composer

composer

Composer bridges — convert a Recipe into EvalSuiteConfig or OperatorManifest.

These are pure-function transformations that let the unified Recipe format drive both the eval framework and the operator system without those systems needing to know about recipes directly.

Classes

Functions

recipe_to_eval_suite

recipe_to_eval_suite(recipe: Recipe, benchmarks: Optional[List[str]] = None, max_samples: Optional[int] = None, judge_model: Optional[str] = None) -> EvalSuiteConfig

Build an EvalSuiteConfig from a recipe.

The recipe's model / engine become the single [[models]] entry. The recipe's eval_benchmarks (or the benchmarks override) become [[benchmarks]] entries. Agent type and tools are inherited so the eval runner constructs the right backend automatically.

Args: recipe: Source recipe. benchmarks: Override benchmark list (defaults to recipe.eval_benchmarks). max_samples: Override per-benchmark sample cap. judge_model: Override LLM judge model.

Raises: ValueError: If no model or benchmarks can be resolved.

Source code in src/openjarvis/recipes/composer.py

def recipe_to_eval_suite(
    recipe: Recipe,
    benchmarks: Optional[List[str]] = None,
    max_samples: Optional[int] = None,
    judge_model: Optional[str] = None,
) -> EvalSuiteConfig:
    """Build an ``EvalSuiteConfig`` from a recipe.

    The recipe's model / engine become the single ``[[models]]`` entry.
    The recipe's ``eval_benchmarks`` (or the *benchmarks* override) become
    ``[[benchmarks]]`` entries.  Agent type and tools are inherited so the
    eval runner constructs the right backend automatically.

    Args:
        recipe: Source recipe.
        benchmarks: Override benchmark list (defaults to ``recipe.eval_benchmarks``).
        max_samples: Override per-benchmark sample cap.
        judge_model: Override LLM judge model.

    Raises:
        ValueError: If no model or benchmarks can be resolved.
    """
    from openjarvis.evals.core.types import (
        BenchmarkConfig,
        DefaultsConfig,
        EvalSuiteConfig,
        ExecutionConfig,
        JudgeConfig,
        MetaConfig,
        ModelConfig,
    )

    bench_names = benchmarks or list(recipe.eval_benchmarks)
    if not bench_names:
        bench_names = list(recipe.eval_suites)
    if not bench_names:
        raise ValueError(
            f"Recipe '{recipe.name}' has no benchmarks defined and none were "
            "provided.  Set [eval] benchmarks in the TOML or pass benchmarks=."
        )

    model_name = recipe.model
    if not model_name:
        raise ValueError(
            f"Recipe '{recipe.name}' has no model defined.  "
            "Set [intelligence] model in the TOML."
        )

    has_agent = recipe.agent_type is not None
    backend = recipe.eval_backend or ("jarvis-agent" if has_agent else "jarvis-direct")

    model_cfg = ModelConfig(
        name=model_name,
        engine=recipe.engine_key,
        provider=recipe.provider,
        temperature=recipe.temperature,
    )

    bench_cfgs: list[BenchmarkConfig] = []
    for bname in bench_names:
        bench_cfgs.append(BenchmarkConfig(
            name=bname,
            backend=backend,
            max_samples=max_samples or recipe.eval_max_samples,
            agent=recipe.agent_type if has_agent else None,
            tools=list(recipe.tools) if has_agent else [],
            judge_model=judge_model or recipe.eval_judge_model,
        ))

    return EvalSuiteConfig(
        meta=MetaConfig(
            name=f"{recipe.name}-eval",
            description=f"Auto-generated eval suite from recipe '{recipe.name}'",
        ),
        defaults=DefaultsConfig(
            temperature=recipe.temperature or 0.0,
            max_tokens=2048,
        ),
        judge=JudgeConfig(
            model=judge_model or recipe.eval_judge_model or "gpt-5-mini-2025-08-07",
        ),
        run=ExecutionConfig(),
        models=[model_cfg],
        benchmarks=bench_cfgs,
    )

recipe_to_operator

recipe_to_operator(recipe: Recipe) -> OperatorManifest

Build an OperatorManifest from a recipe.

Maps the recipe's agent, schedule, and channel fields into the operator manifest format used by OperatorManager.

Raises: ValueError: If schedule information is missing.

Source code in src/openjarvis/recipes/composer.py

def recipe_to_operator(recipe: Recipe) -> OperatorManifest:
    """Build an ``OperatorManifest`` from a recipe.

    Maps the recipe's agent, schedule, and channel fields into the
    operator manifest format used by ``OperatorManager``.

    Raises:
        ValueError: If schedule information is missing.
    """
    from openjarvis.operators.types import OperatorManifest

    if not recipe.schedule_type:
        raise ValueError(
            f"Recipe '{recipe.name}' has no [schedule] section.  "
            "Operator recipes must define schedule_type and schedule_value."
        )

    prompt = recipe.system_prompt or ""
    prompt_path = recipe.system_prompt_path or ""

    return OperatorManifest(
        id=recipe.name,
        name=recipe.name,
        version=recipe.version,
        description=recipe.description,
        tools=list(recipe.tools),
        system_prompt=prompt,
        system_prompt_path=prompt_path,
        max_turns=recipe.max_turns or 20,
        temperature=recipe.temperature or 0.3,
        schedule_type=recipe.schedule_type,
        schedule_value=recipe.schedule_value or "300",
        required_capabilities=list(recipe.required_capabilities),
    )