Reddit Comments Scraper & API: Export Full Comment Threads

Reddit Comments Scraper: Full Comment Threads

Paste Reddit post links and get all their comments: text, score, author, date, depth, parent and root comment, with the post itself. Or get each thread as one Markdown record for an LLM. No login, no cookies, no API key. Export as JSON, CSV or Excel, or read it through the Apify API.

• The whole tree, no comment left out: on a 1,650-comment thread, 263 of 263 recent comments down to depth 12 in our tests, each with parentId, depth and rootCommentId, so the thread can be rebuilt in a spreadsheet.
• Huge threads, fast: on October 21, 2026, the 2012 Obama AMA came back with 22,754 comments (Reddit's counter showed 22,717) in 2 minutes 40 seconds in our tests, and a 1,560-comment thread took 18 seconds on Apify, start included.
• Threads as Markdown for LLMs (outputFormat: threadMarkdown): one record per post with its comment tree, 7.2 times fewer characters than the same thread as JSON records, at the same price.
• Comment date filters: only the comments written after or before a date; the tree is still walked, so a new reply under an old comment is found.
• Only new comments on a schedule: turn on Only new since the last run and each run returns and charges only the comments earlier runs did not return.

$2.50 per 1,000 comments, $2.50 per 1,000 posts, less on paid plans, plus $0.005 per run. Posts that fail, duplicates and comments outside your dates are not charged.

Optional AI analysis of each post and comment (sentiment, intent, entities, summary, pain point, business signals, your own labels): $0.50 per 1,000 analyzed records.

What people use it for

• Reading what buyers say about a product: export the threads where it is discussed, then sort the comments by score or let the AI analysis flag complaints, praise and switching intent.
• Finding leads: comments where someone asks for a recommendation or names a budget come out with wantsHelp and buyingSignal set to true when the AI analysis is on.
• Feeding an LLM or a RAG index: one Markdown record per thread keeps who answered whom, in about 7 times fewer characters than JSON.
• Archiving an AMA or a support thread, or following a launch thread hour by hour with Only new since the last run.
• Research on public discussions, with the full reply tree so a conversation can be studied as it happened.

How to scrape all the comments of a Reddit post

1. Paste post links into Post URLs, one per line.
2. Max comments per post comes set to 50 for the example: empty it for the whole thread, or set your own cap.
3. Click Start and read the comments in the run's Output tab.

Through the API:

{
  "startUrls": ["https://www.reddit.com/r/IAmA/comments/z1c9z/"],
  "commentSort": "top",
  "maxCommentDepth": 2
}

New comments of a launch thread, each hour:

{
  "startUrls": ["https://www.reddit.com/r/technology/comments/1x3b0mi/"],
  "onlyNew": true,
  "watchName": "launch-thread"
}

Like any Apify Actor, it can be scheduled, connected to webhooks, Make, Zapier, n8n, or called by AI agents through Apify's MCP server.

Output

One record for the post, then one per comment. A real comment from our benchmark thread (author replaced, some fields left out):

{
  "type": "comment",
  "id": "pd0smho",
  "url": "https://www.reddit.com/r/technology/comments/1wu4lnz/palantir_ceo_alex_karp_quietly_purchased_37000/pd0smho/",
  "postId": "1wu4lnz",
  "postTitle": "Palantir CEO Alex Karp quietly purchased 37,000 acres of forests in Sweden—and locals found out when hunters lost leases to use the property",
  "parentId": "t1_pd02xdu",
  "parentType": "comment",
  "rootCommentId": "pczzr78",
  "depth": 3,
  "subreddit": "technology",
  "author": "example_user",
  "body": "Not a Swede, but don’t want him there either. ",
  "createdAt": "2026-09-30T15:31:09Z",
  "score": 920,
  "scorePerHour": 8.72,
  "isDeletedOrRemoved": false,
  "scrapedAt": "2026-10-05T00:58:22Z"
}

Field	What it holds
body, score, createdAt	the comment as written, its score and date
parentId, parentType, rootCommentId, depth	where it sits in the tree (0 = a top-level comment)
postId, postTitle	the post it belongs to
isDeletedOrRemoved	true for a comment deleted or removed after it got replies; its score can then be null

With threadMarkdown, each post is one thread record: title, counters, and the comment tree as indented Markdown (author, score, date, text). The run summary is the OUTPUT record of the run's key-value store.

Export Reddit comments to CSV or Excel

Open the run's Output tab, then Export: CSV, Excel, JSON, XML or HTML. Each comment is one row; parentId and depth let you rebuild the tree in a spreadsheet, and sorting by score puts the most upvoted on top.

Scraping Reddit comments without the Reddit API

Since November 11, 2025, every new app needs Reddit's approval before it gets an API key (the Responsible Builder Policy). Self-service keys are gone. The other old route, adding .json to a Reddit URL, also closed: on October 14 and 15, 2026, Reddit stopped serving those listings and the "load more comments" endpoint to readers without an account, from every IP we tried. Scripts built on them now get a login page or a 403.

This Actor reads what any logged-out visitor can see on Reddit, with no account, no cookies and no API key, and it walks the "load more" and "continue this thread" branches itself. We test every route it depends on each morning and fix it when Reddit changes something.

How complete is it? Measured against other Reddit scrapers

The three most used Reddit scrapers on the Apify Store and ours, run with their default settings on October 4 and 5, 2026. We compared every returned comment with what Reddit showed at the same minute.

Test	This Actor	The three most used scrapers
A 1,650-comment thread: the 263 comments written since October 1	263 of 263	245 and 224 (the third was not run)
Deepest reply level reached in that thread	12	9 and 9
A 137-comment thread (6 comments were deleted on Reddit)	131	131, 131, and 25 with the run marked as a success

We looked up the 40 comments the others missed on the big thread: all of them exist on Reddit, under the same parent, with the same text. Scrapers change, so these are the results on those dates; we keep the raw runs.

Huge threads, measured on October 21, 2026 (whole thread, default settings):

Thread	Comments returned	Time
r/technology, 1,669 comments on Reddit's counter	1,560, every branch opened	18 seconds
The 2012 Obama AMA in r/IAmA, 22,717 on Reddit's counter	22,754	2 minutes 40 seconds

An AMA comes back nearly whole, not cut at the first few thousand comments.

AI analysis (optional)

Turn on AI analysis and every post and comment gets an aiAnalysis object next to its own fields: $0.50 per 1,000 analyzed records, every field and signal included. A real result (post "Best and inexpensive CRM for small businesses" in r/CRM, analyzed October 14, 2026, with two labels of our own; some entities left out):

"aiAnalysis": {
  "language": "en",
  "sentiment": "mixed",
  "sentimentScore": -0.2,
  "emotion": "anticipation",
  "intent": "seeking_recommendation",
  "category": "software",
  "topics": ["crm alternatives", "hubspot pricing", "small business software"],
  "entities": [{"name": "HubSpot", "type": "brand"}, {"name": "Zoho", "type": "brand"}, {"name": "Pipedrive", "type": "brand"}],
  "summary": "A user seeks a cheaper CRM alternative to HubSpot that retains key features like lead tracking and reporting for their small businesses.",
  "painPoint": "HubSpot is becoming too expensive",
  "wantsHelp": true,
  "buyingSignal": true,
  "mentionsPrice": true,
  "featureRequest": false,
  "isVendor": false,
  "relevance": null,
  "labels": {"switching": true, "budget": true}
}

Field	What it holds
sentiment, sentimentScore	positive, negative, neutral or mixed, and a score from -1 to 1
emotion	the main emotion (joy, frustration, anger, curiosity, confusion, none…)
intent	what the author wants: question, seeking_recommendation, complaint, praise, purchase_intent, comparison, announcement, self_promotion, discussion, humor
category, topics, entities	a category, up to 3 topics, and the brands, products, companies, people and places named
summary, painPoint	one English sentence, and the problem or frustration the author expresses (or null)
wantsHelp, buyingSignal, mentionsPrice, featureRequest, isVendor	business signals, true or false: asks for help, ready to buy or pay, names a price or budget, asks for a feature, sells or builds what they talk about
relevance	search results only (in Reddit Search Scraper): how much the record is about the query, 0 to 1
labels	your own yes/no labels

Your own labels: up to 5, as {"label_name": "what makes it true"}, for example {"switching": "The author wants to switch from one product to another"}. Included in the price.

• Charged once per record analyzed in full. Deleted, removed, bot (AutoModerator) and empty records are not analyzed and not charged. If an analysis fails, the record is still delivered (and charged as usual), its aiAnalysisStatus is failed with the reason in aiAnalysisError, and the analysis is not charged.
• Any language in, summaries in English. The first ~2,500 characters of a very long text are analyzed.
• Works with the one-record-per-item output format (not the Markdown thread format).
• It adds time: about a minute and a half per 100 records.

Inputs

• Post URLs: reddit.com and old.reddit.com links, redd.it links, share links, gallery or v.redd.it links, or a comment permalink.
• Max comments per post: empty = the whole thread, even with tens of thousands of comments; 0 = the post only.
• Max comment depth: empty = all levels; 0 = top-level comments only.
• Comment sort: Reddit's default order, top, new, controversial, old, Q&A.
• Comments posted after / before.
• AI analysis and Your own labels: the optional analysis described above.
• Output format: one record per item, or each thread as one Markdown record.
• Only new since the last run and Watch name, for scheduled runs.

Pricing

Pay per event:

Apify plan	Comments	Posts
Free	$2.50 / 1,000	$2.50 / 1,000
Starter	$2.40 / 1,000	$2.40 / 1,000
Scale	$2.30 / 1,000	$2.30 / 1,000
Business and above	$2.20 / 1,000	$2.20 / 1,000

AI analysis (optional): $0.50 / 1,000 analyzed records on every plan.

Plus the start: $0.005 per GB of run memory. A full 1,650-comment thread came to about $3.91 on the Free plan in our tests. The Markdown format is charged the same way (one post, plus one comment per comment in the record). Not charged: posts that fail, error records, duplicates, comments outside your dates, comments already returned to a watch. Set Maximum cost per run in the run options and the run stops before going over it.

Run it in production

To follow a thread, or a list of threads, keep one saved input and let Apify run it on a schedule.

1. In Apify Console open Schedules and create a schedule with a cron time (0 */6 * * * is every six hours).
2. Add this Actor with your post links. Turn on Only new since the last run and give a Watch name: each run returns and charges only the comments the earlier runs did not return, even new replies under old comments.
3. Choose the rhythm from how fast the thread moves. Each run costs the start ($0.005) even when nothing is new, so an hourly schedule is $3.60 a month in starts alone; every six hours is $0.60 and daily is $0.15.
4. Without Only new, set Comments posted after to the length of your rhythm (12 hours, 1 day) and remove the overlap on id in your own storage.

What it costs per month (Free plan prices; Business prices in brackets), for 1,000 new comments in the month:

Schedule	Cost per month
Daily (30 runs)	$2.65 ($2.35)
Every 6 hours (120 runs)	$3.10 ($2.80)
Every hour (720 runs)	$6.10 ($5.80)

A full thread read once: 1,650 comments came to about $3.91 on the Free plan. Optional AI analysis adds $0.50 per 1,000 records analyzed. Set Maximum cost per run so that a run never goes over what you planned.

Call it from code. Start a run and wait for the results in one request (this call waits up to 300 seconds, so use it for small jobs):

curl -X POST "https://api.apify.com/v2/acts/quarra~reddit-comments-scraper/run-sync-get-dataset-items?token=<YOUR_APIFY_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"startUrls": ["https://www.reddit.com/r/technology/comments/1x3b0mi/"], "onlyNew": true, "watchName": "launch-thread"}'

For bigger jobs, start the run, wait for it to end, then read its dataset:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("quarra/reddit-comments-scraper").call(run_input={"startUrls": ["https://www.reddit.com/r/technology/comments/1x3b0mi/"], "onlyNew": True, "watchName": "launch-thread"})
items = list(client.dataset(run["defaultDatasetId"]).iterate_items())

Connect it. Add a webhook on the event ACTOR.RUN.SUCCEEDED (Console, Integrations tab) to be called when a run ends, then read the dataset of that run. Make, Zapier and n8n have Apify modules: run the Actor, get the dataset items, then send each one to Slack, Google Sheets, Airtable or your CRM. AI agents can call the Actor through Apify's MCP server.

Send results to Slack, Notion or Discord

Pick one of your Apify MCP connectors in "Send results to your app" (Notion connects in one click), or paste a Slack, Discord, Make, Zapier or n8n webhook URL. After each successful run, a summary lands there: the number of results, the first 25 with their links, and a link to the full dataset. Schedule the Actor with "Only new since the last run (monitoring)" turned on and new results reach your team every day without opening Apify.

Limits

• Reddit does not serve deleted comments that have no replies; they are counted in the run summary, not returned.
• Quarantined communities are visible to logged-in accounts only. The Actor reports them and does not log in.

FAQ

Why did I get zero comments? The usual causes: the post has no comments yet, or only deleted ones with no replies (they are counted in the run summary, not returned); Comments posted after / before leaves nothing in the window; Max comments per post is set to 0, which returns the post only; with Only new, every comment was already returned under the same Watch name. A link that cannot be read gets a free error record, and a run with no result costs the start only.

How fast is it? A 1,560-comment thread in 18 seconds, a 22,000-comment AMA in under 3 minutes (October 21, 2026).

Can I get every comment of a big thread, including the ones behind "load more comments"? Yes. Leave Max comments per post empty: the Actor opens every "load more" and "continue this thread" branch, even on AMAs with tens of thousands of comments.

Can I scrape the comments of many posts in one run? Yes, paste as many post links as you want, one per line. Each post is followed by its own comments in the output.

Does it work for AI agents? Yes, through Apify's MCP server or the API. Use the Markdown thread format to give a model a whole discussion in one record.

Do I need a Reddit account or API key? No account, no cookies, no API key. Platform costs are included in the price.

Is it legal? It collects public comments any logged-out visitor can see. Reddit's terms and privacy law (GDPR in the EU) apply to how you use the data.

Does the Actor send anything to Quarra? Only when a run fails or runs out of time: the Actor, its build, the error type, the line of our code where it happened, and the memory and time the run was given, so we can fix it fast. Never your input, your results, your account or the run ID.

Related Actors

• Reddit Scraper: subreddits, searches, post URLs and users in one Actor.
• Reddit Search Scraper: posts and comments by keyword.
• Reddit Comments Search Scraper: comments by keyword.
• Reddit Subreddit Scraper: the posts of any subreddit.
• Reddit Post Scraper: posts from their links, or refreshed by ID to follow their score.
• Reddit Lead Finder: the posts and comments where people ask for a recommendation or show buying intent.