I found Ruby LLM to be surprisingly good - in terms of usability it's close to Vercel's AI framework.

It tries to strike a balance between working out of the box and being flexible... which has its challenges, still nice overall.

One big real-life pain I experienced is that caches don't always work, e.g. for xAI, since it only supports completions API and thought signatures are returned wrong.

We use and love RubyLLM! A wonderful and easy to use framework.

Agreed with another commenter on the frustration with the responses API not being naively supported; that seems like a huge miss. There is a connector from another dev, but it's buggy and not as high quality as the main gem.

Really looking forward to future development and especially 2.0!

Edit: Just saw that responses API is now native? I will definitely check that out.

I have an open source gem called Raix that builds on top of RubyLLM's abstractions and is quite popular. https://github.com/OlympiaAI/raix

RubyLLM is very easy to use. Made extensive use of it for a project last year. Drawbacks are it was difficult to instrument for true trace observability and it has a pattern where retries will delete the underlying models so the history you see is clean but not necessarily great for seeing exactly what the sequence of API calls was.

We use RubyLLM in production too, the most elegant library in this space I've seen so far.

I also liked how they run the issue tracker. If you select "Feature Request", it makes you explain how you explored workarounds, why you believe it belongs in RubyLLM etc to prevent scope creep.

We use this in production for a few apps. Great project.

thank you for bringing ruby into AI community and your open-source work. Great language must be explored and get more attention :)

I have been a fan of Ruby for many years, but in this fast paced era the Ruby ecosystem always struggled with the dependency versioning. Gems I relied on were never available or compatible with the rest of the ecosystem.

I built a similar Ruby based agent development kit that has a different focus and feature set:

https://github.com/tweibley/legate

I haven't tried it but it looks promising.

Using RubyLLM in production for https://usetix.io It drives our event chat agent that is enhanced with toolcalls etc. Super happy with it.

I have created an open source chatgpt clone with rubyllm, check it out here: https://www.railschat.org/

It is quite nice, but not as nice as you'd want. You still have to set platform specifics when running completions when you want to tune things like temperature, effort, max tokens, etc.

"What is the best language in the world (say ruby)" ;)

Thank you

In case you're using PHP or Node.js, we've made a similar toolkit free and open source on github: https://github.com/Qbix/AI/tree/main/classes/AI

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Why would anyone still build in dynamically typed languages in 2026? Why relinquish the crystal clear signals that static typing is able to provide to the LLM?