frontend/src/components/Agent/ProvidersSettings.tsx

@@ -182,7 +182,6 @@ export function ProvidersSettings() {
                 >
                   <option value="openai">OpenAI</option>
                   <option value="gemini">Gemini</option>
-                  <option value="deepseek">DeepSeek</option>
                   <option value="claude">Claude</option>
                   <option value="local">Local</option>
                 </select>

frontend/src/types/index.ts

@@ -14,7 +14,7 @@ export interface User {
 
 // Provider types
 export interface Provider {
-  provider_type: 'openai' | 'gemini' | 'claude' | 'local' | 'deepseek';
+  provider_type: 'openai' | 'gemini' | 'claude' | 'local';
   provider_title: string;
   provider_url?: string;
   provider_owner?: string;
@@ -25,7 +25,7 @@ export interface Provider {
 }
 
 export interface ProviderRegisterRequest {
-  provider_type: 'openai' | 'gemini' | 'claude' | 'local' | 'deepseek';
+  provider_type: 'openai' | 'gemini' | 'claude' | 'local';
   provider_title: string;
   provider_url: string;
   provider_apikey: string;

pretor/adapter/model_adapter/agent_factory.py

@@ -67,8 +67,6 @@ class AgentFactory:
                                               output_type=output_type,
                                               deps_type=deps_type,
                                               system_prompt=system_prompt,
-                                              tools=tools,
-                                              retries=3,
                                               )
             case _:
                 agent = Agent(model=model,

pretor/adapter/model_adapter/deepseek_reasoner.py

@@ -1,149 +1,60 @@
 import re
-import json
+import os
 from typing import Type, TypeVar, Any, Generic
 from pydantic import BaseModel, ValidationError
 from pydantic_ai import Agent, RunContext
-from pydantic_ai.run import AgentRunResult
 
 T = TypeVar('T', bound=BaseModel)
 
+
 class DeepSeekReasonerAgent(Generic[T]):
     """
     专为 DeepSeek-V4/R1 设计的适配器。
-    将结构化输出降级为文本解析模式，并支持重试逻辑以确保系统兼容性。
+    将结构化输出降级为文本解析模式，以规避工具调用(Tool Calling)的兼容性问题。
     """
 
     def __init__(
             self,
             model,
             name,
-            output_type: Any = str,
+            output_type: Type[T] = str,
             system_prompt: str = "",
-            deps_type: Type[Any] = None,
+            deps_type: Type[Any] = None
-            tools: list = None,
-            retries: int = 3,
-            **kwargs
     ):
+        # 1. 强制声明输出为 str，确保底层不发送 tools 字段
         self.output_schema = output_type
-        self.has_custom_output = output_type is not str and output_type is not None
-        self.tools = tools or []
-        self.retries = retries
 
-        format_instruction = ""
+        # 2. 注入强制格式指令到 System Prompt
-        if self.has_custom_output:
+        format_instruction = (
-            try:
+            f"\n\nCRITICAL: 你必须输出且只能输出一段纯 JSON 格式的数据，"
-                if hasattr(self.output_schema, 'model_json_schema'):
+            f"并包裹在 ```json 和 ``` 之间。格式必须符合以下 Pydantic 模型结构：\n"
-                    schema_str = json.dumps(self.output_schema.model_json_schema(), ensure_ascii=False)
+            f"{self.output_schema.model_json_schema()}"
-                else:
+        )
-                    # Don't inject <class 'dict'> into prompt
-                    schema_str = str(getattr(self.output_schema, '__name__', str(self.output_schema)))
-
-                format_instruction = (
-                    f"\n\nCRITICAL: 你必须输出且只能输出一段纯 JSON 格式的数据，"
-                    f"并包裹在 ```json 和 ``` 之间。格式必须符合以下 JSON Schema 结构（或对应数据类型）：\n"
-                    f"{schema_str}"
-                )
-            except Exception:
-                pass
-
-        tool_instruction = ""
-        if self.tools:
-            tool_descs = []
-            for t in self.tools:
-                desc = getattr(t, '__name__', str(t))
-                if hasattr(t, '__doc__') and t.__doc__:
-                    desc += f": {t.__doc__.strip()}"
-                tool_descs.append(f"- {desc}")
-            tool_instruction = (
-                "\n\n系统为您提供了以下工具。由于当前处于结构化降级模式，无法原生调用。"
-                "但如果您在思考过程中判断必须使用这些工具，请在返回的结构中（或如果是自由文本）注明意图，由外层逻辑进行调度：\n" +
-                "\n".join(tool_descs)
-            )
 
         self.agent = Agent(
             model=model,
             name=name,
-            output_type=str,  # Force native agent to return str to disable function calling
+            output_type=str,  # 内部通信用字符串
-            system_prompt=system_prompt + format_instruction + tool_instruction,
+            system_prompt=system_prompt + format_instruction,
             deps_type=deps_type,
-            **kwargs
+            retries=0
         )
 
-    def _parse_output(self, text: str) -> Any:
+    async def run(self, user_prompt: str, deps: Any = None) -> T:
-        if not self.has_custom_output:
+        # 调用 PydanticAI 原生 run
-            return text
+        result = await self.agent.run(user_prompt, deps=deps)
+        return self._parse_json(result.output)
 
+    def _parse_json(self, text: str) -> T:
+        # 使用正则提取 JSON 块
         match = re.search(r'```json\s*(.*?)\s*```', text, re.DOTALL)
         json_str = match.group(1).strip() if match else text
 
-        if not json_str.startswith('{') and not json_str.startswith('['):
+        # 如果正则没抓到，尝试寻找首尾大括号
-            start_obj = json_str.find('{')
+        if not json_str.startswith('{'):
-            start_arr = json_str.find('[')
+            json_str = text[text.find('{'):text.rfind('}') + 1]
-            start = -1
-            end = -1
-            if start_obj != -1 and (start_arr == -1 or start_obj < start_arr):
-                start = start_obj
-                end = json_str.rfind('}')
-            elif start_arr != -1:
-                start = start_arr
-                end = json_str.rfind(']')
-
-            if start != -1 and end != -1 and end > start:
-                json_str = json_str[start:end+1]
-
-        if not json_str:
-            raise ValueError("未找到有效的 JSON 块。请将结果包装在 ```json 中。")
 
         try:
-            if hasattr(self.output_schema, 'model_validate_json'):
+            return self.output_schema.model_validate_json(json_str)
-                return self.output_schema.model_validate_json(json_str)
-            else:
-                return json.loads(json_str)
         except ValidationError as e:
-            raise ValueError(f"返回的 JSON 无法匹配所需结构：{e}")
+            raise ValueError(f"DeepSeek 返回格式非法: {e}\n原文: {text}")
-        except json.JSONDecodeError as e:
-            raise ValueError(f"返回的不是合法的 JSON：{e}")
-
-    async def run(self, user_prompt: str, deps: Any = None, message_history: list = None, **kwargs) -> Any:
-        # Custom retry loop
-        current_history = message_history or []
-        last_exception = None
-
-        for attempt in range(self.retries + 1):
-            result = await self.agent.run(
-                user_prompt,
-                deps=deps,
-                message_history=current_history,
-                **kwargs
-            )
-
-            raw_text = result.data if hasattr(result, 'data') else getattr(result, 'output', str(result))
-
-            try:
-                parsed_data = self._parse_output(raw_text)
-
-                # Proxy the result to inject the parsed data seamlessly
-                class AgentRunResultProxy:
-                    def __init__(self, original, parsed):
-                        self._original = original
-                        self._parsed = parsed
-                    def __getattr__(self, name):
-                        if name == 'data':
-                            return self._parsed
-                        if name == 'output':
-                            return self._parsed
-                        return getattr(self._original, name)
-
-                return AgentRunResultProxy(result, parsed_data)
-
-            except ValueError as e:
-                last_exception = e
-                # Prepare retry prompt
-                user_prompt = f"你的上一次输出解析失败，错误原因是: {e}\n请修正格式后重新输出。"
-
-                # We need to maintain history manually so the model sees what it did wrong
-                # Actually, pydantic-ai manages history inside the result. Let's use the all_messages from result
-                if hasattr(result, 'all_messages'):
-                    current_history = result.all_messages()
-
-        raise ValueError(f"Exceeded maximum retries ({self.retries}) for output validation. Last error: {last_exception}")

pretor/api/provider.py

@@ -25,7 +25,7 @@ from pretor.utils.ray_hook import ray_actor_hook
 provider_router = APIRouter(prefix="/api/v1/provider", tags=["provider"])
 
 class ProviderRegister(BaseModel):
-    provider_type: Literal["openai", "gemini", "claude", "deepseek"]
+    provider_type: Literal["openai", "gemini", "claude"]
     provider_title: str
     provider_url: str
     provider_apikey: str