Package 'rtemis.llm'

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 [email protected] (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

x	Character or list: Values to iterate over.
model_or_agent	Character or Agent: Either the name of a model (a string) or a pre-built Agent object from create_agent.
backend	Character {"ollama", "openai", "anthropic"}: Backend to use when model_or_agent is a string. Ignored when model_or_agent is an Agent object.
system_prompt	Character: System prompt for the on-the-fly Agent.
tools	Optional list of Tool objects: Tools available to the on-the-fly Agent.
use_memory	Logical: Whether the on-the-fly Agent should keep conversation memory.
max_tool_rounds	Integer [1, Inf): Maximum number of tool call rounds per query.
output_schema	Optional Schema: Output schema for the on-the-fly Agent.
verbosity	Integer [0, Inf): Verbosity level.
extract_responses	Logical: If TRUE, return a character vector of assistant responses. If FALSE, return the raw list of lists of Message objects.
...	Additional per-call arguments forwarded to generate.

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

x	Character: Name of the model.
base_url	Character: Base URL of the Anthropic API.
api_key	Optional character: API key.
api_key_env	Character: Environment variable containing the API key.
keychain_service	Optional character: macOS Keychain service containing the API key.
anthropic_version	Character: anthropic-version header value.

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

base_url	Character: Base URL of the Anthropic API.
api_key	Optional character: API key.
api_key_env	Character: Environment variable containing the API key.
keychain_service	Optional character: macOS Keychain service containing the API key.
anthropic_version	Character: anthropic-version header value.

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 to R list

Description

Generic method to convert various objects to R lists

Usage

as_list(x, ...)

Arguments

x	An object to convert
...	Additional arguments for specific methods

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)

---

Convert Message to List

Description

Convert Message to List

Usage

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

Arguments

x	Message object
...	Additional arguments (not used)

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)

---

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

verbosity

Integer: Verbosity level.

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

model_name	Character: The name of the Anthropic model to use (for example "claude-sonnet-4-6").
temperature	Numeric [0, 2]: The temperature for the model.
base_url	Character: Base URL of the Anthropic API.
api_key	Optional character: API key.
api_key_env	Character: Environment variable containing the API key.
keychain_service	Optional character: macOS Keychain service containing the API key.
anthropic_version	Character: Value of the required anthropic-version header.
anthropic_beta	Optional character: Value(s) for the anthropic-beta header. A character vector is comma-joined.
max_tokens	Integer [1, Inf): Maximum number of tokens the model may generate. Required by the Messages API.
timeout	Numeric (0, Inf): Request timeout in seconds.
extra_headers	Optional list: Additional HTTP headers.
extra_body	Optional list: Additional request body fields.
thinking_budget_tokens	Optional integer [1024, Inf): Budget for extended thinking. When set, each request enables extended thinking with this budget.
validate_model	Logical: Whether to validate model availability using ⁠/models⁠.

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

model_name	Character: The name of the LLM model to use. Must be an Ollama model.
temperature	Numeric: The temperature for the model.
base_url	Character: Base URL of Ollama server.
think	Optional Logical or Character {"low", "medium", "high"}: Default thinking mode for this config. Logical values target models like deepseek or qwen3; character values target gpt-oss. Can be overridden per call.

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

model_name	Character: The name of the LLM model to use.
temperature	Numeric [0, 2]: The temperature for the model.
base_url	Character: Base URL of the OpenAI-compatible server.
api_key	Optional character: API key.
api_key_env	Character: Environment variable containing the API key.
keychain_service	Optional character: macOS Keychain service containing the API key.
organization	Optional character: OpenAI organization id.
project	Optional character: OpenAI project id.
timeout	Numeric (0, Inf): Request timeout in seconds.
extra_headers	Optional list: Additional HTTP headers.
extra_body	Optional list: Additional request body fields.
enable_thinking	Optional logical: Whether to enable model thinking for compatible local servers.
validate_model	Logical: Whether to validate model availability using the models endpoint.

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 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

llmconfig	LLMConfig: The LLM configuration to use. Create using one of config_Ollama, config_OpenAI, or config_Anthropic.
system_prompt	Optional character: The system prompt to use.
use_memory	Logical: Whether to use conversation memory.
tools	Optional list of Tool objects: The tools available to the agent.
max_tool_rounds	Integer: Maximum number of tool call rounds per query.
output_schema	Optional Schema: The output schema to enforce on the agent's response created using schema and field.
name	Optional character: The name of the agent.
allow_custom_tools	Logical: If TRUE, allow the agent to carry tools whose function_name is not in the package allowlist. Such tools must be built via create_custom_tool and supply their own function body. The caller vouches for that code: built-in package guarantees (allowlist + hash verification) do not apply to it. Defaults to FALSE.
logfile	Optional character: Path to the agent's security log. Important! If NULL, the value will be set to getOption("rtemis_security_logfile", tempfile("rtemis_security_log_", fileext = ".jsonl")) to satisfy CRAN policy. It is important to set it to a non-temporary location that will persist and you can access. Otherwise, security incidents may be missed. Can be overridden per call on generate.
verbosity	Integer: Verbosity level.

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 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

model_name	Character: The name of the Anthropic model to use.
system_prompt	Character: The system prompt to use.
temperature	Numeric [0, 2]: The temperature for the model.
output_schema	Optional Schema: Output schema created using schema. Structured output is implemented by forcing a single synthetic tool call whose input_schema is this schema.
name	Optional character: Name for the LLM object.
base_url	Character: Base URL of the Anthropic API.
api_key	Optional character: API key.
api_key_env	Character: Environment variable containing the API key.
keychain_service	Optional character: macOS Keychain service containing the API key.
anthropic_version	Character: Value of the required anthropic-version header.
anthropic_beta	Optional character: Value(s) for the anthropic-beta header.
max_tokens	Integer [1, Inf): Maximum number of tokens the model may generate.
timeout	Numeric (0, Inf): Request timeout in seconds.
extra_headers	Optional list: Additional HTTP headers.
extra_body	Optional list: Additional request body fields.
thinking_budget_tokens	Optional integer [1024, Inf): Extended-thinking budget.
validate_model	Logical: Whether to validate model availability using ⁠/models⁠.

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_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

name	Character: The name of the tool, e.g. "Addition".
function_name	Character: The name to expose to the model, e.g. "add_numbers".
description	Character: The description of the tool.
parameters	List of ToolParameter: The parameters of the tool, each defined using tool_param.
impl	Function: The R function to invoke when the tool is called. Its formal argument names must match the name fields of parameters.

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 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

model_name	Character: The name of the LLM model to use. Must be an Ollama model.
system_prompt	Character: The system prompt to use.
temperature	Numeric: The temperature for the model.
output_schema	Optional Schema: An optional output schema created using schema.
name	Character or NULL: An optional name for the Ollama object.
base_url	Character: Base URL of Ollama server.
think	Optional Logical or Character {"low", "medium", "high"}: Default thinking mode. Logical values target models like deepseek or qwen3; character values target gpt-oss.

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

model_name	Character: The name of the LLM model to use.
system_prompt	Character: The system prompt to use.
temperature	Numeric [0, 2]: The temperature for the model.
output_schema	Optional Schema: Output schema created using schema.
name	Optional character: Name for the LLM object.
base_url	Character: Base URL of the OpenAI-compatible server.
api_key	Optional character: API key.
api_key_env	Character: Environment variable containing the API key.
keychain_service	Optional character: macOS Keychain service containing the API key.
organization	Optional character: OpenAI organization id.
project	Optional character: OpenAI project id.
timeout	Numeric (0, Inf): Request timeout in seconds.
extra_headers	Optional list: Additional HTTP headers.
extra_body	Optional list: Additional request body fields.
enable_thinking	Optional logical: Whether to enable model thinking for compatible local servers.
validate_model	Logical: Whether to validate model availability using the models endpoint.

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_tool

Description

Define a tool for an agent

Usage

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

Arguments

name	Character: The name of the tool, e.g. "Wikipedia Search".
function_name	Character: The name of the function to call, e.g. "query_wikipedia".
description	Character: The description of the tool.
parameters	List of ToolParameter: The parameters of the tool, each defined using tool_param.

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

name	Optional Character: The name of the field.
description	Optional Character: A brief description of the field.
type	Character {"string", "number", "integer", "boolean", "array", "object"}: The field type.
required	Logical: Whether the field is required.

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

x	An object of class LLM or Agent.
prompt	Character: The prompt to pass to the model or agent.
temperature	Optional numeric [0, 2]: Per-call sampling temperature.
top_p	Optional numeric [0, 1]: Nucleus sampling cutoff.
max_tokens	Optional integer [1, Inf): Maximum tokens to generate. For Anthropic, this overrides the config-level value (which is required); for Ollama this maps to options.num_predict; for OpenAI-compatible backends this maps to max_tokens.
stop	Optional character: Stop sequence(s). Mapped to stop_sequences on Anthropic and options.stop on Ollama.
think	Optional logical or character: Whether to enable model thinking (reasoning trace) for this call. Character values target gpt-oss-style local models.
output_schema	Optional Schema: Output schema to enforce on this call's response. If omitted, the object's default schema (if any) is used.
verbosity	Integer: Verbosity level.
...	Additional backend-specific per-call arguments. See Details.

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

x	Character or list: Values to iterate over. Each element forms the user prompt for one call to the LLM.
model_or_llm	Character or LLM: Either the name of a model (a string) or a pre-built LLM object (for example from create_Ollama, create_OpenAI, or create_Anthropic).
backend	Character {"ollama", "openai", "anthropic"}: Backend to use when model_or_llm is a string. Ignored when model_or_llm is an LLM object.
system_prompt	Character: System prompt to use when building the LLM from a model name. Ignored when model_or_llm is an LLM object.
output_schema	Optional Schema: Output schema to enforce, created with schema. When model_or_llm is a string, this is baked into the built LLM. When model_or_llm is a pre-built LLM, supplying this here is a conflict and will error.
verbosity	Integer [0, Inf): Verbosity level. The per-call verbosity is verbosity - 1L.
extract_responses	Logical: If TRUE, return a character vector of assistant responses (with NA_character_ for missing assistant content). If FALSE, return the raw list of Message objects from each call.
...	Additional per-call arguments forwarded to generate (e.g. temperature, top_p, max_tokens, stop, think, top_k, seed).

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

x	A character vector or list to map over.
f	An LLM or Agent object.
...	Additional arguments passed to generate().

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

x	Character: Name of model.

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

x	Optional character vector: Name of model(s) to get info for. If NULL, all available models' info is returned.
base_url	Character: Base URL of Ollama server.

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

base_url

Character: Base URL of Ollama server.

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

x	Character: Name of model.
base_url	Character: Base URL of the OpenAI-compatible server.
api_key	Optional character: API key.
api_key_env	Character: Environment variable containing the API key.
keychain_service	Optional character: macOS Keychain service containing the API key.
organization	Optional character: OpenAI organization id.
project	Optional character: OpenAI project id.

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

base_url	Character: Base URL of the OpenAI-compatible server.
api_key	Optional character: API key.
api_key_env	Character: Environment variable containing the API key.
keychain_service	Optional character: macOS Keychain service containing the API key.
organization	Optional character: OpenAI organization id.
project	Optional character: OpenAI project id.

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

x	Message object, list of Message objects, or list of lists of Message objects.

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

x	Message object, list of Message objects, or list of lists of Message objects.

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

name	Optional Character: The name of the schema.
...	Field objects defining the schema fields. Create using field.
description	Optional Character: A brief description of the schema.

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

name	Character: The name of the parameter.
type	Character: The type of the parameter.
description	Character: The description of the parameter.
required	Logical: Whether the parameter is required.

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)

---