# Academic Paper Resolver — DOI/arXiv to Citations for AI (`haketa/academic-resolver`) Actor Resolve any DOI, arXiv, PubMed ID or paper title into rich metadata: authors, abstract, venue, citation count, references, open-access PDF and cross-IDs. Merged from multiple scholarly sources into RAG-ready Markdown for AI agents & research tools. - **URL**: https://apify.com/haketa/academic-resolver.md - **Developed by:** [Haketa](https://apify.com/haketa) (community) - **Categories:** AI, Developer tools, Agents - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.40 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## Academic Paper Resolver — DOI, arXiv & PubMed to Metadata & Citations Turn any **DOI, arXiv ID, PubMed ID, or paper title** into a rich, structured record: **authors, abstract, venue, year, citation count, references, open-access PDF, topics and cross-IDs** — merged from several scholarly databases into one clean answer, plus a **RAG-ready Markdown card**. Built for **AI agents, research assistants, literature reviews and RAG pipelines**. No API key, no setup. --- ### Why this Actor? An LLM can't reliably recall a paper's exact citation count, its DOI, or who it cites — that data lives across OpenAlex, Crossref, arXiv and PubMed, each with a different format and different gaps. This Actor is the **scholarly grounding** an agent needs: give it an identifier (or a title) and get back verified metadata it can cite, with a PDF link when the paper is open access. - **RAG & literature review** — resolve a batch of DOIs, embed the abstracts and reference lists, and ground your assistant in real papers. - **AI research agents** — a tool an agent calls to answer "what is this paper, how many times is it cited, and what does it reference?" - **Citation graphs** — walk references (and, optionally, citing papers) to map a research area. - **Reference management & enrichment** — clean up a messy bibliography into structured, deduplicated records. Because it merges multiple sources, a gap in one is filled by another: OpenAlex provides the backbone and topics, Crossref adds a titled reference list, arXiv supplies the abstract and PDF for preprints. --- ### What you get For every query, one structured record: | Field | Description | | --- | --- | | `query` | What you asked for | | `resolved` | Whether a paper was found | | `title` | Paper title | | `authors` | Author list | | `year` | Publication year | | `venue` | Journal / conference / source | | `publisher` | Publisher | | `doi`, `arxivId`, `pmid`, `openAlexId` | Cross-identifiers | | `citationCount` | Times cited | | `referenceCount` | Number of references | | `influentialCitationCount` | Influential citations (when available) | | `isOpenAccess`, `oaStatus` | Open-access status | | `pdfUrl` | Open-access PDF link (when available) | | `abstract` | Abstract | | `concepts` | Topics / fields | | `tldr` | One-line summary (when available) | | `references` | Full reference list (titles, years, DOIs) | | `citations` | Papers that cite this one (optional) | | `markdown` | A **RAG-ready paper card** | | `url` | Landing page | | `sources` | Which databases contributed | | `scrapedAt` | Timestamp | --- ### Example output ```json { "query": "10.1038/nature14539", "resolved": "true", "title": "Deep learning", "authors": "Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton", "year": "2015", "venue": "Nature", "doi": "10.1038/nature14539", "pmid": "26017442", "citationCount": "81929", "referenceCount": "50", "isOpenAccess": "true", "pdfUrl": "https://hal.science/hal-04206682", "concepts": "Computer science, Deep learning, Artificial intelligence", "sources": "OpenAlex, Crossref", "markdown": "# Deep learning\n\n**Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton · 2015 · Nature**\n\nCited by 81929 · ...", "scrapedAt": "2026-07-06T15:00:00.000Z" } ```` The `markdown` field renders as a self-contained card: > # Attention Is All You Need > > **Ashish Vaswani, Noam Shazeer, … · 2017 · arXiv** > Cited by 100000+ · Open access > [PDF](https://arxiv.org/pdf/1706.03762) > > ## Abstract > > The dominant sequence transduction models are based on complex recurrent or convolutional neural networks … *** ### Input | Field | Type | Default | Description | | --- | --- | --- | --- | | `queries` | array | — | **Required.** DOIs, arXiv IDs, PubMed IDs, or titles — mixed is fine. | | `includeReferences` | boolean | `true` | Include the paper's reference list. | | `includeCitations` | boolean | `false` | Also fetch papers that cite this one (extra lookup). | | `maxReferences` | integer | `50` | Cap references / citations per paper. | #### The Actor auto-detects each identifier - **DOI** — `10.1038/nature14539` or a `https://doi.org/…` URL - **arXiv** — `1706.03762`, `arXiv:1706.03762`, or old-style `hep-th/9901001` - **PubMed** — a bare PMID like `26017442` - **Title** — any free text, resolved by best match #### Example input ```json { "queries": [ "10.1038/nature14539", "1706.03762", "26017442", "attention is all you need" ], "includeReferences": true } ``` *** ### Use cases in detail #### 1. RAG & literature review Feed a list of DOIs, take the `abstract` and `markdown` fields, embed them, and your assistant now answers from real papers — with citation counts and DOIs for provenance. The `references` array gives you the next hop for expanding a review. #### 2. AI research agents Expose this as a tool so an agent can resolve a paper mid-task: "summarize this DOI and tell me how influential it is." The agent gets structured, sourced data instead of guessing. #### 3. Citation-graph exploration Turn on `includeCitations` to fetch papers that cite a work, and use `references` for papers it cites — build a two-way citation graph around a seed paper. #### 4. Bibliography cleanup & enrichment Have a messy list of titles or half-formed references? Resolve them into clean records with DOIs, years and venues, deduplicated by identifier. #### 5. Open-access discovery `isOpenAccess` and `pdfUrl` tell you instantly whether a paper is freely readable and where to get the PDF. *** ### How to use it 1. Click **Try for free**. 2. Paste identifiers or titles into `queries`. 3. Click **Start**. 4. Read the results as **JSON, CSV, Excel**, or via the Apify API. Batches resolve in parallel — a hundred papers finish in well under a minute. *** ### Calling from the API ```bash curl -X POST "https://api.apify.com/v2/acts/YOUR_ACTOR_ID/runs?token=YOUR_APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "queries": ["10.1038/nature14539", "1706.03762"] }' ``` Then fetch the dataset: ```bash curl "https://api.apify.com/v2/acts/YOUR_ACTOR_ID/runs/last/dataset/items?token=YOUR_APIFY_TOKEN" ``` Works with webhooks, Zapier, Make and n8n. *** ### Frequently asked questions **What can I search by?** DOI, arXiv ID, PubMed ID, or a plain title — mixed in one run. Each is auto-detected. **Where does the data come from?** Multiple open scholarly databases are merged so gaps in one are covered by another. The `sources` field lists which contributed to each record. **Why is an abstract sometimes missing?** Some publishers (e.g. certain Nature titles) don't release abstracts to any open database. When that happens the field is empty even though the rest of the metadata is complete. **Do I get the full reference list?** When the publisher deposits references (most do), yes — titled and dated. `referenceCount` always reflects the total. **Can I get citing papers too?** Yes — set `includeCitations: true`. **Is it good for RAG?** Yes — use the `markdown` card or the `abstract` for embeddings, and keep `doi`/`url` for citations. **Can an AI agent call this automatically?** Yes — it's keyless and returns structured JSON, ideal as an agent/MCP tool. *** ### Tips for great results - **Prefer identifiers over titles.** A DOI or arXiv ID resolves the exact paper; a title returns the best match, which may differ for very common titles. - **Batch your DOIs.** Resolving 100 at once is far faster than one at a time. - **Chunk by reference.** The `references` array is a natural boundary for building citation-aware embeddings. - **Keep the DOI.** Store it with each embedding so your assistant can cite its source. *** ### Notes This Actor retrieves bibliographic metadata from open scholarly sources for research, indexing and AI-grounding purposes. Metadata may be incomplete or contain source-side errors; verify against the publisher of record for critical use. Abstracts and content remain the rights of their respective publishers and authors. *Metadata aggregated from open scholarly databases including OpenAlex, Crossref and arXiv.* *** ### Support Want another identifier type, more citation depth, or an extra field? Open an issue from the Actor's page. Happy researching. # Actor input Schema ## `queries` (type: `array`): DOIs, arXiv IDs, PubMed IDs, or paper titles — mixed is fine. E.g. \["10.1038/nature14539", "1706.03762", "attention is all you need"]. ## `includeReferences` (type: `boolean`): Include the paper's reference list (works it cites). ## `includeCitations` (type: `boolean`): Also fetch papers that cite this one (adds an extra lookup per paper). ## `maxReferences` (type: `integer`): Cap the number of references and citing papers returned per paper. ## `proxyConfiguration` (type: `object`): Apify Proxy settings. The scholarly sources are public, so a proxy is OFF by default. ## Actor input object example ```json { "queries": [ "10.1038/nature14539", "1706.03762", "attention is all you need" ], "includeReferences": true, "includeCitations": false, "maxReferences": 50, "proxyConfiguration": { "useApifyProxy": false } } ``` # Actor output Schema ## `query` (type: `string`): What you asked for ## `resolved` (type: `string`): Whether a paper was found ## `title` (type: `string`): Paper title ## `authors` (type: `string`): Authors ## `year` (type: `string`): Publication year ## `venue` (type: `string`): Journal / venue ## `doi` (type: `string`): DOI ## `arxivId` (type: `string`): arXiv ID ## `pmid` (type: `string`): PubMed ID ## `citationCount` (type: `string`): Times cited ## `referenceCount` (type: `string`): Number of references ## `isOpenAccess` (type: `string`): Open access ## `pdfUrl` (type: `string`): Open-access PDF URL ## `abstract` (type: `string`): Abstract ## `concepts` (type: `string`): Topics / concepts ## `url` (type: `string`): Landing page ## `markdown` (type: `string`): RAG-ready paper card ## `scrapedAt` (type: `string`): Timestamp # 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": [ "10.1038/nature14539", "1706.03762", "attention is all you need" ], "maxReferences": 50 }; // Run the Actor and wait for it to finish const run = await client.actor("haketa/academic-resolver").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": [ "10.1038/nature14539", "1706.03762", "attention is all you need", ], "maxReferences": 50, } # Run the Actor and wait for it to finish run = client.actor("haketa/academic-resolver").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": [ "10.1038/nature14539", "1706.03762", "attention is all you need" ], "maxReferences": 50 }' | apify call haketa/academic-resolver --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=haketa/academic-resolver", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Academic Paper Resolver — DOI/arXiv to Citations for AI", "description": "Resolve any DOI, arXiv, PubMed ID or paper title into rich metadata: authors, abstract, venue, citation count, references, open-access PDF and cross-IDs. Merged from multiple scholarly sources into RAG-ready Markdown for AI agents & research tools.", "version": "0.1", "x-build-id": "vxWgzgpi85x2kexms" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/haketa~academic-resolver/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-haketa-academic-resolver", "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/haketa~academic-resolver/runs": { "post": { "operationId": "runs-sync-haketa-academic-resolver", "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/haketa~academic-resolver/run-sync": { "post": { "operationId": "run-sync-haketa-academic-resolver", "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": "Papers to resolve", "type": "array", "description": "DOIs, arXiv IDs, PubMed IDs, or paper titles — mixed is fine. E.g. [\"10.1038/nature14539\", \"1706.03762\", \"attention is all you need\"].", "items": { "type": "string" } }, "includeReferences": { "title": "Include references", "type": "boolean", "description": "Include the paper's reference list (works it cites).", "default": true }, "includeCitations": { "title": "Include citing papers", "type": "boolean", "description": "Also fetch papers that cite this one (adds an extra lookup per paper).", "default": false }, "maxReferences": { "title": "Max references / citations", "minimum": 0, "type": "integer", "description": "Cap the number of references and citing papers returned per paper.", "default": 50 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Apify Proxy settings. The scholarly sources are public, so a proxy is OFF by default.", "default": { "useApifyProxy": 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 } } } } } } } } } } ```