README

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LLMR

LLMR gives R a single tidy-friendly interface to hosted and local language models. One llm_config() object selects the provider; every other function works the same regardless of which model is behind it.

Providers: OpenAI, Anthropic, Gemini (incl. Vertex AI), Groq, Together AI, DeepSeek, xAI, Voyage AI, Ollama, Xiaomi MiMo, Alibaba (Qwen), Zhipu, Moonshot.

Install

install.packages("LLMR")                        # CRAN
# remotes::install_github("asanaei/LLMR")       # dev

Setup

Store API keys as environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, etc.) in ~/.Renviron.

library(LLMR)

cfg <- llm_config("openai", "gpt-4.1-nano", temperature = 0.2, max_tokens = 256)

One call

r <- call_llm(cfg, c(system = "Be concise.", user = "Capital of Mongolia?"))
r                    # prints text + status line
as.character(r)      # just the text
tokens(r)            # token counts

Data-frame pipelines

llm_mutate() adds LLM-generated columns. The shorthand puts the output name and glue prompt in one argument.

library(dplyr)

df <- tibble::tibble(city = c("Cairo", "Lima", "Oslo"))

df |>
  llm_mutate(country = "What country is {city} in? One word.", .config = cfg)

Structured output (JSON)

schema <- list(
  type = "object",
  properties = list(
    answer = list(type = "string"),
    confidence = list(type = "number")
  ),
  required = list("answer", "confidence"),
  additionalProperties = FALSE
)

df |>
  llm_mutate(
    result = "What country is {city} in?",
    .config = cfg,
    .structured = TRUE,
    .schema = schema
  )

Soft structured output (XML-like tags)

df |>
  llm_mutate(
    geo = "Where is {city}? Give country and continent.",
    .config = cfg,
    .tags = c("country", "continent")
  )

Embeddings

emb_cfg <- llm_config("voyage", "voyage-3.5-lite", embedding = TRUE)

texts <- c("Quiet rivers mirror bright skies.", "Thunder shakes the mountain path.")
emb   <- get_batched_embeddings(texts, emb_cfg, batch_size = 8)
dim(emb)

Chat sessions

chat <- chat_session(cfg, system = "You teach statistics tersely.")
chat$send("Explain p-values in 12 words.")
chat$send("Now give a three-word analogy.")

Parallel experiments

setup_llm_parallel(workers = 4)

experiments <- build_factorial_experiments(
  configs = list(cfg),
  user_prompts = c("Summarize: The Apollo program.", "Summarize: The Manhattan Project."),
  system_prompts = "One sentence."
)

res <- call_llm_par(experiments, progress = TRUE)
reset_llm_parallel()

Which function do I need?

The LLMR ecosystem

Task	Function
Single prompt	`call_llm()` / `call_llm_robust()`
Vector of prompts	`llm_fn()`
Data-frame pipeline	`llm_mutate()`
JSON with schema	`.structured = TRUE` / `llm_mutate_structured()`
Tag-based extraction	`.tags` / `llm_mutate_tags()`
Parse existing column	`llm_parse_structured_col()` / `llm_parse_tags_col()`
Factorial designs	`build_factorial_experiments()` + `call_llm_par()`

LLMR is the provider layer of a family of packages for LLM-assisted research. LLMRAgent builds agents and multi-agent designs on top of it. LLMRcontent carries the measurement workflow in one package: codebook-first annotation with sealed gold-set validation, robustness audits of LLM-mediated estimates, and verifiable replication archives built from LLMR’s audit logs. LLMRpanel provides calibrated silicon samples for survey and experiment design, and FocusGroup supports moderated multi-agent discussions. An overview of the family lives at the ecosystem page.

Contributing

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.