing of credit card statement emails from multiple banks. It extracts important payment details, stores them in Google Sheets, and creates calendar reminders in Google Calendar. Additionally, it allows users to update the payment status once the bill has been paid. Key Features Email Processing: Retrieves credit card statement emails from eight Taiwanese banks. PDF Parsing: Extracts payment due date and amount from email content or attached PDF files. Google Sheets Integration: Logs extracted data into a Google Sheets document for record-keeping. Google Calendar Integration: Creates Google Calendar events as reminders for due payments. Webhook for Payment Updates: Allows users to update the payment status via a webhook. Node Configurations 1. Email Retrieval Purpose**: Fetches credit card statement emails from Gmail. Configuration**: Email Filters: SinoPac Bank: from:(newebill.banksinopac.com.tw) SinoPac Bank Credit Card E-Statement Notification Cathay United Bank: from:(service@pxbillrc01.cathaybk.com.tw) Cathay United Bank Monthly E-Statement CTBC Bank: from:(ebill@estats.ctbcbank.com) CTBC Bank Credit Card E-Statement Taipei Fubon Bank: from:(rs@cf.taipeifubon.com.tw) Taipei Fubon Bank Credit Card Statement E.SUN Commercial Bank: from:(estatement@esunbank.com) E.SUN Commercial Bank Credit Card E-Statement DBS Bank: from:(eservicetw@dbs.com) DBS Bank Credit Card E-Statement Union Bank of Taiwan: from:(聯邦銀行信用卡) Union Bank of Taiwan Credit Card E-Statement (Year Month) Taishin International Bank: from:(webmaster@bhurecv.taishinbank.com.tw) Taishin International Bank Credit Card E-Statement 2. Extract Payment Information Purpose**: Extracts payment due date, total amount, and minimum payment amount. Methods**: Text-based Extraction: Uses regex to parse email body. PDF Parsing: Extracts text from PDF attachments. 3. Data Processing and Storage 3.1. Consolidate Extracted Data Purpose**: Standardizes extracted payment details. Data Fields**: payment_due_date payment_amount minimum_payment email_id bank email_subject 3.2. Google Sheets Integration Purpose**: Stores the extracted data in a structured format. Configuration**: Sheet Name: n8n-Credit Card Payment Reminder Columns: calendar_id Paid Billing Period Amount Minimum Payment Bank email_id 4. Google Calendar Integration 4.1. Create Calendar Reminders Purpose**: Generates reminders for upcoming payments. Configuration**: Event Title: Credit Card Payment - {{ bank }} Due Date: payment_due_date Reminders: 30 minutes before 60 minutes before 1 day before 4.2. Update Payment Status Purpose**: Updates the calendar event once payment is made. Configuration**: Webhook URL: Automatically updates the Google Calendar event title and description. 5. Webhook for Payment Updates Purpose: Users can mark a payment as **paid via a webhook. Configuration**: Webhook Path: darrell_demo_creditcard_pay_update_path Updates: Marks the payment as Paid Updates Google Calendar and Google Sheets

Based on Jonathan & Solomon work. > The only addition I've made is a Set node. This node organizes workflows into subfolders within the GitHub repository based on their respective tags. How it works This workflow will backup your workflows to GitHub. It uses the n8n API node to export all workflows. It then loops over the data, checks in GitHub to see if a file exists that uses the credential's ID. Once checked it will: update the file on GitHub if it exists; create a new file if it doesn't exist; ignore if it's the same. Who is this for? People wanting to backup their workflows outside the server for safety purposes or to migrate to another server.

Using PostBin to Test Webhooks Without Changing WEBHOOK_URL How it Works Many new n8n users struggle with testing webhooks when running n8n on localhost, as external services cannot reach localhost. This workflow introduces a technique using PostBin, which provides a temporary, publicly accessible URL to receive webhook requests. Generates a temporary webhook endpoint via PostBin. Uses this endpoint in place of localhost to test webhooks. Captures and displays the incoming webhook request data. Enables debugging and iterating without modifying the WEBHOOK_URL environment variable. Set Up Steps Estimated time:** ~5–10 minutes Create a PostBin instance to generate a publicly accessible webhook URL. Copy the PostBin URL and use it as the webhook destination in n8n. Trigger the webhook from an external service or manually. Inspect the request payload in PostBin to verify data reception. (EXAMPLE) Using PostBin for Webhook Testing in a BambooHR Integration How it Works In this example, we apply the PostBin technique to a BambooHR integration. Instead of manually configuring a webhook in BambooHR, this workflow automates webhook registration using the BambooHR API. The workflow: Uses the BambooHR API to programmatically register the PostBin URL as a webhook. Retrieves the most recent webhook calls made by BambooHR to the PostBin URL. (Optional) Sends a personalized Slack message for new employees using OpenAI. Set Up Steps Estimated time:** ~15–20 minutes Set up PostBin using the steps from the first section. Log into BambooHR to generate an API key for authentication. Run the workflow to register the PostBin URL as a webhook in BambooHR via the API. Retrieve recent webhook calls from PostBin to validate data reception. (Optional) Send a Slack notification using the processed data.

Workflow based on the following article. https://www.anthropic.com/news/contextual-retrieval This n8n automation is designed to extract, process, and store content from documents into a Pinecone vector store using context-based chunking. The workflow enhances retrieval accuracy in RAG (Retrieval-Augmented Generation) setups by ensuring each chunk retains meaningful context. Workflow Breakdown: 🔹 Google Drive - Retrieve Document: The automation starts by fetching a source document from Google Drive. This document contains structured content, with predefined boundary markers for easy segmentation. 🔹 Extract Text Content - Once retrieved, the document’s text is extracted for processing. Special section boundary markers are used to divide the text into logical sections. 🔹 Code Node - Create Context-Based Chunks: A custom code node processes the extracted text, identifying section boundaries and splitting the document into meaningful chunks. Each chunk is structured to retain its context within the entire document. 🔹 Loop Node - Process Each Chunk: The workflow loops through each chunk, ensuring they are processed individually while maintaining a connection to the overall document context. 🔹 Agent Node - Generate Context for Each Chunk: We use an Agent node powered by OpenAI’s GPT-4.0-mini via OpenRouter to generate contextual metadata for each chunk, ensuring better retrieval accuracy. 🔹 Prepend Context to Chunks & Create Embeddings - The generated context is prepended to the original chunk, creating context-rich embeddings that improve searchability. 🔹 Google Gemini - Text Embeddings: The processed text is passed through Google Gemini text-embedding-004, which converts the text into semantic vector representations. 🔹 Pinecone Vector Store - Store Embeddings: The final embeddings, along with the enriched chunk content and metadata, are stored in Pinecone, making them easily retrievable for RAG-based AI applications. Use Case: This automation enhances RAG retrieval by ensuring each chunk is contextually aware of the entire document, leading to more accurate AI responses. It’s perfect for applications that require semantic search, AI-powered knowledge management, or intelligent document retrieval. By implementing context-based chunking, this workflow ensures that LLMs retrieve the most relevant data, improving response quality and accuracy in AI-driven applications.

What it is: An automation to plan→draft→finalize and publish your textual blog post ideas to your wordpress blog Works in stages and hand back control to you in between those You can use a Google Spreadsheet for planning topics and configuring LLM models and prompts What it does: plans→drafts→finalizes blog post topics you specify in a Google Spreadsheet using an LLM with prompts that also ar configured in that spreadsheet (even which model to use) savs the results in the corresponding columns of the "Schedule" sheet in the spreadsheet hands control back to the user for inspecting or changing the results and for setting the next "Action" for th workflow Finally publishes the blog post to your Wordpress instance Limitations Probably slightly over-engineered ;-) No media generation yet some LLM models don't work because of their output format How it works: The Workflow is triggered manually or scheduled every hour It ingests a Google Spreadsheet to get Config for prompts/context tc Blog-Topics and their status and next action Depending on each blog topics "Status" and "Action" it then either uses an LLM for th next action ("plan"→"draft"→"final" actions) or publishes the written content to your Wordpress instance ("publish" actions) Set up steps: Import the workflow Make your own copy of the Google Spreadsheet Update the credentials using your individual credentials for: Google Spreadsheets OpenRouter Edit the "Settings" node and enter your individual values for Your spreadsheet copy URL Your wordpress blog URL Your wordpress blog username Your wordpress blog app password (a 4x4 alphanumeric sequence), that you probably have to create first, for which your wordpress user has to have 2-factor-authentication enabled. In your own copy of the spreadsheet: individualize the "Config" sheet's "Value" column for the prompts/context/etc Populate the "Schedule" sheet with at least one line in which you specify a "Topic" a "Schedulded" date (YYYY-MM-DD HH:mm:ss) a "Status" of "idea" an "Action" of "plan" (to kick off that action)

Template Description WDF Top Keywords: This workflow is designed to streamline keyword research by automating the process of generating, filtering, and analyzing Google and YouTube keyword data. Ensure compliance with local regulations and API terms of service when using this workflow. 📌 Purpose The WDF Top Keywords workflow automates collecting, processing, and managing keyword data for both Google and YouTube platforms. Leveraging multiple data sources and APIs ensures an efficient and scalable approach to identifying high-impact keywords for SEO, content creation, and marketing campaigns. Key Features Automates the generation of keyword suggestions using autocomplete APIs. Integrates with NocoDB to store and manage keyword data. Filters keywords based on monthly search volume and cost-per-click (CPC). Supports bulk import of keyword data into structured databases. Outputs both Google and YouTube keyword insights, enabling informed decision-making. 🎯 Target Audience This workflow is ideal for: Digital marketers aiming to optimize ad campaigns with data-driven insights. SEO specialists looking to identify high-potential keywords efficiently. Content creators seeking trending and relevant topics for their platforms. Agencies managing keyword research for multiple clients. ⚙️ How It Works Trigger: The workflow runs on-demand or at scheduled intervals. Keyword Generation: Retrieves base keywords from NocoDB. Generates autocomplete suggestions for Google and YouTube. Data Processing: Filters and formats keyword data based on specific criteria (e.g., search volume, CPC). Consolidates results for efficient storage and analysis. Storage and Output: Saves data into structured NocoDB tables for tracking and reuse. Bulk imports monthly search volume statistics for detailed analysis. 🛠️ Key APIs and Tools Used NocoDB**: Stores and organizes base and processed keyword data. DataForSEO API**: Provides search volume and keyword performance metrics. Google Autocomplete API**: Suggests relevant Google search terms. YouTube Autocomplete API**: Suggests trending YouTube keywords. Social Flood Docker Instance**: Serves as the local integration hub. Setup Instructions Required Tools: NocoDB n8n DataForSEO Account Social Flood Docker Instance Create the following NocoDB tables: Base Keyword Search Second Order Google Keywords Second Order YouTube Keywords Search Volume This template empowers users to handle complex keyword research tasks effortlessly, saving time and providing actionable insights. Share this template to enhance your workflow efficiency!

This workflow takes off the task of backing up workflows regularly on Github and uses Google Drive as the main tool to host these. This can be a good way to keep track of your workflows so that you never lose any workflows in case your n8n goes down. How does it work Creates a new folder within a specified folder with the time its backed up Loops around all workflows, converts them to a JSON file and uploads them to the created folder Gets the previous backups and deletes them This has a clean feel and look as it simplifies the backup while not keeping a cache of workflows on your drive. Setup Create a new folder Create new service account credentials Share the folder with the service account email Upload this workflow to your canvas and map the credentials Set the schedule that you need your workflows to run and manage your backups Activate the workflow Happy Productivity! @Imperol

This workflow automates the retrieval of invoice PDFs from the Smartbill API and saves them to Google Drive in a dynamically created folder based on last month’s date. It also generates a range of invoice numbers, ensuring proper formatting, and uploads each PDF with a structured filename. Overview Trigger and Data Setup: The workflow is manually triggered. It sets the invoice range (start and end numbers), invoice series, the parent Google Drive folder ID and the folder where to save the PDF files. Folder Handling: Folder Name Calculation: Calculates a default folder name based on last month’s date (formatted as YYYY-MM). Invoice Generation: A code node generates invoice items by iterating over a specified range of numbers. Each invoice number is padded to custom number of digits (e.g., 0013), and the invoice series and folder ID are attached to each item. Retrieving Invoice PDFs: For each generated invoice, the Smartbill API is called (using an HTTP Request node) to retrieve the corresponding invoice PDF. How to Use Credentials:** Ensure you have configured the Smartbill API (HTTP Basic Auth) and Google API credentials correctly. Parameter Adjustment:** Modify the SetData node if you need to change the invoice range, series, or target parent folder. Execution:** Click Execute Workflow to run the workflow manually.

What this does Show you how to us XMLRPC APIs via the generic HTTP-Request-node, by the example of posting to a wordpress blog This is also a feasible workaround if a specific n8n integration does not work or stops working (which happens e.g. with the Wordpress node) How it works First, the XML payload for the request is being prepared (in a code node, which also properly escapes special character in the values that you want to send to the XMLRPC endpoint) Then, the HTTP Request node sends the request using the HTTP post method Last, the returned XML response is converted to JSON which a conditional node uses to determine whether th operation was successful or not Setup steps: Import workflow Ensure you have a wordpress blog with a user that has an app-Password Edit the "Settings"-node and enter your individual values for url/user/app-pw

Overview This n8n workflow automates the process of extracting published WordPress posts, converting them into a CSV file, and uploading it to Google Drive. It’s perfect for content backups, SEO audits, and data migration. Features Fetches all published posts from a WordPress website Extracts key post details (ID, Title, Link) Converts the extracted data into a CSV file Uploads the CSV file to Google Drive for easy access and storage Use Cases SEO Optimization**: Export post data for keyword analysis and performance tracking Automated Content Backup**: Store WordPress post details in Google Drive. You can add more fields to the Csv file if needed Workflow Steps 1. Trigger Workflow Manually The workflow starts when triggered manually in n8n. 2. Retrieve WordPress Posts The workflow fetches all published posts using the WordPress API. It extracts: Post ID Title Link Rendered Content 3. Format Data The retrieved data is structured to ensure correct CSV formatting. 4. Convert to CSV File The formatted data is transformed into a downloadable CSV file. 5. Upload to Google Drive The CSV file is automatically uploaded to a specified Google Drive folder for easy access and storage. How to Use Connect your WordPress and Google Drive accounts to n8n. Run the workflow manually or set up a scheduled trigger. Access the CSV file from your Google Drive folder.

This AI Agent is designed to streamline the process of creating realistic images of clothing products worn by models, eliminating the need for expensive photoshoots. The primary goal is to automate the generation of virtual try-on images for an eCommerce store selling clothing, using advanced image processing technologies. Example of results How It Works Triggering the Workflow: The workflow can be triggered manually using the When clicking ‘Test workflow’ node or automatically via the Schedule Trigger node, which runs the workflow at regular intervals (e.g., every 5 minutes). Data Retrieval: The Get new product node retrieves data from a Google Sheets document containing the URLs of the model image, the clothing product image, and the WooCommerce product ID. The document also includes a column for the resulting virtual try-on image URL, which is initially empty. Setting Up the Request: The Set data node prepares the data for the AI request by assigning the model image URL and the product image URL to variables. AI Image Generation: The Create Image node sends a request to API to generate a virtual try-on image. The request includes the URLs of the model and the clothing product. When you register for the API service you will get 1$ for free. For continuous work add API credits to your account. Image Retrieval: The Get Url image node retrieves the URL of the generated virtual try-on image. The Get File image node downloads the generated image from the provided URL. Image Storage: The Upload Image node uploads the generated image to a specified Google Drive folder for storage. Updating Google Sheets: The Update result node updates the Google Sheets document with the URL of the generated virtual try-on image. Updating WooCommerce: The Update product node updates the corresponding product in WooCommerce by adding the generated virtual try-on image to the product's image gallery. Functionality This AI Agent is designed to streamline the process of creating realistic images of clothing products worn by models, eliminating the need for expensive photoshoots. The primary goal is to automate the generation of virtual try-on images for an eCommerce store selling clothing, using advanced image processing technologies. By offering realistic images of clothing items worn by models, this automation process saves time and resources, making product catalog management more efficient. This approach not only enhances the competitiveness of the eCommerce store but also provides greater flexibility in creating high-quality visual content adaptable to various digital marketing campaigns. Key Features Automated Virtual Try-On**: Generates realistic images of models wearing clothing items using AI. Google Sheets Integration**: Retrieves and updates data in Google Sheets for seamless data management. Image Storage**: Uploads generated images to Google Drive for easy access and archiving. WooCommerce Integration**: Updates product pages with virtual try-on images, enhancing the shopping experience. Scheduled Automation**: Can be triggered manually or run at regular intervals for continuous processing. This workflow is ideal for eCommerce businesses looking to enhance their product catalogs with high-quality, realistic images without the need for costly photoshoots. It leverages AI to provide a cost-effective and efficient solution for virtual try-on image generation.

Who Is This For? This workflow is designed for AI engineers, automation specialists, and content creators who need a scalable system to dynamically manage prompts stored in GitHub. It eliminates manual updates, enforces required variable checks, and ensures that AI interactions always receive fully processed prompts. 🚀 What Problem Does This Solve? Manually managing AI prompts can be inefficient and error-prone. This workflow: ✅ Fetches dynamic prompts from GitHub ✅ Auto-populates placeholders with values from the setVars node ✅ Ensures all required variables are present before execution ✅ Processes the formatted prompt through an AI agent 🛠 How This Workflow Works This workflow consists of three key branches, ensuring smooth prompt retrieval, variable validation, and AI processing. 1️⃣ Retrieve the Prompt from GitHub (HTTP Request → Extract from File → SetPrompt) The workflow starts manually or via an external trigger. It fetches a text-based prompt stored in a GitHub repository. The Extract from File Node retrieves the content from the GitHub file. The SetPrompt Node stores the prompt, making it accessible for processing. 📌 Note: The prompt must contain n8n expression format variables (e.g., {{ $json.company }}) so they can be dynamically replaced. 2️⃣ Extract & Auto-Populate Variables (Check All Prompt Vars → Replace Variables) A Code Node scans the prompt for placeholders in the n8n expression format ({{ $json.variableName }}). The workflow compares required variables against the setVars node: ✅ If all variables are present, it proceeds to variable replacement. ❌ If any variables are missing, the workflow stops and returns an error listing them. The Replace Variables Node replaces all placeholders with values from setVars. 📌 Example of a properly formatted GitHub prompt: Hello {{ $json.company }}, your product {{ $json.features }} launches on {{ $json.launch_date }}. This ensures seamless replacement when processed in n8n. 3️⃣ AI Processing & Output (AI Agent → Prompt Output) The Set Completed Prompt Node stores the final, processed prompt. The AI Agent Node (Ollama Chat Model) processes the prompt. The Prompt Output Node returns the fully formatted response. 📌 Optional: Modify this to use OpenAI, Claude, or other AI models. ⚠️ Error Handling: Missing Variables If a required variable is missing, the workflow stops execution and provides an error message: ⚠️ Missing Required Variables: ["launch_date"] This ensures no incomplete prompts are sent to AI agents. ✅ Example Use Case 📜 GitHub Prompt File (Using n8n Expressions) Hello {{ $json.company }}, your product {{ $json.features }} launches on {{ $json.launch_date }}. 🔹 Variables in setVars Node { "company": "PropTechPro", "features": "AI-powered Property Management", "launch_date": "March 15, 2025" } ✅ Successful Output Hello PropTechPro, your product AI-powered Property Management launches on March 15, 2025. 🚨 Error Output (If Missing launch_date) ⚠️ Missing Required Variables: ["launch_date"] 🔧 Setup Instructions 1️⃣ Connect Your GitHub Repository Store your prompt in a public or private GitHub repo. The workflow will fetch the raw file using the GitHub API. 2️⃣ Configure the SetVars Node Define the required variables in the SetVars Node. Make sure the variable names match those used in the prompt. 3️⃣ Test & Run Click Test Workflow to execute. If variables are missing, it will show an error. If everything is correct, it will output the fully formatted prompt. ⚡ How to Customize This Workflow 💡 Need CRM or Database Integration? Connect the setVars node to an Airtable, Google Sheets, or HubSpot API to pull variables dynamically. 💡 Want to Modify the AI Model? Replace the Ollama Chat Model with OpenAI, Claude, or a custom LLM endpoint. 📌 Why Use This Workflow? ✅ No Manual Updates Required – Fetches prompts dynamically from GitHub. ✅ Prevents Broken Prompts – Ensures required variables exist before execution. ✅ Works for Any Use Case – Handles AI chat prompts, marketing messages, and chatbot scripts. ✅ Compatible with All n8n Deployments – Works on Cloud, Self-Hosted, and Desktop versions.

N8N for Beginners: Looping Over Items Description This workflow is designed for n8n beginners to understand how n8n handles looping (iteration) over multiple items. It highlights two key behaviors: Built-In Looping:** By default, most n8n nodes iterate over each item in an input array. Explicit Looping:* The *Loop Over Items* node allows controlled iteration, enabling *custom batch processing** and multi-step workflows. This workflow demonstrates the difference between processing an unsplit array of strings (single item) vs. a split array (multiple items). Setup 1. Input Data To begin, paste the following JSON into the Manual Trigger node: { "urls": [ "https://www.reddit.com", "https://www.n8n.io/", "https://n8n.io/", "https://supabase.com/", "https://duckduckgo.com/" ] } 📌 Steps to Paste Data: Double-click** the "Manual Trigger" node. Click "Edit Output" (top-right corner). Paste the JSON and Save. The node turns purple, indicating that test data is pinned. 1. Click "Test Workflow" button at the bottom of the canvas Explanation of the n8n Nodes in the Workflow | Node Name | Purpose | Documentation Link | |-----------|---------|--------------------| | Manual Trigger | Starts the workflow manually and sends test data | Docs | | Split Out | Converts an array of strings into separate JSON objects | Docs | | Loop Over Items (Loop Over Items 1) | Demonstrates how an unsplit array is treated as one item | Docs | | Loop Over Items (Loop Over Items 2) | Iterates over each item separately | Docs | | Wait | Introduces a delay per iteration (set to 1 second) | Docs | | Code | Adds a constant parameter (param1) to each item | Docs | | NoOp (Result Nodes) | Displays output for inspection | Docs | Execution Details 1. How the Workflow Runs Manual Trigger starts execution** with the pasted JSON data. The workflow follows two paths: Unsplit Array Path → Loop Over Items 1 Processes the entire array as a single item. Result1 & Result5: Show that the array was not split. Split Array Path → Split Out → Loop Over Items 2 Splits the array into separate objects. Result2, Result3, Result4: Show that each item is processed individually. A Wait node (1 sec delay) demonstrates controlled execution. Code nodes modify the JSON, adding a parameter (param1). 2. What You Will See | Node | Expected Output | |------|---------------| | Result1 & Result5 | The entire array is processed as one item. | | Result2, Result3, Result4 | The array is split and processed as individual items. | | Wait Node | Adds a 1-second delay per item in Loop Over Items 2. | Use Cases This workflow is useful for: ✅ API Data Processing: Loop through API responses containing arrays. ✅ Web Scraping: Process multiple URLs individually. ✅ Task Automation: Execute a sequence of actions per item. ✅ Workflow Optimization: Control execution order, delays, and dependencies. Notes Sticky notes are included in the workflow for easy reference. The Wait node is optional—remove it for faster execution. This template is structured for beginners but serves as a building block for more advanced automations.