This is documentation for Rasa Documentation v2.x, which is no longer actively maintained.
For up-to-date documentation, see the latest version (3.x).

Version: 2.x

Rasa Telemetry

Rasa uses telemetry to report anonymous usage information. This information is essential to help improve Rasa Open Source for all users.

For the team working on Rasa Open Source it is important to understand how the product is used. It allows us to properly prioritize our research efforts and feature development.

You will be notified about the telemetry reporting when running Rasa Open Source for the first time.

How to opt-out

You can opt out of telemetry reporting at any time by running the command:

rasa telemetry disable

or by defining RASA_TELEMETRY_ENABLED=false as an environment variable. If you want to enable reporting again, you can run:

rasa telemetry enable

Why do we use telemetry reporting?

Anonymous telemetry data allow us to prioritize our research efforts and feature development based on usage. We want to collect aggregated information on usage and reliability so that we can ensure a high-quality product.

So how will we use the reported telemetry data? Here are some examples of what we use the data for:

  • We will be able to know which languages, pipelines and policies are used. This will enable us to direct our research efforts towards text and dialogue handling projects that will have the biggest impact for our users.
  • We will be able to know data set sizes and general structure (e.g. the number of intents). This allows us to better test our software on different types of data sets and optimize the frameworks performance.
  • We will be able to get more detail on the types of errors you are running into while building an assistant (e.g. initialization, training, etc.). This will let us improve the quality of our framework and better focus our time on solving more common, frustrating issues.

What about sensitive data?

Your sensitive data never leaves your machine. We:

  • don't report any personal identifiable information
  • don't report your training data
  • don't report any messages your assistant receives or sends
Inspect what is reported

You can view all the telemetry information that is reported by defining the environment variable RASA_TELEMETRY_DEBUG=true, for example when running the train command:

RASA_TELEMETRY_DEBUG=true rasa train

When you set RASA_TELEMETRY_DEBUG no information will be sent to any server, instead it will be logged to the commandline as a json dump for you to inspect.

What do we report?

Rasa reports aggregated usage details, command invocations, performance measurements and errors. We use the telemetry data to better understand usage patterns. The reported data will directly allow us to better decide how to design future features and prioritize current work.

Specifically, we collect the following information for all telemetry events:

  • Type of the reported event (e.g. Training Started)
  • Rasa machine ID: This is generated with a UUID and stored in the global Rasa config at ~/.config/rasa/global.yml and sent as metrics_id
  • One-way hash of the current working directory or a hash of the git remote
  • General OS level information (operating system, number of CPUs, number of GPUs and whether the command is run inside a CI)
  • Current Rasa Open Source and Python version

Here is an example report that shows the data reported to Rasa after running rasa train:

"userId": "38d23c36c9be443281196080fcdd707d",
"event": "Training Started",
"properties": {
"language": "en",
"num_intent_examples": 68,
"num_entity_examples": 0,
"num_actions": 17,
"num_templates": 6,
"num_conditional_response_variations": 5,
"num_slots": 0,
"num_forms": 0,
"num_intents": 6,
"num_entities": 0,
"num_story_steps": 5,
"num_lookup_tables": 0,
"num_synonyms": 0,
"num_regexes": 0,
"metrics_id": "38d23c36c9be443281196080fcdd707d"
"context": {
"os": {
"name": "Darwin",
"version": "19.4.0"
"ci": false,
"project": "a0a7178e6e5f9e6484c5cfa3ea4497ffc0c96d0ad3f3ad8e9399a1edd88e3cf4",
"python": "3.7.5",
"rasa_open_source": "2.0.0",
"cpu": 16

We cannot identify individual users from the dataset. It is anonymized and untraceable back to the user.