minions

minions

MinionsAgent — port of HazyResearch Minions protocol.

Cloud supervisor decomposes the task and reads back local-worker output; local worker(s) do the bulk reading/extraction. Multi-turn loop until the supervisor commits to a final answer.

Two modes (cfg["mode"]):

• "minion" — single local worker, one cloud supervisor (cheaper). Default.
• "minions" — parallel local workers, cloud aggregator.

Hybrid harness result: minions-swebenchverified-qwen27b-opus-500 = 0.274 acc / $0.09 per task — beats baseline-cloud's 0.236 / $0.95 on both accuracy and cost. GAIA at n=165 ties baseline-cloud at 0.576 acc / $0.67 (vs $1.09).

Requires the minions library from https://github.com/HazyResearch/minions installed in the same env (e.g. uv pip install -e /matx/u/aspark/hybrid-local-cloud-compute/external/minions). Import is lazy — the agent class registers without minions available, and the import error only fires on run().

Compatibility patches applied at first run() (idempotent):

• Strip temperature for Opus 4.7+ (rejected with 400).
• Inject server-side output_config JSON schema on supervisor turns so Opus replies in the shape Minions's parser expects (per-turn schema picked by sniffing the prompt for "decision": "provide_final_answer").
• Replace Minions's _extract_json with a wrapper that short-circuits when the response is already valid JSON.
• Inject timeout=600/max_retries=5 defaults into anthropic.Anthropic() — Minions builds bare clients which 60s-timeout under SWE-bench concurrency=8.

Ported from hybrid-local-cloud-compute/adapters/minions_adapter.py.

Classes

MinionsAgent

MinionsAgent(engine: InferenceEngine, model: str, *, local_model: Optional[str] = None, local_endpoint: Optional[str] = None, cloud_endpoint: str = 'anthropic', cfg: Optional[Dict[str, Any]] = None, bus: Optional[Any] = None, temperature: Optional[float] = None, max_tokens: Optional[int] = None)

Bases: LocalCloudAgent

HazyResearch Minions supervisor/worker protocol. See module docstring.

Source code in src/openjarvis/agents/hybrid/_base.py

def __init__(
    self,
    engine: InferenceEngine,
    model: str,
    *,
    local_model: Optional[str] = None,
    local_endpoint: Optional[str] = None,
    cloud_endpoint: str = "anthropic",
    cfg: Optional[Dict[str, Any]] = None,
    bus: Optional[Any] = None,
    temperature: Optional[float] = None,
    max_tokens: Optional[int] = None,
) -> None:
    super().__init__(
        engine,
        model,
        bus=bus,
        temperature=temperature,
        max_tokens=max_tokens,
    )
    self._cloud_model = model
    self._cloud_endpoint = (cloud_endpoint or "anthropic").lower()
    self._local_model = local_model
    self._local_endpoint = local_endpoint
    self._cfg: Dict[str, Any] = dict(cfg or {})

Functions