# Agent

class `rasa.core.agent.Agent`( _domain=None_, _policies=None_, _interpreter=None_, _generator=None_, _tracker_store=None_, _lock_store=None_, _action_endpoint=None_, _fingerprint=None_, _model_directory=None_, _model_server=None_, _remote_storage=None_, _path_to_model_archive=None_)

The Agent class provides a convenient interface for the most important Rasa functionality.

This includes training, handling messages, loading a dialogue model, getting the next action, and handling a channel.

`create_processor`( _preprocessor=None_)

Instantiates a processor based on the set state of the agent.

Return type

`MessageProcessor`

async `execute_action`( _sender_id_, _action_, _output_channel_, _policy_, _confidence_)

Handle a single message.

Return type

`DialogueStateTracker`

`handle_channels`( _channels_, _http_port=5005_, _route='/webhooks/'_, _cors=None_)

Start a webserver attaching the input channels and handling msgs.

Return type

`Sanic`

async `handle_message`( _message_, _message_preprocessor=None_, **kwargs_)

Handle a single message.

Return type

Optional[List[Dict[str, Any]]]

async `handle_text`( _text_message_, _message_preprocessor=None_, _output_channel=None_, _sender_id='default_)

Handle a single message.

If a message preprocessor is passed, the message will be passed to that function first and the return value is then used as the input for the dialogue engine.

Return type

Optional[List[Dict[str, Any]]]

is_core_ready()

Check if all necessary components and policies are ready to use the agent.

Return type

bool

is_ready()

Check if all necessary components are instantiated to use agent.

Return type

bool

classmethod `load`( _model_path_, _interpreter=None_, _generator=None_, _tracker_store=None_, _lock_store=None_, _action_endpoint=None_, _model_server=None_, _remote_storage=None_, _path_to_model_archive=None_)

Load a persisted model from the passed path.

Return type

`Agent`

async `load_data`( _training_resource_, _remove_duplicates=True_, _unique_last_num_states=None_, _augmentation_factor=50_, _tracker_limit=None_, _use_story_concatenation=True_, _debug_plots=False_, _exclusion_percentage=None_)

Load training data from a resource.

Return type

List[ `DialogueStateTracker`]

async `log_message`( _message_, _message_preprocessor=None_, **kwargs_)

Append a message to a dialogue - does not predict actions.

Return type

`DialogueStateTracker`

async `parse_message_using_nlu_interpreter`( _message_data_, _tracker=None_)

Handles message text and intent payload input messages.

Return type

Dict[str, Any]

`persist`( _model_path_)

Persists this agent into a directory for later loading and usage.

Return type

None

async `predict_next`( _sender_id_, **kwargs_)

Handle a single message.

Return type

Optional[Dict[str, Any]]

`toggle_memoization`( _activate_)

Toggles the memoization on and off.

Return type

None

`train`( _training_trackers_, **kwargs_)

Train the policies / policy ensemble using dialogue data from file.

Return type

None

async `trigger_intent`( _intent_name_, _entities_, _output_channel_, _tracker_)

Trigger a user intent, e.g. triggered by an external event.

Return type

None