Welcome

Welcome to the homepage for llm.rb

llm.rb is an advanced runtime for building capable AI applications on CRuby. By default it has zero runtime dependencies although certain functionality – such as ActiveRecord support – require optional dependencies that are opt-in.

Features

The runtime supports OpenAI, OpenAI-compatible endpoints, Anthropic, Google Gemini, Mistral, DeepSeek, DeepInfra, xAI, Z.ai, AWS Bedrock, Ollama, and llama.cpp. It has first-class support for streaming, tool calls, MCP and A2A, embeddings, vector stores and the RAG pattern.

There are multiple HTTP backends to choose from, tools can be run concurrently or in parallel via threads, async tasks, fibers, ractors, and fork, and it is also possible to make a tool call while the model is still streaming.

The runtime builds on top of three core concepts: providers, contexts, and agents, so once you learn the fundamentals, everything else falls into place naturally. And once you learn llm.rb, you will also be able to use mruby-llm and wasm-llm because the API is pretty much identical.

Install

Source code and releases are available from github.com/r-uby-dev/llm.

$ gem install llm.rb

Quick start

require "llm"

llm = LLM.deepseek(key: ENV["KEY"])
agent = LLM::Agent.new(llm, stream: $stdout)
agent.talk "Hello world"

require "llm"

llm = LLM.deepseek(key: ENV["KEY"])
ctx = LLM::Context.new(llm, stream: $stdout)
ctx.talk "Hello world"

llm = LLM.deepseek(key: ENV["KEY"])
agent = LLM::Agent.new(llm)
agent.repl

llm = LLM.deepseek(key: ENV["KEY"])
agent = LLM::Agent.new(llm)
agent.repl(path: "session.json")

require "llm"

llm = LLM.deepseek(key: ENV["KEY"])
agent = LLM::Agent.new(llm)
agent.repl(tools: [Debugger])

require "llm"
require "lll/tools"

llm = LLM.deepseek(key: ENV["KEY"])
agent = LLM::Agent.new(llm)
agent.repl(tools: LLM::Tool.subclasses)

require "llm"

llm = LLM.deepseek(key: ENV["KEY"])
agent = LLM::Agent.new(llm)
agent.repl(skills: [__dir__])

require "llm"

llm    = LLM.deepseek(key: ENV["KEY"])
tracer = LLM.logger(llm, path: "agent.log")
agent  = LLM::Agent.new(llm, tracer:)
agent.repl(tracer: true, tools: [Debugger])

require "llm"
require "llm/repl"

class Greeter < LLM::Command
  name "greet"
  description "Greets the given name"
  parameter :name, String, "The person's name"
  required %i[name]

  def call(name:)
    write("Welcome #{name}!\n")
  end
end

class ReadFile < LLM::Tool
  name "read-file"
  description "Read a file"
  parameter :path, String, "The filename or path"
  required %i[path]

  def call(path:)
    {contents: File.read(path)}
  end
end

LLM::Stream

Streams can be simple IO objects or subclasses of LLM::Stream with structured callbacks for content, reasoning, tool calls, tool returns, and compaction.

class MyStream < LLM::Stream
  def on_content(content)
    print content
  end

  def on_reasoning_content(content)
    warn content
  end
end

llm = LLM.deepseek(key: ENV["KEY"])
agent = LLM::Agent.new(llm, stream: MyStream.new)
agent.talk "Explain Ruby fibers."

LLM::MCP

The Model Context Protocol (MCP) has first-class support in llm.rb. The stdio and http transports work out of the box. MCP tools are translated into subclasses of LLM::Tool that can be used with LLM::Context or LLM::Agent.

require "llm"

llm   = LLM.deepseek(key: ENV["KEY"])
mcp   = LLM::MCP.stdio(argv: ["ruby", "server.rb"])
agent = LLM::Agent.new(llm, stream: $stdout, tools: mcp.tools)
agent.talk "Run the tool"

LLM::A2A

The Agent 2 Agent (A2A) protocol has first-class support in llm.rb. The http and jsonrpc transports work out of the box. A2A skills are translated into subclasses of LLM::Tool that can be used with LLM::Context or LLM::Agent.

require "llm"

llm   = LLM.deepseek(key: ENV["KEY"])
a2a   = LLM::A2A.rest(url: "https://remote-agent.example.com")
agent = LLM::Agent.new(llm, stream: $stdout, tools: a2a.skills)
agent.talk "Run the skill"

RAG

Most providers offer an embedding model that can be used for semantic search, or similarity search. An embedding model can generate embeddings that can then be stored in a database that is optimized for storing and querying vectors, such as SQLite's sqlite-vec or PostgreSQL's pg-vector.

llm.rb also includes support for OpenAI's vector store API. It provides a vector database as a HTTP service but we won't cover that here.

require "llm"

llm  = LLM.openai(key: ENV["KEY"])
body = "llm.rb is Ruby's capable AI runtime."
embedding = llm.embed([body]).embeddings.first

Document.create!(
  title: "llm.rb",
  body:,
  embedding:,
)

Concurrency

The runtime supports five different concurrency strategies that have different attributes. The choice between all of them often depends on the requirements of your application.

IO-bound tools are a good fit for the :task, :thread, and :fiber strategies while true parallelism can be achieved with the :fork and :ractor strategies. The :fork strategy also provides a separate process that offers isolation from its parent.

require "llm"

llm   = LLM.deepseek(key: ENV["KEY"])
tools = [FetchNews, FetchStocks, FetchFeeds]
agent = LLM::Agent.new(llm, tools:, concurrency: :fork)
agent.talk "Run the tools in parallel"

ORM

Because both LLM::Context, and LLM::Agent can be serialized to JSON and stored in a simple string both ActiveRecord and Sequel support can be implemented within a single column on a single row.

The runtime includes first-class support for both ActiveRecord and Sequel, and for both Rack-based applications and Rails-based applications. On databases where it is supported, such as PostgresSQL, the column can be optimized by using the jsonb type.

require "active_record"
require "llm"
require "llm/active_record"

class Agent < ApplicationRecord
  acts_as_agent do |agent|
    agent.model "deepseek-v4-pro"
    agent.instructions "solve the user's query"
    agent.tools [Research, FinalizeResearch, ActOnResearch]
  end

  private

  ##
  # By convention, this method defines the provider
  # for a model. If neccessary, it can be renamed and
  # configured via `provider: :your_method` instead.
  def set_provider
    LLM.deepseek(key: ENV["KEY"])
  end

  ##
  # By convention, this method should return what is
  # given as the second argument to `LLM::Context` or
  # `LLM::Agent`.
  #
  # Often, there is no need to set it, so it can be left
  # undefined or it can be reassigned in the same way as
  # `set_provider`. For example: `context: :your_method`
  def set_context
    {}
  end
end

agent = Agent.create!
agent.talk "perform research"

Learn more

The Quick Start guide is intended to be a quick overview of the runtime, it lacks depth, and omits certain features. The deepdive provides a thorough, deep overview of the runtime that is recommended as the next stop for learning more about llm.rb.

Applications

mruby-llm is a port of llm.rb to the mruby runtime, and it has been used to build novel applications that are available to the general public via SSH.