spartnernl/laravel-excel

🚀 Supercharged Excel exports and imports in Laravel

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Summary Information

Updated 51 seconds ago
Added to GitGenius on September 4th, 2026
Created on November 9th, 2013
Open Issues & Pull Requests: 1 (+0)
GitHub issues: Enabled
Number of forks: 1,982
Total Stargazers: 12,702 (+0)
Total Subscribers: 229 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 5.1 hours
Mean response time: 23.3 days
90th percentile: 51.8 days
Tracked items: 115

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How this project is maintained

Around half of the issues opened in the past year never receive a reply. Only 4% of issues opened in the past year have been closed. Three people close 69% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 0
New in 7 days: 0
Closed in 7 days: 0
Avg open age: N/A days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • bug (100)
  • stale (48)
  • question (7)
  • more information needed (5)
  • help wanted (3)
  • proposal (2)
  • enhancement (1)

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Detailed Description

Laravel Excel is a Laravel package that provides Excel and CSV export and import functionality.

The package solves the problem of handling spreadsheet operations in Laravel applications by wrapping PhpSpreadsheet with a Laravel-friendly interface. For exports, it allows developers to export collections and query results directly to Excel or CSV files, with built-in support for chunking large datasets and queueing exports to run in the background. For imports, it enables reading workbooks and worksheets into Eloquent models using chunk reading and batch inserts, with the ability to queue import chunks for background processing. The tool also supports exporting custom HTML table layouts from Blade views directly to Excel.

Developers should choose this package if they need straightforward spreadsheet handling in Laravel applications without wrestling with low-level PhpSpreadsheet APIs. It suits projects ranging from simple exports of small datasets to complex operations involving large files that benefit from background processing. The package is particularly valuable when performance matters, as its automatic chunking and queueing capabilities handle memory-intensive operations gracefully.

The project maintains active test coverage through continuous integration workflows and enforces code style standards. Documentation is comprehensive and actively maintained in a separate repository, with additional learning resources available through a video course and blog articles. The project welcomes community contributions through a documented contributing process and provides dedicated support channels.