This article provides a complete, practical walkthrough of the Executeworkflow Hackernews Create 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:  
   Automating Hacker News Insights: Using AI Agents and Custom Tools in n8n
   
   Meta Description:  
   Learn how to use LangChain-powered AI agents inside n8n to query and process top Hacker News posts. This workflow demonstrates OpenAI integration, data transformation, and seamless sub-workflow execution.
   
   Keywords:  
   n8n, LangChain, OpenAI API, GPT-4, automation, AI agent, Hacker News API, workflow automation, custom tool, chat integration
   
   Third-party APIs Used:
   
   - Hacker News API
   - OpenAI API
   
   Article:
   
   Harnessing Hacker News with AI Agents in n8n: A Workflow Walkthrough
   
   In the rapidly evolving world of automation and artificial intelligence, platforms like n8n have empowered developers and technologists to build modular, scalable solutions by simply connecting nodes. In this article, we examine a unique real-world n8n workflow that leverages the power of AI via LangChain, integrates OpenAI's GPT models, and retrieves top posts from Hacker News—all in a chat-driven experience.
   
   Overview
   
   This n8n workflow demonstrates how to design an AI agent capable of responding to natural language prompts by querying live data through a custom sub-workflow. Specifically, the agent can answer questions like:
   
   “What is the 5th most popular post ever on Hacker News?”
   
   By using LangChain architecture, the workflow links a Language Model (GPT-4 via OpenAI), a custom tool sub-workflow that fetches Hacker News top posts, and automates the data processing pipeline for clean and accurate responses. Let’s dissect how it's built.
   
   Main Components
   
   1. AI Agent with LangChain and OpenAI GPT
   At the heart of the experience is the AI Agent node, powered by LangChain capabilities. It leverages the OpenAI GPT-4o-mini model as its language model. This agent is triggered by a chat interface (via the LangChain “When chat message received” node), which allows users to type in natural language prompts.
   
   Whenever a new chat message is received, the agent interprets the intent and decides if it needs a tool or action to assist in constructing a valid response.
   
   2. Custom Tool: Hacker News Data Sub-workflow
   This workflow takes full advantage of n8n’s modularity through a "Custom Tool to call the wf below" node. This lets the AI agent call another embedded sub-workflow that fetches data from the Hacker News API.
   
   A trigger node (“When Executed by Another Workflow”) waits for the main agent to invoke the sub-workflow. Once executed, it proceeds through a well-defined data pipeline:
   
   - Hacker News Node: Fetches the top 50 all-time posts by popularity.
   - Clean up Data: Uses a Set node to extract and transform essential data points like title, score, URL, author, and timestamp from the API response.
   - Stringify: Converts the resulting JSON data into a format suitable for AI consumption.
   
   Ultimately, this data is sent back to the main agent for interpretation and user-facing response via the chat interface.
   
   3. Workflow Flow
   Here’s how the sequence unfolds:
   
   - A chat message is submitted (e.g., “What is the 5th most popular post ever on Hacker News?”).
   - The LangChain-based AI agent, using GPT-4, parses the query and recognizes a need to use its tool (hn_tool).
   - The tool executes the Hacker News sub-workflow, retrieving cleaned-up data of top posts.
   - The agent processes the JSON response and identifies the 5th most popular post.
   - A concise, natural language answer is returned to the user through the chat interface.
   
   Customizable and Extensible
   
   What makes this workflow particularly exciting is its customizability. Developers can:
   
   - Swap or extend the data source (e.g., Reddit, Twitter, GitHub).
   - Replace the API (e.g., financial data, company metrics, weather).
   - Refine tool logic (e.g., sorting algorithms, keyword filtering).
   - Use richer language models like GPT-4 or Claude for better contextual understanding.
   
   n8n’s node-based visual builder makes it easy to test and debug each part, providing clear insight into every step—from input triggers to final responses.
   
   Why This Matters
   
   This example is more than a fun experiment with Hacker News. It illustrates an expanding paradigm in digital automation:
   
   - AI agents can be enriched with custom tools for real-time answers.
   - Workflows are no longer just static; they’re dynamic and intelligent.
   - Users interact via intuitive chat interfaces, reducing learning curves.
   - APIs, AI, and automation blend to build responsive, data-driven microservices.
   
   In real-world scenarios, this architecture could be applied to sales insights ("What was our best-selling product last month?"), customer support ("Summarize today’s unresolved tickets"), or even research (“Find the most cited papers on quantum computing”).
   
   Final Thoughts
   
   By marrying LangChain, OpenAI, and n8n’s sub-workflows, developers can build intelligent assistants capable of real-time, data-rich interactions. This Hacker News example is just the beginning—limitless integration potential awaits.
   
   Try it out, customize it, and see how intelligent agents can power up your workflows.
   
   —  
   Written by your AI Automation Assistant
   
   Looking to automate knowledge tasks or business processes with AI? Start building your first workflow on n8n.io today.

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.