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Local Ollama chat agent with weather and Wikipedia tools

by Mindketn8n workflowProductivity
Newversion 1 · updated
Nodes6Apps3Like

Overview

  1. On new manual Chat Message
  2. Wikipedia
  3. Window Buffer Memory
  4. Weather HTTP Request
  5. Ollama Chat Model
  6. AI Agent
Local Ollama chat agent with weather and Wikipedia tools, picture 1
‹2 / 2›

An n8n workflow with a chat agent that runs on a local Ollama model. It works out a place’s coordinates, reads a day’s temperatures from Open-Meteo and searches Wikipedia for general questions. Memory keeps the last 20 exchanges.

Local Ollama chat agent with weather and Wikipedia tools is an n8n workflow in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page or copy its JSON, then import it into an n8n canvas. Nothing runs until you activate it in n8n.

At a glance

Runs in
  • n8n on your computer: Ollama’s default address works as it is
  • Your own n8n server: point the credential at an address that server can reach
  • n8n in Docker: use an address other than localhost for Ollama
  • n8n Cloud: it cannot reach an Ollama on your computer unless you expose it

Platform

n8nWorkflow automation, in n8n’s cloud or on your own server
Connects 3 apps

How it works

Trigger

Chat message

It starts when you send a message in n8n’s chat:

  • A question about the weather somewhere, or about any topic

Actions

  1. The agent reads your message and chooses a tool
  2. For weather, it works out the place’s coordinates and asks Open-Meteo for the day’s hourly temperatures
  3. For general questions, it searches Wikipedia
  4. llama3.2 on your Ollama writes the answer, with the last 20 exchanges as memory

Result

A chat answer with the day’s temperatures or Wikipedia facts, from a model running on your own machine

Integrations

What you need before you start.

Ollama

Language model

Runs llama3.2 on your own machine as the agent’s model.

NeedsOllama installed with llama3.2 pulled, and its address in an Ollama credential

Open-Meteo

Weather data

Returns the day’s hourly temperatures for the latitude and longitude the model gives.

NeedsNo key. The free service is for non-commercial use

Wikipedia

Encyclopedia

Searches Wikipedia for the string the model passes and returns what it finds.

NeedsNo key

Workflow check

Checked by Mindket on 13 Nov 2026

Steps
2 steps, starting on a Manual Chat event
AI
1 step calls an AI model: AI Agent
Sign-ins
Signs in to: Ollama and Manual Chat
API keys
No API key is left in the file
Step types
Mindket’s checker reads all 2 of its step types

Read from the workflow’s file, not from a run.

About

What it is

This n8n workflow is a small chat agent that runs on a model of your own through Ollama. The model is llama3.2:latest, and a memory node keeps the last 20 exchanges. For weather, the agent works out a place’s latitude and longitude, then asks Open-Meteo for one day of hourly temperatures. For anything else it searches Wikipedia. Six nodes do the work, beside four sticky notes.

Who it suits

Hobbyists, students and developers with Ollama on their own computer who want to try an n8n agent without a paid model. Skip it if you need exact forecasts or a bigger model than your machine runs. Open-Meteo’s free service is for non-commercial use.

Who uses it, and how
IndustryWho uses itHow they use itExample
EducationStudentsSee how an agent picks between two toolsA student asks for a city’s temperature today, then for its history.
Hobby projectsHome lab usersRun an assistant whose model stays on their own machineA user asks what the day is like in Oslo.
TravelTrip plannersCheck a destination’s temperatures for the dayA planner asks for today’s temperatures in Lisbon.
SoftwareDevelopersUse it as a base for more toolsA developer adds a calendar tool next to the two it has.

You need a little n8n and a local model: import a file, pull llama3.2 in Ollama and add one credential.

Where to use it

Any n8n that can reach your Ollama and the internet will run it. The answers appear in the chat, and the model runs on your own machine.

  • n8n on your computer: Ollama’s default address works as it is
  • Your own n8n server: point the credential at an address that server can reach
  • n8n in Docker: use an address other than localhost for Ollama
  • n8n Cloud: it cannot reach an Ollama on your computer unless you expose it

Setup

Download saves a zip with workflow.json and Mindket’s setup notes in your downloads folder. Setup is one model and one credential.

What you need first
NeedDetail
n8nOn your computer, on a server or in Docker, able to reach Ollama
OllamaInstalled, with the model llama3.2 pulled
InternetFor Open-Meteo and Wikipedia, neither of which needs a key
  1. In n8n, create a workflow, open its menu, choose Import from File and pick workflow.json.
  2. In a terminal, run ollama pull llama3.2.
  3. Add an Ollama credential with the address of your Ollama, http://localhost:11434 by default, and select it in Ollama Chat Model.
  4. Save, press Chat on the canvas and ask for the temperature in a city and what it is known for.

How to use it

Press Chat and ask about the weather in a place, or ask a general question. The agent chooses the tool. Ask follow-ups in the same chat, since it remembers the last 20 exchanges.

What to change for a different result
To changeWhere
The modelllama3.2:latest in Ollama Chat Model. Pick one that supports tool calling
Memory lengthcontextWindowLength, 20, in Window Buffer Memory
Weather dataThe fixed query values forecast_days and hourly in Weather HTTP Request
What the agent doesThe system message in AI Agent

Example

A student asks for the temperature in Lisbon today and what the city is known for.

The agent works out Lisbon’s coordinates, calls Open-Meteo with them and reads the hourly temperatures. For the second part it searches Wikipedia. It answers in the chat from both. The coordinates come from the model’s own memory, so check them for a small place.

For newcomers and experts

If you are new. n8n draws a workflow as boxes joined by lines, and each box, a node, does one job. The agent sits in the middle with its model, its memory and two tools plugged into it. Try a weather question and a general one in the same chat, and watch which tool each calls.

For experts. The weather tool is a Tool HTTP Request node. The model fills latitude and longitude, and the node fixes the other two query values.

Where to look closer
NodeWhat it doesWhat to know
Weather HTTP RequestA GET to api.open-meteo.com/v1/forecast with forecast_days 1 and hourly set to temperature_2mIt returns the day’s hourly temperatures in GMT, not a current reading
WikipediaThe Wikipedia tool node, with no settingsThe model passes it one search string
Window Buffer MemoryKeeps the last 20 exchanges under the key chat_historyEvery chat shares that one key
AI AgentA Tools Agent with the prompt type Define, reading inputA model without tool calling cannot use either tool

Good to know

Small local models call tools unevenly, and the weather tool returns hours, not a current reading.

  • Coordinates: there is no geocoding tool, so the model supplies the numbers from what it knows. Check them for a small place
  • Hours: the tool’s description says current temperature, but the reply is a list of hours in GMT. Add a timezone to the query, or let the agent pick the hour
  • Terms: Open-Meteo’s terms, read on 12 November 2026, limit the free service to non-commercial use and fewer than 10,000 calls a day, and its data is under CC BY 4.0
  • The model: a small local model can ignore a tool or send it bad values. Try a larger one if it does
  • Memory: one key, chat_history, so every chat shares the same history
  • The chat: the trigger works only from n8n’s editor
  • Your data: your messages go to your own Ollama, coordinates to Open-Meteo and search words to Wikipedia
  • Credentials: the JSON holds none

README and files

README

Local Ollama chat agent with weather and Wikipedia tools

An n8n workflow with a small chat agent that runs on a model served by Ollama. For weather it works out a place's
coordinates and asks Open-Meteo. For general questions it searches Wikipedia. It remembers the last 20 exchanges.

What is inside

  • workflow.json: the workflow as n8n exports it, ten nodes, four of them sticky notes. It holds no credential.
  • README.md: these notes.

What we changed from the original copy

  • Ollama Chat Model was linked to a credential in its author's n8n. The link is gone, so select your own.
  • The original copy had no settings block. Mindket's sets the execution order to v1.
  • Nothing else. The file has no Code node, and its expressions needed no repair.

Before you start

  • An n8n instance that can reach your Ollama. Ollama's default address is http://localhost:11434, which n8n's
    credential suggests too, so n8n and Ollama on one machine work as they are.
  • For other setups, n8n in Docker needs an address other than localhost. n8n Cloud cannot reach an Ollama on your
    computer unless you expose it.
  • Ollama with the model installed: ollama pull llama3.2.
  • Internet access for the two tools. Neither needs a key.

Set it up

  1. In n8n, create a workflow, open its menu and choose Import from File. Pick workflow.json.
  2. Add an Ollama credential with the address of your Ollama and select it in Ollama Chat Model.
  3. Save, press Chat on the canvas and ask, for example, for the temperature in a city and what it is known for.

How a run goes

  1. On new manual Chat Message starts a run when you send a message in the chat. It works only from the editor.
  2. AI Agent reads your message from input. Its system message tells it to find a place's latitude and longitude, use the
    weather tool for the weather and the Wikipedia tool for general information.
  3. Ollama Chat Model runs llama3.2:latest. Window Buffer Memory gives it the last 20 exchanges.
  4. Weather HTTP Request calls https://api.open-meteo.com/v1/forecast. The model fills in latitude and longitude,
    and the node adds forecast_days=1 and hourly=temperature_2m.
  5. Wikipedia searches Wikipedia for the string the model gives it.
  6. The agent writes the answer in the chat.

Change the look

  • The model: llama3.2:latest in Ollama Chat Model. Pick one that supports tool calling.
  • The memory: contextWindowLength in Window Buffer Memory.
  • The weather data: the fixed query values in Weather HTTP Request. Add more hourly variables there, or a timezone.
  • What the agent says: the system message in AI Agent.

Good to know

  • There is no geocoding tool. The model supplies the coordinates from what it knows, so check them for a small place.
  • The weather tool asks for one forecast day with hourly temperatures only. Its description says "current temperature", but
    the reply is a list of hours. Open-Meteo's times are GMT unless a timezone is set, so the agent has to pick the right
    hour itself.
  • Open-Meteo's terms, read on 12 November 2026, limit the free service to non-commercial use and fewer than 10,000 calls a
    day, and its data is under CC BY 4.0.
  • A small local model can ignore a tool or call it with bad values. If it does, try a larger model.
  • The memory uses one key, chat_history, so every chat shares the same history.
  • Your messages go to your own Ollama. Place coordinates go to Open-Meteo, and search words to Wikipedia.
Files
NOTICE.md  632 B
README.md  3 KB
workflow.json  5 KB

3 files, 10 KB, downloaded as a zip.

What’s new

  • Version 1 · 2026-11-12

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