# LinkedIn Post Scraper (`scrapeengine/linkedin-post-scraper`) Actor ๐Ÿ“ LinkedIn Post Scraper (linkedin-post-scraper) extracts post text, author, date, reactions, comments, shares, media, links & hashtags. ๐Ÿ” Ideal for content research, social listening, competitor analysis & lead gen. ๐Ÿ“Š Fast, scalable, API-ready JSON/CSV exports. ๐Ÿš€ - **URL**: https://apify.com/scrapeengine/linkedin-post-scraper.md - **Developed by:** [ScrapeEngine](https://apify.com/scrapeengine) (community) - **Categories:** Automation, Lead generation, Social media - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $19.99/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 ### LinkedIn Post Scraper LinkedIn Post Scraper is a fast, scalable Apify actor that collects public LinkedIn posts from company and personal profile URLs. It solves the manual, repetitive work of capturing post text, author details, timestamps, engagement, images, comments, and reactions โ€” exporting clean, structured results ready for analysis or automation. Built for marketers, developers, data analysts, and researchers, it enables content research, social listening, competitor tracking, and API-ready workflows at scale. ๐Ÿš€ ### What data / output can you get? Here are the key fields the actor saves for each post in the dataset and in the linkedin_posts.json snapshot. Example values illustrate structure. - Data field | Description | Example value - urn | LinkedIn URN for the post (detected from URL or hashed) | urn:li:activity:7191234567890 - text | Full post text content | โ€œWeโ€™re excited to unveil our latest AI researchโ€ฆโ€ - url | Canonical URL of the post | https://www.linkedin.com/feed/update/urn:li:activity:7191234567890 - postedAtTimestamp | Post time in milliseconds since epoch | 1778862600000 - postedAtISO | ISO timestamp of post time | 2026-03-15T08:30:00.000Z - timeSincePosted | Relative time descriptor | 3d - authorType | Author category | Company - authorProfileUrl | Source profile URL scraped | https://www.linkedin.com/company/google/ - authorFullName | Display name of the author | Google - authorHeadline | Author description/headline (if available) | โ€œOur mission is to organize the worldโ€™s informationโ€ฆโ€ - type | Post type derived from media presence | image - images | Array of post image URLs | ["https://media.licdn.com/dms/image/.../feedshare-image-1.png"] Bonus fields and objects: - image (primary image URL), author (nested profile object), attributes (PROFILE_MENTION entities), comments (with author object, text, link, time), reactions (with reactor profile and type) - numLikes, numComments, numShares (numShares defaults to 0) - commentsTruncated, commentsComplete, reactionsTruncated - canReact, canPostComments, canShare, commentingDisabled, allowedCommentersScope, rootShare, shareAudience - rawHtml (included only when rawData is true) Exports: - Dataset: export individual post records as JSON, CSV, or Excel via Apify - Key-value store: full array mirror saved to linkedin_posts.json ### Key features - โšก Batch collection per source URL Gather up to your specified limitPerSource posts per LinkedIn company or person profile in one run โ€” ideal for monitoring and reporting. - ๐Ÿ” Deep scraping mode Enable deepScrape to follow additional activity links and enrich the dataset with more posts and contextual signals (comments, reactions, images). - ๐Ÿ“… Date-based filtering Use scrapeUntil to include only posts from a specific date onwards, keeping your output timely and relevant. - ๐Ÿ“ฆ Raw data option (advanced) Turn on rawData to include rawHtml in each post object for custom parsing or auditing needs. - ๐ŸŒ Proxy-ready reliability Configure proxyConfiguration in the Apify UI for better stability and to reduce rate limiting. - ๐Ÿ’พ Flexible exports Download results as JSON, CSV, or Excel from the Apify dataset, with a complete array mirror saved to linkedin_posts.json. - ๐Ÿ’ป Developer-friendly and API-ready Built on the Apify platform โ€” trigger runs programmatically, stream results via the Dataset API, and wire into n8n or Make.com workflows. - ๐Ÿ›ก๏ธ Production-focused Stable HTTP session with retries and backoff, plus optional proxy use for resilient scraping on public LinkedIn pages. ### How to use LinkedIn Post Scraper - step by step 1. Sign in to your Apify account. 2. Open the โ€œLinkedIn Post Scraperโ€ actor. 3. In the Input tab, paste one or more LinkedIn company or personal profile URLs into urls. 4. Set limitPerSource to control how many posts to collect per URL. 5. (Optional) Set scrapeUntil to only include posts from that date onward (YYYY-MM-DD). 6. Choose whether to enable deepScrape for richer data and rawData to include rawHtml. 7. (Optional) Configure proxyConfiguration in the Apify UI for higher reliability and throughput. 8. Click Start to run the scraper. Progress logs will appear in real time. 9. When finished, open the Dataset to preview and export results in JSON/CSV/Excel. 10. Download the complete post array from the Key-value store as linkedin_posts.json. Pro Tip: Trigger runs and pull datasets via the Apify API, then connect to n8n or Make.com for hands-free reporting and enrichment. ### Use cases - Use case | Description - Marketing + Social listening | Monitor competitorsโ€™ posts and engagement to inform content strategy and campaign timing. - Content research for teams | Aggregate post text and images to extract themes and winning formats across brands or authors. - Sales + Lead intelligence | Track executive or company posts to spot product signals and market moves. - Recruitment branding analysis | Analyze company updates and engagement over time to benchmark employer brand activity. - Academic & policy research | Build corpora of public LinkedIn posts with timestamps and authors for longitudinal studies. - Data engineering pipeline | Feed structured post data into analytics stacks via Apify API and schedule automated runs. ### Why choose LinkedIn Post Scraper? Purpose-built for public LinkedIn pages, this actor emphasizes precision, reliability, and automation-ready outputs. - โœ… Accurate structured output: Captures post text, timestamps, author details, images, comments, and reactions. - ๐ŸŒ Scales across profiles: Set per-source limits and run on many company/person URLs in one job. - ๐Ÿ”Œ API & workflow friendly: Export to JSON/CSV/Excel and integrate with n8n or Make.com. - ๐Ÿ›ก๏ธ Safe & robust: Works on public pages with retry/backoff and optional proxy support. - ๐Ÿ’ฐ Cost-efficient control: Tune depth and limits to match your budget and data needs. - ๐Ÿงฐ Developer-first: Clean JSON schema with a run-level snapshot in linkedin_posts.json for reproducibility. Bottom line: A production-ready alternative to fragile extensions, designed for consistent outputs and automation. ### Is it legal / ethical to use LinkedIn Post Scraper? Yes โ€” when used responsibly. This actor accesses publicly available LinkedIn pages and does not log in or access private data. Guidelines for compliant use: - Scrape only public data youโ€™re permitted to process. - Follow applicable data protection laws (e.g., GDPR, CCPA). - Respect LinkedInโ€™s terms and avoid abusive request patterns. - Do not use scraped content for spam or misuse. - Consult your legal team for edge cases or regulated use. ### Input parameters & output format #### Example JSON input ```json { "urls": ["https://www.linkedin.com/company/google/"], "limitPerSource": 10, "scrapeUntil": "2026-03-01", "deepScrape": true, "rawData": false, "proxyConfiguration": { "useApifyProxy": true } } ```` #### Input fields - Field: urls - Type: array - Description: Add LinkedIn company or person profile URLs to scrape โ€” one or many! - Default: โ€” - Required: Yes - Field: limitPerSource - Type: integer - Description: How many posts to collect from each URL. More = more data! - Default: 10 - Required: No - Field: scrapeUntil - Type: string - Description: Filter posts โ€” only include content from this date onwards. Pick from calendar! - Default: null - Required: No - Field: deepScrape - Type: boolean - Description: Get richer data โ€” more metadata & engagement details. Recommended! - Default: true - Required: No - Field: rawData - Type: boolean - Description: For power users โ€” include extra raw data in output. Turn off for cleaner results. - Default: false - Required: No - Field: proxyConfiguration - Type: object - Description: Optional โ€” use proxy for better reliability & to avoid rate limits. - Default: null - Required: No Notes: - urls must be non-empty; the run will end early if none are provided. - scrapeUntil is applied as a timestamp cutoff; posts older than the date are excluded. - rawData=true adds a rawHtml field to each output item. #### Example JSON output ```json { "urn": "urn:li:activity:7191234567890", "text": "Weโ€™re excited to unveil our latest AI research. Read more below!", "url": "https://www.linkedin.com/feed/update/urn:li:activity:7191234567890", "postedAtTimestamp": 1778862600000, "postedAtISO": "2026-03-15T08:30:00.000Z", "timeSincePosted": "3d", "isRepost": false, "authorType": "Company", "authorProfileUrl": "https://www.linkedin.com/company/google/", "authorProfileId": "google", "authorHeadline": "Our mission is to organize the worldโ€™s information and make it universally accessible and useful.", "authorFullName": "Google", "image": "https://media.licdn.com/dms/image/.../feedshare-image-1.png", "type": "image", "images": [ "https://media.licdn.com/dms/image/.../feedshare-image-1.png", "https://media.licdn.com/dms/image/.../feedshare-image-2.png" ], "author": { "firstName": null, "lastName": null, "occupation": "Our mission is to organize the worldโ€™s information and make it universally accessible and useful.", "id": "google", "publicId": "google", "trackingId": "Q29DgqS1Vq9W5oQkZB1yHA", "profileId": "google", "picture": "https://media.licdn.com/dms/image/.../logo.png", "backgroundImage": "" }, "authorName": "Google", "authorTitle": "Our mission is to organize the worldโ€™s information and make it universally accessible and useful.", "attributes": [ { "start": 0, "length": 7, "type": "PROFILE_MENTION", "profile": { "firstName": "openai", "lastName": "", "occupation": "", "id": "user-0", "publicId": "openai", "trackingId": "WsT2b8hQ9z1lV3nPq6YqVw", "profileId": "user-0", "picture": "", "backgroundImage": "" } } ], "comments": [ { "time": 1778949000000, "link": "https://www.linkedin.com/feed/update/urn:li:activity:7191234567890", "text": "Congrats on the launch!", "entities": [], "pinned": false, "originalLanguage": "English", "author": { "firstName": "Jane", "lastName": "Doe", "occupation": "", "id": "commenter-a1b2c3d4e5f6", "publicId": "jane-doe", "trackingId": "XyZ1nOaB7pQ6lMdRtUeFrw", "profileId": "commenter-a1b2c3d4e5f6", "picture": "", "backgroundImage": "", "distance": "OUT_OF_NETWORK" } } ], "reactions": [ { "type": "LIKE", "profile": { "firstName": "John", "lastName": "Smith", "occupation": "", "id": "reactor-0f9e8d7c6b5a", "publicId": "john-smith-12345", "trackingId": "R2F0eSdUd1BqTnN5QXhZdw", "profileId": "ACoAA0f9e8d7c", "picture": "", "backgroundImage": "" } } ], "numShares": 0, "numLikes": 128, "numComments": 12, "commentsTruncated": true, "commentsComplete": false, "reactionsTruncated": false, "canReact": true, "canPostComments": true, "canShare": true, "commentingDisabled": false, "allowedCommentersScope": "ALL", "rootShare": true, "shareAudience": "PUBLIC" } ``` Notes: - authorFullName may be โ€œUnknownโ€ if not found in page metadata. - postedAtTimestamp/postedAtISO may be 0/empty when the date is unavailable. - numShares defaults to 0 when share counts arenโ€™t present in the HTML. - rawHtml appears only when rawData is true. Outputs are written to: - Dataset: individual post records (export as JSON/CSV/Excel) - Key-value store: linkedin\_posts.json containing the full array ### FAQ #### Do I need to log in or provide cookies to use this actor? No. The LinkedIn Post Scraper works on publicly available LinkedIn pages without authentication. It uses standard HTTP requests with retry/backoff and optional proxy support. #### What types of LinkedIn URLs are supported? Company and person profile pages, as well as feed URLs, are supported as sources. Unsupported URL types (e.g., direct post, school, group) are skipped and not included in the final dataset. #### How many posts can I collect per source? You control this with limitPerSource. Set it to the number of posts you want per URL, and the actor will collect up to that amount, subject to availability and page content. #### How does the date filter work? If you set scrapeUntil (YYYY-MM-DD), the actor computes a timestamp cutoff and includes only posts at or after that date. Posts older than the cutoff are excluded from the output. #### What engagement data is included? The actor extracts likes and comments counts when detectable from the page HTML, plus parsed comments and reactions lists. numShares is included and defaults to 0 when not found. #### Can I export the results to CSV or Excel? Yes. Open the runโ€™s Dataset in Apify and export in JSON, CSV, or Excel. A complete JSON array is also saved to the Key-value store as linkedin\_posts.json. #### Is there a way to include the raw HTML? Yes. Set rawData to true. Each post in the dataset will include a rawHtml field containing the page snapshot used for parsing. #### Does it support proxies and automation tools? Yes. Configure proxyConfiguration in the Apify input UI for reliable scraping, and integrate outputs with automation platforms like n8n and Make.com via the Apify API. ### Closing CTA / Final thoughts The LinkedIn Post Scraper is built for structured, reliable extraction of public LinkedIn posts at scale. It delivers clean JSON suitable for analytics, enrichment, and automated workflows. Set your per-source limits, optionally enable deepScrape and rawData, and export to JSON/CSV/Excel for use by marketing teams, analysts, developers, and researchers. Developers can automate runs via the Apify API and wire outputs into n8n or Make.com. Start extracting smarter LinkedIn insights today โ€” from public posts to structured datasets, ready for your next analysis or pipeline. # Actor input Schema ## `urls` (type: `array`): ๐Ÿ“Ž Add LinkedIn company or person profile URLs to scrape โ€” one or many! ## `limitPerSource` (type: `integer`): ๐ŸŽฏ How many posts to collect from each URL. More = more data! ## `scrapeUntil` (type: `string`): ๐Ÿ—“๏ธ Filter posts โ€” only include content from this date onwards. Pick from calendar! ## `deepScrape` (type: `boolean`): โœจ Get richer data โ€” more metadata & engagement details. Recommended! ๐Ÿ‘ ## `rawData` (type: `boolean`): ๐Ÿ”ง For power users โ€” include extra raw data in output. Turn off for cleaner results. ## `proxyConfiguration` (type: `object`): ๐Ÿ›ก๏ธ Optional โ€” use proxy for better reliability & to avoid rate limits. ## Actor input object example ```json { "urls": [ "https://www.linkedin.com/company/google/" ], "limitPerSource": 10, "deepScrape": true, "rawData": false } ``` # 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 = { "urls": [ "https://www.linkedin.com/company/google/" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapeengine/linkedin-post-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "urls": ["https://www.linkedin.com/company/google/"] } # Run the Actor and wait for it to finish run = client.actor("scrapeengine/linkedin-post-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "urls": [ "https://www.linkedin.com/company/google/" ] }' | apify call scrapeengine/linkedin-post-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapeengine/linkedin-post-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Post Scraper", "description": "๐Ÿ“ LinkedIn Post Scraper (linkedin-post-scraper) extracts post text, author, date, reactions, comments, shares, media, links & hashtags. ๐Ÿ” Ideal for content research, social listening, competitor analysis & lead gen. ๐Ÿ“Š Fast, scalable, API-ready JSON/CSV exports. ๐Ÿš€", "version": "0.2", "x-build-id": "qeaMHJCgYFVtY1rwU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapeengine~linkedin-post-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapeengine-linkedin-post-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapeengine~linkedin-post-scraper/runs": { "post": { "operationId": "runs-sync-scrapeengine-linkedin-post-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapeengine~linkedin-post-scraper/run-sync": { "post": { "operationId": "run-sync-scrapeengine-linkedin-post-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "urls" ], "properties": { "urls": { "title": "๐Ÿ”— Source URLs (required)", "type": "array", "description": "๐Ÿ“Ž Add LinkedIn company or person profile URLs to scrape โ€” one or many!", "items": { "type": "string" } }, "limitPerSource": { "title": "๐Ÿ“Š Limit per source", "minimum": 1, "type": "integer", "description": "๐ŸŽฏ How many posts to collect from each URL. More = more data!", "default": 10 }, "scrapeUntil": { "title": "๐Ÿ“… Scrape until date", "pattern": "^(\\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[12]\\d|3[01])$", "type": "string", "description": "๐Ÿ—“๏ธ Filter posts โ€” only include content from this date onwards. Pick from calendar!" }, "deepScrape": { "title": "๐Ÿ” Scrape additional information", "type": "boolean", "description": "โœจ Get richer data โ€” more metadata & engagement details. Recommended! ๐Ÿ‘", "default": true }, "rawData": { "title": "๐Ÿ“ฆ Get raw data (Advanced)", "type": "boolean", "description": "๐Ÿ”ง For power users โ€” include extra raw data in output. Turn off for cleaner results.", "default": false }, "proxyConfiguration": { "title": "๐ŸŒ Proxy configuration", "type": "object", "description": "๐Ÿ›ก๏ธ Optional โ€” use proxy for better reliability & to avoid rate limits." } } }, "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 } } } } } } } } } } ```