Monthly-SWEBench-2026-04

Monthly-SWEBench-2026-04 is a curated benchmark of 90 real-world software engineering tasks, sourced from GitHub pull requests merged in April 2026. Tasks are in Harbor format and can be run with any Harbor-compatible agent.

View leaderboard and results →

• 90 tasks — 43 bugfix + 47 non-bugfix
• Tasks span diverse open-source repositories
• Each task includes a runnable environment, test suite, and reference solution

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Task Structure

Each task is a self-contained directory:

<task_id>/
├── task.toml        # Task metadata and resource limits
├── instruction.md   # Natural-language description given to the agent
├── environment/     # Dockerfile + setup scripts defining the starting state
├── tests/           # Verifier scripts run after the agent finishes
└── solution/        # Reference solution (ground-truth patch)

task.toml specifies the task name, difficulty, category, and execution constraints — agent timeout, verifier timeout, CPU/memory/disk limits for the container.

instruction.md is what the agent sees. It describes what needs to be implemented or fixed, the expected behavior, and acceptance criteria — written as a natural language engineering spec, not as a hint or walkthrough.

environment/ contains the Dockerfile (and any supporting files) that builds the container the agent works inside. The environment captures the exact repository state before the PR was merged — the agent starts from a broken or incomplete codebase.

tests/ contains the verifier. After the agent finishes, Harbor runs the test script inside the container. The script writes 1.0 to /logs/verifier/reward.txt if the task passes, 0.0 if it fails. Tests are derived from the actual CI checks in the original PR.

solution/ contains the reference patch — the actual code change from the merged PR. This is used by the oracle agent and can be used for debugging or analysis.

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Run with Harbor

Install

pip install harbor

Download

huggingface-cli download UnipatAI/Monthly-SWEBench-2026-04 \
  --repo-type dataset --local-dir ./monthly-swebench-2026-04

cd monthly-swebench-2026-04
tar -I zstd -xf bugfix.tar.zst
tar -I zstd -xf non_bugfix.tar.zst

Run

export ANTHROPIC_API_KEY=<your-key>

# Run bugfix tasks
harbor run \
  -p ./monthly-swebench-2026-04/bugfix \
  --agent terminus-2 \
  --model anthropic/claude-opus-4-5 \
  --n-concurrent 4

# Run non-bugfix tasks
harbor run \
  -p ./monthly-swebench-2026-04/non_bugfix \
  --agent terminus-2 \
  --model anthropic/claude-opus-4-5 \
  --n-concurrent 4

terminus-2 is Harbor's built-in multi-turn agent and works with any model supported by LiteLLM. Swap --model to evaluate different models, or use --agent to try other agents (claude-code, codex, openhands, aider, etc.).

View results

harbor view

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Citation

@dataset{monthly_swebench_2026_04,
  title  = {Monthly-SWEBench-2026-04},
  author = {UnipatAI},
  year   = {2026},
  url    = {https://huggingface.co/datasets/UnipatAI/Monthly-SWEBench-2026-04}
}