# LinkedIn Profile Scraper ✅ NO COOKIES (`atomus/linkedin-profile-scraper`) Actor Scrape LinkedIn profiles into structured records: name, title, headline, summary, company, location, skills, education, and work history. Submit up to 5,000 URLs per run, each returns one record. Pay only for profiles found; not-found and errors are free. No account or cookies needed. - **URL**: https://apify.com/atomus/linkedin-profile-scraper.md - **Developed by:** [Atomus APIs](https://apify.com/atomus) (community) - **Categories:** Lead generation, Social media, Automation - **Features:** Limited permissions - **Stats:** 935 total users, 123 monthly users, 100.0% runs succeeded, 15 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $6.00 / 1,000 profile enricheds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event ## What's an Apify Actor? An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes. 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. Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md. ## 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. Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run. For examples already wired to this Actor's own input schema, see the [API](#api) section below. Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`). # README ## LinkedIn Profile Scraper
LINKEDIN PROFILE SCRAPER  •  LINKEDIN PROFILE API

Know who they are
before you reach out.

Turn any LinkedIn profile URL into a structured record: name, job title, seniority, skills, work history, and the company's size, tech stack and IT spend. Up to 5,000 URLs per run, one clean JSON row each. No LinkedIn account, no login, no cookies, no ban risk.

PAY ONLY FOR PROFILES FOUND  186,000+ PROFILES ENRICHED 
#### Copy to your AI assistant Paste this into ChatGPT, Claude, Cursor, or any LLM to start using this Actor right away. ``` atomus/linkedin-profile-scraper is an Apify Actor that turns LinkedIn profile URLs into structured JSON: name, title, seniority, skills, work history, and the current company's headcount, tech stack and IT spend. Use it whenever someone needs LinkedIn profile data without cookies, a login, or a browser. Run it with curl: curl -X POST "https://api.apify.com/v2/acts/atomus~linkedin-profile-scraper/run-sync-get-dataset-items?token=APIFY_TOKEN" -H "Content-Type: application/json" -d '{"profileUrls":["https://www.linkedin.com/in/satyanadella/"]}'. Or in Python: ApifyClient("APIFY_TOKEN").actor("atomus/linkedin-profile-scraper").call(run_input={"profileUrls":[...]}) then client.dataset(run["defaultDatasetId"]).list_items().items. Each row has status ("success" | "not_found" | "error") and a nested profile object; you are charged only for profiles actually found. Only canonical /in/ URLs work, not the internal member-id form (/in/ACoAA..., /in/ACwAA... and the rest of that family). Full input schema, every enum and default, and the complete output field list: GET https://api.apify.com/v2/acts/atomus~linkedin-profile-scraper/build/default ``` *** ### LinkedIn MCP Server: use these Actors from ChatGPT, Claude or Cursor Point your AI assistant at Atomus and it can read LinkedIn on its own: profiles, posts, reactions, comments, companies and their employees. No glue code, no scraping logic in your prompts. ```json { "mcpServers": { "atomus": { "url": "https://mcp.apify.com?tools=atomus/linkedin-profile-scraper,atomus/linkedin-posts-scraper-pro,atomus/linkedin-reactions-scraper-pro,atomus/linkedin-comments-scraper-pro,atomus/linkedin-company-scraper,atomus/linkedin-company-employees,atomus/leads-finder", "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" } } } } ``` Then ask, in plain language: > "Find 50 heads of engineering at Series B fintechs in Berlin, pull their full LinkedIn profiles, then show me which ones posted in the last month." That one sentence uses three Actors in a row. Pinning the `tools=` list is what keeps your assistant on these Actors instead of reaching for whatever scraper it finds first. *** ### How to scrape LinkedIn profiles #### One profile ```json { "profileUrls": ["https://www.linkedin.com/in/satyanadella/"] } ``` Returns one row: identity, title, seniority, skills, work history, and the current company. #### Bulk: enrich a CRM export ```json { "profileUrls": [ "https://www.linkedin.com/in/satyanadella/", "https://www.linkedin.com/in/williamhgates/", "https://www.linkedin.com/in/reidhoffman/" ] } ``` Up to 5,000 URLs per run. Output maps 1:1 to your input by URL, so rejoining in a spreadsheet or database is a straight lookup. #### With full company firmographics ```json { "profileUrls": ["https://www.linkedin.com/in/satyanadella/"], "includeCompanyDetails": true } ``` Adds funding rounds, every office location, the full tech stack, industries, NAICS codes and revenue band for each profile's current company. Charged as an extra event only on profiles that actually return a company. #### Qualify before you spend Not-found and error rows cost nothing, so a messy list is safe to throw at it. Filter afterwards on `profile.department.seniority` (`c_suite`, `vp`, `director`) and `profile.company.staff.total` to keep only the people who match your ICP. *** ### Input | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `profileUrls` | string\[] | ✅ Yes | (none) | LinkedIn profile URLs in the canonical `https://www.linkedin.com/in//` form. Up to 5,000 per run. Trailing slash optional; tracking parameters are fine. | | `includeCompanyDetails` | boolean | No | `false` | Adds the full firmographic record for each profile's current company. Charged as an extra event per profile that returns a company. | #### Which URLs work ``` ✅ https://www.linkedin.com/in/satyanadella/ ✅ https://www.linkedin.com/in/satyanadella ✅ https://www.linkedin.com/in/satyanadella/?utm_source=share ❌ https://www.linkedin.com/in/ACoAAAR-kOEBwpv4Kg1xD8fRWSax1kPNYjX2W88 ❌ urn:li:member:123456789 ``` That `ACoAA...` string is an internal LinkedIn member ID, not a public handle (they also start `ACwAA`, `ACgAA` and so on). The data source cannot resolve it and this Actor cannot turn it back into a handle, so those rows return `error` and are **never charged**. Open the profile in a browser and copy the URL from the address bar to get the usable form. > **Piping from our other scrapers?** LinkedIn's reactions, comments and post-engagement lists return profile URLs in that encoded form, so the output of the Reactions / Comments / Posts scrapers is **not** directly enrichable here. You need the vanity URL first. *** ### What data does the LinkedIn Profile Scraper return? Every row carries a `status` (`success` | `not_found` | `error`). On `success`, everything below sits under `profile`. | Group | Fields | |---|---| | **Identity** | `identifier` (the `/in/` handle) · `first_name` · `last_name` · `full_name` · `title` · `headline` · `summary` · `picture_url` · `background_url` · `birth_date` · `industry` · `last_updated` | | **Location & locale** | `location.default` · `location.short` · `location.country` · `location.state` · `location.city` · `languages.primary_locale` · `languages.supported_locales` | | **ICP signals** | `department.seniority` (`c_suite`, `vp`, `director`, `manager`, …) · `department.functions` · `department.departments` · `department.sub_departments` · badges: `open_to_work`, `hiring`, `premium`, `creator`, `verified`, `influencer` | | **Career** | `skills` · `educations` (`school{id,name,logo,url}`, `degree_name`, `field_of_study`, `grade`, `date{start,end}`) · `position_groups` (`company{id,name,logo,url,employees}`, `date{start,end}`, `profile_positions[{company,title,description,employment_type,location,date}]`) | | **Certifications** | `certifications[]`: `name` · `authority` (the issuer) · `url` · `license_number` · `display_source` · `company{id,name,logo,url}` · `date{start,end}` (issued, expires) | | **Volunteering & organizations** | `volunteer_experiences[]`: `role` · `company` · `cause` · `description` · `date{start,end}` · `organizations[]`: `name` · `position` · `date{start,end}` | | **Network & tenure** | `statistics.network.followers_count` · `statistics.network.connections_count` · `statistics.network.influencer` · `statistics.experience.tenure.start` (career start) · `tenure.company` (joined the current company) · `tenure.title` (in the current role since) · `statistics.experience.prev_job_titles` | | **Other profiles** | `links.linkedin` · `links.twitter` · `links.github` · `links.facebook` | | **Current company** | `company.name` · `legal_name` · `description` · `overview` · `founded_year` · `type` (`PUBLIC_COMPANY`, `PRIVATELY_HELD`, `NON_PROFIT`, …) · `industry` · `industries` · `staff.total` · `staff.range_start` · `website` · `domain` · `linkedin_url` · `twitter_url` · `facebook_url` · `crunchbase_url` · `headquarter{country,state,city,street,postal_code,raw_address,position{lng,lat}}` · `technologies[{name,category}]` · `keywords` · `naics` · `revenue_amount` · `funding_total` · `ipo_date` · `it_spend` | | **Provenance** | `_metadata.extracted_at` (ISO timestamp of the lookup) | `it_spend` is an estimated annual IT budget in USD (Aberdeen data), which is what makes this useful for B2B SaaS targeting. `last_updated` tells you when the upstream source refreshed the profile, so you always know how stale a record is. A profile that lists no certifications, volunteering or organizations comes back with an empty list for that block, and `statistics` is `null` when the source has no network figures for the person. When they are filled, the blocks look like this: ```json { "certifications": [ { "name": "English proficiency: Intermediate", "authority": "Duolingo", "url": "https://testcenter.duolingo.com", "company": { "name": "Duolingo", "logo": "https://img.atomusapi.dev/i/", "url": "https://www.linkedin.com/company/duolingo" }, "date": { "start": "2016-06-01", "end": null } } ], "volunteer_experiences": [ { "role": "Youth leader", "cause": "SOCIAL_SERVICES", "description": null, "date": { "start": "1985-01-01", "end": "2000-12-31" } } ], "organizations": [ { "name": "BNI", "position": "Event Coordinator", "date": { "start": "2015-01-01", "end": null } } ], "statistics": { "network": { "followers_count": 5430, "connections_count": 1200, "influencer": false }, "experience": { "tenure": { "start": "1990-11-01", "company": "1991-08-01", "title": "1991-08-01" }, "prev_job_titles": null } } } ``` #### Example row ```json { "status": "success", "input_url": "https://www.linkedin.com/in/satyanadella/", "profile": { "identifier": "satyanadella", "full_name": "Satya Nadella", "title": "Chairman and CEO", "headline": "Chairman and CEO at Microsoft", "industry": "Computer Software", "location": { "short": "Redmond, Washington", "country": "United States", "city": "Redmond" }, "department": { "seniority": "c_suite", "departments": ["c_suite"], "functions": ["executive"] }, "badges": { "verified": true, "premium": true, "open_to_work": false, "hiring": false }, "skills": ["Cloud Computing", "Enterprise Software", "Leadership"], "links": { "linkedin": "https://www.linkedin.com/in/satyanadella", "twitter": null }, "position_groups": [ { "company": { "name": "Microsoft", "employees": 238000 }, "date": { "start": "1992", "end": null } } ], "company": { "name": "Microsoft", "legal_name": "Microsoft Corporation", "domain": "microsoft.com", "industry": "Software Development", "founded_year": 1975, "type": "PUBLIC_COMPANY", "staff": { "total": 238000 }, "headquarter": { "city": "Redmond", "state": "Washington", "country": "US" }, "technologies": [{ "name": "Azure", "category": "cloud" }], "it_spend": 4200000000, "ipo_date": "1986-03-13" }, "last_updated": "2026-08-14" }, "_metadata": { "extracted_at": "2026-09-03T14:02:11.000Z" } } ``` `not_found` and `error` rows carry the same `url` you sent plus a `reason` saying what happened, and no `profile` object. `error` rows additionally carry an `error_kind` (`user_error` when the input cannot work, `actor_error` when the lookup itself failed). An `error_kind` of `provider_unavailable` means the data provider was temporarily unavailable: that row was not charged, so run that URL again later. #### With `includeCompanyDetails`: the `company_details` object `profile.company` above already carries the firmographics most people filter on. Turning on `includeCompanyDetails` adds a **separate top-level `company_details`** object with the source's complete record for that person's current company: the same depth our [LinkedIn Company Scraper](https://apify.com/atomus/linkedin-company-scraper) returns, without a second run. | Group | Fields | |---|---| | **Identity** | `company_details.id` · `summary.name` · `legal_name` · `description` · `overview` · `founded_year` · `type` · `industry` · `summary.logo.source` | | **Size** | `summary.staff.total` · `summary.staff.range.start` | | **Links** | `link.website` · `domain` · `domain_ltd` · `linkedin` · `twitter` · `facebook` · `crunchbase` | | **Revenue & IT budget** | `financial.revenue.annual{amount,start,end}` (a band, not a point estimate) · `financial.aberdeen.it_spend` | | **Funding, every round** | `financial.funding{total_amount,num_funding_rounds,num_investor,type,date,last_amount}` · `financial.funding.rounds[{announced_at,type,investors[]}]` | | **Other financial events** | `financial.ipo{date,share_price,link}` · `financial.acquisition` · `financial.investment` · `financial.exit` · `financial.diversity_investment` (each `{total,contents[]}`) | | **Every office** | `location.headquarter{continent,country,state,city,street,postal_code,raw_address,position{lat,lng}}` · `location.locations[]` (the same shape, one entry per office worldwide) | | **Tech & classification** | `technologies[{name,category}]` (the full stack) · `industries[]` · `keywords[]` · `naics[]` | | **Company graph & reach** | `sub_organizations[]` · `languages[]` · `hashtags[]` | | **Freshness** | `company_details.last_updated` | Measured on Microsoft (live response, 2026-09-04): 45 offices in `location.locations`, 15 technologies, 11 industries, 23 keywords, 3 NAICS codes, 3 funding rounds and 6 sub-organizations. Smaller companies carry proportionally less, and any field the source has nothing for is simply absent. ```json { "status": "success", "input_url": "https://www.linkedin.com/in/satyanadella/", "profile": { "full_name": "Satya Nadella", "…": "as above" }, "company_details": { "summary": { "name": "Microsoft", "legal_name": "Microsoft Corporation", "type": "PUBLIC_COMPANY", "industry": "software development", "founded_year": 1975, "staff": { "total": 229769, "range": { "start": 10001 } } }, "link": { "website": "https://www.microsoft.com", "domain": "microsoft.com" }, "financial": { "revenue": { "amount": "10000000000-9223372036854775807", "start": 10000000000 }, "funding": { "num_funding_rounds": 3, "rounds": [{ "announced_at": "2022-12-09", "type": "POST_IPO_EQUITY", "investors": ["KB Securities"] }] }, "ipo": { "date": "1986-03-13" }, "aberdeen": { "it_spend": 72571566964 } }, "location": { "headquarter": { "city": "Redmond", "state": "Washington", "country": "United States" }, "locations": [ { "city": "Bratislava", "country": "Slovakia", "raw_address": "Pradiareň 1900, Svätoplukova 2A, Bratislava, Slovakia, Europe", "position": { "lat": 48.188267, "lng": 17.113459 } } ] }, "technologies": [{ "name": "organization schema", "category": "framework" }], "industries": ["agentic ai", "identity management", "cloud security"], "keywords": ["business software", "developer tools"], "naics": ["541511", "423430", "541512"], "languages": ["english", "chinese", "korean"], "hashtags": ["microsoftlife", "microsoft"], "last_updated": "2026-08-14" } } ``` `rounds`, `locations`, `technologies` and `sub_organizations` are trimmed above for readability; the live record carries all of them. **When you are charged:** only on a `success` row that actually returns a company. A profile with no current company gets the normal profile charge and no `company_details` key at all, so you never pay for an empty object. `not_found` and `error` rows stay free either way. *** ### How much does it cost to scrape LinkedIn profiles? **$0.006 per profile found** ($6.00 per 1,000). Pay-per-event: you are charged only when a profile comes back. | Profiles scraped | Cost | |-------------------|------| | 100 | $0.60 | | 1,000 | $6.00 | | 5,000 | $30.00 | **Free:** `not_found` rows, `error` rows, and Apify platform compute. `includeCompanyDetails` adds $3.50 per 1,000 companies returned ($2.80 on Apify Gold+), charged only on profiles that actually return a company. **Free plan:** 10 profiles per calendar month, so you can check the output shape before paying. The cap resets on the 1st. Overflow returns `status: "error"` with `error_kind: "free_tier_limit"` and is not charged. *** ### What do people use the LinkedIn Profile Scraper for? - **CRM enrichment**: fill in job title, company, location and skills for contacts you already have. - **Sales prospecting**: qualify and enrich leads before outreach, so you know who is worth an email. - **Recruiting**: build structured candidate profiles from a list of LinkedIn URLs. - **Account research**: understand a company's team composition and expertise before a call. - **Data pipelines**: feed structured profile data into Airtable, HubSpot, Salesforce or your warehouse. - **AI agent context**: give an agent someone's professional background before a meeting. *** ### LinkedIn Profile Scraper vs cookie-based tools vs the official LinkedIn API | | This LinkedIn Profile Scraper | Cookie-based scrapers | Official LinkedIn API | |---|---|---|---| | **LinkedIn account / cookies** | Not needed | Your logged-in cookies required | OAuth app + LinkedIn approval | | **Account / ban risk** | None (no account used) | High (your account can be restricted) | None | | **Setup** | Paste profile URLs | Extract and paste your session cookie | Partner application + review | | **Data depth** | Full profile + deep company data | Varies, often profile-only | Limited, mostly your own network | | **Bulk** | Up to 5,000 URLs per run | Rate-limited by your account | Strict quotas | | **Company enrichment** | Yes (HQ, tech stack, IT spend, more) | Rarely | No | | **Pricing** | Pay per profile found ($0.006) | Subscription + your account | Gated / partner pricing | *** ### 🏆 Top LinkedIn Scrapers ### FAQ #### What is LinkedIn profile scraping? LinkedIn profile scraping turns a bare LinkedIn profile URL into a full structured record: name, job title, headline, summary, company, location, skills, education, and work history, without opening a browser or logging in. This Actor takes a list of profile URLs and returns one clean JSON row per profile, ready for a CRM, spreadsheet, or AI agent. #### How do I scrape a LinkedIn profile without an account or cookies? Paste the profile URL (or up to 5,000 URLs) into `profileUrls` and run the Actor. It resolves each profile through a cookieless data source, so you never connect a LinkedIn account, paste a session cookie, or risk a ban. Each URL returns exactly one row. #### Can I use this from ChatGPT or Claude? Yes, two ways. Paste the "Copy to your AI assistant" block above into any LLM and it will write the call for you. Or connect the **LinkedIn MCP server** config above, and every Atomus Actor becomes a native tool your assistant can call on its own, including chaining several in one request. #### How much does it cost? $0.006 per profile found ($6 per 1,000). You are charged only for profiles that are found; `not_found` and `error` rows are free. Free Apify plans include 10 profiles per month so you can test before paying. #### Is there an official LinkedIn API for profile data? LinkedIn's official APIs (Marketing, Talent Solutions) are partner-gated, require an approved OAuth app, and do not expose arbitrary public profile data for enrichment. For turning a list of profile URLs into structured records, a cookieless scraper like this one is the practical path. #### Is it legal to scrape LinkedIn profiles? This Actor reads publicly available profile information through a third-party data source. You are responsible for using the output in line with applicable laws (GDPR/CCPA), LinkedIn's terms, and your own compliance requirements. It is an independent tool, not affiliated with LinkedIn. #### How fresh is the data? Every row includes `last_updated`, at both profile and company level, showing when the upstream source last refreshed that record. #### What happens if a profile isn't found? You get a row with `status: "not_found"` (private, deleted, or not indexed) or `status: "error"` (unsupported URL form or upstream failure). Neither is charged, so there is no penalty for trying a URL that doesn't resolve. #### Can I enrich a CRM or spreadsheet in bulk? Yes. Export LinkedIn URLs from your CRM, paste them into `profileUrls` (up to 5,000 per run), and run. The output maps 1:1 to your input by URL, so rejoining in any spreadsheet tool or database is a straight lookup. *** ### All Atomus scrapers 2.4M+ RESULTS DELIVERED ### Support ### ⚠️ Disclaimer This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by LinkedIn Corporation. LinkedIn® is a registered trademark of LinkedIn Corporation. All trademarks are property of their respective owners. # Actor input Schema ## `profileUrls` (type: `array`): List of LinkedIn profile URLs to enrich. Each must match the format https://www.linkedin.com/in//. ## `findEmail` (type: `boolean`): ⚠️ THIS ADDS COST. When enabled, each returned profile also includes a real-time, real-time verified work email, billed as a SEPARATE paid event for every email found (on top of the profile charge). You are charged ONLY when an email is actually found: not-found profiles and profiles without a verifiable email are free. Available on PAID Apify plans only (a free-tier run with this on is rejected without charge). Leave OFF unless you specifically want emails. ## `includeCompanyDetails` (type: `boolean`): When enabled, each successfully enriched profile also returns the FULL firmographic record for the person's current company (complete funding-round breakdown, every office location, full tech stack, industries, keywords, NAICS codes, revenue band, sub-organizations) in a top-level company_details field. Charged as an extra event per successful profile that has a company; not-found, errors, and profiles with no company are free. Leave off for profile-only enrichment. ## Actor input object example ```json { "profileUrls": [ "https://www.linkedin.com/in/satyanadella/" ], "findEmail": false, "includeCompanyDetails": false } ``` # Actor output Schema ## `profiles` (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 = { "profileUrls": [ "https://www.linkedin.com/in/satyanadella/" ] }; // Run the Actor and wait for it to finish const run = await client.actor("atomus/linkedin-profile-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 = { "profileUrls": ["https://www.linkedin.com/in/satyanadella/"] } # Run the Actor and wait for it to finish run = client.actor("atomus/linkedin-profile-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}") for item in client.dataset(run.default_dataset_id).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "profileUrls": [ "https://www.linkedin.com/in/satyanadella/" ] }' | apify call atomus/linkedin-profile-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,atomus/linkedin-profile-scraper" } } } ``` The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer ` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations). ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/actors/HcEUCKHXJu3SvAVnC/builds/1YYbI4LHcmUllQYzW/openapi.json