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This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions. This template is intended to help introduce n8n users interested in building with WhatsApp. How it works This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot. A product brochure is imported via HTTP request node and its text contents extracted. The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot. A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out. The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool. The Agent's response is sent back to the user via the WhatsApp node. How to use Once you've setup and configured your WhatsApp account and credentials First, populate the vector store by clicking the "Test Workflow" button. Next, activate the workflow to enable the WhatsApp chatbot. Message your designated WhatsApp number and you should receive a message from the AI sales agent. Tweak datasource and behaviour as required. Requirements WhatsApp Business Account OpenAI for LLM Customising this workflow Upgrade the vector store to Qdrant for persistance and production use-cases. Handle different WhatsApp message types for a more rich and engaging experience for customers.

Enrich your company lists with OpenAI GPT-3 ↓ You’ll get valuable information such as: Market (B2B or B2C) Industry Target Audience Value Proposition This will help you to: add more personalization to your outreach make informed decisions about which accounts to target I've made the process easy with an n8n workflow. Here is what it does: Retrieve website URLs from Google Sheets Extract the content for each website Analyze it with GPT-3 Update Google Sheets with GPT-3 data

How it works: The workflow starts by sending a request to a website to retrieve its HTML content. It then parses the HTML extracting the relevant information The extracted data is storted and converted into a CSV file. The CSV file is attached to an email and sent to your specified address. The data is simultaneously saved to both Google Sheets and Microsoft Excel for further analysis or use. Set-up steps: Change the website to scrape in the "Fetch website content" node Configure Microsoft Azure credentials with Microsoft Graph permissions (required for the Save to Microsoft Excel 365 node) Configure Google Cloud credentials with access to Google Drive, Google Sheets and Gmail APIs (the latter is required for the Send CSV via e-mail node).

Who is this for? This workflow is for all sales reps and lead generation manager who need to prepare their prospecting activities, and find relevant information to personalize their outreach. Use Case This workflow allows you to do account research with the web using AI. It has the potential to replace manual work done by sales rep when preparing their prospecting activities by searching complex information available online. What this workflow does The advanced AI module has 2 capabilities: Research Google using SerpAPI Visit and get website content using a sub-workflow From an unstructured input like a domain or a company name. It will return the following properties: domain company Linkedin Url cheapest plan has free trial has entreprise plan has API market (B2B or B2C) The strength of n8n here is that you can adapt this workflow to research whatever information you need. You just have to precise it in the prompt and to precise the output format in the "Strutured Output Parser" module. Detailed instructions + video guide can be found by following this link.

Who is this template for? This workflow template is designed for sales, marketing, and business development professionals who want a cost-effective and efficient way to generate leads. By leveraging n8n core nodes, it scrapes business emails from Google Maps without relying on third-party APIs or paid services, ensuring there are no additional costs involved. Ideal for small business owners, freelancers, and agencies, this template automates the process of collecting contact information for targeted outreach, making it a powerful tool for anyone looking to scale their lead generation efforts without incurring extra expenses. How it works This template streamlines email scraping from Google Maps using only n8n core nodes, ensuring a completely free and self-contained solution. Here’s how it operates: Input Queries You provide a list of queries, each consisting of keywords related to the type of business you want to target and the specific region or subregion you’re interested in. Iterates through Queries The workflow processes each query one at a time. For each query, it triggers a sub-workflow dedicated to handling the scraping tasks. Scrapes Google Maps for URLs Using these queries, the workflow scrapes Google Maps to collect URLs of business listings matching the provided criteria. Fetches HTML Content The workflow then fetches the HTML pages of the collected URLs for further processing. Extracts Emails Using a Code Node with custom JavaScript, the workflow runs regular expressions on the HTML content to extract business email addresses. Setup Add Queries: Open the first node, "Run Workflow" and input a list of queries, each containing the business keywords and the target region. Configure the Google Sheets Node: Open the Google Sheets node and select a document and specific sheet where the scraped results will be saved. Run the workflow: Click on "Test workflow" and watch your Google Sheets document gradually receive business email addresses. Customize as Needed: You can adjust the regular expressions in the Code Node to refine the email extraction logic or add logic to extract other kinds of information.

This workflow automatically generates draft replies in Gmail. It's designed for anyone who manages a high volume of emails or often face writer's block when crafting responses. Since it doesn't send the generated message directly, you're still in charge of editing and approving emails before they go out. How It Works: Email Trigger: activates when new emails reach the Gmail inbox Assessment: uses OpenAI gpt-4o and a JSON parser to determine if a response is necessary. Reply Generation: crafts a reply with OpenAI GPT-4 Turbo Draft Integration: after converting the text to html, it places the draft into the Gmail thread as a reply to the first message Set Up Overview (~10 minutes): OAuth Configuration (follow n8n instructions here): Setup Google OAuth in Google Cloud console. Make sure to add Gmail API with the modify scope. Add Google OAuth credentials in n8n. Make sure to add the n8n redirect URI to the Google Cloud Console consent screen settings. OpenAI Configuration: add OpenAI API Key in the credentials Tweaking the prompt: edit the system prompt in the "Generate email reply" node to suit your needs Detailed Walkthrough Check out this blog post where I go into more details on how I built this workflow. Reach out to me here if you need help building automations for your business.

Task: Create a simple API endpoint using the Webhook and Respond to Webhook nodes Why: You can prototype or replace a backend process with a single workflow Main use cases: Replace backend logic with a workflow

Want to learn the basics of n8n? Our comprehensive quick quickstart tutorial is here to guide you through the basics of n8n, step by step. Designed with beginners in mind, this tutorial provides a hands-on approach to learning n8n's basic functionalities.

You still can use the app in a workflow even if we don’t have a node for that or the existing operation for that. With the HTTP Request node, it is possible to call any API point and use the incoming data in your workflow Main use cases: Connect with apps and services that n8n doesn’t have integration with Web scraping How it works This workflow can be divided into three branches, each serving a distinct purpose: 1.Splitting into Items (HTTP Request - Get Mock Albums): The workflow initiates with a manual trigger (On clicking 'execute'). It performs an HTTP request to retrieve mock albums data from "https://jsonplaceholder.typicode.com/albums." The obtained data is split into items using the Item Lists node, facilitating easier management. 2.Data Scraping (HTTP Request - Get Wikipedia Page and HTML Extract): Another branch of the workflow involves fetching a random Wikipedia page using an HTTP request to "https://en.wikipedia.org/wiki/Special:Random." The HTML Extract node extracts the article title from the fetched Wikipedia page. 3.Handling Pagination (The final branch deals with handling pagination for a GitHub API request): It sends an HTTP request to "https://api.github.com/users/that-one-tom/starred," with parameters like the page number and items per page dynamically set by the Set node. The workflow uses conditions (If - Are we finished?) to check if there are more pages to retrieve and increments the page number accordingly (Set - Increment Page). This process repeats until all pages are fetched, allowing for comprehensive data retrieval.

Task: Merge two datasets into one based on matching rules Why: A powerful capability of n8n is to easily branch out the workflow in order to process different datasets. Even more powerful is the ability to join them back together with SQL-like joining logic. Main use cases: Appending data sets Keep only new items Keep only existing items

This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions. This template is intended to help introduce n8n users interested in building with WhatsApp. How it works This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot. A product brochure is imported via HTTP request node and its text contents extracted. The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot. A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out. The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool. The Agent's response is sent back to the user via the WhatsApp node. How to use Once you've setup and configured your WhatsApp account and credentials First, populate the vector store by clicking the "Test Workflow" button. Next, activate the workflow to enable the WhatsApp chatbot. Message your designated WhatsApp number and you should receive a message from the AI sales agent. Tweak datasource and behaviour as required. Requirements WhatsApp Business Account OpenAI for LLM Customising this workflow Upgrade the vector store to Qdrant for persistance and production use-cases. Handle different WhatsApp message types for a more rich and engaging experience for customers.

This workflow will backup your workflows to Github. It uses the public api to export all of the workflow data using the n8n node. It then loops over the data checks in Github to see if a file exists that uses the workflow name. Once checked it will then update the file on Github if it exists, Create a new file if it doesn't exist and if it's the same it will ignore the file. Config Options repo_owner - Github owner repo_name - Github repository name repo_path - Path within the Github repository >This workflow has been updated to use the n8n node and the code node so requires at least version 0.198.0 of n8n

Video Guide I prepared a detailed guide explaining how to set up and implement this scenario, enabling you to chat with your documents stored in Supabase using n8n. Youtube Link Who is this for? This workflow is ideal for researchers, analysts, business owners, or anyone managing a large collection of documents. It's particularly beneficial for those who need quick contextual information retrieval from text-heavy files stored in Supabase, without needing additional services like Google Drive. What problem does this workflow solve? Manually retrieving and analyzing specific information from large document repositories is time-consuming and inefficient. This workflow automates the process by vectorizing documents and enabling AI-powered interactions, making it easy to query and retrieve context-based information from uploaded files. What this workflow does The workflow integrates Supabase with an AI-powered chatbot to process, store, and query text and PDF files. The steps include: Fetching and comparing files to avoid duplicate processing. Handling file downloads and extracting content based on the file type. Converting documents into vectorized data for contextual information retrieval. Storing and querying vectorized data from a Supabase vector store. File Extraction and Processing: Automates handling of multiple file formats (e.g., PDFs, text files), and extracts document content. Vectorized Embeddings Creation: Generates embeddings for processed data to enable AI-driven interactions. Dynamic Data Querying: Allows users to query their document repository conversationally using a chatbot. Setup N8N Workflow Fetch File List from Supabase: Use Supabase to retrieve the stored file list from a specified bucket. Add logic to manage empty folder placeholders returned by Supabase, avoiding incorrect processing. Compare and Filter Files: Aggregate the files retrieved from storage and compare them to the existing list in the Supabase files table. Exclude duplicates and skip placeholder files to ensure only unprocessed files are handled. Handle File Downloads: Download new files using detailed storage configurations for public/private access. Adjust the storage settings and GET requests to match your Supabase setup. File Type Processing: Use a Switch node to target specific file types (e.g., PDFs or text files). Employ relevant tools to process the content: For PDFs, extract embedded content. For text files, directly process the text data. Content Chunking: Break large text data into smaller chunks using the Text Splitter node. Define chunk size (default: 500 tokens) and overlap to retain necessary context across chunks. Vector Embedding Creation: Generate vectorized embeddings for the processed content using OpenAI's embedding tools. Ensure metadata, such as file ID, is included for easy data retrieval. Store Vectorized Data: Save the vectorized information into a dedicated Supabase vector store. Use the default schema and table provided by Supabase for seamless setup. AI Chatbot Integration: Add a chatbot node to handle user input and retrieve relevant document chunks. Use metadata like file ID for targeted queries, especially when multiple documents are involved. Testing Upload sample files to your Supabase bucket. Verify if files are processed and stored successfully in the vector store. Ask simple conversational questions about your documents using the chatbot (e.g., "What does Chapter 1 say about the Roman Empire?"). Test for accuracy and contextual relevance of retrieved results.

Temporary solution using the undocumented REST API for backups using Google drive. Please note that there are issues with this workflow. It does not support versioning, so please know that it will create multiple copies of the workflows so if you run this daily it will make the folder grow quickly. Once I figure out how to version in Gdrive I'll update it here.

This n8n workflow template lets teams easily generate a custom AI chat assistant based on the schema of any Notion database. Simply provide the Notion database URL, and the workflow downloads the schema and creates a tailored AI assistant designed to interact with that specific database structure. Set Up Watch this quick set up video 👇 Key Features Instant Assistant Generation**: Enter a Notion database URL, and the workflow produces an AI assistant configured to the database schema. Advanced Querying**: The assistant performs flexible queries, filtering records by multiple fields (e.g., tags, names). It can also search inside Notion pages to pull relevant content from specific blocks. Schema Awareness**: Understands and interacts with various Notion column types like text, dates, and tags for accurate responses. Reference Links**: Each query returns direct links to the exact Notion pages that inform the assistant’s response, promoting transparency and easy access. Self-Validation**: The workflow has logic to check the generated assistant, and if any errors are detected, it reruns the agent to fix them. Ideal for Product Managers**: Easily access and query product data across Notion databases. Support Teams**: Quickly search through knowledge bases for precise information to enhance support accuracy. Operations Teams**: Streamline access to HR, finance, or logistics data for fast, efficient retrieval. Data Teams**: Automate large dataset queries across multiple properties and records. How It Works This AI assistant leverages two HTTP request tools—one for querying the Notion database and another for retrieving data within individual pages. It’s powered by the Anthropic LLM (or can be swapped for GPT-4) and always provides reference links for added transparency.

A robust n8n workflow designed to enhance Telegram bot functionality for user management and broadcasting. It facilitates automatic support ticket creation, efficient user data storage in Redis, and a sophisticated system for message forwarding and broadcasting. How It Works Telegram Bot Setup: Initiate the workflow with a Telegram bot configured for handling different chat types (private, supergroup, channel). User Data Management: Formats and updates user data, storing it in a Redis database for efficient retrieval and management. Support Ticket Creation: Automatically generates chat tickets for user messages and saves the corresponding topic IDs in Redis. Message Forwarding: Forwards new messages to the appropriate chat thread, or creates a new thread if none exists. Support Forum Management: Handles messages within a support forum, differentiating between various chat types and user statuses. Broadcasting System: Implements a broadcasting mechanism that sends channel posts to all previous bot users, with a system to filter out blocked users. Blocked User Management: Identifies and manages blocked users, preventing them from receiving broadcasted messages. Versatile Channel Handling: Ensures that messages from verified channels are properly managed and broadcasted to relevant users. Set Up Steps Estimated Time**: Around 30 minutes. Requirements**: A Telegram bot, a Redis database, and Telegram group/channel IDs are necessary. Configuration**: Input the Telegram bot token and relevant group/channel IDs. Configure message handling and user data processing according to your needs. Detailed Instructions**: Sticky notes within the workflow provide extensive setup information and guidance. Live Demo Workflow Bot: Telegram Bot Link (Click here) Support Group: Telegram Group Link (Click here) Broadcasting Channel: Telegram Channel Link (Click here) Keywords: n8n workflow, Telegram bot, chat ticket system, Redis database, message broadcasting, user data management, support forum automation

Who is this for This workflow is perfect for teams and individuals who manage extensive data in Notion and need a quick, AI-powered way to interact with their databases. If you're looking to streamline your knowledge management, automate searches, and get faster insights from your Notion databases, this workflow is for you. It’s ideal for support teams, project managers, or anyone who needs to query specific data across multiple records or within individual pages of their Notion setup. Check out the Notion template this Assistant is set up to use: https://www.notion.so/templates/knowledge-base-ai-assistant-with-n8n How it works The Notion Database Assistant uses an AI Agent built with Retrieval-Augmented Generation (RAG) to query this Knowledge Base style Notion database. The assistant can search across multiple properties like tags or question and retrieves content from inside individual Notion pages for additional context. Key features include: Querying the database with flexible filters. Searching within individual Notion pages and extracting relevant blocks. Providing a reference link to the exact Notion pages used to inform its responses, ensuring transparency and easy verification. This assistant uses two HTTP request tools—one for querying the Notion database and another for pulling data from within specific pages. It streamlines knowledge retrieval, offering a conversational, AI-driven way to interact with large datasets. Set up Find basic set up instructions inside the workflow itself or watch a quickstart video 👇

This n8n workflow shows how using multimodal LLMs with AI vision can tackle tricky image validation tasks which are near impossible to achieve with code and often impractical to be done by humans at scale. You may need image validation when users submitted photos or images are required to meet certain criteria before being accepted. A wine review website may require users only submit photos of wine with labels, a bank may require account holders to submit scanned documents for verification etc. In this demonstration, our scenario will be to analyse a set of portraits to verify if they meet the criteria for valid passport photos according to the UK government website (https://www.gov.uk/photos-for-passports). How it works Our set of portaits are jpg files downloaded from our Google Drive using the Google Drive node. Each image is resized using the Edit Image node to ensure a balance between resolution and processing speed. Using the Basic LLM node, we'll define a "user message" option with the type of binary (data). This will allow us to pass our portrait to the LLM as an input. With our prompt containing the criteria pulled off the passport photo requirements webpage, the LLM is able to validate the photo does or doesn't meet its criteria. A structured output parser is used to structure the LLM's response to a JSON object which has the "is_valid" boolean property. This can be useful to further extend the workflow. Requirements Google Gemini API key Google Drive account Customising this workflow Not using Gemini? n8n's LLM node works with any compatible multimodal LLM so feel free to swap Gemini out for OpenAI's GPT4o or Antrophic's Claude Sonnet. Don't need to validate portraits? Try other use cases such as document classification, security footage analysis, people tagging in photos and more.