This is documentation for Rasa Open Source Documentation v2.0.x, which is no longer actively maintained.
For up-to-date documentation, see the latest version (2.6.x).

Version: 2.0.x


Stories are a type of training data used to train your assistant's dialogue management model. Stories can be used to train models that are able to generalize to unseen conversation paths.


A story is a representation of a conversation between a user and an AI assistant, converted into a specific format where user inputs are expressed as intents (and entities when necessary), while the assistant's responses and actions are expressed as action names.

Here's an example of a dialogue in the Rasa story format:

- story: collect restaurant booking info # name of the story - just for debugging
- intent: greet # user message with no entities
- action: utter_ask_howcanhelp
- intent: inform # user message with no entities
- location: "rome"
- price: "cheap"
- action: utter_on_it # action that the bot should execute
- action: utter_ask_cuisine
- intent: inform
- cuisine: "spanish"
- action: utter_ask_num_people

User Messages

While writing stories, you do not have to deal with the specific contents of the messages that the users send. Instead, you can take advantage of the output from the NLU pipeline, which lets you use just the combination of an intent and entities to refer to all the possible messages the users can send to mean the same thing.

It is important to include the entities here as well because the policies learn to predict the next action based on a combination of both the intent and entities (you can, however, change this behavior using the use_entities attribute).


All actions executed by the bot, including responses are listed in stories under the action key.

You can use a response from your domain as an action by listing it as one in a story. Similarly, you can indicate that a story should call a custom action by including the name of the custom action from the actions list in your domain.


During training, Rasa Open Source does not call the action server. This means that your assistant's dialogue management model doesn't know which events a custom action will return.

Because of this, events such as setting a slot or activating/deactivating a form have to be explicitly written out as part of the stories. For more info, see the documentation on Events.

Slot Events

Slot events are written under slot_was_set in a story. If this slot is set inside a custom action, add the slot_was_set event immediately following the custom action call. If your custom action resets a slot value to None, the corresponding event for that would look like this:

- story: set slot to none
# ... other story steps
- action: my_custom_action
- slot_was_set:
- my_slot: null

Form Events

There are three kinds of events that need to be kept in mind while dealing with forms in stories.

  • A form action event (e.g. - action: restaurant_form) is used in the beginning when first starting a form, and also while resuming the form action when the form is already active.

  • A form activation event (e.g. - active_loop: restaurant_form) is used right after the first form action event.

  • A form deactivation event (e.g. - active_loop: null), which is used to deactivate the form.

writing form stories

In order to get around the pitfall of forgetting to add events, the recommended way to write these stories is to use interactive learning.

Checkpoints and OR statements

Checkpoints and OR statements should be used with caution, if at all. There is usually a better way to achieve what you want by using Rules or the ResponseSelector.


You can use checkpoints to modularize and simplify your training data. Checkpoints can be useful, but do not overuse them. Using lots of checkpoints can quickly make your example stories hard to understand, and will slow down training.

Here is an example of stories that contain checkpoints:

- story: beginning of flow
- intent: greet
- action: action_ask_user_question
- checkpoint: check_asked_question
- story: handle user affirm
- checkpoint: check_asked_question
- intent: affirm
- action: action_handle_affirmation
- checkpoint: check_flow_finished
- story: handle user deny
- checkpoint: check_asked_question
- intent: deny
- action: action_handle_denial
- checkpoint: check_flow_finished
- story: finish flow
- checkpoint: check_flow_finished
- intent: goodbye
- action: utter_goodbye

Unlike regular stories, checkpoints are not restricted to starting with user input. As long as the checkpoint is inserted at the right points in the main stories, the first event can be a custom action or a response as well.

Or Statements

Another way to write shorter stories, or to handle multiple intents the same way, is to use an or statement. For example, if you ask the user to confirm something, and you want to treat the affirm and thankyou intents in the same way. The story below will be converted into two stories at training time:

- story:
# ... previous steps
- action: utter_ask_confirm
- or:
- intent: affirm
- intent: thankyou
- action: action_handle_affirmation

or statements can be useful, but if you are using a lot of them, it is probably better to restructure your domain and/or intents. Overusing OR statements will slow down training.

Test Conversation Format

The test conversation format is a format that combines both NLU data and stories into a single file for evaluation. Read more about this format in Testing Your Assistant.

testing only

This format is only used for testing and cannot be used for training.