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Unity-Technologies/ml-agents

Decliningrank #74

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.

19.8k+27 · 7d 4.5k 155est. NOASSERTIONC#pushed 7m ago
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Composite

66

conf 65%

Momentum

39

conf 60%

Vitality (PVI)

80

conf 67%

Community

74

conf 60%

Security & maint.

78

conf 85%

Ecosystem influence

76

conf 60%

Opportunity

57

conf 60%

Breakout · 7d

2%

conf 71%

Executive summary

ml-agents is currently classified as declining with a composite score of 66/100. Momentum (39) and vitality (80) are aligned — growth is backed by sustained maintenance. Estimated 7-day gain of 27 stars against a 19.8k-star base. Risk is assessed as low; the interpretable model puts its probability of entering the top trend cohort within 7 days at 2%.

90-day trajectory

+211 · 30d
2026-08-012026-10-29

Score breakdown

Every input, weight and normalized value — nothing hidden. Inputs marked est. are estimated until the full ingestion pipeline lands.

Momentum

Star velocityest.25%40
Star accelerationest.15%27
Fork velocityest.10%22
Contributor growthest.10%77
Issue activity growthest.10%33
PR activity growthest.10%61
External mention velocityest.10%1
Release event impactest.5%64
Trending-rank persistenceest.5%45

Model tn-interpretable-1.0.0 · confidence 60% · coverage 100%

Project Vitality Index

Commit consistency20%100
Release cadenceest.15%91
PR responsivenessest.15%75
Issue responsivenessest.15%77
Contributor diversityest.10%81
Maintainer continuityest.10%68
Documentation qualityest.5%55
Dependency freshnessest.5%62
Security hygieneest.5%49

Model tn-interpretable-1.0.0 · confidence 67% · coverage 92% · missing: licenseClarity

Security & maintenance

Release recency25%100
License clarity20%30
Maintenance status25%100
Dependency stalenessest.15%50
Bus factorest.15%98

Model tn-interpretable-1.0.0 · confidence 85% · coverage 100%

Ecosystem influence

Dependent repositoriesest.30%89
Quality-weighted dependentsest.20%68
Documentation citationsest.15%54
Contributor overlapest.15%74
Registry adoptionest.10%60
Topic centralityest.10%98

Model tn-interpretable-1.0.0 · confidence 60% · coverage 92% · missing: packageRegistryAdoption