Integrating AI with Open-Meteo API for Enhanced Weather Forecasting
This workflow integrates artificial intelligence with a weather API to provide an intelligent weather inquiry service. Users only need to enter the city name and the number of days for the query in the chat interface, and the system will automatically retrieve the city's latitude and longitude, as well as future weather information, offering accurate weather forecasts. It supports multi-turn dialogue memory, enhancing the user experience. This service is suitable for scenarios such as travel planning, education and training, and intelligent customer service, allowing users to quickly obtain the weather data they need, thereby assisting with daily travel and decision-making.
Workflow Name
Integrating AI with Open-Meteo API for Enhanced Weather Forecasting
Key Features and Highlights
This workflow integrates OpenAI’s chat model with Open-Meteo’s weather API to enable intelligent weather queries based on natural language input. Users simply enter a city name and the desired forecast duration in the chat interface. The system automatically invokes two tools—geolocation and weather forecasting—to intelligently retrieve the weather information for the specified city over the upcoming days. The workflow supports multi-turn conversation memory to enhance the interactive experience.
Core Problems Addressed
- Simplifies traditional complex weather query processes by enabling one-stop intelligent conversational weather inquiries
- Utilizes AI to automatically determine the call sequence, first obtaining geographic coordinates and then fetching weather data, thereby improving workflow automation and intelligence
- Allows users to quickly access accurate future weather forecasts, aiding daily travel and trip planning
Application Scenarios
- Travel Planning: Users can check the future weather of destinations anytime to arrange itineraries reasonably
- Education and Training: Serves as a teaching case demonstrating how to combine AI with external APIs to design automated tools
- Intelligent Customer Service: Provides a natural language interface for meteorological-related services
- Personal and Enterprise Daily Weather Query Needs
Main Workflow Steps
- The user inputs a weather query (e.g., “São Paulo weather for the next 7 days”) at the chat trigger node
- The AI agent node parses the request and first calls the “Geolocation Tool” to send an HTTP request to the Open-Meteo geocoding API to obtain the city’s latitude and longitude
- Using the acquired coordinates, the “Weather Forecast Tool” calls the Open-Meteo weather forecast API to retrieve temperature, precipitation, and other data for the specified number of days
- The AI model integrates the weather data and generates a natural language response returned to the user
- The chat memory buffer node saves the conversation context to support continuous multi-turn interactions
Systems or Services Involved
- OpenAI Chat Model (language model and AI agent)
- Open-Meteo Geocoding API (city coordinate lookup)
- Open-Meteo Weather Forecast API (retrieving future weather data)
- n8n Workflow Automation Platform (trigger nodes, HTTP requests, chat memory management, etc.)
Target Users and Value
- Automation developers and AI enthusiasts: Learn how to combine language models with third-party APIs to build intelligent toolchains
- Travelers and outdoor event organizers: Quickly obtain accurate weather information to support decision-making
- Enterprise customer service teams: Build natural language weather query bots to enhance user experience
- Educational institutions: Use as a demonstration of AI tool applications to help learners understand AI and API integration methods
Designed by Davi Saranszky Mesquita, this workflow leverages AI intelligent agents to automatically invoke different tools, achieving full-process automation from city name input to precise weather forecasting. It greatly enhances user interaction convenience and intelligence. Users only need to input simple natural language commands to instantly receive detailed weather information, making it suitable for a variety of practical application scenarios.