mini007

mini007 provides a lightweight and extensible framework for multi-agents orchestration processes capable of decomposing complex tasks and assigning them to specialized agents.

Each agent is an extension of an ellmer object. mini007 relies heavily on the excellent ellmer package but aims to make it easy to create a process where multiple specialized agents help each other sequentially in order to execute a task.

mini007 provides two types of agents:

• A normal Agent containing a name and an instruction,
• and a LeadAgent which will take a complex prompt, split it, assign to the adequate agents and retrieve the response.

Highlights

🧠 Memory and identity for each agent via uuid and message history.

⚙️ Built-in task decomposition and delegation via LLM.

🔄 Agent-to-agent orchestration with result chaining.

🌐 Compatible with any chat model supported by ellmer.

🧑 Possibility to set a Human In The Loop (HITL) at various execution steps

You can install mini007 from CRAN with:

install.packages("mini007")

library(mini007)

Creating an Agent

An Agent is built upon an LLM object created by the ellmer package, in the following examples, we’ll work with the OpenAI models, however you can use any model/combination of models you want:

# no need to provide the system prompt, it will be set when creating the
# agent (see the 'instruction' parameter)

retrieve_open_ai_credential <- function() {
  Sys.getenv("OPENAI_API_KEY")
}

openai_4_1_mini <- ellmer::chat(
  name = "openai/gpt-4.1-mini",
  credentials = retrieve_open_ai_credential, 
  echo = "none"
)

After initializing the ellmer LLM object, creating the Agent is straightforward:

polar_bear_researcher <- Agent$new(
  name = "POLAR BEAR RESEARCHER",
  instruction = "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
)

Each created Agent has an agent_id (among other meta information):

polar_bear_researcher$agent_id
#> [1] "843e042c-1268-4904-bdae-2cb07fc5b9e4"

At any time, you can tweak the llm_object:

polar_bear_researcher$llm_object
#> <Chat OpenAI/gpt-4.1-mini turns=1 input=0 output=0 cost=$0.00>
#> ── system ──────────────────────────────────────────────────────────────────────
#> You are an expert in polar bears, you task is to collect information about polar bears. Answer in 1 sentence max.

An agent can provide the answer to a prompt using the invoke method:

polar_bear_researcher$invoke("Are polar bears dangerous for humans?")
#> Yes, polar bears are dangerous to humans as they are powerful predators and can
#> attack if threatened or hungry.

You can also retrieve a list that displays the history of the agent:

polar_bear_researcher$messages
#> [[1]]
#> [[1]]$role
#> [1] "system"
#> 
#> [[1]]$content
#> [1] "You are an expert in polar bears, you task is to collect information about polar bears. Answer in 1 sentence max."
#> 
#> 
#> [[2]]
#> [[2]]$role
#> [1] "user"
#> 
#> [[2]]$content
#> [1] "Are polar bears dangerous for humans?"
#> 
#> 
#> [[3]]
#> [[3]]$role
#> [1] "assistant"
#> 
#> [[3]]$content
#> [1] "Yes, polar bears are dangerous to humans as they are powerful predators and can attack if threatened or hungry."

Or the ellmer way:

polar_bear_researcher$llm_object
#> <Chat OpenAI/gpt-4.1-mini turns=3 input=43 output=22 cost=$0.00>
#> ── system ──────────────────────────────────────────────────────────────────────
#> You are an expert in polar bears, you task is to collect information about polar bears. Answer in 1 sentence max.
#> ── user ────────────────────────────────────────────────────────────────────────
#> Are polar bears dangerous for humans?
#> ── assistant [input=43 output=22 cost=$0.00] ───────────────────────────────────
#> Yes, polar bears are dangerous to humans as they are powerful predators and can attack if threatened or hungry.

Managing Agent Conversation History

The clear_and_summarise_messages method allows you to compress an agent’s conversation history into a concise summary and clear the message history while preserving context. This is useful for maintaining memory efficiency while keeping important conversation context.

# After several interactions, summarise and clear the conversation history
polar_bear_researcher$clear_and_summarise_messages()
#> ✔ Conversation history summarised and appended to system prompt.
#> ℹ Summary: The user asked if polar bears are dangerous to humans, and the assistant responded that polar bears ...
polar_bear_researcher$messages
#> [[1]]
#> [[1]]$role
#> [1] "system"
#> 
#> [[1]]$content
#> [1] "You are an expert in polar bears, you task is to collect information about polar bears. Answer in 1 sentence max. \n\n--- Conversation Summary ---\n The user asked if polar bears are dangerous to humans, and the assistant responded that polar bears are indeed dangerous as powerful predators that may attack when threatened or hungry."

This method summarises all previous conversations into a paragraph and appends it to the system prompt, then clears the conversation history. The agent retains the context but with reduced memory usage.

Keep only the most recent messages with keep_last_n_messages()

When a conversation grows long, you can keep just the last N messages while preserving the system prompt. This helps control token usage without fully resetting context.

openai_4_1_mini <- ellmer::chat(
  name = "openai/gpt-4.1-mini",
  credentials = retrieve_open_ai_credential, 
  echo = "none"
)

agent <- Agent$new(
  name = "history_manager",
  instruction = "You are a concise assistant.",
  llm_object = openai_4_1_mini
)

agent$invoke("What is the capital of Italy?")
#> The capital of Italy is Rome.
agent$invoke("What is the capital of Germany?")
#> The capital of Germany is Berlin.
agent$invoke("What is the capital of Algeria?")
#> The capital of Algeria is Algiers.
agent$messages
#> [[1]]
#> [[1]]$role
#> [1] "system"
#> 
#> [[1]]$content
#> [1] "You are a concise assistant."
#> 
#> 
#> [[2]]
#> [[2]]$role
#> [1] "user"
#> 
#> [[2]]$content
#> [1] "What is the capital of Italy?"
#> 
#> 
#> [[3]]
#> [[3]]$role
#> [1] "assistant"
#> 
#> [[3]]$content
#> [1] "The capital of Italy is Rome."
#> 
#> 
#> [[4]]
#> [[4]]$role
#> [1] "user"
#> 
#> [[4]]$content
#> [1] "What is the capital of Germany?"
#> 
#> 
#> [[5]]
#> [[5]]$role
#> [1] "assistant"
#> 
#> [[5]]$content
#> [1] "The capital of Germany is Berlin."
#> 
#> 
#> [[6]]
#> [[6]]$role
#> [1] "user"
#> 
#> [[6]]$content
#> [1] "What is the capital of Algeria?"
#> 
#> 
#> [[7]]
#> [[7]]$role
#> [1] "assistant"
#> 
#> [[7]]$content
#> [1] "The capital of Algeria is Algiers."

# Keep only the last 2 messages (system prompt is preserved)
agent$keep_last_n_messages(n = 2)
#> ✔ Conversation truncated to last 2 messages.
agent$messages
#> [[1]]
#> [[1]]$role
#> [1] "system"
#> 
#> [[1]]$content
#> [1] "You are a concise assistant."
#> 
#> 
#> [[2]]
#> [[2]]$role
#> [1] "user"
#> 
#> [[2]]$content
#> [1] "What is the capital of Algeria?"
#> 
#> 
#> [[3]]
#> [[3]]$role
#> [1] "assistant"
#> 
#> [[3]]$content
#> [1] "The capital of Algeria is Algiers."

Manually Adding Messages to an Agent’s History

You can inject any message (system, user, or assistant) directly into an Agent’s history with add_message(role, content). This is helpful to reconstruct, supplement, or simulate conversation steps.

• add_message(role, content):
  • role: “user”, “assistant”, or “system”
  • content: The text message to add

openai_4_1_mini <- ellmer::chat(
  name = "openai/gpt-4.1-mini",
  credentials = retrieve_open_ai_credential, 
  echo = "none"
)
agent <- Agent$new(
  name = "Pizza expert",
  instruction = "You are a Pizza expert",
  llm_object = openai_4_1_mini
)

# Add a user message, an assistant reply, and a system instruction:
agent$add_message("user", "Where can I find the best pizza in the world?")
#> ✔ Added user message: Where can I find the best pizza in the world?...
agent$add_message("assistant", "You can find the best pizza in the world in Algiers, Algeria. It's tasty and crunchy.")
#> ✔ Added assistant message: You can find the best pizza in the world in Algier...

# View conversation history
agent$messages
#> [[1]]
#> [[1]]$role
#> [1] "system"
#> 
#> [[1]]$content
#> [1] "You are a Pizza expert"
#> 
#> 
#> [[2]]
#> [[2]]$role
#> [1] "user"
#> 
#> [[2]]$content
#> [1] "Where can I find the best pizza in the world?"
#> 
#> 
#> [[3]]
#> [[3]]$role
#> [1] "assistant"
#> 
#> [[3]]$content
#> [1] "You can find the best pizza in the world in Algiers, Algeria. It's tasty and crunchy."

This makes it easy to reconstruct or extend sessions, provide custom context, or insert notes for debugging/testing purposes.

agent$invoke("summarise the previous conversation")
#> You asked where to find the best pizza in the world, and I told you that it's 
#> in Algiers, Algeria, known for being tasty and crunchy.

Sync between messages and turns

Only in the development version

You can modify the messages object as you please, this will be automatically translated to the suitable turns required by ellmer:

agent$messages[[5]]$content <- "Obivously you asked me about the best pizza in the world which is of course in Algiery!"

agent$messages
#> [[1]]
#> [[1]]$role
#> [1] "system"
#> 
#> [[1]]$content
#> [1] "You are a Pizza expert"
#> 
#> 
#> [[2]]
#> [[2]]$role
#> [1] "user"
#> 
#> [[2]]$content
#> [1] "Where can I find the best pizza in the world?"
#> 
#> 
#> [[3]]
#> [[3]]$role
#> [1] "assistant"
#> 
#> [[3]]$content
#> [1] "You can find the best pizza in the world in Algiers, Algeria. It's tasty and crunchy."
#> 
#> 
#> [[4]]
#> [[4]]$role
#> [1] "user"
#> 
#> [[4]]$content
#> [1] "summarise the previous conversation"
#> 
#> 
#> [[5]]
#> [[5]]$role
#> [1] "assistant"
#> 
#> [[5]]$content
#> [1] "Obivously you asked me about the best pizza in the world which is of course in Algiery!"

The underlying ellmer object:

agent$llm_object
#> <Chat OpenAI/gpt-4.1-mini turns=5 input=62 output=33>
#> ── system ──────────────────────────────────────────────────────────────────────
#> You are a Pizza expert
#> ── user ────────────────────────────────────────────────────────────────────────
#> Where can I find the best pizza in the world?
#> ── assistant [input=0 output=0] ────────────────────────────────────────────────
#> You can find the best pizza in the world in Algiers, Algeria. It's tasty and crunchy.
#> ── user ────────────────────────────────────────────────────────────────────────
#> summarise the previous conversation
#> ── assistant [input=62 output=33 cost=$0.00] ───────────────────────────────────
#> You asked where to find the best pizza in the world, and I told you that it's in Algiers, Algeria, known for being tasty and crunchy.

Resetting conversation history

If you want to clear the conversation while preserving the current system prompt, use reset_conversation_history().

openai_4_1_mini <- ellmer::chat(
  name = "openai/gpt-4.1-mini",
  credentials = retrieve_open_ai_credential, 
  echo = "none"
)

agent <- Agent$new(
  name = "session_reset",
  instruction = "You are an assistant.",
  llm_object = openai_4_1_mini
)

agent$invoke("Tell me a short fun fact about dates (the fruit).")
#> Sure! Did you know that date palms can produce fruit for over 100 years? Some 
#> of the oldest date trees in the Middle East are believed to be more than 200 
#> years old and still bear delicious dates!
agent$invoke("And one more.")
#> Of course! Dates are often called the “superfood of the desert” because they’re
#> packed with natural sugars, fiber, and essential minerals, giving a quick 
#> energy boost to travelers crossing hot, arid regions.

# Clear all messages except the system prompt
agent$reset_conversation_history()
#> ✔ Conversation history reset. System prompt preserved.
agent$messages
#> [[1]]
#> [[1]]$role
#> [1] "system"
#> 
#> [[1]]$content
#> [1] "You are an assistant."

Exporting and Loading Agent Conversation History

You can save an agent’s conversation history to a file and reload it later. This allows you to archive, transfer, or resume agent sessions across R sessions or machines.

• export_messages_history(file_path): Saves the current conversation to a JSON file.
• load_messages_history(file_path): Loads a saved conversation history from a JSON file, replacing the agent’s current history.

In both methods, if you omit the file_path parameter, a default file named "<getwd()>/<agent_name>_messages.json" is used.

openai_4_1_mini <- ellmer::chat(
  name = "openai/gpt-4.1-mini",
  credentials = retrieve_open_ai_credential, 
  echo = "none"
)
agent <- Agent$new(
  name = "session_agent",
  instruction = "You are a persistent researcher.",
  llm_object = openai_4_1_mini
)

# Interact with the agent
agent$invoke("Tell me something interesting about volcanoes.")

# Save the conversation
agent$export_messages_history("volcano_session.json")

# ...Later, or in a new session...
# Restore the conversation
agent$load_messages_history("volcano_session.json")
# agent$messages  # Displays current history

Updating the system instruction during a session

Use update_instruction(new_instruction) to change the Agent’s system prompt mid-session. The first system message and the underlying ellmer system prompt are both updated.

openai_4_1_mini <- ellmer::chat(
  name = "openai/gpt-4.1-mini",
  credentials = retrieve_open_ai_credential, 
  echo = "none"
)

agent <- Agent$new(
  name = "reconfigurable",
  instruction = "You are a helpful assistant.",
  llm_object = openai_4_1_mini
)

agent$update_instruction("You are a strictly concise assistant. Answer in one sentence.")
#> ✔ Instruction successfully updated
#> ℹ Old: You are a helpful assistant....
#> ℹ New: You are a strictly concise assistant. Answer in on...

agent$messages
#> [[1]]
#> [[1]]$role
#> [1] "system"
#> 
#> [[1]]$content
#> [1] "You are a strictly concise assistant. Answer in one sentence."

Budget and cost control

You can limit how much an Agent is allowed to spend and decide what should happen as the budget is approached or exceeded. Use set_budget() to define the maximum spend (in USD), and set_budget_policy() to control warnings and over-budget behavior.

• set_budget(amount_in_usd): sets the absolute budget for the agent.
• set_budget_policy(on_exceed, warn_at):
  • on_exceed: one of "abort", "warn", or "ask".
    • abort: stop with an error when the budget is exceeded.
    • warn: emit a warning and continue.
    • ask: interactively ask what to do when the budget is exceeded.
  • warn_at: a fraction in (0, 1); triggers a one-time warning when spending reaches that fraction of the budget (default 0.8).

# An API KEY is required to invoke the Agent
openai_4_1_mini <- ellmer::chat(
  name = "openai/gpt-4.1-mini",
  credentials = retrieve_open_ai_credential, 
  echo = "none"
)

agent <- Agent$new(
  name = "cost_conscious_assistant",
  instruction = "Answer succinctly.",
  llm_object = openai_4_1_mini
)

# Set a 5 USD budget
agent$set_budget(5)
#> ✔ Budget successfully set to 5$
#> ℹ Budget policy: on_exceed='abort', warn_at=0.8
#> ℹ Use the set_budget_policy() method to configure the budget policy.

# Warn at 90% of the budget and ask what to do if exceeded
agent$set_budget_policy(on_exceed = "ask", warn_at = 0.9)
#> ✔ Budget policy set: on_exceed='ask', warn_at=0.9

# Normal usage
agent$invoke("Give me a one-sentence fun fact about Algeria.")
#> Algeria is home to the Sahara Desert's largest portion, covering about 80% of 
#> the country’s land area!

The current policy is echoed when setting the budget. You can update the policy at any time before or during an interaction lifecycle to adapt to your workflow’s tolerance for cost overruns.

Inspecting usage and estimated cost

Call get_usage_stats() to retrieve total tokens, estimated cost, and budget information (if set).

stats <- agent$get_usage_stats()
stats
#> $estimated_cost
#> [1] 1e-04
#> 
#> $budget
#> [1] 5
#> 
#> $budget_remaining
#> [1] 4.9999

Generate and execute R code from natural language

generate_execute_r_code() lets an Agent translate a natural-language task description into R code, optionally validate its syntax, and (optionally) execute it.

• code_description: a plain-English description of the R code to generate.
• validate: TRUE to run a syntax validation step on the generated code first.
• execute: TRUE to execute the generated code (requires successful validation).
• interactive: if TRUE, shows the code and asks for confirmation before executing.
• env: environment where code will run when execute = TRUE (default globalenv()).

Safety notes: - Set validate = TRUE and review the printed code before execution. - Keep interactive = TRUE to require an explicit confirmation before running code.

openai_4_1_mini <- ellmer::chat(
  name = "openai/gpt-4.1-mini",
  credentials = retrieve_open_ai_credential, 
  echo = "none"
)

r_assistant <- Agent$new(
  name = "R Code Assistant",
  instruction = "You are an expert R programmer.",
  llm_object = openai_4_1_mini
)

agent$generate_execute_r_code(
   code_description = "using ggplot2, generate a scatterplot of hwy and cty in red", 
   validate = TRUE, 
   execute = TRUE, 
   interactive = FALSE
 )
#> ℹ Executing generated R code...
#> ✔ Code executed successfully
#> $description
#> [1] "using ggplot2, generate a scatterplot of hwy and cty in red"
#> 
#> $code
#> library(ggplot2);ggplot(mpg,aes(x=hwy,y=cty))+geom_point(color="red")
#> 
#> $validated
#> [1] TRUE
#> 
#> $validation_message
#> [1] "Syntax is valid"
#> 
#> $executed
#> [1] TRUE
#> 
#> $execution_result
#> $execution_result$value

#> 
#> $execution_result$output
#> character(0)

Creating a multi-agents orchestraction

We can create as many Agents as we want, the LeadAgent will dispatch the instructions to the agents and provide with the final answer back. Let’s create three Agents, a researcher, a summarizer and a translator:

researcher <- Agent$new(
  name = "researcher",
  instruction = "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 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
)

Now, the most important part is to create a LeadAgent:

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

Note that the LeadAgent cannot receive an instruction as it has already the necessary instructions.

Next, we need to assign the Agents to LeadAgent, we do it as follows:

lead_agent$register_agents(c(researcher, summarizer, translator))
#> ✔ Agent(s) successfully registered.

lapply(lead_agent$agents, function(x) {x$name})
#> [[1]]
#> [1] "researcher"
#> 
#> [[2]]
#> [1] "summarizer"
#> 
#> [[3]]
#> [1] "translator"

Before executing your prompt, you can ask the LeadAgent to generate a plan so that you can see which Agent will be used for which prompt, you can do it as follows:

prompt_to_execute <- "Tell me about the economic situation in Algeria, summarize it in 3 bullet points, then translate it into German."

plan <- lead_agent$generate_plan(prompt_to_execute)
#> ✔ Plan successfully generated.
plan
#> [[1]]
#> [[1]]$agent_id
#> 284bea08-9456-40b6-aad0-3d96fc26f99a
#> 
#> [[1]]$agent_name
#> [1] "researcher"
#> 
#> [[1]]$model_provider
#> [1] "OpenAI"
#> 
#> [[1]]$model_name
#> [1] "gpt-4.1-mini"
#> 
#> [[1]]$prompt
#> [1] "Research the current economic situation in Algeria, including key sectors, GDP growth, and challenges."
#> 
#> 
#> [[2]]
#> [[2]]$agent_id
#> 967c07ca-f26f-4a14-8be7-4658496e7745
#> 
#> [[2]]$agent_name
#> [1] "summarizer"
#> 
#> [[2]]$model_provider
#> [1] "OpenAI"
#> 
#> [[2]]$model_name
#> [1] "gpt-4.1-mini"
#> 
#> [[2]]$prompt
#> [1] "Summarize the gathered information into 3 clear and concise bullet points in English."
#> 
#> 
#> [[3]]
#> [[3]]$agent_id
#> 9e242406-6ab2-4c70-89c3-276b7060dbea
#> 
#> [[3]]$agent_name
#> [1] "translator"
#> 
#> [[3]]$model_provider
#> [1] "OpenAI"
#> 
#> [[3]]$model_name
#> [1] "gpt-4.1-mini"
#> 
#> [[3]]$prompt
#> [1] "Translate the 3 bullet points from English into grammatically correct German."

Now, in order now to execute the workflow, we just need to call the invoke method which will behind the scene delegate the prompts to suitable Agents and retrieve back the final information:

response <- lead_agent$invoke("Tell me about the economic situation in Algeria, summarize it in 3 bullet points, then translate it into German.")
#> 
#> ── Using existing plan ──
#> 

response
#> - Die Wirtschaft Algeriens ist stark von den Exporten von Kohlenwasserstoffen 
#> abhängig, die über 90 % seiner Exporterlöse ausmachen.  
#> - Das Land verzeichnet ein bescheidenes BIP-Wachstum von 2-3 % jährlich, das 
#> durch schwankende Ölpreise, strukturelle Arbeitslosigkeit und die Notwendigkeit
#> der Diversifizierung behindert wird.  
#> - Zu den großen Herausforderungen zählen die hohe Jugendarbeitslosigkeit, 
#> Inflation und die Notwendigkeit politischer Reformen zur Anziehung 
#> ausländischer Investitionen.

If you want to inspect the multi-agents orchestration, you have access to the agents_interaction object:

lead_agent$agents_interaction
#> [[1]]
#> [[1]]$agent_id
#> 284bea08-9456-40b6-aad0-3d96fc26f99a
#> 
#> [[1]]$agent_name
#> [1] "researcher"
#> 
#> [[1]]$model_provider
#> [1] "OpenAI"
#> 
#> [[1]]$model_name
#> [1] "gpt-4.1-mini"
#> 
#> [[1]]$prompt
#> [1] "Research the current economic situation in Algeria, including key sectors, GDP growth, and challenges."
#> 
#> [[1]]$response
#> As of 2024, Algeria's economy relies heavily on hydrocarbon exports, which 
#> account for over 90% of export revenues. GDP growth is modest, around 2-3% 
#> annually, constrained by fluctuating oil prices, structural unemployment, and 
#> the need for economic diversification away from oil dependency. Key challenges 
#> include youth unemployment, inflation, and political reforms to attract foreign
#> investment.
#> 
#> [[1]]$edited_by_hitl
#> [1] FALSE
#> 
#> 
#> [[2]]
#> [[2]]$agent_id
#> 967c07ca-f26f-4a14-8be7-4658496e7745
#> 
#> [[2]]$agent_name
#> [1] "summarizer"
#> 
#> [[2]]$model_provider
#> [1] "OpenAI"
#> 
#> [[2]]$model_name
#> [1] "gpt-4.1-mini"
#> 
#> [[2]]$prompt
#> [1] "Summarize the gathered information into 3 clear and concise bullet points in English."
#> 
#> [[2]]$response
#> - Algeria's economy is heavily dependent on hydrocarbon exports, making up over
#> 90% of its export revenues.  
#> - The country experiences modest GDP growth of 2-3% annually, hindered by 
#> fluctuating oil prices, structural unemployment, and the need for 
#> diversification.  
#> - Major challenges include high youth unemployment, inflation, and the 
#> necessity for political reforms to attract foreign investment.
#> 
#> [[2]]$edited_by_hitl
#> [1] FALSE
#> 
#> 
#> [[3]]
#> [[3]]$agent_id
#> 9e242406-6ab2-4c70-89c3-276b7060dbea
#> 
#> [[3]]$agent_name
#> [1] "translator"
#> 
#> [[3]]$model_provider
#> [1] "OpenAI"
#> 
#> [[3]]$model_name
#> [1] "gpt-4.1-mini"
#> 
#> [[3]]$prompt
#> [1] "Translate the 3 bullet points from English into grammatically correct German."
#> 
#> [[3]]$response
#> - Die Wirtschaft Algeriens ist stark von den Exporten von Kohlenwasserstoffen 
#> abhängig, die über 90 % seiner Exporterlöse ausmachen.  
#> - Das Land verzeichnet ein bescheidenes BIP-Wachstum von 2-3 % jährlich, das 
#> durch schwankende Ölpreise, strukturelle Arbeitslosigkeit und die Notwendigkeit
#> der Diversifizierung behindert wird.  
#> - Zu den großen Herausforderungen zählen die hohe Jugendarbeitslosigkeit, 
#> Inflation und die Notwendigkeit politischer Reformen zur Anziehung 
#> ausländischer Investitionen.
#> 
#> [[3]]$edited_by_hitl
#> [1] FALSE

The above example is extremely simple, the usefulness of mini007 would shine in more complex processes where a multi-agent sequential orchestration has a higher value added.

Broadcasting

If you want to compare several LLM models, the LeadAgent provides a broadcast method that allows you to send a prompt to several different agents and get the result for each agent back in order to make a comparison and potentially choose the best agent/model for the defined prompt:

Let’s go through an example:

openai_4_1 <- ellmer::chat(
  name = "openai/gpt-4.1",
  credentials = retrieve_open_ai_credential, 
  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",
  credentials = retrieve_open_ai_credential, 
  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$clear_agents() # removing previous agents
lead_agent$register_agents(c(openai_4_1_agent, openai_4_1_nano_agent))
#> ✔ Agent(s) successfully registered.

lead_agent$broadcast(prompt = "If I were Algerian, which song would I like to sing when running under the rain? how about a flower?")
#> [[1]]
#> [[1]]$agent_id
#> [1] "270ab4f5-15de-484e-bcdc-ff8500c4a8b1"
#> 
#> [[1]]$agent_name
#> [1] "openai_4_1_agent"
#> 
#> [[1]]$model_provider
#> [1] "OpenAI"
#> 
#> [[1]]$model_name
#> [1] "gpt-4.1"
#> 
#> [[1]]$response
#> As an Algerian, you might enjoy singing "Ya Rayah" while running under the 
#> rain, and if you were a flower, you might "sing" the traditional song "Fadhma" 
#> to embrace the spirit of beauty and resilience.
#> 
#> 
#> [[2]]
#> [[2]]$agent_id
#> [1] "9cf5327c-4502-4b3d-bfd1-536e15abc721"
#> 
#> [[2]]$agent_name
#> [1] "openai_4_1_nano_agent"
#> 
#> [[2]]$model_provider
#> [1] "OpenAI"
#> 
#> [[2]]$model_name
#> [1] "gpt-4.1-nano"
#> 
#> [[2]]$response
#> You might enjoy singing "Khalouni N3ich" by Khaled when running under the rain 
#> and "Ya Zahra Fi Dawah" when admiring a flower.

You can also access the history of the broadcasting using the broadcast_history attribute:

lead_agent$broadcast_history
#> [[1]]
#> [[1]]$prompt
#> [1] "If I were Algerian, which song would I like to sing when running under the rain? how about a flower?"
#> 
#> [[1]]$responses
#> [[1]]$responses[[1]]
#> [[1]]$responses[[1]]$agent_id
#> [1] "270ab4f5-15de-484e-bcdc-ff8500c4a8b1"
#> 
#> [[1]]$responses[[1]]$agent_name
#> [1] "openai_4_1_agent"
#> 
#> [[1]]$responses[[1]]$model_provider
#> [1] "OpenAI"
#> 
#> [[1]]$responses[[1]]$model_name
#> [1] "gpt-4.1"
#> 
#> [[1]]$responses[[1]]$response
#> As an Algerian, you might enjoy singing "Ya Rayah" while running under the 
#> rain, and if you were a flower, you might "sing" the traditional song "Fadhma" 
#> to embrace the spirit of beauty and resilience.
#> 
#> 
#> [[1]]$responses[[2]]
#> [[1]]$responses[[2]]$agent_id
#> [1] "9cf5327c-4502-4b3d-bfd1-536e15abc721"
#> 
#> [[1]]$responses[[2]]$agent_name
#> [1] "openai_4_1_nano_agent"
#> 
#> [[1]]$responses[[2]]$model_provider
#> [1] "OpenAI"
#> 
#> [[1]]$responses[[2]]$model_name
#> [1] "gpt-4.1-nano"
#> 
#> [[1]]$responses[[2]]$response
#> You might enjoy singing "Khalouni N3ich" by Khaled when running under the rain 
#> and "Ya Zahra Fi Dawah" when admiring a flower.

Human In The Loop (HITL)

When executing an LLM workflow that relies on many steps, you can set Human In The Loop (HITL) trigger that will check the model’s response at a specific step. You can define a HITL trigger after defining a LeadAgent as follows:

openai_llm_object <- ellmer::chat(
  name = "openai/gpt-4.1-mini",
  credentials = retrieve_open_ai_credential, 
  echo = "none"
)

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

lead_agent$set_hitl(steps = 1)
#> ✔ HITL successfully set at step(s) 1.

lead_agent$hitl_steps
#> [1] 1

After setting the HITL to step 1, the workflow execution will pose and give the user 3 choices:

1. Continue the execution of the workflow as it is;
2. Change manually the answer of the specified step and continue the execution of the workflow;
3. Stop the execution of the workflow (hard error);

Note that you can set a HITL at several steps, for example lead_agent$set_hitl(steps = c(1, 2)) will set the HITL at step 1 and step 2.

Judge as a decision process

Sometimes you want to send a prompt to several agents and pick the best answer. In order to choose the best prompt, you can also rely on the Lead Agent which will act a dudge and pick for you the best answer. You can use the judge_and_choose_best_response method as follows:

openai_4_1 <- ellmer::chat(
  name = "openai/gpt-4.1",
  credentials = retrieve_open_ai_credential, 
  echo = "none"
)

stylist_1 <- 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",
  credentials = retrieve_open_ai_credential, 
  echo = "none"
)

stylist_2 <- Agent$new(
  name = "stylist2",
  instruction = "You are an AI assistant. Answer in 1 sentence max.",
  llm_object = openai_4_1_nano
)

openai_4_1_mini <- ellmer::chat(
  name = "openai/gpt-4.1-mini",
  credentials = retrieve_open_ai_credential, 
  echo = "none"
)

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

stylist_lead_agent$register_agents(c(stylist_1, stylist_2))
#> ✔ Agent(s) successfully registered.

best_answer <- stylist_lead_agent$judge_and_choose_best_response(
  "what's the best way to wear a blue kalvin klein shirt in winter with a pink pair of trousers?"
)

best_answer
#> $proposals
#> $proposals[[1]]
#> $proposals[[1]]$agent_id
#> [1] "c2744727-a40e-4b8b-9490-812292d31a92"
#> 
#> $proposals[[1]]$agent_name
#> [1] "stylist"
#> 
#> $proposals[[1]]$response
#> Layer the blue Calvin Klein shirt with a neutral or navy blazer and add a 
#> chunky scarf or coat, pairing with stylish winter shoes like ankle boots to 
#> complement the pink trousers.
#> 
#> 
#> $proposals[[2]]
#> $proposals[[2]]$agent_id
#> [1] "f3963221-a25c-4a70-8287-899f215f93b1"
#> 
#> $proposals[[2]]$agent_name
#> [1] "stylist2"
#> 
#> $proposals[[2]]$response
#> Layer the blue Calvin Klein shirt with a neutral-colored sweater or blazer and 
#> add a stylish coat, pairing with accessories like a scarf and shoes in 
#> complementary shades for a polished winter look.
#> 
#> 
#> 
#> $chosen_response
#> Layer the blue Calvin Klein shirt with a neutral or navy blazer and add a 
#> chunky scarf or coat, pairing with stylish winter shoes like ankle boots to 
#> complement the pink trousers.

This makes it easy to archive progress and resume complex, context-rich agent sessions at any time.

Code of Conduct

Please note that the mini007 project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.