# Indiamart Discovery Scraper (`getdataforme/indiamart-discovery-scraper`) Actor The INDIAMART DISCOVERY SCRAPER efficiently extracts detailed product data from IndiaMART, aiding strategic insights. Key features include customizable searches, high data quality, proxy support, and scalable performance. Export data in JSON, CSV, or Excel for seamless integration. - **URL**: https://apify.com/getdataforme/indiamart-discovery-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, E-commerce, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results 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 ## INDIAMART DISCOVERY SCRAPER ### Introduction The INDIAMART DISCOVERY SCRAPER is designed to efficiently extract product information from IndiaMART, one of India's largest online marketplaces. This Actor enables users to gather detailed data on products, sellers, and pricing, facilitating informed decision-making and strategic business insights. ### Features - **Comprehensive Data Extraction**: Scrapes detailed product information including title, price, seller details, and more. - **High Data Quality**: Ensures accurate and up-to-date information from IndiaMART listings. - **Customizable Search**: Allows users to specify search queries and limit the number of results. - **Proxy Support**: Utilizes Apify's residential proxies for secure and anonymous scraping. - **Scalable Performance**: Capable of handling large volumes of data efficiently. - **User-Friendly Interface**: Easy setup and configuration through Apify's platform. - **Versatile Output Formats**: Export data in JSON, CSV, or Excel formats for seamless integration. ### Input Parameters | Parameter | Type | Required | Description | Example | |---------------------|----------|----------|--------------------------------------------------------------|-----------------------| | `searchQuery` | String | Yes | Keyword to search for products on IndiaMART. | "washing machine" | | `maxItems` | Integer | No | Maximum number of auction results to scrape. | 100 | | `proxyConfiguration`| Object | No | Specifies proxy servers used by the scraper. | `{ "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] }` | ### Example Usage #### Input JSON ```json { "searchQuery": "washing machine", "maxItems": 100, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] } } ```` #### Output JSON ```json [ { "id": "2849500901273", "scraped_at": "2026-04-20T10:28:31.733Z", "product_id": "2849500901273", "title": "Automatic Vegetable Washing Machine", "price": "₹600000/Piece", "price_value": 600000, "moq": "1", "company_name": "Dairy Man Engineering", "seller_rating": 4.2, "city": "Coimbatore", "state": "Tamil Nadu", "images": [ "http://5.imimg.com/data5/SELLER/Default/2022/12/VJ/NE/LD/3378846/vegetable-washer-125x125.jpg", "http://5.imimg.com/data5/SELLER/Default/2022/12/VJ/NE/LD/3378846/vegetable-washer-250x250.jpg", "http://5.imimg.com/data5/SELLER/Default/2022/12/VJ/NE/LD/3378846/vegetable-washer-500x500.jpg", "http://5.imimg.com/data5/SELLER/Default/2022/12/VJ/NE/LD/3378846/vegetable-washer-1000x1000.jpg" ], "product_url": "https://www.indiamart.com/proddetail/automatic-vegetable-washing-machine-2849500901273.html", "description": "Suitable for washing all kinds of fruits and vegetables. The unique feature of the machine is that it can be used to wash leafy vegetables as well, that normally have a tendency to float on water." } ] ``` ### Use Cases - **Market Research and Analysis**: Gather comprehensive product data for market trends and consumer preferences. - **Competitive Intelligence**: Monitor competitors' product offerings and pricing strategies. - **Price Monitoring**: Track price changes and availability of products over time. - **Content Aggregation**: Compile product information for websites or applications. - **Academic Research**: Analyze market dynamics and consumer behavior. - **Business Automation**: Automate data collection for business intelligence systems. ### Installation and Usage ``` 1. Search for "INDIAMART DISCOVERY SCRAPER" in the Apify Store 2. Click "Try for free" or "Run" 3. Configure input parameters 4. Click "Start" to begin extraction 5. Monitor progress in the log 6. Export results in your preferred format (JSON, CSV, Excel) ``` ### Output Format The output is a JSON array containing objects with fields such as `id`, `scraped_at`, `product_id`, `title`, `price`, `price_value`, `moq`, `company_name`, `seller_rating`, `city`, `state`, `images`, `product_url`, and `description`. Each field provides specific details about the product and seller. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! # Actor input Schema ## `searchQuery` (type: `string`): Keyword to search ## `maxItems` (type: `integer`): Maximum number of auction results to scrape. ## `proxyConfiguration` (type: `object`): Specifies proxy servers that will be used by the scraper in order to hide its origin. ## Actor input object example ```json { "searchQuery": "washing machine", "maxItems": 100, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "searchQuery": "washing machine", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/indiamart-discovery-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 = { "searchQuery": "washing machine", "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("getdataforme/indiamart-discovery-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 '{ "searchQuery": "washing machine", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call getdataforme/indiamart-discovery-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/indiamart-discovery-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Indiamart Discovery Scraper", "description": "The INDIAMART DISCOVERY SCRAPER efficiently extracts detailed product data from IndiaMART, aiding strategic insights. Key features include customizable searches, high data quality, proxy support, and scalable performance. Export data in JSON, CSV, or Excel for seamless integration.", "version": "0.0", "x-build-id": "oVs5bUulorX9WOYKX" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~indiamart-discovery-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-indiamart-discovery-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~indiamart-discovery-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-indiamart-discovery-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~indiamart-discovery-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-indiamart-discovery-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "searchQuery" ], "properties": { "searchQuery": { "title": "Search Query", "type": "string", "description": "Keyword to search " }, "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of auction results to scrape.", "default": 100 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Specifies proxy servers that will be used by the scraper in order to hide its origin.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "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 } } } } } } } } } } ```