The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Choose Semantic Scholar when your agent needs structured scholarly records—papers, authors, citations, references, and related literature. Choose Valyu when it needs to search across web and specialist sources, extract supplied URLs, synthesize sourced answers, or run a multi-step research task. These APIs serve different workflows; official materials do not establish a universal winner or a controlled head-to-head result.
How the APIs differ
Semantic Scholar: scholarly graph data
Semantic Scholar’s Academic Graph API is organized around scholarly publication data: papers, authors, citations, venues, and embeddings. Its documented operations include relevance search, title matching, paper and author lookup, batch requests, and citation or reference fields. Paper search supports filters such as year, open-access PDF availability, publication type, venue, and field of study; the documented paper-search endpoint returns up to 1,000 relevance-ranked results. For larger-scale use, Semantic Scholar points to bulk search and its Datasets API. Semantic Scholar API overview Academic Graph API reference
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The official tutorial separates three API families: Academic Graph for scholarly records, Recommendations for related papers, and Datasets for downloading data to host and query locally. Semantic Scholar API tutorial
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWhen checked in 2026, Semantic Scholar’s overview displayed 214 million papers, 2.49 billion citations, and 79 million authors. These are publisher-displayed corpus counts, not independently audited measurements; verify the live page before relying on them.
Valyu: search, extraction, synthesis, and research workflows
Valyu documents a Search API, Contents API, Answer API, and DeepResearch API. The examples cover searching web and other sources, extracting content from URLs, producing search-grounded answers, and starting an asynchronous research task that returns a report. Python and JavaScript SDKs and hosted MCP access are also documented. Valyu describes coverage across web, academic, financial, biomedical, legal, and economic material, but source availability depends on plan and data source. Valyu API documentation Valyu APIs product page
Which API fits your agent’s task?
| Agent task | Better first candidate | Why |
|---|---|---|
| Find scholarly papers and retrieve metadata | Semantic Scholar | Its Academic Graph API is built around scholarly records and provides paper search and details. |
| Resolve a known paper title or follow citation and reference links | Semantic Scholar | Title matching and citation/reference fields are documented; check the chosen endpoint’s field availability and limits. |
| Retrieve author records or paper recommendations | Semantic Scholar | Author endpoints and a separate Recommendations API are documented. |
| Search the open web alongside specialist sources | Valyu | Its product materials describe cross-domain search; source availability and cost vary. |
| Extract structured content from URLs supplied to the agent | Valyu | The Contents API is documented for URL extraction. |
| Return a synthesized answer grounded in retrieved sources | Valyu | The Answer API combines search and synthesis, but the agent should still inspect citations and source quality. |
| Produce a multi-step research report | Valyu | DeepResearch is documented as an asynchronous research workflow with report outputs. |
| Run repeated or large scholarly queries locally | Semantic Scholar Datasets may fit | Datasets can be downloaded for local hosting and custom queries; factor in ingestion and maintenance. |
Compare the integration, not the labels
Before choosing, define what the agent must retrieve and what evidence it must return. The meaningful comparison is not simply “research API versus research API”: Semantic Scholar emphasizes structured scholarly graph data, while Valyu documents a broader retrieval-to-report workflow.
- Domain coverage: Is the needed evidence scholarly literature only, or also web and specialist sources?
- Output shape: Does the agent need graph fields and relationships, page content, a synthesized answer, or a finished report?
- Retrieval controls: Check filters, batch support, result ceilings, and the fields exposed by the exact endpoint.
- Operational fit: Review authentication, rate limits, asynchronous task handling, and how the service behaves under your expected load.
- Evidence quality: Determine whether citations are inspectable and whether returned evidence actually supports the answer.
- Cost model: Compare billable retrievals, sources, tokens, tasks, or other units against your expected workload.
Pricing and access considerations
Semantic Scholar access
Semantic Scholar says most endpoints are publicly accessible without authentication, though they may be throttled, and some endpoints require an API key. Its overview recommends sending a key and states an introductory keyed rate of 1 request per second across endpoints. Treat this as current access guidance, not an uptime or throughput guarantee; confirm the applicable limits in the documentation before deployment. Semantic Scholar API overview
Valyu usage and plans
Valyu’s pricing page describes pay-as-you-go and monthly-credit plans, $10 signup credits, and source access that varies by plan. It prices Search by retrieval/source, Contents per successful URL plus AI processing, Answer as search charges plus token charges, and DeepResearch per task. The page lists Search retrieval at $0.50–$30 CPM (cost per thousand), depending on source, and DeepResearch at $0.10–$15 per task. Displayed monthly-credit plans include $29/month for $50 credits, $89/month for $130 credits, and $449/month for $750 credits. These are vendor-posted prices and plan details; confirm current terms and your expected billable units before budgeting. Valyu pricing
Valyu’s product page also displays 94% SimpleQA precision and 79% FreshQA accuracy, and reports a 72.7% DRACO result for its DeepResearch Heavy mode in a comparison the company says used the benchmark’s 100 tasks. These are vendor-published benchmark claims, not an independent comparison with Semantic Scholar or a guarantee for a particular agent workload. Valyu APIs product page
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run a workload-specific evaluation
Neither vendor’s published figures answer which API will be more accurate or economical for your agent. Test the candidates against the same representative work before committing. Include overlapping capabilities where possible, while recognizing that a scholarly graph lookup and a cross-domain research report are not identical tasks.
Quick Recap
- Build a small test set: include a known-paper lookup, broad scholarly discovery, a current cross-domain fact, extraction from a supplied URL, and a question requiring synthesis across sources.
- Hold the test conditions constant: use the same prompts, required source domains, quality rubric, latency target, and expected monthly volume for both candidates wherever their capabilities overlap.
- Inspect the evidence: record whether the necessary source exists, whether the returned material supports the answer, and how easily citations can be checked.
- Measure operations and spend: track latency, errors, rate limiting, and the actual billable units for each request or task.
- Route by task if needed: if the results show different strengths, call the scholarly graph API for literature operations and the broader workflow API for cross-domain research rather than forcing one tool to handle every job.
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