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Reddit Subreddits Search API

Returns up to 25 communities matching a topic, each with the name at author.id, subscriber count, description and icon. include=details adds the creation date, weekly activity, rules and language.

GET /v1/reddit/subreddits/search

Last updated November 2026Maintained by the SocialCrawl team

When to use the Reddit Subreddits Search API

Use it to find which communities discuss a subject. Send include=details instead of one subreddit/details call per community, at 1 credit plus 1 per row filled, 26 at most.

From the API reference

Finds communities by topic. Every other Reddit endpoint on this API needs you to already know the subreddit you want; this is the one that answers which communities discuss a subject. Returns up to 25 communities with the name, the subscriber count, the description, the icon, the NSFW flag and the community URL. author.id is the bare community name in Reddit's own casing, which is exactly what /v1/reddit/subreddit, /v1/reddit/subreddit/details and /v1/reddit/subreddit/search take as their subreddit parameter, so a discovery call feeds a listing call with no re-mapping in between. The row is the same Author shape /v1/reddit/subreddit/details returns. The search surface publishes nothing else, so on a plain call author.joined_at, author.ext.weekly_active_users, author.ext.weekly_contributions, author.ext.rules_text and author.ext.language are null on every row. Send include=details and every row is joined, in the same call, to the details lookup that carries them: the community's creation date, weekly active users, weekly contributions and rules land on each row (rules stay null for a community that publishes none), and the language on most rows (it comes from a second source that can miss a row on a busy page, so treat it as usually present). When the details lookup's main source cannot answer a row, its second source still fills the creation date and the language and that row is billed as filled, but the weekly numbers and the rules stay null on it; data.hydration counts it among the filled rows. Cost: 1 credit for the page plus 1 credit per row filled from a fresh lookup (25 rows, so 26 at most); rows already in cache are filled for free, rows that could not be filled are refunded, and a repeat of the same call within the cache window is 0. Time: a plain call is about 1 second; include=details adds 3 to 9 seconds on a fresh page (the lookups run in parallel and the call waits for the slowest, never more than 12 seconds) and nothing when the rows are already cached. limit=N (1 to 25) takes the top N communities of the page and, with include, caps the extra credits at N. The response carries a hydration block itemising rows, lookups, cache hits, credits and milliseconds. The subscriber count on a search row is the search index's own figure; the join never overwrites a value the row already carries. Note the name: this is subreddits/search and it finds COMMUNITIES, where the singular subreddit/search searches posts INSIDE one community. Paginate with cursor. When the main source is unavailable, a backup answers the first page with the same fields and no cursor. A query that matches nothing still returns communities rather than an empty list, which is Reddit's own search behaviour, so judge relevance on the rows.

Searching 69 platforms in parallel

·TikTok logoTikTok·Instagram logoInstagram·YouTube logoYouTube·Facebook logoFacebook·X logoX·LinkedIn logoLinkedIn·Reddit logoReddit·Threads logoThreads·Pinterest logoPinterest·Twitch logoTwitch·Truth Social logoTruth Social·Snapchat logoSnapchat·Kick logoKick·Bluesky logoBluesky·Kwai logoKwai·Rumble logoRumble·Spotify logoSpotify·Apple Music logoApple Music·TikTok Shop logoTikTok Shop·Amazon Shop logoAmazon Shop·Google Shopping logoGoogle Shopping·Trustpilot logoTrustpilot·TripAdvisor logoTripAdvisor·Yelp logoYelp·Linktree logoLinktree·Komi logoKomi·Pillar logoPillar·lnk.bio logolnk.bio·Facebook Ads logoFacebook Ads·Google Ads logoGoogle Ads·LinkedIn Ads logoLinkedIn Ads·Google Search logoGoogle Search·Google News logoGoogle News·Finance logoFinance·Polymarket logoPolymarket·Tavily logoTavily·Hacker News logoHacker News·GitHub logoGitHub·Perplexity logoPerplexity·Naver logoNaver·Utility logoUtility·Universal Search logoUniversal Search
·TikTok logoTikTok·Instagram logoInstagram·YouTube logoYouTube·Facebook logoFacebook·X logoX·LinkedIn logoLinkedIn·Reddit logoReddit·Threads logoThreads·Pinterest logoPinterest·Twitch logoTwitch·Truth Social logoTruth Social·Snapchat logoSnapchat·Kick logoKick·Bluesky logoBluesky·Kwai logoKwai·Rumble logoRumble·Spotify logoSpotify·Apple Music logoApple Music·TikTok Shop logoTikTok Shop·Amazon Shop logoAmazon Shop·Google Shopping logoGoogle Shopping·Trustpilot logoTrustpilot·TripAdvisor logoTripAdvisor·Yelp logoYelp·Linktree logoLinktree·Komi logoKomi·Pillar logoPillar·lnk.bio logolnk.bio·Facebook Ads logoFacebook Ads·Google Ads logoGoogle Ads·LinkedIn Ads logoLinkedIn Ads·Google Search logoGoogle Search·Google News logoGoogle News·Finance logoFinance·Polymarket logoPolymarket·Tavily logoTavily·Hacker News logoHacker News·GitHub logoGitHub·Perplexity logoPerplexity·Naver logoNaver·Utility logoUtility·Universal Search logoUniversal Search
·TikTok logoTikTok·Instagram logoInstagram·YouTube logoYouTube·Facebook logoFacebook·X logoX·LinkedIn logoLinkedIn·Reddit logoReddit·Threads logoThreads·Pinterest logoPinterest·Twitch logoTwitch·Truth Social logoTruth Social·Snapchat logoSnapchat·Kick logoKick·Bluesky logoBluesky·Kwai logoKwai·Rumble logoRumble·Spotify logoSpotify·Apple Music logoApple Music·TikTok Shop logoTikTok Shop·Amazon Shop logoAmazon Shop·Google Shopping logoGoogle Shopping·Trustpilot logoTrustpilot·TripAdvisor logoTripAdvisor·Yelp logoYelp·Linktree logoLinktree·Komi logoKomi·Pillar logoPillar·lnk.bio logolnk.bio·Facebook Ads logoFacebook Ads·Google Ads logoGoogle Ads·LinkedIn Ads logoLinkedIn Ads·Google Search logoGoogle Search·Google News logoGoogle News·Finance logoFinance·Polymarket logoPolymarket·Tavily logoTavily·Hacker News logoHacker News·GitHub logoGitHub·Perplexity logoPerplexity·Naver logoNaver·Utility logoUtility·Universal Search logoUniversal Search
·TikTok logoTikTok·Instagram logoInstagram·YouTube logoYouTube·Facebook logoFacebook·X logoX·LinkedIn logoLinkedIn·Reddit logoReddit·Threads logoThreads·Pinterest logoPinterest·Twitch logoTwitch·Truth Social logoTruth Social·Snapchat logoSnapchat·Kick logoKick·Bluesky logoBluesky·Kwai logoKwai·Rumble logoRumble·Spotify logoSpotify·Apple Music logoApple Music·TikTok Shop logoTikTok Shop·Amazon Shop logoAmazon Shop·Google Shopping logoGoogle Shopping·Trustpilot logoTrustpilot·TripAdvisor logoTripAdvisor·Yelp logoYelp·Linktree logoLinktree·Komi logoKomi·Pillar logoPillar·lnk.bio logolnk.bio·Facebook Ads logoFacebook Ads·Google Ads logoGoogle Ads·LinkedIn Ads logoLinkedIn Ads·Google Search logoGoogle Search·Google News logoGoogle News·Finance logoFinance·Polymarket logoPolymarket·Tavily logoTavily·Hacker News logoHacker News·GitHub logoGitHub·Perplexity logoPerplexity·Naver logoNaver·Utility logoUtility·Universal Search logoUniversal Search
Reddit API

What can you do with the Subreddits Search API?

The Subreddits Search endpoint gives you structured Reddit data with computed fields in a single request. No scraping infrastructure to build or maintain.

Example Request

# GET /v1/reddit/subreddits/search: Metered: holds 1 credit per page up front and refunds what the call does not use. Cache hits are free and failed calls are refunded.
# Check the price first for 0 credits: add dry_run=1 to the query. Nothing is fetched or charged.
# --max-time 29: this endpoint's measured p99 latency x 1.5.
# --retry 3: retries timeouts, 429 and 5xx, honoring Retry-After.
# -o response.json keeps only the final attempt's body.
# One Idempotency-Key per request, reused on every retry, so a retried call is never charged twice.
# Next page: send pagination.next_cursor back as cursor until pagination.has_more is false.
IDEMPOTENCY_KEY=$(od -An -N16 -tx1 /dev/urandom | tr -d ' \n')
curl -sS --max-time 29 --retry 3 -o response.json \
  -H "x-api-key: YOUR_API_KEY" \
  -H "Idempotency-Key: $IDEMPOTENCY_KEY" \
  "https://www.socialcrawl.dev/v1/reddit/subreddits/search?query=machine+learning" && cat response.json
# GET /v1/reddit/subreddits/search: Metered: holds 1 credit per page up front and refunds what the call does not use. Cache hits are free and failed calls are refunded.
# Check the price first for 0 credits: add dry_run=1 to the query. Nothing is fetched or charged.
import os
import random
import time
import uuid

import requests

API_KEY = os.environ.get("SOCIALCRAWL_API_KEY", "YOUR_API_KEY")
TIMEOUT_S = 29  # this endpoint's measured p99 latency x 1.5.
MAX_RETRIES = 3
URL = "https://www.socialcrawl.dev/v1/reddit/subreddits/search"


def backoff(attempt):
    # Exponential backoff with jitter: about 1 s, 2 s, then 4 s.
    return 2 ** attempt * (0.5 + random.random() / 2)


def socialcrawl(params):
    # One Idempotency-Key per request, reused on every retry, so a retried call is never charged twice.
    idempotency_key = str(uuid.uuid4())
    for attempt in range(MAX_RETRIES + 1):
        try:
            res = requests.get(
                URL,
                params=params,
                headers={"x-api-key": API_KEY, "Idempotency-Key": idempotency_key},
                timeout=TIMEOUT_S,
            )
            body = res.json()
        except (requests.RequestException, ValueError):
            # A timeout, a dropped connection or a non-JSON gateway page: retry with the same key.
            if attempt == MAX_RETRIES:
                raise
            time.sleep(backoff(attempt))
            continue
        if body["success"]:
            return body
        error = body["error"]
        # Branch on error["type"], never on the message text.
        if error["type"] == "RESOURCE_NOT_FOUND":
            return None  # refunded: nothing to read
        retry_after = res.headers.get("Retry-After")
        # SERVICE_UNAVAILABLE without Retry-After is a withdrawn endpoint: permanent.
        permanent = error["type"] == "SERVICE_UNAVAILABLE" and retry_after is None
        if not error.get("retryable") or permanent or attempt == MAX_RETRIES:
            raise RuntimeError(f"{error['type']}: {error['message']} (request_id {body['request_id']})")
        # Retry-After (seconds) wins; otherwise back off.
        time.sleep(float(retry_after) if retry_after else backoff(attempt))


params = {
    "query": "machine learning",
}

# Walk the list: send pagination.next_cursor back as cursor until has_more is false.
MAX_PAGES = 10  # each page is one call
items = []
for _ in range(MAX_PAGES):
    body = socialcrawl(params)
    if body is None:
        break
    items.extend(body["data"]["items"])
    page = body.get("pagination") or {}
    if not page.get("has_more") or not page.get("next_cursor"):
        break
    params["cursor"] = page["next_cursor"]
print(f"{len(items)} items")
// GET /v1/reddit/subreddits/search: Metered: holds 1 credit per page up front and refunds what the call does not use. Cache hits are free and failed calls are refunded.
// Check the price first for 0 credits: add dry_run=1 to the query. Nothing is fetched or charged.
const API_KEY = process.env.SOCIALCRAWL_API_KEY ?? "YOUR_API_KEY";
const TIMEOUT_MS = 29_000; // this endpoint's measured p99 latency x 1.5.
const MAX_RETRIES = 3;

type Page = { next_cursor: string | null; has_more: boolean };
type SocialCrawlResponse =
  | { success: true; data: any; pagination?: Page; credits_used: number; request_id: string }
  | { success: false; error: { type: string; message: string; retryable: boolean }; request_id: string };

const sleep = (ms: number) => new Promise((resolve) => setTimeout(resolve, ms));
// Exponential backoff with jitter: about 1 s, 2 s, then 4 s.
const backoffMs = (attempt: number) => 2 ** attempt * 1000 * (0.5 + Math.random() / 2);

async function socialcrawl(url: URL) {
  // One Idempotency-Key per request, reused on every retry, so a retried call is never charged twice.
  const idempotencyKey = crypto.randomUUID();
  for (let attempt = 0; ; attempt++) {
    let res: Response;
    let body: SocialCrawlResponse;
    try {
      res = await fetch(url, {
        headers: {
          "x-api-key": API_KEY,
          "Idempotency-Key": idempotencyKey,
        },
        signal: AbortSignal.timeout(TIMEOUT_MS),
      });
      body = (await res.json()) as SocialCrawlResponse;
    } catch (error) {
      // A timeout, a dropped connection or a non-JSON gateway page: retry with the same key.
      if (attempt >= MAX_RETRIES) throw error;
      await sleep(backoffMs(attempt));
      continue;
    }
    if (body.success) return body;
    // Branch on error.type, never on the message text.
    if (body.error.type === "RESOURCE_NOT_FOUND") return null; // refunded: nothing to read
    const retryAfter = res.headers.get("Retry-After");
    // SERVICE_UNAVAILABLE without Retry-After is a withdrawn endpoint: permanent.
    const permanent = body.error.type === "SERVICE_UNAVAILABLE" && retryAfter === null;
    if (!body.error.retryable || permanent || attempt >= MAX_RETRIES) {
      throw new Error(`${body.error.type}: ${body.error.message} (request_id ${body.request_id})`);
    }
    // Retry-After (seconds) wins; otherwise back off.
    await sleep(retryAfter ? Number(retryAfter) * 1000 : backoffMs(attempt));
  }
}

const url = new URL("https://www.socialcrawl.dev/v1/reddit/subreddits/search");
url.searchParams.set("query", "machine learning");

// Walk the list: send pagination.next_cursor back as cursor until has_more is false.
const MAX_PAGES = 10; // each page is one call
const items: unknown[] = [];
for (let page = 0; page < MAX_PAGES; page++) {
  const body = await socialcrawl(url);
  if (!body) break;
  items.push(...body.data.items);
  const next = body.pagination?.next_cursor;
  if (!body.pagination?.has_more || !next) break;
  url.searchParams.set("cursor", next);
}
console.log(`${items.length} items`);

Parameters

ParameterRequiredDescription
queryYesTopic or keyword to find communities for.
cursorNoCursor to get more results. Get 'cursor' from the previous response.
includeNoSet to `details` (one token only) to fill `author.joined_at`, `author.ext.weekly_active_users`, `author.ext.weekly_contributions`, `author.ext.rules_text` and `author.ext.language` on every row in this one call (a row the details lookup's main source cannot answer gets only the creation date and the language, and is still billed). Holds 1 credit per row (at most 25, or `limit`) and keeps only the rows filled from a fresh lookup; rows already in cache and rows that could not be filled are refunded, so the most a call can cost is 1 + rows. Adds 3 to 9 seconds on a fresh page (never more than 12) and nothing when the rows are already cached; read `data.hydration` for the rows, the credits held and kept, and the time. (details)
limitNoTake the top N communities of the page (1 to 25). With `include=details` it also caps the extra credits at N, so `limit=5&include=details` holds 6 credits, not 26. It is not a page size: the cursor still advances past the whole page, so rows beyond N are not returned by the next page.
max_pagesNo1 to 50 (default 1, or 50 when `max_items` or `max_credits` is sent). Walk up to this many pages in one call and return the rows from all of them. Each page walked is billed exactly as one call to this endpoint (a cached page is free) and counts against your rate limit; its charge and any refund are recorded under the page's own request id, listed in `data.walk.request_ids`. 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`, `max_items`, `max_credits`, `time_budget`, `page_error`) and `pagination.next_cursor` continues from where it stopped.
max_itemsNo1 to 5000. Get more in one call: keep walking pages until the response holds at least this many rows, then stop with `data.walk.stopped: "max_items"`. The last page is returned whole (it was billed), so `data.items` can hold up to one page more than this, and `pagination.next_cursor` resumes right after it without skipping a row. Walks up to 50 pages unless `max_pages` says fewer; each page is billed as one call (a cached page is free). Add `max_credits` to cap the spend. A long walk can stop on the time budget first (`"time_budget"`): send the returned cursor with the same params to continue.
max_creditsNo1 to 100000. The most this call may spend, across every page it walks. A page starts only while the credits already spent plus that page's price (or, when higher, the most any page of this walk has cost) still fit, so the call never goes past it; the walk then stops with `data.walk.stopped: "max_credits"` and the cursor to resume. Sent alone, it walks as far as the budget goes (up to 50 pages). A value below the price of one page is a free 400 whose `error.details.page_credits` names that price. Credits a row join (`include=`) adds are known only once a page has run, so page 1 is checked against its own price.
Example Response

What does the Reddit Subreddits Search API return?

Every response follows one unified schema. Here is a real, unmodified response body, so you can see the exact fields you get back before spending a credit.

Example response
{
  "success": true,
  "platform": "reddit",
  "endpoint": "/v1/reddit/subreddits/search",
  "data": {
    "items": [
      {
        "author": {
          "id": "user_0b2f67",
          "username": "user_0b2f67",
          "display_name": "user_0b2f67",
          "avatar_url": "https://example.com/avatars/user_37b18d.png",
          "bio": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted).",
          "verified": null,
          "followers": 3075004,
          "following": null,
          "posts_count": null,
          "likes_count": null,
          "url": "https://example.com/user_05e113",
          "private": null,
          "joined_at": null,
          "ext": {
            "is_nsfw": false,
            "weekly_active_users": null,
            "weekly_contributions": null,
            "rules_text": null,
            "language": null
          }
        }
      },
      {
        "author": {
          "id": "user_21b48e",
          "username": "user_21b48e",
          "display_name": "user_21b48e",
          "avatar_url": null,
          "bio": "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).",
          "verified": null,
          "followers": 682525,
          "following": null,
          "posts_count": null,
          "likes_count": null,
          "url": "https://example.com/user_5311fd",
          "private": null,
          "joined_at": null,
          "ext": {
            "is_nsfw": false,
            "weekly_active_users": null,
            "weekly_contributions": null,
            "rules_text": null,
            "language": null
          }
        }
      }
    ],
    "next_cursor": "sc1.eyJjIjoiTWpRPSIsInMiOltdfQ",
    "total": null,
    "dropped": 0
  },
  "credits_used": 1,
  "request_id": "req_example000000",
  "cached": false,
  "pagination": {
    "next_cursor": "sc.eyJ2IjoyLCJjIjoic2MxLmV5SmpJam9pVFdwUlBTSXNJbk1pT2x0ZGZRIiwiZSI6InJlZGRpdC9zdWJyZWRkaXRzL3NlYXJjaCIsInAiOiJjdXJzb3IifQ",
    "has_more": true,
    "page_size": 2
  },
  "credits_remaining": 9999,
  "source": "captured",
  "captured_at": "2026-10-02T07:33:19.717Z",
  "redacted": true
}

Live sample illustrating the unified response shape. Field values reflect the record you query.

API Details

How does the Reddit Subreddits Search API work?

Send a GET request with your API key and get back clean, structured JSON in our unified schema. Supported computed fields are populated when the source provides the required inputs.

Method

GET

Response

JSON

Why SocialCrawl

Why use SocialCrawl for Reddit Subreddits Search data?

We handle the complexity of Reddit data extraction so you can focus on building. Unified schema, AI enrichment, and zero platform logic in your code.

Developer First

How do you scrape social media data in seconds?

The fastest social media scraping API for developers. Scrape profiles, posts, comments, and analytics from 69 platforms covering 10B+ monthly active users.

One schema, every platform

Query 69 platforms with identical response structures. Write your integration once.

Computed fields, not just scraped

When an endpoint supports these metrics and the source provides the required inputs, the normalized record includes engagement_rate, estimated_reach, content_category, and language. Ready to use.

See your data before you code

Visual Data Explorer. Paste any URL, get rich result cards, sortable tables, CSV export.

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()
[ .JSON ]
{
  "success": true,
  "platform": "tiktok",
  "data": {
    "author": {
      "username": "charlidamelio",
      "followers": 152400000
    },
    "engagement": {
      "likes": 12400000000,
      "engagement_rate": 0.087
    },
    "metadata": {
      "language": "en",
      "content_category": "lifestyle"
    }
  }
}
+ 69 platforms
FAQ

Have a question? We got answers

Find answers to frequently asked questions about SocialCrawl's API, pricing, and capabilities.

Contact us
How do I find subreddits about a topic?
Send a GET request to /v1/reddit/subreddits/search with a query. Each row carries the community name at author.id, the subscriber count, description, icon and NSFW flag, up to 25 communities per page.
How do I get weekly active users for each community?
Add include=details to the same call. Every community on the page is joined to the subreddit details lookup, so the creation date, weekly active users, weekly contributions, rules and language land on each row.
How much does the Subreddit Search API cost?
A plain search costs 1 credit on the standard tier. With include=details you pay the page price plus one credit per community filled from a fresh lookup, so at most 26 credits; communities already in cache are free and ones that could not be filled are refunded.
Can I cap how many communities are returned?
Yes. limit takes the top N communities of the page, 1 to 25, and with include=details it caps the extra credits at N as well, so asking for five holds five extra credits rather than the whole ceiling.
Can I feed the results into the other Reddit endpoints?
Yes. author.id is the bare community name in Reddit's own casing, which is exactly the subreddit parameter that /v1/reddit/subreddit, /v1/reddit/subreddit/details and /v1/reddit/subreddit/search take.

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