AstraBert

AstraBert / better-result-py

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Yet another implementation of Rust's Result type, with type annotations and async support

16
2
100% credibility
Found Mar 23, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

better-result-py is a minimal, dependency-free Python library inspired by Rust's Result type that wraps synchronous and asynchronous functions to treat exceptions as return values rather than control flow interruptions.

How It Works

1
📰 Discover a helpful tool

You hear about better-result-py, a simple way to make your programs handle mistakes without crashing everything.

2
📦 Add it to your project

You easily include this lightweight helper into your Python work with a quick add command.

3
✨ Wrap your tasks

You wrap your regular calculations or data fetches with this tool, so errors turn into useful info instead of surprises.

4
🔍 Check what happened

You peek to see if it succeeded or grab the mistake details safely, without any drama.

5
⚡ Handle waiting tasks

For tasks that take time like grabbing info from the web, you use the waiting version the same easy way.

6
🎁 Pull out the good stuff

You get the successful result or a backup plan if something went off, keeping everything smooth.

🏆 Programs run reliably

Now your code deals with problems gracefully, making it stronger and easier to manage.

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Star Growth

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AI-Generated Review

What is better-result-py?

better-result-py brings Rust's Result type to Python, turning exceptions from sync or async functions into typed values instead of control flow interrupts. You wrap any callable with Result or AsyncResult, passing args directly, and get back an object with ok/err fields, is_ok checks, unwrap, unwrap_or defaults, and expect for custom errors. It's dependency-free, needs Python 3.10+, and uses full type annotations for IDE safety—no more unhandled exception surprises in yet another GitHub take on robust error handling.

Why is it gaining traction?

In a sea of GitHub yet another libs like config tools or exporters, this stands out with zero deps, async support, and seamless typing that catches errors at dev time via annotations. Developers hook on the Rust-like ergonomics: no verbose try/except blocks, just value-based flows that play nice with type checkers. Minimal API means instant productivity without learning curves.

Who should use this?

Python backend devs building APIs or services with async I/O, tired of exception ladders in data fetches or computations. Async-heavy teams using asyncio for web scraping, DB queries, or microservices will dig the clean error propagation. Avoid if you're in simple scripts or need stack traces for debugging.

Verdict

With 16 stars and 1.0% credibility score, it's early-stage—solid docs and pytest coverage, but low adoption means watch for edge cases. Try it for Rust-inspired hygiene in Python error flows if boilerplate bugs you; otherwise, stick to established patterns.

(178 words)

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