treelite
Treelite: Universal model exchange format for decision tree forests
Decision gist · record as of 2026-08-14
Yes, if you need to serialize tree models for C++ integration or cross-platform exchange. The package is actively maintained, has no known vulnerabilities, and carries a permissive license. Install friction is moderate due to compiled wheels, but pre-built binaries exist for common platforms. Not necessary if your workflow stays within a single Python framework.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later; compiled wheels available for common platforms but may require compilation on unsupported architectures.
- Medium install friction due to compiled wheels for multiple platforms (macOS, Linux, Windows).
- Active maintenance with a recent release (161 days ago) and steady repository activity.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for most projects.
last release 2026-03-06 (161 days) · last repo commit 2026-08-12 · 828 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 674,951 downloads/mo, #5,386 on PyPI
Alternatives
Verify before relying
pip install treelite
import treelite
# Load or create a tree model and serialize it
model = treelite.Model.load('model.txt')
model.export_lib('output')- Whether the package provides Python APIs for programmatic tree construction or only serialization/loading of externally-trained models.
- Performance characteristics and typical use patterns for large-scale model interchange.
- Compatibility with specific tree training frameworks (XGBoost, LightGBM, etc.) beyond generic forest formats.
What it is and what it does
Treelite is a model exchange and serialization library designed to store and transmit decision tree forests in a standardized format. It acts as a bridge for C++ applications and other tools to work with trained tree models without reimplementing the forest logic. The package depends on numpy, scipy, and packaging, and supports Python 3.8 through 3.10 across macOS, Linux, and Windows.
The library is intended for scenarios where you need to persist tree models to disk, share them across systems, or integrate them into C++ pipelines. It provides a universal format that decouples model storage from the training framework, making it useful in production environments where model portability and interoperability matter.
Use it for
- Serialize trained tree ensemble models from Python for deployment in C++ production systems.
- Exchange decision tree forests between different machine learning frameworks or applications.
- Store trained tree models in a portable format for long-term archival or version control.
- Integrate pre-trained tree models into C++ services without re-training or format conversion.
- Enable cross-platform model sharing in teams using heterogeneous development environments.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to serialize tree models for C++ integration or cross-platform exchange.
The package is actively maintained, has no known vulnerabilities, and carries a permissive license. Install friction is moderate due to compiled wheels, but pre-built binaries exist for common platforms. Not necessary if your workflow stays within a single Python framework.
Install
treelite on PyPI
Before you install
Medium install friction due to compiled wheels for multiple platforms (macOS, Linux, Windows). Active maintenance with a recent release (161 days ago) and steady repository activity.
Requires Python 3.8 or later; compiled wheels available for common platforms but may require compilation on unsupported architectures.
License in practice
Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for most projects.
Quickstart
pip install treelite
import treelite
# Load or create a tree model and serialize it
model = treelite.Model.load('model.txt')
model.export_lib('output')Verify before relying
- Whether the package provides Python APIs for programmatic tree construction or only serialization/loading of externally-trained models.
- Performance characteristics and typical use patterns for large-scale model interchange.
- Compatibility with specific tree training frameworks (XGBoost, LightGBM, etc.) beyond generic forest formats.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagesnumpypackagingscipy |
| Maintenance | Actively maintained 161 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 674,951 / month, #5,386 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: treelite-4.7.0-py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.macosx_12_0_x86_64.whl; treelite-4.7.0-py3-none-macosx_12_0_arm64.whl; treelite-4.7.0-py3-none-manylinux2014_aarch64.whl; treelite-4.7.0-py3-none-manylinux2014_x86_64.whl; treelite-4.7.0-py3-none-win_amd64.whl
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See also interchange · treelite-runtime · ydf · tensorflow-decision-forests · dtreeviz · treeinterpreter · py_trees · coremltools · sklearn2pmml · skope-rules