diff --git a/envs/pydantic-v1/poetry.lock b/envs/pydantic-v1/poetry.lock
deleted file mode 100644
index 0d3f31a2..00000000
--- a/envs/pydantic-v1/poetry.lock
+++ /dev/null
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diff --git a/envs/pydantic-v1/pyproject.toml b/envs/pydantic-v1/pyproject.toml
index 1bb9489d..8c2aabb6 100644
--- a/envs/pydantic-v1/pyproject.toml
+++ b/envs/pydantic-v1/pyproject.toml
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 cn2an = "^0.5.22"
 dateparser = "^1.2.0"
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 python-jose = { extras = ["cryptography"], version = "^3.3.0" }
 python-multipart = "^0.0.9"
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 pydantic = ">=1.0.0, <2.0.0"
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 asyncpg = { version = ">=0.20.0", optional = true }
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 [tool.poetry.group.dev.dependencies]
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deleted file mode 100644
index 565bc543..00000000
--- a/envs/pydantic-v2/poetry.lock
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-    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:14a85f3bd2d7bb255be7183e5d7d6e70add151a98edf56a770d6140f5d5f4010"},
-    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:2c89b5c792685dd9cd3fa9761c1b9f46fc240c2a3265483acc1565769996a3f8"},
-    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:69e9b141de5511021942a6866990aea6d111c9042235de90e08f94cf972ca03d"},
-    {file = "yarl-1.20.1-cp39-cp39-win32.whl", hash = "sha256:b5f307337819cdfdbb40193cad84978a029f847b0a357fbe49f712063cfc4f06"},
-    {file = "yarl-1.20.1-cp39-cp39-win_amd64.whl", hash = "sha256:eae7bfe2069f9c1c5b05fc7fe5d612e5bbc089a39309904ee8b829e322dcad00"},
-    {file = "yarl-1.20.1-py3-none-any.whl", hash = "sha256:83b8eb083fe4683c6115795d9fc1cfaf2cbbefb19b3a1cb68f6527460f483a77"},
-    {file = "yarl-1.20.1.tar.gz", hash = "sha256:d017a4997ee50c91fd5466cef416231bb82177b93b029906cefc542ce14c35ac"},
-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-propcache = ">=0.2.1"
-
-[package.source]
-type = "legacy"
-url = "https://mirrors.aliyun.com/pypi/simple"
-reference = "aliyun"
-
-[[package]]
-name = "zipp"
-version = "3.23.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"},
-    {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"},
-]
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
-
-[package.source]
-type = "legacy"
-url = "https://mirrors.aliyun.com/pypi/simple"
-reference = "aliyun"
-
-[metadata]
-lock-version = "2.1"
-python-versions = "^3.10"
-content-hash = "f43caf65fcde0c63698c5fdef8da3cb9d6325d986abc85f4adabe883e9ae725f"
diff --git a/envs/pydantic-v2/pyproject.toml b/envs/pydantic-v2/pyproject.toml
index 481e58a3..d13e2f74 100644
--- a/envs/pydantic-v2/pyproject.toml
+++ b/envs/pydantic-v2/pyproject.toml
@@ -36,7 +36,6 @@ feedparser = "^6.0.11"
 imagehash = "^4.3.1"
 cn2an = "^0.5.22"
 dateparser = "^1.2.0"
-bilireq = ">=0.2.10"
 python-jose = { extras = ["cryptography"], version = "^3.3.0" }
 python-multipart = "^0.0.9"
 aiocache = {extras = ["redis"], version = "^0.12.3"}
@@ -47,10 +46,10 @@ nonebot-plugin-uninfo = ">=0.7.3"
 nonebot-plugin-waiter = "^0.8.1"
 multidict = ">=6.0.0,!=6.3.2"
 pydantic = ">=2.0.0, <3.0.0"
-
 redis = { version = ">=5", optional = true }
 asyncpg = { version = ">=0.20.0", optional = true }
 alibabacloud-devops20210625 = "^5.0.2"
+json_repair = "^0.54.0"
 
 [tool.poetry.group.dev.dependencies]
 nonebug = "^0.4"
diff --git a/pyproject.toml b/pyproject.toml
index a2dfbc82..615cad87 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,7 +36,6 @@ feedparser = "^6.0.11"
 imagehash = "^4.3.1"
 cn2an = "^0.5.22"
 dateparser = "^1.2.0"
-bilireq = ">=0.2.10"
 python-jose = { extras = ["cryptography"], version = "^3.3.0" }
 python-multipart = "^0.0.9"
 aiocache = {extras = ["redis"], version = "^0.12.3"}
@@ -46,6 +45,7 @@ tenacity = "^9.0.0"
 nonebot-plugin-uninfo = ">=0.7.3"
 nonebot-plugin-waiter = "^0.8.1"
 multidict = ">=6.0.0,!=6.3.2"
+json_repair = "^0.54.0"
 
 redis = { version = ">=5", optional = true }
 asyncpg = { version = ">=0.20.0", optional = true }
diff --git a/requirements.txt b/requirements.txt
index 8948f2f4..26a01df4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -21,7 +21,6 @@ feedparser>=6.0.11,<7.0.0
 ImageHash>=4.3.1,<5.0.0
 cn2an>=0.5.22,<0.6.0
 dateparser>=1.2.0,<2.0.0
-bilireq>=0.2.10
 python-jose[cryptography]>=3.3.0,<4.0.0
 python-multipart>=0.0.9,<0.1.0
 aiocache[redis]>=0.12.3,<0.13.0
@@ -32,6 +31,6 @@ nonebot-plugin-uninfo>=0.7.3
 nonebot-plugin-waiter>=0.8.1,<0.9.0
 multidict>=6.0.0,<7.0.0,!=6.3.2
 alibabacloud-devops20210625>=5.0.2,<6.0.0
-
+json_repair>=0.54.0,<0.55.0
 redis>=5
 asyncpg>=0.20.0
diff --git a/zhenxun/builtin_plugins/superuser/reload_setting.py b/zhenxun/builtin_plugins/superuser/reload_setting.py
index 019e4c37..2bf821cb 100644
--- a/zhenxun/builtin_plugins/superuser/reload_setting.py
+++ b/zhenxun/builtin_plugins/superuser/reload_setting.py
@@ -7,6 +7,8 @@ from nonebot_plugin_session import EventSession
 
 from zhenxun.configs.config import Config
 from zhenxun.configs.utils import PluginExtraData, RegisterConfig
+from zhenxun.services.llm.config.providers import get_llm_config
+from zhenxun.services.llm.manager import clear_model_cache
 from zhenxun.services.log import logger
 from zhenxun.utils.enum import PluginType
 from zhenxun.utils.message import MessageUtils
@@ -54,6 +56,8 @@ _matcher = on_alconna(
 @_matcher.handle()
 async def _(session: EventSession, arparma: Arparma):
     Config.reload()
+    get_llm_config.cache_clear()
+    clear_model_cache()
     logger.debug("自动重载配置文件", arparma.header_result, session=session)
     await MessageUtils.build_message("重载完成!").send(reply_to=True)
 
@@ -65,4 +69,6 @@ async def _(session: EventSession, arparma: Arparma):
 async def _():
     if Config.get_config("reload_setting", "AUTO_RELOAD"):
         Config.reload()
+        get_llm_config.cache_clear()
+        clear_model_cache()
         logger.debug("已自动重载配置文件...")
diff --git a/zhenxun/services/llm/__init__.py b/zhenxun/services/llm/__init__.py
index 3bce85ce..a870cae7 100644
--- a/zhenxun/services/llm/__init__.py
+++ b/zhenxun/services/llm/__init__.py
@@ -9,13 +9,15 @@ from .api import (
     code,
     create_image,
     embed,
+    embed_documents,
+    embed_query,
     generate,
     generate_structured,
-    run_with_tools,
     search,
 )
 from .config import (
     CommonOverrides,
+    GenConfigBuilder,
     LLMGenerationConfig,
     register_llm_configs,
 )
@@ -32,8 +34,14 @@ from .manager import (
     list_model_identifiers,
     set_global_default_model_name,
 )
-from .session import AI, AIConfig
-from .tools import function_tool, tool_provider_manager
+from .memory import (
+    AIConfig,
+    BaseMemory,
+    MemoryProcessor,
+    set_default_memory_backend,
+)
+from .session import AI
+from .tools import RunContext, ToolInvoker, function_tool, tool_provider_manager
 from .types import (
     EmbeddingTaskType,
     LLMContentPart,
@@ -50,26 +58,39 @@ from .types import (
     ToolMetadata,
     UsageInfo,
 )
+from .types.models import (
+    GeminiCodeExecution,
+    GeminiGoogleSearch,
+    GeminiUrlContext,
+)
 from .utils import create_multimodal_message, message_to_unimessage, unimsg_to_llm_parts
 
 __all__ = [
     "AI",
     "AIConfig",
+    "BaseMemory",
     "CommonOverrides",
     "EmbeddingTaskType",
+    "GeminiCodeExecution",
+    "GeminiGoogleSearch",
+    "GeminiUrlContext",
+    "GenConfigBuilder",
     "LLMContentPart",
     "LLMErrorCode",
     "LLMException",
     "LLMGenerationConfig",
     "LLMMessage",
     "LLMResponse",
+    "MemoryProcessor",
     "ModelDetail",
     "ModelInfo",
     "ModelName",
     "ModelProvider",
     "ResponseFormat",
+    "RunContext",
     "TaskType",
     "ToolCategory",
+    "ToolInvoker",
     "ToolMetadata",
     "UsageInfo",
     "chat",
@@ -78,6 +99,8 @@ __all__ = [
     "create_image",
     "create_multimodal_message",
     "embed",
+    "embed_documents",
+    "embed_query",
     "function_tool",
     "generate",
     "generate_structured",
@@ -89,8 +112,8 @@ __all__ = [
     "list_model_identifiers",
     "message_to_unimessage",
     "register_llm_configs",
-    "run_with_tools",
     "search",
+    "set_default_memory_backend",
     "set_global_default_model_name",
     "tool_provider_manager",
     "unimsg_to_llm_parts",
diff --git a/zhenxun/services/llm/adapters/__init__.py b/zhenxun/services/llm/adapters/__init__.py
index 773d3ed2..d296fb33 100644
--- a/zhenxun/services/llm/adapters/__init__.py
+++ b/zhenxun/services/llm/adapters/__init__.py
@@ -7,16 +7,18 @@ LLM 适配器模块
 from .base import BaseAdapter, OpenAICompatAdapter, RequestData, ResponseData
 from .factory import LLMAdapterFactory, get_adapter_for_api_type, register_adapter
 from .gemini import GeminiAdapter
-from .openai import OpenAIAdapter
+from .openai import DeepSeekAdapter, OpenAIAdapter, OpenAIImageAdapter
 
 LLMAdapterFactory.initialize()
 
 __all__ = [
     "BaseAdapter",
+    "DeepSeekAdapter",
     "GeminiAdapter",
     "LLMAdapterFactory",
     "OpenAIAdapter",
     "OpenAICompatAdapter",
+    "OpenAIImageAdapter",
     "RequestData",
     "ResponseData",
     "get_adapter_for_api_type",
diff --git a/zhenxun/services/llm/adapters/base.py b/zhenxun/services/llm/adapters/base.py
index 67888816..ca19aaeb 100644
--- a/zhenxun/services/llm/adapters/base.py
+++ b/zhenxun/services/llm/adapters/base.py
@@ -3,24 +3,26 @@ LLM 适配器基类和通用数据结构
 """
 
 from abc import ABC, abstractmethod
-import base64
-import binascii
 import json
+from pathlib import Path
 from typing import TYPE_CHECKING, Any
+import uuid
 
+import httpx
 from pydantic import BaseModel
 
+from zhenxun.configs.path_config import TEMP_PATH
 from zhenxun.services.log import logger
 
+from ..types import LLMContentPart
 from ..types.exceptions import LLMErrorCode, LLMException
 from ..types.models import LLMToolCall
 
 if TYPE_CHECKING:
-    from ..config.generation import LLMGenerationConfig
+    from ..config.generation import LLMEmbeddingConfig, LLMGenerationConfig
     from ..service import LLMModel
-    from ..types.content import LLMMessage
-    from ..types.enums import EmbeddingTaskType
-    from ..types.protocols import ToolExecutable
+    from ..types import LLMMessage
+    from ..types.models import ToolChoice
 
 
 class RequestData(BaseModel):
@@ -29,19 +31,23 @@ class RequestData(BaseModel):
     url: str
     headers: dict[str, str]
     body: dict[str, Any]
+    files: dict[str, Any] | list[tuple[str, Any]] | None = None
 
 
 class ResponseData(BaseModel):
     """响应数据封装 - 支持所有高级功能"""
 
     text: str
-    images: list[bytes] | None = None
+    content_parts: list[LLMContentPart] | None = None
+    images: list[bytes | Path] | None = None
     usage_info: dict[str, Any] | None = None
     raw_response: dict[str, Any] | None = None
     tool_calls: list[LLMToolCall] | None = None
     code_executions: list[Any] | None = None
     grounding_metadata: Any | None = None
     cache_info: Any | None = None
+    thought_text: str | None = None
+    thought_signature: str | None = None
 
     code_execution_results: list[dict[str, Any]] | None = None
     search_results: list[dict[str, Any]] | None = None
@@ -50,9 +56,33 @@ class ResponseData(BaseModel):
     citations: list[dict[str, Any]] | None = None
 
 
+def process_image_data(image_data: bytes) -> bytes | Path:
+    """
+    处理图片数据：若超过 2MB 则保存到临时目录，避免占用内存。
+    """
+    max_inline_size = 2 * 1024 * 1024
+    if len(image_data) > max_inline_size:
+        save_dir = TEMP_PATH / "llm"
+        save_dir.mkdir(parents=True, exist_ok=True)
+        file_name = f"{uuid.uuid4()}.png"
+        file_path = save_dir / file_name
+        file_path.write_bytes(image_data)
+        logger.info(
+            f"图片数据过大 ({len(image_data)} bytes)，已保存到临时文件: {file_path}",
+            "LLMAdapter",
+        )
+        return file_path.resolve()
+    return image_data
+
+
 class BaseAdapter(ABC):
     """LLM API适配器基类"""
 
+    @property
+    def log_sanitization_context(self) -> str:
+        """用于日志清洗的上下文名称，默认 'default'"""
+        return "default"
+
     @property
     @abstractmethod
     def api_type(self) -> str:
@@ -77,7 +107,7 @@ class BaseAdapter(ABC):
         默认实现：将简单请求转换为高级请求格式
         子类可以重写此方法以提供特定的优化实现
         """
-        from ..types.content import LLMMessage
+        from ..types import LLMMessage
 
         messages: list[LLMMessage] = []
 
@@ -107,8 +137,8 @@ class BaseAdapter(ABC):
         api_key: str,
         messages: list["LLMMessage"],
         config: "LLMGenerationConfig | None" = None,
-        tools: dict[str, "ToolExecutable"] | None = None,
-        tool_choice: str | dict[str, Any] | None = None,
+        tools: list[Any] | None = None,
+        tool_choice: "str | dict[str, Any] | ToolChoice | None" = None,
     ) -> RequestData:
         """准备高级请求"""
         pass
@@ -129,8 +159,7 @@ class BaseAdapter(ABC):
         model: "LLMModel",
         api_key: str,
         texts: list[str],
-        task_type: "EmbeddingTaskType | str",
-        **kwargs: Any,
+        config: "LLMEmbeddingConfig",
     ) -> RequestData:
         """准备文本嵌入请求"""
         pass
@@ -142,9 +171,16 @@ class BaseAdapter(ABC):
         """解析文本嵌入响应"""
         pass
 
+    @abstractmethod
+    def convert_generation_config(
+        self, config: "LLMGenerationConfig", model: "LLMModel"
+    ) -> dict[str, Any]:
+        """将通用生成配置转换为特定API的参数字典"""
+        pass
+
     def validate_embedding_response(self, response_json: dict[str, Any]) -> None:
         """验证嵌入API响应"""
-        if "error" in response_json:
+        if response_json.get("error"):
             error_info = response_json["error"]
             msg = (
                 error_info.get("message", str(error_info))
@@ -179,158 +215,9 @@ class BaseAdapter(ABC):
         )
         return headers
 
-    def convert_messages_to_openai_format(
-        self, messages: list["LLMMessage"]
-    ) -> list[dict[str, Any]]:
-        """将LLMMessage转换为OpenAI格式 - 通用方法"""
-        openai_messages: list[dict[str, Any]] = []
-        for msg in messages:
-            openai_msg: dict[str, Any] = {"role": msg.role}
-
-            if msg.role == "tool":
-                openai_msg["tool_call_id"] = msg.tool_call_id
-                openai_msg["name"] = msg.name
-                openai_msg["content"] = msg.content
-            else:
-                if isinstance(msg.content, str):
-                    openai_msg["content"] = msg.content
-                else:
-                    content_parts = []
-                    for part in msg.content:
-                        if part.type == "text":
-                            content_parts.append({"type": "text", "text": part.text})
-                        elif part.type == "image":
-                            content_parts.append(
-                                {
-                                    "type": "image_url",
-                                    "image_url": {"url": part.image_source},
-                                }
-                            )
-                    openai_msg["content"] = content_parts
-
-            if msg.role == "assistant" and msg.tool_calls:
-                assistant_tool_calls = []
-                for call in msg.tool_calls:
-                    assistant_tool_calls.append(
-                        {
-                            "id": call.id,
-                            "type": "function",
-                            "function": {
-                                "name": call.function.name,
-                                "arguments": call.function.arguments,
-                            },
-                        }
-                    )
-                openai_msg["tool_calls"] = assistant_tool_calls
-
-            if msg.name and msg.role != "tool":
-                openai_msg["name"] = msg.name
-
-            openai_messages.append(openai_msg)
-        return openai_messages
-
-    def parse_openai_response(self, response_json: dict[str, Any]) -> ResponseData:
-        """解析OpenAI格式的响应 - 通用方法"""
-        self.validate_response(response_json)
-
-        try:
-            choices = response_json.get("choices", [])
-            if not choices:
-                logger.debug("OpenAI响应中没有choices，可能为空回复或流结束。")
-                return ResponseData(text="", raw_response=response_json)
-
-            choice = choices[0]
-            message = choice.get("message", {})
-            content = message.get("content", "")
-
-            if content:
-                content = content.strip()
-
-            images_bytes: list[bytes] = []
-            if content and content.startswith("{") and content.endswith("}"):
-                try:
-                    content_json = json.loads(content)
-                    if "b64_json" in content_json:
-                        images_bytes.append(base64.b64decode(content_json["b64_json"]))
-                        content = "[图片已生成]"
-                    elif "data" in content_json and isinstance(
-                        content_json["data"], str
-                    ):
-                        images_bytes.append(base64.b64decode(content_json["data"]))
-                        content = "[图片已生成]"
-
-                except (json.JSONDecodeError, KeyError, binascii.Error):
-                    pass
-            elif (
-                "images" in message
-                and isinstance(message["images"], list)
-                and message["images"]
-            ):
-                image_info = message["images"][0]
-                if image_info.get("type") == "image_url":
-                    image_url_obj = image_info.get("image_url", {})
-                    url_str = image_url_obj.get("url", "")
-                    if url_str.startswith("data:image/png;base64,"):
-                        try:
-                            b64_data = url_str.split(",", 1)[1]
-                            images_bytes.append(base64.b64decode(b64_data))
-                            content = content if content else "[图片已生成]"
-                        except (IndexError, binascii.Error) as e:
-                            logger.warning(f"解析OpenRouter Base64图片数据失败: {e}")
-
-            parsed_tool_calls: list[LLMToolCall] | None = None
-            if message_tool_calls := message.get("tool_calls"):
-                from ..types.models import LLMToolFunction
-
-                parsed_tool_calls = []
-                for tc_data in message_tool_calls:
-                    try:
-                        if tc_data.get("type") == "function":
-                            parsed_tool_calls.append(
-                                LLMToolCall(
-                                    id=tc_data["id"],
-                                    function=LLMToolFunction(
-                                        name=tc_data["function"]["name"],
-                                        arguments=tc_data["function"]["arguments"],
-                                    ),
-                                )
-                            )
-                    except KeyError as e:
-                        logger.warning(
-                            f"解析OpenAI工具调用数据时缺少键: {tc_data}, 错误: {e}"
-                        )
-                    except Exception as e:
-                        logger.warning(
-                            f"解析OpenAI工具调用数据时出错: {tc_data}, 错误: {e}"
-                        )
-                if not parsed_tool_calls:
-                    parsed_tool_calls = None
-
-            final_text = content if content is not None else ""
-            if not final_text and parsed_tool_calls:
-                final_text = f"请求调用 {len(parsed_tool_calls)} 个工具。"
-
-            usage_info = response_json.get("usage")
-
-            return ResponseData(
-                text=final_text,
-                tool_calls=parsed_tool_calls,
-                usage_info=usage_info,
-                images=images_bytes if images_bytes else None,
-                raw_response=response_json,
-            )
-
-        except Exception as e:
-            logger.error(f"解析OpenAI格式响应失败: {e}", e=e)
-            raise LLMException(
-                f"解析API响应失败: {e}",
-                code=LLMErrorCode.RESPONSE_PARSE_ERROR,
-                cause=e,
-            )
-
     def validate_response(self, response_json: dict[str, Any]) -> None:
         """验证API响应，解析不同API的错误结构"""
-        if "error" in response_json:
+        if response_json.get("error"):
             error_info = response_json["error"]
 
             if isinstance(error_info, dict):
@@ -341,12 +228,15 @@ class BaseAdapter(ABC):
                 error_code_mapping = {
                     "invalid_api_key": LLMErrorCode.API_KEY_INVALID,
                     "authentication_failed": LLMErrorCode.API_KEY_INVALID,
+                    "insufficient_quota": LLMErrorCode.API_QUOTA_EXCEEDED,
                     "rate_limit_exceeded": LLMErrorCode.API_RATE_LIMITED,
                     "quota_exceeded": LLMErrorCode.API_RATE_LIMITED,
                     "model_not_found": LLMErrorCode.MODEL_NOT_FOUND,
                     "invalid_model": LLMErrorCode.MODEL_NOT_FOUND,
                     "context_length_exceeded": LLMErrorCode.CONTEXT_LENGTH_EXCEEDED,
                     "max_tokens_exceeded": LLMErrorCode.CONTEXT_LENGTH_EXCEEDED,
+                    "invalid_request_error": LLMErrorCode.INVALID_PARAMETER,
+                    "invalid_parameter": LLMErrorCode.INVALID_PARAMETER,
                 }
 
                 llm_error_code = error_code_mapping.get(
@@ -405,23 +295,12 @@ class BaseAdapter(ABC):
     ) -> dict[str, Any]:
         """通用的配置应用逻辑"""
         if config is not None:
-            return config.to_api_params(model.api_type, model.model_name)
+            return self.convert_generation_config(config, model)
 
-        if model._generation_config is not None:
-            return model._generation_config.to_api_params(
-                model.api_type, model.model_name
-            )
+        if model._generation_config:
+            return self.convert_generation_config(model._generation_config, model)
 
-        base_config = {}
-        if model.temperature is not None:
-            base_config["temperature"] = model.temperature
-        if model.max_tokens is not None:
-            if model.api_type == "gemini":
-                base_config["maxOutputTokens"] = model.max_tokens
-            else:
-                base_config["max_tokens"] = model.max_tokens
-
-        return base_config
+        return {}
 
     def apply_config_override(
         self,
@@ -434,12 +313,96 @@ class BaseAdapter(ABC):
         body.update(config_params)
         return body
 
+    def handle_http_error(self, response: httpx.Response) -> LLMException | None:
+        """
+        处理 HTTP 错误响应。
+        如果响应状态码表示成功 (200)，返回 None；否则构造 LLMException 供外部捕获。
+        """
+        if response.status_code == 200:
+            return None
+
+        error_text = response.content.decode("utf-8", errors="ignore")
+        error_status = ""
+        error_msg = error_text
+        try:
+            error_json = json.loads(error_text)
+            if isinstance(error_json, dict) and "error" in error_json:
+                error_info = error_json["error"]
+                if isinstance(error_info, dict):
+                    error_msg = error_info.get("message", error_msg)
+                    raw_status = error_info.get("status") or error_info.get("code")
+                    error_status = str(raw_status) if raw_status is not None else ""
+                elif error_info is not None:
+                    error_msg = str(error_info)
+                    error_status = error_msg
+        except Exception:
+            pass
+
+        status_upper = error_status.upper() if error_status else ""
+        text_upper = error_text.upper()
+
+        error_code = LLMErrorCode.API_REQUEST_FAILED
+        if response.status_code == 400:
+            if (
+                "FAILED_PRECONDITION" in status_upper
+                or "LOCATION IS NOT SUPPORTED" in text_upper
+            ):
+                error_code = LLMErrorCode.USER_LOCATION_NOT_SUPPORTED
+            elif "INVALID_ARGUMENT" in status_upper:
+                error_code = LLMErrorCode.INVALID_PARAMETER
+            elif "API_KEY_INVALID" in text_upper or "API KEY NOT VALID" in text_upper:
+                error_code = LLMErrorCode.API_KEY_INVALID
+            else:
+                error_code = LLMErrorCode.INVALID_PARAMETER
+        elif response.status_code in [401, 403]:
+            if error_msg and (
+                "country" in error_msg.lower()
+                or "region" in error_msg.lower()
+                or "unsupported" in error_msg.lower()
+            ):
+                error_code = LLMErrorCode.USER_LOCATION_NOT_SUPPORTED
+            elif "PERMISSION_DENIED" in status_upper:
+                error_code = LLMErrorCode.API_KEY_INVALID
+            else:
+                error_code = LLMErrorCode.API_KEY_INVALID
+        elif response.status_code == 404:
+            error_code = LLMErrorCode.MODEL_NOT_FOUND
+        elif response.status_code == 429:
+            if (
+                "RESOURCE_EXHAUSTED" in status_upper
+                or "INSUFFICIENT_QUOTA" in status_upper
+                or ("quota" in error_msg.lower() if error_msg else False)
+            ):
+                error_code = LLMErrorCode.API_QUOTA_EXCEEDED
+            else:
+                error_code = LLMErrorCode.API_RATE_LIMITED
+        elif response.status_code in [402, 413]:
+            error_code = LLMErrorCode.API_QUOTA_EXCEEDED
+        elif response.status_code == 422:
+            error_code = LLMErrorCode.GENERATION_FAILED
+        elif response.status_code >= 500:
+            error_code = LLMErrorCode.API_TIMEOUT
+
+        return LLMException(
+            f"HTTP请求失败: {response.status_code} ({error_status or 'Unknown'})",
+            code=error_code,
+            details={
+                "status_code": response.status_code,
+                "api_status": error_status,
+                "response": error_text,
+            },
+        )
+
 
 class OpenAICompatAdapter(BaseAdapter):
     """
     处理所有 OpenAI 兼容 API 的通用适配器。
     """
 
+    @property
+    def log_sanitization_context(self) -> str:
+        return "openai_request"
+
     @abstractmethod
     def get_chat_endpoint(self, model: "LLMModel") -> str:
         """子类必须实现，返回 chat completions 的端点"""
@@ -481,8 +444,8 @@ class OpenAICompatAdapter(BaseAdapter):
         api_key: str,
         messages: list["LLMMessage"],
         config: "LLMGenerationConfig | None" = None,
-        tools: dict[str, "ToolExecutable"] | None = None,
-        tool_choice: str | dict[str, Any] | None = None,
+        tools: list[Any] | None = None,
+        tool_choice: "str | dict[str, Any] | ToolChoice | None" = None,
     ) -> RequestData:
         """准备高级请求 - OpenAI兼容格式"""
         url = self.get_api_url(model, self.get_chat_endpoint(model))
@@ -494,28 +457,44 @@ class OpenAICompatAdapter(BaseAdapter):
                     "X-Title": "Zhenxun Bot",
                 }
             )
-        openai_messages = self.convert_messages_to_openai_format(messages)
+        from .components.openai_components import OpenAIMessageConverter
+
+        converter = OpenAIMessageConverter()
+        openai_messages = converter.convert_messages(messages)
 
         body = {
             "model": model.model_name,
             "messages": openai_messages,
         }
 
+        openai_tools: list[dict[str, Any]] | None = None
+        executables: list[Any] = []
         if tools:
+            for tool in tools:
+                if hasattr(tool, "get_definition"):
+                    executables.append(tool)
+
+        if executables:
             import asyncio
 
             from zhenxun.utils.pydantic_compat import model_dump
 
             definition_tasks = [
-                executable.get_definition() for executable in tools.values()
+                executable.get_definition() for executable in executables
             ]
-            openai_tools = await asyncio.gather(*definition_tasks)
-            if openai_tools:
-                body["tools"] = [
+            tool_defs = []
+            if definition_tasks:
+                tool_defs = await asyncio.gather(*definition_tasks)
+
+            if tool_defs:
+                openai_tools = [
                     {"type": "function", "function": model_dump(tool)}
-                    for tool in openai_tools
+                    for tool in tool_defs
                 ]
 
+        if openai_tools:
+            body["tools"] = openai_tools
+
         if tool_choice:
             body["tool_choice"] = tool_choice
 
@@ -528,20 +507,21 @@ class OpenAICompatAdapter(BaseAdapter):
         response_json: dict[str, Any],
         is_advanced: bool = False,
     ) -> ResponseData:
-        """解析响应 - 直接使用基类的 OpenAI 格式解析"""
+        """解析响应 - 直接使用组件化 ResponseParser"""
         _ = model, is_advanced
-        return self.parse_openai_response(response_json)
+        from .components.openai_components import OpenAIResponseParser
+
+        parser = OpenAIResponseParser()
+        return parser.parse(response_json)
 
     def prepare_embedding_request(
         self,
         model: "LLMModel",
         api_key: str,
         texts: list[str],
-        task_type: "EmbeddingTaskType | str",
-        **kwargs: Any,
+        config: "LLMEmbeddingConfig",
     ) -> RequestData:
         """准备嵌入请求 - OpenAI兼容格式"""
-        _ = task_type
         url = self.get_api_url(model, self.get_embedding_endpoint(model))
         headers = self.get_base_headers(api_key)
 
@@ -550,8 +530,14 @@ class OpenAICompatAdapter(BaseAdapter):
             "input": texts,
         }
 
-        if kwargs:
-            body.update(kwargs)
+        if config.output_dimensionality:
+            body["dimensions"] = config.output_dimensionality
+
+        if config.task_type:
+            body["task"] = config.task_type
+
+        if config.encoding_format and config.encoding_format != "float":
+            body["encoding_format"] = config.encoding_format
 
         return RequestData(url=url, headers=headers, body=body)
 
diff --git a/zhenxun/services/llm/adapters/components/__init__.py b/zhenxun/services/llm/adapters/components/__init__.py
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/zhenxun/services/llm/adapters/components/__init__.py
@@ -0,0 +1 @@
+
diff --git a/zhenxun/services/llm/adapters/components/gemini_components.py b/zhenxun/services/llm/adapters/components/gemini_components.py
new file mode 100644
index 00000000..b2b9015f
--- /dev/null
+++ b/zhenxun/services/llm/adapters/components/gemini_components.py
@@ -0,0 +1,606 @@
+import base64
+import json
+from pathlib import Path
+from typing import Any
+
+from zhenxun.services.llm.adapters.base import ResponseData, process_image_data
+from zhenxun.services.llm.adapters.components.interfaces import (
+    ConfigMapper,
+    MessageConverter,
+    ResponseParser,
+    ToolSerializer,
+)
+from zhenxun.services.llm.config.generation import (
+    ImageAspectRatio,
+    LLMGenerationConfig,
+    ReasoningEffort,
+    ResponseFormat,
+)
+from zhenxun.services.llm.config.providers import get_gemini_safety_threshold
+from zhenxun.services.llm.types import (
+    CodeExecutionOutcome,
+    LLMContentPart,
+    LLMMessage,
+)
+from zhenxun.services.llm.types.capabilities import ModelCapabilities
+from zhenxun.services.llm.types.exceptions import LLMErrorCode, LLMException
+from zhenxun.services.llm.types.models import (
+    LLMGroundingAttribution,
+    LLMGroundingMetadata,
+    LLMToolCall,
+    LLMToolFunction,
+    ModelDetail,
+    ToolDefinition,
+)
+from zhenxun.services.llm.utils import (
+    resolve_json_schema_refs,
+    sanitize_schema_for_llm,
+)
+from zhenxun.services.log import logger
+from zhenxun.utils.http_utils import AsyncHttpx
+from zhenxun.utils.pydantic_compat import model_copy, model_dump
+
+
+class GeminiConfigMapper(ConfigMapper):
+    def map_config(
+        self,
+        config: LLMGenerationConfig,
+        model_detail: ModelDetail | None = None,
+        capabilities: ModelCapabilities | None = None,
+    ) -> dict[str, Any]:
+        params: dict[str, Any] = {}
+
+        if config.core:
+            if config.core.temperature is not None:
+                params["temperature"] = config.core.temperature
+            if config.core.max_tokens is not None:
+                params["maxOutputTokens"] = config.core.max_tokens
+            if config.core.top_k is not None:
+                params["topK"] = config.core.top_k
+            if config.core.top_p is not None:
+                params["topP"] = config.core.top_p
+
+        if config.output:
+            if config.output.response_format == ResponseFormat.JSON:
+                params["responseMimeType"] = "application/json"
+                if config.output.response_schema:
+                    params["responseJsonSchema"] = config.output.response_schema
+            elif config.output.response_mime_type is not None:
+                params["responseMimeType"] = config.output.response_mime_type
+
+            if (
+                config.output.response_schema is not None
+                and "responseJsonSchema" not in params
+            ):
+                params["responseJsonSchema"] = config.output.response_schema
+            if config.output.response_modalities:
+                params["responseModalities"] = config.output.response_modalities
+
+        if config.tool_config:
+            fc_config: dict[str, Any] = {"mode": config.tool_config.mode}
+            if (
+                config.tool_config.allowed_function_names
+                and config.tool_config.mode == "ANY"
+            ):
+                builtins = {"code_execution", "google_search", "google_map"}
+                user_funcs = [
+                    name
+                    for name in config.tool_config.allowed_function_names
+                    if name not in builtins
+                ]
+                if user_funcs:
+                    fc_config["allowedFunctionNames"] = user_funcs
+            params["toolConfig"] = {"functionCallingConfig": fc_config}
+
+        if config.reasoning:
+            thinking_config = params.setdefault("thinkingConfig", {})
+
+            if config.reasoning.budget_tokens is not None:
+                if (
+                    config.reasoning.budget_tokens <= 0
+                    or config.reasoning.budget_tokens >= 1
+                ):
+                    budget_value = int(config.reasoning.budget_tokens)
+                else:
+                    budget_value = int(config.reasoning.budget_tokens * 32768)
+                thinking_config["thinkingBudget"] = budget_value
+            elif config.reasoning.effort:
+                if config.reasoning.effort == ReasoningEffort.MEDIUM:
+                    thinking_config["thinkingLevel"] = "HIGH"
+                else:
+                    thinking_config["thinkingLevel"] = config.reasoning.effort.value
+
+            if config.reasoning.show_thoughts is not None:
+                thinking_config["includeThoughts"] = config.reasoning.show_thoughts
+            elif capabilities and capabilities.reasoning_visibility == "visible":
+                thinking_config["includeThoughts"] = True
+
+        if config.visual:
+            image_config: dict[str, Any] = {}
+
+            if config.visual.aspect_ratio is not None:
+                ar_value = (
+                    config.visual.aspect_ratio.value
+                    if isinstance(config.visual.aspect_ratio, ImageAspectRatio)
+                    else config.visual.aspect_ratio
+                )
+                image_config["aspectRatio"] = ar_value
+
+            if config.visual.resolution:
+                image_config["imageSize"] = config.visual.resolution
+
+            if image_config:
+                params["imageConfig"] = image_config
+
+            if config.visual.media_resolution:
+                media_value = config.visual.media_resolution.upper()
+                if not media_value.startswith("MEDIA_RESOLUTION_"):
+                    media_value = f"MEDIA_RESOLUTION_{media_value}"
+                params["mediaResolution"] = media_value
+
+        if config.custom_params:
+            mapped_custom = config.custom_params.copy()
+            if "max_tokens" in mapped_custom:
+                mapped_custom["maxOutputTokens"] = mapped_custom.pop("max_tokens")
+            if "top_k" in mapped_custom:
+                mapped_custom["topK"] = mapped_custom.pop("top_k")
+            if "top_p" in mapped_custom:
+                mapped_custom["topP"] = mapped_custom.pop("top_p")
+
+            for key in (
+                "code_execution_timeout",
+                "grounding_config",
+                "dynamic_threshold",
+                "user_location",
+                "reflexion_retries",
+            ):
+                mapped_custom.pop(key, None)
+
+            for unsupported in [
+                "frequency_penalty",
+                "presence_penalty",
+                "repetition_penalty",
+            ]:
+                if unsupported in mapped_custom:
+                    mapped_custom.pop(unsupported)
+
+            params.update(mapped_custom)
+
+        safety_settings: list[dict[str, Any]] = []
+        if config.safety and config.safety.safety_settings:
+            for category, threshold in config.safety.safety_settings.items():
+                safety_settings.append({"category": category, "threshold": threshold})
+        else:
+            threshold = get_gemini_safety_threshold()
+            for category in [
+                "HARM_CATEGORY_HARASSMENT",
+                "HARM_CATEGORY_HATE_SPEECH",
+                "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+                "HARM_CATEGORY_DANGEROUS_CONTENT",
+            ]:
+                safety_settings.append({"category": category, "threshold": threshold})
+
+        if safety_settings:
+            params["safetySettings"] = safety_settings
+
+        return params
+
+
+class GeminiMessageConverter(MessageConverter):
+    async def convert_part(self, part: LLMContentPart) -> dict[str, Any]:
+        """将单个内容部分转换为 Gemini API 格式"""
+
+        def _get_gemini_resolution_dict() -> dict[str, Any]:
+            if part.media_resolution:
+                value = part.media_resolution.upper()
+                if not value.startswith("MEDIA_RESOLUTION_"):
+                    value = f"MEDIA_RESOLUTION_{value}"
+                return {"media_resolution": {"level": value}}
+            return {}
+
+        if part.type == "text":
+            return {"text": part.text}
+
+        if part.type == "thought":
+            return {"text": part.thought_text, "thought": True}
+
+        if part.type == "image":
+            if not part.image_source:
+                raise ValueError("图像类型的内容必须包含image_source")
+
+            if part.is_image_base64():
+                base64_info = part.get_base64_data()
+                if base64_info:
+                    mime_type, data = base64_info
+                    payload = {"inlineData": {"mimeType": mime_type, "data": data}}
+                    payload.update(_get_gemini_resolution_dict())
+                    return payload
+                raise ValueError(f"无法解析Base64图像数据: {part.image_source[:50]}...")
+            if part.is_image_url():
+                logger.debug(f"正在为Gemini下载并编码URL图片: {part.image_source}")
+                try:
+                    image_bytes = await AsyncHttpx.get_content(part.image_source)
+                    mime_type = part.mime_type or "image/jpeg"
+                    base64_data = base64.b64encode(image_bytes).decode("utf-8")
+                    payload = {
+                        "inlineData": {"mimeType": mime_type, "data": base64_data}
+                    }
+                    payload.update(_get_gemini_resolution_dict())
+                    return payload
+                except Exception as e:
+                    logger.error(f"下载或编码URL图片失败: {e}", e=e)
+                    raise ValueError(f"无法处理图片URL: {e}")
+            raise ValueError(f"不支持的图像源格式: {part.image_source[:50]}...")
+
+        if part.type == "video":
+            if not part.video_source:
+                raise ValueError("视频类型的内容必须包含video_source")
+
+            if part.video_source.startswith("data:"):
+                try:
+                    header, data = part.video_source.split(",", 1)
+                    mime_type = header.split(";")[0].replace("data:", "")
+                    payload = {"inlineData": {"mimeType": mime_type, "data": data}}
+                    payload.update(_get_gemini_resolution_dict())
+                    return payload
+                except (ValueError, IndexError):
+                    raise ValueError(
+                        f"无法解析Base64视频数据: {part.video_source[:50]}..."
+                    )
+            raise ValueError(
+                "Gemini API 的视频处理需要通过 File API 上传，不支持直接 URL"
+            )
+
+        if part.type == "audio":
+            if not part.audio_source:
+                raise ValueError("音频类型的内容必须包含audio_source")
+
+            if part.audio_source.startswith("data:"):
+                try:
+                    header, data = part.audio_source.split(",", 1)
+                    mime_type = header.split(";")[0].replace("data:", "")
+                    payload = {"inlineData": {"mimeType": mime_type, "data": data}}
+                    payload.update(_get_gemini_resolution_dict())
+                    return payload
+                except (ValueError, IndexError):
+                    raise ValueError(
+                        f"无法解析Base64音频数据: {part.audio_source[:50]}..."
+                    )
+            raise ValueError(
+                "Gemini API 的音频处理需要通过 File API 上传，不支持直接 URL"
+            )
+
+        if part.type == "file":
+            if part.file_uri:
+                payload = {
+                    "fileData": {"mimeType": part.mime_type, "fileUri": part.file_uri}
+                }
+                payload.update(_get_gemini_resolution_dict())
+                return payload
+            if part.file_source:
+                file_name = (
+                    part.metadata.get("name", "file") if part.metadata else "file"
+                )
+                return {"text": f"[文件: {file_name}]\n{part.file_source}"}
+            raise ValueError("文件类型的内容必须包含file_uri或file_source")
+
+        raise ValueError(f"不支持的内容类型: {part.type}")
+
+    async def convert_messages_async(
+        self, messages: list[LLMMessage]
+    ) -> list[dict[str, Any]]:
+        gemini_contents: list[dict[str, Any]] = []
+
+        for msg in messages:
+            current_parts: list[dict[str, Any]] = []
+            if msg.role == "system":
+                continue
+
+            elif msg.role == "user":
+                if isinstance(msg.content, str):
+                    current_parts.append({"text": msg.content})
+                elif isinstance(msg.content, list):
+                    for part_obj in msg.content:
+                        current_parts.append(await self.convert_part(part_obj))
+                gemini_contents.append({"role": "user", "parts": current_parts})
+
+            elif msg.role == "assistant" or msg.role == "model":
+                if isinstance(msg.content, str) and msg.content:
+                    current_parts.append({"text": msg.content})
+                elif isinstance(msg.content, list):
+                    for part_obj in msg.content:
+                        part_dict = await self.convert_part(part_obj)
+
+                        if "executableCode" in part_dict:
+                            part_dict["executable_code"] = part_dict.pop(
+                                "executableCode"
+                            )
+
+                        if "codeExecutionResult" in part_dict:
+                            part_dict["code_execution_result"] = part_dict.pop(
+                                "codeExecutionResult"
+                            )
+
+                        if (
+                            part_obj.metadata
+                            and "thought_signature" in part_obj.metadata
+                        ):
+                            part_dict["thoughtSignature"] = part_obj.metadata[
+                                "thought_signature"
+                            ]
+                        current_parts.append(part_dict)
+
+                if msg.tool_calls:
+                    for call in msg.tool_calls:
+                        fc_part = {
+                            "functionCall": {
+                                "name": call.function.name,
+                                "args": json.loads(call.function.arguments),
+                            }
+                        }
+                        if call.thought_signature:
+                            fc_part["thoughtSignature"] = call.thought_signature
+                        current_parts.append(fc_part)
+                if current_parts:
+                    gemini_contents.append({"role": "model", "parts": current_parts})
+
+            elif msg.role == "tool":
+                if not msg.name:
+                    raise ValueError("Gemini 工具消息必须包含 'name' 字段（函数名）。")
+
+                try:
+                    content_str = (
+                        msg.content
+                        if isinstance(msg.content, str)
+                        else str(msg.content)
+                    )
+                    tool_result_obj = json.loads(content_str)
+                except json.JSONDecodeError:
+                    content_str = (
+                        msg.content
+                        if isinstance(msg.content, str)
+                        else str(msg.content)
+                    )
+                    tool_result_obj = {"raw_output": content_str}
+
+                if isinstance(tool_result_obj, list):
+                    final_response_payload = {"result": tool_result_obj}
+                elif not isinstance(tool_result_obj, dict):
+                    final_response_payload = {"result": tool_result_obj}
+                else:
+                    final_response_payload = tool_result_obj
+
+                current_parts.append(
+                    {
+                        "functionResponse": {
+                            "name": msg.name,
+                            "response": final_response_payload,
+                        }
+                    }
+                )
+                if gemini_contents and gemini_contents[-1]["role"] == "function":
+                    gemini_contents[-1]["parts"].extend(current_parts)
+                else:
+                    gemini_contents.append({"role": "function", "parts": current_parts})
+
+        return gemini_contents
+
+    def convert_messages(self, messages: list[LLMMessage]) -> list[dict[str, Any]]:
+        raise NotImplementedError("Use convert_messages_async for Gemini")
+
+
+class GeminiToolSerializer(ToolSerializer):
+    def serialize_tools(self, tools: list[ToolDefinition]) -> list[dict[str, Any]]:
+        function_declarations: list[dict[str, Any]] = []
+        for tool_def in tools:
+            tool_copy = model_copy(tool_def)
+            tool_copy.parameters = resolve_json_schema_refs(tool_copy.parameters)
+            tool_copy.parameters = sanitize_schema_for_llm(
+                tool_copy.parameters, api_type="gemini"
+            )
+            function_declarations.append(model_dump(tool_copy))
+        return function_declarations
+
+
+class GeminiResponseParser(ResponseParser):
+    def validate_response(self, response_json: dict[str, Any]) -> None:
+        if error := response_json.get("error"):
+            code = error.get("code")
+            message = error.get("message", "")
+            status = error.get("status")
+            details = error.get("details", [])
+
+            if code == 429 or status == "RESOURCE_EXHAUSTED":
+                is_quota = any(
+                    d.get("reason") in ("QUOTA_EXCEEDED", "SERVICE_DISABLED")
+                    for d in details
+                    if isinstance(d, dict)
+                )
+                if is_quota or "quota" in message.lower():
+                    raise LLMException(
+                        f"Gemini配额耗尽: {message}",
+                        code=LLMErrorCode.API_QUOTA_EXCEEDED,
+                        details=error,
+                    )
+                raise LLMException(
+                    f"Gemini速率限制: {message}",
+                    code=LLMErrorCode.API_RATE_LIMITED,
+                    details=error,
+                )
+
+            if code == 400 or status in ("INVALID_ARGUMENT", "FAILED_PRECONDITION"):
+                raise LLMException(
+                    f"Gemini参数错误: {message}",
+                    code=LLMErrorCode.INVALID_PARAMETER,
+                    details=error,
+                    recoverable=False,
+                )
+
+        if prompt_feedback := response_json.get("promptFeedback"):
+            if block_reason := prompt_feedback.get("blockReason"):
+                raise LLMException(
+                    f"内容被安全过滤: {block_reason}",
+                    code=LLMErrorCode.CONTENT_FILTERED,
+                    details={
+                        "block_reason": block_reason,
+                        "safety_ratings": prompt_feedback.get("safetyRatings"),
+                    },
+                )
+
+    def parse(self, response_json: dict[str, Any]) -> ResponseData:
+        self.validate_response(response_json)
+
+        if "image_generation" in response_json and isinstance(
+            response_json["image_generation"], dict
+        ):
+            candidates_source = response_json["image_generation"]
+        else:
+            candidates_source = response_json
+
+        candidates = candidates_source.get("candidates", [])
+        usage_info = response_json.get("usageMetadata")
+
+        if not candidates:
+            return ResponseData(text="", raw_response=response_json)
+
+        candidate = candidates[0]
+        thought_signature: str | None = None
+
+        content_data = candidate.get("content", {})
+        parts = content_data.get("parts", [])
+
+        text_content = ""
+        images_payload: list[bytes | Path] = []
+        parsed_tool_calls: list[LLMToolCall] | None = None
+        parsed_code_executions: list[dict[str, Any]] = []
+        content_parts: list[LLMContentPart] = []
+        thought_summary_parts: list[str] = []
+        answer_parts = []
+
+        for part in parts:
+            part_signature = part.get("thoughtSignature")
+            if part_signature and thought_signature is None:
+                thought_signature = part_signature
+            part_metadata: dict[str, Any] | None = None
+            if part_signature:
+                part_metadata = {"thought_signature": part_signature}
+
+            if part.get("thought") is True:
+                t_text = part.get("text", "")
+                thought_summary_parts.append(t_text)
+                content_parts.append(LLMContentPart.thought_part(t_text))
+
+            elif "text" in part:
+                answer_parts.append(part["text"])
+                c_part = LLMContentPart(
+                    type="text", text=part["text"], metadata=part_metadata
+                )
+                content_parts.append(c_part)
+
+            elif "thoughtSummary" in part:
+                thought_summary_parts.append(part["thoughtSummary"])
+                content_parts.append(
+                    LLMContentPart.thought_part(part["thoughtSummary"])
+                )
+
+            elif "inlineData" in part:
+                inline_data = part["inlineData"]
+                if "data" in inline_data:
+                    decoded = base64.b64decode(inline_data["data"])
+                    images_payload.append(process_image_data(decoded))
+
+            elif "functionCall" in part:
+                if parsed_tool_calls is None:
+                    parsed_tool_calls = []
+                fc_data = part["functionCall"]
+                fc_sig = part_signature
+                try:
+                    call_id = f"call_gemini_{len(parsed_tool_calls)}"
+                    parsed_tool_calls.append(
+                        LLMToolCall(
+                            id=call_id,
+                            thought_signature=fc_sig,
+                            function=LLMToolFunction(
+                                name=fc_data["name"],
+                                arguments=json.dumps(fc_data["args"]),
+                            ),
+                        )
+                    )
+                except Exception as e:
+                    logger.warning(
+                        f"解析Gemini functionCall时出错: {fc_data}, 错误: {e}"
+                    )
+            elif "executableCode" in part:
+                exec_code = part["executableCode"]
+                lang = exec_code.get("language", "PYTHON")
+                code = exec_code.get("code", "")
+                content_parts.append(LLMContentPart.executable_code_part(lang, code))
+                answer_parts.append(f"\n[生成代码 ({lang})]:\n```python\n{code}\n```\n")
+
+            elif "codeExecutionResult" in part:
+                result = part["codeExecutionResult"]
+                outcome = result.get("outcome", CodeExecutionOutcome.OUTCOME_UNKNOWN)
+                output = result.get("output", "")
+
+                content_parts.append(
+                    LLMContentPart.execution_result_part(outcome, output)
+                )
+
+                parsed_code_executions.append(result)
+
+                if outcome == CodeExecutionOutcome.OUTCOME_OK:
+                    answer_parts.append(f"\n[代码执行结果]:\n```\n{output}\n```\n")
+                else:
+                    answer_parts.append(f"\n[代码执行失败 ({outcome})]:\n{output}\n")
+
+        full_answer = "".join(answer_parts).strip()
+        text_content = full_answer
+        final_thought_text = (
+            "\n\n".join(thought_summary_parts).strip()
+            if thought_summary_parts
+            else None
+        )
+
+        grounding_metadata_obj = None
+        if grounding_data := candidate.get("groundingMetadata"):
+            try:
+                sep_content = None
+                sep_field = grounding_data.get("searchEntryPoint")
+                if isinstance(sep_field, dict):
+                    sep_content = sep_field.get("renderedContent")
+
+                attributions = []
+                if chunks := grounding_data.get("groundingChunks"):
+                    for chunk in chunks:
+                        if web := chunk.get("web"):
+                            attributions.append(
+                                LLMGroundingAttribution(
+                                    title=web.get("title"),
+                                    uri=web.get("uri"),
+                                    snippet=web.get("snippet"),
+                                    confidence_score=None,
+                                )
+                            )
+
+                grounding_metadata_obj = LLMGroundingMetadata(
+                    web_search_queries=grounding_data.get("webSearchQueries"),
+                    grounding_attributions=attributions or None,
+                    search_suggestions=grounding_data.get("searchSuggestions"),
+                    search_entry_point=sep_content,
+                    map_widget_token=grounding_data.get("googleMapsWidgetContextToken"),
+                )
+            except Exception as e:
+                logger.warning(f"无法解析Grounding元数据: {grounding_data}, {e}")
+
+        return ResponseData(
+            text=text_content,
+            tool_calls=parsed_tool_calls,
+            code_executions=parsed_code_executions if parsed_code_executions else None,
+            content_parts=content_parts if content_parts else None,
+            images=images_payload if images_payload else None,
+            usage_info=usage_info,
+            raw_response=response_json,
+            grounding_metadata=grounding_metadata_obj,
+            thought_text=final_thought_text,
+            thought_signature=thought_signature,
+        )
diff --git a/zhenxun/services/llm/adapters/components/interfaces.py b/zhenxun/services/llm/adapters/components/interfaces.py
new file mode 100644
index 00000000..d5eb2be1
--- /dev/null
+++ b/zhenxun/services/llm/adapters/components/interfaces.py
@@ -0,0 +1,43 @@
+from abc import ABC, abstractmethod
+from typing import Any
+
+from zhenxun.services.llm.adapters.base import ResponseData
+from zhenxun.services.llm.config.generation import LLMGenerationConfig
+from zhenxun.services.llm.types import LLMMessage
+from zhenxun.services.llm.types.capabilities import ModelCapabilities
+from zhenxun.services.llm.types.models import ModelDetail, ToolDefinition
+
+
+class ConfigMapper(ABC):
+    @abstractmethod
+    def map_config(
+        self,
+        config: LLMGenerationConfig,
+        model_detail: ModelDetail | None = None,
+        capabilities: ModelCapabilities | None = None,
+    ) -> dict[str, Any]:
+        """将通用生成配置转换为特定 API 的参数字典"""
+        ...
+
+
+class MessageConverter(ABC):
+    @abstractmethod
+    def convert_messages(
+        self, messages: list[LLMMessage]
+    ) -> list[dict[str, Any]] | dict[str, Any]:
+        """将通用消息列表转换为特定 API 的消息格式"""
+        ...
+
+
+class ToolSerializer(ABC):
+    @abstractmethod
+    def serialize_tools(self, tools: list[ToolDefinition]) -> Any:
+        """将通用工具定义转换为特定 API 的工具格式"""
+        ...
+
+
+class ResponseParser(ABC):
+    @abstractmethod
+    def parse(self, response_json: dict[str, Any]) -> ResponseData:
+        """将特定 API 的响应解析为通用响应数据"""
+        ...
diff --git a/zhenxun/services/llm/adapters/components/openai_components.py b/zhenxun/services/llm/adapters/components/openai_components.py
new file mode 100644
index 00000000..fcbb9997
--- /dev/null
+++ b/zhenxun/services/llm/adapters/components/openai_components.py
@@ -0,0 +1,347 @@
+import base64
+import binascii
+import json
+from pathlib import Path
+from typing import Any
+
+from zhenxun.services.llm.adapters.base import ResponseData, process_image_data
+from zhenxun.services.llm.adapters.components.interfaces import (
+    ConfigMapper,
+    MessageConverter,
+    ResponseParser,
+    ToolSerializer,
+)
+from zhenxun.services.llm.config.generation import (
+    ImageAspectRatio,
+    LLMGenerationConfig,
+    ResponseFormat,
+    StructuredOutputStrategy,
+)
+from zhenxun.services.llm.types import LLMMessage
+from zhenxun.services.llm.types.capabilities import ModelCapabilities
+from zhenxun.services.llm.types.exceptions import LLMErrorCode, LLMException
+from zhenxun.services.llm.types.models import (
+    LLMToolCall,
+    LLMToolFunction,
+    ModelDetail,
+    ToolDefinition,
+)
+from zhenxun.services.llm.utils import sanitize_schema_for_llm
+from zhenxun.services.log import logger
+from zhenxun.utils.pydantic_compat import model_dump
+
+
+class OpenAIConfigMapper(ConfigMapper):
+    def __init__(self, api_type: str = "openai"):
+        self.api_type = api_type
+
+    def map_config(
+        self,
+        config: LLMGenerationConfig,
+        model_detail: ModelDetail | None = None,
+        capabilities: ModelCapabilities | None = None,
+    ) -> dict[str, Any]:
+        params: dict[str, Any] = {}
+        strategy = config.output.structured_output_strategy if config.output else None
+        if strategy is None:
+            strategy = (
+                StructuredOutputStrategy.TOOL_CALL
+                if self.api_type == "deepseek"
+                else StructuredOutputStrategy.NATIVE
+            )
+
+        if config.core:
+            if config.core.temperature is not None:
+                params["temperature"] = config.core.temperature
+            if config.core.max_tokens is not None:
+                params["max_tokens"] = config.core.max_tokens
+            if config.core.top_k is not None:
+                params["top_k"] = config.core.top_k
+            if config.core.top_p is not None:
+                params["top_p"] = config.core.top_p
+            if config.core.frequency_penalty is not None:
+                params["frequency_penalty"] = config.core.frequency_penalty
+            if config.core.presence_penalty is not None:
+                params["presence_penalty"] = config.core.presence_penalty
+            if config.core.stop is not None:
+                params["stop"] = config.core.stop
+
+            if config.core.repetition_penalty is not None:
+                if self.api_type == "openai":
+                    logger.warning("OpenAI官方API不支持repetition_penalty参数，已忽略")
+                else:
+                    params["repetition_penalty"] = config.core.repetition_penalty
+
+        if config.reasoning and config.reasoning.effort:
+            params["reasoning_effort"] = config.reasoning.effort.value.lower()
+
+        if config.output:
+            if isinstance(config.output.response_format, dict):
+                params["response_format"] = config.output.response_format
+            elif (
+                config.output.response_format == ResponseFormat.JSON
+                and strategy == StructuredOutputStrategy.NATIVE
+            ):
+                if config.output.response_schema:
+                    sanitized = sanitize_schema_for_llm(
+                        config.output.response_schema, api_type="openai"
+                    )
+                    params["response_format"] = {
+                        "type": "json_schema",
+                        "json_schema": {
+                            "name": "structured_response",
+                            "schema": sanitized,
+                            "strict": True,
+                        },
+                    }
+                else:
+                    params["response_format"] = {"type": "json_object"}
+
+        if config.tool_config:
+            mode = config.tool_config.mode
+            if mode == "NONE":
+                params["tool_choice"] = "none"
+            elif mode == "AUTO":
+                params["tool_choice"] = "auto"
+            elif mode == "ANY":
+                params["tool_choice"] = "required"
+
+        if config.visual and config.visual.aspect_ratio:
+            size_map = {
+                ImageAspectRatio.SQUARE: "1024x1024",
+                ImageAspectRatio.LANDSCAPE_16_9: "1792x1024",
+                ImageAspectRatio.PORTRAIT_9_16: "1024x1792",
+            }
+            ar = config.visual.aspect_ratio
+            if isinstance(ar, ImageAspectRatio):
+                mapped_size = size_map.get(ar)
+                if mapped_size:
+                    params["size"] = mapped_size
+            elif isinstance(ar, str):
+                params["size"] = ar
+
+        if config.custom_params:
+            mapped_custom = config.custom_params.copy()
+            if "repetition_penalty" in mapped_custom and self.api_type == "openai":
+                mapped_custom.pop("repetition_penalty")
+
+            if "stop" in mapped_custom:
+                stop_value = mapped_custom["stop"]
+                if isinstance(stop_value, str):
+                    mapped_custom["stop"] = [stop_value]
+
+            params.update(mapped_custom)
+
+        return params
+
+
+class OpenAIMessageConverter(MessageConverter):
+    def convert_messages(self, messages: list[LLMMessage]) -> list[dict[str, Any]]:
+        openai_messages: list[dict[str, Any]] = []
+        for msg in messages:
+            openai_msg: dict[str, Any] = {"role": msg.role}
+
+            if msg.role == "tool":
+                openai_msg["tool_call_id"] = msg.tool_call_id
+                openai_msg["name"] = msg.name
+                openai_msg["content"] = msg.content
+            else:
+                if isinstance(msg.content, str):
+                    openai_msg["content"] = msg.content
+                else:
+                    content_parts = []
+                    for part in msg.content:
+                        if part.type == "text":
+                            content_parts.append({"type": "text", "text": part.text})
+                        elif part.type == "image":
+                            content_parts.append(
+                                {
+                                    "type": "image_url",
+                                    "image_url": {"url": part.image_source},
+                                }
+                            )
+                    openai_msg["content"] = content_parts
+
+            if msg.role == "assistant" and msg.tool_calls:
+                assistant_tool_calls = []
+                for call in msg.tool_calls:
+                    assistant_tool_calls.append(
+                        {
+                            "id": call.id,
+                            "type": "function",
+                            "function": {
+                                "name": call.function.name,
+                                "arguments": call.function.arguments,
+                            },
+                        }
+                    )
+                openai_msg["tool_calls"] = assistant_tool_calls
+
+            if msg.name and msg.role != "tool":
+                openai_msg["name"] = msg.name
+
+            openai_messages.append(openai_msg)
+        return openai_messages
+
+
+class OpenAIToolSerializer(ToolSerializer):
+    def serialize_tools(
+        self, tools: list[ToolDefinition]
+    ) -> list[dict[str, Any]] | None:
+        if not tools:
+            return None
+
+        openai_tools = []
+        for tool in tools:
+            tool_dict = model_dump(tool)
+            parameters = tool_dict.get("parameters")
+            if parameters:
+                tool_dict["parameters"] = sanitize_schema_for_llm(
+                    parameters, api_type="openai"
+                )
+            tool_dict["strict"] = True
+            openai_tools.append({"type": "function", "function": tool_dict})
+        return openai_tools
+
+
+class OpenAIResponseParser(ResponseParser):
+    def validate_response(self, response_json: dict[str, Any]) -> None:
+        if response_json.get("error"):
+            error_info = response_json["error"]
+            if isinstance(error_info, dict):
+                error_message = error_info.get("message", "未知错误")
+                error_code = error_info.get("code", "unknown")
+
+                error_code_mapping = {
+                    "invalid_api_key": LLMErrorCode.API_KEY_INVALID,
+                    "authentication_failed": LLMErrorCode.API_KEY_INVALID,
+                    "insufficient_quota": LLMErrorCode.API_QUOTA_EXCEEDED,
+                    "rate_limit_exceeded": LLMErrorCode.API_RATE_LIMITED,
+                    "quota_exceeded": LLMErrorCode.API_RATE_LIMITED,
+                    "model_not_found": LLMErrorCode.MODEL_NOT_FOUND,
+                    "invalid_model": LLMErrorCode.MODEL_NOT_FOUND,
+                    "context_length_exceeded": LLMErrorCode.CONTEXT_LENGTH_EXCEEDED,
+                    "max_tokens_exceeded": LLMErrorCode.CONTEXT_LENGTH_EXCEEDED,
+                    "invalid_request_error": LLMErrorCode.INVALID_PARAMETER,
+                    "invalid_parameter": LLMErrorCode.INVALID_PARAMETER,
+                }
+
+                llm_error_code = error_code_mapping.get(
+                    error_code, LLMErrorCode.API_RESPONSE_INVALID
+                )
+            else:
+                error_message = str(error_info)
+                error_code = "unknown"
+                llm_error_code = LLMErrorCode.API_RESPONSE_INVALID
+
+            raise LLMException(
+                f"API请求失败: {error_message}",
+                code=llm_error_code,
+                details={"api_error": error_info, "error_code": error_code},
+            )
+
+    def parse(self, response_json: dict[str, Any]) -> ResponseData:
+        self.validate_response(response_json)
+
+        choices = response_json.get("choices", [])
+        if not choices:
+            return ResponseData(text="", raw_response=response_json)
+
+        choice = choices[0]
+        message = choice.get("message", {})
+        content = message.get("content", "")
+        reasoning_content = message.get("reasoning_content", None)
+        refusal = message.get("refusal")
+
+        if refusal:
+            raise LLMException(
+                f"模型拒绝生成请求: {refusal}",
+                code=LLMErrorCode.CONTENT_FILTERED,
+                details={"refusal": refusal},
+                recoverable=False,
+            )
+
+        if content:
+            content = content.strip()
+
+        images_payload: list[bytes | Path] = []
+        if content and content.startswith("{") and content.endswith("}"):
+            try:
+                content_json = json.loads(content)
+                if "b64_json" in content_json:
+                    b64_str = content_json["b64_json"]
+                    if isinstance(b64_str, str) and b64_str.startswith("data:"):
+                        b64_str = b64_str.split(",", 1)[1]
+                    decoded = base64.b64decode(b64_str)
+                    images_payload.append(process_image_data(decoded))
+                    content = "[图片已生成]"
+                elif "data" in content_json and isinstance(content_json["data"], str):
+                    b64_str = content_json["data"]
+                    if b64_str.startswith("data:"):
+                        b64_str = b64_str.split(",", 1)[1]
+                    decoded = base64.b64decode(b64_str)
+                    images_payload.append(process_image_data(decoded))
+                    content = "[图片已生成]"
+
+            except (json.JSONDecodeError, KeyError, binascii.Error):
+                pass
+        elif (
+            "images" in message
+            and isinstance(message["images"], list)
+            and message["images"]
+        ):
+            for image_info in message["images"]:
+                if image_info.get("type") == "image_url":
+                    image_url_obj = image_info.get("image_url", {})
+                    url_str = image_url_obj.get("url", "")
+                    if url_str.startswith("data:image"):
+                        try:
+                            b64_data = url_str.split(",", 1)[1]
+                            decoded = base64.b64decode(b64_data)
+                            images_payload.append(process_image_data(decoded))
+                        except (IndexError, binascii.Error) as e:
+                            logger.warning(f"解析OpenRouter Base64图片数据失败: {e}")
+
+            if images_payload:
+                content = content if content else "[图片已生成]"
+
+        parsed_tool_calls: list[LLMToolCall] | None = None
+        if message_tool_calls := message.get("tool_calls"):
+            parsed_tool_calls = []
+            for tc_data in message_tool_calls:
+                try:
+                    if tc_data.get("type") == "function":
+                        parsed_tool_calls.append(
+                            LLMToolCall(
+                                id=tc_data["id"],
+                                function=LLMToolFunction(
+                                    name=tc_data["function"]["name"],
+                                    arguments=tc_data["function"]["arguments"],
+                                ),
+                            )
+                        )
+                except KeyError as e:
+                    logger.warning(
+                        f"解析OpenAI工具调用数据时缺少键: {tc_data}, 错误: {e}"
+                    )
+                except Exception as e:
+                    logger.warning(
+                        f"解析OpenAI工具调用数据时出错: {tc_data}, 错误: {e}"
+                    )
+            if not parsed_tool_calls:
+                parsed_tool_calls = None
+
+        final_text = content if content is not None else ""
+        if not final_text and parsed_tool_calls:
+            final_text = f"请求调用 {len(parsed_tool_calls)} 个工具。"
+
+        usage_info = response_json.get("usage")
+
+        return ResponseData(
+            text=final_text,
+            tool_calls=parsed_tool_calls,
+            usage_info=usage_info,
+            images=images_payload if images_payload else None,
+            raw_response=response_json,
+            thought_text=reasoning_content,
+        )
diff --git a/zhenxun/services/llm/adapters/factory.py b/zhenxun/services/llm/adapters/factory.py
index 9f2a8b64..a21349e4 100644
--- a/zhenxun/services/llm/adapters/factory.py
+++ b/zhenxun/services/llm/adapters/factory.py
@@ -2,10 +2,17 @@
 LLM 适配器工厂类
 """
 
-from typing import ClassVar
+import fnmatch
+from typing import TYPE_CHECKING, Any, ClassVar
 
 from ..types.exceptions import LLMErrorCode, LLMException
-from .base import BaseAdapter
+from ..types.models import ToolChoice
+from .base import BaseAdapter, RequestData, ResponseData
+
+if TYPE_CHECKING:
+    from ..config.generation import LLMEmbeddingConfig, LLMGenerationConfig
+    from ..service import LLMModel
+    from ..types import LLMMessage
 
 
 class LLMAdapterFactory:
@@ -21,10 +28,13 @@ class LLMAdapterFactory:
             return
 
         from .gemini import GeminiAdapter
-        from .openai import OpenAIAdapter
+        from .openai import DeepSeekAdapter, OpenAIAdapter, OpenAIImageAdapter
 
         cls.register_adapter(OpenAIAdapter())
+        cls.register_adapter(DeepSeekAdapter())
         cls.register_adapter(GeminiAdapter())
+        cls.register_adapter(SmartAdapter())
+        cls.register_adapter(OpenAIImageAdapter())
 
     @classmethod
     def register_adapter(cls, adapter: BaseAdapter) -> None:
@@ -74,3 +84,100 @@ def get_adapter_for_api_type(api_type: str) -> BaseAdapter:
 def register_adapter(adapter: BaseAdapter) -> None:
     """注册新的适配器"""
     LLMAdapterFactory.register_adapter(adapter)
+
+
+class SmartAdapter(BaseAdapter):
+    """
+    智能路由适配器。
+    本身不处理序列化，而是根据规则委托给 OpenAIAdapter 或 GeminiAdapter。
+    """
+
+    @property
+    def log_sanitization_context(self) -> str:
+        return "openai_request"
+
+    _ROUTING_RULES: ClassVar[list[tuple[str, str]]] = [
+        ("*nano-banana*", "gemini"),
+        ("*gemini*", "gemini"),
+    ]
+    _DEFAULT_API_TYPE: ClassVar[str] = "openai"
+
+    def __init__(self):
+        self._adapter_cache: dict[str, BaseAdapter] = {}
+
+    @property
+    def api_type(self) -> str:
+        return "smart"
+
+    @property
+    def supported_api_types(self) -> list[str]:
+        return ["smart"]
+
+    def _get_delegate_adapter(self, model: "LLMModel") -> BaseAdapter:
+        """
+        核心路由逻辑：决定使用哪个适配器 (带缓存)
+        """
+        if model.model_detail.api_type:
+            return get_adapter_for_api_type(model.model_detail.api_type)
+
+        model_name = model.model_name
+        if model_name in self._adapter_cache:
+            return self._adapter_cache[model_name]
+
+        target_api_type = self._DEFAULT_API_TYPE
+        model_name_lower = model_name.lower()
+
+        for pattern, api_type in self._ROUTING_RULES:
+            if fnmatch.fnmatch(model_name_lower, pattern):
+                target_api_type = api_type
+                break
+
+        adapter = get_adapter_for_api_type(target_api_type)
+        self._adapter_cache[model_name] = adapter
+        return adapter
+
+    async def prepare_advanced_request(
+        self,
+        model: "LLMModel",
+        api_key: str,
+        messages: list["LLMMessage"],
+        config: "LLMGenerationConfig | None" = None,
+        tools: list[Any] | None = None,
+        tool_choice: "str | dict[str, Any] | ToolChoice | None" = None,
+    ) -> RequestData:
+        adapter = self._get_delegate_adapter(model)
+        return await adapter.prepare_advanced_request(
+            model, api_key, messages, config, tools, tool_choice
+        )
+
+    def parse_response(
+        self,
+        model: "LLMModel",
+        response_json: dict[str, Any],
+        is_advanced: bool = False,
+    ) -> ResponseData:
+        adapter = self._get_delegate_adapter(model)
+        return adapter.parse_response(model, response_json, is_advanced)
+
+    def prepare_embedding_request(
+        self,
+        model: "LLMModel",
+        api_key: str,
+        texts: list[str],
+        config: "LLMEmbeddingConfig",
+    ) -> RequestData:
+        adapter = self._get_delegate_adapter(model)
+        return adapter.prepare_embedding_request(model, api_key, texts, config)
+
+    def parse_embedding_response(
+        self, response_json: dict[str, Any]
+    ) -> list[list[float]]:
+        return get_adapter_for_api_type("openai").parse_embedding_response(
+            response_json
+        )
+
+    def convert_generation_config(
+        self, config: "LLMGenerationConfig", model: "LLMModel"
+    ) -> dict[str, Any]:
+        adapter = self._get_delegate_adapter(model)
+        return adapter.convert_generation_config(config, model)
diff --git a/zhenxun/services/llm/adapters/gemini.py b/zhenxun/services/llm/adapters/gemini.py
index 444a9023..720bdbe4 100644
--- a/zhenxun/services/llm/adapters/gemini.py
+++ b/zhenxun/services/llm/adapters/gemini.py
@@ -2,27 +2,35 @@
 Gemini API 适配器
 """
 
-import base64
 from typing import TYPE_CHECKING, Any
 
 from zhenxun.services.log import logger
 
+from ..config.generation import ResponseFormat
+from ..types import LLMContentPart
 from ..types.exceptions import LLMErrorCode, LLMException
-from ..utils import sanitize_schema_for_llm
+from ..types.models import BasePlatformTool, ToolChoice
 from .base import BaseAdapter, RequestData, ResponseData
+from .components.gemini_components import (
+    GeminiConfigMapper,
+    GeminiMessageConverter,
+    GeminiResponseParser,
+    GeminiToolSerializer,
+)
 
 if TYPE_CHECKING:
-    from ..config.generation import LLMGenerationConfig
+    from ..config.generation import LLMEmbeddingConfig, LLMGenerationConfig
     from ..service import LLMModel
-    from ..types.content import LLMMessage
-    from ..types.enums import EmbeddingTaskType
-    from ..types.models import LLMToolCall
-    from ..types.protocols import ToolExecutable
+    from ..types import LLMMessage
 
 
 class GeminiAdapter(BaseAdapter):
     """Gemini API 适配器"""
 
+    @property
+    def log_sanitization_context(self) -> str:
+        return "gemini_request"
+
     @property
     def api_type(self) -> str:
         return "gemini"
@@ -47,110 +55,75 @@ class GeminiAdapter(BaseAdapter):
         api_key: str,
         messages: list["LLMMessage"],
         config: "LLMGenerationConfig | None" = None,
-        tools: dict[str, "ToolExecutable"] | None = None,
-        tool_choice: str | dict[str, Any] | None = None,
+        tools: list[Any] | None = None,
+        tool_choice: str | dict[str, Any] | ToolChoice | None = None,
     ) -> RequestData:
         """准备高级请求"""
         effective_config = config if config is not None else model._generation_config
 
+        if tools:
+            from ..types.models import GeminiUrlContext
+
+            context_urls: list[str] = []
+            for tool in tools:
+                if isinstance(tool, GeminiUrlContext):
+                    context_urls.extend(tool.urls)
+
+            if context_urls and messages:
+                last_msg = messages[-1]
+                if last_msg.role == "user":
+                    url_text = "\n\n[Context URLs]:\n" + "\n".join(context_urls)
+                    if isinstance(last_msg.content, str):
+                        last_msg.content += url_text
+                    elif isinstance(last_msg.content, list):
+                        last_msg.content.append(LLMContentPart.text_part(url_text))
+
+        has_function_tools = False
+        if tools:
+            has_function_tools = any(hasattr(tool, "get_definition") for tool in tools)
+
+        is_structured = False
+        if effective_config and effective_config.output:
+            if (
+                effective_config.output.response_schema
+                or effective_config.output.response_format == ResponseFormat.JSON
+                or effective_config.output.response_mime_type == "application/json"
+            ):
+                is_structured = True
+
+        if (has_function_tools or is_structured) and effective_config:
+            if effective_config.reasoning is None:
+                from ..config.generation import ReasoningConfig
+
+                effective_config.reasoning = ReasoningConfig()
+
+            if (
+                effective_config.reasoning.budget_tokens is None
+                and effective_config.reasoning.effort is None
+            ):
+                reason_desc = "工具调用" if has_function_tools else "结构化输出"
+                logger.debug(
+                    f"检测到{reason_desc}，自动为模型 {model.model_name} 开启思维链增强"
+                )
+                effective_config.reasoning.budget_tokens = -1
+
         endpoint = self._get_gemini_endpoint(model, effective_config)
         url = self.get_api_url(model, endpoint)
         headers = self.get_base_headers(api_key)
 
-        gemini_contents: list[dict[str, Any]] = []
+        converter = GeminiMessageConverter()
         system_instruction_parts: list[dict[str, Any]] | None = None
-
         for msg in messages:
-            current_parts: list[dict[str, Any]] = []
             if msg.role == "system":
                 if isinstance(msg.content, str):
                     system_instruction_parts = [{"text": msg.content}]
                 elif isinstance(msg.content, list):
                     system_instruction_parts = [
-                        await part.convert_for_api_async("gemini")
-                        for part in msg.content
+                        await converter.convert_part(part) for part in msg.content
                     ]
                 continue
 
-            elif msg.role == "user":
-                if isinstance(msg.content, str):
-                    current_parts.append({"text": msg.content})
-                elif isinstance(msg.content, list):
-                    for part_obj in msg.content:
-                        current_parts.append(
-                            await part_obj.convert_for_api_async("gemini")
-                        )
-                gemini_contents.append({"role": "user", "parts": current_parts})
-
-            elif msg.role == "assistant" or msg.role == "model":
-                if isinstance(msg.content, str) and msg.content:
-                    current_parts.append({"text": msg.content})
-                elif isinstance(msg.content, list):
-                    for part_obj in msg.content:
-                        current_parts.append(
-                            await part_obj.convert_for_api_async("gemini")
-                        )
-
-                if msg.tool_calls:
-                    import json
-
-                    for call in msg.tool_calls:
-                        current_parts.append(
-                            {
-                                "functionCall": {
-                                    "name": call.function.name,
-                                    "args": json.loads(call.function.arguments),
-                                }
-                            }
-                        )
-                if current_parts:
-                    gemini_contents.append({"role": "model", "parts": current_parts})
-
-            elif msg.role == "tool":
-                if not msg.name:
-                    raise ValueError("Gemini 工具消息必须包含 'name' 字段（函数名）。")
-
-                import json
-
-                try:
-                    content_str = (
-                        msg.content
-                        if isinstance(msg.content, str)
-                        else str(msg.content)
-                    )
-                    tool_result_obj = json.loads(content_str)
-                except json.JSONDecodeError:
-                    content_str = (
-                        msg.content
-                        if isinstance(msg.content, str)
-                        else str(msg.content)
-                    )
-                    logger.warning(
-                        f"工具 {msg.name} 的结果不是有效的 JSON: {content_str}. "
-                        f"包装为原始字符串。"
-                    )
-                    tool_result_obj = {"raw_output": content_str}
-
-                if isinstance(tool_result_obj, list):
-                    logger.debug(
-                        f"工具 '{msg.name}' 的返回结果是列表，"
-                        f"正在为Gemini API包装为JSON对象。"
-                    )
-                    final_response_payload = {"result": tool_result_obj}
-                elif not isinstance(tool_result_obj, dict):
-                    final_response_payload = {"result": tool_result_obj}
-                else:
-                    final_response_payload = tool_result_obj
-
-                current_parts.append(
-                    {
-                        "functionResponse": {
-                            "name": msg.name,
-                            "response": final_response_payload,
-                        }
-                    }
-                )
-                gemini_contents.append({"role": "function", "parts": current_parts})
+        gemini_contents = await converter.convert_messages_async(messages)
 
         body: dict[str, Any] = {"contents": gemini_contents}
 
@@ -158,75 +131,78 @@ class GeminiAdapter(BaseAdapter):
             body["systemInstruction"] = {"parts": system_instruction_parts}
 
         all_tools_for_request = []
+        has_user_functions = False
         if tools:
-            import asyncio
+            from ..types.protocols import ToolExecutable
 
-            from zhenxun.utils.pydantic_compat import model_dump
+            function_tools: list[ToolExecutable] = []
+            gemini_tools_dict: dict[str, Any] = {}
 
-            definition_tasks = [
-                executable.get_definition() for executable in tools.values()
-            ]
-            tool_definitions = await asyncio.gather(*definition_tasks)
+            for tool in tools:
+                if isinstance(tool, BasePlatformTool):
+                    declaration = tool.get_tool_declaration()
+                    if declaration:
+                        gemini_tools_dict.update(declaration)
+                elif hasattr(tool, "get_definition"):
+                    function_tools.append(tool)
 
-            function_declarations = []
-            for tool_def in tool_definitions:
-                tool_def.parameters = sanitize_schema_for_llm(
-                    tool_def.parameters, api_type="gemini"
-                )
-                function_declarations.append(model_dump(tool_def))
+            if function_tools:
+                import asyncio
 
-            if function_declarations:
-                all_tools_for_request.append(
-                    {"functionDeclarations": function_declarations}
-                )
+                definition_tasks = [
+                    executable.get_definition() for executable in function_tools
+                ]
+                tool_definitions = await asyncio.gather(*definition_tasks)
 
-        if effective_config:
-            if getattr(effective_config, "enable_grounding", False):
-                has_explicit_gs_tool = any(
-                    "googleSearch" in tool_item for tool_item in all_tools_for_request
-                )
-                if not has_explicit_gs_tool:
-                    all_tools_for_request.append({"googleSearch": {}})
-                    logger.debug("隐式启用 Google Search 工具进行信息来源关联。")
+                serializer = GeminiToolSerializer()
+                function_declarations = serializer.serialize_tools(tool_definitions)
 
-            if getattr(effective_config, "enable_code_execution", False):
-                has_explicit_ce_tool = any(
-                    "codeExecution" in tool_item for tool_item in all_tools_for_request
-                )
-                if not has_explicit_ce_tool:
-                    all_tools_for_request.append({"codeExecution": {}})
-                    logger.debug("隐式启用代码执行工具。")
+                if function_declarations:
+                    gemini_tools_dict["functionDeclarations"] = function_declarations
+                    has_user_functions = True
+
+            if gemini_tools_dict:
+                all_tools_for_request.append(gemini_tools_dict)
 
         if all_tools_for_request:
             body["tools"] = all_tools_for_request
 
-        final_tool_choice = tool_choice
-        if final_tool_choice is None and effective_config:
-            final_tool_choice = getattr(effective_config, "tool_choice", None)
+        tool_config_updates: dict[str, Any] = {}
+        if (
+            effective_config
+            and effective_config.custom_params
+            and "user_location" in effective_config.custom_params
+        ):
+            tool_config_updates["retrievalConfig"] = {
+                "latLng": effective_config.custom_params["user_location"]
+            }
 
-        if final_tool_choice:
-            if isinstance(final_tool_choice, str):
-                mode_upper = final_tool_choice.upper()
-                if mode_upper in ["AUTO", "NONE", "ANY"]:
-                    body["toolConfig"] = {"functionCallingConfig": {"mode": mode_upper}}
-                else:
-                    body["toolConfig"] = self._convert_tool_choice_to_gemini(
-                        final_tool_choice
-                    )
-            else:
-                body["toolConfig"] = self._convert_tool_choice_to_gemini(
-                    final_tool_choice
+        if tool_config_updates:
+            body.setdefault("toolConfig", {}).update(tool_config_updates)
+
+        converted_params: dict[str, Any] = {}
+        if effective_config:
+            converted_params = self.convert_generation_config(effective_config, model)
+
+        if converted_params:
+            if "toolConfig" in converted_params:
+                tool_config_payload = converted_params.pop("toolConfig")
+                fc_config = tool_config_payload.get("functionCallingConfig")
+                should_apply_fc = has_user_functions or (
+                    fc_config and fc_config.get("mode") == "NONE"
                 )
+                if should_apply_fc:
+                    body.setdefault("toolConfig", {}).update(tool_config_payload)
+                elif fc_config and fc_config.get("mode") != "AUTO":
+                    logger.debug(
+                        "Gemini: 忽略针对纯内置工具的 functionCallingConfig (API限制)"
+                    )
 
-        final_generation_config = self._build_gemini_generation_config(
-            model, effective_config
-        )
-        if final_generation_config:
-            body["generationConfig"] = final_generation_config
+            if "safetySettings" in converted_params:
+                body["safetySettings"] = converted_params.pop("safetySettings")
 
-        safety_settings = self._build_safety_settings(effective_config)
-        if safety_settings:
-            body["safetySettings"] = safety_settings
+            if converted_params:
+                body["generationConfig"] = converted_params
 
         return RequestData(url=url, headers=headers, body=body)
 
@@ -242,317 +218,56 @@ class GeminiAdapter(BaseAdapter):
     def _get_gemini_endpoint(
         self, model: "LLMModel", config: "LLMGenerationConfig | None" = None
     ) -> str:
-        """根据配置选择Gemini API端点"""
-        if config:
-            if getattr(config, "enable_code_execution", False):
-                return f"/v1beta/models/{model.model_name}:generateContent"
-
-            if getattr(config, "enable_grounding", False):
-                return f"/v1beta/models/{model.model_name}:generateContent"
-
+        """返回Gemini generateContent 端点"""
         return f"/v1beta/models/{model.model_name}:generateContent"
 
-    def _convert_tool_choice_to_gemini(
-        self, tool_choice_value: str | dict[str, Any]
-    ) -> dict[str, Any]:
-        """转换工具选择策略为Gemini格式"""
-        if isinstance(tool_choice_value, str):
-            mode_upper = tool_choice_value.upper()
-            if mode_upper in ["AUTO", "NONE", "ANY"]:
-                return {"functionCallingConfig": {"mode": mode_upper}}
-            else:
-                logger.warning(
-                    f"不支持的 tool_choice 字符串值: '{tool_choice_value}'。"
-                    f"回退到 AUTO。"
-                )
-                return {"functionCallingConfig": {"mode": "AUTO"}}
-
-        elif isinstance(tool_choice_value, dict):
-            if (
-                tool_choice_value.get("type") == "function"
-                and "function" in tool_choice_value
-            ):
-                func_name = tool_choice_value["function"].get("name")
-                if func_name:
-                    return {
-                        "functionCallingConfig": {
-                            "mode": "ANY",
-                            "allowedFunctionNames": [func_name],
-                        }
-                    }
-                else:
-                    logger.warning(
-                        f"tool_choice dict 中的函数名无效: {tool_choice_value}。"
-                        f"回退到 AUTO。"
-                    )
-                    return {"functionCallingConfig": {"mode": "AUTO"}}
-
-            elif "functionCallingConfig" in tool_choice_value:
-                return {
-                    "functionCallingConfig": tool_choice_value["functionCallingConfig"]
-                }
-
-            else:
-                logger.warning(
-                    f"不支持的 tool_choice dict 值: {tool_choice_value}。回退到 AUTO。"
-                )
-                return {"functionCallingConfig": {"mode": "AUTO"}}
-
-        logger.warning(
-            f"tool_choice 的类型无效: {type(tool_choice_value)}。回退到 AUTO。"
-        )
-        return {"functionCallingConfig": {"mode": "AUTO"}}
-
-    def _build_gemini_generation_config(
-        self, model: "LLMModel", config: "LLMGenerationConfig | None" = None
-    ) -> dict[str, Any]:
-        """构建Gemini生成配置"""
-        effective_config = config if config is not None else model._generation_config
-
-        if not effective_config:
-            return {}
-
-        generation_config = effective_config.to_api_params(
-            api_type="gemini", model_name=model.model_name
-        )
-
-        if generation_config:
-            param_keys = list(generation_config.keys())
-            logger.debug(
-                f"构建Gemini生成配置完成，包含 {len(generation_config)} 个参数: "
-                f"{param_keys}"
-            )
-
-        return generation_config
-
-    def _build_safety_settings(
-        self, config: "LLMGenerationConfig | None" = None
-    ) -> list[dict[str, Any]] | None:
-        """构建安全设置"""
-        if not config:
-            return None
-
-        safety_settings = []
-
-        safety_categories = [
-            "HARM_CATEGORY_HARASSMENT",
-            "HARM_CATEGORY_HATE_SPEECH",
-            "HARM_CATEGORY_SEXUALLY_EXPLICIT",
-            "HARM_CATEGORY_DANGEROUS_CONTENT",
-        ]
-
-        custom_safety_settings = getattr(config, "safety_settings", None)
-        if custom_safety_settings:
-            for category, threshold in custom_safety_settings.items():
-                safety_settings.append({"category": category, "threshold": threshold})
-        else:
-            from ..config.providers import get_gemini_safety_threshold
-
-            threshold = get_gemini_safety_threshold()
-            for category in safety_categories:
-                safety_settings.append({"category": category, "threshold": threshold})
-
-        return safety_settings if safety_settings else None
-
-    def validate_response(self, response_json: dict[str, Any]) -> None:
-        """验证 Gemini API 响应，增加对 promptFeedback 的检查"""
-        super().validate_response(response_json)
-
-        if prompt_feedback := response_json.get("promptFeedback"):
-            if block_reason := prompt_feedback.get("blockReason"):
-                logger.warning(
-                    f"Gemini 内容因 promptFeedback 被安全过滤: {block_reason}"
-                )
-                raise LLMException(
-                    f"内容被安全过滤: {block_reason}",
-                    code=LLMErrorCode.CONTENT_FILTERED,
-                    details={
-                        "block_reason": block_reason,
-                        "safety_ratings": prompt_feedback.get("safetyRatings"),
-                    },
-                )
-
     def parse_response(
         self,
         model: "LLMModel",
         response_json: dict[str, Any],
         is_advanced: bool = False,
-    ) -> ResponseData:
-        """解析API响应"""
-        return self._parse_response(model, response_json, is_advanced)
-
-    def _parse_response(
-        self,
-        model: "LLMModel",
-        response_json: dict[str, Any],
-        is_advanced: bool = False,
     ) -> ResponseData:
         """解析 Gemini API 响应"""
-        _ = is_advanced
-        self.validate_response(response_json)
-
-        try:
-            if "image_generation" in response_json and isinstance(
-                response_json["image_generation"], dict
-            ):
-                candidates_source = response_json["image_generation"]
-            else:
-                candidates_source = response_json
-
-            candidates = candidates_source.get("candidates", [])
-            usage_info = response_json.get("usageMetadata")
-
-            if not candidates:
-                logger.debug("Gemini响应中没有candidates。")
-                return ResponseData(text="", raw_response=response_json)
-
-            candidate = candidates[0]
-
-            if candidate.get("finishReason") in [
-                "RECITATION",
-                "OTHER",
-            ] and not candidate.get("content"):
-                logger.warning(
-                    f"Gemini candidate finished with reason "
-                    f"'{candidate.get('finishReason')}' and no content."
-                )
-                return ResponseData(
-                    text="",
-                    raw_response=response_json,
-                    usage_info=response_json.get("usageMetadata"),
-                )
-
-            content_data = candidate.get("content", {})
-            parts = content_data.get("parts", [])
-
-            text_content = ""
-            images_bytes: list[bytes] = []
-            parsed_tool_calls: list["LLMToolCall"] | None = None
-            thought_summary_parts = []
-            answer_parts = []
-
-            for part in parts:
-                if "text" in part:
-                    answer_parts.append(part["text"])
-                elif "thought" in part:
-                    thought_summary_parts.append(part["thought"])
-                elif "thoughtSummary" in part:
-                    thought_summary_parts.append(part["thoughtSummary"])
-                elif "inlineData" in part:
-                    inline_data = part["inlineData"]
-                    if "data" in inline_data:
-                        images_bytes.append(base64.b64decode(inline_data["data"]))
-
-                elif "functionCall" in part:
-                    if parsed_tool_calls is None:
-                        parsed_tool_calls = []
-                    fc_data = part["functionCall"]
-                    try:
-                        import json
-
-                        from ..types.models import LLMToolCall, LLMToolFunction
-
-                        call_id = f"call_{model.provider_name}_{len(parsed_tool_calls)}"
-                        parsed_tool_calls.append(
-                            LLMToolCall(
-                                id=call_id,
-                                function=LLMToolFunction(
-                                    name=fc_data["name"],
-                                    arguments=json.dumps(fc_data["args"]),
-                                ),
-                            )
-                        )
-                    except KeyError as e:
-                        logger.warning(
-                            f"解析Gemini functionCall时缺少键: {fc_data}, 错误: {e}"
-                        )
-                    except Exception as e:
-                        logger.warning(
-                            f"解析Gemini functionCall时出错: {fc_data}, 错误: {e}"
-                        )
-                elif "codeExecutionResult" in part:
-                    result = part["codeExecutionResult"]
-                    if result.get("outcome") == "OK":
-                        output = result.get("output", "")
-                        answer_parts.append(f"\n[代码执行结果]:\n```\n{output}\n```\n")
-                    else:
-                        answer_parts.append(
-                            f"\n[代码执行失败]: {result.get('outcome', 'UNKNOWN')}\n"
-                        )
-
-            if thought_summary_parts:
-                full_thought_summary = "\n".join(thought_summary_parts).strip()
-                full_answer = "".join(answer_parts).strip()
-
-                formatted_parts = []
-                if full_thought_summary:
-                    formatted_parts.append(f"🤔 **思考过程**\n\n{full_thought_summary}")
-                if full_answer:
-                    separator = "\n\n---\n\n" if full_thought_summary else ""
-                    formatted_parts.append(f"{separator}✅ **回答**\n\n{full_answer}")
-
-                text_content = "".join(formatted_parts)
-            else:
-                text_content = "".join(answer_parts)
-
-            usage_info = response_json.get("usageMetadata")
-
-            grounding_metadata_obj = None
-            if grounding_data := candidate.get("groundingMetadata"):
-                try:
-                    from ..types.models import LLMGroundingMetadata
-
-                    grounding_metadata_obj = LLMGroundingMetadata(**grounding_data)
-                except Exception as e:
-                    logger.warning(f"无法解析Grounding元数据: {grounding_data}, {e}")
-
-            return ResponseData(
-                text=text_content,
-                tool_calls=parsed_tool_calls,
-                images=images_bytes if images_bytes else None,
-                usage_info=usage_info,
-                raw_response=response_json,
-                grounding_metadata=grounding_metadata_obj,
-            )
-
-        except Exception as e:
-            logger.error(f"解析 Gemini 响应失败: {e}", e=e)
-            raise LLMException(
-                f"解析API响应失败: {e}",
-                code=LLMErrorCode.RESPONSE_PARSE_ERROR,
-                cause=e,
-            )
+        _ = model, is_advanced
+        parser = GeminiResponseParser()
+        return parser.parse(response_json)
 
     def prepare_embedding_request(
         self,
         model: "LLMModel",
         api_key: str,
         texts: list[str],
-        task_type: "EmbeddingTaskType | str",
-        **kwargs: Any,
+        config: "LLMEmbeddingConfig",
     ) -> RequestData:
         """准备文本嵌入请求"""
         api_model_name = model.model_name
         if not api_model_name.startswith("models/"):
             api_model_name = f"models/{api_model_name}"
 
-        url = self.get_api_url(model, f"/{api_model_name}:batchEmbedContents")
+        if not model.api_base:
+            raise LLMException(
+                f"模型 {model.model_name} 的 api_base 未设置",
+                code=LLMErrorCode.CONFIGURATION_ERROR,
+            )
+
+        base_url = model.api_base.rstrip("/")
+        url = f"{base_url}/v1beta/{api_model_name}:batchEmbedContents"
         headers = self.get_base_headers(api_key)
 
         requests_payload = []
         for text_content in texts:
+            safe_text = text_content if text_content else " "
             request_item: dict[str, Any] = {
-                "content": {"parts": [{"text": text_content}]},
+                "model": api_model_name,
+                "content": {"parts": [{"text": safe_text}]},
             }
 
-            from ..types.enums import EmbeddingTaskType
-
-            if task_type and task_type != EmbeddingTaskType.RETRIEVAL_DOCUMENT:
-                request_item["task_type"] = str(task_type).upper()
-            if title := kwargs.get("title"):
-                request_item["title"] = title
-            if output_dimensionality := kwargs.get("output_dimensionality"):
-                request_item["output_dimensionality"] = output_dimensionality
+            if config.task_type:
+                request_item["task_type"] = str(config.task_type).upper()
+            if config.title:
+                request_item["title"] = config.title
+            if config.output_dimensionality:
+                request_item["output_dimensionality"] = config.output_dimensionality
 
             requests_payload.append(request_item)
 
@@ -601,3 +316,9 @@ class GeminiAdapter(BaseAdapter):
                     code=LLMErrorCode.RESPONSE_PARSE_ERROR,
                     details=response_json,
                 )
+
+    def convert_generation_config(
+        self, config: "LLMGenerationConfig", model: "LLMModel"
+    ) -> dict[str, Any]:
+        mapper = GeminiConfigMapper()
+        return mapper.map_config(config, model.model_detail, model.capabilities)
diff --git a/zhenxun/services/llm/adapters/openai.py b/zhenxun/services/llm/adapters/openai.py
index a992d7ee..16613524 100644
--- a/zhenxun/services/llm/adapters/openai.py
+++ b/zhenxun/services/llm/adapters/openai.py
@@ -1,15 +1,181 @@
 """
 OpenAI API 适配器
 
-支持 OpenAI、DeepSeek、智谱AI 和其他 OpenAI 兼容的 API 服务。
+支持 OpenAI、智谱AI 等 OpenAI 兼容的 API 服务。
 """
 
-from typing import TYPE_CHECKING
+from abc import ABC, abstractmethod
+import base64
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
 
-from .base import OpenAICompatAdapter
+import json_repair
+
+from zhenxun.services.llm.config.generation import ImageAspectRatio
+from zhenxun.services.llm.types.exceptions import LLMErrorCode, LLMException
+from zhenxun.services.log import logger
+from zhenxun.utils.http_utils import AsyncHttpx
+
+from ..types import StructuredOutputStrategy
+from ..types.models import ToolChoice
+from ..utils import sanitize_schema_for_llm
+from .base import (
+    BaseAdapter,
+    OpenAICompatAdapter,
+    RequestData,
+    ResponseData,
+    process_image_data,
+)
+from .components.openai_components import (
+    OpenAIConfigMapper,
+    OpenAIMessageConverter,
+    OpenAIResponseParser,
+    OpenAIToolSerializer,
+)
 
 if TYPE_CHECKING:
+    from ..config.generation import LLMEmbeddingConfig, LLMGenerationConfig
     from ..service import LLMModel
+    from ..types import LLMMessage
+
+
+class APIProtocol(ABC):
+    """API 协议策略基类"""
+
+    @abstractmethod
+    def build_request_body(
+        self,
+        model: "LLMModel",
+        messages: list["LLMMessage"],
+        tools: list[dict[str, Any]] | None,
+        tool_choice: Any,
+    ) -> dict[str, Any]:
+        """构建不同协议下的请求体"""
+        pass
+
+    @abstractmethod
+    def parse_response(self, response_json: dict[str, Any]) -> ResponseData:
+        """解析不同协议下的响应"""
+        pass
+
+
+class StandardProtocol(APIProtocol):
+    """标准 OpenAI 协议策略"""
+
+    def __init__(self, adapter: "OpenAICompatAdapter"):
+        self.adapter = adapter
+
+    def build_request_body(
+        self,
+        model: "LLMModel",
+        messages: list["LLMMessage"],
+        tools: list[dict[str, Any]] | None,
+        tool_choice: Any,
+    ) -> dict[str, Any]:
+        converter = OpenAIMessageConverter()
+        openai_messages = converter.convert_messages(messages)
+        body: dict[str, Any] = {
+            "model": model.model_name,
+            "messages": openai_messages,
+        }
+        if tools:
+            body["tools"] = tools
+        if tool_choice:
+            body["tool_choice"] = tool_choice
+        return body
+
+    def parse_response(self, response_json: dict[str, Any]) -> ResponseData:
+        parser = OpenAIResponseParser()
+        return parser.parse(response_json)
+
+
+class ResponsesProtocol(APIProtocol):
+    """/v1/responses 新版协议策略"""
+
+    def __init__(self, adapter: "OpenAICompatAdapter"):
+        self.adapter = adapter
+
+    def build_request_body(
+        self,
+        model: "LLMModel",
+        messages: list["LLMMessage"],
+        tools: list[dict[str, Any]] | None,
+        tool_choice: Any,
+    ) -> dict[str, Any]:
+        input_items: list[dict[str, Any]] = []
+
+        for msg in messages:
+            role = msg.role
+            content_list: list[dict[str, Any]] = []
+            raw_contents = (
+                msg.content if isinstance(msg.content, list) else [msg.content]
+            )
+
+            for part in raw_contents:
+                if part is None:
+                    continue
+                if isinstance(part, str):
+                    content_list.append({"type": "input_text", "text": part})
+                    continue
+
+                if hasattr(part, "type"):
+                    part_type = getattr(part, "type", None)
+                    if part_type == "text":
+                        content_list.append(
+                            {"type": "input_text", "text": getattr(part, "text", "")}
+                        )
+                    elif part_type == "image":
+                        content_list.append(
+                            {
+                                "type": "input_image",
+                                "image_url": getattr(part, "image_source", ""),
+                            }
+                        )
+                    continue
+
+                if isinstance(part, dict):
+                    part_type = part.get("type")
+                    if part_type == "text":
+                        content_list.append(
+                            {"type": "input_text", "text": part.get("text", "")}
+                        )
+                    elif part_type in {"image", "image_url"}:
+                        image_src = part.get("image_url") or part.get(
+                            "image_source", ""
+                        )
+                        content_list.append(
+                            {
+                                "type": "input_image",
+                                "image_url": image_src,
+                            }
+                        )
+
+            input_items.append({"role": role, "content": content_list})
+
+        body: dict[str, Any] = {
+            "model": model.model_name,
+            "input": input_items,
+        }
+        if tools:
+            body["tools"] = tools
+        if tool_choice:
+            body["tool_choice"] = tool_choice
+        return body
+
+    def parse_response(self, response_json: dict[str, Any]) -> ResponseData:
+        self.adapter.validate_response(response_json)
+        text_content = ""
+        for item in response_json.get("output", []):
+            if item.get("type") == "message" and item.get("role") == "assistant":
+                for content_item in item.get("content", []):
+                    if content_item.get("type") == "output_text":
+                        text_content += content_item.get("text", "")
+
+        return ResponseData(
+            text=text_content,
+            usage_info=response_json.get("usage"),
+            raw_response=response_json,
+        )
 
 
 class OpenAIAdapter(OpenAICompatAdapter):
@@ -23,23 +189,411 @@ class OpenAIAdapter(OpenAICompatAdapter):
     def supported_api_types(self) -> list[str]:
         return [
             "openai",
-            "deepseek",
             "zhipu",
-            "general_openai_compat",
             "ark",
             "openrouter",
+            "openai_responses",
         ]
 
     def get_chat_endpoint(self, model: "LLMModel") -> str:
         """返回聊天完成端点"""
-        if model.api_type == "ark":
+        if model.model_detail.endpoint:
+            return model.model_detail.endpoint
+
+        current_api_type = model.model_detail.api_type or model.api_type
+
+        if current_api_type == "openai_responses":
+            return "/v1/responses"
+        if current_api_type == "ark":
             return "/api/v3/chat/completions"
-        if model.api_type == "zhipu":
+        if current_api_type == "zhipu":
             return "/api/paas/v4/chat/completions"
         return "/v1/chat/completions"
 
+    def _get_protocol_strategy(self, model: "LLMModel") -> APIProtocol:
+        """根据 API 类型获取对应的处理策略"""
+        current_api_type = model.model_detail.api_type or model.api_type
+        if current_api_type == "openai_responses":
+            return ResponsesProtocol(self)
+        return StandardProtocol(self)
+
     def get_embedding_endpoint(self, model: "LLMModel") -> str:
         """根据API类型返回嵌入端点"""
         if model.api_type == "zhipu":
             return "/v4/embeddings"
         return "/v1/embeddings"
+
+    def convert_generation_config(
+        self, config: "LLMGenerationConfig", model: "LLMModel"
+    ) -> dict[str, Any]:
+        mapper = OpenAIConfigMapper(api_type=self.api_type)
+        return mapper.map_config(config, model.model_detail, model.capabilities)
+
+    async def prepare_advanced_request(
+        self,
+        model: "LLMModel",
+        api_key: str,
+        messages: list["LLMMessage"],
+        config: "LLMGenerationConfig | None" = None,
+        tools: list[Any] | None = None,
+        tool_choice: str | dict[str, Any] | ToolChoice | None = None,
+    ) -> "RequestData":
+        """根据不同协议策略构建高级请求"""
+        url = self.get_api_url(model, self.get_chat_endpoint(model))
+        headers = self.get_base_headers(api_key)
+        if model.api_type == "openrouter":
+            headers.update(
+                {
+                    "HTTP-Referer": "https://github.com/zhenxun-org/zhenxun_bot",
+                    "X-Title": "Zhenxun Bot",
+                }
+            )
+
+        default_config = getattr(model, "_generation_config", None)
+        effective_config = config if config is not None else default_config
+        structured_strategy = (
+            effective_config.output.structured_output_strategy
+            if effective_config and effective_config.output
+            else None
+        )
+        if structured_strategy is None:
+            structured_strategy = StructuredOutputStrategy.NATIVE
+
+        openai_tools: list[dict[str, Any]] | None = None
+        executables: list[Any] = []
+        if tools:
+            if isinstance(tools, dict):
+                executables = list(tools.values())
+            else:
+                for tool in tools:
+                    if hasattr(tool, "get_definition"):
+                        executables.append(tool)
+
+        definition_tasks = [executable.get_definition() for executable in executables]
+        tool_defs: list[Any] = []
+        if definition_tasks:
+            import asyncio
+
+            tool_defs = await asyncio.gather(*definition_tasks)
+
+        if tool_defs:
+            serializer = OpenAIToolSerializer()
+            openai_tools = serializer.serialize_tools(tool_defs)
+
+        final_tool_choice = tool_choice
+        if final_tool_choice is None:
+            if (
+                effective_config
+                and effective_config.tool_config
+                and effective_config.tool_config.mode == "ANY"
+            ):
+                allowed = effective_config.tool_config.allowed_function_names
+                if allowed:
+                    if len(allowed) == 1:
+                        final_tool_choice = {
+                            "type": "function",
+                            "function": {"name": allowed[0]},
+                        }
+                    else:
+                        logger.warning(
+                            "OpenAI API 不支持多个 allowed_function_names，降级为"
+                            " required。"
+                        )
+                        final_tool_choice = "required"
+                else:
+                    final_tool_choice = "required"
+
+        if (
+            structured_strategy == StructuredOutputStrategy.TOOL_CALL
+            and effective_config
+            and effective_config.output
+            and effective_config.output.response_schema
+        ):
+            sanitized_schema = sanitize_schema_for_llm(
+                effective_config.output.response_schema, api_type="openai"
+            )
+            structured_tool = {
+                "type": "function",
+                "function": {
+                    "name": "return_structured_response",
+                    "description": "Return the final structured response.",
+                    "parameters": sanitized_schema,
+                    "strict": True if model.api_type != "deepseek" else False,
+                },
+            }
+            if openai_tools is None:
+                openai_tools = []
+            openai_tools.append(structured_tool)
+            final_tool_choice = {
+                "type": "function",
+                "function": {"name": "return_structured_response"},
+            }
+
+        protocol_strategy = self._get_protocol_strategy(model)
+        body = protocol_strategy.build_request_body(
+            model=model,
+            messages=messages,
+            tools=openai_tools,
+            tool_choice=final_tool_choice,
+        )
+
+        body = self.apply_config_override(model, body, config)
+
+        if final_tool_choice is not None:
+            body["tool_choice"] = final_tool_choice
+
+        response_format = body.get("response_format", {})
+        inject_prompt = (
+            structured_strategy == StructuredOutputStrategy.NATIVE
+            and isinstance(response_format, dict)
+            and response_format.get("type") == "json_object"
+        )
+
+        if inject_prompt:
+            messages_list = body.get("messages", [])
+            has_json_keyword = False
+            for msg in messages_list:
+                content = msg.get("content")
+                if isinstance(content, str) and "json" in content.lower():
+                    has_json_keyword = True
+                    break
+                if isinstance(content, list):
+                    for part in content:
+                        if (
+                            isinstance(part, dict)
+                            and part.get("type") == "text"
+                            and "json" in part.get("text", "").lower()
+                        ):
+                            has_json_keyword = True
+                            break
+                    if has_json_keyword:
+                        break
+
+            if not has_json_keyword:
+                injection_text = (
+                    "请务必输出合法的 JSON 格式，避免额外的文本、Markdown 或解释。"
+                )
+                system_msg = next(
+                    (m for m in messages_list if m.get("role") == "system"), None
+                )
+                if system_msg:
+                    if isinstance(system_msg.get("content"), str):
+                        system_msg["content"] += " " + injection_text
+                    elif isinstance(system_msg.get("content"), list):
+                        system_msg["content"].append(
+                            {"type": "text", "text": injection_text}
+                        )
+                else:
+                    messages_list.insert(
+                        0, {"role": "system", "content": injection_text}
+                    )
+                body["messages"] = messages_list
+
+        return RequestData(url=url, headers=headers, body=body)
+
+    def parse_response(
+        self,
+        model: "LLMModel",
+        response_json: dict[str, Any],
+        is_advanced: bool = False,
+    ) -> ResponseData:
+        """解析响应 - 使用策略模式委托处理"""
+        _ = is_advanced
+        protocol_strategy = self._get_protocol_strategy(model)
+        response_data = protocol_strategy.parse_response(response_json)
+
+        if response_data.tool_calls:
+            target_tool = next(
+                (
+                    tc
+                    for tc in response_data.tool_calls
+                    if tc.function.name == "return_structured_response"
+                ),
+                None,
+            )
+            if target_tool:
+                response_data.text = json_repair.repair_json(
+                    target_tool.function.arguments
+                )
+                remaining = [
+                    tc
+                    for tc in response_data.tool_calls
+                    if tc.function.name != "return_structured_response"
+                ]
+                response_data.tool_calls = remaining or None
+
+        return response_data
+
+
+class DeepSeekAdapter(OpenAIAdapter):
+    """DeepSeek 专用适配器 (基于 OpenAI 协议)"""
+
+    @property
+    def api_type(self) -> str:
+        return "deepseek"
+
+    @property
+    def supported_api_types(self) -> list[str]:
+        return ["deepseek"]
+
+
+class OpenAIImageAdapter(BaseAdapter):
+    """OpenAI 图像生成/编辑适配器"""
+
+    @property
+    def api_type(self) -> str:
+        return "openai_image"
+
+    @property
+    def log_sanitization_context(self) -> str:
+        return "openai_request"
+
+    @property
+    def supported_api_types(self) -> list[str]:
+        return ["openai_image", "nano_banana"]
+
+    async def prepare_advanced_request(
+        self,
+        model: "LLMModel",
+        api_key: str,
+        messages: list["LLMMessage"],
+        config: "LLMGenerationConfig | None" = None,
+        tools: list[Any] | None = None,
+        tool_choice: "str | dict[str, Any] | ToolChoice | None" = None,
+    ) -> RequestData:
+        _ = tools, tool_choice
+        effective_config = config if config is not None else model._generation_config
+        headers = self.get_base_headers(api_key)
+
+        prompt = ""
+        images_bytes_list: list[bytes] = []
+
+        for msg in reversed(messages):
+            if msg.role != "user":
+                continue
+            if isinstance(msg.content, str):
+                prompt = msg.content
+            elif isinstance(msg.content, list):
+                for part in msg.content:
+                    if part.type == "text" and not prompt:
+                        prompt = part.text
+                    elif part.type == "image":
+                        if part.is_image_base64():
+                            if b64_data := part.get_base64_data():
+                                _, b64_str = b64_data
+                                images_bytes_list.append(base64.b64decode(b64_str))
+                        elif part.is_image_url() and part.image_source:
+                            images_bytes_list.append(
+                                await AsyncHttpx.get_content(part.image_source)
+                            )
+            if prompt:
+                break
+
+        if not prompt and not images_bytes_list:
+            raise LLMException(
+                "图像生成需要提供 Prompt",
+                code=LLMErrorCode.CONFIGURATION_ERROR,
+            )
+
+        body: dict[str, Any] = {
+            "model": model.model_name,
+            "prompt": prompt,
+            "response_format": "b64_json",
+        }
+
+        if effective_config:
+            if effective_config.visual:
+                if effective_config.visual.aspect_ratio:
+                    ar = effective_config.visual.aspect_ratio
+                    size_map = {
+                        ImageAspectRatio.SQUARE: "1024x1024",
+                        ImageAspectRatio.LANDSCAPE_16_9: "1792x1024",
+                        ImageAspectRatio.PORTRAIT_9_16: "1024x1792",
+                    }
+                    if isinstance(ar, ImageAspectRatio) and ar in size_map:
+                        body["size"] = size_map[ar]
+                        body["aspect_ratio"] = ar.value
+                    elif isinstance(ar, str):
+                        if "x" in ar:
+                            body["size"] = ar
+                        else:
+                            body["aspect_ratio"] = ar
+
+                if effective_config.visual.resolution:
+                    res_val = effective_config.visual.resolution
+                    if not isinstance(res_val, str):
+                        res_val = getattr(res_val, "value", res_val)
+                    body["image_size"] = res_val
+
+            if effective_config.custom_params:
+                body.update(effective_config.custom_params)
+
+        if images_bytes_list:
+            b64_images = []
+            for img_bytes in images_bytes_list:
+                b64_str = base64.b64encode(img_bytes).decode("utf-8")
+                b64_images.append(b64_str)
+            body["image"] = b64_images
+
+        endpoint = "/v1/images/generations"
+        url = self.get_api_url(model, endpoint)
+        return RequestData(url=url, headers=headers, body=body)
+
+    def parse_response(
+        self,
+        model: "LLMModel",
+        response_json: dict[str, Any],
+        is_advanced: bool = False,
+    ) -> ResponseData:
+        _ = model, is_advanced
+        self.validate_response(response_json)
+
+        images_data: list[bytes | Path] = []
+        data_list = response_json.get("data", [])
+
+        for item in data_list:
+            if "b64_json" in item:
+                try:
+                    b64_str = item["b64_json"]
+                    if b64_str.startswith("data:"):
+                        b64_str = b64_str.split(",", 1)[1]
+                    img = base64.b64decode(b64_str)
+                    images_data.append(process_image_data(img))
+                except Exception as exc:
+                    logger.error(f"Base64 解码失败: {exc}")
+            elif "url" in item:
+                logger.warning(
+                    f"API 返回了 URL 而不是 Base64: {item.get('url', 'unknown')}"
+                )
+
+        text_summary = (
+            f"已生成 {len(images_data)} 张图片。"
+            if images_data
+            else "图像生成接口调用成功，但未解析到图片数据。"
+        )
+
+        return ResponseData(
+            text=text_summary,
+            images=images_data if images_data else None,
+            raw_response=response_json,
+        )
+
+    def prepare_embedding_request(
+        self,
+        model: "LLMModel",
+        api_key: str,
+        texts: list[str],
+        config: "LLMEmbeddingConfig",
+    ) -> RequestData:
+        raise NotImplementedError("OpenAIImageAdapter 不支持 Embedding")
+
+    def parse_embedding_response(
+        self, response_json: dict[str, Any]
+    ) -> list[list[float]]:
+        raise NotImplementedError("OpenAIImageAdapter 不支持 Embedding")
+
+    def convert_generation_config(
+        self, config: "LLMGenerationConfig", model: "LLMModel"
+    ) -> dict[str, Any]:
+        _ = config, model
+        return {}
diff --git a/zhenxun/services/llm/api.py b/zhenxun/services/llm/api.py
index 23da7f1b..d56d9a91 100644
--- a/zhenxun/services/llm/api.py
+++ b/zhenxun/services/llm/api.py
@@ -2,6 +2,7 @@
 LLM 服务的高级 API 接口 - 便捷函数入口 (无状态)
 """
 
+from collections.abc import Awaitable, Callable
 from pathlib import Path
 from typing import Any, TypeVar, overload
 
@@ -11,19 +12,24 @@ from pydantic import BaseModel
 from zhenxun.services.log import logger
 
 from .config import CommonOverrides
-from .config.generation import LLMGenerationConfig, create_generation_config_from_kwargs
+from .config.generation import (
+    GenConfigBuilder,
+    LLMEmbeddingConfig,
+    LLMGenerationConfig,
+    OutputConfig,
+)
 from .manager import get_model_instance
 from .session import AI
-from .tools.manager import tool_provider_manager
 from .types import (
-    EmbeddingTaskType,
     LLMContentPart,
     LLMErrorCode,
     LLMException,
     LLMMessage,
     LLMResponse,
     ModelName,
+    ToolChoice,
 )
+from .types.exceptions import get_user_friendly_error_message
 from .utils import create_multimodal_message
 
 T = TypeVar("T", bound=BaseModel)
@@ -34,9 +40,10 @@ async def chat(
     *,
     model: ModelName = None,
     instruction: str | None = None,
-    tools: list[dict[str, Any] | str] | None = None,
-    tool_choice: str | dict[str, Any] | None = None,
-    **kwargs: Any,
+    tools: list[Any] | None = None,
+    tool_choice: str | dict[str, Any] | ToolChoice | None = None,
+    config: LLMGenerationConfig | GenConfigBuilder | None = None,
+    timeout: float | None = None,
 ) -> LLMResponse:
     """
     无状态的聊天对话便捷函数，通过临时的AI会话实例与LLM模型交互。
@@ -47,14 +54,13 @@ async def chat(
         instruction: 系统指令，用于指导AI的行为和回复风格。
         tools: 可用的工具列表，支持字典配置或字符串标识符。
         tool_choice: 工具选择策略，控制AI如何选择和使用工具。
-        **kwargs: 额外的生成配置参数，会被转换为LLMGenerationConfig。
+        config: (可选) 生成配置对象，将与默认配置合并后传递。
+        timeout: (可选) HTTP 请求超时时间（秒）。
 
     返回:
         LLMResponse: 包含AI回复内容、使用信息和工具调用等的完整响应对象。
     """
     try:
-        config = create_generation_config_from_kwargs(**kwargs) if kwargs else None
-
         ai_session = AI()
 
         return await ai_session.chat(
@@ -64,12 +70,14 @@ async def chat(
             tools=tools,
             tool_choice=tool_choice,
             config=config,
+            timeout=timeout,
         )
     except LLMException:
         raise
     except Exception as e:
-        logger.error(f"执行 chat 函数失败: {e}", e=e)
-        raise LLMException(f"聊天执行失败: {e}", cause=e)
+        friendly_msg = get_user_friendly_error_message(e)
+        logger.error(f"执行 chat 函数失败: {e} | 建议: {friendly_msg}", e=e)
+        raise LLMException(f"聊天执行失败: {friendly_msg}", cause=e)
 
 
 async def code(
@@ -77,7 +85,6 @@ async def code(
     *,
     model: ModelName = None,
     timeout: int | None = None,
-    **kwargs: Any,
 ) -> LLMResponse:
     """
     无状态的代码执行便捷函数，支持在沙箱环境中执行代码。
@@ -86,66 +93,25 @@ async def code(
         prompt: 代码执行的提示词，描述要执行的代码任务。
         model: 要使用的模型名称，默认使用Gemini/gemini-2.0-flash。
         timeout: 代码执行超时时间（秒），防止长时间运行的代码阻塞。
-        **kwargs: 额外的生成配置参数。
 
     返回:
         LLMResponse: 包含代码执行结果的完整响应对象。
     """
-    resolved_model = model or "Gemini/gemini-2.0-flash"
+    resolved_model = model
 
     config = CommonOverrides.gemini_code_execution()
     if timeout:
         config.custom_params = config.custom_params or {}
         config.custom_params["code_execution_timeout"] = timeout
 
-    final_config = config.to_dict()
-    final_config.update(kwargs)
-
-    return await chat(prompt, model=resolved_model, **final_config)
-
-
-async def search(
-    query: str | UniMessage | LLMMessage | list[LLMContentPart],
-    *,
-    model: ModelName = None,
-    instruction: str = (
-        "你是一位强大的信息检索和整合专家。请利用可用的搜索工具，"
-        "根据用户的查询找到最相关的信息，并进行总结和回答。"
-    ),
-    **kwargs: Any,
-) -> LLMResponse:
-    """
-    无状态的信息搜索便捷函数，利用搜索工具获取实时信息。
-
-    参数:
-        query: 搜索查询内容，支持多种输入格式。
-        model: 要使用的模型名称，如果为None则使用默认模型。
-        instruction: 搜索任务的系统指令，指导AI如何处理搜索结果。
-        **kwargs: 额外的生成配置参数。
-
-    返回:
-        LLMResponse: 包含搜索结果和AI整合回复的完整响应对象。
-    """
-    logger.debug("执行无状态 'search' 任务...")
-    search_config = CommonOverrides.gemini_grounding()
-
-    final_config = search_config.to_dict()
-    final_config.update(kwargs)
-
-    return await chat(
-        query,
-        model=model,
-        instruction=instruction,
-        **final_config,
-    )
+    return await chat(prompt, model=resolved_model, config=config)
 
 
 async def embed(
     texts: list[str] | str,
     *,
     model: ModelName = None,
-    task_type: EmbeddingTaskType | str = EmbeddingTaskType.RETRIEVAL_DOCUMENT,
-    **kwargs: Any,
+    config: LLMEmbeddingConfig | None = None,
 ) -> list[list[float]]:
     """
     无状态的文本嵌入便捷函数，将文本转换为向量表示。
@@ -153,8 +119,7 @@ async def embed(
     参数:
         texts: 要生成嵌入的文本内容，支持单个字符串或字符串列表。
         model: 要使用的嵌入模型名称，如果为None则使用默认模型。
-        task_type: 嵌入任务类型，影响向量的优化方向（如检索、分类等）。
-        **kwargs: 额外的模型配置参数。
+        config: 嵌入配置对象。
 
     返回:
         list[list[float]]: 文本对应的嵌入向量列表，每个向量为浮点数列表。
@@ -164,27 +129,71 @@ async def embed(
     if not texts:
         return []
 
+    final_config = config or LLMEmbeddingConfig()
+
     try:
         async with await get_model_instance(model) as model_instance:
-            return await model_instance.generate_embeddings(
-                texts, task_type=task_type, **kwargs
-            )
+            return await model_instance.generate_embeddings(texts, config=final_config)
     except LLMException:
         raise
     except Exception as e:
-        logger.error(f"文本嵌入失败: {e}", e=e)
+        friendly_msg = get_user_friendly_error_message(e)
+        logger.error(f"文本嵌入失败: {e} | 建议: {friendly_msg}", e=e)
         raise LLMException(
-            f"文本嵌入失败: {e}", code=LLMErrorCode.EMBEDDING_FAILED, cause=e
+            f"文本嵌入失败: {friendly_msg}",
+            code=LLMErrorCode.EMBEDDING_FAILED,
+            cause=e,
         )
 
 
+async def embed_query(
+    text: str,
+    *,
+    model: ModelName = None,
+    dimensions: int | None = None,
+) -> list[float]:
+    """
+    语义化便捷 API：为检索查询生成嵌入。
+    """
+    config = LLMEmbeddingConfig(
+        task_type="RETRIEVAL_QUERY",
+        output_dimensionality=dimensions,
+    )
+    vectors = await embed([text], model=model, config=config)
+    return vectors[0] if vectors else []
+
+
+async def embed_documents(
+    texts: list[str],
+    *,
+    model: ModelName = None,
+    dimensions: int | None = None,
+    title: str | None = None,
+) -> list[list[float]]:
+    """
+    语义化便捷 API：为文档集合生成嵌入。
+    """
+    config = LLMEmbeddingConfig(
+        task_type="RETRIEVAL_DOCUMENT",
+        output_dimensionality=dimensions,
+        title=title,
+    )
+    return await embed(texts, model=model, config=config)
+
+
 async def generate_structured(
     message: str | LLMMessage | list[LLMContentPart],
     response_model: type[T],
     *,
     model: ModelName = None,
+    tools: list[Any] | None = None,
+    tool_choice: str | dict[str, Any] | ToolChoice | None = None,
+    max_validation_retries: int | None = None,
+    validation_callback: Callable[[T], Any | Awaitable[Any]] | None = None,
+    error_prompt_template: str | None = None,
+    auto_thinking: bool = False,
     instruction: str | None = None,
-    **kwargs: Any,
+    timeout: float | None = None,
 ) -> T:
     """
     无状态地生成结构化响应，并自动解析为指定的Pydantic模型。
@@ -192,39 +201,48 @@ async def generate_structured(
     参数:
         message: 用户输入的消息内容，支持多种格式。
         response_model: 用于解析和验证响应的Pydantic模型类。
+        max_validation_retries: 校验失败时的最大重试次数，默认为 None (使用全局配置)。
+        validation_callback: 自定义校验回调函数，抛出异常视为校验失败。
+        error_prompt_template: 自定义错误反馈提示词模板。
+        auto_thinking: 是否自动开启思维链 (CoT) 包装。适用于不支持原生思考的模型
         model: 要使用的模型名称，如果为None则使用默认模型。
         instruction: 系统指令，用于指导AI生成符合要求的结构化输出。
-        **kwargs: 额外的生成配置参数。
+        timeout: HTTP 请求超时时间（秒）。
 
     返回:
         T: 解析后的Pydantic模型实例，类型为response_model指定的类型。
     """
     try:
-        config = create_generation_config_from_kwargs(**kwargs) if kwargs else None
-
         ai_session = AI()
 
         return await ai_session.generate_structured(
             message,
             response_model,
             model=model,
+            tools=tools,
+            tool_choice=tool_choice,
+            max_validation_retries=max_validation_retries,
+            validation_callback=validation_callback,
+            error_prompt_template=error_prompt_template,
+            auto_thinking=auto_thinking,
             instruction=instruction,
-            config=config,
+            timeout=timeout,
         )
     except LLMException:
         raise
     except Exception as e:
-        logger.error(f"生成结构化响应失败: {e}", e=e)
-        raise LLMException(f"生成结构化响应失败: {e}", cause=e)
+        friendly_msg = get_user_friendly_error_message(e)
+        logger.error(f"生成结构化响应失败: {e} | 建议: {friendly_msg}", e=e)
+        raise LLMException(f"生成结构化响应失败: {friendly_msg}", cause=e)
 
 
 async def generate(
     messages: list[LLMMessage],
     *,
     model: ModelName = None,
-    tools: list[dict[str, Any] | str] | None = None,
-    tool_choice: str | dict[str, Any] | None = None,
-    **kwargs: Any,
+    tools: list[Any] | None = None,
+    tool_choice: str | dict[str, Any] | ToolChoice | None = None,
+    config: LLMGenerationConfig | GenConfigBuilder | None = None,
 ) -> LLMResponse:
     """
     根据完整的消息列表生成一次性响应，这是一个无状态的底层函数。
@@ -234,109 +252,56 @@ async def generate(
         model: 要使用的模型名称，如果为None则使用默认模型。
         tools: 可用的工具列表，支持字典配置或字符串标识符。
         tool_choice: 工具选择策略，控制AI如何选择和使用工具。
-        **kwargs: 额外的生成配置参数，会覆盖默认配置。
+        config: (可选) 生成配置对象，将与默认配置合并后传递。
 
     返回:
         LLMResponse: 包含AI回复内容、使用信息和工具调用等的完整响应对象。
     """
     try:
+        if isinstance(config, GenConfigBuilder):
+            config = config.build()
+
         async with await get_model_instance(
-            model, override_config=kwargs
+            model, override_config=None
         ) as model_instance:
             return await model_instance.generate_response(
                 messages,
-                tools=tools,  # type: ignore
+                config=config,
+                tools=tools,  # type: ignore[arg-type]
                 tool_choice=tool_choice,
             )
     except LLMException:
         raise
     except Exception as e:
-        logger.error(f"生成响应失败: {e}", e=e)
-        raise LLMException(f"生成响应失败: {e}", cause=e)
-
-
-async def run_with_tools(
-    message: str | UniMessage | LLMMessage | list[LLMContentPart],
-    *,
-    model: ModelName = None,
-    instruction: str | None = None,
-    tools: list[str],
-    max_cycles: int = 5,
-    **kwargs: Any,
-) -> LLMResponse:
-    """
-    无状态地执行一个带本地Python函数的LLM调用循环。
-
-    参数:
-        message: 用户输入。
-        model: 使用的模型。
-        instruction: 系统指令。
-        tools: 要使用的本地函数工具名称列表 (必须已通过 @function_tool 注册)。
-        max_cycles: 最大工具调用循环次数。
-        **kwargs: 额外的生成配置参数。
-
-    返回:
-        LLMResponse: 包含最终回复的响应对象。
-    """
-    from .executor import ExecutionConfig, LLMToolExecutor
-    from .utils import normalize_to_llm_messages
-
-    messages = await normalize_to_llm_messages(message, instruction)
-
-    async with await get_model_instance(
-        model, override_config=kwargs
-    ) as model_instance:
-        resolved_tools = await tool_provider_manager.get_function_tools(tools)
-        if not resolved_tools:
-            logger.warning(
-                "run_with_tools 未找到任何可用的本地函数工具，将作为普通聊天执行。"
-            )
-            return await model_instance.generate_response(messages, tools=None)
-
-        executor = LLMToolExecutor(model_instance)
-        config = ExecutionConfig(max_cycles=max_cycles)
-        final_history = await executor.run(messages, resolved_tools, config)
-
-        for msg in reversed(final_history):
-            if msg.role == "assistant":
-                text = msg.content if isinstance(msg.content, str) else str(msg.content)
-                return LLMResponse(text=text, tool_calls=msg.tool_calls)
-
-    raise LLMException(
-        "带工具的执行循环未能产生有效的助手回复。", code=LLMErrorCode.GENERATION_FAILED
-    )
+        friendly_msg = get_user_friendly_error_message(e)
+        logger.error(f"生成响应失败: {e} | 建议: {friendly_msg}", e=e)
+        raise LLMException(f"生成响应失败: {friendly_msg}", cause=e)
 
 
 async def _generate_image_from_message(
     message: UniMessage,
     model: ModelName = None,
-    **kwargs: Any,
+    config: LLMGenerationConfig | GenConfigBuilder | None = None,
 ) -> LLMResponse:
     """
     [内部] 从 UniMessage 生成图片的核心辅助函数。
     """
     from .utils import normalize_to_llm_messages
 
-    config = (
-        create_generation_config_from_kwargs(**kwargs)
-        if kwargs
-        else LLMGenerationConfig()
-    )
+    if isinstance(config, GenConfigBuilder):
+        config = config.build()
+
+    config = config or LLMGenerationConfig()
 
     config.validation_policy = {"require_image": True}
-    config.response_modalities = ["IMAGE", "TEXT"]
+    if config.output is None:
+        config.output = OutputConfig()
+    config.output.response_modalities = ["IMAGE", "TEXT"]
 
     try:
         messages = await normalize_to_llm_messages(message)
 
         async with await get_model_instance(model) as model_instance:
-            if not model_instance.can_generate_images():
-                raise LLMException(
-                    f"模型 '{model_instance.provider_name}/{model_instance.model_name}'"
-                    f"不支持图片生成",
-                    code=LLMErrorCode.CONFIGURATION_ERROR,
-                )
-
             response = await model_instance.generate_response(messages, config=config)
 
             if not response.images:
@@ -347,8 +312,9 @@ async def _generate_image_from_message(
     except LLMException:
         raise
     except Exception as e:
-        logger.error(f"执行图片生成时发生未知错误: {e}", e=e)
-        raise LLMException(f"图片生成失败: {e}", cause=e)
+        friendly_msg = get_user_friendly_error_message(e)
+        logger.error(f"执行图片生成时发生未知错误: {e} | 建议: {friendly_msg}", e=e)
+        raise LLMException(f"图片生成失败: {friendly_msg}", cause=e)
 
 
 @overload
@@ -357,7 +323,6 @@ async def create_image(
     *,
     images: None = None,
     model: ModelName = None,
-    **kwargs: Any,
 ) -> LLMResponse:
     """根据文本提示生成一张新图片。"""
     ...
@@ -369,7 +334,6 @@ async def create_image(
     *,
     images: list[Path | bytes | str] | Path | bytes | str,
     model: ModelName = None,
-    **kwargs: Any,
 ) -> LLMResponse:
     """在给定图片的基础上，根据文本提示进行编辑或重新生成。"""
     ...
@@ -380,7 +344,7 @@ async def create_image(
     *,
     images: list[Path | bytes | str] | Path | bytes | str | None = None,
     model: ModelName = None,
-    **kwargs: Any,
+    config: LLMGenerationConfig | GenConfigBuilder | None = None,
 ) -> LLMResponse:
     """
     智能图片生成/编辑函数。
@@ -400,4 +364,42 @@ async def create_image(
 
     message = create_multimodal_message(text=text_prompt, images=image_list)
 
-    return await _generate_image_from_message(message, model=model, **kwargs)
+    return await _generate_image_from_message(message, model=model, config=config)
+
+
+async def search(
+    query: str | UniMessage | LLMMessage | list[LLMContentPart],
+    *,
+    model: ModelName = None,
+    instruction: str = (
+        "你是一位强大的信息检索和整合专家。请利用可用的搜索工具，"
+        "根据用户的查询找到最相关的信息，并进行总结和回答。"
+    ),
+    config: LLMGenerationConfig | GenConfigBuilder | None = None,
+) -> LLMResponse:
+    """
+    无状态的信息搜索便捷函数，利用搜索工具获取实时信息。
+
+    参数:
+        query: 搜索查询内容，支持多种输入格式。
+        model: 要使用的模型名称，如果为None则使用默认模型。
+        config: (可选) 生成配置对象，将与预设配置合并后传递。
+        instruction: 搜索任务的系统指令，指导AI如何处理搜索结果。
+
+    返回:
+        LLMResponse: 包含搜索结果和AI整合回复的完整响应对象。
+    """
+    logger.debug("执行无状态 'search' 任务...")
+    search_config = CommonOverrides.gemini_grounding()
+
+    if isinstance(config, GenConfigBuilder):
+        config = config.build()
+
+    final_config = search_config.merge_with(config)
+
+    return await chat(
+        query,
+        model=model,
+        instruction=instruction,
+        config=final_config,
+    )
diff --git a/zhenxun/services/llm/config/__init__.py b/zhenxun/services/llm/config/__init__.py
index f43792c4..345347a0 100644
--- a/zhenxun/services/llm/config/__init__.py
+++ b/zhenxun/services/llm/config/__init__.py
@@ -5,13 +5,12 @@ LLM 配置模块
 """
 
 from .generation import (
+    CommonOverrides,
+    GenConfigBuilder,
+    LLMEmbeddingConfig,
     LLMGenerationConfig,
-    ModelConfigOverride,
-    apply_api_specific_mappings,
-    create_generation_config_from_kwargs,
     validate_override_params,
 )
-from .presets import CommonOverrides
 from .providers import (
     LLMConfig,
     get_gemini_safety_threshold,
@@ -23,11 +22,10 @@ from .providers import (
 
 __all__ = [
     "CommonOverrides",
+    "GenConfigBuilder",
     "LLMConfig",
+    "LLMEmbeddingConfig",
     "LLMGenerationConfig",
-    "ModelConfigOverride",
-    "apply_api_specific_mappings",
-    "create_generation_config_from_kwargs",
     "get_gemini_safety_threshold",
     "get_llm_config",
     "register_llm_configs",
diff --git a/zhenxun/services/llm/config/generation.py b/zhenxun/services/llm/config/generation.py
index 9f132b8a..45560b89 100644
--- a/zhenxun/services/llm/config/generation.py
+++ b/zhenxun/services/llm/config/generation.py
@@ -3,209 +3,397 @@ LLM 生成配置相关类和函数
 """
 
 from collections.abc import Callable
-from typing import Any
+from enum import Enum
+from typing import Any, Literal
+from typing_extensions import Self
 
 from pydantic import BaseModel, ConfigDict, Field
 
 from zhenxun.services.log import logger
-from zhenxun.utils.pydantic_compat import model_dump
+from zhenxun.utils.pydantic_compat import model_copy, model_dump, model_validate
 
-from ..types import LLMResponse
-from ..types.enums import ResponseFormat
+from ..types import LLMResponse, ResponseFormat, StructuredOutputStrategy
 from ..types.exceptions import LLMErrorCode, LLMException
+from .providers import get_gemini_safety_threshold
 
 
-class ModelConfigOverride(BaseModel):
-    """模型配置覆盖参数"""
+class ReasoningEffort(str, Enum):
+    """推理努力程度枚举"""
+
+    LOW = "LOW"
+    MEDIUM = "MEDIUM"
+    HIGH = "HIGH"
+
+
+class ImageAspectRatio(str, Enum):
+    """图像宽高比枚举"""
+
+    SQUARE = "1:1"
+    LANDSCAPE_16_9 = "16:9"
+    PORTRAIT_9_16 = "9:16"
+    LANDSCAPE_4_3 = "4:3"
+    PORTRAIT_3_4 = "3:4"
+    LANDSCAPE_3_2 = "3:2"
+    PORTRAIT_2_3 = "2:3"
+
+
+class ImageResolution(str, Enum):
+    """图像分辨率/质量枚举"""
+
+    STANDARD = "STANDARD"
+    HD = "HD"
+
+
+class CoreConfig(BaseModel):
+    """核心生成参数"""
 
     temperature: float | None = Field(
         default=None, ge=0.0, le=2.0, description="生成温度"
     )
+    """生成温度"""
     max_tokens: int | None = Field(default=None, gt=0, description="最大输出token数")
+    """最大输出token数"""
     top_p: float | None = Field(default=None, ge=0.0, le=1.0, description="核采样参数")
+    """核采样参数"""
     top_k: int | None = Field(default=None, gt=0, description="Top-K采样参数")
+    """Top-K采样参数"""
     frequency_penalty: float | None = Field(
         default=None, ge=-2.0, le=2.0, description="频率惩罚"
     )
+    """频率惩罚"""
     presence_penalty: float | None = Field(
         default=None, ge=-2.0, le=2.0, description="存在惩罚"
     )
+    """存在惩罚"""
     repetition_penalty: float | None = Field(
         default=None, ge=0.0, le=2.0, description="重复惩罚"
     )
-
+    """重复惩罚"""
     stop: list[str] | str | None = Field(default=None, description="停止序列")
+    """停止序列"""
+
+
+class ReasoningConfig(BaseModel):
+    """推理能力配置"""
+
+    effort: ReasoningEffort | None = Field(
+        default=None, description="推理努力程度 (适用于 O1, Gemini 3)"
+    )
+    """推理努力程度 (适用于 O1, Gemini 3)"""
+    budget_tokens: int | None = Field(
+        default=None, description="具体的思考 Token 预算 (适用于 Gemini 2.5)"
+    )
+    """具体的思考 Token 预算 (适用于 Gemini 2.5)"""
+    show_thoughts: bool | None = Field(
+        default=None, description="是否在响应中显式包含思维链内容"
+    )
+    """是否在响应中显式包含思维链内容"""
+
+
+class VisualConfig(BaseModel):
+    """视觉生成配置"""
+
+    aspect_ratio: ImageAspectRatio | str | None = Field(
+        default=None, description="宽高比"
+    )
+    """宽高比"""
+    resolution: ImageResolution | str | None = Field(
+        default=None, description="生成质量/分辨率"
+    )
+    """生成质量/分辨率"""
+    media_resolution: str | None = Field(
+        default=None,
+        description="输入媒体的解析度 (Gemini 3+): 'LOW', 'MEDIUM', 'HIGH'",
+    )
+    """输入媒体的解析度 (Gemini 3+): 'LOW', 'MEDIUM', 'HIGH'"""
+    style: str | None = Field(
+        default=None, description="图像风格 (如 DALL-E 3 vivid/natural)"
+    )
+    """图像风格 (如 DALL-E 3 vivid/natural)"""
+
+
+class OutputConfig(BaseModel):
+    """输出格式控制"""
 
     response_format: ResponseFormat | dict[str, Any] | None = Field(
         default=None, description="期望的响应格式"
     )
+    """期望的响应格式"""
     response_mime_type: str | None = Field(
         default=None, description="响应MIME类型（Gemini专用）"
     )
+    """响应MIME类型（Gemini专用）"""
     response_schema: dict[str, Any] | None = Field(
         default=None, description="JSON响应模式"
     )
-    thinking_budget: float | None = Field(
-        default=None, ge=0.0, le=1.0, description="思考预算"
-    )
-    include_thoughts: bool | None = Field(
-        default=None, description="是否在响应中包含思维过程（Gemini专用）"
-    )
-    safety_settings: dict[str, str] | None = Field(default=None, description="安全设置")
+    """JSON响应模式"""
     response_modalities: list[str] | None = Field(
-        default=None, description="响应模态类型"
+        default=None, description="响应模态类型 (TEXT, IMAGE, AUDIO)"
     )
+    """响应模态类型 (TEXT, IMAGE, AUDIO)"""
+    structured_output_strategy: StructuredOutputStrategy | str | None = Field(
+        default=None, description="结构化输出策略 (NATIVE/TOOL_CALL/PROMPT)"
+    )
+    """结构化输出策略 (NATIVE/TOOL_CALL/PROMPT)"""
 
-    enable_code_execution: bool | None = Field(
-        default=None, description="是否启用代码执行"
+
+class SafetyConfig(BaseModel):
+    """安全设置"""
+
+    safety_settings: dict[str, str] | None = Field(default=None, description="安全设置")
+    """安全设置"""
+
+
+class ToolConfig(BaseModel):
+    """工具调用控制配置"""
+
+    mode: Literal["AUTO", "ANY", "NONE"] = Field(
+        default="AUTO",
+        description="工具调用模式: AUTO(自动), ANY(强制), NONE(禁用)",
     )
-    enable_grounding: bool | None = Field(
-        default=None, description="是否启用信息来源关联"
+    """工具调用模式: AUTO(自动), ANY(强制), NONE(禁用)"""
+    allowed_function_names: list[str] | None = Field(
+        default=None,
+        description="当 mode 为 ANY 时，允许调用的函数名称白名单",
     )
+    """当 mode 为 ANY 时，允许调用的函数名称白名单"""
+
+
+class LLMGenerationConfig(BaseModel):
+    """
+    LLM 生成配置
+    采用组件化设计，不再扁平化参数。
+    """
+
+    core: CoreConfig | None = Field(default=None, description="基础生成参数")
+    """基础生成参数"""
+    reasoning: ReasoningConfig | None = Field(default=None, description="推理能力配置")
+    """推理能力配置"""
+    visual: VisualConfig | None = Field(default=None, description="视觉生成配置")
+    """视觉生成配置"""
+    output: OutputConfig | None = Field(default=None, description="输出格式配置")
+    """输出格式配置"""
+    safety: SafetyConfig | None = Field(default=None, description="安全配置")
+    """安全配置"""
+    tool_config: ToolConfig | None = Field(default=None, description="工具调用策略配置")
+    """工具调用策略配置"""
+
     enable_caching: bool | None = Field(default=None, description="是否启用响应缓存")
+    """是否启用响应缓存"""
 
     custom_params: dict[str, Any] | None = Field(default=None, description="自定义参数")
+    """自定义参数"""
 
     validation_policy: dict[str, Any] | None = Field(
         default=None, description="声明式的响应验证策略 (例如: {'require_image': True})"
     )
+    """声明式的响应验证策略 (例如: {'require_image': True})"""
     response_validator: Callable[[LLMResponse], None] | None = Field(
-        default=None, description="一个高级回调函数，用于验证响应，验证失败时应抛出异常"
+        default=None,
+        description="一个高级回调函数，用于验证响应，验证失败时应抛出异常",
     )
+    """一个高级回调函数，用于验证响应，验证失败时应抛出异常"""
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
+    @classmethod
+    def builder(cls) -> "GenConfigBuilder":
+        """创建一个新的配置构建器"""
+        return GenConfigBuilder()
+
     def to_dict(self) -> dict[str, Any]:
-        """转换为字典，排除None值"""
+        """
+        转换为字典，排除None值。
+        注意：这会返回嵌套结构的字典。适配器需要处理这种嵌套。
+        """
+        return model_dump(self, exclude_none=True)
 
-        model_data = model_dump(self, exclude_none=True)
+    def merge_with(self, other: "LLMGenerationConfig | None") -> "LLMGenerationConfig":
+        """
+        与另一个配置对象进行深度合并。
+        other 中的非 None 字段会覆盖当前配置中的对应字段。
+        返回一个新的配置对象，原对象不变。
+        """
+        if not other:
+            return model_copy(self, deep=True)
 
-        result = {}
-        for key, value in model_data.items():
-            if key == "custom_params" and isinstance(value, dict):
-                result.update(value)
-            else:
-                result[key] = value
+        new_config = model_copy(self, deep=True)
 
-        return result
+        def _merge_component(base_comp, override_comp, comp_cls):
+            if override_comp is None:
+                return base_comp
+            if base_comp is None:
+                return override_comp
+            updates = model_dump(override_comp, exclude_none=True)
+            return model_copy(base_comp, update=updates)
 
-    def merge_with_base_config(
+        new_config.core = _merge_component(new_config.core, other.core, CoreConfig)
+        new_config.reasoning = _merge_component(
+            new_config.reasoning, other.reasoning, ReasoningConfig
+        )
+        new_config.visual = _merge_component(
+            new_config.visual, other.visual, VisualConfig
+        )
+        new_config.output = _merge_component(
+            new_config.output, other.output, OutputConfig
+        )
+        new_config.safety = _merge_component(
+            new_config.safety, other.safety, SafetyConfig
+        )
+        new_config.tool_config = _merge_component(
+            new_config.tool_config, other.tool_config, ToolConfig
+        )
+
+        if other.enable_caching is not None:
+            new_config.enable_caching = other.enable_caching
+
+        if other.custom_params:
+            if new_config.custom_params is None:
+                new_config.custom_params = {}
+            new_config.custom_params.update(other.custom_params)
+
+        if other.validation_policy:
+            if new_config.validation_policy is None:
+                new_config.validation_policy = {}
+            new_config.validation_policy.update(other.validation_policy)
+
+        if other.response_validator:
+            new_config.response_validator = other.response_validator
+
+        return new_config
+
+
+class LLMEmbeddingConfig(BaseModel):
+    """Embedding 专用配置"""
+
+    task_type: str | None = Field(default=None, description="任务类型 (Gemini/Jina)")
+    """任务类型 (Gemini/Jina)"""
+    output_dimensionality: int | None = Field(
+        default=None, description="输出维度/压缩维度 (Gemini/Jina/OpenAI)"
+    )
+    """输出维度/压缩维度 (Gemini/Jina/OpenAI)"""
+    title: str | None = Field(
+        default=None, description="仅用于 Gemini RETRIEVAL_DOCUMENT 任务的标题"
+    )
+    """仅用于 Gemini RETRIEVAL_DOCUMENT 任务的标题"""
+    encoding_format: str | None = Field(
+        default="float", description="编码格式 (float/base64)"
+    )
+    """编码格式 (float/base64)"""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+
+class GenConfigBuilder:
+    """
+    LLM 生成配置的语义化构建器。
+    设计原则：高频业务场景优先，低频参数命名空间化。
+    """
+
+    def __init__(self):
+        self._config = LLMGenerationConfig()
+
+    def _ensure_core(self) -> CoreConfig:
+        if self._config.core is None:
+            self._config.core = CoreConfig()
+        return self._config.core
+
+    def _ensure_output(self) -> OutputConfig:
+        if self._config.output is None:
+            self._config.output = OutputConfig()
+        return self._config.output
+
+    def _ensure_reasoning(self) -> ReasoningConfig:
+        if self._config.reasoning is None:
+            self._config.reasoning = ReasoningConfig()
+        return self._config.reasoning
+
+    def as_json(self, schema: dict[str, Any] | None = None) -> Self:
+        """
+        [高频] 强制模型输出 JSON 格式。
+        """
+        out = self._ensure_output()
+        out.response_format = ResponseFormat.JSON
+        if schema:
+            out.response_schema = schema
+        return self
+
+    def enable_thinking(
+        self, budget_tokens: int = -1, show_thoughts: bool = False
+    ) -> Self:
+        """
+        [高频] 启用模型的思考/推理能力 (如 Gemini 2.0 Flash Thinking, DeepSeek R1)。
+        """
+        reasoning = self._ensure_reasoning()
+        reasoning.budget_tokens = budget_tokens
+        reasoning.show_thoughts = show_thoughts
+        return self
+
+    def config_core(
         self,
-        base_temperature: float | None = None,
-        base_max_tokens: int | None = None,
-    ) -> dict[str, Any]:
-        """与基础配置合并，覆盖参数优先"""
-        merged = {}
+        temperature: float | None = None,
+        max_tokens: int | None = None,
+        top_p: float | None = None,
+        top_k: int | None = None,
+        stop: list[str] | str | None = None,
+        frequency_penalty: float | None = None,
+        presence_penalty: float | None = None,
+    ) -> Self:
+        """
+        [低频] 配置核心生成参数。
+        """
+        core = self._ensure_core()
+        if temperature is not None:
+            core.temperature = temperature
+        if max_tokens is not None:
+            core.max_tokens = max_tokens
+        if top_p is not None:
+            core.top_p = top_p
+        if top_k is not None:
+            core.top_k = top_k
+        if stop is not None:
+            core.stop = stop
+        if frequency_penalty is not None:
+            core.frequency_penalty = frequency_penalty
+        if presence_penalty is not None:
+            core.presence_penalty = presence_penalty
+        return self
 
-        if base_temperature is not None:
-            merged["temperature"] = base_temperature
-        if base_max_tokens is not None:
-            merged["max_tokens"] = base_max_tokens
+    def config_safety(self, settings: dict[str, str]) -> Self:
+        """
+        [低频] 配置安全过滤设置。
+        """
+        if self._config.safety is None:
+            self._config.safety = SafetyConfig()
+        self._config.safety.safety_settings = settings
+        return self
 
-        override_dict = self.to_dict()
-        merged.update(override_dict)
+    def config_visual(
+        self,
+        aspect_ratio: ImageAspectRatio | str | None = None,
+        resolution: ImageResolution | str | None = None,
+    ) -> Self:
+        """
+        [低频] 配置视觉生成参数 (DALL-E 3 / Gemini Imagen)。
+        """
+        if self._config.visual is None:
+            self._config.visual = VisualConfig()
+        if aspect_ratio:
+            self._config.visual.aspect_ratio = aspect_ratio
+        if resolution:
+            self._config.visual.resolution = resolution
+        return self
 
-        return merged
+    def set_custom_param(self, key: str, value: Any) -> Self:
+        """设置特定于厂商的自定义参数"""
+        if self._config.custom_params is None:
+            self._config.custom_params = {}
+        self._config.custom_params[key] = value
+        return self
 
-
-class LLMGenerationConfig(ModelConfigOverride):
-    """LLM 生成配置，继承模型配置覆盖参数"""
-
-    def to_api_params(self, api_type: str, model_name: str) -> dict[str, Any]:
-        """转换为API参数，支持不同API类型的参数名映射"""
-        _ = model_name
-        params = {}
-
-        if self.temperature is not None:
-            params["temperature"] = self.temperature
-
-        if self.max_tokens is not None:
-            if api_type == "gemini":
-                params["maxOutputTokens"] = self.max_tokens
-            else:
-                params["max_tokens"] = self.max_tokens
-
-        if api_type == "gemini":
-            if self.top_k is not None:
-                params["topK"] = self.top_k
-            if self.top_p is not None:
-                params["topP"] = self.top_p
-        else:
-            if self.top_k is not None:
-                params["top_k"] = self.top_k
-            if self.top_p is not None:
-                params["top_p"] = self.top_p
-
-        if api_type in ["openai", "deepseek", "zhipu", "general_openai_compat"]:
-            if self.frequency_penalty is not None:
-                params["frequency_penalty"] = self.frequency_penalty
-            if self.presence_penalty is not None:
-                params["presence_penalty"] = self.presence_penalty
-
-            if self.repetition_penalty is not None:
-                if api_type == "openai":
-                    logger.warning("OpenAI官方API不支持repetition_penalty参数，已忽略")
-                else:
-                    params["repetition_penalty"] = self.repetition_penalty
-
-        if self.response_format is not None:
-            if isinstance(self.response_format, dict):
-                if api_type in ["openai", "zhipu", "deepseek", "general_openai_compat"]:
-                    params["response_format"] = self.response_format
-                    logger.debug(
-                        f"为 {api_type} 使用自定义 response_format: "
-                        f"{self.response_format}"
-                    )
-            elif self.response_format == ResponseFormat.JSON:
-                if api_type in ["openai", "zhipu", "deepseek", "general_openai_compat"]:
-                    params["response_format"] = {"type": "json_object"}
-                    logger.debug(f"为 {api_type} 启用 JSON 对象输出模式")
-                elif api_type == "gemini":
-                    params["responseMimeType"] = "application/json"
-                    if self.response_schema:
-                        params["responseSchema"] = self.response_schema
-                    logger.debug(f"为 {api_type} 启用 JSON MIME 类型输出模式")
-
-        if self.custom_params:
-            custom_mapped = apply_api_specific_mappings(self.custom_params, api_type)
-            params.update(custom_mapped)
-
-        if api_type == "gemini":
-            if (
-                self.response_format != ResponseFormat.JSON
-                and self.response_mime_type is not None
-            ):
-                params["responseMimeType"] = self.response_mime_type
-                logger.debug(
-                    f"使用显式设置的 responseMimeType: {self.response_mime_type}"
-                )
-
-            if self.response_schema is not None and "responseSchema" not in params:
-                params["responseSchema"] = self.response_schema
-
-            if self.thinking_budget is not None or self.include_thoughts is not None:
-                thinking_config = params.setdefault("thinkingConfig", {})
-
-                if self.thinking_budget is not None:
-                    max_budget = 24576
-                    budget_value = int(self.thinking_budget * max_budget)
-                    thinking_config["thinkingBudget"] = budget_value
-                    logger.debug(
-                        f"已将 thinking_budget (float: {self.thinking_budget}) "
-                        f"转换为 Gemini API 的整数格式: {budget_value}"
-                    )
-
-                if self.include_thoughts is not None:
-                    thinking_config["includeThoughts"] = self.include_thoughts
-                    logger.debug(f"已设置 includeThoughts: {self.include_thoughts}")
-
-            if self.safety_settings is not None:
-                params["safetySettings"] = self.safety_settings
-            if self.response_modalities is not None:
-                params["responseModalities"] = self.response_modalities
-
-        logger.debug(f"为{api_type}转换配置参数: {len(params)}个参数")
-        return params
+    def build(self) -> LLMGenerationConfig:
+        """构建最终的配置对象"""
+        return self._config
 
 
 def validate_override_params(
@@ -215,12 +403,12 @@ def validate_override_params(
     if override_config is None:
         return LLMGenerationConfig()
 
+    if isinstance(override_config, LLMGenerationConfig):
+        return override_config
+
     if isinstance(override_config, dict):
         try:
-            filtered_config = {
-                k: v for k, v in override_config.items() if v is not None
-            }
-            return LLMGenerationConfig(**filtered_config)
+            return model_validate(LLMGenerationConfig, override_config)
         except Exception as e:
             logger.warning(f"覆盖配置参数验证失败: {e}")
             raise LLMException(
@@ -229,56 +417,107 @@ def validate_override_params(
                 cause=e,
             )
 
-    return override_config
+    raise LLMException(
+        f"不支持的配置类型: {type(override_config)}",
+        code=LLMErrorCode.CONFIGURATION_ERROR,
+    )
 
 
-def apply_api_specific_mappings(
-    params: dict[str, Any], api_type: str
-) -> dict[str, Any]:
-    """应用API特定的参数映射"""
-    mapped_params = params.copy()
+class CommonOverrides:
+    """常用的配置覆盖预设"""
 
-    if api_type == "gemini":
-        if "max_tokens" in mapped_params:
-            mapped_params["maxOutputTokens"] = mapped_params.pop("max_tokens")
-        if "top_k" in mapped_params:
-            mapped_params["topK"] = mapped_params.pop("top_k")
-        if "top_p" in mapped_params:
-            mapped_params["topP"] = mapped_params.pop("top_p")
+    @staticmethod
+    def gemini_json() -> LLMGenerationConfig:
+        """Gemini JSON模式：强制JSON输出"""
+        return LLMGenerationConfig(
+            core=CoreConfig(),
+            output=OutputConfig(
+                response_format=ResponseFormat.JSON,
+                response_mime_type="application/json",
+            ),
+        )
 
-        unsupported = ["frequency_penalty", "presence_penalty", "repetition_penalty"]
-        for param in unsupported:
-            if param in mapped_params:
-                logger.warning(f"Gemini 原生API不支持参数 '{param}'，已忽略")
-                mapped_params.pop(param)
+    @staticmethod
+    def gemini_2_5_thinking(tokens: int = -1) -> LLMGenerationConfig:
+        """Gemini 2.5 思考模式：默认 -1 (动态思考)，0 为禁用，>=1024 为固定预算"""
+        return LLMGenerationConfig(
+            core=CoreConfig(temperature=1.0),
+            reasoning=ReasoningConfig(budget_tokens=tokens, show_thoughts=True),
+        )
 
-    elif api_type in ["openai", "deepseek", "zhipu", "general_openai_compat"]:
-        if "repetition_penalty" in mapped_params and api_type == "openai":
-            logger.warning("OpenAI官方API不支持repetition_penalty参数，已忽略")
-            mapped_params.pop("repetition_penalty")
+    @staticmethod
+    def gemini_3_thinking(level: str = "HIGH") -> LLMGenerationConfig:
+        """Gemini 3 深度思考模式：使用思考等级"""
+        try:
+            effort = ReasoningEffort(level.upper())
+        except ValueError:
+            effort = ReasoningEffort.HIGH
 
-        if "stop" in mapped_params:
-            stop_value = mapped_params["stop"]
-            if isinstance(stop_value, str):
-                mapped_params["stop"] = [stop_value]
+        return LLMGenerationConfig(
+            core=CoreConfig(),
+            reasoning=ReasoningConfig(effort=effort, show_thoughts=True),
+        )
 
-    return mapped_params
+    @staticmethod
+    def gemini_structured(schema: dict[str, Any]) -> LLMGenerationConfig:
+        """Gemini 结构化输出：自定义JSON模式"""
+        return LLMGenerationConfig(
+            core=CoreConfig(),
+            output=OutputConfig(
+                response_mime_type="application/json", response_schema=schema
+            ),
+        )
 
+    @staticmethod
+    def gemini_safe() -> LLMGenerationConfig:
+        """Gemini 安全模式：使用配置的安全设置"""
+        threshold = get_gemini_safety_threshold()
+        return LLMGenerationConfig(
+            core=CoreConfig(),
+            safety=SafetyConfig(
+                safety_settings={
+                    "HARM_CATEGORY_HARASSMENT": threshold,
+                    "HARM_CATEGORY_HATE_SPEECH": threshold,
+                    "HARM_CATEGORY_SEXUALLY_EXPLICIT": threshold,
+                    "HARM_CATEGORY_DANGEROUS_CONTENT": threshold,
+                }
+            ),
+        )
 
-def create_generation_config_from_kwargs(**kwargs) -> LLMGenerationConfig:
-    """从关键字参数创建生成配置"""
-    model_fields = getattr(LLMGenerationConfig, "model_fields", {})
-    known_fields = set(model_fields.keys())
-    known_params = {}
-    custom_params = {}
+    @staticmethod
+    def gemini_code_execution() -> LLMGenerationConfig:
+        """Gemini 代码执行模式：启用代码执行功能"""
+        return LLMGenerationConfig(
+            core=CoreConfig(),
+            custom_params={"code_execution_timeout": 30},
+        )
 
-    for key, value in kwargs.items():
-        if key in known_fields:
-            known_params[key] = value
-        else:
-            custom_params[key] = value
+    @staticmethod
+    def gemini_grounding() -> LLMGenerationConfig:
+        """Gemini 信息来源关联模式：启用Google搜索"""
+        return LLMGenerationConfig(
+            core=CoreConfig(),
+            custom_params={
+                "grounding_config": {"dynamicRetrievalConfig": {"mode": "MODE_DYNAMIC"}}
+            },
+        )
 
-    if custom_params:
-        known_params["custom_params"] = custom_params
+    @staticmethod
+    def gemini_nano_banana(aspect_ratio: str = "16:9") -> LLMGenerationConfig:
+        """Gemini Nano Banana Pro：自定义比例生图"""
+        try:
+            ar = ImageAspectRatio(aspect_ratio)
+        except ValueError:
+            ar = ImageAspectRatio.LANDSCAPE_16_9
 
-    return LLMGenerationConfig(**known_params)
+        return LLMGenerationConfig(
+            core=CoreConfig(),
+            visual=VisualConfig(aspect_ratio=ar),
+        )
+
+    @staticmethod
+    def gemini_high_res() -> LLMGenerationConfig:
+        """Gemini 3: 强制使用高解析度处理输入媒体"""
+        return LLMGenerationConfig(
+            visual=VisualConfig(media_resolution="HIGH", resolution=ImageResolution.HD)
+        )
diff --git a/zhenxun/services/llm/config/presets.py b/zhenxun/services/llm/config/presets.py
deleted file mode 100644
index aa4b6c21..00000000
--- a/zhenxun/services/llm/config/presets.py
+++ /dev/null
@@ -1,172 +0,0 @@
-"""
-LLM 预设配置
-
-提供常用的配置预设，特别是针对 Gemini 的高级功能。
-"""
-
-from typing import Any
-
-from .generation import LLMGenerationConfig
-
-
-class CommonOverrides:
-    """常用的配置覆盖预设"""
-
-    @staticmethod
-    def creative() -> LLMGenerationConfig:
-        """创意模式：高温度，鼓励创新"""
-        return LLMGenerationConfig(temperature=0.9, top_p=0.95, frequency_penalty=0.1)
-
-    @staticmethod
-    def precise() -> LLMGenerationConfig:
-        """精确模式：低温度，确定性输出"""
-        return LLMGenerationConfig(temperature=0.1, top_p=0.9, frequency_penalty=0.0)
-
-    @staticmethod
-    def balanced() -> LLMGenerationConfig:
-        """平衡模式：中等温度"""
-        return LLMGenerationConfig(temperature=0.5, top_p=0.9, frequency_penalty=0.0)
-
-    @staticmethod
-    def concise(max_tokens: int = 100) -> LLMGenerationConfig:
-        """简洁模式：限制输出长度"""
-        return LLMGenerationConfig(
-            temperature=0.3,
-            max_tokens=max_tokens,
-            stop=["\n\n", "。", "！", "？"],
-        )
-
-    @staticmethod
-    def detailed(max_tokens: int = 2000) -> LLMGenerationConfig:
-        """详细模式：鼓励详细输出"""
-        return LLMGenerationConfig(
-            temperature=0.7, max_tokens=max_tokens, frequency_penalty=-0.1
-        )
-
-    @staticmethod
-    def gemini_json() -> LLMGenerationConfig:
-        """Gemini JSON模式：强制JSON输出"""
-        return LLMGenerationConfig(
-            temperature=0.3, response_mime_type="application/json"
-        )
-
-    @staticmethod
-    def gemini_thinking(budget: float = 0.8) -> LLMGenerationConfig:
-        """Gemini 思考模式：使用思考预算"""
-        return LLMGenerationConfig(temperature=0.7, thinking_budget=budget)
-
-    @staticmethod
-    def gemini_creative() -> LLMGenerationConfig:
-        """Gemini 创意模式：高温度创意输出"""
-        return LLMGenerationConfig(temperature=0.9, top_p=0.95)
-
-    @staticmethod
-    def gemini_structured(schema: dict[str, Any]) -> LLMGenerationConfig:
-        """Gemini 结构化输出：自定义JSON模式"""
-        return LLMGenerationConfig(
-            temperature=0.3,
-            response_mime_type="application/json",
-            response_schema=schema,
-        )
-
-    @staticmethod
-    def gemini_safe() -> LLMGenerationConfig:
-        """Gemini 安全模式：使用配置的安全设置"""
-        from .providers import get_gemini_safety_threshold
-
-        threshold = get_gemini_safety_threshold()
-        return LLMGenerationConfig(
-            temperature=0.5,
-            safety_settings={
-                "HARM_CATEGORY_HARASSMENT": threshold,
-                "HARM_CATEGORY_HATE_SPEECH": threshold,
-                "HARM_CATEGORY_SEXUALLY_EXPLICIT": threshold,
-                "HARM_CATEGORY_DANGEROUS_CONTENT": threshold,
-            },
-        )
-
-    @staticmethod
-    def gemini_multimodal() -> LLMGenerationConfig:
-        """Gemini 多模态模式：优化多模态处理"""
-        return LLMGenerationConfig(temperature=0.6, max_tokens=2048, top_p=0.8)
-
-    @staticmethod
-    def gemini_code_execution() -> LLMGenerationConfig:
-        """Gemini 代码执行模式：启用代码执行功能"""
-        return LLMGenerationConfig(
-            temperature=0.3,
-            max_tokens=4096,
-            enable_code_execution=True,
-            custom_params={"code_execution_timeout": 30},
-        )
-
-    @staticmethod
-    def gemini_grounding() -> LLMGenerationConfig:
-        """Gemini 信息来源关联模式：启用Google搜索"""
-        return LLMGenerationConfig(
-            temperature=0.5,
-            max_tokens=4096,
-            enable_grounding=True,
-            custom_params={
-                "grounding_config": {"dynamicRetrievalConfig": {"mode": "MODE_DYNAMIC"}}
-            },
-        )
-
-    @staticmethod
-    def gemini_cached() -> LLMGenerationConfig:
-        """Gemini 缓存模式：启用响应缓存"""
-        return LLMGenerationConfig(
-            temperature=0.3,
-            max_tokens=2048,
-            enable_caching=True,
-        )
-
-    @staticmethod
-    def gemini_advanced() -> LLMGenerationConfig:
-        """Gemini 高级模式：启用所有高级功能"""
-        return LLMGenerationConfig(
-            temperature=0.5,
-            max_tokens=4096,
-            enable_code_execution=True,
-            enable_grounding=True,
-            enable_caching=True,
-            custom_params={
-                "code_execution_timeout": 30,
-                "grounding_config": {
-                    "dynamicRetrievalConfig": {"mode": "MODE_DYNAMIC"}
-                },
-            },
-        )
-
-    @staticmethod
-    def gemini_research() -> LLMGenerationConfig:
-        """Gemini 研究模式：思考+搜索+结构化输出"""
-        return LLMGenerationConfig(
-            temperature=0.6,
-            max_tokens=4096,
-            thinking_budget=0.8,
-            enable_grounding=True,
-            response_mime_type="application/json",
-            custom_params={
-                "grounding_config": {"dynamicRetrievalConfig": {"mode": "MODE_DYNAMIC"}}
-            },
-        )
-
-    @staticmethod
-    def gemini_analysis() -> LLMGenerationConfig:
-        """Gemini 分析模式：深度思考+详细输出"""
-        return LLMGenerationConfig(
-            temperature=0.4,
-            max_tokens=6000,
-            thinking_budget=0.9,
-            top_p=0.8,
-        )
-
-    @staticmethod
-    def gemini_fast_response() -> LLMGenerationConfig:
-        """Gemini 快速响应模式：低延迟+简洁输出"""
-        return LLMGenerationConfig(
-            temperature=0.3,
-            max_tokens=512,
-            top_p=0.8,
-        )
diff --git a/zhenxun/services/llm/config/providers.py b/zhenxun/services/llm/config/providers.py
index f3895481..b6cc0c16 100644
--- a/zhenxun/services/llm/config/providers.py
+++ b/zhenxun/services/llm/config/providers.py
@@ -13,6 +13,7 @@ from zhenxun.configs.config import Config
 from zhenxun.configs.utils import parse_as
 from zhenxun.services.log import logger
 from zhenxun.utils.manager.priority_manager import PriorityLifecycle
+from zhenxun.utils.pydantic_compat import model_dump
 
 from ..core import key_store
 from ..tools import tool_provider_manager
@@ -22,6 +23,39 @@ AI_CONFIG_GROUP = "AI"
 PROVIDERS_CONFIG_KEY = "PROVIDERS"
 
 
+class DebugLogOptions(BaseModel):
+    """调试日志细粒度控制"""
+
+    show_tools: bool = Field(
+        default=True, description="是否在日志中显示工具定义(JSON Schema)"
+    )
+    show_schema: bool = Field(
+        default=True, description="是否在日志中显示结构化输出Schema(response_format)"
+    )
+    show_safety: bool = Field(
+        default=True, description="是否在日志中显示安全设置(safetySettings)"
+    )
+
+    def __bool__(self) -> bool:
+        """支持 bool(debug_options) 的语法，方便兼容旧逻辑。"""
+        return self.show_tools or self.show_schema or self.show_safety
+
+
+class ClientSettings(BaseModel):
+    """LLM 客户端通用设置"""
+
+    timeout: int = Field(default=300, description="API请求超时时间（秒）")
+    max_retries: int = Field(default=3, description="请求失败时的最大重试次数")
+    retry_delay: int = Field(default=2, description="请求重试的基础延迟时间（秒）")
+    structured_retries: int = Field(
+        default=2, description="结构化生成校验失败时的最大重试次数 (IVR)"
+    )
+    proxy: str | None = Field(
+        default=None,
+        description="网络代理，例如 http://127.0.0.1:7890",
+    )
+
+
 class LLMConfig(BaseModel):
     """LLM 服务配置类"""
 
@@ -29,20 +63,16 @@ class LLMConfig(BaseModel):
         default=None,
         description="LLM服务全局默认使用的模型名称 (格式: ProviderName/ModelName)",
     )
-    proxy: str | None = Field(
-        default=None,
-        description="LLM服务请求使用的网络代理，例如 http://127.0.0.1:7890",
-    )
-    timeout: int = Field(default=180, description="LLM服务API请求超时时间（秒）")
-    max_retries_llm: int = Field(
-        default=3, description="LLM服务请求失败时的最大重试次数"
-    )
-    retry_delay_llm: int = Field(
-        default=2, description="LLM服务请求重试的基础延迟时间（秒）"
+    client_settings: ClientSettings = Field(
+        default_factory=ClientSettings, description="客户端连接与重试配置"
     )
     providers: list[ProviderConfig] = Field(
         default_factory=list, description="配置多个 AI 服务提供商及其模型信息"
     )
+    debug_log: DebugLogOptions | bool = Field(
+        default_factory=DebugLogOptions,
+        description="LLM请求日志详情开关。支持 bool (全开/全关) 或 dict (细粒度控制)。",
+    )
 
     def get_provider_by_name(self, name: str) -> ProviderConfig | None:
         """根据名称获取提供商配置
@@ -226,36 +256,29 @@ def register_llm_configs():
     )
     Config.add_plugin_config(
         AI_CONFIG_GROUP,
-        "proxy",
-        llm_config.proxy,
-        help="LLM服务请求使用的网络代理，例如 http://127.0.0.1:7890",
-        type=str,
+        "client_settings",
+        model_dump(llm_config.client_settings),
+        help=(
+            "LLM客户端高级设置。\n"
+            "包含: timeout(超时秒数), max_retries(重试次数), "
+            "retry_delay(重试延迟), structured_retries(结构化生成重试), proxy(代理)"
+        ),
+        type=dict,
     )
     Config.add_plugin_config(
         AI_CONFIG_GROUP,
-        "timeout",
-        llm_config.timeout,
-        help="LLM服务API请求超时时间（秒）",
-        type=int,
-    )
-    Config.add_plugin_config(
-        AI_CONFIG_GROUP,
-        "max_retries_llm",
-        llm_config.max_retries_llm,
-        help="LLM服务请求失败时的最大重试次数",
-        type=int,
-    )
-    Config.add_plugin_config(
-        AI_CONFIG_GROUP,
-        "retry_delay_llm",
-        llm_config.retry_delay_llm,
-        help="LLM服务请求重试的基础延迟时间（秒）",
-        type=int,
+        "debug_log",
+        {"show_tools": True, "show_schema": True, "show_safety": True},
+        help=(
+            "LLM日志详情开关。示例: {'show_tools': True, 'show_schema': False, "
+            "'show_safety': False}"
+        ),
+        type=dict,
     )
     Config.add_plugin_config(
         AI_CONFIG_GROUP,
         "gemini_safety_threshold",
-        "BLOCK_MEDIUM_AND_ABOVE",
+        "BLOCK_NONE",
         help=(
             "Gemini 安全过滤阈值 "
             "(BLOCK_LOW_AND_ABOVE: 阻止低级别及以上, "
@@ -270,7 +293,20 @@ def register_llm_configs():
         AI_CONFIG_GROUP,
         PROVIDERS_CONFIG_KEY,
         get_default_providers(),
-        help="配置多个 AI 服务提供商及其模型信息",
+        help=(
+            "配置多个 AI 服务提供商及其模型信息。\n"
+            "注意：可以在特定模型配置下添加 'api_type' 以覆盖提供商的全局设置。\n"
+            "支持的 api_type 包括:\n"
+            "- 'openai': 标准 OpenAI 格式 (DeepSeek, SiliconFlow, Moonshot 等)\n"
+            "- 'gemini': Google Gemini API\n"
+            "- 'zhipu': 智谱 AI (GLM)\n"
+            "- 'ark': 字节跳动火山引擎 (Doubao)\n"
+            "- 'openrouter': OpenRouter 聚合平台\n"
+            "- 'openai_image': OpenAI 兼容的图像生成接口 (DALL-E)\n"
+            "- 'openai_responses': 支持新版 responses 格式的 OpenAI 兼容接口\n"
+            "- 'smart': 智能路由模式 (主要用于第三方中转场景，自动根据模型名"
+            "分发请求到 openai 或 gemini)"
+        ),
         default_value=[],
         type=list[ProviderConfig],
     )
@@ -278,15 +314,21 @@ def register_llm_configs():
 
 @lru_cache(maxsize=1)
 def get_llm_config() -> LLMConfig:
-    """获取 LLM 配置实例，不再加载 MCP 工具配置"""
+    """获取 LLM 配置实例"""
     ai_config = get_ai_config()
 
+    raw_debug = ai_config.get("debug_log", False)
+    if isinstance(raw_debug, bool):
+        debug_log_val = DebugLogOptions(
+            show_tools=raw_debug, show_schema=raw_debug, show_safety=raw_debug
+        )
+    else:
+        debug_log_val = raw_debug
+
     config_data = {
         "default_model_name": ai_config.get("default_model_name"),
-        "proxy": ai_config.get("proxy"),
-        "timeout": ai_config.get("timeout", 180),
-        "max_retries_llm": ai_config.get("max_retries_llm", 3),
-        "retry_delay_llm": ai_config.get("retry_delay_llm", 2),
+        "client_settings": ai_config.get("client_settings", {}),
+        "debug_log": debug_log_val,
         PROVIDERS_CONFIG_KEY: ai_config.get(PROVIDERS_CONFIG_KEY, []),
     }
 
@@ -314,14 +356,14 @@ def validate_llm_config() -> tuple[bool, list[str]]:
     try:
         llm_config = get_llm_config()
 
-        if llm_config.timeout <= 0:
+        if llm_config.client_settings.timeout <= 0:
             errors.append("timeout 必须大于 0")
 
-        if llm_config.max_retries_llm < 0:
-            errors.append("max_retries_llm 不能小于 0")
+        if llm_config.client_settings.max_retries < 0:
+            errors.append("max_retries 不能小于 0")
 
-        if llm_config.retry_delay_llm <= 0:
-            errors.append("retry_delay_llm 必须大于 0")
+        if llm_config.client_settings.retry_delay <= 0:
+            errors.append("retry_delay 必须大于 0")
 
         if not llm_config.providers:
             errors.append("至少需要配置一个 AI 服务提供商")
diff --git a/zhenxun/services/llm/core.py b/zhenxun/services/llm/core.py
index 6ab846d0..03781cbe 100644
--- a/zhenxun/services/llm/core.py
+++ b/zhenxun/services/llm/core.py
@@ -254,7 +254,7 @@ class KeyStats:
         if total_calls == 0:
             return KeyStatus.UNUSED
 
-        if self.success_rate < 80:
+        if self.success_rate < 70:
             return KeyStatus.ERROR
 
         if total_calls >= 5 and self.avg_latency > 15000:
@@ -292,96 +292,6 @@ class RetryConfig:
         self.key_rotation = key_rotation
 
 
-async def with_smart_retry(
-    func,
-    *args,
-    retry_config: RetryConfig | None = None,
-    key_store: "KeyStatusStore | None" = None,
-    provider_name: str | None = None,
-    **kwargs: Any,
-) -> Any:
-    """
-    智能重试装饰器 - 支持Key轮询和错误分类
-
-    参数:
-        func: 要重试的异步函数。
-        *args: 传递给函数的位置参数。
-        retry_config: 重试配置。
-        key_store: API密钥状态存储。
-        provider_name: 提供商名称。
-        **kwargs: 传递给函数的关键字参数。
-
-    返回:
-        Any: 函数执行结果。
-    """
-    config = retry_config or RetryConfig()
-    last_exception: Exception | None = None
-    failed_keys: set[str] = set()
-
-    model_instance = next((arg for arg in args if hasattr(arg, "api_keys")), None)
-    all_provider_keys = model_instance.api_keys if model_instance else []
-
-    for attempt in range(config.max_retries + 1):
-        try:
-            if config.key_rotation and "failed_keys" in func.__code__.co_varnames:
-                kwargs["failed_keys"] = failed_keys
-
-            start_time = time.monotonic()
-            result = await func(*args, **kwargs)
-            latency = (time.monotonic() - start_time) * 1000
-
-            if key_store and isinstance(result, tuple) and len(result) == 2:
-                _, api_key_used = result
-                if api_key_used:
-                    await key_store.record_success(api_key_used, latency)
-                return result
-            else:
-                return result
-
-        except LLMException as e:
-            last_exception = e
-            api_key_in_use = e.details.get("api_key")
-
-            if api_key_in_use:
-                failed_keys.add(api_key_in_use)
-                if key_store and provider_name and len(all_provider_keys) > 1:
-                    status_code = e.details.get("status_code")
-                    error_message = f"({e.code.name}) {e.message}"
-                    await key_store.record_failure(
-                        api_key_in_use, status_code, error_message
-                    )
-
-            should_retry = _should_retry_llm_error(e, attempt, config.max_retries)
-            if not should_retry:
-                logger.error(f"不可重试的错误，停止重试: {e}")
-                raise
-
-            if attempt < config.max_retries:
-                wait_time = config.retry_delay
-                if config.exponential_backoff:
-                    wait_time *= 2**attempt
-                logger.warning(
-                    f"请求失败，{wait_time:.2f}秒后重试 (第{attempt + 1}次): {e}"
-                )
-                await asyncio.sleep(wait_time)
-            else:
-                logger.error(f"重试{config.max_retries}次后仍然失败: {e}")
-
-        except Exception as e:
-            last_exception = e
-            logger.error(f"非LLM异常，停止重试: {e}")
-            raise LLMException(
-                f"操作失败: {e}",
-                code=LLMErrorCode.GENERATION_FAILED,
-                cause=e,
-            )
-
-    if last_exception:
-        raise last_exception
-    else:
-        raise RuntimeError("重试函数未能正常执行且未捕获到异常")
-
-
 def _should_retry_llm_error(
     error: LLMException, attempt: int, max_retries: int
 ) -> bool:
@@ -390,7 +300,9 @@ def _should_retry_llm_error(
         LLMErrorCode.MODEL_NOT_FOUND,
         LLMErrorCode.CONTEXT_LENGTH_EXCEEDED,
         LLMErrorCode.USER_LOCATION_NOT_SUPPORTED,
+        LLMErrorCode.INVALID_PARAMETER,
         LLMErrorCode.CONFIGURATION_ERROR,
+        LLMErrorCode.API_KEY_INVALID,
     }
 
     if error.code in non_retryable_errors:
@@ -404,15 +316,12 @@ def _should_retry_llm_error(
         LLMErrorCode.RESPONSE_PARSE_ERROR,
         LLMErrorCode.GENERATION_FAILED,
         LLMErrorCode.CONTENT_FILTERED,
-        LLMErrorCode.API_KEY_INVALID,
         LLMErrorCode.API_QUOTA_EXCEEDED,
     }
 
     if error.code in retryable_errors:
         if error.code == LLMErrorCode.API_QUOTA_EXCEEDED:
             return attempt < min(2, max_retries)
-        elif error.code == LLMErrorCode.CONTENT_FILTERED:
-            return attempt < min(1, max_retries)
         return True
 
     return False
@@ -558,14 +467,68 @@ class KeyStatusStore:
         now = time.time()
         cooldown_duration = 300
 
-        if status_code in [401, 403, 404]:
+        location_not_supported = error_message and (
+            "USER_LOCATION_NOT_SUPPORTED" in error_message
+            or "User location is not supported" in error_message
+        )
+        if location_not_supported:
+            logger.warning(
+                f"API Key {key_id} 请求失败，原因是地区不支持 (Gemini)。"
+                " 这通常是代理节点问题，Key 本身可能是正常的。跳过冷却。"
+            )
+            async with self._lock:
+                stats = self._key_stats.setdefault(api_key, KeyStats())
+                stats.failure_count += 1
+                stats.last_error_info = error_message[:256]
+                await self._save_to_file_internal()
+            return
+
+        if error_message and (
+            "API_QUOTA_EXCEEDED" in error_message
+            or "insufficient_quota" in error_message.lower()
+        ):
+            cooldown_duration = 3600
+            logger.warning(f"API Key {key_id} 额度耗尽，冷却 1 小时。")
+
+        is_key_invalid = status_code == 401 or (
+            status_code == 400
+            and error_message
+            and (
+                "API_KEY_INVALID" in error_message
+                or "API key not valid" in error_message
+            )
+        )
+
+        if is_key_invalid:
             cooldown_duration = 31536000
             log_level = "error"
             log_message = f"API密钥认证/权限/路径错误，将永久禁用: {key_id}"
+        elif status_code == 403:
+            cooldown_duration = 3600
+            log_level = "warning"
+            log_message = f"API密钥权限不足或地区不支持(403)，冷却1小时: {key_id}"
+        elif status_code == 404:
+            log_level = "error"
+            log_message = "API请求返回 404 (未找到)，可能是模型名称错误或接口地址"
+            f"错误，不冷却密钥: {key_id}"
+        elif status_code == 422:
+            cooldown_duration = 0
+            log_level = "warning"
+            log_message = f"API请求无法处理(422)，可能是生成故障，不冷却密钥: {key_id}"
         elif status_code == 429:
             cooldown_duration = 60
             log_level = "warning"
             log_message = f"API密钥被限流，冷却60秒: {key_id}"
+        elif error_message and (
+            "ConnectError" in error_message
+            or "NetworkError" in error_message
+            or "Connection refused" in error_message
+            or "RemoteProtocolError" in error_message
+            or "ProxyError" in error_message
+        ):
+            cooldown_duration = 0
+            log_level = "warning"
+            log_message = f"网络连接层异常(代理/DNS)，不冷却密钥: {key_id}"
         else:
             log_level = "warning"
             log_message = f"API密钥遇到临时性错误，冷却{cooldown_duration}秒: {key_id}"
diff --git a/zhenxun/services/llm/executor.py b/zhenxun/services/llm/executor.py
deleted file mode 100644
index b731e520..00000000
--- a/zhenxun/services/llm/executor.py
+++ /dev/null
@@ -1,193 +0,0 @@
-"""
-LLM 轻量级工具执行器
-
-提供驱动 LLM 与本地函数工具之间交互的核心循环。
-"""
-
-import asyncio
-from enum import Enum
-import json
-from typing import Any
-
-from pydantic import BaseModel, Field
-
-from zhenxun.services.log import logger
-from zhenxun.utils.decorator.retry import Retry
-from zhenxun.utils.pydantic_compat import model_dump
-
-from .service import LLMModel
-from .types import (
-    LLMErrorCode,
-    LLMException,
-    LLMMessage,
-    ToolExecutable,
-    ToolResult,
-)
-
-
-class ExecutionConfig(BaseModel):
-    """
-    轻量级执行器的配置。
-    """
-
-    max_cycles: int = Field(default=5, description="工具调用循环的最大次数。")
-
-
-class ToolErrorType(str, Enum):
-    """结构化工具错误的类型枚举。"""
-
-    TOOL_NOT_FOUND = "ToolNotFound"
-    INVALID_ARGUMENTS = "InvalidArguments"
-    EXECUTION_ERROR = "ExecutionError"
-    USER_CANCELLATION = "UserCancellation"
-
-
-class ToolErrorResult(BaseModel):
-    """一个结构化的工具执行错误模型，用于返回给 LLM。"""
-
-    error_type: ToolErrorType = Field(..., description="错误的类型。")
-    message: str = Field(..., description="对错误的详细描述。")
-    is_retryable: bool = Field(False, description="指示这个错误是否可能通过重试解决。")
-
-    def model_dump(self, **kwargs):
-        return model_dump(self, **kwargs)
-
-
-def _is_exception_retryable(e: Exception) -> bool:
-    """判断一个异常是否应该触发重试。"""
-    if isinstance(e, LLMException):
-        retryable_codes = {
-            LLMErrorCode.API_REQUEST_FAILED,
-            LLMErrorCode.API_TIMEOUT,
-            LLMErrorCode.API_RATE_LIMITED,
-        }
-        return e.code in retryable_codes
-    return True
-
-
-class LLMToolExecutor:
-    """
-    一个通用的执行器，负责驱动 LLM 与工具之间的多轮交互。
-    """
-
-    def __init__(self, model: LLMModel):
-        self.model = model
-
-    async def run(
-        self,
-        messages: list[LLMMessage],
-        tools: dict[str, ToolExecutable],
-        config: ExecutionConfig | None = None,
-    ) -> list[LLMMessage]:
-        """
-        执行完整的思考-行动循环。
-        """
-        effective_config = config or ExecutionConfig()
-        execution_history = list(messages)
-
-        for i in range(effective_config.max_cycles):
-            response = await self.model.generate_response(
-                execution_history, tools=tools
-            )
-
-            assistant_message = LLMMessage(
-                role="assistant",
-                content=response.text,
-                tool_calls=response.tool_calls,
-            )
-            execution_history.append(assistant_message)
-
-            if not response.tool_calls:
-                logger.info("✅ LLMToolExecutor：模型未请求工具调用，执行结束。")
-                return execution_history
-
-            logger.info(
-                f"🛠️ LLMToolExecutor：模型请求并行调用 {len(response.tool_calls)} 个工具"
-            )
-            tool_results = await self._execute_tools_parallel_safely(
-                response.tool_calls,
-                tools,
-            )
-            execution_history.extend(tool_results)
-
-        raise LLMException(
-            f"超过最大工具调用循环次数 ({effective_config.max_cycles})。",
-            code=LLMErrorCode.GENERATION_FAILED,
-        )
-
-    async def _execute_single_tool_safely(
-        self, tool_call: Any, available_tools: dict[str, ToolExecutable]
-    ) -> tuple[Any, ToolResult]:
-        """安全地执行单个工具调用。"""
-        tool_name = tool_call.function.name
-        arguments = {}
-
-        try:
-            if tool_call.function.arguments:
-                arguments = json.loads(tool_call.function.arguments)
-        except json.JSONDecodeError as e:
-            error_result = ToolErrorResult(
-                error_type=ToolErrorType.INVALID_ARGUMENTS,
-                message=f"参数解析失败: {e}",
-                is_retryable=False,
-            )
-            return tool_call, ToolResult(output=model_dump(error_result))
-
-        try:
-            executable = available_tools.get(tool_name)
-            if not executable:
-                raise LLMException(
-                    f"Tool '{tool_name}' not found.",
-                    code=LLMErrorCode.CONFIGURATION_ERROR,
-                )
-
-            @Retry.simple(
-                stop_max_attempt=2, wait_fixed_seconds=1, return_on_failure=None
-            )
-            async def execute_with_retry():
-                return await executable.execute(**arguments)
-
-            execution_result = await execute_with_retry()
-            if execution_result is None:
-                raise LLMException("工具执行在多次重试后仍然失败。")
-
-            return tool_call, execution_result
-        except Exception as e:
-            error_type = ToolErrorType.EXECUTION_ERROR
-            is_retryable = _is_exception_retryable(e)
-            if (
-                isinstance(e, LLMException)
-                and e.code == LLMErrorCode.CONFIGURATION_ERROR
-            ):
-                error_type = ToolErrorType.TOOL_NOT_FOUND
-                is_retryable = False
-
-            error_result = ToolErrorResult(
-                error_type=error_type, message=str(e), is_retryable=is_retryable
-            )
-            return tool_call, ToolResult(output=model_dump(error_result))
-
-    async def _execute_tools_parallel_safely(
-        self,
-        tool_calls: list[Any],
-        available_tools: dict[str, ToolExecutable],
-    ) -> list[LLMMessage]:
-        """并行执行所有工具调用，并对每个调用的错误进行隔离。"""
-        if not tool_calls:
-            return []
-
-        tasks = [
-            self._execute_single_tool_safely(call, available_tools)
-            for call in tool_calls
-        ]
-        results = await asyncio.gather(*tasks)
-
-        tool_messages = [
-            LLMMessage.tool_response(
-                tool_call_id=original_call.id,
-                function_name=original_call.function.name,
-                result=result.output,
-            )
-            for original_call, result in results
-        ]
-        return tool_messages
diff --git a/zhenxun/services/llm/manager.py b/zhenxun/services/llm/manager.py
index dbe6d675..e69f9cec 100644
--- a/zhenxun/services/llm/manager.py
+++ b/zhenxun/services/llm/manager.py
@@ -13,15 +13,19 @@ from zhenxun.services.log import logger
 from zhenxun.utils.pydantic_compat import dump_json_safely
 
 from .config import validate_override_params
-from .config.providers import AI_CONFIG_GROUP, PROVIDERS_CONFIG_KEY, get_ai_config
+from .config.generation import LLMGenerationConfig
+from .config.providers import (
+    AI_CONFIG_GROUP,
+    PROVIDERS_CONFIG_KEY,
+    get_ai_config,
+    get_llm_config,
+)
 from .core import http_client_manager, key_store
 from .service import LLMModel
 from .types import LLMErrorCode, LLMException, ModelDetail, ProviderConfig
 from .types.capabilities import get_model_capabilities
 
 DEFAULT_MODEL_NAME_KEY = "default_model_name"
-PROXY_KEY = "proxy"
-TIMEOUT_KEY = "timeout"
 
 _model_cache: dict[str, tuple[LLMModel, float]] = {}
 _cache_ttl = 3600
@@ -39,7 +43,8 @@ def parse_provider_model_string(name_str: str | None) -> tuple[str | None, str |
 
 
 def _make_cache_key(
-    provider_model_name: str | None, override_config: dict | None
+    provider_model_name: str | None,
+    override_config: dict | LLMGenerationConfig | None,
 ) -> str:
     """生成缓存键"""
     config_str = (
@@ -115,11 +120,12 @@ def get_default_api_base_for_type(api_type: str) -> str | None:
     """根据API类型获取默认的API基础地址"""
     default_api_bases = {
         "openai": "https://api.openai.com",
-        "deepseek": "https://api.deepseek.com",
+        "deepseek": "https://api.deepseek.com/beta",
         "zhipu": "https://open.bigmodel.cn",
         "gemini": "https://generativelanguage.googleapis.com",
         "openrouter": "https://openrouter.ai/api",
-        "general_openai_compat": None,
+        "smart": None,
+        "openai_responses": None,
     }
 
     return default_api_bases.get(api_type)
@@ -244,7 +250,7 @@ def list_embedding_models() -> list[dict[str, Any]]:
 
 async def get_model_instance(
     provider_model_name: str | None = None,
-    override_config: dict[str, Any] | None = None,
+    override_config: dict[str, Any] | LLMGenerationConfig | None = None,
 ) -> LLMModel:
     """
     根据 'ProviderName/ModelName' 字符串获取并实例化 LLMModel (异步版本)
@@ -303,21 +309,20 @@ async def get_model_instance(
 
     model_detail_found.is_embedding_model = capabilities.is_embedding_model
 
-    ai_config = get_ai_config()
-    global_proxy_setting = ai_config.get(PROXY_KEY)
+    llm_config = get_llm_config()
+    client_settings = llm_config.client_settings
     default_timeout = (
         provider_config_found.timeout
         if provider_config_found.timeout is not None
-        else 180
+        else client_settings.timeout
     )
-    global_timeout_setting = ai_config.get(TIMEOUT_KEY, default_timeout)
 
     config_for_http_client = ProviderConfig(
         name=provider_config_found.name,
         api_key=provider_config_found.api_key,
         models=provider_config_found.models,
-        timeout=global_timeout_setting,
-        proxy=global_proxy_setting,
+        timeout=default_timeout,
+        proxy=client_settings.proxy,
         api_base=provider_config_found.api_base,
         api_type=provider_config_found.api_type,
         openai_compat=provider_config_found.openai_compat,
diff --git a/zhenxun/services/llm/memory.py b/zhenxun/services/llm/memory.py
index d983090d..c0b8b80f 100644
--- a/zhenxun/services/llm/memory.py
+++ b/zhenxun/services/llm/memory.py
@@ -1,55 +1,243 @@
+"""
+LLM 服务 - 会话记忆模块
+
+定义了LLM会话记忆的存储、策略和处理接口。
+"""
+
 from abc import ABC, abstractmethod
 from collections import defaultdict
+from collections.abc import Callable
 from typing import Any
 
-from .types import LLMMessage
+from pydantic import BaseModel, Field
+
+from zhenxun.services.llm.types import LLMMessage
+from zhenxun.services.log import logger
+
+
+class AIConfig(BaseModel):
+    """AI配置类 (为保持独立性而在此处保留一个副本，实际使用中可能来自更高层)"""
+
+    model: Any = None
+    default_embedding_model: Any = None
+    default_preserve_media_in_history: bool = False
+    tool_providers: list[Any] = Field(default_factory=list)
+
+    def __post_init__(self):
+        """初始化后从配置中读取默认值"""
+        pass
+
+
+class BaseMessageStore(ABC):
+    """
+    底层存储接口 (DAO - Data Access Object)。
+
+    这是一个抽象基类，定义了消息数据最底层的 **持久化与检索 (CRUD)** 接口。
+    它只关心数据的存取，不涉及任何业务逻辑（如历史记录修剪）。
+
+    开发者如果希望将对话历史存储到 Redis、数据库或其他持久化后端，
+    应当实现这个接口。
+    """
+
+    @abstractmethod
+    async def get_messages(self, session_id: str) -> list[LLMMessage]:
+        """
+        根据会话ID获取完整的消息列表。
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    async def add_messages(self, session_id: str, messages: list[LLMMessage]) -> None:
+        """追加消息"""
+        raise NotImplementedError
+
+    @abstractmethod
+    async def set_messages(self, session_id: str, messages: list[LLMMessage]) -> None:
+        """
+        完全覆盖指定会话ID的消息列表。
+        主要用于历史记录修剪等场景。
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    async def clear(self, session_id: str) -> None:
+        """清空指定会话ID的所有消息数据。"""
+        raise NotImplementedError
+
+
+class InMemoryMessageStore(BaseMessageStore):
+    """
+    一个基于内存的 `BaseMessageStore` 实现。
+
+    它使用一个Python字典来存储所有会话的消息，提供了最简单、最快速的存储方案。
+    这是框架的默认存储方式，实现了开箱即用。
+
+    注意：此实现是 **非持久化** 的，当应用程序重启时，所有对话历史都会丢失。
+    适用于测试、简单应用或不需要长期记忆的场景。
+    """
+
+    def __init__(self):
+        self._data: dict[str, list[LLMMessage]] = defaultdict(list)
+
+    async def get_messages(self, session_id: str) -> list[LLMMessage]:
+        """从内存字典中获取消息列表的副本。"""
+        return self._data.get(session_id, []).copy()
+
+    async def add_messages(self, session_id: str, messages: list[LLMMessage]) -> None:
+        """向内存中的消息列表追加消息。"""
+        self._data[session_id].extend(messages)
+
+    async def set_messages(self, session_id: str, messages: list[LLMMessage]) -> None:
+        """在内存中直接替换指定会话的消息列表。"""
+        self._data[session_id] = messages
+
+    async def clear(self, session_id: str) -> None:
+        """从内存字典中删除指定会话的条目。"""
+        if session_id in self._data:
+            del self._data[session_id]
 
 
 class BaseMemory(ABC):
     """
-    记忆系统的抽象基类。
-    定义了任何记忆后端都必须实现的接口。
+    记忆系统上层逻辑基类 (Strategy Layer)。
+
+    此抽象基类定义了记忆系统的 **策略层** 接口。它负责对外提供统一的记忆操作
+    接口，并封装了具体的记忆管理策略，如历史记录的修剪、摘要生成等。
+
+    `AI` 会话客户端直接与此接口交互，而不关心底层的存储实现。
+
+    开发者可以通过实现此接口来创建自定义的记忆管理策略，例如：
+    - `SummarizationMemory`: 在历史记录过长时，自动调用LLM生成摘要来压缩历史。
+    - `VectorStoreMemory`: 将对话历史向量化并存入向量数据库，实现长期记忆检索。
     """
 
     @abstractmethod
     async def get_history(self, session_id: str) -> list[LLMMessage]:
-        """根据会话ID获取历史记录。"""
+        """获取用于构建模型输入的完整历史消息列表。"""
         raise NotImplementedError
 
-    @abstractmethod
     async def add_message(self, session_id: str, message: LLMMessage) -> None:
-        """向指定会话添加一条消息。"""
-        raise NotImplementedError
+        """向记忆中添加单条消息。默认实现是调用 `add_messages`。"""
+        await self.add_messages(session_id, [message])
 
     @abstractmethod
     async def add_messages(self, session_id: str, messages: list[LLMMessage]) -> None:
-        """向指定会话添加多条消息。"""
+        """向记忆中添加多条消息，并可能触发内部的记忆管理策略（如修剪）。"""
         raise NotImplementedError
 
     @abstractmethod
     async def clear_history(self, session_id: str) -> None:
-        """清空指定会话的历史记录。"""
+        """清空指定会话的全部记忆。"""
         raise NotImplementedError
 
 
-class InMemoryMemory(BaseMemory):
+class ChatMemory(BaseMemory):
     """
-    一个简单的、默认的内存记忆后端。
-    将历史记录存储在进程内存中的字典里。
+    标准聊天记忆实现：组合 Store + 滑动窗口策略。
+
+    这是 `BaseMemory` 的默认实现，它通过组合一个 `BaseMessageStore` 实例来
+    完成实际的数据存储，并在此之上实现了一个简单的“滑动窗口”记忆修剪策略。
     """
 
-    def __init__(self, **kwargs: Any):
-        self._history: dict[str, list[LLMMessage]] = defaultdict(list)
+    def __init__(self, store: BaseMessageStore, max_messages: int = 50):
+        self.store = store
+        self._max_messages = max_messages
+
+    async def _trim_history(self, session_id: str) -> None:
+        """
+        记忆修剪策略：确保历史记录不超过 `_max_messages` 条。
+
+        如果存在系统消息 (System Prompt)，它将被永久保留在列表的第一位。
+        """
+        history = await self.store.get_messages(session_id)
+        if len(history) <= self._max_messages:
+            return
+
+        has_system = history and history[0].role == "system"
+        new_history: list[LLMMessage] = []
+
+        if has_system:
+            keep_count = max(0, self._max_messages - 1)
+            new_history = [history[0], *history[-keep_count:]]
+        else:
+            new_history = history[-self._max_messages :]
+
+        await self.store.set_messages(session_id, new_history)
 
     async def get_history(self, session_id: str) -> list[LLMMessage]:
-        return self._history.get(session_id, []).copy()
-
-    async def add_message(self, session_id: str, message: LLMMessage) -> None:
-        self._history[session_id].append(message)
+        """直接从底层存储获取历史记录。"""
+        return await self.store.get_messages(session_id)
 
     async def add_messages(self, session_id: str, messages: list[LLMMessage]) -> None:
-        self._history[session_id].extend(messages)
+        """添加消息到历史记录，并立即执行修剪策略。"""
+        await self.store.add_messages(session_id, messages)
+        await self._trim_history(session_id)
 
     async def clear_history(self, session_id: str) -> None:
-        if session_id in self._history:
-            del self._history[session_id]
+        """清空底层存储中的历史记录。"""
+        await self.store.clear(session_id)
+
+
+class MemoryProcessor(ABC):
+    """
+    记忆处理器接口 (Hook/Observer)。
+
+    这是一个扩展接口，允许开发者创建自定义的“记忆处理器”，以在记忆被修改后
+    执行额外的操作（“钩子”）。
+
+    当 `AI` 实例的记忆更新时，它会依次调用所有注册的 `MemoryProcessor`。
+
+    使用场景示例：
+    - `LoggingMemoryProcessor`: 将每一轮对话异步记录到外部日志系统。
+    - `SummarizationProcessor`: 在后台任务中检查对话长度，并在需要时生成摘要。
+    - `EntityExtractionProcessor`: 从对话中提取关键实体（如人名、地名）并存储。
+    """
+
+    @abstractmethod
+    async def process(self, session_id: str, new_messages: list[LLMMessage]) -> None:
+        """处理新添加到记忆中的消息。"""
+        pass
+
+
+_default_memory_factory: Callable[[], BaseMemory] | None = None
+
+
+def set_default_memory_backend(factory: Callable[[], BaseMemory]):
+    """
+    设置全局默认记忆后端工厂，允许统一替换会话的记忆实现。
+
+    这是一个高级依赖注入函数，允许插件或项目在启动时用自定义的 `BaseMemory`
+    实现替换掉默认的 `ChatMemory(InMemoryMessageStore())`。
+
+    Args:
+        factory: 一个无参数的、返回 `BaseMemory` 实例的函数或类。
+    """
+    global _default_memory_factory
+    _default_memory_factory = factory
+
+
+def _get_default_memory() -> BaseMemory:
+    """
+    [内部函数] 获取一个默认的记忆后端实例。
+
+    它会首先检查是否有通过 `set_default_memory_backend` 设置的全局工厂，
+    如果有，则使用该工厂创建实例；否则，返回一个标准的内存记忆实例。
+    """
+    if _default_memory_factory:
+        logger.debug("使用自定义的默认记忆后端工厂构建实例。")
+        return _default_memory_factory()
+
+    logger.debug("未配置自定义记忆后端，使用默认的 ChatMemory。")
+    return ChatMemory(store=InMemoryMessageStore())
+
+
+__all__ = [
+    "AIConfig",
+    "BaseMemory",
+    "BaseMessageStore",
+    "ChatMemory",
+    "InMemoryMessageStore",
+    "MemoryProcessor",
+    "_get_default_memory",
+    "set_default_memory_backend",
+]
diff --git a/zhenxun/services/llm/service.py b/zhenxun/services/llm/service.py
index 5b95bdf4..ecefd3a0 100644
--- a/zhenxun/services/llm/service.py
+++ b/zhenxun/services/llm/service.py
@@ -5,41 +5,87 @@ LLM 模型实现类
 """
 
 from abc import ABC, abstractmethod
+import asyncio
 from collections.abc import Awaitable, Callable
 import json
-from typing import Any, TypeVar
+import re
+import time
+from typing import Any, Literal, TypeVar, cast
 
-from pydantic import BaseModel
+import httpx
+from pydantic import BaseModel, ConfigDict, Field
 
 from zhenxun.services.log import logger
+from zhenxun.utils.http_utils import AsyncHttpx
 from zhenxun.utils.log_sanitizer import sanitize_for_logging
 from zhenxun.utils.pydantic_compat import dump_json_safely
 
-from .adapters.base import RequestData
+from .adapters.base import BaseAdapter, RequestData, process_image_data
 from .config import LLMGenerationConfig
-from .config.providers import get_ai_config
+from .config.generation import LLMEmbeddingConfig
+from .config.providers import get_llm_config
 from .core import (
     KeyStatusStore,
     LLMHttpClient,
     RetryConfig,
+    _should_retry_llm_error,
     http_client_manager,
-    with_smart_retry,
 )
 from .types import (
-    EmbeddingTaskType,
     LLMErrorCode,
     LLMException,
     LLMMessage,
     LLMResponse,
+    LLMToolCall,
     ModelDetail,
     ProviderConfig,
-    ToolExecutable,
+    ToolChoice,
 )
 from .types.capabilities import ModelCapabilities, ModelModality
 
 T = TypeVar("T", bound=BaseModel)
 
 
+class LLMContext(BaseModel):
+    """LLM 执行上下文，用于在中间件管道中传递请求状态"""
+
+    messages: list[LLMMessage]
+    config: LLMGenerationConfig | LLMEmbeddingConfig
+    tools: list[Any] | None
+    tool_choice: str | dict[str, Any] | ToolChoice | None
+    timeout: float | None
+    extra: dict[str, Any] = Field(default_factory=dict)
+    request_type: Literal["generation", "embedding"] = "generation"
+    runtime_state: dict[str, Any] = Field(
+        default_factory=dict,
+        description="中间件运行时的临时状态存储(api_key, retry_count等)",
+    )
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+
+NextCall = Callable[[LLMContext], Awaitable[LLMResponse]]
+LLMMiddleware = Callable[[LLMContext, NextCall], Awaitable[LLMResponse]]
+
+
+class BaseLLMMiddleware(ABC):
+    """LLM 中间件抽象基类"""
+
+    @abstractmethod
+    async def __call__(self, context: LLMContext, next_call: NextCall) -> LLMResponse:
+        """
+        执行中间件逻辑
+
+        Args:
+            context: 请求上下文，包含配置和运行时状态
+            next_call: 调用链中的下一个处理函数
+
+        Returns:
+            LLMResponse: 模型响应结果
+        """
+        pass
+
+
 class LLMModelBase(ABC):
     """LLM模型抽象基类"""
 
@@ -48,9 +94,9 @@ class LLMModelBase(ABC):
         self,
         messages: list[LLMMessage],
         config: LLMGenerationConfig | None = None,
-        tools: dict[str, ToolExecutable] | None = None,
-        tool_choice: str | dict[str, Any] | None = None,
-        **kwargs: Any,
+        tools: list[Any] | None = None,
+        tool_choice: str | dict[str, Any] | ToolChoice | None = None,
+        timeout: float | None = None,
     ) -> LLMResponse:
         """生成高级响应"""
         pass
@@ -59,8 +105,7 @@ class LLMModelBase(ABC):
     async def generate_embeddings(
         self,
         texts: list[str],
-        task_type: EmbeddingTaskType | str = EmbeddingTaskType.RETRIEVAL_DOCUMENT,
-        **kwargs: Any,
+        config: LLMEmbeddingConfig,
     ) -> list[list[float]]:
         """生成文本嵌入向量"""
         pass
@@ -98,35 +143,115 @@ class LLMModel(LLMModelBase):
         self.max_tokens = model_detail.max_tokens
 
         self._is_closed = False
+        self._ref_count = 0
+        self._middlewares: list[LLMMiddleware] = []
 
+    def _has_modality(self, modality: ModelModality, is_input: bool = True) -> bool:
+        target_set = (
+            self.capabilities.input_modalities
+            if is_input
+            else self.capabilities.output_modalities
+        )
+        return modality in target_set
+
+    @property
     def can_process_images(self) -> bool:
         """检查模型是否支持图片作为输入。"""
-        return ModelModality.IMAGE in self.capabilities.input_modalities
+        return self._has_modality(ModelModality.IMAGE)
 
+    @property
     def can_process_video(self) -> bool:
         """检查模型是否支持视频作为输入。"""
-        return ModelModality.VIDEO in self.capabilities.input_modalities
+        return self._has_modality(ModelModality.VIDEO)
 
+    @property
     def can_process_audio(self) -> bool:
         """检查模型是否支持音频作为输入。"""
-        return ModelModality.AUDIO in self.capabilities.input_modalities
+        return self._has_modality(ModelModality.AUDIO)
 
+    @property
     def can_generate_images(self) -> bool:
         """检查模型是否支持生成图片。"""
-        return ModelModality.IMAGE in self.capabilities.output_modalities
+        return self._has_modality(ModelModality.IMAGE, is_input=False)
 
+    @property
     def can_generate_audio(self) -> bool:
         """检查模型是否支持生成音频 (TTS)。"""
-        return ModelModality.AUDIO in self.capabilities.output_modalities
-
-    def can_use_tools(self) -> bool:
-        """检查模型是否支持工具调用/函数调用。"""
-        return self.capabilities.supports_tool_calling
+        return self._has_modality(ModelModality.AUDIO, is_input=False)
 
+    @property
     def is_embedding_model(self) -> bool:
         """检查这是否是一个嵌入模型。"""
         return self.capabilities.is_embedding_model
 
+    def add_middleware(self, middleware: LLMMiddleware) -> None:
+        """注册一个中间件到处理管道的最外层"""
+        self._middlewares.append(middleware)
+
+    def _build_pipeline(self) -> NextCall:
+        """
+        构建完整的中间件调用链。顺序为：
+        用户自定义中间件 -> Retry -> Logging -> KeySelection -> Network (终结者)
+        """
+        from .adapters import get_adapter_for_api_type
+
+        client_settings = get_llm_config().client_settings
+        retry_config = RetryConfig(
+            max_retries=client_settings.max_retries,
+            retry_delay=client_settings.retry_delay,
+        )
+        adapter = get_adapter_for_api_type(self.api_type)
+
+        network_middleware = NetworkRequestMiddleware(self, adapter)
+
+        async def terminal_handler(ctx: LLMContext) -> LLMResponse:
+            async def _noop(_: LLMContext) -> LLMResponse:
+                raise RuntimeError("NetworkRequestMiddleware 不应调用 next_call")
+
+            return await network_middleware(ctx, _noop)
+
+        def _wrap(middleware: LLMMiddleware, next_call: NextCall) -> NextCall:
+            async def _handler(inner_ctx: LLMContext) -> LLMResponse:
+                return await middleware(inner_ctx, next_call)
+
+            return _handler
+
+        handler: NextCall = terminal_handler
+        handler = _wrap(
+            KeySelectionMiddleware(self.key_store, self.provider_name, self.api_keys),
+            handler,
+        )
+        handler = _wrap(
+            LoggingMiddleware(self.provider_name, self.model_name),
+            handler,
+        )
+        handler = _wrap(
+            RetryMiddleware(retry_config, self.key_store),
+            handler,
+        )
+
+        for middleware in reversed(self._middlewares):
+            handler = _wrap(middleware, handler)
+
+        return handler
+
+    def _get_effective_api_type(self) -> str:
+        """
+        获取实际生效的 API 类型。
+        主要用于 Smart 模式下，判断日志净化应该使用哪种格式。
+        """
+        if self.api_type != "smart":
+            return self.api_type
+
+        if self.model_detail.api_type:
+            return self.model_detail.api_type
+        if (
+            "gemini" in self.model_name.lower()
+            and "openai" not in self.model_name.lower()
+        ):
+            return "gemini"
+        return "openai"
+
     async def _get_http_client(self) -> LLMHttpClient:
         """获取HTTP客户端"""
         if self.http_client.is_closed:
@@ -163,307 +288,6 @@ class LLMModel(LLMModelBase):
 
         return selected_key
 
-    async def _perform_api_call(
-        self,
-        prepare_request_func: Callable[[str], Awaitable["RequestData"]],
-        parse_response_func: Callable[[dict[str, Any]], Any],
-        http_client: "LLMHttpClient",
-        failed_keys: set[str] | None = None,
-        log_context: str = "API",
-    ) -> tuple[Any, str]:
-        """执行API调用的通用核心方法"""
-        api_key = await self._select_api_key(failed_keys)
-
-        try:
-            request_data = await prepare_request_func(api_key)
-
-            logger.info(
-                f"🌐 发起LLM请求 - 模型: {self.provider_name}/{self.model_name} "
-                f"[{log_context}]"
-            )
-            logger.debug(f"📡 请求URL: {request_data.url}")
-            masked_key = (
-                f"{api_key[:8]}...{api_key[-4:] if len(api_key) > 12 else '***'}"
-            )
-            logger.debug(f"🔑 API密钥: {masked_key}")
-            logger.debug(f"📋 请求头: {dict(request_data.headers)}")
-
-            sanitizer_req_context_map = {"gemini": "gemini_request"}
-            sanitizer_req_context = sanitizer_req_context_map.get(
-                self.api_type, "openai_request"
-            )
-            sanitized_body = sanitize_for_logging(
-                request_data.body, context=sanitizer_req_context
-            )
-            request_body_str = dump_json_safely(
-                sanitized_body, ensure_ascii=False, indent=2
-            )
-            logger.debug(f"📦 请求体: {request_body_str}")
-
-            http_response = await http_client.post(
-                request_data.url,
-                headers=request_data.headers,
-                content=dump_json_safely(request_data.body, ensure_ascii=False),
-            )
-
-            logger.debug(f"📥 响应状态码: {http_response.status_code}")
-            logger.debug(f"📄 响应头: {dict(http_response.headers)}")
-
-            response_bytes = await http_response.aread()
-            logger.debug(f"📦 响应体已完整读取 ({len(response_bytes)} bytes)")
-
-            if http_response.status_code != 200:
-                error_text = response_bytes.decode("utf-8", errors="ignore")
-                logger.error(
-                    f"❌ HTTP请求失败: {http_response.status_code} - {error_text} "
-                    f"[{log_context}]"
-                )
-                logger.debug(f"💥 完整错误响应: {error_text}")
-
-                await self.key_store.record_failure(
-                    api_key, http_response.status_code, error_text
-                )
-
-                if http_response.status_code in [401, 403]:
-                    error_code = LLMErrorCode.API_KEY_INVALID
-                elif http_response.status_code == 429:
-                    error_code = LLMErrorCode.API_RATE_LIMITED
-                elif http_response.status_code in [402, 413]:
-                    error_code = LLMErrorCode.API_QUOTA_EXCEEDED
-                else:
-                    error_code = LLMErrorCode.API_REQUEST_FAILED
-
-                raise LLMException(
-                    f"HTTP请求失败: {http_response.status_code}",
-                    code=error_code,
-                    details={
-                        "status_code": http_response.status_code,
-                        "response": error_text,
-                        "api_key": api_key,
-                    },
-                )
-
-            try:
-                response_json = json.loads(response_bytes)
-
-                sanitizer_context_map = {"gemini": "gemini_response"}
-                sanitizer_context = sanitizer_context_map.get(
-                    self.api_type, "openai_response"
-                )
-
-                sanitized_for_log = sanitize_for_logging(
-                    response_json, context=sanitizer_context
-                )
-
-                response_json_str = json.dumps(
-                    sanitized_for_log, ensure_ascii=False, indent=2
-                )
-                logger.debug(f"📋 响应JSON: {response_json_str}")
-                parsed_data = parse_response_func(response_json)
-            except Exception as e:
-                logger.error(f"解析 {log_context} 响应失败: {e}", e=e)
-                await self.key_store.record_failure(api_key, None, str(e))
-                if isinstance(e, LLMException):
-                    raise
-                else:
-                    raise LLMException(
-                        f"解析API {log_context} 响应失败: {e}",
-                        code=LLMErrorCode.RESPONSE_PARSE_ERROR,
-                        cause=e,
-                    )
-
-            logger.info(f"🎯 LLM响应解析完成 [{log_context}]")
-            return parsed_data, api_key
-
-        except LLMException:
-            raise
-        except Exception as e:
-            error_log_msg = f"生成 {log_context.lower()} 时发生未预期错误: {e}"
-            logger.error(error_log_msg, e=e)
-            await self.key_store.record_failure(api_key, None, str(e))
-            raise LLMException(
-                error_log_msg,
-                code=LLMErrorCode.GENERATION_FAILED
-                if log_context == "Generation"
-                else LLMErrorCode.EMBEDDING_FAILED,
-                cause=e,
-            )
-
-    async def _execute_embedding_request(
-        self,
-        adapter,
-        texts: list[str],
-        task_type: EmbeddingTaskType | str,
-        http_client: LLMHttpClient,
-        failed_keys: set[str] | None = None,
-    ) -> list[list[float]]:
-        """执行单次嵌入请求 - 供重试机制调用"""
-
-        async def prepare_request(api_key: str) -> RequestData:
-            return adapter.prepare_embedding_request(
-                model=self,
-                api_key=api_key,
-                texts=texts,
-                task_type=task_type,
-            )
-
-        def parse_response(response_json: dict[str, Any]) -> list[list[float]]:
-            adapter.validate_embedding_response(response_json)
-            return adapter.parse_embedding_response(response_json)
-
-        parsed_data, _api_key_used = await self._perform_api_call(
-            prepare_request_func=prepare_request,
-            parse_response_func=parse_response,
-            http_client=http_client,
-            failed_keys=failed_keys,
-            log_context="Embedding",
-        )
-        return parsed_data
-
-    async def _execute_with_smart_retry(
-        self,
-        adapter,
-        messages: list[LLMMessage],
-        config: LLMGenerationConfig | None,
-        tools: dict[str, ToolExecutable] | None,
-        tool_choice: str | dict[str, Any] | None,
-        http_client: LLMHttpClient,
-    ):
-        """智能重试机制 - 使用统一的重试装饰器"""
-        ai_config = get_ai_config()
-        max_retries = ai_config.get("max_retries_llm", 3)
-        retry_delay = ai_config.get("retry_delay_llm", 2)
-        retry_config = RetryConfig(max_retries=max_retries, retry_delay=retry_delay)
-
-        return await with_smart_retry(
-            self._execute_single_request,
-            adapter,
-            messages,
-            config,
-            tools,
-            tool_choice,
-            http_client,
-            retry_config=retry_config,
-            key_store=self.key_store,
-            provider_name=self.provider_name,
-        )
-
-    async def _execute_single_request(
-        self,
-        adapter,
-        messages: list[LLMMessage],
-        config: LLMGenerationConfig | None,
-        tools: dict[str, ToolExecutable] | None,
-        tool_choice: str | dict[str, Any] | None,
-        http_client: LLMHttpClient,
-        failed_keys: set[str] | None = None,
-    ) -> tuple[LLMResponse, str]:
-        """执行单次请求 - 供重试机制调用，直接返回 LLMResponse 和使用的 key"""
-
-        async def prepare_request(api_key: str) -> RequestData:
-            return await adapter.prepare_advanced_request(
-                model=self,
-                api_key=api_key,
-                messages=messages,
-                config=config,
-                tools=tools,
-                tool_choice=tool_choice,
-            )
-
-        def parse_response(response_json: dict[str, Any]) -> LLMResponse:
-            response_data = adapter.parse_response(
-                model=self,
-                response_json=response_json,
-                is_advanced=True,
-            )
-            from .types.models import LLMToolCall
-
-            response_tool_calls = []
-            if response_data.tool_calls:
-                for tc_data in response_data.tool_calls:
-                    if isinstance(tc_data, LLMToolCall):
-                        response_tool_calls.append(tc_data)
-                    elif isinstance(tc_data, dict):
-                        try:
-                            response_tool_calls.append(LLMToolCall(**tc_data))
-                        except Exception as e:
-                            logger.warning(
-                                f"无法将工具调用数据转换为LLMToolCall: {tc_data}, "
-                                f"error: {e}"
-                            )
-                    else:
-                        logger.warning(f"工具调用数据格式未知: {tc_data}")
-
-            return LLMResponse(
-                text=response_data.text,
-                usage_info=response_data.usage_info,
-                images=response_data.images,
-                raw_response=response_data.raw_response,
-                tool_calls=response_tool_calls if response_tool_calls else None,
-                code_executions=response_data.code_executions,
-                grounding_metadata=response_data.grounding_metadata,
-                cache_info=response_data.cache_info,
-            )
-
-        parsed_data, api_key_used = await self._perform_api_call(
-            prepare_request_func=prepare_request,
-            parse_response_func=parse_response,
-            http_client=http_client,
-            failed_keys=failed_keys,
-            log_context="Generation",
-        )
-
-        if config:
-            if config.response_validator:
-                try:
-                    config.response_validator(parsed_data)
-                except Exception as e:
-                    raise LLMException(
-                        f"响应内容未通过自定义验证器: {e}",
-                        code=LLMErrorCode.API_RESPONSE_INVALID,
-                        details={"validator_error": str(e)},
-                        cause=e,
-                    ) from e
-
-            policy = config.validation_policy
-            if policy:
-                if policy.get("require_image") and not parsed_data.images:
-                    if self.api_type == "gemini" and parsed_data.raw_response:
-                        usage_metadata = parsed_data.raw_response.get(
-                            "usageMetadata", {}
-                        )
-                        prompt_token_details = usage_metadata.get(
-                            "promptTokensDetails", []
-                        )
-                        prompt_had_image = any(
-                            detail.get("modality") == "IMAGE"
-                            for detail in prompt_token_details
-                        )
-
-                        if prompt_had_image:
-                            raise LLMException(
-                                "响应验证失败：模型接收了图片输入但未生成图片。",
-                                code=LLMErrorCode.API_RESPONSE_INVALID,
-                                details={
-                                    "policy": policy,
-                                    "text_response": parsed_data.text,
-                                    "raw_response": parsed_data.raw_response,
-                                },
-                            )
-                        else:
-                            logger.debug("Gemini提示词中未包含图片，跳过图片要求重试。")
-                    else:
-                        raise LLMException(
-                            "响应验证失败：要求返回图片但未找到图片数据。",
-                            code=LLMErrorCode.API_RESPONSE_INVALID,
-                            details={
-                                "policy": policy,
-                                "text_response": parsed_data.text,
-                            },
-                        )
-
-        return parsed_data, api_key_used
-
     async def close(self):
         """标记模型实例的当前使用周期结束"""
         if self._is_closed:
@@ -481,108 +305,102 @@ class LLMModel(LLMModelBase):
             )
             self._is_closed = False
         self._check_not_closed()
+        self._ref_count += 1
         return self
 
     async def __aexit__(self, exc_type, exc_val, exc_tb):
         """异步上下文管理器出口"""
         _ = exc_type, exc_val, exc_tb
-        await self.close()
+        self._ref_count -= 1
+        if self._ref_count <= 0:
+            self._ref_count = 0
+            await self.close()
 
     def _check_not_closed(self):
         """检查实例是否已关闭"""
         if self._is_closed:
             raise RuntimeError(f"LLMModel实例已关闭: {self}")
 
+    async def _execute_core_generation(self, context: LLMContext) -> LLMResponse:
+        """
+        [内核] 执行核心生成逻辑：构建管道并执行。
+        此方法作为中间件管道的终点被调用。
+        """
+        pipeline_handler = self._build_pipeline()
+        return await pipeline_handler(context)
+
     async def generate_response(
         self,
         messages: list[LLMMessage],
         config: LLMGenerationConfig | None = None,
-        tools: dict[str, ToolExecutable] | None = None,
-        tool_choice: str | dict[str, Any] | None = None,
-        **kwargs: Any,
+        tools: list[Any] | None = None,
+        tool_choice: str | dict[str, Any] | ToolChoice | None = None,
+        timeout: float | None = None,
     ) -> LLMResponse:
         """
-        生成高级响应。
-        此方法现在只执行 *单次* LLM API 调用，并将结果（包括工具调用请求）返回。
+        生成高级响应 (支持中间件管道)。
         """
         self._check_not_closed()
 
-        from .adapters import get_adapter_for_api_type
-        from .config.generation import create_generation_config_from_kwargs
+        if self._generation_config and config:
+            final_request_config = self._generation_config.merge_with(config)
+        elif config:
+            final_request_config = config
+        else:
+            final_request_config = self._generation_config or LLMGenerationConfig()
 
-        final_request_config = self._generation_config or LLMGenerationConfig()
-        if kwargs:
-            kwargs_config = create_generation_config_from_kwargs(**kwargs)
-            merged_dict = final_request_config.to_dict()
-            merged_dict.update(kwargs_config.to_dict())
-            final_request_config = LLMGenerationConfig(**merged_dict)
+        normalized_tools: list[Any] | None = None
+        if tools:
+            if isinstance(tools, dict):
+                normalized_tools = list(tools.values())
+            elif isinstance(tools, list):
+                normalized_tools = tools
+            else:
+                normalized_tools = [tools]
 
-        if config is not None:
-            merged_dict = final_request_config.to_dict()
-            merged_dict.update(config.to_dict())
-            final_request_config = LLMGenerationConfig(**merged_dict)
-
-        adapter = get_adapter_for_api_type(self.api_type)
-        http_client = await self._get_http_client()
-
-        response, _ = await self._execute_with_smart_retry(
-            adapter,
-            messages,
-            final_request_config,
-            tools,
-            tool_choice,
-            http_client,
+        context = LLMContext(
+            messages=messages,
+            config=final_request_config,
+            tools=normalized_tools,
+            tool_choice=tool_choice,
+            timeout=timeout,
         )
 
-        return response
+        return await self._execute_core_generation(context)
 
     async def generate_embeddings(
         self,
         texts: list[str],
-        task_type: EmbeddingTaskType | str = EmbeddingTaskType.RETRIEVAL_DOCUMENT,
-        **kwargs: Any,
+        config: LLMEmbeddingConfig | None = None,
     ) -> list[list[float]]:
         """生成文本嵌入向量"""
         self._check_not_closed()
         if not texts:
             return []
 
-        from .adapters import get_adapter_for_api_type
+        final_config = config or LLMEmbeddingConfig()
 
-        adapter = get_adapter_for_api_type(self.api_type)
-        if not adapter:
+        context = LLMContext(
+            messages=[],
+            config=final_config,
+            tools=None,
+            tool_choice=None,
+            timeout=None,
+            request_type="embedding",
+            extra={"texts": texts},
+        )
+
+        pipeline = self._build_pipeline()
+        response = await pipeline(context)
+        embeddings = (
+            response.cache_info.get("embeddings") if response.cache_info else None
+        )
+        if embeddings is None:
             raise LLMException(
-                f"未找到适用于 API 类型 '{self.api_type}' 的嵌入适配器",
-                code=LLMErrorCode.CONFIGURATION_ERROR,
+                "嵌入请求未返回 embeddings 数据",
+                code=LLMErrorCode.EMBEDDING_FAILED,
             )
-
-        http_client = await self._get_http_client()
-
-        ai_config = get_ai_config()
-        default_max_retries = ai_config.get("max_retries_llm", 3)
-        default_retry_delay = ai_config.get("retry_delay_llm", 2)
-        max_retries_embed = kwargs.get(
-            "max_retries_embed", max(1, default_max_retries // 2)
-        )
-        retry_delay_embed = kwargs.get("retry_delay_embed", default_retry_delay / 2)
-
-        retry_config = RetryConfig(
-            max_retries=max_retries_embed,
-            retry_delay=retry_delay_embed,
-            exponential_backoff=True,
-            key_rotation=True,
-        )
-
-        return await with_smart_retry(
-            self._execute_embedding_request,
-            adapter,
-            texts,
-            task_type,
-            http_client,
-            retry_config=retry_config,
-            key_store=self.key_store,
-            provider_name=self.provider_name,
-        )
+        return embeddings
 
     def __str__(self) -> str:
         status = "closed" if self._is_closed else "active"
@@ -594,3 +412,401 @@ class LLMModel(LLMModelBase):
             f"LLMModel(provider={self.provider_name}, model={self.model_name}, "
             f"api_type={self.api_type}, status={status})"
         )
+
+
+class RetryMiddleware(BaseLLMMiddleware):
+    """
+    重试中间件：处理异常捕获与重试循环
+    """
+
+    def __init__(self, retry_config: RetryConfig, key_store: KeyStatusStore):
+        self.retry_config = retry_config
+        self.key_store = key_store
+
+    async def __call__(self, context: LLMContext, next_call: NextCall) -> LLMResponse:
+        last_exception: Exception | None = None
+        total_attempts = self.retry_config.max_retries + 1
+
+        for attempt in range(total_attempts):
+            try:
+                context.runtime_state["attempt"] = attempt + 1
+                return await next_call(context)
+
+            except LLMException as e:
+                last_exception = e
+                api_key = context.runtime_state.get("api_key")
+
+                if api_key:
+                    status_code = e.details.get("status_code")
+                    error_msg = f"({e.code.name}) {e.message}"
+                    await self.key_store.record_failure(api_key, status_code, error_msg)
+
+                if not _should_retry_llm_error(
+                    e, attempt, self.retry_config.max_retries
+                ):
+                    raise e
+
+                if attempt == total_attempts - 1:
+                    raise e
+
+                wait_time = self.retry_config.retry_delay
+                if self.retry_config.exponential_backoff:
+                    wait_time *= 2**attempt
+
+                logger.warning(
+                    f"请求失败，{wait_time:.2f}秒后重试"
+                    f" (第{attempt + 1}/{self.retry_config.max_retries}次重试): {e}"
+                )
+                await asyncio.sleep(wait_time)
+
+            except Exception as e:
+                logger.error(f"非预期异常，停止重试: {e}", e=e)
+                raise e
+
+        if last_exception:
+            raise last_exception
+        raise LLMException("重试循环异常结束")
+
+
+class KeySelectionMiddleware(BaseLLMMiddleware):
+    """
+    密钥选择中间件：负责轮询获取可用 API Key
+    """
+
+    def __init__(
+        self, key_store: KeyStatusStore, provider_name: str, api_keys: list[str]
+    ):
+        self.key_store = key_store
+        self.provider_name = provider_name
+        self.api_keys = api_keys
+        self._failed_keys: set[str] = set()
+
+    async def __call__(self, context: LLMContext, next_call: NextCall) -> LLMResponse:
+        selected_key = await self.key_store.get_next_available_key(
+            self.provider_name, self.api_keys, exclude_keys=self._failed_keys
+        )
+
+        if not selected_key:
+            raise LLMException(
+                f"提供商 {self.provider_name} 无可用 API Key",
+                code=LLMErrorCode.NO_AVAILABLE_KEYS,
+            )
+
+        context.runtime_state["api_key"] = selected_key
+
+        try:
+            response = await next_call(context)
+            return response
+        except LLMException as e:
+            self._failed_keys.add(selected_key)
+            masked = f"{selected_key[:8]}..."
+            if isinstance(e.details, dict):
+                e.details["api_key"] = masked
+            raise e
+
+
+class LoggingMiddleware(BaseLLMMiddleware):
+    """
+    日志中间件：负责请求和响应的日志记录与脱敏
+    """
+
+    def __init__(
+        self, provider_name: str, model_name: str, log_context: str = "Generation"
+    ):
+        self.provider_name = provider_name
+        self.model_name = model_name
+        self.log_context = log_context
+
+    async def __call__(self, context: LLMContext, next_call: NextCall) -> LLMResponse:
+        attempt = context.runtime_state.get("attempt", 1)
+        api_key = context.runtime_state.get("api_key", "unknown")
+        masked_key = f"{api_key[:8]}..."
+
+        logger.info(
+            f"🌐 发起LLM请求 (尝试 {attempt}) - {self.provider_name}/{self.model_name} "
+            f"[{self.log_context}] Key: {masked_key}"
+        )
+
+        try:
+            start_time = time.monotonic()
+            response = await next_call(context)
+            duration = (time.monotonic() - start_time) * 1000
+            logger.info(f"🎯 LLM响应成功 [{self.log_context}] 耗时: {duration:.2f}ms")
+            return response
+        except Exception as e:
+            logger.error(f"❌ 请求异常 [{self.log_context}]: {type(e).__name__} - {e}")
+            raise e
+
+
+class NetworkRequestMiddleware(BaseLLMMiddleware):
+    """
+    网络请求中间件：执行 Adapter 转换和 HTTP 请求
+    """
+
+    def __init__(self, model_instance: "LLMModel", adapter: "BaseAdapter"):
+        self.model = model_instance
+        self.http_client = model_instance.http_client
+        self.adapter = adapter
+        self.key_store = model_instance.key_store
+
+    async def __call__(self, context: LLMContext, next_call: NextCall) -> LLMResponse:
+        api_key = context.runtime_state["api_key"]
+
+        request_data: RequestData
+        gen_config: LLMGenerationConfig | None = None
+        embed_config: LLMEmbeddingConfig | None = None
+
+        if context.request_type == "embedding":
+            embed_config = cast(LLMEmbeddingConfig, context.config)
+            texts = (context.extra or {}).get("texts", [])
+            request_data = self.adapter.prepare_embedding_request(
+                model=self.model,
+                api_key=api_key,
+                texts=texts,
+                config=embed_config,
+            )
+        else:
+            gen_config = cast(LLMGenerationConfig, context.config)
+            request_data = await self.adapter.prepare_advanced_request(
+                model=self.model,
+                api_key=api_key,
+                messages=context.messages,
+                config=gen_config,
+                tools=context.tools,
+                tool_choice=context.tool_choice,
+            )
+
+        masked_key = (
+            f"{api_key[:8]}...{api_key[-4:] if len(api_key) > 12 else '***'}"
+            if api_key
+            else "N/A"
+        )
+        logger.debug(f"🔑 API密钥: {masked_key}")
+        logger.debug(f"📡 请求URL: {request_data.url}")
+        logger.debug(f"📋 请求头: {dict(request_data.headers)}")
+
+        if self.model.api_type == "smart":
+            effective_type = self.model._get_effective_api_type()
+            sanitizer_req_context = f"{effective_type}_request"
+        else:
+            sanitizer_req_context = self.adapter.log_sanitization_context
+        sanitized_body = sanitize_for_logging(
+            request_data.body, context=sanitizer_req_context
+        )
+
+        if request_data.files and isinstance(sanitized_body, dict):
+            file_info: list[str] = []
+            file_count = 0
+            if isinstance(request_data.files, list):
+                file_count = len(request_data.files)
+                for key, value in request_data.files:
+                    filename = (
+                        value[0]
+                        if isinstance(value, tuple) and len(value) > 0
+                        else "..."
+                    )
+                    file_info.append(f"{key}='{filename}'")
+            elif isinstance(request_data.files, dict):
+                file_count = len(request_data.files)
+                file_info = list(request_data.files.keys())
+
+            sanitized_body["[MULTIPART_FILES]"] = f"Count: {file_count} | {file_info}"
+
+        request_body_str = dump_json_safely(
+            sanitized_body, ensure_ascii=False, indent=2
+        )
+        logger.debug(f"📦 请求体: {request_body_str}")
+
+        start_time = time.monotonic()
+        try:
+            http_response = await self.http_client.post(
+                request_data.url,
+                headers=request_data.headers,
+                content=dump_json_safely(request_data.body, ensure_ascii=False)
+                if not request_data.files
+                else None,
+                data=request_data.body if request_data.files else None,
+                files=request_data.files,
+                timeout=context.timeout,
+            )
+
+            logger.debug(f"📥 响应状态码: {http_response.status_code}")
+
+            if exception := self.adapter.handle_http_error(http_response):
+                error_text = http_response.content.decode("utf-8", errors="ignore")
+                logger.debug(f"💥 完整错误响应: {error_text}")
+                await self.key_store.record_failure(
+                    api_key, http_response.status_code, error_text
+                )
+                raise exception
+
+            response_bytes = await http_response.aread()
+            logger.debug(f"📦 响应体已完整读取 ({len(response_bytes)} bytes)")
+
+            response_json = json.loads(response_bytes)
+
+            sanitizer_resp_context = sanitizer_req_context.replace(
+                "_request", "_response"
+            )
+            if sanitizer_resp_context == sanitizer_req_context:
+                sanitizer_resp_context = f"{sanitizer_req_context}_response"
+
+            sanitized_response = sanitize_for_logging(
+                response_json, context=sanitizer_resp_context
+            )
+            response_json_str = json.dumps(
+                sanitized_response, ensure_ascii=False, indent=2
+            )
+            logger.debug(f"📋 响应JSON: {response_json_str}")
+
+            if context.request_type == "embedding":
+                self.adapter.validate_embedding_response(response_json)
+                embeddings = self.adapter.parse_embedding_response(response_json)
+                latency = (time.monotonic() - start_time) * 1000
+                await self.key_store.record_success(api_key, latency)
+
+                return LLMResponse(
+                    text="",
+                    raw_response=response_json,
+                    cache_info={"embeddings": embeddings},
+                )
+
+            response_data = self.adapter.parse_response(
+                self.model, response_json, is_advanced=True
+            )
+
+            should_rescue_image = (
+                gen_config
+                and gen_config.validation_policy
+                and gen_config.validation_policy.get("require_image")
+            )
+            if (
+                should_rescue_image
+                and not response_data.images
+                and response_data.text
+                and gen_config
+            ):
+                markdown_matches = re.findall(
+                    r"(!?\[.*?\]\((https?://[^\)]+)\))", response_data.text
+                )
+                if markdown_matches:
+                    logger.info(
+                        f"检测到 {len(markdown_matches)} "
+                        "个资源链接，尝试自动下载并清洗。"
+                    )
+                    if response_data.images is None:
+                        response_data.images = []
+
+                    downloaded_urls = set()
+                    for full_tag, url in markdown_matches:
+                        try:
+                            if url not in downloaded_urls:
+                                content = await AsyncHttpx.get_content(url)
+                                response_data.images.append(process_image_data(content))
+                                downloaded_urls.add(url)
+                            response_data.text = response_data.text.replace(
+                                full_tag, ""
+                            )
+                        except Exception as exc:
+                            logger.warning(
+                                f"自动下载生成的图片失败: {url}, 错误: {exc}"
+                            )
+                    response_data.text = response_data.text.strip()
+
+            latency = (time.monotonic() - start_time) * 1000
+            await self.key_store.record_success(api_key, latency)
+
+            response_tool_calls: list[LLMToolCall] = []
+            if response_data.tool_calls:
+                for tc_data in response_data.tool_calls:
+                    if isinstance(tc_data, LLMToolCall):
+                        response_tool_calls.append(tc_data)
+                    elif isinstance(tc_data, dict):
+                        try:
+                            response_tool_calls.append(LLMToolCall(**tc_data))
+                        except Exception:
+                            pass
+
+            final_response = LLMResponse(
+                text=response_data.text,
+                content_parts=response_data.content_parts,
+                usage_info=response_data.usage_info,
+                images=response_data.images,
+                raw_response=response_data.raw_response,
+                tool_calls=response_tool_calls if response_tool_calls else None,
+                code_executions=response_data.code_executions,
+                grounding_metadata=response_data.grounding_metadata,
+                cache_info=response_data.cache_info,
+                thought_text=response_data.thought_text,
+                thought_signature=response_data.thought_signature,
+            )
+
+            if context.request_type == "generation" and gen_config:
+                if gen_config.response_validator:
+                    try:
+                        gen_config.response_validator(final_response)
+                    except Exception as exc:
+                        raise LLMException(
+                            f"响应内容未通过自定义验证器: {exc}",
+                            code=LLMErrorCode.API_RESPONSE_INVALID,
+                            details={"validator_error": str(exc)},
+                            cause=exc,
+                        ) from exc
+
+                policy = gen_config.validation_policy
+                if policy:
+                    effective_type = self.model._get_effective_api_type()
+                    if policy.get("require_image") and not final_response.images:
+                        if effective_type == "gemini" and response_data.raw_response:
+                            usage_metadata = response_data.raw_response.get(
+                                "usageMetadata", {}
+                            )
+                            prompt_token_details = usage_metadata.get(
+                                "promptTokensDetails", []
+                            )
+                            prompt_had_image = any(
+                                detail.get("modality") == "IMAGE"
+                                for detail in prompt_token_details
+                            )
+
+                            if prompt_had_image:
+                                raise LLMException(
+                                    "响应验证失败：模型接收了图片输入但未生成图片。",
+                                    code=LLMErrorCode.API_RESPONSE_INVALID,
+                                    details={
+                                        "policy": policy,
+                                        "text_response": final_response.text,
+                                        "raw_response": response_data.raw_response,
+                                    },
+                                )
+                            else:
+                                logger.debug(
+                                    "Gemini提示词中未包含图片，跳过图片要求重试。"
+                                )
+                        else:
+                            raise LLMException(
+                                "响应验证失败：要求返回图片但未找到图片数据。",
+                                code=LLMErrorCode.API_RESPONSE_INVALID,
+                                details={
+                                    "policy": policy,
+                                    "text_response": final_response.text,
+                                },
+                            )
+
+            return final_response
+
+        except Exception as e:
+            if isinstance(e, LLMException):
+                raise e
+
+            logger.error(f"解析响应失败或发生未知错误: {e}")
+
+            if not isinstance(e, httpx.NetworkError | httpx.TimeoutException):
+                await self.key_store.record_failure(api_key, None, str(e))
+
+            raise LLMException(
+                f"网络请求异常: {type(e).__name__} - {e}",
+                code=LLMErrorCode.API_REQUEST_FAILED,
+                details={"api_key": masked_key},
+                cause=e,
+            )
diff --git a/zhenxun/services/llm/session.py b/zhenxun/services/llm/session.py
index 59937cf0..a07a7987 100644
--- a/zhenxun/services/llm/session.py
+++ b/zhenxun/services/llm/session.py
@@ -4,30 +4,37 @@ LLM 服务 - 会话客户端
 提供一个有状态的、面向会话的 LLM 客户端，用于进行多轮对话和复杂交互。
 """
 
+from collections.abc import Awaitable, Callable
 import copy
-from dataclasses import dataclass, field
 import json
-from typing import Any, TypeVar
+from typing import Any, TypeVar, cast
 import uuid
 
-from jinja2 import Environment
-from nonebot.compat import type_validate_json
+from jinja2 import Template
+from nonebot.utils import is_coroutine_callable
 from nonebot_plugin_alconna.uniseg import UniMessage
-from pydantic import BaseModel, ValidationError
+from pydantic import BaseModel
 
 from zhenxun.services.log import logger
-from zhenxun.utils.pydantic_compat import model_copy, model_dump, model_json_schema
+from zhenxun.utils.pydantic_compat import model_json_schema
 
 from .config import (
     CommonOverrides,
+    GenConfigBuilder,
+    LLMEmbeddingConfig,
     LLMGenerationConfig,
 )
-from .config.providers import get_ai_config
+from .config.generation import OutputConfig
+from .config.providers import get_llm_config
 from .manager import get_global_default_model_name, get_model_instance
-from .memory import BaseMemory, InMemoryMemory
-from .tools.manager import tool_provider_manager
+from .memory import (
+    AIConfig,
+    BaseMemory,
+    MemoryProcessor,
+    _get_default_memory,
+)
+from .tools import tool_provider_manager
 from .types import (
-    EmbeddingTaskType,
     LLMContentPart,
     LLMErrorCode,
     LLMException,
@@ -35,30 +42,31 @@ from .types import (
     LLMResponse,
     ModelName,
     ResponseFormat,
+    StructuredOutputStrategy,
+    ToolChoice,
     ToolExecutable,
-    ToolProvider,
 )
-from .utils import normalize_to_llm_messages
+from .types.models import (
+    GeminiCodeExecution,
+    GeminiGoogleSearch,
+)
+from .utils import (
+    create_cot_wrapper,
+    normalize_to_llm_messages,
+    parse_and_validate_json,
+    should_apply_autocot,
+)
 
 T = TypeVar("T", bound=BaseModel)
 
-jinja_env = Environment(autoescape=False)
-
-
-@dataclass
-class AIConfig:
-    """AI配置类 - [重构后] 简化版本"""
-
-    model: ModelName = None
-    default_embedding_model: ModelName = None
-    default_preserve_media_in_history: bool = False
-    tool_providers: list[ToolProvider] = field(default_factory=list)
-
-    def __post_init__(self):
-        """初始化后从配置中读取默认值"""
-        ai_config = get_ai_config()
-        if self.model is None:
-            self.model = ai_config.get("default_model_name")
+DEFAULT_IVR_TEMPLATE = (
+    "你的响应未能通过结构校验。\n"
+    "错误详情: {error_msg}\n\n"
+    "请执行以下步骤进行修正：\n"
+    "1. 反思：分析为什么会出现这个错误。\n"
+    "2. 修正：生成一个新的、符合 Schema 要求的 JSON 对象。\n"
+    "请直接输出修正后的 JSON，不要包含 Markdown 标记或其他解释。"
+)
 
 
 class AI:
@@ -73,6 +81,7 @@ class AI:
         config: AIConfig | None = None,
         memory: BaseMemory | None = None,
         default_generation_config: LLMGenerationConfig | None = None,
+        processors: list[MemoryProcessor] | None = None,
     ):
         """
         初始化AI服务
@@ -80,25 +89,47 @@ class AI:
         参数:
             session_id: 唯一的会话ID，用于隔离记忆。
             config: AI 配置.
-            memory: 可选的自定义记忆后端。如果为None，则使用默认的InMemoryMemory。
-            default_generation_config: (新增) 此AI实例的默认生成配置。
+            memory: 可选的自定义记忆后端。如果为None，则使用默认的 ChatMemory
+            (InMemoryMessageStore)。
+            default_generation_config: 此AI实例的默认生成配置。
+            processors: 记忆处理器列表，在添加记忆后触发。
         """
         self.session_id = session_id or str(uuid.uuid4())
         self.config = config or AIConfig()
-        self.memory = memory or InMemoryMemory()
+        self.memory = memory or _get_default_memory()
         self.default_generation_config = (
             default_generation_config or LLMGenerationConfig()
         )
+        self.processors = processors or []
 
         global_providers = tool_provider_manager._providers
         config_providers = self.config.tool_providers
         self._tool_providers = list(dict.fromkeys(global_providers + config_providers))
+        self.message_buffer: list[LLMMessage] = []
 
     async def clear_history(self):
         """清空当前会话的历史记录。"""
         await self.memory.clear_history(self.session_id)
         logger.info(f"AI会话历史记录已清空 (session_id: {self.session_id})")
 
+    async def add_observation(
+        self, message: str | UniMessage | LLMMessage | list[LLMContentPart]
+    ):
+        """
+        将一条观察消息加入缓冲区，不立即触发模型调用。
+
+        返回:
+            int: 缓冲区中消息的数量。
+        """
+        current_message = await self._normalize_input_to_message(message)
+        self.message_buffer.append(current_message)
+        content_preview = str(current_message.content)[:50]
+        logger.debug(
+            f"[放入观察] {content_preview} (缓冲区大小: {len(self.message_buffer)})",
+            "AI_MEMORY",
+        )
+        return len(self.message_buffer)
+
     async def add_user_message_to_history(
         self, message: str | LLMMessage | list[LLMContentPart]
     ):
@@ -161,7 +192,7 @@ class AI:
         self, message: str | UniMessage | LLMMessage | list[LLMContentPart]
     ) -> LLMMessage:
         """
-        [重构后] 内部辅助方法，将各种输入类型统一转换为单个 LLMMessage 对象。
+        内部辅助方法，将各种输入类型统一转换为单个 LLMMessage 对象。
         它调用共享的工具函数并提取最后一条消息（通常是用户输入）。
         """
         messages = await normalize_to_llm_messages(message)
@@ -172,17 +203,79 @@ class AI:
             )
         return messages[-1]
 
+    async def generate_internal(
+        self,
+        messages: list[LLMMessage],
+        *,
+        model: ModelName = None,
+        config: LLMGenerationConfig | GenConfigBuilder | None = None,
+        tools: list[Any] | dict[str, ToolExecutable] | None = None,
+        tool_choice: str | dict[str, Any] | ToolChoice | None = None,
+        timeout: float | None = None,
+        model_instance: Any = None,
+    ) -> LLMResponse:
+        """
+        内部生成核心方法，负责配置合并、工具解析和模型调用。
+        此方法不处理历史记录的存储，供 AgentExecutor 或 chat 方法调用。
+        """
+        final_config = self.default_generation_config
+        if isinstance(config, GenConfigBuilder):
+            config = config.build()
+
+        if config:
+            final_config = final_config.merge_with(config)
+
+        final_tools_list = []
+        if tools:
+            if isinstance(tools, dict):
+                final_tools_list = list(tools.values())
+            elif isinstance(tools, list):
+                to_resolve: list[Any] = []
+                for t in tools:
+                    if isinstance(t, str | dict):
+                        to_resolve.append(t)
+                    else:
+                        final_tools_list.append(t)
+
+                if to_resolve:
+                    resolved_dict = await self._resolve_tools(to_resolve)
+                    final_tools_list.extend(resolved_dict.values())
+
+        if model_instance:
+            return await model_instance.generate_response(
+                messages,
+                config=final_config,
+                tools=final_tools_list if final_tools_list else None,
+                tool_choice=tool_choice,
+                timeout=timeout,
+            )
+
+        resolved_model_name = self._resolve_model_name(model or self.config.model)
+        async with await get_model_instance(
+            resolved_model_name,
+            override_config=None,
+        ) as instance:
+            return await instance.generate_response(
+                messages,
+                config=final_config,
+                tools=final_tools_list if final_tools_list else None,
+                tool_choice=tool_choice,
+                timeout=timeout,
+            )
+
     async def chat(
         self,
-        message: str | UniMessage | LLMMessage | list[LLMContentPart],
+        message: str | UniMessage | LLMMessage | list[LLMContentPart] | None,
         *,
         model: ModelName = None,
         instruction: str | None = None,
         template_vars: dict[str, Any] | None = None,
         preserve_media_in_history: bool | None = None,
-        tools: list[dict[str, Any] | str] | dict[str, ToolExecutable] | None = None,
-        tool_choice: str | dict[str, Any] | None = None,
-        config: LLMGenerationConfig | None = None,
+        tools: list[Any] | dict[str, ToolExecutable] | None = None,
+        tool_choice: str | dict[str, Any] | ToolChoice | None = None,
+        config: LLMGenerationConfig | GenConfigBuilder | None = None,
+        use_buffer: bool = False,
+        timeout: float | None = None,
     ) -> LLMResponse:
         """
         核心交互方法，管理会话历史并执行单次LLM调用。
@@ -198,18 +291,27 @@ class AI:
             tools: 可用的工具列表或工具字典，支持临时工具和预配置工具。
             tool_choice: 工具选择策略，控制AI如何选择和使用工具。
             config: 生成配置对象，用于覆盖默认的生成参数。
+            use_buffer: 是否刷新并包含消息缓冲区的内容，在此次对话中一次性提交。
+            timeout: HTTP 请求超时时间（秒）。
 
         返回:
             LLMResponse: 包含AI回复、工具调用请求、使用信息等的完整响应对象。
         """
-        current_message = await self._normalize_input_to_message(message)
+        messages_to_add: list[LLMMessage] = []
+        if message:
+            current_message = await self._normalize_input_to_message(message)
+            messages_to_add.append(current_message)
+
+        if use_buffer and self.message_buffer:
+            messages_to_add = self.message_buffer + messages_to_add
+            self.message_buffer.clear()
 
         messages_for_run = []
         final_instruction = instruction
 
         if final_instruction and template_vars:
             try:
-                template = jinja_env.from_string(final_instruction)
+                template = Template(final_instruction)
                 final_instruction = template.render(**template_vars)
                 logger.debug(f"渲染后的系统指令: {final_instruction}")
             except Exception as e:
@@ -220,51 +322,55 @@ class AI:
 
         current_history = await self.memory.get_history(self.session_id)
         messages_for_run.extend(current_history)
-        messages_for_run.append(current_message)
+        messages_for_run.extend(messages_to_add)
 
         try:
-            resolved_model_name = self._resolve_model_name(model or self.config.model)
-
-            final_config = model_copy(self.default_generation_config, deep=True)
-            if config:
-                update_dict = model_dump(config, exclude_unset=True)
-                final_config = model_copy(final_config, update=update_dict)
-
-            ad_hoc_tools = None
-            if tools:
-                if isinstance(tools, dict):
-                    ad_hoc_tools = tools
-                else:
-                    ad_hoc_tools = await self._resolve_tools(tools)
-
-            async with await get_model_instance(
-                resolved_model_name,
-                override_config=final_config.to_dict(),
-            ) as model_instance:
-                response = await model_instance.generate_response(
-                    messages_for_run, tools=ad_hoc_tools, tool_choice=tool_choice
-                )
+            response = await self.generate_internal(
+                messages_for_run,
+                model=model,
+                config=config,
+                tools=tools,
+                tool_choice=tool_choice,
+                timeout=timeout,
+            )
 
             should_preserve = (
                 preserve_media_in_history
                 if preserve_media_in_history is not None
                 else self.config.default_preserve_media_in_history
             )
-            user_msg_to_store = (
-                current_message
-                if should_preserve
-                else self._sanitize_message_for_history(current_message)
-            )
-            assistant_response_msg = LLMMessage.assistant_text_response(response.text)
-            if response.tool_calls:
-                assistant_response_msg = LLMMessage.assistant_tool_calls(
-                    response.tool_calls, response.text
+            msgs_to_store: list[LLMMessage] = []
+            for msg in messages_to_add:
+                store_msg = (
+                    msg if should_preserve else self._sanitize_message_for_history(msg)
                 )
+                msgs_to_store.append(store_msg)
+
+            if response.content_parts:
+                assistant_response_msg = LLMMessage(
+                    role="assistant",
+                    content=response.content_parts,
+                    tool_calls=response.tool_calls,
+                )
+            else:
+                assistant_response_msg = LLMMessage.assistant_text_response(
+                    response.text
+                )
+                if response.tool_calls:
+                    assistant_response_msg = LLMMessage.assistant_tool_calls(
+                        response.tool_calls, response.text
+                    )
 
             await self.memory.add_messages(
-                self.session_id, [user_msg_to_store, assistant_response_msg]
+                self.session_id, [*msgs_to_store, assistant_response_msg]
             )
 
+            if self.processors:
+                for processor in self.processors:
+                    await processor.process(
+                        self.session_id, [*msgs_to_store, assistant_response_msg]
+                    )
+
             return response
 
         except Exception as e:
@@ -280,7 +386,7 @@ class AI:
         *,
         model: ModelName = None,
         timeout: int | None = None,
-        config: LLMGenerationConfig | None = None,
+        config: LLMGenerationConfig | GenConfigBuilder | None = None,
     ) -> LLMResponse:
         """
         代码执行
@@ -294,16 +400,18 @@ class AI:
         返回:
             LLMResponse: 包含执行结果的完整响应对象。
         """
-        resolved_model = model or self.config.model or "Gemini/gemini-2.0-flash"
+        resolved_model = model or self.config.model
 
         code_config = CommonOverrides.gemini_code_execution()
         if timeout:
             code_config.custom_params = code_config.custom_params or {}
             code_config.custom_params["code_execution_timeout"] = timeout
 
+        if isinstance(config, GenConfigBuilder):
+            config = config.build()
+
         if config:
-            update_dict = model_dump(config, exclude_unset=True)
-            code_config = model_copy(code_config, update=update_dict)
+            code_config = code_config.merge_with(config)
 
         return await self.chat(prompt, model=resolved_model, config=code_config)
 
@@ -317,7 +425,7 @@ class AI:
             "根据用户的查询找到最相关的信息，并进行总结和回答。"
         ),
         template_vars: dict[str, Any] | None = None,
-        config: LLMGenerationConfig | None = None,
+        config: LLMGenerationConfig | GenConfigBuilder | None = None,
     ) -> LLMResponse:
         """
         信息搜索的便捷入口，原生支持多模态查询。
@@ -325,9 +433,11 @@ class AI:
         logger.info("执行 'search' 任务...")
         search_config = CommonOverrides.gemini_grounding()
 
+        if isinstance(config, GenConfigBuilder):
+            config = config.build()
+
         if config:
-            update_dict = model_dump(config, exclude_unset=True)
-            search_config = model_copy(search_config, update=update_dict)
+            search_config = search_config.merge_with(config)
 
         return await self.chat(
             query,
@@ -339,21 +449,31 @@ class AI:
 
     async def generate_structured(
         self,
-        message: str | LLMMessage | list[LLMContentPart],
+        message: str | LLMMessage | list[LLMContentPart] | None,
         response_model: type[T],
         *,
         model: ModelName = None,
+        tools: list[Any] | dict[str, ToolExecutable] | None = None,
+        tool_choice: str | dict[str, Any] | ToolChoice | None = None,
         instruction: str | None = None,
-        config: LLMGenerationConfig | None = None,
+        timeout: float | None = None,
+        template_vars: dict[str, Any] | None = None,
+        config: LLMGenerationConfig | GenConfigBuilder | None = None,
+        max_validation_retries: int | None = None,
+        validation_callback: Callable[[T], Any | Awaitable[Any]] | None = None,
+        error_prompt_template: str | None = None,
+        auto_thinking: bool = False,
     ) -> T:
         """
         生成结构化响应，并自动解析为指定的Pydantic模型。
 
         参数:
-            message: 用户输入的消息内容，支持多种格式。
+            message: 用户输入的消息内容，支持多种格式。为None时只使用历史+缓冲区。
             response_model: 用于解析和验证响应的Pydantic模型类。
             model: 要使用的模型名称，如果为None则使用配置中的默认模型。
             instruction: 本次调用的特定系统指令，会与JSON Schema指令合并。
+            timeout: HTTP 请求超时时间（秒）。
+            template_vars: 系统指令中的模板变量，用于动态渲染。
             config: 生成配置对象，用于覆盖默认的生成参数。
 
         返回:
@@ -362,6 +482,46 @@ class AI:
         异常:
             LLMException: 如果模型返回的不是有效的JSON或验证失败。
         """
+        if isinstance(config, GenConfigBuilder):
+            config = config.build()
+
+        final_config = self.default_generation_config.merge_with(config)
+
+        if final_config is None:
+            final_config = LLMGenerationConfig()
+
+        if max_validation_retries is None:
+            max_validation_retries = get_llm_config().client_settings.structured_retries
+
+        resolved_model_name = self._resolve_model_name(model or self.config.model)
+
+        request_autocot = True if auto_thinking is False else auto_thinking
+        effective_auto_thinking = should_apply_autocot(
+            request_autocot, resolved_model_name, final_config
+        )
+
+        target_model: type[T] = response_model
+        if effective_auto_thinking:
+            target_model = cast(type[T], create_cot_wrapper(response_model))
+            response_model = target_model
+
+            cot_instruction = (
+                "请务必先在 `reasoning` 字段中进行详细的一步步推理，确保逻辑正确，"
+                "然后再填充 `result` 字段。"
+            )
+            if instruction:
+                instruction = f"{instruction}\n\n{cot_instruction}"
+            else:
+                instruction = cot_instruction
+
+        final_instruction = instruction
+        if final_instruction and template_vars:
+            try:
+                template = Template(final_instruction)
+                final_instruction = template.render(**template_vars)
+            except Exception as e:
+                logger.error(f"渲染结构化指令模板失败: {e}", e=e)
+
         try:
             json_schema = model_json_schema(response_model)
         except AttributeError:
@@ -369,41 +529,149 @@ class AI:
 
         schema_str = json.dumps(json_schema, ensure_ascii=False, indent=2)
 
-        system_prompt = (
-            (f"{instruction}\n\n" if instruction else "")
-            + "你必须严格按照以下 JSON Schema 格式进行响应。"
-            + "不要包含任何额外的解释、注释或代码块标记，只返回纯粹的 JSON 对象。\n\n"
+        prompt_prefix = f"{final_instruction}\n\n" if final_instruction else ""
+        structured_strategy = (
+            final_config.output.structured_output_strategy
+            if final_config.output
+            else None
         )
-        system_prompt += f"JSON Schema:\n```json\n{schema_str}\n```"
+        if structured_strategy == StructuredOutputStrategy.TOOL_CALL:
+            system_prompt = prompt_prefix + "请调用提供的工具提交结构化数据。"
+        else:
+            system_prompt = (
+                prompt_prefix
+                + "请严格按照以下 JSON Schema 格式进行响应。不应包含任何额外的解释、"
+                "注释或代码块标记，只返回一个合法的 JSON 对象。\n\n"
+            )
+            system_prompt += f"JSON Schema:\n```json\n{schema_str}\n```"
 
-        final_config = model_copy(config) if config else LLMGenerationConfig()
-
-        final_config.response_format = ResponseFormat.JSON
-        final_config.response_schema = json_schema
-
-        response = await self.chat(
-            message, model=model, instruction=system_prompt, config=final_config
+        structured_strategy = (
+            final_config.output.structured_output_strategy
+            if final_config.output
+            else StructuredOutputStrategy.NATIVE
         )
 
-        try:
-            return type_validate_json(response_model, response.text)
-        except ValidationError as e:
-            logger.error(f"LLM结构化输出验证失败: {e}", e=e)
-            raise LLMException(
-                "LLM返回的JSON未能通过结构验证。",
-                code=LLMErrorCode.RESPONSE_PARSE_ERROR,
-                details={"raw_response": response.text, "validation_error": str(e)},
-                cause=e,
-            )
-        except Exception as e:
-            logger.error(f"解析LLM结构化输出时发生未知错误: {e}", e=e)
-            raise LLMException(
-                "解析LLM的JSON输出时失败。",
-                code=LLMErrorCode.RESPONSE_PARSE_ERROR,
-                details={"raw_response": response.text},
-                cause=e,
+        final_tools_list: list[ToolExecutable] | None = None
+        if structured_strategy != StructuredOutputStrategy.NATIVE:
+            if tools:
+                final_tools_list = []
+                if isinstance(tools, dict):
+                    final_tools_list = list(tools.values())
+                elif isinstance(tools, list):
+                    to_resolve: list[Any] = []
+                    for t in tools:
+                        if isinstance(t, str | dict):
+                            to_resolve.append(t)
+                        else:
+                            final_tools_list.append(t)
+                    if to_resolve:
+                        resolved_dict = await self._resolve_tools(to_resolve)
+                        final_tools_list.extend(resolved_dict.values())
+        elif tools:
+            logger.warning(
+                "检测到在 generate_structured (NATIVE 策略) 中传入了 tools。"
+                "为了避免 API 冲突(Gemini)及输出歧义(OpenAI)，这些"
+                "tools 将被本次请求忽略。"
+                "若需使用工具，请使用 chat() 方法或 Agent 流程。"
             )
 
+        if final_config.output is None:
+            final_config.output = OutputConfig()
+
+        final_config.output.response_format = ResponseFormat.JSON
+        final_config.output.response_schema = json_schema
+
+        messages_for_run = [LLMMessage.system(system_prompt)]
+        current_history = await self.memory.get_history(self.session_id)
+        messages_for_run.extend(current_history)
+        messages_for_run.extend(self.message_buffer)
+        if message:
+            normalized_message = await self._normalize_input_to_message(message)
+            messages_for_run.append(normalized_message)
+
+        ivr_messages = list(messages_for_run)
+        last_exception: Exception | None = None
+
+        for attempt in range(max_validation_retries + 1):
+            current_response_text: str = ""
+
+            async with await get_model_instance(
+                resolved_model_name,
+                override_config=None,
+            ) as model_instance:
+                response = await model_instance.generate_response(
+                    ivr_messages,
+                    config=final_config,
+                    tools=final_tools_list if final_tools_list else None,
+                    tool_choice=tool_choice,
+                    timeout=timeout,
+                )
+                current_response_text = response.text
+
+            try:
+                parsed_obj = parse_and_validate_json(response.text, target_model)
+
+                final_obj: T = cast(T, parsed_obj)
+                if effective_auto_thinking:
+                    logger.debug(
+                        f"AutoCoT 思考过程: {getattr(parsed_obj, 'reasoning', '')}"
+                    )
+                    final_obj = cast(T, getattr(parsed_obj, "result"))
+
+                if validation_callback:
+                    if is_coroutine_callable(validation_callback):
+                        await validation_callback(final_obj)
+                    else:
+                        validation_callback(final_obj)
+
+                return final_obj
+
+            except Exception as e:
+                is_llm_error = isinstance(e, LLMException)
+                llm_error: LLMException | None = (
+                    cast(LLMException, e) if is_llm_error else None
+                )
+                last_exception = e
+
+                if attempt < max_validation_retries:
+                    error_msg = (
+                        llm_error.details.get("validation_error", str(e))
+                        if llm_error
+                        else str(e)
+                    )
+                    raw_response = current_response_text or (
+                        llm_error.details.get("raw_response", "") if llm_error else ""
+                    )
+                    logger.warning(
+                        f"结构化校验失败 (尝试 {attempt + 1}/"
+                        f"{max_validation_retries + 1})。正在尝试 IVR 修复... 错误:"
+                        f"{error_msg}"
+                    )
+
+                    if raw_response:
+                        ivr_messages.append(
+                            LLMMessage.assistant_text_response(raw_response)
+                        )
+                    else:
+                        logger.warning(
+                            "IVR 警告: 无法获取上一轮生成的原始文本，"
+                            "模型将在无上下文情况下尝试修复。"
+                        )
+
+                    template = error_prompt_template or DEFAULT_IVR_TEMPLATE
+                    feedback_prompt = template.format(error_msg=error_msg)
+                    ivr_messages.append(LLMMessage.user(feedback_prompt))
+                    continue
+
+                if llm_error and not llm_error.recoverable:
+                    raise llm_error
+
+        if last_exception:
+            raise last_exception
+        raise LLMException(
+            "IVR 循环异常结束，未能生成有效结果。", code=LLMErrorCode.GENERATION_FAILED
+        )
+
     def _resolve_model_name(self, model_name: ModelName) -> str:
         """解析模型名称"""
         if model_name:
@@ -423,8 +691,7 @@ class AI:
         texts: list[str] | str,
         *,
         model: ModelName = None,
-        task_type: EmbeddingTaskType | str = EmbeddingTaskType.RETRIEVAL_DOCUMENT,
-        **kwargs: Any,
+        config: LLMEmbeddingConfig | None = None,
     ) -> list[list[float]]:
         """
         生成文本嵌入向量，将文本转换为数值向量表示。
@@ -432,14 +699,13 @@ class AI:
         参数:
             texts: 要生成嵌入的文本内容，支持单个字符串或字符串列表。
             model: 嵌入模型名称，如果为None则使用配置中的默认嵌入模型。
-            task_type: 嵌入任务类型，影响向量的优化方向（如检索、分类等）。
-            **kwargs: 传递给嵌入模型的额外参数。
+            config: 嵌入配置
 
         返回:
             list[list[float]]: 文本对应的嵌入向量列表，每个向量为浮点数列表。
 
         异常:
-            LLMException: 如果嵌入生成失败或模型配置错误。
+            LLMException: 当嵌入生成失败或模型配置错误时抛出
         """
         if isinstance(texts, str):
             texts = [texts]
@@ -452,18 +718,20 @@ class AI:
             )
             if not resolved_model_str:
                 raise LLMException(
-                    "使用 embed 功能时必须指定嵌入模型名称，"
-                    "或在 AIConfig 中配置 default_embedding_model。",
+                    "使用 embed 方法时未指定嵌入模型名称，"
+                    "且 AIConfig 未设置 default_embedding_model。",
                     code=LLMErrorCode.MODEL_NOT_FOUND,
                 )
             resolved_model_str = self._resolve_model_name(resolved_model_str)
 
+            final_config = config or LLMEmbeddingConfig()
+
             async with await get_model_instance(
                 resolved_model_str,
                 override_config=None,
             ) as embedding_model_instance:
                 return await embedding_model_instance.generate_embeddings(
-                    texts, task_type=task_type, **kwargs
+                    texts, config=final_config
                 )
         except LLMException:
             raise
@@ -484,6 +752,15 @@ class AI:
         resolved: dict[str, ToolExecutable] = {}
 
         for config in tool_configs:
+            if isinstance(config, str):
+                if config == "google_search":
+                    resolved[config] = GeminiGoogleSearch()  # type: ignore[arg-type]
+                    continue
+                elif config == "code_execution":
+                    resolved[config] = GeminiCodeExecution()  # type: ignore[arg-type]
+                    continue
+                elif config == "url_context":
+                    pass
             name = config if isinstance(config, str) else config.get("name")
             if not name:
                 raise LLMException(
diff --git a/zhenxun/services/llm/tools.py b/zhenxun/services/llm/tools.py
new file mode 100644
index 00000000..bbf1b9ed
--- /dev/null
+++ b/zhenxun/services/llm/tools.py
@@ -0,0 +1,839 @@
+"""
+工具模块
+
+整合了工具参数解析器、工具提供者管理器与工具执行逻辑，便于在 LLM 服务层统一调用。
+"""
+
+import asyncio
+from collections.abc import Callable
+from enum import Enum
+import inspect
+import json
+import re
+import time
+from typing import (
+    Annotated,
+    Any,
+    Optional,
+    Union,
+    cast,
+    get_args,
+    get_origin,
+    get_type_hints,
+)
+from typing_extensions import override
+
+from httpx import NetworkError, TimeoutException
+
+try:
+    import ujson as fast_json
+except ImportError:
+    fast_json = json
+
+import nonebot
+from nonebot.dependencies import Dependent, Param
+from nonebot.internal.adapter import Bot, Event
+from nonebot.internal.params import (
+    BotParam,
+    DefaultParam,
+    DependParam,
+    DependsInner,
+    EventParam,
+    StateParam,
+)
+from pydantic import BaseModel, Field, ValidationError, create_model
+from pydantic.fields import FieldInfo
+
+from zhenxun.services.log import logger
+from zhenxun.utils.decorator.retry import Retry
+from zhenxun.utils.pydantic_compat import model_dump, model_fields, model_json_schema
+
+from .types import (
+    LLMErrorCode,
+    LLMException,
+    LLMMessage,
+    LLMToolCall,
+    ToolExecutable,
+    ToolProvider,
+    ToolResult,
+)
+from .types.models import ToolDefinition
+from .types.protocols import BaseCallbackHandler, ToolCallData
+
+
+class ToolParam(Param):
+    """
+    工具参数提取器。
+
+    用于在自定义工具函数（Function Tool）中，从 LLM 解析出的参数字典
+    (`state["_tool_params"]`)
+    中提取特定的参数值。通常配合 `Annotated` 和依赖注入系统使用。
+    """
+
+    def __init__(self, *args: Any, name: str, **kwargs: Any):
+        super().__init__(*args, **kwargs)
+        self.name = name
+
+    def __repr__(self) -> str:
+        return f"ToolParam(name={self.name})"
+
+    @classmethod
+    @override
+    def _check_param(
+        cls, param: inspect.Parameter, allow_types: tuple[type[Param], ...]
+    ) -> Optional["ToolParam"]:
+        if param.default is not inspect.Parameter.empty and isinstance(
+            param.default, DependsInner
+        ):
+            return None
+
+        if get_origin(param.annotation) is Annotated:
+            for arg in get_args(param.annotation):
+                if isinstance(arg, DependsInner):
+                    return None
+
+        if param.kind not in (
+            inspect.Parameter.VAR_POSITIONAL,
+            inspect.Parameter.VAR_KEYWORD,
+        ):
+            return cls(name=param.name)
+        return None
+
+    @override
+    async def _solve(self, **kwargs: Any) -> Any:
+        state: dict[str, Any] = kwargs.get("state", {})
+        tool_params = state.get("_tool_params", {})
+        if self.name in tool_params:
+            return tool_params[self.name]
+        return None
+
+
+class RunContext(BaseModel):
+    """
+    依赖注入容器（DI Container），保留原有上下文信息的同时提升获取类型的能力。
+    """
+
+    session_id: str | None = None
+    scope: dict[str, Any] = Field(default_factory=dict)
+    extra: dict[str, Any] = Field(default_factory=dict)
+
+    class Config:
+        arbitrary_types_allowed = True
+
+
+class RunContextParam(Param):
+    """自动注入 RunContext 的参数解析器"""
+
+    @classmethod
+    def _check_param(
+        cls, param: inspect.Parameter, allow_types: tuple[type[Param], ...]
+    ) -> Optional["RunContextParam"]:
+        if param.annotation is RunContext:
+            return cls()
+        return None
+
+    async def _solve(self, **kwargs: Any) -> Any:
+        state = kwargs.get("state", {})
+        return state.get("_agent_context")
+
+
+def _parse_docstring_params(docstring: str | None) -> dict[str, str]:
+    """
+    解析文档字符串，提取参数描述。
+    支持 Google Style (Args:), ReST Style (:param:), 和中文风格 (参数:)。
+    """
+    if not docstring:
+        return {}
+
+    params: dict[str, str] = {}
+    lines = docstring.splitlines()
+
+    rest_pattern = re.compile(r"[:@]param\s+(\w+)\s*:?\s*(.*)")
+    found_rest = False
+    for line in lines:
+        match = rest_pattern.search(line)
+        if match:
+            params[match.group(1)] = match.group(2).strip()
+            found_rest = True
+
+    if found_rest:
+        return params
+
+    section_header_pattern = re.compile(
+        r"^\s*(?:Args|Arguments|Parameters|参数)\s*[:：]\s*$"
+    )
+
+    param_section_active = False
+    google_pattern = re.compile(r"^\s*(\**\w+)(?:\s*\(.*?\))?\s*[:：]\s*(.*)")
+
+    for line in lines:
+        stripped_line = line.strip()
+        if not stripped_line:
+            continue
+
+        if section_header_pattern.match(line):
+            param_section_active = True
+            continue
+
+        if param_section_active:
+            if (
+                stripped_line.endswith(":") or stripped_line.endswith("：")
+            ) and not google_pattern.match(line):
+                param_section_active = False
+                continue
+
+            match = google_pattern.match(line)
+            if match:
+                name = match.group(1).lstrip("*")
+                desc = match.group(2).strip()
+                params[name] = desc
+
+    return params
+
+
+def _create_dynamic_model(func: Callable) -> type[BaseModel]:
+    """根据函数签名动态创建 Pydantic 模型"""
+    sig = inspect.signature(func)
+    doc_params = _parse_docstring_params(func.__doc__)
+    type_hints = get_type_hints(func, include_extras=True)
+
+    fields = {}
+    for name, param in sig.parameters.items():
+        if name in ("self", "cls"):
+            continue
+
+        annotation = type_hints.get(name, Any)
+        default = param.default
+
+        is_run_context = False
+        if annotation is RunContext:
+            is_run_context = True
+        else:
+            origin = get_origin(annotation)
+            if origin is Union:
+                args = get_args(annotation)
+                if RunContext in args:
+                    is_run_context = True
+
+        if is_run_context:
+            continue
+
+        if default is not inspect.Parameter.empty and isinstance(default, DependsInner):
+            continue
+
+        if get_origin(annotation) is Annotated:
+            args = get_args(annotation)
+            if any(isinstance(arg, DependsInner) for arg in args):
+                continue
+
+        description = doc_params.get(name)
+        if isinstance(default, FieldInfo):
+            if description and not getattr(default, "description", None):
+                default.description = description
+            fields[name] = (annotation, default)
+        else:
+            if default is inspect.Parameter.empty:
+                default = ...
+            fields[name] = (annotation, Field(default, description=description))
+
+    return create_model(f"{func.__name__}Params", **fields)
+
+
+class FunctionExecutable(ToolExecutable):
+    """一个 ToolExecutable 的实现，用于包装一个普通的 Python 函数。"""
+
+    def __init__(
+        self,
+        func: Callable,
+        name: str,
+        description: str,
+        params_model: type[BaseModel] | None = None,
+        unpack_args: bool = False,
+    ):
+        self._func = func
+        self._name = name
+        self._description = description
+        self._params_model = params_model
+        self._unpack_args = unpack_args
+
+        self.dependent = Dependent[Any].parse(
+            call=func,
+            allow_types=(
+                DependParam,
+                BotParam,
+                EventParam,
+                StateParam,
+                RunContextParam,
+                ToolParam,
+                DefaultParam,
+            ),
+        )
+
+    async def get_definition(self) -> ToolDefinition:
+        if not self._params_model:
+            return ToolDefinition(
+                name=self._name,
+                description=self._description,
+                parameters={"type": "object", "properties": {}},
+            )
+
+        schema = model_json_schema(self._params_model)
+
+        return ToolDefinition(
+            name=self._name,
+            description=self._description,
+            parameters={
+                "type": "object",
+                "properties": schema.get("properties", {}),
+                "required": schema.get("required", []),
+            },
+        )
+
+    async def execute(
+        self, context: RunContext | None = None, **kwargs: Any
+    ) -> ToolResult:
+        context = context or RunContext()
+
+        tool_arguments = kwargs
+
+        if self._params_model:
+            try:
+                _fields = model_fields(self._params_model)
+                validation_input = {
+                    key: value for key, value in kwargs.items() if key in _fields
+                }
+
+                validated_params = self._params_model(**validation_input)
+
+                if not self._unpack_args:
+                    pass
+                else:
+                    validated_dict = model_dump(validated_params)
+                    tool_arguments = validated_dict
+
+            except ValidationError as e:
+                error_msgs = []
+                for err in e.errors():
+                    loc = ".".join(str(x) for x in err["loc"])
+                    msg = err["msg"]
+                    error_msgs.append(f"Parameter '{loc}': {msg}")
+
+                formatted_error = "; ".join(error_msgs)
+                error_payload = {
+                    "error_type": "InvalidArguments",
+                    "message": f"Parameter validation failed: {formatted_error}",
+                    "is_retryable": True,
+                }
+                return ToolResult(
+                    output=json.dumps(error_payload, ensure_ascii=False),
+                    display_content=f"Validation Error: {formatted_error}",
+                )
+            except Exception as e:
+                logger.error(
+                    f"执行工具 '{self._name}' 时参数验证或实例化失败: {e}", e=e
+                )
+                raise
+
+        state = {
+            "_tool_params": tool_arguments,
+            "_agent_context": context,
+        }
+
+        bot: Bot | None = None
+        if context and context.scope.get("bot"):
+            bot = context.scope.get("bot")
+        if not bot:
+            try:
+                bot = nonebot.get_bot()
+            except ValueError:
+                pass
+
+        event: Event | None = None
+        if context and context.scope.get("event"):
+            event = context.scope.get("event")
+
+        raw_result = await self.dependent(
+            bot=bot,
+            event=event,
+            state=state,
+        )
+
+        return ToolResult(output=raw_result, display_content=str(raw_result))
+
+
+class BuiltinFunctionToolProvider(ToolProvider):
+    """一个内置的 ToolProvider，用于处理通过装饰器注册的函数。"""
+
+    def __init__(self):
+        self._functions: dict[str, dict[str, Any]] = {}
+
+    def register(
+        self,
+        name: str,
+        func: Callable,
+        description: str,
+        params_model: type[BaseModel] | None = None,
+        unpack_args: bool = False,
+    ):
+        self._functions[name] = {
+            "func": func,
+            "description": description,
+            "params_model": params_model,
+            "unpack_args": unpack_args,
+        }
+
+    async def initialize(self) -> None:
+        pass
+
+    async def discover_tools(
+        self,
+        allowed_servers: list[str] | None = None,
+        excluded_servers: list[str] | None = None,
+    ) -> dict[str, ToolExecutable]:
+        executables = {}
+        for name, info in self._functions.items():
+            executables[name] = FunctionExecutable(
+                func=info["func"],
+                name=name,
+                description=info["description"],
+                params_model=info["params_model"],
+                unpack_args=info.get("unpack_args", False),
+            )
+        return executables
+
+    async def get_tool_executable(
+        self, name: str, config: dict[str, Any]
+    ) -> ToolExecutable | None:
+        if config.get("type", "function") == "function" and name in self._functions:
+            info = self._functions[name]
+            return FunctionExecutable(
+                func=info["func"],
+                name=name,
+                description=info["description"],
+                params_model=info["params_model"],
+                unpack_args=info.get("unpack_args", False),
+            )
+        return None
+
+
+class ToolProviderManager:
+    """工具提供者的中心化管理器，采用单例模式。"""
+
+    _instance: "ToolProviderManager | None" = None
+
+    def __new__(cls) -> "ToolProviderManager":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __init__(self):
+        if hasattr(self, "_initialized") and self._initialized:
+            return
+
+        self._providers: list[ToolProvider] = []
+        self._resolved_tools: dict[str, ToolExecutable] | None = None
+        self._init_lock = asyncio.Lock()
+        self._init_promise: asyncio.Task | None = None
+        self._builtin_function_provider = BuiltinFunctionToolProvider()
+        self.register(self._builtin_function_provider)
+        self._initialized = True
+
+    def register(self, provider: ToolProvider):
+        """注册一个新的 ToolProvider。"""
+        if provider not in self._providers:
+            self._providers.append(provider)
+            logger.info(f"已注册工具提供者: {provider.__class__.__name__}")
+
+    def function_tool(
+        self,
+        name: str,
+        description: str,
+        params_model: type[BaseModel] | None = None,
+    ):
+        """装饰器：将一个函数注册为内置工具。"""
+
+        def decorator(func: Callable):
+            if name in self._builtin_function_provider._functions:
+                logger.warning(f"正在覆盖已注册的函数工具: {name}")
+
+            final_model = params_model
+            unpack_args = False
+            if final_model is None:
+                final_model = _create_dynamic_model(func)
+                unpack_args = True
+
+            self._builtin_function_provider.register(
+                name=name,
+                func=func,
+                description=description,
+                params_model=final_model,
+                unpack_args=unpack_args,
+            )
+            logger.info(f"已注册函数工具: '{name}'")
+            return func
+
+        return decorator
+
+    async def initialize(self) -> None:
+        """懒加载初始化所有已注册的 ToolProvider。"""
+        if not self._init_promise:
+            async with self._init_lock:
+                if not self._init_promise:
+                    self._init_promise = asyncio.create_task(
+                        self._initialize_providers()
+                    )
+        await self._init_promise
+
+    async def _initialize_providers(self) -> None:
+        """内部初始化逻辑。"""
+        logger.info(f"开始初始化 {len(self._providers)} 个工具提供者...")
+        init_tasks = [provider.initialize() for provider in self._providers]
+        await asyncio.gather(*init_tasks, return_exceptions=True)
+        logger.info("所有工具提供者初始化完成。")
+
+    async def get_resolved_tools(
+        self,
+        allowed_servers: list[str] | None = None,
+        excluded_servers: list[str] | None = None,
+    ) -> dict[str, ToolExecutable]:
+        """
+        获取所有已发现和解析的工具。
+        此方法会触发懒加载初始化，并根据是否传入过滤器来决定是否使用全局缓存。
+        """
+        await self.initialize()
+
+        has_filters = allowed_servers is not None or excluded_servers is not None
+
+        if not has_filters and self._resolved_tools is not None:
+            logger.debug("使用全局工具缓存。")
+            return self._resolved_tools
+
+        if has_filters:
+            logger.info("检测到过滤器，执行临时工具发现 (不使用缓存)。")
+            logger.debug(
+                f"过滤器详情: allowed_servers={allowed_servers}, "
+                f"excluded_servers={excluded_servers}"
+            )
+        else:
+            logger.info("未应用过滤器，开始全局工具发现...")
+
+        all_tools: dict[str, ToolExecutable] = {}
+
+        discover_tasks = []
+        for provider in self._providers:
+            sig = inspect.signature(provider.discover_tools)
+            params_to_pass = {}
+            if "allowed_servers" in sig.parameters:
+                params_to_pass["allowed_servers"] = allowed_servers
+            if "excluded_servers" in sig.parameters:
+                params_to_pass["excluded_servers"] = excluded_servers
+
+            discover_tasks.append(provider.discover_tools(**params_to_pass))
+
+        results = await asyncio.gather(*discover_tasks, return_exceptions=True)
+
+        for i, provider_result in enumerate(results):
+            provider_name = self._providers[i].__class__.__name__
+            if isinstance(provider_result, dict):
+                logger.debug(
+                    f"提供者 '{provider_name}' 发现了 {len(provider_result)} 个工具。"
+                )
+                for name, executable in provider_result.items():
+                    if name in all_tools:
+                        logger.warning(
+                            f"发现重复的工具名称 '{name}'，后发现的将覆盖前者。"
+                        )
+                    all_tools[name] = executable
+            elif isinstance(provider_result, Exception):
+                logger.error(
+                    f"提供者 '{provider_name}' 在发现工具时出错: {provider_result}"
+                )
+
+        if not has_filters:
+            self._resolved_tools = all_tools
+            logger.info(f"全局工具发现完成，共找到并缓存了 {len(all_tools)} 个工具。")
+        else:
+            logger.info(f"带过滤器的工具发现完成，共找到 {len(all_tools)} 个工具。")
+
+        return all_tools
+
+    async def resolve_specific_tools(
+        self, tool_names: list[str]
+    ) -> dict[str, ToolExecutable]:
+        """
+        仅解析指定名称的工具，避免触发全量工具发现。
+        """
+        resolved: dict[str, ToolExecutable] = {}
+        if not tool_names:
+            return resolved
+
+        await self.initialize()
+
+        for name in tool_names:
+            config: dict[str, Any] = {"name": name}
+            for provider in self._providers:
+                try:
+                    executable = await provider.get_tool_executable(name, config)
+                except Exception as exc:
+                    logger.error(
+                        f"provider '{provider.__class__.__name__}' 在解析工具 '{name}'"
+                        f"时出错: {exc}",
+                        e=exc,
+                    )
+                    continue
+
+                if executable:
+                    resolved[name] = executable
+                    break
+            else:
+                logger.warning(f"没有找到名为 '{name}' 的工具，已跳过。")
+
+        return resolved
+
+    async def get_function_tools(
+        self, names: list[str] | None = None
+    ) -> dict[str, ToolExecutable]:
+        """
+        仅从内置的函数提供者中解析指定的工具。
+        """
+        all_function_tools = await self._builtin_function_provider.discover_tools()
+        if names is None:
+            return all_function_tools
+
+        resolved_tools = {}
+        for name in names:
+            if name in all_function_tools:
+                resolved_tools[name] = all_function_tools[name]
+            else:
+                logger.warning(
+                    f"本地函数工具 '{name}' 未通过 @function_tool 注册，将被忽略。"
+                )
+        return resolved_tools
+
+
+tool_provider_manager = ToolProviderManager()
+function_tool = tool_provider_manager.function_tool
+
+
+class ToolErrorType(str, Enum):
+    """结构化工具错误的类型枚举。"""
+
+    TOOL_NOT_FOUND = "ToolNotFound"
+    INVALID_ARGUMENTS = "InvalidArguments"
+    EXECUTION_ERROR = "ExecutionError"
+    USER_CANCELLATION = "UserCancellation"
+
+
+class ToolErrorResult(BaseModel):
+    """一个结构化的工具执行错误模型。"""
+
+    error_type: ToolErrorType = Field(..., description="错误的类型。")
+    message: str = Field(..., description="对错误的详细描述。")
+    is_retryable: bool = Field(False, description="指示这个错误是否可能通过重试解决。")
+
+
+class ToolInvoker:
+    """
+    全能工具执行器。
+    负责接收工具调用请求，解析参数，触发回调，执行工具，并返回标准化的结果。
+    """
+
+    def __init__(self, callbacks: list[BaseCallbackHandler] | None = None):
+        self.callbacks = callbacks or []
+
+    async def _trigger_callbacks(self, event_name: str, *args, **kwargs: Any) -> None:
+        if not self.callbacks:
+            return
+        tasks = [
+            getattr(handler, event_name)(*args, **kwargs)
+            for handler in self.callbacks
+            if hasattr(handler, event_name)
+        ]
+        await asyncio.gather(*tasks, return_exceptions=True)
+
+    async def execute_tool_call(
+        self,
+        tool_call: LLMToolCall,
+        available_tools: dict[str, ToolExecutable],
+        context: Any | None = None,
+    ) -> tuple[LLMToolCall, ToolResult]:
+        tool_name = tool_call.function.name
+        arguments_str = tool_call.function.arguments
+        arguments: dict[str, Any] = {}
+
+        try:
+            if arguments_str:
+                arguments = json.loads(arguments_str)
+        except json.JSONDecodeError as e:
+            error_result = ToolErrorResult(
+                error_type=ToolErrorType.INVALID_ARGUMENTS,
+                message=f"参数解析失败: {e}",
+                is_retryable=False,
+            )
+            return tool_call, ToolResult(output=model_dump(error_result))
+
+        tool_data = ToolCallData(tool_name=tool_name, tool_args=arguments)
+        pre_calculated_result: ToolResult | None = None
+        for handler in self.callbacks:
+            res = await handler.on_tool_start(tool_call, tool_data)
+            if isinstance(res, ToolCallData):
+                tool_data = res
+                arguments = tool_data.tool_args
+                tool_call.function.arguments = json.dumps(arguments, ensure_ascii=False)
+            elif isinstance(res, ToolResult):
+                pre_calculated_result = res
+                break
+
+        if pre_calculated_result:
+            return tool_call, pre_calculated_result
+
+        executable = available_tools.get(tool_name)
+        if not executable:
+            error_result = ToolErrorResult(
+                error_type=ToolErrorType.TOOL_NOT_FOUND,
+                message=f"Tool '{tool_name}' not found.",
+                is_retryable=False,
+            )
+            return tool_call, ToolResult(output=model_dump(error_result))
+
+        from .config.providers import get_llm_config
+
+        if not get_llm_config().debug_log:
+            try:
+                definition = await executable.get_definition()
+                schema_payload = getattr(definition, "parameters", {})
+                schema_json = fast_json.dumps(
+                    schema_payload,
+                    ensure_ascii=False,
+                )
+                logger.debug(
+                    f"🔍 [JIT Schema] {tool_name}: {schema_json}",
+                    "ToolInvoker",
+                )
+            except Exception as e:
+                logger.trace(f"JIT Schema logging failed: {e}")
+
+        start_t = time.monotonic()
+        result: ToolResult | None = None
+        error: Exception | None = None
+
+        try:
+
+            @Retry.simple(stop_max_attempt=2, wait_fixed_seconds=1)
+            async def execute_with_retry():
+                return await executable.execute(context=context, **arguments)
+
+            result = await execute_with_retry()
+        except ValidationError as e:
+            error = e
+            error_msgs = []
+            for err in e.errors():
+                loc = ".".join(str(x) for x in err["loc"])
+                msg = err["msg"]
+                error_msgs.append(f"参数 '{loc}': {msg}")
+
+            formatted_error = "; ".join(error_msgs)
+            error_result = ToolErrorResult(
+                error_type=ToolErrorType.INVALID_ARGUMENTS,
+                message=f"参数验证失败。请根据错误修正你的输入: {formatted_error}",
+                is_retryable=True,
+            )
+            result = ToolResult(output=model_dump(error_result))
+        except (TimeoutException, NetworkError) as e:
+            error = e
+            error_result = ToolErrorResult(
+                error_type=ToolErrorType.EXECUTION_ERROR,
+                message=f"工具执行网络超时或连接失败: {e!s}",
+                is_retryable=False,
+            )
+            result = ToolResult(output=model_dump(error_result))
+        except Exception as e:
+            error = e
+            error_type = ToolErrorType.EXECUTION_ERROR
+            if (
+                isinstance(e, LLMException)
+                and e.code == LLMErrorCode.CONFIGURATION_ERROR
+            ):
+                error_type = ToolErrorType.TOOL_NOT_FOUND
+                is_retryable = False
+
+            is_retryable = False
+
+            error_result = ToolErrorResult(
+                error_type=error_type, message=str(e), is_retryable=is_retryable
+            )
+            result = ToolResult(output=model_dump(error_result))
+
+        duration = time.monotonic() - start_t
+
+        await self._trigger_callbacks(
+            "on_tool_end",
+            result=result,
+            error=error,
+            tool_call=tool_call,
+            duration=duration,
+        )
+
+        if result is None:
+            raise LLMException("工具执行未返回任何结果。")
+
+        return tool_call, result
+
+    async def execute_batch(
+        self,
+        tool_calls: list[LLMToolCall],
+        available_tools: dict[str, ToolExecutable],
+        context: Any | None = None,
+    ) -> list[LLMMessage]:
+        if not tool_calls:
+            return []
+
+        tasks = [
+            self.execute_tool_call(call, available_tools, context)
+            for call in tool_calls
+        ]
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+
+        tool_messages: list[LLMMessage] = []
+        for index, result_pair in enumerate(results):
+            original_call = tool_calls[index]
+
+            if isinstance(result_pair, Exception):
+                logger.error(
+                    f"工具执行发生未捕获异常: {original_call.function.name}, "
+                    f"错误: {result_pair}"
+                )
+                tool_messages.append(
+                    LLMMessage.tool_response(
+                        tool_call_id=original_call.id,
+                        function_name=original_call.function.name,
+                        result={
+                            "error": f"System Execution Error: {result_pair}",
+                            "status": "failed",
+                        },
+                    )
+                )
+                continue
+
+            tool_call_result = cast(tuple[LLMToolCall, ToolResult], result_pair)
+            _, tool_result = tool_call_result
+            tool_messages.append(
+                LLMMessage.tool_response(
+                    tool_call_id=original_call.id,
+                    function_name=original_call.function.name,
+                    result=tool_result.output,
+                )
+            )
+        return tool_messages
+
+
+__all__ = [
+    "RunContext",
+    "RunContextParam",
+    "ToolErrorResult",
+    "ToolErrorType",
+    "ToolInvoker",
+    "ToolParam",
+    "function_tool",
+    "tool_provider_manager",
+]
diff --git a/zhenxun/services/llm/tools/__init__.py b/zhenxun/services/llm/tools/__init__.py
deleted file mode 100644
index ffb1c691..00000000
--- a/zhenxun/services/llm/tools/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""
-工具模块导出
-"""
-
-from .manager import tool_provider_manager
-
-function_tool = tool_provider_manager.function_tool
-
-
-__all__ = [
-    "function_tool",
-    "tool_provider_manager",
-]
diff --git a/zhenxun/services/llm/tools/manager.py b/zhenxun/services/llm/tools/manager.py
deleted file mode 100644
index 6cbf3136..00000000
--- a/zhenxun/services/llm/tools/manager.py
+++ /dev/null
@@ -1,293 +0,0 @@
-"""
-工具提供者管理器
-
-负责注册、生命周期管理（包括懒加载）和统一提供所有工具。
-"""
-
-import asyncio
-from collections.abc import Callable
-import inspect
-from typing import Any
-
-from pydantic import BaseModel
-
-from zhenxun.services.log import logger
-from zhenxun.utils.pydantic_compat import model_json_schema
-
-from ..types import ToolExecutable, ToolProvider
-from ..types.models import ToolDefinition, ToolResult
-
-
-class FunctionExecutable(ToolExecutable):
-    """一个 ToolExecutable 的实现，用于包装一个普通的 Python 函数。"""
-
-    def __init__(
-        self,
-        func: Callable,
-        name: str,
-        description: str,
-        params_model: type[BaseModel] | None,
-    ):
-        self._func = func
-        self._name = name
-        self._description = description
-        self._params_model = params_model
-
-    async def get_definition(self) -> ToolDefinition:
-        if not self._params_model:
-            return ToolDefinition(
-                name=self._name,
-                description=self._description,
-                parameters={"type": "object", "properties": {}},
-            )
-
-        schema = model_json_schema(self._params_model)
-
-        return ToolDefinition(
-            name=self._name,
-            description=self._description,
-            parameters={
-                "type": "object",
-                "properties": schema.get("properties", {}),
-                "required": schema.get("required", []),
-            },
-        )
-
-    async def execute(self, **kwargs: Any) -> ToolResult:
-        raw_result: Any
-
-        if self._params_model:
-            try:
-                params_instance = self._params_model(**kwargs)
-
-                if inspect.iscoroutinefunction(self._func):
-                    raw_result = await self._func(params_instance)
-                else:
-                    loop = asyncio.get_event_loop()
-                    raw_result = await loop.run_in_executor(
-                        None, lambda: self._func(params_instance)
-                    )
-            except Exception as e:
-                logger.error(
-                    f"执行工具 '{self._name}' 时参数验证或实例化失败: {e}", e=e
-                )
-                raise
-        else:
-            if inspect.iscoroutinefunction(self._func):
-                raw_result = await self._func(**kwargs)
-            else:
-                loop = asyncio.get_event_loop()
-                raw_result = await loop.run_in_executor(
-                    None, lambda: self._func(**kwargs)
-                )
-
-        return ToolResult(output=raw_result, display_content=str(raw_result))
-
-
-class BuiltinFunctionToolProvider(ToolProvider):
-    """一个内置的 ToolProvider，用于处理通过装饰器注册的函数。"""
-
-    def __init__(self):
-        self._functions: dict[str, dict[str, Any]] = {}
-
-    def register(
-        self,
-        name: str,
-        func: Callable,
-        description: str,
-        params_model: type[BaseModel] | None,
-    ):
-        self._functions[name] = {
-            "func": func,
-            "description": description,
-            "params_model": params_model,
-        }
-
-    async def initialize(self) -> None:
-        pass
-
-    async def discover_tools(
-        self,
-        allowed_servers: list[str] | None = None,
-        excluded_servers: list[str] | None = None,
-    ) -> dict[str, ToolExecutable]:
-        executables = {}
-        for name, info in self._functions.items():
-            executables[name] = FunctionExecutable(
-                func=info["func"],
-                name=name,
-                description=info["description"],
-                params_model=info["params_model"],
-            )
-        return executables
-
-    async def get_tool_executable(
-        self, name: str, config: dict[str, Any]
-    ) -> ToolExecutable | None:
-        if config.get("type") == "function" and name in self._functions:
-            info = self._functions[name]
-            return FunctionExecutable(
-                func=info["func"],
-                name=name,
-                description=info["description"],
-                params_model=info["params_model"],
-            )
-        return None
-
-
-class ToolProviderManager:
-    """工具提供者的中心化管理器，采用单例模式。"""
-
-    _instance: "ToolProviderManager | None" = None
-
-    def __new__(cls) -> "ToolProviderManager":
-        if cls._instance is None:
-            cls._instance = super().__new__(cls)
-        return cls._instance
-
-    def __init__(self):
-        if hasattr(self, "_initialized") and self._initialized:
-            return
-
-        self._providers: list[ToolProvider] = []
-        self._resolved_tools: dict[str, ToolExecutable] | None = None
-        self._init_lock = asyncio.Lock()
-        self._init_promise: asyncio.Task | None = None
-        self._builtin_function_provider = BuiltinFunctionToolProvider()
-        self.register(self._builtin_function_provider)
-        self._initialized = True
-
-    def register(self, provider: ToolProvider):
-        """注册一个新的 ToolProvider。"""
-        if provider not in self._providers:
-            self._providers.append(provider)
-            logger.info(f"已注册工具提供者: {provider.__class__.__name__}")
-
-    def function_tool(
-        self,
-        name: str,
-        description: str,
-        params_model: type[BaseModel] | None = None,
-    ):
-        """装饰器：将一个函数注册为内置工具。"""
-
-        def decorator(func: Callable):
-            if name in self._builtin_function_provider._functions:
-                logger.warning(f"正在覆盖已注册的函数工具: {name}")
-
-            self._builtin_function_provider.register(
-                name=name,
-                func=func,
-                description=description,
-                params_model=params_model,
-            )
-            logger.info(f"已注册函数工具: '{name}'")
-            return func
-
-        return decorator
-
-    async def initialize(self) -> None:
-        """懒加载初始化所有已注册的 ToolProvider。"""
-        if not self._init_promise:
-            async with self._init_lock:
-                if not self._init_promise:
-                    self._init_promise = asyncio.create_task(
-                        self._initialize_providers()
-                    )
-        await self._init_promise
-
-    async def _initialize_providers(self) -> None:
-        """内部初始化逻辑。"""
-        logger.info(f"开始初始化 {len(self._providers)} 个工具提供者...")
-        init_tasks = [provider.initialize() for provider in self._providers]
-        await asyncio.gather(*init_tasks, return_exceptions=True)
-        logger.info("所有工具提供者初始化完成。")
-
-    async def get_resolved_tools(
-        self,
-        allowed_servers: list[str] | None = None,
-        excluded_servers: list[str] | None = None,
-    ) -> dict[str, ToolExecutable]:
-        """
-        获取所有已发现和解析的工具。
-        此方法会触发懒加载初始化，并根据是否传入过滤器来决定是否使用全局缓存。
-        """
-        await self.initialize()
-
-        has_filters = allowed_servers is not None or excluded_servers is not None
-
-        if not has_filters and self._resolved_tools is not None:
-            logger.debug("使用全局工具缓存。")
-            return self._resolved_tools
-
-        if has_filters:
-            logger.info("检测到过滤器，执行临时工具发现 (不使用缓存)。")
-            logger.debug(
-                f"过滤器详情: allowed_servers={allowed_servers}, "
-                f"excluded_servers={excluded_servers}"
-            )
-        else:
-            logger.info("未应用过滤器，开始全局工具发现...")
-
-        all_tools: dict[str, ToolExecutable] = {}
-
-        discover_tasks = []
-        for provider in self._providers:
-            sig = inspect.signature(provider.discover_tools)
-            params_to_pass = {}
-            if "allowed_servers" in sig.parameters:
-                params_to_pass["allowed_servers"] = allowed_servers
-            if "excluded_servers" in sig.parameters:
-                params_to_pass["excluded_servers"] = excluded_servers
-
-            discover_tasks.append(provider.discover_tools(**params_to_pass))
-
-        results = await asyncio.gather(*discover_tasks, return_exceptions=True)
-
-        for i, provider_result in enumerate(results):
-            provider_name = self._providers[i].__class__.__name__
-            if isinstance(provider_result, dict):
-                logger.debug(
-                    f"提供者 '{provider_name}' 发现了 {len(provider_result)} 个工具。"
-                )
-                for name, executable in provider_result.items():
-                    if name in all_tools:
-                        logger.warning(
-                            f"发现重复的工具名称 '{name}'，后发现的将覆盖前者。"
-                        )
-                    all_tools[name] = executable
-            elif isinstance(provider_result, Exception):
-                logger.error(
-                    f"提供者 '{provider_name}' 在发现工具时出错: {provider_result}"
-                )
-
-        if not has_filters:
-            self._resolved_tools = all_tools
-            logger.info(f"全局工具发现完成，共找到并缓存了 {len(all_tools)} 个工具。")
-        else:
-            logger.info(f"带过滤器的工具发现完成，共找到 {len(all_tools)} 个工具。")
-
-        return all_tools
-
-    async def get_function_tools(
-        self, names: list[str] | None = None
-    ) -> dict[str, ToolExecutable]:
-        """
-        仅从内置的函数提供者中解析指定的工具。
-        """
-        all_function_tools = await self._builtin_function_provider.discover_tools()
-        if names is None:
-            return all_function_tools
-
-        resolved_tools = {}
-        for name in names:
-            if name in all_function_tools:
-                resolved_tools[name] = all_function_tools[name]
-            else:
-                logger.warning(
-                    f"本地函数工具 '{name}' 未通过 @function_tool 注册，将被忽略。"
-                )
-        return resolved_tools
-
-
-tool_provider_manager = ToolProviderManager()
diff --git a/zhenxun/services/llm/types/__init__.py b/zhenxun/services/llm/types/__init__.py
index 183e71db..4a16924a 100644
--- a/zhenxun/services/llm/types/__init__.py
+++ b/zhenxun/services/llm/types/__init__.py
@@ -5,30 +5,32 @@ LLM 类型定义模块
 """
 
 from .capabilities import ModelCapabilities, ModelModality, get_model_capabilities
-from .content import (
-    LLMContentPart,
-    LLMMessage,
-    LLMResponse,
-)
-from .enums import (
-    EmbeddingTaskType,
-    ModelProvider,
-    ResponseFormat,
-    TaskType,
-    ToolCategory,
-)
 from .exceptions import LLMErrorCode, LLMException, get_user_friendly_error_message
 from .models import (
+    CodeExecutionOutcome,
+    EmbeddingTaskType,
+    GeminiCodeExecution,
+    GeminiGoogleSearch,
+    GeminiUrlContext,
     LLMCacheInfo,
     LLMCodeExecution,
+    LLMContentPart,
     LLMGroundingAttribution,
     LLMGroundingMetadata,
+    LLMMessage,
+    LLMResponse,
     LLMToolCall,
     LLMToolFunction,
     ModelDetail,
     ModelInfo,
     ModelName,
+    ModelProvider,
     ProviderConfig,
+    ResponseFormat,
+    StructuredOutputStrategy,
+    TaskType,
+    ToolCategory,
+    ToolChoice,
     ToolMetadata,
     ToolResult,
     UsageInfo,
@@ -36,7 +38,11 @@ from .models import (
 from .protocols import ToolExecutable, ToolProvider
 
 __all__ = [
+    "CodeExecutionOutcome",
     "EmbeddingTaskType",
+    "GeminiCodeExecution",
+    "GeminiGoogleSearch",
+    "GeminiUrlContext",
     "LLMCacheInfo",
     "LLMCodeExecution",
     "LLMContentPart",
@@ -56,8 +62,10 @@ __all__ = [
     "ModelProvider",
     "ProviderConfig",
     "ResponseFormat",
+    "StructuredOutputStrategy",
     "TaskType",
     "ToolCategory",
+    "ToolChoice",
     "ToolExecutable",
     "ToolMetadata",
     "ToolProvider",
diff --git a/zhenxun/services/llm/types/capabilities.py b/zhenxun/services/llm/types/capabilities.py
index 2e083708..4e7ccd09 100644
--- a/zhenxun/services/llm/types/capabilities.py
+++ b/zhenxun/services/llm/types/capabilities.py
@@ -6,6 +6,7 @@ LLM 模型能力定义模块
 
 from enum import Enum
 import fnmatch
+from typing import Literal
 
 from pydantic import BaseModel, Field
 
@@ -20,6 +21,35 @@ class ModelModality(str, Enum):
     EMBEDDING = "embedding"
 
 
+class ReasoningMode(str, Enum):
+    """推理/思考模式类型"""
+
+    NONE = "none"
+    BUDGET = "budget"
+    LEVEL = "level"
+    EFFORT = "effort"
+
+
+PATTERNS_GEMINI_2_5 = [
+    "gemini-2.5*",
+    "gemini-flash*",
+    "gemini*lite*",
+    "gemini-flash-latest",
+]
+
+PATTERNS_GEMINI_3 = [
+    "gemini-3*",
+    "gemini-exp*",
+]
+
+PATTERNS_OPENAI_REASONING = [
+    "o1-*",
+    "o3-*",
+    "deepseek-r1*",
+    "deepseek-reasoner",
+]
+
+
 class ModelCapabilities(BaseModel):
     """定义一个模型的核心、稳定能力。"""
 
@@ -27,6 +57,8 @@ class ModelCapabilities(BaseModel):
     output_modalities: set[ModelModality] = Field(default={ModelModality.TEXT})
     supports_tool_calling: bool = False
     is_embedding_model: bool = False
+    reasoning_mode: ReasoningMode = ReasoningMode.NONE
+    reasoning_visibility: Literal["visible", "hidden", "none"] = "none"
 
 
 STANDARD_TEXT_TOOL_CAPABILITIES = ModelCapabilities(
@@ -35,7 +67,7 @@ STANDARD_TEXT_TOOL_CAPABILITIES = ModelCapabilities(
     supports_tool_calling=True,
 )
 
-GEMINI_CAPABILITIES = ModelCapabilities(
+CAP_GEMINI_2_5 = ModelCapabilities(
     input_modalities={
         ModelModality.TEXT,
         ModelModality.IMAGE,
@@ -44,21 +76,44 @@ GEMINI_CAPABILITIES = ModelCapabilities(
     },
     output_modalities={ModelModality.TEXT},
     supports_tool_calling=True,
+    reasoning_mode=ReasoningMode.BUDGET,
+    reasoning_visibility="visible",
 )
 
-GEMINI_IMAGE_GEN_CAPABILITIES = ModelCapabilities(
+CAP_GEMINI_3 = ModelCapabilities(
+    input_modalities={
+        ModelModality.TEXT,
+        ModelModality.IMAGE,
+        ModelModality.AUDIO,
+        ModelModality.VIDEO,
+    },
+    output_modalities={ModelModality.TEXT},
+    supports_tool_calling=True,
+    reasoning_mode=ReasoningMode.LEVEL,
+    reasoning_visibility="visible",
+)
+
+CAP_GEMINI_IMAGE_GEN = ModelCapabilities(
     input_modalities={ModelModality.TEXT, ModelModality.IMAGE},
     output_modalities={ModelModality.TEXT, ModelModality.IMAGE},
     supports_tool_calling=True,
 )
 
-GPT_ADVANCED_TEXT_IMAGE_CAPABILITIES = ModelCapabilities(
+CAP_OPENAI_REASONING = ModelCapabilities(
+    input_modalities={ModelModality.TEXT, ModelModality.IMAGE},
+    output_modalities={ModelModality.TEXT},
+    supports_tool_calling=True,
+    reasoning_mode=ReasoningMode.EFFORT,
+    reasoning_visibility="hidden",
+)
+
+CAP_GPT_ADVANCED = ModelCapabilities(
     input_modalities={ModelModality.TEXT, ModelModality.IMAGE},
     output_modalities={ModelModality.TEXT},
     supports_tool_calling=True,
 )
 
-GPT_MULTIMODAL_IO_CAPABILITIES = ModelCapabilities(
+CAP_GPT_MULTIMODAL_IO = ModelCapabilities(
     input_modalities={ModelModality.TEXT, ModelModality.AUDIO, ModelModality.IMAGE},
     output_modalities={ModelModality.TEXT, ModelModality.AUDIO},
     supports_tool_calling=True,
@@ -76,6 +131,12 @@ GPT_VIDEO_GENERATION_CAPABILITIES = ModelCapabilities(
     supports_tool_calling=True,
 )
 
+EMBEDDING_CAPABILITIES = ModelCapabilities(
+    input_modalities={ModelModality.TEXT},
+    output_modalities={ModelModality.EMBEDDING},
+    is_embedding_model=True,
+)
+
 DEFAULT_PERMISSIVE_CAPABILITIES = ModelCapabilities(
     input_modalities={
         ModelModality.TEXT,
@@ -107,17 +168,33 @@ MODEL_ALIAS_MAPPING: dict[str, str] = {
 }
 
 
-MODEL_CAPABILITIES_REGISTRY: dict[str, ModelCapabilities] = {
-    "gemini-*-tts": ModelCapabilities(
+def _build_registry() -> dict[str, ModelCapabilities]:
+    """构建模型能力注册表，展开模式列表以减少冗余"""
+    registry: dict[str, ModelCapabilities] = {}
+
+    def register_family(patterns: list[str], cap: ModelCapabilities) -> None:
+        for pattern in patterns:
+            registry[pattern] = cap
+
+    register_family(
+        ["*gemini-*-image-preview*", "gemini-*-image*"], CAP_GEMINI_IMAGE_GEN
+    )
+
+    register_family(PATTERNS_GEMINI_2_5, CAP_GEMINI_2_5)
+    register_family(PATTERNS_GEMINI_3, CAP_GEMINI_3)
+
+    register_family(PATTERNS_OPENAI_REASONING, CAP_OPENAI_REASONING)
+
+    registry["gemini-*-tts"] = ModelCapabilities(
         input_modalities={ModelModality.TEXT},
         output_modalities={ModelModality.AUDIO},
-    ),
-    "gemini-*-native-audio-*": ModelCapabilities(
+    )
+    registry["gemini-*-native-audio-*"] = ModelCapabilities(
         input_modalities={ModelModality.TEXT, ModelModality.AUDIO, ModelModality.VIDEO},
         output_modalities={ModelModality.TEXT, ModelModality.AUDIO},
         supports_tool_calling=True,
-    ),
-    "gemini-2.0-flash-preview-image-generation": ModelCapabilities(
+    )
+    registry["gemini-2.0-flash-preview-image-generation"] = ModelCapabilities(
         input_modalities={
             ModelModality.TEXT,
             ModelModality.IMAGE,
@@ -126,39 +203,39 @@ MODEL_CAPABILITIES_REGISTRY: dict[str, ModelCapabilities] = {
         },
         output_modalities={ModelModality.TEXT, ModelModality.IMAGE},
         supports_tool_calling=True,
-    ),
-    "gemini-embedding-exp": ModelCapabilities(
-        input_modalities={ModelModality.TEXT},
-        output_modalities={ModelModality.EMBEDDING},
-        is_embedding_model=True,
-    ),
-    "*gemini-*-image-preview*": GEMINI_IMAGE_GEN_CAPABILITIES,
-    "gemini-*-pro*": GEMINI_CAPABILITIES,
-    "gemini-*-flash*": GEMINI_CAPABILITIES,
-    "GLM-4V-Flash": ModelCapabilities(
+    )
+
+    registry["GLM-4V-Flash"] = ModelCapabilities(
         input_modalities={ModelModality.TEXT, ModelModality.IMAGE},
         output_modalities={ModelModality.TEXT},
         supports_tool_calling=True,
-    ),
-    "GLM-4V-Plus*": ModelCapabilities(
+    )
+    registry["GLM-4V-Plus*"] = ModelCapabilities(
         input_modalities={ModelModality.TEXT, ModelModality.IMAGE, ModelModality.VIDEO},
         output_modalities={ModelModality.TEXT},
         supports_tool_calling=True,
-    ),
-    "glm-4-*": STANDARD_TEXT_TOOL_CAPABILITIES,
-    "glm-z1-*": STANDARD_TEXT_TOOL_CAPABILITIES,
-    "doubao-seed-*": DOUBAO_ADVANCED_MULTIMODAL_CAPABILITIES,
-    "doubao-1-5-thinking-vision-pro": DOUBAO_ADVANCED_MULTIMODAL_CAPABILITIES,
-    "deepseek-chat": STANDARD_TEXT_TOOL_CAPABILITIES,
-    "deepseek-reasoner": STANDARD_TEXT_TOOL_CAPABILITIES,
-    "gpt-5*": GPT_ADVANCED_TEXT_IMAGE_CAPABILITIES,
-    "gpt-4.1*": GPT_ADVANCED_TEXT_IMAGE_CAPABILITIES,
-    "gpt-4o*": GPT_MULTIMODAL_IO_CAPABILITIES,
-    "o3*": GPT_ADVANCED_TEXT_IMAGE_CAPABILITIES,
-    "o4-mini*": GPT_ADVANCED_TEXT_IMAGE_CAPABILITIES,
-    "gpt image*": GPT_IMAGE_GENERATION_CAPABILITIES,
-    "sora*": GPT_VIDEO_GENERATION_CAPABILITIES,
-}
+    )
+
+    register_family(
+        ["glm-4-*", "glm-z1-*", "deepseek-chat"], STANDARD_TEXT_TOOL_CAPABILITIES
+    )
+    register_family(
+        ["doubao-seed-*", "doubao-1-5-thinking-vision-pro"],
+        DOUBAO_ADVANCED_MULTIMODAL_CAPABILITIES,
+    )
+
+    register_family(["gpt-5*", "gpt-4.1*", "o4-mini*"], CAP_GPT_ADVANCED)
+    registry["gpt-4o*"] = CAP_GPT_MULTIMODAL_IO
+
+    registry["gpt image*"] = GPT_IMAGE_GENERATION_CAPABILITIES
+    registry["sora*"] = GPT_VIDEO_GENERATION_CAPABILITIES
+
+    registry["*embedding*"] = EMBEDDING_CAPABILITIES
+
+    return registry
+
+
+MODEL_CAPABILITIES_REGISTRY = _build_registry()
 
 
 def get_model_capabilities(model_name: str) -> ModelCapabilities:
diff --git a/zhenxun/services/llm/types/content.py b/zhenxun/services/llm/types/content.py
deleted file mode 100644
index 2ceee5a4..00000000
--- a/zhenxun/services/llm/types/content.py
+++ /dev/null
@@ -1,434 +0,0 @@
-"""
-LLM 内容类型定义
-
-包含多模态内容部分、消息和响应的数据模型。
-"""
-
-import base64
-import mimetypes
-from pathlib import Path
-from typing import Any
-
-import aiofiles
-from pydantic import BaseModel
-
-from zhenxun.services.log import logger
-
-
-class LLMContentPart(BaseModel):
-    """LLM 消息内容部分 - 支持多模态内容"""
-
-    type: str
-    text: str | None = None
-    image_source: str | None = None
-    audio_source: str | None = None
-    video_source: str | None = None
-    document_source: str | None = None
-    file_uri: str | None = None
-    file_source: str | None = None
-    url: str | None = None
-    mime_type: str | None = None
-    metadata: dict[str, Any] | None = None
-
-    def model_post_init(self, /, __context: Any) -> None:
-        """验证内容部分的有效性"""
-        _ = __context
-        validation_rules = {
-            "text": lambda: self.text,
-            "image": lambda: self.image_source,
-            "audio": lambda: self.audio_source,
-            "video": lambda: self.video_source,
-            "document": lambda: self.document_source,
-            "file": lambda: self.file_uri or self.file_source,
-            "url": lambda: self.url,
-        }
-
-        if self.type in validation_rules:
-            if not validation_rules[self.type]():
-                raise ValueError(f"{self.type}类型的内容部分必须包含相应字段")
-
-    @classmethod
-    def text_part(cls, text: str) -> "LLMContentPart":
-        """创建文本内容部分"""
-        return cls(type="text", text=text)
-
-    @classmethod
-    def image_url_part(cls, url: str) -> "LLMContentPart":
-        """创建图片URL内容部分"""
-        return cls(type="image", image_source=url)
-
-    @classmethod
-    def image_base64_part(
-        cls, data: str, mime_type: str = "image/png"
-    ) -> "LLMContentPart":
-        """创建Base64图片内容部分"""
-        data_url = f"data:{mime_type};base64,{data}"
-        return cls(type="image", image_source=data_url)
-
-    @classmethod
-    def audio_url_part(cls, url: str, mime_type: str = "audio/wav") -> "LLMContentPart":
-        """创建音频URL内容部分"""
-        return cls(type="audio", audio_source=url, mime_type=mime_type)
-
-    @classmethod
-    def video_url_part(cls, url: str, mime_type: str = "video/mp4") -> "LLMContentPart":
-        """创建视频URL内容部分"""
-        return cls(type="video", video_source=url, mime_type=mime_type)
-
-    @classmethod
-    def video_base64_part(
-        cls, data: str, mime_type: str = "video/mp4"
-    ) -> "LLMContentPart":
-        """创建Base64视频内容部分"""
-        data_url = f"data:{mime_type};base64,{data}"
-        return cls(type="video", video_source=data_url, mime_type=mime_type)
-
-    @classmethod
-    def audio_base64_part(
-        cls, data: str, mime_type: str = "audio/wav"
-    ) -> "LLMContentPart":
-        """创建Base64音频内容部分"""
-        data_url = f"data:{mime_type};base64,{data}"
-        return cls(type="audio", audio_source=data_url, mime_type=mime_type)
-
-    @classmethod
-    def file_uri_part(
-        cls,
-        file_uri: str,
-        mime_type: str | None = None,
-        metadata: dict[str, Any] | None = None,
-    ) -> "LLMContentPart":
-        """创建Gemini File API URI内容部分"""
-        return cls(
-            type="file",
-            file_uri=file_uri,
-            mime_type=mime_type,
-            metadata=metadata or {},
-        )
-
-    @classmethod
-    async def from_path(
-        cls, path_like: str | Path, target_api: str | None = None
-    ) -> "LLMContentPart | None":
-        """
-        从本地文件路径创建 LLMContentPart。
-        自动检测MIME类型，并根据类型（如图片）可能加载为Base64。
-        target_api 可以用于提示如何最好地准备数据（例如 'gemini' 可能偏好 base64）
-        """
-        try:
-            path = Path(path_like)
-            if not path.exists() or not path.is_file():
-                logger.warning(f"文件不存在或不是一个文件: {path}")
-                return None
-
-            mime_type, _ = mimetypes.guess_type(path.resolve().as_uri())
-
-            if not mime_type:
-                logger.warning(
-                    f"无法猜测文件 {path.name} 的MIME类型，将尝试作为文本文件处理。"
-                )
-                try:
-                    async with aiofiles.open(path, encoding="utf-8") as f:
-                        text_content = await f.read()
-                    return cls.text_part(text_content)
-                except Exception as e:
-                    logger.error(f"读取文本文件 {path.name} 失败: {e}")
-                    return None
-
-            if mime_type.startswith("image/"):
-                if target_api == "gemini" or not path.is_absolute():
-                    try:
-                        async with aiofiles.open(path, "rb") as f:
-                            img_bytes = await f.read()
-                        base64_data = base64.b64encode(img_bytes).decode("utf-8")
-                        return cls.image_base64_part(
-                            data=base64_data, mime_type=mime_type
-                        )
-                    except Exception as e:
-                        logger.error(f"读取或编码图片文件 {path.name} 失败: {e}")
-                        return None
-                else:
-                    logger.warning(
-                        f"为本地图片路径 {path.name} 生成 image_url_part。"
-                        "实际API可能不支持 file:// URI。考虑使用Base64或公网URL。"
-                    )
-                    return cls.image_url_part(url=path.resolve().as_uri())
-            elif mime_type.startswith("audio/"):
-                return cls.audio_url_part(
-                    url=path.resolve().as_uri(), mime_type=mime_type
-                )
-            elif mime_type.startswith("video/"):
-                if target_api == "gemini":
-                    # 对于 Gemini API，将视频转换为 base64
-                    try:
-                        async with aiofiles.open(path, "rb") as f:
-                            video_bytes = await f.read()
-                        base64_data = base64.b64encode(video_bytes).decode("utf-8")
-                        return cls.video_base64_part(
-                            data=base64_data, mime_type=mime_type
-                        )
-                    except Exception as e:
-                        logger.error(f"读取或编码视频文件 {path.name} 失败: {e}")
-                        return None
-                else:
-                    return cls.video_url_part(
-                        url=path.resolve().as_uri(), mime_type=mime_type
-                    )
-            elif (
-                mime_type.startswith("text/")
-                or mime_type == "application/json"
-                or mime_type == "application/xml"
-            ):
-                try:
-                    async with aiofiles.open(path, encoding="utf-8") as f:
-                        text_content = await f.read()
-                    return cls.text_part(text_content)
-                except Exception as e:
-                    logger.error(f"读取文本类文件 {path.name} 失败: {e}")
-                    return None
-            else:
-                logger.info(
-                    f"文件 {path.name} (MIME: {mime_type}) 将作为通用文件URI处理。"
-                )
-                return cls.file_uri_part(
-                    file_uri=path.resolve().as_uri(),
-                    mime_type=mime_type,
-                    metadata={"name": path.name, "source": "local_path"},
-                )
-
-        except Exception as e:
-            logger.error(f"从路径 {path_like} 创建LLMContentPart时出错: {e}")
-            return None
-
-    def is_image_url(self) -> bool:
-        """检查图像源是否为URL"""
-        if not self.image_source:
-            return False
-        return self.image_source.startswith(("http://", "https://"))
-
-    def is_image_base64(self) -> bool:
-        """检查图像源是否为Base64 Data URL"""
-        if not self.image_source:
-            return False
-        return self.image_source.startswith("data:")
-
-    def get_base64_data(self) -> tuple[str, str] | None:
-        """从Data URL中提取Base64数据和MIME类型"""
-        if not self.is_image_base64() or not self.image_source:
-            return None
-
-        try:
-            header, data = self.image_source.split(",", 1)
-            mime_part = header.split(";")[0].replace("data:", "")
-            return mime_part, data
-        except (ValueError, IndexError):
-            logger.warning(f"无法解析Base64图像数据: {self.image_source[:50]}...")
-            return None
-
-    async def convert_for_api_async(self, api_type: str) -> dict[str, Any]:
-        """根据API类型转换多模态内容格式"""
-        from zhenxun.utils.http_utils import AsyncHttpx
-
-        if self.type == "text":
-            if api_type == "openai":
-                return {"type": "text", "text": self.text}
-            elif api_type == "gemini":
-                return {"text": self.text}
-            else:
-                return {"type": "text", "text": self.text}
-
-        elif self.type == "image":
-            if not self.image_source:
-                raise ValueError("图像类型的内容必须包含image_source")
-
-            if api_type == "openai":
-                return {"type": "image_url", "image_url": {"url": self.image_source}}
-            elif api_type == "gemini":
-                if self.is_image_base64():
-                    base64_info = self.get_base64_data()
-                    if base64_info:
-                        mime_type, data = base64_info
-                        return {"inlineData": {"mimeType": mime_type, "data": data}}
-                    else:
-                        raise ValueError(
-                            f"无法解析Base64图像数据: {self.image_source[:50]}..."
-                        )
-                elif self.is_image_url():
-                    logger.debug(f"正在为Gemini下载并编码URL图片: {self.image_source}")
-                    try:
-                        image_bytes = await AsyncHttpx.get_content(self.image_source)
-                        mime_type = self.mime_type or "image/jpeg"
-                        base64_data = base64.b64encode(image_bytes).decode("utf-8")
-                        return {
-                            "inlineData": {"mimeType": mime_type, "data": base64_data}
-                        }
-                    except Exception as e:
-                        logger.error(f"下载或编码URL图片失败: {e}", e=e)
-                        raise ValueError(f"无法处理图片URL: {e}")
-                else:
-                    raise ValueError(f"不支持的图像源格式: {self.image_source[:50]}...")
-            else:
-                return {"type": "image_url", "image_url": {"url": self.image_source}}
-
-        elif self.type == "video":
-            if not self.video_source:
-                raise ValueError("视频类型的内容必须包含video_source")
-
-            if api_type == "gemini":
-                # Gemini 支持视频，但需要通过 File API 上传
-                if self.video_source.startswith("data:"):
-                    # 处理 base64 视频数据
-                    try:
-                        header, data = self.video_source.split(",", 1)
-                        mime_type = header.split(";")[0].replace("data:", "")
-                        return {"inlineData": {"mimeType": mime_type, "data": data}}
-                    except (ValueError, IndexError):
-                        raise ValueError(
-                            f"无法解析Base64视频数据: {self.video_source[:50]}..."
-                        )
-                else:
-                    # 对于 URL 或其他格式，暂时不支持直接内联
-                    raise ValueError(
-                        "Gemini API 的视频处理需要通过 File API 上传，不支持直接 URL"
-                    )
-            else:
-                # 其他 API 可能不支持视频
-                raise ValueError(f"API类型 '{api_type}' 不支持视频内容")
-
-        elif self.type == "audio":
-            if not self.audio_source:
-                raise ValueError("音频类型的内容必须包含audio_source")
-
-            if api_type == "gemini":
-                # Gemini 支持音频，处理方式类似视频
-                if self.audio_source.startswith("data:"):
-                    try:
-                        header, data = self.audio_source.split(",", 1)
-                        mime_type = header.split(";")[0].replace("data:", "")
-                        return {"inlineData": {"mimeType": mime_type, "data": data}}
-                    except (ValueError, IndexError):
-                        raise ValueError(
-                            f"无法解析Base64音频数据: {self.audio_source[:50]}..."
-                        )
-                else:
-                    raise ValueError(
-                        "Gemini API 的音频处理需要通过 File API 上传，不支持直接 URL"
-                    )
-            else:
-                raise ValueError(f"API类型 '{api_type}' 不支持音频内容")
-
-        elif self.type == "file":
-            if api_type == "gemini" and self.file_uri:
-                return {
-                    "fileData": {"mimeType": self.mime_type, "fileUri": self.file_uri}
-                }
-            elif self.file_source:
-                file_name = (
-                    self.metadata.get("name", "file") if self.metadata else "file"
-                )
-                if api_type == "gemini":
-                    return {"text": f"[文件: {file_name}]\n{self.file_source}"}
-                else:
-                    return {
-                        "type": "text",
-                        "text": f"[文件: {file_name}]\n{self.file_source}",
-                    }
-            else:
-                raise ValueError("文件类型的内容必须包含file_uri或file_source")
-
-        else:
-            raise ValueError(f"不支持的内容类型: {self.type}")
-
-
-class LLMMessage(BaseModel):
-    """LLM 消息"""
-
-    role: str
-    content: str | list[LLMContentPart]
-    name: str | None = None
-    tool_calls: list[Any] | None = None
-    tool_call_id: str | None = None
-
-    def model_post_init(self, /, __context: Any) -> None:
-        """验证消息的有效性"""
-        _ = __context
-        if self.role == "tool":
-            if not self.tool_call_id:
-                raise ValueError("工具角色的消息必须包含 tool_call_id")
-            if not self.name:
-                raise ValueError("工具角色的消息必须包含函数名 (在 name 字段中)")
-        if self.role == "tool" and not isinstance(self.content, str):
-            logger.warning(
-                f"工具角色消息的内容期望是字符串，但得到的是: {type(self.content)}. "
-                "将尝试转换为字符串。"
-            )
-            try:
-                self.content = str(self.content)
-            except Exception as e:
-                raise ValueError(f"无法将工具角色的内容转换为字符串: {e}")
-
-    @classmethod
-    def user(cls, content: str | list[LLMContentPart]) -> "LLMMessage":
-        """创建用户消息"""
-        return cls(role="user", content=content)
-
-    @classmethod
-    def assistant_tool_calls(
-        cls,
-        tool_calls: list[Any],
-        content: str | list[LLMContentPart] = "",
-    ) -> "LLMMessage":
-        """创建助手请求工具调用的消息"""
-        return cls(role="assistant", content=content, tool_calls=tool_calls)
-
-    @classmethod
-    def assistant_text_response(
-        cls, content: str | list[LLMContentPart]
-    ) -> "LLMMessage":
-        """创建助手纯文本回复的消息"""
-        return cls(role="assistant", content=content, tool_calls=None)
-
-    @classmethod
-    def tool_response(
-        cls,
-        tool_call_id: str,
-        function_name: str,
-        result: Any,
-    ) -> "LLMMessage":
-        """创建工具执行结果的消息"""
-        import json
-
-        try:
-            content_str = json.dumps(result)
-        except TypeError as e:
-            logger.error(
-                f"工具 '{function_name}' 的结果无法JSON序列化: {result}. 错误: {e}"
-            )
-            content_str = json.dumps(
-                {"error": "工具结果无法JSON序列化", "details": str(e)}
-            )
-
-        return cls(
-            role="tool",
-            content=content_str,
-            tool_call_id=tool_call_id,
-            name=function_name,
-        )
-
-    @classmethod
-    def system(cls, content: str) -> "LLMMessage":
-        """创建系统消息"""
-        return cls(role="system", content=content)
-
-
-class LLMResponse(BaseModel):
-    """LLM 响应"""
-
-    text: str
-    images: list[bytes] | None = None
-    usage_info: dict[str, Any] | None = None
-    raw_response: dict[str, Any] | None = None
-    tool_calls: list[Any] | None = None
-    code_executions: list[Any] | None = None
-    grounding_metadata: Any | None = None
-    cache_info: Any | None = None
diff --git a/zhenxun/services/llm/types/enums.py b/zhenxun/services/llm/types/enums.py
deleted file mode 100644
index 82cb49b0..00000000
--- a/zhenxun/services/llm/types/enums.py
+++ /dev/null
@@ -1,78 +0,0 @@
-"""
-LLM 枚举类型定义
-"""
-
-from enum import Enum, auto
-
-
-class ModelProvider(Enum):
-    """模型提供商枚举"""
-
-    OPENAI = "openai"
-    GEMINI = "gemini"
-    ZHIXPU = "zhipu"
-    CUSTOM = "custom"
-
-
-class ResponseFormat(Enum):
-    """响应格式枚举"""
-
-    TEXT = "text"
-    JSON = "json"
-    MULTIMODAL = "multimodal"
-
-
-class EmbeddingTaskType(str, Enum):
-    """文本嵌入任务类型 (主要用于Gemini)"""
-
-    RETRIEVAL_QUERY = "RETRIEVAL_QUERY"
-    RETRIEVAL_DOCUMENT = "RETRIEVAL_DOCUMENT"
-    SEMANTIC_SIMILARITY = "SEMANTIC_SIMILARITY"
-    CLASSIFICATION = "CLASSIFICATION"
-    CLUSTERING = "CLUSTERING"
-    QUESTION_ANSWERING = "QUESTION_ANSWERING"
-    FACT_VERIFICATION = "FACT_VERIFICATION"
-
-
-class ToolCategory(Enum):
-    """工具分类枚举"""
-
-    FILE_SYSTEM = auto()
-    NETWORK = auto()
-    SYSTEM_INFO = auto()
-    CALCULATION = auto()
-    DATA_PROCESSING = auto()
-    CUSTOM = auto()
-
-
-class TaskType(Enum):
-    """任务类型枚举"""
-
-    CHAT = "chat"
-    CODE = "code"
-    SEARCH = "search"
-    ANALYSIS = "analysis"
-    GENERATION = "generation"
-    MULTIMODAL = "multimodal"
-
-
-class LLMErrorCode(Enum):
-    """LLM 服务相关的错误代码枚举"""
-
-    MODEL_INIT_FAILED = 2000
-    MODEL_NOT_FOUND = 2001
-    API_REQUEST_FAILED = 2002
-    API_RESPONSE_INVALID = 2003
-    API_KEY_INVALID = 2004
-    API_QUOTA_EXCEEDED = 2005
-    API_TIMEOUT = 2006
-    API_RATE_LIMITED = 2007
-    NO_AVAILABLE_KEYS = 2008
-    UNKNOWN_API_TYPE = 2009
-    CONFIGURATION_ERROR = 2010
-    RESPONSE_PARSE_ERROR = 2011
-    CONTEXT_LENGTH_EXCEEDED = 2012
-    CONTENT_FILTERED = 2013
-    USER_LOCATION_NOT_SUPPORTED = 2014
-    GENERATION_FAILED = 2015
-    EMBEDDING_FAILED = 2016
diff --git a/zhenxun/services/llm/types/exceptions.py b/zhenxun/services/llm/types/exceptions.py
index 623d4c26..4168adf3 100644
--- a/zhenxun/services/llm/types/exceptions.py
+++ b/zhenxun/services/llm/types/exceptions.py
@@ -2,9 +2,31 @@
 LLM 异常类型定义
 """
 
+from enum import Enum
 from typing import Any
 
-from .enums import LLMErrorCode
+
+class LLMErrorCode(Enum):
+    """LLM 服务相关的错误代码枚举"""
+
+    MODEL_INIT_FAILED = 2000
+    MODEL_NOT_FOUND = 2001
+    API_REQUEST_FAILED = 2002
+    API_RESPONSE_INVALID = 2003
+    API_KEY_INVALID = 2004
+    API_QUOTA_EXCEEDED = 2005
+    API_TIMEOUT = 2006
+    API_RATE_LIMITED = 2007
+    NO_AVAILABLE_KEYS = 2008
+    UNKNOWN_API_TYPE = 2009
+    CONFIGURATION_ERROR = 2010
+    RESPONSE_PARSE_ERROR = 2011
+    CONTEXT_LENGTH_EXCEEDED = 2012
+    CONTENT_FILTERED = 2013
+    USER_LOCATION_NOT_SUPPORTED = 2014
+    INVALID_PARAMETER = 2017
+    GENERATION_FAILED = 2015
+    EMBEDDING_FAILED = 2016
 
 
 class LLMException(Exception):
@@ -27,7 +49,11 @@ class LLMException(Exception):
 
     def __str__(self) -> str:
         if self.details:
-            return f"{self.message} (错误码: {self.code.name}, 详情: {self.details})"
+            safe_details = {k: v for k, v in self.details.items() if k != "api_key"}
+            if safe_details:
+                return (
+                    f"{self.message} (错误码: {self.code.name}, 详情: {safe_details})"
+                )
         return f"{self.message} (错误码: {self.code.name})"
 
     @property
@@ -46,10 +72,13 @@ class LLMException(Exception):
                 "当前所有API密钥均不可用，请稍后再试或联系管理员。"
             ),
             LLMErrorCode.USER_LOCATION_NOT_SUPPORTED: (
-                "当前地区暂不支持此AI服务，请联系管理员或尝试其他模型。"
+                "当前网络环境不支持此 AI 模型 (如 Gemini/OpenAI)。\n"
+                "原因: 代理节点所在地区（如香港/国内/非支持区）被服务商屏蔽。\n"
+                "建议: 请尝试更换代理节点至支持的地区（如美国/日本/新加坡）。"
             ),
             LLMErrorCode.API_REQUEST_FAILED: "AI服务请求失败，请稍后再试。",
             LLMErrorCode.API_RESPONSE_INVALID: "AI服务响应异常，请稍后再试。",
+            LLMErrorCode.INVALID_PARAMETER: "请求参数错误，请检查输入内容。",
             LLMErrorCode.CONFIGURATION_ERROR: "AI服务配置错误，请联系管理员。",
             LLMErrorCode.CONTEXT_LENGTH_EXCEEDED: "输入内容过长，请缩短后重试。",
             LLMErrorCode.CONTENT_FILTERED: "内容被安全过滤，请修改后重试。",
@@ -66,15 +95,19 @@ def get_user_friendly_error_message(error: Exception) -> str:
 
     error_str = str(error).lower()
 
-    if "timeout" in error_str or "超时" in error_str:
-        return "请求超时，请稍后再试。"
-    elif "connection" in error_str or "连接" in error_str:
-        return "网络连接失败，请检查网络后重试。"
-    elif "permission" in error_str or "权限" in error_str:
-        return "权限不足，请联系管理员。"
-    elif "not found" in error_str or "未找到" in error_str:
-        return "请求的资源未找到，请检查配置。"
-    elif "invalid" in error_str or "无效" in error_str:
+    if "timeout" in error_str or "timed out" in error_str:
+        return "网络请求超时，请检查服务器网络或代理连接。"
+    if "connect" in error_str and ("refused" in error_str or "error" in error_str):
+        return "无法连接到 AI 服务商，请检查网络连接或代理设置。"
+    if "proxy" in error_str:
+        return "代理连接失败，请检查代理服务器是否正常运行。"
+    if "ssl" in error_str or "certificate" in error_str:
+        return "SSL 证书验证失败，请检查网络环境。"
+    if "permission" in error_str or "forbidden" in error_str:
+        return "权限不足，可能是 API Key 权限受限。"
+    if "not found" in error_str:
+        return "请求的资源未找到 (404)，请检查模型名称或端点配置。"
+    if "invalid" in error_str or "无效" in error_str:
         return "请求参数无效，请检查输入。"
-    else:
-        return "服务暂时不可用，请稍后再试。"
+
+    return f"服务暂时不可用 ({type(error).__name__})，请稍后再试。"
diff --git a/zhenxun/services/llm/types/models.py b/zhenxun/services/llm/types/models.py
index 9ff56158..e8c79078 100644
--- a/zhenxun/services/llm/types/models.py
+++ b/zhenxun/services/llm/types/models.py
@@ -4,12 +4,459 @@ LLM 数据模型定义
 包含模型信息、配置、工具定义和响应数据的模型类。
 """
 
+import base64
 from dataclasses import dataclass, field
-from typing import Any
+from enum import Enum, auto
+import mimetypes
+from pathlib import Path
+import sys
+from typing import Any, Literal
 
+import aiofiles
 from pydantic import BaseModel, Field
 
-from .enums import ModelProvider, ToolCategory
+from zhenxun.services.log import logger
+
+if sys.version_info >= (3, 11):
+    from enum import StrEnum
+else:
+    from strenum import StrEnum
+
+
+class ModelProvider(Enum):
+    """模型提供商枚举"""
+
+    OPENAI = "openai"
+    GEMINI = "gemini"
+    ZHIXPU = "zhipu"
+    CUSTOM = "custom"
+
+
+class ResponseFormat(Enum):
+    """响应格式枚举"""
+
+    TEXT = "text"
+    JSON = "json"
+    MULTIMODAL = "multimodal"
+
+
+class StructuredOutputStrategy(str, Enum):
+    """结构化输出策略"""
+
+    NATIVE = "native"
+    """使用原生 API (如 OpenAI json_object/json_schema, Gemini mime_type)"""
+    TOOL_CALL = "tool_call"
+    """构造虚假工具调用来强制输出结构化数据 (适用于指令跟随弱但工具调用强的模型)"""
+    PROMPT = "prompt"
+    """仅在 Prompt 中追加 Schema 说明，依赖文本补全"""
+
+
+class EmbeddingTaskType(str, Enum):
+    """文本嵌入任务类型 (主要用于Gemini)"""
+
+    RETRIEVAL_QUERY = "RETRIEVAL_QUERY"
+    RETRIEVAL_DOCUMENT = "RETRIEVAL_DOCUMENT"
+    SEMANTIC_SIMILARITY = "SEMANTIC_SIMILARITY"
+    CLASSIFICATION = "CLASSIFICATION"
+    CLUSTERING = "CLUSTERING"
+    QUESTION_ANSWERING = "QUESTION_ANSWERING"
+    FACT_VERIFICATION = "FACT_VERIFICATION"
+
+
+class ToolCategory(Enum):
+    """工具分类枚举"""
+
+    FILE_SYSTEM = auto()
+    NETWORK = auto()
+    SYSTEM_INFO = auto()
+    CALCULATION = auto()
+    DATA_PROCESSING = auto()
+    CUSTOM = auto()
+
+
+class CodeExecutionOutcome(StrEnum):
+    """代码执行结果状态枚举"""
+
+    OUTCOME_OK = "OUTCOME_OK"
+    OUTCOME_FAILED = "OUTCOME_FAILED"
+    OUTCOME_DEADLINE_EXCEEDED = "OUTCOME_DEADLINE_EXCEEDED"
+    OUTCOME_COMPILATION_ERROR = "OUTCOME_COMPILATION_ERROR"
+    OUTCOME_RUNTIME_ERROR = "OUTCOME_RUNTIME_ERROR"
+    OUTCOME_UNKNOWN = "OUTCOME_UNKNOWN"
+
+
+class TaskType(Enum):
+    """任务类型枚举"""
+
+    CHAT = "chat"
+    CODE = "code"
+    SEARCH = "search"
+    ANALYSIS = "analysis"
+    GENERATION = "generation"
+    MULTIMODAL = "multimodal"
+
+
+class LLMContentPart(BaseModel):
+    """
+    LLM 消息内容部分 - 支持多模态内容。
+
+    这是一个联合体模型，`type` 字段决定了哪些其他字段是有效的。
+    例如：
+    - type='text': 使用 `text` 字段。
+    - type='image': 使用 `image_source` 字段。
+    - type='executable_code': 使用 `code_language` 和 `code_content` 字段。
+    """
+
+    type: str
+    text: str | None = None
+    image_source: str | None = None
+    audio_source: str | None = None
+    video_source: str | None = None
+    document_source: str | None = None
+    file_uri: str | None = None
+    file_source: str | None = None
+    url: str | None = None
+    mime_type: str | None = None
+    thought_text: str | None = None
+    media_resolution: str | None = None
+    code_language: str | None = None
+    code_content: str | None = None
+    execution_outcome: str | None = None
+    execution_output: str | None = None
+    metadata: dict[str, Any] | None = None
+
+    def model_post_init(self, /, __context: Any) -> None:
+        """验证内容部分的有效性"""
+        _ = __context
+        validation_rules = {
+            "text": lambda: self.text is not None,
+            "image": lambda: self.image_source,
+            "audio": lambda: self.audio_source,
+            "video": lambda: self.video_source,
+            "document": lambda: self.document_source,
+            "file": lambda: self.file_uri or self.file_source,
+            "url": lambda: self.url,
+            "thought": lambda: self.thought_text,
+            "executable_code": lambda: self.code_content is not None,
+            "execution_result": lambda: self.execution_outcome is not None,
+        }
+
+        if self.type in validation_rules:
+            if not validation_rules[self.type]():
+                raise ValueError(f"{self.type}类型的内容部分必须包含相应字段")
+
+    @classmethod
+    def text_part(cls, text: str) -> "LLMContentPart":
+        """创建文本内容部分"""
+        return cls(type="text", text=text)
+
+    @classmethod
+    def thought_part(cls, text: str) -> "LLMContentPart":
+        """创建思考过程内容部分"""
+        return cls(type="thought", thought_text=text)
+
+    @classmethod
+    def image_url_part(cls, url: str) -> "LLMContentPart":
+        """创建图片URL内容部分"""
+        return cls(type="image", image_source=url)
+
+    @classmethod
+    def image_base64_part(
+        cls, data: str, mime_type: str = "image/png"
+    ) -> "LLMContentPart":
+        """创建Base64图片内容部分"""
+        data_url = f"data:{mime_type};base64,{data}"
+        return cls(type="image", image_source=data_url)
+
+    @classmethod
+    def audio_url_part(cls, url: str, mime_type: str = "audio/wav") -> "LLMContentPart":
+        """创建音频URL内容部分"""
+        return cls(type="audio", audio_source=url, mime_type=mime_type)
+
+    @classmethod
+    def video_url_part(cls, url: str, mime_type: str = "video/mp4") -> "LLMContentPart":
+        """创建视频URL内容部分"""
+        return cls(type="video", video_source=url, mime_type=mime_type)
+
+    @classmethod
+    def video_base64_part(
+        cls, data: str, mime_type: str = "video/mp4"
+    ) -> "LLMContentPart":
+        """创建Base64视频内容部分"""
+        data_url = f"data:{mime_type};base64,{data}"
+        return cls(type="video", video_source=data_url, mime_type=mime_type)
+
+    @classmethod
+    def audio_base64_part(
+        cls, data: str, mime_type: str = "audio/wav"
+    ) -> "LLMContentPart":
+        """创建Base64音频内容部分"""
+        data_url = f"data:{mime_type};base64,{data}"
+        return cls(type="audio", audio_source=data_url, mime_type=mime_type)
+
+    @classmethod
+    def file_uri_part(
+        cls,
+        file_uri: str,
+        mime_type: str | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> "LLMContentPart":
+        """创建Gemini File API URI内容部分"""
+        return cls(
+            type="file",
+            file_uri=file_uri,
+            mime_type=mime_type,
+            metadata=metadata or {},
+        )
+
+    @classmethod
+    def executable_code_part(cls, language: str, code: str) -> "LLMContentPart":
+        """创建可执行代码内容部分"""
+        return cls(type="executable_code", code_language=language, code_content=code)
+
+    @classmethod
+    def execution_result_part(
+        cls, outcome: str, output: str | None
+    ) -> "LLMContentPart":
+        """创建代码执行结果部分"""
+        return cls(
+            type="execution_result", execution_outcome=outcome, execution_output=output
+        )
+
+    @classmethod
+    async def from_path(
+        cls, path_like: str | Path, target_api: str | None = None
+    ) -> "LLMContentPart | None":
+        """
+        从本地文件路径创建 LLMContentPart。
+        自动检测MIME类型，并根据类型（如图片）可能加载为Base64。
+        target_api 可以用于提示如何最好地准备数据（例如 'gemini' 可能偏好 base64）
+        """
+        try:
+            path = Path(path_like)
+            if not path.exists() or not path.is_file():
+                logger.warning(f"文件不存在或不是一个文件: {path}")
+                return None
+
+            mime_type, _ = mimetypes.guess_type(path.resolve().as_uri())
+
+            if not mime_type:
+                logger.warning(
+                    f"无法猜测文件 {path.name} 的MIME类型，将尝试作为文本文件处理。"
+                )
+                try:
+                    async with aiofiles.open(path, encoding="utf-8") as f:
+                        text_content = await f.read()
+                    return cls.text_part(text_content)
+                except Exception as e:
+                    logger.error(f"读取文本文件 {path.name} 失败: {e}")
+                    return None
+
+            if mime_type.startswith("image/"):
+                if target_api == "gemini" or not path.is_absolute():
+                    try:
+                        async with aiofiles.open(path, "rb") as f:
+                            img_bytes = await f.read()
+                        base64_data = base64.b64encode(img_bytes).decode("utf-8")
+                        return cls.image_base64_part(
+                            data=base64_data, mime_type=mime_type
+                        )
+                    except Exception as e:
+                        logger.error(f"读取或编码图片文件 {path.name} 失败: {e}")
+                        return None
+                else:
+                    logger.warning(
+                        f"为本地图片路径 {path.name} 生成 image_url_part。"
+                        "实际API可能不支持 file:// URI。考虑使用Base64或公网URL。"
+                    )
+                    return cls.image_url_part(url=path.resolve().as_uri())
+            elif mime_type.startswith("audio/"):
+                return cls.audio_url_part(
+                    url=path.resolve().as_uri(), mime_type=mime_type
+                )
+            elif mime_type.startswith("video/"):
+                if target_api == "gemini":
+                    try:
+                        async with aiofiles.open(path, "rb") as f:
+                            video_bytes = await f.read()
+                        base64_data = base64.b64encode(video_bytes).decode("utf-8")
+                        return cls.video_base64_part(
+                            data=base64_data, mime_type=mime_type
+                        )
+                    except Exception as e:
+                        logger.error(f"读取或编码视频文件 {path.name} 失败: {e}")
+                        return None
+                else:
+                    return cls.video_url_part(
+                        url=path.resolve().as_uri(), mime_type=mime_type
+                    )
+            elif (
+                mime_type.startswith("text/")
+                or mime_type == "application/json"
+                or mime_type == "application/xml"
+            ):
+                try:
+                    async with aiofiles.open(path, encoding="utf-8") as f:
+                        text_content = await f.read()
+                    return cls.text_part(text_content)
+                except Exception as e:
+                    logger.error(f"读取文本类文件 {path.name} 失败: {e}")
+                    return None
+            else:
+                logger.info(
+                    f"文件 {path.name} (MIME: {mime_type}) 将作为通用文件URI处理。"
+                )
+                return cls.file_uri_part(
+                    file_uri=path.resolve().as_uri(),
+                    mime_type=mime_type,
+                    metadata={"name": path.name, "source": "local_path"},
+                )
+
+        except Exception as e:
+            logger.error(f"从路径 {path_like} 创建LLMContentPart时出错: {e}")
+            return None
+
+    def is_image_url(self) -> bool:
+        """检查图像源是否为URL"""
+        if not self.image_source:
+            return False
+        return self.image_source.startswith(("http://", "https://"))
+
+    def is_image_base64(self) -> bool:
+        """检查图像源是否为Base64 Data URL"""
+        if not self.image_source:
+            return False
+        return self.image_source.startswith("data:")
+
+    def get_base64_data(self) -> tuple[str, str] | None:
+        """从Data URL中提取Base64数据和MIME类型"""
+        if not self.is_image_base64() or not self.image_source:
+            return None
+
+        try:
+            header, data = self.image_source.split(",", 1)
+            mime_part = header.split(";")[0].replace("data:", "")
+            return mime_part, data
+        except (ValueError, IndexError):
+            logger.warning(f"无法解析Base64图像数据: {self.image_source[:50]}...")
+            return None
+
+
+class LLMMessage(BaseModel):
+    """
+    LLM 消息对象，用于构建对话历史。
+
+    核心字段说明：
+    - role: 消息角色，推荐值为 'user', 'assistant', 'system', 'tool'。
+    - content: 消息内容，可以是纯文本字符串，也可以是 LLMContentPart 列表（用于多模态）
+    - tool_calls: (仅 assistant) 包含模型生成的工具调用请求。
+    - tool_call_id: (仅 tool) 对应 tool 消息响应的调用 ID。
+    - name: (仅 tool) 对应 tool 消息响应的函数名称。
+    """
+
+    role: str
+    content: str | list[LLMContentPart]
+    name: str | None = None
+    tool_calls: list[Any] | None = None
+    tool_call_id: str | None = None
+    thought_signature: str | None = None
+
+    def model_post_init(self, /, __context: Any) -> None:
+        """验证消息的有效性"""
+        _ = __context
+        if self.role == "tool":
+            if not self.tool_call_id:
+                raise ValueError("工具角色的消息必须包含 tool_call_id")
+            if not self.name:
+                raise ValueError("工具角色的消息必须包含函数名 (在 name 字段中)")
+        if self.role == "tool" and not isinstance(self.content, str):
+            logger.warning(
+                f"工具角色消息的内容期望是字符串，但得到的是: {type(self.content)}. "
+                "将尝试转换为字符串。"
+            )
+            try:
+                self.content = str(self.content)
+            except Exception as e:
+                raise ValueError(f"无法将工具角色的内容转换为字符串: {e}")
+
+    @classmethod
+    def user(cls, content: str | list[LLMContentPart]) -> "LLMMessage":
+        """创建用户消息"""
+        return cls(role="user", content=content)
+
+    @classmethod
+    def assistant_tool_calls(
+        cls,
+        tool_calls: list[Any],
+        content: str | list[LLMContentPart] = "",
+    ) -> "LLMMessage":
+        """创建助手请求工具调用的消息"""
+        return cls(role="assistant", content=content, tool_calls=tool_calls)
+
+    @classmethod
+    def assistant_text_response(
+        cls, content: str | list[LLMContentPart]
+    ) -> "LLMMessage":
+        """创建助手纯文本回复的消息"""
+        return cls(role="assistant", content=content, tool_calls=None)
+
+    @classmethod
+    def tool_response(
+        cls,
+        tool_call_id: str,
+        function_name: str,
+        result: Any,
+    ) -> "LLMMessage":
+        """创建工具执行结果的消息"""
+        import json
+
+        try:
+            content_str = json.dumps(result)
+        except TypeError as e:
+            logger.error(
+                f"工具 '{function_name}' 的结果无法JSON序列化: {result}. 错误: {e}"
+            )
+            content_str = json.dumps(
+                {"error": "工具结果无法JSON序列化", "details": str(e)}
+            )
+
+        return cls(
+            role="tool",
+            content=content_str,
+            tool_call_id=tool_call_id,
+            name=function_name,
+        )
+
+    @classmethod
+    def system(cls, content: str) -> "LLMMessage":
+        """创建系统消息"""
+        return cls(role="system", content=content)
+
+
+class LLMResponse(BaseModel):
+    """
+    LLM 响应对象，封装了模型生成的全部信息。
+
+    核心字段说明：
+    - text: 模型生成的文本内容。如果是纯文本回复，此字段即为结果。
+    - tool_calls: 如果模型决定调用工具，此列表包含调用详情。
+    - content_parts: 包含多模态或结构化内容的原始部分列表（如思维链、代码块）。
+    - raw_response: 原始的第三方 API 响应字典（用于调试）。
+    - images: 如果请求涉及生图，此处包含生成的图片数据。
+    """
+
+    text: str
+    content_parts: list[Any] | None = None
+    images: list[bytes | Path] | None = None
+    usage_info: dict[str, Any] | None = None
+    raw_response: dict[str, Any] | None = None
+    tool_calls: list[Any] | None = None
+    code_executions: list[Any] | None = None
+    grounding_metadata: Any | None = None
+    cache_info: Any | None = None
+    thought_text: str | None = None
+    thought_signature: str | None = None
+
 
 ModelName = str | None
 
@@ -26,6 +473,64 @@ class ToolDefinition(BaseModel):
     )
 
 
+class ToolChoice(BaseModel):
+    """统一的工具选择配置"""
+
+    mode: Literal["auto", "none", "any", "required"] = Field(
+        default="auto", description="工具调用模式"
+    )
+    allowed_function_names: list[str] | None = Field(
+        default=None, description="允许调用的函数名称列表"
+    )
+
+
+class BasePlatformTool(BaseModel):
+    """平台原生工具基类"""
+
+    class Config:
+        extra = "forbid"
+
+    def get_tool_declaration(self) -> dict[str, Any]:
+        """获取放入 'tools' 列表中的声明对象 (Snake Case)"""
+        raise NotImplementedError
+
+    def get_tool_config(self) -> dict[str, Any] | None:
+        """获取放入 'toolConfig' 中的配置对象 (Snake Case)"""
+        return None
+
+
+class GeminiCodeExecution(BasePlatformTool):
+    """Gemini 代码执行工具"""
+
+    def get_tool_declaration(self) -> dict[str, Any]:
+        return {"code_execution": {}}
+
+
+class GeminiGoogleSearch(BasePlatformTool):
+    """Gemini 谷歌搜索 (Grounding) 工具"""
+
+    mode: Literal["MODE_DYNAMIC"] = "MODE_DYNAMIC"
+    dynamic_threshold: float | None = Field(default=None)
+
+    def get_tool_declaration(self) -> dict[str, Any]:
+        return {"google_search": {}}
+
+    def get_tool_config(self) -> dict[str, Any] | None:
+        return None
+
+
+class GeminiUrlContext(BasePlatformTool):
+    """Gemini 网址上下文工具"""
+
+    urls: list[str] = Field(..., description="作为上下文的 URL 列表", max_length=20)
+
+    def get_tool_declaration(self) -> dict[str, Any]:
+        return {"google_search": {}, "url_context": {}}
+
+    def get_tool_config(self) -> dict[str, Any] | None:
+        return None
+
+
 class ToolResult(BaseModel):
     """
     一个结构化的工具执行结果模型。
@@ -87,6 +592,8 @@ class ModelDetail(BaseModel):
     is_embedding_model: bool = False
     temperature: float | None = None
     max_tokens: int | None = None
+    api_type: str | None = None
+    endpoint: str | None = None
 
 
 class ProviderConfig(BaseModel):
@@ -116,6 +623,7 @@ class LLMToolCall(BaseModel):
 
     id: str
     function: LLMToolFunction
+    thought_signature: str | None = None
 
 
 class LLMCodeExecution(BaseModel):
@@ -143,6 +651,12 @@ class LLMGroundingMetadata(BaseModel):
     web_search_queries: list[str] | None = None
     grounding_attributions: list[LLMGroundingAttribution] | None = None
     search_suggestions: list[dict[str, Any]] | None = None
+    search_entry_point: str | None = Field(
+        default=None, description="Google搜索建议的HTML片段(renderedContent)"
+    )
+    map_widget_token: str | None = Field(
+        default=None, description="Google Maps 前端组件令牌"
+    )
 
 
 class LLMCacheInfo(BaseModel):
diff --git a/zhenxun/services/llm/types/protocols.py b/zhenxun/services/llm/types/protocols.py
index d7e295fc..d63aedcd 100644
--- a/zhenxun/services/llm/types/protocols.py
+++ b/zhenxun/services/llm/types/protocols.py
@@ -2,10 +2,97 @@
 LLM 模块的协议定义
 """
 
-from typing import Any, Protocol
+from abc import ABC
+from typing import TYPE_CHECKING, Any, Protocol, Union
+
+from pydantic import BaseModel
 
 from .models import ToolDefinition, ToolResult
 
+if TYPE_CHECKING:
+    from .models import LLMMessage, LLMResponse, LLMToolCall
+
+
+class ToolCallData(BaseModel):
+    """传递给 on_tool_start 的数据模型"""
+
+    tool_name: str
+    tool_args: dict[str, Any]
+
+
+class ToolCallCompleteData(BaseModel):
+    """传递给 on_tool_call_complete 的数据模型"""
+
+    id: str
+    name: str
+    arguments: str
+    result: "ToolResult"
+
+
+class BaseCallbackHandler(ABC):
+    """
+    Agent/LLM 生命周期回调处理器的基类。
+    下沉至 LLM 层以允许 ToolInvoker 直接调用。
+    """
+
+    async def on_agent_start(self, messages: list["LLMMessage"], **kwargs: Any) -> None:
+        """在 AgentExecutor 开始运行时调用。"""
+        pass
+
+    async def on_model_start(
+        self, model_name: str, messages: list["LLMMessage"], **kwargs: Any
+    ) -> None:
+        """在向LLM发起请求之前调用。"""
+        pass
+
+    async def on_model_end(
+        self, response: "LLMResponse", duration: float, **kwargs: Any
+    ) -> None:
+        """在收到LLM响应之后调用。"""
+        pass
+
+    async def on_tool_start(
+        self, tool_call: "LLMToolCall", data: ToolCallData, **kwargs: Any
+    ) -> Union[ToolCallData, "ToolResult", None]:
+        """
+        在单个工具即将被执行时调用。
+
+        返回:
+            ToolCallData: 修改参数并继续执行
+            ToolResult: 拦截执行并直接返回给模型
+            None: 正常继续
+        """
+        pass
+
+    async def on_tool_end(
+        self,
+        result: Union["ToolResult", None],
+        error: Exception | None,
+        tool_call: "LLMToolCall",
+        duration: float,
+        **kwargs: Any,
+    ) -> None:
+        """在单个工具执行完毕后调用，无论成功或失败。"""
+        pass
+
+    async def on_tool_call_complete(
+        self, data: ToolCallCompleteData, **kwargs: Any
+    ) -> None:
+        """在工具调用完成并准备创建响应消息时调用。"""
+        pass
+
+    async def on_human_input_request(self, query: str, **kwargs: Any) -> str | None:
+        """
+        当 Agent 需要人类输入时调用。
+        """
+        return None
+
+    async def on_agent_end(
+        self, final_history: list["LLMMessage"], duration: float, **kwargs: Any
+    ) -> None:
+        """在 AgentExecutor 运行结束时调用。"""
+        pass
+
 
 class ToolExecutable(Protocol):
     """
@@ -19,10 +106,14 @@ class ToolExecutable(Protocol):
         """
         ...
 
-    async def execute(self, **kwargs: Any) -> ToolResult:
+    async def execute(self, context: Any | None = None, **kwargs: Any) -> ToolResult:
         """
         异步执行工具并返回一个结构化的结果。
         参数由LLM根据工具定义生成。
+
+        Args:
+            context: 运行时上下文 (RunContext)，可选注入
+            **kwargs: 工具参数
         """
         ...
 
diff --git a/zhenxun/services/llm/utils.py b/zhenxun/services/llm/utils.py
index 624d1557..aed1dc39 100644
--- a/zhenxun/services/llm/utils.py
+++ b/zhenxun/services/llm/utils.py
@@ -3,26 +3,176 @@ LLM 模块的工具和转换函数
 """
 
 import base64
-import copy
+from collections.abc import Awaitable, Callable
+import io
 from pathlib import Path
-from typing import Any
+from typing import Any, TypeVar
 
+import aiofiles
+import json_repair
 from nonebot.adapters import Message as PlatformMessage
+from nonebot.compat import type_validate_json
 from nonebot_plugin_alconna.uniseg import (
     At,
     File,
     Image,
     Reply,
+    Segment,
     Text,
     UniMessage,
     Video,
     Voice,
 )
+from PIL.Image import Image as PILImageType
+from pydantic import BaseModel, Field, ValidationError, create_model
 
 from zhenxun.services.log import logger
 from zhenxun.utils.http_utils import AsyncHttpx
+from zhenxun.utils.pydantic_compat import model_validate
 
-from .types import LLMContentPart, LLMMessage
+from .types import LLMContentPart, LLMErrorCode, LLMException, LLMMessage
+from .types.capabilities import ReasoningMode, get_model_capabilities
+
+T = TypeVar("T", bound=BaseModel)
+
+
+S = TypeVar("S", bound=Segment)
+_SEGMENT_HANDLERS: dict[
+    type[Segment], Callable[[Any], Awaitable[LLMContentPart | None]]
+] = {}
+
+
+def register_segment_handler(seg_type: type[S]):
+    """装饰器：注册 Uniseg 消息段的处理器"""
+
+    def decorator(func: Callable[[S], Awaitable[LLMContentPart | None]]):
+        _SEGMENT_HANDLERS[seg_type] = func
+        return func
+
+    return decorator
+
+
+async def _process_media_data(seg: Any, default_mime: str) -> tuple[str, str] | None:
+    """
+    [内部复用] 通用媒体数据处理：获取 Base64 数据和 MIME 类型。
+    优先顺序：Raw -> Path -> URL (下载)
+    """
+    mime_type = getattr(seg, "mimetype", None) or default_mime
+    b64_data = None
+
+    if hasattr(seg, "raw") and seg.raw:
+        if isinstance(seg.raw, bytes):
+            b64_data = base64.b64encode(seg.raw).decode("utf-8")
+
+    elif getattr(seg, "path", None):
+        try:
+            path = Path(seg.path)
+            if path.exists():
+                async with aiofiles.open(path, "rb") as f:
+                    content = await f.read()
+                b64_data = base64.b64encode(content).decode("utf-8")
+        except Exception as e:
+            logger.error(f"读取媒体文件失败: {seg.path}, 错误: {e}")
+
+    elif getattr(seg, "url", None):
+        try:
+            logger.debug(f"检测到媒体URL，开始下载: {seg.url}")
+            media_bytes = await AsyncHttpx.get_content(seg.url)
+            b64_data = base64.b64encode(media_bytes).decode("utf-8")
+            logger.debug(f"媒体文件下载成功，大小: {len(media_bytes)} bytes")
+        except Exception as e:
+            logger.error(f"从URL下载媒体失败: {seg.url}, 错误: {e}")
+            return None
+
+    if b64_data:
+        return mime_type, b64_data
+    return None
+
+
+@register_segment_handler(Text)
+async def _handle_text(seg: Text) -> LLMContentPart | None:
+    if seg.text.strip():
+        return LLMContentPart.text_part(seg.text)
+    return None
+
+
+@register_segment_handler(Image)
+async def _handle_image(seg: Image) -> LLMContentPart | None:
+    media_info = await _process_media_data(seg, "image/png")
+    if media_info:
+        mime, data = media_info
+        return LLMContentPart.image_base64_part(data, mime)
+    return None
+
+
+@register_segment_handler(Voice)
+async def _handle_voice(seg: Voice) -> LLMContentPart | None:
+    media_info = await _process_media_data(seg, "audio/wav")
+    if media_info:
+        mime, data = media_info
+        return LLMContentPart.audio_base64_part(data, mime)
+    return LLMContentPart.text_part(f"[语音消息: {seg.id or 'unknown'}]")
+
+
+@register_segment_handler(Video)
+async def _handle_video(seg: Video) -> LLMContentPart | None:
+    media_info = await _process_media_data(seg, "video/mp4")
+    if media_info:
+        mime, data = media_info
+        return LLMContentPart.video_base64_part(data, mime)
+    return LLMContentPart.text_part(f"[视频消息: {seg.id or 'unknown'}]")
+
+
+@register_segment_handler(File)
+async def _handle_file(seg: File) -> LLMContentPart | None:
+    if seg.path:
+        return await LLMContentPart.from_path(seg.path)
+    return LLMContentPart.text_part(f"[文件: {seg.name} (ID: {seg.id})]")
+
+
+@register_segment_handler(At)
+async def _handle_at(seg: At) -> LLMContentPart | None:
+    if seg.flag == "all":
+        return LLMContentPart.text_part("[提及所有人]")
+    return LLMContentPart.text_part(f"[提及用户: {seg.target}]")
+
+
+@register_segment_handler(Reply)
+async def _handle_reply(seg: Reply) -> LLMContentPart | None:
+    text = str(seg.msg) if seg.msg else ""
+    if text:
+        return LLMContentPart.text_part(f'[回复消息: "{text[:50]}..."]')
+    return LLMContentPart.text_part("[回复了一条消息]")
+
+
+async def _transform_to_content_part(item: Any) -> LLMContentPart:
+    """
+    将混合输入转换为统一的 LLMContentPart，便于 normalize_to_llm_messages 使用。
+    """
+    if isinstance(item, LLMContentPart):
+        return item
+
+    if isinstance(item, str):
+        return LLMContentPart.text_part(item)
+
+    if isinstance(item, Path):
+        part = await LLMContentPart.from_path(item)
+        if part is None:
+            raise ValueError(f"无法从路径加载内容: {item}")
+        return part
+
+    if isinstance(item, dict):
+        return LLMContentPart(**item)
+
+    if PILImageType and isinstance(item, PILImageType):
+        buffer = io.BytesIO()
+        fmt = item.format or "PNG"
+        item.save(buffer, format=fmt)
+        b64_data = base64.b64encode(buffer.getvalue()).decode("utf-8")
+        mime_type = f"image/{fmt.lower()}"
+        return LLMContentPart.image_base64_part(b64_data, mime_type)
+
+    raise TypeError(f"不支持的输入类型用于构建 ContentPart: {type(item)}")
 
 
 async def unimsg_to_llm_parts(message: UniMessage) -> list[LLMContentPart]:
@@ -36,110 +186,25 @@ async def unimsg_to_llm_parts(message: UniMessage) -> list[LLMContentPart]:
     返回:
         list[LLMContentPart]: 转换后的内容部分列表。
     """
+    if not _SEGMENT_HANDLERS:
+        pass
+
     parts: list[LLMContentPart] = []
     for seg in message:
-        part = None
-        if isinstance(seg, Text):
-            if seg.text.strip():
-                part = LLMContentPart.text_part(seg.text)
-        elif isinstance(seg, Image):
-            if seg.path:
-                part = await LLMContentPart.from_path(seg.path, target_api="gemini")
-            elif seg.url:
-                part = LLMContentPart.image_url_part(seg.url)
-            elif hasattr(seg, "raw") and seg.raw:
-                mime_type = (
-                    getattr(seg, "mimetype", "image/png")
-                    if hasattr(seg, "mimetype")
-                    else "image/png"
-                )
-                if isinstance(seg.raw, bytes):
-                    b64_data = base64.b64encode(seg.raw).decode("utf-8")
-                    part = LLMContentPart.image_base64_part(b64_data, mime_type)
-
-        elif isinstance(seg, File | Voice | Video):
-            if seg.path:
-                part = await LLMContentPart.from_path(seg.path)
-            elif seg.url:
-                try:
-                    logger.debug(f"检测到媒体URL，开始下载: {seg.url}")
-                    media_bytes = await AsyncHttpx.get_content(seg.url)
-
-                    new_seg = copy.copy(seg)
-                    new_seg.raw = media_bytes
-                    seg = new_seg
-                    logger.debug(f"媒体文件下载成功，大小: {len(media_bytes)} bytes")
-                except Exception as e:
-                    logger.error(f"从URL下载媒体失败: {seg.url}, 错误: {e}")
-                    part = LLMContentPart.text_part(
-                        f"[下载媒体失败: {seg.name or seg.url}]"
-                    )
-
+        handler = _SEGMENT_HANDLERS.get(type(seg))
+        if handler:
+            try:
+                part = await handler(seg)
                 if part:
                     parts.append(part)
-                    continue
-
-            if hasattr(seg, "raw") and seg.raw:
-                mime_type = getattr(seg, "mimetype", None)
-                if isinstance(seg.raw, bytes):
-                    b64_data = base64.b64encode(seg.raw).decode("utf-8")
-
-                    if isinstance(seg, Video):
-                        if not mime_type:
-                            mime_type = "video/mp4"
-                        part = LLMContentPart.video_base64_part(
-                            data=b64_data, mime_type=mime_type
-                        )
-                        logger.debug(
-                            f"处理视频字节数据: {mime_type}, 大小: {len(seg.raw)} bytes"
-                        )
-                    elif isinstance(seg, Voice):
-                        if not mime_type:
-                            mime_type = "audio/wav"
-                        part = LLMContentPart.audio_base64_part(
-                            data=b64_data, mime_type=mime_type
-                        )
-                        logger.debug(
-                            f"处理音频字节数据: {mime_type}, 大小: {len(seg.raw)} bytes"
-                        )
-                    else:
-                        part = LLMContentPart.text_part(
-                            f"[FILE: {mime_type or 'unknown'}, {len(seg.raw)} bytes]"
-                        )
-                        logger.debug(
-                            f"处理其他文件字节数据: {mime_type}, "
-                            f"大小: {len(seg.raw)} bytes"
-                        )
-
-        elif isinstance(seg, At):
-            if seg.flag == "all":
-                part = LLMContentPart.text_part("[提及所有人]")
-            else:
-                part = LLMContentPart.text_part(f"[提及用户: {seg.target}]")
-
-        elif isinstance(seg, Reply):
-            if seg.msg:
-                try:
-                    extract_method = getattr(seg.msg, "extract_plain_text", None)
-                    if extract_method and callable(extract_method):
-                        reply_text = str(extract_method()).strip()
-                    else:
-                        reply_text = str(seg.msg).strip()
-                    if reply_text:
-                        part = LLMContentPart.text_part(
-                            f'[回复消息: "{reply_text[:50]}..."]'
-                        )
-                except Exception:
-                    part = LLMContentPart.text_part("[回复了一条消息]")
-
-        if part:
-            parts.append(part)
+            except Exception as e:
+                logger.warning(f"处理消息段 {seg} 失败: {e}", "LLMUtils")
 
     return parts
 
 
 async def normalize_to_llm_messages(
-    message: str | UniMessage | LLMMessage | list[LLMContentPart] | list[LLMMessage],
+    message: str | UniMessage | LLMMessage | list[Any],
     instruction: str | None = None,
 ) -> list[LLMMessage]:
     """
@@ -167,7 +232,10 @@ async def normalize_to_llm_messages(
         content_parts = await unimsg_to_llm_parts(message)
         messages.append(LLMMessage.user(content_parts))
     elif isinstance(message, list):
-        messages.append(LLMMessage.user(message))  # type: ignore
+        parts = []
+        for item in message:
+            parts.append(await _transform_to_content_part(item))
+        messages.append(LLMMessage.user(parts))
     else:
         raise TypeError(f"不支持的消息类型: {type(message)}")
 
@@ -255,53 +323,271 @@ def message_to_unimessage(message: PlatformMessage) -> UniMessage:
     返回:
         UniMessage: 转换后的通用消息对象。
     """
-    uni_segments = []
-    for seg in message:
-        if seg.type == "text":
-            uni_segments.append(Text(seg.data.get("text", "")))
-        elif seg.type == "image":
-            uni_segments.append(Image(url=seg.data.get("url")))
-        elif seg.type == "record":
-            uni_segments.append(Voice(url=seg.data.get("url")))
-        elif seg.type == "video":
-            uni_segments.append(Video(url=seg.data.get("url")))
-        elif seg.type == "at":
-            uni_segments.append(At("user", str(seg.data.get("qq", ""))))
-        else:
-            logger.debug(f"跳过不支持的平台消息段类型: {seg.type}")
+    return UniMessage.of(message)
 
-    return UniMessage(uni_segments)
+
+def resolve_json_schema_refs(schema: dict) -> dict:
+    """
+    递归解析 JSON Schema 中的 $ref，将其替换为 $defs/definitions 中的定义。
+    用于兼容不支持 $ref 的 Gemini API。
+    """
+    definitions = schema.get("$defs") or schema.get("definitions") or {}
+
+    def _resolve(node: Any) -> Any:
+        if isinstance(node, dict):
+            if "$ref" in node:
+                ref_name = node["$ref"].split("/")[-1]
+                if ref_name in definitions:
+                    return _resolve(definitions[ref_name])
+
+            return {
+                key: _resolve(value)
+                for key, value in node.items()
+                if key not in ("$defs", "definitions")
+            }
+
+        if isinstance(node, list):
+            return [_resolve(item) for item in node]
+
+        return node
+
+    return _resolve(schema)
 
 
 def sanitize_schema_for_llm(schema: Any, api_type: str) -> Any:
     """
     递归地净化 JSON Schema，移除特定 LLM API 不支持的关键字。
-
-    参数:
-        schema: 要净化的 JSON Schema (可以是字典、列表或其它类型)。
-        api_type: 目标 API 的类型，例如 'gemini'。
-
-    返回:
-        Any: 净化后的 JSON Schema。
     """
-    if isinstance(schema, dict):
-        schema_copy = {}
-        for key, value in schema.items():
-            if api_type == "gemini":
-                unsupported_keys = ["exclusiveMinimum", "exclusiveMaximum", "default"]
-                if key in unsupported_keys:
-                    continue
-
-                if key == "format" and isinstance(value, str):
-                    supported_formats = ["enum", "date-time"]
-                    if value not in supported_formats:
-                        continue
-
-            schema_copy[key] = sanitize_schema_for_llm(value, api_type)
-        return schema_copy
-
-    elif isinstance(schema, list):
+    if isinstance(schema, list):
         return [sanitize_schema_for_llm(item, api_type) for item in schema]
+    if isinstance(schema, dict):
+        schema_copy = schema.copy()
 
+        if api_type == "gemini":
+            if "const" in schema_copy:
+                schema_copy["enum"] = [schema_copy.pop("const")]
+
+            if "type" in schema_copy and isinstance(schema_copy["type"], list):
+                types_list = schema_copy["type"]
+                if "null" in types_list:
+                    schema_copy["nullable"] = True
+                    types_list = [t for t in types_list if t != "null"]
+                    if len(types_list) == 1:
+                        schema_copy["type"] = types_list[0]
+                    else:
+                        schema_copy["type"] = types_list
+
+            if "anyOf" in schema_copy:
+                any_of = schema_copy["anyOf"]
+                has_null = any(
+                    isinstance(x, dict) and x.get("type") == "null" for x in any_of
+                )
+                if has_null:
+                    schema_copy["nullable"] = True
+                    new_any_of = [
+                        x
+                        for x in any_of
+                        if not (isinstance(x, dict) and x.get("type") == "null")
+                    ]
+                    if len(new_any_of) == 1:
+                        schema_copy.update(new_any_of[0])
+                        schema_copy.pop("anyOf", None)
+                    else:
+                        schema_copy["anyOf"] = new_any_of
+
+            unsupported_keys = [
+                "exclusiveMinimum",
+                "exclusiveMaximum",
+                "default",
+                "title",
+                "additionalProperties",
+                "$schema",
+                "$id",
+            ]
+            for key in unsupported_keys:
+                schema_copy.pop(key, None)
+
+            if schema_copy.get("format") and schema_copy["format"] not in [
+                "enum",
+                "date-time",
+            ]:
+                schema_copy.pop("format", None)
+
+        elif api_type == "openai":
+            unsupported_keys = [
+                "default",
+                "minLength",
+                "maxLength",
+                "pattern",
+                "format",
+                "minimum",
+                "maximum",
+                "multipleOf",
+                "patternProperties",
+                "minItems",
+                "maxItems",
+                "uniqueItems",
+                "$schema",
+                "title",
+            ]
+            for key in unsupported_keys:
+                schema_copy.pop(key, None)
+
+            if "$ref" in schema_copy:
+                ref_key = schema_copy["$ref"].split("/")[-1]
+                defs = schema_copy.get("$defs") or schema_copy.get("definitions")
+                if defs and ref_key in defs:
+                    schema_copy.pop("$ref", None)
+                    schema_copy.update(defs[ref_key])
+                else:
+                    return {"$ref": schema_copy["$ref"]}
+
+            is_object = (
+                schema_copy.get("type") == "object" or "properties" in schema_copy
+            )
+            if is_object:
+                schema_copy["type"] = "object"
+                schema_copy["additionalProperties"] = False
+
+                properties = schema_copy.get("properties", {})
+                required = schema_copy.get("required", [])
+                if properties:
+                    existing_req = set(required)
+                    for prop in properties.keys():
+                        if prop not in existing_req:
+                            required.append(prop)
+                    schema_copy["required"] = required
+
+        for def_key in ["$defs", "definitions"]:
+            if def_key in schema_copy and isinstance(schema_copy[def_key], dict):
+                schema_copy[def_key] = {
+                    k: sanitize_schema_for_llm(v, api_type)
+                    for k, v in schema_copy[def_key].items()
+                }
+
+        recursive_keys = ["properties", "items", "allOf", "anyOf", "oneOf"]
+        for key in recursive_keys:
+            if key in schema_copy:
+                if key == "properties" and isinstance(schema_copy[key], dict):
+                    schema_copy[key] = {
+                        k: sanitize_schema_for_llm(v, api_type)
+                        for k, v in schema_copy[key].items()
+                    }
+                else:
+                    schema_copy[key] = sanitize_schema_for_llm(
+                        schema_copy[key], api_type
+                    )
+
+        return schema_copy
     else:
         return schema
+
+
+def extract_text_from_content(
+    content: str | list[LLMContentPart] | None,
+) -> str:
+    """
+    从消息内容中提取纯文本，自动过滤非文本部分，防止污染 Prompt。
+    """
+    if content is None:
+        return ""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        return " ".join(
+            part.text for part in content if part.type == "text" and part.text
+        )
+    return str(content)
+
+
+def parse_and_validate_json(text: str, response_model: type[T]) -> T:
+    """
+    通用工具：尝试将文本解析为指定的 Pydantic 模型，并统一处理异常。
+    """
+    try:
+        return type_validate_json(response_model, text)
+    except (ValidationError, ValueError) as e:
+        try:
+            logger.warning(f"标准JSON解析失败，尝试使用json_repair修复: {e}")
+            repaired_obj = json_repair.loads(text, skip_json_loads=True)
+            return model_validate(response_model, repaired_obj)
+        except Exception as repair_error:
+            logger.error(
+                f"LLM结构化输出校验最终失败: {repair_error}",
+                e=repair_error,
+            )
+            raise LLMException(
+                "LLM返回的JSON未能通过结构验证。",
+                code=LLMErrorCode.RESPONSE_PARSE_ERROR,
+                details={
+                    "raw_response": text,
+                    "validation_error": str(repair_error),
+                    "original_error": repair_error,
+                },
+                cause=repair_error,
+            )
+    except Exception as e:
+        logger.error(f"解析LLM结构化输出时发生未知错误: {e}", e=e)
+        raise LLMException(
+            "解析LLM的JSON输出时失败。",
+            code=LLMErrorCode.RESPONSE_PARSE_ERROR,
+            details={"raw_response": text},
+            cause=e,
+        )
+
+
+def create_cot_wrapper(inner_model: type[BaseModel]) -> type[BaseModel]:
+    """
+    [动态运行时封装]
+    创建一个包含思维链 (Chain of Thought) 的包装模型。
+    强制模型在生成最终 JSON 结构前，先输出一个 reasoning 字段进行思考。
+    """
+    wrapper_name = f"CoT_{inner_model.__name__}"
+
+    return create_model(
+        wrapper_name,
+        reasoning=(
+            str,
+            Field(
+                ...,
+                min_length=10,
+                description=(
+                    "在生成最终结果之前，请务必在此字段中详细描述你的推理步骤、计算过程或思考逻辑。禁止留空。"
+                ),
+            ),
+        ),
+        result=(
+            inner_model,
+            Field(
+                ...,
+            ),
+        ),
+    )
+
+
+def should_apply_autocot(
+    requested: bool,
+    model_name: str | None,
+    config: Any,
+) -> bool:
+    """
+    [智能决策管道]
+    判断是否应该应用 AutoCoT (显式思维链包装)。
+    防止在模型已有原生思维能力时进行“双重思考”。
+    """
+    if not requested:
+        return False
+
+    if config:
+        thinking_budget = getattr(config, "thinking_budget", 0) or 0
+        if thinking_budget > 0:
+            return False
+        if getattr(config, "thinking_level", None) is not None:
+            return False
+
+    if model_name:
+        caps = get_model_capabilities(model_name)
+        if caps.reasoning_mode != ReasoningMode.NONE:
+            return False
+
+    return True
diff --git a/zhenxun/utils/log_sanitizer.py b/zhenxun/utils/log_sanitizer.py
index 2884da5c..9d8a5c2b 100644
--- a/zhenxun/utils/log_sanitizer.py
+++ b/zhenxun/utils/log_sanitizer.py
@@ -14,9 +14,34 @@ def _truncate_base64_string(value: str, threshold: int = 256) -> str:
     if value.startswith(prefixes) and len(value) > threshold:
         prefix = next((p for p in prefixes if value.startswith(p)), "base64")
         return f"[{prefix}_data_omitted_len={len(value)}]"
+
+    if len(value) > 1000:
+        return f"[long_string_omitted_len={len(value)}] {value[:20]}...{value[-20:]}"
+
+    if len(value) > 2000:
+        return f"[long_string_omitted_len={len(value)}] {value[:50]}...{value[-20:]}"
+
     return value
 
 
+def _truncate_vector_list(vector: list, threshold: int = 10) -> list:
+    """如果列表过长（通常是embedding向量），则截断它用于日志显示。"""
+    if isinstance(vector, list) and len(vector) > threshold:
+        return [*vector[:3], f"...({len(vector)} floats omitted)...", *vector[-3:]]
+    return vector
+
+
+def _recursive_sanitize_any(obj: Any) -> Any:
+    """递归清洗任何对象中的长字符串"""
+    if isinstance(obj, dict):
+        return {k: _recursive_sanitize_any(v) for k, v in obj.items()}
+    elif isinstance(obj, list):
+        return [_recursive_sanitize_any(v) for v in obj]
+    elif isinstance(obj, str):
+        return _truncate_base64_string(obj)
+    return obj
+
+
 def _sanitize_ui_html(html_string: str) -> str:
     """
     专门用于净化UI渲染调试HTML的函数。
@@ -64,6 +89,37 @@ def _sanitize_openai_response(response_json: dict) -> dict:
                                 message["images"][i]["image_url"]["url"] = (
                                     _truncate_base64_string(url)
                                 )
+                    if "reasoning_details" in message and isinstance(
+                        message["reasoning_details"], list
+                    ):
+                        for detail in message["reasoning_details"]:
+                            if isinstance(detail, dict):
+                                if "data" in detail and isinstance(detail["data"], str):
+                                    if len(detail["data"]) > 100:
+                                        detail["data"] = (
+                                            f"[encrypted_data_omitted_len={len(detail['data'])}]"
+                                        )
+                                if "text" in detail and isinstance(detail["text"], str):
+                                    detail["text"] = _truncate_base64_string(
+                                        detail["text"], threshold=2000
+                                    )
+        if "data" in sanitized_json and isinstance(sanitized_json["data"], list):
+            for item in sanitized_json["data"]:
+                if "embedding" in item and isinstance(item["embedding"], list):
+                    item["embedding"] = _truncate_vector_list(item["embedding"])
+                if "b64_json" in item and isinstance(item["b64_json"], str):
+                    if len(item["b64_json"]) > 256:
+                        item["b64_json"] = (
+                            f"[base64_json_omitted_len={len(item['b64_json'])}]"
+                        )
+        if "input" in sanitized_json and isinstance(sanitized_json["input"], list):
+            for item in sanitized_json["input"]:
+                if "content" in item and isinstance(item["content"], list):
+                    for part in item["content"]:
+                        if isinstance(part, dict) and part.get("type") == "input_image":
+                            image_url = part.get("image_url")
+                            if isinstance(image_url, str):
+                                part["image_url"] = _truncate_base64_string(image_url)
         return sanitized_json
     except Exception:
         return response_json
@@ -71,22 +127,44 @@ def _sanitize_openai_response(response_json: dict) -> dict:
 
 def _sanitize_openai_request(body: dict) -> dict:
     """净化OpenAI兼容API的请求体，主要截断图片base64。"""
+    from zhenxun.services.llm.config.providers import (
+        DebugLogOptions,
+        get_llm_config,
+    )
+
+    debug_conf = get_llm_config().debug_log
+    if isinstance(debug_conf, bool):
+        debug_conf = DebugLogOptions(
+            show_tools=debug_conf, show_schema=debug_conf, show_safety=debug_conf
+        )
+
     try:
-        sanitized_json = copy.deepcopy(body)
-        if "messages" in sanitized_json and isinstance(
-            sanitized_json["messages"], list
-        ):
-            for message in sanitized_json["messages"]:
-                if "content" in message and isinstance(message["content"], list):
-                    for i, part in enumerate(message["content"]):
-                        if part.get("type") == "image_url":
-                            if "image_url" in part and isinstance(
-                                part["image_url"], dict
-                            ):
-                                url = part["image_url"].get("url", "")
-                                message["content"][i]["image_url"]["url"] = (
-                                    _truncate_base64_string(url)
-                                )
+        sanitized_json = _recursive_sanitize_any(copy.deepcopy(body))
+        if "tools" in sanitized_json and not debug_conf.show_tools:
+            tools = sanitized_json["tools"]
+            if isinstance(tools, list):
+                tool_names = []
+                for t in tools:
+                    if isinstance(t, dict):
+                        name = None
+                        if "function" in t and isinstance(t["function"], dict):
+                            name = t["function"].get("name")
+                        if not name and "name" in t:
+                            name = t.get("name")
+                        tool_names.append(name or "unknown")
+                sanitized_json["tools"] = (
+                    f"<{len(tool_names)} tools hidden: {', '.join(tool_names)}>"
+                )
+
+        if "response_format" in sanitized_json and not debug_conf.show_schema:
+            response_format = sanitized_json["response_format"]
+            if isinstance(response_format, dict):
+                if response_format.get("type") == "json_schema":
+                    sanitized_json["response_format"] = {
+                        "type": "json_schema",
+                        "json_schema": "<JSON Schema Hidden>",
+                    }
+
         return sanitized_json
     except Exception:
         return body
@@ -94,6 +172,9 @@ def _sanitize_openai_request(body: dict) -> dict:
 
 def _sanitize_gemini_response(response_json: dict) -> dict:
     """净化Gemini API的响应体，处理文本和图片生成两种格式。"""
+    from zhenxun.services.llm.config.providers import get_llm_config
+
+    debug_mode = get_llm_config().debug_log
     try:
         sanitized_json = copy.deepcopy(response_json)
 
@@ -114,6 +195,15 @@ def _sanitize_gemini_response(response_json: dict) -> dict:
                                     content["parts"][i]["inlineData"]["data"] = (
                                         f"[base64_data_omitted_len={len(data)}]"
                                     )
+                            if "thoughtSignature" in part:
+                                signature = part.get("thoughtSignature", "")
+                                if isinstance(signature, str) and len(signature) > 256:
+                                    content["parts"][i]["thoughtSignature"] = (
+                                        f"[signature_omitted_len={len(signature)}]"
+                                    )
+                if not debug_mode and isinstance(candidate, dict):
+                    if "safetyRatings" in candidate:
+                        candidate["safetyRatings"] = "<Safety Ratings Hidden>"
 
         if "candidates" in sanitized_json:
             _process_candidates(sanitized_json["candidates"])
@@ -124,6 +214,19 @@ def _sanitize_gemini_response(response_json: dict) -> dict:
             if "candidates" in sanitized_json["image_generation"]:
                 _process_candidates(sanitized_json["image_generation"]["candidates"])
 
+        if "embeddings" in sanitized_json and isinstance(
+            sanitized_json["embeddings"], list
+        ):
+            for embedding in sanitized_json["embeddings"]:
+                if "values" in embedding and isinstance(embedding["values"], list):
+                    embedding["values"] = _truncate_vector_list(embedding["values"])
+
+        if not debug_mode and "promptFeedback" in sanitized_json:
+            prompt_feedback = sanitized_json.get("promptFeedback") or {}
+            if isinstance(prompt_feedback, dict) and "safetyRatings" in prompt_feedback:
+                prompt_feedback["safetyRatings"] = "<Safety Ratings Hidden>"
+                sanitized_json["promptFeedback"] = prompt_feedback
+
         return sanitized_json
     except Exception:
         return response_json
@@ -131,8 +234,46 @@ def _sanitize_gemini_response(response_json: dict) -> dict:
 
 def _sanitize_gemini_request(body: dict) -> dict:
     """净化Gemini API的请求体，进行结构转换和总结。"""
+    from zhenxun.services.llm.config.providers import (
+        DebugLogOptions,
+        get_llm_config,
+    )
+
+    debug_conf = get_llm_config().debug_log
+    if isinstance(debug_conf, bool):
+        debug_conf = DebugLogOptions(
+            show_tools=debug_conf, show_schema=debug_conf, show_safety=debug_conf
+        )
+
     try:
         sanitized_body = copy.deepcopy(body)
+        if "tools" in sanitized_body and not debug_conf.show_tools:
+            tool_summary = []
+            for tool_group in sanitized_body["tools"]:
+                if (
+                    isinstance(tool_group, dict)
+                    and "functionDeclarations" in tool_group
+                ):
+                    declarations = tool_group["functionDeclarations"]
+                    if isinstance(declarations, list):
+                        for func in declarations:
+                            if isinstance(func, dict):
+                                tool_summary.append(func.get("name", "unknown"))
+            sanitized_body["tools"] = (
+                f"<{len(tool_summary)} functions hidden: {', '.join(tool_summary)}>"
+            )
+
+        if not debug_conf.show_safety and "safetySettings" in sanitized_body:
+            sanitized_body["safetySettings"] = "<Safety Settings Hidden>"
+
+        if not debug_conf.show_schema and "generationConfig" in sanitized_body:
+            generation_config = sanitized_body["generationConfig"]
+            if (
+                isinstance(generation_config, dict)
+                and "responseJsonSchema" in generation_config
+            ):
+                generation_config["responseJsonSchema"] = "<JSON Schema Hidden>"
+
         if "contents" in sanitized_body and isinstance(
             sanitized_body["contents"], list
         ):
@@ -153,6 +294,13 @@ def _sanitize_gemini_request(body: dict) -> dict:
                                 continue
                         new_parts.append(part)
 
+                        if "thoughtSignature" in part:
+                            sig = part["thoughtSignature"]
+                            if isinstance(sig, str) and len(sig) > 64:
+                                part["thoughtSignature"] = (
+                                    f"[signature_omitted_len={len(sig)}]"
+                                )
+
                     if media_summary:
                         summary_text = (
                             f"[多模态内容: {len(media_summary)}个文件 - "
@@ -195,8 +343,5 @@ def sanitize_for_logging(data: Any, context: str | None = None) -> Any:
     elif context == "ui_html":
         if isinstance(data, str):
             return _sanitize_ui_html(data)
-    else:
-        if isinstance(data, str):
-            return _truncate_base64_string(data)
 
-    return data
+    return _recursive_sanitize_any(data)
diff --git a/zhenxun/utils/pydantic_compat.py b/zhenxun/utils/pydantic_compat.py
index 878e12d6..5c68cbff 100644
--- a/zhenxun/utils/pydantic_compat.py
+++ b/zhenxun/utils/pydantic_compat.py
@@ -10,8 +10,14 @@ from enum import Enum
 from pathlib import Path
 from typing import Any, TypeVar, get_args, get_origin
 
-from nonebot.compat import PYDANTIC_V2, model_dump
-from pydantic import VERSION, BaseModel
+from nonebot.compat import (
+    PYDANTIC_V2,
+    model_dump,
+    model_fields,
+    type_validate_json,
+    type_validate_python,
+)
+from pydantic import BaseModel
 import ujson as json
 
 T = TypeVar("T", bound=BaseModel)
@@ -27,9 +33,13 @@ __all__ = [
     "model_construct",
     "model_copy",
     "model_dump",
+    "model_dump_json",
+    "model_fields",
     "model_json_schema",
     "model_validate",
     "parse_as",
+    "type_validate_json",
+    "type_validate_python",
 ]
 
 
@@ -58,12 +68,18 @@ def model_construct(model_class: type[T], **kwargs: Any) -> T:
 
 def model_validate(model_class: type[T], obj: Any) -> T:
     """
-    Pydantic `model_validate` (v2) 与 `parse_obj` (v1) 的兼容函数。
+    Pydantic 模型验证兼容函数。
+    """
+    return type_validate_python(model_class, obj)
+
+
+def model_dump_json(model: BaseModel, **kwargs: Any) -> str:
+    """
+    Pydantic `model.json()` (v1) 和 `model.model_dump_json()` (v2) 的兼容函数。
     """
     if PYDANTIC_V2:
-        return model_class.model_validate(obj)
-    else:
-        return model_class.parse_obj(obj)
+        return model.model_dump_json(**kwargs)
+    return model.json(**kwargs)
 
 
 if PYDANTIC_V2:
@@ -78,8 +94,7 @@ def model_json_schema(model_class: type[BaseModel], **kwargs: Any) -> dict[str,
     """
     if PYDANTIC_V2:
         return model_class.model_json_schema(**kwargs)
-    else:
-        return model_class.schema(by_alias=kwargs.get("by_alias", True))
+    return model_class.schema(by_alias=kwargs.get("by_alias", True))
 
 
 def _is_pydantic_type(t: Any) -> bool:
@@ -108,18 +123,7 @@ def _dump_pydantic_obj(obj: Any) -> Any:
     return obj
 
 
-def parse_as(type_: type[V], obj: Any) -> V:
-    """
-    一个兼容 Pydantic V1 的 parse_obj_as 和V2的TypeAdapter.validate_python 的辅助函数。
-    """
-    if VERSION.startswith("1"):
-        from pydantic import parse_obj_as
-
-        return parse_obj_as(type_, obj)
-    else:
-        from pydantic import TypeAdapter  # type: ignore
-
-        return TypeAdapter(type_).validate_python(obj)
+parse_as = type_validate_python
 
 
 def dump_json_safely(obj: Any, **kwargs) -> str: