Best Facebook Posts Scraper Tools in 2026: Top 5 Compared

Facebook posts contain useful signals for competitor research, brand monitoring, content analysis, audience research, and campaign reporting. But manually copying post text, timestamps, reactions, shares, comments, and media links becomes impractical once a team needs to study dozens or hundreds of posts.

A Facebook posts scraper automates that collection and turns accessible post information into structured records. The best tool is not necessarily the one that extracts the most fields. It should match the team’s technical skills, required Facebook sources, output format, and how the data will be analyzed afterward.

What Makes a Good Facebook Posts Scraper?

For practical business use, four factors matter most: post-data coverage, structured output, ease of use, and automation.

A useful dataset should clearly separate fields such as post text, URL, publishing date, reactions, comments, shares, media, author, and source Page. Clean fields are easier to filter, compare, export, and connect with reporting or AI analysis than raw webpage content.

Top 5 Facebook Posts Scraper Tools Compared

1. CoreClaw

CoreClaw’s Facebook Posts Scraper collects post content and engagement data from supported public Facebook Pages, groups, and profiles. Available fields include post text, links, author and Page information, likes, reaction breakdowns, comment counts, top comments, shares, external links, and related metadata.

Best for: Marketing teams, researchers, agencies, and non-technical users.

Key features: Batch URLs, no-code execution, structured data, CSV/JSON output, API workflows, and access to additional Facebook tools through the Facebook Scraper Store.

Pros: Simple setup; purpose-built Facebook Workers; cleaned and organized fields; no scraper infrastructure to maintain.

Cons: Highly specialized workflows may require a custom or developer-built Worker.

Use case: Collect competitor posts from several Pages, filter by engagement, and export the strongest posts for content research.

CoreClaw also uses a pay-only-for-successful-results pricing model across applicable Worker usage rather than treating failed runs as successful records.

2. Apify

Apify provides an officially maintained Facebook Posts Scraper Actor for public Pages and profiles. It extracts post text, URLs, timestamps, engagement metrics, thumbnails, media assets, and video transcripts and supports date filtering. Results can be exported in JSON, CSV, Excel, or HTML and connected through APIs, SDKs, or webhooks.

Best for: Developers and growth teams that want configurable cloud workflows.

Pros: Broad export support and strong automation options.

Cons: Users must understand Actor configuration, platform usage, and the differences between Facebook Actors.

Use case: Schedule recurring competitor Page monitoring and send results into an internal analytics pipeline.

3. Bright Data

Bright Data provides a dedicated Facebook Posts Scraper through both API and no-code workflows. Its current product supports bulk requests of up to 5,000 URLs and returns public post information such as comments, likes, shares, images, and other structured fields.

Best for: Enterprises and data teams with high-volume collection requirements.

Pros: Dedicated infrastructure, API and no-code options, and broader Facebook scraper coverage.

Cons: The platform may provide more infrastructure and configuration than smaller research projects require.

Use case: Build a recurring Facebook post-data pipeline across a large collection of URLs.

4. Data365

Data365 approaches Facebook collection as a social-media API. Its Facebook product supports publicly available profiles, posts, engagement metrics, reactions, shares, comments, trends, and related data for analytics applications and reporting workflows.

Best for: Development teams building social listening or analytics products.

Pros: API-first design and broader social-analysis positioning.

Cons: Less suitable for users who simply want to paste URLs and download a spreadsheet.

Use case: Feed Facebook post and engagement information into a custom monitoring dashboard.

5. ScrapeCreators

ScrapeCreators provides one API covering multiple social networks, including Facebook. Its platform supports profiles, posts, videos, comments, ads, and metrics and currently lists 11 Facebook endpoints. Responses are delivered as structured API data rather than through a traditional visual scraper.

Best for: Developers building products that need Facebook plus other social platforms.

Pros: Unified API model and straightforward developer integration.

Cons: Business users without API experience may prefer a ready-made no-code Worker.

Use case: Add Facebook post data to an application that already consumes TikTok, Instagram, YouTube, or other social data.

Which Facebook Post Scraper Fits Your Workflow?

CoreClaw is the practical choice when the goal is to move quickly from Facebook URLs to cleaned, structured post records without building infrastructure. Apify provides more configurable Actor automation, while Bright Data is oriented toward larger data operations. Data365 and ScrapeCreators make more sense when Facebook data needs to become part of a developer-managed social analytics product.

The decision should therefore start with the output: a spreadsheet for research, or an API feeding another system.

How to Collect Facebook Post Data With CoreClaw

1. Open the Facebook Posts Scraper and add the supported Page, profile, or post inputs required by the Worker.

2. Run a small sample first and inspect post text, URLs, timestamps, engagement, media, and source fields.

3. Remove irrelevant columns, duplicates, and records outside the required campaign or research period.

4. Export the dataset for analysis, or use the CoreClaw API for recurring workflows. CoreClaw’s broader platform also supports spreadsheet-oriented export formats such as XLS/XLSX.

5. If deeper audience analysis is needed, combine post data with the Facebook Comments Scraper. Developers with specialized requirements can also build and publish Workers.

Data Quality and Responsible Facebook Data Collection

A large dataset is not automatically a useful dataset. Teams should sample important results against their source posts, check timestamps and engagement fields, remove duplicates, and verify data before making significant business decisions.

Teams also need to consider permissions and platform rules. Meta defines scraping as automated data collection and states that automated collection from its products without permission can violate its terms. Collection practices should therefore be reviewed against current Meta requirements, applicable privacy rules, and the intended use of the data.

Conclusion

The best Facebook posts scraper depends on what happens after the data is collected.

With CoreClaw, teams can use ready-made Facebook Workers to collect structured public post and engagement data without building their own scraping infrastructure. Results can be cleaned and filtered before analysis, exported for business workflows, or connected through an API. When a standard Worker is not enough, CoreClaw also provides a developer path for building and publishing specialized Workers.