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How to run a Codex candidate on one Terminal-Bench task with Rsi Eval

Score a candidate agent on a single Terminal-Bench 2.0 task and read the per-task result.

Credentials. The Codex login directory and the profile hold account access, so keep them outside the repository and out of candidate artifacts.

Who it is forA software engineer testing a coding agent setup who needs a real score on one task before running a larger suite.

The problemSetting up Docker, Harbor, and a dedicated Codex login is easy to get wrong, and a first run can be hard to diagnose.

The outcomeA result.json and resolved.json for the candidate, plus a reference oracle run to compare against.

What you need first

  • Python 3.12 or newer and git
  • Docker Engine with Compose and Buildx, or Docker Desktop, for benchmark runs
  • Codex CLI on PATH and access to a model for candidate runs
  • Harbor 0.18.0 installed through the harbor extra for benchmark runs

Steps

  1. python -m pip install -e '.[harbor]'
  2. docker info
  3. CODEX_HOME="$HOME/.codex-rsi-eval" codex -c 'cli_auth_credentials_store="file"' login (inferred, check the README)
  4. rsi-eval configure --model YOUR_MODEL --auth-dir "$HOME/.codex-rsi-eval" --output profile.json (inferred, check the README)
  5. rsi-eval check --config examples/evaluate.json
  6. rsi-eval run --config examples/evaluate.json --output runs/tb20-candidate (inferred, check the README)

Prompt for your agent

In the rsi_eval repo, set target.model and the Codex login directory in examples/evaluate.json for <model-name>, then run `rsi-eval run --config examples/evaluate.json --output runs/<new-output-dir>`. Report status and per-task reward from result.json. Verify that the output directory did not exist before the run.

Caveat. The candidate run consumes model quota, and the README says the bundled subscription Codex target requires concurrency=1.

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