# Replicate ModelInfo Scraper (`getdataforme/replicate-modelinfo-scraper`) Actor The Replicate ModelInfo Scraper efficiently extracts detailed data on machine learning models from the Replicate platform. It offers comprehensive model specs, usage stats, and outputs, supporting JSON, CSV, and Excel formats.... - **URL**: https://apify.com/getdataforme/replicate-modelinfo-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** Automation, Lead generation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 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 ## Replicate ModelInfo Scraper ### Introduction The Replicate ModelInfo Scraper is a powerful tool designed to extract detailed information about machine learning models hosted on the Replicate platform. It provides comprehensive data on model specifications, usage statistics, and example outputs, making it an invaluable resource for developers, researchers, and businesses looking to leverage cutting-edge AI technologies. ### Features - **Comprehensive Data Extraction**: Retrieves detailed model information, including descriptions, usage statistics, and example outputs. - **High Data Quality**: Ensures accurate and up-to-date information by scraping directly from the source. - **Efficient Performance**: Optimized for fast and reliable data retrieval. - **User-Friendly Interface**: Easy to configure and use, even for non-technical users. - **Versatile Output Formats**: Supports multiple export formats, including JSON, CSV, and Excel. - **Scalable**: Capable of handling multiple URLs and large datasets efficiently. - **Customizable**: Allows for tailored scraping based on specific user needs. ### Input Parameters | Parameter | Type | Required | Description | Example | |------------|--------|----------|-----------------------|----------------------------------------------| | startUrls | array | Yes | URLs to start with. | `["https://replicate.com/google/nano-banana"]` | ### Example Usage #### Example Input ```json { "startUrls": [ "https://replicate.com/google/nano-banana" ] } ```` #### Example Output ```json [ { "url": "https://replicate.com/google/nano-banana", "model": "/nano-banana", "description": "Google's latest image editing model in Gemini 2.5", "runs": "105.5M\n runs", "price": null, "inputSchema": null, "badges": [ "{\"anchorText\": \"Official\", \"anchorHref\": \"https://replicate.com/docs/topics/models/official-models\", \"tooltipText\": \"Official models are always on, maintained, and have predictable pricing.\", \"__flags\": {\"show-open-ai-api-instructions\": false, \"show-pylon-widget\": false, \"assign-support-tickets-to-ai\": false}}", "105.5M\n runs" ], "exampleImages": [ "https://replicate.delivery/xezq/eQ2MQYrD6XzheEgCe7OcHlUJAXYc8HaMJmGPmbTOCClZS7dqA/tmp4vqrduzh.jpg", "https://replicate.delivery/xezq/mbE0pfJqUtTfu0vp7gjG93nkNYNsy78a1onho9oos56fkTjqA/tmpgxxuv701.jpg" ], "timestamps": [ { "label": "created", "datetime": "2025-08-26T21:08:24.983047+00:00", "display": "8 months, 2 weeks ago" }, { "label": "updated", "datetime": "2026-02-06T04:33:54.610363+00:00", "display": "3 months, 1 week ago" } ], "readmeText": "Nano Banana\nGoogle’s state-of-the-art image generation and editing model designed for fast, conversational, and multi-turn creative workflows.", "scraped_at": "2026-05-14T10:53:17.410Z" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather insights on the latest AI models and their capabilities. - **Competitive Intelligence**: Monitor developments in AI technologies and model performance. - **Content Aggregation**: Compile comprehensive datasets for academic or business purposes. - **Academic Research**: Access detailed model information for research and development. - **Business Automation**: Integrate model data into automated workflows for enhanced decision-making. ### Installation and Usage 1. Search for "Replicate ModelInfo Scraper" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). ### Output Format The output is a JSON array containing objects with the following fields: - **url**: The URL of the model page. - **model**: The model identifier. - **description**: A brief description of the model. - **runs**: The number of times the model has been run. - **badges**: Additional metadata about the model. - **exampleImages**: URLs of example images generated by the model. - **timestamps**: Creation and update timestamps. - **readmeText**: Detailed model documentation. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! # Actor input Schema ## `startUrls` (type: `array`): URLs to start with. ## Actor input object example ```json { "startUrls": [ { "url": "https://replicate.com/google/nano-banana" } ] } ``` # Actor output Schema ## `results` (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 = { "startUrls": [ { "url": "https://replicate.com/google/nano-banana" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/replicate-modelinfo-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "startUrls": [{ "url": "https://replicate.com/google/nano-banana" }] } # Run the Actor and wait for it to finish run = client.actor("getdataforme/replicate-modelinfo-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ { "url": "https://replicate.com/google/nano-banana" } ] }' | apify call getdataforme/replicate-modelinfo-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/replicate-modelinfo-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Replicate ModelInfo Scraper", "description": "The Replicate ModelInfo Scraper efficiently extracts detailed data on machine learning models from the Replicate platform. It offers comprehensive model specs, usage stats, and outputs, supporting JSON, CSV, and Excel formats....", "version": "0.0", "x-build-id": "t6gBh54vdXolTa21j" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~replicate-modelinfo-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-replicate-modelinfo-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~replicate-modelinfo-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-replicate-modelinfo-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~replicate-modelinfo-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-replicate-modelinfo-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "startUrls": { "title": "Start URLs", "type": "array", "description": "URLs to start with.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } } } }, "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 } } } } } } } } } } ```