Merge remote-tracking branch 'origin/main' into deploy/dev

- implement a test to verify the display of the credits exhausted badge when AI credits are depleted and no API key is present
- refactor ModelSelectorTrigger to use a resolved provider for credential panel state
- enhance tooltip labels for deprecated status and credits exhausted scenarios

.agents/skills/frontend-testing/assets/component-test.template.tsx

@@ -41,7 +41,7 @@ import userEvent from '@testing-library/user-event'
 // Router (if component uses useRouter, usePathname, useSearchParams)
 // WHY: Isolates tests from Next.js routing, enables testing navigation behavior
 // const mockPush = vi.fn()
-// vi.mock('@/next/navigation', () => ({
+// vi.mock('next/navigation', () => ({
 //   useRouter: () => ({ push: mockPush }),
 //   usePathname: () => '/test-path',
 // }))

api/README.md

@@ -42,7 +42,7 @@ The scripts resolve paths relative to their location, so you can run them from a
 
 1. Set up your application by visiting `http://localhost:3000`.
 
-1. Start the worker service (async and scheduler tasks, runs from `api`).
+1. Optional: start the worker service (async tasks, runs from `api`).
 
    ```bash
    ./dev/start-worker
@@ -54,6 +54,87 @@ The scripts resolve paths relative to their location, so you can run them from a
    ./dev/start-beat
    ```
 
+### Manual commands
+
+<details>
+<summary>Show manual setup and run steps</summary>
+
+These commands assume you start from the repository root.
+
+1. Start the docker-compose stack.
+
+   The backend requires middleware, including PostgreSQL, Redis, and Weaviate, which can be started together using `docker-compose`.
+
+   ```bash
+   cp docker/middleware.env.example docker/middleware.env
+   # Use mysql or another vector database profile if you are not using postgres/weaviate.
+   docker compose -f docker/docker-compose.middleware.yaml --profile postgresql --profile weaviate -p dify up -d
+   ```
+
+1. Copy env files.
+
+   ```bash
+   cp api/.env.example api/.env
+   cp web/.env.example web/.env.local
+   ```
+
+1. Install UV if needed.
+
+   ```bash
+   pip install uv
+   # Or on macOS
+   brew install uv
+   ```
+
+1. Install API dependencies.
+
+   ```bash
+   cd api
+   uv sync --group dev
+   ```
+
+1. Install web dependencies.
+
+   ```bash
+   cd web
+   pnpm install
+   cd ..
+   ```
+
+1. Start backend (runs migrations first, in a new terminal).
+
+   ```bash
+   cd api
+   uv run flask db upgrade
+   uv run flask run --host 0.0.0.0 --port=5001 --debug
+   ```
+
+1. Start Dify [web](../web) service (in a new terminal).
+
+   ```bash
+   cd web
+   pnpm dev:inspect
+   ```
+
+1. Set up your application by visiting `http://localhost:3000`.
+
+1. Optional: start the worker service (async tasks, in a new terminal).
+
+   ```bash
+   cd api
+   # Note: enterprise_telemetry queue is only used in Enterprise Edition
+   uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention,enterprise_telemetry
+   ```
+
+1. Optional: start Celery Beat (scheduled tasks, in a new terminal).
+
+   ```bash
+   cd api
+   uv run celery -A app.celery beat
+   ```
+
+</details>
+
 ### Environment notes
 
 > [!IMPORTANT]

api/app_factory.py

@@ -143,6 +143,7 @@ def initialize_extensions(app: DifyApp):
         ext_commands,
         ext_compress,
         ext_database,
+        ext_enterprise_telemetry,
         ext_fastopenapi,
         ext_forward_refs,
         ext_hosting_provider,
@@ -193,6 +194,7 @@ def initialize_extensions(app: DifyApp):
         ext_commands,
         ext_fastopenapi,
         ext_otel,
+        ext_enterprise_telemetry,
         ext_request_logging,
         ext_session_factory,
     ]

api/configs/app_config.py

@@ -8,7 +8,7 @@ from pydantic_settings import BaseSettings, PydanticBaseSettingsSource, Settings
 from libs.file_utils import search_file_upwards
 
 from .deploy import DeploymentConfig
-from .enterprise import EnterpriseFeatureConfig
+from .enterprise import EnterpriseFeatureConfig, EnterpriseTelemetryConfig
 from .extra import ExtraServiceConfig
 from .feature import FeatureConfig
 from .middleware import MiddlewareConfig
@@ -73,6 +73,8 @@ class DifyConfig(
     # Enterprise feature configs
     # **Before using, please contact business@dify.ai by email to inquire about licensing matters.**
     EnterpriseFeatureConfig,
+    # Enterprise telemetry configs
+    EnterpriseTelemetryConfig,
 ):
     model_config = SettingsConfigDict(
         # read from dotenv format config file

api/configs/enterprise/__init__.py

@@ -22,3 +22,49 @@ class EnterpriseFeatureConfig(BaseSettings):
     ENTERPRISE_REQUEST_TIMEOUT: int = Field(
         ge=1, description="Maximum timeout in seconds for enterprise requests", default=5
     )
+
+
+class EnterpriseTelemetryConfig(BaseSettings):
+    """
+    Configuration for enterprise telemetry.
+    """
+
+    ENTERPRISE_TELEMETRY_ENABLED: bool = Field(
+        description="Enable enterprise telemetry collection (also requires ENTERPRISE_ENABLED=true).",
+        default=False,
+    )
+
+    ENTERPRISE_OTLP_ENDPOINT: str = Field(
+        description="Enterprise OTEL collector endpoint.",
+        default="",
+    )
+
+    ENTERPRISE_OTLP_HEADERS: str = Field(
+        description="Auth headers for OTLP export (key=value,key2=value2).",
+        default="",
+    )
+
+    ENTERPRISE_OTLP_PROTOCOL: str = Field(
+        description="OTLP protocol: 'http' or 'grpc' (default: http).",
+        default="http",
+    )
+
+    ENTERPRISE_OTLP_API_KEY: str = Field(
+        description="Bearer token for enterprise OTLP export authentication.",
+        default="",
+    )
+
+    ENTERPRISE_INCLUDE_CONTENT: bool = Field(
+        description="Include input/output content in traces (privacy toggle).",
+        default=True,
+    )
+
+    ENTERPRISE_SERVICE_NAME: str = Field(
+        description="Service name for OTEL resource.",
+        default="dify",
+    )
+
+    ENTERPRISE_OTEL_SAMPLING_RATE: float = Field(
+        description="Sampling rate for enterprise traces (0.0 to 1.0, default 1.0 = 100%).",
+        default=1.0,
+    )

api/controllers/console/datasets/rag_pipeline/rag_pipeline.py

@@ -46,8 +46,6 @@ class PipelineTemplateDetailApi(Resource):
         type = request.args.get("type", default="built-in", type=str)
         rag_pipeline_service = RagPipelineService()
         pipeline_template = rag_pipeline_service.get_pipeline_template_detail(template_id, type)
-        if pipeline_template is None:
-            return {"error": "Pipeline template not found from upstream service."}, 404
         return pipeline_template, 200
 
 

api/controllers/trigger/webhook.py

@@ -70,14 +70,7 @@ def handle_webhook(webhook_id: str):
 
 @bp.route("/webhook-debug/<string:webhook_id>", methods=["GET", "POST", "PUT", "PATCH", "DELETE", "HEAD", "OPTIONS"])
 def handle_webhook_debug(webhook_id: str):
-    """Handle webhook debug calls without triggering production workflow execution.
-
-    The debug webhook endpoint is only for draft inspection flows. It never enqueues
-    Celery work for the published workflow; instead it dispatches an in-memory debug
-    event to an active Variable Inspector listener. Returning a clear error when no
-    listener is registered prevents a misleading 200 response for requests that are
-    effectively dropped.
-    """
+    """Handle webhook debug calls without triggering production workflow execution."""
     try:
         webhook_trigger, _, node_config, webhook_data, error = _prepare_webhook_execution(webhook_id, is_debug=True)
         if error:
@@ -101,32 +94,11 @@ def handle_webhook_debug(webhook_id: str):
                 "method": webhook_data.get("method"),
             },
         )
-        dispatch_count = TriggerDebugEventBus.dispatch(
+        TriggerDebugEventBus.dispatch(
             tenant_id=webhook_trigger.tenant_id,
             event=event,
             pool_key=pool_key,
         )
-        if dispatch_count == 0:
-            logger.warning(
-                "Webhook debug request dropped without an active listener for webhook %s (tenant=%s, app=%s, node=%s)",
-                webhook_trigger.webhook_id,
-                webhook_trigger.tenant_id,
-                webhook_trigger.app_id,
-                webhook_trigger.node_id,
-            )
-            return (
-                jsonify(
-                    {
-                        "error": "No active debug listener",
-                        "message": (
-                            "The webhook debug URL only works while the Variable Inspector is listening. "
-                            "Use the published webhook URL to execute the workflow in Celery."
-                        ),
-                        "execution_url": webhook_trigger.webhook_url,
-                    }
-                ),
-                409,
-            )
         response_data, status_code = WebhookService.generate_webhook_response(node_config)
         return jsonify(response_data), status_code
 

api/core/app/apps/advanced_chat/app_generator.py

@@ -5,7 +5,7 @@ import logging
 import threading
 import uuid
 from collections.abc import Generator, Mapping, Sequence
-from typing import TYPE_CHECKING, Any, Literal, Union, overload
+from typing import TYPE_CHECKING, Any, Literal, TypeVar, Union, overload
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -47,6 +47,7 @@ from extensions.ext_database import db
 from factories import file_factory
 from libs.flask_utils import preserve_flask_contexts
 from models import Account, App, Conversation, EndUser, Message, Workflow, WorkflowNodeExecutionTriggeredFrom
+from models.base import Base
 from models.enums import WorkflowRunTriggeredFrom
 from services.conversation_service import ConversationService
 from services.workflow_draft_variable_service import (
@@ -521,9 +522,8 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
 
         # release database connection, because the following new thread operations may take a long time
         with Session(bind=db.engine, expire_on_commit=False) as session:
-            workflow = _refresh_model(session=session, model=workflow)
-            message = _refresh_model(session=session, model=message)
-        assert message is not None
+            workflow = _refresh_model(session, workflow)
+            message = _refresh_model(session, message)
         #     workflow_ = session.get(Workflow, workflow.id)
         #     assert workflow_ is not None
         #     workflow = workflow_
@@ -690,21 +690,11 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                 raise e
 
 
-@overload
-def _refresh_model(*, session: Session | None = None, model: Workflow) -> Workflow: ...
+_T = TypeVar("_T", bound=Base)
 
 
-@overload
-def _refresh_model(*, session: Session | None = None, model: Message) -> Message: ...
-
-
-def _refresh_model(*, session: Session | None = None, model: Any) -> Any:
-    if session is not None:
-        detached_model = session.get(type(model), model.id)
-        assert detached_model is not None
-        return detached_model
-
-    with Session(bind=db.engine, expire_on_commit=False) as refresh_session:
-        detached_model = refresh_session.get(type(model), model.id)
-        assert detached_model is not None
-        return detached_model
+def _refresh_model(session, model: _T) -> _T:
+    with Session(bind=db.engine, expire_on_commit=False) as session:
+        detach_model = session.get(type(model), model.id)
+        assert detach_model is not None
+        return detach_model

api/core/app/apps/advanced_chat/generate_response_converter.py

@@ -1,4 +1,4 @@
-from collections.abc import Generator, Iterator
+from collections.abc import Generator
 from typing import Any, cast
 
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
@@ -56,8 +56,8 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_full_response(
-        cls, stream_response: Iterator[AppStreamResponse]
-    ) -> Generator[dict | str, None, None]:
+        cls, stream_response: Generator[AppStreamResponse, None, None]
+    ) -> Generator[dict | str, Any, None]:
         """
         Convert stream full response.
         :param stream_response: stream response
@@ -87,8 +87,8 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_simple_response(
-        cls, stream_response: Iterator[AppStreamResponse]
-    ) -> Generator[dict | str, None, None]:
+        cls, stream_response: Generator[AppStreamResponse, None, None]
+    ) -> Generator[dict | str, Any, None]:
         """
         Convert stream simple response.
         :param stream_response: stream response

api/core/app/apps/agent_chat/generate_response_converter.py

@@ -1,4 +1,4 @@
-from collections.abc import Generator, Iterator
+from collections.abc import Generator
 from typing import cast
 
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
@@ -55,7 +55,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_full_response(
-        cls, stream_response: Iterator[AppStreamResponse]
+        cls, stream_response: Generator[AppStreamResponse, None, None]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream full response.
@@ -86,7 +86,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_simple_response(
-        cls, stream_response: Iterator[AppStreamResponse]
+        cls, stream_response: Generator[AppStreamResponse, None, None]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream simple response.

api/core/app/apps/base_app_generate_response_converter.py

@@ -1,7 +1,7 @@
 import logging
 from abc import ABC, abstractmethod
-from collections.abc import Generator, Iterator, Mapping
-from typing import Any
+from collections.abc import Generator, Mapping
+from typing import Any, Union
 
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.entities.task_entities import AppBlockingResponse, AppStreamResponse
@@ -16,26 +16,24 @@ class AppGenerateResponseConverter(ABC):
 
     @classmethod
     def convert(
-        cls, response: AppBlockingResponse | Iterator[AppStreamResponse], invoke_from: InvokeFrom
-    ) -> Mapping[str, Any] | Generator[str | Mapping[str, Any], None, None]:
+        cls, response: Union[AppBlockingResponse, Generator[AppStreamResponse, Any, None]], invoke_from: InvokeFrom
+    ) -> Mapping[str, Any] | Generator[str | Mapping[str, Any], Any, None]:
         if invoke_from in {InvokeFrom.DEBUGGER, InvokeFrom.SERVICE_API}:
             if isinstance(response, AppBlockingResponse):
                 return cls.convert_blocking_full_response(response)
             else:
-                stream_response = response
 
-                def _generate_full_response() -> Generator[dict[str, Any] | str, None, None]:
-                    yield from cls.convert_stream_full_response(stream_response)
+                def _generate_full_response() -> Generator[dict | str, Any, None]:
+                    yield from cls.convert_stream_full_response(response)
 
                 return _generate_full_response()
         else:
             if isinstance(response, AppBlockingResponse):
                 return cls.convert_blocking_simple_response(response)
             else:
-                stream_response = response
 
-                def _generate_simple_response() -> Generator[dict[str, Any] | str, None, None]:
-                    yield from cls.convert_stream_simple_response(stream_response)
+                def _generate_simple_response() -> Generator[dict | str, Any, None]:
+                    yield from cls.convert_stream_simple_response(response)
 
                 return _generate_simple_response()
 
@@ -52,14 +50,14 @@ class AppGenerateResponseConverter(ABC):
     @classmethod
     @abstractmethod
     def convert_stream_full_response(
-        cls, stream_response: Iterator[AppStreamResponse]
+        cls, stream_response: Generator[AppStreamResponse, None, None]
     ) -> Generator[dict | str, None, None]:
         raise NotImplementedError
 
     @classmethod
     @abstractmethod
     def convert_stream_simple_response(
-        cls, stream_response: Iterator[AppStreamResponse]
+        cls, stream_response: Generator[AppStreamResponse, None, None]
     ) -> Generator[dict | str, None, None]:
         raise NotImplementedError
 

api/core/app/apps/base_app_generator.py

@@ -224,7 +224,6 @@ class BaseAppGenerator:
     def _get_draft_var_saver_factory(invoke_from: InvokeFrom, account: Account | EndUser) -> DraftVariableSaverFactory:
         if invoke_from == InvokeFrom.DEBUGGER:
             assert isinstance(account, Account)
-            debug_account = account
 
             def draft_var_saver_factory(
                 session: Session,
@@ -241,7 +240,7 @@ class BaseAppGenerator:
                     node_type=node_type,
                     node_execution_id=node_execution_id,
                     enclosing_node_id=enclosing_node_id,
-                    user=debug_account,
+                    user=account,
                 )
         else:
 

api/core/app/apps/chat/app_generator.py

@@ -166,19 +166,15 @@ class ChatAppGenerator(MessageBasedAppGenerator):
 
         # init generate records
         (conversation, message) = self._init_generate_records(application_generate_entity, conversation)
-        assert conversation is not None
-        assert message is not None
-        generated_conversation_id = str(conversation.id)
-        generated_message_id = str(message.id)
 
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
             task_id=application_generate_entity.task_id,
             user_id=application_generate_entity.user_id,
             invoke_from=application_generate_entity.invoke_from,
-            conversation_id=generated_conversation_id,
+            conversation_id=conversation.id,
             app_mode=conversation.mode,
-            message_id=generated_message_id,
+            message_id=message.id,
         )
 
         # new thread with request context
@@ -188,8 +184,8 @@ class ChatAppGenerator(MessageBasedAppGenerator):
                 flask_app=current_app._get_current_object(),  # type: ignore
                 application_generate_entity=application_generate_entity,
                 queue_manager=queue_manager,
-                conversation_id=generated_conversation_id,
-                message_id=generated_message_id,
+                conversation_id=conversation.id,
+                message_id=message.id,
             )
 
         worker_thread = threading.Thread(target=worker_with_context)

api/core/app/apps/chat/generate_response_converter.py

@@ -1,4 +1,4 @@
-from collections.abc import Generator, Iterator
+from collections.abc import Generator
 from typing import cast
 
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
@@ -55,7 +55,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_full_response(
-        cls, stream_response: Iterator[AppStreamResponse]
+        cls, stream_response: Generator[AppStreamResponse, None, None]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream full response.
@@ -86,7 +86,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_simple_response(
-        cls, stream_response: Iterator[AppStreamResponse]
+        cls, stream_response: Generator[AppStreamResponse, None, None]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream simple response.

api/core/app/apps/completion/app_generator.py

@@ -149,8 +149,6 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
 
         # init generate records
         (conversation, message) = self._init_generate_records(application_generate_entity)
-        assert conversation is not None
-        assert message is not None
 
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
@@ -314,19 +312,15 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
 
         # init generate records
         (conversation, message) = self._init_generate_records(application_generate_entity)
-        assert conversation is not None
-        assert message is not None
-        conversation_id = str(conversation.id)
-        message_id = str(message.id)
 
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
             task_id=application_generate_entity.task_id,
             user_id=application_generate_entity.user_id,
             invoke_from=application_generate_entity.invoke_from,
-            conversation_id=conversation_id,
+            conversation_id=conversation.id,
             app_mode=conversation.mode,
-            message_id=message_id,
+            message_id=message.id,
         )
 
         # new thread with request context
@@ -336,7 +330,7 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
                 flask_app=current_app._get_current_object(),  # type: ignore
                 application_generate_entity=application_generate_entity,
                 queue_manager=queue_manager,
-                message_id=message_id,
+                message_id=message.id,
             )
 
         worker_thread = threading.Thread(target=worker_with_context)

api/core/app/apps/completion/generate_response_converter.py

@@ -1,4 +1,4 @@
-from collections.abc import Generator, Iterator
+from collections.abc import Generator
 from typing import cast
 
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
@@ -54,7 +54,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_full_response(
-        cls, stream_response: Iterator[AppStreamResponse]
+        cls, stream_response: Generator[AppStreamResponse, None, None]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream full response.
@@ -84,7 +84,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_simple_response(
-        cls, stream_response: Iterator[AppStreamResponse]
+        cls, stream_response: Generator[AppStreamResponse, None, None]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream simple response.

api/core/app/apps/pipeline/generate_response_converter.py

@@ -1,4 +1,4 @@
-from collections.abc import Generator, Iterator
+from collections.abc import Generator
 from typing import cast
 
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
@@ -36,7 +36,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_full_response(
-        cls, stream_response: Iterator[AppStreamResponse]
+        cls, stream_response: Generator[AppStreamResponse, None, None]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream full response.
@@ -65,7 +65,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_simple_response(
-        cls, stream_response: Iterator[AppStreamResponse]
+        cls, stream_response: Generator[AppStreamResponse, None, None]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream simple response.

api/core/app/apps/workflow/app_generator.py

@@ -159,9 +159,12 @@ class WorkflowAppGenerator(BaseAppGenerator):
 
         inputs: Mapping[str, Any] = args["inputs"]
 
-        extras = {
+        extras: dict[str, Any] = {
             **extract_external_trace_id_from_args(args),
         }
+        parent_trace_context = args.get("_parent_trace_context")
+        if parent_trace_context:
+            extras["parent_trace_context"] = parent_trace_context
         workflow_run_id = str(workflow_run_id or uuid.uuid4())
         # FIXME (Yeuoly): we need to remove the SKIP_PREPARE_USER_INPUTS_KEY from the args
         # trigger shouldn't prepare user inputs

api/core/app/apps/workflow/generate_response_converter.py

@@ -1,4 +1,4 @@
-from collections.abc import Generator, Iterator
+from collections.abc import Generator
 from typing import cast
 
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
@@ -36,7 +36,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_full_response(
-        cls, stream_response: Iterator[AppStreamResponse]
+        cls, stream_response: Generator[AppStreamResponse, None, None]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream full response.
@@ -65,7 +65,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
 
     @classmethod
     def convert_stream_simple_response(
-        cls, stream_response: Iterator[AppStreamResponse]
+        cls, stream_response: Generator[AppStreamResponse, None, None]
     ) -> Generator[dict | str, None, None]:
         """
         Convert stream simple response.

api/core/app/apps/workflow_app_runner.py

@@ -1,17 +1,13 @@
 import logging
 import time
 from collections.abc import Mapping, Sequence
-from typing import Protocol, TypeAlias
+from typing import Any, cast
 
 from pydantic import ValidationError
 
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.entities.agent_strategy import AgentStrategyInfo
-from core.app.entities.app_invoke_entities import (
-    InvokeFrom,
-    UserFrom,
-    build_dify_run_context,
-)
+from core.app.entities.app_invoke_entities import InvokeFrom, UserFrom, build_dify_run_context
 from core.app.entities.queue_entities import (
     AppQueueEvent,
     QueueAgentLogEvent,
@@ -40,7 +36,7 @@ from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.workflow.node_factory import DifyNodeFactory, get_default_root_node_id, resolve_workflow_node_class
 from core.workflow.workflow_entry import WorkflowEntry
 from dify_graph.entities import GraphInitParams
-from dify_graph.entities.graph_config import NodeConfigDict, NodeConfigDictAdapter
+from dify_graph.entities.graph_config import NodeConfigDictAdapter
 from dify_graph.entities.pause_reason import HumanInputRequired
 from dify_graph.graph import Graph
 from dify_graph.graph_engine.layers.base import GraphEngineLayer
@@ -79,14 +75,6 @@ from tasks.mail_human_input_delivery_task import dispatch_human_input_email_task
 
 logger = logging.getLogger(__name__)
 
-GraphConfigObject: TypeAlias = dict[str, object]
-GraphConfigMapping: TypeAlias = Mapping[str, object]
-
-
-class SingleNodeRunEntity(Protocol):
-    node_id: str
-    inputs: Mapping[str, object]
-
 
 class WorkflowBasedAppRunner:
     def __init__(
@@ -110,7 +98,7 @@ class WorkflowBasedAppRunner:
 
     def _init_graph(
         self,
-        graph_config: GraphConfigMapping,
+        graph_config: Mapping[str, Any],
         graph_runtime_state: GraphRuntimeState,
         user_from: UserFrom,
         invoke_from: InvokeFrom,
@@ -166,8 +154,8 @@ class WorkflowBasedAppRunner:
     def _prepare_single_node_execution(
         self,
         workflow: Workflow,
-        single_iteration_run: SingleNodeRunEntity | None = None,
-        single_loop_run: SingleNodeRunEntity | None = None,
+        single_iteration_run: Any | None = None,
+        single_loop_run: Any | None = None,
     ) -> tuple[Graph, VariablePool, GraphRuntimeState]:
         """
         Prepare graph, variable pool, and runtime state for single node execution
@@ -220,88 +208,11 @@ class WorkflowBasedAppRunner:
         # This ensures all nodes in the graph reference the same GraphRuntimeState instance
         return graph, variable_pool, graph_runtime_state
 
-    @staticmethod
-    def _get_graph_items(graph_config: GraphConfigMapping) -> tuple[list[GraphConfigMapping], list[GraphConfigMapping]]:
-        nodes = graph_config.get("nodes")
-        edges = graph_config.get("edges")
-        if not isinstance(nodes, list):
-            raise ValueError("nodes in workflow graph must be a list")
-        if not isinstance(edges, list):
-            raise ValueError("edges in workflow graph must be a list")
-
-        validated_nodes: list[GraphConfigMapping] = []
-        for node in nodes:
-            if not isinstance(node, Mapping):
-                raise ValueError("nodes in workflow graph must be mappings")
-            validated_nodes.append(node)
-
-        validated_edges: list[GraphConfigMapping] = []
-        for edge in edges:
-            if not isinstance(edge, Mapping):
-                raise ValueError("edges in workflow graph must be mappings")
-            validated_edges.append(edge)
-
-        return validated_nodes, validated_edges
-
-    @staticmethod
-    def _extract_start_node_id(node_config: GraphConfigMapping | None) -> str | None:
-        if node_config is None:
-            return None
-        node_data = node_config.get("data")
-        if not isinstance(node_data, Mapping):
-            return None
-        start_node_id = node_data.get("start_node_id")
-        return start_node_id if isinstance(start_node_id, str) else None
-
-    @classmethod
-    def _build_single_node_graph_config(
-        cls,
-        *,
-        graph_config: GraphConfigMapping,
-        node_id: str,
-        node_type_filter_key: str,
-    ) -> tuple[GraphConfigObject, NodeConfigDict]:
-        node_configs, edge_configs = cls._get_graph_items(graph_config)
-        main_node_config = next((node for node in node_configs if node.get("id") == node_id), None)
-        start_node_id = cls._extract_start_node_id(main_node_config)
-
-        filtered_node_configs = [
-            dict(node)
-            for node in node_configs
-            if node.get("id") == node_id
-            or (isinstance(node_data := node.get("data"), Mapping) and node_data.get(node_type_filter_key) == node_id)
-            or (start_node_id and node.get("id") == start_node_id)
-        ]
-        if not filtered_node_configs:
-            raise ValueError(f"node id {node_id} not found in workflow graph")
-
-        filtered_node_ids = {
-            str(node_id_value) for node in filtered_node_configs if isinstance((node_id_value := node.get("id")), str)
-        }
-        filtered_edge_configs = [
-            dict(edge)
-            for edge in edge_configs
-            if (edge.get("source") is None or edge.get("source") in filtered_node_ids)
-            and (edge.get("target") is None or edge.get("target") in filtered_node_ids)
-        ]
-
-        target_node_config = next((node for node in filtered_node_configs if node.get("id") == node_id), None)
-        if target_node_config is None:
-            raise ValueError(f"node id {node_id} not found in workflow graph")
-
-        return (
-            {
-                "nodes": filtered_node_configs,
-                "edges": filtered_edge_configs,
-            },
-            NodeConfigDictAdapter.validate_python(target_node_config),
-        )
-
     def _get_graph_and_variable_pool_for_single_node_run(
         self,
         workflow: Workflow,
         node_id: str,
-        user_inputs: Mapping[str, object],
+        user_inputs: dict[str, Any],
         graph_runtime_state: GraphRuntimeState,
         node_type_filter_key: str,  # 'iteration_id' or 'loop_id'
         node_type_label: str = "node",  # 'iteration' or 'loop' for error messages
@@ -325,14 +236,41 @@ class WorkflowBasedAppRunner:
         if not graph_config:
             raise ValueError("workflow graph not found")
 
+        graph_config = cast(dict[str, Any], graph_config)
+
         if "nodes" not in graph_config or "edges" not in graph_config:
             raise ValueError("nodes or edges not found in workflow graph")
 
-        graph_config, target_node_config = self._build_single_node_graph_config(
-            graph_config=graph_config,
-            node_id=node_id,
-            node_type_filter_key=node_type_filter_key,
-        )
+        if not isinstance(graph_config.get("nodes"), list):
+            raise ValueError("nodes in workflow graph must be a list")
+
+        if not isinstance(graph_config.get("edges"), list):
+            raise ValueError("edges in workflow graph must be a list")
+
+        # filter nodes only in the specified node type (iteration or loop)
+        main_node_config = next((n for n in graph_config.get("nodes", []) if n.get("id") == node_id), None)
+        start_node_id = main_node_config.get("data", {}).get("start_node_id") if main_node_config else None
+        node_configs = [
+            node
+            for node in graph_config.get("nodes", [])
+            if node.get("id") == node_id
+            or node.get("data", {}).get(node_type_filter_key, "") == node_id
+            or (start_node_id and node.get("id") == start_node_id)
+        ]
+
+        graph_config["nodes"] = node_configs
+
+        node_ids = [node.get("id") for node in node_configs]
+
+        # filter edges only in the specified node type
+        edge_configs = [
+            edge
+            for edge in graph_config.get("edges", [])
+            if (edge.get("source") is None or edge.get("source") in node_ids)
+            and (edge.get("target") is None or edge.get("target") in node_ids)
+        ]
+
+        graph_config["edges"] = edge_configs
 
         # Create required parameters for Graph.init
         graph_init_params = GraphInitParams(
@@ -361,6 +299,18 @@ class WorkflowBasedAppRunner:
         if not graph:
             raise ValueError("graph not found in workflow")
 
+        # fetch node config from node id
+        target_node_config = None
+        for node in node_configs:
+            if node.get("id") == node_id:
+                target_node_config = node
+                break
+
+        if not target_node_config:
+            raise ValueError(f"{node_type_label} node id not found in workflow graph")
+
+        target_node_config = NodeConfigDictAdapter.validate_python(target_node_config)
+
         # Get node class
         node_type = target_node_config["data"].type
         node_version = str(target_node_config["data"].version)

api/core/app/entities/app_invoke_entities.py

@@ -213,7 +213,7 @@ class AdvancedChatAppGenerateEntity(ConversationAppGenerateEntity):
         """
 
         node_id: str
-        inputs: Mapping[str, object]
+        inputs: Mapping
 
     single_iteration_run: SingleIterationRunEntity | None = None
 
@@ -223,7 +223,7 @@ class AdvancedChatAppGenerateEntity(ConversationAppGenerateEntity):
         """
 
         node_id: str
-        inputs: Mapping[str, object]
+        inputs: Mapping
 
     single_loop_run: SingleLoopRunEntity | None = None
 
@@ -243,7 +243,7 @@ class WorkflowAppGenerateEntity(AppGenerateEntity):
         """
 
         node_id: str
-        inputs: Mapping[str, object]
+        inputs: dict
 
     single_iteration_run: SingleIterationRunEntity | None = None
 
@@ -253,7 +253,7 @@ class WorkflowAppGenerateEntity(AppGenerateEntity):
         """
 
         node_id: str
-        inputs: Mapping[str, object]
+        inputs: dict
 
     single_loop_run: SingleLoopRunEntity | None = None
 

api/core/entities/provider_configuration.py

@@ -30,7 +30,6 @@ from dify_graph.model_runtime.model_providers.__base.ai_model import AIModel
 from dify_graph.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
 from libs.datetime_utils import naive_utc_now
 from models.engine import db
-from models.enums import CredentialSourceType
 from models.provider import (
     LoadBalancingModelConfig,
     Provider,
@@ -547,7 +546,7 @@ class ProviderConfiguration(BaseModel):
                 self._update_load_balancing_configs_with_credential(
                     credential_id=credential_id,
                     credential_record=credential_record,
-                    credential_source=CredentialSourceType.PROVIDER,
+                    credential_source="provider",
                     session=session,
                 )
             except Exception:
@@ -624,7 +623,7 @@ class ProviderConfiguration(BaseModel):
                 LoadBalancingModelConfig.tenant_id == self.tenant_id,
                 LoadBalancingModelConfig.provider_name.in_(self._get_provider_names()),
                 LoadBalancingModelConfig.credential_id == credential_id,
-                LoadBalancingModelConfig.credential_source_type == CredentialSourceType.PROVIDER,
+                LoadBalancingModelConfig.credential_source_type == "provider",
             )
             lb_configs_using_credential = session.execute(lb_stmt).scalars().all()
             try:
@@ -1044,7 +1043,7 @@ class ProviderConfiguration(BaseModel):
                 self._update_load_balancing_configs_with_credential(
                     credential_id=credential_id,
                     credential_record=credential_record,
-                    credential_source=CredentialSourceType.CUSTOM_MODEL,
+                    credential_source="custom_model",
                     session=session,
                 )
             except Exception:
@@ -1074,7 +1073,7 @@ class ProviderConfiguration(BaseModel):
                 LoadBalancingModelConfig.tenant_id == self.tenant_id,
                 LoadBalancingModelConfig.provider_name.in_(self._get_provider_names()),
                 LoadBalancingModelConfig.credential_id == credential_id,
-                LoadBalancingModelConfig.credential_source_type == CredentialSourceType.CUSTOM_MODEL,
+                LoadBalancingModelConfig.credential_source_type == "custom_model",
             )
             lb_configs_using_credential = session.execute(lb_stmt).scalars().all()
 
@@ -1712,7 +1711,7 @@ class ProviderConfiguration(BaseModel):
                     provider_model_lb_configs = [
                         config
                         for config in model_setting.load_balancing_configs
-                        if config.credential_source_type != CredentialSourceType.CUSTOM_MODEL
+                        if config.credential_source_type != "custom_model"
                     ]
 
                     load_balancing_enabled = model_setting.load_balancing_enabled
@@ -1770,7 +1769,7 @@ class ProviderConfiguration(BaseModel):
                 custom_model_lb_configs = [
                     config
                     for config in model_setting.load_balancing_configs
-                    if config.credential_source_type != CredentialSourceType.PROVIDER
+                    if config.credential_source_type != "provider"
                 ]
 
                 load_balancing_enabled = model_setting.load_balancing_enabled

api/core/llm_generator/entities.py

@@ -9,6 +9,7 @@ class RuleGeneratePayload(BaseModel):
     instruction: str = Field(..., description="Rule generation instruction")
     model_config_data: ModelConfig = Field(..., alias="model_config", description="Model configuration")
     no_variable: bool = Field(default=False, description="Whether to exclude variables")
+    app_id: str | None = Field(default=None, description="App ID for prompt generation tracing")
 
 
 class RuleCodeGeneratePayload(RuleGeneratePayload):
@@ -18,3 +19,4 @@ class RuleCodeGeneratePayload(RuleGeneratePayload):
 class RuleStructuredOutputPayload(BaseModel):
     instruction: str = Field(..., description="Structured output generation instruction")
     model_config_data: ModelConfig = Field(..., alias="model_config", description="Model configuration")
+    app_id: str | None = Field(default=None, description="App ID for prompt generation tracing")

api/core/ops/entities/trace_entity.py

@@ -9,8 +9,8 @@ from pydantic import BaseModel, ConfigDict, field_serializer, field_validator
 class BaseTraceInfo(BaseModel):
     message_id: str | None = None
     message_data: Any | None = None
-    inputs: Union[str, dict[str, Any], list] | None = None
-    outputs: Union[str, dict[str, Any], list] | None = None
+    inputs: Union[str, dict[str, Any], list[Any]] | None = None
+    outputs: Union[str, dict[str, Any], list[Any]] | None = None
     start_time: datetime | None = None
     end_time: datetime | None = None
     metadata: dict[str, Any]
@@ -18,7 +18,7 @@ class BaseTraceInfo(BaseModel):
 
     @field_validator("inputs", "outputs")
     @classmethod
-    def ensure_type(cls, v):
+    def ensure_type(cls, v: str | dict[str, Any] | list[Any] | None) -> str | dict[str, Any] | list[Any] | None:
         if v is None:
             return None
         if isinstance(v, str | dict | list):
@@ -27,6 +27,48 @@ class BaseTraceInfo(BaseModel):
 
     model_config = ConfigDict(protected_namespaces=())
 
+    @property
+    def resolved_trace_id(self) -> str | None:
+        """Get trace_id with intelligent fallback.
+
+        Priority:
+        1. External trace_id (from X-Trace-Id header)
+        2. workflow_run_id (if this trace type has it)
+        3. message_id (as final fallback)
+        """
+        if self.trace_id:
+            return self.trace_id
+
+        # Try workflow_run_id (only exists on workflow-related traces)
+        workflow_run_id = getattr(self, "workflow_run_id", None)
+        if workflow_run_id:
+            return workflow_run_id
+
+        # Final fallback to message_id
+        return str(self.message_id) if self.message_id else None
+
+    @property
+    def resolved_parent_context(self) -> tuple[str | None, str | None]:
+        """Resolve cross-workflow parent linking from metadata.
+
+        Extracts typed parent IDs from the untyped ``parent_trace_context``
+        metadata dict (set by tool_node when invoking nested workflows).
+
+        Returns:
+            (trace_correlation_override, parent_span_id_source) where
+            trace_correlation_override is the outer workflow_run_id and
+            parent_span_id_source is the outer node_execution_id.
+        """
+        parent_ctx = self.metadata.get("parent_trace_context")
+        if not isinstance(parent_ctx, dict):
+            return None, None
+        trace_override = parent_ctx.get("parent_workflow_run_id")
+        parent_span = parent_ctx.get("parent_node_execution_id")
+        return (
+            trace_override if isinstance(trace_override, str) else None,
+            parent_span if isinstance(parent_span, str) else None,
+        )
+
     @field_serializer("start_time", "end_time")
     def serialize_datetime(self, dt: datetime | None) -> str | None:
         if dt is None:
@@ -48,7 +90,10 @@ class WorkflowTraceInfo(BaseTraceInfo):
     workflow_run_version: str
     error: str | None = None
     total_tokens: int
+    prompt_tokens: int | None = None
+    completion_tokens: int | None = None
     file_list: list[str]
+    invoked_by: str | None = None
     query: str
     metadata: dict[str, Any]
 
@@ -59,7 +104,7 @@ class MessageTraceInfo(BaseTraceInfo):
     answer_tokens: int
     total_tokens: int
     error: str | None = None
-    file_list: Union[str, dict[str, Any], list] | None = None
+    file_list: Union[str, dict[str, Any], list[Any]] | None = None
     message_file_data: Any | None = None
     conversation_mode: str
     gen_ai_server_time_to_first_token: float | None = None
@@ -106,7 +151,7 @@ class ToolTraceInfo(BaseTraceInfo):
     tool_config: dict[str, Any]
     time_cost: Union[int, float]
     tool_parameters: dict[str, Any]
-    file_url: Union[str, None, list] = None
+    file_url: Union[str, None, list[str]] = None
 
 
 class GenerateNameTraceInfo(BaseTraceInfo):
@@ -114,6 +159,79 @@ class GenerateNameTraceInfo(BaseTraceInfo):
     tenant_id: str
 
 
+class PromptGenerationTraceInfo(BaseTraceInfo):
+    """Trace information for prompt generation operations (rule-generate, code-generate, etc.)."""
+
+    tenant_id: str
+    user_id: str
+    app_id: str | None = None
+
+    operation_type: str
+    instruction: str
+
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
+
+    model_provider: str
+    model_name: str
+
+    latency: float
+
+    total_price: float | None = None
+    currency: str | None = None
+
+    error: str | None = None
+
+    model_config = ConfigDict(protected_namespaces=())
+
+
+class WorkflowNodeTraceInfo(BaseTraceInfo):
+    workflow_id: str
+    workflow_run_id: str
+    tenant_id: str
+    node_execution_id: str
+    node_id: str
+    node_type: str
+    title: str
+
+    status: str
+    error: str | None = None
+    elapsed_time: float
+
+    index: int
+    predecessor_node_id: str | None = None
+
+    total_tokens: int = 0
+    total_price: float = 0.0
+    currency: str | None = None
+
+    model_provider: str | None = None
+    model_name: str | None = None
+    prompt_tokens: int | None = None
+    completion_tokens: int | None = None
+
+    tool_name: str | None = None
+
+    iteration_id: str | None = None
+    iteration_index: int | None = None
+    loop_id: str | None = None
+    loop_index: int | None = None
+    parallel_id: str | None = None
+
+    node_inputs: Mapping[str, Any] | None = None
+    node_outputs: Mapping[str, Any] | None = None
+    process_data: Mapping[str, Any] | None = None
+
+    invoked_by: str | None = None
+
+    model_config = ConfigDict(protected_namespaces=())
+
+
+class DraftNodeExecutionTrace(WorkflowNodeTraceInfo):
+    pass
+
+
 class TaskData(BaseModel):
     app_id: str
     trace_info_type: str
@@ -128,11 +246,31 @@ trace_info_info_map = {
     "DatasetRetrievalTraceInfo": DatasetRetrievalTraceInfo,
     "ToolTraceInfo": ToolTraceInfo,
     "GenerateNameTraceInfo": GenerateNameTraceInfo,
+    "PromptGenerationTraceInfo": PromptGenerationTraceInfo,
+    "WorkflowNodeTraceInfo": WorkflowNodeTraceInfo,
+    "DraftNodeExecutionTrace": DraftNodeExecutionTrace,
 }
 
 
+class OperationType(StrEnum):
+    """Operation type for token metric labels.
+
+    Used as a metric attribute on ``dify.tokens.input`` / ``dify.tokens.output``
+    counters so consumers can break down token usage by operation.
+    """
+
+    WORKFLOW = "workflow"
+    NODE_EXECUTION = "node_execution"
+    MESSAGE = "message"
+    RULE_GENERATE = "rule_generate"
+    CODE_GENERATE = "code_generate"
+    STRUCTURED_OUTPUT = "structured_output"
+    INSTRUCTION_MODIFY = "instruction_modify"
+
+
 class TraceTaskName(StrEnum):
     CONVERSATION_TRACE = "conversation"
+    DRAFT_NODE_EXECUTION_TRACE = "draft_node_execution"
     WORKFLOW_TRACE = "workflow"
     MESSAGE_TRACE = "message"
     MODERATION_TRACE = "moderation"
@@ -140,4 +278,6 @@ class TraceTaskName(StrEnum):
     DATASET_RETRIEVAL_TRACE = "dataset_retrieval"
     TOOL_TRACE = "tool"
     GENERATE_NAME_TRACE = "generate_conversation_name"
+    PROMPT_GENERATION_TRACE = "prompt_generation"
+    NODE_EXECUTION_TRACE = "node_execution"
     DATASOURCE_TRACE = "datasource"

api/core/ops/ops_trace_manager.py

@@ -15,22 +15,32 @@ from sqlalchemy import select
 from sqlalchemy.orm import Session, sessionmaker
 
 from core.helper.encrypter import batch_decrypt_token, encrypt_token, obfuscated_token
-from core.ops.entities.config_entity import OPS_FILE_PATH, TracingProviderEnum
+from core.ops.entities.config_entity import (
+    OPS_FILE_PATH,
+    TracingProviderEnum,
+)
 from core.ops.entities.trace_entity import (
     DatasetRetrievalTraceInfo,
+    DraftNodeExecutionTrace,
     GenerateNameTraceInfo,
     MessageTraceInfo,
     ModerationTraceInfo,
+    PromptGenerationTraceInfo,
     SuggestedQuestionTraceInfo,
     TaskData,
     ToolTraceInfo,
     TraceTaskName,
+    WorkflowNodeTraceInfo,
     WorkflowTraceInfo,
 )
 from core.ops.utils import get_message_data
+from extensions.ext_database import db
 from extensions.ext_storage import storage
-from models.engine import db
+from models.account import Tenant
+from models.dataset import Dataset
 from models.model import App, AppModelConfig, Conversation, Message, MessageFile, TraceAppConfig
+from models.provider import Provider, ProviderCredential, ProviderModel, ProviderModelCredential, ProviderType
+from models.tools import ApiToolProvider, BuiltinToolProvider, MCPToolProvider, WorkflowToolProvider
 from models.workflow import WorkflowAppLog
 from tasks.ops_trace_task import process_trace_tasks
 
@@ -40,9 +50,142 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 
+def _lookup_app_and_workspace_names(app_id: str | None, tenant_id: str | None) -> tuple[str, str]:
+    """Return (app_name, workspace_name) for the given IDs. Falls back to empty strings."""
+    app_name = ""
+    workspace_name = ""
+    if not app_id and not tenant_id:
+        return app_name, workspace_name
+    with Session(db.engine) as session:
+        if app_id:
+            name = session.scalar(select(App.name).where(App.id == app_id))
+            if name:
+                app_name = name
+        if tenant_id:
+            name = session.scalar(select(Tenant.name).where(Tenant.id == tenant_id))
+            if name:
+                workspace_name = name
+    return app_name, workspace_name
+
+
+_PROVIDER_TYPE_TO_MODEL: dict[str, type] = {
+    "builtin": BuiltinToolProvider,
+    "plugin": BuiltinToolProvider,
+    "api": ApiToolProvider,
+    "workflow": WorkflowToolProvider,
+    "mcp": MCPToolProvider,
+}
+
+
+def _lookup_credential_name(credential_id: str | None, provider_type: str | None) -> str:
+    if not credential_id:
+        return ""
+    model_cls = _PROVIDER_TYPE_TO_MODEL.get(provider_type or "")
+    if not model_cls:
+        return ""
+    with Session(db.engine) as session:
+        name = session.scalar(select(model_cls.name).where(model_cls.id == credential_id))  # type: ignore[attr-defined]
+        return str(name) if name else ""
+
+
+def _lookup_llm_credential_info(
+    tenant_id: str | None, provider: str | None, model: str | None, model_type: str | None = "llm"
+) -> tuple[str | None, str]:
+    """
+    Lookup LLM credential ID and name for the given provider and model.
+    Returns (credential_id, credential_name).
+
+    Handles async timing issues gracefully - if credential is deleted between lookups,
+    returns the ID but empty name rather than failing.
+    """
+    if not tenant_id or not provider:
+        return None, ""
+
+    try:
+        with Session(db.engine) as session:
+            # Try to find provider-level or model-level configuration
+            provider_record = session.scalar(
+                select(Provider).where(
+                    Provider.tenant_id == tenant_id,
+                    Provider.provider_name == provider,
+                    Provider.provider_type == ProviderType.CUSTOM,
+                )
+            )
+
+            if not provider_record:
+                return None, ""
+
+            # Check if there's a model-specific config
+            credential_id = None
+            credential_name = ""
+            is_model_level = False
+
+            if model:
+                # Try model-level first
+                model_record = session.scalar(
+                    select(ProviderModel).where(
+                        ProviderModel.tenant_id == tenant_id,
+                        ProviderModel.provider_name == provider,
+                        ProviderModel.model_name == model,
+                        ProviderModel.model_type == model_type,
+                    )
+                )
+
+                if model_record and model_record.credential_id:
+                    credential_id = model_record.credential_id
+                    is_model_level = True
+
+            if not credential_id and provider_record.credential_id:
+                # Fall back to provider-level credential
+                credential_id = provider_record.credential_id
+                is_model_level = False
+
+            # Lookup credential_name if we have credential_id
+            if credential_id:
+                try:
+                    if is_model_level:
+                        # Query ProviderModelCredential
+                        cred_name = session.scalar(
+                            select(ProviderModelCredential.credential_name).where(
+                                ProviderModelCredential.id == credential_id
+                            )
+                        )
+                    else:
+                        # Query ProviderCredential
+                        cred_name = session.scalar(
+                            select(ProviderCredential.credential_name).where(ProviderCredential.id == credential_id)
+                        )
+
+                    if cred_name:
+                        credential_name = str(cred_name)
+                except Exception as e:
+                    # Credential might have been deleted between lookups (async timing)
+                    # Return ID but empty name rather than failing
+                    logger.warning(
+                        "Failed to lookup credential name for credential_id=%s (provider=%s, model=%s): %s",
+                        credential_id,
+                        provider,
+                        model,
+                        str(e),
+                    )
+
+            return credential_id, credential_name
+    except Exception as e:
+        # Database query failed or other unexpected error
+        # Return empty rather than propagating error to telemetry emission
+        logger.warning(
+            "Failed to lookup LLM credential info for tenant_id=%s, provider=%s, model=%s: %s",
+            tenant_id,
+            provider,
+            model,
+            str(e),
+        )
+        return None, ""
+
+
 class OpsTraceProviderConfigMap(collections.UserDict[str, dict[str, Any]]):
-    def __getitem__(self, key: str) -> dict[str, Any]:
-        match key:
+    def __getitem__(self, provider: str) -> dict[str, Any]:
+        match provider:
             case TracingProviderEnum.LANGFUSE:
                 from core.ops.entities.config_entity import LangfuseConfig
                 from core.ops.langfuse_trace.langfuse_trace import LangFuseDataTrace
@@ -149,7 +292,7 @@ class OpsTraceProviderConfigMap(collections.UserDict[str, dict[str, Any]]):
                 }
 
             case _:
-                raise KeyError(f"Unsupported tracing provider: {key}")
+                raise KeyError(f"Unsupported tracing provider: {provider}")
 
 
 provider_config_map = OpsTraceProviderConfigMap()
@@ -314,6 +457,10 @@ class OpsTraceManager:
         if app_id is None:
             return None
 
+        # Handle storage_id format (tenant-{uuid}) - not a real app_id
+        if isinstance(app_id, str) and app_id.startswith("tenant-"):
+            return None
+
         app: App | None = db.session.query(App).where(App.id == app_id).first()
 
         if app is None:
@@ -466,8 +613,6 @@ class TraceTask:
 
     @classmethod
     def _get_workflow_run_repo(cls):
-        from repositories.factory import DifyAPIRepositoryFactory
-
         if cls._workflow_run_repo is None:
             with cls._repo_lock:
                 if cls._workflow_run_repo is None:
@@ -478,6 +623,56 @@ class TraceTask:
                     cls._workflow_run_repo = DifyAPIRepositoryFactory.create_api_workflow_run_repository(session_maker)
         return cls._workflow_run_repo
 
+    @classmethod
+    def _get_user_id_from_metadata(cls, metadata: dict[str, Any]) -> str:
+        """Extract user ID from metadata, prioritizing end_user over account.
+
+        Returns the actual user ID (end_user or account) who invoked the workflow,
+        regardless of invoke_from context.
+        """
+        # Priority 1: End user (external users via API/WebApp)
+        if user_id := metadata.get("from_end_user_id"):
+            return f"end_user:{user_id}"
+
+        # Priority 2: Account user (internal users via console/debugger)
+        if user_id := metadata.get("from_account_id"):
+            return f"account:{user_id}"
+
+        # Priority 3: User (internal users via console/debugger)
+        if user_id := metadata.get("user_id"):
+            return f"user:{user_id}"
+
+        return "anonymous"
+
+    @classmethod
+    def _calculate_workflow_token_split(cls, workflow_run_id: str, tenant_id: str) -> tuple[int, int]:
+        from dify_graph.enums import WorkflowNodeExecutionMetadataKey
+        from models.workflow import WorkflowNodeExecutionModel
+
+        with Session(db.engine) as session:
+            node_executions = session.scalars(
+                select(WorkflowNodeExecutionModel).where(
+                    WorkflowNodeExecutionModel.tenant_id == tenant_id,
+                    WorkflowNodeExecutionModel.workflow_run_id == workflow_run_id,
+                )
+            ).all()
+
+            total_prompt = 0
+            total_completion = 0
+
+            for node_exec in node_executions:
+                metadata = node_exec.execution_metadata_dict
+
+                prompt = metadata.get(WorkflowNodeExecutionMetadataKey.PROMPT_TOKENS)
+                if prompt is not None:
+                    total_prompt += prompt
+
+                completion = metadata.get(WorkflowNodeExecutionMetadataKey.COMPLETION_TOKENS)
+                if completion is not None:
+                    total_completion += completion
+
+            return (total_prompt, total_completion)
+
     def __init__(
         self,
         trace_type: Any,
@@ -498,6 +693,8 @@ class TraceTask:
         self.app_id = None
         self.trace_id = None
         self.kwargs = kwargs
+        if user_id is not None and "user_id" not in self.kwargs:
+            self.kwargs["user_id"] = user_id
         external_trace_id = kwargs.get("external_trace_id")
         if external_trace_id:
             self.trace_id = external_trace_id
@@ -511,7 +708,7 @@ class TraceTask:
             TraceTaskName.WORKFLOW_TRACE: lambda: self.workflow_trace(
                 workflow_run_id=self.workflow_run_id, conversation_id=self.conversation_id, user_id=self.user_id
             ),
-            TraceTaskName.MESSAGE_TRACE: lambda: self.message_trace(message_id=self.message_id),
+            TraceTaskName.MESSAGE_TRACE: lambda: self.message_trace(message_id=self.message_id, **self.kwargs),
             TraceTaskName.MODERATION_TRACE: lambda: self.moderation_trace(
                 message_id=self.message_id, timer=self.timer, **self.kwargs
             ),
@@ -527,6 +724,9 @@ class TraceTask:
             TraceTaskName.GENERATE_NAME_TRACE: lambda: self.generate_name_trace(
                 conversation_id=self.conversation_id, timer=self.timer, **self.kwargs
             ),
+            TraceTaskName.PROMPT_GENERATION_TRACE: lambda: self.prompt_generation_trace(**self.kwargs),
+            TraceTaskName.NODE_EXECUTION_TRACE: lambda: self.node_execution_trace(**self.kwargs),
+            TraceTaskName.DRAFT_NODE_EXECUTION_TRACE: lambda: self.draft_node_execution_trace(**self.kwargs),
         }
 
         return preprocess_map.get(self.trace_type, lambda: None)()
@@ -562,6 +762,10 @@ class TraceTask:
 
         total_tokens = workflow_run.total_tokens
 
+        prompt_tokens, completion_tokens = self._calculate_workflow_token_split(
+            workflow_run_id=workflow_run_id, tenant_id=tenant_id
+        )
+
         file_list = workflow_run_inputs.get("sys.file") or []
         query = workflow_run_inputs.get("query") or workflow_run_inputs.get("sys.query") or ""
 
@@ -582,7 +786,14 @@ class TraceTask:
                 )
                 message_id = session.scalar(message_data_stmt)
 
-        metadata = {
+        from core.telemetry.gateway import is_enterprise_telemetry_enabled
+
+        if is_enterprise_telemetry_enabled():
+            app_name, workspace_name = _lookup_app_and_workspace_names(workflow_run.app_id, tenant_id)
+        else:
+            app_name, workspace_name = "", ""
+
+        metadata: dict[str, Any] = {
             "workflow_id": workflow_id,
             "conversation_id": conversation_id,
             "workflow_run_id": workflow_run_id,
@@ -595,8 +806,14 @@ class TraceTask:
             "triggered_from": workflow_run.triggered_from,
             "user_id": user_id,
             "app_id": workflow_run.app_id,
+            "app_name": app_name,
+            "workspace_name": workspace_name,
         }
 
+        parent_trace_context = self.kwargs.get("parent_trace_context")
+        if parent_trace_context:
+            metadata["parent_trace_context"] = parent_trace_context
+
         workflow_trace_info = WorkflowTraceInfo(
             trace_id=self.trace_id,
             workflow_data=workflow_run.to_dict(),
@@ -611,6 +828,8 @@ class TraceTask:
             workflow_run_version=workflow_run_version,
             error=error,
             total_tokens=total_tokens,
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
             file_list=file_list,
             query=query,
             metadata=metadata,
@@ -618,10 +837,11 @@ class TraceTask:
             message_id=message_id,
             start_time=workflow_run.created_at,
             end_time=workflow_run.finished_at,
+            invoked_by=self._get_user_id_from_metadata(metadata),
         )
         return workflow_trace_info
 
-    def message_trace(self, message_id: str | None):
+    def message_trace(self, message_id: str | None, **kwargs):
         if not message_id:
             return {}
         message_data = get_message_data(message_id)
@@ -644,6 +864,19 @@ class TraceTask:
 
         streaming_metrics = self._extract_streaming_metrics(message_data)
 
+        tenant_id = ""
+        with Session(db.engine) as session:
+            tid = session.scalar(select(App.tenant_id).where(App.id == message_data.app_id))
+            if tid:
+                tenant_id = str(tid)
+
+        from core.telemetry.gateway import is_enterprise_telemetry_enabled
+
+        if is_enterprise_telemetry_enabled():
+            app_name, workspace_name = _lookup_app_and_workspace_names(message_data.app_id, tenant_id)
+        else:
+            app_name, workspace_name = "", ""
+
         metadata = {
             "conversation_id": message_data.conversation_id,
             "ls_provider": message_data.model_provider,
@@ -655,7 +888,14 @@ class TraceTask:
             "workflow_run_id": message_data.workflow_run_id,
             "from_source": message_data.from_source,
             "message_id": message_id,
+            "tenant_id": tenant_id,
+            "app_id": message_data.app_id,
+            "user_id": message_data.from_end_user_id or message_data.from_account_id,
+            "app_name": app_name,
+            "workspace_name": workspace_name,
         }
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
 
         message_tokens = message_data.message_tokens
 
@@ -672,7 +912,9 @@ class TraceTask:
             outputs=message_data.answer,
             file_list=file_list,
             start_time=created_at,
-            end_time=created_at + timedelta(seconds=message_data.provider_response_latency),
+            end_time=message_data.updated_at
+            if message_data.updated_at and message_data.updated_at > created_at
+            else created_at + timedelta(seconds=message_data.provider_response_latency),
             metadata=metadata,
             message_file_data=message_file_data,
             conversation_mode=conversation_mode,
@@ -697,6 +939,8 @@ class TraceTask:
             "preset_response": moderation_result.preset_response,
             "query": moderation_result.query,
         }
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
 
         # get workflow_app_log_id
         workflow_app_log_id = None
@@ -738,6 +982,8 @@ class TraceTask:
             "workflow_run_id": message_data.workflow_run_id,
             "from_source": message_data.from_source,
         }
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
 
         # get workflow_app_log_id
         workflow_app_log_id = None
@@ -777,6 +1023,52 @@ class TraceTask:
         if not message_data:
             return {}
 
+        tenant_id = ""
+        with Session(db.engine) as session:
+            tid = session.scalar(select(App.tenant_id).where(App.id == message_data.app_id))
+            if tid:
+                tenant_id = str(tid)
+
+        from core.telemetry.gateway import is_enterprise_telemetry_enabled
+
+        if is_enterprise_telemetry_enabled():
+            app_name, workspace_name = _lookup_app_and_workspace_names(message_data.app_id, tenant_id)
+        else:
+            app_name, workspace_name = "", ""
+
+        doc_list = [doc.model_dump() for doc in documents] if documents else []
+        dataset_ids: set[str] = set()
+        for doc in doc_list:
+            doc_meta = doc.get("metadata") or {}
+            did = doc_meta.get("dataset_id")
+            if did:
+                dataset_ids.add(did)
+
+        embedding_models: dict[str, dict[str, str]] = {}
+        if dataset_ids:
+            with Session(db.engine) as session:
+                rows = session.execute(
+                    select(Dataset.id, Dataset.embedding_model, Dataset.embedding_model_provider).where(
+                        Dataset.id.in_(list(dataset_ids))
+                    )
+                ).all()
+                for row in rows:
+                    embedding_models[str(row[0])] = {
+                        "embedding_model": row[1] or "",
+                        "embedding_model_provider": row[2] or "",
+                    }
+
+        # Extract rerank model info from retrieval_model kwargs
+        rerank_model_provider = ""
+        rerank_model_name = ""
+        if "retrieval_model" in kwargs:
+            retrieval_model = kwargs["retrieval_model"]
+            if isinstance(retrieval_model, dict):
+                reranking_model = retrieval_model.get("reranking_model")
+                if isinstance(reranking_model, dict):
+                    rerank_model_provider = reranking_model.get("reranking_provider_name", "")
+                    rerank_model_name = reranking_model.get("reranking_model_name", "")
+
         metadata = {
             "message_id": message_id,
             "ls_provider": message_data.model_provider,
@@ -787,13 +1079,23 @@ class TraceTask:
             "agent_based": message_data.agent_based,
             "workflow_run_id": message_data.workflow_run_id,
             "from_source": message_data.from_source,
+            "tenant_id": tenant_id,
+            "app_id": message_data.app_id,
+            "user_id": message_data.from_end_user_id or message_data.from_account_id,
+            "app_name": app_name,
+            "workspace_name": workspace_name,
+            "embedding_models": embedding_models,
+            "rerank_model_provider": rerank_model_provider,
+            "rerank_model_name": rerank_model_name,
         }
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
 
         dataset_retrieval_trace_info = DatasetRetrievalTraceInfo(
             trace_id=self.trace_id,
             message_id=message_id,
             inputs=message_data.query or message_data.inputs,
-            documents=[doc.model_dump() for doc in documents] if documents else [],
+            documents=doc_list,
             start_time=timer.get("start"),
             end_time=timer.get("end"),
             metadata=metadata,
@@ -836,6 +1138,10 @@ class TraceTask:
             "error": error,
             "tool_parameters": tool_parameters,
         }
+        if message_data.workflow_run_id:
+            metadata["workflow_run_id"] = message_data.workflow_run_id
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
 
         file_url = ""
         message_file_data = db.session.query(MessageFile).filter_by(message_id=message_id).first()
@@ -890,6 +1196,8 @@ class TraceTask:
             "conversation_id": conversation_id,
             "tenant_id": tenant_id,
         }
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
 
         generate_name_trace_info = GenerateNameTraceInfo(
             trace_id=self.trace_id,
@@ -904,6 +1212,182 @@ class TraceTask:
 
         return generate_name_trace_info
 
+    def prompt_generation_trace(self, **kwargs) -> PromptGenerationTraceInfo | dict:
+        tenant_id = kwargs.get("tenant_id", "")
+        user_id = kwargs.get("user_id", "")
+        app_id = kwargs.get("app_id")
+        operation_type = kwargs.get("operation_type", "")
+        instruction = kwargs.get("instruction", "")
+        generated_output = kwargs.get("generated_output", "")
+
+        prompt_tokens = kwargs.get("prompt_tokens", 0)
+        completion_tokens = kwargs.get("completion_tokens", 0)
+        total_tokens = kwargs.get("total_tokens", 0)
+
+        model_provider = kwargs.get("model_provider", "")
+        model_name = kwargs.get("model_name", "")
+
+        latency = kwargs.get("latency", 0.0)
+
+        timer = kwargs.get("timer")
+        start_time = timer.get("start") if timer else None
+        end_time = timer.get("end") if timer else None
+
+        total_price = kwargs.get("total_price")
+        currency = kwargs.get("currency")
+
+        error = kwargs.get("error")
+
+        app_name = None
+        workspace_name = None
+        if app_id:
+            app_name, workspace_name = _lookup_app_and_workspace_names(app_id, tenant_id)
+
+        metadata = {
+            "tenant_id": tenant_id,
+            "user_id": user_id,
+            "app_id": app_id or "",
+            "app_name": app_name,
+            "workspace_name": workspace_name,
+            "operation_type": operation_type,
+            "model_provider": model_provider,
+            "model_name": model_name,
+        }
+        if node_execution_id := kwargs.get("node_execution_id"):
+            metadata["node_execution_id"] = node_execution_id
+
+        return PromptGenerationTraceInfo(
+            trace_id=self.trace_id,
+            inputs=instruction,
+            outputs=generated_output,
+            start_time=start_time,
+            end_time=end_time,
+            metadata=metadata,
+            tenant_id=tenant_id,
+            user_id=user_id,
+            app_id=app_id,
+            operation_type=operation_type,
+            instruction=instruction,
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            total_tokens=total_tokens,
+            model_provider=model_provider,
+            model_name=model_name,
+            latency=latency,
+            total_price=total_price,
+            currency=currency,
+            error=error,
+        )
+
+    def node_execution_trace(self, **kwargs) -> WorkflowNodeTraceInfo | dict:
+        node_data: dict = kwargs.get("node_execution_data", {})
+        if not node_data:
+            return {}
+
+        from core.telemetry.gateway import is_enterprise_telemetry_enabled
+
+        if is_enterprise_telemetry_enabled():
+            app_name, workspace_name = _lookup_app_and_workspace_names(
+                node_data.get("app_id"), node_data.get("tenant_id")
+            )
+        else:
+            app_name, workspace_name = "", ""
+
+        # Try tool credential lookup first
+        credential_id = node_data.get("credential_id")
+        if is_enterprise_telemetry_enabled():
+            credential_name = _lookup_credential_name(credential_id, node_data.get("credential_provider_type"))
+            # If no credential_id found (e.g., LLM nodes), try LLM credential lookup
+            if not credential_id:
+                llm_cred_id, llm_cred_name = _lookup_llm_credential_info(
+                    tenant_id=node_data.get("tenant_id"),
+                    provider=node_data.get("model_provider"),
+                    model=node_data.get("model_name"),
+                    model_type="llm",
+                )
+                if llm_cred_id:
+                    credential_id = llm_cred_id
+                    credential_name = llm_cred_name
+        else:
+            credential_name = ""
+        metadata: dict[str, Any] = {
+            "tenant_id": node_data.get("tenant_id"),
+            "app_id": node_data.get("app_id"),
+            "app_name": app_name,
+            "workspace_name": workspace_name,
+            "user_id": node_data.get("user_id"),
+            "invoke_from": node_data.get("invoke_from"),
+            "credential_id": credential_id,
+            "credential_name": credential_name,
+            "dataset_ids": node_data.get("dataset_ids"),
+            "dataset_names": node_data.get("dataset_names"),
+            "plugin_name": node_data.get("plugin_name"),
+        }
+
+        parent_trace_context = node_data.get("parent_trace_context")
+        if parent_trace_context:
+            metadata["parent_trace_context"] = parent_trace_context
+
+        message_id: str | None = None
+        conversation_id = node_data.get("conversation_id")
+        workflow_execution_id = node_data.get("workflow_execution_id")
+        if conversation_id and workflow_execution_id and not parent_trace_context:
+            with Session(db.engine) as session:
+                msg_id = session.scalar(
+                    select(Message.id).where(
+                        Message.conversation_id == conversation_id,
+                        Message.workflow_run_id == workflow_execution_id,
+                    )
+                )
+                if msg_id:
+                    message_id = str(msg_id)
+                    metadata["message_id"] = message_id
+            if conversation_id:
+                metadata["conversation_id"] = conversation_id
+
+        return WorkflowNodeTraceInfo(
+            trace_id=self.trace_id,
+            message_id=message_id,
+            start_time=node_data.get("created_at"),
+            end_time=node_data.get("finished_at"),
+            metadata=metadata,
+            workflow_id=node_data.get("workflow_id", ""),
+            workflow_run_id=node_data.get("workflow_execution_id", ""),
+            tenant_id=node_data.get("tenant_id", ""),
+            node_execution_id=node_data.get("node_execution_id", ""),
+            node_id=node_data.get("node_id", ""),
+            node_type=node_data.get("node_type", ""),
+            title=node_data.get("title", ""),
+            status=node_data.get("status", ""),
+            error=node_data.get("error"),
+            elapsed_time=node_data.get("elapsed_time", 0.0),
+            index=node_data.get("index", 0),
+            predecessor_node_id=node_data.get("predecessor_node_id"),
+            total_tokens=node_data.get("total_tokens", 0),
+            total_price=node_data.get("total_price", 0.0),
+            currency=node_data.get("currency"),
+            model_provider=node_data.get("model_provider"),
+            model_name=node_data.get("model_name"),
+            prompt_tokens=node_data.get("prompt_tokens"),
+            completion_tokens=node_data.get("completion_tokens"),
+            tool_name=node_data.get("tool_name"),
+            iteration_id=node_data.get("iteration_id"),
+            iteration_index=node_data.get("iteration_index"),
+            loop_id=node_data.get("loop_id"),
+            loop_index=node_data.get("loop_index"),
+            parallel_id=node_data.get("parallel_id"),
+            node_inputs=node_data.get("node_inputs"),
+            node_outputs=node_data.get("node_outputs"),
+            process_data=node_data.get("process_data"),
+            invoked_by=self._get_user_id_from_metadata(metadata),
+        )
+
+    def draft_node_execution_trace(self, **kwargs) -> DraftNodeExecutionTrace | dict:
+        node_trace = self.node_execution_trace(**kwargs)
+        if not isinstance(node_trace, WorkflowNodeTraceInfo):
+            return node_trace
+        return DraftNodeExecutionTrace(**node_trace.model_dump())
+
     def _extract_streaming_metrics(self, message_data) -> dict:
         if not message_data.message_metadata:
             return {}
@@ -937,13 +1421,17 @@ class TraceQueueManager:
         self.user_id = user_id
         self.trace_instance = OpsTraceManager.get_ops_trace_instance(app_id)
         self.flask_app = current_app._get_current_object()  # type: ignore
+
+        from core.telemetry.gateway import is_enterprise_telemetry_enabled
+
+        self._enterprise_telemetry_enabled = is_enterprise_telemetry_enabled()
         if trace_manager_timer is None:
             self.start_timer()
 
     def add_trace_task(self, trace_task: TraceTask):
         global trace_manager_timer, trace_manager_queue
         try:
-            if self.trace_instance:
+            if self._enterprise_telemetry_enabled or self.trace_instance:
                 trace_task.app_id = self.app_id
                 trace_manager_queue.put(trace_task)
         except Exception:
@@ -979,20 +1467,27 @@ class TraceQueueManager:
     def send_to_celery(self, tasks: list[TraceTask]):
         with self.flask_app.app_context():
             for task in tasks:
-                if task.app_id is None:
-                    continue
+                storage_id = task.app_id
+                if storage_id is None:
+                    tenant_id = task.kwargs.get("tenant_id")
+                    if tenant_id:
+                        storage_id = f"tenant-{tenant_id}"
+                    else:
+                        logger.warning("Skipping trace without app_id or tenant_id, trace_type: %s", task.trace_type)
+                        continue
+
                 file_id = uuid4().hex
                 trace_info = task.execute()
 
                 task_data = TaskData(
-                    app_id=task.app_id,
+                    app_id=storage_id,
                     trace_info_type=type(trace_info).__name__,
                     trace_info=trace_info.model_dump() if trace_info else None,
                 )
-                file_path = f"{OPS_FILE_PATH}{task.app_id}/{file_id}.json"
+                file_path = f"{OPS_FILE_PATH}{storage_id}/{file_id}.json"
                 storage.save(file_path, task_data.model_dump_json().encode("utf-8"))
                 file_info = {
                     "file_id": file_id,
-                    "app_id": task.app_id,
+                    "app_id": storage_id,
                 }
                 process_trace_tasks.delay(file_info)  # type: ignore

api/core/rag/datasource/vdb/weaviate/weaviate_vector.py

@@ -5,7 +5,6 @@ This module provides integration with Weaviate vector database for storing and r
 document embeddings used in retrieval-augmented generation workflows.
 """
 
-import atexit
 import datetime
 import json
 import logging
@@ -38,32 +37,6 @@ _weaviate_client: weaviate.WeaviateClient | None = None
 _weaviate_client_lock = threading.Lock()
 
 
-def _shutdown_weaviate_client() -> None:
-    """
-    Best-effort shutdown hook to close the module-level Weaviate client.
-
-    This is registered with atexit so that HTTP/gRPC resources are released
-    when the Python interpreter exits.
-    """
-    global _weaviate_client
-
-    # Ensure thread-safety when accessing the shared client instance
-    with _weaviate_client_lock:
-        client = _weaviate_client
-        _weaviate_client = None
-
-    if client is not None:
-        try:
-            client.close()
-        except Exception:
-            # Best-effort cleanup; log at debug level and ignore errors.
-            logger.debug("Failed to close Weaviate client during shutdown", exc_info=True)
-
-
-# Register the shutdown hook once per process.
-atexit.register(_shutdown_weaviate_client)
-
-
 class WeaviateConfig(BaseModel):
     """
     Configuration model for Weaviate connection settings.
@@ -112,6 +85,18 @@ class WeaviateVector(BaseVector):
         self._client = self._init_client(config)
         self._attributes = attributes
 
+    def __del__(self):
+        """
+        Destructor to properly close the Weaviate client connection.
+        Prevents connection leaks and resource warnings.
+        """
+        if hasattr(self, "_client") and self._client is not None:
+            try:
+                self._client.close()
+            except Exception as e:
+                # Ignore errors during cleanup as object is being destroyed
+                logger.warning("Error closing Weaviate client %s", e, exc_info=True)
+
     def _init_client(self, config: WeaviateConfig) -> weaviate.WeaviateClient:
         """
         Initializes and returns a connected Weaviate client.

api/core/rag/index_processor/index_processor.py

@@ -9,7 +9,6 @@ from flask import current_app
 from sqlalchemy import delete, func, select
 
 from core.db.session_factory import session_factory
-from core.rag.index_processor.index_processor_base import SummaryIndexSettingDict
 from core.workflow.nodes.knowledge_index.exc import KnowledgeIndexNodeError
 from core.workflow.nodes.knowledge_index.protocols import Preview, PreviewItem, QaPreview
 from models.dataset import Dataset, Document, DocumentSegment
@@ -52,7 +51,7 @@ class IndexProcessor:
         original_document_id: str,
         chunks: Mapping[str, Any],
         batch: Any,
-        summary_index_setting: SummaryIndexSettingDict | None = None,
+        summary_index_setting: dict | None = None,
     ):
         with session_factory.create_session() as session:
             document = session.query(Document).filter_by(id=document_id).first()
@@ -132,12 +131,7 @@ class IndexProcessor:
         }
 
     def get_preview_output(
-        self,
-        chunks: Any,
-        dataset_id: str,
-        document_id: str,
-        chunk_structure: str,
-        summary_index_setting: SummaryIndexSettingDict | None,
+        self, chunks: Any, dataset_id: str, document_id: str, chunk_structure: str, summary_index_setting: dict | None
     ) -> Preview:
         doc_language = None
         with session_factory.create_session() as session:

api/core/rag/index_processor/index_processor_base.py

@@ -7,11 +7,10 @@ import os
 import re
 from abc import ABC, abstractmethod
 from collections.abc import Mapping
-from typing import TYPE_CHECKING, Any, NotRequired, Optional
+from typing import TYPE_CHECKING, Any, Optional
 from urllib.parse import unquote, urlparse
 
 import httpx
-from typing_extensions import TypedDict
 
 from configs import dify_config
 from core.entities.knowledge_entities import PreviewDetail
@@ -37,13 +36,6 @@ if TYPE_CHECKING:
     from core.model_manager import ModelInstance
 
 
-class SummaryIndexSettingDict(TypedDict):
-    enable: bool
-    model_name: NotRequired[str]
-    model_provider_name: NotRequired[str]
-    summary_prompt: NotRequired[str]
-
-
 class BaseIndexProcessor(ABC):
     """Interface for extract files."""
 
@@ -60,7 +52,7 @@ class BaseIndexProcessor(ABC):
         self,
         tenant_id: str,
         preview_texts: list[PreviewDetail],
-        summary_index_setting: SummaryIndexSettingDict,
+        summary_index_setting: dict,
         doc_language: str | None = None,
     ) -> list[PreviewDetail]:
         """

api/core/rag/index_processor/processor/paragraph_index_processor.py

@@ -23,7 +23,7 @@ from core.rag.extractor.entity.extract_setting import ExtractSetting
 from core.rag.extractor.extract_processor import ExtractProcessor
 from core.rag.index_processor.constant.doc_type import DocType
 from core.rag.index_processor.constant.index_type import IndexStructureType
-from core.rag.index_processor.index_processor_base import BaseIndexProcessor, SummaryIndexSettingDict
+from core.rag.index_processor.index_processor_base import BaseIndexProcessor
 from core.rag.models.document import AttachmentDocument, Document, MultimodalGeneralStructureChunk
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.utils.text_processing_utils import remove_leading_symbols
@@ -279,7 +279,7 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
         self,
         tenant_id: str,
         preview_texts: list[PreviewDetail],
-        summary_index_setting: SummaryIndexSettingDict,
+        summary_index_setting: dict,
         doc_language: str | None = None,
     ) -> list[PreviewDetail]:
         """
@@ -363,7 +363,7 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
     def generate_summary(
         tenant_id: str,
         text: str,
-        summary_index_setting: SummaryIndexSettingDict | None = None,
+        summary_index_setting: dict | None = None,
         segment_id: str | None = None,
         document_language: str | None = None,
     ) -> tuple[str, LLMUsage]:

api/core/rag/index_processor/processor/parent_child_index_processor.py

@@ -19,7 +19,7 @@ from core.rag.extractor.entity.extract_setting import ExtractSetting
 from core.rag.extractor.extract_processor import ExtractProcessor
 from core.rag.index_processor.constant.doc_type import DocType
 from core.rag.index_processor.constant.index_type import IndexStructureType
-from core.rag.index_processor.index_processor_base import BaseIndexProcessor, SummaryIndexSettingDict
+from core.rag.index_processor.index_processor_base import BaseIndexProcessor
 from core.rag.models.document import AttachmentDocument, ChildDocument, Document, ParentChildStructureChunk
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from extensions.ext_database import db
@@ -362,7 +362,7 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
         self,
         tenant_id: str,
         preview_texts: list[PreviewDetail],
-        summary_index_setting: SummaryIndexSettingDict,
+        summary_index_setting: dict,
         doc_language: str | None = None,
     ) -> list[PreviewDetail]:
         """

api/core/rag/index_processor/processor/qa_index_processor.py

@@ -22,7 +22,7 @@ from core.rag.docstore.dataset_docstore import DatasetDocumentStore
 from core.rag.extractor.entity.extract_setting import ExtractSetting
 from core.rag.extractor.extract_processor import ExtractProcessor
 from core.rag.index_processor.constant.index_type import IndexStructureType
-from core.rag.index_processor.index_processor_base import BaseIndexProcessor, SummaryIndexSettingDict
+from core.rag.index_processor.index_processor_base import BaseIndexProcessor
 from core.rag.models.document import AttachmentDocument, Document, QAStructureChunk
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.utils.text_processing_utils import remove_leading_symbols
@@ -245,7 +245,7 @@ class QAIndexProcessor(BaseIndexProcessor):
         self,
         tenant_id: str,
         preview_texts: list[PreviewDetail],
-        summary_index_setting: SummaryIndexSettingDict,
+        summary_index_setting: dict,
         doc_language: str | None = None,
     ) -> list[PreviewDetail]:
         """

api/core/rag/summary_index/summary_index.py

@@ -2,7 +2,6 @@ import concurrent.futures
 import logging
 
 from core.db.session_factory import session_factory
-from core.rag.index_processor.index_processor_base import SummaryIndexSettingDict
 from models.dataset import Dataset, Document, DocumentSegment, DocumentSegmentSummary
 from services.summary_index_service import SummaryIndexService
 from tasks.generate_summary_index_task import generate_summary_index_task
@@ -12,11 +11,7 @@ logger = logging.getLogger(__name__)
 
 class SummaryIndex:
     def generate_and_vectorize_summary(
-        self,
-        dataset_id: str,
-        document_id: str,
-        is_preview: bool,
-        summary_index_setting: SummaryIndexSettingDict | None = None,
+        self, dataset_id: str, document_id: str, is_preview: bool, summary_index_setting: dict | None = None
     ) -> None:
         if is_preview:
             with session_factory.create_session() as session:

api/core/telemetry/__init__.py

@@ -0,0 +1,43 @@
+"""Telemetry facade.
+
+Thin public API for emitting telemetry events.  All routing logic
+lives in ``core.telemetry.gateway`` which is shared by both CE and EE.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from core.ops.entities.trace_entity import TraceTaskName
+from core.telemetry.events import TelemetryContext, TelemetryEvent
+from core.telemetry.gateway import emit as gateway_emit
+from core.telemetry.gateway import get_trace_task_to_case
+
+if TYPE_CHECKING:
+    from core.ops.ops_trace_manager import TraceQueueManager
+
+
+def emit(event: TelemetryEvent, trace_manager: TraceQueueManager | None = None) -> None:
+    """Emit a telemetry event.
+
+    Translates the ``TelemetryEvent`` (keyed by ``TraceTaskName``) into a
+    ``TelemetryCase`` and delegates to ``core.telemetry.gateway.emit()``.
+    """
+    case = get_trace_task_to_case().get(event.name)
+    if case is None:
+        return
+
+    context: dict[str, object] = {
+        "tenant_id": event.context.tenant_id,
+        "user_id": event.context.user_id,
+        "app_id": event.context.app_id,
+    }
+    gateway_emit(case, context, event.payload, trace_manager)
+
+
+__all__ = [
+    "TelemetryContext",
+    "TelemetryEvent",
+    "TraceTaskName",
+    "emit",
+]

api/core/telemetry/events.py

@@ -0,0 +1,21 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from core.ops.entities.trace_entity import TraceTaskName
+
+
+@dataclass(frozen=True)
+class TelemetryContext:
+    tenant_id: str | None = None
+    user_id: str | None = None
+    app_id: str | None = None
+
+
+@dataclass(frozen=True)
+class TelemetryEvent:
+    name: TraceTaskName
+    context: TelemetryContext
+    payload: dict[str, Any]

api/core/telemetry/gateway.py

@@ -0,0 +1,239 @@
+"""Telemetry gateway — single routing layer for all editions.
+
+Maps ``TelemetryCase`` → ``CaseRoute`` and dispatches events to either
+the CE/EE trace pipeline (``TraceQueueManager``) or the enterprise-only
+metric/log Celery queue.
+
+This module lives in ``core/`` so both CE and EE share one routing table
+and one ``emit()`` entry point.  No separate enterprise gateway module is
+needed — enterprise-specific dispatch (Celery task, payload offloading)
+is handled here behind lazy imports that no-op in CE.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import uuid
+from typing import TYPE_CHECKING, Any
+
+from core.ops.entities.trace_entity import TraceTaskName
+from enterprise.telemetry.contracts import SignalType
+from extensions.ext_storage import storage
+
+if TYPE_CHECKING:
+    from core.ops.ops_trace_manager import TraceQueueManager
+    from enterprise.telemetry.contracts import TelemetryCase
+
+logger = logging.getLogger(__name__)
+
+PAYLOAD_SIZE_THRESHOLD_BYTES = 1 * 1024 * 1024
+
+# ---------------------------------------------------------------------------
+# Routing table — authoritative mapping for all editions
+# ---------------------------------------------------------------------------
+
+_case_to_trace_task: dict | None = None
+_case_routing: dict | None = None
+
+
+def _get_case_to_trace_task() -> dict:
+    global _case_to_trace_task
+    if _case_to_trace_task is None:
+        from enterprise.telemetry.contracts import TelemetryCase
+
+        _case_to_trace_task = {
+            TelemetryCase.WORKFLOW_RUN: TraceTaskName.WORKFLOW_TRACE,
+            TelemetryCase.MESSAGE_RUN: TraceTaskName.MESSAGE_TRACE,
+            TelemetryCase.NODE_EXECUTION: TraceTaskName.NODE_EXECUTION_TRACE,
+            TelemetryCase.DRAFT_NODE_EXECUTION: TraceTaskName.DRAFT_NODE_EXECUTION_TRACE,
+            TelemetryCase.PROMPT_GENERATION: TraceTaskName.PROMPT_GENERATION_TRACE,
+            TelemetryCase.TOOL_EXECUTION: TraceTaskName.TOOL_TRACE,
+            TelemetryCase.MODERATION_CHECK: TraceTaskName.MODERATION_TRACE,
+            TelemetryCase.SUGGESTED_QUESTION: TraceTaskName.SUGGESTED_QUESTION_TRACE,
+            TelemetryCase.DATASET_RETRIEVAL: TraceTaskName.DATASET_RETRIEVAL_TRACE,
+            TelemetryCase.GENERATE_NAME: TraceTaskName.GENERATE_NAME_TRACE,
+        }
+    return _case_to_trace_task
+
+
+def get_trace_task_to_case() -> dict:
+    """Return TraceTaskName → TelemetryCase (inverse of _get_case_to_trace_task)."""
+    return {v: k for k, v in _get_case_to_trace_task().items()}
+
+
+def _get_case_routing() -> dict:
+    global _case_routing
+    if _case_routing is None:
+        from enterprise.telemetry.contracts import CaseRoute, SignalType, TelemetryCase
+
+        _case_routing = {
+            # TRACE — CE-eligible (flow in both CE and EE)
+            TelemetryCase.WORKFLOW_RUN: CaseRoute(signal_type=SignalType.TRACE, ce_eligible=True),
+            TelemetryCase.MESSAGE_RUN: CaseRoute(signal_type=SignalType.TRACE, ce_eligible=True),
+            TelemetryCase.TOOL_EXECUTION: CaseRoute(signal_type=SignalType.TRACE, ce_eligible=True),
+            TelemetryCase.MODERATION_CHECK: CaseRoute(signal_type=SignalType.TRACE, ce_eligible=True),
+            TelemetryCase.SUGGESTED_QUESTION: CaseRoute(signal_type=SignalType.TRACE, ce_eligible=True),
+            TelemetryCase.DATASET_RETRIEVAL: CaseRoute(signal_type=SignalType.TRACE, ce_eligible=True),
+            TelemetryCase.GENERATE_NAME: CaseRoute(signal_type=SignalType.TRACE, ce_eligible=True),
+            # TRACE — enterprise-only
+            TelemetryCase.NODE_EXECUTION: CaseRoute(signal_type=SignalType.TRACE, ce_eligible=False),
+            TelemetryCase.DRAFT_NODE_EXECUTION: CaseRoute(signal_type=SignalType.TRACE, ce_eligible=False),
+            TelemetryCase.PROMPT_GENERATION: CaseRoute(signal_type=SignalType.TRACE, ce_eligible=False),
+            # METRIC_LOG — enterprise-only (signal-driven, not trace)
+            TelemetryCase.APP_CREATED: CaseRoute(signal_type=SignalType.METRIC_LOG, ce_eligible=False),
+            TelemetryCase.APP_UPDATED: CaseRoute(signal_type=SignalType.METRIC_LOG, ce_eligible=False),
+            TelemetryCase.APP_DELETED: CaseRoute(signal_type=SignalType.METRIC_LOG, ce_eligible=False),
+            TelemetryCase.FEEDBACK_CREATED: CaseRoute(signal_type=SignalType.METRIC_LOG, ce_eligible=False),
+        }
+    return _case_routing
+
+
+def __getattr__(name: str) -> dict:
+    """Lazy module-level access to routing tables."""
+    if name == "CASE_ROUTING":
+        return _get_case_routing()
+    if name == "CASE_TO_TRACE_TASK":
+        return _get_case_to_trace_task()
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def is_enterprise_telemetry_enabled() -> bool:
+    try:
+        from enterprise.telemetry.exporter import is_enterprise_telemetry_enabled
+
+        return is_enterprise_telemetry_enabled()
+    except Exception:
+        return False
+
+
+def _handle_payload_sizing(
+    payload: dict[str, Any],
+    tenant_id: str,
+    event_id: str,
+) -> tuple[dict[str, Any], str | None]:
+    """Inline or offload payload based on size.
+
+    Returns ``(payload_for_envelope, storage_key | None)``.  Payloads
+    exceeding ``PAYLOAD_SIZE_THRESHOLD_BYTES`` are written to object
+    storage and replaced with an empty dict in the envelope.
+    """
+    try:
+        payload_json = json.dumps(payload)
+        payload_size = len(payload_json.encode("utf-8"))
+    except (TypeError, ValueError):
+        logger.warning("Failed to serialize payload for sizing: event_id=%s", event_id)
+        return payload, None
+
+    if payload_size <= PAYLOAD_SIZE_THRESHOLD_BYTES:
+        return payload, None
+
+    storage_key = f"telemetry/{tenant_id}/{event_id}.json"
+    try:
+        storage.save(storage_key, payload_json.encode("utf-8"))
+        logger.debug("Stored large payload to storage: key=%s, size=%d", storage_key, payload_size)
+        return {}, storage_key
+    except Exception:
+        logger.warning("Failed to store large payload, inlining instead: event_id=%s", event_id, exc_info=True)
+        return payload, None
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+
+def emit(
+    case: TelemetryCase,
+    context: dict[str, Any],
+    payload: dict[str, Any],
+    trace_manager: TraceQueueManager | None = None,
+) -> None:
+    """Route a telemetry event to the correct pipeline.
+
+    TRACE events are enqueued into ``TraceQueueManager`` (works in both CE
+    and EE).  Enterprise-only traces are silently dropped when EE is
+    disabled.
+
+    METRIC_LOG events are dispatched to the enterprise Celery queue;
+    silently dropped when enterprise telemetry is unavailable.
+    """
+    route = _get_case_routing().get(case)
+    if route is None:
+        logger.warning("Unknown telemetry case: %s, dropping event", case)
+        return
+
+    if not route.ce_eligible and not is_enterprise_telemetry_enabled():
+        logger.debug("Dropping EE-only event: case=%s (EE disabled)", case)
+        return
+
+    if route.signal_type == SignalType.TRACE:
+        _emit_trace(case, context, payload, trace_manager)
+    else:
+        _emit_metric_log(case, context, payload)
+
+
+def _emit_trace(
+    case: TelemetryCase,
+    context: dict[str, Any],
+    payload: dict[str, Any],
+    trace_manager: TraceQueueManager | None,
+) -> None:
+    from core.ops.ops_trace_manager import TraceQueueManager as LocalTraceQueueManager
+    from core.ops.ops_trace_manager import TraceTask
+
+    trace_task_name = _get_case_to_trace_task().get(case)
+    if trace_task_name is None:
+        logger.warning("No TraceTaskName mapping for case: %s", case)
+        return
+
+    queue_manager = trace_manager or LocalTraceQueueManager(
+        app_id=context.get("app_id"),
+        user_id=context.get("user_id"),
+    )
+    queue_manager.add_trace_task(TraceTask(trace_task_name, user_id=context.get("user_id"), **payload))
+    logger.debug("Enqueued trace task: case=%s, app_id=%s", case, context.get("app_id"))
+
+
+def _emit_metric_log(
+    case: TelemetryCase,
+    context: dict[str, Any],
+    payload: dict[str, Any],
+) -> None:
+    """Build envelope and dispatch to enterprise Celery queue.
+
+    No-ops when the enterprise telemetry task is not importable (CE mode).
+    """
+    try:
+        from tasks.enterprise_telemetry_task import process_enterprise_telemetry
+    except ImportError:
+        logger.debug("Enterprise metric/log dispatch unavailable, dropping: case=%s", case)
+        return
+
+    tenant_id = context.get("tenant_id") or ""
+    event_id = str(uuid.uuid4())
+
+    payload_for_envelope, payload_ref = _handle_payload_sizing(payload, tenant_id, event_id)
+
+    from enterprise.telemetry.contracts import TelemetryEnvelope
+
+    envelope = TelemetryEnvelope(
+        case=case,
+        tenant_id=tenant_id,
+        event_id=event_id,
+        payload=payload_for_envelope,
+        metadata={"payload_ref": payload_ref} if payload_ref else None,
+    )
+
+    process_enterprise_telemetry.delay(envelope.model_dump_json())
+    logger.debug(
+        "Enqueued metric/log event: case=%s, tenant_id=%s, event_id=%s",
+        case,
+        tenant_id,
+        event_id,
+    )

api/core/tools/tool_manager.py

@@ -1045,10 +1045,9 @@ class ToolManager:
                         continue
                     tool_input = ToolNodeData.ToolInput.model_validate(tool_configurations.get(parameter.name, {}))
                     if tool_input.type == "variable":
-                        variable_selector = tool_input.require_variable_selector()
-                        variable = variable_pool.get(variable_selector)
+                        variable = variable_pool.get(tool_input.value)
                         if variable is None:
-                            raise ToolParameterError(f"Variable {variable_selector} does not exist")
+                            raise ToolParameterError(f"Variable {tool_input.value} does not exist")
                         parameter_value = variable.value
                     elif tool_input.type == "constant":
                         parameter_value = tool_input.value

api/core/workflow/nodes/agent/entities.py

@@ -1,24 +1,13 @@
-from __future__ import annotations
-
 from enum import IntEnum, StrEnum, auto
-from typing import Literal, TypeAlias
+from typing import Any, Literal, Union
 
-from pydantic import BaseModel, TypeAdapter, field_validator
-from pydantic_core.core_schema import ValidationInfo
+from pydantic import BaseModel
 
 from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 from core.tools.entities.tool_entities import ToolSelector
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import BuiltinNodeTypes, NodeType
 
-AgentInputConstantValue: TypeAlias = (
-    list[ToolSelector] | str | int | float | bool | dict[str, object] | list[object] | None
-)
-VariableSelector: TypeAlias = list[str]
-
-_AGENT_INPUT_VALUE_ADAPTER: TypeAdapter[AgentInputConstantValue] = TypeAdapter(AgentInputConstantValue)
-_AGENT_VARIABLE_SELECTOR_ADAPTER: TypeAdapter[VariableSelector] = TypeAdapter(VariableSelector)
-
 
 class AgentNodeData(BaseNodeData):
     type: NodeType = BuiltinNodeTypes.AGENT
@@ -32,20 +21,8 @@ class AgentNodeData(BaseNodeData):
     tool_node_version: str | None = None
 
     class AgentInput(BaseModel):
+        value: Union[list[str], list[ToolSelector], Any]
         type: Literal["mixed", "variable", "constant"]
-        value: AgentInputConstantValue | VariableSelector
-
-        @field_validator("value", mode="before")
-        @classmethod
-        def validate_value(
-            cls, value: object, validation_info: ValidationInfo
-        ) -> AgentInputConstantValue | VariableSelector:
-            input_type = validation_info.data.get("type")
-            if input_type == "variable":
-                return _AGENT_VARIABLE_SELECTOR_ADAPTER.validate_python(value)
-            if input_type in {"mixed", "constant"}:
-                return _AGENT_INPUT_VALUE_ADAPTER.validate_python(value)
-            raise ValueError(f"Unknown agent input type: {input_type}")
 
     agent_parameters: dict[str, AgentInput]
 

api/core/workflow/nodes/agent/runtime_support.py

@@ -1,17 +1,16 @@
 from __future__ import annotations
 
 import json
-from collections.abc import Mapping, Sequence
-from typing import TypeAlias
+from collections.abc import Sequence
+from typing import Any, cast
 
 from packaging.version import Version
-from pydantic import TypeAdapter, ValidationError
+from pydantic import ValidationError
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 
 from core.agent.entities import AgentToolEntity
 from core.agent.plugin_entities import AgentStrategyParameter
-from core.app.entities.app_invoke_entities import InvokeFrom
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance, ModelManager
 from core.plugin.entities.request import InvokeCredentials
@@ -29,14 +28,6 @@ from .entities import AgentNodeData, AgentOldVersionModelFeatures, ParamsAutoGen
 from .exceptions import AgentInputTypeError, AgentVariableNotFoundError
 from .strategy_protocols import ResolvedAgentStrategy
 
-JsonObject: TypeAlias = dict[str, object]
-JsonObjectList: TypeAlias = list[JsonObject]
-VariableSelector: TypeAlias = list[str]
-
-_JSON_OBJECT_ADAPTER = TypeAdapter(JsonObject)
-_JSON_OBJECT_LIST_ADAPTER = TypeAdapter(JsonObjectList)
-_VARIABLE_SELECTOR_ADAPTER = TypeAdapter(VariableSelector)
-
 
 class AgentRuntimeSupport:
     def build_parameters(
@@ -48,12 +39,12 @@ class AgentRuntimeSupport:
         strategy: ResolvedAgentStrategy,
         tenant_id: str,
         app_id: str,
-        invoke_from: InvokeFrom,
+        invoke_from: Any,
         for_log: bool = False,
-    ) -> dict[str, object]:
+    ) -> dict[str, Any]:
         agent_parameters_dictionary = {parameter.name: parameter for parameter in agent_parameters}
 
-        result: dict[str, object] = {}
+        result: dict[str, Any] = {}
         for parameter_name in node_data.agent_parameters:
             parameter = agent_parameters_dictionary.get(parameter_name)
             if not parameter:
@@ -63,10 +54,9 @@ class AgentRuntimeSupport:
             agent_input = node_data.agent_parameters[parameter_name]
             match agent_input.type:
                 case "variable":
-                    variable_selector = _VARIABLE_SELECTOR_ADAPTER.validate_python(agent_input.value)
-                    variable = variable_pool.get(variable_selector)
+                    variable = variable_pool.get(agent_input.value)  # type: ignore[arg-type]
                     if variable is None:
-                        raise AgentVariableNotFoundError(str(variable_selector))
+                        raise AgentVariableNotFoundError(str(agent_input.value))
                     parameter_value = variable.value
                 case "mixed" | "constant":
                     try:
@@ -89,38 +79,60 @@ class AgentRuntimeSupport:
 
             value = parameter_value
             if parameter.type == "array[tools]":
-                tool_payloads = _JSON_OBJECT_LIST_ADAPTER.validate_python(value)
-                value = self._normalize_tool_payloads(
-                    strategy=strategy,
-                    tools=tool_payloads,
-                    variable_pool=variable_pool,
-                )
+                value = cast(list[dict[str, Any]], value)
+                value = [tool for tool in value if tool.get("enabled", False)]
+                value = self._filter_mcp_type_tool(strategy, value)
+                for tool in value:
+                    if "schemas" in tool:
+                        tool.pop("schemas")
+                    parameters = tool.get("parameters", {})
+                    if all(isinstance(v, dict) for _, v in parameters.items()):
+                        params = {}
+                        for key, param in parameters.items():
+                            if param.get("auto", ParamsAutoGenerated.OPEN) in (
+                                ParamsAutoGenerated.CLOSE,
+                                0,
+                            ):
+                                value_param = param.get("value", {})
+                                if value_param and value_param.get("type", "") == "variable":
+                                    variable_selector = value_param.get("value")
+                                    if not variable_selector:
+                                        raise ValueError("Variable selector is missing for a variable-type parameter.")
+
+                                    variable = variable_pool.get(variable_selector)
+                                    if variable is None:
+                                        raise AgentVariableNotFoundError(str(variable_selector))
+
+                                    params[key] = variable.value
+                                else:
+                                    params[key] = value_param.get("value", "") if value_param is not None else None
+                            else:
+                                params[key] = None
+                        parameters = params
+                    tool["settings"] = {k: v.get("value", None) for k, v in tool.get("settings", {}).items()}
+                    tool["parameters"] = parameters
 
             if not for_log:
                 if parameter.type == "array[tools]":
-                    value = _JSON_OBJECT_LIST_ADAPTER.validate_python(value)
+                    value = cast(list[dict[str, Any]], value)
                     tool_value = []
                     for tool in value:
-                        provider_type = self._coerce_tool_provider_type(tool.get("type"))
-                        setting_params = self._coerce_json_object(tool.get("settings")) or {}
-                        parameters = self._coerce_json_object(tool.get("parameters")) or {}
+                        provider_type = ToolProviderType(tool.get("type", ToolProviderType.BUILT_IN))
+                        setting_params = tool.get("settings", {})
+                        parameters = tool.get("parameters", {})
                         manual_input_params = [key for key, value in parameters.items() if value is not None]
 
                         parameters = {**parameters, **setting_params}
-                        provider_id = self._coerce_optional_string(tool.get("provider_name")) or ""
-                        tool_name = self._coerce_optional_string(tool.get("tool_name")) or ""
-                        plugin_unique_identifier = self._coerce_optional_string(tool.get("plugin_unique_identifier"))
-                        credential_id = self._coerce_optional_string(tool.get("credential_id"))
                         entity = AgentToolEntity(
-                            provider_id=provider_id,
+                            provider_id=tool.get("provider_name", ""),
                             provider_type=provider_type,
-                            tool_name=tool_name,
+                            tool_name=tool.get("tool_name", ""),
                             tool_parameters=parameters,
-                            plugin_unique_identifier=plugin_unique_identifier,
-                            credential_id=credential_id,
+                            plugin_unique_identifier=tool.get("plugin_unique_identifier", None),
+                            credential_id=tool.get("credential_id", None),
                         )
 
-                        extra = self._coerce_json_object(tool.get("extra")) or {}
+                        extra = tool.get("extra", {})
 
                         runtime_variable_pool: VariablePool | None = None
                         if node_data.version != "1" or node_data.tool_node_version is not None:
@@ -133,9 +145,8 @@ class AgentRuntimeSupport:
                             runtime_variable_pool,
                         )
                         if tool_runtime.entity.description:
-                            description_override = self._coerce_optional_string(extra.get("description"))
                             tool_runtime.entity.description.llm = (
-                                description_override or tool_runtime.entity.description.llm
+                                extra.get("description", "") or tool_runtime.entity.description.llm
                             )
                         for tool_runtime_params in tool_runtime.entity.parameters:
                             tool_runtime_params.form = (
@@ -156,13 +167,13 @@ class AgentRuntimeSupport:
                             {
                                 **tool_runtime.entity.model_dump(mode="json"),
                                 "runtime_parameters": runtime_parameters,
-                                "credential_id": credential_id,
+                                "credential_id": tool.get("credential_id", None),
                                 "provider_type": provider_type.value,
                             }
                         )
                     value = tool_value
                 if parameter.type == AgentStrategyParameter.AgentStrategyParameterType.MODEL_SELECTOR:
-                    value = _JSON_OBJECT_ADAPTER.validate_python(value)
+                    value = cast(dict[str, Any], value)
                     model_instance, model_schema = self.fetch_model(tenant_id=tenant_id, value=value)
                     history_prompt_messages = []
                     if node_data.memory:
@@ -188,27 +199,17 @@ class AgentRuntimeSupport:
 
         return result
 
-    def build_credentials(self, *, parameters: Mapping[str, object]) -> InvokeCredentials:
+    def build_credentials(self, *, parameters: dict[str, Any]) -> InvokeCredentials:
         credentials = InvokeCredentials()
         credentials.tool_credentials = {}
-        tools = parameters.get("tools")
-        if not isinstance(tools, list):
-            return credentials
-
-        for raw_tool in tools:
-            tool = self._coerce_json_object(raw_tool)
-            if tool is None:
-                continue
+        for tool in parameters.get("tools", []):
             if not tool.get("credential_id"):
                 continue
             try:
                 identity = ToolIdentity.model_validate(tool.get("identity", {}))
             except ValidationError:
                 continue
-            credential_id = self._coerce_optional_string(tool.get("credential_id"))
-            if credential_id is None:
-                continue
-            credentials.tool_credentials[identity.provider] = credential_id
+            credentials.tool_credentials[identity.provider] = tool.get("credential_id", None)
         return credentials
 
     def fetch_memory(
@@ -231,14 +232,14 @@ class AgentRuntimeSupport:
 
         return TokenBufferMemory(conversation=conversation, model_instance=model_instance)
 
-    def fetch_model(self, *, tenant_id: str, value: Mapping[str, object]) -> tuple[ModelInstance, AIModelEntity | None]:
+    def fetch_model(self, *, tenant_id: str, value: dict[str, Any]) -> tuple[ModelInstance, AIModelEntity | None]:
         provider_manager = ProviderManager()
         provider_model_bundle = provider_manager.get_provider_model_bundle(
             tenant_id=tenant_id,
-            provider=str(value.get("provider", "")),
+            provider=value.get("provider", ""),
             model_type=ModelType.LLM,
         )
-        model_name = str(value.get("model", ""))
+        model_name = value.get("model", "")
         model_credentials = provider_model_bundle.configuration.get_current_credentials(
             model_type=ModelType.LLM,
             model=model_name,
@@ -248,7 +249,7 @@ class AgentRuntimeSupport:
         model_instance = ModelManager().get_model_instance(
             tenant_id=tenant_id,
             provider=provider_name,
-            model_type=ModelType(str(value.get("model_type", ""))),
+            model_type=ModelType(value.get("model_type", "")),
             model=model_name,
         )
         model_schema = model_type_instance.get_model_schema(model_name, model_credentials)
@@ -267,88 +268,9 @@ class AgentRuntimeSupport:
     @staticmethod
     def _filter_mcp_type_tool(
         strategy: ResolvedAgentStrategy,
-        tools: JsonObjectList,
-    ) -> JsonObjectList:
+        tools: list[dict[str, Any]],
+    ) -> list[dict[str, Any]]:
         meta_version = strategy.meta_version
         if meta_version and Version(meta_version) > Version("0.0.1"):
             return tools
         return [tool for tool in tools if tool.get("type") != ToolProviderType.MCP]
-
-    def _normalize_tool_payloads(
-        self,
-        *,
-        strategy: ResolvedAgentStrategy,
-        tools: JsonObjectList,
-        variable_pool: VariablePool,
-    ) -> JsonObjectList:
-        enabled_tools = [dict(tool) for tool in tools if bool(tool.get("enabled", False))]
-        normalized_tools = self._filter_mcp_type_tool(strategy, enabled_tools)
-        for tool in normalized_tools:
-            tool.pop("schemas", None)
-            tool["parameters"] = self._resolve_tool_parameters(tool=tool, variable_pool=variable_pool)
-            tool["settings"] = self._resolve_tool_settings(tool)
-        return normalized_tools
-
-    def _resolve_tool_parameters(self, *, tool: Mapping[str, object], variable_pool: VariablePool) -> JsonObject:
-        parameter_configs = self._coerce_named_json_objects(tool.get("parameters"))
-        if parameter_configs is None:
-            raw_parameters = self._coerce_json_object(tool.get("parameters"))
-            return raw_parameters or {}
-
-        resolved_parameters: JsonObject = {}
-        for key, parameter_config in parameter_configs.items():
-            if parameter_config.get("auto", ParamsAutoGenerated.OPEN) in (ParamsAutoGenerated.CLOSE, 0):
-                value_param = self._coerce_json_object(parameter_config.get("value"))
-                if value_param and value_param.get("type") == "variable":
-                    variable_selector = _VARIABLE_SELECTOR_ADAPTER.validate_python(value_param.get("value"))
-                    variable = variable_pool.get(variable_selector)
-                    if variable is None:
-                        raise AgentVariableNotFoundError(str(variable_selector))
-                    resolved_parameters[key] = variable.value
-                else:
-                    resolved_parameters[key] = value_param.get("value", "") if value_param is not None else None
-            else:
-                resolved_parameters[key] = None
-
-        return resolved_parameters
-
-    @staticmethod
-    def _resolve_tool_settings(tool: Mapping[str, object]) -> JsonObject:
-        settings = AgentRuntimeSupport._coerce_named_json_objects(tool.get("settings"))
-        if settings is None:
-            return {}
-        return {key: setting.get("value") for key, setting in settings.items()}
-
-    @staticmethod
-    def _coerce_json_object(value: object) -> JsonObject | None:
-        try:
-            return _JSON_OBJECT_ADAPTER.validate_python(value)
-        except ValidationError:
-            return None
-
-    @staticmethod
-    def _coerce_optional_string(value: object) -> str | None:
-        return value if isinstance(value, str) else None
-
-    @staticmethod
-    def _coerce_tool_provider_type(value: object) -> ToolProviderType:
-        if isinstance(value, ToolProviderType):
-            return value
-        if isinstance(value, str):
-            return ToolProviderType(value)
-        return ToolProviderType.BUILT_IN
-
-    @classmethod
-    def _coerce_named_json_objects(cls, value: object) -> dict[str, JsonObject] | None:
-        if not isinstance(value, dict):
-            return None
-
-        coerced: dict[str, JsonObject] = {}
-        for key, item in value.items():
-            if not isinstance(key, str):
-                return None
-            json_object = cls._coerce_json_object(item)
-            if json_object is None:
-                return None
-            coerced[key] = json_object
-        return coerced

api/core/workflow/nodes/knowledge_index/entities.py

@@ -2,7 +2,6 @@ from typing import Literal, Union
 
 from pydantic import BaseModel
 
-from core.rag.index_processor.index_processor_base import SummaryIndexSettingDict
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.workflow.nodes.knowledge_index import KNOWLEDGE_INDEX_NODE_TYPE
 from dify_graph.entities.base_node_data import BaseNodeData
@@ -162,4 +161,4 @@ class KnowledgeIndexNodeData(BaseNodeData):
     chunk_structure: str
     index_chunk_variable_selector: list[str]
     indexing_technique: str | None = None
-    summary_index_setting: SummaryIndexSettingDict | None = None
+    summary_index_setting: dict | None = None

api/core/workflow/nodes/knowledge_index/knowledge_index_node.py

@@ -3,7 +3,6 @@ from collections.abc import Mapping
 from typing import TYPE_CHECKING, Any
 
 from core.rag.index_processor.index_processor import IndexProcessor
-from core.rag.index_processor.index_processor_base import SummaryIndexSettingDict
 from core.rag.summary_index.summary_index import SummaryIndex
 from core.workflow.nodes.knowledge_index import KNOWLEDGE_INDEX_NODE_TYPE
 from dify_graph.entities.graph_config import NodeConfigDict
@@ -128,7 +127,7 @@ class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
         is_preview: bool,
         batch: Any,
         chunks: Mapping[str, Any],
-        summary_index_setting: SummaryIndexSettingDict | None = None,
+        summary_index_setting: dict | None = None,
     ):
         if not document_id:
             raise KnowledgeIndexNodeError("document_id is required.")

api/core/workflow/workflow_entry.py

@@ -1,7 +1,7 @@
 import logging
 import time
 from collections.abc import Generator, Mapping, Sequence
-from typing import Any, TypeAlias, cast
+from typing import Any, cast
 
 from configs import dify_config
 from core.app.apps.exc import GenerateTaskStoppedError
@@ -32,13 +32,6 @@ from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
 
-SpecialValueScalar: TypeAlias = str | int | float | bool | None
-SpecialValue: TypeAlias = SpecialValueScalar | File | Mapping[str, "SpecialValue"] | list["SpecialValue"]
-SerializedSpecialValue: TypeAlias = (
-    SpecialValueScalar | dict[str, "SerializedSpecialValue"] | list["SerializedSpecialValue"]
-)
-SingleNodeGraphConfig: TypeAlias = dict[str, list[dict[str, object]]]
-
 
 class _WorkflowChildEngineBuilder:
     @staticmethod
@@ -283,10 +276,10 @@ class WorkflowEntry:
     @staticmethod
     def _create_single_node_graph(
         node_id: str,
-        node_data: Mapping[str, object],
+        node_data: dict[str, Any],
         node_width: int = 114,
         node_height: int = 514,
-    ) -> SingleNodeGraphConfig:
+    ) -> dict[str, Any]:
         """
         Create a minimal graph structure for testing a single node in isolation.
 
@@ -296,14 +289,14 @@ class WorkflowEntry:
         :param node_height: height for UI layout (default: 100)
         :return: graph dictionary with start node and target node
         """
-        node_config: dict[str, object] = {
+        node_config = {
             "id": node_id,
             "width": node_width,
             "height": node_height,
             "type": "custom",
-            "data": dict(node_data),
+            "data": node_data,
         }
-        start_node_config: dict[str, object] = {
+        start_node_config = {
             "id": "start",
             "width": node_width,
             "height": node_height,
@@ -328,12 +321,7 @@ class WorkflowEntry:
 
     @classmethod
     def run_free_node(
-        cls,
-        node_data: Mapping[str, object],
-        node_id: str,
-        tenant_id: str,
-        user_id: str,
-        user_inputs: Mapping[str, object],
+        cls, node_data: dict[str, Any], node_id: str, tenant_id: str, user_id: str, user_inputs: dict[str, Any]
     ) -> tuple[Node, Generator[GraphNodeEventBase, None, None]]:
         """
         Run free node
@@ -351,8 +339,6 @@ class WorkflowEntry:
         graph_dict = cls._create_single_node_graph(node_id, node_data)
 
         node_type = node_data.get("type", "")
-        if not isinstance(node_type, str):
-            raise ValueError("Node type must be a string")
         if node_type not in {BuiltinNodeTypes.PARAMETER_EXTRACTOR, BuiltinNodeTypes.QUESTION_CLASSIFIER}:
             raise ValueError(f"Node type {node_type} not supported")
 
@@ -383,7 +369,7 @@ class WorkflowEntry:
         graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter())
 
         # init workflow run state
-        node_config = NodeConfigDictAdapter.validate_python({"id": node_id, "data": dict(node_data)})
+        node_config = NodeConfigDictAdapter.validate_python({"id": node_id, "data": node_data})
         node_factory = DifyNodeFactory(
             graph_init_params=graph_init_params,
             graph_runtime_state=graph_runtime_state,
@@ -419,34 +405,30 @@ class WorkflowEntry:
             raise WorkflowNodeRunFailedError(node=node, err_msg=str(e))
 
     @staticmethod
-    def handle_special_values(value: Mapping[str, SpecialValue] | None) -> dict[str, SerializedSpecialValue] | None:
+    def handle_special_values(value: Mapping[str, Any] | None) -> Mapping[str, Any] | None:
         # NOTE(QuantumGhost): Avoid using this function in new code.
         # Keep values structured as long as possible and only convert to dict
         # immediately before serialization (e.g., JSON serialization) to maintain
         # data integrity and type information.
         result = WorkflowEntry._handle_special_values(value)
-        if result is None:
-            return None
-        if isinstance(result, dict):
-            return result
-        raise TypeError("handle_special_values expects a mapping input")
+        return result if isinstance(result, Mapping) or result is None else dict(result)
 
     @staticmethod
-    def _handle_special_values(value: SpecialValue) -> SerializedSpecialValue:
+    def _handle_special_values(value: Any):
         if value is None:
             return value
-        if isinstance(value, Mapping):
-            res: dict[str, SerializedSpecialValue] = {}
+        if isinstance(value, dict):
+            res = {}
             for k, v in value.items():
                 res[k] = WorkflowEntry._handle_special_values(v)
             return res
         if isinstance(value, list):
-            res_list: list[SerializedSpecialValue] = []
+            res_list = []
             for item in value:
                 res_list.append(WorkflowEntry._handle_special_values(item))
             return res_list
         if isinstance(value, File):
-            return dict(value.to_dict())
+            return value.to_dict()
         return value
 
     @classmethod

api/dify_graph/enums.py

@@ -248,6 +248,8 @@ class WorkflowNodeExecutionMetadataKey(StrEnum):
     """
 
     TOTAL_TOKENS = "total_tokens"
+    PROMPT_TOKENS = "prompt_tokens"
+    COMPLETION_TOKENS = "completion_tokens"
     TOTAL_PRICE = "total_price"
     CURRENCY = "currency"
     TOOL_INFO = "tool_info"

api/dify_graph/file/file_manager.py

@@ -112,8 +112,6 @@ def _get_encoded_string(f: File, /) -> str:
             data = _download_file_content(f.storage_key)
         case FileTransferMethod.DATASOURCE_FILE:
             data = _download_file_content(f.storage_key)
-        case _:
-            raise ValueError(f"Unsupported transfer method: {f.transfer_method}")
 
     return base64.b64encode(data).decode("utf-8")
 

api/dify_graph/graph_engine/layers/execution_limits.py

@@ -133,8 +133,6 @@ class ExecutionLimitsLayer(GraphEngineLayer):
         elif limit_type == LimitType.TIME_LIMIT:
             elapsed_time = time.time() - self.start_time if self.start_time else 0
             reason = f"Maximum execution time exceeded: {elapsed_time:.2f}s > {self.max_time}s"
-        else:
-            return
 
         self.logger.warning("Execution limit exceeded: %s", reason)
 

api/dify_graph/nodes/base/node.py

@@ -336,7 +336,12 @@ class Node(Generic[NodeDataT]):
 
     def _restore_execution_id_from_runtime_state(self) -> str | None:
         graph_execution = self.graph_runtime_state.graph_execution
-        node_executions = graph_execution.node_executions
+        try:
+            node_executions = graph_execution.node_executions
+        except AttributeError:
+            return None
+        if not isinstance(node_executions, dict):
+            return None
         node_execution = node_executions.get(self._node_id)
         if node_execution is None:
             return None
@@ -390,7 +395,8 @@ class Node(Generic[NodeDataT]):
                 if isinstance(event, NodeEventBase):  # pyright: ignore[reportUnnecessaryIsInstance]
                     yield self._dispatch(event)
                 elif isinstance(event, GraphNodeEventBase) and not event.in_iteration_id and not event.in_loop_id:  # pyright: ignore[reportUnnecessaryIsInstance]
-                    yield event.model_copy(update={"id": self.execution_id})
+                    event.id = self.execution_id
+                    yield event
                 else:
                     yield event
         except Exception as e:

api/dify_graph/nodes/document_extractor/node.py

@@ -443,10 +443,7 @@ def _extract_text_from_docx(file_content: bytes) -> str:
         # Keep track of paragraph and table positions
         content_items: list[tuple[int, str, Table | Paragraph]] = []
 
-        doc_body = getattr(doc.element, "body", None)
-        if doc_body is None:
-            raise TextExtractionError("DOCX body not found")
-        it = iter(doc_body)
+        it = iter(doc.element.body)
         part = next(it, None)
         i = 0
         while part is not None:

api/dify_graph/nodes/llm/entities.py

@@ -1,8 +1,7 @@
 from collections.abc import Mapping, Sequence
-from typing import Literal, NotRequired
+from typing import Any, Literal
 
 from pydantic import BaseModel, Field, field_validator
-from typing_extensions import TypedDict
 
 from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate, MemoryConfig
 from dify_graph.entities.base_node_data import BaseNodeData
@@ -11,17 +10,11 @@ from dify_graph.model_runtime.entities import ImagePromptMessageContent, LLMMode
 from dify_graph.nodes.base.entities import VariableSelector
 
 
-class StructuredOutputConfig(TypedDict):
-    schema: Mapping[str, object]
-    name: NotRequired[str]
-    description: NotRequired[str]
-
-
 class ModelConfig(BaseModel):
     provider: str
     name: str
     mode: LLMMode
-    completion_params: dict[str, object] = Field(default_factory=dict)
+    completion_params: dict[str, Any] = Field(default_factory=dict)
 
 
 class ContextConfig(BaseModel):
@@ -40,7 +33,7 @@ class VisionConfig(BaseModel):
 
     @field_validator("configs", mode="before")
     @classmethod
-    def convert_none_configs(cls, v: object):
+    def convert_none_configs(cls, v: Any):
         if v is None:
             return VisionConfigOptions()
         return v
@@ -51,7 +44,7 @@ class PromptConfig(BaseModel):
 
     @field_validator("jinja2_variables", mode="before")
     @classmethod
-    def convert_none_jinja2_variables(cls, v: object):
+    def convert_none_jinja2_variables(cls, v: Any):
         if v is None:
             return []
         return v
@@ -74,7 +67,7 @@ class LLMNodeData(BaseNodeData):
     memory: MemoryConfig | None = None
     context: ContextConfig
     vision: VisionConfig = Field(default_factory=VisionConfig)
-    structured_output: StructuredOutputConfig | None = None
+    structured_output: Mapping[str, Any] | None = None
     # We used 'structured_output_enabled' in the past, but it's not a good name.
     structured_output_switch_on: bool = Field(False, alias="structured_output_enabled")
     reasoning_format: Literal["separated", "tagged"] = Field(
@@ -97,30 +90,11 @@ class LLMNodeData(BaseNodeData):
 
     @field_validator("prompt_config", mode="before")
     @classmethod
-    def convert_none_prompt_config(cls, v: object):
+    def convert_none_prompt_config(cls, v: Any):
         if v is None:
             return PromptConfig()
         return v
 
-    @field_validator("structured_output", mode="before")
-    @classmethod
-    def convert_legacy_structured_output(cls, v: object) -> StructuredOutputConfig | None | object:
-        if not isinstance(v, Mapping):
-            return v
-
-        schema = v.get("schema")
-        if schema is None:
-            return None
-
-        normalized: StructuredOutputConfig = {"schema": schema}
-        name = v.get("name")
-        description = v.get("description")
-        if isinstance(name, str):
-            normalized["name"] = name
-        if isinstance(description, str):
-            normalized["description"] = description
-        return normalized
-
     @property
     def structured_output_enabled(self) -> bool:
         return self.structured_output_switch_on and self.structured_output is not None

api/dify_graph/nodes/llm/node.py

@@ -9,7 +9,6 @@ import time
 from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Literal
 
-from pydantic import TypeAdapter
 from sqlalchemy import select
 
 from core.llm_generator.output_parser.errors import OutputParserError
@@ -75,7 +74,6 @@ from .entities import (
     LLMNodeChatModelMessage,
     LLMNodeCompletionModelPromptTemplate,
     LLMNodeData,
-    StructuredOutputConfig,
 )
 from .exc import (
     InvalidContextStructureError,
@@ -90,7 +88,6 @@ if TYPE_CHECKING:
     from dify_graph.runtime import GraphRuntimeState
 
 logger = logging.getLogger(__name__)
-_JSON_OBJECT_ADAPTER = TypeAdapter(dict[str, object])
 
 
 class LLMNode(Node[LLMNodeData]):
@@ -357,7 +354,7 @@ class LLMNode(Node[LLMNodeData]):
         stop: Sequence[str] | None = None,
         user_id: str,
         structured_output_enabled: bool,
-        structured_output: StructuredOutputConfig | None = None,
+        structured_output: Mapping[str, Any] | None = None,
         file_saver: LLMFileSaver,
         file_outputs: list[File],
         node_id: str,
@@ -370,10 +367,8 @@ class LLMNode(Node[LLMNodeData]):
         model_schema = llm_utils.fetch_model_schema(model_instance=model_instance)
 
         if structured_output_enabled:
-            if structured_output is None:
-                raise LLMNodeError("Please provide a valid structured output schema")
             output_schema = LLMNode.fetch_structured_output_schema(
-                structured_output=structured_output,
+                structured_output=structured_output or {},
             )
             request_start_time = time.perf_counter()
 
@@ -925,12 +920,6 @@ class LLMNode(Node[LLMNodeData]):
             # Extract clean text and reasoning from <think> tags
             clean_text, reasoning_content = LLMNode._split_reasoning(full_text, reasoning_format)
 
-        structured_output = (
-            dict(invoke_result.structured_output)
-            if isinstance(invoke_result, LLMResultWithStructuredOutput) and invoke_result.structured_output is not None
-            else None
-        )
-
         event = ModelInvokeCompletedEvent(
             # Use clean_text for separated mode, full_text for tagged mode
             text=clean_text if reasoning_format == "separated" else full_text,
@@ -939,7 +928,7 @@ class LLMNode(Node[LLMNodeData]):
             # Reasoning content for workflow variables and downstream nodes
             reasoning_content=reasoning_content,
             # Pass structured output if enabled
-            structured_output=structured_output,
+            structured_output=getattr(invoke_result, "structured_output", None),
         )
         if request_latency is not None:
             event.usage.latency = round(request_latency, 3)
@@ -973,18 +962,27 @@ class LLMNode(Node[LLMNodeData]):
     @staticmethod
     def fetch_structured_output_schema(
         *,
-        structured_output: StructuredOutputConfig,
-    ) -> dict[str, object]:
+        structured_output: Mapping[str, Any],
+    ) -> dict[str, Any]:
         """
         Fetch the structured output schema from the node data.
 
         Returns:
-            dict[str, object]: The structured output schema
+            dict[str, Any]: The structured output schema
         """
-        schema = structured_output.get("schema")
-        if not schema:
+        if not structured_output:
             raise LLMNodeError("Please provide a valid structured output schema")
-        return _JSON_OBJECT_ADAPTER.validate_python(schema)
+        structured_output_schema = json.dumps(structured_output.get("schema", {}), ensure_ascii=False)
+        if not structured_output_schema:
+            raise LLMNodeError("Please provide a valid structured output schema")
+
+        try:
+            schema = json.loads(structured_output_schema)
+            if not isinstance(schema, dict):
+                raise LLMNodeError("structured_output_schema must be a JSON object")
+            return schema
+        except json.JSONDecodeError:
+            raise LLMNodeError("structured_output_schema is not valid JSON format")
 
     @staticmethod
     def _save_multimodal_output_and_convert_result_to_markdown(

api/dify_graph/nodes/loop/entities.py

@@ -1,10 +1,7 @@
-from __future__ import annotations
-
 from enum import StrEnum
-from typing import Annotated, Any, Literal, TypeAlias, cast
+from typing import Annotated, Any, Literal
 
-from pydantic import AfterValidator, BaseModel, Field, TypeAdapter, field_validator
-from pydantic_core.core_schema import ValidationInfo
+from pydantic import AfterValidator, BaseModel, Field, field_validator
 
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import BuiltinNodeTypes, NodeType
@@ -12,12 +9,6 @@ from dify_graph.nodes.base import BaseLoopNodeData, BaseLoopState
 from dify_graph.utils.condition.entities import Condition
 from dify_graph.variables.types import SegmentType
 
-LoopValue: TypeAlias = str | int | float | bool | None | dict[str, Any] | list[Any]
-LoopValueMapping: TypeAlias = dict[str, LoopValue]
-VariableSelector: TypeAlias = list[str]
-
-_VARIABLE_SELECTOR_ADAPTER: TypeAdapter[VariableSelector] = TypeAdapter(VariableSelector)
-
 _VALID_VAR_TYPE = frozenset(
     [
         SegmentType.STRING,
@@ -38,36 +29,6 @@ def _is_valid_var_type(seg_type: SegmentType) -> SegmentType:
     return seg_type
 
 
-def _validate_loop_value(value: object) -> LoopValue:
-    if value is None or isinstance(value, (str, int, float, bool)):
-        return cast(LoopValue, value)
-
-    if isinstance(value, list):
-        return [_validate_loop_value(item) for item in value]
-
-    if isinstance(value, dict):
-        normalized: dict[str, LoopValue] = {}
-        for key, item in value.items():
-            if not isinstance(key, str):
-                raise TypeError("Loop values only support string object keys")
-            normalized[key] = _validate_loop_value(item)
-        return normalized
-
-    raise TypeError("Loop values must be JSON-like primitives, arrays, or objects")
-
-
-def _validate_loop_value_mapping(value: object) -> LoopValueMapping:
-    if not isinstance(value, dict):
-        raise TypeError("Loop outputs must be an object")
-
-    normalized: LoopValueMapping = {}
-    for key, item in value.items():
-        if not isinstance(key, str):
-            raise TypeError("Loop output keys must be strings")
-        normalized[key] = _validate_loop_value(item)
-    return normalized
-
-
 class LoopVariableData(BaseModel):
     """
     Loop Variable Data.
@@ -76,29 +37,7 @@ class LoopVariableData(BaseModel):
     label: str
     var_type: Annotated[SegmentType, AfterValidator(_is_valid_var_type)]
     value_type: Literal["variable", "constant"]
-    value: LoopValue | VariableSelector | None = None
-
-    @field_validator("value", mode="before")
-    @classmethod
-    def validate_value(cls, value: object, validation_info: ValidationInfo) -> LoopValue | VariableSelector | None:
-        value_type = validation_info.data.get("value_type")
-        if value_type == "variable":
-            if value is None:
-                raise ValueError("Variable loop inputs require a selector")
-            return _VARIABLE_SELECTOR_ADAPTER.validate_python(value)
-        if value_type == "constant":
-            return _validate_loop_value(value)
-        raise ValueError(f"Unknown loop variable value type: {value_type}")
-
-    def require_variable_selector(self) -> VariableSelector:
-        if self.value_type != "variable":
-            raise ValueError(f"Expected variable loop input, got {self.value_type}")
-        return _VARIABLE_SELECTOR_ADAPTER.validate_python(self.value)
-
-    def require_constant_value(self) -> LoopValue:
-        if self.value_type != "constant":
-            raise ValueError(f"Expected constant loop input, got {self.value_type}")
-        return _validate_loop_value(self.value)
+    value: Any | list[str] | None = None
 
 
 class LoopNodeData(BaseLoopNodeData):
@@ -107,14 +46,14 @@ class LoopNodeData(BaseLoopNodeData):
     break_conditions: list[Condition]  # Conditions to break the loop
     logical_operator: Literal["and", "or"]
     loop_variables: list[LoopVariableData] | None = Field(default_factory=list[LoopVariableData])
-    outputs: LoopValueMapping = Field(default_factory=dict)
+    outputs: dict[str, Any] = Field(default_factory=dict)
 
     @field_validator("outputs", mode="before")
     @classmethod
-    def validate_outputs(cls, value: object) -> LoopValueMapping:
-        if value is None:
+    def validate_outputs(cls, v):
+        if v is None:
             return {}
-        return _validate_loop_value_mapping(value)
+        return v
 
 
 class LoopStartNodeData(BaseNodeData):
@@ -138,8 +77,8 @@ class LoopState(BaseLoopState):
     Loop State.
     """
 
-    outputs: list[LoopValue] = Field(default_factory=list)
-    current_output: LoopValue | None = None
+    outputs: list[Any] = Field(default_factory=list)
+    current_output: Any = None
 
     class MetaData(BaseLoopState.MetaData):
         """
@@ -148,7 +87,7 @@ class LoopState(BaseLoopState):
 
         loop_length: int
 
-    def get_last_output(self) -> LoopValue | None:
+    def get_last_output(self) -> Any:
         """
         Get last output.
         """
@@ -156,7 +95,7 @@ class LoopState(BaseLoopState):
             return self.outputs[-1]
         return None
 
-    def get_current_output(self) -> LoopValue | None:
+    def get_current_output(self) -> Any:
         """
         Get current output.
         """

api/dify_graph/nodes/loop/loop_node.py

@@ -3,7 +3,7 @@ import json
 import logging
 from collections.abc import Callable, Generator, Mapping, Sequence
 from datetime import datetime
-from typing import TYPE_CHECKING, Literal, cast
+from typing import TYPE_CHECKING, Any, Literal, cast
 
 from dify_graph.entities.graph_config import NodeConfigDictAdapter
 from dify_graph.enums import (
@@ -29,7 +29,7 @@ from dify_graph.node_events import (
 )
 from dify_graph.nodes.base import LLMUsageTrackingMixin
 from dify_graph.nodes.base.node import Node
-from dify_graph.nodes.loop.entities import LoopCompletedReason, LoopNodeData, LoopValue, LoopVariableData
+from dify_graph.nodes.loop.entities import LoopCompletedReason, LoopNodeData, LoopVariableData
 from dify_graph.utils.condition.processor import ConditionProcessor
 from dify_graph.variables import Segment, SegmentType
 from factories.variable_factory import TypeMismatchError, build_segment_with_type, segment_to_variable
@@ -60,7 +60,7 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
         break_conditions = self.node_data.break_conditions
         logical_operator = self.node_data.logical_operator
 
-        inputs: dict[str, object] = {"loop_count": loop_count}
+        inputs = {"loop_count": loop_count}
 
         if not self.node_data.start_node_id:
             raise ValueError(f"field start_node_id in loop {self._node_id} not found")
@@ -68,14 +68,12 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
         root_node_id = self.node_data.start_node_id
 
         # Initialize loop variables in the original variable pool
-        loop_variable_selectors: dict[str, list[str]] = {}
+        loop_variable_selectors = {}
         if self.node_data.loop_variables:
             value_processor: dict[Literal["constant", "variable"], Callable[[LoopVariableData], Segment | None]] = {
-                "constant": lambda var: self._get_segment_for_constant(var.var_type, var.require_constant_value()),
+                "constant": lambda var: self._get_segment_for_constant(var.var_type, var.value),
                 "variable": lambda var: (
-                    self.graph_runtime_state.variable_pool.get(var.require_variable_selector())
-                    if var.value is not None
-                    else None
+                    self.graph_runtime_state.variable_pool.get(var.value) if isinstance(var.value, list) else None
                 ),
             }
             for loop_variable in self.node_data.loop_variables:
@@ -97,7 +95,7 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
         condition_processor = ConditionProcessor()
 
         loop_duration_map: dict[str, float] = {}
-        single_loop_variable_map: dict[str, dict[str, LoopValue]] = {}  # single loop variable output
+        single_loop_variable_map: dict[str, dict[str, Any]] = {}  # single loop variable output
         loop_usage = LLMUsage.empty_usage()
         loop_node_ids = self._extract_loop_node_ids_from_config(self.graph_config, self._node_id)
 
@@ -148,7 +146,7 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
                 loop_usage = self._merge_usage(loop_usage, graph_engine.graph_runtime_state.llm_usage)
 
                 # Collect loop variable values after iteration
-                single_loop_variable: dict[str, LoopValue] = {}
+                single_loop_variable = {}
                 for key, selector in loop_variable_selectors.items():
                     segment = self.graph_runtime_state.variable_pool.get(selector)
                     single_loop_variable[key] = segment.value if segment else None
@@ -299,29 +297,20 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
     def _extract_variable_selector_to_variable_mapping(
         cls,
         *,
-        graph_config: Mapping[str, object],
+        graph_config: Mapping[str, Any],
         node_id: str,
         node_data: LoopNodeData,
     ) -> Mapping[str, Sequence[str]]:
-        variable_mapping: dict[str, Sequence[str]] = {}
+        variable_mapping = {}
 
         # Extract loop node IDs statically from graph_config
 
         loop_node_ids = cls._extract_loop_node_ids_from_config(graph_config, node_id)
 
         # Get node configs from graph_config
-        raw_nodes = graph_config.get("nodes")
-        node_configs: dict[str, Mapping[str, object]] = {}
-        if isinstance(raw_nodes, list):
-            for raw_node in raw_nodes:
-                if not isinstance(raw_node, dict):
-                    continue
-                raw_node_id = raw_node.get("id")
-                if isinstance(raw_node_id, str):
-                    node_configs[raw_node_id] = raw_node
+        node_configs = {node["id"]: node for node in graph_config.get("nodes", []) if "id" in node}
         for sub_node_id, sub_node_config in node_configs.items():
-            sub_node_data = sub_node_config.get("data")
-            if not isinstance(sub_node_data, dict) or sub_node_data.get("loop_id") != node_id:
+            if sub_node_config.get("data", {}).get("loop_id") != node_id:
                 continue
 
             # variable selector to variable mapping
@@ -352,8 +341,9 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
 
         for loop_variable in node_data.loop_variables or []:
             if loop_variable.value_type == "variable":
+                assert loop_variable.value is not None, "Loop variable value must be provided for variable type"
                 # add loop variable to variable mapping
-                selector = loop_variable.require_variable_selector()
+                selector = loop_variable.value
                 variable_mapping[f"{node_id}.{loop_variable.label}"] = selector
 
         # remove variable out from loop
@@ -362,7 +352,7 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
         return variable_mapping
 
     @classmethod
-    def _extract_loop_node_ids_from_config(cls, graph_config: Mapping[str, object], loop_node_id: str) -> set[str]:
+    def _extract_loop_node_ids_from_config(cls, graph_config: Mapping[str, Any], loop_node_id: str) -> set[str]:
         """
         Extract node IDs that belong to a specific loop from graph configuration.
 
@@ -373,19 +363,12 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
         :param loop_node_id: the ID of the loop node
         :return: set of node IDs that belong to the loop
         """
-        loop_node_ids: set[str] = set()
+        loop_node_ids = set()
 
         # Find all nodes that belong to this loop
-        raw_nodes = graph_config.get("nodes")
-        if not isinstance(raw_nodes, list):
-            return loop_node_ids
-
-        for node in raw_nodes:
-            if not isinstance(node, dict):
-                continue
-            node_data = node.get("data")
-            if not isinstance(node_data, dict):
-                continue
+        nodes = graph_config.get("nodes", [])
+        for node in nodes:
+            node_data = node.get("data", {})
             if node_data.get("loop_id") == loop_node_id:
                 node_id = node.get("id")
                 if node_id:
@@ -394,7 +377,7 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
         return loop_node_ids
 
     @staticmethod
-    def _get_segment_for_constant(var_type: SegmentType, original_value: LoopValue | None) -> Segment:
+    def _get_segment_for_constant(var_type: SegmentType, original_value: Any) -> Segment:
         """Get the appropriate segment type for a constant value."""
         # TODO: Refactor for maintainability:
         # 1. Ensure type handling logic stays synchronized with _VALID_VAR_TYPE (entities.py)
@@ -406,12 +389,11 @@ class LoopNode(LLMUsageTrackingMixin, Node[LoopNodeData]):
             SegmentType.ARRAY_OBJECT,
             SegmentType.ARRAY_STRING,
         ]:
-            # New typed payloads may already provide native lists, while legacy
-            # configs still serialize array constants as JSON strings.
-            if isinstance(original_value, str):
-                value = json.loads(original_value) if original_value else []
+            if original_value and isinstance(original_value, str):
+                value = json.loads(original_value)
             else:
-                value = original_value
+                logger.warning("unexpected value for LoopNode, value_type=%s, value=%s", original_value, var_type)
+                value = []
         else:
             raise AssertionError("this statement should be unreachable.")
         try:

api/dify_graph/nodes/parameter_extractor/entities.py

@@ -1,4 +1,4 @@
-from typing import Annotated, Literal
+from typing import Annotated, Any, Literal
 
 from pydantic import (
     BaseModel,
@@ -6,7 +6,6 @@ from pydantic import (
     Field,
     field_validator,
 )
-from typing_extensions import TypedDict
 
 from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 from dify_graph.entities.base_node_data import BaseNodeData
@@ -56,7 +55,7 @@ class ParameterConfig(BaseModel):
 
     @field_validator("name", mode="before")
     @classmethod
-    def validate_name(cls, value: object) -> str:
+    def validate_name(cls, value) -> str:
         if not value:
             raise ValueError("Parameter name is required")
         if value in {"__reason", "__is_success"}:
@@ -80,23 +79,6 @@ class ParameterConfig(BaseModel):
         return element_type
 
 
-class JsonSchemaArrayItems(TypedDict):
-    type: str
-
-
-class ParameterJsonSchemaProperty(TypedDict, total=False):
-    description: str
-    type: str
-    items: JsonSchemaArrayItems
-    enum: list[str]
-
-
-class ParameterJsonSchema(TypedDict):
-    type: Literal["object"]
-    properties: dict[str, ParameterJsonSchemaProperty]
-    required: list[str]
-
-
 class ParameterExtractorNodeData(BaseNodeData):
     """
     Parameter Extractor Node Data.
@@ -113,19 +95,19 @@ class ParameterExtractorNodeData(BaseNodeData):
 
     @field_validator("reasoning_mode", mode="before")
     @classmethod
-    def set_reasoning_mode(cls, v: object) -> str:
-        return str(v) if v else "function_call"
+    def set_reasoning_mode(cls, v) -> str:
+        return v or "function_call"
 
-    def get_parameter_json_schema(self) -> ParameterJsonSchema:
+    def get_parameter_json_schema(self):
         """
         Get parameter json schema.
 
         :return: parameter json schema
         """
-        parameters: ParameterJsonSchema = {"type": "object", "properties": {}, "required": []}
+        parameters: dict[str, Any] = {"type": "object", "properties": {}, "required": []}
 
         for parameter in self.parameters:
-            parameter_schema: ParameterJsonSchemaProperty = {"description": parameter.description}
+            parameter_schema: dict[str, Any] = {"description": parameter.description}
 
             if parameter.type == SegmentType.STRING:
                 parameter_schema["type"] = "string"
@@ -136,7 +118,7 @@ class ParameterExtractorNodeData(BaseNodeData):
                     raise AssertionError("element type should not be None.")
                 parameter_schema["items"] = {"type": element_type.value}
             else:
-                parameter_schema["type"] = parameter.type.value
+                parameter_schema["type"] = parameter.type
 
             if parameter.options:
                 parameter_schema["enum"] = parameter.options

api/dify_graph/nodes/parameter_extractor/parameter_extractor_node.py

@@ -5,8 +5,6 @@ import uuid
 from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any, cast
 
-from pydantic import TypeAdapter
-
 from core.model_manager import ModelInstance
 from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
 from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate
@@ -65,7 +63,6 @@ from .prompts import (
 )
 
 logger = logging.getLogger(__name__)
-_JSON_OBJECT_ADAPTER = TypeAdapter(dict[str, object])
 
 if TYPE_CHECKING:
     from dify_graph.entities import GraphInitParams
@@ -73,7 +70,7 @@ if TYPE_CHECKING:
     from dify_graph.runtime import GraphRuntimeState
 
 
-def extract_json(text: str) -> str | None:
+def extract_json(text):
     """
     From a given JSON started from '{' or '[' extract the complete JSON object.
     """
@@ -395,15 +392,10 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         )
 
         # generate tool
-        parameter_schema = node_data.get_parameter_json_schema()
         tool = PromptMessageTool(
             name=FUNCTION_CALLING_EXTRACTOR_NAME,
             description="Extract parameters from the natural language text",
-            parameters={
-                "type": parameter_schema["type"],
-                "properties": dict(parameter_schema["properties"]),
-                "required": list(parameter_schema["required"]),
-            },
+            parameters=node_data.get_parameter_json_schema(),
         )
 
         return prompt_messages, [tool]
@@ -610,21 +602,19 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         else:
             return None
 
-    def _transform_result(self, data: ParameterExtractorNodeData, result: Mapping[str, object]) -> dict[str, object]:
+    def _transform_result(self, data: ParameterExtractorNodeData, result: dict):
         """
         Transform result into standard format.
         """
-        transformed_result: dict[str, object] = {}
+        transformed_result: dict[str, Any] = {}
         for parameter in data.parameters:
             if parameter.name in result:
                 param_value = result[parameter.name]
                 # transform value
                 if parameter.type == SegmentType.NUMBER:
-                    if isinstance(param_value, (bool, int, float, str)):
-                        numeric_value: bool | int | float | str = param_value
-                        transformed = self._transform_number(numeric_value)
-                        if transformed is not None:
-                            transformed_result[parameter.name] = transformed
+                    transformed = self._transform_number(param_value)
+                    if transformed is not None:
+                        transformed_result[parameter.name] = transformed
                 elif parameter.type == SegmentType.BOOLEAN:
                     if isinstance(result[parameter.name], (bool, int)):
                         transformed_result[parameter.name] = bool(result[parameter.name])
@@ -671,7 +661,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
 
         return transformed_result
 
-    def _extract_complete_json_response(self, result: str) -> dict[str, object] | None:
+    def _extract_complete_json_response(self, result: str) -> dict | None:
         """
         Extract complete json response.
         """
@@ -682,11 +672,11 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
                 json_str = extract_json(result[idx:])
                 if json_str:
                     with contextlib.suppress(Exception):
-                        return _JSON_OBJECT_ADAPTER.validate_python(json.loads(json_str))
+                        return cast(dict, json.loads(json_str))
         logger.info("extra error: %s", result)
         return None
 
-    def _extract_json_from_tool_call(self, tool_call: AssistantPromptMessage.ToolCall) -> dict[str, object] | None:
+    def _extract_json_from_tool_call(self, tool_call: AssistantPromptMessage.ToolCall) -> dict | None:
         """
         Extract json from tool call.
         """
@@ -700,16 +690,16 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
                 json_str = extract_json(result[idx:])
                 if json_str:
                     with contextlib.suppress(Exception):
-                        return _JSON_OBJECT_ADAPTER.validate_python(json.loads(json_str))
+                        return cast(dict, json.loads(json_str))
 
         logger.info("extra error: %s", result)
         return None
 
-    def _generate_default_result(self, data: ParameterExtractorNodeData) -> dict[str, object]:
+    def _generate_default_result(self, data: ParameterExtractorNodeData):
         """
         Generate default result.
         """
-        result: dict[str, object] = {}
+        result: dict[str, Any] = {}
         for parameter in data.parameters:
             if parameter.type == "number":
                 result[parameter.name] = 0

api/dify_graph/nodes/tool/entities.py

@@ -1,66 +1,12 @@
-from __future__ import annotations
+from typing import Any, Literal, Union
 
-from typing import Literal, TypeAlias, cast
-
-from pydantic import BaseModel, TypeAdapter, field_validator
+from pydantic import BaseModel, field_validator
 from pydantic_core.core_schema import ValidationInfo
-from typing_extensions import TypedDict
 
 from core.tools.entities.tool_entities import ToolProviderType
 from dify_graph.entities.base_node_data import BaseNodeData
 from dify_graph.enums import BuiltinNodeTypes, NodeType
 
-ToolConfigurationValue: TypeAlias = str | int | float | bool
-ToolInputConstantValue: TypeAlias = str | int | float | bool | dict[str, object] | list[object] | None
-VariableSelector: TypeAlias = list[str]
-
-_TOOL_INPUT_MIXED_ADAPTER: TypeAdapter[str] = TypeAdapter(str)
-_TOOL_INPUT_CONSTANT_ADAPTER: TypeAdapter[ToolInputConstantValue] = TypeAdapter(ToolInputConstantValue)
-_VARIABLE_SELECTOR_ADAPTER: TypeAdapter[VariableSelector] = TypeAdapter(VariableSelector)
-
-
-class WorkflowToolInputValue(TypedDict):
-    type: Literal["mixed", "variable", "constant"]
-    value: ToolInputConstantValue | VariableSelector
-
-
-ToolConfigurationEntry: TypeAlias = ToolConfigurationValue | WorkflowToolInputValue
-ToolConfigurations: TypeAlias = dict[str, ToolConfigurationEntry]
-
-
-class ToolInputPayload(BaseModel):
-    type: Literal["mixed", "variable", "constant"]
-    value: ToolInputConstantValue | VariableSelector
-
-    @field_validator("value", mode="before")
-    @classmethod
-    def validate_value(
-        cls, value: object, validation_info: ValidationInfo
-    ) -> ToolInputConstantValue | VariableSelector:
-        input_type = validation_info.data.get("type")
-        if input_type == "mixed":
-            return _TOOL_INPUT_MIXED_ADAPTER.validate_python(value)
-        if input_type == "variable":
-            return _VARIABLE_SELECTOR_ADAPTER.validate_python(value)
-        if input_type == "constant":
-            return _TOOL_INPUT_CONSTANT_ADAPTER.validate_python(value)
-        raise ValueError(f"Unknown tool input type: {input_type}")
-
-    def require_variable_selector(self) -> VariableSelector:
-        if self.type != "variable":
-            raise ValueError(f"Expected variable tool input, got {self.type}")
-        return _VARIABLE_SELECTOR_ADAPTER.validate_python(self.value)
-
-
-def _validate_tool_configuration_entry(value: object) -> ToolConfigurationEntry:
-    if isinstance(value, (str, int, float, bool)):
-        return cast(ToolConfigurationEntry, value)
-
-    if isinstance(value, dict):
-        return cast(ToolConfigurationEntry, ToolInputPayload.model_validate(value).model_dump())
-
-    raise TypeError("Tool configuration values must be primitives or workflow tool input objects")
-
 
 class ToolEntity(BaseModel):
     provider_id: str
@@ -68,29 +14,52 @@ class ToolEntity(BaseModel):
     provider_name: str  # redundancy
     tool_name: str
     tool_label: str  # redundancy
-    tool_configurations: ToolConfigurations
+    tool_configurations: dict[str, Any]
     credential_id: str | None = None
     plugin_unique_identifier: str | None = None  # redundancy
 
     @field_validator("tool_configurations", mode="before")
     @classmethod
-    def validate_tool_configurations(cls, value: object, _validation_info: ValidationInfo) -> ToolConfigurations:
+    def validate_tool_configurations(cls, value, values: ValidationInfo):
         if not isinstance(value, dict):
-            raise TypeError("tool_configurations must be a dictionary")
+            raise ValueError("tool_configurations must be a dictionary")
 
-        normalized: ToolConfigurations = {}
-        for key, item in value.items():
-            if not isinstance(key, str):
-                raise TypeError("tool_configurations keys must be strings")
-            normalized[key] = _validate_tool_configuration_entry(item)
-        return normalized
+        for key in values.data.get("tool_configurations", {}):
+            value = values.data.get("tool_configurations", {}).get(key)
+            if not isinstance(value, str | int | float | bool):
+                raise ValueError(f"{key} must be a string")
+
+        return value
 
 
 class ToolNodeData(BaseNodeData, ToolEntity):
     type: NodeType = BuiltinNodeTypes.TOOL
 
-    class ToolInput(ToolInputPayload):
-        pass
+    class ToolInput(BaseModel):
+        # TODO: check this type
+        value: Union[Any, list[str]]
+        type: Literal["mixed", "variable", "constant"]
+
+        @field_validator("type", mode="before")
+        @classmethod
+        def check_type(cls, value, validation_info: ValidationInfo):
+            typ = value
+            value = validation_info.data.get("value")
+
+            if value is None:
+                return typ
+
+            if typ == "mixed" and not isinstance(value, str):
+                raise ValueError("value must be a string")
+            elif typ == "variable":
+                if not isinstance(value, list):
+                    raise ValueError("value must be a list")
+                for val in value:
+                    if not isinstance(val, str):
+                        raise ValueError("value must be a list of strings")
+            elif typ == "constant" and not isinstance(value, (allowed_types := (str, int, float, bool, dict, list))):
+                raise ValueError(f"value must be one of: {', '.join(t.__name__ for t in allowed_types)}")
+            return typ
 
     tool_parameters: dict[str, ToolInput]
     # The version of the tool parameter.
@@ -100,7 +69,7 @@ class ToolNodeData(BaseNodeData, ToolEntity):
 
     @field_validator("tool_parameters", mode="before")
     @classmethod
-    def filter_none_tool_inputs(cls, value: object) -> object:
+    def filter_none_tool_inputs(cls, value):
         if not isinstance(value, dict):
             return value
 
@@ -111,10 +80,8 @@ class ToolNodeData(BaseNodeData, ToolEntity):
         }
 
     @staticmethod
-    def _has_valid_value(tool_input: object) -> bool:
+    def _has_valid_value(tool_input):
         """Check if the value is valid"""
         if isinstance(tool_input, dict):
             return tool_input.get("value") is not None
-        if isinstance(tool_input, ToolNodeData.ToolInput):
-            return tool_input.value is not None
-        return False
+        return getattr(tool_input, "value", None) is not None

api/dify_graph/nodes/tool/tool_node.py

@@ -81,7 +81,9 @@ class ToolNode(Node[ToolNodeData]):
         tool_info = {
             "provider_type": self.node_data.provider_type.value,
             "provider_id": self.node_data.provider_id,
+            "tool_name": self.node_data.tool_name,
             "plugin_unique_identifier": self.node_data.plugin_unique_identifier,
+            "credential_id": self.node_data.credential_id,
         }
 
         # get tool runtime
@@ -225,11 +227,10 @@ class ToolNode(Node[ToolNodeData]):
                 continue
             tool_input = node_data.tool_parameters[parameter_name]
             if tool_input.type == "variable":
-                variable_selector = tool_input.require_variable_selector()
-                variable = variable_pool.get(variable_selector)
+                variable = variable_pool.get(tool_input.value)
                 if variable is None:
                     if parameter.required:
-                        raise ToolParameterError(f"Variable {variable_selector} does not exist")
+                        raise ToolParameterError(f"Variable {tool_input.value} does not exist")
                     continue
                 parameter_value = variable.value
             elif tool_input.type in {"mixed", "constant"}:
@@ -511,9 +512,8 @@ class ToolNode(Node[ToolNodeData]):
                     for selector in selectors:
                         result[selector.variable] = selector.value_selector
                 case "variable":
-                    variable_selector = input.require_variable_selector()
-                    selector_key = ".".join(variable_selector)
-                    result[f"#{selector_key}#"] = variable_selector
+                    selector_key = ".".join(input.value)
+                    result[f"#{selector_key}#"] = input.value
                 case "constant":
                     pass
 

api/dify_graph/nodes/variable_assigner/v1/node.py

@@ -9,7 +9,7 @@ from dify_graph.node_events import NodeRunResult
 from dify_graph.nodes.base.node import Node
 from dify_graph.nodes.variable_assigner.common import helpers as common_helpers
 from dify_graph.nodes.variable_assigner.common.exc import VariableOperatorNodeError
-from dify_graph.variables import Segment, SegmentType, VariableBase
+from dify_graph.variables import SegmentType, VariableBase
 
 from .node_data import VariableAssignerData, WriteMode
 
@@ -74,29 +74,23 @@ class VariableAssignerNode(Node[VariableAssignerData]):
         if not isinstance(original_variable, VariableBase):
             raise VariableOperatorNodeError("assigned variable not found")
 
-        income_value: Segment
-        updated_variable: VariableBase
         match self.node_data.write_mode:
             case WriteMode.OVER_WRITE:
-                input_value = self.graph_runtime_state.variable_pool.get(self.node_data.input_variable_selector)
-                if input_value is None:
+                income_value = self.graph_runtime_state.variable_pool.get(self.node_data.input_variable_selector)
+                if not income_value:
                     raise VariableOperatorNodeError("input value not found")
-                income_value = input_value
                 updated_variable = original_variable.model_copy(update={"value": income_value.value})
 
             case WriteMode.APPEND:
-                input_value = self.graph_runtime_state.variable_pool.get(self.node_data.input_variable_selector)
-                if input_value is None:
+                income_value = self.graph_runtime_state.variable_pool.get(self.node_data.input_variable_selector)
+                if not income_value:
                     raise VariableOperatorNodeError("input value not found")
-                income_value = input_value
                 updated_value = original_variable.value + [income_value.value]
                 updated_variable = original_variable.model_copy(update={"value": updated_value})
 
             case WriteMode.CLEAR:
                 income_value = SegmentType.get_zero_value(original_variable.value_type)
                 updated_variable = original_variable.model_copy(update={"value": income_value.to_object()})
-            case _:
-                raise VariableOperatorNodeError(f"unsupported write mode: {self.node_data.write_mode}")
 
         # Over write the variable.
         self.graph_runtime_state.variable_pool.add(assigned_variable_selector, updated_variable)

api/dify_graph/runtime/graph_runtime_state.py

@@ -66,11 +66,6 @@ class GraphExecutionProtocol(Protocol):
     exceptions_count: int
     pause_reasons: list[PauseReason]
 
-    @property
-    def node_executions(self) -> Mapping[str, NodeExecutionProtocol]:
-        """Return node execution state keyed by node id for resume support."""
-        ...
-
     def start(self) -> None:
         """Transition execution into the running state."""
         ...
@@ -96,12 +91,6 @@ class GraphExecutionProtocol(Protocol):
         ...
 
 
-class NodeExecutionProtocol(Protocol):
-    """Structural interface for per-node execution state used during resume."""
-
-    execution_id: str | None
-
-
 class ResponseStreamCoordinatorProtocol(Protocol):
     """Structural interface for response stream coordinator."""
 

api/enterprise/telemetry/DATA_DICTIONARY.md

@@ -0,0 +1,525 @@
+# Dify Enterprise Telemetry Data Dictionary
+
+Quick reference for all telemetry signals emitted by Dify Enterprise. For configuration and architecture details, see [README.md](./README.md).
+
+## Resource Attributes
+
+Attached to every signal (Span, Metric, Log).
+
+| Attribute | Type | Example |
+|-----------|------|---------|
+| `service.name` | string | `dify` |
+| `host.name` | string | `dify-api-7f8b` |
+
+## Traces (Spans)
+
+### `dify.workflow.run`
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.trace_id` | string | Business trace ID (Workflow Run ID) |
+| `dify.tenant_id` | string | Tenant identifier |
+| `dify.app_id` | string | Application identifier |
+| `dify.workflow.id` | string | Workflow definition ID |
+| `dify.workflow.run_id` | string | Unique ID for this run |
+| `dify.workflow.status` | string | `succeeded`, `failed`, `stopped`, etc. |
+| `dify.workflow.error` | string | Error message if failed |
+| `dify.workflow.elapsed_time` | float | Total execution time (seconds) |
+| `dify.invoke_from` | string | `api`, `webapp`, `debug` |
+| `dify.conversation.id` | string | Conversation ID (optional) |
+| `dify.message.id` | string | Message ID (optional) |
+| `dify.invoked_by` | string | User ID who triggered the run |
+| `gen_ai.usage.total_tokens` | int | Total tokens across all nodes (optional) |
+| `gen_ai.user.id` | string | End-user identifier (optional) |
+| `dify.parent.trace_id` | string | Parent workflow trace ID (optional) |
+| `dify.parent.workflow.run_id` | string | Parent workflow run ID (optional) |
+| `dify.parent.node.execution_id` | string | Parent node execution ID (optional) |
+| `dify.parent.app.id` | string | Parent app ID (optional) |
+
+### `dify.node.execution`
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.trace_id` | string | Business trace ID |
+| `dify.tenant_id` | string | Tenant identifier |
+| `dify.app_id` | string | Application identifier |
+| `dify.workflow.id` | string | Workflow definition ID |
+| `dify.workflow.run_id` | string | Workflow Run ID |
+| `dify.message.id` | string | Message ID (optional) |
+| `dify.conversation.id` | string | Conversation ID (optional) |
+| `dify.node.execution_id` | string | Unique node execution ID |
+| `dify.node.id` | string | Node ID in workflow graph |
+| `dify.node.type` | string | Node type (see appendix) |
+| `dify.node.title` | string | Display title |
+| `dify.node.status` | string | `succeeded`, `failed` |
+| `dify.node.error` | string | Error message if failed |
+| `dify.node.elapsed_time` | float | Execution time (seconds) |
+| `dify.node.index` | int | Execution order index |
+| `dify.node.predecessor_node_id` | string | Triggering node ID |
+| `dify.node.iteration_id` | string | Iteration ID (optional) |
+| `dify.node.loop_id` | string | Loop ID (optional) |
+| `dify.node.parallel_id` | string | Parallel branch ID (optional) |
+| `dify.node.invoked_by` | string | User ID who triggered execution |
+| `gen_ai.usage.input_tokens` | int | Prompt tokens (LLM nodes only) |
+| `gen_ai.usage.output_tokens` | int | Completion tokens (LLM nodes only) |
+| `gen_ai.usage.total_tokens` | int | Total tokens (LLM nodes only) |
+| `gen_ai.request.model` | string | LLM model name (LLM nodes only) |
+| `gen_ai.provider.name` | string | LLM provider name (LLM nodes only) |
+| `gen_ai.user.id` | string | End-user identifier (optional) |
+
+### `dify.node.execution.draft`
+
+Same attributes as `dify.node.execution`. Emitted during Preview/Debug runs.
+
+## Counters
+
+All counters are cumulative and emitted at 100% accuracy.
+
+### Token Counters
+
+| Metric | Unit | Description |
+|--------|------|-------------|
+| `dify.tokens.total` | `{token}` | Total tokens consumed |
+| `dify.tokens.input` | `{token}` | Input (prompt) tokens |
+| `dify.tokens.output` | `{token}` | Output (completion) tokens |
+
+**Labels:**
+
+- `tenant_id`, `app_id`, `operation_type`, `model_provider`, `model_name`, `node_type` (if node_execution)
+
+⚠️ **Warning:** `dify.tokens.total` at workflow level includes all node tokens. Filter by `operation_type` to avoid double-counting.
+
+#### Token Hierarchy & Query Patterns
+
+Token metrics are emitted at multiple layers. Understanding the hierarchy prevents double-counting:
+
+```
+App-level total
+├── workflow          ← sum of all node_execution tokens (DO NOT add both)
+│   └── node_execution ← per-node breakdown
+├── message           ← independent (non-workflow chat apps only)
+├── rule_generate     ← independent helper LLM call
+├── code_generate     ← independent helper LLM call
+├── structured_output ← independent helper LLM call
+└── instruction_modify← independent helper LLM call
+```
+
+**Key rule:** `workflow` tokens already include all `node_execution` tokens. Never sum both.
+
+**Available labels on token metrics:** `tenant_id`, `app_id`, `operation_type`, `model_provider`, `model_name`, `node_type`.
+App name is only available on span attributes (`dify.app.name`), not metric labels — use `app_id` for metric queries.
+
+**Common queries** (PromQL):
+
+```promql
+# ── Totals ──────────────────────────────────────────────────
+# App-level total (exclude node_execution to avoid double-counting)
+sum by (app_id) (dify_tokens_total{operation_type!="node_execution"})
+
+# Single app total
+sum (dify_tokens_total{app_id="<app_id>", operation_type!="node_execution"})
+
+# Per-tenant totals
+sum by (tenant_id) (dify_tokens_total{operation_type!="node_execution"})
+
+# ── Drill-down ──────────────────────────────────────────────
+# Workflow-level tokens for an app
+sum (dify_tokens_total{app_id="<app_id>", operation_type="workflow"})
+
+# Node-level breakdown within an app
+sum by (node_type) (dify_tokens_total{app_id="<app_id>", operation_type="node_execution"})
+
+# Model breakdown for an app
+sum by (model_provider, model_name) (dify_tokens_total{app_id="<app_id>"})
+
+# Input vs output per model
+sum by (model_name) (dify_tokens_input_total{app_id="<app_id>"})
+sum by (model_name) (dify_tokens_output_total{app_id="<app_id>"})
+
+# ── Rates ───────────────────────────────────────────────────
+# Token consumption rate (per hour)
+sum(rate(dify_tokens_total{operation_type!="node_execution"}[1h]))
+
+# Per-app consumption rate
+sum by (app_id) (rate(dify_tokens_total{operation_type!="node_execution"}[1h]))
+```
+
+**Finding `app_id` from app name** (trace query — Tempo / Jaeger):
+
+```
+{ resource.dify.app.name = "My Chatbot" } | select(resource.dify.app.id)
+```
+
+### Request Counters
+
+| Metric | Unit | Description |
+|--------|------|-------------|
+| `dify.requests.total` | `{request}` | Total operations count |
+
+**Labels by type:**
+
+| `type` | Additional Labels |
+|--------|-------------------|
+| `workflow` | `tenant_id`, `app_id`, `status`, `invoke_from` |
+| `node` | `tenant_id`, `app_id`, `node_type`, `model_provider`, `model_name`, `status` |
+| `draft_node` | `tenant_id`, `app_id`, `node_type`, `model_provider`, `model_name`, `status` |
+| `message` | `tenant_id`, `app_id`, `model_provider`, `model_name`, `status`, `invoke_from` |
+| `tool` | `tenant_id`, `app_id`, `tool_name` |
+| `moderation` | `tenant_id`, `app_id` |
+| `suggested_question` | `tenant_id`, `app_id`, `model_provider`, `model_name` |
+| `dataset_retrieval` | `tenant_id`, `app_id` |
+| `generate_name` | `tenant_id`, `app_id` |
+| `prompt_generation` | `tenant_id`, `app_id`, `operation_type`, `model_provider`, `model_name`, `status` |
+
+### Error Counters
+
+| Metric | Unit | Description |
+|--------|------|-------------|
+| `dify.errors.total` | `{error}` | Total failed operations |
+
+**Labels by type:**
+
+| `type` | Additional Labels |
+|--------|-------------------|
+| `workflow` | `tenant_id`, `app_id` |
+| `node` | `tenant_id`, `app_id`, `node_type`, `model_provider`, `model_name` |
+| `draft_node` | `tenant_id`, `app_id`, `node_type`, `model_provider`, `model_name` |
+| `message` | `tenant_id`, `app_id`, `model_provider`, `model_name` |
+| `tool` | `tenant_id`, `app_id`, `tool_name` |
+| `prompt_generation` | `tenant_id`, `app_id`, `operation_type`, `model_provider`, `model_name` |
+
+### Other Counters
+
+| Metric | Unit | Labels |
+|--------|------|--------|
+| `dify.feedback.total` | `{feedback}` | `tenant_id`, `app_id`, `rating` |
+| `dify.dataset.retrievals.total` | `{retrieval}` | `tenant_id`, `app_id`, `dataset_id`, `embedding_model_provider`, `embedding_model`, `rerank_model_provider`, `rerank_model` |
+| `dify.app.created.total` | `{app}` | `tenant_id`, `app_id`, `mode` |
+| `dify.app.updated.total` | `{app}` | `tenant_id`, `app_id` |
+| `dify.app.deleted.total` | `{app}` | `tenant_id`, `app_id` |
+
+## Histograms
+
+| Metric | Unit | Labels |
+|--------|------|--------|
+| `dify.workflow.duration` | `s` | `tenant_id`, `app_id`, `status` |
+| `dify.node.duration` | `s` | `tenant_id`, `app_id`, `node_type`, `model_provider`, `model_name`, `plugin_name` |
+| `dify.message.duration` | `s` | `tenant_id`, `app_id`, `model_provider`, `model_name` |
+| `dify.message.time_to_first_token` | `s` | `tenant_id`, `app_id`, `model_provider`, `model_name` |
+| `dify.tool.duration` | `s` | `tenant_id`, `app_id`, `tool_name` |
+| `dify.prompt_generation.duration` | `s` | `tenant_id`, `app_id`, `operation_type`, `model_provider`, `model_name` |
+
+## Structured Logs
+
+### Span Companion Logs
+
+Logs that accompany spans. Signal type: `span_detail`
+
+#### `dify.workflow.run` Companion Log
+
+**Common attributes:** All span attributes (see Traces section) plus:
+
+| Additional Attribute | Type | Always Present | Description |
+|---------------------|------|----------------|-------------|
+| `dify.app.name` | string | No | Application display name |
+| `dify.workspace.name` | string | No | Workspace display name |
+| `dify.workflow.version` | string | Yes | Workflow definition version |
+| `dify.workflow.inputs` | string/JSON | Yes | Input parameters (content-gated) |
+| `dify.workflow.outputs` | string/JSON | Yes | Output results (content-gated) |
+| `dify.workflow.query` | string | No | User query text (content-gated) |
+
+**Event attributes:**
+
+- `dify.event.name`: `"dify.workflow.run"`
+- `dify.event.signal`: `"span_detail"`
+- `trace_id`, `span_id`, `tenant_id`, `user_id`
+
+#### `dify.node.execution` and `dify.node.execution.draft` Companion Logs
+
+**Common attributes:** All span attributes (see Traces section) plus:
+
+| Additional Attribute | Type | Always Present | Description |
+|---------------------|------|----------------|-------------|
+| `dify.app.name` | string | No | Application display name |
+| `dify.workspace.name` | string | No | Workspace display name |
+| `dify.invoke_from` | string | No | Invocation source |
+| `gen_ai.tool.name` | string | No | Tool name (tool nodes only) |
+| `dify.node.total_price` | float | No | Cost (LLM nodes only) |
+| `dify.node.currency` | string | No | Currency code (LLM nodes only) |
+| `dify.node.iteration_index` | int | No | Iteration index (iteration nodes) |
+| `dify.node.loop_index` | int | No | Loop index (loop nodes) |
+| `dify.plugin.name` | string | No | Plugin name (tool/knowledge nodes) |
+| `dify.credential.name` | string | No | Credential name (plugin nodes) |
+| `dify.credential.id` | string | No | Credential ID (plugin nodes) |
+| `dify.dataset.ids` | JSON array | No | Dataset IDs (knowledge nodes) |
+| `dify.dataset.names` | JSON array | No | Dataset names (knowledge nodes) |
+| `dify.node.inputs` | string/JSON | Yes | Node inputs (content-gated) |
+| `dify.node.outputs` | string/JSON | Yes | Node outputs (content-gated) |
+| `dify.node.process_data` | string/JSON | No | Processing data (content-gated) |
+
+**Event attributes:**
+
+- `dify.event.name`: `"dify.node.execution"` or `"dify.node.execution.draft"`
+- `dify.event.signal`: `"span_detail"`
+- `trace_id`, `span_id`, `tenant_id`, `user_id`
+
+### Standalone Logs
+
+Logs without structural spans. Signal type: `metric_only`
+
+#### `dify.message.run`
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.event.name` | string | `"dify.message.run"` |
+| `dify.event.signal` | string | `"metric_only"` |
+| `trace_id` | string | OTEL trace ID (32-char hex) |
+| `span_id` | string | OTEL span ID (16-char hex) |
+| `tenant_id` | string | Tenant identifier |
+| `user_id` | string | User identifier (optional) |
+| `dify.app_id` | string | Application identifier |
+| `dify.message.id` | string | Message identifier |
+| `dify.conversation.id` | string | Conversation ID (optional) |
+| `dify.workflow.run_id` | string | Workflow run ID (optional) |
+| `dify.invoke_from` | string | `service-api`, `web-app`, `debugger`, `explore` |
+| `gen_ai.provider.name` | string | LLM provider |
+| `gen_ai.request.model` | string | LLM model |
+| `gen_ai.usage.input_tokens` | int | Input tokens |
+| `gen_ai.usage.output_tokens` | int | Output tokens |
+| `gen_ai.usage.total_tokens` | int | Total tokens |
+| `dify.message.status` | string | `succeeded`, `failed` |
+| `dify.message.error` | string | Error message (if failed) |
+| `dify.message.duration` | float | Duration (seconds) |
+| `dify.message.time_to_first_token` | float | TTFT (seconds) |
+| `dify.message.inputs` | string/JSON | Inputs (content-gated) |
+| `dify.message.outputs` | string/JSON | Outputs (content-gated) |
+
+#### `dify.tool.execution`
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.event.name` | string | `"dify.tool.execution"` |
+| `dify.event.signal` | string | `"metric_only"` |
+| `trace_id` | string | OTEL trace ID |
+| `span_id` | string | OTEL span ID |
+| `tenant_id` | string | Tenant identifier |
+| `dify.app_id` | string | Application identifier |
+| `dify.message.id` | string | Message identifier |
+| `dify.tool.name` | string | Tool name |
+| `dify.tool.duration` | float | Duration (seconds) |
+| `dify.tool.status` | string | `succeeded`, `failed` |
+| `dify.tool.error` | string | Error message (if failed) |
+| `dify.tool.inputs` | string/JSON | Inputs (content-gated) |
+| `dify.tool.outputs` | string/JSON | Outputs (content-gated) |
+| `dify.tool.parameters` | string/JSON | Parameters (content-gated) |
+| `dify.tool.config` | string/JSON | Configuration (content-gated) |
+
+#### `dify.moderation.check`
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.event.name` | string | `"dify.moderation.check"` |
+| `dify.event.signal` | string | `"metric_only"` |
+| `trace_id` | string | OTEL trace ID |
+| `span_id` | string | OTEL span ID |
+| `tenant_id` | string | Tenant identifier |
+| `dify.app_id` | string | Application identifier |
+| `dify.message.id` | string | Message identifier |
+| `dify.moderation.type` | string | `input`, `output` |
+| `dify.moderation.action` | string | `pass`, `block`, `flag` |
+| `dify.moderation.flagged` | boolean | Whether flagged |
+| `dify.moderation.categories` | JSON array | Flagged categories |
+| `dify.moderation.query` | string | Content (content-gated) |
+
+#### `dify.suggested_question.generation`
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.event.name` | string | `"dify.suggested_question.generation"` |
+| `dify.event.signal` | string | `"metric_only"` |
+| `trace_id` | string | OTEL trace ID |
+| `span_id` | string | OTEL span ID |
+| `tenant_id` | string | Tenant identifier |
+| `dify.app_id` | string | Application identifier |
+| `dify.message.id` | string | Message identifier |
+| `dify.suggested_question.count` | int | Number of questions |
+| `dify.suggested_question.duration` | float | Duration (seconds) |
+| `dify.suggested_question.status` | string | `succeeded`, `failed` |
+| `dify.suggested_question.error` | string | Error message (if failed) |
+| `dify.suggested_question.questions` | JSON array | Questions (content-gated) |
+
+#### `dify.dataset.retrieval`
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.event.name` | string | `"dify.dataset.retrieval"` |
+| `dify.event.signal` | string | `"metric_only"` |
+| `trace_id` | string | OTEL trace ID |
+| `span_id` | string | OTEL span ID |
+| `tenant_id` | string | Tenant identifier |
+| `dify.app_id` | string | Application identifier |
+| `dify.message.id` | string | Message identifier |
+| `dify.dataset.id` | string | Dataset identifier |
+| `dify.dataset.name` | string | Dataset name |
+| `dify.dataset.embedding_providers` | JSON array | Embedding model providers (one per dataset) |
+| `dify.dataset.embedding_models` | JSON array | Embedding models (one per dataset) |
+| `dify.retrieval.rerank_provider` | string | Rerank model provider |
+| `dify.retrieval.rerank_model` | string | Rerank model name |
+| `dify.retrieval.query` | string | Search query (content-gated) |
+| `dify.retrieval.document_count` | int | Documents retrieved |
+| `dify.retrieval.duration` | float | Duration (seconds) |
+| `dify.retrieval.status` | string | `succeeded`, `failed` |
+| `dify.retrieval.error` | string | Error message (if failed) |
+| `dify.dataset.documents` | JSON array | Documents (content-gated) |
+
+#### `dify.generate_name.execution`
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.event.name` | string | `"dify.generate_name.execution"` |
+| `dify.event.signal` | string | `"metric_only"` |
+| `trace_id` | string | OTEL trace ID |
+| `span_id` | string | OTEL span ID |
+| `tenant_id` | string | Tenant identifier |
+| `dify.app_id` | string | Application identifier |
+| `dify.conversation.id` | string | Conversation identifier |
+| `dify.generate_name.duration` | float | Duration (seconds) |
+| `dify.generate_name.status` | string | `succeeded`, `failed` |
+| `dify.generate_name.error` | string | Error message (if failed) |
+| `dify.generate_name.inputs` | string/JSON | Inputs (content-gated) |
+| `dify.generate_name.outputs` | string | Generated name (content-gated) |
+
+#### `dify.prompt_generation.execution`
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.event.name` | string | `"dify.prompt_generation.execution"` |
+| `dify.event.signal` | string | `"metric_only"` |
+| `trace_id` | string | OTEL trace ID |
+| `span_id` | string | OTEL span ID |
+| `tenant_id` | string | Tenant identifier |
+| `dify.app_id` | string | Application identifier |
+| `dify.prompt_generation.operation_type` | string | Operation type (see appendix) |
+| `gen_ai.provider.name` | string | LLM provider |
+| `gen_ai.request.model` | string | LLM model |
+| `gen_ai.usage.input_tokens` | int | Input tokens |
+| `gen_ai.usage.output_tokens` | int | Output tokens |
+| `gen_ai.usage.total_tokens` | int | Total tokens |
+| `dify.prompt_generation.duration` | float | Duration (seconds) |
+| `dify.prompt_generation.status` | string | `succeeded`, `failed` |
+| `dify.prompt_generation.error` | string | Error message (if failed) |
+| `dify.prompt_generation.instruction` | string | Instruction (content-gated) |
+| `dify.prompt_generation.output` | string/JSON | Output (content-gated) |
+
+#### `dify.app.created`
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.event.name` | string | `"dify.app.created"` |
+| `dify.event.signal` | string | `"metric_only"` |
+| `tenant_id` | string | Tenant identifier |
+| `dify.app_id` | string | Application identifier |
+| `dify.app.mode` | string | `chat`, `completion`, `agent-chat`, `workflow` |
+| `dify.app.created_at` | string | Timestamp (ISO 8601) |
+
+#### `dify.app.updated`
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.event.name` | string | `"dify.app.updated"` |
+| `dify.event.signal` | string | `"metric_only"` |
+| `tenant_id` | string | Tenant identifier |
+| `dify.app_id` | string | Application identifier |
+| `dify.app.updated_at` | string | Timestamp (ISO 8601) |
+
+#### `dify.app.deleted`
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.event.name` | string | `"dify.app.deleted"` |
+| `dify.event.signal` | string | `"metric_only"` |
+| `tenant_id` | string | Tenant identifier |
+| `dify.app_id` | string | Application identifier |
+| `dify.app.deleted_at` | string | Timestamp (ISO 8601) |
+
+#### `dify.feedback.created`
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.event.name` | string | `"dify.feedback.created"` |
+| `dify.event.signal` | string | `"metric_only"` |
+| `trace_id` | string | OTEL trace ID |
+| `span_id` | string | OTEL span ID |
+| `tenant_id` | string | Tenant identifier |
+| `dify.app_id` | string | Application identifier |
+| `dify.message.id` | string | Message identifier |
+| `dify.feedback.rating` | string | `like`, `dislike`, `null` |
+| `dify.feedback.content` | string | Feedback text (content-gated) |
+| `dify.feedback.created_at` | string | Timestamp (ISO 8601) |
+
+#### `dify.telemetry.rehydration_failed`
+
+Diagnostic event for telemetry system health monitoring.
+
+| Attribute | Type | Description |
+|-----------|------|-------------|
+| `dify.event.name` | string | `"dify.telemetry.rehydration_failed"` |
+| `dify.event.signal` | string | `"metric_only"` |
+| `tenant_id` | string | Tenant identifier |
+| `dify.telemetry.error` | string | Error message |
+| `dify.telemetry.payload_type` | string | Payload type (see appendix) |
+| `dify.telemetry.correlation_id` | string | Correlation ID |
+
+## Content-Gated Attributes
+
+When `ENTERPRISE_INCLUDE_CONTENT=false`, these attributes are replaced with reference strings (`ref:{id_type}={uuid}`).
+
+| Attribute | Signal |
+|-----------|--------|
+| `dify.workflow.inputs` | `dify.workflow.run` |
+| `dify.workflow.outputs` | `dify.workflow.run` |
+| `dify.workflow.query` | `dify.workflow.run` |
+| `dify.node.inputs` | `dify.node.execution` |
+| `dify.node.outputs` | `dify.node.execution` |
+| `dify.node.process_data` | `dify.node.execution` |
+| `dify.message.inputs` | `dify.message.run` |
+| `dify.message.outputs` | `dify.message.run` |
+| `dify.tool.inputs` | `dify.tool.execution` |
+| `dify.tool.outputs` | `dify.tool.execution` |
+| `dify.tool.parameters` | `dify.tool.execution` |
+| `dify.tool.config` | `dify.tool.execution` |
+| `dify.moderation.query` | `dify.moderation.check` |
+| `dify.suggested_question.questions` | `dify.suggested_question.generation` |
+| `dify.retrieval.query` | `dify.dataset.retrieval` |
+| `dify.dataset.documents` | `dify.dataset.retrieval` |
+| `dify.generate_name.inputs` | `dify.generate_name.execution` |
+| `dify.generate_name.outputs` | `dify.generate_name.execution` |
+| `dify.prompt_generation.instruction` | `dify.prompt_generation.execution` |
+| `dify.prompt_generation.output` | `dify.prompt_generation.execution` |
+| `dify.feedback.content` | `dify.feedback.created` |
+
+## Appendix
+
+### Operation Types
+
+- `workflow`, `node_execution`, `message`, `rule_generate`, `code_generate`, `structured_output`, `instruction_modify`
+
+### Node Types
+
+- `start`, `end`, `answer`, `llm`, `knowledge-retrieval`, `knowledge-index`, `if-else`, `code`, `template-transform`, `question-classifier`, `http-request`, `tool`, `datasource`, `variable-aggregator`, `loop`, `iteration`, `parameter-extractor`, `assigner`, `document-extractor`, `list-operator`, `agent`, `trigger-webhook`, `trigger-schedule`, `trigger-plugin`, `human-input`
+
+### Workflow Statuses
+
+- `running`, `succeeded`, `failed`, `stopped`, `partial-succeeded`, `paused`
+
+### Payload Types
+
+- `workflow`, `node`, `message`, `tool`, `moderation`, `suggested_question`, `dataset_retrieval`, `generate_name`, `prompt_generation`, `app`, `feedback`
+
+### Null Value Behavior
+
+**Spans:** Attributes with `null` values are omitted.
+
+**Logs:** Attributes with `null` values appear as `null` in JSON.
+
+**Content-Gated:** Replaced with reference strings, not set to `null`.

api/enterprise/telemetry/README.md

@@ -0,0 +1,121 @@
+# Dify Enterprise Telemetry
+
+This document provides an overview of the Dify Enterprise OpenTelemetry (OTEL) exporter and how to configure it for integration with observability stacks like Prometheus, Grafana, Jaeger, or Honeycomb.
+
+## Overview
+
+Dify Enterprise uses a "slim span + rich companion log" architecture to provide high-fidelity observability without overwhelming trace storage.
+
+- **Traces (Spans)**: Capture the structure, identity, and timing of high-level operations (Workflows and Nodes).
+- **Structured Logs**: Provide deep context (inputs, outputs, metadata) for every event, correlated to spans via `trace_id` and `span_id`.
+- **Metrics**: Provide 100% accurate counters and histograms for usage, performance, and error tracking.
+
+### Signal Architecture
+
+```mermaid
+graph TD
+    A[Workflow Run] -->|Span| B(dify.workflow.run)
+    A -->|Log| C(dify.workflow.run detail)
+    B ---|trace_id| C
+    
+    D[Node Execution] -->|Span| E(dify.node.execution)
+    D -->|Log| F(dify.node.execution detail)
+    E ---|span_id| F
+    
+    G[Message/Tool/etc] -->|Log| H(dify.* event)
+    G -->|Metric| I(dify.* counter/histogram)
+```
+
+## Configuration
+
+The Enterprise OTEL exporter is configured via environment variables.
+
+| Variable | Description | Default |
+|----------|-------------|---------|
+| `ENTERPRISE_ENABLED` | Master switch for all enterprise features. | `false` |
+| `ENTERPRISE_TELEMETRY_ENABLED` | Master switch for enterprise telemetry. | `false` |
+| `ENTERPRISE_OTLP_ENDPOINT` | OTLP collector endpoint (e.g., `http://otel-collector:4318`). | - |
+| `ENTERPRISE_OTLP_HEADERS` | Custom headers for OTLP requests (e.g., `x-scope-orgid=tenant1`). | - |
+| `ENTERPRISE_OTLP_PROTOCOL` | OTLP transport protocol (`http` or `grpc`). | `http` |
+| `ENTERPRISE_OTLP_API_KEY` | Bearer token for authentication. | - |
+| `ENTERPRISE_INCLUDE_CONTENT` | Whether to include sensitive content (inputs/outputs) in logs. | `true` |
+| `ENTERPRISE_SERVICE_NAME` | Service name reported to OTEL. | `dify` |
+| `ENTERPRISE_OTEL_SAMPLING_RATE` | Sampling rate for traces (0.0 to 1.0). Metrics are always 100%. | `1.0` |
+
+## Correlation Model
+
+Dify uses deterministic ID generation to ensure signals are correlated across different services and asynchronous tasks.
+
+### ID Generation Rules
+
+- `trace_id`: Derived from the correlation ID (workflow_run_id or node_execution_id for drafts) using `int(UUID(correlation_id))`
+- `span_id`: Derived from the source ID using the lower 64 bits of `UUID(source_id)`
+
+### Scenario A: Simple Workflow
+
+A single workflow run with multiple nodes. All spans and logs share the same `trace_id` (derived from `workflow_run_id`).
+
+```
+trace_id = UUID(workflow_run_id)
+├── [root span] dify.workflow.run (span_id = hash(workflow_run_id))
+│   ├── [child] dify.node.execution - "Start" (span_id = hash(node_exec_id_1))
+│   ├── [child] dify.node.execution - "LLM" (span_id = hash(node_exec_id_2))
+│   └── [child] dify.node.execution - "End" (span_id = hash(node_exec_id_3))
+```
+
+### Scenario B: Nested Sub-Workflow
+
+A workflow calling another workflow via a Tool or Sub-workflow node. The child workflow's spans are linked to the parent via `parent_span_id`. Both workflows share the same trace_id.
+
+```
+trace_id = UUID(outer_workflow_run_id)     ← shared across both workflows
+├── [root] dify.workflow.run (outer) (span_id = hash(outer_workflow_run_id))
+│   ├── dify.node.execution - "Start Node"
+│   ├── dify.node.execution - "Tool Node" (triggers sub-workflow)
+│   │   └── [child] dify.workflow.run (inner) (span_id = hash(inner_workflow_run_id))
+│   │       ├── dify.node.execution - "Inner Start"
+│   │       └── dify.node.execution - "Inner End"
+│   └── dify.node.execution - "End Node"
+```
+
+**Key attributes for nested workflows:**
+
+- Inner workflow's `dify.parent.trace_id` = outer `workflow_run_id`
+- Inner workflow's `dify.parent.node.execution_id` = tool node's `execution_id`
+- Inner workflow's `dify.parent.workflow.run_id` = outer `workflow_run_id`
+- Inner workflow's `dify.parent.app.id` = outer `app_id`
+
+### Scenario C: Draft Node Execution
+
+A single node run in isolation (debugger/preview mode). It creates its own trace where the node span is the root.
+
+```
+trace_id = UUID(node_execution_id)   ← own trace, NOT part of any workflow
+└── dify.node.execution.draft (span_id = hash(node_execution_id))
+```
+
+**Key difference:** Draft executions use `node_execution_id` as the correlation_id, so they are NOT children of any workflow trace.
+
+## Content Gating
+
+When `ENTERPRISE_INCLUDE_CONTENT` is set to `false`, sensitive content attributes (inputs, outputs, queries) are replaced with reference strings (e.g., `ref:workflow_run_id=...`) to prevent data leakage to the OTEL collector.
+
+**Reference String Format:**
+
+```
+ref:{id_type}={uuid}
+```
+
+**Examples:**
+
+```
+ref:workflow_run_id=550e8400-e29b-41d4-a716-446655440000
+ref:node_execution_id=660e8400-e29b-41d4-a716-446655440001
+ref:message_id=770e8400-e29b-41d4-a716-446655440002
+```
+
+To retrieve actual content when gating is enabled, query the Dify database using the provided UUID.
+
+## Reference
+
+For a complete list of telemetry signals, attributes, and data structures, see [DATA_DICTIONARY.md](./DATA_DICTIONARY.md).

api/enterprise/telemetry/contracts.py

@@ -0,0 +1,73 @@
+"""Telemetry gateway contracts and data structures.
+
+This module defines the envelope format for telemetry events and the routing
+configuration that determines how each event type is processed.
+"""
+
+from __future__ import annotations
+
+from enum import StrEnum
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict
+
+
+class TelemetryCase(StrEnum):
+    """Enumeration of all known telemetry event cases."""
+
+    WORKFLOW_RUN = "workflow_run"
+    NODE_EXECUTION = "node_execution"
+    DRAFT_NODE_EXECUTION = "draft_node_execution"
+    MESSAGE_RUN = "message_run"
+    TOOL_EXECUTION = "tool_execution"
+    MODERATION_CHECK = "moderation_check"
+    SUGGESTED_QUESTION = "suggested_question"
+    DATASET_RETRIEVAL = "dataset_retrieval"
+    GENERATE_NAME = "generate_name"
+    PROMPT_GENERATION = "prompt_generation"
+    APP_CREATED = "app_created"
+    APP_UPDATED = "app_updated"
+    APP_DELETED = "app_deleted"
+    FEEDBACK_CREATED = "feedback_created"
+
+
+class SignalType(StrEnum):
+    """Signal routing type for telemetry cases."""
+
+    TRACE = "trace"
+    METRIC_LOG = "metric_log"
+
+
+class CaseRoute(BaseModel):
+    """Routing configuration for a telemetry case.
+
+    Attributes:
+        signal_type: The type of signal (trace or metric_log).
+        ce_eligible: Whether this case is eligible for community edition tracing.
+    """
+
+    signal_type: SignalType
+    ce_eligible: bool
+
+
+class TelemetryEnvelope(BaseModel):
+    """Envelope for telemetry events.
+
+    Attributes:
+        case: The telemetry case type.
+        tenant_id: The tenant identifier.
+        event_id: Unique event identifier for deduplication.
+        payload: The main event payload (inline for small payloads,
+            empty when offloaded to storage via ``payload_ref``).
+        metadata: Optional metadata dictionary.  When the gateway
+            offloads a large payload to object storage, this contains
+            ``{"payload_ref": "<storage_key>"}``.
+    """
+
+    model_config = ConfigDict(extra="forbid", use_enum_values=False)
+
+    case: TelemetryCase
+    tenant_id: str
+    event_id: str
+    payload: dict[str, Any]
+    metadata: dict[str, Any] | None = None

api/enterprise/telemetry/draft_trace.py

@@ -0,0 +1,77 @@
+from __future__ import annotations
+
+from collections.abc import Mapping
+from typing import Any
+
+from core.telemetry import TelemetryContext, TelemetryEvent, TraceTaskName
+from core.telemetry import emit as telemetry_emit
+from dify_graph.enums import WorkflowNodeExecutionMetadataKey
+from models.workflow import WorkflowNodeExecutionModel
+
+
+def enqueue_draft_node_execution_trace(
+    *,
+    execution: WorkflowNodeExecutionModel,
+    outputs: Mapping[str, Any] | None,
+    workflow_execution_id: str | None,
+    user_id: str,
+) -> None:
+    node_data = _build_node_execution_data(
+        execution=execution,
+        outputs=outputs,
+        workflow_execution_id=workflow_execution_id,
+    )
+    telemetry_emit(
+        TelemetryEvent(
+            name=TraceTaskName.DRAFT_NODE_EXECUTION_TRACE,
+            context=TelemetryContext(
+                tenant_id=execution.tenant_id,
+                user_id=user_id,
+                app_id=execution.app_id,
+            ),
+            payload={"node_execution_data": node_data},
+        )
+    )
+
+
+def _build_node_execution_data(
+    *,
+    execution: WorkflowNodeExecutionModel,
+    outputs: Mapping[str, Any] | None,
+    workflow_execution_id: str | None,
+) -> dict[str, Any]:
+    metadata = execution.execution_metadata_dict
+    node_outputs = outputs if outputs is not None else execution.outputs_dict
+    execution_id = workflow_execution_id or execution.workflow_run_id or execution.id
+
+    return {
+        "workflow_id": execution.workflow_id,
+        "workflow_execution_id": execution_id,
+        "tenant_id": execution.tenant_id,
+        "app_id": execution.app_id,
+        "node_execution_id": execution.id,
+        "node_id": execution.node_id,
+        "node_type": execution.node_type,
+        "title": execution.title,
+        "status": execution.status,
+        "error": execution.error,
+        "elapsed_time": execution.elapsed_time,
+        "index": execution.index,
+        "predecessor_node_id": execution.predecessor_node_id,
+        "created_at": execution.created_at,
+        "finished_at": execution.finished_at,
+        "total_tokens": metadata.get(WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS, 0),
+        "total_price": metadata.get(WorkflowNodeExecutionMetadataKey.TOTAL_PRICE, 0.0),
+        "currency": metadata.get(WorkflowNodeExecutionMetadataKey.CURRENCY),
+        "tool_name": (metadata.get(WorkflowNodeExecutionMetadataKey.TOOL_INFO) or {}).get("tool_name")
+        if isinstance(metadata.get(WorkflowNodeExecutionMetadataKey.TOOL_INFO), dict)
+        else None,
+        "iteration_id": metadata.get(WorkflowNodeExecutionMetadataKey.ITERATION_ID),
+        "iteration_index": metadata.get(WorkflowNodeExecutionMetadataKey.ITERATION_INDEX),
+        "loop_id": metadata.get(WorkflowNodeExecutionMetadataKey.LOOP_ID),
+        "loop_index": metadata.get(WorkflowNodeExecutionMetadataKey.LOOP_INDEX),
+        "parallel_id": metadata.get(WorkflowNodeExecutionMetadataKey.PARALLEL_ID),
+        "node_inputs": execution.inputs_dict,
+        "node_outputs": node_outputs,
+        "process_data": execution.process_data_dict,
+    }

api/enterprise/telemetry/enterprise_trace.py

@@ -0,0 +1,938 @@
+"""Enterprise trace handler — duck-typed, NOT a BaseTraceInstance subclass.
+
+Invoked directly in the Celery task, not through OpsTraceManager dispatch.
+Only requires a matching ``trace(trace_info)`` method signature.
+
+Signal strategy:
+- **Traces (spans)**: workflow run, node execution, draft node execution only.
+- **Metrics + structured logs**: all other event types.
+
+Token metric labels (unified structure):
+All token metrics (dify.tokens.input, dify.tokens.output, dify.tokens.total) use the
+same label set for consistent filtering and aggregation:
+- tenant_id: Tenant identifier
+- app_id: Application identifier
+- operation_type: Source of token usage (workflow | node_execution | message | rule_generate | etc.)
+- model_provider: LLM provider name (empty string if not applicable)
+- model_name: LLM model name (empty string if not applicable)
+- node_type: Workflow node type (empty string if not node_execution)
+
+This unified structure allows filtering by operation_type to separate:
+- Workflow-level aggregates (operation_type=workflow)
+- Individual node executions (operation_type=node_execution)
+- Direct message calls (operation_type=message)
+- Prompt generation operations (operation_type=rule_generate, code_generate, etc.)
+
+Without this, tokens are double-counted when querying totals (workflow totals include
+node totals, since workflow.total_tokens is the sum of all node tokens).
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any, cast
+
+from opentelemetry.util.types import AttributeValue
+
+from core.ops.entities.trace_entity import (
+    BaseTraceInfo,
+    DatasetRetrievalTraceInfo,
+    DraftNodeExecutionTrace,
+    GenerateNameTraceInfo,
+    MessageTraceInfo,
+    ModerationTraceInfo,
+    OperationType,
+    PromptGenerationTraceInfo,
+    SuggestedQuestionTraceInfo,
+    ToolTraceInfo,
+    WorkflowNodeTraceInfo,
+    WorkflowTraceInfo,
+)
+from enterprise.telemetry.entities import (
+    EnterpriseTelemetryCounter,
+    EnterpriseTelemetryEvent,
+    EnterpriseTelemetryHistogram,
+    EnterpriseTelemetrySpan,
+    TokenMetricLabels,
+)
+from enterprise.telemetry.telemetry_log import emit_metric_only_event, emit_telemetry_log
+
+logger = logging.getLogger(__name__)
+
+
+class EnterpriseOtelTrace:
+    """Duck-typed enterprise trace handler.
+
+    ``*_trace`` methods emit spans (workflow/node only) or structured logs
+    (all other events), plus metrics at 100 % accuracy.
+    """
+
+    def __init__(self) -> None:
+        from extensions.ext_enterprise_telemetry import get_enterprise_exporter
+
+        exporter = get_enterprise_exporter()
+        if exporter is None:
+            raise RuntimeError("EnterpriseOtelTrace instantiated but exporter is not initialized")
+        self._exporter = exporter
+
+    def trace(self, trace_info: BaseTraceInfo) -> None:
+        if isinstance(trace_info, WorkflowTraceInfo):
+            self._workflow_trace(trace_info)
+        elif isinstance(trace_info, MessageTraceInfo):
+            self._message_trace(trace_info)
+        elif isinstance(trace_info, ToolTraceInfo):
+            self._tool_trace(trace_info)
+        elif isinstance(trace_info, DraftNodeExecutionTrace):
+            self._draft_node_execution_trace(trace_info)
+        elif isinstance(trace_info, WorkflowNodeTraceInfo):
+            self._node_execution_trace(trace_info)
+        elif isinstance(trace_info, ModerationTraceInfo):
+            self._moderation_trace(trace_info)
+        elif isinstance(trace_info, SuggestedQuestionTraceInfo):
+            self._suggested_question_trace(trace_info)
+        elif isinstance(trace_info, DatasetRetrievalTraceInfo):
+            self._dataset_retrieval_trace(trace_info)
+        elif isinstance(trace_info, GenerateNameTraceInfo):
+            self._generate_name_trace(trace_info)
+        elif isinstance(trace_info, PromptGenerationTraceInfo):
+            self._prompt_generation_trace(trace_info)
+
+    def _common_attrs(self, trace_info: BaseTraceInfo) -> dict[str, Any]:
+        metadata = self._metadata(trace_info)
+        tenant_id, app_id, user_id = self._context_ids(trace_info, metadata)
+        return {
+            "dify.trace_id": trace_info.resolved_trace_id,
+            "dify.tenant_id": tenant_id,
+            "dify.app_id": app_id,
+            "dify.app.name": metadata.get("app_name"),
+            "dify.workspace.name": metadata.get("workspace_name"),
+            "gen_ai.user.id": user_id,
+            "dify.message.id": trace_info.message_id,
+        }
+
+    def _metadata(self, trace_info: BaseTraceInfo) -> dict[str, Any]:
+        return trace_info.metadata
+
+    def _context_ids(
+        self,
+        trace_info: BaseTraceInfo,
+        metadata: dict[str, Any],
+    ) -> tuple[str | None, str | None, str | None]:
+        tenant_id = getattr(trace_info, "tenant_id", None) or metadata.get("tenant_id")
+        app_id = getattr(trace_info, "app_id", None) or metadata.get("app_id")
+        user_id = getattr(trace_info, "user_id", None) or metadata.get("user_id")
+        return tenant_id, app_id, user_id
+
+    def _labels(self, **values: AttributeValue) -> dict[str, AttributeValue]:
+        return dict(values)
+
+    def _safe_payload_value(self, value: Any) -> str | dict[str, Any] | list[object] | None:
+        if isinstance(value, str):
+            return value
+        if isinstance(value, dict):
+            return cast(dict[str, Any], value)
+        if isinstance(value, list):
+            items: list[object] = []
+            for item in cast(list[object], value):
+                items.append(item)
+            return items
+        return None
+
+    def _content_or_ref(self, value: Any, ref: str) -> Any:
+        if self._exporter.include_content:
+            return self._maybe_json(value)
+        return ref
+
+    def _maybe_json(self, value: Any) -> str | None:
+        if value is None:
+            return None
+        if isinstance(value, str):
+            return value
+        try:
+            return json.dumps(value, default=str)
+        except (TypeError, ValueError):
+            return str(value)
+
+    # ------------------------------------------------------------------
+    # SPAN-emitting handlers (workflow, node execution, draft node)
+    # ------------------------------------------------------------------
+
+    def _workflow_trace(self, info: WorkflowTraceInfo) -> None:
+        metadata = self._metadata(info)
+        tenant_id, app_id, user_id = self._context_ids(info, metadata)
+        # -- Span attrs: identity + structure + status + timing + gen_ai scalars --
+        span_attrs: dict[str, Any] = {
+            "dify.trace_id": info.resolved_trace_id,
+            "dify.tenant_id": tenant_id,
+            "dify.app_id": app_id,
+            "dify.workflow.id": info.workflow_id,
+            "dify.workflow.run_id": info.workflow_run_id,
+            "dify.workflow.status": info.workflow_run_status,
+            "dify.workflow.error": info.error,
+            "dify.workflow.elapsed_time": info.workflow_run_elapsed_time,
+            "dify.invoke_from": metadata.get("triggered_from"),
+            "dify.conversation.id": info.conversation_id,
+            "dify.message.id": info.message_id,
+            "dify.invoked_by": info.invoked_by,
+            "gen_ai.usage.total_tokens": info.total_tokens,
+            "gen_ai.user.id": user_id,
+        }
+
+        trace_correlation_override, parent_span_id_source = info.resolved_parent_context
+
+        parent_ctx = metadata.get("parent_trace_context")
+        if isinstance(parent_ctx, dict):
+            parent_ctx_dict = cast(dict[str, Any], parent_ctx)
+            span_attrs["dify.parent.trace_id"] = parent_ctx_dict.get("trace_id")
+            span_attrs["dify.parent.node.execution_id"] = parent_ctx_dict.get("parent_node_execution_id")
+            span_attrs["dify.parent.workflow.run_id"] = parent_ctx_dict.get("parent_workflow_run_id")
+            span_attrs["dify.parent.app.id"] = parent_ctx_dict.get("parent_app_id")
+
+        self._exporter.export_span(
+            EnterpriseTelemetrySpan.WORKFLOW_RUN,
+            span_attrs,
+            correlation_id=info.workflow_run_id,
+            span_id_source=info.workflow_run_id,
+            start_time=info.start_time,
+            end_time=info.end_time,
+            trace_correlation_override=trace_correlation_override,
+            parent_span_id_source=parent_span_id_source,
+        )
+
+        # -- Companion log: ALL attrs (span + detail) for full picture --
+        log_attrs: dict[str, Any] = {**span_attrs}
+        log_attrs.update(
+            {
+                "dify.app.name": metadata.get("app_name"),
+                "dify.workspace.name": metadata.get("workspace_name"),
+                "gen_ai.user.id": user_id,
+                "gen_ai.usage.total_tokens": info.total_tokens,
+                "dify.workflow.version": info.workflow_run_version,
+            }
+        )
+
+        ref = f"ref:workflow_run_id={info.workflow_run_id}"
+        log_attrs["dify.workflow.inputs"] = self._content_or_ref(info.workflow_run_inputs, ref)
+        log_attrs["dify.workflow.outputs"] = self._content_or_ref(info.workflow_run_outputs, ref)
+        log_attrs["dify.workflow.query"] = self._content_or_ref(info.query, ref)
+
+        emit_telemetry_log(
+            event_name=EnterpriseTelemetryEvent.WORKFLOW_RUN,
+            attributes=log_attrs,
+            signal="span_detail",
+            trace_id_source=info.workflow_run_id,
+            span_id_source=info.workflow_run_id,
+            tenant_id=tenant_id,
+            user_id=user_id,
+        )
+
+        # -- Metrics --
+        labels = self._labels(
+            tenant_id=tenant_id or "",
+            app_id=app_id or "",
+        )
+        token_labels = TokenMetricLabels(
+            tenant_id=tenant_id or "",
+            app_id=app_id or "",
+            operation_type=OperationType.WORKFLOW,
+            model_provider="",
+            model_name="",
+            node_type="",
+        ).to_dict()
+        self._exporter.increment_counter(EnterpriseTelemetryCounter.TOKENS, info.total_tokens, token_labels)
+        if info.prompt_tokens is not None and info.prompt_tokens > 0:
+            self._exporter.increment_counter(EnterpriseTelemetryCounter.INPUT_TOKENS, info.prompt_tokens, token_labels)
+        if info.completion_tokens is not None and info.completion_tokens > 0:
+            self._exporter.increment_counter(
+                EnterpriseTelemetryCounter.OUTPUT_TOKENS, info.completion_tokens, token_labels
+            )
+        invoke_from = metadata.get("triggered_from", "")
+        self._exporter.increment_counter(
+            EnterpriseTelemetryCounter.REQUESTS,
+            1,
+            self._labels(
+                **labels,
+                type="workflow",
+                status=info.workflow_run_status,
+                invoke_from=invoke_from,
+            ),
+        )
+        # Prefer wall-clock timestamps over the elapsed_time field: elapsed_time defaults
+        # to 0 in the DB and can be stale if the Celery write races with the trace task.
+        # start_time = workflow_run.created_at, end_time = workflow_run.finished_at.
+        if info.start_time and info.end_time:
+            workflow_duration = (info.end_time - info.start_time).total_seconds()
+        elif info.workflow_run_elapsed_time:
+            workflow_duration = float(info.workflow_run_elapsed_time)
+        else:
+            workflow_duration = 0.0
+        self._exporter.record_histogram(
+            EnterpriseTelemetryHistogram.WORKFLOW_DURATION,
+            workflow_duration,
+            self._labels(
+                **labels,
+                status=info.workflow_run_status,
+            ),
+        )
+
+        if info.error:
+            self._exporter.increment_counter(
+                EnterpriseTelemetryCounter.ERRORS,
+                1,
+                self._labels(
+                    **labels,
+                    type="workflow",
+                ),
+            )
+
+    def _node_execution_trace(self, info: WorkflowNodeTraceInfo) -> None:
+        self._emit_node_execution_trace(info, EnterpriseTelemetrySpan.NODE_EXECUTION, "node")
+
+    def _draft_node_execution_trace(self, info: DraftNodeExecutionTrace) -> None:
+        self._emit_node_execution_trace(
+            info,
+            EnterpriseTelemetrySpan.DRAFT_NODE_EXECUTION,
+            "draft_node",
+            correlation_id_override=info.node_execution_id,
+            trace_correlation_override_param=info.workflow_run_id,
+        )
+
+    def _emit_node_execution_trace(
+        self,
+        info: WorkflowNodeTraceInfo,
+        span_name: EnterpriseTelemetrySpan,
+        request_type: str,
+        correlation_id_override: str | None = None,
+        trace_correlation_override_param: str | None = None,
+    ) -> None:
+        metadata = self._metadata(info)
+        tenant_id, app_id, user_id = self._context_ids(info, metadata)
+        # -- Span attrs: identity + structure + status + timing + gen_ai scalars --
+        span_attrs: dict[str, Any] = {
+            "dify.trace_id": info.resolved_trace_id,
+            "dify.tenant_id": tenant_id,
+            "dify.app_id": app_id,
+            "dify.workflow.id": info.workflow_id,
+            "dify.workflow.run_id": info.workflow_run_id,
+            "dify.message.id": info.message_id,
+            "dify.conversation.id": metadata.get("conversation_id"),
+            "dify.node.execution_id": info.node_execution_id,
+            "dify.node.id": info.node_id,
+            "dify.node.type": info.node_type,
+            "dify.node.title": info.title,
+            "dify.node.status": info.status,
+            "dify.node.error": info.error,
+            "dify.node.elapsed_time": info.elapsed_time,
+            "dify.node.index": info.index,
+            "dify.node.predecessor_node_id": info.predecessor_node_id,
+            "dify.node.iteration_id": info.iteration_id,
+            "dify.node.loop_id": info.loop_id,
+            "dify.node.parallel_id": info.parallel_id,
+            "dify.node.invoked_by": info.invoked_by,
+            "gen_ai.usage.input_tokens": info.prompt_tokens,
+            "gen_ai.usage.output_tokens": info.completion_tokens,
+            "gen_ai.usage.total_tokens": info.total_tokens,
+            "gen_ai.request.model": info.model_name,
+            "gen_ai.provider.name": info.model_provider,
+            "gen_ai.user.id": user_id,
+        }
+
+        resolved_override, _ = info.resolved_parent_context
+        trace_correlation_override = trace_correlation_override_param or resolved_override
+
+        effective_correlation_id = correlation_id_override or info.workflow_run_id
+        self._exporter.export_span(
+            span_name,
+            span_attrs,
+            correlation_id=effective_correlation_id,
+            span_id_source=info.node_execution_id,
+            start_time=info.start_time,
+            end_time=info.end_time,
+            trace_correlation_override=trace_correlation_override,
+        )
+
+        # -- Companion log: ALL attrs (span + detail) --
+        log_attrs: dict[str, Any] = {**span_attrs}
+        log_attrs.update(
+            {
+                "dify.app.name": metadata.get("app_name"),
+                "dify.workspace.name": metadata.get("workspace_name"),
+                "dify.invoke_from": metadata.get("invoke_from"),
+                "gen_ai.user.id": user_id,
+                "gen_ai.usage.total_tokens": info.total_tokens,
+                "dify.node.total_price": info.total_price,
+                "dify.node.currency": info.currency,
+                "gen_ai.provider.name": info.model_provider,
+                "gen_ai.request.model": info.model_name,
+                "gen_ai.tool.name": info.tool_name,
+                "dify.node.iteration_index": info.iteration_index,
+                "dify.node.loop_index": info.loop_index,
+                "dify.plugin.name": metadata.get("plugin_name"),
+                "dify.credential.name": metadata.get("credential_name"),
+                "dify.credential.id": metadata.get("credential_id"),
+                "dify.dataset.ids": self._maybe_json(metadata.get("dataset_ids")),
+                "dify.dataset.names": self._maybe_json(metadata.get("dataset_names")),
+            }
+        )
+
+        ref = f"ref:node_execution_id={info.node_execution_id}"
+        log_attrs["dify.node.inputs"] = self._content_or_ref(info.node_inputs, ref)
+        log_attrs["dify.node.outputs"] = self._content_or_ref(info.node_outputs, ref)
+        log_attrs["dify.node.process_data"] = self._content_or_ref(info.process_data, ref)
+
+        emit_telemetry_log(
+            event_name=span_name.value,
+            attributes=log_attrs,
+            signal="span_detail",
+            trace_id_source=info.workflow_run_id,
+            span_id_source=info.node_execution_id,
+            tenant_id=tenant_id,
+            user_id=user_id,
+        )
+
+        # -- Metrics --
+        labels = self._labels(
+            tenant_id=tenant_id or "",
+            app_id=app_id or "",
+            node_type=info.node_type,
+            model_provider=info.model_provider or "",
+        )
+        if info.total_tokens:
+            token_labels = TokenMetricLabels(
+                tenant_id=tenant_id or "",
+                app_id=app_id or "",
+                operation_type=OperationType.NODE_EXECUTION,
+                model_provider=info.model_provider or "",
+                model_name=info.model_name or "",
+                node_type=info.node_type,
+            ).to_dict()
+            self._exporter.increment_counter(EnterpriseTelemetryCounter.TOKENS, info.total_tokens, token_labels)
+            if info.prompt_tokens is not None and info.prompt_tokens > 0:
+                self._exporter.increment_counter(
+                    EnterpriseTelemetryCounter.INPUT_TOKENS, info.prompt_tokens, token_labels
+                )
+            if info.completion_tokens is not None and info.completion_tokens > 0:
+                self._exporter.increment_counter(
+                    EnterpriseTelemetryCounter.OUTPUT_TOKENS, info.completion_tokens, token_labels
+                )
+        self._exporter.increment_counter(
+            EnterpriseTelemetryCounter.REQUESTS,
+            1,
+            self._labels(
+                **labels,
+                type=request_type,
+                status=info.status,
+                model_name=info.model_name or "",
+            ),
+        )
+        duration_labels = dict(labels)
+        duration_labels["model_name"] = info.model_name or ""
+        plugin_name = metadata.get("plugin_name")
+        if plugin_name and info.node_type in {"tool", "knowledge-retrieval"}:
+            duration_labels["plugin_name"] = plugin_name
+        self._exporter.record_histogram(EnterpriseTelemetryHistogram.NODE_DURATION, info.elapsed_time, duration_labels)
+
+        if info.error:
+            self._exporter.increment_counter(
+                EnterpriseTelemetryCounter.ERRORS,
+                1,
+                self._labels(
+                    **labels,
+                    type=request_type,
+                    model_name=info.model_name or "",
+                ),
+            )
+
+    # ------------------------------------------------------------------
+    # METRIC-ONLY handlers (structured log + counters/histograms)
+    # ------------------------------------------------------------------
+
+    def _message_trace(self, info: MessageTraceInfo) -> None:
+        metadata = self._metadata(info)
+        tenant_id, app_id, user_id = self._context_ids(info, metadata)
+        attrs = self._common_attrs(info)
+        attrs.update(
+            {
+                "dify.invoke_from": metadata.get("from_source"),
+                "dify.conversation.id": metadata.get("conversation_id"),
+                "dify.conversation.mode": info.conversation_mode,
+                "gen_ai.provider.name": metadata.get("ls_provider"),
+                "gen_ai.request.model": metadata.get("ls_model_name"),
+                "gen_ai.usage.input_tokens": info.message_tokens,
+                "gen_ai.usage.output_tokens": info.answer_tokens,
+                "gen_ai.usage.total_tokens": info.total_tokens,
+                "dify.message.status": metadata.get("status"),
+                "dify.message.error": info.error,
+                "dify.message.from_source": metadata.get("from_source"),
+                "dify.message.from_end_user_id": metadata.get("from_end_user_id"),
+                "dify.message.from_account_id": metadata.get("from_account_id"),
+                "dify.streaming": info.is_streaming_request,
+                "dify.message.time_to_first_token": info.gen_ai_server_time_to_first_token,
+                "dify.message.streaming_duration": info.llm_streaming_time_to_generate,
+                "dify.workflow.run_id": metadata.get("workflow_run_id"),
+            }
+        )
+        node_execution_id = metadata.get("node_execution_id")
+        if node_execution_id:
+            attrs["dify.node.execution_id"] = node_execution_id
+
+        ref = f"ref:message_id={info.message_id}"
+        inputs = self._safe_payload_value(info.inputs)
+        outputs = self._safe_payload_value(info.outputs)
+        attrs["dify.message.inputs"] = self._content_or_ref(inputs, ref)
+        attrs["dify.message.outputs"] = self._content_or_ref(outputs, ref)
+
+        emit_metric_only_event(
+            event_name=EnterpriseTelemetryEvent.MESSAGE_RUN,
+            attributes=attrs,
+            trace_id_source=metadata.get("workflow_run_id") or (str(info.message_id) if info.message_id else None),
+            span_id_source=node_execution_id,
+            tenant_id=tenant_id,
+            user_id=user_id,
+        )
+
+        labels = self._labels(
+            tenant_id=tenant_id or "",
+            app_id=app_id or "",
+            model_provider=metadata.get("ls_provider") or "",
+            model_name=metadata.get("ls_model_name") or "",
+        )
+        token_labels = TokenMetricLabels(
+            tenant_id=tenant_id or "",
+            app_id=app_id or "",
+            operation_type=OperationType.MESSAGE,
+            model_provider=metadata.get("ls_provider") or "",
+            model_name=metadata.get("ls_model_name") or "",
+            node_type="",
+        ).to_dict()
+        self._exporter.increment_counter(EnterpriseTelemetryCounter.TOKENS, info.total_tokens, token_labels)
+        if info.message_tokens > 0:
+            self._exporter.increment_counter(EnterpriseTelemetryCounter.INPUT_TOKENS, info.message_tokens, token_labels)
+        if info.answer_tokens > 0:
+            self._exporter.increment_counter(EnterpriseTelemetryCounter.OUTPUT_TOKENS, info.answer_tokens, token_labels)
+        invoke_from = metadata.get("from_source", "")
+        self._exporter.increment_counter(
+            EnterpriseTelemetryCounter.REQUESTS,
+            1,
+            self._labels(
+                **labels,
+                type="message",
+                status=metadata.get("status", ""),
+                invoke_from=invoke_from,
+            ),
+        )
+
+        if info.start_time and info.end_time:
+            duration = (info.end_time - info.start_time).total_seconds()
+            self._exporter.record_histogram(EnterpriseTelemetryHistogram.MESSAGE_DURATION, duration, labels)
+
+        if info.gen_ai_server_time_to_first_token is not None:
+            self._exporter.record_histogram(
+                EnterpriseTelemetryHistogram.MESSAGE_TTFT, info.gen_ai_server_time_to_first_token, labels
+            )
+
+        if info.error:
+            self._exporter.increment_counter(
+                EnterpriseTelemetryCounter.ERRORS,
+                1,
+                self._labels(
+                    **labels,
+                    type="message",
+                ),
+            )
+
+    def _tool_trace(self, info: ToolTraceInfo) -> None:
+        metadata = self._metadata(info)
+        tenant_id, app_id, user_id = self._context_ids(info, metadata)
+        attrs = self._common_attrs(info)
+        attrs.update(
+            {
+                "gen_ai.tool.name": info.tool_name,
+                "dify.tool.time_cost": info.time_cost,
+                "dify.tool.error": info.error,
+                "dify.workflow.run_id": metadata.get("workflow_run_id"),
+            }
+        )
+        node_execution_id = metadata.get("node_execution_id")
+        if node_execution_id:
+            attrs["dify.node.execution_id"] = node_execution_id
+
+        ref = f"ref:message_id={info.message_id}"
+        attrs["dify.tool.inputs"] = self._content_or_ref(info.tool_inputs, ref)
+        attrs["dify.tool.outputs"] = self._content_or_ref(info.tool_outputs, ref)
+        attrs["dify.tool.parameters"] = self._content_or_ref(info.tool_parameters, ref)
+        attrs["dify.tool.config"] = self._content_or_ref(info.tool_config, ref)
+
+        emit_metric_only_event(
+            event_name=EnterpriseTelemetryEvent.TOOL_EXECUTION,
+            attributes=attrs,
+            trace_id_source=info.resolved_trace_id,
+            span_id_source=node_execution_id,
+            tenant_id=tenant_id,
+            user_id=user_id,
+        )
+
+        labels = self._labels(
+            tenant_id=tenant_id or "",
+            app_id=app_id or "",
+            tool_name=info.tool_name,
+        )
+        self._exporter.increment_counter(
+            EnterpriseTelemetryCounter.REQUESTS,
+            1,
+            self._labels(
+                **labels,
+                type="tool",
+            ),
+        )
+        self._exporter.record_histogram(EnterpriseTelemetryHistogram.TOOL_DURATION, float(info.time_cost), labels)
+
+        if info.error:
+            self._exporter.increment_counter(
+                EnterpriseTelemetryCounter.ERRORS,
+                1,
+                self._labels(
+                    **labels,
+                    type="tool",
+                ),
+            )
+
+    def _moderation_trace(self, info: ModerationTraceInfo) -> None:
+        metadata = self._metadata(info)
+        tenant_id, app_id, user_id = self._context_ids(info, metadata)
+        attrs = self._common_attrs(info)
+        attrs.update(
+            {
+                "dify.moderation.flagged": info.flagged,
+                "dify.moderation.action": info.action,
+                "dify.moderation.preset_response": info.preset_response,
+                "dify.workflow.run_id": metadata.get("workflow_run_id"),
+            }
+        )
+        node_execution_id = metadata.get("node_execution_id")
+        if node_execution_id:
+            attrs["dify.node.execution_id"] = node_execution_id
+
+        attrs["dify.moderation.query"] = self._content_or_ref(
+            info.query,
+            f"ref:message_id={info.message_id}",
+        )
+
+        emit_metric_only_event(
+            event_name=EnterpriseTelemetryEvent.MODERATION_CHECK,
+            attributes=attrs,
+            trace_id_source=info.resolved_trace_id,
+            span_id_source=node_execution_id,
+            tenant_id=tenant_id,
+            user_id=user_id,
+        )
+
+        labels = self._labels(
+            tenant_id=tenant_id or "",
+            app_id=app_id or "",
+        )
+        self._exporter.increment_counter(
+            EnterpriseTelemetryCounter.REQUESTS,
+            1,
+            self._labels(
+                **labels,
+                type="moderation",
+            ),
+        )
+
+    def _suggested_question_trace(self, info: SuggestedQuestionTraceInfo) -> None:
+        metadata = self._metadata(info)
+        tenant_id, app_id, user_id = self._context_ids(info, metadata)
+        attrs = self._common_attrs(info)
+        attrs.update(
+            {
+                "gen_ai.usage.total_tokens": info.total_tokens,
+                "dify.suggested_question.status": info.status,
+                "dify.suggested_question.error": info.error,
+                "gen_ai.provider.name": info.model_provider,
+                "gen_ai.request.model": info.model_id,
+                "dify.suggested_question.count": len(info.suggested_question),
+                "dify.workflow.run_id": metadata.get("workflow_run_id"),
+            }
+        )
+        node_execution_id = metadata.get("node_execution_id")
+        if node_execution_id:
+            attrs["dify.node.execution_id"] = node_execution_id
+
+        attrs["dify.suggested_question.questions"] = self._content_or_ref(
+            info.suggested_question,
+            f"ref:message_id={info.message_id}",
+        )
+
+        emit_metric_only_event(
+            event_name=EnterpriseTelemetryEvent.SUGGESTED_QUESTION_GENERATION,
+            attributes=attrs,
+            trace_id_source=info.resolved_trace_id,
+            span_id_source=node_execution_id,
+            tenant_id=tenant_id,
+            user_id=user_id,
+        )
+
+        labels = self._labels(
+            tenant_id=tenant_id or "",
+            app_id=app_id or "",
+        )
+        self._exporter.increment_counter(
+            EnterpriseTelemetryCounter.REQUESTS,
+            1,
+            self._labels(
+                **labels,
+                type="suggested_question",
+                model_provider=info.model_provider or "",
+                model_name=info.model_id or "",
+            ),
+        )
+
+    def _dataset_retrieval_trace(self, info: DatasetRetrievalTraceInfo) -> None:
+        metadata = self._metadata(info)
+        tenant_id, app_id, user_id = self._context_ids(info, metadata)
+        attrs = self._common_attrs(info)
+        attrs["dify.dataset.error"] = info.error
+        attrs["dify.workflow.run_id"] = metadata.get("workflow_run_id")
+        node_execution_id = metadata.get("node_execution_id")
+        if node_execution_id:
+            attrs["dify.node.execution_id"] = node_execution_id
+
+        docs: list[dict[str, Any]] = []
+        documents_any: Any = info.documents
+        documents_list: list[Any] = cast(list[Any], documents_any) if isinstance(documents_any, list) else []
+        for entry in documents_list:
+            if isinstance(entry, dict):
+                entry_dict: dict[str, Any] = cast(dict[str, Any], entry)
+                docs.append(entry_dict)
+        dataset_ids: list[str] = []
+        dataset_names: list[str] = []
+        structured_docs: list[dict[str, Any]] = []
+        for doc in docs:
+            meta_raw = doc.get("metadata")
+            meta: dict[str, Any] = cast(dict[str, Any], meta_raw) if isinstance(meta_raw, dict) else {}
+            did = meta.get("dataset_id")
+            dname = meta.get("dataset_name")
+            if did and did not in dataset_ids:
+                dataset_ids.append(did)
+            if dname and dname not in dataset_names:
+                dataset_names.append(dname)
+            structured_docs.append(
+                {
+                    "dataset_id": did,
+                    "document_id": meta.get("document_id"),
+                    "segment_id": meta.get("segment_id"),
+                    "score": meta.get("score"),
+                }
+            )
+
+        attrs["dify.dataset.ids"] = self._maybe_json(dataset_ids)
+        attrs["dify.dataset.names"] = self._maybe_json(dataset_names)
+        attrs["dify.retrieval.document_count"] = len(docs)
+
+        embedding_models_raw: Any = metadata.get("embedding_models")
+        embedding_models: dict[str, Any] = (
+            cast(dict[str, Any], embedding_models_raw) if isinstance(embedding_models_raw, dict) else {}
+        )
+        if embedding_models:
+            providers: list[str] = []
+            models: list[str] = []
+            for ds_info in embedding_models.values():
+                if isinstance(ds_info, dict):
+                    ds_info_dict: dict[str, Any] = cast(dict[str, Any], ds_info)
+                    p = ds_info_dict.get("embedding_model_provider", "")
+                    m = ds_info_dict.get("embedding_model", "")
+                    if p and p not in providers:
+                        providers.append(p)
+                    if m and m not in models:
+                        models.append(m)
+            attrs["dify.dataset.embedding_providers"] = self._maybe_json(providers)
+            attrs["dify.dataset.embedding_models"] = self._maybe_json(models)
+
+        # Add rerank model to logs
+        rerank_provider = metadata.get("rerank_model_provider", "")
+        rerank_model = metadata.get("rerank_model_name", "")
+        if rerank_provider or rerank_model:
+            attrs["dify.retrieval.rerank_provider"] = rerank_provider
+            attrs["dify.retrieval.rerank_model"] = rerank_model
+
+        ref = f"ref:message_id={info.message_id}"
+        retrieval_inputs = self._safe_payload_value(info.inputs)
+        attrs["dify.retrieval.query"] = self._content_or_ref(retrieval_inputs, ref)
+        attrs["dify.dataset.documents"] = self._content_or_ref(structured_docs, ref)
+
+        emit_metric_only_event(
+            event_name=EnterpriseTelemetryEvent.DATASET_RETRIEVAL,
+            attributes=attrs,
+            trace_id_source=metadata.get("workflow_run_id") or (str(info.message_id) if info.message_id else None),
+            span_id_source=node_execution_id or (str(info.message_id) if info.message_id else None),
+            tenant_id=tenant_id,
+            user_id=user_id,
+        )
+
+        labels = self._labels(
+            tenant_id=tenant_id or "",
+            app_id=app_id or "",
+        )
+        self._exporter.increment_counter(
+            EnterpriseTelemetryCounter.REQUESTS,
+            1,
+            self._labels(
+                **labels,
+                type="dataset_retrieval",
+            ),
+        )
+
+        for did in dataset_ids:
+            # Get embedding model for this specific dataset
+            ds_embedding_info = embedding_models.get(did, {})
+            embedding_provider = ds_embedding_info.get("embedding_model_provider", "")
+            embedding_model = ds_embedding_info.get("embedding_model", "")
+
+            # Get rerank model (same for all datasets in this retrieval)
+            rerank_provider = metadata.get("rerank_model_provider", "")
+            rerank_model = metadata.get("rerank_model_name", "")
+
+            self._exporter.increment_counter(
+                EnterpriseTelemetryCounter.DATASET_RETRIEVALS,
+                1,
+                self._labels(
+                    **labels,
+                    dataset_id=did,
+                    embedding_model_provider=embedding_provider,
+                    embedding_model=embedding_model,
+                    rerank_model_provider=rerank_provider,
+                    rerank_model=rerank_model,
+                ),
+            )
+
+    def _generate_name_trace(self, info: GenerateNameTraceInfo) -> None:
+        metadata = self._metadata(info)
+        tenant_id, app_id, user_id = self._context_ids(info, metadata)
+        attrs = self._common_attrs(info)
+        attrs["dify.conversation.id"] = info.conversation_id
+        node_execution_id = metadata.get("node_execution_id")
+        if node_execution_id:
+            attrs["dify.node.execution_id"] = node_execution_id
+
+        ref = f"ref:conversation_id={info.conversation_id}"
+        inputs = self._safe_payload_value(info.inputs)
+        outputs = self._safe_payload_value(info.outputs)
+        attrs["dify.generate_name.inputs"] = self._content_or_ref(inputs, ref)
+        attrs["dify.generate_name.outputs"] = self._content_or_ref(outputs, ref)
+
+        emit_metric_only_event(
+            event_name=EnterpriseTelemetryEvent.GENERATE_NAME_EXECUTION,
+            attributes=attrs,
+            trace_id_source=info.resolved_trace_id,
+            span_id_source=node_execution_id,
+            tenant_id=tenant_id,
+            user_id=user_id,
+        )
+
+        labels = self._labels(
+            tenant_id=tenant_id or "",
+            app_id=app_id or "",
+        )
+        self._exporter.increment_counter(
+            EnterpriseTelemetryCounter.REQUESTS,
+            1,
+            self._labels(
+                **labels,
+                type="generate_name",
+            ),
+        )
+
+    def _prompt_generation_trace(self, info: PromptGenerationTraceInfo) -> None:
+        metadata = self._metadata(info)
+        tenant_id, app_id, user_id = self._context_ids(info, metadata)
+        attrs = {
+            "dify.trace_id": info.resolved_trace_id,
+            "dify.tenant_id": tenant_id,
+            "dify.user.id": user_id,
+            "dify.app.id": app_id or "",
+            "dify.app.name": metadata.get("app_name"),
+            "dify.workspace.name": metadata.get("workspace_name"),
+            "dify.operation.type": info.operation_type,
+            "gen_ai.provider.name": info.model_provider,
+            "gen_ai.request.model": info.model_name,
+            "gen_ai.usage.input_tokens": info.prompt_tokens,
+            "gen_ai.usage.output_tokens": info.completion_tokens,
+            "gen_ai.usage.total_tokens": info.total_tokens,
+            "dify.prompt_generation.latency": info.latency,
+            "dify.prompt_generation.error": info.error,
+        }
+        node_execution_id = metadata.get("node_execution_id")
+        if node_execution_id:
+            attrs["dify.node.execution_id"] = node_execution_id
+
+        if info.total_price is not None:
+            attrs["dify.prompt_generation.total_price"] = info.total_price
+            attrs["dify.prompt_generation.currency"] = info.currency
+
+        ref = f"ref:trace_id={info.trace_id}"
+        outputs = self._safe_payload_value(info.outputs)
+        attrs["dify.prompt_generation.instruction"] = self._content_or_ref(info.instruction, ref)
+        attrs["dify.prompt_generation.output"] = self._content_or_ref(outputs, ref)
+
+        emit_metric_only_event(
+            event_name=EnterpriseTelemetryEvent.PROMPT_GENERATION_EXECUTION,
+            attributes=attrs,
+            trace_id_source=info.resolved_trace_id,
+            span_id_source=node_execution_id,
+            tenant_id=tenant_id,
+            user_id=user_id,
+        )
+
+        token_labels = TokenMetricLabels(
+            tenant_id=tenant_id or "",
+            app_id=app_id or "",
+            operation_type=info.operation_type,
+            model_provider=info.model_provider,
+            model_name=info.model_name,
+            node_type="",
+        ).to_dict()
+
+        labels = self._labels(
+            tenant_id=tenant_id or "",
+            app_id=app_id or "",
+            operation_type=info.operation_type,
+            model_provider=info.model_provider,
+            model_name=info.model_name,
+        )
+
+        self._exporter.increment_counter(EnterpriseTelemetryCounter.TOKENS, info.total_tokens, token_labels)
+        if info.prompt_tokens > 0:
+            self._exporter.increment_counter(EnterpriseTelemetryCounter.INPUT_TOKENS, info.prompt_tokens, token_labels)
+        if info.completion_tokens > 0:
+            self._exporter.increment_counter(
+                EnterpriseTelemetryCounter.OUTPUT_TOKENS, info.completion_tokens, token_labels
+            )
+
+        status = "failed" if info.error else "success"
+        self._exporter.increment_counter(
+            EnterpriseTelemetryCounter.REQUESTS,
+            1,
+            self._labels(
+                **labels,
+                type="prompt_generation",
+                status=status,
+            ),
+        )
+
+        self._exporter.record_histogram(
+            EnterpriseTelemetryHistogram.PROMPT_GENERATION_DURATION,
+            info.latency,
+            labels,
+        )
+
+        if info.error:
+            self._exporter.increment_counter(
+                EnterpriseTelemetryCounter.ERRORS,
+                1,
+                self._labels(
+                    **labels,
+                    type="prompt_generation",
+                ),
+            )

api/enterprise/telemetry/entities/__init__.py

@@ -0,0 +1,121 @@
+from enum import StrEnum
+from typing import cast
+
+from opentelemetry.util.types import AttributeValue
+from pydantic import BaseModel, ConfigDict
+
+
+class EnterpriseTelemetrySpan(StrEnum):
+    WORKFLOW_RUN = "dify.workflow.run"
+    NODE_EXECUTION = "dify.node.execution"
+    DRAFT_NODE_EXECUTION = "dify.node.execution.draft"
+
+
+class EnterpriseTelemetryEvent(StrEnum):
+    """Event names for enterprise telemetry logs."""
+
+    APP_CREATED = "dify.app.created"
+    APP_UPDATED = "dify.app.updated"
+    APP_DELETED = "dify.app.deleted"
+    FEEDBACK_CREATED = "dify.feedback.created"
+    WORKFLOW_RUN = "dify.workflow.run"
+    MESSAGE_RUN = "dify.message.run"
+    TOOL_EXECUTION = "dify.tool.execution"
+    MODERATION_CHECK = "dify.moderation.check"
+    SUGGESTED_QUESTION_GENERATION = "dify.suggested_question.generation"
+    DATASET_RETRIEVAL = "dify.dataset.retrieval"
+    GENERATE_NAME_EXECUTION = "dify.generate_name.execution"
+    PROMPT_GENERATION_EXECUTION = "dify.prompt_generation.execution"
+    REHYDRATION_FAILED = "dify.telemetry.rehydration_failed"
+
+
+class EnterpriseTelemetryCounter(StrEnum):
+    TOKENS = "tokens"
+    INPUT_TOKENS = "input_tokens"
+    OUTPUT_TOKENS = "output_tokens"
+    REQUESTS = "requests"
+    ERRORS = "errors"
+    FEEDBACK = "feedback"
+    DATASET_RETRIEVALS = "dataset_retrievals"
+    APP_CREATED = "app_created"
+    APP_UPDATED = "app_updated"
+    APP_DELETED = "app_deleted"
+
+
+class EnterpriseTelemetryHistogram(StrEnum):
+    WORKFLOW_DURATION = "workflow_duration"
+    NODE_DURATION = "node_duration"
+    MESSAGE_DURATION = "message_duration"
+    MESSAGE_TTFT = "message_ttft"
+    TOOL_DURATION = "tool_duration"
+    PROMPT_GENERATION_DURATION = "prompt_generation_duration"
+
+
+class TokenMetricLabels(BaseModel):
+    """Unified label structure for all dify.token.* metrics.
+
+    All token counters (dify.tokens.input, dify.tokens.output, dify.tokens.total) MUST
+    use this exact label set to ensure consistent filtering and aggregation across
+    different operation types.
+
+    Attributes:
+        tenant_id: Tenant identifier.
+        app_id: Application identifier.
+        operation_type: Source of token usage (workflow | node_execution | message |
+            rule_generate | code_generate | structured_output | instruction_modify).
+        model_provider: LLM provider name. Empty string if not applicable (e.g., workflow-level).
+        model_name: LLM model name. Empty string if not applicable (e.g., workflow-level).
+        node_type: Workflow node type. Empty string unless operation_type=node_execution.
+
+    Usage:
+        labels = TokenMetricLabels(
+            tenant_id="tenant-123",
+            app_id="app-456",
+            operation_type=OperationType.WORKFLOW,
+            model_provider="",
+            model_name="",
+            node_type="",
+        )
+        exporter.increment_counter(
+            EnterpriseTelemetryCounter.INPUT_TOKENS,
+            100,
+            labels.to_dict()
+        )
+
+    Design rationale:
+        Without this unified structure, tokens get double-counted when querying totals
+        because workflow.total_tokens is already the sum of all node tokens. The
+        operation_type label allows filtering to separate workflow-level aggregates from
+        node-level detail, while keeping the same label cardinality for consistent queries.
+    """
+
+    tenant_id: str
+    app_id: str
+    operation_type: str
+    model_provider: str
+    model_name: str
+    node_type: str
+
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    def to_dict(self) -> dict[str, AttributeValue]:
+        return cast(
+            dict[str, AttributeValue],
+            {
+                "tenant_id": self.tenant_id,
+                "app_id": self.app_id,
+                "operation_type": self.operation_type,
+                "model_provider": self.model_provider,
+                "model_name": self.model_name,
+                "node_type": self.node_type,
+            },
+        )
+
+
+__all__ = [
+    "EnterpriseTelemetryCounter",
+    "EnterpriseTelemetryEvent",
+    "EnterpriseTelemetryHistogram",
+    "EnterpriseTelemetrySpan",
+    "TokenMetricLabels",
+]

api/enterprise/telemetry/event_handlers.py

@@ -0,0 +1,99 @@
+"""Blinker signal handlers for enterprise telemetry.
+
+Registered at import time via ``@signal.connect`` decorators.
+Import must happen during ``ext_enterprise_telemetry.init_app()`` to
+ensure handlers fire.  Each handler delegates to ``core.telemetry.gateway``
+which handles routing, EE-gating, and dispatch.
+
+All handlers are best-effort: exceptions are caught and logged so that
+telemetry failures never break user-facing operations.
+"""
+
+from __future__ import annotations
+
+import logging
+
+from events.app_event import app_was_created, app_was_deleted, app_was_updated
+from events.feedback_event import feedback_was_created
+
+logger = logging.getLogger(__name__)
+
+__all__ = [
+    "_handle_app_created",
+    "_handle_app_deleted",
+    "_handle_app_updated",
+    "_handle_feedback_created",
+]
+
+
+@app_was_created.connect
+def _handle_app_created(sender: object, **kwargs: object) -> None:
+    try:
+        from core.telemetry.gateway import emit as gateway_emit
+        from enterprise.telemetry.contracts import TelemetryCase
+
+        gateway_emit(
+            case=TelemetryCase.APP_CREATED,
+            context={"tenant_id": str(getattr(sender, "tenant_id", "") or "")},
+            payload={
+                "app_id": getattr(sender, "id", None),
+                "mode": getattr(sender, "mode", None),
+            },
+        )
+    except Exception:
+        logger.warning("Failed to emit app_created telemetry", exc_info=True)
+
+
+@app_was_deleted.connect
+def _handle_app_deleted(sender: object, **kwargs: object) -> None:
+    try:
+        from core.telemetry.gateway import emit as gateway_emit
+        from enterprise.telemetry.contracts import TelemetryCase
+
+        gateway_emit(
+            case=TelemetryCase.APP_DELETED,
+            context={"tenant_id": str(getattr(sender, "tenant_id", "") or "")},
+            payload={"app_id": getattr(sender, "id", None)},
+        )
+    except Exception:
+        logger.warning("Failed to emit app_deleted telemetry", exc_info=True)
+
+
+@app_was_updated.connect
+def _handle_app_updated(sender: object, **kwargs: object) -> None:
+    try:
+        from core.telemetry.gateway import emit as gateway_emit
+        from enterprise.telemetry.contracts import TelemetryCase
+
+        gateway_emit(
+            case=TelemetryCase.APP_UPDATED,
+            context={"tenant_id": str(getattr(sender, "tenant_id", "") or "")},
+            payload={"app_id": getattr(sender, "id", None)},
+        )
+    except Exception:
+        logger.warning("Failed to emit app_updated telemetry", exc_info=True)
+
+
+@feedback_was_created.connect
+def _handle_feedback_created(sender: object, **kwargs: object) -> None:
+    try:
+        from core.telemetry.gateway import emit as gateway_emit
+        from enterprise.telemetry.contracts import TelemetryCase
+
+        tenant_id = str(kwargs.get("tenant_id", "") or "")
+        gateway_emit(
+            case=TelemetryCase.FEEDBACK_CREATED,
+            context={"tenant_id": tenant_id},
+            payload={
+                "message_id": getattr(sender, "message_id", None),
+                "app_id": getattr(sender, "app_id", None),
+                "conversation_id": getattr(sender, "conversation_id", None),
+                "from_end_user_id": getattr(sender, "from_end_user_id", None),
+                "from_account_id": getattr(sender, "from_account_id", None),
+                "rating": getattr(sender, "rating", None),
+                "from_source": getattr(sender, "from_source", None),
+                "content": getattr(sender, "content", None),
+            },
+        )
+    except Exception:
+        logger.warning("Failed to emit feedback_created telemetry", exc_info=True)

api/enterprise/telemetry/exporter.py

@@ -0,0 +1,289 @@
+"""Enterprise OTEL exporter — shared by EnterpriseOtelTrace, event handlers, and direct instrumentation.
+
+Uses dedicated TracerProvider and MeterProvider instances (configurable sampling,
+independent from ext_otel.py infrastructure).
+
+Initialized once during Flask extension init (single-threaded via ext_enterprise_telemetry.py).
+Accessed via ``ext_enterprise_telemetry.get_enterprise_exporter()`` from any thread/process.
+"""
+
+import logging
+import socket
+import uuid
+from datetime import UTC, datetime
+from typing import Any, cast
+
+from opentelemetry import trace
+from opentelemetry.context import Context
+from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter as GRPCMetricExporter
+from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter as GRPCSpanExporter
+from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter as HTTPMetricExporter
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter as HTTPSpanExporter
+from opentelemetry.sdk.metrics import MeterProvider
+from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
+from opentelemetry.sdk.resources import Resource
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+from opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatio
+from opentelemetry.semconv.resource import ResourceAttributes
+from opentelemetry.trace import SpanContext, TraceFlags
+from opentelemetry.util.types import Attributes, AttributeValue
+
+from configs import dify_config
+from enterprise.telemetry.entities import EnterpriseTelemetryCounter, EnterpriseTelemetryHistogram
+from enterprise.telemetry.id_generator import (
+    CorrelationIdGenerator,
+    compute_deterministic_span_id,
+    set_correlation_id,
+    set_span_id_source,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def is_enterprise_telemetry_enabled() -> bool:
+    return bool(dify_config.ENTERPRISE_ENABLED and dify_config.ENTERPRISE_TELEMETRY_ENABLED)
+
+
+def _parse_otlp_headers(raw: str) -> dict[str, str]:
+    """Parse ``key=value,key2=value2`` into a dict."""
+    if not raw:
+        return {}
+    headers: dict[str, str] = {}
+    for pair in raw.split(","):
+        if "=" not in pair:
+            continue
+        k, v = pair.split("=", 1)
+        headers[k.strip().lower()] = v.strip()
+    return headers
+
+
+def _datetime_to_ns(dt: datetime) -> int:
+    """Convert a datetime to nanoseconds since epoch (OTEL convention)."""
+    # Ensure we always interpret naive datetimes as UTC instead of local time.
+    if dt.tzinfo is None:
+        dt = dt.replace(tzinfo=UTC)
+    else:
+        dt = dt.astimezone(UTC)
+    return int(dt.timestamp() * 1_000_000_000)
+
+
+class _ExporterFactory:
+    def __init__(self, protocol: str, endpoint: str, headers: dict[str, str], insecure: bool):
+        self._protocol = protocol
+        self._endpoint = endpoint
+        self._headers = headers
+        self._grpc_headers = tuple(headers.items()) if headers else None
+        self._http_headers = headers or None
+        self._insecure = insecure
+
+    def create_trace_exporter(self) -> HTTPSpanExporter | GRPCSpanExporter:
+        if self._protocol == "grpc":
+            return GRPCSpanExporter(
+                endpoint=self._endpoint or None,
+                headers=self._grpc_headers,
+                insecure=self._insecure,
+            )
+        trace_endpoint = f"{self._endpoint}/v1/traces" if self._endpoint else ""
+        return HTTPSpanExporter(endpoint=trace_endpoint or None, headers=self._http_headers)
+
+    def create_metric_exporter(self) -> HTTPMetricExporter | GRPCMetricExporter:
+        if self._protocol == "grpc":
+            return GRPCMetricExporter(
+                endpoint=self._endpoint or None,
+                headers=self._grpc_headers,
+                insecure=self._insecure,
+            )
+        metric_endpoint = f"{self._endpoint}/v1/metrics" if self._endpoint else ""
+        return HTTPMetricExporter(endpoint=metric_endpoint or None, headers=self._http_headers)
+
+
+class EnterpriseExporter:
+    """Shared OTEL exporter for all enterprise telemetry.
+
+    ``export_span`` creates spans with optional real timestamps, deterministic
+    span/trace IDs, and cross-workflow parent linking.
+    ``increment_counter`` / ``record_histogram`` emit OTEL metrics at 100% accuracy.
+    """
+
+    def __init__(self, config: object) -> None:
+        endpoint: str = getattr(config, "ENTERPRISE_OTLP_ENDPOINT", "")
+        headers_raw: str = getattr(config, "ENTERPRISE_OTLP_HEADERS", "")
+        protocol: str = (getattr(config, "ENTERPRISE_OTLP_PROTOCOL", "http") or "http").lower()
+        service_name: str = getattr(config, "ENTERPRISE_SERVICE_NAME", "dify")
+        sampling_rate: float = getattr(config, "ENTERPRISE_OTEL_SAMPLING_RATE", 1.0)
+        self.include_content: bool = getattr(config, "ENTERPRISE_INCLUDE_CONTENT", True)
+        api_key: str = getattr(config, "ENTERPRISE_OTLP_API_KEY", "")
+
+        # Auto-detect TLS: https:// uses secure, everything else is insecure
+        insecure = not endpoint.startswith("https://")
+
+        resource = Resource(
+            attributes={
+                ResourceAttributes.SERVICE_NAME: service_name,
+                ResourceAttributes.HOST_NAME: socket.gethostname(),
+            }
+        )
+        sampler = ParentBasedTraceIdRatio(sampling_rate)
+        id_generator = CorrelationIdGenerator()
+        self._tracer_provider = TracerProvider(resource=resource, sampler=sampler, id_generator=id_generator)
+
+        headers = _parse_otlp_headers(headers_raw)
+        if api_key:
+            if "authorization" in headers:
+                logger.warning(
+                    "ENTERPRISE_OTLP_API_KEY is set but ENTERPRISE_OTLP_HEADERS also contains "
+                    "'authorization'; the API key will take precedence."
+                )
+            headers["authorization"] = f"Bearer {api_key}"
+        factory = _ExporterFactory(protocol, endpoint, headers, insecure=insecure)
+
+        trace_exporter = factory.create_trace_exporter()
+        self._tracer_provider.add_span_processor(BatchSpanProcessor(trace_exporter))
+        self._tracer = self._tracer_provider.get_tracer("dify.enterprise")
+
+        metric_exporter = factory.create_metric_exporter()
+        self._meter_provider = MeterProvider(
+            resource=resource,
+            metric_readers=[PeriodicExportingMetricReader(metric_exporter)],
+        )
+        meter = self._meter_provider.get_meter("dify.enterprise")
+        self._counters = {
+            EnterpriseTelemetryCounter.TOKENS: meter.create_counter("dify.tokens.total", unit="{token}"),
+            EnterpriseTelemetryCounter.INPUT_TOKENS: meter.create_counter("dify.tokens.input", unit="{token}"),
+            EnterpriseTelemetryCounter.OUTPUT_TOKENS: meter.create_counter("dify.tokens.output", unit="{token}"),
+            EnterpriseTelemetryCounter.REQUESTS: meter.create_counter("dify.requests.total", unit="{request}"),
+            EnterpriseTelemetryCounter.ERRORS: meter.create_counter("dify.errors.total", unit="{error}"),
+            EnterpriseTelemetryCounter.FEEDBACK: meter.create_counter("dify.feedback.total", unit="{feedback}"),
+            EnterpriseTelemetryCounter.DATASET_RETRIEVALS: meter.create_counter(
+                "dify.dataset.retrievals.total", unit="{retrieval}"
+            ),
+            EnterpriseTelemetryCounter.APP_CREATED: meter.create_counter("dify.app.created.total", unit="{app}"),
+            EnterpriseTelemetryCounter.APP_UPDATED: meter.create_counter("dify.app.updated.total", unit="{app}"),
+            EnterpriseTelemetryCounter.APP_DELETED: meter.create_counter("dify.app.deleted.total", unit="{app}"),
+        }
+        self._histograms = {
+            EnterpriseTelemetryHistogram.WORKFLOW_DURATION: meter.create_histogram("dify.workflow.duration", unit="s"),
+            EnterpriseTelemetryHistogram.NODE_DURATION: meter.create_histogram("dify.node.duration", unit="s"),
+            EnterpriseTelemetryHistogram.MESSAGE_DURATION: meter.create_histogram("dify.message.duration", unit="s"),
+            EnterpriseTelemetryHistogram.MESSAGE_TTFT: meter.create_histogram(
+                "dify.message.time_to_first_token", unit="s"
+            ),
+            EnterpriseTelemetryHistogram.TOOL_DURATION: meter.create_histogram("dify.tool.duration", unit="s"),
+            EnterpriseTelemetryHistogram.PROMPT_GENERATION_DURATION: meter.create_histogram(
+                "dify.prompt_generation.duration", unit="s"
+            ),
+        }
+
+    def export_span(
+        self,
+        name: str,
+        attributes: dict[str, Any],
+        correlation_id: str | None = None,
+        span_id_source: str | None = None,
+        start_time: datetime | None = None,
+        end_time: datetime | None = None,
+        trace_correlation_override: str | None = None,
+        parent_span_id_source: str | None = None,
+    ) -> None:
+        """Export an OTEL span with optional deterministic IDs and real timestamps.
+
+        Args:
+            name: Span operation name.
+            attributes: Span attributes dict.
+            correlation_id: Source for trace_id derivation (groups spans in one trace).
+            span_id_source: Source for deterministic span_id (e.g. workflow_run_id or node_execution_id).
+            start_time: Real span start time. When None, uses current time.
+            end_time: Real span end time. When None, span ends immediately.
+            trace_correlation_override: Override trace_id source (for cross-workflow linking).
+                When set, trace_id is derived from this instead of ``correlation_id``.
+            parent_span_id_source: Override parent span_id source (for cross-workflow linking).
+                When set, parent span_id is derived from this value. When None and
+                ``correlation_id`` is set, parent is the workflow root span.
+        """
+        effective_trace_correlation = trace_correlation_override or correlation_id
+        set_correlation_id(effective_trace_correlation)
+        set_span_id_source(span_id_source)
+
+        try:
+            parent_context: Context | None = None
+            # A span is the "root" of its correlation group when span_id_source == correlation_id
+            # (i.e. a workflow root span).  All other spans are children.
+            if parent_span_id_source:
+                # Cross-workflow linking: parent is an explicit span (e.g. tool node in outer workflow)
+                parent_span_id = compute_deterministic_span_id(parent_span_id_source)
+                try:
+                    parent_trace_id = int(uuid.UUID(effective_trace_correlation)) if effective_trace_correlation else 0
+                except (ValueError, AttributeError):
+                    logger.warning(
+                        "Invalid trace correlation UUID for cross-workflow link: %s, span=%s",
+                        effective_trace_correlation,
+                        name,
+                    )
+                    parent_trace_id = 0
+                if parent_trace_id:
+                    parent_span_context = SpanContext(
+                        trace_id=parent_trace_id,
+                        span_id=parent_span_id,
+                        is_remote=True,
+                        trace_flags=TraceFlags(TraceFlags.SAMPLED),
+                    )
+                    parent_context = trace.set_span_in_context(trace.NonRecordingSpan(parent_span_context))
+            elif correlation_id and correlation_id != span_id_source:
+                # Child span: parent is the correlation-group root (workflow root span)
+                parent_span_id = compute_deterministic_span_id(correlation_id)
+                try:
+                    parent_trace_id = int(uuid.UUID(effective_trace_correlation or correlation_id))
+                except (ValueError, AttributeError):
+                    logger.warning(
+                        "Invalid trace correlation UUID for child span link: %s, span=%s",
+                        effective_trace_correlation or correlation_id,
+                        name,
+                    )
+                    parent_trace_id = 0
+                if parent_trace_id:
+                    parent_span_context = SpanContext(
+                        trace_id=parent_trace_id,
+                        span_id=parent_span_id,
+                        is_remote=True,
+                        trace_flags=TraceFlags(TraceFlags.SAMPLED),
+                    )
+                    parent_context = trace.set_span_in_context(trace.NonRecordingSpan(parent_span_context))
+
+            span_start_time = _datetime_to_ns(start_time) if start_time is not None else None
+            span_end_on_exit = end_time is None
+
+            with self._tracer.start_as_current_span(
+                name,
+                context=parent_context,
+                start_time=span_start_time,
+                end_on_exit=span_end_on_exit,
+            ) as span:
+                for key, value in attributes.items():
+                    if value is not None:
+                        span.set_attribute(key, value)
+                if end_time is not None:
+                    span.end(end_time=_datetime_to_ns(end_time))
+        except Exception:
+            logger.exception("Failed to export span %s", name)
+        finally:
+            set_correlation_id(None)
+            set_span_id_source(None)
+
+    def increment_counter(
+        self, name: EnterpriseTelemetryCounter, value: int, labels: dict[str, AttributeValue]
+    ) -> None:
+        counter = self._counters.get(name)
+        if counter:
+            counter.add(value, cast(Attributes, labels))
+
+    def record_histogram(
+        self, name: EnterpriseTelemetryHistogram, value: float, labels: dict[str, AttributeValue]
+    ) -> None:
+        histogram = self._histograms.get(name)
+        if histogram:
+            histogram.record(value, cast(Attributes, labels))
+
+    def shutdown(self) -> None:
+        self._tracer_provider.shutdown()
+        self._meter_provider.shutdown()

api/enterprise/telemetry/id_generator.py

@@ -0,0 +1,75 @@
+"""Custom OTEL ID Generator for correlation-based trace/span ID derivation.
+
+Uses contextvars for thread-safe correlation_id -> trace_id mapping.
+When a span_id_source is set, the span_id is derived deterministically
+from that value, enabling any span to reference another as parent
+without depending on span creation order.
+"""
+
+import random
+import uuid
+from contextvars import ContextVar
+
+from opentelemetry.sdk.trace.id_generator import IdGenerator
+
+_correlation_id_context: ContextVar[str | None] = ContextVar("correlation_id", default=None)
+_span_id_source_context: ContextVar[str | None] = ContextVar("span_id_source", default=None)
+
+
+def set_correlation_id(correlation_id: str | None) -> None:
+    _correlation_id_context.set(correlation_id)
+
+
+def get_correlation_id() -> str | None:
+    return _correlation_id_context.get()
+
+
+def set_span_id_source(source_id: str | None) -> None:
+    """Set the source for deterministic span_id generation.
+
+    When set, ``generate_span_id()`` derives the span_id from this value
+    (lower 64 bits of the UUID).  Pass the ``workflow_run_id`` for workflow
+    root spans or ``node_execution_id`` for node spans.
+    """
+    _span_id_source_context.set(source_id)
+
+
+def compute_deterministic_span_id(source_id: str) -> int:
+    """Derive a deterministic span_id from any UUID string.
+
+    Uses the lower 64 bits of the UUID, guaranteeing non-zero output
+    (OTEL requires span_id != 0).
+    """
+    span_id = uuid.UUID(source_id).int & ((1 << 64) - 1)
+    return span_id if span_id != 0 else 1
+
+
+class CorrelationIdGenerator(IdGenerator):
+    """ID generator that derives trace_id and optionally span_id from context.
+
+    - trace_id: always derived from correlation_id (groups all spans in one trace)
+    - span_id: derived from span_id_source when set (enables deterministic
+      parent-child linking), otherwise random
+    """
+
+    def generate_trace_id(self) -> int:
+        correlation_id = _correlation_id_context.get()
+        if correlation_id:
+            try:
+                return uuid.UUID(correlation_id).int
+            except (ValueError, AttributeError):
+                pass
+        return random.getrandbits(128)
+
+    def generate_span_id(self) -> int:
+        source = _span_id_source_context.get()
+        if source:
+            try:
+                return compute_deterministic_span_id(source)
+            except (ValueError, AttributeError):
+                pass
+
+        span_id = random.getrandbits(64)
+        while span_id == 0:
+            span_id = random.getrandbits(64)
+        return span_id

api/enterprise/telemetry/metric_handler.py

@@ -0,0 +1,381 @@
+"""Enterprise metric/log event handler.
+
+This module processes metric and log telemetry events after they've been
+dequeued from the enterprise_telemetry Celery queue. It handles case routing,
+idempotency checking, and payload rehydration.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any
+
+from enterprise.telemetry.contracts import TelemetryCase, TelemetryEnvelope
+from extensions.ext_redis import redis_client
+from extensions.ext_storage import storage
+
+logger = logging.getLogger(__name__)
+
+
+class EnterpriseMetricHandler:
+    """Handler for enterprise metric and log telemetry events.
+
+    Processes envelopes from the enterprise_telemetry queue, routing each
+    case to the appropriate handler method. Implements idempotency checking
+    and payload rehydration with fallback.
+    """
+
+    def _increment_diagnostic_counter(self, counter_name: str, labels: dict[str, str] | None = None) -> None:
+        """Increment a diagnostic counter for operational monitoring.
+
+        Args:
+            counter_name: Name of the counter (e.g., 'processed_total', 'deduped_total').
+            labels: Optional labels for the counter.
+        """
+        try:
+            from extensions.ext_enterprise_telemetry import get_enterprise_exporter
+
+            exporter = get_enterprise_exporter()
+            if not exporter:
+                return
+
+            full_counter_name = f"enterprise_telemetry.handler.{counter_name}"
+            logger.debug(
+                "Diagnostic counter: %s, labels=%s",
+                full_counter_name,
+                labels or {},
+            )
+        except Exception:
+            logger.debug("Failed to increment diagnostic counter: %s", counter_name, exc_info=True)
+
+    def handle(self, envelope: TelemetryEnvelope) -> None:
+        """Main entry point for processing telemetry envelopes.
+
+        Args:
+            envelope: The telemetry envelope to process.
+        """
+        # Check for duplicate events
+        if self._is_duplicate(envelope):
+            logger.debug(
+                "Skipping duplicate event: tenant_id=%s, event_id=%s",
+                envelope.tenant_id,
+                envelope.event_id,
+            )
+            self._increment_diagnostic_counter("deduped_total")
+            return
+
+        # Route to appropriate handler based on case
+        case = envelope.case
+        if case == TelemetryCase.APP_CREATED:
+            self._on_app_created(envelope)
+            self._increment_diagnostic_counter("processed_total", {"case": "app_created"})
+        elif case == TelemetryCase.APP_UPDATED:
+            self._on_app_updated(envelope)
+            self._increment_diagnostic_counter("processed_total", {"case": "app_updated"})
+        elif case == TelemetryCase.APP_DELETED:
+            self._on_app_deleted(envelope)
+            self._increment_diagnostic_counter("processed_total", {"case": "app_deleted"})
+        elif case == TelemetryCase.FEEDBACK_CREATED:
+            self._on_feedback_created(envelope)
+            self._increment_diagnostic_counter("processed_total", {"case": "feedback_created"})
+        elif case == TelemetryCase.MESSAGE_RUN:
+            self._on_message_run(envelope)
+            self._increment_diagnostic_counter("processed_total", {"case": "message_run"})
+        elif case == TelemetryCase.TOOL_EXECUTION:
+            self._on_tool_execution(envelope)
+            self._increment_diagnostic_counter("processed_total", {"case": "tool_execution"})
+        elif case == TelemetryCase.MODERATION_CHECK:
+            self._on_moderation_check(envelope)
+            self._increment_diagnostic_counter("processed_total", {"case": "moderation_check"})
+        elif case == TelemetryCase.SUGGESTED_QUESTION:
+            self._on_suggested_question(envelope)
+            self._increment_diagnostic_counter("processed_total", {"case": "suggested_question"})
+        elif case == TelemetryCase.DATASET_RETRIEVAL:
+            self._on_dataset_retrieval(envelope)
+            self._increment_diagnostic_counter("processed_total", {"case": "dataset_retrieval"})
+        elif case == TelemetryCase.GENERATE_NAME:
+            self._on_generate_name(envelope)
+            self._increment_diagnostic_counter("processed_total", {"case": "generate_name"})
+        elif case == TelemetryCase.PROMPT_GENERATION:
+            self._on_prompt_generation(envelope)
+            self._increment_diagnostic_counter("processed_total", {"case": "prompt_generation"})
+        else:
+            logger.warning(
+                "Unknown telemetry case: %s (tenant_id=%s, event_id=%s)",
+                case,
+                envelope.tenant_id,
+                envelope.event_id,
+            )
+
+    def _is_duplicate(self, envelope: TelemetryEnvelope) -> bool:
+        """Check if this event has already been processed.
+
+        Uses Redis with TTL for deduplication. Returns True if duplicate,
+        False if first time seeing this event.
+
+        Args:
+            envelope: The telemetry envelope to check.
+
+        Returns:
+            True if this event_id has been seen before, False otherwise.
+        """
+        dedup_key = f"telemetry:dedup:{envelope.tenant_id}:{envelope.event_id}"
+
+        try:
+            # Atomic set-if-not-exists with 1h TTL
+            # Returns True if key was set (first time), None if already exists (duplicate)
+            was_set = redis_client.set(dedup_key, b"1", nx=True, ex=3600)
+            return was_set is None
+        except Exception:
+            # Fail open: if Redis is unavailable, process the event
+            # (prefer occasional duplicate over lost data)
+            logger.warning(
+                "Redis unavailable for deduplication check, processing event anyway: %s",
+                envelope.event_id,
+                exc_info=True,
+            )
+            return False
+
+    def _rehydrate(self, envelope: TelemetryEnvelope) -> dict[str, Any]:
+        """Rehydrate payload from storage reference or inline data.
+
+        If the envelope payload is empty and metadata contains a
+        ``payload_ref``, the full payload is loaded from object storage
+        (where the gateway wrote it as JSON).  When both the inline
+        payload and storage resolution fail, a degraded-event marker
+        is emitted so the gap is observable.
+
+        Args:
+            envelope: The telemetry envelope containing payload data.
+
+        Returns:
+            The rehydrated payload dictionary, or ``{}`` on total failure.
+        """
+        payload = envelope.payload
+
+        # Resolve from object storage when the gateway offloaded a large payload.
+        if not payload and envelope.metadata:
+            payload_ref = envelope.metadata.get("payload_ref")
+            if payload_ref:
+                try:
+                    payload_bytes = storage.load(payload_ref)
+                    payload = json.loads(payload_bytes.decode("utf-8"))
+                    logger.debug("Loaded payload from storage: key=%s", payload_ref)
+                except Exception:
+                    logger.warning(
+                        "Failed to load payload from storage: key=%s, event_id=%s",
+                        payload_ref,
+                        envelope.event_id,
+                        exc_info=True,
+                    )
+
+        if not payload:
+            # Storage resolution failed or no data available — emit degraded event.
+            logger.error(
+                "Payload rehydration failed for event_id=%s, tenant_id=%s, case=%s",
+                envelope.event_id,
+                envelope.tenant_id,
+                envelope.case,
+            )
+            from enterprise.telemetry.entities import EnterpriseTelemetryEvent
+            from enterprise.telemetry.telemetry_log import emit_metric_only_event
+
+            emit_metric_only_event(
+                event_name=EnterpriseTelemetryEvent.REHYDRATION_FAILED,
+                attributes={
+                    "dify.tenant_id": envelope.tenant_id,
+                    "dify.event_id": envelope.event_id,
+                    "dify.case": envelope.case,
+                    "rehydration_failed": True,
+                },
+                tenant_id=envelope.tenant_id,
+            )
+            self._increment_diagnostic_counter("rehydration_failed_total")
+            return {}
+
+        return payload
+
+    # Stub methods for each metric/log case
+    # These will be implemented in later tasks with actual emission logic
+
+    def _on_app_created(self, envelope: TelemetryEnvelope) -> None:
+        """Handle app created event."""
+        from enterprise.telemetry.entities import EnterpriseTelemetryCounter, EnterpriseTelemetryEvent
+        from enterprise.telemetry.telemetry_log import emit_metric_only_event
+        from extensions.ext_enterprise_telemetry import get_enterprise_exporter
+
+        exporter = get_enterprise_exporter()
+        if not exporter:
+            logger.debug("No exporter available for APP_CREATED: event_id=%s", envelope.event_id)
+            return
+
+        payload = self._rehydrate(envelope)
+        if not payload:
+            return
+
+        attrs = {
+            "dify.app.id": payload.get("app_id"),
+            "dify.tenant_id": envelope.tenant_id,
+            "dify.event.id": envelope.event_id,
+            "dify.app.mode": payload.get("mode"),
+        }
+
+        emit_metric_only_event(
+            event_name=EnterpriseTelemetryEvent.APP_CREATED,
+            attributes=attrs,
+            tenant_id=envelope.tenant_id,
+        )
+        exporter.increment_counter(
+            EnterpriseTelemetryCounter.APP_CREATED,
+            1,
+            {
+                "tenant_id": envelope.tenant_id,
+                "app_id": str(payload.get("app_id", "")),
+                "mode": str(payload.get("mode", "")),
+            },
+        )
+
+    def _on_app_updated(self, envelope: TelemetryEnvelope) -> None:
+        """Handle app updated event."""
+        from enterprise.telemetry.entities import EnterpriseTelemetryCounter, EnterpriseTelemetryEvent
+        from enterprise.telemetry.telemetry_log import emit_metric_only_event
+        from extensions.ext_enterprise_telemetry import get_enterprise_exporter
+
+        exporter = get_enterprise_exporter()
+        if not exporter:
+            logger.debug("No exporter available for APP_UPDATED: event_id=%s", envelope.event_id)
+            return
+
+        payload = self._rehydrate(envelope)
+        if not payload:
+            return
+
+        attrs = {
+            "dify.app.id": payload.get("app_id"),
+            "dify.tenant_id": envelope.tenant_id,
+            "dify.event.id": envelope.event_id,
+        }
+
+        emit_metric_only_event(
+            event_name=EnterpriseTelemetryEvent.APP_UPDATED,
+            attributes=attrs,
+            tenant_id=envelope.tenant_id,
+        )
+        exporter.increment_counter(
+            EnterpriseTelemetryCounter.APP_UPDATED,
+            1,
+            {
+                "tenant_id": envelope.tenant_id,
+                "app_id": str(payload.get("app_id", "")),
+            },
+        )
+
+    def _on_app_deleted(self, envelope: TelemetryEnvelope) -> None:
+        """Handle app deleted event."""
+        from enterprise.telemetry.entities import EnterpriseTelemetryCounter, EnterpriseTelemetryEvent
+        from enterprise.telemetry.telemetry_log import emit_metric_only_event
+        from extensions.ext_enterprise_telemetry import get_enterprise_exporter
+
+        exporter = get_enterprise_exporter()
+        if not exporter:
+            logger.debug("No exporter available for APP_DELETED: event_id=%s", envelope.event_id)
+            return
+
+        payload = self._rehydrate(envelope)
+        if not payload:
+            return
+
+        attrs = {
+            "dify.app.id": payload.get("app_id"),
+            "dify.tenant_id": envelope.tenant_id,
+            "dify.event.id": envelope.event_id,
+        }
+
+        emit_metric_only_event(
+            event_name=EnterpriseTelemetryEvent.APP_DELETED,
+            attributes=attrs,
+            tenant_id=envelope.tenant_id,
+        )
+        exporter.increment_counter(
+            EnterpriseTelemetryCounter.APP_DELETED,
+            1,
+            {
+                "tenant_id": envelope.tenant_id,
+                "app_id": str(payload.get("app_id", "")),
+            },
+        )
+
+    def _on_feedback_created(self, envelope: TelemetryEnvelope) -> None:
+        """Handle feedback created event."""
+        from enterprise.telemetry.entities import EnterpriseTelemetryCounter, EnterpriseTelemetryEvent
+        from enterprise.telemetry.telemetry_log import emit_metric_only_event
+        from extensions.ext_enterprise_telemetry import get_enterprise_exporter
+
+        exporter = get_enterprise_exporter()
+        if not exporter:
+            logger.debug("No exporter available for FEEDBACK_CREATED: event_id=%s", envelope.event_id)
+            return
+
+        payload = self._rehydrate(envelope)
+        if not payload:
+            return
+
+        include_content = exporter.include_content
+        attrs: dict = {
+            "dify.message.id": payload.get("message_id"),
+            "dify.tenant_id": envelope.tenant_id,
+            "dify.event.id": envelope.event_id,
+            "dify.app_id": payload.get("app_id"),
+            "dify.conversation.id": payload.get("conversation_id"),
+            "gen_ai.user.id": payload.get("from_end_user_id") or payload.get("from_account_id"),
+            "dify.feedback.rating": payload.get("rating"),
+            "dify.feedback.from_source": payload.get("from_source"),
+        }
+        if include_content:
+            attrs["dify.feedback.content"] = payload.get("content")
+
+        user_id = payload.get("from_end_user_id") or payload.get("from_account_id")
+        emit_metric_only_event(
+            event_name=EnterpriseTelemetryEvent.FEEDBACK_CREATED,
+            attributes=attrs,
+            tenant_id=envelope.tenant_id,
+            user_id=str(user_id or ""),
+        )
+        exporter.increment_counter(
+            EnterpriseTelemetryCounter.FEEDBACK,
+            1,
+            {
+                "tenant_id": envelope.tenant_id,
+                "app_id": str(payload.get("app_id", "")),
+                "rating": str(payload.get("rating", "")),
+            },
+        )
+
+    def _on_message_run(self, envelope: TelemetryEnvelope) -> None:
+        """Handle message run event (stub)."""
+        logger.debug("Processing MESSAGE_RUN: event_id=%s", envelope.event_id)
+
+    def _on_tool_execution(self, envelope: TelemetryEnvelope) -> None:
+        """Handle tool execution event (stub)."""
+        logger.debug("Processing TOOL_EXECUTION: event_id=%s", envelope.event_id)
+
+    def _on_moderation_check(self, envelope: TelemetryEnvelope) -> None:
+        """Handle moderation check event (stub)."""
+        logger.debug("Processing MODERATION_CHECK: event_id=%s", envelope.event_id)
+
+    def _on_suggested_question(self, envelope: TelemetryEnvelope) -> None:
+        """Handle suggested question event (stub)."""
+        logger.debug("Processing SUGGESTED_QUESTION: event_id=%s", envelope.event_id)
+
+    def _on_dataset_retrieval(self, envelope: TelemetryEnvelope) -> None:
+        """Handle dataset retrieval event (stub)."""
+        logger.debug("Processing DATASET_RETRIEVAL: event_id=%s", envelope.event_id)
+
+    def _on_generate_name(self, envelope: TelemetryEnvelope) -> None:
+        """Handle generate name event (stub)."""
+        logger.debug("Processing GENERATE_NAME: event_id=%s", envelope.event_id)
+
+    def _on_prompt_generation(self, envelope: TelemetryEnvelope) -> None:
+        """Handle prompt generation event (stub)."""
+        logger.debug("Processing PROMPT_GENERATION: event_id=%s", envelope.event_id)

api/enterprise/telemetry/telemetry_log.py

@@ -0,0 +1,122 @@
+"""Structured-log emitter for enterprise telemetry events.
+
+Emits structured JSON log lines correlated with OTEL traces via trace_id.
+Picked up by ``StructuredJSONFormatter`` → stdout/Loki/Elastic.
+"""
+
+from __future__ import annotations
+
+import logging
+import uuid
+from functools import lru_cache
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from enterprise.telemetry.entities import EnterpriseTelemetryEvent
+
+logger = logging.getLogger("dify.telemetry")
+
+
+@lru_cache(maxsize=4096)
+def compute_trace_id_hex(uuid_str: str | None) -> str:
+    """Convert a business UUID string to a 32-hex OTEL-compatible trace_id.
+
+    Returns empty string when *uuid_str* is ``None`` or invalid.
+    """
+    if not uuid_str:
+        return ""
+    normalized = uuid_str.strip().lower()
+    if len(normalized) == 32 and all(ch in "0123456789abcdef" for ch in normalized):
+        return normalized
+    try:
+        return f"{uuid.UUID(normalized).int:032x}"
+    except (ValueError, AttributeError):
+        return ""
+
+
+@lru_cache(maxsize=4096)
+def compute_span_id_hex(uuid_str: str | None) -> str:
+    if not uuid_str:
+        return ""
+    normalized = uuid_str.strip().lower()
+    if len(normalized) == 16 and all(ch in "0123456789abcdef" for ch in normalized):
+        return normalized
+    try:
+        from enterprise.telemetry.id_generator import compute_deterministic_span_id
+
+        return f"{compute_deterministic_span_id(normalized):016x}"
+    except (ValueError, AttributeError):
+        return ""
+
+
+def emit_telemetry_log(
+    *,
+    event_name: str | EnterpriseTelemetryEvent,
+    attributes: dict[str, Any],
+    signal: str = "metric_only",
+    trace_id_source: str | None = None,
+    span_id_source: str | None = None,
+    tenant_id: str | None = None,
+    user_id: str | None = None,
+) -> None:
+    """Emit a structured log line for a telemetry event.
+
+    Parameters
+    ----------
+    event_name:
+        Canonical event name, e.g. ``"dify.workflow.run"``.
+    attributes:
+        All event-specific attributes (already built by the caller).
+    signal:
+        ``"metric_only"`` for events with no span, ``"span_detail"``
+        for detail logs accompanying a slim span.
+    trace_id_source:
+        A UUID string (e.g. ``workflow_run_id``) used to derive a 32-hex
+        trace_id for cross-signal correlation.
+    tenant_id:
+        Tenant identifier (for the ``IdentityContextFilter``).
+    user_id:
+        User identifier (for the ``IdentityContextFilter``).
+    """
+    if not logger.isEnabledFor(logging.INFO):
+        return
+    attrs = {
+        "dify.event.name": event_name,
+        "dify.event.signal": signal,
+        **attributes,
+    }
+
+    extra: dict[str, Any] = {"attributes": attrs}
+
+    trace_id_hex = compute_trace_id_hex(trace_id_source)
+    if trace_id_hex:
+        extra["trace_id"] = trace_id_hex
+    span_id_hex = compute_span_id_hex(span_id_source)
+    if span_id_hex:
+        extra["span_id"] = span_id_hex
+    if tenant_id:
+        extra["tenant_id"] = tenant_id
+    if user_id:
+        extra["user_id"] = user_id
+
+    logger.info("telemetry.%s", signal, extra=extra)
+
+
+def emit_metric_only_event(
+    *,
+    event_name: str | EnterpriseTelemetryEvent,
+    attributes: dict[str, Any],
+    trace_id_source: str | None = None,
+    span_id_source: str | None = None,
+    tenant_id: str | None = None,
+    user_id: str | None = None,
+) -> None:
+    emit_telemetry_log(
+        event_name=event_name,
+        attributes=attributes,
+        signal="metric_only",
+        trace_id_source=trace_id_source,
+        span_id_source=span_id_source,
+        tenant_id=tenant_id,
+        user_id=user_id,
+    )

api/events/app_event.py

@@ -3,6 +3,12 @@ from blinker import signal
 # sender: app
 app_was_created = signal("app-was-created")
 
+# sender: app
+app_was_deleted = signal("app-was-deleted")
+
+# sender: app
+app_was_updated = signal("app-was-updated")
+
 # sender: app, kwargs: app_model_config
 app_model_config_was_updated = signal("app-model-config-was-updated")
 

api/events/feedback_event.py

@@ -0,0 +1,4 @@
+from blinker import signal
+
+# sender: MessageFeedback, kwargs: tenant_id
+feedback_was_created = signal("feedback-was-created")

api/extensions/ext_celery.py

@@ -204,6 +204,8 @@ def init_app(app: DifyApp) -> Celery:
             "schedule": timedelta(minutes=dify_config.API_TOKEN_LAST_USED_UPDATE_INTERVAL),
         }
 
+    if dify_config.ENTERPRISE_ENABLED and dify_config.ENTERPRISE_TELEMETRY_ENABLED:
+        imports.append("tasks.enterprise_telemetry_task")
     celery_app.conf.update(beat_schedule=beat_schedule, imports=imports)
 
     return celery_app

api/extensions/ext_enterprise_telemetry.py

@@ -0,0 +1,50 @@
+"""Flask extension for enterprise telemetry lifecycle management.
+
+Initializes the EnterpriseExporter singleton during ``create_app()``
+(single-threaded), registers blinker event handlers, and hooks atexit
+for graceful shutdown.
+
+Skipped entirely when ``ENTERPRISE_ENABLED`` and ``ENTERPRISE_TELEMETRY_ENABLED``
+are false (``is_enabled()`` gate).
+"""
+
+from __future__ import annotations
+
+import atexit
+import logging
+from typing import TYPE_CHECKING
+
+from configs import dify_config
+
+if TYPE_CHECKING:
+    from dify_app import DifyApp
+    from enterprise.telemetry.exporter import EnterpriseExporter
+
+logger = logging.getLogger(__name__)
+
+_exporter: EnterpriseExporter | None = None
+
+
+def is_enabled() -> bool:
+    return bool(dify_config.ENTERPRISE_ENABLED and dify_config.ENTERPRISE_TELEMETRY_ENABLED)
+
+
+def init_app(app: DifyApp) -> None:
+    global _exporter
+
+    if not is_enabled():
+        return
+
+    from enterprise.telemetry.exporter import EnterpriseExporter
+
+    _exporter = EnterpriseExporter(dify_config)
+    atexit.register(_exporter.shutdown)
+
+    # Import to trigger @signal.connect decorator registration
+    import enterprise.telemetry.event_handlers  # noqa: F401  # type: ignore[reportUnusedImport]
+
+    logger.info("Enterprise telemetry initialized")
+
+
+def get_enterprise_exporter() -> EnterpriseExporter | None:
+    return _exporter

api/extensions/ext_otel.py

@@ -78,16 +78,24 @@ def init_app(app: DifyApp):
     protocol = (dify_config.OTEL_EXPORTER_OTLP_PROTOCOL or "").lower()
     if dify_config.OTEL_EXPORTER_TYPE == "otlp":
         if protocol == "grpc":
+            # Auto-detect TLS: https:// uses secure, everything else is insecure
+            endpoint = dify_config.OTLP_BASE_ENDPOINT
+            insecure = not endpoint.startswith("https://")
+
             exporter = GRPCSpanExporter(
-                endpoint=dify_config.OTLP_BASE_ENDPOINT,
+                endpoint=endpoint,
                 # Header field names must consist of lowercase letters, check RFC7540
-                headers=(("authorization", f"Bearer {dify_config.OTLP_API_KEY}"),),
-                insecure=True,
+                headers=(
+                    (("authorization", f"Bearer {dify_config.OTLP_API_KEY}"),) if dify_config.OTLP_API_KEY else None
+                ),
+                insecure=insecure,
             )
             metric_exporter = GRPCMetricExporter(
-                endpoint=dify_config.OTLP_BASE_ENDPOINT,
-                headers=(("authorization", f"Bearer {dify_config.OTLP_API_KEY}"),),
-                insecure=True,
+                endpoint=endpoint,
+                headers=(
+                    (("authorization", f"Bearer {dify_config.OTLP_API_KEY}"),) if dify_config.OTLP_API_KEY else None
+                ),
+                insecure=insecure,
             )
         else:
             headers = {"Authorization": f"Bearer {dify_config.OTLP_API_KEY}"} if dify_config.OTLP_API_KEY else None

api/extensions/otel/parser/__init__.py

@@ -5,7 +5,7 @@ This module provides parsers that extract node-specific metadata and set
 OpenTelemetry span attributes according to semantic conventions.
 """
 
-from extensions.otel.parser.base import DefaultNodeOTelParser, NodeOTelParser, safe_json_dumps
+from extensions.otel.parser.base import DefaultNodeOTelParser, NodeOTelParser, safe_json_dumps, should_include_content
 from extensions.otel.parser.llm import LLMNodeOTelParser
 from extensions.otel.parser.retrieval import RetrievalNodeOTelParser
 from extensions.otel.parser.tool import ToolNodeOTelParser
@@ -17,4 +17,5 @@ __all__ = [
     "RetrievalNodeOTelParser",
     "ToolNodeOTelParser",
     "safe_json_dumps",
+    "should_include_content",
 ]

api/extensions/otel/parser/base.py

@@ -1,5 +1,10 @@
 """
 Base parser interface and utilities for OpenTelemetry node parsers.
+
+Content gating: ``should_include_content()`` controls whether content-bearing
+span attributes (inputs, outputs, prompts, completions, documents) are written.
+Gate is only active in EE (``ENTERPRISE_ENABLED=True``) when
+``ENTERPRISE_INCLUDE_CONTENT=False``; CE behaviour is unchanged.
 """
 
 import json
@@ -9,6 +14,7 @@ from opentelemetry.trace import Span
 from opentelemetry.trace.status import Status, StatusCode
 from pydantic import BaseModel
 
+from configs import dify_config
 from dify_graph.enums import BuiltinNodeTypes
 from dify_graph.file.models import File
 from dify_graph.graph_events import GraphNodeEventBase
@@ -17,6 +23,17 @@ from dify_graph.variables import Segment
 from extensions.otel.semconv.gen_ai import ChainAttributes, GenAIAttributes
 
 
+def should_include_content() -> bool:
+    """Return True if content should be written to spans.
+
+    CE (ENTERPRISE_ENABLED=False): always True — no behaviour change.
+    EE: follows ENTERPRISE_INCLUDE_CONTENT (default True).
+    """
+    if not dify_config.ENTERPRISE_ENABLED:
+        return True
+    return dify_config.ENTERPRISE_INCLUDE_CONTENT
+
+
 def safe_json_dumps(obj: Any, ensure_ascii: bool = False) -> str:
     """
     Safely serialize objects to JSON, handling non-serializable types.
@@ -101,10 +118,11 @@ class DefaultNodeOTelParser:
         # Extract inputs and outputs from result_event
         if result_event and result_event.node_run_result:
             node_run_result = result_event.node_run_result
-            if node_run_result.inputs:
-                span.set_attribute(ChainAttributes.INPUT_VALUE, safe_json_dumps(node_run_result.inputs))
-            if node_run_result.outputs:
-                span.set_attribute(ChainAttributes.OUTPUT_VALUE, safe_json_dumps(node_run_result.outputs))
+            if should_include_content():
+                if node_run_result.inputs:
+                    span.set_attribute(ChainAttributes.INPUT_VALUE, safe_json_dumps(node_run_result.inputs))
+                if node_run_result.outputs:
+                    span.set_attribute(ChainAttributes.OUTPUT_VALUE, safe_json_dumps(node_run_result.outputs))
 
         if error:
             span.record_exception(error)

api/extensions/otel/semconv/dify.py

@@ -21,3 +21,15 @@ class DifySpanAttributes:
 
     INVOKE_FROM = "dify.invoke_from"
     """Invocation source, e.g. SERVICE_API, WEB_APP, DEBUGGER."""
+
+    INVOKED_BY = "dify.invoked_by"
+    """Invoked by, e.g. end_user, account, user."""
+
+    USAGE_INPUT_TOKENS = "gen_ai.usage.input_tokens"
+    """Number of input tokens (prompt tokens) used."""
+
+    USAGE_OUTPUT_TOKENS = "gen_ai.usage.output_tokens"
+    """Number of output tokens (completion tokens) generated."""
+
+    USAGE_TOTAL_TOKENS = "gen_ai.usage.total_tokens"
+    """Total number of tokens used."""

api/models/enums.py

@@ -11,13 +11,6 @@ class CreatorUserRole(StrEnum):
     ACCOUNT = "account"
     END_USER = "end_user"
 
-    @classmethod
-    def _missing_(cls, value):
-        if value == "end-user":
-            return cls.END_USER
-        else:
-            return super()._missing_(value)
-
 
 class WorkflowRunTriggeredFrom(StrEnum):
     DEBUGGING = "debugging"

api/models/provider.py

@@ -13,7 +13,6 @@ from libs.uuid_utils import uuidv7
 
 from .base import TypeBase
 from .engine import db
-from .enums import CredentialSourceType, PaymentStatus
 from .types import EnumText, LongText, StringUUID
 
 
@@ -238,9 +237,7 @@ class ProviderOrder(TypeBase):
     quantity: Mapped[int] = mapped_column(sa.Integer, nullable=False, server_default=text("1"))
     currency: Mapped[str | None] = mapped_column(String(40))
     total_amount: Mapped[int | None] = mapped_column(sa.Integer)
-    payment_status: Mapped[PaymentStatus] = mapped_column(
-        EnumText(PaymentStatus, length=40), nullable=False, server_default=text("'wait_pay'")
-    )
+    payment_status: Mapped[str] = mapped_column(String(40), nullable=False, server_default=text("'wait_pay'"))
     paid_at: Mapped[datetime | None] = mapped_column(DateTime)
     pay_failed_at: Mapped[datetime | None] = mapped_column(DateTime)
     refunded_at: Mapped[datetime | None] = mapped_column(DateTime)
@@ -303,9 +300,7 @@ class LoadBalancingModelConfig(TypeBase):
     name: Mapped[str] = mapped_column(String(255), nullable=False)
     encrypted_config: Mapped[str | None] = mapped_column(LongText, nullable=True, default=None)
     credential_id: Mapped[str | None] = mapped_column(StringUUID, nullable=True, default=None)
-    credential_source_type: Mapped[CredentialSourceType | None] = mapped_column(
-        EnumText(CredentialSourceType, length=40), nullable=True, default=None
-    )
+    credential_source_type: Mapped[str | None] = mapped_column(String(40), nullable=True, default=None)
     enabled: Mapped[bool] = mapped_column(sa.Boolean, nullable=False, server_default=text("true"), default=True)
     created_at: Mapped[datetime] = mapped_column(
         DateTime, nullable=False, server_default=func.current_timestamp(), init=False

api/pyrefly-local-excludes.txt

@@ -13,6 +13,21 @@ controllers/console/workspace/trigger_providers.py
 controllers/service_api/app/annotation.py
 controllers/web/workflow_events.py
 core/agent/fc_agent_runner.py
+core/app/apps/advanced_chat/app_generator.py
+core/app/apps/advanced_chat/app_runner.py
+core/app/apps/advanced_chat/generate_task_pipeline.py
+core/app/apps/agent_chat/app_generator.py
+core/app/apps/base_app_generate_response_converter.py
+core/app/apps/base_app_generator.py
+core/app/apps/chat/app_generator.py
+core/app/apps/common/workflow_response_converter.py
+core/app/apps/completion/app_generator.py
+core/app/apps/pipeline/pipeline_generator.py
+core/app/apps/pipeline/pipeline_runner.py
+core/app/apps/workflow/app_generator.py
+core/app/apps/workflow/app_runner.py
+core/app/apps/workflow/generate_task_pipeline.py
+core/app/apps/workflow_app_runner.py
 core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py
 core/datasource/datasource_manager.py
 core/external_data_tool/api/api.py
@@ -93,6 +108,35 @@ core/tools/workflow_as_tool/provider.py
 core/trigger/debug/event_selectors.py
 core/trigger/entities/entities.py
 core/trigger/provider.py
+core/workflow/workflow_entry.py
+dify_graph/entities/workflow_execution.py
+dify_graph/file/file_manager.py
+dify_graph/graph_engine/error_handler.py
+dify_graph/graph_engine/layers/execution_limits.py
+dify_graph/nodes/agent/agent_node.py
+dify_graph/nodes/base/node.py
+dify_graph/nodes/code/code_node.py
+dify_graph/nodes/datasource/datasource_node.py
+dify_graph/nodes/document_extractor/node.py
+dify_graph/nodes/human_input/human_input_node.py
+dify_graph/nodes/if_else/if_else_node.py
+dify_graph/nodes/iteration/iteration_node.py
+dify_graph/nodes/knowledge_index/knowledge_index_node.py
+core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
+dify_graph/nodes/list_operator/node.py
+dify_graph/nodes/llm/node.py
+dify_graph/nodes/loop/loop_node.py
+dify_graph/nodes/parameter_extractor/parameter_extractor_node.py
+dify_graph/nodes/question_classifier/question_classifier_node.py
+dify_graph/nodes/start/start_node.py
+dify_graph/nodes/template_transform/template_transform_node.py
+dify_graph/nodes/tool/tool_node.py
+dify_graph/nodes/trigger_plugin/trigger_event_node.py
+dify_graph/nodes/trigger_schedule/trigger_schedule_node.py
+dify_graph/nodes/trigger_webhook/node.py
+dify_graph/nodes/variable_aggregator/variable_aggregator_node.py
+dify_graph/nodes/variable_assigner/v1/node.py
+dify_graph/nodes/variable_assigner/v2/node.py
 extensions/logstore/repositories/logstore_api_workflow_run_repository.py
 extensions/otel/instrumentation.py
 extensions/otel/runtime.py

api/services/model_load_balancing_service.py

@@ -19,7 +19,6 @@ from dify_graph.model_runtime.entities.provider_entities import (
 from dify_graph.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
 from extensions.ext_database import db
 from libs.datetime_utils import naive_utc_now
-from models.enums import CredentialSourceType
 from models.provider import LoadBalancingModelConfig, ProviderCredential, ProviderModelCredential
 
 logger = logging.getLogger(__name__)
@@ -104,9 +103,9 @@ class ModelLoadBalancingService:
             is_load_balancing_enabled = True
 
         if config_from == "predefined-model":
-            credential_source_type = CredentialSourceType.PROVIDER
+            credential_source_type = "provider"
         else:
-            credential_source_type = CredentialSourceType.CUSTOM_MODEL
+            credential_source_type = "custom_model"
 
         # Get load balancing configurations
         load_balancing_configs = (
@@ -422,11 +421,7 @@ class ModelLoadBalancingService:
                     raise ValueError("Invalid load balancing config name")
 
                 if credential_id:
-                    credential_source = (
-                        CredentialSourceType.PROVIDER
-                        if config_from == "predefined-model"
-                        else CredentialSourceType.CUSTOM_MODEL
-                    )
+                    credential_source = "provider" if config_from == "predefined-model" else "custom_model"
                     assert credential_record is not None
                     load_balancing_model_config = LoadBalancingModelConfig(
                         tenant_id=tenant_id,

api/services/rag_pipeline/pipeline_template/remote/remote_retrieval.py

@@ -15,8 +15,7 @@ class RemotePipelineTemplateRetrieval(PipelineTemplateRetrievalBase):
     Retrieval recommended app from dify official
     """
 
-    def get_pipeline_template_detail(self, template_id: str) -> dict | None:
-        result: dict | None
+    def get_pipeline_template_detail(self, template_id: str):
         try:
             result = self.fetch_pipeline_template_detail_from_dify_official(template_id)
         except Exception as e:
@@ -36,23 +35,17 @@ class RemotePipelineTemplateRetrieval(PipelineTemplateRetrievalBase):
         return PipelineTemplateType.REMOTE
 
     @classmethod
-    def fetch_pipeline_template_detail_from_dify_official(cls, template_id: str) -> dict:
+    def fetch_pipeline_template_detail_from_dify_official(cls, template_id: str) -> dict | None:
         """
         Fetch pipeline template detail from dify official.
-
-        :param template_id: Pipeline template ID
-        :return: Template detail dict
-        :raises ValueError: When upstream returns a non-200 status code
+        :param template_id: Pipeline ID
+        :return:
         """
         domain = dify_config.HOSTED_FETCH_PIPELINE_TEMPLATES_REMOTE_DOMAIN
         url = f"{domain}/pipeline-templates/{template_id}"
         response = httpx.get(url, timeout=httpx.Timeout(10.0, connect=3.0))
         if response.status_code != 200:
-            raise ValueError(
-                "fetch pipeline template detail failed,"
-                + f" status_code: {response.status_code},"
-                + f" response: {response.text[:1000]}"
-            )
+            return None
         data: dict = response.json()
         return data
 

api/services/rag_pipeline/rag_pipeline.py

@@ -117,21 +117,13 @@ class RagPipelineService:
     def get_pipeline_template_detail(cls, template_id: str, type: str = "built-in") -> dict | None:
         """
         Get pipeline template detail.
-
         :param template_id: template id
-        :param type: template type, "built-in" or "customized"
-        :return: template detail dict, or None if not found
+        :return:
         """
         if type == "built-in":
             mode = dify_config.HOSTED_FETCH_PIPELINE_TEMPLATES_MODE
             retrieval_instance = PipelineTemplateRetrievalFactory.get_pipeline_template_factory(mode)()
             built_in_result: dict | None = retrieval_instance.get_pipeline_template_detail(template_id)
-            if built_in_result is None:
-                logger.warning(
-                    "pipeline template retrieval returned empty result, template_id: %s, mode: %s",
-                    template_id,
-                    mode,
-                )
             return built_in_result
         else:
             mode = "customized"

api/services/summary_index_service.py

@@ -12,7 +12,6 @@ from core.db.session_factory import session_factory
 from core.model_manager import ModelManager
 from core.rag.datasource.vdb.vector_factory import Vector
 from core.rag.index_processor.constant.doc_type import DocType
-from core.rag.index_processor.index_processor_base import SummaryIndexSettingDict
 from core.rag.models.document import Document
 from dify_graph.model_runtime.entities.llm_entities import LLMUsage
 from dify_graph.model_runtime.entities.model_entities import ModelType
@@ -31,7 +30,7 @@ class SummaryIndexService:
     def generate_summary_for_segment(
         segment: DocumentSegment,
         dataset: Dataset,
-        summary_index_setting: SummaryIndexSettingDict,
+        summary_index_setting: dict,
     ) -> tuple[str, LLMUsage]:
         """
         Generate summary for a single segment.
@@ -601,7 +600,7 @@ class SummaryIndexService:
     def generate_and_vectorize_summary(
         segment: DocumentSegment,
         dataset: Dataset,
-        summary_index_setting: SummaryIndexSettingDict,
+        summary_index_setting: dict,
     ) -> DocumentSegmentSummary:
         """
         Generate summary for a segment and vectorize it.
@@ -706,7 +705,7 @@ class SummaryIndexService:
     def generate_summaries_for_document(
         dataset: Dataset,
         document: DatasetDocument,
-        summary_index_setting: SummaryIndexSettingDict,
+        summary_index_setting: dict,
         segment_ids: list[str] | None = None,
         only_parent_chunks: bool = False,
     ) -> list[DocumentSegmentSummary]:

api/tasks/enterprise_telemetry_task.py

@@ -0,0 +1,52 @@
+"""Celery worker for enterprise metric/log telemetry events.
+
+This module defines the Celery task that processes telemetry envelopes
+from the enterprise_telemetry queue. It deserializes envelopes and
+dispatches them to the EnterpriseMetricHandler.
+"""
+
+import json
+import logging
+
+from celery import shared_task
+
+from enterprise.telemetry.contracts import TelemetryEnvelope
+from enterprise.telemetry.metric_handler import EnterpriseMetricHandler
+
+logger = logging.getLogger(__name__)
+
+
+@shared_task(queue="enterprise_telemetry")
+def process_enterprise_telemetry(envelope_json: str) -> None:
+    """Process enterprise metric/log telemetry envelope.
+
+    This task is enqueued by the TelemetryGateway for metric/log-only
+    events. It deserializes the envelope and dispatches to the handler.
+
+    Best-effort processing: logs errors but never raises, to avoid
+    failing user requests due to telemetry issues.
+
+    Args:
+        envelope_json: JSON-serialized TelemetryEnvelope.
+    """
+    try:
+        # Deserialize envelope
+        envelope_dict = json.loads(envelope_json)
+        envelope = TelemetryEnvelope.model_validate(envelope_dict)
+
+        # Process through handler
+        handler = EnterpriseMetricHandler()
+        handler.handle(envelope)
+
+        logger.debug(
+            "Successfully processed telemetry envelope: tenant_id=%s, event_id=%s, case=%s",
+            envelope.tenant_id,
+            envelope.event_id,
+            envelope.case,
+        )
+    except Exception:
+        # Best-effort: log and drop on error, never fail user request
+        logger.warning(
+            "Failed to process enterprise telemetry envelope, dropping event",
+            exc_info=True,
+        )

api/tasks/ops_trace_task.py

@@ -39,17 +39,36 @@ def process_trace_tasks(file_info):
         trace_info["documents"] = [Document.model_validate(doc) for doc in trace_info["documents"]]
 
     try:
+        trace_type = trace_info_info_map.get(trace_info_type)
+        if trace_type:
+            trace_info = trace_type(**trace_info)
+
+        from extensions.ext_enterprise_telemetry import is_enabled as is_ee_telemetry_enabled
+
+        if is_ee_telemetry_enabled():
+            from enterprise.telemetry.enterprise_trace import EnterpriseOtelTrace
+
+            try:
+                EnterpriseOtelTrace().trace(trace_info)
+            except Exception:
+                logger.exception("Enterprise trace failed for app_id: %s", app_id)
+
         if trace_instance:
             with current_app.app_context():
-                trace_type = trace_info_info_map.get(trace_info_type)
-                if trace_type:
-                    trace_info = trace_type(**trace_info)
                 trace_instance.trace(trace_info)
+
         logger.info("Processing trace tasks success, app_id: %s", app_id)
     except Exception as e:
-        logger.info("error:\n\n\n%s\n\n\n\n", e)
+        logger.exception("Processing trace tasks failed, app_id: %s", app_id)
         failed_key = f"{OPS_TRACE_FAILED_KEY}_{app_id}"
         redis_client.incr(failed_key)
-        logger.info("Processing trace tasks failed, app_id: %s", app_id)
     finally:
-        storage.delete(file_path)
+        try:
+            storage.delete(file_path)
+        except Exception as e:
+            logger.warning(
+                "Failed to delete trace file %s for app_id %s: %s",
+                file_path,
+                app_id,
+                e,
+            )

api/tests/unit_tests/controllers/console/datasets/rag_pipeline/test_rag_pipeline.py

@@ -59,44 +59,6 @@ class TestPipelineTemplateDetailApi:
         assert status == 200
         assert response == template
 
-    def test_get_returns_404_when_template_not_found(self, app):
-        api = PipelineTemplateDetailApi()
-        method = unwrap(api.get)
-
-        service = MagicMock()
-        service.get_pipeline_template_detail.return_value = None
-
-        with (
-            app.test_request_context("/?type=built-in"),
-            patch(
-                "controllers.console.datasets.rag_pipeline.rag_pipeline.RagPipelineService",
-                return_value=service,
-            ),
-        ):
-            response, status = method(api, "non-existent-id")
-
-        assert status == 404
-        assert "error" in response
-
-    def test_get_returns_404_for_customized_type_not_found(self, app):
-        api = PipelineTemplateDetailApi()
-        method = unwrap(api.get)
-
-        service = MagicMock()
-        service.get_pipeline_template_detail.return_value = None
-
-        with (
-            app.test_request_context("/?type=customized"),
-            patch(
-                "controllers.console.datasets.rag_pipeline.rag_pipeline.RagPipelineService",
-                return_value=service,
-            ),
-        ):
-            response, status = method(api, "non-existent-id")
-
-        assert status == 404
-        assert "error" in response
-
 
 class TestCustomizedPipelineTemplateApi:
     def test_patch_success(self, app):

api/tests/unit_tests/controllers/trigger/test_webhook.py

@@ -23,7 +23,6 @@ def mock_jsonify():
 
 class DummyWebhookTrigger:
     webhook_id = "wh-1"
-    webhook_url = "http://localhost:5001/triggers/webhook/wh-1"
     tenant_id = "tenant-1"
     app_id = "app-1"
     node_id = "node-1"
@@ -105,32 +104,7 @@ class TestHandleWebhookDebug:
     @patch.object(module.WebhookService, "get_webhook_trigger_and_workflow")
     @patch.object(module.WebhookService, "extract_and_validate_webhook_data")
     @patch.object(module.WebhookService, "build_workflow_inputs", return_value={"x": 1})
-    @patch.object(module.TriggerDebugEventBus, "dispatch", return_value=0)
-    def test_debug_requires_active_listener(
-        self,
-        mock_dispatch,
-        mock_build_inputs,
-        mock_extract,
-        mock_get,
-    ):
-        mock_get.return_value = (DummyWebhookTrigger(), None, "node_config")
-        mock_extract.return_value = {"method": "POST"}
-
-        response, status = module.handle_webhook_debug("wh-1")
-
-        assert status == 409
-        assert response["error"] == "No active debug listener"
-        assert response["message"] == (
-            "The webhook debug URL only works while the Variable Inspector is listening. "
-            "Use the published webhook URL to execute the workflow in Celery."
-        )
-        assert response["execution_url"] == DummyWebhookTrigger.webhook_url
-        mock_dispatch.assert_called_once()
-
-    @patch.object(module.WebhookService, "get_webhook_trigger_and_workflow")
-    @patch.object(module.WebhookService, "extract_and_validate_webhook_data")
-    @patch.object(module.WebhookService, "build_workflow_inputs", return_value={"x": 1})
-    @patch.object(module.TriggerDebugEventBus, "dispatch", return_value=1)
+    @patch.object(module.TriggerDebugEventBus, "dispatch")
     @patch.object(module.WebhookService, "generate_webhook_response")
     def test_debug_success(
         self,

api/tests/unit_tests/core/app/apps/advanced_chat/test_app_generator.py

@@ -1013,7 +1013,7 @@ class TestAdvancedChatAppGeneratorInternals:
         monkeypatch.setattr("core.app.apps.advanced_chat.app_generator.Session", _Session)
         monkeypatch.setattr("core.app.apps.advanced_chat.app_generator.db", SimpleNamespace(engine=object()))
 
-        refreshed = _refresh_model(session=None, model=source_model)
+        refreshed = _refresh_model(session=SimpleNamespace(), model=source_model)
 
         assert refreshed is detached_model
 

api/tests/unit_tests/core/app/apps/test_workflow_app_runner_single_node.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from copy import deepcopy
+from typing import Any
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -33,8 +33,8 @@ def _make_graph_state():
     ],
 )
 def test_run_uses_single_node_execution_branch(
-    single_iteration_run: WorkflowAppGenerateEntity.SingleIterationRunEntity | None,
-    single_loop_run: WorkflowAppGenerateEntity.SingleLoopRunEntity | None,
+    single_iteration_run: Any,
+    single_loop_run: Any,
 ) -> None:
     app_config = MagicMock()
     app_config.app_id = "app"
@@ -130,23 +130,10 @@ def test_single_node_run_validates_target_node_config(monkeypatch) -> None:
                     "break_conditions": [],
                     "logical_operator": "and",
                 },
-            },
-            {
-                "id": "other-node",
-                "data": {
-                    "type": "answer",
-                    "title": "Answer",
-                },
-            },
-        ],
-        "edges": [
-            {
-                "source": "other-node",
-                "target": "loop-node",
             }
         ],
+        "edges": [],
     }
-    original_graph_dict = deepcopy(workflow.graph_dict)
 
     _, _, graph_runtime_state = _make_graph_state()
     seen_configs: list[object] = []
@@ -156,19 +143,13 @@ def test_single_node_run_validates_target_node_config(monkeypatch) -> None:
         seen_configs.append(value)
         return original_validate_python(value)
 
-    class FakeNodeClass:
-        @staticmethod
-        def extract_variable_selector_to_variable_mapping(**_kwargs):
-            return {}
-
     monkeypatch.setattr(NodeConfigDictAdapter, "validate_python", record_validate_python)
 
     with (
         patch("core.app.apps.workflow_app_runner.DifyNodeFactory"),
-        patch("core.app.apps.workflow_app_runner.Graph.init", return_value=MagicMock()) as graph_init,
+        patch("core.app.apps.workflow_app_runner.Graph.init", return_value=MagicMock()),
         patch("core.app.apps.workflow_app_runner.load_into_variable_pool"),
         patch("core.app.apps.workflow_app_runner.WorkflowEntry.mapping_user_inputs_to_variable_pool"),
-        patch("core.app.apps.workflow_app_runner.resolve_workflow_node_class", return_value=FakeNodeClass),
     ):
         runner._get_graph_and_variable_pool_for_single_node_run(
             workflow=workflow,
@@ -180,8 +161,3 @@ def test_single_node_run_validates_target_node_config(monkeypatch) -> None:
         )
 
     assert seen_configs == [workflow.graph_dict["nodes"][0]]
-    assert workflow.graph_dict == original_graph_dict
-    graph_config = graph_init.call_args.kwargs["graph_config"]
-    assert graph_config is not workflow.graph_dict
-    assert graph_config["nodes"] == [workflow.graph_dict["nodes"][0]]
-    assert graph_config["edges"] == []

api/tests/unit_tests/core/entities/test_entities_provider_configuration.py

@@ -35,7 +35,6 @@ from dify_graph.model_runtime.entities.provider_entities import (
     ProviderCredentialSchema,
     ProviderEntity,
 )
-from models.enums import CredentialSourceType
 from models.provider import ProviderType
 from models.provider_ids import ModelProviderID
 
@@ -515,7 +514,7 @@ def test_get_custom_provider_models_sets_status_for_removed_credentials_and_inva
                         id="lb-base",
                         name="LB Base",
                         credentials={},
-                        credential_source_type=CredentialSourceType.PROVIDER,
+                        credential_source_type="provider",
                     )
                 ],
             ),
@@ -529,7 +528,7 @@ def test_get_custom_provider_models_sets_status_for_removed_credentials_and_inva
                         id="lb-custom",
                         name="LB Custom",
                         credentials={},
-                        credential_source_type=CredentialSourceType.CUSTOM_MODEL,
+                        credential_source_type="custom_model",
                     )
                 ],
             ),
@@ -827,7 +826,7 @@ def test_update_load_balancing_configs_updates_all_matching_configs() -> None:
         configuration._update_load_balancing_configs_with_credential(
             credential_id="cred-1",
             credential_record=credential_record,
-            credential_source=CredentialSourceType.PROVIDER,
+            credential_source="provider",
             session=session,
         )
 
@@ -845,7 +844,7 @@ def test_update_load_balancing_configs_returns_when_no_matching_configs() -> Non
     configuration._update_load_balancing_configs_with_credential(
         credential_id="cred-1",
         credential_record=SimpleNamespace(encrypted_config="{}", credential_name="Main"),
-        credential_source=CredentialSourceType.PROVIDER,
+        credential_source="provider",
         session=session,
     )
 

api/tests/unit_tests/core/ops/test_ops_trace_manager.py

@@ -107,6 +107,7 @@ def make_message_data(**overrides):
         "agent_thoughts": [],
         "query": "sample-query",
         "inputs": "sample-input",
+        "app_id": "app-id",
     }
     base.update(overrides)
 
@@ -171,10 +172,10 @@ def configure_db_query(session, *, message_file=None, workflow_app_log=None):
 
 class DummySessionContext:
     scalar_values = []
+    _shared_index = 0
 
     def __init__(self, engine):
-        self._values = list(self.scalar_values)
-        self._index = 0
+        self._values = self.scalar_values
 
     def __enter__(self):
         return self
@@ -183,12 +184,28 @@ class DummySessionContext:
         return False
 
     def scalar(self, *args, **kwargs):
-        if self._index >= len(self._values):
+        if DummySessionContext._shared_index >= len(self._values):
             return None
-        value = self._values[self._index]
-        self._index += 1
+        value = self._values[DummySessionContext._shared_index]
+        DummySessionContext._shared_index += 1
         return value
 
+    def scalars(self, *args, **kwargs):
+        class ScalarsResult:
+            def __init__(self, context):
+                self._context = context
+
+            def all(self):
+                if DummySessionContext._shared_index >= len(self._context._values):
+                    return []
+                value = self._context._values[DummySessionContext._shared_index]
+                DummySessionContext._shared_index += 1
+                if isinstance(value, list):
+                    return value
+                return [value] if value is not None else []
+
+        return ScalarsResult(self)
+
 
 @pytest.fixture(autouse=True)
 def patch_provider_map(monkeypatch):
@@ -216,6 +233,8 @@ def patch_timer_and_current_app(monkeypatch):
 
 @pytest.fixture(autouse=True)
 def patch_sqlalchemy_session(monkeypatch):
+    DummySessionContext.scalar_values = []
+    DummySessionContext._shared_index = 0
     monkeypatch.setattr("core.ops.ops_trace_manager.Session", DummySessionContext)
 
 
@@ -453,7 +472,7 @@ def test_trace_task_message_trace(trace_task_message, mock_db):
 
 
 def test_trace_task_workflow_trace(workflow_repo_fixture, mock_db):
-    DummySessionContext.scalar_values = ["wf-app-log", "message-ref"]
+    DummySessionContext.scalar_values = [[], "wf-app-log", "message-ref"]
     execution = SimpleNamespace(id_="run-id")
     task = TraceTask(
         trace_type=TraceTaskName.WORKFLOW_TRACE, workflow_execution=execution, conversation_id="conv", user_id="user"

api/tests/unit_tests/core/ops/test_trace_queue_manager.py

@@ -0,0 +1,200 @@
+"""Unit tests for TraceQueueManager telemetry guard.
+
+This test suite verifies that TraceQueueManager correctly drops trace tasks
+when telemetry is disabled, proving Bug 1 from code review is a false positive.
+
+The guard logic moved from persistence.py to TraceQueueManager.add_trace_task()
+at line 1282 of ops_trace_manager.py:
+    if self._enterprise_telemetry_enabled or self.trace_instance:
+        trace_task.app_id = self.app_id
+        trace_manager_queue.put(trace_task)
+
+Tasks are only enqueued if EITHER:
+- Enterprise telemetry is enabled (_enterprise_telemetry_enabled=True), OR
+- A third-party trace instance (Langfuse, etc.) is configured
+
+When BOTH are false, tasks are silently dropped (correct behavior).
+"""
+
+import queue
+import sys
+import types
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+
+@pytest.fixture
+def trace_queue_manager_and_task(monkeypatch):
+    """Fixture to provide TraceQueueManager and TraceTask with delayed imports."""
+    module_name = "core.ops.ops_trace_manager"
+    if module_name not in sys.modules:
+        ops_stub = types.ModuleType(module_name)
+
+        class StubTraceTask:
+            def __init__(self, trace_type):
+                self.trace_type = trace_type
+                self.app_id = None
+
+        class StubTraceQueueManager:
+            def __init__(self, app_id=None):
+                self.app_id = app_id
+                from core.telemetry.gateway import is_enterprise_telemetry_enabled
+
+                self._enterprise_telemetry_enabled = is_enterprise_telemetry_enabled()
+                self.trace_instance = StubOpsTraceManager.get_ops_trace_instance(app_id)
+
+            def add_trace_task(self, trace_task):
+                if self._enterprise_telemetry_enabled or self.trace_instance:
+                    trace_task.app_id = self.app_id
+                    from core.ops.ops_trace_manager import trace_manager_queue
+
+                    trace_manager_queue.put(trace_task)
+
+        class StubOpsTraceManager:
+            @staticmethod
+            def get_ops_trace_instance(app_id):
+                return None
+
+        ops_stub.TraceQueueManager = StubTraceQueueManager
+        ops_stub.TraceTask = StubTraceTask
+        ops_stub.OpsTraceManager = StubOpsTraceManager
+        ops_stub.trace_manager_queue = MagicMock(spec=queue.Queue)
+        monkeypatch.setitem(sys.modules, module_name, ops_stub)
+
+    from core.ops.entities.trace_entity import TraceTaskName
+
+    ops_module = __import__(module_name, fromlist=["TraceQueueManager", "TraceTask"])
+    TraceQueueManager = ops_module.TraceQueueManager
+    TraceTask = ops_module.TraceTask
+
+    return TraceQueueManager, TraceTask, TraceTaskName
+
+
+class TestTraceQueueManagerTelemetryGuard:
+    """Test TraceQueueManager's telemetry guard in add_trace_task()."""
+
+    def test_task_not_enqueued_when_telemetry_disabled_and_no_trace_instance(self, trace_queue_manager_and_task):
+        """Verify task is NOT enqueued when telemetry disabled and no trace instance.
+
+        This is the core guard: when _enterprise_telemetry_enabled=False AND
+        trace_instance=None, the task should be silently dropped.
+        """
+        TraceQueueManager, TraceTask, TraceTaskName = trace_queue_manager_and_task
+
+        mock_queue = MagicMock(spec=queue.Queue)
+
+        trace_task = TraceTask(trace_type=TraceTaskName.WORKFLOW_TRACE)
+
+        with (
+            patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=False),
+            patch("core.ops.ops_trace_manager.OpsTraceManager.get_ops_trace_instance", return_value=None),
+            patch("core.ops.ops_trace_manager.trace_manager_queue", mock_queue),
+        ):
+            manager = TraceQueueManager(app_id="test-app-id")
+            manager.add_trace_task(trace_task)
+
+            mock_queue.put.assert_not_called()
+
+    def test_task_enqueued_when_telemetry_enabled(self, trace_queue_manager_and_task):
+        """Verify task IS enqueued when enterprise telemetry is enabled.
+
+        When _enterprise_telemetry_enabled=True, the task should be enqueued
+        regardless of trace_instance state.
+        """
+        TraceQueueManager, TraceTask, TraceTaskName = trace_queue_manager_and_task
+
+        mock_queue = MagicMock(spec=queue.Queue)
+
+        trace_task = TraceTask(trace_type=TraceTaskName.WORKFLOW_TRACE)
+
+        with (
+            patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=True),
+            patch("core.ops.ops_trace_manager.OpsTraceManager.get_ops_trace_instance", return_value=None),
+            patch("core.ops.ops_trace_manager.trace_manager_queue", mock_queue),
+        ):
+            manager = TraceQueueManager(app_id="test-app-id")
+            manager.add_trace_task(trace_task)
+
+            mock_queue.put.assert_called_once()
+            called_task = mock_queue.put.call_args[0][0]
+            assert called_task.app_id == "test-app-id"
+
+    def test_task_enqueued_when_trace_instance_configured(self, trace_queue_manager_and_task):
+        """Verify task IS enqueued when third-party trace instance is configured.
+
+        When trace_instance is not None (e.g., Langfuse configured), the task
+        should be enqueued even if enterprise telemetry is disabled.
+        """
+        TraceQueueManager, TraceTask, TraceTaskName = trace_queue_manager_and_task
+
+        mock_queue = MagicMock(spec=queue.Queue)
+
+        mock_trace_instance = MagicMock()
+
+        trace_task = TraceTask(trace_type=TraceTaskName.WORKFLOW_TRACE)
+
+        with (
+            patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=False),
+            patch(
+                "core.ops.ops_trace_manager.OpsTraceManager.get_ops_trace_instance", return_value=mock_trace_instance
+            ),
+            patch("core.ops.ops_trace_manager.trace_manager_queue", mock_queue),
+        ):
+            manager = TraceQueueManager(app_id="test-app-id")
+            manager.add_trace_task(trace_task)
+
+            mock_queue.put.assert_called_once()
+            called_task = mock_queue.put.call_args[0][0]
+            assert called_task.app_id == "test-app-id"
+
+    def test_task_enqueued_when_both_telemetry_and_trace_instance_enabled(self, trace_queue_manager_and_task):
+        """Verify task IS enqueued when both telemetry and trace instance are enabled.
+
+        When both _enterprise_telemetry_enabled=True AND trace_instance is set,
+        the task should definitely be enqueued.
+        """
+        TraceQueueManager, TraceTask, TraceTaskName = trace_queue_manager_and_task
+
+        mock_queue = MagicMock(spec=queue.Queue)
+
+        mock_trace_instance = MagicMock()
+
+        trace_task = TraceTask(trace_type=TraceTaskName.WORKFLOW_TRACE)
+
+        with (
+            patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=True),
+            patch(
+                "core.ops.ops_trace_manager.OpsTraceManager.get_ops_trace_instance", return_value=mock_trace_instance
+            ),
+            patch("core.ops.ops_trace_manager.trace_manager_queue", mock_queue),
+        ):
+            manager = TraceQueueManager(app_id="test-app-id")
+            manager.add_trace_task(trace_task)
+
+            mock_queue.put.assert_called_once()
+            called_task = mock_queue.put.call_args[0][0]
+            assert called_task.app_id == "test-app-id"
+
+    def test_app_id_set_before_enqueue(self, trace_queue_manager_and_task):
+        """Verify app_id is set on the task before enqueuing.
+
+        The guard logic sets trace_task.app_id = self.app_id before calling
+        trace_manager_queue.put(trace_task). This test verifies that behavior.
+        """
+        TraceQueueManager, TraceTask, TraceTaskName = trace_queue_manager_and_task
+
+        mock_queue = MagicMock(spec=queue.Queue)
+
+        trace_task = TraceTask(trace_type=TraceTaskName.WORKFLOW_TRACE)
+
+        with (
+            patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=True),
+            patch("core.ops.ops_trace_manager.OpsTraceManager.get_ops_trace_instance", return_value=None),
+            patch("core.ops.ops_trace_manager.trace_manager_queue", mock_queue),
+        ):
+            manager = TraceQueueManager(app_id="expected-app-id")
+            manager.add_trace_task(trace_task)
+
+            called_task = mock_queue.put.call_args[0][0]
+            assert called_task.app_id == "expected-app-id"

api/tests/unit_tests/core/telemetry/test_facade.py

@@ -0,0 +1,181 @@
+"""Unit tests for core.telemetry.emit() routing and enterprise-only filtering."""
+
+from __future__ import annotations
+
+import queue
+import sys
+import types
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from core.ops.entities.trace_entity import TraceTaskName
+from core.telemetry.events import TelemetryContext, TelemetryEvent
+
+
+@pytest.fixture
+def telemetry_test_setup(monkeypatch):
+    module_name = "core.ops.ops_trace_manager"
+    ops_stub = types.ModuleType(module_name)
+
+    class StubTraceTask:
+        def __init__(self, trace_type, **kwargs):
+            self.trace_type = trace_type
+            self.app_id = None
+            self.kwargs = kwargs
+
+    class StubTraceQueueManager:
+        def __init__(self, app_id=None, user_id=None):
+            self.app_id = app_id
+            self.user_id = user_id
+            self.trace_instance = StubOpsTraceManager.get_ops_trace_instance(app_id)
+
+        def add_trace_task(self, trace_task):
+            trace_task.app_id = self.app_id
+            from core.ops.ops_trace_manager import trace_manager_queue
+
+            trace_manager_queue.put(trace_task)
+
+    class StubOpsTraceManager:
+        @staticmethod
+        def get_ops_trace_instance(app_id):
+            return None
+
+    ops_stub.TraceQueueManager = StubTraceQueueManager
+    ops_stub.TraceTask = StubTraceTask
+    ops_stub.OpsTraceManager = StubOpsTraceManager
+    ops_stub.trace_manager_queue = MagicMock(spec=queue.Queue)
+    monkeypatch.setitem(sys.modules, module_name, ops_stub)
+
+    from core.telemetry import emit
+
+    return emit, ops_stub.trace_manager_queue
+
+
+class TestTelemetryEmit:
+    @patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=True)
+    def test_emit_enterprise_trace_creates_trace_task(self, mock_ee, telemetry_test_setup):
+        emit_fn, mock_queue = telemetry_test_setup
+
+        event = TelemetryEvent(
+            name=TraceTaskName.DRAFT_NODE_EXECUTION_TRACE,
+            context=TelemetryContext(
+                tenant_id="test-tenant",
+                user_id="test-user",
+                app_id="test-app",
+            ),
+            payload={"key": "value"},
+        )
+
+        emit_fn(event)
+
+        mock_queue.put.assert_called_once()
+        called_task = mock_queue.put.call_args[0][0]
+        assert called_task.trace_type == TraceTaskName.DRAFT_NODE_EXECUTION_TRACE
+
+    def test_emit_community_trace_enqueued(self, telemetry_test_setup):
+        emit_fn, mock_queue = telemetry_test_setup
+
+        event = TelemetryEvent(
+            name=TraceTaskName.WORKFLOW_TRACE,
+            context=TelemetryContext(
+                tenant_id="test-tenant",
+                user_id="test-user",
+                app_id="test-app",
+            ),
+            payload={},
+        )
+
+        emit_fn(event)
+
+        mock_queue.put.assert_called_once()
+
+    def test_emit_enterprise_only_trace_dropped_when_ee_disabled(self, telemetry_test_setup):
+        emit_fn, mock_queue = telemetry_test_setup
+
+        event = TelemetryEvent(
+            name=TraceTaskName.DRAFT_NODE_EXECUTION_TRACE,
+            context=TelemetryContext(
+                tenant_id="test-tenant",
+                user_id="test-user",
+                app_id="test-app",
+            ),
+            payload={},
+        )
+
+        emit_fn(event)
+
+        mock_queue.put.assert_not_called()
+
+    @patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=True)
+    def test_emit_all_enterprise_only_traces_allowed_when_ee_enabled(self, mock_ee, telemetry_test_setup):
+        emit_fn, mock_queue = telemetry_test_setup
+
+        enterprise_only_traces = [
+            TraceTaskName.DRAFT_NODE_EXECUTION_TRACE,
+            TraceTaskName.NODE_EXECUTION_TRACE,
+            TraceTaskName.PROMPT_GENERATION_TRACE,
+        ]
+
+        for trace_name in enterprise_only_traces:
+            mock_queue.reset_mock()
+
+            event = TelemetryEvent(
+                name=trace_name,
+                context=TelemetryContext(
+                    tenant_id="test-tenant",
+                    user_id="test-user",
+                    app_id="test-app",
+                ),
+                payload={},
+            )
+
+            emit_fn(event)
+
+            mock_queue.put.assert_called_once()
+            called_task = mock_queue.put.call_args[0][0]
+            assert called_task.trace_type == trace_name
+
+    @patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=True)
+    def test_emit_passes_name_directly_to_trace_task(self, mock_ee, telemetry_test_setup):
+        emit_fn, mock_queue = telemetry_test_setup
+
+        event = TelemetryEvent(
+            name=TraceTaskName.DRAFT_NODE_EXECUTION_TRACE,
+            context=TelemetryContext(
+                tenant_id="test-tenant",
+                user_id="test-user",
+                app_id="test-app",
+            ),
+            payload={"extra": "data"},
+        )
+
+        emit_fn(event)
+
+        mock_queue.put.assert_called_once()
+        called_task = mock_queue.put.call_args[0][0]
+        assert called_task.trace_type == TraceTaskName.DRAFT_NODE_EXECUTION_TRACE
+        assert isinstance(called_task.trace_type, TraceTaskName)
+
+    @patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=True)
+    def test_emit_with_provided_trace_manager(self, mock_ee, telemetry_test_setup):
+        emit_fn, mock_queue = telemetry_test_setup
+
+        mock_trace_manager = MagicMock()
+        mock_trace_manager.add_trace_task = MagicMock()
+
+        event = TelemetryEvent(
+            name=TraceTaskName.NODE_EXECUTION_TRACE,
+            context=TelemetryContext(
+                tenant_id="test-tenant",
+                user_id="test-user",
+                app_id="test-app",
+            ),
+            payload={},
+        )
+
+        emit_fn(event, trace_manager=mock_trace_manager)
+
+        mock_trace_manager.add_trace_task.assert_called_once()
+        called_task = mock_trace_manager.add_trace_task.call_args[0][0]
+        assert called_task.trace_type == TraceTaskName.NODE_EXECUTION_TRACE

api/tests/unit_tests/core/telemetry/test_gateway_integration.py

@@ -0,0 +1,225 @@
+from __future__ import annotations
+
+import sys
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from core.telemetry.gateway import emit, is_enterprise_telemetry_enabled
+from enterprise.telemetry.contracts import TelemetryCase
+
+
+class TestTelemetryCoreExports:
+    def test_is_enterprise_telemetry_enabled_exported(self) -> None:
+        from core.telemetry.gateway import is_enterprise_telemetry_enabled as exported_func
+
+        assert callable(exported_func)
+
+
+@pytest.fixture
+def mock_ops_trace_manager():
+    mock_module = MagicMock()
+    mock_trace_task_class = MagicMock()
+    mock_trace_task_class.return_value = MagicMock()
+    mock_module.TraceTask = mock_trace_task_class
+    mock_module.TraceQueueManager = MagicMock()
+
+    mock_trace_entity = MagicMock()
+    mock_trace_task_name = MagicMock()
+    mock_trace_task_name.return_value = "workflow"
+    mock_trace_entity.TraceTaskName = mock_trace_task_name
+
+    with (
+        patch.dict(sys.modules, {"core.ops.ops_trace_manager": mock_module}),
+        patch.dict(sys.modules, {"core.ops.entities.trace_entity": mock_trace_entity}),
+    ):
+        yield mock_module, mock_trace_entity
+
+
+class TestGatewayIntegrationTraceRouting:
+    @pytest.fixture
+    def mock_trace_manager(self) -> MagicMock:
+        return MagicMock()
+
+    @pytest.mark.usefixtures("mock_ops_trace_manager")
+    def test_ce_eligible_trace_routed_to_trace_manager(
+        self,
+        mock_trace_manager: MagicMock,
+    ) -> None:
+        with patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=True):
+            context = {"app_id": "app-123", "user_id": "user-456", "tenant_id": "tenant-789"}
+            payload = {"workflow_run_id": "run-abc"}
+
+            emit(TelemetryCase.WORKFLOW_RUN, context, payload, mock_trace_manager)
+
+            mock_trace_manager.add_trace_task.assert_called_once()
+
+    @pytest.mark.usefixtures("mock_ops_trace_manager")
+    def test_ce_eligible_trace_routed_when_ee_disabled(
+        self,
+        mock_trace_manager: MagicMock,
+    ) -> None:
+        with patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=False):
+            context = {"app_id": "app-123", "user_id": "user-456"}
+            payload = {"workflow_run_id": "run-abc"}
+
+            emit(TelemetryCase.WORKFLOW_RUN, context, payload, mock_trace_manager)
+
+            mock_trace_manager.add_trace_task.assert_called_once()
+
+    @pytest.mark.usefixtures("mock_ops_trace_manager")
+    def test_enterprise_only_trace_dropped_when_ee_disabled(
+        self,
+        mock_trace_manager: MagicMock,
+    ) -> None:
+        with patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=False):
+            context = {"app_id": "app-123", "user_id": "user-456"}
+            payload = {"node_id": "node-abc"}
+
+            emit(TelemetryCase.NODE_EXECUTION, context, payload, mock_trace_manager)
+
+            mock_trace_manager.add_trace_task.assert_not_called()
+
+    @pytest.mark.usefixtures("mock_ops_trace_manager")
+    def test_enterprise_only_trace_routed_when_ee_enabled(
+        self,
+        mock_trace_manager: MagicMock,
+    ) -> None:
+        with patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=True):
+            context = {"app_id": "app-123", "user_id": "user-456"}
+            payload = {"node_id": "node-abc"}
+
+            emit(TelemetryCase.NODE_EXECUTION, context, payload, mock_trace_manager)
+
+            mock_trace_manager.add_trace_task.assert_called_once()
+
+
+class TestGatewayIntegrationMetricRouting:
+    @patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=True)
+    def test_metric_case_routes_to_celery_task(
+        self,
+        mock_ee_enabled: MagicMock,
+    ) -> None:
+        from enterprise.telemetry.contracts import TelemetryEnvelope
+
+        with patch("tasks.enterprise_telemetry_task.process_enterprise_telemetry.delay") as mock_delay:
+            context = {"tenant_id": "tenant-123"}
+            payload = {"app_id": "app-abc", "name": "My App"}
+
+            emit(TelemetryCase.APP_CREATED, context, payload)
+
+            mock_delay.assert_called_once()
+            envelope_json = mock_delay.call_args[0][0]
+            envelope = TelemetryEnvelope.model_validate_json(envelope_json)
+            assert envelope.case == TelemetryCase.APP_CREATED
+            assert envelope.tenant_id == "tenant-123"
+            assert envelope.payload["app_id"] == "app-abc"
+
+    @pytest.mark.usefixtures("mock_ops_trace_manager")
+    @patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=True)
+    def test_tool_execution_trace_routed(
+        self,
+        mock_ee_enabled: MagicMock,
+    ) -> None:
+        mock_trace_manager = MagicMock()
+        context = {"tenant_id": "tenant-123", "app_id": "app-123"}
+        payload = {"tool_name": "test_tool", "tool_inputs": {}, "tool_outputs": "result"}
+
+        emit(TelemetryCase.TOOL_EXECUTION, context, payload, mock_trace_manager)
+
+        mock_trace_manager.add_trace_task.assert_called_once()
+
+    @pytest.mark.usefixtures("mock_ops_trace_manager")
+    @patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=True)
+    def test_moderation_check_trace_routed(
+        self,
+        mock_ee_enabled: MagicMock,
+    ) -> None:
+        mock_trace_manager = MagicMock()
+        context = {"tenant_id": "tenant-123", "app_id": "app-123"}
+        payload = {"message_id": "msg-123", "moderation_result": {"flagged": False}}
+
+        emit(TelemetryCase.MODERATION_CHECK, context, payload, mock_trace_manager)
+
+        mock_trace_manager.add_trace_task.assert_called_once()
+
+
+class TestGatewayIntegrationCEEligibility:
+    @pytest.fixture
+    def mock_trace_manager(self) -> MagicMock:
+        return MagicMock()
+
+    @pytest.mark.usefixtures("mock_ops_trace_manager")
+    def test_workflow_run_is_ce_eligible(
+        self,
+        mock_trace_manager: MagicMock,
+    ) -> None:
+        with patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=False):
+            context = {"app_id": "app-123", "user_id": "user-456"}
+            payload = {"workflow_run_id": "run-abc"}
+
+            emit(TelemetryCase.WORKFLOW_RUN, context, payload, mock_trace_manager)
+
+            mock_trace_manager.add_trace_task.assert_called_once()
+
+    @pytest.mark.usefixtures("mock_ops_trace_manager")
+    def test_message_run_is_ce_eligible(
+        self,
+        mock_trace_manager: MagicMock,
+    ) -> None:
+        with patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=False):
+            context = {"app_id": "app-123", "user_id": "user-456"}
+            payload = {"message_id": "msg-abc", "conversation_id": "conv-123"}
+
+            emit(TelemetryCase.MESSAGE_RUN, context, payload, mock_trace_manager)
+
+            mock_trace_manager.add_trace_task.assert_called_once()
+
+    @pytest.mark.usefixtures("mock_ops_trace_manager")
+    def test_node_execution_not_ce_eligible(
+        self,
+        mock_trace_manager: MagicMock,
+    ) -> None:
+        with patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=False):
+            context = {"app_id": "app-123", "user_id": "user-456"}
+            payload = {"node_id": "node-abc"}
+
+            emit(TelemetryCase.NODE_EXECUTION, context, payload, mock_trace_manager)
+
+            mock_trace_manager.add_trace_task.assert_not_called()
+
+    @pytest.mark.usefixtures("mock_ops_trace_manager")
+    def test_draft_node_execution_not_ce_eligible(
+        self,
+        mock_trace_manager: MagicMock,
+    ) -> None:
+        with patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=False):
+            context = {"app_id": "app-123", "user_id": "user-456"}
+            payload = {"node_execution_data": {}}
+
+            emit(TelemetryCase.DRAFT_NODE_EXECUTION, context, payload, mock_trace_manager)
+
+            mock_trace_manager.add_trace_task.assert_not_called()
+
+    @pytest.mark.usefixtures("mock_ops_trace_manager")
+    def test_prompt_generation_not_ce_eligible(
+        self,
+        mock_trace_manager: MagicMock,
+    ) -> None:
+        with patch("core.telemetry.gateway.is_enterprise_telemetry_enabled", return_value=False):
+            context = {"app_id": "app-123", "user_id": "user-456", "tenant_id": "tenant-789"}
+            payload = {"operation_type": "generate", "instruction": "test"}
+
+            emit(TelemetryCase.PROMPT_GENERATION, context, payload, mock_trace_manager)
+
+            mock_trace_manager.add_trace_task.assert_not_called()
+
+
+class TestIsEnterpriseTelemetryEnabled:
+    def test_returns_false_when_exporter_import_fails(self) -> None:
+        with patch.dict(sys.modules, {"enterprise.telemetry.exporter": None}):
+            result = is_enterprise_telemetry_enabled()
+            assert result is False
+
+    def test_function_is_callable(self) -> None:
+        assert callable(is_enterprise_telemetry_enabled)

api/tests/unit_tests/core/workflow/nodes/test_loop_node.py

@@ -1,9 +1,6 @@
-import pytest
-
 from dify_graph.entities.graph_config import NodeConfigDictAdapter
-from dify_graph.nodes.loop.entities import LoopNodeData, LoopValue
+from dify_graph.nodes.loop.entities import LoopNodeData
 from dify_graph.nodes.loop.loop_node import LoopNode
-from dify_graph.variables.types import SegmentType
 
 
 def test_extract_variable_selector_to_variable_mapping_validates_child_node_configs(monkeypatch) -> None:
@@ -53,21 +50,3 @@ def test_extract_variable_selector_to_variable_mapping_validates_child_node_conf
     )
 
     assert seen_configs == [child_node_config]
-
-
-@pytest.mark.parametrize(
-    ("var_type", "original_value", "expected_value"),
-    [
-        (SegmentType.ARRAY_STRING, ["alpha", "beta"], ["alpha", "beta"]),
-        (SegmentType.ARRAY_NUMBER, [1, 2.5], [1, 2.5]),
-        (SegmentType.ARRAY_OBJECT, [{"name": "item"}], [{"name": "item"}]),
-        (SegmentType.ARRAY_STRING, '["legacy", "json"]', ["legacy", "json"]),
-    ],
-)
-def test_get_segment_for_constant_accepts_native_array_values(
-    var_type: SegmentType, original_value: LoopValue, expected_value: LoopValue
-) -> None:
-    segment = LoopNode._get_segment_for_constant(var_type, original_value)
-
-    assert segment.value_type == var_type
-    assert segment.value == expected_value

api/tests/unit_tests/enterprise/telemetry/test_contracts.py

@@ -0,0 +1,230 @@
+"""Unit tests for telemetry gateway contracts."""
+
+from __future__ import annotations
+
+import pytest
+from pydantic import ValidationError
+
+from core.telemetry.gateway import CASE_ROUTING
+from enterprise.telemetry.contracts import CaseRoute, SignalType, TelemetryCase, TelemetryEnvelope
+
+
+class TestTelemetryCase:
+    """Tests for TelemetryCase enum."""
+
+    def test_all_cases_defined(self) -> None:
+        """Verify all 14 telemetry cases are defined."""
+        expected_cases = {
+            "WORKFLOW_RUN",
+            "NODE_EXECUTION",
+            "DRAFT_NODE_EXECUTION",
+            "MESSAGE_RUN",
+            "TOOL_EXECUTION",
+            "MODERATION_CHECK",
+            "SUGGESTED_QUESTION",
+            "DATASET_RETRIEVAL",
+            "GENERATE_NAME",
+            "PROMPT_GENERATION",
+            "APP_CREATED",
+            "APP_UPDATED",
+            "APP_DELETED",
+            "FEEDBACK_CREATED",
+        }
+        actual_cases = {case.name for case in TelemetryCase}
+        assert actual_cases == expected_cases
+
+    def test_case_values(self) -> None:
+        """Verify case enum values are correct."""
+        assert TelemetryCase.WORKFLOW_RUN.value == "workflow_run"
+        assert TelemetryCase.NODE_EXECUTION.value == "node_execution"
+        assert TelemetryCase.DRAFT_NODE_EXECUTION.value == "draft_node_execution"
+        assert TelemetryCase.MESSAGE_RUN.value == "message_run"
+        assert TelemetryCase.TOOL_EXECUTION.value == "tool_execution"
+        assert TelemetryCase.MODERATION_CHECK.value == "moderation_check"
+        assert TelemetryCase.SUGGESTED_QUESTION.value == "suggested_question"
+        assert TelemetryCase.DATASET_RETRIEVAL.value == "dataset_retrieval"
+        assert TelemetryCase.GENERATE_NAME.value == "generate_name"
+        assert TelemetryCase.PROMPT_GENERATION.value == "prompt_generation"
+        assert TelemetryCase.APP_CREATED.value == "app_created"
+        assert TelemetryCase.APP_UPDATED.value == "app_updated"
+        assert TelemetryCase.APP_DELETED.value == "app_deleted"
+        assert TelemetryCase.FEEDBACK_CREATED.value == "feedback_created"
+
+
+class TestCaseRoute:
+    """Tests for CaseRoute model."""
+
+    def test_valid_trace_route(self) -> None:
+        """Verify valid trace route creation."""
+        route = CaseRoute(signal_type=SignalType.TRACE, ce_eligible=True)
+        assert route.signal_type == SignalType.TRACE
+        assert route.ce_eligible is True
+
+    def test_valid_metric_log_route(self) -> None:
+        """Verify valid metric_log route creation."""
+        route = CaseRoute(signal_type=SignalType.METRIC_LOG, ce_eligible=False)
+        assert route.signal_type == SignalType.METRIC_LOG
+        assert route.ce_eligible is False
+
+    def test_invalid_signal_type(self) -> None:
+        """Verify invalid signal_type is rejected."""
+        with pytest.raises(ValidationError):
+            CaseRoute(signal_type="invalid", ce_eligible=True)
+
+
+class TestTelemetryEnvelope:
+    """Tests for TelemetryEnvelope model."""
+
+    def test_valid_envelope_minimal(self) -> None:
+        """Verify valid minimal envelope creation."""
+        envelope = TelemetryEnvelope(
+            case=TelemetryCase.WORKFLOW_RUN,
+            tenant_id="tenant-123",
+            event_id="event-456",
+            payload={"key": "value"},
+        )
+        assert envelope.case == TelemetryCase.WORKFLOW_RUN
+        assert envelope.tenant_id == "tenant-123"
+        assert envelope.event_id == "event-456"
+        assert envelope.payload == {"key": "value"}
+        assert envelope.metadata is None
+
+    def test_valid_envelope_full(self) -> None:
+        """Verify valid envelope with all fields."""
+        metadata = {"payload_ref": "telemetry/tenant-789/event-012.json"}
+        envelope = TelemetryEnvelope(
+            case=TelemetryCase.MESSAGE_RUN,
+            tenant_id="tenant-789",
+            event_id="event-012",
+            payload={"message": "hello"},
+            metadata=metadata,
+        )
+        assert envelope.case == TelemetryCase.MESSAGE_RUN
+        assert envelope.tenant_id == "tenant-789"
+        assert envelope.event_id == "event-012"
+        assert envelope.payload == {"message": "hello"}
+        assert envelope.metadata == metadata
+
+    def test_missing_required_case(self) -> None:
+        """Verify missing case field is rejected."""
+        with pytest.raises(ValidationError):
+            TelemetryEnvelope(
+                tenant_id="tenant-123",
+                event_id="event-456",
+                payload={"key": "value"},
+            )
+
+    def test_missing_required_tenant_id(self) -> None:
+        """Verify missing tenant_id field is rejected."""
+        with pytest.raises(ValidationError):
+            TelemetryEnvelope(
+                case=TelemetryCase.WORKFLOW_RUN,
+                event_id="event-456",
+                payload={"key": "value"},
+            )
+
+    def test_missing_required_event_id(self) -> None:
+        """Verify missing event_id field is rejected."""
+        with pytest.raises(ValidationError):
+            TelemetryEnvelope(
+                case=TelemetryCase.WORKFLOW_RUN,
+                tenant_id="tenant-123",
+                payload={"key": "value"},
+            )
+
+    def test_missing_required_payload(self) -> None:
+        """Verify missing payload field is rejected."""
+        with pytest.raises(ValidationError):
+            TelemetryEnvelope(
+                case=TelemetryCase.WORKFLOW_RUN,
+                tenant_id="tenant-123",
+                event_id="event-456",
+            )
+
+    def test_metadata_none(self) -> None:
+        """Verify metadata can be None."""
+        envelope = TelemetryEnvelope(
+            case=TelemetryCase.WORKFLOW_RUN,
+            tenant_id="tenant-123",
+            event_id="event-456",
+            payload={"key": "value"},
+            metadata=None,
+        )
+        assert envelope.metadata is None
+
+
+class TestCaseRouting:
+    """Tests for CASE_ROUTING table."""
+
+    def test_all_cases_routed(self) -> None:
+        """Verify all 14 cases have routing entries."""
+        assert len(CASE_ROUTING) == 14
+        for case in TelemetryCase:
+            assert case in CASE_ROUTING
+
+    def test_trace_ce_eligible_cases(self) -> None:
+        """Verify trace cases with CE eligibility."""
+        ce_eligible_trace_cases = {
+            TelemetryCase.WORKFLOW_RUN,
+            TelemetryCase.MESSAGE_RUN,
+        }
+        for case in ce_eligible_trace_cases:
+            route = CASE_ROUTING[case]
+            assert route.signal_type == SignalType.TRACE
+            assert route.ce_eligible is True
+
+    def test_trace_enterprise_only_cases(self) -> None:
+        """Verify trace cases that are enterprise-only."""
+        enterprise_only_trace_cases = {
+            TelemetryCase.NODE_EXECUTION,
+            TelemetryCase.DRAFT_NODE_EXECUTION,
+            TelemetryCase.PROMPT_GENERATION,
+        }
+        for case in enterprise_only_trace_cases:
+            route = CASE_ROUTING[case]
+            assert route.signal_type == SignalType.TRACE
+            assert route.ce_eligible is False
+
+    def test_metric_log_cases(self) -> None:
+        """Verify metric/log-only cases."""
+        metric_log_cases = {
+            TelemetryCase.APP_CREATED,
+            TelemetryCase.APP_UPDATED,
+            TelemetryCase.APP_DELETED,
+            TelemetryCase.FEEDBACK_CREATED,
+        }
+        for case in metric_log_cases:
+            route = CASE_ROUTING[case]
+            assert route.signal_type == SignalType.METRIC_LOG
+            assert route.ce_eligible is False
+
+    def test_routing_table_completeness(self) -> None:
+        """Verify routing table covers all cases with correct types."""
+        trace_cases = {
+            TelemetryCase.WORKFLOW_RUN,
+            TelemetryCase.MESSAGE_RUN,
+            TelemetryCase.NODE_EXECUTION,
+            TelemetryCase.DRAFT_NODE_EXECUTION,
+            TelemetryCase.PROMPT_GENERATION,
+            TelemetryCase.TOOL_EXECUTION,
+            TelemetryCase.MODERATION_CHECK,
+            TelemetryCase.SUGGESTED_QUESTION,
+            TelemetryCase.DATASET_RETRIEVAL,
+            TelemetryCase.GENERATE_NAME,
+        }
+        metric_log_cases = {
+            TelemetryCase.APP_CREATED,
+            TelemetryCase.APP_UPDATED,
+            TelemetryCase.APP_DELETED,
+            TelemetryCase.FEEDBACK_CREATED,
+        }
+
+        all_cases = trace_cases | metric_log_cases
+        assert len(all_cases) == 14
+        assert all_cases == set(TelemetryCase)
+
+        for case in trace_cases:
+            assert CASE_ROUTING[case].signal_type == SignalType.TRACE
+
+        for case in metric_log_cases:
+            assert CASE_ROUTING[case].signal_type == SignalType.METRIC_LOG

api/tests/unit_tests/enterprise/telemetry/test_event_handlers.py

@@ -0,0 +1,121 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from enterprise.telemetry import event_handlers
+from enterprise.telemetry.contracts import TelemetryCase
+
+
+@pytest.fixture
+def mock_gateway_emit():
+    with patch("core.telemetry.gateway.emit") as mock:
+        yield mock
+
+
+def test_handle_app_created_calls_task(mock_gateway_emit):
+    sender = MagicMock()
+    sender.id = "app-123"
+    sender.tenant_id = "tenant-456"
+    sender.mode = "chat"
+
+    event_handlers._handle_app_created(sender)
+
+    mock_gateway_emit.assert_called_once_with(
+        case=TelemetryCase.APP_CREATED,
+        context={"tenant_id": "tenant-456"},
+        payload={"app_id": "app-123", "mode": "chat"},
+    )
+
+
+def test_handle_app_created_no_exporter(mock_gateway_emit):
+    """Gateway handles exporter availability internally; handler always calls gateway."""
+    sender = MagicMock()
+    sender.id = "app-123"
+    sender.tenant_id = "tenant-456"
+
+    event_handlers._handle_app_created(sender)
+
+    mock_gateway_emit.assert_called_once()
+
+
+def test_handle_app_updated_calls_task(mock_gateway_emit):
+    sender = MagicMock()
+    sender.id = "app-123"
+    sender.tenant_id = "tenant-456"
+
+    event_handlers._handle_app_updated(sender)
+
+    mock_gateway_emit.assert_called_once_with(
+        case=TelemetryCase.APP_UPDATED,
+        context={"tenant_id": "tenant-456"},
+        payload={"app_id": "app-123"},
+    )
+
+
+def test_handle_app_deleted_calls_task(mock_gateway_emit):
+    sender = MagicMock()
+    sender.id = "app-123"
+    sender.tenant_id = "tenant-456"
+
+    event_handlers._handle_app_deleted(sender)
+
+    mock_gateway_emit.assert_called_once_with(
+        case=TelemetryCase.APP_DELETED,
+        context={"tenant_id": "tenant-456"},
+        payload={"app_id": "app-123"},
+    )
+
+
+def test_handle_feedback_created_calls_task(mock_gateway_emit):
+    sender = MagicMock()
+    sender.message_id = "msg-123"
+    sender.app_id = "app-456"
+    sender.conversation_id = "conv-789"
+    sender.from_end_user_id = "user-001"
+    sender.from_account_id = None
+    sender.rating = "like"
+    sender.from_source = "api"
+    sender.content = "Great response!"
+
+    event_handlers._handle_feedback_created(sender, tenant_id="tenant-456")
+
+    mock_gateway_emit.assert_called_once_with(
+        case=TelemetryCase.FEEDBACK_CREATED,
+        context={"tenant_id": "tenant-456"},
+        payload={
+            "message_id": "msg-123",
+            "app_id": "app-456",
+            "conversation_id": "conv-789",
+            "from_end_user_id": "user-001",
+            "from_account_id": None,
+            "rating": "like",
+            "from_source": "api",
+            "content": "Great response!",
+        },
+    )
+
+
+def test_handle_feedback_created_no_exporter(mock_gateway_emit):
+    """Gateway handles exporter availability internally; handler always calls gateway."""
+    sender = MagicMock()
+    sender.message_id = "msg-123"
+
+    event_handlers._handle_feedback_created(sender, tenant_id="tenant-456")
+
+    mock_gateway_emit.assert_called_once()
+
+
+def test_handlers_create_valid_envelopes(mock_gateway_emit):
+    """Verify handlers pass correct TelemetryCase and payload structure."""
+    sender = MagicMock()
+    sender.id = "app-123"
+    sender.tenant_id = "tenant-456"
+    sender.mode = "chat"
+
+    event_handlers._handle_app_created(sender)
+
+    call_kwargs = mock_gateway_emit.call_args[1]
+    assert call_kwargs["case"] == TelemetryCase.APP_CREATED
+    assert call_kwargs["context"]["tenant_id"] == "tenant-456"
+    assert call_kwargs["payload"]["app_id"] == "app-123"
+    assert call_kwargs["payload"]["mode"] == "chat"

api/tests/unit_tests/enterprise/telemetry/test_exporter.py

@@ -0,0 +1,263 @@
+"""Unit tests for EnterpriseExporter and _ExporterFactory."""
+
+from types import SimpleNamespace
+from unittest.mock import MagicMock, patch
+
+from configs.enterprise import EnterpriseTelemetryConfig
+from enterprise.telemetry.exporter import EnterpriseExporter
+
+
+def test_config_api_key_default_empty():
+    """Test that ENTERPRISE_OTLP_API_KEY defaults to empty string."""
+    config = EnterpriseTelemetryConfig()
+    assert config.ENTERPRISE_OTLP_API_KEY == ""
+
+
+@patch("enterprise.telemetry.exporter.GRPCSpanExporter")
+@patch("enterprise.telemetry.exporter.GRPCMetricExporter")
+def test_api_key_only_injects_bearer_header(mock_metric_exporter: MagicMock, mock_span_exporter: MagicMock) -> None:
+    """Test that API key alone injects Bearer authorization header."""
+    mock_config = SimpleNamespace(
+        ENTERPRISE_OTLP_ENDPOINT="https://collector.example.com",
+        ENTERPRISE_OTLP_HEADERS="",
+        ENTERPRISE_OTLP_PROTOCOL="grpc",
+        ENTERPRISE_SERVICE_NAME="dify",
+        ENTERPRISE_OTEL_SAMPLING_RATE=1.0,
+        ENTERPRISE_INCLUDE_CONTENT=True,
+        ENTERPRISE_OTLP_API_KEY="test-secret-key",
+    )
+
+    EnterpriseExporter(mock_config)
+
+    # Verify span exporter was called with Bearer header
+    assert mock_span_exporter.call_args is not None
+    headers = mock_span_exporter.call_args.kwargs.get("headers")
+    assert headers is not None
+    assert ("authorization", "Bearer test-secret-key") in headers
+
+
+@patch("enterprise.telemetry.exporter.GRPCSpanExporter")
+@patch("enterprise.telemetry.exporter.GRPCMetricExporter")
+def test_empty_api_key_no_auth_header(mock_metric_exporter: MagicMock, mock_span_exporter: MagicMock) -> None:
+    """Test that empty API key does not inject authorization header."""
+    mock_config = SimpleNamespace(
+        ENTERPRISE_OTLP_ENDPOINT="https://collector.example.com",
+        ENTERPRISE_OTLP_HEADERS="",
+        ENTERPRISE_OTLP_PROTOCOL="grpc",
+        ENTERPRISE_SERVICE_NAME="dify",
+        ENTERPRISE_OTEL_SAMPLING_RATE=1.0,
+        ENTERPRISE_INCLUDE_CONTENT=True,
+        ENTERPRISE_OTLP_API_KEY="",
+    )
+
+    EnterpriseExporter(mock_config)
+
+    # Verify span exporter was called without authorization header
+    assert mock_span_exporter.call_args is not None
+    headers = mock_span_exporter.call_args.kwargs.get("headers")
+    # Headers should be None or not contain authorization
+    if headers is not None:
+        assert not any(key == "authorization" for key, _ in headers)
+
+
+@patch("enterprise.telemetry.exporter.GRPCSpanExporter")
+@patch("enterprise.telemetry.exporter.GRPCMetricExporter")
+def test_api_key_and_custom_headers_merge(mock_metric_exporter: MagicMock, mock_span_exporter: MagicMock) -> None:
+    """Test that API key and custom headers are merged correctly."""
+    mock_config = SimpleNamespace(
+        ENTERPRISE_OTLP_ENDPOINT="https://collector.example.com",
+        ENTERPRISE_OTLP_HEADERS="x-custom=foo",
+        ENTERPRISE_OTLP_PROTOCOL="grpc",
+        ENTERPRISE_SERVICE_NAME="dify",
+        ENTERPRISE_OTEL_SAMPLING_RATE=1.0,
+        ENTERPRISE_INCLUDE_CONTENT=True,
+        ENTERPRISE_OTLP_API_KEY="test-key",
+    )
+
+    EnterpriseExporter(mock_config)
+
+    # Verify both headers are present
+    assert mock_span_exporter.call_args is not None
+    headers = mock_span_exporter.call_args.kwargs.get("headers")
+    assert headers is not None
+    assert ("authorization", "Bearer test-key") in headers
+    assert ("x-custom", "foo") in headers
+
+
+@patch("enterprise.telemetry.exporter.logger")
+@patch("enterprise.telemetry.exporter.GRPCSpanExporter")
+@patch("enterprise.telemetry.exporter.GRPCMetricExporter")
+def test_api_key_overrides_conflicting_header(
+    mock_metric_exporter: MagicMock, mock_span_exporter: MagicMock, mock_logger: MagicMock
+) -> None:
+    """Test that API key overrides conflicting authorization header and logs warning."""
+    mock_config = SimpleNamespace(
+        ENTERPRISE_OTLP_ENDPOINT="https://collector.example.com",
+        ENTERPRISE_OTLP_HEADERS="authorization=Basic old",
+        ENTERPRISE_OTLP_PROTOCOL="grpc",
+        ENTERPRISE_SERVICE_NAME="dify",
+        ENTERPRISE_OTEL_SAMPLING_RATE=1.0,
+        ENTERPRISE_INCLUDE_CONTENT=True,
+        ENTERPRISE_OTLP_API_KEY="test-key",
+    )
+
+    EnterpriseExporter(mock_config)
+
+    # Verify Bearer header takes precedence
+    assert mock_span_exporter.call_args is not None
+    headers = mock_span_exporter.call_args.kwargs.get("headers")
+    assert headers is not None
+    assert ("authorization", "Bearer test-key") in headers
+    # Verify old authorization header is not present
+    assert ("authorization", "Basic old") not in headers
+
+    # Verify warning was logged
+    mock_logger.warning.assert_called_once()
+    assert mock_logger.warning.call_args is not None
+    warning_message = mock_logger.warning.call_args[0][0]
+    assert "ENTERPRISE_OTLP_API_KEY is set" in warning_message
+    assert "authorization" in warning_message
+
+
+@patch("enterprise.telemetry.exporter.GRPCSpanExporter")
+@patch("enterprise.telemetry.exporter.GRPCMetricExporter")
+def test_https_endpoint_uses_secure_grpc(mock_metric_exporter: MagicMock, mock_span_exporter: MagicMock) -> None:
+    """Test that https:// endpoint enables TLS (insecure=False) for gRPC."""
+    mock_config = SimpleNamespace(
+        ENTERPRISE_OTLP_ENDPOINT="https://collector.example.com",
+        ENTERPRISE_OTLP_HEADERS="",
+        ENTERPRISE_OTLP_PROTOCOL="grpc",
+        ENTERPRISE_SERVICE_NAME="dify",
+        ENTERPRISE_OTEL_SAMPLING_RATE=1.0,
+        ENTERPRISE_INCLUDE_CONTENT=True,
+        ENTERPRISE_OTLP_API_KEY="test-key",
+    )
+
+    EnterpriseExporter(mock_config)
+
+    # Verify insecure=False for both exporters (https:// scheme)
+    assert mock_span_exporter.call_args is not None
+    assert mock_span_exporter.call_args.kwargs["insecure"] is False
+
+    assert mock_metric_exporter.call_args is not None
+    assert mock_metric_exporter.call_args.kwargs["insecure"] is False
+
+
+@patch("enterprise.telemetry.exporter.GRPCSpanExporter")
+@patch("enterprise.telemetry.exporter.GRPCMetricExporter")
+def test_http_endpoint_uses_insecure_grpc(mock_metric_exporter: MagicMock, mock_span_exporter: MagicMock) -> None:
+    """Test that http:// endpoint uses insecure gRPC (insecure=True)."""
+    mock_config = SimpleNamespace(
+        ENTERPRISE_OTLP_ENDPOINT="http://collector.example.com",
+        ENTERPRISE_OTLP_HEADERS="",
+        ENTERPRISE_OTLP_PROTOCOL="grpc",
+        ENTERPRISE_SERVICE_NAME="dify",
+        ENTERPRISE_OTEL_SAMPLING_RATE=1.0,
+        ENTERPRISE_INCLUDE_CONTENT=True,
+        ENTERPRISE_OTLP_API_KEY="",
+    )
+
+    EnterpriseExporter(mock_config)
+
+    # Verify insecure=True for both exporters (http:// scheme)
+    assert mock_span_exporter.call_args is not None
+    assert mock_span_exporter.call_args.kwargs["insecure"] is True
+
+    assert mock_metric_exporter.call_args is not None
+    assert mock_metric_exporter.call_args.kwargs["insecure"] is True
+
+
+@patch("enterprise.telemetry.exporter.HTTPSpanExporter")
+@patch("enterprise.telemetry.exporter.HTTPMetricExporter")
+def test_insecure_not_passed_to_http_exporters(mock_metric_exporter: MagicMock, mock_span_exporter: MagicMock) -> None:
+    """Test that insecure parameter is not passed to HTTP exporters."""
+    mock_config = SimpleNamespace(
+        ENTERPRISE_OTLP_ENDPOINT="http://collector.example.com",
+        ENTERPRISE_OTLP_HEADERS="",
+        ENTERPRISE_OTLP_PROTOCOL="http",
+        ENTERPRISE_SERVICE_NAME="dify",
+        ENTERPRISE_OTEL_SAMPLING_RATE=1.0,
+        ENTERPRISE_INCLUDE_CONTENT=True,
+        ENTERPRISE_OTLP_API_KEY="test-key",
+    )
+
+    EnterpriseExporter(mock_config)
+
+    # Verify insecure kwarg is NOT in HTTP exporter calls
+    assert mock_span_exporter.call_args is not None
+    assert "insecure" not in mock_span_exporter.call_args.kwargs
+
+    assert mock_metric_exporter.call_args is not None
+    assert "insecure" not in mock_metric_exporter.call_args.kwargs
+
+
+@patch("enterprise.telemetry.exporter.GRPCSpanExporter")
+@patch("enterprise.telemetry.exporter.GRPCMetricExporter")
+def test_api_key_with_special_chars_preserved(mock_metric_exporter: MagicMock, mock_span_exporter: MagicMock) -> None:
+    """Test that API key with special characters is preserved without mangling."""
+    special_key = "abc+def/ghi=jkl=="
+    mock_config = SimpleNamespace(
+        ENTERPRISE_OTLP_ENDPOINT="https://collector.example.com",
+        ENTERPRISE_OTLP_HEADERS="",
+        ENTERPRISE_OTLP_PROTOCOL="grpc",
+        ENTERPRISE_SERVICE_NAME="dify",
+        ENTERPRISE_OTEL_SAMPLING_RATE=1.0,
+        ENTERPRISE_INCLUDE_CONTENT=True,
+        ENTERPRISE_OTLP_API_KEY=special_key,
+    )
+
+    EnterpriseExporter(mock_config)
+
+    # Verify special characters are preserved in Bearer header
+    assert mock_span_exporter.call_args is not None
+    headers = mock_span_exporter.call_args.kwargs.get("headers")
+    assert headers is not None
+    assert ("authorization", f"Bearer {special_key}") in headers
+
+
+@patch("enterprise.telemetry.exporter.GRPCSpanExporter")
+@patch("enterprise.telemetry.exporter.GRPCMetricExporter")
+def test_no_scheme_localhost_uses_insecure(mock_metric_exporter: MagicMock, mock_span_exporter: MagicMock) -> None:
+    """Test that endpoint without scheme defaults to insecure for localhost."""
+    mock_config = SimpleNamespace(
+        ENTERPRISE_OTLP_ENDPOINT="localhost:4317",
+        ENTERPRISE_OTLP_HEADERS="",
+        ENTERPRISE_OTLP_PROTOCOL="grpc",
+        ENTERPRISE_SERVICE_NAME="dify",
+        ENTERPRISE_OTEL_SAMPLING_RATE=1.0,
+        ENTERPRISE_INCLUDE_CONTENT=True,
+        ENTERPRISE_OTLP_API_KEY="",
+    )
+
+    EnterpriseExporter(mock_config)
+
+    # Verify insecure=True for localhost without scheme
+    assert mock_span_exporter.call_args is not None
+    assert mock_span_exporter.call_args.kwargs["insecure"] is True
+
+    assert mock_metric_exporter.call_args is not None
+    assert mock_metric_exporter.call_args.kwargs["insecure"] is True
+
+
+@patch("enterprise.telemetry.exporter.GRPCSpanExporter")
+@patch("enterprise.telemetry.exporter.GRPCMetricExporter")
+def test_no_scheme_production_uses_insecure(mock_metric_exporter: MagicMock, mock_span_exporter: MagicMock) -> None:
+    """Test that endpoint without scheme defaults to insecure (not https://)."""
+    mock_config = SimpleNamespace(
+        ENTERPRISE_OTLP_ENDPOINT="collector.example.com:4317",
+        ENTERPRISE_OTLP_HEADERS="",
+        ENTERPRISE_OTLP_PROTOCOL="grpc",
+        ENTERPRISE_SERVICE_NAME="dify",
+        ENTERPRISE_OTEL_SAMPLING_RATE=1.0,
+        ENTERPRISE_INCLUDE_CONTENT=True,
+        ENTERPRISE_OTLP_API_KEY="",
+    )
+
+    EnterpriseExporter(mock_config)
+
+    # Verify insecure=True for any endpoint without https:// scheme
+    assert mock_span_exporter.call_args is not None
+    assert mock_span_exporter.call_args.kwargs["insecure"] is True
+
+    assert mock_metric_exporter.call_args is not None
+    assert mock_metric_exporter.call_args.kwargs["insecure"] is True