# Civitai Models Scraper (`automation-lab/civitai-scraper`) Actor Scrape AI models from Civitai. Extract names, types, download stats, ratings, creators, versions, tags, trained words, and licensing info. No auth needed. - **URL**: https://apify.com/automation-lab/civitai-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** AI - **Stats:** 2 total users, 1 monthly users, 100.0% 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 ## Civitai Models Scraper Scrape AI models from [Civitai](https://civitai.com) — the largest AI model sharing platform. Extract model names, types, download counts, ratings, creator info, version details, file metadata, tags, trained words, and licensing information. ### What does Civitai Models Scraper do? Civitai Models Scraper uses Civitai's public REST API to extract structured data about AI models — Stable Diffusion checkpoints, LoRAs, ControlNets, VAEs, and more. It collects complete model metadata including download statistics, community ratings, version history, file sizes, trained trigger words, base model compatibility, and commercial licensing terms. The scraper supports keyword search, model type filtering, configurable sorting, and cursor-based pagination to handle large result sets efficiently. No browser automation required — it communicates directly with the Civitai API for fast, reliable extraction. ### Who is it for? - 🤖 **AI researchers** — cataloging model architectures, tracking popularity trends, and analyzing the AI art model ecosystem - 📊 **Data analysts** — building datasets of model statistics for market research and trend analysis - 🎨 **AI artists** — finding the best-rated models for specific styles, subjects, or base model architectures - 🏢 **AI companies** — monitoring the competitive landscape of open-source AI models - 🔧 **Tool developers** — building model recommendation engines, comparison tools, or aggregation platforms - 📈 **Market researchers** — understanding adoption patterns across model types and base architectures ### Why scrape Civitai? Civitai is the #1 hub for sharing AI image generation models, with millions of monthly visitors and tens of thousands of models. It hosts checkpoints, LoRAs, textual inversions, ControlNets, and other model types for Stable Diffusion, SDXL, Flux, and more. Key reasons to scrape Civitai: - **Model discovery** — Find the most downloaded or highest-rated models for any style or subject - **Trend analysis** — Track which base models (SD 1.5, SDXL, Flux) are gaining or losing popularity - **Competitive intelligence** — Monitor new model releases and creator activity - **Dataset building** — Collect model metadata for training recommendation systems or search engines - **License auditing** — Check commercial use permissions across models for compliance - **Community insights** — Analyze download counts, ratings, and tipping patterns ### What data can you extract from Civitai? | Field | Type | Description | |-------|------|-------------| | `modelId` | number | Unique model identifier | | `name` | string | Model name | | `type` | string | Model type (Checkpoint, LORA, ControlNet, etc.) | | `description` | string | Model description (HTML stripped, max 1000 chars) | | `tags` | array | User-assigned tags | | `nsfw` | boolean | NSFW flag | | `nsfwLevel` | number | NSFW severity level | | `creatorUsername` | string | Creator's username | | `creatorImageUrl` | string | Creator's avatar URL | | `downloadCount` | number | Total downloads | | `thumbsUpCount` | number | Total likes | | `thumbsDownCount` | number | Total dislikes | | `commentCount` | number | Total comments | | `tippedAmountCount` | number | Total tip amount | | `rating` | number | Approval ratio (likes / total votes, 0–1) | | `allowNoCredit` | boolean | Can use without credit | | `allowCommercialUse` | string | Commercial use permissions | | `allowDerivatives` | boolean | Derivative works allowed | | `allowDifferentLicense` | boolean | Different license allowed | | `latestVersionName` | string | Latest version name | | `latestVersionId` | number | Latest version ID | | `baseModel` | string | Base model (SD 1.5, SDXL, Flux, etc.) | | `publishedAt` | string | Version publish date (ISO 8601) | | `downloadUrl` | string | Direct download URL for latest version | | `trainedWords` | array | Trigger words for the model | | `files` | array | File details (name, size, format, download URL) | | `modelUrl` | string | Civitai model page URL | | `scrapedAt` | string | Extraction timestamp | ### How much does it cost to scrape Civitai models? Civitai Models Scraper uses Apify's pay-per-event pricing. You only pay for what you extract: - **Run start:** $0.005 per run - **Per model:** $0.003 per model extracted **Example costs:** - 20 models (quick search): $0.005 + 20 × $0.003 = **$0.065** - 100 models (standard run): $0.005 + 100 × $0.003 = **$0.305** - 1,000 models (bulk extraction): $0.005 + 1,000 × $0.003 = **$3.005** 💡 Apify's free plan includes $5/month in platform credits — enough to extract ~1,600 models per month at no cost. ### How to scrape Civitai models 1. Go to [Civitai Models Scraper](https://apify.com/automation-lab/civitai-scraper) on Apify Store 2. Enter a search keyword (e.g., "flux lora") or leave empty to browse all models 3. Optionally filter by model type (Checkpoint, LORA, ControlNet, etc.) 4. Choose how to sort results (Most Downloaded, Highest Rated, Newest) 5. Set the maximum number of models to extract 6. Click **Start** and wait for results 7. Download your data as JSON, CSV, or Excel ### Input parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `searchQuery` | string | `""` | Keyword to search for models. Leave empty to browse all. | | `modelTypes` | string[] | `[]` | Filter by type: Checkpoint, LORA, ControlNet, VAE, etc. | | `sort` | string | `"Most Downloaded"` | Sort by: Most Downloaded, Highest Rated, or Newest | | `period` | string | `"AllTime"` | Time period for stats: AllTime, Year, Month, Week, Day | | `includeNsfw` | boolean | `false` | Include NSFW models in results | | `maxModels` | integer | `100` | Maximum models to extract (1–10,000) | | `includeVersionDetails` | boolean | `true` | Include file metadata and trained words | | `maxRequestRetries` | integer | `3` | Retry attempts for failed API requests | ### Output example ```json { "modelId": 827184, "name": "WAI-illustrious-SDXL", "type": "Checkpoint", "description": "High-quality illustrious checkpoint for anime-style generation...", "tags": ["base model", "anime"], "nsfw": false, "nsfwLevel": 1, "creatorUsername": "WAI0731", "creatorImageUrl": "https://image.civitai.com/...", "downloadCount": 1277231, "thumbsUpCount": 79400, "thumbsDownCount": 204, "commentCount": 0, "tippedAmountCount": 157252, "rating": 1.0, "allowNoCredit": true, "allowCommercialUse": "{Image,RentCivit}", "allowDerivatives": true, "allowDifferentLicense": true, "latestVersionName": "v16.0", "latestVersionId": 2514310, "baseModel": "Illustrious", "publishedAt": "2025-12-18T09:16:12.062Z", "downloadUrl": "https://civitai.com/api/download/models/2514310", "trainedWords": [], "files": [ { "fileName": "waiIllustriousSDXL_v160.safetensors", "fileSizeKB": 6775430, "fileType": "Model", "format": "SafeTensor", "downloadUrl": "https://civitai.com/api/download/models/2514310" } ], "modelUrl": "https://civitai.com/models/827184", "scrapedAt": "2026-04-19T08:24:22.468Z" } ```` ### Tips for scraping Civitai - 🔍 **Use specific search terms** — "flux lora portrait" gives more targeted results than just "flux" - 📦 **Filter by model type** — narrow results to Checkpoints, LoRAs, or other specific types - 📊 **Sort by Most Downloaded** for popular models, **Highest Rated** for quality, **Newest** for recent releases - ⏱️ **Use time periods** — filter by Week or Month to find trending models - 📉 **Disable version details** (`includeVersionDetails: false`) for lighter output when you only need model metadata - 🔞 **NSFW filtering** is on by default — enable it explicitly if needed for research - 🔄 **The API returns up to 100 models per page** — large extractions paginate automatically via cursor ### Integrations Connect Civitai Models Scraper to your workflows: - **Google Sheets** — automatically export model data to spreadsheets for tracking and analysis - **Slack/Discord** — get notifications when new high-rated models appear in your category - **Airtable** — build a searchable model database with ratings, downloads, and compatibility info - **Webhooks** — trigger downstream processing when new data is available - **Zapier/Make** — connect to 5,000+ apps for automated model monitoring pipelines ### API usage #### Node.js ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const run = await client.actor('automation-lab/civitai-scraper').call({ searchQuery: 'flux lora', modelTypes: ['LORA'], sort: 'Most Downloaded', maxModels: 50, }); 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/civitai-scraper").call(run_input={ "searchQuery": "flux lora", "modelTypes": ["LORA"], "sort": "Most Downloaded", "maxModels": 50, }) items = client.dataset(run["defaultDatasetId"]).list_items().items print(items) ``` #### cURL ```bash curl -X POST "https://api.apify.com/v2/acts/automation-lab~civitai-scraper/runs?token=YOUR_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "searchQuery": "flux lora", "modelTypes": ["LORA"], "sort": "Most Downloaded", "maxModels": 50 }' ``` ### Use with MCP (Model Context Protocol) #### Claude Code ```bash claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/civitai-scraper" ``` #### Claude Desktop / Cursor / VS Code Add to your MCP config file: ```json { "mcpServers": { "apify": { "url": "https://mcp.apify.com?tools=automation-lab/civitai-scraper", "headers": { "Authorization": "Bearer YOUR_API_TOKEN" } } } } ``` **Example prompts:** - "Find the top 20 most downloaded Flux LoRAs on Civitai" - "Get all Checkpoint models tagged 'anime' sorted by rating" - "Extract model details for SDXL ControlNets published this month" ### Is it legal to scrape Civitai? This scraper uses Civitai's **official public REST API** (`https://civitai.com/api/v1/models`), which is freely available without authentication. The API is documented and intended for public use. The scraper only extracts publicly available model metadata — it does not download model files, bypass authentication, or access private data. Always review Civitai's Terms of Service before using extracted data commercially. ### FAQ **Q: Do I need a Civitai account to use this scraper?** A: No. The Civitai API is completely public and requires no authentication. The scraper works without any Civitai credentials. **Q: Can I download model files with this scraper?** A: The scraper extracts download URLs for model files, but does not download the files themselves. You can use the extracted URLs to download models separately. **Q: Why am I getting fewer results than expected?** A: Some model types may have limited listings. Try broadening your search query, removing type filters, or enabling NSFW models to increase results. The Civitai API may also return fewer results for very specific searches. **Q: What model types are available?** A: Checkpoint, LORA, TextualInversion, Hypernetwork, AestheticGradient, ControlNet, Poses, Wildcards, Workflows, VAE, Upscaler, MotionModule, LoCon, and DoRA. **Q: How often is Civitai data updated?** A: Civitai's API reflects real-time data. Run the scraper on a schedule (daily, weekly) to track changes in download counts, ratings, and new model releases. **Q: The scraper returned 0 results — what happened?** A: This is rare since the Civitai API returns results even for broad queries. Check your model type filter — some types may have very few listings. Try removing filters or using a different search query. ### Related scrapers - [ArXiv Scraper](https://apify.com/automation-lab/arxiv-scraper) — scrape research papers from ArXiv - [GitHub Repo Scraper](https://apify.com/automation-lab/github-repo-scraper) — extract repository data from GitHub - [HuggingFace Scraper](https://apify.com/automation-lab/huggingface-scraper) — scrape models and datasets from HuggingFace # Actor input Schema ## `searchQuery` (type: `string`): Keyword to search for models on Civitai. Leave empty to browse all models. ## `modelTypes` (type: `array`): Filter by model type. Leave empty for all types. ## `sort` (type: `string`): How to sort results. ## `period` (type: `string`): Time period for sorting stats. ## `includeNsfw` (type: `boolean`): Whether to include NSFW (Not Safe For Work) models in results. ## `maxModels` (type: `integer`): Maximum number of models to extract. Each API page returns up to 100 models. ## `includeVersionDetails` (type: `boolean`): Include detailed version information (files, trained words, base model) for each model. Adds more data but increases output size. ## `maxRequestRetries` (type: `integer`): Number of retry attempts for failed API requests. ## Actor input object example ```json { "searchQuery": "flux lora", "modelTypes": [], "sort": "Most Downloaded", "period": "AllTime", "includeNsfw": false, "maxModels": 20, "includeVersionDetails": true, "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": "flux lora", "modelTypes": [], "sort": "Most Downloaded", "period": "AllTime", "includeNsfw": false, "maxModels": 20, "includeVersionDetails": true, "maxRequestRetries": 3 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/civitai-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": "flux lora", "modelTypes": [], "sort": "Most Downloaded", "period": "AllTime", "includeNsfw": False, "maxModels": 20, "includeVersionDetails": True, "maxRequestRetries": 3, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/civitai-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": "flux lora", "modelTypes": [], "sort": "Most Downloaded", "period": "AllTime", "includeNsfw": false, "maxModels": 20, "includeVersionDetails": true, "maxRequestRetries": 3 }' | apify call automation-lab/civitai-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/civitai-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Civitai Models Scraper", "description": "Scrape AI models from Civitai. Extract names, types, download stats, ratings, creators, versions, tags, trained words, and licensing info. No auth needed.", "version": "0.1", "x-build-id": "3FIsN0T0Juqw8PDYB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~civitai-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-civitai-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~civitai-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-civitai-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~civitai-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-civitai-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 query", "type": "string", "description": "Keyword to search for models on Civitai. Leave empty to browse all models." }, "modelTypes": { "title": "Model types", "type": "array", "description": "Filter by model type. Leave empty for all types.", "items": { "type": "string", "enum": [ "Checkpoint", "TextualInversion", "Hypernetwork", "AestheticGradient", "LORA", "Controlnet", "Poses", "Wildcards", "Workflows", "VAE", "Upscaler", "MotionModule", "LoCon", "DoRA" ], "enumTitles": [ "Checkpoint", "Textual Inversion", "Hypernetwork", "Aesthetic Gradient", "LoRA", "ControlNet", "Poses", "Wildcards", "Workflows", "VAE", "Upscaler", "Motion Module", "LoCon", "DoRA" ] } }, "sort": { "title": "Sort by", "enum": [ "Highest Rated", "Most Downloaded", "Newest" ], "type": "string", "description": "How to sort results.", "default": "Most Downloaded" }, "period": { "title": "Time period", "enum": [ "AllTime", "Year", "Month", "Week", "Day" ], "type": "string", "description": "Time period for sorting stats.", "default": "AllTime" }, "includeNsfw": { "title": "Include NSFW models", "type": "boolean", "description": "Whether to include NSFW (Not Safe For Work) models in results.", "default": false }, "maxModels": { "title": "Max models", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of models to extract. Each API page returns up to 100 models.", "default": 100 }, "includeVersionDetails": { "title": "Include version details", "type": "boolean", "description": "Include detailed version information (files, trained words, base model) for each model. Adds more data but increases output size.", "default": true }, "maxRequestRetries": { "title": "Max request retries", "minimum": 1, "maximum": 10, "type": "integer", "description": "Number of retry attempts for failed API 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 } } } } } } } } } } ```