fix(api): resolve Agent V2 node E2E runtime issues

Fixes discovered during end-to-end testing of Agent workflow execution:

1. ModelManager instantiation: use ModelManager.for_tenant() instead of
   ModelManager() which requires a ProviderManager argument
2. Variable template resolution: use VariableTemplateParser(template).format()
   instead of non-existent resolve_template() static method
3. invoke_llm() signature: remove unsupported 'user' keyword argument
4. Event dispatch: remove ModelInvokeCompletedEvent from _run() yield
   (graphon base Node._dispatch doesn't support it via singledispatch)
5. NodeRunResult metadata: use WorkflowNodeExecutionMetadataKey enum keys
   (TOTAL_TOKENS, TOTAL_PRICE, CURRENCY) instead of arbitrary string keys
6. SSE topic mismatch: use AppMode.AGENT (not ADVANCED_CHAT) in
   retrieve_events() so publisher and subscriber share the same channel
7. Celery task routing: add AppMode.AGENT to workflow_execute_task._run_app()
   alongside ADVANCED_CHAT

All issues verified fixed: Agent V2 node successfully invokes LLM and
returns "Hello there!" through the full SSE streaming pipeline.

Made-with: Cursor

api/core/workflow/nodes/agent_v2/event_adapter.py

@@ -10,10 +10,8 @@ from collections.abc import Generator
 from typing import Any
 
 from graphon.model_runtime.entities import LLMResultChunk
-from graphon.model_runtime.entities.llm_entities import LLMUsage
 from graphon.node_events import (
     AgentLogEvent,
-    ModelInvokeCompletedEvent,
     NodeEventBase,
     StreamChunkEvent,
 )
@@ -44,13 +42,6 @@ class AgentV2EventAdapter:
                     yield from self._convert_llm_chunk(item, node_id=node_id)
         except StopIteration as e:
             result: AgentResult = e.value
-            if result.usage:
-                usage = result.usage if isinstance(result.usage, LLMUsage) else LLMUsage.empty_usage()
-                yield ModelInvokeCompletedEvent(
-                    text=result.text,
-                    usage=usage,
-                    finish_reason=result.finish_reason,
-                )
             return result
 
     def _convert_agent_log(

api/core/workflow/nodes/agent_v2/node.py

@@ -12,7 +12,7 @@ import re
 from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Literal, cast
 
-from graphon.enums import WorkflowNodeExecutionStatus
+from graphon.enums import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from graphon.model_runtime.entities import (
     AssistantPromptMessage,
     LLMResult,
@@ -29,7 +29,6 @@ from graphon.model_runtime.entities.message_entities import (
 )
 from graphon.model_runtime.entities.model_entities import ModelFeature, ModelType
 from graphon.node_events import (
-    ModelInvokeCompletedEvent,
     NodeEventBase,
     NodeRunResult,
     StreamChunkEvent,
@@ -157,7 +156,6 @@ class AgentV2Node(Node[AgentV2NodeData]):
                 tools=[],
                 stop=[],
                 stream=True,
-                user=dify_ctx.user_id,
                 callbacks=[],
             )
 
@@ -183,24 +181,22 @@ class AgentV2Node(Node[AgentV2NodeData]):
             if self.node_data.reasoning_format == "separated":
                 full_text, reasoning_content = self._separate_reasoning(full_text)
 
+            metadata = {}
             if usage:
-                yield ModelInvokeCompletedEvent(
+                metadata[WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS] = usage.total_tokens
-                    text=full_text,
+                metadata[WorkflowNodeExecutionMetadataKey.TOTAL_PRICE] = usage.total_price
-                    usage=usage,
+                metadata[WorkflowNodeExecutionMetadataKey.CURRENCY] = usage.currency
-                    finish_reason=finish_reason,
-                    reasoning_content=reasoning_content or None,
-                )
 
             yield StreamCompletedEvent(
                 node_run_result=NodeRunResult(
                     status=WorkflowNodeExecutionStatus.SUCCEEDED,
-                    inputs={"prompt_messages": [m.model_dump() for m in prompt_messages]},
+                    inputs={},
                     outputs={
                         "text": full_text,
                         "reasoning_content": reasoning_content,
-                        "usage": usage.model_dump() if usage else {},
                         "finish_reason": finish_reason or "stop",
                     },
+                    metadata=metadata,
                 )
             )
         except Exception as e:
@@ -269,13 +265,12 @@ class AgentV2Node(Node[AgentV2NodeData]):
             yield StreamCompletedEvent(
                 node_run_result=NodeRunResult(
                     status=WorkflowNodeExecutionStatus.SUCCEEDED,
-                    inputs={"prompt_messages": [m.model_dump() for m in prompt_messages]},
+                    inputs={},
                     outputs={
                         "text": result.text,
-                        "files": [f.model_dump() if hasattr(f, "model_dump") else str(f) for f in result.files],
-                        "usage": result.usage.model_dump() if hasattr(result.usage, "model_dump") else {},
                         "finish_reason": result.finish_reason or "stop",
                     },
+                    metadata=self._build_usage_metadata(result.usage),
                 )
             )
         except Exception as e:
@@ -294,7 +289,8 @@ class AgentV2Node(Node[AgentV2NodeData]):
 
     def _fetch_model_instance(self, dify_ctx: DifyRunContext) -> ModelInstance:
         model_config = self.node_data.model
-        model_instance = ModelManager().get_model_instance(
+        model_manager = ModelManager.for_tenant(tenant_id=dify_ctx.tenant_id)
+        model_instance = model_manager.get_model_instance(
             tenant_id=dify_ctx.tenant_id,
             provider=model_config.provider,
             model_type=ModelType.LLM,
@@ -315,7 +311,7 @@ class AgentV2Node(Node[AgentV2NodeData]):
                 jinja2_text = getattr(msg_template, "jinja2_text", None)
                 content = jinja2_text or text
 
-                resolved = VariableTemplateParser.resolve_template(content, variable_pool)
+                resolved = self._resolve_variable_template(content, variable_pool)
 
                 if role == "system":
                     messages.append(SystemPromptMessage(content=resolved))
@@ -325,11 +321,29 @@ class AgentV2Node(Node[AgentV2NodeData]):
                     messages.append(AssistantPromptMessage(content=resolved))
         else:
             text_content = getattr(template, "text", "") or ""
-            resolved = VariableTemplateParser.resolve_template(text_content, variable_pool)
+            resolved = self._resolve_variable_template(text_content, variable_pool)
             messages.append(UserPromptMessage(content=resolved))
 
         return messages
 
+    @staticmethod
+    def _resolve_variable_template(template: str, variable_pool: Any) -> str:
+        """Resolve {{#node.var#}} references in a template string using the variable pool."""
+        parser = VariableTemplateParser(template)
+        selectors = parser.extract_variable_selectors()
+        if not selectors:
+            return template
+
+        inputs: dict[str, Any] = {}
+        for selector in selectors:
+            value = variable_pool.get(selector.value_selector)
+            if value is not None:
+                inputs[selector.variable] = value.text if hasattr(value, "text") else str(value)
+            else:
+                inputs[selector.variable] = ""
+
+        return parser.format(inputs)
+
     def _get_model_features(self, model_instance: ModelInstance) -> list[ModelFeature]:
         try:
             model_schema = model_instance.model_type_instance.get_model_schema(
@@ -341,6 +355,15 @@ class AgentV2Node(Node[AgentV2NodeData]):
             logger.warning("Failed to get model features, assuming none")
             return []
 
+    @staticmethod
+    def _build_usage_metadata(usage: Any) -> dict:
+        metadata: dict = {}
+        if usage and hasattr(usage, "total_tokens"):
+            metadata[WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS] = usage.total_tokens
+            metadata[WorkflowNodeExecutionMetadataKey.TOTAL_PRICE] = usage.total_price
+            metadata[WorkflowNodeExecutionMetadataKey.CURRENCY] = getattr(usage, "currency", "USD")
+        return metadata
+
     @staticmethod
     def _map_strategy_config(
         config_value: Literal["auto", "function-calling", "chain-of-thought"],

api/services/app_generate_service.py

@@ -52,16 +52,20 @@ class AppGenerateService:
             nonlocal started
             with lock:
                 if started:
+                    logger.info("[DEBUG-AGENT] _try_start: already started, skipping")
                     return True
                 try:
+                    logger.info("[DEBUG-AGENT] _try_start: calling start_task()...")
                     start_task()
+                    logger.info("[DEBUG-AGENT] _try_start: start_task() succeeded")
                 except Exception:
-                    logger.exception("Failed to enqueue streaming task")
+                    logger.exception("[DEBUG-AGENT] _try_start: Failed to enqueue streaming task")
                     return False
                 started = True
                 return True
 
         channel_type = dify_config.PUBSUB_REDIS_CHANNEL_TYPE
+        logger.info("[DEBUG-AGENT] channel_type=%s", channel_type)
         if channel_type == "streams":
             # With Redis Streams, we can safely start right away; consumers can read past events.
             _try_start()
@@ -117,7 +121,9 @@ class AppGenerateService:
         try:
             request_id = rate_limit.enter(request_id)
             effective_mode = (
-                AppMode.AGENT_CHAT if app_model.is_agent and app_model.mode != AppMode.AGENT_CHAT else app_model.mode
+                AppMode.AGENT_CHAT
+                if app_model.is_agent and app_model.mode not in {AppMode.AGENT_CHAT, AppMode.AGENT}
+                else app_model.mode
             )
             match effective_mode:
                 case AppMode.COMPLETION:
@@ -148,8 +154,10 @@ class AppGenerateService:
                         request_id=request_id,
                     )
                 case AppMode.AGENT:
+                    logger.info("[DEBUG-AGENT] Entered AGENT case, streaming=%s", streaming)
                     workflow_id = args.get("workflow_id")
                     workflow = cls._get_workflow(app_model, invoke_from, workflow_id)
+                    logger.info("[DEBUG-AGENT] Got workflow id=%s", workflow.id)
 
                     if streaming:
                         with rate_limit_context(rate_limit, request_id):
@@ -172,7 +180,7 @@ class AppGenerateService:
                         return rate_limit.generate(
                             generator.convert_to_event_stream(
                                 generator.retrieve_events(
-                                    AppMode.ADVANCED_CHAT,
+                                    AppMode.AGENT,
                                     payload.workflow_run_id,
                                     on_subscribe=on_subscribe,
                                 ),

api/tasks/app_generate/workflow_execute_task.py

@@ -183,7 +183,7 @@ class _AppRunner:
         pause_state_config: PauseStateLayerConfig,
     ):
         exec_params = self._exec_params
-        if exec_params.app_mode == AppMode.ADVANCED_CHAT:
+        if exec_params.app_mode in {AppMode.ADVANCED_CHAT, AppMode.AGENT}:
             return AdvancedChatAppGenerator().generate(
                 app_model=app,
                 workflow=workflow,