Momentum API: Score research momentum for any technology from OpenAlex (CC0)

Score research momentum for any technology from OpenAlex (CC0) — returns momentum_score, acceleration_score, trend_label, growth series, leading institutions and researchers, key works, confidence and provenance. Use before betting a field is accelerating.

$0.05 per call — pay with prepaid card credits (Authorization: Bearer ck_live_…, packs from $1) or with USDC over x402: call it, get 402 with the price, retry signed. Failed calls are never charged.

How do you call it?

GET /v1/technology/momentum — product id technology_momentum. Over MCP, call answerpool_get with product_id=technology_momentum.

curl "https://answerpool.io/v1/technology/momentum?topic=photonic+computing"

# with a prepaid credit key
curl -H "Authorization: Bearer ck_live_..." "https://answerpool.io/v1/technology/momentum?topic=photonic+computing"

Live call: https://answerpool.io/v1/technology/momentum?topic=photonic+computing · JSON sample: /v1/samples/technology_momentum

What does the answer look like?

A representative response, the same static sample served at /v1/samples/technology_momentum.

matched_topics

{
 "result_id": "res_a1b2c3",
 "topic": "photonic computing",
 "query_match": {
  "mode": "phrase",
  "total_works_10y": 4210,
  "focus_topic_ids": [
   "T10412"
  ]
 },
 "matched_topics": [
  {
   "topic_id": "T10412",
   "name": "Photonic and Optical Computing",
   "works": 2914,
   "share": 0.69
  }
 ],
 "momentum_score": 0.81,
 "acceleration_score": 0.64,
 "trend_label": "accelerating",
 "confidence": 0.72,
 "research_growth": {
  "1y": 0.21,
  "3y": 0.86,
  "5y": 1.7
 },
 "citation_growth": {
  "1y": 0.18,
  "3y": 0.7,
  "5y": 1.2
 },
 "citation_growth_as_of_year": 2024,
 "institution_growth": 0.35,
 "leading_institutions": [
  {
   "id": "I63966007",
   "name": "Massachusetts Institute of Technology",
   "recent_works": 210
  }
 ],
 "leading_researchers": [
  {
   "id": "A5012345678",
   "name": "J. Doe",
   "recent_works": 34
  }
 ],
 "important_recent_works": [
  {
   "id": "W4400000001",
   "title": "On-chip photonic tensor cores",
   "year": 2026,
   "cited_by": 89
  }
 ],
 "rationale": "Publication and top-decile citation growth accelerated over the last 3 years.",
 "key_drivers": [
  "AI inference energy limits"
 ],
 "risks": [
  "fabrication cost"
 ],
 "warnings": [],
 "evidence_count": 412,
 "data_as_of": "2026-08-24",
 "methodology_version": "1.2.0",
 "schema_version": "1",
 "computed_at": "2026-08-30T18:00:00Z",
 "refresh_after": "2026-09-18T00:00:00.000Z"
}

How fresh is it, and where does the data come from?

The fastest source behind this answer can change daily, so every response carries a refresh_after timestamp — right now it would be 2026-09-18T00:00:00.000Z. Schedule the next call on that value rather than on a guess.

• OpenAlex (CC0) — upstream: https://api.openalex.org

Method version 0.1.0 · serving cache 30 days · every response carries a result_id you can resolve at /v1/provenance.

When should you use it?

You need a quantified, source-cited answer to "is this research or technology field accelerating, growing, stable, or declining?" for one named topic, with growth ratios, leaders, and recent influential works, in one call.

What you skip building

• dozens of paginated scholarly API searches
• aggregating thousands of works by year, institution, and author
• age-normalized citation momentum computation
• a long-context model call to interpret the aggregates
• retries and rate-limit handling against upstream sources

When should you not use it?

You need one paper's prospects, a literature review, raw bibliographic records, or a topic OpenAlex does not model (brand names, very new jargon) — the response will report a weak match.

Where else can you find this?

• Catalog entry (JSON, free)
• OpenAPI schema — input and output types, prices, the 402 flow
• llms.txt — every product in one plain-text document