# Weather Scraper (Open-Meteo) (`benthepythondev/open-meteo-weather-scraper`) Actor Get current weather and a multi-day forecast for any city or coordinates: temperature, feels-like, humidity, precipitation, wind, UV index, sunrise/sunset and conditions. Fast and reliable via the public Open-Meteo API, no key, no login. Just pass city names. - **URL**: https://apify.com/benthepythondev/open-meteo-weather-scraper.md - **Developed by:** [ben](https://apify.com/benthepythondev) (community) - **Categories:** Travel, Other, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.50 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 🌦️ Weather Scraper (Open-Meteo) Get **current weather** and a **multi-day forecast** for any city or coordinates — temperature, feels-like, humidity, precipitation, wind, UV index, sunrise/sunset and conditions. Powered by the public Open-Meteo API, so it's fast and reliable: no browser, no login, no API key. Just pass city names and it handles the geocoding for you. Built for travel and logistics tools, dashboards, event planning and weather datasets. Export to JSON/CSV/Excel, run on a schedule, call via API, or connect to Make, Zapier or n8n. ### 🔎 What is the Weather Scraper? Give it a list of locations (e.g. "Berlin", "London", or a `lat,lon` pair) and it returns one clean record per location: the current conditions plus a daily forecast for the days you ask for. Great for building a weather feed for many cities at once. #### What data does it extract? - **Location**, **country**, **region** and **timezone** - **Latitude**, **longitude** and **elevation** - **Current**: temperature, feels-like, humidity, precipitation, wind speed/direction, condition, day/night - **Daily forecast** (per day): condition, max/min temperature, feels-like max, precipitation sum and probability, max wind, **UV index**, **sunrise** and **sunset** ### ⬇️ Input | Field | Type | Description | |-------|------|-------------| | `locations` | array | City names (e.g. `Berlin`) or `lat,lon` pairs. | | `forecastDays` | integer | Number of forecast days (1–16). Default `7`. | | `temperatureUnit` | string | `celsius` or `fahrenheit`. | | `windSpeedUnit` | string | `kmh`, `mph`, `ms` or `kn`. | #### Example input ```json { "locations": ["Berlin", "London", "New York"], "forecastDays": 3, "temperatureUnit": "celsius", "windSpeedUnit": "kmh" } ```` ### ⬆️ Output One record per location: ```json { "location": "Berlin", "country": "Germany", "region": "Land Berlin", "latitude": 52.52, "longitude": 13.41, "elevation": 38.0, "timezone": "Europe/Berlin", "temperature_unit": "°C", "current": { "time": "2026-06-29T13:00", "temperature": 26.0, "apparent_temperature": 27.3, "humidity": 58, "precipitation": 0.0, "weather_code": 1, "weather": "Mainly clear", "wind_speed": 9.0, "wind_direction": 210, "is_day": true }, "daily": [ { "date": "2026-06-29", "weather": "Partly cloudy", "temp_max": 28.4, "temp_min": 16.1, "precipitation_sum": 0.0, "precipitation_probability_max": 10, "wind_speed_max": 14.2, "uv_index_max": 7.1, "sunrise": "2026-06-29T04:45", "sunset": "2026-06-29T21:33" } ], "query": "Berlin" } ``` ### 💡 Use cases - ✈️ **Travel & events** — show forecasts for destinations or venues. - 🚚 **Logistics** — plan routes and operations around weather. - 📊 **Dashboards** — build a multi-city weather feed. - 🤖 **Automation** — trigger alerts when rain or heat is forecast. ### ❓ FAQ **Do I need an API key or login?** No — it uses the public Open-Meteo API. **Can I pass city names?** Yes — they're geocoded automatically. You can also pass `lat,lon`. **How many days of forecast?** Up to 16 — set `forecastDays`. **Celsius or Fahrenheit?** Either — set `temperatureUnit`. **Do I get the current conditions too?** Yes — both current and a daily forecast. **Is the UV index included?** Yes — the daily maximum UV index. **How does pricing work?** Pay per location returned. No subscription. **Is it legal?** Open-Meteo offers free weather data for non-commercial and commercial use within their terms. Use responsibly. ### ⚙️ How it works The scraper geocodes each city through Open-Meteo's geocoding API, then calls the forecast API for the resulting coordinates — no browser and no key. It normalizes the response into a tidy record with a `current` block and a `daily` array, mapping WMO weather codes to readable text, so you get clean data instead of parallel arrays. Runs are fast and dependable, which is why the actor keeps passing its daily health check. The same input shape works for one city or a long list. ### 👥 Who uses weather data? Weather data is valuable to travel apps, logistics teams, event planners and analysts. A travel app shows destination forecasts; a logistics team plans around storms; an event planner checks venue conditions; an analyst correlates weather with sales. Because every record is plain JSON with consistent fields, it drops straight into a spreadsheet, database, BI tool or automation with no custom parsing. ### 📤 Export, schedule & integrate Every run is saved to a dataset you can export to **JSON, CSV, Excel, XML or RSS**, or pull through the **Apify API**. Wire it into **Make, Zapier, n8n, Google Sheets, Slack** or your **own database**, run it on a **schedule** to refresh forecasts, and call it from AI agents through the **Apify MCP server**. ### 💡 Tips for best results - Pass many cities in one run to build a regional weather feed. - Use a `lat,lon` pair for precise, non-city locations. - Schedule a morning run to feed a daily forecast into Slack or email. - Switch `temperatureUnit` to match your audience's region. ### ❓ More FAQ **How fresh is the data?** It is fetched live on each run. **Can I run it automatically?** Yes — use Apify Schedules (cron). **What if a city can't be found?** It's skipped and logged — the run continues. **Which export formats?** JSON, CSV, Excel, XML and RSS, plus the Apify API. **Can AI agents use it?** Yes — via the Apify API and MCP server. ### 🔗 You might also like - [Federal Register Scraper](https://apify.com/benthepythondev/federal-register-scraper) — US government documents. - [Open Library Book Scraper](https://apify.com/benthepythondev/open-library-scraper) — book & ISBN data. - [TheSportsDB Scraper](https://apify.com/benthepythondev/thesportsdb-scraper) — teams & players. *** **Keywords:** weather scraper, open-meteo api, weather api, weather forecast, weather data, current weather, daily forecast, temperature data, precipitation, wind data, uv index, city weather, weather feed, forecast api, no api key weather # Actor input Schema ## `locations` (type: `array`): City names (e.g. 'Berlin') or 'lat,lon' pairs. ## `forecastDays` (type: `integer`): Number of forecast days (1-16). ## `temperatureUnit` (type: `string`): Temperature unit for the forecast. ## `windSpeedUnit` (type: `string`): Wind speed unit for the forecast. ## Actor input object example ```json { "locations": [ "Berlin", "London" ], "forecastDays": 3, "temperatureUnit": "celsius", "windSpeedUnit": "kmh" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "locations": [ "Berlin", "London" ], "forecastDays": 3, "temperatureUnit": "celsius", "windSpeedUnit": "kmh" }; // Run the Actor and wait for it to finish const run = await client.actor("benthepythondev/open-meteo-weather-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "locations": [ "Berlin", "London", ], "forecastDays": 3, "temperatureUnit": "celsius", "windSpeedUnit": "kmh", } # Run the Actor and wait for it to finish run = client.actor("benthepythondev/open-meteo-weather-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "locations": [ "Berlin", "London" ], "forecastDays": 3, "temperatureUnit": "celsius", "windSpeedUnit": "kmh" }' | apify call benthepythondev/open-meteo-weather-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=benthepythondev/open-meteo-weather-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Weather Scraper (Open-Meteo)", "description": "Get current weather and a multi-day forecast for any city or coordinates: temperature, feels-like, humidity, precipitation, wind, UV index, sunrise/sunset and conditions. Fast and reliable via the public Open-Meteo API, no key, no login. Just pass city names.", "version": "1.0", "x-build-id": "rDKHy911fEpmdKHII" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/benthepythondev~open-meteo-weather-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-benthepythondev-open-meteo-weather-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/benthepythondev~open-meteo-weather-scraper/runs": { "post": { "operationId": "runs-sync-benthepythondev-open-meteo-weather-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/benthepythondev~open-meteo-weather-scraper/run-sync": { "post": { "operationId": "run-sync-benthepythondev-open-meteo-weather-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "locations": { "title": "Locations", "type": "array", "description": "City names (e.g. 'Berlin') or 'lat,lon' pairs.", "items": { "type": "string" } }, "forecastDays": { "title": "Forecast days", "minimum": 1, "maximum": 16, "type": "integer", "description": "Number of forecast days (1-16).", "default": 7 }, "temperatureUnit": { "title": "Temperature unit", "enum": [ "celsius", "fahrenheit" ], "type": "string", "description": "Temperature unit for the forecast.", "default": "celsius" }, "windSpeedUnit": { "title": "Wind speed unit", "enum": [ "kmh", "mph", "ms", "kn" ], "type": "string", "description": "Wind speed unit for the forecast.", "default": "kmh" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```