G2 Products Reviews Scraper

Export public G2 reviews for known products, find software by keyword, or list products matching a category. G2 Products Reviews Scraper returns review text and available reviewer context, or product names, vendors, categories and ratings, in an Apify dataset for CSV, Excel, JSON and API use.

Coverage depends on G2's publicly available records. Review results are not guaranteed to be complete or newest-first, and some fields are unavailable in the shorter review feed.

Quick start

1. Choose product_reviews and enter a G2 product slug or product URL.
2. Start with a small total record limit:

{
  "mode": "product_reviews",
  "productSlugs": ["slack"],
  "maxItems": 10
}

3. Open the dataset after the run and export the rows. For discovery instead, choose product_search with searchQueries, or category_browse with categoryNames.

Prefer a video guide? This short Apify walkthrough shows you how to set up and run an Actor. It uses a different Actor, but the setup steps are the same.

Ready-to-run examples

Choose an example to start with a small, preconfigured input.

• Slack recent reviews — A bounded review lookup for a known software product.
• CRM software search — Find product records matching a CRM keyword.
• CRM category products — Export products matching the CRM category name.

What you get

• Product reviews: Collect available review text, dates, scores and reviewer context for named G2 products.
• Software discovery: Find product names, vendors, categories and aggregate ratings by keyword.
• Category matching: Retrieve product records matching category text. This is not an exhaustive category directory.
• Reusable data: Export structured rows for your own comparison, reporting or text-analysis workflow.

Who is it for?

• Product researchers can review available feedback about competing software and take the returned text into their own analysis.
• Software buyers can build a shortlist from product names, vendors, categories and aggregate ratings.
• Market analysts can combine category matches with their own research. Review counts and ratings describe available records, not market share.

Practical workflows

Review feedback for a known product

Choose product_reviews, supply one known product slug and set the shared maxItems limit to 10. Export available reviewText, reviewId, reviewUrl and source, then group themes in your own spreadsheet or analysis tool. Keep the source field because the shorter feed and richer records do not expose the same context. A small sample is not a complete or representative review history.

Build a software shortlist

Use product_search for one software keyword or category_browse for a category name, with maxItems set to 10. Compare available product names, vendors, categories and aggregate ratings in your own research. Matching category text does not establish complete category coverage or a product's market share.

Input settings

Setting / JSON key	Behavior
Mode / mode	product_reviews by default. Alternatively product_search or category_browse.
Product URLs / startUrls	Product or review-feed URLs for review mode, such as https://www.g2.com/products/slack/reviews.
Product slugs / productSlugs	Bare product slugs for review mode. Use URLs, slugs, or both.
Search queries / searchQueries	Required in search mode. Matches product name, vendor, description and category text.
Category names / categoryNames	Required in category mode. Matches category text, not a guarantee of an exhaustive category directory.
Maximum records / maxItems	Total saved rows across all inputs, from 1 to 500. The form default is 10. An API call omitting the field uses 100.
Lookback days / lookbackDays	Optional submitted-date filter for richer review records only. Not applied to the shorter review feed.
Skip failed products / skipFailedProducts	Legacy accepted field with no effect. It does not suppress request errors.
Use RSS fallback / useRssFallback	Enabled by default for review mode when the initial lookup yields no review rows. It is not recovery from a failed request.

Only the fields for the chosen mode are used. No field is globally required because each mode has different inputs. The run validates its selected mode before extraction. Input prefills are form examples, not values supplied to an API call. Connection settings are not exposed by this Actor.

Example inputs

Product discovery:

{"mode":"product_search","searchQueries":["crm"],"maxItems":10}

Category matching:

{"mode":"category_browse","categoryNames":["CRM"],"maxItems":10}

Reviews from a product URL:

{"mode":"product_reviews","startUrls":[{"url":"https://www.g2.com/products/postman/reviews"}],"maxItems":10}

Output data

Each saved row has itemType set to product or review. The following is a synthetic shape-only example, not an observed review:

{
  "itemType": "review",
  "productSlug": "example-product",
  "productName": "Example product",
  "reviewId": "example-id",
  "reviewUrl": "https://www.g2.com/products/example-product/reviews/example-id",
  "title": null,
  "rating": null,
  "reviewText": null,
  "publishedAt": null,
  "source": "g2_elasticsearch"
}

Records	Available fields
Product identity	productId, productSlug, productName, productUrl, vendorName, description, categories, imageUrl
Product metrics	reviewCount, averageRating, g2Rating. These are product aggregates, not individual review ratings.
Discovery context	searchQuery or categoryName
Review identity and text	reviewId, reviewUrl, reviewText, title, publishedAt, submittedAt
Reviewer context	reviewerName, reviewerRole, reviewerIndustry, reviewerCompanySize when supplied. Some context values may be numeric codes.
Review scores and themes	rating, ratingText, nps, npsScore, helpful, loveTheme, hateTheme, easeOfUse, qualityOfSupport, easeOfSetup, easeOfAdmin, meetsRequirements when available
Text summaries	likes, dislikes, problemsSolved are best-effort fields and may be empty. Use reviewText for analysis.
Feed-specific fields	rssUrl, guid. The shorter feed does not supply the richer reviewer and score fields.
Provenance	source: g2_elasticsearch or g2_rss

Fields may be absent or null depending on mode and source. Rich review records have a null title. Their rating is derived from nps / 2, while feed ratings are parsed when present. Do not treat the two sources as equally complete.

Download your results

Open your run's dataset, choose Export and select JSON, CSV or Excel. JSON keeps nested fields together. CSV and Excel are useful when you want to review the records in a spreadsheet.

See Apify's download and export guide for the available formats and field options.

Pricing

This Actor uses pay-per-event pricing. The start event is charged once per run. The item event is charged for each saved record, including product rows in search and category modes even though the Pricing tab labels it “Review saved”.

An empty result has no saved-item events, but may still incur the start event.

See the live Pricing tab for current rates and discounts.

Tips for best results

• Start with a known product. Use a G2 slug or product URL to check whether the available review data fits your needs.
• Set maxItems explicitly. The limit is shared across inputs, so earlier inputs can consume it before later ones are reached.
• Keep discovery queries focused. Search and category matching can return overlapping products. Deduplicate in your own workflow when combining queries.
• Use the full review text. Best-effort summary fields may be empty even when a record contains useful text.

Limits and caveats

• Limited discovery: Search and category modes return at most 100 records per query, even when the total limit is higher. Products matching multiple queries can appear more than once.
• Ordering: Discovery results use review-count order within each query. Review order is source-defined, not guaranteed chronological. There is no sorting control.
• Date coverage: lookbackDays checks available submitted dates in richer review records, not publication dates, and does not constrain feed results.
• Access limits: G2 changes, blocks or request errors can fail a run. Empty completed lookups are possible. A failure does not establish that a product is private or deleted.
• Public sources only: No G2 login, cookies or private credentials are accepted. This is not an official G2 API and does not promise full review history.

Automation and integrations

Save your settings for next time

Once you have an input that works for you, save it as an Apify task. This keeps your settings together so you can use them again without filling in the form from scratch.

Run on a schedule

Need fresh results regularly? You can schedule the Actor or a saved task to run at a time that suits you.

Each scheduled run collects the data again. If you want to compare results or highlight changes, add that step in your own workflow.

Send results to your other tools

Use n8n, Make or Zapier to connect this scraper to the rest of your workflow. For example, you could pass its results to an AI assistant or add records to a spreadsheet. Follow the Apify integration guides for your chosen tool.

For a custom connection, an Apify webhook can notify your app when a run finishes or fails. Your app can then fetch the results or handle the failure.

API usage

Want to collect this data from your own app or automation? The examples below send input to the scraper and show how to collect its results.

The Node.js and Python examples wait for the run to finish before reading its results. The cURL example starts the run and returns its details. Wait for that run to finish, then use its defaultDatasetId to fetch the results.

For setup instructions, see Apify's JavaScript client guide, Python client guide and Actor runs API reference.

cURL

curl -X POST "https://api.apify.com/v2/acts/fetch_cat~g2-products-reviews-scraper/runs" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"mode":"product_reviews","productSlugs":["slack"],"maxItems":10}'

Node.js

import { ApifyClient } from "apify-client";
const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor("fetch_cat/g2-products-reviews-scraper").call({
  mode: "product_reviews", productSlugs: ["slack"], maxItems: 10
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

import os
from apify_client import ApifyClient
client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("fetch_cat/g2-products-reviews-scraper").call(run_input={
    "mode": "product_search", "searchQueries": ["crm"], "maxItems": 10
})
print(client.dataset(run["defaultDatasetId"]).list_items().items)

MCP and AI agents

You can ask your AI assistant to collect data with G2 Products Reviews Scraper and help you work with the results. The scraper returns the data, and your assistant can then summarize it, compare records or prepare notes.

Connect your assistant through the Apify MCP server. MCP gives your assistant a way to find this scraper, run it and read the results. Follow the official setup and sign-in guide for your app.

Easier setup with an Apify plugin

If you use a supported coding assistant, the official Apify plugin can set up the connection for you. It connects your assistant to Apify and includes instructions for common tasks. Follow the guide for your particular app.

Use Apify AI in Console

Prefer to keep everything in Apify? Open Apify AI in Console and ask it to find Fetch Cat's G2 Products Reviews Scraper. You can describe the data you want and ask questions about the results.

Apify AI is currently in beta. It asks for confirmation before starting an Actor, and normal run charges apply.

Choose your assistant

• Claude and Claude Code (Anthropic): Apify setup guide.
• ChatGPT and Codex (OpenAI): Apify setup guides.
• Grok Bot (xAI): Apify plugin guide.
• Gemini CLI (Google): MCP server setup guide. Use the server URL below with the HTTP configuration supported by Gemini CLI.

Connect just this scraper

To keep your setup focused on this scraper, add this server configuration to a compatible MCP client:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=fetch_cat/g2-products-reviews-scraper"
    }
  }
}

Sign in to Apify when your client asks. If your client uses a Bearer token instead, follow its setup guide and keep the token in its secure configuration. Configuration formats vary by app, so use the linked guide if your client needs a different format.

Use Apify's general server

Already connected to the general Apify MCP server at https://mcp.apify.com? You can use it to find this scraper without adding another server. Ask your assistant to use G2 Products Reviews Scraper by Fetch Cat, or give it the identifier fetch_cat/g2-products-reviews-scraper.

Your assistant can check the available settings and retrieve the results from the returned run. If the run is still working, it should follow that run rather than start it again. Both connection options use Apify's hosted service.

Example requests

• “Get up to ten available reviews for Slack and summarize the themes in the returned text.”
• “Find up to ten CRM software products and put their names, vendors and review counts in a comparison table.”
• “Retrieve up to ten products matching the CRM category and help me shortlist them using their available ratings.”

These requests combine extraction with follow-up work by your assistant. The scraper itself does not create summaries or comparison reports.

Normal Apify charges apply. Check the live Pricing tab before asking your assistant to run a larger batch.

Pay with x402

If your AI agent has a funded wallet, it can pay for Apify usage through x402. This experimental option lets an agent use Apify without a conventional account.

Apify AGI provides a prepaid API token with a spending cap after payment. Your agent can use that token to run this scraper through the Apify API or an authenticated MCP connection.

Some MCP setups also support paying for each request directly. That is a different payment option, so check the official guide for its availability.

Follow the official guide for wallet setup, supported payment options and current terms. Apify handles the payments. Keep wallet keys and payment tokens in your agent's secure configuration.

Other ways for agents to pay

Apify also supports MPP for prepaid access through AGI and Skyfire for eligible Actors. If your agent already uses one of these, see the agentic payment guide to choose the right setup.

FAQ

Can I export G2 reviews as CSV or JSON?

Yes. Export the saved dataset through Apify Console or retrieve it using the API. The Actor itself does not generate analysis or a comparison report.

Can I search G2 software products, or only retrieve reviews?

Both. Use product_search for keyword matches and category_browse for category-name matches. These modes return product records rather than reviews.

Does it return every historical review or newest reviews first?

No completeness or newest-first guarantee is made. Available coverage and ordering depend on the source. The shorter review feed has fewer fields and does not apply the lookback filter.

Why are reviewer details or pros and cons missing?

Fields vary by source and record. The review feed does not provide rich reviewer context, and text summary fields are best-effort rather than a guarantee of separate answers.

Is this a G2 API alternative for an AI workflow?

It provides structured public-source exports through Apify API and MCP, not official G2 API access. Start with a bounded input and handle empty or unavailable fields without guessing.

Related Actors

• Trustpilot Reviews Scraper — Public consumer review research.
• Google Maps Reviews Scraper — Reviews of places and businesses.
• Google Play Reviews Scraper — Android app feedback.
• Amazon Reviews Scraper — Ecommerce product feedback.
• Amazon Products & Search Scraper — Product discovery beyond software.

Support and feedback

For custom additions or integrations, email hello@fetchcat.dev.

Found an error or have a feature request? Open an issue with the run URL, a non-sensitive input example and what you expected.

Need help sharing it? See Apify’s guide to sharing a run when reporting an issue. Sharing a run can give the developer access to its input, logs and results, so use a run that contains no sensitive data. Please leave out API tokens, passwords, cookies, private documents and personal data. Use a public sample URL or a redacted example instead.

Tried this scraper? We'd love to hear how it worked for you. Leave a review from the Actor page to help others choose the right tool and help us improve it. Your feedback means a lot to us.

Made with care for curious people, AI agents and the possibilities in data. ♥