# Semantic Scholar Scraper (`crawlerbros/semanticscholar-scraper`) Actor Scrape Semantic Scholar with 200M+ academic papers and authors with full citation graph. Search, fetch by paper/author ID, get citations / references / recommendations, with abstracts, TLDRs, fields-of-study, open-access PDFs, h-index, affiliations, and more - **URL**: https://apify.com/crawlerbros/semanticscholar-scraper.md - **Developed by:** [Crawler Bros](https://apify.com/crawlerbros) (community) - **Categories:** AI, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 16 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $3.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Semantic Scholar Scraper Scrape **Semantic Scholar** — Allen Institute for AI's open catalog of 200M+ academic papers and authors with a full citation graph — directly via the official Semantic Scholar Graph API. ### What you get For every paper: - `paperId`, `corpusId`, `externalIds` (DOI, arXiv, MAG, PMID, ACL, DBLP) - `title`, `abstract`, `tldr` (AI-generated summary) - `year`, `publicationDate`, `venue`, `publicationVenue`, `journal` - `authors` — list of `{authorId, name}`, plus `primaryAuthor` - `fieldsOfStudy`, `s2FieldsOfStudy` (with source attribution) - `publicationTypes` (`Review`, `JournalArticle`, `Conference`, …) - `referenceCount`, `citationCount`, `influentialCitationCount` - `isOpenAccess`, `openAccessPdf` (`{url, status, license}`) - `semanticScholarUrl` For every author: - `authorId`, `name`, `aliases`, `affiliations`, `homepage` - `paperCount`, `citationCount`, `hIndex` - `externalIds` (ORCID, DBLP) - `semanticScholarUrl` For citation/reference relations: - The full paper record of the citing/cited paper - `citationContexts` (text snippets of where it was cited) - `citationIntents` (`background`, `methodology`, `result`) - `isInfluentialCitation` ### Modes | Mode | What it does | |---|---| | `searchPaper` | Relevance-ranked paper search via `/paper/search`. Best for "find me the top N papers about X". | | `searchPaperBulk` | Bulk paper search via `/paper/search/bulk` — 1000 results per page, full-corpus pagination. Best for "give me everything about X". | | `byPaper` | Look up papers by ID. Accepts the 40-char Semantic Scholar SHA, plus prefixed external IDs: `DOI:`, `ARXIV:`, `MAG:`, `PMID:`, `PMCID:`, `ACL:`, `DBLP:`. Bare DOIs / arXiv IDs are auto-prefixed. | | `byPaperCitations` | All papers that cite the given paper (with citation contexts and intents). | | `byPaperReferences` | All papers cited by the given paper. | | `searchAuthor` | Search authors by name. | | `byAuthor` | Look up authors by Semantic Scholar author ID. | | `byAuthorPapers` | All papers authored by the given Semantic Scholar author ID. | | `recommendations` | Get related/similar papers via `/recommendations/v1/papers/forpaper/{id}`. | | `byUrl` | Auto-route from Semantic Scholar / DOI / arXiv URLs. | ### Filters Search modes accept: - `year` — single year (`2023`), open range (`2018-`, `-2010`), or closed range (`2015-2020`) - `fieldsOfStudy` — multi-select: Computer Science, Medicine, Chemistry, Biology, … - `publicationTypes` — multi-select: Review, JournalArticle, Conference, … - `venues` — free-text list (e.g., `Nature`, `NeurIPS`) - `openAccessOnly` — drop papers without an open-access PDF - `minCitationCount` — minimum citation count - `sort` (bulk search only) — `relevance`, `citationCount:desc/asc`, `publicationDate:desc/asc` ### API key (optional) The Semantic Scholar Graph API is **public and free**. An API key is **not required**, but raises rate limits 10x. Free signup: . Without a key the actor enforces a polite ~1.5s delay between requests so a single run stays under the 100-requests-per-5-minutes budget. ### Example inputs #### Search the literature on attention mechanisms ```json { "mode": "searchPaper", "searchQuery": "transformer attention", "fieldsOfStudy": ["Computer Science"], "year": "2017-", "minCitationCount": 50, "maxItems": 100 } ```` #### Fetch the "Attention Is All You Need" paper ```json { "mode": "byPaper", "paperIds": ["ARXIV:1706.03762"], "includeReferencesOnPaper": true, "maxItems": 1 } ``` #### All citations of a foundational paper ```json { "mode": "byPaperCitations", "paperIds": ["DOI:10.1145/3065386"], "maxItems": 500 } ``` #### All papers by Geoffrey Hinton ```json { "mode": "byAuthorPapers", "authorIds": ["1741101"], "maxItems": 200 } ``` #### Recommendations for a paper ```json { "mode": "recommendations", "paperIds": ["ARXIV:1706.03762"], "maxItems": 50 } ``` ### FAQ **How do I find a paper's Semantic Scholar ID?** Use the URL on `semanticscholar.org` — the 40-char hex at the end is the ID. Or use a DOI / arXiv ID with the `DOI:` / `ARXIV:` prefix. The `byUrl` mode accepts any of these URL forms directly. **Why does my run say "0 records emitted"?** Either the search query had no matches, or the filter combination was too narrow (e.g., `minCitationCount: 100000` will drop almost everything). Loosen filters or check the status message. **Are abstracts always available?** No. Older papers and some publishers don't share abstracts via the API. The actor omits the `abstract` field when missing rather than returning `null`. **What happens on rate-limit?** The actor honours the `Retry-After` header on 429 responses and retries with exponential backoff. With a key you almost never hit the limit; without a key, large jobs slow to a crawl after 100 requests in any 5-min window. **Can I get reference / citation counts without fetching all the papers?** Yes — `searchPaper` and `byPaper` already return `citationCount`, `referenceCount`, and `influentialCitationCount` in the default field set. **Are the open-access PDF URLs hotlink-blocked?** No. They point at the original publisher / arXiv / preprint server and resolve from a clean shell. ### Limitations - The `recommendations` endpoint returns up to 100 recommendations per source paper. - The Graph API limits each call to 1000 records max; bulk search can paginate beyond 1000. - `tldr` (AI summary) is only generated for a subset of papers. ### Source Data is fetched from the official Semantic Scholar API: . The Allen Institute for AI publishes the API for academic and non-commercial use. See the [API terms](https://www.semanticscholar.org/product/api#Partner-form). # Actor input Schema ## `mode` (type: `string`): What to fetch. ## `searchQuery` (type: `string`): Free-text query for searchPaper / searchPaperBulk / searchAuthor modes. Examples: `transformer attention`, `geoffrey hinton`, `crispr cas9`. ## `year` (type: `string`): Filter papers by publication year. Accepts a single year (`2023`), an open range (`2018-`, `-2010`), or a closed range (`2015-2020`). ## `fieldsOfStudy` (type: `array`): Filter to one or more Semantic Scholar fields-of-study. Multi-select. ## `publicationTypes` (type: `array`): Filter by publication type. Multi-select. ## `venues` (type: `array`): Filter by publication venue (e.g. `Nature`, `NeurIPS`, `IEEE Transactions on Pattern Analysis`). ## `openAccessOnly` (type: `boolean`): Drop papers without an open-access PDF. ## `minCitationCount` (type: `integer`): Drop papers with fewer than this many citations. ## `sort` (type: `string`): Sort order for bulk search. Relevance-ranked search uses an internal score and ignores this field. ## `paperIds` (type: `array`): Semantic Scholar paper IDs (40-char hex), or prefixed external IDs: `DOI:10.1145/...`, `ARXIV:1706.03762`, `MAG:...`, `PMID:...`, `PMCID:PMC...`, `ACL:...`. Bare DOIs and arXiv IDs are auto-prefixed. ## `authorIds` (type: `array`): Numeric Semantic Scholar author IDs (e.g. `1741101`). ## `urls` (type: `array`): Semantic Scholar / DOI / arXiv URLs. Examples: `https://www.semanticscholar.org/paper/`, `https://arxiv.org/abs/1706.03762`, `https://doi.org/10.1145/...`, `https://www.semanticscholar.org/author/1741101`. ## `includeCitationsOnPaper` (type: `boolean`): Embed up to 200 citing-paper summaries inside each paper record (byPaper mode only). Increases payload size. ## `includeReferencesOnPaper` (type: `boolean`): Embed up to 200 cited-paper summaries inside each paper record (byPaper mode only). ## `paperFields` (type: `array`): Override the default `fields=` list sent to the API for paper endpoints. Leave empty to use the curated default. See https://api.semanticscholar.org/api-docs/graph for the full list. ## `authorFields` (type: `array`): Override the default `fields=` list sent to the API for author endpoints. ## `semanticScholarApiKey` (type: `string`): Raises rate limits 10x. Free signup: https://www.semanticscholar.org/product/api#api-key-form. The actor works without a key. ## `requestDelaySeconds` (type: `integer`): Delay between API calls. Defaults to 0 (with API key) or 2 (without). Honour the source's rate-limit budget. ## `maxItems` (type: `integer`): Hard cap on emitted records. ## `useProxy` (type: `boolean`): Force routing through Apify proxy from the first request. Recommended when running without an API key from datacenter IPs (Apify cloud is rate-limited heavily by Semantic Scholar's free tier). ## `autoEscalateOnBlock` (type: `boolean`): If the API responds 429 from direct IP, automatically rotate through Apify proxy sessions for the rest of the run. Helps avoid getting stuck on the unauthenticated 100-req/5-min cap from a single datacenter IP. ## `proxyConfiguration` (type: `object`): Apify proxy configuration. Used only when useProxy is enabled. Auto-escalation falls back to the default Apify proxy group automatically. ## Actor input object example ```json { "mode": "byPaper", "searchQuery": "transformer attention", "fieldsOfStudy": [], "publicationTypes": [], "venues": [], "openAccessOnly": false, "sort": "", "paperIds": [ "10.1145/3065386", "ARXIV:1706.03762" ], "authorIds": [], "urls": [], "includeCitationsOnPaper": false, "includeReferencesOnPaper": false, "paperFields": [], "authorFields": [], "maxItems": 2, "useProxy": false, "autoEscalateOnBlock": true, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [] } } ``` # Actor output Schema ## `records` (type: `string`): Dataset containing all scraped Semantic Scholar records (papers, authors, citations, references, recommendations). # 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 = { "mode": "byPaper", "fieldsOfStudy": [], "publicationTypes": [], "venues": [], "openAccessOnly": false, "paperIds": [ "10.1145/3065386", "ARXIV:1706.03762" ], "authorIds": [], "urls": [], "includeCitationsOnPaper": false, "includeReferencesOnPaper": false, "paperFields": [], "authorFields": [], "maxItems": 2, "useProxy": false, "autoEscalateOnBlock": true, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [] } }; // Run the Actor and wait for it to finish const run = await client.actor("crawlerbros/semanticscholar-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 = { "mode": "byPaper", "fieldsOfStudy": [], "publicationTypes": [], "venues": [], "openAccessOnly": False, "paperIds": [ "10.1145/3065386", "ARXIV:1706.03762", ], "authorIds": [], "urls": [], "includeCitationsOnPaper": False, "includeReferencesOnPaper": False, "paperFields": [], "authorFields": [], "maxItems": 2, "useProxy": False, "autoEscalateOnBlock": True, "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": [], }, } # Run the Actor and wait for it to finish run = client.actor("crawlerbros/semanticscholar-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "mode": "byPaper", "fieldsOfStudy": [], "publicationTypes": [], "venues": [], "openAccessOnly": false, "paperIds": [ "10.1145/3065386", "ARXIV:1706.03762" ], "authorIds": [], "urls": [], "includeCitationsOnPaper": false, "includeReferencesOnPaper": false, "paperFields": [], "authorFields": [], "maxItems": 2, "useProxy": false, "autoEscalateOnBlock": true, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [] } }' | apify call crawlerbros/semanticscholar-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=crawlerbros/semanticscholar-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Semantic Scholar Scraper", "description": "Scrape Semantic Scholar with 200M+ academic papers and authors with full citation graph. Search, fetch by paper/author ID, get citations / references / recommendations, with abstracts, TLDRs, fields-of-study, open-access PDFs, h-index, affiliations, and more", "version": "1.0", "x-build-id": "6Ux96rY9ZEia5xQr5" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/crawlerbros~semanticscholar-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-crawlerbros-semanticscholar-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/crawlerbros~semanticscholar-scraper/runs": { "post": { "operationId": "runs-sync-crawlerbros-semanticscholar-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/crawlerbros~semanticscholar-scraper/run-sync": { "post": { "operationId": "run-sync-crawlerbros-semanticscholar-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "searchPaper", "searchPaperBulk", "byPaper", "byPaperCitations", "byPaperReferences", "searchAuthor", "byAuthor", "byAuthorPapers", "recommendations", "byUrl" ], "type": "string", "description": "What to fetch.", "default": "byPaper" }, "searchQuery": { "title": "Search query", "type": "string", "description": "Free-text query for searchPaper / searchPaperBulk / searchAuthor modes. Examples: `transformer attention`, `geoffrey hinton`, `crispr cas9`.", "default": "transformer attention" }, "year": { "title": "Year filter (search modes)", "type": "string", "description": "Filter papers by publication year. Accepts a single year (`2023`), an open range (`2018-`, `-2010`), or a closed range (`2015-2020`)." }, "fieldsOfStudy": { "title": "Fields of study (search modes)", "uniqueItems": true, "type": "array", "description": "Filter to one or more Semantic Scholar fields-of-study. Multi-select.", "items": { "type": "string", "enum": [ "Computer Science", "Medicine", "Chemistry", "Biology", "Materials Science", "Physics", "Geology", "Psychology", "Art", "History", "Geography", "Sociology", "Business", "Political Science", "Economics", "Philosophy", "Mathematics", "Engineering", "Environmental Science", "Agricultural and Food Sciences", "Education", "Law", "Linguistics" ], "enumTitles": [ "Computer Science", "Medicine", "Chemistry", "Biology", "Materials Science", "Physics", "Geology", "Psychology", "Art", "History", "Geography", "Sociology", "Business", "Political Science", "Economics", "Philosophy", "Mathematics", "Engineering", "Environmental Science", "Agricultural and Food Sciences", "Education", "Law", "Linguistics" ] }, "default": [] }, "publicationTypes": { "title": "Publication types (search modes)", "uniqueItems": true, "type": "array", "description": "Filter by publication type. Multi-select.", "items": { "type": "string", "enum": [ "Review", "JournalArticle", "CaseReport", "ClinicalTrial", "Conference", "Dataset", "Editorial", "LettersAndComments", "MetaAnalysis", "News", "Study", "Book", "BookSection" ], "enumTitles": [ "Review", "Journal Article", "Case Report", "Clinical Trial", "Conference", "Dataset", "Editorial", "Letters / Comments", "Meta-Analysis", "News", "Study", "Book", "Book Section" ] }, "default": [] }, "venues": { "title": "Venues (search modes)", "type": "array", "description": "Filter by publication venue (e.g. `Nature`, `NeurIPS`, `IEEE Transactions on Pattern Analysis`).", "default": [], "items": { "type": "string" } }, "openAccessOnly": { "title": "Open-access only (search modes)", "type": "boolean", "description": "Drop papers without an open-access PDF.", "default": false }, "minCitationCount": { "title": "Min citation count (search modes)", "minimum": 0, "maximum": 10000000, "type": "integer", "description": "Drop papers with fewer than this many citations." }, "sort": { "title": "Sort (searchPaperBulk only)", "enum": [ "", "relevance", "citationCount:desc", "citationCount:asc", "publicationDate:desc", "publicationDate:asc" ], "type": "string", "description": "Sort order for bulk search. Relevance-ranked search uses an internal score and ignores this field.", "default": "" }, "paperIds": { "title": "Paper IDs (byPaper / byPaperCitations / byPaperReferences / recommendations)", "type": "array", "description": "Semantic Scholar paper IDs (40-char hex), or prefixed external IDs: `DOI:10.1145/...`, `ARXIV:1706.03762`, `MAG:...`, `PMID:...`, `PMCID:PMC...`, `ACL:...`. Bare DOIs and arXiv IDs are auto-prefixed.", "default": [], "items": { "type": "string" } }, "authorIds": { "title": "Author IDs (byAuthor / byAuthorPapers)", "type": "array", "description": "Numeric Semantic Scholar author IDs (e.g. `1741101`).", "default": [], "items": { "type": "string" } }, "urls": { "title": "URLs (byUrl mode)", "type": "array", "description": "Semantic Scholar / DOI / arXiv URLs. Examples: `https://www.semanticscholar.org/paper/`, `https://arxiv.org/abs/1706.03762`, `https://doi.org/10.1145/...`, `https://www.semanticscholar.org/author/1741101`.", "default": [], "items": { "type": "string" } }, "includeCitationsOnPaper": { "title": "Include citations on paper records (byPaper)", "type": "boolean", "description": "Embed up to 200 citing-paper summaries inside each paper record (byPaper mode only). Increases payload size.", "default": false }, "includeReferencesOnPaper": { "title": "Include references on paper records (byPaper)", "type": "boolean", "description": "Embed up to 200 cited-paper summaries inside each paper record (byPaper mode only).", "default": false }, "paperFields": { "title": "Custom paper fields (advanced)", "type": "array", "description": "Override the default `fields=` list sent to the API for paper endpoints. Leave empty to use the curated default. See https://api.semanticscholar.org/api-docs/graph for the full list.", "default": [], "items": { "type": "string" } }, "authorFields": { "title": "Custom author fields (advanced)", "type": "array", "description": "Override the default `fields=` list sent to the API for author endpoints.", "default": [], "items": { "type": "string" } }, "semanticScholarApiKey": { "title": "Semantic Scholar API key (optional)", "type": "string", "description": "Raises rate limits 10x. Free signup: https://www.semanticscholar.org/product/api#api-key-form. The actor works without a key." }, "requestDelaySeconds": { "title": "Request delay (seconds)", "minimum": 0, "maximum": 30, "type": "integer", "description": "Delay between API calls. Defaults to 0 (with API key) or 2 (without). Honour the source's rate-limit budget." }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Hard cap on emitted records.", "default": 50 }, "useProxy": { "title": "Use Apify proxy", "type": "boolean", "description": "Force routing through Apify proxy from the first request. Recommended when running without an API key from datacenter IPs (Apify cloud is rate-limited heavily by Semantic Scholar's free tier).", "default": false }, "autoEscalateOnBlock": { "title": "Auto-escalate to proxy on rate-limit", "type": "boolean", "description": "If the API responds 429 from direct IP, automatically rotate through Apify proxy sessions for the rest of the run. Helps avoid getting stuck on the unauthenticated 100-req/5-min cap from a single datacenter IP.", "default": true }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Apify proxy configuration. Used only when useProxy is enabled. Auto-escalation falls back to the default Apify proxy group automatically.", "default": { "useApifyProxy": true, "apifyProxyGroups": [] } } } }, "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 } } } } } } } } } } ```