LLMRagent

Language-model agents for R, built on LLMR.

What LLMRagent supports

LLMRagent defines governed single-agent and multi-agent studies. An agent combines a model configuration and persona with memory, declared tool powers, boundary checks, and a budget. Designed conversations and factorial studies return classed results with transcripts, question-and-answer tables, votes, or condition-level results. Calls, tool use, and state changes can be collected in an analysis-ready run record.

Install and configure a model

# install.packages("remotes")
remotes::install_github("asanaei/LLMRagent")

library(LLMRagent)
cfg <- LLMR::llm_config("groq", "openai/gpt-oss-20b")

agent() accepts any model configuration supported by LLMR. Provider credentials remain in the environment rather than in the agent definition.

Build an agent with explicit limits

ada <- agent(
  "Ada",
  cfg,
  persona = "A meticulous statistician. Be brief.",
  memory = memory_buffer(keep = 20L),
  budget = budget(
    max_calls = 6L,
    max_tokens = 8000L,
    max_tool_calls = 4L,
    max_seconds = 120
  )
)

ada$chat("What is overfitting?")
ada$chat("How do I detect it?")
ada$usage()
ada$trace()

chat() stores successful exchanges in the selected memory policy. Call and tool-call ceilings are checked before the corresponding operation. Token and elapsed-time limits stop the next model round after recorded use reaches the limit. Failures raise conditions and are not stored as model replies.

Give tools declared powers

agent_tool() exposes an R function while declaring its side_effects, approval requirement, and per-tool limits. side_effects is one of "none", "read", "write", or "external". A guardrail() checks an agent input, output, or tool call at a named boundary.

lookup_gdp <- agent_tool(
  fn = function(country) {
    values <- c(chile = 335, uruguay = 81, bolivia = 47)
    value <- values[tolower(country)]
    if (is.na(value)) "unknown" else paste0("$", value, " billion")
  },
  name = "lookup_gdp",
  description = "Look up a country's illustrative GDP in USD billions.",
  parameters = list(country = list(type = "string")),
  required = "country",
  side_effects = "read",
  requires_approval = FALSE,
  max_calls = 5L
)

no_file_writes <- guardrail(
  "no_file_writes",
  check = function(payload, context) {
    if (identical(payload$name, "write_file")) "file writes are not allowed" else TRUE
  },
  stage = "tool"
)

analyst <- agent(
  "Analyst",
  cfg,
  tools = list(lookup_gdp),
  guardrails = guardrails(no_file_writes)
)

Use human_gate() to add the same requirement as requires_approval = TRUE. A gated call pauses before the function runs. The checkpoint exposes the proposed tool name and arguments for a decision.

write_note <- human_gate(agent_tool(
  function(path, text) writeLines(text, path),
  name = "write_note",
  description = "Write text to a file.",
  parameters = list(
    path = list(type = "string"),
    text = list(type = "string")
  ),
  required = c("path", "text"),
  side_effects = "write"
))

scribe <- agent("Scribe", cfg, tools = list(write_note))
pending <- tryCatch(
  scribe$chat("Write a short note to notes.txt."),
  llmragent_pending_approval = function(e) e$checkpoint
)
pending$pending

approved <- approve_tool_call(pending, decision = "approve")
resumed <- resume_run(approved)
resumed$text

Approval can approve, reject, or edit the proposed arguments. Plain LLMR::llm_tool() objects remain accepted, but they do not carry the declared powers and limits supplied by agent_tool().

Run designed multi-agent studies

conversation() provides general dialogue over one attributed transcript. The study presets define turn structure according to the result sought.

panel <- list(
  agent("Morgan", cfg, persona = "An operations manager who values predictability."),
  agent("Sam", cfg, persona = "A young engineer who values flexibility."),
  agent("Ren", cfg, persona = "A finance director focused on costs.")
)

d <- deliberate(panel, "Adopt a four-day work week for a one-year pilot.")
d$transcript
d$votes
d$decision

Vary conditions and assess robustness

agent_experiment() expands a design by replication, runs one fresh procedure per cell, and records cell-level errors without stopping the study.

design <- expand.grid(
  framing = c("benefit", "cost"),
  stringsAsFactors = FALSE
)

study <- agent_experiment(design, reps = 3L, run_fn = function(cond, rep) {
  subject <- agent("Subject", cfg, quiet = TRUE)
  subject$reply(paste("Assess the proposal using a", cond$framing, "frame."))
})

agent_robustness() applies declared perturbation axes and reports how a chosen measure changes. The axis helpers are vary_models(), vary_temperature(), vary_prompt(), vary_persona(), and vary_option_order(). persona_variants() builds planned persona contrasts. mark_claim_type() records whether a run is an instrument pilot, theory probe, or coding exercise; none of these labels turns model output into a population estimate.

Inspect and archive run records

as_agent_run() gives high-level results a common record with utterance, event, call, tool, and state views. Diagnostics, methods text, and a manifest are derived from that record.

run <- as_agent_run(d)
tibble::as_tibble(run, level = "utterance")
tibble::as_tibble(run, level = "tool")
diagnostics(run)
report(run)

manifest <- agent_manifest(run)
archive <- archive_agent_study(
  run,
  path = "four-day-study",
  include_messages = FALSE
)

archive_agent_study() writes an inspectable directory containing projected run data, call records, the study manifest, methods text, artifacts, and file hashes. The archive omits live agents and functions. It is a record for inspection, not a general mechanism for executing the study again.

Delegate and coordinate models

agent_as_tool() lets a supervisor decide when to consult a specialist. The specialist’s calls count against its own budget and appear in its usage record.

stat <- agent(
  "Stat",
  cfg,
  persona = "A statistician who states assumptions and threats to inference."
)
lead <- agent(
  "Lead",
  cfg,
  persona = "A research lead who consults specialists before synthesis.",
  tools = list(agent_as_tool(stat))
)
lead$chat("Could falling crime cause rising policing budgets, rather than vice versa?")

agent_pipeline() runs a fixed sequence of specialists and retains each intermediate result in its steps table. agent_fanout_synthesis() assigns several independent approaches to one worker configuration, then uses another configuration for planning, synthesis, and optional verification.

Run resumable procedures

Workflows represent procedures that need branches, checkpoints, forks, or resumption beyond the higher-level study functions.

wf <- agent_workflow("triage")
wf <- add_node(wf, "clean", function(state) {
  state$text <- trimws(state$input)
  state
})
wf <- add_node(wf, "review", function(state) {
  state$ok <- nzchar(state$text)
  state
})
wf <- add_edge(wf, "clean", "review")

wrun <- run_workflow(wf, input = " a claim ")
replayed <- replay_run(wrun, wf, verify = "structural")

run_workflow() records state transitions and can write checkpoints. resume_workflow() continues a checkpointed workflow, and fork_workflow() branches from recorded state. replay_run() re-executes a workflow run and checks it at the requested verification level. It does not re-execute an arbitrary archived agent run.

External tools and inspection

mcp_tools() exposes tools from a Model Context Protocol server under a read-only or read-write policy, with optional approval for writes and schema pinning. view_run() writes a self-contained HTML view of a run record.

Agent persistence is separate from study archives. save_agent() stores an agent’s configuration, memory, budget, guardrails, and accounting, but omits tool functions. load_agent() restores that state and accepts tools to reattach.

Vignettes

• LLMRagent in 10 minutes: agents, declared tools, memory, and budgets.
• Governed tools and human review: tool powers, boundary checks, and approval.
• Designed conversations: debates, focus groups, interviews, and deliberations.
• A deliberation experiment: a factorial study with analysis.
• Validity: robustness, personas, and scoped claims: perturbations and claim scope.
• Provenance and archiving: run views, manifests, reports, and archives.
• Fan-out synthesis: coordination across worker and synthesis models.
• Workflows: checkpoints, branches, resumption, forks, and workflow replay.

All articles and reference: https://asanaei.github.io/LLMRagent/

Relation to LLMR

LLMR version 0.8.11 or later supplies the common provider interface used by LLMRagent. agent() accepts an LLMR::llm_config() object, so provider and model settings are shared between the packages.

The LLMR ecosystem

LLMRagent is one of a family of packages for LLM-assisted research built on LLMR, the shared provider interface. LLMRcontent codes text from a codebook and evaluates the resulting labels against held-out human labels. LLMRpanel administers survey and experimental instruments to panels of model personas for design-stage studies. FocusGroup runs simulated moderated discussions and supports experiments on how one turn changes the next. The ecosystem page introduces the whole family.

Contributing

Report bugs and feature requests in the GitHub repository. Pull requests may be submitted there.

License

This project uses the MIT License; see LICENSE.