When using google.adk.agents.ParallelAgent with multiple google.adk.agents.LlmAgent instances as sub-agents, ValueError exceptions from the opentelemetry.context module are consistently logged. These errors occur after each sub-agent completes its processing. While the core functionality of the agents (e.g., LLM calls and output generation) appears to be unaffected, these errors create noise in the logs and suggest a potential issue with context management within the ADK framework or its interaction with opentelemetry.

Steps to Reproduce:

1. Define multiple LlmAgent instances, each with its own model, instructions, and output schema.
2. Define an AsyncExitStack and initialize tools (e.g., MCPToolset).
3. Instantiate a ParallelAgent, providing the LlmAgent instances to its sub_agents parameter.
4. The root agent setup returns the ParallelAgent instance and the AsyncExitStack.
5. Run the agent (e.g., by making a request to the FastAPI endpoint if using google.adk.cli.fast_api).

Expected Behavior:
The ParallelAgent and its sub-agents should execute without opentelemetry context errors being logged.

Actual Behavior:
After each sub-agent finishes its execution and its output is generated, the following type of error is logged multiple times:

ERROR - __init__.py:157 - Failed to detach context
Traceback (most recent call last):
  File "/path/to/your/env/lib/pythonX.Y/site-packages/opentelemetry/context/__init__.py", line 155, in detach
    _RUNTIME_CONTEXT.detach(token)
  File "/path/to/your/env/lib/pythonX.Y/site-packages/opentelemetry/context/contextvars_context.py", line 53, in detach
    self._current_context.reset(token)
ValueError: <Token var=<ContextVar name='current_context' default={} at 0xXXXXXXXXX> at 0xYYYYYYYYY> was created in a different Context

(Note: Replace /path/to/your/env/lib/pythonX.Y/ and memory addresses with actuals from your logs if desired)

Environment:

• Operating System: macOS (darwin 23.5.0)
• Python Version: 3.12
• Google ADK Version: 0.4.0
• Minimal Reproducible Code (agent.py):

  import asyncio
  from google.adk.agents import LlmAgent, BaseAgent, ParallelAgent
  from pydantic import BaseModel, Field
  from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset, SseServerParams
  from contextlib import AsyncExitStack
  import enum
  
  # ... (Out_of_office_category, Out_of_office_output_schema definitions) ...
  # ... (New_lead_category, New_lead_output_schema definitions) ...
  # ... (Book_meeting_category, Book_meeting_output_schema definitions) ...
  # ... (General_type_reply_category, General_type_reply_output_schema definitions) ...
  
  # [PASTE YOUR Enum and BaseModel Schema definitions here for completeness]
  
  class Out_of_office_category(enum.Enum):
      resignation = "resignation"
      holiday = "holiday"
      other = "other"
      not_out_of_office = "not_out_of_office"
  
  class Out_of_office_output_schema(BaseModel):
      out_of_office: bool = Field(description="Whether the email is out of office or not")
      category: Out_of_office_category = Field(description="The category of the email")
      reason: str = Field(description="The reason for the email being out of office")
  
  class New_lead_category(enum.Enum):
      not_right_person_add_new_lead_new_email = "not_right_person_add_new_lead_new_email"
      right_person_add_new_lead_new_email = "right_person_add_new_lead_new_email"
      right_person_add_new_lead_existing_email = "right_person_add_new_lead_existing_email"
      not_add_new_lead = "not_add_new_lead"
  
  class New_lead_output_schema(BaseModel):
      new_lead: bool = Field(description="Whether the email contains a new lead or not")
      new_lead_category: New_lead_category = Field(description="The category of the email")
      reason: str = Field(description="The reason for the email being a new lead or not")
  
  class Book_meeting_category(enum.Enum):
      book_meeting = "book_meeting"
      book_meeting_details = "book_meeting_details"
      not_book_meeting = "not_book_meeting"
  
  class Book_meeting_output_schema(BaseModel):
      book_meeting: bool = Field(description="Whether the email contains a request to book a meeting or not")
      book_meeting_category: Book_meeting_category = Field(description="The category of the email")
      reason: str = Field(description="The reason for the email being a book meeting or not")
  
  class General_type_reply_category(enum.Enum):
      positive = "positive"
      negative = "negative"
  
  class General_type_reply_output_schema(BaseModel):
      general_type_reply_category: General_type_reply_category = Field(description="The category of the email")
      reason: str = Field(description="The reason for the email being a positive or negative reply")
  
  
  async def reply_agent():
      common_exit_stack = AsyncExitStack()
      tools, _ = await MCPToolset.from_server(
          connection_params=SseServerParams(
            url="http://44.214.120.160:8002/sse" # Example URL
        ),
        async_exit_stack=common_exit_stack
      )
  
      out_of_office_agent = LlmAgent(
              model="gemini-2.0-flash",
              name="out_of_office_agent",
              description="check whether the email is out of office or not...",
              instruction=\"\"\"...\"\"\", # Shorten for brevity or refer to full agent code
              output_schema=Out_of_office_output_schema,
              output_key="out_of_office_agent"
          )
      
      new_lead_agent = LlmAgent(
          model="gemini-2.0-flash",
          name="new_lead_agent",
          description="Check whether the email contains a new lead...",
          instruction=\"\"\"...\"\"\",
          output_schema=New_lead_output_schema,
          output_key="new_lead_agent"
      )
  
      book_meeting_agent = LlmAgent(
          model="gemini-2.0-flash",
          name="book_meeting_agent",
          description="Check whether the email contains a request to book a meeting...",
          instruction=\"\"\"...\"\"\",
          output_schema=Book_meeting_output_schema,
          output_key="book_meeting_agent"
      )
      
      general_type_reply_agent = LlmAgent(
          model="gemini-2.0-flash",
          name="general_type_reply_agent",
          description="Classify the email into 'positive', 'negative'...",
          instruction=\"\"\"...\"\"\",
          output_schema=General_type_reply_output_schema,
          output_key="general_type_reply_agent"
      )
  
      return out_of_office_agent, new_lead_agent, book_meeting_agent, general_type_reply_agent, common_exit_stack
  
  
  async def main():
      out_of_office_agent, new_lead_agent, book_meeting_agent, general_type_reply_agent, common_exit_stack = await reply_agent()
      reply_intent_agent = ParallelAgent(
              name="replyagent",
              description="Analyze the email and determine the intent of the email.",
              sub_agents=[out_of_office_agent, new_lead_agent, book_meeting_agent, general_type_reply_agent]
          )
      return reply_intent_agent, common_exit_stack
  
  root_agent = main()

Additional Context:

• The sub-agents themselves successfully process inputs and produce the expected outputs via LLM calls. The opentelemetry errors seem to occur around the completion/logging phase of each sub-agent's lifecycle within the ParallelAgent.