# 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.

## Methods

### `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`](https://legacy-docs-v1.rasa.com/1.9.4/api/tracker/#rasa.core.trackers.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.

Example

```python
>>> from rasa.core.agent import Agent
>>> from rasa.core.interpreter import RasaNLUInterpreter
>>> agent = Agent.load("examples/restaurantbot/models/current")
>>> await agent.handle_text("hello")
[u'how can I help you?']
```

**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`

### `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`](https://legacy-docs-v1.rasa.com/1.9.4/api/agent/#rasa.core.agent.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`](https://legacy-docs-v1.rasa.com/1.9.4/api/tracker/#rasa.core.trackers.DialogueStateTracker)

### _async_ `log_message`( _message_, _message_preprocessor=None_, _**kwargs_)
Append a message to a dialogue - does not predict actions.

**Return type**: [`DialogueStateTracker`](https://legacy-docs-v1.rasa.com/1.9.4/api/tracker/#rasa.core.trackers.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`