ruby-openai VS RubyLLM

What’s the relationship, if any, with https://github.com/alexrudall/ruby-openai ?

That gem is actively maintained and widely used. It’s a good gem! But by definition they’re always playing catch up.

If you are using Open AI's API, this tokenizer is more accurate than the one coming with `ruby-openai` gem.

ruby-openai

If you can just interact with OpenAI api directly without langchain then that eliminates need for python. Here’s a gem for interacting with the OpenAI api : https://github.com/alexrudall/ruby-openai

There is a gem developed by Alex - https://github.com/alexrudall/ruby-openai.

I've been playing with ruby-openai, a Ruby gem (ie, library) that interfaces with the OpenAI API. I'm building myself a small movie-recommendation tool, just a personal need, as well as to teach myself how this stuff works. It's unlikely I'll ever build it into a proper app, as I'm okay with interacting in the shell and with text files, versus a web-based thing. But if there's interest, I'll push my tool up to Github.

Can use https://github.com/alexrudall/ruby-openai to do this sort of thing also :)

OpenAI and OpenRouter are also supported. It runs on top of RubyLLM, so any model it supports works here too. You can assign different models per role: a smarter model for query generation, a cheaper one for writing the response.

RubyLLM is a Ruby gem and an AI interface for GPT, Claude, and Gemini to give us an easy way to run LLM chat inside a Ruby application. It allows us to avoid writing JSON and lets us work with AI using beautiful Ruby DSL.

If you're using RubyLLM to build AI-powered applications in Ruby, you're already enjoying a clean, unified API across multiple providers. But there's one problem RubyLLM doesn't solve: every API call costs money.

I have been programming with Ruby for 11 years with most of the time in a professional context. It's my favorite language :).

I don't care much for types, but it can be useful with denser libraries where IDE's can assist with writing code. It has been helpful in my professional life with regards to typed Python and Typescript.

One potential example that would be interesting is utilizing types for reflection for AI tool calling, the python library for Ollama already supports this[0].

It would make it easier to use such tools in a Ruby context and potentially enhance libraries like ruby-llm [1] and ollama-ruby [2].

[0] https://docs.ollama.com/capabilities/tool-calling#using-func...

[1] https://rubyllm.com/

[2] https://github.com/flori/ollama-ruby

ruby_llm seems to be the most popular "AI integration" gem these days.

Then, building RubyLLM and Chat with Work, I noticed that LLM communication is async Ruby's killer app. The unique demands of streaming AI responses -- long-lived connections, token-by-token delivery, thousands of concurrent conversations -- expose exactly why async matters.

Want to shape RubyLLM's future? Join us on GitHub.