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SimPy, a free discrete-event simulation package based on Python
SimPy Logo.png
Original author(s)Klaus G. Müller, Tony Vignaux
Developer(s)Ontje Lünsdorf, Stefan Scherfke
Initial releaseSeptember 17, 2002 (2002-09-17)
Stable release
3.0.11 / November 16, 2018; 5 months ago (2018-11-16)
Repository Edit this at Wikidata
Written inPython
Operating systemCross-platform
TypeDiscrete event simulation

SimPy is a process-based discrete-event simulation framework based on standard Python. Its event dispatcher is based on Python’s generators and can also be used for asynchronous networking or to implement multi-agent systems (with both, simulated and real communication).

Processes in SimPy are simple Python generator functions and are used to model active components like customers, vehicles or agents. SimPy also provides various types of shared resources to model limited capacity congestion points (like servers, checkout counters and tunnels). From version 3.1, it will also provide monitoring capabilities to aid in gathering statistics about resources and processes.

Simulations can be performed “as fast as possible”, in real time (wall clock time) or by manually stepping through the events.

Though it is theoretically possible to do continuous simulations with SimPy, it has no features to carry out that. However, SimPy is overkill for simulations with a fixed step size where your processes don’t interact with each other or with shared resources — use a simple while loop in this case.

The SimPy distribution contains tutorials, in-depth documentation, and a large number of examples.

SimPy is released as open source software under the MIT License. The first version was released in December 2002.


One of SimPy's main goals is to be easy to use. Here is an example for a simple SimPy simulation:[1] a clock process that prints the current simulation time at each step:

>>> import simpy
>>> def clock(env, name, tick):
...     while True:
...         print(name,
...         yield env.timeout(tick)
>>> env = simpy.Environment()
>>> env.process(clock(env, 'fast', 0.5))
<Process(clock) object at 0x...>
>>> env.process(clock(env, 'slow', 1))
<Process(clock) object at 0x...>
fast 0
slow 0 
fast 0.5 
slow 1 
fast 1.0 
fast 1.5


  1. ^ Scherfke, Stefan (July 25, 2014). "Discrete-event simulation with SimPy" (PDF). p. 5. Retrieved August 10, 2016.