RubyLLM 1.12: Agents + Full Cloud Provider Coverage + New instructions semantics and contributor guidelines 🎉🤖☁️

This is a big one.

RubyLLM 1.12 brings a new Agent interface and concludes cloud provider coverage:

GCP coverage via Vertex AI (already supported)
New: full AWS coverage via Bedrock Converse API
New: full Azure coverage via Azure AI Foundry API

🤖 New Agent Interface

Agents are now a first-class way to define reusable AI behavior once and use it everywhere.

class WorkAssistant < RubyLLM::Agent
  chat_model Chat
  model "gpt-4.1-nano"
  instructions "You are a concise work assistant."
  tools TodoTool, GoogleDriveSearchTool
end

Use it directly:

response = WorkAssistant.new.ask("What should I work on today?")

Or with Rails-backed chats:

chat = WorkAssistant.create!(user: current_user)
WorkAssistant.find(chat.id).complete

Prompt conventions are built in (app/prompts/<agent_name>/instructions.txt.erb).

More on agents: https://rubyllm.com/agents

☁️ Bedrock Converse API: Full Bedrock Coverage

RubyLLM now uses Bedrock Converse API, which means every Bedrock chat model is supported through one consistent path.

chat = RubyLLM.chat(
  model: "anthropic.claude-haiku-4-5-20251001-v1:0",
  provider: :bedrock
)

response = chat.ask("Give me three ideas for reducing API latency.")

If it runs on Bedrock, RubyLLM can talk to it.

☁️ Azure Foundry API Support

RubyLLM now supports Azure Foundry AI, giving you broad model access on Azure with the same RubyLLM interface.

RubyLLM.configure do |config|
  config.azure_api_key = ENV["AZURE_API_KEY"]
  config.azure_api_base = ENV["AZURE_API_BASE"]
end

chat = RubyLLM.chat(model: "gpt-4.1", provider: :azure)
response = chat.ask("Summarize this architecture in one paragraph.")

Same API, Azure-wide model availability.

🧠 Instruction Semantics Improved

with_instructions behavior is now clearer:

default call replaces the active system instruction
append behavior is explicit
instructions are always sent before other messages

chat.with_instructions("You are concise.")
chat.with_instructions("Use bullet points.", append: true)

🤝 Contributor + Provider Guidance Expanded

We clarified how contributions should flow so reviews are faster and less surprising.

What we ask now:

Open an issue first and wait for maintainer feedback before coding new features.
Keep PRs focused and reasonably sized.
If you used AI tooling, you still own the code: understand every line before opening the PR.

Provider-specific direction is also clearer:

Core providers have a high acceptance bar.
For smaller or emerging providers, we usually prefer a community gem over adding it to RubyLLM core.

Net effect: less churn in review, clearer expectations up front.

📚 Docs & DX Polishes

A bunch of quality-of-life improvements shipped alongside core features:

updated guides around agents and configuration
docs UX improvements (copy page button, dark mode polish)

Installation

gem "ruby_llm", "1.12.0"

Upgrading from 1.11.x

bundle update ruby_llm

Full Changelog: 1.11.0...1.12.0

crmne/ruby_llm 1.12.0 on GitHub