What are slots?

Slots are your bot’s memory. They act as a key-value store which can be used to store information the user provided (e.g their home city) as well as information gathered about the outside world (e.g. the result of a database query).

Most of the time, you want slots to influence how the dialogue progresses. There are different slot types for different behaviors.

For example, if your user has provided their home city, you might have a text slot called home_city. If the user asks for the weather, and you don’t know their home city, you will have to ask them for it. A text slot only tells Rasa Core whether the slot has a value. The specific value of a text slot (e.g. Bangalore or New York or Hong Kong) doesn’t make any difference.

If the value itself is important, use a categorical or a bool slot. There are also float, and list slots. If you just want to store some data, but don’t want it to affect the flow of the conversation, use an unfeaturized slot.

How Rasa Uses Slots

The Policy doesn’t have access to the value of your slots. It receives a featurized representation. As mentioned above, for a text slot the value is irrelevant. The policy just sees a 1 or 0 depending on whether it is set.

You should choose your slot types carefully!

How Slots Get Set

You can provide an initial value for a slot in your domain file:

    type: text
    initial_value: "human"

You can get the value of a slot using .get_slot() inside for example:

data = tracker.get_slot("slot-name")

There are multiple ways that slots are set during a conversation:

Slots Set from NLU

If your NLU model picks up an entity, and your domain contains a slot with the same name, the slot will be set automatically. For example:

# story_01
* greet{"name": "Ali"}
  - slot{"name": "Ali"}
  - utter_greet

In this case, you don’t have to include the - slot{} part in the story, because it is automatically picked up.

To disable this behavior for a particular slot, you can set the auto_fill attribute to False in the domain file:

    type: text
    auto_fill: False

Slots Set By Clicking Buttons

You can use buttons as a shortcut. Rasa Core will send messages starting with a / to the RegexInterpreter, which expects NLU input in the same format as in story files, e.g. /intent{entities}. For example, if you let users choose a color by clicking a button, the button payloads might be /choose{"color": "blue"} and /choose{"color": "red"}.

You can specify this in your domain file like this: (see details in Domains)

- text: "what color would you like?"
  - title: "blue"
    payload: '/choose{"color": "blue"}'
  - title: "red"
    payload: '/choose{"color": "red"}'

Slots Set by Actions

The second option is to set slots by returning events in custom actions. In this case, your stories need to include the slots. For example, you have a custom action to fetch a user’s profile, and you have a categorical slot called account_type. When the fetch_profile action is run, it returns a event:

      type: categorical
      - premium
      - basic
from rasa_sdk.actions import Action
from import SlotSet
import requests

class FetchProfileAction(Action):
    def name(self):
        return "fetch_profile"

    def run(self, dispatcher, tracker, domain):
        url = ""
        data = requests.get(url).json
        return [SlotSet("account_type", data["account_type"])]
# story_01
* greet
  - action_fetch_profile
  - slot{"account_type" : "premium"}
  - utter_welcome_premium

# story_02
* greet
  - action_fetch_profile
  - slot{"account_type" : "basic"}
  - utter_welcome_basic

In this case you do have to include the - slot{} part in your stories. Rasa Core will learn to use this information to decide on the correct action to take (in this case, utter_welcome_premium or utter_welcome_basic).


It is very easy to forget about slots if you are writing stories by hand. We strongly recommend that you build up these stories using Interactive Learning with Forms rather than writing them.

Slot Types

Text Slot

Use For

User preferences where you only care whether or not they’ve been specified.

      type: text

Results in the feature of the slot being set to 1 if any value is set. Otherwise the feature will be set to 0 (no value is set).

Boolean Slot

Use For

True or False

      type: bool

Checks if slot is set and if True

Categorical Slot

Use For

Slots which can take one of N values

      type: categorical
      - low
      - medium
      - high

Creates a one-hot encoding describing which of the values matched. A default value __other__ is automatically added to the user-defined values. All values encountered which are not explicitly defined in the domain are mapped to __other__ for featurization. The value __other__ should not be used as a user-defined value; if it is, it will still behave as the default to which all unseen values are mapped.

Float Slot

Use For

Continuous values

      type: float
      min_value: -100.0
      max_value:  100.0

max_value=1.0, min_value=0.0


All values below min_value will be treated as min_value, the same happens for values above max_value. Hence, if max_value is set to 1, there is no difference between the slot values 2 and 3.5 in terms of featurization (e.g. both values will influence the dialogue in the same way and the model can not learn to differentiate between them).

List Slot

Use For

Lists of values

      type: list

The feature of this slot is set to 1 if a value with a list is set, where the list is not empty. If no value is set, or the empty list is the set value, the feature will be 0. The length of the list stored in the slot does not influence the dialogue.

Unfeaturized Slot

Use For

Data you want to store which shouldn’t influence the dialogue flow

      type: unfeaturized

There will not be any featurization of this slot, hence its value does not influence the dialogue flow and is ignored when predicting the next action the bot should run.

Custom Slot Types

Maybe your restaurant booking system can only handle bookings for up to 6 people. In this case you want the value of the slot to influence the next selected action (and not just whether it’s been specified). You can do this by defining a custom slot class.

In the code below, we define a slot class called NumberOfPeopleSlot. The featurization defines how the value of this slot gets converted to a vector to our machine learning model can deal with. Our slot has three possible “values”, which we can represent with a vector of length 2.


not yet set


between 1 and 6


more than 6

from rasa.core.slots import Slot

class NumberOfPeopleSlot(Slot):

    def feature_dimensionality(self):
        return 2

    def as_feature(self):
        r = [0.0] * self.feature_dimensionality()
        if self.value:
            if self.value <= 6:
                r[0] = 1.0
                r[1] = 1.0
        return r

Now we also need some training stories, so that Rasa Core can learn from these how to handle the different situations:

# story1
* inform{"people": "3"}
  - action_book_table
# story2
* inform{"people": "9"}
  - action_explain_table_limit