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What you can do with Rsi Eval

A lightweight Python SDK and CLI for evaluating recursive self-improvement methods across agent benchmarks.

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Danielxu0208/rsi_eval on GitHub

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What do you want to do?

4 use cases

  • Preview benchmark tasks before spending model quota

    Check which tasks, candidate hash, and settings a run would use, with no Docker or model calls.

    View prompt
    Clone https://github.com/Danielxu0208/rsi_eval, install it with `python -m pip install -e .` in a Python 3.12 virtual environment, then run `rsi-eval preview --config <config-path>` for the benchmark config I choose. Report the resolved task references and candidate hash. Verify that the preview ran without Docker or a model call.

  • Run a Codex candidate on one Terminal-Bench task

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

    View prompt
    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.

  • Build your own self-improvement loop around evaluate()

    Call evaluate() from your own loop to test each new candidate and feed scores into the next one.

    View prompt
    Using the rsi_eval SDK, write a loop that generates a candidate in <candidate-dir>, calls evaluate() with a new output directory per candidate, and collects per-task rewards. Report the reward for each candidate across iterations. Verify that each evaluation wrote its own result.json and resolved.json.

  • Run a bundled RSI method on a prepared experiment

    Prepare, check, run, and independently test a Meta-Harness, RRSI, AHE, or HarnessX workflow.

    View prompt
    In the rsi_eval repo, prepare a <method> experiment at runs/<experiment-name> using profile.json, then run method check, run, and test in that order. Report the artifact selected by the method and the test output. Verify that the experiment directory contains the frozen configuration and seed.

How to use Rsi Eval

  1. Check you have what it needs: 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.
  2. Open Danielxu0208/rsi_eval on GitHub and read the README and license.
  3. git clone https://github.com/Danielxu0208/rsi_eval.git
  4. cd rsi_eval
  5. python3.12 -m venv .venv
  6. source .venv/bin/activate

These steps come from our top use case, Preview benchmark tasks before spending model quota, which also has a copy-paste agent prompt.

Rsi Eval alternatives · Best AI repos for Software and DevOps

About this repo

  • Who it is forDevelopers and AI researchers who want to test agent candidates or self-improvement loops on existing benchmarks without writing their own execution and result handling.
  • Why it is hot
  • LanguagePython
  • Sourcegithub.com/Danielxu0208/rsi_eval · Back to Hot repos

Requirements

  • 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
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Questions about Rsi Eval

What is Rsi Eval?

A lightweight Python SDK and CLI for evaluating recursive self-improvement methods across agent benchmarks. It is an open-source project on GitHub: https://github.com/Danielxu0208/rsi_eval.

Who is Rsi Eval for?

Developers and AI researchers who want to test agent candidates or self-improvement loops on existing benchmarks without writing their own execution and result handling.

What can I do with Rsi Eval?

4 use cases so far, for example: Preview benchmark tasks before spending model quota; Run a Codex candidate on one Terminal-Bench task; Build your own self-improvement loop around evaluate(). Each has steps and a copy-paste agent prompt.

What do I need to run Rsi Eval?

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.

What are alternatives to Rsi Eval?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Rsi Eval safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.