Workflow Templates

This workflow streamlines your WooCommerce product creation process by integrating directly with Google Sheets. Simply input product details into your spreadsheet, and the workflow takes care of the rest-automatically creating new products on your WooCommerce store with inventory management. But it doesn’t stop there. A dedicated SEO expert chain analyzes each product’s content and generates optimized meta titles and meta descriptions for the plugin Yoast SEO, enhancing visibility and ranking potential on search engines. Key Benefits: 🔄 Automation: No more manual uploads—save time and reduce errors by syncing Google Sheets directly with WooCommerce. ⚡ Speed: Instantly publish multiple products with just one action. 🧠 Built-in SEO Intelligence: Automatically generate SEO-friendly meta titles and descriptions tailored to each product. 📈 Improved Search Visibility: Boost your store's traffic with optimized product listings. 🧩 Customizable: Easily adapt the workflow to your specific needs or integrate with other platforms. How It Works This workflow automates the creation of WooCommerce products and generates optimized SEO meta tags (title and description) using AI. Here’s the step-by-step process: Data Retrieval**: The workflow starts by fetching product details (title, category, description, price, etc.) from a Google Sheets document. Product Creation**: Each product is created in WooCommerce using the retrieved data, including categories, pricing, stock details, and images. AI-Powered SEO Optimization**: An AI model (Google Gemini via OpenRouter) analyzes the product details and generates SEO-optimized meta titles (≤60 chars) and meta descriptions (≤160 chars). Meta Tag Assignment**: The generated meta tags are saved back to the Google Sheets and applied to the WooCommerce product using Yoast SEO metadata. Completion Tracking**: The workflow marks completed entries in Google Sheets and sends a Telegram notification upon finishing all products. Set Up Steps Before running the workflow, ensure the following steps are completed: Step 1**: Install the Yoast SEO plugin on WordPress and add the provided PHP code to functions.php to enable meta tag API support. Step 2**: Enable the WooCommerce REST API in WordPress and configure the Telegram node with a valid CHAT_ID for notifications. Step 3**: Prepare a Google Sheet with product data (columns A-I in specific formats) and share its ID in the workflow. Ensure columns B, E, and F are in text format, and column I is numeric. Once set up, the workflow can be triggered manually or scheduled to run automatically, streamlining product creation and SEO optimization. Who is it useful for? Ideal for eCommerce managers, digital marketers, or anyone managing large product catalogs-this workflow turns your spreadsheet into a powerful product launcher. Need help customizing? Contact me for consulting and support or add me on Linkedin.

This n8n workflow sends SMS messages through the Textbelt API by accepting phone numbers, messages, and API keys as inputs. It uses a manual trigger to start the process, sets the necessary data, and executes an HTTP POST request to deliver the SMS. Step-by-Step Explanation: Manual Trigger: Starts the workflow manually by clicking ‘Execute workflow’. Set Data Node: Defines the required input parameters (phone, message, and key) that will be sent to the SMS API. You can populate these fields with your target phone number, the text message, and your Textbelt API key. HTTP Request Node: Sends a POST request to https://textbelt.com/tex with the phone number, message, and API key in the request body to send the SMS. The response from the API confirms whether the message was successfully sent.

Telegram Tasker Bot — это сценарий n8n, который принимает голосовые сообщения в Telegram, автоматически превращает их в текст, извлекает из него ключевые поля задачи и создаёт карточку в нужной доске Trello. Пользователь просто говорит задачу — бот сам оформляет её и присылает ссылку на готовую карточку. Для использования вам потребуется telegram bot. Его можно создать через бота BotFather Так же понадобится доступ к API chatgpt - он используется только для транскрибции аудио в речь. Вы можете использовать любой другой сервис, по вашему выбору. И аккаунт в trello, с доступом к API. !Внимание! ID доски в trello можно взять из url ID столбца на доске трелло можно взять через инструменты разработчика (по крайней мере я так получал эти данные)

This is the first version of a template for a RAG/GenAI App using WordPress content. As creating, sharing, and improving templates brings me joy 😄, feel free to reach out on LinkedIn if you have any ideas to enhance this template! How It Works This template includes three workflows: Workflow 1**: Generate embeddings for your WordPress posts and pages, then store them in the Supabase vector store. Workflow 2**: Handle upserts for WordPress content when edits are made. Workflow 3**: Enable chat functionality by performing Retrieval-Augmented Generation (RAG) on the embedded documents. Why use this template? This template can be applied to various use cases: Build a GenAI application that requires embedded documents from your website's content. Embed or create a chatbot page on your website to enhance user experience as visitors search for information. Gain insights into the types of questions visitors are asking on your website. Simplify content management by asking the AI for related content ideas or checking if similar content already exists. Useful for internal linking. Prerequisites Access to Supabase for storing embeddings. Basic knowledge of Postgres and pgvector. A WordPress website with content to be embedded. An OpenAI API key Ensure that your n8n workflow, Supabase instance, and WordPress website are set to the same timezone (or use GMT) for consistency. Workflow 1 : Initial Embedding This workflow retrieves your WordPress pages and posts, generates embeddings from the content, and stores them in Supabase using pgvector. Step 0 : Create Supabase tables Nodes : Postgres - Create Documents Table: This table is structured to support OpenAI embedding models with 1536 dimensions Postgres - Create Workflow Execution History Table These two nodes create tables in Supabase: The documents table, which stores embeddings of your website content. The n8n_website_embedding_histories table, which logs workflow executions for efficient management of upserts. This table tracks the workflow execution ID and execution timestamp. Step 1 : Retrieve and Merge WordPress Pages and Posts Nodes : WordPress - Get All Posts WordPress - Get All Pages Merge WordPress Posts and Pages These three nodes retrieve all content and metadata from your posts and pages and merge them. Important: ** **Apply filters to avoid generating embeddings for all site content. Step 2 : Set Fields, Apply Filter, and Transform HTML to Markdown Nodes : Set Fields Filter - Only Published & Unprotected Content HTML to Markdown These three nodes prepare the content for embedding by: Setting up the necessary fields for content embeddings and document metadata. Filtering to include only published and unprotected content (protected=false), ensuring private or unpublished content is excluded from your GenAI application. Converting HTML to Markdown, which enhances performance and relevance in Retrieval-Augmented Generation (RAG) by optimizing document embeddings. Step 3: Generate Embeddings, Store Documents in Supabase, and Log Workflow Execution Nodes: Supabase Vector Store Sub-nodes: Embeddings OpenAI Default Data Loader Token Splitter Aggregate Supabase - Store Workflow Execution This step involves generating embeddings for the content and storing it in Supabase, followed by logging the workflow execution details. Generate Embeddings: The Embeddings OpenAI node generates vector embeddings for the content. Load Data: The Default Data Loader prepares the content for embedding storage. The metadata stored includes the content title, publication date, modification date, URL, and ID, which is essential for managing upserts. ⚠️ Important Note : Be cautious not to store any sensitive information in metadata fields, as this information will be accessible to the AI and may appear in user-facing answers. Token Management: The Token Splitter ensures that content is segmented into manageable sizes to comply with token limits. Aggregate: Ensure the last node is run only for 1 item. Store Execution Details: The Supabase - Store Workflow Execution node saves the workflow execution ID and timestamp, enabling tracking of when each content update was processed. This setup ensures that content embeddings are stored in Supabase for use in downstream applications, while workflow execution details are logged for consistency and version tracking. This workflow should be executed only once for the initial embedding. Workflow 2, described below, will handle all future upserts, ensuring that new or updated content is embedded as needed. Workflow 2: Handle document upserts Content on a website follows a lifecycle—it may be updated, new content might be added, or, at times, content may be deleted. In this first version of the template, the upsert workflow manages: Newly added content** Updated content** Step 1: Retrieve WordPress Content with Regular CRON Nodes: CRON - Every 30 Seconds Postgres - Get Last Workflow Execution WordPress - Get Posts Modified After Last Workflow Execution WordPress - Get Pages Modified After Last Workflow Execution Merge Retrieved WordPress Posts and Pages A CRON job (set to run every 30 seconds in this template, but you can adjust it as needed) initiates the workflow. A Postgres SQL query on the n8n_website_embedding_histories table retrieves the timestamp of the latest workflow execution. Next, the HTTP nodes use the WordPress API (update the example URL in the template with your own website’s URL and add your WordPress credentials) to request all posts and pages modified after the last workflow execution date. This process captures both newly added and recently updated content. The retrieved content is then merged for further processing. Step 2 : Set fields, use filter Nodes : Set fields2 Filter - Only published and unprotected content The same that Step 2 in Workflow 1, except that HTML To Makrdown is used in further Step. Step 3: Loop Over Items to Identify and Route Updated vs. Newly Added Content Here, I initially aimed to use 'update documents' instead of the delete + insert approach, but encountered challenges, especially with updating both content and metadata columns together. Any help or suggestions are welcome! :) Nodes: Loop Over Items Postgres - Filter on Existing Documents Switch Route existing_documents (if documents with matching IDs are found in metadata): Supabase - Delete Row if Document Exists: Removes any existing entry for the document, preparing for an update. Aggregate2: Used to aggregate documents on Supabase with ID to ensure that Set Fields3 is executed only once for each WordPress content to avoid duplicate execution. Set Fields3: Sets fields required for embedding updates. Route new_documents (if no matching documents are found with IDs in metadata): Set Fields4: Configures fields for embedding newly added content. In this step, a loop processes each item, directing it based on whether the document already exists. The Aggregate2 node acts as a control to ensure Set Fields3 runs only once per WordPress content, effectively avoiding duplicate execution and optimizing the update process. Step 4 : HTML to Markdown, Supabase Vector Store, Update Workflow Execution Table The HTML to Markdown node mirrors Workflow 1 - Step 2. Refer to that section for a detailed explanation on how HTML content is converted to Markdown for improved embedding performance and relevance. Following this, the content is stored in the Supabase vector store to manage embeddings efficiently. Lastly, the workflow execution table is updated. These nodes mirros the **Workflow 1 - Step 3 nodes. Workflow 3 : An example of GenAI App with Wordpress Content : Chatbot to be embed on your website Step 1: Retrieve Supabase Documents, Aggregate, and Set Fields After a Chat Input Nodes: When Chat Message Received Supabase - Retrieve Documents from Chat Input Embeddings OpenAI1 Aggregate Documents Set Fields When a user sends a message to the chat, the prompt (user question) is sent to the Supabase vector store retriever. The RPC function match_documents (created in Workflow 1 - Step 0) retrieves documents relevant to the user’s question, enabling a more accurate and relevant response. In this step: The Supabase vector store retriever fetches documents that match the user’s question, including metadata. The Aggregate Documents node consolidates the retrieved data. Finally, Set Fields organizes the data to create a more readable input for the AI agent. Directly using the AI agent without these nodes would prevent metadata from being sent to the language model (LLM), but metadata is essential for enhancing the context and accuracy of the AI’s response. By including metadata, the AI’s answers can reference relevant document details, making the interaction more informative. Step 2: Call AI Agent, Respond to User, and Store Chat Conversation History Nodes: AI Agent** Sub-nodes: OpenAI Chat Model Postgres Chat Memories Respond to Webhook** This step involves calling the AI agent to generate an answer, responding to the user, and storing the conversation history. The model used is gpt4-o-mini, chosen for its cost-efficiency.

BACKGROUND Malaysia's Inland Revenue (LHDN) provides an API to get the tax id number for a business entity, based on a given Business Registration number (BRN or SSM), or NRIC (MyKad). PROBLEM However, the API only allows one search at a time. SOLUTION This free workflow lets you do a batch search to get TIN for multiple SSM or NRIC. This is useful if you need to prepare your internal DB for e-invoicing PRE-REQUISITES 1) Get your connection client id and client secret from myhasil.gov.my website 2) Prepare your Google Sheet containing a list of SSM and NRIC you want to get the TIN 3) Create N8N credential to connect to your google sheet above SUPPORT Questions? Ask alex at halfborg dot com

This workflow template creates an audio stream session on UltraVox compatible with Plivo and sends it to Plivo. How It Works : Plivo initiates a call and requests the Answer URL. The workflow responds with Plivo XML to join the session. Note: Ensure you update the UltraVox API Key in the credentials. Update System Prompt based on your requirements. Check Youtube Video

PersonalNotesAssistant – Organize and Understand Your Thoughts with Local AI PersonalNotesAssistant is an offline-capable, AI-powered agent that helps you store, summarize, retrieve, and reflect on your personal notes and voice memos — all processed locally and sent via Telegram. Built to run efficiently on a Raspberry Pi 5, this agent supports a variety of note-taking styles and acts as your private memory extension. 🧠 What It Can Do Accept voice or text notes via Telegram Transcribe audio messages into clean, structured text (using Whisper) Automatically summarize or categorize notes with a local LLM Answer questions based on your past notes Retrieve relevant entries by topic, date, or keyword Help you journal or reflect by asking follow-up questions Work completely offline — no cloud or external APIs 🔧 How It Works Capture Notes via Telegram You send a voice message or text to your Telegram bot. The assistant supports both quick thoughts and long-form content. Transcription with Whisper (Local) If the input is a voice message, it is transcribed into text using Whisper running locally on your Raspberry Pi. AI Summarization & Tagging The transcribed or typed note is sent to LLaMA 3.2 via Ollama, which summarizes it, suggests tags, and stores it with metadata (e.g., timestamp, mood, theme). Storage & Retrieval Notes are stored in a local database (e.g., SQLite or JSON). You can later query the assistant with prompts like: “What did I say about stress last week?” “Summarize my ideas from this month.” “Show notes tagged with 'travel'.” Follow-Up & Reflection The agent can optionally engage with reflective prompts to help you deepen your thoughts or gain insight from what you’ve recorded. 💡 Use Cases Track personal growth, habits, or therapy progress Create voice memos while walking or commuting Maintain a structured journal without typing Use as a second brain to help you remember and revisit important thoughts 🔐 Privacy by Default Everything runs locally: No notes are uploaded to cloud platforms No audio is sent to third-party transcription services No LLM processing happens outside your device Ideal for privacy-minded users, psychologists, researchers, or digital minimalists who want AI assistance without surveillance. ⚙️ Technical Stack Raspberry Pi 5: Low-power edge device Whisper (local): For voice-to-text conversion Ollama + LLaMA 3.2: For summarization, classification, and retrieval Telegram Bot API: For input/output Custom Database (e.g., JSON/SQLite): For storing and querying notes 🧪 Real-Life Use This agent is actively used daily by the developer to log ideas, emotions, and plans. It has proven effective for lightweight journaling and context-aware memory assistance, even when offline.

CallForge - AI-Powered Product Insights Processor from Sales Calls Automate product feedback extraction from AI-analyzed sales calls and store structured insights in Notion for data-driven product decisions. 🎯 Who is This For? This workflow is designed for: ✅ Product managers tracking customer feedback and feature requests. ✅ Engineering teams identifying usability issues and AI/ML-related mentions. ✅ Customer success teams monitoring product pain points from real sales conversations. It streamlines product intelligence gathering, ensuring customer insights are structured, categorized, and easily accessible in Notion for better decision-making. 🔍 What Problem Does This Workflow Solve? Product teams often struggle to capture, categorize, and act on valuable feedback from sales calls. With CallForge, you can: ✔ Automatically extract and categorize product feedback from AI-analyzed sales calls. ✔ Track AI/ML-related mentions to gauge customer demand for AI-driven features. ✔ Identify feature requests and pain points for product development prioritization. ✔ Store structured feedback in Notion, reducing manual tracking and increasing visibility across teams. This workflow eliminates manual feedback tracking, allowing product teams to focus on innovation and customer needs. 📌 Key Features & Workflow Steps 🎙️ AI-Powered Product Feedback Processing This workflow processes AI-generated sales call insights and organizes them in Notion databases: Triggers when AI sales call data is received. Detects product-related feedback (feature requests, bug reports, usability issues). Extracts key product insights, categorizing feedback based on customer needs. Identifies AI/ML-related mentions, tracking customer interest in AI-driven solutions. Aggregates feedback and categorizes it by sentiment (positive, neutral, negative). Logs insights in Notion, making them accessible for product planning discussions. 📊 Notion Database Integration Product Feedback** → Logs feature requests, usability issues, and bug reports. AI Use Cases** → Tracks AI-related discussions and customer interest in machine learning solutions. 🛠 How to Set Up This Workflow 1. Prepare Your AI Call Analysis Data Ensure AI-generated sales call insights are available. Compatible with Gong, Fireflies.ai, Otter.ai, and other AI transcription tools. 2. Connect Your Notion Database Set up Notion databases for: 🔹 Product Feedback (logs feature requests and bug reports). 🔹 AI Use Cases (tracks AI/ML mentions and customer demand). 3. Configure n8n API Integrations Connect your Notion API key** in n8n under “Notion API Credentials.” Set up webhook triggers** to receive AI-generated sales insights. Test the workflow** using a sample AI sales call analysis. 🔧 How to Customize This Workflow 💡 Modify Notion Data Structure – Adjust fields to align with your product team's workflow. 💡 Refine AI Data Processing Rules – Customize how feature requests and pain points are categorized. 💡 Integrate with Slack or Email – Notify teams when recurring product issues emerge. 💡 Expand with Project Management Tools – Sync insights with Jira, Trello, or Asana to create product tickets automatically. ⚙️ Key Nodes Used in This Workflow 🔹 If Nodes – Detect if product feedback, AI mentions, or feature requests exist in AI data. 🔹 Notion Nodes – Create and update structured feedback entries in Notion. 🔹 Split Out & Aggregate Nodes – Process multiple insights and consolidate AI-generated data. 🔹 Wait Nodes – Ensure smooth sequencing of API calls and database updates. 🚀 Why Use This Workflow? ✔ Eliminates manual sales call review for product teams. ✔ Provides structured, AI-driven insights for feature planning and prioritization. ✔ Tracks AI/ML mentions to assess demand for AI-powered solutions. ✔ Improves product development strategies by leveraging real customer insights. ✔ Scalable for teams using n8n Cloud or self-hosted deployments. This workflow empowers product teams by transforming sales call data into actionable intelligence, optimizing feature planning, bug tracking, and AI/ML strategy. 🚀

How it Works This workflow is designed to automate the process of handling incoming emails, summarizing their content, generating appropriate responses, and obtaining approval before sending replies. Below are the key operational steps: Email Reception and Summarization: The workflow starts with an Email Trigger (IMAP) node that listens for new emails in a specified inbox. Once an email is received, its HTML content is processed by a Markdown node to convert it into plain text if necessary, followed by an Email Summarization Chain node which uses AI to create a concise summary of the email's content using prompts tailored for this purpose. Response Generation and Approval: A Write email node generates a professional response based on the summarized content, utilizing predefined templates and guidelines such as keeping responses under 100 words and ensuring they're formatted correctly in HTML. Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through a Gmail node configured with double-approval settings. If approved (Approve?), the finalized email is sent back to the original sender using the Send Email node; otherwise, it loops back for further edits or manual intervention. Set Up Steps To replicate this workflow within your own n8n environment, follow these essential configuration steps: Configuration: Begin by setting up an n8n instance either locally or via cloud services offered directly from their official site. Import the provided JSON configuration file into your workspace, making sure all required credentials like IMAP, SMTP, OpenAI API keys, etc., are properly set up under Credentials section since multiple nodes rely heavily on external integrations for functionalities like reading emails, generating summaries, crafting replies, and managing approvals. Customization: Adjust parameters according to specific business needs, including but not limited to adjusting the conditions used during conditional checks performed by nodes like Approve?. Modify the template messages given to AI models so they align closely with organizational tone & style preferences while maintaining professionalism expected in business communications. Ensure correct mappings between fields when appending data to external systems like Google Sheets or similar platforms where records might need tracking post-interaction completion.

Who is this template for? This workflow is perfect for competitive‑intel analysts, product managers, content marketers, and anyone who tracks multiple company blogs or news sources. If you need a weekly snapshot of fresh, on‑topic articles—without wading through dozens of tabs—this template is for you. What does it do? The workflow reads a curated list of candidate URLs from Google Sheets, filters out duplicates and off‑topic pages with an AI agent, scrapes the surviving links, generates three‑sentence summaries, logs the results back to Sheets, and delivers a polished HTML digest to your inbox every week. Why is it useful? Instead of manually opening competitor links, checking for relevance, copying highlights, and pasting them into reports, this automation does the grunt work for you. It turns scattered URLs into a searchable knowledge base and a ready‑to‑share email, freeing you to focus on insights and strategy—not housekeeping. How does it work? A Sunday‑morning cron trigger kicks things off. The workflow pulls links from the Input Links tab, compares them to the existing Summary tab, and passes fresh candidates to an AI “bouncer” that keeps only blog posts, tutorials, news, and product updates. Firecrawl then scrapes each page; Gemini 2.5‑Flash and OpenAI condense the content into title, author, date, and summary. The structured data is appended to your Summary sheet and formatted into a company‑grouped HTML digest, which lands in your email before the workweek starts. Set up steps Clone the workflow Import the JSON into your n8n Cloud workspace. Create the Google Sheet Make a new spreadsheet with two tabs: Input Links and Summary (names must match). In Input Links, add columns Company, Page Type, and Link (or rename to match the node mapping). Leave Summary blank—the workflow will populate it. Copy the Sheet URL; you’ll paste it into two Google Sheets nodes. Add credentials (n8n ▸ Credentials) Google Sheets OAuth2 – Authorise with the Google account that owns the spreadsheet. Gmail OAuth2 – Authorise the Gmail account that should send the digest. Firecrawl HTTP Header Auth – Set Authorization: Bearer <YOUR_FIRECRAWL_API_KEY>. Point nodes to your Sheet Open each Google Sheets node (Input Links, Read_Url_Summary_Tool, Append row in sheet, Get row(s) in sheet). Paste the Document ID (found in the Sheet URL) and select the correct tab (Input Links or Summary). Update email recipients In the Send a message (Gmail) node, replace the sample addresses with your own distribution list. Adjust scheduling (optional) Double‑click the Schedule Trigger node and change the cron expression if you prefer a different day/time. Tune AI models (optional) OpenAI o4‑mini and Gemini 2.5‑Flash nodes default to cost‑efficient settings. Feel free to switch models or tweak temperature to suit your tone. Test with a single URL Add one row in Input Links, then execute the workflow manually (▶ Run). Verify that a new row appears in Summary and an email lands in your inbox. Go live Activate the workflow (toggle in top bar). Confirm the green status badge and wait for the next scheduled run. Tip: The Firecrawl Free tier limits you to ~10 requests/min. If you scale beyond that, raise the batching interval in both Firecrawl nodes or upgrade your Firecrawl plan.

This n8n workflow collects and summarizes news from multiple RSS feeds, using OpenAI to generate a concise summary that can be sent to WhatsApp or other destinations. Perfect for automating your daily news digest. 🔁 Workflow Breakdown: Schedule Trigger Start the workflow on your desired schedule (daily, hourly, etc.). 🟨 Note: Set the trigger however you wish. RSS Feeds (My RSS 01–04) Fetches articles from four different RSS sources. 🟨 Note: You can add as many RSS feeds as you want. Edit Fields (Edit Fields1–3) Normalizes RSS fields (title, link, etc.) to ensure consistency across different sources. Merge (append mode) Combines the RSS items into a single unified list. Filter Optionally filter articles by keywords, date, or categories. Limit Limits the analysis to the 10 most recent articles. 🟨 Note: This keeps the result concise and avoids overloading the summary. Aggregate Prepares the selected news for summarization by combining them into a single content block. OpenAI (Message Assistant) Summarizes the aggregated news items in a clean and readable format using AI. Send Summary to WhatsApp Sends the AI-generated summary to a WhatsApp endpoint via webhook (yoururlapi.com). You can replace this with an email service, Google Drive, or any other destination. 🟨 Note: You can send it to your WhatsApp API, email, drive, etc. No Operation (End) Final placeholder to safely close the workflow. You may expand from here if needed.

**Get Started ** Creator: @obisdev This workflow powers a fully automated WhatsApp chatbot using a self-hosted Venom Bot instead of the official WhatsApp Business API. It integrates Google Gemini AI to generate intelligent, conversational responses and optionally pulls factual information from a Google Docs-based knowledge base. Designed for small businesses and creators, the bot can maintain contextual memory across messages and act as a smart virtual assistant for sales, support, and lead generation. Overview This n8n workflow connects with a custom-hosted Venom Bot that simulates WhatsApp Web to send and receive messages. It uses a Webhook trigger to receive incoming messages, processes them with an AI Agent powered by Gemini, optionally pulls extra data from a Google Doc or Google Sheet, and sends a smart reply back through the Venom Bot. The workflow also includes a memory system to retain user context, making it capable of handling follow-up questions and dynamic conversations. Who this workflow is for Small Business Owners: Offer 24/7 customer service on WhatsApp without paying for Meta’s Business API. Freelancers & Developers: Build, test, and monetize intelligent bots without the approval process of WhatsApp’s API. Online Sellers & Creators: Handle FAQs, orders, and customer inquiries via WhatsApp on autopilot. Marketers: Deploy campaign bots that respond to DMs with personalized product suggestions or lead captures. Hackers & Builders: Experiment with unofficial APIs to control WhatsApp reliably without breaking TOS for small-scale use. Tools Used n8n: The automation platform managing flow, context, and decision logic. Venom Bot: A Node.js-based, self-hosted WhatsApp Web bot used to send/receive messages. Google Gemini: AI engine for generating context-aware replies. Google Docs (Optional): Acts as a structured knowledge base for business info or FAQs. Google Sheets (Optional): Feeds real-time or structured data into your AI responses. How to Install Import the Workflow: Download the .json and import it into your n8n instance. Set Up Venom Bot: Deploy Venom Bot (on VPS or local) and set it to send messages to your Webhook URL. Webhook Configuration: Update the Webhook node in n8n and set 'Respond' to "Using Respond to Webhook Node". Connect Google Gemini: Add your Gemini API key in n8n credentials. Set Up Google Docs (Optional): Link the document containing your knowledge base. Enable Conversational Memory: Use ={{ $("Process Message").first().json.from }} as the session ID. Check API Key Matching: Ensure the API_SECRET_KEY in Venom .env matches the authorization header in n8n. Customize Persona & Prompts: Update the AI Agent system message to fit your brand tone. Use Cases Customer service without WhatsApp Business API Smart lead generation bots E-commerce order responders AI-powered chatbot for DMs FAQ responder with knowledge base support Connect with Me Email: obisdev@gmail.com Twitter/X: @obisdev GitHub: github.com/obisdev Visit: obisdev.vercel.app #n8n #whatsappautomation #venombot #chatbots #noapi #geminiapi #googleworkspace #aiassistant #nocode #vpsautomation #chatbotwithoutapi #automationtools #customerbot #salesautomation #googleintegration