Fix cloud playground env detection

src/memos/mem_scheduler/general_scheduler.py

@@ -1,7 +1,6 @@
 import concurrent.futures
 import contextlib
 import json
-import os
 import traceback
 
 from memos.configs.mem_scheduler import GeneralSchedulerConfig
@@ -30,7 +29,10 @@
     is_all_english,
     transform_name_to_key,
 )
-from memos.mem_scheduler.utils.misc_utils import group_messages_by_user_and_mem_cube
+from memos.mem_scheduler.utils.misc_utils import (
+    group_messages_by_user_and_mem_cube,
+    is_cloud_env,
+)
 from memos.memories.textual.item import TextualMemoryItem
 from memos.memories.textual.preference import PreferenceTextMemory
 from memos.memories.textual.tree import TreeTextMemory
@@ -194,9 +196,9 @@ def _add_message_consumer(self, messages: list[ScheduleMessageItem]) -> None:
                             f"prepared_add_items: {prepared_add_items};\n prepared_update_items_with_original: {prepared_update_items_with_original}"
                         )
                         # Conditional Logging: Knowledge Base (Cloud Service) vs. Playground/Default
-                        is_cloud_env = os.getenv("MEMSCHEDULER_RABBITMQ_EXCHANGE_NAME")
+                        cloud_env = is_cloud_env()
 
-                        if is_cloud_env:
+                        if cloud_env:
                             self.send_add_log_messages_to_cloud_env(
                                 msg, prepared_add_items, prepared_update_items_with_original
                             )
@@ -615,8 +617,8 @@ def _mem_feedback_message_consumer(self, messages: list[ScheduleMessageItem]) ->
                 f"Successfully processed feedback for user_id={user_id}, mem_cube_id={mem_cube_id}"
             )
 
-            is_cloud_env = os.getenv("MEMSCHEDULER_RABBITMQ_EXCHANGE_NAME")
-            if is_cloud_env:
+            cloud_env = is_cloud_env()
+            if cloud_env:
                 record = feedback_result.get("record") if isinstance(feedback_result, dict) else {}
                 add_records = record.get("add") if isinstance(record, dict) else []
                 update_records = record.get("update") if isinstance(record, dict) else []
@@ -733,7 +735,7 @@ def _extract_fields(mem_item):
             else:
                 logger.info(
                     "Skipping web log for feedback. Not in a cloud environment (is_cloud_env=%s)",
-                    is_cloud_env,
+                    cloud_env,
                 )
 
         except Exception as e:
@@ -893,8 +895,8 @@ def _process_memories_with_reader(
 
                     # LOGGING BLOCK START
                     # This block is replicated from _add_message_consumer to ensure consistent logging
-                    is_cloud_env = os.getenv("MEMSCHEDULER_RABBITMQ_EXCHANGE_NAME")
-                    if is_cloud_env:
+                    cloud_env = is_cloud_env()
+                    if cloud_env:
                         # New: Knowledge Base Logging (Cloud Service)
                         kb_log_content = []
                         for item in flattened_memories:
@@ -1013,8 +1015,8 @@ def _process_memories_with_reader(
                 f"Error in _process_memories_with_reader: {traceback.format_exc()}", exc_info=True
             )
             with contextlib.suppress(Exception):
-                is_cloud_env = os.getenv("MEMSCHEDULER_RABBITMQ_EXCHANGE_NAME")
-                if is_cloud_env:
+                cloud_env = is_cloud_env()
+                if cloud_env:
                     if not kb_log_content:
                         trigger_source = (
                             info.get("trigger_source", "Messages") if info else "Messages"

src/memos/mem_scheduler/task_schedule_modules/dispatcher.py

@@ -1,5 +1,4 @@
 import concurrent
-import os
 import threading
 import time
 
@@ -25,7 +24,7 @@
 from memos.mem_scheduler.task_schedule_modules.orchestrator import SchedulerOrchestrator
 from memos.mem_scheduler.task_schedule_modules.redis_queue import SchedulerRedisQueue
 from memos.mem_scheduler.task_schedule_modules.task_queue import ScheduleTaskQueue
-from memos.mem_scheduler.utils.misc_utils import group_messages_by_user_and_mem_cube
+from memos.mem_scheduler.utils.misc_utils import group_messages_by_user_and_mem_cube, is_cloud_env
 from memos.mem_scheduler.utils.monitor_event_utils import emit_monitor_event, to_iso
 from memos.mem_scheduler.utils.status_tracker import TaskStatusTracker
 
@@ -351,8 +350,8 @@ def _maybe_emit_task_completion(
         mem_cube_id = first.mem_cube_id
 
         try:
-            is_cloud_env = os.getenv("MEMSCHEDULER_RABBITMQ_EXCHANGE_NAME")
-            if not is_cloud_env:
+            cloud_env = is_cloud_env()
+            if not cloud_env:
                 return
 
             for task_id in task_ids:

src/memos/mem_scheduler/utils/misc_utils.py

@@ -1,4 +1,5 @@
 import json
+import os
 import re
 import traceback
 
@@ -17,6 +18,40 @@
 logger = get_logger(__name__)
 
 
+def _normalize_env_value(value: str | None) -> str:
+    """Normalize environment variable values for comparison."""
+    return value.strip().lower() if isinstance(value, str) else ""
+
+
+def is_playground_env() -> bool:
+    """Return True when ENV_NAME indicates a Playground environment."""
+    env_name = _normalize_env_value(os.getenv("ENV_NAME"))
+    return env_name.startswith("playground")
+
+
+def is_cloud_env() -> bool:
+    """
+    Determine whether the scheduler should treat the runtime as a cloud environment.
+
+    Rules:
+    - Any Playground ENV_NAME is explicitly NOT cloud.
+    - MEMSCHEDULER_RABBITMQ_EXCHANGE_NAME must be set to enable cloud behavior.
+    - The default memos-fanout/fanout combination is treated as non-cloud.
+    """
+    if is_playground_env():
+        return False
+
+    exchange_name = _normalize_env_value(os.getenv("MEMSCHEDULER_RABBITMQ_EXCHANGE_NAME"))
+    exchange_type = _normalize_env_value(os.getenv("MEMSCHEDULER_RABBITMQ_EXCHANGE_TYPE"))
+
+    if not exchange_name:
+        return False
+
+    return not (
+        exchange_name == "memos-fanout" and (not exchange_type or exchange_type == "fanout")
+    )
+
+
 def extract_json_obj(text: str):
     """
     Safely extracts JSON from LLM response text with robust error handling.

src/memos/mem_scheduler/webservice_modules/rabbitmq_service.py

@@ -13,6 +13,7 @@
 from memos.mem_scheduler.general_modules.base import BaseSchedulerModule
 from memos.mem_scheduler.general_modules.misc import AutoDroppingQueue
 from memos.mem_scheduler.schemas.general_schemas import DIRECT_EXCHANGE_TYPE, FANOUT_EXCHANGE_TYPE
+from memos.mem_scheduler.utils.misc_utils import is_cloud_env
 
 
 logger = get_logger(__name__)
@@ -291,7 +292,7 @@ def rabbitmq_publish_message(self, message: dict):
 
         # Cloud environment override: applies to specific message types if MEMSCHEDULER_RABBITMQ_EXCHANGE_NAME is set
         env_exchange_name = os.getenv("MEMSCHEDULER_RABBITMQ_EXCHANGE_NAME")
-        if env_exchange_name and label in ["taskStatus", "knowledgeBaseUpdate"]:
+        if is_cloud_env() and env_exchange_name and label in ["taskStatus", "knowledgeBaseUpdate"]:
             exchange_name = env_exchange_name
             routing_key = ""  # Routing key is always empty in cloud environment for these types