Warning: This document is for an old version of Rasa Core.


The job of interpreting text is mostly outside the scope of Rasa Core. To turn text into structured data you can use Rasa NLU, or a cloud service like wit.ai. If your bot uses button clicks or other input which isn’t natural language, you don’t need an interepreter at all. You can define your own Interpreter subclass which does any custom logic you may need. You can look at the RegexInterpreter class as an example.

To use something other than Rasa NLU, you just need to implement a subclass of Interpreter which has a method parse(message) which takes a single string argument and returns a dict in the following format:

  "text": "show me chinese restaurants",
  "intent": "restaurant_search",
  "entities": [
      "start": 8,
      "end": 15,
      "value": "chinese",
      "entity": "cuisine"


For testing and for writing stories, Rasa Core has a RegexInterpreter. This matches strings in the format _intent[entity1=value, entity2=value].