# templates

name: AI Timesheet Generator with Gmail, Calendar & GitHub to Google Sheets
author_name: Luka Zivkovic
author_username: zivkovic58
categories: []
created_at: 2025-06-27T00:52:47.921Z
updated_at: 2025-06-27T00:52:47.921Z
url: https://n8n.io/workflows/5396
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  AI-Powered Automatic Timesheet Generator for Google Sheets

  Stop wasting billable hours on manual time-tracking. AutoTimesheet Pro uses AI to collect emails, meetings, and GitHub work, then writes a clean timesheet straight into Google Sheets. Perfect for developers, consultants, agencies, and remote teams.

  🚀 Key Features

  Automated Google Sheets time-tracking** — zero spreadsheet prep.  
  AI-generated activity summaries** (≤ 120 chars) via OpenAI GPT-4o-mini.  
  Gmail integration** — logs only important emails, skipping newsletters & no-replies.  
  Google Calendar time logger** — captures confirmed events, duration, and attendees.  
  GitHub commit & PR tracker** — records your commits plus opened/closed PRs.  
  Daily 7 PM cron trigger** (easily adjustable).  
  Month-based sheet creation** — new tab spins up on the first run each month.  
  No-code n8n template* — just connect credentials and tweak one *Set Variables** node.
  🔌 Easily extensible** — drag-and-drop extra n8n nodes to add Slack, Jira, Notion, Asana, Trello, Toggl, or any other data source you need.

  🔍 How It Works

  Collect — n8n pulls data from Gmail, Google Calendar, and chosen GitHub repos.  
  Clean — filters remove noise (newsletters, irrelevant commits, etc.).  
  Condense — OpenAI rewrites each item into a concise, SEO-friendly description.  
  Write — workflow appends Date, Type, and Description to your Timesheet Google Sheet.
  Extend — simply insert new n8n nodes (e.g., Slack, Notion, Jira) and merge them into the same pipeline.

  📈 Benefits for SEO-Minded Professionals

  Keyword-rich activity log** improves internal search and reporting.  
  Structured data** in Sheets simplifies export to accounting or PM tools.  
  Consistent naming** (CALENDAR_EVENT, EMAIL, COMMIT, PR) makes analytics easy.

  ✅ Why Choose AutoTimesheet Pro?

  Zero manual entry — just open the sheet and bill clients.  
  Immediate visibility into where your hours went.  
  Works with any GitHub repo list and any inbox you own.  
  100 % no-code setup — activate in minutes.
  Built on n8n, so you can customize and scale without limits.
  📥 Get Started

  Ready to replace manual time-tracking with smart automation?  

  https://n8n.partnerlinks.io/ds9podzjls6d

  Join N8N now, connect your Google & GitHub accounts, and let AI handle your daily log.

nodes_used:
  ["n8n-nodes-base.googleCalendar","n8n-nodes-base.gmail","n8n-nodes-base.github","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.aggregate","n8n-nodes-base.merge","n8n-nodes-base.aggregate","n8n-nodes-base.aggregate","n8n-nodes-base.cron","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.splitOut","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.splitOut"]

workflow_json_compressed:
  [encoded data, 8348 characters]

metadata_json:
  {"categories":["automation","integration","data-processing","time-tracking"],"complexity":"medium","use_cases":["Automated billable time tracking","Daily activity logging for developers/teams","Generating concise activity summaries for reports/invoicing","Consolidating emails, meetings and repo activity into one sheet","Monthly timesheet generation for accounting/clients"],"estimated_setup_minutes":20,"required_services":["n8n account","Google (Gmail, Calendar, Sheets)","GitHub","OpenAI (GPT-4o-mini or compatible model)"],"key_features":["Automated Google Sheets timesheet creation (month-based tabs)","AI-generated activity summaries (≤120 chars)","Gmail noise filtering (skip newsletters/no-replies)","Google Calendar event logging with attendees and duration","GitHub commits and PR tracking (opened/closed) plus repo list support','Daily cron trigger (default 7 PM) and adjustable schedule"],"target_audience":["Developers","Consultants & freelancers","Agencies and studios that bill hours','Remote teams and managers"]}


name: Auto-Generate & Approve Social Media Posts from RSS Feeds with OpenAI & Telegram
author_name: The Higher Pitch
author_username: digvijay
categories: []
created_at: 2025-06-27T07:19:47.539Z
updated_at: 2025-06-27T07:19:47.539Z
url: https://n8n.io/workflows/5397
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  Overview
  This workflow automates the process of converting RSS feed articles into ready-to-publish social media posts using OpenAI, NocoDB, and Telegram. It's ideal for content teams, marketing managers, or news portals seeking to automate content curation while maintaining control through a human approval system.

  Features
  RSS Feed Monitoring
  Polls a specified RSS feed every 20 minutes.

  Detects new articles automatically.

  AI-Powered Content Processing
  Summarizes the full article using OpenAI's Assistant API.

  Creates an image prompt based on the article summary.

  Generates a platform-specific post for Facebook and LinkedIn using AI.

  Image Generation
  Leverages OpenAI's image model to generate a relevant image from the prompt.

  Retrieves and stores the featured image from the original article (via custom code node).

  Post Management with NocoDB
  Stores all content in NocoDB including:

  Article URL

  AI-generated summary

  Image prompt

  Post content per platform

  Generated image URL

  Post status (Pending, Approved, Declined)

  Human Approval via Telegram
  Sends post preview to a Telegram group or channel with inline buttons:

  ✅ Approve

  ❌ Decline

  On approval: Posts to Facebook, LinkedIn, and optionally Twitter.

  On rejection: Marks the NocoDB record as "Declined".

  Conditional Twitter (X) Posting
  Asks user whether to post with or without a link.

  Posts accordingly based on user’s Telegram response.

  No-Code Backend
  NocoDB acts as a lightweight CMS to manage, edit, and review AI-generated content before publishing.

  Setup Instructions
  Clone the workflow in your n8n instance.

  Configure the following credentials under Credentials &gt; New:

  OpenAI API Key

  Facebook Graph API

  LinkedIn Access Token

  Twitter (X) OAuth credentials

  Telegram Bot Token and Chat ID

  NocoDB API Token and Base URL

  Set the RSS feed URL in the trigger node to your preferred news source.

  Adjust the NocoDB API node with your table and field names (see below).

  Deploy the workflow on an interval trigger (20-minute polling recommended).

  NocoDB Database Structure
  Column Name	Description
  url	Original article URL
  summary	AI-generated summary
  image_prompt	Prompt used for generating image
  image_url	Final image URL (from OpenAI)
  post_content	Formatted social media post
  platform	Social platform (Facebook, LinkedIn, etc.)
  status	Current status (Pending, Approved, Declined)
  date_created	Date article was fetched

  Requirements
  An active n8n instance (cloud or self-hosted).

  API credentials for:

  OpenAI

  Telegram Bot

  Facebook Graph API

  LinkedIn Developer App

  Twitter/X Developer App

  NocoDB (self-hosted or cloud)

  A Telegram chat (group or user) where the bot is added.

  Customization Guidance
  Add more platforms: Extend the logic to other platforms like Instagram, Threads, or Mastodon.

  Customize AI tone: Adjust the prompt to OpenAI for a specific writing style (e.g., formal, casual, humorous).

  Adjust scheduling: Modify interval time or RSS feed frequency as needed.

  Add post delay: Schedule posts using a delay node to spread them over time.

  Use Cases
  Auto-publish summarized news articles to multiple social platforms.

  Reduce effort for social teams by automating draft creation and media.

  Maintain editorial control using the Telegram approval step.

  Repurpose blog or article content into engaging posts with minimal effort.

  Integrations Used
  OpenAI (Assistants & DALL·E Image Generation)

  Telegram Bot (Inline approval workflow)

  Facebook Graph API (Post publishing)

  LinkedIn API (Company or personal posts)

  Twitter/X API (Optional conditional post publishing)

  RSS Feed Reader (Article fetching)

  NocoDB (Content repository and status manager)

nodes_used:
  ["@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.if","n8n-nodes-base.linkedIn","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.code","n8n-nodes-base.twitter","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.wait","n8n-nodes-base.if","n8n-nodes-base.twitter","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.nocoDb","n8n-nodes-base.telegram","n8n-nodes-base.wait","n8n-nodes-base.rssFeedReadTrigger","n8n-nodes-base.nocoDb","n8n-nodes-base.nocoDb","n8n-nodes-base.telegram","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["automation","social media","content curation","AI-assisted publishing"],"complexity":"complex","use_cases":["Automatically convert RSS articles into social media drafts","AI summarization and image generation for posts","Human-in-the-loop approval workflow for published content","Multi-platform cross-posting (Facebook, LinkedIn, optional Twitter/X)"],"estimated_setup_minutes":60,"required_services":["n8n instance","OpenAI (Assistant & Image API)","Telegram Bot (token & chat ID)","Facebook Graph API","LinkedIn API / Access Token","Twitter/X API (optional)","NocoDB (API token & base URL)","RSS feed source"],"key_features":["Periodic RSS feed polling and new-article detection","AI-generated article summaries and platform-specific post copy","AI-generated image prompts and images","Retrieve and store original article featured image via custom code","Store content, metadata and status in NocoDB (Pending/Approved/Declined)"],"target_audience":["Content teams","Marketing managers / Social media managers","News portals and publishers looking to automate curation workflows that keep editorial control intact (human approval)"]}


name: AI Assistant which answers questions with a RAG MCP and a Search Engine MCP
author_name: Thomas Janssen
author_username: thomasjanssen-tech
categories: []
created_at: 2025-06-27T09:04:57.719Z
updated_at: 2025-06-27T09:04:57.719Z
url: https://n8n.io/workflows/5398
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  Build an AI Agent which accesses two MCP Servers: a RAG MCP Server and a Search Engine API MCP Server.

  This workflow contains community nodes that are only compatible with the self-hosted version of n8n.

  Tutorial

  Click here to watch the full tutorial on YouTube!

  How it works
  We build an AI Agent which has access to two MCP servers:
  An MCP Server with a RAG database (click here for the RAG MCP Server
  An MCP Server which can access a Search Engine, so the AI Agent also has access to data about more current events

  Installation
  In order to use the MCP Client, you also have to use MCP Server Template.

  Open the MCP Client "MCP Client: RAG" node and update the SSE Endpoint to the MCP Server workflow

  Install the "n8n-nodes-mcp" community node via settings &gt; community nodes


  ONLY FOR SELF-HOSTING: In Docker, click on your n8n container. Navigate to "Exec" and execute the below command to allow community nodes:

  N8N_COMMUNITY_PACKAGES_ALLOW_TOOL_USAGE=true



  Navigate to Bright Data and create a new  "Web Unlocker API" with the name "mcp_unlocker".

  Open the "MCP Client" and add the following credentials:

  How to use it
  Run the Chat node and start asking questions

  More detailed instructions
  Missed a step? Find more detailed instructions here: Personal Newsfeed With Bright Data and n8n

  What is Retrievel Augmented Generation (RAG)?
  Large Language Models (LLM's) are trained on data until a specific cutoff date. Imagine a model is trained in December 2023 based data until September 2023. This means the model doesn't have any knowledge about events which happened in 2024. So if you ask the LLM who was the Formula 1 World Champion of 2024, it doesn't know the answer.

  The solution? Retrieval Augmented Generation. When using Retrieval Augmented Generation, a user's question is being sent to a semantic database. The LLM will use the information retrieved from the semantic database to answer the user's question.

  What is Model Context Protocol (MCP)?
  MCP is a communication protocol which is used by AI agents to call tools hosted on external servers.

  When an MCP client communicates with an MCP server, the server will provide an overview of all its tools, prompts and resources. The MCP server can then choose which tools to execute (based on the user's request) and execute the tools.

  An MCP client can communicate with multiple MCP servers, which can all host multiple tools.

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

workflow_json_compressed:
  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

metadata_json:
  {"categories":["automation","integration","AI/ML","chatbot","data retrieval"],"complexity":"medium","use_cases":["Retrieval-Augmented Generation (RAG) QA","Up-to-date question answering via web search","Knowledge-base augmented chatbot","Personal newsfeed / research assistant","Customer support agent with external data access"],"estimated_setup_minutes":45,"required_services":["Self-hosted n8n (community nodes enabled)","n8n-nodes-mcp community node","MCP Server (RAG / semantic vector DB)","MCP Server (Search Engine API)","Bright Data Web Unlocker API (mcp_unlocker)","OpenAI (or compatible LLM) API for lmChat node","SSE endpoint configured for MCP Client"],"key_features":["Agent that talks to multiple MCP servers (RAG + Search)","Retrieval-Augmented Generation using a semantic DB","Live web-search access via Search Engine MCP","MCP Client integration with streaming (SSE)","Memory buffer window for conversational context preservation (memoryBufferWindow)"],"target_audience":["Developers and AI engineers","Self-hosted n8n users","Data engineers and integrators building RAG systems and agents with external tool access"]}


name: The title is very good, clearly conveying the template's purpose while mentioning key technologies
author_name: Falk
author_username: falks-ai-workbench
categories: []
created_at: 2025-06-27T09:16:38.121Z
updated_at: 2025-06-27T09:16:38.121Z
url: https://n8n.io/workflows/5400
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  How it works
  Collects articles from your preferred RSS feeds. 
  Rates and tags each article using an AI model (e.g., QWEN 14B-s4), filtering for relevance and quality. 
  Summarizes high-rated articles with a language model (e.g., Gemma3 4B) for quick, digestible reading. 
  Checks for duplicates to avoid sending the same article twice. 
  Formats and sends the top articles as an HTML newsletter via Gmail, using OAuth2 authentication. 
  Stores records in a Postgres database, tracking which articles have been sent and their ratings.

  Requirements
  Postgres Account
  AI Models (if you work localy use Ollama)
  In the cloud you have to change Ollama node to your prefered Model Node
  RSS Feed of your desire
  Google Auth2, if you want to use Gmail

  Recommendations
  Use n8n local version for this workflow
  Here are some more informations: https://github.com/falks-ai-workbench/n8n_newsletter

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.rssFeedRead","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.if","n8n-nodes-base.noOp","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.noOp","n8n-nodes-base.code","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.lmChatOllama","@n8n/n8n-nodes-langchain.lmChatOllama","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.compareDatasets","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","content curation","AI/ML"],"complexity":"complex","use_cases":["Automated newsletter generation from RSS feeds","AI-powered article rating and tagging","Summarizing articles for digestible reading","Duplicate detection to avoid repeated sends","Persisting and tracking articles/ratings in Postgres"],"estimated_setup_minutes":60,"required_services":["Postgres database","RSS feeds (user-provided)","AI model provider (Ollama for local or cloud models like QWEN 14B-s4 / Gemma3 4B)","Google OAuth2 (Gmail API)","n8n (local recommended)"],"key_features":["Aggregates articles from RSS feeds","AI-based relevance/quality rating and tagging","Language-model summarization of top articles","Duplicate detection and deduplication logic","HTML newsletter formatting and Gmail sending via OAuth2 authentication​"],"target_audience":["Developers","Marketers","Content curators/newsletter editors」「Data engineers"]}


name: Automate GoDaddy Subdomain Management via Email Requests
author_name: Oneclick AI Squad
author_username: oneclick-ai
categories: []
created_at: 2025-06-27T09:21:35.955Z
updated_at: 2025-06-27T09:21:35.955Z
url: https://n8n.io/workflows/5401
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:34
description:
  This n8n workflow automates subdomain creation and deletion on GoDaddy using their API, triggered via email requests. This empowers developers to manage subdomains directly without involving DevOps for minor tasks.

  Good to know

  Ensure GoDaddy API credentials are securely configured to avoid unauthorized access.  
  Email parsing accuracy depends on the consistency of request formats.

  How it works

  Detect new email requests using the Start Workflow (GET Request) node.  
  Use the Extract Data from Email node to parse relevant details (e.g., subdomain name, action type).  
  Validate the action type with the Validate Action Type node to proceed with create (true) or delete (false).  
  If true, the Create Subdomain node sends a POST request to GoDaddy’s API to create the subdomain.  
  If false, the Delete Subdomain node sends a DELETE request to remove the subdomain.  
  The Send Email Response node notifies the requester of the action’s success or failure.

  How to use

  Import the workflow into n8n and configure the nodes with your GoDaddy API and email credentials.  
  Test with sample email requests to ensure proper parsing and API calls.

  Requirements

  GoDaddy API credentials  
  Email service (e.g., SMTP or API) for notifications  

  Customising this workflow

  Adjust the Extract Data from Email node to match your email format or add additional validation steps for security.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.emailReadImap","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.emailSend"]

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metadata_json:
  {"categories":["automation","integration","devops"],"complexity":"medium","use_cases":["Self-service creation and deletion of GoDaddy subdomains via email requests","Automating DNS/subdomain management to reduce DevOps workload","Email-driven approval or request workflows for domain changes","Notifying requesters of success/failure of domain operations"],"estimated_setup_minutes":20,"required_services":["GoDaddy API (API key/secret)","Email provider (SMTP/IMAP or email API such as SendGrid/SES)","Publicly accessible n8n instance or webhook tunnel (for incoming requests)"],"key_features":["Parse structured email requests to extract subdomain and action","Validate requested action (create vs delete)","Create subdomain via GoDaddy API (POST)","Delete subdomain via GoDaddy API (DELETE)","Send email notifications with success/failure results"],"target_audience":["Developers","DevOps engineers","IT administrators / Site reliability engineers (SREs)"]}


name: Build an MCP Server which answers questions with Retrieval Augmented Generation
author_name: Thomas Janssen
author_username: thomasjanssen-tech
categories: []
created_at: 2025-06-27T09:39:46.246Z
updated_at: 2025-06-27T09:39:46.246Z
url: https://n8n.io/workflows/5403
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  Build an MCP Server which has access to a semantic database to perform Retrieval Augmented Generation (RAG)

  Tutorial

  Click here to watch the full tutorial on YouTube

  How it works
  This MCP Server has access to a local semantic database (Qdrant) and answers questions being asked to the MCP Client.

  AI Agent Template
  Click here to navigate to the AI Agent n8n workflow which uses this MCP server

  Warning
  This flow only runs local and cannot be executed on the n8n cloud platform because of the MCP Client Community Node.

  Installation
  Install n8n + Ollama + Qdrant using the Self-hosted AI starter kit

  Make sure to install Llama 3.2 and mxbai-embed-large as embeddings model.

  Activate the n8n flow



  Run the "RAG Ingestion Pipeline" and upload some PDF documents

  How to use it
  Run the MCP Client workflow and ask a question. It will be either answered by using the semantic database or the search engine API.

  More detailed instructions
  Missed a step? Find more detailed instructions here: https://brightdata.com/blog/ai/news-feed-n8n-openai-bright-data

nodes_used:
  ["@n8n/n8n-nodes-langchain.mcpTrigger","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.embeddingsOllama","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.formTrigger","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.documentDefaultDataLoader"]

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metadata_json:
  {"categories":["automation","AI/ML","data processing","knowledge management"],"complexity":"complex","use_cases":["Document Q&A and knowledge base (RAG)","Semantic search over PDFs and documents","Internal support/chat assistants","Research assistance and summarization","Local/private LLM deployments for sensitive data"],"estimated_setup_minutes":90,"required_services":["n8n (self-hosted)","Ollama (local model server) with Llama 3.2","Qdrant (local vector/semantic database)","mxbai-embed-large embeddings model","PDF/document sources for ingestion","Optional: external search engine API (fallback)"],"key_features":["Retrieval-Augmented Generation (RAG) using a local vector DB","Semantic indexing and search via Qdrant","Embeddings generation with Ollama","PDF ingestion pipeline to populate the semantic DB","MCP Server that answers MCP Client queries locally (privacy-first)"],"target_audience":["developers","AI/ML engineers","data engineers / ETL specialists"]}


name: B2B lead qualification
author_name: Oneclick AI Squad
author_username: oneclick-ai
categories: []
created_at: 2025-06-27T09:49:43.541Z
updated_at: 2025-06-27T09:49:43.541Z
url: https://n8n.io/workflows/5404
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  This automated n8n workflow qualifies B2B leads via voice calls using the VAPI API and integrates the collected data into Google Sheets. It triggers when a new lead’s phone number is added, streamlining lead qualification and data capture.

  What is VAPI?
  VAPI is an API service that enables voice call automation, used here to qualify leads by capturing structured data through interactive calls.

  Good to Know

  VAPI API calls may incur costs based on usage; check VAPI pricing for details.
  Ensure Google Sheets access is properly authorized to avoid data issues.
  Use credential fields for the HTTP Request node 'Bearer token' instead of hardcoding.
  Use a placeholder Google Sheet document ID (e.g., "your-sheet-id-placeholder") to avoid leaking private data.

  How It Works

  Detect when a new phone number is added for a lead using the New Lead Captured node.
  Use the Receive Lead Details from VAPI node to capture structured data (name, company, challenges) via a POST request.
  Trigger an outbound VAPI call to qualify the lead with the Initiate Voice Call (VAPI) node.
  Store the collected data into a Google Sheet using the Save Qualified Lead to CRM Sheet node.
  Send a success response back to VAPI with the Send Call Data Acknowledgement node.

  How to Use

  Import the workflow into n8n.
  Configure VAPI API credentials in the HTTP Request node using credential fields.
  Set up Google Sheets API access and authorize the app.
  Create a Google Sheet with the following columns: Name (text), Company (text), Challenges (text), Date (date).
  Test with a sample lead phone number to verify call initiation and data storage.
  Adjust the workflow as needed and retest.

  Requirements

  VAPI API credentials
  Google Sheets API access

  Customizing This Workflow
  Modify the Receive Lead Details from VAPI node to capture additional lead fields or adjust call scripts for specific industries.

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

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metadata_json:
  {"categories":["automation","integration","telephony/voice","CRM/data capture"],"complexity":"medium","use_cases":["Automatically qualify B2B leads via voice calls","Capture structured lead details (name, company, challenges) from calls","Populate Google Sheets / lightweight CRM with qualified leads","Automate outbound call initiation and acknowledgement to a voice API"],"estimated_setup_minutes":20,"required_services":["VAPI (voice call automation) API","Google Sheets API","n8n instance with webhook/HTTP Request capability"],"key_features":["Triggers on new lead phone number","Initiates outbound voice call via VAPI","Receives structured lead data via POST from VAPI","Persists qualified lead rows to Google Sheets","Sends acknowledgement back to VAPI after successful save"],"target_audience":["Sales teams / SDRs","Marketers running outbound lead qualification","Small business owners capturing lead data cheaply in Sheets"]}


name: Daily Financial News Summary with Ollama LLM - Automated Email Report
author_name: Oneclick AI Squad
author_username: oneclick-ai
categories: []
created_at: 2025-06-27T10:14:05.504Z
updated_at: 2025-06-27T10:14:05.504Z
url: https://n8n.io/workflows/5405
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  This workflow auto-fetches top financial headlines, cleans the content, and uses AI to summarize it into a short investor-friendly email.

  Good to know

  The workflow runs daily and relies on stable webpage access; check the URL (e.g., https://www.ft.com/) for availability.  
  AI costs may apply depending on the LLM model used (e.g., GPT-4 or Gemini); refer to provider pricing.

  How it works

  Trigger the workflow daily with the Schedule Daily Trigger node.  
  Fetch financial news from a webpage using the Fetch Webpage News node.  
  Add a Delay to Ensure Page Load node to ensure content is fully loaded.  
  Extract and clean headlines with the Extract News Headlines & Clean Extracted Data node.  
  Process the data with the LLM Chat Model node to generate a summary.  
  Send the summarized report via email using the Email Daily Financial Summary node.

  How to use

  Import the workflow into n8n and configure the nodes with your webpage URL and email credentials.  
  Test the workflow to verify content fetching and email delivery.

  Requirements

  Webpage access (e.g., financial news site API or RSS)  
  Email service (e.g., SMTP or API)  
  LLM model credentials (e.g., GPT-4 or Gemini)  

  Customising this workflow

  Adjust the Fetch Webpage News node to target different news sources or modify the LLM Chat Model prompt for a different summary style.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.html","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.emailSend","@n8n/n8n-nodes-langchain.lmChatOllama","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data processing","reporting","AI/ML"],"complexity":"medium","use_cases":["Daily investor-friendly email digests of top financial headlines","Executive or analyst briefings summarizing market news","Automated monitoring and reporting of financial news for portfolios","Creating concise summaries for internal distribution"],"estimated_setup_minutes":20,"required_services":["Financial news website or RSS/API (e.g., ft.com or other news sources)","Email service (SMTP or API such as SendGrid, Mailgun, Gmail SMTP)","LLM provider credentials (Ollama, or alternatives like OpenAI/GPT-4 or Google Gemini)","Running n8n instance with internet access"],"key_features":["Daily scheduled trigger for automated runs","Fetches and waits for webpage content to fully load","Extracts and cleans headlines/content from HTML","Uses an LLM (Ollama/chat model) to generate concise investor-friendly summaries","Sends the summarized report via email automatically"],"target_audience":["investors and portfolio managers","financial analysts","operations or communications teams preparing daily briefs or newsletters"]}


name: Automated Server Log Cleanup via Email Alerts with SSH - Nginx, Docker, System
author_name: Oneclick AI Squad
author_username: oneclick-ai
categories: []
created_at: 2025-06-27T10:32:16.522Z
updated_at: 2025-06-27T10:32:16.522Z
url: https://n8n.io/workflows/5406
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  This n8n workflow monitors email alerts for disk utilization exceeding 80%, extracts the server IP, logs into the server, and purges logs from Nginx, PM2, Docker, and system files to clear disk space.

  Key Insights

  Ensure email alerts are consistently formatted with server IP details.  
  SSH access must be properly configured to avoid authentication failures.

  Workflow Process

  Initiate the workflow with the Check Disk Alert Emails node when an email triggers on high disk usage.  
  Parse the email to extract the server IP using the Extract Server IP from Email node.  
  Set up SSH credentials and paths manually with the Prepare SSH Variables node.  
  Execute cleanup commands to delete logs from Nginx, PM2, Docker, and system files using the Run LogCleanup Commands via SSH node.

  Usage Guide

  Import the workflow into n8n and configure email and SSH credentials.  
  Test with a sample email alert to verify IP extraction and log deletion.

  Prerequisites

  Email service (e.g., IMAP or API) for alert monitoring  
  SSH access with valid credentials  

  Customization Options

  Modify the Prepare SSH Variables node to target specific log directories or adjust cleanup commands for different server setups.

nodes_used:
  ["n8n-nodes-base.emailReadImap","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.ssh","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","infrastructure","monitoring","incident response"],"complexity":"medium","use_cases":["Automatic remediation of high disk usage alerts","Freeing disk space by cleaning Nginx/PM2/Docker/system logs","Automated incident response for overloaded servers","Centralized log-cleanup triggered from alert emails"],"estimated_setup_minutes":30,"required_services":["Email provider (IMAP or email-alert API)","n8n instance","SSH-accessible Linux servers (with Nginx, Docker, PM2 or system logs)"],"key_features":["Monitors incoming disk-usage alert emails","Parses emails to extract affected server IP","Prepares SSH credentials and target paths","Runs remote SSH commands to purge Nginx, PM2, Docker and system logs","Configurable cleanup paths and commands for different server setups"],"target_audience":["DevOps engineers","SREs","System administrators (sysadmins)"]}


name: 🎓 Learn Code Node (JavaScript) with an Interactive Hands-On Tutorial
author_name: Lucas Peyrin
author_username: lucaspeyrin
categories: []
created_at: 2025-06-27T11:22:16.781Z
updated_at: 2025-06-27T11:22:16.781Z
url: https://n8n.io/workflows/5407
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  How it works

  This workflow is a hands-on tutorial for the Code node in n8n, covering both basic and advanced concepts through a simple data processing task.

  Provides Sample Data: The workflow begins with a sample list of users.
  Processes Each Item (Run Once for Each Item): The first Code node iterates through each user to calculate their fullName and age. This demonstrates basic item-by-item data manipulation using $input.item.json.
  Fetches External Data (Advanced): The second Code node showcases a more advanced feature. For each user, it uses the built-in this.helpers.httpRequest function to call an external API (genderize.io) to enrich the data with a predicted gender.
  Processes All Items at Once (Run Once for All Items): The third Code node receives the fully enriched list of users and runs only once. It uses $items() to access the entire list and calculate the averageAge, returning a single summary item.
  Create a Binary File: The final Code node gets the fully enriched list of users once again and creates a binary CSV file to show how to use binary data Buffer in JavaScript.

  Set up steps

  Setup time: &lt; 1 minute

  This workflow is a self-contained tutorial and requires no setup.

  Explore the Nodes: Click on each of the Code nodes to read the code and the comments explaining each step, from basic to advanced.
  Run the Workflow: Click "Execute Workflow" to see it in action.
  Check the Output: Click on each node after the execution to see how the data is transformed at each stage. Notice how the data is progressively enriched.
  Experiment! Try changing the data in the 1. Sample Data node, or modify the code in the Code nodes to see what happens.

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

workflow_json_compressed:
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metadata_json:
  {"categories":["education","automation","data processing","integration"],"complexity":"medium","use_cases":["Learn and practice using the n8n Code node","Item-by-item data transformation using $input.item.json","Enrich data via external API calls (genderize.io)","Aggregate data across all items using $items()","Generate binary files (CSV) from workflow data"],"estimated_setup_minutes":10,"required_services":["None (self-contained)","Optional: genderize.io (external API used in the tutorial)"],"key_features":["Provides sample data for hands-on learning","Per-item processing with Code node ($input.item.json)","Demonstrates this.helpers.httpRequest for external API enrichment","Shows run-once-for-all-items usage with $items() to compute aggregates","Creates a binary CSV file using Buffer to demonstrate binary data handling in JavaScript"],"target_audience":["n8n users","Developers learning to write JavaScript in n8n","Automation engineers building custom data transforms and integrations\",\"Analysts who need to process and export workflow data"]}


name: Transform Images with AI Image Editor using FLUX.1 Kontext
author_name: Nasser
author_username: nasser
categories: []
created_at: 2025-06-27T12:00:44.532Z
updated_at: 2025-06-27T12:00:44.532Z
url: https://n8n.io/workflows/5409
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  For Who?

  Content Creators
  Youtube Automation
  Marketing Team

  How it works?

  1 - Retrieve Base Image, Image Description and Situation from Airtable
  2 - Generate Image Prompt
  3 - Generate Image via Fal AI
  4 - Verify if Image is generated
  5 - Upload Image on Airtable

  📺 YouTube Video Tutorial: 

  SETUP

  Setup Input : The first part of the workflow can be replaced with anything else. You need as input a Prompt and the Base Image URL (publicly available).

  Setup Output : In this Workflow, the output is storing the image on Airtable but you can replace that with anything else but basically you have two options : 
  Store the Generated Image somewhere : Keep everything like this and replace the last Airtable node with the Third Party you want to use.
  Use the Image directly in n8n : In HTTP Request "Generate Image" switch sync_mode to "true", remove all the following nodes and add "Extract form File" node (convert to Base64 String)


  APIs : For the following third-party integrations, replace ==[YOUR_API_TOKEN]== with your API Token or connect your account via Client ID / Secret to your n8n instance:
  Fal AI (FLUX KONTEXT MAX) : https://fal.ai/models/fal-ai/flux-pro/kontext/max/api#schema-input
  Airtable : https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.airtable/?utm_source=n8n_app&utm_medium=node_settings_modal-credential_link&utm_campaign=n8n-nodes-base.airtable

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.airtable","n8n-nodes-base.httpRequest","n8n-nodes-base.airtable","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","@n8n/n8n-nodes-langchain.lmChatMistralCloud","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set"]

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metadata_json:
  {"categories":["automation","image processing","AI","content creation"],"complexity":"medium","use_cases":["Automate AI-based image generation","Create YouTube thumbnails and channel assets","Scale marketing creative production","Store generated images in Airtable or other storage","Convert generated images to Base64 for in-workflow use"],"estimated_setup_minutes":30,"required_services":["Fal AI (FLUX KONTEXT MAX)","Airtable","n8n instance (HTTP request + nodes)","Publicly accessible base image URL or storage endpoint"],"key_features":["Retrieve base image, description and context from Airtable","Generate image prompt automatically","Generate image via Fal AI (Flux Kontext)","Verify image generation and handle failures","Upload/store generated image back to Airtable (replaceable)"],"target_audience":["Content creators","YouTube automation teams","Marketing teams and social media managers"]}


name: Optimize SEO Meta Tags in Google Sheets with Google Gemini
author_name: Federico De Ponte
author_username: fede
categories: []
created_at: 2025-06-27T14:16:14.638Z
updated_at: 2025-06-27T14:16:14.638Z
url: https://n8n.io/workflows/5411
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  🔁 Loop & Optimize Meta Tags with Google Gemini

  This workflow automates the shortening of meta titles and descriptions for SEO—directly from your Google Sheet, row by row, using Google Gemini.

  ✅ What it does
  Reads rows from a Google Sheet (meta_title, meta_description, row_index)
  Loops through each row and checks if content exists
  Sends the data to Google Gemini for length-optimized output
  Cleans and parses the response
  Updates the original sheet with the shortened results

  🛠️ Setup Requirements
  Google Sheets (OAuth2 credentials connected in n8n)
  Google Gemini API key (configured in n8n credentials)

  Sheet must contain:
  row_index
  meta_title
  meta_description

  Output will be written into:
  meta_titleFixed
  meta_descriptionFixed

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.if","n8n-nodes-base.aggregate","n8n-nodes-base.googleSheets","n8n-nodes-base.splitInBatches","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","seo","data processing"],"complexity":"medium","use_cases":["Bulk optimize/shorten meta titles and descriptions","Automate SEO meta tag cleanup from a Google Sheet","Schedule periodic SEO updates for large content lists","Integrate LLM-based content length optimization into workflows"],"estimated_setup_minutes":15,"required_services":["Google Sheets (OAuth2 credentials)","Google Gemini (PaLM) API key","n8n instance"],"key_features":["Reads rows (row_index, meta_title, meta_description) from Google Sheets","Loops row-by-row and skips empty entries","Sends content to Google Gemini for length-optimized output","Cleans and parses model response","Writes optimized values back to meta_titleFixed and meta_descriptionFixed columns in the sheet (supports batching and scheduling)"],"target_audience":["SEO specialists","Content marketers","Webmasters / site owners managing large content lists"]}


name: Summarize YouTube Videos with Transcription, DeepSeek AI and Google Sheets
author_name: Jacob
author_username: jacobgad
categories: []
created_at: 2025-06-27T15:31:00.808Z
updated_at: 2025-06-27T15:31:00.808Z
url: https://n8n.io/workflows/5412
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Tired of manually watching long YouTube videos just to extract the main points?

  With our YouTube Transcript & Summary Automation, you can instantly turn any video into an actionable, AI-generated summary — all from a simple Google Sheet.

  What this automation does:
  Reads video URLs from Google Sheets (just add your links!)

  Generates accurate transcripts using Supadata.ai — with 100 free uses/month

  Creates a smart summary using DeepSeek AI:

  🔹 Short summary of the video

  🔹 Key points

  🔹 Main topics

  Youtube tutorial:
  https://www.youtube.com/watch?v=uj7KaFSh95Y


  Automatically updates your Google Sheet with the transcript and the AI-generated summary

  How to set it up:
  Create a simple Google Sheet with these columns:

  Url – link to the YouTube video

  Status – set to pending to trigger the automation, updated to done after completion

  Transcript – filled automatically

  Summary – filled automatically

  Once your sheet is ready, the automation takes care of the rest — no technical skills needed.

  Why you'll love it:
  This is the perfect tool for content creators, researchers, marketers, and educators who want to save time, extract insights faster, and never miss key ideas from long videos.

  Want something custom?
  Got feedback or want to build your own custom automation or workflow?
  We’d love to hear from you! Reach out to us at jacobmarketingservice@gmail.com — let’s bring your idea to life.

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

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metadata_json:
  {"categories":["automation","content-processing","AI","data-integration"],"complexity":"medium","use_cases":["Summarizing long YouTube videos","Generating transcripts for research or repurposing","Content research and note-taking","Marketing content analysis and highlights","Educational material extraction"],"estimated_setup_minutes":15,"required_services":["Google Sheets","Supadata.ai (transcription)","DeepSeek AI (summary)","YouTube (video URLs)","n8n account"],"key_features":["Reads video URLs from a Google Sheet","Generates accurate transcripts via Supadata.ai","Creates AI summaries with DeepSeek AI (short summary, key points, main topics)","Automatically writes transcript and summary back to the Google Sheet","Uses a Status column (pending → done) to trigger and track runs"],"target_audience":["Content creators","Researchers","Marketers and social media managers ','Educators and students','Knowledge workers"]}


name: Extract Text from Images with Telegram Bot & OCR Tesseractjs
author_name: Rudi Afandi
author_username: fanfanra
categories: []
created_at: 2025-06-27T16:25:27.361Z
updated_at: 2025-06-27T16:25:27.361Z
url: https://n8n.io/workflows/5413
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  Description
  This n8n workflow enables users to send an image to a Telegram bot and receive the extracted text using Tesseract OCR (via the n8n-nodes-tesseractjs Community Node). It's a quick and straightforward way to convert images into readable text directly through chat.

  How it Works

  The workflow listens for new image messages coming in via the Telegram bot.

  Once an image is received, it downloads the image file from Telegram (which initially arrives as application/octet-stream).

  The image data, now properly identified, is then sent to the Tesseract OCR node to extract the text.

  Finally, the recognized text is sent back as a reply to the Telegram user.

  Setup Steps

  Install Community Node: Ensure you have installed n8n-nodes-tesseractjs in your n8n instance.

  Connect Telegram Bot: Configure the Telegram Trigger node with your Telegram bot.

  Bot Token: Add your Telegram bot token to the Send Message node to send replies.

  Deploy & Test: Activate (deploy) the workflow and send an image to your Telegram bot to test.

nodes_used:
  ["n8n-nodes-base.telegramTrigger","n8n-nodes-base.set","n8n-nodes-base.telegram","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.code","n8n-nodes-tesseractjs.tesseractNode"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","OCR","messaging","chatbot"],"complexity":"simple","use_cases":["Convert images to editable text via Telegram chat","Automated data entry from photos (receipts, invoices, notes)","Accessibility: extract text for screen readers or exports","Quick OCR replies for customer support or chat workflows","Archiving and indexing scanned images"],"estimated_setup_minutes":15,"required_services":["n8n instance","Telegram Bot (bot token)","n8n-nodes-tesseractjs (Community Node for Tesseract OCR)"],"key_features":["Listens for incoming Telegram image messages","Downloads image from Telegram and corrects MIME type","Performs OCR using Tesseract (tesseractjs node)","Replies to the user with extracted text","Simple, chat-driven image-to-text conversion"],"target_audience":["Developers / automation engineers","Customer support teams","Small businesses digitizing paper documents"]}


name: Scrape AI News from Multiple Sources to Markdown & Google Drive with RSS.app
author_name: Lucas Walter
author_username: lucaswalter
categories: []
created_at: 2025-06-27T19:09:44.689Z
updated_at: 2025-06-27T19:09:44.689Z
url: https://n8n.io/workflows/5416
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  AI News Scraping System

  This n8n workflow automates the process of pulling in breaking AI-related headlines from curated RSS feeds, scraping their full content, and saving readable Markdown versions directly to Google Drive.

  Use cases include:
  Creating a personal newsletter curation system
  Automating blog post research workflows
  Archiving news content for later summarization or AI use

  How it Works

  Scheduled Triggers  
     The workflow runs every 3–4 hours using multiple Schedule Trigger nodes. Each trigger targets a different news source (e.g., Google News, OpenAI Blog, Hugging Face, etc.).

  Fetch and Parse Feeds  
     RSS feeds are fetched via the HTTP Request node. Items from the feed are split into individual entries using the Split Out node.

  Scrape Article Content  
     Each article URL is sent to the Firecrawl API with a prompt to extract only the main content in Markdown. The scraping skips navigation, headers, footers, and ads.

  Convert and Save  
     The extracted Markdown is converted into a .md file using the Convert to File node. The file is then uploaded to a Google Drive folder.

  Good to Know

  This workflow uses the Firecrawl API for web scraping. Be sure to configure a Generic HTTP Header credential with your API key.
  Output files are saved in Markdown format
  You can add more Schedule Trigger + HTTP Request pairs to extend this workflow to additional feeds.

  Requirements

  Firecrawl API account for scraping
  Google Drive account (OAuth2 credentials must be configured in n8n)
  n8n instance (self-hosted or cloud)

  Customization Ideas

  Replace or extend RSS feeds with sources relevant to your niche
  Load up scraped news stories into a prompt to create new content like TikToks and Reels
  Add a summarization step using an LLM like GPT or Claude
  Send the Markdown files to Notion, Slack, or a blog CMS

  Example Feeds

  | Feed Name        | URL                                                                 |
  |------------------|----------------------------------------------------------------------|
  | Google News (AI) | https://rss.app/feeds/v1.1/AkOariu1C7YyUUMv.json                    |
  | OpenAI Blog      | https://rss.app/feeds/v1.1/xNVg2hbY14Z7Gpva.json                    |
  | Hugging Face     | https://rss.app/feeds/v1.1/sgHcE2ehHQMTWhrL.json                    |

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.convertToFile","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data processing","content curation"],"complexity":"medium","use_cases":["Personal newsletter curation","Automating blog post research workflows","Archiving news content for later summarization or AI use","Monitoring AI industry headlines across sources","Feeding curated content into CMS or downstream workflows"],"estimated_setup_minutes":20,"required_services":["Firecrawl API (or equivalent web-scraping API)","Google Drive (OAuth2 credentials)","RSS feeds provider (rss.app or other RSS sources)","n8n instance (self-hosted or cloud)"],"key_features":["Scheduled multi-source RSS fetching (multiple Schedule Trigger nodes)","Per-item splitting and HTTP-based feed parsing","Focused article scraping via Firecrawl to extract main content as Markdown","Convert extracted content to .md files and upload to Google Drive","Extensible: add more feeds, or downstream steps (summarize, publish, notify)"],"target_audience":["Content creators and newsletter curators","Researchers and analysts","Marketers and community managers for content monitoring/repurposing tasks"]}


name: AI Facebook Ad Spy Tool with Apify, OpenAI, Gemini & Google Sheets
author_name: Nick Saraev
author_username: nicksaraev
categories: []
created_at: 2025-06-27T22:59:40.896Z
updated_at: 2025-06-27T22:59:40.896Z
url: https://n8n.io/workflows/5417
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  AI Facebook Ad Spy Tool with Apify, OpenAI, Gemini & Google Sheets

  Categories: Competitive Intelligence, Marketing Automation, AI Analysis

  This workflow creates a comprehensive Facebook ad spy tool that scrapes competitor ads from Facebook's ad library and generates detailed analysis with rewritten versions. The system processes text, image, and video ads using different AI models, providing strategic intelligence for PPC agencies and marketers. Built to be sold as a premium service for $2,000+, this tool combines web scraping, multi-modal AI analysis, and competitor intelligence into one powerful automation.

  Benefits

  Complete Competitive Intelligence** - Analyze competitor strategies across all ad formats (text, image, video)
  Multi-Modal AI Analysis** - Uses GPT-4 Vision for images and Gemini for video content understanding
  Automated Ad Rewriting** - Generates inspired variations of successful competitor ads
  Quality Filtering** - Targets high-performing advertisers with significant page likes
  Scalable Processing** - Handle hundreds of competitor ads with detailed strategic analysis
  Premium Service Potential** - Easily sold to agencies and marketers for $2,000+ implementations

  How It Works

  Facebook Ad Library Scraping:
  Connects to Facebook's public ad library through Apify's specialized scraper
  Searches for active ads using customizable keywords and targeting parameters
  Extracts comprehensive ad data including creative assets, targeting info, and engagement metrics
  Filters results to focus on high-quality advertisers with substantial page followings

  Intelligent Content Routing:
  Automatically categorizes ads into text-only, image-based, or video content types
  Routes each ad type to specialized processing pipelines optimized for that content format
  Ensures appropriate AI models are used for each type of creative analysis
  Maintains data integrity while processing different content formats simultaneously

  Advanced Video Analysis Pipeline:
  Downloads video ads directly from Facebook's content delivery network
  Uploads videos to Google Drive for temporary storage and processing
  Initiates Gemini AI video upload sessions for multi-modal analysis
  Uses Gemini's advanced video understanding to generate detailed content descriptions
  Processes video narrative, visual elements, messaging strategy, and target audience insights

  Image and Text Processing:
  Analyzes image ads using GPT-4 Vision for comprehensive visual content understanding
  Processes text-only ads using GPT-4 for messaging strategy and copywriting analysis
  Identifies key persuasion techniques, target demographics, and messaging frameworks
  Generates detailed competitive intelligence reports for each ad format

  Strategic Intelligence Generation:
  Creates comprehensive summaries analyzing competitor messaging strategies and target audiences
  Generates rewritten ad copy that captures successful elements while avoiding direct copying
  Produces recreation prompts for images and videos that can be used with AI generation tools
  Organizes all insights in structured Google Sheets database for easy analysis and reporting

  Required Setup Configuration

  Apify Integration:
  Sign up for Apify account and obtain API key
  Replace &lt;your-apify-api-key-here&gt; in "Run Ad Library Scraper" node
  Customize Facebook Ad Library search URLs with your target keywords and regions

  AI Service Configuration:
  OpenAI API**: Set up for text analysis and image understanding with GPT-4 Vision
  Gemini API**: Configure for advanced video content analysis and description
  Replace &lt;your-gemini-api-key-here&gt; in all Gemini-related nodes

  Google Services Setup:
  Google Drive**: Configure OAuth for temporary video storage during Gemini processing
  Google Sheets**: Create results database with proper column structure for ad intelligence storage

  Facebook Ad Library Search Configuration:
  Customize the search parameters in the Apify scraper

  Google Sheets Database Structure:

  Create a sheet with these columns:

  ad_archive_id - Unique Facebook ad identifier
  page_id - Advertiser's Facebook page ID
  page_name - Advertiser's business name
  page_url - Link to advertiser's Facebook page
  type - Ad format (text, image, or video)
  date_added - When ad was analyzed
  summary - Detailed competitive intelligence analysis
  rewritten_ad_copy - AI-generated inspired version
  image_prompt - Description for recreating image ads
  video_prompt - Description for recreating video ads

  Business Use Cases

  PPC Agencies - Offer comprehensive competitor analysis services to clients for strategic advantage
  Marketing Teams - Research competitor strategies and messaging before launching new campaigns
  E-commerce Businesses - Analyze successful ads in your industry for creative inspiration
  SaaS Companies - Study how competitors position their products and target audiences
  Course Creators - Research educational content marketing approaches and messaging strategies
  Affiliate Marketers - Identify successful promotional strategies and high-converting ad formats

  Difficulty Level: Advanced
  Estimated Build Time: 3-4 hours
  Monthly Operating Cost: ~$200 (Apify + OpenAI + Gemini + Google Workspace APIs)

  Watch My Complete Build Process
  Want to see exactly how I built this entire Facebook ad spy system from scratch? I walk through the complete development process live, including API integrations, multi-modal AI setup, error handling, and the exact business strategy for selling this as a premium service.
  🎥 Watch My Live Build: "Build A Facebook Ads Spy Tool With N8N (Sell for $2k+)"
  This comprehensive tutorial shows the real development process - including complex API orchestration, multi-modal AI integration, and proven strategies for monetizing competitive intelligence systems.

  Set Up Steps

  Apify Scraper Configuration:

  Set up Apify account and configure Facebook Ad Library scraper
  Customize search parameters for your target industries and regions
  Configure result limits and filtering parameters for quality control
  Test scraper with sample searches to verify data quality

  Multi-Modal AI Setup:

  Configure OpenAI API credentials for text and image analysis
  Set up Gemini API access for advanced video content understanding
  Configure appropriate rate limits and error handling for API stability
  Test AI analysis with sample ads to optimize prompt quality

  Google Services Integration:

  Set up Google Drive OAuth for temporary video storage during processing
  Create Google Sheets database with proper column structure for intelligence storage
  Configure sharing permissions and access controls for team collaboration
  Test complete data flow from scraping to final intelligence reports

  Quality Control and Filtering:

  Configure page likes threshold in "Filter For Likes" node (recommend 1,000+ for quality)
  Adjust content routing logic in Switch node based on your analysis needs
  Set up error handling and retry logic for reliable large-scale processing
  Test complete workflow with various ad types to ensure proper routing

  Advanced Customization:

  Customize AI prompts for your specific industry analysis needs
  Configure additional filtering criteria beyond page likes
  Set up automated scheduling for regular competitor monitoring
  Add custom fields to database for tracking specific competitive metrics

  Advanced Features

  Scale the system with additional capabilities:

  Industry-Specific Analysis - Customize prompts and filters for different verticals
  Trend Tracking - Monitor messaging changes over time for strategic insights
  Performance Correlation - Cross-reference ad engagement with business outcomes
  Alert Systems - Notify when competitors launch new campaign types
  Custom Reporting - Generate client-ready intelligence reports automatically
  Integration Extensions - Connect to CRM and marketing platforms for strategic workflow

  Important Considerations

  API Rate Limits - Built-in delays and error handling prevent service interruptions
  Content Rights - System generates inspired variations, not direct copies, for legal compliance
  Data Storage - Organize intelligence database for easy client reporting and analysis
  Scalability - Batch processing handles hundreds of ads efficiently without blocking
  Quality Assurance - Filtering logic ensures analysis focuses on successful, high-quality advertisers

  Why This System Works

  The competitive advantage lies in comprehensive multi-modal analysis:

  Complete format coverage - analyzes text, image, and video ads with appropriate AI models
  Strategic depth - goes beyond basic scraping to provide actionable intelligence
  Automation scale - processes competitor research that would take weeks manually
  Premium positioning - advanced AI analysis justifies higher service pricing
  Immediate value - clients receive actionable insights within hours of setup
  Check Out My Channel
  For more advanced automation systems that generate real business results and premium service opportunities, explore my YouTube channel where I share proven strategies for building profitable automation businesses.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.switch","n8n-nodes-base.filter","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.splitInBatches","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.wait"]

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metadata_json:
  {"categories":["Competitive Intelligence","Marketing Automation","AI Analysis"],"complexity":"complex","use_cases":["Competitor ad research and intelligence","Automated ad copy rewriting and inspiration","Multi-modal creative analysis (text, image, video)","Agency client reporting and premium consulting","Trend monitoring and alerting for new competitor campaigns"],"estimated_setup_minutes":210,"required_services":["Apify (Facebook Ad Library scraper)","Facebook Ad Library (public data)","OpenAI (GPT-4 / GPT-4 Vision)","Google Gemini API (video analysis)","Google Drive (temporary video storage / OAuth)","Google Sheets (results database)"],"key_features":["Web scraping of Facebook Ad Library via Apify","Automated routing by ad format (text/image/video)","Multi-modal AI analysis (GPT-4 Vision for images, Gemini for video, GPT-4 for text)","Automated rewritten ad copy and creative recreation prompts","Video download/upload pipeline with temporary Drive storage and Gemini sessions"],"target_audience":["PPC agencies","Marketing teams","E‑commerce businesses and growth marketers\",\"SaaS companies\",\"Affiliate marketers"]}


name: FinnHub API and Slack Template
author_name: Fan Luo
author_username: luofan189
categories: []
created_at: 2025-06-28T00:09:21.530Z
updated_at: 2025-06-28T00:09:21.530Z
url: https://n8n.io/workflows/5418
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:34
description:
  Daily Company News Bot
  This n8n template demonstrates how to use Free FinnHub API to retrieve the company news from a list stock tickers and post messages in Slack channel with a pre-scheduled time.

  How it works
  We firstly define the list of stock tickers you are interested
  Loop over items to call FinnHub API to get the latest company news for the ticker
  Then we format the company news as a markdown text content which could be sent to Slack
  Post a new message in Slack channel
  Wait for 5 seconds, then move to the next ticker

  How to use
  Simply setup a scheduler trigger to automatically trigger the workflow

  Requirements
  FinnHub API Key
  Slack channel webhook

  Need Help?
  Contact me via My Blog or ask in the Forum!

  Happy Hacking!

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.wait","n8n-nodes-base.slack","n8n-nodes-base.code","n8n-nodes-base.noOp","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","notifications","finance"],"complexity":"medium","use_cases":["Daily company/stock news digest","Investor or finance team alerts","Automated Slack reporting","Monitoring news for a watchlist of tickers"],"estimated_setup_minutes":10,"required_services":["FinnHub API (API key)","Slack (incoming webhook or Slack app)"],"key_features":["Scheduled trigger to run at a pre-set time","Loop over a list of stock tickers and fetch company news from FinnHub","Format news as Markdown for Slack messages","Post formatted messages to a Slack channel","Delay between requests to avoid rate limits (5s wait per ticker)"],"target_audience":["Developers/automation engineers","Finance/IR teams","Product/operations teams who need automated notifications"]}


name: Process Multiple Files with Forms: A Tutorial on Binary Data and Loops
author_name: Wyeth
author_username: wyeth
categories: []
created_at: 2025-06-28T01:17:18.053Z
updated_at: 2025-06-28T01:17:18.053Z
url: https://n8n.io/workflows/5419
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Let a user load multiple files with a Form node, and process the binary data. A very important workflow for many tools. 

  This is a learning example of several core concepts that are hard to grasp in n8n:
  $binary data
  Loop and $runIndex
  Split Out

  The Save File deomonstrates how to access the binary data correctly, but could be swapped to POST the files to an AI, for example.

nodes_used:
  ["n8n-nodes-base.splitInBatches","n8n-nodes-base.readWriteFile","n8n-nodes-base.splitOut","n8n-nodes-base.formTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","data processing","file handling","tutorial/learning"],"complexity":"medium","use_cases":["Accept multiple file uploads via a Form node and process each file","Save uploaded binary files to disk or cloud storage","Send individual files to external APIs (e.g., AI file processing) via POST","Learn core n8n concepts: binary data handling, looping with $runIndex, splitOut/splitInBatches"],"estimated_setup_minutes":15,"required_services":["None (local file system)","Optional: external APIs for file processing (e.g., AI services, cloud storage)"],"key_features":["Handle multiple file uploads from a Form node","Correct access and processing of binary data","Looping through items using $runIndex and splitInBatches","splitOut usage to expand items for per-file processing","Example Save File node (can be swapped to POST files to an API)"],"target_audience":["developers","automation engineers","integration builders','n8n learners"]}


name: Edit & Deliver Images with DALL-E 2, Google Drive & Telegram Messaging
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-06-28T01:46:29.266Z
updated_at: 2025-06-28T01:46:29.266Z
url: https://n8n.io/workflows/5420
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  🎨 AI Image Editor with Form Upload + Telegram Delivery 🚀

  Who’s it for? 👥

  This workflow is built for content creators, social media managers, designers, and agencies who need fast, AI-powered image editing without the hassle. Whether you're batch-editing for clients or spicing up personal projects, this tool gets it done — effortlessly.

  What it does 🛠️

  A seamless pipeline that:

  📥 Accepts uploads + prompts via a clean form
  ☁️ Saves images to Google Drive automatically
  🧠 Edits images with OpenAI’s image API
  📁 Converts results to downloadable PNGs
  📬 Delivers the final image instantly via Telegram
  Perfect for AI-enhanced workflows that need speed, structure, and simplicity.

  How it works ⚙️

  User Uploads: Fill a form with an image + editing prompt
  Cloud Save: Auto-upload to your Google Drive folder
  AI Editing: OpenAI processes the image with your prompt
  Convert & Format: Image saved as PNG
  Telegram Delivery: Final result sent straight to your chat 💬
  You’ll need ✅

  🔑 OpenAI API key
  📂 Google Drive OAuth2 setup
  🤖 Telegram bot token & chat ID
  ⚙️ n8n instance (self-hosted or cloud)
  Setup in 4 Easy Steps 🛠️

  1. Connect APIs

  Add OpenAI, Google Drive, and Telegram credentials to n8n
  Store keys securely (avoid hardcoding!)
  2. Configure Settings

  Set Google Drive folder ID
  Add Telegram chat ID
  Tweak image size (default: 1024×1024)
  3. Deploy the Form

  Add a Webhook Trigger node
  Test with a sample image
  Share the form link with users 🎯
  Fine-Tune Variables
  In the Set node, customize:

  📐 Image size
  📁 Folder path
  📲 Delivery options
  ⏱️ Timeout duration
  Want to customize more? 🎛️

  🖼️ Image Settings

  Change size (e.g. 512x512 or 2048x2048)
  Update the model (when new versions drop)
  📂 Storage

  Auto-organize files by date/category
  Add dynamic file names using n8n expressions
  📤 Delivery

  Swap Telegram with Slack, email, Discord
  Add multiple delivery channels
  Include image prompt or metadata in messages
  📝 Form Upgrades

  Add fields for advanced editing
  Validate file types (e.g. PNG/JPEG only)
  Show a progress bar for long edits
  ⚡ Advanced Features

  Add error handling or retry flows
  Support batch editing
  Include approvals or watermarking before delivery
  ⚠️ Notes & Best Practices
  ✅ Check OpenAI credit balance
  🖼️ Test with different image sizes/types
  ⏱️ Adjust timeout settings for larger files
  🔐 Always secure your API keys

nodes_used:
  ["n8n-nodes-base.formTrigger","n8n-nodes-base.set","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.httpRequest","n8n-nodes-base.convertToFile","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","image processing","AI"],"complexity":"medium","use_cases":["On-demand AI image editing from user uploads","Automated delivery of edited images to Telegram","Saving and organizing original/edited images in Google Drive","Batch or single-client asset processing for social media or agencies","Rapid prototyping of visual assets using prompts"],"estimated_setup_minutes":25,"required_services":["OpenAI (Images / DALL·E API)","Google Drive (OAuth2)","Telegram (bot token & chat ID)","n8n instance (self-hosted or cloud)"],"key_features":["Web form for image + prompt uploads","Automatic upload of originals to Google Drive","Image editing via OpenAI image API","Conversion/formatting to PNG","Instant delivery of final image via Telegram message/file attachment (configurable)"],"target_audience":["Content creators","Social media managers","Designers and creative agencies」「Developers / automation engineers integrating AI image workflows"]}


name: AI-Powered Lead Enrichment with Explorium MCP & Telegram
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-06-28T02:07:14.053Z
updated_at: 2025-06-28T02:07:14.053Z
url: https://n8n.io/workflows/5421
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  🤖 AI-Powered Lead Enrichment with Explorium MCP & Telegram

  Who it's for

  Sales reps, agencies, and growth teams who want to turn basic company info into qualified leads with automated research . Perfect for B2B prospecting.

  What it does

  This workflow lets you send a company name or domain via Telegram, and instantly returns:

  ✅ Enriched company profile (industry, size, tech, pain points)

  ✅ A clean, structured JSON — ready for your CRM or sales tools
  How it works

  💬 Send company info to your Telegram bot
  🔎 Workflow pulls data from Explorium MCP + Tavily
  🧠 AI analyzes model, tools, pain points & goals

  📤 JSON response sent back via Telegram or logged to your database
  Requirements

  🔐 OpenAI API (GPT-4)
  🧠 Explorium MCP API
  🌐 Tavily Web Search API
  🤖 Telegram Bot API
  🗃️ PostgreSQL (for memory/logging)
  How to set up

  Add API keys in n8n
  Connect Telegram bot to webhook
  Set up PostgreSQL for memory persistence
  Customize prompts (tone, niche, etc.)
  Test by sending a company name via Telegram
  Customization Options

  🎯 Focus enrichment on specific industries or keywords
  💬 Adjust the email sequence structure & style
  🧩 Add extra data sources (e.g. Clearbit, Crunchbase)
  🧾 Format JSON to match your CRM schema
  ⚙️ Add approval step before sending emails
  Highlights

  ✅ Uses multi-source enrichment
  ✅ Works 100% from Telegram
  ✅ Integrates into any sales pipeline

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.mcpClientTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequestTool","@n8n/n8n-nodes-langchain.memoryPostgresChat","n8n-nodes-base.telegramTrigger","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5692 characters]

metadata_json:
  {"categories":["automation","integration","data enrichment","sales enablement"],"complexity":"medium","use_cases":["B2B lead enrichment","Automated prospect research","Populating CRM with structured company profiles","Alerting or logging enriched leads via Telegram"],"estimated_setup_minutes":45,"required_services":["OpenAI API (GPT-4)","Explorium MCP API","Tavily Web Search API","Telegram Bot API","PostgreSQL (for memory/logging)","n8n"],"key_features":["Send company name/domain via Telegram to trigger enrichment","Multi-source data enrichment (Explorium + web search)","AI analysis of industry, tech stack, pain points and goals","Returns clean, structured JSON ready for CRM ingestion","Logs/enrichments persisted in PostgreSQL for memory/audit trails or later useable context in workflows"],"target_audience":["Sales reps","Growth teams","Marketing/agencies focused on B2B prospecting"]}


name: PT Clinic AI Cold Email Sequence Generator With GPT4
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-06-28T02:56:22.373Z
updated_at: 2025-06-28T02:56:22.373Z
url: https://n8n.io/workflows/5422
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  🎯 PT Clinic AI Email Sequence Generator

  🧠 What it does

  This automation generates personalized cold email sequences for uncontacted physical therapy clinics, pulling lead info from Google Sheets and using GPT-4 to create 4 highly-targeted outreach emails — all tailored for the PT industry.

  ✅ Key Benefits

  Saves 25–40 hours/week of manual email writing
  Delivers niche, pain-point-driven messages to PT clinic directors
  Auto-updates your campaign sheet with verified, ready-to-send sequences
  Scales cold outreach with quality and speed
  ⚙️ How it works

  Fetch Prospects
  Pulls leads from a Google Sheet tab (e.g. Sports & Orthopedic PT)
  Filters only those with Email Sent Status = "No"
  Batch Processing
  Runs one lead at a time to avoid API throttling and preserve accuracy
  AI-Powered Generation
  GPT-4 generates a 4-part email sequence:
  Email 1: Pain point + intro
  Email 2: Social proof (success story)
  Email 3: Objection handling
  Email 4: Final CTA with opt-out or redirect option
  Content Validation
  Extracts emails using regex
  Checks for name mismatches
  Ensures all 4 emails exist before saving
  Data Update
  Auto-saves all email subjects and bodies to Google Sheets
  Marks lead as updated and moves on to the next
  🧾 Requirements

  Google Sheets with required fields (link to make a copy is in the workflow)
  OpenAI GPT-4 API key
  SMTP service (optional, for sending later)
  Telegram/Slack bot (optional for alerts)
  📦 Output

  A fully populated sheet with:

  Four cold emails per clinic
  Subject lines and message bodies
  Quality-checked entries, ready to be scheduled or sent manually

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.splitInBatches","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.stickyNote","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":["automation","integration","sales outreach","AI content generation"],"complexity":"medium","use_cases":["Generate personalized 4-part cold email sequences for physical therapy clinics","Scale B2B outreach and prospecting","Populate and maintain campaign data in Google Sheets","Validate email content (regex, name matching) before sending","Prepare ready-to-send sequences for SMTP dispatch or manual review"],"estimated_setup_minutes":20,"required_services":["Google Sheets","OpenAI GPT-4 API","SMTP service (optional — e.g., SendGrid, Gmail)","Telegram or Slack bot (optional for alerts)"],"key_features":["AI-powered 4-email sequence generation (pain, social proof, objection handling, final CTA)","Pulls and filters leads from Google Sheets","Batch processing to avoid API throttling","Content validation (regex email extraction, name mismatch checks, ensures 4 emails exist)","Auto-updates Google Sheet with subjects and message bodies for each lead"],"target_audience":["Marketing agencies and consultants targeting PT clinics","Sales/BD teams and SDRs doing cold outreach","Founders and growth hackers running niche outbound campaigns"]}


name: Gmail MCP Workflow - AI-Powered Email Management
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-06-28T03:32:17.894Z
updated_at: 2025-06-28T03:32:17.894Z
url: https://n8n.io/workflows/5423
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 

  MCP Gmail Workflow – AI-Powered Email Management

  ✨ What It Does
  A smart n8n workflow that connects Gmail with an AI agent (via MCP), letting you send, read, and organize emails using natural language.

  ⚙️ Key Features
  🧠 AI Commands: “Send email to John about the budget”
  📥 Inbox Control: Mark read/unread, apply/remove labels
  🗂 Smart Organization: Auto-label based on content
  🤖 MCP-Ready: Works with Claude, ChatGPT, etc.
  🎯 Use Cases
  “📤 Send a follow-up to the client about yesterday’s meeting”
  “📬 Mark all newsletters as read and label ‘Newsletter’”
  “🧾 Summarize latest email from Sarah”
  “🗃 Label all Project X emails as ‘Project-X-2024’”
  “⭐ Find unread emails from my manager and mark as important”
  🛠 Setup Guide
  🔑 Prerequisites

  n8n (self-hosted or cloud)
  Gmail API credentials
  MCP-compatible AI (optional but powerful)
  📥 1. Import Workflow

  Copy JSON → Open n8n → Import → Paste → Done ✅
  🔐 2. Gmail OAuth2 Setup

  Create Google project → Enable Gmail API
  Create OAuth2 creds → Add n8n redirect URI
  In n8n: Add Gmail OAuth2 → Paste Client ID/Secret → Connect
  🧩 3. Update Credential References

  Find your credential ID in n8n
  Update each Gmail node with your ID
  🧠 4. MCP Trigger (Optional)

  Use provided webhook URL in your AI system
  Send test prompts to verify connection
  🧪 5. Test Key Actions

  ✅ “Send a test email”
  ✅ “Read latest email”
  ✅ “Label last email as ‘Test’”
  ✅ “Mark latest email as unread”
  ⚙️ 6. Advanced Tips

  Create custom labels in Gmail
  Use HTTPS + webhook auth
  Add retries and error handling in n8n
  🧯 Troubleshooting
  ❗ Gmail Auth Error? → Re-auth and check redirect URI
  ❗ Webhook not firing? → Check endpoint + manual test
  ❗ Label errors? → Use correct label names or IDs
  ✅ Required Gmail Scopes:
  gmail.modify
  gmail.send
  📈 Best Practices
  🔁 Test regularly
  🔒 Use minimal permissions
  🏷 Consistent label naming
  🔍 Monitor execution + webhook logs
  🎉 You’re All Set!
  Control Gmail with your voice or text through AI. Make managing emails smarter, faster, and 100% automated 💌

nodes_used:
  ["@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4408 characters]

metadata_json:
  {"categories":["automation","email management","AI integration"],"complexity":"medium","use_cases":["Send follow-ups or routine emails via natural language","Automatically label and organize incoming emails","Summarize email threads or individual messages","Bulk mark newsletters or managerial emails as read/important","Trigger email actions from AI prompts/webhooks"],"estimated_setup_minutes":30,"required_services":["Gmail / Google API (OAuth2 credentials)","n8n (self-hosted recommended for community nodes)","MCP-compatible AI provider (Claude, ChatGPT via MCP)","Public webhook endpoint (for MCP trigger)"],"key_features":["Natural-language AI commands to send/read/organize email","Mark read/unread and apply/remove Gmail labels","Auto-labeling based on message content","Summarization of emails","MCP webhook integration for AI-driven triggers"],"target_audience":["n8n users / automators","Developers or sysadmins (for OAuth and self-hosting)","Productivity-focused professionals and execs','Support and ops teams"]}


name: Google Sheets MCP - AI-Powered Spreadsheet Management
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-06-28T03:43:07.882Z
updated_at: 2025-06-28T03:43:07.882Z
url: https://n8n.io/workflows/5424
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  📊 Google Sheets MCP Workflow – AI Meets Spreadsheets! 😄

  ✨ What It Does

  This n8n workflow lets you chat with your spreadsheets using AI + MCP! From reading and updating data to creating sheets, it’s your smart assistant for Google Sheets 📈🤖

  🚀 Cool Features

  💬 Natural language commands (e.g. "Add a new lead: John Doe")
  ✏️ Full CRUD (Create, Read, Update, Delete)
  🧠 AI-powered analysis & smart workflows
  🗂️ Multi-sheet support
  🔗 Works with ChatGPT, Claude, and more (via MCP)

  💡 Use Cases

  Data Tasks: “Update status to 'Done' in row 3”
  Sheet Ops: “Create a ‘Marketing 2024’ sheet”
  Business Flows: “Summarize top sales from Q2”
  🛠️ Quick Setup

  Import Workflow into n8n
  Copy the JSON
  In n8n → Import JSON → Paste & Save ✅
  Connect Google Sheets
  Create a project in Google Cloud
  Enable Sheets & Drive APIs
  Create OAuth2 credentials
  In n8n → Add Google Sheets OAuth2 credential → Connect 🔐
  Add Your Credentials
  Get your credential ID
  Open each Google Sheets node → Update with your new credential ID
  Link to AI (Optional 😊)
  MCP webhook is pre-set
  Plug it into your AI tool (like ChatGPT)
  Send test command → Watch the magic happen ✨
  ✅ Test It Out

  Try these fun commands:

  🆕 "Add entry: Jane Doe, janed@example.com"
  🔍 "Read data from Sales 2024"
  🧹 "Clear data from A1:C5"
  ➕ "Create sheet 'Budget 2025'"
  ❌ "Delete sheet 'Test'"

  🧠 MCP Command List (AI-Callable Functions)

  These are the tasks the AI can perform via MCP:

  Add a new entry to a sheet
  Read data from a sheet
  Update a row in a sheet
  Delete a row from a sheet
  Create a new sheet
  Delete an existing sheet
  Clear data from a specific range
  Summarize data from a sheet using AI
  ⚙️ Tips & Fixes

  OAuth2 Errors?

  Re-authenticate and check scopes
  Confirm redirect URI is exact
  Permissions?

  Spreadsheet must be shared with edit access
  Use service accounts for production
  Webhook Not Firing?

  Double-check the URL
  Trigger it manually to test

nodes_used:
  ["@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","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.googleSheetsTool","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5496 characters]

metadata_json:
  {"categories":["automation","integration","data processing","ai","spreadsheets"],"complexity":"medium","use_cases":["Natural-language spreadsheet editing and querying","Full CRUD operations on Google Sheets via AI","Summarizing or analyzing sheet data with AI","Multi-sheet management (create, delete, clear ranges)","Business workflows (leads, sales summaries, budgets)"],"estimated_setup_minutes":30,"required_services":["n8n","Google Sheets","Google Drive","Google Cloud Console (enable Sheets & Drive APIs, OAuth2)","MCP (webhook connector)","AI provider (ChatGPT, Claude, or other via MCP)"],"key_features":["Natural language commands to interact with sheets","Full CRUD: add, read, update, delete rows and sheets","AI-powered data summarization and analysis","Multi-sheet support and range clearing","Preconfigured MCP webhook for AI integration (ChatGPT/Claude compatible)"],"target_audience":["analysts","marketers","product managers / operations teams managing spreadsheets via automation bots"]}


name: AI-Powered File Management Automation for Google Drive with MCP
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-06-28T03:58:19.167Z
updated_at: 2025-06-28T03:58:19.167Z
url: https://n8n.io/workflows/5425
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 

  📁 Google Drive MCP Workflow – AI-Powered File Management Automation 🚀

  🧠 Overview
  A secure and intelligent n8n workflow that connects with Google Drive via MCP (Model Control Protocol). Ideal for AI agent tasks, compliance-driven storage, and document automation.

  🌟 Key Features
  🔒 Built-In Safety

  Backs up files before edits (timestamped)
  Supports rollback using file history
  Validates file size, type, and permissions
  📁 Smart Organization

  Automatically converts file types (PDF, DOCX, etc.)
  Moves files to structured folders
  Auto-archives old files based on age or rules
  🔄 MCP Integration

  Accepts standardized JSON via webhook
  Real-time execution for AI agents
  Fully customizable input (action, fileId, format, etc.)
  ✅ AI Callable MCP Actions
  These are the commands AI agents can perform via MCP:

  Download a file (with optional format conversion)
  Upload a new file to Google Drive
  Copy a file for backup
  Move a file to a specific folder
  Archive old or inactive files
  Organize documents into folders
  Convert files to a new format (PDF, DOCX, etc.)
  Retrieve and review file history for rollback
  📝 Example Input
  {
    "action": "download",
    "fileId": "abc123",
    "folderPath": "/projects/clientA",
    "convertFormat": "pdf"
  }
  🔐 Security & Performance
  OAuth2 secured access to Google Drive API
  No sensitive data stored in transit
  Real-time audit logs and alerts
  Batch-friendly with built-in rate limiting
  📌 Ideal For
  Businesses automating file management
  AI Agents retrieving, sorting, converting, or archiving files
  Compliance teams needing file versioning and backups
  ⚙️ Requirements
  n8n + Google Drive API v3
  MCP server + Webhook integration
  Google OAuth2 Credentials

nodes_used:
  ["@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.googleDriveTool","n8n-nodes-base.googleDriveTool","n8n-nodes-base.googleDriveTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDriveTool","n8n-nodes-base.googleDriveTool"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data-processing","document-management","ai-integration"],"complexity":"complex","use_cases":["Automated file management and organization in Google Drive","AI agents retrieving, converting and manipulating files via MCP","Compliance-focused versioning, backups and rollback of documents","Scheduled or rule-based archiving and lifecycle management","Batch processing and format conversion (PDF, DOCX, etc.)"],"estimated_setup_minutes":60,"required_services":["n8n (self-hosted with community nodes)","Google Drive API v3","Google OAuth2 credentials","MCP server (Model Control Protocol) + webhook endpoint"],"key_features":["OAuth2-secured Google Drive access","Pre-edit backups and timestamped copies","Rollback via file history retrieval","File validation (size, type, permissions)","Automatic format conversion (PDF, DOCX, etc.) and uploads/downloads via MCP actions and webhook JSON input support (action, fileId, format, folderPath, etc.)"],"target_audience":["Developers / DevOps implementing automation","AI/ML engineers integrating agents with storage","Compliance and security teams needing file versioning and audit trails"]}


name: Create a Webhook-Ready Conversational Assistant with Google Gemini and Session Memory
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-06-28T04:06:54.178Z
updated_at: 2025-06-28T04:06:54.178Z
url: https://n8n.io/workflows/5426
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Universal AI Assistant - Webhook-Ready Conversational AI

  Transform any platform into an intelligent conversational experience with this plug-and-play n8n workflow. This AI assistant can be seamlessly integrated into websites, mobile apps, or any system that supports webhook connections.
  Key Features:

  🔗 Universal Integration - Connect to any platform via webhook (websites, apps, bots)
  🧠 Powered by Google Gemini 2.0 Flash - Fast, accurate, and context-aware responses
  💾 Session Memory - Maintains conversation context for natural follow-up interactions
  ⚡ Real-time Responses - Instant webhook responses for smooth user experiences
  🎯 Customizable Personality - Easy prompt modification for brand-specific tone

  Perfect For:

  Website Chat Widgets - Add AI support to any website instantly
  WhatsApp/Telegram Bots - Power messaging platforms with intelligent responses
  Mobile App Integration - Embed conversational AI into iOS/Android apps
  Customer Support Systems - Automate first-line support with context retention
  Lead Qualification - Intelligent pre-screening of prospects before human handoff

  Simple Implementation:
  Just send POST requests to the webhook URL with:
  json{
    "message": "User's question here",
    "sessionId": "unique-session-identifier"
  }
  Ready to deploy in minutes - No complex setup required. Perfect for small businesses wanting to add AI capabilities without technical overhead.

  This workflow gives you a production-ready foundation that you can customize for specific client needs. The session-based memory makes it ideal for customer support scenarios where context matters, and the webhook approach means it integrates with virtually any platform your clients are already using.

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

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metadata_json:
  {"categories":["integration","conversational-ai","automation","customer-support"],"complexity":"simple","use_cases":["Website chat widgets","WhatsApp/Telegram bots","Mobile app conversational UI","First-line customer support automation","Lead qualification and pre-screening"],"estimated_setup_minutes":15,"required_services":["Google Gemini (lmChatGoogleGemini) API / Google Cloud access","n8n instance (self-hosted or cloud)","Any platform capable of sending POST webhooks (website, mobile app, messaging platform)"],"key_features":["Webhook-based universal integration","Session memory / context retention (memoryBufferWindow)","Real-time webhook responses","Powered by Google Gemini 2.0 Flash for fast, accurate replies","Customizable prompts/personality for brand tone"],"target_audience":["Developers and integrators","Small businesses looking for quick AI chat solutions","Customer support teams and ops engineers interested in automation and context-aware responses\",\"No-code/low-code product builders"]}


name: Conversational Lead Capture with Gemini 2.0 Flash AI and Google Sheets
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-06-28T04:28:00.336Z
updated_at: 2025-06-28T04:28:00.336Z
url: https://n8n.io/workflows/5427
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  AI Lead Capture System - Complete Setup Guide
  Prerequisites

  n8n instance (cloud or self-hosted)
  Google AI Studio account (free tier available)
  Google account for Sheets integration
  Website with chat widget capability

  Phase 1: Core Infrastructure Setup
  Step 1: Set Up Google AI Studio

  Go to Google AI Studio
  Create account or sign in with Google
  Navigate to "Get API Key"
  Create new API key for your project
  Copy and securely store the API key
  Free tier limits: 15 requests/minute, 1 million tokens/month

  Step 2: Configure Google Sheets

  Create new Google Sheet for lead storage
  Add column headers (exact names):

  Full Name
  Company Name
  Email Address
  Phone Number
  Project Intent/Needs
  Project Timeline
  Budget Range
  Preferred Communication Channel
  How they heard about DAEX AI


  Copy the Google Sheet ID from URL (between /d/ and /edit)
  Ensure sheet is accessible to your Google account

   Step 3: Import n8n Workflow

  Open your n8n instance
  Create new workflow
  Click "..." menu → Import from JSON
  Paste the provided workflow JSON
  Workflow will appear with all nodes connected

  Phase 2: Credential Configuration
   Step 4: Set Up Google Gemini API

  In n8n, go to Credentials → Add Credential
  Search for "Google PaLM API"
  Enter your API key from Step 1
  Test connection
  Link to the "Google Gemini Chat Model" node

   Step 5: Configure Google Sheets Access

  Go to Credentials → Add Credential
  Select "Google Sheets OAuth2 API"
  Follow OAuth flow to authorize your Google account
  Test connection with your sheet
  Link to the "Google Sheets" node

  Phase 3: Workflow Customization
   Step 6: Update Company Information

  Open the AI Agent node
  In the system message, replace all mentions of:

  Company name and description
  Service offerings and specializations
  FAQ knowledge base
  Typical project timelines and pricing ranges


  Adjust conversation tone to match your brand voice

   Step 7: Configure Lead Qualification Fields

  In the AI Agent system message, modify the required information list:

  Add/remove qualification questions
  Adjust budget ranges for your services
  Customize timeline options
  Update communication channel preferences


  In Google Sheets node, update column mappings if you changed fields

   Step 8: Set Up Sheet Integration

  Open Google Sheets node
  Click on Document ID dropdown
  Select your lead capture sheet
  Verify all column mappings match your sheet headers
  Test with sample data

  Phase 4: Website Integration
   Step 9: Get Webhook URL

  Open Webhook node in n8n
  Copy the webhook URL (starts with your n8n domain)
  Note: URL format is https://your-n8n-domain.com/webhook/[unique-id]

  Step 10: Connect Your Chat Widget
  Choose your integration method:
  Option A: Direct JavaScript Integration
  javascript// Add to your website
  function sendMessage(message, sessionId) {
      fetch('YOUR_WEBHOOK_URL', {
          method: 'POST',
          headers: { 'Content-Type': 'application/json' },
          body: JSON.stringify({
              message: message,
              sessionId: sessionId || 'visitor-' + Date.now()
          })
      })
      .then(response =&gt; response.json())
      .then(data =&gt; {
          // Display AI response in your chat widget
          displayMessage(data.message);
      });
  }
  Option B: Chat Platform Webhook

  Open your chat platform settings (Intercom, Crisp, etc.)
  Find webhook/integration section
  Add webhook URL pointing to your n8n endpoint
  Configure to send message and session data

  Option C: Zapier/Make.com Integration

  Create new Zap/Scenario
  Trigger: New chat message from your platform
  Action: HTTP POST to your n8n webhook
  Map message content and session ID

  Phase 5: Testing & Optimization
  Step 11: Test Complete Flow

  Send test message through your chat widget
  Verify AI responds appropriately
  Check conversation context is maintained
  Confirm lead data appears in Google Sheets
  Test with various conversation scenarios

  Step 12: Monitor Performance

  Check n8n execution logs for errors
  Monitor Google Sheets for data quality
  Review conversation logs for improvement opportunities
  Track response times and conversion rates

  Step 13: Fine-Tune Conversations

  Analyze real conversation logs
  Update system prompts based on common questions
  Add new FAQ knowledge to the AI agent
  Adjust qualification questions based on lead quality
  Optimize for your specific customer patterns

  Phase 6: Advanced Features (Optional)
  Step 14: Add Lead Scoring

  Create new column in Google Sheets for "Lead Score"
  Update AI agent to calculate scores based on:

  Budget range (higher budget = higher score)
  Timeline urgency (sooner = higher score)
  Project complexity (complex = higher score)


  Add conditional formatting in Google Sheets to highlight high-value leads

  Step 15: Set Up Notifications

  Add email notification node after Google Sheets
  Configure to send alerts for high-priority leads
  Include lead details and conversation summary
  Set up different notification rules for different lead scores

  Step 16: Analytics Dashboard

  Connect Google Sheets to Google Data Studio or similar
  Create dashboard showing:

  Daily lead volume
  Conversion rates by source
  Average qualification time
  Lead quality scores
  Revenue pipeline from captured leads



  Troubleshooting Common Issues
  AI Not Responding

  Check Google Gemini API key validity
  Verify API quota not exceeded
  Review n8n execution logs for errors

  Data Not Saving to Sheets

  Confirm Google Sheets permissions
  Check column name matching
  Verify sheet ID is correct

  Chat Widget Not Connecting

  Test webhook URL directly with curl/Postman
  Verify JSON format matches expected structure
  Check CORS settings if browser-based integration

  Conversation Context Lost

  Ensure sessionId is unique per visitor
  Check memory node configuration
  Verify sessionId is passed consistently

nodes_used:
  ["n8n-nodes-base.respondToWebhook","n8n-nodes-base.webhook","n8n-nodes-base.googleSheetsTool","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 10240 characters]

metadata_json:
  {"categories":["automation","lead generation","AI integration","customer engagement"],"complexity":"medium","use_cases":["Conversational lead capture on websites","Automated lead qualification","Storing leads in Google Sheets","Triggering notifications for high-value leads","Feeding analytics dashboards (Data Studio)"],"estimated_setup_minutes":60,"required_services":["n8n (cloud or self-hosted)","Google AI Studio / Gemini (PaLM) API key","Google Account with Google Sheets (OAuth2)","Website or chat widget platform (custom JS, Intercom, Crisp, etc.)","Optional: Zapier/Make.com for integrations, Google Data Studio for analytics"],"key_features":["Real-time conversational AI using Google Gemini","Webhook endpoint to receive chat messages","Session memory (memoryBufferWindow) to maintain context","Automatic mapping and appending of qualified leads to Google Sheets","Customizable system prompts for branding and qualification logic」「Optional lead scoring, email notifications, and analytics pipeline"],"target_audience":["Marketers and demand-gen teams","Sales teams and SDRs","Small and medium businesses with a website chat widget','Developers/implementers integrating AI chat into sites"]}


name: Qualify Real Estate Leads Automatically with OpenAI, Gmail & Airtable CRM
author_name: Marth
author_username: marth
categories: []
created_at: 2025-06-28T06:14:18.530Z
updated_at: 2025-06-28T06:14:18.530Z
url: https://n8n.io/workflows/5428
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  🧠 How It Works
  This AI Agent automatically qualifies property buyer leads from form submissions.
  Form Submission Trigger
  When a user submits their details via a property inquiry form, the workflow is triggered.
  AI Lead Classification
  The buyer's input (budget, location, timeline, etc.) is analyzed by OpenAI to extract structured data and generate a lead score (0–100).
  Lead Qualification Logic
  Leads with a score of 70 or above are marked as qualified, the rest are ignored or stored separately.
  Follow-Up Action
  Qualified leads trigger:
  Email notification to the agent
  Record creation in Airtable as CRM

  ⚙️ How to Set Up
  Form Setup
  Replace the form trigger with your preferred source (Typeform, Google Form, etc.)
  Make sure the form includes: Name, Email, Budget, Location, Timeline, Property Type
  Connect Your Credentials
  Add your OpenAI API key for the LLM node
  Connect your Gmail account for notifications
  Link your Airtable base and table to store qualified leads
  Customize Scoring Logic (Optional)
  You can tweak the prompt in the Information Extractor node to change how scoring works
  Test the Workflow
  Submit a test entry via the form
  Check if you receive an email and see the lead in Airtable
  Activate & Go Live
  Turn on the workflow and start qualifying real buyer leads in real time

  Connect with my linkedin: https://www.linkedin.com/in/bheta-dwiki-maranatha-15654b227/

nodes_used:
  ["n8n-nodes-base.formTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.gmail","n8n-nodes-base.airtable","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","lead management","CRM","AI/ML"],"complexity":"medium","use_cases":["Automated lead qualification for property buyer inquiries","Prioritizing and scoring incoming leads","Automatically creating CRM records for qualified leads","Sending agent notifications for high-quality leads","Extracting structured lead data (budget, location, timeline, etc.) from free-text form input"],"estimated_setup_minutes":20,"required_services":["OpenAI API (LLM)","Gmail (or other email provider)","Airtable","Form provider (Typeform, Google Forms, custom form)","n8n (workflow automation platform)"],"key_features":["AI-powered information extraction and lead scoring (0–100)","Configurable qualification threshold (default: 70)","Conditional logic to filter qualified vs unqualified leads","Automatic Airtable CRM record creation for qualified leads","Email notifications to agents for qualified leads (via Gmail node)"],"target_audience":["Real estate agents and brokers","Sales and lead qualification teams","Small agencies and CRM administrators using Airtable and n8n no-code automation"]}


name: Market Research & Business Case Generator with GPT-4o, Perplexity & Claude
author_name: Automate With Marc
author_username: marconi
categories: []
created_at: 2025-06-28T10:25:17.885Z
updated_at: 2025-06-28T10:25:17.885Z
url: https://n8n.io/workflows/5430
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 

  🧠 Market Research & Business Case Study Generator
  Category: AI + Research | GPT + Perplexity | Business Strategy
  Skill Level: Intermediate
  Use Case: Market Research, Business Planning, Strategic Analysis

  📌 Description:
  This template automates the creation of comprehensive, data-backed business case studies—perfect for entrepreneurs, analysts, consultants, and market researchers.

  For more of such build + step-by-step video tutorials, check out:
  https://www.youtube.com/@Automatewithmarc

  Just send a simple message like:

  “Give me a market opportunity analysis of a bicycle rental business in North Africa.”

  And the workflow does the rest. It scopes your research topic, performs live web research, and crafts a well-structured 1500-word business case study—all automatically saved to Google Docs.

  🔧 How It Works:
  🟢 Chat Trigger:
  Start the workflow by sending a prompt via the built-in Chat interface (Langchain Chat Trigger).

  🧭 Research Scope Definer (GPT-4o):
  Breaks down the user input into structured components like industry, geography, trends, and challenges.

  🌐 Deep Research (Perplexity Sonar):
  Performs live research to retrieve relevant industry data, consumer trends, competitive insights, and more.

  📘 Business Case Writer (Claude Sonnet):
  Synthesizes the findings into a detailed case study with sections including:
  Executive Summary
  Market Overview
  Opportunity Analysis
  Competitive Landscape
  Risks & Challenges
  Strategic Recommendations
  Conclusion
  📄 Google Docs Integration:
  The final output is appended to a connected Google Doc, so all your insights are neatly stored and ready to share.
  🧰 Tools Used:
  OpenAI GPT-4o
  Perplexity Sonar Deep Research
  Anthropic Claude Sonnet
  Google Docs
  Chat Trigger
  ✅ Ideal For:
  Business consultants & strategy teams
  Market researchers & analysts
  Startup founders & product managers
  Educators & MBA students

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatAnthropic","n8n-nodes-base.googleDocs","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.perplexity","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","AI research","business strategy","document generation","web research"],"complexity":"medium","use_cases":["Market research","Business planning","Strategic analysis","Competitive analysis","Automated report generation"],"estimated_setup_minutes":45,"required_services":["OpenAI (GPT-4o)","Perplexity Sonar (live web research)","Anthropic Claude Sonnet","Google Docs API / Google account","n8n self-hosted with community nodes","LangChain Chat Trigger"],"key_features":["Breaks user prompt into research scope (GPT-4o)","Performs live web research (Perplexity Sonar)","Synthesizes findings into a structured ~1500-word business case (Claude)","Appends final report to Google Docs automatically","Multi-model orchestration (GPT, Perplexity, Anthropic) integrated in one workflow"],"target_audience":["Business consultants","Market researchers & analysts","Startup founders & product managers」「Educators & MBA students"]}


name: Scrape Reuters News & Send AI Summaries with Brightdata, Claude 4 & Telegram
author_name: ankitkansaldev
author_username: ankitkansaldev
categories: []
created_at: 2025-06-28T10:54:11.258Z
updated_at: 2025-06-28T10:54:11.258Z
url: https://n8n.io/workflows/5431
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  📰 Comprehensive Reuters News Intelligence System With Brightdata & Telegram Alerts

  A powerful n8n automation workflow that scrapes the latest Reuters news articles using Bright Data's web scraping capabilities and delivers intelligent news summaries directly to your Telegram chat.

  📋 Overview

  This workflow provides an automated news intelligence solution that monitors Reuters for breaking news, analyzes content using Claude AI, and delivers personalized news alerts. Perfect for journalists, researchers, traders, and anyone who needs real-time access to Reuters content with AI-powered insights.

  ✨ Key Features

  🎯 Form-Based Input: Easy web form to specify keywords and news type preferences  
  🤖 AI-Powered Processing: Uses Claude 4 Sonnet for intelligent content analysis  
  🌐 Professional Scraping: Leverages Bright Data's Reuters dataset for reliable data extraction  
  📱 Telegram Integration: Instant notifications delivered to your preferred chat  
  ⏰ Smart Waiting: Built-in delays to ensure data processing completion  
  🔄 Status Monitoring: Automatic scraping status checks with retry logic  
  📊 Data Formatting: Clean, structured output with essential article fields  
  🚀 Scalable Design: Handles multiple articles with batch processing  

  🎯 What This Workflow Does

  Input
  Keywords**: Search terms for Reuters articles (e.g., "Election", "Gas shocks", "Technology")
  News Type**: Sorting preference (newest, oldest, relevance)
  Form Submission**: Web-based interface for easy interaction

  Processing
  Form Trigger: Captures user input via web form interface
  AI Agent Orchestration: Claude processes requirements and coordinates actions
  Bright Data Request: Initiates Reuters scraping with specified keywords
  Status Monitoring: Checks scraping progress with smart retry logic
  Data Retrieval: Fetches completed article data when ready
  Content Processing: Extracts and formats essential article information
  Telegram Delivery: Sends structured news updates to specified chat

  Output Data Points

  | Field | Description | Example |
  |-------|-------------|---------|
  | article_title | The main headline of the article | "Global Energy Markets Face Uncertainty" |
  | headline | Reuters display headline | "Oil Prices Surge Amid Supply Concerns" |
  | description | Article summary/meta description | "Energy markets react to geopolitical tensions..." |
  | content | Full article body text | "LONDON (Reuters) - Oil prices jumped 3%..." |
  | article_url | Direct link to Reuters article | "https://reuters.com/business/energy/..." |

  🚀 Setup Instructions

  Prerequisites
  n8n instance (self-hosted or cloud)
  Bright Data account with Reuters dataset access
  Telegram bot and channel setup
  Claude API access (Anthropic)
  15-20 minutes for complete setup

  Step 1: Import the Workflow
  Copy the JSON workflow code from the provided file
  In n8n: Workflows → + Add workflow → Import from JSON
  Paste JSON content and click Import
  Save the workflow with a descriptive name

  Step 2: Configure Bright Data Integration
  Set up Bright Data credentials:
     In n8n: Credentials → + Add credential → HTTP Header Auth
     Name: "Bright Data API"
     Add header: Authorization: Bearer YOUR_BRIGHT_DATA_API_KEY
     Test the connection

  Configure Reuters dataset:
     Ensure access to dataset ID: gd_lyptx9h74wtlvpnfu
     Verify Reuters scraping permissions in Bright Data dashboard
     Check monthly quota and usage limits

  Step 3: Configure Anthropic Claude Integration
  Set up Anthropic credentials:
     In n8n: Credentials → + Add credential → Anthropic API
     Enter your Anthropic API key
     Test the connection

  Update model settings:
     Open "Anthropic Chat Model" node
     Verify model is set to: claude-sonnet-4-20250514
     Adjust temperature and other parameters if needed

  Step 4: Configure Telegram Notifications
  Create Telegram Bot:
     Message @BotFather on Telegram
     Use /newbot command and follow instructions
     Save the bot token provided

  Get Chat ID:
     Add your bot to desired channel/group
     Send a test message
     Visit: https://api.telegram.org/bot{BOT_TOKEN}/getUpdates
     Find your chat ID in the response

  Set up Telegram credentials:
     In n8n: Credentials → + Add credential → Telegram API
     Enter bot token from BotFather
     Test the connection

  Update Telegram node:
     Open "Telegram" node
     Replace DEMO_CHAT_ID with your actual chat ID
     Customize message format if needed

  Step 5: Configure Web Form
  Set up form trigger:
     Open "On form submission" node
     Note the webhook URL provided
     Customize form title and fields if needed

  Test form functionality:
     Access the webhook URL in your browser
     Fill out test form with sample keywords
     Verify form submission triggers workflow

  Step 6: Update Node Configurations
  Update HTTP Request nodes:
     Replace BRIGHT_DATA_API_KEY with actual credentials reference
     Verify dataset ID matches your Bright Data setup
     Check request parameters and headers

  Configure Data Formatting:
     Open "Data Formatting" node
     Review JavaScript code for field extraction
     Modify output fields if additional data needed

  Step 7: Test & Activate
  Run initial test:
     Submit form with test keywords (e.g., "Technology")
     Monitor workflow execution in n8n
     Check for Telegram message delivery

  Verify data flow:
     Confirm Bright Data snapshot creation
     Check status monitoring functionality
     Validate final data formatting

  Activate workflow:
     Toggle workflow to "Active" status
     Monitor for any execution errors
     Set up error notifications if needed

  📖 Usage Guide

  Submitting News Requests
  Access the form:
     Navigate to your webhook URL
     Form title: "Reuters News Intelligence"

  Fill required fields:
     Keywords: Enter search terms (e.g., "Climate Change", "Tech Earnings")
     News Type: Select sorting preference:
       newest: Most recent articles first
       oldest: Historical articles first  
       relevance: Best matching articles

  Submit and wait:
     Click submit to trigger workflow
     Expect 1-3 minutes for processing
     Check Telegram for article delivery

  Understanding the Process
  The workflow follows this sequence:
  Form submission triggers Claude AI agent
  Claude coordinates all scraping and processing steps
  Bright Data scrapes Reuters with your keywords
  System waits for scraping completion (60 seconds)
  Status check confirms data readiness
  Article data is retrieved and formatted
  Telegram message delivers final results

  Reading Telegram Results
  Each article includes:
  Clickable URL** to full Reuters article
  Headline** for quick scanning
  Description** with article summary
  Content preview** with key details

  🔧 Customization Options

  Modifying Search Parameters
  Edit the "HTTP Request" node to adjust:
  {
    "keyword": "Your search terms",
    "sort": "newest|oldest|relevance",
    "limit_per_input": "2-10 articles"
  }

  Customizing Telegram Messages
  Update the "Telegram" node message format:
  🗞️ {{ $json.heading }}

  📖 {{ $json.description }}

  🔗 Read Full Article

  📅 Retrieved: {{ $now.format('YYYY-MM-DD HH:mm') }}

  Adding Email Notifications
  Add "Email" node after "Data Formatting"
  Configure SMTP credentials
  Create HTML email template with article data
  Connect to same input as Telegram node

  Enhancing AI Processing
  Modify the MCP Agent prompt to:
  Request specific article sections
  Add sentiment analysis
  Include market impact assessment
  Generate executive summaries
  Extract key quotes and statistics

  Adding Data Storage
  Include database storage by:
  Adding "Postgres" or "MySQL" node
  Creating articles table with schema
  Storing full article data for analysis
  Building historical news database

  🚨 Troubleshooting

  Common Issues & Solutions

  1. "Bright Data snapshot failed"
  Cause**: Invalid API key or dataset access
  Solution**: Verify credentials and dataset permissions in Bright Data dashboard

  2. "No articles found"
  Cause**: Keywords too specific or no matching content
  Solution**: Try broader search terms, check Reuters availability

  3. "Telegram message not sent"
  Cause**: Invalid bot token or chat ID
  Solution**: Re-verify bot setup with @BotFather, confirm chat ID

  4. "Workflow timeout"
  Cause**: Bright Data scraping taking too long
  Solution**: Increase timeout in "sleep tool" or add retry logic

  5. "Data formatting errors"
  Cause**: Unexpected response structure from Bright Data
  Solution**: Check "Data Formatting" node logs, adjust parsing logic

  6. "Claude API errors"
  Cause**: API key issues or rate limiting
  Solution**: Verify Anthropic credentials, check usage limits

  Advanced Troubleshooting
  Monitor execution logs** in n8n for detailed error messages
  Test individual nodes** by running them separately
  Verify JSON structures** ensure data flows correctly between nodes
  Check rate limits** for both Bright Data and Claude API
  Add error handling** implement try-catch logic for robust operation

  📊 Use Cases & Examples

  1. Financial News Monitoring
  Goal: Track market-moving Reuters financial news
  Keywords: "earnings", "fed rates", "market outlook"
  Instant alerts for breaking financial news
  Support trading and investment decisions

  2. Competitive Intelligence
  Goal: Monitor industry-specific news for business insights
  Keywords: Company names, industry terms
  Track competitor mentions and market developments
  Generate competitive analysis reports

  3. Crisis Communications
  Goal: Stay informed during breaking news events
  Keywords: "breaking", location names, event types
  Rapid response to developing situations
  Crisis management team notifications

  4. Research & Academia
  Goal: Gather news data for academic research
  Keywords: Research topics, geographic regions
  Build datasets for media analysis
  Track news coverage patterns over time

  ⚙ Advanced Configuration

  Scaling for High Volume
  To handle larger news monitoring needs:

  Increase batch processing:
     Modify limit_per_input parameter
     Add parallel processing branches
     Implement queue management

  Add rate limiting:
     Insert delays between requests
     Monitor API usage quotas
     Implement exponential backoff

  Database integration:
     Store articles in PostgreSQL/MySQL
     Add deduplication logic
     Create search and filter capabilities

  Multi-Channel Distribution
  Expand beyond Telegram:

  Slack integration:
     Add Slack webhook node
     Format messages for team channels
     Include interactive buttons

  Email newsletters:
     Compile daily/weekly summaries
     HTML formatting with images
     Subscriber management

  API endpoints:
     Create webhook responses
     Build news API for other systems
     Real-time data streaming

  AI Enhancement Options
  Leverage Claude's capabilities further:

  Sentiment analysis:
     Add sentiment scoring to articles
     Track market sentiment trends
     Generate mood indicators

  Summarization:
     Create executive summaries
     Extract key points
     Generate abstracts

  Classification:
     Categorize articles by topic
     Tag with relevant industries
     Priority scoring system

  📈 Performance & Limits

  Expected Performance
  Single request**: 60-120 seconds average processing time
  Articles per request**: 2-10 (configurable)
  Data accuracy**: 95%+ for standard Reuters articles
  Success rate**: 90%+ for accessible content
  Daily capacity**: Limited by Bright Data quotas

  Resource Usage
  Memory**: ~200MB per execution
  API calls**: 1 Bright Data + 1 Claude + 1 Telegram per execution
  Bandwidth**: ~5-10MB per article scraped
  Execution time**: 1-3 minutes per request

  Scaling Considerations
  Rate limiting**: Respect API quotas and limits
  Error handling**: Implement comprehensive retry logic
  Data validation**: Verify article quality and completeness
  Cost monitoring**: Track API usage across services
  Performance optimization**: Cache common requests when possible

  🤝 Support & Community

  Getting Help
  n8n Community**: community.n8n.io
  Bright Data Support**: Contact through dashboard
  Anthropic Documentation**: docs.anthropic.com
  Telegram Bot API**: core.telegram.org/bots

  Contributing
  Share workflow improvements with the community
  Report issues and suggest enhancements  
  Create variations for specific news sources
  Document best practices and optimizations

  📋 Quick Setup Checklist

  Before You Start
  ☐ n8n instance running (self-hosted or cloud)  
  ☐ Bright Data account with Reuters dataset access  
  ☐ Anthropic API key for Claude access  
  ☐ Telegram bot created via @BotFather  
  ☐ 20 minutes for complete setup  

  Setup Steps
  ☐ Import Workflow - Copy JSON and import to n8n  
  ☐ Configure Bright Data - Set up API credentials and test  
  ☐ Configure Claude - Add Anthropic API credentials  
  ☐ Setup Telegram - Create bot and get chat ID  
  ☐ Update Credentials - Replace all demo values with real ones  
  ☐ Test Form - Submit test request and verify flow  
  ☐ Check Telegram - Confirm message delivery  
  ☐ Activate Workflow - Turn on for production use  

  Ready to Use! 🎉
  Your workflow form URL: https://your-n8n-instance.com/webhook/your-webhook-id

  🎯 Happy News Monitoring! 

  This workflow provides a solid foundation for automated Reuters news intelligence. Customize it to fit your specific monitoring needs and use cases. The combination of Bright Data's reliable scraping, Claude's AI analysis, and Telegram's instant delivery creates a powerful news monitoring solution.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatAnthropic","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.formTrigger","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.toolCode","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.code"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","web scraping","data processing","AI processing"],"complexity":"complex","use_cases":["Real-time news monitoring and alerts","Financial/market news monitoring","Competitive intelligence","Crisis communications and rapid response","Research and academic news collection"],"estimated_setup_minutes":20,"required_services":["n8n instance (self-hosted or cloud)","Bright Data (Reuters dataset)","Anthropic Claude API (claude-sonnet-4)","Telegram Bot API"],"key_features":["Form-based input for search keywords and sort preferences","Bright Data Reuters scraping with status monitoring and retry logic","Claude 4 (Anthropic) AI-powered summarization and analysis","Telegram notification delivery of structured article summaries","Batch processing and scalable design (limit_per_input configurable)」「Data formatting and extraction of article fields (title, headline, description, content, url)"],"target_audience":["Journalists and newsrooms","Researchers and academics","Traders and financial analysts」「Competitive intelligence and corporate strategy teams"]}


name: Centralize your forms and reply automatically with Tally + Airtable + Gmail
author_name: Baptiste Fort
author_username: baptistefort
categories: []
created_at: 2025-06-28T12:58:39.946Z
updated_at: 2025-06-28T12:58:39.946Z
url: https://n8n.io/workflows/5433
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Still manually copy-pasting your Tally form responses?

  What if every submission went straight into Airtable — and the user got an automatic email right after?

  That’s exactly what this workflow does.
  No code, no headache — just a simple and fast automation:

  Tally → Airtable → Gmail.

  STEP 1 — Capture Tally Form Responses

  Goal
  Trigger the workflow automatically every time someone submits your Tally form.

  What we're setting up
  A webhook that catches form responses and kicks off the rest of the flow.

  Steps to follow
  Add a Webhook node

  Parameter :	Value
  Method :	POST
  Path :	formulaire-tally
  Authentication : None
  Respond : Immediately


  Save the workflow
  → This will generate a URL like:

  https://your-workspace.n8n.cloud/webhook-test/formulaire-tally
  💡 Use the Test URL first (found under Parameters &gt; Test URL)

  Head over to Tally
  Go to your form → Form Settings &gt; Integrations &gt; Webhooks
  Paste the Test URL into the Webhook field
  Enable the webhook ✅

  Submit a test entry
  → Tally won’t send anything until a real submission is made.
  This step is required for n8n to capture the structure.

  Expected output



  n8n receives a JSON object containing:

  General info (IDs, timestamps, etc.)

  A fields[] array with all the form inputs (name, email, etc.)

  Each field is nicely structured with a label, key, type, and most importantly, a value.

  Perfect foundation for the next step: data cleanup.

  STEP 2 — Clean and Structure the Form Data (Set node)

  Goal

  Take the raw data sent by Tally and turn it into clean, readable JSON that's easy to use in the rest of the workflow.

  Tally sends the responses inside a big array called field.

  Can you grab a field directly with something like {{$json"fields"["value"]}}? Yes.

  But a good workflow is like a sock drawer — when everything’s folded and labeled, life’s just easier.

  So we’re going to clean it up using a Set node.

  Steps to follow

  Add a Set node right after the Webhook.

  Enable the “Keep only set” option.

  Define the following fields in the Set node:

  Field name:	Expression
  full_name:	{{$json"fields"["value"]}}
  company_name:	{{$json"fields"["value"]}}
  job_title:	{{$json"fields"["value"]}}
  email:	{{$json"fields"["value"]}}
  phone_number:	{{$json"fields"["value"] ?? ""}}
  submission_date:	{{$now.toISOString()}}

  ⚠️ The order of fields[] depends on your Tally form. If you change the question order, make sure to update these indexes accordingly.

  Expected output

  You’ll get a clean, structured JSON like this:



  Now your data is clear, labeled, and ready for the rest of your workflow.

  STEP 3 — Save Data in Airtable

  Goal

  Every time someone submits your Tally form, their info is automatically added to an Airtable base. No more copy-pasting — everything lands right where it should.

  Steps to follow
  Create your Airtable base
  Start by creating a base named Leads (or whatever you prefer), with a table called Form Submissions.

  Add the following columns in this exact order so everything maps correctly later:

  Generate an Airtable token
  So n8n can send data into your base:

  Go to 👉 [ https://airtable.com/create/tokens](https://airtable.com/create/tokens
  )

  Click Create token

  Give it a name (e.g. Tally Automation)

  Check the following permissions:

  data.records:read

  data.records:write

  schema.bases:read

  Under Base access, either choose your base manually or select “All current and future bases”

  Click Create token and copy the generated key

  Add configure the Airtable node in n8n

  Node: Airtable

  Operation: Create

  Authentication: Personal Access Token

  Paste your token

  n8n will suggest your base and table (or you can manually grab the IDs from the URL:
  https://airtable.com/appXXXXXXXX/tblYYYYYYYY/...)

  Map your fields
  Inside the Airtable node, add the following field mappings:




  Every new Tally form submission automatically creates a new row in your Airtable base.

  STEP 4 — Send an Automatic Confirmation Email

  Goal

  Send a professional email as soon as a form is completed

  Steps to follow

  Add a Wait node

  You don’t want the email to go out instantly — it feels cold and robotic.
  → Add a Wait node right after Airtable.

  Mode: Wait for a period of time

  Delay: 5 to 10 minutes

  Unit: Minutes

  Add a Gmail &gt; Send Email node
  Authentication: OAuth2

  Connect a Gmail account (business or test)
  ⚠️ No API keys here — Gmail requires OAuth.

  Configure the Send Email node



  Field	Value

  Credential to connect with Gmail account via OAuth2

  Resource :	Message
  Operation :	Send
  To :	{{ $json.fields["Email"] }}
  Subject : Thanks for reaching out!
  Email Type : HTML
  Message: (but do the mapping correctly using the Input so that lead receives its name correctly )

  End of the Workflow

  And that’s it — your automation is live!

  Your lead fills out the Tally form → the info goes to Airtable → they get a clean, professional email without you doing a thing.

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.wait","n8n-nodes-base.webhook","n8n-nodes-base.airtable","n8n-nodes-base.gmail","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, 5916 characters]

metadata_json:
  {"categories":["automation","integration","data processing","lead management","CRM"],"complexity":"medium","use_cases":["Automatically capture form responses into a database","Send confirmation emails to form submitters","Centralize lead data for follow-up and analytics","Replace manual copy-paste form handling"],"estimated_setup_minutes":20,"required_services":["n8n (cloud or self-hosted)","Tally (form with webhook support)","Airtable (base + Personal Access Token)","Gmail (OAuth2 account for sending email)"],"key_features":["Webhook trigger to capture Tally submissions","Set node to clean and structure form data","Create records automatically in an Airtable base","Wait node to delay email for a human touch","Gmail Send Email via OAuth2 for confirmations (HTML support)"],"target_audience":["marketers","sales teams / SDRs","operations / growth teams at SMBs and startups ￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼"]}


name: Scrape TikTok Influencer Profiles with Bright Data API to Google Sheets
author_name: ankitkansaldev
author_username: ankitkansaldev
categories: []
created_at: 2025-06-28T13:13:34.718Z
updated_at: 2025-06-28T13:13:34.718Z
url: https://n8n.io/workflows/5434
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  🎬 TikTok Influencer Scraper (URL Input) via Bright Data + n8n & Sheets

  A comprehensive n8n automation that scrapes TikTok influencer profiles using Bright Data's TikTok dataset and automatically saves detailed profile information to Google Sheets.

  📋 Overview

  This workflow provides an automated TikTok influencer data collection solution that scrapes comprehensive profile information and saves it to Google Sheets. Perfect for influencer marketing research, competitor analysis, social media monitoring, and marketing campaign planning.

  ✨ Key Features

  📝 Form-Based Input: Simple web form to submit TikTok profile URLs
  🤖 Bright Data Integration: Uses Bright Data's TikTok dataset for reliable scraping
  ⏳ Status Monitoring: Intelligent polling system to check scraping progress
  🔄 Retry Logic: Automatic retry mechanism with 30-second intervals
  📊 Data Extraction: Comprehensive profile data including engagement metrics
  📈 Google Sheets Storage: Automatic data storage and organization
  ⚡ Error Handling: Built-in error handling and status reporting
  🎯 Custom Fields: Configurable output fields for specific data needs

  🎯 What This Workflow Does

  Input
  Profile URLs**: TikTok profile URLs submitted through web form
  Custom Fields**: Configurable data fields for extraction
  Country Settings**: Geo-targeting for accurate data collection

  Processing
  Form Submission: User submits TikTok profile URL through web form
  API Trigger: Sends profile data to Bright Data for scraping
  Status Polling: Continuously checks scraping progress
  Wait & Retry: Implements 30-second delays between status checks
  Data Retrieval: Fetches complete profile data when ready
  Sheet Update: Saves extracted data to Google Sheets
  Status Reporting: Provides completion status and messages

  Output Data Points

  | Field | Description | Example |
  |-------|-------------|---------|
  | Account ID | Unique TikTok account identifier | @username123 |
  | Nickname | Display name on profile | "John Doe" |
  | Biography | Profile bio/description | "Content creator & influencer" |
  | Followers | Number of followers | 1,250,000 |
  | Following | Number of accounts following | 500 |
  | Likes | Total likes across all videos | 50,000,000 |
  | Videos Count | Total number of videos posted | 1,200 |
  | Profile URL | Direct link to TikTok profile | https://www.tiktok.com/@username |
  | Profile Picture | Profile image URL | https://p16-sign-sg.tiktokcdn.com/... |
  | Profile Picture HD | High-definition profile image | https://p16-sign-sg.tiktokcdn.com/... |
  | Is Verified | Verification status | true/false |
  | Bio Link | External link in bio | https://linktr.ee/username |
  | Like Engagement Rate | Engagement rate based on likes | 5.2% |
  | Comment Engagement Rate | Engagement rate based on comments | 2.1% |
  | Top Videos | List of top performing videos | [video_objects] |
  | Region | Geographic region | "US" |
  | Is Under Age 18 | Age status indicator | true/false |

  🚀 Setup Instructions

  Prerequisites
  n8n instance (self-hosted or cloud)
  Google account with Sheets access
  Bright Data account with TikTok dataset access
  Valid TikTok profile URLs for testing
  10-15 minutes for setup

  Step 1: Import the Workflow
  Copy the JSON workflow code from the provided file
  In n8n: Workflows → + Add workflow → Import from JSON
  Paste JSON and click Import

  Step 2: Configure Bright Data
  Set up Bright Data credentials:
     In n8n: Credentials → + Add credential → HTTP Request Generic Credential
     Name: "Bright Data API"
     Authentication: Bearer Token
     Token: Your Bright Data API key
     Test the connection

  Configure dataset:
     Ensure you have access to TikTok dataset (gd_l1villgoiiidt09ci)
     Verify dataset permissions in Bright Data dashboard
     Check dataset limits and pricing

  Step 3: Configure Google Sheets Integration
  Create a Google Sheet:
     Go to Google Sheets
     Create a new spreadsheet named "TikTok Influencer Data"
     Create a sheet tab named "TikTok profile by url"
     Copy the Sheet ID from URL: https://docs.google.com/spreadsheets/d/SHEET_ID_HERE/edit

  Set up Google Sheets credentials:
     In n8n: Credentials → + Add credential → Google Sheets OAuth2 API
     Complete OAuth setup and test connection

  Prepare your data sheet with columns:
     Column A: Account ID
     Column B: Nickname
     Column C: Biography
     Column D: Followers
     Column E: Following
     Column F: Likes
     Column G: Videos Count
     Column H: Profile URL
     Column I: Is Verified
     Column J: Bio Link
     Column K: Like Engagement Rate
     Column L: Comment Engagement Rate
     Column M: Region
     Column N: Status
     Column O: Message

  Step 4: Update Workflow Settings
  Update API credentials:
     Open "Sends profile URLs to Bright Data to trigger scraping" node
     Replace BRIGHT_DATA_API_KEY with your actual API key
     Update dataset ID if different

  Update Google Sheets nodes:
     Open "Google Sheets" node
     Replace document ID: 1OeqtCFm4Wek9DI5YFOWQXTpQJS-SJxC10iAPKEKkmiY
     Select your Google Sheets credential
     Choose the correct sheet/tab name

  Configure form settings:
     Open "Search by Profile URL" node
     Customize form title and field labels as needed
     Note the webhook URL for form access

  Step 5: Test & Activate
  Add test profiles:
     Access the form using the webhook URL
     Submit 1-2 TikTok profile URLs for testing
     Use full URLs (e.g., https://www.tiktok.com/@username)

  Test the workflow:
     Submit a test profile through the form
     Monitor execution in n8n
     Verify data appears in Google Sheet
     Check for any error messages

  📖 Usage Guide

  Submitting TikTok Profiles
  Navigate to your form URL (found in Form Trigger node)
  Enter TikTok profile URL in the format: https://www.tiktok.com/@username
  Click Submit to start the scraping process
  Wait for processing (typically 1-3 minutes)

  Understanding the Process
  The workflow follows this sequence:
  Form Submission → Profile URL captured
  API Trigger → Scraping job submitted to Bright Data
  Status Polling → Checks every 30 seconds if data is ready
  Data Retrieval → Fetches complete profile information
  Sheet Update → Saves data to Google Sheets

  Monitoring Progress
  Check n8n execution logs for real-time status
  Bright Data dashboard shows scraping progress
  Google Sheets will populate when data is ready
  Status column shows "ready" when complete

  Reading the Results
  Your Google Sheet will show:
  Complete TikTok profile information
  Engagement metrics and statistics
  Profile verification status
  Bio links and external connections
  Timestamp of data collection

  🔧 Customization Options

  Adding More Data Points
  Edit the JSON body in "Sends profile URLs to Bright Data" node to include additional fields:

  "custom_output_fields": [
    "account_id",
    "nickname",
    "biography",
    "followers",
    "following",
    "likes",
    "videos_count",
    "language",
    "creation_time",
    "last_post_time",
    "avg_video_duration",
    "hashtags_used",
    "music_used"
  ]

  Modifying Input Parameters
  Customize the scraping parameters:
  Country targeting**: Change "country" field in input
  Search limits**: Adjust "limit_per_input" value
  Discovery method**: Modify "discover_by" parameter
  Error handling**: Toggle "include_errors" setting

  Batch Processing Multiple Profiles
  To process multiple profiles simultaneously:
  Modify the input array in the API call
  Add multiple profile URLs in single request
  Implement loop logic for processing results
  Add rate limiting between requests

  Custom Form Fields
  Enhance the form with additional inputs:
  Open "Search by Profile URL" node
  Add form fields for:
     Country selection
     Number of videos to analyze
     Specific date ranges
     Custom tags or categories

  🚨 Troubleshooting

  Common Issues & Solutions

  "Bright Data connection failed"
     Cause: Invalid API credentials or dataset access
     Solution: Verify API key in Bright Data dashboard, check dataset permissions

  "Profile not found or private"
     Cause: Invalid TikTok URL or private profile
     Solution: Verify profile URL format, ensure profile is public

  "Google Sheets permission denied"
     Cause: Incorrect credentials or sheet permissions
     Solution: Re-authenticate Google Sheets, check sheet sharing settings

  "Scraping timeout"
     Cause: Profile data taking too long to process
     Solution: Increase wait time or implement longer polling intervals

  "Invalid dataset ID"
     Cause: Incorrect or expired dataset configuration
     Solution: Check Bright Data dashboard for correct dataset ID

  "Form submission failed"
     Cause: Webhook configuration issues
     Solution: Verify webhook URL and form trigger settings

  Advanced Troubleshooting
  Check execution logs** in n8n for detailed error messages
  Test individual nodes** by running them separately
  Verify data formats** ensure URLs are properly formatted
  Monitor API limits** check Bright Data usage quotas
  Add error handling** implement try-catch logic for robust operation

  📊 Use Cases & Examples

  1. Influencer Marketing Research
  Goal: Identify and analyze potential influencers for campaigns
  Research influencers in specific niches
  Analyze engagement rates and audience size
  Compare multiple influencers for campaign selection
  Track influencer growth over time

  2. Competitive Analysis
  Goal: Monitor competitors' TikTok presence and performance
  Track competitor follower growth
  Analyze content strategies and engagement
  Monitor posting frequency and timing
  Identify trending content themes

  3. Social Media Monitoring
  Goal: Track brand mentions and user-generated content
  Monitor branded hashtag usage
  Track brand advocates and micro-influencers
  Analyze sentiment and engagement patterns
  Identify trending topics in your industry

  4. Market Research Pipeline
  Goal: Gather social media intelligence for business decisions
  Analyze target audience behavior
  Study content preferences and trends
  Generate reports for stakeholders
  Support marketing strategy development

  ⚙ Advanced Configuration

  Rate Limiting and Performance
  To optimize for large-scale scraping:
  Adjust wait times between status checks
  Implement exponential backoff for retries
  Add batch processing for multiple profiles
  Monitor API usage to avoid limits

  Data Validation and Cleaning
  Enhance data quality with validation:
  Add data type validation for numeric fields
  Implement URL format checking
  Clean and standardize text fields
  Add data completeness checks

  Integration with Business Tools
  Connect the workflow to your existing systems:
  CRM Integration**: Update customer records with influencer data
  Slack Notifications**: Send alerts when new data is available
  Database Storage**: Store data in PostgreSQL/MySQL for analysis
  BI Tools**: Connect to Tableau/Power BI for visualization

  Webhook Integration
  For real-time updates:
  Add webhook triggers for immediate profile checks
  Integrate with external systems via webhooks
  Create API endpoints for programmatic access
  Implement authentication for secure access

  📈 Performance & Limits

  Expected Performance
  Single Profile**: 30-60 seconds average processing time
  Concurrent Requests**: 5-10 simultaneous (depends on Bright Data plan)
  Data Accuracy**: 95%+ for public TikTok profiles
  Success Rate**: 90%+ for accessible profiles
  Daily Capacity**: 100-1000 profiles (depends on rate limits)

  Resource Usage
  Memory**: ~50MB per execution
  Storage**: Minimal (data stored in Google Sheets)
  API Calls**: 3-5 Bright Data calls per profile (including status checks)
  Bandwidth**: ~1-2MB per profile scraped
  Execution Time**: 1-2 minutes per profile

  Scaling Considerations
  Rate Limiting**: Add delays for high-volume scraping
  Error Handling**: Implement retry logic for failed requests
  Data Validation**: Add checks for malformed profile data
  Monitoring**: Track success/failure rates over time
  Cost Optimization**: Monitor API usage to control costs

  🤝 Support & Community

  Getting Help
  n8n Community Forum**: community.n8n.io
  Documentation**: docs.n8n.io
  Bright Data Support**: Contact through your dashboard
  GitHub Issues**: Report bugs and feature requests

  Contributing
  Share improvements with the community
  Report issues and suggest enhancements
  Create variations for specific use cases
  Document best practices and lessons learned

  📋 Quick Setup Checklist

  Before You Start
  ☐ n8n instance running (self-hosted or cloud)  
  ☐ Google account with Sheets access  
  ☐ Bright Data account with TikTok dataset access  
  ☐ Valid TikTok profile URLs for testing  
  ☐ 15 minutes for setup  

  Setup Steps
  ☐ Import Workflow - Copy JSON and import to n8n  
  ☐ Configure Bright Data - Set up API credentials and test  
  ☐ Create Google Sheet - New sheet with proper column structure  
  ☐ Set up Google Sheets credentials - OAuth setup and test  
  ☐ Update workflow settings - Replace sheet ID and API keys  
  ☐ Test with sample profiles - Submit 1-2 URLs and verify results  
  ☐ Activate workflow - Enable form trigger for production use  

  Ready to Use! 🎉
  Your form URL: https://your-n8n-instance.com/form/[webhook-id]

  🎯 Happy TikTok Scraping! 

  This workflow provides a solid foundation for automated TikTok influencer data collection. Customize it to fit your specific needs and use cases for influencer marketing, competitive analysis, and social media research.

nodes_used:
  ["n8n-nodes-base.googleSheets","n8n-nodes-base.formTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data processing","social media scraping","marketing"],"complexity":"medium","use_cases":["Influencer marketing research","Competitive analysis","Social media monitoring","Market research and reporting","CRM enrichment / lead generation"],"estimated_setup_minutes":15,"required_services":["Bright Data API (TikTok dataset)","Google Sheets (Google OAuth2 API)","n8n instance (self-hosted or cloud)"],"key_features":["Form-based URL input (webhook)","Bright Data TikTok dataset integration","Status polling with retry logic (30s intervals)","Comprehensive profile data extraction (followers, likes, videos, engagement rates, etc.)","Automatic Google Sheets storage and mapping to columns (append/update) and status reporting columns (Status, Message) ]"],"target_audience":["influencer marketers","social media analysts","growth teams and marketers at agencies or startups ]"]}


name: AI Agent to chat with Snowflake database
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2025-06-28T13:33:34.514Z
updated_at: 2025-06-28T13:33:34.514Z
url: https://n8n.io/workflows/5435
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  Video Guide

  I prepared a detailed guide showcasing the process of building an AI agent that interacts with a Snowflake database using n8n. This setup enables conversational querying, secure execution of SQL queries, and dynamic report generation with rich visualization capabilities.



  Youtube Link

  Who is this for?

  This workflow is designed for developers, data analysts, and business professionals who want to interact with their Snowflake data conversationally. It suits users looking to automate SQL query generation with AI, manage large datasets efficiently, and produce interactive reports without deep technical knowledge.

  What problem does this workflow solve?

  Querying Snowflake databases typically requires SQL proficiency and can lead to heavy token usage if large datasets are sent to AI models directly. This workflow addresses these challenges by:

  Guiding AI to generate accurate SQL queries based on user chat input while referencing live database schema to avoid errors.
  Executing queries safely on Snowflake with proper credential management.
  Aggregating large result sets to reduce token consumption.
  Offering a user-friendly report link with pagination, filtering, charts, and CSV export instead of returning overwhelming raw data.
  Providing an error-resilient environment that prompts regenerations for SQL errors or connectivity issues.

  What this workflow does

  The scenario consists of multiple focused n8n workflows orchestrated for smooth, secure, and scalable interactions:

  Agent Workflow  
     Starts with a chat node and sets the system role as "Snowflake SQL assistant."  
     AI generates SQL after verifying database schema and table definitions to avoid hallucinations.  
     Reinforcement rules ensure schema validation before query creation.

  Data Retrieval Workflow  
     Receives SQL queries from the agent workflow.  
     Executes them against the Snowflake database using user-provided credentials (hostname, account, warehouse, database, schema, username, password).  
     Optionally applies safety checks on SQL to prevent injection attacks.

  Aggregation and Reporting Decision  
     Aggregates returned data into arrays for efficient processing.  
     Applies a threshold (default 100 records) to decide whether to return raw data or generate a dynamic report link.  
     Prepares report links embedding URL-encoded SQL queries to securely invoke a separate report workflow.

  Report Viewing Workflow  
     Triggered via webhook from the report link.  
     Re-executes SQL queries to fetch fresh data.  
     Displays data with pagination, column filtering, and selectable chart visualizations.  
     Supports CSV export and custom HTML layouts for tailored user experience.  
     Provides proper error pages in case of SQL or data issues.

  Schema and Table Definition Retrieval Tools  
     Two helper workflows that fetch the list of tables and column metadata from Snowflake.  
     Require the user to replace placeholders with actual database and data source names.  
     Crucial for AI to maintain accurate understanding of the database structure.

  N8N Workflow

  Preparation  
  Create your Snowflake credentials in n8n with required host and account details, warehouse (e.g., "computer_warehouse"), database, schema, username, and password.  
  Replace placeholder variables in schema retrieval workflows with your actual database and data source names.  
  Verify the credentials by testing the connection; reset passwords if needed.

  Workflow Logic  
  The Agent Workflow listens to user chats, employs system role "Snowflake SQL assistant," and ensures schema validation before generating SQL queries.  
  Generated SQL queries pass to the Data Retrieval Workflow, which executes them against Snowflake securely.  
  Retrieved data is aggregated and evaluated against a configurable threshold to decide between returning raw data or creating a report link.  
  When a report link is generated, the Report Viewing Workflow renders a dynamic interactive HTML-based report webpage, including pagination, filters, charts, and CSV export options.  
  Helper workflows periodically fetch or update the current database schema and table definitions to maintain AI accuracy and prevent hallucinations in SQL generation.  
  Error handling mechanisms provide user-friendly messages both in the agent chat and report pages when issues arise with SQL or connectivity.

  This modular, secure, and extensible setup empowers you to build intelligent AI-driven data interactions with Snowflake through n8n automations and custom reporting.

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.snowflakeTool","n8n-nodes-base.snowflakeTool","n8n-nodes-base.webhook","n8n-nodes-base.set","n8n-nodes-base.respondToWebhook","n8n-nodes-base.snowflake","n8n-nodes-base.aggregate","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.snowflake","n8n-nodes-base.aggregate","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","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"]

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metadata_json:
  {"categories":["automation","integration","data processing","analytics","reporting','ai'.replace("],"complexity":"complex","use_cases":["Conversational querying of Snowflake using natural language","AI-assisted SQL generation with schema validation","Secure execution of SQL against Snowflake","Aggregating large result sets to reduce LLM token usage","Generating interactive, paginated reports with charts and CSV export"],"estimated_setup_minutes":60,"required_services":["Snowflake (account, warehouse, database, schema)","n8n (self-hosted or cloud instance with public webhook URL)","OpenAI or compatible LLM connector (lmChatOpenAi or similar)"],"key_features":["System role set to 'Snowflake SQL assistant' to guide SQL generation","Schema/table metadata lookup to prevent hallucinated SQL","Safe SQL execution against Snowflake with credential management","Aggregation and thresholding to avoid sending large raw datasets to LLMs","Dynamic report links that re-run queries via webhook for fresh data retrieval"],"target_audience":["Developers","Data analysts","Business professionals and product owners"]}


name: Automate a Tally Form: Store with Airtable, Notify via Slack
author_name: Baptiste Fort
author_username: baptistefort
categories: []
created_at: 2025-06-28T16:58:08.775Z
updated_at: 2025-06-28T16:58:08.775Z
url: https://n8n.io/workflows/5437
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  🎯 Workflow Goal

  Still manually checking form responses in your inbox?

  What if every submission landed neatly in Airtable — and you got a clean Slack message instantly?

  That’s exactly what this workflow does.

  No code, no delay — just a smooth automation to keep your team in the loop:

  Tally → Airtable → Slack

  Build an automated flow that:
  receives Tally form submissions,
  cleans up the data into usable fields,
  stores the results in Airtable,
  and automatically notifies a Slack channel.

  Step 1 – Connect Tally to n8n

  What we’re setting up

  A Webhook node in POST mode.

  Technical

  Add a Webhook node.  
  Set it to POST.  
  Copy the generated URL.  
  In Tally → Integrations → Webhooks → paste this URL.  
  Submit a test response on your form to capture a sample structure.

  Step 2 – Clean the data



  After connecting Tally, you now receive raw data inside a fields[] array.

  Let’s convert that into something clean and structured.

  Goal

  Extract key info like Full Name, Email, Phone, etc. into simple keys.

  What we’re doing

  Add a Set node to remap and clean the fields.

  Technical

  Add a Set node right after the Webhook.  
  Add new values (String type) manually:
     Name: Full Name → Value: {{$json"fields"["value"]}}  
     Name: Email → Value: {{$json"fields"["value"]}}  
     Name: Phone → Value: {{$json"fields"["value"]}}  
     (Adapt the indexes based on your form structure.)

  Use the data preview in the Webhook node to check the correct order.

  Output

  You now get clean data like:

  { "Full Name": "Jane Doe",
    "Email": "jane@example.com",
    "Phone": "+123456789" }

  Step 3 – Send to Airtable



  ✅ Once the data is cleaned, let’s store it in Airtable automatically.

  Goal

  Create one new Airtable row for each form submission.

  What we’re setting up

  An Airtable – Create Record node.

  Technical

  Add an Airtable node.  
  Authenticate or connect your API token.  
  Choose the base and table.  
  Map the fields:
     Name: {{$json["Full Name"]}}  
     Email: {{$json["Email"]}}  
     Phone: {{$json["Phone"]}}

  Output

  Each submission creates a clean new row in your Airtable table.

  Step 4 – Add a delay



  ⌛ After saving to Airtable, it’s a good idea to insert a short pause — this prevents actions like Slack messages from stacking too fast.

  Goal

  Wait a few seconds before sending a Slack notification.

  What we’re setting up

  A Wait node for X seconds.

  ✅ Technical
  Add a Wait node.  
  Choose Wait for X minutes.  

  Step 5 – Send a message to Slack



  💬 Now that the record is stored, let’s send a Slack message to notify your team.

  Goal
  Automatically alert your team in Slack when someone fills the form.

  What we’re setting up

  A Slack – Send Message node.

  Technical

  Add a Slack node.  
  Connect your account.  
  Choose the target channel, like #leads.  
  Use this message format:

  New lead received!

  Name: {{$json["Full Name"]}}  

  Email: {{$json["Email"]}}  

  Phone: {{$json["Phone"]}}  


  Output

  Your Slack team is notified instantly, with all lead info in one clean message.

  Workflow Complete

  Your automation now looks like this:  

  Tally → Clean → Airtable → Wait → Slack

  Every submission turns into clean data, gets saved in Airtable, and alerts your team on Slack — fully automated, no extra work.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.airtable","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","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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  [encoded data, 5024 characters]

metadata_json:
  {"categories":["automation","integration","data processing","notifications"],"complexity":"simple","use_cases":["Lead capture and CRM entry","Contact form handling","Customer inquiry routing","Event or registration sign-ups","Team notification for new submissions"],"estimated_setup_minutes":20,"required_services":["Tally (form)","Airtable","Slack","n8n (webhook endpoint)"],"key_features":["Receive Tally form submissions via webhook","Parse and remap fields[] into clean keys","Create a new Airtable record per submission","Configurable short delay before downstream actions","Send formatted Slack notification to a channel"],"target_audience":["Marketers","Sales teams","Operations/ops teams / business admins who manage forms and leads/no-code builders"]}


name: Get IPO Calendar Alerts via Telegram with Finnhub API and Gemini AI
author_name: Malik Hashir
author_username: malikx
categories: []
created_at: 2025-06-28T18:00:48.859Z
updated_at: 2025-06-28T18:00:48.859Z
url: https://n8n.io/workflows/5439
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Get instant notifications about new US companies going public (IPOs) sent straight to your Telegram—easy setup, no programming skills needed. 

  Perfect for investors, traders, and anyone interested in the latest stock market opportunities.

  Purpose & Audience
  This n8n workflow is designed for investors, financial analysts, and community managers who want real-time, structured US companies recent and upcoming IPO (Initial Public Offerings) updates directly to their Telegram channels or chats. It’s perfect for anyone running a financial community, managing a trading group, or investing in new companies for personal investment decisions.

  What It Does
  Fetches recent and upcoming IPO calendar updates for US companies expected to be listed on US exchanges using the Finnhub API (free-tier), with a default window of the next 7 days (customizable to any date range).
  Uses Google Gemini AI to automatically format and structure the calendar data into a clean, grouped, and enhanced message, including company names, tickers, total number of shares, total value of shares, and expected stock price (with intelligent scaling for billions/millions).
  Sends the formatted IPO updates directly to your chosen Telegram chat or channel via a Telegram bot. (can be customised for email updates or any other preferred channel for updates)

  Who Is It For?
  Retail and professional investors
  Financial influencers and Telegram group admins
  Trading desk or community managers
  Anyone who wants hands-off, AI-powered IPO calendar alerts for US companies

  How to Set Up
  Get Finnhub API Key:
  Sign up at https://www.finnhub.io and copy your API key.

  Connect Google Gemini (or OpenAI) Model:
  Add your Gemini API credentials in n8n for advanced AI formatting and structuring of earnings updates.

  Set Up Your Telegram Bot:
  Create a bot with @BotFather, get your API token, and add it to n8n’s Telegram credentials.
  Obtain your Telegram chat or group ID (use @RawDataBot for this).

  Customize Date Range (Optional):
  By default, the workflow fetches upcoming IPOs for the next 7 days, but you can adjust it to any custom window (e.g., today to 3 days in the future).

  Activate the Workflow:
  Schedule how often you want updates (default: every 7 days; can be as frequent as daily).
  The workflow will automatically fetch, format, and send the latest US IPO calendar updates to your Telegram.

  Key Features
  No coding required—fully automated and customizable.
  Handles Telegram’s message length limit by default.
  AI-enhanced formatting for maximum readability and engagement.
  Free to use with Finnhub’s free API tier.

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.set","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.telegram"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","finance","notifications","data-enrichment"],"complexity":"medium","use_cases":["Real-time IPO alerts to Telegram channels/chats","Investor research and deal discovery","Community or trading-group notifications","Automated content generation for newsletters/social posts","Monitoring upcoming US IPOs within a custom date window"],"estimated_setup_minutes":20,"required_services":["Finnhub API (IPO calendar)","Google Gemini (or OpenAI) API for formatting","Telegram Bot API (BotFather + chat/group ID)","n8n instance"],"key_features":["Fetches recent and upcoming US IPOs with configurable date range","AI-powered formatting and grouping of IPO data (company, ticker, shares, value, expected price)","Scheduled automation to send periodic alerts","Respects Telegram message length limits and batches messages","No-code setup in n8n with configurable nodes and schedule"],"target_audience":["Retail investors","Professional investors and financial analysts","Trading desk/community managers and Telegram admins/ moderators of finance groups / influencers"]}


name: Generate Professional Document Drafts from PDFs with Google Drive, GPT-4 & Email Notifications
author_name: Michael Gullo
author_username: mgullo
categories: []
created_at: 2025-06-28T20:41:22.710Z
updated_at: 2025-06-28T20:41:22.710Z
url: https://n8n.io/workflows/5441
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Automate Drafts From Google Drive 

  This workflow automates the end-to-end process of extracting and summarizing information from PDFs stored in a specific Google Drive folder. When a new PDF or any binary data is added, the workflow is triggered and begins by downloading and processing the PDF to extract all available text. If multiple PDFs are detected, their content is aggregated into a single, combined dataset. This automation eliminates the time consuming task of manually reading, taking notes, and drafting documents. By removing this burden, users can focus on more meaningful tasks while the workflow handles the repetitive, tedious work.

  The extracted content is then passed through an AI-powered information extractor that identifies key details such as names, dates, addresses, and any other structured data points the user wants to extract from the PDF. This step is highly customizable, allowing the user to define exactly what type of information should be extracted. While the workflow is designed to extract all available content from the PDF, specifying additional structured data points ensures that critical details are accurately captured. A second OpenAI Node uses the extracted information to draft a professional, formal summary suitable for documentation. This is the most important part of the workflow and can be fully customized to meet the user's specific needs. By editing the prompts, users can tailor the workflow to generate a wide variety of draft formats based on the extracted content. 

  The workflow then generates a new Google Document containing the full draft and composes an email summarizing the key points in 3 to 5 bullet points. This email is automatically sent to the designated recipient along with a direct link to the Google Doc. This solution is ideal for insurance, legal, or administrative use cases where timely, accurate extraction and reporting from incoming PDFs is essential.

  How To Use The Workflow
  Step 1 - Place any binary data (e.g., PDF files) into the designated Google Drive folder.

  Step 2 - The workflow will automatically download each PDF, extract the text, and if multiple PDFs are present combine them into a single dataset for analysis.

  Step 3 - The OpenAI Draft Agent will analyze the extracted information, generate a formal draft, and create a Google Document. This document will be updated with the draft content and saved back into the same Google Drive folder.

  Step 4 - An email will be sent to the designated recipient(s), including a summary of the draft and key extracted information, along with a link to view the Google Document.

  Need Help? Have Questions?
  For consulting and support, or if you have questions, please feel free to connect with me on LinkedIn  or email michael.gullo@outlook.com.

nodes_used:
  ["n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleDocs","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDocs","n8n-nodes-base.set","n8n-nodes-base.gmail"]

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metadata_json:
  {"categories":["automation","document processing","AI summarization","integration","notifications"],"complexity":"medium","use_cases":["Automated PDF-to-draft conversion","Contract or legal document summarization","Insurance claim intake and reporting","Administrative report preparation","Automated notification with document links"],"estimated_setup_minutes":30,"required_services":["Google Drive API","Google Docs API","OpenAI (GPT-4) API","Email service (Gmail/SMTP)","n8n"],"key_features":["Automatically detects and downloads new PDFs from a Drive folder","Extracts and aggregates text from single or multiple PDFs","Customizable structured data extraction (names, dates, addresses, etc.)","Uses GPT-4 to generate professional/formal draft documents","Creates and saves a Google Doc with the draft in Drive','Sends email notifications with 3–5 bullet summary and link to the Google Doc"],"target_audience":["Legal teams","Insurance adjusters and processors","Administrative staff and office managers」「Operations teams and knowledge workers"]}


name: Google Maps Business Scraper & Lead Enricher with Bright Data & Google Gemini
author_name: Ranjan Dailata
author_username: ranjancse
categories: []
created_at: 2025-06-29T03:29:25.021Z
updated_at: 2025-06-29T03:29:25.021Z
url: https://n8n.io/workflows/5443
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  Notice
  Community nodes can only be installed on self-hosted instances of n8n.

  Description

  This workflow automates the process of scraping local business data from Google Maps and enriching it using AI to generate lead profiles. It's designed to help sales, marketing, and outreach teams collect high-quality B2B leads from Google Maps and enrich them with contextual insights without manual data entry.

  Overview

  This workflow scrapes business listings from Google Maps, extracts critical information like name, category, phone, address, and website using Bright Data, and passes the results to Google Gemini to generate enriched summaries and lead insights such as company description, potential services offered, and engagement score. The data is then structured and stored in spreadsheets for outreach.

  Tools Used

  n8n: The core automation engine to manage flow and trigger actions.

  Bright Data: Scrapes business information from Google Maps at scale with proxy rotation and CAPTCHA-solving.

  Google Gemini: Enriches the raw scraped data with smart business summaries, categorization, and lead scoring.

  Google Sheets : For storing and acting upon the enriched leads.

  How to Install

  Import the Workflow: Download the .json file and import it into your n8n instance.

  Set Up Bright Data: Insert your Bright Data credentials and configure the Google Maps scraping proxy endpoint.

  Configure Gemini API: Add your Google Gemini API key (or use via Make.com plugin).

  Customize the Inputs: Choose your target location, business category, and number of results per query.

  Choose Storage: Connect to your preferred storage like Google Sheets.

  Test and Deploy: Run a test scrape and enrichment before deploying for bulk runs.

  Use Cases

  Sales Teams: Auto-generate warm B2B lead lists with company summaries and relevance scores.

  Marketing Agencies: Identify local business prospects for SEO, web development, or ads services.

  Freelancers: Find high-potential clients in specific niches or cities.

  Business Consultants: Collect and categorize local businesses for competitive analysis or partnerships.

  Recruitment Firms: Identify and score potential company clients for talent acquisition.

  Connect with Me

  Email: ranjancse@gmail.com

  LinkedIn: https://www.linkedin.com/in/ranjan-dailata/

  Get Bright Data: Bright Data (Supports free workflows with a small commission)

  #n8n #automation #leadscraping #googlemaps #brightdata #leadgen #b2bleads #salesautomation #nocode #leadprospecting #marketingautomation #googlemapsdata #geminiapi #googlegemini #aiworkflow #scrapingworkflow #businessleads #datadrivenoutreach #crm #workflowautomation #salesintelligence #b2bmarketing

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.function","n8n-nodes-base.readWriteFile","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-mcp.mcpClient","n8n-nodes-mcp.mcpClient","n8n-nodes-base.function","n8n-nodes-base.readWriteFile","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data processing","web scraping","lead enrichment"],"complexity":"complex","use_cases":["Automated B2B lead discovery from Google Maps","Enriching scraped business listings with AI-generated summaries and scores","Populating CRM or outreach spreadsheets with structured leads","Local market research and competitive analysis","High-volume prospecting with proxy-backed scraping"],"estimated_setup_minutes":60,"required_services":["n8n (self-hosted with community nodes enabled)","Bright Data (Google Maps scraping + proxy/CAPTCHA handling)","Google Gemini API (or Make.com Gemini integration)","Google Sheets (or other spreadsheet/storage)"],"key_features":["Scales Google Maps scraping via Bright Data with proxy rotation and CAPTCHA handling","AI enrichment using Google Gemini to generate company descriptions, categorizations and engagement scores","Configurable inputs: location, business category, result limit","Structured output saved to Google Sheets for outreach/CRM use","End-to-end automation in n8n with scheduling and bulk runs"],"target_audience":["Sales teams and SDRs","Marketing agencies and consultants","Freelancers doing client prospecting (SEO, dev, ads)  "]}


name: AI Email Assistant: Prioritize Gmail with ChatGPT Summaries and Slack Digests
author_name: Darsheel
author_username: n8ndarsheel
categories: []
created_at: 2025-06-29T06:47:29.729Z
updated_at: 2025-06-29T06:47:29.729Z
url: https://n8n.io/workflows/5446
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  This n8n workflow acts as an AI-powered Inbox Assistant that automatically summarizes and classifies Gmail emails, prioritizes important messages, and sends a daily digest to Slack. It’s ideal for startup founders and small teams juggling investor intros, customer leads, and support queries — all from a busy Gmail inbox.

  Each email is processed using ChatGPT to generate a concise summary, classify the message (e.g., Support, Investor, Spam), and determine its urgency. High and medium priority messages are forwarded to Slack instantly. Lower priority emails are logged to Google Sheets for review. A daily 7 PM digest summarizes the day’s most important messages.

  💡 Use Cases
  Preventing missed investor or lead emails

  Lightweight CRM alternative using Google Sheets

  Slack summaries of critical Gmail activity

  🔧 How It Works
  Gmail node fetches new messages

  ChatGPT summarizes and extracts urgency + type

  High/medium urgency → sent to Slack + labeled in Gmail

  Low urgency → logged in Google Sheets

  Cron node triggers a daily 7 PM Slack summary

  ✅ Requirements
  OpenAI API Key (GPT-4 or GPT-4o recommended)

  Gmail access with read and label permission

  Slack Bot Token or Webhook URL

  Google Sheets integration (optional)

  🛠 Customization Ideas
  Replace Slack with Telegram or WhatsApp

  Route investor leads to Airtable or Notion

  Add multi-language support in ChatGPT prompt

  Create weekly summaries via email

nodes_used:
  ["n8n-nodes-base.gmailTrigger","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.slack","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.slack"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","email management","AI","productivity","integration"],"complexity":"medium","use_cases":["Prevent missing investor or lead emails","Lightweight CRM / lead logging into Google Sheets","Triage and prioritize support requests","Send Slack summaries of critical Gmail activity","Daily digest of important messages"],"estimated_setup_minutes":45,"required_services":["OpenAI API (GPT-4 / GPT-4o recommended)","Gmail (read & label access)","Slack (Bot token or Webhook URL)","Google Sheets API (optional for logging)","n8n account (to run workflow and schedule)"],"key_features":["ChatGPT-generated concise summaries for each email","Automatic classification (Support, Investor, Spam, etc.)","Urgency detection and priority-based routing","Immediate Slack notifications for high/medium priority","Labeling emails in Gmail according to classification/priority and forwarding where needed (optional forwarding behavior implied by Slack alerts or email forwarding node usage). Note: explicit forwarding to Slack via messages.)"],"target_audience":["Startup founders","Small teams","Customer support reps / managers handling inboxes at scale-ish volumes (SMB level rather than enterprise volumes without adjustments.)"]}


name: Advanced Retry and Delay Logic
author_name: Viktor Klepikovskyi
author_username: vklepikovskyi
categories: []
created_at: 2025-06-29T08:01:28.282Z
updated_at: 2025-06-29T08:01:28.282Z
url: https://n8n.io/workflows/5447
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Advanced Retry and Delay Logic

  This template provides a robust solution for handling API rate limits and temporary service outages in n8n workflows. It overcomes the limitations of the default node retry settings, which cap retries at 5 and delays at 5 seconds. By using a custom loop with a Set, If, and Wait node, this workflow gives you complete control over the number of retries and the delay between them.

  Instructions:

  Replace the placeholder HTTP Request node with your target node (the one that might fail).
  In the initial Set Fields node, modify the max_tries value to set the total number of attempts for your workflow.
  Adjust the delay_seconds value to define the initial delay between retries.
  Optionally, configure the Edit Fields node to implement exponential backoff by adjusting the delay_seconds expression (e.g., {{$json.delay_seconds * 2}}).

  For a more detailed breakdown and tutorial of this template, you can find additional information here.

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

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metadata_json:
  {"categories":["automation","integration","error-handling","workflow-orchestration"],"complexity":"medium","use_cases":["Handling API rate limits","Recovering from temporary service outages","Implementing exponential backoff for retries","Reliable automated HTTP/third‑party API calls","Longer/more flexible retry strategies than built‑in node settings"],"estimated_setup_minutes":10,"required_services":["n8n (self-hosted or cloud)","Target external API or service you are calling (e.g., REST API)"],"key_features":["Custom loop-based retry control beyond built‑in limits","Configurable max attempts and delay between retries","Support for exponential backoff via editable expressions","Use of Set, If and Wait nodes to manage retry logic","Optional stop/error node to exit after final failure or bubble errors up"],"target_audience":["Developers","DevOps / SREs","Integration engineers / iPaaS users"]}


name: Clone Voices from Text to Speech with Zyphra Zonos API
author_name: Tiartyos
author_username: tiartyos
categories: []
created_at: 2025-06-29T08:44:47.300Z
updated_at: 2025-06-29T08:44:47.300Z
url: https://n8n.io/workflows/5448
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Voice Cloning Workflow - Zyphra Zonos API

  Who is this for?
  This workflow is designed for developers, content creators, and businesses looking to automate high-quality voice synthesis using AI voice cloning technology.

  What problem does this solve?
  It automates the process of generating natural-sounding speech from text using a sample voice file, eliminating the need for manual voice recording and providing consistent voice output for applications like audiobooks, virtual assistants, or content localization.

  What this workflow does
  The workflow receives text and voice cloning parameters via webhook, reads a sample voice file from your storage, sends the data to Zyphra's Zonos API for voice synthesis, and saves the generated audio file to your specified output location.

  Prerequisites
  You'll need:
  API key from Zyphra (obtain from https://playground.zyphra.com/settings/api-keys)
  Account registration at https://playground.zyphra.com  
  Sample voice file stored on accessible disk/cloud storage
  n8n instance running with webhook capabilities

  Setup
  Configure your Zyphra API key in the "Call Zyphra Clone API" node under Header Parameters (Name: X-API-Key, Value: your-api-key)
  Ensure your sample voice files are accessible at the paths you'll specify
  Test the webhook endpoint is accessible

  Supported Audio Formats
  The API supports multiple output formats through the mime_type parameter:

  WebM** (default) - audio/webm
  Ogg** - audio/ogg
  WAV** - audio/wav
  MP3** - audio/mp3 or audio/mpeg
  MP4/AAC** - audio/mp4 or audio/aac

  Usage Example

  Endpoint: POST http://localhost:5678/webhook-test/voice-clone

  Headers: Content-Type: application/json

  Request Body:
  {
  "text": "Hello there! This voice sounds just like the sample!",
  "speaking_rate": 18,
  "sample_voice_path": "/data/output/sampleVoice.wav",
  "output_path": "/data/output/",
  "language_iso_code": "en-us",
  "mime_type": "audio/wav",
  "model": "zonos-v0.1-transformer",
  "emotion": {
  "happiness": 0.8,
  "neutral": 0.3,
  "sadness": 0.05,
  "disgust": 0.05,
  "fear": 0.05,
  "surprise": 0.05,
  "anger": 0.05,
  "other": 0.5
  }
  }

  Parameters

  Required Parameters
  text**: Text to synthesize into speech
  sample_voice_path**: Path to your voice sample file
  output_path**: Directory where generated audio will be saved

  Optional Parameters (with defaults)
  speaking_rate**: 15 - Speech speed
  language_iso_code**: "en-us" - Language code
  mime_type**: "audio/wav" - Output audio format
  model**: "zonos-v0.1-transformer" - AI model to use
  emotion**: Object with emotion levels (0-1 scale)

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

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metadata_json:
  {"categories":["integration","automation","speech synthesis","content creation","media processing"],"complexity":"medium","use_cases":["Generate audiobooks or narrated content from text","Create consistent voice for virtual assistants and IVR","Automated voiceovers for videos and marketing content","Content localization with cloned voice","Personalized messages and notifications"],"estimated_setup_minutes":20,"required_services":["Zyphra Zonos API (API key and account)","n8n instance with webhook exposure (or tunneling service like ngrok)","Accessible storage for sample voice and output files (local disk, S3, GCS, etc.)"],"key_features":["Webhook-triggered workflow to synthesize speech from text","Voice cloning using a user-provided sample voice file","Configurable parameters: model, speaking_rate, language, mime_type, emotion","Supports multiple output audio formats (WebM, Ogg, WAV, MP3, MP4/AAC)","Reads sample voice file from storage and saves generated audio to specified output path"],"target_audience":["Developers","Content creators","Businesses and product teams implementing voice features"]}


name: Automate Sales Cold Calling Pipeline with Apify, GPT-4o, and WhatsApp
author_name: Khairul Muhtadin
author_username: khmuhtadin
categories: []
created_at: 2025-06-29T08:45:59.943Z
updated_at: 2025-06-29T08:45:59.943Z
url: https://n8n.io/workflows/5449
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 

  Cold Calling Automation - End-to-End Automated Cold Calling with Apify, RAG, and WhatsApp



  The "Cold Calling Automation" workflow is designed to fully automate the end-to-end cold calling process by intelligently combining web scraping, AI-powered research, and WhatsApp messaging. Leveraging key technologies such as Apify for data scraping, RAG (Retrieval-Augmented Generation) for intelligent content creation, and WhatsApp integration for automated outreach, this workflow transforms raw prospect data into personalized, high-converting cold calling campaigns with minimal manual intervention.

  💡 Why Use Cold Calling Automation?
  Scale Your Outreach:** Automate hundreds of personalized cold calls without manual effort or hiring additional staff.  
  Intelligent Personalization:** RAG technology creates highly relevant, personalized messages based on prospect research.  
  Multi-Channel Approach:** Seamlessly integrate WhatsApp messaging with traditional cold calling methods.  
  Real-Time Optimization:** Continuously improve message performance and conversion rates through AI analysis.  
  Cost-Effective:** Reduce cold calling costs while dramatically increasing reach and response rates.

  ⚡ Who Is This For?
  Sales Teams:** Looking to scale their cold calling efforts with intelligent automation and personalization.  
  Lead Generation Agencies:** Needing to deliver high-volume, high-quality cold calling services to clients.  
  Business Development Professionals:** Seeking to maximize outreach efficiency while maintaining personal touch.  
  Small Business Owners:** Who want professional-grade cold calling capabilities without hiring expensive sales teams.  
  Marketing Agencies:** Offering comprehensive lead generation and conversion services to clients.

  ❓ What Problem Does It Solve?
  Traditional cold calling is time-consuming, expensive, and often ineffective due to lack of personalization and poor timing. Manual prospect research, script writing, and call execution create bottlenecks that limit outreach scale. Generic messages result in low response rates and damaged brand reputation. This workflow solves these problems by automating the entire cold calling pipeline - from prospect identification and research to personalized message creation and delivery - while maintaining high quality and relevance that converts prospects into qualified leads.

  🔧 What This Workflow Does
  ⏱ Prospect Scraping: Uses Apify to automatically scrape and identify high-quality prospects based on your target criteria.  
  🔍 Intelligent Research: Employs RAG technology to research each prospect and gather relevant business intelligence.  
  ✍️ Personalized Content: Automatically generates custom messages, scripts, and talking points for each prospect.  
  📱 WhatsApp Integration: Delivers personalized messages through WhatsApp automation for maximum engagement.  
  📊 Performance Tracking: Monitors response rates, engagement metrics, and conversion data for continuous optimization.  
  🤖 AI-Powered Follow-up: Automatically handles initial responses and schedules appropriate follow-up actions.  
  📈 Campaign Analytics: Provides detailed insights on campaign performance and ROI metrics.  
  🔄 Continuous Learning: Improves message effectiveness and targeting based on campaign results.

  This workflow also using community node: `@devlikeapro/n8n-nodes-waha`

  🔐 Setup Instructions
  Import the provided workflow JSON into your n8n instance (Cloud or self-hosted).  
  Set up credentials:  
      Apify API credentials for prospect scraping  
      OpenAI API key for RAG and content generation  
      WhatsApp Business API credentials or WAHA integration  
      Database credentials for prospect and campaign tracking  
      Email credentials for notifications and reporting  
  Customize parameters:  
      Target prospect criteria and scraping parameters  
      Message templates and personalization rules  
      Campaign timing and frequency settings  
      Response handling and follow-up logic  
      Performance tracking and reporting preferences  
  Test the complete workflow with a small prospect list to verify scraping, personalization, and delivery.

  🧩 Pre-Requirements
  Active n8n instance (Cloud or Self-hosted)  
  Apify account with appropriate scraping credits  
  OpenAI API key with sufficient usage limits  
  WhatsApp Business account or WAHA setup  
  Database system for prospect and campaign management  
  Basic understanding of your target audience and value proposition  

  🛠️ Customize It Further
  Integrate with CRM systems to sync prospects and track conversion through sales pipeline.  
  Add voice calling capabilities using VoIP services for complete omnichannel outreach.  
  Implement A/B testing for message templates and timing optimization.  
  Connect with social media platforms for multi-channel prospecting and engagement.  
  Add sentiment analysis to optimize message tone and approach for different prospect types.  
  Integrate with calendar systems for automatic meeting scheduling from qualified responses.

  🧠 Nodes Used
  Apify nodes for prospect scraping and data collection  
  OpenAI Chat Model and Embeddings for RAG implementation  
  WhatsApp/WAHA nodes for message delivery and response handling  
  Database nodes for prospect storage and campaign tracking  
  HTTP Request nodes for API integrations and webhooks  
  Code nodes for data processing and personalization logic  
  Schedule Trigger for automated campaign execution  
  Conditional nodes for response handling and follow-up logic  
  Set nodes for parameter configuration and data transformation  
  Split In Batches for efficient bulk processing

  📊 Expected Results
  50-80% increase** in cold calling efficiency and prospect reach  
  25-40% higher** response rates compared to generic cold calling  
  60-75% reduction** in manual research and message preparation time  
  Real-time insights** into campaign performance and prospect engagement  
  Scalable system** that grows with your business needs

  📞 Support
  Made by: khaisa Studio  
  Tag: automation, cold calling, lead generation, apify, RAG, whatsapp, AI, sales automation, outreach  
  Category: Sales Automation & Lead Generation  
  Need a custom? contact me for more tailored templates

nodes_used:
  ["n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.splitInBatches","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryPostgresChat","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.webhook","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.code","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@devlikeapro/n8n-nodes-waha.wahaTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryPostgresChat","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.rerankerCohere","@n8n/n8n-nodes-langchain.rerankerCohere","n8n-nodes-base.set"]

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metadata_json:
  {"categories":["automation","sales","lead generation","integration","data processing"],"complexity":"complex","use_cases":["Automated cold outreach via WhatsApp","Prospect scraping and enrichment","RAG-powered personalized message and script generation","Automated follow-ups and response handling","Campaign performance tracking and optimization"],"estimated_setup_minutes":120,"required_services":["n8n (self-hosted for community nodes)","Apify (scraping)","OpenAI / GPT-4o (RAG & embeddings)","WhatsApp Business API or WAHA (@devlikeapro/n8n-nodes-waha)","Database / Vector store (e.g., Supabase)","Google Sheets / Google Drive (optional)","Email service for notifications (optional)"],"key_features":["End-to-end prospect scraping with Apify","Retrieval-Augmented Generation (RAG) for contextual personalization","Embeddings & vector store for similarity search (Supabase)","Automated WhatsApp outreach and response handling (WAHA)","Batch processing and scheduling for high-volume campaigns"],"target_audience":["Sales teams","Lead generation agencies","Business development professionals / SDRs」、「Marketing agencies"]}


name: Send AI-Enhanced Economic Calendar Alerts to Telegram with Gemini-2.0-Flash
author_name: Malik Hashir
author_username: malikx
categories: []
created_at: 2025-06-29T10:23:41.436Z
updated_at: 2025-06-29T10:23:41.436Z
url: https://n8n.io/workflows/5450
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Stay ahead of the markets with this fully automated n8n workflow that delivers real-time, AI-formatted economic calendar updates directly to your Telegram channel or group. 

  Powered by the Economic Events Calendar API via RapidAPI, this workflow is perfect for traders, investors, and financial community managers who want timely notifications about high- and medium-impact global economic events—no coding required.

  Key Features
  Automated Alerts:** Receive upcoming economic calendar updates for the next 3 days (customizable window) straight to Telegram.
  AI-Enhanced Formatting:** Economic news are grouped by date and importance, with clear, emoji-rich summaries for maximum readability and engagement.
  Flexible Scheduling:** Default update interval is every 3 days, adjustable to daily or any custom frequency.
  Customizable Date Range:** Easily set the window for economic event coverage (e.g., today to 7 days ahead).
  No Coding Needed:** Simple setup—just plug in your API keys and Telegram credentials.

  How to Set Up
  ++Step 1:++ Get Your RapidAPI Key & Subscribe to the Economic Calendar Endpoint
  Visit RapidAPI (https://rapidapi.com/) and sign up for a free account (free plan includes limited requests per month).
  After registering, click your profile icon in the top-right corner and select "User Settings".
  From the left menu, choose "Applications".
  Click on "default app" and copy your API Key.
  Paste this key inside the workflow node labeled "Set API Key for RapidAPI & Dates" under the "x-rapidapi-key" field.

  ++Step 2:++ Connect Google Gemini (or OpenAI) Model
  Add your Gemini API credentials in n8n for advanced AI formatting and structuring of economic updates.

  ++Step 3:++ Set Up Your Telegram Bot
  Create a bot with @BotFather, get your API token, and add it to n8n’s Telegram credentials.
  Obtain your Telegram chat or group ID (use @RawDataBot for this).

  ++Step 4:++ Customize Date Range (Optional)
  By default, the workflow fetches economic calendar updates for the next 3 days, but you can adjust it to any custom window (e.g., today to 7 days ahead).

  ++Step 5:++ Activate the Workflow
  Schedule how often you want news updates (default: every 3 days; can be as frequent as daily).
  The workflow will automatically fetch, format, and send the latest economic calendar updates to your Telegram.

  Who Is This For?
  Retail and professional traders
  Financial influencers and Telegram group admins
  Community managers seeking hands-off, real-time macroeconomic alerts

  Why Choose This Workflow?
  Save Time: No more manual economic calendar checks.
  Stay Informed: Instantly know about key market-moving events.
  Easy Integration: Works out-of-the-box with n8n and Telegram.

  Get started today and never miss a critical economic event again!

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.code","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.code","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.telegram","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.code"]

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metadata_json:
  {"categories":["automation","integration","finance","notifications","AI"],"complexity":"medium","use_cases":["Real-time economic calendar alerts","Market monitoring for traders and investors","Automated, AI-formatted news delivery to Telegram communities","Community/Telegram group engagement with concise summaries","Scheduled macroeconomic event notifications"],"estimated_setup_minutes":20,"required_services":["Economic Events Calendar API via RapidAPI","Google Gemini (or OpenAI) API for AI formatting","Telegram Bot API (bot token and chat/group ID)","n8n instance/workflow hosting"],"key_features":["Automated fetch of upcoming economic events (default 3-day window, customizable)","AI-enhanced grouping and emoji-rich summaries by date and importance","Flexible scheduling (default every 3 days, adjustable)","No-code setup — configure API keys and Telegram credentials","Sends formatted messages directly to Telegram channels/groups"],"target_audience":["Retail traders","Professional traders and investors","Financial influencers and Telegram group admins/community managers"]}


name: AI Auto-Save Gmail Receipts to Google Sheets + Google Drive
author_name: Matt Chong | n8n Creator
author_username: mattxchong
categories: []
created_at: 2025-06-29T12:07:36.666Z
updated_at: 2025-06-29T12:07:36.666Z
url: https://n8n.io/workflows/5451
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  Who is this for?

  This workflow is ideal for:

  For freelancers, business owners, and finance teams who receive receipts via Gmail  
  Automatically logs expenses for tax, 
  bookkeeping and year-end audits

  What problem is this workflow solving?

  When tax season hits, missing receipts create panic.

  This workflow keeps everything in one place. It uses AI to extract details from Gmail attachments, logs them in a Google Sheet, and stores the PDFs in Google Drive.

  No digging. No copying. Just everything where it should be.

  How it works?

  Apply the label receipt to any incoming Gmail email. Do not mark it as read.

  On a schedule (e.g. daily at 8:00 AM), the workflow triggers.

  It searches for unread emails with the label receipt.

  For each matching email, it downloads the attached receipt file.

  It extracts text content from the receipt file.

  It uploads the original receipt file to a specified folder in Google Drive.

  It merges the extracted text with email metadata.

  It sends this combined data to OpenAI.  
     OpenAI extracts structured fields:
     date
     merchant
     category
     description
     subtotal
     tax
     total

  The extracted data is appended as a new row in the specific Google Sheet.

  Finally, the email is marked as read to prevent it from being processed again.

  How to set up?

  Connect these services in your n8n credentials:
     Gmail (OAuth2)
     Google Drive
     Google Sheets
     OpenAI
  Configure the Google Drive upload:
     In the “Upload File” node, select the target folder where you want receipt PDFs stored.
  Set your execution schedule:
     Open the “Schedule Trigger” node and choose when it should run (default is once daily at 8:00 AM).
  Choose your Google Sheet and tab:
     In the “Append to Google Sheet” node, select your document and tab
     Ensure the sheet contains these columns:  
       Date, Merchant, Category, Description, Subtotal, Tax, Total.

  How to customize this workflow to your needs? 
  Change the Gmail label or search filter** to match your needs.
  Modify the OpenAI schema** to extract additional fields like currency, project, or notes.

nodes_used:
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metadata_json:
  {"categories":["automation","integration","data processing","finance"],"complexity":"medium","use_cases":["Automatically log receipts and expenses","Centralize receipt PDFs in Google Drive","Extract structured receipt data for bookkeeping or tax","Reduce manual data entry for finance teams"],"estimated_setup_minutes":20,"required_services":["Gmail (OAuth2)","Google Drive","Google Sheets","OpenAI"],"key_features":["Scheduled search for labeled unread Gmail receipts","Download and extract text from attached receipt files","Upload original PDFs to a specified Google Drive folder","Use OpenAI to parse and return structured fields (date, merchant, category, description, subtotal, tax, total)","Append extracted data as rows in a Google Sheet tab with required columns set up (Date, Merchant, Category, Description, Subtotal, Tax, Total)"],"target_audience":["freelancers","small business owners","finance teams and accountants/bookkeepers"]}


