refactor: remove stream_options from model adapters and consolidate t…

agentrun/integration/crewai/model_adapter.py

@@ -17,11 +17,15 @@ def wrap_model(self, common_model: Any) -> Any:
         from crewai import LLM
 
         info = common_model.get_model_info()  # 确保模型可用
+
+        # 注意：不在此处设置 stream_options，因为：
+        # 1. CrewAI 内部决定是否使用流式请求
+        # 2. 在非流式请求中传递 stream_options 不符合 OpenAI API 规范
+        # 3. CrewAI 会自行处理 usage 信息
         return LLM(
             api_key=info.api_key,
             model=f"{info.provider or 'openai'}/{info.model}",
             base_url=info.base_url,
             default_headers=info.headers,
-            stream_options={"include_usage": True},
             # async_client=AsyncClient(headers=info.headers),
         )

agentrun/integration/google_adk/model_adapter.py

@@ -34,10 +34,13 @@ def wrap_model(self, common_model: CommonModel) -> Any:
 
         info = common_model.get_model_info()
 
+        # 注意：不在此处设置 stream_options，因为：
+        # 1. Google ADK 内部决定是否使用流式请求
+        # 2. 在非流式请求中传递 stream_options 不符合 OpenAI API 规范
+        # 3. Google ADK 会自行处理 usage 信息
         return LiteLlm(
             model=f"{info.provider or 'openai'}/{info.model}",
             api_base=info.base_url,
             api_key=info.api_key,
             extra_headers=info.headers,
-            stream_options={"include_usage": True},
         )

agentrun/integration/pydantic_ai/model_adapter.py

@@ -17,7 +17,6 @@ def wrap_model(self, common_model: CommonModel) -> Any:
         try:
             from pydantic_ai.models.openai import OpenAIChatModel
             from pydantic_ai.providers.openai import OpenAIProvider
-            from pydantic_ai.settings import ModelSettings
         except Exception as e:
             raise ImportError(
                 "PydanticAI is not installed. "
@@ -28,16 +27,17 @@ def wrap_model(self, common_model: CommonModel) -> Any:
 
         info = common_model.get_model_info()
 
+        # 注意：不在此处设置 stream_options，因为：
+        # 1. run_sync() 使用非流式请求，不需要 stream_options
+        # 2. run_stream() 使用流式请求，PydanticAI 会自行处理 usage 信息
+        # 3. 在非流式请求中传递 stream_options 不符合 OpenAI API 规范
         return OpenAIChatModel(
             info.model or "",
             provider=OpenAIProvider(
                 base_url=info.base_url,
                 api_key=info.api_key,
                 http_client=AsyncClient(headers=info.headers),
             ),
-            settings=ModelSettings(
-                extra_body={"stream_options": {"include_usage": True}}
-            ),
         )
 
 

tests/unittests/integration/base.py

@@ -0,0 +1,236 @@
+"""Integration 测试基类和统一响应模型
+
+为所有 integration 测试提供统一的基类，屏蔽框架特定逻辑，
+提供类似 AgentServer.invoke 的统一调用接口。
+
+使用方式:
+    class TestLangChain(IntegrationTestBase):
+        def create_agent(self, model, tools=None, system_prompt="..."):
+            ...
+
+        def invoke(self, agent, message):
+            ...
+
+        async def ainvoke(self, agent, message):
+            ...
+"""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Any, Dict, Iterator, List, Optional
+
+from agentrun.integration.builtin.model import CommonModel
+from agentrun.integration.utils.tool import CommonToolSet
+
+
+@dataclass
+class ToolCallInfo:
+    """工具调用信息"""
+
+    name: str
+    arguments: Dict[str, Any]
+    id: str
+    result: Optional[str] = None
+
+
+@dataclass
+class IntegrationTestResult:
+    """统一的 Integration 测试结果
+
+    将不同框架的响应格式统一为标准格式，便于测试验证。
+    """
+
+    final_text: str
+    """最终文本响应"""
+
+    tool_calls: List[ToolCallInfo] = field(default_factory=list)
+    """所有工具调用信息"""
+
+    messages: List[Dict[str, Any]] = field(default_factory=list)
+    """完整消息历史（框架特定格式）"""
+
+    raw_response: Any = None
+    """原始框架响应"""
+
+    def has_tool_calls(self) -> bool:
+        """是否有工具调用"""
+        return len(self.tool_calls) > 0
+
+    def get_tool_call(self, name: str) -> Optional[ToolCallInfo]:
+        """获取指定名称的工具调用"""
+        for tc in self.tool_calls:
+            if tc.name == name:
+                return tc
+        return None
+
+
+@dataclass
+class StreamChunk:
+    """流式输出的单个块"""
+
+    content: Optional[str] = None
+    """文本内容"""
+
+    tool_call_id: Optional[str] = None
+    """工具调用 ID"""
+
+    tool_call_name: Optional[str] = None
+    """工具调用名称"""
+
+    tool_call_args_delta: Optional[str] = None
+    """工具调用参数增量"""
+
+    is_final: bool = False
+    """是否是最后一个块"""
+
+
+class IntegrationTestBase(ABC):
+    """Integration 测试基类
+
+    每个框架的测试类需要继承此基类并实现以下抽象方法：
+    - create_agent(): 创建框架特定的 Agent
+    - invoke(): 同步调用 Agent
+    - ainvoke(): 异步调用 Agent
+    - stream(): 流式调用 Agent（可选）
+
+    基类提供统一的测试方法和验证逻辑。
+    """
+
+    @abstractmethod
+    def create_agent(
+        self,
+        model: CommonModel,
+        tools: Optional[CommonToolSet] = None,
+        system_prompt: str = "You are a helpful assistant.",
+    ) -> Any:
+        """创建框架特定的 Agent
+
+        Args:
+            model: AgentRun 通用模型
+            tools: 可选的工具集
+            system_prompt: 系统提示词
+
+        Returns:
+            框架特定的 Agent 对象
+        """
+        pass
+
+    @abstractmethod
+    def invoke(self, agent: Any, message: str) -> IntegrationTestResult:
+        """同步调用 Agent
+
+        Args:
+            agent: 框架特定的 Agent 对象
+            message: 用户消息
+
+        Returns:
+            统一的测试结果
+        """
+        pass
+
+    @abstractmethod
+    async def ainvoke(self, agent: Any, message: str) -> IntegrationTestResult:
+        """异步调用 Agent
+
+        Args:
+            agent: 框架特定的 Agent 对象
+            message: 用户消息
+
+        Returns:
+            统一的测试结果
+        """
+        pass
+
+    def stream(self, agent: Any, message: str) -> Iterator[StreamChunk]:
+        """流式调用 Agent（可选实现）
+
+        Args:
+            agent: 框架特定的 Agent 对象
+            message: 用户消息
+
+        Yields:
+            流式输出块
+
+        Raises:
+            NotImplementedError: 如果框架不支持流式调用
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} does not support streaming"
+        )
+
+    async def astream(self, agent: Any, message: str) -> Iterator[StreamChunk]:
+        """异步流式调用 Agent（可选实现）
+
+        Args:
+            agent: 框架特定的 Agent 对象
+            message: 用户消息
+
+        Yields:
+            流式输出块
+
+        Raises:
+            NotImplementedError: 如果框架不支持流式调用
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} does not support async streaming"
+        )
+
+    # =========================================================================
+    # 验证辅助方法
+    # =========================================================================
+
+    def assert_final_text(self, result: IntegrationTestResult, expected: str):
+        """验证最终文本"""
+        assert (
+            result.final_text == expected
+        ), f"Expected '{expected}', got '{result.final_text}'"
+
+    def assert_final_text_contains(
+        self, result: IntegrationTestResult, substring: str
+    ):
+        """验证最终文本包含指定字符串"""
+        assert (
+            substring in result.final_text
+        ), f"Expected '{substring}' in '{result.final_text}'"
+
+    def assert_tool_called(
+        self,
+        result: IntegrationTestResult,
+        tool_name: str,
+        expected_args: Optional[Dict[str, Any]] = None,
+    ):
+        """验证工具被调用"""
+        tool_call = result.get_tool_call(tool_name)
+        assert tool_call is not None, (
+            f"Tool '{tool_name}' was not called. Called tools:"
+            f" {[tc.name for tc in result.tool_calls]}"
+        )
+
+        if expected_args is not None:
+            assert (
+                tool_call.arguments == expected_args
+            ), f"Expected args {expected_args}, got {tool_call.arguments}"
+
+    def assert_tool_not_called(
+        self, result: IntegrationTestResult, tool_name: str
+    ):
+        """验证工具未被调用"""
+        tool_call = result.get_tool_call(tool_name)
+        assert tool_call is None, f"Tool '{tool_name}' was unexpectedly called"
+
+    def assert_no_tool_calls(self, result: IntegrationTestResult):
+        """验证没有工具调用"""
+        assert not result.has_tool_calls(), (
+            "Expected no tool calls, "
+            f"got {[tc.name for tc in result.tool_calls]}"
+        )
+
+    def assert_tool_call_count(
+        self, result: IntegrationTestResult, expected_count: int
+    ):
+        """验证工具调用次数"""
+        actual_count = len(result.tool_calls)
+        assert actual_count == expected_count, (
+            f"Expected {expected_count} tool calls, got {actual_count}. "
+            f"Tools: {[tc.name for tc in result.tool_calls]}"
+        )