# HuggingFace Trending Models, Datasets & Spaces Scraper (`outofboundslab/hf-trending-scraper`) Actor Scrape trending models, datasets, and spaces from HuggingFace Hub. Get download counts, likes, tags, pipeline types, licenses, and more. Sort by downloads, likes, or trending. Filter by task type. - **URL**: https://apify.com/outofboundslab/hf-trending-scraper.md - **Developed by:** [Julian Bracaglia](https://apify.com/outofboundslab) (community) - **Categories:** AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## HuggingFace Trending Models, Datasets & Spaces Scraper Scrape trending models, datasets, and spaces from the [HuggingFace Hub](https://huggingface.co) — the world's largest AI model repository with 1M+ models, 300K+ datasets, and 500K+ spaces. ### Features - **3 data types in one run:** Models, Datasets, and Spaces - **Flexible sorting:** By downloads, likes, or trending - **Task filtering:** Filter models by pipeline task (text-generation, image-classification, etc.) - **Up to 1,000 results per type** per run - **No API key needed** — uses public HuggingFace API - **Clean structured output** with normalized fields across all types - **License extraction** from tags - **Author extraction** from item IDs ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `type` | select | `all` | What to scrape: `all`, `models`, `datasets`, or `spaces` | | `sort` | select | `downloads` | Sort by: `downloads`, `likes`, or `trending` | | `limit` | integer | `100` | Max results per type (1-1000) | | `filter` | textfield | (empty) | Filter models by pipeline task (e.g. `text-generation`, `image-classification`, `translation`) | ### Sample Input ```json { "type": "all", "sort": "downloads", "limit": 50, "filter": "" } ```` ### Output Each result item includes: ```json { "type": "model", "id": "meta-llama/Llama-3.3-70B-Instruct", "author": "meta-llama", "downloads": 1234567, "likes": 1234, "tags": ["safetensors", "text-generation", "transformers", "license:llama3.3"], "pipeline_tag": "text-generation", "last_modified": "2026-06-15T10:30:00.000Z", "created_at": "2024-12-01T00:00:00.000Z", "library_name": "transformers", "license": "llama3.3", "private": false, "gated": false, "scraped_at": "2026-07-14T20:00:00.000Z" } ``` Spaces also include: `sdk` (gradio, streamlit, docker, etc.). ### Use Cases - **AI model research:** Track which models are gaining traction - **Market intelligence:** Monitor the AI landscape for investment signals - **Content creation:** Find trending models for tutorials and reviews - **MLOps:** Discover new models for your pipeline - **Competitive analysis:** See what organizations are publishing - **Data for AI agents:** Feed model discovery data into your AI workflows via MCP ### Pricing Pay-per-result: **$0.50 per 1,000 results** ### Links - [HuggingFace Hub](https://huggingface.co) - [HuggingFace API Docs](https://huggingface.co/docs/hub/en/api) # Actor input Schema ## `type` (type: `string`): What to scrape from HuggingFace Hub ## `sort` (type: `string`): How to sort results ## `limit` (type: `integer`): Maximum number of items to return per type (models, datasets, spaces). E.g. limit=100 with type=all returns up to 300 items total. ## `filter` (type: `string`): Filter models by pipeline task. Leave empty for all. Examples: text-generation, text-classification, image-classification, translation, summarization, fill-mask, tokenization, feature-extraction, image-generation, text-to-image, automatic-speech-recognition, text-to-speech ## Actor input object example ```json { "type": "all", "sort": "downloads", "limit": 100, "filter": "" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("outofboundslab/hf-trending-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("outofboundslab/hf-trending-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 '{}' | apify call outofboundslab/hf-trending-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=outofboundslab/hf-trending-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "HuggingFace Trending Models, Datasets & Spaces Scraper", "description": "Scrape trending models, datasets, and spaces from HuggingFace Hub. Get download counts, likes, tags, pipeline types, licenses, and more. Sort by downloads, likes, or trending. Filter by task type.", "version": "0.1", "x-build-id": "GG2W9CfXfBxt4E1cl" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/outofboundslab~hf-trending-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-outofboundslab-hf-trending-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/outofboundslab~hf-trending-scraper/runs": { "post": { "operationId": "runs-sync-outofboundslab-hf-trending-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/outofboundslab~hf-trending-scraper/run-sync": { "post": { "operationId": "run-sync-outofboundslab-hf-trending-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "type", "sort", "limit" ], "properties": { "type": { "title": "Item Type", "enum": [ "all", "models", "datasets", "spaces" ], "type": "string", "description": "What to scrape from HuggingFace Hub", "default": "all" }, "sort": { "title": "Sort By", "enum": [ "downloads", "likes", "trending" ], "type": "string", "description": "How to sort results", "default": "downloads" }, "limit": { "title": "Max Results Per Type", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of items to return per type (models, datasets, spaces). E.g. limit=100 with type=all returns up to 300 items total.", "default": 100 }, "filter": { "title": "Task Filter (Models only)", "type": "string", "description": "Filter models by pipeline task. Leave empty for all. Examples: text-generation, text-classification, image-classification, translation, summarization, fill-mask, tokenization, feature-extraction, image-generation, text-to-image, automatic-speech-recognition, text-to-speech", "default": "" } } }, "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 } } } } } } } } } } ```