An AEA developer writes skills that the framework can call.

When you add a skill with the CLI, a directory is created which includes modules for the Behaviour, Task, and Handler classes as well as a configuration file skill.yaml.


The skill has a SkillContext object which is shared by all Handler, Behaviour, and Task objects. The skill context also has a link to the AgentContext. The AgentContext provides read access to AEA specific information like the public key and address of the AEA, its preferences and ownership state. It also provides access to the OutBox.

This means it is possible to, at any point, grab the context and have access to the code in other parts of the skill and the AEA.

For example, in the ErrorHandler(Handler) class, the code often grabs a reference to its context and by doing so can access initialised and running framework objects such as an OutBox for putting messages into.

self.context.outbox.put_message(to=recipient, sender=self.context.agent_address, protocol_id=DefaultMessage.protocol_id, message=DefaultSerializer().encode(reply))

Importantly, however, a skill does not have access to the context of another skill or protected AEA components like the DecisionMaker.

What to code

Each of the skill classes has three methods that must be implemented. All of them include a setup() and teardown() method which the developer must implement.

Then there is a specific method that the framework requires for each class.

There can be none, one or more Handler class per skill.

Handler classes can receive Message objects of one protocol type only. However, Handler classes can send Envelope objects of any type of protocol they require.

  • handle(self, message: Message): is where the skill receives a Message of the specified protocol and decides what to do with it.


For example.

Conceptually, a Behaviour class contains the business logic specific to initial actions initiated by the AEA rather than reactions to other events.

There can be one or more Behaviour classes per skill. The developer must create a subclass from the abstract class Behaviour to create a new Behaviour.

A behaviour can be registered in two ways:

  • By declaring it in the skill configuration file skill.yaml (see below)
  • In any part of the code of the skill, by enqueuing new Behaviour instances in the queue context.new_behaviours.

  • act(self): is how the framework calls the Behaviour code.

The framework supports different types of behaviours: - OneShotBehaviour: this behaviour is executed only once. - CyclicBehaviour: this behaviour is executed many times, as long as done() returns True.) - TickerBehaviour: the act() method is called every tick_interval. E.g. if the TickerBehaviour subclass is instantiated

There is another category of behaviours, called CompositeBehaviour. - SequenceBehaviour: a sequence of Behaviour classes, executed one after the other. - FSMBehaviour_: a state machine ofState` behaviours. A state is in charge of scheduling the next state.

If your behaviour fits one of the above, we suggest subclassing your behaviour class with that behaviour class. Otherwise, you can always subclass the general-purpose Behaviour class.

!! Follows an example of a custom behaviour:

from aea.skills.base import Behaviour

class HelloWorldBehaviour(OneShotBehaviour):

    def setup(self):
        """This method is called once, when the behaviour gets loaded."""

    def act(self): 
        """This methods is called in every iteration of the agent main loop."""
        print("Hello, World!")

    def teardown(self): 
        """This method is called once, when the behaviour is teared down."""

If we want to register this behaviour dynamically, in any part of the skill code (i.e. wherever the skill context is available), we can write:


Or, equivalently:

def hello():
    print("Hello, World!")


The callable passed to the act parameter is equivalent to the implementation of the act method described above.

The framework is then in charge of registering the behaviour and scheduling it for execution.

Conceptually, a Task is where the developer codes any internal tasks the AEA requires.

There can be one or more Task classes per skill. The developer subclasses abstract class Task to create a new Task.

  • execute(self): is how the framework calls a Task.

The Task class implements the functor pattern. An instance of the Task class can be invoked as if it were an ordinary function. Once completed, it will store the result in the property result. Raises error if the task has not been executed yet, or an error occurred during computation.

We suggest using the task_manager, accessible through the skill context, to manage long-running tasks. The task manager uses multiprocessing to schedule tasks, so be aware that the changes on the task object will not be updated.

Here's an example:


from aea.skills.tasks import Task

def nth_prime_number(n: int) -> int:
    """A naive algorithm to find the n_th prime number."""
    primes = [2]
    num = 3
    while len(primes) < n:
        for p in primes:
            if num % p == 0:
        num += 2
    return primes[-1]

class LongTask(Task):

    def setup(self):
        """Set the task up before execution."""

    def execute(self, n: int):
        return nth_prime_number(n)

    def teardown(self):
        """Clean the task up after execution."""


from aea.skills.behaviours import TickerBehaviour
from packages.my_author_name.skills.my_skill.tasks import LongTask

class MyBehaviour(TickerBehaviour):

    def setup(self):
        my_task = LongTask()
        task_id = self.context.task_manager.enqueue_task(my_task, args=(10000, ))
        self.async_result = self.context.task_manager.get_task_result(task_id)  # type: multiprocessing.pool.AsyncResult

    def act(self):
        if self.async_result.ready() is False:
            print("The task is not finished yet.")
            completed_task = self.async_result.get()  # type: LongTask
            print("The result is:", completed_task.result)
            # Stop the skill
            self.context.is_active = False

    def teardown(self):


The developer might want to add other classes on the context level which are shared equally across the Handler, Behaviour and Task classes. To this end, the developer can subclass an abstract Model. These models are made available on the context level upon initialization of the AEA.

Say, the developer has a class called SomeModel

class SomeModel(Model):

Then, an instance of this class is available on the context level like so:

some_model = self.context.some_model

Skill config

Each skill has a skill.yaml configuration file which lists all Behaviour, Handler, and Task objects pertaining to the skill.

It also details the protocol types used in the skill and points to shared modules, i.e. modules of type Model, which allow custom classes within the skill to be accessible in the skill context.

name: echo
authors: fetchai
version: 0.1.0
license: Apache-2.0
    class_name: EchoBehaviour
      tick_interval: 1.0
    class_name: EchoHandler
      foo: bar
models: {}
dependencies: {}
protocols: ["fetchai/default:0.1.0"]

Error skill

All top level AEA skills directories receive a default error skill that contains error handling code for a number of scenarios:

  • Received envelopes with unsupported protocols
  • Received envelopes with unsupported skills (i.e. protocols for which no handler is registered)
  • Envelopes with decoding errors
  • Invalid messages with respect to the registered protocol

The error skill relies on the default protocol which provides error codes for the above.