This article provides a complete, practical walkthrough of the Splitout Code Automation Webhook 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 WordPress Blogging: How to Generate SEO-Friendly Posts with AI and n8n  
   
   Meta Description:  
   Discover how to automate the creation of SEO-optimized WordPress blog posts using n8n, OpenAI, and DALL·E. From keywords to published drafts, this AI-powered workflow does it all—with no code required.  
   
   Keywords:  
   n8n, WordPress automation, AI blog writer, OpenAI GPT, DALL·E, blog post generation, workflow automation, content marketing automation, LangChain, AI writing tool  
   
   Third-Party APIs Used:  
   
   - OpenAI API (via LangChain) – GPT-4 for text generation & DALL·E for image generation  
   - Wikipedia (via LangChain Tool) – For providing verified contextual information  
   - WordPress REST API – For creating and updating posts and uploading media  
   
   —
   
   Article:  
   
   Automating WordPress Blogging with AI and n8n: From Keywords to Draft in Minutes  
   
   For digital marketers, bloggers, and SEO professionals, the demand to publish high-quality content consistently is ever-growing. But what happens when you could delegate the bulk of content creation—idea generation, writing, structuring, and even image creation—to artificial intelligence?
   
   This is the power behind a cutting-edge n8n workflow that fully automates the process of drafting a WordPress post from just a few keywords using OpenAI’s GPT models, DALL·E image generation, and the WordPress REST API.
   
   Let's explore how this no-code solution works and what makes it revolutionary for content marketing and SEO teams.
   
   🎯 What the Workflow Does  
   
   This n8n workflow titled “Write a WordPress Post with AI (starting from a few keywords)” transforms a simple form submission into a fully structured article—complete with a featured image—and then publishes it as a draft on a WordPress blog.  
   
   It eliminates the need for manual writing, research, formatting, and image sourcing by using generative AI models and automation logic. Here's the full process explained in steps.
   
   🧩 Step 1: User Input via Form  
   
   The first node is a form trigger presented to the user. It asks for:
   - Keywords (comma-separated)
   - Number of chapters
   - Maximum word count
   
   Once this form is submitted, the workflow springs into action.
   
   ⚙️ Step 2: Settings Initialization  
   
   The next step involves setting environment variables like the WordPress URL and mapping user input values to internal variables. This ensures that the next steps run smoothly based on user-defined parameters.
   
   🧠 Step 3: Content Outline Generation via GPT-4  
   
   A specially crafted prompt is sent to OpenAI’s GPT-4 model using a LangChain node. This prompt instructs the AI to:
   - Title the article
   - Generate a compelling subtitle
   - Write an introduction and conclusion (each ~60 words)
   - Create detailed prompts for each chapter (based on the specified number of chapters)
   - Build a prompt for the DALL·E engine to later generate a relevant image
   
   Importantly, it also fetches data or validates content using Wikipedia to ensure factual correctness and depth—leveraging the LangChain Wikipedia tool.
   
   🔍 Step 4: Data Validation  
   
   Before continuing, the workflow performs a stringent check to ensure all necessary data was generated correctly—title, subtitle, image prompt, structured chapters, etc. If any component is missing or malformed, the system aborts execution and returns an error response to the user.
   
   📚 Step 5: Chapter Text Generation  
   
   Each chapter created in the outline is processed in a loop. GPT-4 is called again—this time using contextual writing rules that consider the previous and next chapters (ensuring logical flow).  
   
   All chapter texts are formatted using HTML (limited to bold, italic, and lists) and structured not as isolated chunks but as a coherent narrative body.
   
   🧵 Step 6: Merging and Structuring Content  
   
   Once all the chapter texts are ready, the workflow merges them with the original introduction and conclusion to create a unified article body. The final content is structured with basic HTML formatting for WordPress compatibility.
   
   📤 Step 7: Posting the Draft to WordPress  
   
   The article is then posted to the connected WordPress site as a draft. This means users can review and fine-tune the article before publication, giving them full editorial control.
   
   🖼️ Step 8: Featured Image Generation with DALL·E  
   
   Simultaneously (after the draft is created), DALL·E—OpenAI’s image generation model—creates a photo-realistic featured image based on the image prompt generated earlier. The specifications include realistic style, photography metadata (Sigma 85mm f/1.4), and a resolution of 1792x1024.
   
   The image is uploaded to WordPress via its media API, and the draft post is updated to set this as the featured image.
   
   ✅ Step 9: Final User Feedback  
   
   Once everything is successfully executed, the user receives a JSON response confirming that their article was created as a draft—ready for review and publishing.
   
   💡 Why This Workflow is a Game-Changer  
   
   This workflow allows content teams to:
   - Rapidly prototype blog posts using AI
   - Generate SEO-optimized structures with minimal input
   - Eliminate writer's block and image sourcing delays
   - Maintain posting consistency even with minimal staff
   - Automatically pull in verified data from Wikipedia to ensure accuracy
   
   And all of this is accomplished through n8n—an open-source, node-based workflow automation tool. No custom development, no complex scripts. Just smart orchestration of best-in-class APIs.
   
   🔐 Security & Credentials  
   
   The workflow requires WordPress and OpenAI API credentials. All sensitive actions, like media upload or post creation, are authenticated, ensuring only authorized users can create content on the platform.
   
   🚀 Final Thoughts  
   
   This template is an excellent example of using automation and AI not just for productivity but also for creativity. Whether you’re a solo blogger or managing multi-client content calendars, this n8n workflow will help you deliver polished WordPress drafts at scale—based on just a few inputs.
   
   Want to try it yourself? All you need are your OpenAI and WordPress credentials and a self-hosted or cloud n8n instance. Happy automating!
   
   —
   
   If you're interested in exploring or customizing this workflow, it can be cloned and modified easily via the n8n editor for tailored use cases—from newsletters to landing pages, the possibilities are endless.

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.