# templates

name: Telegram AI Bot: NeurochainAI Text & Image - NeurochainAI Basic API Integration
author_name: NeurochainAI
author_username: neurochainai
categories: []
created_at: 2024-11-28T14:46:40.622Z
updated_at: 2024-11-28T14:46:40.622Z
url: https://n8n.io/workflows/2584
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  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:

  &gt; 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

nodes_used:
  ["n8n-nodes-base.telegramTrigger","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.httpRequest","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.switch"]

workflow_json_compressed:
  [encoded data, 6120 characters]

metadata_json:
  {"categories":["integration","automation","AI","messaging","image-generation"],"complexity":"medium","use_cases":["Telegram AI chatbot (text understanding & replies)","On-demand AI image generation from prompts","Automated content generation and multimedia responses","Customer support or interactive bots with AI enrichment","Prototyping and testing NeurochainAI inference features"],"estimated_setup_minutes":30,"required_services":["Telegram Bot API (Bot Token)","NeurochainAI API (API Key)","n8n instance with public webhook access (or tunneling service like ngrok)"],"key_features":["NeurochainAI text inference integration","Flux-based AI image generation","Telegram message handling and reply flows","Webhook and HTTP nodes for API calls","Conditional routing (switch nodes) and custom code for logic"],"target_audience":["developers","chatbot builders","product owners/prototypers\",\"AI/ML enthusiasts"]}


name: Upload images to an S3 Bucket via a Slack Bot
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2024-11-29T06:14:01.540Z
updated_at: 2024-11-29T06:14:01.540Z
url: https://n8n.io/workflows/2585
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Upload Public-Facing Images to an S3 Cloudflare Bucket via Slack Modal

  🛠 Who is this for?

  This workflow is for teams that use Slack for internal communication and need a streamlined way to upload public-facing images to an S3 Cloudflare bucket. It's especially beneficial for DevOps, marketing, or content management teams who frequently share assets and require efficient cloud storage integration.

  💡 What problem does this workflow solve?

  Manually uploading images to cloud storage can be time-consuming and disruptive, especially if you're already working in Slack. This workflow automates the process, allowing you to upload images directly from Slack via a modal popup. It reduces friction and keeps your workflow within a single platform.

  🔍 What does this workflow do?

  This workflow connects Slack with an S3 Cloudflare bucket to simplify the image-uploading process:
  Slack Modal Interaction**: Users trigger a Slack modal to select images for upload.
  Dynamic Folder Management**: Choose to create a new folder or use an existing one for uploads.
  S3 Integration**: Automatically uploads the images to a specified S3 Cloudflare bucket.
  Slack Confirmation**: After upload, Slack sends a confirmation with the uploaded file URLs.

  🚀 Setup Instructions

  Prerequisites
  Slack Bot with the following permissions:
     commands
     files:write
     files:read
     chat:write
  Cloudflare S3 Credentials: Create an API token with write access to your S3 bucket.
  n8n Instance: Ensure n8n is properly set up with webhook capabilities.

  Steps
  Configure Slack Bot:
     Set up a Slack app and enable the Events API.
     Add your n8n webhook URL to the Events Subscription section.
  Add Credentials:
     Add your Slack API and S3 Cloudflare credentials to n8n.
  Customize the Workflow:
     Open the Idea Selector Modal node and update folder options to suit your needs.
     Update the Post Image to Channel node with your Slack channel ID.
  Deploy the Workflow:
     Activate the workflow and test by triggering the Slack modal.

  🛠 How to Customize This Workflow

  Adjust the Slack Modal
  You can modify the modal layout in the Idea Selector Modal node to add additional fields or adjust the styling.

  Change the Bucket Structure
  Update the Upload to S3 Bucket node to customize the folder paths or change naming conventions.

  🔗 References and Helpful Links
  Slack API Documentation
  Cloudflare S3 Setup
  n8n Documentation

  📓 Workflow Notes
  Key Features:
  Slack Integration**: Uses Slack modal interactions to streamline the upload process.
  Cloud Storage**: Automatically uploads to a Cloudflare S3 bucket.
  User Feedback**: Sends a Slack message with file URLs upon successful upload.

  Setup Dependencies:
  Slack API token
  Cloudflare S3 credentials
  n8n webhook configuration

  Sticky Notes Included
  Sticky notes are embedded within the workflow to guide you through configuration and explain node functionality.

  🌟 Why Use This Workflow?

  This workflow keeps your image-uploading process intuitive, efficient, and fully integrated with tools you already use. By leveraging n8n's flexibility, you can ensure smooth collaboration and quick sharing of public-facing assets without switching contexts.

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.switch","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.aggregate","n8n-nodes-base.switch","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.httpRequest","n8n-nodes-base.s3","n8n-nodes-base.slack","n8n-nodes-base.splitOut"]

workflow_json_compressed:
  [encoded data, 7512 characters]

metadata_json:
  {"categories":["automation","integration","content-management","file-storage"],"complexity":"medium","use_cases":["Upload public-facing images from Slack to cloud storage","Streamline asset management for marketing/content teams","Quickly share hosted image URLs back to Slack","Organize uploads into dynamic folders (new or existing)","Batch upload and handle multiple image files"],"estimated_setup_minutes":30,"required_services":["Slack (bot with commands, files:write, files:read, chat:write)","Cloudflare S3 (S3-compatible bucket and API token with write access)","n8n instance with webhook/public endpoint"],"key_features":["Slack modal to select and submit images","Dynamic folder management (create new or use existing)","Automatic upload to Cloudflare S3 bucket","Slack confirmation message with uploaded file URLs","Batch processing and conditional logic (splitInBatches, switch, aggregate) to handle multiple files reliably\",\"Embedded sticky notes guiding configuration"],"target_audience":["DevOps teams","Marketing and content teams","Product/content managers handling assets directly in Slack\",\"Developers/automation engineers setting up integrations"]}


name: Weekly dinner meal plan using recipes from Mealie
author_name: Damian Karzon
author_username: dkarzon
categories: []
created_at: 2024-11-29T06:35:38.804Z
updated_at: 2024-11-29T06:35:38.804Z
url: https://n8n.io/workflows/2586
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  This workflow randomly select recipes from a Mealie instance (can use a specific category) and then creates a meal plan in Mealie with those recipes.

  How it works:
  Workflow has a scheduled trigger (set to run weekly on a Friday)
  Config node sets a few properties to configure the workflow
  A call to the Mealie API to get the list of recipes
  The code node holds most of the logic, this will loop through the number of recipes defined in the config node and randomly select a recipe from the list (making sure not to double up any recipes)
  Once all the recipes are selected it will call the Mealie API to set up the meal plan on the days

  Setup
  Add your Mealie API token as a credential and set it on the Http Request nodes
  Set the relevant schedule trigger to run when you like
  Update the Config node with the config you want
  	numberOfRecipes - Number of recipes to populate for the meal plan
  	offsetPlanDays - Number of days in the future to start the plan (0 will start it today, 1 tomorrow, etc.)
  	mealieCategoryId - A category id of the category you want to pull in recipes from (default to select from all recipes)
  	mealieBaseUrl - The base url of your Mealie instance

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  H4sIAAAAAAAAE71YbW/bOBL+KzzuAbFb06Ek6s29fOim6LbAdjfXdrs4xPlAkSObjUxpKao+I/B/P1CSbcWxs9fF4T7F4Qzn5ZmHw6EesC4l1Hh2+4CVxDMcpFGW+UlGgFFJWAaccJ5QwngmKZMZZSHHE6z5CvAMvzVK8g1KqhWeYLup3JpONGmNkozXMK3FEmRTwGejFgsweIKrslZWlRrPbj1KJ55P7ya44oavwIKp8ewBm6YA91dpC+YbL9r4cgWFC3ENcF87f53F1/YN3+DZbXg3WHpXNgbPfLq92267yL6AqVun3tTbTvps0zTIqQw8EkZBSpgATjI/T4j0OPNASMiS4JDttQFuAX0AXqCbguuzOS+trT7CHw3U9jjf5HTCjSnwDF89PKC/jy6uS52rxcV4mitT29F4+rUu9XQFvFDwI6/hN1Og7faSV+pyWTY1LMtC1pdOXhVcO2hWYJelS/Dm10+f8QSXlQuggxbqqtQ1nPv9tjQrbvEMO6d46+Bzv34s5WYXofsfbbd4gmvQspNY08AE1xUIlW965dbCBPPGLkFbJXiHAl6ABqPEtQHp1nnx2aE42a2/buzyc4erA/IdcAnGLeLtBIv9pjabI4VZX9fyy/qtt/wiN8En802lN4cafmhRRN0e1Fk9ZggbMITLMAg9X5LYZxFhac4IT6KM5H6YZLHwBIvhYP33JWgkCiXulV6gOf4MtUXr0tznRbme47OEWXHd8OLsEQmeMuYJq/cRCyo9GQMlKfM8wiDiJIk9TmgQsyzhaR7K7BDxT2DRRxCqgvr76RyxP2XzeeqavdcDO7cdpf7ZgNlz6n9Hnwn+wxm+eRTrMPLbhx0sFZgbvnBWv/GicSsepdhh3CsIbmFRGtUmsNM5zvi6U9q8l+64dL3o/01gP0/jMM4FieM8JCyTnHDwgcQ0SYCHwvOYHLS4tvWcb+ZwxIDgTD97VFJe12qhV6BtK3v07+7eEdKPolBKkoU0JMwDRtIgkkTk3JcQZ5KmA9bqZpWB+TV/ytxWcChJuMcBaCp5KELiOj5hQgYkpakkEMjcT5KEZ3JwkMs8r8G6Jv+Gb56xH+ztJxTS0AtSkgQyJyzkQNKUU5KLkMoAUhaH+cH+MT8OHmprlF4MSBWB8GI/iwlPYklYEsckySEgARMghE9zX4R4H0Yeeh4kMSMiiAPCIpaQFFhKPJ8GUcxiJiN5HEZ/MJ+JwbF0dnnppf7Ui5KpNw1naeqH+MT1GkzZIRiQCQ2BExq6EYIxSZLA8wmPkswPIfWyTA5bkQbjLtiPXMtyhd5bWJ1vSqJs6zXgYhKd5uLX+trpzrAodW3REXfQ1fkr90j11Vx3Jh6z4zkLjzX3BpRLze1Tumrs4y2t7NVcz3UBFrkb/a0Tv3HIXCENa+R+jsav5vqR0B3OVvJ4ddGvjtHLo7jHrZMuoK4Z/6xqi67Q7d0+UNOW4pemjbZdd0GJxhjQ1hUIXSHa2lkvVQFodDA0LUAv7BL94xjxMXqYa4SOHKAr9IHb5TQvytKM2p+dbDRGL4aiFp/e+LjLASGVo9HfDsFOlRZFI6Ee7dfGvdudY7tUdRcQuurqcbvXveut7nQ1rI/hfwSyi8Jp93r/RSUGCHabh+6qtkCtv9tu/WB6AfZtUxT/Am5G48lOOrqgF+glGg20PpTaLltf3ng8rQslYET8J1sGO7rgBrqd6t30a6n06IJcnIt0g6522CI0x5JbmOPZPo3JQQTamo27nJ18jqXSGswcDzQ6/ryXTuFQoqmSAx3XL47k7VKrsD3U7oDxy5d9hQb0rJp6OeoT2BVwwKBWfGBPq7Cd662zbsA2Rg+MvcLHbdA/NEHGqIBUEiH8mLA08QmnmSQZFeBD6HuxTA9N8JNV4n6DfiktnL+FW51eZdAAGZ2Q6Gn/E2VRGjxjE7wEtVhaPAsYnbhuaEG7Sf+HH1A/es71J3BHA1D/kkK2RKbRaO0G203ZoELdw5NkB9NGxoWf04T4aZQSlrOYpLkAQiGWwLxISHo6We+7s/WTZ9MNJ3itpBupAkqfS/23yhG2JRPqmvgBBRcAcjNrmbvsDeqnL6Vry7WATrPrb06nn2kdaovdbca1HGhId2HYsm/FrRPHQTSiaK2KAtWWG4tsKflmPJ3rnwzPEEf9tLlBSqLclKtdHKPSoAL4N0BZwfU9ykuDeFGgw3Q6fqZY3E9CCkFKhB/mhPkeJUmeAuFRGAsqoiDLw5PF8v8SNf3kabH6EoVsUCLfPy6Re6S0wICc6xfIYd5UDpb9LN1mPqzQ65v3yJb3oN2G11VVbFqsBzts2a746J21FTLd+wa1ueyc7L5d7E+Dq6WEWhmQrgO6UfmZs0BlHFBPkCR1T8dQBCTJw5zkGfi5SKjIg/gkvMF3wxs+fxbi/VmIomfPwu/9UxUV5UIJB4OD3jFqx+xH7Fsru1R6wM4c1U1VFQrk2G3uBrligyol7lHZdHzvD4Ojv9JdVfpj9wL1n1i6iwG58bQ9HnVbX6faVKJcuce1O0gnwL9rM9Ig9h88+ifN7AGvuHJguVdeNxCefPy2WhOstIR/4xnd3t1tJ8MPXacMHT+bTtoYejsdzbPz7xmbp/acDPHsx6uThv/sS8ZfRGG7/Q/5fihYchQAAA==

metadata_json:
  {"categories":["automation","integration","meal planning","home"],"complexity":"medium","use_cases":["Generate a weekly dinner plan from recipe database","Randomly select non-duplicate recipes (optionally by category)","Automatically create meal plans in Mealie on a schedule","Customize start day and number of recipes via config"],"estimated_setup_minutes":15,"required_services":["Mealie API (self‑hosted or hosted instance) - API token required","n8n (workflow runner)"],"key_features":["Scheduled weekly trigger","Configurable number of recipes and start offset","Optional category filtering for recipe selection","Random selection with duplicate avoidance","HTTP calls to Mealie to fetch recipes and create meal plans"],"target_audience":["Home cooks and meal planners","n8n users and automation builders","People self-hosting Mealie who want automated meal planning"]}


name: Sync Entra User to Zammad User
author_name: Sirhexalot
author_username: sirhexalot
categories: []
created_at: 2024-11-29T09:59:23.069Z
updated_at: 2024-11-29T09:59:23.069Z
url: https://n8n.io/workflows/2587
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  This workflow facilitates seamless synchronization between Entra (Microsoft Azure AD) and Zammad. It automates the following processes:

  Fetch Entra Group Members: Retrieves users from a designated Entra group. These users are candidates for synchronization.
  Create Universal User Object: Extracts key user information, such as email, phone, and name, and formats it for Zammad compatibility.
  Synchronize with Zammad:
     Identifies users in Zammad who need updates based on Entra data.
     Adds new users from Entra to Zammad.
     Deactivates users in Zammad if they are no longer in the Entra group.

  Key Features

  Dynamic Matching**: Compares users from Entra with existing Zammad users based on email and updates records accordingly.
  Efficient Management**: Automatically creates, updates, or deactivates Zammad users based on their status in Entra.
  Custom Fields**: Supports custom field mapping, ensuring enriched user profiles in Zammad.

  Setup Instructions

  Microsoft Entra Integration:
     Ensure proper API permissions for accessing Entra groups and members.
     Configure Microsoft OAuth2 credentials in n8n.

  Zammad Integration:
     Set up Zammad API credentials with appropriate access rights.
     Customize the workflow to include additional fields or map existing fields as needed.

  Run Workflow:
     Trigger the workflow manually or set up an automation schedule (e.g., daily sync).
     Review created/updated/deactivated users in Zammad.

  Use Cases

  IT Administration**: Keep your support system in sync with the organization’s Entra data.
  User Onboarding**: Automatically onboard new hires into Zammad based on Entra groups.
  Access Management**: Ensure accurate and up-to-date user records in Zammad.

  Prerequisites

  Access to an Entra (Azure AD) environment with group data.
  A Zammad instance with API credentials for user management.
  A custom field in Zammad User Object (entra_key) of type String.



  A custom field in Zammad User Object (entra_object_type) of type `Single selection field with two key value pairs
    user = User
    contact = Contact`




  This workflow is fully customizable and can be adapted to your organization’s specific needs. Save time and reduce manual errors by automating your user sync process with this template!

  If you have found an error or have any suggestions, please report them here on Github.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.if","n8n-nodes-base.splitOut","n8n-nodes-base.set","n8n-nodes-base.zammad","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.compareDatasets","n8n-nodes-base.zammad","n8n-nodes-base.zammad","n8n-nodes-base.zammad","n8n-nodes-base.compareDatasets","n8n-nodes-base.if","n8n-nodes-base.if"]

workflow_json_compressed:
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metadata_json:
  {"categories":["integration","user provisioning","IT automation","helpdesk"],"complexity":"medium","use_cases":["Keep support system user records in sync with Azure AD group membership","Automated onboarding of new hires into Zammad","Automated offboarding/deactivation when users leave a group","Maintain accurate access and contact information in helpdesk system"],"estimated_setup_minutes":30,"required_services":["Microsoft Entra (Azure AD) API (OAuth2)","Zammad API","n8n workflow runner"],"key_features":["Fetch Entra group members and normalize into a universal user object","Dynamic matching by email to identify create/update candidates","Create new Zammad users, update existing ones, and deactivate removed users","Support for custom field mapping (e.g., entra_key, entra_object_type)","Can run manually or on a schedule for regular syncs"],"target_audience":["IT administrators","Identity/access managers","Helpdesk/support managers/operators who run Zammad integrations」「System integrators/DevOps engineers responsible for automation"]}


name: Assign Zammad Users to Organizations Based on Email Domain
author_name: Sirhexalot
author_username: sirhexalot
categories: []
created_at: 2024-11-29T10:07:15.757Z
updated_at: 2024-11-29T10:07:15.757Z
url: https://n8n.io/workflows/2588
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  This n8n workflow automates the assignment of existing users to organizations in Zammad based on their email domains, utilizing Zammad’s "domain-based assignment" feature.

  Use Case
  Automate the post-hoc assignment of existing users to organizations in Zammad by leveraging their email domains. This ensures efficient management of user-organization relationships using Zammad’s "domain-based assignment" feature.

  Requirements
  An active Zammad account with API access.
  Existing users and organizations in Zammad with proper email and domain configurations.
  Zammad organizations must have domain-based assignment enabled.

  Credentials
  Set up your Zammad API credentials in n8n to allow the workflow to interact with your Zammad instance securely.

  If you have found an error or have any suggestions, please report them here on Github.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.zammad","n8n-nodes-base.zammad","n8n-nodes-base.merge","n8n-nodes-base.zammad"]

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metadata_json:
  {"categories":["automation","integration","ticketing","customer-support"],"complexity":"medium","use_cases":["Automatically assign existing Zammad users to organizations based on their email domain","Post-hoc cleanup of user-organization relationships after user imports","Enforce organization membership rules for incoming users","Onboarding automation to map users to the correct org by domain"],"estimated_setup_minutes":20,"required_services":["Zammad API","n8n"],"key_features":["Domain-based assignment of users to organizations in Zammad","Uses Zammad API to find and update users and organizations","Conditional checks to avoid unnecessary updates","Manual trigger available for controlled runs","Merge and set nodes to prepare and combine data for API calls"],"target_audience":["Support administrators","IT/system administrators","DevOps / platform engineers implementing automation in n8n"]}


name: Daily GitHub Release notification by Email
author_name: Dionysus
author_username: dionysus
categories: []
created_at: 2024-11-30T13:14:35.977Z
updated_at: 2024-11-30T13:14:35.977Z
url: https://n8n.io/workflows/2590
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Automating daily notifications of the latest releases from a GitHub repository. This template is ideal for developers and project managers looking to stay up-to-date with software updates.

  How it Works:

  Daily Trigger: The workflow initiates daily using the Schedule Trigger node.
  Fetch Repository Data: The HTTP Request node retrieves the latest release details from the specified GitHub repository.
  Check if new: The IF node check if the release was done in the last 24 hours.
  Split Content: The Split Out node processes the JSON response to extract and structure relevant data.
  Convert Markdown: The Markdown node converts release notes from Markdown format to HTML, making them ready to use in emails.
  Send a notification by email

  Key Features:

  Simple to customize by modifying the GitHub URL.
  Automatically processes and formats release notes for better readability.
  Modular design, allowing integration with other workflows like Gmail or Slack notifications.

  Setup Steps:

  Modify Repository URL: Update the Sticky Note node with the URL of the repository you want to monitor.
  Modify SMTP details: Update the Send Email node with your SMTP details.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.markdown","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.emailSend","n8n-nodes-base.if"]

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metadata_json:
  {"categories":["automation","integration","notifications","monitoring"],"complexity":"simple","use_cases":["Daily release notifications for a GitHub repository","Keeping teams informed about new releases and changelogs","Automated distribution of release notes via email","Integrating release alerts into other workflows (Gmail, Slack)"],"estimated_setup_minutes":10,"required_services":["GitHub (public repository API access)","Email provider / SMTP server (or Gmail SMTP/API)","n8n instance"],"key_features":["Daily scheduled trigger to check for new releases","Fetches latest release data from GitHub via HTTP","Filters releases within the last 24 hours","Splits and structures JSON release data for processing","Converts Markdown release notes to HTML for email formatting"],"target_audience":["Developers","Project managers","Release managers / DevOps engineers"]}


name: Send Emails via Gmail from Obsidian
author_name: Obsidi8n
author_username: derruttie
categories: []
created_at: 2024-11-30T13:25:25.711Z
updated_at: 2024-11-30T13:25:25.711Z
url: https://n8n.io/workflows/2591
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  How it Works
  This n8n template makes it possible to send emails directly from your Obsidian notes. 
  It leverages the power of the Obsidian Post Webhook plugin, allowing seamless integration between your notes and the email workflow. 

  What it does:
  Receives note content and metadata from Obsidian via a Webhook.
  Parses YAML frontmatter to define email recipients, subject, and more.
  Automatically processes attachments, encoding them into an email-friendly format.
  Sends emails via Gmail and confirms the status back to Obsidian.
  Includes a testing feature to verify everything works before going live.

  Set-up Steps
  Webhook Configuration: Set your n8n POST Webhook URL in the Obsidian Obsidian Post Webhook plugin settings.
  Email Integration: Submit the Gmail credentials in n8n email nodes.
  Test the Workflow: Run a test from Obsidian to ensure the template functions correctly.
  Activate and Enjoy: Start sending customized emails with attachments from your notes in no time!

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.splitOut","n8n-nodes-base.stickyNote","n8n-nodes-base.splitInBatches","n8n-nodes-base.convertToFile","n8n-nodes-base.aggregate","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.respondToWebhook","n8n-nodes-base.set","n8n-nodes-base.respondToWebhook","n8n-nodes-base.dateTime","n8n-nodes-base.webhook"]

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metadata_json:
  {"categories":["integration","automation","communication","productivity"],"complexity":"medium","use_cases":["Send notes or templates from Obsidian as emails","Share meeting notes, reports or drafts directly from a vault","Automate email notifications based on note metadata","Attach files referenced in notes and deliver them via email","Test and validate email formatting before sending"],"estimated_setup_minutes":20,"required_services":["Obsidian with Post Webhook plugin","n8n instance (self-hosted or cloud)","Gmail account / Google credentials (for n8n Gmail nodes)"],"key_features":["Receives note content and metadata via a POST webhook","Parses YAML frontmatter to define recipients, subject, etc.","Processes and encodes attachments for email delivery","Sends emails through Gmail nodes and reports status","Includes a test mode to verify setup before going live"],"target_audience":["Obsidian users","Knowledge workers and writers","n8n users and workflow builders"]}


name: Agentic Telegram AI bot with with LangChain nodes and new tools
author_name: Yulia
author_username: yulia
categories: []
created_at: 2024-11-30T17:40:46.911Z
updated_at: 2024-11-30T17:40:46.911Z
url: https://n8n.io/workflows/2592
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  Create a Telegram bot that combines advanced AI functionalities with LangChain nodes and new tools.

  Nodes as tools and the HTTP request tool are a new n8n feature that extend custom workflow tool and simplify your setup. We used the workflow tool in the previous Telegram template to call the Dalle-3 model.

  In the new version, we've achieved similar results using the HTTP Request tool and the Telegram node tool instead. The main difference is that Telegram bot becomes more flexible. The LangChain Agent node can decide which tool to use and when. In the previous version, all steps inside the custom workflow tool were executed sequentially.

  ⚠️ Note that you'd need to select the Tools Agent to work with new tools. 

  Before launching the template, make sure to set up your OpenAI and Telegram credentials.

  Here’s how the new Telegram bot works:

  Telegram Trigger listens for new messages in a specified Telegram chat. This node activates the rest of the workflow after receiving a message.

  AI Tool Agent receives input text, processes it using the OpenAI model and replies to a user. It addresses users by name and sends image links when an image is requested.

  The OpenAI GPT-4o model generates context-aware responses. You can configure the model parameters or swap this node entirely. 

  Window buffer memory helps maintain context across conversations. It stores the last 10 interactions and ensures that the agent can access previous messages within a session. Conversations from different users are stored in different buffers.

  The HTTP request tool connects with OpenAI's DALL-E-3 API to generate images based on user prompts. The tool is called when the user asks for an image.

  Telegram node tool sends generated images back to the user in a Telegram chat. It retrieves the image from the URL returned by the DALL-E-3 model. This does not happen directly, however. The response from the HTTP request tool is first stored in the Agent’s scratchpad (think of it as a short-term memory). In the next iteration, the Agent sends the updated response to the GPT model once again. The GPT model will then create a new tool request to send the image back to the user. To pass the image URL, the tool uses the new $fromAI() expression. 

  Send final reply node sends the final response message created by the agent back to the user on Telegram. Even though the image was already passed to the user, the Agent always stops with the final response that comes from dedicated output.

  ⚠️ Note, that the Agent may not adhere to the same sequence of actions in 100% of situations. For example, sometimes it could skip sending the file via the Telegram node tool and instead just send an URL in the final reply. If you have a longer series of predefined steps, it may be better to use the “old” custom workflow tool.

  This template is perfect as a starting point for building AI agentic workflow. Take a look at another agentic Telegram AI template that can handle both text and voice messages.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.telegramTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.telegramTool","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.agent"]

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metadata_json:
  {"categories":["automation","integration","AI/ML","conversational AI"],"complexity":"complex","use_cases":["Agentic conversational bot that decides which tools to call","On-demand image generation via DALL·E-3 from chat prompts","Context-aware customer support or personal assistant workflows","Prototyping LangChain-based tool-using agents","Multimodal chat experiences (text + generated images)"],"estimated_setup_minutes":20,"required_services":["OpenAI (GPT-4o and DALL·E-3 API)","Telegram Bot API (Telegram credentials)","n8n instance (cloud or self-hosted)"],"key_features":["LangChain Agent node that chooses tools dynamically","HTTP Request tool used as DALL·E-3 image generator","Telegram node/tool for receiving messages and sending images/replies","Window buffer memory to preserve last 10 interactions per user","Agent scratchpad + $fromAI() expression to pass image URLs between tools and model calls','Configurable OpenAI model parameters (swap GPT model)"],"target_audience":["developers","AI/automation engineers","chatbot builders and integrators who use n8n','product managers and prototypers looking to demo agentic workflows"]}


name: Export Zammad Objects (Users, Roles, Groups, Organizations) to Excel
author_name: Sirhexalot
author_username: sirhexalot
categories: []
created_at: 2024-12-01T11:12:12.671Z
updated_at: 2024-12-01T11:12:12.671Z
url: https://n8n.io/workflows/2596
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n workflow enables you to export data from Zammad, including Users, Roles, Groups, and Organizations, into individual Excel files. It simplifies data handling and reporting by creating structured outputs for further processing or sharing.

  Features
  Export Users with associated details such as email, firstname, lastname, role_ids, and group_ids.
  Export Roles and Organizations with their respective identifiers and names.
  Convert all data into separate Excel files for easy access and use.

  Usage
  Import this workflow into your n8n instance.
  Configure the required Zammad API credentials (zammad_base_url and zammad_api_key) in the Basic Variables node.
  Run the workflow to generate Excel files containing Zammad data.

  Issues and Suggestions
  If you encounter any issues or have suggestions for improvement, please report them on the GitHub repository.

  We appreciate your feedback to help enhance this workflow!

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.zammad","n8n-nodes-base.httpRequest","n8n-nodes-base.convertToFile","n8n-nodes-base.convertToFile","n8n-nodes-base.convertToFile","n8n-nodes-base.zammad","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.convertToFile","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.if"]

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metadata_json:
  {"categories":["integration","data processing","reporting","automation","backup"],"complexity":"medium","use_cases":["Exporting Zammad data for reporting","Auditing and compliance (user/role/group snapshots)","Data migration from Zammad to other systems","Sharing structured Zammad data with stakeholders","Periodic backups of Zammad objects"],"estimated_setup_minutes":15,"required_services":["Zammad API","n8n instance"],"key_features":["Export Users with email, firstname, lastname, role_ids and group_ids","Export Roles, Groups and Organizations with identifiers and names","Convert each object type into separate Excel files","Configurable Zammad credentials via Basic Variables node","Runs within n8n using built-in convertToFile and HTTP nodes"],"target_audience":["System administrators","Support/team managers using Zammad","IT/DevOps engineers handling exports and backups」、「Data analysts needing structured Zammad exports"]}


name: Update all Zammad Roles to default values
author_name: Sirhexalot
author_username: sirhexalot
categories: []
created_at: 2024-12-01T12:02:43.884Z
updated_at: 2024-12-01T12:02:43.884Z
url: https://n8n.io/workflows/2597
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  This n8n workflow allows you to reset all user roles in Zammad to specified default roles. It ensures consistency in role management across your Zammad instance.

  Features
  Retrieve all active users from Zammad.
  Update each user's roles to predefined default role IDs.
  Exclude specific users by their IDs from the update process.
  Simple configuration for default roles and excluded users.

  Usage
  Import the Workflow: Upload the provided .json file into your n8n instance.
  Configure Variables:
     zammad_base_url: Your Zammad instance URL.
     zammad_api_key: Your Zammad API key.
     default_roles: List of default role IDs to apply to all users.
     exclude_zammad_users_by_id: List of user IDs to exclude from the update.
  Run the Workflow: Execute the workflow to update roles automatically.

  Issues and Suggestions
  For issues or suggestions, visit the GitHub Repository.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.zammad","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.convertToFile","n8n-nodes-base.if","n8n-nodes-base.httpRequest"]

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metadata_json:
  {"categories":["automation","user-management","integration"],"complexity":"medium","use_cases":["Reset all Zammad user roles to a standard set","Enforce consistent role assignments for compliance or audits","Bulk onboarding/offboarding role standardization","Exclude specific admin or service accounts from bulk changes"],"estimated_setup_minutes":10,"required_services":["Zammad (API access)","n8n instance"],"key_features":["Fetch all active Zammad users","Apply predefined default role IDs to each user","Exclude specific user IDs from updates","Simple variable-based configuration (base URL, API key, default roles, excluded users)","Supports manual or webhook-triggered execution"],"target_audience":["IT administrators","Helpdesk/support managers","DevOps engineers who manage Zammad instances"]}


name: Update Zammad Roles by Excel
author_name: Sirhexalot
author_username: sirhexalot
categories: []
created_at: 2024-12-01T12:18:42.597Z
updated_at: 2024-12-01T12:18:42.597Z
url: https://n8n.io/workflows/2598
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  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 &lt;put here your zammad api token&gt;
  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.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.extractFromFile","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["integration","automation","data processing","IT administration"],"complexity":"medium","use_cases":["Bulk update user roles in Zammad from spreadsheet","Automate onboarding/offboarding role assignments","Synchronize role assignments from HR spreadsheets","Periodic audits/cleanup of user roles"],"estimated_setup_minutes":15,"required_services":["Zammad (API access with token)","Excel file hosting or accessible URL (e.g., cloud storage or web server)","n8n instance with internet access"],"key_features":["Import user data from Excel file (email + roles)","Find Zammad users by email and update roles via API","Configurable variables for Zammad URL, API token, and Excel URL","Header-based Bearer token authentication for Zammad nodes","Resilient error handling to continue on individual update failures"],"target_audience":["System administrators","IT/DevOps teams","HR or operations staff managing user access lists locally before syncs to Zammad"]}


name: Entra Contacts to Zammad User Sync
author_name: Sirhexalot
author_username: sirhexalot
categories: []
created_at: 2024-12-01T13:44:32.857Z
updated_at: 2024-12-01T13:44:32.857Z
url: https://n8n.io/workflows/2599
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow facilitates seamless synchronization between Entra (Microsoft Azure AD) and Zammad. It automates the following processes:

  Fetch Entra Contacts: 
  Create Universal User Object: Extracts key user information, such as email, phone, and name, and formats it for Zammad compatibility.
  Synchronize with Zammad:
     Identifies users in Zammad who need updates based on Entra data.
     Adds new users from Entra to Zammad.
     Deactivates users in Zammad if they are no longer in Entra.

  Key Features

  Dynamic Matching**: Compares contacts from Entra with existing Zammad users based on email and updates records accordingly.
  Efficient Management**: Automatically creates, updates, or deactivates Zammad users based on their status in Entra.
  Custom Fields**: Supports custom field mapping, ensuring enriched user profiles in Zammad.

  Setup Instructions

  Microsoft Entra Integration:
     Ensure proper API permissions for accessing Entra contacts.
     Configure Microsoft OAuth2 credentials in n8n.

  Zammad Integration:
     Set up Zammad API credentials with appropriate access rights.
     Customize the workflow to include additional fields or map existing fields as needed.

  Run Workflow:
     Trigger the workflow manually or set up an automation schedule (e.g., daily sync).
     Review created/updated/deactivated users in Zammad.

  Use Cases

  IT Administration**: Keep your support system in sync with the organization’s Entra data.
  Customer Management**: Ensure accurate and up-to-date user records in Zammad.

  Prerequisites

  Access to an Entra (Azure AD) environment with contacts data.
  A Zammad instance with API credentials for user management.
  A custom field in Zammad User Object (entra_key) of type String.



  A custom field in Zammad User Object (entra_object_type) of type `Single selection field with two key value pairs
    user = User
    contact = Contact`



  This workflow is fully customizable and can be adapted to your organization’s specific needs. Save time and reduce manual errors by automating your user sync process with this template!

  If you have found an error or have any suggestions, please report them here on Github.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.zammad","n8n-nodes-base.merge","n8n-nodes-base.compareDatasets","n8n-nodes-base.zammad","n8n-nodes-base.zammad","n8n-nodes-base.zammad","n8n-nodes-base.compareDatasets","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.if","n8n-nodes-base.if"]

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metadata_json:
  {"categories":["integration","automation","identity management","ITSM","user management"],"complexity":"medium","use_cases":["Keep helpdesk users in sync with Azure AD/Entra","Automated user provisioning for Zammad","Deactivate Zammad accounts when users are removed from Entra","Maintain enriched user profiles via custom field mapping"],"estimated_setup_minutes":30,"required_services":["Microsoft Entra (Azure AD / Microsoft Graph API)","Zammad (API access)","n8n (workflow automation platform)"],"key_features":["Fetch Entra contacts and normalize to a universal user object","Dynamic matching by email to detect creates/updates","Create new Zammad users and update existing profiles","Deactivate Zammad users when no longer present in Entra","Custom field mapping (entra_key, entra_object_type) support for enriched profiles"],"target_audience":["IT administrators","Helpdesk/support teams","DevOps/system integrators responsible for identity synchronization"]}


name: Generate n8n Forms from Airtable and BaseRow Tables
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-12-03T14:34:43.509Z
updated_at: 2024-12-03T14:34:43.509Z
url: https://n8n.io/workflows/2603
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n template showcases a cool feature of n8n Forms where the form itself can be defined dynamically using the form fields schema.

  It may be debateable how useful this template actually is since both Airtable and Baserow provide form interfaces already but still a great exercise and demonstration if ever the use-case comes around.

  How it works
  A form trigger is used to dynamically select a database/table from which to build the n8n form from.
  the table's schema is imported into the workflow and using the code node, is converted into the n8n form fields schema.
  This let's us dynamically build the fields in our n8n form when we choose to define the form using the JSON option.
  Once the n8n form submits, we convert the values back into our table's API schema so that we can create a new row.
  Note any files/attachments fields are removed as they need to be handled separately.
  Files are processed separately as they may first need to be stored. Once complete, the reference is saved into the newly created row.

  Check out the example Airtable here - https://airtable.com/appfP15Xd0aVZR9xV/shrGFgXLyQ4Jg58SU

  How to use
  The n8n form is autogenerated which means you only need provide access to the table. Using this approach, this template can be reused for any number of Airtable and/or Baserow tables.

  Requirements
  You'll need either an Airtable account or a Baserow account to use this template.
  Accessible n8n instance to your users

  Customising this workflow
  Not using either Airtable or Baserow? Theoretically any datastore which provides a fields schema can be used with this template.
  If you're feeling creative, split the table into multiple forms for a better user experience.

nodes_used:
  ["n8n-nodes-base.airtable","n8n-nodes-base.formTrigger","n8n-nodes-base.filter","n8n-nodes-base.aggregate","n8n-nodes-base.form","n8n-nodes-base.code","n8n-nodes-base.splitOut","n8n-nodes-base.code","n8n-nodes-base.filter","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.aggregate","n8n-nodes-base.filter","n8n-nodes-base.form","n8n-nodes-base.httpRequest","n8n-nodes-base.formTrigger","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.airtable","n8n-nodes-base.form","n8n-nodes-base.form"]

workflow_json_compressed:
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metadata_json:
  {"categories":["integration","form-generation","data-processing","automation"],"complexity":"complex","use_cases":["Dynamically generate n8n forms from Airtable or Baserow table schema","Collect user input and create rows in Airtable/Baserow","Reusable form builder for multiple tables/datastores","Handle file/attachment workflows separately and reference them in created rows","Prototype or expose table-backed forms without building front-end UI"],"estimated_setup_minutes":30,"required_services":["n8n instance (accessible to users)","Airtable account (or API key) or Baserow account (or API key)"],"key_features":["Reads table schema and converts it into n8n form fields JSON","Dynamically builds forms at runtime based on chosen table","Converts form submissions back to table API payloads","Handles attachments/files as a separate processing step","Reusable for any datastore that exposes a fields schema"],"target_audience":["n8n users","integration developers","automation engineers / ops team members"]}


name: Time logging on Clockify using Slack
author_name: Blockia Labs
author_username: blockia
categories: []
created_at: 2024-12-03T18:12:28.591Z
updated_at: 2024-12-03T18:12:28.591Z
url: https://n8n.io/workflows/2604
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Time Logging on Clockify Using Slack

  How it works
  This workflow simplifies time tracking for teams and agencies by integrating Slack with Clockify. It enables users to log, update, or delete time entries directly within Slack, leveraging an AI-powered assistant for seamless and conversational interactions. Key features include:

  Effortless Time Logging**: Create and manage time entries in Clockify without leaving Slack.
  AI-Powered Assistant**: Get step-by-step guidance to ensure accurate and efficient time logging.
  Project and Client Management**: Retrieve project and client information from Clockify effortlessly.
  Overlap Prevention**: Avoid overlapping entries with built-in time validation.
  Automated Descriptions**: Generate ethical, grammatically correct descriptions for time logs.

  Set up steps
  1. Prepare your integrations
  Ensure you have active accounts for both Slack and Clockify.
  Generate your Clockify API credentials for integration.

  2. Import the workflow
  Download and import the workflow template into your n8n instance.
  Configure the workflow to connect with your Slack and Clockify accounts.

  3. Configure the workflow
  Add your Clockify API credentials in the workflow settings.
  Set up the Slack Trigger to listen for app mentions or specific commands.

  4. Test the workflow
  Use Slack to create a time entry and verify it in Clockify.
  Test updating and deleting existing entries to ensure smooth functionality.
  Check for any overlapping time logs or incorrect data entries.

  Why use this workflow?
  Efficiency**: Eliminate the need to switch between tools for time tracking.
  Accuracy**: AI-driven validation ensures error-free entries.
  Automation**: Simplify repetitive tasks like updating or deleting time logs.
  Proactive Guidance**: Conversational assistant ensures smooth operations.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolCalculator","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolCode","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.slackTrigger","n8n-nodes-base.executionData","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.slack","n8n-nodes-base.slack"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","productivity","time tracking"],"complexity":"medium","use_cases":["Log time entries from Slack into Clockify","Update or delete Clockify time entries via Slack","Generate descriptive, grammatically-correct time entry descriptions using AI","Prevent overlapping time entries and validate time logs","Retrieve projects and clients from Clockify for accurate logging"],"estimated_setup_minutes":20,"required_services":["Slack (workspace & app with appropriate scopes)","Clockify (account & API key)","LM/AI provider (e.g., OpenAI) for the AI assistant","n8n instance"],"key_features":["Create, update, and delete Clockify time entries directly from Slack","AI-powered conversational assistant to guide time logging","Project and client lookup from Clockify","Overlap detection/validation to prevent conflicting entries","Automated, ethical and grammatically-correct descriptions for time logs"],"target_audience":["Teams and agencies tracking billable hours","Project managers and operations teams","Developers/automation engineers implementing Slack integrations and workflows"]}


name: Generate Leads with Google Maps
author_name: Alex Kim
author_username: alexk1919
categories: []
created_at: 2024-12-04T22:16:13.814Z
updated_at: 2024-12-04T22:16:13.814Z
url: https://n8n.io/workflows/2605
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 19:29:42
description:
  This workflow leverages n8n to perform automated Google Maps API queries and manage data efficiently in Google Sheets. It's designed to extract specific location data based on a given list of ZIP codes and categories.

  Features
  Queries the Google Maps API for location data using predefined ZIP codes and subcategories.
  Filters, de-duplicates, and organizes data into structured rows in Google Sheets.
  Implements exponential backoff retries to handle API rate limits.
  Logs and updates statuses directly in Google Sheets for easy tracking.

  Prerequisites
  Google OAuth Credentials: A configured Google Cloud project for Google Maps API and Sheets API access.
  Google Sheets: A sheet with ZIP codes and categories defined (e.g., "AZ Zips").
  n8n Setup: A running instance of n8n with credentials configured for Google OAuth.

  Setup Instructions

  1. Prepare Google Sheets
  Add the ZIP codes to the "AZ Zips" sheet.
  Define subcategories in another sheet (e.g., "Google Maps Categories").
  Provide the sheet's URL in the Settings node of the workflow.

  2. Configure API Access
  Set up Google OAuth credentials for Maps and Sheets APIs in n8n.
  Ensure your API key has access to the places.searchText endpoint.

  3. Workflow Customization
  Modify textQuery parameters in the GMaps API node to match your query needs.
  Adjust trigger intervals as required (e.g., manual or scheduled execution).

  4. Run the Workflow
  Execute the workflow manually or schedule periodic runs to keep your data updated.

  Notes
  This workflow includes robust error handling to retry failed API calls with exponential backoff.
  All data is organized and logged directly in Google Sheets for easy reference and updates.

  For more information or issues, feel free to reach out!

nodes_used:
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name: AI Youtube Trend Finder Based On Niche
author_name: Leonardo Grigorio
author_username: leonardogrig
categories: []
created_at: 2024-12-05T03:44:49.638Z
updated_at: 2024-12-05T03:44:49.638Z
url: https://n8n.io/workflows/2606
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 19:51:23
description:
  Youtube Video

  This n8n workflow is designed to assist YouTube content creators in identifying trending topics within a specific niche. By leveraging YouTube's search and data APIs, it gathers and analyzes video performance metrics from the past two days to provide insights into what content is gaining traction. Here's how the workflow operates:

  Trigger Setup: The workflow begins when a user sends a query through the chat_message_received node. If no niche is provided, the AI prompts the user to select or input one.

  AI Agent (Language Model): The central node utilizes a GPT-based AI agent to:
     Understand the user's niche or content preferences.
     Generate tailored search terms related to the niche.
     Process YouTube API responses and summarize trends using insights such as common themes, tags, and audience engagement metrics (views, likes, and comments).

  YouTube Search: The youtube_search node runs a secondary workflow to query YouTube for relevant videos published within the last two days. It retrieves basic video data such as video IDs, relevance scores, and publication dates.

  Video Details Retrieval: The workflow fetches additional details for each video:
     Video Snippet: Metadata like title, description, and tags.
     Video Statistics: Metrics such as views, likes, and comments.
     Content Details: Video duration, ensuring only content longer than 3 minutes and 30 seconds is analyzed.

  Data Processing:
     Video metadata is cleaned, sanitized, and stored in memory.
     Tags, titles, and descriptions are analyzed to identify patterns and trends across multiple videos.

  Output: The workflow compiles insights and presents them to the user, highlighting:
     The most common themes or patterns within the niche.
     URLs to trending videos and their respective channels.
     Engagement statistics, helping the user understand the popularity of the content.

  Key Notes for Setup:
  API Keys**: Ensure valid YouTube API credentials are configured in the get_videos, find_video_snippet, find_video_statistics, and find_video_data nodes.
  Memory Buffer**: The window_buffer_memory node ensures the AI agent retains context during analysis, enhancing the quality of the generated insights.
  Search Term Customization**: The AI agent dynamically creates search terms based on the user’s niche to improve search precision.

  Use Case:
  This workflow is ideal for YouTubers or marketers seeking data-driven inspiration for creating content that aligns with current trends, maximizing the potential to engage their audience.

  Example Output:
  For the niche "digital marketing":
  Trending Topic: Videos about "mental triggers" and "psychological marketing."
  Tags: "SEO," "Conversion Rates," "Social Proof."
  Engagement: Videos with over 200K views and high likes/comment ratios are leading trends.
  Video links:
    https://www.youtube.com/watch?v=video_id1
    https://www.youtube.com/watch?v=video_id2

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.youTube","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.if","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","content-research","data-analysis","social-media"],"complexity":"medium","use_cases":["Identify trending YouTube topics within a niche","Content ideation and topic discovery for creators","Competitive/market research on recent video performance","Optimize titles, tags and topics based on engagement signals","Surface high-performing videos and channels for outreach"],"estimated_setup_minutes":30,"required_services":["YouTube Data API (API key)","GPT-based language model (e.g., OpenAI API or compatible LLM)","n8n instance (workflow automation platform)","Optional: Webhook/HTTP endpoint for secondary workflow execution"],"key_features":["Generates niche-specific search terms via AI","Searches YouTube for videos published in the last 48 hours","Fetches and aggregates video snippets, statistics, and content details","Filters videos by minimum duration ( > 3m30s )","Analyzes tags, titles, and descriptions to identify common themes and trends"],"target_audience":["YouTube creators","Content marketers","Social media managers/strategists"]}


name: Research AI Agent Team with auto citations using OpenRouter and Perplexity
author_name: Derek Cheung
author_username: derekcheungsa
categories: []
created_at: 2024-12-05T04:31:18.702Z
updated_at: 2024-12-05T04:31:18.702Z
url: https://n8n.io/workflows/2607
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  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 &lt;api-key value&gt;

nodes_used:
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metadata_json:
  {"categories":["automation","content generation","AI research","integration"],"complexity":"complex","use_cases":["Automated long-form article generation","Content marketing and SEO writing","Research assistance and literature summaries","Knowledge base or documentation creation","Automated report generation with citations"],"estimated_setup_minutes":45,"required_services":["Perplexity (accessed via OpenRouter.ai)","OpenRouter API key","OpenAI or compatible LLM (lmChatOpenAi node) — optional depending on configuration","n8n platform (to run the workflow)"],"key_features":["Multi-agent orchestration (research leader, planner, assistants, editor)","Internet search and automatic citations via Perplexity","Customizable inputs: topic, tone, word count, number of sections","Automated table-of-contents generation and section breakdown","Final compilation and editing step to ensure coherence and citation formatting"],"target_audience":["Content marketers","Technical and non-technical writers","Researchers and analysts needing summarized sources with citations"]}


name: Automatically Correct Wrong Shipping Addresses in Billbee Orders
author_name: Simon
author_username: simonfes
categories: []
created_at: 2024-12-05T10:23:11.248Z
updated_at: 2024-12-05T10:23:11.248Z
url: https://n8n.io/workflows/2609
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Address Validation Workflow

  About
  This workflow automates the process of validating and correcting client shipping addresses in Billbee, ensuring accurate delivery information. It's ideal for e-commerce businesses looking to save time and reduce errors in their order fulfillment process. The workflow uses Billbee, an order management platform for small to medium-sized online retailers, and the Endereco API for address validation.

  Who Is This For?
  E-Commerce Businesses**: Streamline order fulfillment by automatically correcting common shipping address errors.
  Warehouse Teams**: Reduce manual work and ensure packages are shipped to the correct address.
  Small to Medium-Sized Retailers**: Businesses using Billbee to manage orders and requiring efficient, automated solutions for address validation.

  How it Works
  Trigger: Workflow starts via a Billbee Webhook when an order is imported.  
  Fetch Data: Retrieve the client's shipping address using the Order ID.  
  Validate Address: Send the address to the Endereco API for validation and correction (e.g., house number errors).  
  Conditional Actions:  
     Valid Address: Update the address in Billbee.  
     Invalid Address: Tag the order with "Validation Error."  
  Track Status: Add tags in Billbee for processed orders.

  Setup Steps
  API Keys: Obtain Billbee Developer/User API Keys and Endereco API Key.  
  Billbee Rule: Create an automation rule:  
     Trigger: Order imported.  
     Action: Call External URL with OrderId to trigger n8n workflow.  
  Optional: Use a secondary trigger (e.g., order state changes to "gepackt") for manual corrections.

  Customization Options
  Filter Delivery Addresses:  
     Customize filters to exclude specific delivery types, such as pickup shops ("Postfiliale," "Paketshop," or "Packstation").  
     Filters can be adjusted within Billbee or in the workflow.

  Error Handling:  
     Configure additional actions for orders that fail validation, such as notifying your team or flagging orders for manual review.

  Order Tags:  
     Define custom tags in Billbee to better track order statuses (e.g., "Address Corrected," "Validation Error").

  Trigger Types:  
     Use additional triggers such as changes to order states (e.g., "gepackt" or "In Fulfillment") for manual corrections or validations.

  Address Fields:  
     Modify the workflow to focus on specific address components, such as postal codes, city names, or country codes.

  Validation Rules:  
     Adjust Endereco API settings or add custom logic to refine validation criteria based on your business needs.

  API Documentation
  Endereco**: Endereco API Docs  
  Billbee**: Billbee API Docs

nodes_used:
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metadata_json:
  {"categories":["automation","integration","data validation","e-commerce"],"complexity":"medium","use_cases":["Automatically validate and correct customer shipping addresses","Update Billbee orders with corrected addresses","Tag and track orders with validation results","Filter out pickup-shop addresses from validation","Notify or flag orders that require manual review"],"estimated_setup_minutes":30,"required_services":["Billbee (API access)","Endereco API (address validation)","n8n (automation platform)"],"key_features":["Webhook trigger from Billbee on order import","Fetch and validate shipping addresses via Endereco API","Update addresses in Billbee when validated","Tag orders for 'Address Corrected' or 'Validation Error'","Configurable filters to exclude pickup/shop delivery types and target address fields (postal code, city, etc.)"],"target_audience":["E-commerce businesses","Warehouse / fulfillment teams","Small to medium-sized retailers using Billbee to manage orders (operations)"]}


name: Smartlead to HubSpot Performance Analytics
author_name: victor de coster
author_username: vliegendepater
categories: []
created_at: 2024-12-05T13:51:28.938Z
updated_at: 2024-12-05T13:51:28.938Z
url: https://n8n.io/workflows/2610
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  *Smartlead to HubSpot Performance Analytics
  A streamlined workflow to analyze your Smartlead performance metrics by tracking lifecycle stages in HubSpot and generating automated reports.*

  Who is this for?

  (Outbound) Automation Agencies, Sales and marketing teams using Smartlead for outreach campaigns who want to track their performance metrics and lead progression in HubSpot.

  What problem does this workflow solve?

  Manual tracking of lead performance across Smartlead and HubSpot is time-consuming and error-prone. This workflow automates performance reporting by connecting your Smartlead data with HubSpot lifecycle stages, providing clear insights into your outreach campaign effectiveness.

  What this workflow does

  Automatically pulls performance data from your Smartlead campaigns
  Cross-references contact status with HubSpot lifecycle stages
  Generates comprehensive performance reports in Google Sheets
  Provides customizable reporting schedules to match your team's needs

  Setup Requirements

  PostgreSQL Database

  Set up your PostgreSQL instance (includes $300 free GCP credits)
  Follow our step-by-step setup guide: Find a step-by-step guide here

  Google Account Integration

  Connect your Google Account to n8n
  Find the guide here

  Smartlead Configuration

  Connect your Smartlead instance:
  Detailed connection guide included in workflow

  How to customize this workflow

  Configure the Trigger node to adjust report frequency
  Modify the Google Sheets template to match your specific KPIs
  Customize HubSpot lifecycle stage mapping in the Function node
  Adjust PostgreSQL queries to track additional metrics

  Need assistance or have suggestions? 
  lmk here

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name: AI Agent to chat with Supabase/PostgreSQL DB
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2024-12-06T14:08:05.408Z
updated_at: 2024-12-06T14:08:05.408Z
url: https://n8n.io/workflows/2612
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:31
description:
  Video Guide

  I prepared a detailed guide that showed the whole process of building a resume analyzer.

  Who is this for?
  This workflow is ideal for developers, data analysts, and business owners who want to enable conversational interactions with their database. It’s particularly useful for cases where users need to extract, analyze, or aggregate data without writing SQL queries manually.

  What problem does this workflow solve?
  Accessing and analyzing database data often requires SQL expertise or dedicated reports, which can be time-consuming. This workflow empowers users to interact with a database conversationally through an AI-powered agent. It dynamically generates SQL queries based on user requests, streamlining data retrieval and analysis.

  What this workflow does
  This workflow integrates OpenAI with a Supabase database, enabling users to interact with their data via an AI agent. The agent can:
  Retrieve records from the database.
  Extract and analyze JSON data stored in tables.
  Provide summaries, aggregations, or specific data points based on user queries.

  Dynamic SQL Querying: The agent uses user prompts to create and execute SQL queries on the database.
  Understand JSON Structure: The workflow identifies JSON schema from sample records, enabling the agent to parse and analyze JSON fields effectively.
  Database Schema Exploration: It provides the agent with tools to retrieve table structures, column details, and relationships for precise query generation.

  Setup

  Preparation
  Create Accounts:
     N8N: For workflow automation.
     Supabase: For database hosting and management.
     OpenAI: For building the conversational AI agent.
  Configure Database Connection:
     Set up a PostgreSQL database in Supabase.
     Use appropriate credentials (username, password, host, and database name) in your workflow.

  N8N Workflow

  AI agent with tools:

  Code Tool:
     Execute SQL queries based on user input.
  Database Schema Tool:
     Retrieve a list of all tables in the database.
     Use a predefined SQL query to fetch table definitions, including column names, types, and references.
  Table Definition:
     Retrieve a list of columns with types for one table.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.postgresTool","n8n-nodes-base.postgresTool","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.postgresTool"]

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metadata_json:
  {"categories":["automation","integration","data processing","conversational AI"],"complexity":"medium","use_cases":["Conversational database querying (natural language -> SQL)","Ad-hoc reporting and aggregation without writing SQL","Extracting and analyzing JSON fields stored in Postgres","Building AI-driven data assistants (e.g., resume analyzer)","Exploring database schema and generating context-aware queries"],"estimated_setup_minutes":45,"required_services":["n8n","Supabase (PostgreSQL)","OpenAI (Chat/LM API)"],"key_features":["Dynamic SQL generation from user prompts","Execute SQL queries and return results via agent","Automatic JSON structure/schema inference from sample records","Tools to retrieve table/column definitions and relationships","Natural-language chat interface powered by OpenAI agent"],"target_audience":["Developers","Data analysts","Business owners"]}


name: Export Search Console Results to Google Sheets
author_name: Zacharia Kimotho
author_username: imperolq
categories: []
created_at: 2024-12-07T05:11:48.246Z
updated_at: 2024-12-07T05:11:48.246Z
url: https://n8n.io/workflows/2613
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:31
description:
  How it works

  This workflow gets the search console results data and exports this to google sheets. This makes it easier to visualize and do other SEO related tasks and activities without having to log into Search Console

  Setup and use

  Set your desired schedule
  Enter your desired domain
  Connect to your Google sheets or make a copy of this sheet. 

  Detailed Setup

  Inputs and Outputs:**
  Input: API response from Google Search Console regarding keywords, page data, and date data.
  Output: Entries written to Google Sheets containing keyword data, clicks, impressions, CTR, and positions.

  Setup Instructions:
  Prerequisites:**
  An n8n instance set up and running.
  Active Google Account with access to Google Search Console and Google Sheets.
  Google OAuth 2.0 credentials for API access.

  Step-by-Step Setup:**
  Open n8n and create a new workflow.
  Add the nodes as described in the JSON.
  Configure the Google OAuth2 credentials in n8n to enable API access.
  Set your domain in the Set your domain node.
  Customize the Google Sheets document URLs to your personal sheets.
  Adjust the schedule in the Schedule Trigger node as per your requirements.
  Save the workflow.

  Configuration Options:**
  You can customize the date ranges in the body of the HttpRequest nodes.
  Adjust any fields in the Edit Fields nodes based on different data requirements.

  Use Case Examples:
  Useful in tracking website performance over time using Search Console metrics.
  Ideal for digital marketers, SEO specialists, and web analytics professionals.
  Offers value in compiling performance reports for stakeholders or team reviews.

  Running and Troubleshooting:
  Running the Workflow:**
  Trigger the workflow manually or wait for the schedule to run it automatically.

  Monitoring Execution:**
  Check the execution logs in n8n's dashboard to ensure all nodes complete successfully.

  Common Issues:**
  Invalid OAuth credentials – ensure credentials are set up correctly.
  Incorrect Google Sheets URLs – double-check document links and permissions.
  Scheduling conflicts – make sure the schedule set does not overlap with other workflows.

nodes_used:
  ["n8n-nodes-base.splitOut","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets"]

workflow_json_compressed:
  [encoded data, 4904 characters]

metadata_json:
  {"categories":["integration","automation","data processing","reporting"],"complexity":"medium","use_cases":["Automate periodic export of Google Search Console data","Compile SEO performance reports in Google Sheets","Track keyword-level metrics (clicks, impressions, CTR, position) over time","Share and visualize Search Console data with stakeholders","Feed downstream analytics or dashboards from a spreadsheet"],"estimated_setup_minutes":20,"required_services":["n8n instance","Google Search Console access (site property)","Google Sheets","Google OAuth2 credentials (Google API access enabled)"],"key_features":["Scheduled automated pulls from Google Search Console","Writes keyword/page/date metrics to Google Sheets","Customizable domain and date ranges","Uses Google OAuth2 for authenticated API requests","Editable nodes (HTTP requests, Set, SplitOut, Google Sheets) for customization and mapping"],"target_audience":["SEO specialists","Digital marketers","Web analysts/data analysts tracking organic performance"]}


name: Extract text from PDF and image using Vertex AI (Gemini) into CSV
author_name: Keith Rumjahn
author_username: rumjahn
categories: []
created_at: 2024-12-08T13:07:12.471Z
updated_at: 2024-12-08T13:07:12.471Z
url: https://n8n.io/workflows/2614
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:31
description:
  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.

  Click here to watch Youtube tutorial

  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

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.switch","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.extractFromFile","n8n-nodes-base.httpRequest","n8n-nodes-base.convertToFile","n8n-nodes-base.googleDrive","n8n-nodes-base.convertToFile","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm"]

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metadata_json:
  {"categories":["automation","data extraction","AI/ML","integration","OCR"],"complexity":"medium","use_cases":["Automated expense extraction from bank PDFs and screenshots","Receipt/invoice OCR and data capture","Bulk document to CSV conversion for budgeting or accounting tools","Digitizing paper or image-based transaction records"],"estimated_setup_minutes":15,"required_services":["Google Drive","Vertex AI (Gemini)","n8n (self-hosted or cloud) "],"key_features":["Upload PDFs/screenshots to Google Drive to trigger workflow","Use Vertex Gemini for image/PDF text recognition and extraction","Convert extracted data into CSV format","Save CSV outputs to a designated Google Drive folder","Customizable AI prompt to adapt extraction rules/fields"],"target_audience":["individuals tracking expenses","freelancers and small business owners","finance/operations teams looking to automate data entry into budgeting tools"]}


name: Get Airtable data via AI and Obsidian Notes
author_name: Obsidi8n
author_username: derruttie
categories: []
created_at: 2024-12-08T19:21:21.748Z
updated_at: 2024-12-08T19:21:21.748Z
url: https://n8n.io/workflows/2615
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  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.

nodes_used:
  ["n8n-nodes-base.airtableTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.respondToWebhook","n8n-nodes-base.webhook"]

workflow_json_compressed:
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metadata_json:
  {"categories":["integration","automation","knowledge management","AI"],"complexity":"medium","use_cases":["Retrieve Airtable records by natural-language query from Obsidian","Enrich notes with structured data from Airtable","Enable quick ad-hoc data lookups inside a note-taking workflow","Convert highlighted questions into formatted Airtable results"],"estimated_setup_minutes":20,"required_services":["Airtable","OpenAI (GPT) or compatible LLM via n8n","Obsidian (Post Webhook plugin)","n8n instance / webhook endpoint"],"key_features":["Highlight-to-webhook workflow from Obsidian","LLM interpretation of user queries to determine Airtable query parameters","Airtable data retrieval and filtering","Formatting of results for direct insertion into Obsidian notes","Webhook-based, real-time interaction"],"target_audience":["Obsidian users","Knowledge workers and researchers","n8n users and workflow builders using Airtable and AI integrations"]}


name: Email Subscription Service with n8n Forms, Airtable and AI
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-12-10T10:42:16.009Z
updated_at: 2024-12-10T10:42:16.009Z
url: https://n8n.io/workflows/2618
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n template shows how anyone can build a simple newsletter-like subscription service where users can enrol themselves to receive messages/content on a regular basis. It uses n8n forms for data capture, Airtable for database, AI for content generation and Gmail for email sending.

  How it works
  An n8n form is setup up to allow users to subscribe with a desired topic and interval of which to recieve messages via n8n forms which is then added to the Airtable.
  A scheduled trigger is executed every morning and searches for subscribers to send messages for based on their desired intervals.
  Once found, Subscribers are sent to a subworkflow which performs the text content generation via an AI agent and also uses a vision model to generate an image.
  Both are attached to an email which is sent to the subscriber. This email also includes an unsubscribe link.
  The unsubscribe flow works similarly via n8n form interface which when submitted disables further scheduled emails to the user.

  How to use
  Make a copy of sample Airtable here: https://airtable.com/appL3dptT6ZTSzY9v/shrLukHafy5bwDRfD
  Make sure the workflow is "activated" and the forms are available and reachable by your audience.

  Requirements
  Airtable for Database
  OpenAI for LLM (but compatible with others)
  Gmail for Email (but can be replaced with others)

  Customising this workflow
  This simple use can be extended to deliver any types of content such as your company newsletter, promotions, social media posts etc.
  Doesn't have to be limited to just email - try social messaging, Whatsapp, Telegram and others.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.gmail","n8n-nodes-base.executeWorkflow","n8n-nodes-base.set","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.formTrigger","n8n-nodes-base.set","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.filter","n8n-nodes-base.code","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.formTrigger","n8n-nodes-base.executionData","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolWikipedia","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGroq","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.editImage","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 8240 characters]

metadata_json:
  {"categories":["automation","integration","content-generation","email-marketing"],"complexity":"medium","use_cases":["Newsletter/drip email delivery","Automated content generation for subscribers","Delivering image+text updates to audiences","Subscriber management with unsubscribe handling","Repurposing content for social or messaging channels"],"estimated_setup_minutes":45,"required_services":["n8n Forms (n8n)","Airtable","OpenAI (or compatible LLM/vision API)","Gmail (or other SMTP/email provider)"],"key_features":["Form-based subscriber capture","Airtable-backed subscriber database","Scheduled trigger to find due subscribers","Subworkflow that generates text via LLM and images via vision model","Emails assembled with attachments and unsubscribe link and sent via Gmail node"],"target_audience":["Marketers","Content creators","Small business owners who send newsletters/promotions/apps updates via email automation builders/automation engineers"]}


name: Deduplicate Scraping AI Grants for Eligibility using AI
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-12-10T11:00:25.179Z
updated_at: 2024-12-10T11:00:25.179Z
url: https://n8n.io/workflows/2619
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n template scrapes a list of AI grants from grants.gov and qualifies them using AI; determining interest and eligibility for the business. It then sends an email alert of interesting items to team members in an email.

  The template also shows how you can use the "Remove Duplicates" node to simplify deduplication of external listings without the need to manage this yourself.

  Not particularly interested in AI Grants? This template works for other tender websites as long as you're able to scrape them.

  How it works
  A scheduled trigger is set to fetch a list of AI grants listed on the grants.gov website in the past day.
  A Remove Duplicates node is used to track Grant IDs to filter out those already processed by the workflow.
  New grants are summarized and analysed by AI nodes to determine eligibility and interest which is then saved to an Airtable database.
  Another scheduled trigger starts a little later  than the first to collect and summarize the new grants
  The results are then compiled into an email template using the HTML node, in the form of a newsletter designed to alert and brief team members of new AI grants.
  This email is then sent to a list of subscribers using the gmail node.

  How to use
  Make a copy of sample Airtable here: https://airtable.com/appiNoPRvhJxz9crl/shrRdP6zstgsxjDKL
  The filters for fetching the grants is currently set to the "AI" category. Feel free to change this to include more categories.
  Not interested in grants, this template can works for other sources of leads just change the endpoint and how you're defining the item ID to track.

  Requirements
  Airtable for database
  OpenAI for LLM

  Note: These are not hard requirements and can be exchanged for services available to you.

  customising the workflow
  "Eligibility" criteria at this stage may be better served by identifying hard blockers instead ie. certifications, geographical considerations or certain legal checks. Be sure to mention any hard blockers into the Eligibility prompt.
  Not particularly interested in AI prompts? This template works for other tender websites as long as you're able to scrape them.

nodes_used:
  ["n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.merge","n8n-nodes-base.airtable","n8n-nodes-base.removeDuplicates","n8n-nodes-base.httpRequest","n8n-nodes-base.airtable","n8n-nodes-base.html","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.airtable","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 11772 characters]

metadata_json:
  {"categories":["automation","integration","data processing","lead generation","monitoring"],"complexity":"medium","use_cases":["Monitor grants/tenders and surface new opportunities","Automatically deduplicate external listings","AI-based summarization and eligibility scoring","Save qualified results to a database (Airtable)","Send email alerts/newsletter to team subscribers"],"estimated_setup_minutes":45,"required_services":["Grants.gov (or other target tender/lead website)","Airtable (API access)","OpenAI (or another LLM provider)","Gmail (or other SMTP/email service)"],"key_features":["Scheduled scraping of new listings","Remove Duplicates node to track/process only new items","AI summarization and eligibility/interest classification","Save enriched records to Airtable","Compile HTML newsletter and send email alerts"],"target_audience":["Business development teams","Grant managers and researchers","Startups looking for funding opportunities or tender leads」「n8n users/automation builders"]}


name: Calendly to KlickTipp Integration
author_name: KlickTipp
author_username: KlickTipp
categories: []
created_at: 2024-12-10T15:13:32.783Z
updated_at: 2024-12-10T15:13:32.783Z
url: https://n8n.io/workflows/2620
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Community Node Disclaimer: This workflow uses KlickTipp community nodes.

  How It Works  

  Enhanced Calendly Integration: This workflow processes bookings and cancellations in Calendly, dynamically managing invitee and guest data with KlickTipp.  

  Data Transformation: Dates and times are converted into formats (UNIX timestamps) compatible with KlickTipp’s API, ensuring seamless data integration.  

  Key Features  

  Calendly Trigger:  
  Captures new bookings or cancellations of events, including participant details.  

  Invitee and Guest Subscription in KlickTipp:  
  Adds or updates invitees and guests in KlickTipp based on booking details (event name, time, join link, reschedule link, cancel link, etc.).  
  Tracks and processes cancellations for both invitees and guests.  
  Handles rescheduling intelligently to avoid redundant operations.  

  Guest-Specific Operations:  
  Processes guests individually for bookings and cancellations using dynamic arrays of email addresses.  
  Recovers guest data from invitee records for cancellations since Calendly does not provide guest data upon cancellation.  

  Data Processing:  
  Standardizes and validates input fields  
  Converts phone numbers to numeric-only format with international prefixes.  
  Transforms dates into UNIX timestamps.  
  Reads out the name of the invitee based on both possible input fields for name (name vs. firstname and lastname field setup).  

  Error Handling:  
  Validates critical fields like phone numbers, URLs, and dates to prevent incorrect data submissions.  

  Setup Instructions  

  Authentication:  
  Set up the Calendly and KlickTipp nodes in your n8n instance.  
  Configure authentication for both Calendly and KlickTipp nodes.  

  Custom Field Preparation in KlickTipp:  
  Create the following custom fields in KlickTipp to align with workflow requirements:  

  | Field Name                        | Field Type   |
  |---------------------------------------|------------------|
  | Calendly \| event name                | Text             |
  | Calendly \| join url                  | URL              |
  | Calendly \| reschedule url            | URL              |
  | Calendly \| cancel url                | URL              |
  | Calendly \| event start datetime      | Date & Time      |
  | Calendly \| event end datetime        | Date & Time      |
  | Calendly \| invitee start date        | Date             |
  | Calendly \| invitee end date          | Date             |
  | Calendly \| invitee start time        | Time             |
  | Calendly \| invitee end time          | Time             |
  | Calendly \| invitee timezone          | Text             |
  | Calendly \| invitee guests addresses  | Text             |


  After creating fields, allow 10-15 minutes for them to sync. If fields don’t appear, reconnect your KlickTipp credentials.  

  Field Mapping and Adjustments:  
  Open each KlickTipp node and map fields to match your setup. The workflow includes placeholders for:  
  Invitee details (first name, last name, email, and phone).  
  Event details (start/end times, timezone, etc.).  

  Workflow Logic  

  Trigger via Calendly event Booking:  
     A new form event booking or cancellation from Calendly initiates the workflow  

  Data Transformation:  
     Processes raw Calendly event data to ensure compatibility with KlickTipp’s API.  

  Add to KlickTipp Subscriber List:  
     Adds invitees and guests to the designated KlickTipp list, including event-specific details.  

  Benefits  

  Efficient lead generation: Contacts from event bookings are automatically imported into KlickTipp and can be used immediately, saving time and increasing the conversion rate.  
  Automated processes: Experts can start workflows directly, such as reminder emails or course admissions, reducing administrative effort.  
  Error-free data management: The template ensures precise data mapping, avoids manual corrections and reinforces a professional appearance.  

  Testing and Deployment  

  Test the workflow by triggering a Calendly event and verifying data updates in KlickTipp.  

  Notes:  

  Customization:  
  Update field mappings within the KlickTipp nodes to align with your account setup. This ensures accurate data syncing.  

  Resources:
  Calendly KlickTipp Knowledge Base help article
  Use KlickTipp Community Node in n8n
  Automate Workflows: KlickTipp Integration in n8n

nodes_used:
  ["n8n-nodes-klicktipp.klicktipp","n8n-nodes-klicktipp.klicktipp","n8n-nodes-klicktipp.klicktipp","n8n-nodes-klicktipp.klicktipp","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.if","n8n-nodes-base.calendlyTrigger","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 7040 characters]

metadata_json:
  {"categories":["integration","automation","data processing","lead generation"],"complexity":"medium","use_cases":["Sync Calendly bookings and cancellations to email/CRM lists","Automatic lead capture from event attendees","Manage attendee guests and recover guest data on cancellations","Trigger follow-up workflows (reminders, admissions, onboarding) based on bookings","Normalize and validate contact and event data for downstream systems"],"estimated_setup_minutes":30,"required_services":["n8n (self-hosted or cloud)","Calendly API / Calendly account","KlickTipp (uses KlickTipp community node)"],"key_features":["Calendly trigger for new bookings and cancellations","Adds/updates invitees and guests in KlickTipp lists","Handles guest arrays and recovers guest data on cancellations","Transforms dates to UNIX timestamps and converts time fields","Normalizes phone numbers to numeric international format and validates inputs (URLs, dates) and names (supports different name field setups)  orchestrates reschedule logic to avoid duplicate operations  error handling/validation to prevent bad submissions  custom field mapping for KlickTipp"],"target_audience":["Automation engineers / developers","Marketers and growth teams","Sales and customer success teams handling event leads and follow-ups (event organizers)  Agencies building integrations for clients"]}


name: AI Agent To Chat With Files In Supabase Storage
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2024-12-10T19:04:24.160Z
updated_at: 2024-12-10T19:04:24.160Z
url: https://n8n.io/workflows/2621
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 19:35:01
description:
  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.

nodes_used:
  ["n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.supabase","n8n-nodes-base.if","n8n-nodes-base.supabase","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.manualTrigger","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolVectorStore","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.vectorStoreSupabase","n8n-nodes-base.merge","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.vectorStoreSupabase","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6544 characters]

metadata_json:
  {"categories":["automation","ai","knowledge management","data processing","integration"],"complexity":"medium","use_cases":["Conversational search over files stored in Supabase","Internal knowledge base / document Q&A","Customer support assistant that answers from product docs","Automated ingestion and embedding of uploaded documents","Summarization and context retrieval for workflows"],"estimated_setup_minutes":30,"required_services":["Supabase (Storage and/or Database)","OpenAI API (embeddings and/or LLM) or other embeddings/LLM HTTP endpoint","n8n with LangChain nodes enabled"],"key_features":["Ingest files directly from Supabase Storage","Extract text from multiple file types","Recursive character text splitting for chunking documents","Generate embeddings (OpenAI) and store/query vectors","Batch processing for scalable file handling (splitInBatches node)"],"target_audience":["Developers integrating AI into apps","Data engineers building knowledge pipelines","Product and support teams creating doc-based assistants"]}


name: Generate Amazon FBA keywords using Amazon Completion API
author_name: Keith Rumjahn
author_username: rumjahn
categories: []
created_at: 2024-12-11T02:15:20.745Z
updated_at: 2024-12-11T02:15:20.745Z
url: https://n8n.io/workflows/2629
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Use Case
  Generate keywords for your Amazon product.

  You want to find more keywords for advertising your product on Amazon.

  Click here to watch youtube tutorial

  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

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.airtable","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.aggregate","n8n-nodes-base.code","n8n-nodes-base.airtable","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","e-commerce","marketing","data enrichment","integration"],"complexity":"medium","use_cases":["Generate Amazon product keywords for advertising","Expand keyword lists for PPC campaigns","Automate keyword research and enrichment","Sync generated keywords to Airtable for tracking"],"estimated_setup_minutes":20,"required_services":["Amazon Completion API (Amazon Bedrock/Completions)","Airtable API"],"key_features":["Send seed keywords to Amazon Completion API to retrieve related keywords","Batch processing (split/aggregate) of keyword lists","Store results back into Airtable","Configurable API endpoint/parameters","Includes code node for custom processing and transformations"],"target_audience":["Amazon sellers","E-commerce managers","PPC/advertising specialists and marketers」「Growth hackers and product managers"]}


name: Screen Applicants With AI, notify HR and save them in a Google Sheet
author_name: Sarfaraz Muhammad Sajib
author_username: sarfarazmuhammad
categories: []
created_at: 2024-12-11T11:14:52.865Z
updated_at: 2024-12-11T11:14:52.865Z
url: https://n8n.io/workflows/2632
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:31
description:
  What this workflow does
  This workflow helps HR teams screen CVs with AI, store compatibility ratings in Google Sheets, and send email notifications to candidates and HR. It simplifies the recruitment process.

  CV Submission Form:
  Candidates submit their details and CV (PDF) through a web form, triggering the workflow in n8n.
  PDF Extraction & AI Rating:
  The submitted CV is processed to extract text, and AI analyzes it to generate a compatibility rating.
  Results Storage & Notifications:
  Ratings are stored in a Google Sheet for easy access and organization.
  Confirmation emails are automatically sent to both HR and the candidate.

  Setup
  Use the provided template to configure your form and connect it to n8n.
  Ensure your Google Sheets and email service integrations are active.

  Customization Instructions:

  Modify the email template to match your organization’s branding.
  Adjust the AI compatibility rating thresholds based on your requirements.
  Ensure you have updated the prompt for cv screening.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.gmail","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.extractFromFile","n8n-nodes-base.formTrigger","n8n-nodes-base.googleSheets"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","recruitment","HR","AI screening","integration"],"complexity":"medium","use_cases":["Automated CV screening and ranking","Storing candidate compatibility scores in Google Sheets","Sending confirmation and HR notification emails","Reducing manual pre-screening workload","Customizable AI-based screening criteria"],"estimated_setup_minutes":20,"required_services":["n8n instance","Google Sheets","Email service (SMTP/Gmail/SendGrid/etc.)","Web form provider (n8n Form, Typeform, Google Forms, etc.)","LLM provider (Google Gemini / lmChatGoogleGemini or equivalent)","PDF text extraction capability (extractFromFile or equivalent)"],"key_features":["Extract text from submitted CV PDFs","AI-generated compatibility rating for each candidate","Save ratings and candidate data to Google Sheets","Automatic email notifications to candidate and HR","Customizable screening prompt and rating thresholds"],"target_audience":["HR teams","Recruiters","Hiring managers and talent sourcers」「Small to medium businesses streamlining hiring processes"]}


name: INSEE Company Data Enrichment for Agile CRM (For French companies only)
author_name: Johan Denoyer
author_username: jdenoy
categories: []
created_at: 2024-12-11T16:16:09.355Z
updated_at: 2024-12-11T16:16:09.355Z
url: https://n8n.io/workflows/2633
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  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)

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.noOp","n8n-nodes-base.agileCrm","n8n-nodes-base.agileCrm","n8n-nodes-base.merge","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","CRM integration","data enrichment","data quality"],"complexity":"medium","use_cases":["Enrich Agile CRM company records with official French company data (SIREN and address)","Maintain data quality and standardize company addresses in CRM","Automate periodic enrichment of French company entries","Protect specific records from being overwritten via a readonly flag"],"estimated_setup_minutes":20,"required_services":["Agile CRM (account & API credentials)","INSEE OpenData / French SIREN API (API access/credentials)"],"key_features":["Bulk extraction of company entries from Agile CRM","Search INSEE OpenData by company name to retrieve SIREN and address","Update Agile CRM company records with returned data","Readonly/custom field support to prevent overwriting specific records","Supports manual and scheduled runs"],"target_audience":["CRM administrators","Sales operations / RevOps teams","Data quality managers / data engineers working with CRM data"]}


name: Spotify Sync Liked Songs to Playlist
author_name: Dustin
author_username: dh2io
categories: []
created_at: 2024-12-11T21:47:10.634Z
updated_at: 2024-12-11T21:47:10.634Z
url: https://n8n.io/workflows/2634
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Short an simple:
  This Workflow will sync (add and delete) your Liked Songs to an custom playlist that can be shared.

  Setup:
  Create an app on the Spotify Developer Dashboard.
  Create Spotify Credentials - Just click on one of the Spotify Nodes in the Workflow an click on "create new credentials" and follow the guide.
  Create the Spotify Playlist that you want to sync to.
  Copy the exact name of you playlist, go into Node "Edit set Vars" and replace the value "CHANGE MEEEE" with your playlist name.
  Set your Spotify Credentiels on every Spotify Node. (Should be marekd with Yellow and Red Notes)

  Do you use Gotify?
  - No: Delete the Gotify Nodes (all the way to the right end of the Workflow)
  - Yes: Customize the Gotify Nodes to your needs.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.sort","n8n-nodes-base.gotify","n8n-nodes-base.splitInBatches","n8n-nodes-base.spotify","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.filter","n8n-nodes-base.compareDatasets","n8n-nodes-base.summarize","n8n-nodes-base.splitInBatches","n8n-nodes-base.spotify","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.summarize","n8n-nodes-base.sort","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.gotify","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.spotify","n8n-nodes-base.spotify","n8n-nodes-base.spotify"]

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metadata_json:
  {"categories":["automation","integration","music"],"complexity":"medium","use_cases":["Sync/backup Liked Songs to a shareable playlist","Keep a public playlist in sync with personal likes","Automate playlist management (add/delete)","Send notifications on sync (optional)"],"estimated_setup_minutes":15,"required_services":["Spotify (Developer Dashboard / API)","n8n instance","Gotify (optional, for notifications)"],"key_features":["Two‑way sync: add and delete liked songs to a specified playlist","Configurable target playlist via variable","Uses Spotify OAuth credentials created in Developer Dashboard","Scheduled or manual runs with batching and filtering","Optional Gotify notification nodes (can be removed if unused)"],"target_audience":["n8n users","Spotify listeners who want to share/backup liked songs","Music curators and playlist managers"]}


name: Extract insights & analyse YouTube comments via AI Agent chat
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2024-12-13T12:04:55.292Z
updated_at: 2024-12-13T12:04:55.292Z
url: https://n8n.io/workflows/2636
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Video Guide

  I prepared a detailed guide to help you set up your workflow effectively, enabling you to extract insights from YouTube for content generation using an AI agent.



  Youtube Link

  Who is this for?
  This workflow is ideal for content creators, marketers, and analysts looking to enhance their YouTube strategies through data-driven insights. It’s particularly beneficial for individuals wanting to understand audience preferences and improve their video content.

  What problem does this workflow solve?
  Navigating the content generation and optimization process can be complex, especially without significant audience insight. This workflow automates insights extraction from YouTube videos and comments, empowering users to create more engaging and relevant content effectively.

  What this workflow does
  The workflow integrates various APIs to gather insights from YouTube videos, enabling automated commentary analysis, video transcription, and thumbnail evaluation. The main functionalities include:
  Extracting user preferences from comments.
  Transcribing video content for enhanced understanding.
  Analyzing thumbnails via AI for maximum viewer engagement insights.

  AI Insights Extraction: Automatically pulls comments and metrics from selected YouTube creators to evaluate trends and gaps.
  Dynamic Video Planning: Uses transcriptions to help creators outline video scripts and topics based on audience interest.
  Thumbnail Assessment: Provides analysis on thumbnail designs to improve click-through rates and viewer attraction.

  Setup

  N8N Workflow
  API Setup:
     Create a Google Cloud project and enable the YouTube Data API.
     Generate an API key to be included in your workflow requests.

  YouTube Creator and Video Selection:
     Start by defining a request to identify top creators based on their video views.
     Capture the YouTube video IDs for further analysis of comments and other video metrics.

  Comment Analysis:
     Gather comments associated with the selected videos and analyze them for user insights.

  Video Transcription:
     Utilize the insights from transcriptions to formulate content plans.

  Thumbnail Analysis:
     Evaluate your video thumbnails by submitting the URL through the OpenAI API to gain insights into their effectiveness.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.memoryPostgresChat","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data-processing","ai-insights","content-marketing"],"complexity":"medium","use_cases":["Extract audience sentiment and preferences from YouTube comments","Generate data-driven video topic and script ideas","Transcribe videos for repurposing and analysis","Evaluate thumbnails to improve click-through rates","Monitor creator performance and trend gaps"],"estimated_setup_minutes":45,"required_services":["YouTube Data API (Google Cloud) - API key","OpenAI API (or equivalent AI provider) for chat, transcription, and image analysis","PostgreSQL (for memoryPostgresChat node)","n8n instance (automation runtime)"],"key_features":["Automated comment harvesting and sentiment/insight extraction","Video transcription to support content planning","Thumbnail analysis via AI for CTR insights","AI agent chat for interactive analysis and planning","Selection of top creators/videos by views for targeted analysis"],"target_audience":["Content creators / YouTubers","Marketing teams and social media managers","Growth analysts and data-driven strategists"]}


name: Chat Assistant (OpenAI assistant) with Postgres Memory And API Calling Capabalities
author_name: Fernanda Silva
author_username: ferzinia
categories: []
created_at: 2024-12-13T14:59:00.443Z
updated_at: 2024-12-13T14:59:00.443Z
url: https://n8n.io/workflows/2637
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:31
description:
  Workflow Description

  Your workflow is an intelligent chatbot, using ++OpenAI assistant++, integrated with a backend that supports WhatsApp Business, designed to handle various use cases such as sales and customer support. Below is a breakdown of its functionality and key components:

  Workflow Structure and Functionality

  Chat Input (Chat Trigger)
  The flow starts by receiving messages from customers via WhatsApp Business.
  Collects basic information, such as session_id, to organize interactions.

  Condition Check (If Node)
  Checks if additional customer data (e.g., name, age, dependents) is sent along with the message.
  If additional data is present, a customized prompt is generated, which includes this information. The prompt specifies that this data is for the assistant's awareness and doesn’t require a response.

  Data Preparation (Edit Fields Nodes)
  Formats customer data and the interaction details to be processed by the AI assistant.
  Compiles the customer data and their query into a single text block.

  AI Responses (OpenAI Nodes)
  The assistant’s prompt is carefully designed to guide the AI in providing accurate and relevant responses based on the customer’s query and data provided.
  Prompts describe the available functionalities, including which APIs to call and their specific purposes, helping to prevent “hallucinated” or irrelevant responses.

  Memory and Context (Postgres Chat Memory)
  Stores context and messages in continuous sessions using a database, ensuring the chatbot maintains conversation history.

  API Calls
  The workflow allows the use of APIs with any endpoints you choose, depending on your specific use case. This flexibility enables integration with various services tailored to your needs.
  The OpenAI assistant understands JSON structures, and you can define in the prompt how the responses should be formatted. This allows you to structure responses neatly for the client, ensuring clarity and professionalism.
  Make sure to describe the purpose of each endpoint in the assistant’s prompt to help guide the AI and prevent misinterpretation.

  Customer Response Delivery
  After processing and querying APIs, the generated response is sent to the backend and ultimately delivered to the customer through WhatsApp Business.

  Best Practices Implemented

  Preventing Hallucinations**  
    Every API has a clear description in its prompt, ensuring the AI understands its intended use case.

  Versatile Functionality**  
    The chatbot is modular and flexible, capable of handling both sales and general customer inquiries.

  Context Persistence**  
    By utilizing persistent memory, the flow maintains continuous interaction context, which is crucial for longer conversations or follow-up queries.

  Additional Recommendations

  Include practical examples in the assistant’s prompt, such as frequently asked questions or decision-making flows based on API calls.
  Ensure all responses align with the customer’s objectives (e.g., making a purchase or resolving technical queries).
  Log interactions in detail for future analysis and workflow optimization.

  This workflow provides a solid foundation for a robust and multifunctional virtual assistant 🚀

nodes_used:
  ["n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryPostgresChat","@n8n/n8n-nodes-langchain.memoryPostgresChat","n8n-nodes-base.mySqlTool","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi"]

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metadata_json:
  {"categories":["automation","integration","conversational AI","data persistence","customer support"],"complexity":"complex","use_cases":["Sales conversations and lead qualification","Customer support and troubleshooting","Transactional workflows with API calls (orders, pricing, status)","Context-aware follow-ups and personalized responses"],"estimated_setup_minutes":60,"required_services":["OpenAI API (assistant)","Postgres database for chat memory","WhatsApp Business API or gateway (backend)","HTTP APIs/endpoints for custom integrations","Optional: MySQL if using mySqlTool"],"key_features":["Persistent session memory in Postgres for context continuity","Conditional personalization based on supplied customer data","Controlled prompt design to reduce hallucinations and enforce JSON/structured output","Flexible HTTP API calling from prompts to perform transactions or fetch data","Session and message formatting before sending to AI and back to the client"],"target_audience":["Developers/engineering teams implementing chatbots","Customer support teams seeking automation","Sales teams using conversational qualification and conversions"]}


name: Analyze tradingview.com charts with Chrome extension, N8N and OpenAI
author_name: Hans Blaauw
author_username: thingsio
categories: []
created_at: 2024-12-16T10:08:04.476Z
updated_at: 2024-12-16T10:08:04.476Z
url: https://n8n.io/workflows/2642
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  This flow is supported by a Chrome plugin created with Cursor AI.

  The idea was to create a Chrome plugin and a backend service in N8N to do chart analytics with OpenAI. It's a good sample on how to submit a screenshot from the browser to N8N.

  Who is it for?
  N8N developers who want to learn about using a Chrome plugin, an N8N webhook and OpenAI.

  What opportunity does it present?
  This sample opens up a whole range of N8N connected Chrome extensions that can analyze screenshots by using OpenAI.

  What this workflow does?
  The workflow contains:

  a webhook trigger
  an OpenAI node with GPT-4O-MINI and Analyze Image selected
  a response node to send back the Text that was created after analysing the screenshot.




  All this is needed to talk to the Chrome extension which is created with Cursor AI.

  The idea is to visit the tradingview.com crypto charts, click the Chrome plugin and get back analytics about the shown chart in understandable language. This is driven by the N8N flow.

  With the new image analytics capabilities of OpenAI this opens up a world of opportunities.

  Requirements/setup

  OpenAI API key
  Cursor AI installed
  The Chrome extension. Download
  The N8N JSON code. Download

  How to customize it to your needs?
  Both the Chrome extension and N8N flow can be adapted to use on other websites. You can consider:

  analyzing a financial screen and ask questions about the data shown
  analyzing other charts
  extending the N8N workflow with other AI nodes

  With AI and image analytics the sky is the limit and in some cases it saves you from creating complex API integrations.

  Download Chrome extension

nodes_used:
  ["n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","image analysis","browser-extension"],"complexity":"medium","use_cases":["Analyze trading charts from screenshots","Prototype Chrome extension <> n8n integrations","Generate human-readable chart insights using AI","Build image-based analytics workflows for other websites"],"estimated_setup_minutes":20,"required_services":["OpenAI API key (GPT-4o-mini with Analyze Image)","n8n instance (webhook endpoint)","Cursor AI Chrome extension","Chrome browser / tradingview.com"],"key_features":["Webhook trigger to receive screenshots from the browser extension","OpenAI node using Analyze Image to interpret chart screenshots","Respond-to-webhook node to return textual analysis to the extension","Example Chrome extension integration (Cursor AI) to submit screenshots","Extensible flow to support other sites or additional AI processing nodes"],"target_audience":["n8n developers","Browser extension builders","Quantitative traders and analysts exploring quick chart insights with AI\",\"AI/ML enthusiasts integrating image analytics"]}


name: Intelligent Web Query and Semantic Re-Ranking Flow using Brave and Google Gemini
author_name: Mind-Front
author_username: mind-front
categories: []
created_at: 2024-12-16T13:55:44.230Z
updated_at: 2024-12-16T13:55:44.230Z
url: https://n8n.io/workflows/2643
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:31
description:
  Workflow Description


  This workflow is a powerful, fully automated web query and semantic reranking system that allows users to perform precise, detailed searches, intelligently rank search results and provide high-quality, structured output. Built with AI-powered components, the workflow leverages semantic query generation, result re-ranking, and real-time reporting to deliver actionable insights.

  It is particularly well-suited for real-time data retrieval, market research, and any domain requiring automated yet customizable search result processing.

  How It Works

  Webhook Integration for Input:  
     The workflow begins with a Webhook Node that captures the user's search query as input, enabling seamless integration with other systems.  

  Step 1: Semantic Query Generation (Powered by "Semantic Search - Query Maker"):  
     Using AI (Google Gemini), the initial query is refined and transformed into a context-aware, expert-level search query.  
     The process ensures that the search engine retrieves the most relevant and precise results.

  Step 2: Web Search Execution:  
     A free Brave Search API processes the refined query to fetch search results, ensuring speed and cost efficiency.

  Step 3: Semantic Re-Ranking of Results (Powered by "Semantic Search - Result Re-Ranker"):  
     The workflow reranks the search results based on relevance to the original question, prioritizing the most relevant URLs dynamically.  
     Results are passed through AI-powered intelligent reranking to ensure the final output reflects optimal relevance and quality.

  Step 4: Structured Output Generation:  
     Results are converted into a well-structured, organized JSON format, ranking the top 10 search results with their titles, links, and descriptions.  
     Missing ranks (if fewer than 10 results) are handled gracefully with placeholders, ensuring consistency.

  Step 5: Real-Time Reporting:  
     The reranked search results are sent back to the user or integrated system via the Webhook Node in a JSON-formatted response.  
     Reports are highly structured and ready for downstream processing or consumption.

  Key Features

  AI-Powered Query Refinement:  
     Transforms basic queries into detailed, expert-level search terms for optimal results.

  Dual-Stage Semantic Search:  
     Combines query generation and result reranking for precise, high-relevance outputs.

  Top 10 Result Reranking:  
     Dynamically ranks and organizes the top 10 results based on semantic relevance to the query.

  Customizable Integration:  
     Fully modifiable for alternative APIs or integrations, such as other search engines or custom ranking logic.

  JSON-Formatted Structured Results:  
     Outputs reranked results in a standardized format, ideal for integration into systems requiring machine-readable data.

  Webhook-Based Flexibility:  
     Works seamlessly with Webhook inputs for easy deployment in diverse workflows.

  Cost-Effective API Usage:  
     Pre-integrated with the free Brave Search API, minimizing operational costs while delivering accurate search results.

  Instructions for API Setup

  Brave Search API:  
     Visit api.search.brave.com to obtain a free-tier API key for web search.  
  AI Integration (Google Gemini):  
     Visit Google AI Studio and generate an API key for semantic query generation and reranking.  
  Webhook Configuration:  
     Set up the input Webhook to capture search queries and the output Webhook to deliver reranked results.

  Why Choose This Workflow?

  Precision and Relevance**: Combines AI-based query generation with advanced reranking for accurate results.  
  Fully Customizable**: Easily adapt the workflow to alternative APIs, search engines, or ranking logic.  
  Real-Time Insights**: Provides structured, real-time output ready for immediate use.  
  Scalable and Modular**: Ideal for businesses, researchers, and data analysts needing a robust, repeatable solution.

  Tags  
  AI Workflow, Semantic Search, Query Refinement, Search Result Reranking, Real-Time Search, Web Search Automation, Google Search, Brave Search, News Search, API Integration, Market Research, Competitive Intelligence, Business Intelligence,Google Gemini, Anthropic Claude, OpenAI, GPT, LLM

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.dateTime","n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.outputParserAutofixing","@n8n/n8n-nodes-langchain.outputParserAutofixing","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.code","n8n-nodes-base.respondToWebhook","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatAnthropic","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","AI/ML","search"],"complexity":"complex","use_cases":["Market research","Competitive intelligence","Real-time information retrieval","Automated search result processing","Business intelligence reporting"],"estimated_setup_minutes":30,"required_services":["Brave Search API","Google Gemini (Google AI Studio)","Anthropic Claude (optional)","Webhook endpoint (to receive input and deliver output)"],"key_features":["AI-powered semantic query refinement","Semantic re-ranking of search results","Top-10 results structured JSON output","Webhook-based input/output for real-time integration","Configurable to use alternate search engines or ranking logic','Cost-efficient use of Brave Search free tier"],"target_audience":["Developers","Data analysts","Market researchers/competitive intelligence teams','Business intelligence teams"]}


name: Flux Dev Image Generation (Fal.ai) to Google Drive
author_name: Sira Ekabut
author_username: siraekabut
categories: []
created_at: 2024-12-16T16:52:49.861Z
updated_at: 2024-12-16T16:52:49.861Z
url: https://n8n.io/workflows/2644
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:31
description:
  This workflow automates AI-based image generation using the Fal.ai Flux API. Define custom prompts, image parameters, and effortlessly generate, monitor, and save the output directly to Google Drive. Streamline your creative automation with ease and precision.

  Who is this for?

  This template is for content creators, developers, automation experts, and creative professionals looking to integrate AI-based image generation into their workflows. It’s ideal for generating custom visuals with the Fal.ai Flux API and automating storage in Google Drive.

  What problem is this workflow solving?

  Manually generating AI-based images, checking their status, and saving results can be tedious. This workflow automates the entire process — from requesting image generation, monitoring its progress, downloading the result, and saving it directly to a Google Drive folder.

  What this workflow does
  	Sets Custom Image Parameters: Allows you to define the prompt, resolution, guidance scale, and steps for AI image generation.
  	Sends a Request to Fal.ai: Initiates the image generation process using the Fal.ai Flux API.
  	Monitors Image Status: Checks for completion and waits if needed.
  	Downloads the Generated Image: Fetches the completed image once ready.
  	Saves to Google Drive: Automatically uploads the generated image to a specified Google Drive folder.

  Setup
  	1.	Prerequisites:
  	•	Fal.ai API Key: Obtain it from the Fal.ai platform and set it as the Authorization header in HTTP Header Auth credentials.
  	•	Google Drive OAuth Credentials: Connect your Google Drive account in n8n.
  	2.	Configuration:
  	•	Update the “Edit Fields” node with your desired image parameters:
  	•	Prompt: Describe the image (e.g., “Thai young woman net idol 25 yrs old, walking on the street”).
  	•	Width/Height: Define image resolution (default: 1024x768).
  	•	Steps: Number of inference steps (e.g., 30).
  	•	Guidance Scale: Controls image adherence to the prompt (e.g., 3.5).
  	•	Set your Google Drive folder ID in the “Google Drive” node to save the image.
  	3.	Run the Workflow:
  	•	Trigger the workflow manually to generate the image.
  	•	The workflow waits, checks status, and saves the final output seamlessly.

  Customization
  	•	Modify Image Parameters: Adjust the prompt, resolution, steps, and guidance scale in the “Edit Fields” node.
  	•	Change Storage Location: Update the Google Drive node with a different folder ID.
  	•	Add Notifications: Integrate an email or messaging node to alert you when the image is ready.
  	•	Additional Outputs: Expand the workflow to send the generated image to Slack, Dropbox, or other platforms.

  This workflow streamlines AI-based image generation and storage, offering flexibility and customization for creative automation.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set"]

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metadata_json:
  {"categories":["automation","AI/ML","integration","file storage"],"complexity":"medium","use_cases":["Automated AI image generation from prompts","Saving generated images to cloud storage","Rapid prototyping of visuals","Integrating AI-generated assets into content workflows","Batch or repeated image creation with status monitoring"],"estimated_setup_minutes":15,"required_services":["Fal.ai Flux API (API key)","Google Drive (OAuth)","n8n instance"],"key_features":["Parameterize prompt, resolution, steps, and guidance scale","Initiate image generation via Fal.ai Flux API","Poll/monitor generation status until completion","Download generated image when ready","Upload and save image to specified Google Drive folder (by folder ID)"],"target_audience":["Content creators","Developers","Automation engineers/experts」、「Creative professionals"]}


name: Automatically prune n8n execution history
author_name: Danger
author_username: dangerblack
categories: []
created_at: 2024-12-17T09:10:02.525Z
updated_at: 2024-12-17T09:10:02.525Z
url: https://n8n.io/workflows/2646
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Automated Execution Pruning

  This workflow is designed to help you manage and optimize your n8n instance by automatically pruning old workflow executions, ensuring a cleaner environment and improved performance. You can customize the retention period to suit your needs.

  Key Features:
  Configurable Retention Period:  
     The workflow is preconfigured to delete workflow executions older than 10 days. You can easily adjust this duration by modifying the condition in the If node.

  Daily Automation:  
     Using the Schedule Trigger, the workflow runs daily at the specified time (default: 4:44 AM), retrieving all workflow executions and identifying those that are older than the defined retention period.

  On-Demand Testing:  
     The Manual Trigger allows you to test the workflow at any time, ensuring everything is working as expected.

  Decision Making:  
     The If node evaluates each execution based on its start date and determines whether it should be deleted or retained.

  Execution Pruning:  
     Delete Action: Executions meeting the criteria are removed via the Delete Execution node.  
     No-Operation: Executions that don't meet the criteria remain untouched.

  Workflow Nodes:
  Manual Trigger: Enables on-demand testing of the workflow.  
  Schedule Trigger: Runs the workflow daily at the configured time.  
  n8n List Execution: Fetches all executions in your n8n instance.  
  If Node: Compares the execution's start date with the configured retention period.  
  Delete Execution: Deletes executions older than the specified retention period.  
  No Operation: Serves as a placeholder for executions that don't meet the pruning criteria.  

  How to Customize:
  Retention Period**:  
     Update the If node's condition to modify the retention period. For instance, change 10 * 24 * 60 * 60 * 1000 to the desired number of days in milliseconds.
     
  Schedule**:  
     Adjust the timing of the Schedule Trigger to match your preferred automation schedule.

  This workflow ensures your instance remains efficient by keeping only the relevant execution logs. Use it to maintain a streamlined and clutter-free environment effortlessly.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.n8n","n8n-nodes-base.n8n","n8n-nodes-base.if","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.n8n","n8n-nodes-base.noOp"]

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metadata_json:
  {"categories":["maintenance","automation","devops"],"complexity":"simple","use_cases":["Automatic cleanup of old execution logs","Reduce database/storage usage and improve n8n performance","Enforce execution log retention policies","On-demand testing of pruning logic"],"estimated_setup_minutes":10,"required_services":["n8n instance (with permissions to list and delete executions)"],"key_features":["Configurable retention period (default 10 days)","Daily scheduled pruning (Schedule Trigger)","Manual trigger for on-demand testing","Lists all executions and conditionally deletes old ones","Safe no-op branch for executions retained"],"target_audience":["n8n administrators","DevOps engineers","Platform/system administrators"]}


name: Sentiment Analysis Tracking on Support Issues with Linear and Slack
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-12-17T10:19:26.716Z
updated_at: 2024-12-17T10:19:26.716Z
url: https://n8n.io/workflows/2647
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n template monitors active support issues in Linear.app to track the mood of their ongoing conversation between reporter and assignee using Sentiment Analysis. When sentiment dips into the negative, a notification is sent via Slack to alert the team.

  How it works
  A scheduled trigger is used to fetch recently updated issues in Linear using the GraphQL node.
  Each issue's comments thread is passed into a simple Information Extractor node to identify the overall sentiment.
  The resulting sentiment analysis combined with the some issue details are uploaded to Airtable for review.
  When the template is re-run at a later date, each issue is re-analysed for sentiment
  Each issue's new sentiment state is saved to the airtable whilst its previous state is moved to the "previous sentiment" column.
  An Airtable trigger is used to watch for recently updated rows
  Each matching Airtable row is filtered to check if it has a previous non-negative state but now has a negative state in its current sentiment.
  The results are sent via notification to a team slack channel for priority.

  Check out the sample Airtable here: https://airtable.com/appViDaeaFw4qv9La/shrq6HgeYzpW6uwXL

  How to use
  Modify the GraphQL filter to fetch issues to a relevant issue type, team or person.
  Update the Slack channel to ensure messages are sent to the correct location or persons.
  The Airtable also serves to give a snapshot of Sentiment across support tickets for a given period. It's possible to use this to assess the daily operations.

  Requirements
  Linear for issue tracking (but feel free to use another system if preferred)
  Airtable for Database
  OpenAI for LLM and Sentiment Analysis

  Customising the workflow
  Add more granular levels of sentiment to reduce the number of alerts.
  Explore different types of sentiment based on issue types and customer types. This may help prioritise alerts and response.
  Run across teams or categories of issues to get an overview of sentiment across the support organisation.

nodes_used:
  ["n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.set","n8n-nodes-base.splitInBatches","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.airtableTrigger","n8n-nodes-base.switch","n8n-nodes-base.graphql","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.removeDuplicates","n8n-nodes-base.slack","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","monitoring","sentiment-analysis"],"complexity":"medium","use_cases":["Monitor sentiment on support tickets","Alert team when conversation sentiment turns negative","Track sentiment trends over time","Prioritise tickets based on customer mood"],"estimated_setup_minutes":45,"required_services":["Linear","Airtable","OpenAI (or other LLM/sentiment API)","Slack","n8n"],"key_features":["Scheduled GraphQL fetch of recently updated Linear issues","LLM-based sentiment extraction from comment threads","Persist current and previous sentiment states in Airtable","Filter/compare sentiment changes and trigger alerts","Slack notifications for negative sentiment escalations"],"target_audience":["Support teams","Customer success managers","Engineering/ops on-call teams`,`Automation builders`"]}


name: Automate Blog Creation in Brand Voice with AI
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-12-17T10:37:34.169Z
updated_at: 2024-12-17T10:37:34.169Z
url: https://n8n.io/workflows/2648
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  This n8n template demonstrates a simple approach to using AI to automate the generation of blog content which aligns to your organisation's brand voice and style by using examples of previously published articles.

  In a way, it's quick and dirty "training" which can get your automated content generation strategy up and running for very little effort and cost whilst you evaluate our AI content pipeline.

  How it works
  In this demonstration, the n8n.io blog is used as the source of existing published content and 5 of the latest articles are imported via the HTTP node.
  The HTML node is extract the article  bodies which are then converted to markdown for our LLMs.
  We use LLM nodes to (1) understand the article structure and writing style and (2) identify the brand voice characteristics used in the posts.
  These are then used as guidelines in our final LLM node when generating new articles.
  Finally, a draft is saved to Wordpress for human editors to review or use as starting point for their own articles.

  How to use
  Update Step 1 to fetch data from your desired blog or change to fetch existing content in a different way.
  Update Step 5 to provide your new article instruction. For optimal output, theme topics relevant to your brand.

  Requirements
  A source of text-heavy content is required to accurately breakdown the brand voice and article style. Don't have your own? Maybe try your competitors?
  OpenAI for LLM - though I recommend exploring other models which may give subjectively better results.
  Wordpress for blog but feel free to use other preferred publishing platforms.

  Customising this workflow
  Ideally, you'd want to "train" your agent on material which is similar to your output ie. your social media post may not get the best results from your blog content due to differing formats.
  Typically, this brand voice extraction exercise should run once and then be cached somewhere for reuse later. This would save on generation time and overall cost of the workflow. 

nodes_used:
  ["n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.splitOut","n8n-nodes-base.limit","n8n-nodes-base.html","n8n-nodes-base.aggregate","n8n-nodes-base.merge","n8n-nodes-base.wordpress","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.markdown","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set"]

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metadata_json:
  {"categories":["automation","content-generation","AI","CMS-integration"],"complexity":"medium","use_cases":["Automated blog draft generation","Extracting and replicating brand voice/style","Content pipeline prototyping","Editorial workflow for human review","Content repurposing from existing articles"],"estimated_setup_minutes":45,"required_services":["n8n instance","LLM provider (e.g., OpenAI) or compatible model","WordPress (or other publishing platform) account","Source blog or website (HTTP-accessible content)"],"key_features":["Fetches recent articles via HTTP","HTML extraction and conversion to Markdown","LLM-based analysis of article structure and brand voice","Generates new article drafts guided by extracted voice","Saves drafts to WordPress for editorial review"],"target_audience":["Content marketers","Editors and content teams","Growth teams and founders prototyping content strategies"]}


name: Prompt-based Object Detection with Gemini 2.0
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-12-17T11:53:59.121Z
updated_at: 2024-12-17T11:53:59.121Z
url: https://n8n.io/workflows/2649
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n template demonstrates how to get started with Gemini 2.0's new Bounding Box detection capabilities in your workflows.

  The key difference being this enables prompt-based object detection for images which is pretty powerful for things like contextual search over an image. eg. "Put a bounding box around all adults with children in this image" or "Put a bounding box around cars parked out of bounds of a parking space".

  How it works
  An image is downloaded via the HTTP node and an "Edit Image" node is used to extract the file's width and height.
  The image is then given to the Gemini 2.0 API to parse and return coordinates of the bounding box of the requested subjects. In this demo, we've asked for the AI to identify all bunnies.
  The coordinates are then rescaled with the original image's width and height to correctl align them.
  Finally to measure the accuracy of the object detection, we use the "Edit Image" node to draw the bounding boxes onto the original image.

  How to use
  Really up to the imagination! Perhaps a form of grounding for evidence based workflows or a higher form of image search can be built.

  Requirements
  Google Gemini for LLM

  Customising the workflow
  This template is just a demonstration of an experimental version of Gemini 2.0. It is recommended to wait for Gemini 2.0 to come out of this stage before using in production.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.editImage","n8n-nodes-base.editImage","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5040 characters]

metadata_json:
  {"categories":["computer vision","image processing","automation","AI/ML","integration"],"complexity":"medium","use_cases":["Contextual image search (find objects by natural-language prompts)","Visual evidence extraction and grounding","Automated image annotation and tagging","Monitoring/compliance (e.g., parking/parking-boundary violations)","Prototyping object-detection workflows or POCs"],"estimated_setup_minutes":15,"required_services":["Google Gemini 2.0 API (Gemini for LLM) - API access required","Image hosting or storage with publicly accessible URLs (S3, GCS, HTTP)","n8n instance (self-hosted or cloud)"],"key_features":["Prompt-based bounding-box object detection using Gemini 2.0","Downloads image and extracts original width/height","Rescales normalized coordinates to match original image dimensions","Draws bounding boxes on the original image for visual verification","Supports complex natural-language prompts for contextual detection (e.g., \"adults with children\")"],"target_audience":["Developers","ML / Computer Vision engineers","Automation / Integration builders using n8n workflows"]}


name: Extract Information from a Logo Sheet using forms, AI, Google Sheet and Airtable
author_name: Marcel Claus-Ahrens
author_username: geckse
categories: []
created_at: 2024-12-17T12:35:08.190Z
updated_at: 2024-12-17T12:35:08.190Z
url: https://n8n.io/workflows/2650
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Instructions

  This automation enables you to just upload any Image (via Form) of a Logo Sheet, containing multiple Images of Product Logos (most likely) which brings them in some context to one another. 

  After submitting an AI-Agent eats that Logo Sheet, turning it into an List of "Productname" and "Attributes", also checks if Tools are kind of similar to another, given the Context of the Image.

  We utilize AI Vision capabilities for that. NOTE: It might not be able to extract all informations. For a "upload and forget it" Workflow it works for me. You can even run it multiple times, to be sure. 

  But if you need to make sure it extracts everything you might need to think about an Multi-Agent Setup with Validation-Agent Steps.

  Once the Agent finishes the extraction, it will traditionally and deterministicly add those Attributes to Airtable (Creates those, if not already existing.) and also Upserts the Tool Informations.

  It uses MD5 Hashes for turning Product Names into.. something fancy really, you could also use it without that, but I wanted to have something that looks atleast like an ID.  

  Setup

  Set Up the Airtable like shown below.
  Update and set Credentials for all Airtable Nodes.
  Check or Adjust the Prompt of the Agent matching your use-case.
  Activate the Workflow. 
  Open the Form (default: https://your-n8n.io/form/logo-sheet-feeder)
  Enjoy growing your Airtable.

    
  Enjoy the workflow! ❤️  
  let the work flow — Workflow Automation & Development

nodes_used:
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workflow_json_compressed:
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metadata_json:
  {"categories":["automation","data processing","AI / Vision","integration"],"complexity":"complex","use_cases":["Extract product names and attributes from multi-logo images","Build or update a product/logo catalog from uploaded sheets","Detect similarity/duplicates among logos or tools","Populate and upsert records in Airtable (and optionally Google Sheets)","Quick 'upload-and-forget' brand asset ingestion for marketing/ops"],"estimated_setup_minutes":30,"required_services":["Airtable","n8n Form (or equivalent upload form)","AI Vision / Agent provider (e.g., OpenAI vision, other AI/ML provider)","Google Sheets (optional, depending on template usage)"],"key_features":["Upload logo sheet via form","AI Vision agent parses sheet into product names and attributes","Similarity checking between logos/tools within the image context","Deterministic upsert/create of attributes and tool records in Airtable","MD5-based ID generation for product names (configurable pattern/persistence)"],"target_audience":["automation engineers / n8n developers","marketing and brand teams","product managers maintaining logo/product catalogs/sheets"]}


name: AI Agent for realtime insights on meetings
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2024-12-17T16:48:41.040Z
updated_at: 2024-12-17T16:48:41.040Z
url: https://n8n.io/workflows/2651
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:31
description:
  Video Guide


  I prepared a detailed guide explaining how to build an AI-powered meeting assistant that provides real-time transcription and insights during virtual meetings.



  Youtube Link

  Who is this for?
  This workflow is ideal for business professionals, project managers, and team leaders who require effective transcription of meetings for improved documentation and note-taking. It's particularly beneficial for those who conduct frequent virtual meetings across various platforms like Zoom and Google Meet.

  What problem does this workflow solve?
  Transcribing meetings manually can be tedious and prone to error. This workflow automates the transcription process in real-time, ensuring that key discussions and decisions are accurately captured and easily accessible for later review, thus enhancing productivity and clarity in communications.

  What this workflow does
  The workflow employs an AI-powered assistant to join virtual meetings and capture discussions through real-time transcription. Key functionalities include:
  Automatic joining of meetings on platforms like Zoom, Google Meet, and others with the ability to provide real-time transcription.
  Integration with transcription APIs (e.g., AssemblyAI) to deliver seamless and accurate capture of dialogue.
  Structuring and storing transcriptions efficiently in a database for easy retrieval and analysis.

  Real-Time Transcription: The assistant captures audio during meetings and transcribes it in real-time, allowing participants to focus on discussions.
  Keyword Recognition: Key phrases can trigger specific actions, such as noting important points or making prompts to the assistant.
  Structured Data Management: The assistant maintains a database of transcriptions linked to meeting details for organized storage and quick access later.

  Setup

  Preparation

  Create Recall.ai API key
  Setup Supabase account and table
  create table
    public.data (
      id uuid not null default gen_random_uuid (),
      date_created timestamp with time zone not null default (now() at time zone 'utc'::text),
      input jsonb null,
      output jsonb null,
      constraint data_pkey primary key (id),
    ) tablespace pg_default;

  Create OpenAI API key

  Development

  Bot Creation: 
     Use a node to create the bot that will join meetings. Provide the meeting URL and set transcription options within the API request.

  Authentication: 
     Configure authentication settings via a Bearer token for interacting with your transcription service.

  Webhook Setup: 
     Create a webhook to receive real-time transcription updates, ensuring timely data capture during meetings.

  Join Meeting: 
     Set the bot to join the specified meeting and actively listen to capture conversations.

  Transcription Handling: 
     Combine transcription fragments into cohesive sentences and manage dialog arrays for coherence.

  Trigger Actions on Keywords: 
     Set up keyword recognition that can initiate requests to the OpenAI API for additional interactions based on captured dialogue.

  Output and Summary Generation: 
     Produce insights and summary notes from the transcriptions that can be stored back into the database for future reference.

nodes_used:
  ["@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.postgres","n8n-nodes-base.postgresTool","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.supabase","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.webhook","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","AI/ML","real-time collaboration"],"complexity":"complex","use_cases":["Real-time meeting transcription","Automated meeting summaries and insights","Action-item and keyword detection during calls","Searchable meeting archive and storage","Triggering follow-up workflows based on keywords"],"estimated_setup_minutes":90,"required_services":["Recall.ai (or equivalent meeting bot/transcription service)","Supabase (or other Postgres-compatible database)","OpenAI API (for summarization/insights)","Zoom / Google Meet / other meeting platforms (meeting URLs)","Webhook-accessible endpoint (public URL)"],"key_features":["AI-powered bot joins meetings and captures audio in real time","Streaming transcription and assembly of fragments into coherent text","Keyword recognition to trigger actions or OpenAI prompts","Webhook integration for receiving live transcription updates","Structured storage of transcriptions and metadata in a database (Supabase) for retrieval and analysis"],"target_audience":["Business professionals","Project managers and team leads","Product managers and ops teams needing meeting records and summaries"]}


name: Backup your workflows to GitHub
author_name: Solomon
author_username: solomon
categories: []
created_at: 2024-12-18T15:05:17.137Z
updated_at: 2024-12-18T15:05:17.137Z
url: https://n8n.io/workflows/2652
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:31
description:
  Based on Jonathan's work. Check out his templates.

  How it works
  This workflow will backup your workflows to GitHub. It uses the n8n API node to export all workflows.

  It then loops over the data, checks in GitHub to see if a file exists that uses the credential's ID. Once checked it will:
  update the file on GitHub if it exists;
  create a new file if it doesn't exist;
  ignore if it's the same.

  Who is this for?
  People wanting to backup their workflows outside the server for safety purposes or to migrate to another server.

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

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.n8n","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.switch","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.github","n8n-nodes-base.github","n8n-nodes-base.splitInBatches","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.github","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.executeWorkflow","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","backup"],"complexity":"medium","use_cases":["Periodic backups of n8n workflows","Migrate workflows between n8n instances","Disaster recovery and versioning","Store workflow backups in GitHub for audit/history"],"estimated_setup_minutes":15,"required_services":["GitHub (repository and Personal Access Token)","n8n server with API access/credentials"],"key_features":["Exports all workflows via the n8n API","Checks GitHub for existing files using credential IDs","Creates new files or updates existing ones","Skips uploading when workflow content is unchanged","Processes multiple workflows with loop and conditional logic"],"target_audience":["n8n users","Developers","Sysadmins/DevOps engineers"]}


name: AI Data Analyst Agent and Visualization Agent for Large Spreadsheets
author_name: Derek Cheung
author_username: derekcheungsa
categories: []
created_at: 2024-12-18T23:58:03.508Z
updated_at: 2024-12-18T23:58:03.508Z
url: https://n8n.io/workflows/2653
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:31
description:
  Purpose of workflow:

  This workflow transforms spreadsheet data into an interactive, AI-powered knowledge base that enables users to gain deep insights through natural language queries, searchability, and comparative analysis.

  How it works:

  Data Storage & Integration:
  Spreadsheet data is imported into a no-code database (NocoDB)
  System connects with an AI data analyst agent
  Agent accesses table metadata and column information

  Query Processing:
  Users input natural language questions
  AI agent interprets queries and converts them to database filters
  System retrieves relevant data using filter formulas
  AI synthesizes responses with analysis and insights

  Advanced Capabilities:
  Performs comparative analysis across multiple data points
  Handles complex multi-part queries

  Automatically creates visualizations:
  Visualization AI Agent figures out the data and the chart type and generates professional visualization using Quickchart


  Step by step setup:
  Create account on nocodb.com
  Create table by importing csv, copy table id
  Create API token https://app.nocodb.com/#/account/tokens
  In workflow, settings node, update with table id
  In NocoDB tool node, setup authentication with API token created in step 3
  Specify the workspace and base fields after connecting to NocoDB 

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.set","n8n-nodes-base.nocoDbTool","n8n-nodes-base.set","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data processing","analytics","AI"],"complexity":"complex","use_cases":["Natural-language querying of large spreadsheets","Interactive data exploration and comparative analysis","Automated chart generation from query results","Creating a searchable AI-powered knowledge base from CSVs/spreadsheets","Business reporting and ad-hoc analysis for non-technical users"],"estimated_setup_minutes":30,"required_services":["NocoDB (no-code database)","OpenAI (lmChat/OpenAI API) or compatible LLM","QuickChart (chart image generation)","n8n (workflow runner)","Optional: public HTTP/Webhook endpoint for external triggers"],"key_features":["Import spreadsheet data into NocoDB and expose via API","AI agent converts natural-language queries into DB filters","Retrieves table metadata and column info for accurate querying","Performs comparative and multi-part analyses across fields","Synthesizes human-readable insights and explanations from results"],"target_audience":["Data analysts and business analysts","Product managers and non-technical business users","Developers implementing AI-enabled data workflows and integrations"]}


name: Vector Database as a Big Data Analysis Tool for AI Agents [1/3 anomaly][1/2 KNN]
author_name: Jenny
author_username: mrscoopers
categories: []
created_at: 2024-12-19T09:36:22.024Z
updated_at: 2024-12-19T09:36:22.024Z
url: https://n8n.io/workflows/2654
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Vector Database as a Big Data Analysis Tool for AI Agents

  Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n".

  This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases.

  Uploading (image) datasets to Qdrant
  Set up meta-variables for anomaly detection in Qdrant
  Anomaly detection tool
  KNN classifier tool

  For anomaly detection
  1. This is the first pipeline to upload an image dataset to Qdrant.
  The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection.
  The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset.

  For KNN (k nearest neighbours) classification
  1. This is the first pipeline to upload an image dataset to Qdrant.
  The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset.

  To recreate both
  You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. 

  [This workflow] Batch Uploading Images Dataset to Qdrant 
  This template imports dataset images from Google Could Storage, creates Voyage AI embeddings for them in batches, and uploads them to Qdrant, also in batches. In this particular template, we work with crops dataset. However, it's analogous to uploading lands dataset, and in general, it's adaptable to any dataset consisting of image URLs (as the following pipelines are).

  First, check for an existing Qdrant collection to use; otherwise, create it here. Additionally, when creating the collection, we'll create a payload index, which is required for a particular type of Qdrant requests we will use later.
  Next, import all (dataset) images from Google Cloud Storage but keep only non-tomato-related ones (for anomaly detection testing).
  Create (per batch) embeddings for all imported images using the Voyage AI multimodal embeddings API.
  Finally, upload the resulting embeddings and image descriptors to Qdrant via batch upload.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleCloudStorage","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.filter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["data processing","integration","machine learning","computer vision","vector-databases"],"complexity":"medium","use_cases":["Build vector DB-backed data store for AI agents","Batch ingestion of image datasets into Qdrant","Create embeddings for large image datasets","Prepare metadata/indices for anomaly detection","KNN classification using vector search"],"estimated_setup_minutes":60,"required_services":["n8n","Qdrant Cloud","Voyage AI (embeddings API)","Google Cloud Storage (GCS)","Kaggle (dataset source)","Google Cloud project credentials / service account"],"key_features":["Batch import of images from Google Cloud Storage","Create multimodal embeddings (Voyage AI) in batches","Create/check Qdrant collection and payload index","Batch upload of vectors and payloads to Qdrant","Filtering pipeline (e.g., exclude tomato images) for anomaly testing"],"target_audience":["ML engineers","Data engineers","MLOps engineers implementing production AI agents developers"]}


name: Vector Database as a Big Data Analysis Tool for AI Agents [2/3 - anomaly]
author_name: Jenny
author_username: mrscoopers
categories: []
created_at: 2024-12-19T09:41:43.006Z
updated_at: 2024-12-19T09:41:43.006Z
url: https://n8n.io/workflows/2655
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Vector Database as a Big Data Analysis Tool for AI Agents

  Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n".

  This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases.

  Uploading (image) datasets to Qdrant
  Set up meta-variables for anomaly detection in Qdrant
  Anomaly detection tool
  KNN classifier tool

  For anomaly detection
  The first pipeline to upload an image dataset to Qdrant.
  2. This is the second pipeline to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection.
  The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset.

  For KNN (k nearest neighbours) classification
  The first pipeline to upload an image dataset to Qdrant.
  The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset.

  To recreate both
  You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. 

  [This workflow] Setting Up Cluster (Class) Centres & Cluster (Class) Threshold Scores for Anomaly Detection
  Preparatory workflow to set cluster centres and cluster threshold scores so anomalies can be detected based on these thresholds.
  Here, we're using two approaches to set up these centres: the "distance matrix approach" and the "multimodal embedding model approach".

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["data processing","integration","machine learning","anomaly detection","vector-database"],"complexity":"complex","use_cases":["Upload and index large image datasets into a vector DB (Qdrant)","Prepare cluster/class centres and threshold scores for anomaly detection","Run anomaly detection on incoming images using precomputed centres/thresholds","KNN (k-nearest neighbours) image classification using vectors","Build production-ready AI agent pipelines that use vector search and embeddings"],"estimated_setup_minutes":90,"required_services":["n8n (self-hosted or cloud)","Qdrant Cloud (or self-hosted Qdrant)","Google Cloud Storage (GCS)","Voyage AI API","Kaggle (dataset source)","API keys/credentials for the above services"],"key_features":["Image dataset ingestion and vectorization into Qdrant","Two approaches to set cluster centres: distance-matrix and multimodal embedding model","Compute cluster/class threshold scores for anomaly detection","Anomaly detection workflow that scores new images against clusters","KNN classifier workflow for image classification using vector search in Qdrant

   (production-ready) integrations with cloud storage and external APIs (Voyage AI)"],"target_audience":["ML engineers","Data engineers","MLOps practitioners implementing vector-search pipelines for images

   (AI) developers building agent workflows using vector DBs"]}


