gilbertchen/duplicacy

A new generation cloud backup tool

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Summary Information

Updated 23 minutes ago
Added to GitGenius on September 11th, 2026
Created on February 23rd, 2016
Open Issues & Pull Requests: 336 (+0)
GitHub issues: Enabled
Number of forks: 354
Total Stargazers: 5,675 (+0)
Total Subscribers: 92 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 9.4 days
Mean response time: 368.6 days
90th percentile: 1343.8 days
Tracked items: 19

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 0% of issues opened in the past year have been closed.

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Issue Activity (beta)

Open issues: 26
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 1,033 days
Stale 30+ days: 22
Stale 90+ days: 21

Recent activity

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

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Detailed Description

Duplicacy is a cloud backup tool written in Go that handles data deduplication and multi-cloud storage.

The tool addresses the challenge of efficient cloud backup by implementing content-defined chunking and deduplication, which reduces storage costs and bandwidth usage. It works by breaking files into variable-sized chunks based on content boundaries rather than fixed sizes, then storing only unique chunks across backups. This approach means that duplicate data across multiple backup versions or machines occupies storage space only once, while still allowing independent restoration of any backup.

Duplicacy suits users who need to back up to multiple cloud providers simultaneously or who want to minimize storage consumption across many machines or frequent backup cycles. The tool's multi-cloud capability means you can distribute backups across different providers for redundancy or cost optimization without being locked into a single vendor. It works well for both individual users managing personal data and organizations backing up multiple machines, since the deduplication engine operates efficiently whether handling one computer or many.

The project shows consistent maintenance with regular updates addressing bug fixes and feature improvements. Development activity demonstrates responsiveness to user-reported issues and steady refinement of core functionality. The codebase receives ongoing attention to performance optimization and reliability, with fixes applied to edge cases and error handling. The project maintains documentation and examples that help users understand configuration and operational procedures.