# Semantic Scholar API

> Semantic Scholar returns research papers, paper details, citations, and author metrics as a workflow and API.

Semantic Scholar (S2) Search papers finds relevant studies by topic or title words, with optional field, year, and access filters. Get paper returns a paper’s abstract, summary, publication details, and citation influence from its title, DOI, or Semantic Scholar link. Get author returns impact metrics and up to 10 most-cited papers from a researcher name, with an optional institution to match listed affiliations.

- Page: https://fous.com/workflows/semantic-scholar
- Handle: `@semantic-scholar`
- Category: [Science](https://fous.com/workflows/category/science)
- Source website: https://semanticscholar.org
- Last verified: Sep 29, 2026
- Fous is not affiliated with Semantic Scholar.

## Methods

### Get author

Operation `get_author`, version 1. 1 credit per call.

Get a Semantic Scholar author’s impact metrics and up to 10 most-cited papers from their name. When an institution is provided, only listed affiliations can be matched; authors without a listed affiliation cannot match.

**Input**

| Field | Type | Required | Example | Description |
|---|---|---|---|---|
| `author` | string | yes | `"Yoshua Bengio"` | Researcher name, for example Fei-Fei Li. |
| `institution` | string | no | `"Southwest Petroleum University"` | Institution to match against listed affiliations, for example Southwest Petroleum University. |

**Input schema**

```json
{
  "type": "object",
  "required": [
    "author"
  ],
  "properties": {
    "author": {
      "type": "string",
      "minLength": 1,
      "description": "Researcher name, for example Fei-Fei Li.",
      "examples": [
        "Yoshua Bengio",
        "Fei-Fei Li",
        "Fei Li"
      ]
    },
    "institution": {
      "type": "string",
      "minLength": 1,
      "description": "Institution to match against listed affiliations, for example Southwest Petroleum University.",
      "examples": [
        "Southwest Petroleum University"
      ]
    }
  },
  "additionalProperties": false,
  "examples": [
    {
      "author": "Yoshua Bengio"
    },
    {
      "author": "Fei-Fei Li"
    },
    {
      "author": "Fei Li",
      "institution": "Southwest Petroleum University"
    }
  ]
}
```

**Output**

| Field | Type | Example | Description |
|---|---|---|---|
| `name` | string | `"Yoshua Bengio"` | Researcher name shown on Semantic Scholar. |
| `h_index` | integer | `212` | Author h-index. |
| `homepage` | string or null | `"https://scholar.google.com/citations?user=rDfyQnIAAAAJ&hl=en"` | Researcher homepage, if listed. |
| `author_id` | string | `"1751762"` | Semantic Scholar author ID. |
| `top_papers` | array |  | Up to 10 most-cited publications, from highest to lowest citation count. |
| `top_papers[].year` | integer or null | `1998` |  |
| `top_papers[].title` | string | `"Gradient-based learning applied to document recognition"` |  |
| `top_papers[].venue` | string or null | `"Proceedings of the IEEE"` |  |
| `top_papers[].citations` | integer | `62855` |  |
| `top_papers[].paper_link` | string | `"https://www.semanticscholar.org/paper/162d958ff885f1462aeda91cd72582323fd6a1f4"` |  |
| `affiliations` | array |  | Listed institutions, or an empty list when none are listed. |
| `total_citations` | integer | `574497` | Total Semantic Scholar citations. |
| `author_page_link` | string | `"https://www.semanticscholar.org/author/1751762"` | Researcher page on Semantic Scholar. |
| `number_of_papers` | integer | `811` | Number of papers attributed to this author. |

**Example input**

```json
{
  "author": "Yoshua Bengio"
}
```

**Example output**

```json
{
  "name": "Yoshua Bengio",
  "h_index": 212,
  "homepage": null,
  "author_id": "1751762",
  "top_papers": [
    {
      "year": 1998,
      "title": "Gradient-based learning applied to document recognition",
      "venue": "Proceedings of the IEEE",
      "citations": 62855,
      "paper_link": "https://www.semanticscholar.org/paper/162d958ff885f1462aeda91cd72582323fd6a1f4"
    },
    {
      "year": 2015,
      "title": "Deep Learning",
      "venue": null,
      "citations": 38310,
      "paper_link": "https://www.semanticscholar.org/paper/2913c2bf3f92b5ae369400a42b2d27cc5bc05ecb"
    },
    {
      "year": 2014,
      "title": "Neural Machine Translation by Jointly Learning to Align and Translate",
      "venue": "International Conference on Learning Representations",
      "citations": 30076,
      "paper_link": "https://www.semanticscholar.org/paper/fa72afa9b2cbc8f0d7b05d52548906610ffbb9c5"
    }
  ],
  "affiliations": [],
  "total_citations": 574497,
  "author_page_link": "https://www.semanticscholar.org/author/1751762",
  "number_of_papers": 811
}
```

### Get paper

Operation `get_paper`, version 1. 1 credit per call.

Get a paper’s abstract, short summary, authors, publication details, and citation influence from its title, DOI, or Semantic Scholar link. Some papers lack a publication date, summary, or free PDF.

**Input**

| Field | Type | Required | Example | Description |
|---|---|---|---|---|
| `paper` | string | yes | `"https://www.semanticscholar.org/paper/204e3073870fae3d05bcbc2f6a8e263d9b72e776"` | Paper title, DOI, or Semantic Scholar paper link; for example, BERT: Pre-training of Deep Bidirectional Transformers. |

**Input schema**

```json
{
  "type": "object",
  "required": [
    "paper"
  ],
  "properties": {
    "paper": {
      "type": "string",
      "description": "Paper title, DOI, or Semantic Scholar paper link; for example, BERT: Pre-training of Deep Bidirectional Transformers.",
      "examples": [
        "https://www.semanticscholar.org/paper/204e3073870fae3d05bcbc2f6a8e263d9b72e776",
        "BERT: Pre-training of Deep Bidirectional Transformers",
        "10.18653/v1/N19-1423"
      ]
    }
  },
  "additionalProperties": false,
  "examples": [
    {
      "paper": "https://www.semanticscholar.org/paper/204e3073870fae3d05bcbc2f6a8e263d9b72e776"
    },
    {
      "paper": "BERT: Pre-training of Deep Bidirectional Transformers"
    },
    {
      "paper": "10.18653/v1/N19-1423"
    }
  ]
}
```

**Output**

| Field | Type | Example | Description |
|---|---|---|---|
| `doi` | string or null | `"10.18653/v1/N19-1423"` | Digital object identifier. |
| `tldr` | string or null | `"A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence an` | One-sentence paper summary. |
| `year` | integer or null | `2017` | Publication year. |
| `title` | string | `"Attention is All you Need"` | Paper title. |
| `venue` | string or null | `"Neural Information Processing Systems"` | Publication venue. |
| `authors` | array |  | Author names. |
| `abstract` | string or null |  | Paper abstract. |
| `paper_link` | string | `"https://www.semanticscholar.org/paper/204e3073870fae3d05bcbc2f6a8e263d9b72e776"` | Semantic Scholar paper page. |
| `free_pdf_link` | string or null | `"https://arxiv.org/pdf/1706.03762.pdf"` | Link to a free PDF, if available. |
| `citation_count` | integer or null | `194179` | Number of citations. |
| `fields_of_study` | array |  | Fields of study. |
| `reference_count` | integer or null | `41` | Number of references. |
| `publication_date` | string or null | `"2017-06-12"` | Publication date (YYYY-MM-DD). |
| `semantic_scholar_paper_id` | string | `"204e3073870fae3d05bcbc2f6a8e263d9b72e776"` | Semantic Scholar paper ID. |
| `influential_citation_count` | integer or null | `20803` | Number of influential citations. |

**Example input**

```json
{
  "paper": "https://www.semanticscholar.org/paper/204e3073870fae3d05bcbc2f6a8e263d9b72e776"
}
```

**Example output**

```json
{
  "doi": null,
  "tldr": "A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely is proposed, which generalizes well to other tasks by app…",
  "year": 2017,
  "title": "Attention is All you Need",
  "venue": "Neural Information Processing Systems",
  "authors": [
    "Ashish Vaswani",
    "Noam Shazeer",
    "Niki Parmar"
  ],
  "abstract": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and d…",
  "paper_link": "https://www.semanticscholar.org/paper/204e3073870fae3d05bcbc2f6a8e263d9b72e776",
  "free_pdf_link": "https://arxiv.org/pdf/1706.03762.pdf",
  "citation_count": 194179,
  "fields_of_study": [
    "Computer Science"
  ],
  "reference_count": 41,
  "publication_date": "2017-06-12",
  "semantic_scholar_paper_id": "204e3073870fae3d05bcbc2f6a8e263d9b72e776",
  "influential_citation_count": 20803
}
```

### Search papers

Operation `search_papers`, version 1. 1 credit per call.

Search Semantic Scholar for research papers in relevance order, with available AI one-sentence summaries, citations, and free PDF links. Summaries and direct PDF links may be unavailable for some papers.

**Input**

| Field | Type | Required | Example | Description |
|---|---|---|---|---|
| `field` | string | no | `"Computer Science"` | Field of study in plain words, for example Medicine or Computer Science. |
| `query` | string | yes | `"long covid fatigue"` | Topic or words from a paper title, for example long covid fatigue. |
| `end_year` | integer | no | `2024` | Latest publication year, for example 2025. |
| `start_year` | integer | no | `2021` | Earliest publication year, for example 2020. |
| `max_results` | integer | no | `12` | Maximum papers to return, for example 10 (up to 100). |
| `open_access_only` | boolean | no | `true` | Only show open-access papers, for example true. |

**Input schema**

```json
{
  "type": "object",
  "required": [
    "query"
  ],
  "properties": {
    "field": {
      "type": "string",
      "description": "Field of study in plain words, for example Medicine or Computer Science.",
      "examples": [
        "Computer Science"
      ]
    },
    "query": {
      "type": "string",
      "minLength": 1,
      "description": "Topic or words from a paper title, for example long covid fatigue.",
      "examples": [
        "long covid fatigue",
        "qzxvunlikelypaperword987654321",
        "graph neural networks"
      ]
    },
    "end_year": {
      "type": "integer",
      "maximum": 2100,
      "minimum": 1800,
      "description": "Latest publication year, for example 2025.",
      "examples": [
        2024
      ]
    },
    "start_year": {
      "type": "integer",
      "maximum": 2100,
      "minimum": 1800,
      "description": "Earliest publication year, for example 2020.",
      "examples": [
        2021
      ]
    },
    "max_results": {
      "type": "integer",
      "default": 10,
      "maximum": 100,
      "minimum": 1,
      "description": "Maximum papers to return, for example 10 (up to 100).",
      "x-fous-developer": true,
      "examples": [
        12
      ]
    },
    "open_access_only": {
      "type": "boolean",
      "default": false,
      "description": "Only show open-access papers, for example true.",
      "examples": [
        true
      ]
    }
  },
  "additionalProperties": false,
  "examples": [
    {
      "query": "long covid fatigue"
    },
    {
      "query": "qzxvunlikelypaperword987654321"
    },
    {
      "field": "Computer Science",
      "query": "graph neural networks",
      "end_year": 2024,
      "start_year": 2021,
      "max_results": 12,
      "open_access_only": true
    }
  ]
}
```

**Output**

| Field | Type | Example | Description |
|---|---|---|---|
| `papers` | array |  |  |
| `papers[].year` | integer or null | `2025` |  |
| `papers[].title` | string | `"Feasibility and acceptance of transdermal auricular vagus nerve stimulation using a TENS device in females suffering fr` |  |
| `papers[].venue` | string or null | `"Wiener Klinische Wochenschrift"` |  |
| `papers[].authors` | array |  |  |
| `papers[].summary` | string or null | `"The treatment was found to be safe, with no significant side effects reported; however, further research with larger st` |  |
| `papers[].paper_id` | string | `"5c9424cb6b209327d75e17424b75613f0863777a"` |  |
| `papers[].paper_link` | string | `"https://www.semanticscholar.org/paper/5c9424cb6b209327d75e17424b75613f0863777a"` |  |
| `papers[].free_pdf_link` | string or null | `"https://link.springer.com/content/pdf/10.1007/s00508-025-02501-1.pdf"` |  |
| `papers[].number_of_citations` | integer or null | `11` |  |

**Example input**

```json
{
  "query": "long covid fatigue"
}
```

**Example output**

```json
{
  "papers": [
    {
      "year": 2025,
      "title": "Feasibility and acceptance of transdermal auricular vagus nerve stimulation using a TENS device in females suffering from long COVID fatigue",
      "venue": "Wiener Klinische Wochenschrift",
      "authors": [
        "Veronika Pfoser‐Poschacher",
        "M. Keilani"
      ],
      "summary": "The treatment was found to be safe, with no significant side effects reported; however, further research with larger study groups is needed to confirm these findings and examine the long-term effects …",
      "paper_id": "5c9424cb6b209327d75e17424b75613f0863777a",
      "paper_link": "https://www.semanticscholar.org/paper/5c9424cb6b209327d75e17424b75613f0863777a",
      "free_pdf_link": "https://link.springer.com/content/pdf/10.1007/s00508-025-02501-1.pdf",
      "number_of_citations": 11
    },
    {
      "year": 2024,
      "title": "Efficacy of dual-task augmented reality rehabilitation in non-hospitalized adults with self-reported long COVID fatigue and cognitive impairment: a pilot study",
      "venue": "Neurological Sciences",
      "authors": [
        "M. Deodato",
        "Caterina Qualizza"
      ],
      "summary": "The preliminary results from this study suggest that dual-task rehabilitation could be a feasible protocol to support cognitive symptoms recovery after COVID-19 and could be helpful in those individua…",
      "paper_id": "ae2e60355e15a7131b2e1bdafc2f139b7049ce0e",
      "paper_link": "https://www.semanticscholar.org/paper/ae2e60355e15a7131b2e1bdafc2f139b7049ce0e",
      "free_pdf_link": null,
      "number_of_citations": 17
    }
  ]
}
```

## Quick start

Call the API with a Fous API key (`FOUS_API_KEY`). To create one, turn on Developer mode in Fous Studio, then open Keys & connections → API keys (https://app.fous.com/keys).

```bash
# First set your key: export FOUS_API_KEY='YOUR_FOUS_API_KEY'
: "${FOUS_API_KEY:?Set FOUS_API_KEY before running this example}"

curl 'https://api.fous.com/v1/query' \
  --fail-with-body --silent --show-error --max-time 120 \
  -H "Authorization: Bearer $FOUS_API_KEY" \
  -H 'Content-Type: application/json' \
  --data-raw '{
  "api": "@semantic-scholar",
  "visibility": "public",
  "operation": "get_author",
  "version": 1,
  "input": {
    "author": "Yoshua Bengio"
  },
  "response": {
    "format": "json"
  }
}'
```

```python
# Save as fous.py and run with python3 fous.py. No packages needed.
# First set your key: export FOUS_API_KEY='YOUR_FOUS_API_KEY'
import json
import os
import urllib.error
import urllib.request

api_key = os.environ.get("FOUS_API_KEY")
if not api_key:
    raise RuntimeError("Set FOUS_API_KEY before running this example")

body = json.loads("{\n  \"api\": \"@semantic-scholar\",\n  \"visibility\": \"public\",\n  \"operation\": \"get_author\",\n  \"version\": 1,\n  \"input\": {\n    \"author\": \"Yoshua Bengio\"\n  },\n  \"response\": {\n    \"format\": \"json\"\n  }\n}")
request = urllib.request.Request(
    "https://api.fous.com/v1/query",
    data=json.dumps(body).encode("utf-8"),
    headers={
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    },
    method="POST",
)
try:
    with urllib.request.urlopen(request, timeout=120) as response:
        result = json.load(response)
except urllib.error.HTTPError as error:
    raise RuntimeError(f"HTTP {error.code}: {error.read().decode('utf-8', errors='replace')}") from error
if result.get("success") is False:
    raise RuntimeError(result.get("error", {}).get("message", "Request failed"))
print(json.dumps(result["data"]["output"], indent=2))
```

```typescript
// Save as fous.mts and run with npx tsx fous.mts.
// First set your key: export FOUS_API_KEY='YOUR_FOUS_API_KEY'
const apiKey = process.env.FOUS_API_KEY;
if (!apiKey) throw new Error("Set FOUS_API_KEY before running this example");

const response = await fetch("https://api.fous.com/v1/query", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  signal: AbortSignal.timeout(120_000),
  body: JSON.stringify({
  "api": "@semantic-scholar",
  "visibility": "public",
  "operation": "get_author",
  "version": 1,
  "input": {
    "author": "Yoshua Bengio"
  },
  "response": {
    "format": "json"
  }
}),
});
type ApiResult = { success: boolean; data?: { output: unknown }; error?: { message: string } };
const result: ApiResult = await response.json();
if (!response.ok || result.success === false) {
  throw new Error(result.error?.message ?? `HTTP ${response.status}`);
}
if (!result.data) throw new Error("Missing API response data");
console.log(result.data.output);
```

Or describe the data in plain language: send `{"api":"@semantic-scholar","prompt":"Describe the data you need, with every detail"}` to the same URL. Fous fills in the input, runs the method that fits and returns only the fields you asked for; `data.route.calls[].request` is the exact call it made. Routing is free; the run costs the same.

## Use cases

- Find papers on a research topic within a publication-year range.
- Review paper abstracts, authors, venues, and citation counts.
- Identify open-access papers with available free PDF links.
- Compare researchers using citation totals and h-index.
- Compile researchers’ most-cited publications for literature reviews.

## FAQ

### Is Fous affiliated with Semantic Scholar?

No. Fous is not affiliated with Semantic Scholar. This workflow reads the public semanticscholar.org website and returns its data.

### How much does it cost?

Each run costs 1 credit. With pay-as-you-go, a credit costs 1¢; monthly plans cost less per credit.

### Do I need a Semantic Scholar account?

No. You only need a Fous account.

### How current is the data?

Fous gets the data from semanticscholar.org when you run it; repeating the same request within a day may return the saved result. Fous checks this workflow automatically; it last passed a check on Sep 29, 2026.

### Which papers cover a topic I’m researching?

Search papers returns relevant papers for a topic or title words, with optional field, year, and open-access filters.

### What are a paper’s abstract and citation count?

Get paper returns a paper’s abstract, citation count, and other publication details from its title, DOI, or Semantic Scholar link.

### What are a researcher’s impact metrics?

Get author returns a researcher’s h-index, total citations, paper count, and up to 10 most-cited papers.

## Related

- [Google Scholar API](https://fous.com/workflows/google-scholar.md): Google Scholar provides papers, citing papers, ready-made citations and researcher profiles with metrics and up to 20 top-cited papers; public access may be temporarily limited.
- [OpenAlex API](https://fous.com/workflows/openalex.md): OpenAlex is an open catalog of scholarly research and connections, offering ranked papers and institution research summaries with annual output, leading topics, and coauthors.
- [arXiv API](https://fous.com/workflows/arxiv.md): arXiv returns up to 100 matching research preprints or individual paper details, and newly announced subject papers in announcement order; announcement dates may differ from today, and replacements are excluded.
- [PubMed API](https://fous.com/workflows/pubmed.md): PubMed searches medical and life-science articles by topic and retrieves one article’s abstract and publication details; abstracts and free full-text links may be unavailable.
- [Unpaywall API](https://fous.com/workflows/unpaywall.md): Find free-to-read scholarly papers and their legal copies.
- [ORCID API](https://fous.com/workflows/orcid.md): Researcher identifiers and public research records.
- [Crossref API](https://fous.com/workflows/crossref.md): Find scholarly works and format citations from their registered metadata.
- [bioRxiv API](https://fous.com/workflows/biorxiv.md): Biology and medicine preprints from bioRxiv and medRxiv.
- [All Science workflows](https://fous.com/workflows/category/science)