This article provides a complete, practical walkthrough of the Wait Code Sync Scheduled n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automating Product Sync from Shopify to Google Sheets Using n8n
   
   Meta Description:  
   Discover how this n8n workflow automates the synchronization of Shopify product listings to Google Sheets. Learn how product data like titles, tags, descriptions, and prices are effortlessly updated using GraphQL, batch processing, and Google Sheets integration.
   
   Keywords:  
   n8n automation, Shopify workflow, Google Sheets sync, Shopify GraphQL API, Shopify to Google Sheets, product data sync, no-code automation, eCommerce automation, batch processing, Shopify integration
   
   Third-Party APIs Used:
   
   1. Shopify GraphQL API
   2. Google Sheets API (via n8n’s Google Sheets node using OAuth2 authentication)
   
   Article:
   
   Automating Shopify Product Sync to Google Sheets with n8n
   
   In the ever-evolving landscape of eCommerce, accurate and real-time data synchronization is crucial. Whether you're a store owner managing dozens of SKUs or a marketing team tracking product data for reporting, automating routine data transfers isn’t just a convenience—it's a necessity. This is where no-code automation tools like n8n come into play. In this article, we’ll explore a powerful n8n workflow that automatically syncs product data from a Shopify store to a Google Sheets document. By leveraging Shopify's GraphQL API, batch processing, and Google Sheets integration, this automation ensures your product data stays up-to-date and easily accessible for reporting or analysis.
   
   Overview: What This Workflow Does
   
   This n8n workflow is designed to pull product information—including title, tags, description, and price—from a Shopify store and append it to a designated Google Sheets document. It supports pagination and tracks where it left off, ensuring efficiency and avoiding duplicates or missed records.
   
   Let’s break down the key components and functionalities of this automation.
   
   1. Scheduled Automation Trigger
   
   The process starts with a Schedule Trigger node set to run the workflow every day at 7:00 AM. This ensures that your Google Sheets document is refreshed regularly with the latest product data from Shopify without any manual intervention.
   
   2. Batch Size Configuration
   
   To handle large datasets efficiently, a BatchSize node defines the number of products to be retrieved per request from Shopify. This is currently set to 100, which is below the Shopify GraphQL API’s limit of 250 items per query. This setting allows quick adjustments based on store size and performance needs.
   
   3. Cursor Handling for Pagination
   
   Shopify’s API returns product data in paginated responses. The LastCursor node fetches the last known endCursor value from the "Cursor" sheet inside the Google Sheets document. This endCursor acts like a bookmark that tells the workflow where to resume fetching data.
   
   A conditional If node checks whether the cursor is available. If not, it initiates an initial fetch using the "shopify-initial" node which queries the first product set from the Shopify store.
   
   4. Merging Inputs for Query Execution
   
   A Code node cleverly merges inputs from BatchSize, LastCursor, and Shopify-initial to prepare a comprehensive query request. If no cursor is found, it pulls the first page. Otherwise, it resumes from where it last stopped—ensuring data continuity.
   
   5. Product Data Retrieval via GraphQL
   
   At the heart of the workflow lies a robust "Shopify get products" node. This node uses a GraphQL query tailored to extract specific product information: title, tags, description, and price (via the first variant edge). It dynamically inserts the batch size and cursor into the query to control pagination.
   
   6. Data Parsing and Splitting
   
   The retrieved batch of product data is passed through a Python-based "Split output" node, which separates the batch into individual product records. This enables each entry to be independently appended to Google Sheets in the next steps.
   
   7. Storing Product Data into Google Sheets
   
   There are two storage nodes:
   - "writing first product details" stores the very first product in the batch (from initial cursor).
   - "writing remaining product info to google sheets" appends all other product entries parsed by the Split node.
   
   Each entry includes:
   - Product Title  
   - Description  
   - Tags  
   - Price  
   
   8. Updating Cursor for Next Run
   
   After each batch, a node checks if more pages are available (`hasNextPage`). If so, the workflow:
   - Waits 10 seconds (to respect rate limits)
   - Updates the cursor using the "Set cursor" node
   - Loops back to retrieve the next page
   
   If there are no more pages, the workflow completes and waits until the next scheduled run.
   
   9. Cursor Update in Google Sheets
   
   The new cursor value is also written back into the Google Sheets "Cursor" tab via the "update Curser" node. This ensures the next day's sync starts exactly where the last one ended.
   
   Why This Workflow Is Valuable
   
   This automation brings multiple benefits:
   - Saves hours of manual data export/import
   - Ensures real-time synchronization for better analytics and reporting
   - Reduces risk of data duplication
   - Fully customizable: You can add more product fields or adjust batch size as needed
   
   Tools and APIs Under the Hood
   
   This workflow hinges on the robust capabilities of:
   - Shopify’s GraphQL API: for efficient, structured product queries  
   - Google Sheets API: via n8n’s native integration for reading/writing sheet data  
   - n8n’s built-in logic nodes: for scheduling, looping, splitting, and conditional branching
   
   Conclusion
   
   For Shopify merchants and teams that rely on updated product data for operations, marketing, or analytics, this n8n workflow provides a seamless, low-maintenance solution. By connecting Shopify and Google Sheets via automation, businesses can focus on strategic decisions without getting bogged down by repetitive tasks.
   
   Whether you’re scaling up your store or simply looking to streamline your reporting, this kind of workflow demonstrates the power of no-code automation when combined with modern APIs and thoughtful logic.
   
   Deploy it, modify it, and automate with confidence.
   
   🔗 Relevant Resources:
   - Shopify GraphQL API Docs  
   - Google Sheets API Overview  
   - n8n Documentation on Workflows and API Integration

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.