This article provides a complete, practical walkthrough of the Manual Code Automate Triggered 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:  
   Creating Multilingual Greetings in n8n with a Simple Merge Workflow
   
   Meta Description:  
   Learn how to build a basic multilingual automation using n8n. This workflow merges user names with corresponding language-specific greetings using n8n’s Merge node—no external APIs required.
   
   Keywords:  
   n8n, n8n workflow, merge node, code node, no-code automation, low-code, data merge, multilingual greetings, automation tutorial, n8n tutorial
   
   Third-Party APIs Used:  
   None
   
   Article:
   
   Creating Multilingual Greetings in n8n with a Simple Merge Workflow
   
   n8n is a powerful automation tool allowing users to create workflows using a drag-and-drop interface combined with JavaScript code for custom logic. In this article, we’ll explore a simple yet useful workflow that demonstrates how to merge two sets of sample data based on a common field—in this case, a language code—to produce personalized multilingual greetings.
   
   This workflow is a great introduction to n8n’s Merge node and can be adapted to a variety of use cases where data needs to be matched and combined conditionally.
   
   👨‍🏫 Overview of the Workflow
   
   This n8n workflow consists of four main nodes:
   
   1. Manual Trigger Node (“When clicking ‘Test workflow’”)  
   2. Sample Data Node (User Names + Language Codes)  
   3. Sample Data Node (Greetings + Language Codes)  
   4. Merge Node (Combines Greeting and User Based on Language)
   
   Let’s break down each part of the workflow and how it contributes to the final output.
   
   🚀 Step 1: Starting the Workflow Manually
   
   The entry point of this workflow is the Manual Trigger node, labeled “When clicking ‘Test workflow’.” It allows the workflow to be tested interactively within the n8n editor. This is especially handy during development—and since the workflow uses only static sample data, no external event or input is required.
   
   🧑‍💻 Step 2: Providing Sample User Data
   
   The first Code node is titled “Sample data (name + language)” and simulates a list of users, each with a name and a preferred language code. Here’s the dataset used:
   
   ```javascript
   return [
     {
       json: {
         name: 'Stefan',
         language: 'de',
       }
     },
     {
       json: {
         name: 'Jim',
         language: 'en',
       }
     },
     {
       json: {
         name: 'Hans',
         language: 'de',
       }
     }
   ];
   ```
   
   This sets up three users, two of whom speak German and one who speaks English.
   
   💬 Step 3: Defining Greetings for Each Language
   
   The second Code node, “Sample data (greeting + language),” defines greetings corresponding to the supported languages. The dataset looks like this:
   
   ```javascript
   return [
     {
       json: {
         greeting: 'Hello',
         language: 'en',
       }
     },
     {
       json: {
         greeting: 'Hallo',
         language: 'de',
       }
     }
   ];
   ```
   
   These greetings serve as dictionary mappings. More languages can easily be added in the same format for scalability.
   
   🔀 Step 4: Merging Data Based on Language
   
   The final component is the Merge node titled “Merge (name + language + greeting).” It’s configured in ‘Combine’ mode with the matching field set to language. Essentially, this means:
   
   - It pairs each record from the first dataset (user names + language) with the appropriate greeting from the second dataset (greeting + language).
   - If a match exists, the Merge node outputs a new JSON object that combines user-specific data with the appropriate greeting.
   
   For example, Stefan and Hans both speak German (“de”), so they will receive the greeting “Hallo.” Jim speaks English (“en”) and will receive “Hello.”
   
   📝 Final Output: Personalized Greetings
   
   After passing through the Merge node, the output consists of user records enriched with greetings:
   
   - { name: 'Stefan', language: 'de', greeting: 'Hallo' }
   - { name: 'Jim', language: 'en', greeting: 'Hello' }
   - { name: 'Hans', language: 'de', greeting: 'Hallo' }
   
   You can further fine-tune the workflow to concatenate the greeting and name into a complete message (e.g., “Hello Jim”) using another Code or Set node. This flexibility is what makes n8n so powerful—it gives you full control over processing logic while maintaining readability and modularity.
   
   🧩 Why This Workflow is Valuable
   
   Although simple, this demonstration is a foundational example of working with structured data in n8n. You'll learn:
   
   - How to generate and use static test data with the Code node.
   - The basics of data merging with the Merge node.
   - Filtering and pairing records based on shared fields such as language or user IDs.
   
   This pattern can be extended to:
   
   - Personalize messages for users based on preferences.
   - Match leads with sales representatives by region.
   - Automate email or chatbot responses in the appropriate language.
   
   ✅ No External APIs Required
   
   A unique aspect of this workflow is that it runs entirely on mock data. There's no dependency on third-party APIs, making it ideal for learning and testing n8n’s core functionality without worrying about credentials, rate limits, or internet availability.
   
   📌 Conclusion
   
   Merging datasets based on a shared key is a fundamental requirement in many automation workflows. Using n8n’s Merge node along with some sample data and the Manual Trigger, you can quickly create a scalable and powerful automation that handles language-based personalization.
   
   Whether you're a beginner exploring automation or a seasoned developer building more complex workflows, understanding how to elegantly combine data using n8n will serve you well in numerous applications.
   
   Now go ahead—extend this project, and maybe try adding support for Spanish or French. The possibilities are wide open with n8n.
   
   Happy automating!

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.