Coding agents as solvers — agent

claude_code() and codex() are solvers that evaluate the Claude Code and Codex coding agents on your dataset, allowing you to compare off-the-shelf agent harnesses against your own with the same scorer.

These solvers bridge to Python Inspect's inspect_swe package, which runs the agent's command line interface in a Docker sandbox and proxies its model calls. The agent's transcript is then read back into ellmer Chat objects so that scoring and logging work exactly as they do for any other solver.

Usage

claude_code(solver_chat = NULL, ..., version = "auto", sandbox = "docker")

codex(solver_chat = NULL, ..., version = "auto", sandbox = "docker")

Arguments

solver_chat: An ellmer chat object, such as from ellmer::chat_anthropic(), or a zero-argument function that returns one. Its provider and model choose the model that powers the agent, and its system prompt and ellmer::params() are passed along to Inspect; the same chat is then reused to reconstruct the agent's transcript. The agent reaches the model through Inspect rather than directly, so credentials are read on the host from the usual environment variables, and any agent can be powered by any of the providers Inspect and ellmer agree on: ellmer::chat_anthropic(), ellmer::chat_openai(), ellmer::chat_google_gemini(), ellmer::chat_google_vertex(), ellmer::chat_aws_bedrock(), ellmer::chat_groq(), ellmer::chat_mistral(), ellmer::chat_ollama(), ellmer::chat_openrouter(), and ellmer::chat_perplexity().
...: Additional named arguments, routed by name to either the inspect_swe agent—e.g. system_prompt, disallowed_tools, cwd, or env, documented in inspect_swe's reference—or to Python Inspect's eval(), e.g. max_samples, max_sandboxes, time_limit, or token_limit. (epochs is the exception: pass it to Task's $eval() method as usual.)
version: A string specifying the agent CLI version to use. "auto" (the default) uses a version already installed in the sandbox, falling back to the current stable (Claude Code) or latest (Codex) release. Pass a specific version (e.g. "2.1.37") for reproducibility.
sandbox: The Inspect sandbox in which the agent runs: a string naming the sandbox type, or a length-2 vector pairing a type with a configuration file, e.g. c("docker", "compose.yaml"). Defaults to "docker", which is required on macOS and Windows; on Linux hosts, "local" runs the agent directly on the host.

Value

A solver function that can be passed directly to the solver argument of Task's $new() method. Since the agent runs in a sandbox rather than through ellmer, the solver's solver_chat output contains copies of solver_chat whose turns come from the agent's transcript. Each sample's solver_metadata records the path to the intermediate Inspect log, the sample's token usage by model, and the agent's error message (if any).

Token usage is recorded in the task's log and in solver_metadata, but not in Task's $token_usage() method, which only reflects API calls made through ellmer in the current R session.

Requirements

These solvers require the reticulate package and a running Docker daemon (Docker Desktop or similar). Python dependencies are resolved automatically with reticulate::py_require(). The first evaluation additionally pulls the sandbox image and downloads the agent's command line interface into it, so it takes a few minutes longer than the ones that follow.

The agent's workspace

Each sample gets its own container, discarded when the sample completes, so the agent's edits never touch your machine and never leak from one sample to the next. The agent starts in its image's working directory, falling back to the sandbox user's home directory when the image sets none; pass cwd to place it somewhere else.

By default that image is Inspect's own, which contains little more than a Python installation. To give the agent a repository to work in, or any other starting state, put a Dockerfile or compose.yaml in your working directory and Inspect will build the sandbox from it, or point sandbox at one directly:

claude_code(
  chat_anthropic(model = "claude-sonnet-4-5"),
  sandbox = c("docker", "path/to/compose.yaml")
)

See Inspect's sandboxing documentation for the configuration these files support.

Examples

if (FALSE) {
  library(tibble)
  library(ellmer)

  simple_addition <- tibble(
    input = c("What's 2+2?", "What's 2+3?"),
    target = c("4", "5")
  )

  tsk <- Task$new(
    dataset = simple_addition,
    solver = claude_code(chat_anthropic(model = "claude-sonnet-4-5")),
    scorer = detect_includes()
  )

  tsk$eval()
}