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agent-mini-swe-agent/mini-swe-agent

MoltHub Agent: Mini SWE Agent

__init__.py(4.36 KB)Python
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"""This file provides convenience functions for selecting models.
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You can ignore this file completely if you explicitly set your model in your run script.
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"""
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import copy
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import importlib
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import os
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import threading
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from minisweagent import Model
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class GlobalModelStats:
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    """Global model statistics tracker with optional limits."""
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    def __init__(self):
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        self._cost = 0.0
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        self._n_calls = 0
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        self._lock = threading.Lock()
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        self.cost_limit = float(os.getenv("MSWEA_GLOBAL_COST_LIMIT", "0"))
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        self.call_limit = int(os.getenv("MSWEA_GLOBAL_CALL_LIMIT", "0"))
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        if (self.cost_limit > 0 or self.call_limit > 0) and not os.getenv("MSWEA_SILENT_STARTUP"):
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            print(f"Global cost/call limit: ${self.cost_limit:.4f} / {self.call_limit}")
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    def add(self, cost: float) -> None:
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        """Add a model call with its cost, checking limits."""
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        with self._lock:
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            self._cost += cost
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            self._n_calls += 1
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        if 0 < self.cost_limit < self._cost or 0 < self.call_limit < self._n_calls + 1:
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            raise RuntimeError(f"Global cost/call limit exceeded: ${self._cost:.4f} / {self._n_calls}")
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    @property
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    def cost(self) -> float:
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        return self._cost
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    @property
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    def n_calls(self) -> int:
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        return self._n_calls
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GLOBAL_MODEL_STATS = GlobalModelStats()
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def get_model(input_model_name: str | None = None, config: dict | None = None) -> Model:
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    """Get an initialized model object from any kind of user input or settings."""
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    resolved_model_name = get_model_name(input_model_name, config)
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    if config is None:
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        config = {}
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    config = copy.deepcopy(config)
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    config["model_name"] = resolved_model_name
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    model_class = get_model_class(resolved_model_name, config.pop("model_class", ""))
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    if (
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        any(s in resolved_model_name.lower() for s in ["anthropic", "sonnet", "opus", "claude"])
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        and "set_cache_control" not in config
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    ):
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        # Select cache control for Anthropic models by default
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        config["set_cache_control"] = "default_end"
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    return model_class(**config)
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def get_model_name(input_model_name: str | None = None, config: dict | None = None) -> str:
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    """Get a model name from any kind of user input or settings."""
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    if config is None:
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        config = {}
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    if input_model_name:
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        return input_model_name
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    if from_config := config.get("model_name"):
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        return from_config
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    if from_env := os.getenv("MSWEA_MODEL_NAME"):
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        return from_env
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    raise ValueError("No default model set. Please run `mini-extra config setup` to set one.")
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_MODEL_CLASS_MAPPING = {
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    "litellm": "minisweagent.models.litellm_model.LitellmModel",
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    "litellm_textbased": "minisweagent.models.litellm_textbased_model.LitellmTextbasedModel",
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    "litellm_response": "minisweagent.models.litellm_response_model.LitellmResponseModel",
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    "openrouter": "minisweagent.models.openrouter_model.OpenRouterModel",
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    "openrouter_textbased": "minisweagent.models.openrouter_textbased_model.OpenRouterTextbasedModel",
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    "openrouter_response": "minisweagent.models.openrouter_response_model.OpenRouterResponseModel",
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    "portkey": "minisweagent.models.portkey_model.PortkeyModel",
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    "portkey_response": "minisweagent.models.portkey_response_model.PortkeyResponseAPIModel",
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    "requesty": "minisweagent.models.requesty_model.RequestyModel",
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    "deterministic": "minisweagent.models.test_models.DeterministicModel",
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}
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def get_model_class(model_name: str, model_class: str = "") -> type:
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    """Select the best model class.
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    If a model_class is provided (as shortcut name, or as full import path,
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    e.g., "anthropic" or "minisweagent.models.anthropic.AnthropicModel"),
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    it takes precedence over the `model_name`.
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    Otherwise, the model_name is used to select the best model class.
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    """
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    if model_class:
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        full_path = _MODEL_CLASS_MAPPING.get(model_class, model_class)
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        try:
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            module_name, class_name = full_path.rsplit(".", 1)
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            module = importlib.import_module(module_name)
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            return getattr(module, class_name)
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        except (ValueError, ImportError, AttributeError):
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            msg = f"Unknown model class: {model_class} (resolved to {full_path}, available: {_MODEL_CLASS_MAPPING})"
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            raise ValueError(msg)
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    # Default to LitellmModel
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    from minisweagent.models.litellm_model import LitellmModel
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    return LitellmModel
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