# Reddit Scraper (`alwaysprimedev/reddit-scraper`) Actor

Scrape Reddit posts, threads, and comments from any subreddit, search, or user — clean structured JSON, fast.

- **URL**: https://apify.com/alwaysprimedev/reddit-scraper.md
- **Developed by:** [Always Prime](https://apify.com/alwaysprimedev) (community)
- **Categories:** Automation, Lead generation, Social media
- **Stats:** 22 total users, 1 monthly users, 97.7% runs succeeded, 1 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.50 / 1,000 posts

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

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

[![Apify](https://img.shields.io/badge/Run%20on-Apify-00b96b?logo=apify\&logoColor=white)](https://apify.com/)
[![Python](https://img.shields.io/badge/Built%20with-Python%203.11-3776AB?logo=python\&logoColor=white)](https://www.python.org/)
[![Output: JSON · CSV · Excel](https://img.shields.io/badge/Output-JSON%20%7C%20CSV%20%7C%20Excel-orange)](#-sample-output)

> 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

| Field | Type | Description |
|---|---|---|
| **What to scrape** (`searchType`) | enum | `subreddit` · `search` · `user` · `urls` |
| **Subreddits** (`subreddits`) | string list | e.g. `python`, `programming` (no `r/` prefix) |
| **Search query** (`query`) | string | Keywords. Reddit operators work: `author:`, `subreddit:`, `self:yes`, `flair:`. |
| **Users** (`users`) | string list | Usernames to scrape (no `u/` prefix) |
| **User content type** (`userContent`) | enum | `submitted` (posts) or `comments` |
| **Sort by** (`sortBy`) | enum | `hot` · `new` · `top` · `rising` · `controversial` · `relevance` · `comments` |
| **Time window** (`time`) | enum | `hour` · `day` · `week` · `month` · `year` · `all` (only matters for `top`/`controversial`) |
| **Max items** (`maxItems`) | int | Stop after N posts. `0` = unlimited. Default `50`. |
| **Scrape comments** (`scrapeComments`) | bool | Fetch the comment tree for every post. Default off (cheaper for indexing). |
| **Max comments per post** (`commentDepth`) | int | Cap on comments per post (BFS). Default `200`. |
| **Only posts newer than** (`since`) | datetime | ISO 8601 cutoff for incremental runs. |
| **Concurrency** (`concurrency`) | int | Parallel fetches. Default `5`, max `25`. |
| **Start URLs** (`startUrls`) | string list | Advanced override — paste any reddit URLs and ignore the search-type builder. |

***

### 📦 Sample output

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

```json
{
  "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

| Who | What for |
|---|---|
| 📈 **Market researchers** | Track sentiment, competitor mentions and product feedback across niche subreddits. |
| 🤖 **AI / ML teams** | Build training corpora from focused subreddits — clean text, threading preserved. |
| 📰 **Journalists & analysts** | Monitor breaking-story subreddits and surface trending discussions for coverage. |
| 💼 **Brand / community managers** | Find unanswered support questions about your product across Reddit, on a daily cron. |
| 🏷️ **Recruiters & talent intel** | Pull discussions in tech-job subreddits to track skill demand and salary chatter. |
| 🧑‍🔬 **Academic researchers** | Public-discourse datasets for sociolinguistics, network analysis, opinion mining. |

***

### 🧰 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.

# Actor input Schema

## `searchType` (type: `string`):

Choose the source of posts: a subreddit's listing, a keyword search, a user's submissions/comments, or a custom list of Reddit URLs.

## `subreddits` (type: `array`):

List of subreddit names to scrape (without the r/ prefix). Used when search type is 'subreddit', or to scope a search to specific subreddits.

## `query` (type: `string`):

Keywords to search for. Required when search type is 'search'. Reddit's search supports operators like author:USERNAME, subreddit:NAME, self:yes/no, flair:NAME.

## `users` (type: `array`):

Reddit usernames to scrape (without the u/ prefix). Used when search type is 'user'.

## `userContent` (type: `string`):

When scraping a user, choose whether to fetch their submitted posts or their comments.

## `sortBy` (type: `string`):

How Reddit should order results before scraping. Available options depend on search type: subreddits use hot/new/top/rising/controversial; search adds 'relevance' and 'comments'; users use hot/new/top/controversial.

## `time` (type: `string`):

Time range filter — only matters when sort is 'top' or 'controversial'. Otherwise ignored by Reddit.

## `maxItems` (type: `integer`):

Stop after this many posts have been scraped. Set to 0 for unlimited (the scraper will paginate until Reddit runs out of results).

## `scrapeComments` (type: `boolean`):

If on, fetches each post's full comment thread (one extra request per post — slower and ~2× the cost). If off, you get post metadata only, which is much cheaper for large indexing runs.

## `commentDepth` (type: `integer`):

Cap on how many comments to keep per post (counted breadth-first across the comment tree). Only applies when 'Scrape comments' is on.

## `since` (type: `string`):

Optional ISO 8601 cutoff (e.g. 2026-01-01T00:00:00Z). Posts created before this timestamp are skipped — perfect for incremental/diff runs that only pull what's new since last time.

## `concurrency` (type: `integer`):

How many simultaneous detail fetches to run. Higher is faster but burns Reddit's per-IP rate budget — leave at 5 unless you've configured a proxy pool.

## `startUrls` (type: `array`):

Bypass the searchType-based URL builder and scrape exactly these Reddit URLs. Any reddit.com or old.reddit.com URL works — the scraper appends .json automatically.

## Actor input object example

```json
{
  "searchType": "subreddit",
  "subreddits": [
    "python",
    "programming"
  ],
  "query": "fastapi",
  "users": [
    "spez"
  ],
  "userContent": "submitted",
  "sortBy": "hot",
  "time": "all",
  "maxItems": 50,
  "scrapeComments": false,
  "commentDepth": 200,
  "since": "2026-01-01T00:00:00Z",
  "concurrency": 5,
  "startUrls": [
    "https://www.reddit.com/r/python/top/?t=week"
  ]
}
```

# Actor output Schema

## `posts` (type: `string`):

All scraped posts as a JSON array.

## `postsCsv` (type: `string`):

All scraped posts as a CSV — opens directly in Excel or Google Sheets.

## `postsXlsx` (type: `string`):

All scraped posts as an XLSX workbook.

## `consoleView` (type: `string`):

Browse, filter and re-export results in the Apify web console.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "subreddits": [
        "python",
        "programming"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("alwaysprimedev/reddit-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "subreddits": [
        "python",
        "programming",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("alwaysprimedev/reddit-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "subreddits": [
    "python",
    "programming"
  ]
}' |
apify call alwaysprimedev/reddit-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=alwaysprimedev/reddit-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/4zdi8V53fa7ftfcvu/builds/r7cAxLOfrKbPze0pR/openapi.json