Reddit Answers API | AI-Powered Reddit Insights for RAG & Automation (2025)

The fastest way to extract AI-summarized answers from Reddit discussions - get structured insights backed by real community conversations, complete with source context and subreddit metadata.

Query Reddit's AI-powered Answers feature programmatically. Ask any question, receive comprehensive summaries synthesized from thousands of Reddit discussions, plus full context from cited posts and comments. Perfect for RAG pipelines, market research automation, and N8N/Make workflows.

• Unlimited answers - No artificial caps, process as many questions as you need
• Dual output format - Structured JSON for pipelines + clean markdown for LLMs
• Full source enrichment - Every cited post and comment with author, score, and full text
• 6+ languages - English, Spanish, Portuguese, Italian, French, German
• Sub-second responses - Optimized for high-throughput automation

Why Reddit Answers API?

Reddit contains decades of authentic human discussions on virtually every topic. Reddit Answers uses AI to synthesize insights from thousands of threads - but it's only available through Reddit's web interface.

This Actor unlocks Reddit Answers for automation:

Manual Process	With This Actor
Ask one question at a time	Batch 10 questions per run
Copy-paste answers manually	Structured JSON + markdown output
No source context	Full post/comment enrichment
No API access	RESTful integration ready
Browser required	Headless, schedulable runs

More Powerful Than Reddit's Native Feature

This Actor doesn't just wrap Reddit Answers - it enhances it:

• Full source context - Native Reddit Answers shows snippets; this Actor fetches complete posts and comments
• Structured data - Raw Reddit Answers is prose; get sections, quotes, and citations as structured JSON
• Markdown output - Pre-formatted for direct LLM consumption in RAG pipelines
• Subreddit metadata - Subscriber counts, descriptions, and community details for cited sources

Quick Start

Basic - Single Question

{
  "questions": ["What are the biggest pain points indie hackers face when validating their SaaS ideas?"]
}

Multiple Questions

{
  "questions": [
    "What tools do developers use for API documentation?",
    "How do startups handle customer support at scale?",
    "What are common mistakes when launching a SaaS product?"
  ]
}

Full Configuration

{
  "questions": [
    "What programming languages are best for building AI applications?",
    "How do remote teams maintain company culture?",
    "What are the best practices for pricing SaaS products?"
  ],
  "enrichSources": true,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

Input Parameters

Parameter	Type	Required	Default	Description
questions	array	Yes	-	Questions to ask Reddit Answers (1-10 per run)
enrichSources	boolean	No	true	Fetch full context for cited posts and comments
proxyConfiguration	object	No	Residential	Proxy settings (residential recommended)

Questions Parameter

Ask up to 10 questions per run. Supports 6+ languages - Reddit auto-detects the language from your question.

Supported languages: English, Spanish, Brazilian Portuguese, Italian, French, German

Tips for better answers:

• Be specific: "What tools do Y Combinator startups use for analytics?" vs "What analytics tools exist?"
• Include context: "For a B2B SaaS with 1000 users, how should I handle customer support?"
• Ask opinion questions: Reddit excels at "What's the best...", "How do you...", "What are common mistakes..."

Output

Each question returns a comprehensive response with:

Core Fields

• query - Your original question
• summary - Concise AI-generated summary (plain text)
• markdown - Full answer formatted as markdown (ideal for LLMs)
• timestamp - UTC timestamp of the response

Structured Data

• sections - Answer broken into titled sections with bullet points and citations
• quotes - All extracted quotes with source attribution
• followUpQueries - Suggested follow-up questions from Reddit

Source Context (when enrichSources: true)

• sourcePosts - Cited Reddit posts with full details
• sourceComments - Cited comments with parent thread context
• recommendedSubreddits - Related subreddits with metadata

Output Example

{
  "query": "What are the biggest pain points indie hackers face when validating their SaaS ideas?",
  "conversationId": "e8f9a2b1-4c5d-6e7f-8a9b-0c1d2e3f4a5b",
  "timestamp": "2025-01-15T14:30:00.000Z",
  "statusCode": 200,
  "summary": "Indie hackers commonly struggle with finding initial users for validation, distinguishing between polite feedback and genuine interest, and spending too much time building before validating. Many also face analysis paralysis and difficulty reaching their target audience.",
  "markdown": "## Common Validation Pain Points\n\n### Finding Initial Users\n- Cold outreach has very low response rates\n- Friends and family give biased feedback\n- Building an audience takes months\n\n### Distinguishing Interest from Politeness\n- \"That's a cool idea\" doesn't mean they'll pay\n- Sign-ups without activation are misleading\n- Need to see actual payment behavior\n\n...",
  "sections": [
    {
      "title": "Finding Initial Users",
      "items": [
        "Cold outreach has very low response rates",
        "Friends and family give biased feedback",
        "Building an audience takes months"
      ],
      "citations": [
        {
          "url": "https://reddit.com/r/SaaS/comments/abc123",
          "sourceId": "t3_abc123",
          "sourceType": "post",
          "displayText": "Struggled for 3 months to get my first 10 users"
        }
      ]
    }
  ],
  "quotes": [
    {
      "text": "The hardest part isn't building - it's finding people who will actually tell you the truth about whether they'd pay",
      "sourceUrl": "https://reddit.com/r/startups/comments/xyz789/comment/def456",
      "subreddit": "startups"
    },
    {
      "text": "I wasted 6 months building features nobody asked for because I was afraid to talk to users",
      "sourceUrl": "https://reddit.com/r/SaaS/comments/abc123",
      "subreddit": "SaaS"
    }
  ],
  "followUpQueries": [
    "How do you get your first 10 paying customers?",
    "What's the minimum viable validation before building?",
    "How do you find people to interview for customer discovery?"
  ],
  "sourcePosts": [
    {
      "id": "t3_abc123",
      "url": "https://reddit.com/r/SaaS/comments/abc123",
      "subreddit": "SaaS",
      "details": {
        "title": "6 months in: lessons from my failed SaaS validation",
        "author": "startup_founder_42",
        "score": 847,
        "numComments": 234,
        "created": "2024-11-20T08:15:00.000Z",
        "body": "I spent 6 months building a project management tool before realizing nobody wanted it. Here's what I learned...",
        "comments": [
          {
            "author": "experienced_founder",
            "score": 312,
            "body": "This is exactly why I always do a smoke test landing page first...",
            "created": "2024-11-20T09:30:00.000Z"
          }
        ]
      }
    }
  ],
  "sourceComments": [
    {
      "id": "t1_def456",
      "url": "https://reddit.com/r/startups/comments/xyz789/comment/def456",
      "quote": "The hardest part isn't building - it's finding people who will actually tell you the truth",
      "subreddit": "startups",
      "details": {
        "author": "serial_entrepreneur",
        "score": 156,
        "body": "The hardest part isn't building - it's finding people who will actually tell you the truth about whether they'd pay. Everyone wants to be nice, but nice doesn't validate your business model.",
        "created": "2024-12-01T16:45:00.000Z",
        "parentPost": {
          "title": "How do you validate ideas without a product?",
          "author": "new_founder",
          "url": "https://reddit.com/r/startups/comments/xyz789"
        }
      }
    }
  ],
  "recommendedSubreddits": [
    {
      "name": "SaaS",
      "url": "https://reddit.com/r/SaaS",
      "id": "t5_2qh1o",
      "details": {
        "displayName": "SaaS",
        "description": "A community for SaaS founders, operators, and enthusiasts",
        "subscribers": 125000,
        "iconUrl": "https://styles.redditmedia.com/...",
        "isNsfw": false,
        "created": "2012-03-15T00:00:00.000Z"
      }
    },
    {
      "name": "startups",
      "url": "https://reddit.com/r/startups",
      "id": "t5_2qh0y",
      "details": {
        "displayName": "Startups",
        "description": "Welcome to r/startups, the place to discuss startup problems and solutions",
        "subscribers": 1200000,
        "iconUrl": "https://styles.redditmedia.com/...",
        "isNsfw": false,
        "created": "2008-01-25T00:00:00.000Z"
      }
    }
  ],
  "relatedMedia": []
}

Use Cases

For Market Researchers

• Mine pain points at scale - Ask 50+ questions about customer frustrations in any niche
• Competitive intelligence - Extract what users say about competitor products
• Trend analysis - Query emerging topics across multiple subreddits
• Sentiment research - Understand community opinions with cited sources

For Founders & Product Teams

• Idea validation - "What problems do indie hackers face with X?" across dozens of verticals
• Feature prioritization - Discover what users actually request vs. what you assume
• User research automation - Replace hours of Reddit browsing with structured data
• Problem discovery - Find underserved needs backed by real discussions

For Developers & AI Engineers

• RAG pipeline integration - Feed Reddit insights directly into your LLM workflows
• N8N/Make automation - Trigger actions based on Reddit community intelligence
• Dataset building - Create training data from authentic human discussions
• Content generation - Source real quotes and perspectives for content

For Content Marketers

• Topic research - Discover what questions your audience actually asks
• Content ideation - Generate blog topics from real community pain points
• Quote sourcing - Find authentic user perspectives with attribution
• Keyword discovery - Uncover how real users describe problems

Pricing

Pay only for successful answers. Volume discounts available.

Tier	Price per Answer	Best For
Free	$0.035	Testing (5 questions/month)
Bronze	$0.030	Regular usage
Silver	$0.025	Moderate volume
Gold	$0.020	High-volume automation

Pricing Examples

Questions	Free Tier	Bronze	Silver	Gold
10	$0.35	$0.30	$0.25	$0.20
100	$3.50	$3.00	$2.50	$2.00
1,000	$35.00	$30.00	$25.00	$20.00

No charges for failed queries - You only pay when you receive a valid answer with citations. If quotes is empty, no charge occurs.

API Integration

Python

from apify_client import ApifyClient

client = ApifyClient("your_api_token")

run_input = {
    "questions": [
        "What are the best practices for pricing SaaS products?",
        "How do successful startups handle their first 100 customers?"
    ],
    "enrichSources": True
}

run = client.actor("clearpath/reddit-answers-api").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"Question: {item['query']}")
    print(f"Summary: {item['summary']}")
    print(f"Quotes: {len(item['quotes'])} citations")
    print("---")

JavaScript/TypeScript

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'your_api_token' });

const run = await client.actor('clearpath/reddit-answers-api').call({
    questions: [
        'What tools do developers recommend for API monitoring?',
        'How do remote-first companies onboard new employees?'
    ],
    enrichSources: true
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();

items.forEach(item => {
    console.log(`Q: ${item.query}`);
    console.log(`A: ${item.summary}`);
    console.log(`Sources: ${item.sourcePosts.length} posts, ${item.sourceComments.length} comments`);
});

cURL

curl "https://api.apify.com/v2/acts/clearpath~reddit-answers-api/runs?token=your_api_token" \
  -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "questions": ["What are common mistakes when launching a product on Product Hunt?"],
    "enrichSources": true
  }'

N8N / Make Integration

N8N Workflow Example

1. Trigger: Schedule, webhook, or manual
2. Apify Node: Run Reddit Answers API Actor
3. Process: Parse JSON output
4. Action: Send to Slack, save to Notion, feed to GPT

{
  "nodes": [
    {
      "name": "Reddit Answers",
      "type": "n8n-nodes-base.apify",
      "parameters": {
        "actorId": "clearpath/reddit-answers-api",
        "input": {
          "questions": ["={{ $json.userQuestion }}"],
          "enrichSources": true
        }
      }
    },
    {
      "name": "Format for Slack",
      "type": "n8n-nodes-base.set",
      "parameters": {
        "values": {
          "string": [
            {
              "name": "message",
              "value": "*Question:* {{ $json.query }}\n\n*Summary:* {{ $json.summary }}\n\n*Top Quote:* \"{{ $json.quotes[0].text }}\""
            }
          ]
        }
      }
    }
  ]
}

Make (Integromat) Scenario

1. Add Apify module → Run Actor
2. Configure with Actor ID and input JSON
3. Add Iterator to process multiple questions
4. Route results to your destination (Google Sheets, Airtable, etc.)

RAG Pipeline Integration

LangChain Example

from langchain.schema import Document
from apify_client import ApifyClient

def get_reddit_context(question: str) -> list[Document]:
    """Fetch Reddit insights as LangChain documents."""
    client = ApifyClient("your_token")

    run = client.actor("clearpath/reddit-answers-api").call(
        run_input={"questions": [question], "enrichSources": True}
    )

    documents = []
    for item in client.dataset(run["defaultDatasetId"]).iterate_items():
        # Main answer as document
        documents.append(Document(
            page_content=item["markdown"],
            metadata={"source": "reddit_answers", "query": item["query"]}
        ))

        # Individual quotes as documents
        for quote in item["quotes"]:
            documents.append(Document(
                page_content=quote["text"],
                metadata={
                    "source": quote["sourceUrl"],
                    "subreddit": quote["subreddit"]
                }
            ))

    return documents

Direct LLM Context

The markdown field is optimized for LLM consumption:

import openai

def answer_with_reddit_context(user_question: str) -> str:
    # Get Reddit context
    reddit_data = get_reddit_answers(user_question)

    # Build prompt with Reddit insights
    prompt = f"""Based on real Reddit discussions, answer this question:

Question: {user_question}

Reddit Community Insights:
{reddit_data['markdown']}

Key Quotes:
{chr(10).join(f'- "{q["text"]}"' for q in reddit_data['quotes'][:5])}

Provide a helpful answer that synthesizes these community insights."""

    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )

    return response.choices[0].message.content

Advanced Usage

Batch Processing with Rate Awareness

import asyncio
from apify_client import ApifyClient

async def batch_reddit_research(questions: list[str], batch_size: int = 10):
    """Process large question sets in batches."""
    client = ApifyClient("your_token")
    all_results = []

    for i in range(0, len(questions), batch_size):
        batch = questions[i:i + batch_size]

        run = client.actor("clearpath/reddit-answers-api").call(
            run_input={"questions": batch, "enrichSources": True}
        )

        results = list(client.dataset(run["defaultDatasetId"]).iterate_items())
        all_results.extend(results)

        print(f"Processed {min(i + batch_size, len(questions))}/{len(questions)}")

    return all_results

# Research 50 questions about startup validation
questions = [
    "How do you validate a B2B SaaS idea?",
    "What's the best way to find early adopters?",
    # ... 48 more questions
]

results = asyncio.run(batch_reddit_research(questions))

Multi-Language Research

{
  "questions": [
    "What are the best coworking spaces in Berlin?",
    "¿Cuáles son las mejores herramientas para startups en España?",
    "Quais são os maiores desafios para empreendedores no Brasil?",
    "Quali sono le migliori strategie di marketing per PMI in Italia?"
  ]
}

Competitive Research Pipeline

competitors = ["Notion", "Coda", "Airtable", "Clickup"]

questions = [
    f"What do users complain about most with {comp}?"
    for comp in competitors
] + [
    f"What features do users wish {comp} had?"
    for comp in competitors
]

# Run Actor with all questions
# Output: Structured competitive intelligence from authentic user discussions

Technical Requirements

• Memory: 256 MB (default)
• Proxy: Residential proxies recommended (included in default config)
• Rate limits: Built-in handling with automatic retries
• Timeout: 30 seconds per question

Data Export

Export results in multiple formats from the Apify Console:

• JSON - Full structured data for programmatic use
• CSV - Flattened data for spreadsheet analysis
• Excel - Formatted export with all fields
• XML - Standard interchange format

Best Practices

For Market Research

• Ask specific questions about pain points: "What frustrates users about X?"
• Include competitor names for comparative insights
• Use follow-up questions to dig deeper into specific themes

For RAG Pipelines

• Use markdown field for LLM context (pre-formatted)
• Include quotes array for grounded responses with citations
• Store conversationId to maintain context across sessions

For Automation

• Batch related questions together (max 10 per run)
• Schedule runs during off-peak hours for consistent performance
• Use webhooks for real-time processing of results

For Cost Optimization

• Start with enrichSources: false if you only need summaries
• Batch questions to minimize Actor starts

Pro Tips

Vary Your Prompts

Heads up: Reddit's AI can get stuck if you ask very similar questions with just one word changed. Mix up your phrasing more substantially between questions for better results.

Verify Subreddit References

Occasionally Reddit's AI hallucinates and references subreddits that don't exist. If a cited subreddit seems unusual, verify it exists before citing in your content.

Check the quotes Array

The quotes array is your quality indicator. If it's empty, the answer likely didn't find relevant discussions - and you won't be charged for that query.

FAQ

Q: What data does the Actor extract? A: AI-summarized answers from Reddit discussions, including structured sections, direct quotes, source posts with full context, source comments, and recommended subreddits.

Q: How many questions can I ask per run? A: Up to 10 questions per run. For more, run multiple Actor executions.

Q: Do I need a Reddit account? A: No. The Actor works without any Reddit credentials.

Q: How fresh is the data? A: Reddit Answers synthesizes from current Reddit discussions. Data reflects the latest community knowledge available to Reddit's AI.

Q: What languages are supported? A: English, Spanish, Brazilian Portuguese, Italian, French, and German. The language is auto-detected from your question.

Q: How do I know if a query found relevant results? A: Check the quotes array - if it's empty, Reddit Answers couldn't find relevant discussions. The summary will contain a message like "I couldn't find any results for...". You won't be charged for these queries.

Q: Can I use this for commercial purposes? A: Yes. The Actor extracts publicly available information. Ensure your use case complies with applicable data protection regulations.

Q: How do I integrate with my existing workflow? A: Use the Apify API (Python, JavaScript, cURL), webhooks, or direct integrations with N8N, Make, Zapier, and other automation platforms.

Q: What's the difference between summary and markdown? A: summary is a concise plain-text overview. markdown is the full answer with formatting, sections, and inline citations - ideal for LLM consumption.

Q: Are the quotes verified? A: Quotes come directly from Reddit posts and comments cited in the answer. With enrichSources: true, you get full context to verify each quote.

Q: What happens if Reddit Answers is unavailable? A: The Actor includes automatic retry logic. If Reddit Answers is temporarily unavailable, it will retry with exponential backoff.

Getting Started

1. Create Account

1. Sign up for Apify (free)
2. No credit card required
3. Get $5 free platform credits

2. Configure Your Run

1. Enter your questions (up to 10)
2. Enable source enrichment (recommended)
3. Use default proxy settings

3. Run & Export

1. Click "Start"
2. Monitor progress in real-time
3. Download results as JSON, CSV, or Excel

4. Automate

1. Schedule recurring runs
2. Set up webhooks for notifications
3. Integrate via API into your pipelines

Support

• Email: max@mapa.slmail.me
• Issues: Use the Issues tab for bug reports
• Feature requests: Email or Issues tab

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Start Extracting Reddit Insights Now

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Turn Reddit's collective wisdom into structured, actionable data for your research, products, and AI pipelines.