# Reddit Comments Scraper (`scrapecraze/reddit-comments-scraper`) Actor 🔍 Reddit Comments Scraper pulls insightful Reddit comment threads fast—clean, structured data for sentiment, trend & community analysis. 🧠 Great for research, marketing insights, and competitive intelligence. 🚀 Easy to run, export-ready results. - **URL**: https://apify.com/scrapecraze/reddit-comments-scraper.md - **Developed by:** [ScrapeCraze](https://apify.com/scrapecraze) (community) - **Categories:** Social media, AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.99 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ### Reddit Comments Scraper 🚀 Struggling to collect Reddit thread insights fast enough for your research and outreach workflows? **Reddit Comments Scraper** pulls comments (including nested replies) from one or more Reddit posts and returns a flat record per comment. With a **Reddit comments scraper**, you can scrape Reddit comments for analysis and export ready-to-use data. It’s built for marketers, data analysts, and researchers who want structured Reddit comment extraction without manual copy-pasting. In a single run, you can extract hundreds of comment records per post—quickly enough to iterate on your dataset the same day. --- ### See the Data: Sample Output Here's a real record from a single run: ```json { "postUrl": "https://www.reddit.com/r/AskMec/comments/14990m6/les_applications_de_rencontres_fonctionnent_telles/", "postTitle": "Les applications de rencontres fonctionnent-elles vraiment ?", "postAuthor": "u/relationship_nerd", "postScore": 1834, "subreddit": "AskMec", "commentDepth": 0, "commentAuthor": "u/curious_user_27", "commentText": "From my experience, the apps work best when you treat them like conversations—not like instant dating slots.", "commentTimestamp": "2024-05-12T09:21:17.000Z", "commentPath": "12", "parentPath": null, "isTopLevel": true, "replyCount": 3, "scrapedAt": "2026-06-07T12:34:56.000Z" } ```` | Field | Type | What It Tells You | |---|---|---| | `postUrl` | string | The Reddit post the comment belongs to, so you can trace context. | | `postTitle` | string | The post title for reporting and dataset labeling. | | `postAuthor` | string | Who authored the Reddit post you scraped. | | `postScore` | number | A useful signal for prioritizing threads (score at scrape time). | | `subreddit` | string | The community the post lives in, handy for segmentation. | | `commentDepth` | number | Whether the comment is top-level or nested within the thread tree. | | `commentAuthor` | string | The Reddit user who posted the comment (useful for author-level analysis). | | `commentText` | string | The actual comment content text for NLP, sentiment, or thematic coding. | | `commentTimestamp` | string | UTC timestamp (ISO-8601) of when the comment was created. | | `commentPath` | string | Encoded position in the tree so you can reconstruct the thread structure in analysis. | | `parentPath` | string | null | The parent’s `commentPath` (or `null` for top-level comments). | | `isTopLevel` | boolean | Quick flag to separate top-level comments from replies. | | `replyCount` | number | Count of immediate replies (direct children) for each comment. | | `scrapedAt` | string | UTC timestamp (ISO-8601) when the record was scraped. | | `status` | (not in dataset) | Failures are handled per post via logs; successful comment records are pushed to the dataset. | | `error_message` | (not in dataset) | Error details are logged when a post fails after retries; successful posts still contribute records. | Export your full dataset as JSON, CSV, or Excel from the Apify dashboard. *** ### Setting It Up Drop this into your `input.json` and you're ready to go: ```json { "postUrls": [ "https://www.reddit.com/r/AskMec/comments/14990m6/les_applications_de_rencontres_fonctionnent_telles/", "https://www.reddit.com/r/programming/comments/abcdef/example_thread_title/" ], "maxComments": 500, "includeNestedReplies": true, "sortBy": "top", "maxConcurrentPosts": 2 } ``` | Parameter | Required | What It Does | |---|---|---| | `postUrls` | ✅ | One or more Reddit post URLs to scrape comments from. | | `maxComments` | ⬜ | Maximum number of comments to extract per post (counts nested replies too). | | `includeNestedReplies` | ⬜ | When enabled, replies to comments are also extracted (the full thread tree). When disabled, only top-level comments are returned. | | `sortBy` | ⬜ | How Reddit orders the comments before they are collected (`top`, `best`, `new`, `controversial`, `old`, `qa`). | | `maxConcurrentPosts` | ⬜ | How many posts to scrape in parallel. Each post runs its own browser, so higher values need more memory. | *** ### What It Does Reddit Comments Scraper is a comment extraction tool that returns one flat dataset record per Reddit comment, with path/depth metadata for easy downstream analysis. #### Scrape Reddit comments with thread structure metadata Each extracted comment includes `commentDepth`, `commentPath`, `parentPath`, and `isTopLevel`, so you can analyze conversations as threads—not just a list of messages. If you enable nested replies, you’ll capture the full reply tree and preserve where every comment sits in the discussion. #### Control volume to match your research goals Use `maxComments` to cap how many comments you extract per post (nested replies are counted too). This makes it easier to build focused datasets for topic modeling, Reddit comments sentiment analysis, or content/theme research without overwhelming your pipeline. #### Choose comment ordering for consistent comparisons Set `sortBy` to control how comments are ordered before collection (such as `top` or `new`). This is helpful when you want repeatable datasets for A/B analyses or for comparing different subreddits and time windows. #### Built for export-ready, integration-friendly output The actor pushes structured records to the Apify dataset via `Actor.push_data(records, charged_event_name="result")`. That means your output is immediately usable for reporting, ETL jobs, or further processing (like tagging by subreddit or reconstructing reply chains). #### Includes nested reply extraction when you need it With `includeNestedReplies: true`, replies are collected and flattened into records with correct `replyCount` for immediate children. With nested replies disabled, you get a simpler dataset containing only top-level comments, which can speed up review and annotation. Overall, Reddit Comments Scraper turns Reddit comment extraction into a clean, analysis-ready dataset you can collect and export repeatedly. *** ### Why Reddit Comments Scraper? There are plenty of ways to pull data from Reddit—here’s why Reddit Comments Scraper stands out. #### Flat records per comment, with usable path context Instead of delivering nested blobs that are painful to process, the Reddit Comments Scraper flattens the thread into consistent records and keeps `commentPath`/`parentPath` so you can still reason about the conversation structure. #### Flexible collection depth without losing control You decide whether to include nested replies (`includeNestedReplies`) and cap output with `maxComments`. That’s especially useful for Reddit comments data mining when you need predictable dataset sizes. #### Built for resilience on each post The actor includes multiple attempts per post (up to 4) and logs warnings when it can’t fetch expected data. When a post succeeds, its comments are pushed to the dataset right away, so partial results don’t stall your entire run. *** ### Real-World Use Cases Here's how different teams put Reddit Comments Scraper to work: **Marketing Analysts**\ A marketing team needs to understand what people actually say about a product category in specific subreddits. They scrape Reddit comments from a handful of high-signal posts, turn `commentText` into themes, and use `subreddit` plus `commentDepth` to separate top-level opinions from reply-driven nuance—without manual scraping. **Research Teams & Community Managers**\ A researcher running qualitative analysis wants a thread-level view of how discussions evolve. They run the actor with nested replies enabled to collect a complete conversation tree, then use `commentPath` and `parentPath` to reconstruct conversation order during coding. **Sales & Outreach Ops**\ An outreach coordinator wants to find objection patterns and recurring concerns mentioned in comments across multiple threads. They use `sortBy` and `maxComments` to standardize what gets collected, then export the dataset and apply filtering logic downstream to focus on the most actionable comment segments. **Data Engineers / Automation Specialists**\ A data engineer integrates scraping into an ETL schedule and needs consistent schema fields. They trigger the Reddit comment extraction job with a controlled `maxConcurrentPosts`, then pipe the dataset records into their warehouse—leveraging the consistent output fields (`postUrl`, `commentTimestamp`, `scrapedAt`) for incremental refresh. **NLP Practitioners**\ An applied ML workflow needs clean text inputs and timestamps for time-aware models. They collect comment datasets for Reddit comments sentiment analysis or summarization, using `commentTimestamp` and `commentText` to prepare training and evaluation splits. *** ### How to Run It No code required. Here's how to get your first results in under 5 minutes: 1. **Open the actor on Apify**\ Go to the actor page in Apify Console: . 2. **Enter your inputs**\ Provide `postUrls` (required). Optionally set `maxComments`, `includeNestedReplies`, `sortBy`, and `maxConcurrentPosts`. 3. **Configure proxy settings (if needed)**\ Use the Apify proxy configuration options to improve reliability for larger scraping jobs. 4. **Start the run and watch the live log**\ The run logs show the progress per post and report extraction counts when successful. 5. **Open the Dataset tab**\ Comments records appear as the actor pushes `records` for each processed post. 6. **Export your results**\ Download your dataset from the Apify dashboard in your preferred format (JSON, CSV, or Excel). The whole setup takes under 5 minutes — results start appearing within seconds of launch. *** ### Export & Integration Options Once your data is collected, Reddit Comments Scraper fits directly into your existing workflow. Export your dataset from the Apify dashboard as JSON, CSV, or Excel. That makes it easy to build dashboards, perform manual review, or feed the results into your own analysis notebooks. If you automate reporting or pipelines, you can integrate via Apify’s API, webhooks, and no-code tools like Zapier/Make to move results into your CRM, storage, or analytics stack. For deeper integration details, use the Apify developer documentation. *** ### Pricing Reddit Comments Scraper runs on Apify, which includes a **free tier**—no credit card needed to start. You can use free credits to test runs (including different `sortBy` settings and nested reply collection) and then scale up as your dataset needs grow. When you move beyond the free tier, pricing is based on Apify compute usage (Actor compute units), with subscription options available for heavier workloads. There’s no per-row markup—so you pay for platform compute, not for each comment record returned. Start free at [apify.com](https://apify.com) — scale up when you need to. *** ### Reliability & Limitations | What We Handle | How | |---|---| | Rate limits & anti-bot friction | Uses a real browser session approach to stay robust on the platform. | | Proxy reliability | Supports proxy configuration for more consistent scraping runs. | | Post-level failures | Retries up to 4 attempts per post before giving up. | | Partial run recovery | If a post succeeds, its comment records are pushed to the dataset. | | Error handling | Unexpected response formats and non-200 fetch outcomes are logged. | | Scale within bounds | `maxConcurrentPosts` is capped by input constraints and memory needs. | Limitations: This actor works on public Reddit post pages. It can’t scrape login-gated/private content or scenarios where the platform blocks access completely for a given run. For enterprise-scale needs or custom configurations, reach out and we'll help. *** ### Frequently Asked Questions #### Is there a free plan? Yes, Apify offers a free tier with credits for trying out Reddit Comments Scraper. You can run small batches first (for example, a couple of `postUrls`) and validate the dataset schema before scaling. #### Do I need to log in or create an account on Reddit? No. The actor is designed to scrape publicly available Reddit post data without requiring you to log in through the actor. #### How accurate is the extracted data? The extracted fields come directly from the scraped Reddit content and metadata included in each comment record (for example `commentText`, `commentTimestamp`, and the thread position fields). Accuracy reflects what is publicly available on the page at the time of the scrape. #### How many results can I get per run? You control volume with `maxComments` (maximum number of comments extracted per post, counting nested replies too). For multiple `postUrls`, the total dataset size scales with the number of posts and the per-post cap. #### How fresh is the data? The actor stamps every record with `scrapedAt` in UTC ISO-8601 format. Freshness depends on when you run the actor, so schedule runs when you need up-to-date commentary. #### Is this legal? Does it comply with GDPR / CCPA? You should treat it as collecting **publicly available data** only. Whether your use complies with GDPR, CCPA, Reddit’s Terms of Service, and other applicable regulations is your responsibility; review your workflows and data handling practices accordingly. #### Can I export to Google Sheets or Excel? Yes. You can download the dataset from Apify (JSON, CSV, or Excel) and import it into Google Sheets or any spreadsheet tool you use. #### Can I schedule this to run automatically? Yes. You can schedule Apify actors to run automatically (for example, recurring collection for research). Configure scheduling in the Apify platform based on your needs. #### Can I access results via the API? Yes. Apify supports programmatic access to runs and datasets via the Apify API, so you can integrate Reddit Comments Scraper into your automation stack. #### What happens when the actor encounters an error? If a post fails to fetch or has an unexpected response format, the actor logs warnings and retries (up to 4 attempts) before moving on. Successful posts still push their extracted comment records to the dataset, so you can keep working with partial results. *** ### Get Help & Use Responsibly Got a question about Reddit Comments Scraper or a feature you'd like added? Reach out at . We welcome ideas like improving output usefulness for thread analysis and adding more dataset-friendly fields for research workflows—we actively maintain this actor based on user feedback. **Publicly available data:** This actor collects data only from **publicly available data** on Reddit. It does not access private accounts, login-gated pages, or password-protected content. You are responsible for complying with GDPR, CCPA, and applicable platform Terms of Service when using and storing the data. For data removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `postUrls` (type: `array`): One or more Reddit post URLs to scrape comments from. ## `maxComments` (type: `integer`): Maximum number of comments to extract per post (counts nested replies too). ## `includeNestedReplies` (type: `boolean`): When enabled, replies to comments are also extracted (the full thread tree). When disabled, only top-level comments are returned. ## `sortBy` (type: `string`): How Reddit should order the comments before they are collected. ## `maxConcurrentPosts` (type: `integer`): How many posts to scrape in parallel. Each post runs its own browser, so higher values need more memory. ## Actor input object example ```json { "postUrls": [ { "url": "https://www.reddit.com/r/AskMec/comments/14990m6/les_applications_de_rencontres_fonctionnent_telles/" } ], "maxComments": 500, "includeNestedReplies": true, "sortBy": "top", "maxConcurrentPosts": 2 } ``` # 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 = { "postUrls": [ { "url": "https://www.reddit.com/r/AskMec/comments/14990m6/les_applications_de_rencontres_fonctionnent_telles/" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapecraze/reddit-comments-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 = { "postUrls": [{ "url": "https://www.reddit.com/r/AskMec/comments/14990m6/les_applications_de_rencontres_fonctionnent_telles/" }] } # Run the Actor and wait for it to finish run = client.actor("scrapecraze/reddit-comments-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 '{ "postUrls": [ { "url": "https://www.reddit.com/r/AskMec/comments/14990m6/les_applications_de_rencontres_fonctionnent_telles/" } ] }' | apify call scrapecraze/reddit-comments-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapecraze/reddit-comments-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Comments Scraper", "description": "🔍 Reddit Comments Scraper pulls insightful Reddit comment threads fast—clean, structured data for sentiment, trend & community analysis. 🧠 Great for research, marketing insights, and competitive intelligence. 🚀 Easy to run, export-ready results.", "version": "0.2", "x-build-id": "IQz8mjKFczxwGIC9c" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapecraze~reddit-comments-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapecraze-reddit-comments-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapecraze~reddit-comments-scraper/runs": { "post": { "operationId": "runs-sync-scrapecraze-reddit-comments-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapecraze~reddit-comments-scraper/run-sync": { "post": { "operationId": "run-sync-scrapecraze-reddit-comments-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "postUrls" ], "properties": { "postUrls": { "title": "Reddit Post URLs", "type": "array", "description": "One or more Reddit post URLs to scrape comments from.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxComments": { "title": "Maximum Comments", "minimum": 1, "type": "integer", "description": "Maximum number of comments to extract per post (counts nested replies too).", "default": 500 }, "includeNestedReplies": { "title": "Include Nested Replies", "type": "boolean", "description": "When enabled, replies to comments are also extracted (the full thread tree). When disabled, only top-level comments are returned.", "default": true }, "sortBy": { "title": "Sort Comments By", "enum": [ "top", "best", "new", "controversial", "old", "qa" ], "type": "string", "description": "How Reddit should order the comments before they are collected.", "default": "top" }, "maxConcurrentPosts": { "title": "Maximum Concurrent Posts", "minimum": 1, "maximum": 10, "type": "integer", "description": "How many posts to scrape in parallel. Each post runs its own browser, so higher values need more memory.", "default": 2 } } }, "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 } } } } } } } } } } ```