This article provides a complete, practical walkthrough of the Manual Stickynote 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:  
   Building a Custom LLM Workflow in n8n with OpenAI and Wikipedia Integration
   
   Meta Description:  
   Explore how to create a custom LLM (Large Language Model) chain in n8n, utilizing OpenAI’s GPT-4o-mini and integrating with Wikipedia for intelligent AI responses.
   
   Keywords:  
   n8n, OpenAI, Langchain, GPT-4o-mini, AI agent, Wikipedia API, custom LLM, automation, AI workflow, LangChain integration, no-code AI, GPT-4, n8n tutorial, OpenAI API, Wikipedia tool
   
   Third-party APIs and Tools Used:
   
   - OpenAI API (GPT-4o-mini via LangChain)
   - WikipediaQueryRun (from LangChain’s community tools)
   
   Article:
   
   🚀 Building an Intelligent AI Workflow in n8n Using OpenAI and Wikipedia
   
   As artificial intelligence and automation evolve, no-code tools like n8n are bridging the gap between technical sophistication and user-friendly design. In this article, we'll walk through a powerful custom workflow built in n8n that leverages a combination of OpenAI's GPT-4o-mini model, the LangChain framework, and Wikipedia’s knowledge base to perform real-time question-answering tasks.
   
   The workflow, categorized under two custom branches (LLM Chain and AI Agent), demonstrates how you can orchestrate AI-powered tasks using self-coded nodes and external tools. Let’s explore how this intelligent automation system works and how you can apply similar logic in your own workflows.
   
   🧠 Workflow Overview
   
   This n8n workflow is divided into two primary zones:
   
   1. A self-coded LLM chain that takes a static prompt like “Tell me a joke” and gets a response from GPT-4o-mini.
   2. An AI Agent pipeline that answers a research-based query like “What year was Einstein born?” using Wikipedia as a tool with LangChain integration.
   
   These are activated by a single manual trigger, and each path is built using custom code, demonstrating deep extensibility and flexibility in the no-code paradigm.
   
   📌 The Nodes That Power It All
   
   Here’s a breakdown of the components within this modular automation flow:
   
   ▶ 1. Manual Trigger Node  
   - Node: When clicking "Execute Workflow"  
   - Role: Initiates workflow execution manually, triggering both paths simultaneously (Set2 and Set3).
   
   ▶ 2. Input Set Nodes  
   - Nodes: Set2 and Set3  
   - Role: Define the “chatInput” variable for each part of the workflow. Set2 asks, “Tell me a joke,” while Set3 queries, “What year was Einstein born?”
   
   ▶ 3. LLM Chain Execution  
   - Core Node: Custom - LLM Chain Node1  
   - Powered by: LangChain & GPT-4o-mini  
   - Description: This node is a self-coded LangChain execution module. It builds a prompt using the PromptTemplate utility from LangChain and forwards it to the GPT-4o-mini model. The result is returned as a JSON output.
   
   ▶ 4. OpenAI Chat Model Node  
   - Nodes: OpenAI Chat Model & OpenAI Chat Model1  
   - API: GPT-4o-mini via OpenAI  
   - Role: These nodes are configured to invoke OpenAI’s GPT-4o-mini to generate response text. One feeds into the LLM Chain and the other supports the AI Agent.
   
   ▶ 5. AI Agent Node  
   - Node: AI Agent  
   - Powered by: LangChain Agent  
   - Description: This agent dynamically decides on tools to use for performing tasks. Given a query like “What year was Einstein born?”, it identifies that a factual lookup is needed and makes use of the Wikipedia tool node integrated within the graph.
   
   ▶ 6. Wikipedia Tool Node  
   - Node: Custom - Wikipedia1  
   - API Used: WikipediaQueryRun from LangChain’s community tools  
   - Description: This is a self-coded tool node that uses LangChain’s WikipediaQueryRun class. It fetches up to 3 results with a max document size of 4000+ characters, returning concise Wikipedia-based findings.
   
   🎯 How It All Connects
   
   - When the workflow is manually triggered, Set2 and Set3 are both executed.
   - Set2 directs its “Tell me a joke” input to the custom LLM chain node, which sends it via prompt to OpenAI’s GPT-4o-mini model.
   - Set3 prompts the AI Agent with "What year was Einstein born?” The Agent analyzes the query, determines the best strategy, and invokes the Wikipedia tool for accurate information.
   - Both AI responses are processed in parallel, showcasing how you can unify structured LLM use with autonomous agentic behavior in one seamless system.
   
   ✅ What Makes This Workflow Stand Out?
   
   - Dual use of OpenAI GPT via LangChain for both static and dynamic tasks.
   - Custom tool implementation that wraps LangChain-native modules for niche tasks like Wikipedia search.
   - Agent-based architecture that mimics how human assistants choose tools and data sources based on context.
   - No-code front-end (n8n) meets low-code/JavaScript backend (custom nodes), offering maximum flexibility.
   
   🧩 Use Cases and Applications
   
   - Chatbots with context-aware research tools
   - AI-powered personal assistants capable of web lookup
   - LLM mini projects for learning GPT and LangChain
   - Automation of creative or factual writing workflows
   
   🛠 Final Thoughts
   
   This project showcases the power of modular AI design enabled by n8n’s workflow engine, Go interface, and LangChain-powered plugin environment. By blending agent-level reasoning, structured prompt chains, and real-time knowledge queries, you’re not just building automation—you’re building intelligent systems that think before they act.
   
   Whether you're an AI product developer experimenting with agents or a no-code builder learning how to extend n8n with LangChain tools, this is a brilliant case of automation and AI working hand-in-hand.
   
   Happy building! 💡
   
   —  
   Looking for a tutorial or hands-on help to replicate or deploy this workflow in your environment? Feel free to reach out—we’d love to assist!

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.