# Instagram Hashtag Scraper (`seemuapps/instagram-hashtag-scraper`) Actor Scrape top posts and Reels from any public Instagram hashtag. captions, engagement counts, author profiles, and media URLs. - **URL**: https://apify.com/seemuapps/instagram-hashtag-scraper.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Social media, Lead generation, SEO tools - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $6.00 / 1,000 post 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 ## Instagram Hashtag Scraper Extract posts, Reels, captions, and engagement metrics from any public Instagram hashtag — no login or cookies required. ### What you get For every post under a hashtag, the scraper returns a structured record with: **Post metadata** - Post ID, shortcode, and direct post URL - Media type (photo, video, carousel) and product type (feed, clips/Reels) - Post timestamp (ISO 8601 and Unix) - Caption text with auto-extracted hashtags and @mentions **Engagement stats** - Like count, comment count, view count, play count - Video duration (for Reels and video posts) **Media URLs** - Thumbnail / cover image URL - Video URL (direct MP4, when available) **Author profile** - Author ID, username, full name - Profile picture URL - Verified and private flags **Location** (when set on the post) - Location name and city **Pagination** - `NEXT_PAGE_ID` cursor so you can resume from where a previous run left off ### Use cases - **Trend research** — monitor which posts are gaining traction under a hashtag and spot viral content early - **Influencer discovery** — find creators actively posting in a niche before they break out - **Content strategy** — analyse top-performing posts in a hashtag to understand what hooks, formats, and audio drive engagement - **Competitor analysis** — track how a brand or product hashtag is growing over time - **Audience research** — measure hashtag volume, engagement rates, and creator participation across niches - **Social media monitoring** — track branded or campaign hashtags and pull structured data into your own dashboard ### How to use 1. Enter the hashtag — with or without `#` (e.g. `coffee` or `#travel`) 2. Choose **Top Posts** (mixed photos + Reels) or **Reels Only** 3. Set **Max Posts** — default 100 per run, or 0 for unlimited (will stop ~60s before the actor timeout) 4. Run the actor — results appear in the **Dataset** tab 5. To fetch the next page, copy `NEXT_PAGE_ID` from the **Key-value store** tab and paste it into **Page ID** on the next run ### Output format Each dataset record: ```json { "postId": "3899803439831518781", "shortcode": "DYe4pEFAQ49", "postUrl": "https://www.instagram.com/p/DYe4pEFAQ49/", "mediaType": "photo", "productType": "feed", "takenAt": "2026-05-18T14:00:19.000Z", "takenAtTimestamp": 1779112819, "caption": "Morning brew. #coffee #latteart", "hashtags": ["coffee", "latteart"], "mentions": [], "likeCount": 529, "commentCount": 2, "viewCount": null, "playCount": null, "videoDuration": null, "thumbnailUrl": "https://scontent.cdninstagram.com/...", "videoUrl": null, "authorId": "580240992", "authorUsername": "chrishallbeck", "authorFullName": "Chris Hallbeck", "authorProfilePicUrl": "https://scontent.cdninstagram.com/...", "authorIsVerified": false, "authorIsPrivate": false, "locationName": "Taroudant, Marocco", "locationCity": null } ```` ### Pagination Each run returns up to **Max Posts** records. For large hashtags, use the pagination cursor to collect more across multiple runs: 1. Run the actor — when it finishes, `NEXT_PAGE_ID` is saved in the **Key-value store** tab 2. Copy the `NEXT_PAGE_ID` value 3. Start a new run with the same hashtag and paste it into the **Page ID** field 4. Repeat until `NEXT_PAGE_ID` is `null` (hashtag fully exhausted) If a single run takes you to the actor timeout, the cursor still gets written before the run ends so you can resume on the next run. # Actor input Schema ## `hashtag` (type: `string`): Instagram hashtag to scrape (with or without #), e.g. coffee or #travel. ## `mode` (type: `string`): Which feed to scrape. 'Top Posts' mirrors Instagram's Top tab. 'Reels Only' returns the hashtag's Reels feed. ## `maxItems` (type: `integer`): Maximum number of posts to scrape. Set 0 for unlimited (caps at platform timeout). ## `pageId` (type: `string`): Optional. Paste NEXT\_PAGE\_ID from the previous run's Key-value store to fetch the next page. ## Actor input object example ```json { "hashtag": "coffee", "mode": "top", "maxItems": 100 } ``` # Actor output Schema ## `results` (type: `string`): Each record contains postId, shortcode, postUrl, mediaType, productType, takenAt, caption, hashtags, mentions, likeCount, commentCount, viewCount, playCount, videoDuration, thumbnailUrl, videoUrl, and author fields (authorUsername, authorFullName, authorIsVerified, authorProfilePicUrl), plus locationName and locationCity when available. ## `nextPageId` (type: `string`): NEXT\_PAGE\_ID record in the default key-value store. Paste into Page ID on the next run to resume; null when the hashtag is fully exhausted. # 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 = { "hashtag": "coffee" }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/instagram-hashtag-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 = { "hashtag": "coffee" } # Run the Actor and wait for it to finish run = client.actor("seemuapps/instagram-hashtag-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 '{ "hashtag": "coffee" }' | apify call seemuapps/instagram-hashtag-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/instagram-hashtag-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Hashtag Scraper", "description": "Scrape top posts and Reels from any public Instagram hashtag. captions, engagement counts, author profiles, and media URLs.", "version": "1.0", "x-build-id": "2aTcP5Pup3afgKYrU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~instagram-hashtag-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-instagram-hashtag-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~instagram-hashtag-scraper/runs": { "post": { "operationId": "runs-sync-seemuapps-instagram-hashtag-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~instagram-hashtag-scraper/run-sync": { "post": { "operationId": "run-sync-seemuapps-instagram-hashtag-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "hashtag" ], "properties": { "hashtag": { "title": "Hashtag", "type": "string", "description": "Instagram hashtag to scrape (with or without #), e.g. coffee or #travel." }, "mode": { "title": "Mode", "enum": [ "top", "clips" ], "type": "string", "description": "Which feed to scrape. 'Top Posts' mirrors Instagram's Top tab. 'Reels Only' returns the hashtag's Reels feed.", "default": "top" }, "maxItems": { "title": "Max Posts", "minimum": 0, "type": "integer", "description": "Maximum number of posts to scrape. Set 0 for unlimited (caps at platform timeout).", "default": 100 }, "pageId": { "title": "Page ID (pagination)", "type": "string", "description": "Optional. Paste NEXT_PAGE_ID from the previous run's Key-value store to fetch the next page." } } }, "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 } } } } } } } } } } ```