Semantic Scholar is an AI-powered academic search engine that helps researchers discover and understand scientific literature
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Build and ship the Semantic Scholar plugin to earn 30 points when we merge it to main. You have 1 hour to link an issue, then 3 hours to open a PR with the plugin scaffold.
Examples: <ul> <li><code>https://api.semanticscholar.org/graph/v1/paper/649def34f8be52c8b66281af98ae884c09aef38b</code></li> <ul> <li>Returns a paper with its paperId and title. </li> </ul> <li><code>https://api.semanticscholar.org/graph/v1/paper/649def34f8be52c8b66281af98ae884c09aef38b?fields=url,year,authors</code></li> <ul> <li>Returns the paper's paperId, url, year, and list of authors. </li> <li>Each author has authorId and name.</li> </ul> <li><code>https://api.semanticscholar.org/graph/v1/paper/649def34f8be52c8b66281af98ae884c09aef38b?fields=citations.authors</code></li> <ul> <li>Returns the paper's paperId and list of citations. </li> <li>Each citation has its paperId plus its list of authors.</li> <li>Each author has their 2 always included fields of authorId and name.</li> </ul> <br> Limitations: <ul> <li>Can only return up to 10 MB of data at a time.</li> </ul> </ul>
Retrieves the list of authors for a specific paper identified by its unique paper_id in the Semantic Scholar database. This endpoint returns detailed author information including authorId and name (returned by default), and optionally: url, affiliations, homepage, paperCount, citationCount, hIndex, and papers (with subfields). Use the 'fields' parameter to request additional author fields beyond the defaults. The response is paginated and includes offset/limit parameters for retrieving large author lists. This tool is ideal for exploring paper collaborations, identifying author affiliations, or building author networks. It accepts various paper ID formats including Semantic Scholar IDs, DOI, ARXIV, PMID, and others.
Retrieves a list of citations for a specific academic paper using its unique Semantic Scholar paper ID. This endpoint is useful for researchers and developers who want to explore the impact and connections of a particular academic work within the broader scientific literature. It provides information about other papers that have cited the specified paper, allowing users to trace the influence of research and discover related works. The endpoint should be used when analyzing the reception and impact of a specific paper, building citation networks, or conducting bibliometric studies. It does not provide the full text of citing papers or detailed information about the citations beyond basic metadata.
Retrieves the list of references cited by a specific paper in the Semantic Scholar database. This endpoint allows users to explore the scholarly context of a publication by accessing its bibliography. It's particularly useful for understanding the foundation of a paper's research, tracing the development of ideas, or conducting literature reviews. The tool returns details about the cited papers, which may include their titles, authors, publication dates, and Semantic Scholar IDs. It should be used when analyzing a paper's sources or investigating the connections between different academic works. Note that this endpoint only provides outgoing references (papers cited by the specified paper) and not incoming citations (papers that cite the specified paper).
Retrieve detailed information about an author from Semantic Scholar, including name, affiliations, publication statistics (paperCount, citationCount, h-index), external IDs (ORCID, DBLP), and optionally papers. By default returns authorId and name only. Use 'fields' parameter for additional data: name, url, affiliations, homepage, externalIds, paperCount, citationCount, hIndex, papers (supports nested fields like papers.title, papers.year). Limit: 10 MB per request.
Retrieves a list of papers authored or co-authored by a specific researcher identified by their unique Semantic Scholar author ID. This endpoint is particularly useful for conducting literature reviews, analyzing an author's body of work, or tracking a researcher's publications over time. It provides a comprehensive view of an author's contributions to their field of study, including all papers where the author is listed as an author regardless of their authorship position. The response may be paginated for authors with a large number of publications, and additional API calls might be necessary to retrieve the complete list of papers. Use the offset and limit parameters to control pagination.
Get download links for incremental diffs between dataset releases. Returns a list of diffs required to update a dataset from start_release to end_release, enabling efficient dataset synchronization. Use when you need to update a local dataset copy without re-downloading the entire dataset.
Tool to get download links for a specific dataset within a release. Use when you need to download Semantic Scholar dataset files from S3. Returns pre-signed URLs for all dataset partitions.
Tool to retrieve metadata for a specific Semantic Scholar dataset release. Returns release information including available datasets with their descriptions. Use when you need to discover what datasets are available in a release or get release documentation.
Retrieves detailed information for multiple authors from Semantic Scholar in a single API call. This endpoint allows users to efficiently fetch data for a batch of authors by providing their unique Semantic Scholar IDs. It's particularly useful for applications that need to gather information on multiple authors simultaneously, reducing the number of individual API calls required. The endpoint accepts a list of author IDs and returns comprehensive details for each author, which may include their publications, citations, and other relevant academic information. While the exact response structure is not specified in the given schema, users can expect rich metadata about the requested authors.
Retrieve detailed information for multiple academic papers in a single API call using the Semantic Scholar paper batch endpoint. This endpoint efficiently fetches data for up to 500 papers at once, significantly reducing the number of individual API requests needed. Key features: - Accepts multiple paper ID formats (Semantic Scholar ID, CorpusId, DOI, ArXiv, PMID, etc.) - Customizable field selection to retrieve only needed data - Papers not found return null in the corresponding array position - Results maintain the same order as input IDs - Supports nested field queries (e.g., authors.name, citations.title) Use this endpoint when you have a list of known paper IDs and want to retrieve their details simultaneously, rather than making individual requests for each paper.
Tool to get paper recommendations based on positive and negative example papers. Use when you need to find papers similar to ones you like (positive examples) and optionally dissimilar to ones you don't like (negative examples). The recommendation engine analyzes the provided examples and returns relevant papers from the Semantic Scholar database.
Tool to get recommended papers for a single positive example paper. Use when you need to find papers similar to a given paper based on Semantic Scholar's recommendation algorithm.
Tool to list all available dataset releases from Semantic Scholar. Use when you need to discover available release dates for downloading datasets.
DEPRECATED: Use SEMANTICSCHOLAR_SEARCH_PAPERS instead. The SearchPapers endpoint allows users to search for academic papers within the Semantic Scholar database. It provides a powerful way to discover relevant scientific literature based on user-defined search criteria. This endpoint should be used when researchers, students, or developers need to find papers related to specific topics, authors, or time periods. The search functionality supports various query parameters to refine and customize the search results, making it suitable for both broad exploratory searches and targeted inquiries. However, users should be aware that the search is limited to papers indexed by Semantic Scholar, and very recent publications might not be immediately available. The endpoint returns a list of papers matching the search criteria, along with selected metadata fields, facilitating efficient literature review and analysis.
Behaves similarly to <code>/paper/search</code>, but is intended for retrieval of a single paper based on closest title match to given query. Examples: <ul> <li><code>https://api.semanticscholar.org/graph/v1/paper/search/match?query=Construction of the Literature Graph in Semantic Scholar</code></li> <ul> <li>Returns a single paper that is the closest title match.</li> <li>Each paper has its paperId, title, and matchScore as well as any other requested fields.</li> </ul> <li><code>https://api.semanticscholar.org/graph/v1/paper/search/match?query=totalGarbageNonsense</code></li> <ul> <li>Returns with a 404 error and a "Title match not found" message.</li> </ul> </ul> <br> Limitations: <ul> <li>Will only return the single highest match result.</li> </ul> </ul>
Tool to perform bulk search for academic papers. Intended for bulk retrieval of basic paper data without search relevance scoring. Use when you need to retrieve large sets of papers with optional text filtering and various criteria. Supports token-based pagination for efficient fetching of up to 10 million papers (use Datasets API for larger needs).
Search for academic authors in the Semantic Scholar database by name. This action searches for authors using plain-text name queries. The search is case-insensitive and supports partial name matches (e.g., "Smith" will match "John Smith", "Adam Smith", etc.). Use cases: - Find authors by their name to get their author ID - Discover authors in a specific research area by searching common names - Retrieve author metadata including publications, affiliations, citation counts, and h-index - Build author directories or research networks The response includes pagination metadata (total, offset, next) to help retrieve large result sets. Use the 'fields' parameter to customize which author attributes are returned, and use 'offset' and 'limit' for pagination through result sets larger than 1000 authors. Note: Results are paginated with a maximum of 1000 results per request. Use the 'next' field in the response to determine the offset for the next page.
Tool to search for academic papers by relevance in the Semantic Scholar database. Use when searching for papers on specific topics, keywords, or research areas. Returns papers ordered by relevance score with support for extensive filtering by publication type, date, venue, field of study, and citation metrics.
Get autocomplete suggestions for paper queries. Returns a list of papers matching the partial query string, useful for interactive search experiences. Each suggestion includes the paper ID, title, and authors with publication year. Example: For query "machine learning", returns papers like "Machine learning - a probabilistic perspective" by Murphy, 2012.
Search for text snippets (~500 words) within academic papers that match your natural language query. Returns relevant excerpts from papers' titles, abstracts, and body text, ranked by relevance score. Each result includes: snippet text, location in paper, citation references, and paper metadata (title, authors, corpus ID). Supports filtering by authors, publication date, venue, field of study, citation count, and specific paper IDs. Results sorted by relevance (highest score first). Use limit=10 (default, max 1000) to control result count.
No webhooks.
