Argilla uses telemetry to report anonymous usage and error information. As an open-source software, this type of information is important to improve and understand how the product is used.

How to opt out#

You can opt out of telemetry reporting using the ENV variable ARGILLA_ENABLE_TELEMETRY before launching the server. Setting this variable to 0 will completely disable telemetry reporting.

If you are a Linux/MacOs user, you should run:


If you are a Windows user, you should run:


To opt in again, you can set the variable to 1.

Why reporting telemetry#

Anonymous telemetry information enables us to continuously improve the product and detect recurring problems to better serve all users. We collect aggregated information about general usage and errors. We do NOT collect any information on usersโ€™ data records, datasets, or metadata information.

Sensitive data#

We do not collect any piece of information related to the source data you store in Argilla. We donโ€™t identify individual users. Your data does not leave your server at any time:

  • No dataset record is collected.

  • No dataset names or metadata are collected.

Information reported#

The following usage and error information is reported:

  • The code of the raised error and the entity type related to the error, if any (Dataset, Workspace,โ€ฆ)

  • The user-agent and accept-language http headers

  • Task name and number of records for bulk operations

  • An anonymous generated user uuid

  • The Argilla version running the server

  • The Python version, e.g. 3.8.13

  • The system/OS name, such as Linux, Darwin, Windows

  • The systemโ€™s release version, e.g. Darwin Kernel Version 21.5.0: Tue Apr 26 21:08:22 PDT 2022; root:xnu-8020

  • The machine type, e.g. AMD64

  • The underlying platform spec with as much useful information as possible. (eg. macOS-10.16-x86_64-i386-64bit)

  • The type of deployment: quickstart or server

  • The dockerized deployment flag: True or False

This is performed by registering information from the following API methods:

  • GET /api/me

  • POST /api/dataset/{name}/{task}:bulk

  • POST /api/users

  • Raised server API errors

Additionally, we report the usage of integration for our Python library:

  • ArgillaTrainer framework usage and NLP task-type.

We also report the usage of our tutorials by tutorial_running:

  • The tutorial usage count and its name.

For transparency, you can inspect the source code where this is performed here.

If you have any doubts, donโ€™t hesitate to join our Slack channel or open a GitHub issue. Weโ€™d be very happy to discuss how we can improve this.