# LinkedIn Posts Search Scraper | Social Listening (No Cookies) (`b2b_leads/linkedin-posts-search-scraper-social-listening-no-cookies`) Actor Search LinkedIn posts by keyword, URL, or profile. Export text, author, likes, comments & media as structured JSON. Built for social listening, lead generation, competitive intelligence, market research & LLM/AI workflows. Fast, lightweight — no browser, real-time results. - **URL**: https://apify.com/b2b\_leads/linkedin-posts-search-scraper-social-listening-no-cookies.md - **Developed by:** [Chidubem Aneke](https://apify.com/b2b_leads) (community) - **Categories:** Social media, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.97 / 1,000 posts This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 Posts Search Scraper **The fastest, lightest way to collect LinkedIn posts on Apify.** Turn any topic, profile, or post link into clean, structured JSON — ready for dashboards, alerts, research, and AI workflows. Results land in your dataset **in real time**, as each post is collected. Built for marketers, founders, sales teams, researchers, and builders who need **reliable LinkedIn post data at scale** — without heavy setups, slow runs, or messy exports. --- ### Why this Actor | | LinkedIn Posts Search Scraper | Typical post collector | |---|------------------------------|------------------------| | **Speed** | ~1–3 s per post | 5–15 s per post | | **Memory** | **256–512 MB** default | 2–4 GB+ | | **Setup** | Run in seconds — no login | Often requires accounts & maintenance | | **Cost** | Low compute, efficient runs | High | | **Output** | Structured JSON, LLM-ready | Often messy HTML | | **Scale** | Up to **1,000 posts** per keyword | Often capped lower | | **Filters** | Keyword, author, company, topic, date, sort | Often keyword-only | --- ### What you get — every post, fully structured Each record is one LinkedIn post with `featureType: "post"` and `scrapedAt` so you can sort, filter, and join with your own data. | Field | What it tells you | |-------|-------------------| | `text`, `authorName`, `authorHeadline`, `authorUrl` | Post content and who published it | | `numLikes`, `numComments`, `numShares`, `numReactions` | Engagement at a glance | | `createdAtISO`, `postUrl`, `urn` | When it was posted and direct links | | `comments[]` | Top comments — author, text, likes | | `mediaUrls[]`, `articleTitle`, `articleUrl` | Images, videos, and shared articles | | `searchQuery`, `sourceProfile` | Which search or profile produced this row | Set `includeRaw: true` if you need the extended source payload for custom processing. --- ### Three ways to collect posts #### By keyword (default) Search LinkedIn content the way you would on the platform: - **Keywords** — one search per keyword (e.g. `AI regulation`, `product launch`, `hiring freeze`) - **Location** — narrow by geography - **Author** — filter by person name - **Company** — posts from or about a company - **Topic** — combine with keywords for sharper results - **Date posted** — any time, past month, week, or 24 hours - **Sort** — relevance or most recent - **Volume** — up to **1,000 posts per keyword** #### By post URL Paste LinkedIn post or activity links and get structured data back instantly. #### By profile URL Load recent posts from any public profile's activity — great for tracking executives, competitors, or influencers. --- ### Use cases — real outcomes - **Social listening** — track brand mentions, product launches, and industry conversations - **Competitive intelligence** — monitor what competitors and leaders are saying - **Sales & BD** — spot buying signals, funding news, and hiring announcements in posts - **Content research** — study what topics and formats get engagement in your niche - **Influencer & executive tracking** — follow key profiles and export their latest posts - **PR & comms** — collect posts around a crisis, campaign, or news event - **AI assistants & agents** — summarize trends, draft replies, score sentiment, or build briefings - **Data warehouses & CRMs** — push JSON into Snowflake, BigQuery, HubSpot, Airtable, or your stack via API --- ### LLM & MCP integration Output is **structured JSON** — ideal for ChatGPT, Claude, Gemini, LangChain, LlamaIndex, CrewAI, and custom agents. No cleanup step. No HTML stripping. Just records your model can read. #### Recommended workflow 1. Run the Actor with your keywords, URLs, or profiles. 2. Pull dataset items via [Apify API](https://docs.apify.com/api/v2), webhook, or export JSON/CSV. 3. Pass records to your LLM, vector store, or automation — one post per row. #### Example: one post record for an LLM prompt ```json { "featureType": "post", "authorName": "Jane Doe", "authorHeadline": "VP Marketing at Acme", "authorUrl": "https://www.linkedin.com/in/janedoe", "text": "Excited to share that Acme just launched our new AI platform...", "numLikes": 342, "numComments": 48, "numShares": 12, "createdAtISO": "2026-07-06T10:00:00.000Z", "postUrl": "https://www.linkedin.com/feed/update/urn:li:activity:1234567890/", "comments": [ { "author": "John Smith", "text": "Congrats on the launch!", "likes": 5 } ], "searchQuery": "AI platform launch", "scrapedAt": "2026-07-08T12:00:00.000Z" } ```` #### Apify MCP (Model Context Protocol) Connect the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp) so AI assistants can: - **Run** this Actor from natural-language instructions - **Read** post results directly in the chat - **Chain** with other Actors — enrich contacts, send alerts, update spreadsheets **Example MCP conversation:** ``` User: "Find 30 posts about AI regulation from the past week and summarize top themes" → MCP runs LinkedIn Posts Search Scraper with postKeywords, postDatePosted=week → MCP reads dataset items → LLM summarizes trending topics and key voices ``` #### API quick start ```bash curl -X POST "https://api.apify.com/v2/acts/YOUR_ACTOR_ID/runs?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "postSearchMode": "keyword", "postKeywords": ["AI regulation"], "postDatePosted": "week", "postsPerQuery": 30, "includeComments": true }' ``` **Fetch results:** `GET https://api.apify.com/v2/datasets/{datasetId}/items?format=json` **Dataset views in Apify Console:** Overview (core fields) · Full details (includes media and traceability) Export as **JSON**, **CSV**, **Excel**, **RSS**, or via **API**. *** ### Input reference | Input | Type | Default | Description | |-------|------|---------|-------------| | `postSearchMode` | enum | `keyword` | `keyword`, `url`, or `profile` | | `postKeywords` | string\[] | `["AI regulation"]` | Search keywords — one run per keyword | | `postLocation` | string | `"United States"` | Geographic filter | | `postAuthor` | string | `""` | Author name filter | | `postCompany` | string | `""` | Company filter | | `postTopic` | string | `""` | Topic or theme filter | | `postDatePosted` | enum | `any` | any, month, week, day | | `postSort` | enum | `relevance` | relevance or date | | `postsPerQuery` | integer | `10` | Max posts per keyword (1–1000) | | `postUrls` | string\[] | example post URL | Post URLs (url mode) | | `activityProfiles` | string\[] | example profile URL | Profile URLs or handles (profile mode) | | `postsPerProfile` | integer | `10` | Max posts per profile (1–500) | | `includeComments` | boolean | `true` | Include top comments on each post | | `maxItems` | integer | `25` | Global cap on total posts | | `includeRaw` | boolean | `false` | Attach extended source payload | | `proxyConfiguration` | object | residential US | Connection settings (US residential recommended) | Full schema: **Input** tab on Apify Console or `.actor/input_schema.json` in this repository. *** ### Output reference Every dataset row is a single post. All rows include `featureType: "post"` and `scrapedAt`. | Field | Type | Description | |-------|------|-------------| | `featureType` | string | Always `"post"` | | `scrapedAt` | string | ISO timestamp when the row was collected | | `text` | string | null | Post body text | | `authorName` | string | null | Author display name | | `authorHeadline` | string | null | Author headline or role | | `authorUrl` | string | null | Author profile or company URL | | `authorType` | string | null | Author type when available | | `postUrl` | string | null | Direct link to the post | | `urn` | string | null | LinkedIn activity identifier | | `createdAtISO` | string | null | When the post was published | | `numLikes` | integer | null | Like count | | `numComments` | integer | null | Comment count | | `numShares` | integer | null | Share count | | `numReactions` | integer | null | Total reactions when available | | `mediaType` | string | null | Media type (e.g. image, video) | | `mediaUrls` | string\[] | Attached image or video URLs | | `articleTitle` | string | null | Linked article title | | `articleUrl` | string | null | Linked article URL | | `sharedContentUrl` | string | null | Shared link URL when present | | `comments` | object\[] | Top comments: `author`, `authorUrl`, `text`, `likes` | | `searchQuery` | string | null | Keyword that produced this row | | `sourceProfile` | string | null | Profile handle (profile mode) | | `raw` | object | Extended payload (when `includeRaw` is true) | Full schema: **Dataset** tab on Apify Console or `.actor/dataset_schema.json` in this repository. *** ### Quick start examples **Topic monitoring — past week** ```json { "postSearchMode": "keyword", "postKeywords": ["AI regulation", "EU AI Act"], "postDatePosted": "week", "postSort": "date", "postsPerQuery": 50, "includeComments": true } ``` **Posts from a specific company** ```json { "postSearchMode": "keyword", "postKeywords": ["product launch"], "postCompany": "Stripe", "postsPerQuery": 25 } ``` **Track an executive's recent posts** ```json { "postSearchMode": "profile", "activityProfiles": ["https://www.linkedin.com/in/janedoe"], "postsPerProfile": 30, "includeComments": false } ``` **Collect specific post URLs** ```json { "postSearchMode": "url", "postUrls": [ "https://www.linkedin.com/feed/update/urn:li:activity:1234567890/" ], "includeComments": true } ``` **High-volume industry scan** ```json { "postSearchMode": "keyword", "postKeywords": ["generative AI", "LLM", "machine learning"], "postLocation": "United States", "postsPerQuery": 500, "maxItems": 1500, "includeComments": true } ``` *** ### Performance & reliability - **Lightweight by design** — default **512 MB** memory handles large runs with multiple keywords. - **Fast collection** — posts stream into your dataset as they are found; no waiting until the end. - **Residential proxy (US recommended)** — best results and consistent throughput on LinkedIn. - **Smart coverage** — automatically widens search when you request large result sets. *** ### Limitations & compliance - This is an unofficial tool — not affiliated with LinkedIn. - Use responsibly and comply with applicable laws and LinkedIn's Terms of Service. - Some fields (engagement counts, comments) appear only when publicly visible. - Post availability can change; removed or restricted posts may not return data. *** ### Contact & custom work Need something beyond this Actor? I design and build **custom data solutions** and **full-stack products** for teams that want results — not experiments. **Email:** \ **GitHub:** [github.com/DrunkCodes](https://github.com/DrunkCodes) Reach out for: - **Custom Apify Actors** — any website, social platform, or API - **LinkedIn & social data projects** — listening pipelines, alerts, and dashboards at scale - **LLM & MCP integrations** — connect your data to ChatGPT, Claude, and agent workflows - **Web apps & automation** — data tools, internal platforms, SaaS products, and client-facing apps Whether you need a one-off data feed or a production system, I can help you ship it. *** *LinkedIn Posts Search Scraper · by [DrunkCodes](https://github.com/DrunkCodes)* # Actor input Schema ## `postSearchMode` (type: `string`): How you want to find posts: search by keyword, paste post links, or load from profile pages. ## `postKeywords` (type: `array`): Topics to search for — one run per keyword (e.g. "AI regulation", "product launch", "Series B funding"). ## `postLocation` (type: `string`): Geographic filter to focus results on a region or city. ## `postAuthor` (type: `string`): Filter by author name to find posts from a specific person. ## `postCompany` (type: `string`): Filter by company — finds posts published by or about that company. ## `postTopic` (type: `string`): Additional topic or theme to combine with your keywords for sharper results. ## `postDatePosted` (type: `string`): Only return posts published within this time window. ## `postSort` (type: `string`): Order results by relevance or most recent first. ## `postsPerQuery` (type: `integer`): Maximum posts to collect per keyword (1–1000). ## `postUrls` (type: `array`): LinkedIn post or activity links to collect directly. ## `activityProfiles` (type: `array`): LinkedIn profile URLs or handles — collects recent posts from each profile. ## `postsPerProfile` (type: `integer`): Maximum posts to collect per profile (1–500). ## `includeComments` (type: `boolean`): Add top comments to each post — author name, comment text, and like count. ## `maxItems` (type: `integer`): Global limit on how many posts to return across the entire run. ## `includeRaw` (type: `boolean`): Attach extended source data to each record. Larger output — useful for custom processing. ## `proxyConfiguration` (type: `object`): Residential US proxy recommended for best speed and consistent results on LinkedIn. ## Actor input object example ```json { "postSearchMode": "keyword", "postKeywords": [ "AI regulation" ], "postLocation": "United States", "postAuthor": "", "postCompany": "", "postTopic": "", "postDatePosted": "any", "postSort": "relevance", "postsPerQuery": 10, "postUrls": [ "https://www.linkedin.com/feed/update/urn:li:activity:7152578889167302656/" ], "activityProfiles": [ "https://www.linkedin.com/in/oliver-patel" ], "postsPerProfile": 10, "includeComments": true, "maxItems": 25, "includeRaw": false, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } } ``` # Actor output Schema ## `allResults` (type: `string`): Complete dataset with every post field from this run. ## `overview` (type: `string`): Core post fields — author, text, engagement, URL. ## `full` (type: `string`): All post fields including media and traceability. # 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 = { "postSearchMode": "keyword", "postKeywords": [ "AI regulation" ], "postLocation": "United States", "postAuthor": "", "postCompany": "", "postTopic": "", "postDatePosted": "any", "postSort": "relevance", "postsPerQuery": 10, "postUrls": [ "https://www.linkedin.com/feed/update/urn:li:activity:7152578889167302656/" ], "activityProfiles": [ "https://www.linkedin.com/in/oliver-patel" ], "postsPerProfile": 10, "includeComments": true, "maxItems": 25, "includeRaw": false, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } }; // Run the Actor and wait for it to finish const run = await client.actor("b2b_leads/linkedin-posts-search-scraper-social-listening-no-cookies").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 = { "postSearchMode": "keyword", "postKeywords": ["AI regulation"], "postLocation": "United States", "postAuthor": "", "postCompany": "", "postTopic": "", "postDatePosted": "any", "postSort": "relevance", "postsPerQuery": 10, "postUrls": ["https://www.linkedin.com/feed/update/urn:li:activity:7152578889167302656/"], "activityProfiles": ["https://www.linkedin.com/in/oliver-patel"], "postsPerProfile": 10, "includeComments": True, "maxItems": 25, "includeRaw": False, "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "US", }, } # Run the Actor and wait for it to finish run = client.actor("b2b_leads/linkedin-posts-search-scraper-social-listening-no-cookies").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 '{ "postSearchMode": "keyword", "postKeywords": [ "AI regulation" ], "postLocation": "United States", "postAuthor": "", "postCompany": "", "postTopic": "", "postDatePosted": "any", "postSort": "relevance", "postsPerQuery": 10, "postUrls": [ "https://www.linkedin.com/feed/update/urn:li:activity:7152578889167302656/" ], "activityProfiles": [ "https://www.linkedin.com/in/oliver-patel" ], "postsPerProfile": 10, "includeComments": true, "maxItems": 25, "includeRaw": false, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } }' | apify call b2b_leads/linkedin-posts-search-scraper-social-listening-no-cookies --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=b2b_leads/linkedin-posts-search-scraper-social-listening-no-cookies", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Posts Search Scraper | Social Listening (No Cookies)", "description": "Search LinkedIn posts by keyword, URL, or profile. Export text, author, likes, comments & media as structured JSON. Built for social listening, lead generation, competitive intelligence, market research & LLM/AI workflows. Fast, lightweight — no browser, real-time results.", "version": "0.0", "x-build-id": "mMqSPPgqbe3RlBJzI" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/b2b_leads~linkedin-posts-search-scraper-social-listening-no-cookies/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-b2b_leads-linkedin-posts-search-scraper-social-listening-no-cookies", "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/b2b_leads~linkedin-posts-search-scraper-social-listening-no-cookies/runs": { "post": { "operationId": "runs-sync-b2b_leads-linkedin-posts-search-scraper-social-listening-no-cookies", "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/b2b_leads~linkedin-posts-search-scraper-social-listening-no-cookies/run-sync": { "post": { "operationId": "run-sync-b2b_leads-linkedin-posts-search-scraper-social-listening-no-cookies", "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": { "postSearchMode": { "title": "Search mode", "enum": [ "url", "profile", "keyword" ], "type": "string", "description": "How you want to find posts: search by keyword, paste post links, or load from profile pages.", "default": "keyword" }, "postKeywords": { "title": "Keywords", "type": "array", "description": "Topics to search for — one run per keyword (e.g. \"AI regulation\", \"product launch\", \"Series B funding\").", "default": [ "AI regulation" ], "items": { "type": "string" } }, "postLocation": { "title": "Location", "type": "string", "description": "Geographic filter to focus results on a region or city.", "default": "United States" }, "postAuthor": { "title": "Post author", "type": "string", "description": "Filter by author name to find posts from a specific person.", "default": "" }, "postCompany": { "title": "Post company", "type": "string", "description": "Filter by company — finds posts published by or about that company.", "default": "" }, "postTopic": { "title": "Topic", "type": "string", "description": "Additional topic or theme to combine with your keywords for sharper results.", "default": "" }, "postDatePosted": { "title": "Date posted", "enum": [ "any", "month", "week", "day" ], "type": "string", "description": "Only return posts published within this time window.", "default": "any" }, "postSort": { "title": "Sort posts by", "enum": [ "relevance", "date" ], "type": "string", "description": "Order results by relevance or most recent first.", "default": "relevance" }, "postsPerQuery": { "title": "Posts per keyword", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum posts to collect per keyword (1–1000).", "default": 10 }, "postUrls": { "title": "Post URLs", "type": "array", "description": "LinkedIn post or activity links to collect directly.", "default": [ "https://www.linkedin.com/feed/update/urn:li:activity:7152578889167302656/" ], "items": { "type": "string" } }, "activityProfiles": { "title": "Profile URLs", "type": "array", "description": "LinkedIn profile URLs or handles — collects recent posts from each profile.", "default": [ "https://www.linkedin.com/in/oliver-patel" ], "items": { "type": "string" } }, "postsPerProfile": { "title": "Posts per profile", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum posts to collect per profile (1–500).", "default": 10 }, "includeComments": { "title": "Include top comments", "type": "boolean", "description": "Add top comments to each post — author name, comment text, and like count.", "default": true }, "maxItems": { "title": "Max total posts", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Global limit on how many posts to return across the entire run.", "default": 25 }, "includeRaw": { "title": "Include raw data", "type": "boolean", "description": "Attach extended source data to each record. Larger output — useful for custom processing.", "default": false }, "proxyConfiguration": { "title": "Proxy settings", "type": "object", "description": "Residential US proxy recommended for best speed and consistent results on LinkedIn.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } } } }, "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 } } } } } } } } } } ```