LLMAgentR

Overview

LLMAgentR is an R package for building Language Model Agents using a modular state graph execution framework. Inspired by LangGraph and LangChain architectures, it supports iterative workflows for research, data analysis, and automation.

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Installation

install.packages("LLMAgentR")

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Development version

To get the latest features or bug fixes, you can install the development version of LLMAgentR from GitHub:

# If needed
install.packages("remotes")

# remotes::install_github("knowusuboaky/LLMAgentR")
install.packages("LLMAgentR", repos = c('https://knowusuboaky.r-universe.dev', 'https://cloud.r-project.org'))

See the full function reference or the package website for more details.

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Environment Setup

API Setup

Sys.setenv(
  OPENAI_API_KEY     = "your-openai-key",
  GROQ_API_KEY       = "your-groq-key",
  ANTHROPIC_API_KEY  = "your-anthropic-key",
  DEEPSEEK_API_KEY   = "your-deepseek-key",
  DASHSCOPE_API_KEY  = "your-dashscope-key",
  GH_MODELS_TOKEN    = "your-github-models-token"
)

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LLM Support (Minimal Wrapper)

The chatLLM package allows you to interact with large language models (LLMs) effortlessly - either through direct calls or via reusable minimal wrappers.

Load the Package

library(chatLLM)

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Minimal Wrapper Function

Create a lightweight wrapper around call_llm() for reuse. It optionally provides verbose output:

call_llm(
  prompt     = "Summarize the capital of France.",
  provider   = "groq",
  model      = "llama3-8b",
  temperature = 0.7,
  max_tokens = 200,
  verbose = TRUE
)

my_llm_wrapper <- function(prompt, verbose = FALSE) {
  if (verbose) {
    message("[my_llm_wrapper] Sending prompt to LLM...")
  }

  # Suppress console output but always return the response
  response_text <- if (verbose) {
    call_llm(
      prompt     = prompt,
      provider   = "openai",
      model      = "gpt-4o",
      max_tokens = 3000,
      verbose    = TRUE
    )
  } else {
    suppressMessages(
      suppressWarnings(
        call_llm(
          prompt     = prompt,
          provider   = "openai",
          model      = "gpt-4o",
          max_tokens = 3000,
          verbose    = TRUE
        )
      )
    )
  }

  if (verbose) {
    message("[my_llm_wrapper] Response received.")
  }

  return(response_text)
}

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Quick Access Shortcut

Alternatively, preconfigure an LLM call for one-liners:

my_llm_wrapper <- call_llm(
      provider    = "openai",
      model       = "gpt-4o",
      max_tokens  = 3000,
      verbose = TRUE
)

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Related Package: chatLLM

The chatLLM package (now available on CRAN) offers a modular interface for interacting with LLM providers including OpenAI, Groq, Anthropic, DeepSeek, DashScope, and GitHub Models.

install.packages("chatLLM")

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Agent Articles

Detailed guides now live in pkgdown Articles (one per agent):

• Code Generation Agent
• SQL Query Agent
• Research Agent
• Interpreter Agent
• Document Summarizer Agent
• Data Cleaning Agent
• Forecasting Agent
• Data Wrangling Agent
• Weather Agent
• Feature Engineering Agent
• Visualization Agent

Custom graph workflows:

• Building Custom Agents
• Building Multi-Agent Teams

A full index page is also available:

• Agent Index

License

MIT (c) Kwadwo Daddy Nyame Owusu Boakye