# Shopify Competitor Price Stock Monitor & Catalog Scraper (`leafy-dev-jr/shopify-competitor-price-stock-monitor`) Actor Detect price drops, sales, restocks, sold-outs, and new product launches across competitor Shopify stores. - **URL**: https://apify.com/leafy-dev-jr/shopify-competitor-price-stock-monitor.md - **Developed by:** [Leafy](https://apify.com/leafy-dev-jr) (community) - **Categories:** Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Shopify Competitor Price & Stock Monitor **Detect price drops, sales, restocks, sold-outs, and new product launches across competitor Shopify stores, automatically.** Paste your competitors' Shopify URLs and press **Start**. The first run saves a baseline of their catalog. Every run after that reports exactly what changed, so you know the moment a rival cuts a price, starts a sale, restocks a sold-out size, or launches something new. --- ### What it detects | Event | Meaning | | --- | --- | | `PRICE_DROP` / `PRICE_INCREASE` | A variant got cheaper or more expensive | | `SALE_STARTED` / `SALE_ENDED` | A compare-at ("was") price appeared or disappeared | | `OUT_OF_STOCK` / `RESTOCKED` | A variant sold out or came back | | `NEW_PRODUCT` / `REMOVED_PRODUCT` | A product was added, removed, or unpublished | | `NEW_VARIANT` / `REMOVED_VARIANT` | A size, color, or option was added or removed | | `TITLE_CHANGED` / `VENDOR_CHANGED` / `TAG_CHANGED` | Metadata edits (off by default) | Every change gets a plain-English **summary** like *"Price dropped 20% from 50.00 GBP to 40.00 GBP on Vital Seamless Leggings (Black / M)"*. --- ### Quick start 1. Paste one or more store URLs into **Competitor Shopify store URLs** (the homepage is fine). 2. Press **Start**. 3. *(Optional)* Add **keywords** to watch only certain products, or a **webhook** to get alerts. **First run = baseline.** There is nothing to compare against yet, so the run reports **0 changes** and clearly says a baseline was created. That is expected. Change events start on the second run. **Schedule it** (Apify > **Schedule Actor**) to monitor on autopilot: **hourly** for sales and restocks, **daily** for pricing, **weekly** for new launches. --- ### Output The dataset holds your **change events**, plus **one summary row per store** with a plain-English status (baseline created, number of changes, or an error). The **full catalog** for each store is saved separately in the snapshot Key-Value store, so the dataset stays focused on what actually changed. | `recordType` | What it is | | --- | --- | | `change_event` | One detected change, with severity and summary | | `store_summary` | Per-store status message, totals, and change counts | Example change event: ```json { "recordType": "change_event", "eventType": "PRICE_DROP", "storeName": "Gymshark", "productTitle": "Vital Seamless Leggings", "variantTitle": "Black / M", "sku": "GS-VSL-BLK-M", "oldPrice": 50, "newPrice": 40, "priceChangePercent": -20, "currency": "GBP", "severity": "high", "changeSummary": "Price dropped 20.0% from 50.00 GBP to 40.00 GBP on Vital Seamless Leggings (Black / M)", "detectedAt": "2026-07-04T09:00:00.000Z", "productUrl": "https://gymshark.com/products/vital-seamless-leggings" } ```` If a store cannot be read (invalid URL, blocked, or not Shopify), you still get a `store_summary` row with an `errors` message, and the run keeps going for every other store. Want the whole product list, not just the changes? See **Get the full catalog as a file** below. *** ### Get the full catalog as a file Sometimes you want a competitor's entire catalog, not just what changed. Turn on **Export the full catalog as files** in the input and choose one or more formats: - **Excel** (`catalog.xlsx`) opens straight in Excel or Google Sheets. - **JSON** (`catalog.json`) is a clean array, one row per variant. - **CSV** (`catalog.csv`) opens in any spreadsheet. **Where to get it:** open the run, go to the **Storage** tab (or the **Output** tab), and download `catalog.xlsx`, `catalog.json`, or `catalog.csv`. Each row has store, product, variant, SKU, price, sale price, availability, currency, vendor, type, tags, image URL, and product URL. This runs on **every run, including the very first one**, so you can grab a full catalog immediately even before any changes exist. Prefer the catalog as dataset rows instead (for a one-off export)? Set `outputMode` to `both` or `fullSnapshot` in the JSON input tab. *** ### How it remembers changes To know what changed, the actor remembers what each store looked like last time: - After every run it saves a **snapshot** (a JSON photo of the catalog) to an Apify **Key-Value store** named `shopify-monitor`, with **one record per store**, keyed by domain (for example `gymshark-com`). This is also where you can browse a store's full current catalog. - The next run loads that record and compares it to the live catalog. That comparison is where change events come from, and why the first run finds nothing. - Find it in **Storage > Key-value stores > `shopify-monitor`**. It stores only public catalog data and keeps just the latest snapshot per store, so scheduled runs always compare against the previous run automatically. Need to start a store over? Add its URL to **Reset baseline for these stores** and the next run rebuilds its baseline from scratch. Other stores keep their history. *** ### Options The form shows **store URLs**, **keywords**, **webhook**, **export the full catalog as files**, and **reset baseline for these stores**. Everything below is optional, has a sensible default, and can be set from the **JSON** input tab. | Field | Default | What it does | | --- | --- | --- | | `storeUrls` *(required)* | | Competitor Shopify store URLs | | `keywords` | `[]` | Only track products matching any keyword | | `webhookUrl` | | POST a change summary here (Slack, Zapier, Make) | | `catalogExportFormats` | `[]` | Save the full catalog as files in storage: any of `json`, `xlsx`, `csv` | | `resetBaselineStores` | `[]` | Forget saved history for these stores and start them fresh | | `outputMode` | `changesOnly` | `changesOnly`, `both`, or `fullSnapshot` (also writes catalog rows to the dataset) | | `collectionHandles` | `[]` | Track specific collections instead of the whole catalog | | `minPriceChangePercent` | `0` | Ignore price moves smaller than this % | | `includeNewProducts` / `includeRemovedProducts` | `true` | Toggle new and removed product events | | `includeStockChanges` / `includeSaleChanges` | `true` | Toggle restock/sold-out and sale events | | `includeVariantChanges` / `includeVariants` | `true` | Toggle variant tracking | | `includeMetadataChanges` | `false` | Title, vendor, and tag events (noisy) | | `maxProductsPerStore` | `5000` | Safety cap per store | | `maxConcurrency` | `3` | Stores fetched in parallel | | `requestDelayMs` | `300` | Delay between requests to a store | | `resetBaseline` | `false` | Reset the baseline for every store at once | | `storeSnapshotKeyPrefix` | `shopify-monitor` | Snapshot store name (change it to run separate setups) | **Webhook payload:** when changes are found, the actor POSTs `{ totalChanges, countsByEventType, topChanges (up to 20), runUrl }`. A failed webhook never fails the run. *** ### Pricing This actor uses **pay per event**: you are charged one small flat fee for each competitor **store checked in a run**, no matter how big its catalog is. A store with 50,000 products costs the same as one with 50. - Watching 5 competitors on a daily schedule is about 150 store checks per month. - Stores that error out (blocked, not Shopify, or empty) are **not** charged. - There is **no per-product or per-result fee**, so a large catalog never inflates your bill. - Your run also has a max-cost limit you can set in Apify, so spend is always capped. *** ### Good to know - **Availability, not exact stock counts.** Shopify's public data exposes an in-stock or out-of-stock flag, not quantities. - **Public data only.** No login, Admin API, tokens, or customer and order data. Just the public product JSON. - **Some stores block it.** A minority disable `/products.json` or sit behind a bot wall. Those return an error row instead of crashing the run. - **Currency is best-effort** (read from the storefront when possible, and may be `null`). - **Polite by default:** low concurrency, request delays, and automatic retry with backoff on 429 and 5xx. - Detects and reports changes. It does not reprice your own store. # Actor input Schema ## `storeUrls` (type: `array`): Paste one or more competitor store URLs (the homepage is fine) and press Start. That is the only thing you need. ## `keywords` (type: `array`): Leave empty to track everything. Example: hoodie, leggings. ## `webhookUrl` (type: `string`): Optional. When changes are found, a summary is sent here. Paste a Slack, Zapier, or Make webhook. If it fails, your run still succeeds. ## `catalogExportFormats` (type: `array`): Optional. Also save every monitored product and variant as downloadable file(s) in this run's storage. Pick JSON, Excel (xlsx), and/or CSV. Leave empty to skip. (The dataset still holds only the changes.) ## `resetBaselineStores` (type: `array`): Optional. Paste a store URL here to forget its saved history and start fresh on this run, as if it were brand new. Handy after a store changes a lot, or if its saved data looks wrong. All other stores keep their history. ## Actor input object example ```json { "storeUrls": [ "https://store1.com", "https://store2.myshopify.com" ], "keywords": [], "catalogExportFormats": [], "resetBaselineStores": [] } ``` # Actor output Schema ## `changes` (type: `string`): Change events since the previous run: price drops and increases, sales starting and ending, restocks, sold-outs, and new or removed products and variants. Empty on the first (baseline) run. ## `summary` (type: `string`): One entry per store with a plain-English status message (baseline created, number of changes, or an error), plus counts and the location of the full catalog snapshot. ## `catalogExcel` (type: `string`): Every monitored product and variant as an .xlsx spreadsheet. Only produced when 'xlsx' is chosen in 'Export the full catalog as files'. ## `catalogJson` (type: `string`): The same full catalog as a JSON file. Only produced when 'json' is chosen in 'Export the full catalog as files'. ## `catalogCsv` (type: `string`): The same full catalog as a CSV file for Excel or Google Sheets. Only produced when 'csv' is chosen in 'Export the full catalog as files'. # 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 = { "storeUrls": [ "https://gymshark.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("leafy-dev-jr/shopify-competitor-price-stock-monitor").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 = { "storeUrls": ["https://gymshark.com"] } # Run the Actor and wait for it to finish run = client.actor("leafy-dev-jr/shopify-competitor-price-stock-monitor").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 '{ "storeUrls": [ "https://gymshark.com" ] }' | apify call leafy-dev-jr/shopify-competitor-price-stock-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=leafy-dev-jr/shopify-competitor-price-stock-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Shopify Competitor Price Stock Monitor & Catalog Scraper", "description": "Detect price drops, sales, restocks, sold-outs, and new product launches across competitor Shopify stores.", "version": "1.0", "x-build-id": "9eRdfrDRYfDDOSV8I" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/leafy-dev-jr~shopify-competitor-price-stock-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-leafy-dev-jr-shopify-competitor-price-stock-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/leafy-dev-jr~shopify-competitor-price-stock-monitor/runs": { "post": { "operationId": "runs-sync-leafy-dev-jr-shopify-competitor-price-stock-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/leafy-dev-jr~shopify-competitor-price-stock-monitor/run-sync": { "post": { "operationId": "run-sync-leafy-dev-jr-shopify-competitor-price-stock-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "storeUrls" ], "properties": { "storeUrls": { "title": "Competitor Shopify store URLs", "type": "array", "description": "Paste one or more competitor store URLs (the homepage is fine) and press Start. That is the only thing you need.", "items": { "type": "string" } }, "keywords": { "title": "Only track products matching these words (optional)", "type": "array", "description": "Leave empty to track everything. Example: hoodie, leggings.", "default": [], "items": { "type": "string" } }, "webhookUrl": { "title": "Get notified (webhook URL)", "type": "string", "description": "Optional. When changes are found, a summary is sent here. Paste a Slack, Zapier, or Make webhook. If it fails, your run still succeeds." }, "catalogExportFormats": { "title": "Export the full catalog as files", "type": "array", "description": "Optional. Also save every monitored product and variant as downloadable file(s) in this run's storage. Pick JSON, Excel (xlsx), and/or CSV. Leave empty to skip. (The dataset still holds only the changes.)", "items": { "type": "string", "enum": [ "json", "xlsx", "csv" ], "enumTitles": [ "JSON", "Excel (.xlsx)", "CSV" ] }, "default": [] }, "resetBaselineStores": { "title": "Reset baseline for these stores", "type": "array", "description": "Optional. Paste a store URL here to forget its saved history and start fresh on this run, as if it were brand new. Handy after a store changes a lot, or if its saved data looks wrong. All other stores keep their history.", "default": [], "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```