feat: add deterministic message interaction stages

backend/schemas.py

@@ -100,9 +100,18 @@ class StartupAction(CamelModel):
     required: bool = True
 
 
+class OpeningMessageConfig(CamelModel):
+    """Built-in Prompt opening interaction; it is not a reusable LLM tool."""
+
+    title: str = Field(default="重要提示", min_length=1, max_length=120)
+    message: str = Field(min_length=1, max_length=2000)
+    confirm_label: str = Field(default="确认", min_length=1, max_length=40)
+
+
 class StartupConfig(CamelModel):
     execution_mode: Literal["sequential"] = "sequential"
     actions: list[StartupAction] = Field(default_factory=list, max_length=5)
+    opening_message: OpeningMessageConfig | None = None
 
     @model_validator(mode="after")
     def validate_unique_action_ids(self):
@@ -163,13 +172,17 @@ class AssistantUpsert(CamelModel):
                 setattr(self, field, "")
         if "graph" not in allowed:
             self.graph = {}
+        if self.type == "workflow":
+            self.greeting = ""
         if self.type not in {"prompt", "workflow"}:
             self.tool_ids = []
             self.dynamic_variable_definitions = {}
         if self.type != "prompt":
             self.startup = StartupConfig()
-        if self.runtime_mode == "realtime" and self.startup.actions:
-            raise ValueError("Prompt Realtime 模式暂不支持启动 Action")
+        if self.runtime_mode == "realtime" and (
+            self.startup.actions or self.startup.opening_message is not None
+        ):
+            raise ValueError("Prompt Realtime 模式暂不支持启动阶段")
         # 外部托管大脑只能 cascade,拦住不兼容的 realtime
         if self.runtime_mode == "realtime" and self.type not in REALTIME_CAPABLE_TYPES:
             raise ValueError(f"类型 {self.type} 不支持 realtime 运行模式")

backend/services/action_stage.py

@@ -0,0 +1,100 @@
+"""Shared deterministic stage for one or more tool Actions."""
+
+from __future__ import annotations
+
+from collections.abc import Awaitable, Callable
+from dataclasses import dataclass, field
+from typing import Any, Literal
+
+from models import RuntimeTool
+from services.action_runtime import ActionOutcome, ActionRunner, ActionStatus
+
+
+InputPolicy = Literal["queue", "block"]
+OutcomeHook = Callable[["StageAction", ActionOutcome], Awaitable[None]]
+StartedHook = Callable[[], Awaitable[None]]
+
+
+@dataclass(frozen=True)
+class StageAction:
+    """One deterministic tool invocation inside an Action stage."""
+
+    id: str
+    tool: RuntimeTool | None
+    arguments: dict[str, Any] = field(default_factory=dict)
+    result_assignments: dict[str, str] | None = None
+    required: bool = True
+    invocation_id: str | None = None
+
+
+@dataclass(frozen=True)
+class ActionStageSpec:
+    """Mode-independent description produced by Prompt or Workflow config."""
+
+    actions: tuple[StageAction, ...] = ()
+    input_policy: InputPolicy = "queue"
+
+
+@dataclass(frozen=True)
+class ActionStageResult:
+    """Ordered Action outcomes; optional failures do not fail the stage."""
+
+    succeeded: bool
+    outcomes: tuple[ActionOutcome, ...]
+
+
+class ActionStageRunner:
+    """Run deterministic tool Actions under one optional user-input gate."""
+
+    def __init__(self, actions: ActionRunner) -> None:
+        self._actions = actions
+
+    async def run(
+        self,
+        spec: ActionStageSpec,
+        *,
+        set_input_enabled: Callable[[bool], None] | None = None,
+        input_already_blocked: bool = False,
+        release_input_on_failure: bool = True,
+        on_started: StartedHook | None = None,
+        on_outcome: OutcomeHook | None = None,
+    ) -> ActionStageResult:
+        input_setter = set_input_enabled
+        block_input = spec.input_policy == "block" and input_setter is not None
+        if block_input and not input_already_blocked:
+            input_setter(False)
+
+        result: ActionStageResult | None = None
+        try:
+            if on_started is not None:
+                await on_started()
+
+            outcomes: list[ActionOutcome] = []
+            succeeded = True
+            for action in spec.actions:
+                outcome = await self._actions.execute(
+                    action.tool,
+                    action.arguments,
+                    result_assignments=action.result_assignments,
+                    invocation_id=action.invocation_id,
+                )
+                outcomes.append(outcome)
+                if on_outcome is not None:
+                    await on_outcome(action, outcome)
+                if outcome.status == ActionStatus.SUCCESS:
+                    continue
+                if outcome.status == ActionStatus.CANCELLED or action.required:
+                    succeeded = False
+                    break
+
+            result = ActionStageResult(
+                succeeded=succeeded,
+                outcomes=tuple(outcomes),
+            )
+            return result
+        finally:
+            if block_input and (
+                release_input_on_failure
+                or (result is not None and result.succeeded)
+            ):
+                input_setter(True)

backend/services/brains/base.py

@@ -64,7 +64,7 @@ class CallEndPort(Protocol):
 
     def arm_after_speech(self) -> None: ...
 
-    def track_speech(self) -> None: ...
+    def track_speech(self) -> Awaitable[None] | None: ...
 
     async def arm_after_tracked_speech(self) -> None: ...
 

backend/services/brains/prompt_brain.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import asyncio
+from collections.abc import Awaitable
 from typing import Any
 from uuid import uuid4
 
@@ -25,10 +26,18 @@ from services.brains.base import (
     SessionVariableUpdate,
 )
 from services.action_runtime import (
+    ActionOutcome,
     ActionInvocationCancelled,
     ActionRunner,
     ActionStatus,
 )
+from services.action_stage import ActionStageRunner, ActionStageSpec, StageAction
+from services.fixed_speech import FixedSpeechOutput
+from services.message_stage import (
+    MessageDisplaySpec,
+    MessageStageRunner,
+    MessageStageSpec,
+)
 from services.runtime_variables import DynamicVariableStore
 from services.tool_executor import ToolExecutionError, ToolExecutor
 from services.tool_policy import policy_for_tool
@@ -50,17 +59,31 @@ class PromptBrain(BaseBrain):
         self._store = DynamicVariableStore.from_config(cfg)
         self._tools = ToolExecutor(self._store)
         self._actions = ActionRunner(self._tools)
+        self._action_stages = ActionStageRunner(self._actions)
+        self._message_stages = MessageStageRunner()
         self._tool_by_id = {tool.id: tool for tool in cfg.tools}
         self._runtime: BrainRuntime | None = None
+        self._output: FixedSpeechOutput | None = None
         self._waiting_for_generated_end_speech = False
-        self._greeting_finished = True
         self._preflight_finished = False
         self._opening_started = False
         self._opening_finished = False
+        self._opening_input_blocked = False
         self._startup_failed = False
 
     async def greeting(self, cfg: AssistantConfig) -> str:
-        return self._store.render(cfg.greeting) if self._dynamic_enabled else cfg.greeting
+        # The built-in opening Message owns the greeting so speech and the
+        # client dialog can start as one atomic stage.
+        if self._opening_message() is not None:
+            return ""
+        return self._render_greeting(cfg)
+
+    def _render_greeting(self, cfg: AssistantConfig) -> str:
+        return (
+            self._store.render(cfg.greeting)
+            if self._dynamic_enabled
+            else cfg.greeting
+        )
 
     def system_prompt(self, cfg: AssistantConfig) -> str:
         return self._store.render(cfg.prompt) if self._dynamic_enabled else cfg.prompt
@@ -77,12 +100,15 @@ class PromptBrain(BaseBrain):
             self._tools,
             is_session_ending=lambda: runtime.call_end.ending,
         )
+        self._action_stages = ActionStageRunner(self._actions)
+        self._message_stages = MessageStageRunner(runtime.client_tools)
+        self._output = FixedSpeechOutput(self._store, runtime)
         self._tool_by_id = {tool.id: tool for tool in cfg.tools}
         self._waiting_for_generated_end_speech = False
-        self._greeting_finished = True
         self._preflight_finished = False
         self._opening_started = False
-        self._opening_finished = not bool(self._startup_actions("opening"))
+        self._opening_finished = not self._has_opening_stage()
+        self._opening_input_blocked = False
         self._startup_failed = False
         llm_tool_ids = (
             set(cfg.llm_tool_ids) if cfg.llm_tool_ids is not None else None
@@ -119,32 +145,83 @@ class PromptBrain(BaseBrain):
         self._preflight_finished = True
 
     async def on_connected(self, *, greeting_pending: bool = False) -> None:
-        self._greeting_finished = not greeting_pending
         if (
-            self._startup_actions("opening")
+            self._has_opening_stage()
             and self._runtime is not None
             and self._runtime.set_input_enabled is not None
         ):
             self._runtime.set_input_enabled(False)
+            self._opening_input_blocked = True
 
     async def on_client_ready(self) -> None:
+        if self._output is not None:
+            await self._output.mark_client_ready()
         if self._opening_started or self._opening_finished or self._startup_failed:
             return
         self._opening_started = True
+        runtime = self._runtime
+        if runtime is None:
+            raise RuntimeError("PromptBrain 尚未初始化")
+        opening_message = self._opening_message()
+        opening_actions = self._startup_actions("opening")
+        speech = (
+            self._render_greeting(self._cfg).strip()
+            if opening_message is not None
+            else ""
+        )
+        if speech:
+            self.prepare_greeting_context(speech, runtime.context)
         try:
-            succeeded = await self._run_startup_actions("opening")
+            if opening_message is not None:
+                message_result = await self._message_stages.run(
+                    self._opening_message_stage_spec(speech, opening_message),
+                    speak=self._speak_opening,
+                    set_input_enabled=runtime.set_input_enabled,
+                    input_already_blocked=self._opening_input_blocked,
+                    release_input_on_success=not bool(opening_actions),
+                    release_input_on_failure=False,
+                )
+                if not message_result.succeeded:
+                    await self._fail_opening(
+                        message_result.error or "开场消息显示失败"
+                    )
+                    return
+
+            if opening_actions:
+                result = await self._action_stages.run(
+                    self._opening_actions_stage_spec(),
+                    set_input_enabled=runtime.set_input_enabled,
+                    input_already_blocked=self._opening_input_blocked,
+                    release_input_on_failure=False,
+                    on_outcome=self._publish_opening_outcome,
+                )
+                if not result.succeeded:
+                    await self._fail_opening("必需的开场 Action 执行失败")
+                    return
         except ActionInvocationCancelled:
             self._startup_failed = True
             raise
-        if not succeeded:
-            await self._fail_opening("必需的开场 Action 执行失败")
-            return
         self._opening_finished = True
-        self._release_startup_gate_if_ready()
+        self._opening_input_blocked = False
 
-    async def on_greeting_finished(self) -> None:
-        self._greeting_finished = True
-        self._release_startup_gate_if_ready()
+    async def _speak_opening(self, content: str) -> Awaitable[None] | None:
+        if self._output is None:
+            raise RuntimeError("Prompt 固定播报输出尚未初始化")
+        return await self._output.speak(
+            content,
+            source="prompt-opening-speech",
+            record_history=False,
+        )
+
+    def _opening_message(self) -> dict[str, Any] | None:
+        startup = self._cfg.startup if isinstance(self._cfg.startup, dict) else {}
+        value = startup.get("opening_message", startup.get("openingMessage"))
+        return value if isinstance(value, dict) else None
+
+    def _has_opening_stage(self) -> bool:
+        return self._opening_message() is not None or bool(
+            self._startup_actions("opening")
+        )
 
     def _startup_actions(self, phase: str) -> list[dict[str, Any]]:
         startup = self._cfg.startup if isinstance(self._cfg.startup, dict) else {}
@@ -154,6 +231,78 @@ class PromptBrain(BaseBrain):
             if isinstance(action, dict) and action.get("phase", "opening") == phase
         ]
 
+    def _opening_actions_stage_spec(self) -> ActionStageSpec:
+        actions = tuple(
+            StageAction(
+                id=str(action.get("id") or "startup_action"),
+                tool=self._tool_by_id.get(
+                    str(action.get("tool_id") or action.get("toolId") or "")
+                ),
+                arguments=action.get("arguments") or {},
+                required=bool(action.get("required", True)),
+                invocation_id=self._actions.new_invocation_id(),
+            )
+            for action in self._startup_actions("opening")
+        )
+        return ActionStageSpec(
+            actions=actions,
+            input_policy="block",
+        )
+
+    def _opening_message_stage_spec(
+        self,
+        speech: str,
+        config: dict[str, Any],
+    ) -> MessageStageSpec:
+        return MessageStageSpec(
+            speech=speech,
+            display=MessageDisplaySpec(
+                title=self._store.render(
+                    str(config.get("title") or "重要提示")
+                ).strip(),
+                message=self._store.render(
+                    str(config.get("message") or "")
+                ).strip(),
+                confirm_label=self._store.render(
+                    str(
+                        config.get("confirm_label")
+                        or config.get("confirmLabel")
+                        or "确认"
+                    )
+                ).strip(),
+            ),
+            require_confirmation=True,
+        )
+
+    async def _publish_opening_outcome(
+        self,
+        action: StageAction,
+        outcome: ActionOutcome,
+    ) -> None:
+        if outcome.updated_variables:
+            self._refresh_prompt()
+        if self._runtime is not None:
+            await self._runtime.queue_frame(
+                OutputTransportMessageUrgentFrame(
+                    message={
+                        "type": "startup-action-result",
+                        "actionId": action.id,
+                        "phase": "opening",
+                        "outcome": outcome.trace_payload(),
+                    }
+                )
+            )
+        if outcome.status == ActionStatus.FAILURE and action.required:
+            logger.warning(
+                f"必需的 Prompt opening Action 失败: "
+                f"action={action.id} error={outcome.error}"
+            )
+        elif outcome.status == ActionStatus.FAILURE:
+            logger.warning(
+                f"忽略可选 Prompt opening Action 失败: "
+                f"action={action.id} error={outcome.error}"
+            )
+
     async def _run_startup_actions(self, phase: str) -> bool:
         for action in self._startup_actions(phase):
             action_id = str(action.get("id") or "startup_action")
@@ -170,17 +319,6 @@ class PromptBrain(BaseBrain):
             )
             if outcome.updated_variables:
                 self._refresh_prompt()
-            if phase == "opening" and self._runtime is not None:
-                await self._runtime.queue_frame(
-                    OutputTransportMessageUrgentFrame(
-                        message={
-                            "type": "startup-action-result",
-                            "actionId": action_id,
-                            "phase": phase,
-                            "outcome": outcome.trace_payload(),
-                        }
-                    )
-                )
             if outcome.status == ActionStatus.SUCCESS:
                 continue
             if outcome.status == ActionStatus.CANCELLED:
@@ -197,18 +335,6 @@ class PromptBrain(BaseBrain):
             )
         return True
 
-    def _release_startup_gate_if_ready(self) -> None:
-        runtime = self._runtime
-        if (
-            runtime is not None
-            and runtime.set_input_enabled is not None
-            and self._greeting_finished
-            and self._opening_finished
-            and not self._startup_failed
-            and not runtime.call_end.ending
-        ):
-            runtime.set_input_enabled(True)
-
     async def _fail_opening(self, message: str) -> None:
         self._startup_failed = True
         runtime = self._runtime

backend/services/brains/workflow_brain.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import asyncio
+from collections.abc import Awaitable
 from copy import deepcopy
 from dataclasses import replace
 from typing import Any
@@ -40,7 +41,14 @@ from services.action_runtime import (
     ActionRunner,
     ActionStatus,
 )
+from services.action_stage import ActionStageRunner, ActionStageSpec, StageAction
 from services.knowledge import search as search_knowledge
+from services.message_stage import (
+    MessageDisplaySpec,
+    MessageStageResult,
+    MessageStageRunner,
+    MessageStageSpec,
+)
 from services.runtime_variables import DynamicVariableStore
 from services.tool_executor import ToolExecutionError, ToolExecutor
 from services.tool_policy import policy_for_tool
@@ -110,6 +118,8 @@ class WorkflowBrain(BaseBrain):
         self._store = DynamicVariableStore.from_config(cfg or AssistantConfig(type="workflow"))
         self._tools = ToolExecutor(self._store)
         self._actions = ActionRunner(self._tools)
+        self._action_stages = ActionStageRunner(self._actions)
+        self._message_stages = MessageStageRunner()
         self._tool_by_id: dict[str, RuntimeTool] = {
             tool.id: tool for tool in (cfg.tools if cfg else [])
         }
@@ -126,11 +136,10 @@ class WorkflowBrain(BaseBrain):
         self._output: WorkflowOutput | None = None
         self._agent_stage: WorkflowAgentStage | None = None
         self._ended = False
-        self._greeting_context_message: dict[str, str] | None = None
-        self._startup_waiting_for_greeting = False
 
-    async def greeting(self, cfg: AssistantConfig) -> str:
-        return self._engine.greeting(self._store) or cfg.greeting
+    async def greeting(self, _cfg: AssistantConfig) -> str:
+        """Workflow opening speech belongs to an explicit Message or Agent."""
+        return ""
 
     def system_prompt(self, cfg: AssistantConfig) -> str:
         return self._store.render(self._engine.global_prompt())
@@ -151,6 +160,8 @@ class WorkflowBrain(BaseBrain):
             self._tools,
             is_session_ending=lambda: runtime.call_end.ending,
         )
+        self._action_stages = ActionStageRunner(self._actions)
+        self._message_stages = MessageStageRunner(runtime.client_tools)
         self._tool_by_id = {tool.id: tool for tool in cfg.tools}
         self._router = WorkflowLLMRouter(cfg)
         self._edge_evaluator = WorkflowEdgeEvaluator(
@@ -168,8 +179,6 @@ class WorkflowBrain(BaseBrain):
             runtime=runtime,
         )
         self._ended = False
-        self._greeting_context_message = None
-        self._startup_waiting_for_greeting = False
         self._manager = ConfiguredFlowManager(
             worker=runtime.worker,
             llm=runtime.llm,
@@ -179,15 +188,6 @@ class WorkflowBrain(BaseBrain):
         )
         self._manager.state["variables"] = self._store.values
 
-    def prepare_greeting_context(
-        self,
-        greeting: str,
-        context: LLMContext,
-    ) -> dict[str, str] | None:
-        message = super().prepare_greeting_context(greeting, context)
-        self._greeting_context_message = deepcopy(message) if message else None
-        return message
-
     async def on_connected(self, *, greeting_pending: bool = False) -> None:
         self._state.enter(self._engine.start_id, WorkflowStatus.STARTING)
         await self._emit_node_active(self._engine.start_id)
@@ -198,39 +198,11 @@ class WorkflowBrain(BaseBrain):
         if self._manager is None:
             raise RuntimeError("Workflow FlowManager 尚未初始化")
 
-        self._startup_waiting_for_greeting = greeting_pending
-        if greeting_pending:
-            # Keep the Workflow on Start until the transport confirms that the
-            # shared greeting has finished. This prevents an initial Agent's
-            # fixed speech (or generated reply) from racing the greeting.
-            await self._manager.initialize(
-                self._passive_node_config(self._engine.start_id)
-            )
-            logger.info("工作流等待 Start 开场白播放完毕")
-            return
-
         node_config = await self._initial_node_config()
         await self._manager.initialize(node_config)
         await self._after_node_activated(node_config)
         logger.info(f"工作流模式启用: 当前节点={self._manager.current_node}")
 
-    async def on_greeting_finished(self) -> None:
-        """Enter the first node only after Start's greeting reaches playback end."""
-        if not self._startup_waiting_for_greeting or self._ended:
-            return
-        self._startup_waiting_for_greeting = False
-        manager = self._require_manager()
-        if manager.current_node != self._engine.start_id:
-            return
-
-        node_config = await self._initial_node_config()
-        if node_config.get("name") == self._engine.start_id:
-            self._state.enter(self._engine.start_id, WorkflowStatus.WAITING_USER)
-            return
-        await manager.set_node_from_config(node_config)
-        await self._after_node_activated(node_config)
-        logger.info(f"Start 开场白结束,进入节点: {manager.current_node}")
-
     async def _initial_node_config(self) -> NodeConfig:
         """Only a default-only Start advances before the first user turn."""
         outgoing = self._engine.outgoing(self._engine.start_id)
@@ -409,7 +381,6 @@ class WorkflowBrain(BaseBrain):
         return self._require_agent_stage().node_config(
             node_id,
             functions=functions,
-            greeting_context_message=self._greeting_context_message,
             leading_messages=leading_messages,
         )
 
@@ -455,8 +426,8 @@ class WorkflowBrain(BaseBrain):
         *,
         source: str = "workflow-speech",
         node_id: str | None = None,
-    ) -> None:
-        await self._require_output().speak(
+    ) -> Awaitable[None] | None:
+        return await self._require_output().speak(
             text,
             source=source,
             node_id=node_id,
@@ -700,6 +671,14 @@ class WorkflowBrain(BaseBrain):
                 outcome = await self._enter_action(node_id)
                 if not outcome.should_route:
                     return self._passive_node_config(node_id, context_messages)
+            elif node_type == "message":
+                message_result = await self._enter_message(node_id)
+                if not message_result.succeeded:
+                    return self._passive_node_config(node_id, context_messages)
+                if message_result.speech:
+                    context_messages.append(
+                        {"role": "assistant", "content": message_result.speech}
+                    )
             elif node_type == "handoff":
                 await self._enter_handoff(node_id)
             elif node_type == "start":
@@ -735,29 +714,36 @@ class WorkflowBrain(BaseBrain):
         data = self._engine.data(node_id)
         runtime = self._require_runtime()
         invocation_id = self._actions.new_invocation_id()
-        block_user_input = data.get("userInputPolicy") == "block"
-        if block_user_input and runtime.set_input_enabled:
-            # Blocking only suppresses new audio/text input while the Action
-            # runs.  It deliberately does not cancel the tool.  The default
-            # queue policy leaves input enabled; the turn lock serializes any
-            # completed user turn until this automatic path has finished.
-            runtime.set_input_enabled(False)
         tool_id = str(data.get("toolId") or "")
         tool = self._tool_by_id.get(tool_id)
         try:
-            await self._emit_trace(
-                "action_started",
-                nodeId=node_id,
-                invocationId=invocation_id,
-                toolId=tool_id,
-                toolType=tool.type if tool else None,
-            )
-            outcome = await self._actions.execute(
-                tool,
-                data.get("arguments") or {},
-                result_assignments=self._action_result_assignments(data),
-                invocation_id=invocation_id,
+            stage_result = await self._action_stages.run(
+                ActionStageSpec(
+                    actions=(
+                        StageAction(
+                            id=node_id,
+                            tool=tool,
+                            arguments=data.get("arguments") or {},
+                            result_assignments=self._action_result_assignments(data),
+                            invocation_id=invocation_id,
+                        ),
+                    ),
+                    input_policy=(
+                        "block"
+                        if data.get("userInputPolicy") == "block"
+                        else "queue"
+                    ),
+                ),
+                set_input_enabled=runtime.set_input_enabled,
+                on_started=lambda: self._emit_trace(
+                    "action_started",
+                    nodeId=node_id,
+                    invocationId=invocation_id,
+                    toolId=tool_id,
+                    toolType=tool.type if tool else None,
+                ),
             )
+            outcome = stage_result.outcomes[0]
             updated_variables = list(outcome.updated_variables)
             if updated_variables:
                 await self._emit_variables(
@@ -770,13 +756,74 @@ class WorkflowBrain(BaseBrain):
             self._set_last_action(outcome)
             await self._emit_action_outcome(node_id, outcome)
             raise
-        finally:
-            if block_user_input and runtime.set_input_enabled:
-                runtime.set_input_enabled(True)
         self._set_last_action(outcome)
         await self._emit_action_outcome(node_id, outcome)
         return outcome
 
+    async def _enter_message(self, node_id: str) -> MessageStageResult:
+        self._state.enter(node_id, WorkflowStatus.RUNNING_MESSAGE)
+        await self._emit_node_active(node_id)
+        data = self._engine.data(node_id)
+        runtime = self._require_runtime()
+        speech = self._store.render(str(data.get("speech") or "")).strip()
+        show_message = bool(data.get("showMessage", False))
+        require_confirmation = bool(data.get("requireConfirmation", False))
+        display = (
+            MessageDisplaySpec(
+                title=self._store.render(
+                    str(data.get("title") or "重要提示")
+                ).strip(),
+                message=self._store.render(
+                    str(data.get("message") or "")
+                ).strip(),
+                confirm_label=self._store.render(
+                    str(data.get("confirmLabel") or "确认")
+                ).strip(),
+            )
+            if show_message
+            else None
+        )
+        result = await self._message_stages.run(
+            MessageStageSpec(
+                speech=speech,
+                display=display,
+                require_confirmation=require_confirmation,
+            ),
+            speak=lambda content: self._queue_visible_speech(
+                content,
+                source="workflow-message-speech",
+                node_id=node_id,
+            ),
+            set_input_enabled=runtime.set_input_enabled,
+            on_started=lambda: self._emit_trace(
+                "message_started",
+                nodeId=node_id,
+                hasSpeech=bool(speech),
+                showsMessage=show_message,
+                requiresConfirmation=require_confirmation,
+            ),
+        )
+        if result.succeeded:
+            await self._emit_trace(
+                "message_completed",
+                nodeId=node_id,
+                action=result.action,
+            )
+            return result
+
+        self._state.enter(node_id, WorkflowStatus.WAITING_USER)
+        await self._emit_trace(
+            "message_failed",
+            nodeId=node_id,
+            error=result.error or "Message 节点执行失败",
+        )
+        await self._require_output().emit_error(
+            result.error or "Message 节点执行失败",
+            node_id=node_id,
+            code="workflow_message_error",
+        )
+        return result
+
     def _set_last_action(self, outcome: ActionOutcome) -> None:
         legacy_status = {
             ActionStatus.SUCCESS: "ok",

backend/services/fixed_speech.py

@@ -0,0 +1,73 @@
+"""Shared client-visible output for deterministic fixed speech."""
+
+from __future__ import annotations
+
+from collections.abc import Awaitable
+from typing import Any
+
+from pipecat.frames.frames import OutputTransportMessageUrgentFrame, TTSSpeakFrame
+from pipecat.utils.time import time_now_iso8601
+
+from services.brains.base import BrainRuntime
+from services.runtime_variables import DynamicVariableStore
+
+
+class FixedSpeechOutput:
+    """Display and synthesize fixed speech without waiting for playback."""
+
+    def __init__(
+        self,
+        store: DynamicVariableStore,
+        runtime: BrainRuntime,
+    ) -> None:
+        self._store = store
+        self._runtime = runtime
+        self._client_ready = False
+        self._pending_transcripts: list[dict[str, Any]] = []
+
+    async def mark_client_ready(self) -> None:
+        self._client_ready = True
+        pending = self._pending_transcripts
+        self._pending_transcripts = []
+        for message in pending:
+            await self.emit(message)
+
+    async def speak(
+        self,
+        text: str,
+        *,
+        source: str,
+        node_id: str | None = None,
+        record_history: bool = True,
+    ) -> Awaitable[None] | None:
+        content = text.strip()
+        if not content:
+            return None
+        if record_history:
+            self._store.record("agent", content)
+        transcript = {
+            "type": "transcript",
+            "role": "assistant",
+            "content": content,
+            "timestamp": time_now_iso8601(),
+            "source": source,
+            **({"nodeId": node_id} if node_id else {}),
+        }
+        if self._client_ready:
+            await self.emit(transcript)
+        else:
+            self._pending_transcripts.append(transcript)
+
+        track_speech = getattr(self._runtime.call_end, "track_speech", None)
+        playback_completion: Awaitable[None] | None = None
+        if callable(track_speech):
+            playback_completion = track_speech()
+        await self._runtime.queue_frame(
+            TTSSpeakFrame(content, append_to_context=False)
+        )
+        return playback_completion
+
+    async def emit(self, message: dict[str, Any]) -> None:
+        await self._runtime.queue_frame(
+            OutputTransportMessageUrgentFrame(message=message)
+        )

backend/services/message_stage.py

@@ -0,0 +1,197 @@
+"""Deterministic speech and client-message interaction shared by all brains."""
+
+from __future__ import annotations
+
+import asyncio
+from collections.abc import Awaitable, Callable
+from dataclasses import dataclass
+
+from services.client_tools import ClientToolError, ClientToolPort
+
+
+BUILTIN_SHOW_MESSAGE = "show_message"
+SpeechCompletion = Awaitable[None] | None
+Speak = Callable[[str], Awaitable[SpeechCompletion]]
+StartedHook = Callable[[], Awaitable[None]]
+
+
+@dataclass(frozen=True)
+class MessageDisplaySpec:
+    """Content rendered by the platform-provided client message dialog."""
+
+    title: str
+    message: str
+    confirm_label: str = "确认"
+
+
+@dataclass(frozen=True)
+class MessageStageSpec:
+    """Mode-independent fixed speech and optional client interaction."""
+
+    speech: str = ""
+    display: MessageDisplaySpec | None = None
+    require_confirmation: bool = False
+
+
+@dataclass(frozen=True)
+class MessageStageResult:
+    """Result used by Workflow routing and Prompt opening failure handling."""
+
+    succeeded: bool
+    speech: str = ""
+    action: str | None = None
+    error: str | None = None
+
+
+class MessageStageRunner:
+    """Run one atomic user-visible message stage.
+
+    Speech is queued before the client message is dispatched. A confirmation
+    stage completes when the user confirms, even if audio is still playing. A
+    speech-only stage completes at the real transport playback boundary.
+    """
+
+    def __init__(self, client_tools: ClientToolPort | None = None) -> None:
+        self._client_tools = client_tools
+
+    def set_client_tools(self, client_tools: ClientToolPort | None) -> None:
+        self._client_tools = client_tools
+
+    async def run(
+        self,
+        spec: MessageStageSpec,
+        *,
+        speak: Speak | None = None,
+        set_input_enabled: Callable[[bool], None] | None = None,
+        input_already_blocked: bool = False,
+        release_input_on_success: bool = True,
+        release_input_on_failure: bool = True,
+        on_started: StartedHook | None = None,
+    ) -> MessageStageResult:
+        input_setter = set_input_enabled
+        if input_setter is not None and not input_already_blocked:
+            input_setter(False)
+
+        result: MessageStageResult | None = None
+        try:
+            if on_started is not None:
+                await on_started()
+
+            speech = spec.speech.strip()
+            if spec.require_confirmation and spec.display is None:
+                result = MessageStageResult(
+                    succeeded=False,
+                    speech=speech,
+                    error="等待用户确认时必须显示客户端消息",
+                )
+                return result
+            if not speech and spec.display is None:
+                result = MessageStageResult(
+                    succeeded=False,
+                    error="Message 阶段至少需要播报或客户端消息",
+                )
+                return result
+            playback_completion: SpeechCompletion = None
+            if speech and speak is not None:
+                playback_completion = await speak(speech)
+
+            action: str | None = None
+            if spec.display is not None:
+                result = await self._show_message(spec, speech=speech)
+                if not result.succeeded:
+                    return result
+                action = result.action
+
+            # Confirmation is the gate. It deliberately does not wait for the
+            # audio completion future, so the user can continue immediately.
+            if (
+                playback_completion is not None
+                and not spec.require_confirmation
+            ):
+                await playback_completion
+
+            result = MessageStageResult(
+                succeeded=True,
+                speech=speech,
+                action=action,
+            )
+            return result
+        except asyncio.CancelledError:
+            raise
+        except Exception as exc:  # noqa: BLE001 - surface deterministic stage failure
+            result = MessageStageResult(
+                succeeded=False,
+                speech=spec.speech.strip(),
+                error=str(exc),
+            )
+            return result
+        finally:
+            should_release = (
+                result is not None
+                and (
+                    (result.succeeded and release_input_on_success)
+                    or (not result.succeeded and release_input_on_failure)
+                )
+            )
+            if input_setter is not None and should_release:
+                input_setter(True)
+
+    async def _show_message(
+        self,
+        spec: MessageStageSpec,
+        *,
+        speech: str,
+    ) -> MessageStageResult:
+        display = spec.display
+        if display is None:
+            return MessageStageResult(succeeded=True, speech=speech)
+        if self._client_tools is None:
+            return MessageStageResult(
+                succeeded=False,
+                speech=speech,
+                error="当前运行模式不支持客户端消息",
+            )
+        try:
+            response = await self._client_tools.call(
+                BUILTIN_SHOW_MESSAGE,
+                {
+                    "title": display.title,
+                    "message": display.message,
+                    "actions": [
+                        {
+                            "id": "confirmed",
+                            "label": display.confirm_label,
+                            "style": "primary",
+                        }
+                    ],
+                    "dismissible": not spec.require_confirmation,
+                },
+                timeout_seconds=3,
+                wait_for_response=spec.require_confirmation,
+                response_wait_mode=(
+                    "session" if spec.require_confirmation else "timeout"
+                ),
+            )
+        except ClientToolError as exc:
+            return MessageStageResult(
+                succeeded=False,
+                speech=speech,
+                error=str(exc),
+            )
+        if response.get("status") != "ok":
+            return MessageStageResult(
+                succeeded=False,
+                speech=speech,
+                error=str(response.get("message") or "客户端消息显示失败"),
+            )
+        data = response.get("data")
+        action = (
+            str(data.get("action") or "") or None
+            if isinstance(data, dict)
+            else None
+        )
+        return MessageStageResult(
+            succeeded=True,
+            speech=speech,
+            action=action,
+        )

backend/services/node_specs.py

@@ -8,12 +8,12 @@ from typing import Any
 
 
 SPEC_VERSION = "3"
-NODE_TYPES = {"start", "agent", "action", "handoff", "end"}
+NODE_TYPES = {"start", "agent", "message", "action", "handoff", "end"}
 EDGE_MODES = {"llm", "expression", "always"}
 AGENT_ENTRY_MODES = {"wait_user", "generate", "fixed_speech"}
 ACTION_RESULT_ASSIGNMENT_MODES = {"inherit", "override", "none"}
 ACTION_USER_INPUT_POLICIES = {"queue", "block"}
-AUTOMATIC_NODE_TYPES = {"start", "action", "handoff"}
+AUTOMATIC_NODE_TYPES = {"start", "message", "action", "handoff"}
 EXPRESSION_OPERATORS = {
     "eq",
     "neq",
@@ -31,7 +31,7 @@ NODE_SPECS: list[dict[str, Any]] = [
         "name": "start",
         "displayName": "Start",
         "category": "control_node",
-        "description": "初始化会话、动态变量和全局观察器，可播放固定开场白。",
+        "description": "初始化会话、动态变量和全局观察器。",
         "icon": "Play",
         "accent": "mint",
         "addable": False,
@@ -44,7 +44,6 @@ NODE_SPECS: list[dict[str, Any]] = [
         },
         "fields": [
             {"key": "name", "label": "节点名称", "type": "text", "default": "Start"},
-            {"key": "greeting", "label": "固定开场白", "type": "textarea", "default": ""},
         ],
     },
     {
@@ -67,6 +66,19 @@ NODE_SPECS: list[dict[str, Any]] = [
             },
         ],
     },
+    {
+        "name": "message",
+        "displayName": "Message",
+        "category": "interaction_node",
+        "description": "固定播报，并可同时显示内置客户端消息、等待用户确认。",
+        "icon": "MessageSquareText",
+        "accent": "lavender",
+        "addable": True,
+        "constraints": {"minIncoming": 1, "minOutgoing": 0},
+        "fields": [
+            {"key": "name", "label": "节点名称", "type": "text", "default": "Message"},
+        ],
+    },
     {
         "name": "action",
         "displayName": "Action",
@@ -172,6 +184,17 @@ def _normalize_action_data(data: dict[str, Any]) -> None:
         )
     data.setdefault("resultAssignments", {})
     data.setdefault("userInputPolicy", "queue")
+    data.pop("speech", None)
+
+
+def _normalize_message_data(data: dict[str, Any]) -> None:
+    """Fill the small built-in Message contract used by runtime and editor."""
+    data.setdefault("speech", "")
+    data.setdefault("showMessage", False)
+    data.setdefault("title", "重要提示")
+    data.setdefault("message", "")
+    data.setdefault("confirmLabel", "确认")
+    data.setdefault("requireConfirmation", False)
 
 
 def _normalize_settings(settings: dict[str, Any], *, global_prompt: str = "") -> None:
@@ -200,8 +223,12 @@ def normalize_graph(graph: dict[str, Any] | None) -> dict[str, Any]:
         source.setdefault("edges", [])
         for node in source["nodes"]:
             data = node.setdefault("data", {})
-            if node.get("type") == "agent":
+            if node.get("type") == "start":
+                data.pop("greeting", None)
+            elif node.get("type") == "agent":
                 _normalize_agent_data(data)
+            elif node.get("type") == "message":
+                _normalize_message_data(data)
             elif node.get("type") == "action":
                 _normalize_action_data(data)
         return source
@@ -218,6 +245,7 @@ def normalize_graph(graph: dict[str, Any] | None) -> dict[str, Any]:
         "endCall": "end",
         "start": "start",
         "agent": "agent",
+        "message": "message",
         "action": "action",
         "handoff": "handoff",
         "end": "end",
@@ -236,10 +264,13 @@ def normalize_graph(graph: dict[str, Any] | None) -> dict[str, Any]:
             data.setdefault("scope", "session")
         elif new_type == "agent":
             _normalize_agent_data(data)
+        elif new_type == "message":
+            _normalize_message_data(data)
         elif new_type == "action":
             _normalize_action_data(data)
         elif new_type == "start":
             prompt = str(data.pop("prompt", "") or "").strip()
+            data.pop("greeting", None)
             if prompt:
                 start_prompt_nodes[str(node.get("id"))] = prompt
             for key in ("allowInterrupt", "addGlobalPrompt"):
@@ -349,6 +380,49 @@ def validate_graph(graph: dict[str, Any]) -> list[str]:
                 data.get("entrySpeech") or ""
             ).strip():
                 errors.append(f"Agent 节点 {node_id} 的固定进入语不能为空")
+        elif node_type == "message":
+            data = node.get("data") or {}
+            speech = data.get("speech")
+            show_message = data.get("showMessage")
+            require_confirmation = data.get("requireConfirmation")
+            if not isinstance(speech, str):
+                errors.append(f"Message 节点 {node_id} 的播报内容必须是文本")
+            if not isinstance(show_message, bool):
+                errors.append(f"Message 节点 {node_id} 的弹窗开关必须是布尔值")
+            if not isinstance(require_confirmation, bool):
+                errors.append(f"Message 节点 {node_id} 的确认开关必须是布尔值")
+            if require_confirmation and show_message is not True:
+                errors.append(f"Message 节点 {node_id} 等待确认时必须显示弹窗")
+            if not str(speech or "").strip() and show_message is not True:
+                errors.append(f"Message 节点 {node_id} 至少需要播报或显示弹窗")
+            if show_message is True:
+                title = data.get("title")
+                message = data.get("message")
+                confirm_label = data.get("confirmLabel")
+                if (
+                    not isinstance(title, str)
+                    or not title.strip()
+                    or len(title) > 120
+                ):
+                    errors.append(
+                        f"Message 节点 {node_id} 的弹窗标题必须为 1-120 个字符"
+                    )
+                if (
+                    not isinstance(message, str)
+                    or not message.strip()
+                    or len(message) > 2000
+                ):
+                    errors.append(
+                        f"Message 节点 {node_id} 的弹窗消息必须为 1-2000 个字符"
+                    )
+                if (
+                    not isinstance(confirm_label, str)
+                    or not confirm_label.strip()
+                    or len(confirm_label) > 40
+                ):
+                    errors.append(
+                        f"Message 节点 {node_id} 的按钮文字必须为 1-40 个字符"
+                    )
         elif node_type == "action":
             data = node.get("data") or {}
             assignment_mode = data.get("resultAssignmentMode")
@@ -477,7 +551,7 @@ def validate_graph(graph: dict[str, Any]) -> list[str]:
         if node.get("type") != "agent"
     )
     if any(visit(node_id) for node_id in automatic_node_ids):
-        errors.append("Start/Action/Handoff/End 之间不能形成无等待循环")
+        errors.append("自动节点之间不能形成无等待循环")
     return list(dict.fromkeys(errors))
 
 

backend/services/pipecat/call_lifecycle.py

@@ -2,6 +2,8 @@
 
 from __future__ import annotations
 
+import asyncio
+from collections import deque
 from collections.abc import Awaitable, Callable
 
 from loguru import logger
@@ -19,6 +21,7 @@ class CallEndCoordinator:
         self._speaking = False
         self._response_speech_started = False
         self._tracked_speeches = 0
+        self._tracked_speech_completions: deque[asyncio.Future[None]] = deque()
         self._finish_after_tracked_speech = False
         self._finished = False
         self._reason = "completed"
@@ -39,9 +42,12 @@ class CallEndCoordinator:
         """Wait for the next observed bot speech to finish."""
         self._armed = True
 
-    def track_speech(self) -> None:
-        """Register one fixed utterance before its TTSSpeakFrame is queued."""
+    def track_speech(self) -> Awaitable[None]:
+        """Register fixed speech and return its transport completion signal."""
+        completion = asyncio.get_running_loop().create_future()
+        self._tracked_speech_completions.append(completion)
         self._tracked_speeches += 1
+        return completion
 
     async def arm_after_tracked_speech(self) -> None:
         """Finish after every already queued fixed utterance has played."""
@@ -73,6 +79,9 @@ class CallEndCoordinator:
             self._speaking = False
             if self._tracked_speeches > 0:
                 self._tracked_speeches -= 1
+                completion = self._tracked_speech_completions.popleft()
+                if not completion.done():
+                    completion.set_result(None)
             if (
                 self._finish_after_tracked_speech
                 and self._tracked_speeches == 0

backend/services/workflow/agent.py

@@ -2,8 +2,6 @@
 
 from __future__ import annotations
 
-from copy import deepcopy
-
 from models import AssistantConfig
 from pipecat.flows import ContextStrategy, ContextStrategyConfig, NodeConfig
 from pipecat.frames.frames import LLMUpdateSettingsFrame
@@ -108,7 +106,6 @@ class WorkflowAgentStage:
         node_id: str,
         *,
         functions: list,
-        greeting_context_message: dict[str, str] | None,
         leading_messages: list[dict[str, str]] | None = None,
     ) -> NodeConfig:
         data = self._engine.data(node_id)
@@ -119,11 +116,6 @@ class WorkflowAgentStage:
             if data.get("contextPolicy") == "fresh"
             else ContextStrategy.APPEND
         )
-        greeting_messages = (
-            [deepcopy(greeting_context_message)]
-            if strategy == ContextStrategy.RESET and greeting_context_message
-            else []
-        )
         fixed_reply_messages = (
             [{"role": "assistant", "content": entry_speech}]
             if entry_mode == "fixed_speech" and entry_speech
@@ -133,7 +125,6 @@ class WorkflowAgentStage:
             "name": node_id,
             "role_message": self.role_message(node_id),
             "task_messages": [
-                *greeting_messages,
                 *(leading_messages or []),
                 *fixed_reply_messages,
             ],

backend/services/workflow/models.py

@@ -15,6 +15,7 @@ class WorkflowStatus(StrEnum):
     ROUTING = "routing"
     RUNNING_AGENT = "running_agent"
     RUNNING_ACTION = "running_action"
+    RUNNING_MESSAGE = "running_message"
     HANDOFF = "handoff"
     ENDED = "ended"
 

backend/services/workflow/output.py

@@ -5,65 +5,15 @@ from __future__ import annotations
 from typing import Any
 from uuid import uuid4
 
-from pipecat.frames.frames import OutputTransportMessageUrgentFrame, TTSSpeakFrame
+from pipecat.frames.frames import OutputTransportMessageUrgentFrame
 from pipecat.utils.time import time_now_iso8601
 
-from services.brains.base import BrainRuntime
-from services.runtime_variables import DynamicVariableStore
+from services.fixed_speech import FixedSpeechOutput
 
 
-class WorkflowOutput:
+class WorkflowOutput(FixedSpeechOutput):
     """Publish debug events and fixed speech without duplicating persistence."""
 
-    def __init__(
-        self,
-        store: DynamicVariableStore,
-        runtime: BrainRuntime,
-    ) -> None:
-        self._store = store
-        self._runtime = runtime
-        self._client_ready = False
-        self._pending_transcripts: list[dict[str, Any]] = []
-
-    async def mark_client_ready(self) -> None:
-        self._client_ready = True
-        pending = self._pending_transcripts
-        self._pending_transcripts = []
-        for message in pending:
-            await self.emit(message)
-
-    async def speak(
-        self,
-        text: str,
-        *,
-        source: str,
-        node_id: str | None = None,
-    ) -> None:
-        """Record, display and synthesize one Workflow-owned utterance."""
-        content = text.strip()
-        if not content:
-            return
-        self._store.record("agent", content)
-        transcript = {
-            "type": "transcript",
-            "role": "assistant",
-            "content": content,
-            "timestamp": time_now_iso8601(),
-            "source": source,
-            **({"nodeId": node_id} if node_id else {}),
-        }
-        if self._client_ready:
-            await self.emit(transcript)
-        else:
-            self._pending_transcripts.append(transcript)
-
-        track_speech = getattr(self._runtime.call_end, "track_speech", None)
-        if callable(track_speech):
-            track_speech()
-        await self._runtime.queue_frame(
-            TTSSpeakFrame(content, append_to_context=False)
-        )
-
     async def emit_node_active(self, node_id: str | None) -> None:
         if node_id:
             await self.emit({"type": "node-active", "nodeId": node_id})

backend/services/workflow_engine.py

@@ -190,20 +190,12 @@ class WorkflowEngine:
             sections.append(f"[当前阶段任务]\n{prompt}")
         return "\n\n".join(sections)
 
-    def greeting(self, store: DynamicVariableStore) -> str:
-        return store.render(str(self.data(self.start_id).get("greeting") or ""))
-
     def routing_prompt(self, node_id: str, store: DynamicVariableStore) -> str:
         """Describe the current node to the small LLM edge router."""
         if self.node_type(node_id) == "agent":
             return self.prompt_for(node_id, store)
         data = self.data(node_id)
-        details = (
-            data.get("greeting")
-            or data.get("message")
-            or data.get("target")
-            or ""
-        )
+        details = data.get("message") or data.get("target") or ""
         rendered = store.render(str(details)).strip()
         return f"{self.node_type(node_id) or 'workflow'} 节点：{rendered or self.name(node_id)}"
 

backend/tests/test_brains.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import asyncio
 import unittest
 from types import SimpleNamespace
 from unittest.mock import AsyncMock, patch
@@ -29,7 +30,7 @@ from services.brains.dify_llm import (
 )
 from services.brains.workflow_brain import WorkflowBrain
 from services.runtime_variables import prepare_dynamic_config
-from services.action_runtime import ActionError, ActionOutcome, ActionStatus
+from services.action_runtime import ActionOutcome, ActionStatus
 from services.workflow.models import (
     LLMRouteResult,
     RouteStatus,
@@ -350,30 +351,32 @@ class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
             ["preflight_1", "preflight_2"],
         )
 
-    async def test_opening_actions_wait_for_confirmation_and_greeting(self):
-        tools = [
-            RuntimeTool(
-                id=f"opening_{index}",
-                name=f"开场动作 {index}",
-                function_name="show_message" if index == 1 else "load_opening_data",
-                type="client" if index == 1 else "http",
-            )
-            for index in (1, 2)
-        ]
+    async def test_opening_stage_starts_speech_and_releases_on_confirmation(self):
+        tool = RuntimeTool(
+            id="opening_data",
+            name="加载开场数据",
+            function_name="load_opening_data",
+            type="http",
+        )
         cfg = AssistantConfig(
             type="prompt",
-            tools=tools,
+            greeting="请阅读并确认重要信息",
+            tools=[tool],
             startup={
                 "execution_mode": "sequential",
+                "opening_message": {
+                    "title": "重要提示",
+                    "message": "请确认已阅读。",
+                    "confirm_label": "确认",
+                },
                 "actions": [
                     {
-                        "id": f"opening_{index}",
+                        "id": "opening_data",
                         "phase": "opening",
-                        "tool_id": f"opening_{index}",
+                        "tool_id": "opening_data",
                         "arguments": {},
                         "required": True,
                     }
-                    for index in (1, 2)
                 ],
             },
         )
@@ -384,6 +387,17 @@ class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
         async def queue_frame(frame):
             queued.append(frame)
 
+        confirmation_started = asyncio.Event()
+        user_confirmed = asyncio.Event()
+        client_calls = []
+
+        class FakeClientTools:
+            async def call(self, function_name, arguments, **options):
+                client_calls.append((function_name, arguments, options))
+                confirmation_started.set()
+                await user_confirmed.wait()
+                return {"status": "ok", "data": {"action": "confirmed"}}
+
         await brain.setup(
             cfg,
             BrainRuntime(
@@ -393,28 +407,51 @@ class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
                 set_system_prompt=lambda _prompt: None,
                 set_tools=lambda _tools: None,
                 call_end=FakeCallEnd(),
+                client_tools=FakeClientTools(),
                 set_input_enabled=input_states.append,
             ),
         )
-        brain._actions.execute = AsyncMock(
-            side_effect=[
-                ActionOutcome(
-                    invocation_id=f"act_{index}",
-                    status=ActionStatus.SUCCESS,
-                    duration_ms=index,
-                )
-                for index in (1, 2)
-            ]
-        )
+        called_tool_ids = []
 
-        await brain.on_connected(greeting_pending=True)
-        await brain.on_client_ready()
+        async def execute(tool, *_args, **_kwargs):
+            called_tool_ids.append(tool.id)
+            return ActionOutcome(
+                invocation_id=f"act_{len(called_tool_ids)}",
+                status=ActionStatus.SUCCESS,
+                duration_ms=len(called_tool_ids),
+            )
+
+        brain._actions.execute = AsyncMock(side_effect=execute)
+
+        self.assertEqual(await brain.greeting(cfg), "")
+        await brain.on_connected(greeting_pending=False)
+        opening_task = asyncio.create_task(brain.on_client_ready())
+        await confirmation_started.wait()
 
         self.assertEqual(input_states, [False])
-        called_tool_ids = [
-            call.args[0].id for call in brain._actions.execute.await_args_list
-        ]
-        self.assertEqual(called_tool_ids, ["opening_1", "opening_2"])
+        self.assertEqual(called_tool_ids, [])
+        self.assertEqual(client_calls[0][0], "show_message")
+        self.assertFalse(client_calls[0][1]["dismissible"])
+        self.assertTrue(
+            any(
+                isinstance(frame, TTSSpeakFrame)
+                and frame.text == "请阅读并确认重要信息"
+                for frame in queued
+            )
+        )
+        self.assertTrue(
+            any(
+                isinstance(frame, OutputTransportMessageUrgentFrame)
+                and frame.message.get("type") == "transcript"
+                and frame.message.get("content") == "请阅读并确认重要信息"
+                for frame in queued
+            )
+        )
+
+        user_confirmed.set()
+        await opening_task
+        self.assertEqual(input_states, [False, True])
+        self.assertEqual(called_tool_ids, ["opening_data"])
         self.assertEqual(
             len(
                 [
@@ -424,36 +461,22 @@ class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
                     and frame.message.get("type") == "startup-action-result"
                 ]
             ),
-            2,
+            1,
         )
 
-        await brain.on_greeting_finished()
-        self.assertEqual(input_states, [False, True])
-
         # Replayed client-ready must not execute startup actions twice.
         await brain.on_client_ready()
-        self.assertEqual(brain._actions.execute.await_count, 2)
+        self.assertEqual(brain._actions.execute.await_count, 1)
 
     async def test_required_opening_failure_keeps_input_blocked_and_ends_call(self):
-        tool = RuntimeTool(
-            id="opening_message",
-            name="开场确认",
-            function_name="show_message",
-            type="client",
-        )
         cfg = AssistantConfig(
             type="prompt",
-            tools=[tool],
             startup={
-                "actions": [
-                    {
-                        "id": "opening_message",
-                        "phase": "opening",
-                        "tool_id": tool.id,
-                        "arguments": {},
-                        "required": True,
-                    }
-                ]
+                "opening_message": {
+                    "title": "重要提示",
+                    "message": "请确认已阅读。",
+                    "confirm_label": "确认",
+                }
             },
         )
         brain = build_brain(cfg)
@@ -463,6 +486,10 @@ class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
         async def queue_frame(_frame):
             pass
 
+        class FailingClientTools:
+            async def call(self, *_args, **_kwargs):
+                return {"status": "error", "message": "客户端未显示消息"}
+
         await brain.setup(
             cfg,
             BrainRuntime(
@@ -472,20 +499,10 @@ class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
                 set_system_prompt=lambda _prompt: None,
                 set_tools=lambda _tools: None,
                 call_end=call_end,
+                client_tools=FailingClientTools(),
                 set_input_enabled=input_states.append,
             ),
         )
-        brain._actions.execute = AsyncMock(
-            return_value=ActionOutcome(
-                invocation_id="act_failed",
-                status=ActionStatus.FAILURE,
-                duration_ms=1,
-                error=ActionError(
-                    code="tool_error",
-                    message="用户未确认",
-                ),
-            )
-        )
 
         await brain.on_connected(greeting_pending=False)
         await brain.on_client_ready()
@@ -948,7 +965,7 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
         self.assertNotIn("fetch_user_image", custom_config["role_message"])
         self.assertFalse(scopes[-1]["enabled"])
 
-    async def test_initial_fixed_speech_waits_for_start_greeting_to_finish(self):
+    async def test_initial_fixed_speech_starts_without_workflow_greeting(self):
         brain = WorkflowBrain(
             {
                 "specVersion": 3,
@@ -1006,12 +1023,14 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
         )
         brain._manager = FakeManager()
 
-        await brain.on_connected(greeting_pending=True)
-
-        self.assertEqual(brain._manager.current_node, "start")
-        self.assertFalse(any(isinstance(frame, TTSSpeakFrame) for frame in queued))
-
-        await brain.on_greeting_finished()
+        self.assertNotIn("greeting", brain._engine.data("start"))
+        self.assertEqual(
+            await brain.greeting(
+                AssistantConfig(type="workflow", greeting="旧助手级开场白")
+            ),
+            "",
+        )
+        await brain.on_connected()
 
         self.assertEqual(brain._manager.current_node, "agent")
         fixed_speech_frames = [
@@ -1020,8 +1039,8 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
         self.assertEqual(len(fixed_speech_frames), 1)
         self.assertEqual(fixed_speech_frames[0].text, "请问您怎么称呼？")
 
-        # Playback notifications may be duplicated by a transport reconnect;
-        # the initial entry behavior must still run only once.
+        # Workflow no longer owns a greeting playback lifecycle. Stray generic
+        # transport notifications must not repeat Agent entry behavior.
         await brain.on_greeting_finished()
         self.assertEqual(
             len([frame for frame in queued if isinstance(frame, TTSSpeakFrame)]),
@@ -1372,6 +1391,148 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
         await brain._enter_action("queue_action")
         self.assertEqual(input_states, ["executing"])
 
+    async def test_message_starts_speech_and_releases_on_confirmation(self):
+        brain = WorkflowBrain(
+            AssistantConfig(
+                type="workflow",
+                graph={
+                    "specVersion": 3,
+                    "settings": {},
+                    "nodes": [
+                        {"id": "start", "type": "start", "data": {}},
+                        {
+                            "id": "message",
+                            "type": "message",
+                            "data": {
+                                "speech": "请先确认 {{customer}} 的重要信息。",
+                                "showMessage": True,
+                                "title": "重要提示",
+                                "message": "请核对客户信息。",
+                                "confirmLabel": "确认",
+                                "requireConfirmation": True,
+                            },
+                        },
+                    ],
+                    "edges": [],
+                },
+            )
+        )
+        brain._store.values["customer"] = "王先生"
+        events = []
+
+        async def queue_frame(frame):
+            if isinstance(frame, TTSSpeakFrame):
+                events.append(("speech", frame.text))
+
+        class OrderedCallEnd(FakeCallEnd):
+            def __init__(self):
+                super().__init__()
+                self.playback_completion = None
+
+            def track_speech(self):
+                self.tracked_speeches += 1
+                events.append("tracked")
+                self.playback_completion = asyncio.get_running_loop().create_future()
+                return self.playback_completion
+
+        message_started = asyncio.Event()
+        user_confirmed = asyncio.Event()
+
+        class FakeClientTools:
+            async def call(self, function_name, arguments, **options):
+                self.function_name = function_name
+                self.arguments = arguments
+                self.options = options
+                events.append("message_displayed")
+                message_started.set()
+                await user_confirmed.wait()
+                return {"status": "ok", "data": {"action": "confirmed"}}
+
+        input_states = []
+        call_end = OrderedCallEnd()
+        client_tools = FakeClientTools()
+        brain._runtime = BrainRuntime(
+            context=LLMContext(messages=[]),
+            llm=FakeLLM(),
+            queue_frame=queue_frame,
+            set_system_prompt=lambda _prompt: None,
+            set_tools=lambda _tools: None,
+            call_end=call_end,
+            client_tools=client_tools,
+            set_input_enabled=input_states.append,
+        )
+        brain._message_stages.set_client_tools(client_tools)
+
+        message_task = asyncio.create_task(brain._enter_message("message"))
+        await message_started.wait()
+
+        self.assertEqual(
+            events,
+            [
+                "tracked",
+                ("speech", "请先确认 王先生 的重要信息。"),
+                "message_displayed",
+            ],
+        )
+        self.assertEqual(input_states, [False])
+        self.assertEqual(client_tools.function_name, "show_message")
+        self.assertEqual(client_tools.options["response_wait_mode"], "session")
+
+        user_confirmed.set()
+        result = await message_task
+        self.assertTrue(result.succeeded)
+        self.assertEqual(result.action, "confirmed")
+        self.assertFalse(call_end.playback_completion.done())
+        self.assertEqual(input_states, [False, True])
+
+    async def test_speech_only_message_waits_for_transport_playback(self):
+        brain = WorkflowBrain(
+            {
+                "specVersion": 3,
+                "settings": {},
+                "nodes": [
+                    {"id": "start", "type": "start", "data": {}},
+                    {
+                        "id": "message",
+                        "type": "message",
+                        "data": {"speech": "正在为您准备服务。"},
+                    },
+                ],
+                "edges": [],
+            }
+        )
+
+        class PlaybackCallEnd(FakeCallEnd):
+            def __init__(self):
+                super().__init__()
+                self.completion = None
+
+            def track_speech(self):
+                self.completion = asyncio.get_running_loop().create_future()
+                return self.completion
+
+        call_end = PlaybackCallEnd()
+        input_states = []
+        brain._runtime = BrainRuntime(
+            context=LLMContext(messages=[]),
+            llm=FakeLLM(),
+            queue_frame=noop_queue_frame,
+            set_system_prompt=lambda _prompt: None,
+            set_tools=lambda _tools: None,
+            call_end=call_end,
+            set_input_enabled=input_states.append,
+        )
+
+        message_task = asyncio.create_task(brain._enter_message("message"))
+        await asyncio.sleep(0)
+        self.assertFalse(message_task.done())
+        self.assertEqual(input_states, [False])
+
+        call_end.completion.set_result(None)
+        result = await message_task
+        self.assertTrue(result.succeeded)
+        self.assertEqual(input_states, [False, True])
+
     async def test_nodes_without_outgoing_edges_remain_active(self):
         queued = []
 
@@ -1501,7 +1662,7 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
                     {
                         "id": "start",
                         "type": "start",
-                        "data": {"name": "Start", "greeting": "你想做什么？"},
+                        "data": {"name": "Start"},
                     },
                     {
                         "id": "eat",
@@ -1815,10 +1976,7 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
                 {
                     "id": "start",
                     "type": "start",
-                    "data": {
-                        "name": "Start",
-                        "greeting": "欢迎，{{user_name}}",
-                    },
+                    "data": {"name": "Start"},
                 },
                 {
                     "id": "agent",
@@ -1925,13 +2083,8 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
         )
         await brain.setup(cfg, runtime)
         greeting = await brain.greeting(cfg)
-        self.assertEqual(greeting, "欢迎，王先生")
-        greeting_message = {
-            "role": "system",
-            "content": f"{GREETING_CONTEXT_MARKER}\n欢迎，王先生",
-        }
-        brain.prepare_greeting_context(greeting, context)
-        self.assertEqual(context.get_messages(), [greeting_message])
+        self.assertEqual(greeting, "")
+        self.assertEqual(context.get_messages(), [])
         await brain.on_connected()
         self.assertEqual(brain._manager.current_node, "agent")
         variable_events = [
@@ -1980,7 +2133,7 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
         agent_config = brain._agent_config("agent")
         self.assertIn("王先生", agent_config["role_message"])
         self.assertIn("工作流路由已在用户一轮输入结束时完成", agent_config["role_message"])
-        self.assertEqual(agent_config["task_messages"], [greeting_message])
+        self.assertEqual(agent_config["task_messages"], [])
         self.assertFalse(agent_config["respond_immediately"])
         self.assertFalse(any(isinstance(frame, LLMRunFrame) for frame in worker.frames))
         self.assertEqual(
@@ -2005,10 +2158,7 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
         self.assertNotIn("pre_actions", fixed_config)
         self.assertEqual(
             fixed_config["task_messages"],
-            [
-                greeting_message,
-                {"role": "assistant", "content": "您好，王先生"},
-            ],
+            [{"role": "assistant", "content": "您好，王先生"}],
         )
         self.assertEqual(
             brain._agent_config(
@@ -2016,7 +2166,6 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
                 [{"role": "assistant", "content": "正在进入下一阶段"}],
             )["task_messages"],
             [
-                greeting_message,
                 {"role": "assistant", "content": "正在进入下一阶段"},
                 {"role": "assistant", "content": "您好，王先生"},
             ],
@@ -2034,10 +2183,7 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
         ]
         self.assertEqual(
             context_updates[-1].messages,
-            [
-                greeting_message,
-                {"role": "assistant", "content": "您好，王先生"},
-            ],
+            [{"role": "assistant", "content": "您好，王先生"}],
         )
         self.assertFalse(
             any(

backend/tests/test_call_lifecycle.py

@@ -45,17 +45,20 @@ class CallEndCoordinatorTest(unittest.IsolatedAsyncioTestCase):
         self.assertEqual(self.reasons, ["tool_only"])
 
     async def test_workflow_end_waits_for_every_queued_fixed_speech(self):
-        self.coordinator.track_speech()
-        self.coordinator.track_speech()
+        first_completion = self.coordinator.track_speech()
+        second_completion = self.coordinator.track_speech()
         self.coordinator.begin("workflow_completed")
         await self.coordinator.arm_after_tracked_speech()
 
         await self.coordinator.observe(BotStartedSpeakingFrame())
         await self.coordinator.observe(BotStoppedSpeakingFrame())
+        self.assertTrue(first_completion.done())
+        self.assertFalse(second_completion.done())
         self.assertEqual(self.reasons, [])
 
         await self.coordinator.observe(BotStartedSpeakingFrame())
         await self.coordinator.observe(BotStoppedSpeakingFrame())
+        self.assertTrue(second_completion.done())
         self.assertEqual(self.reasons, ["workflow_completed"])
 
 

backend/tests/test_runtime_variables.py

@@ -89,7 +89,7 @@ class DynamicVariableTests(unittest.TestCase):
                     {
                         "id": "start",
                         "type": "start",
-                        "data": {"greeting": "您好 {{nickname}}"},
+                        "data": {"name": "Start"},
                     }
                 ],
                 "edges": [],

backend/tests/test_startup_actions.py

@@ -77,6 +77,39 @@ class StartupActionValidationTests(unittest.IsolatedAsyncioTestCase):
                 startup=startup_body().startup,
             )
 
+    def test_realtime_rejects_builtin_opening_message(self):
+        with self.assertRaisesRegex(ValueError, "Realtime"):
+            AssistantUpsert(
+                name="Realtime 开场消息",
+                type="prompt",
+                runtimeMode="realtime",
+                startup={
+                    "openingMessage": {
+                        "title": "重要提示",
+                        "message": "请确认已阅读。",
+                        "confirmLabel": "确认",
+                    }
+                },
+            )
+
+    async def test_builtin_opening_message_does_not_reference_a_tool(self):
+        body = AssistantUpsert(
+            name="内置开场消息",
+            type="prompt",
+            startup={
+                "openingMessage": {
+                    "title": "重要提示",
+                    "message": "请确认已阅读。",
+                    "confirmLabel": "确认",
+                }
+            },
+        )
+
+        await _validate_startup_actions(FakeSession(None), body)
+
+        self.assertEqual(body.startup.actions, [])
+        self.assertEqual(body.startup.opening_message.confirm_label, "确认")
+
     async def test_startup_tool_does_not_need_conversation_binding(self):
         tool = SimpleNamespace(
             id="tool_message",

backend/tests/test_workflow_v3.py

@@ -55,6 +55,20 @@ def valid_graph():
 
 
 class WorkflowGraphTests(unittest.TestCase):
+    def test_workflow_removes_assistant_and_start_greetings(self):
+        body = AssistantUpsert(
+            name="无开场白工作流",
+            type="workflow",
+            greeting="旧助手级开场白",
+            graph=valid_graph(),
+        )
+        body.graph["nodes"][0]["data"]["greeting"] = "旧 Start 开场白"
+
+        normalized = normalize_graph(body.graph)
+
+        self.assertEqual(body.greeting, "")
+        self.assertNotIn("greeting", normalized["nodes"][0]["data"])
+
     def test_revision_pins_the_normalized_workflow_snapshot(self):
         first = WorkflowEngine(valid_graph())
         same_graph = deepcopy(valid_graph())
@@ -147,6 +161,7 @@ class WorkflowGraphTests(unittest.TestCase):
         )
         self.assertEqual(actions["legacy_none"]["resultAssignments"], {})
         self.assertEqual(actions["legacy_none"]["userInputPolicy"], "queue")
+        self.assertNotIn("speech", actions["legacy_none"])
 
     def test_action_advanced_settings_are_validated(self):
         graph = valid_graph()
@@ -167,6 +182,46 @@ class WorkflowGraphTests(unittest.TestCase):
         self.assertTrue(any("结果变量映射必须是对象" in error for error in errors))
         self.assertTrue(any("用户输入策略无效" in error for error in errors))
 
+    def test_message_defaults_and_validation(self):
+        graph = valid_graph()
+        graph["nodes"].append(
+            {
+                "id": "message",
+                "type": "message",
+                "data": {"speech": "请确认重要信息"},
+            }
+        )
+        normalized = normalize_graph(graph)
+        message = next(
+            node for node in normalized["nodes"] if node["type"] == "message"
+        )
+        self.assertFalse(message["data"]["showMessage"])
+        self.assertFalse(message["data"]["requireConfirmation"])
+        self.assertEqual(message["data"]["confirmLabel"], "确认")
+
+        message["data"].update(
+            {
+                "speech": "",
+                "showMessage": True,
+                "title": "重要提示",
+                "message": "",
+                "confirmLabel": "确认",
+                "requireConfirmation": True,
+            }
+        )
+        errors = validate_graph(normalized)
+        self.assertTrue(any("弹窗消息必须为" in error for error in errors))
+
+        message["data"].update(
+            {
+                "speech": "请确认",
+                "showMessage": False,
+                "requireConfirmation": True,
+            }
+        )
+        errors = validate_graph(normalized)
+        self.assertTrue(any("等待确认时必须显示弹窗" in error for error in errors))
+
     def test_voice_resource_creates_isolated_runtime_config(self):
         base = AssistantConfig(type="workflow", asr="default", voice="default")
         asr = RuntimeModelResource(

frontend/src/components/assistant-editor/prompt-editor.tsx

@@ -85,55 +85,18 @@ export function PromptEditor({
   handlePromptVisionEnabledChange,
   handlePromptModelChange,
 }: PromptEditorProps) {
-  const openingMessage = form.startup.actions.find(
-    (action) => action.id === "opening_message" && action.phase === "opening",
-  );
-  const openingArguments = openingMessage?.arguments ?? {};
-  const openingButtons = Array.isArray(openingArguments.actions)
-    ? openingArguments.actions
-    : [];
-  const openingButton =
-    openingButtons[0] && typeof openingButtons[0] === "object"
-      ? (openingButtons[0] as Record<string, unknown>)
-      : {};
-  const showMessageTools = tools
-    .filter(
-      (tool) =>
-        tool.status === "active" &&
-        tool.type === "client" &&
-        tool.functionName === "show_message" &&
-        tool.definition.type === "client" &&
-        tool.definition.config.waitForResponse &&
-        tool.definition.config.responseWaitMode === "session",
-    )
-    .map((tool) => ({ value: tool.id, label: tool.name }));
+  const openingMessage = form.startup.openingMessage;
 
   function setOpeningMessage(enabled: boolean) {
-    const otherActions = form.startup.actions.filter(
-      (action) => action.id !== "opening_message",
-    );
-    const defaultToolId = showMessageTools[0]?.value ?? "";
     updateForm("startup", {
-      executionMode: "sequential",
-      actions: enabled
-        ? [
-            ...otherActions,
-            {
-              id: "opening_message",
-              phase: "opening",
-              toolId: defaultToolId,
-              required: true,
-              arguments: {
-                title: "重要提示",
-                message: "请确认已阅读以上信息。",
-                actions: [
-                  { id: "confirmed", label: "确认", style: "primary" },
-                ],
-                dismissible: false,
-              },
-            },
-          ]
-        : otherActions,
+      ...form.startup,
+      openingMessage: enabled
+        ? {
+            title: "重要提示",
+            message: "请确认已阅读以上信息。",
+            confirmLabel: "确认",
+          }
+        : null,
     });
   }
 
@@ -143,16 +106,10 @@ export function PromptEditor({
     if (!openingMessage) return;
     updateForm("startup", {
       ...form.startup,
-      actions: form.startup.actions.map((action) =>
-        action.id === openingMessage.id ? { ...action, ...patch } : action,
-      ),
+      openingMessage: { ...openingMessage, ...patch },
     });
   }
 
-  function updateOpeningArguments(patch: Record<string, unknown>) {
-    updateOpeningMessage({ arguments: { ...openingArguments, ...patch } });
-  }
-
   return (
     <div className="-mt-6 flex h-full flex-col gap-4">
       <div className="flex shrink-0 items-center justify-between gap-6 border-b border-hairline pb-3 pt-1">
@@ -235,33 +192,21 @@ export function PromptEditor({
               <div className="mt-4 space-y-3 border-t border-hairline pt-4">
                 <ToggleRow
                   title="开场确认弹窗"
-                  description="与开场白同时显示，确认且播报完成后才允许用户开始对话。"
+                  description="与开场白同时显示，用户确认后即可开始对话，不等待播报完成。"
                   checked={Boolean(openingMessage)}
                   onChange={setOpeningMessage}
                 />
 
                 {openingMessage && (
                   <div className="space-y-3 rounded-xl border border-hairline bg-canvas-soft p-4">
-                    <ResourceSelectField
-                      label="消息工具"
-                      value={openingMessage.toolId}
-                      options={showMessageTools}
-                      noneLabel="请选择 show_message 工具"
-                      onChange={(toolId) => updateOpeningMessage({ toolId })}
-                    />
-                    {showMessageTools.length === 0 && (
-                      <p className="text-xs leading-5 text-muted-foreground">
-                        请先在组件管理中创建 functionName 为 show_message、会话内等待的 Client Tool。
-                      </p>
-                    )}
                     <label className="block">
                       <span className="mb-1.5 block text-sm font-medium text-foreground">
                         弹窗标题
                       </span>
                       <Input
-                        value={String(openingArguments.title ?? "")}
+                        value={openingMessage.title}
                         onChange={(event) =>
-                          updateOpeningArguments({ title: event.target.value })
+                          updateOpeningMessage({ title: event.target.value })
                         }
                         placeholder="重要提示"
                         className="border-hairline-strong bg-background"
@@ -269,8 +214,8 @@ export function PromptEditor({
                     </label>
                     <TextAreaField
                       label="重要信息"
-                      value={String(openingArguments.message ?? "")}
-                      onChange={(message) => updateOpeningArguments({ message })}
+                      value={openingMessage.message}
+                      onChange={(message) => updateOpeningMessage({ message })}
                       placeholder="请输入需要用户确认的重要信息"
                       rows={4}
                     />
@@ -279,16 +224,10 @@ export function PromptEditor({
                         确认按钮
                       </span>
                       <Input
-                        value={String(openingButton.label ?? "")}
+                        value={openingMessage.confirmLabel}
                         onChange={(event) =>
-                          updateOpeningArguments({
-                            actions: [
-                              {
-                                id: "confirmed",
-                                label: event.target.value,
-                                style: "primary",
-                              },
-                            ],
+                          updateOpeningMessage({
+                            confirmLabel: event.target.value,
                           })
                         }
                         placeholder="确认"
@@ -296,8 +235,8 @@ export function PromptEditor({
                       />
                     </label>
                     <p className="text-xs leading-5 text-muted-foreground">
-                      弹窗不可跳过；失败时将结束本次通话。该工具仅用于开场，
-                      如需允许模型后续调用，请另外在“工具”区域勾选。
+                      弹窗不可跳过；失败时将结束本次通话。这是平台内置开场阶段，
+                      不会作为工具暴露给模型。
                     </p>
                   </div>
                 )}
@@ -318,10 +257,14 @@ export function PromptEditor({
               value={form.runtimeMode}
               onChange={(runtimeMode) => {
                 updateForm("runtimeMode", runtimeMode);
-                if (runtimeMode === "realtime" && form.startup.actions.length) {
+                if (
+                  runtimeMode === "realtime" &&
+                  (form.startup.actions.length || form.startup.openingMessage)
+                ) {
                   updateForm("startup", {
                     executionMode: "sequential",
                     actions: [],
+                    openingMessage: null,
                   });
                 }
               }}

frontend/src/components/pages/AssistantPage.tsx

@@ -177,7 +177,11 @@ function blankPromptForm(name: string): AssistantForm {
     knowledgeRetrievalConfig: defaultKnowledgeRetrievalConfig(),
     enableInterrupt: true,
     turnConfig: defaultTurnConfig(),
-    startup: { executionMode: "sequential", actions: [] },
+    startup: {
+      executionMode: "sequential",
+      actions: [],
+      openingMessage: null,
+    },
     visionEnabled: false,
     visionModelResourceId: "",
     toolIds: [],
@@ -466,7 +470,11 @@ export function AssistantPage(props: AssistantPageProps) {
         a.knowledgeRetrievalConfig ?? defaultKnowledgeRetrievalConfig(),
       enableInterrupt: a.enableInterrupt,
       turnConfig: normalizeTurnConfig(a.turnConfig),
-      startup: a.startup ?? { executionMode: "sequential", actions: [] },
+      startup: {
+        executionMode: "sequential",
+        actions: a.startup?.actions ?? [],
+        openingMessage: a.startup?.openingMessage ?? null,
+      },
       visionEnabled: a.visionEnabled,
       visionModelResourceId: a.visionModelResourceId ?? "",
       toolIds: a.toolIds ?? [],
@@ -536,7 +544,11 @@ export function AssistantPage(props: AssistantPageProps) {
       greeting: "",
       enableInterrupt: true,
       turnConfig: defaultTurnConfig(),
-      startup: { executionMode: "sequential", actions: [] },
+      startup: {
+        executionMode: "sequential",
+        actions: [],
+        openingMessage: null,
+      },
       visionEnabled: false,
       visionModelResourceId: null,
       modelResourceIds: {},

frontend/src/components/workflow/GenericNode.tsx

@@ -27,7 +27,12 @@ export function GenericNode({ id, type, data, selected }: NodeProps) {
 
   const nodeData = data as WorkflowNodeData;
   const Icon = spec.icon;
-  const preview = (nodeData.greeting || nodeData.prompt || nodeData.message || "")
+  const preview = (
+    nodeData.prompt ||
+    nodeData.speech ||
+    nodeData.message ||
+    ""
+  )
     .toString()
     .trim();
   const entryModeLabel = {
@@ -50,8 +55,17 @@ export function GenericNode({ id, type, data, selected }: NodeProps) {
           nodeData.visionEnabled ? "视觉理解" : null,
         ].filter(Boolean)
     : type === "action" && nodeData.toolId
-      ? ["确定性工具"]
-      : [];
+      ? [
+          "确定性工具",
+          nodeData.userInputPolicy === "block" ? "禁止输入" : null,
+        ].filter(Boolean)
+      : type === "message"
+        ? [
+            nodeData.speech ? "固定播报" : null,
+            nodeData.showMessage ? "客户端消息" : null,
+            nodeData.requireConfirmation ? "等待确认" : null,
+          ].filter(Boolean)
+        : [];
 
   return (
     <div
@@ -192,6 +206,7 @@ export function GenericNode({ id, type, data, selected }: NodeProps) {
 export const nodeTypes = {
   start: GenericNode,
   agent: GenericNode,
+  message: GenericNode,
   action: GenericNode,
   handoff: GenericNode,
   end: GenericNode,

frontend/src/components/workflow/WorkflowCanvas.tsx

@@ -63,21 +63,30 @@ let nodeSeq = 0;
 function defaultNodeData(spec: RuntimeNodeSpec): WorkflowNodeData {
   const data: WorkflowNodeData = {
     name: spec.displayName,
-    ...(spec.type === "agent"
-      ? {
-          contextPolicy: "inherit",
-          inheritGlobalConfig: true,
-          entryMode: "wait_user",
-          entrySpeech: "",
-        }
-      : spec.type === "action"
-        ? {
-            arguments: {},
-            resultAssignmentMode: "inherit",
-            userInputPolicy: "queue",
-          }
-      : {}),
   };
+  if (spec.type === "agent") {
+    Object.assign(data, {
+      contextPolicy: "inherit",
+      inheritGlobalConfig: true,
+      entryMode: "wait_user",
+      entrySpeech: "",
+    });
+  } else if (spec.type === "action") {
+    Object.assign(data, {
+      arguments: {},
+      resultAssignmentMode: "inherit",
+      userInputPolicy: "queue",
+    });
+  } else if (spec.type === "message") {
+    Object.assign(data, {
+      speech: "",
+      showMessage: false,
+      title: "重要提示",
+      message: "",
+      confirmLabel: "确认",
+      requireConfirmation: false,
+    });
+  }
   for (const field of spec.fields) {
     if (field.default !== undefined) data[field.key] = field.default;
   }

frontend/src/components/workflow/panels/MessageNodePanel.tsx

@@ -0,0 +1,128 @@
+"use client";
+
+import { MessageSquareText } from "lucide-react";
+
+import { SectionCard } from "@/components/editor/section-card";
+import { Input } from "@/components/ui/input";
+import { Switch } from "@/components/ui/switch";
+import { Textarea } from "@/components/ui/textarea";
+
+import type { WorkflowNodeData } from "../specs";
+
+export function MessageNodePanel({
+  draft,
+  set,
+  setPatch,
+}: {
+  draft: WorkflowNodeData;
+  set: (key: string, value: unknown) => void;
+  setPatch: (patch: Partial<WorkflowNodeData>) => void;
+}) {
+  const showMessage = Boolean(draft.showMessage);
+  const requireConfirmation = Boolean(draft.requireConfirmation);
+
+  return (
+    <SectionCard
+      icon={<MessageSquareText size={15} />}
+      title="播报与确认"
+      description="播放固定话术，并可同时显示平台内置的客户端消息"
+    >
+      <label className="block">
+        <div className="mb-1.5 text-sm font-medium text-foreground">
+          固定播报（可选）
+        </div>
+        <Textarea
+          rows={4}
+          value={draft.speech ?? ""}
+          onChange={(event) => set("speech", event.target.value)}
+          placeholder="例如：您好，在开始服务前请确认以下重要信息。"
+          className="field-sizing-fixed min-h-28 resize-y border-hairline-strong bg-background text-sm text-foreground placeholder:text-muted-soft"
+        />
+        <span className="mt-1.5 block text-xs leading-5 text-muted-foreground">
+          支持 {"{{variable}}"} 动态变量。仅播报时，播放完成后进入下一节点。
+        </span>
+      </label>
+
+      <label className="flex items-start justify-between gap-4 rounded-xl border border-hairline bg-canvas-soft px-4 py-3">
+        <span>
+          <span className="block text-sm font-medium text-foreground">
+            显示客户端消息
+          </span>
+          <span className="mt-1 block text-xs leading-5 text-muted-foreground">
+            使用平台内置弹窗，不需要创建或绑定 Client Tool。
+          </span>
+        </span>
+        <Switch
+          checked={showMessage}
+          onCheckedChange={(checked) =>
+            setPatch({
+              showMessage: checked,
+              ...(!checked ? { requireConfirmation: false } : {}),
+            })
+          }
+        />
+      </label>
+
+      {showMessage && (
+        <div className="space-y-3 rounded-xl border border-hairline bg-canvas-soft p-4">
+          <label className="block">
+            <span className="mb-1.5 block text-sm font-medium text-foreground">
+              弹窗标题
+            </span>
+            <Input
+              value={draft.title ?? ""}
+              onChange={(event) => set("title", event.target.value)}
+              placeholder="重要提示"
+              className="border-hairline-strong bg-background"
+            />
+          </label>
+          <label className="block">
+            <span className="mb-1.5 block text-sm font-medium text-foreground">
+              重要信息 <span className="text-destructive">*</span>
+            </span>
+            <Textarea
+              rows={4}
+              value={draft.message ?? ""}
+              onChange={(event) => set("message", event.target.value)}
+              placeholder="请输入需要向用户展示的重要信息"
+              className="field-sizing-fixed min-h-28 resize-y border-hairline-strong bg-background text-sm text-foreground placeholder:text-muted-soft"
+            />
+          </label>
+          <label className="block">
+            <span className="mb-1.5 block text-sm font-medium text-foreground">
+              按钮文字
+            </span>
+            <Input
+              value={draft.confirmLabel ?? ""}
+              onChange={(event) => set("confirmLabel", event.target.value)}
+              placeholder="确认"
+              className="border-hairline-strong bg-background"
+            />
+          </label>
+          <label className="flex items-start justify-between gap-4 border-t border-hairline pt-3">
+            <span>
+              <span className="block text-sm font-medium text-foreground">
+                等待用户确认
+              </span>
+              <span className="mt-1 block text-xs leading-5 text-muted-foreground">
+                确认前禁止语音、文字和图片输入；确认后立即进入下一节点，不等待播报结束。
+              </span>
+            </span>
+            <Switch
+              checked={requireConfirmation}
+              onCheckedChange={(checked) =>
+                set("requireConfirmation", checked)
+              }
+            />
+          </label>
+        </div>
+      )}
+
+      {!draft.speech?.trim() && !showMessage && (
+        <p role="alert" className="text-xs leading-5 text-destructive">
+          Message 至少需要固定播报或客户端消息。
+        </p>
+      )}
+    </SectionCard>
+  );
+}

frontend/src/components/workflow/panels/NodeSettingsPanel.tsx

@@ -1,6 +1,6 @@
 "use client";
 
-import { Flag, PhoneForwarded, Play, Tag } from "lucide-react";
+import { Flag, PhoneForwarded, Tag } from "lucide-react";
 import { useState } from "react";
 
 import { SectionCard } from "@/components/editor/section-card";
@@ -11,6 +11,7 @@ import { Textarea } from "@/components/ui/textarea";
 
 import { ActionNodePanel } from "./ActionNodePanel";
 import { AgentNodePanel } from "./AgentNodePanel";
+import { MessageNodePanel } from "./MessageNodePanel";
 import { NodeSelect, ToolOptionPicker } from "./controls";
 import type { RuntimeNodeSpec, WorkflowNodeData } from "../specs";
 import type { ModelOption, WorkflowSettings } from "../types";
@@ -82,6 +83,7 @@ export function NodeSettingsPanel({
           spec={spec}
           draft={draft}
           set={set}
+          setPatch={setPatch}
           toolOptions={toolOptions}
           argumentsJson={argumentsJson}
           assignmentsJson={assignmentsJson}
@@ -247,6 +249,10 @@ export function NodeSettingsPanel({
           />
         )}
 
+        {spec.type === "message" && (
+          <MessageNodePanel draft={draft} set={set} setPatch={setPatch} />
+        )}
+
         {spec.type === "handoff" && (
           <NodeSelect
             label="转交类型"
@@ -283,6 +289,7 @@ function WorkflowNodePanelForm({
   spec,
   draft,
   set,
+  setPatch,
   toolOptions,
   argumentsJson,
   assignmentsJson,
@@ -294,6 +301,7 @@ function WorkflowNodePanelForm({
   spec: RuntimeNodeSpec;
   draft: WorkflowNodeData;
   set: (key: string, val: unknown) => void;
+  setPatch: (patch: Partial<WorkflowNodeData>) => void;
   toolOptions: ModelOption[];
   argumentsJson: string;
   assignmentsJson: string;
@@ -321,27 +329,6 @@ function WorkflowNodePanelForm({
         </label>
       </SectionCard>
 
-      {spec.type === "start" && (
-        <SectionCard
-          icon={<Play size={15} />}
-          title="开场白"
-          description="会话建立后首先向用户播放的固定内容"
-        >
-          <label className="block">
-            <div className="mb-1.5 text-sm font-medium text-foreground">
-              固定开场白
-            </div>
-            <Textarea
-              rows={5}
-              value={draft.greeting ?? ""}
-              onChange={(event) => set("greeting", event.target.value)}
-              placeholder="例如：你好，我是 AI 视频助手，有什么可以帮你？"
-              className="field-sizing-fixed min-h-28 resize-y border-hairline-strong bg-background text-sm text-foreground placeholder:text-muted-soft"
-            />
-          </label>
-        </SectionCard>
-      )}
-
       {spec.type === "action" && (
         <ActionNodePanel
           draft={draft}
@@ -356,6 +343,10 @@ function WorkflowNodePanelForm({
         />
       )}
 
+      {spec.type === "message" && (
+        <MessageNodePanel draft={draft} set={set} setPatch={setPatch} />
+      )}
+
       {spec.type === "handoff" && (
         <SectionCard
           icon={<PhoneForwarded size={15} />}

frontend/src/components/workflow/specs.ts

@@ -6,7 +6,13 @@ import { Circle, type LucideIcon } from "lucide-react";
 import type { NodeSpecDto, TurnConfig } from "@/lib/api";
 import { defaultTurnConfig } from "@/lib/turn-config";
 
-export type WorkflowNodeType = "start" | "agent" | "action" | "handoff" | "end";
+export type WorkflowNodeType =
+  | "start"
+  | "agent"
+  | "message"
+  | "action"
+  | "handoff"
+  | "end";
 export type ContextPolicy = "inherit" | "fresh";
 export type KnowledgeMode = "automatic" | "on_demand" | "disabled";
 export type AgentEntryMode = "wait_user" | "generate" | "fixed_speech";
@@ -26,7 +32,6 @@ export type ExpressionOperator =
 
 export type WorkflowNodeData = {
   name: string;
-  greeting?: string;
   prompt?: string;
   contextPolicy?: ContextPolicy;
   inheritGlobalConfig?: boolean;
@@ -49,6 +54,11 @@ export type WorkflowNodeData = {
   resultAssignmentMode?: ActionResultAssignmentMode;
   resultAssignments?: Record<string, string>;
   userInputPolicy?: ActionUserInputPolicy;
+  speech?: string;
+  showMessage?: boolean;
+  title?: string;
+  confirmLabel?: string;
+  requireConfirmation?: boolean;
   targetType?: "ai" | "human" | "queue" | "phone";
   target?: string;
   message?: string;
@@ -224,13 +234,26 @@ export function defaultGraph(): WorkflowGraph {
         position: { x: 360, y: 60 },
         data: {
           name: "Start",
-          greeting: "你好，我是 AI 视频助手，有什么可以帮你？",
+        },
+      },
+      {
+        id: "message-1",
+        type: "message",
+        position: { x: 360, y: 260 },
+        data: {
+          name: "开场消息",
+          speech: "你好，我是 AI 视频助手，有什么可以帮你？",
+          showMessage: false,
+          title: "重要提示",
+          message: "",
+          confirmLabel: "确认",
+          requireConfirmation: false,
         },
       },
       {
         id: "agent-1",
         type: "agent",
-        position: { x: 360, y: 300 },
+        position: { x: 360, y: 480 },
         data: {
           name: "Agent",
           prompt: "了解用户需求并提供清晰、准确的帮助。",
@@ -243,7 +266,7 @@ export function defaultGraph(): WorkflowGraph {
       {
         id: "end",
         type: "end",
-        position: { x: 360, y: 540 },
+        position: { x: 360, y: 700 },
         data: {
           name: "End",
           message: "感谢你的来电，再见。",
@@ -253,8 +276,14 @@ export function defaultGraph(): WorkflowGraph {
     ],
     edges: [
       {
-        id: "e-start-agent",
+        id: "e-start-message",
         source: "start",
+        target: "message-1",
+        data: { mode: "always", priority: 0 },
+      },
+      {
+        id: "e-message-agent",
+        source: "message-1",
         target: "agent-1",
         data: { mode: "always", priority: 0 },
       },

frontend/src/lib/api.ts

@@ -207,6 +207,11 @@ export type StartupAction = {
 export type StartupConfig = {
   executionMode: "sequential";
   actions: StartupAction[];
+  openingMessage: {
+    title: string;
+    message: string;
+    confirmLabel: string;
+  } | null;
 };
 
 /** 后端 AssistantOut(宽表 STI:瘦字段平铺,workflow 用 graph)。apiKey 读时打码 */