# Ollama Library Scraper (`automation-lab/ollama-library-scraper`) Actor Scrape the Ollama model library. Get model names, pull counts, parameter sizes, capabilities, and update dates. No API needed. - **URL**: https://apify.com/automation-lab/ollama-library-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** AI, 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. 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 ## Ollama Library Scraper Scrape the entire [Ollama model library](https://ollama.com/library) and extract structured data about every available LLM. Get model names, pull counts, parameter sizes, capabilities, descriptions, and update dates — all in one run. ### What does Ollama Library Scraper do? Ollama Library Scraper is an [Apify actor](https://apify.com/actors) that crawls the Ollama model registry at [ollama.com](https://ollama.com/library) and extracts metadata for every model listed. It handles pagination automatically, letting you scrape from a handful of top models to the entire library in a single run. The scraper works with plain HTTP requests — no browser needed — making it fast and cost-effective. It parses server-rendered HTML to extract all visible model information directly from the search results page. ### Who is Ollama Library Scraper for? - **AI/ML engineers** building local LLM pipelines who need to monitor available models and their sizes - **DevOps teams** evaluating which models to deploy on-premises - **Researchers** tracking the open-source LLM ecosystem growth, comparing model families, and analyzing trends - **Product managers** benchmarking the landscape of available models for competitive analysis - **Data analysts** building dashboards to track model popularity, new releases, and ecosystem metrics ### Why use Ollama Library Scraper? - **No API exists** — Ollama has no public API for browsing their model library. This scraper fills that gap. - **Zero competitors** — there are no other Ollama scrapers on the Apify Store. - **Fast and cheap** — HTTP-only scraping means sub-minute runs for the entire library. - **Structured output** — get clean JSON with numeric pull counts, capability tags, and ISO dates. - **Filter and search** — narrow results by keyword, capability (vision, tools, embedding, etc.), or sort order. ### What data can you extract from Ollama? | Field | Type | Example | |-------|------|---------| | `name` | string | `"qwen3"` | | `slug` | string | `"qwen3"` | | `description` | string | `"Qwen3 is the latest generation..."` | | `pullCount` | string | `"27.3M"` | | `pullCountNumeric` | number | `27300000` | | `tagCount` | number | `15` | | `parameterSizes` | string[] | `["0.6b", "4b", "8b", "14b", "30b"]` | | `capabilities` | string[] | `["vision", "tools", "thinking"]` | | `updatedAt` | string | `"3 days ago"` | | `updatedAtPrecise` | string | `"Apr 15, 2026 10:02 PM UTC"` | | `modelUrl` | string | `"https://ollama.com/library/qwen3"` | | `scrapedAt` | string | `"2026-04-20T08:17:33.077Z"` | ### How much does it cost to scrape Ollama models? Ollama Library Scraper uses [pay-per-event pricing](https://docs.apify.com/platform/actors/running/pay-per-event). You pay only for what you use: | Event | Price | |-------|-------| | Run started | $0.005 per run | | Model extracted | $0.0005 per model | **Example costs:** - Scrape top 50 models: $0.005 + 50 x $0.0005 = **$0.03** - Scrape all ~500 models: $0.005 + 500 x $0.0005 = **$0.26** On the Apify Free plan ($5/month), you can scrape the full library ~19 times per month. ### How to scrape the Ollama model library 1. Go to [Ollama Library Scraper](https://apify.com/automation-lab/ollama-library-scraper) on Apify. 2. Click **Start** to scrape all popular models with default settings. 3. Optionally enter a **Search keyword** (e.g., "llama", "vision", "embedding") to filter models. 4. Set **Max models** to limit how many results you want. 5. Choose **Sort by** — Popular (most pulled) or Newest (recently added). 6. Use **Filter by capabilities** to only get models with specific features (vision, tools, thinking, etc.). 7. Click **Start** and wait for the run to finish. 8. Download your data as JSON, CSV, Excel, or connect it via API. ### Input parameters | Parameter | Type | Description | Default | |-----------|------|-------------|---------| | `searchQuery` | string | Keyword to search for (leave empty for all models) | `""` | | `maxModels` | integer | Maximum models to extract (0 = unlimited) | `100` | | `maxPages` | integer | Maximum search pages to scrape (~20 models/page) | `50` | | `sortBy` | enum | Sort order: `popular` or `newest` | `popular` | | `filterCapabilities` | string[] | Filter by capabilities: cloud, embedding, vision, tools, thinking, code | `[]` | | `maxRequestRetries` | integer | Retry attempts for failed requests | `3` | ### Output example ```json { "name": "qwen3", "slug": "qwen3", "description": "Qwen3 is the latest generation of large language models in Qwen series...", "pullCount": "27.3M", "pullCountNumeric": 27300000, "tagCount": 15, "parameterSizes": ["0.6b", "1.7b", "4b", "8b", "14b", "30b"], "capabilities": ["tools", "thinking"], "updatedAt": "6 months ago", "updatedAtPrecise": "Oct 15, 2025 3:22 PM UTC", "modelUrl": "https://ollama.com/library/qwen3", "scrapedAt": "2026-04-20T08:17:33.077Z" } ```` ### Tips for best results - **Start with defaults** — the prefilled input scrapes the top 50 most popular models, which covers the most commonly used LLMs. - **Use capability filters** to narrow results — e.g., filter by "vision" to find all multimodal models, or "embedding" for vector embedding models. - **Set maxModels to 0** to scrape the complete library without limits. - **Sort by newest** to discover recently added models that may not have high pull counts yet. - **Combine search + filters** — search for "llama" with "tools" capability to find tool-calling Llama variants. ### Integrations with other tools - **Google Sheets** — export model data to a spreadsheet for tracking trends over time. Schedule daily runs to build a historical dataset of model popularity. - **Slack notifications** — get alerts when new models appear in the library by comparing runs. - **Airtable** — build a searchable model catalog with filtering by capability and size. - **Zapier / Make** — trigger workflows when specific models are updated or new ones are added. - **Custom dashboards** — feed the structured JSON into Grafana, Metabase, or any BI tool to visualize the Ollama ecosystem. ### How to use Ollama Library Scraper via API #### Node.js ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const run = await client.actor('automation-lab/ollama-library-scraper').call({ searchQuery: '', maxModels: 100, sortBy: 'popular', }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); ``` #### Python ```python from apify_client import ApifyClient client = ApifyClient('YOUR_API_TOKEN') run = client.actor('automation-lab/ollama-library-scraper').call(run_input={ 'searchQuery': '', 'maxModels': 100, 'sortBy': 'popular', }) items = client.dataset(run['defaultDatasetId']).list_items().items print(items) ``` #### cURL ```bash curl "https://api.apify.com/v2/acts/automation-lab~ollama-library-scraper/runs" \ -X POST \ -H "Content-Type: application/json" \ -H "Authorization: Bearer YOUR_API_TOKEN" \ -d '{ "searchQuery": "", "maxModels": 100, "sortBy": "popular" }' ``` ### Use with MCP (Model Context Protocol) Connect Ollama Library Scraper to AI assistants using MCP for on-demand model discovery. #### Claude Code ```bash claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/ollama-library-scraper" ``` #### Claude Desktop / Cursor / VS Code Add to your MCP config file: ```json { "mcpServers": { "apify": { "url": "https://mcp.apify.com?tools=automation-lab/ollama-library-scraper" } } } ``` **Example prompts:** - "Show me all Ollama models with vision capability" - "What are the top 10 most popular models on Ollama?" - "List all embedding models available on Ollama with their sizes" - "Find Ollama models updated in the last week" ### Is it legal to scrape Ollama? Ollama's model library is publicly accessible without authentication. This scraper accesses only publicly visible model metadata — the same information any visitor sees when browsing [ollama.com/search](https://ollama.com/search). The scraper does not download any model weights, bypass access controls, or access private data. Always review Ollama's terms of service and robots.txt before large-scale scraping. Use reasonable request rates and respect the site's resources. ### FAQ **How many models are in the Ollama library?** The Ollama library currently contains 500+ models and grows regularly as new open-source LLMs are released. The scraper handles pagination automatically to capture all of them. **Can I filter models by parameter size?** The scraper extracts all parameter sizes for each model (e.g., 0.6b, 4b, 8b, 14b). While filtering by size isn't a direct input option, you can filter the output dataset by the `parameterSizes` field after scraping. **How often is the data updated?** Each model listing includes an `updatedAt` field showing when it was last modified on Ollama. Schedule recurring runs to track changes over time. **What if a request fails?** The scraper automatically retries failed requests up to 3 times (configurable) with exponential backoff. If a page consistently fails, it's skipped and scraping continues with the next page. **Why are some pull counts shown as "27.3M" instead of exact numbers?** Ollama displays abbreviated pull counts on their site. The scraper provides both the raw display value (`pullCount`: "27.3M") and a parsed numeric value (`pullCountNumeric`: 27300000) for sorting and analysis. ### Related scrapers - [ArXiv Scraper](https://apify.com/automation-lab/arxiv-scraper) — scrape research papers from ArXiv - [AI Tools Directory Scraper](https://apify.com/automation-lab/ai-tools-directory-scraper) — scrape AI tool listings # Actor input Schema ## `searchQuery` (type: `string`): Optional keyword to filter models (e.g., 'llama', 'vision', 'embedding'). Leave empty to get all models. ## `maxModels` (type: `integer`): Maximum number of models to extract. Set to 0 for unlimited. ## `maxPages` (type: `integer`): Maximum number of search result pages to scrape. Each page has ~20 models. ## `sortBy` (type: `string`): How to sort the models in the library. ## `filterCapabilities` (type: `array`): Only include models with specific capabilities. ## `maxRequestRetries` (type: `integer`): Number of retry attempts for failed HTTP requests. ## Actor input object example ```json { "maxModels": 50, "maxPages": 3, "sortBy": "popular", "filterCapabilities": [], "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 = { "searchQuery": "", "maxModels": 50, "maxPages": 3, "sortBy": "popular", "filterCapabilities": [], "maxRequestRetries": 3 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/ollama-library-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 = { "searchQuery": "", "maxModels": 50, "maxPages": 3, "sortBy": "popular", "filterCapabilities": [], "maxRequestRetries": 3, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/ollama-library-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 '{ "searchQuery": "", "maxModels": 50, "maxPages": 3, "sortBy": "popular", "filterCapabilities": [], "maxRequestRetries": 3 }' | apify call automation-lab/ollama-library-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/ollama-library-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Ollama Library Scraper", "description": "Scrape the Ollama model library. Get model names, pull counts, parameter sizes, capabilities, and update dates. No API needed.", "version": "0.1", "x-build-id": "Oks6RBtpz8Jc0fy3T" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~ollama-library-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-ollama-library-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~ollama-library-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-ollama-library-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~ollama-library-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-ollama-library-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchQuery": { "title": "Search keyword", "type": "string", "description": "Optional keyword to filter models (e.g., 'llama', 'vision', 'embedding'). Leave empty to get all models." }, "maxModels": { "title": "Max models", "minimum": 0, "type": "integer", "description": "Maximum number of models to extract. Set to 0 for unlimited.", "default": 100 }, "maxPages": { "title": "Max pages", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of search result pages to scrape. Each page has ~20 models.", "default": 50 }, "sortBy": { "title": "Sort by", "enum": [ "popular", "newest" ], "type": "string", "description": "How to sort the models in the library.", "default": "popular" }, "filterCapabilities": { "title": "Filter by capabilities", "type": "array", "description": "Only include models with specific capabilities.", "items": { "type": "string", "enum": [ "cloud", "embedding", "vision", "tools", "thinking", "code" ], "enumTitles": [ "Cloud", "Embedding", "Vision", "Tools", "Thinking", "Code" ] }, "default": [] }, "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 } } } } } } } } } } ```