Pre-release features

At Streamlit, we like to move quick while keeping things stable. In our latest effort to move even faster without sacrificing stability, we're offering our bold and fearless users two ways to try out Streamlit's bleeding-edge features:

1. Nightly releases
2. Beta and experimental features

Nightly releases

At the end of each day (at night 🌛), our bots run automated tests against the latest Streamlit code and, if everything looks good, it publishes them as the streamlit-nightly package. This means the nightly build includes all our latest features, bug fixes, and other enhancements on the same day they land on our codebase.

How does this differ from official releases?

Official Streamlit releases go not only through both automated tests but also rigorous manual testing, while nightly releases only have automated tests. It's important to keep in mind that new features introduced in nightly releases often lack polish. In our official releases, we always make double-sure all new features are ready for prime time.

How do I use the nightly release?

All you need to do is install the streamlit-nightly package:

pip uninstall streamlit
pip install streamlit-nightly --upgrade

Why should I use the nightly release?

Because you can't wait for official releases, and you want to help us find bugs early!

Why shouldn't I use the nightly release?

While our automated tests have high coverage, there's still a significant likelihood that there will be some bugs in the nightly code.

Can I choose which nightly release I want to install?

If you'd like to use a specific version, you can find the version number in our Release history. Specify the desired version using pip as usual: pip install streamlit-nightly==x.yy.zz-123456.

Can I compare changes between releases?

If you'd like to review the changes for a nightly release, you can use the comparison tool on GitHub.

Beta and Experimental Features

In addition to nightly releases, we also have two naming conventions for less stable Streamlit features: st.beta_ and st.experimental_. These distinctions are prefixes we attach to our function names to make sure their status is clear to everyone.

Here's a quick rundown of what you get from each naming convention:

• st: this is where our core features like st.write and st.dataframe live. If we ever make backward-incompatible changes to these, they will take place gradually and with months of announcements and warnings.
• beta: this is where all new features land before they becoming part of Streamlit core. This gives you a chance to try the next big thing we're cooking up weeks or months before we're ready to stabilize its API.
• experimental: this is where we'll put features that may or may not ever make it into Streamlit core. We don't know whether these features have a future, but we want you to have access to everything we're trying, and work with us to figure them out.

The main difference between beta_ and experimental_ is that beta features are expected to make it into Streamlit core at some point soon, while experimental features may never make it.

Beta

Features with the beta_ naming convention are all scheduled to become part of Streamlit core. While in beta, a feature's API and behaviors may not be stable, and it's possible they could change in ways that aren't backward-compatible.

The lifecycle of a beta feature

1. A feature is added with the beta_ prefix.

2. The feature's API stabilizes and the feature is cloned without the beta_ prefix, so it exists as both st and beta_. At this point, users will see a warning when using the version of the feature with the beta_ prefix -- but the feature will still work.

3. At some point, the code of the beta_-prefixed feature is removed, but there will still be a stub of the function prefixed with beta_ that shows an error with appropriate instructions.

4. Finally, at a later date the beta_ version is removed.

Experimental

Features with the experimental_ naming convention are things that we're still working on or trying to understand. If these features are successful, at some point they'll become part of Streamlit core, by moving to the beta_ naming convention and then to Streamlit core. If unsuccessful, these features are removed without much notice.

The lifecycle of an experimental feature

1. A feature is added with the experimental_ prefix.
2. The feature is potentially tweaked over time, with possible API/behavior breakages.
3. At some point, we either promote the feature to beta_ or remove it from experimental_. Either way, we leave a stub in experimental_ that shows an error with instructions.