# Facebook Posts Scraper (No Login Required) (`iron-crawler/facebook-page-posts`) Actor Bulk scrape Facebook page posts in seconds. Zero-config API pipeline exports viral content metrics to JSON/CSV for rapid social media strategy optimization. - **URL**: https://apify.com/iron-crawler/facebook-page-posts.md - **Developed by:** [Iron Crawler](https://apify.com/iron-crawler) (community) - **Categories:** Social media, Automation, Developer tools - **Stats:** 21 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.50 / 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. 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 Posts Scraper (No Login Required) ### What does Facebook Posts Scraper (No Login Required) do? This tool allows you to extract public post data from any Facebook page without needing to log in or manage cookies. Built with a cookieless architecture, the scraper bypasses authentication barriers while respecting Facebook's public data access policies. Whether you're analyzing competitor content, tracking engagement trends, or building a content database, this tool delivers structured data in formats ready for analysis. **Key Features:** - **No Login Required:** Scrape Facebook pages without authentication or cookie management - **Bulk Post Extraction:** Collect multiple posts from any public Facebook page - **Engagement Metrics:** Capture likes, shares, and comment counts for each post - **Media URLs:** Extract direct links to photos and videos - **Flexible Export:** Download data in JSON, CSV, or Excel formats - **Date Filtering:** Target posts from specific time periods - **Post Type Detection:** Identify whether posts are photos, videos, links, or text-only ### Why scrape Facebook? Social media content creators need to scrape Facebook page posts to analyze engagement metrics, track viral content trends, and optimize their content strategy across multiple platforms. Understanding what resonates with audiences on Facebook helps creators replicate success patterns, identify optimal posting times, and benchmark their performance against competitors. This data-driven approach transforms guesswork into strategic decision-making. **Primary Use Cases:** - **Content Strategy Optimization:** Analyze which post types, topics, and formats generate the highest engagement to refine your content calendar and maximize reach across social platforms - **Competitive Intelligence:** Monitor competitor pages to identify their most successful content, posting frequency, and audience engagement patterns to stay ahead in your niche - **Trend Analysis:** Track viral content patterns, hashtag performance, and emerging topics within your industry to create timely, relevant content that captures audience attention ### How to scrape Facebook using this tool? **Step 1: Find the Page ID** Navigate to the Facebook page you want to scrape. The Page ID is typically found in the page's ""About"" section or in the URL. You can also use online tools to extract the numeric Page ID from a page name. **Step 2: Configure Input Parameters** Enter the Page ID into the scraper's input field. Optionally, set the maximum number of posts to retrieve. Note: **1 page ≈ 50 items** depending on the page's posting frequency and Facebook's pagination. **Step 3: Run and Download** Click ""Start"" to begin scraping. Once complete, download your data in JSON, CSV, or Excel format. The scraper typically processes 50-100 posts per minute. ### What are the input parameters? | Field | Type | Description | |-------|------|-------------| | `pageId` | String | The numeric Page ID of the Facebook page to fetch posts from (e.g., 61553294532484). This is required to identify which page's content you want to scrape. | ### What data can you extract? You can download the following data in JSON, CSV, or Excel formats: ```json { ""page_id"": ""103856482910234"", ""post_id"": ""507123456789012"", ""content"": ""Check out our latest product launch! 🚀 Link in bio."", ""created_time"": ""2025-12-18T14:30:00Z"", ""likes_count"": 342, ""shares_count"": 28, ""comments_count"": 47, ""post_type"": ""photo"", ""media_url"": ""https://scontent.xx.fbcdn.net/v/t39/123456789.jpg"" } ```` **Extracted Fields:** - `page_id`: Unique identifier for the Facebook page - `post_id`: Unique identifier for each individual post - `content`: Full text content of the post including emojis and hashtags - `created_time`: ISO 8601 timestamp of when the post was published - `likes_count`: Total number of reactions/likes on the post - `shares_count`: Number of times the post has been shared - `comments_count`: Total number of comments on the post - `post_type`: Format of the post (photo, video, link, status) - `media_url`: Direct URL to attached images or videos *** This Facebook web scraper serves as a powerful web scraping tool for anyone looking to extract data from web pages and scrape website data at scale. Whether you're using it as a blog post scraper, social media post scraper, or lead generation scraper, this tool streamlines the process of collecting actionable insights from one of the world's largest social platforms." # Actor input Schema ## `pageId` (type: `string`): Page ID of the Facebook page to fetch posts from ## `maxPages` (type: `integer`): Maximum number of pages to fetch (pagination handled automatically) ## Actor input object example ```json { "pageId": "100064860875397", "maxPages": 1 } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("iron-crawler/facebook-page-posts").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 = {} # Run the Actor and wait for it to finish run = client.actor("iron-crawler/facebook-page-posts").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 '{}' | apify call iron-crawler/facebook-page-posts --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=iron-crawler/facebook-page-posts", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Facebook Posts Scraper (No Login Required)", "description": "Bulk scrape Facebook page posts in seconds. Zero-config API pipeline exports viral content metrics to JSON/CSV for rapid social media strategy optimization.", "version": "1.0", "x-build-id": "KgNY8uh0im2Uwwm6R" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/iron-crawler~facebook-page-posts/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-iron-crawler-facebook-page-posts", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/iron-crawler~facebook-page-posts/runs": { "post": { "operationId": "runs-sync-iron-crawler-facebook-page-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/iron-crawler~facebook-page-posts/run-sync": { "post": { "operationId": "run-sync-iron-crawler-facebook-page-posts", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "pageId" ], "properties": { "pageId": { "title": "Page ID", "type": "string", "description": "Page ID of the Facebook page to fetch posts from", "default": "100064860875397" }, "maxPages": { "title": "Maximum Pages", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of pages to fetch (pagination handled automatically)", "default": 1 } } }, "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 } } } } } } } } } } ```