# Cloudflare Workers AI Models Scraper (`automation-lab/cloudflare-workers-ai-scraper`) Actor Scrapes the full catalog of AI models available on Cloudflare Workers AI, including model IDs, task categories, descriptions, context lengths, and capabilities. - **URL**: https://apify.com/automation-lab/cloudflare-workers-ai-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Cloudflare Workers AI Models Scraper ### What does Cloudflare Workers AI Models Scraper do? **Cloudflare Workers AI Models Scraper** extracts the complete catalog of AI models available on [Cloudflare Workers AI](https://developers.cloudflare.com/workers-ai/models/) — no API key, no login, and no coding required. Run the actor and get structured data for every model including **model IDs, task categories, context lengths, async support, and hosting details**. Cloudflare Workers AI is Cloudflare's edge-hosted AI inference platform, running 97+ models globally across text generation, image generation, speech recognition, embeddings, translation, and more. The actor fetches their official models catalog using a single HTTP request, parses the server-rendered data, and outputs clean JSON records ready to export or integrate into any pipeline. Use this actor to monitor Cloudflare's model catalog changes, track when new models are added or deprecated, or build comparisons across edge AI providers. --- ### Who is it for? **🤖 AI developers building on Cloudflare Workers** - Find the exact model ID before integrating Cloudflare Workers AI into your application - Verify context lengths and async support for planning request budgets - Automate checks for new model releases or deprecations **📊 ML researchers and data scientists** - Track which models Cloudflare adds to their edge inference platform over time - Compare model availability across Cloudflare, Groq, DeepInfra, and OpenRouter - Build datasets for cross-provider model capability analysis **💰 Platform teams and FinOps** - Monitor model availability changes that might affect your production stack - Filter models by task category to discover alternatives for your use case - Track deprecated models before they break your integration **🏢 AI product managers and strategists** - Understand what AI capabilities Cloudflare offers for edge deployment - Identify emerging model categories being added to the platform - Build dashboards comparing Cloudflare Workers AI to other inference providers --- ### Why use Cloudflare Workers AI Models Scraper? - **No API key required** — Cloudflare's models catalog is fully public - **Single HTTP request** — fetches all 97+ models in one call with zero JS rendering - **Zero proxy cost** — no browser automation or residential proxies needed - **Covers all model types** — text generation, image, embedding, speech, translation, object detection, image classification, and more - **Task category filter** — narrow results to a specific category like "Text Generation" or "Text-to-Image" - **Deprecated model filter** — optionally include or exclude deprecated models - **Pay-per-event pricing** — pay only for models extracted, not idle compute time - **Schedule and automate** — run daily or weekly to track catalog changes over time - **Export anywhere** — JSON, CSV, Excel, Google Sheets, or push via API and webhook --- ### What data does it extract? | Field | Type | Description | |-------|------|-------------| | `modelId` | string | API identifier (e.g. `@cf/meta/llama-3.3-70b-instruct`) | | `displayName` | string | Human-readable model name | | `description` | string | Full model description | | `taskName` | string | Task category (e.g. Text Generation, Text-to-Image) | | `taskDescription` | string | Description of the task category | | `contextLength` | number \| null | Context window size in tokens (null if not applicable) | | `maxOutputTokens` | number \| null | Maximum output tokens per request (null if not applicable) | | `supportsAsync` | boolean | Whether the model supports asynchronous execution | | `hosting` | string | Hosting type (e.g. `hosted`) | | `tags` | string[] | Model tags (e.g. `deprecated`, `recommended`) | | `sourceUrl` | string | Source URL of the Cloudflare Workers AI models catalog | --- ### How much does it cost to scrape Cloudflare Workers AI models? 🟢 **Very cheap.** Cloudflare's catalog currently lists 97 models. At **$0.002 per model** extracted, a full run costs approximately **$0.19** plus the **$0.005 start fee** — under $0.20 total. | Run type | Models | Estimated cost | |----------|--------|----------------| | One-time full scrape | ~97 models | ~$0.20 | | Weekly monitoring (52 runs/year) | ~97 models | ~$10/year | | Daily monitoring (365 runs/year) | ~97 models | ~$73/year | | Filtered (Text Generation only) | ~60 models | ~$0.13/run | All Apify platform compute costs are included. No proxy fees since the page is accessible without proxies. --- ### How to use Cloudflare Workers AI Models Scraper **Step 1:** Open the actor on [Apify Store](https://apify.com/automation-lab/cloudflare-workers-ai-scraper). **Step 2:** Click **Try for free** — no configuration needed. The actor scrapes all models by default. **Step 3:** Click **Start** and wait 5–15 seconds. **Step 4:** View results in the **Dataset** tab. Export to JSON, CSV, or Excel. **Step 5 (optional):** Set the **Task category filter** to narrow down results (e.g. `Text Generation` to get only LLMs). **Step 6 (optional):** Schedule recurring runs in the **Schedules** tab to monitor catalog changes over time. --- ### Input parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `taskCategory` | string | `""` (all) | Filter by task category name. Case-insensitive exact match. Examples: `Text Generation`, `Text-to-Image`, `Text Embeddings`, `Speech-to-Text`, `Text-to-Speech`, `Translation`, `Object Detection` | | `includeDeprecated` | boolean | `false` | Include deprecated models in results. Deprecated models are tagged with `deprecated` and excluded by default. | | `maxRequestRetries` | integer | `3` | Number of retry attempts if the Cloudflare models page fails to load. | No mandatory input is required. Clicking **Start** with defaults returns all non-deprecated models. --- ### Output example ```json { "modelId": "@cf/meta/llama-3.3-70b-instruct", "displayName": "@cf/meta/llama-3.3-70b-instruct", "description": "Llama 3.3 is a 70B parameter multilingual large language model...", "taskName": "Text Generation", "taskDescription": "Family of generative text models, such as large language models (LLM)...", "contextLength": 131072, "maxOutputTokens": null, "supportsAsync": true, "hosting": "hosted", "tags": [], "sourceUrl": "https://developers.cloudflare.com/workers-ai/models/" } ```` **Embedding model example:** ```json { "modelId": "@cf/baai/bge-base-en-v1.5", "displayName": "@cf/baai/bge-base-en-v1.5", "description": "BAAI general embedding (Base) model that transforms any given text into a 768-dimensional vector", "taskName": "Text Embeddings", "taskDescription": "Feature extraction models transform raw data into numerical features...", "contextLength": 153600, "maxOutputTokens": null, "supportsAsync": true, "hosting": "hosted", "tags": [], "sourceUrl": "https://developers.cloudflare.com/workers-ai/models/" } ``` *** ### Tips and best practices 💡 **Schedule for catalog monitoring** — Cloudflare frequently adds new models and deprecates old ones. Schedule weekly runs and compare datasets to track changes automatically. 💡 **Use taskCategory to filter** — Set `taskCategory` to `Text Generation` to get only LLMs, or `Text Embeddings` to find all embedding models. This reduces cost when you only need a subset. 💡 **Set includeDeprecated to true for audits** — When auditing your integrations, enable deprecated model inclusion to see the full catalog including models being phased out. 💡 **Combine with other model scrapers** — Pair this actor with [Groq Models Scraper](https://apify.com/automation-lab/groq-models-scraper) or [OpenRouter Models Scraper](https://apify.com/automation-lab/openrouter-models-scraper) to build a comprehensive cross-provider model catalog. 💡 **Check `supportsAsync` for production planning** — Async execution support matters for batch inference workloads. Filter `supportsAsync: true` models for high-throughput use cases. *** ### Integrations 🔄 **Zapier / Make.com** — Trigger downstream workflows when new models appear in Cloudflare's catalog. Connect model data to Slack notifications, Airtable, or Google Sheets automatically. 📊 **Google Sheets** — Export results directly to a Google Sheet for team-visible model tracking dashboards. Use Apify's native Google Sheets integration or schedule the actor and auto-push results. 🤖 **AI platforms** — Feed model catalog data into your own model selection tools or cost calculators to power real-time model availability checks in your app. 🗄️ **Data pipelines** — Push results to PostgreSQL, BigQuery, or Snowflake via Apify webhooks for long-term catalog trend analysis. 📬 **Webhook notifications** — Set up Apify webhooks to POST results to your endpoint whenever a run completes, enabling real-time model catalog updates in your infrastructure. *** ### API usage #### Node.js (Apify client) ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' }); const run = await client.actor('automation-lab/cloudflare-workers-ai-scraper').call({ taskCategory: 'Text Generation', }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(`Extracted ${items.length} Cloudflare Workers AI models`); items.forEach(m => { console.log(`${m.modelId}: context=${m.contextLength}`); }); ``` #### Python ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("automation-lab/cloudflare-workers-ai-scraper").call( run_input={"taskCategory": "Text Generation"} ) items = client.dataset(run["defaultDatasetId"]).list_items().items print(f"Extracted {len(items)} Cloudflare Workers AI models") for m in items: print(f"{m['modelId']}: contextLength={m.get('contextLength')}") ``` #### cURL ```bash ## Start the actor curl -X POST \ "https://api.apify.com/v2/acts/automation-lab~cloudflare-workers-ai-scraper/runs?token=YOUR_APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{"taskCategory": "Text Generation"}' ## Get results (replace DATASET_ID with the run's defaultDatasetId) curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_APIFY_TOKEN" ``` *** ### Use with Claude AI (MCP) You can use Cloudflare Workers AI Models Scraper directly inside Claude via the [Apify MCP server](https://mcp.apify.com). #### Claude Code (terminal) ```bash claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/cloudflare-workers-ai-scraper" ``` #### Claude Desktop / Cursor / VS Code Add to your MCP config: ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com?tools=automation-lab/cloudflare-workers-ai-scraper", "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" } } } } ``` **Example prompts to use with Claude:** - *"Use the Cloudflare Workers AI scraper to list all available text generation models and their context lengths."* - *"Scrape Cloudflare Workers AI models and tell me which embedding models support async execution."* - *"Run the Cloudflare Workers AI scraper and compare the model types available to what Groq offers."* *** ### Legality and terms of service This actor scrapes publicly accessible data from Cloudflare's developer documentation (`developers.cloudflare.com/workers-ai/models/`). The page is fully public, requires no authentication, and contains no personal data. Scraping publicly available developer documentation for informational, research, and competitive intelligence purposes is generally accepted under fair use principles. Always review [Cloudflare's Terms of Service](https://www.cloudflare.com/terms/) before using the data commercially. The actor does not log in, bypass access controls, or scrape any user-specific or private data. *** ### FAQ **Q: How often should I run this actor to stay up to date?** A: Cloudflare adds new models and deprecates old ones every few weeks. A weekly schedule is sufficient for most use cases. For tighter monitoring, run daily. **Q: Why are some fields null for certain models?** A: Not all fields apply to every model type. Image generation and speech models don't have context lengths — those fields are null. The `contextLength` and `maxOutputTokens` fields are only populated for text models where they're applicable. **Q: The actor returned fewer models than I expected. What happened?** A: If you set `includeDeprecated: false` (the default), deprecated models are excluded. Enable `includeDeprecated: true` to see the full catalog including deprecated entries. **Q: I'm getting an error or empty results. What should I do?** A: Cloudflare occasionally updates their documentation page structure. If the actor returns 0 items or throws an error, please [open an issue](https://apify.com/automation-lab/cloudflare-workers-ai-scraper) in the actor's review section. You can also try increasing `maxRequestRetries` to 5 to handle transient network issues. **Q: Can I filter by multiple task categories?** A: Currently the actor filters by a single task category at a time. To get models from multiple categories, run the actor multiple times with different `taskCategory` values, or omit the filter to get all models and filter the output yourself. **Q: Can I get historical data to track model additions?** A: Yes — schedule recurring runs and each run's dataset captures a snapshot. Compare datasets across runs to track when new models are added or deprecated. *** ### Related scrapers - 🔗 [Groq Models Scraper](https://apify.com/automation-lab/groq-models-scraper) — All Groq LLM models with pricing and speed benchmarks - 🔗 [OpenRouter Models Scraper](https://apify.com/automation-lab/openrouter-models-scraper) — All AI models available on OpenRouter with pricing and capability metadata - 🔗 [DeepInfra Models Scraper](https://apify.com/automation-lab/deepinfra-models-scraper) — Models and pricing from the DeepInfra platform - 🔗 [Fireworks AI Scraper](https://apify.com/automation-lab/fireworks-ai-scraper) — Models and pricing from Fireworks AI - 🔗 [Replicate Scraper](https://apify.com/automation-lab/replicate-scraper) — Models and pricing from the Replicate platform # Actor input Schema ## `taskCategory` (type: `string`): Filter models by task category. Leave empty to get all categories. Examples: Text Generation, Text-to-Image, Text Embeddings, Speech-to-Text, Text-to-Speech, Translation, Object Detection, Image-to-Text. ## `includeDeprecated` (type: `boolean`): If enabled, deprecated models are included in the results. By default they are excluded. ## `maxRequestRetries` (type: `integer`): Number of retry attempts for failed HTTP requests. ## Actor input object example ```json { "includeDeprecated": false, "maxRequestRetries": 3 } ``` # Actor output Schema ## `overview` (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 = { "taskCategory": "", "maxRequestRetries": 3 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/cloudflare-workers-ai-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 = { "taskCategory": "", "maxRequestRetries": 3, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/cloudflare-workers-ai-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 '{ "taskCategory": "", "maxRequestRetries": 3 }' | apify call automation-lab/cloudflare-workers-ai-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/cloudflare-workers-ai-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Cloudflare Workers AI Models Scraper", "description": "Scrapes the full catalog of AI models available on Cloudflare Workers AI, including model IDs, task categories, descriptions, context lengths, and capabilities.", "version": "0.1", "x-build-id": "0d7N3jVh3Pb4KyyU8" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~cloudflare-workers-ai-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-cloudflare-workers-ai-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~cloudflare-workers-ai-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-cloudflare-workers-ai-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~cloudflare-workers-ai-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-cloudflare-workers-ai-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "taskCategory": { "title": "Task category filter", "type": "string", "description": "Filter models by task category. Leave empty to get all categories. Examples: Text Generation, Text-to-Image, Text Embeddings, Speech-to-Text, Text-to-Speech, Translation, Object Detection, Image-to-Text." }, "includeDeprecated": { "title": "Include deprecated models", "type": "boolean", "description": "If enabled, deprecated models are included in the results. By default they are excluded.", "default": false }, "maxRequestRetries": { "title": "Max request retries", "minimum": 1, "maximum": 10, "type": "integer", "description": "Number of retry attempts for failed HTTP requests.", "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 } } } } } } } } } } ```