# Weather Forecast API (`agentx/weather-forecast-api`) Actor Worldwide weather feed by country, region, city, or district — returns up to 16 days of forecast (temperature, feels-like, precipitation, humidity, wind, UV index, sunrise/sunset) or any historical date range via Start/End dates, ready for logistics, retail-demand, and energy modeling. - **URL**: https://apify.com/agentx/weather-forecast-api.md - **Developed by:** [AgentX](https://apify.com/agentx) (community) - **Categories:** Other, Developer tools, Automation - **Stats:** 3 total users, 1 monthly users, 100.0% runs succeeded, 2 bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 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 ## Global Weather Scraper - Daily Forecast & Historical Weather Data Extraction API **Global Weather Scraper is a global weather intelligence API that extracts structured daily weather records, multi-variable climate signals, and forecast/historical date ranges for any location worldwide in a single location-driven run.** Global Weather Scraper returns structured records per day, including maximum/minimum/mean temperature, apparent temperature extremes, precipitation sum, rain sum, snowfall sum, precipitation hours, precipitation probability, max wind speed, max wind gusts, dominant wind direction (degrees and compass), UV index maximum, sunrise timestamp, sunset timestamp, daylight hours, sunshine hours, current conditions snapshot, resolved location name, country, region, city, latitude, longitude, and timezone. Coverage spans every country, region, city, and town worldwide via free-text location input, with both forecast (1–16 days) and historical date-range modes. Built for climate modeling, logistics planning, agricultural intelligence, solar energy optimization, weather-aware automation, supply chain risk monitoring, and environmental research pipelines. Pay-per-result pricing with no monthly minimum. [![Weather](https://img.shields.io/badge/Weather-0070c0?style=for-the-badge&color=0070c0)](https://apify.com/agentx/global-weather-scraper?fpr=aiagentapi) [![Forecast & Historical](https://img.shields.io/badge/Forecast_%26_Historical-0070c0?style=for-the-badge&color=0070c0)](https://apify.com/agentx/global-weather-scraper?fpr=aiagentapi) [![Global Coverage](https://img.shields.io/badge/Global_Coverage-0070c0?style=for-the-badge&color=0070c0)](https://apify.com/agentx/global-weather-scraper?fpr=aiagentapi) --- ### Why Choose This API #### Structured Daily Weather Records with Forecast and Historical Range Support **🌡️ Full Daily Temperature Profile** Extract maximum, minimum, and mean temperatures alongside feels-like (apparent) temperature extremes per day — normalized numeric values ready for time-series modeling, climate benchmarking, and logistics planning systems. **🌧️ Precipitation & Snow Data** Capture precipitation sum, rain sum, snowfall sum, precipitation hours, and precipitation probability per day for rainfall event detection, snow routing, and climate analysis pipelines. **💨 Wind Speed, Gusts & Direction** Retrieve maximum wind speed, maximum wind gusts, dominant wind direction (degrees), and compass label per day for wind impact analysis, logistics risk scoring, and weather monitoring feeds. **☀️ UV Index, Sunrise & Sunset** Extract UV index maximum, sunrise timestamp, sunset timestamp, daylight hours, and sunshine hours per day for health intelligence, solar energy optimization, and event planning systems. **📍 Enriched Location Metadata** Each record includes resolved location name, country, region, city, latitude, longitude, and timezone — complete geo-metadata for multi-location weather dataset pipelines. **📅 Forecast & Historical Range Modes** Run in forecast mode (1–16 days from today) or historical date-range mode using ISO dates or relative periods (e.g., `30 days ago`, `2 weeks`). --- ### Quick Start Guide #### How to Extract Weather Data in 3 Steps ##### Step 1: Define Your Location and Date Range Enter a location (any country, region, city, town, or district) and choose between forecast mode (number of days) or historical mode (start and end dates). ##### Step 2: Configure and Run the Actor [Open Actor Input](https://console.apify.com/actors/Yn1ksjorQhCtBcLN6/input?fpr=aiagentapi) Set the location, mode, and date configuration, then click **Start**. ##### Step 3: Download Your Data Export results as JSON or CSV, or access programmatically via the Apify dataset API. Each record represents one calendar day with full weather metrics, location metadata, and current conditions. --- ### Input Parameters #### Configuration Fields | Parameter | Type | Required | Description | Example Values | | ------------ | ---------- | -------- | ---------------------------------------------------------------- | ------------------------------------------------- | | `location` | string | ✅ | Country, region, city, town, or district | `"Tokyo, Japan"`, `"Ontario, Canada"`, `"Munich"` | | `days` | integer | ❌ | Number of forecast days from today (1–16) when no date range set | `3`, `7`, `14` | | `start_date` | datepicker | ❌ | Historical start date (ISO or relative period) | `"2025-04-01"`, `"30 days"` | | `end_date` | datepicker | ❌ | Historical end date (ISO or relative period) | `"2025-04-21"`, `"1 day"` | #### Example Input Configuration — Forecast Mode ```json { "location": "Tokyo, Japan", "days": 7 } ```` #### Example Input Configuration — Historical Mode ```json { "location": "London, UK", "start_date": "2025-04-01", "end_date": "2025-04-30" } ``` *** ### Output Data Schema #### Complete Weather Record Structure Each extracted weather record represents one calendar day and contains the following fields: [Open Actor Output](https://console.apify.com/actors/Yn1ksjorQhCtBcLN6/input?fpr=aiagentapi) #### Run Context Fields | Field | Type | Description | | -------------- | ------ | ------------------------------------------ | | `processor` | string | Apify actor URL that processed this record | | `processed_at` | string | ISO 8601 timestamp (UTC) when scraped | | `status` | string | Lookup status | | `mode` | string | `"forecast"` or `"historical"` | | `start_date` | string | Requested start date (historical mode) | | `end_date` | string | Requested end date (historical mode) | #### Location Metadata Fields | Field | Type | Description | | ------------------ | ------ | --------------------------- | | `location` | string | Resolved place name | | `country` | string | Country name | | `region` | string | State, province, or county | | `city` | string | City/town/municipality name | | `latitude` | number | Latitude coordinate | | `longitude` | number | Longitude coordinate | | `timezone` | string | Location timezone | | `temperature_unit` | string | Temperature unit used | | `wind_speed_unit` | string | Wind speed unit used | #### Daily Weather Fields | Field | Type | Description | | ------------------------------- | ------ | --------------------------------- | | `date` | string | Calendar day (ISO date) | | `weather` | string | Human-readable weather label | | `temperature_max` | number | Maximum temperature | | `temperature_min` | number | Minimum temperature | | `temperature_mean` | number | Mean temperature | | `apparent_temperature_max` | number | Feels-like maximum | | `apparent_temperature_min` | number | Feels-like minimum | | `wind_speed_max` | number | Maximum wind speed | | `wind_gusts_max` | number | Maximum wind gusts | | `wind_direction_dominant` | number | Dominant wind direction (degrees) | | `wind_direction_label` | string | Compass label for wind direction | | `precipitation_sum` | number | Total precipitation | | `precipitation_probability_max` | number | Max precipitation probability (%) | | `rain_sum` | number | Total rain | | `snowfall_sum` | number | Total snowfall | | `precipitation_hours` | number | Hours with precipitation | | `uv_index_max` | number | Maximum UV index | | `sunrise` | string | Sunrise time (local) | | `sunset` | string | Sunset time (local) | | `daylight_hours` | number | Hours of daylight | | `sunshine_hours` | number | Hours of sunshine | #### Current Conditions Fields | Field | Type | Description | | ------------------------------ | ------- | -------------------------------------- | | `current_temperature` | number | Current air temperature | | `current_apparent_temperature` | number | Current feels-like temperature | | `current_humidity` | number | Current relative humidity (%) | | `current_weather` | string | Current weather label | | `current_cloud_cover` | number | Cloud cover (%) | | `current_pressure` | number | Atmospheric pressure (hPa) | | `current_visibility_km` | number | Visibility distance (km) | | `current_uv_index` | number | Current UV index | | `current_wind_speed` | number | Current wind speed | | `current_wind_direction` | number | Current wind direction (degrees) | | `current_wind_direction_label` | string | Compass label | | `current_wind_gusts` | number | Current wind gusts | | `current_is_day` | boolean | Whether current observation is daytime | #### Example JSON Output ```json { "processor": "https://apify.com/agentx/global-weather-scraper?fpr=aiagentapi", "processed_at": "2026-05-01T10:30:00.000Z", "status": "success", "mode": "historical", "start_date": "2026-04-28", "end_date": "2026-04-30", "location": "Tokyo, Japan", "country": "Japan", "region": "Tokyo", "city": "Tokyo", "latitude": 35.6762, "longitude": 139.6503, "timezone": "Asia/Tokyo", "temperature_unit": "celsius", "wind_speed_unit": "kmh", "date": "2026-04-28", "weather": "Mainly clear", "temperature_max": 22.6, "temperature_min": 14.8, "temperature_mean": 18.7, "precipitation_sum": 0, "precipitation_probability_max": 10, "uv_index_max": 7.5, "sunrise": "2026-04-28T04:59+09:00", "sunset": "2026-04-28T18:19+09:00", "daylight_hours": 13.3, "current_temperature": 18.4, "current_humidity": 63, "current_weather": "Mainly clear" } ``` #### Export Formats - **JSON** - Complete structured data with all metadata - **CSV** - Spreadsheet-compatible format for time-series analysis - **API Access** - Programmatic access via Apify Client SDK - **Cloud Storage** - Automatic upload to Apify Dataset *** ### Integration Examples #### Actor ID for Platform Integration ```text Yn1ksjorQhCtBcLN6 ``` #### Ⓜ️ [Make.com Setup:](https://www.make.com/en/register?pc=marketingme) 1. Login to **[Make.com (Get 1000 Free Credits)](https://www.make.com/en/register?pc=marketingme)** 2. Add module **"Run an Actor"** 3. Turn **'Map'** on - right side of the 'Actor\*' 4. Paste **Actor ID** - from above 5. Click the **'⟳ Refresh'** - left side of Map 6. **Input JSON\*** - Modify the parameters as needed 7. Set **"Run synchronously"** to **YES** 8. Add module **"Get Dataset Items"** - receive the result 9. In **Dataset ID\*** select **defaultDatasetId** #### 🎱 [N8N.io Setup:](https://n8n.io/) 1. Add **'Run an Actor and get dataset'** - from the apify node 2. **Actor** → **By ID** → **Paste Actor ID** - from above 3. **Input JSON** - Modify the parameters as needed #### Python Integration Example ```python from apify_client import ApifyClient client = ApifyClient('YOUR_API_TOKEN') run_input = { "location": "Tokyo, Japan", "days": 7 } run = client.actor("Yn1ksjorQhCtBcLN6").call(run_input=run_input) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ``` #### JavaScript/Node.js Integration ```javascript import { ApifyClient } from "apify-client"; const client = new ApifyClient({ token: "YOUR_API_TOKEN" }); const input = { location: "London, UK", start_date: "2025-04-01", end_date: "2025-04-30", }; const run = await client.actor("Yn1ksjorQhCtBcLN6").call(input); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => console.log(item)); ``` #### JSON-LD Metadata ```json { "@context": "https://schema.org", "@graph": [ { "@type": "SoftwareApplication", "@id": "https://apify.com/agentx/global-weather-scraper#software", "name": "Global Weather Scraper", "description": "Global Weather Scraper is a global weather intelligence API that extracts structured daily weather records with temperature, precipitation, wind, UV index, and current conditions for any location worldwide, in both forecast and historical modes.", "applicationCategory": "BusinessApplication", "applicationSubCategory": "Data Extraction API", "operatingSystem": "Web, Cloud", "url": "https://apify.com/agentx/global-weather-scraper?fpr=aiagentapi", "softwareVersion": "1.0.0", "datePublished": "2024-08-01", "dateModified": "2026-05-01", "featureList": [ "Daily max/min/mean temperature plus apparent extremes", "Precipitation sum, rain, snowfall, hours, probability", "Max wind speed, gusts, dominant direction (deg + compass)", "UV index, sunrise, sunset, daylight, sunshine hours", "Forecast mode (1–16 days) and historical date-range mode", "Worldwide location coverage via free-text input", "Resolved geo-metadata: country, region, city, lat/lon, timezone", "Native integrations with Make.com, n8n, LangChain, and CrewAI" ], "offers": { "@type": "Offer", "price": "0.01", "priceCurrency": "USD", "availability": "https://schema.org/InStock" }, "author": { "@id": "https://apify.com/agentx#person" }, "publisher": { "@id": "https://apify.com#organization" } }, { "@type": "Person", "@id": "https://apify.com/agentx#person", "name": "AgentX", "url": "https://apify.com/agentx", "sameAs": [ "https://apify.com/agentx", "https://t.me/AiAgentApi", "https://t.me/Apify_Actor" ], "knowsAbout": [ "weather data", "forecast API", "climate intelligence", "meteorological data", "logistics planning" ] }, { "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Apify", "item": "https://apify.com" }, { "@type": "ListItem", "position": 2, "name": "AgentX", "item": "https://apify.com/agentx" }, { "@type": "ListItem", "position": 3, "name": "Global Weather Scraper", "item": "https://apify.com/agentx/global-weather-scraper" } ] } ] } ``` *** ### Pricing & Cost Calculator #### Pay-Per-Result Pricing | Event | Description | Price | | --------------- | ------------------------------------------------------ | ----- | | **Actor Start** | Charged on each run start (per GB memory, min 1 event) | $0.02 | | **Result** | Charged weather lookup per day-record returned | $0.01 | #### Cost Estimation Examples | Records (Days) | Estimated Cost | | ------------------------------- | -------------- | | 7-day forecast (1 location) | ~$0.09 | | 30-day historical (1 location) | ~$0.32 | | 90-day historical (1 location) | ~$0.92 | | 365-day historical (1 location) | ~$3.67 | | 30-day × 10 locations | ~$3.02 | > Actor Start cost ($0.02) is separate from per-result pricing. *** ### Use Cases & Applications #### Logistics & Operations Intelligence **Weather Risk Scoring for Routes** Extract forecast data for route waypoints to score weather risk per day — enabling proactive routing decisions for freight, last-mile delivery, and field operations. **Weather Event Detection & Alerting** Use precipitation probability, snowfall, and wind gust thresholds to build automated weather alert pipelines that trigger operational responses when conditions exceed risk thresholds. **Construction & Site Management Weather Feeds** Feed daily weather records into project management systems to flag high-risk weather days for outdoor construction, site inspections, or infrastructure maintenance. #### Research & Environmental Intelligence **Historical Climate Dataset Building** Extract months or years of historical daily weather records for any location to build climate research datasets, seasonal pattern analysis, and long-term trend modeling. **Multi-Location Weather Comparison** Run parallel extractions across multiple cities or regions to compare temperature, precipitation, or UV index profiles across locations for environmental research and investment analysis. **Agricultural Weather Intelligence** Extract precipitation, temperature range, sunshine hours, and UV index for agricultural regions to support crop planning, irrigation scheduling, and yield modeling. #### AI & Data Applications **Weather-Correlated Demand Modeling** Combine weather time-series with sales, traffic, or demand data to build regression models quantifying weather impact on business metrics. **Climate NLP Corpus** Build labeled weather description datasets using `weather` and `current_weather` fields for NLP model training on weather language understanding. *** ### FAQ #### Can this actor extract historical weather data? Yes. Set `start_date` and `end_date` to enable historical mode. Accepts ISO dates (`YYYY-MM-DD`) or relative periods like `30 days` or `2 weeks`. #### How far ahead can forecast data be extracted? Up to **16 days** forward from today using the `days` parameter. #### What location formats are supported? Any country, region, city, town, or district name. Examples: `"Tokyo, Japan"`, `"Ontario, Canada"`, `"Munich"`. #### Is current conditions data included? Yes. Each record includes current temperature, humidity, weather label, cloud cover, pressure, visibility, UV index, wind speed, and wind direction. #### What data format does the actor output? Structured **JSON** with one record per calendar day. Data is also available in CSV format and via REST API. *** ### SEO Keywords & Search Terms #### Primary Keywords global weather data API, weather scraper API, historical weather dataset, weather forecast extraction, daily weather API, weather data pipeline, weather intelligence API, meteorological data extraction, weather time-series API, climate dataset builder #### Long-Tail Keywords how to extract historical weather data, daily weather forecast dataset API, multi-location weather data extraction, weather API for logistics planning, precipitation data pipeline, temperature time-series extraction API, weather risk scoring API, climate trend dataset builder, weather NLP corpus API, agricultural weather intelligence API #### Industry Terms weather time-series extraction, meteorological data pipeline, climate dataset API, forecast weather data, historical daily records, precipitation event detection, wind risk intelligence, solar UV index dataset, weather-correlated demand modeling, logistics weather intelligence #### Use Case Keywords weather data tracker, climate dataset builder, weather risk API, multi-city weather monitor, weather pipeline integration, agricultural climate data, daily temperature feed, historical precipitation dataset, weather forecast API, weather alert automation *** ### Trust & Certifications - ✅ **Production-Grade Infrastructure** — runs on the Apify cloud platform with managed proxy rotation and automatic retries - ✅ **Public Open-Data Source** — weather records derived from publicly available meteorological feeds; no personal data processed - ✅ **Pay-Per-Result Billing** — transparent per-day pricing with no monthly minimum or seat fees - ✅ **Continuously Maintained** — schema and feed updates released as upstream weather providers evolve *** ### Legal & Compliance #### Data Rights & Usage All weather data returned by this actor is sourced from publicly available meteorological data providers. Users are responsible for ensuring their use of extracted data complies with applicable laws and the terms of use of underlying weather data sources. #### Privacy Compliance - **GDPR**: Compliant with EU General Data Protection Regulation. No personal data is stored beyond the extraction session. - **CCPA**: Compliant with California Consumer Privacy Act requirements. #### Enterprise Support For enterprise licensing, custom integrations, or compliance inquiries: - **Telegram**: [@AiAgentApi](https://t.me/AiAgentApi) *** ### Related Tools #### Jobs & Hiring - **[All Jobs Scraper](https://apify.com/agentx/all-jobs-scraper?fpr=aiagentapi)** - Multi-Platform Job Listings Intelligence API - **[Glassdoor Hiring Scraper](https://apify.com/agentx/glassdoor-hiring-scraper?fpr=aiagentapi)** - Glassdoor Job Listings Intelligence API - **[Glints Employer Data Extractor](https://apify.com/agentx/glints-employer-data-extractor?fpr=aiagentapi)** - Glints Employer Intelligence API - **[Glints Jobs Data Extractor](https://apify.com/agentx/glints-jobs-data-extractor?fpr=aiagentapi)** - Glints Job Listings Intelligence API - **[Indeed Hiring Scraper](https://apify.com/agentx/indeed-hiring-scraper?fpr=aiagentapi)** - Indeed Job Listings Intelligence API - 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Global Weather Intelligence API *** ### Support & Community - **👥 Community**: [@Apify\_Actor](https://t.me/Apify_Actor) - **👤 Contact Team**: [@AiAgentApi](https://t.me/AiAgentApi) *** **Last Updated:** May 01, 2026 # Actor input Schema ## `location` (type: `string`): Any country, region, city, town, or district to extract weather for. Examples: "Tokyo, Japan", "Ontario, Canada", "Munich" ## `days` (type: `integer`): Number of forecast days to extract from today when start\_date and end\_date are not configured ## `start_date` (type: `string`): Optional historical start date for date-range extraction. Accepts ISO date (YYYY-MM-DD) or relative period like '30 days' or '2 weeks'. ## `end_date` (type: `string`): Optional historical end date to close the date-range extraction window. Accepts ISO date (YYYY-MM-DD) or relative period like '1 day' or '1 week'. ## Actor input object example ```json { "location": "Tokyo, Japan", "days": 3, "start_date": "2025-04-01", "end_date": "2025-04-21" } ``` # Actor output Schema ## `results` (type: `string`): List of daily weather items for the requested location # 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 = { "location": "Tokyo, Japan", "days": 3, "start_date": "", "end_date": "" }; // Run the Actor and wait for it to finish const run = await client.actor("agentx/weather-forecast-api").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 = { "location": "Tokyo, Japan", "days": 3, "start_date": "", "end_date": "", } # Run the Actor and wait for it to finish run = client.actor("agentx/weather-forecast-api").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 '{ "location": "Tokyo, Japan", "days": 3, "start_date": "", "end_date": "" }' | apify call agentx/weather-forecast-api --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=agentx/weather-forecast-api", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Weather Forecast API", "description": "Worldwide weather feed by country, region, city, or district — returns up to 16 days of forecast (temperature, feels-like, precipitation, humidity, wind, UV index, sunrise/sunset) or any historical date range via Start/End dates, ready for logistics, retail-demand, and energy modeling.", "version": "1.0", "x-build-id": "IqEa23i6ETvhccrE3" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/agentx~weather-forecast-api/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-agentx-weather-forecast-api", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/agentx~weather-forecast-api/runs": { "post": { "operationId": "runs-sync-agentx-weather-forecast-api", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/agentx~weather-forecast-api/run-sync": { "post": { "operationId": "run-sync-agentx-weather-forecast-api", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "location" ], "properties": { "location": { "title": "🌍 Location", "type": "string", "description": "Any country, region, city, town, or district to extract weather for. Examples: \"Tokyo, Japan\", \"Ontario, Canada\", \"Munich\"" }, "days": { "title": "📊 Days", "minimum": 1, "maximum": 16, "type": "integer", "description": "Number of forecast days to extract from today when start_date and end_date are not configured" }, "start_date": { "title": "📅 Start Date", "type": "string", "description": "Optional historical start date for date-range extraction. Accepts ISO date (YYYY-MM-DD) or relative period like '30 days' or '2 weeks'." }, "end_date": { "title": "📅 End Date", "type": "string", "description": "Optional historical end date to close the date-range extraction window. Accepts ISO date (YYYY-MM-DD) or relative period like '1 day' or '1 week'." } } }, "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 } } } } } } } } } } ```