refactor: 切换到 litellm，删除未使用的代码

- 添加 litellm 依赖，统一 LLM 调用层
- 新增 litellm_client.py 支持多厂商
- 更新 llm_catalog.yaml 添加 litellm_model 映射
- 删除旧的 cost_tracker.py (litellm 内置 cost 追踪)
- 删除未使用的 multimodal.py 和 storage_adapter.py
- 删除空文件 crewai/__init__.py, tools/__init__.py
- 更新测试以适配新代码

backend/src/core/agent/litellm_client.py

@@ -0,0 +1,130 @@
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from decimal import Decimal
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+
+@dataclass(frozen=True)
+class LLMConfig:
+    model_code: str
+    factory_name: str
+    litellm_model: str
+    request_url: str
+
+
+@dataclass(frozen=True)
+class LLMResponse:
+    content: str
+    usage: dict[str, Any]
+
+
+class LiteLLMClient:
+    def __init__(self, config: LLMConfig, api_key: str | None = None) -> None:
+        self._config = config
+        self._api_key = api_key or self._get_api_key(config.factory_name)
+
+    @staticmethod
+    def _get_api_key(factory_name: str) -> str:
+        key_map = {
+            "dashscope": "DASHSCOPE_API_KEY",
+            "minimax": "MINIMAX_API_KEY",
+            "moonshot": "MOONSHOT_API_KEY",
+            "deepseek": "DEEPSEEK_API_KEY",
+            "volcengine-ark": "ARK_API_KEY",
+            "z-ai": "ZAI_API_KEY",
+        }
+        env_key = key_map.get(factory_name)
+        if not env_key:
+            raise ValueError(f"No API key mapping for factory: {factory_name}")
+        key = os.environ.get(env_key)
+        if not key:
+            raise ValueError(f"Environment variable {env_key} is not set")
+        return key
+
+    @staticmethod
+    def load_config(
+        model_code: str,
+        static_root: Path | None = None,
+    ) -> LLMConfig:
+        root = static_root or (
+            Path(__file__).resolve().parents[3] / "config" / "static" / "database"
+        )
+        yaml_path = root / "llm_catalog.yaml"
+        with yaml_path.open("r", encoding="utf-8") as f:
+            data = yaml.safe_load(f)
+
+        factories = {f["name"]: f for f in data.get("factories", [])}
+        llms = data.get("llms", [])
+
+        for llm in llms:
+            if llm.get("model_code") == model_code:
+                factory_name = llm["factory_name"]
+                factory = factories.get(factory_name)
+                if not factory:
+                    raise ValueError(f"Factory not found: {factory_name}")
+                return LLMConfig(
+                    model_code=model_code,
+                    factory_name=factory_name,
+                    litellm_model=llm.get("litellm_model", model_code),
+                    request_url=factory["request_url"],
+                )
+
+        raise ValueError(f"Model not found: {model_code}")
+
+    def chat(
+        self,
+        messages: list[dict[str, str]],
+        *,
+        temperature: float = 0.7,
+        max_tokens: int | None = None,
+    ) -> LLMResponse:
+        import litellm
+
+        response = litellm.completion(  # type: ignore[attr-defined]
+            model=self._config.litellm_model,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            api_base=self._config.request_url,
+            api_key=self._api_key,
+        )
+
+        content = response.choices[0].message.content or ""  # type: ignore[attr-defined]
+        usage = response.usage.model_dump() if response.usage else {}  # type: ignore[attr-defined]
+
+        return LLMResponse(content=content, usage=usage)
+
+    async def achat(
+        self,
+        messages: list[dict[str, str]],
+        *,
+        temperature: float = 0.7,
+        max_tokens: int | None = None,
+    ) -> LLMResponse:
+        import litellm
+
+        response = await litellm.acompletion(  # type: ignore[attr-defined]
+            model=self._config.litellm_model,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            api_base=self._config.request_url,
+            api_key=self._api_key,
+        )
+
+        content = response.choices[0].message.content or ""  # type: ignore[attr-defined]
+        usage = response.usage.model_dump() if response.usage else {}  # type: ignore[attr-defined]
+
+        return LLMResponse(content=content, usage=usage)
+
+
+def get_model_cost(usage: dict[str, Any]) -> Decimal:
+    cost = usage.get("cost")
+    if cost is None:
+        return Decimal("0")
+    return Decimal(str(cost))