harborforge 1.0.1

Forge Harbor task directories from any evaluation benchmark

HarborForge

Forge Harbor task directories from any evaluation benchmark.

HarborForge provides the abstract contracts (DataMapper, DatasetHandler) for turning raw benchmark datasets into Harbor-compatible task directories, enabling large-scale parallel agent evaluation.

How it works

Raw benchmark data
      ↓  DataMapper.map()
Harbor task directories
      ↓  harbor jobs start
Agent runs in isolated Docker container
      ↓
Verifier scores the output → reward written to Harbor

Each task directory contains an instruction.md (shown to the agent), a Dockerfile (the agent's environment), and a test.sh verifier that writes a float reward to /logs/verifier/reward.txt.

Installation

pip install harborforge

Or with uv:

uv add harborforge

Usage

Implement DatasetHandler for each dataset type in your benchmark, then DataMapper to iterate over tasks:

from harborforge import DataMapper, DatasetHandler

class MyHandler(DatasetHandler):
    dataset_name = "my_dataset"

    def instruction(self, task_data):
        return f"Solve this: {task_data['problem']}"

    def dockerfile(self, task_data):
        return "FROM python:3.12-slim\nWORKDIR /app\n"

    def test_sh(self, task_data):
        answer = task_data["answer"]
        return f"""#!/bin/bash
mkdir -p /logs/verifier
actual=$(cat /output/answer.txt 2>/dev/null)
[ "$actual" = "{answer}" ] && echo 1 || echo 0 > /logs/verifier/reward.txt
"""

class MyMapper(DataMapper):
    def iter_tasks(self):
        for i, task in enumerate(load_my_benchmark()):
            yield f"my_dataset/{i}", f"my_dataset/{i}", MyHandler(), task

# Generate Harbor task directories
MyMapper().run(output_dir=Path(".data/tasks"), registry_path=Path("registry.json"))

Handler contract

Method	Required	Purpose
instruction(task_data)	✅	Content for instruction.md — no answer leakage
test_sh(task_data)	✅	Content for tests/test.sh — must write float reward to /logs/verifier/reward.txt
dockerfile(task_data)	✅	Content for environment/Dockerfile
setup()	optional	Download/prepare data for this dataset
data_files(task_data)	optional	Local files to COPY into the image build context
artifacts()	optional	Container paths to capture after trial
verifier_env_keys()	optional	Env var keys to forward to the SEPARATE verifier
verifier_dockerfile(task_data)	optional	Non-None triggers SEPARATE verifier mode

Reference implementation

g9 — maps DSGym benchmarks to Harbor using HarborForge.