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Reddit CI API

Read-only access to what people say on Reddit about supplements, skincare and pet care — 12 months (Sep 2025 – Aug 2026), filtered from 4.6 billion posts and comments. One category (face creams) is fully analysed by AI; every topic can be searched and counted.

78.3 Mtopic posts (supplements, skincare, pets)

8.1 Mpeople behind them

584,627face-cream posts · 278,566 people

20,120face-cream posts tagged by AI

Your first request

Tip: paste this page's link and your key into Claude and ask in plain words — e.g. "Using this API, what do people dislike about CeraVe?". Claude will call the endpoints for you.

Authentication & limits

Send your personal key in the X-API-Key header (or ?key= in the URL). Keys are personal — please don't share them; every request is logged under your name.

Limit per key	Value	When exceeded
Requests per minute		`429` + `Retry-After: 60`
Requests per day		`429` until midnight UTC
Result rows per day		`429` until midnight UTC
Posts per page	100 (use `offset` to page)	`422`

Check your own use any time: GET /v1/usage. Every response carries X-Quota-Day-Requests.

What data is inside

Layer	What	Size	Endpoints
Topic posts	Every Reddit post/comment in ~150 topic communities (e.g. r/Supplements, r/SkincareAddiction, r/AskVet), ~365 problem communities (e.g. r/PCOS, r/tretinoin, r/loseit, dog breeds) and keyword hits for ~500 products, ingredients and brands anywhere else.	78.3 M	`/v1/topics/*`, `/v1/mentions`
Audience	For any term: where the people who write about it spend their time on Reddit (communities with lift), from the 12-month activity of 8.1 M topic authors. Group level only.	2.4 B activity rows (aggregated)	`/v1/audience`
Face creams (AI-analysed)	584,627 posts about face creams & moisturizers. 20,120 tagged by AI (Gemini 3.8 Flash) with ~25 fields: problem, trigger, verdict, brands, objections, switches, unmet needs, quote. Plus segments, journeys, white spaces, brand trends.	20,120 tagged	`/v1/face_cream/*`

Each topic post carries a layer: core (topic community), problem (problem community) or keyword (keyword hit elsewhere) and topics: supp, skin, pet.

How numbers are computed

Two kinds of numbers. Full dataset counts (mentions, trends, audience, topic counts) use every post. AI-tagged numbers (verdicts, segments, objections, switches, unmet needs) come from a stratified sample of 20,160 face-cream posts.
Weighted shares. The AI sample has equal thirds per layer; real data does not (core 50 %, keyword 33 %, problem 18 %). Every percentage from tags is weighted back to the real mix. n next to a percentage is the raw number of tagged posts — trust shares with n ≥ 100 (±10 pp), better n ≥ 400 (±5 pp).
Verdict = the writer's own experience with the cream they discuss: works · mixed · no_effect · side_effects (posts without a verdict excluded).
Trends = mentions per 1,000 posts by month; growth = Jun–Aug 2026 vs Sep–Nov 2025, so overall volume changes don't distort it.
Lift (audience) = share of the term's writers active in a community ÷ share of all 8.1 M topic authors active there. Lift 5 = five times more likely than average.
Search uses a full-text index: words are matched case-insensitively and all words must appear (q=magnesium glycinate).

Recipes

Question	Calls

Endpoints

All endpoints are GET and return JSON. Examples below are real (shortened). With your key set above, press Run to call the live API.

Schema: tagged face-cream post

Returned by /v1/face_cream/posts. Lists can be empty; fields the model left empty are omitted.

Field	Type	Meaning / values

Segments (face creams)

Each relevant post belongs to exactly one segment, first matching rule wins.

id	Name	Rule

Errors

Code	Meaning
`401`	Missing or wrong key
`404`	Unknown category, brand or segment
`422`	Invalid parameter (e.g. `limit` above 100); the body says which
`429`	Quota reached (per minute, per day or rows per day)
`500`	Query failed or too heavy — narrow the filter or tell us

Privacy

No usernames, Reddit ids or links are returned; u/name mentions inside texts are masked and user-profile communities are hidden. There is no way to look up a single person. Texts are public Reddit posts — please don't try to find their authors. Internal use only; do not share the key or bulk-copy the data.