It may be the current "Zeitgeist", but I find the addiction to AI annoying. I am not denying that there are use cases to be had that can be net-positive, but there are also numerous bad examples of AI use. And these, IMO, are more prevalent than the positive ones overall.
It’s not a good thing.
This reads to me like they think that the response from the tool doesn’t go back to the LLM.
I’ve not worked with tools but my understanding is that they’re a way to allow the LLM to request additional data from the client. Once the client executes the requested function, that response data then goes to the LLM to be further processed into a final response.
Does anyone have a comparison of the two, or any other libraries?
RubyLLM gives you a clean API for LLM calls and tool definitions. You're still writing prompts and managing conversations directly.
DSPy.rb treats prompts as functions with typed signatures. You define inputs/outputs and the framework handles prompt construction, JSON parsing, and structured extraction. Two articles that might help:
1. "Building Your First ReAct Agent" shows how to build tool-using agents with type-safe tool definitions [0].
2. "Building Chat Agents with Ephemeral Memory" demonstrates context engineering patterns (what the LLM sees vs. what you store), cost-based routing between models, and memory management [1].
The article's approach (RubyLLM + single tool) works great for simple cases. DSPy.rb shines when you need to decompose into multiple specialized modules with different concerns. Some examples: separate signatures for classification vs. response generation, each optimized independently with separate context windows and memory to maintain.
Would love to learn how dspy.rb is working for you!
Note that RubyLLM and DSPy.rb aren't mutually exclusive (`gem 'dspy-ruby_llm'`) adapter gives us access to a TON of providers.
[0] https://oss.vicente.services/dspy.rb/blog/articles/react-age... [1] https://oss.vicente.services/dspy.rb/blog/articles/ephemeral...
I liked how well designed the monolith application seems to be from the brief description in the article.
Coincidentally I installed Ruby, first time in years, last week and spent a half hour experimenting the same nicely designed RubyLLM gem used in the article. While slop code can be written in any language, it seems like in general many Ruby devs have excellent style. Clojure is another language where I have noticed a preponderance for great style.
As long as I am rambling, one more thing, a plug for monolith applications: I used to get a lot of pleasure from working as a single dev on monoliths in Java and Ruby, eschewing micro-services, really great to share data and code in one huge usually multithreaded process.
Your single-tool approach is a solid starting point. As it grows, you might hit context window limits and find the prompt getting unwieldy. Things like why is this prompt choking on 1.5MB of JSON coming from this other API/Tool?
When you look at systems like Codex CLI, they run at least four separate LLM subsystems: (1) the main agent prompt, (2) a summarizer model that watches the reasoning trace and produces user-facing updates like "Searching for test files...", (3) compaction and (4) a reviewer agent. Each one only sees the context it needs. Like a function with their inputs and outputs. Total tokens stay similar, but signal density per prompt goes up.
DSPy.rb[0] enables this pattern in Ruby: define typed Signatures for each concern, compose them as Modules/Prompting Techniques (simple predictor, CoT, ReAct, CodeAct, your own, ...), and let each maintain its own memory scope. Three articles that show this:
- "Ephemeral Memory Chat"[1] — the Two-Struct pattern (rich storage vs. lean prompt context) plus cost-based routing between cheap and expensive models.
- "Evaluator Loops"[2] — decompose generation from evaluation: a cheap model drafts, a smarter model critiques, each with its own focused signature.
- "Workflow Router"[3] — route requests to the right model based on complexity, only escalate to expensive LLMs when needed.
And since you're already using RubyLLM, the dspy-ruby_llm adapter lets you keep your provider setup while gaining the decomposition benefits.
Thanks for coming to my TED talk. Let me know if you need someone to bounce ideas off.
[0] https://github.com/vicentereig/dspy.rb
[1] https://oss.vicente.services/dspy.rb/blog/articles/ephemeral...
[2] https://oss.vicente.services/dspy.rb/blog/articles/evaluator...
[3] https://oss.vicente.services/dspy.rb/blog/articles/workflow-...
(edit: minor formatting)
Surely a fuzzy search by name or some other field is a much better UI for this.
We build front ends for the API to make our applications easier to use. This is just another type of front end.
- Made a RAG in ~50 lines of ruby (practical and efficient)
- Perform authorization on chunks in 2 lines of code (!!)
- Offload retrieval to Algolia. Since a RAG is essentially LLM + retriever, the retriever typically ends up being most of the work. So using an existing search tool (rather than setting up a dedicated vector db) could save a lot of time/hassle when building a RAG.
Of course tool calling and MCP are not new. But the smart thing is that by defining the tools in the context of an authenticated request, one can easily enforce the security policy of the monolith.
In my case (we will maybe write a blog post one day), it's even neater as the agent is coded in Python so the php app talks with Python through local HTTP (we are thinking about building a central micro service) and the tool calls are encoded as JSON RPC, and yet it works.
Not all cool code is in new greenfield projects.
I checked a few OpenAI models for this implementation: gpt-5, gpt-4o, gpt4.