problog
ProbLog2: Probabilistic Logic Programming toolbox
Decision gist · record as of 2026-08-14
Yes. ProbLog is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively. It fills a specific niche in probabilistic logic programming with a mature codebase (first released in 2015) and clear documentation. Install it if you need to combine logic programming with probabilistic reasoning; skip it if you're looking for general-purpose Bayesian inference or constraint solving.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction; ships as a pure Python wheel with only setuptools as a runtime dependency.
- Actively maintained with a release 149 days ago.
License · maintenance · safety
Apache Software License (permissive) — Licensed under Apache License 2.0 (permissive), allowing commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
last release 2026-03-18 (149 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 302,512 downloads/mo, #7,821 on PyPI
Alternatives
Verify before relying
pip install problog
from problog.program import PrologString
from problog.engine import DefaultEngine
program = PrologString("""
0.5::heads.
query(heads).
""")
result = DefaultEngine().prepare(program).evaluate()- Whether the package supports Java embedding as claimed in the description excerpt
- Whether CSV and SQLite integration work out-of-the-box or require optional dependencies
- Performance characteristics for large-scale probabilistic programs
What it is and what it does
ProbLog is a Python package for probabilistic logic programming that lets you write logic programs where facts can be annotated with probabilities. It solves inference problems by converting programs and queries into weighted Boolean formulas, then using algorithms from graphical models and knowledge compilation to compute answers. The package handles tasks like computing marginal probabilities given evidence and learning from partial interpretations.
You can represent knowledge bases as Prolog/Datalog facts, CSV files, SQLite tables, or Python functions. It's designed for situations where you need to encode both complex interactions between components and the inherent uncertainties in real-world data. The package is actively maintained, supports Python 3.8 through 3.12, and installs cleanly with minimal dependencies.
Use it for
- Build probabilistic reasoning systems that combine logical rules with uncertain facts to compute outcome probabilities
- Learn probabilistic logic programs from partial interpretations or labeled examples in AI/ML workflows
- Model real-world systems with both deterministic rules and probabilistic components (e.g., sensor fusion, diagnosis)
- Perform inference over knowledge bases stored in Prolog, CSV, or SQLite with probability annotations
- Reduce probabilistic inference to weighted model counting for integration with knowledge compilation solvers
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
ProbLog is actively maintained, has no known vulnerabilities, installs with low friction, and is licensed permissively. It fills a specific niche in probabilistic logic programming with a mature codebase (first released in 2015) and clear documentation. Install it if you need to combine logic programming with probabilistic reasoning; skip it if you're looking for general-purpose Bayesian inference or constraint solving.
Install
problog on PyPI
Before you install
Low install friction; ships as a pure Python wheel with only setuptools as a runtime dependency. Actively maintained with a release 149 days ago.
License in practice
Licensed under Apache License 2.0 (permissive), allowing commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
Quickstart
pip install problog
from problog.program import PrologString
from problog.engine import DefaultEngine
program = PrologString("""
0.5::heads.
query(heads).
""")
result = DefaultEngine().prepare(program).evaluate()Verify before relying
- Whether the package supports Java embedding as claimed in the description excerpt
- Whether CSV and SQLite integration work out-of-the-box or require optional dependencies
- Performance characteristics for large-scale probabilistic programs
Package facts
| License | Apache Software License permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagesetuptools |
| Maintenance | Actively maintained 149 days since the last release |
| First released | |
| Downloads | 302,512 / month, #7,821 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: PrologProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: problog-2.2.10-py3-none-any.whl
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