# Facebook Photos Posts Scraper - Low-costπŸ’²πŸ”₯πŸ”πŸ“Έ (`delectable_incubator/facebook-photos-posts-scraper-low-cost`) Actor Scrape Facebook photos by keyword πŸ”πŸ“Έ with a powerful visual content scraper. Extract photo posts, captions, upload dates, image URLs, and post links from Facebook search results. Ideal for visual trend analysis, content research, image datasets, AI training data, and automated workflows πŸ“ŠπŸš€ - **URL**: https://apify.com/delectable\_incubator/facebook-photos-posts-scraper-low-cost.md - **Developed by:** [Prime Scrape](https://apify.com/delectable_incubator) (community) - **Categories:** Videos, Social media, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.005 / actor start This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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

Facebook Photos Scraper

--- ## Facebook Photos Scraper πŸ“ΈπŸ”πŸ“Š The Facebook Photos Scraper is a powerful and scalable Apify Actor designed to extract structured photo posts from Facebook using keyword-based search. It enables visual content research, trend analysis, dataset creation, competitor monitoring, marketing intelligence, and large-scale image data collection from Facebook. --- ### 🎯 What This Scraper Does Simply provide one or multiple keywords and the scraper handles everything automatically. βœ… Extracts Facebook photo posts from keyword searches βœ… Supports bulk keyword scraping βœ… Automatically processes Facebook search results βœ… Collects public photo metadata and engagement stats βœ… Extracts image URLs and thumbnails βœ… Captures captions, descriptions, and snippets βœ… Retrieves reactions, comments, and shares (when available) βœ… Detects upload dates when available βœ… Generates clean structured datasets βœ… Ready for AI, ML, and analytics workflows βœ… Export-ready output format --- ### πŸ“Š Data Extracted #### πŸ“Έ Facebook Photo Information | Field | Description | | ------------- | ----------------------------------------------- | | πŸ†” photoId | Unique Facebook photo post ID | | 🏷️ title | Photo caption or title | | πŸ”— photoUrl | Facebook photo post URL | | πŸ–ΌοΈ imageUrl | Direct image file URL | | πŸ•’ uploadDate | Upload date (when available) | | πŸ‘ reactions | Number of reactions (when visible) | | πŸ’¬ comments | Number of comments (when visible) | | πŸ” shares | Number of shares (when visible) | | πŸ“ description | Full caption / description | | 🧩 snippet | Related content preview snippet | | 🌐 platform | Facebook Photos | | πŸ–ΌοΈ thumbnail | Thumbnail image URL | --- ### πŸ›  How to Use #### 1️⃣ Configure Input Provide one or multiple keywords: ```` { "keywords": \[ "nature", "travel", "food photos" ], "maxItemsPerKeyword": 100, "timeFilter": "any" } ``` 2️⃣ Run the Actor β€’ Searches Facebook Photos using keywords β€’ Extracts relevant photo posts automatically β€’ Collects captions, images, and engagement metrics β€’ Applies maxItemsPerKeyword limits β€’ Processes multiple keywords in bulk β€’ Stops automatically when limits are reached 3️⃣ Export the Dataset Download your results in multiple formats: βœ… JSON βœ… CSV βœ… Excel βœ… XML βœ… HTML --- ### βš™οΈ Input Configuration πŸ“₯ Input Example ``` { "keywords": \[ "nature", "travel", "food photos" ], "maxItemsPerKeyword": 100, "timeFilter": "any" } ``` πŸ“Œ Input Fields | Field | Type | Description | | ------------------ | ------- | ----------------------------------------- | | keywords | array | List of Facebook search keywords | | maxItemsPerKeyword | integer | Maximum number of photo posts per keyword | | timeFilter | string | Time filter (any, day, week, month, year) | --- ### πŸ“€ Output Example ``` { "photoId": "795483389926725", "title": "Beautiful evening sky πŸŒ…", "photoUrl": "https://www.facebook.com/photo/?fbid=795483389926725", "imageUrl": "https://scontent.xx.fbcdn.net/v/t39.30808-6/example.jpg", "uploadDate": "March 4, 2024", "reactions": "2.4K", "comments": "134", "shares": "22", "description": "Captured this sunset on my evening walk ✨", "snippet": "More like this: sunset photos β€’ sky shots β€’ nature photography...", "platform": "Facebook Photos", "thumbnail": "https://scontent.xx.fbcdn.net/v/t39.30808-6/thumb.jpg" } ```` --- ### πŸ“Š Output Explanation | Use Case | Description | | ----------------------- | ---------------------------------------- | | πŸ“Έ Visual Research | Collect trending or niche photo content | | πŸ“Š Marketing Analysis | Analyze captions and engagement patterns | | πŸ“ˆ Dataset Creation | Build datasets for AI/ML models | | πŸ” Competitor Tracking | Monitor brand visual content | | 🧠 AI Training Data | Image-based dataset generation | | πŸ€– Automation Pipelines | Feed structured photo data into tools | --- ### 🌍 Why Use This Scraper? πŸ“Έ Extract Facebook photo content at scale πŸ“Š Build structured visual datasets πŸ” Analyze engagement and content trends πŸ“ˆ Track competitors and creators ⚑ Fast and automated extraction πŸ€– AI & machine learning ready output πŸ“¦ Bulk keyword scraping support 🧠 Ideal for marketers, researchers, and analysts πŸš€ Scalable for enterprise workflows --- ### ❓ FAQ #### How does this scraper work? The scraper searches Facebook Photos using keywords and extracts structured photo posts including captions, images, and engagement metrics. #### Can I scrape multiple keywords? Yes. You can provide multiple keywords inside the keywords array. #### Does it collect image URLs? Yes. Both direct image URLs and thumbnails are extracted when available. #### Can I filter by time? Yes. The timeFilter option allows filtering by time ranges such as day, week, month, or year. #### Is login required? No Need. #### What export formats are supported? JSON, CSV, Excel, XML, and HTML. #### Can I use this data commercially? Yes. The extracted data can be used for analytics, research, automation, and commercial use cases. #### What happens if the scraper fails? The Actor includes retry mechanisms and error handling for stability. #### How long does a run take? Most runs complete within minutes depending on keyword count and limits. --- ### πŸš€ How to Use 1️⃣ Sign up β€” Create a free Apify account 2️⃣ Find the tool β€” Search for "Facebook Photos Scraper" in the Apify Store 3️⃣ Configure keywords β€” Add one or multiple search keywords 4️⃣ Run it β€” Start the Actor 5️⃣ Export data β€” Download results in JSON, CSV, Excel, XML, or HTML --- ### ⚠️ Disclaimer This tool is an independent solution and is not affiliated with, endorsed by, or sponsored by Facebook or Meta Platforms, Inc. --- ### πŸ’Έ Pricing This scraper runs on a **pay-per-result pricing model**. You only pay for successfully extracted records. πŸ’³ **Price:** $4.99 / 1,000 results --- ### πŸ”— Related Actors Explore more PrimeScrape social media and data extraction tools (coming soon). --- ### πŸ“¬ Support ⭐⭐⭐⭐⭐ Leave a 5-star rating if you like this tool --- ### 🌍 PrimeScrape Built for scalable web data extraction & automation. Contact us for custom scraping solutions, enterprise workflows, or bulk data extraction via Apify or email. # Actor input Schema ## `bulk_keywords` (type: `array`): List of keywords to search relevant Facebook photos. Examples: β€’ volleyball β€’ sunset β€’ wedding ⚠️ One keyword per entry. ## `TIME_FILTER` (type: `string`): Filter photos by post date ## `MAX_ITEMS` (type: `integer`): Maximum number of photos to extract per keyword ## Actor input object example ```json { "bulk_keywords": [ "volleyball", "sunset" ], "TIME_FILTER": "any", "MAX_ITEMS": 10 } ```` # Actor output Schema ## `overview` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "bulk_keywords": [ "volleyball", "sunset" ], "TIME_FILTER": "any", "MAX_ITEMS": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("delectable_incubator/facebook-photos-posts-scraper-low-cost").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "bulk_keywords": [ "volleyball", "sunset", ], "TIME_FILTER": "any", "MAX_ITEMS": 10, } # Run the Actor and wait for it to finish run = client.actor("delectable_incubator/facebook-photos-posts-scraper-low-cost").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "bulk_keywords": [ "volleyball", "sunset" ], "TIME_FILTER": "any", "MAX_ITEMS": 10 }' | apify call delectable_incubator/facebook-photos-posts-scraper-low-cost --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=delectable_incubator/facebook-photos-posts-scraper-low-cost", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Facebook Photos Posts Scraper - Low-costπŸ’²πŸ”₯πŸ”πŸ“Έ", "description": "Scrape Facebook photos by keyword πŸ”πŸ“Έ with a powerful visual content scraper. Extract photo posts, captions, upload dates, image URLs, and post links from Facebook search results. Ideal for visual trend analysis, content research, image datasets, AI training data, and automated workflows πŸ“ŠπŸš€", "version": "0.0", "x-build-id": "gS8bdwqozlU5lGUR3" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/delectable_incubator~facebook-photos-posts-scraper-low-cost/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-delectable_incubator-facebook-photos-posts-scraper-low-cost", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/delectable_incubator~facebook-photos-posts-scraper-low-cost/runs": { "post": { "operationId": "runs-sync-delectable_incubator-facebook-photos-posts-scraper-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/delectable_incubator~facebook-photos-posts-scraper-low-cost/run-sync": { "post": { "operationId": "run-sync-delectable_incubator-facebook-photos-posts-scraper-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "bulk_keywords", "TIME_FILTER", "MAX_ITEMS" ], "properties": { "bulk_keywords": { "title": "Keywords to search photos πŸ”", "type": "array", "description": "List of keywords to search relevant Facebook photos. Examples: β€’ volleyball β€’ sunset β€’ wedding ⚠️ One keyword per entry.", "items": { "type": "string" } }, "TIME_FILTER": { "title": "Time Filter", "enum": [ "any", "month", "year" ], "type": "string", "description": "Filter photos by post date", "default": "any" }, "MAX_ITEMS": { "title": "Max Items", "type": "integer", "description": "Maximum number of photos to extract per keyword", "default": 10 } } }, "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 } } } } } } } } } } ```