Pricing

Rating

Developer

Actor stats

Categories

Share

🔥 Reddit Scraper for LLM & RAG | Posts, Comments & Images (2026)

The fastest, most cost-effective way to extract Reddit data for AI workflows. No browser overhead, no rate limit headaches—just clean JSON + LLM-ready Markdown output that plugs directly into n8n, Make, or any automation platform.

• ⚡ Blazing fast - Pure HTTP requests, no browser simulation
• 💰 Incredibly cheap - $0.30 for 100 posts with comments (flat rate, any comment count)
• 🤖 LLM-optimized - Markdown output ready for GPT, Claude, Gemini
• 🔄 n8n native - Designed for workflow automation

👁 Demo

Copy to your AI assistant

Copy this block into ChatGPT, Claude, Cursor, or any LLM to start using this actor.

clearpath/reddit-to-llm-api on Apify. Call:ApifyClient("TOKEN").actor("clearpath/reddit-to-llm-api").call(run_input={...}), then client.dataset(run["defaultDatasetId"]).list_items().items for results. Key inputs:mode(string:"subreddit"|"search"|"post"),subreddits(array),searchKeywords(array). Full actor spec(input schema with all params/enums/defaults, output dataset fields,README):GEThttps://api.apify.com/v2/acts/clearpath~reddit-to-llm-api/builds/default(Bearer TOKEN) → inputSchema, actorDefinition.storages.dataset, readme. Pricing: pay-per-event, $0.001/post, $0.003/post with comments, $0.0005/image. Get token: https://console.apify.com/account/integrations

Why This Actor?

Most Reddit scrapers give you raw JSON that needs heavy transformation before LLMs can use it. This Actor outputs pre-formatted Markdown alongside structured JSON—feed it directly to an AI node without writing a single line of code.

Perfect for n8n Workflows

Lead Generation from Pain Points

reddit-to-llm(search:"looking for","need help with")
 → LLM qualify leads
 → CRM/Email sequence

Find people actively seeking solutions you provide.

Product Validation Pipeline

Webhook (new idea)
 → reddit-to-llm (search related subreddits)
 → LLM analyze: demand signals, objections, existing solutions
 → Structured report

Before building, validate if people actually want it. The markdown format lets LLMs deeply analyze threaded discussions.

⚡ Key Features

Lightning Fast Extraction

• No browser overhead - Direct data extraction, not Puppeteer/Playwright
• 20 concurrent requests - Process multiple posts simultaneously
• Automatic deduplication - No duplicate posts across modes

🎯 Three Collection Modes

• Search - Global or subreddit-restricted keyword search
• Subreddit Feeds - Hot, new, top, rising posts
• Direct URLs - Scrape specific posts by URL

🤖 LLM-Ready Output

• Markdown field - Formatted for direct AI consumption
• Flat comments with depth - Easy to process, depth signals conviction
• OP markers - Know when the author replies

📸 Optional Image Extraction

• Preview images, galleries, direct links (i.redd.it, imgur)
• Stored to Apify Key-Value Store with public URLs
• Ready for vision models (GPT-4V, Claude)

💰 Pricing (Pay Per Event)

Transparent, predictable pricing. Only pay for what you extract.

Flat rate per post - Whether a post has 1 comment or 500 comments, the price is the same ($0.003).

Cost Examples

Cost optimization tips:

• Set includeComments: false if you only need post titles/content (3x cheaper)
• Comment count doesn't affect price - get as many as you need!
• Filter by subreddit to avoid irrelevant posts

Input Configuration

Search Mode

Subreddit Feed Mode

Direct URLs Mode

Output Settings

Output Schema

Each dataset item is one Reddit post and carries every field Reddit exposes for that post (media, image previews, galleries, awards, full flair, crosspost data, content and moderation flags) plus a full subreddit profile, the comment thread, and the LLM-ready Markdown. The example below is trimmed for readability; large nested objects (preview, media, all_awardings) are shown abbreviated.

{
"id":"1prkwnx",
"title":"Product Developer (15y SaaS/Apps) seeking Marketing/Sales co-builder",
"author":"ManuelWenner",
"author_fullname":"t2_8xk2p",
"created_utc":"2025-12-20T18:18:21+00:00",
"created_utc_epoch":1766254701,
"permalink":"/r/indiehackers/comments/1prkwnx/product_developer_15y_saasapps_seeking/",
"url":"https://www.reddit.com/r/indiehackers/comments/1prkwnx/...",
"domain":"self.indiehackers",
"selftext":"Hey folks,\n\nI've been building digital products for ~15 years...",
"score":2,
"ups":2,
"upvote_ratio":0.75,
"num_comments":8,
"num_crossposts":0,
"view_count":null,
"subreddit":"indiehackers",
"subreddit_id":"t5_3i9si",
"subreddit_subscribers":140617,
"is_self":true,
"is_video":false,
"over_18":false,
"spoiler":false,
"stickied":false,
"locked":false,
"archived":false,
"edited":false,
"gilded":0,
"total_awards_received":0,
"all_awardings":[],
"distinguished":null,
"post_hint":null,
"thumbnail":"self",
"link_flair_text":"General Question",
"link_flair_background_color":"#0079d3",
"is_nsfw":false,
"is_spoiler":false,
"media":null,
"preview":{"images":[{"source":{"url":"https://preview.redd.it/...","width":1200,"height":630},"resolutions":["..."]}]},
"subreddit_details":{
"id":"3i9si",
"fullname":"t5_3i9si",
"name":"indiehackers",
"display_name_prefixed":"r/indiehackers",
"title":"Independent developers building their own way",
"description":"IndieHackers is a subreddit focused on people who bootstrap their way to success.",
"sidebar_description":"# Welcome\n\nFull community rules and resources...",
"subscribers":140617,
"active_users":null,
"created_utc":"2016-09-26T12:05:56+00:00",
"over_18":false,
"subreddit_type":"public",
"lang":"en",
"icon_img":"https://b.thumbs.redditmedia.com/....png",
"community_icon":"https://styles.redditmedia.com/....png",
"banner_background_image":"https://styles.redditmedia.com/....jpg",
"primary_color":"#0079d3",
"key_color":"#222222",
"submission_type":"any",
"allow_images":true,
"allow_videos":true,
"allow_galleries":true,
"spoilers_enabled":true,
"wiki_enabled":true,
"url":"https://www.reddit.com/r/indiehackers/"
},
"comments":[
{
"id":"nv30qrn",
"body":"How do people find Matchplan?",
"author":"scarfwizard",
"author_id":"t2_5h2k9",
"author_flair":"Bootstrapper",
"author_type":"USER",
"score":1,
"depth":0,
"child_count":1,
"created_utc":"2025-12-20T20:02:18+00:00",
"edited_at":null,
"parent_id":"t3_1prkwnx",
"permalink":"/r/indiehackers/comments/1prkwnx/.../nv30qrn/",
"is_submitter":false,
"distinguished":null,
"is_stickied":false,
"language_code":"en"
},
{
"id":"nv33n4g",
"body":"Currently I'm in such an early stage...",
"author":"ManuelWenner",
"score":1,
"depth":1,
"parent_id":"t1_nv30qrn",
"is_submitter":true
}
],
"images":[],
"markdown":"# Product Developer seeking Marketing/Sales co-builder\n\n**2 upvotes** | 8 comments | u/ManuelWenner | 2025-12-20\n\nHey folks...\n\n---\n\n## Comments\n\n**[1] u/scarfwizard** How do people find Matchplan?\n> **[1] u/ManuelWenner (OP)** Currently I'm in such an early stage...\n"
}

Output Fields

Post data: every field the post exposes, including identity (id, title, author, author_fullname, permalink, url, domain), content (selftext, is_self, is_video, media, preview, gallery data, thumbnail, post_hint), engagement (score, ups, upvote_ratio, num_comments, num_crossposts, view_count, gilded, total_awards_received, all_awardings), state (edited, stickied, pinned, locked, archived, distinguished, over_18, spoiler, removed_by_category), flair (link_flair_text + color/css/type), and subreddit context (subreddit, subreddit_id, subreddit_subscribers). Timestamps come as both ISO (created_utc) and epoch (created_utc_epoch).

subreddit_details: full subreddit profile — descriptions, subscriber count, icons and banners, theme colors, content policy (submission_type, allow_images/allow_videos/allow_galleries/allow_polls, spoilers_enabled, wiki_enabled), and timestamps.

Comments: flat list with text, author (author, author_id, author_flair, author_type), score, depth (0 = top-level), child_count, timestamps (created_utc, edited_at), parent_id, permalink, is_submitter (marks OP replies), plus distinguished, is_stickied/is_locked/is_removed, removed_by_category, and language_code.

Markdown: Pre-formatted for LLM consumption with nested blockquotes for replies.

Example Inputs

Search Mode (Global)

{
"searchKeywords":["indiehacker","pain points"],
"searchSort":"relevance",
"searchLimit":50,
"includeComments":true,
"commentsLimit":100
}

Subreddit Feed

{
"subreddits":["indiehackers","SaaS","startups"],
"subredditSort":"top",
"subredditTimeFilter":"week",
"subredditLimit":100
}

Direct URLs

{
"postUrls":[
"https://www.reddit.com/r/indiehackers/comments/abc123/...",
"https://redd.it/xyz789"
],
"includeComments":true,
"commentsLimit":500
}

Combined (All Modes)

{
"searchKeywords":["product feedback"],
"subreddits":["SaaS"],
"postUrls":["https://redd.it/abc123"],
"includeComments":true,
"commentsLimit":100
}

Use Cases

For Product Teams

• Voice of Customer - Extract feature requests and complaints from product subreddits
• Competitor Intelligence - Monitor what users say about alternatives
• Product Validation - Search for demand signals before building

For Marketers

• Content Research - Find top-performing topics in your niche
• Lead Generation - Identify users seeking solutions you provide
• Brand Monitoring - Track mentions and sentiment

For Researchers

• Qualitative Analysis - Reddit comments as interview transcripts
• Trend Detection - Early signals from rising posts
• Sentiment Analysis - Community reactions with depth context

Limitations

• Pagination cap: Max 1,000 posts per mode, 1,000 comments per post
• Comment structure: Flat list with depth field (not nested JSON tree)
• Images: Common sources supported (preview, i.redd.it, imgur, galleries). Videos not downloaded.
• Rate limits: Handled automatically with exponential backoff

FAQ

Q: Do I need a Reddit account or API key? A: No. The Actor extracts publicly available data without authentication.

Q: How fast is it? A: Very fast. No browser overhead means 100 posts with comments typically complete in under 60 seconds.

Q: Why is the markdown field useful? A: LLMs process it directly without transformation. Perfect for n8n/Make workflows where you connect Actor output → AI node.

Q: Can I scrape private subreddits? A: No. Only public subreddits and posts are accessible.

Q: What if a post is deleted? A: The Actor returns null and continues with other posts.

Q: How do I reduce costs? A: Set includeComments: false if you only need posts (3x cheaper: $0.001 vs $0.003). Comment count doesn't affect price, so no need to limit commentsLimit for cost reasons—use it only to reduce processing time.

More Clearpath scrapers for Reddit

🔍 Search & discovery

• 👁 Image
  Reddit Search Scraper
  Keyword search across all of Reddit
• 👁 Image
  Reddit Subreddit Posts Scraper
  Full subreddit feeds: hot, new, top, rising

💬 Threads & comments

• 👁 Image
  Reddit Post Comments Scraper
  Full comment trees and nested replies from any post URL

👤 Users

• 👁 Image
  Reddit Profile Scraper
  Bulk username, karma, and account lookup
• 👁 Image
  Reddit User Posts & Comments Scraper
  A single user's full post and comment history

🤖 AI & LLM tools

• 👁 Image
  Reddit MCP Server
  Live Reddit search inside your AI assistant
• 👁 Image
  Reddit Answers API
  Reddit's AI answer engine for any question

Support

• 📧 Email: max@mapa.slmail.me
• 🐛 Bugs: Use the Issues tab
• 💡 Feature requests: Email or Issues

Legal

This Actor extracts publicly available Reddit data. Users are responsible for compliance with Reddit's Terms of Service and applicable data protection regulations (GDPR, CCPA).

🚀 Start Extracting Reddit Data Now

Turn Reddit discussions into AI-ready insights in minutes, not hours.