pyAgrum-nightly
Bayesian networks and other Probabilistic Graphical Models.
Decision gist · record as of 2026-08-14
Yes, if you need Bayesian Networks or probabilistic graphical models in Python. Active maintenance, no known vulnerabilities, dual licensing (LGPL/MIT) gives flexibility, and broad platform support. Caveat: this is a nightly build (released today), so verify stability for your use case; production users may prefer the stable release track.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- This is a nightly build; consider testing before production deployment.
- Medium install friction due to compiled wheels; nightly build released today (0 days since release).
License · maintenance · safety
LGPL-3.0-only OR MIT (unclear) — Dual-licensed under LGPLv3 and MIT at your choice. License treatment marked unclear in metadata, but description states you can integrate into your software under either license or both. No GPL contamination if you use only the pyAgrum interface, not native third-party interfaces.
last release 2026-08-14 (0 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 310,025 downloads/mo, #7,749 on PyPI
Alternatives
Verify before relying
pip install pyagrum-nightly
import pyagrum as gum
bn = gum.fastBN("w<-r<-c{Yes|No}->s->w")
bn.cpt("c").fillWith([0.5, 0.5])
ie = gum.LazyPropagation(bn)
ie.makeInference()
print(ie.posterior("w"))- Whether the nightly build is suitable for production use or intended for testing only
- Specific performance characteristics or scalability limits for large graphical models
- Whether all four runtime dependencies (numpy, matplotlib, pydot, scikit-learn) are required or optional
What it is and what it does
pyAgrum wraps a C++ probabilistic graphical model library and exposes it through a Python API for Bayesian Networks and related models. It lets you construct networks by adding nodes and arcs, populate conditional probability tables, save and load models in standard formats (BIF), and run inference algorithms to compute posterior probabilities given evidence.
The library is designed for researchers and practitioners in AI, statistics, and decision analysis who need to model uncertainty and reason under incomplete information. It depends on numpy for numerical operations, matplotlib for visualization, pydot for graph layout, and scikit-learn for machine learning integration. The package supports modern Python versions (3.10–3.14) and runs on macOS, Linux, and Windows.
Use it for
- Build and query Bayesian networks to model causal relationships and perform probabilistic inference with hard or soft evidence
- Learn network structure and parameters from data using the library's training capabilities
- Diagnose faults or predict outcomes in systems where uncertainty and conditional dependencies matter
- Visualize probabilistic graphical models and export them to standard formats for sharing or downstream analysis
- Integrate probabilistic reasoning into financial, insurance, or risk-assessment applications
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need Bayesian Networks or probabilistic graphical models in Python.
Active maintenance, no known vulnerabilities, dual licensing (LGPL/MIT) gives flexibility, and broad platform support. Caveat: this is a nightly build (released today), so verify stability for your use case; production users may prefer the stable release track.
Install
pyagrum-nightly on PyPI
Before you install
Medium install friction due to compiled wheels; nightly build released today (0 days since release). Active maintenance status. Requires Python 3.10 or later. Pre-built wheels available for macOS (x86_64, arm64), Linux (x86_64, aarch64), and Windows.
Requires Python 3.10 or later. This is a nightly build; consider testing before production deployment.
License in practice
Dual-licensed under LGPLv3 and MIT at your choice. License treatment marked unclear in metadata, but description states you can integrate into your software under either license or both. No GPL contamination if you use only the pyAgrum interface, not native third-party interfaces.
Quickstart
pip install pyagrum-nightly
import pyagrum as gum
bn = gum.fastBN("w<-r<-c{Yes|No}->s->w")
bn.cpt("c").fillWith([0.5, 0.5])
ie = gum.LazyPropagation(bn)
ie.makeInference()
print(ie.posterior("w"))Verify before relying
- Whether the nightly build is suitable for production use or intended for testing only
- Specific performance characteristics or scalability limits for large graphical models
- Whether all four runtime dependencies (numpy, matplotlib, pydot, scikit-learn) are required or optional
Package facts
| License | LGPL-3.0-only OR MIT unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 4 packagesnumpymatplotlibpydotscikit-learn |
| Maintenance | Actively maintained 0 days since the last release |
| First released | |
| Downloads | 310,025 / month, #7,749 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/StableIntended Audience :: DevelopersIntended Audience :: Financial and Insurance IndustryIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)License :: OSI Approved :: MIT LicenseOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: C++Programming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Information AnalysisTopic :: Scientific/Engineering :: MathematicsTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: pyagrum_nightly-3.0.0.9.dev202608141786444173-cp310-abi3-macosx_10_15_x86_64.whl; pyagrum_nightly-3.0.0.9.dev202608141786444173-cp310-abi3-macosx_11_0_arm64.whl; pyagrum_nightly-3.0.0.9.dev202608141786444173-cp310-abi3-manylinux2014_aarch64.whl; pyagrum_nightly-3.0.0.9.dev202608141786444173-cp310-abi3-manylinux2014_x86_64.whl; pyagrum_nightly-3.0.0.9.dev202608141786444173-cp310-abi3-win_amd64.whl
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