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Reddit Post Comments API

Scrape Reddit Post Comments data with one API call. Returns the full threaded comment tree for a Reddit post. Nested replies are auto-expanded by following upstream pagination, so a single call returns the deep tree (not just top-level comments). Each comment includes the author, body, score, direct-reply count, creation timestamp, its nesting depth at `ext.depth`, and a recursive `replies[]` array. `items[]` holds only the top-level comments, so `items.length` and `pagination.page_size` count those alone; `data.comments_returned` counts every comment in the response, replies included, and is the number to compare with the post's comment count. Comment bodies are Reddit's own markdown source, so `comment.text` can contain markdown such as `**bold**` and `[label](url)`. Comment ids are unique within a response: cursor windows can re-offer a boundary node, and those are de-duplicated before the tree is returned. Very large threads are bounded, and `data.truncated` tells you whether more of the thread exists than the response carries. It is not simply a record of our own paging: a response that reached the source's end of the thread is still reported as `truncated: true` when it falls far short of the post's own comment count, because that end is the end of one ranking window rather than the end of the thread. So `truncated: true` can arrive with no `next_cursor`, which means the thread is incomplete and this endpoint cannot fetch you the rest. A whole thread pages from one top-level cursor, so `ext.replies_cursor` on an individual branch is rare. `truncated: false` means the response looks complete against the post's own count, which is normally still higher than the comments you receive because Reddit counts removed, deleted, and filtered replies that no listing returns to anyone. `items[]` can also contain a comment whose own parent was removed upstream: it sits at the top level beside the real roots, and `parent_id` is what separates them, because a root's `parent_id` equals its `post_id` and an orphan's does not.

Last updated October 2026Maintained by the SocialCrawl team

Start for freeView Documentation

Returns the full comment tree for a post, each comment with author, body, score, direct-reply count, nesting depth, timestamp, and its nested replies.

Use it when you want the whole discussion rather than only top-level comments. Read data.truncated: true means the thread is incomplete, so keep paging while a cursor comes back.

Searching 68 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 Post Comments API?

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

Example Request

curl -H "x-api-key: YOUR_API_KEY" \
  "https://www.socialcrawl.dev/v1/reddit/post/comments?url=https%3A%2F%2Fwww.reddit.com%2Fr%2FNoteTaking%2Fcomments%2F1o9s55r%2Fi_tried_all_popular_ai_notetaking_apps_so_you%2F&label=purchase_intent&trim=true"
import requests

response = requests.get(
    "https://www.socialcrawl.dev/v1/reddit/post/comments",
    params={
    'url': 'https://www.reddit.com/r/NoteTaking/comments/1o9s55r/i_tried_all_popular_ai_notetaking_apps_so_you/',
    'label': 'purchase_intent',
    'trim': 'true',
    },
    headers={"x-api-key": "YOUR_API_KEY"},
)

data = response.json()
const response = await fetch(
  "https://www.socialcrawl.dev/v1/reddit/post/comments?url=https%3A%2F%2Fwww.reddit.com%2Fr%2FNoteTaking%2Fcomments%2F1o9s55r%2Fi_tried_all_popular_ai_notetaking_apps_so_you%2F&label=purchase_intent&trim=true",
  {
    headers: { "x-api-key": "YOUR_API_KEY" },
  },
);

const data = await response.json();

Parameters worth sending

  • label=purchase_intent Marks each comment that shows buying intent, at no extra credits; sentiment, question and complaint are free too.
  • trim=true Same price, lighter payload: drops the bulky raw fields.

Parameters

ParameterRequiredDescription
urlYesFull URL of the Reddit post to fetch comments for
cursorNoCursor to get more comments, or replies.
trimNoAccepted, but not honoured on this endpoint: the source that serves it does not offer a trimmed shape, so the response is the same either way. Kept because removing an accepted parameter would break existing calls.
labelNoOptional CSV of SocialCrawl labels to add to every comment: sentiment, spam, question, purchase_intent, complaint, toxic, low_quality, injection. Without this param every page already carries sentiment, question, purchase_intent and complaint, free; label= adds the labels you name to them (the defaults keep running). Each comment gains computed.labels with probabilities (0 to 1), so you choose the cut-off; a comment that could not be judged carries labels: null. sentiment, question, purchase_intent and complaint are free when asked for too; spam, toxic, low_quality and injection add 1 credit per started 25 newly judged comments (comments already labelled for anyone are free, and so is a cached page). injection flags text that addresses an AI system and tries to direct it (flagged, p); it never drops or rewrites a row. judgments=off (or label=none) turns the default labels off. data.labels reports what was judged and billed.
excludeNoOptional CSV (spam, low_quality), only with the same preset in label. Drops comments whose probability is 0.8 or higher and lists their ids in data.labels.dropped_ids. A comment that could not be judged is never dropped.
judgmentsNoOptional, 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_runNoOptional. 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)
label_evidenceNoOptional, 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)
fitNoOptional. 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)
goalNoRequired by fit=goal. What you are trying to do, in your own words, up to 300 characters.
fit_tokensNoOptional, 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.
sinceNoOnly rows published on or after this date: YYYY-MM-DD (midnight UTC) or an ISO 8601 timestamp. Rows older than it are left off each page. This list is not in date order, so the walk does not stop at the date: keep following `next_cursor`, and each page costs what a page costs even when every row on it is filtered out. 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`.
untilNoOnly 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`.
Example Response

What does the Reddit Post Comments 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/post/comments",
  "data": {
    "items": [
      {
        "comment": {
          "id": "nk4bu00",
          "url": "https://example.com/redacted",
          "parent_id": "1o9s55r",
          "post_id": "1o9s55r",
          "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted).",
          "author": {
            "username": "user_0dc293",
            "display_name": "user_d96928",
            "avatar_url": "https://example.com/avatars/user_918b31.png",
            "verified": null
          },
          "engagement": {
            "likes": 7,
            "replies": 1
          },
          "flags": {
            "pinned": null,
            "deleted": false
          },
          "published_at": "2025-10-18T10:27:59.144Z",
          "ext": {
            "depth": 0,
            "published_at_epoch": 1760783279
          },
          "replies": [
            {
              "id": "nnbr72r",
              "url": "https://example.com/redacted",
              "parent_id": "nk4bu00",
              "post_id": "1o9s55r",
              "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted).",
              "author": {
                "username": "user_bec0fc",
                "display_name": "user_47ef50",
                "avatar_url": "https://example.com/avatars/user_63d5a5.png",
                "verified": null
              },
              "engagement": {
                "likes": 1,
                "replies": null
              },
              "flags": {
                "pinned": null,
                "deleted": false
              },
              "published_at": "2025-11-05T23:14:00.172Z",
              "ext": {
                "depth": 1,
                "published_at_epoch": 1762384440
              }
            }
          ]
        },
        "computed": {
          "language": "en",
          "labels": {
            "sentiment": {
              "level": 3,
              "score_0_1": 0.78,
              "confidence": 0.91
            },
            "question": {
              "p": 0.03
            },
            "purchase_intent": {
              "p": 0.15
            },
            "complaint": {
              "p": 0.03
            }
          }
        }
      },
      {
        "comment": {
          "id": "nk4ee40",
          "url": "https://example.com/redacted",
          "parent_id": "1o9s55r",
          "post_id": "1o9s55r",
          "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted).",
          "author": {
            "username": "user_d43641",
            "display_name": "user_5d003e",
            "avatar_url": "https://example.com/avatars/user_bf53d9.png",
            "verified": null
          },
          "engagement": {
            "likes": 7,
            "replies": null
          },
          "flags": {
            "pinned": null,
            "deleted": false
          },
          "published_at": "2025-10-18T10:52:23.370Z",
          "ext": {
            "depth": 0,
            "published_at_epoch": 1760784743
          }
        },
        "computed": {
          "language": "en",
          "labels": {
            "sentiment": {
              "level": 1,
              "score_0_1": 0.24,
              "confidence": 0.96
            },
            "question": {
              "p": 0.03
            },
            "purchase_intent": {
              "p": 0.03
            },
            "complaint": {
              "p": 0.96
            }
          }
        }
      }
    ],
    "total": null,
    "truncated": false,
    "labels": {
      "presets": [
        "sentiment",
        "question"
      ],
      "rows": 33,
      "labelled": 33,
      "cached": 33,
      "skipped": 0,
      "unjudged": 0,
      "dropped": 0,
      "dropped_ids": [],
      "extra_credits": 0,
      "methodology_version": "labels/1",
      "mode": "default",
      "status": "complete",
      "pending": 0,
      "pending_ids": []
    },
    "label_share": {
      "policy_version": "share/1",
      "presets": {
        "question": {
          "n": 33,
          "counted": 5,
          "abstained": 1,
          "share": 0.1563,
          "low": 0.0782,
          "high": 0.2879,
          "receipts": [
            {
              "id": "nk4qy74",
              "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted)."
            },
            {
              "id": "nk7zp1p",
              "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted)."
            }
          ],
          "policy_version": "share/1",
          "too_few": false,
          "at_cut": 5,
          "nulls": 0,
          "low_confidence": 1
        },
        "purchase_intent": {
          "n": 33,
          "counted": 2,
          "abstained": 3,
          "share": 0.0667,
          "low": 0.0223,
          "high": 0.1827,
          "receipts": [
            {
              "id": "nk4fn67",
              "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)."
            },
            {
              "id": "nlyjukn",
              "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted)."
            }
          ],
          "policy_version": "share/1",
          "too_few": false,
          "at_cut": 3,
          "nulls": 0,
          "low_confidence": 3
        },
        "complaint": {
          "n": 33,
          "counted": 3,
          "abstained": 2,
          "share": 0.0968,
          "low": 0.0393,
          "high": 0.2189,
          "receipts": [
            {
              "id": "nk4ee40",
              "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted)."
            },
            {
              "id": "nlh1o80",
              "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)."
            }
          ],
          "policy_version": "share/1",
          "too_few": false,
          "at_cut": 4,
          "nulls": 0,
          "low_confidence": 2
        },
        "sentiment": {
          "positive": {
            "n": 33,
            "counted": 18,
            "abstained": 5,
            "share": null,
            "low": 0.4875,
            "high": 0.7731,
            "receipts": [
              {
                "id": "nk4bu00",
                "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted)."
              },
              {
                "id": "nk4fn67",
                "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)."
              }
            ],
            "policy_version": "share/1",
            "too_few": true,
            "at_cut": 18,
            "nulls": 0,
            "low_confidence": 5
          },
          "neutral": {
            "n": 33,
            "counted": 7,
            "abstained": 5,
            "share": null,
            "low": 0.1416,
            "high": 0.4024,
            "receipts": [
              {
                "id": "nk4kecb",
                "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted)."
              },
              {
                "id": "nk4qy74",
                "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted)."
              }
            ],
            "policy_version": "share/1",
            "too_few": true,
            "at_cut": 7,
            "nulls": 0,
            "low_confidence": 5
          },
          "negative": {
            "n": 33,
            "counted": 3,
            "abstained": 5,
            "share": 0.1071,
            "low": 0.0436,
            "high": 0.2399,
            "receipts": [
              {
                "id": "nk4ee40",
                "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted)."
              },
              {
                "id": "nk4exbw",
                "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted)."
              }
            ],
            "policy_version": "share/1",
            "too_few": false,
            "at_cut": 3,
            "nulls": 0,
            "low_confidence": 5
          }
        }
      }
    },
    "comment_language": {
      "policy_version": "share/1",
      "n": 33,
      "abstained": 1,
      "judged_by": {
        "script": 0,
        "choice": 32
      },
      "shares": {
        "en": {
          "n": 33,
          "counted": 32,
          "abstained": 1,
          "share": 1,
          "low": 0.922,
          "high": 1,
          "receipts": [
            {
              "id": "nk4bu00",
              "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted)."
            },
            {
              "id": "nk4ee40",
              "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted)."
            }
          ],
          "policy_version": "share/1",
          "too_few": false
        }
      }
    },
    "comment_recency": {
      "policy_version": "d09-1",
      "n": 33,
      "counted": 33,
      "abstained": 0,
      "share_last_24h": 0,
      "share_later_half": null,
      "too_few": false,
      "stage": "unknown",
      "order": "default",
      "recent_share_is_floor": true,
      "receipts": []
    },
    "dropped": 0
  },
  "credits_used": 5,
  "request_id": "req_example000000",
  "cached": false,
  "pagination": {
    "next_cursor": null,
    "has_more": false,
    "page_size": 33
  },
  "credits_remaining": 9999,
  "source": "captured",
  "captured_at": "2026-10-02T15:39:22.882Z",
  "redacted": true
}

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

API Details

How does the Reddit Post Comments 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 Post Comments 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 68 platforms covering 10B+ monthly active users.

One schema, every platform

Query 68 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"
    }
  }
}
+ 68 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 fetch Reddit comments for a post?
Send a GET request to /v1/reddit/post/comments with the full post URL in the url parameter. SocialCrawl returns a threaded comment tree including author, body text, score, reply count, awards, and creation timestamp for every comment.
Are replies nested or flat in the response?
Replies are nested. Each comment has a replies array containing its direct children, which in turn contain their own replies. Walk the tree recursively to process the full conversation, or flatten it if you only need a sentiment snapshot.
How do I load more replies on deep threads?
Usually you don't need to. The endpoint returns the full tree by default, auto-following Reddit's nested reply pages for you. Only on very large threads that hit the expansion limit does the response set data.truncated and include a cursor at each unexpanded branch. Pass that cursor as the cursor parameter to fetch the rest.
Do you return deleted or removed comments?
Reddit replaces deleted comments with placeholder text and removed comments with moderator notices. SocialCrawl returns them as-is so your pipeline can detect and filter them. The original body text is not recoverable from the public endpoint.
How much does the Reddit Comments API cost?
Each call is a flat 5 credits on the advanced tier, regardless of thread size. It costs more than a basic read because it returns the entire comment tree in one call, auto-expanding Reddit's nested reply pages for you across multiple upstream fetches. Very large threads stop at a safety limit and hand back a cursor to continue. New accounts get 100 free credits, and trim=true keeps payloads light.

Ask AI about SocialCrawl

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🤖 AI agent or LLM? Read this page as markdown