gumdrop
Rust option parser with custom derive support (by murarth)
configure_me
A Rust library for processing application configuration easily (by Kixunil)
gumdrop | configure_me | |
---|---|---|
1 | 1 | |
222 | 58 | |
- | - | |
0.0 | 1.1 | |
5 months ago | about 1 year ago | |
Rust | Rust | |
Apache License 2.0 | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
gumdrop
Posts with mentions or reviews of gumdrop.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-03-01.
-
Argument parsers that don't choke on invalid unicode
It looks like there's currently an open pull request for Gumdrop for this.
configure_me
Posts with mentions or reviews of configure_me.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-03-01.
-
Argument parsers that don't choke on invalid unicode
Thanks for your feedback! I decided to write it a bit more succinctly. What do you think?
What are some alternatives?
When comparing gumdrop and configure_me you can also consider the following projects:
xflags
async-graphql - A GraphQL server library implemented in Rust
tangram - Tangram makes it easy for programmers to train, deploy, and monitor machine learning models.