# AI Tools & Models Intelligence (`swerve/aitools-intel-scraper`) Actor Aggregate AI tool and model data from Futurepedia (29 tool categories) and Hugging Face Hub (models, datasets, spaces). Used by AI investors tracking new launches, founders sizing markets, recruiters sourcing AI talent, and SaaS competitive-intelligence teams benchmarking AI product features. - **URL**: https://apify.com/swerve/aitools-intel-scraper.md - **Developed by:** [Swerve](https://apify.com/swerve) (community) - **Categories:** AI, Developer tools, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## AI Tools & Models Intelligence Aggregate AI tool, model, dataset, and space data from two of the largest open catalogs: - **[Futurepedia](https://www.futurepedia.io)** — the largest curated directory of AI tools, with 29 categories (chatbots, code assistants, image generation, video, marketing, productivity, AI agents, etc.) - **[Hugging Face Hub](https://huggingface.co)** — the canonical registry for open-source AI models, datasets, and hosted demos (Spaces) Pick a source, optionally narrow by category and keyword, and get a normalized dataset across all four endpoints. ### Use cases - **AI investors** tracking new tool launches and which models are climbing the popularity rankings - **Founders sizing a market** ("how many code assistants are out there? how many have over 10K likes?") - **Recruiters sourcing AI talent** mining authors / orgs of top-trending models - **Competitive-intelligence teams** benchmarking AI product features against an industry-wide list - **AI directory builders** seeding their own catalog with a clean baseline - **Data scientists** finding the most popular datasets for a given task ### Sources | Source | What it returns | Filters | |--------|-----------------|---------| | `futurepedia` | AI tools across 29 categories | `category` (slug), `keyword` (client-side title filter) | | `huggingface_models` | LLMs, vision, audio, multimodal models | `category` (e.g. `text-generation`), `keyword` (server-side), `sortBy` | | `huggingface_datasets` | Training / evaluation datasets | `category` (e.g. `task_categories:text-classification`), `keyword`, `sortBy` | | `huggingface_spaces` | Hosted Gradio / Streamlit demos | `category` (e.g. `gradio`), `keyword`, `sortBy` | ### Output fields | Field | Type | Description | |-------|------|-------------| | `source` | string | One of: `futurepedia`, `huggingface_models`, `huggingface_datasets`, `huggingface_spaces` | | `id` | string | Unique ID at the source | | `name` | string | Display name | | `url` | string | Direct URL on the source | | `description` | string \| null | Short description (Futurepedia only) | | `categories` | string[] | Human-readable categories | | `tags` | string[] | Source-specific tags | | `popularity` | integer \| null | Favs / likes | | `downloads` | integer \| null | All-time downloads (HF models + datasets) | | `author` | string \| null | Author / organization (HF only) | | `libraryName` | string \| null | Library (HF models: transformers, diffusers, etc.) | | `pipelineTag` | string \| null | Primary task (HF models) | | `sdk` | string \| null | Spaces SDK (HF spaces) | | `imageUrl` | string \| null | Thumbnail (Futurepedia) | | `createdAt` | string \| null | ISO timestamp (HF only) | | `scrapedAt` | string | ISO timestamp of the scrape | ### Examples **Top 100 trending models on Hugging Face:** ```json { "source": "huggingface_models", "sortBy": "trending", "maxItems": 100 } ```` **All text-generation LLMs sorted by downloads:** ```json { "source": "huggingface_models", "category": "text-generation", "sortBy": "downloads", "maxItems": 500 } ``` **AI chatbots on Futurepedia:** ```json { "source": "futurepedia", "category": "chatbots", "maxItems": 50 } ``` **All AI agent tools across Futurepedia:** ```json { "source": "futurepedia", "category": "ai-agents", "maxItems": 100 } ``` **Hugging Face Spaces built on Gradio, searching for "image":** ```json { "source": "huggingface_spaces", "category": "gradio", "keyword": "image", "maxItems": 50 } ``` ### Notes - The keyword filter is **title-first**: every token must appear in the name, ID, categories, or tags. This avoids description-matching false positives. - Futurepedia category slugs include: `productivity`, `chatbots`, `image`, `code-assistant`, `video`, `audio`, `writing-generators`, `design-generators`, `marketing`, `business`, `research-assistant`, `ai-agents`, `avatars`, `data-analysis`, `social-media`, `education-assistant`, `sales`, `customer-support`, `finance`, `legal`, `health`, `translation`, `art`, `gaming`, `real-estate`, `lifestyle`, `human-resources`, `religion`, `fun-tools`. - Hugging Face uses cursor-based pagination via the `Link` header — pages up to the `maxItems` cap are fetched automatically. - No login or API key required. ### Pricing Pay per result. See the Pricing tab. Failed runs cost nothing. # Actor input Schema ## `source` (type: `string`): Which catalog to pull from. Hugging Face is free and fast. Futurepedia is the largest curated directory of AI tools (uses residential proxy). ## `category` (type: `string`): Source-specific filter. Leave empty to pull all. Futurepedia slugs: chatbots, code-assistant, image, video, audio, productivity, ai-agents, marketing, business, writing-generators, design-generators, research-assistant, customer-support, finance, sales, social-media, education-assistant, real-estate, legal, health, art, gaming, lifestyle, translation, avatars, data-analysis, human-resources, religion, fun-tools. Hugging Face Models examples: text-generation, image-classification, automatic-speech-recognition, text-to-image, image-to-text. Hugging Face Datasets examples: task\_categories:text-classification, task\_categories:question-answering. Hugging Face Spaces examples: gradio, streamlit, docker, static. ## `keyword` (type: `string`): Free-text keyword. Examples: llama, image, chatbot, whisper, gpt. For Hugging Face, sent server-side via ?search=. For Futurepedia, applied client-side against title + tags. All tokens must appear in the title or tags (no description-based false positives). ## `sortBy` (type: `string`): Hugging Face sort order. Ignored for Futurepedia (uses the site's default ordering per category). ## `maxItems` (type: `integer`): Maximum number of items to return. Default: 100. Max: 10,000. ## Actor input object example ```json { "source": "huggingface_models", "sortBy": "trending", "maxItems": 100 } ``` # 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("swerve/aitools-intel-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("swerve/aitools-intel-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 swerve/aitools-intel-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=swerve/aitools-intel-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Tools & Models Intelligence", "description": "Aggregate AI tool and model data from Futurepedia (29 tool categories) and Hugging Face Hub (models, datasets, spaces). Used by AI investors tracking new launches, founders sizing markets, recruiters sourcing AI talent, and SaaS competitive-intelligence teams benchmarking AI product features.", "version": "1.0", "x-build-id": "IDzul9VajbPg8VA3x" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/swerve~aitools-intel-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-swerve-aitools-intel-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/swerve~aitools-intel-scraper/runs": { "post": { "operationId": "runs-sync-swerve-aitools-intel-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/swerve~aitools-intel-scraper/run-sync": { "post": { "operationId": "run-sync-swerve-aitools-intel-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "source" ], "properties": { "source": { "title": "1. Source", "enum": [ "huggingface_models", "huggingface_datasets", "huggingface_spaces", "futurepedia" ], "type": "string", "description": "Which catalog to pull from. Hugging Face is free and fast. Futurepedia is the largest curated directory of AI tools (uses residential proxy).", "default": "huggingface_models" }, "category": { "title": "2. Category / Filter (optional)", "type": "string", "description": "Source-specific filter. Leave empty to pull all.\n\nFuturepedia slugs: chatbots, code-assistant, image, video, audio, productivity, ai-agents, marketing, business, writing-generators, design-generators, research-assistant, customer-support, finance, sales, social-media, education-assistant, real-estate, legal, health, art, gaming, lifestyle, translation, avatars, data-analysis, human-resources, religion, fun-tools.\n\nHugging Face Models examples: text-generation, image-classification, automatic-speech-recognition, text-to-image, image-to-text.\n\nHugging Face Datasets examples: task_categories:text-classification, task_categories:question-answering.\n\nHugging Face Spaces examples: gradio, streamlit, docker, static." }, "keyword": { "title": "3. Keyword (optional)", "type": "string", "description": "Free-text keyword. Examples: llama, image, chatbot, whisper, gpt. For Hugging Face, sent server-side via ?search=. For Futurepedia, applied client-side against title + tags. All tokens must appear in the title or tags (no description-based false positives)." }, "sortBy": { "title": "4. Sort Order", "enum": [ "trending", "likes", "downloads", "recent", "default" ], "type": "string", "description": "Hugging Face sort order. Ignored for Futurepedia (uses the site's default ordering per category).", "default": "trending" }, "maxItems": { "title": "5. Max Items", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of items to return. Default: 100. Max: 10,000.", "default": 100 } } }, "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 } } } } } } } } } } ```