# Academic Papers Scraper — OpenAlex, 250M+ Works, No Login (`charliemorrisondev/openalex-papers-scraper`) Actor Search 250M+ scholarly works from OpenAlex — by keyword, author, institution or journal; filter by year, citations, open access and work type. No login, no API key, no captcha. Normalized JSON: DOI, title, authors, venue, year, citations, OA PDF link, abstract. - **URL**: https://apify.com/charliemorrisondev/openalex-papers-scraper.md - **Developed by:** [Petro Pankov](https://apify.com/charliemorrisondev) (community) - **Categories:** Education, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.50 / 1,000 papers This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Academic Papers Scraper — OpenAlex (250M+ works, no login) Search the world's scholarly literature — journal articles, preprints, book chapters, datasets and reviews — and get clean, structured rows back. Built directly on [OpenAlex](https://openalex.org), an open catalogue of over 250 million works. **No login. No API key. No captcha. No proxies needed.** ### Why this instead of a Google Scholar scraper Google Scholar has no public API. Every scraper built on it is reverse-engineering an HTML surface that actively fights automation — which is why those Actors break, rate-limit, and need proxy budgets. OpenAlex publishes the same literature through a documented, open, versioned API with a stable schema. Nothing to fight, nothing to break. That means: predictable runs, no proxy costs, no captcha failures, and results you can actually schedule against. ### What you can do - **Keyword search** across title, abstract and fulltext — `"large language models"` - **By author** — `"Yoshua Bengio"` (name match, no author IDs needed) - **By institution** — `"Massachusetts Institute of Technology"`, `"Max Planck"` - **By journal / conference** — `"Nature"`, `"NeurIPS"` - **Filter** by year range, minimum citations, open-access-only, and work type - **Sort** by relevance, citation count, or newest first ### Output Each row is normalized: | field | description | |---|---| | `id`, `openAlexUrl` | OpenAlex work ID and URL | | `doi` | DOI (bare, no `https://doi.org/` prefix) | | `title` | Work title | | `authors`, `firstAuthor`, `authorCount` | Full author list and convenience fields | | `venue`, `publisher`, `issn` | Journal / conference and publisher | | `year`, `publishedAt` | Publication year and ISO date | | `type`, `language` | Work type (article, preprint, …) and language | | `citedByCount`, `referencedWorksCount`, `fwci` | Citation metrics | | `isOpenAccess`, `oaStatus`, `pdfUrl` | Open-access status and direct free-PDF link | | `landingPageUrl` | Publisher landing page | | `concepts`, `institutions`, `countries` | Topics and affiliations | | `abstract` | Full abstract text (when **Include abstract** is on) | ### Example ```json { "search": "retrieval augmented generation", "fromYear": 2023, "minCitations": 10, "openAccessOnly": true, "sortBy": "citations", "maxResults": 200, "includeAbstract": true } ```` ### Typical uses - Literature reviews and systematic reviews - Citation and research-trend analysis - Tracking a lab's, author's or institution's output - Building datasets of open-access PDFs for downstream NLP - Competitive/landscape research in a scientific field ### Notes - Results are capped at today's date unless you set **To year** — OpenAlex contains a small number of records with future publication dates, and this keeps "newest first" meaningful. - Requests go through OpenAlex's polite pool (a contact address is sent with each call), which is the usage pattern OpenAlex asks for. - Abstracts are stored by OpenAlex as an inverted index for licensing reasons; this Actor reconstructs them into plain text for you. # Actor input Schema ## `search` (type: `string`): Full-text search across title, abstract and fulltext (e.g. "large language models", "CRISPR off-target"). Leave empty if you are filtering by author, institution, venue or year instead. ## `authors` (type: `array`): Match works by author name, e.g. "Yoshua Bengio". Multiple names are OR-ed. ## `institutions` (type: `array`): Match works by author affiliation, e.g. "MIT", "Max Planck". Multiple values are OR-ed. ## `venues` (type: `array`): Match works by journal or conference name, e.g. "Nature", "NeurIPS". Multiple values are OR-ed. ## `fromYear` (type: `integer`): Only works published in this year or later. ## `toYear` (type: `integer`): Only works published in this year or earlier. If omitted, results are capped at today — OpenAlex contains some records with bogus future publication dates. ## `minCitations` (type: `integer`): Only works cited at least this many times. Useful for cutting long-tail noise. ## `openAccessOnly` (type: `boolean`): Only works with a free full text available. ## `types` (type: `array`): Restrict to work types, e.g. article, preprint, book-chapter, dataset, review. ## `sortBy` (type: `string`): relevance (default, requires a search query), citations (most cited first), or date (newest first). ## `maxResults` (type: `integer`): Maximum number of works to return. Results are paged 200 at a time. ## `includeAbstract` (type: `boolean`): Reconstruct and include the full abstract text for each work. Slightly larger dataset rows. ## Actor input object example ```json { "search": "large language models", "authors": [ "Yoshua Bengio" ], "institutions": [ "Massachusetts Institute of Technology" ], "venues": [ "Nature" ], "fromYear": 2023, "minCitations": 10, "openAccessOnly": false, "types": [ "article" ], "sortBy": "relevance", "maxResults": 100, "includeAbstract": false } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("charliemorrisondev/openalex-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 = {} # Run the Actor and wait for it to finish run = client.actor("charliemorrisondev/openalex-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 '{}' | apify call charliemorrisondev/openalex-papers-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=charliemorrisondev/openalex-papers-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Academic Papers Scraper — OpenAlex, 250M+ Works, No Login", "description": "Search 250M+ scholarly works from OpenAlex — by keyword, author, institution or journal; filter by year, citations, open access and work type. No login, no API key, no captcha. Normalized JSON: DOI, title, authors, venue, year, citations, OA PDF link, abstract.", "version": "0.0", "x-build-id": "rCgFtMEldHrD0AabT" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/charliemorrisondev~openalex-papers-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-charliemorrisondev-openalex-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/charliemorrisondev~openalex-papers-scraper/runs": { "post": { "operationId": "runs-sync-charliemorrisondev-openalex-papers-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/charliemorrisondev~openalex-papers-scraper/run-sync": { "post": { "operationId": "run-sync-charliemorrisondev-openalex-papers-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "search": { "title": "Search query", "type": "string", "description": "Full-text search across title, abstract and fulltext (e.g. \"large language models\", \"CRISPR off-target\"). Leave empty if you are filtering by author, institution, venue or year instead." }, "authors": { "title": "Authors", "type": "array", "description": "Match works by author name, e.g. \"Yoshua Bengio\". Multiple names are OR-ed.", "items": { "type": "string" } }, "institutions": { "title": "Institutions", "type": "array", "description": "Match works by author affiliation, e.g. \"MIT\", \"Max Planck\". Multiple values are OR-ed.", "items": { "type": "string" } }, "venues": { "title": "Journals / venues", "type": "array", "description": "Match works by journal or conference name, e.g. \"Nature\", \"NeurIPS\". Multiple values are OR-ed.", "items": { "type": "string" } }, "fromYear": { "title": "From year", "type": "integer", "description": "Only works published in this year or later." }, "toYear": { "title": "To year", "type": "integer", "description": "Only works published in this year or earlier. If omitted, results are capped at today — OpenAlex contains some records with bogus future publication dates." }, "minCitations": { "title": "Minimum citations", "type": "integer", "description": "Only works cited at least this many times. Useful for cutting long-tail noise." }, "openAccessOnly": { "title": "Open access only", "type": "boolean", "description": "Only works with a free full text available.", "default": false }, "types": { "title": "Work types", "type": "array", "description": "Restrict to work types, e.g. article, preprint, book-chapter, dataset, review.", "items": { "type": "string" } }, "sortBy": { "title": "Sort by", "enum": [ "relevance", "citations", "date" ], "type": "string", "description": "relevance (default, requires a search query), citations (most cited first), or date (newest first).", "default": "relevance" }, "maxResults": { "title": "Max results", "type": "integer", "description": "Maximum number of works to return. Results are paged 200 at a time.", "default": 100 }, "includeAbstract": { "title": "Include abstract", "type": "boolean", "description": "Reconstruct and include the full abstract text for each work. Slightly larger dataset rows.", "default": false } } }, "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 } } } } } } } } } } ```