# Polish Premises Prospector - REGON jednostki lokalne (`regdata/polish-premises-prospector`) Actor Build prospect lists at the physical-site level from the Polish REGON registry. One row per premise (jednostka lokalna) with industry (PKD), address, company age, and ownership, filterable by region (TERYT). - **URL**: https://apify.com/regdata/polish-premises-prospector.md - **Developed by:** [getregdata](https://apify.com/regdata) (community) - **Categories:** Automation, Developer tools, Lead generation - **Stats:** 3 total users, 2 monthly users, 95.2% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 premises 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.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 ## Polish Premises Prospector - Site-Level REGON (jednostki lokalne) Build B2B prospect lists in Poland at the **individual physical-site level** - one row per premise (*jednostka lokalna*), not just the registered company HQ. For each company you seed, this actor expands it into **every physical location** registered in the official GUS REGON registry, with the attributes you need to filter and prioritize: - **Industry** - per-site PKD activity code + name (each premise can have its own activity) - **Address** - full readable address (województwo, powiat, gmina, city, street, number, postcode) - **Region** - TERYT symbols for precise filtering by voivodeship / county - **Company age** - registration / start date of both the site and the parent company - **Ownership** - parent legal form and ownership form (e.g. private, foreign, State Treasury) - **Headcount** *(optional, company-level proxy)* - see "Headcount" below Output is one row per premise plus the company HQ, ready for CSV/Excel/API. ### Why site-level? REGON registers **local units** (*jednostki lokalne*) separately from the legal entity. Each gets its own 14-digit REGON, its own address, and its own PKD activity. A bank has hundreds of branches; a manufacturer has plants and warehouses at different addresses. Filtering the legal entity alone only ever returns the HQ - this actor returns the **actual workplaces**. ### How it works ```` seed identifiers ──▶ public GUS REGON registry ──▶ entity report + entity PKD (anonymous, no API key) + local-unit list (every premise) \+ per-site report + PKD ──▶ one row per premise (HQ + jednostki lokalne) ──▶ filter by region (TERYT) and site type (PKD) ──▶ optional per-site headcount overlay (footprint / LinkedIn) ```` **No API key required.** This actor reads the public REGON registry anonymously (it does not use the registered BIR web-service key). You **seed** the companies to examine (see Discovery), and the actor expands each into its premises and filters them. ### Input | Field | Description | |---|---| | `seedIdentifiers` | List of NIP / REGON / KRS (auto-detected by shape). | | `seedDatasetId` | Apify dataset ID to read identifiers from (chain in a regional search / KRS export). | | `seedIdType` | How to read 10-digit ids (`auto` / `nip` / `regon` / `krs`). NIP and KRS are both 10 digits. | | `regions` | Presets `warszawa`, `lodz`, `kutno`, or `{ "woj": "10", "powiat": "03" }`. Empty = all Poland. | | `siteTypes` | `factory`, `warehouse`, `clinic`, `school`, `coliving`. Empty = all industries. | | `pkdCodes` | Explicit PKD prefixes, e.g. `"86"`, `"8610"`, `"5210B"`. | | `fetchSitePkd` | Fetch each premise's own PKD (one extra call/premise). Off = inherit company PKD. | | `includeHeadcount` | Attach a per-site headcount signal (footprint for industrial, LinkedIn for white-collar; see below). | | `headcountMethod` | `auto` (default), `footprint`, `linkedin`, or `none`. | | `maxResults`, `minIntervalMs` | Output cap and politeness delay between requests. | ### Discovery: how to seed a regional list The actor works on real public registry data with **no key or sandbox** to worry about. Get your seed universe from one of: 1. **The companion `polish-regon-scraper` actor (recommended)** - run a by-address / by-PKD discovery there, then pass its dataset id as `seedDatasetId`. This actor reads `nip`/`regon`/`krs` from each item. The two actors share the same anonymous REGON client. 2. **A KRS export or your own prospect list** - drop the identifiers into `seedIdentifiers`. 3. **A paid GUS bulk data order** - the cleanest way to get the full local-unit universe for a region (statistical confidentiality applies, so no headcount). ### Headcount (read this) No source publishes a **verified per-site headcount** for Poland - REGON's employment figure is legally confidential. So instead of pretending otherwise, every enriched row carries a `headcountBasis` that tells you exactly how much of the number is *sourced* vs *modeled*: | `headcountBasis` | What it means | Method | Best for | |---|---|---|---| | `reported-proxy` | A real, sourced **count** (a tally of actual LinkedIn profiles for the company in that city) | LinkedIn employees-by-city | Offices, clinics, schools, universities | | `modeled-estimate` | A number **we compute** (building footprint ÷ employment density, ±50-100%) | OSM footprint | Factories, warehouses, DCs | | *(none, `unknown`)* | No signal available | - | - | `headcountMethod=auto` routes white-collar site types to the LinkedIn city-count (the primary, sourced signal). For each company we **resolve the canonical LinkedIn page** before counting: first by name (`harvestapi/linkedin-company`, validated by website-domain / name overlap), then - if the legal name is too far from the brand - by **website domain** (`s-r/free-linkedin-company-finder`, e.g. `pkobp.pl` -> `/company/pko-bp`). A low-confidence match is rejected (-> `unknown`) rather than returned as a wrong number. We then count employees by the resolved URL, filtered to the site's city. **Caveats:** it's a per-*city* count (not split between two sites in the same city) and undercounts blue-collar sites. Counting uses `harvestapi/linkedin-company-employees` at ~**$0.02/company-city** (the count is read from the run log, so only 1 profile is scraped); resolution adds ~$0.003-0.004/company. Bound LinkedIn spend with `maxEnrich`. For **industrial site types** (factory/warehouse/DC), where LinkedIn is blind, `auto` routes to the **building-footprint** method - the only realistic *per-site* number. It geocodes the site address (OSM Nominatim), reads the building's floor area from OpenStreetMap (Overpass), and divides by an employment-density factor (warehouse ~80, manufacturing ~40 m²/FTE; UK Employment Densities Guide) × building levels. The result is flagged `modeled-estimate` with a **±50-100% band** (`headcountLow`/`headcountHigh`) and a caveat, because the floor area is sourced but the people figure is computed, not reported. It uses **only free OSM APIs** (no Apify helper-actor cost); if no building can be resolved at the address, the row is left as `unknown` rather than guessed. Per OSM usage policy the actor self-throttles to ≤1 request/second and sends a descriptive User-Agent (`footprintUserAgent`). Data © OpenStreetMap contributors (ODbL). ### Output (one row per premise) `regon14`, `parentRegon`, `nip`, `krs`, `isHeadquarters`, `name`, `companyName`, `addressText`, `voivodeship`, `powiat`, `gmina`, `city`, `postalCode`, `street`, `buildingNumber`, `apartmentNumber`, `terytWoj`/`terytPowiat`/`terytGmina`, `pkdMainCode`, `pkdMainName`, `pkdAll`, `siteStartDate`, `companyRegistrationDate`, `companyAgeYears`, `legalForm`, `ownershipForm`, `website`, `localUnitsCount`, `headcountEstimate`, `headcountLow`, `headcountHigh`, `headcountBasis`, `headcountMethod`, `headcountSource`, `headcountConfidence`, `linkedinCityCount`, `headcountCaveat`, `scrapedAt`, `dataSource`. ### Coverage notes - **Strong**: factories (PKD 10-33), warehouses/distribution (52, 46), private clinics (86), private schools/universities (85). These commonly register premises as local units. - **Weak**: co-living - buildings are not entities; only the operator's declared premise (PKD 55/68) may appear. Expect partial coverage. - Only legal entities (`Typ = P`) are expanded. Sole-proprietor businesses are skipped. ### Permissions This Actor requests **full permissions** because its optional LinkedIn headcount enrichment orchestrates third-party LinkedIn Actors (`harvestapi/linkedin-company`, `harvestapi/linkedin-company-employees`), and calling them requires it. The Actor only runs those Actors and writes its own dataset - it does not read or modify any other data in your account. If you don't use LinkedIn headcount, the core REGON extraction and the free OSM building-footprint headcount work the same. ### Source Data: [GUS REGON BIR](https://api.stat.gov.pl/Home/RegonApi) (official Polish statistical business registry). This actor uses the registry's JSON `ajaxEndpoint` interface. # Actor input Schema ## `seedIdentifiers` (type: `array`): List of company identifiers to expand. Auto-detected by shape: 9 or 14 digits = REGON, 10 digits = NIP (override with Seed identifier type). The BIR registry has no geographic search, so you seed the companies to examine here (or via Seed dataset). ## `seedDatasetId` (type: `string`): Optional Apify dataset ID to read identifiers from (fields: nip / regon / regon14 / krs). Use this to chain in a regional REGON web search or a KRS export as the discovery source. ## `seedIdType` (type: `string`): How to interpret 10-digit identifiers (NIP and KRS are both 10 digits). ## `regions` (type: `array`): Filter premises to these regions. Use presets (warszawa, lodz, kutno) or objects like {"woj":"10","powiat":"03"} (TERYT symbols). Empty = all of Poland. ## `siteTypes` (type: `array`): Filter premises by industry preset: factory, warehouse, clinic, school, coliving. Empty = all industries. (Large offices are not cleanly identifiable by PKD - leave empty or use PKD codes.) ## `pkdCodes` (type: `array`): Explicit PKD code prefixes to match (e.g. "86", "8610", "5210B"). Combined with Site types. ## `fetchSitePkd` (type: `boolean`): Fetch each premise's own PKD activity (one extra API call per premise). When off, premises inherit the parent company's main PKD. ## `includeHeadcount` (type: `boolean`): Attach a per-site headcount signal. Each row is labelled with headcountBasis: 'reported-proxy' (LinkedIn employees-by-city, a real sourced count) or 'modeled-estimate' (building footprint ÷ density for industrial sites - computed, ±50-100%). No source publishes a verified per-site count; see README. ## `headcountMethod` (type: `string`): auto = LinkedIn city-count for office/clinic/school sites and a building-footprint estimate for industrial sites (factory/warehouse/DC). Force a single method if desired. ## `linkedinActorId` (type: `string`): Apify actor used for LinkedIn employees-by-city counts. ## `linkedinResolverActorId` (type: `string`): Actor that resolves a company name to its canonical LinkedIn page (URL + company-wide employee count). The match is validated (website domain / name overlap) and rejected if low-confidence, so wrong matches become 'unknown' rather than false counts. Leave default. ## `linkedinDomainResolverActorId` (type: `string`): Fallback resolver: maps a company's website domain (from REGON) to its LinkedIn page when name resolution fails - fixes companies whose brand differs from their legal name (e.g. pkobp.pl -> /company/pko-bp). Pay-per-success. Leave default; clear to disable. ## `linkedinMode` (type: `string`): Profile detail level (cost): short is cheapest and enough for a headcount count. ## `linkedinMaxItems` (type: `integer`): Profiles actually scraped per company+city. The headcount COUNT is read from the run log ('Found N profiles total'), so 1 is enough and cheapest (~$0.02/company-city). Raise only if you also want the profile list. ## `maxEnrich` (type: `integer`): Hard cap on paid LinkedIn calls per run (~$0.02/company-city; 0 = unlimited). The footprint estimator uses free OSM APIs and is NOT counted against this budget. Bounds cost. ## `footprintUserAgent` (type: `string`): Contact User-Agent sent to the free OSM Nominatim/Overpass APIs used by the building-footprint headcount estimator. Their usage policy requires a descriptive identifier with a contact. Leave default unless you want your own contact string. ## `maxResults` (type: `integer`): Maximum premise rows to output (0 = unlimited). ## `minIntervalMs` (type: `integer`): Politeness delay between REGON requests. Keep >= 300ms to avoid the public site's conditional rate limit. ## Actor input object example ```json { "seedIdentifiers": [ "5250007738", "5260250995" ], "seedIdType": "auto", "regions": [ "warszawa", "lodz", "kutno" ], "siteTypes": [ "factory", "warehouse", "clinic", "school" ], "fetchSitePkd": true, "includeHeadcount": false, "headcountMethod": "auto", "linkedinActorId": "harvestapi/linkedin-company-employees", "linkedinResolverActorId": "harvestapi/linkedin-company", "linkedinDomainResolverActorId": "s-r/free-linkedin-company-finder---linkedin-address-from-any-site", "linkedinMode": "short", "linkedinMaxItems": 1, "maxEnrich": 0, "footprintUserAgent": "polish-premises-prospector/1.0 (Apify actor; contact via Apify Store)", "maxResults": 0, "minIntervalMs": 350 } ```` # Actor output Schema ## `premises` (type: `string`): Site-level rows: REGON-14, HQ flag, name, address, PKD, company age, legal form, ownership, headcount band. # 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 = { "seedIdentifiers": [ "5250007738", "5260250995" ], "regions": [ "warszawa", "lodz", "kutno" ], "siteTypes": [ "factory", "warehouse", "clinic", "school" ], "linkedinActorId": "harvestapi/linkedin-company-employees", "linkedinResolverActorId": "harvestapi/linkedin-company", "linkedinDomainResolverActorId": "s-r/free-linkedin-company-finder---linkedin-address-from-any-site" }; // Run the Actor and wait for it to finish const run = await client.actor("regdata/polish-premises-prospector").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 = { "seedIdentifiers": [ "5250007738", "5260250995", ], "regions": [ "warszawa", "lodz", "kutno", ], "siteTypes": [ "factory", "warehouse", "clinic", "school", ], "linkedinActorId": "harvestapi/linkedin-company-employees", "linkedinResolverActorId": "harvestapi/linkedin-company", "linkedinDomainResolverActorId": "s-r/free-linkedin-company-finder---linkedin-address-from-any-site", } # Run the Actor and wait for it to finish run = client.actor("regdata/polish-premises-prospector").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 '{ "seedIdentifiers": [ "5250007738", "5260250995" ], "regions": [ "warszawa", "lodz", "kutno" ], "siteTypes": [ "factory", "warehouse", "clinic", "school" ], "linkedinActorId": "harvestapi/linkedin-company-employees", "linkedinResolverActorId": "harvestapi/linkedin-company", "linkedinDomainResolverActorId": "s-r/free-linkedin-company-finder---linkedin-address-from-any-site" }' | apify call regdata/polish-premises-prospector --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=regdata/polish-premises-prospector", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Polish Premises Prospector - REGON jednostki lokalne", "description": "Build prospect lists at the physical-site level from the Polish REGON registry. One row per premise (jednostka lokalna) with industry (PKD), address, company age, and ownership, filterable by region (TERYT).", "version": "1.0", "x-build-id": "65s5rfESdd531jWhZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/regdata~polish-premises-prospector/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-regdata-polish-premises-prospector", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/regdata~polish-premises-prospector/runs": { "post": { "operationId": "runs-sync-regdata-polish-premises-prospector", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/regdata~polish-premises-prospector/run-sync": { "post": { "operationId": "run-sync-regdata-polish-premises-prospector", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "seedIdentifiers": { "title": "Seed identifiers (NIP / REGON / KRS)", "type": "array", "description": "List of company identifiers to expand. Auto-detected by shape: 9 or 14 digits = REGON, 10 digits = NIP (override with Seed identifier type). The BIR registry has no geographic search, so you seed the companies to examine here (or via Seed dataset).", "items": { "type": "string" } }, "seedDatasetId": { "title": "Seed dataset ID", "type": "string", "description": "Optional Apify dataset ID to read identifiers from (fields: nip / regon / regon14 / krs). Use this to chain in a regional REGON web search or a KRS export as the discovery source." }, "seedIdType": { "title": "Seed identifier type", "enum": [ "auto", "nip", "regon", "krs" ], "type": "string", "description": "How to interpret 10-digit identifiers (NIP and KRS are both 10 digits).", "default": "auto" }, "regions": { "title": "Regions", "type": "array", "description": "Filter premises to these regions. Use presets (warszawa, lodz, kutno) or objects like {\"woj\":\"10\",\"powiat\":\"03\"} (TERYT symbols). Empty = all of Poland.", "items": { "type": "string" } }, "siteTypes": { "title": "Site types", "type": "array", "description": "Filter premises by industry preset: factory, warehouse, clinic, school, coliving. Empty = all industries. (Large offices are not cleanly identifiable by PKD - leave empty or use PKD codes.)", "items": { "type": "string" } }, "pkdCodes": { "title": "PKD codes (advanced)", "type": "array", "description": "Explicit PKD code prefixes to match (e.g. \"86\", \"8610\", \"5210B\"). Combined with Site types.", "items": { "type": "string" } }, "fetchSitePkd": { "title": "Fetch per-site PKD", "type": "boolean", "description": "Fetch each premise's own PKD activity (one extra API call per premise). When off, premises inherit the parent company's main PKD.", "default": true }, "includeHeadcount": { "title": "Include headcount enrichment", "type": "boolean", "description": "Attach a per-site headcount signal. Each row is labelled with headcountBasis: 'reported-proxy' (LinkedIn employees-by-city, a real sourced count) or 'modeled-estimate' (building footprint ÷ density for industrial sites - computed, ±50-100%). No source publishes a verified per-site count; see README.", "default": false }, "headcountMethod": { "title": "Headcount method", "enum": [ "auto", "linkedin", "footprint", "none" ], "type": "string", "description": "auto = LinkedIn city-count for office/clinic/school sites and a building-footprint estimate for industrial sites (factory/warehouse/DC). Force a single method if desired.", "default": "auto" }, "linkedinActorId": { "title": "LinkedIn employees actor ID", "type": "string", "description": "Apify actor used for LinkedIn employees-by-city counts.", "default": "harvestapi/linkedin-company-employees" }, "linkedinResolverActorId": { "title": "LinkedIn company resolver actor ID", "type": "string", "description": "Actor that resolves a company name to its canonical LinkedIn page (URL + company-wide employee count). The match is validated (website domain / name overlap) and rejected if low-confidence, so wrong matches become 'unknown' rather than false counts. Leave default.", "default": "harvestapi/linkedin-company" }, "linkedinDomainResolverActorId": { "title": "LinkedIn domain resolver actor ID", "type": "string", "description": "Fallback resolver: maps a company's website domain (from REGON) to its LinkedIn page when name resolution fails - fixes companies whose brand differs from their legal name (e.g. pkobp.pl -> /company/pko-bp). Pay-per-success. Leave default; clear to disable.", "default": "s-r/free-linkedin-company-finder---linkedin-address-from-any-site" }, "linkedinMode": { "title": "LinkedIn scrape mode", "enum": [ "short", "full" ], "type": "string", "description": "Profile detail level (cost): short is cheapest and enough for a headcount count.", "default": "short" }, "linkedinMaxItems": { "title": "LinkedIn max profiles per company-city", "minimum": 1, "type": "integer", "description": "Profiles actually scraped per company+city. The headcount COUNT is read from the run log ('Found N profiles total'), so 1 is enough and cheapest (~$0.02/company-city). Raise only if you also want the profile list.", "default": 1 }, "maxEnrich": { "title": "Max enrichment calls (shared budget)", "minimum": 0, "type": "integer", "description": "Hard cap on paid LinkedIn calls per run (~$0.02/company-city; 0 = unlimited). The footprint estimator uses free OSM APIs and is NOT counted against this budget. Bounds cost.", "default": 0 }, "footprintUserAgent": { "title": "Footprint contact User-Agent", "type": "string", "description": "Contact User-Agent sent to the free OSM Nominatim/Overpass APIs used by the building-footprint headcount estimator. Their usage policy requires a descriptive identifier with a contact. Leave default unless you want your own contact string.", "default": "polish-premises-prospector/1.0 (Apify actor; contact via Apify Store)" }, "maxResults": { "title": "Max results", "minimum": 0, "type": "integer", "description": "Maximum premise rows to output (0 = unlimited).", "default": 0 }, "minIntervalMs": { "title": "Min interval between requests (ms)", "minimum": 0, "type": "integer", "description": "Politeness delay between REGON requests. Keep >= 300ms to avoid the public site's conditional rate limit.", "default": 350 } } }, "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 } } } } } } } } } } ```