# Agent.ai Scraper (`automation-lab/agent-ai-scraper`) Actor
Scrape the Agent.ai marketplace. Extracts agent names, descriptions, review scores, execution counts, author info, and tags for all 2,380+ public AI agents.
- **URL**: https://apify.com/automation-lab/agent-ai-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
## Agent.ai Scraper
### What does it do?
Agent.ai Scraper extracts structured data from the [Agent.ai](https://agent.ai) marketplace — the largest public directory of AI agents. It crawls the platform's 2,380+ public agents and collects every key attribute: name, description, type, review scores, execution counts, pricing, author info, tags, and timestamps.
By default the scraper discovers all agents automatically via the sitemap. You can also target specific agents by slug or URL. No authentication, no browser — data is parsed directly from the server-side rendered HTML, making runs fast and cheap.
### Who is it for?
- **AI researchers and analysts** tracking the growth and diversity of the AI agent ecosystem
- **Product managers and founders** benchmarking competitor agents or tracking launch activity
- **Marketers and growth teams** monitoring popular agents and engagement trends (execution counts, review scores)
- **Developers** building agent discovery tools, recommendation engines, or aggregator platforms
- **Investors and VCs** tracking which AI agent categories and builders are gaining traction
- **Recruiters and talent scouts** finding active builders with large agent portfolios on Agent.ai
### Why use it?
- ✅ Extracts all 2,380+ public agents from Agent.ai in a single run
- ✅ No login or cookies required — public data only
- ✅ HTTP-only (no browser), so runs are fast and affordable
- ✅ Full data including review scores, execution counts, pricing, and author metadata
- ✅ Supports targeted scraping by providing a list of specific agent slugs or URLs
- ✅ Handles errors gracefully — invalid or private agents are skipped automatically
- ✅ All data flattened and ready for spreadsheets, databases, or BI tools
### What data does it extract?
| Field | Description |
|-------|-------------|
| `url` | Full URL to the agent page |
| `agentId` | Internal agent ID (e.g. `earningschat`) |
| `name` | Display name of the agent |
| `description` | Short description of what the agent does |
| `type` | Agent type (e.g. `studio`, `workflow`, `custom`) |
| `iconUrl` | Agent icon image URL |
| `reviewScore` | Average review score (0–5) |
| `reviewCount` | Total number of reviews |
| `executionCount` | Number of times the agent has been run |
| `tags` | Category tags (e.g. `Sales`, `Marketing`, `Operations`) |
| `price` | Price value (credits per run) |
| `isPremium` | Whether the agent is a premium/paid agent |
| `isIncludedInPro` | Whether included in Agent.ai Pro plan |
| `isMonetized` | Whether the agent has monetization enabled |
| `authorUsername` | Author's Agent.ai username |
| `authorName` | Author's display name or email |
| `authorAvatarUrl` | Author profile picture URL |
| `authorAgentsBuiltCount` | Number of agents the author has built |
| `status` | Agent visibility status (e.g. `public`) |
| `llm` | LLM model used (if specified) |
| `approximateTimeMins` | Approximate time to run in minutes |
| `createdAt` | When the agent was created |
| `updatedAt` | When the agent was last updated |
### How much does it cost to scrape agents from Agent.ai?
This actor uses **pay-per-event (PPE) pricing**. You pay a small fee per extracted agent record, with volume discounts at higher tiers.
| Volume tier | Price per agent |
|-------------|-----------------|
| FREE (first 1,000) | $0.000118 |
| BRONZE | $0.0001026 |
| SILVER | $0.000080 |
| GOLD | $0.0000615 |
| PLATINUM | $0.000041 |
| DIAMOND | $0.0000287 |
A run scraping all 2,380 agents costs approximately **$0.005 (start) + $0.244 (results) = ~$0.25** at BRONZE pricing. With DIAMOND volume pricing for large operations, that drops to under $0.10.
The free plan on Apify gives you $5 of platform credits per month — enough to run a full scrape every two months for free.
### How to scrape Agent.ai agents
1. Go to [Agent.ai Scraper](https://apify.com/automation-lab/agent-ai-scraper) on Apify
2. Leave **Agent slugs or URLs** empty to scrape ALL public agents via the sitemap
3. Set **Max agents** to limit results (e.g. `100` for a sample run)
4. Click **Start** and wait for results
5. Download your data as JSON, CSV, or Excel
For targeted scraping, enter specific agent slugs (e.g. `earningschat`) or full URLs (e.g. `https://agent.ai/agent/earningschat`) in the **Agent slugs or URLs** field.
### Input parameters
| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `agentSlugs` | Array of strings | No | `[]` | List of agent slugs or full URLs. Leave empty to scrape all agents from the sitemap. |
| `maxResults` | Integer | No | `100` | Maximum number of agents to scrape. Set to `0` for no limit (scrapes all 2,380+). |
| `proxyConfiguration` | Object | No | None | Optional proxy settings. Agent.ai does not require proxies for most use cases. |
| `maxRequestRetries` | Integer | No | `3` | Number of retry attempts per failed HTTP request. |
#### Example inputs
**Scrape 50 agents from the sitemap:**
```json
{
"maxResults": 50
}
````
**Scrape specific agents by slug:**
```json
{
"agentSlugs": ["earningschat", "competitoranalyst", "mememaker"]
}
```
**Scrape all agents (no limit):**
```json
{
"maxResults": 0
}
```
### Output format
Each result is a flat JSON object:
```json
{
"url": "https://agent.ai/agent/earningschat",
"agentId": "earningschat",
"name": "Earnings Call Analyzer",
"description": "Ask questions about a public company's earnings call discussion.",
"type": "studio",
"iconUrl": "https://agent.ai/icons/earnings.svg",
"reviewScore": 4.33,
"reviewCount": 229,
"executionCount": 7388,
"tags": ["Operations"],
"price": 1,
"isPremium": false,
"isIncludedInPro": false,
"isMonetized": false,
"authorUsername": "AgentAI",
"authorName": "agents@mail.agent.ai",
"authorAvatarUrl": "https://s3.amazonaws.com/...",
"authorAgentsBuiltCount": 50,
"status": "public",
"llm": null,
"approximateTimeMins": 5,
"createdAt": "Fri, 24 May 2024 00:00:00 GMT",
"updatedAt": "Fri, 17 Apr 2026 20:58:50 GMT"
}
```
### Tips and tricks
- 🔍 **Monitor new agents:** Run on a schedule (e.g. weekly) and compare results to detect newly published agents. Sort by `updatedAt` to find recent changes.
- 📊 **Track popular agents:** Sort by `executionCount` or `reviewScore` to identify top-performing agents in each category.
- 🏷️ **Filter by tag:** Scrape all agents and filter locally by `tags` (e.g. `Sales`, `Marketing`, `Writing`) to build category-specific lists.
- 📈 **Trend analysis:** Download weekly snapshots and compare `executionCount` growth to spot rising agents.
- 💰 **Monetization analysis:** Filter by `isMonetized: true` to track which agent builders are monetizing their work.
- ⚡ **Fast targeted runs:** Use `agentSlugs` for quick lookups of specific agents without crawling the full sitemap.
- 🚀 **Full scrape:** Set `maxResults: 0` to scrape all 2,380+ agents. A full run takes 10–20 minutes at standard pricing.
### Integrations
Agent.ai Scraper outputs to Apify's standard dataset, making integration with downstream tools simple:
- **Google Sheets:** Use the [Apify Google Sheets integration](https://apify.com/apify/google-sheets-import-export) to automatically export results to a spreadsheet. Refresh weekly to track agent growth.
- **Airtable / Notion:** Export as CSV from the dataset, then import into your database. Build a competitor intelligence base tracking top agents by category.
- **Zapier / Make:** Trigger a zap/scenario when new agents are found. For example: "When a new agent with tag `Sales` appears, add it to a Salesforce list."
- **BigQuery / Snowflake:** Use the Apify API to stream new results into your data warehouse for historical trend analysis.
- **Python / Pandas:** Download the dataset as JSON and load it with `pd.DataFrame(results)` for custom analysis (sorting, filtering, visualization).
- **Slack alerts:** Connect to a webhook to post a daily digest of new agents with high execution counts.
### API usage
#### Node.js (Apify SDK)
```javascript
import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('automation-lab/agent-ai-scraper').call({
maxResults: 100,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
```
#### Python
```python
from apify_client import ApifyClient
client = ApifyClient(token="YOUR_APIFY_TOKEN")
run = client.actor("automation-lab/agent-ai-scraper").call(run_input={
"maxResults": 100,
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item["name"], item["executionCount"])
```
#### cURL
```bash
curl -X POST \
"https://api.apify.com/v2/acts/automation-lab~agent-ai-scraper/runs?token=YOUR_APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"maxResults": 100}'
```
### Use with Claude and AI assistants (MCP)
You can use Agent.ai Scraper directly from Claude Code, Claude Desktop, Cursor, or VS Code via the Apify MCP server:
#### Claude Code (terminal)
```bash
claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/agent-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/agent-ai-scraper",
"headers": {
"Authorization": "Bearer YOUR_APIFY_TOKEN"
}
}
}
}
```
#### Example prompts
- *"Scrape the top 50 agents from Agent.ai and show me which categories have the most activity"*
- *"Fetch agents with the tag 'Sales' from Agent.ai and export to a table"*
- *"Get execution counts for agents by earningschat, competitoranalyst, and mememaker"*
- *"Scrape all Agent.ai agents and find which authors have built the most agents"*
### Legality and compliance
Agent.ai Scraper collects **only publicly available data** from [agent.ai](https://agent.ai). No authentication, private accounts, or protected content is accessed.
The data extracted (agent names, descriptions, execution counts, review scores) is public information visible to any visitor without logging in. This scraper respects the site's rate limits with built-in request delays.
Always review [Agent.ai's Terms of Service](https://agent.ai/terms) and their [Privacy Policy](https://agent.ai/privacy-policy) before using scraped data commercially. Ensure your use case complies with applicable data protection laws (GDPR, CCPA, etc.).
### FAQ
#### How many agents can I scrape?
Agent.ai currently lists 2,380+ public agents. Set `maxResults: 0` to scrape all of them in one run.
#### Do I need an Agent.ai account?
No. The scraper only accesses public agent pages that are visible without logging in.
#### How often does Agent.ai update its agent catalog?
New agents are added regularly. The sitemap updates daily. Run on a weekly schedule to stay current.
#### The run returned 0 results — what happened?
Check the logs. Common causes: (1) invalid agent slugs that don't exist on Agent.ai, (2) agents that have been removed or made private, (3) network timeouts. Try increasing `maxRequestRetries` to 5.
#### Can I scrape private or premium agents?
No. This scraper only accesses publicly visible agent pages. Agents with `status: private` or behind a paywall are not accessible.
#### My run is slow — how can I speed it up?
The default concurrency is 5 simultaneous requests. For faster runs on large datasets, consider running multiple actors in parallel with non-overlapping `agentSlugs` lists. Full scrapes of all 2,380 agents take 10–20 minutes.
### Related actors
- [Apify Store Scraper](https://apify.com/automation-lab/apify-store-scraper) — Scrape the Apify actor marketplace
- [HuggingFace Scraper](https://apify.com/automation-lab/huggingface-scraper) — Scrape AI models and datasets from HuggingFace
- [ProductHunt Scraper](https://apify.com/automation-lab/producthunt-scraper) — Track AI product launches on Product Hunt
# Actor input Schema
## `agentSlugs` (type: `array`):
Optional list of specific agents to scrape. Accepts agent slugs (e.g. earningschat) or full URLs (e.g. https://agent.ai/agent/earningschat). Leave empty to scrape all public agents from the sitemap.
## `maxResults` (type: `integer`):
Maximum number of agents to scrape. Set to 0 for unlimited (scrapes all agents found in the sitemap, up to 2,380+). Default is 100.
## `proxyConfiguration` (type: `object`):
Optional proxy settings. Agent.ai is generally accessible without proxies. Enable if you encounter rate limiting.
## `maxRequestRetries` (type: `integer`):
Number of retry attempts for failed HTTP requests.
## Actor input object example
```json
{
"agentSlugs": [
"competitoranalyst",
"mememaker"
],
"maxResults": 20,
"proxyConfiguration": {
"useApifyProxy": false
},
"maxRequestRetries": 3
}
```
# Actor output Schema
## `overview` (type: `string`):
No description
## `results` (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 = {
"agentSlugs": [
"competitoranalyst",
"mememaker"
],
"maxResults": 20,
"proxyConfiguration": {
"useApifyProxy": false
},
"maxRequestRetries": 3
};
// Run the Actor and wait for it to finish
const run = await client.actor("automation-lab/agent-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 = {
"agentSlugs": [
"competitoranalyst",
"mememaker",
],
"maxResults": 20,
"proxyConfiguration": { "useApifyProxy": False },
"maxRequestRetries": 3,
}
# Run the Actor and wait for it to finish
run = client.actor("automation-lab/agent-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 '{
"agentSlugs": [
"competitoranalyst",
"mememaker"
],
"maxResults": 20,
"proxyConfiguration": {
"useApifyProxy": false
},
"maxRequestRetries": 3
}' |
apify call automation-lab/agent-ai-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=automation-lab/agent-ai-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Agent.ai Scraper",
"description": "Scrape the Agent.ai marketplace. Extracts agent names, descriptions, review scores, execution counts, author info, and tags for all 2,380+ public AI agents.",
"version": "0.1",
"x-build-id": "gQQX0ph55a1EDClbo"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/automation-lab~agent-ai-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-automation-lab-agent-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/automation-lab~agent-ai-scraper/runs": {
"post": {
"operationId": "runs-sync-automation-lab-agent-ai-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/automation-lab~agent-ai-scraper/run-sync": {
"post": {
"operationId": "run-sync-automation-lab-agent-ai-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"agentSlugs": {
"title": "🤖 Agent slugs or URLs",
"type": "array",
"description": "Optional list of specific agents to scrape. Accepts agent slugs (e.g. earningschat) or full URLs (e.g. https://agent.ai/agent/earningschat). Leave empty to scrape all public agents from the sitemap.",
"items": {
"type": "string"
}
},
"maxResults": {
"title": "Max agents",
"minimum": 0,
"type": "integer",
"description": "Maximum number of agents to scrape. Set to 0 for unlimited (scrapes all agents found in the sitemap, up to 2,380+). Default is 100.",
"default": 100
},
"proxyConfiguration": {
"title": "Proxy configuration",
"type": "object",
"description": "Optional proxy settings. Agent.ai is generally accessible without proxies. Enable if you encounter rate limiting."
},
"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
}
}
}
}
}
}
}
}
}
}
```