Add simplified run() API for OpenAI Agents SDK integration

- New run() method accepts standard Agent objects from OpenAI SDK
- Automatically converts ModelSettings and FunctionTool to serializable format
- Provides clean DX matching OpenAI Agents SDK with just task_id param
- Works in Temporal workflows, sync agents (FastACP), and standalone scripts

Example usage:
    from agents import Agent, function_tool, ModelSettings

    @function_tool
    def my_tool(param: str) -> str:
        return f"Result: {param}"

    agent = Agent(
        name="My Agent",
        instructions="Help users",
        model="gpt-4o",
        model_settings=ModelSettings(parallel_tool_calls=True),
        tools=[my_tool]
    )

    result = await adk.providers.openai.run(
        agent=agent,
        input="Hello",
        task_id=task_id
    )

Author: danielmillerp

src/agentex/lib/adk/providers/_modules/openai.py

@@ -477,3 +477,167 @@ async def run_agent_streamed_auto_send(
                 max_turns=max_turns,
                 previous_response_id=previous_response_id,
             )
+
+    async def run(
+        self,
+        agent: Agent,
+        input: str | list[dict[str, Any]],
+        task_id: str,
+        *,
+        trace_id: str | None = None,
+        parent_span_id: str | None = None,
+        start_to_close_timeout: timedelta = timedelta(seconds=600),
+        heartbeat_timeout: timedelta = timedelta(seconds=600),
+        retry_policy: RetryPolicy = DEFAULT_RETRY_POLICY,
+        max_turns: int | None = None,
+        mcp_server_params: list[StdioServerParameters] | None = None,
+        previous_response_id: str | None = None,
+    ) -> SerializableRunResultStreaming | RunResultStreaming:
+        """
+        Run an OpenAI Agent with automatic streaming to AgentEx UI.
+
+        This is a simple wrapper that lets you use standard OpenAI Agents SDK
+        patterns while getting AgentEx features (streaming, tracing, TaskMessages).
+
+        Works everywhere: Temporal workflows, sync agents (FastACP), standalone scripts.
+
+        Example:
+            from agents import Agent, function_tool, ModelSettings
+            from openai.types.shared import Reasoning
+
+            @function_tool
+            def get_weather(city: str) -> str:
+                return f"Weather in {city}: Sunny"
+
+            agent = Agent(
+                name="Weather Bot",
+                instructions="Help with weather",
+                model="gpt-4o",
+                model_settings=ModelSettings(
+                    parallel_tool_calls=True,
+                    reasoning=Reasoning(effort="low", summary="auto")
+                ),
+                tools=[get_weather]
+            )
+
+            result = await adk.providers.openai.run(
+                agent=agent,
+                input="What's the weather in Tokyo?",
+                task_id=params.task.id,
+                trace_id=params.task.id,
+                parent_span_id=span.id,
+            )
+
+        Args:
+            agent: Standard OpenAI Agents SDK Agent object
+            input: User message (str) or conversation history (list of dicts)
+            task_id: AgentEx task ID for streaming
+            trace_id: Optional trace ID (defaults to task_id)
+            parent_span_id: Optional parent span for nested tracing
+            start_to_close_timeout: Maximum time allowed for the operation
+            heartbeat_timeout: Maximum time between heartbeats
+            retry_policy: Policy for retrying failed operations
+            max_turns: Max conversation turns (default from Runner)
+            mcp_server_params: Optional MCP server configurations
+            previous_response_id: For conversation continuity
+
+        Returns:
+            RunResult with final_output and conversation history
+        """
+        # 1. Normalize input format
+        if isinstance(input, str):
+            input_list = [{"role": "user", "content": input}]
+        else:
+            input_list = input
+
+        # 2. Extract agent properties
+        agent_name = agent.name
+        agent_instructions = agent.instructions
+
+        # Extract model name
+        if isinstance(agent.model, str):
+            model = agent.model
+        else:
+            model = None  # Will use default
+
+        # Extract model settings and convert to serializable format if needed
+        model_settings = getattr(agent, 'model_settings', None)
+        if model_settings and not isinstance(model_settings, dict):
+            # Convert OpenAI SDK ModelSettings to serializable format
+            from agentex.lib.core.temporal.activities.adk.providers.openai_activities import ModelSettings as SerializableModelSettings
+
+            model_settings = SerializableModelSettings(
+                temperature=getattr(model_settings, 'temperature', None),
+                max_tokens=getattr(model_settings, 'max_tokens', None),
+                top_p=getattr(model_settings, 'top_p', None),
+                frequency_penalty=getattr(model_settings, 'frequency_penalty', None),
+                presence_penalty=getattr(model_settings, 'presence_penalty', None),
+                parallel_tool_calls=getattr(model_settings, 'parallel_tool_calls', None),
+                tool_choice=getattr(model_settings, 'tool_choice', None),
+                reasoning=getattr(model_settings, 'reasoning', None),
+                top_logprobs=getattr(model_settings, 'top_logprobs', None),
+                store=getattr(model_settings, 'store', None),
+                metadata=getattr(model_settings, 'metadata', None),
+                verbosity=getattr(model_settings, 'verbosity', None),
+                extra_headers=getattr(model_settings, 'extra_headers', None),
+                extra_query=getattr(model_settings, 'extra_query', None),
+                extra_body=getattr(model_settings, 'extra_body', None),
+                extra_args=getattr(model_settings, 'extra_args', None),
+            )
+
+        # Extract other properties and convert tools to serializable format
+        tools = agent.tools or []
+        if tools:
+            # Convert tools to ensure they're serializable
+            converted_tools = []
+            for tool in tools:
+                if hasattr(tool, 'to_oai_function_tool'):
+                    # Already a custom tool with conversion method
+                    converted_tools.append(tool.to_oai_function_tool())
+                elif hasattr(tool, 'name') and hasattr(tool, 'description'):
+                    # Standard OpenAI SDK FunctionTool - convert to serializable format
+                    from agentex.lib.core.temporal.activities.adk.providers.openai_activities import FunctionTool as SerializableFunctionTool
+
+                    converted_tools.append(SerializableFunctionTool(
+                        name=tool.name,
+                        description=tool.description,
+                        params_json_schema=getattr(tool, 'params_json_schema', {}),
+                        strict_json_schema=getattr(tool, 'strict_json_schema', True),
+                        on_invoke_tool=getattr(tool, 'on_invoke_tool', None),
+                    ))
+                else:
+                    # Other tool types (WebSearchTool, FileSearchTool, etc.)
+                    converted_tools.append(tool)
+            tools = converted_tools
+
+        handoffs = agent.handoffs or []
+        handoff_description = getattr(agent, 'handoff_description', None)
+        output_type = getattr(agent, 'output_type', None)
+        tool_use_behavior = getattr(agent, 'tool_use_behavior', 'run_llm_again')
+        input_guardrails = getattr(agent, 'input_guardrails', None)
+        output_guardrails = getattr(agent, 'output_guardrails', None)
+
+        # 3. Call the existing service layer
+        return await self.run_agent_streamed_auto_send(
+            task_id=task_id,
+            trace_id=trace_id,
+            parent_span_id=parent_span_id,
+            input_list=input_list,
+            mcp_server_params=mcp_server_params or [],
+            agent_name=agent_name,
+            agent_instructions=agent_instructions,
+            model=model,
+            model_settings=model_settings,
+            tools=tools,
+            handoff_description=handoff_description,
+            handoffs=handoffs,
+            output_type=output_type,
+            tool_use_behavior=tool_use_behavior,
+            start_to_close_timeout=start_to_close_timeout,
+            heartbeat_timeout=heartbeat_timeout,
+            retry_policy=retry_policy,
+            input_guardrails=input_guardrails,
+            output_guardrails=output_guardrails,
+            max_turns=max_turns,
+            previous_response_id=previous_response_id,
+        )