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How to reproduce the AppWorld evaluation with the dedicated setup with Harness Zero

Run AppWorld rollouts through the included mini-SWE-agent and environment to match the reported setup.

  • Research
  • Advanced
  • 4 hours
  • From the README
  • Any agent

Who it is forA benchmark researcher who needs to check AppWorld results against the paper's test_normal split.

The problemAppWorld rollouts fail or give misleading scores when run with a generic harness instead of the dedicated agent and environment.

The outcomeAppWorld rollouts that use the dedicated envs/appworld agent and environment, with SGC scores on the 168 test tasks.

What you need first

  • Python 3.12 to 3.13 and uv installed
  • Docker running, since rollouts run in local Docker sandboxes via Harbor
  • API key for your model route (OpenAI, Azure AI Foundry, or OpenRouter) in a .env file copied from .env.example
  • A Tinker key (TM_API_KEY) if you plan to run training

Steps

  1. Run uv sync in the repo folder.
  2. Read envs/appworld/README.md, which the README says AppWorld rollouts must follow.
  3. Build the AppWorld image and environment as described in envs/appworld/README.md.
  4. Run the AppWorld rollouts with the dedicated mini-SWE-agent and environment, then compare SGC against the test_normal numbers in the README.

Prompt for your agent

Using the repo metaevo-ai/harness-zero, follow envs/appworld/README.md to run AppWorld test_normal rollouts with the dedicated agent and environment. Expected output: SGC scores across the 168 test tasks. Verify that the run uses the dedicated environment and not a generic harness before reporting scores.

Caveat. The README says AppWorld rollouts must use the dedicated environment, so results from other setups are not comparable to the paper.

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