---
title: Trying RubyLLM again
slug: trying-rubyllm-again
published_at: 2025-09-27 00:12:07 +0000
updated_at: 2026-03-04 20:12:31 +0000
summary: 
description: 
tags: []
views: 1703
author: CJ Avilla
url: https://www.cjav.dev/videos/trying-rubyllm-again
youtube_url: https://www.youtube.com/watch?v=M2vkMsEAF7I
youtube_id: M2vkMsEAF7I
embed_url: https://www.youtube.com/embed/M2vkMsEAF7I
thumbnail_url: https://i.ytimg.com/vi/M2vkMsEAF7I/hqdefault.jpg
type: video
---

# Trying RubyLLM again

*Published: September 27, 2025*
*Views: 1703*

## Watch

[Watch on YouTube](https://www.youtube.com/watch?v=M2vkMsEAF7I)

[![Trying RubyLLM again](https://i.ytimg.com/vi/M2vkMsEAF7I/hqdefault.jpg)](https://www.youtube.com/watch?v=M2vkMsEAF7I)

## Description



## Transcript

I'm working on my little podcast generation AI workflow and updating some steps. I'm migrating away from just using raw API calls to OpenAI and starting to use Ruby LLM. Let's take a look. So, I love this developer experience. I just want to walk you through it. It's so clean. We're able to pass in any model we want from the Open Router set of models, which is now over 300. And then we can add tool calls. But what's really nice is that before I was dependent on OpenAI and using their web search preview and it was really easy to write a Tavly search tool with Ruby LLM. Like take a look at this. You inherit from Ruby LLM tool. Give it your description param make your API call out to Tavi and then you can customize how this response is passed back up to LM. This is where you might do your context engineering at the tool level. What's nice about this is I can swap out and use any of those models and still get the benefits of web search. while also having access to engineer my own context.

---

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