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

Active bindings

Methods

Public methods

• Agent$new()

• Agent$invoke()

• Agent$generate_execute_r_code()

• Agent$set_budget()

• Agent$set_budget_policy()

• Agent$keep_last_n_messages()

• Agent$clear_and_summarise_messages()

• Agent$update_instruction()

• Agent$get_usage_stats()

• Agent$add_message()

• Agent$reset_conversation_history()

• Agent$export_messages_history()

• Agent$load_messages_history()

• Agent$clone()

Method new()

Initializes a new Agent with a specific role/instruction.

Usage

Agent$new(name, instruction, llm_object, budget = NA)

Arguments

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

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

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

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

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

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 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

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 add_message()

Add a pre-formatted message to the conversation history

Usage

Agent$add_message(role, content)

Arguments

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 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

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()

Saves the agent's current conversation history as a JSON file on disk.

Usage

Agent$load_messages_history(
  file_path = paste0(getwd(), "/", paste0(self$name, "_messages.json"))
)

Arguments

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$new(
  name = "capital_finder",
  instruction = "You are an assistant.",
  llm_object = openai_4_1_mini
)
agent$load_messages_history("path/to/messages.json")
agent$messages
agent$llm_object
}

Method clone()

The objects of this class are cloneable with this method.

Usage

Agent$clone(deep = FALSE)

Arguments

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$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$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$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$new(
  name = "capital_finder",
  instruction = "You are an assistant.",
  llm_object = openai_4_1_mini
)
agent$load_messages_history("path/to/messages.json")
agent$messages
agent$llm_object

## End(Not run)

LeadAgent: A Multi-Agent Orchestration Coordinator

Description

'LeadAgent' extends 'Agent' to coordinate a group of specialized agents. It decomposes complex prompts into subtasks using LLMs and assigns each subtask to the most suitable registered agent. The lead agent handles response chaining, where each agent can consider prior results.

Details

This class builds intelligent multi-agent workflows by delegating sub-tasks using 'delegate_prompt()', executing them with 'invoke()', and storing the results in the 'agents_interaction' list.

Super class

mini007::Agent -> LeadAgent

Public fields

Methods

Public methods

• LeadAgent$new()

• LeadAgent$clear_agents()

• LeadAgent$remove_agents()

• LeadAgent$register_agents()

• LeadAgent$visualize_plan()

• LeadAgent$invoke()

• LeadAgent$generate_plan()

• LeadAgent$broadcast()

• LeadAgent$set_hitl()

• LeadAgent$judge_and_choose_best_response()

• LeadAgent$clone()

Method new()

Initializes the LeadAgent with a built-in task-decomposition prompt.

Usage

LeadAgent$new(name, llm_object)

Arguments

Examples

  # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )

 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

Method clear_agents()

Clear out the registered Agents

Usage

LeadAgent$clear_agents()

Examples

  # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
 researcher <- Agent$new(
   name = "researcher",
   instruction = paste0(
   "You are a research assistant. ",
   "Your job is to answer factual questions with detailed and accurate information. ",
   "Do not answer with more than 2 lines"
   ),
   llm_object = openai_4_1_mini
 )

 summarizer <- Agent$new(
   name = "summarizer",
   instruction = paste0(
   "You are an agent designed to summarise ",
   "a given text into 3 distinct bullet points."
   ),
   llm_object = openai_4_1_mini
 )

 translator <- Agent$new(
   name = "translator",
   instruction = "Your role is to translate a text from English to German",
   llm_object = openai_4_1_mini
 )
 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

 lead_agent$register_agents(c(researcher, summarizer, translator))

 lead_agent$agents

 lead_agent$clear_agents()

 lead_agent$agents

Method remove_agents()

Remove registered agents by IDs

Usage

LeadAgent$remove_agents(agent_ids)

Arguments

Examples

  # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
 researcher <- Agent$new(
   name = "researcher",
   instruction = paste0(
   "You are a research assistant. ",
   "Your job is to answer factual questions with detailed and accurate information. ",
   "Do not answer with more than 2 lines"
   ),
   llm_object = openai_4_1_mini
 )

 summarizer <- Agent$new(
   name = "summarizer",
   instruction = "You are agent designed to summarise a given text into 3 distinct bullet points.",
   llm_object = openai_4_1_mini
 )

 translator <- Agent$new(
   name = "translator",
   instruction = "Your role is to translate a text from English to German",
   llm_object = openai_4_1_mini
 )


 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

 lead_agent$register_agents(c(researcher, summarizer, translator))

 lead_agent$agents

 # deleting the translator agent

 id_translator_agent <- translator$agent_id

 lead_agent$remove_agents(id_translator_agent)

 lead_agent$agents

Method register_agents()

Register one or more agents for delegation.

Usage

LeadAgent$register_agents(agents)

Arguments

Examples

  # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
 researcher <- Agent$new(
   name = "researcher",
   instruction = paste0(
   "You are a research assistant. ",
   "Your job is to answer factual questions with detailed and accurate information. ",
   "Do not answer with more than 2 lines"
   ),
   llm_object = openai_4_1_mini
 )

 summarizer <- Agent$new(
   name = "summarizer",
   instruction = "You are agent designed to summarise a given text into 3 distinct bullet points.",
   llm_object = openai_4_1_mini
 )

 translator <- Agent$new(
   name = "translator",
   instruction = "Your role is to translate a text from English to German",
   llm_object = openai_4_1_mini
 )

 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

 lead_agent$register_agents(c(researcher, summarizer, translator))

 lead_agent$agents

Method visualize_plan()

Visualizes the orchestration plan Each agent node is shown in sequence (left → right), with tooltips showing the actual prompt delegated to that agent.

Usage

LeadAgent$visualize_plan()

Method invoke()

Executes the full prompt pipeline: decomposition → delegation → invocation.

Usage

LeadAgent$invoke(prompt, force_regenerate_plan = FALSE)

Arguments

Returns

The final response (from the last agent in the sequence).

Examples

\dontrun{
 # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
 researcher <- Agent$new(
   name = "researcher",
   instruction = paste0(
   "You are a research assistant. ",
   "Your job is to answer factual questions with detailed ",
   "and accurate information. Do not answer with more than 2 lines"
   ),
   llm_object = openai_4_1_mini
 )

 summarizer <- Agent$new(
   name = "summarizer",
   instruction = "You are agent designed to summarise a given text into 3 distinct bullet points.",
   llm_object = openai_4_1_mini
 )

 translator <- Agent$new(
   name = "translator",
   instruction = "Your role is to translate a text from English to German",
   llm_object = openai_4_1_mini
 )

 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

 lead_agent$register_agents(c(researcher, summarizer, translator))

 lead_agent$invoke(
 paste0(
  "Describe the economic situation in Algeria in 3 sentences. ",
  "Answer in German"
  )
 )
}

Method generate_plan()

Generates a task execution plan without executing the subtasks. It returns a structured list containing the subtask, the selected agent, and metadata.

Usage

LeadAgent$generate_plan(prompt)

Arguments

Returns

A list of lists containing agent_id, agent_name, model_name, model_provider, and the assigned prompt.

Examples

\dontrun{
 # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
 researcher <- Agent$new(
   name = "researcher",
   instruction = paste0(
   "You are a research assistant. Your job is to answer factual questions ",
   "with detailed and accurate information. Do not answer with more than 2 lines"
   ),
   llm_object = openai_4_1_mini
 )

 summarizer <- Agent$new(
   name = "summarizer",
   instruction = "You are agent designed to summarise a given text into 3 distinct bullet points.",
   llm_object = openai_4_1_mini
 )

 translator <- Agent$new(
   name = "translator",
   instruction = "Your role is to translate a text from English to German",
   llm_object = openai_4_1_mini
 )

 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

 lead_agent$register_agents(c(researcher, summarizer, translator))

 lead_agent$generate_plan(
 paste0(
  "Describe the economic situation in Algeria in 3 sentences. ",
  "Answer in German"
  )
 )
}

Method broadcast()

Broadcasts a prompt to all registered agents and collects their responses. This does not affect the main agent orchestration logic or history.

Usage

LeadAgent$broadcast(prompt)

Arguments

Returns

A list of responses from all agents.

Examples

\dontrun{
 # An API KEY is required in order to invoke the agents
openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
openai_4_1 <- ellmer::chat(
  name = "openai/gpt-4.1",
  api_key = Sys.getenv("OPENAI_API_KEY"),
  echo = "none"
)

openai_4_1_agent <- Agent$new(
  name = "openai_4_1_agent",
  instruction = "You are an AI assistant. Answer in 1 sentence max.",
  llm_object = openai_4_1
)

openai_4_1_nano <- ellmer::chat(
  name = "openai/gpt-4.1-nano",
  api_key = Sys.getenv("OPENAI_API_KEY"),
  echo = "none"
)

openai_4_1_nano_agent <- Agent$new(
  name = "openai_4_1_nano_agent",
  instruction = "You are an AI assistant. Answer in 1 sentence max.",
  llm_object = openai_4_1_nano
  )

 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

lead_agent$register_agents(c(openai_4_1_agent, openai_4_1_nano_agent))
lead_agent$broadcast(
  prompt = paste0(
    "If I were Algerian, which song would I like to sing ",
    "when running under the rain? how about a flower?"
  )
  )
}

Method set_hitl()

Set Human In The Loop (HITL) interaction at determined steps within the workflow

Usage

LeadAgent$set_hitl(steps)

Arguments

Returns

A list of responses from all agents.

Examples

\dontrun{
 # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
 researcher <- Agent$new(
   name = "researcher",
   instruction = paste0(
    "You are a research assistant. ",
    "Your job is to answer factual questions with detailed and accurate information. ",
    "Do not answer with more than 2 lines"
   ),
   llm_object = openai_4_1_mini
 )

 summarizer <- Agent$new(
   name = "summarizer",
   instruction = paste0(
   "You are agent designed to summarise a give text ",
   "into 3 distinct bullet points."
   ),
   llm_object = openai_4_1_mini
 )

 translator <- Agent$new(
   name = "translator",
   instruction = "Your role is to translate a text from English to German",
   llm_object = openai_4_1_mini
 )

 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

 lead_agent$register_agents(c(researcher, summarizer, translator))

 # setting a human in the loop in step 2
 lead_agent$set_hitl(1)

 # The execution will stop at step 2 and a human will be able
 # to either accept the answer, modify it or stop the execution of
 # the workflow

 lead_agent$invoke(
 paste0(
  "Describe the economic situation in Algeria in 3 sentences. ",
  "Answer in German"
  )
 )
}

Method judge_and_choose_best_response()

The Lead Agent send a prompt to its registered agents and choose the best response from the agents' responses

Usage

LeadAgent$judge_and_choose_best_response(prompt)

Arguments

Returns

A list of responses from all agents, including the chosen response

Examples

\dontrun{
openai_4_1_mini <- ellmer::chat(
  name = "openai/gpt-4.1-mini",
  api_key = Sys.getenv("OPENAI_API_KEY"),
  echo = "none"
)
openai_4_1 <- ellmer::chat(
  name = "openai/gpt-4.1",
  api_key = Sys.getenv("OPENAI_API_KEY"),
  echo = "none"
)

stylist <- Agent$new(
  name = "stylist",
  instruction = "You are an AI assistant. Answer in 1 sentence max.",
  llm_object = openai_4_1
)

openai_4_1_nano <- ellmer::chat(
  name = "openai/gpt-4.1-nano",
  api_key = Sys.getenv("OPENAI_API_KEY"),
  echo = "none"
)

stylist2 <- Agent$new(
  name = "stylist2",
  instruction = "You are an AI assistant. Answer in 1 sentence max.",
  llm_object = openai_4_1_nano
)

lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
)

lead_agent$register_agents(c(stylist, stylist2))

lead_agent$judge_and_choose_best_response("what's the best way to war a kalvin klein shirt?")

}

Method clone()

The objects of this class are cloneable with this method.

Usage

LeadAgent$clone(deep = FALSE)

Arguments

Examples

## ------------------------------------------------
## Method `LeadAgent$new`
## ------------------------------------------------


  # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )

 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )



## ------------------------------------------------
## Method `LeadAgent$clear_agents`
## ------------------------------------------------

  # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
 researcher <- Agent$new(
   name = "researcher",
   instruction = paste0(
   "You are a research assistant. ",
   "Your job is to answer factual questions with detailed and accurate information. ",
   "Do not answer with more than 2 lines"
   ),
   llm_object = openai_4_1_mini
 )

 summarizer <- Agent$new(
   name = "summarizer",
   instruction = paste0(
   "You are an agent designed to summarise ",
   "a given text into 3 distinct bullet points."
   ),
   llm_object = openai_4_1_mini
 )

 translator <- Agent$new(
   name = "translator",
   instruction = "Your role is to translate a text from English to German",
   llm_object = openai_4_1_mini
 )
 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

 lead_agent$register_agents(c(researcher, summarizer, translator))

 lead_agent$agents

 lead_agent$clear_agents()

 lead_agent$agents


## ------------------------------------------------
## Method `LeadAgent$remove_agents`
## ------------------------------------------------

  # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
 researcher <- Agent$new(
   name = "researcher",
   instruction = paste0(
   "You are a research assistant. ",
   "Your job is to answer factual questions with detailed and accurate information. ",
   "Do not answer with more than 2 lines"
   ),
   llm_object = openai_4_1_mini
 )

 summarizer <- Agent$new(
   name = "summarizer",
   instruction = "You are agent designed to summarise a given text into 3 distinct bullet points.",
   llm_object = openai_4_1_mini
 )

 translator <- Agent$new(
   name = "translator",
   instruction = "Your role is to translate a text from English to German",
   llm_object = openai_4_1_mini
 )


 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

 lead_agent$register_agents(c(researcher, summarizer, translator))

 lead_agent$agents

 # deleting the translator agent

 id_translator_agent <- translator$agent_id

 lead_agent$remove_agents(id_translator_agent)

 lead_agent$agents


## ------------------------------------------------
## Method `LeadAgent$register_agents`
## ------------------------------------------------

  # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
 researcher <- Agent$new(
   name = "researcher",
   instruction = paste0(
   "You are a research assistant. ",
   "Your job is to answer factual questions with detailed and accurate information. ",
   "Do not answer with more than 2 lines"
   ),
   llm_object = openai_4_1_mini
 )

 summarizer <- Agent$new(
   name = "summarizer",
   instruction = "You are agent designed to summarise a given text into 3 distinct bullet points.",
   llm_object = openai_4_1_mini
 )

 translator <- Agent$new(
   name = "translator",
   instruction = "Your role is to translate a text from English to German",
   llm_object = openai_4_1_mini
 )

 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

 lead_agent$register_agents(c(researcher, summarizer, translator))

 lead_agent$agents

## ------------------------------------------------
## Method `LeadAgent$invoke`
## ------------------------------------------------

## Not run: 
 # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
 researcher <- Agent$new(
   name = "researcher",
   instruction = paste0(
   "You are a research assistant. ",
   "Your job is to answer factual questions with detailed ",
   "and accurate information. Do not answer with more than 2 lines"
   ),
   llm_object = openai_4_1_mini
 )

 summarizer <- Agent$new(
   name = "summarizer",
   instruction = "You are agent designed to summarise a given text into 3 distinct bullet points.",
   llm_object = openai_4_1_mini
 )

 translator <- Agent$new(
   name = "translator",
   instruction = "Your role is to translate a text from English to German",
   llm_object = openai_4_1_mini
 )

 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

 lead_agent$register_agents(c(researcher, summarizer, translator))

 lead_agent$invoke(
 paste0(
  "Describe the economic situation in Algeria in 3 sentences. ",
  "Answer in German"
  )
 )

## End(Not run)

## ------------------------------------------------
## Method `LeadAgent$generate_plan`
## ------------------------------------------------

## Not run: 
 # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
 researcher <- Agent$new(
   name = "researcher",
   instruction = paste0(
   "You are a research assistant. Your job is to answer factual questions ",
   "with detailed and accurate information. Do not answer with more than 2 lines"
   ),
   llm_object = openai_4_1_mini
 )

 summarizer <- Agent$new(
   name = "summarizer",
   instruction = "You are agent designed to summarise a given text into 3 distinct bullet points.",
   llm_object = openai_4_1_mini
 )

 translator <- Agent$new(
   name = "translator",
   instruction = "Your role is to translate a text from English to German",
   llm_object = openai_4_1_mini
 )

 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

 lead_agent$register_agents(c(researcher, summarizer, translator))

 lead_agent$generate_plan(
 paste0(
  "Describe the economic situation in Algeria in 3 sentences. ",
  "Answer in German"
  )
 )

## End(Not run)

## ------------------------------------------------
## Method `LeadAgent$broadcast`
## ------------------------------------------------

## Not run: 
 # An API KEY is required in order to invoke the agents
openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
openai_4_1 <- ellmer::chat(
  name = "openai/gpt-4.1",
  api_key = Sys.getenv("OPENAI_API_KEY"),
  echo = "none"
)

openai_4_1_agent <- Agent$new(
  name = "openai_4_1_agent",
  instruction = "You are an AI assistant. Answer in 1 sentence max.",
  llm_object = openai_4_1
)

openai_4_1_nano <- ellmer::chat(
  name = "openai/gpt-4.1-nano",
  api_key = Sys.getenv("OPENAI_API_KEY"),
  echo = "none"
)

openai_4_1_nano_agent <- Agent$new(
  name = "openai_4_1_nano_agent",
  instruction = "You are an AI assistant. Answer in 1 sentence max.",
  llm_object = openai_4_1_nano
  )

 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

lead_agent$register_agents(c(openai_4_1_agent, openai_4_1_nano_agent))
lead_agent$broadcast(
  prompt = paste0(
    "If I were Algerian, which song would I like to sing ",
    "when running under the rain? how about a flower?"
  )
  )

## End(Not run)

## ------------------------------------------------
## Method `LeadAgent$set_hitl`
## ------------------------------------------------

## Not run: 
 # An API KEY is required in order to invoke the agents
  openai_4_1_mini <- ellmer::chat(
    name = "openai/gpt-4.1-mini",
    api_key = Sys.getenv("OPENAI_API_KEY"),
    echo = "none"
  )
 researcher <- Agent$new(
   name = "researcher",
   instruction = paste0(
    "You are a research assistant. ",
    "Your job is to answer factual questions with detailed and accurate information. ",
    "Do not answer with more than 2 lines"
   ),
   llm_object = openai_4_1_mini
 )

 summarizer <- Agent$new(
   name = "summarizer",
   instruction = paste0(
   "You are agent designed to summarise a give text ",
   "into 3 distinct bullet points."
   ),
   llm_object = openai_4_1_mini
 )

 translator <- Agent$new(
   name = "translator",
   instruction = "Your role is to translate a text from English to German",
   llm_object = openai_4_1_mini
 )

 lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
 )

 lead_agent$register_agents(c(researcher, summarizer, translator))

 # setting a human in the loop in step 2
 lead_agent$set_hitl(1)

 # The execution will stop at step 2 and a human will be able
 # to either accept the answer, modify it or stop the execution of
 # the workflow

 lead_agent$invoke(
 paste0(
  "Describe the economic situation in Algeria in 3 sentences. ",
  "Answer in German"
  )
 )

## End(Not run)

## ------------------------------------------------
## Method `LeadAgent$judge_and_choose_best_response`
## ------------------------------------------------

## Not run: 
openai_4_1_mini <- ellmer::chat(
  name = "openai/gpt-4.1-mini",
  api_key = Sys.getenv("OPENAI_API_KEY"),
  echo = "none"
)
openai_4_1 <- ellmer::chat(
  name = "openai/gpt-4.1",
  api_key = Sys.getenv("OPENAI_API_KEY"),
  echo = "none"
)

stylist <- Agent$new(
  name = "stylist",
  instruction = "You are an AI assistant. Answer in 1 sentence max.",
  llm_object = openai_4_1
)

openai_4_1_nano <- ellmer::chat(
  name = "openai/gpt-4.1-nano",
  api_key = Sys.getenv("OPENAI_API_KEY"),
  echo = "none"
)

stylist2 <- Agent$new(
  name = "stylist2",
  instruction = "You are an AI assistant. Answer in 1 sentence max.",
  llm_object = openai_4_1_nano
)

lead_agent <- LeadAgent$new(
  name = "Leader",
  llm_object = openai_4_1_mini
)

lead_agent$register_agents(c(stylist, stylist2))

lead_agent$judge_and_choose_best_response("what's the best way to war a kalvin klein shirt?")


## End(Not run)