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A lightweight multi-agent system

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A multi-agent platform contrived by Bjorn Madsen

All right reserved © 2016-2019. All code has been written by the author in isolation and any similarity to other systems is purely coincidental.

MASlite explained in 60 seconds:

MASlite is a simle python module for creating multi-agent simulations.

  • Simple API: Only 3 modules to learn: Scheduler, Agent & Agent message
  • Fast: Handles up to 270 million messages per second
  • Lightweight: 52kB.

It only has 3 components:

  • The scheduler (main loop)

    • handles pause and proceed with a single call.
    • assures repeatability in execution, which makes agents easy to debug.
    • handles up to 270 million messages per second.
  • Agent's

    • are python classes that have setup(), update() and teardown() methods that can be customized.
    • can exchange messages using send() and receive().
    • can subscribe/unsubscribe to message classes.
    • have clocks and can set alarms.
    • can be tested individually.
    • can have independent I/O/Database interaction.
  • Messages

    • that have sender and receiver enable direct communication
    • that have topics and no receiver are treated as broadcasts, and sent to subscribers.

The are plenty of use-cases for MASlite:

  • Prototyping MASSIVE™ type games.
  • Creating data processing pipeline
  • Optimisation Engine, for:
    • Scheduling (using Bjorn Madsen's distributed scheduling method)
    • Auctions (using Dimtry Bertsekas alternating iterative auction)

All the user needs to worry about are the protocols of interaction, which conveniently may be summarised as:

  1. Design the messages that an agent will send or receive as regular python objects that inherit the necessary implementation details from a basic AgentMessage. The messages must have an unambiguous topic.
  2. Write the functions that are supposed to execute once an agent receives one of the messages.
  3. Update the agents operations (self.operations) with a dictionary that describes the relationship between topic and function.
  4. Write the update function that maintains the inner state of the agent using send to send messages, and using receive to get messages.

The user can thereby create an agent using just:

class HelloMessage(AgentMessage):
    def __init__(self, sender, receiver)
        super().__init__(sender=sender, receiver=receiver)

class myAgent(Agent):
    def __init__(self):
        self.operations.update({HelloMessage.__name__: self.hello})
    def update(self):
        while self.messages:
            msg = self.receive()
            operation = self.operations.get(msg.topic))
            if operation is not None:
                self.logger.debug("%s: don't know what to do with: %s" % (self.uuid), str(msg)))
    def hello(self, msg)

That simple!

The dictionary self.operations which is inherited from the Agent-class is updated with {HelloMessage.__name__: self.hello}. self.operations thereby acts as a pointer for when a HelloMessage arrives, so when the agents update function is called, it will get the topic from the message's and point to the function self.hello, where self.hello in this simple example just prints the content of the message.

More nuanced behaviour, can also be embedded without the user having to worry about any externals. For example if some messages take precedence over others (priority messages), the inbox should be emptied in the beginning of the update function for sorting.

Here is an example where some topics are treated with priority over others:

class AgentWithPriorityInbox(Agent):
    def __init__(self):
        self.operations.update({"1": self.some_priority_function, 
                                "2": self.some_function, 
                                "3": self.some_function,  # Same function for 2 topics.! 
                                "hello": self.hello, })
        self.priority_topics = ["1","2","3"]
        self.priority_messages = deque()  # from collections import deque
        self.normal_messages = deque()    # deques append and popleft are threadsafe.

    def update(self):
        # 1. Empty the inbox and sort the messages using the topic:
        while self.messages:
            msg = self.receive()
            if msg.topic in self.priority_topics:
        # 2. We've now sorted the incoming messages and can now extend
        # the priority message deque with the normal messages:
        # 3. Next we process them as usual:
        while len(self.priority_messages) > 0:
            msg = self.priority_messages.popleft()
            operation = self.operations.get(msg.topic)
            if operation is not None:

The only thing which the user needs to worry about, is that the update function cannot depend on any externals. The agent is confined to sending (self.send(msg)) and receiving (msg = self.receive()) messages which must be processed within the function self.update. Any responses to sent messages will not happen until the agent runs update again.

If any state needs to be stored within the agent, such as for example memory of messages sent or received, then the agents __init__ should declare the variables as class variables and store the information. Calls to databases, files, etc. can of course happen, including the usage of self.setup() and self.teardown() which are called when the agent is, respectively, started or stopped. See the boiler-plate (below) for a more detailed description.


The following boiler-plate allows the user to manage the whole lifecycle of an agent, including:

  1. add variables to __init__ which can store information between updates.
  2. react to topics by extending self.operations
  3. extend setup and teardown for start and end of the agents lifecycle.
  4. use update with actions before(1), during(2) and after(3) reading messages.

There are no requirements, for using all functions. The boiler-plate merely seeks to illustrate typical usage.

There are also no requirements for the agent to be programmed in procedural, functional or object oriented manner. Doing that is completely up to the user of MASlite.

class MyAgent(Agent):
    def __init__(self):
        # add variables here.
        # remember to register topics and their functions:
        self.operations.update({"topic x": self.x,
                                "topic y": self.y,
                                "topic ...": self....})
    def setup(self):
        # add own setup operations here.
        # register topics with the mailman..!
        # naive:
        for topic in self.operations.keys():
        # selective
        for topic in ["topic x","topic y","topic ..."]:
    def teardown(self):
        # add own teardown operations here.
    def update(self):
        # do something before reading messages
        # read the messages
        while self.messages:
            msg = self.receive()
            # react immediately to some messages:
            operation = self.operations.get(msg.topic)
            if operation is not None:
        # react after reading all messages:
    # Functions added by the user that are not inherited from the 
    # `Agent`-class. If the `update` function should react on these,
    # the topic of the message must be in the self.operations dict.
    def action_before_processing_messages(self)
        # do something.
    def action_after_processing_all_messages(self)
        # do something. Perhaps send a message to somebody that update is done?
        msg = DoneMessages(sender=self, receiver=SomeOtherAgent)
    def x(msg):
        # read msg and send a response
        from_ = msg.sender
        response = SomeMessage(sender=self, receiver=from_) 
    def y(msg):
        # ...


Messages are objects and are required to use the base class AgentMessage.

When agents receive messages they should be interpreted by their topic, which should (by convention) also be the class name of the message. Practice has shown that there are no obvious reasons where this convention shouldn't apply, so messages which don't have a topic declared explicitly inherit the class name. An example is shown below:

>>> from maslite import AgentMessage
>>> class MyMsg(AgentMessage):
...     def __init__(sender, receiver):
...         super().__init__(sender=sender, receiver=receiver)

>>> m = MyMsg(sender=1, receiver=2)
>>> m.topic


Adding functions to messages. Below is an example of a message with it's own function(s):

class DatabaseUpdateMessage(AgentMessage):
    """ Description of the message """
    def __init__(self, sender, senders_db_alias):
        super().__init__(sender=sender, receiver=DatabaseAgent.__name__)
        self._states = {1: 'new', 2: 'read'} 
        self._state = 1
    def get_senders_alias(self):
        return self.senders_db_alias
    def __next__(self)
        if self._state + 1 <= max(self._states.keys()):
            self._state += 1
    def state(self):
        return self._states[self._state]

The class DatabaseUpdateMessage is subclassed from the AgentMessage so that the basic message handling properties are available for the DatabaseUpdateMessage. This helps the user as s/he doesn't need to know anything about how the message handling system works.

The init function requires a sender, which normally defaults to the agent's self. The AgentMessage knows that if it gets an agent in it's __init__ call, it will obtain the agents UUID and use that. Similar applies to a receiver, where the typical operation is based on that the local agent gets a message from the sender and only knows the sender based on msg.get_sender() which returns the sending agents UUID. If the sender might change UUID, in the course of multiple runs, the local agent should be instructed to use, for example, the senders_db_alias. For the purpose of illustration, the message above contains the function get_senders_alias which then can be persistent over multiple runs.

The message is also designed to be returned to save pythons garbage collector: When the DatabaseAgent receives the message, the __next__-function allows the agent to call next(msg) to progress it's self._state from '1' (new) to '2' (read) before returning it to the sender using 'self.send(msg)'. In such case it is important that the DatabaseAgent doesn't store the message in its variables, as the message must not have any open object pointers when sent. This is due to multiprocessing which uses multiprocessing.queues for exchanging messages, which require that Agents and AgentMessages can be pickled.

If an Agent can't be pickled when added to the Scheduler, the scheduler will raise an error explaining that the are open pointer references. Messages are a bit more tolerant as the mailman that manages the messages will try to send the message and hope that the shared pointer will not cause conflicts. If sharing of object pointers is required by the user (for example during prototyping) the scheduler must be set up with number_of_multiprocessors=0 which forces the scheduler to run single-process-single-threaded.

Message Conventions:

  • Messages which have None as receiver are considered broadcasts. The logic is that if you don't know who exactly you are sending it to, send it it to None, and you might get a response if any other agent react on the topic of the message. The magic behind the scenes is handled by the schedulers mailmanager (mailman) which keeps track of all topics that any Agent subscribes to. By convention the topic of the message should be self.__class__.__name__.

  • Messages which have a class.__name__ as receiver, will be received by all agents of that class.

  • Messages which have a particular UUID as receiver, will be received by the agent holding that UUID. If anyone other agent is tracking that UUID, by subscribing to it, then the tracking agent will receive a deepcopy of the message, and not the original.

  • To get the UUID of the sender the method msg.sender is available.

  • To subscribe/unsubscribe to messages the agents should use the subscribe function directly.

These methods are run when the agent is added (setup) to, or removed from (teardown), the scheduler. The internal operation of the agents run method guarantees this:

def run(self):
    """ The main operation of the Agent. """
    if not self.is_setup():
    if not self._quit:
    if self._quit:

The can extend the setup methods either by writing their own self.setup-method (recommended approach).

How to load data from a database connection

When agents are added to the scheduler setup is run. When agents are removed from MASlite teardown is run.

if agents are added and removed iteratively, they will load their state during setup and store it during teardown from some database. It is not necessary to let the scheduler know where the database is. The agents can keep track of this themselves.

Though the user might find it attractice to use uuid to identify, a particular Agent the user should keep in mind that the UUID is unique with every creation and destruction of the agent. To expect or rely on the UUID to be persistent would lead to logical fallacy.

The user must usesetup and teardown and include a naming convention that assures that the agent doesn't depend on the UUID. For example:

# get the data from the previously stored agent
begin transaction:
    id = SELECT agent_id FROM stored_agents WHERE agent_alive == False LIMIT 1;
    UPDATE stored_agents WHERE agent_id == id VALUES (agent_live = TRUE);
    properties = SELECT * FROM stored_agents WHERE agent_id == id;
end transaction;
# Finally let the agent load the properties:

An approach such as above assures that the agent that is revived has no dependency to the UUID.

Getting started

To get started only 3 steps are required:

Step 1. setup a scheduler

>>> from maslite import Agent, Scheduler
>>> s = Scheduler(number_of_multi_processors=0)

Step 2. create agents which have setup, teardown and update methods.

>>> class MyAgent(Agent):
...     def __init__(self):
...         super().__init__()
...     def setup(self):
...         pass
...     def teardown(self):
...         pass
...     def update(self):
...         pass
>>> m = MyAgent()
>>> s.add(m)
2017-02-11 15:05:27,171 - DEBUG - Registering agent MyAgent 331228774898081874557512062996431768652

Step 3. run the scheduler (nothing happens here)

2017-02-11 15:09:20,120 - DEBUG - Pausing Scheduler, use 'run()' to continue. Use 'stop()' to shutdown remote processors.

Other methods such as, iterations=None, pause_if_idle=False) can be applied as the user, finds it suitable.

Step 4. call the schedulers stop method to gracefully execute the teardown method on all agents as a part of the shutdown procedure.

>>> s.stop()
2017-02-11 15:09:37,055 - DEBUG - Scheduler shutdown complete.

Debugging with pdb or breakpoints (PyCharm)

Debugging is easily performed by putting breakpoint at the beginning of the update function. In that way you can watch what happens inside the agent during its state-update.

Typical mistakes

The user constructs the agent correctly with:

  1. the methods update, send, receive, setup and teardown,
  2. adding the agent to the scheduler using scheduler.add(agent).
  3. runs the scheduler using,


Q: The agents don't seem to update?

A: The agents are not getting any messages and are therefore not updated. This is correct behaviour, as update only should run when there are new messages! To force agents to run update in every scheduling cycle, use the hidden method: agent.keep_awake=True. Doing this blindly however is a poor design choice if the agent merely is polling for data. For this purpose agent.set_alarm_clock( should be used, as this allows the agent to sleep for 1 second and the be "woken up" by the alarm message.

The reason it is recommended to use the alarm instead of setting keep_awake=True is that the workload of the system remains transparent at the level of message exchange. Remember that the internal state of the agents should always be hidden whilst the messages should be indicative of any activity.

Adjust runspeed using the clock.

The clock is a powertool in MASlite that should be studied. The clock has the ability to:

  • run at speeds -inf; +inf and any floating point progressing in between.

  • start at time -inf; +inf and any floating point time in between.

The clock is set using the api calls to the clock:

>>> s = Scheduler()
2017-02-11 15:15:00,197 - INFO - Scheduler is running with uuid: 206586991924651126011034509456004484857
2017-02-11 15:15:00,197 - DEBUG - Registering agent Clock 237028863335333747268219642853960174161
2017-02-11 15:15:00,197 - DEBUG - Registering agent MailMan 108593288939288121173991719827939198422
>>> s.clock.time = 1000
2017-02-11 15:16:49,395 - DEBUG - Pausing Scheduler, use 'run()' to continue. Use 'stop()' to shutdown remote processors.
>>> s.clock.clock_speed = 200
2017-02-11 15:17:41,355 - DEBUG - Pausing Scheduler, use 'run()' to continue. Use 'stop()' to shutdown remote processors.

In the calls (above) the scheduler first sets the time to 1000 (whatever that is). using s.clock.time. Next it sets the clock speed using s.clock.clock_speed to 200 times real-time

  1. clock.time =time in seconds. Typically time is set to time since 1970-01-01T00:00:00.000000 - the UNIX epoch - using time.time()
  2. clock.clock_speed = 1.000000

If the clock is set to run with clock.clock_speed = None, the scheduler will ask the clock to progress in jumps, so which behaves like follows: (pseudo code):

if Clock.clock_speed is None:
    if not mailman.messages:
        if self.pending_tasks() == 0:
            Ask clock to set time to the time of the next event.
        else: wait for multiprocessor to return computed agent.
    else: continue until no new messages
else: continue clock as normal.

To adjust the timing during a simulation (for whatever reason), the scheduler should be primed with messages:

  1. run at maximum speed: self.set_new_clock_speed_as_timed_event(start_time=now(), speed=None)
  2. set clock to 10x speed 1 hour into the simulation: set_runtime(start_time=now()+1*60*60, speed=10) This will take 6 minutes in real-time.
  3. set the clock to 1x (real-time) speed 3 hours into the simulation: set_runtime(start_time=now()+3*60*60, speed=1) This will take 1 hour in real time.
  4. set clock to 10x speed 4 hour into the simulation: set_runtime(start_time=now()+4*60*60, speed=10)
  5. set the clock to 'None' to run as fast as possible for the rest of the simulation: set_runtime(start_time=now()+6*60*60, speed=None)

Note: The clock_speed can be set as an argument in the schedulers run function:, iterations=None, pause_if_idle=False,



MASlite only makes one assumption: That it's the primary service on the hardware where it is running.

MASlite running on multiple machines

Not implemented yet.

API access to every agent

Not implemented yet.

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