# G2 Software Reviews Scraper · Battlecards + Switching Data (`factden/g2-reviews-scraper`) Actor
G2.com reviews scraper API — scrape G2 software reviews & ratings for B2B SaaS. 27 fields/review: sub-ratings, pros/cons, NPS, switched-from competitors, reviewer role & size, LLM-ready markdown. Auto-ranked competitor battlecards for competitive intelligence & win-loss. $4/1K.
- **URL**: https://apify.com/factden/g2-reviews-scraper.md
- **Developed by:** [Factden](https://apify.com/factden) (community)
- **Categories:** Business, Lead generation, AI
- **Features:** Limited permissions
- **Stats:** 346 total users, 87 monthly users, 100.0% runs succeeded, 19 bookmarks
- **User rating**: 5.00 out of 5 stars
## Pricing
from $2.50 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.
Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event
## What's an Apify Actor?
An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.
Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.
## 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.
Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.
For examples already wired to this Actor's own input schema, see the [API](#api) section below.
Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).
# README
## G2 Reviews & Products Scraper | AI, Competitor Intel (2026)
**G2 Reviews & Products Scraper** is an Apify actor that extracts public G2 reviews and product data in real time — with ranked top-10 competitors per product and an LLM-ready markdown field for direct RAG ingestion.
> **Real-time** G2 reviews (fresh data on every run, no caching) — no login, no proxy setup, no anti-bot tuning, no CAPTCHA work. Everything's handled inside the actor.
> **$4 / 1,000 rows.** Uniform pricing across both modes (Reviews mode = $0.004 per review row, Products mode = $0.004 per discovered product). **Free tier**: new Apify users get **~1,250 rows free** with the $5 platform credit.
**Contents**: [What's different](#whats-different-about-this-g2-scraper) · [G2 API vs this scraper](#g2s-official-api-vs-this-scraper) · [Use cases](#common-use-cases-for-scraping-g2-reviews) · [Pricing](#pricing---how-much-does-it-cost-to-scrape-g2-reviews) · [FAQ](#faq)
### What's different about this G2 scraper
Two angles vs. every other G2 scraper on Apify Store:
🏆 **Top-10 competitors ranked per product** — mined from each reviewer's switching-from data and resolved to real product names (no opaque IDs). Battlecard data, no aggregation code needed.
🤖 **LLM-ready `markdownContent` per review** — self-contained markdown block, ready for direct vector-DB ingestion / RAG pipelines / LLM prompt context with zero formatting work.
> **Quick start (30 seconds):**
>
> 1. Click **Try for free** above; the input is pre-filled with 2 examples (a full G2 URL and a bare slug) so you can see both accepted formats at a glance.
> 2. Click **Start**. The run takes ~15 seconds and fits inside Apify's $5 free credit.
> 3. Download results as JSON, CSV, or Excel from the **Output** tab.
**No-setup checklist** — what every other G2 scraper makes you build yourself:
- **No login** or G2 account required (public review data only)
- **No proxy** configuration (Apify proxy network bundled)
- **No anti-bot** tuning required (handled inside the actor)
- **No CAPTCHA** handling required (handled inside the actor)
- **No code** required for the simple flow (form-based input)
- **No subscription** (pay-per-event: per-run + per-row only)
### What does G2 Reviews Scraper do?
Two modes:
- **Reviews mode**: Enter G2 product URLs or bare slugs and get every public review back as a clean, structured row.
**27 fields per review** including a nested `subRatings` dict (up to 6 G2 sub-ratings the reviewer gave), structured pros / cons / problemsSolved / recommendations, switching history with named competitors, reviewer industry / role / company size / country, and an LLM-ready `markdownContent` field for direct RAG ingest.
- **Products mode**: Enter a keyword (e.g. `communication`, `CRM`, `project management`, `saas`) and get the top N matching products
with metadata. Useful for competitor discovery before pulling reviews.
Reviews mode - extract every review
Products mode - discover competitors
Works on **every G2 product page** — Slack, Salesforce, HubSpot, Zoom, Notion, Microsoft Teams, Asana, Figma, monday.com, Intercom, and any other G2 listing you paste.
Output renders in the run's **Output tab** with a dataset dropdown (Reviews / Products). Download as JSON, CSV, or Excel.
**Per-product `topCompetitors` view** — top 10 named competitors ranked by reviewer-mention count, populated automatically for every product:
### G2's Official API vs this scraper
Why this actor exists: G2's review data is publicly displayed on their product pages, but G2's official API is enterprise-tier (sales call, MSA, multi-month procurement). This actor gives you the same review data with a 30-second setup.
| | G2 Official API | This actor |
|---|---|---|
| Access | Enterprise contract | Apify account (free signup) |
| Setup time | Weeks (procurement + integration) | 30 seconds |
| Pricing model | Custom contract | $0.004 per row |
| Free trial | None published | ~1,250 rows free on Apify's $5 credit |
| Top-10 competitors ranked per product | — | ✅ `topCompetitors` |
| Named switching-from data (resolved to product names, not opaque IDs) | — | ✅ `previousCompetitors` + `whySwitched` |
| LLM-ready markdown per review | — | ✅ `markdownContent` |
| Schedule / webhook delivery | Bring your own | Native via Apify Schedules + Webhooks |
### Who G2 Reviews Scraper is for
- **Competitive intel analysts** mining switching-from signals and ranked top competitors per product.
- **Product marketers** building battlecards from structured pros / cons / switching-reason fields.
- **AI / RAG engineers** ingesting G2 review data into vector databases - `markdownContent` is chunk-ready.
- **Win-loss researchers** scoping returned reviews to their ICP using `reviewerIndustry`, `companySize`, `reviewerCountry`, and `reviewerRole` (when present) — post-download filtering on the dataset.
- **Sales enablement** mining detractor language with `minRating=1, maxRating=2` and the lowest-rated sort order.
### Common use cases for scraping G2 reviews
#### 1. Build a battlecard from competitor reviews
Pull reviews for your product + top 3 competitors. The per-product `topCompetitors` field surfaces who customers actually switch from. Structured `pros` / `cons` / `recommendations` drop directly into Notion / Slides.
```json
{
"startUrls": ["slack", "microsoft-teams", "zoom-workplace", "rocket-chat"],
"maxReviewsPerProduct": 500,
"sortReviews": "helpful"
}
```
#### 2. Mine objection language for sales enablement
Pull the lowest-rated reviews of your product to find what detractors actually say. `cons` and `whySwitched` are objection-handling gold.
```json
{
"startUrls": ["slack"],
"minRating": 1,
"maxRating": 2,
"sortReviews": "rating_low"
}
```
#### 3. Feed G2 reviews into a RAG / AI agent pipeline
Each review's `markdownContent` is a self-contained markdown chunk ready for vector-DB ingestion. Attach metadata (`productSlug`, `overallRating`, `previousCompetitors`) for filtered retrieval.
```json
{
"startUrls": ["slack", "microsoft-teams"],
"maxReviewsPerProduct": 1000
}
```
#### 4. Monitor competitor G2 reviews with scheduled runs
Run daily with `fromDate` set to yesterday for incremental pulls. Wire an Apify webhook to push new reviews into Slack, Snowflake, BigQuery, or your CRM.
```json
{
"startUrls": ["microsoft-teams"],
"fromDate": "2026-05-22",
"sortReviews": "newest"
}
```
### How to scrape G2 reviews - step-by-step
1. Click **Try for free** on the Apify Store page.
2. Pick a **mode** in the input form (Reviews or Products).
3. Reviews mode: paste G2 product URLs or bare slugs (one per line, up to 100). Products mode: enter a keyword in **Search query**.
4. (Optional) Set filters: max reviews per product, date range, rating range, sort order, verified-reviewers-only.
5. Click **Start**. Results appear in the Output tab as soon as the first product completes.
Measured throughput on a single product: **~60 reviews/sec** sustained (plus ~5s container start).
| Reviews / product | Run time | Cost |
|---|---|---|
| 100 | ~10s | $0.40 |
| 1,000 | ~25s | $4.00 |
| 10,000 | ~3 min | $40.00 |
| 39,000 (Slack) | ~12 min | $156.00 |
**Products run sequentially** for predictable memory and cost.
### Input
| Field | Type | Default | What it does |
|---|---|---|---|
| **Mode** | enum | `reviews` | `reviews` extracts reviews for the products in `startUrls`. `products` finds top products by keyword. |
| **G2 product URLs or slugs** | array | (prefilled with 2 examples: 1 URL + 1 slug) | Reviews mode only. Full G2 URLs or bare slugs, one per line. Up to 100 products per run. Slugs are matched exactly; a renamed or removed product is reported as not found and skipped (other inputs still run). |
| **Search query** | string | `communication` (prefilled) | Products mode only. Keyword for product lookup. Silently ignored in Reviews mode. |
| **Max products** | int | 25 | Products mode only. Up to 1000. |
| **Max reviews per product** | int | 100 | Hard ceiling per product. Default 100 keeps first-click runs under the $5 free credit (100 reviews ≈ $0.40). Raise it for the real pull. |
| **From date** | date | (none) | Only reviews submitted on or after this date. |
| **To date** | date | (none) | Only reviews submitted on or before this date. |
| **Min rating / Max rating** | 1-5 | 1 / 5 | Filter reviews by overall star rating. |
| **Sort reviews by** | enum | `newest` | `newest` / `helpful` / `rating_high` / `rating_low`. `rating_low` is the fastest path to negative-review pain-point mining. |
| **Verified reviewers only** | bool | false | When true, drops vendor-submitted rows; keeps reviews from verified G2 reviewers only. |
### Output
Output renders in the run's **Output tab** with a dataset dropdown:
- **Reviews mode**: dropdown shows `Reviews` (review rows) and `Products` (per-product summary, one row per product).
- **Products mode**: dropdown shows `Reviews` (empty by design - products mode does not extract reviews) and `Products` (the discovered products with G2 catalog metadata).
Download from the Output tab dropdown as JSON, CSV, or Excel.
📊 **Prefer a ready-made dataset?** Grab a free 500-review sample (Slack, Microsoft Teams, Zoom, Google Workspace, Trello) on [HuggingFace](https://huggingface.co/datasets/fact-den/g2-team-collaboration-reviews-sample) or [Kaggle](https://www.kaggle.com/datasets/factden/g2-team-collaboration-reviews-sample) — no run needed.
Note: on the Products dataset, `reviewsExtracted`, `completenessPct`, and `topCompetitors` are mined from review content. In Reviews mode they're fully populated; in Products mode `topCompetitors` is an empty array `[]` while `reviewsExtracted` and `completenessPct` are `0` (no reviews were extracted).
#### Review row sample (`Reviews` tab in the Output dropdown)
Abbreviated — the full row has 27 fields including the `mode` marker. The sub-ratings are clubbed into one nested `subRatings` dict (each on a 1–7 scale; dimensions the reviewer skipped are omitted, so the key set varies per review); the overall `overallRating` is 1–5 stars, and the products dataset's `averageRating` is 0–5 (derived from G2's 0–10 internal rating ÷ 2).
```json
{
"mode": "reviews",
"reviewId": "1234567",
"reviewUrl": "https://www.g2.com/products/slack/reviews/slack-review-1234567",
"productSlug": "slack",
"productName": "Slack",
"submittedAt": "2025-09-12T14:32:00+00:00",
"reviewerName": "Sarah K.",
"reviewerIndustry": "Marketing and Advertising",
"reviewerRole": "Marketing Manager",
"companySize": "Mid-Market",
"reviewerCountry": "United States",
"overallRating": 4,
"subRatings": {
"easeOfUse": 7,
"easeOfSetup": 6,
"meetsRequirements": 7,
"qualityOfSupport": 6
},
"reviewTitle": "Best team chat we've used",
"pros": "Channels keep work organized; integrations with Jira and Salesforce are seamless.",
"cons": "Notification volume is high until you tune it.",
"problemsSolved": "Replaced 80% of internal email and consolidated cross-team coordination.",
"recommendations": "Spend time setting up notification preferences early.",
"didSwitchFromCompetitor": true,
"previousCompetitors": ["Microsoft Teams"],
"whySwitched": "Better thread management and a more responsive mobile app.",
"isIncentivized": false,
"helpfulVotes": 7,
"extractedAt": "2026-05-21T12:00:00+00:00",
"markdownContent": "## Slack review\n\n**Rating**: 4/5\n..."
}
```
#### Product row sample (`Products` tab - either per-product summary in Reviews mode, or discovered products in Products mode)
```json
{
"slug": "slack",
"requestedSlug": "slack",
"matchType": "exact",
"name": "Slack",
"id": 3437,
"url": "https://www.g2.com/products/slack",
"vendorName": "Slack Technologies",
"type": "Software",
"categoryNames": ["Business Instant Messaging"],
"categoryPrimary": "Business Instant Messaging",
"averageRating": 4.5,
"starRating": 5,
"reviewCount": 38966,
"matchingFilterTotal": 38966,
"reviewsExtracted": 1000,
"completenessPct": 2.57,
"topCompetitors": ["Microsoft Teams", "Google Chat", "Discord", "Zoom Workplace"],
"extractedAt": "2026-05-21T12:00:00+00:00"
}
```
> All timestamps are **UTC with explicit `+00:00`** offset.
### Pricing - how much does it cost to scrape G2 reviews
| Event | Price | Fires |
|---|---|---|
| Dataset row | $0.004 | Per output row - in Reviews mode this is per review row; in Products mode this is per discovered product. |
You are charged **$0.004 per row** you actually receive. A small platform start fee of **$0.005 per run** also applies (less on higher plans). Platform usage (compute) is **included** in Apify's per-plan quota; no separate charge.
**Examples**:
- 100 **HubSpot** reviews (Reviews mode) = 100 × $0.004 = **$0.40**
- 1,000 **Slack** reviews (Reviews mode) = 1000 × $0.004 = **$4.00**
- 25-product **`CRM`** keyword search (Products mode) = 25 × $0.004 = **$0.10**
- 4 messaging competitors (Slack + Microsoft Teams + Google Chat + Discord) × 250 reviews each = 1000 × $0.004 = **$4.00**
New Apify users get **$5 platform credit**, which covers ~1,250 reviews at base tier.
### How to run this G2 reviews API on a schedule
This actor doubles as a G2 reviews API - call it from any HTTP client, the [Apify Python client](https://docs.apify.com/api/client/python), or the [Apify JS client](https://docs.apify.com/api/client/js). For incremental pulls:
1. Open the [Apify Schedules tab](https://docs.apify.com/platform/schedules) in your Console.
2. Set the cron (e.g., daily at 06:00 UTC).
3. In the schedule's input override, set `fromDate` to yesterday's date — the actor returns only reviews submitted on or after that.
4. (Optional) Wire an [Apify webhook](https://docs.apify.com/platform/integrations/webhooks) on `ACTOR.RUN.SUCCEEDED` to push results into Snowflake, BigQuery, Sheets, or your CRM.
### AI agents & RAG - using G2 reviews with LLMs
`markdownContent` is a self-contained markdown block per review, designed for direct ingestion into vector databases, RAG pipelines, and LLM context windows.
Examples using the [Apify Python client](https://docs.apify.com/api/client/python) and [Apify JS client](https://docs.apify.com/api/client/js):
**Python**
```python
from apify_client import ApifyClient
## Get your token from https://console.apify.com/account/integrations
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("factden/g2-reviews-scraper").call(run_input={
"startUrls": ["slack", "microsoft-teams"],
"maxReviewsPerProduct": 100,
})
## Each markdownContent is a ready-to-embed chunk for your vector DB
for review in client.dataset(run["defaultDatasetId"]).iterate_items():
chunk = review["markdownContent"]
metadata = {"product": review["productSlug"], "rating": review["overallRating"]}
print(metadata, chunk[:100], "...")
```
**JavaScript / TypeScript**
```javascript
import { ApifyClient } from 'apify-client';
// Get your token from https://console.apify.com/account/integrations
const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('factden/g2-reviews-scraper').call({
startUrls: ['slack', 'microsoft-teams'],
maxReviewsPerProduct: 100,
});
// Each markdownContent is a ready-to-embed chunk for your vector DB
const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const review of items) {
const metadata = { product: review.productSlug, rating: review.overallRating };
console.log(metadata, review.markdownContent.slice(0, 100), '...');
}
```
**Wedge fields glossary** - what AI agents filter / chunk on:
| Field | What it means | Why an AI agent cares |
|---|---|---|
| `topCompetitors` | Top 10 most-cited competitors per product, ranked by mention count | Battlecard generation, competitive-intel agents |
| `previousCompetitors` | Named products the reviewer switched from | Migration-trend analysis, win-loss agents |
| `whySwitched` | Free-text reason for switching | Objection-handling, sales agents |
| `pros` / `cons` | Structured strengths and weaknesses | Sentiment-tagged retrieval, product-research agents |
| `markdownContent` | Self-contained per-review markdown | Vector-DB chunks, RAG pipelines, LLM prompts |
### Data sources & GDPR
Only public, listed-on-G2-product-page review data is extracted. No personal data is scraped beyond what G2 itself displays publicly: reviewer first name + last initial, self-reported role, self-reported industry, company size, and country. G2 collects and publishes this data with reviewer consent as part of their review submission process.
**What this actor does NOT collect:** email addresses, full last names, LinkedIn profiles, account credentials, private messaging, or any field not displayed on a G2 public product page. No logins are used.
**Fields that may constitute personal data under GDPR** (depending on jurisdiction and context): `reviewerName`, `reviewerRole`, `reviewerIndustry`, `reviewerCountry`. All four are publicly displayed by G2 — you are the data controller for any downstream processing. Legitimate interest or contractual necessity are the most common lawful bases cited by customers using this data for competitive research.
You are responsible for complying with G2's terms of service and any applicable data-protection regulations (GDPR, CCPA, etc.) in your jurisdiction. If in doubt, consult counsel.
### FAQ
#### How much does it cost to scrape 10,000 G2 reviews?
10,000 × $0.004 = **$40.00** (10,000 review rows), plus a $0.005 per-run start fee. Apify's $5 free credit covers ~1,250 reviews at base tier. Tiered discounts apply for higher-volume users — Bronze, Silver, Gold, and Platinum tiers each reduce the per-row price.
#### Can I filter G2 reviews by date?
Yes. `From date` and `To date` accept YYYY-MM-DD format and are applied at scrape time — only matching reviews are fetched, which is faster and cheaper than downloading everything and filtering after. Both fields are optional; leave either blank for no lower or upper bound.
#### What if a G2 product slug was renamed?
The actor matches the slug **exactly**. If G2 has renamed or removed the product, that input is reported as "not found" (a warning is logged) and skipped — the run continues with any other inputs. Look up the product's current slug on G2, or use **Products mode** to find it by keyword, and pass that. This strict matching guarantees you never receive a *different* product's reviews by mistake.
#### Can I run the G2 scraper on a schedule?
Yes. Apify's built-in Schedules let you run the actor on any cron interval — daily, weekly, or custom. For incremental monitoring, set `From date` to the previous run's start date so only new reviews are fetched each time. Pair with a webhook on `ACTOR.RUN.SUCCEEDED` to push results automatically into Slack, BigQuery, Snowflake, or your CRM.
#### Why is `reviewerRole` null for some reviews?
G2 makes the role field optional on the review submission form. About 48% of reviewers skip it — this is a G2 platform characteristic, not a scraping gap. The field is never fabricated or inferred: when a reviewer didn't provide a role, the actor returns `null`. The same applies to `reviewerIndustry` and `reviewerCountry`, which are also optional on G2.
#### Does this work for products with thousands of reviews?
Yes. Slack has ~39k reviews and you can extract them all in a single run by setting `maxReviewsPerProduct` to 50000. Expect roughly 12 minutes and ~$156 at base tier. Throughput is ~60 reviews/sec sustained. Products run sequentially, so multi-product runs take proportionally longer but use predictable, flat memory.
#### Can I export G2 reviews to CSV or Excel?
Yes. Every dataset in the Output tab supports JSON, CSV, Excel (XLSX), XML, and JSONL downloads. The `Overview` view uses customer-readable column order so it imports cleanly into Google Sheets or Excel without reformatting. For large exports, the [Apify API](https://docs.apify.com/api/v2) supports direct dataset streaming.
#### Is scraping G2 legal?
This actor extracts only **public** review data already displayed on G2 product pages — no logins, no private fields, no personal data beyond what G2 itself publishes. You are responsible for complying with G2's terms of service and any applicable data-protection regulations in your jurisdiction. If in doubt, consult counsel.
#### Can I monitor G2 reviews continuously?
Yes. Use Apify [Schedules](https://docs.apify.com/platform/schedules) + [Webhooks](https://docs.apify.com/platform/integrations/webhooks) to run the actor daily with `fromDate` set to yesterday. Each run fetches only reviews newer than that date, keeping costs minimal. Wire the webhook to push new rows into Slack, your CRM, Snowflake, or a Google Sheet automatically on run completion.
#### How fresh is G2 review data?
**Real-time** — the actor hits G2's live pages on every run, not a cached copy. A review submitted to G2 minutes ago appears in the next run as soon as G2's own indexing picks it up (typically a 1-2 minute lag on G2's side, nothing on ours).
#### Do I need a G2 account, API key, or proxy to use this scraper?
No. This actor requires only an Apify account (free). No G2 login, no G2 API key, no proxy configuration, and no anti-bot setup — all of that is handled inside the actor using Apify's built-in proxy network. You can run your first G2 review extraction in under 30 seconds with zero infrastructure work.
#### Can I scrape G2 products and reviews in one workflow?
Yes, by chaining two runs: (1) run in **Products mode** with a search keyword (e.g. `CRM`) to discover the top N products and their G2 slugs, then (2) feed those slugs into a second run in **Reviews mode** to extract every review. Total cost: $0.004 per discovered product + $0.004 per review row.
### Related Actors
Pair G2 Reviews Scraper with our other FactDen actors on the Apify Store:
- [MCA India Company & Director Data Scraper](https://apify.com/factden/mca-company-director-scraper) - India MCA company master data + directors' personal email & phone, by CIN, DIN, name, or industry & state
- [Justdial Business Leads Scraper](https://apify.com/factden/justdial-business-leads) - Indian business leads (real phone, email, owner name & GST) from Justdial, by city & category
- [Indeed Jobs Scraper](https://apify.com/factden/indeed-jobs-scraper) - job listings, salaries, and company profiles across 60+ countries
### Changelog
#### Fixed - MCP-triggered runs (2026-07-09)
- Runs started through the **MCP integration** were failing immediately (exit code 91) due to a
compatibility issue with a newly introduced Apify run origin. This is fixed and deployed in the
latest build - MCP-triggered runs now complete normally. If you were affected, just re-run your
input. Runs started via Console, API, and Scheduler were unaffected.
#### 1.2 - Strict slug matching & resilience (2026-07-07)
- **Strict, exact slug matching.** A renamed or removed G2 product is now reported as "not found" and skipped, rather than resolved to a fuzzy best-guess — so you never receive a *different* product's reviews by mistake. Use **Products mode** to find a product's current slug by keyword.
- **More resilient runs.** A transient upstream hiccup now ends the run gracefully with a clear status message instead of failing it.
- Mixed-case G2 URLs (e.g. `HTTPS://www.G2.com/products/Slack`) are now accepted.
#### 1.0 - Initial release (2026-05-21)
- 27 review-row keys (26 review fields + `mode` marker), semantically ordered for readability - including a nested `subRatings` dict and `markdownContent` for LLM/RAG ingestion.
- Per-product `topCompetitors` ranking - top 10 named competitors by mention count, unique to this Actor.
- 2 curated review views (Overview, AI ingest) + 1 product view (Overview, 12 columns - includes matchType plus reviewsExtracted / completenessPct / topCompetitors which populate in Reviews mode; in Products mode `topCompetitors` is an empty array `[]` and the two counters are `0`).
- Resolved competitor display names (`previousCompetitors`) - no opaque IDs, no manual lookup.
- Two modes: Reviews (extract reviews for given products) and Products (keyword discovery).
- Auto-resolve renamed G2 slugs with a 0.6 similarity floor. *(Removed in 1.2 — slugs are now matched exactly; a renamed/removed product is reported "not found" and skipped.)*
- PPE pricing: $0.004 per row (unified across both modes - Reviews mode and Products mode price the same per row).
### Support & maintenance
**Actively maintained.** Updates pushed regularly — see the [Changelog](#changelog) section above. Bug reports are typically triaged within 1-2 business days.
- **Bug reports / feature requests**: use the **Issues** tab on this actor's [Apify Store page](https://apify.com/factden/g2-reviews-scraper/issues)
- **Billing or support questions**:
***
*Built by [factden](https://apify.com/factden) on the Apify platform. **Try the G2 Reviews Scraper free** with Apify's $5 monthly credit - covers ~1,250 reviews on first run.*
# Actor input Schema
## `mode` (type: `string`):
Pick the workflow:
- **Extract reviews** (default): get every public review for the products listed below. Writes review rows to the `Reviews` dataset and a per-product summary (incl. `topCompetitors`) to the `Products` dataset.
- **Discover products**: find the top N products on G2 matching a search query. Writes product rows to the `Products` dataset; the `Reviews` dataset stays empty.
## `startUrls` (type: `array`):
Each entry is either a full G2 URL (`https://www.g2.com/products/` or `.../reviews`) or a bare canonical slug (`slack`, `microsoft-teams` — letters, digits, hyphens, no spaces; case-insensitive). Up to 100 products per run.
## `maxReviewsPerProduct` (type: `integer`):
Hard ceiling per product (1–50,000). Default 100 ≈ $0.40 per run, fits inside the $5 free credit. Raise for the full pull: 1,000 reviews ≈ $4.00, 10,000 ≈ $40.00.
## `fromDate` (type: `string`):
Only return reviews submitted on or after this date. Format: YYYY-MM-DD. Leave blank for no lower bound.
## `toDate` (type: `string`):
Only return reviews submitted on or before this date. Format: YYYY-MM-DD. Leave blank for no upper bound.
## `minRating` (type: `integer`):
Only return reviews with an overall rating ≥ this value. Default 1 = include all detractors.
## `maxRating` (type: `integer`):
Only return reviews with an overall rating ≤ this value. Default 5 = include all promoters.
## `sortReviews` (type: `string`):
How to order extracted reviews. `newest` (default) = most recent first. `rating_low` is the fastest path to negative-review pain-point mining. `helpful` surfaces the reviews G2 readers themselves found most useful.
## `verifiedReviewer` (type: `boolean`):
When on, only reviews from verified G2 reviewers are returned (filters out the vendor-submitted set). Default off = include all.
## `searchQuery` (type: `string`):
Keyword used to look up products on G2 (e.g. `communication`, `CRM`, `project management`, `saas`). 2–200 characters.
## `maxProducts` (type: `integer`):
How many products to return (1–1,000). Each product row costs $0.004.
## Actor input object example
```json
{
"mode": "reviews",
"startUrls": [
"https://www.g2.com/products/slack/reviews",
"microsoft-teams"
],
"maxReviewsPerProduct": 100,
"minRating": 1,
"maxRating": 5,
"sortReviews": "newest",
"verifiedReviewer": false,
"searchQuery": "communication",
"maxProducts": 25
}
```
# Actor output Schema
## `reviews` (type: `string`):
Per-review rows with ratings, a nested subRatings object (up to 6 G2 sub-ratings the reviewer gave), named switching-from competitors, structured pros/cons/problems-solved/recommendations, reviewer firmographics, and an LLM-ready markdownContent block per row. Populated only in Reviews mode (Products mode does not extract reviews).
## `products` (type: `string`):
Per-product rows with topCompetitors ranking, categoryPrimary, averageRating, reviewCount, completenessPct, url. Populated in both modes: Reviews mode writes 1 summary row per slug; Products mode writes the keyword-search results.
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"mode": "reviews",
"startUrls": [
"https://www.g2.com/products/slack/reviews",
"microsoft-teams"
],
"maxReviewsPerProduct": 100,
"minRating": 1,
"maxRating": 5,
"sortReviews": "newest",
"verifiedReviewer": false,
"searchQuery": "communication",
"maxProducts": 25
};
// Run the Actor and wait for it to finish
const run = await client.actor("factden/g2-reviews-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"mode": "reviews",
"startUrls": [
"https://www.g2.com/products/slack/reviews",
"microsoft-teams",
],
"maxReviewsPerProduct": 100,
"minRating": 1,
"maxRating": 5,
"sortReviews": "newest",
"verifiedReviewer": False,
"searchQuery": "communication",
"maxProducts": 25,
}
# Run the Actor and wait for it to finish
run = client.actor("factden/g2-reviews-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"mode": "reviews",
"startUrls": [
"https://www.g2.com/products/slack/reviews",
"microsoft-teams"
],
"maxReviewsPerProduct": 100,
"minRating": 1,
"maxRating": 5,
"sortReviews": "newest",
"verifiedReviewer": false,
"searchQuery": "communication",
"maxProducts": 25
}' |
apify call factden/g2-reviews-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"type": "http",
"url": "https://mcp.apify.com/?tools=fetch-actor-details,factden/g2-reviews-scraper"
}
}
}
```
The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer ` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).
## OpenAPI specification
Download the OpenAPI definition: https://api.apify.com/v2/actors/XKPUnqBYJn8RIEFLs/builds/tBc128EunFh25dQcM/openapi.json
