Refactor workflow agent and routing components for improved functionality

- Introduce WorkflowAgentStage to manage agent stage configurations and enhance interaction with the workflow engine. - Implement WorkflowEdgeEvaluator for priority-aware edge evaluation, improving routing decisions based on conditions and user turns. - Update WorkflowBrain to handle user turns and routing more effectively, ensuring agents cannot have only one default path. - Enhance CallEndCoordinator to track speech events and manage call termination based on queued speech. - Add new models and output handling for workflow interactions, improving clarity and maintainability. - Update tests to validate the new routing logic and agent behavior under various scenarios.
2026-07-17 22:37:15 +08:00
parent 162a3d8bec
commit bdf3d3dd9c
23 changed files with 1374 additions and 475 deletions
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -1,6 +1,6 @@
-你编写具有可维护性、高可读性、可扩展性、模块化的的代码，尽量不去对pipecat框架本身修改
-以MVP构建为目标
+你编写具有可维护性、高可读性、可扩展性、模块化的的代码
 适合CS的本科学生阅读修改
+尽量不去对pipecat框架本身修改
+以MVP构建为目标
 编写代码之前先用易于理解的语言说清楚思路
-
 界面设计要参考 frontend/DESIGN.md
--- a/backend/services/brains/base.py
+++ b/backend/services/brains/base.py
@@ -55,6 +55,10 @@ class CallEndPort(Protocol):

    def arm_after_speech(self) -> None: ...

+    def track_speech(self) -> None: ...
+
+    async def arm_after_tracked_speech(self) -> None: ...
+
    async def finish_after_current_speech(self, *, has_text: bool) -> None: ...

    async def finish(self) -> None: ...
--- a/backend/services/brains/workflow_brain.py
+++ b/backend/services/brains/workflow_brain.py
@@ -2,6 +2,7 @@

 from __future__ import annotations

+import asyncio
 from copy import deepcopy
 from typing import Any

@@ -19,26 +20,26 @@ from pipecat.frames.frames import (
    LLMRunFrame,
    LLMUpdateSettingsFrame,
    OutputTransportMessageUrgentFrame,
-    TTSSpeakFrame,
 )
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.frame_processor import FrameProcessor
-from pipecat.services.settings import LLMSettings
-from pipecat.utils.time import time_now_iso8601
-
 from services.brains.base import BaseBrain, BrainRuntime, BrainSpec
 from services.knowledge import search as search_knowledge
 from services.runtime_variables import DynamicVariableStore
 from services.tool_executor import ToolExecutionError, ToolExecutor
+from services.workflow.agent import WorkflowAgentStage
+from services.workflow.models import (
+    RouteStatus,
+    WorkflowRuntimeState,
+    WorkflowStatus,
+)
+from services.workflow.output import WorkflowOutput
+from services.workflow.routing import WorkflowEdgeEvaluator
 from services.workflow_engine import WorkflowEngine
-from services.workflow_router import STAY_ON_CURRENT_NODE, WorkflowLLMRouter
+from services.workflow_router import WorkflowLLMRouter


 MAX_AUTOMATIC_HOPS = 50
-AGENT_STAGE_INSTRUCTION = (
-    "工作流路由已在用户一轮输入结束时完成。只执行当前阶段任务，"
-    "不要自行解释、模拟或宣布节点切换。"
-)


 class WorkflowBrain(BaseBrain):
@@ -69,11 +70,18 @@ class WorkflowBrain(BaseBrain):
        self._runtime: BrainRuntime | None = None
        self._manager: FlowManager | None = None
        self._router = WorkflowLLMRouter(cfg or AssistantConfig(type="workflow"))
+        self._edge_evaluator = WorkflowEdgeEvaluator(
+            self._engine,
+            self._store,
+            self._router_for_node,
+        )
+        self._state = WorkflowRuntimeState(current_node_id=self._engine.start_id)
+        self._turn_lock = asyncio.Lock()
+        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
-        self._client_ready = False
-        self._pending_visible_speech_events: list[dict[str, Any]] = []

    async def greeting(self, cfg: AssistantConfig) -> str:
        return self._engine.greeting(self._store) or cfg.greeting
@@ -95,10 +103,23 @@ class WorkflowBrain(BaseBrain):
        self._tools = ToolExecutor(self._store)
        self._tool_by_id = {tool.id: tool for tool in cfg.tools}
        self._router = WorkflowLLMRouter(cfg)
+        self._edge_evaluator = WorkflowEdgeEvaluator(
+            self._engine,
+            self._store,
+            self._router_for_node,
+        )
+        self._state = WorkflowRuntimeState(current_node_id=self._engine.start_id)
+        self._turn_lock = asyncio.Lock()
+        self._output = WorkflowOutput(self._store, runtime)
+        self._agent_stage = WorkflowAgentStage(
+            cfg=cfg,
+            engine=self._engine,
+            store=self._store,
+            runtime=runtime,
+        )
+        self._ended = False
        self._greeting_context_message = None
        self._startup_waiting_for_greeting = False
-        self._client_ready = False
-        self._pending_visible_speech_events = []
        self._manager = FlowManager(
            worker=runtime.worker,
            llm=runtime.llm,
@@ -118,6 +139,7 @@ class WorkflowBrain(BaseBrain):
        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)
        await self._emit_variables(
            reason="initialized",
@@ -139,6 +161,7 @@ class WorkflowBrain(BaseBrain):

        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:
@@ -152,17 +175,28 @@ class WorkflowBrain(BaseBrain):

        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:
-        """Resolve the immediate path from Start without evaluating LLM edges."""
-        # Start LLM conditions need an actual user turn. Expression-only and
-        # default-only starts may still advance immediately at connection time.
-        edge = await self._select_edge(
-            self._engine.start_id,
-            evaluate_llm=False,
+        """Only a default-only Start advances before the first user turn."""
+        outgoing = self._engine.outgoing(self._engine.start_id)
+        has_condition = any(
+            self._engine.edge_mode(edge) != "always" for edge in outgoing
+        )
+        if has_condition:
+            self._state.enter(self._engine.start_id, WorkflowStatus.WAITING_USER)
+            return self._passive_node_config(self._engine.start_id)
+        edge = next(
+            (
+                candidate
+                for candidate in outgoing
+                if self._engine.edge_mode(candidate) == "always"
+            ),
+            None,
        )
        return (
            await self._follow_edge(edge)
@@ -172,18 +206,14 @@ class WorkflowBrain(BaseBrain):

    async def on_client_ready(self) -> None:
        """Replay state that may have been emitted before WebRTC data was ready."""
-        self._client_ready = True
-        pending_speech_events = self._pending_visible_speech_events
-        self._pending_visible_speech_events = []
-        for message in pending_speech_events:
-            await self._require_runtime().queue_frame(
-                OutputTransportMessageUrgentFrame(message=message)
-            )
+        await self._require_output().mark_client_ready()
        current_node = (
            str(self._manager.current_node)
            if self._manager and self._manager.current_node
-            else self._engine.start_id
+            else self._state.current_node_id
        )
+        if current_node != self._state.current_node_id:
+            self._state.current_node_id = current_node
        await self._emit_node_active(current_node)
        await self._emit_variables(
            reason="client_ready",
@@ -198,114 +228,69 @@ class WorkflowBrain(BaseBrain):
        """Route a complete user turn before the active stage may reply."""
        if not content or self._ended:
            return True
+        async with self._turn_lock:
+            return await self._handle_user_turn_end(content)
+
+    async def _handle_user_turn_end(self, content: str) -> bool:
+        """Serialized implementation so one user turn cannot transition twice."""
        self.record_user_message(content)
+        self._state.begin_user_turn(content)
        manager = self._require_manager()
-        current = manager.current_node
+        current = self._state.current_node_id
        if not current:
            return True

-        edge = await self._select_edge(current)
+        self._state.status = WorkflowStatus.ROUTING
+        decision = await self._edge_evaluator.evaluate(current)
+        if decision.status == RouteStatus.ERROR:
+            await self._require_output().emit_error(
+                decision.error or "工作流路由失败",
+                node_id=current,
+                code="workflow_routing_error",
+            )
+            return await self._continue_current_node_after_no_transition(current)

-        if edge and manager.current_node == current:
+        if decision.edge and manager.current_node == current:
            next_config = await self._follow_edge(
-                edge,
+                decision.edge,
                triggering_user_text=content,
            )
            await manager.set_node_from_config(next_config)
-            next_node = str(next_config.get("name") or "")
-            if (
-                self._engine.node_type(next_node) == "agent"
-                and self._engine.data(next_node).get("entryMode", "wait_user")
-                == "wait_user"
-            ):
-                await self._require_runtime().queue_frame(LLMRunFrame())
+            await self._after_node_activated(
+                next_config,
+                triggering_user_text=content,
+            )
            return True

-        if self._engine.node_type(current) != "agent":
-            # Start/Action/Handoff have no conversational LLM of their own.
-            # Keep waiting so a later user turn may satisfy another condition.
+        return await self._continue_current_node_after_no_transition(current)
+
+    async def _continue_current_node_after_no_transition(
+        self,
+        node_id: str,
+    ) -> bool:
+        if self._engine.node_type(node_id) != "agent":
+            self._state.enter(node_id, WorkflowStatus.WAITING_USER)
            return True

-        # The incoming LLMContextFrame is intentionally suppressed by the
-        # pipeline router. Queue prompt refresh + inference in this order so
-        # this user turn is answered with the current Agent's latest variables.
-        await self._refresh_agent_prompt(current)
+        await self._refresh_agent_prompt(node_id)
+        self._state.enter(node_id, WorkflowStatus.RUNNING_AGENT)
        await self._require_runtime().queue_frame(LLMRunFrame())
        return True

    async def _select_edge(
        self,
        node_id: str,
-        *,
-        evaluate_llm: bool = True,
    ) -> dict | None:
-        """Resolve conditional paths by priority, then use the default path."""
-        expression_edge = self._engine.deterministic_edge(
-            node_id,
-            self._store,
-            include_default=False,
-        )
-        outgoing = self._engine.outgoing(node_id)
-        all_llm_edges = [
-            candidate
-            for candidate in outgoing
-            if self._engine.edge_mode(candidate) == "llm"
-        ]
-        llm_edges = all_llm_edges
-        default_edge = next(
-            (
-                candidate
-                for candidate in outgoing
-                if self._engine.edge_mode(candidate) == "always"
-            ),
-            None,
-        )
-
-        # A matching expression is a deterministic priority boundary. Only LLM
-        # conditions before it may win; later conditions must not bypass it.
-        if expression_edge:
-            expression_index = outgoing.index(expression_edge)
-            llm_edges = [
-                candidate
-                for candidate in llm_edges
-                if outgoing.index(candidate) < expression_index
-            ]
-
-        if not evaluate_llm:
-            if expression_edge and not llm_edges:
-                return expression_edge
-            if all_llm_edges:
-                return None
-            return default_edge
-
-        if not llm_edges:
-            return expression_edge or default_edge
-
-        selected = await self._router_for_node(node_id).select_edge(
-            node_name=self._engine.name(node_id),
-            node_prompt=self._engine.routing_prompt(node_id, self._store),
-            edges=llm_edges,
-            history=self._store.history,
-            variables={
-                key: value
-                for key, value in self._store.values.items()
-                if not key.startswith("system__")
-            },
-            edge_name=self._engine.edge_fn_name,
-            edge_description=self._engine.edge_description,
-        )
-        if selected == STAY_ON_CURRENT_NODE:
-            return expression_edge or default_edge
-        if not selected:
-            return expression_edge
-        return next(
-            (
-                candidate
-                for candidate in llm_edges
-                if self._engine.edge_fn_name(candidate) == selected
-            ),
-            None,
-        )
+        """Compatibility helper used by automatic-node traversal and tests."""
+        decision = await self._edge_evaluator.evaluate(node_id)
+        if decision.status == RouteStatus.ERROR:
+            await self._require_output().emit_error(
+                decision.error or "工作流路由失败",
+                node_id=node_id,
+                code="workflow_routing_error",
+            )
+            return None
+        return decision.edge

    async def on_assistant_text_end(
        self,
@@ -316,88 +301,29 @@ class WorkflowBrain(BaseBrain):
        if not content or interrupted or self._ended:
            return
        self._store.record("agent", content, completed_agent_turn=True)
+        self._state.consume_user_turn()
+        if self._engine.node_type(self._state.current_node_id) == "agent":
+            self._state.status = WorkflowStatus.WAITING_USER

    async def _refresh_agent_prompt(self, node_id: str) -> None:
-        runtime = self._require_runtime()
-        await runtime.queue_frame(
-            LLMUpdateSettingsFrame(
-                delta=LLMSettings(
-                    system_instruction=self._agent_role_message(node_id)
-                )
-            )
-        )
+        await self._require_agent_stage().refresh_prompt(node_id)

    def _agent_role_message(self, node_id: str) -> str:
-        """Build one provider-compatible system instruction for an Agent stage."""
-        stage_prompt = self._engine.prompt_for(node_id, self._store)
-        return f"{stage_prompt}\n\n[工作流执行规则]\n{AGENT_STAGE_INSTRUCTION}"
+        return self._require_agent_stage().role_message(node_id)

    def _router_for_node(self, node_id: str) -> WorkflowLLMRouter:
-        stage = self._engine.agent_stage_config(node_id)
-        resource_id = stage.llm_resource_id
-        cfg = self._cfg
-        resource = cfg.workflow_model_resources.get(resource_id) if cfg else None
-        if not cfg or not resource:
+        if self._agent_stage is None:
            return self._router
-        from services.pipecat.service_factory import config_with_resource
-
-        return WorkflowLLMRouter(config_with_resource(cfg, resource))
+        return self._agent_stage.router_for_node(node_id, self._router)

    async def _apply_agent_stage(self, node_id: str) -> None:
-        stage = self._engine.agent_stage_config(node_id)
-        await self._emit_node_active(node_id)
-        if self._runtime and self._runtime.set_input_enabled:
-            self._runtime.set_input_enabled(True)
-        runtime = self._require_runtime()
-        if runtime.apply_turn_config:
-            await runtime.apply_turn_config(
-                stage.enable_interrupt,
-                stage.turn_config,
-            )
-        if runtime.switch_services:
-            await runtime.switch_services(
-                stage.llm_resource_id or None,
-                stage.asr_resource_id or None,
-                stage.tts_resource_id or None,
-            )
-        if runtime.set_knowledge_scope:
-            runtime.set_knowledge_scope(
-                {
-                    "knowledge_base_id": stage.knowledge_base_id,
-                    "mode": stage.knowledge_mode,
-                    "top_n": stage.knowledge_top_n,
-                    "score_threshold": stage.knowledge_score_threshold,
-                }
-            )
+        await self._require_agent_stage().apply(node_id)

    def _agent_config(
        self,
        node_id: str,
        leading_messages: list[dict[str, str]] | None = None,
    ) -> NodeConfig:
-        data = self._engine.data(node_id)
-        entry_mode = str(data.get("entryMode") or "wait_user")
-        entry_speech = self._store.render(str(data.get("entrySpeech") or ""))
-        fixed_reply_messages: list[dict[str, str]] = (
-            [{"role": "assistant", "content": entry_speech}]
-            if entry_mode == "fixed_speech" and entry_speech
-            else []
-        )
-        strategy = (
-            ContextStrategy.RESET
-            if data.get("contextPolicy") == "fresh"
-            else ContextStrategy.APPEND
-        )
-        greeting_messages = (
-            [deepcopy(self._greeting_context_message)]
-            if strategy == ContextStrategy.RESET and self._greeting_context_message
-            else []
-        )
-        task_messages = [
-            *greeting_messages,
-            *(leading_messages or []),
-            *fixed_reply_messages,
-        ]
        stage = self._engine.agent_stage_config(node_id)
        functions: list[FlowsFunctionSchema] = []
        for tool_id in stage.tool_ids:
@@ -407,36 +333,49 @@ class WorkflowBrain(BaseBrain):
        knowledge_function = self._knowledge_function(node_id)
        if knowledge_function:
            functions.append(knowledge_function)
-        config: NodeConfig = {
-            "name": node_id,
-            "role_message": self._agent_role_message(node_id),
-            # Flows writes task_messages into the Pipecat LLM context. The
-            # pre-action below is responsible only for display, persistence,
-            # dynamic conversation history, and TTS playback.
-            "task_messages": task_messages,
-            "functions": functions,
-            "context_strategy": ContextStrategyConfig(strategy=strategy),
-            "respond_immediately": entry_mode == "generate",
-        }
-        if entry_mode == "fixed_speech":
-            config["pre_actions"] = [
-                {
-                    "type": "workflow_fixed_speech",
-                    "text": entry_speech,
-                    "node_id": node_id,
-                    "handler": self._play_fixed_speech,
-                }
-            ]
-        return config
-
-    async def _play_fixed_speech(self, action: dict, _flow_manager: FlowManager) -> None:
-        """Play and persist Agent entry speech without creating an LLM turn."""
-        await self._queue_visible_speech(
-            str(action.get("text") or ""),
-            source="workflow-fixed-reply",
-            node_id=str(action.get("node_id") or "") or None,
+        return self._require_agent_stage().node_config(
+            node_id,
+            functions=functions,
+            greeting_context_message=self._greeting_context_message,
+            leading_messages=leading_messages,
        )

+    async def _after_node_activated(
+        self,
+        node_config: NodeConfig,
+        *,
+        triggering_user_text: str = "",
+    ) -> None:
+        """Publish activation and perform exactly one Agent entry behavior."""
+        node_id = str(node_config.get("name") or "")
+        node_type = self._engine.node_type(node_id)
+        if node_type != "agent":
+            if node_type == "start":
+                self._state.enter(node_id, WorkflowStatus.WAITING_USER)
+            return
+
+        await self._emit_node_active(node_id)
+        data = self._engine.data(node_id)
+        entry_mode = str(data.get("entryMode") or "wait_user")
+        if entry_mode == "fixed_speech":
+            entry_speech = self._store.render(str(data.get("entrySpeech") or ""))
+            await self._queue_visible_speech(
+                entry_speech,
+                source="workflow-fixed-reply",
+                node_id=node_id,
+            )
+            self._state.enter(node_id, WorkflowStatus.WAITING_USER)
+            self._state.consume_user_turn()
+            return
+
+        should_run = entry_mode == "generate" or bool(triggering_user_text)
+        if should_run:
+            self._state.enter(node_id, WorkflowStatus.RUNNING_AGENT)
+            await self._require_runtime().queue_frame(LLMRunFrame())
+            return
+
+        self._state.enter(node_id, WorkflowStatus.WAITING_USER)
+
    async def _queue_visible_speech(
        self,
        text: str,
@@ -444,27 +383,11 @@ class WorkflowBrain(BaseBrain):
        source: str = "workflow-speech",
        node_id: str | None = None,
    ) -> None:
-        """Show and persist fixed workflow speech before sending it to TTS."""
-        content = text.strip()
-        if not content:
-            return
-        self._store.record("agent", content)
-        runtime = self._require_runtime()
-        transcript_message = {
-            "type": "transcript",
-            "role": "assistant",
-            "content": content,
-            "timestamp": time_now_iso8601(),
-            "source": source,
-            **({"nodeId": node_id} if node_id else {}),
-        }
-        if self._client_ready:
-            await runtime.queue_frame(
-                OutputTransportMessageUrgentFrame(message=transcript_message)
-            )
-        else:
-            self._pending_visible_speech_events.append(transcript_message)
-        await runtime.queue_frame(TTSSpeakFrame(content, append_to_context=False))
+        await self._require_output().speak(
+            text,
+            source=source,
+            node_id=node_id,
+        )

    def _passive_node_config(
        self,
@@ -486,10 +409,19 @@ class WorkflowBrain(BaseBrain):
        self._tools.register_secrets(tool)

        async def handler(args, _flow_manager):
+            transition_id = self._state.transition_id
            try:
                result = await self._tools.execute(tool, dict(args or {}))
            except ToolExecutionError as exc:
                return {"status": "error", "message": str(exc)}
+            if (
+                self._state.current_node_id != node_id
+                or self._state.transition_id != transition_id
+            ):
+                return {
+                    "status": "stale",
+                    "message": "工具完成时当前 Agent 已经切换，结果不再触发路由。",
+                }
            updated_variables = list(result.get("updated_variables") or [])
            if updated_variables:
                await self._emit_variables(
@@ -504,7 +436,22 @@ class WorkflowBrain(BaseBrain):
                    include_default=False,
                )
                if edge:
-                    return result, await self._follow_edge(edge)
+                    next_config = await self._follow_edge(
+                        edge,
+                        triggering_user_text=(
+                            self._state.pending_user_turn.text
+                            if self._state.pending_user_turn
+                            else ""
+                        ),
+                    )
+                    return result, self._flow_managed_transition_config(
+                        next_config,
+                        triggering_user_text=(
+                            self._state.pending_user_turn.text
+                            if self._state.pending_user_turn
+                            else ""
+                        ),
+                    )
            return result

        return FlowsFunctionSchema(
@@ -516,6 +463,65 @@ class WorkflowBrain(BaseBrain):
            cancel_on_interruption=True,
        )

+    def _flow_managed_transition_config(
+        self,
+        node_config: NodeConfig,
+        *,
+        triggering_user_text: str,
+    ) -> NodeConfig:
+        """Let FlowManager finish a tool result before activating its target.
+
+        Function-returned transitions are the one place where FlowManager must
+        schedule the LLM run. Its pre-action only updates our explicit runtime
+        state; it never performs a second LLM run.
+        """
+        node_id = str(node_config.get("name") or "")
+        if self._engine.node_type(node_id) != "agent":
+            return node_config
+        entry_mode = str(
+            self._engine.data(node_id).get("entryMode") or "wait_user"
+        )
+        should_run = entry_mode == "generate" or bool(triggering_user_text)
+        configured = dict(node_config)
+        configured["respond_immediately"] = (
+            should_run and entry_mode != "fixed_speech"
+        )
+        configured["pre_actions"] = [
+            {
+                "type": "workflow_function_transition_entry",
+                "node_id": node_id,
+                "entry_mode": entry_mode,
+                "should_run": should_run,
+                "handler": self._activate_from_flow_transition,
+            }
+        ]
+        return configured
+
+    async def _activate_from_flow_transition(
+        self,
+        action: dict,
+        _flow_manager: FlowManager,
+    ) -> None:
+        """Apply visible entry state without manually queueing an LLM run."""
+        node_id = str(action.get("node_id") or "")
+        await self._emit_node_active(node_id)
+        entry_mode = str(action.get("entry_mode") or "wait_user")
+        if entry_mode == "fixed_speech":
+            entry_speech = self._store.render(
+                str(self._engine.data(node_id).get("entrySpeech") or "")
+            )
+            await self._queue_visible_speech(
+                entry_speech,
+                source="workflow-fixed-reply",
+                node_id=node_id,
+            )
+            self._state.enter(node_id, WorkflowStatus.WAITING_USER)
+            self._state.consume_user_turn()
+        elif action.get("should_run"):
+            self._state.enter(node_id, WorkflowStatus.RUNNING_AGENT)
+        else:
+            self._state.enter(node_id, WorkflowStatus.WAITING_USER)
+
    def _knowledge_function(self, node_id: str) -> FlowsFunctionSchema | None:
        stage = self._engine.agent_stage_config(node_id)
        knowledge_id = str(stage.knowledge_base_id or "")
@@ -562,6 +568,7 @@ class WorkflowBrain(BaseBrain):
        *,
        triggering_user_text: str = "",
    ) -> NodeConfig:
+        self._state.begin_transition()
        leading_messages: list[dict[str, str]] = []
        speech = self._engine.edge_transition_speech(edge)
        if speech:
@@ -589,7 +596,8 @@ class WorkflowBrain(BaseBrain):
        triggering_user_text: str = "",
    ) -> NodeConfig:
        context_messages = list(leading_messages or [])
-        for _ in range(MAX_AUTOMATIC_HOPS):
+        for hop in range(MAX_AUTOMATIC_HOPS):
+            self._state.automatic_hops = hop
            node_type = self._engine.node_type(node_id)
            if node_type == "agent":
                await self._apply_agent_stage(node_id)
@@ -611,6 +619,7 @@ class WorkflowBrain(BaseBrain):
            elif node_type == "handoff":
                await self._enter_handoff(node_id)
            elif node_type == "start":
+                self._state.enter(node_id, WorkflowStatus.WAITING_USER)
                await self._emit_node_active(node_id)
            else:
                raise RuntimeError(f"工作流指向未知节点:{node_id}")
@@ -619,6 +628,7 @@ class WorkflowBrain(BaseBrain):
            edge = await self._select_edge(node_id)
            if not edge:
                return self._passive_node_config(node_id, context_messages)
+            self._state.begin_transition()
            speech = self._engine.edge_transition_speech(edge)
            if speech:
                content = self._store.render(speech).strip()
@@ -636,6 +646,7 @@ class WorkflowBrain(BaseBrain):
        raise RuntimeError("工作流连续自动跳转超过安全上限")

    async def _enter_action(self, node_id: str) -> None:
+        self._state.enter(node_id, WorkflowStatus.RUNNING_ACTION)
        await self._emit_node_active(node_id)
        data = self._engine.data(node_id)
        tool_id = str(data.get("toolId") or "")
@@ -665,6 +676,7 @@ class WorkflowBrain(BaseBrain):
            self._store.values["system__last_action_error"] = str(exc)[:2048]

    async def _enter_handoff(self, node_id: str) -> None:
+        self._state.enter(node_id, WorkflowStatus.HANDOFF)
        await self._emit_node_active(node_id)
        data = self._engine.data(node_id)
        message = self._store.render(str(data.get("message") or ""))
@@ -685,6 +697,8 @@ class WorkflowBrain(BaseBrain):

    async def _enter_end(self, node_id: str) -> None:
        self._ended = True
+        self._state.enter(node_id, WorkflowStatus.ENDED)
+        self._state.finish()
        await self._emit_node_active(node_id)
        runtime = self._require_runtime()
        if runtime.set_knowledge_scope:
@@ -705,27 +719,17 @@ class WorkflowBrain(BaseBrain):
            return
        runtime.call_end.begin("workflow_completed")
        if message:
-            runtime.call_end.arm_after_speech()
            await self._queue_visible_speech(message)
+        arm_tracked = getattr(runtime.call_end, "arm_after_tracked_speech", None)
+        if callable(arm_tracked):
+            await arm_tracked()
+        elif message:
+            runtime.call_end.arm_after_speech()
        else:
            await runtime.call_end.finish()

    async def _emit_node_active(self, node_id: str | None) -> None:
-        if node_id:
-            await self._require_runtime().queue_frame(
-                OutputTransportMessageUrgentFrame(
-                    message={"type": "node-active", "nodeId": node_id}
-                )
-            )
-
-    def _public_variables(self) -> dict[str, str | int | float | bool]:
-        """Return the browser-safe part of this session's variable state."""
-        return {
-            name: value
-            for name, value in self._store.values.items()
-            if not name.startswith(("system__", "secret__"))
-            and isinstance(value, (str, int, float, bool))
-        }
+        await self._require_output().emit_node_active(node_id)

    async def _emit_variables(
        self,
@@ -735,21 +739,10 @@ class WorkflowBrain(BaseBrain):
        changed: list[str] | None = None,
    ) -> None:
        """Publish a safe snapshot so Workflow debug mirrors runtime state."""
-        message: dict[str, Any] = {
-            "type": "workflow-variables",
-            "reason": reason,
-            "variables": self._public_variables(),
-        }
-        if node_id:
-            message["nodeId"] = node_id
-        if changed:
-            message["changed"] = [
-                name
-                for name in changed
-                if not name.startswith(("system__", "secret__"))
-            ]
-        await self._require_runtime().queue_frame(
-            OutputTransportMessageUrgentFrame(message=message)
+        await self._require_output().emit_variables(
+            reason=reason,
+            node_id=node_id,
+            changed=changed,
        )

    def _require_runtime(self) -> BrainRuntime:
@@ -761,3 +754,20 @@ class WorkflowBrain(BaseBrain):
        if self._manager is None:
            raise RuntimeError("Workflow FlowManager 尚未初始化")
        return self._manager
+
+    def _require_output(self) -> WorkflowOutput:
+        if self._output is None:
+            # A few focused unit tests bind BrainRuntime directly. Lazily create
+            # the output adapter so those tests exercise the same production path.
+            self._output = WorkflowOutput(self._store, self._require_runtime())
+        return self._output
+
+    def _require_agent_stage(self) -> WorkflowAgentStage:
+        if self._agent_stage is None:
+            self._agent_stage = WorkflowAgentStage(
+                cfg=self._cfg or AssistantConfig(type="workflow"),
+                engine=self._engine,
+                store=self._store,
+                runtime=self._require_runtime(),
+            )
+        return self._agent_stage
--- a/backend/services/node_specs.py
+++ b/backend/services/node_specs.py
@@ -73,7 +73,7 @@ NODE_SPECS: list[dict[str, Any]] = [
        "icon": "Zap",
        "accent": "peach",
        "addable": True,
-        "constraints": {"minIncoming": 1, "minOutgoing": 1},
+        "constraints": {"minIncoming": 1, "minOutgoing": 0},
        "fields": [
            {"key": "name", "label": "节点名称", "type": "text", "default": "Action"},
        ],
@@ -352,18 +352,19 @@ def validate_graph(graph: dict[str, Any]) -> list[str]:
        if mode == "expression":
            expression_errors = _validate_expression(data.get("expression"))
            errors.extend(f"边 {edge_id}:{item}" for item in expression_errors)
-        try:
-            priority = int(data.get("priority", 10))
-        except (TypeError, ValueError):
-            errors.append(f"边 {edge_id} 的优先级必须是整数")
-            priority = 10
-        if priority in priorities[source_id]:
-            errors.append(f"节点 {source_id} 的出边优先级不能重复:{priority}")
-        priorities[source_id].add(priority)
        if mode == "always":
            always_counts[source_id] += 1
            if always_counts[source_id] > 1:
                errors.append(f"节点 {source_id} 最多只能有一条默认路径")
+        else:
+            try:
+                priority = int(data.get("priority", 10))
+            except (TypeError, ValueError):
+                errors.append(f"边 {edge_id} 的优先级必须是整数")
+                priority = 10
+            if priority in priorities[source_id]:
+                errors.append(f"节点 {source_id} 的条件边优先级不能重复:{priority}")
+            priorities[source_id].add(priority)
        incoming[target_id] += 1
        outgoing[source_id] += 1
        adj[source_id].append(target_id)
@@ -387,6 +388,15 @@ def validate_graph(graph: dict[str, Any]) -> list[str]:
                errors.append(f"节点 {node_id} 的{label}不能少于 {lo}")
            if hi is not None and actual > hi:
                errors.append(f"节点 {node_id} 的{label}不能多于 {hi}")
+        if (
+            node.get("type") == "agent"
+            and outgoing[node_id] == 1
+            and always_counts[node_id] == 1
+        ):
+            errors.append(
+                f"Agent 节点 {node_id} 不能只有默认路径；"
+                "请改为条件路径，或删除该路径以持续对话"
+            )
    start_id = next(
        (node_id for node_id, node in node_by_id.items() if node.get("type") == "start"),
        None,
--- a/backend/services/pipecat/call_lifecycle.py
+++ b/backend/services/pipecat/call_lifecycle.py
@@ -18,6 +18,8 @@ class CallEndCoordinator:
        self._armed = False
        self._speaking = False
        self._response_speech_started = False
+        self._tracked_speeches = 0
+        self._finish_after_tracked_speech = False
        self._finished = False
        self._reason = "completed"

@@ -37,6 +39,16 @@ 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."""
+        self._tracked_speeches += 1
+
+    async def arm_after_tracked_speech(self) -> None:
+        """Finish after every already queued fixed utterance has played."""
+        self._finish_after_tracked_speech = True
+        if self._tracked_speeches == 0:
+            await self.finish()
+
    async def finish_after_current_speech(self, *, has_text: bool) -> None:
        """Finish now if speech is absent/done, otherwise wait for its stop."""
        if not has_text:
@@ -59,7 +71,15 @@ class CallEndCoordinator:
            self._response_speech_started = True
        elif isinstance(frame, BotStoppedSpeakingFrame) and self._speaking:
            self._speaking = False
-            if self._armed:
+            if self._tracked_speeches > 0:
+                self._tracked_speeches -= 1
+            if (
+                self._finish_after_tracked_speech
+                and self._tracked_speeches == 0
+            ):
+                logger.info("所有工作流结束语播报完毕,挂断通话")
+                await self.finish()
+            elif self._armed:
                logger.info("结束语播报完毕,挂断通话")
                await self.finish()

--- a/backend/services/workflow/init.py
+++ b/backend/services/workflow/init.py
@@ -0,0 +1,19 @@
+"""Small, explicit building blocks for the local Workflow runtime."""
+
+from services.workflow.models import (
+    EdgeEvaluation,
+    LLMRouteResult,
+    RouteStatus,
+    UserTurn,
+    WorkflowRuntimeState,
+    WorkflowStatus,
+)
+
+__all__ = [
+    "EdgeEvaluation",
+    "LLMRouteResult",
+    "RouteStatus",
+    "UserTurn",
+    "WorkflowRuntimeState",
+    "WorkflowStatus",
+]
--- a/backend/services/workflow/agent.py
+++ b/backend/services/workflow/agent.py
@@ -0,0 +1,134 @@
+"""Resolve and apply one Workflow Agent's complete stage configuration."""
+
+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
+from pipecat.services.settings import LLMSettings
+
+from services.brains.base import BrainRuntime
+from services.runtime_variables import DynamicVariableStore
+from services.workflow_engine import WorkflowEngine
+from services.workflow_router import WorkflowLLMRouter
+
+
+AGENT_STAGE_INSTRUCTION = (
+    "工作流路由已在用户一轮输入结束时完成。只执行当前阶段任务，"
+    "不要自行解释、模拟或宣布节点切换。"
+)
+
+
+class WorkflowAgentStage:
+    """Translate graph-level Agent data into Pipecat Flows configuration."""
+
+    def __init__(
+        self,
+        *,
+        cfg: AssistantConfig,
+        engine: WorkflowEngine,
+        store: DynamicVariableStore,
+        runtime: BrainRuntime,
+    ) -> None:
+        self._cfg = cfg
+        self._engine = engine
+        self._store = store
+        self._runtime = runtime
+
+    def role_message(self, node_id: str) -> str:
+        stage_prompt = self._engine.prompt_for(node_id, self._store)
+        return (
+            f"{stage_prompt}\n\n[工作流执行规则]\n"
+            f"{AGENT_STAGE_INSTRUCTION}"
+        )
+
+    async def refresh_prompt(self, node_id: str) -> None:
+        await self._runtime.queue_frame(
+            LLMUpdateSettingsFrame(
+                delta=LLMSettings(
+                    system_instruction=self.role_message(node_id)
+                )
+            )
+        )
+
+    def router_for_node(
+        self,
+        node_id: str,
+        default_router: WorkflowLLMRouter,
+    ) -> WorkflowLLMRouter:
+        resource_id = self._engine.agent_stage_config(node_id).llm_resource_id
+        resource = self._cfg.workflow_model_resources.get(resource_id)
+        if not resource:
+            return default_router
+        from services.pipecat.service_factory import config_with_resource
+
+        return WorkflowLLMRouter(config_with_resource(self._cfg, resource))
+
+    async def apply(self, node_id: str) -> None:
+        stage = self._engine.agent_stage_config(node_id)
+        if self._runtime.set_input_enabled:
+            self._runtime.set_input_enabled(True)
+        if self._runtime.apply_turn_config:
+            await self._runtime.apply_turn_config(
+                stage.enable_interrupt,
+                stage.turn_config,
+            )
+        if self._runtime.switch_services:
+            await self._runtime.switch_services(
+                stage.llm_resource_id or None,
+                stage.asr_resource_id or None,
+                stage.tts_resource_id or None,
+            )
+        if self._runtime.set_knowledge_scope:
+            self._runtime.set_knowledge_scope(
+                {
+                    "knowledge_base_id": stage.knowledge_base_id,
+                    "mode": stage.knowledge_mode,
+                    "top_n": stage.knowledge_top_n,
+                    "score_threshold": stage.knowledge_score_threshold,
+                }
+            )
+
+    def node_config(
+        self,
+        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)
+        entry_mode = str(data.get("entryMode") or "wait_user")
+        entry_speech = self._store.render(str(data.get("entrySpeech") or ""))
+        strategy = (
+            ContextStrategy.RESET
+            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
+            else []
+        )
+        return {
+            "name": node_id,
+            "role_message": self.role_message(node_id),
+            "task_messages": [
+                *greeting_messages,
+                *(leading_messages or []),
+                *fixed_reply_messages,
+            ],
+            "functions": functions,
+            "context_strategy": ContextStrategyConfig(strategy=strategy),
+            # Direct node activations let WorkflowRuntime decide whether to run
+            # the LLM. A Pipecat function transition may explicitly override
+            # this because FlowManager must coordinate the tool result first.
+            "respond_immediately": False,
+        }
--- a/backend/services/workflow/models.py
+++ b/backend/services/workflow/models.py
@@ -0,0 +1,92 @@
+"""Readable runtime values shared by Workflow orchestration modules."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import StrEnum
+from typing import Any
+
+
+class WorkflowStatus(StrEnum):
+    """The small set of states useful to operators and future debug tooling."""
+
+    STARTING = "starting"
+    WAITING_USER = "waiting_user"
+    ROUTING = "routing"
+    RUNNING_AGENT = "running_agent"
+    RUNNING_ACTION = "running_action"
+    HANDOFF = "handoff"
+    ENDED = "ended"
+
+
+class RouteStatus(StrEnum):
+    """A routing error is deliberately different from a valid no-match."""
+
+    MATCHED = "matched"
+    NO_MATCH = "no_match"
+    ERROR = "error"
+
+
+@dataclass(frozen=True)
+class UserTurn:
+    """One committed user turn that may cross automatic Workflow nodes."""
+
+    id: int
+    text: str
+
+
+@dataclass
+class WorkflowRuntimeState:
+    """Mutable per-call state; graph definitions remain immutable."""
+
+    current_node_id: str
+    status: WorkflowStatus = WorkflowStatus.STARTING
+    pending_user_turn: UserTurn | None = None
+    transition_id: int = 0
+    automatic_hops: int = 0
+    ended: bool = False
+    _next_turn_id: int = 1
+
+    def begin_user_turn(self, text: str) -> UserTurn:
+        turn = UserTurn(id=self._next_turn_id, text=text)
+        self._next_turn_id += 1
+        self.pending_user_turn = turn
+        self.automatic_hops = 0
+        return turn
+
+    def enter(self, node_id: str, status: WorkflowStatus) -> None:
+        self.current_node_id = node_id
+        self.status = status
+
+    def begin_transition(self) -> int:
+        self.transition_id += 1
+        return self.transition_id
+
+    def consume_user_turn(self) -> UserTurn | None:
+        turn = self.pending_user_turn
+        self.pending_user_turn = None
+        return turn
+
+    def finish(self) -> None:
+        self.ended = True
+        self.status = WorkflowStatus.ENDED
+        self.pending_user_turn = None
+
+
+@dataclass(frozen=True)
+class LLMRouteResult:
+    """Control-plane result returned by the small routing LLM."""
+
+    status: RouteStatus
+    function_name: str | None = None
+    error: str | None = None
+
+
+@dataclass(frozen=True)
+class EdgeEvaluation:
+    """Final graph-level decision after expression, LLM and default handling."""
+
+    status: RouteStatus
+    edge: dict[str, Any] | None = None
+    error: str | None = None
+
--- a/backend/services/workflow/output.py
+++ b/backend/services/workflow/output.py
@@ -0,0 +1,119 @@
+"""Client-visible Workflow output and fixed speech in one place."""
+
+from __future__ import annotations
+
+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 WorkflowOutput:
+    """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})
+
+    async def emit_variables(
+        self,
+        *,
+        reason: str,
+        node_id: str | None,
+        changed: list[str] | None = None,
+    ) -> None:
+        message: dict[str, Any] = {
+            "type": "workflow-variables",
+            "reason": reason,
+            "variables": self.public_variables(),
+        }
+        if node_id:
+            message["nodeId"] = node_id
+        if changed:
+            message["changed"] = [
+                name
+                for name in changed
+                if not name.startswith(("system__", "secret__"))
+            ]
+        await self.emit(message)
+
+    async def emit_error(
+        self,
+        message: str,
+        *,
+        node_id: str | None,
+        code: str = "workflow_runtime_error",
+    ) -> None:
+        payload: dict[str, Any] = {
+            "type": "workflow-error",
+            "code": code,
+            "message": message,
+        }
+        if node_id:
+            payload["nodeId"] = node_id
+        await self.emit(payload)
+
+    def public_variables(self) -> dict[str, str | int | float | bool]:
+        return {
+            name: value
+            for name, value in self._store.values.items()
+            if not name.startswith(("system__", "secret__"))
+            and isinstance(value, (str, int, float, bool))
+        }
+
+    async def emit(self, message: dict[str, Any]) -> None:
+        await self._runtime.queue_frame(
+            OutputTransportMessageUrgentFrame(message=message)
+        )
--- a/backend/services/workflow/routing.py
+++ b/backend/services/workflow/routing.py
@@ -0,0 +1,97 @@
+"""Priority-aware Workflow edge evaluation."""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+
+from services.runtime_variables import DynamicVariableStore
+from services.workflow.models import EdgeEvaluation, RouteStatus
+from services.workflow_engine import WorkflowEngine
+from services.workflow_router import WorkflowLLMRouter
+
+
+class WorkflowEdgeEvaluator:
+    """Evaluate one node's paths without changing runtime state."""
+
+    def __init__(
+        self,
+        engine: WorkflowEngine,
+        store: DynamicVariableStore,
+        router_for_node: Callable[[str], WorkflowLLMRouter],
+    ) -> None:
+        self._engine = engine
+        self._store = store
+        self._router_for_node = router_for_node
+
+    async def evaluate(self, node_id: str) -> EdgeEvaluation:
+        """Select the first matching conditional path, then the default path."""
+        outgoing = self._engine.outgoing(node_id)
+        expression_edge = self._engine.deterministic_edge(
+            node_id,
+            self._store,
+            include_default=False,
+        )
+        default_edge = next(
+            (
+                edge
+                for edge in outgoing
+                if self._engine.edge_mode(edge) == "always"
+            ),
+            None,
+        )
+        llm_edges = [
+            edge for edge in outgoing if self._engine.edge_mode(edge) == "llm"
+        ]
+
+        # A matching expression is a priority boundary. A later LLM condition
+        # cannot bypass it, while an earlier LLM condition still gets one chance.
+        if expression_edge:
+            expression_index = outgoing.index(expression_edge)
+            llm_edges = [
+                edge
+                for edge in llm_edges
+                if outgoing.index(edge) < expression_index
+            ]
+
+        if not llm_edges:
+            return self._matched_or_no_match(expression_edge or default_edge)
+
+        result = await self._router_for_node(node_id).select_edge(
+            node_name=self._engine.name(node_id),
+            node_prompt=self._engine.routing_prompt(node_id, self._store),
+            edges=llm_edges,
+            history=self._store.history,
+            variables={
+                key: value
+                for key, value in self._store.values.items()
+                if not key.startswith(("system__", "secret__"))
+            },
+            edge_name=self._engine.edge_fn_name,
+            edge_description=self._engine.edge_description,
+        )
+        if result.status == RouteStatus.ERROR:
+            return EdgeEvaluation(status=RouteStatus.ERROR, error=result.error)
+        if result.status == RouteStatus.NO_MATCH:
+            return self._matched_or_no_match(expression_edge or default_edge)
+
+        selected = next(
+            (
+                edge
+                for edge in llm_edges
+                if self._engine.edge_fn_name(edge) == result.function_name
+            ),
+            None,
+        )
+        if selected is None:
+            return EdgeEvaluation(
+                status=RouteStatus.ERROR,
+                error="路由模型返回了不属于当前节点的连接",
+            )
+        return EdgeEvaluation(status=RouteStatus.MATCHED, edge=selected)
+
+    @staticmethod
+    def _matched_or_no_match(edge: dict | None) -> EdgeEvaluation:
+        if edge is None:
+            return EdgeEvaluation(status=RouteStatus.NO_MATCH)
+        return EdgeEvaluation(status=RouteStatus.MATCHED, edge=edge)
+
--- a/backend/services/workflow_engine.py
+++ b/backend/services/workflow_engine.py
@@ -177,6 +177,11 @@ class WorkflowEngine:
            try:
                if operator == "exists":
                    matched = exists if expected is not False else not exists
+                elif not exists:
+                    # Missing values are never equal, unequal, greater or
+                    # contained. Use the explicit ``exists`` operator when the
+                    # distinction between missing and present is important.
+                    matched = False
                elif operator == "eq":
                    matched = actual == expected
                elif operator == "neq":
--- a/backend/services/workflow_router.py
+++ b/backend/services/workflow_router.py
@@ -15,6 +15,8 @@ from loguru import logger
 from models import AssistantConfig
 from openai import AsyncOpenAI

+from services.workflow.models import LLMRouteResult, RouteStatus
+

 STAY_ON_CURRENT_NODE = "workflow_stay_on_current_node"
 # Compatibility alias for callers saved before all source nodes supported LLM edges.
@@ -38,10 +40,10 @@ class WorkflowLLMRouter:
        variables: dict[str, Any],
        edge_name: Callable[[dict[str, Any]], str],
        edge_description: Callable[[dict[str, Any]], str],
-    ) -> str | None:
-        """Return an edge function name, STAY, or None when routing failed."""
+    ) -> LLMRouteResult:
+        """Return a typed match, no-match or technical error."""
        if not edges:
-            return STAY_ON_CURRENT_NODE
+            return LLMRouteResult(status=RouteStatus.NO_MATCH)

        names = {edge_name(edge) for edge in edges}
        stay_name = STAY_ON_CURRENT_NODE
@@ -116,16 +118,28 @@ class WorkflowLLMRouter:
            tool_calls = response.choices[0].message.tool_calls or []
            if not tool_calls:
                logger.warning("Workflow 路由 LLM 未返回函数调用，留在当前节点")
-                return STAY_ON_CURRENT_NODE
+                return LLMRouteResult(
+                    status=RouteStatus.ERROR,
+                    error="路由模型没有返回函数调用",
+                )
            selected = str(tool_calls[0].function.name or "")
            if selected == stay_name:
-                return STAY_ON_CURRENT_NODE
+                return LLMRouteResult(status=RouteStatus.NO_MATCH)
            if selected not in names:
                logger.warning(f"Workflow 路由 LLM 返回未知函数:{selected}")
-                return STAY_ON_CURRENT_NODE
-            return selected
+                return LLMRouteResult(
+                    status=RouteStatus.ERROR,
+                    error=f"路由模型返回未知函数:{selected}",
+                )
+            return LLMRouteResult(
+                status=RouteStatus.MATCHED,
+                function_name=selected,
+            )
        except Exception as exc:  # noqa: BLE001 - routing failure must not end the call
            logger.warning(f"Workflow LLM 边判断失败，留在当前节点:{exc}")
-            return None
+            return LLMRouteResult(
+                status=RouteStatus.ERROR,
+                error=f"大模型路由失败:{type(exc).__name__}",
+            )
        finally:
            await client.close()
--- a/backend/tests/test_brains.py
+++ b/backend/tests/test_brains.py
@@ -28,7 +28,7 @@ from services.brains.dify_llm import (
 )
 from services.brains.workflow_brain import WorkflowBrain
 from services.runtime_variables import prepare_dynamic_config
-from services.workflow_router import STAY_ON_CURRENT_NODE
+from services.workflow.models import LLMRouteResult, RouteStatus


 class FakeLLM:
@@ -47,6 +47,7 @@ class FakeCallEnd:
        self.finished = False
        self.response_started = False
        self.waited_for_text: bool | None = None
+        self.tracked_speeches = 0

    def begin(self, reason: str) -> None:
        self.ending = True
@@ -58,6 +59,14 @@ class FakeCallEnd:
    def arm_after_speech(self) -> None:
        self.armed = True

+    def track_speech(self) -> None:
+        self.tracked_speeches += 1
+
+    async def arm_after_tracked_speech(self) -> None:
+        self.armed = True
+        if self.tracked_speeches == 0:
+            await self.finish()
+
    async def finish_after_current_speech(self, *, has_text: bool) -> None:
        self.waited_for_text = has_text
        if has_text:
@@ -824,7 +833,10 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):

            async def select_edge(self, **_kwargs):
                self.calls += 1
-                return "goto_eat"
+                return LLMRouteResult(
+                    status=RouteStatus.MATCHED,
+                    function_name="goto_eat",
+                )

        manager = FakeManager()
        router = FakeRouter()
@@ -847,6 +859,45 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
        self.assertTrue(any(isinstance(frame, LLMRunFrame) for frame in queued))
        self.assertIn("我想吃饭", brain._store.values["system__conversation_history"])

+    async def test_start_expression_condition_also_waits_for_user_turn(self):
+        brain = WorkflowBrain(
+            {
+                "specVersion": 3,
+                "settings": {},
+                "nodes": [
+                    {"id": "start", "type": "start", "data": {}},
+                    {"id": "agent", "type": "agent", "data": {}},
+                ],
+                "edges": [
+                    {
+                        "id": "route",
+                        "source": "start",
+                        "target": "agent",
+                        "data": {
+                            "mode": "expression",
+                            "priority": 10,
+                            "expression": {
+                                "combinator": "and",
+                                "rules": [
+                                    {
+                                        "variable": "route",
+                                        "operator": "eq",
+                                        "value": "agent",
+                                    }
+                                ],
+                            },
+                        },
+                    }
+                ],
+            }
+        )
+        brain._store.values["route"] = "agent"
+
+        config = await brain._initial_node_config()
+
+        self.assertEqual(config["name"], "start")
+        self.assertEqual(brain._state.status.value, "waiting_user")
+
    async def test_automatic_node_can_follow_llm_condition(self):
        brain = WorkflowBrain(
            {
@@ -896,7 +947,10 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
        class FakeRouter:
            async def select_edge(self, **kwargs):
                self.node_name = kwargs["node_name"]
-                return "goto_to_agent"
+                return LLMRouteResult(
+                    status=RouteStatus.MATCHED,
+                    function_name="goto_to_agent",
+                )

        router = FakeRouter()
        brain._router = router
@@ -961,7 +1015,7 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):

        class FakeRouter:
            def __init__(self):
-                self.result = STAY_ON_CURRENT_NODE
+                self.result = LLMRouteResult(status=RouteStatus.NO_MATCH)
                self.edge_ids = []

            async def select_edge(self, **kwargs):
@@ -975,7 +1029,10 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
        self.assertEqual(router.edge_ids, ["llm"])
        self.assertEqual(selected["id"], "expression")

-        router.result = "goto_llm"
+        router.result = LLMRouteResult(
+            status=RouteStatus.MATCHED,
+            function_name="goto_llm",
+        )
        selected = await brain._select_edge("agent")
        self.assertEqual(selected["id"], "llm")

@@ -1183,21 +1240,19 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):

        brain._engine.data("agent")["entryMode"] = "generate"
        generate_config = brain._agent_config("agent")
-        self.assertTrue(generate_config["respond_immediately"])
+        self.assertFalse(generate_config["respond_immediately"])
        worker.frames.clear()
        await brain._manager.set_node_from_config(generate_config)
-        self.assertTrue(any(isinstance(frame, LLMRunFrame) for frame in worker.frames))
+        self.assertFalse(any(isinstance(frame, LLMRunFrame) for frame in worker.frames))
+        await brain._after_node_activated(generate_config)
+        self.assertTrue(any(isinstance(frame, LLMRunFrame) for frame in queued))

        brain._engine.data("agent").update(
            {"entryMode": "fixed_speech", "entrySpeech": "您好，{{user_name}}"}
        )
        fixed_config = brain._agent_config("agent")
        self.assertFalse(fixed_config["respond_immediately"])
-        self.assertEqual(
-            fixed_config["pre_actions"][0]["type"],
-            "workflow_fixed_speech",
-        )
-        self.assertEqual(fixed_config["pre_actions"][0]["text"], "您好，王先生")
+        self.assertNotIn("pre_actions", fixed_config)
        self.assertEqual(
            fixed_config["task_messages"],
            [
@@ -1216,10 +1271,10 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
                {"role": "assistant", "content": "您好，王先生"},
            ],
        )
-        self.assertEqual(fixed_config["pre_actions"][0]["node_id"], "agent")
        worker.frames.clear()
        queued.clear()
        await brain._manager.set_node_from_config(fixed_config)
+        await brain._after_node_activated(fixed_config)
        self.assertTrue(any(isinstance(frame, TTSSpeakFrame) for frame in queued))
        self.assertFalse(any(isinstance(frame, LLMRunFrame) for frame in worker.frames))
        context_updates = [
@@ -1263,7 +1318,10 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):

        class FakeRouter:
            async def select_edge(self, **_kwargs):
-                return "goto_finish"
+                return LLMRouteResult(
+                    status=RouteStatus.MATCHED,
+                    function_name="goto_finish",
+                )

        brain._router = FakeRouter()
        handled = await brain.on_user_turn_end("我的需求已经说完了")
--- a/backend/tests/test_call_lifecycle.py
+++ b/backend/tests/test_call_lifecycle.py
@@ -44,6 +44,20 @@ 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()
+        self.coordinator.begin("workflow_completed")
+        await self.coordinator.arm_after_tracked_speech()
+
+        await self.coordinator.observe(BotStartedSpeakingFrame())
+        await self.coordinator.observe(BotStoppedSpeakingFrame())
+        self.assertEqual(self.reasons, [])
+
+        await self.coordinator.observe(BotStartedSpeakingFrame())
+        await self.coordinator.observe(BotStoppedSpeakingFrame())
+        self.assertEqual(self.reasons, ["workflow_completed"])
+

 if __name__ == "__main__":
    unittest.main()
--- a/backend/tests/test_workflow_router.py
+++ b/backend/tests/test_workflow_router.py
@@ -5,6 +5,7 @@ from types import SimpleNamespace
 from unittest.mock import patch

 from models import AssistantConfig
+from services.workflow.models import RouteStatus
 from services.workflow_router import WorkflowLLMRouter


@@ -62,7 +63,8 @@ class WorkflowLLMRouterTest(unittest.IsolatedAsyncioTestCase):
                edge_description=lambda _edge: "用户已经回答姓名",
            )

-        self.assertEqual(selected, "goto_age")
+        self.assertEqual(selected.status, RouteStatus.MATCHED)
+        self.assertEqual(selected.function_name, "goto_age")
        self.assertEqual(requests[0]["tool_choice"], "required")
        self.assertEqual(
            [message["role"] for message in requests[0]["messages"]],
--- a/backend/tests/test_workflow_routing.py
+++ b/backend/tests/test_workflow_routing.py
@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+import unittest
+
+from services.runtime_variables import DynamicVariableStore
+from services.workflow.models import LLMRouteResult, RouteStatus
+from services.workflow.routing import WorkflowEdgeEvaluator
+from services.workflow_engine import WorkflowEngine
+
+
+def routing_graph() -> dict:
+    return {
+        "specVersion": 3,
+        "settings": {},
+        "nodes": [
+            {"id": "start", "type": "start", "data": {}},
+            {"id": "agent", "type": "agent", "data": {"name": "Agent"}},
+            {"id": "matched", "type": "end", "data": {}},
+            {"id": "fallback", "type": "end", "data": {}},
+        ],
+        "edges": [
+            {
+                "id": "condition",
+                "source": "agent",
+                "target": "matched",
+                "data": {
+                    "mode": "llm",
+                    "priority": 10,
+                    "condition": "用户要求结束",
+                },
+            },
+            {
+                "id": "default",
+                "source": "agent",
+                "target": "fallback",
+                "data": {"mode": "always", "priority": 10},
+            },
+        ],
+    }
+
+
+class WorkflowEdgeEvaluatorTest(unittest.IsolatedAsyncioTestCase):
+    async def test_router_error_does_not_take_default_path(self):
+        class ErrorRouter:
+            async def select_edge(self, **_kwargs):
+                return LLMRouteResult(
+                    status=RouteStatus.ERROR,
+                    error="provider unavailable",
+                )
+
+        evaluator = WorkflowEdgeEvaluator(
+            WorkflowEngine(routing_graph()),
+            DynamicVariableStore({}),
+            lambda _node_id: ErrorRouter(),
+        )
+
+        decision = await evaluator.evaluate("agent")
+
+        self.assertEqual(decision.status, RouteStatus.ERROR)
+        self.assertIsNone(decision.edge)
+
+    async def test_explicit_no_match_takes_default_path(self):
+        class NoMatchRouter:
+            async def select_edge(self, **_kwargs):
+                return LLMRouteResult(status=RouteStatus.NO_MATCH)
+
+        evaluator = WorkflowEdgeEvaluator(
+            WorkflowEngine(routing_graph()),
+            DynamicVariableStore({}),
+            lambda _node_id: NoMatchRouter(),
+        )
+
+        decision = await evaluator.evaluate("agent")
+
+        self.assertEqual(decision.status, RouteStatus.MATCHED)
+        self.assertEqual(decision.edge["id"], "default")
+
+
+if __name__ == "__main__":
+    unittest.main()
--- a/backend/tests/test_workflow_v3.py
+++ b/backend/tests/test_workflow_v3.py
@@ -211,7 +211,7 @@ class WorkflowGraphTests(unittest.TestCase):
            "smart_turn",
        )

-    def test_start_agent_and_handoff_may_have_no_outgoing_edge(self):
+    def test_start_agent_action_and_handoff_may_have_no_outgoing_edge(self):
        terminal_graphs = [
            {
                "specVersion": 3,
@@ -277,13 +277,47 @@ class WorkflowGraphTests(unittest.TestCase):
                }
            ],
        }
+        self.assertEqual(validate_graph(action_without_exit), [])
+
+    def test_agent_cannot_have_only_one_default_path(self):
+        graph = {
+            "specVersion": 3,
+            "settings": {},
+            "nodes": [
+                {"id": "start", "type": "start", "data": {}},
+                {"id": "agent", "type": "agent", "data": {}},
+                {"id": "end", "type": "end", "data": {}},
+            ],
+            "edges": [
+                {
+                    "id": "begin",
+                    "source": "start",
+                    "target": "agent",
+                    "data": {"mode": "always", "priority": 0},
+                },
+                {
+                    "id": "leave",
+                    "source": "agent",
+                    "target": "end",
+                    "data": {"mode": "always", "priority": 0},
+                },
+            ],
+        }
+
        self.assertTrue(
            any(
-                "action 的出边不能少于 1" in error
-                for error in validate_graph(action_without_exit)
+                "Agent 节点 agent 不能只有默认路径" in error
+                for error in validate_graph(graph)
            )
        )

+        graph["edges"][1]["data"] = {
+            "mode": "llm",
+            "priority": 10,
+            "condition": "当前阶段已经完成",
+        }
+        self.assertEqual(validate_graph(graph), [])
+
    def test_all_source_nodes_allow_multiple_conditional_paths(self):
        expression = {
            "combinator": "and",
@@ -448,6 +482,41 @@ class WorkflowGraphTests(unittest.TestCase):
        self.assertNotIn("服务 王先生", custom_prompt)
        self.assertIn("处理订单", custom_prompt)

+    def test_missing_variable_only_matches_explicit_exists_rule(self):
+        engine = WorkflowEngine(valid_graph())
+        values = {}
+
+        self.assertFalse(
+            engine.expression_matches(
+                {
+                    "combinator": "and",
+                    "rules": [
+                        {
+                            "variable": "order_status",
+                            "operator": "neq",
+                            "value": "paid",
+                        }
+                    ],
+                },
+                values,
+            )
+        )
+        self.assertTrue(
+            engine.expression_matches(
+                {
+                    "combinator": "and",
+                    "rules": [
+                        {
+                            "variable": "order_status",
+                            "operator": "exists",
+                            "value": False,
+                        }
+                    ],
+                },
+                values,
+            )
+        )
+

 if __name__ == "__main__":
    unittest.main()
--- a/frontend/src/components/assistant-editor/use-workflow-editor-state.ts
+++ b/frontend/src/components/assistant-editor/use-workflow-editor-state.ts
@@ -1,14 +1,19 @@
 "use client";

-import { useState } from "react";
+import { useCallback, useMemo, useState } from "react";

 import type { WorkflowSettings } from "@/components/workflow/types";
 import { defaultGraph, type WorkflowGraph } from "@/components/workflow/specs";
 import type { DynamicVariableDefinition } from "@/lib/api";
-import { defaultTurnConfig } from "@/lib/turn-config";

-function initialWorkflowSettings(): WorkflowSettings {
-  const graph = defaultGraph();
+type WorkflowPanel =
+  | { type: "debug" }
+  | { type: "settings" }
+  | { type: "node"; nodeId: string }
+  | { type: "edge"; edgeId: string }
+  | null;
+
+function settingsFromGraph(graph: WorkflowGraph): WorkflowSettings {
  return {
    globalPrompt: graph.settings.globalPrompt,
    llm: graph.settings.defaultLlmResourceId,
@@ -21,8 +26,8 @@ function initialWorkflowSettings(): WorkflowSettings {
      topN: graph.settings.knowledgeTopN,
      scoreThreshold: graph.settings.knowledgeScoreThreshold,
    },
-    allowInterrupt: true,
-    turnConfig: defaultTurnConfig(),
+    allowInterrupt: graph.settings.enableInterrupt,
+    turnConfig: graph.settings.turnConfig,
  };
 }

@@ -32,19 +37,67 @@ export function useWorkflowEditorState() {
  const [workflowGraph, setWorkflowGraph] = useState<WorkflowGraph>(() =>
    defaultGraph(),
  );
-  const [workflowSettings, setWorkflowSettings] = useState<WorkflowSettings>(
-    initialWorkflowSettings,
+  const workflowSettings = useMemo(
+    () => settingsFromGraph(workflowGraph),
+    [workflowGraph],
  );
+  const setWorkflowSettings = useCallback((settings: WorkflowSettings) => {
+    setWorkflowGraph((graph) => ({
+      ...graph,
+      settings: {
+        globalPrompt: settings.globalPrompt,
+        defaultLlmResourceId: settings.llm ?? "",
+        defaultAsrResourceId: settings.asr ?? "",
+        defaultTtsResourceId: settings.tts ?? "",
+        toolIds: settings.toolIds,
+        knowledgeBaseId: settings.knowledgeBaseId,
+        knowledgeMode: settings.knowledgeRetrievalConfig.mode,
+        knowledgeTopN: settings.knowledgeRetrievalConfig.topN,
+        knowledgeScoreThreshold:
+          settings.knowledgeRetrievalConfig.scoreThreshold,
+        enableInterrupt: settings.allowInterrupt,
+        turnConfig: settings.turnConfig,
+      },
+    }));
+  }, []);
  const [workflowDynamicVariableDefinitions, setWorkflowDynamicVariableDefinitions] =
    useState<Record<string, DynamicVariableDefinition>>({});
-  const [workflowDebugOpen, setWorkflowDebugOpen] = useState(false);
-  const [workflowSettingsOpen, setWorkflowSettingsOpen] = useState(false);
-  const [workflowEditingNodeId, setWorkflowEditingNodeId] = useState<string | null>(
-    null,
-  );
-  const [workflowEditingEdgeId, setWorkflowEditingEdgeId] = useState<string | null>(
-    null,
-  );
+  const [workflowPanel, setWorkflowPanel] = useState<WorkflowPanel>(null);
+  const workflowDebugOpen = workflowPanel?.type === "debug";
+  const workflowSettingsOpen = workflowPanel?.type === "settings";
+  const workflowEditingNodeId =
+    workflowPanel?.type === "node" ? workflowPanel.nodeId : null;
+  const workflowEditingEdgeId =
+    workflowPanel?.type === "edge" ? workflowPanel.edgeId : null;
+
+  const setWorkflowDebugOpen = useCallback((open: boolean) => {
+    setWorkflowPanel((panel) =>
+      open ? { type: "debug" } : panel?.type === "debug" ? null : panel,
+    );
+  }, []);
+  const setWorkflowSettingsOpen = useCallback((open: boolean) => {
+    setWorkflowPanel((panel) =>
+      open ? { type: "settings" } : panel?.type === "settings" ? null : panel,
+    );
+  }, []);
+  const setWorkflowEditingNodeId = useCallback((nodeId: string | null) => {
+    setWorkflowPanel((panel) =>
+      nodeId
+        ? { type: "node", nodeId }
+        : panel?.type === "node"
+          ? null
+          : panel,
+    );
+  }, []);
+  const setWorkflowEditingEdgeId = useCallback((edgeId: string | null) => {
+    setWorkflowPanel((panel) =>
+      edgeId
+        ? { type: "edge", edgeId }
+        : panel?.type === "edge"
+          ? null
+          : panel,
+    );
+  }, []);
  const [activeNodeId, setActiveNodeId] = useState<string | null>(null);
  const [dynamicVariablesOpen, setDynamicVariablesOpen] = useState(false);

@@ -65,6 +118,7 @@ export function useWorkflowEditorState() {
    setWorkflowEditingNodeId,
    workflowEditingEdgeId,
    setWorkflowEditingEdgeId,
+    workflowPanel,
    activeNodeId,
    setActiveNodeId,
    dynamicVariablesOpen,
--- a/frontend/src/components/workflow/AddNodeDialog.tsx
+++ b/frontend/src/components/workflow/AddNodeDialog.tsx
@@ -0,0 +1,116 @@
+"use client";
+
+import { Plus } from "lucide-react";
+
+import {
+  Dialog,
+  DialogContent,
+  DialogDescription,
+  DialogHeader,
+  DialogTitle,
+} from "@/components/ui/dialog";
+
+import { accentVar, type RuntimeNodeSpec } from "./specs";
+
+export function AddNodeDialog({
+  open,
+  connectingFromSource,
+  specs,
+  canAdd,
+  onOpenChange,
+  onSelect,
+}: {
+  open: boolean;
+  connectingFromSource: boolean;
+  specs: RuntimeNodeSpec[];
+  canAdd: (spec: RuntimeNodeSpec) => boolean;
+  onOpenChange: (open: boolean) => void;
+  onSelect: (spec: RuntimeNodeSpec) => void;
+}) {
+  return (
+    <Dialog open={open} onOpenChange={onOpenChange}>
+      <DialogContent className="gap-0 overflow-hidden border border-hairline bg-card p-0 shadow-2xl sm:max-w-[500px]">
+        <DialogHeader className="relative overflow-hidden border-b border-hairline px-6 py-6 pr-16">
+          <div
+            aria-hidden
+            className="pointer-events-none absolute -right-14 -top-16 h-40 w-40 rounded-full opacity-40 blur-3xl"
+            style={{
+              background:
+                "radial-gradient(circle, var(--gradient-sky), transparent 68%)",
+            }}
+          />
+          <div className="relative flex items-start gap-4">
+            <div className="flex h-11 w-11 shrink-0 items-center justify-center rounded-full bg-surface-strong text-foreground">
+              <Plus size={18} />
+            </div>
+            <div>
+              <div className="caption-label text-muted-soft">节点目录</div>
+              <DialogTitle className="font-display display-sm mt-1 text-ink">
+                添加节点
+              </DialogTitle>
+              <DialogDescription className="mt-2 leading-6">
+                {connectingFromSource
+                  ? "选择节点类型，将在当前节点下方创建并自动连接。"
+                  : "选择节点类型并添加到画布中央，随后可编辑内容并建立连线。"}
+              </DialogDescription>
+            </div>
+          </div>
+        </DialogHeader>
+
+        <div className="flex max-h-[440px] flex-col gap-3 overflow-y-auto bg-canvas-soft/70 p-4">
+          {specs.length === 0 && (
+            <p className="rounded-2xl border border-dashed border-hairline-strong bg-card px-4 py-8 text-center text-sm text-muted-soft">
+              暂无可添加的节点类型。
+            </p>
+          )}
+          {specs.map((spec) => {
+            const Icon = spec.icon;
+            const allowed = canAdd(spec);
+            return (
+              <button
+                key={spec.type}
+                type="button"
+                disabled={!allowed}
+                className="group relative flex items-start gap-4 overflow-hidden rounded-2xl border border-hairline bg-card p-4 text-left shadow-sm transition-[border-color,box-shadow,transform] hover:-translate-y-0.5 hover:border-hairline-strong hover:shadow-md disabled:cursor-not-allowed disabled:opacity-55 disabled:hover:translate-y-0 disabled:hover:border-hairline disabled:hover:shadow-sm"
+                onClick={() => onSelect(spec)}
+              >
+                <span
+                  aria-hidden
+                  className="absolute left-5 right-5 top-0 h-px"
+                  style={{
+                    background: `linear-gradient(90deg, transparent, var(${accentVar(spec.accent)}), transparent)`,
+                  }}
+                />
+                <div
+                  className="flex h-10 w-10 shrink-0 items-center justify-center rounded-full text-foreground transition-transform group-hover:scale-105"
+                  style={{
+                    background: `color-mix(in srgb, var(${accentVar(spec.accent)}) 28%, var(--surface-strong))`,
+                  }}
+                >
+                  <Icon size={17} />
+                </div>
+                <div className="min-w-0 flex-1">
+                  <div className="flex items-center gap-2 text-sm font-medium text-foreground">
+                    <span>{spec.displayName}</span>
+                    {!allowed && (
+                      <span className="rounded-full bg-surface-strong px-2 py-0.5 text-[10px] font-normal text-muted-foreground">
+                        已添加
+                      </span>
+                    )}
+                  </div>
+                  <div className="mt-1 text-xs leading-5 text-muted-foreground">
+                    {spec.description}
+                  </div>
+                </div>
+                <Plus
+                  size={15}
+                  className="mt-1 shrink-0 text-muted-soft transition-colors group-hover:text-foreground"
+                />
+              </button>
+            );
+          })}
+        </div>
+      </DialogContent>
+    </Dialog>
+  );
+}
--- a/frontend/src/components/workflow/WorkflowCanvas.tsx
+++ b/frontend/src/components/workflow/WorkflowCanvas.tsx
@@ -16,7 +16,7 @@ import {
  useNodesState,
  useReactFlow,
 } from "@xyflow/react";
-import { Braces, Plus, Settings2, X } from "lucide-react";
+import { Braces, Plus, Settings2 } from "lucide-react";
 import {
  useCallback,
  useEffect,
@@ -26,13 +26,6 @@ import {
 } from "react";

 import { Button } from "@/components/ui/button";
-import {
-  Dialog,
-  DialogContent,
-  DialogDescription,
-  DialogHeader,
-  DialogTitle,
-} from "@/components/ui/dialog";
 import {
  Tooltip,
  TooltipContent,
@@ -40,6 +33,7 @@ import {
  TooltipTrigger,
 } from "@/components/ui/tooltip";

+import { AddNodeDialog } from "./AddNodeDialog";
 import { edgeTypes } from "./ConditionEdge";
 import { hasDefaultPath, newEdgeData } from "./edge-rules";
 import {
@@ -52,8 +46,8 @@ import { nodeTypes } from "./GenericNode";
 import { EdgeSettingsPanel } from "./panels/EdgeSettingsPanel";
 import { GlobalSettingsPanel } from "./panels/GlobalSettingsPanel";
 import { NodeSettingsPanel } from "./panels/NodeSettingsPanel";
+import { WorkflowSidePanel } from "./WorkflowSidePanel";
 import {
-  accentVar,
  defaultGraph,
  type NodeSpecMap,
  type RuntimeNodeSpec,
@@ -195,7 +189,9 @@ export function WorkflowCanvas({
    settings.tts,
    settings.toolIds,
    settings.knowledgeBaseId,
-    settings.knowledgeRetrievalConfig,
+    settings.knowledgeRetrievalConfig.mode,
+    settings.knowledgeRetrievalConfig.topN,
+    settings.knowledgeRetrievalConfig.scoreThreshold,
    settings.allowInterrupt,
    settings.turnConfig,
  ]);
@@ -203,6 +199,9 @@ export function WorkflowCanvas({
  const onConnect = useCallback(
    (connection: Connection) => {
      if (!connection.source || !connection.target) return;
+      const sourceType = nodes.find(
+        (node) => node.id === connection.source,
+      )?.type;
      setEdges((eds) =>
        addEdge(
          {
@@ -210,13 +209,13 @@ export function WorkflowCanvas({
            id: `e-${connection.source}-${connection.target}-${Date.now()}`,
            type: "condition",
            animated: true,
-            data: newEdgeData(eds, connection.source),
+            data: newEdgeData(eds, connection.source, sourceType),
          },
          eds,
        ),
      );
    },
-    [setEdges],
+    [nodes, setEdges],
  );

  // 连线约束:不能连入开始节点(无入边句柄),不能自连。
@@ -277,7 +276,7 @@ export function WorkflowCanvas({
              target: id,
              type: "condition",
              animated: true,
-              data: newEdgeData(currentEdges, source.id),
+              data: newEdgeData(currentEdges, source.id, source.type),
            },
            currentEdges,
          );
@@ -577,9 +576,11 @@ export function WorkflowCanvas({
              </ReactFlow>
            </section>

-            {/* 添加节点弹窗 */}
-            <Dialog
+            <AddNodeDialog
              open={addOpen}
+              connectingFromSource={Boolean(addSourceId)}
+              specs={addableSpecs}
+              canAdd={canAddSpec}
              onOpenChange={(open) => {
                setAddOpen(open);
                if (!open) {
@@ -587,131 +588,43 @@ export function WorkflowCanvas({
                  setAddPosition(null);
                }
              }}
-            >
-              <DialogContent className="gap-0 overflow-hidden border border-hairline bg-card p-0 shadow-2xl sm:max-w-[500px]">
-                <DialogHeader className="relative overflow-hidden border-b border-hairline px-6 py-6 pr-16">
-                  <div
-                    aria-hidden
-                    className="pointer-events-none absolute -right-14 -top-16 h-40 w-40 rounded-full opacity-40 blur-3xl"
-                    style={{
-                      background:
-                        "radial-gradient(circle, var(--gradient-sky), transparent 68%)",
-                    }}
-                  />
-                  <div className="relative flex items-start gap-4">
-                    <div className="flex h-11 w-11 shrink-0 items-center justify-center rounded-full bg-surface-strong text-foreground">
-                      <Plus size={18} />
-                    </div>
-                    <div>
-                      <div className="caption-label text-muted-soft">
-                        节点目录
-                      </div>
-                      <DialogTitle className="font-display display-sm mt-1 text-ink">
-                        添加节点
-                      </DialogTitle>
-                      <DialogDescription className="mt-2 leading-6">
-                        {addSourceId
-                          ? "选择节点类型，将在当前节点下方创建并自动连接。"
-                          : "选择节点类型并添加到画布中央，随后可编辑内容并建立连线。"}
-                      </DialogDescription>
-                    </div>
-                  </div>
-                </DialogHeader>
-                <div className="flex max-h-[440px] flex-col gap-3 overflow-y-auto bg-canvas-soft/70 p-4">
-                  {addableSpecs.length === 0 ? (
-                    <p className="rounded-2xl border border-dashed border-hairline-strong bg-card px-4 py-8 text-center text-sm text-muted-soft">
-                      暂无可添加的节点类型。
-                    </p>
-                  ) : null}
-                  {addableSpecs.map((spec) => {
-                    const Icon = spec.icon;
-                    const canAdd = canAddSpec(spec);
-                    return (
-                      <button
-                        key={spec.type}
-                        type="button"
-                        disabled={!canAdd}
-                        className="group relative flex items-start gap-4 overflow-hidden rounded-2xl border border-hairline bg-card p-4 text-left shadow-sm transition-[border-color,box-shadow,transform] hover:-translate-y-0.5 hover:border-hairline-strong hover:shadow-md disabled:cursor-not-allowed disabled:opacity-55 disabled:hover:translate-y-0 disabled:hover:border-hairline disabled:hover:shadow-sm"
-                        onClick={() => addNode(spec)}
-                      >
-                        <span
-                          aria-hidden
-                          className="absolute left-5 right-5 top-0 h-px"
-                          style={{
-                            background: `linear-gradient(90deg, transparent, var(${accentVar(spec.accent)}), transparent)`,
-                          }}
-                        />
-                        <div
-                          className="flex h-10 w-10 shrink-0 items-center justify-center rounded-full text-foreground transition-transform group-hover:scale-105"
-                          style={{
-                            background: `color-mix(in srgb, var(${accentVar(spec.accent)}) 28%, var(--surface-strong))`,
-                          }}
-                        >
-                          <Icon size={17} />
-                        </div>
-                        <div className="min-w-0 flex-1">
-                          <div className="flex items-center gap-2 text-sm font-medium text-foreground">
-                            <span>{spec.displayName}</span>
-                            {!canAdd && (
-                              <span className="rounded-full bg-surface-strong px-2 py-0.5 text-[10px] font-normal text-muted-foreground">
-                                已添加
-                              </span>
-                            )}
-                          </div>
-                          <div className="mt-1 text-xs leading-5 text-muted-foreground">
-                            {spec.description}
-                          </div>
-                        </div>
-                        <Plus
-                          size={15}
-                          className="mt-1 shrink-0 text-muted-soft transition-colors group-hover:text-foreground"
-                        />
-                      </button>
-                    );
-                  })}
-                </div>
-              </DialogContent>
-            </Dialog>
+              onSelect={addNode}
+            />

            {(debugOpen ||
              settingsOpen ||
              (editingNode && editingSpec) ||
              editingEdge) && (
-              <aside className="absolute inset-y-0 right-0 z-40 flex w-1/2 flex-col overflow-hidden rounded-r-2xl border-l border-hairline bg-card shadow-2xl">
+              <WorkflowSidePanel
+                title={
+                  debugOpen
+                    ? undefined
+                    : settingsOpen
+                      ? "工作流设置"
+                      : editingEdge
+                        ? "编辑连接条件"
+                        : `编辑${editingSpec?.displayName ?? "节点"}`
+                }
+                closeLabel={
+                  settingsOpen
+                    ? "关闭工作流设置"
+                    : editingEdge
+                      ? "关闭边编辑"
+                      : "关闭节点编辑"
+                }
+                onClose={
+                  debugOpen
+                    ? undefined
+                    : () => {
+                        if (settingsOpen) setSettingsOpen(false);
+                        else if (editingEdge) onEditingEdgeIdChange(null);
+                        else onEditingNodeIdChange(null);
+                      }
+                }
+              >
                {debugOpen ? (
                  debugPanel
-                ) : settingsOpen ||
-                  (editingNode && editingSpec) ||
-                  editingEdge ? (
-                  <>
-                    <div className="flex min-h-14 shrink-0 items-center gap-3 border-b border-hairline px-4 py-3">
-                      <button
-                        type="button"
-                        aria-label={
-                          settingsOpen
-                            ? "关闭工作流设置"
-                            : editingEdge
-                              ? "关闭边编辑"
-                              : "关闭节点编辑"
-                        }
-                        title="关闭"
-                        className="flex h-8 w-8 shrink-0 items-center justify-center rounded-full border border-hairline-strong bg-card text-muted-foreground shadow-sm transition-colors hover:text-foreground"
-                        onClick={() => {
-                          if (settingsOpen) setSettingsOpen(false);
-                          else if (editingEdge) onEditingEdgeIdChange(null);
-                          else onEditingNodeIdChange(null);
-                        }}
-                      >
-                        <X size={16} />
-                      </button>
-                      <h2 className="min-w-0 truncate text-sm font-medium text-foreground">
-                        {settingsOpen
-                          ? "工作流设置"
-                          : editingEdge
-                            ? "编辑连接条件"
-                            : `编辑${editingSpec?.displayName ?? "节点"}`}
-                      </h2>
-                    </div>
+                ) : (
                    <div className="scrollbar-subtle min-h-0 flex-1 overflow-y-auto bg-canvas-soft px-4 pb-5 pt-4">
                      {settingsOpen ? (
                        <GlobalSettingsPanel
@@ -741,6 +654,16 @@ export function WorkflowCanvas({
                        <EdgeSettingsPanel
                          key={editingEdge.id}
                          edge={editingEdge}
+                          sourceType={
+                            nodes.find(
+                              (node) => node.id === editingEdge.source,
+                            )?.type as string | undefined
+                          }
+                          isOnlyOutgoing={
+                            edges.filter(
+                              (edge) => edge.source === editingEdge.source,
+                            ).length === 1
+                          }
                          hasOtherDefaultPath={hasDefaultPath(
                            edges,
                            editingEdge.source,
@@ -752,9 +675,8 @@ export function WorkflowCanvas({
                        />
                      ) : null}
                    </div>
-                  </>
-                ) : null}
-              </aside>
+                )}
+              </WorkflowSidePanel>
            )}
          </div>
        </EdgeActionContext.Provider>
--- a/frontend/src/components/workflow/WorkflowSidePanel.tsx
+++ b/frontend/src/components/workflow/WorkflowSidePanel.tsx
@@ -0,0 +1,38 @@
+"use client";
+
+import { X } from "lucide-react";
+import type { ReactNode } from "react";
+
+export function WorkflowSidePanel({
+  title,
+  closeLabel,
+  onClose,
+  children,
+}: {
+  title?: string;
+  closeLabel?: string;
+  onClose?: () => void;
+  children: ReactNode;
+}) {
+  return (
+    <aside className="absolute inset-y-0 right-0 z-40 flex w-1/2 flex-col overflow-hidden rounded-r-2xl border-l border-hairline bg-card shadow-2xl">
+      {title && onClose && (
+        <div className="flex min-h-14 shrink-0 items-center gap-3 border-b border-hairline px-4 py-3">
+          <button
+            type="button"
+            aria-label={closeLabel ?? "关闭面板"}
+            title="关闭"
+            className="flex h-8 w-8 shrink-0 items-center justify-center rounded-full border border-hairline-strong bg-card text-muted-foreground shadow-sm transition-colors hover:text-foreground"
+            onClick={onClose}
+          >
+            <X size={16} />
+          </button>
+          <h2 className="min-w-0 truncate text-sm font-medium text-foreground">
+            {title}
+          </h2>
+        </div>
+      )}
+      {children}
+    </aside>
+  );
+}
--- a/frontend/src/components/workflow/edge-rules.ts
+++ b/frontend/src/components/workflow/edge-rules.ts
@@ -31,11 +31,19 @@ export function nextEdgePriority(edges: Edge[], sourceId: string): number {
 }

 /**
- * The first path from a node is deterministic. Further paths start as an
- * incomplete LLM condition so the graph never silently gains two defaults.
+ * Agent nodes need a real condition before they can leave; a lone default path
+ * would consume the next user turn without letting the Agent answer it.
+ * Automatic nodes may use a default as their first deterministic path.
 */
-export function newEdgeData(edges: Edge[], sourceId: string): WorkflowEdgeData {
+export function newEdgeData(
+  edges: Edge[],
+  sourceId: string,
+  sourceType?: string,
+): WorkflowEdgeData {
  const priority = nextEdgePriority(edges, sourceId);
+  if (sourceType === "agent" && !hasDefaultPath(edges, sourceId)) {
+    return { mode: "llm", priority, condition: "" };
+  }
  return hasDefaultPath(edges, sourceId)
    ? { mode: "llm", priority, condition: "" }
    : { mode: "always", priority };
--- a/frontend/src/components/workflow/panels/EdgeSettingsPanel.tsx
+++ b/frontend/src/components/workflow/panels/EdgeSettingsPanel.tsx
@@ -27,10 +27,14 @@ import type { ExpressionRule, WorkflowEdgeData } from "../specs";

 export function EdgeSettingsPanel({
  edge,
+  sourceType,
+  isOnlyOutgoing,
  hasOtherDefaultPath,
  onChange,
 }: {
  edge: Edge;
+  sourceType?: string;
+  isOnlyOutgoing: boolean;
  hasOtherDefaultPath: boolean;
  onChange: (patch: WorkflowEdgeData) => void;
 }) {
@@ -109,7 +113,10 @@ export function EdgeSettingsPanel({
            {
              value: "always",
              label: "默认路径",
-              disabled: mode !== "always" && hasOtherDefaultPath,
+              disabled:
+                mode !== "always" &&
+                (hasOtherDefaultPath ||
+                  (sourceType === "agent" && isOnlyOutgoing)),
            },
            { value: "llm", label: "大模型判断" },
            { value: "expression", label: "表达式" },
@@ -127,24 +134,31 @@ export function EdgeSettingsPanel({
            当前节点已经有一条默认路径；其它出边需要使用大模型判断或表达式。
          </span>
        )}
-        <label className="block">
-          <div className="mb-1.5 text-sm font-medium text-foreground">
-            优先级
-          </div>
-          <Input
-            type="number"
-            value={priority}
-            onChange={(event) => {
-              const nextPriority = Number(event.target.value) || 0;
-              setPriority(nextPriority);
-              publish({ nextPriority });
-            }}
-            className="border-hairline-strong bg-background text-foreground"
-          />
-          <span className="mt-1.5 block text-xs text-muted-foreground">
-            条件路径按数字从小到大判断，默认路径始终作为兜底。
+        {sourceType === "agent" && isOnlyOutgoing && mode === "always" && (
+          <span className="-mt-1 block text-xs text-destructive">
+            Agent 不能只有默认路径；请改为条件路径，或删除连接以持续对话。
          </span>
-        </label>
+        )}
+        {mode !== "always" && (
+          <label className="block">
+            <div className="mb-1.5 text-sm font-medium text-foreground">
+              优先级
+            </div>
+            <Input
+              type="number"
+              value={priority}
+              onChange={(event) => {
+                const nextPriority = Number(event.target.value) || 0;
+                setPriority(nextPriority);
+                publish({ nextPriority });
+              }}
+              className="border-hairline-strong bg-background text-foreground"
+            />
+            <span className="mt-1.5 block text-xs text-muted-foreground">
+              条件路径按数字从小到大判断；默认路径不参与优先级。
+            </span>
+          </label>
+        )}
      </SectionCard>

      <SectionCard