Semantic Scholar is a free AI-powered academic search engine built by the Allen Institute for AI. It indexes 200 million papers across all disciplines, generates TLDR summaries for every paper, maps citation networks visually, and provides AI-powered recommendations — all at no cost.
Free Academic Search200M+ papersTLDR summariesCompletely freeLast reviewed: April 2026
What is Semantic Scholar?
Semantic Scholar is a free AI-powered academic search engine built and maintained by the Allen Institute for AI (AI2), a Seattle-based nonprofit research organisation. It indexes over 200 million academic papers across all disciplines — from computer science and biomedical research to humanities and social sciences — and makes them searchable at no cost, with no account required for basic searches.
What distinguishes Semantic Scholar from Google Scholar is AI. Every paper in Semantic Scholar has a TLDR summary — a one or two sentence AI-generated summary of the paper's core contribution, generated using the full text. These summaries let you scan dozens of papers in minutes to identify the most relevant ones before reading. Google Scholar shows you titles and snippets; Semantic Scholar shows you what each paper actually contributes.
The citation graph features are particularly powerful: you can see which papers cited a given work, which papers it cited, and how papers in a field connect through shared citations. This is how researchers find the papers that connect their specific topic to adjacent fields they may not know to search for.
Who Semantic Scholar is for
Any researcher who needs to find academic papers, understand a field's landscape, or follow citation networks — at no cost. It is the best starting point for any literature search, especially for researchers without institutional access to paid databases like Web of Science or Scopus. The free API makes it particularly valuable for developers building research tools or researchers doing computational bibliometric analysis.
Getting started
Go to semanticscholar.org. No account required. Enter search terms or a research question. The results page shows papers with TLDR summaries visible immediately — scan these to assess relevance. Click any paper for the full detail view: abstract, citations received, references, related papers and the citation graph visualisation.
12 Semantic Scholar workflows
Initial literature discovery
Search your topic using key terms. Scan the TLDR summaries of the top 20 results. Identify the 5–8 most relevant papers. For each: check citation count (influential papers = cited frequently), publication year, and the TLDR. This gives you your core reading list in 15 minutes.
Find the foundational papers
Search your topic. Sort by 'Most Cited'. The top 10–15 most-cited papers are the foundational literature of your field. Read their TLDRs. These are the papers you need to cite and engage with in any serious work on this topic.
Find the most recent work
Search your topic. Filter by date: last 12 months. Sort by 'Most Cited' within that range. This shows you the most influential recent papers — the current frontier of the field rather than its historical foundations.
Trace a citation network
Open any key paper. Scroll to 'Citations' — papers that cite this work. Scroll to 'References' — papers this work builds on. Click 'View in Citation Graph' for the visual network. Follow the network to find: papers you did not know to search for, researchers working in adjacent areas, and the intellectual lineage of an idea.
Find highly influential papers in your references
Open a paper you are analysing. Look for citations marked 'Highly Influential' — papers where Semantic Scholar's AI has determined this paper was a significant intellectual influence (not just a passing mention). These are the papers you should read to understand the foundation of the work you are studying.
Discover related papers
Open any paper. Scroll to 'Related Papers'. Semantic Scholar generates these using AI similarity matching — papers that are conceptually similar but may not share keywords. This surfaces work from adjacent fields or using different terminology that a keyword search would miss.
Author search
Search by author name to find all papers by a specific researcher. Useful for: tracking a leading researcher's work over time, identifying their most-cited contributions, and finding their most recent work even before it appears in other databases.
Set up research alerts
Create a free Semantic Scholar account. Save papers and search terms to receive email alerts when new papers matching your searches are published. This is how researchers stay current in fast-moving fields without manually checking databases.
API for computational research
Register for a free Semantic Scholar API key at api.semanticscholar.org. The API gives programmatic access to paper metadata, citations and recommendations for up to 1 million requests per day free. Use it for: building literature review tools, computational bibliometrics, or integrating academic search into your own applications.
Export for reference manager
From any search results page or paper view: click the cite button. Export as BibTeX, RIS or MLA. Import directly into Zotero, Mendeley or EndNote. For bulk exports, use the API.
Use TLDRs for rapid reading
Upload a large reading list to Semantic Scholar by searching each paper title. Use the TLDR for each to decide reading priority. Papers where the TLDR directly addresses your research question get read in full. Papers where it is tangential get skimmed or noted for later. This is how you process 50 papers in a day.
Identify review papers on your topic
In the search filters, select 'Paper Type: Review'. Semantic Scholar identifies review and survey papers. These aggregate and synthesise the primary literature on a topic — reading one good review paper can replace reading 30 individual studies when mapping a new field.
Tips
TLDR summaries are AI-generated and occasionally imprecise. They are excellent for scanning relevance but not for citing. Always read the abstract or full paper before citing a specific claim from it.
Use the API for scale. If you are processing hundreds of papers — for a systematic review, bibliometric study, or building a research tool — the free Semantic Scholar API handles volumes that the web interface cannot. It is one of the most generous free academic data APIs available.
Pair with Elicit for extraction and Scite for verification. Semantic Scholar finds papers. Elicit extracts structured data from them. Scite verifies how they have been received. This three-tool combination covers the full research workflow at near-zero cost.
Technical background
Semantic Scholar is built and maintained by the Allen Institute for AI (AI2), a nonprofit research organisation founded by Microsoft co-founder Paul Allen. Per the Allen Institute's official documentation, the platform indexes 200 million academic papers and uses AI models to generate TLDR summaries, detect highly influential citations, and power semantic search. The TLDR summaries use models trained on the full text of papers, not just abstracts.
The Semantic Scholar Open Research Corpus (S2ORC) and API are available for academic and research use. The API provides programmatic access to paper metadata, abstracts, citations and recommendations, and is free with registration up to 1 million requests per day, per the official API documentation at api.semanticscholar.org.
Pricing (verified April 2026)
Web search: Completely free, no account required for basic search
Account features (free): Saved papers, research alerts, reading lists
API (free): Up to 1M requests/day with free registration
Institutional partnerships: Contact Allen Institute for data partnerships