pool

pool

Model-alias pool for the SkillOrchestra eval orchestrator.

The original SkillOrchestra (config/models.py + config/pool_config.json) maps stage aliases — search-1/2/3, reasoner-1/2/3, answer-1/2/3/4, answer-math-1/2 — onto a pool of 6+ models served via SGLang. OpenJarvis runs a 2-model world (one local vLLM student + one cloud model), so the default pool collapses the alias tiers onto local/cloud by cost rank: the dearer -1 / -2 aliases (and answer-math-1) route to the cloud model, the cheaper -3 / -4 aliases (and answer-math-2) route to the local model. This mirrors stage_router.WeightedAverageStrategy.COST_TIERS.

A cell overrides any alias through method_cfg.model_pool::

method_cfg.model_pool = {
    "search-1" = { model = "claude-opus-4-7", endpoint = "anthropic" },
    "search-3" = { model = "Qwen/Qwen3.5-27B-FP8", endpoint = "http://localhost:8001/v1" },
    ...
}

endpoint is anthropic / openai / gemini for a cloud model, or an OpenAI-compatible base URL (http://...) for a local vLLM model.

Classes

ModelSpec dataclass

ModelSpec(alias: str, model: str, endpoint: str, kind: str)

A resolved alias: which concrete model on which endpoint.

Functions

build_pool

build_pool(*, local_model: Optional[str], local_endpoint: Optional[str], cloud_model: str, cloud_endpoint: str, overrides: Optional[Dict[str, Dict[str, str]]] = None) -> Dict[str, ModelSpec]

Resolve every alias to a :class:ModelSpec.

Default mapping collapses the alias tiers onto the cell's local/cloud pair; overrides (from method_cfg.model_pool) wins per alias.

Source code in src/openjarvis/agents/hybrid/skillorchestra/pool.py

def build_pool(
    *,
    local_model: Optional[str],
    local_endpoint: Optional[str],
    cloud_model: str,
    cloud_endpoint: str,
    overrides: Optional[Dict[str, Dict[str, str]]] = None,
) -> Dict[str, ModelSpec]:
    """Resolve every alias to a :class:`ModelSpec`.

    Default mapping collapses the alias tiers onto the cell's local/cloud
    pair; ``overrides`` (from ``method_cfg.model_pool``) wins per alias.
    """
    pool: Dict[str, ModelSpec] = {}
    have_local = bool(local_model and local_endpoint)
    for alias in ALL_ALIASES:
        to_cloud = alias in _CLOUD_ALIASES or not have_local
        if to_cloud:
            pool[alias] = ModelSpec(alias, cloud_model, cloud_endpoint, "cloud")
        else:
            pool[alias] = ModelSpec(
                alias, local_model, local_endpoint, "local"  # type: ignore[arg-type]
            )

    for alias, spec in (overrides or {}).items():
        if alias not in pool:
            continue
        model = spec.get("model")
        endpoint = spec.get("endpoint")
        if not model or not endpoint:
            continue
        pool[alias] = ModelSpec(alias, model, endpoint, _endpoint_kind(endpoint))
    return pool

call_alias

call_alias(agent: Any, spec: ModelSpec, *, user: str, system: Optional[str] = None, max_tokens: int = 8000, temperature: float = 1.0) -> Tuple[str, int, int, float]

Single text-generation call through a resolved alias.

Returns (text, tokens_in, tokens_out, cost_usd). Dispatches to the right :class:LocalCloudAgent SDK helper by endpoint. temperature defaults to 1.0 — the value the original call_tool uses for every worker call (eval_frames.py:657).

Source code in src/openjarvis/agents/hybrid/skillorchestra/pool.py

def call_alias(
    agent: Any,
    spec: ModelSpec,
    *,
    user: str,
    system: Optional[str] = None,
    max_tokens: int = 8000,
    temperature: float = 1.0,
) -> Tuple[str, int, int, float]:
    """Single text-generation call through a resolved alias.

    Returns ``(text, tokens_in, tokens_out, cost_usd)``. Dispatches to the
    right :class:`LocalCloudAgent` SDK helper by endpoint. ``temperature``
    defaults to 1.0 — the value the original ``call_tool`` uses for every
    worker call (``eval_frames.py:657``).
    """
    if spec.is_local:
        text, p, c = agent._call_vllm(
            spec.model,
            spec.endpoint,
            user=user,
            system=system,
            max_tokens=max_tokens,
            temperature=temperature,
            enable_thinking=False,
            trace_role="local",
        )
        return text, p, c, 0.0

    ep = spec.endpoint.lower()
    if ep == "anthropic":
        text, p, c, _ = agent._call_anthropic(
            spec.model, user=user, system=system,
            max_tokens=max_tokens, temperature=temperature, trace_role="cloud",
        )
    elif ep == "openai":
        text, p, c = agent._call_openai(
            spec.model, user=user, system=system,
            max_tokens=max_tokens, temperature=temperature, trace_role="cloud",
        )
    elif ep == "gemini":
        text, p, c = agent._call_gemini(
            spec.model, user=user, system=system,
            max_tokens=max_tokens, temperature=temperature, trace_role="cloud",
        )
    else:
        raise ValueError(f"unsupported pool endpoint: {spec.endpoint!r}")
    return text, p, c, agent.cost_usd(spec.model, p, c)