libact
libact: A free, self-hosted Python package with varied query strategies and scikit-learn adapters. Ranked #10 of 16 in Active Learning Tools by our editors (6.3/10); pricing: Free plan; best for scikit-learn users needing varied query strategies.
At a glance
- Editor score6.3 / 10
- PricingFree plan
- Best forScikit-learn users needing varied query strategies
- Free planYes
- Paid fromNone
- Human annotation workflowExternal
- Facts checked24 Sep 2026
Where it wins
- Offers uncertainty, diversity, BALD, and committee-based query strategies
- Includes ActiveLearningByLearning to select among strategies
- Supports custom strategies, models, and labelers alongside scikit-learn adapters
Where it doesn't
- Human annotation happens outside the package through a labeler or workflow
- Self-hosted use requires a Python environment and workflow management
- Its focus is active-learning workflows, not a hosted annotation service
Our verdict on libact
libact is an open-source Python package for pool-based active learning, aimed at Python users who want to choose unlabeled samples for human labeling within their own workflows. It provides a unified interface for datasets, models, and labelers, and can wrap scikit-learn estimators. The package runs on Linux, macOS, and Windows and is self-hosted, so it suits teams that want to build active-learning workflows in code rather than use a hosted annotation service.
The strategy range is a central strength. Options include uncertainty sampling and its epsilon-greedy variant, CoreSet for diversity-based selection, BALD and information-density sampling, query by committee, QUIRE, and other approaches such as RandomSampling and VarianceReduction. ActiveLearningByLearning is a meta-algorithm for selecting among strategies. Interfaces for custom strategies, models, and labelers give users room to adapt the workflow, while scikit-learn adapters connect it to an existing estimator-based stack. This breadth is useful when a project calls for comparing query approaches or combining them with custom components; users who need only a hosted labeling interface may find the package is pointed at the wrong part of the process.
libact is free and open source, with documentation and community support channels. Because it is self-hosted, users manage their own environment and the surrounding workflow. Annotation is external: a user-defined labeler or separate workflow handles human labeling, rather than a built-in hosted annotation service. That division is important when evaluating fit. Choose libact if you can work in Python and want control over query strategy, model, and labeler components, especially around scikit-learn. Consider another option if you need annotation and active-learning orchestration bundled in a managed service.
libact pricing
libact fact sheet
| Free plan | Yes |
|---|---|
| Paid from | None |
| Query strategies | UncertaintySampling; EpsilonUncertaintySampling; CoreSet; BALD; InformationDensity; QueryByCommittee; QUIRE; RandomSampling; ActiveLearningByLearning; VarianceReduction; HintSVM; DensityWeightedMeta; DWUS |
| Human annotation workflow | External |
| Supported data types | Not verified |
| Model frameworks | scikit-learn |
| Deployment options | Self_hosted |
| Deployment | Self-hosted |
| Platforms | Linux, macOS, Windows |
| Support | Community, Docs |
| Built for | Solo, Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 1 integrations: scikit-learn |
| Pricing | Free plan |
| Website | github.com |
| Facts checked | 24 Sep 2026 |
libact integrations
libact lists 1 integrations on its own site.
- scikit-learn
Alternatives to libact
- ActiveTiggerA broad annotation and modeling workflow for teams working with text corpora.9.0
- PotatoA self-hosted annotation toolkit for teams combining active learning with varied media.8.2
- ProdigyA paid, developer-oriented annotator spanning NLP, vision, audio, and video.7.7
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Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026
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