This article provides a complete, practical walkthrough of the Stickynote Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Simple setup in 5-15 minutes. One‑time purchase: €9.

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 Conversational Weather and Wiki Assistant Using n8n and LangChain
   
   Meta Description:  
   Explore how to create an intelligent AI agent in n8n using LangChain, Wikipedia, and weather API integration. Learn how the workflow operates with memory management and real-time responses.
   
   Keywords:  
   n8n, LangChain, AI agent, workflow automation, Wikipedia API, Open-Meteo API, weather assistant, intelligent chatbot, local LLM, AI with memory, llama3, Ollama AI, conversational AI, low-code automation
   
   Third-Party APIs Used:
   
   - Wikipedia API (via LangChain tool)
   - Open-Meteo API (https://api.open-meteo.com/v1/forecast)
   - Ollama (local language model integration via Llama3)
   
   Article:
   
   Creating a Conversational Weather and Wiki Assistant with n8n and LangChain Tools
   
   In today’s rapidly evolving automation landscape, intelligent agents that can comprehend conversations, access multiple tools, and offer relevant, real-time answers have become highly valuable across industries. Using n8n—a powerful open-source workflow automation platform—and LangChain—an AI orchestration framework—you can now build your own conversational AI agent that doesn’t just talk, but thinks, recalls, and acts.
   
   In this article, we’ll walk you through an n8n workflow designed to create a conversational AI assistant capable of handling queries related to both weather forecasts and general knowledge, powered by Wikipedia and Open-Meteo APIs. It also utilizes a local LLM through Ollama to ensure flexible and private language model capabilities.
   
   Overview of the Workflow
   
   At the core of this n8n workflow is a conversational AI agent that dynamically interacts with users, processes messages, accesses external tools (Wikipedia and weather data), and maintains memory to offer contextual and coherent conversations.
   
   Here’s how each part of the workflow functions:
   
   1. Starting the Conversation: Manual Chat Trigger  
   The process is initiated using the “On new manual Chat Message” node. This feature waits for user interaction—perfect for testing during development or creating controlled conversational environments.
   
   2. AI Agent Configuration  
   The "AI Agent" node is the brain of the workflow. It is configured to process user inputs and respond based on context, instruction (system message), and tool usage capabilities. The system message explicitly guides the behavior of the agent:
   
   "You are a helpful assistant, with weather tool and wiki tool. Find out the latitude and longitude information of a location then use the weather tool for current weather and weather forecast. For general info, use the wiki tool."
   
   This clear behavioral prompt ensures the agent knows when and how to use which tools.
   
   3. Local Language Model with Ollama  
   To process natural language inputs and generate human-like text, the workflow utilizes the “Ollama Chat Model” node. This incorporates a local instance of the Llama3.2 model. The advantage of using a local model is increased control and privacy while reducing dependency on third-party cloud services.
   
   4. Memory Buffer Setup  
   Real conversational agents must remember recent context to emulate human-like conversations. The “Window Buffer Memory” node stores up to 20 recent messages, allowing the agent to reference earlier parts of the conversation and maintain coherence over multiple turns. This is critical for rich, layered dialogue.
   
   5. Wikipedia Tool Integration  
   When users request general information—like "Tell me about the Eiffel Tower"—the AI agent leverages the “Wikipedia” node. This integration queries Wikipedia through LangChain's tooling, ensuring accurate and summarized responses directly from the world's largest free encyclopedia.
   
   6. Weather Forecast Tool via HTTP Request  
   For location-specific weather services, the “Weather HTTP Request” node connects to the Open-Meteo API. Based on input (latitude and longitude), this node fetches one day's worth of hourly forecast data. It returns temperature data, which the AI then interprets and returns to the user in a conversational way.
   
   7. Smart Tool Usage Control  
   The agent intelligently selects tools according to the task. It first performs location-based processing (e.g., determining coordinates), then decides whether to reach out to the Wikipedia API or weather service.
   
   Why This Workflow is Powerful
   
   By combining the strengths of multiple technologies, this setup enables users to experience:
   
   - Conversational interaction with memory and context logic
   - Custom responses leveraging real-time data
   - A secure, local AI model (Llama3 via Ollama)
   - Access to structured internet data sources for general and weather-specific information
   
   Use Cases
   
   This workflow is ideal for businesses or personal use cases that demand informative virtual assistants. Examples include:
   
   - Travel assistants providing location-based weather forecasts
   - Educational tools answering general knowledge questions
   - Customer service bots offering quick, reliable data-driven responses
   
   Conclusion
   
   With this n8n workflow, you’re not just automating—you’re empowering a conversational agent with real tools, intelligence, and memory. Whether you are a developer, a data enthusiast, or an enterprise looking to enrich your customer engagement, this easy-to-configure and open-source solution offers a scalable and secure entry into intelligent conversational automation.
   
   The best part? Everything is done locally, giving you full control in how data is processed, actions are taken, and insights are delivered.
   
   Get started by replicating this workflow in your own n8n instance and experience the future of automated conversations 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.