# Semantic Scholar API Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/semantic-scholar-api
> Markdown URL: https://aitinkerers.org/technologies/semantic-scholar-api.md
> Technology record last updated: 2026-05-28T20:22:23Z
> Generated: 2026-09-21T16:42:15Z

A powerful REST API providing programmatic access to a graph of over 200 million academic papers, authors, citations, and references.

The Semantic Scholar Academic Graph (S2AG) API allows developers to query a massive corpus of scientific literature using standard HTTP requests. Built by the Allen Institute for AI, the service offers endpoints to fetch paper details, map citation graphs, retrieve author profiles, and generate personalized recommendations. Unauthenticated requests are rate-limited to 1,000 requests per second globally, while registered developers can secure API keys for dedicated access (starting at 1 request per second) to power custom academic search engines, research analysis tools, and literature discovery platforms.

- Official technology site: https://www.semanticscholar.org/product/api
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Selling to Scientists: Sales Intent Identification for Super Technical Buyers](https://seattle.aitinkerers.org/talks/rsvp_zsBLWVQ1iE4)

Sales intent tools watch behavior — they tell you an account is researching your category. They can't tell you what a researcher is working on, what they're stuck on, or what would make them reply. I sold to AI researchers for a decade by reading their papers and asking questions only someone who read them would ask. I built the system I wish I'd had the whole time: a seller defines their product, target accounts, and ideal buying signals in plain English; the engine mines papers, linked repos, and adjacent sources, then surfaces named opportunities with openers grounded in real pain quotes from the buyer's own writing. The novel part isn't the retrieval. It's that the seller's domain expertise becomes the system's input — captured as natural language signals, not keywords or trained models — and the LLM applies that expertise across a corpus the seller could never read on their own.

- Event context: GTM / Growth Track - Seattle — 2026-05-27 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_zsBLWVQ1iE4

## Related Technologies

- [arXiv API](https://aitinkerers.org/technologies/arxiv-api) ([Markdown](https://aitinkerers.org/technologies/arxiv-api.md)) — 1 public demo
- [Claude Haiku](https://aitinkerers.org/technologies/claude-haiku) ([Markdown](https://aitinkerers.org/technologies/claude-haiku.md)) — 10 public demos
- [Claude Sonnet 4](https://aitinkerers.org/technologies/claude-sonnet-4) ([Markdown](https://aitinkerers.org/technologies/claude-sonnet-4.md)) — 18 public demos
- [GitHub API](https://aitinkerers.org/technologies/github-api) ([Markdown](https://aitinkerers.org/technologies/github-api.md)) — 6 public demos
- [OpenAlex API](https://aitinkerers.org/technologies/openalex-api) ([Markdown](https://aitinkerers.org/technologies/openalex-api.md)) — 1 public demo
- [PyMuPDF](https://aitinkerers.org/technologies/pymupdf) ([Markdown](https://aitinkerers.org/technologies/pymupdf.md)) — 2 public demos
- [Python asyncio](https://aitinkerers.org/technologies/python-asyncio) ([Markdown](https://aitinkerers.org/technologies/python-asyncio.md)) — 1 public demo