Cleanlab is the standard open-source library for data-centric AI: it automatically finds the errors hiding in your datasets — mislabeled examples, outliers, duplicates and near-duplicates — so models trained on messy, real-world data perform the way they should. It has earned about 11,700 stars on GitHub.
Most model failures are data failures in disguise, and real datasets are full of wrong labels that accuracy metrics never reveal. Cleanlab is built on confident learning, a theoretically grounded approach from the team’s research: give it your dataset and the predicted probabilities from any classifier you already trained, and it estimates which examples are likely mislabeled, ranks them for review, and can prune or correct them — often improving model accuracy more than switching algorithms. The library covers classification, multi-label, token classification and other task types, along with tools for detecting outliers, out-of-distribution examples and near-duplicates, estimating dataset health and improving annotation workflows. It works with scikit-learn, PyTorch, TensorFlow or any framework that can produce predictions, and it fits the workflow data teams actually have: labels from multiple annotators, weak supervision sources and years of accumulated noise. The repository stays actively maintained in 2026 as data quality has become a first-class concern in the LLM era.
The Cleanlab library is free and open source under the Apache-2.0 license. The company also sells Cleanlab Studio, a no-code platform that applies the same technology to improving data and fine-tuning models, so the overall offering is freemium.
Cleanlab is for data scientists and ML teams whose models underperform for unknown reasons, annotation teams managing labeling quality at scale, and anyone who suspects — correctly, most of the time — that their data, not their model, is the problem.