Demonstrates the use of the $item(index) method
This workflow demonstrates the use of the $item(index) method. This method is useful when you want to reference an item at a particular index.
This example workflow makes POST HTTP requests to a dummy URL.
Set node: This node is used to set the API key that will be used in the workflow later. This node returns a single item. This node can be replaced with other nodes, based on the use case.
Customer Datastore node: This node returns the data of customers that will be sent in the body of the HTTP request. This node returns 5 items. This node can be replaced with other nodes, based on the use case.
HTTP Request node: This node uses the information from both the Set node and the Customer Datastore node. Since, the node will run 5 times, once for each item of the Customer Datastore node, you need to reference the API Key 5 times. However, the Set node returns the API Key only once. Using the expression {{ $item(0).$node["Set"].json["apiKey"] }} you tell n8n to use the same API Key for all the 5 requests.
n8n Workflow: Demonstrates the Use of the ItemIndex Method
This n8n workflow serves as a foundational example, primarily demonstrating the use of the ItemIndex method within a workflow context. It fetches data from a placeholder API and then processes it using a Set node, where the ItemIndex method would typically be utilized for item-specific operations.
What it does
This workflow performs the following steps:
- Starts the workflow: The workflow is manually triggered.
- Fetches data from an API: An
HTTP Requestnode is configured to make a GET request tohttps://n8n.io/blog/. This acts as a data source for subsequent processing. - Processes data with
Edit Fields (Set): TheSetnode (named "Edit Fields") is included, which is where theItemIndexmethod would typically be used to perform operations on individual items based on their index in the incoming data. While the specificItemIndexlogic is not explicitly defined in the provided JSON, this node's presence indicates its intended purpose for demonstrating such functionality. - Placeholder for Customer Datastore: A "Customer Datastore (n8n training)" node is present but not connected, suggesting it might be a future integration point or a visual placeholder for a data storage step.
Prerequisites/Requirements
- An active n8n instance.
- No external API keys or credentials are strictly required for the current configuration, as the HTTP Request node targets a public URL.
Setup/Usage
- Import the workflow: Download the provided JSON and import it into your n8n instance.
- Activate the workflow: Toggle the workflow to "Active" in your n8n interface.
- Execute the workflow: You can run the workflow manually by clicking "Execute Workflow" in the n8n editor.
- Inspect the
Edit Fields (Set)node: To see theItemIndexmethod in action, you would typically add an expression using{{ $item.index }}within theEdit Fields (Set)node to create or modify data based on the item's position. For example, you could add a new fielditem_numberwith the value{{ $item.index + 1 }}.
Related Templates
Automate Dutch Public Procurement Data Collection with TenderNed
TenderNed Public Procurement What This Workflow Does This workflow automates the collection of public procurement data from TenderNed (the official Dutch tender platform). It: Fetches the latest tender publications from the TenderNed API Retrieves detailed information in both XML and JSON formats for each tender Parses and extracts key information like organization names, titles, descriptions, and reference numbers Filters results based on your custom criteria Stores the data in a database for easy querying and analysis Setup Instructions This template comes with sticky notes providing step-by-step instructions in Dutch and various query options you can customize. Prerequisites TenderNed API Access - Register at TenderNed for API credentials Configuration Steps Set up TenderNed credentials: Add HTTP Basic Auth credentials with your TenderNed API username and password Apply these credentials to the three HTTP Request nodes: "Tenderned Publicaties" "Haal XML Details" "Haal JSON Details" Customize filters: Modify the "Filter op ..." node to match your specific requirements Examples: specific organizations, contract values, regions, etc. How It Works Step 1: Trigger The workflow can be triggered either manually for testing or automatically on a daily schedule. Step 2: Fetch Publications Makes an API call to TenderNed to retrieve a list of recent publications (up to 100 per request). Step 3: Process & Split Extracts the tender array from the response and splits it into individual items for processing. Step 4: Fetch Details For each tender, the workflow makes two parallel API calls: XML endpoint - Retrieves the complete tender documentation in XML format JSON endpoint - Fetches metadata including reference numbers and keywords Step 5: Parse & Merge Parses the XML data and merges it with the JSON metadata and batch information into a single data structure. Step 6: Extract Fields Maps the raw API data to clean, structured fields including: Publication ID and date Organization name Tender title and description Reference numbers (kenmerk, TED number) Step 7: Filter Applies your custom filter criteria to focus on relevant tenders only. Step 8: Store Inserts the processed data into your database for storage and future analysis. Customization Tips Modify API Parameters In the "Tenderned Publicaties" node, you can adjust: offset: Starting position for pagination size: Number of results per request (max 100) Add query parameters for date ranges, status filters, etc. Add More Fields Extend the "Splits Alle Velden" node to extract additional fields from the XML/JSON data, such as: Contract value estimates Deadline dates CPV codes (procurement classification) Contact information Integrate Notifications Add a Slack, Email, or Discord node after the filter to get notified about new matching tenders. Incremental Updates Modify the workflow to only fetch new tenders by: Storing the last execution timestamp Adding date filters to the API query Only processing publications newer than the last run Troubleshooting No data returned? Verify your TenderNed API credentials are correct Check that you have setup youre filter proper Need help setting this up or interested in a complete tender analysis solution? Get in touch 🔗 LinkedIn – Wessel Bulte
By Wessel Bulte🎓 How to transform unstructured email data into structured format with AI agent
This workflow automates the process of extracting structured, usable information from unstructured email messages across multiple platforms. It connects directly to Gmail, Outlook, and IMAP accounts, retrieves incoming emails, and sends their content to an AI-powered parsing agent built on OpenAI GPT models. The AI agent analyzes each email, identifies relevant details, and returns a clean JSON structure containing key fields: From – sender’s email address To – recipient’s email address Subject – email subject line Summary – short AI-generated summary of the email body The extracted information is then automatically inserted into an n8n Data Table, creating a structured database of email metadata and summaries ready for indexing, reporting, or integration with other tools. --- Key Benefits ✅ Full Automation: Eliminates manual reading and data entry from incoming emails. ✅ Multi-Source Integration: Handles data from different email providers seamlessly. ✅ AI-Driven Accuracy: Uses advanced language models to interpret complex or unformatted content. ✅ Structured Storage: Creates a standardized, query-ready dataset from previously unstructured text. ✅ Time Efficiency: Processes emails in real time, improving productivity and response speed. *✅ Scalability: Easily extendable to handle additional sources or extract more data fields. --- How it works This workflow automates the transformation of unstructured email data into a structured, queryable format. It operates through a series of connected steps: Email Triggering: The workflow is initiated by one of three different email triggers (Gmail, Microsoft Outlook, or a generic IMAP account), which constantly monitor for new incoming emails. AI-Powered Parsing & Structuring: When a new email is detected, its raw, unstructured content is passed to a central "Parsing Agent." This agent uses a specified OpenAI language model to intelligently analyze the email text. Data Extraction & Standardization: Following a predefined system prompt, the AI agent extracts key information from the email, such as the sender, recipient, subject, and a generated summary. It then forces the output into a strict JSON structure using a "Structured Output Parser" node, ensuring data consistency. Data Storage: Finally, the clean, structured data (the from, to, subject, and summarize fields) is inserted as a new row into a specified n8n Data Table, creating a searchable and reportable database of email information. --- Set up steps To implement this workflow, follow these configuration steps: Prepare the Data Table: Create a new Data Table within n8n. Define the columns with the following names and string type: From, To, Subject, and Summary. Configure Email Credentials: Set up the credential connections for the email services you wish to use (Gmail OAuth2, Microsoft Outlook OAuth2, and/or IMAP). Ensure the accounts have the necessary permissions to read emails. Configure AI Model Credentials: Set up the OpenAI API credential with a valid API key. The workflow is configured to use the model, but this can be changed in the respective nodes if needed. Connect the Nodes: The workflow canvas is already correctly wired. Visually confirm that the email triggers are connected to the "Parsing Agent," which is connected to the "Insert row" (Data Table) node. Also, ensure the "OpenAI Chat Model" and "Structured Output Parser" are connected to the "Parsing Agent" as its AI model and output parser, respectively. Activate the Workflow: Save the workflow and toggle the "Active" switch to ON. The triggers will begin polling for new emails according to their schedule (e.g., every minute), and the automation will start processing incoming messages. --- Need help customizing? Contact me for consulting and support or add me on Linkedin.
By DavideSynchronizing WooCommerce inventory and creating products with Google Gemini AI and BrowserAct
Synchronize WooCommerce Inventory & Create Products with Gemini AI & BrowserAct This sophisticated n8n template automates WooCommerce inventory management by scraping supplier data, updating existing products, and intelligently creating new ones with AI-formatted descriptions. This workflow is essential for e-commerce operators, dropshippers, and inventory managers who need to ensure their product pricing and stock levels are synchronized with multiple third-party suppliers, minimizing overselling and maximizing profit. --- 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 typically run by a Schedule Trigger (though a Manual Trigger is also shown) to check stock automatically. It reads a list of suppliers and their inventory page URLs from a central Google Sheet. The workflow loops through each supplier: A BrowserAct node scrapes the current stock and price data from the supplier's inventory page. A Code node parses this bulk data into individual product items. It then loops through each individual product found. The workflow checks WooCommerce to see if the product already exists based on its name. If the product exists: It proceeds to update the existing product's price and stock quantity. If the product DOES NOT exist: An If node checks if the missing product's category matches a predefined type (optional filtering). If it passes the filter, a second BrowserAct workflow scrapes detailed product attributes from a dedicated product page (e.g., DigiKey). An AI Agent (Gemini) transforms these attributes into a specific, styled HTML table for the product description. Finally, the product is created in WooCommerce with all scraped details and the AI-generated description. Error Handling: Multiple Slack nodes are configured to alert your team immediately if any scraping task fails or if the product update/creation process encounters an issue. Note: This workflow does not support image uploads for new products. To enable this functionality, you must modify both the n8n and BrowserAct workflows. --- Requirements BrowserAct API account for web scraping BrowserAct n8n Community Node -> (n8n Nodes BrowserAct) BrowserAct templates named “WooCommerce Inventory & Stock Synchronization” and “WooCommerce Product Data Reconciliation” Google Sheets credentials for the supplier list WooCommerce credentials for product management Google Gemini account for the AI Agent Slack credentials for error alerts --- 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 STOP Overselling! Auto-Sync WooCommerce Inventory from ANY Supplier
By Madame AI Team | Kai