This article provides a complete, practical walkthrough of the Stickynote Hackernews 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:  
   Enhancing AI-Powered Workflows with n8n and Hacker News: Building a Custom AI Tool with LangChain and OpenAI
   
   Meta Description:  
   Discover how to build an AI agent using n8n, LangChain, and OpenAI to interact with real-time Hacker News data. Learn how this workflow fetches and processes the top 50 HN posts to answer user queries intelligently.
   
   Keywords:  
   n8n automation, LangChain, OpenAI, Hacker News API, AI assistant, no-code workflow, AI integration, chatbot workflow, Hacker News data, custom AI tool
   
   Third-Party APIs Used:
   
   - Hacker News (via n8n’s Hacker News node)
   - OpenAI API (via LangChain integration in n8n)
   
   Article:
   
   How to Build a Custom AI Agent That Answers Hacker News Questions Using n8n, LangChain, and OpenAI
   
   In the rapidly evolving world of automation and artificial intelligence, there’s an increasing demand for workflows that combine smart querying with live data access. In this article, we'll walk you through a powerful n8n workflow that utilizes LangChain’s AI agent capabilities, OpenAI’s chat model, and real-time data from Hacker News to answer user questions like, “What is the 5th most popular post ever on Hacker News?”
   
   ⛓️ Overview of the Workflow
   
   This n8n workflow is divided into two core components:
   
   1. A main chat workflow driven by a LangChain-powered AI agent using OpenAI's language model.
   2. A sub-workflow serving as a custom tool for fetching and processing Hacker News data.
   
   These two components are connected to allow natural language user queries to dynamically pull relevant data from external sources—in this case, the Hacker News API—and respond with formatted, human-readable information.
   
   💬 The Chatbot Interface
   
   The entry point for this workflow is the “On new manual Chat Message” trigger, which activates whenever a user sends a message, such as:  
   > “What is the 5th most popular post ever on Hacker News?”
   
   This input message is passed to the “AI Agent” node, a LangChain integration within n8n, that interprets the prompt and delegates tasks as needed.
   
   🧠 LangChain-Powered AI Agent
   
   The backbone of interpretation and decision-making is the “AI Agent” node. It’s equipped with an OpenAI chat model via the “OpenAI Chat Model” node using a linked OpenAI API credential. This allows it to process natural language instructions and intelligently call tools to fetch or manipulate data.
   
   The agent is limited to 10 iterations to prevent unnecessary loops and ensure performance stability.
   
   ⚙️ Custom Tool: Hacker News Data Fetcher
   
   To give the agent a way to pull relevant Hacker News data, the “Custom tool to call the wf below” node is used. This is a LangChain ‘toolWorkflow’ that allows the agent to delegate specific tasks—in this case, executing a sub-workflow to retrieve and clean up Hacker News data.
   
   🔁 Sub-Workflow: Fetching and Preparing Hacker News Data
   
   This sub-workflow starts with an “Execute Workflow Trigger,” followed by a “Hacker News” node configured to retrieve the top 50 stories across all categories. It then passes the data to a “Clean up data” node, which selects and standardizes key fields including:
   
   - Title
   - Points
   - URL
   - Created date
   - Author
   
   Next, a “Stringify” node consolidates the cleaned JSON into a single stringified response, making it digestible by the AI agent.
   
   🗨️ AI-Powered Responses in Real-Time
   
   Once the cleaned Hacker News data is returned to the AI agent, the OpenAI language model uses this information to answer the user's question appropriately. For example, when asked about the 5th most popular post, it can now analyze the points field and respond with precision:
   
   > “The 5th most popular post ever on Hacker News is titled 'Show HN: Personal Finance Tracker Using GPT-4' with 3,493 points, authored by @fintechdev. Read it here: https://news.ycombinator.com/item?id=12345678.”
   
   🔖 Sticky Notes for Documentation
   
   The workflow includes several sticky notes for clarity and ease of use. These are helpful hints explaining:
   
   - How to test the workflow
   - What each component does
   - How to extend or replace the data source
   
   This makes the workflow exceptionally beginner-friendly for those new to n8n or AI integrations.
   
   🌍 Real-World Applications
   
   This example isn’t limited to Hacker News—by replacing the data-fetching sub-workflow, you could build similar tools using:
   
   - Internal business metrics
   - CRM data (e.g., from Salesforce or HubSpot)
   - News aggregators or social media APIs
   - E-commerce analytics or trending product reports
   
   💡 Final Thoughts
   
   This use case highlights the power of integrating AI agents into no-code or low-code environments like n8n. By connecting OpenAI’s sophisticated language understanding with real-time data sources using LangChain, you can create highly interactive, intelligent systems without writing a single line of front-end code.
   
   Whether you’re a developer, marketer, or data analyst, workflows like this bring advanced AI capabilities within reach—fast, flexible, and customizable to your needs.
   
   Try modifying the workflow with your own tool integrations and see how AI agents can transform your automation game.
   
   —  
   
   Ready to build your own AI-powered assistant? Start with this Hacker News integration and watch your chatbot go from reactive to remarkably insightful.

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.