Building a Conversational AI Weather Agent with n8n: A Step-by-Step Tutorial

Transform your automation game by creating an intelligent chatbot that can discuss weather and suggest activities - all without writing a single line of code!

INTRODUCTION

Have you ever wanted to build your own AI assistant but felt overwhelmed by the complexity of coding? What if I told you that you could create a sophisticated conversational AI agent that not only provides weather information but also suggests activities based on current conditions - all using a visual, no-code approach?

In this tutorial, we'll walk through building a complete weather-aware AI agent using n8n, the popular workflow automation platform. By the end, you'll have a chatbot that can understand natural language queries about weather and provide intelligent, contextual responses.

I got this tutorial from Nate Herk Youtube channel - a n8n enthusiast and trainer — from his video (links provide at the end of this post). Shout out to him 👏👏

WHAT WE'RE BUILDING

Our AI weather agent will be capable of:

- Natural conversation about weather in any city

- Real-time weather data retrieval from WeatherAPI

- Activity suggestions based on current weather conditions

- Memory retention across conversations

- Multi-step reasoning using Google Gemini AI

The best part? Everything is built visually using n8n's intuitive workflow designer.

PREREQUISITES

Before we start, you'll need:

- An n8n instance (cloud or self-hosted)

- A free WeatherAPI account (get your API key at weatherapi.com)

- Google Gemini API access

- Basic familiarity with n8n workflows

ARCHITECTURE OVERVIEW

Our solution consists of two interconnected workflows:

1. Core Weather Service (tutorial_get_weather): Handles API calls and data processing

2. Conversational Interface (Weather_Agent): Manages chat interactions and AI reasoning

This modular approach ensures scalability and maintainability - you can easily extend the weather service or add new conversational capabilities independently.

PART 1: BUILDING THE WEATHER DATA SERVICE

Step 1: Create the Weather Workflow Foundation

Start by creating a new workflow called "get_weather". This will be our core weather data processing engine.

Node 1: Workflow Trigger

- Add an "Execute Workflow Trigger" node

- Set input source to "passthrough"

- This allows other workflows to call this service

Node 2: Data Preparation

- Add a "Set" node named "Edit Fields"

- Extract the city parameter: {{ $json.values() }}

- This normalizes input data from the calling workflow

Step 2: Weather API Integration

Note

For weather forecase API, I use weatherAPI initially I tried to do the same. Their swagger API doc can be found here . I depart from Nate’s tutorial using OpenWeather as for some reason, my API request keep returning 404 error (invalid API KEY) eventhough I have waited for more than 10 minutes.

Node 3: Weather Data Retrieval

Go to www.weatherapi.com. Register and after logged in you can get an API key as below:

Then go back to n8n workspace

- Add an "HTTP Request" node

- Configure the WeatherAPI call:

- URL: http://api.weatherapi.com/v1/current.json

- Method: GET

- Query parameters:

- q: {{ $json.city }}

- key: YOUR_WEATHERAPI_KEY

Pro tip: Store your API key in n8n's credential system for security!

Step 3: AI-Powered Weather Analysis

Node 4: Weather Summary Generation

- Add a "Basic LLM Chain" node

- Connect a "Google Gemini Chat Model"

- Configure the prompt to structure weather data:

"Put the parameters below into a single 'query' field:

weather_description: {{ $json.current.condition.text }}

temp: {{ $json.current.temp_c }} [C] / {{ $json.current.temp_f }}[F]

humidity: {{ $json.current.humidity }}

wind_speed: {{ $json.current.wind_mph }}

city: {{ $json.location.name }}

country: {{ $json.location.country }}"

Node 5: Activity Suggestion Engine

- Add another "Basic LLM Chain" node

- Connect a second Google Gemini model

- Prompt: "Suggest up to 3 suitable activities given the weather of the city"

- Input: {{ $json.text }} from the previous chain

Node 6: Output Formatting

- Add a final "Set" node

- Format the response: {{ $json.text }}

- This creates clean output for the calling workflow

PART 2: BUILDING THE CONVERSATIONAL INTERFACE

Step 4: Create the Chat Agent Workflow

Create a new workflow called "Weather_Agent". This will be your user-facing chatbot.

Node 1: Chat Interface

- Add a "When chat message received" trigger

- This creates a webhook endpoint for chat interactions

- Note the webhook URL - you'll use this to interact with your agent

Node 2: AI Agent Core

- Add an "AI Agent" node

- System message: "You are a helpful assistant"

- This orchestrates the entire conversation flow

Step 5: Memory and Intelligence

Node 3: Conversation Memory

- Add a "Simple Memory" node

- Connect to the AI Agent

- This enables context retention across messages

Node 4: Language Model

- Add a "Google Gemini Chat Model"

- Connect to the AI Agent

- This powers the natural language understanding

Step 6: Tool Integration

Node 5: Weather Tool

- Add a "Call n8n Workflow Tool"

- Description: "Call this tool to get information about current weather. If message requires calling this tool more than once (for example: request information about two cities) then repeat using the tools accordingly to achieve your goal/user request"

- Target workflow: Select your tutorial_get_weather workflow

- This creates a seamless connection between chat and weather service

Node 6: Knowledge Enhancement

- Add a "Wikipedia" tool

- Connect to the AI Agent

- This provides additional context for location-based queries

STEP 5: TESTING YOUR WEATHER AGENT

Basic Functionality Test

1. Deploy both workflows

2. Activate the Weather_Agent workflow

3. Use the chat webhook URL to send test messages:

"What's the weather like in Tokyo?"

"How's the weather in London and Paris?"

"Should I go hiking in Melbourne today?"

Advanced Conversation Examples

Your agent should handle complex queries like:

- Multi-city comparisons: "Compare weather in NYC and LA"

- Activity planning: "I'm in Miami, what outdoor activities do you recommend?"

- Follow-up questions: "What about tomorrow?" (maintains context)

KEY FEATURES AND BENEFITS

MODULAR ARCHITECTURE

The two-workflow design allows independent scaling and maintenance. You can enhance the weather service without touching the chat interface.

AI-POWERED INTELLIGENCE

Google Gemini processes both weather data interpretation and activity suggestions, providing human-like responses.

CONVERSATION MEMORY

The agent remembers previous interactions, enabling natural follow-up questions and context-aware responses.

EASY EXTENSION

Adding new data sources or capabilities is as simple as creating new tools and connecting them to the agent.

TROUBLESHOOTING COMMON ISSUES

Weather API Problems

- Invalid API key: Verify your WeatherAPI credentials

- City not found: The agent should gracefully handle invalid locations

- Rate limiting: Consider caching frequently requested cities

AI Model Issues

- Slow responses: Check your Google Gemini API quotas

- Inconsistent formatting: Refine your prompt templates

- Context loss: Verify memory node connections

Workflow Connectivity

- Tool not working: Ensure the weather workflow is active

- Missing parameters: Check data mapping between nodes

- Webhook errors: Verify the chat trigger configuration

ADVANCED ENHANCEMENTS

Weather Alerts and Notifications

Extend the weather service to include:

- Severe weather warnings

- Air quality index

- UV index recommendations

- Hourly forecasts

Multi-Language Support

Enhance the AI agent to:

- Detect user language

- Respond in multiple languages

- Handle location names in different languages

Integration Possibilities

Connect your weather agent to:

- Slack/Discord: For team weather updates

- Calendar apps: Weather-aware event planning

- IoT devices: Smart home automation based on weather

- Email: Daily weather summaries

PERFORMANCE OPTIMIZATION

Caching Strategy

Implement caching to reduce API calls:

- Store recent weather queries

- Cache city coordinates

- Implement time-based cache expiration

Error Handling

Add robust error handling:

- Graceful API failure responses

- Alternative data sources

- User-friendly error messages

CONCLUSION

You've successfully built a sophisticated conversational AI weather agent using n8n's visual workflow approach! This project demonstrates the power of no-code automation for creating intelligent applications.

The modular architecture you've implemented provides a solid foundation for future enhancements. Whether you want to add new data sources, integrate with other platforms, or enhance the AI capabilities, you now have the framework to build upon.

WHAT'S NEXT FROM HERE?

Consider these exciting expansion possibilities:

- Travel Planning Agent: Combine weather with flight and hotel data

- Outdoor Activity Coordinator: Integrate with sports and event APIs

- Smart Home Controller: Connect to IoT devices for weather-based automation

- Business Intelligence: Create weather impact reports for retail or agriculture

The skills you've learned here - API integration, AI orchestration, and conversational design - are transferable to countless other automation projects.

RESOURCES AND LINKS

• n8n Documentation: docs.n8n.io

• WeatherAPI: weatherapi.com

• Google Gemini API: ai.google.dev

• n8n Community: Join thousands of automation enthusiasts sharing ideas and solutions

• Nate Herk tutorial video: link

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