Lookup API: Look up statistical research momentum for one OpenAlex topic (free)
Look up statistical research momentum for one OpenAlex topic (free) — returns rank among ~4,500 topics, momentum z-score, 1y/3y/5y growth, acceleration and per-year counts. Use to screen a field before paying for a full judgment.
Free — no key, no signup, rate-limited to 60 calls per minute per client.
How do you call it?
GET /v1/technology/lookup — product id
technology_lookup. Over MCP, call answerpool_get with
product_id=technology_lookup.
curl "https://answerpool.io/v1/technology/lookup?topic=photonic+computing"
Live call: https://answerpool.io/v1/technology/lookup?topic=photonic+computing · JSON sample: /v1/samples/technology_lookup
What does the answer look like?
A representative response, the same static sample served at /v1/samples/technology_lookup.
scan_id | scan_2026w35 |
|---|---|
computed_at | 2026-08-24T06:00:00Z |
data_as_of | 2026-08-24 |
last_full_year | 2025 |
universe_size | 4516 |
method_id | technology_momentum_stat_v1 |
topic.topic_id | T12026 |
topic.name | Quantum error correction |
topic.field | Physical Sciences |
topic.subfield | Condensed Matter Physics |
topic.rank | 3 |
topic.momentum_stat | 3.41 |
topic.growth_1y | 0.38 |
topic.growth_3y | 1.9 |
topic.growth_5y | 3.2 |
topic.acceleration_3y | 0.6 |
topic.decile_growth_3y | 1.4 |
topic.works_last_full_year | 1841 |
topic.works_10y | 6210 |
topic.is_emerging | true |
works_by_year.2023 | 1102 |
works_by_year.2024 | 1490 |
works_by_year.2025 | 1841 |
top_decile_by_year.2023 | 118 |
top_decile_by_year.2024 | 166 |
top_decile_by_year.2025 | 204 |
refresh_after | 2026-09-18T00:00:00.000Z |
The same sample as raw JSON
{
"scan_id": "scan_2026w35",
"computed_at": "2026-08-24T06:00:00Z",
"data_as_of": "2026-08-24",
"last_full_year": 2025,
"universe_size": 4516,
"method_id": "technology_momentum_stat_v1",
"topic": {
"topic_id": "T12026",
"name": "Quantum error correction",
"field": "Physical Sciences",
"subfield": "Condensed Matter Physics",
"rank": 3,
"momentum_stat": 3.41,
"growth_1y": 0.38,
"growth_3y": 1.9,
"growth_5y": 3.2,
"acceleration_3y": 0.6,
"decile_growth_3y": 1.4,
"works_last_full_year": 1841,
"works_10y": 6210,
"is_emerging": true
},
"works_by_year": {
"2023": 1102,
"2024": 1490,
"2025": 1841
},
"top_decile_by_year": {
"2023": 118,
"2024": 166,
"2025": 204
},
"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 7 days ·
every response carries a result_id you can resolve at
/v1/provenance.
When should you use it?
You have a topic and want its numbers now, or you are screening many topics.
What you skip building
- per-year aggregation
- universe normalization
- rate-limit handling
When should you not use it?
You need leaders, influential works and a reasoned judgment — use technology_momentum.
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