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What you can do with Simreal ML Bench

Simreal-MLBench is a public Python package with a 60-task agentic ML research benchmark, its scoring code, and a client SDK, while the official evaluation runs on a Simreal-operated service.

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Simreal-AI/Simreal-MLBench on GitHub

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What do you want to do?

4 use cases

  • Validate the 60-task benchmark catalog locally

    Check the public task catalog and protocol config before building on the benchmark.

    View prompt
    In the Simreal-MLBench repository, create a virtual environment, install the package with `python -m pip install -e '.[test]'`, and run `mleb validate configs/catalog.json`. Report whether validation passed and how many tasks the catalog lists. Confirm the count is 60 before reporting success.

  • Recompute a published benchmark score from saved counts

    Rebuild a reported task score from its reference counts to check the number yourself.

    View prompt
    In the Simreal-MLBench repository, run `mleb grade examples/snapshot.json examples/result.json` and then `python scripts/verify_report.py`. Replace the example files with the saved reference counts for <target task> if you have them. Report the recomputed score and whether the verification script passed.

  • Get an agent officially scored on the 60 tasks

    Run an agent under the official protocol through the Simreal evaluation service.

    View prompt
    Read AGENT_GUIDE.md and docs/GATEWAY.md in the Simreal-MLBench repository. Summarize how an agent calls the mleb-remote SDK, the tier budgets, and the two-submission rule for <target agent>. Expected output: a short integration checklist. Verify that the checklist names the business contact before any run is planned.

  • Run the scoring test suite before changing the rule

    Run the 35-test suite to confirm the scoring code still behaves as published.

    View prompt
    In the Simreal-MLBench repository, set up the virtual environment, install with `python -m pip install -e '.[test]'`, and run `python -m pytest -q`. Report the number of tests passed and any failures. Expected output: a pass count. Verify the count matches the 35 tests listed in the release status.

How to use Simreal ML Bench

  1. Check you have what it needs: Python 3.11 or newer (Python 3.12 is the locally tested version); A local copy of the repository from github.com/Simreal-AI/Simreal-MLBench; A terminal with a shell that supports the commands in the README.
  2. Open Simreal-AI/Simreal-MLBench on GitHub and read the README and license.
  3. Create a virtual environment with python3 -m venv .venv.
  4. Activate it with source .venv/bin/activate.
  5. Install the package and test extras with python -m pip install -e '.[test]'.
  6. Validate the catalog with mleb validate configs/catalog.json.

These steps come from our top use case, Validate the 60-task benchmark catalog locally, which also has a copy-paste agent prompt.

Simreal ML Bench alternatives · Best AI repos for Research

About this repo

Requirements

  • Python 3.11 or newer (Python 3.12 is the locally tested version)
  • A local copy of the repository from github.com/Simreal-AI/Simreal-MLBench
  • A terminal with a shell that supports the commands in the README
  • For full agent evaluation only: contact with [email protected] to get access to the Simreal evaluation service
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Questions about Simreal ML Bench

What is Simreal ML Bench?

Simreal-MLBench is a public Python package with a 60-task agentic ML research benchmark, its scoring code, and a client SDK, while the official evaluation runs on a Simreal-operated service. It is an open-source project on GitHub: https://github.com/Simreal-AI/Simreal-MLBench.

Who is Simreal ML Bench for?

Written for ML researchers, benchmark reviewers, and AI labs that want to check or run agentic ML research evaluations.

What can I do with Simreal ML Bench?

4 use cases so far, for example: Validate the 60-task benchmark catalog locally; Recompute a published benchmark score from saved counts; Get an agent officially scored on the 60 tasks. Each has steps and a copy-paste agent prompt.

What do I need to run Simreal ML Bench?

Python 3.11 or newer (Python 3.12 is the locally tested version); A local copy of the repository from github.com/Simreal-AI/Simreal-MLBench; A terminal with a shell that supports the commands in the README; For full agent evaluation only: contact with [email protected] to get access to the Simreal evaluation service.

What are alternatives to Simreal ML Bench?

Other hot AI repos in Research: Scientific Agent Skills, Speaker Mem R1, DSH Council, Evo Bot, AI Engineering Course.

Is Simreal ML Bench 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.