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    • Automate UX Persona Creation with Data-Driven Insights

      Streamline the creation of UX personas using market data and AI-generated insights, complete with visual representations and documentation.

      n8nFree

    • Automate YouTube Thumbnail Creation with OpenAI and Apify

      This n8n workflow automates the creation of custom YouTube thumbnails using OpenAI's DALL·E. It extracts video metadata and transcripts via Apify, formulates a prompt, and generates a high-resolution image tailored for YouTube.

      n8nFree

    • Auto-Generate Product Comparison Pages with OpenAI & Google Sheets

      ## Auto-generate product comparison pages that help users buy faster This workflow creates detailed X vs Y product comparison pages designed to help readers make faster, more confident purchase decisions - all with zero manual writing. --- ### How it works - Triggered manually or via Google Sheets row - Takes two product names as input (e.g., "Notion vs Evernote") - Uses AI to generate: - A compelling title and meta description - Clear feature-by-feature comparison - Use-case-based recommendations - An FAQ section tailored to user pain points - Saves each section into a Google Sheet for review or publishing - Final output can be exported to your CMS or website builder (like Dorik, Webflow, etc.) --- ### Set up steps - You'll need OpenAI and Google Sheets credentials - Takes **10-15 minutes** to plug in your keys and connect the sheet - Adjust prompts to match your brand tone or SEO goals You can easily expand this to generate pricing tables, testimonials, or even localized versions using the same structure. --- Ideal for SaaS companies, affiliate marketers, or content teams who want to scale up comparison content - without spending hours writing.

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  1. Automate Blog Content Creation and Publishing with AI and Elementor

    Streamline your agency's content operations by automating the creation, image generation, and publishing of SEO-optimized blog posts directly to Elementor-powered WordPress sites using AI technology.

    n8nFree

  2. Automate Post-Meeting Tasks Using AI and Transcripts

    This n8n workflow automates the organization of follow-up meetings by analyzing video transcripts with AI. It saves time by summarizing meetings and scheduling necessary follow-ups.

    n8nFree

  3. Chat with AI Models via OpenRouter Using Mistral

    This n8n workflow demonstrates how to build an automated AI chat system using OpenRouter.ai. It includes a manual trigger, sets a model and user message, sends a POST request to the OpenRouter chat API, and summarizes the response. Workflow Steps: 1. Manual Trigger - Starts the workflow when executed manually. 2. Set Node - Defines: - Model: mistralai/mistral-small-3.2-24b-instruct:free - Message: What is the meaning of life? 3. HTTP Request - Sends a POST request to https://openrouter.ai/api/v1/chat/completions using Bearer Token Authentication with the model and message as JSON. 4. Summarize - Extracts and summarizes the AI's response (choices[0].message.content). Use Cases: - AI chatbot automation - Content summarization - Testing AI prompts in real-time - Educational demos using OpenRouter.ai - Lightweight conversational tools with no external server

    n8nFree

  4. Generate Professional Document Drafts from PDFs with Google Drive, GP-4, and Email Notifications

    # 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](www.linkedin.com/in/michael-gullo-46b3b7274) or email [michael.gullo@outlook.com](michael.gullo@outlook.com).

    n8nFree

  5. Adaptive RAG Strategy with Query Classification & Retrieval (Gemini & Qdrant)

    This n8n workflow implements a version of the Adaptive Retrieval-Augmented Generation (RAG) framework. It recognizes that the best way to retrieve information often depends on the type of question asked. Instead of a one-size-fits-all approach, this workflow adapts its strategy based on the user's query intent. ## How it Works 1. **Receive Query:** Takes a user query as input (along with context like a chat session ID and Vector Store collection ID if used as a sub-workflow). 2. **Classify Query:** First, the workflow classifies the query into a predefined category. This template uses four examples: * **Factual:** For specific facts. * **Analytical:** For deeper explanations or comparisons. * **Opinion:** For subjective viewpoints. * **Contextual:** For questions relying on specific background. 3. **Select & Adapt Strategy:** Based on the classification, it selects a corresponding strategy to prepare for information retrieval. The example strategies aim to: * **Factual:** Refine the query for precision. * **Analytical:** Break the query into sub-questions for broad coverage. * **Opinion:** Identify different viewpoints to look for. * **Contextual:** Incorporate implied or user-specific context. 4. **Retrieve Info:** Uses the output of the selected strategy to search the specified knowledge base (Qdrant vector store - change as needed) for relevant documents. 5. **Generate Response:** Constructs a response using the retrieved documents, guided by a prompt tailored to the original query type. By adapting the retrieval strategy, this workflow aims to provide more relevant results tailored to the user's intent. ## Usage & Flexibility * **Sub-Workflow:** Designed to be called from other n8n workflows, passing `user_query`, `chat_memory_key`, and `vector_store_id` as inputs. * **Chat Testing:** Can also be triggered directly via the n8n Chat interface for easy testing and interaction. * **Customizable Framework:** The query categories (Factual, Analytical, etc.) and the associated retrieval strategies are examples. You can modify or replace them entirely to fit your specific domain or requirements. ## Requirements * **Credentials:** You will need API credentials configured in your n8n instance for: * Google Gemini (AI Models) * Qdrant (Vector Store)

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  6. Extract Invoice Data from PDFs with AI - Google Sheets Email Alerts

    Built by Setidure Technologies This smart n8n automation extracts invoice details from PDF files uploaded to Google Drive using AI, logs them to a Google Sheet, and notifies the billing team via email - all without manual intervention. Note: This workflow requires a self-hosted n8n instance with LangChain, LLM, and Google integrations configured. **What This Workflow Does** - Monitors a Google Drive folder for new invoice uploads - Extracts text and parses key invoice details using LLM via LangChain - Logs extracted data into a Google Sheet (Invoice Database) - Generates a summary email using GP-4O-MINI (Greenie) - Sends the email to the billing team via Gmail **Prerequisites** - A Google Drive folder to monitor for PDF uploads - A Google Sheet named Invoice Database with the following columns: - Invoice Number, Client Name, Client Email, Client Address, Client Phone, Invoice Date, Due Date, Total Amount - Service account or OAuth credentials for: - Google Drive - Google Sheets - Gmail - LangChain + Ollama integration for LLM responses **Step-by-Step Setup Instructions** 1. Clone this workflow into your self-hosted n8n instance 2. Set up credentials: - Google Drive (for folder trigger) - Google Sheets (for data logging) - Gmail (for sending email) - Ollama (local LLM) or any connected LangChain provider 3. Configure the trigger node to watch your specific Invoice Uploads folder 4. Update the Google Sheet node with your Invoice Database sheet URL and column mapping 5. Test with a sample invoice to validate the AI extraction and email generation **Workflow Steps** ## Step 1: Trigger on New File in Google Drive Node Name: Watch for New Invoices Type: Google Drive Trigger Event: fileCreated Triggers when a new PDF file is uploaded to a designated folder ## Step 2: Download the Uploaded File Node Name: Download Invoice PDF Type: Download Binary Downloads the invoice file from Google Drive ## Step 3: Extract Raw Text from PDF Node Name: Extract PDF Text Type: Extract from File Extracts unstructured text content from the downloaded PDF ## Step 4: Parse Invoice Fields Using AI Node Name: Parse Invoice Data with LLM Type: LangChain Agent LLM is prompted to extract: - Invoice Number - Client Name, Email, Address, Phone - Invoice Date, Due Date, Total Amount Fields not found are skipped ## Step 5: Log Extracted Data to Google Sheet Node Name: Log to Invoice Database Type: Google Sheets Appends a new row with the extracted fields to the Invoice Database spreadsheet ## Step 6: Create Email Notification via LLM Node Name: Generate Billing Email Summary Type: LangChain Agent (GP-4O-MINI) Prompt instructs AI to: - Act as "Greenie" from Green Grass Corp - Inform billing that a new invoice was processed - Confirm logging into the Invoice Database ## Step 7: Send the Email to Billing Team Node Name: Email Billing Team Type: Gmail Send To: billing@example.com Subject and body injected from LLM output ## Step 8: End Workflow Gracefully Node Name: End Type: No Operation Used to cleanly terminate the flow ## Example Output (Email) Subject: New Invoice Logged - Client: ABC Corp Hi Billing Team, A new invoice has been received and processed automatically. The following details have been extracted and logged into the Invoice Database: - Invoice Number: INV-1024 - Client: ABC Corp - Amount: $1,450 - Due Date: July 15, 2025 Please review the [Invoice Database](https://docs.google.com/spreadsheets/d/14FtgVUjy0tw4EQOB28wuCYOe6CELtt3Ujn-GsuDWY/edit?gid=0#gid=0) for full details. Regards, Greenie Green Grass Corp

    n8nFree

  7. Automate WhatsApp Chatbot with Venom Bot and Google Gemini AI

    Create a fully automated WhatsApp chatbot using Venom Bot and Google Gemini AI to handle customer interactions intelligently. This workflow is ideal for small businesses and creators looking to provide 24/7 customer service without the official WhatsApp Business API.

    n8nFree

  8. Integrate Flowise Multi-Agent Chatflows into a Custom-Branded n8n Chatbot

    Create a custom-branded n8n chatbot that integrates Flowise Multi-Agent Chatflows for real-time user interaction with AI agents powered by large language models.

    n8nFree

  9. Chat with a Google Sheet Using AI

    This workflow allows you to ask questions about the data in a Google Sheet over a chat interface. It uses n8n's built-in chat, but could be modified to work with Slack, Teams, WhatsApp, etc. Behind the scenes, the workflow uses GPT-4, so you'll need to have an OpenAI API key that supports it. ### How it works The workflow uses an AI agent with custom tools that call a sub-workflow. That sub-workflow reads the Google Sheet and returns information from it. Because models have a context window (and therefore a maximum number of characters they can accept), we can't pass the whole Google Sheet to GPT-4 - at least not for big sheets. So we provide three ways of querying less data, that can be used in combination to answer questions. Those three functions are: 1. List all the columns in the sheet 2. Get all values of a single column 3. Get all values of a single row Note that to use this template, you need to be on n8n version 1.19.4 or later.

    n8nFree

  10. Automate AI-Powered Newsletter Drafts from Google Sheets to Notion

    Transform Google Sheets entries into polished newsletter drafts in Notion using AI. This workflow automates the drafting process, leveraging GPT-3.5 for content creation and updating statuses in Google Sheets.

    n8nFree

  11. Automate Image Generation with Bytedance Seedream 3 via Replicate API

    This workflow automates image generation using the Bytedance Seedream 3 AI model through the Replicate API, streamlining the process within your n8n automation workflows.

    n8nFree

  12. Automate Email History Retrieval with OpenAI and Qdrant

    This workflow automates the retrieval and analysis of Gmail history using OpenAI embeddings and stores them in a Qdrant vector database. It enables real-time email updates and facilitates a Retrieval-Augmented-Generation (RAG) agent to efficiently respond to user queries.

    n8nFree

  13. Integrate Telegram AI Bot with LangChain for Dynamic Conversations and Image Generation

    This workflow connects a Telegram bot with LangChain nodes in n8n to create a dynamic AI-driven conversation agent. It utilizes OpenAI's GPT-4 for generating responses and Dall-E 3 for image creation upon user requests.

    n8nFree

  14. Automate Email Categorization, PDF Processing, and Appointment Booking with AI

    This workflow automates the categorization of Gmail emails, processes PDFs into a Pinecone vector store for AI interaction, and facilitates appointment booking via Google Calendar using AI agents.

    n8nFree

  15. Automate Book Data Extraction and Email Delivery from URLs

    Streamline the process of extracting book information from websites, organizing it into a CSV, and emailing it automatically. Ideal for those needing efficient data handling from catalog-based sites.

    n8nFree

  16. Evaluation Metric Example: RAG Document Relevance

    ## AI evaluation in n8n This is a template for n8n's [evaluation feature](https://docs.n8n.io/advanced-ai/evaluations/overview). Evaluation is a technique for getting confidence that your AI workflow performs reliably, by running a test dataset containing different inputs through the workflow. By calculating a metric (score) for each input, you can see where the workflow is performing well and where it isn't. ## How it works This template shows how to calculate a workflow evaluation metric: **retrieved document relevance** (i.e., whether the information retrieved from a vector store is relevant to the question). The workflow takes a question and checks whether the information retrieved to answer it is relevant. To run this workflow, you need to insert documents into a vector data store, so that they can be retrieved by the agent to answer questions. You can do this by running the top part of the workflow once. The main workflow works as follows: - We use an evaluation trigger to read in our dataset. - It is wired up in parallel with the regular trigger so that the workflow can be started from either one. [More info](https://docs.n8n.io/advanced-ai/evaluations/tips-and-common-issues/#combining-multiple-triggers). - We make sure that the agent outputs the list data from the tools that it used. - If we're evaluating (i.e., the execution started from the evaluation trigger), we calculate the relevance metric using AI to compare the retrieved documents with the question. - We pass this information back to n8n as a metric. - If we're not evaluating, we avoid calculating the metric, to reduce cost.

    n8nFree

  17. Create a Personal Portfolio Chatbot with Automated Email Summaries

    Develop a professional personal portfolio chatbot that integrates RAG functionality, stores conversations, and sends daily email summaries.

    n8nFree

  18. Automate Job-Resume Matching with Bright Data and OpenAI

    Streamline the process of matching resumes to job descriptions using real-time data from LinkedIn and job boards, powered by Bright Data and OpenAI.

    n8nFree

  19. Automate Lead Qualification & Customer Support with GP-40-mini Agents

    ### Beginner AI Agent Duo: Lead-Qualifier and Automator & Ecommerce Chatbot **Status:** Ready for Use **Note:** This template is built entirely with **official n8n nodes**—no community-node installation required. --- ### Description This template demonstrates two beginner-friendly AI-agent patterns that cover the most common use cases: | Agent | Purpose | Flow Highlights | |-------|---------|-----------------| | **Lead-Qualifier and Automator** | Classifies phone-call transcripts to decide if the caller is a good bulk-order lead. | Manual trigger → Code (sample data) → AI Agent (GPT-4-mini) → Structured Output Parser → Set (clean fields) | | **Ecommerce Chatbot** | Answers customer questions about products, bulk pricing, shipping, and returns. | Chat trigger (webhook) → AI Agent (GPT-4-mini) with Memory → If node → Order-placed reply or no-op | Both agents run on **GPT-4-mini** and use n8n's LangChain-powered nodes for quick, low-code configuration. --- ### How to Install & Run 1. **Import the Workflow** - In n8n, go to **Workflows → Import from File** or **Paste JSON**, then save. 2. **Add Your OpenAI API Key** - Go to **Credentials → New → OpenAI API**. - Paste your key from <https://platform.openai.com>. - Select this credential in both **OpenAI Chat Model** nodes. 3. **(Optional) Select a Different Model** - Default model is **gpt-4-mini**. - Change to GPT-4, GPT-3.5-turbo, or any available model in each OpenAI node. 4. **Test the Lead-Qualifier Agent** - Click **Activate**. - Press **Test workflow**. - The Code node feeds four sample transcripts; the AI Agent returns JSON like: ```json { "Name": "Jordan Lee", "Is Good Lead": "Yes", "Reasoning": "Customer requests 300 custom mugs, indicating a bulk order." } ``` 5. **Test the Ecommerce Chatbot** - Copy the **Webhook URL** from the **When chat message received** trigger. - POST a payload like: ```json { "message": "Hi, do you offer discounts if I buy 120 notebooks?" } ``` - The AI Agent replies with bulk-pricing info. - If the customer confirms an order, it appends `*****`; the If node then sends “Your order has been placed”. --- ### Customization Ideas - **Refine Qualification Logic**—Edit the Lead Agents system prompt to match your own lead criteria. - **Save Leads Automatically**—Add Google Sheets, Airtable, or a database node after the Set node. - **Expand the Chatbot**—Connect inventory APIs, payment gateways, or CRM integrations. - **Adjust Memory Length**—Change the *Simple Memory* nodes window to retain more conversation context. --- ## Connect with Me ### Description I'm Robert Breen, founder of Ynteractive—a consulting firm that helps businesses automate operations using **n8n**, **AI agents**, and custom workflows. I've helped clients build everything from intelligent chatbots to complex sales automations, and I'm always excited to collaborate or support new projects. If you found this workflow helpful or want to talk through an idea, I'd love to hear from you. ### Links - Website: [https://www.ynteractive.com](https://www.ynteractive.com) - YouTube: [@ynteractivetraining](https://www.youtube.com/@ynteractivetraining) - LinkedIn: [https://www.linkedin.com/in/robert-breen](https://www.linkedin.com/in/robert-breen) - Email: rbreen@ynteractive.com

    n8nFree

  20. Automate TikTok Video Transcription with RapidAPI and Google Sheets

    # TikTok Transcript Generator ## Overview This automated workflow extracts transcripts from TikTok videos by reading video URLs from a Google Sheet, calling the **API via [TikTok Transcript Generator](https://rapidapi.com/PrineshPatel/api/tiktok-transcript-generator)**, cleaning the subtitle data, and updating the sheet with transcripts. It efficiently handles batches, errors, and rate limits to provide a seamless transcription process. ## Key Features - **Batch processing:** Reads and processes multiple TikTok video URLs from Google Sheets. - **Automatic transcript generation:** Uses the **[TikTok Transcript Generator API on RapidAPI](https://rapidapi.com/PrineshPatel/api/tiktok-transcript-generator)**. - **Clean subtitle output:** Removes timestamps and headers for clear transcripts. - **Error handling:** Marks videos with no available transcript. - **Rate limiting:** Implements wait times to avoid API throttling on **[RapidAPI](https://rapidapi.com/PrineshPatel/api/tiktok-transcript-generator)**. - **Seamless Google Sheets integration:** Updates the same sheet with transcript results and statuses. ## API Used - **[TikTok Transcript Generator API](https://rapidapi.com/PrineshPatel/api/tiktok-transcript-generator)** ## Google Sheet Columns | Column Name | Description | |----------------|-----------------------------------------------| | Video Url | URL of the TikTok video to transcribe | | Transcript | Generated transcript text (updated by workflow) | | Generated Date | Date when the transcript was generated (YYYY-MM-DD) | ## Workflow Nodes Explanation | Node Name | Type | Purpose | |-------------------------|-----------------------|-----------------------------------------------------------------------| | When clicking “Execute workflow” | Manual Trigger | Manually starts the entire transcription workflow. | | Google Sheets2 | Google Sheets (Read) | Reads TikTok video URLs and transcript data from Google Sheets. | | Loop Over Items | Split In Batches | Processes rows in smaller batches to control execution speed. | | If | Conditional Check | Filters videos needing transcription (URL present, transcript empty). | | HTTP Request | HTTP Request | Calls the **TikTok Transcript Generator API on [RapidAPI](https://rapidapi.com/PrineshPatel/api/tiktok-transcript-generator)** to fetch transcripts. | | If1 | Conditional Check | Checks for valid API responses (handles 404 errors). | | Code | Code (JavaScript) | Cleans and formats raw subtitle text by removing timestamps. | | Google Sheets | Google Sheets (Update)| Updates the sheet with cleaned transcripts and generation dates. | | Google Sheets1 | Google Sheets (Update)| Updates sheet with “No transcription available” message on error. | | Wait | Wait | Adds delay between batches to avoid API rate limits on **[RapidAPI](https://rapidapi.com/PrineshPatel/api/tiktok-transcript-generator)**. | ## Challenges Resolved - **Manual Transcription Effort:** Eliminates the need to manually transcribe TikTok videos, saving time and reducing errors. - **API Rate Limits:** Introduces batching and wait periods to avoid exceeding API usage limits on **[RapidAPI](https://rapidapi.com/PrineshPatel/api/tiktok-transcript-generator)**, ensuring smooth execution. - **Incomplete or Missing Data:** Filters out videos already transcribed and handles missing transcripts gracefully by logging appropriate messages. - **Data Formatting Issues:** Cleans raw subtitle data to provide readable, timestamp-free transcripts. - **Data Synchronization:** Updates transcripts back into the same Google Sheet row, maintaining data consistency and ease of access. ## Use Cases - Content creators wanting to transcribe TikTok videos automatically. - Social media analysts extracting text data for research. - Automation enthusiasts integrating transcript generation into workflows. ## How to Use 1. Prepare a Google Sheet with the columns: **Video Url**, **Transcript**, and **Generated Date**. 2. Connect your Google Sheets account in the workflow. 3. Enter your **RapidAPI** key for the TikTok Transcript Generator API. 4. Execute the workflow to generate transcripts. 5. View transcripts and generated dates directly in your Google Sheet. --- **Try this workflow to automate your TikTok video transcriptions efficiently!** Create your **free n8n account** and set up the workflow in just a few minutes using the link below: ☀️🌟 [Start Automating with n8n](https://n8n.partnerlinks.io/7fuarh1e6dz5) Save time, stay consistent, and grow your LinkedIn presence effortlessly!

    n8nFree

  21. Automate Video and Thumbnail Updates in Airtable with Luma AI Webhook

    This workflow captures webhook responses from Luma AI after video generation, processes the data, and updates Airtable with video and thumbnail URLs, completing the video creation and tracking process.

    n8nFree
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