simpy
Event discrete, process based simulation for Python.
Decision gist · record as of 2026-08-14
Yes. SimPy is a mature, actively maintained framework with zero dependencies, permissive licensing, and broad Python version support. It solves a specific, well-defined problem—discrete-event simulation—and does so with a clean, Pythonic API. Install it if you need to model systems with interacting processes and shared resources; skip it if your problem is continuous simulation or fixed-step modeling.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.8; not designed for continuous simulations or fixed-step simulations where processes don't interact.
- Low friction: pure Python wheel with no runtime dependencies, requires Python >= 3.8, and actively maintained with a recent release.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute SimPy with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2026-05-24 (82 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 527,054 downloads/mo, #6,171 on PyPI
Alternatives
Verify before relying
pip install simpy
import simpy
def clock(env, name, tick):
while True:
print(name, env.now)
yield env.timeout(tick)
env = simpy.Environment()
env.process(clock(env, 'fast', 0.5))
env.run(until=2)- Performance characteristics and scalability limits for large-scale simulations.
- Whether the package supports parallel or distributed simulation execution.
- Typical use-case performance benchmarks compared to other discrete-event simulators.
What it is and what it does
SimPy is a discrete-event simulation framework built on standard Python that lets you model systems as processes defined by generator functions. You define active components (customers, vehicles, agents) and shared resources (servers, counters, tunnels) and then run the simulation to observe how the system behaves over time. The framework handles the event scheduling and process coordination for you.
It's designed for scenarios where you want to understand system behavior through simulation—how queues form, how resources get congested, how timing affects outcomes. You can run simulations as fast as the computer allows, synchronized to wall-clock time, or step through events manually for debugging. The framework is not suited for continuous simulations or fixed-step models where processes don't interact.
Use it for
- Model customer flows through a bank or retail checkout to understand queue lengths and service times.
- Simulate vehicle routing and congestion in a transportation network to optimize dispatch.
- Test agent-based models where multiple autonomous entities interact with shared resources.
- Prototype manufacturing or production systems to find bottlenecks before building.
- Evaluate IT infrastructure (server load, request queuing) under different traffic patterns.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
SimPy is a mature, actively maintained framework with zero dependencies, permissive licensing, and broad Python version support. It solves a specific, well-defined problem—discrete-event simulation—and does so with a clean, Pythonic API. Install it if you need to model systems with interacting processes and shared resources; skip it if your problem is continuous simulation or fixed-step modeling.
Install
simpy on PyPI
Before you install
Low friction: pure Python wheel with no runtime dependencies, requires Python >= 3.8, and actively maintained with a recent release.
Requires Python >= 3.8; not designed for continuous simulations or fixed-step simulations where processes don't interact.
License in practice
MIT license is permissive; you can use, modify, and distribute SimPy with minimal restrictions, making it suitable for both open-source and commercial projects.
Quickstart
pip install simpy
import simpy
def clock(env, name, tick):
while True:
print(name, env.now)
yield env.timeout(tick)
env = simpy.Environment()
env.process(clock(env, 'fast', 0.5))
env.run(until=2)Verify before relying
- Performance characteristics and scalability limits for large-scale simulations.
- Whether the package supports parallel or distributed simulation execution.
- Typical use-case performance benchmarks compared to other discrete-event simulators.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 82 days since the last release |
| First released | |
| Downloads | 527,054 / month, #6,171 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/StableEnvironment :: ConsoleIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Scientific/Engineering |
Evidence: simpy-4.1.2-py3-none-any.whl
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