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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.

his workflow automates time tracking using location-based triggers. How it works Trigger: It starts when you enter or exit a specified location, triggering a shortcut on your iPhone. Webhook: The shortcut sends a request to a webhook in n8n. Check-In/Check-Out: The webhook receives the request and records the time and whether it was a "Check-In" or "Check-Out" event. Google Sheets: This data is then logged into a Google Sheet, creating a record of your work hours. Set up steps Google Drive: Connect your Google Drive account. Google Sheets: Connect your Google Sheets account. Webhook: Set up a webhook node in n8n. iPhone Shortcuts: Create two shortcuts on your iPhone, one for "Check-In" and one for "Check-Out." Configure Shortcuts: Configure each shortcut to send a request to the webhook with the appropriate "Direction" header. It's easy to setup, around 5 minutes.

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.

Transforming Emails into Podcasts 🎙️ Check out this channel for example. The n8n workflow described here aims to revolutionize the way users engage with promotional emails by converting them into entertaining audio podcasts. This innovative project leverages automation through n8n to streamline tasks and enhance user experience. Project Benefit 🎧🌟 The primary goal of this project is to transform "CATEGORY_PROMOTIONS" emails into engaging audio content. By converting text into speech, users can enjoy promotional material hands-free, making it easier to consume information while on the go or relaxing. The workflow consists of several key steps orchestrated seamlessly to deliver a delightful experience to users. How to Use the Workflow: Gmail trigger Node: Initiates the workflow by fetching "CATEGORY_PROMOTIONS" emails at regular intervals. The Gmail Trigger node in your N8N workflow is set to poll for new emails every minute and is configured to filter emails with the label "CATEGORY_PROMOTIONS" before triggering the workflow. Steps to Use Filters Inside the Gmail Trigger Node: Configure Gmail Trigger Node: Set "Poll Times" to "Every Minute" to check for new emails at regular intervals. Enable the "Simple" toggle if you want to simplify the node interface. Under "Filters", specify the label IDs you want to filter by. In this case, it's set to "CATEGORY_PROMOTIONS". Adjust any additional options as needed. // Configure Gmail Trigger node pollTimes: { item: [ { mode: "everyMinute" } ] }, simple: false, filters: { labelIds: [ "CATEGORY_PROMOTIONS" ] }, options: {} Save and Execute: Save your workflow and execute it to start monitoring your Gmail account for new emails with the specified label filter. By following these steps, your workflow will effectively trigger based on new emails that match the "CATEGORY_PROMOTIONS" label in your Gmail account. Get message content Node: Extracts the email content for processing. Summarization Chain Node: Generates concise summaries using advanced methods for better readability. Delete the unnecessary items Node: Removes irrelevant details from the email content. Text to Free TTS Node: Converts the summary text into speech using Free TTS technology. Convert from base64 to File Node: Transforms the audio data into a compatible file format. Merge Text with Audio Node: Combines the text and audio components seamlessly. Aggregate in same cell Node: Gathers all processed data for finalization. Send Message to Telegram Node: Dispatches the audio message along with a caption to a designated Telegram chat ID. By automating these tasks, the workflow ensures efficient communication and delivers content in a more engaging format, fostering a positive user experience. Configuration Instructions: The configuration of this workflow involves setting up the necessary nodes and establishing connections between them. Each node performs a specific function crucial to the overall operation of the workflow. Additionally, credentials need to be provided for accessing Gmail and OpenAI services to enable seamless data processing and summarization. Utilizing Text-to-Speech API 🎧 In addition to n8n automation, an external Text-to-Speech API plays a pivotal role in generating audio content from text data. By sending a POST request with JSON data containing the text and voice preferences, users can quickly receive audio files of the converted content. The API offers a straightforward interface for text-to-speech conversion, making it ideal for creating audio clips efficiently. To access this API, simply submit the desired text and voice selection to receive the generated speech audio file. The API endpoint can be accessed at https://tiktok-tts.weilnet.workers.dev/api/generation or through https://tiktokvoicegenerator.com/. In conclusion, this n8n workflow coupled with a Text-to-Speech API presents a powerful solution for transforming emails into captivating podcasts, enhancing user engagement and communication effectiveness. By embracing automation and innovative technologies, this project aims to improve user experience and streamline content delivery processes. 🌈✨🚀

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 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.

Purpose of workflow: This AI-powered workflow is designed to automatically generate comprehensive, well-researched articles on any given topic. It utilizes a team of AI agents to streamline the research and writing process, producing high-quality content with proper citations and credible sources. How it works Multi-agent team: Research Leader: Plans and conducts initial research, creating a table of contents. Project Planner: Breaks down the table of contents into manageable sections. Research Assistants: Multiple agents that conduct in-depth research on assigned sections. Editor: Compiles and refines the final article, ensuring coherence and proper citations. Key features: Utilizes Perplexity AI for internet search and citation capabilities Produces well-structured articles with proper citations Customizable parameters (topic, tone, word count, number of sections) Step by step setup: Get account from OpenRouter.ai to access Perplexity API Set API key in the Perplexity API node Credential key name : Authorization and key value Bearer <api-key value>

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

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

Who is this for? This template is designed for businesses and organizations that use Mautic for email marketing and want to automate the process of removing contacts from specific segments when they receive an unsubscribe request via email. What problem is this workflow solving? / use case Many email recipients, especially those who are less tech-savvy, may not follow the standard unsubscribe link provided in emails. Instead, for example in Gmail, they click the "Unsubscribe" button in the Gmail web interface, which in turn sends an email with a consistent format, these emails contain the word unsubscribe in the 'To' field using the following structure: [email protected] This workflow automates the process of identifying such unsubscribe emails and removing the contact from the relevant Mautic segments, ensuring compliance with unsubscribe requests and maintaining a clean mailing list. What this workflow does Monitors a Gmail account for incoming emails. Identifies unsubscribe emails based on specific patterns in the "To" field (e.g., containing the word "unsubscribe"). Retrieves the contact's ID from Mautic based on the email address. Removes the contact from the specified "newsletter" segment in Mautic. Adds the contact to the "unsubscribed" segment in Mautic. Sends a confirmation email to the contact, acknowledging their unsubscribe request. Setup Configure your email address and unsubscribe message in the "Edit Fields" node. Set your credentials in the Gmail trigger and in the Mautic nodes. Set the segments for the "newsletter" and "unsubscribed" in the Mautic nodes. Make sure your n8n installation has a public endpoint for your Gmail trigger to work correctly. Deploy the workflow. How to customize this workflow to your needs Adjust the conditions for identifying unsubscribe emails based on your specific requirements. Modify the segments or actions taken in Mautic according to your desired behavior. Customize the confirmation email message and sender details. Note: This workflow assumes a consistent structure for unsubscribe emails, where the "From" field contains the word "unsubscribe" using the "+" sign. If your email provider follows a different convention, adjust the conditions in the "Is automated unsubscribe?" node accordingly.

Video Guide I prepared a detailed guide that showed the whole process of building an AI bot, from the simplest version to the most complex in a template. .png) Who is this for? This workflow is ideal for developers, chatbot enthusiasts, and businesses looking to build a dynamic Telegram bot with memory capabilities. The bot leverages OpenAI's assistant to interact with users and stores user data in Supabase for personalized conversations. What problem does this workflow solve? Many simple chatbots lack context awareness and user memory. This workflow solves that by integrating Supabase to keep track of user sessions (via What this workflow does This Telegram bot template connects with OpenAI to answer user queries while storing and retrieving user information from a Supabase database. The memory component ensures that the bot can reference past interactions, making it suitable for use cases such as customer support, virtual assistants, or any application where context retention is crucial. 1.Receive New Message: The bot listens for incoming messages from users in Telegram. Check User in Database: The workflow checks if the user is already in the Supabase database using the Create New User (if necessary): If the user does not exist, a new record is created in Supabase with the telegram_id and a unique Start or Continue Conversation with OpenAI: Based on the user’s context, the bot either creates a new thread or continues an existing one using the stored Merge Data: User-specific data and conversation context are merged. Send and Receive Messages: The message is sent to OpenAI, and the response is received and processed. Reply to User: The bot sends OpenAI’s response back to the user in Telegram. Setup Create a Telegram Bot using the Botfather and obtain the bot token. Set up Supabase: Create a new project and generate a Create a new table named create table public.telegram_users ( id uuid not null default gen_random_uuid (), date_created timestamp with time zone not null default (now() at time zone 'utc'::text), telegram_id bigint null, openai_thread_id text null, constraint telegram_users_pkey primary key (id) ) tablespace pg_default; OpenAI Setup: Create an OpenAI assistant and obtain the Customize your assistant’s personality or use cases according to your requirements. Environment Configuration in n8n: Configure the Telegram, Supabase, and OpenAI nodes with the appropriate credentials. Set up triggers for receiving messages and handling conversation logic. Set up OpenAI assistant ID in "++OPENAI - Run assistant++" node.

OpenAI Assistant is a powerful tool, but at the time of writing it doesn't automatically remember past messages from a conversation. This workflow demonstrates how to get around this, by managing the chat history in n8n and passing it to the assistant when required. This makes it possible to use OpenAI Assistant for chatbot use cases. Note that to use this template, you need to be on n8n version 1.28.0 or later.

This workflow is designed for dynamic and intelligent conversational capabilities. It incorporates OpenAI's GPT-4o model for natural language understanding and generation. Additional tools include SerpAPI and Wikipedia for enriched, data-driven responses. The workflow is triggered manually, and utilizes a 'Window Buffer Memory' to maintain the context of the last 20 interactions for better conversational continuity. All these components are orchestrated through n8n nodes, ensuring seamless interconnectivity. To use this template, you need to be on n8n version 1.50.0 or later.

How it works: This workflow automates your customer service with built in notifications for your users & ticket creation with Asana. If a user submits a form, he gets send a confirmation message via WhatsApp a task is opened in Asana with his request in it. Setup: You need to add your credentials to the WhatsApp Business Cloud node. You need to add your credentials to the Asana node. Replace the placeholders with the correct phone number, id, and so on. Change the confirmation message to your liking. Optional Changes: You could extend this workflow to update your user on the progress of the ticket in Asana. You can change the messaging from WhatsApp to E-Mail. You can change the form submission service from n8n-native to Typeform or similar. You can change the task management software from Asana to the one you use. Click here to find a blog post with additional information.

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

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

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.

This n8n workflow simplifies the process of importing CSV data into a PostgreSQL database, making it ideal for users searching for an efficient "CSV to Postgres" solution. Key features include: Automatic Google Drive Integration: Detects new CSV files uploaded to a specific Google Drive folder, eliminating manual intervention. Dynamic Table Creation: Automatically analyzes the CSV structure, infers column names and data types (integer, text, float, etc.) from sample data, and generates a PostgreSQL table creation query. Data Insertion: Dynamically inserts all rows from the CSV into the newly created PostgreSQL table, ensuring seamless data migration. Focused Processing: Handles the first detected CSV file in the folder, perfect for scenarios requiring controlled batch processing. Ideal for businesses, developers, or data engineers needing a reliable, automated pipeline for transferring CSV data into PostgreSQL databases. Save time and avoid errors with this streamlined "CSV to Postgres" workflow.

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

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.

In Grist, when I mark a row as confirmed (via a toggle): a webhook is set up to notify n8n, and this workflow will create derived records in the destination table. Design decisions Confirmation-based In the source table there is a boolean column "Confirmed" that will trigger the transfer. This way there is a manual check involved & it's a conscious step to trigger the workflow. Runs once If the destination table already contains an entry, we will not re-create/update it (as it might've already been changed manually) Setup Create a boolean column Confirmed in source table Add a webhook in Grist Settings Add grist API credentials in n8n Set document ID & source table ID/Name in the 'get existing' node Set docID, the destination table ID/Name - and the columns & values you want in the Create Row node

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

Use Case When tracking your contacts and leads in Hubspot CRM, every new contact might be a potential customer. To guarantee that you're keeping the overview you'd normally need to look at every new lead that is coming in manually to identify high-quality leads to prioritize their engagement and optimize the sales process. This workflow saves the work and does it for you. What this workflow does The workflow runs every 5 minutes. On every run, it checks the Hubspot CRM for contacts that were added since the last check. It then checks if they meet certain criteria (in this case if they are making +5m annual revenue) and alerts you in Slack for every match. Setup Add Hubspot, and Slack credentials. Click on Test workflow. How to adjust this workflow to your needs Change the schedule interval Adjust the criteria to send alerts

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.

Description This workflow automatically sends a Telegram notification when an order status in WooCommerce changes to "Processing." Perfect for online store owners who want instant updates on order fulfillment. ⚙️ Set Up Telegram Alerts for WooCommerce Orders Configure WooCommerce Webhook to trigger on order updates. Create a Telegram Bot and obtain the API token. Set Up Telegram Credentials in n8n. Configure the Telegram Node with your chat ID. Activate and Test the workflow by placing a new order. ##💡 Notes You can customize the message format in the 🖋️ Design Message Template node to include additional order details. Contact me on [Telegram]: https://t.me/amir676080 Message structure includes the following details 🆔 Order Number: 11234 👦🏻 Customer Name: John Doe 💵 Amount: 299.99 USD 📅 Order Date: ➖ 25th November 2024 at 14:42 🏙 City: New York 📞 Phone: +1 555-1234 ✍🏻 Order Note: Fast delivery requested 📦 Ordered Products: 🔹 Wireless Earbuds (2 items) 📝 Type: Premium Sound Edition Contact me on [Telegram]: https://t.me/amir676080

Using the Systeme API can be challenging due to its pagination settings and low rate limit. This requires a bit more knowledge about API requests than a beginner might have. This template provides preconfigured HTTP Request nodes to help you work more efficiently. Pagination settings, item limits, and rate limits are all configured for you, making it easier to get started. How to configure Systeme.io credentials The Systeme API uses the Header Auth method. So create a Header Auth credential in your n8n with the name "X-API-Key". . Check out my other templates 👉 https://n8n.io/creators/solomon/

Image Creation with OpenAI and Telegram Check this channel: AutoTechAi_bot Description: In the realm of automation and artificial intelligence, n8n offers a sophisticated platform for seamlessly integrating AI algorithms to enhance image creation and communication processes. This innovative workflow leverages the capabilities of OpenAI and Telegram to facilitate creative image generation and streamline communication channels, ultimately enhancing user engagement and interaction. How to Use: Set Up Credentials: Configure credentials for the Telegram account and OpenAI API to enable seamless integration. Configure Nodes: Telegram Trigger Node: Set up the node to initiate the workflow based on incoming messages from users on Telegram. OpenAI Node: Utilize advanced AI algorithms to analyze text content from messages and generate intelligent responses. Telegram Node: Send processed data, including images and responses, back to users on Telegram for seamless communication. Merge Node: Organize and combine processed data for efficient handling and integration within the workflow. Aggregate Node: Aggregate all item data, including binaries if specified, for comprehensive reporting and analysis purposes. Run Workflow: Initiate the workflow to leverage AI-enhanced image processing and communication capabilities for enhanced user interactions. Monitor Execution: Keep an eye on the workflow execution for any errors or issues that may occur during processing. Customize Workflow: Tailor the workflow nodes, parameters, or AI models to align with specific business objectives and user engagement strategies. Experience Benefits: Embrace the power of AI-driven image processing and interactive communication on Telegram to elevate user engagement and satisfaction levels. By following these steps, businesses can unlock the transformative potential of AI integration in image creation and communication workflows using n8n. Elevate your user engagement strategies and deliver exceptional experiences to your audience through innovative AI-driven solutions. Embark on a journey of innovation and efficiency with AI integration in image creation and communication workflows using n8n!

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! 🎉

This workflow is a supporting automation to a common Airtable situation, that as of this writing, has no direct solution but has great demand. Interfaces are your secret weapon for managing a variety of tasks – from sales funnels and task tracking to creating dynamic dashboards. But here's a common situation: how do you efficiently bulk upload records (like contacts, leads, or clients) from an interface with just a click? Once set up, you'll be able to upload CSV files directly to your tables from the Interfaces with ease. Workflow Key Points: 1. Bulk Upload Functionality: Say goodbye to the limitations of standard Airtable interfaces. Now, you can upload multiple leads or contacts simultaneously, making your work swift and efficient. 2. Customizable Fields: Tailor the base to meet your specific data needs. This ensures seamless integration with your existing systems and simplifies data management. ​ Perfect for teams in e-commerce, CRM, or any sector where managing a high volume of leads or contacts is key. Our Airtable Base is designed to eliminate the tediousness of importing contacts. It makes large-scale data management straightforward, saving you precious time and hassle. ​ Get ready to streamline your operations and boost your productivity! 🚀💡

Who is this for? This workflow is designed for: Database administrators and developers working with MongoDB Content managers handling movie databases Organizations looking to implement AI-powered search and recommendation systems Developers interested in combining LangChain, OpenAI, and MongoDB capabilities What problem does this workflow solve? Traditional database queries can be complex and require specific MongoDB syntax knowledge. This workflow addresses: The complexity of writing MongoDB aggregation pipelines The need for natural language interaction with movie databases The challenge of maintaining user preferences and favorites The gap between AI language models and database operations What this workflow does This workflow creates an intelligent agent that: Accepts natural language queries about movies Translates user requests into MongoDB aggregation pipelines Queries a movie database containing detailed information including: Plot summaries Genre classifications Cast and director information Runtime and release dates Ratings and awards Provides contextual responses using OpenAI's language model Allows users to save favorite movies to the database Maintains conversation context using a window buffer memory Setup Required Credentials: OpenAI API credentials MongoDB connection details Node Configuration: Configure the MongoDB connection in the MongoDBAggregate node Set up the OpenAI Chat Model with your API key Ensure the webhook trigger is properly configured for receiving chat messages Database Requirements: A MongoDB collection named "movies" with the specified document structure Proper indexes for efficient querying Appropriate user permissions for read/write operations How to customize this workflow Modify the Document Structure: Update the tool description in the MongoDBAggregate node to match your collection schema Adjust the aggregation pipeline templates for your specific use case Enhance the AI Agent: Customize the prompt in the "AI Agent - Movie Recommendation" node Modify the window buffer memory size based on your context needs Add additional tools for more functionality Extend Functionality: Add more MongoDB operations beyond aggregation Implement additional workflows for different types of queries Create custom error handling and validation Add user authentication and rate limiting Integration Options: Connect to external APIs for additional movie data Add webhook endpoints for different platforms Implement caching mechanisms for frequent queries Add data transformation nodes for specific output formats This workflow serves as a foundation that can be adapted to various use cases beyond movie recommendations, such as e-commerce product search, content management systems, or any scenario requiring intelligent database interaction.

Who might benfit from this workflow? Do you have to record your working hours yourself? Then this n8n workflow in combination with an iOS shortcut will definitely help you. Once set up, you can use a shortcut, which can be stored as an app icon on your home screen, to record the start, end and duration of your break. How it works Once setup you can tap the iOS shortcut on your iPhone. You will see a menu containing three options: "Track Start", "Track Break" and "Track End". After time is tracked iOS will display you a notification about the successful operation. How to set it up Copy the notion database to your notion workspace (Top right corner). Copy the n8n workflow to your n8n workspace In the notion nodes in the n8n workflow, add your notion credentials and select the copied notion database. Download the iOS Shortcut from our documentation page Edit the shortcut and paste the url of your n8n Webhook trigger node to the first "Text" node of the iOS shortcut flow. It is a best practice to use authentication. You can do so by adding "Header" auth to the webhook node and to the shrotcut. You need help implementing this or any other n8n workflow? Feel free to contact me via LinkedIn or my business website. You want to start using n8n? Use this link to register for n8n (This is an affiliate link)

How it works: Webhook URL that responds to Requests with an AI generated Image based on the prompt provided in the URL. Setup Steps: Ideate your prompt URL Encode The Prompt (as shown in the Template) Authenticate with your OpenAI Credentials Put together the Webhook URL with your prompt and enter into a webbrowser In this way you can expose a public url to users, employee's etc. without exposing your OpenAI API Key to them. Click here to find a blog post with additional information.

Source code, I maintain this worflow here. Usage Guide This workflow backs up all workflows as JSON files named in the [workflow_name].json format. Steps Create GitHub Repository Skip this step if using an existing repository. Add GitHub Credentials In Credentials, add the GitHub credential for the repository owner. Download and Import Workflow Import this workflow into n8n. Set Global Values In the Globals node, set the following: repo.owner: GitHub username of the repository owner. repo.name: Name of the repository for backups. repo.path: Path to the folder within the repository where workflows will be saved. Configure GitHub Nodes Edit each GitHub node in the workflow to use the added credentials. Workflow Logic Each workflow run handles files based on their status: New Workflow If a workflow is new, create a new file in the repository. Unchanged Workflow If the workflow is unchanged, skip to the next item. Changed Workflow If a workflow has changes, update the corresponding file in the repository. Current Limitations / Needs work Name Change of Workflows If a workflow is renamed or deleted in n8n, the old file remains in the repository. Deleted Workflows Deleted workflows in n8n are not removed from the repository.

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.

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

How it works: 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).

Use Case Automatically archive emails in your Gmail inbox from the last day, unless they have been starred. Been using this with my personal and work emails to stick to an Inbox Zero strategy, without having to click or swipe a lot. Setup Add your Gmail creds How to adjust this template Set your own schedule for when to run this. Otherwise, should be good to go. 🤞🏽

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

This template provides a workflow to integrate a Telegram bot with NeurochainAI's inference capabilities, supporting both text processing and image generation. Follow these steps to get started: > Purpose: Enables seamless integration between your Telegram bot and NeurochainAI for advanced AI-driven text and image tasks. Requirements Telegram Bot Token. NeurochainAI API Key. Sufficient credits to utilize NeurochainAI services. Features Text processing through NeurochainAI's inference engine. AI-powered image generation (Flux). Easy customization and scalability for your use case. Setup Import the template into N8N. Add your Telegram Bot Token and NeurochainAI API Key where prompted. Follow the step-by-step instructions embedded in the template for configuration. [NeurochainAI Website](https://www.neurochain.ai/ ) NeurochainAI Guides

Overview The [n8n] YouTube Channel Advanced RSS Feeds Generator workflow facilitates the generation of various RSS feed formats for YouTube channels without requiring API access or administrative permissions. It utilizes third-party services to extract data, making it extremely user-friendly and accessible. Key Use Cases and Benefits Content Aggregation**: Easily gather and syndicate content from any public YouTube channel. No API Key Required**: Avoid the complexities and limitations of Google's API. Multiple Formats**: Supports ATOM, JSON, MRSS, Plaintext, Sfeed, and direct YouTube XML feeds. Flexibility**: Input can be a YouTube channel or video URL, ID, or username. Services/APIs Utilized This workflow integrates with: commentpicker.com**: For retrieving YouTube channel IDs. rss-bridge.org**: To generate various RSS formats. Configuration Instructions Start the Workflow: Activate the workflow in your n8n instance. Input Details: Enter the YouTube channel or video URL, ID, or username via the provided form trigger. Run the Workflow: Execute the workflow to receive links to 13 different RSS feeds, including community and video content feeds. Screenshots Additional Notes Customization**: You can modify the RSS feed formats or integrate additional services as needed. Support and Contributions For support, questions, or contributions, please visit the n8n community forum or the GitHub repository. We welcome contributions from the community!

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.

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

Enhance Security Operations with the Venafi Slack CertBot! Venafi Presentation - Watch Video Our Venafi Slack CertBot is strategically designed to facilitate immediate security operations directly from Slack. This tool allows end users to request Certificate Signing Requests that are automatically approved or passed to the Secops team for manual approval depending on the Virustotal analysis of the requested domain. Not only does this help centralize requests, but it helps an organization maintain the security certifications by allowing automated processes to log and analyze requests in real time. Workflow Highlights: Interactive Modals**: Utilizes Slack modals to gather user inputs for scan configurations and report generation, providing a user-friendly interface for complex operations. Dynamic Workflow Execution**: Integrates seamlessly with Venafi to execute CSR generation and if any issues are found, AI can generate a custom report that is then passed to a slack teams channel for manual approval with the press of a single button. Operational Flow: Parse Webhook Data**: Captures and parses incoming data from Slack to understand user commands accurately. Execute Actions**: Depending on the user's selection, the workflow triggers other actions within the flow like automatic Virustotal Scanning. Respond to Slack**: Ensures that every interaction is acknowledged, maintaining a smooth user experience by managing modal popups and sending appropriate responses. Setup Instructions: Verify that Slack and Qualys API integrations are correctly configured for seamless interaction. Customize the modal interfaces to align with your organization's operational protocols and security policies. Test the workflow to ensure that it responds accurately to Slack commands and that the integration with Qualys is functioning as expected. Need Assistance? Explore Venafi's Documentation or get help from the n8n Community for more detailed guidance on setup and customization. Deploy this bot within your Slack environment to significantly enhance the efficiency and responsiveness of your security operations, enabling proactive management of CSR's.

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.

Purpose This workflow allows you to transfer credentials from one n8n instance to another. How it works A multi-form setup guides you through the entire process You get to choose one of your predefined (in the Settings node) remote instances first Then all credentials of the current instance are being retrieved using the Execute Command node On the next form page you can select one of the credentials by their name and initiate the transfer Finally the credential is being created on the remote instance using the n8n API. A final form ending indicates if that action succeeded or not. Setup Select your credentials in the nodes which require those Configure your remote instance(s) in the Settings node Every instance is defined as object with the keys name, apiKey and baseUrl. Those instances are then wrapped inside an array. You can find an example described within a note on the workflow canvas. How to use Grab the (production) URL of the Form from the first node Open the URL and follow the instructions given in the multi-form Disclaimer Please note, that this workflow can only run on self-hosted n8n instances, since it requires the Execute Command Node. Security: Beware, that all credentials are being decrypted and processed within the workflow. Also the API keys to other n8n instances are stored within the workflow. This solution is primarily meant for transferring data between testing environments. For production use consider the n8n enterprise edition which provides a reliable way to manage credentials across different environments.

This template can be used as a real-time listening and processing of search results with Icypeas. Be sure to have an active account to use this template. How it works This workflow can be divided into two steps : A webhook node to link your Icypeas account with your n8n workflow. A set node to retrieve the relevant informations. Set up steps You will need a working icypeas account to run the workflow and you will have to paste the production URL provided by the n8n webhook node.

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.

Pull data using the Facebook Graph API and get your Meta Ads insights into your Google Sheets for analysis. Get metrics like impressions, spend, reach, conversions, CTR, and CPC. Automate your data collection. Who is this template for? Marketing professionals looking to analyze Meta Ads data. You can also replace the Google Sheets node for a database node like MySQL or Postgres, and then use your BI tool (Power BI, Looker Studio, Tableau) to analyze the data. How it works Every day at 3am, the flow gets Meta Ads insights from the previous day and saves them to Google Sheets. How to set up All information you need to set up this workflow is already inside the template. Check out my other templates 👉 https://n8n.io/creators/solomon/

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.

Use Case Following up at the right time is one of the most important parts of sales. This workflow uses Gmail to send outreach emails to Hubspot contacts that have already been contacted only once more than a month ago, and records the engagement in Hubspot. Setup Setup HubSpot Oauth2 creds (Be careful with scopes. They have to be exact, not less or more. Yes, it’s not simple, but it’s well documented in the n8n docs. Be smarter than me, read the docs) Setup Gmail creds. Change the email variables in the Set keys node How to adjust this template There's plenty to do here because the approach here is really just a starting point. Most important here is to figure out what your rules are to follow up. After a month? More than once? Also, remember to update the follow-up email! Unless you want to sell n8n 😉

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

Boost engagement on your Discord server by automatically sharing new YouTube videos along with AI generated summaries of their content. This workflow is ideal for content creators and community managers looking to provide value and spark interest through summarized content, making it easier for community members to decide if a video is of interest to them. Watch this video tutorial to learn more about the template. How it works RSS Feed Trigger**: Monitors your YouTube channel for new uploads using the RSS feed. Video Captions Retrieval**: Fetches video captions using the YouTube API to get detailed content data. AI Summary Generation**: Uses an AI model to generate concise summaries from the video captions, highlighting key points. Discord Notification**: Posts video announcements along with their AI generated summaries to a specified Discord channel using a webhook. Set up steps Configure YouTube RSS Feed: Set up the RSS feed node to detect new video uploads. Add your YouTube channel ID to the URL in the first node: https://www.youtube.com/feeds/videos.xml?channel_id=YOUR_CHANNEL_ID. Connect OpenAI Account: To enable AI summary generation, connect your OpenAI account in n8n. Set Up Discord Webhook: Create a webhook in your Discord server and configure it in the Discord node. Design the Message: Format the Discord message as you like to include the video title, link, and the AI generated summary. Example This template empowers you to maintain a highly engaging Discord community, ensuring members receive not only regular updates but also valuable insights into each video's content without needing to watch immediately.

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 node is designed to cleanse URLs and extract their domain names efficiently. It effectively handles a wide range of URL formats, including those with unconventional or complex top-level domains (TLDs), such as 'co.uk'. You can also use it to extract the domain from an email. The node will also check if the domain is from a free email provider (gmail.com / outlook.com...etc) or not. How It Works The node analyzes the provided URL, removing any unnecessary elements. It then identifies and extracts the domain name, ensuring compatibility with a diverse array of TLDs. The node utilizes an extensive list of TLDs to guarantee accurate domain extraction for virtually any URL. To view the complete list of supported top-level domains, please visit: TLD List on GitHub How to use it Call this workflow using the "execute workflow" node You can pass either an email variable or a url variable. For email, the node also detect free mail provider such as Yahoo / Google...etc

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 workflow is aimed at generating keywords for SEO and articles To get started, you need to use the workflow as it is. You just call the webhook URL with a query parameter as q={{ $keywords}} For example, you can call it using ?q=keyword research This will give you a list of keywords back as an array. This system can be used by SEO pros, content marketers and also social media marketers to generate relevant keywords for their user needs

Overview This workflow aims at providing data visualization to a native SQL Agent. Together, they can help with fostering data analysis and data visualization within a team. It uses the native SQL Agent that works well and adds some visualization capabilities thanks to OpenAI Structured Output and Quickchart.io. How it works The first part of the workflow is a regular SQL Agent: it connects to a Database and is able to query it and translate the response in a human format. Then, the Text Classifier is deciding if the user would benefit from a chart, supporting the SQL Agent's response. If it does, then it executes the subworkflow to dynamically generate a chart and append the chart to the response from the SQL Agent. If it doesn't, then the SQL Agent response is directly outputted. The sub-workflow calls OpenAI through the HTTP Request node to retrieve a chart definition. In the "set response" node, the chart definition is added at the end of a quickchart.io URL - the URL to the chart image. It is sent back to the AI Agent. How to use it Use an existing or create a new database. For example, I've used this Kaggle dataset and uploaded it to a Supabase DB. Add the PostgreSQL or MySQL credentials. Alternatively, you can use SQLite binary files (check this template). Activate the workflow. Start chatting with the AI SQL Agent. If the Text Classifier considers a chart would be useful, it will generate a chart in addition to the response from the SQL Agent. Notes The full Quickchart.io specifications have not been integrated, thus there are some possible glitches (e.g., due to the size of the graph, radar graphs are not displayed properly).

Automated YouTube Video Promotion Workflow Automate the promotion of new YouTube videos on X (formerly Twitter) with minimal effort. This workflow is perfect for content creators, marketers, and social media managers who want to keep their audience updated with fresh content consistently. How it works This workflow triggers every 30 minutes to check for new YouTube videos from a specified channel. If a new video is found, it utilizes OpenAI's ChatGPT to craft an engaging, promotional message for X. Finally, the workflow posts the generated message to Twitter, ensuring your latest content is shared with your audience promptly. Set up steps Schedule the workflow to run at your desired frequency. Connect to your YouTube account and set up the node to fetch new videos based on your Channel ID. Integrate with OpenAI to generate promotional messages using GPT-3.5 turbo. Link to your X account and set up the node to post the generated content. Please note, you'll need API keys and credentials for YouTube, OpenAI, and X. Check out this quick video tutorial to make the setup process a breeze. Additional Tips Customize the workflow to match your branding and messaging tone. Test each step to ensure your workflow runs smoothly before going live.

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

This workflow sends a OpenAI GPT reply when an email is received from specific email recipients. It then saves the initial email and the GPT response to an automatically generated Google spreadsheet. Subsequent GPT responses will be added to the same spreadsheet. Additionally, when feedback is given for any of the GPT responses, it will be recorded to the spreasheet, which can then be used later to fine-tune the GPT model. Prerequisites OpenAI credentials Google credentials How it works This workflow is essentially a two-in-one workflow. It triggers off from two different nodes and have very different functionality from each trigger. The flow triggered from On email received node is as follows: Triggers off on the On email received node. Extract the email body from the email. Generate a response from the email body using the OpenAI node. Reply to the email sender using the Send reply to recipient node. A feedback link is also included in the email body which will trigger the On feedback given node. This is used to fine-tune the GPT model. Save the email body and OpenAI response to a Google Sheet. If a sheet does not exist, it will be created. The flow triggered from On feedback given node is as follows: Triggers off when a feedback link is clicked in the emailed GPT response. The feedback, either positive or negative, for that specific GPT response is then recorded to the Google Sheet.

This workflow uses OpenAI Assistant to compose draft replies for labeled email messages. It automatically connects the drafts to Gmail threads. 💡 You can add knowledge base to your OpenAI Assistant and make your reply drafts very customized (e.g. compose response with product information in response to inquiry from customer). 🎬 See this workflow in action in my YouTube video about automating Gmail. How it works? The workflow is triggered at regular intervals (default: every 1 minute – you can change this value) to check for messages with a specific label (e.g., "AI"). The content of the retrieved email message is then forwarded to the OpenAI Assistant node, and a reply draft is generated. Next, the response from the Assistant is converted to HTML, and a raw message in RFC standard is composed. 💡 You can learn more about composing drafts with the Gmail API in the official Google documentation. The raw email message (reply draft) is encoded and attached to the original thread ID. Finally, the trigger label (in this case: "AI") is removed to prevent the workflow from looping. Set up steps Set credentials for Gmail and OpenAI. Add new label in Gmail account for messages that should be handled by the workflow (e.g. name it "AI"). Select this label in the first and last Gmail nodes in workflow. Create and configure your OpenAI Assistant. Select your assistant in "OpenAI Assistant" node. Optionally: change trigger interval (by default interval is 1 minute). If you like this workflow, please subscribe to my YouTube channel and/or my newsletter.

Who is this for? This workflow is designed for businesses or developers looking to integrate voice-based chat applications with dynamic responses and conversational memory. What problem does this solve? It automates AI-powered voice conversations, maintaining context between sessions and converting speech-to-text and text-to-speech. What this workflow does: The workflow receives audio input, transcribes it using OpenAI, and processes the conversation using Google Gemini Chat Model (you can use OpenAI Chat Model). Responses are converted back to speech using ElevenLabs. Prerequisites: You'll need API keys for: OpenAI (you can obtain it from OpenAI website) ElevenLabs (you can obtain it from their website) Google Gemini (You can obtain it from Google AI Studio) Setup: Configure you API keys Ensure that the value (voice_message) in the "Path" parameter in the Webhook node is used as the name of the parameter that will contain the voice message you are sending via the HTTP Post request.

This n8n workflow demonstrates how to automate customer interactions and appointment management via WhatsApp Business bot. After submitting a Google Form, the user receives a notification via WhatsApp. These notifications are sent via a template message. In case user sends a message to the bot, the text and user data is stored in Google Sheets. To reply back to the user, fill in the ReplyText column and change the Status to 'Ready'. In a few seconds n8n will fetch the unsent replies and deliver them one by one via WhatsApp Business node. Customize this workflow to fit your specific needs, connect different online services and enhance your customer communication! 🎉 Setup Instructions To get this workflow up and running, you'll need to: 👇 Create a WhatsApp template message on the Meta Business portal. Obtain an Access Token and WhatsApp Business Account ID from the Meta Developers Portal. This is needed for the WhatsApp Business Node to send messages. Set up a WhatsApp Trigger node with App ID and App Secret from the Meta Developers Portal. Right after that copy the WhatsApp Trigger URL and add it as a Callback URL in the Meta Developers Portal. This trigger is needed to receive incoming messages and their status updates. Connect your Google Sheets account for data storage and management. Check out the documentation page. ⚠️ Important Notes WhatsApp allows automatic custom text messages only within 24 hours of the last user message. Outside with time frame only approved template messages can be sent. The workflow uses a Google Sheet to manage form submissions, incoming messages and prepare responses. You can replace these nodes and connect the WhatsApp bot with other systems.

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

Who is this template for? This workflow template is designed for Sales and Customer Success professionals seeking alerts when potential high-value users, prospects, or existing customers register for a Discourse community. Leveraging Clearbit, it retrieves enriched data for the new member to assess their value. Example result in Slack How it works Each time a new member is created in Discourse, the workflow runs (powered by Discourse's native Webhooks feature). After filtering out popular private email accounts, we run the member's email through Clearbit to fetch available information on the member as well as their organization. If the enriched data meets certain criteria, we send a Slack message to a channel. This message has a few quick actions: Open LinkedIn profile and Email member Set up instructions Overview is below. Watch this 🎥 quick set up video for detailed instructions on how to get the template running, as well as how to customize it. Complete the Set up credentials step when you first open the workflow. You'll need a Discourse (admin user), Clearbit, and Slack account. Set up the Webhook in Discourse, linking the On new Discourse user Trigger with your Discourse community. Set the correct channel to send to in the Post message in channel step After testing your workflow, swap the Test URL to Production URL in Discourse and activate your workflow Template was created in n8n v1.29.1

Easily generate images with Black Forest's Flux Text-to-Image AI models using Hugging Face’s Inference API. This template serves a webform where you can enter prompts and select predefined visual styles that are customizable with no-code. The workflow integrates seamlessly with Hugging Face's free tier, and it’s easy to modify for any Text-to-Image model that supports API access. Try it Curious what this template does? Try a public version here: https://devrel.app.n8n.cloud/form/flux Set Up Watch this quick set up video 👇 Accounts required Huggingface.co account (free) Cloudflare.com account (free - used for storage; but can be swapped easily e.g. GDrive) Key Features: Text-to-Image Creation**: Generates unique visuals based on your prompt and style. Hugging Face Integration**: Utilizes Hugging Face’s Inference API for reliable image generation. Customizable Visual Styles**: Select from preset styles or easily add your own. Adaptable**: Swap in any Hugging Face Text-to-Image model that supports API calls. Ideal for: Creators**: Rapidly create visuals for projects. Marketers**: Prototype campaign visuals. Developers**: Test different AI image models effortlessly. How It Works: You submit an image prompt via the webform and select a visual style, which appends style instructions to your prompt. The Hugging Face Inference API then generates and returns the image, which gets hosted on Cloudflare S3. The workflow can be easily adjusted to use other models and styles for complete flexibility.

LOOM Walkthrough: https://www.loom.com/share/4fbe06872cb8483993e7792018594f08 How it works Create a form for customer feedback, have OpenAI classify the sentiment of the feedback (positive/neutral/negative) and store it in Google sheets! Set up steps Connect Google sheets Connect your OpenAi account (api key + org Id) Create a customer feedback form, use an existing one or use the one below as example. All set! Here is the example google sheet being used in this workflow: https://docs.google.com/spreadsheets/d/1omWdRbiT6z6GNZ6JClu9gEsRhPQ6J0EJ2yXyFH9Zng4/edit?usp=sharing. You can download it to your account.

Case Study I'm too lazy to record every transaction for my expense tracking. Since all my expenses are digital, I just extract the transactions from bank PDF statements and screenshots into CSV to import into my budgeting software. Read more -> How I used A.I. to track all my expenses What this workflow does Upload your PDF or screenshots into Google Drive It then passes the PDF/image to Vertex Gemini to do some A.I. image recognition It then sends the transactions as CSV and stores it into another Google Drive folder Setup Set up 2 google drive folders. 1 for uploading and 1 for the output. Input your Google Drive crendtials Input your Vertex Gemini credentials How to adjust it to your needs You can upload other types of documents for information extraction. You can extract any text data from any image or PDF You can adjust the A.I. prompt to do different things

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.

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.

Flow Start: The flow starts upon receiving an HTTP GET call. Webhook: Receives the HTTP GET call and triggers the flow. Database: Connects to the database (Customer Datastore) to retrieve all necessary information (getAllPeople). Data Processing: Variable Insertion: The retrieved data is inserted into a variable. Variable Aggregation: The variables are aggregated and prepared for use in FlutterFlow. Webhook Response: Sends the response back through the Webhook with the processed data ready for use in FlutterFlow.

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.

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

Linear Project Status and End Date to Productboard feature Sync Sync project data between Linear and Productboard to keep teams aligned. This workflow updates Productboard features with the status and end date from Linear projects. It ensures consistent data and sends a Slack notification whenever changes are made. Features Listens for updates in Linear projects. Maps Linear statuses to Productboard feature statuses. Updates Productboard feature details including timeframe. Sends a Slack notification summarizing the updates. Setup Linear Credentials: Add your Linear API credentials in n8n. Productboard Credentials: Configure the Productboard API credentials in n8n. Linear Projects: Select the Linear project(s) you want to monitor for updates. Productboard Custom Field: Create a custom field in Productboard named "Linear Project". This field should store the URL of the Linear project you want to sync. Retrieve the UUID of the custom field in Productboard and set it up in the "Get Productboard Feature ID" node. Slack Notification: Update the Slack node with the desired Slack channel ID. Activate the Workflow: Enable the workflow to automatically sync data when triggered by updates in Linear.

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.

Task: Merge two datasets into one based on matching rules Why: A powerful capability of n8n is to easily branch out the workflow in order to process different datasets. Even more powerful is the ability to join them back together with SQL-like joining logic. Main use cases: Appending data sets Keep only new items Keep only existing items

Use case This workflow uses Gmail to send outreach emails to new Hubspot contacts that have yet to be contacted (usually unknown contacts), and records the outreach in Hubspot. Setup Setup HubSpot Oauth2 creds (Be careful with scopes. They have to be exact, not less or more. Yes, it's not simple, but it's well documented in the n8n docs. Be smarter than me, read the docs) Setup Gmail creds. Change the from email and from name in the Record outreach in HubSpot node How to adjust this template to your needs Change the email message in the Set keys node Think about your criteria to reach out to new contacts. Here we simply filter for only contacts with unknown dates.

This workflow will allow you to enrich in real-time a form submission from Webflow using Datagma. Based on the result of this workflow, a specific Calendly link will be shown on the website. If the process outcome is '1', a link for a one-on-one demo will be provided. If the process outcome is '2', a link for a group demo will be shown. Full guide here: Real-time Lead Routing

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

Description This template creates a nightly backup of all n8n workflows and saves them to a Google Drive folder. Each night, the previous night's backups are moved to an “n8n_old” folder and renamed with the corresponding date. Backups older than a specified age are automatically deleted (this feature is active for 30 days, you can remove it if you don't want the backups to be deleted). Prerequisites Google Drive account and credentials Get from the following link. Link n8n version from v 1.63.4 to 1.70.1 or higher N8n api key Guide from the following link. Link A destination folder for backups: “n8n_old” “n8n_backups” (if it doesn't exist, create it) Configuration Update all Google Drive nodes with your credentials. Edit the Schedule Trigger node with the desired time to run the backup. If you want to automatically purge old backups. Edit the “PURGE DAYS” node to specify the age of the backups you want to delete. Enable the “PURGE DAYS” node and the 3 subsequent nodes. Enable the workflow to run on the specified schedule. Last updated December 2024

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 n8n template demonstrates the beginnings of building your own n8n-powered WhatsApp chatbot! Under the hood, utilise n8n's powerful AI features to handle different message types and use an AI agent to respond to the user. A powerful tool for any use-case! How it works Incoming WhatsApp Trigger provides a way to get messages into the workflow. The message received is extracted and sent through 1 of 4 branches for processing. Each processing branch uses AI to analyse, summarize or transcribe the message so that the AI agent can understand it. The supported types are text, image, audio (voice notes) and video. The AI Agent is used to generate a response generally and uses a wikipedia tool for more complex queries. Finally, the response message is sent back to the WhatsApp user using the WhatsApp node. How to use Once you have setup and configured your WhatsApp account, you'll need to activate your workflow to start processing messages. Good to know: Large media files may negatively impact workflow performance. Requirements WhatsApp Buisness account Google Gemini for LLM. Gemini is used specifically because it can accept audio and video files whereas at time of writing, many other providers like OpenAI's GPT, do not. Customising this workflow For performance reasons, consider detecting large audio and video before sending to the LLM. Pre-processing such files may allow your agent to perform better. Go beyond and create rich and engagement customer experiences by responding using images, audio and video instead of just text!

What this template does This workflow uses the Amadeus API, every day to check for bargain flights for an itinerary and price target of your choice. It then automatically emails you once it found a match. Setup Create an api account on https://developers.amadeus.com/ In Amadeus Flight Search, connect to Oauth2 API: -- Grant Type - Client Credentials -- Access Token URL - https://test.api.amadeus.com/v1/security/oauth2/token -- Client ID/Secret - from your account Set your details in Gmail Set your desired Origin/Destination airports in FromTo Set the dates ahead you wish to search in Get Dates (default is 7 days and 14 days) Set the price target in Under Price How to test it After completing the setup steps above, just hit 'Test workflow'!

Hello there! This is a supporting workflow for an Airtable Base that handles Recurring Tasks. The objective of the workflow is to handle creating tasks on a recurring basis depending on the Airtable Setup You can access that Airtable Template here for complete context- Airtable Universe The functionality of the workflow can be easliy adapted to any data source. Feel free to contact us with any doubts or questions at http://sidetool.co ​ Use this as is, or adapted to your existing Airtable Base – embrace automated simplicity! 🚀🌟

This template will create a nightly backup of all your n8n workflows to a Dropbox folder. Each night, the previous night's backups are moved into an "old" folder, and renamed with the date they were taken. Backups over a specified age are deleted. (this is disabled by default for safety until you manually enable and verify it with your own setup) Prerequisites Dropbox account and credentials A destination folder for backups Setup Update all dropbox nodes with your credential Edit the Schedule Trigger node with the desired time to run the backup Edit the DESTINATION FOLDER node to specify the path in dropbox to upload to. This should be a folder and include the trailing / If you want to automatically purge old backups Edit the PURGE DAYS node to specify the age to purge Enable the PURGE DAYS node, and the 3 subsequent nodes Enable the workflow to run on the specified schedule

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.

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.

This updated workflow will automatically archive your Spotify Discover Weekly tracks to another manually created playlist, without the nuisance of duplicate tracks. It utilizes the latest verisons of n8n's Schedule trigger, Spotify, Switch, Merge, and IF nodes. Special thanks to trey for their original version of the workflow, as well as ihortom for their help with navigating the Switch node's outputs. To use this workflow, you'll need to: Create a playlist for use as the archive playlist within your Spotify account Create and select your Spotify credentials within each Spotify node within the workflow See workflow README for additional information and optional setup steps.

Use case Error workflows are an important part of running workflows in production. Make sure to set them up for all your important workflows. The message links directly to the execution. How to setup Add Telegram creds Set chat id in Telegram node Add this error workflow to other workflows https://docs.n8n.io/flow-logic/error-handling/#create-and-set-an-error-workflow

Free template for voice & text messages with short-term memory This n8n workflow template is a blueprint for an AI Telegram bot that processes both voice and text messages. Ready to use with minimal setup. The bot remembers the last several messages (10 by default), understands commands and provides responses in HTML. You can easily swap GPT-4 and Whisper for other language and speech-to-text models to suit your needs. Core Features Text: send or forward messages Voice: transcription via Whisper Extend this template by adding LangChain tools. Requirements Telegram Bot API OpenAI API (for GPT-4 and Whisper) 💡 New to Telegram bots? Check our step-by-step guide on creating your first bot and setting up OpenAI access. Use Cases Personal AI assistant Customer support automation Knowledge base interface Integration hub for services that you use: Connect to any API via HTTP Request Tool Trigger other n8n workflows with Workflow Tool

How it works Automates the retrieval of Calvin and Hobbes daily comics. Extracts the comic image URL from the website. Translates comic dialogues to English and Korean. Posts the comic and translations to Discord daily. Set up steps Estimated setup time: ~10-15 minutes. Use a Schedule Trigger to automate the workflow at 9 AM daily. Add nodes for parameter setup, HTTP request, data extraction, and integration with Discord. Add detailed notes to each node in the workflow for easy understanding.

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.

Use case If you have a form where potential leads reach out, then you probably want to analyze those leads and send a notification if certain requirements are met, e.g. employee number is high enough. MadKudu is built exactly to solve this problem. We use it along with Hunter to alert on Slack for high quality leads. How to setup Add you MadKudu, Hunter, and Slack credentials Set the Slack channel Click the Test Workflow button, enter your email and check the Slack channel Activate the workflow and use the form trigger production URL to collect your leads in a smart way How to adjust this template You may want to raise or lower the threshold for your leads, as you see fit.

Sometimes you need the rich text field to be in HTML instead of Markdown. This template either syncs a single record or all records at once. Youtube tutorial

Use Case: This n8n workflow automates the process of extracting information from emails. It uses OpenAI to summarize sales emails and adds this information in Odoo. How it works: When an email is received with a certain label, it sends the email to OpenAI for summarization A sales opportunity is created in Odoo with the email subject as title and the email summary as internal note Set up steps: Configure Google Cloud credentials with Gmail access In the Gmail node, choose an email label in the filter section Configure OpenAI credentials Configure Odoo credentials

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

This workflows helps with processing binary data. You'll often have binary objects with keys such as attachment_0, attachment_1, attachment_2, etc. attached to your items, for example when reading an incoming email. This binary data is hard to process because it's not an array you can simply loop through. This workflow solves this problem by providing a Function node that takes all incoming items and all their binary data and then returning a single item for each file with a data key containing your binary file. Incoming binary data: Processed binary data:

Use Case Transform and optimize images for web use: You need to host local images online You want to reduce image file sizes automatically You need image URLs for web content You want to generate and optimize AI-created images What this Workflow Does The workflow processes images through two services: Uploads images to ImgBB for hosting and URL generation (free but need API key) Optimizes images using ReSmush.it to reduce file size (free) Optional: Creates images using OpenAI's image generation Returns optimized image URLs ready for use Setup Create an ImgBB account and get your API key Add your ImgBB API key to the HTTP Request node (key parameter) Optional: Configure OpenAI credentials for image generation Connect your image input source How to Adjust it to Your Needs Skip OpenAI nodes if using your own image files Adjust image optimization parameters Customize image hosting settings Modify output format for your needs Made by Simon @ automake.io

Use case When collecting leads via an online form, you often need to manually add those new leads into your Pipedrive CRM. This not only takes a lot of time but is also error-prone. This workflow automates this tedious work for you. What this workflow does The workflow is triggered each time a form is submitted in n8n. It validates the email address using Hunter.io. If the email is valid, the workflow checks for an existing person with that email in Pipedrive. If no existing person is found, it utilizes Clearbit to enrich the person's information. It then verifies if the person's organization already exists in Pipedrive, creating a new organization if necessary. The workflow then registers the person in Pipedrive. Lastly, it creates a lead in Pipedrive using information from the person and organization. Setup This workflow is very quick to set up. Add your Hunter.io, Clearbit and Pipedrive credentials Click the test workflow button Activate the workflow and use the form trigger production URL to collect your leads in a smart way How to adjust it to your needs Exchange the n8n form trigger with your form of choice (Typeform, Google Forms, SurveyMonkey...) Add a filter criteria to only add new leads if they match certain requirements Remove the email check with Hunter.io if you don't own this tool and expect new form submission to have a correct email anyways Add ways to handle invalid emails or existing Persons

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.

Product Introduction: You can create a form on n8n through which you can collect leads from interested user's. You can also verify each user's email address to validate whether it's a valid email to make sure it lands on their inbox(suitable for those who have newletter / want's to send bulk email) The leads with valid email will then get saved as a leads with the relevant field data to gmail, google sheets and discord(through webhook). You can add/remove any integration you feel like. Setup Guide: Create Your Form: Begin by logging into your n8n account and navigating to the workflow editor. You can then customize your form fields to gather relevant information from your prospects, such as name, email address, and any additional data you require. Email Verification: After collecting the lead data, add an Email Verification node to validate each user's email address. This crucial step ensures that only genuine and deliverable email addresses are included in your lead database, maximizing the effectiveness of your outreach efforts. We have used hunter.io service in this, you can change it to any other service you need. Save Leads: Next, incorporate nodes to save your verified leads to various platforms for streamlined management. Utilize the Gmail node to store lead information directly in your Gmail account, ensuring easy access and organization. Additionally, leverage the Google Sheets node to archive lead data in a spreadsheet for comprehensive tracking and analysis. Integrate with Discord: For real-time notifications and collaboration, integrate n8n with Discord using the Webhook node. Configure the webhook to send lead information to your designated Discord channel, enabling your team to stay informed and coordinate effectively. Customize Integrations: Tailor your workflow by adding or removing integrations based on your specific requirements. Whether you prefer to connect with additional platforms or streamline your workflow further, n8n offers unparalleled flexibility to adapt to your business needs. Experience the simplicity and efficiency of n8n to revolutionize your lead generation strategy and propel your success to new heights.

This n8n workflow was developed to evaluate and categorize incoming leads based on certain criteria. The workflow is triggered by adding a new row in a Google Sheets document. The workflow uses the OpenAI node to process the lead information. The system query contains detailed qualification rules and the response format. The user message contains the data for the individual lead. The JSON response from the OpenAI node is then processed by the Edit Fields node to extract the response. This response is merged together with the original lead data by the Merge node. Finally, the Google Sheets node updates the original lead entry in the Google Sheets document with the qualification result ("qualified" or "not qualified") in a separate column. This allows for easy tracking and sorting of the qualified leads.

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.

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.

Overview This workflow automates the process of notifying users about new emails via Telegram and temporarily hosting the email content on a secret HTML page. It is ideal for users who need immediate notifications and a secure, temporary web view of their email content. Use Cases Immediate notification of new emails in Telegram with the ability to preview the email content in a secure, temporary HTML format. Automation for users who need to keep track of their emails without constantly checking their email client. Useful for teams or individuals who require instant updates on critical emails and prefer a quick preview through a web interface. My Personal Use Case: Secure Subscription Sharing From time to time, I find myself wanting to share my paid subscriptions with friends, but giving out OTP codes manually or sharing my email isn't a good idea due to security concerns. I attempted to use the IMAP node to integrate with Telegram secret channel for this purpose but encountered numerous problems, such as difficulties in scraping content from emails. Additionally, the Telegram API sometimes rejects certain special characters found within email contents. After facing these challenges, I discovered that rendering emails as HTML pages and sharing them directly is the most effective solution. This approach bypasses the issues with character limitations and content scraping, providing a seamless way to share subscription benefits securely. Services/APIs Used | Service/API | Node Type | Description | |----------------------|-------------------------|------------------------------------------------------| | IMAP Email Server | Email Trigger (IMAP) | Triggers the workflow on receiving a new email. | | Telegram API | Telegram | Sends notifications and manages messages in Telegram.| | GitHub Gist API | HTTP Request (Github Gist) | Temporarily hosts email content on GitHub Gist. | | GitHub Gist API (Deletion) | HTTP Request (Github Gist ‌) | Deletes the temporary GitHub Gist after a specified time. | | Wait | Wait | Delays the workflow for a specified period. | Configuration Steps Email Trigger (IMAP): Configure your IMAP email credentials to enable the workflow to check for new emails. Telegram Nodes: Insert your Telegram bot's API credentials and your chat ID in both Telegram nodes to send and delete messages. Github Gist Nodes: Provide your GitHub API credentials to create and delete Gists. Ensure the GitHub token has the necessary permissions to create and delete Gists. Wait Node: Adjust the wait time according to your preference for how long the email content should be accessible via the HTML page. Screenshot Additional Notes Ensure all credentials are securely stored and have the minimum necessary permissions for the workflow to function. Test the workflow with non-sensitive emails to ensure it operates as expected before using it with critical email accounts. Consider the privacy and security implications of temporarily hosting email content on GitHub Gist. For any questions or issues, refer to the respective API documentation for each service used or consult the n8n community for support.

Workflow Description: This workflow automates the synchronization of product data from a Shopify store to a Google Sheets document, ensuring seamless management and tracking. It retrieves product details such as title, tags, description, and price from Shopify via GraphQL queries. The outcome is a comprehensive list of products neatly organized in Google Sheets for easy access and analysis. Key Features: Automated: Runs on a schedule you define (e.g., daily, hourly) to keep your product data fresh. Complete Product Details: Retrieves titles, descriptions, variants, images, inventory, and more. Cursor-Based Pagination: Efficiently handles large product sets by navigating pages without starting from scratch. Google Sheets Integration: Writes product data directly to your designated sheets. Set up Instructions: Set up GraphQL node with Header Authentication for Shopify: Create Google Sheet Credentials: Follow this guide to set up your Google Sheet credentials for n8n: https://docs.n8n.io/integrations/builtin/credentials/google/ Choose your Google Sheet: Select the sheet where you want product information written. For the setup, we need a document with two sheets: 1. for storing Shopify data 2. for storing cursor details. Google sheet template : https://docs.google.com/spreadsheets/d/1I6JnP8ugqmMD5ktJlNB84J1MlSkoCHhAEuCofSa3OSM Schedule and run: Decide how often you want the data refreshed (daily, hourly, etc.) and let n8n do its magic!

Template Description This n8n workflow template allows you to create a masked email address using the Fastmail API, triggered by a webhook. This is especially useful for generating disposable email addresses for privacy-conscious users or for testing purposes. Workflow Details: Webhook Trigger: The workflow is initiated by sending a POST request to a specific webhook. You can include state and description in your request body to customize the masked email's state and description. Session Retrieval: The workflow makes an HTTP request to the Fastmail API to retrieve session information. It uses this data to authenticate further requests. Create Masked Email: Using the retrieved session data, the workflow sends a POST request to Fastmail's JMAP API to create a masked email. It uses the provided state and description from the webhook payload. Prepare Output: Once the masked email is successfully created, the workflow extracts the email address and attaches the description for further processing. Respond to Webhook: Finally, the workflow responds to the original POST request with the newly created masked email and its description. Requirements: Fastmail API Access**: You will need valid API credentials for Fastmail configured with HTTP Header Authentication. Authorization Setup**: Optionally set up authorization if your webhook is exposed to the internet to prevent misuse. Custom Webhook Request**: Use a tool like curl or create a shortcut on macOS/iOS to send the POST request to the webhook with the necessary JSON payload, like so: curl -X POST -H 'Content-Type: application/json' https://your-n8n-instance/webhook/87f9abd1-2c9b-4d1f-8c7f-2261f4698c3c -d '{"state": "pending", "description": "my mega fancy masked email"}' This template simplifies the process of integrating masked email functionality into your projects or workflows and can be extended for various use cases. Feel free to use the companion shortcut I've also created. Please update the authorization header in the shortcut if needed. https://www.icloud.com/shortcuts/ac249b50eab34c04acd9fb522f9f7068

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

Remember when you were doing some large research and wanted to quickly bookmark a page and save it, only to find premium options? Worry not; n8n got you covered. You can now create a simple bookmarking app straight to your browser using simple scrips on your browser called bookmarklets. A bookmarklet is a bookmark stored in a web browser that contains JavaScript commands that add new features to the browser. To create one, we need to add a short script to the bookmark tab of our browser like below A simple hack is to open a new tab and click on the star that appears on the right side Now that we have our bookmark, it's time for the fun part. Right-click on the bookmark we just created and select the edit option. This will allow you to set the name you want for your bookmark and the destination URL. The URL used here will be the script that shall "capture" the page we want to bookmark. The code below has been used and tested to work for this example javascript:(() => { var currentUrl = window.location.href; var webhookUrl = 'https://$yourN8nInstanceUrl/webhook/1rxsxc04b027-39d2-491a-a9c6-194289fe400c'; var xhr = new XMLHttpRequest(); xhr.open('POST', webhookUrl, true); xhr.setRequestHeader('Content-Type', 'application/json'); var data = JSON.stringify({ url: currentUrl }); xhr.send(data); })(); Your Bookmark should look like something like this Now that we have this setup, we are now going to n8n to receive the data sent by this script. Create a new webhook node that receives the POST request as in the workflow and replace $yourN8nInstanceUrl with your actual n8n instance. This workflow can then be configured to send this data to a notion database. Make sure the notion database has all the required permissions before executing the workflow. Otherwise the URLs will not be saved

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 Made by Simon @ automake.io

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/

Use case When collecting new leads via a form, you need to follow up on new submissions. Often, this required a lot of manual work that includes reviewing each submission, checking if they meet your criteria and then outreaching. With this workflow you can do all of that fully automatically and save a lot of your valuable time. What this workflow does This workflow runs every time you're receiving a new submission from an n8n form. It then filters out typical personal emails (such as Gmail, Hotmail, Yahoo etc.) before enriching the submission via Clearbit. It then checks, if the company of the submitter is a B2B company and has more than 499 employees. If it does, it sends an email via Gmail to the user. Setup Add the Clearbit and Gmail credentials Click on Test Workflow Enter your own email (which needs to be a business email to work) in the Form Check your email Once you're happy don't forget to activate this workflow How to adjust this template Replace the form trigger with your form provider of choice (e.g. Typeform, SurveyMonkey, Google Forms etc.) Adjust the criteria to your needs via the If node Adjust the email you're sending in the Gmail node

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 workflow creates a Slack thread when a new ticket is created in Zendesk. Subsequent comments on the ticket in Zendesk are added as replies to the thread in Slack. Prerequisites Zendesk account and Zendesk credentials. Slack account and Slack credentials. Slack channel to create threads in. How it works The workflow listens for new tickets in Zendesk. When a new ticket is created, the workflow creates a new thread/message in Slack. The Slack thread ID is then saved in one of the ticket's fields called "Slack thread ID". The next time a comment is added to the ticket, the workflow retrieves the Slack thread ID from the ticket's field and adds the comment to the thread/message in Slack as a reply. Setup This workflow requires that you set up a webhook in Zendesk. To do so, follow the steps below: In the workflow, open the On new Zendesk ticket node and copy the webhook URL. In Zendesk, navigate to Admin Center > Apps and integrations > Webhooks > Actions > Create Webhook. Add all the required details which can be retrieved from the On new Zendesk ticket node. The webhook URL gets added to the “Endpoint URL” field, and the “Request method” should match what is shown in n8n. Save the webhook. In Zendesk, navigate to Admin Center > Objects and rules > Business rules > Triggers > Add trigger. Give trigger a name such as “New tickets”. Under “Conditions” in “Meet ALL of the following conditions”, add “Status is New”. Under “Actions”, select “Notify active webhook” and select the webhook you created previously. In the JSON body, add the following: { "id": "{{ticket.id}}", "comment": "{{ticket.latest_comment_html}}" } Save the Zendesk trigger. You will also need to set up a field in Zendesk to store the Slack thread ID. To do so, follow the steps below: In Zendesk, navigate to Admin Center > Objects and rules > Tickets > Fields > Add field. Use the text field option and give the field a name such as “Slack thread ID”. Save the field. In n8n, open the Update ticket node and select the field you created in Zendesk.

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

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/

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

I am submitting this workflow for the Obsidian community to showcase the potential of integrating Obsidian with n8n. While straightforward, it serves as a compelling demonstration of the potential unlocked by integrating Obsidian with n8n. How it works This workflow lets you retrieve specific Airtable data you need in seconds, directly within your Obsidian note, using n8n. By highlighting a question in Obsidian and sending it to a webhook via the Post Webhook Plugin, you can fetch specific data from your Airtable base and instantly insert the response back into your note. The workflow leverages OpenAI’s GPT model to interpret your query, extract relevant data from Airtable, and format the result for seamless integration into your note. Set up steps Install the Post Webhook Plugin: Add this plugin to your Obsidian vault from the plugin store or GitHub. Set up the n8n Webhook: Copy the webhook URL generated in this workflow and insert it into the Post Webhook Plugin's settings in Obsidian. Configure Airtable Access: Link your Airtable account and specify the desired base and table to pull data from. Test the Workflow: Highlight a question in your Obsidian note, use the “Send Selection to Webhook” command, and verify that data is returned as expected.

Workflow Overview This workflow automates the process of forwarding e-book files to a Kindle device using a Telegram bot and Outlook email. Setup Steps: Telegram Bot Setup: Create a Telegram bot via BotFather and configure its credentials in the workflow. Outlook Email Configuration: Set up your Outlook email credentials. (Currently, only Outlook is supported, but you can modify the workflow to support other email providers.) Amazon Kindle Email Setup: Find your Kindle device's email address from your Amazon account. This will be the recipient address for the e-books. Allow Email Sending to Kindle: Ensure your Amazon account is configured to allow emails from your Outlook address to send files to your Kindle. Workflow Explanation: The workflow begins with a Telegram bot trigger node that listens for new chat messages. When a new message is received, the workflow checks if the message contains a file attachment. If no file is detected, the bot will send a warning reply to the user in the chat. If a file is found, it will be renamed to ensure it appears correctly on the Kindle device when sent. The workflow then composes an email with the file attached and sends it to the Kindle's receiving address. If the email is sent successfully, the bot will notify the user with a success message in the chat. Only Amazon-supported file types will be accepted by Kindle. If sending fails, you will receive a notification email from Amazon in your Outlook inbox. In case of delivery issues, retry sending the file as network issues may occasionally interfere with the process.

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 n8n workflow automates the process of monitoring inventory levels for Shopify products, ensuring timely updates and efficient stock management. It is designed to alert users when inventory levels are low or out of stock, integrating with Shopify's webhook system and providing notifications through Discord (can be changed to any messaging platform) with product images and details. Workflow Overview Webhook Node (Shopify Listener): This node is set up to listen for Shopify's inventory level webhook. It triggers the workflow whenever there is an update in the inventory levels. The webhook is configured in Shopify settings, where the n8n URL is specified to receive inventory level updates. Function Node (Inventory Check): This node processes the data received from the Shopify webhook. It extracts the available inventory and the inventory item ID, and determines whether the inventory is low (less than 4 items) or out of stock. Condition Nodes (Inventory Level Check): Two condition nodes follow the function node. One checks if the inventory is low (low_inventory equals true), and the other checks if the inventory is out of stock (out_of_stock equals true). GraphQL Node (Product Details Retrieval): Connected to the condition nodes, this node fetches detailed information about the product using Shopify's GraphQL API. It retrieves the product variant, title, current inventory quantity, and the first product image. HTTP Node (Discord Notification): The final node in the workflow sends a notification to Discord. It includes an embed with the product title, a warning message ("This product is running out of stock!"), the remaining inventory quantity, product variant details, and the product image. The notification ensures that relevant stakeholders are immediately informed about critical inventory levels.

Use Case This workflow is a slight variation of a workflow we're using at n8n. In most companies, employees have a lot of great ideas. That was the same for us at n8n. We wanted to make it as easy as possible to allow everyone to add their ideas to some formatted database - it should be somewhere where everyone is all the time and could add a new idea without much extra effort. Since we're using Slack, this seemed to be the perfect place to easily add ideas. In this example, we're adding the ideas to Google Sheets instead of Notion, like we do. What this workflow does This workflow waits for a webhook call within Slack, that gets fired when users use the /idea command on a bot that you will create as part of this template. It then checks the command, adds the idea to Google Sheets and notifies the user about the newly added idea as you can see below: Creating your Slack bot Visit https://api.slack.com/apps, click on New App and choose a name and workspace. Click on OAuth & Permissions and scroll down to Scopes -> Bot token Scopes Add the chat:write scope Head over to Slash Commands and click on Create New Command Use /idea as the command Copy the test URL from the Webhook node into Request URL Add whatever feels best to the description and usage hint Go to Install app and click install Setup Create a Google Sheets document with the columns Name and Creator Add your Google credentials Fill the Set me up node. Create your Slack app (see other sticky) Click Test workflow and use the /idea comment in Slack Activate the workflow and exchange the Request URL with the production URL from the webhook How to adjust it to your needs You can adjust the table in Google Sheets and for example, add different types of ideas or areas that they impact Rename the Slack command as it works best for you How to enhance this workflow At n8n we use this workflow in combination with some others. E.g. we have the following things on top: We additionally have a /bug Slack command that adds a new bug to Linear. Here we're using AI to classify the bugs and move it to the right team. (Bug command workflow and Ai Classifier workflow) We also added other types, like /pain to be less solution-driven To make it easier for everyone to give input, we added a Votes column that allows everyone to vote on ideas/pain points in the list We're also running a workflow once a week that highlights the most popular new ideas and the most active voters

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

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

This workflow syncs your GitHub issues to your Notion database. Whenever a new issue is opened in your GitHub repository, it will be shown in your Notion database, syncing the status property (opened/edited/closed/deleted). In case there’s no Notion database existing yet, a new one will be created automatically. Prerequisites Notion account and Notion credentials GitHub account and GitHub credentials How it works Github trigger starts the workflow when a new issue is created in a GitHub repository. If node splits the workflow conditionally, showing whether the issue is new or an update of an existing issue. If data is new, the Notion node will create a new database page in Notion. If the data is not new, the Function node will create a Notion filter that will find its specific database page by issue ID. Switch node will then conditionally route the data into the appropriate Notion page, based on the update made upon it.

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 n8n workflow takes Slack conversations and turns them into Calendar events complete with accurate date and times and location information. Adding and removing attendees are also managed automatically. How it works Workflow monitors a Slack channel for invite messages with a "📅" reaction and sends this to the AI agent. AI agent parses the message determining the time, date and location. Using its Location tool, the AI agent searches for the precise location address from Google Maps. Using its Calendar tool, the AI agent creates a Google Calendar invite with the title, description and location address for the user. Back in the Slack channel, others can RSVP to the invite by reacting with the "✅" emjoi. The workflow polls the message after a while and adds the users who have reacted to the Calendar Invite as attendees. Conversely, removing any attendees who have since removed their reaction. Examples Jill: "Hey team, I'm organising a round of Laser Tag (Bunker 51) next Thursday around 6pm. Please RSVP with a ✅" AI: "I've helped you create an event in your calendar https://cal.google.com/..." Jack: "✅" AI: "I've added Jack to the event as an attendee". Requirements Slack channel to attach the workflow OpenAI account to use a GPT model Google Calendar to create and update events Customising the Workflow This workflow can work with other messaging platforms that support reactions or tagging like features such as discord. Don't use Google Calendar? Swap it out for Outlook or your own. Use any combinations of emjoi reactions and add new rules like "RSVP maybe" which could send reminder updates nearer the event date.

This n8n workflow demonstrates a simple approach to improve chat UX by staggering an AI Agent's reply for users who send in a sequence of partial messages and in short bursts. How it works Twilio webhook receives user's messages which are recorded in a message stack powered by Redis. The execution is immediately paused for 5 seconds and then another check is done against the message stack for the latest message. The purpose of this check lets use know if the user is sending more messages or if they are waiting for a reply. The execution is aborted if the latest message on the stack differs from the incoming message and continues if they are the same. For the latter, the agent receives the buffered messages up to that point and is able to respond to them in a single reply. Requirements A Twilio account and SMS-enabled phone number to receive messages. Redis instance for the messages stack. OpenAI account for the language model. Customising the workflow This workflow should work for other common messaging platforms such as Whatsapp and Telegram. 5 seconds too long or too short? Adjust the wait threshold to suit your customers.

This n8n workflow verifies the deliverability of mailing addresses stored in Groundhogg CRM by integrating with Lob’s address verification service. Who is this for? This template is designed for Groundhogg CRM users who need to ensure the accuracy of mailing addresses stored in their CRM systems. What problem is this workflow solving? / Use Case This workflow addresses the challenge of maintaining accurate mailing addresses in CRM databases by verifying the deliverability of addresses. What this workflow does A new contact is created in Groundhogg CRM Webhook sent to n8n Verify if the address is deliverable via LOB Report back to Groundhogg CRM Set Up Steps Watch this setup video: https://www.youtube.com/watch?v=nrV0P0Yz8FI Takes 10-30 minutes to set up Accounts Needed: Groundhogg CRM LOB Account (https://www.lob.com $0.00/mo 300 US addresses Verifications) n8n Before using this template, ensure you have API keys for your Groundhogg CRM app and Lob. Set up authentication for both services within n8n. How to customize this workflow to your needs You can customize this workflow by adjusting the trigger settings to match Groundhogg CRM’s workflow configuration. Additionally, you can modify the actions taken based on the deliverability outcome, such as updating custom fields or sending notifications.

Purpose of the workflow Most scraping workflows get blocked by anti-bot technologies. To avoid this, you can use Scrappey to scrape every website you want. How it works: We use Test Data and make a API Call to the Scrappey service. We get the scraped website data back as a result. Setup Steps: Replace YOUR_API_KEY in the "Scrappey API Call" node with your Scrappey API Key (Register For Free) Replace the test data with your production data. You can plug in any type of data connector at this point of your workflow.

This workflow will allow you to use OpenAI Assistant API together with a chatting platform. This version is configured to work with Hubspot, however, the Hubspot modules can be replaced by other platform and it will work similarly. Prerequisites: Create a Hubspot Chat (Live chat available on free plan) or Chatflow (paid hubspot only) and configure it to send all replies toward an n8n webhook (you need to create a custom app for that. I will create a separate article on how to do it, meanwhile, feel free to message me if you need support. Setup: Create a OpenAI Assistant, define its functionality and functions Update the Hubspot modules with the Hubspot API Key Update the OpenAI modules with OpenAI API Key Create an Airtable or any other database where you keep a reference between the thread id in Hubspot and Assistant API If you need help deploying this solution don't hesitate to email me or schedule a call here.

This is an example of how we can build a slack bot in a few easy steps Before you can start, you need to o a few things Create a copy of this workflow Create a slack bot Create a slash command on slack and paste the webhook url to the slack command Note Make sure to configure this webhook using a https:// wrapper and don't use the default http://localhost:5678 as that will not be recognized by your slack webhook. Once the data has been sent to your webhook, the next step will be passing it via an AI Agent to process data based on the queries we pass to our agent. To have some sort of a memory, be sure to set the slack token to the memory node. This way you can refer to other chats from the history. The final message is relayed back to slack as a new message. Since we can not wait longer than 3000 ms for slack response, we will create a new message with reference to the input we passed. We can advance this using the tools or data sources for it to be more custom tailored for your company. Usage To use the slackbot, go to slack and click on your set slash command eg /Bob and send your desired message. This will send the message to your endpoint and get return the processed results as the message. If you would like help setting this up, feel free to reach out to [email protected]

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

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.

This workflow helps marketers verify and update data using EffiBotics Email Verifier API. Copy and create a list with emails as on this one https://docs.google.com/spreadsheets/d/1rzuojNGTaBvaUEON6cakQRDva3ueGg5kNu9v12aaSP4/edit#gid=0 The trigger checks for any updates in the number of rows that are present in a sheet and updates the verified emails on Google sheets Once you update a new cell, the new data is read, and the email is checked for its validity before. The results are then updated in real-time on the sheet. Happy Emailing!

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

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

This 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.

Streamline Case Management in TheHive via Slack! Our TheHive Slack Integration empowers SOC analysts by allowing them to efficiently manage and update case attributes directly within Slack, reducing the need to switch contexts and enhancing response time. Key Features: Direct Case Management**: Modify case details such as assignee, severity, status, and more through intuitive form inputs embedded within Slack messages. Seamless Integration**: Assumes matching email addresses between TheHive and Slack users for straightforward assignee updates. Note: Ensure email consistency to avoid assignment errors. Instant Case Actions**: Quickly close cases as false positives or adjust threat levels with minimal clicks, directly impacting case status in TheHive and reflecting updates immediately in Slack. Task Management**: Add tasks to cases through a user-friendly modal popup, fostering better task tracking and delegation within your team. Operational Benefits: Efficiency**: Enables analysts to perform multiple case actions without leaving Slack, streamlining workflows and saving valuable time. Accuracy**: Reduces the chances of human error by providing a controlled interface for case updates. Agility**: Enhances the SOC team's agility by providing tools for rapid response and case management, crucial for effective security operations. Setup Tips: Verify that all SOC team members have matching email IDs in TheHive and Slack. Familiarize your team with the Slack form inputs and ensure they understand the importance of accurate data entry. Regularly review and update the integration settings to accommodate any changes in your security operations protocols. Need Help? For detailed setup instructions or troubleshooting, refer to our Integration Guide or reach out on our Support Forum. Leverage this integration to maximize your SOC team's efficiency and responsiveness, ensuring that case management is as streamlined and effective as possible.

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 Made by Simon @ automake.io

Description This n8n workflow verifies the deliverability of mailing addresses stored in Keap/Infusionsoft by integrating with Lob’s address verification service. Who is this for? This template is designed for Keap/Infusionsoft users who need to ensure the accuracy of mailing addresses stored in their CRM systems. What problem is this workflow solving? / Use Case This workflow addresses the challenge of maintaining accurate mailing addresses in CRM databases by verifying the deliverability of addresses. What this workflow does A new contact is created in Keap/Infusionsoft Webhook sent to n8n Verify if the address is deliverable via LOB Report back to Keap/Infusionsoft Set Up Steps Watch this setup video: https://www.youtube.com/watch?v=T7Baopubc-0 Takes 10-30 minutes to set up Accounts Needed: Keap/Infusionsoft LOB Account (https://www.lob.com $0.00/mo 300 US addresses Verifications) n8n Before using this template, ensure you have API keys for your Keap/Infusionsoft app and Lob. Set up authentication for both services within n8n. How to customize this workflow to your needs You can customize this workflow by adjusting the trigger settings to match Keap/Infusionsoft’s workflow configuration. Additionally, you can modify the actions taken based on the deliverability outcome, such as updating custom fields or sending notifications.

Workflow Description: This n8n workflow automates the drafting of email replies for Fastmail using OpenAI's GPT-4 model. Here’s the overall process: Email Monitoring: The workflow continuously monitors a specified IMAP inbox for new, unread emails. Email Data Extraction: When a new email is detected, it extracts relevant details such as the sender, subject, email body, and metadata. AI Response Generation: The extracted email content is sent to OpenAI's GPT-4, which generates a personalized draft response. Get Fastmail Session and Mailbox IDs: Connects to the Fastmail API to retrieve necessary session details and mailbox IDs. Draft Identification: Identifies the "Drafts" folder in the mailbox. Draft Preparation: Compiles all the necessary information to create the draft, including the generated response, original email details, and specified recipient. Draft Uploading: Uploads the prepared draft email to the "Drafts" folder in the Fastmail mailbox. Prerequisites: IMAP Email Account: You need to configure an IMAP email account in n8n to monitor incoming emails. Fastmail API Credentials: A Fastmail account with JMAP API enabled. You should set up HTTP Header authentication in n8n with your Fastmail API credentials. OpenAI API Key: An API key from OpenAI to access GPT-4. Make sure to configure the OpenAI credentials in n8n. Configuration Steps: Email Trigger (IMAP) Node: Provide your email server settings and credentials to monitor emails. HTTP Request Nodes for Fastmail: Set up HTTP Header authentication in n8n using your Fastmail API credentials. Replace the httpHeaderAuth credential IDs with your configured credential IDs. OpenAI Node: Configure the OpenAI API key in n8n. Replace the openAiApi credential ID with your configured credential ID. By following these steps and setting up the necessary credentials, you can seamlessly automate the creation of email drafts in response to new emails using AI-generated content. This workflow helps improve productivity and ensures timely, personalized communication.

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

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

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

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

Unlock streamlined Zoom Meeting organization and exclusive access management with this n8n workflow. Designed for educators, event organizers, and businesses, this tool automates your event logistics, so you can focus on delivering valuable content. Features Zoom Meetings Creation:** Instantly generate new Zoom meetings with the n8n built-in form. Collect Payments Using Stripe:** Effortlessly monetize your events with secure, automatically created Stripe payment pages for each meeting. Exclusive Gated Access:** Ensure your content remains exclusive by sending Zoom meeting passwords only to verified subscribers who have completed their payment through Stripe. Participants Email Notifications:** Automate the distribution of Zoom meeting details post-payment, eliminating the need for manual email management and ensuring participants are promptly informed. Instant and Easy Participants Overview:** Manage and track your event registrations with ease. All related data is stored in a Google Sheets document that you own. You're notified via email with each new subscription, simplifying participant management. Set Up Steps Connect your Zoom, Stripe, Gmail and Google Sheet credentials. Create an empty Google Sheet in your Google Drive. Fill the config node (Sheet URL, email and currency). Edit email text. This n8n workflow template is designed to minimize setup time and maximize efficiency, allowing you to focus on delivering value to your subscribers. With just a few clicks, you can automate the entire process of organizing and monetizing your Zoom meetings. Created by the n8ninja.

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!

Telegram User Registration module Offers an efficient way to manage new and returning users in your Telegram bot workflow. It checks user data against a Google Sheets database, saving essential user information and ensuring personalized interactions. Key Features: User Lookup: Searches for users in a Google Sheets database based on their unique Telegram ID. New User Handling: Automatically registers new users, capturing details such as first name, last name, username, and language code. Returning User Recognition: Detects when an existing user returns and updates their status. Data Storage: Safely stores user information in a structured format, with fields for status and date of registration. Personalized Greetings: Delivers customized welcome messages for both new and returning users, promoting engagement. Setup Instructions: Copy this Telegram User Registration Workflow Follow the instructions inside: - Use Google Sheet template - Set up your credentials - Use example data to test workflow Customization: Adjust the status and messages for users based on your bot's needs. Connecting to the bot Workflow: Copy my Telegram bot starter template Copy Workflow ID of your Telegram User Registration Workflow Find the Register module in Telegram bot starter template and paste your Workflow ID Now the user's data is entered into the Register Workflow. This module provides a scalable foundation for managing user registration, whether your bot is for meal planning, customer support, or other interactive services. My easy to set up Telegram bot modules: 🏁 Telegram bot starter template 📝 Telegram callback menu (soon) Please reach out to Victor if you need further assistance with you n8n workflows and automations!

This workflow imports a JSON file from an email attachment and saves the content into a spreadsheet.

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 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.

Example: @SubAlertMe_Bot Summary: The automated image analysis and response workflow using n8n is a sophisticated solution designed to streamline the process of analyzing images sent via Telegram and delivering insightful responses based on the analysis outcomes. This cutting-edge workflow employs a series of meticulously orchestrated nodes to ensure seamless automation and efficiency in image processing tasks. Use Cases: This advanced workflow caters to a myriad of scenarios where real-time image analysis and response mechanisms are paramount. The use cases include: Providing immediate feedback on images shared within Telegram groups. Enabling automated content moderation based on the analysis of image content. Facilitating rapid categorization and tagging of images based on the results of the analysis. Detailed Workflow Setup: To effectively implement this workflow, users must adhere to a meticulous setup process, which includes: Access to the versatile n8n platform, ensuring seamless workflow orchestration. Integration of a Telegram account to facilitate image reception and communication. Utilization of an OpenAI account for sophisticated image analysis capabilities. Configuration of Telegram and OpenAI credentials within the n8n environment for seamless integration. Proficiency in creating and interconnecting nodes within the n8n workflow for optimal functionality. Detailed Node Description: Get the Image (Telegram Trigger): Actively triggers upon receipt of an image via Telegram, ensuring prompt processing. Extracts essential information from the received image message to initiate further actions. Merge all fields To get data from trigger: Seamlessly amalgamates all relevant data fields extracted from the trigger node for comprehensive data consolidation. Analyze Image (OpenAI): Harnesses the powerful capabilities of OpenAI services to conduct in-depth analysis of the received image. Processes the image data in base64 format to derive meaningful insights from the visual content. Aggregate all fields: Compiles and consolidates all data items for subsequent processing and analysis, ensuring comprehensive data aggregation. Send Content for the Analyzed Image (Telegram): Transmits the analyzed content back to the Telegram chat interface for seamless communication. Delivers the analyzed information in textual format, enhancing user understanding and interaction. Switch Node: The Switch node is pivotal for decision-making based on predefined conditions within the workflow. It evaluates incoming data to determine the existence or absence of specific elements, such as images in this context. Utilizes a set of rules to assess the presence of image data in the message payload and distinguishes between cases where images are detected and when they are not. This crucial node plays a pivotal role in directing the flow of the workflow based on the outcomes of its evaluations. Conclusion: The automation of image analysis processes through this sophisticated workflow not only enhances operational efficiency but also revolutionizes communication dynamics within Telegram interactions. By incorporating this advanced workflow solution, users can optimize their image analysis workflows, bolster communication efficacy, and unlock new levels of automation in image processing tasks.

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 workflow was presented at the AI Developers meet up in San Fransico on 24 July, 2024. AI workflows Categorize incoming Gmail emails and assign custom Gmail labels. This example uses the Text Classifier node, simplifying this usecase. Ingest a PDF into a Pinecone vector store and chat with it (RAG example) AI Agent example showcasing the HTTP Request tool. We teach the agent how to check availability on a Google Calendar and book an appointment.

This n8n workflow simplifies the process of removing backgrounds from images stored in Google Drive. By leveraging the PhotoRoom API, this template enables automatic background removal, padding adjustments, and output formatting, all while storing the updated images back in a designated Google Drive folder. This workflow is very useful for companies or individuals that are spending a lot of time into removing the background from product images. How it Works The workflow begins with a Google Drive Trigger node that monitors a specific folder for new image uploads. Upon detecting a new image, the workflow downloads the file and extracts essential metadata, such as the file size. Configurations are set for background color, padding, output size, and more, which are all customizable to match specific requirements. The PhotoRoom API is called to process the image by removing its background and adding padding based on the settings. The processed image is saved back to Google Drive in the specified output folder with an updated name indicating the background has been removed. Requirements PhotoRoom API Key Google Drive API Access Customizing the Workflow Easily adjust the background color, padding, and output size using the configuration node. Modify the output folder path in Google Drive or replace Google Drive with another storage service if needed. For advanced use cases, integrate further image processing steps, such as adding captions or analyzing content using AI.

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 reads PDF textual content and sends the text to OpenAI. Attachments of interest will then be uploaded to a specified Google Drive folder. For example, you may wish to send invoices received from an email to an inbox folder in Google Drive for later processing. This workflow has been designed to easily change the search term to match your needs. See the workflow for more details. Prerequisites OpenAI credentials. Google credentials. How it works Triggers off on the On email received node. Iterates over the attachments in the email. Uses the OpenAI node to filter out the attachments that do not match the search term set in the Configure node. You could match on various PDF files (i.e. invoice, receipt, or contract). If the PDF attachment matches the search term, the workflow uses the Google Drive node to upload the PDF attachment to a specific Google Drive folder.

Video Demo: Click here to see a video of this workflow in action. Summary Description: The "IT Department Q&A Workflow" is designed to streamline and automate the process of handling IT-related inquiries from employees through Slack. When an employee sends a direct message (DM) to the IT department's Slack channel, the workflow is triggered. The initial step involves the "Receive DMs" node, which listens for new messages. Upon receiving a message, the workflow verifies the webhook by responding to Slack's challenge request, ensuring that the communication channel is active and secure. Once the webhook is verified, the workflow checks if the message sender is a bot using the "Check if Bot" node. If the sender is identified as a bot, the workflow terminates the process to avoid unnecessary actions. If the sender is a human, the workflow sends an acknowledgment message back to the user, confirming that their query is being processed. This is achieved through the "Send Initial Message" node, which posts a simple message like "On it!" to the user's Slack channel. The core functionality of the workflow is powered by the "AI Agent" node, which utilizes the OpenAI GPT-4 model to interpret and respond to the user's query. This AI-driven node processes the text of the received message, generating an appropriate response based on the context and information available. To maintain conversation context, the "Window Buffer Memory" node stores the last five messages from each user, ensuring that the AI agent can provide coherent and contextually relevant answers. Additionally, the workflow includes a custom Knowledge Base (KB) tool (see that tool template here) that integrates with the AI agent, allowing it to search the company's internal KB for relevant information. After generating the response, the workflow cleans up the initial acknowledgment message using the "Delete Initial Message" node to keep the conversation thread clean. Finally, the generated response is sent back to the user via the "Send Message" node, providing them with the information or assistance they requested. This workflow effectively automates the IT support process, reducing response times and improving efficiency. To quickly deploy the Knowledge Ninja app in Slack, use the app manifest below and don't forget to replace the two sample urls: { "display_information": { "name": "Knowledge Ninja", "description": "IT Department Q&A Workflow", "background_color": "#005e5e" }, "features": { "bot_user": { "display_name": "IT Ops AI SlackBot Workflow", "always_online": true } }, "oauth_config": { "redirect_urls": [ "Replace everything inside the double quotes with your slack redirect oauth url, for example: https://n8n.domain.com/rest/oauth2-credential/callback" ], "scopes": { "user": [ "search:read" ], "bot": [ "chat:write", "chat:write.customize", "groups:history", "groups:read", "groups:write", "groups:write.invites", "groups:write.topic", "im:history", "im:read", "im:write", "mpim:history", "mpim:read", "mpim:write", "mpim:write.topic", "usergroups:read", "usergroups:write", "users:write", "channels:history" ] } }, "settings": { "event_subscriptions": { "request_url": "Replace everything inside the double quotes with your workflow webhook url, for example: https://n8n.domain.com/webhook/99db3e73-57d8-4107-ab02-5b7e713894ad", "bot_events": [ "message.im" ] }, "org_deploy_enabled": false, "socket_mode_enabled": false, "token_rotation_enabled": false } }

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 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.

This is an add-on for the template Check if workflows contain build-in nodes that are not of the latest version Purpose This workflow highlights outdated nodes within all workflows of a single n8n instance and places an updated preconfigured node right next to it, so it can be swapped easily. How it works The parent workflow checks the entire n8n instance for outdated nodes within all workflows and passes a list of those alongside some metadata to this workflow This workflow then processes that data and updates the affected workflows Outdated nodes are renamed by prepending an emoji (default: ⚠️) - this is also used for future checks to prevent from double-processing The latest version of each outdated node is added to the workflow canvas (not wired up) behind the old one, slightly shifted in position An Email is sent with a list of modified workflows In the settings it is possible to define: which symbol/emoji should be prepended to outdated notes whether to include only major node updates or all of them whether to add the new nodes to the canvas or not Setup Clone this template to your n8n instance Update the Settings node by setting at least the base URL of your n8n instance Set a recipient in the Gmail node Clone the parent template to your n8n instance and configure it as described in it’s description Add an “Execute Workflow” node to the end of the parent workflow and configure it, so it calls this workflow How to use Execute the parent workflow and check your Email Inbox. All linked workflows should contain one or more updated nodes with an emoji prepended to their names. Disclaimer Beware, that major updates can cause migrations of nodes to fail, since their structure can differ. So always compare the old nodes with the newly created, if all parameters still meet the requirements. Be careful when executing this workflow on a production environment, since it directly modifies your workflows. It is advisable to run this on your testing environment and migrate successfully tested workflows to your production environment using git or manually.

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

How it works Weekly triggered Fetches all previous executions of a given workflow Filter for failures and aggregate them into a single report Sends them to a given Telegram chat. Set up steps Create a new N8N api token in the settings panel. Add new N8N credentials in the credentials panel. Add new Telegram credentials in the credentials panel. Select N8N credentials and select the workflow ID in the "Get all previous executions" node. Select Telegram credentials and enter the chat-id in the "Telegram" node.

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. 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

The Stripe API does not provide custom fields in invoice or charge data. So you have to get it from the Checkout Sessions endpoint. But that endpoint is not easy for begginners. It has dictionary parameters and pagination settings. This workflows solves that problem by having a preconfigured GET request that gets all the checkout sessions from the last 7 days. It then transforms the data to make it easier to work with and allows you to filter by the custom_fields you want to get. . Check out my other templates 👉 https://n8n.io/creators/solomon/

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

Who this is for This workflow is for sales people who want to quickly and efficiently follow up with their leads What this workflow does This workflow starts every time a new reply is received in lemlist. It then classifies the response using openAI and creates the correct follow up task. The follow-up tasks currently include: Slack alerts when a lead for each new replies Tag interested leads in lemlist Unsubscription of leads when they request it The Slack alerts include: Lead email address Sender email address Reply type (positive, not interested...etc) A preview of the reply Setup To set this template up, simply follow the stickies steps in it How to customize this workflow to your needs Adjust the follow up tasks to your needs Change the Slack notification to your needs ...

This workflow allows you to send multi-step email campaigns using n8n, Gmail and Google Sheets. You define a sequence of emails, and a list of email addresses to send them to. The first email is sent to everyone, but the later emails in the sequence are only sent if no-one has replied to the thread yet. This means you only need to worry about replying to people who respond to your email, not chasing people who don’t. Requirements A list of emails in a Google sheet. You can define extra info that will be available to your email templates (e.g. name, company, etc.) A sequence of emails to send, plus how long to wait to send each one, e.g. On day 0:** “Hi, {name} — nice to meet you at the conference. Was wondering whether {company} would be interested in a quick call about X?” On day 3:** “Hi, {name}, just wanted to check in on this. Let me know if there’s any interest!” On day 7:** “{name}, just wanted to give this one last try” A Gmail account (although you could also swap out that part for any other email service) How it works When sending the emails, n8n inserts a hidden attribute in each one that tags it as being part of the email campaign. It then regularly looks through the email threads with that tag and checks whether: No-one has replied yet It’s time to send the next message The workflow doesn’t send emails on weekends. Notes This workflow is not intended for spam! Please use responsibly You can use this workflow for multiple different campaigns by making copies of the workflow and changing the sequence / Google Sheet that it uses

Use Case This workflow aims to enrich new contacts in HubSpot. The more relevant the HubSpot profile, the more useful it is. Once active, this n8n workflow will update the social profiles, contact data (phone, email) as well as location data from ExactBuyer. Setup Add HubSpot trigger credential (be careful, scopes must be exactly as in n8n docs ) Add your Exact Buyer API key Add HubSpot credential for update node (be careful, scopes must be same as n8n docs for this. This is different from the trigger cred) Activate workflow How to adjust this template There's plenty of interesting info that ExactBuyer returns that could be helpful. Take a look and update this workflow to add what you need.

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:

n8n Creators Template: Creator Profile Stats Updater This n8n workflow template is designed to automate the process of updating a creator's profile statistics, including total workflows, complex workflows, approved workflows, pending workflows, total nodes, and total views. It utilizes various nodes to fetch data, process it, and update a SVG file hosted on GitHub to reflect the latest stats. Workflow Overview Schedule Trigger: Triggers the workflow execution at specified intervals. Config: Sets up configuration details like creator username, colors for text, icons, border, and card. Get Workflows: Fetches workflows associated with the creator from the n8n API. Workflows Data: Processes the fetched data to calculate various statistics. Get User: Fetches user details from the n8n API. Download Image: Downloads the creator's profile image. Extract From File: Extracts binary data from the downloaded image file. SVG: Generates an SVG file with updated stats and visual representation. GitHub: Commits the updated SVG file to the specified GitHub repository. Final: Prepares the final data set for further processing or output. Sticky Note: Provides a visual note or reminder within the workflow editor. Embed & Live Preview Since it's a .SVG format you can host it anywhere. treat it like normal image so you can embed it with any site, forum, page that support posting images. here's example code for markdown: Here's the result Or served through CDN & Cache Setup Instructions GitHub Credentials: Ensure you have GitHub credentials set up in your n8n instance to allow the workflow to commit changes to your repository. Configure Trigger: Adjust the Schedule Trigger node to set the desired execution intervals for the workflow. Set Configuration: Customize the Config node with your GitHub username and preferred aesthetic options for the SVG. Deploy Workflow: Import the workflow into your n8n instance and deploy it. Customization Options Text and Icon Colors**: Customize the colors used in the SVG by modifying the respective fields in the Config node. Profile Image Size**: Adjust the image size in the Download Image node URL if needed. Commit Messages**: Modify the commit messages in the GitHub nodes to suit your version control conventions [I've used $now funaction to include current time in message which will gives allways a diffrent commit value]. Requirements n8n (Self-hosted or Cloud version compatible with 2024 releases and up) GitHub account and repository Basic understanding of n8n workflow configuration Support and Contributions For support, please refer to the n8n community forum or the official n8n documentation. Contributions to the template can be made you're allowed to reuse this workflow and reshare with edit (like new design/colors etc..) under your name.

What this does: This automation will dynamically create channels on your Discord server for each of your Webflow forms then send formatted form submissions as messages in those channels. This is useful for Webflow will only notify a single email of a form submission. By using this workflow you can enhance your Webflow form management by receiving them in Discord. This is great if you need to notify multiple team members or communities of your form submissions. Usage guide Full written and video guide Simply create credentials for Webflow and Discord and connect them to the nodes. The video guide demonstrates a realworld usecase using a Webflow template and breaks down each node in detail about how it works.

This template demonstrates how to trigger an AI Agent with Siri and Apple Shortcuts, showing a simple pattern for voice-activated workflows in n8n. It's easy to customize—add app nodes before the AI Agent step to pass additional context, or modify the Apple Shortcut to send inputs like text, geolocation, images, or files. Set Up Basic instructions in template itself. Requirements n8n account** (cloud or self-hosted) Apple Shortcuts app** on iOS or macOS. Dictation ("Siri") must be activated. Download the Shortcuts template here. Key Features: Voice-Controlled AI:** Trigger AI Agent via Siri for real-time voice replies. Customizable Inputs:** Modify Apple Shortcut to send text, images, geolocation, and more. Flexible Outputs:** Siri can return the AI’s response as text, files, or customize it to trigger CRUD actions in connected apps. Context-Aware:** Automatically feeds the current date and time to the AI Agent, with easy options to pass in more data. How It Works: Activate Siri and speak your request. Siri sends the transcribed text to the n8n workflow via Apple Shortcuts. AI Agent processes the request and generates a response. Siri reads the response, or the workflow can return geolocation, files, or even perform CRUD actions in apps. Inspiration: Custom Use Cases Tweak this template and make it your own. Capture Business Cards:** Snap a photo of a business card and record a voice note. Have the AI Agent draft a follow-up email in Gmail, ready to send. Voice-to-Task Automation:** Speak a new to-do item, and the workflow will add it to a Notion task board. Business English on the Fly:** Convert casual speech into polished business language, and save the refined text directly to your pasteboard, ready to be pasted into any app. "It's late because of you" -> "There has been a delay, and I believe your input may have contributed to it."

Purpose of workflow: The purpose of this workflow is to automatically transform complex, lengthy PDFs into concise, insightful blog posts. This process aims to make dense information more accessible and understandable to a broader audience. How it works: Multi-agent team: Working together to transform PDF to insightful blog Content Analyst: Extracts key information and structure from the PDF. Project Planner: Organizes the workflow and ensures efficient processing. Writers: Multiple agents that craft engaging content based on the analyzed information. Editor: Polishes the final output for accuracy, reliability, and readability. Step by step User inputs parameters via a n8n form (tone, word count, sections, PDF file). System extracts text from the PDF. Content Analyst AI agent analyzes the extracted text, identifying main topics and key points. Project Planner AI agent organizes the workflow, creating a structure for the blog post. Multiple Writer AI agents (based on the number of sections specified) each write a portion of the blog post. Editor AI agent combines all sections and polishes the final draft. Generates a title for the blog post. Final content is uploaded to a publishing Ghost platform

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/

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

How It Works 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.

Task: Conditional filtering and branching items Why: Filtering and branching data based on conditions allows you to build complex workflows that work with more than one data flow scenario Main use cases: Filter out data that is not relevant for the rest of the workflow Split data to several branches of the workflow, where we want the data to be treated differently in the rest of the workflow

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 automated workflow takes a Typeform form, and once it is filled out, it is automatically uploaded as a Lead in Pipedrive. There is an option for custom fields (this workflow works with company size), and leaves notes in the note section based on questions answered. Prerequisites Typeform account and Typeform credentials and a form for people to fill out Pipedrive account and Pipedrive credentials Nodes Typeform node gets the data after the survey is completed Set node extracts data from the Typeform node and keeps only relevant data Function node maps the company size Pipedrive node populates a pipeline with a deal and adds custom fields

This workflow creates/updates/deletes a Notion database page when an issue is created/updated/deleted in Jira. Subsequent updates to the issue's title or status in Jira are updated in the Notion database. If you require more fields to send to Notion, this template is easily extendible which will be described in setup. The Notion database will require setup before the workflow can be used. Prerequisites Notion account and Notion credentials. Jira account and Jira credentials. How it works When a new issue is created in Jira, the workflow creates a new page in the Notion database will all the required fields. When the issue's title or status is updated in Jira, the workflow updates the specific Notion database page identified by the "Issue Key" field in Notion. If the status in Jira is set to "Done", the workflow will mark the Notion database page "Done" field as true. When the issue is deleted in Jira, the workflow archives the Notion database page. Setup This workflow requires that you set up a Notion database. To do so, follow the steps below: In Notion, create a new database. Add the following columns to the database: Done (with type "Checkbox") Title (renamed from "Name") Status (with the following options: "To Do", "In Progress", "Done") Link (with type "URL") Issue ID (with type "Number") Issue Key (with type "Text") Add any other fields you require to the database. Your database should look something like this Share the database to n8n. By default, the workflow will fill all the fields provided above, except for any other additional fields you add.

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

This workflow is an experiment to integrate charts in AI Agents, using the new Structured Output from OpenAI and Quickchart.io. How it works Users chat with an AI Agent. Anytime the AI Agent considers a chart is needed, it calls a tool to generate a chart OpenAI generates a chart using the Quickchart definition This object is added at the end of a Quickchart.io URL (see documentation) The url is added in the conversation via the AI Agent as markdown. Set up steps Create an OpenAI API Key Create the OpenAI credentials Use the credentials for the HTTP Request node (as Predefined Credential type) Activate your workflow Start chatting For example, you can ask the AI Agent to generate a chart about the top 5 movies at the box office Start exploring the limits Shout-out Quickchart.io is an amazing open source project that provides a free API to test. Go check them out! Example of chart

This workflow automates daily LinkedIn posts using Notion. It starts by fetching the day's post from a Notion database, processes and formats the content, including images, then publishes it on LinkedIn. Finally, it updates the post status in the Notion database. Set up Notion and LinkedIn credentials as required.

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) Made by Simon @ automake.io

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 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/

This workflow creates a new item in a Monday.com board when a new contact is created in Mautic. Additional fields can be added to the workflow to send more data to Monday.com. Prerequisites Monday account and Monday credentials. Mautic account and Mautic credentials. How it works When a new contact is created in Mautic, the workflow creates a new item in the Monday.com board. By default the workflow will send the contact's email address to Monday.com and name the item after the contact's first and last name. Setup This workflow requires that you set up a Monday.com board. To do so, follow the steps below: In Monday.com, create a new item board. In the board, add the following columns: Email (with type "Email") Any other fields you require to the board. If more columns are created, you will need to find out what their ID's are by turning on developer mode as explained in this Monday article. With these new IDs, you can add them to the Create item Monday.com node, this is explained further in the workflow as a sticky note.

Overview This n8n workflow is specifically designed to monitor USDT TRC20 transactions within a specified wallet. It utilizes the public blockchain database of TronScan, requiring no API authentication, to periodically check and process transaction data. This workflow is ideal for users who need an automated solution to track their TRC20 wallet transactions. Features Automated Tracking**: Executes every 15 minutes to capture new transactions. Customizable Filters**: Tailors the tracking based on specific parameters like transaction time and wallet addresses. Data Aggregation**: Compiles transaction data into a single, structured list. Formatted Outputs**: Presents transaction data in an organized and comprehensible format. Requirements N8N (self-hosted or cloud version) setup and operational. Basic understanding of N8N workflows and nodes. Setup and Configuration Import Workflow: Load the provided JSON workflow into your N8N instance. Configure Edit Fields Node: Enter your TRC20 wallet address in the 'Your Wallet Address' field. Adjust 'Number of transactions to retrieve per request' if necessary. (Default one set to 20 which is recommanded) TronScan Data Access: The workflow accesses TronScan's public blockchain data, so no additional configuration is required for API access. Schedule Trigger Node: Defaulted to trigger every 15 minutes. Modify as per your requirements. Test the Workflow: Execute the workflow manually to ensure everything is operating correctly. How it Works Schedule Trigger: Initiates the workflow at predetermined intervals. Edit Fields: Sets up the wallet address and transaction retrieval count. TronScan Data Retrieval: Gathers transaction data from the TRC20 wallet using TronScan's public database. Split Out & Filter: Processes and filters the transaction data. Final Results: Organizes and formats the required transaction data for review. Aggregate: Consolidates all records (items) into a one comprehensive list (item). Customization Modify the filter conditions and fields to suit your tracking needs. (for example you can higher or lower the number of time to filter or IN / OUT transactions - Default is 15m/IN) Adjust the schedule trigger frequency according to your preference (default is 15m). Best Practices Regularly test the workflow to ensure consistent performance. Stay updated with any changes to the structure of TronScan's public data that might affect the workflow. Contributing Your feedback and contributions are greatly appreciated. Feel free to adapt, modify, and share enhancements with the n8n community.

What this template does This template uses GROQ LLAVA V1.5 7B API that offers fast inference for multimodal models with vision capabilities for understanding and interpreting visual data from images. . The users send a image and get a description of the image from the model. Setup Open the Telegram app and search for the BotFather user (@BotFather) Start a chat with the BotFather Type /newbot to create a new bot Follow the prompts to name your bot and get a unique API token Save your access token and username Once you set your bot, you can send the image, and get the descriptions.

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 template allows you to create a CRUD endpoint that performs the following actions: Create a new record Get a record Get many records Update a record Delete a record This template is connected with Airtable but you can replace the Airtable nodes with anything you need to interact with (e.g. Postgres, MySQL, Notion, Coda...). The template uses the n8n Webhook node setting 'Allow Multiple HTTP Methods' to enable multiple HTTP methods on the same node. Features Just two nodes to create 5 endpoints Use it with Airtable or replace the Airtable nodes for your own customization Add your custom logic exploiting all n8n's possibilities Workflow Steps Webhook node**: exposes the endpoints to get many records and create a new record Webhook (with ID) node**: exposes the endpoints to get, update, and delete a record. Due to a n8n limitation, this endpoint will have an additional code in the path (e.g. https://my.app.n8n.cloud/webhook/580ccc56-f308-4b64-961d-38323501a170/customers/:id). Keep this in mind when using these endpoints in your application Various Airtable nodes**: execute various specific operations to interact with Airtable records Getting Started To deploy and use this template: Import the workflow into your n8n workspace Customize the endpoint paths by tweaking the 'Path' parameters in the 'Webhook' and 'Webhook (with ID)' nodes (currently customers) Set up your Airtable credentials by following this guide and customize the Airtable nodes by selecting your base, table, and the correct fields to update. ...or... replace the Airtable nodes and connect the endpoint to any other service (e.g. Postgres, MySQL, Notion, Coda) How to use the workflow Activate the workflow Connect your app to the endpoints (production URLs) to perform the various operations allowed by the workflow Note that the Webhook nodes have two URLs, one for testing and one for production. The testing URL is activated when you click on 'Test workflow' button and can't be used for production. The production URL is available after you activate the workflow. More info here. Feel free to get in touch with me if you have questions about this workflow.

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

Use Case When trying to maximize your outreach, website visitors are often an overlooked source of qualified new leads. This workflow allows your to track and enrich new website visitors and saves them to a Google Sheet once they meet a pre-defined criteria. What this workflow does This workflow fires once a day and gets all your leads saved in Leadfeeder. It then takes the leads that meet a pre-defined engagement criteria, e.g. that they visited your site 3 times, and enriches them additionally with Clearbit. From there it filters the leads again by a criteria on the company, e.g. a minimum employee count, and saves matching leads into a Google Sheet document. Setup Add your Leedfeeder credentials. The name should be Authorization and the value Token token=yourapitoken. You can find your token via Settings -> Personal -> API-Token Add your Google Sheet credentials Save the Leedfeeder account names you want to use in the Setup node Copy the Google Sheets Template and add its URL to the Setup node How to adjust this to your needs Adjust and/or remove the engagement and company criteria Add more ways to enrich a company Potential ideas to enhance the use of this workflow Automatically reach out to users that meet the criteria / that get added to the sheet Create a workflow that finds the right employee in companies that are identified by this workflow

Use case This workflow snoozes any Todoist tasks, by moving them into a Snoozed todoist list and unsnoozes them 3 days before due date. Helps keep inbox clear only of tasks you need to worry about soon. How to setup Add your Todoist creds Create a Todoist project called snoozed Set the project ids in the relevant nodes Add due dates to your tasks in Inbox. Watch them disappear to snoozed. Set their date to tomorrow, watch it return to inbox. How to adjust this template Adjust the timeline.. Maybe 3 days is too close for you. Works mostly for me :)

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 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

This workflow creates or updates a Mautic contact when a new event is scheduled in Calendly. The first name and email address are the only two fields that get updated. Prerequisites Calendly account and Calendly credentials. Mautic account and Mautic credentials. How it works Triggers on a new event in Calendly. Creates a new contact in Mautic if the email address does not exist in Mautic. Updates the contact's first name in Mautic if the email address exists in Mautic.

Use case Error workflows are an important part of running workflows in production. How to setup Add Slack creds Add this error workflow to other workflows (docs)

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

This workflow shows how you can get your OpenAI assistant to call an n8n workflow as a tool. Since you can put almost any functionality in an n8n workflow, this means you can give your assistant access to almost any data source. Note that to use this template, you need to be on n8n version 1.19.4 or later.

This workflow demonstrates how to use the workflowStaticData() function to set any type of variable that will persist within workflow executions. https://docs.n8n.io/code/cookbook/builtin/get-workflow-static-data/ This can be useful for example when working with access tokens that expire after a certain time period. Using staticData we can keep a record of that access token and the expiry time and build our workflow logic around it. Important Static Data only persists across production executions, i.e. triggered by Webhooks or Schedule Triggers (not manual executions!) For this the workflow will have to be activated. Setup configure HTTP Request node to fetch access token from your API (optional) activate workflow test the workflow with the webhook production link you can check the population of the static data in the single executions Feedback If you found this useful or want to report some missing information - I'd be happy to hear from you at [email protected]

Who is this for? This workflow template is perfect for content creators, researchers, students, or anyone who regularly works with audio files and needs to transcribe and summarize them for easy reference and organization. What problem does this workflow solve? Transcribing audio files and summarizing their content can be time-consuming and tedious when done manually. This workflow automates the process, saving users valuable time and effort while ensuring accurate transcriptions and concise summaries. What this workflow does This template automates the following steps: Monitors a specified Google Drive folder for new audio files Sends the audio file to OpenAI's Whisper API for transcription Passes the transcribed text to GPT-4 for summarization Creates a new page in Notion with the summary Setup To set up this workflow: Connect your Google Drive, OpenAI, and Notion accounts to n8n Configure the Google Drive node with the folder you want to monitor for new audio files Set up the OpenAI node with your API key and desired parameters for Whisper and GPT-4 Specify the Notion database where you want the summaries to be stored How to customize this workflow Adjust the Google Drive folder being monitored Modify the OpenAI node parameters to fine-tune the transcription and summarization process Change the Notion database or page properties to match your preferred structure With this AI-powered workflow, you can effortlessly transcribe audio files, generate concise summaries, and store them in a structured manner within Notion. Streamline your audio content processing and organization with this automated template.

About the workflow The workflow reads every reply that is received from a cold email campaign and qualifies if the lead is interested in a meeting. If the lead is interested, a deal is made in pipedrive. You can add as many email inboxes as you need! Setup: Add credentials to the Gmail, OpenAI and Pipedrive Nodes. Add a in_campaign field in Pipedrive for persons. In Pipedrive click on your credentials at the top right, go to company settings > Data fields > Person and click on add custom field. Single option [TRUE/FALSE]. If you have only one email inbox, you can delete one of the Gmail nodes. If you have more than two email inboxes, you can duplicate a Gmail node as many times as you like. Just connect it to the Get email node, and you are good to go! In the Gmail inbox nodes, select Inbox under label names and uncheck Simplify.

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.

Edit 19/11/2024: As explained on the workflow, the AI Agent with the original system prompt was not effective when using gpt4-o-mini. To address this, I optimized the prompt to work better with this model. You can find the prompts I’ve tested on this Notion Page. And yes, there is one that works well with gpt4-o-mini. AI Agent to chat with you Search Console Data, using OpenAI and Postgres This AI Agent enables you to interact with your Search Console data through a chat interface. Each node is documented within the template, providing sufficient information for setup and usage. You will also need to configure Search Console OAuth credentials. Follow this n8n documentation to set up the OAuth credentials. Important Notes Correctly Configure Scopes for Search Console API Calls It’s essential to configure the scopes correctly in your Google Search Console API OAuth2 credentials. Incorrect configuration can cause issues with the refresh token, requiring frequent reconnections. Below is the configuration I use to avoid constant re-authentication: Of course, you'll need to add your client_id and client_secret from the Google Cloud Platform app you created to access your Search Console data. Configure Authentication for the Webhook Since the webhook will be publicly accessible, don’t forget to set up authentication. I’ve used Basic Auth, but feel free to choose the method that best meets your security requirements. 🤩💖 Example of awesome things you can do with this AI Agent

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!

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

This is a demo workflow to showcase how to use Supabase to embed a document, retrieve information from the vector store via chat and update the database. Setup steps: set your credentials for Supabase set your credentials for an AI model of your choice set credentials for any service you want to use to upload documents please follow the guidelines in the workflow itself (Sticky Notes) Feedback & Questions If you have any questions or feedback about this workflow - Feel free to get in touch at [email protected]

Template for Kids' Story in Arabic The n8n template for creating kids' stories in Arabic offers a versatile platform for storytellers to captivate young audiences with educational and interactive tales. It allows for customization to suit various use cases and can be set up effortlessly. Check this example: https://t.me/st0ries95 Use Cases Educational Platforms: Educational platforms can automate the creation and distribution of educational stories in Arabic for children using this template. By incorporating visual and auditory elements into the storytelling process, educational platforms can enhance learning experiences and engage young learners effectively. Children's Libraries: Children's libraries can utilize this template to curate and share a diverse collection of Arabic stories with young readers. The automated generation of visual content and audio files enhances the storytelling experience, encouraging children to immerse themselves in new worlds and characters through captivating narratives. Language Learning Apps: Language learning apps focused on Arabic can integrate this template to offer culturally rich storytelling experiences for children learning the language. By translating stories into Arabic and supplementing them with visual and auditory components, these apps can facilitate language acquisition in an enjoyable and interactive manner. Configuration Guide for Nodes OpenAI Chat Model Nodes: Functionality**: Allows interaction with the OpenAI GPT-4 Turbo model. Purpose**: Enables communication with advanced chat capabilities. Create a Prompt for DALL-E Node: Customization**: Tailor prompts for generating relevant visual content. Summarization**: Define prompts for visual content generation without text. Generate an Image for the Story Node: Resource Type**: Specifies image as the resource. Prompt Setup**: Configures prompt for textless image creation within the visual content. Generate Audio for the Story Node: Resource Type**: Chooses audio as the resource. Input Definition**: Sets input text for audio file generation. Translate the Story to Arabic Node: Chunking Mode Selection**: Allows advanced chunking mode choice. Summarization Configuration**: Sets method and prompts for story translation into Arabic. Send the Story To Channel Node: Channel ID**: Specifies the channel ID for sending the story text. Text Configuration**: Sets up the text to be sent to the channel. By following these node descriptions, users can effectively configure the n8n template for kids' stories in Arabic, tailoring it to specific use cases for a seamless and engaging storytelling experience for young audiences.

Accepting payments via credit card online is a crucial component for the majority of businesses. Stripe provides a robust suite of tools for processing payments, yet many people still find it challenging to create a simple payment page and distribute it to their customers. 📋 Blog post 📺 Youtube Video This n8n workflow aims to offer the simplest and most direct method for generating a Stripe payment link. Features Quick Stripe Payment Link Creation:** Simply enter a title and select a price to create a Stripe payment link in seconds. Set Up Steps Connect your Stripe credentials. Fill the config node (currency). This n8n workflow template is crafted to significantly reduce the creation time of a Stripe Payment link. Created by the n8ninja.

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.

This n8n template leverages n8n's multi-form feature to build a 2 part job application submission journey which aims to eliminate the need for applicants to re-enter data found on their CVs/Resumes. How it works The application submission process starts with an n8n form trigger to accept CV files in the form of PDFs. The PDF is validated using the text classifier node to determine if it is a valid CV else the applicant is asked to reupload. A basic LLM node is used to extract relevant information from the CV as data capture. A copy of the original job post is included to ensure relevancy. Applicant's data is then sent to an ATS for processing. For our demo, we used airtable because we could attach PDFs to rows. Finally, a second form trigger is used for the actual application form. However, it is prefilled to save the applicant's time and allow them to amend any of the generated application fields. How to use Ensure to change the redirect URL in the form ending node to use the host domain of your n8n instance. Requirements OpenAI for LLM Airtable to capture applicant data Customising the workflow Application form is pretty basic for this demonstration but could be extended to ask more in-depth questions. If it fits the job, why not ask applicants to upload portfolio works and have AI describe/caption them.

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 demonstrates how to enrich data from a list of companies in a spreadsheet. While this workflow is production-ready if all steps are followed, adding error handling would enhance its robustness. Important notes Check legal regulations**: This workflow involves scraping, so make sure to check the legal regulations around scraping in your country before getting started. Better safe than sorry! Mind those tokens**: OpenAI tokens can add up fast, so keep an eye on usage unless you want a surprising bill that could knock your socks off! 💸 Main Workflow Node 1 - Webhook This node triggers the workflow via a webhook call. You can replace it with any other trigger of your choice, such as form submission, a new row added in Google Sheets, or a manual trigger. Node 2 - Get Rows from Google Sheet This node retrieves the list of companies from your spreadsheet. here is the Google Sheet Template you can use. The columns in this Google Sheet are: Company**: The name of the company Website**: The website URL of the company These two fields are required at this step. Business Area**: The business area deduced by OpenAI from the scraped data Offer**: The offer deduced by OpenAI from the scraped data Value Proposition**: The value proposition deduced by OpenAI from the scraped data Business Model**: The business model deduced by OpenAI from the scraped data ICP**: The Ideal Customer Profile deduced by OpenAI from the scraped data Additional Information**: Information related to the scraped data, including: Information Sufficiency: Description: Indicates if the information was sufficient to provide a full analysis. Options: "Sufficient" or "Insufficient" Insufficient Details: Description: If labeled "Insufficient," specifies what information was missing or needed to complete the analysis. Mismatched Content: Description: Indicates whether the page content aligns with that of a typical company page. Suggested Actions: Description: Provides recommendations if the page content is insufficient or mismatched, such as verifying the URL or searching for alternative sources. Node 3 - Loop Over Items This node ensures that, in subsequent steps, the website in "extra workflow input" corresponds to the row being processed. You can delete this node, but you'll need to ensure that the "query" sent to the scraping workflow corresponds to the website of the specific company being scraped (rather than just the first row). Node 4 - AI Agent This AI agent is configured with a prompt to extract data from the content it receives. The node has three sub-nodes: OpenAI Chat Model: The model used is currently gpt4-o-mini. Call n8n Workflow: This sub-node calls the workflow to use ScrapingBee and retrieves the scraped data. Structured Output Parser: This parser structures the output for clarity and ease of use, and then adds rows to the Google Sheet. Node 5 - Update Company Row in Google Sheet This node updates the specific company's row in Google Sheets with the enriched data. Scraper Agent Workflow Node 1 - Tool Called from Agent This is the trigger for when the AI Agent calls the Scraper. A query is sent with: Company name Website (the URL of the website) Node 2 - Set Company URL This node renames a field, which may seem trivial but is useful for performing transformations on data received from the AI Agent. Node 3 - ScrapingBee: Scrape Company's Website This node scrapes data from the URL provided using ScrapingBee. You can use any scraper of your choice, but ScrapingBee is recommended, as it allows you to configure scraper behavior directly. Once configured, copy the provided "curl" command and import it into n8n. Node 4 - HTML to Markdown This node converts the scraped HTML data to Markdown, which is then sent to OpenAI. The Markdown format generally uses fewer tokens than HTML. Improving the Workflow It's always a pleasure to share workflows, but creators sometimes want to keep some magic to themselves ✨. Here are some ways you can enhance this workflow: Handle potential errors Configure the scraper tool to scrape other pages on the website. Although this will cost more tokens, it can be useful (e.g., scraping "Pricing" or "About Us" pages in addition to the homepage). Instead of Google Sheets, connect directly to your CRM to enrich company data. Trigger the workflow from form submissions on your website and send the scraped data about the lead to a Slack or Teams channel.

This workflow automatically sends you a list of your daily meetings every morning via a Telegram bot. Use Cases: This workflow is useful for anyone who wants to be automatically informed of their daily meetings, especially for busy professionals, students, and anyone with a hectic schedule. Setup: Google Calendar connected to n8n A Telegram bot created and connected to n8n Your Telegram user ID specified Notes: You need to replace the placeholder in the Telegram node with your actual Telegram user ID. You can customize the formatting of the Telegram message in the JavaScript Code node.

When you collect leads from a form, you need to format the incoming data such as the lead's name and also apply a basic validation of the email entered. Lucky for us, N8N offers all of these features with simple expressions that can easily be applied to data. This workflow aims to show how you can process your lead data before saving it in Mautic. How it Works This workflow receives data from a Wordpress form; applies name formatting and basic validation to the email; Creates the contact in Mautic; If e-mail is invalid, add the lead in Dot Not Contact list. Setup Steps Set up credentials when you first open the workflow. You'll need a Mautic account. You need to configure a form in Wordpress (Elementor, WPForms, etc.) and send it to the N8N Webhook address; Now map the fields you need to apply formatting and validation. After testing your workflow, swap the Test URL to Production URL in Discourse and activate your workflow

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!

Send multiple emails in Gmail directly via Google Sheets [Video for workflow process](https://www.canva.com/design/DAF8VnLBBQA/BKog1CSHs7_goAYse3mEzQ/watch?utm_content=DAF8VnLBBQA&utm_campaign=designshare&utm_medium=link&utm_source=editor ) In today's fast-paced digital world, businesses are constantly seeking ways to streamline their processes and enhance customer engagement. One powerful tool that facilitates these goals is n8n, an automation platform that allows users to create workflows to automate tasks and workflows. Benefits of the Workflow: Efficiency**: By automating the process of sending emails to customers based on data from Google Sheets, this n8n workflow significantly reduces manual effort and saves time. Accuracy**: The workflow ensures that emails are sent to the right recipients at the right time by filtering items based on specific conditions and the current date. Personalization**: Personalized emails can be sent to customers based on the information provided in the Google Sheet, resulting in enhanced customer engagement. Real-time Updates**: The workflow updates the Google Sheet with the status of the sent emails, providing real-time insights into the communication process. Consistency**: Through automation, this workflow helps maintain consistency in communication with customers, ensuring a seamless experience. Workflow Overview: The workflow begins with the "Google Sheets Trigger" node, which monitors a specified Google Sheet for new row additions. Upon detection of a new row, the workflow progresses to the "Filter Status (Waiting for sending)" node, where items are filtered based on specific conditions. Subsequently, the workflow moves to the "Filter Items by Current Date" node, which filters items based on the current date. Items matching the current date are then processed further. The filtered items are then forwarded to the "Gmail" node, where personalized emails are composed and sent to recipients based on the Google Sheet data. Finally, the workflow updates the Google Sheet using the "Google Sheets" node with the status of the sent emails and other relevant information. Copy this template to get started : Google Sheets Workflow Nodes Documentation: Schedule Trigger Filter Items by Current Date Gmail Google Sheets Filter Status (Waiting for sending) Set data Merge feild Conclusion: In conclusion, this n8n workflow presents a powerful solution for automating email communication processes based on Google Sheets data. By leveraging automation, businesses can enhance their operational efficiency, accuracy, and customer engagement. The seamless integration of nodes in this workflow streamlines the communication process and ensures timely and personalized interactions with customers. As businesses continue to prioritize efficiency and customer satisfaction, n8n workflows offer a versatile and effective means to achieve these objectives.

This workflow pushes Stripe charges to HubSpot contacts. It uses the Stripe API to get all charges and the HubSpot API to update the contacts. The workflow will create a new HubSpot property to store the total amount charged. If the property already exists, it will update the property. Prerequisites Stripe credentials. HubSpot credentials. How it works On a schedule, check if the property exists in HubSpot. If it doesn't exist, create it. The default schedule is once a day at midnight. Once property is acertained, the first Stripe node gets all charges. Once the charges are returned, the second Stripe node gets extra customer information. Once the customer information is returned, Merge data node will merge the customer information with the charges so that the next node Aggregate totals can calculate the total amount charged per contact. Once we have the total amount charged per contact, the Create or update customer node will create a new HubSpot contact if it doesn't exist or update the contact if it does exist with the total amount charged.

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

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 Made by Simon @ automake.io

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: [email protected] Linkedin: https://www.linkedin.com/in/anthony-sidashin/

Task: Make sure that data is in the right format before injecting it into a database/spreadsheet/CRM/etc. Why: Spreadsheets and databases require the incoming data to have the same fields as the headers of the destination table. You can decide which fields you would like to send with the database and rename them by using the set node Main use cases: Change fields names to match a database or a spreadsheet table structure Keep only the fields that are needed at the destination table

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)

Purpose This enables webhooks for nearly realtime updates (every 5 seconds) from Notion Databases. Problem Notion does not offer webhooks. Even worse, the “Last edited time” property, we could use for polling, only updates every minute. This gives us a polling interval only as low as 2 minutes and we still need to implement a comparing mechanism to detect changes. Solution This workflow caches states in between while doing efficient polling & comparing. It brings down the update latency from 2 minutes to 5 seconds and also provides the output of the changes only. Demo How it works Database Pages are frequently polled while filtered by a last modified time stamp for more efficiency Retrieved pages get compared with previously cached versions in Supabase Only new and changed pages are pushed to a registered webhook Setup Create a new project in Supabase and import the DB schema (provided through Gumroad) Add a "Last edited time" property to your Notion Database, if it has none yet Define the dynamically generated settings_id from the settings table (Supabase) in the Globals node Define the Notion Database URL in the Globals node Define your custom Webhook URL in the last node where the results should be pushed to It is recommended to call this workflow using this template to prevent simultaneous workflow executions Set the Schedule Trigger to every 5 seconds or less frequent More detailed instructions provided within the workflow file and the illustrated instructions provided during the download Example output [ { "action": "changed", "changes": { "property_modified_at": "2024-06-04T17:59:00.000Z", "property_priority": "important" }, "data": { "id": "ba761e03-7d6d-44c2-8e8d-c8a4fb930d0f", "name": "Try out n8n", "url": "https://www.notion.so/Try-out-n8n-ba761e037d6d44c28e8dc8a4fb930d0f", "property_todoist_id": "", "property_id": "ba761e037d6d44c28e8dc8a4fb930d0f", "property_modified_at": "2024-06-04T17:59:00.000Z", "property_status": "Backlog", "property_priority": "important", "property_due": { "start": "2024-06-05", "end": null, "time_zone": null }, "property_focus": false, "property_name": "Try out n8n" }, "updated_at": "2024-06-04T17:59:42.144+00:00" } ]

This template shows how you can create reports on data in an app and share a summary in another app. Specifically, this example checks a Notion database for new submissions, filters for submissions with a specific tag, and then sends a Slack message with the number created this week. Setup instructions are located inside the workflow template.

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.

Send Webflow form data to Google Sheets 🚀 How It Works This workflow connects your Webflow form submissions to Google Sheets with style and efficiency. Here's the magic in action: Form Submitted 🎉 – A Webflow form submission triggers the flow. Fields Prepared 🛠️ – Data gets organized, and a submission date is added. Data Logged 📋 – Your data finds a cozy new home in Google Sheets. Set-Up Steps ⏱️ Estimated Time: 5-10 minutes Connect Webflow 🌐 – Hook up your Webflow account and disable legacy APIs (instructions inside the workflow). Set Up Google Sheets 🧾 – Choose your spreadsheet and authenticate. You're Ready! 💫 – Let the workflow take it from here. Why You'll Love It ❤️ No More Manual Copy-Paste:** Save time and keep data clean. Smart Field Mapping:** Automatically creates columns and fits your data. Helpful Notes Along the Way:** Friendly tips guide you through setup like a pro. Enjoy the smooth ride from Webflow to Google Sheets! 🚀

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

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. 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.

Automating your marketing campaign management process can streamline your workflow and save you valuable time. With the combination of Baserow and n8n, you can efficiently handle your campaign data and seamlessly publish content to your Shopify store. In this workflow template, I demonstrate how to leverage Baserow as a centralized platform for organizing your marketing campaign assets, including copy and images. By utilizing n8n, we automate the process of fetching images and campaign descriptions from Baserow and uploading them directly to your Shopify store. With this automated solution, you can expedite the publishing process, ensuring that your campaigns are launched swiftly across your sales channels. Additionally, this workflow serves as a foundational step towards further automation in campaign management, allowing you to dynamically generate and upload content to your Shopify store with ease. This template will help you: Use n8n to get images for marketing campaigns from Baserow and upload them to your Shopify media library Dynamically inject data from Baserow into a template file Upload a template file to your Shopify theme This template will demonstrate the follwing concepts in n8n: use the Webhook node use the IF node to control the execution flow of the workflow do time calculation using expressions and javascript use the GraphQL node to upload images to your Shopify media files create a dynamic template file for your Shopify theme use the HTTP Reqest node to upload your template file to your Shopify store How to get started? Create a custom app in Shopify get the credentials needed to connect n8n to Shopify This is needed for the Shopify Trigger Create Shopify Acces Token API credentials n n8n for the Shopify trigger node Create Header Auth credentials: Use X-Shopify-Access-Token as the name and the Acces-Token from the Shopify App you created as the value. The Header Auth is neccessary for the GraphQL nodes. You will need a running Baserow instance for this. You can also sign up for a free account at https://baserow.io/ Please make sure to read the notes in the template. For a detailed explanation please check the corresponding video: https://youtu.be/Ky-dYlljGiY

Use Case Generate keywords for your Amazon product. You want to find more keywords for advertising your product on Amazon. What this Workflow Does The workflow connects to Amazon's completion API to retrieve keywords for your advertising campaigns Use Airtable to send the workflow your keywords. Download airtable here. It sends the keyword to Amazon and returns keywords related to your keyword. It stores the results back into Airtable. How to Adjust it to Your Needs You can modify the Amazon Completion API URL to get more data back from Amazon. Made by Rumjahn

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

Use case When working with multiple teams, bugs must get in front of the right team as quickly as possible to be resolved. Normally this includes a manual grooming of new bugs that have arrived in your ticketing system (in our case Linear). We found this way too time-consuming. That's why we built this workflow. What this workflow does This workflow triggers every time a Linear issue is created or updated within a certain team. For us at n8n, we created one general team called Engineering where all bugs get added in the beginning. The workflow then checks if the issue meets the criteria to be auto-moved to a certain team. In our case, that means that the description is filled, that it has the bug label, and that it's in the Triage state. The workflow then classifies the bug using OpenAI's GPT-4 model before updating the team property of the Linear issue. If the AI fails to classify a team, the workflow sends an alert to Slack. Setup Add your Linear and OpenAi credentials Change the team in the Linear Trigger to match your needs Customize your teams and their areas of responsibility in the Set me up node. Please use the format Teamname. Also, make sure that the team names match the names in Linear exactly. Change the Slack channel in the Set me up node to your Slack channel of choice. How to adjust it to your needs Play around with the context that you're giving to OpenAI, to make sure the model has enough knowledge about your teams and their areas of responsibility Adjust the handling of AI failures to your needs How to enhance this workflow At n8n we use this workflow in combination with some others. E.g. we have the following things on top: We're using an automation that enables everyone to add new bugs easily with the right data via a /bug command in Slack (check out this template if that's interesting to you) This workflow was built using n8n version 1.30.0