mhagenta 1.3.8
MHAgentA allows you to design and run simulations with containerized Modular Hybrid Agents.
Requires Python: <4.0,>=3.12

# MHAgentA

**MHAgentA** (Modular Hybrid Agent Architecture) is a framework for developing container-based agents with complex
behaviors. Each agent is composed of a number of semi-autonomous modules, each focusing on their own tasks (either 
reactively or proactively) and communicating with each other.

The framework handles all the internal workings of each agent automatically, but leaves the exact implementations of 
their modules' behaviors up to the user. This way agents of varying levels of complexity can be developed, from simple
reactive ones, to sophisticated deliberative (or hybrid) ones. 

## High-level Model

Full high-level model of MHAgentA modules and their interaction scheme is shown below. An agent can have instances of
each of them (including multiple instances of modules of the same type) or just a subset. Note that it is not necessary 
to use all of these module types, as simplest behaviors can be implemented even with just one of them.

![](https://raw.githubusercontent.com/Auron-tliar/mhagenta/b57b514aec2e54f0b0a4cbf366f6b098ab0e82f6/docs/images/MHAgentA_modules.png)

There are 8 types of agent modules, defined by their role in the agent's internal communication scheme. Each module is
run as a separate process communicating with others as necessary. Each of them can be purely reactive, or can have
additional periodic internal computations. Essentially, one can view each agent's module as an agent in itw own right,
and a MHAgentA agent – as a closed multi-agent system with strongly defined communication scheme.

1. **Low-level reasoner**. Handles basic fast decision-making and reactions to the environment.
2. **Perceptor**. Observes the agent's environment.
3. **Actuator**. Acts upon the environment.
4. **Knowledge model**. Contains and processes inherent and acquired knowledge of the agent and/or the world model.
5. **High-level reasoner**. Strategizes and comes up with long-term plans.
6. **Goal graph**. Represents agent's plan structure and handles its updates.
7. **Memory**. Stores, processes, and allows access to old observation data and outdated knowledge.
8. **Learner**. Handles (machine learning-based) training of models used by the reasoners.

When interacting with each other, each model can either request or send some data as per the communication scheme above.

## Agent Implementation and Execution

To define an agent, you need to define all the relevant modules. You can to that by extending base classes for them from
the `mhagenta.bases` submodule. Each of these base classes have the nameplate functions related to the module's periodic
step function, utility functions, and reaction to messages from other modules functions. Override their default (empty) 
implementations to define a desired behavior.

The functions to override are:

- `step(state: State) -> State` – is called in set intervals as defined by the `step_frequency` parameter when defining 
an agent;
- `on_init(**kwargs) -> None` – is called when the module has finished initializing, before the agent execution start 
is scheduled. Use it if your custom module needs additional setup steps;
- `on_first(state: State) -> State` – is called right after the execution start before the first call to the `step`
function
- `on_last(state: State) -> State` – is called when the stop signal is received (or when the timeout is reached), right 
before the module execution stops;
- `on_<message_type>(state: State, ...) -> State` – is called when a message of a predefined type received.

Each module base has its own set of nameplates for the message reactions that you can override. Additionally, when 
overriding these functions, it is recommended to use module specific State classes from `mhagenta.states` as type hints,
such as `PerceptorState` or `LLState` instead of the default one, as they will provide hints for outboxes (see below).   

All the functions executed during the agent execution have State as their first argument. States contain user defined
fields (you can specify them during the module initialization), additional useful information, such as module ID, time 
since the start of the agent execution, a directory of all other module IDs organized by module types, and the `outbox` 
object. The latter is used to send out messages to other modules. If module-specific state class is used, it will 
display the functions for sending all the established message types to their designated recipients. For instance, to 
send a set of beliefs from a low-level reasoner to a knowledge module, you type:

```python
state.outbox.send_beliefs(knowledge_id='knowledge_module_id', beliefs=[...], ...)
```

Be sure to return the modified state at the end of the function, and all the outgoing messages will get processed 
automatically.

After defining all the necessary module classes, you need an `Orchestrator` (available from module's root) object to 
handle the creation and execution of the agents. When creating an orchestrator you can also redefine default values of
agent parameters if different agents share them a lot. Full signature of the Orchestrator's init function is 
provided in the MHAgentA's API documentation.

Use `Orchestrator.add_agent(...)` then to compose an agent. To it you need to pass on instances of the relevant modules 
and various parameters, such as agent's ID, frequencies of periodic functions, number of copies of this agent to run, 
whether to resume execution with previously saved stated, etc.

Note that when instantiating modules, you need to define a unique ID for the module, any keyword arguments for its 
`on_init` function (if any), and initial fields and their value for the module's state as a `dict[str, Any]`. These
fields will be added to the state's field dictionary and so that they can be accessed at the runtime with 
`state.field_name`.

When done, you can run all the agents togather with

```python
orchestrator.run()
```

The internal run function is asynchronous. If you want to hande its execution yourself (i.e. add it to another task 
group), you can use `orchestrator.arun()` instead.