Reddit Search API
API 한 번의 호출로 Reddit Search 데이터를 받아 가세요. Searches Reddit for posts matching a keyword query. Returns a list of matching posts with titles, scores, comment counts, subreddit names, and permalinks. One result page is assembled from more than one index, so a few leaves are reported for only part of a page and are `null` on the rest: `post.flags.spoiler`, `post.engagement.shares` and `post.engagement.saves`. A `null` there means the index that returned that row does not report the field, never that the value is false or zero — treat those three as opportunistic and do not aggregate over them. The post body is at `ext.selftext` (`content.text` is the title and body joined, matching `/v1/reddit/post`); this surface sends it on self-posts without any extra flag. A page holds up to 25 posts and the exact count varies between calls, so do not assume a fixed page size: pages of about 7 are common and a sparse query can return fewer. Rows beyond the first few, and the newest rows on a `sort=new` page, may carry fewer fields than the rest: `flags.spoiler` in particular is present on some rows and null on others within one response. Where a row carries them you also get the author's avatar at `author.avatar_url`, images or video at `content.media_urls`, and the upvote ratio, post flair and Reddit's own content language at `ext.upvote_ratio`, `ext.flair` and `ext.content_language`. `include_body=true` is still accepted and rarely needed now: bodies arrive on the rows themselves, so on most pages the flag finds nothing to fetch and its extra credits are refunded in full. It still hydrates a row that does arrive without a body, at 1 extra credit per row hydrated, up to 25 per page. Link posts have no body, so `ext.selftext` is null. Ranking is a voice-of-customer sweep rather than precision retrieval: expect some off-topic matches on short or ambiguous queries, and use quoted phrases to tighten them.
2026년 10월 업데이트SocialCrawl 팀이 직접 관리해요
Reddit 전체에서 키워드에 맞는 게시물을 돌려줍니다. 제목, 점수, 댓글 수, 서브레딧 이름, permalink가 붙고 본문은 ext.selftext에 담깁니다. 한 페이지는 최대 25개이고 호출마다 개수가 다릅니다.
Reddit 전체를 한 번에 검색할 때 사용하세요. 본문이 그대로 따라오니 include_body=true는 거의 필요 없고, 쓰지 않은 크레딧은 환불됩니다. 커뮤니티 하나로 좁히려면 subreddit/search를 쓰세요.
68개 플랫폼을 병렬로 검색합니다
Search API로 무엇을 할 수 있을까요
Search 엔드포인트가 통합 스키마와 계산 필드를 담은 Reddit 데이터를 한 번의 요청으로 보내드려요. 스크래핑 인프라를 직접 만들거나 유지할 필요가 없어요.
요청 예시
curl -H "x-api-key: YOUR_API_KEY" \
"https://www.socialcrawl.dev/v1/reddit/search?query=%22claude+code%22&label=sponsored&relevance=filter&trim=true"import requests
response = requests.get(
"https://www.socialcrawl.dev/v1/reddit/search",
params={
'query': '"claude code"',
'label': 'sponsored',
'relevance': 'filter',
'trim': 'true',
},
headers={"x-api-key": "YOUR_API_KEY"},
)
data = response.json()const response = await fetch(
"https://www.socialcrawl.dev/v1/reddit/search?query=%22claude+code%22&label=sponsored&relevance=filter&trim=true",
{
headers: { "x-api-key": "YOUR_API_KEY" },
},
);
const data = await response.json();함께 보내면 좋은 파라미터
label=sponsored게시물마다 유료 광고나 협찬인지 표시합니다. 추가 크레딧은 들지 않으며 의도와 분야 판정도 무료입니다.relevance=filter검색어와 관련 없는 결과를 제외합니다. 추가 크레딧은 들지 않습니다. relevance=score를 보내면 결과를 모두 남기고 관련도 점수를 붙입니다.trim=true가격은 같습니다. 용량이 큰 원본 필드를 빼서 응답이 가벼워집니다.
파라미터
| 파라미터 | 필수 | 설명 |
|---|---|---|
| query | 예 | Search words or a phrase. Words match any, not all: vision pro also returns posts that mention only one of them. Wrap a phrase in double quotes to match it exactly (query="vision pro"). Reddit operators such as subreddit:name work too. When fewer than half the rows contain every word of an unquoted query, the response says so in _warnings. |
| sort | 아니오 | Sort by (relevance | new | top | comment_count) |
| timeframe | 아니오 | Only return posts from this window. Applied on sort=relevance, sort=top and sort=comment_count. It has no effect with sort=new, which is already ordered newest-first and returns recent posts regardless. (all | day | week | month | year) |
| after | 아니오 | Used to paginate to next page |
| trim | 아니오 | Set to true for a trimmed down version of the response. On this endpoint it also skips the second source that widens the page, so a trimmed page is both thinner and usually shorter. Measured 09/09/2026: default 29 rows, `trim=true` 25 rows. It does not drop `author.avatar_url` or `ext.*` — those leaves stay when the first source already carried them. Use it to skip the widening pass, not to strip fields. |
| include_body | 아니오 | Set to true to include each post's body (selftext) on the search page. Rarely needed now: bodies arrive on the rows themselves. 1 extra credit per row that actually comes back with a body, unused credits refunded. Link posts have no body, so they are looked up and not charged for. |
| relevance | 아니오 | Optional. Without this param every row already carries computed.relevance against your query, free ({ p, sense, depth, spam }: is this row about what your query means, or a different thing that shares its words?), and nothing is dropped or reordered. score asks for it explicitly and waits for every row; filter also drops the rows that are not about your query, and lists their ids in data.relevance.dropped_ids. Your query is the topic; nothing to configure. A row that could not be judged is never dropped and carries relevance: null. Pagination is unchanged, so a filtered page can hold fewer rows. Free with your query as the topic; with relevant_to it adds 1 credit per started 25 newly judged rows (rows already judged for the same topic are free, and so is a cached page). (score | filter) |
| relevance_threshold | 아니오 | Optional, only with relevance. The probability (0 to 1) a row must reach to be kept by relevance=filter. Default 0.5. Lower keeps more rows, higher keeps fewer. |
| relevant_to | 아니오 | Optional, only with relevance. Up to 200 characters describing what you mean, used as the topic instead of the query. Use it when the query is ambiguous, for example query=cleopatra with relevant_to=Cleopatra, the IGT slot game. |
| judgments | 아니오 | Optional, on (the default) or off. By default every row gains free SocialCrawl judgments (computed.labels, and computed.relevance on search endpoints), reported in data.labels (mode default) and data.relevance (origin default), each with a status (complete, partial or skipped) and pending: the rows still being judged when the page was sent, which carry null now and are filled on your next call or cached read. Default judgments never add credits, never change an existing field, and never drop or reorder a row. off returns the page exactly as before, with none of those keys. label=none does the same. (on | off) |
| dry_run | 아니오 | Optional. When 1, return a cost preview for this labelled or relevance-filtered request without fetching the page or judging any row. data.estimate reports rows_expected, rows_cached, label_credits_min, label_credits_max and base_credits. 0 credits charged. (1) |
| fit | 아니오 | Optional. When goal, keep the rows and fields needed for the goal you pass in goal= (plus any that are uncertain, and the first and last), and replace the rest with a stub. data.held_back lists the held ids and a recall id that re-reads the full page from cache at no extra charge. Without this param the page is unchanged. (goal) |
| goal | 아니오 | Required by fit=goal. What you are trying to do, in your own words, up to 300 characters. |
| fit_tokens | 아니오 | Optional, only with fit=goal. Soft cap on how much of the page to keep, in tokens. Uncertain blocks and the first and last block are kept even if they exceed it. |
| label | 아니오 | Optional CSV of SocialCrawl labels to add to every post. Without this param every page already carries sponsored, intent and niche, free; label= adds the labels you name to them (the defaults keep running). sponsored, intent and niche are free when asked for too; mention, quality, intent with offer=, and injection add 1 credit per started 25 newly judged posts. judgments=off (or label=none) turns the default labels off. sponsored: is the post a paid or gifted promotion (p, 0 to 1), did it carry a disclosure marker such as #ad or 광고 (disclosed), is it likely paid with no marker (undisclosed), and which of the accounts it mentions does it promote (brand, or null). These are signals to review, never a finding. mention (needs brand=): is the post about that brand rather than something that shares its name (about_brand, 0 to 1), how it feels about the brand on five levels (sentiment_level 0 to 4 and sentiment_score 0 to 1, null when the post is not about the brand), is it sarcastic, which aspect it talks about (taste_or_quality, price_or_value, availability_or_delivery, health_or_safety, advertising_or_campaign, customer_service, none), and did the author buy or use it (first_hand). intent: what the author is mainly doing (label: asking_for_recommendation, comparing_options, switching_away, complaining, promoting, news_or_discussion, other, or null when unsure, with confidence), whether they read as a potential buyer rather than a seller (buyer, seller), how pressing the need is (urgency, 0 to 3), and, when you pass offer=, whether your offer would plausibly help them (fits_offer). niche: which of the 33 niches of the published taxonomy sc-niche-v1 the caption belongs to, or personal_no_niche, or other (label, confidence, taxonomy), with label null when the caption is too thin to tell or the pick is unsure. It reads the caption only, not the video. quality: how much checkable detail the caption carries (fact_density 0 to 3), whether it mainly asks for likes, replies, shares, follows or tags (engagement_bait, 0 to 1), whether it is written to provoke anger as a way to get engagement (rage_bait, 0 to 1; about the writing, never the side taken), whether it only repeats someone else's news or view (secondhand, 0 to 1), and what the post is mainly doing (post_aim: inform, opinion, sell, entertain, provoke, other). exclude=engagement_bait drops posts whose engagement_bait is 0.8 or higher. injection flags text that addresses an AI system and tries to direct it (flagged, p); it never drops or rewrites a row. A post that could not be judged carries labels: null. Posts already labelled for anyone are free, and so is a cached page. data.labels reports what was judged and billed. |
| exclude | 아니오 | Optional CSV (engagement_bait), only with label=quality. Drops posts whose engagement_bait probability is 0.8 or higher and lists their ids in data.labels.dropped_ids. A post that could not be judged is never dropped. |
| brand | 아니오 | Required by label=mention, ignored otherwise. The brand or product the posts are judged against, up to 300 characters, for example brand=Buldak. Without it label=mention is skipped with the warning label_mention_needs_brand and is not billed. |
| brand_description | 아니오 | Optional, with label=mention. One plain sentence saying what the brand is, for example brand_description=Samyang's spicy instant noodle brand. Use it when the name is also an ordinary word or another company's name. |
| offer | 아니오 | Optional, with label=intent. One or two plain sentences saying what you sell, up to 300 characters, for example offer=A web design agency that builds websites for small businesses. Adds fits_offer to every post; without it fits_offer is null. |
| label_evidence | 아니오 | Optional, only with label=. When 1, every labelled row also carries computed.labels_evidence.<preset> = { quote, sentence_index }: the sentence in the row that most clearly shows the label, copied verbatim. Absent or null when no single sentence shows it. (1) |
| since | 아니오 | Only rows published on or after this date: YYYY-MM-DD (midnight UTC) or an ISO 8601 timestamp. Rows older than it are left off the page, and the page that reaches one ends the walk: `next_cursor` is not returned and `pagination.stopped_at` is `since`. Sending it sets the newest-first sort shown in this endpoint's sort parameter (any other sort value is a free 400). The page still costs what a page costs, so you pay for the pages you walk and no more. A row with no date is kept. `published_after`, `start_date`, `from_date`, `publish_time` (`yesterday`, `this-week`, `this-month`, `last-3-months`, `last-6-months`) and `max_age_days` are accepted and applied as `since`. |
| until | 아니오 | Only rows published on or before this date: YYYY-MM-DD (that whole UTC day is included) or an ISO 8601 timestamp. Newer rows are left off each page and the walk does not stop, so older pages follow. A row with no date is kept. `published_before`, `end_date` and `to_date` are accepted and applied as `until`. |
| max_age_days | 아니오 | Keep only rows published within this many days (`post.published_at`), 1 to 3650. A row with no date is discarded. Filtering runs on our side after each page is fetched, so every page walked is billed as usual; `data.walk.discarded` counts what was removed and why. |
| max_pages | 아니오 | 1 to 5 (default 1). Walk up to this many pages in one call and return the rows from all of them (after any filter). Each page walked is billed exactly as one call to this endpoint (a cached page is free) and counts against your rate limit. The walk stops early at the last page, or when the request's time budget runs low; `data.walk.stopped` says why (`end`, `max_pages`, `time_budget`, `page_error`) and `data.next_cursor` continues from where it stopped. |
| seen | 아니오 | An id you choose (1 to 64 letters, digits, `.`, `_` or `-`). Rows your account already received under the same `seen` id are removed from the page, and the page price falls with the share of repeats: page credits x new rows / rows on the page, rounded up, so a page of nothing but repeats is free. Only the page's own price is discounted: credits for joined data (`include=`) are charged only for rows you have not received, and are never discounted. The memory lasts 24 hours from the last call that used the id, holds row ids only, and is private to your account. Use one id across a set of related searches; `data.walk.repeats` counts what was removed. |
Reddit Search API는 무엇을 돌려주나요
모든 응답은 하나의 통합 스키마를 따라요. 크레딧을 쓰기 전에 어떤 필드가 돌아오는지, 실제 응답 본문 그대로 확인해 보세요.
응답 예시 보기
{
"success": true,
"platform": "reddit",
"endpoint": "/v1/reddit/search",
"data": {
"items": [
{
"post": {
"id": "1wrckhp",
"url": "https://www.reddit.com/r/ClaudeAI/comments/1wrckhp/what_tool_have_you_built_for_yourself_with_claude/",
"content": {
"text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted).",
"media_urls": null,
"thumbnail_url": null,
"duration_seconds": null
},
"author": {
"username": "user_1c226d",
"display_name": "user_37da46",
"avatar_url": "https://example.com/avatars/user_246eb3.png",
"verified": null
},
"engagement": {
"views": null,
"likes": 1183,
"comments": 581,
"shares": null,
"saves": null
},
"flags": {
"nsfw": false,
"spoiler": false,
"pinned": null,
"deleted": false
},
"published_at": "2026-09-27T06:12:55.000Z",
"ext": {
"subreddit": "ClaudeAI",
"title": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted).",
"selftext": "I built a tool that reads emails from my kid’s school and other activities (swimming, music classes, scouts) etc and then: - Adds them to our shared calendar- Sends us a WhatsApp message to remind us the day before of what to pack in his school bag- Tells us what is on his lunch menu (helps us to avoid making the same dinner for him as he would have had for lunch) This all saves so much headache and head space in our daily routine. No more flapping around in the morning!!! I’ve also build tools that help with invoicing and proposals for example. Those have single-handedly removed a lot of daily frustration and friction. For example sometimes I wouldn’t bid on a job because I knew it would take a few hours of my day to write the proposal. And that would leave money on the table. Not it takes 5 mins and more work is coming in as a result simply because more proposals are going through the door. I’d I love to hear what you guys are making and things that really help you out. My main aim is to get my evenings and weekends back and be able to spend more time with my family whilst still scaling my business. Maybe they are lofty goals!!",
"upvote_ratio": 0.9750402576489532,
"flair": "Claude Code Workflow",
"content_language": "en",
"published_at_epoch": 1790489575
}
},
"computed": {
"engagement_rate": null,
"language": "en",
"content_category": "food",
"estimated_reach": null,
"relevance": {
"p": 0.95,
"sense": "target",
"depth": 1.52,
"spam": 0.18
},
"labels": {
"sponsored": {
"p": 0.04,
"disclosed": false,
"undisclosed": false,
"brand": null
},
"intent": {
"label": "other",
"confidence": 0.55,
"buyer": false,
"seller": 0.06,
"fits_offer": null,
"urgency": 0.4
},
"niche": {
"label": "technology",
"confidence": 0.89,
"taxonomy": "sc-niche-v1"
}
}
}
},
{
"post": {
"id": "1wqnn5x",
"url": "https://www.reddit.com/r/ClaudeAI/comments/1wqnn5x/claude_code_users_are_getting_up_to_250_in_free/",
"content": {
"text": "Sample comment text (redacted).",
"media_urls": "https://madrobot.blog/2026/09/24/claude-code-cloud-sessions-free-credit-how-to-claim/",
"thumbnail_url": "https://external-preview.redd.it/a5YzxKLxe6DjLWqf21T52VT5R2_j8clQxEGkWpNkADY.jpeg?auto=webp&s=c739062d30747aaa35f78bfb4348eff896509a17",
"duration_seconds": null
},
"author": {
"username": "user_15284e",
"display_name": "user_72b766",
"avatar_url": "https://example.com/avatars/user_d9b374.png",
"verified": null
},
"engagement": {
"views": null,
"likes": 110,
"comments": 53,
"shares": null,
"saves": null
},
"flags": {
"nsfw": false,
"spoiler": false,
"pinned": null,
"deleted": false
},
"published_at": "2026-09-26T10:58:20.000Z",
"ext": {
"subreddit": "ClaudeAI",
"title": "Sample comment text (redacted).",
"selftext": "Don't forget to claim it guys!",
"upvote_ratio": 0.8089887640449438,
"flair": "Promotion",
"content_language": "en",
"published_at_epoch": 1790420300
}
},
"computed": {
"engagement_rate": null,
"language": "en",
"content_category": "other",
"estimated_reach": null,
"relevance": {
"p": 0.89,
"sense": "target",
"depth": 0.96,
"spam": 0.85
},
"labels": null
}
}
],
"next_cursor": "sc2.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",
"total": null,
"relevance": {
"mode": "score",
"topic_source": "query",
"rows": 25,
"scored": 25,
"cached": 0,
"kept": 25,
"dropped": 0,
"dropped_ids": [],
"unjudged": 0,
"too_short": 0,
"threshold": 0.5,
"extra_credits": 0,
"methodology_version": "relevance/2",
"origin": "default",
"pending": 0,
"pending_ids": [],
"status": "complete"
},
"labels": {
"presets": [
"sponsored",
"intent"
],
"rows": 25,
"labelled": 3,
"cached": 3,
"skipped": 0,
"unjudged": 0,
"dropped": 0,
"dropped_ids": [],
"extra_credits": 0,
"methodology_version": "labels/1",
"mode": "default",
"status": "partial",
"pending": 22,
"pending_ids": [
"1wqnn5x",
"1wrcmjn"
]
},
"dropped": 0
},
"credits_used": 1,
"request_id": "req_example000000",
"cached": false,
"pagination": {
"next_cursor": "sc.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",
"has_more": true,
"page_size": 25
},
"credits_remaining": 9999,
"source": "captured",
"captured_at": "2026-10-02T07:26:20.953Z",
"redacted": true
}통합 응답 구조를 보여 주는 실제 샘플이에요. 필드 값은 조회한 대상에 따라 달라져요.
Reddit Search API는 어떻게 동작하나요
API 키와 함께 GET 요청을 보내면, 통합 스키마와 계산 필드를 담은 깔끔한 JSON이 돌아와요.
메서드
GET
응답 형식
JSON
소셜 미디어 데이터를 몇 초 만에 수집하는 방법
개발자를 위한 가장 빠른 소셜 미디어 스크래핑 API. 월간 활성 사용자 100억 명 이상을 포괄하는 68개 플랫폼에서 프로필, 게시물, 댓글, 분석 데이터를 수집하세요.
모든 플랫폼을 하나의 스키마로
동일한 응답 구조로 68개 플랫폼을 조회하세요. 연동은 한 번이면 충분합니다.
단순 수집을 넘어 계산된 필드 제공
엔드포인트가 해당 지표를 지원하고 계산에 필요한 원본 값이 있을 때, 정규화된 레코드에 engagement_rate, estimated_reach, content_category, language를 함께 담아 바로 활용할 수 있습니다.
코드 한 줄 쓰기 전에, 데이터부터
Visual Data Explorer에 URL만 붙여넣으면 결과 카드와 정형화된 테이블, CSV 내보내기를 바로 사용할 수 있습니다.
import requests
response = requests.get(
'https://www.socialcrawl.dev/v1/tiktok/profile',
params={'handle': 'charlidamelio'},
headers={'x-api-key': 'sc_YOUR_API_KEY'}
)
data = response.json(){
"success": true,
"platform": "tiktok",
"data": {
"author": {
"username": "charlidamelio",
"followers": 152400000
},
"engagement": {
"likes": 12400000000,
"engagement_rate": 0.087
},
"metadata": {
"language": "en",
"content_category": "lifestyle"
}
}
}자주 묻는 질문
API, 요금제, 기능에 대한 질문과 답변입니다.
문의하기Reddit 게시물은 어떻게 검색하나요?
검색 결과 정렬은 어떻게 바꾸나요?
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검색 결과는 어떻게 페이지네이션하나요?
Reddit Search API는 요금이 얼마인가요?
AI에게 SocialCrawl을 물어보세요
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API 키 받고 60초 안에 Reddit 데이터를 받아 가세요.
