PyRank
ADA-research

sparkle

Sparkle is a Programming by Optimisation (PbO)-based problem-solving platform designed to enable the widespread and effective use of PbO techniques for improving the state-of-the-art in solving a broad range of prominent AI problems, including SAT and AI Planning.
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Download Stats Changelog Documentation Issues
Verified repo
Monthly downloads
572
Weekly downloads
139
GitHub stars
8

Downloads in the 30 and 7 days to October 10, 2026. Refreshed weekly.

Package Information

Latest Version
0.9.7
Requires Python
>=3.10
Licence
MIT
Last Release
1 month ago
Author
Thijs Snelleman, Jeroen Rook, Koen van der Blom, Jeremie Gobeil, Chuan Luo, Holger H. Hoos
First Release
May 9, 2015
Total Releases
6
Python Versions
3.10 3.11 3.12 3.13
No reported malware or high/critical advisories · Details

The current version has no OSV.dev malware reports and no critical or high-severity vulnerabilities.

Package Insights

What's this?
Score breakdown - each component scores 0-100, weighted by percentage
Maintenance
60
Popularity
18
Adoption
27
Community
50
Provenance
100
How the maintenance status was determined
Last commit
38 days ago
Monthly downloads
572
Active
Activity within 30 days
• Stable
Activity within 31-180 days
Minimal
Activity within 181-365 days, or no activity data
At Risk
No activity for > 365 days, but > 10,000 monthly downloads
Archived
Repository flagged as archived on GitHub
How the popularity category was determined
Monthly downloads
572
Stars
8
Stars per 1k downloads
13.99
Industry Standard
> 10,000,000 monthly downloads
Hidden Gem
< 0.5 stars per 1k downloads and > 100,000 monthly downloads
Community Favorite
> 5 stars per 1k downloads
Rising Star
Balanced ratio of stars to downloads
• Unknown
Fewer than 1,000 monthly downloads, or no star data
Needs at least 1,000 monthly downloads to classify popularity.
How download momentum is calculated
Week daily avg
20
Month daily avg
19
Momentum
—
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100
Momentum not reported for packages with < 5,000 monthly downloads — percentages are noisy at low volume.
More insights and metrics
Python version support tiers
requires_python
>=3.10
Cutting Edge
Minimum supported version is 3.14 or newer
Modern
Minimum supported version is 3.11 to 3.13
• Legacy
Supports 3.10 or older (or Python 2)
Unknown
No requires_python specified or unparseable
License categories
License
MIT
• Permissive
MIT, Apache, BSD, ISC, Unlicense, CC0, etc.
Copyleft
GPL, LGPL, AGPL, MPL, EPL, etc.
Proprietary
Commercial, all rights reserved, custom EULAs
Unknown
No license specified, or none of the above match
How the release cadence was determined
Last release
38 days ago
Total releases
6
Releases/year
0.5
Rapid
Last release within 365 days and > 12 releases/year
• Regular
Last release within 365 days
Slow
Last release 366-730 days ago
Stalled
Last release > 730 days ago, or no releases
Unknown
No usable release date available
How contributor risk was determined
Contributors
21
Monthly downloads
572
High Load
≤ 2 contributors and ≥ 50,000 monthly downloads
Moderate Load
< 10 contributors and ≥ 500,000 monthly downloads
Low Load
< 25 contributors and ≥ 5,000,000 monthly downloads, or sole maintainer with ≥ 10,000 monthly downloads
• Balanced
Enough contributors for the package's popularity
Unknown
No contributor data available
Engagement
23
 
Issue Burden
-
per 1k stars
Discussions
-
per 1k stars
Fork Ratio
1050.0%
contrib / forks
Stale Release
No
 
PR Velocity
Quiet
 
Not all downloads include detailed metadata. Breakdowns reflect the subset that does.

Downloads by OS

Linux 4

Python Versions

2.7
50.0% 2
3.7
50.0% 2

Top Countries

1. US US
25
2. DE DE
1

Weekly Downloads

GitHub Stars

Dependencies

14
14 optional dependencies

Used By

0
Not used by any tracked packages

Repository details

Forks
2
Issues
6
Last Commit
1 month ago
Contributors
21
Open PRs
0
Archived
No

Data and sources

Tracking Since
September 26, 2026
Data Updated
October 11, 2026
Data Freshness
Fresh
Keywords
algorithm configuration selection hyperparameter optimization empirical evaluation solver sat ai planning
Sources and methodology