adaptive-machine-learning/CapyMOA
CapyMOA does efficient machine learning for data streams in Python. CapyMOA is a toolbox of methods and evaluators for: classification, regression, clustering, anomaly detection, semi-supervised learning, online continual learning, and drift detection for data streams.
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+10
STARS · LAST 30 DAYS
1
PER DAY
#1,871
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +7 | +10 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +1 | +3 | +10 |
CapyMOA gained 10 stars in the last 30 days, about 1 a day, and now has 151. It is about 3 years old and has averaged roughly 50 stars a year. It ranks #1,871 among Python repositories and #14,642 across all languages on GitHubRepo.
Trending Record
4
DAYS ON TRENDING
#13207
BEST RANK
Oct 14, 2026
FIRST APPEARANCE
Active
STATUS TODAY
CapyMOA has maintained a continuous presence across global trending indexes, peaking at #13207. Below is the 30-day activity profile:
💡 Overview
CapyMOA is an open-source project written in Python: CapyMOA does efficient machine learning for data streams in Python. CapyMOA is a toolbox of methods and evaluators for: classification, regression, clustering, anomaly detection, semi-supervised learning, online continual learning, and drift detection for data streams.
Engineered for speed, consistency, and developer ease, it solves common hurdles in anomaly-detection, concept-drift, continual-learning. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install CapyMOA⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
CapyMOA coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Production System Integration
Embed CapyMOA into Python backend services to handle core application logic.
CI/CD Automated Pipelines
Run automated validation, builds, and integration suites during deployments.
Developer Tooling & Workflows
Accelerate developer onboarding with pre-configured project utilities.
Open Source Extension
Fork and customize internal modules under the repository's open BSD-3-Clause license.
🚀 Getting Started
Install CapyMOA using your package manager: `pip install CapyMOA`
Initialize your project workspace or configuration file for CapyMOA.
Import CapyMOA into your codebase or invoke it directly from your terminal.
Execute your test suite or run `CapyMOA --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
adaptive-machine-learning/CapyMOA is currently ranked #14,642 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #14,596 | sorsama/deepseek-harness-mobile | Kotlin | ★ 152 | Compare ↗ |
| #14,596 | newdee/magpie | Rust | ★ 152 | Compare ↗ |
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| #14,596 | Sheltercosmo/jev4pg | Python | ★ 152 | Compare ↗ |
| #14,642 | adaptive-machine-learning/CapyMOA This Project | Python | ★ 151 | |
| #14,642 | drogus/apache-upload-progress-module | C | ★ 151 | Compare ↗ |
| #14,642 | jaz303/boxy | JavaScript | ★ 151 | Compare ↗ |
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Frequently Asked Questions
What does CapyMOA do? +
CapyMOA does efficient machine learning for data streams in Python. CapyMOA is a toolbox of methods and evaluators for: classification, regression, clustering, anomaly detection, semi-supervised learning, online continual learning, and drift detection for data streams.
What language is CapyMOA written in? +
The primary language is Python. Topics include: anomaly-detection, concept-drift, continual-learning, data-science, data-shift.
Is CapyMOA actively maintained? +
Yes, the last recorded push was on Oct 14, 2026 with 14 open issues being tracked.
How many stars does CapyMOA have? +
CapyMOA has 151 stars and 49 forks on GitHub.
How does CapyMOA rank among GitHub repositories? +
With 151 stars, adaptive-machine-learning/CapyMOA is ranked #14,642 globally across all repositories tracked on GitHubRepo and #1,871 among Python projects.
What license is CapyMOA distributed under? +
The repository reports a BSD-3-Clause license. Always verify the repository LICENSE file for legal terms.
