Valyu vs Semantic Scholar: Choosing the Right Research API for AI Agents

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Developers building AI research agents face a foundational choice between two distinct API products: Valyu, which returns full-text passages with structured citations across academic, clinical, patent, and regulatory sources, and Semantic Scholar, a free metadata API from the Allen Institute for AI covering 214 million papers and 2.49 billion citations. Semantic Scholar excels at paper discovery, citation graph traversal, author profiling, and influence ranking, but does not deliver full text at query time — bulk full-text access requires hosting the S2ORC dataset separately. Valyu indexes roughly 4 million open-access papers alongside licensed journal content, ClinicalTrials.gov, FDA drug labels, SEC filings, and USPTO and EPO patents, enabling a single query to span multiple source types simultaneously. The core distinction is the unit of retrieval: Valyu returns the specific passage that answers a question with its citation attached, while Semantic Scholar returns paper records that the agent must then process further. Choosing the wrong tool can result in an unplanned RAG pipeline being built weeks into a project, making the decision an important architectural consideration from the outset.

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