Agent: Agent: A General-Purpose LLM Agent
In mini007: Lightweight Framework for Orchestrating Multi-Agent Large Language Models
Agent R Documentation
Agent: A General-Purpose LLM Agent
Description
The 'Agent' class defines a modular LLM-based agent capable of responding to prompts using a defined role/instruction.
It wraps an OpenAI-compatible chat model via the ['ellmer'](https://github.com/llrs/ellmer) package.
Each agent maintains its own message history and unique identity.
Public fields
nameThe agent's name.
instructionThe agent's role/system prompt.
llm_objectThe underlying 'ellmer::chat_openai' object.
agent_idA UUID uniquely identifying the agent.
model_providerThe name of the entity providing the model (eg. OpenAI)
model_nameThe name of the model to be used (eg. gpt-4.1-mini)
broadcast_historyA list of all past broadcast interactions.
budgetA budget in $ that the agent should not exceed.
budget_policyA list controlling budget behavior: on_exceed and warn_at.
budget_warnedInternal flag indicating whether warn_at notice was emitted.
costThe current cost of the agent
toolsA list of registered tools available to the agent
Active bindings
messagesPublic active binding for the conversation history.
Assignment is validated automatically.
Methods
Public methods
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Method new()
Initializes a new Agent with a specific role/instruction.
Usage
Agent$new(name, instruction, llm_object, budget = NA)
Arguments
nameA short identifier for the agent (e.g. '"translator"').
instructionThe system prompt that defines the agent's role.
llm_objectThe LLM object generate by ellmer (eg. output of ellmer::chat_openai)
budgetNumerical value denoting the amount to set for the budget in US$ to a specific agent,
if the budget is reached, an error will be thrown.
Examples
# An API KEY is required in order to invoke the Agent
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
polar_bear_researcher <- Agent$new(
name = "POLAR BEAR RESEARCHER",
instruction = paste0(
"You are an expert in polar bears, ",
"you task is to collect information about polar bears. Answer in 1 sentence max."
),
llm_object = openai_4_1_mini
)
Method invoke()
Sends a user prompt to the agent and returns the assistant's response.
Usage
Agent$invoke(prompt)
Arguments
promptA character string prompt for the agent to respond to.
Returns
The LLM-generated response as a character string.
Examples
\dontrun{
# An API KEY is required in order to invoke the Agent
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "translator",
instruction = "You are an Algerian citizen",
llm_object = openai_4_1_mini
)
agent$invoke("Continue this sentence: 1 2 3 viva")
}
Method generate_execute_r_code()
Generate R code from natural language descriptions and optionally validate/execute it
Usage
Agent$generate_execute_r_code(
code_description,
validate = FALSE,
execute = FALSE,
interactive = TRUE,
env = globalenv()
)
Arguments
code_descriptionCharacter string describing the R code to generate
validateLogical indicating whether to validate the generated code syntax
executeLogical indicating whether to execute the generated code (use with caution)
interactiveLogical; if TRUE, ask for user confirmation before executing generated code
envEnvironment in which to execute the code if execute = TRUE. Default to globalenv
Returns
A list containing the generated code and validation/execution results
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
r_assistant <- Agent$new(
name = "R Code Assistant",
instruction = paste("You are an expert R programmer",
llm_object = openai_4_1_mini
)
# Generate code for data manipulation
result <- r_assistant$generate_execute_r_code(
code_description = "Calculate the summary of the mtcars dataframe",
validate = TRUE,
execute = TRUE,
interactive = TRUE
)
print(result)
}
Method set_budget()
Set a budget to a specific agent, if the budget is reached, an error will be thrown
Usage
Agent$set_budget(amount_in_usd)
Arguments
amount_in_usdNumerical value denoting the amount to set for the budget,
Examples
\dontrun{
# An API KEY is required in order to invoke the Agent
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "translator",
instruction = "You are an Algerian citizen",
llm_object = openai_4_1_mini
)
agent$set_budget(amount_in_usd = 10.5) # this is equivalent to 10.5$
}
Method set_budget_policy()
Configure how the agent behaves as it approaches or exceeds its budget.
Use 'warn_at' (0-1) to emit a one-time warning when spending reaches the
specified fraction of the budget. When the budget is exceeded, 'on_exceed'
controls behavior: abort, warn and proceed, or ask interactively.
Usage
Agent$set_budget_policy(on_exceed = "abort", warn_at = 0.8)
Arguments
on_exceedOne of "abort", "warn", or "ask".
warn_atNumeric in (0,1); fraction of budget to warn at. Default 0.8.
Examples
\dontrun{
agent$set_budget(5)
agent$set_budget_policy(on_exceed = "ask", warn_at = 0.9)
}
Method keep_last_n_messages()
Keep only the most recent 'n' messages, discarding older ones while keeping
the system prompt.
Usage
Agent$keep_last_n_messages(n = 2)
Arguments
nNumber of most recent messages to keep.
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "capital finder",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$invoke("What is the capital of Algeria")
agent$invoke("What is the capital of Germany")
agent$invoke("What is the capital of Italy")
agent$keep_last_n_messages(n = 2)
}
Method add_message()
Add a pre-formatted message to the conversation history
Usage
Agent$add_message(role, content)
Arguments
roleThe role of the message ("user", "assistant", or "system")
contentThe content of the message
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "AI assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$add_message("user", "Hello, how are you?")
agent$add_message("assistant", "I'm doing well, thank you!")
}
Method clear_and_summarise_messages()
Summarises the agent's conversation history into a concise form and appends it
to the system prompt. Unlike 'update_instruction()', this method does not override
the existing instruction but augments it with a summary for future context.
After creating the summary, the method clears the conversation history and
retains only the updated system prompt. This ensures that subsequent interactions
start fresh but with the summary preserved as context.
Usage
Agent$clear_and_summarise_messages()
Examples
\dontrun{
# Requires an OpenAI-compatible LLM from `ellmer`
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "summariser",
instruction = "You are a summarising assistant",
llm_object = openai_4_1_mini
)
agent$invoke("The quick brown fox jumps over the lazy dog.")
agent$invoke("This is another example sentence.")
# Summarises and resets history
agent$summarise_messages()
# Now only the system prompt (with summary) remains
agent$messages
}
Method update_instruction()
Update the system prompt/instruction
Usage
Agent$update_instruction(new_instruction)
Arguments
new_instructionNew instruction to use. Not that the new instruction
will override the old one
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$update_instruction("You are a concise assistant.")
}
Method get_usage_stats()
Get the current token count and estimated cost of the conversation
Usage
Agent$get_usage_stats()
Returns
A list with token counts and cost information
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$set_budget(1)
agent$invoke("What is the capital of Algeria?")
stats <- agent$get_usage_stats()
stats
}
Method reset_conversation_history()
Reset the agent's conversation history while keeping the system instruction
Usage
Agent$reset_conversation_history()
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "AI assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$invoke("Hello, how are you?")
agent$invoke("Tell me about machine learning")
agent$reset_conversation_history() # Clears all messages except system prompt
}
Method export_messages_history()
Saves the agent's current conversation history as a JSON file on disk.
Usage
Agent$export_messages_history(
file_path = paste0(getwd(), "/", paste0(self$name, "_messages.json"))
)
Arguments
file_pathCharacter string specifying the file path where the JSON
file should be saved. Defaults to a file named
'"<agent_name>_messages.json"' in the current working directory.
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "capital_finder",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$invoke("What is the capital of Algeria")
agent$invoke("What is the capital of Italy")
agent$export_messages_history()
}
Method load_messages_history()
Load an agent's conversation history as a JSON file from disk.
Usage
Agent$load_messages_history(
file_path = paste0(getwd(), "/", paste0(self$name, "_messages.json"))
)
Arguments
file_pathCharacter string specifying the file path where the JSON
file is stored. Defaults to a file named
'"<agent_name>_messages.json"' in the current working directory.
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "capital_finder",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
# use the export_messages_history to save the interaction first
agent$load_messages_history("path/to/messages.json")
agent$messages
agent$llm_object
}
Method validate_response()
Validates an agent's response against custom criteria using LLM-based validation.
This method uses the agent's LLM to evaluate whether a response meets specified
validation criteria (e.g., accuracy, completeness, tone, format).
Usage
Agent$validate_response(
prompt,
response,
validation_criteria,
validation_score = 0.8
)
Arguments
promptThe prompt used to generate the response.
responseThe response text to validate.
validation_criteriaThe criteria for validation.
(e.g., "The response should be accurate and complete", "The response must be under 100 words").
validation_scoreA numeric from 0 to 1 denoting the score to consider for
the evaluation. During the evaluation, the agent will provide a score from 0 to 1,
a provided score greater or equal to the 'validation_score' will result in a 'valid' response.
Defaults to 0.8.
Returns
list object
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "fact_checker",
instruction = "You are a factual assistant.",
llm_object = openai_4_1_mini
)
prompt <- "What is the capital of Algeria?"
response <- agent$invoke(prompt)
validation <- agent$validate_response(
response = response,
prompt = prompt,
validation_criteria = "The response must be accurate and mention Algiers",
validation_score = 0.8
)
print(validation)
}
Method register_tools()
Register one or more tools with the agent
Usage
Agent$register_tools(tools)
Arguments
toolsA list of ellmer tool objects or a single tool object
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "file_assistant",
instruction = "You are a file management assistant.",
llm_object = openai_4_1_mini
)
# Register predefined tools
agent$register_tools(list(change_directory_tool(), list_files_tool()))
}
Method list_tools()
List all registered tools
Usage
Agent$list_tools()
Returns
Character vector of tool names
Examples
\dontrun{
agent$list_tools()
}
Method remove_tools()
Remove tools from the agent
Usage
Agent$remove_tools(tool_names)
Arguments
tool_namesCharacter vector of tool names to remove
Examples
\dontrun{
agent$remove_tools(c("change_directory", "list_files"))
}
Method clear_tools()
Clear all registered tools
Usage
Agent$clear_tools()
Examples
\dontrun{
agent$clear_tools()
}
Method generate_and_register_tool()
Generate and register a tool from a natural language description.
This method uses the agent's LLM to create both the R function and the
ellmer tool definition based on your description, then automatically registers it.
Usage
Agent$generate_and_register_tool(description)
Arguments
descriptionCharacter string describing what the tool should do
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "file_assistant",
instruction = "You are a helpful assistant.",
llm_object = openai_4_1_mini
)
# Generate and register a tool from description
agent$generate_and_register_tool(
description = "Create a tool that saves text content to a file on disk"
)
# Now the tool is available to use
agent$list_tools()
}
Method clone_agent()
Create a copy of the agent with the same instruction and configuration but a new unique ID.
Useful for creating multiple instances of the same agent type.
Usage
Agent$clone_agent(new_name = NULL)
Arguments
new_nameOptional character string to assign a new name to the cloned agent.
If NULL, the cloned agent retains the original name.
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "translator",
instruction = "You are a translator.",
llm_object = openai_4_1_mini
)
# Clone with same name
agent_copy <- agent$clone_agent()
}
Method clone()
The objects of this class are cloneable with this method.
Usage
Agent$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
[load_messages_history()] for reloading a saved message history.
[export_messages_history()] for exporting the messages object to json.
Examples
## ------------------------------------------------
## Method `Agent$new`
## ------------------------------------------------
# An API KEY is required in order to invoke the Agent
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
polar_bear_researcher <- Agent$new(
name = "POLAR BEAR RESEARCHER",
instruction = paste0(
"You are an expert in polar bears, ",
"you task is to collect information about polar bears. Answer in 1 sentence max."
),
llm_object = openai_4_1_mini
)
## ------------------------------------------------
## Method `Agent$invoke`
## ------------------------------------------------
## Not run:
# An API KEY is required in order to invoke the Agent
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "translator",
instruction = "You are an Algerian citizen",
llm_object = openai_4_1_mini
)
agent$invoke("Continue this sentence: 1 2 3 viva")
## End(Not run)
## ------------------------------------------------
## Method `Agent$generate_execute_r_code`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
r_assistant <- Agent$new(
name = "R Code Assistant",
instruction = paste("You are an expert R programmer",
llm_object = openai_4_1_mini
)
# Generate code for data manipulation
result <- r_assistant$generate_execute_r_code(
code_description = "Calculate the summary of the mtcars dataframe",
validate = TRUE,
execute = TRUE,
interactive = TRUE
)
print(result)
## End(Not run)
## ------------------------------------------------
## Method `Agent$set_budget`
## ------------------------------------------------
## Not run:
# An API KEY is required in order to invoke the Agent
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "translator",
instruction = "You are an Algerian citizen",
llm_object = openai_4_1_mini
)
agent$set_budget(amount_in_usd = 10.5) # this is equivalent to 10.5$
## End(Not run)
## ------------------------------------------------
## Method `Agent$set_budget_policy`
## ------------------------------------------------
## Not run:
agent$set_budget(5)
agent$set_budget_policy(on_exceed = "ask", warn_at = 0.9)
## End(Not run)
## ------------------------------------------------
## Method `Agent$keep_last_n_messages`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "capital finder",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$invoke("What is the capital of Algeria")
agent$invoke("What is the capital of Germany")
agent$invoke("What is the capital of Italy")
agent$keep_last_n_messages(n = 2)
## End(Not run)
## ------------------------------------------------
## Method `Agent$add_message`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "AI assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$add_message("user", "Hello, how are you?")
agent$add_message("assistant", "I'm doing well, thank you!")
## End(Not run)
## ------------------------------------------------
## Method `Agent$clear_and_summarise_messages`
## ------------------------------------------------
## Not run:
# Requires an OpenAI-compatible LLM from `ellmer`
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "summariser",
instruction = "You are a summarising assistant",
llm_object = openai_4_1_mini
)
agent$invoke("The quick brown fox jumps over the lazy dog.")
agent$invoke("This is another example sentence.")
# Summarises and resets history
agent$summarise_messages()
# Now only the system prompt (with summary) remains
agent$messages
## End(Not run)
## ------------------------------------------------
## Method `Agent$update_instruction`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$update_instruction("You are a concise assistant.")
## End(Not run)
## ------------------------------------------------
## Method `Agent$get_usage_stats`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$set_budget(1)
agent$invoke("What is the capital of Algeria?")
stats <- agent$get_usage_stats()
stats
## End(Not run)
## ------------------------------------------------
## Method `Agent$reset_conversation_history`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "AI assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$invoke("Hello, how are you?")
agent$invoke("Tell me about machine learning")
agent$reset_conversation_history() # Clears all messages except system prompt
## End(Not run)
## ------------------------------------------------
## Method `Agent$export_messages_history`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "capital_finder",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$invoke("What is the capital of Algeria")
agent$invoke("What is the capital of Italy")
agent$export_messages_history()
## End(Not run)
## ------------------------------------------------
## Method `Agent$load_messages_history`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "capital_finder",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
# use the export_messages_history to save the interaction first
agent$load_messages_history("path/to/messages.json")
agent$messages
agent$llm_object
## End(Not run)
## ------------------------------------------------
## Method `Agent$validate_response`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "fact_checker",
instruction = "You are a factual assistant.",
llm_object = openai_4_1_mini
)
prompt <- "What is the capital of Algeria?"
response <- agent$invoke(prompt)
validation <- agent$validate_response(
response = response,
prompt = prompt,
validation_criteria = "The response must be accurate and mention Algiers",
validation_score = 0.8
)
print(validation)
## End(Not run)
## ------------------------------------------------
## Method `Agent$register_tools`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "file_assistant",
instruction = "You are a file management assistant.",
llm_object = openai_4_1_mini
)
# Register predefined tools
agent$register_tools(list(change_directory_tool(), list_files_tool()))
## End(Not run)
## ------------------------------------------------
## Method `Agent$list_tools`
## ------------------------------------------------
## Not run:
agent$list_tools()
## End(Not run)
## ------------------------------------------------
## Method `Agent$remove_tools`
## ------------------------------------------------
## Not run:
agent$remove_tools(c("change_directory", "list_files"))
## End(Not run)
## ------------------------------------------------
## Method `Agent$clear_tools`
## ------------------------------------------------
## Not run:
agent$clear_tools()
## End(Not run)
## ------------------------------------------------
## Method `Agent$generate_and_register_tool`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "file_assistant",
instruction = "You are a helpful assistant.",
llm_object = openai_4_1_mini
)
# Generate and register a tool from description
agent$generate_and_register_tool(
description = "Create a tool that saves text content to a file on disk"
)
# Now the tool is available to use
agent$list_tools()
## End(Not run)
## ------------------------------------------------
## Method `Agent$clone_agent`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "translator",
instruction = "You are a translator.",
llm_object = openai_4_1_mini
)
# Clone with same name
agent_copy <- agent$clone_agent()
## End(Not run)
mini007 documentation built on Jan. 12, 2026, 5:08 p.m.
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| Agent | R Documentation |
Agent: A General-Purpose LLM Agent
Description
The 'Agent' class defines a modular LLM-based agent capable of responding to prompts using a defined role/instruction. It wraps an OpenAI-compatible chat model via the ['ellmer'](https://github.com/llrs/ellmer) package.
Each agent maintains its own message history and unique identity.
Public fields
nameThe agent's name.
instructionThe agent's role/system prompt.
llm_objectThe underlying 'ellmer::chat_openai' object.
agent_idA UUID uniquely identifying the agent.
model_providerThe name of the entity providing the model (eg. OpenAI)
model_nameThe name of the model to be used (eg. gpt-4.1-mini)
broadcast_historyA list of all past broadcast interactions.
budgetA budget in $ that the agent should not exceed.
budget_policyA list controlling budget behavior: on_exceed and warn_at.
budget_warnedInternal flag indicating whether warn_at notice was emitted.
costThe current cost of the agent
toolsA list of registered tools available to the agent
Active bindings
messagesPublic active binding for the conversation history. Assignment is validated automatically.
Methods
Public methods
Method new()
Initializes a new Agent with a specific role/instruction.
Usage
Agent$new(name, instruction, llm_object, budget = NA)
Arguments
nameA short identifier for the agent (e.g. '"translator"').
instructionThe system prompt that defines the agent's role.
llm_objectThe LLM object generate by ellmer (eg. output of ellmer::chat_openai)
budgetNumerical value denoting the amount to set for the budget in US$ to a specific agent, if the budget is reached, an error will be thrown.
Examples
# An API KEY is required in order to invoke the Agent
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
polar_bear_researcher <- Agent$new(
name = "POLAR BEAR RESEARCHER",
instruction = paste0(
"You are an expert in polar bears, ",
"you task is to collect information about polar bears. Answer in 1 sentence max."
),
llm_object = openai_4_1_mini
)
Method invoke()
Sends a user prompt to the agent and returns the assistant's response.
Usage
Agent$invoke(prompt)
Arguments
promptA character string prompt for the agent to respond to.
Returns
The LLM-generated response as a character string.
Examples
\dontrun{
# An API KEY is required in order to invoke the Agent
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "translator",
instruction = "You are an Algerian citizen",
llm_object = openai_4_1_mini
)
agent$invoke("Continue this sentence: 1 2 3 viva")
}
Method generate_execute_r_code()
Generate R code from natural language descriptions and optionally validate/execute it
Usage
Agent$generate_execute_r_code( code_description, validate = FALSE, execute = FALSE, interactive = TRUE, env = globalenv() )
Arguments
code_descriptionCharacter string describing the R code to generate
validateLogical indicating whether to validate the generated code syntax
executeLogical indicating whether to execute the generated code (use with caution)
interactiveLogical; if TRUE, ask for user confirmation before executing generated code
envEnvironment in which to execute the code if execute = TRUE. Default to
globalenv
Returns
A list containing the generated code and validation/execution results
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
r_assistant <- Agent$new(
name = "R Code Assistant",
instruction = paste("You are an expert R programmer",
llm_object = openai_4_1_mini
)
# Generate code for data manipulation
result <- r_assistant$generate_execute_r_code(
code_description = "Calculate the summary of the mtcars dataframe",
validate = TRUE,
execute = TRUE,
interactive = TRUE
)
print(result)
}
Method set_budget()
Set a budget to a specific agent, if the budget is reached, an error will be thrown
Usage
Agent$set_budget(amount_in_usd)
Arguments
amount_in_usdNumerical value denoting the amount to set for the budget,
Examples
\dontrun{
# An API KEY is required in order to invoke the Agent
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "translator",
instruction = "You are an Algerian citizen",
llm_object = openai_4_1_mini
)
agent$set_budget(amount_in_usd = 10.5) # this is equivalent to 10.5$
}
Method set_budget_policy()
Configure how the agent behaves as it approaches or exceeds its budget. Use 'warn_at' (0-1) to emit a one-time warning when spending reaches the specified fraction of the budget. When the budget is exceeded, 'on_exceed' controls behavior: abort, warn and proceed, or ask interactively.
Usage
Agent$set_budget_policy(on_exceed = "abort", warn_at = 0.8)
Arguments
on_exceedOne of "abort", "warn", or "ask".
warn_atNumeric in (0,1); fraction of budget to warn at. Default 0.8.
Examples
\dontrun{
agent$set_budget(5)
agent$set_budget_policy(on_exceed = "ask", warn_at = 0.9)
}
Method keep_last_n_messages()
Keep only the most recent 'n' messages, discarding older ones while keeping the system prompt.
Usage
Agent$keep_last_n_messages(n = 2)
Arguments
nNumber of most recent messages to keep.
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "capital finder",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$invoke("What is the capital of Algeria")
agent$invoke("What is the capital of Germany")
agent$invoke("What is the capital of Italy")
agent$keep_last_n_messages(n = 2)
}
Method add_message()
Add a pre-formatted message to the conversation history
Usage
Agent$add_message(role, content)
Arguments
roleThe role of the message ("user", "assistant", or "system")
contentThe content of the message
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "AI assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$add_message("user", "Hello, how are you?")
agent$add_message("assistant", "I'm doing well, thank you!")
}
Method clear_and_summarise_messages()
Summarises the agent's conversation history into a concise form and appends it to the system prompt. Unlike 'update_instruction()', this method does not override the existing instruction but augments it with a summary for future context.
After creating the summary, the method clears the conversation history and retains only the updated system prompt. This ensures that subsequent interactions start fresh but with the summary preserved as context.
Usage
Agent$clear_and_summarise_messages()
Examples
\dontrun{
# Requires an OpenAI-compatible LLM from `ellmer`
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "summariser",
instruction = "You are a summarising assistant",
llm_object = openai_4_1_mini
)
agent$invoke("The quick brown fox jumps over the lazy dog.")
agent$invoke("This is another example sentence.")
# Summarises and resets history
agent$summarise_messages()
# Now only the system prompt (with summary) remains
agent$messages
}
Method update_instruction()
Update the system prompt/instruction
Usage
Agent$update_instruction(new_instruction)
Arguments
new_instructionNew instruction to use. Not that the new instruction will override the old one
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$update_instruction("You are a concise assistant.")
}
Method get_usage_stats()
Get the current token count and estimated cost of the conversation
Usage
Agent$get_usage_stats()
Returns
A list with token counts and cost information
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$set_budget(1)
agent$invoke("What is the capital of Algeria?")
stats <- agent$get_usage_stats()
stats
}
Method reset_conversation_history()
Reset the agent's conversation history while keeping the system instruction
Usage
Agent$reset_conversation_history()
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "AI assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$invoke("Hello, how are you?")
agent$invoke("Tell me about machine learning")
agent$reset_conversation_history() # Clears all messages except system prompt
}
Method export_messages_history()
Saves the agent's current conversation history as a JSON file on disk.
Usage
Agent$export_messages_history( file_path = paste0(getwd(), "/", paste0(self$name, "_messages.json")) )
Arguments
file_pathCharacter string specifying the file path where the JSON file should be saved. Defaults to a file named '"<agent_name>_messages.json"' in the current working directory.
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "capital_finder",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$invoke("What is the capital of Algeria")
agent$invoke("What is the capital of Italy")
agent$export_messages_history()
}
Method load_messages_history()
Load an agent's conversation history as a JSON file from disk.
Usage
Agent$load_messages_history( file_path = paste0(getwd(), "/", paste0(self$name, "_messages.json")) )
Arguments
file_pathCharacter string specifying the file path where the JSON file is stored. Defaults to a file named '"<agent_name>_messages.json"' in the current working directory.
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "capital_finder",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
# use the export_messages_history to save the interaction first
agent$load_messages_history("path/to/messages.json")
agent$messages
agent$llm_object
}
Method validate_response()
Validates an agent's response against custom criteria using LLM-based validation. This method uses the agent's LLM to evaluate whether a response meets specified validation criteria (e.g., accuracy, completeness, tone, format).
Usage
Agent$validate_response( prompt, response, validation_criteria, validation_score = 0.8 )
Arguments
promptThe prompt used to generate the response.
responseThe response text to validate.
validation_criteriaThe criteria for validation. (e.g., "The response should be accurate and complete", "The response must be under 100 words").
validation_scoreA numeric from 0 to 1 denoting the score to consider for the evaluation. During the evaluation, the agent will provide a score from 0 to 1, a provided score greater or equal to the 'validation_score' will result in a 'valid' response. Defaults to 0.8.
Returns
list object
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "fact_checker",
instruction = "You are a factual assistant.",
llm_object = openai_4_1_mini
)
prompt <- "What is the capital of Algeria?"
response <- agent$invoke(prompt)
validation <- agent$validate_response(
response = response,
prompt = prompt,
validation_criteria = "The response must be accurate and mention Algiers",
validation_score = 0.8
)
print(validation)
}
Method register_tools()
Register one or more tools with the agent
Usage
Agent$register_tools(tools)
Arguments
toolsA list of ellmer tool objects or a single tool object
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "file_assistant",
instruction = "You are a file management assistant.",
llm_object = openai_4_1_mini
)
# Register predefined tools
agent$register_tools(list(change_directory_tool(), list_files_tool()))
}
Method list_tools()
List all registered tools
Usage
Agent$list_tools()
Returns
Character vector of tool names
Examples
\dontrun{
agent$list_tools()
}
Method remove_tools()
Remove tools from the agent
Usage
Agent$remove_tools(tool_names)
Arguments
tool_namesCharacter vector of tool names to remove
Examples
\dontrun{
agent$remove_tools(c("change_directory", "list_files"))
}
Method clear_tools()
Clear all registered tools
Usage
Agent$clear_tools()
Examples
\dontrun{
agent$clear_tools()
}
Method generate_and_register_tool()
Generate and register a tool from a natural language description. This method uses the agent's LLM to create both the R function and the ellmer tool definition based on your description, then automatically registers it.
Usage
Agent$generate_and_register_tool(description)
Arguments
descriptionCharacter string describing what the tool should do
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "file_assistant",
instruction = "You are a helpful assistant.",
llm_object = openai_4_1_mini
)
# Generate and register a tool from description
agent$generate_and_register_tool(
description = "Create a tool that saves text content to a file on disk"
)
# Now the tool is available to use
agent$list_tools()
}
Method clone_agent()
Create a copy of the agent with the same instruction and configuration but a new unique ID. Useful for creating multiple instances of the same agent type.
Usage
Agent$clone_agent(new_name = NULL)
Arguments
new_nameOptional character string to assign a new name to the cloned agent. If NULL, the cloned agent retains the original name.
Examples
\dontrun{
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "translator",
instruction = "You are a translator.",
llm_object = openai_4_1_mini
)
# Clone with same name
agent_copy <- agent$clone_agent()
}
Method clone()
The objects of this class are cloneable with this method.
Usage
Agent$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
[load_messages_history()] for reloading a saved message history.
[export_messages_history()] for exporting the messages object to json.
Examples
## ------------------------------------------------
## Method `Agent$new`
## ------------------------------------------------
# An API KEY is required in order to invoke the Agent
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
polar_bear_researcher <- Agent$new(
name = "POLAR BEAR RESEARCHER",
instruction = paste0(
"You are an expert in polar bears, ",
"you task is to collect information about polar bears. Answer in 1 sentence max."
),
llm_object = openai_4_1_mini
)
## ------------------------------------------------
## Method `Agent$invoke`
## ------------------------------------------------
## Not run:
# An API KEY is required in order to invoke the Agent
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "translator",
instruction = "You are an Algerian citizen",
llm_object = openai_4_1_mini
)
agent$invoke("Continue this sentence: 1 2 3 viva")
## End(Not run)
## ------------------------------------------------
## Method `Agent$generate_execute_r_code`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
r_assistant <- Agent$new(
name = "R Code Assistant",
instruction = paste("You are an expert R programmer",
llm_object = openai_4_1_mini
)
# Generate code for data manipulation
result <- r_assistant$generate_execute_r_code(
code_description = "Calculate the summary of the mtcars dataframe",
validate = TRUE,
execute = TRUE,
interactive = TRUE
)
print(result)
## End(Not run)
## ------------------------------------------------
## Method `Agent$set_budget`
## ------------------------------------------------
## Not run:
# An API KEY is required in order to invoke the Agent
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "translator",
instruction = "You are an Algerian citizen",
llm_object = openai_4_1_mini
)
agent$set_budget(amount_in_usd = 10.5) # this is equivalent to 10.5$
## End(Not run)
## ------------------------------------------------
## Method `Agent$set_budget_policy`
## ------------------------------------------------
## Not run:
agent$set_budget(5)
agent$set_budget_policy(on_exceed = "ask", warn_at = 0.9)
## End(Not run)
## ------------------------------------------------
## Method `Agent$keep_last_n_messages`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "capital finder",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$invoke("What is the capital of Algeria")
agent$invoke("What is the capital of Germany")
agent$invoke("What is the capital of Italy")
agent$keep_last_n_messages(n = 2)
## End(Not run)
## ------------------------------------------------
## Method `Agent$add_message`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "AI assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$add_message("user", "Hello, how are you?")
agent$add_message("assistant", "I'm doing well, thank you!")
## End(Not run)
## ------------------------------------------------
## Method `Agent$clear_and_summarise_messages`
## ------------------------------------------------
## Not run:
# Requires an OpenAI-compatible LLM from `ellmer`
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "summariser",
instruction = "You are a summarising assistant",
llm_object = openai_4_1_mini
)
agent$invoke("The quick brown fox jumps over the lazy dog.")
agent$invoke("This is another example sentence.")
# Summarises and resets history
agent$summarise_messages()
# Now only the system prompt (with summary) remains
agent$messages
## End(Not run)
## ------------------------------------------------
## Method `Agent$update_instruction`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$update_instruction("You are a concise assistant.")
## End(Not run)
## ------------------------------------------------
## Method `Agent$get_usage_stats`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$set_budget(1)
agent$invoke("What is the capital of Algeria?")
stats <- agent$get_usage_stats()
stats
## End(Not run)
## ------------------------------------------------
## Method `Agent$reset_conversation_history`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "AI assistant",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$invoke("Hello, how are you?")
agent$invoke("Tell me about machine learning")
agent$reset_conversation_history() # Clears all messages except system prompt
## End(Not run)
## ------------------------------------------------
## Method `Agent$export_messages_history`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "capital_finder",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
agent$invoke("What is the capital of Algeria")
agent$invoke("What is the capital of Italy")
agent$export_messages_history()
## End(Not run)
## ------------------------------------------------
## Method `Agent$load_messages_history`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "capital_finder",
instruction = "You are an assistant.",
llm_object = openai_4_1_mini
)
# use the export_messages_history to save the interaction first
agent$load_messages_history("path/to/messages.json")
agent$messages
agent$llm_object
## End(Not run)
## ------------------------------------------------
## Method `Agent$validate_response`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "fact_checker",
instruction = "You are a factual assistant.",
llm_object = openai_4_1_mini
)
prompt <- "What is the capital of Algeria?"
response <- agent$invoke(prompt)
validation <- agent$validate_response(
response = response,
prompt = prompt,
validation_criteria = "The response must be accurate and mention Algiers",
validation_score = 0.8
)
print(validation)
## End(Not run)
## ------------------------------------------------
## Method `Agent$register_tools`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "file_assistant",
instruction = "You are a file management assistant.",
llm_object = openai_4_1_mini
)
# Register predefined tools
agent$register_tools(list(change_directory_tool(), list_files_tool()))
## End(Not run)
## ------------------------------------------------
## Method `Agent$list_tools`
## ------------------------------------------------
## Not run:
agent$list_tools()
## End(Not run)
## ------------------------------------------------
## Method `Agent$remove_tools`
## ------------------------------------------------
## Not run:
agent$remove_tools(c("change_directory", "list_files"))
## End(Not run)
## ------------------------------------------------
## Method `Agent$clear_tools`
## ------------------------------------------------
## Not run:
agent$clear_tools()
## End(Not run)
## ------------------------------------------------
## Method `Agent$generate_and_register_tool`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "file_assistant",
instruction = "You are a helpful assistant.",
llm_object = openai_4_1_mini
)
# Generate and register a tool from description
agent$generate_and_register_tool(
description = "Create a tool that saves text content to a file on disk"
)
# Now the tool is available to use
agent$list_tools()
## End(Not run)
## ------------------------------------------------
## Method `Agent$clone_agent`
## ------------------------------------------------
## Not run:
openai_4_1_mini <- ellmer::chat(
name = "openai/gpt-4.1-mini",
api_key = Sys.getenv("OPENAI_API_KEY"),
echo = "none"
)
agent <- Agent$new(
name = "translator",
instruction = "You are a translator.",
llm_object = openai_4_1_mini
)
# Clone with same name
agent_copy <- agent$clone_agent()
## End(Not run)
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