Reddit Comments & Posts Scraper — Full Threads, Every Comment

Scrape Reddit posts, complete comment trees, subreddits, users, and search results. No API key, no login, no rate limits. Pure HTTP — fast and cheap.

Why this scraper

🧵 100% of the comment tree. Most Reddit scrapers silently return only the comments that load on the page and skip the "load more comments" branches. This Actor recursively expands every morechildren stub until the tree is exhausted. If a cap is hit, items are explicitly flagged commentsTruncated: true — you always know whether a thread is complete.

📡 Monitor mode. Turn any input into a scheduled monitor: on each run, only posts and comments that are NEW since the previous run are returned. Brand mentions, keyword alerts, community tracking — without deduping downstream.

🤖 LLM-ready output. outputFormat: "markdown" adds one clean Markdown document per thread (post + threaded comments, ordered by score, with a tokensEstimate field), ideal for RAG pipelines, AI agents, and NotebookLM-style workflows. Token budgeting via markdownMaxTopComments. The doc comes on top of the structured items at no extra charge.

🎯 Search that respects your keywords. Reddit search is notoriously loose — the most common complaint against Reddit scrapers is "I searched X and got unrelated Y". Turn on strictKeywordFilter and every word of your query must actually appear in the post's title, body, or URL.

📅 Date-range filters. postedAfter / postedBefore and commentedAfter / commentedBefore (date or full ISO 8601). With sort: "new", pagination stops early once posts fall outside your window — you don't pay for pages you don't need.

🏷️ Flair filters. onlyWithFlair: true or target specific flairs with flairFilter: ["Discussion", "Help"] (case-insensitive).

🔎 Deep scan. Reddit caps any listing at ~1,000 posts. deepScan: true combines every sort and time window and dedupes, surfacing several times more unique posts per subreddit.

⚡ Pure HTTP. No headless browser: faster runs, lower platform usage, fewer failures. Automatic anti-blocking with tiered proxy escalation — a 403 never kills your run.

💶 Honest billing. You pay per scraped item, with maxItems as a hard cost cap you control. Filtered-out posts don't count against your bill, truncation is always flagged, and the run status message reports exactly what happened.

Live benchmark (July 12, 2026)

Real thread from r/AskReddit with 1,014 declared comments:

Method	Comments retrieved	Coverage
Inline page load only (what most scrapers return)	491	48%
This Actor (full 3-phase expansion)	990	97.6%

The remaining ~2% are deleted/removed comments — counted in Reddit's total but not retrievable by anyone. Total cost: 28 HTTP requests, ~40 seconds. Runs are always flagged commentsTruncated if any cap was hit, so partial data is never silent.

Input examples

Monitor a keyword in one community (schedule this daily):

{
  "searches": ["shopify"],
  "searchCommunityName": "ecommerce",
  "sort": "new",
  "monitorMode": true,
  "scrapeComments": false,
  "maxItems": 200
}

Full thread as LLM Markdown:

{
  "startUrls": [{ "url": "https://www.reddit.com/r/webscraping/comments/abc123/example/" }],
  "outputFormat": "markdown",
  "maxCommentsPerPost": 2000
}

Archive a whole subreddit beyond the 1k cap:

{
  "subreddits": ["MachineLearning"],
  "deepScan": true,
  "scrapeComments": false,
  "maxItems": 5000
}

Precise search: only posts that really mention your brand, from June 2026 onward:

{
  "searches": ["acme widgets"],
  "strictKeywordFilter": true,
  "postedAfter": "2026-06-01",
  "sort": "new",
  "scrapeComments": false,
  "maxItems": 500
}

Flair-targeted community research:

{
  "subreddits": ["ecommerce"],
  "flairFilter": ["Discussion", "Question"],
  "postedAfter": "2026-01-01",
  "scrapeComments": true,
  "maxCommentsPerPost": 300
}

Output

Structured items with dataType: post, comment, community, user, or thread_markdown. Every item also carries a unified content field (post title / comment body) so mixed exports render cleanly in one column. Comments carry parentId, depth, and threadPath (e.g. "c1/c2/c5") so you can rebuild the exact tree in one pass. All timestamps are ISO 8601 UTC. Export JSON, CSV, Excel via the Apify API or console.

{
  "dataType": "comment",
  "id": "c2",
  "parentId": "t1_c1",
  "postId": "p1",
  "author": "alice_dev",
  "body": "Thanks, trying that now.",
  "score": 7,
  "depth": 1,
  "threadPath": "c1/c2",
  "isSubmitter": true,
  "createdAt": "2026-07-12T06:43:20+00:00",
  "permalink": "https://www.reddit.com/r/webscraping/comments/p1/x/c2/"
}

Integrations

Works with the Apify MCP server (give AI agents direct access), n8n, Make, Zapier, LangChain, and the Apify API (Python/JS clients). Schedules + monitor mode = zero-infrastructure Reddit alerting.

FAQ

Do I need a Reddit account or API key? No. Only publicly visible content is scraped.

Legal? Scraping publicly available data is generally permissible, but you are responsible for complying with applicable laws (including GDPR when storing usernames) and Reddit's terms for your use case. Intended uses: research, sentiment analysis, brand monitoring, AI/RAG datasets.

Why are some threads flagged truncated? You hit maxCommentsPerPost. Raise it (cost scales with results) — the flag exists so partial data is never silent.