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Ai agent with Ollama for current weather and wiki

Thomas ChanThomas Chan
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2/3/2026
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This workflow template demonstrates how to create an AI-powered agent that provides users with current weather information and Wikipedia summaries. By integrating n8n with Ollama's local Large Language Models (LLMs), this template offers a seamless and privacy-conscious solution for real-time data retrieval and summarization.

Who is this for?

Developers and Enthusiasts: Individuals interested in building AI-driven workflows without relying on external APIs. Privacy-Conscious Users: Those who prefer processing data locally to maintain control over their information. Educators and Students: Learners seeking hands-on experience with AI integrations and workflow automation.

What problem does this workflow solve?

Accessing up-to-date weather information and concise Wikipedia summaries typically requires multiple API calls to external services, which can raise privacy concerns and incur costs. This workflow addresses these issues by utilizing Ollama's self-hosted LLMs within n8n, enabling users to retrieve and process information locally.

What this workflow does:

User Input Capture: Begins with a chat interface where users can input queries. AI Processing: The input is sent to an AI Agent node configured with Ollama's LLMs, which interprets the query and determines the required actions. Weather Retrieval: For weather-related queries, the workflow fetches current weather data from a specified source. Wikipedia Summarization: For queries seeking information, it retrieves relevant Wikipedia content and generates concise summaries.

Setup:

Install Required Tools: Ollama: Install and run Ollama to manage local LLMs. Configure n8n Workflow: Import the provided workflow template into your n8n instance. Set up the AI Agent node to connect with Ollama's API. Ensure nodes responsible for fetching weather data and Wikipedia content are correctly configured. Run the Workflow: Start the workflow and interact with the chat interface to test various queries.

How to customize this workflow to your needs:

Automate Triggers: Set up scheduled triggers to provide users with regular updates, such as daily weather forecasts or featured Wikipedia articles.

AI Agent with Ollama for Current Weather and Wikipedia

This n8n workflow demonstrates how to build a powerful AI agent using Ollama as the chat model, capable of answering questions about current weather and general knowledge using Wikipedia. It leverages LangChain nodes within n8n to create a conversational agent with tool-use capabilities and memory.

What it does

This workflow simplifies and automates the process of interacting with an AI agent that can:

  1. Receive Chat Messages: It starts by listening for incoming chat messages, acting as the trigger for the AI agent's response.
  2. Process with AI Agent: An AI Agent node (powered by LangChain) takes the user's input and, based on its understanding, decides which tools to use.
  3. Utilize Ollama Chat Model: The agent uses an Ollama Chat Model for its core language understanding and generation, enabling it to process natural language queries.
  4. Maintain Conversation Memory: A Simple Memory node ensures the agent remembers past interactions, allowing for more coherent and context-aware conversations.
  5. Access Real-time Weather Data: An HTTP Request Tool is configured to fetch current weather information, enabling the agent to answer weather-related questions.
  6. Query Wikipedia: A Wikipedia Tool is integrated, allowing the agent to retrieve information from Wikipedia for general knowledge questions.

Prerequisites/Requirements

To run this workflow, you will need:

  • n8n Instance: A running n8n instance (self-hosted or cloud).
  • Ollama Installation: An Ollama server running locally or accessible from your n8n instance, with a suitable chat model (e.g., llama2, mistral) downloaded and available.
  • Internet Access: For the Wikipedia and HTTP Request tools to function correctly.

Setup/Usage

  1. Import the Workflow:
    • Download the provided JSON file.
    • In your n8n instance, click on "Workflows" in the left sidebar.
    • Click "New" -> "Import from JSON" and paste the workflow JSON or upload the file.
  2. Configure Ollama Chat Model:
    • Locate the "Ollama Chat Model" node.
    • Ensure the "Base URL" points to your running Ollama server (e.g., http://localhost:11434).
    • Select the "Model" you have downloaded and want to use (e.g., llama2).
  3. Configure HTTP Request Tool (for Weather):
    • Locate the "HTTP Request" tool node.
    • This tool is pre-configured to fetch weather data. You might need to adjust the API endpoint or add API keys if you intend to use a specific weather API that requires authentication. The current setup implies a generic HTTP request capability that the AI agent will leverage.
  4. Configure Wikipedia Tool:
    • Locate the "Wikipedia" node. No specific configuration is usually needed for this tool as it directly interfaces with the public Wikipedia API.
  5. Activate the Workflow:
    • Click the "Activate" toggle in the top right corner of the workflow editor.
  6. Interact with the AI Agent:
    • Use the "When chat message received" trigger node to send test messages to your AI agent.
    • Ask questions like:
      • "What is the current weather in London?"
      • "Tell me about the Eiffel Tower."
      • "Who was Albert Einstein?"
      • "What's the temperature like in New York today?"

The AI agent will process your query, decide whether to use the HTTP Request Tool (for weather) or the Wikipedia Tool (for general knowledge), and provide a coherent response.

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