Build a literature review agent

Outcome: a loop that takes a research question, searches at increasing depth, screens the papers, and returns a cited summary your users can verify.

What you need

Key with search:write, papers:read; credits for at least one deep_review.

Approach

Literature review agent — escalation flow

Escalate depth only when the cheap pass is thin. That single decision dominates cost.

Walkthrough

Expand the question into topics to catch vocabulary you would have missed
Run a standard search per topic
Screen: drop retracted papers, apply year and open-access filters
Escalate to deep_review only where evidence is thin
Render the answer with clickable citations

Handling the hard parts

Deep reviews take minutes — stream or webhook, never block a request thread (streaming-deep-review)
Deduplicate papers across topic searches by pap_ ID, not title
Always surface is_retracted
Never drop the citations to make the summary tidier — that is the product

Cost

As written — one topic search plus three standard searches, escalating one to deep_review — the walkthrough costs 4 + (3 × 5) + 60 = 79 credits. Without the escalation it is 19.

At 1,000 questions a day with a 10% escalation rate: roughly 10,500 credits a day, ~315,000 a month. The escalation rate is the number to watch — see cost-control.

Production checklist

Idempotency keys · webhooks over polling · cache paper metadata, not full text (see content-licensing) · alert on X-Credits-Remaining · handle 402 gracefully

searches · topics · depth · cost-control

Last updated 2026-08-10