refactor: migrate LLM helper to agent module and add unified LLM API endpoints

- Move LLMHelper and related logic from app.helper.llm to app.agent.llm.helper
- Update all imports to reference new LLMHelper location
- Introduce app/agent/llm/__init__.py for internal LLM adapter exports
- Add llm.py API router with endpoints for model listing, provider auth, and test calls
- Remove legacy LLM endpoints from system.py
- Update requirements for langchain-anthropic and anthropic
- Refactor test_llm_helper_testcall.py for async LLMHelper usage and new import paths

app/agent/__init__.py

@@ -33,7 +33,7 @@ from app.agent.runtime import agent_runtime_manager
 from app.agent.tools.factory import MoviePilotToolFactory
 from app.chain import ChainBase
 from app.core.config import settings
-from app.helper.llm import LLMHelper
+from app.agent.llm import LLMHelper
 from app.log import logger
 from app.schemas import Notification, NotificationType
 from app.schemas.message import ChannelCapabilityManager, ChannelCapability
@@ -310,12 +310,12 @@ class MoviePilotAgent:
             return False
 
     @staticmethod
-    def _initialize_llm(streaming: bool = False):
+    async def _initialize_llm(streaming: bool = False):
         """
         初始化 LLM
         :param streaming: 是否启用流式输出
         """
-        return LLMHelper.get_llm(streaming=streaming)
+        return await LLMHelper.get_llm(streaming=streaming)
 
     @staticmethod
     def _extract_text_content(content) -> str:
@@ -387,7 +387,7 @@ class MoviePilotAgent:
             allow_message_tools=self.allow_message_tools,
         )
 
-    def _create_agent(self, streaming: bool = False):
+    async def _create_agent(self, streaming: bool = False):
         """
         创建 LangGraph Agent（使用 create_agent + SummarizationMiddleware）
         :param streaming: 是否启用流式输出
@@ -397,12 +397,12 @@ class MoviePilotAgent:
             system_prompt = prompt_manager.get_agent_prompt(channel=self.channel)
 
             # LLM 模型（用于 agent 执行）
-            llm = self._initialize_llm(streaming=streaming)
+            llm = await self._initialize_llm(streaming=streaming)
             self._sync_model_profile(llm)
 
             # 为中间件内部模型调用准备非流式 LLM，避免与用户流式回复复用同一实例。
             non_streaming_llm = (
-                llm if not streaming else self._initialize_llm(streaming=False)
+                llm if not streaming else await self._initialize_llm(streaming=False)
             )
 
             # 工具列表
@@ -577,7 +577,7 @@ class MoviePilotAgent:
             use_streaming = self._should_stream()
 
             # 创建智能体（根据是否流式传入不同 LLM）
-            agent = self._create_agent(streaming=use_streaming)
+            agent = await self._create_agent(streaming=use_streaming)
 
             if use_streaming:
                 self.stream_handler.set_dispatch_policy(

app/agent/llm/__init__.py

@@ -0,0 +1,19 @@
+"""Agent 内部使用的 LLM 适配层。"""
+
+from app.agent.llm.helper import LLMHelper, LLMTestError, LLMTestTimeout
+from app.agent.llm.provider import (
+    LLMProviderAuthError,
+    LLMProviderError,
+    LLMProviderManager,
+    render_auth_result_html,
+)
+
+__all__ = [
+    "LLMHelper",
+    "LLMProviderAuthError",
+    "LLMProviderError",
+    "LLMProviderManager",
+    "LLMTestError",
+    "LLMTestTimeout",
+    "render_auth_result_html",
+]

app/agent/llm/helper.py

@@ -342,7 +342,7 @@ class LLMHelper:
             return {}
 
         # OpenAI 原生推理模型优先走 LangChain 内置 reasoning_effort。
-        if provider_name == "openai" and model_name.startswith(
+        if provider_name in {"openai", "chatgpt"} and model_name.startswith(
                 ("gpt-5", "o1", "o3", "o4")
         ):
             openai_effort = cls._normalize_openai_reasoning_effort(
@@ -366,11 +366,89 @@ class LLMHelper:
         return bool(settings.LLM_SUPPORT_IMAGE_INPUT)
 
     @staticmethod
-    def get_llm(
+    def _build_legacy_runtime(
+            provider_name: str,
+            model_name: str | None,
+            api_key: str | None = None,
+            base_url: str | None = None,
+    ) -> dict[str, Any]:
+        """
+        在 provider 目录不可用时回退到旧的直接构造逻辑。
+
+        这主要用于单测 stub 环境以及极端的最小运行环境，正常生产路径仍优先
+        走 `LLMProviderManager.resolve_runtime()`。
+        """
+        api_key_value = api_key if api_key is not None else settings.LLM_API_KEY
+        base_url_value = base_url if base_url is not None else settings.LLM_BASE_URL
+        if not api_key_value:
+            raise ValueError("未配置LLM API Key")
+
+        runtime_name = provider_name if provider_name in {"google", "deepseek"} else "openai_compatible"
+        return {
+            "provider_id": provider_name,
+            "runtime": runtime_name,
+            "model_id": model_name,
+            "api_key": api_key_value,
+            "base_url": base_url_value,
+            "default_headers": None,
+            "use_responses_api": None,
+            "model_record": None,
+            "model_metadata": None,
+        }
+
+    @classmethod
+    def _resolve_thinking_level(
+            cls,
+            thinking_level: str | None = None,
+            disable_thinking: bool | None = None,
+            reasoning_effort: str | None = None,
+    ) -> str | None:
+        """
+        统一兼容新旧 thinking 参数。
+
+        新前端只会传 `thinking_level`，但测试和部分旧调用仍可能带
+        `disable_thinking` / `reasoning_effort`，这里集中做一次归一化。
+        """
+
+        def _normalize(value: str | None) -> str | None:
+            normalized = str(value or "").strip().lower()
+            if not normalized:
+                return None
+            alias_map = {
+                "none": "off",
+                "disabled": "off",
+                "disable": "off",
+                "enabled": "auto",
+                "enable": "auto",
+                "default": "auto",
+                "dynamic": "auto",
+            }
+            normalized = alias_map.get(normalized, normalized)
+            if normalized in cls._SUPPORTED_THINKING_LEVELS:
+                return normalized
+            logger.warning(f"忽略不支持的思考级别: {value}")
+            return None
+
+        normalized_thinking_level = _normalize(thinking_level)
+        if normalized_thinking_level:
+            return normalized_thinking_level
+
+        legacy_effort = _normalize(reasoning_effort)
+        if disable_thinking:
+            return "off"
+        if disable_thinking is False:
+            return legacy_effort or "auto"
+        return legacy_effort
+
+    @classmethod
+    async def get_llm(
+            cls,
             streaming: bool = False,
             provider: str | None = None,
             model: str | None = None,
             thinking_level: str | None = None,
+            disable_thinking: bool | None = None,
+            reasoning_effort: str | None = None,
             api_key: str | None = None,
             base_url: str | None = None,
     ):
@@ -383,28 +461,50 @@ class LLMHelper:
             是否启用思考模式）。支持的级别包括 "off"（关闭）、"auto"（自动）、"minimal"、"low"、"medium"、"high"、"max"/"xhigh"（最大）。
             不同模型对思考模式的支持和表现不同，具体映射关系请
             参考代码实现。对于不支持思考模式的模型，该参数将被忽略。
+        :param disable_thinking: 兼容旧参数，若传入则会被转换为新的
+            `thinking_level` 语义。
+        :param reasoning_effort: 兼容旧参数，若传入则会被转换为新的
+            `thinking_level` 语义。
         :param api_key: API Key，默认为
             配置项LLM_API_KEY。对于某些提供商（
             如 DeepSeek），可能需要同时提供 base_url。
         :param base_url: API Base URL，默认为配置项LLM_BASE_URL。
         :return: LLM实例
         """
-        provider_name = str(
-            provider if provider is not None else settings.LLM_PROVIDER
-        ).lower()
+        provider_name = str(provider if provider is not None else settings.LLM_PROVIDER).lower()
         model_name = model if model is not None else settings.LLM_MODEL
-        api_key_value = api_key if api_key is not None else settings.LLM_API_KEY
-        base_url_value = base_url if base_url is not None else settings.LLM_BASE_URL
-        thinking_kwargs = LLMHelper._build_thinking_kwargs(
+        normalized_thinking_level = cls._resolve_thinking_level(
+            thinking_level=thinking_level,
+            disable_thinking=disable_thinking,
+            reasoning_effort=reasoning_effort,
+        )
+        try:
+            # 延迟导入，避免单测在最小 stub 环境下 import `llm.py` 时被 provider
+            # 目录依赖链拖住。
+            from app.agent.llm.provider import LLMProviderManager
+
+            runtime = await LLMProviderManager().resolve_runtime(
+                provider_id=provider_name,
+                model=model_name,
+                api_key=api_key,
+                base_url=base_url,
+            )
+        except Exception as err:
+            logger.debug(f"LLM provider 目录不可用，回退到旧运行时逻辑: {err}")
+            runtime = cls._build_legacy_runtime(
+                provider_name=provider_name,
+                model_name=model_name,
+                api_key=api_key,
+                base_url=base_url,
+            )
+        model_name = runtime.get("model_id") or model_name
+        thinking_kwargs = cls._build_thinking_kwargs(
             provider=provider_name,
             model=model_name,
-            thinking_level=thinking_level
+            thinking_level=normalized_thinking_level,
         )
 
-        if not api_key_value:
-            raise ValueError("未配置LLM API Key")
-
-        if provider_name == "google":
+        if runtime["runtime"] == "google":
             # 修补 Gemini 2.5 思考模型的 thought_signature 兼容性
             _patch_gemini_thought_signature()
 
@@ -420,49 +520,76 @@ class LLMHelper:
 
             model = ChatGoogleGenerativeAI(
                 model=model_name,
-                api_key=api_key_value,
+                api_key=runtime["api_key"],
                 retries=3,
                 temperature=settings.LLM_TEMPERATURE,
                 streaming=streaming,
                 client_args=client_args,
                 **thinking_kwargs,
             )
-        elif provider_name == "deepseek":
+        elif runtime["runtime"] == "deepseek":
             from langchain_deepseek import ChatDeepSeek
 
             _patch_deepseek_reasoning_content_support()
             model = ChatDeepSeek(
                 model=model_name,
-                api_key=api_key_value,
-                api_base=base_url_value,
+                api_key=runtime["api_key"],
+                api_base=runtime["base_url"],
                 max_retries=3,
                 temperature=settings.LLM_TEMPERATURE,
                 streaming=streaming,
                 stream_usage=True,
                 **thinking_kwargs,
             )
+        elif runtime["runtime"] in {"anthropic_compatible", "copilot_anthropic"}:
+            from langchain_anthropic import ChatAnthropic
+
+            model = ChatAnthropic(
+                model=model_name,
+                api_key=runtime["api_key"],
+                base_url=runtime["base_url"],
+                max_retries=3,
+                temperature=settings.LLM_TEMPERATURE,
+                streaming=streaming,
+                stream_usage=True,
+                anthropic_proxy=settings.PROXY_HOST,
+                default_headers=runtime.get("default_headers"),
+                **thinking_kwargs,
+            )
         else:
             from langchain_openai import ChatOpenAI
 
             model = ChatOpenAI(
                 model=model_name,
-                api_key=api_key_value,
+                api_key=runtime["api_key"],
                 max_retries=3,
-                base_url=base_url_value,
+                base_url=runtime.get("base_url"),
                 temperature=settings.LLM_TEMPERATURE,
                 streaming=streaming,
                 stream_usage=True,
                 openai_proxy=settings.PROXY_HOST,
+                default_headers=runtime.get("default_headers"),
+                use_responses_api=runtime.get("use_responses_api"),
                 **thinking_kwargs,
             )
 
-        # 检查是否有profile
-        if hasattr(model, "profile") and model.profile:
+        # 优先使用 provider / models.dev 目录中的上下文上限，减少用户手填成本。
+        model_profile = getattr(model, "profile", None)
+        if model_profile:
             logger.debug(f"使用LLM模型: {model.model}，Profile: {model.profile}")
         else:
+            model_record = runtime.get("model_record") or {}
+            model_metadata = runtime.get("model_metadata") or {}
+            metadata_limit = model_metadata.get("limit") or {}
+            max_input_tokens = (
+                    model_record.get("input_tokens")
+                    or model_record.get("context_tokens")
+                    or metadata_limit.get("input")
+                    or metadata_limit.get("context")
+                    or settings.LLM_MAX_CONTEXT_TOKENS * 1000
+            )
             model.profile = {
-                "max_input_tokens": settings.LLM_MAX_CONTEXT_TOKENS
-                                    * 1000,  # 转换为token单位
+                "max_input_tokens": int(max_input_tokens),
             }
 
         return model
@@ -514,6 +641,8 @@ class LLMHelper:
             provider: str | None = None,
             model: str | None = None,
             thinking_level: str | None = None,
+            disable_thinking: bool | None = None,
+            reasoning_effort: str | None = None,
             api_key: str | None = None,
             base_url: str | None = None,
     ) -> dict:
@@ -522,16 +651,16 @@ class LLMHelper:
         """
         provider_name = provider if provider is not None else settings.LLM_PROVIDER
         model_name = model if model is not None else settings.LLM_MODEL
-        api_key_value = api_key if api_key is not None else settings.LLM_API_KEY
-        base_url_value = base_url if base_url is not None else settings.LLM_BASE_URL
         start = time.perf_counter()
-        llm = LLMHelper.get_llm(
+        llm = await LLMHelper.get_llm(
             streaming=False,
             provider=provider_name,
             model=model_name,
             thinking_level=thinking_level,
-            api_key=api_key_value,
-            base_url=base_url_value,
+            disable_thinking=disable_thinking,
+            reasoning_effort=reasoning_effort,
+            api_key=api_key,
+            base_url=base_url,
         )
         try:
             response = await asyncio.wait_for(llm.ainvoke(prompt), timeout=timeout)
@@ -556,18 +685,47 @@ class LLMHelper:
             data["reply_preview"] = reply_text[:120]
         return data
 
-    def get_models(
-            self, provider: str, api_key: str, base_url: str = None
-    ) -> List[str]:
-        """获取模型列表"""
+    async def get_models(
+            self,
+            provider: str,
+            api_key: str | None = None,
+            base_url: str | None = None,
+            force_refresh: bool = False,
+    ) -> List[dict[str, Any]]:
+        """
+        获取模型列表。
+
+        返回值会带上 context/supports_reasoning 等元数据，供前端直接渲染并自动
+        回填上下文大小。
+        """
         logger.info(f"获取 {provider} 模型列表...")
-        if provider == "google":
-            return self._get_google_models(api_key)
-        else:
-            return self._get_openai_compatible_models(provider, api_key, base_url)
+        try:
+            from app.agent.llm.provider import LLMProviderManager
+
+            return await LLMProviderManager().list_models(
+                provider_id=provider,
+                api_key=api_key,
+                base_url=base_url,
+                force_refresh=force_refresh,
+            )
+        except Exception as err:
+            logger.debug(f"LLM provider 目录不可用，回退旧模型列表逻辑: {err}")
+            if provider == "google":
+                return [
+                    {"id": model_id, "name": model_id}
+                    for model_id in await self._get_google_models(api_key or "")
+                ]
+            return [
+                {"id": model_id, "name": model_id}
+                for model_id in await self._get_openai_compatible_models(
+                    provider,
+                    api_key or "",
+                    base_url,
+                )
+            ]
 
     @staticmethod
-    def _get_google_models(api_key: str) -> List[str]:
+    async def _get_google_models(api_key: str) -> List[str]:
         """获取Google模型列表（使用 google-genai SDK v1）"""
         try:
             from google import genai
@@ -583,29 +741,32 @@ class LLMHelper:
                 )
 
             client = genai.Client(api_key=api_key, http_options=http_options)
-            models = client.models.list()
-            return [
+            models = await client.aio.models.list()
+            result = [
                 m.name
-                for m in models
+                for m in models.page
                 if m.supported_actions and "generateContent" in m.supported_actions
             ]
+            await client.aio.aclose()
+            return result
         except Exception as e:
             logger.error(f"获取Google模型列表失败：{e}")
             raise e
 
     @staticmethod
-    def _get_openai_compatible_models(
+    async def _get_openai_compatible_models(
             provider: str, api_key: str, base_url: str = None
     ) -> List[str]:
         """获取OpenAI兼容模型列表"""
         try:
-            from openai import OpenAI
+            from openai import AsyncOpenAI
 
             if provider == "deepseek":
                 base_url = base_url or "https://api.deepseek.com"
 
-            client = OpenAI(api_key=api_key, base_url=base_url)
-            models = client.models.list()
+            client = AsyncOpenAI(api_key=api_key, base_url=base_url)
+            models = await client.models.list()
+            await client.close()
             return [model.id for model in models.data]
         except Exception as e:
             logger.error(f"获取 {provider} 模型列表失败：{e}")

app/agent/middleware/activity_log.py

@@ -158,9 +158,9 @@ async def _summarize_with_llm(conversation_text: str) -> str | None:
         LLM 生成的摘要字符串，失败时返回 None。
     """
     try:
-        from app.helper.llm import LLMHelper
+        from app.agent.llm import LLMHelper
 
-        llm = LLMHelper.get_llm(streaming=False)
+        llm = await LLMHelper.get_llm(streaming=False)
         prompt = SUMMARY_PROMPT.format(conversation=conversation_text)
         response = await llm.ainvoke(prompt)
         summary = response.content.strip()

app/api/apiv1.py

@@ -2,7 +2,7 @@ from fastapi import APIRouter
 
 from app.api.endpoints import login, user, webhook, message, site, subscribe, \
     media, douban, search, plugin, tmdb, history, system, download, dashboard, \
-    transfer, mediaserver, bangumi, storage, discover, recommend, workflow, torrent, mcp, mfa, openai, anthropic
+    transfer, mediaserver, bangumi, storage, discover, recommend, workflow, torrent, mcp, mfa, openai, anthropic, llm
 
 api_router = APIRouter()
 api_router.include_router(login.router, prefix="/login", tags=["login"])
@@ -18,6 +18,7 @@ api_router.include_router(douban.router, prefix="/douban", tags=["douban"])
 api_router.include_router(tmdb.router, prefix="/tmdb", tags=["tmdb"])
 api_router.include_router(history.router, prefix="/history", tags=["history"])
 api_router.include_router(system.router, prefix="/system", tags=["system"])
+api_router.include_router(llm.router, prefix="/llm", tags=["llm"])
 api_router.include_router(plugin.router, prefix="/plugin", tags=["plugin"])
 api_router.include_router(download.router, prefix="/download", tags=["download"])
 api_router.include_router(dashboard.router, prefix="/dashboard", tags=["dashboard"])

app/api/endpoints/llm.py

@@ -0,0 +1,290 @@
+import re
+from typing import Annotated, Optional
+
+from fastapi import APIRouter, Body, Depends, Request
+from fastapi.responses import HTMLResponse
+from pydantic import BaseModel
+
+from app import schemas
+from app.agent.llm import (
+    LLMHelper,
+    LLMProviderManager,
+    LLMTestTimeout,
+    render_auth_result_html,
+)
+from app.core.config import settings
+from app.db.models import User
+from app.db.user_oper import (
+    get_current_active_superuser_async,
+    get_current_active_user_async,
+)
+from app.log import logger
+
+router = APIRouter()
+
+
+class LlmTestRequest(BaseModel):
+    enabled: Optional[bool] = None
+    provider: Optional[str] = None
+    model: Optional[str] = None
+    thinking_level: Optional[str] = None
+    disable_thinking: Optional[bool] = None
+    reasoning_effort: Optional[str] = None
+    api_key: Optional[str] = None
+    base_url: Optional[str] = None
+
+
+class LlmProviderAuthStartRequest(BaseModel):
+    provider: str
+    method: str
+
+
+def _sanitize_llm_test_error(message: str, api_key: Optional[str] = None) -> str:
+    """
+    清理错误信息中的敏感字段，避免回显密钥。
+    """
+    if not message:
+        return "LLM 调用失败"
+
+    sanitized = message
+    if api_key:
+        sanitized = sanitized.replace(api_key, "***")
+    sanitized = re.sub(
+        r"(?i)(api[_-]?key\s*[:=]\s*)([^\s,;]+)",
+        r"\1***",
+        sanitized,
+    )
+    sanitized = re.sub(
+        r"(?i)authorization\s*:\s*bearer\s+[^\s,;]+",
+        "Authorization: ***",
+        sanitized,
+    )
+    return sanitized
+
+
+@router.get("/models", summary="获取LLM模型列表", response_model=schemas.Response)
+async def get_llm_models(
+        provider: str,
+        api_key: Optional[str] = None,
+        base_url: Optional[str] = None,
+        force_refresh: Optional[bool] = False,
+        _: User = Depends(get_current_active_user_async),
+):
+    """
+    获取指定 provider 的模型目录。
+    """
+    try:
+        provider_manager = LLMProviderManager()
+        models = await LLMHelper().get_models(
+            provider=provider,
+            api_key=api_key,
+            base_url=base_url,
+            force_refresh=bool(force_refresh),
+        )
+        return schemas.Response(
+            success=True,
+            data={
+                "provider": provider,
+                "models": models,
+                "auth_status": provider_manager.get_auth_status(provider),
+            },
+        )
+    except Exception as err:
+        return schemas.Response(success=False, message=str(err))
+
+
+@router.get("/providers", summary="获取LLM提供商目录", response_model=schemas.Response)
+async def get_llm_providers(
+        _: User = Depends(get_current_active_user_async),
+):
+    """
+    返回前端可直接渲染的 provider 目录。
+    """
+    try:
+        providers = LLMProviderManager().list_providers()
+        return schemas.Response(success=True, data=providers)
+    except Exception as err:
+        return schemas.Response(success=False, message=str(err))
+
+
+@router.post(
+    "/provider-auth/start",
+    summary="启动LLM提供商授权",
+    response_model=schemas.Response,
+)
+async def start_llm_provider_auth(
+        payload: LlmProviderAuthStartRequest,
+        request: Request,
+        _: User = Depends(get_current_active_superuser_async),
+):
+    """
+    启动 provider 授权会话。
+    """
+    try:
+        callback_url = None
+        if payload.provider == "chatgpt" and payload.method == "browser_oauth":
+            callback_url = str(
+                request.url_for("llm_provider_auth_callback", provider_id=payload.provider)
+            )
+        result = await LLMProviderManager().start_auth(
+            payload.provider,
+            payload.method,
+            callback_url,
+        )
+        return schemas.Response(success=True, data=result)
+    except Exception as err:
+        return schemas.Response(success=False, message=str(err))
+
+
+@router.get(
+    "/provider-auth/{session_id}",
+    summary="获取LLM提供商授权会话状态",
+    response_model=schemas.Response,
+)
+async def get_llm_provider_auth_session(
+        session_id: str,
+        _: User = Depends(get_current_active_superuser_async),
+):
+    """
+    查询授权会话状态。
+    """
+    try:
+        result = LLMProviderManager().get_session_status(session_id)
+        return schemas.Response(success=True, data=result)
+    except Exception as err:
+        return schemas.Response(success=False, message=str(err))
+
+
+@router.post(
+    "/provider-auth/{session_id}/poll",
+    summary="轮询LLM提供商授权会话",
+    response_model=schemas.Response,
+)
+async def poll_llm_provider_auth_session(
+        session_id: str,
+        _: User = Depends(get_current_active_superuser_async),
+):
+    """
+    轮询 device code / OAuth 会话状态。
+    """
+    try:
+        result = await LLMProviderManager().poll_auth_session(session_id)
+        return schemas.Response(success=True, data=result)
+    except Exception as err:
+        return schemas.Response(success=False, message=str(err))
+
+
+@router.delete(
+    "/provider-auth/{provider_id}",
+    summary="断开LLM提供商授权",
+    response_model=schemas.Response,
+)
+async def delete_llm_provider_auth(
+        provider_id: str,
+        _: User = Depends(get_current_active_superuser_async),
+):
+    """
+    删除已保存的 provider 授权信息。
+    """
+    try:
+        await LLMProviderManager().clear_auth(provider_id)
+        return schemas.Response(success=True)
+    except Exception as err:
+        return schemas.Response(success=False, message=str(err))
+
+
+@router.get(
+    "/provider-auth/callback/{provider_id}",
+    summary="LLM提供商OAuth回调",
+    response_class=HTMLResponse,
+    name="llm_provider_auth_callback",
+)
+async def llm_provider_auth_callback(
+        provider_id: str,
+        code: Optional[str] = None,
+        state: Optional[str] = None,
+        error: Optional[str] = None,
+        error_description: Optional[str] = None,
+):
+    """
+    处理需要浏览器回跳的 OAuth provider。
+    """
+    success, message = await LLMProviderManager().handle_chatgpt_callback(
+        provider_id,
+        code,
+        state,
+        error,
+        error_description,
+    )
+    return HTMLResponse(content=render_auth_result_html(success, message))
+
+
+@router.post("/test", summary="测试LLM调用", response_model=schemas.Response)
+async def llm_test(
+        payload: Annotated[Optional[LlmTestRequest], Body()] = None,
+        _: User = Depends(get_current_active_superuser_async),
+):
+    """
+    使用传入配置或当前已保存配置执行一次最小 LLM 调用。
+    """
+    payload = payload or LlmTestRequest(
+        enabled=settings.AI_AGENT_ENABLE,
+        provider=settings.LLM_PROVIDER,
+        model=settings.LLM_MODEL,
+        thinking_level=getattr(settings, "LLM_THINKING_LEVEL", None),
+        disable_thinking=getattr(settings, "LLM_DISABLE_THINKING", None),
+        reasoning_effort=getattr(settings, "LLM_REASONING_EFFORT", None),
+        api_key=settings.LLM_API_KEY,
+        base_url=settings.LLM_BASE_URL,
+    )
+
+    if not payload.provider:
+        return schemas.Response(success=False, message="请配置LLM提供商和模型")
+    if not payload.model or not payload.model.strip():
+        return schemas.Response(success=False, message="请先配置 LLM 模型")
+
+    data = {
+        "provider": payload.provider,
+        "model": payload.model,
+    }
+    if not payload.enabled:
+        return schemas.Response(success=False, message="请先启用智能助手", data=data)
+
+    if (
+            payload.provider not in {"chatgpt", "github-copilot"}
+            and (not payload.api_key or not payload.api_key.strip())
+    ):
+        return schemas.Response(
+            success=False,
+            message="请先配置 LLM API Key",
+            data=data,
+        )
+
+    try:
+        result = await LLMHelper.test_current_settings(
+            provider=payload.provider,
+            model=payload.model,
+            thinking_level=payload.thinking_level,
+            disable_thinking=payload.disable_thinking,
+            reasoning_effort=payload.reasoning_effort,
+            api_key=payload.api_key,
+            base_url=payload.base_url,
+        )
+        if not result.get("reply_preview"):
+            return schemas.Response(
+                success=False,
+                message="模型响应为空",
+                data=result,
+            )
+        return schemas.Response(success=True, data=result)
+    except (LLMTestTimeout, TimeoutError) as err:
+        logger.warning(err)
+        return schemas.Response(
+            success=False,
+            message="LLM 调用超时",
+        )
+    except Exception as err:
+        return schemas.Response(
+            success=False,
+            message=_sanitize_llm_test_error(str(err), payload.api_key),
+        )

app/api/endpoints/system.py

@@ -1,6 +1,5 @@
 import asyncio
 import json
-import re
 from collections import deque
 from datetime import datetime
 from typing import Any, Optional, Union, Annotated
@@ -12,7 +11,6 @@ from anyio import Path as AsyncPath
 from app.helper.sites import SitesHelper  # noqa  # noqa
 from fastapi import APIRouter, Body, Depends, HTTPException, Header, Request, Response
 from fastapi.responses import StreamingResponse
-from pydantic import BaseModel
 
 from app import schemas
 from app.chain.mediaserver import MediaServerChain
@@ -31,7 +29,6 @@ from app.db.user_oper import (
     get_current_active_user_async,
 )
 from app.helper.image import ImageHelper
-from app.helper.llm import LLMHelper, LLMTestTimeout
 from app.helper.mediaserver import MediaServerHelper
 from app.helper.message import MessageHelper
 from app.helper.progress import ProgressHelper
@@ -53,15 +50,6 @@ router = APIRouter()
 _NETTEST_REDIRECT_STATUS_CODES = {301, 302, 303, 307, 308}
 
 
-class LlmTestRequest(BaseModel):
-    enabled: Optional[bool] = None
-    provider: Optional[str] = None
-    model: Optional[str] = None
-    thinking_level: Optional[str] = None
-    api_key: Optional[str] = None
-    base_url: Optional[str] = None
-
-
 def _match_nettest_prefix(url: str, prefix: str) -> bool:
     """
     判断目标URL是否仍然落在允许的协议、主机、端口和路径前缀内。
@@ -268,30 +256,6 @@ def _build_nettest_rules() -> list[dict[str, Any]]:
         )
     return rules
 
-
-def _sanitize_llm_test_error(message: str, api_key: Optional[str] = None) -> str:
-    """
-    清理错误信息中的敏感字段，避免回显密钥。
-    """
-    if not message:
-        return "LLM 调用失败"
-
-    sanitized = message
-    if api_key:
-        sanitized = sanitized.replace(api_key, "***")
-    sanitized = re.sub(
-        r"(?i)(api[_-]?key\s*[:=]\s*)([^\s,;]+)",
-        r"\1***",
-        sanitized,
-    )
-    sanitized = re.sub(
-        r"(?i)authorization\s*:\s*bearer\s+[^\s,;]+",
-        "Authorization: ***",
-        sanitized,
-    )
-    return sanitized
-
-
 def _validate_nettest_url(url: str) -> Optional[str]:
     """
     对实际请求地址做基础安全校验。
@@ -643,87 +607,6 @@ async def set_setting(
         return schemas.Response(success=False, message=f"配置项 '{key}' 不存在")
 
 
-@router.get("/llm-models", summary="获取LLM模型列表", response_model=schemas.Response)
-async def get_llm_models(
-        provider: str,
-        api_key: str,
-        base_url: Optional[str] = None,
-        _: User = Depends(get_current_active_user_async),
-):
-    """
-    获取LLM模型列表
-    """
-    try:
-        models = await asyncio.to_thread(
-            LLMHelper().get_models, provider, api_key, base_url
-        )
-        return schemas.Response(success=True, data=models)
-    except Exception as e:
-        return schemas.Response(success=False, message=str(e))
-
-
-@router.post("/llm-test", summary="测试LLM调用", response_model=schemas.Response)
-async def llm_test(
-        payload: Annotated[Optional[LlmTestRequest], Body()] = None,
-        _: User = Depends(get_current_active_superuser_async),
-):
-    """
-    使用传入配置或当前已保存配置执行一次最小 LLM 调用。
-    """
-    if not payload:
-        return schemas.Response(success=False, message="请配置智能助手LLM相关参数后再进行测试")
-
-    if not payload.provider or not payload.model:
-        return schemas.Response(success=False, message="请配置LLM提供商和模型")
-
-    data = {
-        "provider": payload.provider,
-        "model": payload.model,
-    }
-    if not payload.enabled:
-        return schemas.Response(success=False, message="请先启用智能助手", data=data)
-
-    if not payload.api_key or not payload.api_key.strip():
-        return schemas.Response(
-            success=False,
-            message="请先配置 LLM API Key",
-            data=data,
-        )
-
-    if not payload.model or not payload.model.strip():
-        return schemas.Response(
-            success=False,
-            message="请先配置 LLM 模型",
-            data=data,
-        )
-
-    try:
-        result = await LLMHelper.test_current_settings(
-            provider=payload.provider,
-            model=payload.model,
-            thinking_level=payload.thinking_level,
-            api_key=payload.api_key,
-            base_url=payload.base_url,
-        )
-        if not result.get("reply_preview"):
-            return schemas.Response(
-                success=False,
-                message="模型响应为空"
-            )
-        return schemas.Response(success=True, data=result)
-    except (LLMTestTimeout, TimeoutError) as err:
-        logger.warning(err)
-        return schemas.Response(
-            success=False,
-            message="LLM 调用超时"
-        )
-    except Exception as err:
-        return schemas.Response(
-            success=False,
-            message=_sanitize_llm_test_error(str(err), payload.api_key)
-        )
-
-
 @router.get("/message", summary="实时消息")
 async def get_message(
         request: Request,

app/chain/message.py

@@ -21,7 +21,7 @@ from app.chain.transfer import TransferChain
 from app.core.config import settings, global_vars
 from app.db.models import TransferHistory
 from app.db.transferhistory_oper import TransferHistoryOper
-from app.helper.llm import LLMHelper
+from app.agent.llm import LLMHelper
 from app.helper.voice import VoiceHelper
 from app.log import logger
 from app.schemas import Notification, CommingMessage

requirements.in

@@ -79,10 +79,12 @@ httpx[socks]~=0.28.1
 langchain~=1.2.15
 langchain-core~=1.3.2
 langchain-community~=0.4.1
+langchain-anthropic~=1.1.0
 langchain-openai~=1.2.1
 langchain-google-genai~=4.2.2
 langchain-deepseek~=1.0.1
 langgraph~=1.1.9
+anthropic>=0.57,<1
 openai~=2.32.0
 google-genai~=1.73.1
 ddgs~=9.10.0

tests/test_agent_image_support.py

@@ -13,7 +13,7 @@ from app.agent.tools.impl.send_local_file import SendLocalFileInput
 from app.agent import MoviePilotAgent, AgentChain
 from app.chain.message import MessageChain
 from app.core.config import settings
-from app.helper.llm import LLMHelper
+from app.agent.llm import LLMHelper
 from app.modules.discord import DiscordModule
 from app.modules.qqbot import QQBotModule
 from app.modules.slack import SlackModule

tests/test_llm_helper_testcall.py

@@ -4,7 +4,7 @@ import sys
 import unittest
 from pathlib import Path
 from types import ModuleType, SimpleNamespace
-from unittest.mock import Mock, patch
+from unittest.mock import AsyncMock, patch
 
 
 def _stub_module(name: str, **attrs):
@@ -30,7 +30,7 @@ class _FakeModel:
         return SimpleNamespace(content=self._content)
 
 
-sys.modules.pop("app.helper.llm", None)
+sys.modules.pop("app.agent.llm.helper", None)
 _stub_module(
     "app.core.config",
     settings=SimpleNamespace(
@@ -46,7 +46,7 @@ _stub_module(
 )
 _stub_module("app.log", logger=_DummyLogger())
 
-module_path = Path(__file__).resolve().parents[1] / "app" / "helper" / "llm.py"
+module_path = Path(__file__).resolve().parents[1] / "app" / "agent" / "llm" / "helper.py"
 spec = importlib.util.spec_from_file_location("test_llm_module", module_path)
 llm_module = importlib.util.module_from_spec(spec)
 assert spec and spec.loader
@@ -67,7 +67,7 @@ class LlmHelperTestCallTest(unittest.TestCase):
 
     def test_test_current_settings_uses_explicit_snapshot(self):
         fake_model = _FakeModel("OK")
-        get_llm_mock = Mock(return_value=fake_model)
+        get_llm_mock = AsyncMock(return_value=fake_model)
 
         with patch.object(llm_module.LLMHelper, "get_llm", get_llm_mock):
             result = asyncio.run(
@@ -79,7 +79,7 @@ class LlmHelperTestCallTest(unittest.TestCase):
                 )
             )
 
-        get_llm_mock.assert_called_once_with(
+        get_llm_mock.assert_awaited_once_with(
             streaming=False,
             provider="deepseek",
             model="deepseek-chat",
@@ -101,7 +101,9 @@ class LlmHelperTestCallTest(unittest.TestCase):
             ]
         )
 
-        with patch.object(llm_module.LLMHelper, "get_llm", return_value=fake_model):
+        with patch.object(
+            llm_module.LLMHelper, "get_llm", AsyncMock(return_value=fake_model)
+        ):
             result = asyncio.run(
                 llm_module.LLMHelper.test_current_settings(
                     provider="deepseek",
@@ -126,12 +128,14 @@ class LlmHelperTestCallTest(unittest.TestCase):
             sys.modules,
             {"langchain_openai": SimpleNamespace(ChatOpenAI=_FakeChatOpenAI)},
         ):
-            llm_module.LLMHelper.get_llm(
-                provider="openai",
-                model="kimi-k2.6",
-                disable_thinking=True,
-                api_key="sk-test",
-                base_url="https://kimi.example.com/v1",
+            asyncio.run(
+                llm_module.LLMHelper.get_llm(
+                    provider="openai",
+                    model="kimi-k2.6",
+                    disable_thinking=True,
+                    api_key="sk-test",
+                    base_url="https://kimi.example.com/v1",
+                )
             )
 
         self.assertEqual(len(calls), 1)
@@ -158,12 +162,14 @@ class LlmHelperTestCallTest(unittest.TestCase):
             "_patch_deepseek_reasoning_content_support",
             side_effect=lambda: patch_calls.append(True),
         ):
-            llm_module.LLMHelper.get_llm(
-                provider="deepseek",
-                model="deepseek-v4-pro",
-                thinking_level="xhigh",
-                api_key="sk-test",
-                base_url="https://api.deepseek.com",
+            asyncio.run(
+                llm_module.LLMHelper.get_llm(
+                    provider="deepseek",
+                    model="deepseek-v4-pro",
+                    thinking_level="xhigh",
+                    api_key="sk-test",
+                    base_url="https://api.deepseek.com",
+                )
             )
 
         self.assertEqual(len(calls), 1)
@@ -193,12 +199,14 @@ class LlmHelperTestCallTest(unittest.TestCase):
             "_patch_deepseek_reasoning_content_support",
             side_effect=lambda: patch_calls.append(True),
         ):
-            llm_module.LLMHelper.get_llm(
-                provider="deepseek",
-                model="deepseek-v4-flash",
-                thinking_level="off",
-                api_key="sk-test",
-                base_url="https://proxy.example.com",
+            asyncio.run(
+                llm_module.LLMHelper.get_llm(
+                    provider="deepseek",
+                    model="deepseek-v4-flash",
+                    thinking_level="off",
+                    api_key="sk-test",
+                    base_url="https://proxy.example.com",
+                )
             )
 
         self.assertEqual(len(calls), 1)
@@ -223,12 +231,14 @@ class LlmHelperTestCallTest(unittest.TestCase):
             sys.modules,
             {"langchain_openai": SimpleNamespace(ChatOpenAI=_FakeChatOpenAI)},
         ):
-            llm_module.LLMHelper.get_llm(
-                provider="openai",
-                model="gpt-5-mini",
-                thinking_level="off",
-                api_key="sk-test",
-                base_url="https://api.openai.com/v1",
+            asyncio.run(
+                llm_module.LLMHelper.get_llm(
+                    provider="openai",
+                    model="gpt-5-mini",
+                    thinking_level="off",
+                    api_key="sk-test",
+                    base_url="https://api.openai.com/v1",
+                )
             )
 
         self.assertEqual(len(calls), 1)
@@ -247,12 +257,14 @@ class LlmHelperTestCallTest(unittest.TestCase):
             sys.modules,
             {"langchain_openai": SimpleNamespace(ChatOpenAI=_FakeChatOpenAI)},
         ):
-            llm_module.LLMHelper.get_llm(
-                provider="openai",
-                model="gpt-5.4",
-                thinking_level="max",
-                api_key="sk-test",
-                base_url="https://api.openai.com/v1",
+            asyncio.run(
+                llm_module.LLMHelper.get_llm(
+                    provider="openai",
+                    model="gpt-5.4",
+                    thinking_level="max",
+                    api_key="sk-test",
+                    base_url="https://api.openai.com/v1",
+                )
             )
 
         self.assertEqual(len(calls), 1)
@@ -275,12 +287,14 @@ class LlmHelperTestCallTest(unittest.TestCase):
                 )
             },
         ):
-            llm_module.LLMHelper.get_llm(
-                provider="google",
-                model="gemini-2.5-flash",
-                thinking_level="off",
-                api_key="sk-test",
-                base_url=None,
+            asyncio.run(
+                llm_module.LLMHelper.get_llm(
+                    provider="google",
+                    model="gemini-2.5-flash",
+                    thinking_level="off",
+                    api_key="sk-test",
+                    base_url=None,
+                )
             )
 
         self.assertEqual(len(calls), 1)
@@ -304,12 +318,14 @@ class LlmHelperTestCallTest(unittest.TestCase):
                 )
             },
         ):
-            llm_module.LLMHelper.get_llm(
-                provider="google",
-                model="gemini-3.1-flash",
-                thinking_level="xhigh",
-                api_key="sk-test",
-                base_url=None,
+            asyncio.run(
+                llm_module.LLMHelper.get_llm(
+                    provider="google",
+                    model="gemini-3.1-flash",
+                    thinking_level="xhigh",
+                    api_key="sk-test",
+                    base_url=None,
+                )
             )
 
         self.assertEqual(len(calls), 1)