# OpenAlex Academic Papers Scraper - 250M+ Papers (`wetyr_corporation/openalex-academic-papers-scraper`) Actor Bulk extract 250M+ academic papers from OpenAlex. Free open replacement for Web of Science / Scopus. Citations, abstracts, OA links, concepts. For AI/ML researchers and citation analysis. - **URL**: https://apify.com/wetyr\_corporation/openalex-academic-papers-scraper.md - **Developed by:** [WETYR CORPORATION](https://apify.com/wetyr_corporation) (community) - **Categories:** Education, AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## OpenAlex Academic Papers Scraper Bulk extract academic papers from OpenAlex (250M+ scholarly works across ALL fields - science, humanities, medicine, engineering). Free public API. No key needed. ### Pricing - $0.05 per actor start - $0.005 per paper extracted Typical run: 1,000 LLM papers with abstracts = **~$5.05**. ### Why OpenAlex (vs PubMed, arXiv) | Source | Coverage | Best For | |--------|----------|----------| | **OpenAlex (this)** | 250M papers, ALL fields | General academic research | | PubMed | 35M biomedical | Pharma, healthcare, medicine | | arXiv | 2.5M physics/CS/math | AI/ML cutting-edge research | OpenAlex is a free open replacement for Web of Science / Scopus (which cost $50K+/year per institution). ### Output Per paper: - Reconstructed abstract (OpenAlex stores as inverted index) - Authors + institutions - DOI, OpenAlex ID, journal info - Citations count + references count - Open Access status + free PDF URL - Top 5 concepts with scores (TF-IDF over OpenAlex's concept tree) - Publisher, language, type ### Use cases - AI/ML researcher tools (find related work) - Citation analysis at scale - Institution research output tracking - Building literature review automation - Academic publishing competitive intel # Actor input Schema ## `queries` (type: `array`): Free-text searches across 250M+ academic works. ## `filterFromYear` (type: `integer`): Only papers published from this year. 0 = no filter. ## `filterToYear` (type: `integer`): Only papers published up to this year. 0 = no filter. ## `filterMinCitations` (type: `integer`): Skip papers below this citation count. 0 = no filter. ## `filterOpenAccess` (type: `boolean`): Only return papers with free open-access PDF/HTML. ## `sortBy` (type: `string`): How to sort results. ## `maxResultsPerQuery` (type: `integer`): Total cap per query. OpenAlex allows large pulls. ## `contactEmail` (type: `string`): OpenAlex offers faster rate limits (the 'polite pool') if you provide an email. Recommended. ## Actor input object example ```json { "queries": [ "large language models" ], "filterFromYear": 2020, "filterToYear": 0, "filterMinCitations": 0, "filterOpenAccess": false, "sortBy": "cited_by_count:desc", "maxResultsPerQuery": 50, "contactEmail": "info@wetyr.com" } ```` # 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 = { "queries": [ "large language models" ] }; // Run the Actor and wait for it to finish const run = await client.actor("wetyr_corporation/openalex-academic-papers-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 = { "queries": ["large language models"] } # Run the Actor and wait for it to finish run = client.actor("wetyr_corporation/openalex-academic-papers-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 '{ "queries": [ "large language models" ] }' | apify call wetyr_corporation/openalex-academic-papers-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=wetyr_corporation/openalex-academic-papers-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenAlex Academic Papers Scraper - 250M+ Papers", "description": "Bulk extract 250M+ academic papers from OpenAlex. Free open replacement for Web of Science / Scopus. Citations, abstracts, OA links, concepts. For AI/ML researchers and citation analysis.", "version": "0.1", "x-build-id": "EUFbnMBoPa059dgFZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/wetyr_corporation~openalex-academic-papers-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-wetyr_corporation-openalex-academic-papers-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/wetyr_corporation~openalex-academic-papers-scraper/runs": { "post": { "operationId": "runs-sync-wetyr_corporation-openalex-academic-papers-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/wetyr_corporation~openalex-academic-papers-scraper/run-sync": { "post": { "operationId": "run-sync-wetyr_corporation-openalex-academic-papers-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "queries" ], "properties": { "queries": { "title": "Search Queries", "type": "array", "description": "Free-text searches across 250M+ academic works.", "items": { "type": "string" } }, "filterFromYear": { "title": "Publication Year Min", "minimum": 0, "maximum": 2100, "type": "integer", "description": "Only papers published from this year. 0 = no filter.", "default": 2020 }, "filterToYear": { "title": "Publication Year Max", "minimum": 0, "maximum": 2100, "type": "integer", "description": "Only papers published up to this year. 0 = no filter.", "default": 0 }, "filterMinCitations": { "title": "Minimum Citations", "minimum": 0, "type": "integer", "description": "Skip papers below this citation count. 0 = no filter.", "default": 0 }, "filterOpenAccess": { "title": "Open Access Only", "type": "boolean", "description": "Only return papers with free open-access PDF/HTML.", "default": false }, "sortBy": { "title": "Sort By", "enum": [ "relevance_score:desc", "cited_by_count:desc", "publication_date:desc" ], "type": "string", "description": "How to sort results.", "default": "cited_by_count:desc" }, "maxResultsPerQuery": { "title": "Max Results Per Query", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Total cap per query. OpenAlex allows large pulls.", "default": 50 }, "contactEmail": { "title": "Contact Email (optional but recommended)", "type": "string", "description": "OpenAlex offers faster rate limits (the 'polite pool') if you provide an email. Recommended.", "default": "info@wetyr.com" } } }, "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 } } } } } } } } } } ```