baseline_local

baseline_local

BaselineLocalAgent — local-only reference for the hybrid ablation.

Mirror of :class:BaselineCloudAgent (baseline_cloud.py) but the entire trajectory runs on the local vLLM model. No cloud teacher / router / advisor is involved — this is the "what does the local model do by itself?" floor in the n=100 ablation matrix.

On GAIA the agent makes one local call with the formatted prompt (which already carries the FINAL ANSWER: reminder from _prompts.format_gaia) and returns the text. On SWE-bench-Verified the agent delegates to :func:run_swe_agent_loop with backbone="local" so the model gets to run bash and read the repo — same wiring as the mini-swe-agent cells but driven by the local model.

Construction args mirror :class:LocalCloudAgent. The local block in the cell registry determines the local model + endpoint; cloud is accepted for schema compatibility but unused (and cost_usd is always 0 — local inference is free).

Classes

BaselineLocalAgent

BaselineLocalAgent(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

Local-only baseline. cloud_* fields are ignored.

Configurable knobs via cfg:

• local_max_tokens (int, default 4096): max_tokens per GAIA call and per turn of the SWE agent loop.
• local_temperature (float, default 0.0): sampling temperature for the local model.
• swe_use_agent_loop (bool, default True for SWE): if False the SWE branch falls back to a one-shot blind patch (not recommended; kept for parity with other agents).
• swe_max_turns (int, default 30): SWE-bench loop turn cap.
• swe_bash_timeout_s (int, default 120): bash timeout per turn.
• swe_turn_max_tokens (int, default 4096): max_tokens per agent turn inside the SWE loop. Falls back to local_max_tokens.

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