# templates

name: Build an MCP Server with Airtable
author_name: Aitor | 1Node
author_username: aitoralonso
categories: []
created_at: 2025-05-05T17:38:07.713Z
updated_at: 2025-05-05T17:38:07.713Z
url: https://n8n.io/workflows/3879
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  Who is this for?
  This template is designed for anyone who wants to integrate MCP with their AI Agents using Airtable. Whether you're a developer, a data analyst, or an automation enthusiast, if you're looking to leverage the power of MCP and Airtable in your n8n workflows, this template is for you.

  What problem is this workflow solving?
  This template caters to MCP beginners seeking a hands-on example and developers looking to integrate Airtable MCP service. When integrating MCP with Airtable, manually updating AI Agents after changes to Airtable data on the MCP Server is time-consuming and error-prone.

  This template automates the process, enabling the AI Agent to instantly recognize changes made to Airtable on the MCP Server. In data management, for example, it ensures that record updates or additions in Airtable are automatically detected by the AI Agent. With detailed steps, it simplifies the integration process for all users.

  What this workflow does
  This workflow focuses on integrating MCP with Airtable within n8n. Specifically, it allows you to build an MCP Server and Client using Airtable nodes in n8n. Any changes made to the Airtable Base/Table on the MCP Server are automatically recognized by the MCP Client in the workflow. This means that you can make changes to your Airtable (such as adding, deleting, or modifying records) on the MCP Server, and the MCP Client in the n8n workflow will immediately detect these changes without any manual intervention.

  Setup

  Requirements
  An active n8n account.
  Access to Airtable API.
  A sample base and rows in Airtable that you can use to test.
  An API key from your preferred LLM to power the AI agent.

  Step-by-step guide
  Create a new workflow in n8n: Log in to your n8n account and create a new workflow.
  Add Airtable nodes: Search for and add the Airtable nodes to your workflow that you wish the MCP client to have access to.
  Set up the MCP Server and Client: Use the appropriate nodes in n8n to set up the MCP Server and Client. Connect the Airtable nodes to the MCP nodes as required.
  Activate and test the workflow: Talk to the chat trigger once all credentials have been updated and table data synced and try adding some rows, deleting or finding and updating cells.

  How to customize this workflow to your needs
  If you want to customize this workflow, you can:
  Modify the triggers:** You can change the conditions under which the MCP Client detects changes. For example, you can set it to detect changes only in specific fields or based on certain record values in Airtable.
  Integrate with other services:** You can add more nodes to the workflow to integrate with other services, such as sending notifications to Slack or triggering further actions based on the detected Airtable changes.

  Need help?

  Feel free to contact us at 1 Node.
  Get instant access to a library of free resources we created.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","AI/ML","data synchronization"],"complexity":"medium","use_cases":["Automatically sync Airtable changes to AI agents","Keep AI Agents updated with Airtable record additions/edits/deletions","Build an MCP Server/Client pair to expose Airtable data to agents","Trigger downstream actions (notifications, workflows) when Airtable changes occur"],"estimated_setup_minutes":30,"required_services":["n8n account","Airtable (API access and Base/Table)","LLM provider API key (e.g., OpenAI)","MCP components within n8n"],"key_features":["MCP Server and Client setup to expose Airtable data to agents","Automatic detection of Airtable record adds/updates/deletes","Integration with LLM-powered AI agent (chat/agent nodes)","Configurable triggers/field-level filtering","Easily extendable to other services (Slack, webhooks)"],"target_audience":["Developers","Data analysts","Automation enthusiasts / no-code builders who use n8n or Airtable (power users)"]}


name: Monitor Server Uptime & Get Email Alerts with Google Sheets
author_name: Khaled
author_username: khaled
categories: []
created_at: 2025-05-05T17:52:03.128Z
updated_at: 2025-05-05T17:52:03.128Z
url: https://n8n.io/workflows/3880
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  🌐 Web Server Monitor & Alert System

  This automation pings web servers at regular intervals, logs their status, and sends email alerts if a server goes down. It’s perfect for maintaining visibility over server uptime — without complex monitoring tools.
  🧠 How It Works

  This workflow performs minute-by-minute checks on all listed servers in a Google Sheet and:

      ✅ Logs all reachable servers in an “Alive” log.

      🔻 Sends an email alert if a server is unreachable.

      📄 Logs failed servers in a “Down” sheet with timestamps.

  🧩 Key Components
  ⏰ 1. Schedule Trigger

  Runs the workflow every minute for real-time monitoring.
  📄 2. Web Servers List (Google Sheets)

  Pulls server IPs or hostnames from a Google Sheet named Server_List.
  Each row = one server to monitor. This makes adding/removing servers effortless — just update the sheet.
  🌐 3. Servers Alive Check (HTTP Request)

  Performs an HTTP GET request to each server (e.g., http://your-server.com).
  If the request fails, it automatically triggers the error path (handled via continueOnFail).
  ✅ 4. Web Server Alive Log (Google Sheets)

  Records successful pings in Server_Status_Alive with:

      Timestamp

      Server IP

      Status = Alive

  This log can be used for uptime reports or audits.
  📧 5. Server Down Notification (Gmail)

  If a server fails, this node sends an email to the admin.
  It includes:

      Server address

      Timestamp

      Suggested action

  📄 6. Web Server Down Log (Google Sheets)

  Logs failed pings in a separate sheet for historical tracking and debugging.
  ✅ Main Advantages

      Live Server Monitoring
      Stay informed about server health in near real-time.

      No-Code Configuration
      Add/remove servers from the Google Sheet — no need to touch the workflow.

      Email Alerts on Failure
      Proactively notifies you before users report the issue.

      Audit-Ready Logging
      Maintains logs for both healthy and failed checks for documentation or reporting.

      Flexible & Scalable
      Monitor 1 or 100 servers with the same template — just scale the list.

  ⚙️ Setup Steps
  🔑 Prerequisites

      Google Sheet with server list (column name = “Server”)

      Gmail OAuth2 Connection for alerts

      n8n Instance running regularly

  🛠 Configuration

      Google Sheets

          Sheet 1 (Server_List): Your list of servers.

          Sheet 2 (Server_Status_Alive): Log for reachable servers.

          Sheet 3 (Server_Status_Down): Log for unreachable servers.

      Gmail Integration

          Connect your Gmail account in the Server Down Notification node.

          Edit recipient email and message content as needed.

      HTTP Check

          Adjust the HTTP request URL template if using port numbers or paths (e.g., http://{{Server}}:8080/status).

      Schedule

          Default is every 1 minute. Change via Schedule Trigger if needed.

  🧪 Testing

      Input a reachable server (e.g., example.com) and an unreachable IP.

      Run the workflow manually or wait for the next scheduled run.

      Check:

          Alive log updates correctly.

          Down log records failures.

          Email alert is received.

  🚀 Deployment

  Activate the workflow, and it will quietly run in the background, notifying you of any server downtime instantly while keeping logs for future review.

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

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metadata_json:
  {"categories":["monitoring","automation","integration","alerting"],"complexity":"medium","use_cases":["Server uptime monitoring","Realtime alerting for service outages","Logging uptime/down events for audits","Lightweight SRE/DevOps checks for small teams","Notification-driven incident response"],"estimated_setup_minutes":15,"required_services":["n8n instance","Google Sheets (Google Sheets API / OAuth2)","Gmail (Gmail API / OAuth2)","Publicly reachable HTTP servers to check"],"key_features":["Minute-by-minute scheduled HTTP checks","Reads server list from Google Sheet for easy scaling","Logs successful checks to an \"Alive\" sheet","Logs failures to a separate \"Down\" sheet with timestamps","Sends email alerts on failure via Gmail node (continueOnFail handling)"],"target_audience":["System administrators","DevOps / SRE engineers","IT managers / small ops teams"]}


name: Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI
author_name: Elliot Scribner
author_username: ejscribner
categories: []
created_at: 2025-05-05T18:21:37.826Z
updated_at: 2025-05-05T18:21:37.826Z
url: https://n8n.io/workflows/3881
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  &gt; Disclaimer: this workflow template uses the n8n-nodes-couchbase community package. Community nodes are unverified and usage of them comes with some risks. See here for instructions on installing n8n community nodes.

  This template is intended for use by those interested in learning more about Agentic AI workflow development, as well as those interested in learning how to use the Couchbase Search Vector Store node for practical applications.

  This workflow helps users decide on travel destinations based on descriptions of several points of interest loaded into Couchbase and retrieved using Vector Search.

  How it Works

  This template contains two workflows:

  The Data Ingestion workflow uses the following nodes
      Webhook node (to listen for HTTP requests)
      OpenAI Embeddings node (to generate embeddings on document insertion)
          Note: You’ll need to configure OpenAI credentials for this node
      Couchbase Vector node (configured for document insertion)
      Default Data Loader and Recursive Character Text Splitter
  The Chat Application workflow uses the following nodes
      Chat Trigger node
      AI Tools Agent node connect to:
          Gemini (as the Chat Model, for generating responses)
              Note: You will have to configure Gemini credentials for this node
          Simple Memory (as the Memory, to maintain conversation context)
          Couchbase Search Vector node (as the Tool, for search)
          OpenAI Embeddings node (as the Embedding model for the Couchbase Search Vector node, to convert queries to vectors)
              Note: You’ll need to configure OpenAI credentials for this node

  Set up

  Setting up this workflow is easy and only takes around 10 minutes.

  Prerequisites

  A Couchbase Cluster running the Search Service, and corresponding database access credentials
      Be sure the Couchbase cluster allows the incoming IP address for n8n
      Create a Vector Search Index using this index definition
      Create a bucket (called travel-agent), scope (called vectors), and collection (called points-of-interest) in your Cluster
  OpenAI API Key
  Gemini API Key

  Steps

  Configure all necessary credentials (Couchbase, OpenAI, and Gemini)
  Select your bucket, scope, and collection for each of the Couchbase vector nodes
  Ingest data, either using the cURL statements found on the sticky note within the workflow, or using this shell script to ingest 6 points of interest
  Open the chat and test out your travel agent!

  Customization and Next Steps
  This workflow template can be made more robust by enhancing the data model to include more information about each point of interest. For example, the addition of price ranges, ideal seasons to visit, activity types, and accomodation options can help inform the LLM further about each destination, and in turn allow it to provide a more tailored response and be more helpful for travel planning.
  Alternatively, the data model could be entirely re-configured to suit a wide variety of other use cases. This template can serve as a building block for all sorts of AI Agent applications using RAG and is not limited to only travel recommendations.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote","n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.agent","n8n-nodes-couchbase.vectorStoreCouchbaseSearch","n8n-nodes-couchbase.vectorStoreCouchbaseSearch","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.embeddingsOpenAi"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","AI / ML","search & retrieval","integration","knowledge management"],"complexity":"medium","use_cases":["Travel destination recommendation and planning","Retrieval-Augmented Generation (RAG) agent demos","Prototype agentic AI workflows with vector search","Learning embeddings + vector search integration","Building conversational tools with memory and retrieval"],"estimated_setup_minutes":10,"required_services":["Couchbase Cluster with Search Service (vector index, bucket/scope/collection)","OpenAI API (embeddings)","Google Gemini API (Gemini 2.0 Flash for chat)","n8n instance with n8n-nodes-couchbase community package installed","Public/accessible HTTP endpoint for n8n webhook (or tunneling)"],"key_features":["Two-workflow design: data ingestion (embeddings -> Couchbase) and chat agent (agent + vector search tool)","Uses OpenAI embeddings to vectorize documents and Couchbase Vector Search for retrieval","Agentic chat via AI Tools Agent + Gemini chat model with conversational memory","Retrieval tool integrated into agent to perform RAG over points of interest","Simple data loader and text splitter for document ingestion; customizable data model"],"target_audience":["developers building conversational agents","AI engineers / ML practitioners experimenting with RAG","data engineers setting up vector search pipelines in Couchbase"]}


name: Error Handling System with PostgreSQL Logging and Rate-Limited Notifications
author_name: Davi Saranszky Mesquita
author_username: frkr
categories: []
created_at: 2025-05-05T21:38:38.282Z
updated_at: 2025-05-05T21:38:38.282Z
url: https://n8n.io/workflows/3882
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Log errors and avoid sending too many emails

  Use case

  Most of the time, it’s necessary to log all errors that occur. However, in some cases, a scheduled task or service consuming excessive resources might trigger a surge of errors.

  To address this, we can log all errors but limit alerts to a maximum of one notification every 5 minutes.

  What this workflow does

  This workflow can be configured to receive error events, or you can integrate it before your own error-handling logic.  

  If used as the primary error handler, note that this flow will only add a database log entry and take no further action. You’ll need to add your own alerts (e.g., email or push notifications). Below is an example of a notification setup I prefer to use.  

  At the end, there’s an error cleanup option. This feature is particularly useful in development environments.  

  If you already have an error-handling workflow, you can call this one as a sub-workflow. Its final steps include cleanup logic to reset the execution state and terminate the workflow.

  Setup

  Verify all Postgres nodes and credentials when using the 'Error Handling Sample'

  How to adjust it to your needs

  1) You can set this workflow as a sub-workflow within your existing error-handling setup.

  2) Alternatively, you can add the "Error Handling Sample" at the end of this workflow, which sends email and push notifications.

  Configuration Requirements:

  ⚠️ You must create a database table for this to work!



  DDL of this sample:

  create table p1gq6ljdsam3x1m."N8Err"
  (
      id         serial
          primary key,
      created_at timestamp,
      updated_at timestamp,
      created_by varchar,
      updated_by varchar,
      nc_order   numeric,
      title      text,
      "URL"      text,
      "Stack"    text,
      json       json,
      "Message"  text,
      "LastNode" text
  );

  alter table p1gq6ljdsam3x1m."N8Err"
      owner to postgres;

  create index "N8Err_order_idx"
      on p1gq6ljdsam3x1m."N8Err" (nc_order);

  by Davi Saranszky Mesquita
  https://www.linkedin.com/in/mesquitadavi/

nodes_used:
  ["n8n-nodes-base.errorTrigger","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.stickyNote","n8n-nodes-base.emailSend","n8n-nodes-base.emailSend","n8n-nodes-base.pushover","n8n-nodes-base.postgres","n8n-nodes-base.manualTrigger","n8n-nodes-base.executeWorkflow","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4984 characters]

metadata_json:
  {"categories":["error handling","logging","notification","integration","monitoring"],"complexity":"medium","use_cases":["Centralized logging of workflow/application errors","Throttling/limiting alert noise (rate-limited notifications)","Using as a sub-workflow within existing error-handling pipelines","Development environment error cleanup and reset","Persisting error details for debugging and auditing"],"estimated_setup_minutes":25,"required_services":["PostgreSQL (required) - database and credentials","Notification service (optional) - e.g., Pushover","Email provider / SMTP (optional) for alerts","n8n instance"],"key_features":["Logs all errors to a PostgreSQL table (DDL sample provided)","Rate-limited notifications (example: max one alert every 5 minutes)","Can run as primary error handler or be invoked as a sub-workflow","Optional cleanup/reset logic for development/test environments","Includes example notification setup (email/push) and configurable nodes"],"target_audience":["Developers","SREs / DevOps","QA engineers / Testers who run scheduled tasks/services"]}


name: Auto-Post Dev.to Articles to LinkedIn with Airtable Tracking & Telegram Alerts
author_name: Krishna Kumar Eswaran
author_username: mrprogrammer
categories: []
created_at: 2025-05-06T02:00:24.554Z
updated_at: 2025-05-06T02:00:24.554Z
url: https://n8n.io/workflows/3883
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  🧠 Problem This Solves:
  For developers and creators, consistently posting quality content on LinkedIn can be time-consuming. This workflow automates the process by:

  Fetching the latest Dev.to articles

  Posting them to LinkedIn twice daily

  Preventing duplicates using Airtable

  Sending success alerts to Telegram

  This ensures you're always active on LinkedIn, with zero manual effort.

  👥 Who This Template Is For
  Developers who want to build their presence on LinkedIn

  Tech creators or solo founders looking to grow an audience

  Community/page managers who want regular, curated content

  Busy professionals aiming for consistent LinkedIn engagement without doing it manually

  ⚙️ Workflow Breakdown
  This automation runs twice a day (9:00 AM and 7:00 PM) and performs the following steps:

  Fetches Dev.to articles based on a tag

  Checks Airtable to avoid reposting the same article

  Posts to LinkedIn if it’s new

  Sends a Telegram message after posting successfully

  🧩 Step-by-Step Setup Instructions
  ✅ 1. Airtable Configuration
  Create a new base in Airtable with just one table and one column:

  Table Name: PostedArticles

  Column:

  ArticleID (Single line text – stores the unique ID of each Dev.to article posted)

  This column is used to track posted articles and prevent duplicates.

  🔗 2. Dev.to API Setup
  Use the following endpoint in the HTTP Request node:

  arduino
  Copy
  Edit
  https://dev.to/api/articles?tag=YOUR_TAG_HERE&per_page=10
  Replace YOUR_TAG_HERE with a tag like android, webdev, ai, etc.

  💬 3. Telegram Bot Setup
  Open @BotFather in Telegram and create a new bot

  Save the bot token

  Get your chat ID using @userinfobot or via Telegram API

  Add a Telegram node in n8n using this token and chat ID

  This will notify you when a post is successfully published.

  🧾 4. LinkedIn Setup
  Create a LinkedIn Developer App

  Use OAuth2 to connect it in n8n

  Choose to post on either a user profile or a company page

  🧱 5. n8n Workflow Structure
  Here’s the basic structure of the workflow:

  Cron Node – Triggers at 9:00 AM and 7:00 PM daily

  HTTP Request – Fetches latest articles from Dev.to

  Airtable Search – Checks if ArticleID already exists

  IF Node – Filters new vs. already-posted articles

  LinkedIn Post – Publishes new article

  Airtable Create – Saves the new ArticleID

  Telegram Message – Sends success confirmation

  🛠️ Customization Tips
  Change the Dev.to tag in the API URL

  Modify LinkedIn post format (add hashtags, emojis, personal notes)

  Adjust posting times in the Cron node

  Use additional filters (e.g., only post articles with a cover image or certain word count)

nodes_used:
  ["n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.filter","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.telegram","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.linkedIn","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","social media","content-distribution"],"complexity":"medium","use_cases":["Automated cross-posting of Dev.to articles to LinkedIn","Scheduled posting (twice daily)","Preventing duplicate posts via Airtable tracking","Sending success notifications to Telegram","Curating consistent LinkedIn activity for audience growth"],"estimated_setup_minutes":30,"required_services":["Dev.to API","LinkedIn (Developer App / OAuth)","Airtable","Telegram Bot API","n8n"],"key_features":["Fetch latest Dev.to articles by tag","Scheduled triggers (Cron) for twice-daily posting","Duplicate detection using Airtable ArticleID records","Post to LinkedIn (user profile or company page)","Send Telegram alerts on successful posts"],"target_audience":["Developers","Tech creators / solo founders","Community or page managers」「Social media managers」「Growth marketers"]}


name: Automate URL Shortening with Bitly Using Llama3 Chat Interface
author_name: Ghufran Ridhawi
author_username: ghufran-ridhawi
categories: []
created_at: 2025-05-06T06:21:57.919Z
updated_at: 2025-05-06T06:21:57.919Z
url: https://n8n.io/workflows/3885
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Who is this for?
  This workflow is intended for online users who want and need workflow automation that minimizes link creation time and shortens links. This has an impact on the speed of work time and energy. In reality, links created one by one and are very long are very time consuming and impractical, especially if we have to compile them first and send them. This is also a form of service to the community at n8n and the n8n company, and so that reality is no longer tiring and is able to answer existing reality problems. It can also be used to learn how automatic link building works.

  How it works?
  Easy explanation:

  1) Firstly, open chat is intended to trigger commands that are sent to the AI ​​model and Bitly Tools, you can do any command and just include the link. Later here we will also create a link.
  2) The two, AI Agents will send AI models to work according to input commands and produce output via open chat.
  3) Third, the Bitly tool that is set up to create links will do its job. Because it is tied to the AI ​​Agent and AI model.
  4) Finally, you can immediately see and open the link that has been created.

  This is very simple and very simple. Makes it easy for everyone.

  Set up instructions
  Complete what is in the nodes as stated in the notes column.

  1) First, you download Ollama, using the guide that has been provided. Then after you run the model, you can create and connect a "Credential Account".
  2) Second, you need an open chat trigger node or you can use other triggers too.

  3) Third, the AI ​​Agent selects the Ollama model, and the Bitly tools that have been prepared are set up here.

  4) Fourth, just run it and it works.

  This is very simple and very practical.

  Requirements
  As a reminder:

  It should be set in each node, like what your goal is, also according to the conditions of your goal.
  There must be (if not, make sure it is registered) in each "Account Credential" by following the guide on how to do it n8n the guide is very complete
  Don't forget to save, and make sure the workflow is active.

  How to customize this workflow to your needs
  You can immediately add other nodes or other triggers to be able to send this link, such as email and so on, which will provide knowledge for your needs in additional nodes, so that accuracy is also high when carrying out tasks, task goals and responsibilities.

  Congratulations on this automation and Thank you to n8n and the n8n community

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

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metadata_json:
  {"categories":["automation","integration","AI-assisted","productivity"],"complexity":"medium","use_cases":["Automated URL shortening and management","Speeding up link creation for marketing and sharing","Generating short links via conversational (chat) commands","Integrating short links into downstream workflows (email, Slack, CRM)","Learning and prototyping AI-driven link-generation workflows"],"estimated_setup_minutes":20,"required_services":["n8n instance","Bitly account / API token","Ollama (Llama3) model runtime or equivalent local LLM","n8n credentials configured for nodes used"],"key_features":["Create Bitly short links automatically from chat input","Use Llama3 (Ollama) chat agent to accept natural-language commands and extract links","Triggerable via open-chat trigger (or other n8n triggers)","Credential-based integration for secure API access","Easy to extend with additional nodes (email, Slack, storage) to distribute links"],"target_audience":["n8n users and automators","marketers and content creators","developers and operations engineers prototyping AI workflows"]}


name: Send YouTube Video Summaries to Obsidian via Dropbox
author_name: I versus AI
author_username: iversusai
categories: []
created_at: 2025-05-06T07:18:28.095Z
updated_at: 2025-05-06T07:18:28.095Z
url: https://n8n.io/workflows/3886
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  How it works

  This workflow runs on a schedule you set (default is every 10 minutes). It fetches all videos from a specific YouTube playlist, retrieves details and transcripts for each video using RapidAPI, cleans the transcript text, and then uses OpenAI (GPT models) to generate detailed notes, YAML frontmatter, and internal links formatted for Obsidian. Finally, it assembles everything into a Markdown file, saves it to your designated Dropbox folder, and removes the processed video from the source YouTube playlist.

  Set up steps

  Getting started should take about 5 minutes. A Quick Start Guide (PDF) is included in the download with step-by-step instructions!

  Here's a quick overview of the setup:

  Connect Credentials: Add your n8n credentials for YouTube (OAuth2), OpenAI, and Dropbox (OAuth2).
  Add API Keys:
      RapidAPI: You'll need a key for the yt-api service. The template uses ={{ $env["RAPIDAPI_API_KEY"] }}. You can either set up an environment variable in n8n named RAPIDAPI_API_KEY or simply delete that expression in the "Get Video Details" and "Get Video Transcript" nodes and paste your API key directly into the x-rapidapi-key field.
  Configure Nodes:
      Update the Playlist ID in the "Get Playlist Videos" node to your target YouTube playlist.
      (Optional) Adjust the Save Path in the "Save Note to Dropbox" node if you want to save files to a different folder.
  Activate: Turn on the workflow.

  &gt; Bonus Trick: Symlink the Dropbox folder (using the Dropbox desktop app) to a folder in your Obsidian vault for beautifully organized YouTube summary notes!

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.youTube","n8n-nodes-base.code","n8n-nodes-base.convertToFile","n8n-nodes-base.dropbox","n8n-nodes-base.set","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.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.youTube"]

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metadata_json:
  {"categories":["automation","integration","content-generation","knowledge-management"],"complexity":"medium","use_cases":["Automatically summarize YouTube videos into Obsidian-ready notes","Archive video transcripts and metadata as Markdown","Create searchable knowledge base from playlists","Automate content curation and note-taking workflows"],"estimated_setup_minutes":5,"required_services":["YouTube (OAuth2)","OpenAI (GPT)","Dropbox (OAuth2)","RapidAPI (yt-api)"],"key_features":["Scheduled polling of a YouTube playlist","Fetch video details and transcripts via RapidAPI","Clean and normalize transcript text","Use OpenAI to generate detailed notes, YAML frontmatter, and Obsidian-style internal links","Assemble Markdown file and save to Dropbox folder"],"target_audience":["content creators","Obsidian users","knowledge workers and researchers」「automation enthusiasts"]}


name: Auto-Generate & Publish SEO Articles to WordPress with GPT-4 + Postgres Tracking
author_name: AlexWantMoreB
author_username: alexwmb
categories: []
created_at: 2025-05-06T07:24:39.990Z
updated_at: 2025-05-06T07:24:39.990Z
url: https://n8n.io/workflows/3887
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  🚀 What this flow does
  • 🔎 Selects the least-used WordPress category (tracked in PostgreSQL)
  • 🤖 Uses GPT (4-mini or better) to generate a fully formatted SEO article with headings, TOC, lists, CTA, and Yoast blocks
  • 🖼️ Creates a placeholder cover image and uploads it to WordPress Media
  • 📬 Publishes the final post via /wp-json/wp/v2/posts with correct category + featured image
  • 🧠 Logs the used category for future rotation (zero duplicates!)

  ⚙️ Setup in 3 mins

  🏷️ Add your WordPress domain with a simple Set node:
      domain=https://yourdomain.com

  🔐 Create these 3 credentials in n8n:
      YOUR_WORDPRESS_CREDENTIAL — for /media, /posts
      YOUR_POSTGRES_CREDENTIAL — for category tracking
      YOUR_OPENAI_CREDENTIAL — GPT-4-mini or better

  🧱 Run the SQL from docs to create the used_categories table

  ✅ Manually test first 3–5 nodes to check WP auth, OpenAI response, and DB connection

  🕒 Then just schedule it and let the bot write for you.

  🎯 Why it's awesome
  This is your personal AI content writer + publisher — perfect for:
  • 📰 SEO content farms
  • 📈 Affiliate blogs
  • 🧰 Micro niche sites
  • 🤫 PBNs with rotation-safe automation

  No more manual uploads, broken categories, or GPT spam. Every post is structured, beautiful, and intelligently categorized.

nodes_used:
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metadata_json:
  {"categories":["automation","content-generation","CMS","SEO","integration"],"complexity":"complex","use_cases":["Automated SEO article creation","Scheduled WordPress publishing","Category rotation to avoid duplicates","Affiliate/micro-niche blog content generation","PBN/content farm automation"],"estimated_setup_minutes":300,"required_services":["WordPress (REST API)","PostgreSQL","OpenAI (GPT-4-mini or better)","n8n"],"key_features":["Selects least-used WordPress category tracked in Postgres","Generates fully formatted SEO articles (headings, TOC, lists, CTA, Yoast blocks) via GPT","Creates and uploads placeholder cover image to WordPress Media","Publishes posts through /wp-json/wp/v2/posts with correct category and featured image","Logs used category in database to enforce rotation/no duplicates"],"target_audience":["SEO specialists","Content managers","Affiliate marketers and bloggers running multiple sites or niches increased throughput via automation and rotation-safe publishing"]}


name: AI Speech Coach & Generator using Telegram, Open AI and Gemini
author_name: NanaB
author_username: nanabrown
categories: []
created_at: 2025-05-06T08:07:44.488Z
updated_at: 2025-05-06T08:07:44.488Z
url: https://n8n.io/workflows/3889
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Description
  This n8n workflow acts as your personal AI speechwriting coach, directly accessible through Telegram. It listens to your spoken or typed drafts, provides insightful feedback on clarity, engagement, structure, and content, and iteratively refines your message based on your updates. Once you're ready, it synthesizes a brand-new speech or talk incorporating all the improvements and your accumulated ideas. This tool streamlines the speechwriting process, offering on-demand AI assistance to help you craft impactful and well-structured presentations.

  How it Works
  Input via Telegram: You interact with the workflow by sending your speech drafts or talking points directly to a designated Telegram bot.
  AI Feedback: The workflow processes your input using AI models (OpenAI and/or Google Gemini) to analyze various aspects of your speech and provides constructive feedback via Telegram.
  Iterative Refinement: You can then send updated versions of your speech to the bot, receiving further feedback to guide your revisions.
  Speech Synthesis: When you send the command to "generate speech," the workflow compiles all your previous input and the AI's feedback to synthesize a new, improved speech or talk, which is then sent back to you via Telegram.
  New Speech Cycle: By sending the command "new speech," the workflow clears its memory, allowing you to start the process anew for a different topic.
  Set Up Steps (Takes Approximatly 5 Minutes)
  Step 1: Create a Telegram Bot and Obtain its ID
  Open the Telegram application and search for "BotFather". Start a chat with BotFather by clicking "Start" or sending the /start command. Create a new bot by sending the command /newbot. Follow BotFather's instructions to choose a name and username for your bot. Once your bot is created, BotFather will provide you with an API token. Keep this token secure as it's required to connect your n8n workflow to your bot.

  Step 2: Obtain an OpenAI API Key
  Go to the OpenAI website (https://platform.openai.com/) and sign up for an account if you don't already have one.
  Navigate to the API keys section (usually under your profile settings or a "Developers" tab). Click on "Create new secret key". Copy the generated API key and store it securely. You will need to provide this key to your n8n workflow to access OpenAI's language models.

  Step 3: Obtain a Google Gemini LLM API Key 
  Go to the Google Cloud AI Platform or Google AI Studio website (the specific platform may vary depending on the current Google AI offerings; search for "Google AI API"). Sign up or log in with your Google account. Follow the instructions to enable the Gemini API and create an API key. This might involve creating a project if you haven't already. Copy the generated API key and store it securely. You can then configure your n8n workflow to utilize Google Gemini's language models as well.

  Customization Options
  This n8n workflow offers significant flexibility, below are a few options:
  Modify AI prompts to tailor feedback and generation for presentations, storytelling, interviews, sales pitches, academic talks, and creative writing.
  Switch the interface from Telegram to Slack, WhatsApp, or even a web interface by replacing the relevant n8n nodes.
  Integrate analysis for sentiment, keyword density, pacing (with voice input), and filler word detection by adjusting the workflow.
  Connect to external data sources to provide context to the AI for more targeted feedback and generation.

  This adaptability allows you to re use this workflow for a wide range of specific use cases and communication environments.

nodes_used:
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metadata_json:
  {"categories":["automation","integration","content creation","AI"],"complexity":"medium","use_cases":["Speech and talk drafting","Iterative AI feedback/coaching","Presentation and pitch preparation","Converting notes/voice into polished speeches","Teaching and coaching speaking skills"],"estimated_setup_minutes":5,"required_services":["Telegram Bot API","OpenAI API","Google Gemini (Google Cloud AI)","n8n instance"],"key_features":["Accepts typed or voice drafts via Telegram","Provides AI feedback on clarity, engagement, structure, content","Iterative revision loop storing prior inputs and feedback","Generates a polished speech from accumulated inputs","Supports both OpenAI and Google Gemini LLMs (multi-model)"],"target_audience":["Public speakers","Content creators and marketers","Coaches and trainers (speaking/presentation)`,`n8n users and automation builders"]}


name: Create Customized Google Slides Presentations from CSV Data for Cold Outreach 🚀
author_name: JPres
author_username: stardawnai
categories: []
created_at: 2025-05-06T08:08:57.852Z
updated_at: 2025-05-06T08:08:57.852Z
url: https://n8n.io/workflows/3890
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  👥 Who Is This For?

  Sales and marketing teams seeking efficient, hands‑free generation of personalized slide decks for each prospect from CSV lead lists.

  🛠 What Problem Does This Solve?

  Manually editing presentation decks for large lead lists is slow and error‑prone. This workflow fully automates:

  Importing and parsing CSV lead data
  Logging leads and outputs in Google Sheets
  Duplicating a master Slides template per lead
  Injecting lead‑specific variables into slides

  🔄 Node‑by‑Node Breakdown

  | Step | Node                                     | Purpose                                                  |
  | ---- | ---------------------------------------- | -------------------------------------------------------- |
  | 1    | New Leads Arrived                        | Detect new CSV uploads in Drive                          |
  | 2    | File Type?                               | Filter for .csv files only                             |
  | 3    | Download by ID                           | Download the CSV content                                 |
  | 4    | Create new Sheet                         | Create a Google Sheet to record lead data                |
  | 5    | Combine Empty New Document with CSV Data | Structure each lead record for slide creation            |
  | 6    | Merge Data for new Lead Document         | Map template placeholders to lead values                 |
  | 7    | Get all Leads                            | Retrieve sheet rows to iterate through each lead         |
  | 8    | MoveToLeadListFolder                     | Move processed CSV to an archive folder                  |
  | 9    | Copy Slides Template                     | Make a copy of the master Slides deck                    |
  | 10   | Create Custom Presentation               | Replace placeholders in the copied deck with lead data   |
  | 11   | Add Presentation ID to Lead              | Write the generated presentation URL back into the Sheet |

  ⚙️ Pre‑conditions / Requirements

  n8n with Google Drive, Sheets, and Slides credentials
  A master Google Slides deck with placeholder tokens (e.g. {{Name}}, {{Company}})
  A Drive folder for incoming CSV lead files

  ⚙️ Setup Instructions

  Import this workflow into your n8n instance.
  Configure the New Leads Arrived node to watch your CSV folder.
  Enter your Google credentials in the Drive, Sheets, and Slides nodes.
  Specify the master Slides template ID in the Copy Slides Template node.
  In Create Custom Presentation, map slide tokens to sheet column names.
  Disable “Keep Binary Data” in Copy Slides Template to conserve memory.
  Upload a sample CSV (with headers like Name, Company, Metric) to test.

  🎨 How to Customize

  Add or remove variables by editing the CSV headers and updating the mapping in Merge Data for new Lead Document.
  Insert an AI/natural‑language node before slide creation to generate more advanced and personalized text blocks.
  Use SplitInBatches to throttle API calls and avoid rate‑limit errors.
  Add error‑handling branches to capture and log failed operations.

  🔐 Security and Privacy

  The workflow uses placeholder variables for file and folder IDs, so no actual IDs are exposed in the template.
  Ensure OAuth scopes are limited to only the required Google APIs.

nodes_used:
  ["n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.switch","n8n-nodes-base.merge","n8n-nodes-base.googleSlides","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.extractFromFile","n8n-nodes-base.googleDrive"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["automation","integration","document generation","sales & marketing"],"complexity":"medium","use_cases":["Generate personalized Google Slides decks from CSV lead lists","Automated cold outreach collateral creation","Bulk creation of prospect-specific presentations","Logging and tracking generated outputs in Google Sheets"],"estimated_setup_minutes":20,"required_services":["n8n instance","Google Drive API (OAuth)","Google Sheets API (OAuth)","Google Slides API (OAuth)"],"key_features":["Detects and imports CSV files from a Drive folder","Parses CSV rows and logs leads to Google Sheets","Duplicates a master Slides template per lead","Replaces placeholder tokens (e.g. {{Name}}) with lead data","Writes generated presentation IDs/URLs back to the sheet and archives processed CSVs"],"target_audience":["Sales teams (SDRs, account execs)","Marketing teams doing personalized outreach","Operations/automation engineers implementing workflows in n8n"]}


name: Explore n8n Nodes in a Visual Reference Library
author_name: I versus AI
author_username: iversusai
categories: []
created_at: 2025-05-06T08:57:36.890Z
updated_at: 2025-05-06T08:57:36.890Z
url: https://n8n.io/workflows/3891
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  WATCH THE n8n STARTER GUIDE 👇



  This template is featured in the n8n Starter Guide series. The template is free, but comes with two additional PDFs and a Quick Start video if you grab the full download pack on gumroad.

  How it works

  This template is a visual map of many useful n8n nodes. It groups nodes like Triggers, AI tools, and App connectors onto the canvas. Explore the sections to learn about different nodes and easily copy them for your own workflows. It acts as a handy visual reference guide.

  Set up steps

  • Setup takes about 5 minutes.
  • Import the template into your n8n instance.
  • Explore the node categories visually on the canvas.
  • A Quick Start video is included in the download pack, along with a prompts PDF and PDF with links to other awesome n8n templates here on the n8n template gallery.

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.chainRetrievalQa","@n8n/n8n-nodes-langchain.sentimentAnalysis","@n8n/n8n-nodes-langchain.chainSummarization","@n8n/n8n-nodes-langchain.textClassifier","@n8n/n8n-nodes-langchain.memoryManager","n8n-nodes-base.bitly","n8n-nodes-base.dropbox","n8n-nodes-base.gmail","n8n-nodes-base.googleCalendar","n8n-nodes-base.googleDocs","n8n-nodes-base.googleSheets","n8n-nodes-base.pushbullet","n8n-nodes-base.youTube","@muench-dev/n8n-nodes-bluesky.bluesky","@watzon/n8n-nodes-perplexity.perplexity","n8n-nodes-elevenlabs.elevenLabs","n8n-nodes-base.reddit","n8n-nodes-base.gmailTrigger","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolCode","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.postgresTool","n8n-nodes-base.redisTool","n8n-nodes-base.emailSendTool","@n8n/n8n-nodes-langchain.toolSerpApi","@n8n/n8n-nodes-langchain.toolWikipedia","@n8n/n8n-nodes-langchain.toolWolframAlpha","n8n-nodes-base.gmailTool","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.googleDocsTool","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.outputParserAutofixing","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.vectorStoreInMemory","@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.vectorStorePGVector","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.lmChatAnthropic","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.memoryPostgresChat","@n8n/n8n-nodes-langchain.memoryRedisChat","@n8n/n8n-nodes-langchain.outputParserItemList","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","@n8n/n8n-nodes-langchain.embeddingsOpenAi","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.twitter","n8n-nodes-base.calendlyTrigger","n8n-nodes-base.emailReadImap","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.gumroadTrigger","n8n-nodes-base.localFileTrigger","n8n-nodes-base.formTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.manualTrigger","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.gmailTrigger","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.dateTime","n8n-nodes-base.set","n8n-nodes-base.filter","n8n-nodes-base.limit","n8n-nodes-base.removeDuplicates","n8n-nodes-base.splitOut","n8n-nodes-base.aggregate","n8n-nodes-base.merge","n8n-nodes-base.summarize","n8n-nodes-base.convertToFile","n8n-nodes-base.extractFromFile","n8n-nodes-base.html","n8n-nodes-base.markdown","n8n-nodes-base.renameKeys","n8n-nodes-base.sort","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.splitInBatches","n8n-nodes-base.noOp","n8n-nodes-base.executeWorkflow","n8n-nodes-base.wait","n8n-nodes-base.executeCommand","n8n-nodes-base.httpRequest","n8n-nodes-base.executionData","n8n-nodes-base.ftp","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.rssFeedRead","n8n-nodes-base.stickyNote","n8n-nodes-base.aiTransform","@n8n/n8n-nodes-langchain.mcpClientTool","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 10244 characters]

metadata_json:
  {"categories":["education","reference","automation","onboarding"],"complexity":"simple","use_cases":["Learn n8n node capabilities and structure","Visual reference for node selection and workflow design","Onboarding new n8n users","Quickly copy nodes into your own workflows","Explore examples of triggers, AI tools, and app connectors"],"estimated_setup_minutes":5,"required_services":["None required to explore (n8n instance only)","Google Sheets (optional, for Google Sheets nodes)","OpenAI API (optional, for AI nodes)","HTTP/Webhook endpoints (optional, for HTTP/Webhook nodes)","Gumroad (optional, to download the full pack)"],"key_features":["Visual map of 113 example nodes grouped by category (Triggers, AI, App connectors, etc.)","Easy to navigate canvas for learning and copying nodes into workflows","Includes Quick Start video and two PDF companion resources (prompts and links)","Highlights common node types and how they connect","Lightweight import and immediate exploration within n8n"],"target_audience":["n8n beginners and learners","Workflow designers and automation engineers","Developers exploring integrations and AI nodes within n8n','Educators and technical trainers"]}


name: PostgreSQL Conversational Agent with Claude & DeepSeek (Multi-KPI, Secure)
author_name: hippolyte-hu
author_username: hippolyte-hu
categories: []
created_at: 2025-05-06T09:56:26.479Z
updated_at: 2025-05-06T09:56:26.479Z
url: https://n8n.io/workflows/3892
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  🧠 Conversational PostgreSQL Agent 

  Enable AI-driven conversations with your PostgreSQL database using a secure and visual-free agent powered by n8n’s Model Context Protocol (MCP). This template allows users to ask multiple KPIs in a single message, returning consolidated insights — more efficient than the original Conversing with Data template.

  🚀 Why This Template

  Unlike the Conversing with Data workflow, which handles one KPI per message, this version:

  ✅ Supports multi-KPI questions  
  ✅ Returns structured, human-readable reports  
  ✅ Uses fewer AI calls, making it faster and cheaper  
  ✅ Avoids raw SQL execution for enhanced security

  💲 Estimated cost per full multi-request run: ~$0.01

  This template is optimized for efficiency. Each message can return 2–4 KPIs (You can change the MaxIteration of the Agent to make it more, it is currently set up at 30 iterations) using a single Claude 3.5 Haiku session and DeepSeek-based SQL generation — balancing speed, reasoning, and affordability.

  💬 Sample Use Case

  User:  
  “Can you show product performance, revenue trends, and top 5 customers?”

  Agent:  
  Uses ListTables and GetTableSchema  
  Generates three SQL queries using get_query_and_data  
  Returns:

  📊 Product Performance  
  High-Waist Jeans — 10 units, $1,027 revenue  
  Denim Jacket — 10 units, $783 revenue  

  📈 Sales Trends  
  Peak Month: January 2024 — 32 units, $2,378  
  Average Monthly Units: 10–16  

  🧍 Customer Insights  
  Bob Brown — $1,520 spent  
  Diana Wilson — $925 spent  

  All from one natural prompt.

  🖼️ Real-World Interaction Screenshot

  🧰 What’s Inside

  | Node                        | Purpose                                                   |
  |----------------------------|-----------------------------------------------------------|
  | MCP Server Trigger         | Receives user queries via /mcp/...                      |
  | AI Agent + Memory          | Understands and plans multi-step queries                  |
  | Think Tool                 | Breaks down the user’s question into structured goals     |
  | get_query_and_data         | Generates SQL securely from natural language              |
  | ListTables, GetSchema      | AI tools to explore DB safely                             |
  | Read/Insert/Update Tools   | Execute structured operations (never raw SQL)             |
  | checkdatabase Subflow      | Validates SQL, formats response as clean text             |

  🤖 Model Selection Recommendations

  This template uses two types of models, selected for cost-performance balance and role alignment:

  1. Claude 3.5 Haiku (Anthropic) – for the MCP Agent  
  The main conversational agent uses Claude 3.5 Haiku, ideal for MCP because it was built by Anthropic — the creators of the MCP standard. It’s fast, affordable, and performs excellently in tool-calling and reasoning tasks.

  2. DeepSeek – for the SQL subworkflow  
  The subworkflow that turns natural language into SQL uses DeepSeek. It’s one of the most affordable and performant models available today for structured outputs like SQL, making it a perfect fit for utility logic.

  ✅ This setup provides top-tier reasoning + low-cost execution.

  🔐 Security Benefits

  No raw SQL accepted from the user or LLM  
  All queries are parameterized  
  Schema is dynamically retrieved  
  Final output is clean, safe, and human-readable

  🧪 Try a Prompt

  &gt; “Show me the top 5 products by units sold and revenue, total monthly sales trend, and top 5 customers by spending.”

  In one message, the agent will:  
  Generate and run multiple queries  
  Use the schema to validate logic  
  Return a single, comprehensive answer

  🛠 How to Use

  📥 Upload both workflow files into your n8n instance:
     Build_your_own_PostgreSQL_MCP_server_No_visuals_.json
     checkdatabase.json
  🔐 Set up PostgreSQL credentials (e.g. “Postgres account 3”)
  🧠 Confirm model setup:
     Claude 3.5 Haiku for the main agent
     DeepSeek for the subflow
  🌐 Use the /mcp/... URL from the MCP Server Trigger to connect your frontend or chatbot
  🗣 Ask questions naturally — the agent takes care of planning, querying, and formatting

  🔄 Customization Ideas

  Swap Claude or DeepSeek for OpenAI, Mistral, Gemini, etc.  
  Export insights to Slack, Notion, or Google Sheets  
  Add Switch nodes to control access to specific tables  
  Integrate with any front-end app, internal dashboard, or bot

  📦 What's Included

  Build_your_own_PostgreSQL_MCP_server_No_visuals_.json – MCP agent logic  
  checkdatabase.json – SQL generation and formatting utility workflow

  📝 These must be uploaded into your n8n workspace for the template to function.

  📊 Comparison: Conversing with Data vs This Workflow

  | Feature                       | Conversing with Data      | This Workflow                   |
  |------------------------------|---------------------------|---------------------------------|
  | Handles multi-KPI questions  | ❌ No                     | ✅ Yes                          |
  | Secure query execution       | ✅ Yes                    | ✅ Yes                          |
  | Structured response          | ⚠️ JSON / raw            | ✅ Clean natural language       |
  | Cost-efficiency              | ⚠️ More calls            | ✅ Optimized with fewer calls   |
  | Endpoint support             | ❌ Manual interaction     | ✅ MCP-ready (/mcp/...)       |

  🔗 Prefer something more lightweight and cost-sensitive?  
  Try the original Conversing with Data template (single KPI + chart support):  
  Conversing with Data: Transforming Text into SQL Queries and Visual Curves

  &gt; I used this version for over 3 months and only spent $0.80 total, making it a great entry point if you're just getting started or on a limited budget.

  📚 More from the Same Creator

  Looking for a different kind of AI reporting workflow?

  Explore:  
  Customer Feedback Analysis with AI, QuickChart & HTML Report Generator  
  → Automatically analyze customer input and generate full reports with insights and charts.  
  Customer Feedback Analysis with AI, QuickChart & HTML Report Generator

nodes_used:
  ["n8n-nodes-base.postgresTool","n8n-nodes-base.postgresTool","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.postgres","n8n-nodes-base.switch","n8n-nodes-base.postgres","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.postgres","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.lmChatAnthropic","@n8n/n8n-nodes-langchain.toolThink","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","AI / conversational agents","databases"],"complexity":"complex","use_cases":["Natural-language querying of PostgreSQL (multi-KPI reports)","Automated executive-style summaries (sales, product, customer insights)","Secure SQL generation and execution with schema-aware validation","Embedding the DB agent into chatbots or internal tools via MCP endpoint","Cost-efficient multi-query reasoning with reduced LLM calls"],"estimated_setup_minutes":30,"required_services":["n8n instance","PostgreSQL database (with credentials)","Anthropic Claude 3.5 Haiku (or MCP-compatible model)","DeepSeek (or equivalent model for SQL generation)","Optional: frontend/chat interface that calls the /mcp/... endpoint"],"key_features":["Multi-KPI single-prompt handling (2–4 KPIs by default)","n8n MCP Agent + memory for multi-step planning","DeepSeek-driven SQL generation subflow (checkdatabase) — no raw SQL from users","Schema discovery (ListTables, GetTableSchema) and parameterized queries","Structured, human-readable report output (aggregated results in one message)"],"target_audience":["Developers building internal AI assistants","Data analysts / BI engineers needing quick natural-language reports","Product managers and non-technical stakeholders wanting ad-hoc insights"]}


name: Automatically Create JIRA Issues from Outlook Email Support Requests
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-05-06T10:26:52.630Z
updated_at: 2025-05-06T10:26:52.630Z
url: https://n8n.io/workflows/3893
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n template watches an outlook shared inbox for support messages and creates an equivalent issue item in JIRA.

  How it works
  A scheduled trigger fetches recent Outlook messages from an shared inbox which collects support requests.
  These support requests are filtered to ensure they are only processed once and their HTML body is converted to markdown for easier parsing.
  Each support request is then triaged via an AI Agent which adds appropriate labels, assesses priority and summarises a title and description of the original request.
  Finally, the AI generated values are used to create an issue in JIRA to be actioned.

  How to use
  Ensure the messages fetched are solely support requests otherwise you'll need to classify messages before processing them.
  Specify the labels and priorities to use in the system prompt of the AI agent.

  Requirements
  Outlook for incoming support
  OpenAI for LLM
  JIRA for issue management

  Customising this workflow
  Consider automating more steps after the issue is created such as attempting issue resolution or capacity planning.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.microsoftOutlook","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","n8n-nodes-base.markdown","n8n-nodes-base.jira","n8n-nodes-base.removeDuplicates","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","integration","AI/ML","email-processing","ITSM"],"complexity":"medium","use_cases":["Automatically convert incoming support emails into JIRA issues","AI-assisted triage: generate issue title, description, labels and priority","Prevent duplicate processing of the same support message","Standardise email bodies by converting HTML to Markdown for downstream parsing","Integrate scheduled polling of a shared inbox with ticketing systems"],"estimated_setup_minutes":30,"required_services":["Outlook","OpenAI","JIRA"],"key_features":["Scheduled watcher for a shared Outlook inbox","Duplicate message filtering","HTML-to-Markdown conversion of email bodies","AI agent generates title, description, labels and priority","Creates issues in JIRA using AI-generated fields and configured labels/priorities"],"target_audience":["support teams","IT/ops administrators","customer success teams', 'helpdesk managers"]}


name: Convert Images to 3D Models with Fal AI Trellis and Store in Google Drive
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-05-06T10:59:27.867Z
updated_at: 2025-05-06T10:59:27.867Z
url: https://n8n.io/workflows/3894
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  This workflow allows users to convert a 2D image into a 3D model by integrating multiple AI and web services. The process begins with a user uploading or providing an image URL, which is then sent to a generative AI model capable of interpreting the content and generating a 3D representation in .glb format. The model is then stored and a download link is returned to the user.

  Main Steps

  Trigger Node: Initiates the workflow either via HTTP request, webhook, or manual execution.
  Image Upload or Input: The image is acquired via direct upload or URL input.
  API Integration: The image is sent to a 3D generation API (e.g., a service like Kaedim, Luma Labs, or a custom AI model).
  Model Generation: The external API processes the image and creates a 3D model.
  File Storage: The resulting 3D model is stored in cloud storage (e.g., S3, Google Drive, or a local server).
  Response to User: A download link for the 3D model is returned to the user via the same communication channel (HTTP response, email, or chat).

  Advantages

  Automation**: Eliminates the need for manual 3D modeling, saving time for artists, developers, and designers.
  AI-Powered**: Leverages AI to generate realistic and usable 3D models from simple 2D inputs.
  Scalability**: Can be triggered automatically and scaled up to handle many requests via n8n's automation.
  Integration-Friendly**: Easily extendable with other services like Discord, Telegram, or marketplaces for 3D assets.
  No-Code Configuration**: Built with n8n’s visual interface, making it editable without programming knowledge.
  How It Works  
  Trigger: The workflow can be started manually ("When clicking ‘Test workflow’") or automatically at scheduled intervals ("Schedule Trigger").  
  Data Retrieval: The "Get new image" node fetches data from a Google Sheet, including the model image, product image, and product ID.  
  3D Image Creation: The "Create 3D Image" node sends the image data to the Fal.run API (Trellis) to generate a 3D model.  
  Status Check: The workflow periodically checks the request status ("Get status" and "Wait 60 sec.") until the job is marked as "COMPLETED."  
  Result Processing: Once completed, the 3D model URL is retrieved ("Get Url 3D image"), the file is downloaded ("Get File 3D image"), and uploaded to Google Drive ("Upload 3D Image").  
  Sheet Update: The final 3D model URL is written back to the Google Sheet ("Update result").  

  Set Up Steps  
  Prepare Google Sheet:  
     Create a Google Sheet with columns: IMAGE MODEL and 3D RESULT (empty).  
     Example sheet: Google Sheet Template.  

  Obtain Fal.run API Key:  
     Sign up at Fal.ai and get an API key.  
     Configure the Authorization header in the "Create 3D Image" node with Key YOURAPIKEY.  

  Configure Workflow Execution:  
     Run manually via the Test workflow button.  
     For automation, set up the Schedule Trigger node (e.g., every 5 minutes).  

  Verify Credentials: Ensure Google Sheets, Google Drive, and Fal.run API credentials are correctly set in n8n.  

  Once configured, the workflow processes new entries in the Google Sheet, generates 3D models, and updates the results automatically.

  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin. 

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

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metadata_json:
  {"categories":["automation","AI/ML","integration","file management","media processing"],"complexity":"medium","use_cases":["Generate 3D models (.glb) from 2D images for product catalogs","Automate 3D asset creation for games, AR/VR, and marketing","Batch process image-to-3D conversions from a spreadsheet","Store and share generated 3D assets via cloud storage"],"estimated_setup_minutes":20,"required_services":["Fal.run (Trellis) API or equivalent 3D generation API","Google Sheets","Google Drive","n8n (workflow runner)"],"key_features":["Upload or reference image via URL or Google Sheet input","Calls external generative AI API to produce .glb 3D models","Polls job status until completion","Downloads generated file and uploads to Google Drive","Writes resulting 3D model URL back to Google Sheet (automated feedback loop)"],"target_audience":["3D artists and designers","Game and AR/VR developers","E‑commerce/product teams looking to create 3D assets from images"]}


name: 📢 Multi-Platform Video Publisher – YouTube, Instagram & TikTok
author_name: Amanda Benks
author_username: amanda
categories: []
created_at: 2025-05-06T11:21:23.115Z
updated_at: 2025-05-06T11:21:23.115Z
url: https://n8n.io/workflows/3895
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  Hi! I'm Amanda ❤️
  I build intelligent automation flows with n8n and Make. This one is for all content creators, marketing teams, and agencies who want to publish once and post everywhere.

  With this workflow, you can upload a single video to YouTube, Instagram Reels, and TikTok — simultaneously and automatically.

  ✅ What the workflow does

  Downloads a video from a provided URL  
  Uploads the video to your YouTube channel with title and description  
  Publishes it as a Reel on Instagram via the Meta Graph API  
  Sends the same video to TikTok using their official API  
  Supports credential input via Set node (tokens, titles, descriptions)

  ⚙️ Nodes & Tech Used

  HTTP Request – Download video and handle uploads to Instagram & TikTok  
  YouTube node – Official n8n integration for video upload  
  Set node – For handling user inputs (tokens, titles, video URLs)  
  Switch, Wait, Merge – Logic to control publishing status  
  Manual or webhook start available

  🛠️ Setup Instructions

  Open the workflow in your n8n (Cloud or self-hosted) instance  
  Edit the Set node called Credentials and fill in:
     Token Instagram  
     Token Tiktok  
     YouTube title, description, and video URL  
     Instagram account ID  
  Connect your YouTube OAuth credentials in the YouTube node  
  Optionally, trigger via webhook to automate from other apps (Typebot, CRM, Drive)  
  Hit "Execute Workflow" or schedule via cron

  👥 Who this is for

  Content creators who want to post everywhere at once  
  Agencies managing video distribution across platforms  
  Social media managers and freelancers  
  Anyone wanting a one-click multi-platform publishing workflow

  🌐 Explore more workflows

  ❤️ Buy workflows: https://iloveflows.com  
  ☁️ Try n8n Cloud: https://n8n.partnerlinks.io/amanda

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

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metadata_json:
  {"categories":["automation","integration","content_publishing","social_media"],"complexity":"medium","use_cases":["Simultaneous multi-platform video publishing","Repurposing single video across YouTube, Instagram Reels, and TikTok","Automating social distribution workflows from webhooks or manual triggers","Centralized credential-driven publishing for teams/agencies"],"estimated_setup_minutes":30,"required_services":["n8n (Cloud or self-hosted)","YouTube API (OAuth credentials)","Instagram / Meta Graph API (access token & account ID)","TikTok API (access token)","Hosting or accessible URL for source video (HTTP)"],"key_features":["Download video from a provided URL","Upload video to YouTube with title and description (official n8n YouTube node)","Publish Instagram Reels via Meta Graph API","Send video to TikTok using official API","Credential management via Set node (tokens, titles, descriptions)"],"target_audience":["Content creators","Social media managers","Marketing teams and agencies handling multi-platform distribution`,`Freelancers managing client accounts"]}


name: 🤖 Instagram MCP AI Agent – Read, Reply & Manage Comments with GPT-4o
author_name: Amanda Benks
author_username: amanda
categories: []
created_at: 2025-05-06T12:33:19.751Z
updated_at: 2025-05-06T12:33:19.751Z
url: https://n8n.io/workflows/3896
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  🤖 Instagram AI Agent with MCP Server – Built for Smart Engagement and Automation

  Hi! I’m Amanda 🥰
  I build intelligent automations with n8n and Make.  
  This powerful workflow was designed to help teams automatically handle Instagram interactions with AI. Using Meta Graph API, LangChain, MCP Server, and GPT-4o, it allows your AI agent to search for posts, read captions, fetch comments, and even reply or message followers, all through structured tools.

  🔧 What the workflow does

  Searches for recent media using Instagram ID and access token  
  Reads and extracts captions or media URLs  
  Fetches comments and specific replies from each post  
  Replies to comments automatically with GPT-generated responses  
  Sends direct messages to followers who commented  
  Maps user input and session to keep memory context via LangChain  
  Communicates via Server-Sent Events (SSE) using your MCP Server URL

  🧰 Nodes & Tech Used

  LangChain Agent + Chat Model with GPT-4o  
  Memory Buffer for session memory  
  toolHttpRequest to search media, comments, and send replies  
  MCP Trigger and MCP Tool (custom SSE connection)  
  Set node for input and variable assignment  
  Webhook and JSON for Instagram API structure

  ⚙️ Setup Instructions

  Create your Instagram App in Meta Developer Portal  
  Add your Instagram ID and Access Token in the Set node  
  Update the MCP Server Tool URL in the MCP Instagram node  
     Use your n8n server URL (e.g. https://yourdomain.com/mcp/server/instagram/sse)  
  Trigger the workflow using the included LangChain Chat Trigger  
  Interact via text to ask the agent to:
     “Get latest posts”  
     “Reply to comment X with this message”  
     “Send DM to this user about...”  

  👥 Who this is for

  Social media teams managing multiple comments  
  Brands automating engagement with followers  
  Agencies creating smart, autonomous digital assistants  
  Developers building conversational Instagram bots

  ✅ Requirements

  Meta Graph API access  
  Instagram Business account  
  n8n instance (Cloud or Self-hosted)  
  MCP Server configured (SSE Endpoint enabled)  
  OpenAI API Key (for GPT-4o + LangChain)

  🌐 Want to use this workflow?

  ❤️ Buy workflows: https://iloveflows.com  
  ☁️ Try n8n Cloud: https://n8n.partnerlinks.io/amanda

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

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metadata_json:
  {"categories":["automation","integration","social_media","conversational_ai"],"complexity":"complex","use_cases":["Automated comment moderation and reply on Instagram","Sending targeted direct messages to commenters","Maintaining conversational session memory for follow-ups","Building AI-driven social media assistants/agents","Scaling engagement for brands and agencies"],"estimated_setup_minutes":90,"required_services":["Meta Graph API / Instagram Business account","n8n (Cloud or self-hosted) instance","MCP Server with SSE endpoint configured","OpenAI API (GPT-4o) / LangChain integration"],"key_features":["Search recent media and extract captions/media URLs","Fetch comments and replies per post","Generate and post GPT-4o powered comment replies","Send direct messages to users who commented","Session memory via LangChain memory buffer for context persistence and follow-ups"],"target_audience":["Social media managers and teams","Brands and marketing agencies","Developers building conversational Instagram bots or automations"]}


name: Sync Youtube Video URLs with Google Sheets
author_name: Aayushman Sharma
author_username: ajstyle0411
categories: []
created_at: 2025-05-06T13:06:20.813Z
updated_at: 2025-05-06T13:06:20.813Z
url: https://n8n.io/workflows/3897
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Sync Youtube Videos with Google Sheets
  (Part 1 of Youtube comments sentiment analyze automation along with detailed dashboard)

  This workflow is the first part of a multi-part automation system designed to perform large-scale YouTube comment sentiment analysis alongwith detailed dashboard. It solves the problem of manually tracking new videos across multiple YouTube channels by automatically fetching and organizing video URLs in a Google Sheet, setting the stage for deeper analysis in Part 2.

  What It Does
  Reads Channel IDs from Sheet3 of a connected Google Sheet.
  Fetches the latest videos from each Channel ID using the YouTube Data API.
  Extracts video URLs and metadata (like title and publish date).
  Appends the video data to Sheet2 of the same Google Sheet — this sheet is later used by Part 2 for further processing.

  Part of a Multi-Step System
  This is Part 1 of a 2-workflow system:
  Part 1 (this workflow)** populates a sheet with the latest videos from a list of channels.
  Part 2* reads the video URLs from Sheet2, fetches comments for each video, analyzes their sentiment using *OpenAI**, and stores structured results in Sheet1.

  👉 Continue to Part 2 – YouTube Comment Sentiment Analyzer with Google Sheets & OpenAI

  ✅ Use Cases
  Monitor and organize new videos from a list of YouTube channels
  Automate content pipelines for social media teams and analysts
  Build scalable datasets for comment and sentiment analysis
  Perfect for creators, agencies, or data analysts managing multiple YouTube accounts

  🔧 Apps Used
  Google Sheets** – To read and write channel/video data
  YouTube** – To fetch video data from public channels

  💡 Why Use This?
  Manually checking YouTube channels for new content is time-consuming and error-prone. This automation ensures your data stays current and structured — enabling consistent tracking and deeper analysis (especially when paired with Part 2). It brings speed, scale, and automation to your YouTube content operations.

  How to Customize

  1. Modify Trigger Settings
  Change the Google Sheet (Sheet 3) channel ID entry to track other channels.
  Use a time-based trigger to fetch new videos regularly, ensuring your data stays up to date.

  2. Adjust Output Fields
  Fetch additional details from YouTube, such as view count, description, or thumbnails.
  Add custom columns in Sheet 2 for organizing videos by different criteria, such as:
    "Published Date"
    "Video Type"
    "View Count"
    "Video Description"

  3. Extend with Integrations
  Integrate with other workflows like YouTube Comment Sentiment Analysis (Part 2) for a deeper dive into content analysis.
  Use filters to fetch videos by certain tags, keywords, or publish dates.

  4. Adjust Sheet Structure
  Modify the structure of Sheet 2 to categorize videos based on criteria like:
    Channel
    Video Status (e.g., "Published," "Scheduled")
    Video Type (e.g., "Tutorial," "Review")

  5. Schedule Regular Fetching
  Set a schedule trigger to fetch videos at regular intervals (e.g., daily or weekly), ensuring new content is automatically added to your sheet.

  6. Customize Google Sheet Layout
  Change the layout of Sheet 2 to better fit your needs. For example, you can add additional columns for

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

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metadata_json:
  {"categories":["integration","automation","data processing"],"complexity":"medium","use_cases":["Monitor and organize new videos from multiple YouTube channels","Automate content pipelines for social media teams","Build scalable datasets for comment and sentiment analysis","Feed downstream workflows (e.g., comment scraping and sentiment analysis)","Maintain an up-to-date inventory of channel videos for reporting or analytics"],"estimated_setup_minutes":20,"required_services":["Google Sheets","YouTube Data API"],"key_features":["Reads channel IDs from a Google Sheet (Sheet3)","Fetches latest videos and metadata (title, publish date, URL) via YouTube Data API","Appends video rows to a target Google Sheet (Sheet2) for downstream processing","Supports scheduling/time-based triggers to keep data current","Easily customizable output fields and Sheet structure for additional metadata or categorization"],"target_audience":["social media managers","content creators","digital/marketing agencies managing multiple channels`,`data analysts`"]}


name: Automatically Create Linear Issues from Gmail Support Request Messages
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-05-06T14:28:52.150Z
updated_at: 2025-05-06T14:28:52.150Z
url: https://n8n.io/workflows/3899
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n template watches a Gmail inbox for support messages and creates an equivalent issue item in Linear.

  How it works
  A scheduled trigger fetches recent Gmail messages from the inbox which collects support requests.
  These support requests are filtered to ensure they are only processed once and their HTML body is converted to markdown for easier parsing.
  Each support request is then triaged via an AI Agent which adds appropriate labels, assesses priority and summarises a title and description of the original request.
  Finally, the AI generated values are used to create an issue in Linear to be actioned.

  How to use
  Ensure the messages fetched are solely support requests otherwise you'll need to classify messages before processing them.
  Specify the labels and priorities to use in the system prompt of the AI agent.

  Requirements
  Gmail for incoming support messages
  OpenAI for LLM
  Linear for issue management

  Customising this workflow
  Consider automating more steps after the issue is created such as attempting issue resolution or capacity planning.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","n8n-nodes-base.markdown","n8n-nodes-base.removeDuplicates","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.gmail","n8n-nodes-base.linear","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","customer support","issue tracking","AI triage"],"complexity":"medium","use_cases":["Automatically create Linear issues from incoming support emails","AI-driven triage: generate titles, descriptions, labels and priorities","Convert HTML email bodies to markdown for better readability/storage","De-duplicate and ensure each support request is processed once","Integrate email-driven workflows with engineering backlog management"],"estimated_setup_minutes":30,"required_services":["Gmail","OpenAI (or compatible LLM)","Linear","n8n"],"key_features":["Scheduled polling of Gmail inbox for support messages","Duplicate detection to avoid processing the same message twice","HTML-to-markdown conversion of email body","AI agent triage that produces title, summary, labels and priority","Creates issues in Linear using AI-generated metadata and contentable prompts for customization"],"target_audience":["support teams","customer success managers","product managers / PMs who manage backlogs of issues from email reports"]}


name: Automated YouTube Video Scheduling & AI Metadata Generation 🎬
author_name: JPres
author_username: stardawnai
categories: []
created_at: 2025-05-06T14:51:43.081Z
updated_at: 2025-05-06T14:51:43.081Z
url: https://n8n.io/workflows/3900
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  👥 Who Is This For?
  Content creators, marketing teams, and channel managers who need to streamline video publishing with optimized metadata and scheduled releases across multiple videos.

  🛠 What Problem Does This Solve?
  Manual YouTube video publishing is time-consuming and often results in inconsistent descriptions, tags, and scheduling. This workflow fully automates:
  Extracting video transcripts via Apify for metadata generation
  Creating SEO-optimized descriptions and tags for each video
  Setting videos to private during initial upload (critical for scheduling)
  Implementing scheduled publishing at strategic times
  Maintaining consistent branding and formatting across all content

  🔄 Node-by-Node Breakdown
  | Step | Node Purpose |
  |------|--------------|
  | 1 | Every Day (Scheduler) | Trigger workflow on a regular schedule |
  | 2 | Get Videos to Harmonize | Retrieve videos requiring metadata enhancement |
  | 3 | Get Video IDs (Unpublished) | Filter for videos that need publishing |
  | 4 | Loop over Video IDs | Process each video individually |
  | 5 | Get Video Data | Retrieve metadata for the current video |
  | 6 | Loop over Videos with Parameter IS | Set parameters for processing |
  | 7 | Set Videos to Private | Ensure videos are private (required for scheduling) |
  | 8 | Apify: Get Transcript | Extract video transcript via Apify |
  | 9 | Fetch Latest Videos | Get most recent channel content |
  | 10 | Loop Over Items | Process each video item |
  | 11 | Generate Description, Tags, etc. | Create optimized metadata from transcript |
  | 12 | AP Clean ID | Format identifiers |
  | 13 | Retrieve Generated Data | Collect the enhanced metadata |
  | 14 | Adjust Transcript Format | Format transcript for better processing |
  | 15 | Update Video's Metadata | Apply generated description and tags to video |

  ⚙️ Pre-conditions / Requirements
  n8n with YouTube API credentials configured
  Apify account with API access for transcript extraction
  YouTube channel with upload permissions
  Master templates for description formatting
  Videos must be initially set to private for scheduling to work

  ⚙️ Setup Instructions
  Import this workflow into your n8n instance.
  Configure YouTube API credentials with proper channel access.
  Set up Apify integration with appropriate actor for transcript extraction.
  Define scheduling parameters in the Every Day node.
  Configure description templates with placeholders for dynamic content.
  Set default tags and customize tag generation rules.
  Test with a single video before batch processing.

  🎨 How to Customize
  Adjust prompt templates for description generation to match your brand voice.
  Modify tag selection algorithms based on your channel's SEO strategy.
  Create multiple publishing schedules for different content categories.
  Integrate with analytics tools to optimize publishing times.
  Add notification nodes to alert when videos are successfully scheduled.

  ⚠️ Important Notes
  Videos MUST be uploaded as private initially - the Publish At logic only works for private videos that haven't been published before.
  Publishing schedules require videos to remain private until their scheduled time.
  Transcript quality affects metadata generation results.
  Consider YouTube API quotas when scheduling large batches of videos.

  🔐 Security and Privacy
  API credentials are stored securely within n8n.
  Transcripts are processed temporarily and not stored permanently.
  Webhook URLs should be protected to prevent unauthorized triggering.
  Access to the workflow should be limited to authorized team members only.

nodes_used:
  ["n8n-nodes-base.youTube","n8n-nodes-base.splitInBatches","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.wait","n8n-nodes-base.youTube","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.youTube","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.youTube","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.youTube","n8n-nodes-base.code","n8n-nodes-base.wait","n8n-nodes-base.splitInBatches","n8n-nodes-base.set","n8n-nodes-base.removeDuplicates","n8n-nodes-base.if","n8n-nodes-base.youTube","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.youTube"]

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metadata_json:
  {"categories":["automation","content management","integration","media publishing","AI-assisted content"],"complexity":"medium","use_cases":["Automated scheduling of YouTube uploads","Generate SEO-optimized descriptions and tags from transcripts","Batch processing and harmonization of multiple videos","Extracting transcripts for metadata generation","Maintaining consistent branding/description templates"],"estimated_setup_minutes":45,"required_services":["n8n instance","YouTube Data API (channel upload permissions)","Apify (transcript extraction actor)","LLM provider (OpenAI, Google Gemini, or equivalent)"],"key_features":["Transcript extraction via Apify","AI-generated SEO descriptions and tags","Set videos to private and schedule publish times","Batch looping and per-video processing","Template-driven description formatting and placeholders support`,`ID formatting and metadata update nodes"],"target_audience":["Content creators","YouTube channel managers","Marketing teams and social media managers`,`Video production teams"]}


name: Amazon Product Search Scraper with BrightData, GPT-4, and Google Sheets
author_name: phil
author_username: phil
categories: []
created_at: 2025-05-06T14:51:48.388Z
updated_at: 2025-05-06T14:51:48.388Z
url: https://n8n.io/workflows/3901
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  This workflow automates web scraping of Amazon search result pages by retrieving raw HTML, cleaning it to retain only the relevant product elements, and then using an LLM to extract structured product data (name, description, rating, reviews, and price), before saving the results back to Google Sheets.

  It integrates Google Sheets to supply and collect URLs, BrightData to fetch page HTML, a custom n8n Function node to sanitize the HTML, LangChain (OpenRouter GPT-4) to parse product details, and Google Sheets again to store the output.


  URL to scape

  .


  Result

  Who Needs Amazon Search Result Scraping?

  This scraping workflow is ideal for teams and businesses that need to monitor Amazon product listings at scale:

  E-commerce Analysts** – Track competitor pricing, ratings, and inventory trends.  
  Market Researchers** – Collect data on product popularity and reviews for market analysis.  
  Data Teams** – Automate ingestion of product metadata into BI pipelines or data lakes.  
  Affiliate Marketers** – Keep affiliate catalogs up to date with latest product details and prices.

  If you need reliable, structured data from Amazon search results delivered directly into your spreadsheets, this workflow saves you hours of manual copy-and-paste.

  Why Use This Workflow?

  End-to-End Automation** – From URL list to clean JSON output in Sheets.  
  Robust HTML Cleaning** – Strips scripts, styles, unwanted tags, and noise.  
  Accurate Structured Parsing** – Leverages GPT-4 via LangChain for reliable extraction.  
  Scalable & Repeatable** – Processes thousands of URLs in batches.

  Step-by-Step: How This Workflow Scrapes Amazon

  Get URLs from Google Sheets – Reads a list of search result URLs.  
  Loop Over Items – Iterates through each URL in controlled batches.  
  Fetch Raw HTML – Uses BrightData’s Web Unlocker proxy to retrieve the page.  
  Clean HTML – A Function node removes doctype, scripts, styles, head, comments, classes, and non-whitelisted tags, collapsing extra whitespace.  
  Extract with LLM – Passes cleaned HTML into LangChain → GPT-4 to output JSON for each product:  
     name, description, rating, reviews, price  
  Save Results – Appends the JSON fields as columns back into a “results” sheet in Google Sheets.

  Customization: Tailor to Your Needs

  Adaptable Sites** – This workflow can be adapted to any e-commerce or other website, for example Walmart or eBay. 
  Whitelist Tags** – Modify the allowedTags array in the Code node to keep additional HTML elements.  
  Schema Changes** – Update the Structured Output Parser schema to include more fields (e.g., availability, SKU).  
  Alternate Data Sink** – Instead of Sheets, route output to a database, CSV file, or webhook.

  🔑 Prerequisites

  Google Sheets Credentials** – OAuth credentials configured in n8n.  
  BrightData API token** – Stored in n8n credentials as BRIGHTDATA_TOKEN.  
  OpenRouter API Key** – Configured for the LangChain node to call GPT-4.  
  n8n Instance** – Self-hosted or cloud with sufficient quota for HTTP requests and LLM calls.

  🚀 Installation & Setup

  Configure Credentials**  
    In n8n, set up Google Sheets OAuth under “Credentials.”  
    Add BrightData token as a new HTTP Request credential.  
    Create an OpenRouter API key credential for the LangChain node.  
  Import the Workflow**  
    Copy the JSON workflow into n8n’s “Import” dialog.  
    Map your Google Sheet IDs and GIDs to the {{WEB_SHEET_ID}}, {{TRACK_SHEET_GID}}, and {{RESULTS_SHEET_GID}} placeholders.  
    Ensure the BRIGHTDATA_TOKEN credential is selected on the HTTP Request node.  
  Test & Run**  
    Add a few Amazon search URLs to your “track” sheet.  
    Execute the workflow and verify product data appears in your “results” sheet.  
    Tweak batch size or parser schema as needed.

  ⚠ Important

  API Rate Limits** – Monitor your BrightData and OpenRouter usage to avoid throttling.  
  Amazon’s Terms** – Ensure your scraping complies with Amazon’s policies and legal requirements.

  Summary

  This workflow delivers a fully automated, scalable solution to extract structured product data from Amazon search pages directly into Google Sheets—streamlining your competitive analysis and data collection. 🚀

  Phil | Inforeole 

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.splitInBatches","n8n-nodes-base.googleSheets","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.googleSheets","n8n-nodes-base.splitOut"]

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metadata_json:
  {"categories":["web scraping","automation","data extraction","integration"],"complexity":"complex","use_cases":["Competitive price and listing monitoring","Market research and product trend analysis","Automated ingestion of product metadata into BI pipelines","Maintaining affiliate product catalogs","Bulk extraction of structured product data from search results"],"estimated_setup_minutes":30,"required_services":["Google Sheets (OAuth credentials)","BrightData (Web Unlocker / API token)","OpenRouter / GPT-4 (API key used via LangChain)","n8n instance (self-hosted or cloud)"],"key_features":["Reads URLs from Google Sheets and writes structured results back to Sheets","Fetches page HTML through BrightData proxy to avoid blocking","Custom Function node that cleans/sanitizes HTML (whitelist tags, strip scripts/styles/comments)","Uses LangChain + GPT-4 to parse cleaned HTML into structured JSON (name, description, rating, reviews, price)","Batch processing and controlled looping over many URLs (splitInBatches node)** for scale** (sic)**"],"target_audience":["E-commerce analysts","Market researchers","Data engineering teams / data analysts automating ingestion pipelines"]}


name: Conversational PostgreSQL Agent with Visuals, Multi-KPI, and Data Editing (MCP)
author_name: hippolyte-hu
author_username: hippolyte-hu
categories: []
created_at: 2025-05-06T15:59:21.804Z
updated_at: 2025-05-06T15:59:21.804Z
url: https://n8n.io/workflows/3903
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Ask your PostgreSQL database complex questions and receive clear summaries, charts, and even update or insert data — all through one smart agent powered by n8n’s Model Context Protocol (MCP).

  Supports:
  Multi-KPI insights in one prompt  
  Auto-generated QuickChart bar/pie charts  
  Natural-language inserts and updates  
  Markdown-friendly output for dashboards

  🚀 Why This Version Stands Out

  This version goes beyond reporting:
  📈 Auto-generates charts (QuickChart)
  🧮 Answers multiple KPIs in one message
  ✍️ Add and update records securely
  🧠 Uses up to 30 planned steps for smart reasoning

  💰 Estimated cost per run: ~$0.02

  💬 Example Output

  🧰 Key Components

  MCP Server Trigger → Receives natural queries  
  Claude 3.5 Haiku → Plans, reasons, splits tasks  
  DeepSeek → SQL and QuickChart generation  
  checkdatabase subflow → Validates SQL  
  Plot Tool → Converts data to QuickChart URLs  
  Insert/Update nodes → Edits PostgreSQL records  
  Markdown Formatter → Combines output into readable message

  🤖 Model Configuration Notes

  This workflow uses two models:

  Claude 3.5 Haiku (Anthropic)  
  Used as the MCP agent for reasoning, planning, and tool calling. Claude is the native model for MCP and delivers reliable results in fewer steps.

  DeepSeek  
  Used in:
  checkdatabase for SQL generation  
  Plot Tool for QuickChart JSON generation

  🧠 All models are modular — you can plug in OpenAI, Gemini, or Mistral if desired.

  🔐 Security by Design

  No raw SQL from user input  
  Fully parameterized queries  
  Structured tool calling with validation  
  Safe output format (text + chart links)

  🧪 Try This Prompt

  &gt; “Show me top 5 products by revenue, revenue per month chart, and best customers.”

  Expected output:
  3 KPIs
  Multiple SQL queries
  2–3 QuickChart links
  Markdown summary for dashboard/Slack

  🛠 How to Use

  Import:
     Build_your_own_PostgreSQL_MCP_server__visuals_capable_.json
     checkdatabase.json
     Plot_tool.json

  Create your PostgreSQL credential under “Credentials” in n8n:
     Must match the name used in the workflow (e.g., Postgres account 3)

  Assign AI models:
     Claude 3.5 Haiku → MCP agent (Claude 3.5 MCP Agent)
     DeepSeek → LLM nodes inside checkdatabase and Plot Tool

  Trigger the workflow using the URL from the MCP Server Trigger node  
     (e.g., in a chatbot, HTTP request, or Webhook UI)

  📦 End-User Setup Guide

  If you're using this template for the first time, follow these exact steps:

  Go to your n8n dashboard and import all three workflows (main + subflows)
  Create a PostgreSQL credential using your host, database, user, and password
  Go to the Claude and DeepSeek nodes, and connect them to your account(s)
  Use the Webhook URL in the MCP Server Trigger to connect your chatbot or frontend
  Send a prompt like:  
     “Show me revenue per month, top 5 products, and a chart of best customers.”

  Optional:  
  You can increase the MCP Agent’s MaxIterations to go deeper (default is 30)
  You can use Switch nodes to limit access to certain tables or actions
  Insert/Update nodes are already included and can be safely enabled

  ✅ Once this is done, your AI assistant will:
  Read from your database  
  Visualize data via QuickChart  
  Insert or update rows  
  Respond in clear, markdown-formatted summaries

  🔗 More Templates by the Same Creator

  PostgreSQL Conversational Agent with Claude & DeepSeek (Multi-KPI, Secure)

  Conversing with Data: Transforming Text into SQL Queries and Visual Curves

  Customer Feedback Analysis with AI, QuickChart & HTML Report Generator

nodes_used:
  ["n8n-nodes-base.postgresTool","n8n-nodes-base.postgresTool","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.postgres","n8n-nodes-base.switch","n8n-nodes-base.postgres","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.postgres","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.lmChatAnthropic","@n8n/n8n-nodes-langchain.toolThink","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.chatTrigger"]

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metadata_json:
  {"categories":["automation","data processing","database","visualization","AI/assistant"],"complexity":"complex","use_cases":["Conversational SQL querying and natural-language analytics","Multi-KPI reporting in a single prompt","Auto-generated charts for dashboards or chat output","Safe inserts and updates to PostgreSQL via natural language","Embedding an AI data assistant into chatbots or internal tools"],"estimated_setup_minutes":20,"required_services":["n8n","PostgreSQL","Anthropic Claude 3.5 Haiku (or alternative MCP-capable model)","DeepSeek (or substitute LLM for SQL/plot generation)","QuickChart (or equivalent chart URL generator)","Webhook/chatbot/frontend to call MCP Server Trigger"],"key_features":["MCP-based agent planning and tool orchestration (up to 30 iterations)","Multi-KPI answers in one prompt","Auto-generated QuickChart bar/pie chart URLs","Natural-language insert and update operations with parameterized queries","SQL validation subflow to prevent raw user SQL execution (safe, parameterized queries only)"],"target_audience":["developers building conversational data tools","data analysts and BI engineers","product teams adding natural-language data access to apps/dashboards"]}


name: Search LinkedIn companies, Score with AI and add them to Google Sheet CRM
author_name: Matthieu
author_username: yaznow
categories: []
created_at: 2025-05-06T16:14:52.314Z
updated_at: 2025-05-06T16:14:52.314Z
url: https://n8n.io/workflows/3904
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  Search LinkedIn companies, Score with AI and add them to Google Sheet CRM

  Setup Video: https://youtube.com/watch?v=m904RNxtF0w&t

  Who is this for?
  This template is ideal for sales teams, business development professionals, and marketers looking to build a targeted prospect database with automatic qualification. Perfect for agencies, consultants, and B2B companies wanting to identify and prioritize the most promising potential clients.

  What problem does this workflow solve?
  Manually researching companies on LinkedIn, evaluating their fit for your services, and tracking them in your CRM is time-consuming and subjective. This automation streamlines lead generation by automatically finding, scoring, and importing qualified prospects into your database.

  What this workflow does
  This workflow automatically searches for companies on LinkedIn based on your criteria, retrieves detailed information about each company, filters them based on quality indicators, uses AI to score how well they match your ideal customer profile, and adds them to your Google Sheet CRM while preventing duplicates.

  Setup
  Create a Ghost Genius API account and get your API key
  Configure HTTP Request nodes with Header Auth credentials
  Create a copy of the provided Google Sheet template
  Set up your Google Sheet and OpenAI credentials following n8n documentation
  Customize the "Set Variables" node to match your target audience and scoring criteria

  How to customize this workflow
  Modify search parameters to target different industries, locations, or company sizes
  Adjust the follower count threshold based on your qualification criteria
  Customize the AI scoring system to align with your specific product or service offering
  Add notification nodes to alert you when high-scoring companies are identified

nodes_used:
  ["n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.wait","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 8096 characters]

metadata_json:
  {"categories":["automation","lead generation","CRM integration","sales intelligence","data enrichment"],"complexity":"medium","use_cases":["Automated prospect discovery from LinkedIn","AI-based lead qualification and scoring","Populating and maintaining a Google Sheets CRM","Prioritizing outreach for sales and BD teams","Agency/consultant client sourcing"],"estimated_setup_minutes":30,"required_services":["Ghost Genius API (LinkedIn/company data)","LinkedIn (data source)","Google Sheets","OpenAI (or compatible AI for scoring)","n8n"],"key_features":["Automated company search on LinkedIn based on customizable criteria","AI scoring of companies against an ideal customer profile","Filtering by quality indicators (e.g., follower count)","De-duplication when adding records to Google Sheets CRM","Batch processing and enrichment of company data via HTTP requests and AI nodes (splitInBatches)"],"target_audience":["Sales teams","Business development professionals","Marketers and growth teams looking for qualified prospects`,`Agencies and consultants targeting B2B clients"]}


name: Build a Personal Assistant with Google Gemini, Gmail and Calendar using MCP
author_name: Aitor | 1Node
author_username: aitoralonso
categories: []
created_at: 2025-05-06T17:01:07.757Z
updated_at: 2025-05-06T17:01:07.757Z
url: https://n8n.io/workflows/3905
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  Talk to Your Apps: Building a Personal Assistant MCP Server with Google Gemini

  Wouldn't it be cool to just tell your computer or phone to "schedule a meeting with Sarah next Tuesday at 3 PM" or "find John Doe's email address" and have it actually do it? That's the dream of a personal assistant!

  With n8n and the power of MCP and AI models like Google Gemini, you can actually build something pretty close to that. We've put together a workflow that shows you how you can use a natural language chat interface to interact with your other apps, like your CRM, email, and calendar.

  What You Need to Get Started

  Before you dive in, you'll need a few things:

  n8n:** An n8n instance (either cloud or self-hosted) to build and run your workflow.
  Google Gemini Access:** Access to the Google Gemini model via an API key.
  Credentials for Your Apps:** API keys or login details for the specific CRM, Email, and Calendar services you want to connect (like Google Sheets for CRM, Gmail, Google Calendar, etc., depending on your chosen nodes).
  A Chat Interface:** A way to send messages to n8n to trigger the workflow (e.g., via a chat app node or webhook).

  How it Works (In Simple Terms)

  Imagine this workflow is like a helpful assistant who sits between you and your computer.

  Step 1: You Talk, the AI Agent Listens

  It all starts when you send a message through your connected chat interface. Think of this as you speaking directly to your assistant.

  Step 2: The Assistant's Brain (Google Gemini)

  Your message goes straight to the assistant's "brain." In this case, the brain is powered by a smart AI model like Google Gemini. In our template we are using the latest Gemini 2.5 Pro. But this is totally up to you. Experiment and track which model fits the kind of tasks you will pass to the agent. Its job is to understand exactly what you're asking for.

  Are you asking to create something?
  Are you asking to find information?
  Are you asking to update something?

  The brain also uses a "memory" so it can remember what you've talked about recently, making the conversation feel more natural. We are using the default context window, which is the past 5 interactions.

  Step 3: The Assistant Decides What Tool to Use

  Once the brain understands your request, the assistant figures out the best way to help you. It looks at the request and thinks, "Okay, to do this, I need to use one of my tools."

  Step 4: The Assistant's Toolbox (MCP & Your Apps)

  Here's where the "MCP" part comes in. Think of "MCP" (Model Context Protocol) as the assistant's special toolbox. Inside this toolbox are connections to all the different apps and services you use – your CRM for contacts, your email service, and your calendar.

  The MCP system acts like a manager for these tools, making them available to the assistant whenever they're needed.

  Step 5: Using the Right Tool for the Job

  Based on what you asked for, the assistant picks the correct tool from the toolbox.

  If you asked to find a contact, it grabs the "Get Contact" node from the CRM section.
  If you wanted to schedule a meeting, it picks the "Create Event" node from the Calendar section.
  If you asked to draft an email, it uses the "Draft Email" node.

  Step 6: The Tool Takes Action

  Now, the node or set of nodes get to work! It performs the action you requested within the specific app.

  The CRM tool finds or adds the contact.
  The Email tool drafts the message.
  The Calendar tool creates the event.

  Step 7: Task Completed!

  And just like that, your request is handled automatically, all because you simply told your assistant what you wanted in plain language.

  Why This is Awesome

  This kind of workflow shows the power of combining AI with automation platforms like n8n. You can move beyond clicking buttons and filling out forms, and instead, interact with your digital life using natural conversation. n8n makes it possible to visually build these complex connections between your chat, the AI brain, and all your different apps.

  Taking it Further (Possible Enhancements)

  This is just the start! You could enhance this personal assistant by:

  Connecting more apps and services (task managers, project tools, etc.).
  Adding capabilities to search the web or internal documents.
  Implementing more sophisticated memory or context handling.
  Getting a notification when the AI agent is done completing each task such as in Slack or Microsoft Teams.
  Allowing the assistant to ask clarifying questions if needed. Building a robust prompt for the AI agent.

  Ready to Automate Your Workflow?

  Imagine the dozens of hours your team could save weekly by automating repetitive tasks through a simple, natural language interface. 

  Need help?
  Feel free to contact us at 1 Node.
  Get instant access to a library of free resources we created.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.mcpTrigger","@n8n/n8n-nodes-langchain.mcpClientTool","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.gmailTool","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.gmailTool","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.googleCalendarTool"]

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metadata_json:
  {"categories":["automation","integration","AI assistant","productivity"],"complexity":"complex","use_cases":["Schedule meetings via natural language","Find or update contact information in CRM","Draft and send emails","Create and manage calendar events","Automate repetitive workflows through chat commands"],"estimated_setup_minutes":90,"required_services":["n8n instance (cloud or self-hosted)","Google Gemini (Google Cloud AI) API access","Gmail API or SMTP credentials","Google Calendar API","Google Sheets or CRM API (e.g., Google Sheets as a CRM)","Chat interface or webhook (Slack, Microsoft Teams, web chat)","MCP (Model Context Protocol) server/component"],"key_features":["Natural-language interface powered by Google Gemini","MCP-based tool orchestration to call app-specific nodes","Memory buffer for conversational context (last 5 interactions)","Multi-app integration: Calendar, Email, CRM/Sheets","Agent logic to choose and execute appropriate nodes (create event, get contact, draft email)"],"target_audience":["Developers and automation engineers","Productivity/product managers building assistant features","IT/operations teams automating workflows for teams or SMBs"]}


name: Automate YouTube Uploads with AI-Generated Metadata from Google Drive
author_name: JPres
author_username: stardawnai
categories: []
created_at: 2025-05-06T17:06:31.065Z
updated_at: 2025-05-06T17:06:31.065Z
url: https://n8n.io/workflows/3906
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  👥 Who Is This For?  
  Content creators, marketing teams, and channel managers who want a simple, hands‑off solution to upload videos and automatically generate optimized metadata from video transcripts.

  🛠 What Problem Does This Solve?  
  Manual video uploads with proper metadata creation is time‑consuming and repetitive. This workflow fully automates:  
  Monitoring a specific Google Drive folder for new video uploads  
  Seamless YouTube upload processing  
  Transcript extraction for context understanding  
  AI‑powered generation of titles, descriptions, and tags  
  Metadata application to uploaded videos without manual intervention  

  🔄 Node‑by‑Node Breakdown  
  | Step | Node Purpose                                                        |
  |------|---------------------------------------------------------------------|
  | 1    | New Video? (Trigger) – Monitors specified Google Drive folder       |
  | 2    | Download New Video – Retrieves the video file from Google Drive     |
  | 3    | Upload to YouTube – Uploads the video to YouTube with initial settings |
  | 4    | Get Transcript – Extracts transcript from the uploaded video        |
  | 5    | Adjust Transcript Format – Formats raw transcript for processing    |
  | 6    | Create Description – Generates SEO‑optimized description            |
  | 7    | YT Tags (Message Model) – Creates relevant tags based on content    |
  | 8    | YT Title (Message Model) – Generates compelling title               |
  | 9    | Define File Path Upload Format (Optional) – Structures data paths   |
  | 10   | Update Video’s Metadata – Applies generated title, description, tags|

  ⚙️ Pre‑conditions / Requirements  
  n8n with Google Drive and YouTube API credentials configured (stored as n8n credentials/variables; no hard‑coded IDs)  
  Dedicated Google Drive folder for video uploads  
  YouTube channel with proper upload permissions  
  AI service access for transcript processing and metadata generation  
  Sufficient storage for temporary video handling  

  ⚙️ Setup Instructions  
  Import this workflow into your n8n instance.  
  Configure Google Drive credentials; reference folder ID via n8n variable (do not hard‑code).  
  Set up YouTube API credentials with upload and edit permissions.  
  Specify the target Google Drive folder ID in the New Video? trigger node (via variable).  
  Configure AI service credentials for transcript and metadata generation.  
  Adjust message templates for title, description, and tag creation.  
  Test with a small video file before production use.  

  🎨 How to Customize  
  Modify AI prompts to match your channel’s tone and style.  
  Add conditional logic based on video categories or naming conventions.  
  Implement notification systems to alert when uploads complete.  
  Create custom metadata templates for different content types.  
  Include timestamps or chapter markers based on transcript analysis.  
  Add social media sharing nodes to announce new uploads.  

  ⚠️ Important Notes  
  Video quality is preserved through the upload process.  
  Consider YouTube API quotas when handling multiple uploads.  
  Transcript quality affects metadata generation results.  
  Videos are initially uploaded without visibility adjustments.  
  Processing time depends on video length and transcript complexity.  

  🔐 Security and Privacy  
  Store API credentials and folder IDs as n8n Credentials/Variables—remove any hard‑coded tokens or IDs.  
  Video files are processed temporarily and not stored permanently.  
  Limit Google Drive folder access to authorized users only.  
  Manage YouTube upload permissions carefully (use OAuth/service accounts).  
  Ensure compliance with organizational data‑handling policies.  

nodes_used:
  ["n8n-nodes-base.googleDrive","n8n-nodes-base.googleDriveTrigger","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.youTube","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleDrive","n8n-nodes-base.youTube","n8n-nodes-base.set"]

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metadata_json:
  {"categories":["automation","content management","video publishing","AI-generated content","integration"],"complexity":"medium","use_cases":["Automate YouTube video uploads from a Drive folder","Generate SEO-optimized titles, descriptions, and tags from transcripts","Batch or scheduled video publishing workflows","Ensure consistent channel metadata and branding","Reduce manual metadata creation for creators/marketing teams"],"estimated_setup_minutes":45,"required_services":["Google Drive API (folder monitoring & file download)","YouTube Data API (upload + edit permissions)","AI provider for text generation (e.g., OpenAI, Google Gemini)","Transcription service or captioning (YouTube captions or external STT)","Temporary file storage for video processing (local or cloud)"],"key_features":["Monitors a Drive folder and auto-triggers uploads","Uploads videos to YouTube and retains quality","Extracts and formats transcripts for context","AI-powered title, description, and tag generation","Applies generated metadata to uploaded videos automatically (update video fields)"],"target_audience":["Content creators","Channel managers","Marketing teams/social media managers`,`Video editors`"]}


name: Generate Riddle Shorts & Post on YouTube with Sonnet 3.5, Pinecone & Creatomate
author_name: Joseph
author_username: mjomba
categories: []
created_at: 2025-05-06T17:37:42.403Z
updated_at: 2025-05-06T17:37:42.403Z
url: https://n8n.io/workflows/3907
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow allows you to generate riddle-themed vertical videos (9:16), render them using Creatomate, and upload them directly to YouTube — all automatically. It's optimized for low-cost operation while still integrating AI and automation at key stages.

  Here is a sample: https://youtube.com/shorts/svhgti9L6Fw?feature=share

  🔄 Workflow Overview

  This is a schedule-triggered workflow that:

  Generates riddles with answers using an AI agent.
  Checks Pinecone vector store to avoid repeating riddles.
  Inserts new riddles into Pinecone under a namespace structured by month/year for easier indexing.
  Fetches an unused music track URL for video background.
  Generates a video using Creatomate with the riddles and audio.
  Uploads the video to YouTube via a custom HTTP request with OAuth.
  Updates the worksheet and riddle numbering, and notifies via email.

  🔗 Integration Guide

  🔎 Pinecone Setup

  You need an active Pinecone account with an index created.
  The vector index should be accessible from n8n.
  Follow \[n8n's Pinecone Node Documentation] for help setting up credentials and connecting.

  📝 Google Sheet Setup

  Open the sheets template provided in the Gumroad documentation pdf.
  Make a copy to your Google Drive.
  Set up the required credentials in n8n and link the Google Sheets node to your new copy.

  🎧 Audio Tracks

  Get copyright-free tracks from Royalty Free Music Library.
  Ensure files are in .mp3 format for compatibility.

  🎞️ Background Videos (Optional)

  For optional background visuals, use free stock videos from Pexels.

  💡 Creatomate Setup

  Create a Creatomate account at \[creatomate.com].
  Create a 9:16 aspect ratio template.
  Press F12 to open the source editor.
  Download the Creatomate JSON file from the Gumroad page and paste its contents into the source editor.
  Refresh the page and click "Use Template" &gt; API Integration.
  Copy the cURL.
  From the cURL, extract your Bearer Token and Template ID.
  Paste them in the respective fields in the Creatomate Render Node inside n8n.

  📉 YouTube Upload Configuration

  Inside n8n, set up a YouTube OAuth credential.
  Search for "YouTube" in the credentials list and select the OAuth type.
  Use the Custom HTTP Request node with multipart/form-data to upload the video using:

     Title (e.g., "Riddles4U #001")
     Description
     Video file binary
     Category ID (find ID here: https://gist.github.com/dgp/1b24bf2961521bd75d6c)

  🔄 Workflow Logic Detail

  Riddle Generation

     Checks Pinecone vector store for existing riddles (to avoid duplicates).
     Generates 4 new riddles with answers.
     Stores them in Pinecone under month/year namespace.
     Extracts a numbered title for current riddle post (e.g., Riddles4U #001).
     Prepares next riddle title counter (but only updates after YouTube post succeeds).

  Track URL Fetching

     Pulls the first unused track from Google Sheets.

  Render & Upload

     Sends the riddles + track to Creatomate for rendering.
     Fetches binary video and uploads it directly to YouTube via HTTP.
     Note: For video source url, I downloaded a video, uploaded it to drive and used that video drive link. You can add a new sheet and rotate videos too, although youtube likes consistency in shorts so I think it is best to have one very beautiful and calm background video then change to a new one after some months or when you generate like 50 shorts. 


  Post Actions

     Sends email with YouTube video link.
     Marks used audio row as "used".
     Updates to next riddle title.
     If all tracks have been used, resets them to "unused".

  🚀 Customization Ideas

  Add AI-generated voiceover to the riddles.
  Replace riddles with trivia, quotes, or jokes.
  Integrate TikTok or Instagram upload APIs for multichannel publishing.

  ✉️ Support

  Refer to the comprehensive setup guide included in the Gumroad download folder to configure your template, credentials, and API keys correctly.

  This is a beginner-friendly, low-cost automation you can build on for more advanced content pipelines. Happy building!

  You can find me on twitter and via email incase you need any custom n8n workflows or wanna chat.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.lmChatAnthropic","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.merge","n8n-nodes-base.wait","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.vectorStorePinecone","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.vectorStorePinecone","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.gmail","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","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","content-creation","video-production","social-media","ai-integration"],"complexity":"medium","use_cases":["Automated generation of short-form riddle videos","Scheduled YouTube Shorts publishing","Avoiding duplicate content via vector search","Low-cost AI-driven video content pipeline","Repurposing text content into vertical videos"],"estimated_setup_minutes":90,"required_services":["n8n","Pinecone (vector DB)","Creatomate (rendering API)","YouTube API (OAuth upload)","Google Sheets / Google Drive","LLM provider (Sonnet 3.5 / Anthropic, OpenAI for embeddings)","Royalty-free music source (MP3)","Optional: Pexels (stock video)","SMTP/email service (for notifications)"],"key_features":["Schedule-triggered end-to-end workflow","AI riddle generation with answers","Duplicate-checks via Pinecone embeddings and namespace-by-month/year","Selects unused background music from Google Sheets","Renders 9:16 videos in Creatomate and returns binary output for upload"],"target_audience":["YouTube creators and Shorts publishers","Social media managers and marketers","No-code/low-code builders using n8n (beginners → intermediate)  ,content creators looking to scale output"]}


name: Comprehensive SEO Keyword Research with OpenAI & DataForSEO Analytics to NocoDB
author_name: phil
author_username: philrox
categories: []
created_at: 2025-05-06T18:12:18.353Z
updated_at: 2025-05-06T18:12:18.353Z
url: https://n8n.io/workflows/3908
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  AI-Powered SEO Keyword Research Workflow with n8n

  &gt; automates comprehensive keyword research for content creation

  Table of Contents

  Introduction
  Workflow Architecture
  NocoDB Integration
  Data Flow
  Core Components
  Setup Requirements
  Possible Improvements

  Introduction

  This n8n workflow automates SEO keyword research using AI and data-driven analytics. It combines OpenAI's language models with DataForSEO's analytics to generate comprehensive keyword strategies for content creation. The workflow is triggered by a webhook from NocoDB, processes the input data through multiple stages, and returns a detailed content brief with optimized keywords.

  Workflow Architecture

  The workflow follows a structured process:

  Input Collection: Receives data via webhook from NocoDB
  Topic Expansion: Generates keywords using AI
  Keyword Metrics Analysis: Gathers search volume, CPC, and difficulty metrics
  Competitor Analysis: Analyzes competitor content for ranking keywords
  Final Strategy Creation: Combines all data to generate a comprehensive keyword strategy
  Output Storage: Saves results back to NocoDB and sends notifications

  NocoDB Integration

  Database Structure

  The workflow integrates with two tables in NocoDB:

  Input Table Schema

  This table collects the input parameters for the keyword research:

  | Field Name      | Type          | Description                                                                 |
  | --------------- | ------------- | --------------------------------------------------------------------------- |
  | ID              | Auto Number   | Unique identifier                                                           |
  | Primary Topic   | Text          | The main keyword/topic to research                                          |
  | Competitor URLs | Text          | Comma-separated list of competitor websites                                 |
  | Target Audience | Single Select | Description of the target audience (Solopreneurs, Marketing Managers, etc.) |
  | Content Type    | Single Select | Type of content (Blog, Product page, etc.)                                  |
  | Location        | Single Select | Target geographic location                                                  |
  | Language        | Single Select | Target language for keywords                                                |
  | Status          | Single Select | Workflow status (Pending, Started, Done)                                    |
  | Start Research  | Checkbox      | Active Workflow when you set this to true                                   |

  Output Table Schema

  This table stores the generated keyword strategy:

  | Field Name         | Type        | Description                                      |
  | ------------------ | ----------- | ------------------------------------------------ |
  | ID                 | Auto Number | Unique identifier                                |
  | primary_topic_used | Text        | The topic that was researched                    |
  | report_content     | Long Text   | The complete keyword strategy in Markdown format |
  | generatedAt        | Datetime    | Automatically generated by NocoDb                |

  Webhook Settings

  NocoDB Webhook Settings

  Data Flow

  The workflow handles data in the following sequence:

  Webhook Trigger: Receives input from NocoDB when a new keyword research request is created
     
  Field Extraction: Extracts primary topic, competitor URLs, audience, and other parameters
  AI Topic Expansion: Uses OpenAI to generate related keywords, categorized by type and intent
  Keyword Analysis: Sends primary keywords to DataForSEO to get search volume, CPC, and difficulty
  Competitor Research: Analyzes competitor pages to identify their keyword rankings
  Strategy Generation: Combines all data to create a comprehensive keyword strategy
  Storage & Notification: Saves the strategy to NocoDB and sends a notification to Slack

  Core Components

  1. Topic Expansion

  This component uses OpenAI and a structured output parser to generate:

  20 primary keywords
  30 long-tail keywords with search intent
  15 question-based keywords
  10 related topics

  2. DataForSEO Integration

  Two API endpoints are used:

  Search Volume & CPC**: Gets monthly search volume and cost-per-click data
  Keyword Difficulty**: Evaluates how difficult it would be to rank for each keyword

  3. Competitor Analysis

  This component:

  Analyzes competitor URLs to identify which keywords they rank for
  Identifies content gaps or opportunities
  Determines the search intent their content targets

  4. Final Keyword Strategy

  The AI-generated strategy includes:

  Top 10 primary keywords with metrics
  15 long-tail opportunities with low competition
  5 question-based keywords to address in content
  Content structure recommendations
  3 potential content titles optimized for SEO

  Setup Requirements

  To use this workflow, you'll need:

  n8n Instance: Either cloud or self-hosted
  NocoDB Account: For data input and storage
  API Keys:
     OpenAI API key
     DataForSEO API credentials
     Slack API token (for notifications)
  Database Setup: Create the required tables in NocoDB as described above

  Possible Improvements

  The workflow could be enhanced with the following improvements:

  Enhanced Keyword Strategy

  Add topic clustering to group related keywords
  Enhance the final output with more specific content structure suggestions
  Include word count recommendations for each content section

  Additional Data Sources

  Integrate Google Search Console data for existing content optimization
  Add Google Trends data to identify rising topics
  Include sentiment analysis for different keyword groups

  Improved Competitor Analysis

  Analyze content length and structure from top-ranking pages
  Identify common backlink sources for competitor content
  Extract content headings to better understand content organization

  Automation Enhancements

  Add scheduling capabilities to run updates on existing content
  Implement content performance tracking over time
  Create alert thresholds for changes in keyword difficulty or search volume

  Example Output

  Here is an example Output the Workflow generated based on the following inputs.

  Inputs:
  Primary Topic: AI Automation
  Competitor URLs: n8n.io, zapier.com, make.com
  Target Audience: Small Business Owners
  Content Type: Landing Page
  Location: United States
  Language: English

  Output: Final Keyword Strategy 

  The workflow provides a powerful automation for content marketers and SEO specialists to develop data-driven keyword strategies with minimal manual effort.

  &gt; Original Workflow: AI-Powered SEO Keyword Research Automation - The vibe Marketer

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","n8n-nodes-dataforseo.dataForSeo","n8n-nodes-dataforseo.dataForSeo","n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-dataforseo.dataForSeo","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.webhook","n8n-nodes-base.splitOut","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.code","n8n-nodes-base.aggregate","n8n-nodes-base.aggregate","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.aggregate","n8n-nodes-base.merge","n8n-nodes-base.nocoDb","n8n-nodes-base.nocoDb","n8n-nodes-base.slack","n8n-nodes-base.slack","n8n-nodes-base.nocoDb","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":["automation","integration","data processing","seo","ai"],"complexity":"complex","use_cases":["SEO keyword research","Content strategy and brief generation","Competitor keyword analysis","Marketing automation for content teams","Periodic keyword monitoring and reporting"],"estimated_setup_minutes":45,"required_services":["n8n instance (cloud or self-hosted)","NocoDB","OpenAI API key","DataForSEO API credentials","Slack API token / workspace"],"key_features":["Webhook trigger from NocoDB to start research","AI-driven topic expansion (primary, long-tail, question-based keywords)","Search volume, CPC and difficulty metrics from DataForSEO","Competitor URL analysis to surface ranked keywords and content gaps","Structured output parsing to produce a content brief/strategy in Markdown format and store in NocoDB table(s)"],"target_audience":["SEO specialists","Content marketers","Digital marketing agencies and consultants"]}


name: Automate Agile Refinement Prep with Gmail, OpenAI & Google Sheets
author_name: Willemijn
author_username: willemijn
categories: []
created_at: 2025-05-06T19:11:29.244Z
updated_at: 2025-05-06T19:11:29.244Z
url: https://n8n.io/workflows/3909
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  👤 Who is this for?  
  This workflow is designed for Scrum Masters, Agile Coaches, and Product Owners who want to automate backlog refinement preparation using Google Sheets, Gmail, and OpenAI. It’s ideal for teams seeking consistent, high-quality refinement sessions with minimal manual effort.

  🧩 What problem is this workflow solving?  
  Many Agile teams struggle with unprepared refinement sessions, unclear user stories, and inconsistent feedback. This workflow solves that by automating the selection, validation, and communication process—saving time while improving backlog quality and team alignment.

  ⚙️ What this workflow does  
  The workflow checks the Scrum Master’s Google Calendar for an upcoming refinement event. It then pulls potential user stories from a Google Sheets backlog, filters them by status, and validates them using OpenAI agents for Scrum, business, and technical feedback. Finally, it compiles everything into a structured HTML email, either creating a draft or sending it upon approval.

  🚀 Setup  
  Use consistent event naming in Google Calendar  
  Configure environment variables for your project (e.g., sheet names, statuses)  
  Connect your own backlog and Definition of Ready  
  Customize the AI prompts and email layout

  🛠️ How to customize this workflow to your needs  
  Replace Google Sheets with Jira or Airtable  
  Switch Gmail to Outlook, SMTP, or Mailgun  
  Extend error handling or approval logic  
  Tailor the email tone and AI prompts to match your team

  🔥 Unique Selling Points (USPs)
  AI-Powered Multi-Perspective Feedback
  Automated Definition of Ready (DoR) Checks
  Calendar-Aware Triggering
  Fully Formatted Gmail Emails, With Approval Flow
  Plug-and-Play Customizability

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.noOp","n8n-nodes-base.googleCalendar","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.filter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.splitInBatches","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.noOp","n8n-nodes-base.googleSheets","n8n-nodes-base.googleDrive","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.stickyNote","n8n-nodes-base.splitInBatches","n8n-nodes-base.stickyNote","n8n-nodes-base.merge","n8n-nodes-base.aggregate","n8n-nodes-base.merge","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.splitInBatches","n8n-nodes-base.aggregate","n8n-nodes-base.stickyNote","n8n-nodes-base.errorTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.googleDrive","n8n-nodes-base.gmail"]

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metadata_json:
  {"categories":["automation","project-management","integration","ai-assistance","collaboration"],"complexity":"complex","use_cases":["Automate backlog refinement preparation","Validate and improve user stories (DoR checks)","Generate multi-perspective feedback (scrum, business, technical)","Create and send structured pre-meeting emails/drafts","Reduce manual prep for refinement meetings"],"estimated_setup_minutes":60,"required_services":["Google Calendar","Google Sheets","Gmail","OpenAI API","Google Drive"],"key_features":["Calendar-aware triggering for upcoming refinement events","Filters and selects backlog items from Google Sheets","Multi-agent OpenAI validation (scrum, business, technical)","Automated Definition of Ready (DoR) checks","Generates formatted HTML Gmail drafts or sends emails with approval flow and optional customization"],"target_audience":["Scrum Masters","Agile Coaches","Product Owners/Product Managers (PMs) \",\"Engineering Managers/Tech Leads\",\"n8n automation builders/DevOps engineers"]}


name: Client Feedback Collector & Analyzer (Form → AI Summary → Email + Social Draft)
author_name: Rodrigue Gbadou
author_username: gbadou
categories: []
created_at: 2025-05-06T19:48:38.245Z
updated_at: 2025-05-06T19:48:38.245Z
url: https://n8n.io/workflows/3910
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  What this workflow does

  This n8n workflow collects client feedback through a form (Tally, Typeform, or Google Forms) and uses AI to analyze it.  
  It automatically generates a summary of the positive points, highlights areas for improvement, and drafts a short social media post based on the feedback.

  Ideal for:
  Freelancers
  Customer support teams
  Online service providers
  Coaches and educators

  Setup steps

  Connect your form tool to the Webhook node (POST method) and make sure it sends a feedback field.
  Add your DeepSeek (or other GPT-compatible) API key to the AI request node.
  Configure the email node with your SMTP credentials and desired recipient address.
  Replace the Telegram node with Slack, Buffer, or another integration if you prefer.
  (Optional) Customize the prompt in the function node for different tone/language.

  🕐 Estimated setup time: ~15 minutes  
  💬 Sticky notes are included and clearly positioned to guide you.

  Technologies used

  n8n Webhook node
  n8n Function node
  DeepSeek Chat or compatible AI API
  Email node (SMTP)
  Telegram node (or other integration)
  Sticky Notes for setup guidance

  Use cases

  Analyze feedback from onboarding or satisfaction surveys
  Create ready-to-publish social media content from real customer praise
  Help support or marketing teams act on feedback immediately

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.function","n8n-nodes-base.httpRequest","n8n-nodes-base.function","n8n-nodes-base.emailSend","n8n-nodes-base.telegram"]

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metadata_json:
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name: Automate Lead Qualification with RetellAI Phone Agent, OpenAI GPT & Google Sheet
author_name: Dr. Firas
author_username: drfiras
categories: []
created_at: 2025-05-06T23:32:32.465Z
updated_at: 2025-05-06T23:32:32.465Z
url: https://n8n.io/workflows/3912
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  👉 Build a Phone Agent to qualify outbound leads and schedule inbound calls

  Who is this for?

  This workflow is designed for sales teams, call centers, and businesses handling both outbound and inbound lead calls who want to automate their qualification, follow-up, and call documentation process without manual intervention. It’s ideal for teams using Google Sheets, RetellAI, OpenAI, and Gmail as part of their tech stack.

  Real-World Use Cases

  🛍 E-commerce – Instantly handle product FAQs and order status checks, 24/7.
  🏬 Retail Stores – Share store hours, directions, and return policies without lifting a finger.
  🍽 Restaurants – Take reservations or answer menu questions automatically.
  💼 Service Providers – Book appointments or consultations while you focus on your craft.
  📞 Any Local Business – Deliver friendly, consistent phone support — no live agent required.

  What problem is this workflow solving?

  Managing lead calls at scale can be chaotic—between scheduling outbound qualification calls, handling inbound appointment requests, and making sure every call is documented and followed up. This workflow automates the entire process, reducing human error and saving time by:

  ✅ Sending reminders to reps for outbound calls
  ✅ Automatically placing calls with RetellAI
  ✅ Handling inbound calls and checking caller details
  ✅ Generating and emailing call summaries automatically

  What this workflow does

  This n8n template connects Google Sheets, RetellAI, OpenAI, and Gmail into a seamless workflow:

  Outbound Lead Qualification Workflow
     Triggers when a new lead is added to Google Sheets
     Sends an SMS notification to remind the rep to call in 5 minutes
     (Optional) Waits 5 minutes
     Initiates an automated call to the lead via RetellAI

  Inbound Call Appointment Scheduler
     Receives inbound calls from RetellAI (via webhook)
     Checks if the caller’s number exists in Google Sheets
     Responds to RetellAI with a success or error message

  Post-Call Workflow
     Receives post-call data from RetellAI
     Filters only analyzed calls
     Updates the lead’s record in Google Sheets
     Uses OpenAI to generate a call summary
     Emails the summary to a team inbox or rep

  Setup

  ✅ You need an active RetellAI API key

  Sign up for RetellAI, create an agent, and set the webhook URLs (n8n_call for call events).
  Purchase a Twilio phone number and link it to the agent.  

  ✅ Your Google Sheet must have a column for phone numbers (e.g., "Phone")  
  ✅ Gmail account connected and authorized in n8n  
  ✅ OpenAI API key added to your environment variables or credentials

  Configure your Google Sheets node with the correct spreadsheet ID and range
  Add your RetellAI API key to the HTTP request nodes
  Connect your Gmail account in the Gmail node
  Add your OpenAI key in the OpenAI node

  👉 See full setup guide here: Notion Documentation

  How to customize this workflow to your needs

  Change SMS content**: Edit the text in the “Send SMS reminder” node to match your team’s tone
  Modify call wait time**: Enable and adjust the “Wait 5 minutes” node to any delay you prefer
  Add CRM integration**: Replace or extend the Google Sheets node to update your CRM instead of a spreadsheet
  Customize call summary prompts**: Edit the prompt sent to OpenAI to change the summary style or add extra insights
  Send email to different recipients**: Change the recipient address in the Gmail node or make it dynamic from the lead record

  Need help customizing?
  Contact me for consulting and support : Linkedin

nodes_used:
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metadata_json:
  {"categories":["automation","telephony","crm-integration","lead-management","customer-support"],"complexity":"complex","use_cases":["Outbound lead qualification and reminders","Inbound appointment scheduling via phone agent","Automated call handling and IVR-style responses","Post-call documentation and AI-generated summaries","Emailing call summaries and follow-ups"],"estimated_setup_minutes":60,"required_services":["RetellAI (phone agent & webhooks)","OpenAI (GPT for call summaries)","Google Sheets (lead datastore)","Gmail (send summary emails)","Twilio (phone number and SMS/calling)","n8n (workflow runner)"],"key_features":["Triggers on new Google Sheet leads and sends SMS reminders","Initiates automated outbound calls via RetellAI","Receives inbound calls and validates caller against Google Sheets","Processes post-call data, filters analyzed calls, and updates sheet records","Generates call summaries using OpenAI and emails them via Gmail to reps or team inboxes (configurable)"],"target_audience":["Sales teams","Call centers","Small and local businesses handling leads/calls (e.g., retail, restaurants, services)  | Customer support teams"]}


name: Transparent Tracking Pixel for Email Open Detection
author_name: Julien DEL RIO
author_username: juliendelrio
categories: []
created_at: 2025-05-07T09:05:44.942Z
updated_at: 2025-05-07T09:05:44.942Z
url: https://n8n.io/workflows/3913
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  📌 Description
  This workflow serves a 1x1 transparent PNG image via a webhook, which can be embedded in an email to track when the email is opened. When the image is loaded by the recipient's email client, the webhook is triggered, optionally capturing a userId to identify who opened the email.

  📂 Workflow Steps

  Webhook Trigger (Request img)
     Path: /webhook/change-with-your-id
     Triggered by an HTTP request (e.g. when the image is loaded in an email).
     Accepts a query parameter id to identify the recipient.

  Set Base64 Data (Create data pix)
     Creates a variable data containing a Base64-encoded transparent PNG image (1x1 pixel).

  Convert to Binary (Create img bin)
     Converts the Base64 data string into a binary file.
     Sets MIME type to image/png.

  Respond to Webhook (Respond to Webhook)
     Sends the binary image file in the HTTP response.

  Logging (Do anything to log)
     Placeholder node to log or process the id or request metadata.
     You can access the id using {{$json"query"}}.
     You can also use any parameter you want

  ✉️ How to Use in Emails

  Embed the image in an HTML email like this:



  When the email is opened and the image is loaded, the workflow will be triggered.

  🛠️ Notes
  Some email clients block images by default; this may prevent tracking.
  You can enhance the workflow to store open events in a database, log the timestamp, IP, or user agent.
  Make sure to comply with data privacy and consent regulations (e.g. GDPR).

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.convertToFile","n8n-nodes-base.respondToWebhook","n8n-nodes-base.webhook","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","email","tracking"],"complexity":"simple","use_cases":["Email open tracking for campaigns","User engagement analytics","Lightweight read receipts","Logging email opens for audit or testing"],"estimated_setup_minutes":10,"required_services":["n8n instance with a publicly accessible webhook endpoint","Email service/provider to embed the tracking image","Optional: database or analytics service to store events"],"key_features":["Serves a 1x1 transparent PNG via webhook","Accepts query parameter (id) to identify recipient","Converts Base64 image data to binary and responds with correct MIME type","Placeholder logging node to capture id, timestamp, IP, user agent","Easy to extend to store events in a database or analytics tool"],"target_audience":["Marketers","Developers","Growth/product teams"]}


name: 💾 Backup Automation for n8n Workflows to Google Drive (Daily or Manual)
author_name: Amanda Benks
author_username: amanda
categories: []
created_at: 2025-05-07T11:36:46.111Z
updated_at: 2025-05-07T11:36:46.111Z
url: https://n8n.io/workflows/3918
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  💾 Backup Automation for n8n Workflows to Google Drive – No Risk, No Stress

  Hi! I’m Amanda,
  I build automation workflows for n8n and Make.  
  This ready-to-use workflow is designed to automatically export and back up all your n8n workflows to a designated Google Drive folder, organized by date.

  It’s perfect for agencies, developers, and teams that want to protect their automation assets — without relying on manual exports or risking losing their work.

  ✅ What this workflow does

  Fetches all your existing workflows from the n8n API  
  Compiles each workflow as a .json file  
  Creates a new folder in Google Drive using the current date  
  Uploads each .json file to that folder  
  Can be triggered manually or automatically via cron (date-time node)

  ⚙️ How to set it up

  Connect your Google Drive in the “Folder Creation” and “Save to Drive” nodes  
  Add your n8n API Key in the “Search All Workflows” and “Compiles Individual Data” nodes  
  Define your time zone and execution method:
     Run manually via the “Manual Trigger” node  
     Or schedule backups using the “Date & Time” and “Schedule Trigger” nodes  
  (Optional) Change the parent folder ID in the “Folder Creation in Drive” node to set your destination

  Once configured, the workflow will back up your entire workflow list daily — or whenever you run it — and send the files to your Google Drive!

  🧰 Nodes & Tech Used

  Schedule Trigger + Manual Trigger  
  Date & Time – For timestamp folder naming  
  n8n API – To list and fetch each workflow  
  Google Drive – Folder creation and file upload  
  Merge, Move Binary Data, Set – Workflow compilation and formatting  

  🧑‍💻 Who this is for

  n8n developers who want peace of mind  
  Agencies managing many client automations  
  Anyone worried about losing their workflow work  
  Teams needing version control and backup history

  🛠️ Requirements

  Google Drive OAuth2 credential**  
  n8n API Key** (from your account settings)  
  n8n Cloud or self-hosted instance

  🌐 Want to use this workflow?

  ❤️ Buy workflows: https://iloveflows.com  
  ☁️ Try n8n Cloud with my partner link: https://n8n.partnerlinks.io/amanda

nodes_used:
  ["n8n-nodes-base.moveBinaryData","n8n-nodes-base.dateTime","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote","n8n-nodes-base.n8n","n8n-nodes-base.n8n","n8n-nodes-base.merge","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","backup","integration","data protection"],"complexity":"medium","use_cases":["Automated daily backups of n8n workflows","Manual on-demand exports for versioning","Disaster recovery and migration of workflows","Audit/compliance archive of workflow configurations"],"estimated_setup_minutes":15,"required_services":["Google Drive (OAuth2)","n8n API (n8n Cloud or self-hosted)"],"key_features":["Fetches all workflows from n8n API","Compiles each workflow as a .json file","Creates date-stamped folder in Google Drive","Uploads workflow files to designated Drive folder","Supports manual trigger or scheduled (cron) runs with timezone control\",\"Configurable parent folder ID for destination"],"target_audience":["n8n developers","Agencies managing client automations","DevOps/teams responsible for backups and recovery"]}


name: Lead Tracking System for HubSpot with Automated Notifications via Gmail & Slack
author_name: dataplusminus+-
author_username: dataplusminus
categories: []
created_at: 2025-05-07T12:02:27.662Z
updated_at: 2025-05-07T12:02:27.662Z
url: https://n8n.io/workflows/3919
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  🎯 Project Purpose

  This project automates the process of collecting and managing new leads submitted through a web form. It eliminates the need for manual data entry and ensures that each lead is:

  Properly recorded and time-stamped in a structured format
  Automatically communicated to the sales or support team
  Ready for follow-up, with a reminder system in place

  It’s a lightweight but effective solution suitable for freelancers, small teams, and growing businesses that want to streamline their lead intake process.

  🛠️ Tools & Technologies Used

  Google Forms / Web Form** – Frontend for capturing leads
  Google Sheets** – Central database for storing lead information
  n8n** – Automation platform that connects and coordinates all services
  Gmail** – Handles email notifications for new leads
  Slack* *(optional) – Provides instant team notifications
  Date & Time nodes** – Tracks and manages lead response timing
  Conditional (IF) nodes** – Filters out duplicate and incomplete entries

  🔄 Workflow Overview

  ✨ Key Features

  ✅ No-code integration using n8n
  ✅ Instant alerts via Gmail and/or Slack
  ✅ Google Sheets as an easily accessible backend
  ✅ Modular design — easy to expand with CRM tools (like HubSpot)
  ✅ Clean JSON structure and logic, beginner-friendly

  📈 Possible Improvements

  Add email validation via external API (e.g., NeverBounce, Hunter)
  Integrate with a CRM for deeper automation
  Add lead scoring based on answers
  Include automatic follow-up emails after X days
  Schedule weekly summary reports via email

  🧑🏻‍💻 Creator Information
  Developed by: Adem Tasin
   Adem T.

  🌐 Website: Dataplusminus+-
  📧 Email:dataplusminuss@gmail.com
   💼 LinkedIn: Adem Tasin

nodes_used:
  ["n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.slack","n8n-nodes-base.gmail","n8n-nodes-base.hubspot","n8n-nodes-base.if","n8n-nodes-base.noOp","n8n-nodes-base.gmail","n8n-nodes-base.wait","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:
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metadata_json:
  {"categories":["automation","integration","lead management","notifications","CRM integration"],"complexity":"medium","use_cases":["Capture leads from web forms","Automatically store leads in a central sheet","Send instant team notifications (Gmail/Slack)","Filter duplicates and incomplete entries","Trigger follow-ups and reminders / time-based actions"],"estimated_setup_minutes":30,"required_services":["Google Forms / any web form","Google Sheets","n8n","Gmail (for email notifications)","Slack (optional)","HubSpot (optional CRM integration)"],"key_features":["No-code n8n workflow to collect and route leads","Stores leads with timestamps in Google Sheets","Automated notifications via Gmail and Slack","Conditional checks to filter duplicates and incomplete entries","Date & Time based reminder scheduling for follow-up actions (reminders)"],"target_audience":["Freelancers","Small teams","Sales teams / SDRs / Account executives looking to capture leads quickly from forms into a simple pipeline"]}


name: Collect LinkedIn Profiles with AI Processing using SerpAPI, OpenAI, and NocoDB
author_name: Askan
author_username: askans
categories: []
created_at: 2025-05-07T12:26:15.551Z
updated_at: 2025-05-07T12:26:15.551Z
url: https://n8n.io/workflows/3920
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  What problem does this solve? 

  It fetches LinkedIn profiles for a multitude of purposes based on a keyword and location via Google search and stores them in an Excel file for download and in a NocoDB database.
  It tries to avoid using costly services and should be n8n beginner friendly.
  It uses the serpapi.com to avoid being blocked by Google Search and to process the data in an easier way.

  What does it do?

  Based on criteria input, it searches LinkedIn profiles
  It discards unnecessary data and turns the follower count into a real number
  The output is provided as an Excel table for download and in a NocoDB database

  How does it do it?

  Based on criteria input, it uses serpAPI.com to conduct Google search of the respective LinkedI profiles
  With OpenAI.com the name of the respective company is being added
  With OpenAI.com the follower number e.g., 300+ is turned into a real number: 300
  All unnecessary metadata is being discarded
  As an output an Excel file is being created
  The output is stored in a nocodb.com table

  Step-by-step instruction


  Import the Workflow:
  Copy the workflow JSON from the "Template Code" section below.
  Import it into n8n via "Import from File" or "Import from URL".

  Set up a free account at serpapi.com and get API credentials to enable good Google search results

  Set up an API account at openai.com and get API key

  Set up a nocodb.com account (or self-host) and get the API credentials

  Create the credentials for serpapi.com, opemnai.com and nocodb.com in n8n.

  Set up a table in NocoDB with the fields indicated in the note above the NocoDB node

  Follow the instructions as detailed in the notes above individual nodes 

  When the workflow is finished, open the Excel node and click download if you need the Excel file

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.convertToFile","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.merge","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.nocoDb","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6528 characters]

metadata_json:
  {"categories":["data extraction","integration","automation","data enrichment"],"complexity":"medium","use_cases":["lead generation / prospecting","recruiter sourcing / talent discovery","market and competitor research","contact list building for outreach","data enrichment and normalization"],"estimated_setup_minutes":30,"required_services":["SerpAPI (serpapi.com) for Google searches","OpenAI (openai.com) for text parsing/enrichment","NocoDB (nocodb.com) for storing results","n8n (for workflow execution) — Excel export uses n8n convertToFile node"],"key_features":["Searches LinkedIn profiles via Google (SerpAPI) based on keyword + location","Cleans and discards unnecessary metadata","Uses OpenAI to infer company names and convert fuzzy follower counts to numeric values","Outputs results as downloadable Excel file","Stores structured results in a NocoDB table for further use/automation"],"target_audience":["marketers and demand generation teams","sales and SDR teams","recruiters and talent sourcers"]}


name: Upload Large Files to Kommo/AmoCRM with Automatic File Chunking
author_name: yatolstoy
author_username: yatolstoy
categories: []
created_at: 2025-05-07T15:10:30.214Z
updated_at: 2025-05-07T15:10:30.214Z
url: https://n8n.io/workflows/3922
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Upload files from any source to your account Kommo or AmoCRM with a simple and reusable workflow.
  Advantages of workflow
  It can split a large file into small ones and upload chunks.
  Works for Kommo and amoCRM
  There are 3 required parameters in total (drive_url, file_name and file_base64)
  Where it can be useful
  To send files in a message to clients chat in Kommo or amoCRM
  To upload the generated files to the transaction or contact fields
  To upload files to notes
  How it works
  Checking the file availability
  Opening the session to download the file
  Split the file into chunks, if necessary.
  Send parts of the file to CRM one at a time.
  Installation Steps
  Import workflow
  Configuring Oauth2 credentials in Kommo or amoCRM
  Applying credentials for specific workflow steps
  Specifying the link to the file and the disk_url address
  Launching a workflow to upload a file to a Kommo or amoCRM account

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.stopAndError","n8n-nodes-base.code","n8n-nodes-base.limit","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.splitInBatches","n8n-nodes-base.convertToFile","n8n-nodes-base.if","n8n-nodes-base.stopAndError","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.extractFromFile","n8n-nodes-base.httpRequest","n8n-nodes-base.executeWorkflow","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.convertToFile","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["integration","automation","file processing","CRM"],"complexity":"medium","use_cases":["Upload large files to Kommo/AmoCRM records (contacts, deals/transactions)","Send large files in client chat messages in Kommo/AmoCRM","Attach generated files to notes or custom fields","Upload files from any HTTP/drive source into CRM with automatic chunking"],"estimated_setup_minutes":15,"required_services":["Kommo / AmoCRM (OAuth2)","Source file hosting accessible by URL (HTTP/drive — e.g., Google Drive, S3, public file URL)","n8n instance with HTTP access"],"key_features":["Automatic splitting of large files into CRM-supported chunk sizes","Sequential upload of file parts to CRM via API","Works for both Kommo and AmoCRM accounts","Checks file availability and opens download session before upload","Reusable workflow accepting drive_url, file_name, file_base64 parameters"],"target_audience":["Developers / Integrators","CRM administrators","Sales/Marketing operations teams who manage CRM assets"]}


name: Sync HubSpot Contacts with LinkedIn URLs, Detect Changes & Send Gmail Alerts
author_name: PollupAI
author_username: zeerobug
categories: []
created_at: 2025-05-07T15:29:02.022Z
updated_at: 2025-05-07T15:29:02.022Z
url: https://n8n.io/workflows/3923
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Automate CSV imports into HubSpot without the mess. Powered by n8n. Supercharged by Pollup AI.
  Who’s this for?
  This workflow is perfect for Ops-minded folks in Customer Success, Marketing, and Sales – especially if you’re juggling CSV lists, CRM updates, and team-wide reporting. Whether you're a CS ops lead syncing product usage, or a RevOps manager updating contact records, this will save you hours.

  🧠 Why it matters
  Manually importing CSVs into HubSpot is a headache:
   ❌ Copy-paste errors
   ❌ Mismatched fields
   ❌ Wasted time formatting every upload
  This workflow does the heavy lifting for you:
   ✅ Pulls fields dynamically from your HubSpot instance
   ✅ Preps and maps CSV data in minutes
   ✅ Syncs with Google Sheets to keep your source of truth aligned

  🔄 What the workflow actually does
  Pulls live field names from HubSpot’s API (no guesswork)
  Indexes and organizes your CSV for import
  Processes & transforms raw data (optional filters, cleanup, etc.)
  Dynamically maps CSV fields to HubSpot fields
  Executes the upload and logs results

  ⚙️ How to get started
  Prerequisites:
  HubSpot private app token
  Google Sheets API credentials
  Access to an n8n instance (cloud or self-hosted)

  Setup in 3 steps:
  Import the JSON into your n8n
  Connect your HubSpot and Sheets accounts
  Upload a CSV through the built-in form — you’re live 🎉

  ✏️ Make it yours
  Add filters** (e.g., remove duplicates, format fields)
  Add more HubSpot objects** to match your use case
  Trigger automation** via webhook, schedule, or custom input

  Works beautifully with other Pollup AI flows – so you can scale CS ops with less stress!

  About Pollup AI
  Pollup builds hands-on automations for B2B communication teams. This n8n workflow is one of many tools helping lean CS teams deliver high-touch outcomes at scale.

  Find out more at pollup.ai 

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.executeWorkflow","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.executeWorkflow","n8n-nodes-base.gmail","n8n-nodes-base.httpRequest","n8n-nodes-base.noOp","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.filter","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.splitOut","n8n-nodes-base.filter","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":["integration","automation","data synchronization","CRM management"],"complexity":"medium","use_cases":["Automate CSV imports into HubSpot","Sync and enrich contacts with LinkedIn URLs","Detect contact changes and notify teams via email","Maintain Google Sheets as source-of-truth for contacts","RevOps/CS ops reporting and audit logs"],"estimated_setup_minutes":20,"required_services":["HubSpot API (private app token)","Google Sheets API/Google account","Gmail API (for alert emails)","n8n instance (cloud or self-hosted)","Optional: Pollup AI / Pollup integrations"],"key_features":["Dynamically pulls HubSpot field schema","Parses, indexes and maps CSV fields to HubSpot","Data transformation and optional filtering (dedupe, formatting)","Syncs source-of-truth to Google Sheets","Detects changes and logs results/updates status fields/alerts via Gmail notifications (change detection)"],"target_audience":["Customer Success operations","Marketing operations","Sales operations / RevOps managers / Ops-minded teams"]}


name: Wix Chat Auto-Responder with OpenAI GPT and Email Fallback
author_name: Ryan McMahan
author_username: owner232
categories: []
created_at: 2025-05-07T16:06:00.304Z
updated_at: 2025-05-07T16:06:00.304Z
url: https://n8n.io/workflows/3925
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Wix Chat Auto-Responder with OpenAI GPT and Email Fallback

  This template connects Wix Chat with OpenAI via n8n to automate intelligent customer responses on your website only when no human has responded recently. It uses smart throttling, checks for member vs. anonymous visitors, pulls chat history, and optionally alerts support staff via email if AI shouldn't reply.

  Perfect for solopreneurs, agencies, or customer support teams looking to auto-handle conversations while maintaining a fallback system.

  Key Features

  🔌 Connects Wix Chat API to n8n seamlessly
  🧠 Uses GPT-4 (via OpenAI) to auto-generate replies
  🧍‍♂️ Detects member vs. guest via webhook payload
  📬 Optional email alert to support staff if AI should not reply
  ⏱️ Only responds if a human hasn't answered in the past 12 hours
  ✂️ Splits long AI messages into chunks to fit Wix API constraints
  🧱 Modular & customizable—adapt for CRM, Slack, SMS, etc.

  Requirements

  Published Wix site with Wix Inbox enabled**
  Wix Developer App credentials** (Client ID, Secret, Instance ID)
  OpenAI API Key**
  Active n8n instance** (self-hosted or cloud)
  Working SMTP credentials** (for fallback email alert)

  Setup Instructions

  1. Import the Workflow

  Download and import the .json file into your n8n instance. All nodes should appear in the visual editor.

  2. Rename Key Nodes (Recommended for Clarity)

  Rename the following nodes for easier maintenance:
  If → Check Member vs Visitor
  Execute Workflow → Generate OAuth Token
  Send Email → Alert Technician via Email

  3. Configure OAuth for Wix API

  In the node HTTP Request1, replace the placeholder values:
  "client_id": "YOUR_WIX_APP_ID",
  "client_secret": "YOUR_WIX_APP_SECRET",
  "instance_id": "YOUR_WIX_INSTANCE_ID"

  💡 You may move this to a separate workflow and call it using Execute Workflow.

  4. Set Up the Webhook in Wix

  Copy the Webhook URL from the Webhook node.
  Go to your Wix dashboard:
     Navigate to: Settings &gt; Automations &gt; Create New
     Trigger: "When someone sends a message via chat"
     Action: "Send a Webhook"
  Paste the n8n Webhook URL in the configuration.

  5. Add Your OpenAI API Key

  Open both OpenAI Chat Model1 and OpenAI Chat Model2 nodes:
  Add your OpenAI credentials.
  Adjust the model (e.g., GPT-4 or GPT-4o) and temperature as needed.

  6. Customize Response Logic (Optional)

  In the Code node labeled Response Throttle, modify:
  const allowChat = true;
  const allowEmail = false;
  const humanResponseTimeWindow = 43200000; // 12 hours in ms

  Change values to:
  Disable chat or email
  Modify the delay window before AI is allowed to respond

  7. Set Up Email Fallback (Optional)

  If human support is required, the workflow sends an email:
  Add SMTP credentials in the Send Email node
  Customize the message, or replace the node with Slack, webhook, or SMS alert

  8. Test

  Open your Wix site in an incognito browser tab.
  Use the chat and monitor the bot's response.
  Check Executions in n8n to debug or verify the flow.

  How to Customize

  Trigger follow-up actions** (e.g., Zapier, CRM sync) after AI response
  Customize GPT prompt** via AI Agent nodes
  Prevent replies by channel** (e.g., don’t reply via email)
  Add filters** for keywords, lead scoring, or VIP contacts with If or Code nodes

  Includes

  ✅ 1 Workflow JSON file
  🔐 OAuth2 logic (can be modularized into a separate workflow)
  📝 Sticky notes and comments to guide usage
  ⚙️ Production-ready, extensible logic for any support stack

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.httpRequest","n8n-nodes-base.executeWorkflow","n8n-nodes-base.emailSendTool","n8n-nodes-base.if","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.noOp","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.noOp","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.emailSendTool","n8n-nodes-base.httpRequest","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","customer support","integration"],"complexity":"medium","use_cases":["Automated website chat responses using GPT","Triage and escalate conversations to human support via email","Prevent AI reply if human responded recently (throttling)","Enrich workflows by routing messages to CRM/Slack/SMS after AI reply"],"estimated_setup_minutes":30,"required_services":["Wix site with Wix Inbox (Wix Chat)","Wix Developer App credentials (Client ID, Secret, Instance ID)","OpenAI API Key (GPT-4/GPT-4o)","n8n instance (self-hosted or cloud)","SMTP credentials (or other alert channel like Slack/webhook)"],"key_features":["Wix Chat ↔ n8n integration via webhook and OAuth","GPT-4 powered auto-generated replies","Detects member vs. anonymous visitor from webhook payload","Optional email alert fallback to support staff","Human-response throttle (default: 12 hours) to avoid double-replies by AI when a human already responded','Splits long AI messages to fit Wix API limits','Modular design — OAuth/token fetch can be separated into its own workflow and nodes for CRM/Slack/SMS can be added"],"target_audience":["Solopreneurs","Agencies","Customer support teams/operators (SMB) who want partial automation while preserving human fallback options (developers/admins)"]}


name: Voice-to-Email Response System with Telegram, OpenAI Whisper & Gmail
author_name: Adam Janes
author_username: adamjanes
categories: []
created_at: 2025-05-08T04:01:50.996Z
updated_at: 2025-05-08T04:01:50.996Z
url: https://n8n.io/workflows/3930
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  This workflow gives you the ability to reply to a long email with a voice note, rather than having to type everything out. 

  ChatGPT will format your audio response and create an email draft for you.

  How it works
  When a new email arrives in your inbox, the workflow checks if it needs a response, and it it does, it sends a message to you on Telegram via a VoiceEmailer bot.

  When you reply to that message with an audio message, the second part of this workflow is triggered. It checks if the message is in the right format, transcribes the audio, and creates a draft response that shows up in the same email thread.

  Set up steps
  Add your credentials for Gmail and OpenAI
  Create an Telegram bot following the instructions here.
  Connect your telegram credentials so the workflow will use your bot.
  Turn on the workflow, and message the bot from your telegram. Find the Chat ID from the Executions tab of your workflow, and enter it in as a variable.

nodes_used:
  ["@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.gmailTrigger","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.telegramTrigger","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.gmail","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 10108 characters]

metadata_json:
  {"categories":["automation","communication","integration","AI-assisted"],"complexity":"medium","use_cases":["Reply to email using voice notes instead of typing","Create AI-formatted email drafts from transcribed audio","Mobile or hands-free email responses via Telegram","Accessibility-friendly email composition for users with limited typing ability"],"estimated_setup_minutes":20,"required_services":["Gmail (Google account)","Telegram Bot API (Telegram bot)","OpenAI (Whisper for transcription and ChatGPT for drafting)"],"key_features":["Monitors inbox and detects emails that need a reply","Sends Telegram notification via a VoiceEmailer bot","Accepts Telegram voice messages and validates format","Uses OpenAI Whisper to transcribe audio","Uses ChatGPT to format and generate an email draft in the same thread (creates Gmail draft)"],"target_audience":["knowledge workers and professionals who handle many emails","mobile-first users who prefer voice input","people requiring accessibility-friendly email options (limited typing)`,`n8n users and developers deploying automated integrations"]}


name: Auto-Post Medium.com Articles to LinkedIn with Telegram Alerts
author_name: Krishna Kumar Eswaran
author_username: mrprogrammer
categories: []
created_at: 2025-05-08T06:38:45.420Z
updated_at: 2025-05-08T06:38:45.420Z
url: https://n8n.io/workflows/3931
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  🧠 Problem This Solves
  Manually sharing Medium articles to LinkedIn daily can be repetitive and time-consuming. This automation:

  Fetches the latest Medium articles based on a tag (e.g., android)

  Posts them on LinkedIn twice daily

  Uses Airtable to prevent duplicates

  Sends a confirmation to Telegram once posted

  Stay consistently active on LinkedIn without lifting a finger.

  👥 Who This Template Is For
  Developers who write or follow Medium content

  Tech creators or founders looking to grow an audience

  Community or page managers needing regular curated posts

  Busy professionals who want hands-free LinkedIn engagement

  ⚙️ Workflow Breakdown
  This automation runs at 9:00 AM and 7:00 PM daily and performs these steps:

  Fetch articles from MediumAPI.com by tag

  Check Airtable to prevent reposting the same article

  Post on LinkedIn if it’s new

  Store the article ID in Airtable

  Send a Telegram message after successful posting

  🧾 Step-by-Step Setup Instructions

  ✅ 1. Airtable Configuration
  Create a base with:

  Table Name: PostedArticles

  Column: ArticleID (Single line text – to track posted articles)

  🔗 2. MediumAPI Setup
  Go to https://mediumapi.com

  Sign up and generate your API key from the dashboard

  Use this API endpoint in an HTTP node:

  GET https://mediumapi.com/api/tag/YOUR_TAG/latest

  Headers:
    Authorization: Bearer YOUR_API_KEY
  Replace YOUR_TAG with a topic like android, ai, webdev, etc.

  💬 3. Telegram Bot Setup
  Go to @BotFather and create a new bot

  Save the bot token

  Use @userinfobot to get your Telegram chat ID

  Add a Telegram node in n8n with the token + chat ID

  🔗 4. LinkedIn Setup
  Create a LinkedIn Developer App

  Connect it via OAuth2 in n8n

  Choose to post on your profile or company page

  🧱 5. n8n Workflow Structure
  Node Type	Description
  Cron	Triggers the flow twice a day
  HTTP Request	Fetches articles from MediumAPI.com
  Airtable Search	Checks if article ID already exists
  IF Node	Skips duplicates
  LinkedIn Post	Publishes to your LinkedIn profile/page
  Airtable Create	Stores posted article ID
  Telegram Node	Sends success notification

  🛠️ Customization Tips
  Change the tag in the API URL to match your niche

  Add hashtags or personal comments to the LinkedIn message

  Schedule different posting times in the Cron node

  Filter Medium posts based on length or title keywords (optional)

nodes_used:
  ["n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.code","n8n-nodes-base.filter","n8n-nodes-base.code","n8n-nodes-base.telegram","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.linkedIn","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","social media","content distribution","integration"],"complexity":"medium","use_cases":["Automatically share latest Medium articles to LinkedIn","Maintain consistent LinkedIn activity without manual posting","Curate and repost niche/tagged Medium content","Avoid duplicate posts using a datastore","Receive confirmation alerts in Telegram"],"estimated_setup_minutes":45,"required_services":["n8n","MediumAPI (https://mediumapi.com)","Airtable","LinkedIn Developer App (OAuth)","Telegram Bot API"],"key_features":["Scheduled twice-daily fetch and post","Fetch latest Medium articles by tag via API","Duplicate prevention using Airtable (ArticleID tracking)","Post to LinkedIn profile or company page via OAuth","Send Telegram confirmation after successful post"],"target_audience":["Developers who publish or follow Medium content","Tech creators and founders","Community or page managers handling social presence`,`Busy professionals wanting hands-free LinkedIn posting"]}


name: Track Personal Finances with GPT-4 via Telegram & Google Sheets
author_name: Md. Nazmul Islam
author_username: pomegai
categories: []
created_at: 2025-05-08T07:20:03.997Z
updated_at: 2025-05-08T07:20:03.997Z
url: https://n8n.io/workflows/3932
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Track your expenses, gain insights, and manage money smarter – right from Telegram.
  Who is this For
  This workflow is perfect for individuals and freelancers who want financial control without app overload. Launch it in your n8n environment and start tracking your money smarter today!

  Features
  Natural Language Expense Input: Log expenses by simply messaging “Spent $50 on groceries”.
  Income Logging: Add income sources such as salary, freelance, or passive income.
  Voice Support: Speak to the bot—Whisper API converts voice to text.
  Google Sheets Integration: All data is synced to a spreadsheet in real time.

  How It Works
  User Input (Text or Voice):
   Send messages on your telegram bot like:
  	“Spent $25 on transportation”
  	“Received $300 from freelancing”
  	Or just voice record a message
  AI Parsing & Categorization:
  	GPT-4 processes and categorizes the entry using pre-trained financial logic.
  Data Logging:
   	n8n stores data in: Google Sheets (via Google Sheets API)
  Video Demo:
  	See this youtube Video to explore "how it works".

  Set Up Steps
  Import the Workflow
  	Create a new workflow
  	Import the JSON file by clicking "three dot" (upper right corner) &gt; "import from file..." (I will provide the JSON file after buying).
  Set Up Telegram Bot 
     	Create a bot via @BotFather
     	Create a telegram credential to connect telegram with n8n.
     	Grab the token from @BotFather and paste into the Telegram credential.
     	Connect that credential with telegram nodes.
  Enable Google Sheets Integration
  	Create a google sheet credential to connect google sheet with n8n.
  	Connect that credential with Google sheets tools.
  	Create a google sheet including these columns: "chat_id", "date_and_time", "income (dollar)", "income source", "cost (dollar)", "cost category"		
  	Provide access that sheet to n8n.															
  Configure OpenAI (GPT & Whisper)
  	Create an OpenAi credential to connect openai with n8n.
  	Add your OpenAI API Key in the credentials tab
  	Connect that credential with openai nodes.
  Pre-requisites
  	Necessary credentials:
  		Telegram Account
  		OpenAi Account
  		Google Sheets Account
  	N8N version 1.92.2 or upper

  Customization Guidance
  You can customize the ai agent prompt based on your needs.
  And also you can customize the sheet's columns based on your needs.
  And you can add additional features like:
  	Monthly budgeting alert
  	Expense report generation
  	Smart Recommendations for cost cutting tips
  Ready to Automate Your Finances?
  Buy the workflow for only 30$, You will get full setup guide after buying.

nodes_used:
  ["n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.telegram","n8n-nodes-base.set","n8n-nodes-base.telegram","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","personal finance","chatbot","voice input"],"complexity":"medium","use_cases":["Log expenses via Telegram chat","Log income and track sources","Voice-to-text expense entry (Whisper)","Automatic AI parsing and categorization of transactions","Real-time sync to Google Sheets for reporting/analysis"],"estimated_setup_minutes":30,"required_services":["Telegram Bot (BotFather)","OpenAI (GPT-4 and Whisper)","Google Sheets (Google account & API access)","n8n (v1.92.2 or higher)"],"key_features":["Natural-language expense and income input via Telegram","Voice support using Whisper for speech-to-text","GPT-4 parses and categorizes entries","Saves transactions to Google Sheets in real time","Configurable prompts and sheet columns for customization','Supports additional features like budgeting alerts and reports"],"target_audience":["Individuals","Freelancers","Solopreneurs and independent contractors','Users who prefer messaging-based financial tracking"]}


name: Generate Lessons Learned Reports from Jira Epics with AI and Google Docs
author_name: Tarek Mustafa
author_username: tarekadam
categories: []
created_at: 2025-05-08T07:35:46.686Z
updated_at: 2025-05-08T07:35:46.686Z
url: https://n8n.io/workflows/3934
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  Who is this for?
  Jira users who want to automate the generation of a Lessons Learned or Retrospective report after an Epic is Done.

  What problem is this workflow solving? / use case
  Lessons Learned / Retrospective reports are often omitted in Agile teams because they take time to write. With the use of n8n and AI this process can be automated.

  What is this workflow doing
  Triggers automatically upon an Epic reaching the "Done" status in Jira.
  Collects all related tasks and comments associated with the completed Epic.
  Intelligently filters the gathered data to provide the LLM with the most relevant information.
  Utilizes an LLM with a structured System Message to generate insightful reports.
  Delivers the finalized report directly to your specified Google Docs document.

  Setup
  Create a Jira API key and follow the Credentials Setup in the Jira trigger node.
  Create credentials for Google Docs and paste your document ID into the Node.

  How to customize this workflow to your needs
  Change the System Message in the AI Agent to fit your needs.

nodes_used:
  ["n8n-nodes-base.jira","n8n-nodes-base.jira","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.googleDocs","n8n-nodes-base.jiraTrigger","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","knowledge management","AI-generated content"],"complexity":"medium","use_cases":["Automatically generate Lessons Learned/Retrospective reports when a Jira Epic is completed","Aggregate and summarize related tasks and comments for post‑mortem documentation","Save standardized retrospective reports into Google Docs for team sharing and archiving","Reduce manual reporting overhead and improve team learning capture","Customize AI System Prompts to enforce company templates or tone"],"estimated_setup_minutes":20,"required_services":["Jira (API access)","Google Docs / Google Drive API credentials","OpenAI or compatible LLM API (lmChat)","n8n instance with credentials configured"],"key_features":["Triggers on Jira Epic status change to Done","Collects and filters related issues and comments","Uses an LLM with a structured system message to produce a focused report","Summarizes and formats output then writes directly to a Google Docs document","Includes conditional logic, memory buffer and summarization nodes to refine input for the LLM"],"target_audience":["Agile teams and Scrum Masters","Project/Product Managers","Engineering managers and team leads performing retrospectives or post‑mortems`,`n8n automation builders and DevOps engineers"]}


name: AI Marketing Agent for Lead Generation: Reddit + OpenRouter + Gmail
author_name: Ari Nakos
author_username: just-aristides
categories: []
created_at: 2025-05-08T09:13:21.340Z
updated_at: 2025-05-08T09:13:21.340Z
url: https://n8n.io/workflows/3935
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  What it is
  This n8n workflow monitors Reddit for posts relevant to a specific business or industry, identifies potential leads, and delivers them directly to your inbox.

  Full tutorial (https://findleads.agency/blog/n8n-ai-agent-for-lead-generation-using-reddit-openai-gmail)  for those wanting to build it themselves.

  How It Works
  A user submits their website URL and email through a form
  The workflow analyzes the website to determine the industry and extract relevant keywords
  It searches Reddit for posts containing those keywords and filters them based on custom engagement metrics (i.e. upvotes &gt; 15, non-empty text content, posted within the last 90 days)
  An AI agent (using OpenRouter's GPT-4.1-mini) analyzes each post to determine relevance and summarizes the key points
  Relevant posts are stored in Google Sheets and formatted into a professional HTML email
  The email is sent to the user's provided email address with a summary of potential leads

  Setup
  To run this workflow, you need to set up credentials in n8n for:

  Reddit: Uses OAuth 2.0. Requires creating an app on Reddit to get a Client ID & Secret. (YT Tutorial for Reddit App Creation: https://youtu.be/zlGXtW4LAK8)
  OpenRouter: Uses an API Key. Generate this key directly from your OpenRouter account settings. (YT Tutorial: https://youtu.be/Cq5Y3zpEhlc)
  Google Sheets: Recommend OAuth2 (just connect by authenticating) or setup in Google Cloud Console (enable Sheets API, create OAuth Client ID with n8n redirect URI) to get a Client ID & Secret.
  Gmail: Uses OAuth 2.0. Requires setup in Google Cloud Console to enable Gmail API and create OAuth credentials.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenRouter","n8n-nodes-base.httpRequestTool","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.splitOut","n8n-nodes-base.reddit","n8n-nodes-base.code","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.removeDuplicates","n8n-nodes-base.formTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.gmail"]

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metadata_json:
  {"categories":["automation","integration","lead generation","social listening"],"complexity":"medium","use_cases":["Automated lead generation from Reddit","Social listening for industry-relevant posts","Marketing outreach and prospect discovery","Content/market research to find potential opportunities"],"estimated_setup_minutes":60,"required_services":["Reddit (OAuth app)","OpenRouter (API key for GPT-4.1-mini)","Google Sheets (OAuth / API)","Gmail (OAuth / Gmail API)","A form or webhook endpoint to collect user URL and email","Target website accessible for content analysis"],"key_features":["Extracts industry and keywords from a submitted website","Searches and filters Reddit posts by keywords, upvotes, recency, and content","AI agent scores relevance and summarizes posts (OpenRouter GPT-4.1-mini)","Stores relevant leads in Google Sheets","Formats and sends professional HTML email summaries via Gmail"],"target_audience":["marketers","lead generation agencies","growth hackers / demand gen teams"]}


name: YouTube Comment Sentiment Analysis with Google Gemini AI and Google Sheets
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-05-08T09:43:30.770Z
updated_at: 2025-05-08T09:43:30.770Z
url: https://n8n.io/workflows/3936
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow automatically collects all comments from a specified YouTube video and analyzes the sentiment of each comment using an AI model (e.g., GPT, Claude or Gemini). The sentiment (Positive, Neutral, or Negative), its strength, and confidence score are extracted and saved into a connected Google Sheet for easy access, reporting, and visualization.

  Advantages:

  🧠 AI-Powered Sentiment Analysis

     Uses modern language models (LLMs) to categorize comments with high accuracy.

  📺 Ideal for YouTube Creators & Marketers

     Provides insights into audience perception of videos, campaigns, or products.

  📈 Real-Time Feedback Monitoring

     Quickly identify trends in viewer sentiment across large comment volumes.

  📊 Automatic Reporting

     Saves results in Google Sheets for easy sharing or dashboard integration.

  🔁 Handles Pagination

     Automatically fetches all comments, even from multi-page videos.

  ⚙️ No-Code Customization

     Easily adaptable to other platforms (e.g., TikTok, Instagram) or data sources.

  📥 Simple Setup

     Requires just a YouTube video ID and API key — no coding needed.

  🔁 Loop and Update Logic

     Continuously updates sheet with new results, avoiding duplicate processing.

  🧩 Modular Design

     Easy to expand (e.g., reply classification, toxic comment detection, translation).

  💬 Multi-Language Compatibility

  AI can be configured to analyze comments in different languages with minimal setup.

  How It Works  
  Trigger: The workflow starts manually ("When clicking ‘Test workflow’") or can be scheduled.  
  Fetch Comments: The "Get API Comments" node retrieves comments from a YouTube video using the YouTube API.  
  Process Comments:  
     Extracts comments and replies via the "Comments" node.  
     Splits them into individual entries ("Split comments").  
     Saves raw comments to Google Sheets ("Save comments").  
  Sentiment Analysis:  
     Uses Google Gemini AI (or another model) to classify each comment as Positive, Neutral, or Negative.  
     Captures strength and confidence metrics for deeper insights.  
  Update Results: The "Update sentiment" node writes the analysis back to Google Sheets, marking processed rows.  
  Pagination Handling: Checks for multiple pages of comments ("Multipage?") and loops until all are processed.  

  Set Up Steps  
  Prepare Google Sheet:  
     Clone the template: YouTube Comments Sheet.  
     Ensure columns exist: VIDEO_ID, COMMENTS, SENTIMENT, STRENGTH, CONFIDENCE, and DO (tracking column).  

  Configure YouTube API:  
     Obtain a YouTube API key from Google Developers Console.  
     Add it to the "Get API Comments" node under Youtube Query Auth (parameter: key).  

  Set Video ID:  
     Replace the default xxxxxxxx in the "ID Video" node with your target YouTube video ID.  

  AI Integration:  
     Ensure Google Gemini API credentials are configured in the "Google Gemini" node.  

  Run the Workflow:  
     Test manually or automate execution (e.g., hourly/daily) to analyze new comments.  

  Output: A Google Sheet with categorized sentiments, enabling trend analysis and audience engagement tracking.  

  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin. 

nodes_used:
  ["n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.sentimentAnalysis","n8n-nodes-base.code","n8n-nodes-base.splitOut","n8n-nodes-base.googleSheets","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.splitInBatches","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote","n8n-nodes-base.quickChart"]

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metadata_json:
  {"categories":["automation","integration","data processing","analytics","ai"],"complexity":"medium","use_cases":["YouTube comment sentiment tracking","Audience perception analysis","Brand monitoring","Content and campaign optimization","Community moderation and toxic content detection"],"estimated_setup_minutes":20,"required_services":["YouTube Data API (API key)","Google Gemini / PaLM API (LLM credentials) or alternative LLM","Google Sheets (Google account and destination sheet)","n8n (cloud or self‑hosted)"],"key_features":["Fetches all YouTube comments with pagination handling","AI-powered sentiment classification (Positive/Neutral/Negative) with strength and confidence scores","Writes raw comments and analysis back to Google Sheets","Deduplication / processed-row tracking to avoid reprocessing","Batch splitting and loop logic for large volumes of comments','Supports manual test trigger or scheduled runs','Modular design—easy to extend (translation, toxicity, reply classification)"],"target_audience":["YouTube creators","Social media managers","Marketing teams and growth marketers`,`Community moderators`,`Data analysts and researchers`,`n8n users / automation builders"]}


name: Send Personalized WhatsApp Templates Triggered by KlickTipp with Auto-Responses
author_name: KlickTipp
author_username: KlickTipp
categories: []
created_at: 2025-05-08T10:59:08.584Z
updated_at: 2025-05-08T10:59:08.584Z
url: https://n8n.io/workflows/3937
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Community Node Disclaimer: This workflow uses KlickTipp community nodes.

  How It Works

  This workflow automates personalized WhatsApp message template delivery triggered by events in KlickTipp or by messages sent to the Whatsapp Business account. When a contact triggers an Outbound, the workflow uses a pre-approved WhatsApp message template to send dynamic, real-time messages through the WhatsApp Business Cloud API. When receiving messages it checks whether a cancellation should be processed or if a auto-response is sent.

  This setup is ideal for time-sensitive campaigns such as:
  Birthday greetings
  Discount or promo notifications
  Follow-ups on product or service interest

  Key Features

  KlickTipp Trigger
  Starts the workflow when a specific outbound is triggered
  Typical use case: subcriber receives activation Tag and triggers an Outbound which sends a webhook call to trigger WhatsApp messaging.

  WhatsApp Business Cloud - Message Trigger
  Listens to messages from the contact and processes answers with answering auto-responder or by tagging the contact in KlickTipp.

  WhatsApp Business Cloud - Sending Template Messages
  Sends  WhatsApp message templates using a pre-approved template.
  Template placeholders are filled with data from KlickTipp custom fields.

  Setup Instructions

  Set up the KlickTipp and Whatsapp nodes in your n8n instance.
  Authenticate your WhatsApp and KlickTipp accounts.
  Create the necessary custom fields to match the data structure
  Verify and customize field assignments in the workflow to align with your specific form and subscriber list setup.

  | Field Label                 | Field Type  |
  |-----------------------------|-------------|
  | Whatsapp \| Product/service | Single line |
  | Whatsapp \| URL             | URL         |
  | Whatsapp \| Name/company    | Single line |
  | Whatsapp \| URL ending      | Single line |

  Testing & Deployment

  Use a real test contact with all required fields filled.
  Trigger the Outbound in KlickTipp using the activation tag and answer with a message to the template.
  Run the scenario once in n8n to verify successful delivery of the whatsapp message template to your test contact as well as the reception of the auto-responder and the subscription and tagging in KlickTipp to stop further messages.

  Campaign Expansion Ideas

  Connect campaign to process keywords like "STOP" from WhatsApp messages
  Pair WhatsApp with welcome email series for onboarding.
  Use tags like product_interest_X for precise segmentation.
  A/B test templates with different CTA formats or timings.
  Monitor CTRs via dynamic URLs in WhatsApp templates.

  Benefits

  Multi-channel engagement:** Adds WhatsApp to your marketing toolkit.
  Dynamic content:** Personalizes messages using contact data.
  KlickTipp campaign control** Whatsapp contacts can for example signal with messages like "STOP" to receive the according Tag in KlickTipp in order to start/end automations.

  &gt; 💡 Pro Tip: Customize the domain link ending per campaign or product line. This allows targeted redirects, e.g., meinshop.de/ProduktA or `mein

  Ressources:
  Send WhatsApp Templates with KlickTipp
  Use KlickTipp Community Node in n8n
  Automate Workflows: KlickTipp Integration in n8n

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.whatsAppTrigger","n8n-nodes-base.switch","CUSTOM.klicktippTrigger","n8n-nodes-base.whatsApp","n8n-nodes-base.whatsApp","CUSTOM.klicktipp","n8n-nodes-base.filter","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","marketing","customer engagement","messaging"],"complexity":"medium","use_cases":["Time-sensitive campaigns (birthdays, promos, limited offers)","Personalized WhatsApp template delivery triggered from KlickTipp","Automated follow-ups and lead nurturing","Auto-responses and opt-out handling (e.g., STOP)","Tag-based subscription control and segmentation"],"estimated_setup_minutes":45,"required_services":["n8n instance (with KlickTipp community node)","KlickTipp account (with outbound/webhook capability and custom fields)","WhatsApp Business Cloud API (Meta) and pre-approved message templates","Hosting/endpoint for webhooks (if not using n8n cloud)"],"key_features":["KlickTipp-triggered workflow start (outbound activation tag)","Send pre-approved WhatsApp message templates with dynamic placeholders","Listen to inbound WhatsApp messages and send auto-responses","Process keywords (e.g., STOP) and apply KlickTipp tags to control subscriptions","Field mapping from KlickTipp custom fields to template placeholders (URL, product, name/company, URL ending)"],"target_audience":["Marketers running personalized messaging campaigns","E‑commerce and small business owners","Growth teams and customer success managers for timely outreach and opt-out handling"]}


name: Jira MCP Server
author_name: Tamer
author_username: tamerelzein
categories: []
created_at: 2025-05-08T12:10:18.039Z
updated_at: 2025-05-08T12:10:18.039Z
url: https://n8n.io/workflows/3939
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  What it does
  This n8n workflow creates a powerful AI-powered Jira management system that allows you to use Claude or other AI assistants to create, update, and manage Jira tickets through natural language requests. The workflow exposes key Jira functions as AI tools, enabling you to interact with your Jira instance through conversational commands.
  How it works

  The workflow sets up an MCP (Model Control Protocol) server, allowing compatible AI assistants to use a suite of Jira management tools
  The AI assistant can perform various Jira operations including:

  Creating new tickets with customized fields
  Adding comments to existing tickets
  Retrieving available status transitions for tickets
  Attaching files to tickets
  Changing ticket statuses
  Retrieving detailed information about tickets
  Getting available projects and issue types


  When you make a request to your AI assistant, it determines which Jira operation to perform and executes it through this workflow

  Setup Steps

  Prerequisites:

  Active Jira Cloud account with admin access
  n8n instance with the Langchain and MCP nodes installed
  Claude Desktop App or another compatible AI assistant


  Jira Credentials Setup:

  Configure your Jira Cloud API credentials in n8n 
  Ensure your Jira account has sufficient permissions for all operations


  Workflow Configuration:

  Import this template into your n8n instance
  Set up the MCP Trigger node with your desired path (currently "test_mcp")
  Verify that all Jira tool nodes are correctly connected to the MCP Server node
  Activate the workflow


  Using Claude as an MCP Client:

  Open your Claude Desktop App
  Navigate to Settings &gt; Developer Settings
  Enable "Connect to local MCP servers"
  Add a new connection with the URL path to your n8n MCP server (e.g., "http://localhost:5678/webhook/test_mcp")
  Start a new conversation and ask Claude to perform Jira tasks



  Example Usage with Claude
  Once you've set up the connection between Claude and your MCP server, you can use natural language to manage your Jira tickets. Here are some examples:

  Creating a ticket:
  "Claude, please create a new Jira ticket in the Web Development project with bug issue type. The summary should be 'Homepage loading slowly' and the description should be 'Users are experiencing delays of 5+ seconds when loading the homepage on mobile devices.'"
  Adding a comment:
  "Claude, please add a comment to Jira ticket WEB-123 saying 'This issue has been reproduced on multiple devices and browsers. Priority should be increased.'"
  Checking status:
  "Claude, can you get the details of ticket WEB-123 and tell me its current status?"
  Changing status:
  "Claude, please move ticket WEB-123 to 'In Progress' status."

  This workflow creates a seamless bridge between your AI assistant and Jira, making project management more efficient through natural language interactions.

nodes_used:
  ["n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","n8n-nodes-base.jiraTool","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.jiraTool","n8n-nodes-base.httpRequestTool"]

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metadata_json:
  {"categories":["integration","automation","AI-powered project management"],"complexity":"medium","use_cases":["Create and update Jira tickets via natural language","Add comments and attach files to issues","Change issue status and perform transitions","Retrieve ticket details, projects, and issue types","Expose Jira functions as tools for AI assistants (MCP)"],"estimated_setup_minutes":25,"required_services":["Jira Cloud account with admin/API access","n8n instance with LangChain and MCP nodes installed","MCP-compatible AI assistant (e.g., Claude Desktop)","HTTP/Webhook access to the n8n MCP endpoint"],"key_features":["Runs an MCP server to expose Jira tools to AI assistants","Create new tickets with custom fields via natural language","Add comments and attachments to issues","Retrieve available status transitions and change issue statuses","Fetch detailed ticket information and list projects/issue types"],"target_audience":["Developers/DevOps engineers","Project managers and product owners","Support and QA teams looking to automate ticketing workflows"]}


