harness-eval

Harness Eval — End-to-End Feature Validation

Validate OpenHarness features by running real agent loops against an unfamiliar codebase with actual LLM API calls. Every test exercises the full stack: API client → model → tool calls → execution → result.

Core Principles

1. Test on an unfamiliar project — never test on OpenHarness itself (the agent modifies its own code). Clone a real project as the workspace.
2. Use real API calls — no mocks. Configure a real LLM endpoint.
3. Multi-turn conversations — always test 2+ turns where the model needs prior context.
4. Combine features — test hooks+skills+agent loop together, not in isolation.
5. Verify tool execution — inspect tool call lists and output files, not just model text.

Workflow

1. Prepare Workspace

Clone an unfamiliar project (do not use OpenHarness):

git clone https://github.com/HKUDS/AutoAgent /tmp/eval-workspace

2. Configure Environment

export ANTHROPIC_API_KEY=sk-xxx
export ANTHROPIC_BASE_URL=https://api.moonshot.cn/anthropic  # or any provider
export ANTHROPIC_MODEL=kimi-k2.5

For long-running real evals, do not artificially lower max_turns. Use the product default (200) unless the user explicitly wants a tighter bound.

3. Prepare Real Sandbox Runtime When Relevant

If the task is validating sandbox behavior, install and verify the actual runtime before running agent loops:

npm install -g @anthropic-ai/sandbox-runtime
sudo apt-get update
sudo apt-get install -y bubblewrap ripgrep
which srt
which bwrap
which rg
srt --version

Then run a minimal smoke check through OpenHarness, not just raw srt, so you verify the real adapter path:

from pathlib import Path
from openharness.config.settings import Settings, SandboxSettings, save_settings
from openharness.tools.bash_tool import BashTool

cfg = Path("/tmp/openharness-sandbox-settings.json")
save_settings(Settings(sandbox=SandboxSettings(enabled=True, fail_if_unavailable=True)), cfg)
# Point config loader at this file, then run BashTool on a tiny command such as `pwd`.