# Hugging Face Organization Scraper — AI Company Leads (`gocreative.ai/huggingface-org-leads`) Actor Find AI companies, research labs, and ML teams publishing models on HuggingFace Hub. Extract org profiles, model portfolios, download stats, and AI focus areas for B2B prospecting. - **URL**: https://apify.com/gocreative.ai/huggingface-org-leads.md - **Developed by:** [GoCreative AI](https://apify.com/gocreative.ai) (community) - **Categories:** Lead generation, Developer tools - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN 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.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 ## Hugging Face Organization Scraper — AI Company Leads **Find AI companies, research labs, and ML teams publishing models on HuggingFace Hub. Extract org profiles, model portfolios, download stats, and AI focus areas for B2B prospecting.** No API key, no signup, no subscription — pay only for what you scrape. Clean, structured output ready for CSV, JSON, Excel, or direct API export into your own pipeline. ### What this scraper does Find AI companies, research labs, and ML teams publishing models on HuggingFace Hub. Extract org profiles, model portfolios, download stats, and AI focus areas for B2B prospecting. Every run pulls fresh data straight from the source and pushes clean, typed records to the dataset — ready for your CRM, spreadsheet, AI agent, or data pipeline. ### Use cases - **B2B lead generation** — build targeted prospect lists with verified, structured data - **Sales prospecting** — find companies and contacts that match your ICP - **Market research & competitive intelligence** — track an industry or niche in structured form - **AI agents & automation** — feed agents clean external data without scraping infra - **Data enrichment** — append fresh fields to your existing lists ### Input | Field | Description | |---|---| | `taskCategory` | Filter by HuggingFace pipeline tag (e.g. text-generation, image-classification). Leave blank for all tasks. | | `searchQuery` | Keyword search to filter models/orgs (e.g. llama, mistral, biomedical). | | `maxResults` | Maximum number of unique organizations or authors to export. | | `organizationsOnly` | When enabled, skips individual user accounts and only returns verified HuggingFace organizations. | | `enrichOrgProfile` | Fetch additional org-level data (full name, website, location, followers) for each author. | ### Output Every result is a clean structured record. Export the full dataset as **CSV, JSON, or Excel** from the Apify console, or pull it via the Apify API straight into your own tools. ### Why use this actor - **Scrape huggingface org leads** — fast, structured, reliable. - **Huggingface org leads data export (csv, json, excel)** — fast, structured, reliable. - **Huggingface org leads api alternative — no key required** — fast, structured, reliable. - **Automated huggingface org leads monitoring on a schedule** — fast, structured, reliable. - **Structured huggingface org leads records for ai agents and pipelines** — fast, structured, reliable. ### How it works This actor pulls data directly from the source, structures it into clean rows, and pushes each result to the dataset. It runs on Apify's infrastructure — reliable, schedulable, and pay-per-result so you only pay for data you actually get. ### Pricing Pay-per-result via the Apify Store. No monthly subscription, no minimums — run it once or schedule it daily; you're only charged for the results returned. ### FAQ **Do I need an API key or account?** No. Just provide your input and run it. **Can I schedule it to run automatically?** Yes — use Apify Schedules to run it hourly, daily, or weekly and get fresh data on autopilot. **What formats can I export?** CSV, JSON, Excel, or via the Apify API. **Can I integrate it into my own app?** Yes — call it via the Apify API and pull results directly into your pipeline. **Is the data accurate and fresh?** Data is pulled live from the source on each run, so it reflects what's available at run time. # Actor input Schema ## `taskCategory` (type: `string`): Filter by HuggingFace pipeline tag (e.g. text-generation, image-classification). Leave blank for all tasks. ## `searchQuery` (type: `string`): Keyword search to filter models/orgs (e.g. llama, mistral, biomedical). ## `maxResults` (type: `integer`): Maximum number of unique organizations or authors to export. ## `organizationsOnly` (type: `boolean`): When enabled, skips individual user accounts and only returns verified HuggingFace organizations. ## `enrichOrgProfile` (type: `boolean`): Fetch additional org-level data (full name, website, location, followers) for each author. ## Actor input object example ```json { "taskCategory": "text-generation", "searchQuery": "llama", "maxResults": 200, "organizationsOnly": false, "enrichOrgProfile": true } ```` # 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("gocreative.ai/huggingface-org-leads").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("gocreative.ai/huggingface-org-leads").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 gocreative.ai/huggingface-org-leads --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=gocreative.ai/huggingface-org-leads", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hugging Face Organization Scraper — AI Company Leads", "description": "Find AI companies, research labs, and ML teams publishing models on HuggingFace Hub. Extract org profiles, model portfolios, download stats, and AI focus areas for B2B prospecting.", "version": "0.1", "x-build-id": "Ekqr9HC3Lfw31Z9Os" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/gocreative.ai~huggingface-org-leads/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-gocreative.ai-huggingface-org-leads", "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/gocreative.ai~huggingface-org-leads/runs": { "post": { "operationId": "runs-sync-gocreative.ai-huggingface-org-leads", "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/gocreative.ai~huggingface-org-leads/run-sync": { "post": { "operationId": "run-sync-gocreative.ai-huggingface-org-leads", "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": { "taskCategory": { "title": "AI Task Category", "type": "string", "description": "Filter by HuggingFace pipeline tag (e.g. text-generation, image-classification). Leave blank for all tasks." }, "searchQuery": { "title": "Search Query", "type": "string", "description": "Keyword search to filter models/orgs (e.g. llama, mistral, biomedical)." }, "maxResults": { "title": "Max Unique Authors / Orgs", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Maximum number of unique organizations or authors to export.", "default": 200 }, "organizationsOnly": { "title": "Organizations Only", "type": "boolean", "description": "When enabled, skips individual user accounts and only returns verified HuggingFace organizations.", "default": false }, "enrichOrgProfile": { "title": "Enrich Organization Profile", "type": "boolean", "description": "Fetch additional org-level data (full name, website, location, followers) for each author.", "default": true } } }, "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 } } } } } } } } } } ```