LLMConversation: LLMConversation Class for Coordinating Agents

In LLMR: Interface for Large Language Model APIs in R

LLMConversation Class for Coordinating Agents

Description

An R6 class for managing a conversation among multiple Agent objects. Includes optional conversation-level summarization if 'summarizer_config' is provided:

1. summarizer_config: A list that can contain:

   • llm_config: The llm_config used for the summarizer call (default a basic OpenAI).

   • prompt: A custom summarizer prompt (default provided).

   • threshold: Word-count threshold (default 3000 words).

   • summary_length: Target length in words for the summary (default 400).

2. Once the total conversation word count exceeds 'threshold', a summarization is triggered.

3. The conversation is replaced with a single condensed message that keeps track of who said what.

Public fields

agents

A named list of Agent objects.

conversation_history

A list of speaker/text pairs for the entire conversation.

conversation_history_full

A list of speaker/text pairs for the entire conversation that is never modified and never used directly.

topic

A short string describing the conversation's theme.

prompts

An optional list of prompt templates (may be ignored).

shared_memory

Global store that is also fed into each agent's memory.

last_response

last response received

total_tokens_sent

total tokens sent in conversation

total_tokens_received

total tokens received in conversation

summarizer_config

Config list controlling optional conversation-level summarization.

Methods

Public methods

• LLMConversation$new()

• LLMConversation$add_agent()

• LLMConversation$add_message()

• LLMConversation$converse()

• LLMConversation$run()

• LLMConversation$print_history()

• LLMConversation$reset_conversation()

• LLMConversation$|>()

• LLMConversation$maybe_summarize_conversation()

• LLMConversation$summarize_conversation()

• LLMConversation$clone()

---

Method new()

Create a new conversation.

Usage

LLMConversation$new(topic, prompts = NULL, summarizer_config = NULL)

Arguments

topic

Character. The conversation topic.

prompts

Optional named list of prompt templates.

summarizer_config

Optional list controlling conversation-level summarization.

---

Method add_agent()

Add an Agent to this conversation. The agent is stored by agent$id.

Usage

LLMConversation$add_agent(agent)

Arguments

agent

An Agent object.

---

Method add_message()

Add a message to the global conversation log. Also appended to shared memory. Then possibly trigger summarization if configured.

Usage

LLMConversation$add_message(speaker, text)

Arguments

speaker

Character. Who is speaking?

text

Character. What they said.

---

Method converse()

Have a specific agent produce a response. The entire global conversation plus shared memory is temporarily loaded into that agent. Then the new message is recorded in the conversation. The agent's memory is then reset except for its new line.

Usage

LLMConversation$converse(
  agent_id,
  prompt_template,
  replacements = list(),
  verbose = FALSE
)

Arguments

agent_id

Character. The ID of the agent to converse.

prompt_template

Character. The prompt template for the agent.

replacements

A named list of placeholders to fill in the prompt.

verbose

Logical. If TRUE, prints extra info.

---

Method run()

Run a multi-step conversation among a sequence of agents.

Usage

LLMConversation$run(
  agent_sequence,
  prompt_template,
  replacements = list(),
  verbose = FALSE
)

Arguments

agent_sequence

Character vector of agent IDs in the order they speak.

prompt_template

Single string or named list of strings keyed by agent ID.

replacements

Single list or list-of-lists with per-agent placeholders.

verbose

Logical. If TRUE, prints extra info.

---

Method print_history()

Print the conversation so far to the console.

Usage

LLMConversation$print_history()

---

Method reset_conversation()

Clear the global conversation and reset all agents' memories.

Usage

LLMConversation$reset_conversation()

---

Method |>()

Pipe-like operator to chain conversation steps. E.g., conv |> "Solver"(...)

Usage

LLMConversation$|>(agent_id)

Arguments

agent_id

Character. The ID of the agent to call next.

Returns

A function that expects (prompt_template, replacements, verbose).

---

Method maybe_summarize_conversation()

Possibly summarize the conversation if summarizer_config is non-null and the word count of conversation_history exceeds summarizer_config$threshold.

Usage

LLMConversation$maybe_summarize_conversation()

---

Method summarize_conversation()

Summarize the conversation so far into one condensed message. The new conversation history becomes a single message with speaker = "summary".

Usage

LLMConversation$summarize_conversation()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LLMConversation$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

LLMR documentation built on April 4, 2025, 1:11 a.m.