codeagent

An R-native agentic coding assistant built on ellmer and btw. It reimplements a coding-agent harness in R: the agent loop, permission system, context compaction, hook system, skill system, tool execution, session management, multi-agent coordination, a CLI REPL, and an interactive Shiny UI.

Not a wrapper. codeagent reimplements the harness from scratch rather than shelling out to an external CLI.

Installation

pak::pak(c("tidyverse/ellmer", "kaipingyang/codeagent"))

# Recommended: btw adds R-environment tools (docs, git, pkg, file editing, etc.)
# Without btw the agent has minimal tooling for real R projects.
pak::pak("posit-dev/btw")

Configuration

Step 1 — Create settings file

codeagent::use_codeagent_settings()   # creates ~/.codeagent/settings.json

Edit the generated file with your endpoint:

{
  "provider": "openai_compatible",
  "model": "main",
  "env": {
    "CODEAGENT_BASE_URL": "https://YOUR-WORKSPACE/serving-endpoints",
    "CODEAGENT_MODEL": "your-main-endpoint",
    "CODEAGENT_FAST_MODEL": "your-fast-endpoint",
    "CODEAGENT_API_KEY": "your-token"
  }
}

CODEAGENT_* vars in the env block are loaded at startup even under --vanilla, so you do not need to set them separately in .Renviron.

Step 2 — Install the CLI (once)

codeagent::install_codeagent_cli()   # puts `codeagent` on your PATH

codeagent           # interactive REPL (default permission mode)
codeagent -y        # bypass mode (skip all permission prompts)
codeagent "query"   # one-shot query

Quick start (R)

library(codeagent)

# Option A: auto-build client from settings.json
client <- codeagent_client()

# Option B: explicit ellmer Chat
chat   <- ellmer::chat_openai_compatible(
  base_url    = Sys.getenv("CODEAGENT_BASE_URL"),
  model       = Sys.getenv("CODEAGENT_MODEL"),
  credentials = function() Sys.getenv("CODEAGENT_API_KEY")
)
client <- codeagent_client(chat)

# One-shot query
codeagent(client, "List all .R files in R/")

# Interactive Shiny app
codeagent_app(client)

# Interactive CLI REPL
codeagent_console(client)

Features

Agent harness

Feature	Details
Agent loop	agent_loop() with max_turns, budget tracking, compaction
Permissions	7 modes: default, plan, accept_edits, bypass, dont_ask, auto, bubble; fine-grained rules match tool arguments
Hooks	12 lifecycle events (tool, permission, message, session), configurable from settings.json
Compaction	Dynamic per-model context window + two-level flow (session-memory summary → full 9-section summary), real token counts via get_tokens(), PTL/413 fallback, an "N% context left" indicator (REPL + Shiny), and mid-loop compaction between tool rounds
System prompt	Tone, task, convention, tool-use, and R-specific behavioural guidance
Error recovery	PTL/rate-limit/network/auth classification; exponential backoff
Verification	verify_fn param + verify_r_tests() re-enters loop on test failures
Plan mode	Model enters/exits read-only planning mid-turn
Rewind	truncate_chat_turns() / REPL /rewind roll the conversation back
Model switch	switch_model(client, model) swaps provider/model mid-session

Tools

Group	Source	Tools
Core	codeagent	Bash, Read, Write, Edit, MultiEdit, Glob, Grep, LS
docs	btw	help pages, vignettes, NEWS
env	btw	describe data frames / R environment
files	btw	hash-anchored precise editing + atomic multi-file patch
git	btw	status, diff, log, commit, branches
pkg	btw	document, check, test, coverage, load_all
web	btw	URL → Markdown
agent	btw	hierarchical subagent delegation
data	codeagent	ExploreData — sandboxed data.frame queries

Skill system

Compatible with Claude Code and btw skill format (name/SKILL.md directories).

# Install Posit's data-science skill collection
install_ds_skills()

# Install from a package or GitHub
btw::btw_skill_install_package("btw")
btw::btw_skill_install_github("org/repo")

Built-in slash commands: /compact, /plan, /verify, /simplify, /loop, /remember

Multi-agent teams

# Work-stealing over a shared SQLite board
team_coordinate(c("task 1", "task 2", "task 3"))

# LLM-lead coordinator: decomposes goal into DAG, runs team, re-plans
team_lead("Refactor the parser and add tests", max_rounds = 3)

MCP server

codeagent_mcp_server()
# Claude Desktop config:
# {"mcpServers": {"codeagent": {"command": "Rscript",
#   "args": ["-e", "codeagent::codeagent_mcp_server()"]}}}

Shiny app

codeagent_app(
  client,
  theme         = "default",   # "default" | "flatly" | "darkly" | "glass"
  pinned_skills = c("plan", "compact")
)

Configuration reference

Precedence (low → high): package defaults → ~/.codeagent/settings.json → .codeagent/settings.json → environment variables.

{
  "provider": "openai_compatible",
  "model": "main",
  "env": {
    "CODEAGENT_BASE_URL": "https://YOUR-WORKSPACE/serving-endpoints",
    "CODEAGENT_MODEL":       "your-main-endpoint",
    "CODEAGENT_HEAVY_MODEL": "your-heavy-endpoint",
    "CODEAGENT_FAST_MODEL":  "your-fast-endpoint"
  },
  "permissions": {
    "allow": [],
    "deny":  [],
    "ask":   [],
    "defaultMode": "default"
  },
  "effortLevel": "high",
  "hooks": {}
}

API key: keep CODEAGENT_API_KEY in ~/.Renviron, not in settings.json.

Supported providers

Provider	"provider" value
OpenAI-compatible (Databricks, Azure, vLLM, …)	"openai_compatible"
Anthropic	"anthropic"
OpenAI	"openai"
Google Gemini	"google_gemini"
Ollama	"ollama"
Posit AI	"posit"
AWS Bedrock	"aws_bedrock"
Azure OpenAI	"azure_openai"

Related

• ellmer — LLM client for R
• btw — R-environment tools for LLMs
• shinychat — Chat UI components

License

MIT