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🚀 Reddit Scraper — every post, comment & thread, as clean JSON

👁 Apify
👁 Python
👁 Output: JSON · CSV · Excel

Pull structured Reddit data at speed — posts, comments, scores, flairs, awards, media, timestamps. No login. No code. No babysitting.

🏠 Subreddits · 🔍 Keyword search · 👤 User submissions/comments · 🔗 Custom URLs — all four sources, one input form.

⚡️ Why this scraper

• 🎯 50+ fields per post — full title and body, score breakdown, upvote ratio, flair, awards, removal status, media URLs, edit timestamps. Nothing dropped on the floor.
• 💬 Comment threads on demand — flip one switch and get the full comment tree per post, threaded via parent_id and depth.
• 🚄 Fast — ~3 posts/second steady-state on default settings; ~250ms median per detail fetch.
• 🧠 Smart pagination — stops the moment your Max items budget is reached. Never over-fetches, never wastes Apify Compute Units.
• 🔁 Incremental mode — pass a since timestamp and only get posts newer than your last run. Perfect for daily monitoring jobs.
• 🛡️ Built-in failure budget — if Reddit starts pushing back (challenges, hard 4xx), the actor aborts cleanly instead of burning through your CU on a broken extractor.
• 📊 Three export formats out of the box — JSON, CSV, Excel. Direct download links from the run page.

🚀 Quick start

1. Click Try for free (top-right). No code, no API key.
2. Pick a search type — Subreddit, Search, User, or paste your own URLs.
3. Hit Start and let it run.
4. Download as JSON / CSV / Excel from the run page.

📥 Input

📦 Sample output

One record per post — flat, JSON-friendly, ready to load into BigQuery / Postgres / pandas.

{
"id":"1t3x7ba",
"fullname":"t3_1t3x7ba",
"url":"https://www.reddit.com/r/Python/comments/1t3x7ba/whos_going_to_pycon_us_next_week/",
"subreddit":"Python",
"subreddit_prefixed":"r/Python",
"subreddit_id":"t5_2qh0y",
"title":"Who's going to PyCon US next week?",
"selftext":"Me ✋ I hope to see a good number of you all in Long Beach, too! ...",
"is_self":true,
"domain":"self.Python",
"post_hint":"self",
"link_url":null,
"author":"Loren-PSF",
"author_fullname":"t2_so0s40st",
"author_flair_text":":pythonLogo: Python Software Foundation Staff",
"distinguished":null,
"score":46,
"ups":46,
"upvote_ratio":0.91,
"num_comments":35,
"num_crossposts":0,
"total_awards_received":0,
"gilded":0,
"over_18":false,
"spoiler":false,
"locked":false,
"stickied":true,
"archived":false,
"is_video":false,
"is_original_content":false,
"link_flair_text":"Discussion",
"link_flair_css_class":"discussion",
"link_flair_background_color":"#f50057",
"thumbnail":null,
"preview_image_url":"https://external-preview.redd.it/FBtD3iI-OdRHdmfJbVushiwzLeMcmgTx-Ff3FnwUUg0.jpeg",
"video_url":null,
"removed_by_category":null,
"removal_reason":null,
"created_at":"2026-05-04T22:40:29+00:00",
"edited_at":null,
"scraped_at":"2026-05-09T13:43:47+00:00",
"comments":[
{
"id":"myz2pn1",
"parent_id":"t3_1t3x7ba",
"depth":0,
"author":"vintagegeek",
"body":"I'll be there with bells on. Looking forward to meeting people!",
"score":19,
"is_submitter":false,
"stickied":false,
"permalink":"https://www.reddit.com/r/Python/comments/1t3x7ba/.../myz2pn1/",
"created_at":"2026-05-04T23:01:14+00:00",
"edited_at":null
}
],
"comments_count_scraped":35
}

💡 Use cases

🧰 Tips & tricks

• 🪶 Index-first, hydrate later. Run with scrapeComments: false and maxItems: 0 to cheaply enumerate everything. Then a second run with startUrls and scrapeComments: true only on the posts you care about.
• ⏱️ Daily diffs. Save the timestamp of your last successful run, then pass it as since next time. The actor short-circuits old posts before fetching them.
• 🎛️ Subreddit-scoped search. Set searchType: search, fill query, and add subreddits to subreddits — the actor automatically scopes search to those subreddits.
• 🔗 Mix custom URLs. Drop any reddit.com/... URL into startUrls (a thread, a multireddit, a sort variant) — the actor strips/appends .json itself.

❓ FAQ

Does it need a Reddit account? No.

What about the new Reddit API limits? This actor doesn't use Reddit's Data API, so the post-2023 commercial pricing tiers don't apply.

Can I scrape NSFW subreddits? Yes. NSFW posts are returned with over_18: true so you can filter downstream.

Will it get all comments on a huge thread? Up to your commentDepth cap (default 200, max 5000), breadth-first across the tree. For Reddit's truly massive megathreads (>10K comments), Reddit itself paginates and not every comment is reachable in one fetch — that's a Reddit limitation, not the scraper's.

What if a post is deleted while scraping? Deleted posts come through with author: "[deleted]", selftext: "[deleted]", and removed_by_category: "deleted". They're not skipped — you get the metadata Reddit still surfaces.

How fresh is the data? Real-time. Each record carries a scraped_at UTC timestamp.

📅 Changelog

0.1 (initial release)

• Subreddit, search, user, and start-URL modes
• Configurable comment-tree scraping with depth cap
• Incremental since filter, maxItems cap, dedup, failure budget
• JSON / CSV / Excel exports

⚖️ Legal

This scraper accesses Reddit through public, non-authenticated requests. Reddit's robots.txt disallows automated crawling, and Reddit's User Agreement and Public Content Policy restrict automated/commercial use of Reddit content. By using this scraper you take on responsibility for the legality of your specific use case in your jurisdiction (including GDPR / CCPA where applicable). The scraper does not bypass authentication, paywalls, or technical access controls. Use it for research, journalism, internal analytics, ML/AI training datasets, or other lawful purposes — and confirm that those purposes are compatible with Reddit's policies and any applicable law before running large-scale jobs. Personal data scraped from Reddit (usernames, comment bodies, flair) may constitute PII under GDPR even though usernames are pseudonymous; treat the output dataset accordingly.