AI agent that can scrape webpages
⚙️🛠️🚀🤖🦾 This template is a PoC of a ReAct AI Agent capable of fetching random pages (not only Wikipedia or Google search results). On the top part there's a manual chat node connected to a LangChain ReAct Agent. The agent has access to a workflow tool for getting page content. The page content extraction starts with converting query parameters into a JSON object. There are 3 pre-defined parameters: url – an address of the page to fetch method = full / simplified maxlimit - maximum length for the final page. For longer pages an error message is returned back to the agent Page content fetching is a multistep process: An HTTP Request mode tries to get the page content. If the page content was successfuly retrieved, a series of post-processing begin: Extract HTML BODY; content Remove all unnecessary tags to recuce the page size Further eliminate external URLs and IMG scr values (based on the method query parameter) Remaining HTML is converted to Markdown, thus recuding the page lengh even more while preserving the basic page structure The remaining content is sent back to an Agent if it's not too long (maxlimit = 70000 by default, see CONFIG node). NB: You can isolate the HTTP Request part into a separate workflow. Check the Workflow Tool description, it guides the agent to provide a query string with several parameters instead of a JSON object. Please reach out to Eduard is you need further assistance with you n8n workflows and automations! Note that to use this template, you need to be on n8n version 1.19.4 or later.
By EduardQualify new leads in Google Sheets via OpenAI's GPT-4
This n8n workflow was developed to evaluate and categorize incoming leads based on certain criteria. The workflow is triggered by adding a new row in a Google Sheets document. The workflow uses the OpenAI node to process the lead information. The system query contains detailed qualification rules and the response format. The user message contains the data for the individual lead. The JSON response from the OpenAI node is then processed by the Edit Fields node to extract the response. This response is merged together with the original lead data by the Merge node. Finally, the Google Sheets node updates the original lead entry in the Google Sheets document with the qualification result ("qualified" or "not qualified") in a separate column. This allows for easy tracking and sorting of the qualified leads.
By YuliaApi schema extractor
This workflow automates the process of discovering and extracting APIs from various services, followed by generating custom schemas. It works in three distinct stages: research, extraction, and schema generation, with each stage tracking progress in a Google Sheet. 🙏 Jim Le deserves major kudos for helping to build this sophisticated three-stage workflow that cleverly automates API documentation processing using a smart combination of web scraping, vector search, and LLM technologies. How it works Stage 1 - Research: Fetches pending services from a Google Sheet Uses Google search to find API documentation Employs Apify for web scraping to filter relevant pages Stores webpage contents and metadata in Qdrant (vector database) Updates progress status in Google Sheet (pending, ok, or error) Stage 2 - Extraction: Processes services that completed research successfully Queries vector store to identify products and offerings Further queries for relevant API documentation Uses Gemini (LLM) to extract API operations Records extracted operations in Google Sheet Updates progress status (pending, ok, or error) Stage 3 - Generation: Takes services with successful extraction Retrieves all API operations from the database Combines and groups operations into a custom schema Uploads final schema to Google Drive Updates final status in sheet with file location Ideal for: Development teams needing to catalog multiple APIs API documentation initiatives Creating standardized API schema collections Automating API discovery and documentation Accounts required: Google account (for Sheets and Drive access) Apify account (for web scraping) Qdrant database Gemini API access Set up instructions: Prepare your Google Sheets document with the services information. Here's an example of a Google Sheet – you can copy it and change or remove the values under the columns. Also, make sure to update Google Sheets nodes with the correct Google Sheet ID. Configure Google Sheets OAuth2 credentials, required third-party services (Apify, Qdrant) and Gemini. Ensure proper permissions for Google Drive access.
By Polina MedvedievaSimple social: Instagram single image post with Facebook API
Simple Social: Instagram Single Image Post with Facebook API Who is this workflow for? This workflow is designed for businesses, social media managers, content creators, and developers who need to automate the process of posting single images to Instagram using the Facebook API. It is ideal for anyone looking to streamline their social media posting process, saving time and ensuring consistent content delivery. Use Case / Problem Solved Manually posting images and captions on Instagram can be time-consuming, especially for businesses and content creators managing multiple accounts. This workflow automates the process from image preparation to publishing, reducing manual effort and increasing efficiency. What this workflow does Trigger Initialization: The workflow starts with a manual trigger that can be adapted to other triggers (e.g., HTTP webhook or schedule). Set Parameters: The workflow includes a node that sets essential parameters, such as the image URL, Instagram business account ID, and caption. Prepare Instagram Media: A node prepares the media for upload using the Facebook API, sending the image and caption for pre-publication processing. Check Media Upload Status: The workflow verifies if the media preparation is complete. Conditional Check: If the media preparation is successful, the workflow proceeds to publish; otherwise, it triggers an error-handling path. Publish Media: The media is published on Instagram if the conditions are met. Post-Publish Check: The workflow checks the status after publication. Conditional Check for Publication: If the publication status is "PUBLISHED," it triggers a success path; otherwise, it triggers a failure handling. Email Notifications: The workflow sends email notifications to indicate successful or unsuccessful outcomes. Setup Here is a quick video in italian language with sub eng(https://youtu.be/obWJFJvg_6g) Add API Credentials: Ensure that valid Facebook API credentials are added and configured for use. Permissions Required: Ensure your app has the necessary permissions (adsmanagement, businessmanagement, instagrambasic, instagramcontentpublish, pagesread_engagement). App review may be required for external user access. Node Configuration: Customize the Set Instagram Parameters node to specify the image URL, caption, and Instagram business account ID. Trigger Adaptation: Adapt the initial trigger if needed to fit your workflow's requirements (e.g., schedule, webhook). How to customize this workflow Change the Image URL and Caption: Modify the Set Instagram Parameters node to change the image and caption. Trigger Customization: Replace the manual trigger with other triggers like a webhook to automate posting based on external events. Notifications: Adjust the email nodes to send customized messages or trigger other workflows based on the outcome. Limitations Image Format: Only JPEG images are supported. Extended JPEG formats such as MPO and JPS are not compatible. Unsupported Tags: Shopping tags, branded content tags, and filters are not supported. Instagram TV: Publishing to Instagram TV is not supported. Rate Limit: Instagram accounts are limited to 50 API-published posts within a rolling 24-hour period. Carousels count as a single post. Check usage with GET /{ig-user-id}/contentpublishinglimit. Example Usage Imagine managing a business account that needs consistent posts. You can schedule this workflow or trigger it manually to automatically post images with captions at the right time, ensuring that your audience stays engaged without manual posting efforts.
By Edoardo GuzziPersonal shopper chatbot for WooCommerce with RAG using Google Drive and openAI
This workflow combines OpenAI, Retrieval-Augmented Generation (RAG), and WooCommerce to create an intelligent personal shopping assistant. It handles two scenarios: Product Search: Extracts user intent (keywords, price ranges, SKUs) and fetches matching products from WooCommerce. General Inquiries: Answers store-related questions (e.g., opening hours, policies) using RAG and documents stored in Google Drive. --- How It Works Chat Interaction & Intent Detection Chat Trigger: Starts when a user sends a message ("When chat message received"). Information Extractor: Uses OpenAI to analyze the message and determine if the user is searching for a product or asking a general question. Extracts: search (true/false). keyword, priceRange, SKU, category (if product-related). Example: json { "search": true, "keyword": "red handbags", "priceRange": { "min": 50, "max": 100 }, "SKU": "BAG123", "category": "women's accessories" } Product Search (WooCommerce Integration) AI Agent: If search: true, routes the request to the personal_shopper tool. WooCommerce Node: Queries the WooCommerce store using extracted parameters (keyword, priceRange, SKU). Filters products in stock (stockStatus: "instock"). Returns matching products (e.g., "red handbags under €100"). General Inquiries (RAG System) RAG Tool: If search: false, uses the Qdrant Vector Store to retrieve store information from documents. Google Drive Integration: Documents (e.g., store policies, FAQs) are stored in Google Drive. Downloaded, split into chunks, and embedded into Qdrant for semantic search. OpenAI Chat Model: Generates answers based on retrieved documents (e.g., "Our store opens at 9 AM"). Set Up Steps Configure the RAG System Google Drive Setup: Upload store documents . Update the Google Drive2 node with your folder ID. Qdrant Vector Database: Clean the collection (update Qdrant Vector Store node with your URL). Use Embeddings OpenAI to convert documents into vectors. Configure OpenAI & WooCommerce OpenAI Credentials: Add your API key to all OpenAI nodes (OpenAI Chat Model, Embeddings OpenAI, etc.). WooCommerce Integration: Connect your WooCommerce store (credentials in the personal_shopper node). Ensure product data is synced and accessible. Customize the AI Agent Intent Detection: Modify the Information Extractor’s system prompt to align with your store’s terminology. RAG Responses: Update the tool description to reflect your store’s documents. --- Notes This template is ideal for e-commerce businesses needing a hybrid assistant for product discovery and customer support. Need help customizing? Contact me for consulting and support or add me on Linkedin.
By DavideSave your workflows into a GitHub repository
Basics Provides a mechanism to save all your workflows into a github repository and path (of your choosing). These can then be shared through your entire org and used to track changes (if you make any sad 'oopsies'. Flow Obtains and creates listing of currently configured workflows. Iterates through each workflow looking at the following Github source (if present) Actual workflow code (from N8N) Workflow code is sorted and compared for any changes If changed (or new) the workflows are saved / archived into github. Configuration Most of the configuration is done in the Globals node which houses the repo detail for github nodes. The only other dependency is that it by default looks for a GitHub credential, if you use something other than that precise wording you will need to change the credential used on the respective nodes. We gave it 'Manage' rights, but that was only so that it was able to override a requirement for checks to complete? Most would probably only need 'Write' privileges. Background Well, so we initially started using N8N just as a kubernetes-based service housed with its DB running inside the pod. Worked great for getting to know N8N and we jut kept all our workflows and credentials listed in a readme. Fast forward about a year... We have migrated this into our 'production' toolsets and maintain a bunch of team worflows inside it (not company-wide, but LOTS of team fun). While trying to spin a copy of our production RDS database, the ++actual++ production database was deleted, and in doing so AWS was nice enough to wipe our snapshots too!! Yea! Thankfully it only took us a few hours to get everything back up and running thanks to this, so I'm sharing it for everyone to benefit. We have used it to restore old workflows, changes, and now to test our full DR proceedures! (Ok, I might have taken that a bit far)
By Brian BurnettWebsite scam risk detector with GPT-4o and SerpAPI
What It Does This intelligent workflow simplifies the complex task of determining whether a website is legitimate or potentially a scam. By simply submitting a URL through a form, the system initiates a multi-agent evaluation process. Four dedicated AI agents—each powered by GPT-4o and connected to SerpAPI—analyze different dimensions of the website: domain and technical details, search engine signals, product and pricing patterns, and on-site content analysis. Their findings are then passed to a fifth AI agent, the Analyzer, powered by GPT-4o mini, which consolidates the data, scores the site on a scale of 1–10 for scam likelihood, and presents the findings in a clear, structured format for the user. Who It's For This workflow is ideal for anyone who needs to quickly and reliably assess the trustworthiness of a website. Whether you're a consumer double-checking a store before making a purchase, a small business owner validating supplier sites, a cybersecurity analyst conducting threat assessments, or a developer building fraud detection into your platform — this tool offers fast, AI-powered insights without the need for manual research or technical expertise. It's designed for both individuals and teams who value accurate, scalable scam detection. How It Works The process begins with a simple form submission where the user enters the URL of the website they want to investigate. Once submitted, the workflow activates four specialized AI agents—each powered by GPT-4o and connected to SerpAPI—to independently analyze the site from different angles: Agent 1 examines domain age, SSL certificates, and TLD trustworthiness. Agent 2 reviews search engine results, forum mentions, and public scam reports. Agent 3 analyzes product pricing patterns and brand authenticity. Agent 4 assesses on-site content quality, grammar, legitimacy of claims, and presence of business info. Each agent returns its findings, which are then aggregated and passed to a fifth AI agent—the Analyzer. This final agent, powered by GPT-4o mini, evaluates all the input, assigns a scam likelihood score from 1 to 10, and compiles a neatly formatted summary with organized insights and a disclaimer for context. Set UP You will need to obtain an Open AI API key from platform.openai.com/api-keys After you obtain this Open AI API key you will need to connect it to the Open AI Chat Model for all of the Tools agents (Analyzer, Domain & Technical Details, Search Engine Signals, Product & Pricing Patterns, and Content Analysis Tools Agents). You will now need to fund your Open AI account. GPT 4o costs ~$0.01 to run the workflow. Next you will need to create a SerpAPI account at https://serpapi.com/users/sign_up After you create an account you will need to obtain a SerpAPI key. You will then need to use this key to connect to the SerpAPI tool for each of the tools agents (Domain & Technical Details, Search Engine Signals, Product & Pricing Patterns, and Content Analysis Tools Agents) Tip: SerpAPI will allow you to run 100 free searches each month. This workflow uses ~5-15 SerpAPI searches per run. If you would like to utilize the workflow more than that each month, create multiple SerpAPI accounts and have an API key for each account. When you utilize all 100 free searches for an account, switch to the API key for another account within the workflow. Disclaimer This tool is designed to assist in evaluating the potential risk of websites using AI-generated insights. The scam likelihood score and analysis provided are based on publicly available information and should not be considered a definitive or authoritative assessment. This tool does not guarantee the accuracy, safety, or legitimacy of any website. Users should perform their own due diligence and use independent judgment before engaging with any site. N8N, OpenAI, its affiliates, and the creators of this workflow are not responsible for any loss, damages, or consequences arising from the use of this tool or the actions taken based on its results.
By AiAgentFlux dev image generation (Fal.ai) to Google Drive
This workflow automates AI-based image generation using the Fal.ai Flux API. Define custom prompts, image parameters, and effortlessly generate, monitor, and save the output directly to Google Drive. Streamline your creative automation with ease and precision. Who is this for? This template is for content creators, developers, automation experts, and creative professionals looking to integrate AI-based image generation into their workflows. It’s ideal for generating custom visuals with the Fal.ai Flux API and automating storage in Google Drive. What problem is this workflow solving? Manually generating AI-based images, checking their status, and saving results can be tedious. This workflow automates the entire process — from requesting image generation, monitoring its progress, downloading the result, and saving it directly to a Google Drive folder. What this workflow does Sets Custom Image Parameters: Allows you to define the prompt, resolution, guidance scale, and steps for AI image generation. Sends a Request to Fal.ai: Initiates the image generation process using the Fal.ai Flux API. Monitors Image Status: Checks for completion and waits if needed. Downloads the Generated Image: Fetches the completed image once ready. Saves to Google Drive: Automatically uploads the generated image to a specified Google Drive folder. Setup Prerequisites: • Fal.ai API Key: Obtain it from the Fal.ai platform and set it as the Authorization header in HTTP Header Auth credentials. • Google Drive OAuth Credentials: Connect your Google Drive account in n8n. Configuration: • Update the “Edit Fields” node with your desired image parameters: • Prompt: Describe the image (e.g., “Thai young woman net idol 25 yrs old, walking on the street”). • Width/Height: Define image resolution (default: 1024x768). • Steps: Number of inference steps (e.g., 30). • Guidance Scale: Controls image adherence to the prompt (e.g., 3.5). • Set your Google Drive folder ID in the “Google Drive” node to save the image. Run the Workflow: • Trigger the workflow manually to generate the image. • The workflow waits, checks status, and saves the final output seamlessly. Customization • Modify Image Parameters: Adjust the prompt, resolution, steps, and guidance scale in the “Edit Fields” node. • Change Storage Location: Update the Google Drive node with a different folder ID. • Add Notifications: Integrate an email or messaging node to alert you when the image is ready. • Additional Outputs: Expand the workflow to send the generated image to Slack, Dropbox, or other platforms. This workflow streamlines AI-based image generation and storage, offering flexibility and customization for creative automation.
By Sira EkabutSync Google Sheets data with MySQL
This workflow performs several data integration and synchronization tasks between Google Sheets and a MySQL database. Here is a step-by-step description of what this workflow does: Manual Trigger: The workflow starts when the user clicks "Execute Workflow." Schedule Trigger: This node schedules the workflow to run at specific intervals on weekdays (Monday to Friday) between 6 AM and 10 PM. It ensures regular data synchronization. Google Sheet Data: This node connects to a specific Google Sheets document and retrieves data from the "Form Responses 1" sheet, filtering by the "DB Status" column. SQL Get inquiries from Google: This node retrieves data from a MySQL database table named "ConcertInquiries" where the "source_name" is "GoogleForm." Rename GSheet variables: This node renames the columns retrieved from Google Sheets and transforms the data into a format suitable for MySQL, assigning a value for "source_name" as "GoogleForm." Compare Datasets: This node compares the data retrieved from Google Sheets and the MySQL database based on timestamp and source_name fields. It identifies changes and updates. No reply too long?: This node checks if there has been no reply within the last four hours, using the "timestamp" field from the Google Sheets data. DB Status assigned?: This node checks if the "DB Status" field is not empty in the compared dataset. Update GSheet status: If conditions are met in the previous nodes, this node updates the "DB Status" field in Google Sheets with the corresponding value from the MySQL dataset. DB Status in sync?: This node checks if the "source_name" field in Google Sheets is not empty. Sync MySQL data: If conditions are met in the previous nodes, this node updates the "source_name" field in the MySQL database to "GoogleFormSync." Send Notifications: If conditions are met in the "No reply too long?" node, this node sends notifications or performs actions as needed. Sticky Notes: These nodes provide additional information and documentation links for users.
By n8n TeamSplit out binary data
This workflows helps with processing binary data. You'll often have binary objects with keys such as attachment0, attachment1, attachment_2, etc. attached to your items, for example when reading an incoming email. This binary data is hard to process because it's not an array you can simply loop through. This workflow solves this problem by providing a Function node that takes all incoming items and all their binary data and then returning a single item for each file with a data key containing your binary file. Incoming binary data: Processed binary data:
By TomSuspicious login detection
This n8n workflow is designed for security monitoring and incident response when suspicious login events are detected. It can be initiated either manually from within the n8n UI for testing or automatically triggered by a webhook when a new login event occurs. The workflow first extracts relevant data from the incoming webhook payload, including the IP address, user agent, timestamp, URL, and user ID. It then splits into three parallel processing paths. In the first path, it queries GreyNoise's Community API to retrieve information about the investigated IP address. Depending on the classification and trust level received from GreyNoise, the alert is given a High, Medium, or Low priority. This priority is assigned based on the best practices documentation from GreyNoise on how to apply their data to analysis. Once a priority is assigned, a message is sent to a Slack channel to notify users about the alert. The second path involves fetching geolocation data about the IP address using IP-API's Geolocation API and merging it with data from the UserParser node. This data is then combined with the data obtained from GreyNoise. In the third path, the UserParser node queries the Userparser IP address and user agent lookup API to obtain information about the user's IP and user agent. This data is merged with the IP-API data and GreyNoise data. The workflow then checks if the IP address is considered an unknown threat by examining both the noise and riot fields from GreyNoise. If it is considered an unknown threat, the workflow proceeds to retrieve the last 10 login records for the same user from a Postgres database. If there are any discrepancies in the login information, indicating a new location or device/browser, the user is informed via email. Potential issues when setting up this workflow include ensuring that credentials are correctly entered for GreyNoise and UserParser nodes, and addressing any discrepancies in the data sources that could lead to false positives or negatives in threat detection. Additionally, the usage of hardcoded API keys should be replaced with credentials for security and flexibility. Thorough testing and validation with sample data are crucial to ensure the workflow performs as expected and aligns with security incident response procedures.
By n8n TeamAmazon affiliate marketing automation
Who this is for This workflow is for Amazon affiliate marketers and social media managers who want to quickly turn product links into engaging Facebook posts with AI-generated captions and images — fully automated. --- What this workflow does This workflow starts every time a new Product Link is added to a connected Google Sheet. It then: Extracts the ASIN from the product link. Fetches product details from Amazon using RapidAPI. Generates a short, scroll-stopping Facebook caption with OpenRouter AI. Creates a product image prompt and sends it to Google Gemini for high-quality image generation. Uploads the creative directly to Facebook via the Facebook Graph API. Marks the Google Sheet row as “Done ✅” after posting. The Facebook post includes: Product image (AI-enhanced) Short, benefit-focused caption with emojis Affiliate link --- Setup To set this template up, follow the sticky notes inside the workflow and: Google Sheets → Connect your sheet and ensure the columns “Product Link” & “Facebook Upload” exist. RapidAPI → Add your API key in the “Amazon Product Details” node. OpenRouter → Add credentials for caption generation. Google Gemini → Add your API key for image generation. Facebook Graph API → Connect your Facebook account for posting. --- How to customize this workflow to your needs Change the Facebook caption prompt to match your tone or branding. Adjust the AI image generation prompt to fit your preferred photography style. Modify the Google Sheet update logic if you want to track additional info (e.g., posting date, engagement stats). Switch Facebook posting to Instagram or another platform by replacing the final API call.
By Rakin Jakaria