# templates

name: AI Chatbot Call Center: Taxi Service (Production-Ready, Part 3)
author_name: ChatPayLabs
author_username: chatpaylabs
categories: []
created_at: 2025-05-14T15:27:06.322Z
updated_at: 2025-05-14T15:27:06.322Z
url: https://n8n.io/workflows/4046
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Workflow Name: 🛎️ Taxi Service

  Template was created in n8n v1.90.2

  Skill Level: High

  Categories: n8n, Chatbot

  Stacks

  Execute Sub-workflow Trigger node
  Chat Trigger node
  Redis node
  Postgres node
  AI Agent node
  If node, Switch node, Code node, Edit Fields (Set)

  Prerequisite

  Execute Sub-workflow Trigger: Taxi Service Workflow (or your own node)
  Sub-workflow: Taxi Service Provider (or your own node)
  Sub-workflow: Demo Call Back (or your own node)

  Production Features

  Scaling Design* for n8n *Queue mode** in production environment
  Service Data* from *external Database* with *Caching Mechanism**
  Optional Long Terms Memory design
  Find Route Distance* using *Google Map API**
  Optional Multi-Language Wait Output example
  Error Management**

  What this workflow does?

  This is a n8n Taxi Service Workflow demo. It is the core node for Taxi Service. It will receive message from the Call Center Workflow, handling the QA from the caller, and pass to each of the Taxi Service Provider Workflow to process the estimation.

  How it works

  The Flow Trigger node will wait for the message from Call Center or other Sub-workflow.
  When message is received, it will first check for the matching Service from the PostgreSQL database.
  If no service or service is inactive, output Error.
  Next, always reset the Session Data in Cache, with channel_no set to taxi
  Next, delete the previous Route Data in Cache
  Trigger a AI Agent to process the fare estimation question to create the Route Data
  Use the Google Map Route API to calculate the distance.
  Repeat until created the route data, then pass to all the Taxi Service Provider for an estimation.

  Set up instructions

  Pull and Set up the required SQL from our Github repository.
  Create you Redis credentials, refer to n8n integration documentation for more information.
  Select your Credentials in Service Cache, Save Service Cache, Reset Session, Delete Route Data, Route Data, Update User Session and Create Route Data.
  Create you Postgres credentials, refer to n8n integration documentation for more information.
  Select your Credentials in Load Service Data, Postgres Chat Memory, Load User Memory and Save User Memory.
  Modify the AI Agent prompt to fit your need
  Set you Google Map API key in Find Route Distance

  How to adjust it to your needs

  By default, this template will use the sys_service table provider information, you could change it for your own design.
  You can use any AI Model for the AI Agent node
  Learn we use the prompt for the Load/Save User Memory on demand.
  Include is our prompt for the taxi service. It is a flexible design which use the data from the Service node to customize the prompt, so you could duplicate this workflow as another service.
  Create difference Taxi Providers to process the and feedback the estimate.

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryPostgresChat","n8n-nodes-base.set","n8n-nodes-base.redisTool","@n8n/n8n-nodes-langchain.lmChatXAiGrok","n8n-nodes-base.postgresTool","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.redis","n8n-nodes-base.postgres","n8n-nodes-base.redis","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.postgresTool","n8n-nodes-base.redisTool","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","n8n-nodes-base.redis","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflow","n8n-nodes-base.executeWorkflow"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","chatbot","customer support"],"complexity":"complex","use_cases":["AI-driven call center for taxi/ride-hailing services","Fare estimation and route calculation","Orchestrating multiple service providers for bids/estimations","Session management and caching for conversational flows","Scaling workflows using n8n queue mode in production"],"estimated_setup_minutes":120,"required_services":["n8n (Queue mode / production instance)","PostgreSQL (service and chat memory tables)","Redis (cache/session storage)","Google Maps Routes API (distance/route calculation)","AI model/provider (e.g., OpenAI, Anthropic or custom agent)","GitHub (to pull SQL/setup scripts)"],"key_features":["Production-ready scaling design for n8n queue mode","External service data lookup from PostgreSQL","Caching mechanism with Redis (session reset, route cache deletion)","AI Agent-driven QA and route data generation","Google Maps-based route distance calculation and retry loop until route produced"],"target_audience":["developers implementing conversational call center workflows","DevOps/engineers deploying n8n in production","Contact center architects and integrators for taxi/transport services"]}


name: Taxi Service Provider (Production-Ready, Part 4)
author_name: ChatPayLabs
author_username: chatpaylabs
categories: []
created_at: 2025-05-14T15:28:48.092Z
updated_at: 2025-05-14T15:28:48.092Z
url: https://n8n.io/workflows/4047
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Workflow Name: 🤖 Taxi Service Provider

  Template was created in n8n v1.90.2

  Skill Level: High

  Categories: n8n, Chatbot

  Stacks

  Execute Sub-workflow Trigger node
  Chat Trigger node
  Redis node
  Postgres node
  AI Agent node
  Calculator node
  If node, Switch node, Code node, Edit Fields (Set)

  Prerequisite

  Execute Sub-workflow Trigger: Taxi Service Workflow (or your own node)
  Sub-workflow: Demo Call Back (or your own node)

  Production Features

  Scaling Design* for n8n *Queue mode** in production environment
  Provider Data* from *external Database* with *Caching Mechanism**
  Optional Score design output
  Optimize Taxi Provider Prompt from database
  Error Management**

  What this workflow does?

  This is a n8n Taxi Provider Workflow demo. It will receive message from the Taxi Service Workflow, process the estimation and return to the caller.

  How it works

  The Flow Trigger node will wait for the message from other Sub-workflow.
  When message is received, it will first check for the matching Provider from the PostgreSQL database.
  Then it will increase a Provider Number in Redis for selection later
  Trigger a AI Agent to process the fare estimation and create a NEW booking
  Finally, send the AI response to the Call Output
  Note: if the Provider is set to inactive, it will do nothing

  Set up instructions

  Pull and Set up the required SQL from our Github repository.
  Create you Redis credentials, refer to n8n integration documentation for more information.
  Select your Credentials in Provider Cache, Save Provider Cache and Provider Number.
  Create you Postgres credentials, refer to n8n integration documentation for more information.
  Select your Credentials in Load Provider Data and Create Booking Data.
  Modify the AI Agent prompt to fit your need

  How to adjust it to your needs

  By default, this template will use the sys_provider table provider information, you could change it for your own design.
  You can use any AI Model for the AI Agent node
  Include is our prompt for the taxi service provider. It is a flexible design which use the data from the Provider Cache to customize the prompt, so you could create more Taxi Service Providers by duplicate this workflow in the Taxi Service node.

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.lmChatXAiGrok","n8n-nodes-base.postgresTool","n8n-nodes-base.stickyNote","n8n-nodes-base.redis","@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.redis","n8n-nodes-base.postgres","n8n-nodes-base.redis","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflow","n8n-nodes-base.switch"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","chatbot","data processing"],"complexity":"complex","use_cases":["Real-time taxi provider selection and booking","AI-driven fare estimation and response generation","Caching provider data for low-latency selection","Scaling n8n workflows in queue mode for production","Integrating Redis and PostgreSQL with n8n sub-workflows"],"estimated_setup_minutes":60,"required_services":["n8n (v1.90.2 or compatible)","PostgreSQL (provider & booking tables)","Redis (provider counters / cache)","AI model/service (e.g., OpenAI or compatible agent)","GitHub (SQL/schema files repository)"],"key_features":["Production-ready scaling design for n8n queue mode","Provider data retrieval from PostgreSQL with caching in Redis","Provider selection using Redis counters","AI Agent node for fare estimation and booking creation","Sub-workflow execution and callback handling for modular flows"],"target_audience":["developers building transportation/dispatch systems","DevOps engineers deploying n8n in production","AI engineers integrating conversational agents into workflows\",\"integration specialists"]}


name: AI Chatbot Call Center: Taxi Booking Worker (Production-Ready, Part 5)
author_name: ChatPayLabs
author_username: chatpaylabs
categories: []
created_at: 2025-05-14T15:30:14.972Z
updated_at: 2025-05-14T15:30:14.972Z
url: https://n8n.io/workflows/4048
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Workflow Name: 👷‍♂️ Taxi Booking Worker

  Template was created in n8n v1.90.2

  Skill Level: High

  Categories: n8n, Chatbot

  Stacks

  Execute Sub-workflow Trigger node
  Chat Trigger node
  Redis node
  Postgres node
  Google Calendar node
  Execute Sub-workflow
  If node, Switch node, Code node, Edit Fields (Set)

  Prerequisite

  Execute Sub-workflow Trigger: Call In Center (or your own node)
  Sub-workflow: Demo Call Back (or your own node)

  Production Features

  Scaling Design* for n8n *Queue mode** in production environment
  Customize Open Booking Action
  Integrate  with Long Term Memory backup.
  Error Management**

  What this workflow does?

  This is a n8n Taxi Booking Worker, the worker node to process the job. It will wait for message from the Call Center node and handle the real process here. In this particular case, it will check the input number for the selected provider, then update the booking from NEW to OPEN, then clean up the orphan data.

  How it works

  The Form Trigger node will wait for the message from any node with the input in number.
  First check for NEW booking under the same chat session
  For input 0, which is cancel
      Reset the user session in Redis memory
  For input 1 or above, which is the provider selection number
      Set the booking status to OPEN in database
      OPTIONAL Save the summary to User Memory database
      OPTIONAL Create a new Event in Google Calendar and sync to the database
      Reset the user session in Redis memory
  Afterward, process data clean up
      Delete the provider selection number queue in Redis
      Delete the route data in Redis
      OPTIONAL Delete the chat memory with current session
  Finally, output the response to the Call Back node

  Set up instructions

  Pull and Set up the required SQL from our Github repository.
  Create you Postgres credentials, refer to n8n integration documentation for more information.
  Select your Credentials in Booking, Set Open Booking, Sync Booking Google Cal, and Save User Memory.
  Create you Redis credentials, refer to n8n integration documentation for more information.
  Select your Credentials in Reset Session, Reset Session 2, Delete Provider Number and Delete Route Data.
  Create your Google Calendar credentials, refer to n8n integration documentation for more information.
  Create a Google Calendar, e.g. DEMO
  Select your Credentials in Create Event, and select the above Calendar
  FOR TEST ONLY. Enable the Telegram Test Output for testing from the Test Input.

  How to adjust it to your needs

  You can specific a number to trigger the specific action based on your needs.
  The current action only create a event in Google Calendar, you can do more based on your needs.
  You can replace the sub-workflow trigger Flow Trigger and Call Back to another flow as needs.

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.set","n8n-nodes-base.redis","n8n-nodes-base.set","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.redis","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.redis","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.redis","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.postgres","n8n-nodes-base.executeWorkflow","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.googleCalendar","n8n-nodes-base.executeWorkflow","n8n-nodes-base.postgres","n8n-nodes-base.postgres"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["chatbot","contact center automation","integration","scheduling"],"complexity":"complex","use_cases":["Taxi / ride booking worker for call centers","Processing provider selection and opening bookings","Session and memory management for conversational flows","Syncing bookings to Google Calendar","Queue-based scaling for workers in production"],"estimated_setup_minutes":60,"required_services":["n8n (orchestrator)","Postgres (booking database)","Redis (session/queue storage)","Google Calendar API / Google account","Chat/AI provider or Chat Trigger integration","Optional: Telegram (for test output)","Optional: Long-term memory storage/backups"],"key_features":["Queue-mode scaling design for production workers","Listens for sub-workflow/Call Center triggers","Validates provider selection and updates booking status from NEW to OPEN","Resets user session and cleans up Redis queues and route data","Optional: Save conversation summary to user memory database (Postgres)"],"target_audience":["Developers implementing conversational workflows","Contact center engineers / operations teams","Platform integrators building booking systems with n8n"]}


name: AI Chatbot Call Center: Demo Call Back (Production-Ready, Part 6)
author_name: ChatPayLabs
author_username: chatpaylabs
categories: []
created_at: 2025-05-14T15:32:58.059Z
updated_at: 2025-05-14T15:32:58.059Z
url: https://n8n.io/workflows/4050
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Workflow Name: 💬 Demo Call Back

  Template was created in n8n v1.90.2

  Skill Level: High

  Categories: n8n, Chatbot

  Stacks

  Execute Sub-workflow Trigger node
  Chat Trigger node
  Redis node
  Postgres node
  Telegram node
  HTTP Request node
  If node, Code node, Edit Fields (Set)

  Prerequisite

  Execute Sub-workflow Trigger: your own node
  MiniMax Account (https://www.minimax.io/)

  Production Features

  Scaling Design* for n8n *Queue mode** in production environment
  Optional Provider Data from external Database with Caching Mechanism.
  Optional AI Clone Voice Message response via MiniMax API with Multi-Languages support.
  Optional Backup Chat Log to Database, so you can use in APP/API building.
  Testing Flow** with or without dependance on other workflow.
  Multi Chatbot** (This is a demo for Telegram, you can add WhatsApp, Line, etc)
  Error Management**

  What this workflow does?

  This is a n8n Telegram Output Workflow. It will receive message from other Sub-workflow then output to Telegram for Message, or Replay Message and extra Voice Message.

  How it works

  The Flow Trigger node will wait for the message from other Sub-workflow.
  When message is received, it will first check for the matching Provider from the PostgreSQL database.
  Then determine if it is a Voice message to Text message.
  OPTIONAL. For voice message, use the MiniMax API to generate a voice message, then send it to Telegram.
  Finally, send the text to Telegram.

  Set up instructions

  Pull and Set up the required SQL from our Github repository.
  Create you Redis credentials, refer to n8n integration documentation for more information.
  Select your Credentials in Provider Cache and Save Provider Cache.
  Create you Postgres credentials, refer to n8n integration documentation for more information.
  Select your Credentials in Load Member Data, Create Chat Log Input, and Create Chat Log Output.
  Create you Telegram credentials, refer to n8n integration documentation for more information.
  Select your Credentials in Telegram Voice Output, Telegram Reply Output, and Telegram Output.

  AI Clone Voice setup instructions (Optional)

  You can clone any voice with MiniMax

  Go to https://www.minimax.io/ and create a MiniMax account
  Setup the Database with the required variables found in the MiniMax TTS node
  That’s it

  How to adjust it to your needs

  By default, this template will use the sys_provider table provider information, you could change it for your own design.
  The demo use MiniMax API for AI voice cloning, you could implement any other AI your choice.
  The Backup Chat Log will backup all chat conversion line by line. You can use it for you own APP/API development.

nodes_used:
  ["n8n-nodes-base.switch","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.postgres","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.code"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","chatbot","integration","voice/telephony","ai"],"complexity":"complex","use_cases":["Telegram chatbot reply and callback handling","Contact center demo call‑back workflows","Text and optional AI‑generated voice responses (TTS)","Provider lookup with caching and DB backup of chat logs","Multi‑chatbot support and scalable queue processing"],"estimated_setup_minutes":60,"required_services":["n8n (self‑hosted or cloud)","Telegram API (bot)","PostgreSQL","Redis","MiniMax (optional TTS/voice cloning)","GitHub (for SQL scripts / template assets)"],"key_features":["Sub‑workflow trigger to receive messages","Provider lookup from PostgreSQL with caching via Redis","Optional AI voice cloning via MiniMax and multi‑language TTS","Sends text, replies, and voice messages to Telegram","Backup chat logs to database for API/APP use cases"],"target_audience":["Developers","Automation engineers","Contact center engineers / architects implementing chatbot callbacks (production)"]}


name: AI Chatbot Call Center: Taxi Booking Support (Production-Ready, Part 7)
author_name: ChatPayLabs
author_username: chatpaylabs
categories: []
created_at: 2025-05-14T15:34:22.600Z
updated_at: 2025-05-14T15:34:22.600Z
url: https://n8n.io/workflows/4051
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Workflow Name: 🫶 Taxi Booking Support

  Template was created in n8n v1.90.2

  Skill Level: Mid

  Categories: n8n, Chatbot

  Stacks

  Schedule Trigger node
  Postgres node
  AI Agent node
  Google Calendar node
  Execute Sub-workflow
  If node, Switch node, Code node, Edit Fields (Set)

  Prerequisite

  Sub-workflow: Demo Call Back (or your own node)

  Production Features

  Scaling Design* for n8n *Queue mode** in production environment
  Customize Expired Booking Actions example
  Multi-Language** Design

  What this workflow does?

  This is a n8n Taxi Booking Support, the background node to process the job at scheduled. It is scheduled to check the database for outstanding booking and handle the after sales process. In this particular case, it will check for OPEN booking over 10 minutes, then update the booking status from OPEN to CANCELLED, delete the Calendar event and send a reply to the user.

  How it works

  The Schedule Trigger node is scheduled to run every 5 minutes.
  It will check the database for OPEN or HOLD booking.
  For OPEN booking
      update the booking status to CANCELLED
      delete the Calendar event
      send a reply to the user
  Optional: The AI Agent is used to create the reply message to the user in Multi-language based on the language set in the booking.

  Set up instructions

  Pull and Set up the required SQL from our Github repository.
  Create you Postgres credentials, refer to n8n integration documentation for more information.
  Select your Credentials in Open Hold Booking and Set Cancel Booking.
  Create your Google Calendar credentials, refer to n8n integration documentation for more information.
  Create a Google Calendar, e.g. DEMO
  Select your Credentials in Delete Event, and select the above Calendar
  Remember to activate this workflow for schedule to run.

  How to adjust it to your needs

  There should be more status for the booking.
  The current action only check for OPEN and HOLD booking, you can do more based on your needs.
  You can replace the sub-workflow trigger Call Back to another flow as needs.

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatXAiGrok","n8n-nodes-base.switch","n8n-nodes-base.postgres","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.set","n8n-nodes-base.executeWorkflow","n8n-nodes-base.postgres","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleCalendar"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","chatbot","scheduling","database","AI / language model integration"],"complexity":"medium","use_cases":["Automated taxi/ride booking expiration and cleanup","After-sales customer communication (multilingual)","Scheduled background processing of booking records","Calendar event deletion and synchronization","Triggering follow-up workflows (call back, notifications)"],"estimated_setup_minutes":30,"required_services":["Postgres database","Google Calendar (Google API credentials)","n8n (production mode, Queue mode recommended)","AI agent / language model (e.g., lmChatXAiGrok or other LLM connector)","Optional: GitHub or storage for SQL setup scripts"],"key_features":["Scheduled DB polling for OPEN/HOLD bookings","Automatic status update (OPEN -> CANCELLED)","Calendar event deletion","AI-generated multilingual reply messages","Sub-workflow execution for callbacks or follow-ups (executeWorkflow node support and demo sub-workflow included/referenced)"],"target_audience":["developers implementing automation","customer support / contact center engineers","DevOps / n8n administrators deploying production workflows"]}


name: AI Chatbot Call Center: General Exception Flow (Production-Ready, Part 8)
author_name: ChatPayLabs
author_username: chatpaylabs
nodes_used: ["n8n-nodes-base.errorTrigger","n8n-nodes-base.slack"]
categories: []
created_at: 2025-05-14T15:36:39.923Z
updated_at: 2025-05-14T15:36:39.923Z
url: https://n8n.io/workflows/4052
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Workflow Name: 👻 Exception Flow

  Template was created in n8n v1.90.2

  Skill Level: Low

  Categories: n8n, Chatbot

  Stacks

  Error Trigger
  Slack node

  What this workflow does?

  This is a n8n Error Workflow. It will trigger when there is an error in another workflow. When this happens, it then tries to send an error notification to the preset Slack channel.

  How it works

  The Error Trigger node will trigger when there is an error in another workflow, as long as that workflow is set up to do so.
  A error notification will be sent to the Slack Channel.

  Set up instructions

  Create you Slack credentials, refer to n8n integration documentation for more information.
  Set up the Channel in 👻 Exception Alert node.
  For any n8n workflows to trigger this, switch to that workflow, select menu &gt; settings, and set the Error Workflow to 👻 Exception Flow.

  How to adjust it to your needs

  Although this workflow template is part of the AI Chatbot Call Center Series, it could be used with any n8n workflows.

  Update the Channel in 👻 Exception Alert to your own channel

  https://chatpaylabs.com/blog/part-8-build-your-own-ai-chatbot-call-center-general-exception-flow-production-ready-n8n-workflow-free-download-

workflow_json_compressed:
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metadata_json:
  {"categories":["Automation","Monitoring","Notifications","Error handling","Chatbot"],"complexity":"simple","use_cases":["Centralized error handling for n8n workflows","Send incident/error notifications to a Slack channel","Alerting operators/teams about workflow failures","Integrating chatbot workflow errors with team communication"],"estimated_setup_minutes":10,"required_services":["n8n","Slack (workspace/channel and credentials)"],"key_features":["Error Trigger node that catches workflow failures","Sends error notifications to a preset Slack channel","Easy configuration of Slack credentials and channel","Centralized, production-ready exception flow for multiple workflows"],"target_audience":["n8n users","Developers","DevOps / SRE engineers / Site reliability teams supporting automations and chatbots\",\"Support engineers and chatbot maintainers"]}


name: Sort Gmail Emails with GPT-4o into Action Required and No Action Labels
author_name: Khaled
author_username: khaled
categories: []
created_at: 2025-05-14T16:10:55.017Z
updated_at: 2025-05-14T16:10:55.017Z
url: https://n8n.io/workflows/4053
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  🧾 Description:

  This automation uses GPT-4o to scan unread Gmail emails and intelligently classify them as:

      Action → Requires your attention (reply, review, schedule, or respond)

      No Action → Informational or promotional; no action needed

  The result? You eliminate inbox noise and gain a clear daily routine: only check what's in Action Required.
  ⚙️ How It Works:

      Trigger: Runs on a customizable schedule

      Fetch Emails: Pulls unread messages from Gmail

      Classify via GPT-4o: Determines if each email needs action or not

      Sort Emails:

          Labels actionable emails as Action Required

          Labels non-actionable ones as No Action

          Removes the Inbox label to clean your primary inbox view

          ✅ Emails stay in your account—just better organized

  🚀 How to Use:

      Import the workflow into your n8n instance

      Set up Gmail and OpenAI credentials

      Create Gmail labels:

          Action Required

          No Action

      Activate the workflow

      Start your day by checking only the Action Required label

  📦 Requirements:

      n8n (self-hosted or cloud)

      Gmail OAuth2 account

      OpenAI API key (GPT-4o or GPT-4o-mini)

      Gmail labels: Action Required, No Action

  💡 Why It Matters:

  Stop manually filtering emails.
  This workflow helps you focus only on what matters while keeping everything else out of your way—without deleting or archiving anything.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.gmail","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.textClassifier","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","email management","integration","productivity","NLP"],"complexity":"medium","use_cases":["Inbox triage and prioritization","Automatically surface emails that require action","Reduce email noise for daily review","Automated labeling for team or personal workflows"],"estimated_setup_minutes":15,"required_services":["n8n (self-hosted or cloud)","Gmail (OAuth2 account)","OpenAI API (GPT-4o or GPT-4o-mini)"],"key_features":["Scheduled scan of unread Gmail messages","GPT-4o-based classification into Action Required vs No Action","Apply Gmail labels and remove Inbox label to declutter primary inbox","Non-destructive organization (emails retained)","Configurable schedule and credentials"],"target_audience":["Professionals and knowledge workers","Solopreneurs and small teams","Productivity enthusiasts looking to automate inbox triage','IT/automation engineers deploying workflows"]}


name: Automated Replies to X Threads with Airtop Browser Automation
author_name: Airtop
author_username: cesar-at-airtop
categories: []
created_at: 2025-05-14T18:29:50.112Z
updated_at: 2025-05-14T18:29:50.112Z
url: https://n8n.io/workflows/4054
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  Use Case
  Automatically responding to X (formerly Twitter) posts can help you engage with potential customers at scale, saving time while maintaining a personal touch.

  What This Automation Does
  This automation replies to specified X posts using the following input parameters:

  airtop_profile: The name of your Airtop Profile connected to X.
  thread_url: The URL of the X post to reply to. Example
  reply_text: The message you want to post as a reply.

  How It Works
  Creates a browser session using Airtop.
  Navigates to the specified X post.
  Types and submits the reply text.

  Setup Requirements
  Airtop API Key — free to generate.
  An Airtop Profile connected to X (requires one-time login).

  Next Steps
  Combine with X Monitoring**: Use this with the X monitoring automation to create a fully automated engagement pipeline.
  Extend to Other Platforms**: Adapt the automation for use on LinkedIn, Reddit, or any web community.

  Read more about this Airtop Automation.

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.airtop","n8n-nodes-base.airtop","n8n-nodes-base.airtop","n8n-nodes-base.wait","n8n-nodes-base.formTrigger","n8n-nodes-base.set","n8n-nodes-base.airtop","n8n-nodes-base.airtop","n8n-nodes-base.airtop","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","social_media","browser_automation","engagement"],"complexity":"medium","use_cases":["Automatically reply to X (Twitter) threads","Scale customer engagement and outreach","Community management and moderation","Lead generation and follow-ups","Automated response pipeline when paired with X monitoring"],"estimated_setup_minutes":15,"required_services":["Airtop (API key and an Airtop Profile connected to X)","X (Twitter) account","Internet / web access (for browser automation)"],"key_features":["Creates a browser session via Airtop","Navigates to specified X post and posts a reply","Parameterized inputs: airtop_profile, thread_url, reply_text","Can be combined with X monitoring to automate engagement at scale","Easily adaptable to other web platforms (LinkedIn, Reddit, etc.)"],"target_audience":["Marketers","Community managers","Sales and growth teams (outreach automation specialists)"]}


name: AI Talent Screener – CV Parser, Job Fit Evaluator & Email Notifier
author_name: Aayushman Sharma
author_username: ajstyle0411
categories: []
created_at: 2025-05-14T19:24:37.751Z
updated_at: 2025-05-14T19:24:37.751Z
url: https://n8n.io/workflows/4055
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  AI Talent Screener – CV Parser, Job Fit Evaluator & Email Notifier

  Who is this for?
  This template is ideal for HR teams, recruiters, staffing agencies, and tech-enabled hiring managers looking to automate the intake, analysis, and shortlisting of job applicants directly from CV submissions.

  What problem is this workflow solving?
  Manually evaluating resumes for job openings is time-consuming and prone to bias or oversight. This workflow uses AI to extract candidate data, analyze fit based on job descriptions, and make data-backed shortlisting decisions — automatically updating Google Sheets and notifying both Talent Acquisition and candidates.

  What this workflow does
  This workflow automates your recruitment pipeline by:

  1. Accepting CV submissions via an embedded form.

  2. Saving resumes to Google Drive.

  3. Extracting and parsing resume content using AI.

  4. Fetching the relevant job description from Google Sheets.

  5. Summarizing both job and applicant profiles.

  6. Conducting an AI-powered fit evaluation (semantic match, red flags, soft skills, etc.).

  7. Updating detailed analysis in a centralized Google Sheet.

  8. Notifying the TA team for approval.

  9. Automatically sending shortlisting or rejection emails to candidates based on approval.

  Setup
  1. Connect credentials for:

  Google Sheets

  Google Drive

  OpenAI API

  SMTP email (for notification)

  2. Update the Google Sheets document IDs and folder IDs in the relevant nodes.

  3. Adjust the form fields in the "Form Trigger" node if needed.

  4. Customize email sender/receiver addresses in the email nodes.

  5. Enable the workflow and embed the form on your career page.

  For convenience, the sticky notes guide you through what each block does.

  How to customize this workflow to your needs
  Change job roles by editing the dropdown options in the form.

  Modify evaluation criteria in the “Semantic Fit & Evaluation by HR Expert” prompt.

  Add more fields (e.g., GitHub, portfolio links) to the form and extend parsing.

  Connect to ATS or Slack if you'd like to integrate further.

nodes_used:
  ["n8n-nodes-base.formTrigger","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainSummarization","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainSummarization","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.emailSend","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.emailSend","n8n-nodes-base.emailSend","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data processing","recruiting"],"complexity":"medium","use_cases":["Automated resume/CV intake and parsing","AI-powered candidate-job fit evaluation and shortlisting","Centralized candidate tracking in Google Sheets","Automated notifications to TA team and candidates","Reducing manual screening time and bias"],"estimated_setup_minutes":45,"required_services":["Google Sheets","Google Drive","OpenAI API (or compatible LLM)","SMTP/email provider","Form host (n8n Form Trigger or embedded form)"],"key_features":["Accept CV submissions via embedded form","Save resumes to Google Drive","AI resume parsing and structured extraction","Fetch job descriptions from Google Sheets","Summarize job and applicant profiles with AI prompts"],"target_audience":["HR teams","Recruiters","Staffing agencies/security hiring managers and tech-enabled hiring managers"]}


name: Generate Images and Convert to Video Using Flux, Kraken & Runway
author_name: Joseph
author_username: mjomba
categories: []
created_at: 2025-05-14T19:48:15.928Z
updated_at: 2025-05-14T19:48:15.928Z
url: https://n8n.io/workflows/4056
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  (Image Generation → Hosting → Video Generation)

  This workflow is designed for creators, automation enthusiasts, and indie hackers who want to generate image-based videos automatically using AI tools — at a low cost.

  ⚙️ Workflow Overview

  This automation performs the following steps:

  Trigger (Schedule or manual)
  Generate an image using Flux (choose between two APIs)
  Upload the image to Kraken.io to get a public URL
  Send the image to Runway ML (choose between two APIs) to generate a video
  Receive the video as a URL — ready for posting, download, or further automation

  🛠️ Step-by-Step Setup

  🖼️ Flux (Image Generation)

  You can use either of the following providers:

  Option 1: Flux by BlackForest Labs (Direct API)

  🔑 Get your API key here: https://docs.bfl.ml/

  Paste your API key in the HTTP Request node named Flux (Blackforest)
  You can customize prompts or styles inside the JSON body

  Option 2: Flux via RapidAPI

  🔑 Subscribe and get your key here:
  https://rapidapi.com/poorav925/api/ai-text-to-image-generator-flux-free-api/playground/apiendpoint\_e38039ee-1912-4ef9-b4d4-270d72fca851

  Enter your RapidAPI key in the X-RapidAPI-Key header
  Optional: tweak prompts, style, or resolution inside the JSON body

  🐙 Kraken.io (Hosting the Image Publicly)

  Runway ML requires the image to be publicly accessible. We use Kraken.io to host the generated image and return a public URL.

  🔑 Get your API credentials: https://kraken.io/account/api-credentials

  Setup:

  Copy your API Key and API Secret
  Open the Kraken Upload node in n8n
  Replace placeholders with your credentials
  The node uploads your image and gives back a public image URL for Runway to use

  🎬 RunwayML (Video Generation)

  You also have two options here:

  Option 1: Runway Official API

  🔑 Get your credentials at: https://dev.runwayml.com/

  Use the public image URL from Kraken in the JSON body
  Paste your Bearer token in the Authorization header
  Customize other settings like video length, style, FPS, etc.

  Option 2: Runway via RapidAPI

  🔑 Subscribe and get your key here:
  https://rapidapi.com/fortunehoppers/api/runwayml/playground/apiendpoint\_93c8554d-8097-40cd-8252-3d4dec9c0e68

  Paste your RapidAPI key in the request header
  Customize prompt and generation options in the body
  Use the Kraken-generated image URL as the input source

  📤 What to Do with the Video

  Once the video is generated, you’ll get a direct video URL. You can:

  Save it to Google Sheets or Notion
  Send it via email
  Trigger a YouTube upload automation
  Or download manually for editing and reposting

  💡 Optional Tips & Notes

  You can schedule this workflow to generate AI videos daily or weekly
  Combine it with a Google Sheet of prompts for bulk automation
  Try using a consistent visual style or theme for better branding
  This workflow is lightweight and affordable — perfect for indie projects or experimental content generation
  Great for shorts, quote visuals, music loops, AI art promos, etc.

  🔗 Resources

  Flux (Blackforest) Docs
  Flux on RapidAPI
  RunwayML Official Docs
  Runway on RapidAPI
  Kraken.io API Dashboard

  🙋 Need Help?

  Feel free to reach out:

  🐦 Twitter: @juppfy
  📧 Email: joseph@uppfy.com

  If you’d like to hire me for custom n8n workflows or product automations, don’t hesitate to get in touch.

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

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","AI/ML","media processing","content creation"],"complexity":"medium","use_cases":["Automated short video creation from AI-generated images","Social media content generation (Reels/Shorts/TikToks)","Batch/periodic generation of visual promos or art","Prototype or low-cost video production for indie projects","Integrating generated assets into downstream automations (Sheets, Notion, uploads)"],"estimated_setup_minutes":30,"required_services":["Flux (BlackForest Labs) or Flux via RapidAPI","Kraken.io (image hosting API)","RunwayML (official API) or Runway via RapidAPI"],"key_features":["Generate images via Flux and customize prompts/styles","Upload images to Kraken.io to obtain public URLs","Send public image URLs to RunwayML to generate videos","Supports multiple provider options (direct APIs or RapidAPI)","Scheduled or manual triggering and easy customization of generation parameters (length, style, FPS)"],"target_audience":["creators","indie hackers","automation enthusiasts / no-code builders (n8n users)"]}


name: Auto-Respond to Gmail Enquiries using GPT-4o, Dumpling AI & LangChain Agent
author_name: Yang
author_username: yang
categories: []
created_at: 2025-05-14T21:06:14.263Z
updated_at: 2025-05-14T21:06:14.263Z
url: https://n8n.io/workflows/4057
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  Who is this for?

  This workflow is perfect for customer support teams, sales departments, or solopreneurs who receive frequent email enquiries and want to automate the initial response process using AI. If you spend too much time answering similar questions, this system helps respond faster and more intelligently—without writing a single line of code.

  What problem is this workflow solving?

  Manually responding to repeated customer enquiries slows productivity and increases delay. This workflow classifies if an incoming email is a real enquiry, analyzes the content with a LangChain-powered agent, fetches helpful context using Dumpling AI, and sends a personalized reply using Gmail—all within minutes.

  What this workflow does

  Listens for new incoming Gmail messages using the Gmail Trigger node.  
  Classifies whether the email is an enquiry using a GPT-4o classification prompt.  
  Uses a Filter node to continue only if the email was classified as an enquiry.  
  Passes the email content to a LangChain Agent, enhanced with memory, AI tools, and Dumpling AI to search for relevant information.  
  The agent constructs a smart, relevant response, then sends it to the original sender via Gmail.

  Setup

  Connect Gmail  
     Use the Gmail Trigger node to connect to the Gmail account that receives enquiries.  
     Make sure Gmail OAuth2 credentials are authenticated.

  Configure Dumpling AI Agent  
     Sign up at Dumpling AI.  
     Create an agent trained to search your help docs, site content, or FAQs.  
     Copy your Dumpling agent ID and API key.  
     Paste it in the Dumpling AI Agent – Search for Relevant Info HTTP Request node.

  Set Up LangChain Agent  
     No extra setup needed beyond connecting OpenAI credentials.  
     GPT-4o is used for classification and reply generation.

  Enable Gmail Reply Node  
     The final Send Email Response via Gmail node will send the AI-generated reply back to the same thread.

  How to customize this workflow to your needs

  Change the classification prompt to include other email types like “support”, “complaint”, or “sales”.  
  Add additional logic if you want to CC someone or forward certain types of enquiries.  
  Add a Notion or Google Sheets node to log the conversation for analytics.  
  Replace Gmail with Outlook or another email provider by switching the nodes.  
  Improve context by adding more AI tools like database queries or preloaded FAQs.

nodes_used:
  ["n8n-nodes-base.gmailTrigger","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.filter","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolHttpRequest","n8n-nodes-base.gmailTool","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","customer support","AI-assisted communication"],"complexity":"medium","use_cases":["Automated email triage and reply","Initial customer support response","Sales enquiry qualification","Context-aware response generation using internal docs","Logging or forwarding enquiries for follow-up"],"estimated_setup_minutes":30,"required_services":["Gmail (OAuth2)","OpenAI (GPT-4o)","Dumpling AI (agent ID & API key)","LangChain (agent tooling)"],"key_features":["Classifies incoming emails as enquiries using GPT-4o","Filters non-enquiry messages","Uses a LangChain agent with memory and tools to analyze email content","Fetches contextual info via Dumpling AI search","Generates personalized replies and sends them via Gmail reply node','Easily customizable prompts, routing, and logging integrations"],"target_audience":["Customer support teams","Sales teams","Solopreneurs and small business owners handling email enquiries without coding skills"]}


name: Homey Pro - Smarthouse integration with LLM
author_name: Ole Andre Torjussen
author_username: oleandre
categories: []
created_at: 2025-05-14T21:30:37.315Z
updated_at: 2025-05-14T21:30:37.315Z
url: https://n8n.io/workflows/4058
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n workflow sets up a smart home assistant using OpenAI and Homey integration. It uses LangChain agent tools to allow natural language queries (in Norwegian) to trigger workflows for controlling lights, curtains, temperature, TVs, and other devices across different rooms (e.g., living room, bedroom, cinema). The system uses tool-based workflows connected to specific smart home actions and responds in Norwegian. It’s designed to be modular and easily extended with new devices or capabilities.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.set","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.lmChatXAiGrok","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","home-automation","AI/LLM"],"complexity":"complex","use_cases":["Natural language control of smart home devices (Norwegian)","Automating routines and scenes across rooms","Modular extension to add new devices and actions","Orchestrating multi-device workflows (lights, curtains, temperature, TVs)","Tool-based agent actions triggered by LLM queries"],"estimated_setup_minutes":120,"required_services":["Homey Pro (Homey Cloud / Homey apps)","OpenAI or compatible LLM API","LangChain agent tools (or similar agent framework)","n8n platform"],"key_features":["Natural-language (Norwegian) queries routed to tool/workflow actions","LangChain-style agent tooling to map intents to device workflows","Modular tool/workflow design for easy extension","Multi-room device control (living room, bedroom, cinema, etc.)","Executes and chains workflows programmatically (toolWorkflow nodes)"],"target_audience":["Developers and integrators","Home automation enthusiasts and power users","IoT/Smart home installers and system administrators"]}


name: Extract and Save Invoice Data from Google Drive to Sheets with Dumpling AI
author_name: Yang
author_username: yang
categories: []
created_at: 2025-05-14T21:31:31.196Z
updated_at: 2025-05-14T21:31:31.196Z
url: https://n8n.io/workflows/4059
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  Who is this for?

  This workflow is perfect for operations teams, accountants, e-commerce businesses, or finance managers who regularly process digital invoices and need to automate data extraction and record-keeping.

  What problem is this workflow solving?

  Manually reading invoice PDFs, extracting relevant data, and entering it into spreadsheets is time-consuming and error-prone. This workflow automates that process—watching a Google Drive folder, extracting structured invoice data using Dumpling AI, and saving the results into Google Sheets.

  What this workflow does

  Watches a specific Google Drive folder for new invoices.
  Downloads the uploaded invoice file.
  Converts the file into a Base64 format.
  Sends the file to Dumpling AI’s extract-document endpoint with a detailed parsing prompt.
  Parses Dumpling AI’s JSON response using a Code node.
  Splits the items array into individual rows using the Split Out node.
  Appends each invoice item to a preformatted Google Sheet along with the full header metadata (order number, PO, addresses, etc.).

  Setup

  Google Drive Setup
     Create or select a folder in Google Drive and place the folder ID in the trigger node.
     Make sure your n8n Google Drive credentials are authorized for access.

  Google Sheets
     Create a Google Sheet with the following headers:
       Order number, Document Date, Po_number, Sold to name, Sold to address, Ship to name, Ship to address, Model, Description, Quantity, Unity price, Total price
     Paste the Sheet ID and sheet name (Sheet1) into the Google Sheets node.

  Dumpling AI
     Sign up at Dumpling AI
     Go to your account settings and generate your API key.
     Paste this key into the HTTP header of the Dumpling AI request node.
     The endpoint used is: https://app.dumplingai.com/api/v1/extract-document

  Prompt (already included)
     This prompt extracts: order number, document date, PO number, shipping/billing details, and detailed line items (model, quantity, unit price, total).

  How to customize this workflow to your needs

  Adjust the Google Sheet fields to fit your invoice structure.
  Modify the Dumpling AI prompt if your invoices have additional or different data points.
  Add filtering logic if you want to handle different invoice types differently.
  Replace Google Sheets with Airtable or a database if preferred.
  Use a different trigger like an email attachment if invoices come via email.

nodes_used:
  ["n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDrive","n8n-nodes-base.extractFromFile","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.splitOut","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["automation","integration","document processing","data extraction","finance"],"complexity":"medium","use_cases":["Automate invoice data extraction from PDFs","Populate accounting spreadsheets with invoice line items","Reduce manual data entry for accounts payable","E-commerce invoice record-keeping","Preprocess invoices for downstream accounting or ERP systems"],"estimated_setup_minutes":20,"required_services":["Google Drive","Google Sheets","Dumpling AI (API key)","n8n"],"key_features":["Folder trigger to watch Google Drive for new invoices","Download and convert files to Base64","Send files to Dumpling AI extract-document endpoint with custom prompt","Parse Dumpling AI JSON response via Code node","Split line items and append rows to Google Sheets with header metadata (order number, PO, addresses, etc.)"],"target_audience":["Operations teams","Accountants / Finance managers","E-commerce businesses / Sellers who process invoices regularly"]}


name: Automated daily workflow backup to GitHub
author_name: Hugo
author_username: hugop
categories: []
created_at: 2025-05-15T02:33:44.721Z
updated_at: 2025-05-15T02:33:44.721Z
url: https://n8n.io/workflows/4064
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  This workflow provides a robust solution for automatically backing up all your n8n workflows to a designated GitHub repository on a daily basis. By leveraging the n8n API and GitHub API, it ensures your workflows are version-controlled and securely stored, safeguarding against data loss and facilitating disaster recovery.

  How it works

  The automation follows these key steps:

  Scheduled trigger: The workflow is initiated automatically every day at a pre-configured time.
  List existing backups: It first connects to your GitHub repository to retrieve a list of already backed-up workflow files. This helps in determining whether a workflow's backup file needs to be created or updated.
  Retrieve n8n workflows: The workflow then fetches all current workflows directly from your n8n instance using the n8n REST API.
  Process and prepare: Each retrieved workflow is individually processed. Its data is converted into JSON format. This JSON content is then encoded to base64, a format suitable for GitHub API file operations.
  Commit to GitHub: For each n8n workflow:
      A standardized filename is generated (e.g., workflow-name-tag.json).
      The workflow checks if a file with this name already exists in the GitHub repository (based on the list fetched in step 2).
      If the file exists: It updates the existing file with the latest version of the workflow.
      If it's a new workflow (file doesn't exist): A new file is created in the repository.
      Each commit is timestamped for clarity.

  This process ensures that you always have an up-to-date version of all your n8n workflows stored securely in your GitHub version control system, providing peace of mind and a reliable backup history.

  Pre-requisites

  Before you can use this template, please ensure you have the following:

  An active n8n instance (self-hosted or cloud).
  A GitHub account.
  A GitHub repository created where you want to store the workflow backups.
  A GitHub Personal Access Token with repo scope (or fine-grained token with read/write access to the specific backup repository). This token will be used for GitHub API authentication.
  n8n API credentials (API key) for your n8n instance.

  Set up steps

  Setting up this workflow should take approximately 10-15 minutes if you have your credentials ready.

  Import the template: Import this workflow into your n8n instance.
  Configure n8n API credentials:
      Locate the "Retrieve workflows" node.
      In the "Credentials" section for "n8n API", create new credentials (or select existing ones).
      Enter your n8n instance URL and your n8n API Key (you can create your n8n api key in the settings of your n8n instance)
  Configure GitHub credentials:
      Locate the "List files from repo" node (and subsequently "Update file" / "Upload file" nodes which will use the same credential).
      In the "Credentials" section for "GitHub API", create new credentials.
      Select OAuth2/Personal Access Token authentication method.
      Enter the GitHub Personal Access Token you generated as per the pre-requisites.
  Specify repository details:
      In the "List files from repo", "Update file", and "Upload file" GitHub nodes:
          Set the Owner: Your GitHub username or organization name.
          Set the Repository: The name of your GitHub repository dedicated to backups.
          Set the Branch (e.g., main or master) where backups should be stored.
          (Optional) Specify a Path within the repository if you want backups in a specific folder (e.g., n8n_backups/). Leave blank to store in the root.
  Adjust schedule (Optional):
      Select the "Schedule Trigger" node.
      Modify the trigger interval (e.g., change the time of day or frequency) as needed. By default, it's set for a daily run.
  Activate the workflow: Save and activate the workflow.

  Explanation of nodes

  Here's a detailed breakdown of each node used in this workflow:

  Schedule trigger**
      Type: n8n-nodes-base.scheduleTrigger
      Purpose: This node automatically starts the workflow based on a defined schedule (e.g., daily at midnight).
  List files from repo**
      Type: n8n-nodes-base.github
      Purpose: Connects to your specified GitHub repository and lists all files, primarily to check for existing workflow backups.
  Aggregate**
      Type: n8n-nodes-base.aggregate
      Purpose: Consolidates the list of file names obtained from the "List files from repo" node into a single item for easier lookup later in the "Check if file exists" node.
  Retrieve workflows**
      Type: n8n-nodes-base.n8n
      Purpose: Uses the n8n API to fetch a list of all workflows currently present in your n8n instance.
  Json file**
      Type: n8n-nodes-base.convertToFile
      Purpose: Takes the data of each workflow (retrieved by the "Retrieve workflows" node) and converts it into a structured JSON file format.
  To base64**
      Type: n8n-nodes-base.extractFromFile
      Purpose: Converts the binary content of the JSON file (from the "Json file" node) into a base64 encoded string. This encoding is required by the GitHub API for file content.
  Commit date & file name**
      Type: n8n-nodes-base.set
      Purpose: Prepares metadata for the GitHub commit. It generates:
          commitDate: The current date and time for the commit message.
          fileName: A standardized file name for the workflow backup (e.g., my-workflow-vps-backups.json), typically using the workflow's name and its first tag.
  Check if file exists**
      Type: n8n-nodes-base.if
      Purpose: A conditional node. It checks if the fileName (generated by "Commit date & file name") is present in the list of files aggregated by the "Aggregate" node. This determines if the workflow backup already exists in GitHub.
  Update file**
      Type: n8n-nodes-base.github
      Purpose: If the "Check if file exists" node determines the file does exist, this node updates that existing file in your GitHub repository with the latest workflow content (base64 encoded) and a commit message.
  Upload file**
      Type: n8n-nodes-base.github
      Purpose: If the "Check if file exists" node determines the file does not exist, this node creates and uploads a new file to your GitHub repository with the workflow content and a commit message.

  Customization

  Here are a few ways you can customize this template to better fit your needs:

  Backup path**: In the GitHub nodes ("List files from repo", "Update file", "Upload file"), you can specify a Path parameter to store backups in a specific folder within your repository (e.g., workflows/ or daily_backups/).
  Filename convention**: Modify the "Commit date & file name" node (specifically the expression for fileName) to change how backup files are named. For example, you might want to include the workflow ID or a different date format.
  Commit messages**: Customize the commit messages in the "Update file" and "Upload file" GitHub nodes to include more specific information if needed.
  Error handling**: Consider adding error handling branches (e.g., using the "Error Trigger" node or checking for node execution failures) to notify you if a backup fails for any reason.
  Filtering workflows**: If you only want to back up specific workflows (e.g., those with a particular tag or name pattern), you can add a "Filter" node after "Retrieve workflows" to include only the desired workflows in the backup process.
  Backup frequency**: Adjust the "Schedule Trigger" node to change how often the backup runs (e.g., hourly, weekly, or on specific days).


  Template was created in n8n v1.92.2

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.n8n","n8n-nodes-base.aggregate","n8n-nodes-base.github","n8n-nodes-base.github","n8n-nodes-base.github","n8n-nodes-base.if","n8n-nodes-base.convertToFile","n8n-nodes-base.extractFromFile","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","backup","version-control"],"complexity":"medium","use_cases":["Automated backups of n8n workflows","Disaster recovery and restore","Keeping workflow history/versioning in GitHub","Audit and change tracking for workflows","Migrating or syncing workflows between instances"],"estimated_setup_minutes":15,"required_services":["n8n instance / n8n REST API","GitHub repository / GitHub API (Personal Access Token with repo scope)"],"key_features":["Daily scheduled export of all n8n workflows","Checks repo for existing files and updates or creates files accordingly","Converts workflows to JSON and encodes content as base64 for GitHub API","Generates standardized filenames and timestamped commits","Configurable repo/branch/path and schedule frequency"],"target_audience":["n8n users","Developers","DevOps engineers / platform operators"]}


name: Serve Inspirational Quotes On-Demand via Webhook using ZenQuotes API
author_name: ist00dent
author_username: ist00dent
categories: []
created_at: 2025-05-15T04:21:51.123Z
updated_at: 2025-05-15T04:21:51.123Z
url: https://n8n.io/workflows/4065
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  This n8n template lets you instantly serve batches of inspirational quotes via a webhook using the free ZenQuotes API. It’s perfect for developers, content creators, community managers, or educators who want to add dynamic, uplifting content to websites, chatbots, or internal tools—without writing custom backend code.

  🔧 How it works
  A Webhook node listens for incoming HTTP requests on your chosen path.
  Get Random Quote from ZenQuotes sends an HTTP Request to https://zenquotes.io/api/random?count=5 and retrieves five random quotes.
  Format data uses a Set node to combine each quote (q) and author (a) into a single string:
  "“quote” – author".
  Send response returns a JSON array of objects { quote, author } back to the caller.

  👤 Who is it for?
  This workflow is ideal for:

  Developers building motivational Slack or Discord bots.
  Website owners adding on-demand quote widgets.
  Educators or trainers sharing daily inspiration via webhooks.
  Anyone learning webhook handling and API integration in n8n.

  🗂️ Response Structure
  Your webhook response will be a JSON array, for example:

  [
  {
  "quote": "Life is what happens when you're busy making other plans.",
  "author": "John Lennon"
  },
  {
  "quote": "Be yourself; everyone else is already taken.",
  "author": "Oscar Wilde"
  }
  ]

  ⚙️ Setup Instructions
  Import the workflow JSON into your n8n instance.
  In the Webhook node, set your desired path (e.g., /inspire).
  (Optional) Change the count parameter in the HTTP Request node to fetch more or fewer quotes.
  Activate the workflow.
  Test by sending an HTTP GET or POST to https://&lt;your-n8n-domain&gt;/webhook/&lt;path&gt;.

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

workflow_json_compressed:
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metadata_json:
  {"categories":["integration","automation","content delivery","webhook"],"complexity":"simple","use_cases":["Serve inspirational quotes to websites or widgets on demand","Power motivational Slack/Discord bots or chatbots","Provide daily/instant inspiration for internal tools or dashboards","Learning example for webhook handling and API integration in n8n"],"estimated_setup_minutes":5,"required_services":["n8n instance (publicly reachable or via tunnel like ngrok)","ZenQuotes API (https://zenquotes.io/)","Optional: hosting or domain for webhook endpoint"],"key_features":["Webhook-triggered endpoint to request quote batches","Fetches multiple random quotes from ZenQuotes API","Formats quote and author into readable strings","Returns structured JSON array of {quote, author}","Configurable count parameter to control batch size (via HTTP Request node)"],"target_audience":["Developers","Content creators","Community managers / moderators building bots or widgets"]}


name: Track Hourly Weather Conditions with OpenWeatherMap and Google Sheets
author_name: ist00dent
author_username: ist00dent
categories: []
created_at: 2025-05-15T04:33:05.417Z
updated_at: 2025-05-15T04:33:05.417Z
url: https://n8n.io/workflows/4066
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  This n8n template allows you to monitor hourly weather conditions in a specific city using OpenWeatherMap and log the results to a Google Sheet. It’s perfect for anyone needing periodic weather tracking—whether you're managing logistics, travel planning, or environmental monitoring.

  🔧 How it works
  A Schedule Trigger activates the workflow every hour.

  The Get Weather Data from OpenWeatherMap node fetches real-time weather details using the city name you specify.

  An IF node checks if the weather description contains "rain" or the temperature is below a set threshold.

  If the condition is true, the data is formatted with city, temperature, humidity, and conditions.

  The Google Sheets node appends this formatted information to your designated sheet.

  👤 Who is it for?
  This workflow is ideal for:

  Operations teams monitoring weather-sensitive logistics

  Researchers collecting climate data

  Developers and hobbyists learning how to connect APIs with Google Sheets

  🗂️ Google Sheet Structure
  Your Google Sheet should have the following columns:

  city (string)

  temperature (K) (number)

  humidity (number)

  conditions (string)

  status (string)

  ⚙️ Setup Instructions
  Create a Google Sheet with the above columns.

  Set up your Google Service Account credentials in n8n.

  Replace the API key in the HTTP Request node with your own OpenWeatherMap credential.

  Specify your target city and ensure your OpenWeatherMap account is active.

  Adjust the frequency in the Schedule Trigger as needed (default: every hour).

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

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metadata_json:
  {"categories":["automation","integration","monitoring","data-logging"],"complexity":"simple","use_cases":["Weather-sensitive logistics monitoring","Environmental or climate data collection","Automated alerts for rain or low temperature","Learning example for API → Google Sheets integrations"],"estimated_setup_minutes":15,"required_services":["OpenWeatherMap API","Google Sheets (Google Service Account)","n8n instance"],"key_features":["Hourly schedule trigger","Fetch current weather by city from OpenWeatherMap","Conditional check for rain or temperature threshold","Format and append records to Google Sheets","Configurable city, API key and thresholds"],"target_audience":["Operations teams","Researchers and analysts","Developers and hobbyists"]}


name: Automated Email Assistant for Suppliers using OpenAI and Google Sheets
author_name: Omar Hdez
author_username: omarhdez
categories: []
created_at: 2025-05-15T06:22:30.568Z
updated_at: 2025-05-15T06:22:30.568Z
url: https://n8n.io/workflows/4067
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Automated Email Assistant for Busy Professionals

  This assistant is designed for people who don't have time to write and send emails to suppliers. With just one request, it drafts and sends clear, professional messages automatically.

  How It Works
  The user makes a request (e.g., “Send an email to my fruit supplier asking for a quote on 1 crate of mangoes.”).

  Workflow:

  The AI agent searches for the supplier in a Google Sheets database.

  It automatically drafts the email using OpenAI (with the tone and style you define).
   
  It sends the email using your Gmail account connected through n8n.
  This assistant uses:

  Google Sheets to manage your suppliers (name and email).

  OpenAI to generate clear, natural messages.
    
  MCP (client-server logic) to handle request processing.
    
  Gmail as the sending channel for automated emails.
  Setup Instructions

  Create a Google Sheets document with the supplier name and email, like this:

  |Supplier name|Email|
  |-|-|
  |Proveedor de frutas Alvarez|fruteriaalvarez@alvarez.com|
   
  Connect your Google Sheets and Gmail accounts within n8n.
   
  Add your OpenAI API key.

  Test the automation by chatting with the integrated assistant.
  It will generate and send the email automatically to the indicated supplier.

  Requirements
  OpenAI API key to generate email content.
  Gmail account connected via OAuth2.
  Google Sheets document with your supplier database.
  n8n instance (cloud or self-hosted).

  Customization

  Adjust the OpenAI prompt to make the email tone more formal, casual, or technical.

  Add custom fields to your supplier sheet (location, notes, special conditions).

  Replace Google Sheets with a real database like Supabase or PostgreSQL for greater scalability.

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

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metadata_json:
  {"categories":["automation","communication","integration"],"complexity":"medium","use_cases":["Automating supplier outreach and quote requests","Drafting professional emails on behalf of the user","Procurement follow-ups and order inquiries","Saving time for busy professionals handling vendor communication"],"estimated_setup_minutes":20,"required_services":["Google Sheets","OpenAI API","Gmail (OAuth2)","n8n instance","MCP (client-server logic)"],"key_features":["Search supplier records in Google Sheets","Generate email content with OpenAI (customizable tone/style)","Send emails via Gmail automatically","Chat-driven request input (assistant interface)","Easily replaceable data source (e.g., Supabase/Postgres)"],"target_audience":["Busy professionals","Procurement managers and buyers","Small business owners and operators handling vendors (ops)
  Note: for clarity this is a single audience entry that includes non-technical users who rely on assistants and business workflows."]}


name: Find Reddit Prospects & Generate Personalized Responses with Llama3 AI and Google Sheets
author_name: Dhrumil Barot
author_username: barotdhrumil21
categories: []
created_at: 2025-05-15T10:00:08.976Z
updated_at: 2025-05-15T10:00:08.976Z
url: https://n8n.io/workflows/4068
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Turn Reddit into Your Most Powerful Lead Generation Channel

  Discover hot prospects on Reddit who are actively discussing problems your product can solve - without spending hours manually searching and responding. This intelligent workflow automatically finds relevant conversations, analyzes intent with Llama3 AI, and crafts personalized responses that drive conversions.

  🔍 Automated Prospect Discovery
  Stop wasting time manually searching Reddit. Let AI find people who genuinely need your solution - while you focus on higher-value tasks. The workflow monitors target subreddits 24/7, ensuring you never miss a potential lead.

  🧠 Intelligent Relevance Filtering
  Not all mentions are quality leads. Our sophisticated Llama3 AI engine analyzes post content, sentiment, and intent to identify true business opportunities. Say goodbye to false positives and low-quality interactions.

  💬 Personalized Response Generation
  Generic outreach gets ignored. This workflow crafts individualized responses that reference specific pain points from each post, positioning your solution as the perfect answer to their problem - dramatically increasing engagement rates.

  📊 Complete Activity Tracking
  Every interaction is automatically logged in Google Sheets, creating a comprehensive database of your Reddit prospecting activities. Track which posts received responses, measure engagement, and refine your targeting over time.

  🔄 Set It and Forget It
  Once configured, the entire process runs autonomously. No more daily Reddit searches or manual outreach - just check your dashboard to see new prospects and conversations.

  Perfect For:
  SaaS businesses targeting specific use cases
  Consultants and agencies looking for clients with specific problems
  Product companies monitoring customer pain points
  Support teams finding users who need help

  Technical Requirements:
  Reddit API credentials
  Ollama with Llama3 model
  Google account for tracking

  Get started today and transform Reddit from a time-sink into your most efficient lead generation channel!

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.reddit","n8n-nodes-base.if","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.lmChatOllama","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.splitInBatches","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.reddit","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","lead generation","social listening","integration"],"complexity":"medium","use_cases":["Automated prospect discovery on Reddit","Personalized outreach and engagement","Customer pain-point research and product discovery","Support triage by discovering users who need help","Building a tracked lead database in Google Sheets"],"estimated_setup_minutes":45,"required_services":["Reddit API credentials","Ollama running Llama3 model","Google account with Google Sheets access","n8n instance"],"key_features":["24/7 subreddit monitoring for relevant posts","Llama3-based intent and sentiment analysis to filter prospects","Automated, personalized response generation","Activity and interaction logging to Google Sheets","Batch processing and conditional filtering to reduce false positives"],"target_audience":["SaaS founders and growth teams","Consultants and agencies","Product managers and researchers doing customer discovery"]}


name: Complete Shopify to Odoo Integration: Sync Orders, Products & Customers
author_name: Evozard
author_username: evozardconsulting
categories: []
created_at: 2025-05-15T10:58:11.724Z
updated_at: 2025-05-15T10:58:11.724Z
url: https://n8n.io/workflows/4069
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  This workflow functions like the integration of Shopify products with Odoo products, Shopify customers with Odoo customers, and paid Shopify orders with Odoo sales orders containing multiple products.
   
  Trigger (Shopify – Order Created & Paid): The workflow starts when a new order is created in Shopify and its status is marked as paid.
   
  Loop Through Each Line Item in the Order:
      For each product in the order:
          Check if Product Exists in Odoo (via Internal Reference/Default Code)
  	    If the product exists, proceed.
  	    If the product does not exist, create it in Odoo with the following details:
  	        Product Name
  		Description
  		Price
  		Internal Reference (Default Code)
   
  Check if Customer Contact Exists in Odoo (via Email):
      If the contact exists, proceed.
      If the contact does not exist, create a new contact in Odoo with:
          Full Name
          Email Address
          Phone Number
          Shipping or Billing Address (as available)
   
  Create a Sale Order in Odoo:
      Create a new Sale Order in Odoo using:
          The matched or newly created Customer Contact
          The matched or newly created All the Products in the Shopify order (with correct quantities and pricing)
      Each Shopify product in the order is matched to the corresponding Odoo product using the Internal Reference field
      
  Integration Setup References:
   
      Shopify Integration Setup:
      https://docs.n8n.io/integrations/builtin/credentials/shopify/
   
      Odoo Integration Setup:
      https://docs.n8n.io/integrations/builtin/credentials/odoo/

nodes_used:
  ["n8n-nodes-base.shopifyTrigger","n8n-nodes-base.odoo","n8n-nodes-base.code","n8n-nodes-base.splitOut","n8n-nodes-base.odoo","n8n-nodes-base.odoo","n8n-nodes-base.code","n8n-nodes-base.odoo","n8n-nodes-base.filter","n8n-nodes-base.odoo","n8n-nodes-base.odoo","n8n-nodes-base.filter","n8n-nodes-base.code","n8n-nodes-base.splitOut","n8n-nodes-base.code","n8n-nodes-base.odoo","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.odoo"]

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metadata_json:
  {"categories":["integration","e-commerce","automation","ERP/CRM synchronization"],"complexity":"complex","use_cases":["Sync Shopify products to Odoo (create if missing)","Sync Shopify customers to Odoo (create if missing)","Create Odoo sales orders from paid Shopify orders with correct line items and pricing","Automate order processing and catalog consistency between Shopify and Odoo","Match products using Internal Reference/Default Code for reliable mapping"],"estimated_setup_minutes":60,"required_services":["Shopify (API)","Odoo (API/ERP)","n8n (workflow automation platform)"],"key_features":["Trigger on Shopify Order Created & Paid","Loop through order line items and handle each product","Check-for-or-create product in Odoo (name, description, price, internal reference)","Check-for-or-create customer in Odoo (name, email, phone, address)","Create Odoo Sale Order with matched products, quantities and pricing using Internal Reference matching"],"target_audience":["ecommerce managers","ERP/CRM administrators","integration engineers/developers implementing system-to-system syncs\",\"small and medium businesses using Shopify and Odoo"]}


name: Linking NocoDB Records via API 🔗
author_name: Max aka Mosheh
author_username: aiwithapex
categories: []
created_at: 2025-05-15T13:39:15.959Z
updated_at: 2025-05-15T13:39:15.959Z
url: https://n8n.io/workflows/4070
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  How it works:
  The n8n flow grabs the needed IDs, fetches the current links, adds your new one, and sends a single HTTP request to NocoDB to update the record’s linked entries.

  Set up steps:

  Plan for 10 minutes setup if you’re already running n8n and NocoDB.
  You’ll need to copy/paste table IDs, set up your HTTP node, and test once.
  No coding, just copy IDs.

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

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metadata_json:
  {"categories":["integration","automation","databases","no-code"],"complexity":"simple","use_cases":["Add a link between NocoDB records","Update a record's linked entries via API","Automate relational data maintenance between tables","Quickly test and apply record relationships without coding"],"estimated_setup_minutes":10,"required_services":["n8n","NocoDB (API access)","HTTP/Webhooks endpoint (for API requests)"],"key_features":["Retrieves required record/table IDs","Fetches current linked entries and appends a new link","Sends a single HTTP request to update linked records","No-code configuration—copy/paste IDs and test","Uses n8n NocoDB and HTTP nodes for orchestration"],"target_audience":["n8n users","No-code/low-code builders","Database administrators and data stewards`,`Integrators and product managers"]}


name: AI-Powered RAG Q&A Chatbot with OpenAI, Google Sheets, Glide & Supabase
author_name: Sam Yassine
author_username: samyassine
categories: []
created_at: 2025-05-15T14:00:56.738Z
updated_at: 2025-05-15T14:00:56.738Z
url: https://n8n.io/workflows/4071
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Automate AI-Powered RAG System with Contextual Q&A, Google Sheets Integration, and Glide Frontend—Powered by n8n, OpenAI, Supabase, and Google Apps Script.

  Tools & Services Used
  Glide (Frontend for user interactions)

  Google Sheets (Stores questions and answers)

  Google Apps Script (Forms + media upload handling)

  OpenAI (Embeddings + GPT-4 to rank and generate answers)

  Supabase (Optional for image hosting)

  n8n (Automation logic and backend glue)

  Workflow Overview
  This automation performs the following steps:

  Trigger: Webhook receives a user question from a Glide frontend.

  Fetch Data: Retrieves Q&A entries (and optionally, image URLs) from a connected Google Sheet.

  Rank Relevance:

  OpenAI Embeddings rank the relevance of stored questions to the new input.

  Top matches are passed to a GPT-4 prompt for answer generation.

  Generate Answer:

  GPT-4 creates a contextual answer using the best match.

  Optional: Includes media URL if attached to the matched answer.

  Response: Sends the formatted answer back to Glide frontend (text + optional image).

  Prerequisites
  Active accounts and API keys for:

  OpenAI (API key with GPT-4 and embedding access)

  Google Sheets (linked via Service Account or OAuth2 credentials)

  Glide App (with a form to submit questions)

  Supabase (optional, if hosting user-uploaded media)

  n8n Self-hosted (for community nodes and webhook access)

  How to Use This Template
  Step 1: Import the Template
  Upload the provided JSON into your self-hosted n8n instance.

  Requires installation of the Community Node: @n8n/n8n-nodes-openai-embeddings

  Step 2: Configure Credentials
  Webhook Trigger: Replace the Glide webhook URL with your actual endpoint from your app.

  Google Sheets Node: Set the spreadsheet ID and worksheet name to fetch your Q&A dataset.

  OpenAI Nodes:

  Insert your OpenAI API key.

  Confirm that both text-embedding-ada-002 (or similar) and gpt-4 are available.

  Supabase or Apps Script (Optional): Ensure image links are correctly formed in the Sheet.

  Step 3: Customize Prompts and Thresholds
  Update the OpenAI ranking prompt or similarity threshold in the Function or Code node.

  Tailor GPT-4 prompts to return concise or detailed answers based on your use case.

  Initial Test Run
  Simulate a Question:
  Trigger the Glide form with a sample question.

  Check n8n logs for:

  Matching score/ranking success

  Correct retrieval from Sheets

  GPT-4 answer generation

  Image URL handling (if enabled)

  Production Prep
  Add error-handling nodes for cases when Sheets are empty or GPT-4 fails.

  Set up logging via Google Sheets, Supabase, or external database.

  Rate-limit or debounce requests from Glide if needed.

  Use Cases
  Customer Support Portals: Offer instant Q&A with your business data.

  Internal Knowledgebase: Employees can query training manuals and SOPs.

  AI Concierge: Combine structured answers with friendly tone for guest interactions.

  Disclaimer

  Validate Glide form inputs before full deployment.

  Ensure your OpenAI and Google Sheets usage stays within quota limits.

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.googleCalendar","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.webhook","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.respondToWebhook","n8n-nodes-base.httpRequest","n8n-nodes-base.respondToWebhook","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.vectorStoreQdrant","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterTokenSplitter","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","data processing","AI/ML"],"complexity":"complex","use_cases":["Customer support portals (instant contextual Q&A)","Internal knowledgebase / employee helpdesk","AI concierge for apps (contextual responses with media)","FAQ automation and response enrichment"],"estimated_setup_minutes":90,"required_services":["OpenAI (embeddings + GPT-4)","Google Sheets","Glide (frontend)","n8n (self-hosted instance)","Google Apps Script","Supabase (optional - media hosting)","Qdrant (optional - vector store)","Google Drive (optional - media storage)"],"key_features":["RAG-style retrieval using OpenAI embeddings to rank stored Q&A","GPT-4 generated contextual answers built from top matches","Webhook trigger from Glide for real-time Q&A","Google Sheets as the Q&A datastore (with optional image URLs)","Optional media handling and hosting (Supabase / Google Drive / Apps Script integration)"],"target_audience":["Developers implementing conversational AI frontends","Product teams building knowledge-driven features","Support/operations teams automating FAQ and helpdesk responses"]}


name: Enrich Seller Data with Email & Domain Lookup using Bright Data & Google Search
author_name: Cyril Nicko Gaspar
author_username: cngaspar
categories: []
created_at: 2025-05-15T14:35:17.827Z
updated_at: 2025-05-15T14:35:17.827Z
url: https://n8n.io/workflows/4072
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  🔍 Email Lookup with Google Search from Postgres Database

  This N8N workflow is designed to enrich seller data stored in a Postgres database by performing automated Google search lookups. It uses Bright Data's Web Unlocker to bypass search result restrictions and the HTML Extract node to parse and extract relevant information from webpages.

  The main purpose of this workflow is to discover missing contact details, company domains, and secondary emails for businesses or sellers based on existing database entries.

  🎯 Problem This Workflow Solves

  Manually searching for missing seller or business details—like secondary emails, websites, or domain names—can be time-consuming and inefficient, especially for large datasets.
  This workflow automates the search and data enrichment process, significantly reducing manual effort while improving the quality and completeness of your seller database.

  ✅ Prerequisites

  Before using this template, make sure the following requirements are met:

  ✔️ A Bright Data account with access to the Web Unlocker or Amazon Scraper API
  ✔️ A valid Bright Data API key
  ✔️ An active PostgreSQL database with seller data
  ✔️ N8N self-hosted instance (recommended for using community nodes like n8n-nodes-brightdata)
  ✔️ Installed n8n-nodes-brightdata package (custom node for Bright Data integration)

  ⚙️ Setup Instructions

  Step 1: Prepare Your Postgres Table

  Create a table in Postgres with the following structure (you can adjust field names if needed):

  CREATE TABLE sellers (
    seller_id SERIAL PRIMARY KEY,
    seller_name TEXT,
    primary_email TEXT,
    company_info TEXT,
    trade_name TEXT,
    business_address TEXT,
    coc_number TEXT,
    vat_number TEXT,
    commercial_register TEXT,
    secondary_email TEXT,
    domain TEXT,
    seller_slug TEXT,
    source TEXT
  );

  Step 2: Setup Web Unlocker on Bright Data

  Go to your Bright Data dashboard.
  Navigate to Proxies & Scraping → Web Unlocker.
  Create a new zone, selecting Web Unlocker API under Scraping Solutions.
  Whitelist your server IP if required.

  Step 3: Generate API Key

  In the Bright Data dashboard, go to the API section.
  Generate a new API key.
  In N8N, create HTTP Request Credentials using Bearer Authentication with the API key.

  Step 4: Install the Bright Data Node in N8N

  In your N8N self-hosted instance, go to Settings → Community Nodes.
  Search and install n8n-nodes-brightdata.

  🔄 Workflow Functionality

  🔁 Trigger: Can be set to run on a schedule (e.g., daily) or manually.
  📥 Read: Fetches seller records from the Postgres table.
  🌐 Search: Uses Bright Data to perform a Google search based on seller_name, company_info, or trade_name.
  🧾 Extract: Parses the HTML content using the HTML Extract node to identify potential websites and email addresses.
  📝 Update: Writes enriched data (like domain or secondary_email) back to the Postgres table.

  💡 Use Cases

  Lead enrichment for e-commerce sellers
  Domain and contact info discovery for B2B databases
  Email and web domain verification for CRM systems
  Market research automation

  🛠️ Customization Tips

  You can enhance the parsing logic in the HTML Extract node to look for phone numbers, LinkedIn profiles, or social media links.
  Modify the search query logic to include additional parameters like location or industry for more refined results.
  Integrate additional APIs (e.g., Hunter.io, Clearbit) for email validation or social profile enrichment.
  Add filtering to skip entries that already have domain or secondary_email.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.postgres","n8n-nodes-base.wait","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.postgres","n8n-nodes-base.switch","n8n-nodes-base.code","n8n-nodes-base.postgres","n8n-nodes-base.switch","n8n-nodes-base.postgres","n8n-nodes-base.wait","n8n-nodes-base.set","n8n-nodes-base.splitInBatches","n8n-nodes-brightdata.brightData","n8n-nodes-brightdata.brightData","n8n-nodes-base.html","n8n-nodes-base.html","n8n-nodes-base.splitOut","n8n-nodes-base.filter","n8n-nodes-base.aggregate","n8n-nodes-base.code","n8n-nodes-base.splitOut","n8n-nodes-base.filter","n8n-nodes-base.if","n8n-nodes-base.aggregate","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.postgres","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6808 characters]

metadata_json:
  {"categories":["automation","data enrichment","web scraping","database integration"],"complexity":"complex","use_cases":["Lead enrichment for e-commerce sellers","Domain and contact discovery for B2B databases","Email and web domain verification for CRM systems","Market research automation"],"estimated_setup_minutes":45,"required_services":["Bright Data (Web Unlocker / Scraping API)","PostgreSQL database","n8n self-hosted instance","n8n-nodes-brightdata community node (Bright Data integration)"],"key_features":["Automated Google search via Bright Data to bypass result restrictions","HTML extraction and parsing to find domains and secondary emails","Reads from and writes enriched records back to Postgres","Configurable scheduled or manual trigger","Supports custom parsing logic and additional API integrations (e.g., Hunter, Clearbit)"],"target_audience":["Developers / Automation Engineers","Data Engineers","Growth / Marketing teams (lead enrichment)  GMT/CRM admins]"]}


name: Automate GitHub PR Linting with Google Gemini AI and Auto-Fix PRs
author_name: Adam Bertram
author_username: adbertram
categories: []
created_at: 2025-05-15T15:00:58.877Z
updated_at: 2025-05-15T15:00:58.877Z
url: https://n8n.io/workflows/4073
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  LintGuardian: Automated PR Linting with n8n & AI

  What It Does

  LintGuardian is an n8n workflow template that automates code quality enforcement for GitHub repositories. When a pull request is created, the workflow automatically analyzes the changed files, identifies linting issues, fixes them, and submits a new PR with corrections. This eliminates manual code style reviews, reduces back-and-forth comments, and lets your team focus on functionality rather than formatting.

  How It Works

  The workflow is triggered by a GitHub webhook when a PR is created. It fetches all changed files from the PR using the GitHub API, processes them through an AI-powered linting service (Google Gemini), and automatically generates fixes. The AI agent then creates a new branch with the corrected files and submits a "linting fixes" PR against the original branch. Developers can review and merge these fixes with a single click, keeping code consistently formatted with minimal effort.

  Prerequisites

  To use this template, you'll need:

  n8n instance: Either self-hosted or using n8n.cloud
  GitHub repository: Where you want to enforce linting standards
  GitHub Personal Access Token: With permissions for repo access (repo, workflow, admin:repo_hook)
  Google AI API Key: For the Gemini language model that powers the linting analysis
  GitHub webhook: Configured to send PR creation events to your n8n instance

  Setup Instructions

  Import the template into your n8n instance
  Configure credentials:
     Add your GitHub Personal Access Token under Credentials → GitHub API
     Add your Google AI API key under Credentials → Google Gemini API
  Update repository information:
     Locate the "Set Common Fields" code node at the beginning of the workflow
     Change the gitHubRepoName and gitHubOrgName values to match your repository
        const commonFields = {
       'gitHubRepoName': 'your-repo-name',
       'gitHubOrgName': 'your-org-name'
     }
     Configure the webhook:
   Create a file named .github/workflows/lint-guardian.yml in your repository replacing the Trigger n8n Workflow step with your webhook:
            name: Lint Guardian
       
       on:
         pull_request:
           types: [opened, synchronize]
       
       jobs:
         trigger-linting:
           runs-on: ubuntu-latest
           steps:
             name: Trigger n8n Workflow
               uses: fjogeleit/http-request-action@v1
               with:
                 url: 'https://your-n8n-instance.com/webhook/1da5a6e1-9453-4a65-bbac-a1fed633f6ad'
                 method: 'POST'
                 contentType: 'application/json'
                 data: |
                   {
                     "pull_request_number": ${{ github.event.pull_request.number }},
                     "repository": "${{ github.repository }}",
                     "branch": "${{ github.event.pull_request.head.ref }}",
                     "base_branch": "${{ github.event.pull_request.base.ref }}"
                   }
                 preventFailureOnNoResponse: true
       Customize linting rules (optional):
     Modify the AI Agent's system message to specify your team's linting preferences
     Adjust file handling if you have specific file types to focus on or ignore

  Security Considerations

  When creating your GitHub Personal Access Token, remember to:
  Choose the minimal permissions needed (repo, workflow, admin:repo_hook)
  Set an appropriate expiration date
  Treat your token like a password and store it securely
  Consider using GitHub's fine-grained personal access tokens for more limited scope

  As GitHub documentation notes: "Personal access tokens are like passwords, and they share the same inherent security risks."

  Extending the Template

  You can enhance this workflow by:
  Adding Slack notifications when linting fixes are submitted
  Creating custom linting rules specific to your team's needs
  Expanding it to handle different types of code quality checks
  Adding approval steps for more controlled environments

  This template provides an excellent starting point that you can customize to fit your team's exact workflow and code style requirements.

nodes_used:
  ["n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.respondToWebhook","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.convertToFile","n8n-nodes-base.extractFromFile","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequestTool","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.code"]

workflow_json_compressed:
  [encoded data, 4128 characters]

metadata_json:
  {"categories":["automation","integration","code quality","AI-assisted development"],"complexity":"medium","use_cases":["Automatically lint and auto-fix PRs","Enforce code style and consistency","Reduce manual code-review overhead","Generate fix branches and PRs for maintainers"],"estimated_setup_minutes":30,"required_services":["n8n (self-hosted or n8n.cloud)","GitHub (repository + webhook + Personal Access Token)","Google AI / Gemini API (API key)"],"key_features":["Webhook-triggered on PR creation/sync","Fetches changed files via GitHub API","AI-powered lint analysis and auto-fixes (Google Gemini)","Creates branch and opens a \"linting fixes\" PR","Configurable linting rules via AI system message and workflow settings"],"target_audience":["Developers","DevOps/Platform engineers","Repository maintainers and code reviewers"]}


name: Financial News Digest with Google Gemini AI to Outlook Email
author_name: Louis
author_username: louisdl
categories: []
created_at: 2025-05-15T17:15:58.107Z
updated_at: 2025-05-15T17:15:58.107Z
url: https://n8n.io/workflows/4074
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  🧠 Key Features

  Looping source scraping: Collects content from news sites you have selected (it might not work for all of them however)
  HTML extraction & cleaning: Parses, cleans, and filters messy website data to isolate only the most relevant content.
  AI-powered synthesis: Uses Google Gemini (via LangChain agent) to summarize and structure financial news into a clear, bullet-pointed format.
  Email-ready output: Generates styled HTML summaries with coral-colored headings, ideal for daily email reports.
  Fully automated delivery: Sends out beautifully formatted updates via Outlook — no manual work required.

  🔧 Technologies Used

  n8n workflow orchestration
  LangChain agents for prompt logic and formatting
  Google Gemini API for advanced NLP
  Custom JS logic to manage dynamic inputs and cleanup
  Microsoft Outlook API for final distribution

  &gt; Feel free to reach out if needed ! 

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.microsoftOutlook","n8n-nodes-base.manualTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.outputParserItemList","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","news aggregation","reporting"],"complexity":"complex","use_cases":["Daily financial news digest for teams or clients","Investor/portfolio update emails","Market-moving event alerts and summaries","Generating executive-ready market briefings","Automated newsletter or outreach content generation"],"estimated_setup_minutes":90,"required_services":["n8n (workflow host)","Google Gemini API (via LangChain)","LangChain agent integration","Microsoft Outlook API (for sending emails)","Access to target news websites/web scraping endpoints"],"key_features":["Looping source scraping of selected news sites","HTML extraction, cleaning and content filtering","AI-powered synthesis and bullet-point summarization (Google Gemini)","Styled HTML output for email (coral-colored headings)","Automated scheduled delivery via Outlook (manual trigger supported)"],"target_audience":["financial analysts and researchers","portfolio managers and investors","marketing/communications teams producing newsletters\",\"automation engineers and n8n users"]}


name: Automated Tweet Generator & Publisher with GPT-4, Discord, and Google Sheets
author_name: LukaszB
author_username: lukaszb
categories: []
created_at: 2025-05-15T17:47:41.088Z
updated_at: 2025-05-15T17:47:41.088Z
url: https://n8n.io/workflows/4075
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  AI Twitter Content Machine – Write, Refine & Publish Tweets on Autopilot

  This workflow is perfect for creators, solopreneurs, and personal brands who want to consistently publish bold, high-performing content on X (Twitter) — without writing a single line themselves. After a one-time setup, it automatically generates tweet ideas, writes in your voice, evaluates post quality, avoids duplicates, and publishes directly to Twitter. All approvals and rewrites are handled in a conversational loop powered by OpenAI, Discord, and Google Sheets.

  Whether you’re building a personal brand or growing your startup audience, this tool will help you stay active, edgy, and relevant — with zero friction.

  How it works
  Distill what your flow does in a few high-level steps.
  Loads your brand brief from a sub-workflow.
  Generates a tweet idea aligned with your tone.
  Checks Google Sheets to ensure the idea hasn’t been used.
  Writes the post.
  Evaluates it using a feedback sub-workflow — if the quality score is below 0.7, it rewrites the post.
  Refines tone and voice using a Rewriter Agent that mimics your past content (from a Google Sheet).
  Sends the final post to a Discord channel for manual approval.
  On approval, posts directly to Twitter (X) and logs it to Google Sheets (History and Examples tabs).

  Set up steps
  Give users an idea of how long setup will take. Don’t describe every detail.
  Keep detailed descriptions in sticky notes inside your workflow.

  Key benefits
  No burnout, no block – Stop spending energy thinking what to tweet. AI handles everything.
  Style-matching – Posts sound like you, not a generic robot. Based on your real writing.
  Fast & scalable – Publish once or five times a day — it’s up to you.
  Avoid duplicates – Each idea is checked against your post history.
  Human-in-the-loop – You approve final posts via Discord. No rogue tweets.

  Integrations required
  n8n
  OpenAI API
  Google Sheets
  Twitter (OAuth2)
  Discord (for approval)
  Notion (optional for brand brief storage)

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.discord","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.executeWorkflow","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.discord","n8n-nodes-base.discord","n8n-nodes-base.discord","n8n-nodes-base.discord","n8n-nodes-base.twitter","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.notion","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflow","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","social media","AI content generation","integrations"],"complexity":"complex","use_cases":["Automated tweet/post generation and publishing","Personal brand growth and audience engagement","Scaling social content without manual writing","Maintaining consistent voice/style across posts","Human-in-the-loop approval and quality control"],"estimated_setup_minutes":45,"required_services":["n8n","OpenAI (GPT-4) API","Google Sheets","Twitter / X (OAuth2)","Discord (for approvals)","Notion (optional, for brand brief)"],"key_features":["Loads brand brief from sub-workflow to guide tone","Generates tweet ideas and checks Google Sheets for duplicates","Writes and evaluates posts with a quality score (rewrites if score < 0.7)","Rewriter Agent that mimics your past content from Google Sheets","Sends final drafts to Discord for manual approval (human-in-the-loop)"],"target_audience":["Creators and influencers","Solopreneurs and founders","Social media managers and content marketers"]}


name: Query-to-Action Automation with Bright Data MCP & OpenAI GPT
author_name: Cyril Nicko Gaspar
author_username: cngaspar
categories: []
created_at: 2025-05-15T18:00:56.880Z
updated_at: 2025-05-15T18:00:56.880Z
url: https://n8n.io/workflows/4077
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  📌 AI Agent Template with Bright Data MCP Tool Integration

  This template obtains all the possible tools from Bright Data MCP, process this through chatbot, then run any tool based on the user's query

  ❓ Problem It Solves

  The problem that the MCP solves is the complexity and difficulty of traditional automation, where users need to have specific knowledge of APIs or interfaces to trigger backend processes. By allowing interaction through natural language, automatically classifying and routing queries, and managing context and memory effectively, MCP simplifies complex data operations, customer support, and workflow orchestration scenarios where inputs and responses change dynamically.

  🧰 Pre-requisites

  Before deploying this template, ensure you have:

  An active n8n instance (self-hosted or cloud).
  A valid OpenAI API key (or any AI models)
  Access to Bright Data MCP API with credentials.
  Basic familiarity with n8n workflows and nodes.

  ⚙️ Setup Instructions

  **Install the MCP Community Node in N8N

     In your N8N self-hosted instance, go to Settings → Community Nodes.
     Search and install n8n-nodes-mcp.

  Configure Credentials:

     Add your OpenAI API key or any AI mdeols to the relevant nodes. If you want other AI model, please replace all associated nodes of OpenAI in the workflow
     Set up Bright Data MCP client credentials in the installed community node (STDIO)
     Obtain your API in Bright Data and put it in Environment field in the credentials window. It should be written as API_Key=&lt;your api key from Bright Data&gt;

  🔄 Workflow Functionality (Summary)

  User message** triggers the workflow.
  AI Classifier** (OpenAI) interprets the intent and maps it to a tool from Bright Data MCP.
  If no match is found, the user is notified.
  If more information is needed, the AI requests it.
  Memory** preserves context for follow-up actions.
  The tool is executed, and results are returned contextually to the user.

  &gt; 🧠 Optional memory buffer and chat memory manager nodes keep conversations context-aware across multiple messages.

  🧩 Use Cases

  Data Scraping Automation**: Trigger scraping tasks via chat.
  Lead Generation Bots**: Use MCP tools to fetch, enrich, or validate data.
  Customer Support Agents**: Automatically classify and respond to queries with tool-backed answers.
  Internal Workflow Agents**: Let team members trigger backend jobs (e.g., reports, lookups) by chatting naturally.

  🛠️ Customization

  Tool Matching Logic**: Modify the AI classifier prompt and schema to suit different APIs or services.
  Memory Size and Retention**: Adjust memory buffer size and filtering to fit your app’s complexity.
  Tool Execution**: Extend the "Execute the tool" sub-workflow to handle additional actions, fallback strategies, or logging.
  Frontend Integration**: Connect this with various platforms (e.g., WhatsApp, Slack, web chatbots) using the webhook.

  ✅ Summary

  This template delivers a powerful no-code/low-code agent that turns chat into automation, combining AI intelligence with real-world tool execution. With minimal setup, you can build contextual, dynamic assistants that drive backend operations using natural language.

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.if","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.memoryManager","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-mcp.mcpClient","n8n-nodes-mcp.mcpClient"]

workflow_json_compressed:
  [encoded data, 4384 characters]

metadata_json:
  {"categories":["automation","integration","AI agent","data processing","workflow orchestration"],"complexity":"medium","use_cases":["Data scraping automation triggered via chat","Lead generation and enrichment bots","Customer support agents that run backend tools","Internal workflow agents to trigger jobs/reports by natural language"],"estimated_setup_minutes":30,"required_services":["n8n instance (self-hosted or cloud)","OpenAI API or alternate AI model","Bright Data MCP API (n8n-nodes-mcp community node)"],"key_features":["Natural-language intent classification (OpenAI)","Automatic tool discovery and matching (Bright Data MCP)","Remote tool execution from chat","Context and memory management across conversations","Requests for missing info and graceful fallback when no tool matches\",\"No-code/low-code node-based workflow integration"],"target_audience":["Developers and automation engineers","Data analysts and growth/marketing teams","Customer support managers and teams implementing bots"]}


name: Convert Markdown Content to Contentful Rich Text with AI Formatting
author_name: Varritech
author_username: varritech
categories: []
created_at: 2025-05-15T18:02:32.245Z
updated_at: 2025-05-15T18:02:32.245Z
url: https://n8n.io/workflows/4078
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Workflow: Publish to Contentful with Rich Text Formatting

  ⚡ About the Creators
  This workflow was created by Varritech Technologies, an innovative agency that leverages AI to engineer, design, and deliver software development projects 500% faster than traditional agencies. Based in New York City, we specialize in custom software development, web applications, and digital transformation solutions. If you need assistance implementing this workflow or have questions about content management solutions, please reach out to our team.

  🏗️ Architecture Overview  
  This workflow takes a JSON article payload, splits its markdown content into logical chunks, converts each chunk into Contentful Rich Text JSON via an AI agent, merges the resulting rich text nodes back into a single document, formats the entire entry according to Contentful's field schema, and finally publishes it to Contentful.

  Trigger → Executes when called by another workflow  
  Split by Headings → Breaks markdown into ##-delimited chunks  
  Markdown → Rich Text → AI agent converts each chunk to Contentful Rich Text JSON  
  Combine Rich Text Objects → Aggregates all chunk outputs into one document  
  Format Entry → Wraps metadata and rich-text content into Contentful schema  
  Publish Entry → HTTP POST to Contentful API  

  📦 Node-by-Node Breakdown

  flowchart LR
    A[When Executed by Another Workflow] --&gt; B[Split by Headings]
    B --&gt; C[Markdown to Contentful format]
    C --&gt; D[Combine Rich Text Objects]
    D --&gt; E[Merge1]
    E --&gt; F[Format1]
    F --&gt; G[Create newly formatted Contentful Entry]

  1. When Executed by Another Workflow
  Type: Execute Workflow Trigger

  Input Example:
  title, slug, category.id, description, keywords, content, metaTitle, metaDescription, readingTime, difficulty

  Purpose: Receives the JSON payload from the upstream workflow.

  2. Split by Headings
  Type: Code

  Logic:
  Splits input.content into an array of markdown chunks at each second-level heading (##).
  Emits one item per chunk with index, slug, title, and contentChunk.

  3. Markdown to Contentful format
  Type: LangChain Agent (+ OpenAI Chat model)

  System Prompt:
  Defines rules for generating valid Contentful Rich Text JSON (must include nodeType, data:{}, content:[], etc.).
  Provides examples for paragraphs, headings, lists, links, and images.

  User Prompt:

  Here is the markdown content to convert:

  Purpose: Converts each markdown chunk into an array of rich-text nodes.

  4. Combine Rich Text Objects
  Type: Code

  Logic:
  Parses and merges all content arrays returned by the AI agent into one combined content array under a document root.

  5. Merge1
  Type: Merge

  Purpose: Joins the original item (with metadata) and the combined rich-text document into a single data stream.

  6. Format1
  Type: Code

  Logic:
  Maps workflow data into the Contentful entry schema by setting each field (title, slug, category link, description, keywords, rich-text content, metaTitle, metaDescription, readingTime, difficulty) under the appropriate locale and structure required by Contentful.

  7. Create newly formatted Contentful Entry
  Type: HTTP Request

  Method: POST

  URL:
  https://api.contentful.com/spaces

  Headers:
  Authorization: Bearer token for Contentful Management API
  Content-Type: application/vnd.contentful.management.v1+json
  X-Contentful-Version: entry version number
  X-Contentful-Content-Type: content type ID

  Body: The formatted fields object produced by the previous node

  Purpose: Publishes the new entry with rich-text content to Contentful.

  🔍 Design Rationale & Best Practices
  Chunked Conversion
  Splitting by headings prevents AI context limits and keeps conversions modular.

  Strict Rich Text Schema
  Enforcing nodeType, data, and content structure avoids validation errors on Contentful.

  Two-Phase Merge
  Separating "combine AI outputs" and "format entry" keeps transformations clear and testable.

  Idempotent Publish
  Uses explicit versioning and content type headers to ensure correct entry creation.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.merge","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.code","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 8072 characters]

metadata_json:
  {"categories":["content_management","integration","automation","ai_assisted"],"complexity":"complex","use_cases":["Convert Markdown to Contentful Rich Text","Automated CMS entry creation and publishing","Content migration into Contentful","AI-assisted content formatting and structuring","Mapping article metadata to Contentful schema"],"estimated_setup_minutes":45,"required_services":["Contentful Management API (space & content type)","OpenAI API (or compatible LLM) via LangChain/lmChatOpenAi","n8n workflow platform"],"key_features":["Splits markdown into ##-delimited chunks to avoid context limits","Uses an AI agent to convert each chunk into Contentful Rich Text JSON nodes","Merges chunk outputs into a single rich-text document","Maps metadata (title, slug, category, SEO fields, readingTime, difficulty) to Contentful field schema","Publishes entry to Contentful with proper headers/versioning to ensure idempotency"],"target_audience":["Developers integrating CMS workflows","Content engineers and publishers","Agencies automating content ingestion and formatting with AI"]}


name: Automate AI News Videos with GPT-4o, Heygen Avatars, and Blotato
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-05-15T18:43:13.778Z
updated_at: 2025-05-15T18:43:13.778Z
url: https://n8n.io/workflows/4080
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Turn your ideas into engaging videos, talking avatars, and scheduled posts — all on autopilot.

  🚀 How It Works: 3 Steps to AI-Generated Video Posts

  🤖 AI Researches & Creates Your Video Script

  Automatically fetches trending AI/LLM stories from Hacker News
  Generates a 30-second viral video script with:
  Attention-grabbing hook
  Balanced analysis with statistics
  Call-to-action ("Hit follow to stay ahead in AI!")
  Writes two caption versions:
  Long (50 words + hashtags)
  Short (2 sentences for platforms like Twitter/Threads)
  🎥 Heygen Generates the AI Avatar Video

  Uses your preferred Heygen avatar/voice (configure in "Setup Heygen")
  Applies a professional background video (included)
  Processes the video in ~8 minutes (wait time adjustable)
  📣 Auto-Publishes to 10+ Platforms via Blotato

  Uploads video to Blotato’s CDN
  Publishes natively to:
  ✅ Instagram | ✅ YouTube | ✅ TikTok | ✅ LinkedIn
  ✅ Twitter/X | ✅ Facebook | ✅ Threads (coming soon)
  Platform-specific optimizations:
  Instagram/YouTube: Long captions + hashtags
  Twitter/Threads: Short captions
  TikTok: Vertical video formatting
  ⏱️ Full Automation Timeline

  AI Research → Scriptwriting → Video Generation → Multi-Platform Publishing
  (Runs daily at 10 AM or your custom schedule)

  🛠️ Key Features

  No Manual Work: Fully hands-off after setup

  Customizable: Adjust AI prompts, avatars, or add platforms

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.hackerNewsTool","n8n-nodes-base.hackerNewsTool","n8n-nodes-base.wait","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","content creation","social media","AI/video generation","integration"],"complexity":"complex","use_cases":["Automated daily AI/LLM news video production","Multi-platform social video publishing","Creating talking-avatar explainer videos from headlines","Automated caption generation and platform-specific formatting","Hands-off content pipelines for growth/engagement"],"estimated_setup_minutes":45,"required_services":["OpenAI (GPT-4o / lmChatOpenAi)","Heygen (avatar & video generation)","Blotato (CDN + native publishing)","Hacker News (trending stories data source)","Social platform accounts/APIs: Instagram, YouTube, TikTok, LinkedIn, Twitter/X, Facebook, Threads"],"key_features":["Automatically fetches trending AI/LLM stories and generates a 30-second viral script","Generates long and short captions optimized per platform","Uses Heygen avatars/voices and background video to produce talking-avatar clips","Uploads to Blotato CDN and publishes natively to multiple platforms","Platform-specific formatting (vertical for TikTok, long captions for IG/YouTube, short for X/Threads)"],"target_audience":["Social media managers","Content creators and influencers","Growth marketers and community managers"]}


name: Automate SEO Blog Creation + Social Media with GPT-4, Perplexity and WordPress
author_name: LukaszB
author_username: lukaszb
categories: []
created_at: 2025-05-15T18:59:00.587Z
updated_at: 2025-05-15T18:59:00.587Z
url: https://n8n.io/workflows/4082
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  🚀 AI Blog & Social Media Publisher – Fully Automated Workflow

  This workflow is ideal for individuals, marketers, agencies, and brands who want to effortlessly automate the entire blogging and social media process—from idea generation to promotion. Its primary goal is to consistently deliver engaging, SEO-optimized blog posts directly to WordPress, accompanied by professionally crafted social media content ready for Instagram, Facebook, and X (Twitter).

  No writers, editors, designers, or social media managers required. Save countless hours and resources while boosting your brand's online presence.

  🔧 Workflow Capabilities:

  -Automated Blog Topic Generation: Instantly generates unique, high-value blog ideas tailored to your brand.
  -SEO Content Creation: Produces comprehensive, 2000–2500 word articles optimized for search engines.
  -Real-Time Research: Integrates Perplexity AI to ensure factually accurate, authoritative content.
  -Visual Content Generation: Creates custom, AI-generated featured images via GPT-4, with automatic fallback to Pexels.
  -WordPress Publishing: Automatically publishes articles directly to WordPress with complete formatting (HTML), meta descriptions, featured images, and proper categorization.
  -Social Media Automation: Generates and schedules engaging, platform-specific posts for Instagram, Facebook, and X, complete with relevant hashtags, calls-to-action, and links.
  -Detailed Logging & Reporting: Logs every step transparently into Google Sheets and optionally sends publication reports to Gmail.

  ⚙️ How It Works:
  When activated, the workflow checks your content calendar in Google Sheets to avoid duplicate topics. It generates fresh, brand-aligned blog ideas, conducts live research, and creates complete, ready-to-publish articles. It then automatically designs and sources appropriate visuals, publishes content seamlessly to WordPress, and prepares engaging social media posts optimized for each platform.

  🛠️ Easy Setup Steps:
  -Connect your APIs: OpenAI, WordPress, Google Sheets, Gmail, and optionally Pexels.
  -Replace placeholders (WordPress URLs, Google Sheets ID/tab, workflow IDs).
  -Customize AI prompts with your brand’s tone, keywords, and CTAs for personalized content.
  -Configure scheduling to automate daily, weekly, or custom publishing intervals.

  📌 Additional Features:
  -Modular workflow design for easy customization and scalability.
  -Comprehensive Markdown and PDF setup guides included.
  -Bonus: Reference placeholders and quick-edit Sticky Notes for effortless adjustments.

  Launch today. Automate permanently. Amplify your reach effortlessly.

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metadata_json:
  {"categories":["automation","content creation","SEO","social media","publishing"],"complexity":"complex","use_cases":["Fully automated long-form SEO blog production","End-to-end content publishing to WordPress","Platform-specific social media post generation and scheduling (Instagram, Facebook, X)","Automated visual (featured image) generation with fallback sourcing","Content calendar enforcement and duplicate-topic prevention using Google Sheets logging"],"estimated_setup_minutes":90,"required_services":["OpenAI (GPT-4)","Perplexity AI","WordPress (REST API / credentials)","Google Sheets (API access)","Gmail (optional, for reports)","Facebook Graph API (for Facebook/Instagram posting)","Pexels API (optional fallback for images)","n8n webhook/HTTP access (for integrations and scheduling)"],"key_features":["Automated topic ideation tailored to brand voice","Long-form SEO-optimized article generation (2000–2500 words)","Real-time research integration (Perplexity) to improve factual accuracy","AI-generated featured images with automatic Pexels fallback","Direct WordPress publishing with HTML formatting, meta descriptions and categorization"],"target_audience":["Marketers","Agencies","Brands and content teams (in-house marketers, editors and managers)"]}


name: AI Sales Agent: WhatsApp, FB, IG, OpenAI, Airtable, Supabase Auto-Booking
author_name: Sam Yassine
author_username: samyassine
categories: []
created_at: 2025-05-15T19:39:14.260Z
updated_at: 2025-05-15T19:39:14.260Z
url: https://n8n.io/workflows/4083
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  This workflow automates multi-channel AI-driven sales engagement for lead qualification, service information delivery, and consultation booking. It integrates WhatsApp, Facebook Messenger, Instagram DM, and an n8n chat interface with a backend CRM (Airtable), a knowledge base (Supabase), and conversational AI (OpenAI), all orchestrated by n8n.

  Tools & Services Used

  Messaging Platforms: WhatsApp, Facebook Messenger, Instagram DM, n8n Built-in Chat

  AI Core & Processing: OpenAI (GPT-4o for main agent logic, Whisper for audio transcription)

  CRM & Data Management: Airtable (for initial WhatsApp lead lookup, lead form submissions, and as the backend for the crmAgent sub-workflow operations)

  Knowledge Base: Supabase (Vector Store for technical_and_sales_knowledge tool)

  Chat Memory: PostgreSQL (for the main AI Agent's conversation history)

  Orchestration & Automation: n8n (Self-hosted, utilizing Langchain community nodes)

  Calendar Service: Integrated via the calendarAgent sub-workflow

  CRM Service: Integrated via the crmAgent sub-workflow (interacting with Airtable)

  Workflow Overview

  This automation performs the following steps:

  Trigger: A new interaction is initiated through one of the following channels:

  A new message is received via the WhatsApp Trigger.

  A new message is received via the Facebook Trigger (Webhook).

  A new message is received via the Instagram Trigger (Webhook).

  A new message is received via the n8n Chat Trigger.

  Alternatively, a new lead is submitted via the Airtable Form Submitted Webhook.

  Channel-Specific Ingestion & Pre-processing:

  For WhatsApp:

  The system attempts to find an existing lead in Airtable using the sender's phone number.

  Incoming messages are routed by the Handle Message Types switch:

  Text messages are passed to the Edit Fields - chat1 node to prepare input for the AI Agent, including any found lead information.

  Audio messages are processed: the WhatsApp Business Cloud node gets the media URL, the HTTP Request node downloads the audio, OpenAI transcribes it to text, and Edit Fields - chat2 prepares this transcribed text and lead information for the AI Agent.

  Unsupported message types trigger the Reply To User1 node to send a notification that the message type cannot be processed.

  For Facebook Messenger:

  The system responds to webhook verification (Respond to Webhook - facebook get) and acknowledges new messages (Respond to Webhook - facebook post).

  The If is not echo - facebook node filters out messages sent by the page.

  The Sales Agent Demo - typing_on node sends a typing indicator.

  The Edit Fields - facebook node prepares the message text, sender ID, and Facebook-specific context for the AI Agent.

  For Instagram DM:

  The system responds to webhook verification (Respond to Webhook - instagram get) and acknowledges new messages (Respond to Webhook - instagram post).

  The If is not echo - instagram node filters out messages sent by the business account.

  The Edit Fields - instagram node prepares the message text, sender ID, and Instagram-specific context for the AI Agent.

  For n8n Chat:

  The Edit Fields - chat node prepares the user's input and session information for the AI Agent.

  Input Aggregation for AI Agent:

  Processed data from all active messaging channels (WhatsApp text/audio, Facebook, Instagram, n8n Chat) is funneled through the No Operation, do nothing node to the main AI Agent.

  AI Sales Conversation & Tool Utilization:

  The AI Agent (using OpenAI Chat Model - GPT-4o, and Postgres Chat Memory) engages the user according to its system prompt, aiming to qualify them for Paint Protection Film (PPF), Ceramic Coating, or Window Tint.

  The AI Agent uses the technical_and_sales_knowledge tool (which queries the Demo Supabase vector store via Embeddings OpenAI and OpenAI Chat Model1) to provide service details and answer questions.

  The AI Agent uses the crmAgent tool (a sub-workflow) to log contact details (Name, Email, service interest) and update opportunity statuses in Airtable.

  The AI Agent uses the calendarAgent tool (a sub-workflow) to book consultation appointments once preferred dates/times are provided. This occurs after contact details are logged in the CRM.

  Response Delivery:

  The AI Agent's final textual response is passed to the Switch node.

  The Switch node routes the response to the appropriate node for delivery on the original channel:

  Reply To User for WhatsApp.

  Facebook Graph API - Sales Agent Demo for Facebook Messenger.

  Instagram Graph API - smb.sales.agent.demo for Instagram DM.

  Output - chat for the n8n Chat interface.

  Airtable Form Submission Processing (Separate Branch):

  When the Airtable Form Submitted webhook receives data, the Airtable node fetches the full record.

  The Create Contact node creates a new contact in the Airtable 'Contacts' table.

  The Edit Fields - form node prepares data for a notification.

  The WhatsApp Business Cloud2 node sends a templated WhatsApp message to the lead, confirming their form submission.

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metadata_json:
  {"categories":["automation","integration","conversational AI","sales & CRM"],"complexity":"complex","use_cases":["Multi‑channel lead qualification (WhatsApp, FB, IG, n8n Chat)","Automated consultation booking and calendar integration","CRM contact creation and opportunity updates","Technical & sales knowledge lookup via vector search","Audio message transcription to text for AI processing"],"estimated_setup_minutes":180,"required_services":["WhatsApp Business Cloud","Facebook Messenger / Instagram Graph API","n8n (self‑hosted)","OpenAI (GPT-4o for chat, Whisper for transcription, Embeddings)","Airtable (CRM / form webhook)","Supabase (vector store / knowledge base)","PostgreSQL (chat memory)","Calendar service (Google Calendar or other via calendarAgent sub-workflow)","HTTP/Webhook endpoints"],"key_features":["Unified multi‑channel ingestion (WhatsApp, FB, IG, n8n Chat)","Audio download + Whisper transcription pipeline","AI sales agent using GPT-4o with chat memory","Vector search knowledge tool (Supabase + OpenAI embeddings)","CRM tooling via Airtable sub-workflow for contact & opportunity management','Automated appointment booking via calendarAgent sub-workflow'"],"target_audience":["Small/medium businesses and service providers (consultations/appointments)","Sales teams and SDRs automating qualification","Marketing/ops teams wanting multi-channel outreach automation','Developers / automation engineers implementing conversational AI workflows"]}


name: Automatically Store Shopify Orders in Google Sheets with Telegram Notifications
author_name: RedOne
author_username: redone
categories: []
created_at: 2025-05-15T20:17:14.891Z
updated_at: 2025-05-15T20:17:14.891Z
url: https://n8n.io/workflows/4084
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow is designed for e-commerce store owners, operations managers, and developers who use Shopify as their e-commerce platform and want an automated way to track and analyze their order data.

  It is particularly useful for businesses that:

  Need a centralized view of all Shopify orders
  Want to analyze order trends without logging into Shopify
  Need to share order data with team members who don't have Shopify access
  Want to build custom reports based on order information

  What Problem Is This Workflow Solving?

  While Shopify provides excellent order management within its platform, many businesses need their order data available in other systems for various purposes:

  Data accessibility**: Not everyone in your organization may have access to Shopify's admin interface
  Custom reporting**: Google Sheets allows for flexible analysis and report creation
  Data integration**: Having orders in Google Sheets makes it easier to combine with other business data
  Backup**: Creates an additional backup of your critical order information

  What This Workflow Does

  This n8n workflow creates an automated bridge between your Shopify store and Google Sheets:

  Listens for new order notifications from your Shopify store via webhooks
  Processes the incoming order data and transforms it into a structured format
  Stores each new order in a dedicated Google Sheets spreadsheet
  Sends real-time notifications to Telegram when new orders are received or errors occur

  Setup

  Create a Google Sheet

  Create a new Google Sheet to store your orders
  Add a sheet named "orders" with the following columns:

    orderId
    orderNumber
    created_at
    processed
    processed_at
    json
    customer
    shippingAddress
    lineItems
    totalPrice
    currency

  Set Up Telegram Bot

  Create a Telegram bot using BotFather (send /newbot to @BotFather)
  Save your bot token for use in n8n credentials
  Start a chat with your bot and get your chat ID (you can use @userinfobot)

  Configure the Workflow

  Set your Google Sheet ID in the "Edit Variables" node
  Enter your Telegram chat ID in the "Edit Variables" node
  Set up your Telegram API credentials in n8n

  Configure Shopify Webhook

  In your Shopify admin, go to:
    Settings > Notifications > Webhooks
  Create a new webhook for "Order creation"
  Set the URL to your n8n webhook URL (from the "Receive New Shopify Order" node)
  Set the format to JSON

  How to Customize This Workflow to Your Needs

  Additional data**: Modify the "Transform Order Data to Standard Format" function to extract more Shopify data
  Multiple sheets**: Duplicate the Google Sheets node to store different aspects of orders in separate sheets
  Telegram messages**: Customize the text in Telegram nodes to include more details or rich formatting
  Data processing**: Add nodes to perform calculations or transformations on order data
  Additional notifications**: Add more channels like Slack, Discord, or SMS
  Integrations**: Extend the workflow to send order data to other systems like CRMs, ERPs, or accounting software

  Final Notes

  This workflow serves as a foundation that you can build upon to create a comprehensive order management system tailored to your specific business needs.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.webhook","n8n-nodes-base.function","n8n-nodes-base.googleSheets","n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.set"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","e-commerce","notifications"],"complexity":"medium","use_cases":["Centralize Shopify orders in Google Sheets for analysis","Real-time order alerts via Telegram","Share order data with team members who lack Shopify access","Create backups of order data","Enable custom reporting and downstream integrations"],"estimated_setup_minutes":20,"required_services":["Shopify (webhooks)","Google Sheets / Google account","Telegram (Bot via BotFather)","n8n instance with public webhook endpoint"],"key_features":["Listen for Shopify order creation via webhook","Transform raw order JSON into structured rows","Append orders to a dedicated Google Sheets sheet","Send Telegram notifications for new orders and errors","Configurable variables for Sheet ID and Telegram chat ID"],"target_audience":["e-commerce store owners","operations managers","developers implementing automations\",\"data analysts"]}


name: AI Agent To Chat With Files In Supabase Storage and Google Drive
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2025-05-15T20:44:33.064Z
updated_at: 2025-05-15T20:44:33.064Z
url: https://n8n.io/workflows/4086
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  Video Guide

  I prepared a detailed guide that illustrates the entire process of building an AI agent using Supabase and Google Drive within N8N workflows.



  Youtube Link

  Who is this for?
  This workflow is designed for developers, data scientists, and business users who wish to automate document management and enable AI-powered interactions over their stored files. It's especially beneficial for scenarios where users need to process, analyze, and retrieve information from uploaded documents rapidly.

  What problem does this workflow solve?
  Managing files across multiple platforms often involves tedious manual processes. This workflow facilitates automated file handling, making it easier for users to upload, parse, and interact with documents through an AI agent. It reduces redundancy and enhances the efficiency of data retrieval and management tasks.

  What this workflow does
  This workflow integrates Supabase storage with Google Drive and employs an AI agent to manage files effectively. The agent can:
  Upload files to Supabase storage and activate processes based on file changes in Google Drive.
  Retrieve and parse documents, converting them into a structured format for easy querying.
  Utilize an AI agent to answer user queries based on saved document data.

  Data Collection: The workflow initially gathers files from Supabase storage, ensuring no duplicates are processed in the 'files' table.
  File Handling: It processes files to be parsed based on their type, leveraging LlamaParse for effective data transformation.
  Google Drive Integration: The workflow monitors a designated Google Drive folder to upload files automatically and refresh document records in the database with new data.
  AI Interaction: A webhook is established to enable the AI agent to converse with users, facilitating queries and leveraging stored document knowledge.

  Setup

  Supabase Storage Setup:
     Create a private bucket in Supabase storage, modifying the default name in the URL.
     Upload your files using the provided upload options.

  Database Configuration:
     Establish the 'file' and 'document' tables in Supabase with the necessary fields.
     Execute any required SQL queries for enabling vector matching features.

  N8N Workflow Logic:
     Start with a manual trigger for the initial workflow segment or consider alternative triggers like webhooks.
     Replace all relevant credentials across nodes with your own to ensure seamless operation.

  File Processing and Google Drive Monitoring:
     Set up file processing to take care of downloading and parsing files based on their types.
     Create triggers to monitor the designated Google Drive folder for file uploads and updates.

  Integrate AI Agent:
     Configure the webhook for the AI agent to accept chat inputs while maintaining session context for enhanced user interactions.
     Utilize PostgreSQL to store user interactions and manage conversation states effectively.  

  Testing and Adjustments:
     Once everything is set up, run tests with the AI agent to validate its responses based on the documents in your database.
     Fine-tune the workflow and AI model as needed to achieve desired performance.

nodes_used:
  ["n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.supabase","n8n-nodes-base.if","n8n-nodes-base.supabase","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.manualTrigger","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.vectorStoreSupabase","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.vectorStoreSupabase","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.supabase","n8n-nodes-base.set","n8n-nodes-base.supabase","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","@n8n/n8n-nodes-langchain.vectorStoreSupabase","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.switch","n8n-nodes-base.stopAndError","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.merge","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.switch","n8n-nodes-base.stopAndError","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","AI/ML"],"complexity":"complex","use_cases":["Automated multi-source document ingestion and syncing","AI-powered Q&A over stored documents","Document parsing and structured data extraction","Deduplication and database-backed file management","Vector search and semantic retrieval using embeddings"],"estimated_setup_minutes":90,"required_services":["n8n (workflow runner)","Supabase (storage + PostgreSQL)","Google Drive (Google account + API access)","OpenAI (embeddings/LLM)","LlamaParse (document parsing)","Public webhook endpoint (n8n cloud, ngrok, or similar)"],"key_features":["Monitor Google Drive folder and auto-upload files to Supabase","Avoid duplicate processing via file/database checks","Parse documents by type and convert to structured records","Store documents and embeddings in Supabase vector store","Semantic search/QA using embeddings and an AI agent/webhook chat interface with session context management (PostgreSQL)"],"target_audience":["Developers","Data scientists","Business users/product managers who need document search and automation workflows"]}


name: AI Content Creation and Publishing Engine with Mistral, Creatomate, and YouTube
author_name: NanaB
author_username: nanabrown
categories: []
created_at: 2025-05-15T22:06:26.894Z
updated_at: 2025-05-15T22:06:26.894Z
url: https://n8n.io/workflows/4087
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Description

  This n8n workflow automates the entire process of creating and publishing AI-generated videos, triggered by a simple message from a Telegram bot (YTAdmin). It transforms a text prompt into a structured video with scenes, visuals, and voiceover, stores assets in MongoDB, renders the final output using Creatomate, and uploads the video to YouTube. Throughout the process, YTAdmin receives real-time updates on the workflow’s progress. This is ideal for content creators, marketers, or businesses looking to scale video production using automation and AI.

  You can see a video demonstrating this template in action here: https://www.youtube.com/watch?v=EjI-ChpJ4xA&t=200s 

  How it Works

  Trigger: Message from YTAdmin (Telegram Bot)

  The flow starts when YTAdmin sends a content prompt.

  Generate Structured Content

  A Mistral language model processes the input and outputs structured content, typically broken into scenes.

  Split & Process Content into Scenes

  The content is split into categorized parts for scene generation.

  Generate Media Assets

  For each scene:

  Images: Generated using OpenAI’s image model.
  Voiceovers: Created using OpenAI’s text-to-speech.
  Audio files are encoded and stored in MongoDB.

  Scene Composition

  Assets are grouped into coherent scenes.

  Render with Creatomate

  A complete payload is generated and sent to the Creatomate rendering API to produce the video.
  Progress messages are sent to YTAdmin.
  The flow pauses briefly to avoid rate limits.

  Render Callback

  Once Creatomate completes rendering, it sends a callback to the flow.
  If the render fails, an error message is sent to YTAdmin.
  If the render succeeds, the flow proceeds to post-processing.

  Generate Title & Description

  A second Mistral prompt generates a compelling title and description for YouTube.

  Upload to YouTube

  The rendered video is retrieved from Creatomate.
  It’s uploaded to YouTube with the AI-generated metadata.

  Final Update

  A success message is sent to YTAdmin, confirming upload completion.

  Set Up Steps (Approx. 10–15 Minutes)Step 1: Set Up YTAdmin Bot

  Create a Telegram bot via BotFather and get your API token.
  Add this token in n8n's Telegram credentials and link to the "Receive Message from YTAdmin" trigger.

  Step 2: Connect Your AI Providers

  Mistral: Add your API key under HTTP Request or AI Model nodes.
  OpenAI: Create an account at platform.openai.com and obtain an API key. Use it for both image generation and voiceover synthesis.

  Step 3: Configure Audio File Storage with MongoDB via Custom API

  Receives the Base64 encoded audio data sent in the request body.
  Connects to the configured MongoDB instance (connection details are managed securely within the API- code below).
  Uses the MongoDB driver and GridFS to store the audio data.
  Returns the unique _id (ObjectId) of the stored file in GridFS as a response.
  This _id is crucial as it will be used in subsequent steps to generate the download URL for the audio file.
  My API code can be found here for reference: https://github.com/nanabrownsnr/YTAutomation.git

  Step 4: Set Up Creatomate

  Create a Creatomate account, define your video templates, and retrieve your API key.
  Configure the HTTP request node to match your Creatomate payload requirements.

  Step 5: Connect YouTube

  In n8n, add OAuth2 credentials for your YouTube account.
  Make sure your Google Cloud project has YouTube Data API enabled.

  Step 6: Deploy and Test

  Send a message to YTAdmin and monitor the flow in n8n.
  Verify that content is generated, media is created, and the final video is rendered and uploaded.

  Customization Options

  Change the AI Prompts

  Modify the generation prompts to adjust tone, voice, or content type (e.g., news recaps, product videos, educational summaries).

  Switch Messaging Platform

  Replace Telegram (YTAdmin) with Slack, Discord, or WhatsApp by swapping out the trigger and response nodes.

  Add Subtitles or Effects

  Integrate Whisper or another speech-to-text tool to generate subtitles.
  Add overlay or transition effects in the Creatomate video payload.

  Use Local File Storage Instead of MongoDB

  Swap out MongoDB upload http nodes with filesystem or S3-compatible storage.

  Repurpose for Other Platforms

  Swap YouTube upload with TikTok, Instagram, or Vimeo endpoints for broader publishing.

  **Need Help or Want to Customize This Workflow?
  If you'd like assistance setting this up or adapting it for a different use case, feel free to reach out to me at nanabrownsnr@gmail.com. I'm happy to help!**

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatMistralCloud","n8n-nodes-base.httpRequest","n8n-nodes-base.youTube","@n8n/n8n-nodes-langchain.lmChatMistralCloud","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.set","n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.wait","n8n-nodes-base.extractFromFile","n8n-nodes-base.httpRequest","n8n-nodes-base.merge","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.chainLlm","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/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.httpRequest","n8n-nodes-base.webhook"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","content-creation","video-production","AI/integration"],"complexity":"complex","use_cases":["Scale YouTube video production from a text prompt","Automated marketing or product videos","Educational or news summary video generation","Batch social/video content repurposing","Automated publishing and metadata generation"],"estimated_setup_minutes":15,"required_services":["Telegram (Bot/API)","Mistral (LM API)","OpenAI (images + TTS)","MongoDB with GridFS (or custom file storage API)","Creatomate (video rendering API)","YouTube Data API / Google OAuth"],"key_features":["End-to-end prompt → published YouTube video pipeline","Structured content generation (scene segmentation) via Mistral","Per-scene image generation and text-to-speech voiceover","Audio asset storage in MongoDB/GridFS","Creatomate rendering with callback handling and rate-limit pauses\n(plus error reporting and retries flow control)** (note: newline is literal in single feature string)**\nThis line is not desired; keep concise instead"],"target_audience":["Content creators & YouTubers","Marketers & agencies","Social media managers/growth teams"]}


name: Extract and Merge Twitter (X) Threads using TwitterAPI.io
author_name: enes cingoz
author_username: ecingoz
categories: []
created_at: 2025-05-15T22:37:55.997Z
updated_at: 2025-05-15T22:37:55.997Z
url: https://n8n.io/workflows/4088
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  Twitter (X) Thread Fetcher: Extract and Merge Tweets from Threads
  What it does

  Thread Detection:** Automatically detects whether the provided Twitter link is a single tweet or a thread.
  Tweet Extraction:** Fetches and returns the content of a single tweet, or gathers all tweets in a thread by retrieving the first tweet and all connected replies.
  Thread Merging:** Merges all tweets in the correct order to reconstruct the complete thread, filtering out any empty results.
  Seamless Integration:** Easily trigger this workflow from other n8n workflows to automate Twitter thread extraction from various sources.

  How it works
  Accepts a Twitter link as input-either a single tweet or a thread.
  If the link is for a single tweet, fetches and returns the tweet content.
  If the link is for a thread, fetches the first tweet, then iteratively retrieves all connected replies that form the thread, ensuring only relevant tweets are included.
  Merges the first tweet and all subsequent thread tweets in order, filters out any empty results, and returns the complete thread.
  Uses twitterapi.io for all Twitter API requests.

  Set up steps
  Setup typically takes just a few minutes. You’ll need to configure your Twitter API credentials for twitterapi.io.
  You can trigger this workflow manually for testing or call it from another workflow to automate thread fetching from sources like Notion, spreadsheets, or other platforms.
  For best results, create a separate workflow to gather Twitter links from your preferred source, then trigger this workflow to fetch and return the full thread.

  &gt; Detailed configuration instructions and node explanations are included as sticky notes within the workflow canvas.

  Benefits

  Light speed:* Fetches a 15-tweet thread in just *3 seconds** for rapid results.
  Cost effective:* Processes a *15-tweet thread* for only *$0.0027*, making it highly affordable. *(Cost may vary depending on the density of replies in the thread.)

nodes_used:
  ["n8n-nodes-base.merge","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.function","n8n-nodes-base.httpRequest","n8n-nodes-base.function","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.filter"]

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metadata_json:
  {"categories":["integration","automation","social media","data extraction"],"complexity":"medium","use_cases":["Fetch and reconstruct Twitter (X) threads for archiving or republishing","Automate content collection for social media management or newsletters","Feed full thread content into downstream workflows for summarization or analysis","Monitor and extract threaded conversations for research or compliance"],"estimated_setup_minutes":10,"required_services":["twitterapi.io (Twitter API access)","Valid Twitter API credentials (via twitterapi.io)","n8n (workflow automation platform)"],"key_features":["Auto-detects whether a link is a single tweet or a thread","Iteratively fetches first tweet and connected replies to reconstruct threads","Merges tweets in correct order and filters out empty results","Callable from other n8n workflows or run manually","Efficient performance and low per-thread cost"],"target_audience":["developers/automation engineers","social media managers and marketers","content creators and curators"]}


name: Track SEO Keyword Rankings in Google Search with ScrapingBee API
author_name: Gegenfeld
author_username: gegenfeld
categories: []
created_at: 2025-05-15T23:42:27.189Z
updated_at: 2025-05-15T23:42:27.189Z
url: https://n8n.io/workflows/4089
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Who is this template for?

  This SEO Reporting workflow template is ideal for digital marketers, SEO consultants, content strategists, and founders who need to quickly gather, format, and store Google search result data. If you regularly audit SERPs, track keyword performance, or monitor competitors, this automation lets you generate polished SEO reports in seconds—ready to share or analyze further.

  What problem does this workflow solve?

  Scraping and formatting Google search results for SEO insights is often manual, repetitive, and error-prone or requires expensive software. Professionals frequently face challenges like:

  Collecting live, structured data from Google for multiple keywords
  Converting raw search results into readable reports for clients or stakeholders
  Logging changes in rankings or URLs across time for historical tracking
  Exporting SEO data into spreadsheets for deeper analysis
  High monthly software fees

  What this workflow does

  This n8n workflow scrapes the top organic Google search results for a given keyword and automatically creates a downloadable report while also logging the results in a table format for long-term storage or further processing.

  Here’s what it includes:

  A trigger form that accepts a search keyword from the user
  An automated API call to fetch Google’s SERP data
  Two output formats: a formatted HTML table for emails and a Markdown table for download (e.g., for Excel, Airtable or Google Sheets)
  Automatic CSV file generation for download
  Optional email delivery of the report

  Setup

  Getting started is simple:

  Enter your API key

     Add your API key to the “Scrape Google SERPs” HTTP Request node (Step-by-step guide inside the template)
     Replace the default query with your own custom Google search parameters if needed

  Set up delivery options

     Update your email in the “Mail SEO Report” node for report delivery
     Use the downloadable file output from the “Convert to File” node
     Optional: Add a Google Sheets (or similar) node which imports the file

  Test the workflow

     Use the built-in form to input a keyword
     Confirm that results appear in both your email and downloadable file

  Activate the workflow

     Turn on the trigger so your team or clients can submit keywords at any time

  How to customize this workflow

  This template is easy to extend for a variety of SEO automation needs:

  Add a loop to handle multiple keywords at once
  Connect to Airtable, Notion or Google Sheets
  Integrate with Slack or Discord for notifications
  Apply additional filtering to track only new or changed search results
  Schedule it to run daily or weekly with a cron trigger

  By combining live SERP scraping, report formatting, and spreadsheet integration, this workflow gives you a fast and flexible SEO reporting system you can use right away or scale up as needed.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.formTrigger","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.convertToFile","n8n-nodes-base.mailjet","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","data processing","integration","reporting","SEO"],"complexity":"medium","use_cases":["Track keyword rankings in Google SERPs","Generate SEO reports (HTML, Markdown, CSV)","Monitor competitor search results","Log historical ranking changes for analysis","Export results to spreadsheets (Google Sheets, Airtable, Excel)"],"estimated_setup_minutes":15,"required_services":["ScrapingBee API (required)","SMTP / Email provider (for optional email delivery)","Google Sheets, Airtable or other spreadsheet service (optional)","Slack or Discord (optional notifications)"],"key_features":["User trigger form to input keywords","Automated HTTP request to scrape Google SERPs","Outputs as formatted HTML table and Markdown table","CSV file generation for download","Optional email delivery of reports and integrations with sheets/DBs for logging"],"target_audience":["Digital marketers","SEO consultants","Content strategists and writers doing SEO audits/monitoring staff/founders"]}


name: Auto Workflow Backup to Google Drive – Automated Export of All Your Workflows
author_name: LukaszB
author_username: lukaszb
categories: []
created_at: 2025-05-16T01:52:32.526Z
updated_at: 2025-05-16T01:52:32.526Z
url: https://n8n.io/workflows/4090
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  n8n Workflow Backup to Google Drive – Automated Export of All Your Workflows

  This workflow is designed to automatically create backups of all your workflows in n8n and store them as individual .json files in Google Drive. It's a fully automated system that helps developers, agencies, or automation teams ensure their automation logic is always safe, versioned, and ready to restore or share.

  What is this for?
  If you’re building and managing multiple automations inside n8n, losing a workflow due to accidental deletion or misconfiguration can cost you hours of work. This template solves that by exporting all your workflows into separate files and storing them in a dated Google Drive folder. It helps with disaster recovery, version tracking, and team collaboration — without any manual exporting.

  How this works:
  -Once triggered (manually or via a schedule), the workflow performs the following steps:
  -Creates a new folder in your Google Drive, named with today’s date (e.g. “Workflow Backups Monday 16-05-2025”).
  -Connects to your n8n instance using the internal API and retrieves a list of all existing workflows.
  -Iterates over each workflow, converts it into a .json file using the built-in file conversion node.
  -Uploads each individual .json file to the newly created folder in Google Drive.
  -Optionally, the workflow finds and deletes old backup folders to keep your Google Drive clean and avoid clutter.

  You get a clean, timestamped folder with all your flows — ready to restore, send, or store securely.
  You can trigger it manually or schedule it (e.g., to run weekly on Monday mornings).

  How to set it up:
  Import the provided workflow JSON into your n8n instance.
  Set up your credentials:
  -Replace the placeholder “Google demo” with your actual Google Drive OAuth2 credentials in all Google Drive nodes.
  -Replace the placeholder “n8n demo” with your n8n API credentials so the workflow can fetch your flows.
  -Go to the node “Create new folder” and replace the folder ID with your own destination folder in Google Drive where backups should be stored.
  -(Optional) Enable the “Schedule Trigger” to run the backup automatically once a week or on your preferred interval.

  You’re ready to go — test it with the Manual Trigger first and check your Google Drive for results.

nodes_used:
  ["n8n-nodes-base.convertToFile","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.filter","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.manualTrigger","n8n-nodes-base.googleDrive","n8n-nodes-base.splitInBatches","n8n-nodes-base.googleDrive","n8n-nodes-base.limit","n8n-nodes-base.n8n","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","backup"],"complexity":"medium","use_cases":["Automated backups of n8n workflows","Disaster recovery and restore","Version tracking of workflows","Team collaboration and sharing of workflows","Scheduled exports for compliance or archival"],"estimated_setup_minutes":15,"required_services":["Google Drive (OAuth2 credentials)","n8n instance with internal API access"],"key_features":["Creates a dated Google Drive folder for each run","Exports each workflow as an individual .json file","Converts workflows to files and uploads them to Drive","Optional cleanup of old backup folders","Supports manual trigger and scheduled runs"],"target_audience":["Developers","Automation teams","Agencies managing n8n workflows, consultants"]}


name: Tesla Quant Trading AI Agent using Telegram + GPT-4.1 (Main InterFace)
author_name: Don Jayamaha Jr
author_username: don-the-gem-dealer
categories: []
created_at: 2025-05-16T08:44:07.297Z
updated_at: 2025-05-16T08:44:07.297Z
url: https://n8n.io/workflows/4092
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  📈 Get daily and on-demand Tesla (TSLA) trading signals via Telegram—powered by GPT-4.1 and real-time market data.
  This is the central AI supervisor that orchestrates seven sub-agents for technical analysis, price pattern recognition, and news sentiment. Reports are delivered in structured Telegram-ready HTML, optimized for traders seeking fast, intelligent decision-making signals.

  ⚠️ This master agent requires 7 connected sub-workflows to function. One of them, the News & Sentiment Agent, also requires a DeepSeek Chat API key for language processing.

  🔌 Required Sub-Workflows

  You must download and publish the following workflows:

  Tesla Financial Market Data Analyst Tool
  Tesla News and Sentiment Analyst Tool (Requires DeepSeek Chat API Key)
  Tesla 15min Indicators Tool
  Tesla 1hour Indicators Tool
  Tesla 1day Indicators Tool
  Tesla 1hour & 1day Klines Tool
  Tesla Quant Technical Indicators Webhooks Tool (Requires Alpha Vantage Premium API Key)

  📍 See all tools at:
  🔗 https://n8n.io/creators/don-the-gem-dealer/

  🔍 What This Agent Does

  Listens to your trading query via Telegram
  Calls the Financial Analyst and News & Sentiment Analyst
  These agents aggregate:

     RSI, MACD, BBANDS, SMA, EMA, ADX
     Candlestick pattern + volume divergence analysis
     News summaries and sentiment scoring via DeepSeek Chat
  GPT-4.1 composes the final structured TSLA trade report with:

     Spot and leverage setups
     Signal rationale
     Confidence score
     Sentiment tag
     News summary

  🧠 Output Example

  TSLA Trading Report (Daily Summary)
  Spot Trade
  • Action: Buy
  • Entry: 172.45
  • TP: 182.00
  • SL: 169.80
  • Signal: RSI bounce + Bullish Engulfing
  • Sentiment: Neutral

  Leveraged Position
  • Position: Long
  • Leverage: 3x
  • TP: 186
  • SL: 170
  • Confidence: High (83/100)

  📰 Top News
  • Tesla Model Y delivery surge – Electrek
  • Options market pricing in upside – Bloomberg
  • FSD delayed in Canada – TeslaNorth

  🛠️ Setup Instructions

  1. Import All 8 Workflows

  Ensure all sub-agents above are published in your n8n instance.

  2. Create Your Telegram Bot

  Use @BotFather to generate the token and connect to the trigger/send nodes.

  3. Connect OpenAI GPT-4.1

  Add your OpenAI credentials for GPT-4.1 in the designated node.

  4. Add DeepSeek Chat API Key

  Sign up at https://deepseek.com and insert your DeepSeek Chat credentials in the News Agent.

  5. Add Alpha Vantage Premium API Key

  Sign up at https://www.alphavantage.co/premium/
  Use HTTP Header Auth for webhook-based indicator fetchers.

  6. Replace Telegram ID

  Update the placeholder &lt;&lt;replace your ID here&gt;&gt; with your actual Telegram numeric ID in the auth node.

  📌 Included Sticky Notes

  ✅ Telegram Bot Setup
  ✅ Agent Routing & Memory
  ✅ Financial vs. Sentiment Trigger Flow
  ✅ Report Formatting (HTML)
  ✅ API Requirements (GPT-4.1, DeepSeek, Alpha Vantage)
  ✅ Troubleshooting & Licensing

  🧾 Licensing & Attribution

  © 2025 Treasurium Capital Limited Company
  Architecture, prompts, and trade report structure are IP-protected. No unauthorized rebranding permitted.

  🔗 For support: LinkedIn – Don Jayamaha

  🚀 Deploy the Tesla Quant Trading AI system with GPT-4.1, DeepSeek Chat, and Alpha Vantage Premium—right into Telegram.
  All 8 workflows are required.

  🎥 Tesla Quant AI Agent – Live Demo

  Experience the power of the Tesla Quant Trading AI Agent in action.
    

nodes_used:
  ["n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.telegram","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.dateTime","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","financial trading","AI integration","data integration"],"complexity":"complex","use_cases":["Daily and on-demand TSLA trading signal generation","Multi-agent technical analysis aggregation (indicators + patterns)","News summarization and sentiment scoring for trade context","Delivering structured trading reports via Telegram","Orchestrating webhook-based market indicator fetchers"],"estimated_setup_minutes":90,"required_services":["n8n instance with all 8 workflows published","OpenAI GPT-4.1 API key","DeepSeek Chat API key (for News & Sentiment Agent)","Alpha Vantage Premium API key (for technical indicator webhooks)","Telegram Bot API token and numeric chat ID"],"key_features":["Central AI master agent orchestrating seven sub-agents","Aggregates RSI, MACD, BBANDS, SMA, EMA, ADX and candlestick/volume pattern analysis","News summaries and sentiment scoring via DeepSeek Chat","Final report composition by GPT-4.1 into Telegram-ready HTML","Provides spot and leveraged trade setups, confidence scores and sentiment tags"],"target_audience":["quantitative traders","algo developers","trading analysts and researchers"]}


name: Tesla News and Sentiment Analyst Tool (Powered by DeepSeek Chat)
author_name: Don Jayamaha Jr
author_username: don-the-gem-dealer
categories: []
created_at: 2025-05-16T08:49:30.606Z
updated_at: 2025-05-16T08:49:30.606Z
url: https://n8n.io/workflows/4093
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  📰 This AI-powered agent performs real-time sentiment analysis on Tesla (TSLA) news to support trading decisions.
  It aggregates headlines from 5 trusted sources and uses DeepSeek Chat to classify sentiment and generate structured summaries. This tool is a critical sub-agent in the broader Tesla Quant Trading AI Agent system.

   ⚠️ Not standalone — this agent is designed to be executed by the Tesla Quant Trading AI Agent.
   ⚙️ Requires: DeepSeek Chat API Key

  🔌 Workflow Role

  This tool processes Tesla-related news and produces output like:

  {
    "sentiment": "bullish",
    "summary": "Tesla stock rallied today after strong delivery numbers and Cybertruck updates. Analysts remain optimistic.",
    "topHeadlines": [
      "Tesla beats Q2 delivery forecast – Yahoo Finance",
      "Cybertruck ramps up in Texas – Electrek",
      "Berlin Gigafactory expands battery production – CleanTechnica"
    ]
  }

  Its output feeds directly into the master trading agent’s final trade report.

  📰 News Sources Used

  This agent collects real-time headlines from:

  Google News (filtered by “Tesla” or “TSLA”)
  Yahoo Finance (TSLA-specific feed)
  Electrek (Tesla archive)
  CleanTechnica (Tesla sustainability news)
  TeslaNorth (app/product release updates)

  These five tools are always queried together to ensure market-wide signal coverage.

  🤖 What the Agent Does

  Pulls headlines from all 5 Tesla-specific RSS feeds
  Uses DeepSeek Chat to:

     Analyze narrative tone (bullish / bearish / neutral)
     Identify macro/financial drivers
     Generate a 2–3 sentence summary
     Return top 3–5 headlines
  Outputs structured JSON for downstream use

  🛠️ Setup Instructions

  1. Install & Name

  Import this file and name it: Tesla_News_and_Sentiment_Analyst_Tool

  2. Add DeepSeek API Credentials

  Go to: Credentials → Add New → DeepSeek API
  Save as: DeepSeek account

  3. Internet Access Required

  Ensure RSS feeds can fetch live headlines
  Works best with a cloud-hosted n8n instance or tunnel-enabled local install

  4. Must Be Triggered by Parent

  Triggered via Execute Workflow by the Tesla Quant Trading AI Agent
  Requires these inputs:

    message: optional query context
    sessionId: passed to maintain short-term memory across executions

  🧠 Agent Architecture

  | Node Name                          | Function                                         |
  | ---------------------------------- | ------------------------------------------------ |
  | DeepSeek Chat Model              | Performs AI-based sentiment analysis             |
  | Tesla News and Sentiment Analyst | Combines results, formats output in strict JSON  |
  | Simple Memory                    | Stores session-level context (short-term memory) |
  | 5x RSS nodes                     | Aggregate Tesla news from trusted media outlets  |

  📌 Sticky Notes Included

  🟢 Trigger from Parent Workflow – Executed only by main TSLA agent
  🟠 News Feeds Overview – Lists and explains each of the 5 feeds
  🧠 DeepSeek Chat Notes – Describes LLM behavior and parsing role
  🔵 Short-Term Memory – Buffers sentiment context during user session
  📘 Sentiment Analyst Agent – Summarizes key responsibilities

  📎 Licensing & Attribution

  © 2025 Treasurium Capital Limited Company
  This architecture, workflow structure, and prompt design are licensed for educational and operational use only. Commercial resale or rebranding prohibited without authorization.

  🔗 Creator: Don Jayamaha
  🔗 Templates: https://n8n.io/creators/don-the-gem-dealer/

  🚀 Power your TSLA trading with AI-driven sentiment—built with DeepSeek Chat and 5 trusted news sources.
  This tool is required by the Tesla Quant Trading AI Agent.

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metadata_json:
  {"categories":["integration","data processing","automation","financial analysis"],"complexity":"medium","use_cases":["Real-time news aggregation for Tesla (TSLA)","Sentiment analysis to inform trading decisions","Generating concise structured summaries for downstream agents","Feeding market-signal sub-agent within a quant trading system","Short-term session memory for contextual analysis"],"estimated_setup_minutes":15,"required_services":["DeepSeek Chat API (API key)","Google News (Tesla/TSLA RSS)","Yahoo Finance TSLA RSS","Electrek (Tesla archive RSS)","CleanTechnica (Tesla RSS)","TeslaNorth (product/release updates RSS)","n8n instance with internet access","Parent Tesla Quant Trading AI Agent (to trigger execution)"],"key_features":["Aggregates headlines from five Tesla-specific RSS feeds","Uses DeepSeek Chat to classify sentiment (bullish/neutral/bearish)","Identifies macro/financial drivers","Produces 2–3 sentence structured summaries","Returns top 3–5 headlines and strict JSON output for downstream use"],"target_audience":["Quant traders / trading teams","Algo trading engineers","Data analysts monitoring equity news flow (TSLA-focused)"]}


name: Tesla Financial Market Data Analyst Tool (Multi-Timeframe Technical AI Agent)
author_name: Don Jayamaha Jr
author_username: don-the-gem-dealer
categories: []
created_at: 2025-05-16T08:54:17.737Z
updated_at: 2025-05-16T08:54:17.737Z
url: https://n8n.io/workflows/4094
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  📊 This AI sub-agent aggregates Tesla (TSLA) trading signals across multiple timeframes using real-time technical indicators and candlestick behavior.
  It is a core component of the Tesla Quant Trading AI system. Powered by GPT-4.1, it consolidates 15-minute, 1-hour, and 1-day indicators, adds candlestick pattern data, and produces a unified JSON signal for downstream use by the master agent.

   ⚠️ This agent is not standalone. It is triggered by the Tesla Quant Trading AI Agent via Execute Workflow.
   🧠 Requires: 4 connected sub-agents and Alpha Vantage Premium API Key

  🔌 Required Sub-Workflows

  To use this workflow, you must install:

  Tesla 15min Indicators Tool
  Tesla 1hour Indicators Tool
  Tesla 1day Indicators Tool
  Tesla 1hour and 1day Klines Tool
  Tesla Quant Technical Indicators Webhooks Tool (provides Alpha Vantage data)

  🧠 What This Agent Does

  Fetches pre-cleaned 20-point JSON outputs from the 4 sub-agents listed above
  Analyzes each timeframe individually:

     15m: momentum and short-term setups
     1h: confirmation of emerging trends
     1d: macro positioning and trend alignment
     Klines: candlestick reversal patterns and volume divergence
  Generates a structured final signal in JSON with:

     Trading stance: Buy, Sell, Hold, or Cautious
     Confidence score (0.0–1.0)
     Multi-timeframe indicator breakdown
     Candlestick and volume divergence annotations

  📋 Sample Output

  {
    "summary": "TSLA momentum is weakening short-term. 1h MACD shows bearish crossover, RSI declining. 1d candles confirm potential reversal setup.",
    "signal": "Cautious Sell",
    "confidence": 0.81,
    "multiTimeframeInsights": {
      "15m": { "RSI": 68.3, "MACD": { "macd": 0.53, "signal": 0.61 }, ... },
      "1h": { "RSI": 65.0, "MACD": { "macd": -0.32, "signal": 0.11 }, ... },
      "1d": { "BBANDS": { ... }, ... },
      "candlestickPatterns": { "1h": "Doji", "1d": "Bearish Engulfing" },
      "volumeDivergence": { "1h": "Bearish", "1d": "Neutral" }
    }
  }

  🛠️ Setup Instructions

  Import this workflow into n8n

     Name it: Tesla_Financial_Market_Data_Analyst_Tool

  Add Required API Credentials

     Alpha Vantage Premium (via HTTP Query Auth)
     OpenAI GPT-4.1 for reasoning and synthesis

  Link Required Sub-Agents

     Connect the 4 tool workflows listed above to their respective Tool Workflow nodes
     Connect the webhook provider for data fetches

  Set Up as Sub-Agent

     This workflow must be triggered using Execute Workflow from the parent agent
     Pass in:

       message (optional context)
       sessionId (used for memory continuity)

  🧾 Sticky Notes Provided

  📘 Tesla Financial Market Data Analyst — Core logic overview
  📈 15m / 1h / 1d Tool Notes — Indicator lists + use cases
  🕯️ Klines Tool Note — Candlestick and volume divergence patterns
  🧠 GPT Reasoning Note — GPT-4.1 handles final synthesis
  🧩 Sub-Workflow Trigger — Proper integration with parent agent
  🧠 Memory Buffer — Maintains session context across evaluations

  🔒 Licensing & Support

  © 2025 Treasurium Capital Limited Company
  The logic, prompt design, and multi-agent architecture are proprietary and IP-protected.

  For support or collaboration inquiries:
  🔗 Don Jayamaha – LinkedIn
  🔗 n8n Creator Profile

  🚀 Unify your Tesla trading logic across timeframes—automated, AI-powered, and built for scalers and swing traders.

nodes_used:
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  {"categories":["integration","data processing","finance","automation"],"complexity":"complex","use_cases":["Multi-timeframe trading signal generation for TSLA","Automated consolidation of technical indicators across timeframes","Providing structured JSON signals to a master trading agent","Candlestick and volume divergence annotation for trade decisioning","Session-aware iterative analysis for swing/intraday strategies"],"estimated_setup_minutes":45,"required_services":["Alpha Vantage Premium API","OpenAI GPT-4.1","n8n sub-workflows: Tesla 15min Indicators Tool, Tesla 1hour Indicators Tool, Tesla 1day Indicators Tool, Tesla 1hour and 1day Klines Tool","Webhook provider for Alpha Vantage data","n8n Execute Workflow integration (parent agent)"],"key_features":["Aggregates 15m, 1h, and 1d technical indicators into a single view","Identifies candlestick patterns and volume divergence across timeframes","Produces a unified JSON signal with stance (Buy/Sell/Hold/Cautious) and confidence score","Breaks down multi-timeframe indicator values for transparency","Uses GPT-4.1 for final reasoning and synthesis of signals」「Maintains session continuity via memory buffer for contextual evaluations\",\"Designed to be invoked as a sub-agent by a parent Tesla Quant Trading AI agent"],"target_audience":["Quant traders and algo developers","Trading analysts and researchers","Prop trading firms and systematic traders implementing n8n integrations"]}


name: Tesla Quant Technical Indicators Webhooks Tool
author_name: Don Jayamaha Jr
author_username: don-the-gem-dealer
categories: []
created_at: 2025-05-16T08:58:13.199Z
updated_at: 2025-05-16T08:58:13.199Z
url: https://n8n.io/workflows/4095
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  📡 This workflow serves as the central Alpha Vantage API fetcher for Tesla trading indicators, delivering cleaned 20-point JSON outputs for three timeframes: 15min, 1hour, and 1day.
  It is required by the following agents:

  Tesla 15min, 1h, 1d Indicators Tools
  Tesla Financial Market Data Analyst Tool

   ✅ Requires an Alpha Vantage Premium API Key
   🚀 Used as a sub-agent via webhook endpoints triggered by other workflows

  📈 What It Does

  For each timeframe (15min, 1h, 1d), this tool:

  Triggers 6 technical indicators via Alpha Vantage:

     RSI
     MACD
     BBANDS
     SMA
     EMA
     ADX
  Trims the raw response to the latest 20 data points
  Reformats into a clean JSON structure:

        {
       "indicator": "MACD",
       "timeframe": "1hour",
       "data": {
         "timestamp": "...",
         "macd": 0.32,
         "signal": 0.29
       }
     }
     Returns results via Webhook Respond for the calling agent

  📂 Required Credentials

  🔑 Alpha Vantage Premium API Key
    Set up under Credentials &gt; HTTP Query Auth

    Name: Alpha Vantage Premium
    Query Param: apikey

  Get yours here: https://www.alphavantage.co/premium/

  🛠️ Setup Steps

  Import Workflow into n8n
     Name it: Tesla_Quant_Technical_Indicators_Webhooks_Tool

  Add HTTP Query Auth Credential
     Name: Alpha Vantage Premium
     Param key: apikey
     Value: your Alpha Vantage key

  Publish and Use the Webhooks
     This workflow exposes 3 endpoints:

     /15minData → used by 15m Indicator Tool
     /1hourData → used by 1h Indicator Tool
     /1dayData → used by 1d Indicator Tool

  Connect via Execute Workflow or HTTP Request
     Ensure caller sends webhook trigger correctly to the path

  🧱 Architecture Summary

  Each timeframe section includes:

  | Component          | Details                                       |
  | ------------------ | --------------------------------------------- |
  | 📡 Webhook Trigger | Entry node (/15minData, /1hourData, etc.) |
  | 🔄 API Calls       | 6 nodes fetching indicators via Alpha Vantage |
  | 🧹 Formatters      | JS Code nodes to clean and trim responses     |
  | 🧩 Merge Node      | Consolidates cleaned JSONs                    |
  | 🚀 Webhook Respond | Returns structured data to calling workflow   |

  🧾 Sticky Notes Overview

  ✅ Webhook Entry: Instructions per timeframe
  ✅ API Call Summary: Alpha Vantage endpoint for each indicator
  ✅ Format Nodes: Explain JSON parsing and cleaning
  ✅ Merge Logic: Final output format
  ✅ Webhook Response: What gets returned to caller

  All stickies follow n8n standard color-coding:

  Blue = Webhook flow
  Yellow = API request group
  Purple = Formatters
  Green = Merge step
  Gray = Workflow overview and usage

  🔐 Licensing & Support

  © 2025 Treasurium Capital Limited Company
  This agent is part of the Tesla Quant AI Trading System and protected under U.S. copyright.

  For support:
  🔗 Don Jayamaha – LinkedIn
  🔗 n8n Creator Profile

  🚀 Use this API tool to feed Tesla technical indicators into any AI or trading agent across 15m, 1h, and 1d timeframes.
  Required for all Tesla Quant Agent indicator tools.

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metadata_json:
  {"categories":["automation","integration","data processing","finance/trading"],"complexity":"complex","use_cases":["Fetch standardized technical indicators for Tesla across multiple timeframes","Provide cleaned 20-point JSON indicator payloads to other workflows/agents via webhooks","Feed trading agents or AI models with ready-to-use market indicators","Integrate Alpha Vantage indicator data into analysis, signals, or alerting systems","Backtesting or on-demand indicator retrieval for algorithmic strategies"],"estimated_setup_minutes":15,"required_services":["Alpha Vantage Premium API (apikey)","n8n (cloud or self-hosted) with webhook endpoints","Calling workflows/agents that trigger the webhooks (HTTP requests)"],"key_features":["Fetches 6 technical indicators (RSI, MACD, BBANDS, SMA, EMA, ADX) per timeframe","Supports three timeframes: 15min, 1hour, 1day","Trims and reformats raw API responses to latest 20 data points","Returns consistent, typed JSON objects for each indicator/timeframe","Exposed webhook endpoints (/15minData, /1hourData, /1dayData) for sub-agent usage or external callers"],"target_audience":["Quant traders and trading strategy developers","Data engineers and analysts needing cleaned indicator data","n8n workflow authors and automation engineers integrating market data into systems and agents','AI/agent integrators feeding models with market indicators"]}


name: Tesla 15min Indicators Tool (Short-Term AI Technical Analysis)
author_name: Don Jayamaha Jr
author_username: don-the-gem-dealer
categories: []
created_at: 2025-05-16T09:00:47.876Z
updated_at: 2025-05-16T09:00:47.876Z
url: https://n8n.io/workflows/4096
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  ⏱️ Analyze Tesla (TSLA) short-term market structure and momentum using 6 technical indicators on the 15-minute timeframe.
  This AI agent tool is part of the Tesla Quant Trading AI Agent system. It is designed to detect intraday shifts in volatility, trend strength, and potential reversal signals.

   ⚠️ Not standalone. This agent is triggered via Execute Workflow by the Tesla Financial Market Data Analyst Tool.

   🔌 Requires:

   Tesla Quant Technical Indicators Webhooks Tool
   Alpha Vantage Premium API Key

  📊 What It Does

  This workflow pulls the latest 20 data points for 6 key technical indicators from a webhook-powered source, then uses GPT-4.1 to interpret market momentum and structure:

  Connected Indicators:

  RSI (Relative Strength Index)**
  MACD (Moving Average Convergence Divergence)**
  BBANDS (Bollinger Bands)**
  SMA (Simple Moving Average)**
  EMA (Exponential Moving Average)**
  ADX (Average Directional Index)**

  The output is a structured JSON with:

  Market summary
  Timeframe (15m)
  Indicator values

  📋 Sample Output

  {
    "summary": "TSLA shows fading momentum. RSI dropped below 60, MACD is flattening, and BBANDS are tightening. Expect short-term consolidation.",
    "timeframe": "15m",
    "indicators": {
      "RSI": 58.3,
      "MACD": {
        "macd": -0.020,
        "signal": -0.018,
        "histogram": -0.002
      },
      "BBANDS": {
        "upper": 183.10,
        "lower": 176.70,
        "middle": 179.90,
        "close": 177.60
      },
      "SMA": 178.20,
      "EMA": 177.70,
      "ADX": 19.6
    }
  }

  🧠 Agent Components

  | Module                | Role                                                     |
  | --------------------- | -------------------------------------------------------- |
  | Webhook Data Node | Calls /15minData endpoint for Alpha Vantage indicators |
  | LangChain Agent   | Parses indicator payloads and generates reasoning        |
  | OpenAI GPT-4.1    | Powers the AI logic to interpret technical structure     |
  | Memory Module     | Maintains session consistency for multi-agent calls      |

  🛠️ Setup Instructions

  Import Workflow into n8n
     Name it: Tesla_15min_Indicators_Tool

  Configure Webhook Source

     Install and publish: Tesla_Quant_Technical_Indicators_Webhooks_Tool
     Ensure /15minData is publicly reachable (or tunnel-enabled)

  Add Credentials

     Alpha Vantage API Key (HTTP Query Auth)
     OpenAI GPT-4.1 (OpenAI Chat Model)

  Link as Sub-Agent
     This workflow is not triggered manually. It is executed using Execute Workflow by:
     👉 Tesla_Financial_Market_Data_Analyst_Tool
     Pass in:

     message (optional)
     sessionId (for short-term memory linkage)

  📌 Sticky Notes Summary

  🟢 Trigger Integration – Receives sessionId and message from parent
  🟡 Webhook Fetcher – Pulls Alpha Vantage data from /15minData
  🧠 GPT-4.1 Reasoning – Produces structured JSON insight
  🔵 Session Memory – Maintains evaluation flow across tools
  📘 Tool Description – Explains indicator use and AI output format

  🔒 Licensing & Author

  © 2025 Treasurium Capital Limited Company
  All logic, formatting, and agent design are protected under copyright. No resale or public re-use without permission.

  Created by: Don Jayamaha
  Creator Profile: https://n8n.io/creators/don-the-gem-dealer/

  🚀 Build faster intraday Tesla trading models using clean 15-minute indicator insights—processed by AI.
  Required by the Tesla Financial Market Data Analyst Tool.

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metadata_json:
  {"categories":["automation","integration","data processing","trading/finance","AI analysis"],"complexity":"medium","use_cases":["Intraday technical analysis for TSLA (15min)","Detect short-term volatility, momentum and reversal signals","Feed structured indicator insights into larger trading agent/system","Automated sub-agent for multi-agent trading workflows","Generate JSON-formatted trading signals/summary for downstream systems"],"estimated_setup_minutes":30,"required_services":["Tesla Quant Technical Indicators Webhooks Tool (public /15minData endpoint)","Alpha Vantage Premium API Key","OpenAI GPT-4.1 (OpenAI Chat model)","n8n instance (with Execute Workflow enabled)","LangChain agent module / memory module (within workflow)"],"key_features":["Fetches latest 20 data points for 6 indicators (RSI, MACD, BBANDS, SMA, EMA, ADX)","Uses GPT-4.1 via LangChain agent to interpret momentum and market structure","Outputs structured JSON summary (timeframe, indicator values, market summary)","Operates as a webhook-triggered sub-agent (not standalone)","Maintains short-term session memory for continuity across calls"],"target_audience":["Quant/algo traders","Trading system developers","Data analysts focused on market microstructure and intraday signals"]}


name: Tesla 1hour Indicators Tool (Mid-Term Technical Analysis AI)
author_name: Don Jayamaha Jr
author_username: don-the-gem-dealer
categories: []
created_at: 2025-05-16T09:03:30.771Z
updated_at: 2025-05-16T09:03:30.771Z
url: https://n8n.io/workflows/4097
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  🕒 Evaluate Tesla (TSLA) price action and market structure on the 1-hour timeframe using 6 real-time indicators.
  This sub-agent is designed to feed mid-term technical insights into the Tesla Financial Market Data Analyst Tool. It uses GPT-4.1 to interpret Alpha Vantage indicator data delivered via secure webhooks.

   ⚠️ This workflow is not standalone and is executed via Execute Workflow.
   🔌 Requires:

   Tesla Quant Technical Indicators Webhooks Tool
   Alpha Vantage Premium API Key

  🔧 Connected Indicators

  This tool fetches and analyzes the latest 20 datapoints for:

  RSI (Relative Strength Index)**
  MACD (Moving Average Convergence Divergence)**
  BBANDS (Bollinger Bands)**
  SMA (Simple Moving Average)**
  EMA (Exponential Moving Average)**
  ADX (Average Directional Index)**

  📋 Sample Output

  {
    "summary": "TSLA is gaining strength on the 1-hour chart. RSI is rising, MACD has crossed bullish, and BBANDS are widening.",
    "timeframe": "1h",
    "indicators": {
      "RSI": 62.1,
      "BBANDS": {
        "upper": 176.90,
        "lower": 169.70,
        "middle": 173.30,
        "close": 176.30
      },
      "SMA": 174.20,
      "EMA": 175.60,
      "ADX": 27.5,
      "MACD": {
        "macd": 0.84,
        "signal": 0.65,
        "histogram": 0.19
      }
    }
  }

  🧠 Agent Components

  | Component                      | Role                                               |
  | ------------------------------ | -------------------------------------------------- |
  | 1hour Data                   | Pulls Alpha Vantage indicator data via webhook     |
  | Tesla 1hour Indicators Agent | Interprets signals using structured GPT-4.1 prompt |
  | OpenAI Chat Model            | GPT-4.1 LLM performs analysis                      |
  | Simple Memory                | Maintains session context                          |

  🛠️ Setup Instructions

  Import Workflow into n8n
     Name it: Tesla_1hour_Indicators_Tool

  Install the Webhook Fetcher Tool
     👉 Required: Tesla_Quant_Technical_Indicators_Webhooks_Tool

     This agent expects webhook /1hourData to return pre-cleaned data

  Add Credentials

     Alpha Vantage Premium API Key (via HTTP Query Auth)
     OpenAI GPT-4.1 credentials

  Configure for Sub-Agent Use

     Triggered only via Execute Workflow from:
       👉 Tesla Financial Market Data Analyst Tool
     Inputs:

       message (optional)
       sessionId (required for memory linkage)

  📌 Sticky Notes Overview

  🟢 Trigger Setup – Activated only by the parent agent
  📊 1h Webhook Fetcher – Calls Alpha Vantage via secured endpoint
  🧠 AI Agent Summary – Interprets trend/momentum from indicator data
  🔗 GPT Model Notes – GPT-4.1 parses and explains technical alignment
  📘 Documentation Sticky – Embedded in canvas with full walkthrough

  🔐 Licensing & Support

  © 2025 Treasurium Capital Limited Company
  This tool is part of a proprietary multi-agent AI architecture. No commercial reuse or redistribution permitted.

  🔗 Author: Don Jayamaha
  🔗 Templates: https://n8n.io/creators/don-the-gem-dealer/

  🚀 Detect TSLA trend shifts and validate setups with 1-hour technical clarity—powered by Alpha Vantage + GPT-4.1.
  This tool is required by the Tesla Financial Market Data Analyst Tool.

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name: Tesla 1day Indicators Tool (Macro-Level Technical AI)
author_name: Don Jayamaha Jr
author_username: don-the-gem-dealer
categories: []
created_at: 2025-05-16T09:06:41.790Z
updated_at: 2025-05-16T09:06:41.790Z
url: https://n8n.io/workflows/4098
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  📅 Analyze Tesla’s daily trading structure with AI using 6 Alpha Vantage indicators.
  This tool evaluates long-term trend health, volatility patterns, and potential reversal signals at the 1-day timeframe. Designed for use within the Tesla Financial Market Data Analyst Tool, this agent helps swing and position traders anchor macro sentiment.

   ⚠️ Not standalone. Must be executed via Execute Workflow
   🔌 Requires:

   Tesla Quant Technical Indicators Webhooks Tool
   Alpha Vantage Premium API Key
   OpenAI GPT-4.1 credentials

  🔍 What It Does

  This tool queries a secured webhook (/1dayData) to retrieve real-time, trimmed JSON data for:

  RSI (Relative Strength Index)**
  BBANDS (Bollinger Bands)**
  SMA (Simple Moving Average)**
  EMA (Exponential Moving Average)**
  ADX (Average Directional Index)**
  MACD (Moving Average Convergence Divergence)**

  These values are then passed to a LangChain AI Agent powered by GPT-4.1, which returns:

  A 2–3 sentence market condition summary
  Structured indicator values
  Timeframe tag ("1d")

  📋 Sample Output

  {
    "summary": "TSLA shows consolidation on the daily chart. RSI is neutral, BBANDS are contracting, and MACD is flattening.",
    "timeframe": "1d",
    "indicators": {
      "RSI": 51.3,
      "BBANDS": {
        "upper": 192.80,
        "lower": 168.20,
        "middle": 180.50,
        "close": 179.90
      },
      "SMA": 181.10,
      "EMA": 179.75,
      "ADX": 15.8,
      "MACD": {
        "macd": -0.25,
        "signal": -0.20,
        "histogram": -0.05
      }
    }
  }

  🧠 Agent Components

  | Component                     | Description                                        |
  | ----------------------------- | -------------------------------------------------- |
  | 1day Data (HTTP Node)       | Pulls latest data from secured /1dayData webhook |
  | OpenAI Chat Model           | GPT-4.1 powers the analysis logic                  |
  | Tesla 1day Indicators Agent | LangChain agent performing interpretation          |
  | Simple Memory               | Short-term session continuity                      |

  🛠️ Setup Instructions

  Import Workflow into n8n

     Name: Tesla_1day_Indicators_Tool

  Add Required Credentials

     Alpha Vantage Premium (via HTTP Query Auth)
     OpenAI GPT-4.1 (Chat Model)

  Install Webhook Fetcher

     Required: Tesla Quant Technical Indicators Webhooks Tool
     Endpoint /1dayData must be active

  Execution Context
     This tool is only triggered via:
     👉 Tesla Financial Market Data Analyst Tool

     Inputs expected:

       message: optional context
       sessionId: session memory linkage

  📌 Sticky Notes Overview

  📘 Tesla 1-Day Indicators Tool – Purpose and integration
  📡 Webhook Fetcher – Pulls daily Alpha Vantage data via HTTPS
  🧠 GPT-4.1 Model – Reasoning for trend classification
  🔗 Sub-Agent Trigger – Used only by Financial Market Analyst
  🧠 Memory Buffer – Ensures consistent session logic

  🔒 Licensing & Support

  © 2025 Treasurium Capital Limited Company
  This workflow—including prompts, logic, and formatting—is protected IP.

  🔗 Don Jayamaha – LinkedIn
  🔗 Creator Profile

  🚀 Evaluate long-term Tesla price behavior with AI-enhanced technical analysis—critical for swing trading strategy.
  Required by the Tesla Financial Market Data Analyst Tool.

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metadata_json:
  {"categories":["automation","integration","data processing","financial-analysis","AI-assist"],"complexity":"medium","use_cases":["Daily macro-level technical analysis for TSLA","Automated swing/position trading signal generation","Feeding structured indicator data into a larger analyst workflow","Quick market condition summaries for traders/analysts"],"estimated_setup_minutes":20,"required_services":["Tesla Quant Technical Indicators Webhooks Tool (secured /1dayData endpoint)","Alpha Vantage Premium API Key","OpenAI GPT-4.1 credentials","n8n (Execute Workflow context)","Tesla Financial Market Data Analyst Tool (parent workflow integration)"],"key_features":["Retrieves six Alpha Vantage indicators (RSI, BBANDS, SMA, EMA, ADX, MACD) via secured webhook","LangChain/GPT-4.1 agent produces 2–3 sentence market summary and structured JSON output","Returns timeframe tag (1d) plus detailed indicator values (including BBANDS and MACD subfields)","Simple memory buffer for short-term session continuity","Designed as a sub-agent to be invoked by the Tesla Financial Market Data Analyst Tool (not standalone)"],"target_audience":["Swing and position traders","Quantitative/technical analysts","Algo/trading developers integrating AI analysis into workflows"]}


name: Tesla 1hour & 1day Klines Tool (Candlestick & Volume AI Pattern Detector)
author_name: Don Jayamaha Jr
author_username: don-the-gem-dealer
categories: []
created_at: 2025-05-16T09:09:10.055Z
updated_at: 2025-05-16T09:09:10.055Z
url: https://n8n.io/workflows/4099
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  📉 Detect key candlestick reversal patterns and volume divergence on Tesla (TSLA) using GPT-4.1 and real-time OHLCV data.
  This AI agent evaluates 1-hour and 1-day candles and is an essential part of the Tesla Financial Market Data Analyst Tool. It identifies signals like Doji, Engulfing, Hammer, and volume anomalies to support trade entry and exit logic.

   ⚠️ Not a standalone template — must be triggered by the Tesla Financial Market Data Analyst Tool
   🔐 Requires:

   Alpha Vantage Premium API Key
   OpenAI GPT-4.1 access

  🔍 What This Agent Does

  Calls Alpha Vantage to fetch:

     🕐 1-hour OHLCV data
     📅 1-day OHLCV data
  GPT-4.1 evaluates:

     📊 Candlestick patterns like Doji, Engulfing, Shooting Star
     🔄 Volume divergence (price/volume inconsistency)
  Returns a structured JSON output like:

  {
    "summary": "Bearish signs detected on 1-day chart. A shooting star formed on high volume while RSI is elevated. Volume divergence seen on 1h chart as price rises but volume weakens.",
    "candlestickPatterns": {
      "1h": "None",
      "1d": "Shooting Star"
    },
    "volumeDivergence": {
      "1h": "Bearish",
      "1d": "None"
    },
    "ohlcv": {
      "1h": {
        "close": 174.1,
        "volume": 1430000,
        "high": 175.0,
        "low": 173.8
      },
      "1d": {
        "close": 188.3,
        "volume": 21234000,
        "high": 189.9,
        "low": 183.7
      }
    }
  }

  🛠️ Setup Instructions

  Import the Workflow

     Name it: Tesla_1hour_and_1day_Klines_Tool

  Install Dependencies

     ✅ Tesla Financial Market Data Analyst Tool (this is the trigger parent)

  Add Required Credentials

     Alpha Vantage Premium → via HTTP Query Auth
     OpenAI GPT-4.1 → via OpenAI credentials

  Verify Web Access

     This tool fetches data live from Alpha Vantage:

       /query?function=TIME_SERIES_INTRADAY&interval=60min
       /query?function=TIME_SERIES_DAILY

  Run via Execute Workflow Trigger
     This tool will activate only when called by the Financial Analyst Agent. Inputs:

     message (optional)
     sessionId (used for memory continuity)

  🧠 Agent Architecture

  | Component               | Description                                         |
  | ----------------------- | --------------------------------------------------- |
  | Candlestick Data Hour | Fetches 60min TSLA candles via Alpha Vantage        |
  | Candlestick Data Day  | Fetches daily TSLA candles via Alpha Vantage        |
  | OpenAI Chat Model     | GPT-4.1 reasoning engine for pattern detection      |
  | Simple Memory         | Maintains short-term logic context                  |
  | Tesla Klines Agent    | LangChain AI agent analyzing both candle and volume |

  📌 Sticky Notes Overview

  📘 Workflow Purpose
  🧠 Short-Term Memory Notes
  🔍 1h/1d Data Fetch Logic
  📉 Candlestick Pattern Types Detected
  📊 Volume Divergence Definitions
  🤖 GPT-4.1 Prompt Configuration

  🔐 Licensing & Support

  © 2025 Treasurium Capital Limited Company
  Logic, pattern reasoning, and prompt structure are proprietary IP.

  🔗 Don Jayamaha – LinkedIn
  🔗 n8n Creator Profile

  🚀 Automate technical edge: detect TSLA candle reversals and volume anomalies with precision using GPT-4.1 and Alpha Vantage.
  Required by the Tesla Financial Market Data Analyst Tool.

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

workflow_json_compressed:
  [encoded data, 5300 characters]

metadata_json:
  {"categories":["automation","financial-market-analysis","data-processing","AI/ML"],"complexity":"medium","use_cases":["Detect candlestick reversal signals for TSLA","Identify volume divergence for trade entry/exit logic","Provide structured market signal JSON to parent trading agent","Augment algorithmic trading strategies with AI pattern detection","Market research and technical analysis automation"],"estimated_setup_minutes":20,"required_services":["Alpha Vantage Premium API (intraday & daily OHLCV)","OpenAI GPT-4.1 (OpenAI API)","Tesla Financial Market Data Analyst Tool (parent/trigger workflow)","n8n (workflow runtime)"],"key_features":["Fetches 1-hour and 1-day OHLCV from Alpha Vantage","Uses GPT-4.1 to detect candlestick patterns (Doji, Engulfing, Hammer, Shooting Star, etc.)","Detects volume divergence between price and volume","Returns standardized JSON summary and detailed fields (patterns, divergence, OHLCV)","Designed as a triggered agent (not standalone) with short-term memory support"],"target_audience":["traders","quantitative analysts","algo developers/engineers integrating trading signals into systems"]}


