# OpenAlex Scraper - Academic Papers & Citations (`benthepythondev/openalex-scraper`) Actor OpenAlex Scraper to search 250M+ academic papers via the free OpenAlex API. Extract title, authors, institutions, year, venue, DOI, citation count, open-access status, concepts and PDF links. Filter by year and open access. For literature reviews, citation analysis and AI/RAG datasets. - **URL**: https://apify.com/benthepythondev/openalex-scraper.md - **Developed by:** [ben](https://apify.com/benthepythondev) (community) - **Categories:** Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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 Scraper — Academic Papers & Citations **Search [OpenAlex](https://openalex.org) — a free, open index of over 250 million scholarly works.** Built for **literature reviews, research analytics, citation analysis, and academic databases**. Keyless, fast and reliable — no proxy or browser needed. ### What you get Per work: - **title**, **id**, **doi** - **publication_year**, **publication_date**, **type**, **language** - **authors**, **author_count**, **institutions** - **venue**, **venue_type** - **cited_by_count** - **is_open_access**, **open_access_url** - **landing_page_url**, **pdf_url** - **concepts** (topics) - **search_query**, **scraped_at** ### Why this Actor | | OpenAlex Scraper | Manual search | Paid databases | |---|---|---|---| | 250M+ works, free data | Yes | — | $$$ | | Citations + open access | Yes | Slow | Varies | | Filter by year / OA | Yes | Manual | Varies | | Pay per result | Yes | — | Subscription | ### Input | Field | Type | Description | |---|---|---| | `searchQuery` | string | Topic or keyword | | `fromYear` / `toYear` | integer | Publication year range | | `openAccessOnly` | boolean | Only open-access works | | `sortBy` | string | Relevance / Most Cited / Newest | | `maxResults` | integer | Max works to return | #### Example input ```json { "searchQuery": "machine learning", "fromYear": 2018, "sortBy": "citations", "maxResults": 100 } ```` ### Sample output ```json { "title": "Random Forests", "authors": ["Leo Breiman"], "publication_year": 2001, "venue": "Machine Learning", "type": "article", "cited_by_count": 124485, "is_open_access": true, "doi": "https://doi.org/10.1023/a:1010933404324" } ``` ### Use cases - **Literature reviews** — gather and rank relevant papers fast - **Research analytics** — analyse citations, venues and trends - **Academic databases** — build topic and author corpora - **Bibliometrics** — measure impact across fields and years ### Pricing Pay-per-result. You are charged only for the works returned — empty runs cost nothing. ### Notes & legal - Uses the free OpenAlex API. A polite contact email is sent to use the fast pool. - Use data only for lawful purposes. ### Related actors More scrapers from the same author: - [PubMed Scraper](https://apify.com/benthepythondev/pubmed-scraper) — biomedical literature & citations - [arXiv Scraper](https://apify.com/benthepythondev/arxiv-scraper) — 2M+ scientific papers, abstracts & PDFs - [Reddit Archive Scraper](https://apify.com/benthepythondev/reddit-archive-scraper) — years of historical posts & comments # Actor input Schema ## `searchQuery` (type: `string`): Topic or keyword to search (e.g. 'machine learning', 'crispr gene editing'). ## `fromYear` (type: `integer`): Only include works published on or after this year. ## `toYear` (type: `integer`): Only include works published on or before this year. ## `openAccessOnly` (type: `boolean`): Only return works that are open access. ## `sortBy` (type: `string`): How to sort the results. ## `maxResults` (type: `integer`): Maximum number of works to return. ## Actor input object example ```json { "searchQuery": "machine learning", "openAccessOnly": false, "sortBy": "relevance", "maxResults": 25 } ``` # 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 = { "searchQuery": "machine learning", "maxResults": 25 }; // Run the Actor and wait for it to finish const run = await client.actor("benthepythondev/openalex-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 = { "searchQuery": "machine learning", "maxResults": 25, } # Run the Actor and wait for it to finish run = client.actor("benthepythondev/openalex-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 '{ "searchQuery": "machine learning", "maxResults": 25 }' | apify call benthepythondev/openalex-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=benthepythondev/openalex-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenAlex Scraper - Academic Papers & Citations", "description": "OpenAlex Scraper to search 250M+ academic papers via the free OpenAlex API. Extract title, authors, institutions, year, venue, DOI, citation count, open-access status, concepts and PDF links. Filter by year and open access. For literature reviews, citation analysis and AI/RAG datasets.", "version": "1.0", "x-build-id": "MuUaZvFnKPNWCUgza" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/benthepythondev~openalex-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-benthepythondev-openalex-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/benthepythondev~openalex-scraper/runs": { "post": { "operationId": "runs-sync-benthepythondev-openalex-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/benthepythondev~openalex-scraper/run-sync": { "post": { "operationId": "run-sync-benthepythondev-openalex-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": [ "searchQuery" ], "properties": { "searchQuery": { "title": "Search Query", "type": "string", "description": "Topic or keyword to search (e.g. 'machine learning', 'crispr gene editing')." }, "fromYear": { "title": "From Year", "minimum": 1800, "maximum": 2100, "type": "integer", "description": "Only include works published on or after this year." }, "toYear": { "title": "To Year", "minimum": 1800, "maximum": 2100, "type": "integer", "description": "Only include works published on or before this year." }, "openAccessOnly": { "title": "Open Access Only", "type": "boolean", "description": "Only return works that are open access.", "default": false }, "sortBy": { "title": "Sort By", "enum": [ "relevance", "citations", "newest" ], "type": "string", "description": "How to sort the results.", "default": "relevance" }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of works to return.", "default": 50 } } }, "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 } } } } } } } } } } ```