# Dynamic Web Scraper (`josejet/dynamic-web-scraper`) Actor Dynamic Web Scraper is an Apify Actor that gathers information online by simulating user browsing behavior on the web. It reduces the time and amount of scraped web pages by using a model (ChatGPT) to make decisions regarding browser navigation and results evaluation. - **URL**: https://apify.com/josejet/dynamic-web-scraper.md - **Developed by:** [Pepa J](https://apify.com/josejet) (community) - **Categories:** Automation, AI - **Stats:** 349 total users, 1 monthly users, 100.0% runs succeeded, 9 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 ### What does Dynamic Web Scraper do? Dynamic Web Scraper is an open-source Apify Actor that gathers information online by simulating user browsing behavior on the web. It reduces the time and amount of scraped web pages by using a model (ChatGPT) to make decisions regarding browser navigation and results evaluation. Dynamic Web Scraper takes as input a prompt and a list of initial URLs or Google queries. The model then decides which URLs should be visited in order to provide the best answer to the prompt. After you start this Actor, you will see additional Runs of [RAG Web Browser Actor](https://apify.com/apify/rag-web-browser) as it is used to scrape the required informations. ### Example #### An example of the JSON INPUT for the Actor: ```json { "maxDepth": 5, "prompt": "Why do people enjoy this beverage it so much? Answer in 10 words.", "startSources": [ "coca-cola", "pepsi", "7up", "kofola", "https://www.coca-cola.com/us/en/brands/sprite", "https://en.wikipedia.org/wiki/Nestea" ] } ```` #### An example of the JSON results produced by the Actor: ```json [ { "url": "https://www.pepsi.com/en-us/", "inputSource": "pepsi", "depth": 5, "response": "Refreshing taste, nostalgia, social moments, and caffeine boost." }, { "url": "https://www.coca-colacompany.com/", "inputSource": "coca-cola", "depth": 5, "response": "Coca-Cola is refreshing, sweet, and brings joyful moments universally." }, { "url": "https://en.wikipedia.org/wiki/Kofola", "inputSource": "kofola", "depth": 5, "response": "Unique flavor, nostalgic connection, cultural significance, refreshing experience." }, { "url": "https://www.7up.com/", "inputSource": "7up", "depth": 5, "response": "Crisp, refreshing taste brings joy to many occasions." }, { "url": "https://www.coca-cola.com/us/en/brands/sprite", "inputSource": "https://www.coca-cola.com/us/en/brands/sprite", "depth": 5, "response": "Refreshing, crisp taste that satisfies thirst anytime, anywhere." }, { "url": "https://en.wikipedia.org/wiki/Nestea", "inputSource": "https://en.wikipedia.org/wiki/Nestea", "depth": 5, "response": "Refreshing, flavorful, convenient, and widely available iced tea option." } ] ``` ### Development If you are interested in the development check out the [GitHub Repository](https://github.com/apify-projects/store-dynamic-web-scraper). Feel free to inspire yourself or contribute with your own ideas! This Actor uses standard official [openai](https://www.npmjs.com/package/openai) package to evaluate the prompts. You need to use your own token to run this Actor for local development. ### Why scrape websites dynamically? Dynamic web scraping allows for more efficient data extraction by automating the browsing process. It can help gather specific information from websites that may not be easily accessible through traditional scraping methods. ### How to use Dynamic Web Scraper To use Dynamic Web Scraper, follow these steps: 1. Click on Try for free. 2. Enter the prompt and a list of initial URLs or Google queries. 3. Click on Run. 4. Once the actor has finished, preview or download your data from the Dataset tab. ### How much will it cost to use Dynamic Web Scraper? Apify provides $5 free usage credits every month on the Apify Free plan. For more extensive data extraction needs, consider upgrading to a paid Apify subscription. ### Tips for using Dynamic Web Scraper - Ensure that your prompt is clear and specific to get accurate results. - Monitor the actor's progress to ensure it is navigating the web effectively. ### Is it legal to use Dynamic Web Scraper? It is important to be aware of legal considerations when scraping websites, especially regarding data privacy regulations such as GDPR. Ensure that you have a legitimate reason for scraping and consult legal advice if needed. For more information on the legality of web scraping, read our blog post: [Is Web Scraping Legal?](https://blog.apify.com/is-web-scraping-legal/) # Actor input Schema ## `startSources` (type: `array`): Sources to start with, could be . ## `prompt` (type: `string`): Prompt to evaluate on content. ## `maxDepth` (type: `integer`): Maximum depth to scrape. ## Actor input object example ```json { "startSources": [ "Lucerna Bar Cafe, Prague" ], "prompt": "Look for the food menu items and if you find it look for a chicken option", "maxDepth": 3 } ``` # 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 = { "startSources": [ "Lucerna Bar Cafe, Prague" ], "prompt": "Look for the food menu items and if you find it look for a chicken option" }; // Run the Actor and wait for it to finish const run = await client.actor("josejet/dynamic-web-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 = { "startSources": ["Lucerna Bar Cafe, Prague"], "prompt": "Look for the food menu items and if you find it look for a chicken option", } # Run the Actor and wait for it to finish run = client.actor("josejet/dynamic-web-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 '{ "startSources": [ "Lucerna Bar Cafe, Prague" ], "prompt": "Look for the food menu items and if you find it look for a chicken option" }' | apify call josejet/dynamic-web-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=josejet/dynamic-web-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Dynamic Web Scraper", "description": "Dynamic Web Scraper is an Apify Actor that gathers information online by simulating user browsing behavior on the web. It reduces the time and amount of scraped web pages by using a model (ChatGPT) to make decisions regarding browser navigation and results evaluation.", "version": "0.0", "x-build-id": "3RUqw5zwczJUEKCIq" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/josejet~dynamic-web-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-josejet-dynamic-web-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/josejet~dynamic-web-scraper/runs": { "post": { "operationId": "runs-sync-josejet-dynamic-web-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/josejet~dynamic-web-scraper/run-sync": { "post": { "operationId": "run-sync-josejet-dynamic-web-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "startSources", "prompt" ], "properties": { "startSources": { "title": "Start Sources", "minItems": 1, "type": "array", "description": "Sources to start with, could be .", "items": { "type": "string" } }, "prompt": { "title": "Prompt", "type": "string", "description": "Prompt to evaluate on content." }, "maxDepth": { "title": "Max Depth", "type": "integer", "description": "Maximum depth to scrape.", "default": 3 } } }, "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 } } } } } } } } } } ```