by Yulia
This n8n workflow demonstrates how to create an agent using LangChain and SQLite. The agent can understand natural language queries and interact with a SQLite database to provide accurate answers. πͺ π Setup Run the top part of the workflow once. It downloads the example SQLite database, extracts from a ZIP file and saves locally (chinook.db). π£οΈ Chatting with Your Data Send a message in a chat window. Locally saved SQLite database loads automatically. User's chat input is combined with the binary data. The LangChain Agend node gets both data and begins to work. The AI Agent will process the user's message, perform necessary SQL queries, and generate a response based on the database information. ποΈ π Example Queries Try these sample queries to see the AI Agent in action: "Please describe the database" - Get a high-level overview of the database structure, only one or two queries are needed. "What are the revenues by genre?" - Retrieve revenue information grouped by genre, LangChain agent iterates several time before producing the answer. The AI Agent will store the final answer in its memory, allowing for context-aware conversations. π¬ Read the full article: π https://blog.n8n.io/ai-agents/
by Lucas Peyrin
How it works This template launches your very first AI Agent βan AI-powered chatbot that can do more than just talkβ it can take action using tools. Think of an AI Agent as a smart assistant, and the tools are the apps on its phone. By connecting it to other nodes, you give your agent the ability to interact with real-world data and services, like checking the weather, fetching news, or even sending emails on your behalf. This workflow is designed to be the perfect starting point: The Chat Interface:** A Chat Trigger node provides a simple, clean interface for you to talk to your agent. The Brains:** The AI Agent node receives your messages, intelligently decides which tool to use (if any), and formulates a helpful response. Its personality and instructions are fully customizable in the "System Message". The Language Model:* It uses *Google Gemini** to power its reasoning and conversation skills. The Tools:** It comes pre-equipped with two tools to demonstrate its capabilities: Get Weather: Fetches real-time weather forecasts. Get News: Reads any RSS feed to get the latest headlines. The Memory:** A Conversation Memory node allows the agent to remember the last few messages, enabling natural, follow-up conversations. Set up steps Setup time: ~2 minutes You only need one thing to get started: a free Google AI API key. Get Your Google AI API Key: Visit Google AI Studio at aistudio.google.com/app/apikey. Click "Create API key in new project" and copy the key that appears. Add Your Credential in n8n: On the workflow canvas, go to the Connect your model (Google Gemini) node. Click the Credential dropdown and select + Create New Credential. Paste your API key into the API Key field and click Save. Start Chatting! Go to the Example Chat node. Click the "Open Chat" button in its parameter panel. Try asking it one of the example questions, like: "What's the weather in Paris?" or "Get me the latest tech news." That's it! You now have a fully functional AI Agent. Try adding more tools (like Gmail or Google Calendar) to make it even more powerful.
by Keith Rumjahn
Who is this template for? Anyone who is drowning in emails Busy parents who has alot of school emails Busy executives with too many emails Case Study I get too many emails from my kid's school about soccer practice, lunch orders and parent events. I use this workflow to read all the emails and tell me what is important and what requires actioning. Read more -> How I used A.I. to read all my emails What this workflow does It uses IMAP to read the emails from your email account (i.e. Gmail). It then passes the email to Openrouter.ai and uses a free A.I. model to read and summarize the email. It then sends the summary as a message to your messenger (i.e. Line). Setup You need to find your email server IMAP credentials. Input your openrouter.ai API credentials or replace the HTTP request node with an A.I. node such as OpenAI. Input your messenger credentials. I use Line but you can change the node to another messenger line Telegram. You need to change the message ID to your ID inside the http request. You can find your user ID inside the https://developers.line.biz/console/. Change the "to": {insert your user ID}. How to adjust it to your needs You can change the A.I. prompt to fit your needs by telling it to mark emails from a certain address as important. You can change the A.I. model from the current meta-llama/llama-3.1-70b-instruct:free to a paid model or other free models. You can change the messenger node to telegram or any other messenger app you like.
by bangank36
This workflow converts an exported CSV from Squarespace profiles into a Shopify-compatible format for customer import. How It Works Clone this Google Sheets template, which includes two sheets: Squarespace Profiles (Input) Go to Squarespace Dashboard β Contacts Click the three-dot icon β Select Export all Contacts Shopify Customers (Output) This sheet formats the data to match Shopify's customer import CSV. Shopify Dashboard β Customers β Import customers by CSV The workflow can run on-demand or be triggered via webhook. Via webhook Set up webhook node to expect a POST request Trigger the webhook using this code (psuedo) - replace {webhook-url} with the actual URL const formData = new FormData(); formData.append('file', blob, 'profiles_export.csv'); // Add file to FormData fetch('{webhook-url}', { // Replace with your target URL method: 'POST', mode: 'no-cors', body: formData }); The data is processed into the Shopify Customers sheet. Manually trigger Import Squarespace profiles into the sheet. Run the workflow to convert and populate the Shopify Customers sheet. Once workflow is done, export the Shopify to csv and import to Shopify customers Requirements To use this template, you need: Google Sheets API credentials Google Sheets Setup Use this sample Google Sheets template to get started quickly. Who Is This For? For anyone looking to automate Squarespace contact exports into a Shopify-compatible formatβno more manual conversion! Explore More Templates Check out my other n8n templates: π n8n.io/creators/bangank36
by Srinivasan KB
This n8n workflow provides a ready-to-use API endpoint for extracting structured data from images. It processes an image URL using an AI-powered OCR model and returns the extracted details in a structured JSON format. Use Cases Document OCR** β Extract details from ID cards, invoices, receipts, etc. Text Extraction from Images** β Process screenshots, scanned documents, and photos. Automated Form Processing** β Digitize and capture information from paper forms. Business Card Data Extraction** β Extract names, emails, and phone numbers from business cards. How It Works Send a GET request with an image URL and define the required extraction parameters. The image is converted to base64 for processing. The AI model (Gemini API - Flash Lite) extracts relevant text. The response returns structured JSON data containing only the requested fields. Features βοΈ No-Code API Setup β Easily integrate into any application. βοΈ Customizable Extraction β Modify the request parameters to fit your needs. βοΈ AI-Powered OCR β Uses advanced models for accurate text recognition. βοΈ Automated Processing β Ideal for document processing and digitization. Integration Works with any frontend/backend system that supports API calls. Can be used for workflow automation in CRM, ERP, and document management solutions. Supports further customization based on specific OCR requirements.
by Dr. Firas
Who Is This For This workflow is ideal for content creators, bloggers, marketers, and professionals seeking to automate the creation and publication of SEO-optimized articles. It's particularly beneficial for those utilizing Notion for content management and WordPress for publishing.β What Problem Does This Workflow Solve Manually creating SEO-friendly articles is time-consuming and requires consistent effort. This workflow streamlines the entire processβfrom detecting updates in Notion to publishing on WordPressβby leveraging AI for content generation, thereby reducing the time and effort involved.β What This Workflow Does Monitor Notion Updates: Detects changes in a specified Notion database.β AI Content Generation: Utilizes an AI model to produce an SEO-optimized article based on Notion data.β Publish to WordPress: Automatically posts the generated article to a WordPress site.β Email Notification: Sends an email containing the article's title and URL.β Update Notion Database: Updates the corresponding entry in the Notion database with the article details.β Setup Guide Prerequisites WordPress account with API access.β API key for the AI model used.β Notion integration with the relevant database ID.β Credentials for the email service used (e.g., Gmail).β Community Node Requirement: This workflow utilizes the n8n-nodes-mcp community node, which is only compatible with self-hosted instances of n8n. For more information on installing and managing community nodes, refer to the n8n documentation.β n8n Docs Steps Import the workflow into your self-hosted n8n instance.β Install the required community node (n8n-nodes-mcp).β Configure API credentials for WordPress, the AI service, Notion, and the email service.β Define necessary variables, such as the notification email address and Notion database IDs.β Activate the workflow to automate the process.β How to Customize This Workflow AI Prompt: Adjust the prompt used for content generation to align with your preferred tone and style.β Article Structure: Modify the structure of the generated article by tweaking settings in the content generation node.β Notifications: Customize the content and recipients of the emails sent post-publication.β Notion Updates: Tailor the fields updated in Notion to suit your specific requirements.
by Pavel Duchovny
Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. Until now, these pieces had to be custom-wired. But with the new native n8n nodes for MongoDB Atlas, we reduce that overhead dramatically. With just a few clicks: Store and recall long-term memory from MongoDB Query vector embeddings stored in Atlas Vector Search Use these results in your LLM chains and automation logic In this example we present an ingestion and AI Agent flows that focus around Travel Planning. The different interest points that we want the agent to know about can be ingested into the vector store. The AI Agent will use the vector store tool to get relevant context about those points of interest if it needs to. Prerequisites MongoDB Atlas project and Cluster OpenAI Valid API Key for embeddings (can be other provider) Gemini API Key for the LLM (can be other provider) How it works: There are 2 main flows. One is ingesting flow: Gets a document from a webhook and use MongoDB Vector Atlas to embed the document title and description into points_of_interest collection. Embeddings are stored in a field named embedding Embeddings used are OpenAI's but it can be any type of supported embedders. Second flow is an AI Agent node with Chat Memory Stored in MongoDB Atlas and a Vector Search node as a tool: Chat Message Trigger**: Chatting with the AI Agent will trigger the conversation store in the MongoDB Chat Memory node. When data is necessary like a location search or details it will go to the "Vector Search" tool. Vector Search Tool** - uses Atlas Vector Search index created on the points_of_interest collection: // index name : "vector_index" // If you change an embedding provider make sure the numDimensions correspond to the model. { "fields": [ { "type": "vector", "path": "embedding", "numDimensions": 1536, "similarity": "cosine" } ] } Additional Resources MongoDB Atlas Vector Search n8n Atlas Vector Search docs
by Abdullah
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Overview This workflow automates the process of transcribing audio files and summarizing them using OpenAI models, with the final output stored neatly in Notion. Whether you're a researcher, content creator, student, or professional, this automation saves time by converting voice recordings into actionable summaries with zero manual effort. Created by: Abdullah Dilshad Contact: iamabdullahdilshad@gmail.com Who Itβs For This template is ideal for: Researchers**: Transcribe and summarize interviews, lectures, or research recordings. Content Creators**: Convert podcasts or videos into transcripts and social captions/show notes. Students**: Automatically turn lectures or study group audio into summarized notes. Professionals**: Log meeting notes and summaries directly into your Notion workspace. How It Works This four-step workflow performs the following: Step 1:* *Trigger: New Audio in Google Drive** Automatically triggers when a new audio file (MP3/WAV) is uploaded to a specified Google Drive folder.The file is then downloaded for processing. Step 2: Transcribe Audio with Whisper** The audio file is sent to OpenAIβs Whisper model for high-accuracy transcription. Step 3: Summarize Transcript with GPT-4** The transcript is passed to GPT-4, which generates a clean, concise summary. Step 4: Store Summary in Notion** A new Notion page is created with the generated summary and optional metadata (file name, upload time, etc.). Setup Instructions Step 1: Google Drive Trigger** Connect your Google Drive account. Select the folder you want to monitor. This node detects new file uploads and passes the file for download. Step 2: Download File** Downloads the new audio file for transcription. Step 3: Transcribe Recording (OpenAI Whisper) Connect your OpenAI API Key. Ensure this node receives the binary audio file. It will return the transcription as plain text. Step 3: Transcribe Recording (OpenAI Whisper)** Connect your OpenAI API Key. Ensure this node receives the binary audio file. It will return the transcription as plain text. Step 4: Summarize Transcript (GPT-4 via AI Agent)** Use your OpenAI API Key. Configure a summarization prompt like: "Summarize the following transcript in a clear and concise manner:" Connect the output from Whisper into this GPT-4 prompt. Step 5: Notion Integration** Connect your Notion account. Choose or create a database to store summaries. Map the GPT output (summary) to a "Text" or "Rich Text" property. Optionally include metadata like filename, file upload date, etc.
by Adrian Bent
This workflow takes two inputs, YouTube video URL (required) and a description of what information to extract from the video. If the description/"what you want" field is left empty, the default prompt will generate a detailed summary and description of the video's contents. However, you can ask for something more specific using this field/input. ++ Don't forget to make the workflow Active and use the production URL from the form node. Benefits Instant Summary Generation - Convert hours of watching YouTube videos to familiar, structured paragraphs and sentences in less than a minute Live Integration - Generate a summary or extract information on the contents of a YouTube video whenever, wherever Virtually Complete Automation - All that needs to be done is to add the video URL and describe what you want to know from the video Presentation - You can ask for a specific structure or tone to better help you understand or study the contents of the video How It Works Smart Form Interface: Simple N8N form captures video URL and description of what's to be extracted Designed for rapid and repeated completion anywhere and anytime Description Check: Uses JavaScript to determine if the description was filled in or left empty If the description field was left empty, the default prompt is, "Please be as descriptive as possible about the contents being spoken of in this video after giving a detailed summary." If the description field is filled, then the filled input will be used to describe what information to extract from the video HTTP Request: We're using Gemini API, specifically the video understanding endpoint We make a post HTTP request passing the video URL and the description of what information to extract Setup Instructions: HTTP Request Setup: Sign up for a Google Cloud account, join the Developer Program and get your Gemini API key Get curl for Gemini Video Understanding API The video understanding relies on the inputs from the form, code and HTTP request node, so correct mapping is essential for the workflow to function correctly. Feel free to reach out for additional help or clarification at my Gmail: terflix45@gmail.com, and I'll get back to you as soon as I can. Setup Steps: Code Node Setup: The code node is used as a filter to ensure a description prompt is always passed on. Use the JavaScript code below for that effect: // Loop over input items and add a new field called 'myNewField' to the JSON of each one for (const item of $input.all()) { item.json.myNewField = 1; if ($input.first().json['What u want?'].trim() == "") { $input.first().json['What do you want?'] = "Please be as descriptive as possible about the contents being spoken of this video after giving a detailed summary"; } } return $input.all(); // End of Code HTTP Request: To use Gemini Video Understanding, you'll need your Gemini API key Go to https://ai.google.dev/gemini-api/docs/video-understanding#youtube. This link will take you directly to the snippet. Just select REST programming language, copy that curl command, then paste it into the HTTP Request node Replace "Please summarize the video in 3 sentences." with the code node's output, which should either be the default description or the one entered by the user (second output field variable) Replace "https://www.youtube.com/watch?v=9hE5-98ZeCg" with the n8n form node's first output field, which should be the YouTube video URL variable Replace $GEMINI_API_KEY with your API key Redirect: Use n8n form node, page type "Final Ending" to redirect user to the initial n8n form for another analysis or preferred destination
by Aitor | 1Node
This n8n workflow captures Partnerstack events via a webhook, logs the event data into a Google Sheet, and sends a Telegram notification. How it Works: Webhook Node (Trigger): Listens for incoming POST requests. When an event occurs in Partnerstack (e.g., a new referral signs up), the workflow is triggered, capturing the event data. Append Row in Sheets Node: Takes the received Partnerstack event data and appends it as a new row to a designated Google Sheet. This creates a historical log of all captured events. Set Chat ID Node: Defines the specific Telegram chat ID where notifications will be sent. Send Notification Node (Telegram): Sends a message to the specified Telegram chat. The message content includes details from the Partnerstack event, providing real-time alerts. Setup Requirements: Partnerstack Postback: Configure a postback in Partnerstack (My account > Postbacks > Create a postback). Paste the URL provided by n8n's **Webhook node. Select the Partnerstack events you wish to track. Google Sheets Authentication**: Provide n8n with Google credentials that have write access to your target Google Sheet. Specify the sheet name. Telegram Integration**: You'll need a Telegram bot token (from BotFather) and the specific chat ID for the destination Telegram chat/channel. Additional Notes: This workflow efficiently automates logging of Partnerstack activities and provides immediate team awareness through Telegram notifications, streamlining event monitoring and response. π Need Help? Feel free to contact us at 1 Node. Get instant access to a library of free resources we created.
by Ahmed Saadawi
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. π§ Vtiger CRM β Auto-Answer FAQs with DeepSeek AI Description: This workflow automates the process of answering FAQ drafts in Vtiger CRM using DeepSeek LLM via LangChain. It's perfect for teams who want to accelerate knowledge base creation, improve support response consistency, or reduce the manual effort of writing FAQ content. Every 1 minute, this workflow: π₯ Retrieves the most recent FAQ record marked as Draft in Vtiger CRM π§ Sends the question to a LangChain agent powered by DeepSeek AI π Receives a plain-text answer π€ Updates the original FAQ with the generated answer and changes its status to Published βοΈ How It Works Trigger:** Scheduled to run every 1 minute Query:** Pulls the latest FAQ from Vtiger where faqstatus = 'Draft' AI Agent:** Uses LangChain + DeepSeek to generate a natural-language answer Memory Buffer:** Keeps context using LangChain memory Update:** Pushes the answer back to Vtiger and marks it as Published π οΈ Setup Instructions Connect Credentials for: Vtiger CRM API DeepSeek API Ensure your Vtiger CRM has a Faq module with fields: question faq_answer faqstatus Install the required Community Node: Go to Settings β Community Nodes Click Install Node and enter: n8n-nodes-vtiger-crm Restart your instance when prompted. Optionally customize the schedule or field names as needed. π€ Who Is This For? Customer support teams building a knowledge base Businesses using Vtiger as a CRM or internal helpdesk Teams looking to automate repetitive content creation using LLMs π Credentials Required β Vtiger CRM API credentials β DeepSeek AI API key β Highlights Fully automated LLM-powered FAQ generation Uses custom community node for Vtiger support Lightweight and runs on a short interval (1 min) Includes sticky note for clarity and onboarding Clean conditional logic and memory context built-in π· Tags vtiger, crm, faq automation, ai automation, deepseek, langchain, llm, open source crm, faq generation, customer support, n8n, n8n community nodes, workflow automation, ai generated answers, vtiger integration, deepseek ai, langchain integration
by Dmytro
AI-Powered Product Assistant for E-commerce Transform your online store customer service with an intelligent AI assistant that automatically processes customer inquiries, searches your product database, and provides personalized responses about product availability, pricing, and specifications. Perfect for shoe stores, fashion retailers, and any business with extensive product catalogs - this workflow eliminates manual customer service while increasing response speed and accuracy. How it works Customer sends product inquiry via webhook (Instagram DM, website chat, or messaging app) AI extracts key product details (brand, model, size, color) from natural language text System searches your Google Sheets product database with smart filtering AI generates friendly, personalized response with availability, pricing, and stock information Automatic response sent back to customer with product details or alternatives Screenshots: Customer inquiry: "Do you have Nike Air Max 40 size?" AI response: "Nike Air Max 90, size 40 - in stock 3 pieces, price 120$" Set up steps Prepare your product database - Create Google Sheets with columns: Brand, Model, Size, Color, Price, Quantity Configure AI settings - Connect OpenAI API for natural language processing Set up webhook endpoint - Configure trigger for your messaging platform (Instagram, Telegram, website chat) Test with sample inquiries - Verify AI correctly parses requests and finds products Deploy and monitor - Launch your automated assistant and track performance Time investment: 30-45 minutes setup, works immediately with any product catalog up to 1000+ items.