Title:	Large Language Models and Agentic AI
Version:	0.8.1
Date:	2026-04-28
Description:	Unified interface for creating LLM and Agent objects, generating responses and performing batch inference based on a type-checked and validated 'S7' backend. Features reasoning, structured output, memory management, and tool use. Supports 'Ollama' https://docs.ollama.com/api, 'OpenAI'-compatible https://developers.openai.com/api/reference/overview, and 'Anthropic'-compatible https://platform.claude.com/docs/en/api/getting-started endpoints.
License:	GPL (≥ 3)
URL:	https://www.rtemis.org, https://docs.rtemis.org/r/llm, https://docs.rtemis.org/r/llm-api
BugReports:	https://github.com/rtemis-org/llm/issues
Encoding:	UTF-8
RoxygenNote:	7.3.3
Imports:	cli, data.table, digest, httr2, jsonlite, rtemis.core, S7
Suggests:	keyring, testthat (≥ 3.0.0), xml2
Config/testthat/edition:	3
Depends:	R (≥ 4.1.0)
NeedsCompilation:	no
Packaged:	2026-04-29 23:50:32 UTC; sdg
Author:	E.D. Gennatas [aut, cre, cph]
Maintainer:	E.D. Gennatas <gennatas@gmail.com>
Repository:	CRAN
Date/Publication:	2026-05-04 12:10:02 UTC

rtemis.llm: Agentic AI for the rtemis ecosystem

Description

rtemis.llm provides functionality to interface with Large Language Models, part of the rtemis ecosystem. It provides an Agent class with support for reasoning, structured output, memory management, and tool use. Allows creation of custom LLM-based workflows and agentic AI systems using a functional user-facing frontend and an S7 backend. Includes llmapply() for quick batch LLM inference. Supports Ollama, OpenAI, and Anthropic endpoints.

Online Documentation and Vignettes

https://www.rtemis.org

Author(s)

Maintainer: E.D. Gennatas gennatas@gmail.com (ORCID) [copyright holder]

See Also

Useful links:

• https://www.rtemis.org

• https://docs.rtemis.org/r/llm

• https://docs.rtemis.org/r/llm-api

• Report bugs at https://github.com/rtemis-org/llm/issues

Apply an Agent over a vector of prompts

Description

agentapply is the lapply-style entry point for running a single prompt against an Agent repeatedly over a vector of inputs. Pass either a model name (in which case an Agent is built on the fly using backend, system_prompt, tools, use_memory, max_tool_rounds, output_schema) or a pre-built Agent object.

Usage

agentapply(
  x,
  model_or_agent,
  backend = c("ollama", "openai", "anthropic"),
  system_prompt = SYSTEM_PROMPT_DEFAULT,
  tools = NULL,
  use_memory = FALSE,
  max_tool_rounds = 3L,
  output_schema = NULL,
  verbosity = 1L,
  extract_responses = TRUE,
  ...
)

Arguments

Details

Unlike llmapply, this function can carry tools and memory. The default is use_memory = FALSE because the common case for vectorized calls is independent queries.

Value

If extract_responses = TRUE, a character vector the same length as x. Otherwise, a list of lists of Message objects.

Author(s)

EDG

Examples

# Requires running Ollama server and gemma4:e4b model
## Not run: 
  agentapply(
    c("today", "yesterday", "tomorrow"),
    "gemma4:e4b",
    system_prompt = "Return the date in ISO format",
    tools = list(tool_datetime),
    temperature = 0.2
  )

## End(Not run)

Check Anthropic Model Is Available

Description

Check Anthropic Model Is Available

Usage

anthropic_check_model(
  x,
  base_url = ANTHROPIC_URL_DEFAULT,
  api_key = NULL,
  api_key_env = ANTHROPIC_API_KEY_ENV_DEFAULT,
  keychain_service = NULL,
  anthropic_version = ANTHROPIC_API_VERSION_DEFAULT
)

Arguments

Value

NULL, invisibly, if the model is available; otherwise throws an error.

Author(s)

EDG

Examples

# Requires running Anthropic-compatible server with /models endpoint
## Not run: 
  anthropic_check_model(
    x = "test-model",
    base_url = "http://localhost:1234/v1",
    api_key = "test-key"
  )

## End(Not run)

List Anthropic (Anthropic) Models

Description

List Anthropic (Anthropic) Models

Usage

anthropic_list_models(
  base_url = ANTHROPIC_URL_DEFAULT,
  api_key = NULL,
  api_key_env = ANTHROPIC_API_KEY_ENV_DEFAULT,
  keychain_service = NULL,
  anthropic_version = ANTHROPIC_API_VERSION_DEFAULT
)

Arguments

Value

Character vector: Model ids.

Author(s)

EDG

Examples

# Requires running Anthropic-compatible server with /models endpoint
## Not run: 
  anthropic_list_models(
    base_url = "http://localhost:1234/v1",
   api_key = "test-key"
  )

## End(Not run)

Convert Message to List

Description

Convert Message to List

Usage

## S3 method for class 'Message'
as.list(x, ...)

Arguments

Value

A list representation of the Message object

Author(s)

EDG

Examples

# Requires running Ollama server and gemma4:e4b model
  ## Not run: 
  llm <- create_Ollama("gemma4:e4b")
  res <- generate(llm, "How can anything exist?")
  as.list(res)

## End(Not run)

Convert to R list

Description

Generic method to convert various objects to R lists

Usage

as_list(x, ...)

Arguments

Value

A named R list

Author(s)

EDG

Examples

decay_time <- field("decay_time", "Time from peak amplitude to sustain level", type = "number")
as_list(decay_time)

Print built-in tools available for use by agents

Description

Prints the R handle (⁠tool_*⁠), the function_name the model sees, and the description of every built-in Tool exported by the package. Derived at call time from the namespace — no hardcoded list.

Usage

available_tools(verbosity = 1L)

Arguments

Value

A named list of Tool objects keyed by their R handle, invisibly.

Author(s)

EDG

Examples

available_tools()

Create a AnthropicConfig Object

Description

Creates a AnthropicConfig object which can be passed to create_agent()

Usage

config_Anthropic(
  model_name,
  temperature = TEMPERATURE_DEFAULT,
  base_url = ANTHROPIC_URL_DEFAULT,
  api_key = NULL,
  api_key_env = ANTHROPIC_API_KEY_ENV_DEFAULT,
  keychain_service = NULL,
  anthropic_version = ANTHROPIC_API_VERSION_DEFAULT,
  anthropic_beta = NULL,
  max_tokens = ANTHROPIC_MAX_TOKENS_DEFAULT,
  timeout = ANTHROPIC_TIMEOUT_DEFAULT,
  extra_headers = NULL,
  extra_body = NULL,
  thinking_budget_tokens = NULL,
  validate_model = FALSE
)

Arguments

Value

AnthropicConfig object

Author(s)

EDG

Examples

cfg <- config <- config_Anthropic(
   model_name = "claude-sonnet-4-6",
   temperature = 0.4,
   api_key = "test-key",
   max_tokens = 1024L,
   validate_model = FALSE
)

Create an OllamaConfig Object

Description

Creates an OllamaConfig object which can be passed to create_agent()

Usage

config_Ollama(
  model_name,
  temperature = TEMPERATURE_DEFAULT,
  base_url = OLLAMA_URL_DEFAULT,
  think = NULL
)

Arguments

Value

OllamaConfig object

Author(s)

EDG

Examples

# Requires running Ollama server and gemma4:e4b model
## Not run: 
  config_Ollama(
    model_name = "gemma4:e4b",
    temperature = 0.2
  )

## End(Not run)

Create an OpenAI-compatible Config Object

Description

Creates an OpenAIConfig object which can be passed to create_agent()

Usage

config_OpenAI(
  model_name,
  temperature = TEMPERATURE_DEFAULT,
  base_url = OPENAI_URL_DEFAULT,
  api_key = NULL,
  api_key_env = OPENAI_API_KEY_ENV_DEFAULT,
  keychain_service = NULL,
  organization = NULL,
  project = NULL,
  timeout = OPENAI_TIMEOUT_DEFAULT,
  extra_headers = NULL,
  extra_body = NULL,
  enable_thinking = NULL,
  validate_model = FALSE
)

Arguments

Value

OpenAIConfig object

Author(s)

EDG

Examples

cfg <- config_OpenAI(
   model_name = "local-model",
   temperature = 0.4,
   base_url = "http://localhost:1234/v1/",
   validate_model = FALSE
)

Create a Anthropic LLM Object

Description

Create a Anthropic LLM Object

Usage

create_Anthropic(
  model_name,
  system_prompt = SYSTEM_PROMPT_DEFAULT,
  temperature = TEMPERATURE_DEFAULT,
  output_schema = NULL,
  name = NULL,
  base_url = ANTHROPIC_URL_DEFAULT,
  api_key = NULL,
  api_key_env = ANTHROPIC_API_KEY_ENV_DEFAULT,
  keychain_service = NULL,
  anthropic_version = ANTHROPIC_API_VERSION_DEFAULT,
  anthropic_beta = NULL,
  max_tokens = ANTHROPIC_MAX_TOKENS_DEFAULT,
  timeout = ANTHROPIC_TIMEOUT_DEFAULT,
  extra_headers = NULL,
  extra_body = NULL,
  thinking_budget_tokens = NULL,
  validate_model = FALSE
)

Arguments

Value

Anthropic LLM object

Author(s)

EDG

Examples

llm <- create_Anthropic(
   model_name = "claude-sonnet-4-6",
   system_prompt = "You are a meticulous research assistant.",
   api_key = "test-key",
   validate_model = FALSE
)

Create an Ollama Object

Description

Create an Ollama Object

Usage

create_Ollama(
  model_name,
  system_prompt = SYSTEM_PROMPT_DEFAULT,
  temperature = TEMPERATURE_DEFAULT,
  output_schema = NULL,
  name = NULL,
  base_url = OLLAMA_URL_DEFAULT,
  think = NULL
)

Arguments

Value

Ollama LLM object

Author(s)

EDG

Examples

# Requires running Ollama server and gemma4:e4b model
## Not run: 
  llm <- create_Ollama(
    model_name = "gemma4:e4b",
    system_prompt = "You are professor of Drum and Bass at the Institute of Advanced Beat Studies.",
    temperature = 1.0
  )
  generate(llm, "What is your name and who made you?")

## End(Not run)

Create an OpenAI-compatible LLM Object

Description

Create an OpenAI-compatible LLM Object

Usage

create_OpenAI(
  model_name,
  system_prompt = SYSTEM_PROMPT_DEFAULT,
  temperature = TEMPERATURE_DEFAULT,
  output_schema = NULL,
  name = NULL,
  base_url = OPENAI_URL_DEFAULT,
  api_key = NULL,
  api_key_env = OPENAI_API_KEY_ENV_DEFAULT,
  keychain_service = NULL,
  organization = NULL,
  project = NULL,
  timeout = OPENAI_TIMEOUT_DEFAULT,
  extra_headers = NULL,
  extra_body = NULL,
  enable_thinking = NULL,
  validate_model = FALSE
)

Arguments

Value

OpenAI LLM object

Author(s)

EDG

Examples

llm <- create_OpenAI(
   model_name = "local-model",
   base_url = "http://localhost:1234/v1",
   system_prompt = "You are a meticulous research assistant.",
   validate_model = FALSE
)

Create a rtemis.llm Agent

Description

Create a rtemis.llm Agent

Usage

create_agent(
  llmconfig,
  system_prompt = SYSTEM_PROMPT_DEFAULT,
  use_memory = TRUE,
  tools = NULL,
  max_tool_rounds = 3L,
  output_schema = NULL,
  name = NULL,
  allow_custom_tools = FALSE,
  logfile = NULL,
  verbosity = 1L
)

Arguments

Value

Agent object

Author(s)

EDG

Examples

# Requires Ollama server running and gemma4:e4b model available
## Not run: 
  agent <- create_agent(
    config_Ollama(
      model_name = "gemma4:e4b",
      temperature = 0.2
    ),
    system_prompt = "You are professor of Trance at the Institute of Advanced Beat Studies.",
    use_memory = TRUE
  )

## End(Not run)

create_custom_tool

Description

Define a user-supplied tool for an agent. Unlike create_tool, the caller provides the R function to invoke (impl). Custom tools are outside the package's allowlist-and-hash enforcement, so the caller vouches for the code. An agent will refuse to carry a custom tool unless the agent is created with allow_custom_tools = TRUE (see create_agent).

Usage

create_custom_tool(name, function_name, description, parameters = list(), impl)

Arguments

Value

Tool object with impl populated.

Author(s)

EDG

Examples

add_numbers <- function(x, y) x + y
tool_addition <- create_custom_tool(
  name = "Addition",
  function_name = "add_numbers",
  description = "Performs arithmetic addition of two numbers.",
  parameters = list(
    tool_param("x", "number", "The first number to add", required = TRUE),
    tool_param("y", "number", "The second number to add", required = TRUE)
  ),
  impl = add_numbers
)

create_tool

Description

Define a tool for an agent

Usage

create_tool(name, function_name, description, parameters = list())

Arguments

Value

Tool object

Author(s)

EDG

Examples

tool_addition <- create_tool(
  name = "Addition",
  function_name = "add_numbers",
  description = "Performs arithmetic addition of two numbers.",
  parameters = list(
    tool_param(
      name = "x",
      type = "number",
      description = "The first number to add",
      required = TRUE
    ),
    tool_param(
      name = "y",
      type = "number",
      description = "The second number to add",
      required = TRUE
    )
  )
)

Define a schema field

Description

Define a schema field

Usage

field(
  name,
  description = name,
  type = c("string", "number", "integer", "boolean", "array", "object"),
  required = TRUE
)

Arguments

Value

Field object

Author(s)

EDG

Examples

# `type` defaults to "string", `required` defaults to TRUE
field("lab_name", "Name of the lab test")
field("normal_range_low", "Lower bound of normal range", type = "number")

Generate Method

Description

Generic method for generating text or structured output from LLMs and Agents.

Usage

generate(
  x,
  prompt,
  temperature = NULL,
  top_p = NULL,
  max_tokens = NULL,
  stop = NULL,
  think = NULL,
  output_schema = NULL,
  verbosity = 1L,
  ...
)

Arguments

Details

The system prompt is set once at agent (or LLM) construction time and is not overridable per call. Construct a new agent if you need a different system prompt.

Backend-specific extra arguments accepted via ...:

• Ollama: top_k (integer), seed (integer)

• OpenAI: seed (integer)

• Anthropic: top_k (integer)

Any argument set to NULL (the default) falls back to the value baked into the underlying LLMConfig at construction time.

Value

Message object or list of Message objects (for Agent).

Author(s)

EDG

Examples

# Requires running Ollama server and gemma4:e4b model
## Not run: 
  agent <- create_agent(
    config_Ollama(
      model_name = "gemma4:e4b",
      temperature = 0.2
    )
  )
  generate(agent, "What is your name?", temperature = 0.7)

## End(Not run)

Apply an LLM over a vector of prompts

Description

llmapply is the lapply-style entry point for running a single prompt against an LLM repeatedly over a vector of inputs. Pass either a model name (in which case an LLM is built on the fly using backend, system_prompt, output_schema) or a pre-built LLM object.

Usage

llmapply(
  x,
  model_or_llm,
  backend = c("ollama", "openai", "anthropic"),
  system_prompt = SYSTEM_PROMPT_DEFAULT,
  output_schema = NULL,
  verbosity = 1L,
  extract_responses = TRUE,
  ...
)

Arguments

Details

Per-call overrides such as temperature, top_p, max_tokens, stop, think, plus backend-specific options like top_k or seed, are forwarded via ... to generate. Vectors passed via ... are not yet recycled across x — they are forwarded as-is to each call.

Value

If extract_responses = TRUE, a character vector the same length as x. Otherwise, a list of Message objects.

Author(s)

EDG

Examples

# Requires running Ollama server and gemma4:e4b model
## Not run: 
  llmapply(
    c("burgundy", "crimson", "maroon", "ruby", "scarlet"),
    "gemma4:e4b",
    system_prompt = "Return the hexadecimal code for the color provided in format #FFFFFF",
    temperature = 0.2
  )

## End(Not run)

Map

Description

Map

Usage

map(x, f, ...)

Arguments

Details

Use responses to retrieve just the content from the assistant messages, or reasoning to retrieve the reasoning traces (if enabled).

Value

A list of Message objects (for LLM) or list of lists of Message objects (for Agent).

Author(s)

EDG

Examples

# Requires running Ollama server and gemma4:e4b model
## Not run: 
  llm <- create_Ollama(
    "gemma4:e4b",
    system_prompt = "Convert color to hex code using the format #FFFFFF"
  )
  x <- c("ocean teal", "california poppy orange", "bougainvillea pink")
  hex <- map(x, llm)
  hex

## End(Not run)

Check Ollama Model is Available

Description

Check Ollama Model is Available

Usage

ollama_check_model(x)

Arguments

Value

NULL, invisibly if model is available; otherwise throws an error.

Author(s)

EDG

Examples

# Requires running Ollama server
## Not run: 
  ollama_check_model("gemma4:e4b")

## End(Not run)

Get Ollama Model Info

Description

Get Ollama Model Info

Usage

ollama_get_model_info(x = NULL, base_url = OLLAMA_URL_DEFAULT)

Arguments

Value

data.table

Author(s)

EDG

Examples

# Requires a running Ollama server
## Not run: 
  ollama_get_model_info()
  ollama_get_model_info(x = "gemma4:e4b")

## End(Not run)

List Ollama Models

Description

List Ollama Models

Usage

ollama_list_models(base_url = OLLAMA_URL_DEFAULT)

Arguments

Value

Character vector: Model names.

Author(s)

EDG

Examples

# Requires a running Ollama server
## Not run: 
ollama_list_models()

## End(Not run)

Check OpenAI-compatible Model Is Available

Description

Check OpenAI-compatible Model Is Available

Usage

openai_check_model(
  x,
  base_url = OPENAI_URL_DEFAULT,
  api_key = NULL,
  api_key_env = OPENAI_API_KEY_ENV_DEFAULT,
  keychain_service = NULL,
  organization = NULL,
  project = NULL
)

Arguments

Value

NULL, invisibly, if model is available; otherwise throws an error.

Author(s)

EDG

Examples

# Requires running OpenAI-compatible server with /models endpoint
## Not run: 
  openai_check_model(
    x = "local-model",
    base_url = "http://localhost:1234/v1",
    api_key = "test-key"
  )

## End(Not run)

List OpenAI-compatible Models

Description

List OpenAI-compatible Models

Usage

openai_list_models(
  base_url = OPENAI_URL_DEFAULT,
  api_key = NULL,
  api_key_env = OPENAI_API_KEY_ENV_DEFAULT,
  keychain_service = NULL,
  organization = NULL,
  project = NULL
)

Arguments

Value

Character vector: Model ids.

Author(s)

EDG

Examples

# Requires running OpenAI-compatible server with /models endpoint
## Not run: 
  openai_list_models(
    base_url = "http://localhost:1234/v1",
    api_key = "test-key"
  )

## End(Not run)

Extract reasoning trace(s) from a Message or list of Messages

Description

Returns the assistant's reasoning trace (if any). Only LLMMessage objects carry a reasoning field; all other Message subclasses return NA_character_. Messages whose reasoning is unset (NULL) also return NA_character_.

Usage

reasoning(x)

Arguments

Value

Character vector of reasoning traces, with NA_character_ in slots where no reasoning is available.

Author(s)

EDG

Examples

# Requires running Ollama server and gemma4:e4b model
## Not run: 
  llmapply(
    c("burgundy", "crimson", "maroon", "ruby", "scarlet"),
    "gemma4:e4b",
    system_prompt = "Return the hexadecimal code for the color provided in format #FFFFFF",
    temperature = 0.2
  ) |> reasoning()

## End(Not run)

Extract response(s) from a Message or list of Messages

Description

Returns the assistant content from a single Message, from a flat list of Message objects (e.g. the output of llmapply with extract_responses = FALSE), or from a list of lists of Message objects (e.g. the output of map() on an Agent with extract_responses = FALSE).

Usage

responses(x)

Arguments

Value

Character vector of assistant responses. Returns NA_character_ in slots where no assistant message is present, so that the length of the result matches the length of x.

Author(s)

EDG

Examples

# Requires running Ollama server and gemma4:e4b model
## Not run: 
  llmapply(
    c("burgundy", "crimson", "maroon", "ruby", "scarlet"),
    "gemma4:e4b",
    system_prompt = "Return the hexadecimal code for the color provided in format #FFFFFF",
    temperature = 0.2
  ) |> responses()

## End(Not run)

Define output schema for LLM responses

Description

Define output schema for LLM responses

Usage

schema(name = NULL, ..., description = NULL)

Arguments

Value

Schema object, named list, or JSON string.

Author(s)

EDG

Examples

schema(
  "LabSchema",
  field("Lab name"),
  field("normal range low", type = "number"),
  field("normal range high", type = "number")
)

tool_param

Description

Define a tool parameter schema

Usage

tool_param(name, type, description, required = FALSE)

Arguments

Value

ToolParameter object

Author(s)

EDG

Examples

tool_param("query", "string", "search query to send", required = TRUE)

Built-in Agent Tools

Description

Pre-defined Tool objects that can be passed to create_agent() via the tools argument, allowing an agent to search external services or retrieve local information.

Usage

tool_arxiv

tool_datetime

tool_duckduckgo_ia

tool_semanticscholar

tool_wikipedia

Format

Tool S7 objects:

tool_arxiv

Search arXiv.org for academic papers.

tool_wikipedia

Search Wikipedia articles.

tool_semanticscholar

Search Semantic Scholar for academic papers.

tool_duckduckgo_ia

Query the DuckDuckGo Instant Answer API.

tool_datetime

Return the current date, time, and timezone.

Author(s)

EDG

Examples

# Inspect a tool
tool_datetime

## Not run: 
# Requires a running Ollama server and the "gemma4:e4b" model
agent <- create_agent(
  llmconfig = config_Ollama(
    model_name = "gemma4:e4b",
    base_url = "http://localhost:11434"
  ),
  system_prompt = "You are a meticulous research assistant.",
  tools = list(tool_datetime, tool_semanticscholar, tool_wikipedia)
)
generate(agent, "Find recent papers on diffusion models.")

## End(Not run)