# templates

name: Convert GitHub commits into review-ready pull requests with Google Gemini
author_name: Ahmed Salama
author_username: ahmedsalama
categories: []
created_at: 2026-01-30T14:07:54.978Z
updated_at: 2026-01-30T14:07:54.978Z
url: https://n8n.io/workflows/13118
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:22
description:
  Description  
  Categories  
  Developer Automation, AI Agents, GitHub Automation, DevOps Productivity

  Build an AI-Driven GitHub Pull Request Automation with n8n + MCP

  This workflow creates an AI-powered GitHub automation that turns raw commit history into a clean, professional pull request automatically.

  When triggered via MCP or another workflow, it extracts repository details, fetches all commits from a target branch, uses AI to understand the intent behind the changes, and creates a well-structured pull request with a clear title and description.

  The result is a reliable, no-manual-work system that standardizes pull requests and reduces review friction across teams.

  Benefits

  Consistent Pull Requests  
  Every PR follows a clean, readable structure regardless of who triggered it.

  Zero Manual Formatting  
  No copy-pasting commit messages or writing descriptions by hand.

  Faster Review Cycles  
  Reviewers get clear context upfront, reducing back-and-forth.

  AI-Assisted Context Awareness  
  Commit history is summarized intelligently, not blindly concatenated.

  MCP-Ready Automation  
  Can be called directly by AI tools like Cursor through MCP.

  How It Works

  MCP or Workflow Trigger  
  Triggered via MCP server or another n8n workflow  
  Accepts natural language or structured input  

  Repository Information Extraction  
  AI extracts:
    Repository owner  
    Repository name  
    Source branch  
    Base branch  

  Commit Retrieval (GitHub API)  
  Fetches all commits for the source branch  
  Collects commit messages as context  

  Commit Summarization (AI)  
  AI analyzes commit history  
  Generates:
    A concise PR title  
    A clear bullet-point description  

  Pull Request Creation  
  Creates a GitHub pull request automatically  
  Uses correct base and head branches  
  Inserts AI-generated title and description  

  Required Setup

  GitHub  
  Repository access  
  OAuth or personal access token  
  Permission to read commits and create pull requests  

  AI Model  
  Google Gemini or compatible LLM  
  Connected via n8n AI nodes  

  n8n  
  Self-hosted or cloud  
  HTTP access to GitHub APIs  
  MCP Trigger enabled for AI tool access  

  Business Use Cases

  Engineering Teams  
  Standardize PR quality across developers  
  Reduce cognitive load on contributors  

  DevOps & Platform Teams  
  Enforce PR hygiene automatically  
  Improve velocity without extra process  

  Founders & Tech Leads  
  Maintain clean repositories without micromanagement  

  Agencies & Consultants  
  Deliver AI-assisted GitHub automation to clients  

  Difficulty Level  
  Intermediate

  Estimated Build Time  
  45–75 minutes

  Monthly Operating Cost  
  GitHub: Existing plan  
  AI Model: Free tier or usage-based  
  n8n: Self-hosted or cloud  

  Typical range: $0–20/month

  Why This Workflow Works

  Commits are the most reliable source of change intent  
  AI summarizes meaning, not noise  
  MCP enables direct AI-to-automation execution  
  GitHub remains the single source of truth  

  Possible Extensions

  Auto-assign reviewers based on files changed  
  Add PR labels using AI classification  
  Generate changelog entries automatically  
  Post PR summary to Slack or Teams  
  Enforce branch naming or commit standards  

  Details

  Nodes used in workflow
  MCP Trigger  
  AI Agent (Repository Extraction)  
  Structured Output Parser  
  GitHub API (Commits)  
  Summarize  
  AI LLM Chain  
  GitHub API (Create Pull Request)  
  If  
  Edit Fields (Set)  
  Sticky Note

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.mcpTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.githubTool","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.summarize","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5636 characters]

metadata_json:
  {"categories":["AI/ML","DevOps","Developer Automation"],"complexity":"medium","use_cases":["Generate PR descriptions from commits","Create detailed change summaries","Automate PR templates","Clean up commit messages"],"estimated_setup_minutes":15,"required_services":["GitHub","Google Gemini"],"key_features":["AI-powered commit analysis","Automated PR creation","Structured output generation","MCP integration"],"target_audience":["developers","devops engineers","product managers"]}


name: Triage LinkedIn notifications and InMails using Gmail, OpenAI, Notion, and Slack
author_name: Paul Karrmann
author_username: pk-consulting-ai
categories: []
created_at: 2026-01-30T21:08:51.711Z
updated_at: 2026-01-30T21:08:51.711Z
url: https://n8n.io/workflows/13121
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:22
description:
  LinkedIn Inbox Triage (Gmail Label to Notion + Slack)

  This n8n template demonstrates how to use AI to triage LinkedIn emails in your Gmail inbox, so you only see the messages worth your time.  
  It filters out automated noise, scores sales likelihood, drafts quick replies for real conversations, stores everything in Notion, and sends you a Slack DM for items you should answer quickly.

  Good to know
  This workflow sends email content to an LLM. Do not use it with sensitive mailboxes unless you are comfortable with that.
  Cost depends on your model choice and token usage. The body is currently limited to 4000 characters to control spend.
  If you want a shorter run window, adjust the receivedAfter filter.

  How it works
  Runs on a daily schedule.
  Pulls emails from Gmail using a label you define (example: LinkedIn).
  Applies two filters:
    Keeps only invitations and messages
    Removes common automated notifications
  Fetches the full email body for better classification.
  Sends the message to an AI agent that returns strict structured JSON:
    action (reply_quick, review, ignore, block)
    relevancy_score (0 to 100)
    sales_likelihood (0 to 1)
    summary
    optional reply_draft
  Applies a quality gate to keep high signal messages.
  Writes the output to a Notion database as a ticket.
  Sends a Slack DM only for items marked reply_quick.

  How to use
  Create a Gmail label that captures LinkedIn emails, then add the label id to the Gmail node.
  Create a Notion database with fields matching the Notion node mapping.
  Connect your OpenAI, Gmail, Notion, and Slack credentials in n8n.
  Run once manually to verify mapping, then enable the workflow.

  Requirements
  Gmail account
  OpenAI API credentials (or compatible model node)
  Notion database
  Slack account

  Customising this workflow
  Make it more aggressive by increasing the sales threshold or raising the relevancy cutoff.
  Add more filter phrases for your own LinkedIn email language.
  Swap Slack DM for a channel post, or send a daily digest instead of per message.
  Add a redaction step before the AI node if you want to remove signatures or quoted replies.

nodes_used:
  ["@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.gmail","n8n-nodes-base.filter","n8n-nodes-base.filter","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.filter","n8n-nodes-base.notion","n8n-nodes-base.filter","n8n-nodes-base.slack","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, 5792 characters]

metadata_json:
  {"categories":["Communication"],"complexity":"medium","use_cases":["Triage LinkedIn messages","Filter email noise","Draft replies automatically","Track conversations in Notion","Notify team via Slack"],"estimated_setup_minutes":60,"required_services":["Gmail","OpenAI","Notion","Slack"],"key_features":["Automated content scoring","AI-powered reply drafting","Structured data storage","Real-time notifications","Noise filtering"],"target_audience":["Sales managers","Business developers","Customer support teams"]}


name: Summarize Gmail emails with Gemini and send an HTML action digest
author_name: Mohammed Aljer
author_username: m-j
categories: []
created_at: 2026-01-31T00:06:07.342Z
updated_at: 2026-01-31T00:06:07.342Z
url: https://n8n.io/workflows/13126
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:22
description:
  Who’s it for
  This template is for anyone who wants a quick, structured inbox review (founders, freelancers, support, operations) without reading every email.

  What it does
  Every 12 hours, the workflow checks Gmail for emails received in the last 12 hours, then summarizes each email and extracts the next action plus a priority level (high/normal/low). It generates a direct Gmail “Open message” link for each item (using rfc822msgid) and sends one clean HTML digest email. If there are no new emails, it sends a short “No new emails” message instead.

  How to set up
  1) Connect your Gmail OAuth2 credentials in these nodes: Fetch Emails, Send Summary, No Emails.  
  2) Connect your Gemini credentials in Google Gemini Chat Model.  
  3) Update the recipient email in Send Summary and No Emails (sendTo), or replace it with a single variable in a Config/Set node.  
  4) Click Test workflow once, then activate the workflow.

  Requirements
  Gmail account + Gmail OAuth2 credentials in n8n.  
  Google Gemini API access.

  Recommended model
  Use Gemini 2.5 Flash for fast, reliable structured output. If the model is not available in your region/account, select the closest available Gemini model.

  How to customize the workflow
  Change the lookback window (12 hours → 24 hours, etc.) by editing the Date/Time calculation.  
  Adjust the AI prompt rules (priority criteria, action wording, output fields).  
  Modify the HTML digest design to match your branding.  
  Remove or change the Inbox-only filter if you want to include other labels (Sent/Other).

nodes_used:
  ["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.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.manualTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.dateTime","n8n-nodes-base.gmail","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.merge","n8n-nodes-base.aggregate","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.set","n8n-nodes-base.noOp"]

workflow_json_compressed:
  [encoded data, 9352 characters]

metadata_json:
  {"categories":["Communication","Productivity"],"complexity":"medium","use_cases":["daily inbox review","email triage","action item extraction"],"estimated_setup_minutes":15,"required_services":["Gmail API","Google Gemini API","Gmail SMTP server"],"key_features":["hourly email scanning","AI-powered summarization","structured action extraction","HTML digest email formatting"],"target_audience":["freelancers","managers","executives"]}


name: Extract Meta Ads detailed targeting across all endpoints using Google Sheets
author_name: Kirill Khatkevich
author_username: hotgomer
categories: []
created_at: 2026-01-31T19:42:14.129Z
updated_at: 2026-01-31T19:42:14.129Z
url: https://n8n.io/workflows/13134
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:22
description:
  Meta Ads Detailed Targeting Extractor (Universal, Switch by Endpoint)

  This workflow is a universal automation for all four Meta Detailed Targeting API endpoints: Search, Suggestions, Browse, and Validation. You use a single Google Sheets tab with an endpoint column; a Switch node routes each row to the correct branch; results are written to four separate sheets in the same spreadsheet. It is designed for media buyers, performance marketers, and analysts who manage targeting research, audience suggestions, browse trees, and validation in bulk and want one workflow instead of four.

  Use Case
  Working with Meta’s Detailed Targeting API usually means separate flows for search, suggestions, browse, and validation. This workflow is ideal if you want to:
  Centralize targeting operations** in one place: one input sheet, one workflow, four result sheets.
  Drive everything from Google Sheets**: add rows with endpoint (search | suggestions | browse | validation), ad_account_id, and endpoint-specific parameters; run manually or on new rows.
  Keep results organized** by endpoint: search_results, suggestions_results, browse_results, validation_results in the same document.
  Run on demand or on row add**: use Manual Trigger for full-sheet runs or Google Sheets Trigger to process only new rows.

  How it Works

  The workflow is organized into clear blocks:

  1. Trigger & input
  Manual Trigger* → *Read Input (Google Sheets)** — reads the entire targeting_requests sheet for ad-hoc or test runs.
  Google Sheets Trigger** — runs when a new row is added to targeting_requests; only new rows are processed (no re-processing of existing data).
  Read Input (Google Sheets)** always reads from the same sheet: targeting_requests.

  2. Validation & routing
  Valid rows (ad_account_id + endpoint)** — Filter node keeps only rows where both ad_account_id and endpoint are non-empty.
  Switch by endpoint** — routes each row to one of four branches based on endpoint: search, suggestions, browse, or validation (values must match exactly, including case).

  3. Each branch (Search, Suggestions, Browse, Validation)
  API** (Facebook Graph API) — calls the corresponding edge: targetingsearch, targetingsuggestions, targetingbrowse, or targetingvalidation with parameters from the row (act_{ad_account_id}/...).
  Merge** (combine by position) — merges the API response with the original request row so each result keeps context (e.g. ad_account_id, q, targeting_list).
  Split** (field: data) — expands the API data array into one item per targeting result.
  Format** — maps fields to flat columns for the sheet: endpoint, ad_account_id, query, limit_type, targeting_id, targeting_name, audience_size_lower_bound, audience_size_upper_bound, path, description, type; for Validation branch, valid is also included.
  Save to Google Sheets** — appends to the branch-specific sheet: search_results, suggestions_results, browse_results, or validation_results.

  4. Output
  All four Save nodes write to the same spreadsheet (same Document ID), each to its own sheet. The valid column is populated only in validation_results; other sheets leave it empty.

  Input sheet: targeting_requests

  Required columns for every row:

  | Column           | Description |
  |------------------|-------------|
  | endpoint       | One of: search, suggestions, browse, validation (lowercase). |
  | ad_account_id  | Meta ad account ID (without the act_ prefix). |

  Endpoint-specific columns:

  | Endpoint       | Required              | Optional |
  |----------------|-----------------------|----------|
  | search     | q — search query    | limit (default 25), limit_type, locale |
  | suggestions| targeting_list — JSON array, e.g. [{"type":"interests","id":"6003263791114"}] | limit (up to 45), limit_type, locale |
  | browse     | —                     | limit_type, locale |
  | validation | One of: targeting_list, id_list, or name_list (string/JSON per Meta API docs) | locale |

  If search has no q, or suggestions / validation lack the required targeting input, the API call will fail.

  Output sheets (same document)

  Use the same Document ID in Read Input, Google Sheets Trigger, and all four Save nodes. Create (or let n8n create) these sheet names:

  | Sheet                 | Branch      | Notes |
  |-----------------------|-------------|-------|
  | search_results      | Search      | endpoint, ad_account_id, query, limit_type, targeting_id, targeting_name, audience_size_*, path, description, type |
  | suggestions_results | Suggestions | Same columns; query holds the targeting_list from the request |
  | browse_results      | Browse      | Same columns; query empty |
  | validation_results  | Validation  | Same columns + valid (true/false from API) |

  Setup Instructions

  1. Credentials
  Connect Google Sheets OAuth2 in: Read Input (Google Sheets), Google Sheets Trigger (if used), and all four Save nodes.
  Connect Facebook Graph API credentials in each of the four API nodes (e.g. same “Facebook Graph” credential set).

  2. Spreadsheet & sheets
  Set the Document ID in:
    Read Input (Google Sheets) — Document = your spreadsheet, Sheet = targeting_requests.
    Google Sheets Trigger — same Document ID and sheet targeting_requests (if you use the trigger).
    All four Save …_results nodes — same Document ID; each node uses its own Sheet name: search_results, suggestions_results, browse_results, validation_results.
  Create the input sheet targeting_requests with the columns described above and the four result sheets (or allow n8n to create them on first append).

  3. Switch by endpoint
  Ensure the endpoint column in targeting_requests contains exactly: search, suggestions, browse, or validation (lowercase, as in the Switch conditions).

  4. Triggers
  Keep Manual Trigger for full-sheet runs; use Google Sheets Trigger for row-added automation. When using the trigger, run the workflow only when new rows are added so existing rows are not processed again.

  5. Activate
  Save and activate the workflow. Test with a few rows per endpoint before processing large sheets.

  The workflow reuses the same patterns as other Meta Detailed Targeting templates: read from Sheets, call Facebook Graph API, Merge by position, Split Out on data, then append to Sheets. The difference is the single input sheet with an endpoint column, Switch-based routing, and four dedicated branches writing to four sheets in one spreadsheet.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.filter","n8n-nodes-base.switch","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6356 characters]

metadata_json:
  {"categories":["Marketing","Data Processing"],"complexity":"medium","use_cases":["Extract Meta Ads targeting lists","Manage multi-endpoint API calls","Organize audience data rows"],"estimated_setup_minutes":30,"required_services":["Meta Ads API","Google Sheets"],"key_features":["Multi-endpoint API routing","Switch node for dynamic endpoint selection","Data organization in spreadsheet tabs"],"target_audience":["media buyers","performance marketers","analysts"]}


name: Sync Fathom meeting summaries and action items with GoHighLevel contacts
author_name: Dmytro
author_username: dmytroai
categories: []
created_at: 2026-02-01T01:03:45.688Z
updated_at: 2026-02-01T01:03:45.688Z
url: https://n8n.io/workflows/13136
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  This n8n template automatically syncs your Fathom AI meeting summaries, recordings, and action items directly into GoHighLevel CRM contacts.

  Fathom has native integrations for HubSpot and Salesforce, but not for GoHighLevel. This workflow fills that gap, giving GHL users the same automated meeting-to-CRM sync that other platforms enjoy. Perfect for sales teams, agencies, and consultants who want to keep their CRM updated without manual data entry.

  Good to know

  Only external attendees are synced (your team members are filtered out automatically)
  Contacts must already exist in GHL - the workflow matches by email address
  Action items become tasks with a 7-day due date by default (customizable in the Code node)

  How it works

  Fathom sends a webhook when your meeting ends, containing the summary, recording link, and action items
  The workflow extracts external attendees from the calendar invitees list
  For each attendee, it searches GoHighLevel for a matching contact by email
  If found, it creates an internal comment in the contact's conversation with a brief summary and recording link
  A detailed note is added to the contact with the full meeting summary
  Each action item from the meeting becomes a GHL task assigned to that contact

  How to use

  Import this workflow into your n8n instance
  Update the Config node with your GoHighLevel Location ID
  Connect your GoHighLevel OAuth2 credentials to all HTTP Request nodes
  Copy the webhook URL and add it in Fathom: Settings → API → Webhooks → "New meeting content ready"
  Activate the workflow and you're done!

  Requirements

  Fathom account with API access (free tier works)
  GoHighLevel account with Private Integration Token or OAuth app
  GHL API scopes: contacts.readonly, contacts.write, conversations.readonly, conversations.write, conversations/message.write

  Customising this workflow

  Change task due date**: Edit the "Prepare Action Items" Code node to adjust the 7-day default
  Add Slack notifications**: Insert a Slack node after "Create GHL Note" to notify your team
  Create contacts if not found**: Add a branch after "Contact Found?" to create new contacts from meeting attendees
  Sync to Opportunities**: Extend the workflow to also update related deals/opportunities in GHL

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.webhook","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.noOp","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.noOp","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest"]

workflow_json_compressed:
  [encoded data, 6112 characters]

metadata_json:
  {"categories":["CRM","Data Processing","Communication"],"complexity":"medium","use_cases":["Sync meeting summaries","Capture action items","Automate CRM entries","Integrate Fathom with GoHighLevel"],"estimated_setup_minutes":15,"required_services":["Fathom","GoHighLevel"],"key_features":["Fires leading service call","Processes meeting summaries","Extracts action items","Syncs contacts and activities","Handles external attendees only"],"target_audience":["sales teams","agencies","consultants"]}


name: Process data rights requests and governance compliance with Anthropic Claude
author_name: Cheng Siong Chin
author_username: cschin
categories: []
created_at: 2026-02-01T04:46:58.317Z
updated_at: 2026-02-01T04:46:58.317Z
url: https://n8n.io/workflows/13138
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:22
description:
  How It Works
  This workflow automates comprehensive data validation and regulatory compliance reporting through intelligent AI-driven analysis. Designed for compliance officers, data governance teams, and regulatory affairs departments, it solves the critical challenge of ensuring data quality while generating audit-ready compliance documentation across multiple regulatory frameworks.The system receives data through webhook triggers, performs multi-layered validation using AI models to detect anomalies and policy violations, and intelligently routes findings based on validation outcomes. It orchestrates parallel processing streams for content lookup, retention policy enforcement, and rejection handling. The workflow merges validation results, generates governance documentation, and manages compliance notifications through multiple channels. By automating action routing based on compliance status and maintaining detailed audit logs across validation, governance, and action streams, it ensures regulatory adherence while eliminating manual review bottlenecks.

  Setup Steps
  Configure Data Ingestion Webhook trigger endpoint
  Connect Workflow Execution Configuration node with validation parameters
  Set up Fetch Validation Rules node with OpenAI/Nvidia API credentials for AI model access
  Configure parallel AI model nodes with respective API credentials
  Connect Route by Validation Status node with branching logic
  Set up Governance Documentation node with document template configurations
  Configure parallel action nodes

  Prerequisites
  OpenAI/Nvidia/Anthropic API credentials for AI validation models
  Use Cases
  Financial institutions ensuring transaction compliance monitoring, 
  Customization
  Adjust AI model parameters for industry-specific compliance rules
  Benefits
  Reduces compliance review time by 80%, eliminates manual validation errors

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agentTool","@n8n/n8n-nodes-langchain.agentTool","@n8n/n8n-nodes-langchain.agentTool","@n8n/n8n-nodes-langchain.lmChatAnthropic","@n8n/n8n-nodes-langchain.lmChatAnthropic","@n8n/n8n-nodes-langchain.lmChatAnthropic","@n8n/n8n-nodes-langchain.lmChatAnthropic","@n8n/n8n-nodes-langchain.lmChatAnthropic","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.switch","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","n8n-nodes-base.dataTable","n8n-nodes-base.dataTable","n8n-nodes-base.dataTable","n8n-nodes-base.slack","n8n-nodes-base.emailSend","n8n-nodes-base.merge","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, 9656 characters]

metadata_json:
  {"categories":["Compliance","Data Processing","Governance"],"complexity":"complex","use_cases":["automate data rights requests","generate compliance reports","validate data quality","detect regulatory deviations","audit documentation creation"],"estimated_setup_minutes":120,"required_services":["Anthropic","Claude API"],"key_features":["AI-driven data validation","multi-layered compliance checking","audit-ready report generation","regulatory framework mapping","webhook-based data ingestion"],"target_audience":["compliance officers","data governance teams","regulatory affairs"]}


name: Generate and publish Instagram Reels with Gemini and Veo
author_name: Meelioo
author_username: mookielian
categories: []
created_at: 2026-02-01T09:34:07.470Z
updated_at: 2026-02-01T09:34:07.470Z
url: https://n8n.io/workflows/13149
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  How it Works

  The workflow runs automatically on a schedule (daily at a set time).
  It defines the video topic, caption, and aspect ratio.
  Gemini generates a detailed visual prompt for a short-form vertical video.
  Google Veo creates the video based on that prompt.
  The finished video is published to Instagram as a Reel with the preset caption.

  Set-up Steps

  Connect Google Gemini and Veo API credentials.
  Authenticate Instagram publishing access.
  Customize the video topic, caption, and posting schedule.
  Run a quick test to verify everything works.

  Estimated setup time: ~20–40 minutes.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.googleGemini","@n8n/n8n-nodes-langchain.googleGemini","@mookielianhd/n8n-nodes-instagram.instagram","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["AI/ML","Social Media"],"complexity":"medium","use_cases":["auto-generate video content","publish social media clips","create AI short-form videos"],"estimated_setup_minutes":15,"required_services":["Google Gemini API","Google Veo API","Instagram API"],"key_features":["scheduled video generation","AI prompt creation","automatic publishing","aspect ratio customization"],"target_audience":["social media managers","content creators","marketing teams"]}


name: Manage finances, tasks, tweets and Gmail with GPT‑4.1 on WhatsApp
author_name: moosa
author_username: moosa
categories: []
created_at: 2026-02-01T21:52:53.145Z
updated_at: 2026-02-01T21:52:53.145Z
url: https://n8n.io/workflows/13160
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  n8n Wizard 🪄 – Your personal AI assistant inside WhatsApp

  This workflow transforms WhatsApp into a powerful personal AI using n8n + Green-API. Send text or voice messages — the assistant understands intent and handles daily tasks automatically.

  Key features

  💰 Expense & income tracking — record spending, view summaries & category breakdowns (Google Sheets, append-only)
  ✓ Google Tasks management — create, list, update, delete tasks & reminders
  🐦 Post to X/Twitter — write and publish single tweets or short threads
  📧 Gmail search & summaries — find recent/unread emails by sender, label, keyword (read-only)
  🌐 Real-time answers — current weather, news, exchange rates, facts via web search
  🧮 Quick calculations — math, percentages, currency conversions
  🎤 Full voice support — incoming voice messages transcribed (Whisper), replies can be spoken (TTS)

  How it works

  Green-API webhook receives message (text or audio)  
  Voice → transcribed automatically  
  Main intelligent router agent selects one sub-agent/tool  
  Action executed → result sent back as text or voice (if input was voice)

  Setup requirements

  Green-API instance (webhook + send endpoints)  
  OpenAI API key (chat, Whisper, TTS)  
  Google Sheets, Google Tasks, Twitter/X, Gmail (read scope), SerpAPI credentials  

  Strict routing rules prevent misuse — no deletions, no guessing values, one tool per clear intent.

  Start commanding: “spent 3200 on groceries”, “remind dentist tomorrow”, “tweet: loving n8n!”, “weather in Lahore now”

nodes_used:
  ["@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agentTool","@n8n/n8n-nodes-langchain.agentTool","@n8n/n8n-nodes-langchain.agentTool","n8n-nodes-base.twitterTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agentTool","n8n-nodes-base.gmailTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.httpRequest","n8n-nodes-base.switch","n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.httpRequestTool","@n8n/n8n-nodes-langchain.toolSerpApi","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Communication","Productivity","AI/ML"],"complexity":"medium","use_cases":["Track expenses and income","Manage tasks and reminders","Post tweets from phone","Receive WhatsApp messages","Understand user intent"],"estimated_setup_minutes":120,"required_services":["WhatsApp (Green-API)","Google Sheets","Google Tasks","Twitter (X API)","Google Calendar"],"key_features":["Text-to-speech integration","Multi-channel processing","Intelligent intent recognition","Voice message handling","Persistent memory buffer"],"target_audience":["Personal assistants","Creative professionals","Freelancers"]}


name: Create and publish AI recipe infographics with Gemini, Nanobanana Pro and Blotato
author_name: giangxai
author_username: giangxai
categories: []
created_at: 2026-02-02T03:53:07.338Z
updated_at: 2026-02-02T03:53:07.338Z
url: https://n8n.io/workflows/13168
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  Overview

  This workflow automatically creates and publishes AI-generated recipe infographics using n8n and AI agents.

  By entering a single dish name, the system researches the recipe, generates structured cooking content, designs a visual infographic using Nanobanana Pro, uploads the image to Blotato’s media server, and publishes the post to Facebook using Blotato as the posting service.

  The workflow is designed for food content creators, social media managers, and automation builders who want to consistently produce and publish high-quality recipe infographics without manual research, design, or posting.

  What can this workflow do?

  Accept a dish name as the only user input  
  Automatically research ingredients, cooking steps, and timing details  
  Generate social-ready captions optimized for engagement  
  Create clean and readable recipe infographics using Nanobanana Pro  
  Monitor image generation status and handle retries  
  Upload generated images to Blotato’s media server  
  Publish recipe posts to Facebook via Blotato automatically  

  This workflow reduces manual work while keeping recipe content production structured, scalable, and repeatable.

  How it works

  The workflow starts with a simple input form where the user submits a dish name. This dish name is used as the single source of truth for the entire automation.

  An AI research agent analyzes the dish and generates structured recipe data, including ingredients, preparation steps, and cooking time. This data is normalized so it can be reused reliably in later steps.

  Next, an AI agent generates a concise, social-friendly caption optimized for food content and Facebook engagement. In parallel, another AI agent converts the structured recipe data into a detailed infographic prompt.

  This prompt is sent to Nanobanana Pro to generate a complete recipe infographic image. The workflow waits for the rendering process to finish and checks the generation status to ensure only completed images continue.

  Once the infographic is ready, the image is uploaded to Blotato’s media server. Finally, the workflow calls Blotato’s posting API to publish the post to Facebook using the Facebook account already connected inside Blotato.

  Setup steps

  Connect an AI model (Gemini or compatible LLM) for recipe research, caption generation, and prompt creation  
  Configure the input form to accept a dish name  
  Set up Nanobanana Pro as the image generation engine for recipe infographics  
  Connect your Blotato account and ensure Facebook is logged in inside Blotato  
  Configure the Blotato media upload node  
  Configure the Blotato post creation node for Facebook publishing  

  Once configured, the workflow runs end to end with no manual intervention beyond entering a dish name.

  Documentation

  For a full walkthrough, prompt optimization tips, and scaling strategies for food content automation, watch the detailed tutorial on YouTube.

nodes_used:
  ["@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.formTrigger","n8n-nodes-base.perplexityTool","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.switch","n8n-nodes-base.set","@blotato/n8n-nodes-blotato.blotato","@blotato/n8n-nodes-blotato.blotato","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.wait"]

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metadata_json:
  {"categories":["AI/ML","Marketing","Content Creation"],"complexity":"medium","use_cases":["Generate recipe content","Create visual infographics","Publish social posts","Automate content workflows"],"estimated_setup_minutes":30,"required_services":["Google Gemini API","Nanobanana Pro API","Blotato Media Server","Facebook Graph API"],"key_features":["Multi-agent orchestration","Structured data processing","Visual content generation","Automated social publishing","SEO-friendly content creation"],"target_audience":["Content creators","Social media managers","Food bloggers"]}


name: Translate RSS news and publish to WordPress, Facebook, LinkedIn and Telegram
author_name: SpaGreen Creative
author_username: spagreen
categories: []
created_at: 2026-02-02T06:56:28.323Z
updated_at: 2026-02-02T06:56:28.323Z
url: https://n8n.io/workflows/13171
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Who it's for
  This n8n workflow is designed for content curators, digital marketers, and social media managers who want to automate the process of discovering, translating, and publishing news content from multiple reputable sources. It provides a complete end-to-end system capable of reading RSS feeds, extracting articles and images, translating them into a specific language, and automatically publishing the final content to WordPress, Facebook, LinkedIn, and Telegram.

  Features
  Reads news from multiple RSS sources (TechCrunch, The - Verge, Analytics India Mag, Wired, The Next Web)
  Publishes content to WordPress, Facebook, LinkedIn Profile, LinkedIn Page, and Telegram
  Supports multiple languages with automatic translation (Bengali configured)
  Extracts article images, resizes them, adds watermarks, and uploads to WordPress
  Automatically creates WordPress posts and sets featured images
  Sends notification updates through Discord, Telegram, WhatsApp, and Gmail
  Runs automatically every hour
  Processes multiple articles from different feeds at once

  Requirements
  Google Translate API credentials for translation
  WordPress API access for content and media management
  Social media API credentials (Facebook, Telegram, LinkedIn)
  Notification service credentials (Discord, WhatsApp, Gmail)
  Access to the specified RSS feeds

  Important Notes
  Workflow runs on an hourly schedule to fetch the latest news
  Extracts featured images from articles and processes them for publication
  Content is automatically translated to Bengali (language code: "bn")
  Images are resized and watermarked before social media posting
  WordPress posts are created with translated content and featured images
  Notifications are sent after processing all items from the RSS feeds
  The workflow includes wait periods to prevent API rate limiting

  Support & Help
  WhatsApp**: Chat on WhatsApp
  Discord**: SpaGreen Community
  Facebook Group**: SpaGreen Support
  Website**: https://spagreen.net
  Developer Portfolio**: Codecanyon SpaGreen

nodes_used:
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metadata_json:
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name: Send AI-personalized LinkedIn outreach from Google Sheets with Azure OpenAI and ConnectSafely
author_name: Rahul Joshi
author_username: rahul08
categories: []
created_at: 2026-02-02T08:29:56.902Z
updated_at: 2026-02-02T08:29:56.902Z
url: https://n8n.io/workflows/13172
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  📊 Description
  Automate personalized LinkedIn outreach at scale using AI and real prospect data. 🤖📧
  This workflow pulls leads from Google Sheets, generates human-like LinkedIn messages with Azure OpenAI, and sends them safely via the ConnectSafely LinkedIn API. It intelligently avoids duplicate messages, validates delivery, and updates message status back into your sheet automatically. Perfect for consistent, non-salesy B2B outreach with zero manual effort.

  What This Template Does

  ⏰ Triggers automatically on a daily schedule to start LinkedIn outreach.

  📄 Fetches prospect details from Google Sheets for processing.

  🔁 Loops through prospects in controlled batches to avoid API limits.

  🧠 Uses AI to generate personalized LinkedIn messages based on role, company, industry, and activity.

  ✅ Checks if a message already exists to prevent duplicates.

  💾 Saves generated messages back to Google Sheets for tracking.

  🔍 Fetches LinkedIn profile data and profile URNs via ConnectSafely.

  💬 Sends personalized LinkedIn messages safely using the ConnectSafely API.

  📊 Updates Google Sheets with message delivery status and LinkedIn profile links.

  Key Benefits
  ✅ Fully automated LinkedIn outreach with no manual copywriting
  ✅ AI-driven personalization for higher reply rates
  ✅ Prevents duplicate messages automatically
  ✅ Centralized tracking inside Google Sheets
  ✅ Safe LinkedIn messaging using ConnectSafely APIs
  ✅ Scalable daily outreach without risking account limits

  Features
  Scheduled trigger for daily LinkedIn outreach
  Google Sheets integration for lead management
  Azure OpenAI (GPT-4o-mini) for AI message generation
  Batch processing for rate-limit safety
  Conditional logic to validate message creation
  ConnectSafely LinkedIn API for profile lookup and message sending

  Requirements
  Google Sheets account with prospect data
  Azure OpenAI credentials (GPT-4o-mini access)
  ConnectSafely LinkedIn API credentials
  n8n instance with scheduled workflows enabled

  Target Audience
  B2B founders and agency owners doing LinkedIn outreach
  Sales and lead generation teams
  Growth marketers running outbound campaigns
  Automation consultants building LinkedIn workflows

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name: AI WhatsApp Chatbot with RAG: Automate Text, Voice, Image & PDF Responses
author_name: SpaGreen Creative
author_username: spagreen
categories: []
created_at: 2026-02-02T08:47:14.657Z
updated_at: 2026-02-02T08:47:14.657Z
url: https://n8n.io/workflows/13173
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Who it's for
  This n8n workflow creates an intelligent WhatsApp customer support bot that can handle text, image, audio, and document messages. The workflow automatically processes incoming messages through different AI agents, maintains conversation context, accesses company knowledge bases, and provides personalized responses. It's designed for businesses looking to automate customer support across multiple communication channels.

  Features
  Multi-Modal Processing** – Handles text, images, audio, and documents.
  Smart Message Routing** – Categorizes and directs messages automatically.
  AI Analysis** – Uses Google Gemini (images/audio) and Mistral Cloud (text).
  Knowledge Base Access** – Connects with Google Sheets, Docs, and product docs.
  Conversation Memory** – Keeps context across multiple messages.
  Document Recognition** – Supports PDF, XLS/XLSX, CSV, JSON, and more.
  Support Logging** – Tracks customer issues in Google Sheets.
  Product Docs** – Access for Salebot, Delix, Socialvibe, Faculty, Yoori, MeetAir, Oxoo, Flixoo.

  Requirements
  Rapiwa API credentials** for WhatsApp integration
  Google Gemini API credentials** for image and audio analysis
  Mistral Cloud API credentials** for language model processing
  Google Sheets OAuth2 credentials** for accessing and logging data
  Google Docs OAuth2 credentials** for accessing company documentation
  HTTP Request access** to various product documentation sites

  Important Notes
  Message Type Handling**: The workflow includes specialized processing for different message types
  Document Support**: Supports multiple document formats including PDF, XLS, XLSX, CSV, JSON, XML, RTF, TXT, and HTML
  Product Documentation**: Provides access to documentation for multiple products through HTTP requests
  Conversation Memory**: Maintains context across multiple messages from the same session
  Support Logging**: Automatically logs customer issues with categories and suggested solutions
  Error Handling**: Provides appropriate error messages for unsupported file types

  Support & Help
  WhatsApp**: Chat on WhatsApp
  Discord**: SpaGreen Community
  Facebook Group**: SpaGreen Support
  Website**: https://spagreen.net
  Developer Portfolio**: Codecanyon SpaGreen

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name: Escalate payment issues with Azure OpenAI, Google Sheets, Slack and Zendesk
author_name: Rahul Joshi
author_username: rahul08
categories: []
created_at: 2026-02-03T06:40:02.019Z
updated_at: 2026-02-03T06:40:02.019Z
url: https://n8n.io/workflows/13178
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  📘 Description

  This workflow automates payment-related customer support escalation by validating reported issues against transaction data and coordinating all downstream actions in a controlled, auditable way. It is designed for support teams that need to quickly separate valid payment failures from false or incomplete reports—while maintaining consistent customer communication and timely human intervention.

  The workflow is triggered via a webhook whenever an external system reports a customer issue. Incoming data is normalized and cross-checked against Google Sheets, which acts as the single source of truth for transaction status, payment confirmation, and order creation. An AI escalation engine then applies strict, rule-based logic to determine whether the issue is confirmed, needs clarification, or not valid, without assuming or inventing missing information.

  For confirmed cases, the workflow runs parallel actions: it generates a concise escalation summary for human support, logs full context to Slack, creates a Zendesk ticket for traceable resolution, and sends a calm, customer-safe email acknowledging the issue. Transaction records are then updated to reflect confirmation status. Robust error handling ensures any workflow failure is immediately surfaced in Slack.

  ⚙️ What This Workflow Does (Step-by-Step)
   📥 Webhook Intake
   Receives structured payment issue data from external systems.
  🧾 Normalize Incoming Payload
   Prepares a consistent data structure for processing.
  📊 Transaction Lookup (Google Sheets)
   Fetches payment and order details from the source of truth.
  🧠 AI Escalation Decision
   Validates the issue and classifies it as confirmed, needs clarification, or invalid.
  🚨 Notify Human Support
   Sends escalation email and logs detailed context to Slack.
  ✉️ Send Customer-Facing Email
   Delivers a calm, professional acknowledgement to the customer.
  🎫 Create Zendesk Ticket
   Opens a support ticket for confirmed cases requiring manual action.
  📝 Update Transaction Record
   Marks confirmation status in Google Sheets for auditability.
  ⚠️ Error Handling → Slack Alert
   Immediately alerts the team if any workflow step fails.

  🧩 Prerequisites
   • Webhook access for incoming issues
   • Google Sheets OAuth2 (source of truth)
   • Azure OpenAI credentials
   • Gmail OAuth2 for email delivery
   • Slack API access
   • Zendesk API access

  💡 Key Benefits
   ✔ Prevents false or premature escalations
   ✔ Uses Google Sheets as an authoritative data source
   ✔ Consistent, customer-safe communication
   ✔ Parallel handling for speed and clarity
   ✔ Full audit trail across Sheets, Slack, Email, and Zendesk
   ✔ Strong error monitoring and reliability

  👥 Perfect For
  Customer support teams
  Payment and order operations
  SaaS and transaction-heavy platforms
  Businesses handling high-volume support escalations

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metadata_json:
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name: Grok Imagine Video Chatbot 🤖📺: Generate & Modify Videos via Natural Language
author_name: Davide Boizza
author_username: n3witalia
categories: []
created_at: 2026-02-03T09:49:56.613Z
updated_at: 2026-02-03T09:49:56.613Z
url: https://n8n.io/workflows/13182
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  The Video Grok Agent is an AI-powered video generation and editing workflow that uses Grok 4.1 Fast (via OpenRouter) and Grok Imagine Video to create and modify videos through natural language.

  This workflow enables seamless AI-driven video creation and editing through a conversational interface, with built-in validation, async processing, and secure credential management.

  Key Advantages

  1. ✅ Unified Video Creation Pipeline

  A single workflow supports text-to-video, image-to-video, and video editing, reducing complexity and avoiding duplicated logic across multiple automations.

  2. ✅ AI-Guided User Interaction

  The conversational agent ensures:

  the correct tool is selected,
  all mandatory parameters are provided,
  errors due to missing inputs are minimized.

  This makes the workflow usable even by non-technical users.

  3. ✅ Asynchronous & Scalable Execution

  The workflow is designed around queued, non-blocking requests:

  wait nodes + status polling
  no execution timeouts
  scalable for multiple concurrent video jobs

  4. ✅ Automatic Media Handling

  Uploaded images are:

  detected automatically,
  uploaded to external storage,
  converted into usable URLs without manual steps.

  This enables smooth image-to-video generation directly from chat uploads.

  5. ✅ Clear Separation of Responsibilities

  Each step is modular:

  orchestration (agent),
  decision routing (switch),
  media processing (Fal.run APIs),
  status monitoring (HTTP + wait loops).

  This makes the workflow easy to maintain, extend, or debug.

  6. ✅ Strong Guardrails & Validation

  The agent enforces:

  correct tool usage (e.g. video-to-video only for editing),
  duration limits (1–15 seconds),
  mandatory URLs before execution.

  This prevents incorrect API calls and wasted compute.

  7. ✅ Extensible Architecture

  New tools (e.g. different models, resolutions, aspect ratios, or providers) can be added without redesigning the whole system—just plug them into the existing agent + switch logic.

  How it works

  User Interaction  
     The workflow starts with a chat trigger (When chat message received), where users can upload images and submit text prompts.
     If an image is uploaded, it is automatically stored via FTP (BunnyCDN) and its URL is passed to the AI agent.

  AI Agent Orchestration  
     The Grok Imagine Video Agent processes the user’s request and determines which action to perform:
       Text-to-Video: Create a new video from a text prompt. See test result
       Image-to-Video: Animate an existing image. See test result
       Video-to-Video: Edit an existing video. See test result
     The agent follows strict rules to ensure all required parameters (e.g., duration, URLs, prompts) are collected before proceeding.

  Video Processing via Fal.run API  
     Depending on the selected tool, the workflow calls the corresponding Fal.run endpoint:
       text-to-video
       image-to-video
       edit-video
     Each request returns a request_id used to poll for completion.

  Asynchronous Processing & Polling  
     After submission, the workflow enters a polling loop:
       Waits 10–30 seconds.
       Checks the request status via Fal.run’s status endpoint.
       Once the status is COMPLETED, it retrieves the final video URL.

  Result Delivery  
     The final video URL is returned to the user via the chat interface.
     The agent does not proceed further once the video is ready.

  Set Up Steps

  To deploy and use this workflow in n8n:

  Credentials Configuration  
     Set up the following credentials in n8n:
       OpenRouter API (for Grok 4.1 Fast)
       Fal.run API (HTTP Header Auth)
       FTP/BunnyCDN (for image uploads)

  Workflow Activation  
     Ensure all nodes are correctly connected as per the connections mapping.
     Activate the workflow via the Execute Workflow Trigger (Run Text-to-Video1).

  Chat Interface Setup  
     The workflow is designed to be triggered via a chat message.
     Configure the When chat message received node to connect to your frontend or chat platform.

  Parameter Validation  
     The AI agent includes validation rules to ensure:
       Duration is between 1–15 seconds.
       Required URLs (image/video) are provided where needed.
       The correct tool is invoked based on user intent.

  Testing & Monitoring  
     Test each tool separately (text, image, video) to ensure Fal.run API responses are handled correctly.
     Monitor the polling loops to avoid timeouts and ensure video URLs are retrieved successfully.

  👉 Subscribe to my new YouTube channel. Here I’ll share videos and Shorts with practical tutorials and FREE templates for n8n.

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

nodes_used:
  ["@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.httpRequest","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.lmChatOpenRouter","n8n-nodes-base.if","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","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.wait","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.ftp","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, 8140 characters]

metadata_json:
  {"categories":["AI/ML","Media","Communication"],"complexity":"complex","use_cases":["Generate videos via natural language","Edit existing videos through chat","Create videos from text prompts","Transform images into videos","Synthesize video content with conversational AI"],"estimated_setup_minutes":5,"required_services":["OpenRouter","Grok 4.1"],"key_features":["Conversational video generation","Async processing for long workflows","Natural language video editing","Multi-video input support","Secure credential management"],"target_audience":["video creators","content producers","media teams"]}


name: Scrape LinkedIn comments and score lead intent using ConnectSafely, Azure OpenAI, and Google Sheets
author_name: Rahul Joshi
author_username: rahul08
categories: []
created_at: 2026-02-03T11:28:06.148Z
updated_at: 2026-02-03T11:28:06.148Z
url: https://n8n.io/workflows/13185
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  📊 Description
  Automate LinkedIn lead discovery by identifying high-intent prospects directly from post comments using ConnectSafely and AI 🤖. This workflow searches LinkedIn posts related to common sales pain points, extracts all comments safely, and evaluates each commenter’s buying intent using AI. The scored leads are automatically stored in Google Sheets for easy follow-up and prioritization 📈. Perfect for sales, growth, and RevOps teams looking to turn conversations into qualified opportunities.

  🔍 What This Template Does
  🔎 Searches LinkedIn posts via SerpAPI using predefined pain-point keywords
  🔗 Filters and validates relevant LinkedIn post URLs
  🛡️ Fetches all post comments using ConnectSafely (safe, compliant LinkedIn access)
  📄 Flattens comments into individual lead records
  🤖 Uses AI to analyze each comment and score buying intent (0–100)
  📊 Saves enriched lead data into Google Sheets automatically

  ✅ Key Benefits
  ✅ Discover warm LinkedIn leads from real conversations
  ✅ Uses ConnectSafely for reliable and compliant comment extraction
  ✅ AI-powered intent scoring removes manual lead qualification
  ✅ Automatically prioritizes high-intent prospects
  ✅ Centralized lead tracking in Google Sheets
  ✅ Zero manual scraping or copy-pasting

  ⚙️ Features
  LinkedIn post discovery via SerpAPI
  Comment scraping powered by ConnectSafely
  AI intent detection using Azure OpenAI (GPT-4o-mini)
  Intent labels: No-Intent → High-Intent
  Google Sheets integration for lead storage
  Modular, customizable n8n workflow

  🔐 Requirements
  SerpAPI account
  ConnectSafely LinkedIn API credentials
  Azure OpenAI account (GPT-4o-mini deployment)
  Google Sheets OAuth credentials
  n8n instance (self-hosted or cloud)

  🎯 Target Audience
  B2B sales teams & SDRs
  Growth marketers & lead generation agencies
  RevOps & automation specialists
  Founders using LinkedIn for outbound prospecting

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.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-serpapi.serpApi","n8n-nodes-connectsafely-ai.connectSafelyLinkedIn","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.googleSheets"]

workflow_json_compressed:
  [encoded data, 6876 characters]

metadata_json:
  {"categories":["AI/ML","Sales","Marketing"],"complexity":"medium","use_cases":["discover high-intent prospects","extract comments from LinkedIn posts","score lead quality with AI","store leads in spreadsheets"],"estimated_setup_minutes":15,"required_services":["LinkedIn via ConnectSafely","Azure OpenAI","Google Sheets","Serper API"],"key_features":["sentiment analysis on social comments","automatic lead scoring model","structured data export to spreadsheets"],"target_audience":["sales representatives","growth marketers","RevOps managers"]}


name: Track domain expiry dates with Google Sheets and WHOIS API
author_name: isaWOW
author_username: isawow
categories: []
created_at: 2026-02-04T09:33:24.357Z
updated_at: 2026-02-04T09:33:24.357Z
url: https://n8n.io/workflows/13211
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:22
description:
  Automatically track domain expiry dates from Google Sheets, fetch real-time DNS expiry data via WHOIS API, and update expiry details back to your sheet with zero manual effort.

  Automated Domain Expiry Date Tracker with Google Sheets & WHOIS API

  Automate the entire process of monitoring domain expiry dates for all your websites directly from Google Sheets.
  This workflow reads domain names, fetches DNS SOA expiry information using the WHOIS API, converts timestamps into readable dates, and updates expiry details back into your tracking sheet—fully automated and rate-limit safe.

  Perfect for SEO teams, agencies, hosting managers, and businesses managing large domain portfolios.

  What this workflow does

  This automation handles four key tasks:

  Reads domain data

  Pulls all website domains directly from a Google Sheet

  Fetches expiry details

  Uses WHOIS API to retrieve DNS SOA records for each domain

  Processes expiry dates

  Converts expiry timestamps into human-readable DD-MM-YYYY format
  Extracts expiry month and year automatically

  Updates tracking sheet

  Writes expiry date, month name, and year back to Google Sheets
  Processes domains one by one with a controlled delay to avoid API limits

  How it works

  The workflow starts manually and loads configuration values such as Google Sheet ID and sheet name.
  It reads all domains listed in the Websites column and processes them in a loop.

  For each domain, the workflow calls the WHOIS API to fetch DNS SOA records.
  The expiry timestamp is extracted, converted into a readable date format, and enriched with expiry month and year values.

  Once processed, the workflow updates the same Google Sheet row with the new expiry information.
  A 30-second pause is applied before moving to the next domain to ensure API safety and stability.

  Setup requirements

  Accounts needed:

  n8n instance (self-hosted or cloud)
  Google account with Google Sheets access
  RapidAPI account with WHOIS API access

  Estimated setup time: 10 minutes

  Setup steps

  1. Import workflow

  Copy the workflow JSON
  Open n8n → Workflows → Import from JSON
  Paste and import
  Verify all nodes are connected correctly

  2. Configure Google Sheets

  Create a Google Sheet with a Websites column
  Add Google Sheets OAuth2 credential in n8n
  Paste your Sheet ID and sheet name inside Set Sheet Configuration node

  3. Configure WHOIS API

  Get your RapidAPI WHOIS API key
  Add it to the Fetch DNS Records via WHOIS API HTTP Request node
  Test the API request

  4. Verify data mapping

  Ensure expiry values map correctly to:

    Domain Expiry
    Expiry Month
    Expiry Year

  5. Run and monitor

  Run the workflow manually
  Check execution logs
  Verify expiry data updates correctly in Google Sheets

  What data gets updated

  Domain data:

  Domain name
  Expiry date (DD-MM-YYYY)
  Expiry month (January–December)
  Expiry year

  Sheet updates:

  Existing rows are matched using the Websites column
  No duplicate rows are created

  Use cases

  SEO management:** Prevent domain expiries that can hurt rankings
  Agency operations:** Track client domains in one central sheet
  Hosting monitoring:** Stay ahead of renewal deadlines
  Portfolio management:** Manage hundreds of domains automatically

  Important notes

  Replace the WHOIS API key before activating
  Google Sheets column names must match exactly
  Workflow runs sequentially to avoid rate limits
  One domain is processed at a time
  Expiry accuracy depends on DNS SOA availability

  Support
  Need help or custom development?

  📧 Email: info@isawow.com

  🌐 Website: https://isawow.com/

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

workflow_json_compressed:
  [encoded data, 4200 characters]

metadata_json:
  {"categories":["Data Processing","DevOps"],"complexity":"medium","use_cases":["track domain expiration","monitor website DNS updates"],"estimated_setup_minutes":15,"required_services":["Google Sheets","WHOIS API"],"key_features":["automated expiration tracking","real-time DNS lookup","date timestamp conversion","batch processing with HTTP requests","code logic for SOA data extraction"],"target_audience":["domain administrators","web ops engineers","technical managers"]}


name: Track MPESA and Airtel mobile payments in a fundraising WhatsApp group
author_name: Mabura Ze Guru
author_username: zeguru
categories: []
created_at: 2026-02-04T12:39:20.338Z
updated_at: 2026-02-04T12:39:20.338Z
url: https://n8n.io/workflows/13213
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:22
description:
  Try It Out!
  This n8n templates assists with keeping track of mobile payments within a fundraising WhatsApp group.

  Use cases: We fundraise alot using whatsapp groups in East Africa, especially in Kenya ! Keeping track of each payment and the tallying requires alot of manual effort and brings unnecessary tension in cases of Errors of Commision or Ommision.

  Works with MPESA and AIRTEL MONEY, for now.

  How it works
  Connect you twillio account / mobile number to the webhook.
  Send whatsapp message or web chat.
  We use simple regex to classify the text of the message. 
  A switch node routes payment messages based on the payment service provider. The message may be a request for the current total or an instruction to end the campaign and clear the payment logs.
  A Gemini node will handle deviations from payments topic
  Clearing may be necessary in case of mistakes since we provide no edit function. It may also be necessary to avoid mixing payments of a previous fundraising campaign with the current one.
  Payment information is extracted from the message accoding to the SMS format of the service provider. This is then saved to a data table.
  After each payment, or a request for summary, all payments related to the sender/groupid are fetched and taken to the next node for summarization.
  A merge node is used to bring in the message metadata (from, to, ) to assist in whatsapp reply via twillio

  How to use
  As the treasurer / payee keep SMS receipts of all incoming mobile payments.
  Each SMS receipt will contain the amount and the senders details, among other info.
  Send each one by one via whatsapp to the phone number above or web chat.

  Requirements
  Twillio account - for whatsapp
  Accessible webhook url
  This example uses a data table payment_table but an SQL node is recommended for production use

  Need Help?
  Join the Discord or ask in the Forum!

  Happy Hacking!

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.dataTable","n8n-nodes-base.dataTable","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.stickyNote","n8n-nodes-base.twilio","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.switch","n8n-nodes-base.dataTable","n8n-nodes-base.function","n8n-nodes-base.function","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.chat","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.googleGemini","n8n-nodes-base.code"]

workflow_json_compressed:
  [encoded data, 7800 characters]

metadata_json:
  {"categories":["Communication","Fundraising"],"complexity":"medium","use_cases":["Track mobile payments","Manage WhatsApp fundraisers","Tally donor contributions"],"estimated_setup_minutes":15,"required_services":["WhatsApp","Twilio","M-Pesa","Airtel Money"],"key_features":["Automatic donation tracking","Payment verification","WhatsApp group notifications","Error avoidance"],"target_audience":["Non-profit managers","Fundraisers","WhatsApp admins"]}


name: Post AI news to Telegram with Google Gemini and human approval
author_name: Natnail Getachew
author_username: itan
categories: []
created_at: 2026-02-04T14:00:46.519Z
updated_at: 2026-02-04T14:00:46.519Z
url: https://n8n.io/workflows/13216
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:22
description:
  How it works
  This workflow automates the curation and publishing of AI news to a Telegram channel. It follows a four-stage process:

  Aggregation: Monitors RSS feeds from VentureBeat and The AI Blog on a daily schedule.

  Filtering: Merges the feeds and uses a code snippet to select only the single most recent, high-quality article.

  Summarization: Sends the article data to Google Gemini, which generates a professional, emoji-rich summary formatted for social media.

  Human-in-the-loop Approval: Instead of auto-posting, it sends the draft to a private Telegram chat. The post is only published to the public channel once a human clicks "Approve."

  Set up steps
  Time estimate: 10–15 minutes.

  Credentials: You will need to connect your Google Gemini (PaLM) API key and your Telegram Bot API token.

  Telegram IDs: Ensure you have the Chat ID for both your private approval chat and your public destination channel.

  Schedule: The trigger is currently set to 3:56 PM daily; adjust this in the "Schedule Trigger" node to fit your preferred posting time.

nodes_used:
  ["n8n-nodes-base.merge","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.telegram","n8n-nodes-base.scheduleTrigger"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["Communication","AI/ML","Marketing"],"complexity":"medium","use_cases":["Curate AI news","Publish to Telegram","Generate summaries","Human review approval","RSS aggregation"],"estimated_setup_minutes":30,"required_services":["Google Gemini","Telegram","RSS feeds"],"key_features":["Scheduled RSS aggregation","AI-powered summarization","Human-in-the-loop approval","Social media formatting","Content filtering logic"],"target_audience":["Content creators","Marketing teams","Community managers"]}


name: Send pre-meeting Slack briefings using Google Calendar, Notion, GitHub, and Jira
author_name: Intuz
author_username: intuz
categories: []
created_at: 2026-02-05T09:26:41.209Z
updated_at: 2026-02-05T09:26:41.209Z
url: https://n8n.io/workflows/13224
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:22
description:
  This n8n template from Intuz provides a complete and automated solution for preparing and delivering context-rich briefings directly to attendees before every meeting. 

  It acts as an AI-powered executive assistant, gathering relevant information from all your key work tools to ensure everyone arrives prepared and aligned.

  Who's this workflow for?
  Engineering Managers & Team Leads
  Product Managers & Project Managers
  Scrum Masters & Agile Coaches
  Any team that holds regular status, planning, or technical meetings.

  How it works

  1. Trigger on New Calendar Event: The workflow starts automatically whenever a new meeting is created in a designated Google Calendar.

  2. Fetch Previous Context: It immediately connects to Notion to retrieve the notes from the most recent past meeting, ensuring continuity.

  3. Wait for the Right Moment: The workflow calculates a time 15 minutes before the meeting's scheduled start and pauses its execution until then.

  4. Gather Real-Time Project Data: Just before the meeting, the workflow wakes up and:
  Extracts keywords from the meeting title.
  Searches GitHub for recent Pull Requests (PRs) relevant to those keywords.
  Searches Jira for any tickets or issues that match the meeting's topic.

  5. Build the Intelligent Briefing: It assembles all the gathered information—previous notes from Notion, current PRs from GitHub, and relevant tickets from Jira—into a single, beautifully formatted Slack message.

  6. Deliver to Each Attendee: The workflow identifies all attendees from the Google Calendar invite, finds their corresponding Slack profiles via email, and sends the personalized briefing as a Direct Message (DM) to each one, ensuring everyone is prepared just in time.

  Key Requirements to Use This Template
  1. n8n Instance: An active n8n account (Cloud or self-hosted).
  2. Google Calendar Account: To trigger the workflow on new events.
  3. Notion Account: With a dedicated database for storing meeting notes.
  4. GitHub Account: To search for relevant pull requests.
  5. Jira Cloud Account: To search for relevant project tickets.
  6. Slack Workspace & App: A Slack workspace where you have permission to install an app. You will need a Bot Token with the necessary permissions.

  Setup Instructions
  Google Calendar Trigger:
  In the "Capture New Google Calendar Event" node, connect your Google Calendar account and select the calendar you want to monitor.

  Notion Connection:
  In the "Get Last Meeting Notes" node, connect your Notion account.
  Select the Notion Database ID that contains your meeting notes.

  GitHub & Jira Connections:
  In the "Get PRs from Repo" node, connect your GitHub account and select the repository to search.
  In the "Get Jira Issues Related to Meeting" node, connect your Jira Cloud account. You can customize the JQL query if needed.

  Slack Configuration (Crucial Step):
  Create a Slack App: Go to api.slack.com/apps, create a new app, and install it to your workspace.
  Set Permissions: In your app's "OAuth & Permissions" settings, add the following Bot
  Token Scopes: chat:write (to send messages) and users:read.email (this is critical for looking up attendees). Reinstall the app to your workspace.
  Get Bot Token: Copy the "Bot User OAuth Token" (it starts with xoxb-).
  Connect in n8n:
  In the "Get User Slack Info from Email" node, click "Header Parameters" and replace {{ slack oauth token }} with your actual Bot Token.
  In the "Send Meeting Context in Slack DM" node, connect your Slack credentials using the same Bot Token.

  Activate the Workflow:
  Save the workflow and toggle the "Active" switch to ON. Your automated pre-meeting bot is now live!

  Connect with us
  Website: https://www.intuz.com/n8n-workflow-automation-templates
  Email: getstarted@intuz.com
  LinkedIn:
  https://www.linkedin.com/company/intuz
  Get Started: https://n8n.partnerlinks.io/intuz

  For Custom Workflow Automation

  Click here: Get Started

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metadata_json:
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name: Enrich creator handles with cross-platform social data from influencers.club
author_name: Influencers Club
author_username: influencers-club
categories: []
created_at: 2026-02-05T10:48:35.739Z
updated_at: 2026-02-05T10:48:35.739Z
url: https://n8n.io/workflows/13227
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  How it works:

  Get multi social platform data for creators from one social handle.

  Step by step workflow to enrich influencer contacts with multi social (Instagram, Tiktok, Youtube, Twitter, Onlyfans, Twitch and more) profiles, analytics and metrics using the influencer.club API

  Set up:
  Supabase (can be swapped for any DB)
  Influencers.club

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name: Back up n8n workflows to google drive while preserving folder structure
author_name: Adam ABDELMOUMNI
author_username: adamabdelmoumni
categories: []
created_at: 2026-02-06T14:17:49.498Z
updated_at: 2026-02-06T14:17:49.498Z
url: https://n8n.io/workflows/13245
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:21
description:
  Back up & restore n8n workflows with preserved folder structure with GoogleDrive

  A. Backup workflows solution to google Drive

  ✅ What problem does this workflow solve?

  If you’re building and managing multiple automations with well-organized nested folders structure, then, losing a workflow due to accidental deletion or misconfiguration, can cost you hours of work and headache.

  This can be even more impactful for self-hosted n8n instances.

  This template solves that for any n8n setup (cloud or self hosted)  by exporting a perfect mirrored setup of a whole n8n project, preserving the nested folder structure and the workflows within it. 

  All of it is uploaded in google drive under one main backup folder per execution to keep track of different setup versions along time.

  🧑‍💻Who’s it for ?

  This workflow is ideal for any n8n user, from beginner to advanced solo/team “flowgrammer” who wants to have a reliable, safe, automated and easy to access backup solution that reflects a perfect mirror of their n8n setup. 

  ✨What it does

  Scheduled Execution**: The workflow runs automatically according to the schedule, could be up to X times a day (or can be triggered manually).

  Creates backup Folder**: It creates a new overall backup folder with a naming convention as “n8n_backup_folder_structure_DDMMYYYY_HHmmss” (i.e “n8n_backup_folder_structure_02022026_123343”) where the whole n8n nested folder structure along with workflows (JSON files) will be saved.

  For example if an n8n workflow instance look like this, then same structure will be preserved and reflected on google drive along with the workflows within each folder : 

   projects-root-folder/
  	└── Your-project-folder-name/
  		└── Utilities/
  └──Error_management/
  	└── error_alerting.json
  └──Log_analysis/
  └── Reports/
  	└── Clients/
  		└──Client_A/
  		└──Client_B/
  └── client_reporting_standard.json
         └── ...
  └── workflow_test.json


  Fetches the non documented n8n API**: It connects to your n8n instance via the n8n API, not the documented one, but the one used directly from your UI when you manipulate your instance.
  This API has been used instead of the native n8n node because it gives some more features that are being used here such as :

      Retrieve the workflow’s parentFolder name
      Retrieve the workflow’s description
      Retrieve the n8n instance’s projectId
      Create an n8n data table
      Get properties of an n8n data table

  🛠️How to set up ?

  1. Configure Credentials: Make sure you have valid credentials for :
      Google Drive: To allow the workflow to create folders and upload files.

  2. Set Your Variables: In the first Set node named "n8n instance/project access details":
      n8n_instance_URL: Paste your n8n instance URL without the “/” at the end. For example : https://myautomations.app.n8n.cloud
      emailOrLdapLoginId: your login email address
      password: your password

   


  3. Create your main backup folder: Create on your google drive space the main folder where all your backup will be uploaded. For example “my_n8n_backup” and then select it in the node Create backup folder "n8n_backup_folder_structure_ddMMyyyy_HHmmss" ” from the List under “Parent Folder”,

  🚀 Activate the workflow, and you're all set!

  B. Restore Backup workflows to n8n from google Drive

  You can run it manually by adding the  specific google drive backup folder name that you want to restore on n8n inside the node "Search for the backup folder in Drive".





  After running the workflow you'll see on your n8n the backup folder restored with all your saved folder/subfolders and associated workflows.



  And when you open that folder you'll see all your setup successfully restored! 

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name: Collect leads with a conversational GPT-4.1 chatbot and Google Sheets
author_name: Incrementors
author_username: incrementors
categories: []
created_at: 2026-02-07T11:23:43.192Z
updated_at: 2026-02-07T11:23:43.192Z
url: https://n8n.io/workflows/13250
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:22
description:
  Description

  A natural conversational AI chatbot that collects lead information (Name, Phone, Email, Message) one question at a time without feeling like a form. Uses session-based memory to track conversations, intelligently asks only for missing details, and saves complete leads to Google Sheets automatically.

  What this workflow does

  This workflow creates a human-like booking assistant that gathers lead information through natural conversation instead of traditional forms. The AI chatbot asks ONE question at a time, remembers previous answers using session memory, never repeats questions, and only saves data to Google Sheets when all four required fields (Name, Phone Number, Email Address, User Message) are confidently collected. The conversation feels natural and friendly—users engage with the bot as if chatting with a real person, dramatically improving completion rates compared to static forms.

  Perfect for booking systems, consultation requests, event registrations, customer support intake, or any scenario where you need to collect contact information without friction.

  Key features

  One question at a time: The AI never overwhelms users with multiple questions. It asks for Name, then Phone, then Email, then Message—sequentially and naturally, based on what's still missing from the conversation.

  Session-based memory: Uses timestamp-based session tracking so the AI remembers the entire conversation context. If a user says "My name is John" in message 1, the AI won't ask for the name again in message 5.

  Smart field detection: The AI automatically detects which details have been collected and which are still missing. It adapts the conversation flow dynamically instead of following a rigid script.

  Natural language processing: Handles variations in user input ("John Doe", "I'm John", "Call me John") and validates data intelligently before saving.

  Complete data guarantee: Only writes to Google Sheets when all 4 required fields are present. No partial or incomplete leads clutter your tracking sheet.

  Webhook-based integration: Works with any website, app, or platform that can send HTTP requests. Integrate with chatbots, contact forms, booking widgets, or custom applications.

  Instant responses: Real-time conversation with sub-second response times. Users get immediate replies, maintaining engagement throughout the lead collection process.

  How it works

  1. User initiates conversation via webhook
  A user sends a message through your website chat widget, contact form, or booking interface. This triggers a webhook that passes the message along with query parameters (name, email, phone, message, timestamp, source) to n8n.

  2. AI Agent analyzes conversation state
  The Conversational Lead Collection Agent receives the user's message and checks the current state:
  Which fields are already collected (from previous messages in this session)?
  Which fields are still missing?
  What should be asked next?

  The AI uses the system prompt to understand its role as a booking assistant for "Spark Writers' Retreat" and follows strict conversation rules.

  3. Session memory tracks context
  The Buffer Window Memory node uses the timestamp from the webhook as a unique session ID. This allows the AI to:
  Remember all previous messages in this conversation
  Access previously collected information (name, phone, email)
  Never ask the same question twice
  Maintain conversation continuity even if the user takes breaks

  4. One question at a time
  Based on what's missing, the AI asks exactly ONE question in natural, friendly language:
  If Name is missing → "Hi! What's your name?"
  If Phone is missing → "Great! And what's your phone number?"
  If Email is missing → "Perfect! Could you share your email address?"
  If Message is missing → "Thanks! How can I help you today?"

  The AI adapts its language based on previous conversation flow—it doesn't sound robotic or repetitive.

  5. Data validation and collection
  As the user responds, the AI:
  Validates input (checks if phone number looks valid, email has @ symbol, etc.)
  Extracts the information from natural language responses
  Stores it temporarily in session memory
  Continues asking until all 4 fields are complete

  If the user provides unclear input, the AI politely asks again: "I didn't quite catch that. Could you share your phone number?"

  6. Save to Google Sheets (when complete)
  Critical rule: The AI only uses the Google Sheets tool AFTER all four details are confidently collected. This prevents partial or incomplete leads from cluttering your database.

  When all fields are present, the AI:
  Writes exactly ONE row to Google Sheets
  Maps data: Name → Name, Phone → Phone No., Email → Email, Message → Message
  Uses Timestamp as the unique identifier (matching column)
  Updates existing rows if the same timestamp appears again (prevents duplicates)

  7. Confirmation message
  After successfully saving, the AI sends a polite thank you:

  "Thank you! 🙏 We've received your details and our team will get back to you shortly."

  The AI never mentions Google Sheets, tools, backend systems, or automation—it maintains the illusion of human conversation.

  8. Response delivery
  The final AI response is sent back to the user via the webhook response. Your website or app displays this message in the chat interface, completing the conversation loop.

  Setup requirements

  Tools you'll need:

  Active n8n instance (self-hosted or n8n Cloud)
  Google Sheets with OAuth access for lead storage
  OpenAI API key (GPT-4.1-mini access)
  Website or app with chat interface (or any platform that can send webhooks)

  Estimated setup time: 15–20 minutes

  Configuration steps

  1. Connect Google Sheets
  In n8n: Credentials → Add credential → Google Sheets OAuth2 API
  Complete OAuth authentication
  Create a Google Sheet for lead tracking with these columns:
    Timestamp (unique session identifier)
    Name
    Phone No.
    Email
    Message
  Open "Save Lead to Google Sheets" node
  Select your Google Sheet and the correct sheet tab
  Verify column mapping matches your sheet structure

  2. Add OpenAI API credentials
  Get API key: https://platform.openai.com/api-keys
  In n8n: Credentials → Add credential → OpenAI API
  Paste your API key
  Open "OpenAI GPT-4.1 Mini Language Model" node
  Select your OpenAI credential
  Ensure model is set to gpt-4.1-mini

  3. Copy webhook URL
  Open "Receive User Message via Webhook" node
  Copy the Webhook URL (format: https://your-n8n.cloud/webhook/[webhook-id])
  This is the endpoint your website or app will send messages to

  4. Integrate with your chat interface
  You need to send HTTP POST/GET requests to the webhook URL with these query parameters:

  GET https://your-n8n.cloud/webhook/[id]?name=[name]&email=[email]&phone=[phone]&message=[user_message]&timestamp=[unique_timestamp]&source=[source]

  Query parameter details:
  name: User's name (empty string if not yet collected)
  email: User's email (empty string if not yet collected)
  phone: User's phone number (empty string if not yet collected)
  message: Current user message (required)
  timestamp: Unique session ID (use ISO timestamp or UUID)
  source: Source identifier (e.g., "website_chat", "booking_form")

  Example integration (JavaScript):
  const sessionId = new Date().toISOString();
  const userMessage = "Hi, I want to book a retreat";

  fetch(https://your-n8n.cloud/webhook/[id]?message=${encodeURIComponent(userMessage)}&timestamp=${sessionId}&name=&email=&phone=&source=website_chat)
    .then(res =&gt; res.json())
    .then(data =&gt; {
      // Display AI response in your chat UI
      console.log(data.output);
    });

  5. Customize the AI assistant
  Open "Conversational Lead Collection Agent" node and edit the system message to:
  Change the business name (currently "Spark Writers' Retreat")
  Modify conversation tone (formal vs. casual)
  Adjust the fields being collected
  Change the final thank you message

  6. Test the workflow
  Activate the workflow (toggle to Active at the top)
  Send a test message to the webhook URL
  Verify the AI responds appropriately
  Continue the conversation by sending follow-up messages with the same timestamp
  Check that:
    AI asks for missing fields only
    Session memory persists across messages
    Lead saves to Google Sheets when all 4 fields are collected
    Thank you message appears after saving

  Use cases

  Booking and reservations: Hotels, retreat centers, event venues, or appointment-based businesses collect guest details conversationally instead of long booking forms. Higher completion rates mean more confirmed bookings.

  Lead generation for services: Agencies, consultants, coaches, or freelancers capture qualified leads through natural conversation. Users are more likely to complete the process when it feels like chatting instead of form-filling.

  Customer support intake: Support teams collect issue details, contact information, and problem descriptions through chat before routing to the right agent. All data automatically logged in Google Sheets for ticketing.

  Event registration: Conference organizers, workshop hosts, or webinar providers gather attendee information without friction. The conversational approach encourages sign-ups even from mobile users who hate forms.

  Sales qualification: Sales teams use the chatbot to qualify leads by collecting basic information and understanding requirements before human handoff. Complete context stored in Google Sheets for CRM integration.

  Consultation requests: Professional services (legal, medical, financial) collect client details and initial consultation requests through friendly conversation, reducing no-show rates by building rapport early.

  Customization options

  Change collected fields
  Open "Conversational Lead Collection Agent" node and modify the system message:
  Add new fields (e.g., Company Name, Budget, Preferred Date)
  Remove optional fields (e.g., make Message optional)
  Update the field names and data mapping

  Then update the Google Sheets node to include the new columns.

  Adjust conversation tone
  In the system message, change conversation style:
  Formal:** "May I please have your full name?"
  Casual:** "What's your name?"
  Friendly:** "Hey! What should I call you?"

  Add validation rules
  Enhance the system prompt with specific validation:
  Phone format (e.g., 10 digits, US format)
  Email domain restrictions (e.g., only business emails)
  Name length requirements
  Message minimum word count

  Connect to CRM or email
  After "Save Lead to Google Sheets" node, add:
  HTTP Request node** to send data to your CRM API
  Email node** to notify sales team of new leads
  Slack/Discord node** for real-time team alerts
  Webhook node** to trigger other workflows

  Multi-language support
  Modify the system prompt to respond in the user's language:
  Add language detection logic
  Translate questions and responses
  Update thank you message for each language

  Add conversation analytics
  Insert a Set node before saving to track:
  Number of messages per lead
  Time to completion
  Drop-off points
  Source performance

  Troubleshooting

  AI repeats questions already answered
  Memory not persisting:* Verify the *"Session Memory with Timestamp"** node is using the correct timestamp from the webhook query params.
  Timestamp changing:** Ensure your chat interface sends the SAME timestamp for all messages in one conversation. Generate it once and reuse it.
  Memory window size:** Increase the buffer window size in the memory node if conversations are very long.

  Leads not saving to Google Sheets
  Partial data:** The AI only saves when all 4 fields are collected. Check your test conversation actually provided all required information.
  OAuth expired:** Re-authenticate Google Sheets credentials.
  Sheet permissions:** Verify the connected Google account has edit access to the sheet.
  Column names mismatch:** Ensure sheet column names exactly match the mapping in the Google Sheets node (case-sensitive).

  AI saves incomplete data
  System prompt not followed:** Review the "Tool usage (VERY IMPORTANT)" section in the system message. Ensure it clearly states to only use Google Sheets after all fields are collected.
  Validation too lenient:** The AI might be guessing missing fields. Strengthen validation rules in the system prompt.

  Webhook not receiving messages
  URL incorrect:** Double-check the webhook URL in your integration code matches the n8n webhook URL exactly.
  CORS issues:** If calling from a browser, ensure n8n allows cross-origin requests or use server-side integration.
  Query params missing:** Verify all required parameters (message, timestamp) are included in the request.

  AI responses too slow
  OpenAI API latency:** GPT-4.1-mini typically responds in 1-3 seconds. If slower, check OpenAI API status.
  Network delays:** Verify n8n instance has good connectivity.
  Memory lookup slow:** Reduce buffer window size if storing hundreds of messages.

  Session memory not working
  Timestamp format inconsistent:** Use ISO format (e.g., 2026-01-28T14:38:23.720Z) and ensure it's identical across messages.
  Memory node misconfigured:* Check the session key expression in *"Session Memory with Timestamp"** node references the correct webhook query param.

  Resources

  n8n documentation
  OpenAI GPT-4 API
  Google Sheets API
  n8n Webhook node
  n8n AI Agent
  Buffer Window Memory

  Support

  Need help or custom development?

  📧 Email: info@incrementors.com 
  🌐 Website: https://www.incrementors.com/

nodes_used:
  ["n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.googleSheets","n8n-nodes-base.respondToWebhook","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":["AI/ML","Communication"],"complexity":"medium","use_cases":["Lead capture","Customer outreach","Data entry"],"estimated_setup_minutes":15,"required_services":["Google Sheets"],"key_features":["Conversational AI","Natural language processing","Session memory","Automatic data saving"],"target_audience":["Marketers","Sales teams"]}


name: Design UI projects 🎨🖼️ with Google Stitch via Telegram using MCP and Gemini AI
author_name: Davide Boizza
author_username: n3witalia
categories: []
created_at: 2026-02-09T17:13:15.521Z
updated_at: 2026-02-09T17:13:15.521Z
url: https://n8n.io/workflows/13269
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:22
description:
  This workflow implements an AI-powered design and prototyping assistant that integrates Telegram, Google Gemini, and Google Stitch (MCP) to enable conversational UI generation and project management.

  Supported actions include:

  Creating new design projects
  Retrieving existing projects
  Listing projects and screens
  Fetching individual screens
  Generating new UI screens directly from text descriptions

  Key Advantages

  1. ✅ Conversational Design Workflow

  Design and UI prototyping can be driven entirely through natural language. Users can create screens, explore layouts, or manage projects simply by chatting, without opening design tools.

  2. ✅ Tight Integration with Google Stitch

  By leveraging the Stitch MCP API, the workflow provides direct access to structured design capabilities such as screen generation, project management, and UI exploration, avoiding manual API calls or custom scripting.

  3. ✅ Intelligent Tool Selection

  The AI agent does not blindly call APIs. It first analyzes the user request, determines the required level of fidelity and intent, and then selects the most appropriate Stitch function or combination of functions.

  4. ✅ Multi-Channel Support

  The workflow supports both generic chat triggers and Telegram, making it flexible for internal tools, demos, or production chatbots.

  5. ✅ Security and Access Control

  Telegram access is restricted to a specific user ID, and execution only happens when a dedicated command is used. This prevents accidental or unauthorized usage.

  6. ✅ Context Awareness with Memory

  The inclusion of conversational memory allows the agent to maintain context across interactions, enabling iterative design discussions rather than isolated commands.

  7. ✅ Production-Ready Output Formatting

  Responses are automatically converted into Telegram-compatible HTML, ensuring clean, readable, and well-formatted messages without manual post-processing.

  8. ✅ Extensible and Modular Architecture

  The workflow is highly modular: additional Stitch tools, AI models, or communication channels can be added with minimal changes, making it future-proof and easy to extend.

  How It Works

  This workflow functions as a Telegram-powered AI agent that leverages Google Stitch's MCP (Model Context Protocol) tools for design, UI generation, and product prototyping. It combines conversational AI, tool-based actions, and web search capabilities.

  Trigger & Authorization: The workflow is activated by an incoming message from a configured Telegram bot. A code node first checks the sender's Telegram User ID against a hardcoded value (xxx) to restrict access. Only authorized users can proceed.
  Command Parsing: An IF node filters messages, allowing the agent to proceed only if the message text starts with the command /stitch. This ensures the agent is only invoked intentionally.
  Query Preparation: The /stitch prefix is stripped from the message text, and the cleaned query, along with the user's ID (used as a session identifier), is passed to the main agent.
  AI Agent Execution: The core "Google Stitch Agent" node is an LLM-powered agent (using Google Gemini) equipped with:
      Tools: Access to several Google Stitch MCP functions (create_project, get_project, list_projects, list_screens, get_screen, generate_screen_from_text) and a Perplexity web search tool.
      Memory: A conversation buffer window to maintain context within a session.
      System Prompt: Instructs the agent to intelligently select and use the appropriate Stitch tools based on the user's design-related request (e.g., generating screens from text, managing projects). It is directed to use web search when necessary for additional context.
  Response Processing & Delivery: The agent's text output (in Markdown) is passed through another LLM chain ("From MD to HTML") that converts it to Telegram-friendly HTML. Finally, the formatted response is sent back to the user via the Telegram bot.

  Set Up Steps

  To make this workflow operational, you need to configure credentials and update specific nodes:

  Telegram Bot Configuration:
      In the "Code" node (id: 08bfae9e...), replace xxx in the condition $input.first().json.message.from.id !== xxx with your actual Telegram User ID. This ensures only you can trigger the agent.
      Ensure the "Telegram Trigger" and "Send a text message" nodes have valid Telegram Bot credentials configured.

  Google Stitch API Setup:
      Obtain an API key from Google Stitch.
      Configure the HTTP Header Auth credential named "Google Stitch" (referenced by all MCP tool nodes: Create Project, Get Project, etc.).
      Set the Header Auth with:
          Name: X-Goog-Api-Key
          Value: Your actual Google Stitch API Key (YOUR-API-KEY).

  AI Model & Tool Credentials:
      Verify the credentials for the Google Gemini Chat Model nodes are correctly set up for API access.
      Verify the credentials for the Perplexity API node ("Search on web") are configured if web search functionality is required.

  Activation:
      Once all credentials are configured, set the workflow to Active. The Telegram webhook will be registered, and the workflow will listen for authorized messages containing the /stitch command.

  👉 Subscribe to my new YouTube channel. Here I’ll share videos and Shorts with practical tutorials and FREE templates for n8n.

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

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.mcpClientTool","n8n-nodes-base.perplexityTool","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.telegramTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Communication","Design"],"complexity":"medium","use_cases":["Conversational UI generation","Design project management","UI prototyping via text descriptions"],"estimated_setup_minutes":15,"required_services":["Telegram","Google Gemini","Google Stitch","Perplexity"],"key_features":["MCP integration","Conversational design workflow","Text-to-screen generation","Project and screen retrieval"],"target_audience":["UI/UX designers","developers","product managers"]}


name: Use skills In n8n agent node
author_name: Liam McGarrigle
author_username: liammcgarrigle
categories: []
created_at: 2026-02-09T23:35:01.390Z
updated_at: 2026-02-09T23:35:01.390Z
url: https://n8n.io/workflows/13270
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:22
description:
  This template gives you a framework to use skills in any n8n agent. You can use this as a starting point and add any other tools or patterns needed for your use case.

  What are “skills”?

  Skills are a context management standard created by Anthropic for use in Claude code. Basically, instead of having a HUGE system prompt, skills split that into lots of small, structured instruction files that tell an agent how to do a specific kind of task. Instead of stuffing a massive prompt full of rules, the agent:

  finds the relevant skill file
  reads it
  and follows the steps inside

  It’s a simple pattern that makes managing system prompts for general purpose agents much more straightforward.

  See an example of a skills repo here.

  What this workflow does

  Responds to messages in n8n Chat (or Chat Hub)
  Builds an “available skills” index from one or more GitHub repos
  Lets the agent browse folders + fetch skill files (Markdown) as needed
  Uses the skill content to guide how it completes tasks

  How it (roughly) works

  A chat message comes in.
  The workflow lists directories in the configured skills repos (root + /skills if present), filters out noise, and merges everything into one directory map.
  That directory map gets injected into the agent’s system prompt so it knows what skill files exist.
  When it needs instructions, it uses tools:

     List Files by Path Name to explore folders
     Get a File From GitHub to pull the skill file as raw text (no base64)

  Same “skills” pattern as the CLI tools

  The flow is: find a skill → open it → follow the steps, the same way it works in the CLI tools, but running inside n8n, so you don’t need to download or install anything locally.

  How to set it up
  (Required) Add your GitHub credentials to each node that needs it
  (Required) Add your OpenRouter credentials to the chat node or replace with the provider of your choosing
  (Optional) Add more repos to SKILLS_REPOS (any skills GitHub repo works as long as your credentials have access to it, such as any public repo)
  (Optional) Add more tools and turn it into whatever agent you actually need

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.github","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatOpenRouter","n8n-nodes-base.github","n8n-nodes-base.filter","n8n-nodes-base.filter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.githubTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.merge","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Automation"],"complexity":"medium","use_cases":["Define custom AI agent capabilities","Organize AI behavior patterns","Structure agent instructions","Extend agent functionality with tools"],"estimated_setup_minutes":15,"required_services":["GitHub","OpenRouter"],"key_features":["Modular skills architecture","Context management","Dynamic tool integration","Separation of concerns for AI prompts"],"target_audience":["AI/ML engineers","Prompt engineers","Automation developers"]}


name: Chat Hub Web Search Agent with Tavily Search
author_name: Liam McGarrigle
author_username: liammcgarrigle
categories: []
created_at: 2026-02-10T00:28:32.609Z
updated_at: 2026-02-10T00:28:32.609Z
url: https://n8n.io/workflows/13271
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:22
description:
  A blazing fast research agent that works nativly in n8n's Chat Hub.

  This agent is preconfigured with an optional system prompt for both Chat Hub and to use the Tavily search tool.

  Setup Steps
  Sign up for the generous Tavily Free Tier
  Add your Tavily API key in the credentials in the tool
  Never use a search engine again!

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatOpenRouter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chatTrigger","@tavily/n8n-nodes-tavily.tavilyTool"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Communication"],"complexity":"medium","use_cases":["conduct research","generate answers","search web data"],"estimated_setup_minutes":5,"required_services":["OpenRouter API","Tavily API"],"key_features":["web search integration","chat interface","conversational memory"],"target_audience":["developers","reserachers"]}


name: Build a WordPress RAG chatbot with OpenAI, Qdrant or MongoDB
author_name: Paolo Ronco
author_username: paoloronco
categories: []
created_at: 2026-02-10T16:57:20.063Z
updated_at: 2026-02-10T16:57:20.063Z
url: https://n8n.io/workflows/13291
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:22
description:
  Wordpress AI Chatbot

  Build a powerful, customizable AI chatbot for your WordPress website that intelligently retrieves posts, answers questions, and engages in natural conversations. This complete solution handles content indexing with OpenAI embeddings and vector storage (Qdrant or MongoDB), then powers a sophisticated chatbot interface that provides accurate, context-aware responses using RAG (Retrieval-Augmented Generation).


    
      
    


  Two indexing options:

  Quick path:** Automated workflow indexing to Qdrant (15-25 minutes setup)
  Advanced path:** Manual off-workflow indexing to MongoDB with included code and guides (1-2 hours setup)

  Complete package: Includes both indexing and chatbot workflows, WordPress plugin with customizable chat widget, and comprehensive step-by-step guides with screenshots.

  What this workflow does

  This complete solution provides content indexing and a customizable AI chatbot for your WordPress website:

  Indexing Phase (Choose your path):

  Option 1: Automated Qdrant indexing (Recommended):

  Retrieves all published posts from your WordPress site using the REST API
  Cleans and structures the content by extracting titles, URLs, post IDs, and slugs
  Splits text into intelligent chunks (1200 characters with 150-character overlap) for optimal embedding
  Generates vector embeddings using OpenAI's text-embedding model (1536 dimensions)
  Stores vectors in Qdrant Cloud for fast similarity search and retrieval

  Option 2: Advanced MongoDB indexing:

  Manual off-workflow process** for developers who prefer MongoDB Atlas
  Includes complete Node.js embedding scripts and detailed setup guides
  Full control over indexing parameters and storage structure

  Chatbot Phase (Works with both indexing options):

  Once indexed, your WordPress site features a fully customizable AI chatbot that:

  Retrieves relevant posts** using semantic search to answer content-related questions
  Provides accurate information** from your actual WordPress content with source citations
  Engages in natural small talk** for a conversational user experience
  Customizable appearance** to match your website's branding and style
  Delivers context-aware responses** using RAG (Retrieval-Augmented Generation)

  Tools and services used

  Core components:

  WordPress REST API** - Retrieves published posts with full content
  OpenAI Embeddings API** - Generates text-embedding-ada-002 vectors (1536d)
  Qdrant Cloud** - Vector database for storing and searching embeddings
  LangChain nodes** - Document processing and vector store integration
  Recursive Character Text Splitter** - Intelligent text chunking with overlap

  n8n nodes included:

  WordPress node (getAll posts operation)
  Qdrant Vector Store node (insert mode)
  OpenAI Embeddings node
  Default Data Loader with custom metadata
  Text Splitter with configurable chunk sizes
  Manual trigger for on-demand indexing

  Use cases

  Content websites & blogs

  Make your articles semantically searchable
  Power AI chatbots with your blog content
  Help visitors discover relevant posts through natural language

  Business websites

  Enable intelligent search across your service pages
  Answer customer questions using your existing content
  Reduce support load with automated, accurate responses

  Documentation sites

  Semantic search across technical documentation
  Find solutions by meaning, not exact keyword matches
  Combine multiple doc sources in one searchable index

  Portfolio sites

  Make your projects and case studies searchable
  Answer questions about your work with AI
  Showcase expertise through conversational interface

  E-commerce

  Search product descriptions semantically
  Answer product questions using your content
  Guide customers to relevant items

  What's included

  When you purchase this complete pack, you receive:

  Workflows (both included)

  Indexing workflow part** - Prepares WordPress content for semantic search

  Chatbot workflow part** - User-facing AI chat interface with RAG search capabilities

  Both workflows work together as a complete AI chatbot solution

  Documentation & guides

  📚 Clear and complete step-by-step guides included:

  Quick start guide** - Get up and running in 15-25 minutes
  Detailed setup instructions** - With screenshots for every step
  Qdrant configuration guide** - Vector database setup walkthrough
  MongoDB alternative path** - Advanced setup option (1-2 hours)
  Customization guide** - Adjust AI behavior, chunk sizes, and styling
  Troubleshooting guide** - Solutions to common issues
  Security best practices** - GDPR compliance, authentication setup
  API integration guide** - Connect all required services

  WordPress plugin

  Frontend chat widget** - Ready-to-use chat interface
  REST API proxy** - Secure server-side communication
  Admin settings panel** - Easy configuration from WordPress dashboard
  Customizable shortcode** - [wp_ai_chatbot] for any page

  Additional files

  Configuration examples** - Sample credentials and settings templates
  Test micro-website** - Standalone HTML for testing chatbot workflow
  MongoDB embedding scripts** (Node.js) - For advanced setup path
  Sample data files** - For testing and verification

  Support:

  📧 Email support after purchase for installation help
    (may require additional fees)
  📚 Full documentation access with all guides

  Requirements

  Essential services (all have free tiers):

  WordPress site** (5.0+) with REST API enabled
  n8n instance** (cloud or self-hosted v1.0+)
  OpenAI API key** - For generating embeddings
  Qdrant**
    Cloud: Free tier includes 1GB storage
    On-Premise/VM: Dockerized or runned from source

  Optional but recommended:

  HTTPS enabled on WordPress site
  Application password authentication for WordPress
  Cohere API key (for reranking in the chatbot workflow)

  Technical requirements:

  WordPress application password or API credentials
  Basic understanding of API authentication
  Basic understanding and knowledge of automation platforms (n8n)
  Ability to import JSON workflows into n8n

  Cost estimate

  Indexing

  For a typical small site (150 posts, indexed once):

  | Service           | Initial cost | Monthly cost | Notes                         |
  | ----------------- | ------------ | ------------ | ----------------------------- |
  | OpenAI Embeddings | ~$0.01-0.50  | $0           | One-time indexing cost        |
  | Qdrant Cloud      | $0           | $0           | Free tier: 1GB storage        |
  | n8n Self-hosted   | $0           | $0           | If self-hosted                |
  | n8n Cloud         | -            | $20          | If using n8n Cloud            |
  | TOTAL         | ~$0.25   | $0-20    | Depends on n8n hosting choice |

  Re-indexing costs: Run this workflow whenever you publish new content or want to update the index. Each run costs only the OpenAI embedding fee (~$0.01-0.05 per 100 posts).

  Chatbot usage costs

  The chatbot workflow uses AI models to generate responses. Costs vary by provider and model.

  Typical conversation: 1000 tokens input (context + user query) + 500 tokens output (AI response)

  Cost per chat interaction - Monthly cost examples

  Based on different traffic levels:

  | Monthly chats | GPT-4o-mini | Gemini Flash | Claude Haiku | GPT-4o | Claude Sonnet |
  | ------------- | ----------- | ------------ | ------------ | ------ | ------------- |
  | 100           | $0.05       | $0.02        | $0.09        | $0.80  | $1.10         |
  | 500           | $0.25       | $0.10        | $0.45        | $4.00  | $5.50         |
  | 1,000         | $0.50       | $0.20        | $0.90        | $8.00  | $11.00        |
  | 5,000         | $2.50       | $1.00        | $4.50        | $40.00 | $55.00        |
  | 10,000        | $5.00       | $2.00        | $9.00        | $80.00 | $110.00       |

  Note: Prices are approximate and based on January 2025 rates. Check current pricing at:

  OpenAI: https://openai.com/api/pricing/
  Google Gemini: https://ai.google.dev/pricing
  Anthropic Claude: https://www.anthropic.com/pricing

  Setup time

  Indexing:

  Indexing Quick setup:** 15-25 minutes

    Create Qdrant account (5 min)

    Get OpenAI API key (5 min)

    Import workflow and configure credentials (5-10 min)

    Run initial indexing (5 min)


  Indexing Advanced Setup**: 30-60 minutes

  Chatbot:

  First-time users: 5-10 minutes

  Import chatbot workflow
  Configure webhook URL in WordPress plugin
  Add shortcode to pages
  Testing and verification

  📚 Complete guides included: All setup steps are documented with clear instructions and screenshots in the guides you receive after purchase.

  How to use this workflow

  📚 Complete setup guides with screenshots are included with your purchase. Below is a quick overview:

  Step 1: indexing workflow

  Phase 1: Prepare services (10-15 min)

  Create Qdrant Cloud account (or MongoDB Atlas for advanced path)
  Get OpenAI API key from platform.openai.com
  Create WordPress application password
  Configure Qdrant collection (vector size: 1536, distance: Cosine)

  Phase 2: Import and configure workflow (5-10 min)

  Import indexing workflow JSON into n8n
  Add credentials (WordPress, OpenAI, Qdrant)
  Verify collection name matches your Qdrant setup
  Optionally customize chunk size and overlap settings

  Phase 3: Run indexing (5-10 min)

  Execute workflow manually or set up schedule
  Workflow retrieves all published posts from WordPress
  Content is cleaned, chunked, and embedded
  Vectors stored in Qdrant with metadata (title, URL, post ID, slug)
  Check execution log to confirm success

  Phase 4: Maintain index (ongoing)

  Re-run workflow after publishing new posts
  Update index when editing existing content
  Schedule automatic indexing (optional)

  Step 2: Deploy the chatbot workflow

  After indexing is complete, follow these steps to activate your AI chatbot:

  Import the chatbot workflow (included in your purchase) into n8n
  Configure the webhook - The chatbot receives user queries via secure webhook
  Connect to your vector database - Point to the same Qdrant collection you just created
  Test the chatbot - Use the included test micro-website to verify AI responses
  Install the WordPress plugin - Upload and activate on your WordPress site
  Configure plugin settings - Add webhook URL, customize colors and appearance
  Add shortcode to pages - Place [wp_ai_chatbot] where you want the chat widget
  Go live - Your AI chatbot is now answering visitor questions using your content

  How the chatbot works

  When a visitor asks a question on your site:

  Question sent to n8n - WordPress plugin sends query to chatbot workflow via webhook
  Intent classification - AI determines if it's SmallTalk, content question, or profile/skills query
  Vector search - For content questions, searches your indexed posts in Qdrant using semantic similarity
  Context retrieval - Fetches relevant content chunks with metadata (title, URL, post ID)
  AI response generation - Uses retrieved context to generate accurate, contextual answer
  Response delivered - Answer sent back to WordPress plugin and displayed in chat widget

  Result: Visitors get instant, accurate answers based on your actual content, not generic AI responses.

  Compatibility

  Works with:

  WordPress 5.0+ (including WordPress 6.x)
  n8n 1.0+ (Cloud or self-hosted)
  OpenAI API (all embedding models)
  Qdrant Cloud (free and paid tiers)
  MongoDB Atlas (alternative vector database path)

  Tested on:

  WordPress 6.4+
  n8n 1.65+
  OpenAI text-embedding-ada-002
  Qdrant Cloud free tier

  Common questions

  Q: What exactly is included when I purchase?
  A: You get the complete package: both the indexing workflow (shown here) AND the chatbot workflow, the WordPress plugin with chat interface, comprehensive step-by-step setup guides with screenshots, configuration templates, email support, and 12 months of free updates. Everything you need to deploy a fully functional AI chatbot.

  Q: Are the setup guides really clear and complete?
  A: Yes! Clear, detailed step-by-step guides are included with screenshots for every step. We provide both quick setup (15-25 min) and advanced setup paths, troubleshooting guides, customization instructions, and security best practices. Even beginners can follow along.

  Q: Do I need to buy the chatbot workflow separately?
  A: No! Both workflows come together as a complete package. You get everything needed for a fully functional AI chatbot solution.

  Q: How often should I run this workflow?
  A: Run it whenever you publish new content or want to update the index. For active blogs, weekly or after each batch of posts.

  Q: Can I index custom post types?
  A: Yes. Modify the WordPress node to include custom post types in the query parameters.

  Q: What if I have more than 1000 posts?
  A: The workflow handles pagination automatically. Larger sites may take longer to index and may exceed free tier limits.

  Q: Is my content secure?
  A: Your content is sent to OpenAI for embedding generation and stored in Qdrant. Review privacy policies of both services.

  Q: Can I use a different embedding model?
  A: Yes. Update the OpenAI node to use text-embedding-3-small or text-embedding-3-large (adjust vector dimensions accordingly).

  Q: What's the difference between Qdrant and MongoDB?
  A: Qdrant is easier to set up (15 min) and purpose-built for vectors. MongoDB Atlas is more flexible for complex data but takes longer to configure (1-2 hours).

  Q: Do I need coding skills?
  A: No. If you can follow instructions, create API keys, and import workflows, you can set this up.

  Q: Can I use this for multiple WordPress sites?
  A: Yes. Create separate collections in Qdrant for each site, or purchase additional licenses for commercial use.

  Troubleshooting

  "Failed to fetch posts from WordPress"

  Verify WordPress REST API is enabled (visit /wp-json/wp/v2/posts)
  Check application password credentials
  Ensure WordPress site is publicly accessible

  "OpenAI API authentication failed"

  Verify API key is correct and active
  Check account has available credits
  Ensure no rate limits are exceeded

  "Qdrant collection not found"

  Create collection manually in Qdrant dashboard
  Verify collection name matches workflow settings
  Check API key has write permissions

  "Text too large" or chunking errors

  Reduce chunk size in Text Splitter node
  Increase overlap if context is being lost
  Check for unusually large posts (&gt;50k words)

  Indexing takes too long

  Normal for large sites (1000+ posts)
  Consider indexing in batches
  Check network speed and API rate limits

  Complete WordPress AI chatbot solution

  This is a complete, all-in-one package that includes everything you need:

  ✅ What you get:

  Indexing workflow** (shown here) - Prepares your content for semantic search
  Chatbot workflow** - Full RAG-powered chat interface with intent classification
  WordPress plugin** - Frontend chat widget and secure REST API proxy
  Advanced features** - SmallTalk handling, multilingual support, GDPR compliance
  Complete documentation** - Clear, step-by-step setup guides with screenshots
  All configuration files** - Templates, examples, and sample data
  Email support** - Installation and configuration assistance (fees may apply)

  No hidden costs. No separate purchases needed. Everything included.

  License & usage

  Single site license:

  Use on one WordPress site you own or manage
  Modify and customize as needed
  Cannot resell or redistribute the workflow

  Technical architecture

  INDEXING FLOW
  ┌─────────────────────────────────────────────────────────────┐
  │                                                             │
  │  1. Manual Trigger (or scheduled)                          │
  │     ↓                                                       │
  │  2. WordPress Node (Get all published posts)               │
  │     ↓                                                       │
  │  3. Prepare Documents (Extract & structure)                │
  │     ↓                                                       │
  │  4. Default Data Loader (Add metadata: title, URL, ID)     │
  │     ↓                                                       │
  │  5. Text Splitter (1200 chars, 150 overlap)                │
  │     ↓                                                       │
  │  6. OpenAI Embeddings (Generate 1536d vectors)             │
  │     ↓                                                       │
  │  7. Qdrant Vector Store (Insert mode)                      │
  │     ↓                                                       │
  │  ✓ Content indexed and searchable                          │
  │                                                             │
  └─────────────────────────────────────────────────────────────┘

  Data flow:

  Raw WordPress posts (JSON)
  Cleaned content with metadata
  Text chunks (1200 chars each)
  Vector embeddings (1536 dimensions)
  Stored in Qdrant with metadata

  Updates & changelog

  Version included: 1.0.0

  Changelog:

  v1.0.0 (2026-02) - Initial release for n8n creators
    WordPress REST API integration
    OpenAI embedding generation
    Qdrant vector storage
    Metadata preservation
    Configurable chunking

  Planned features:

  Incremental indexing (update only new/modified posts)
  Multi-language support with language detection
  Image and media embedding
  Custom field extraction
  Scheduled automatic indexing

  About the author

  Built by Paolo Ronco, automation specialist and n8n workflow developer.

  More workflows:

  AI-powered contact forms
  Automated content publishing
  Social media automation
  Document processing pipelines

  Get started today

  Ready to make your WordPress content semantically searchable?

  Purchase the complete pack - Receive both workflows, WordPress plugin, and all guides
  Follow the step-by-step guides - Clear instructions with screenshots included
  Import workflows into n8n (5 minutes)
  Configure credentials (10 minutes) - Guides show you exactly how
  Run indexing (5 minutes)
  Deploy chatbot (10 minutes) - Install WordPress plugin and go live
  Start answering user questions with AI-powered responses

  🚀 Transform your WordPress content into an intelligent, searchable knowledge base.

  📚 Everything you need is included: Complete documentation, both workflows, WordPress plugin, and email support.

  © 2026 Paolo Ronco. All rights reserved.

  This workflow is provided as-is for use with n8n. OpenAI, WordPress, and Qdrant are trademarks of their respective owners.

nodes_used:
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metadata_json:
  {"categories":["AI/ML","Web Development","Customer Support"],"complexity":"complex","use_cases":["create custom AI chatbot","index WordPress content","implement RAG architecture","enable natural language questions","provide context-aware responses"],"estimated_setup_minutes":60,"required_services":["WordPress","OpenAI","Qdrant","MongoDB Atlas","Cohere"],"key_features":["automated content indexing","vector database integration","retrieval-augmented generation","reranking for query precision","structured output parsing"],"target_audience":["developers","webmasters","AI engineers"]}


name: Enrich newsletter subscribers with social data and tag in Mailchimp
author_name: Influencers Club
author_username: influencers-club
categories: []
created_at: 2026-02-10T20:59:15.455Z
updated_at: 2026-02-10T20:59:15.455Z
url: https://n8n.io/workflows/13293
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:22
description:
  How it works:

  Get multi social platform data for newsletter subscribers with their email and tag them to enter organic creator, partner and ambassador workflows.

  Step by step workflow to enrich subscriber emails with multi social (Instagram, Tiktok, Youtube, Twitter, Onlyfans, Twitch and more) profiles, analytics and metrics using the influencers.club API and tagging them on Mailchimp to route campaigns.

  Set up:

  Mailchimp (can be swapped for any email marketing sender eg. ActiveCampaign)
  Influencers.club API

nodes_used:
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name: Create an AI content agent with Telegram, Gemini, and Blotato (no-code)
author_name: giangxai
author_username: giangxai
categories: []
created_at: 2026-02-15T14:32:58.645Z
updated_at: 2026-02-15T14:32:58.645Z
url: https://n8n.io/workflows/13409
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:21
description:
  Description

  Automatically turn a simple Telegram message into fully researched, AI-generated visual content and publish it to multiple social platforms using Blotato — with async processing control and built-in status validation.

  This workflow creates a complete:

  Telegram → AI → Research → Visual → Publish → Confirm  

  automation pipeline.

  Who is this for?

  This template is ideal for:

  Content creators automating daily posting  
  Solopreneurs building a 24/7 AI content engine  
  Agencies managing multiple social accounts  
  AI automation builders integrating Telegram + Blotato  
  Teams that want a Chat → Publish pipeline  

  If you want to control your entire content system from a single Telegram chat, this workflow is for you.

  What problem is this workflow solving?

  Manual content production usually requires:

  Researching topics manually  
  Switching between AI writing tools and design tools  
  Waiting for rendering without clear status updates  
  Posting separately to each platform  
  No centralized confirmation system  

  This workflow connects research, media generation, publishing, and confirmation into one automated orchestration system.

  What this workflow does

  This automation workflow:

  Accepts a topic or request via Telegram Bot  
  Sends the message to an AI Content Orchestrator  
  Uses AI model + conversation memory for context  
  Creates a Source job via Blotato (research stage)  
  Waits for structured content extraction  
  Retrieves processed research data  
  Creates a Visual generation job (infographic or slideshow)  
  Waits for AI rendering  
  Retrieves final media asset  
  Publishes automatically to:
    Instagram  
    LinkedIn  
  Sends confirmation back to Telegram  

  Workflow pattern used

  Trigger → Analyze → Create → Wait → Retrieve → Validate → Publish → Confirm  

  Setup

  1. Configure Telegram Trigger

  Use Telegram Bot Trigger

  Connect your Telegram Bot token  
  Set update type to message  
  Ensure bot permissions are enabled  

  This is the entry point of the system.

  2. Configure AI Core (Orchestrator)

  Connect:

  AI Chat Model  
  Conversation Memory storage  

  Ensure:

  User message is mapped correctly into the AI prompt  
  Memory is attached for contextual responses  
  Output is structured for downstream routing  

  3. Configure Blotato Research Engine

  Create Source

  Connect Blotato credentials  
  Map topic/content request correctly  
  Store Source ID  

  Wait for Source Processing

  Add a Wait node  
  Adjust delay based on API performance  

  Get Source

  Retrieve structured research output  
  Validate status = completed  

  Only completed sources proceed to visual generation.

  4. Configure Blotato Media Engine

  Create Visual

  Connect Blotato credentials  
  Map structured content into visual prompt  
  Select infographic or slideshow format  

  Wait for Rendering

  Adjust timing based on rendering duration  

  Get Visual

  Retrieve generated media  
  Validate status = completed  

  5. Configure Distribution

  Publish to Instagram

  Connect Instagram credentials  
  Verify posting permissions  

  Publish to LinkedIn

  Connect LinkedIn credentials  
  Confirm correct profile or page selection  

  Both platforms publish automatically once media is validated.

  6. Configure Telegram Response

  Use Telegram Response Sender

  Send success confirmation  
  Optionally include published links  
  Add failure branch notification if needed  

  This keeps the entire workflow controlled from one chat interface.

  How to customize this workflow

  You can extend this workflow by:

  Adding Telegram approval before publishing  
  Generating AI captions and hashtags dynamically  
  Adding retry logic for failed jobs  
  Logging execution results into Google Sheets  
  Expanding to Facebook or TikTok  
  Adding analytics tracking  

  Best practices

  Keep wait durations aligned with API speed  
  Always validate job status before proceeding  
  Maintain consistent ID mapping (Source ID / Visual ID)  
  Monitor failed execution branches  
  Test with simple content before scaling  

  Nodes used in workflow

  Telegram Trigger  
  AI Chat Model  
  Memory Store  
  HTTP Request (Blotato API)  
  Wait  
  Switch  
  If  
  Sticky Note  

  Free or Paid?

  Depends on:

  Blotato plan  
  AI model usage  
  Social platform API limits  

  This workflow transforms a Telegram message into a fully automated, multi-platform AI content system running 24/7 — including research, visual creation, publishing, and confirmation.

  (Watch the tutorial video on YouTube)[https://youtu.be/WdTimRjiiH8]

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name: Automatically Share Shopify New Products Across All Social Media Platforms
author_name: SpaGreen Creative
author_username: spagreen
categories: []
created_at: 2026-02-16T05:16:25.097Z
updated_at: 2026-02-16T05:16:25.097Z
url: https://n8n.io/workflows/13413
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Who it's for
  This n8n workflow is designed for Shopify store owners, e-commerce managers, and digital marketers who want to automate their product promotion across multiple platforms. The workflow automatically creates promotional content for new products and distributes them across various social media and content management platforms.

  Key Features
  Automatically detects new Shopify products
  AI converts product descriptions into clear bullet points
  Optimizes images for each platform
  Publishes formatted posts to WordPress
  Shares content across multiple social platforms
  Sends notifications after successful posts

  Requirements
  Shopify Access Token**: For detecting new products in your store
  OpenAI API Credentials**: For enhancing product descriptions
  WordPress API Credentials**: For creating posts and uploading media
  Social Media Platform Credentials**:
    Facebook Graph API
    Telegram Bot API
    LinkedIn OAuth2 API
    Discord Bot API
  Notification Service Credentials**:
    Gmail OAuth2
    Rapiwa API (for WhatsApp)

  Workflow Steps
  Shopify Trigger: Detects when a new product is created in your Shopify store
  Image Processing: Extracts and processes product images from the Shopify data
  Content Enhancement: Uses OpenAI to convert HTML product descriptions into concise, customer-friendly bullet points
  WordPress Integration: Creates a blog post on your WordPress site with the product information
  Multi-Platform Sharing: Automatically shares the product on: Facebook page, Telegram channel, LinkedIn profile and page, Discord channel, Instagram
  Notification System: Sends completion notifications through multiple channels (Telegram, Gmail, Discord, WhatsApp)

  Support & Help
  WhatsApp**: Chat on WhatsApp
  Discord**: SpaGreen Community
  Facebook Group**: SpaGreen Support
  Website**: https://spagreen.net
  Developer Portfolio**: Codecanyon SpaGreen

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metadata_json:
  {"categories":["Marketing","E-commerce"],"complexity":"medium","use_cases":["automate product promotion","cross-platform content distribution"],"estimated_setup_minutes":30,"required_services":["Shopify","Facebook","LinkedIn","Discord","Telegram","WordPress","Rapiwa"],"key_features":["detect new products","convert descriptions to bullets","optimize images","distribute to social media"],"target_audience":["Store owners","E-commerce managers","Digital marketers"]}


name: Automatically Share Shopify New Products Across All Social Media Platforms
author_name: SpaGreen Creative
author_username: spagreen
categories: []
created_at: 2026-02-16T05:46:36.305Z
updated_at: 2026-02-16T05:46:36.305Z
url: https://n8n.io/workflows/13414
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:22
description:
  Who Is This For?
  This workflow is perfect for e-commerce store owners using Shopify who want to automatically announce new products to their customer base through personalized WhatsApp messages. It's ideal for businesses looking to increase product visibility and drive sales without manual outreach efforts.

  What This Workflow Does
  This workflow automatically sends WhatsApp product announcements whenever a new product is added to your Shopify store. It processes product data by extracting images, generating product links, and converting HTML descriptions into clean, bullet-point feature lists using OpenAI. Customer data is pulled from Shopify, formatted, and phone numbers are verified on WhatsApp via the Rapiwa API. Personalized messages with product images, features, and purchase links are sent only to verified numbers. All verification and messaging activity is logged in Google Sheets, clearly tracking which contacts were verified and successfully reached.

  Features
  Shopify Integration** that triggers automatically when new products are created
  Image Processing** to extract and filter valid product images from Shopify
  Product URL Generation** to create direct links to new products
  AI-Powered Description Conversion** using OpenAI to transform HTML descriptions into concise bullet points
  Customer Data Retrieval** from Shopify store via API
  WhatsApp Number Verification** using Rapiwa to check if customer numbers are valid WhatsApp accounts
  Personalized WhatsApp Messaging** with product details, features, and images
  Dual-Path Logging** to Google Sheets, tracking both verified (sent) and unverified (not sent) messages
  Batch Processing** to handle multiple customers efficiently
  Rate Limiting** with wait steps to avoid API restrictions

  Requirements
  Shopify Access Token API credentials** for accessing product and customer data
  OpenAI API credentials** for converting HTML descriptions to bullet points
  Rapiwa API credentials** for WhatsApp number verification and messaging
  Google Sheets OAuth2 credentials** for logging message delivery status

  Support & Help
  WhatsApp**: Chat on WhatsApp
  Discord**: SpaGreen Community
  Facebook Group**: SpaGreen Support
  Website**: https://spagreen.net
  Developer Portfolio**: Codecanyon SpaGreen

nodes_used:
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metadata_json:
  {"categories":["E-Commerce","Marketing","Social Media"],"complexity":"medium","use_cases":["announcing new products","driving e-commerce sales","automating product visibility","personalized customer outreach","cross-platform product alerts"],"estimated_setup_minutes":15,"required_services":["WhatsApp Business","Shopify","AI/ML"],"key_features":["automatic product announcements","product image extraction","batch processing","conditional message logic","AI-enhanced content generation"],"target_audience":["e-commerce store owners","social media managers","marketing teams"]}


name: Automate Shopify New Product Promotions via WhatsApp from Sheets
author_name: SpaGreen Creative
author_username: spagreen
categories: []
created_at: 2026-02-16T06:07:30.629Z
updated_at: 2026-02-16T06:07:30.629Z
url: https://n8n.io/workflows/13415
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:22
description:
  Who is this for?
  This workflow is designed for e-commerce store owners, digital marketers, and Shopify merchants who want to automate their product announcement and marketing campaigns. It's particularly useful for businesses that rely on WhatsApp marketing to inform customers about new products and want to streamline their notification process while ensuring message delivery to verified numbers only.

  Key Features
  Triggers automatically for new Shopify products
  Extracts and processes product images and media
  Converts HTML descriptions into concise AI-powered bullet points
  Verifies WhatsApp numbers before sending
  Sends personalized WhatsApp messages with product info and images
  Updates Google Sheets with verification and delivery status
  Adds delays to prevent API rate limits
  Manages both verified and unverified numbers

  Workflow Does
  This workflow automates announcing new Shopify products via WhatsApp. It extracts product images, converts HTML descriptions into AI-powered bullet points, fetches customer contacts from Google Sheets, verifies WhatsApp numbers, and sends personalized messages with product details. Message status is tracked in Google Sheets, marking numbers and messages appropriately, with built-in rate limiting to ensure smooth delivery.

  Requirements
  Shopify store with webhook access for product creation events
  Google Sheets account with customer contact information
  Rapiwa account for WhatsApp verification and messaging
  OpenAI API credentials for text processing
  Properly configured Google Sheets with columns for: WhatsApp numbers, Customer names, Verification status, Message status, Product URLs`

  Support & Help
  WhatsApp**: Chat on WhatsApp
  Discord**: SpaGreen Community
  Facebook Group**: SpaGreen Support
  Website**: https://spagreen.net
  Developer Portfolio**: Codecanyon SpaGreen

nodes_used:
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metadata_json:
  {"categories":["E-commerce","AI/ML","AI-powered Marketing","Marketing Automation"],"complexity":"medium","use_cases":["announcing new products via WhatsApp","extracting product details from Sheets","validating phone numbers","sending targeted marketing messages","automating customer notifications"],"estimated_setup_minutes":15,"required_services":["Shopify","Google Sheets","WhatsApp Business API","Rapiwa","AI Model"],"key_features":["triggers on new products","batch processing messages","conditional logic for messaging","extracts text with AI","integrates external data from spreadsheets"],"target_audience":["e-commerce store owners","digital marketers","Shopify merchants"]}


name: Send WhatsApp new product campaigns from WooCommerce with OpenAI and Sheets
author_name: SpaGreen Creative
author_username: spagreen
categories: []
created_at: 2026-02-16T06:25:36.424Z
updated_at: 2026-02-16T06:25:36.424Z
url: https://n8n.io/workflows/13416
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:22
description:
  Who Is This For?
  This workflow is perfect for e-commerce store owners using WooCommerce who want to automatically announce new products to their customers through personalized WhatsApp messages. It's ideal for businesses looking to increase product visibility and drive sales without manual outreach efforts.

  What This Workflow Does
  This workflow automatically announces new WooCommerce products by extracting product images and converting HTML descriptions into concise, bullet-pointed features using OpenAI. It retrieves and formats customer data, including names, phone numbers, and addresses, and verifies which numbers are registered on WhatsApp via the Rapiwa API. Personalized messages containing product details, images, purchase links, and bullet-point features are sent to verified contacts, while all outreach is logged in Google Sheets, tracking which numbers were verified and messages successfully sent versus unverified or not sent.
  Key Features
  Triggers automatically for new WooCommerce products
  Extracts and processes product images and media
  Converts HTML descriptions into concise AI-powered bullet points
  Verifies WhatsApp numbers before sending
  Sends personalized WhatsApp messages with product info and images
  Updates Google Sheets with verification and delivery status
  Adds delays to prevent API rate limits
  Manages both verified and unverified numbers

  Requirements
  WooCommerce API Access** (to retrieve product and customer data)
  Google Sheets OAuth2** (for tracking outreach)
  OpenAI API** (for processing product descriptions)
  Rapiwa API** (for WhatsApp verification and messaging)
  . Prepare Your Google Sheet
  Create a Google Sheet with columns for: name, number, verify, and status to track customer outreach.
  . Customize the Workflow
  Adjust the WooCommerce Trigger to specify which product events should trigger announcements
  Modify the message template in the Rapiwa node to match your brand voice
  Update the Google Sheet ID in both ""Save data"" nodes to point to your tracking sheet

  Support & Help
  WhatsApp**: Chat on WhatsApp
  Discord**: SpaGreen Community
  Facebook Group**: SpaGreen Support
  Website**: https://spagreen.net
  Developer Portfolio**: Codecanyon SpaGreen

nodes_used:
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metadata_json:
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name: AI Company Research Agent (n8n+Airtable)
author_name: Milan Vasarhelyi - SmoothWork
author_username: vasarmilan
categories: []
created_at: 2026-02-17T10:43:32.640Z
updated_at: 2026-02-17T10:43:32.640Z
url: https://n8n.io/workflows/13457
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:22
description:
  Video Introduction

  Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin

  What This Workflow Does

  This workflow automatically enriches your Airtable company database with AI-powered research. Whenever a new company is added to your Airtable base, an AI agent searches the web to discover the company's website and generates a concise 1-2 sentence summary of their business activities. The workflow then updates the Airtable record with these findings, eliminating manual research and keeping your data complete and current.

  Key Benefits

  Automated data enrichment**: No more manual web searches for company information
  AI-powered accuracy**: Uses OpenAI with web search capabilities to find reliable, up-to-date information
  Structured output**: Returns data in a predictable JSON format for consistent record updates
  Real-time processing**: Triggers immediately when new companies are added

  Setup Requirements

  Airtable Configuration:
  Your Companies table must have a Created time field to trigger the workflow
  Create an Airtable Personal Access Token at airtable.com/create/tokens with these scopes: data.records:read, data.records:write, and schema.bases:read
  Ensure your table has fields for Website and Company Details (or modify the field mappings accordingly)

  OpenAI Setup:
  Register for an OpenAI account and obtain an API key
  Add your OpenAI credentials in n8n

  Workflow Configuration:
  Update the Airtable Trigger node with your base and table URLs
  Update the Airtable Update node with your specific base and table information
  Customize the AI prompt if you need different research parameters or output fields

  Once published, the workflow runs automatically whenever a new company is added to your table.

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

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metadata_json:
  {"categories":["Data Processing","Marketing"],"complexity":"medium","use_cases":["enrich company databases","automate web research","summarize business activities","update records with AI insights"],"estimated_setup_minutes":30,"required_services":["Airtable","AI/ML API"],"key_features":["automatic record enrichment","web scraping","AI text generation","database updates on trigger"],"target_audience":["sales managers","market researchers","customer success teams"]}


name: Sync leads between Google Sheets and HubSpot contacts
author_name: Milan Vasarhelyi - SmoothWork
author_username: vasarmilan
categories: []
created_at: 2026-02-17T10:45:23.233Z
updated_at: 2026-02-17T10:45:23.233Z
url: https://n8n.io/workflows/13460
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Video Introduction

  Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin

  Workflow Overview

  This workflow automatically imports leads from a Google Sheets spreadsheet directly into your HubSpot account. When executed, it reads contact information from a designated spreadsheet and creates or updates corresponding contact records in HubSpot, preventing duplicates by matching on email addresses.

  This automation is ideal for teams that collect leads through external forms, landing pages, or other systems that output to Google Sheets. Instead of manually copying contact information into HubSpot, this workflow handles the entire import process, ensuring your CRM stays up-to-date with minimal effort.

  Key Features

  Automatically syncs contact data including email, name, company, and phone number
  Prevents duplicate records by updating existing contacts based on email addresses
  Works with any Google Sheets document containing lead information
  Can be scheduled to run automatically or triggered manually on demand

  Setup Requirements

  HubSpot Credentials:
  Create a developer account at developers.hubspot.com
  Navigate to Legacy Apps and create a new private app
  In the Scopes tab, add these permissions:
     crm.objects.contacts.read
     crm.objects.contacts.write
     crm.objects.companies.read
     crm.objects.companies.write
  Copy the Access Token from the Auth tab
  In n8n, create a HubSpot credential using APP Token method and paste your token

  Google Sheets:
  Connect your Google account to n8n when configuring the Google Sheets node.

  Configuration

  Update the Google Sheets node to point to your specific spreadsheet containing lead data. The HubSpot node is pre-configured to map common contact properties (Email, Company Name, Name, Phone Number). Adjust these field mappings if your spreadsheet uses different column names or if you need to sync additional contact properties.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.hubspot","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["Data Processing","CRM"],"complexity":"simple","use_cases":["sync contacts from sheets","import leads","update customer records"],"estimated_setup_minutes":5,"required_services":["Google Sheets","HubSpot"],"key_features":["read spreadsheet data","create/update contacts","prevent duplicates","match on email"],"target_audience":["marketing teams","sales teams","admin users"]}


name: Hubspot AI Manager - Automate your CRM!
author_name: Milan Vasarhelyi - SmoothWork
author_username: vasarmilan
categories: []
created_at: 2026-02-17T10:46:04.536Z
updated_at: 2026-02-17T10:46:04.536Z
url: https://n8n.io/workflows/13461
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:22
description:
  Video Introduction

  Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin

  What This Workflow Does

  This workflow creates an intelligent AI assistant that manages your HubSpot contacts through natural conversation. Instead of manually navigating the HubSpot interface, you can simply chat with the agent to search for contacts by email or company name, add new leads, or update existing records. The AI automatically determines which action to take based on your chat input and can handle multiple operations in a single conversation.

  Key Features

  Natural language contact search**: Find contacts by email address or company name using conversational commands
  Smart contact creation**: Add new contacts by providing details in natural language—the AI extracts email, name, and company information automatically
  Duplicate prevention**: Uses HubSpot's ""create or update"" functionality to prevent duplicate entries
  Conversation memory**: Remembers context from previous messages in the same chat session
  Multi-tool intelligence**: The AI agent automatically selects the appropriate tool based on your request

  Common Use Cases

  Quickly add leads captured from conversations, emails, or meetings without opening HubSpot
  Search for contact information during calls or meetings
  Update contact details through simple chat commands
  Bulk contact lookups by company for account research
  Enable non-technical team members to manage CRM data through conversation

  Setup Instructions

  HubSpot Configuration:

  Create a developer account at developers.hubspot.com using your existing HubSpot login
  Navigate to Legacy Apps in the left-hand menu and create a new private app
  Give your app a name and move to the Scopes tab
  Add the following permissions:
     crm.objects.contacts.read
     crm.objects.contacts.write
  Click Create app
  In the Auth tab, reveal and copy your Access token
  In n8n, create a new HubSpot credential using APP Token as the connection method and paste your token

  OpenAI Configuration:

  Ensure you have an OpenAI API account with valid credentials configured in n8n
  The workflow uses GPT-5.2 by default, but you can select any compatible OpenAI model based on your needs and budget

  Configuration Notes

  The workflow includes three HubSpot tools that the AI can use:
  Search contacts by email address
  Search contacts by company name
  Create or update a contact

  All contact properties (email, first name, last name, company name) are set to be automatically defined by the AI model based on your chat input. The agent intelligently chooses which tool to use based on your request—no need to specify which action you want.

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

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metadata_json:
  {"categories":["CRM","AI/ML"],"complexity":"simple","use_cases":["chat with CRM","automate contact management","search leads","update records"],"estimated_setup_minutes":30,"required_services":["HubSpot"],"key_features":["natural language interaction","contact data management","AI-driven automation"],"target_audience":["sales teams","CRM administrators"]}


name: Handle guest inquiries for multiple rentals with Pinecone Assistant and GPT-4.1
author_name: Pinecone
author_username: pinecone
categories: []
created_at: 2026-02-17T16:06:09.713Z
updated_at: 2026-02-17T16:06:09.713Z
url: https://n8n.io/workflows/13467
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:21
description:
  Vacation rental property manager with multiple Assistants

  🛠️ Read about how multi-domain RAG works and other use cases by working through this tutorial on the n8n Blog here.

  This workflow shows you how a vacation rental property manager can manage multiple properties each with different information using Pinecone Assistant. Guests can ask questions about their property and get a personalized answer back.
  What is Pinecone Assistant?

  Pinecone Assistant allows you to build production-grade chat and agent-based applications quickly. It abstracts the complexities of implementing retrieval-augmented (RAG) systems by managing the chunking, embedding, storage, query planning, vector search, model orchestration, reranking for you.

  Try it out

  Prerequisites

  A Pinecone account
  A GCP project with Google Drive API enabled and configured
  An Open AI account and API key

  Setup

  Create three Pinecone Assistants in the Pinecone Console here 
  	Name your Assistants n8n-vacation-rental-property-lakeside, n8n-vacation-rental-property-birchwood, n8n-vacation-rental-property-hillcrest
  Use the Connect to Pinecone button to authenticate to Pinecone or if you self-host n8n, create a Pinecone credential and add your Pinecone API key directly
  Setup your Google Drive OAuth2 API and OpenAI credentials in n8n
  Select your Assistant Name in each of the respective Pinecone Assistant nodes
  Ask Claude or ChatGPT to generate fictional data in markdown file(s) for each property. You'll use this data in the next step. Using this prompt:
  Generate fictional data in markdown format in multiple files for three fictional vacation rental properties in a fictional city. The rental property names are:
  Hillcrest Haven** – cozy hillside cottage
  Birchwood Retreat** – wooded cabin
  Lakeside Loft** – modern loft near the water

  Include house manual and rules, wifi codes, local restaurant, coffee shop, outdoor recreation, and entertainment recommendations, and fictional appliance manuals for the air fryer, coffee pot, tv, and washer and dryer.

  All addresses, cities, names, phone numbers should be fictional. Each set of files should be named based on their property name like "hillcrest_haven_house_manual.md".
  Note: If you don't want to generate your own files, you can use these files.

  Add the files to three separate Drive folders named lakeside, birchwood, and hillcrest
  Activate the workflow to upload the documents to Pinecone
  Once the data is uploaded, ask questions in the chat about a property:
  	I need help with the coffee maker
  	The air fryer isn't working at the Lakeside property

  Ideas for customizing this workflow

  This workflow uses one Assistant per property. You could also use one Assistant and separate the data by setting a metadata field, property, to the name of the property the file is for.
  Use your own data and customize to your use case with multiple store locations, restaurants, teams, etc.

  Need help?

  You can find help by asking in the Pinecone Discord community or filing an issue on this repo.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDriveTrigger","@pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistant","n8n-nodes-base.googleDrive","@pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistant","n8n-nodes-base.googleDrive","@pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistant","n8n-nodes-base.stickyNote","@pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistantTool","@n8n/n8n-nodes-langchain.memoryBufferWindow","@pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistantTool","@pinecone-database/n8n-nodes-pinecone-assistant.pineconeAssistantTool"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Property Management","AI/ML","Communication"],"complexity":"medium","use_cases":["manage multiple property inquiries","provide personalized guest responses","enable automated customer support"],"estimated_setup_minutes":30,"required_services":["Pinecone Assistant","GPT-4.1","Google Drive"],"key_features":["multi-domain RAG","personalized property answers","multi-property support","guest inquiry automation"],"target_audience":["property managers","customer support teams"]}


