salabim
salabim - discrete event simulation in Python
Decision gist · record as of 2026-08-14
Yes. Salabim is a mature, actively maintained library with zero known vulnerabilities, low install friction, and permissive licensing. It is well-suited for anyone building discrete event simulations in Python, particularly those who prefer intuitive process description over yield-based coroutines. The built-in animation and data collection features add significant value for modeling and analysis workflows.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.7 or later; animation features may require additional system libraries depending on your platform.
- Low install friction with no runtime dependencies; wheel-only distribution.
- Package is actively maintained with recent releases and a stable production status.
License · maintenance · safety
permissive license (permissive) — MIT license is permissive, allowing commercial and private use with minimal restrictions; you may use, modify, and distribute salabim freely provided you include the license notice.
last release 2026-06-24 (51 days) · last repo commit 2026-05-29 · 403 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 101,819 downloads/mo, #12,913 on PyPI
Alternatives
Verify before relying
pip install salabim
import salabim as sim
env = sim.Environment()
env.run()- Whether animation features work out-of-the-box on all platforms or require separate graphics library installation.
- Performance characteristics and scalability limits for large-scale simulations.
- Whether the package is suitable for real-time simulation or primarily for offline modeling.
What it is and what it does
Salabim is a discrete event simulation framework designed for modeling systems found in logistics, manufacturing, healthcare, and network analysis. It provides an object-oriented API with components, queues, resources, stores, and state tracking, allowing you to describe processes intuitively without yield statements. The library includes built-in data collection via monitors, 2D and 3D animation capabilities, tracing for debugging, and statistical sampling tools.
The package is cross-platform (Windows, macOS, Linux, iOS/iPadOS) and has minimal dependencies when animation is disabled. It follows a well-established process description method and is actively maintained with comprehensive documentation. The core developer provides direct support and consulting services.
Use it for
- Model production facility workflows, material handling, and resource allocation to optimize throughput and identify bottlenecks.
- Simulate airport operations, hospital patient flows, or warehouse logistics to evaluate system designs before implementation.
- Analyze computer network behavior and packet routing under various load conditions.
- Create animated visualizations of simulations for stakeholder presentations or educational demonstrations.
- Collect and analyze statistical data from repeated simulation runs to support decision-making.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Salabim is a mature, actively maintained library with zero known vulnerabilities, low install friction, and permissive licensing. It is well-suited for anyone building discrete event simulations in Python, particularly those who prefer intuitive process description over yield-based coroutines. The built-in animation and data collection features add significant value for modeling and analysis workflows.
Install
salabim on PyPI
Before you install
Low install friction with no runtime dependencies; wheel-only distribution. Package is actively maintained with recent releases and a stable production status.
Requires Python 3.7 or later; animation features may require additional system libraries depending on your platform.
License in practice
MIT license is permissive, allowing commercial and private use with minimal restrictions; you may use, modify, and distribute salabim freely provided you include the license notice.
Quickstart
pip install salabim
import salabim as sim
env = sim.Environment()
env.run()
Verify before relying
- Whether animation features work out-of-the-box on all platforms or require separate graphics library installation.
- Performance characteristics and scalability limits for large-scale simulations.
- Whether the package is suitable for real-time simulation or primarily for offline modeling.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 51 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 101,819 / month, #12,913 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 :: MIT LicenseProgramming Language :: Python :: 3 :: Only |
Evidence: salabim-26.0.8-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “discrete event simulation python”
- salabimSalabim is a Python library for discrete event simulation (DES) that…
- simpySimPy is a process-based discrete-event simulation framework that…
- datadog-loggerSends Python log messages to Datadog as Events in the Events Explorer…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also simpy · manim · python-motion-planning · NREL-PySAM · pybamm · dwave-optimization · rel · cirq-pasqal · ansys-dpf-core · pymunk