How I Optimized RubyLLM for Coding Agents

You’ve heard it this year. Maybe you’ve said it yourself: “You don’t need frameworks anymore. Just let the agent write it. Why would you depend on somebody else’s gem?” And it’s spreading. My friend Andrzej Krzywda goes to every Ruby conference and talks to everybody, and a few weeks ago he wrote that 40% of the devs he meets are trying to reinvent RubyLLM, my AI framework. One person’s impression, sure. But you can feel it in the air. I think it’s bullshit, and I wanted to prove it, not just say it. Same Agent, Same App, Four Times So I took a coding agent, Codex on GPT-6 Astra, a really good one, and gave it the same app to build four times: with RubyLLM on Rails, with LangChain and LangGraph, with the Vercel AI SDK, and with no framework at all, just plain Ruby with no AI libraries or provider SDKs. Each framework got its own official agent skill where one exists. The app is a real chat app, built over eleven steps, each a new agent session on the same project: streaming,…

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