# OpenAlex — Academic Papers & Citations Search (`omao/openalex`) Actor Search 250M+ scholarly works via OpenAlex into clean JSON: title, authors, venue, year, citation count, open-access status, PDF URL, concepts and reconstructed abstract. Powered by the public OpenAlex API. No API key. - **URL**: https://apify.com/omao/openalex.md - **Developed by:** [Marouane Oulabass](https://apify.com/omao) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $1.00 / 1,000 works 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/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 ## OpenAlex — Academic Papers & Citations Search to JSON **Search hundreds of millions of scholarly works via OpenAlex — as clean JSON.** Pass topics, titles or keywords and get back a tidy row per paper: title, authors, venue, year, citation count, open-access status, PDF URL, concepts and a reconstructed abstract. Powered by the public OpenAlex API — no API key. The easy way to build research and citation datasets for science, RAG and analytics. ### Who uses this - 🎓 **Researchers & academics** — find papers and track citations on any topic. - 🤖 **AI / LLM & RAG pipelines** — ingest abstracts and metadata at scale. - 📊 **Research analysts & librarians** — study venues, citations and open access. - 🏢 **R&D & competitive intel** — monitor publications in your field. - 🧑‍💻 **Developers** — a clean scholarly-search endpoint by keyword. ### What you get — one clean row per paper | Field | Description | |---|---| | `title` | Paper title | | `authors` + `authorCount` | Authors | | `venue` | Journal / venue | | `publicationYear` + `publicationDate` | Publication date | | `citedByCount` | Citation count | | `isOpenAccess` + `oaUrl` | Open-access status & URL | | `concepts` | Topics / concepts | | `abstract` | Reconstructed abstract | | `doi` + `pmid` | External IDs | | `referencedWorksCount` | References count | | `landingPageUrl` + `pdfUrl` | Links | ### Example **Input** ```json { "queries": ["large language models", "crispr gene editing"], "maxResultsPerQuery": 25 } ```` **Output (one item)** ```json { "id": "https://openalex.org/W2741809807", "title": "Attention Is All You Need", "authors": ["Ashish Vaswani", "Noam Shazeer"], "venue": "NeurIPS", "publicationYear": 2017, "citedByCount": 95000, "isOpenAccess": true, "concepts": ["Artificial intelligence", "Transformer"], "doi": "https://doi.org/10.5555/3295222.3295349" } ``` ### Why this actor - ✅ **Massive coverage** — OpenAlex indexes 250M+ scholarly works. - 🔎 **Keyword search** — topics, titles and terms. - 📈 **Citation counts** — measure impact per paper. - 📝 **Reconstructed abstracts** — full abstract text, ready for LLM/RAG. - ⚡ **Fast & affordable** — pay only per paper returned. No API key. ### Input options - `queries` *(required)* — academic search queries. - `maxResultsPerQuery` — cap papers per query. ### FAQ **Do I get the abstract?** Yes — the abstract is reconstructed from OpenAlex's inverted index into readable text. **Are citation counts included?** Yes — `citedByCount` per paper. **Can I find open-access PDFs?** Yes — `isOpenAccess` and `oaUrl`/`pdfUrl` when available. **How fresh is the data?** Live from the OpenAlex API on every run. **Can I export to CSV/Excel/Google Sheets?** Yes — the dataset exports to JSON, CSV, Excel and HTML, or pull it via the API. **What does it cost?** Pay-per-paper — you're only charged for papers actually returned. *** *Tip: search your field's key terms and sort your dataset by `citedByCount` to find the most influential papers.* # Actor input Schema ## `queries` (type: `array`): Academic search queries (topic, title, keyword). One search per query. ## `maxResultsPerQuery` (type: `integer`): Cap the number of papers per query. ## `healthCheckMode` (type: `boolean`): Internal monitoring: run a canary search and FAIL if broken (no billing). ## Actor input object example ```json { "queries": [ "crispr gene editing", "large language models" ], "maxResultsPerQuery": 25, "healthCheckMode": false } ``` # Actor output Schema ## `papers` (type: `string`): All papers (title, authors, venue, citations, open access, abstract). # 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": [ "machine learning" ] }; // Run the Actor and wait for it to finish const run = await client.actor("omao/openalex").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": ["machine learning"] } # Run the Actor and wait for it to finish run = client.actor("omao/openalex").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": [ "machine learning" ] }' | apify call omao/openalex --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=omao/openalex", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenAlex — Academic Papers & Citations Search", "description": "Search 250M+ scholarly works via OpenAlex into clean JSON: title, authors, venue, year, citation count, open-access status, PDF URL, concepts and reconstructed abstract. Powered by the public OpenAlex API. No API key.", "version": "0.1", "x-build-id": "8rxuBBqrJ4h6cgSbh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/omao~openalex/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-omao-openalex", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/omao~openalex/runs": { "post": { "operationId": "runs-sync-omao-openalex", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/omao~openalex/run-sync": { "post": { "operationId": "run-sync-omao-openalex", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Academic search queries (topic, title, keyword). One search per query.", "items": { "type": "string" } }, "maxResultsPerQuery": { "title": "Max results per query", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Cap the number of papers per query.", "default": 25 }, "healthCheckMode": { "title": "Health-check mode", "type": "boolean", "description": "Internal monitoring: run a canary search and FAIL if broken (no billing).", "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 } } } } } } } } } } ```