RuntimeError: Event loop is closed caused by Gemini.api_client @cached_property

[ypenn21]

🔴 Required Information

Please ensure all items in this section are completed to allow for efficient
triaging. Requests without complete information may be rejected / deprioritized.
If an item is not applicable to you - please mark it as N/A

Describe the Bug:

My work flow is cloud run backend api call down stream agent engine agents. This is an architecture with workflow:

iap for auth-> cloud run for ui & backend -> agent engine agent

In the agent stack trace:

File "/usr/local/lib/python3.12/asyncio/base_events.py", line 545, in _check_closed
raise RuntimeError('Event loop is closed')
RuntimeError: Event loop is closed"

This happens constantly after I utilize the agent engine api to prompt agent after sending 3-5 requests.

Issue Summary:

When deployed to multi-threaded environments like Vertex AI Agent Engine, the ADK Runner spawns a fresh OS thread and a brand-new asyncio event loop for every incoming request.

1. Request 1: Creates the api_client (and its underlying aiohttp session). This session is permanently bound to Loop A.
2. Request 2: ADK reuses the same Agent instance. The Gemini model returns the cached api_client.
3. Failure: The client attempts to perform I/O using the now-closed Loop A while running inside the new Loop B, resulting in RuntimeError: Event loop is closed.

Steps to Reproduce:
Please provide a numbered list of steps to reproduce the behavior:

1. create agent..

Agent(
        name="shoper agent",
        model=Gemini(
            model="gemini-3-flash-preview",
            retry_options=types.HttpRetryOptions(attempts=3),
            api_client=None, # Force fresh client creation for this instance
        ),
        description="shopper management agent. Use this agent to look up shopping mall.",
        instruction="""You are the main customer orchestrator. Look up shopping mall using the `get_store` tool.
        """,
        tools=[
            get_store,
            LongRunningFunctionTool(func=request_user_input),
        ],
    )

2. initiate adk agent to be deployed with AdkApp wrapper:

from vertexai.agent_engines import AdkApp
from customers.agent import root_agent

app = AdkApp(agent=root_agent)

3. deploy to agent engine..

import vertexai
from vertexai import agent_engines

REQUIREMENTS = [
    "google-cloud-aiplatform[evaluation,agent-engines]>=1.130.0",
    "google-adk>=1.31.1,<2.0.0",
    "a2a-sdk~=0.3.22",
    "google-auth>=2.29.0",
    "nest-asyncio>=1.6.0,<2.0.0",
    "opentelemetry-instrumentation-google-genai>=0.1.0,<1.0.0",
    "opentelemetry-instrumentation-fastapi",
    "opentelemetry-instrumentation-grpc",
    "opentelemetry-instrumentation-httpx",
    "gcsfs>=2024.11.0",
    "google-cloud-logging>=3.12.0,<4.0.0",
    "protobuf>=6.31.1,<7.0.0",
    "starlette>=0.37.0",
    "PyJWT>=2.8.0",
    "pydantic==2.12.5",
    "cloudpickle==3.1.2",
]

    config = {
        "display_name": DISPLAY_NAME,
        "description": DESCRIPTION,
        "requirements": REQUIREMENTS,
        "extra_packages": EXTRA_PACKAGES,
        "env_vars": environment_variables,
        "identity_type": types.IdentityType.AGENT_IDENTITY,
        "staging_bucket": staging_bucket,
        "max_instances": 3,
    }
    client = vertexai.Client(project=project_id, location=location, credentials=credentials)
        remote_app = client.agent_engines.create(
            agent=app,
            config=config
        )

4. create session and prompt the agent in agent engine using api

import vertexai

logger = logging.getLogger(__name__)

PROJECT_ID = os.environ.get("PROJECT_ID")
LOCATION   = os.environ.get("LOCATION", "us-central1")
ENGINE_ID  = os.environ.get(
    "CUSTOMERS_ENGINE_ID"
)

async def query_agent(
    user_message: str,
    user_id: str = "web_user",
    session_id: Optional[str] = None,
    force_new: bool = False,
    iap_token: Optional[str] = None,
    user_timezone: str = "UTC",
) -> tuple[str, str]:
    try:
        grpc_client = get_grpc_client()

        # 1. Handle Metadata (Headers)
        # In gRPC, headers are passed as a list of tuples (key, value)
        # Key MUST be lowercase
        token_str = iap_token
        if isinstance(token_str, bytes):
            token_str = token_str.decode("utf-8")

        metadata = []
        if token_str:
            logger.info("Raw x-user-identity value setting for customer agent: %s", token_str)
            metadata.append(("x-user-identity", token_str))
        if user_timezone:
            metadata.append(("x-user-timezone", user_timezone))

        # 2. Ensure we have a session ID
        if not session_id or force_new:
            session_client = _get_session_client()
            request = aiplatform_v1.CreateSessionRequest(
                parent=ENGINE_ID,
                session=aiplatform_v1.Session(user_id=user_id)
            )
            # Pass metadata to create_session so the agent receives the identity header
            operation = await session_client.create_session(request=request, metadata=metadata)
            if hasattr(operation, "result"):
                 session_obj = await operation.result()
            else:
                 session_obj = operation
            
            session_id = session_obj.name.split("/")[-1]
            logger.info("Created new session: %s", session_id)

        # 3. Prepare the Input Struct
        input_dict = {"message": user_message, "user_id": user_id}
        if session_id:
            input_dict["session_id"] = session_id
        
        # Explicitly pass run_config in the input struct to ensure ADK receives it
        run_config_metadata = {}
        if token_str:
            run_config_metadata["x-user-identity"] = token_str
        if user_timezone:
            run_config_metadata["x-user-timezone"] = user_timezone

        if run_config_metadata:
            input_dict["run_config"] = {
                "custom_metadata": run_config_metadata
            }
        
        input_struct = struct_pb2.Struct()
        input_struct.update(input_dict)

        # 3. Build the Request
        # Note: StreamQueryReasoningEngineRequest uses the reasoning_engine name
        resource_name = ENGINE_ID

        request = aiplatform_v1.StreamQueryReasoningEngineRequest(
            name=resource_name,
            input=input_struct,
        )

Expected Behavior:
prompt and response without event loop closed error after 3-5 user messages send to the agent engine agent..

Observed Behavior:
What actually happened? Include error messages or crash stack traces here.
Agent engine agent gets stack trace:

RuntimeError: Event loop is closed

(Note* more detailed error message below at Logs section)

Environment Details:

• ADK Library Version (pip show google-adk): "google-adk==1.31.1" (also tried 1.27.4 and 1.28.0)
• Agent Engine Run time in GCP
• Python Version (python -V): 3.12

Model Information:

• Are you using LiteLLM: no
• Which model is being used: gemini-3.0-flash

---

Work Around

added pre llm call hook in the agent:

from google.adk.agents import Agent
from google.adk.apps import App
from google.adk.models import Gemini
from google.adk.tools import LongRunningFunctionTool
from google.adk.tools.tool_context import ToolContext
from google.genai import types
async def detect_loop_mismatch(llm_request: LlmRequest, callback_context: CallbackContext, **kwargs) -> LlmResponse | None:
    """Callback to detect and log potential event loop mismatches before model calls."""
    exec_ctx = get_execution_context()
    logger.info(f"{exec_ctx} Starting model call for agent '{callback_context.agent_name}'")
    
    # In Reasoning Engine, the event loop can change between requests if the runner
    # uses a thread pool. Since the Gemini model's api_client (and its aiohttp session)
    # is cached on the first access, we must ensure it is reset if we are in a new loop
    # to avoid "RuntimeError: ... attached to a different loop".
    try:
        agent = callback_context._invocation_context.agent
        model = agent.model
        if hasattr(model, 'api_client'):
            # Force recreation of the cached api_client if it exists
            if 'api_client' in model.__dict__:
                logger.info(f"{exec_ctx} Resetting cached api_client for model '{callback_context.agent_name}' to avoid loop mismatch.")
                del model.__dict__['api_client']
            if '_api_backend' in model.__dict__:
                 del model.__dict__['_api_backend']
            if '_live_api_client' in model.__dict__:
                 del model.__dict__['_live_api_client']
    except Exception as e:
        logger.warning(f"Failed to reset api_client cache: {e}")

    return None

agent = Agent(
        name="shoper agent",
        model=Gemini(
            model="gemini-3-flash-preview",
            retry_options=types.HttpRetryOptions(attempts=3),
            api_client=None, # Force fresh client creation for this instance
        ),
        description="shopper management agent. Use this agent to look up shopping mall.",
        instruction="""You are the main customer orchestrator. Look up shopping mall using the `get_store` tool.
        """,
        tools=[
            get_store,
            LongRunningFunctionTool(func=request_user_input),
        ],
        before_model_callback=detect_loop_mismatch,
    )

After talking to reviewer the cleaner workaround is to create subclass of Gemini, and overwrite the api_client so it doesn't cache:

class UncachedGemini(Gemini):
    @property
    def api_client(self) -> Client:
        kwargs = {
            'http_options': types.HttpOptions(
                headers=self._tracking_headers(),
                retry_options=self.retry_options,
                base_url=self.base_url,
            )
        }
        if self.model.startswith('projects/'):
            kwargs['vertexai'] = True

        return Client(**kwargs)

def create_agent() -> Agent:
    return Agent(
        name="agent",
        model=UncachedGemini(
            model="gemini-3-flash-preview",
            retry_options=types.HttpRetryOptions(attempts=3),
        ),
        ...
   )

🟡 Optional Information

Providing this information greatly speeds up the resolution process.

Regression:
Did this work in a previous version of ADK? If so, which one?
N/A

Logs:
Please attach relevant logs. Wrap them in code blocks (```) or attach a
text file.

{
insertId: "69f14a6c0009a084e90c708f"
logName: "projects/agent-security-patterns/logs/aiplatform.googleapis.com%2Freasoning_engine_stderr"
receiveTimestamp: "2026-04-29T00:01:48.682175301Z"
resource: {2}
severity: "ERROR"
textPayload: "Traceback (most recent call last):
  File "/usr/local/lib/python3.12/threading.py", line 1075, in _bootstrap_inner
    self.run()
  File "/usr/local/lib/python3.12/threading.py", line 1012, in run
    self._target(*self._args, **self._kwargs)
  File "/code/.venv/lib/python3.12/site-packages/google/adk/runners.py", line 486, in _asyncio_thread_main
    asyncio.run(_invoke_run_async())
  File "/usr/local/lib/python3.12/asyncio/runners.py", line 195, in run
    return runner.run(main)
           ^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/asyncio/runners.py", line 118, in run
    return self._loop.run_until_complete(task)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/asyncio/base_events.py", line 691, in run_until_complete
    return future.result()
           ^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/google/adk/runners.py", line 479, in _invoke_run_async
    async for event in agen:
  File "/code/.venv/lib/python3.12/site-packages/google/adk/runners.py", line 632, in run_async
    async for event in agen:
  File "/code/.venv/lib/python3.12/site-packages/google/adk/runners.py", line 617, in _run_with_trace
    async for event in agen:
  File "/code/.venv/lib/python3.12/site-packages/google/adk/runners.py", line 881, in _exec_with_plugin
    async for event in agen:
  File "/code/.venv/lib/python3.12/site-packages/google/adk/runners.py", line 606, in execute
    async for event in agen:
  File "/code/.venv/lib/python3.12/site-packages/google/adk/agents/base_agent.py", line 297, in run_async
    async for event in agen:
  File "/code/.venv/lib/python3.12/site-packages/google/adk/agents/llm_agent.py", line 487, in _run_async_impl
    async for event in agen:
  File "/code/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py", line 804, in run_async
    async for event in agen:
  File "/code/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py", line 881, in _run_one_step_async
    async for llm_response in agen:
  File "/code/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py", line 1261, in _call_llm_async
    async for event in agen:
  File "/code/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py", line 1239, in _call_llm_with_tracing
    async for llm_response in agen:
  File "/code/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py", line 1322, in _run_and_handle_error
    async for response in agen:
  File "/code/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py", line 406, in _run_and_handle_error
    raise model_error
  File "/code/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py", line 379, in _run_and_handle_error
    async for llm_response in agen:
  File "/code/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py", line 245, in generate_content_async
    response = await self.api_client.aio.models.generate_content(
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/opentelemetry/instrumentation/google_genai/generate_content.py", line 1180, in instrumented_generate_content
    response = await wrapped_func(
               ^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/google/genai/models.py", line 8337, in generate_content
    return await self._generate_content(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/google/genai/models.py", line 6897, in _generate_content
    response = await self._api_client.async_request(
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/google/genai/_api_client.py", line 1583, in async_request
    result = await self._async_request(
             ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/google/genai/_api_client.py", line 1516, in _async_request
    return await self._async_retry(  # type: ignore[no-any-return]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/tenacity/asyncio/__init__.py", line 112, in __call__
    do = await self.iter(retry_state=retry_state)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/tenacity/asyncio/__init__.py", line 157, in iter
    result = await action(retry_state)
             ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/tenacity/_utils.py", line 111, in inner
    return call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/tenacity/__init__.py", line 393, in <lambda>
    self._add_action_func(lambda rs: rs.outcome.result())
                                     ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 449, in result
    return self.__get_result()
           ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
    raise self._exception
  File "/code/.venv/lib/python3.12/site-packages/tenacity/asyncio/__init__.py", line 116, in __call__
    result = await fn(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/google/genai/_api_client.py", line 1440, in _async_request_once
    response = await self._aiohttp_session.request(  # type: ignore[union-attr]
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/google/auth/aio/transport/sessions.py", line 293, in request
    response = await with_timeout(
               ^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/google/auth/aio/transport/sessions.py", line 78, in with_timeout
    response = await asyncio.wait_for(coro, remaining)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/asyncio/tasks.py", line 520, in wait_for
    return await fut
           ^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/google/auth/aio/transport/aiohttp.py", line 174, in __call__
    response = await self._session.request(
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/aiohttp/client.py", line 788, in _request
    resp = await handler(req)
           ^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/aiohttp/client.py", line 742, in _connect_and_send_request
    conn = await self._connector.connect(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/aiohttp/connector.py", line 672, in connect
    proto = await self._create_connection(req, traces, timeout)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/aiohttp/connector.py", line 1251, in _create_connection
    _, proto = await self._create_direct_connection(req, traces, timeout)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/aiohttp/connector.py", line 1574, in _create_direct_connection
    hosts = await self._resolve_host(host, port, traces=traces)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/aiohttp/connector.py", line 1190, in _resolve_host
    return await asyncio.shield(resolved_host_task)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/aiohttp/connector.py", line 1221, in _resolve_host_with_throttle
    addrs = await self._resolver.resolve(host, port, family=self._family)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/code/.venv/lib/python3.12/site-packages/aiohttp/resolver.py", line 40, in resolve
    infos = await self._loop.getaddrinfo(
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/asyncio/base_events.py", line 905, in getaddrinfo
    return await self.run_in_executor(
                 ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/asyncio/base_events.py", line 854, in run_in_executor
    self._check_closed()
  File "/usr/local/lib/python3.12/asyncio/base_events.py", line 545, in _check_closed
    raise RuntimeError('Event loop is closed')
RuntimeError: Event loop is closed"
timestamp: "2026-04-29T00:01:48.630916Z"
}

Additional Context:
Add any other context about the problem here. This is an architecture with workflow:
iap for auth-> cloud run for ui & backend -> agent engine agent

sequenceDiagram
    participant User as User (Browser)
    participant IAP as Identity-Aware Proxy
    participant FastAPI as fast-api-fe (Cloud Run)

    User->>IAP: Access Chat UI / API
    alt Not Authenticated
        IAP-->>User: Redirect to Google Login
        User->>IAP: Authenticate
    end
    IAP->>FastAPI: Forward authenticated request

    User->>FastAPI: Chat Message (UI)
    Note over FastAPI: POST /v1/chat/completions
    FastAPI->>ShoppingAgent: query_agent(message)
    Note over ShoppingAgent: Handles greeting, lookup,<br/>or detects store intent

    alt Needs StoreAgent
        ShoppingAgent->>StoreAgent: Delegate task (Agent Engine REST API)
        StoreAgent-->>ShoppingAgent: Store result
    end

    ShoppingAgent-->>FastAPI: Final text response
    FastAPI-->>User: Chat Bubble

Loading

How often has this issue occurred?:

• Always (100%) after 3-5 user prompts sent to the agent in gcp agent engine

[surajksharma07]

@ypenn21 The @cached_property on api_client stores the aiohttp.ClientSession from the first request's loop, and since Agent Engine spawns a fresh OS thread (and a new asyncio.run() loop) per request, subsequent requests hit the now-closed loop on any new TCP connection.

As a potentially cleaner workaround, try subclassing Gemini and overriding api_client with a plain @Property — that creates a fresh Client per access instead of caching it.

The ADK docs actually hint at this pattern for multithreaded environments, so it's a supported path.

Could you give that a try and let us know if it holds up better than the dict deletion approach?

PR #5543 is already in for a proper fix, but please do a thorough soak test with either workaround on your end before we push anything through!

[ypenn21]

@surajksharma07 thanks for the suggestion your work around indeed works, and I would rather not add logic to pre hook if i can.. I have deployed both workarounds to agent engine and have not seen the event loop error since:

class UncachedGemini(Gemini):
    @property
    def api_client(self):
        # Clear the underlying cache keys to force recreation
        self.__dict__.pop("api_client", None)
        self.__dict__.pop("_api_backend", None)
        self.__dict__.pop("_live_api_client", None)
        return super().api_client

def create_agent() -> Agent:
    return Agent(
        name="agent",
        model=UncachedGemini(
            model="gemini-3-flash-preview",
            retry_options=types.HttpRetryOptions(attempts=3),
        ),
        ...
   )

[surajksharma07]

@ypenn21 consistent clean runs after deployment is the right signal that the @Property override is doing its job!

One small thing worth knowing about your UncachedGemini impl: calling super().api_client still goes through the parent's @cached_property, which re-stores the new client in self.dict["api_client"] on each call — then your @Property getter pops it again on the next access.

It works correctly (the @Property data descriptor always wins over dict), but it's a write-then-pop cycle every call.

A slightly cleaner version is to create the Client directly in the property body without delegating to super() at all — that's what avoids the dict churn entirely.
Also, _api_backend doesn't need to be cleared; it's just a GoogleLLMVariant enum value that doesn't carry any loop binding.

One side effect worth watching at scale: both workaround patterns create a fresh aiohttp.ClientSession on every api_client access, so there's no HTTP connection pooling between LLM calls — each generate_content_async() will do a new TCP+TLS handshake.
For most Agent Engine traffic this is fine, but if you start noticing per-call latency creep at higher concurrency, that's the likely culprit.

Also worth flagging: PR #5543 didn't make it into v1.32.0 — the only event loop fix that landed there targets McpSessionManager, not Gemini.api_client.

Keep the workaround in place until #5543 merges.

Team is looking more into it.
Thanks!

[ypenn21]

class UncachedGemini(Gemini):
    @property
    def api_client(self) -> Client:
        kwargs = {
            'http_options': types.HttpOptions(
                headers=self._tracking_headers(),
                retry_options=self.retry_options,
                base_url=self.base_url,
            )
        }
        if self.model.startswith('projects/'):
            kwargs['vertexai'] = True

        return Client(**kwargs)

Great thanks for the quick feedbacks!

I have also caught wind that this runtime error has been on going since last August with one fix after another. The prior fix was to ensure aiohttp was installed during the AE deploy process by adding it to the list of dependencies. Is there anything that the Agent Engine team and the ADK team are doing to make sure that this is squashed once and for all ??

[surajksharma07]

@ypenn21 That's the right implementation — _tracking_headers() preserves ADK's telemetry headers and retry_options/base_url pull cleanly from the parent init, so the Client is fully configured without anything being silently dropped.

On the recurring-fix concern, that's a fair observation.
The earlier patches targeted different failure surfaces — aiohttp install ordering during AE deploy and the McpSessionManager loop binding that landed in v1.32.0 — rather than this specific Gemini.api_client cache-lifetime root cause.

PR #5543 is the fix aimed directly at that: it addresses the @cached_property on api_client (and _live_api_client) so the framework itself handles loop lifecycle correctly without requiring a workaround subclass.

It's actively in review, and once it merges and cuts a release you should be able to drop UncachedGemini entirely — we'll update this thread when that happens!

Team is looking more into it.
Thanks!

[surajksharma07]

@Jacksunwei Please have a look into it once.