File: README — Documentation by YARD 0.9.37

About

llm.rb is a zero-dependency Ruby toolkit for Large Language Models that includes OpenAI, Gemini, Anthropic, DeepSeek, Ollama, and LlamaCpp. It’s fast, simple and composable – with full support for chat, streaming, tool calling, audio, images, files, and JSON Schema generation.

Features

General

✅ A single unified interface for multiple providers
📦 Zero dependencies outside Ruby’s standard library
🚀 Optimized for performance and low memory usage

Chat, Agents

🧠 Stateless and stateful chat via completions and responses API
🤖 Tool calling and function execution
🗂️ JSON Schema support for structured, validated responses
📡 Streaming support for real-time response updates

Media

🗣️ Text-to-speech, transcription, and translation
🖼️ Image generation, editing, and variation support
📎 File uploads and prompt-aware file interaction
💡 Multimodal prompts (text, images, PDFs, URLs, files)

Miscellaneous

🧮 Text embeddings and vector support
🔌 Retrieve models dynamically for introspection and selection

Demos

1. Tools: "system" function

2. Files: import at runtime

3. Files: import at boot time

Examples

Providers

LLM::Provider

All providers inherit from LLM::Provider – they share a common interface and set of functionality. Each provider can be instantiated using an API key (if required) and an optional set of configuration options via the singleton methods of LLM. For example:

#!/usr/bin/env ruby
require "llm"

##
# remote providers
llm = LLM.openai(key: "yourapikey")
llm = LLM.gemini(key: "yourapikey")
llm = LLM.anthropic(key: "yourapikey")
llm = LLM.deepseek(key: "yourapikey")
llm = LLM.voyageai(key: "yourapikey")

##
# local providers
llm = LLM.ollama(key: nil)
llm = LLM.llamacpp(key: nil)

Conversations

Completions

This example uses the stateless chat completions API that all providers support. A similar example for OpenAI’s stateful responses API is available in the docs/ directory.

The following example creates an instance of LLM::Bot by entering into a conversation where messages are buffered and sent to the provider on-demand. This is the default behavior because it can reduce the number of requests sent to a provider, and avoids unneccessary requests until an attempt to iterate over LLM::Bot#messages is made:

#!/usr/bin/env ruby
require "llm"

llm  = LLM.openai(key: ENV["KEY"])
bot  = LLM::Bot.new(llm)
msgs = bot.chat do |prompt|
  prompt.system File.read("./share/llm/prompts/system.txt")
  prompt.user "Tell me the answer to 5 + 15"
  prompt.user "Tell me the answer to (5 + 15) * 2"
  prompt.user "Tell me the answer to ((5 + 15) * 2) / 10"
end

# At this point, we execute a single request
msgs.each { print "[#{_1.role}] ", _1.content, "\n" }

Streaming

There Is More Than One Way To Do It (TIMTOWTDI) when you are using llm.rb – and this is especially true when it comes to streaming. See the streaming documentation in docs/ for more details.

The following example streams the messages in a conversation as they are generated in real-time. This feature can be useful in case you want to see the contents of a message as it is generated, or in case you want to avoid potential read timeouts during the generation of a response.

The stream option can be set to an IO object, or the value true to enable streaming – and at the end of the request, bot.chat returns the same response as the non-streaming version which allows you to process a response in the same way:

#!/usr/bin/env ruby
require "llm"

llm = LLM.openai(key: ENV["KEY"])
bot = LLM::Bot.new(llm)
bot.chat(stream: $stdout) do |prompt|
  prompt.system "You are my math assistant."
  prompt.user "Tell me the answer to 5 + 15"
  prompt.user "Tell me the answer to (5 + 15) * 2"
  prompt.user "Tell me the answer to ((5 + 15) * 2) / 10"
end.to_a

Schema

Structured

All LLM providers except Anthropic and DeepSeek allow a client to describe the structure of a response that a LLM emits according to a schema that is described by JSON. The schema lets a client describe what JSON object (or value) an LLM should emit, and the LLM will abide by the schema:

#!/usr/bin/env ruby
require "llm"

##
# Objects
llm = LLM.openai(key: ENV["KEY"])
schema = llm.schema.object(answer: llm.schema.integer.required)
bot = LLM::Bot.new(llm, schema:)
bot.chat "Does the earth orbit the sun?", role: :user
bot.messages.find(&:assistant?).content! # => {probability: 1}

##
# Enums
schema = llm.schema.object(fruit: llm.schema.string.enum("Apple", "Orange", "Pineapple"))
bot = LLM::Bot.new(llm, schema:)
bot.chat "Your favorite fruit is Pineapple", role: :system
bot.chat "What fruit is your favorite?", role: :user
bot.messages.find(&:assistant?).content! # => {fruit: "Pineapple"}

##
# Arrays
schema = llm.schema.object(answers: llm.schema.array(llm.schema.integer.required))
bot = LLM::Bot.new(llm, schema:)
bot.chat "Answer all of my questions", role: :system
bot.chat "Tell me the answer to ((5 + 5) / 2)", role: :user
bot.chat "Tell me the answer to ((5 + 5) / 2) * 2", role: :user
bot.chat "Tell me the answer to ((5 + 5) / 2) * 2 + 1", role: :user
bot.messages.find(&:assistant?).content! # => {answers: [5, 10, 11]}

Tools

Functions

All providers support a powerful feature known as tool calling, and although it is a little complex to understand at first, it can be powerful for building agents. The following example demonstrates how we can define a local function (which happens to be a tool), and a provider (such as OpenAI) can then detect when we should call the function.

The LLM::Bot#functions method returns an array of functions that can be called after sending a message and it will only be populated if the LLM detects a function should be called. Each function corresponds to an element in the “tools” array. The array is emptied after a function call, and potentially repopulated on the next message.

The following example defines an agent that can run system commands based on natural language, and it is only intended to be a fun demo of tool calling - it is not recommended to run arbitrary commands from a LLM without sanitizing the input first :) Without further ado:

#!/usr/bin/env ruby
require "llm"

llm  = LLM.openai(key: ENV["KEY"])
tool = LLM.function(:system) do |fn|
  fn.description "Run a shell command"
  fn.params do |schema|
    schema.object(command: schema.string.required)
  end
  fn.define do |params|
    ro, wo = IO.pipe
    re, we = IO.pipe
    Process.wait Process.spawn(params.command, out: wo, err: we)
    [wo,we].each(&:close)
    {stderr: re.read, stdout: ro.read}
  end
end

bot = LLM::Bot.new(llm, tools: [tool])
bot.chat "Your task is to run shell commands via a tool.", role: :system

bot.chat "What is the current date?", role: :user
bot.chat bot.functions.map(&:call) # report return value to the LLM

bot.chat "What operating system am I running? (short version please!)", role: :user
bot.chat bot.functions.map(&:call) # report return value to the LLM

##
# {stderr: "", stdout: "Thu May  1 10:01:02 UTC 2025"}
# {stderr: "", stdout: "FreeBSD"}

Audio

Speech

Some but not all providers implement audio generation capabilities that can create speech from text, transcribe audio to text, or translate audio to text (usually English). The following example uses the OpenAI provider to create an audio file from a text prompt. The audio is then moved to ${HOME}/hello.mp3 as the final step:

#!/usr/bin/env ruby
require "llm"

llm = LLM.openai(key: ENV["KEY"])
res = llm.audio.create_speech(input: "Hello world")
IO.copy_stream res.audio, File.join(Dir.home, "hello.mp3")

Transcribe

The following example transcribes an audio file to text. The audio file (${HOME}/hello.mp3) was theoretically created in the previous example, and the result is printed to the console. The example uses the OpenAI provider to transcribe the audio file:

#!/usr/bin/env ruby
require "llm"

llm = LLM.openai(key: ENV["KEY"])
res = llm.audio.create_transcription(
  file: File.join(Dir.home, "hello.mp3")
)
print res.text, "\n" # => "Hello world."

Translate

The following example translates an audio file to text. In this example the audio file (${HOME}/bomdia.mp3) is theoretically in Portuguese, and it is translated to English. The example uses the OpenAI provider, and at the time of writing, it can only translate to English:

#!/usr/bin/env ruby
require "llm"

llm = LLM.openai(key: ENV["KEY"])
res = llm.audio.create_translation(
  file: File.join(Dir.home, "bomdia.mp3")
)
print res.text, "\n" # => "Good morning."

Images

Create

Some but not all LLM providers implement image generation capabilities that can create new images from a prompt, or edit an existing image with a prompt. The following example uses the OpenAI provider to create an image of a dog on a rocket to the moon. The image is then moved to ${HOME}/dogonrocket.png as the final step:

#!/usr/bin/env ruby
require "llm"
require "open-uri"
require "fileutils"

llm = LLM.openai(key: ENV["KEY"])
res = llm.images.create(prompt: "a dog on a rocket to the moon")
res.urls.each do |url|
  FileUtils.mv OpenURI.open_uri(url).path,
               File.join(Dir.home, "dogonrocket.png")
end

Edit

The following example is focused on editing a local image with the aid of a prompt. The image (/images/cat.png) is returned to us with the cat now wearing a hat. The image is then moved to ${HOME}/catwithhat.png as the final step:

#!/usr/bin/env ruby
require "llm"
require "open-uri"
require "fileutils"

llm = LLM.openai(key: ENV["KEY"])
res = llm.images.edit(
  image: "/images/cat.png",
  prompt: "a cat with a hat",
)
res.urls.each do |url|
  FileUtils.mv OpenURI.open_uri(url).path,
               File.join(Dir.home, "catwithhat.png")
end

Variations

The following example is focused on creating variations of a local image. The image (/images/cat.png) is returned to us with five different variations. The images are then moved to ${HOME}/catvariation0.png, ${HOME}/catvariation1.png and so on as the final step:

#!/usr/bin/env ruby
require "llm"
require "open-uri"
require "fileutils"

llm = LLM.openai(key: ENV["KEY"])
res = llm.images.create_variation(
  image: "/images/cat.png",
  n: 5
)
res.urls.each.with_index do |url, index|
  FileUtils.mv OpenURI.open_uri(url).path,
               File.join(Dir.home, "catvariation#{index}.png")
end

Files

Create

Most LLM providers provide a Files API where you can upload files that can be referenced from a prompt and llm.rb has first-class support for this feature. The following example uses the OpenAI provider to describe the contents of a PDF file after it has been uploaded. The file (an instance of LLM::Response::File) is passed directly to the chat method, and generally any object a prompt supports can be given to the chat method:

#!/usr/bin/env ruby
require "llm"

llm = LLM.openai(key: ENV["KEY"])
bot = LLM::Bot.new(llm)
file = llm.files.create(file: "/documents/openbsd_is_awesome.pdf")
bot.chat(file)
bot.chat("What is this file about?")
bot.messages.select(&:assistant?).each { print "[#{_1.role}] ", _1.content, "\n" }

##
# [assistant] This file is about OpenBSD, a free and open-source Unix-like operating system
#             based on the Berkeley Software Distribution (BSD). It is known for its
#             emphasis on security, code correctness, and code simplicity. The file
#             contains information about the features, installation, and usage of OpenBSD.

Prompts

Multimodal

Generally all providers accept text prompts but some providers can also understand URLs, and various file types (eg images, audio, video, etc). The llm.rb approach to multimodal prompts is to let you pass URI objects to describe links, LLM::File | LLM::Response::File objects to describe files, String objects to describe text blobs, or an array of the aforementioned objects to describe multiple objects in a single prompt. Each object is a first class citizen that can be passed directly to a prompt:

#!/usr/bin/env ruby
require "llm"

llm = LLM.openai(key: ENV["KEY"])
bot = LLM::Bot.new(llm)

bot.chat [URI("https://example.com/path/to/image.png"), "Describe the image in the link"]
bot.messages.select(&:assistant?).each { print "[#{_1.role}] ", _1.content, "\n" }

file = llm.files.create(file: "/documents/openbsd_is_awesome.pdf")
bot.chat [file, "What is this file about?"]
bot.messages.select(&:assistant?).each { print "[#{_1.role}] ", _1.content, "\n" }

bot.chat [LLM.File("/images/puffy.png"), "What is this image about?"]
bot.messages.select(&:assistant?).each { print "[#{_1.role}] ", _1.content, "\n" }

bot.chat [LLM.File("/images/beastie.png"), "What is this image about?"]
bot.messages.select(&:assistant?).each { print "[#{_1.role}] ", _1.content, "\n" }

Embeddings

Text

The LLM::Provider#embed method generates a vector representation of one or more chunks of text. Embeddings capture the semantic meaning of text – a common use-case for them is to store chunks of text in a vector database, and then to query the database for semantically similar text. These chunks of similar text can then support the generation of a prompt that is used to query a large language model, which will go on to generate a response:

#!/usr/bin/env ruby
require "llm"

llm = LLM.openai(key: ENV["KEY"])
res = llm.embed(["programming is fun", "ruby is a programming language", "sushi is art"])
print res.class, "\n"
print res.embeddings.size, "\n"
print res.embeddings[0].size, "\n"

##
# LLM::Response::Embedding
# 3
# 1536

Models

List

Almost all LLM providers provide a models endpoint that allows a client to query the list of models that are available to use. The list is dynamic, maintained by LLM providers, and it is independent of a specific llm.rb release. LLM::Model objects can be used instead of a string that describes a model name (although either works). Let’s take a look at an example:

#!/usr/bin/env ruby
require "llm"

##
# List all models
llm = LLM.openai(key: ENV["KEY"])
llm.models.all.each do |model|
  print "model: ", model.id, "\n"
end

##
# Select a model
model = llm.models.all.find { |m| m.id == "gpt-3.5-turbo" }
bot = LLM::Bot.new(llm, model:)
bot.chat "Hello #{model.id} :)"
bot.messages.select(&:assistant?).each { print "[#{_1.role}] ", _1.content, "\n" }

Documentation

API

The README tries to provide a high-level overview of the library. For everything else there’s the API reference. It covers classes and methods that the README glances over or doesn’t cover at all. The API reference is available at 0x1eef.github.io/x/llm.rb.

Guides

The docs/ directory contains some additional documentation that didn’t quite make it into the README. It covers the design guidelines that the library follows, some strategies for memory management, and other provider-specific features.

Install

llm.rb can be installed via rubygems.org:

gem install llm.rb

License

BSD Zero Clause
See LICENSE

About

Features

General

Chat, Agents

Media

Miscellaneous

Demos

Examples

Providers

LLM::Provider

Conversations

Completions

Streaming

Schema

Structured

Tools

Functions

Audio

Speech

Transcribe

Translate

Images

Create

Edit

Variations

Files

Create

Prompts

Multimodal

Embeddings

Text

Models

List

Documentation

API

Guides

See also

Install

License