Best Facebook Scrapers: Apify, Bright Data & Decodo

Using Python and a managed Facebook scraping API lets you collect public posts, comments, likes, and shares. This tutorial demonstrates how to scrape Facebook posts by keyword and retrieve their URLs via Google search.

Then it explains how to extract detailed post data using the API, along with tips for scaling the process with tools like Apify, Nimble, and Decodo.

Facebook scrapers benchmark results

Pricing of the best Facebook scraper tools 2026

See the best Facebook scraping tools based on supported page types, output formats, pricing, and trial options.

• Dedicated: Returns structured JSON with key data fields from Facebook pages. These APIs are specifically designed for Facebook and provide higher accuracy.
• General-purpose: Not Facebook-specific but can be adapted for Facebook data scraping through custom parsing.
• NDJSON & JSONL: Uses newline-delimited JSON for efficient storage and processing of large datasets, with each line representing one JSON object.

Before examining the top tools below, the easiest way to understand how these APIs handle Facebook scraping is by seeing their output. You can download sample output from all providers.

Features of the best Facebook scraping tools

Bright Data Facebook scraper covers 15 dedicated templates for extracting public data from Facebook Pages, Profiles, Groups, Marketplace, Events, Reels, and Comments. Users can choose between two collection modes:

• Scraper API: allows developers to automate large-scale Facebook data scraping with scheduling, storage, delivery, and integration options.
• No-code scraper: a plug-and-play interface for non-developers to collect data directly from Facebook URLs through a control panel.

In addition to scraping live data, Bright Data also provides ready-to-use Facebook datasets (including posts, comments, marketplace listings, events, and profiles).

Apify Facebook posts scraper can output data in JSON, CSV, or Excel. Inputs for the scraper must be Facebook Page URLs, which can be added manually, uploaded as a list, or provided via API.

The Facebook scraper can extract detailed information, such as page addresses, emails, and phone numbers, from the “About” section, even when this data isn’t available in the intro widget. Social media links are grouped by platform, and additional data is collected from the updated “About” and “Page Transparency” sections.

The Starter plan, which costs $39 per month, reduces the scraping rate to around $10 per 1,000 pages and includes up to 3,900 pages per month. On the Free plan, you can scrape up to 500 pages.

Nimbleway offers a general-purpose scraping API adaptable to Facebook. It’s not specifically tailored for the platform, but it performs well for lightweight HTML-to-JSON scraping.

With the Facebook data scraper, you can target down to specific states and cities. They offer pay-as-you-go and monthly plans.

ScrapingBot is an affordable Facebook scraping software that supports posts and profiles, ideal for startups or small data teams. It handles proxy rotation automatically and outputs clean JSON or HTML for simple integrations.

Crawlbase offers dedicated Facebook scraping via its Crawling API, enabling users to collect structured JSON data from public Facebook pages, groups, profiles, events, and hashtags.

The API returns structured JSON that includes fields such as “title”, “type”, “membersCount”, “url”, and a “feeds” array containing post data like “userName”, “text”, “likesCount”, “commentsCount”, and “sharesCount”.

Pricing: $78/month

Facebook scraper Python tutorial

This step-by-step guide will show you how to scrape Facebook posts, scrape Facebook groups by keyword, fetch URLs via Google, and extract detailed post information using Bright Data’s Facebook post scraper.

How the Facebook scraper works

The Facebook scraper script is divided into four main steps:

1. Setup & configuration: Import libraries, set up Python, and add API credentials.
2. Find Facebook URLs: Use Google search to collect links for scraping Facebook posts.
3. Trigger scraping: Send URLs to the Facebook data scraper API.
4. Retrieve & save results: Download the scraped data and export it to a CSV file.

Step 1: Setup & configuration

Here, we import Python libraries for making HTTP requests, parsing data, and handling JSON. Add your API credentials from the dashboard and configure a proxy server for Google searches, essential for Facebook data scraping.

We then define our search parameters: looking for posts about “agentic frameworks” and collecting five posts (you can increase this number for deeper analysis using your Facebook scraper).

Step 2: Google Search for Facebook URLs

Now we search Google to find Facebook post URLs for Facebook data scraping.

This step performs the actual Facebook scraping setup using Google search. The script constructs a site:facebook.com query to locate relevant public posts, retrieves the HTML results, and extracts the post URLs (including shared posts and videos).

Duplicate links are filtered out, and a 2-second delay ensures that respectful, compliant requests are made to Google.

Step 3: Extracting post data

Next, we send the collected Facebook post URLs to the API for Facebook data scraping and extraction.

This step sends your Facebook URLs to the Facebook scraping API. Each URL is sent as JSON; if successful, the scraper returns a snapshot ID to track your scraping job. If the request fails, the script exits with an error message.

Step 4: Retrieve & save results

This step waits for the API to finish Facebook scraping and saves the collected data.

It extracts post details such as URL, username, date, likes, comments, and shares, then exports everything into a CSV file for analysis. The script includes timeout handling and error checks to keep your Facebook scraper reliable and efficient.

Is Facebook scraping legal?

Scraping Facebook is only legal when it involves collecting publicly available data and complies with Facebook’s Terms of Service. Facebook explicitly prohibits unauthorized data collection, automated scraping, and the access of private user information without consent.¹

However, developers can still access certain types of Facebook data ethically and lawfully using official Facebook APIs. ²

What measures does Facebook take to prevent unauthorized scraping?

Facebook employs several anti-scraping measures to detect and block scraping attempts that violate its terms of service. These include:

1. External Data Misuse (EDM) team: The External Data Misuse (EDM) team at Facebook is responsible for detecting potential data misuse and preventing unauthorized scrapers from violating Facebook’s policies and compromising user privacy.
2. Rate limits refer to the number of times a user can interact with a website’s services within a given period. Facebook applies rate limits to prevent the overuse and abuse of its APIs.
3. Request blocking through pattern recognition: Facebook employs algorithms to prevent automated Facebook scraping tools from overloading its systems. This technique involves analyzing the traffic and requests received by the server by using machine learning algorithms.

What is Facebook scraping?

Facebook scraping involves the automatic collection of publicly available data from Facebook pages, posts, profiles, or groups using code or specialized tools.

Scraping can be done with Python scripts or APIs, which simplify Facebook data scraping by automating proxy management.

Facebook scraper benchmark methodology

We benchmarked web data scrapers to evaluate their ability to scrape Facebook profile data. We executed 500 Facebook profile URLs per provider, with each profile tested once.

• Dataset: We used a curated list of 500 Facebook profile URLs.

• Target: Each provider scraped profile metadata, including follower count, like count, and bio/intro text.

• Runs: We performed 1 run per profile.

Success rates

We defined three levels of success:

1. Submission success: We considered a submission successful if the API accepted our initial request (HTTP 200/202) without authentication or rate limit errors.
2. Execution success: We considered an execution successful if the scraping job completed without timeout or system errors.
3. Validation success: We applied a set of rules to ensure data quality and usability. A result is considered VALID if the mandatory field (page name) is returned in a non-empty, non-redirect format, and the followers field, when present, contains a numeric value.

A trial that fails at any earlier stage cannot proceed to later stages and is recorded as a failed trial in the final validation calculation. For example, if a request fails during submission, it receives a validation score of 0. The final validation success rate includes all trials across all stages.

Validation criteria

We validated four fields per profile to assess data accuracy and completeness. Each field is evaluated independently using the rules below.

1. Name validation

The profile name is the only field that must be present and valid for a result to pass validation. All providers extract the profile name: Nimble and Decodo parse it from HTML meta tags, while SerpAPI and Apify return it as a structured field.

When a scraper is detected or fails to bypass anti-bot measures, the response typically returns the platform’s login page or home page rather than the requested profile. We identify these cases by checking whether the returned name matches known redirect page titles such as “Log in” or “Welcome to Facebook”, and treat any such match as a failure.

2. Followers

• Valid if the value is absent (the field may not be publicly visible on all profiles).
• Valid if present and contains at least one numeric character (e.g., “1.4K”, 500, “2,576”).
• Invalid if present but contains no numeric value.

Extraction varies by provider:

• Nimble: Regex on og:title / og:description HTML meta tags (pattern: \d+[KkMmBb]? followers)
• Decodo: Regex on og:description content (pattern: [\d,.]+ [KkMmBb]?\s*followers)
• SerpAPI: Structured field profile_results.followers
• Apify: Structured field followers

Validation decision logic

is_valid = name_passed AND followers_passed

Where:

• name_passed = True if name is a valid non-redirect string, or if Apify’s profile_info list is non-empty
• followers_passed = True if followers is absent (None) OR present with a numeric value

We automatically skipped profiles with broken or unavailable URLs. Detection was applied at the submission stage using error message matching:

• HTTP 404 errors
• “not found”, “does not exist”, “invalid url”
• “post not available”, “content removed”, “post removed”, “post deleted”
• “page not found”, “post is unavailable”, “this post is no longer available”

However, there were no broken URLs in our dataset, so no profiles were excluded from analysis.

Available metadata fields

We counted the number of non-null structured fields returned by each provider across the normalized output schema. Provider scores differ depending on whether they offer a dedicated Facebook API or rely on general-purpose HTML scraping.

Nimble and Decodo retrieve profile pages as raw HTML and extract fields using regex patterns applied to Open Graph meta tags. 

SerpAPI and Apify use dedicated Facebook data products that return structured JSON with individually labeled fields. This allows them to surface a broader range of metadata without parsing unstructured HTML.

The metadata count per result was averaged across all 500 runs for each provider and reported as available metadata fields in the results summary.

Statistical methodology

Confidence intervals were calculated using bootstrap percentile resampling:

• Method: Bootstrap percentile
• Resamples: 10,000
• Confidence level: 95%
• Metric: Validation success rate (binary: 1 = valid, 0 = invalid)
• Sample size: N = 500 per provider

FAQs about Facebook scrapers

Reference Links

1.

Was ist Scraping und wie kann ich meine Infos auf Facebook schützen? | Facebook-Hilfebereich

2.

Conditions générales et politiques - Développement d’applications avec Meta - Documentation - Meta for Developers

CTO

Sedat Dogan

CTO

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Sedat is a technology and information security leader with experience in software development, web data collection and cybersecurity. Sedat:
- Has ⁠20 years of experience as a white-hat hacker and development guru, with extensive expertise in programming languages and server architectures.
- Is an advisor to C-level executives and board members of corporations with high-traffic and mission-critical technology operations like payment infrastructure.
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Researched by

Gulbahar Karatas

Industry Analyst

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Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security.

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