# G2 Reviews API | Scrape B2B Software Reviews (`johnvc/g2-reviews-api`) Actor Collect G2 product reviews as clean, structured JSON for B2B software intelligence. Send one or many product URLs and get ratings, pros and cons, reviewer role, company size, and dates, with an optional product-metadata add-on. Reliable, pay per review, MCP-ready for Claude and AI agents. - **URL**: https://apify.com/johnvc/g2-reviews-api.md - **Developed by:** [John](https://apify.com/johnvc) (community) - **Categories:** AI - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 1,000 review scrapeds 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 a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 ## ⭐ G2 Reviews API | Scrape B2B Software Reviews The **G2 Reviews API** turns public [G2](https://www.g2.com) product reviews into clean, structured JSON. Use this actor with Claude, Cursor, or any MCP-compatible AI agent to collect ratings, pros, cons, reviewer role, and company size, on reliable infrastructure and billed per review. Send one or many G2 product URLs and get one row per review, plus an optional product-metadata row per product. It works as a straightforward **G2 reviews scraper** for customer sentiment, review monitoring, and competitor analysis, and it is built API-first and MCP-ready so AI agents can call it as a tool. --- ### 📋 What this API returns | Field | Description | |---|---| | `productName` | The G2 product the review belongs to. | | `rating` | The star rating the reviewer gave, 0 to 5. | | `title` | The reviewer's headline. | | `reviewText` | The full review body, joined from G2's structured question-and-answer sections. | | `pros`, `cons` | Best-effort split of what the reviewer liked and disliked. | | `reviewerName`, `reviewerRole` | The reviewer's name and job title, when disclosed. | | `companySize` | The size band of the reviewer's company. | | `datePublished` | When the review was published. | | `tags`, `verified` | Review tags such as "Validated Reviewer", and a verified flag derived from them. | | `reviewUrl` | Direct link to the individual review. | | `summary` | A plain-language one-line interpretation of the review. | With `includeProductMetadata` enabled, each product also yields a `product_metadata` row with category, star rating, review count, vendor, and competitors. --- ### 🎯 Use cases - **Competitive intelligence.** Pull every review for a competitor's product and analyze strengths, weaknesses, and churn signals. - **Win/loss analysis.** Compare how buyers describe your product versus alternatives, by role and company size. - **Customer sentiment.** Track ratings, pros, and cons over time to see how buyers actually feel about a product. - **RAG and LLM pipelines.** Feed structured, deduplicated review text into a retrieval or sentiment pipeline. - **Product marketing.** Surface the exact phrases customers use, segmented by company size. - **Voice-of-customer dashboards.** Batch many products in one run and export ratings and sentiment over time. - **AI agent workflows.** Drive this API over MCP from Claude to pull and summarize reviews in-conversation. --- ### 🔁 Use it as a G2 reviews scraper at scale Paste a batch of G2 product URLs and this G2 reviews scraper collects them in budget-capped chunks, one row per review, so a large run never overshoots the cap you set with `maxReviewsPerProduct`. Every review comes back with the same flat, predictable fields, which is what makes the output easy to load into a sheet, a database, or a customer sentiment pipeline. The result is ready-to-use data for customer sentiment and review monitoring: star rating, title, pros and cons, reviewer role, company size, and the date each review was published. If a URL cannot be collected, you get a clear error row for that URL instead of a failed run, so one bad link never sinks the batch. Run it once for a single product or on a schedule to keep review monitoring current across your whole competitive set. --- ### ⚙️ Input examples #### Reviews for one product ```json { "productUrls": ["https://www.g2.com/products/asana/reviews"] } ```` #### Several products, recent first, capped ```json { "productUrls": [ "https://www.g2.com/products/asana/reviews", "https://www.g2.com/products/trello/reviews" ], "maxReviewsPerProduct": 200, "sortBy": "recent" } ``` #### With product metadata ```json { "productUrls": ["https://www.g2.com/products/asana/reviews"], "includeProductMetadata": true } ``` A plain product URL without `/reviews` is accepted and normalized. URLs that are not G2 product URLs are skipped with a warning. The API returns one row per review. *** ### 📤 Example output Each review is returned as one JSON row: ```json { "result_type": "review", "productName": "Asana", "rating": 4.5, "title": "Simple, team-friendly interface that keeps everyone productive", "reviewText": "Question: What do you like best about Asana? - Answer: The interface is simple enough to learn quickly...", "pros": "The interface is simple enough to learn quickly.", "cons": "More automations would be helpful in all plans.", "reviewerRole": "Program Manager", "companySize": "Small-Business (50 or fewer emp.)", "datePublished": "2026-07-06", "tags": ["Current User", "Validated Reviewer", "Incentivized"], "verified": true, "reviewUrl": "https://www.g2.com/products/asana/reviews/asana-review-13068232", "summary": "4.5-star verified review of Asana from Program Manager: \"Simple, team-friendly interface\"" } ``` *** ### 💰 Pricing (pay-per-event) You pay per review returned, plus an optional per-product metadata fee. There is no per-run setup fee. The current per-event prices are shown on the Store card and in the Actor's Monetization details. Because billing is per review, you only pay for the reviews actually delivered, and `maxReviewsPerProduct` lets you cap both volume and cost. *** ### 🔌 Use the G2 Reviews API from Claude (MCP) This Actor is MCP-server-compatible, so [Claude Code](https://claude.ai/referral/uIlpa7nPLg) (free trial), Claude (desktop and web), Cursor, and other MCP clients can call it as a tool through Apify's hosted MCP server. Add it with this Actor-specific URL: `https://mcp.apify.com/?tools=actors,docs,johnvc/g2-reviews-api` Apify MCP integration docs: https://docs.apify.com/platform/integrations/mcp Setup walkthrough: https://www.youtube.com/watch?v=jREWahDGhJM **New to Claude Code or Claude Cowork?** Start a [free trial](https://claude.ai/referral/uIlpa7nPLg). Then ask Claude: > *"Use the G2 Reviews API to collect the 100 most recent reviews for Asana, and summarize the top three complaints."* *** ### 🔌 Integrations: run review monitoring on a schedule This API is most useful as a recurring step in a pipeline, not a one-off run: - **Tasks and Schedules.** Save a set of G2 product URLs as a Task, then attach a Schedule to refresh that review dataset daily, weekly, or monthly with no code. - **n8n, Make, and Zapier.** Trigger a run when a competitor ships a release or a new review lands, then push the fresh reviews into your own tools. Apify has native connectors for each. - **Supabase, Google Sheets, and databases.** Send the dataset straight to storage with a webhook or the Apify API so the rows land where your team already works. - **MCP.** Call the API as a tool from Claude and other AI agents (see the section above) to pull and summarize reviews inside an agent workflow. - **Webhooks.** Fire a webhook on run completion to kick off the next step in your own system, such as sentiment scoring or alerting. *** ### 🔗 Related Tools Build a fuller picture of the vendors you track by pairing this with the rest of the suite: - [Glassdoor Reviews API](https://apify.com/johnvc/glassdoor-reviews-api?fpr=9n7kx3) - employee reviews and per-category ratings for employers. - [Crunchbase Company API](https://apify.com/johnvc/crunchbase-company-api?fpr=9n7kx3) - firmographics and funding for the vendors you track. Comparison point: some G2 review scrapers on the Store are low-rated (for example [powerai/g2-product-reviews-scraper](https://apify.com/powerai/g2-product-reviews-scraper?fpr=9n7kx3), rated 1.68 stars). This API is built for reliability, pay-per-review billing, and MCP access from AI agents. *** ### 🚀 How to get started 1. Open the Actor and paste one or more G2 product review URLs into `productUrls`. 2. Optionally set `maxReviewsPerProduct`, `sortBy`, and `includeProductMetadata`. 3. Run it and read the results from the dataset (JSON, CSV, Excel, or API). [View on Apify Store](https://apify.com/johnvc/g2-reviews-api?fpr=9n7kx3) **Example code:** [Python quick-start + MCP setup on GitHub](https://github.com/johnisanerd/Apify-G2-Reviews-API) - call the API from Python, or install it as a tool in Claude, Cursor, and ChatGPT. *** ### ❓ FAQ #### What is the G2 Reviews API? It is a hosted endpoint that turns public G2 product reviews into structured JSON. You send one or more G2 product URLs and get one row per review back, with rating, title, pros, cons, reviewer role, company size, and the publish date, plus an optional product-metadata row per product. #### Can I use it as a G2 reviews scraper? Yes. Under the hood it is a G2 reviews scraper that reads public review pages, but you never touch scraping infrastructure. You call it like an API (product URLs in, clean JSON out) and it returns the same flat, one-row-per-review shape whether you pull one product or a whole competitive set. #### Can I run the G2 Reviews API from Claude or another AI agent over MCP? Yes. The Actor is MCP-server-compatible, so [Claude Code](https://claude.ai/referral/uIlpa7nPLg) (free trial) and other MCP clients call it as a tool. See the MCP section above for the setup URL. #### What URL should I provide? A G2 product reviews URL, for example `https://www.g2.com/products/asana/reviews`. A plain product URL without `/reviews` is accepted and normalized automatically. #### How many reviews will I get? As many as are available, capped by `maxReviewsPerProduct` (default 100). Set it to 0 to return everything available. #### Can I sort the reviews? Yes. Leave `sortBy` empty for the default order, or choose recent, helpful, highest, or lowest. #### What if a product returns no reviews? Some product pages carry no reviews of their own. The API returns a clear message for that product rather than a blank result. If a URL cannot be collected at all, you get a clear error row for that URL instead of a failed run. #### How do I use G2 reviews for competitor analysis? Collect reviews for your product and each competitor in one run, then compare ratings, pros, cons, and company-size segments side by side. The flat per-review fields make it easy to answer questions like which HR software has the best user reviews, or how a rival is perceived by enterprise versus small-business buyers. #### Is it legal to scrape public G2 reviews? This actor collects only public review data. Public-data scraping has been addressed in court cases such as [hiQ Labs v. LinkedIn](https://en.wikipedia.org/wiki/HiQ_Labs_v._LinkedIn), but you are responsible for your own use, including [GDPR](https://en.wikipedia.org/wiki/General_Data_Protection_Regulation) and platform terms. Consult your own counsel for your use case. #### Can I schedule it or connect it to my other apps? Yes. Save your product URLs as a Task and attach a Schedule for recurring review monitoring, and connect the output to n8n, Make, Zapier, Supabase, or your own system by webhook. See the Integrations section above. *** Last Updated: 2026.07.11 # Actor input Schema ## `productUrls` (type: `array`): One or more G2 product review URLs, e.g. https://www.g2.com/products/asana/reviews. A plain product URL (without /reviews) is accepted and normalized. URLs that are not G2 product URLs are skipped. Up to 100 per run. Need larger batches? Contact us and we can raise this limit for your account. ## `maxReviewsPerProduct` (type: `integer`): Maximum number of reviews to return per product (1-1000). Caps cost and volume; each product is capped independently. ## `sortBy` (type: `string`): Optional sort order for reviews. Leave empty for the default (most relevant). Use 'recent' to prioritize the newest reviews. ## `includeProductMetadata` (type: `boolean`): When enabled, add one product-metadata row per product (category, star rating, review count, vendor, competitors). Billed as a separate product-metadata event. ## Actor input object example ```json { "productUrls": [ "https://www.g2.com/products/asana/reviews" ], "maxReviewsPerProduct": 100, "sortBy": "", "includeProductMetadata": false } ``` # Actor output Schema ## `allResults` (type: `string`): Every dataset item from this run: collected reviews, any product-metadata rows, and any error rows. ## `overview` (type: `string`): Tabular overview of every dataset item using the dataset schema's default view. Columns: type, product, rating, title, role, company size, date, summary. ## `detail` (type: `string`): Verified flag, reviewer, pros/cons, and direct review link. # 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 = { "productUrls": [ "https://www.g2.com/products/asana/reviews" ], "maxReviewsPerProduct": 100 }; // Run the Actor and wait for it to finish const run = await client.actor("johnvc/g2-reviews-api").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 = { "productUrls": ["https://www.g2.com/products/asana/reviews"], "maxReviewsPerProduct": 100, } # Run the Actor and wait for it to finish run = client.actor("johnvc/g2-reviews-api").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 '{ "productUrls": [ "https://www.g2.com/products/asana/reviews" ], "maxReviewsPerProduct": 100 }' | apify call johnvc/g2-reviews-api --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=johnvc/g2-reviews-api", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "G2 Reviews API | Scrape B2B Software Reviews", "description": "Collect G2 product reviews as clean, structured JSON for B2B software intelligence. Send one or many product URLs and get ratings, pros and cons, reviewer role, company size, and dates, with an optional product-metadata add-on. Reliable, pay per review, MCP-ready for Claude and AI agents.", "version": "0.0", "x-build-id": "4y22toPzDMiTpONgz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/johnvc~g2-reviews-api/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-johnvc-g2-reviews-api", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/johnvc~g2-reviews-api/runs": { "post": { "operationId": "runs-sync-johnvc-g2-reviews-api", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/johnvc~g2-reviews-api/run-sync": { "post": { "operationId": "run-sync-johnvc-g2-reviews-api", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "productUrls" ], "properties": { "productUrls": { "title": "Product Review URLs", "maxItems": 100, "type": "array", "description": "One or more G2 product review URLs, e.g. https://www.g2.com/products/asana/reviews. A plain product URL (without /reviews) is accepted and normalized. URLs that are not G2 product URLs are skipped. Up to 100 per run. Need larger batches? Contact us and we can raise this limit for your account.", "items": { "type": "string" } }, "maxReviewsPerProduct": { "title": "Max Reviews Per Product", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of reviews to return per product (1-1000). Caps cost and volume; each product is capped independently.", "default": 100 }, "sortBy": { "title": "Sort Order", "enum": [ "", "recent", "helpful", "highest", "lowest" ], "type": "string", "description": "Optional sort order for reviews. Leave empty for the default (most relevant). Use 'recent' to prioritize the newest reviews.", "default": "" }, "includeProductMetadata": { "title": "Include Product Metadata", "type": "boolean", "description": "When enabled, add one product-metadata row per product (category, star rating, review count, vendor, competitors). Billed as a separate product-metadata event.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```