Tavily vs Valyu

Valyu is great for AI knowledge work. It combines web search, specialist datasets, covered licensed publications and a firm's knowledge for research and deepresearch. DeepResearch accepts the original files, lets you restrict its searches to selected sources and can return a cited report file.

TL;DR

Choose Valyu when the tasks needs named financial, academic, biomedical and scientific datasets, internal knowledge base (in any format) and a generated cited report. Tavily focuses on web search and research

Tavily and Valyu at a glance

DeepResearch benchmark results

In Valyu's published DRACO evaluation, Valyu DeepResearch (Heavy) scored 72.7%, compared with 38.8% for Tavily Research (pro) — a 33.9-percentage-point lead. DRACO grades 100 long-form research tasks across 10 professional domains against expert-written rubrics. The results are reported in Introducing DeepResearch.

DRACO research scores and cost per 1,000 requests, from Valyu's published launch evaluation.

For the medical-evidence note below, a useful report should keep label statements, study findings and reviewer inference distinct, with unanswered questions visible. DRACO's report-level grading adds context to the file-format and source-control differences discussed here. The score is an overall research result across the benchmark domains, rather than a clinical validation of the evidence-note workflow. View the methodology, raw outputs and evaluations.

Bring the right sources into any research

An example use case: A medical-information team updating a product evidence note has a current label, an internal review document and a list of new studies. It needs to separate what the label states from what a trial reported and what the reviewer inferred. A registered study without posted results should not be treated as evidence of an effect. The team can execute a deepresearch with this brief:

Review this product's current label and published clinical evidence against the attached internal evidence note. Return cited rows distinguishing label statements, study findings and reviewer inference, with population, endpoint, source date and missing results; identify claims in our note that need correction or further review.

Valyu's catalogue includes drug labels from DailyMed, ClinicalTrials.gov records and PubMed. Select those datasets, attach the review note and request the comparison table. The team can open the underlying records, correct the note and send the unresolved questions to its reviewers.

Keep research searches within the required source set

Valyu's search restricts research searches to selected dataset IDs or web domains. The agent cannot override that request-level configuration. For an evidence note with a prescribed source set, specify the permitted search sources in the task. The attached document remains a separate input.

Reuse the team's evidence-review instructions

Valyu's Workflows API stores immutable versions of research templates, with typed inputs and report settings. The team can reuse the same label-versus-study review brief for another product and record later edits. Workflows is in Beta. A pinned version preserves the instructions; source records and findings can still change.

For a larger internal archive, Valyu offers enterprise private indexes.

Which should you choose?

Choose Valyu when AI knowledge work requires specialist datasets, covered licensed literature, the firm's own documents and a repeatable research brief.

Use the report your team already needs. Identify its required sources, attach the relevant internal research and review the returned citations and open questions.

Try Valyu or talk to the team about licensed sources and an enterprise research workflow. To test from an existing agent, follow the MCP setup guide.