This article provides a complete, practical walkthrough of the Manual Http Create 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:
   Demystifying n8n HTTP Workflows: Pagination, Data Scraping & API Automation Explained
   
   Meta Description:
   Learn how to use n8n for real-world HTTP automation tasks including GitHub API pagination, web scraping with HTML extract, and handling dynamic API responses. Discover practical workflow techniques using n8n's low-code interface.
   
   Keywords:
   n8n automation, n8n workflows, HTTP request n8n, GitHub API pagination, web scraping with n8n, n8n HTML extract, API integration n8n, automate GitHub starred repos, fetch Wikipedia title n8n, n8n http examples
   
   Third-Party APIs Used:
   - GitHub API (https://api.github.com)
   - Wikipedia (https://en.wikipedia.org)
   - JSONPlaceholder (https://jsonplaceholder.typicode.com)
   
   Article:
   
   Creating Powerful HTTP Automation in n8n: Pagination, API Calls, and Data Extraction
   
   n8n (short for "node, node") is a powerful, visual, low-code workflow automation tool designed for developers and non-developers alike. Among its most flexible and widely used features is the native HTTP Request node—which lets users communicate with almost any API, fetch web data, or trigger integrations across services.
   
   In this article, we’ll walk through a multi-functional n8n workflow designed to demonstrate three key capabilities:
   1. Making and splitting requests from an API (JSONPlaceholder),
   2. Web scraping titles from Wikipedia pages,
   3. Handling paginated GitHub API responses efficiently.
   
   Let’s break it down step by step.
   
   ⏯️ Manual Trigger: Your Workflow Starting Point
   
   The workflow begins with a Manual Trigger node—ideal for testing and iteration. This node allows you to click “Execute Workflow” to immediately run and observe the automation sequence.
   
   📌 HTTP Request Examples: The Building Blocks
   
   From the start, the workflow launches three separate tasks:
   
   1. Fetch a list of mock album data from the JSONPlaceholder free test API.
   2. Request a random Wikipedia page and extract its main heading.
   3. Initialize GitHub starred repositories retrieval with pagination.
   
   These tasks serve as foundational examples for more advanced logic.
   
   🔹 Splitting API Responses into Manageable Items
   
   The first example hits the endpoint https://jsonplaceholder.typicode.com/albums. JSONPlaceholder is a mock rest API perfect for prototyping.
   
   The node HTTP Request - Get Mock Albums retrieves the full dataset, then passes it to the Item Lists - Create Items from Body node. This node splits the response body into multiple separate items, making them easier to handle downstream in the workflow.
   
   This approach is helpful when dealing with bulk data—allowing each item (e.g., an album) to be manipulated individually or pushed to other systems.
   
   🔹 Scraping Wikipedia: Simple Web Content Extraction
   
   The second use case involves fetching a random page from Wikipedia using the HTTP Request - Get Wikipedia Page node. By enabling redirect-following in the request settings and setting the response to “file” (binary), the node can retrieve the full HTML content of the page.
   
   Once received, the HTML Extract - Extract Article Title node uses a CSS selector (#firstHeading) to extract the wiki page’s article title. This kind of lightweight scraping is ideal for generating content insights, building databases, or collecting search metadata.
   
   🔁 GitHub API Pagination: A Real-World Data Handling Challenge
   
   Perhaps the most powerful part of the workflow is how it handles paginated API responses from GitHub.
   
   GitHub’s /users/{username}/starred endpoint returns a paginated list of starred repositories, which can be navigated using "page" and "per_page" query parameters.
   
   The workflow uses the following key nodes and logic:
   
   - Set node: Initializes user-specific parameters such as username (e.g., that-one-tom), per_page limit (15), and page counter.
   - HTTP Request - Get my Stars node: Retrieves the list of starred repositories using API parameters supplied by that Set node.
   - Item Lists - Fetch Body: Splits the returned data for inspection or integration downstream.
   - If - Are we finished?: Determines whether there are more pages of data by checking if the response body is empty.
   - Set - Increment Page: Adds one to the current page parameter and loops the request if more data exists.
   
   This type of looping plus conditional logic allows complete extraction of paginated data—a must-have skill when working with APIs like GitHub, HubSpot, Salesforce, and many others that use pagination to manage payload sizes.
   
   💡 Purposeful Documentation Throughout
   
   Sticky Notes have been used throughout the visual flow to provide guided explanations for each section:
   - “Split into items” – JSONPlaceholder demo.
   - “Data Scraping” – Wikipedia title extraction.
   - “Handle Pagination” – GitHub API loop explanation.
   
   These annotations turn the workflow into a self-guided learning path, which is especially valuable for teams or onboarding new developers to an automation-first mindset.
   
   🔚 Final Thoughts
   
   This n8n workflow is more than just an example—it’s a blueprint for a wide variety of automation use cases:
   - Pulling datasets from third-party APIs,
   - Handling API pagination at scale,
   - Scraping and parsing web content dynamically.
   
   Whether you’re syncing lists, cleaning data, or just exploring service integration possibilities, n8n and its HTTP module offer incredible freedom. Combined with powerful logic nodes like Set, IF, and Item Lists, you’re one step away from building full-fledged API automation systems—without writing a single line of code.
   
   Want to take it a step further? You could:
   - Store the extracted data in a spreadsheet or database (using Google Sheets or PostgreSQL nodes),
   - Set up a timer instead of manually triggering the workflow,
   - Or add real-time alerts when new data is fetched.
   
   Automation is no longer a luxury; it’s the new normal. And tools like n8n make it remarkably accessible.
   
   —
   
   Explore the full potential of n8n workflows at https://n8n.io, and join their community to share, ask questions, and learn.

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.