# Netflix Catalog & Search Scraper (Cheapest) (`ahmed_jasarevic/netflix-search-scraper`) Actor Fast and reliable scraper to extract movie and series details from the Netflix catalog. Search by keywords to get structured data including titles, ratings, synopsis, IDs (Netflix/IMDb), and release years - **URL**: https://apify.com/ahmed\_jasarevic/netflix-search-scraper.md - **Developed by:** [Ahmed Jasarevic](https://apify.com/ahmed_jasarevic) (community) - **Categories:** Developer tools, Automation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.50 / 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 ## Netflix Search Scraper A high-performance Netflix search scraper that extracts structured movie and TV show data based on keywords. It collects titles, metadata, ratings, images, and availability information from Netflix search results and returns clean structured output. --- ### 🚀 Features - Search Netflix catalog by keyword - Extract movies and TV shows - Get structured metadata (title, year, rating, synopsis) - Image and poster support - Country availability list - IMDb ID extraction when available - Fast and lightweight scraping engine --- ### 📥 Input The actor accepts the following JSON input: ```json { "keywords": ["fashion", "action", "comedy"], "maxItems": 20 } ```` #### Input Fields | Field | Type | Description | | -------- | ------ | ---------------------------------- | | keywords | array | Search keywords for Netflix titles | | maxItems | number | Maximum number of results per run | *** ### 📤 Output Each item in the dataset looks like this: ```json { "keyword": "fashion", "id": 52454, "title": "Maggie and Bianca: Fashion Friends", "slug": "maggie-and-bianca-fashion-friends", "type": "series", "year": 2016, "rating": 2.939394, "synopsis": "Originally polar opposites, an American fashion student and the daughter of an industry mogul form an everlasting bond and start a rock band in Milan.", "image": "https://occ-0-1926-41.1.nflxso.net/dnm/api/v6/evlCitJPPCVCry0BZlEFb5-QjKc/AAAABbK-tMDwG6ld0MhF2X26AUI6DbZgIFFTg_A6XU1ip6NDcCSF0qNVQ5ZGOxiBez-eThwvxkZnW_h5d6tqLts_Wp0ISw.jpg?r=558", "poster": "https://occ-0-1926-41.1.nflxso.net/dnm/api/v6/evlCitJPPCVCry0BZlEFb5-QjKc/AAAABbK-tMDwG6ld0MhF2X26AUI6DbZgIFFTg_A6XU1ip6NDcCSF0qNVQ5ZGOxiBez-eThwvxkZnW_h5d6tqLts_Wp0ISw.jpg?r=558", "netflixId": 80144408, "imdbId": "tt6155478", "clist": { "IT": "Italy" }, "runtime": null, "top250": null, "raw": { "id": 52454, "title": "Maggie and Bianca: Fashion Friends", "slug": "maggie-and-bianca-fashion-friends", "img": "https://occ-0-1926-41.1.nflxso.net/dnm/api/v6/evlCitJPPCVCry0BZlEFb5-QjKc/AAAABbK-tMDwG6ld0MhF2X26AUI6DbZgIFFTg_A6XU1ip6NDcCSF0qNVQ5ZGOxiBez-eThwvxkZnW_h5d6tqLts_Wp0ISw.jpg?r=558", "vtype": "series", "nfid": 80144408, "synopsis": "Originally polar opposites, an American fashion student and the daughter of an industry mogul form an everlasting bond and start a rock band in Milan.", "avgrating": 2.939394, "year": 2016, "runtime": 0, "imdbid": "tt6155478", "poster": "N/A", "top250": 0, "top250tv": 0, "clist": "\"IT\":\"Italy\"", "titledate": "2017-03-31" } } ``` *** ### ⚙️ How it works 1. The scraper searches Netflix catalog using provided keywords 2. It captures search results from the site 3. Extracts structured metadata for each title 4. Normalizes data into clean JSON format 5. Stores results in Apify dataset *** ### 🧠 Use Cases - Movie research - Content discovery tools - Netflix catalog analysis - Market research - Recommendation systems *** ### 📊 Output Storage Results are stored in the default dataset and can be accessed via: ``` https://api.apify.com/datasets/xxx/items ``` *** ### ⚡ Performance - Optimized for batch keyword search - Lightweight browser usage - Built-in retry handling - Stable request interception *** # Actor input Schema ## `keywords` (type: `array`): List of keywords to search on Netflix catalog ## `maxItems` (type: `integer`): Maximum number of results to return ## Actor input object example ```json { "keywords": [ "fashion" ], "maxItems": 20 } ``` # Actor output Schema ## `datasetItems` (type: `string`): No description ## `datasetUrl` (type: `string`): No description ## `totalItems` (type: `string`): No description ## `runId` (type: `string`): No description # 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 = { "keywords": [ "fashion" ] }; // Run the Actor and wait for it to finish const run = await client.actor("ahmed_jasarevic/netflix-search-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 = { "keywords": ["fashion"] } # Run the Actor and wait for it to finish run = client.actor("ahmed_jasarevic/netflix-search-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 '{ "keywords": [ "fashion" ] }' | apify call ahmed_jasarevic/netflix-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ahmed_jasarevic/netflix-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Netflix Catalog & Search Scraper (Cheapest)", "description": "Fast and reliable scraper to extract movie and series details from the Netflix catalog. Search by keywords to get structured data including titles, ratings, synopsis, IDs (Netflix/IMDb), and release years", "version": "1.0", "x-build-id": "rOz7olftkSYTi4McD" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ahmed_jasarevic~netflix-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ahmed_jasarevic-netflix-search-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ahmed_jasarevic~netflix-search-scraper/runs": { "post": { "operationId": "runs-sync-ahmed_jasarevic-netflix-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ahmed_jasarevic~netflix-search-scraper/run-sync": { "post": { "operationId": "run-sync-ahmed_jasarevic-netflix-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "keywords" ], "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "List of keywords to search on Netflix catalog", "items": { "type": "string" }, "default": [ "fashion" ] }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of results to return", "default": 20 } } }, "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 } } } } } } } } } } ```