# online-ml/river

🌊 Online machine learning in Python

Repository: https://github.com/online-ml/river
Canonical: https://ross.abutalabs.com/products/river
Homepage: https://riverml.xyz
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: incremental-learning, machine-learning, python, online-learning, online-statistics, data-science, streaming, online-machine-learning, streaming-data, concept-drift, real-time-processing, stream-processing
Last push: 2026-08-21T19:05:47+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 86, longevity 100
- inputs: {"age_days": 2778, "days_push": 12, "days_rel": 12, "gap_med": 46, "n_releases_24m": 8}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5924, forks 659 (observed 2026-08-28T04:09:32.369823+00:00)

## What it is
River is a Python library for online machine learning, allowing models to learn incrementally from streaming data one observation at a time. It is the merger of the creme and scikit-multiflow projects and provides a scikit-learn-like API for streaming pipelines, metrics, and preprocessing.

## Use cases
- train machine learning models on streaming data in python
- incrementally update models without retraining from scratch
- handle concept drift in real-time predictions
- build online learning pipelines like sklearn but for streams
- compute streaming statistics and metrics on the fly
- classify or regress data one sample at a time

## When to choose
- your data arrives as a stream and doesn't fit in memory
- you need models that update continuously in real time
- you want a scikit-learn-like API for incremental learning
- you need to detect and adapt to concept drift

## When to avoid
- you need batch learning on static datasets with GPU acceleration
- you rely on the full scikit-learn ecosystem of estimators
- your project requires deep learning frameworks like PyTorch

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, streaming, data-science
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: online-learning, incremental-learning, concept-drift, stream-processing, scikit-learn-alternative, real-time

## Member repositories
- online-ml/river (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:32.369823+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:50:49.957471+00:00, confidence not recorded.
  - readme: https://github.com/online-ml/river (fetched 2026-08-28T04:09:32.369823+00:00, sha a6e38b1f0187)
  - homepage: https://riverml.xyz (fetched 2026-08-29T08:46:32.010902+00:00, sha 37b0d4ae981c)
- Data as of 2026-08-30T08:39:29.467469+00:00.