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Responses are messages that your assistant sends to the user. A response is usually only text, but can also include content like images and buttons.

Defining Responses

Responses go under the responses key in your domain file or in a separate "responses.yml" file. Each response name should start with utter_. For example, you could add responses for greeting and saying goodbye under the response names utter_greet and utter_bye:

- greet
- text: "Hi there!"
- text: "See you!"

If you are using retrieval intents in your assistant, you also need to add responses for your assistant's replies to these intents:

- chitchat
- text: Oh yeah, I am called the retrieval bot.
- text: Oh, it does look sunny right now in Berlin.

Notice the special format of response names for retrieval intents. Each name starts with utter_, followed by the retrieval intent's name (here chitchat) and finally a suffix specifying the different response keys (here ask_name and ask_weather). See the documentation for NLU training examples to learn more.

Using Variables in Responses

You can use variables to insert information into responses. Within a response, a variable is enclosed in curly brackets. For example, see the variable name below:

- text: "Hey, {name}. How are you?"

When the utter_greet response is used, Rasa automatically fills in the variable with the value found in the slot called name. If such a slot doesn't exist or is empty, the variable gets filled with None.

Another way to fill in a variable is within a custom action. In your custom action code, you can supply values to a response to fill in specific variables. If you're using the Rasa SDK for your action server, you can pass a value for the variable as a keyword argument to dispatcher.utter_message:


If you use a different custom action server, supply the values by adding extra parameters to the responses your server returns:


Response Variations

You can make your assistant's replies more interesting if you provide multiple response variations to choose from for a given response name:

- text: "Hey, {name}. How are you?"
- text: "Hey, {name}. How is your day going?"

In this example, when utter_greet gets predicted as the next action, Rasa will randomly pick one of the two response variations to use.

Channel-Specific Response Variations

To specify different response variations depending on which channel the user is connected to, use channel-specific response variations.

In the following example, the channel key makes the first response variation channel-specific for the slack channel while the second variation is not channel-specific:

- text: "Which game would you like to play on Slack?"
channel: "slack"
- text: "Which game would you like to play?"

Make sure the value of the channel key matches the value returned by the name() method of your input channel. If you are using a built-in channel, this value will also match the channel name used in your credentials.yml file.

When your assistant looks for suitable response variations under a given response name, it will first try to choose from channel-specific variations for the current channel. If there are no such variations, the assistant will choose from any response variations which are not channel-specific.

In the above example, the second response variation has no channel specified and can be used by your assistant for all channels other than slack.


For each response, try to have at least one response variation without the channel key. This allows your assistant to properly respond in all environments, such as in new channels, in the shell and in interactive learning.

Rich Responses

You can make responses rich by adding visual and interactive elements. There are several types of elements that are supported across many channels:


Here is an example of a response that uses buttons:

- text: "Hey! How are you?"
- title: "great"
payload: "/mood_great"
- title: "super sad"
payload: "/mood_sad"

Each button in the list of buttons should have two keys:

  • title: The text displayed on the buttons that the user sees.
  • payload: The message sent from the user to the assistant when the button is clicked.
bypass nlu with buttons

It's common to use buttons as a shortcut to bypass the machine-learning-based NLU interpreter. Messages starting with / are sent straight to the RegexInterpreter, which expects NLU input in a shortened /intent{entities} format. In the example above, if the user clicks a button, the user input will be directly classified as either the mood_great or mood_sad intent.

Check your channel

Keep in mind that it is up to the implementation of the output channel how to display the defined buttons. For example, some channels have a limit on the number of buttons you can provide. Check your channel's documentation under Concepts > Channel Connectors for any channel-specific restrictions.


You can add images to a response by providing a URL to the image under the image key:

- text: "Here is something to cheer you up:"
image: ""

Custom Output Payloads

You can send any arbitrary output to the output channel using the custom key. The output channel receives the object stored under the custom key as a JSON payload.

Here's an example of how to send a date picker to the Slack Output Channel:

- custom:
- type: section
text: "Make a bet on when the world will end:"
type: mrkdwn
type: datepicker
initial_date: '2019-05-21'
type: plain_text
text: Select a date

Using Responses in Conversations

Calling Responses as Actions

If the name of the response starts with utter_, the response can directly be used as an action, without being listed in the actions section of your domain. You would add the response to the domain:

- text: "Hey! How are you?"

You can use that same response as an action in your stories:

- story: greet user
- intent: greet
- action: utter_greet

When the utter_greet action runs, it will send the message from the response back to the user.

Changing responses

If you want to change the text, or any other part of the response, you need to retrain the assistant before these changes will be picked up.

Calling Responses from Custom Actions

You can use the responses to generate response messages from your custom actions. If you're using Rasa SDK as your action server, you can use the dispatcher to generate the response message, for example:
from rasa_sdk.interfaces import Action
class ActionGreet(Action):
def name(self):
return 'action_greet'
def run(self, dispatcher, tracker, domain):
return []

If you use a different custom action server, your server should return the following JSON to call the utter_greet response: