by Madame AI
Automated B2B Lead Generation from Google Maps to Google Sheets using BrowserAct This n8n template automates local lead generation by scraping Google Maps for businesses, saving them to Google Sheets, and notifying you in real-time via Telegram. This workflow is perfect for sales teams, marketing agencies, and local B2B services looking to build targeted lead lists automatically. Self-Hosted Only This Workflow uses a community contribution and is designed and tested for self-hosted n8n instances only. How it works The workflow is triggered manually. You can set the Location, Bussines_Category, and number of leads (Extracted_Data) in the first BrowserAct node. A BrowserAct node ("Run a workflow task") initiates the scraping job on Google Maps using your specified criteria. A second BrowserAct node ("Get details of a workflow task") pauses the workflow and waits for the scraping task to be 100% complete. A Code node takes the raw JSON string output from the scraper and correctly parses it, splitting the data into individual items (one for each business). A Google Sheets node appends or updates each lead into your spreadsheet, matching on the "Name" column to prevent duplicate entries. Finally, a Telegram node sends a message with the new lead's details to your specified chat, providing instant notification. Requirements BrowserAct** API account for web scraping BrowserAct* "Google Maps Local Lead Finder*" Template BrowserAct** n8n Community Node -> (n8n Nodes BrowserAct) Google Sheets** credentials for saving leads Telegram** credentials for sending notifications Need Help? How to Find Your BrowseAct API Key & Workflow ID How to Connect n8n to Browseract How to Use & Customize BrowserAct Templates How to Use the BrowserAct N8N Community Node Workflow Guidance and Showcase AUTOMATE Local Lead Generation: Google Maps to Sheets & Telegram with n8n
by Harshil Agrawal
This workflow handles the incoming call from Twitter and sends the required response for verification. On registering the webhook with the Twitter Account Activity API, Twitter expects a signature in response. Twitter also randomly ping the webhook to ensure it is active and secure. Webhook node: Use the displayed URL to register with the Account Activity API. Crypto node: In the Secret field, enter your API Key Secret from Twitter. Set node: This node generates the response expected by the Twitter API. Learn more about connecting n8n with Twitter in the Getting Started with Twitter Webhook article.
by mohamed ali
This workflow creates an automatic self-hosted URL shortener. It consists of three sub-workflows: Short URL creation for extracting the provided long URL, generating an ID, and saving the record in the database. It returns a short link as a result. Redirection for extracting the ID value, validating the existence of its correspondent record in the database, and returning a redirection page after updating the visits (click) count. Dashboard for calculating simple statistics about the saved record and displaying them on a dashboard. Read more about this use case and how to set up the workflow in the blog post How to build a low-code, self-hosted URL shortener in 3 steps. Prerequisites A local proxy set up that redirects the n8n.ly domain to your n8n instance An Airtable account and credentials Basic knowledge of JavaScript, HTML, and CSS Nodes Webhook nodes trigger the sub-workflows on calls to a specified link. IF nodes route the workflows based on specified query parameters. Set nodes set the required values returned by the previous nodes (id, longUrl, and shortUrl). Airtable nodes retrieve records (values) from or append records to the database. Function node calculates statistics on link clicks to be displayed on the dashboard, as well as its design. Crypto node generates a SHA256 hash.
by tanaypant
This is Workflow 1 in the blog tutorial Database activity monitoring and alerting. Prerequisites A Postgres database set up and credentials. Basic knowledge of JavaScript and SQL. Nodes Cron node starts the workflow every minute. Function node generates sensor data (sensor id (preset), a randomly generated value, timestamp, and notification (preset as false) ) Postgres node inserts the data into a Postgres database. You can create the database for this workflow with the following SQL statement: CREATE TABLE n8n (id SERIAL, sensor_id VARCHAR, value INT, time_stamp TIMESTAMP, notification BOOLEAN);
by Raquel Giugliano
This minimal utility workflow connects to the SAP Business One Service Layer API to verify login credentials and return the session ID. It's ideal for testing access or using as a sub-workflow to retrieve the B1SESSION token for other operations. ++⚙️ HOW IT WORKS:++ 🔹 1. Trigger Manually The workflow is initiated using a Manual Trigger. Ideal for testing or debugging credentials before automation. 🔹 2. Set SAP Login Data The Set Login Data node defines four key input variables: sap_url: Base URL of the SAP B1 Service Layer (e.g. https://sap-server:50000/b1s/v1/) sap_username: SAP B1 username sap_password: SAP B1 password sap_companydb: SAP B1 Company DB name 🔹 3. Connect to SAP A HTTP Request node performs a POST to the Login endpoint. The body is structured as: { "UserName": "your_sap_username", "Password": "your_sap_password", "CompanyDB": "your_sap_companydb" } If successful, the response contains a SessionId which is essential for authenticated requests. 🔹 4. Return Session or Error The response is branched: On success → the sessionID is extracted and returned. On failure → the error message and status code are stored separately. ++🛠 SETUP STEPS:++ 1️⃣ Create SAP Service Layer Credentials Although this workflow uses manual inputs (via Set), it's best to define your connection details as environment variables for reuse: SAP_URL=https://your-sap-host:50000/b1s/v1/ SAP_USER=your_sapuser SAP_PASSWORD=your_password SAP_COMPANY_DB=your_companyDB Alternatively, update the Set Login Data node directly with your values. 2️⃣ Run the Workflow Click "Execute Workflow" in n8n. Watch the response from SAP: If successful: sessionID will be available in the Success node. If failed: statusCode and errorMessage will be available in the Failed node. ++✅ USE CASES:++ 🔄 Reusable Login Module Export this as a reusable sub-workflow for other SAP-integrated flows. 🔐 Credential Testing Tool Validate new environments, test credentials before deployment.
by Miquel Colomer
This workflow is useful if you have lots of tasks running daily. MySQL node (or the database used to save data shown in n8n - could be Mongo, Postgres, ... -) remove old entries from execution_entity table that contains the history of the executed workflows. If you have multiple tasks executed every minute, 1024 rows will be created every day (60 minutes x 24 hours) per every task. This will increase the table size fastly. SQL query deletes entries older than 30 days taking stoppedAt column as a reference for date calculations. You only have to setup Mysql connection properly and config cron to execute once per day in a low traffic hour, this way
by Francis Njenga
AI Content Generator Workflow Introduction This workflow automates the process of creating high-quality articles using AI, organizing them in Google Drive, and tracking their progress in Google Sheets. It's perfect for marketers, bloggers, and businesses looking to streamline content creation. With minimal setup, you can have a fully operational system to generate, save, and manage your articles in one cohesive workflow. How It Works Collect Inputs: Users fill out a form with details like article title, keywords, and instructions. Generate Content: AI creates an outline and writes the article based on user inputs. Organize Files: Saves the outline and final article in Google Drive for easy access. Track Progress: Updates Google Sheets with links to the generated content for tracking. Set Up Steps Time Required**: Approximately 15–20 minutes to connect all integrations and test the workflow. Steps**: Connect Google Drive and Google Sheets: Authorize access to store files and update the spreadsheet. Set Up OpenAI Integration: Add your OpenAI API key for generating the outline and article content. Customize the Form: Modify the form fields to match the details you want to collect for each article. Test the Workflow: Run the workflow with sample inputs to ensure everything works smoothly. This workflow not only simplifies the process of article creation but also sets a foundation for expanding into additional automations, like posting to social media platforms.
by Askan
The News Site from Colt, a telecom company, does not offer an RSS feed, therefore web scraping is the choice to extract and process the news. The goal is to get only the newest posts, a summary of each post and their respective (technical) keywords. Note that the news site offers the links to each news post, but not the individual news. We collect first the links and dates of each post before extracting the newest ones. The result is sent to a SQL database, in this case a NocoDB database. This process happens each week thru a cron job. Requirements: Basic understanding of CSS selectors and how to get them via browser (usually: right click → inspect) ChatGPT API account - normal account is not sufficient A NocoDB database - of course you may choose any type of output target Assumptions: CSS selectors work on the news site The post has a date with own CSS selector - meaning date is not part of the news content "Warnings" Not every site likes to be scraped, especially not in high frequency Each website is structured in different ways, the workflow may then need several adaptations.
by n8n Team
This n8n workflow is designed to analyze email headers received via a webhook. The workflow splits into two main paths based on the presence of the received and authentication results headers. In the first path, if received headers are present, the workflow extracts IP addresses from these headers and then queries the IP Quality Score API to gather information about the IP addresses, including fraud score, abuse history, organization, and more. Geolocation data is also obtained from the IP-API API. The workflow collects and aggregates this information for each IP address. In the second path, if authentication-results headers are present, the workflow extracts SPF, DKIM, and DMARC authentication results. It then evaluates these results and sets fields accordingly (e.g., SPF pass/fail/neutral). The paths merge their results, and the workflow responds to the original webhook with the aggregated analysis, including IP information and authentication results. Potential issues during setup include ensuring proper configuration of the webhook calls with header authentication, handling authentication and API keys for the IP Quality Score API, and addressing any discrepancies or errors in the logic nodes, such as handling SPF, DKIM, and DMARC results correctly. Additionally, thorough testing with various email header formats is essential to ensure accurate analysis and response.
by Hybroht
Using Mistral API, you can use this n8n workflow to automate the process of: collecting, filtering, analyzing, and summarizing news articles from multiple sources. The sources come from pre-built RSS feeds and a custom DuckDuckGo node, which you can change if you need. It will deliver the most relevant news of the day in a concise manner. ++How It Works++** The workflow begins each weekday at noon. The news are gathered from RSS feeds and a custom DuckDuckGo node, using HTTPS GET when needed. News not from today or containing unwanted keywords are filtered out. The first AI Agent will select the top news from their titles alone and generate a general title & summary. The next AI Agent will summarize the full content of the selected top news articles. The general summary and title will be combined with the top 10 news summaries into a final output. ++Requirements++ An active n8n instance (self-hosted or cloud). Install the custom DuckDuckGo node: n8n-nodes-duckduckgo-search A Mistral API key Configure the Sub-Workflow for the content which requires HTTP GET requests. It is provided in the template itself. ++Fair Notice++ This is an older version of the template. There is a superior updated version which isn't restricted to tech news, with enhanced capabilities such as communication through different channels (email, social media) and advanced keyword filtering. It was recently published in n8n. You can find it here. If you are interested or would like to discuss specific needs, then feel free to contact us.
by Greg Lopez
Workflow Information 📌 Purpose 🎯 The intention of this workflow is to integrate New Shopify Orders into MS Dynamics Business Central: Point-of-Sale (POS):** POS orders will be created in Business Central as Sales Invoices given no fulfillment is expected. Web Orders:** This type of orders will be created as Business Central Sales Orders. How to use it 🚀 Edit the "D365 BC Environment Settings" node with your own account values (Company Id, Tenanant Id, Tax & Discount Items). Go to the "Shopify" node and edit the connection with your environment. More help here. Go to the "Lookup Customers" node to edit the Business Central connection details with your environment settings. Set the required filters on the "Shopify Order Filter" node. Edit the "Schedule Trigger" node with the required frequency. Useful Workflow Links 📚 Step-by-step Guide/ Integro Cloud Solutions Business Central REST API Documentation Video Demo Need Help? Contact me at: ✉️greg.lopez@integrocloudsolutions.com 📥 https://www.linkedin.com/in/greg-lopez-08b5071b/
by InfraNodus
Build a Better AI Chatbot for Your Zendesk Knowledge Portal Simple setup, no vector database needed. Uses GraphRAG to enhance user's prompts and provide high-quality and relevant up-to-date responses from your Zendesk knowledge base. Can be embedded on your Zendesk portal, also accesible via a URL. Can be customized and branded in your style. See example at support.noduslabs.com or a screenshot below: Also, compare it to the original Zendesk AI chatbot available at our other website https://infranodus.com — you will see that the quality of responses in this custom chatbot is much better than in the native Zendesk one, plus you save subscription because you won't need to activate their chat option, which is $25 per agent. Workflow Overview In this workflow, we use the n8n AI Agent Node with a custom prompt that: 1) First consults an "expert" graph from the InfraNodus GraphRAG system using the official InfraNodus GraphRAG node that will extract a reasoning ontology and a general context about your product from the graph that you create manually or automatically as described on our support portal. 2) The augmented user prompt is converted by AI agent node in a Zendesk search query that retrieves the most relevant content using their search API via n8n HTTP node. Both the results from the graph and the search results are combined and shown to the user How it works Receives a request from a user via a webhook that connects to the custom n8n chat widget. The request goes to the AI Agent node from n8n with a custom prompt (provided in the workflow) that orchestrates the following procedure: Sends the request to the knowledge graph in your InfraNodus account using the official InfraNodus GraphRAG node that contains a reasoning ontology represented as a knowledge graph based on your Zendesk knowledge support portal. Read more on how to generate this ontology here. Based on the results from InfraNodus, it reformulates the original prompt to include the reasoning logic as well as provide a fuller context to the model. Sends the request to the Zendesk search API using the n8n custom HTTP node with an enhanced search query to retrieve high-quality results. Combines Zendesk search results with InfraNodus ontology to generate a final response to the user. Sends the response back to the webhook, which is then picked up by the n8n chat widget that is shown to the user wherever the widget is embedded (e.g. on your own support portal). How to use • Get an InfraNodus API key and add it into InfraNodus GraphRAG node. • Edit the InfraNodus Graph node to provide the name of the graph that you will be using as ontology (you need to create it in InfraNodus first. • Edit the AI Agent (Support Agent) prompt to modify our custom instructions for your particular use case (do not change it too much as it works quite well and tells the agent what it should do and in what sequence). • Add the API key for your Zendesk account. In order to get it, go to your support portal Admin > Apps & Integrations > API Tokens. Usually it's located at https://noduslabs.zendesk.com/admin/apps-integrations/apis/api-tokens where instead of noduslabs you need to put the name of your support portal. Note: the official n8n Zendesk node does not have an endpoint to search and extract articles from support portal, so we use the custom HTTP node, but you can still connect to it via the Zendesk API key you have installed in your n8n. Support & Tutorials If you wan to create your own reasoning ontology graphs, please, refer to this article on generating your own knowledge graph ontologies. Specifically for this use case: Building ontology for your n8n AI chat bot. You may also be interested to watch this video that explains the logic of this approach in detail: Our support article for this workflow with real-life example: Building an embeddable AI chatbot agent for your Zendesk knowledge portal. To get support and help, contact us via support.noduslabs.com Learn more about InfraNodus at www.infranodus.com