# Instagram Post Scraper (Metadata) (`scrapecraze/instagram-post-metadata-scraper`) Actor Instagram Post Scraper (Metadata) extracts publicly available post metadata from Instagram at scale. Collect captions, hashtags, likes, comments count, timestamps, usernames, and engagement data for analytics, social media research, competitor tracking, and marketing insights. - **URL**: https://apify.com/scrapecraze/instagram-post-metadata-scraper.md - **Developed by:** [ScrapeCraze](https://apify.com/scrapecraze) (community) - **Categories:** Social media, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.99 / 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 ### Instagram Post Metadata Scraper 🎯 Trying to understand how an Instagram post performs—without manually copying numbers and captions from each URL? **Instagram Post Metadata Scraper** pulls key Instagram post metadata in bulk from a list of post links, including likes, comments, and timestamps. It’s ideal for a **Instagram post metadata scraper**, **Instagram URL to metadata scraper**, and anyone who needs **Instagram engagement metrics scraper** output fast. Use it for influencer research, content audits, competitive analysis, and marketing reporting when you have real post URLs to process—results start appearing almost immediately. --- ### See the Data: Sample Output Here's a real record from a single run: ```json { "original_url": "https://www.instagram.com/p/DWMLuq4Ec7U/", "author_username": "nba", "description": "161K likes, 154 comments - nba on August 5, 2025: \"...\"", "likes": "161K", "comments": "154", "upload_date": "August 5, 2025", "feedback": "Post metadata extracted.", "Post_Metadata": { "caption": "nba on August 5, 2025", "owner_user_id": "1234567890", "image_url": "https://example-cdn.com/path/to/image.jpg", "author_username": "nba", "shortcode": "DWMLuq4Ec7U" }, "status": "success" } ```` | Field | Type | What It Tells You | |---|---|---| | `original_url` | string | The normalized Instagram post URL used for the record (handy for joining back to your source list). | | `author_username` | string | The post author handle extracted from available metadata. Useful for grouping and filtering. | | `description` | string | The full parsed `og:description` text used to derive engagement values. Great for audit trails and QA. | | `likes` | string | Like count (as provided by the parsed metadata). Directly usable for ranking posts. | | `comments` | string | Comment count (as provided by the parsed metadata). Useful for engagement scoring. | | `upload_date` | string | The human-readable date extracted from metadata formatting. Helps build timelines and trend reports. | | `feedback` | string | A simple status message: `"Post metadata extracted."` when extraction succeeds. | | `Post_Metadata` | object | A structured container for additional post metadata found in the page meta tags. | | `Post_Metadata.caption` | string | Caption/title value captured from the page’s metadata. Useful for content analysis. | | `Post_Metadata.owner_user_id` | string | Owner user id (when available), useful for identity resolution across datasets. | | `Post_Metadata.image_url` | string | Main image URL captured from metadata for previews and enrichment. | | `Post_Metadata.shortcode` | string | The Instagram post shortcode when the actor can extract it from the URL. | Export your full dataset as JSON, CSV, or Excel from the Apify dashboard. *** ### Setting It Up Drop this into your `input.json` and you're ready to go: ```json { "post_urls": [ { "url": "https://www.instagram.com/nba/p/DWMLuq4Ec7U/" } ], "proxy_configuration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` | Parameter | Required | What It Does | |---|---|---| | `post_urls` | ✅ | A list of Instagram post URLs to scrape metadata from. | | `proxy_configuration` | ⬜ | Optional proxy settings to help your run stay reliable across larger batches. | | ↳ `proxy support` | ⬜ | When enabled, routes requests through Apify Proxy for improved reliability. | | ↳ `proxy support` | ⬜ | Selects the proxy groups used by the run (for example, residential). | *** ### What It Does The actor fetches each Instagram post page you provide and extracts structured metadata into your dataset. #### Scrape Instagram post metadata from URLs Give the actor a list of Instagram post links and it will return engagement and content fields for each one. This makes it a practical **Instagram post metadata scraper** when you’re building lists from **Instagram URL to metadata scraper** workflows. #### Pull likes, comments, and post timestamp fields The actor parses the page metadata to produce `likes`, `comments`, and `upload_date` values. If you’re running an **Instagram engagement metrics scraper** for reporting, these fields are the core metrics you need. #### Build a structured metadata object in `Post_Metadata` In addition to the top-level fields, results include a `Post_Metadata` object that stores metadata tags collected from the page. This supports **Instagram media metadata extraction** use cases where you want more than just counts. #### Clean, integration-ready output per URL Each input URL produces a single result record written to the dataset immediately after extraction. This is especially helpful for **Instagram caption metadata scraper** style audits where you want consistent records you can sort, filter, and join. #### Includes resilient behavior when extraction fails If metadata extraction fails for a URL, the actor logs an error and continues processing the rest of the input list. You’ll keep captured results from successful URLs even if some links can’t be processed. Overall, **Instagram Post Metadata Scraper** turns a batch of post links into a ready-to-analyze dataset—without manual copy/paste. *** ### Why Instagram Post Metadata Scraper? There are plenty of ways to pull data from Instagram links—here’s why **Instagram Post Metadata Scraper** stands out. #### Designed for fast bulk extraction You can submit many post URLs in one run, and results are pushed to your dataset as each URL is processed. That speed matters when you need **extract Instagram post data** for research cycles, content audits, or competitive reviews. #### Output that’s easy to work with The actor produces clear top-level fields like `likes`, `comments`, and `upload_date`, plus a `Post_Metadata` object for additional page metadata. This structure supports downstream analysis for marketers and data analysts who want reliable columns. #### Built for reliability with proxy support For runs that include many URLs, proxy support can help keep requests reliable. The result is smoother **Instagram media metadata extraction** at scale when you’re scraping more than a handful of posts. *** ### Real-World Use Cases Here's how different teams put Instagram Post Metadata Scraper to work: **Marketing Analysts**\ A marketing analyst is auditing how content themes perform across competitor posts. They feed a list of Instagram post URLs into the actor, then use `likes`, `comments`, and `upload_date` to rank posts and spot which topics consistently drive engagement. **Influencer Research Teams**\ An influencer research manager wants to compare engagement levels quickly across multiple creators. Instead of manually visiting each link, they run an **Instagram post metadata scraper** batch and compile results by `author_username` for a shortlist they can take straight into outreach planning. **Content Auditors for Brands**\ A social media manager runs periodic reviews to understand what formats and captions correlate with higher engagement. They use **Instagram caption metadata scraper** results from `Post_Metadata.caption` alongside `likes` and `comments` to guide next month’s content direction. **Freelance Researchers**\ A freelance researcher is building a dataset for a report and needs consistent metadata from multiple posts. They use **Instagram URL to metadata scraper** output to store timestamps and engagement metrics, then export the dataset for analysis in spreadsheets. **Automation & Data Engineering Workflows**\ A data engineer integrates actor runs into a pipeline that refreshes engagement metrics on a schedule. They call the actor through Apify and push the scraped dataset downstream for dashboards, enrichment, and long-term **Instagram engagement metrics scraper** tracking. *** ### How to Run It No code required. Here's how to get your first results in under 5 minutes: 1. **Open the actor on Apify**\ Go to the actor page on Apify at [console.apify.com](https://console.apify.com). 2. **Enter your inputs**\ In the input fields, add your list of Instagram post links to `post_urls` (each entry should include a `url`). 3. **Configure proxy settings (optional)**\ If you’re processing more URLs, set `proxy_configuration` to match your reliability needs (for example, enabling `proxy support`). 4. **Start the run and watch the live log**\ Launch the run and monitor progress in the logs as each URL is processed. 5. **Open the Dataset tab**\ Check the dataset for records as they are written—this is where your `original_url`, `likes`, `comments`, `upload_date`, and `Post_Metadata` land. 6. **Export your results**\ Export from the Apify dashboard in your preferred format (JSON, CSV, or Excel). The whole setup takes under 5 minutes — results start appearing within seconds of launch. *** ### Export & Integration Options Once your data is collected, Instagram Post Metadata Scraper fits directly into your existing workflow. You can export your full dataset from the Apify dashboard as JSON, CSV, or Excel. That’s useful for reporting, spreadsheets, and quick collaboration with non-technical stakeholders. If you’re running automation, you can connect the actor to your pipeline using Apify’s API-driven runs. You can also use automation tools like Zapier or Make to push new datasets into your CRM or reporting stack after a run completes. *** ### Pricing Instagram Post Metadata Scraper runs on Apify, which includes a **free tier** — no credit card needed to start. Free tier credits are typically enough for several real test runs. For larger batches, you’ll use Apify’s pay-as-you-go compute based on Actor execution. Start free at [apify.com](https://apify.com) — scale up when you need to. *** ### Reliability & Limitations | What We Handle | How | |---|---| | Rate-limit resilience | Uses pacing and stable request handling across runs. | | Proxy support | Optional proxy configuration helps maintain reliability on larger inputs. | | Per-URL extraction outcomes | Each URL is processed and results are pushed when extraction succeeds. | | Error handling | Failures for individual URLs are logged and the run continues. | | Data completeness variance | Some metadata fields may be missing depending on what’s available on the post page. | Limitations: the actor works with public Instagram post pages accessible from the provided URLs. If a post page doesn’t expose the expected metadata, some fields may be empty. If a URL can’t be fetched successfully, that specific record may not be returned. For enterprise-scale needs or custom configurations, reach out and we’ll help. *** ### Frequently Asked Questions #### Is there a free plan? Apify offers a free tier with monthly usage credits, so you can test runs without a credit card. #### Do I need to log in or create an account on Instagram? No. This actor works on the publicly available content that can be accessed from the provided post URLs. #### How accurate is the extracted data? Accuracy depends on what’s available in the post page metadata. The actor parses metadata fields and formats engagement and date values based on the page’s metadata, so results reflect the source content. #### How many results can I get per run? Your results depend on how many `post_urls` you provide and your Apify run configuration. The actor processes each URL in your list and writes results to the dataset as it goes. #### How fresh is the data? The output reflects the state of each post page at the time the actor fetched it during your run. If you need fresher snapshots, run the actor again with the same URLs. #### Is this legal? Does it comply with GDPR / CCPA? This actor extracts **publicly available data** from Instagram post pages. It’s your responsibility to comply with GDPR, CCPA, Instagram’s Terms of Service, and any applicable local regulations when using or storing the results. #### Can I export to Google Sheets or Excel? You can export your dataset as JSON, CSV, or Excel from the Apify dashboard. From there, you can import into Google Sheets or other tools that accept CSV/Excel. #### Can I schedule this to run automatically? Yes, you can schedule Apify actors to run automatically on a schedule using Apify’s scheduling capabilities. This is useful for keeping datasets up to date. #### Can I access results via the API? Yes. You can access run results programmatically via the Apify API and integrate them into your workflows. #### What happens when the actor encounters an error? If a URL fails to fetch or parse, the actor logs the error for that URL and continues processing the remaining input URLs. You still keep results for any posts that were extracted successfully. *** ### Get Help & Use Responsibly Got a question about Instagram Post Metadata Scraper or a feature you'd like added? Email us at . We’re happy to help with run setup, improving how you structure `post_urls`, or requests for additional output fields within the existing extraction approach. **Publicly available data** only: this actor accesses and extracts data from public Instagram post pages. It does not access private accounts, login-gated pages, or password-protected content. You are responsible for GDPR, CCPA, platform ToS, and any other applicable regulations when using the output. For data removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `post_urls` (type: `array`): List of Instagram post URLs to scrape metadata from. ## Actor input object example ```json { "post_urls": [ { "url": "https://www.instagram.com/nba/p/DWMLuq4Ec7U/" } ] } ``` # 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 = { "post_urls": [ { "url": "https://www.instagram.com/nba/p/DWMLuq4Ec7U/" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapecraze/instagram-post-metadata-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 = { "post_urls": [{ "url": "https://www.instagram.com/nba/p/DWMLuq4Ec7U/" }] } # Run the Actor and wait for it to finish run = client.actor("scrapecraze/instagram-post-metadata-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 '{ "post_urls": [ { "url": "https://www.instagram.com/nba/p/DWMLuq4Ec7U/" } ] }' | apify call scrapecraze/instagram-post-metadata-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapecraze/instagram-post-metadata-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Post Scraper (Metadata)", "description": "Instagram Post Scraper (Metadata) extracts publicly available post metadata from Instagram at scale. Collect captions, hashtags, likes, comments count, timestamps, usernames, and engagement data for analytics, social media research, competitor tracking, and marketing insights.", "version": "1.0", "x-build-id": "vaK7HyJLJpGHAbPJB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapecraze~instagram-post-metadata-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapecraze-instagram-post-metadata-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapecraze~instagram-post-metadata-scraper/runs": { "post": { "operationId": "runs-sync-scrapecraze-instagram-post-metadata-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapecraze~instagram-post-metadata-scraper/run-sync": { "post": { "operationId": "run-sync-scrapecraze-instagram-post-metadata-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "post_urls" ], "properties": { "post_urls": { "title": "Post URLs", "type": "array", "description": "List of Instagram post URLs to scrape metadata from.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } } } }, "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 } } } } } } } } } } ```