# Reddit Software Reviews Scraper | Real User Opinions (`zen-studio/reddit-software-reviews-scraper`) Actor

Extract real user opinions on any software product from Reddit. Each result includes sentiment, alternatives mentioned, use cases, and thread context. From Notion to niche tools.

- **URL**: https://apify.com/zen-studio/reddit-software-reviews-scraper.md
- **Developed by:** [Zen Studio](https://apify.com/zen-studio) (community)
- **Categories:** Lead generation, Social media, Automation
- **Stats:** 8 total users, 4 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $9.99 / 1,000 reviews

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 Software Reviews Scraper | Real User Opinions & Alternatives (2026)

**Real-time Reddit opinions on any software product, structured with sentiment, alternatives, and use cases.**

Search Reddit live for what real users think. Not cached data, not a static dump. Every run pulls fresh comments and runs AI extraction to surface genuine evaluations.

<table>
<tr>
<td colspan="3" style="padding:10px 14px;background:#4C945E;border:none;border-radius:4px 4px 0 0">
<span style="color:#FAFAF9;font-size:14px;font-weight:700;letter-spacing:0.5px">Zen Studio Software Reviews</span>
<span style="color:#E8F5E9;font-size:13px">&nbsp;&nbsp;&bull;&nbsp;&nbsp;Real-time review data across every major platform</span>
</td>
</tr>
<tr>
<td style="padding:12px 16px;border:1px solid #E7E5E4;border-radius:0 0 0 4px;background:#D3EDD9;border-right:none;border-top:none;vertical-align:top;width:33%">
<img src="https://cdn-icons-png.flaticon.com/512/2111/2111589.png" width="24" height="24" style="vertical-align:middle"> &nbsp;<a href="https://apify.com/zen-studio/reddit-software-reviews-scraper" style="color:#4C945E;text-decoration:none;font-weight:700;font-size:14px">Reddit Reviews</a><br>
<span style="color:#4C945E;font-size:12px;font-weight:600">&#10148; You are here</span>
</td>
<td style="background:#E8F5E9;padding:12px 16px;border:1px solid #E7E5E4;border-right:none;border-top:none;vertical-align:top;width:33%">
<img src="https://apify-image-uploads-prod.s3.us-east-1.amazonaws.com/NWYsOG96fMDy8ycdf-actor-XQAkoDssJyWZmyR0W-aBi9woFm7e-g2-scraper-logo.png" width="24" height="24" style="vertical-align:middle"> &nbsp;<a href="https://apify.com/zen-studio/g2-reviews-scraper" style="color:#1C1917;text-decoration:none;font-weight:700;font-size:14px">G2 Reviews</a><br>
<span style="color:#78716C;font-size:12px">Ratings, pros/cons, segments</span>
</td>
<td style="background:#E8F5E9;padding:12px 16px;border:1px solid #E7E5E4;border-radius:0 0 4px 0;border-top:none;vertical-align:top;width:33%">
<img src="https://apify-image-uploads-prod.s3.us-east-1.amazonaws.com/NWYsOG96fMDy8ycdf-actor-WLXhoenmc5vv74kRq-UAl3HAjCjZ-trustradius-review-scraper-logo.png" width="24" height="24" style="vertical-align:middle"> &nbsp;<a href="https://apify.com/zen-studio/trustradius-review-scraper" style="color:#1C1917;text-decoration:none;font-weight:700;font-size:14px">TrustRadius Reviews</a><br>
<span style="color:#78716C;font-size:12px">Enterprise reviews, trueScore</span>
</td>
</tr>
</table>

#### Copy to your AI assistant

Copy this block into ChatGPT, Claude, Cursor, or any LLM to start building with this data.

```
Reddit Software Reviews Scraper (zen-studio/reddit-software-reviews-scraper) on Apify extracts real user opinions on any software product from Reddit. Each result includes: product name, raw comment text, sentiment (positive/negative/mixed/neutral), alternatives mentioned, use case classification, subreddit, author, upvotes, comment date, thread title, direct URL. Optional thread context adds original post body/author/upvotes. Input: query (product name, domain, G2/Capterra URL, or Reddit thread URL), maxResults (10-1000, default 500), dateRange (past30days/past90days/pastYear/allTime), includeThreadContext (boolean). Output: JSON dataset. Pricing: $0.10 start + $0.00999 per opinion + $0.00999 per thread context (optional). Free tier: 5 runs, 25 opinions per run. Apify token required.
```

### Key Features

- **Real-time Reddit search** -- every run queries Reddit live, no cached or outdated data
- **AI-powered extraction** -- filters genuine opinions from noise, classifies sentiment, identifies alternatives and use cases
- **Smart product discovery** -- automatically finds the right subreddits, threads, and discussions for any product
- **Works for any software** -- from Notion (1000+ opinions) to GorillaDesk (3 opinions in r/PestControlIndustry)
- **Free tier** -- 5 runs, 25 opinions per run

### How to Get Reddit Software Reviews

#### Search by product name

```json
{
    "query": "Notion"
}
```

#### Search by G2 review page URL

```json
{
    "query": "https://www.g2.com/products/clickup/reviews"
}
```

#### Recent opinions only

```json
{
    "query": "Pipedrive",
    "dateRange": "past30days",
    "maxResults": 100
}
```

#### With full thread context (for LLM pipelines)

```json
{
    "query": "Figma",
    "includeThreadContext": true,
    "maxResults": 50
}
```

### Input Parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `query` | string | *required* | Product name, domain, G2/Capterra/TrustRadius URL, or Reddit thread URL |
| `maxResults` | integer | `500` | Maximum opinions to extract (10-1000) |
| `dateRange` | select | `pastYear` | `past30days`, `past90days`, `pastYear`, or `allTime` |
| `includeThreadContext` | boolean | `false` | Add original post text, author, and upvotes per thread |

### What Data Can You Extract from Reddit?

Every result includes:

- **Opinion data** -- raw comment text, AI-classified sentiment, use case
- **Competitive intelligence** -- other software tools mentioned in the same comment
- **Source metadata** -- subreddit, thread title, author, upvotes, direct URL, date
- **Thread context** (optional) -- original post that started the discussion

#### Demo

![Demo](https://iili.io/BnULBdg.gif)

#### Output Example

```json
{
  "product": "Notion",
  "comment": "Switched from Notion to Obsidian 6 months ago. Notion's web-first approach made it sluggish with large databases. Obsidian is instant because everything is local markdown. Miss the collaboration features though.",
  "sentiment": "mixed",
  "url": "https://reddit.com/r/productivity/comments/1abc123/comment/xyz789/",
  "threadTitle": "Is Notion overrated? What are you using instead?",
  "subreddit": "productivity",
  "author": "u/devtools_fan",
  "upvotes": 47,
  "commentDate": "2026-03-15T14:22:33+00:00",
  "alternativesMentioned": ["Obsidian"],
  "useCase": "knowledge management and databases",
  "threadBody": "I've been using Notion for 2 years and I'm starting to feel like it tries to do too much. The databases are slow, the mobile app is clunky...",
  "threadAuthor": "u/productivity_seeker",
  "threadUpvotes": 234,
  "scrapedAt": "2026-04-03T08:15:00.000Z"
}
```

`threadBody`, `threadAuthor`, and `threadUpvotes` only appear when `includeThreadContext` is enabled.

### How It Works

The scraper runs a 3-stage pipeline per query:

1. **Discovery** -- identifies the product, finds relevant subreddits and high-signal threads
2. **Collection** -- searches multiple subreddits in parallel, harvests full comment trees from targeted threads
3. **Extraction** -- AI analyzes each comment, keeps only genuine evaluations, classifies sentiment and alternatives

The whole process takes 30-120 seconds depending on how much the product is discussed on Reddit.

### Pricing -- Pay Per Event (PPE)

| Event | Cost |
|-------|------|
| Actor start | $0.10 |
| Per opinion extracted | $0.00999 |
| Per thread context (optional) | $0.00999 |

Free tier: 5 runs, 25 opinions per run.

### Advanced Usage

#### Competitive analysis

Compare how Reddit talks about your product vs competitors. Run the scraper for each product and compare `alternativesMentioned` fields.

```json
{
    "query": "Asana",
    "maxResults": 200
}
```

#### Sentiment monitoring

Schedule runs with `past30days` to track how opinions shift over time. Useful for detecting backlash after pricing changes or feature removals.

```json
{
    "query": "Figma",
    "dateRange": "past30days",
    "maxResults": 100
}
```

#### LLM-powered report generation

Enable thread context and feed the results into GPT-4 or Claude for automated "What does Reddit think about X?" reports.

```json
{
    "query": "Linear",
    "includeThreadContext": true,
    "maxResults": 100
}
```

#### Extract from a specific thread

Pass a Reddit thread URL to extract opinions from that specific discussion.

```json
{
    "query": "https://www.reddit.com/r/sysadmin/comments/abc123/best_helpdesk_software/"
}
```

### FAQ

**How many opinions can I get?**

Depends on how much the product is discussed on Reddit. Popular products (Notion, Slack, Airtable) can return 500+. Mid-size products (Pipedrive, Linear) typically yield 50-200. Very niche products may return fewer than 20.

**What if a product has a generic name like "Linear" or "Monday"?**

The scraper uses AI to distinguish between the software product and the common word. Comments about "linear algebra" or "Monday the day of the week" are automatically filtered out.

**What if the product isn't discussed on Reddit?**

You'll get 0 results. The scraper only returns genuine opinions it can verify. No padding with irrelevant data.

**How fresh is the data?**

Every run searches Reddit live. There is no cache. With `past30days`, you get opinions posted in the last 30 days.

**What counts as an "opinion"?**

A comment where someone evaluates the product. "Airtable is solid for project tracking" counts. "I pipe data into Airtable" does not. Questions like "Is Notion worth it?" are excluded.

**Can I use a G2 or Capterra URL as input?**

Yes. The scraper extracts the product name from the URL and searches Reddit for opinions about that product.

**What's in `alternativesMentioned`?**

Other software tools the commenter mentions in the same comment. If someone says "I switched from Notion to Obsidian," the alternatives array will contain `["Obsidian"]`.

**What does `includeThreadContext` add?**

The original post (OP) that started the Reddit discussion. Includes the post body, author, and upvotes. Useful when you need to understand what question or statement triggered the opinions.

**Is there a free tier?**

Yes. 5 runs with up to 25 opinions per run.

**What subreddits does it search?**

The scraper automatically discovers relevant subreddits for each product using AI. It typically searches 10-20 subreddits including the product's own subreddit (e.g., r/Airtable), category subreddits (r/projectmanagement), and general tech communities (r/SaaS).

### Support

- **Bugs**: Issues tab
- **Features**: Issues tab

### Legal Compliance

Extracts publicly available data from Reddit. Users must comply with Reddit's terms of service and applicable data protection regulations (GDPR, CCPA).

***

*Real user opinions on any software product, structured for analysis.*

# Actor input Schema

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

Enter the software product you want Reddit opinions for.<br><br><b>Examples:</b><br>• <code>Notion</code> — product name<br>• <code>monday.com</code> — domain<br>• <code>https://www.g2.com/products/clickup/reviews</code> — auto-detects product from G2, Capterra, or TrustRadius URLs<br>• <code>https://reddit.com/r/sysadmin/comments/...</code> — extracts opinions from a specific thread

## `maxResults` (type: `integer`):

Each result is one Reddit comment with sentiment, alternatives mentioned, and use case.<br><br>Actual results depend on how much the product is discussed on Reddit. Popular products (Notion, Slack) can return 500+. Niche products may return fewer.

## `dateRange` (type: `string`):

<b>Past year</b> gives the best balance of volume and relevance.<br><br>Use <b>Past 30 days</b> for trending sentiment or to monitor new opinions on a schedule. Use <b>All time</b> for maximum coverage on niche products.

## `includeThreadContext` (type: `boolean`):

Adds the original post text, author, and upvotes for each opinion's thread. Useful for LLM pipelines that need the full discussion context to generate reports or summaries.<br><br>Adds <code>threadBody</code>, <code>threadAuthor</code>, and <code>threadUpvotes</code> fields to each result.

## Actor input object example

```json
{
  "query": "Notion",
  "maxResults": 500,
  "dateRange": "pastYear",
  "includeThreadContext": false
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# 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 = {
    "query": "Notion",
    "maxResults": 500
};

// Run the Actor and wait for it to finish
const run = await client.actor("zen-studio/reddit-software-reviews-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 = {
    "query": "Notion",
    "maxResults": 500,
}

# Run the Actor and wait for it to finish
run = client.actor("zen-studio/reddit-software-reviews-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 '{
  "query": "Notion",
  "maxResults": 500
}' |
apify call zen-studio/reddit-software-reviews-scraper --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/rPGHhW9udsiIkM1Xw/builds/dMVDqmDuaSqlVbDGj/openapi.json