This is documentation for Rasa Open Source Documentation v2.0.x, which is no longer actively maintained.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 (2.3.x).

Version: 2.0.x

Rasa as open source alternative to Facebook’s - Migration Guide

To get started with migrating your application from to Rasa:

Step 1: Export your Training Data from

Navigate to your app's setting page by clicking the Settings icon in the upper right corner. Scroll down to Export your data and hit the button Download .zip with your data.

This will download a file with a .zip extension. Unzip this file to create a folder. The file you want from your download is called expressions.json

Step 2: Create a Rasa Project

To create a Rasa project, run:

rasa init

This will create a directory called data. Remove the files in this directory, and move the expressions.json file into this directory.

rm -r data/*
mv /path/to/expressions.json data/

Step 3: Train your NLU model

To train a model using your Wit data, run:

rasa train nlu

Step 4: Test your NLU model

Let's see how your NLU model will interpret some test messages. To start a testing session, run:

rasa shell nlu

This will prompt your for input. Type a test message and press 'Enter'. The output of your NLU model will be printed to the screen. You can keep entering messages and test as many as you like. Press 'control + C' to quit.

Step 5: Start a Server with your NLU Model

To start a server with your NLU model, run:

rasa run nlu

This will start a server listening on port 5005.

To send a request to the server, run:

curl 'localhost:5005/model/parse?emulation_mode=wit' -d '{"text": "hello"}'
The `emulation_mode` parameter tells Rasa that you want your json
response to have the same format as you would get from
You can also leave it out to get the result in the usual Rasa format.
Join the [Rasa Community Forum]( and let us know how your migration went!