Merge branch 'feature/workflow'

backend/models.py

@@ -27,6 +27,21 @@ class RuntimeTool(BaseModel):
     secrets: dict = Field(default_factory=dict)
 
 
+class RuntimeModelResource(BaseModel):
+    id: str
+    name: str = ""
+    capability: str
+    interface_type: str
+    values: dict = Field(default_factory=dict)
+    secrets: dict = Field(default_factory=dict)
+
+
+class RuntimeKnowledgeBase(BaseModel):
+    id: str
+    name: str = ""
+    description: str = ""
+
+
 class AssistantConfig(BaseModel):
     """运行时配置:前端可见部分(name/prompt/...) + 服务端注入部分(*_api_key/*_base_url)。"""
 
@@ -93,6 +108,8 @@ class AssistantConfig(BaseModel):
 
     # workflow 类型:节点图(nodes/edges)。非 workflow 为空,引擎据此决定是否启用。
     graph: dict = {}
+    workflow_model_resources: dict[str, RuntimeModelResource] = Field(default_factory=dict)
+    workflow_knowledge_bases: dict[str, RuntimeKnowledgeBase] = Field(default_factory=dict)
 
     # 外部托管类型(fastgpt/dify/opencode)的连接信息:context/KB/tools 由对方服务端接管。
     dify_api_url: str = ""

backend/requirements.txt

@@ -2,7 +2,7 @@
 # webrtc  -> SmallWebRTCTransport / SmallWebRTCConnection + aiortc
 # silero  -> 本地 VAD(判断用户说话起止),语音必备
 # openai  -> OpenAI 兼容的 LLM/STT/TTS 客户端(DeepSeek、SenseVoice、CosyVoice 都走它)
-pipecat-ai[webrtc,websocket,silero,openai]==1.4.0
+pipecat-ai[webrtc,websocket,silero,openai]==1.5.0
 Pillow>=11.1.0,<13
 
 # FastGPT 类型助手:本地 SDK(包 /api/v1/chat/completions 流式 + chatId 会话)

backend/routes/assistants.py

@@ -15,7 +15,7 @@ from fastapi import APIRouter, Depends, HTTPException
 from schemas import AssistantOut, AssistantUpsert
 from services.auth import require_admin
 from services.masking import mask, resolve_incoming_key
-from services.node_specs import validate_graph
+from services.node_specs import graph_references, normalize_graph, validate_graph
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
@@ -31,9 +31,54 @@ def _validate_workflow(body: AssistantUpsert) -> None:
     """workflow 类型:保存前校验图结构,不通过则 400。其他类型跳过。"""
     if body.type != "workflow":
         return
-    errors = validate_graph(body.graph or {})
+    body.graph = normalize_graph(body.graph or {})
+    errors = validate_graph(body.graph)
     if errors:
         raise HTTPException(400, "工作流校验失败:" + ";".join(errors))
+    refs = graph_references(body.graph)
+    body.tool_ids = list(dict.fromkeys([*body.tool_ids, *sorted(refs["tools"])]))
+
+
+async def _validate_workflow_references(
+    session: AsyncSession, body: AssistantUpsert
+) -> None:
+    if body.type != "workflow" or not body.graph.get("nodes"):
+        return
+    graph = body.graph
+    settings = graph.get("settings") or {}
+    resource_expectations: dict[str, str] = {}
+    for key, capability in (
+        ("defaultLlmResourceId", "LLM"),
+        ("defaultAsrResourceId", "ASR"),
+        ("defaultTtsResourceId", "TTS"),
+    ):
+        if settings.get(key):
+            resource_expectations[str(settings[key])] = capability
+    knowledge_ids: set[str] = (
+        {str(settings["knowledgeBaseId"])}
+        if settings.get("knowledgeBaseId")
+        else set()
+    )
+    for node in graph.get("nodes") or []:
+        data = node.get("data") or {}
+        if node.get("type") == "agent" and data.get("inheritGlobalConfig", True):
+            continue
+        if data.get("llmResourceId"):
+            resource_expectations[str(data["llmResourceId"])] = "LLM"
+        if data.get("asrResourceId"):
+            resource_expectations[str(data["asrResourceId"])] = "ASR"
+        if data.get("ttsResourceId"):
+            resource_expectations[str(data["ttsResourceId"])] = "TTS"
+        if data.get("knowledgeBaseId"):
+            knowledge_ids.add(str(data["knowledgeBaseId"]))
+    for resource_id, capability in resource_expectations.items():
+        resource = await session.get(ModelResource, resource_id)
+        if not resource or not resource.enabled or resource.capability != capability:
+            raise HTTPException(400, f"Workflow 引用了无效的 {capability} 资源:{resource_id}")
+    for knowledge_id in knowledge_ids:
+        knowledge = await session.get(KnowledgeBase, knowledge_id)
+        if not knowledge or knowledge.status != "active":
+            raise HTTPException(400, f"Workflow 引用了无效知识库:{knowledge_id}")
 
 
 async def _validate_vision_model(
@@ -101,7 +146,11 @@ async def _resource_ids(session: AsyncSession, assistant_id: str) -> dict[str, s
 async def _sync_tool_bindings(
     session: AsyncSession, assistant_id: str, assistant_type: str, tool_ids: list[str]
 ) -> None:
-    requested = list(dict.fromkeys(tool_ids)) if assistant_type == "prompt" else []
+    requested = (
+        list(dict.fromkeys(tool_ids))
+        if assistant_type in {"prompt", "workflow"}
+        else []
+    )
     if requested:
         tools = (
             await session.execute(select(Tool).where(Tool.id.in_(requested)))
@@ -158,7 +207,11 @@ async def _to_out(session: AsyncSession, assistant: Assistant) -> AssistantOut:
         api_url=assistant.api_url,
         api_key=mask(assistant.api_key),
         app_id=assistant.app_id,
-        graph=assistant.graph or {},
+        graph=(
+            normalize_graph(assistant.graph or {})
+            if assistant.type == "workflow"
+            else {}
+        ),
         updated_at=assistant.updated_at.isoformat() if assistant.updated_at else None,
     )
 
@@ -176,6 +229,7 @@ async def create_assistant(
     body: AssistantUpsert, session: AsyncSession = Depends(get_session)
 ):
     _validate_workflow(body)
+    await _validate_workflow_references(session, body)
     await _validate_vision_model(session, body)
     await _validate_knowledge_base(session, body)
     data = body.model_dump()
@@ -248,6 +302,7 @@ async def update_assistant(
     if not assistant:
         raise HTTPException(404, "助手不存在")
     _validate_workflow(body)
+    await _validate_workflow_references(session, body)
     await _validate_vision_model(session, body)
     await _validate_knowledge_base(session, body)
     data = body.model_dump()

backend/routes/knowledge_bases.py

@@ -15,6 +15,7 @@ from schemas import (
 )
 from services.auth import require_admin
 from services.knowledge import create_document, delete_storage_object, process_document, search
+from services.node_specs import graph_references
 import settings
 from sqlalchemy import select
 from sqlalchemy.exc import IntegrityError
@@ -123,6 +124,14 @@ async def delete_knowledge_base(
     )).scalar_one_or_none()
     if referenced:
         raise HTTPException(409, "知识库正被助手引用,无法删除")
+    workflows = (
+        await session.execute(select(Assistant).where(Assistant.type == "workflow"))
+    ).scalars().all()
+    if any(
+        kb_id in graph_references(assistant.graph or {})["knowledge_bases"]
+        for assistant in workflows
+    ):
+        raise HTTPException(409, "知识库正被 Workflow 节点引用,无法删除")
     documents = (await session.execute(
         select(KnowledgeDocument).where(KnowledgeDocument.knowledge_base_id == kb_id)
     )).scalars().all()

backend/routes/model_registry.py

@@ -3,6 +3,7 @@
 import uuid
 
 from db.models import (
+    Assistant,
     AssistantModelBinding,
     InterfaceDefinition,
     KnowledgeBase,
@@ -20,6 +21,7 @@ from services.auth import require_admin
 from services.interface_catalog import validate_fields
 from services.masking import mask_secrets, merge_secrets
 from services.model_resource_tester import test_model_resource
+from services.node_specs import graph_references
 from sqlalchemy import delete, select, update
 from sqlalchemy.ext.asyncio import AsyncSession
 
@@ -102,6 +104,14 @@ async def _clear_incompatible_references(
 ) -> None:
     if capability == resource.capability:
         return
+    workflows = (
+        await session.execute(select(Assistant).where(Assistant.type == "workflow"))
+    ).scalars().all()
+    if any(
+        resource.id in graph_references(assistant.graph or {})["model_resources"]
+        for assistant in workflows
+    ):
+        raise HTTPException(409, "模型资源正被 Workflow 节点引用，不能修改能力类型")
     await session.execute(
         delete(AssistantModelBinding).where(
             AssistantModelBinding.model_resource_id == resource.id
@@ -263,6 +273,14 @@ async def delete_model_resource(
     ).scalar_one_or_none()
     if in_use:
         raise HTTPException(409, "该模型资源仍被助手引用")
+    workflows = (
+        await session.execute(select(Assistant).where(Assistant.type == "workflow"))
+    ).scalars().all()
+    if any(
+        resource_id in graph_references(assistant.graph or {})["model_resources"]
+        for assistant in workflows
+    ):
+        raise HTTPException(409, "该模型资源仍被 Workflow 节点引用")
     await session.delete(row)
     await session.commit()
     return {"ok": True}

backend/schemas.py

@@ -138,7 +138,7 @@ class AssistantUpsert(CamelModel):
                 setattr(self, field, "")
         if "graph" not in allowed:
             self.graph = {}
-        if self.type != "prompt":
+        if self.type not in {"prompt", "workflow"}:
             self.tool_ids = []
             self.dynamic_variable_definitions = {}
         # 外部托管大脑只能 cascade,拦住不兼容的 realtime

backend/services/auth.py

@@ -109,6 +109,17 @@ def clear_auth_cookie(response: Response) -> None:
 
 async def require_admin(request: Request) -> AdminUser:
     user = verify_admin_token(request.cookies.get(settings.AUTH_COOKIE_NAME))
+    if not user:
+        authorization = request.headers.get("authorization", "")
+        scheme, _, encoded_credentials = authorization.partition(" ")
+        if scheme.lower() == "basic" and encoded_credentials:
+            try:
+                credentials = base64.b64decode(encoded_credentials).decode("utf-8")
+                username, separator, password = credentials.partition(":")
+                if separator:
+                    user = authenticate_admin(username, password)
+            except (ValueError, UnicodeDecodeError):
+                user = None
     if not user:
         raise HTTPException(
             status_code=status.HTTP_401_UNAUTHORIZED,

backend/services/brains/base.py

@@ -9,7 +9,7 @@ without coupling brains to Pipecat internals more than necessary.
 from __future__ import annotations
 
 from collections.abc import Awaitable, Callable
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from typing import Any, Protocol, runtime_checkable
 
 from models import AssistantConfig
@@ -52,6 +52,15 @@ class BrainRuntime:
     set_system_prompt: Callable[[str], None]
     set_tools: Callable[[list[FunctionSchema] | None], None]
     call_end: CallEndPort
+    worker: Any = None
+    context_aggregator: Any = None
+    transport: Any = None
+    switch_services: (
+        Callable[[str | None, str | None, str | None], Awaitable[None]] | None
+    ) = None
+    set_knowledge_scope: Callable[[dict[str, Any]], None] | None = None
+    set_input_enabled: Callable[[bool], None] | None = None
+    flow_global_functions: list[Any] = field(default_factory=list)
 
 
 class BaseBrain:
@@ -77,6 +86,15 @@ class BaseBrain:
     def record_user_message(self, content: str) -> None:
         """Observe a committed user message for brain-owned routing state."""
 
+    async def on_user_turn_end(self, content: str) -> bool:
+        """Handle a complete user turn before the conversational LLM runs.
+
+        Return True when the brain scheduled the next action itself and the
+        in-flight context frame must not reach the previous Agent's LLM.
+        """
+        self.record_user_message(content)
+        return False
+
     async def on_assistant_text_start(self, turn_id: str) -> None:
         """Observe the start of a generated assistant turn."""
 
@@ -107,6 +125,8 @@ class Brain(Protocol):
 
     def record_user_message(self, content: str) -> None: ...
 
+    async def on_user_turn_end(self, content: str) -> bool: ...
+
     async def on_assistant_text_start(self, turn_id: str) -> None: ...
 
     async def on_assistant_text_end(

backend/services/brains/prompt_brain.py

@@ -2,11 +2,8 @@
 
 from __future__ import annotations
 
-from copy import deepcopy
-from urllib.parse import quote
 from uuid import uuid4
 
-import httpx
 from models import AssistantConfig
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.frames.frames import OutputTransportMessageUrgentFrame, TTSSpeakFrame
@@ -20,10 +17,9 @@ from pipecat.utils.time import time_now_iso8601
 
 from services.brains.base import BaseBrain, BrainRuntime, BrainSpec
 from services.runtime_variables import (
-    DynamicVariableError,
     DynamicVariableStore,
-    value_at_path,
 )
+from services.tool_executor import ToolExecutionError, ToolExecutor
 
 
 class PromptBrain(BaseBrain):
@@ -37,6 +33,7 @@ class PromptBrain(BaseBrain):
         self._cfg = cfg
         self._dynamic_enabled = True
         self._store = DynamicVariableStore.from_config(cfg)
+        self._tools = ToolExecutor(self._store)
         self._runtime: BrainRuntime | None = None
 
     async def greeting(self, cfg: AssistantConfig) -> str:
@@ -85,120 +82,16 @@ class PromptBrain(BaseBrain):
             self._runtime.set_system_prompt(self._store.render(self._cfg.prompt))
 
     def _make_http_tool(self, tool, runtime: BrainRuntime):
-        config = (tool.definition or {}).get("config") or {}
+        properties, required = self._tools.schema_parts(tool)
-        parameters = list(config.get("parameters") or [])
+        self._tools.register_secrets(tool)
-        properties = {
-            str(parameter.get("name")): {
-                "type": str(parameter.get("type") or "string"),
-                "description": str(parameter.get("description") or ""),
-            }
-            for parameter in parameters
-            if parameter.get("name")
-        }
-        required = [
-            str(parameter["name"])
-            for parameter in parameters
-            if parameter.get("name") and parameter.get("required", True)
-        ]
-
-        tool_secrets = tool.secrets or {}
-        dynamic_secrets = tool_secrets.get("dynamic_variables") or {}
-        for name, value in dynamic_secrets.items():
-            if not str(name).startswith("secret__"):
-                raise DynamicVariableError(f"工具密钥变量必须以 secret__ 开头: {name}")
-            self._store.secrets[str(name)] = str(value)
 
         async def call_http(params: FunctionCallParams) -> None:
-            arguments = params.arguments or {}
-            url = self._store.render(str(config.get("url") or ""))
-            configured_headers = self._store.render_data(
-                deepcopy(config.get("headers") or {}), allow_secrets=True
-            )
-            secret_headers = self._store.render_data(
-                deepcopy(tool_secrets.get("headers") or {}), allow_secrets=True
-            )
-            headers: dict[str, str] = {}
-            query: dict[str, object] = {}
-            body = self._store.render_data(deepcopy(config.get("body") or {}))
-
-            for parameter in parameters:
-                name = str(parameter.get("name") or "")
-                if not name or name not in arguments:
-                    continue
-                value = arguments[name]
-                location = str(parameter.get("location") or "body")
-                if location == "path":
-                    encoded = quote(str(value), safe="")
-                    url = url.replace(f"{{{name}}}", encoded)
-                elif location == "query":
-                    query[name] = value
-                elif location == "header":
-                    headers[name] = str(value)
-                else:
-                    body[name] = value
-
-            # Admin-configured headers win over model-provided arguments; secret
-            # headers are applied last so an LLM can never replace credentials.
-            headers.update(
-                {str(key): str(value) for key, value in configured_headers.items()}
-            )
-            headers.update({str(key): str(value) for key, value in secret_headers.items()})
-
             try:
-                async with httpx.AsyncClient(
+                result = await self._tools.execute(tool, dict(params.arguments or {}))
-                    timeout=float(config.get("timeout_seconds") or 15),
+                if result["updated_variables"]:
-                    follow_redirects=False,
-                ) as client:
-                    response = await client.request(
-                        str(config.get("method") or "GET"),
-                        url,
-                        headers=headers,
-                        params=query,
-                        json=body if body else None,
-                    )
-                response.raise_for_status()
-                if len(response.content) > 1_000_000:
-                    raise DynamicVariableError("HTTP 工具响应超过 1 MB 限制")
-                try:
-                    payload = response.json()
-                except ValueError:
-                    payload = {"text": response.text[:8000]}
-
-                updated: list[str] = []
-                assignments = config.get("dynamic_variable_assignments") or {}
-                for variable_name, path in assignments.items():
-                    try:
-                        value = value_at_path(payload, str(path))
-                    except KeyError:
-                        try:
-                            value = value_at_path({"response": payload}, str(path))
-                        except KeyError:
-                            continue
-                    self._store.assign(str(variable_name), value)
-                    updated.append(str(variable_name))
-                if updated:
                     self._refresh_prompt()
-                await params.result_callback(
+                await params.result_callback(result)
-                    {
+            except (ToolExecutionError, ValueError) as exc:
-                        "status": "ok",
-                        "status_code": response.status_code,
-                        "data": payload,
-                        "updated_variables": updated,
-                    }
-                )
-            except httpx.TimeoutException:
-                await params.result_callback(
-                    {"status": "error", "message": "HTTP 工具调用超时"}
-                )
-            except httpx.HTTPStatusError as exc:
-                await params.result_callback(
-                    {
-                        "status": "error",
-                        "status_code": exc.response.status_code,
-                        "message": "HTTP 工具返回错误状态",
-                    }
-                )
-            except (httpx.RequestError, DynamicVariableError) as exc:
                 await params.result_callback(
                     {"status": "error", "message": f"HTTP 工具调用失败: {exc}"}
                 )

backend/services/brains/registry.py

@@ -14,7 +14,7 @@ from services.brains.workflow_brain import WorkflowBrain
 
 
 def _workflow(cfg: AssistantConfig) -> Brain:
-    return WorkflowBrain(cfg.graph)
+    return WorkflowBrain(cfg)
 
 
 BRAIN_FACTORIES: dict[str, Callable[[AssistantConfig], Brain]] = {

backend/services/brains/workflow_brain.py

@@ -1,27 +1,43 @@
-"""Local graph-driven workflow assistant and its per-call state."""
+"""Pipecat Flows-backed Workflow v3 brain."""
 
 from __future__ import annotations
 
-from dataclasses import dataclass
 from typing import Any
 
 from loguru import logger
-from models import AssistantConfig
+from models import AssistantConfig, RuntimeTool
-from pipecat.adapters.schemas.function_schema import FunctionSchema
+from db.session import SessionLocal
-from pipecat.frames.frames import OutputTransportMessageUrgentFrame, TTSSpeakFrame
+from pipecat.flows import (
+    ContextStrategy,
+    ContextStrategyConfig,
+    FlowManager,
+    FlowsFunctionSchema,
+    NodeConfig,
+)
+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_engine import WorkflowEngine
+from services.workflow_router import STAY_ON_CURRENT_AGENT, WorkflowLLMRouter
 
 
-@dataclass
+MAX_AUTOMATIC_HOPS = 50
-class WorkflowState:
+AGENT_STAGE_INSTRUCTION = (
-    current: str
+    "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，"
-    ended: bool = False
+    "不要自行解释、模拟或宣布节点切换。"
-    turns_in_node: int = 0
+)
-    end_turn_id: str | None = None
 
 
 class WorkflowBrain(BaseBrain):
@@ -30,22 +46,29 @@ class WorkflowBrain(BaseBrain):
         supported_runtime_modes=frozenset({"pipeline"}),
         owns_context=True,
     )
-    _FALLBACK_AFTER_TURNS = 2
 
-    def __init__(self, graph: dict[str, Any]):
+    def __init__(self, cfg_or_graph: AssistantConfig | dict[str, Any]):
+        cfg = cfg_or_graph if isinstance(cfg_or_graph, AssistantConfig) else None
+        graph = cfg.graph if cfg is not None else cfg_or_graph
         self._engine = WorkflowEngine(graph or {})
         if not self._engine.has_graph() or not self._engine.start_id:
-            raise ValueError("WorkflowBrain 缺少有效的 startCall 节点")
+            raise ValueError("WorkflowBrain 缺少有效的 Start 节点")
-        self._state = WorkflowState(current=self._engine.start_id)
+        self._cfg = cfg
-        self._history: list[dict[str, str]] = []
+        self._store = DynamicVariableStore.from_config(cfg or AssistantConfig(type="workflow"))
-        self._cfg: AssistantConfig | None = None
+        self._tools = ToolExecutor(self._store)
+        self._tool_by_id: dict[str, RuntimeTool] = {
+            tool.id: tool for tool in (cfg.tools if cfg else [])
+        }
         self._runtime: BrainRuntime | None = None
+        self._manager: FlowManager | None = None
+        self._router = WorkflowLLMRouter(cfg or AssistantConfig(type="workflow"))
+        self._ended = False
 
     async def greeting(self, cfg: AssistantConfig) -> str:
-        return self._engine.greeting() or cfg.greeting
+        return self._engine.greeting(self._store) or cfg.greeting
 
     def system_prompt(self, cfg: AssistantConfig) -> str:
-        return self._engine.system_prompt_for(self._state.current)
+        return self._store.render(self._engine.global_prompt())
 
     def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor:
         from services.pipecat.service_factory import create_llm
@@ -53,72 +76,422 @@ class WorkflowBrain(BaseBrain):
         return create_llm(cfg)
 
     async def setup(self, cfg: AssistantConfig, runtime: BrainRuntime) -> None:
+        if runtime.worker is None or runtime.context_aggregator is None:
+            raise RuntimeError("WorkflowBrain 需要 PipelineWorker 和 context aggregator pair")
         self._cfg = cfg
         self._runtime = runtime
-        for edge in self._engine.edges:
+        self._store = DynamicVariableStore.from_config(cfg)
-            if edge.get("target"):
+        self._tools = ToolExecutor(self._store)
-                runtime.llm.register_function(
+        self._tool_by_id = {tool.id: tool for tool in cfg.tools}
-                    self._engine.edge_fn_name(edge),
+        self._router = WorkflowLLMRouter(cfg)
-                    self._make_transition_handler(edge),
+        self._manager = FlowManager(
-                )
+            worker=runtime.worker,
-        self._apply_node(self._state.current)
+            llm=runtime.llm,
-        logger.info(
+            context_aggregator=runtime.context_aggregator,
-            f"工作流模式启用: 起始节点={self._engine.name(self._state.current)}"
+            transport=runtime.transport,
+            global_functions=runtime.flow_global_functions,
         )
+        self._manager.state["variables"] = self._store.values
 
     async def on_connected(self) -> None:
-        await self._emit_node_active(self._state.current)
+        await self._emit_node_active(self._engine.start_id)
+        edge = self._engine.deterministic_edge(
+            self._engine.start_id,
+            self._store,
+            include_default=True,
+        )
+        if not edge and self._engine.has_outgoing(self._engine.start_id):
+            raise RuntimeError("Start 初始化后没有命中的表达式边或默认边")
+        node_config = (
+            await self._follow_edge(edge)
+            if edge
+            else self._passive_node_config(self._engine.start_id)
+        )
+        if self._manager is None:
+            raise RuntimeError("Workflow FlowManager 尚未初始化")
+        await self._manager.initialize(node_config)
+        logger.info(f"工作流模式启用: 当前节点={self._manager.current_node}")
 
     def record_user_message(self, content: str) -> None:
-        if content:
+        if content and not self._ended:
-            self._history.append({"role": "user", "content": content})
+            self._store.record("user", content)
 
-    async def on_assistant_text_start(self, turn_id: str) -> None:
+    async def on_user_turn_end(self, content: str) -> bool:
-        if self._state.ended and self._state.end_turn_id is None:
+        """Route a complete user turn before any Agent is allowed to reply."""
-            self._state.end_turn_id = turn_id
+        if not content or self._ended:
+            return True
+        self.record_user_message(content)
+        manager = self._require_manager()
+        current = manager.current_node
+        if not current or self._engine.node_type(current) != "agent":
+            return True
+
+        edge = self._engine.deterministic_edge(
+            current,
+            self._store,
+            include_default=False,
+        )
+        outgoing = self._engine.outgoing(current)
+        llm_edges = [
+            candidate
+            for candidate in outgoing
+            if self._engine.edge_mode(candidate) == "llm"
+        ]
+        default_edge = next(
+            (
+                candidate
+                for candidate in outgoing
+                if self._engine.edge_mode(candidate) == "always"
+            ),
+            None,
+        )
+
+        if edge is None and llm_edges:
+            selected = await self._router_for_node(current).select_edge(
+                node_name=self._engine.name(current),
+                node_prompt=self._engine.prompt_for(current, 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 and selected != STAY_ON_CURRENT_AGENT:
+                edge = next(
+                    (
+                        candidate
+                        for candidate in llm_edges
+                        if self._engine.edge_fn_name(candidate) == selected
+                    ),
+                    None,
+                )
+            elif selected == STAY_ON_CURRENT_AGENT:
+                edge = default_edge
+        elif edge is None and not llm_edges:
+            edge = default_edge
+
+        if edge and manager.current_node == current:
+            next_config = await self._follow_edge(edge)
+            await manager.set_node_from_config(next_config)
+            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._require_runtime().queue_frame(LLMRunFrame())
+        return True
 
     async def on_assistant_text_end(
         self,
-        turn_id: str,
+        _turn_id: str,
         content: str,
         interrupted: bool,
     ) -> None:
-        if not content or interrupted:
+        if not content or interrupted or self._ended:
             return
-        self._history.append({"role": "assistant", "content": content})
+        self._store.record("agent", content, completed_agent_turn=True)
-        if turn_id == self._state.end_turn_id:
-            runtime = self._require_runtime()
-            runtime.call_end.begin("completed")
-            runtime.call_end.arm_after_speech()
-        elif not self._state.ended:
-            self._state.turns_in_node += 1
-            await self._fallback_route()
 
-    def _apply_node(self, node_id: str) -> None:
+    async def _refresh_agent_prompt(self, node_id: str) -> None:
         runtime = self._require_runtime()
-        runtime.set_system_prompt(self._engine.system_prompt_for(node_id))
+        await runtime.queue_frame(
-        if self._engine.is_end(node_id):
+            LLMUpdateSettingsFrame(
-            runtime.set_tools([])
+                delta=LLMSettings(
-            return
+                    system_instruction=self._agent_role_message(node_id)
-        runtime.set_tools(
-            [
-                FunctionSchema(
-                    name=self._engine.edge_fn_name(edge),
-                    description=self._engine.edge_description(edge),
-                    properties={},
-                    required=[],
                 )
-                for edge in self._engine.outgoing(node_id)
+            )
-            ]
         )
 
-    async def _go_to_node(self, target: str) -> None:
+    def _agent_role_message(self, node_id: str) -> str:
-        self._state.current = target
+        """Build one provider-compatible system instruction for an Agent stage."""
-        self._state.turns_in_node = 0
+        stage_prompt = self._engine.prompt_for(node_id, self._store)
-        if self._engine.is_end(target):
+        return f"{stage_prompt}\n\n[工作流执行规则]\n{AGENT_STAGE_INSTRUCTION}"
-            self._state.ended = True
+
-        await self._emit_node_active(target)
+    def _router_for_node(self, node_id: str) -> WorkflowLLMRouter:
-        self._apply_node(target)
+        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:
+            return self._router
+        from services.pipecat.service_factory import config_with_resource
+
+        return WorkflowLLMRouter(config_with_resource(cfg, resource))
+
+    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.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,
+                }
+            )
+
+    def _agent_config(self, node_id: str) -> 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
+        )
+        stage = self._engine.agent_stage_config(node_id)
+        functions: list[FlowsFunctionSchema] = []
+        for tool_id in stage.tool_ids:
+            tool = self._tool_by_id.get(str(tool_id))
+            if tool and tool.type == "http":
+                functions.append(self._flow_tool(tool, node_id))
+        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),
+            "task_messages": (
+                [{"role": "assistant", "content": entry_speech}]
+                if entry_mode == "fixed_speech"
+                else []
+            ),
+            "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,
+                    "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 ""))
+
+    async def _queue_visible_speech(self, text: str) -> 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()
+        await runtime.queue_frame(
+            OutputTransportMessageUrgentFrame(
+                message={
+                    "type": "transcript",
+                    "role": "assistant",
+                    "content": content,
+                    "timestamp": time_now_iso8601(),
+                }
+            )
+        )
+        await runtime.queue_frame(TTSSpeakFrame(content, append_to_context=False))
+
+    def _passive_node_config(self, node_id: str) -> NodeConfig:
+        """Keep a non-conversational terminal node active without ending the call."""
+        return {
+            "name": node_id,
+            "role_message": self._store.render(self._engine.global_prompt()),
+            "task_messages": [],
+            "functions": [],
+            "context_strategy": ContextStrategyConfig(strategy=ContextStrategy.APPEND),
+            "respond_immediately": False,
+        }
+
+    def _flow_tool(self, tool: RuntimeTool, node_id: str) -> FlowsFunctionSchema:
+        properties, required = self._tools.schema_parts(tool)
+        self._tools.register_secrets(tool)
+
+        async def handler(args, _flow_manager):
+            try:
+                result = await self._tools.execute(tool, dict(args or {}))
+            except ToolExecutionError as exc:
+                return {"status": "error", "message": str(exc)}
+            if result.get("updated_variables"):
+                await self._refresh_agent_prompt(node_id)
+                edge = self._engine.deterministic_edge(
+                    node_id,
+                    self._store,
+                    include_default=False,
+                )
+                if edge:
+                    return result, await self._follow_edge(edge)
+            return result
+
+        return FlowsFunctionSchema(
+            name=tool.function_name,
+            description=tool.description or f"调用 {tool.name}",
+            properties=properties,
+            required=required,
+            handler=handler,
+            cancel_on_interruption=True,
+        )
+
+    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 "")
+        if not knowledge_id or stage.knowledge_mode != "on_demand":
+            return None
+        cfg = self._cfg or AssistantConfig(type="workflow")
+        knowledge = cfg.workflow_knowledge_bases.get(knowledge_id)
+        description = "在当前 Agent 绑定的知识库中检索资料。"
+        if knowledge:
+            description += f"知识库：{knowledge.name}。{knowledge.description}"
+
+        async def handler(args, _flow_manager):
+            query = str((args or {}).get("query") or "").strip()
+            if not query:
+                return {"status": "error", "message": "检索问题为空"}
+            try:
+                async with SessionLocal() as session:
+                    results = await search_knowledge(
+                        session,
+                        knowledge_id,
+                        query,
+                        top_k=stage.knowledge_top_n,
+                        score_threshold=stage.knowledge_score_threshold,
+                    )
+                return {"status": "ok", "results": results}
+            except Exception as exc:  # noqa: BLE001 - tool errors are returned to the LLM
+                logger.warning(f"Workflow 知识库检索失败:{exc}")
+                return {"status": "error", "message": "知识库检索暂时不可用"}
+
+        return FlowsFunctionSchema(
+            name="search_knowledge_base",
+            description=description,
+            properties={
+                "query": {"type": "string", "description": "完整问题或检索关键词"}
+            },
+            required=["query"],
+            handler=handler,
+            cancel_on_interruption=True,
+        )
+
+    async def _follow_edge(self, edge: dict) -> NodeConfig:
+        speech = self._engine.edge_transition_speech(edge)
+        if speech:
+            await self._queue_visible_speech(self._store.render(speech))
+        return await self._resolve_path(str(edge.get("target") or ""))
+
+    async def _resolve_path(self, node_id: str) -> NodeConfig:
+        for _ in range(MAX_AUTOMATIC_HOPS):
+            node_type = self._engine.node_type(node_id)
+            if node_type == "agent":
+                await self._apply_agent_stage(node_id)
+                return self._agent_config(node_id)
+            if node_type == "end":
+                await self._enter_end(node_id)
+                return self._passive_node_config(node_id)
+            if node_type == "action":
+                await self._enter_action(node_id)
+            elif node_type == "handoff":
+                await self._enter_handoff(node_id)
+            elif node_type == "start":
+                await self._emit_node_active(node_id)
+            else:
+                raise RuntimeError(f"工作流指向未知节点:{node_id}")
+            if not self._engine.has_outgoing(node_id):
+                return self._passive_node_config(node_id)
+            edge = self._engine.deterministic_edge(
+                node_id,
+                self._store,
+                include_default=True,
+            )
+            if not edge:
+                raise RuntimeError(f"自动节点 {node_id} 没有命中的表达式边或默认边")
+            speech = self._engine.edge_transition_speech(edge)
+            if speech:
+                await self._queue_visible_speech(self._store.render(speech))
+            node_id = str(edge.get("target") or "")
+        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:
+        await self._emit_node_active(node_id)
+        data = self._engine.data(node_id)
+        tool_id = str(data.get("toolId") or "")
+        tool = self._tool_by_id.get(tool_id)
+        if not tool:
+            self._store.values["system__last_action_status"] = "error"
+            self._store.values["system__last_action_error"] = f"工具不存在:{tool_id}"
+            return
+        try:
+            arguments = self._store.render_data(data.get("arguments") or {})
+            await self._tools.execute(
+                tool,
+                arguments,
+                result_assignments=data.get("resultAssignments") or {},
+            )
+            self._store.values["system__last_action_status"] = "ok"
+            self._store.values["system__last_action_error"] = ""
+        except (ToolExecutionError, ValueError) as exc:
+            self._store.values["system__last_action_status"] = "error"
+            self._store.values["system__last_action_error"] = str(exc)[:2048]
+
+    async def _enter_handoff(self, node_id: str) -> None:
+        await self._emit_node_active(node_id)
+        data = self._engine.data(node_id)
+        message = self._store.render(str(data.get("message") or ""))
+        await self._require_runtime().queue_frame(
+            OutputTransportMessageUrgentFrame(
+                message={
+                    "type": "handoff-requested",
+                    "nodeId": node_id,
+                    "targetType": data.get("targetType", "human"),
+                    "target": data.get("target", ""),
+                    "message": message,
+                }
+            )
+        )
+        if message:
+            await self._queue_visible_speech(message)
+        self._store.values["system__handoff_status"] = "requested"
+
+    async def _enter_end(self, node_id: str) -> None:
+        self._ended = True
+        await self._emit_node_active(node_id)
+        runtime = self._require_runtime()
+        if runtime.set_knowledge_scope:
+            runtime.set_knowledge_scope({"mode": "disabled"})
+        if runtime.set_input_enabled:
+            runtime.set_input_enabled(False)
+        data = self._engine.data(node_id)
+        message = self._store.render(str(data.get("message") or ""))
+        scope = str(data.get("scope") or "session")
+        if scope == "flow":
+            await runtime.queue_frame(
+                OutputTransportMessageUrgentFrame(
+                    message={"type": "flow-ended", "nodeId": node_id}
+                )
+            )
+            if message:
+                await self._queue_visible_speech(message)
+            return
+        runtime.call_end.begin("workflow_completed")
+        if message:
+            runtime.call_end.arm_after_speech()
+            await self._queue_visible_speech(message)
+        else:
+            await runtime.call_end.finish()
 
     async def _emit_node_active(self, node_id: str | None) -> None:
         if node_id:
@@ -128,61 +501,12 @@ class WorkflowBrain(BaseBrain):
                 )
             )
 
-    async def _speak_transition(self, edge: dict | None) -> None:
-        speech = self._engine.edge_transition_speech(edge)
-        if speech:
-            await self._require_runtime().queue_frame(
-                TTSSpeakFrame(speech, append_to_context=False)
-            )
-
-    def _make_transition_handler(self, edge: dict):
-        target = str(edge.get("target"))
-
-        async def handler(params) -> None:
-            logger.info(f"LLM 触发转移 → {self._engine.name(target)}")
-            if not self._engine.is_end(target):
-                await self._speak_transition(edge)
-            await self._go_to_node(target)
-            await params.result_callback({"status": "ok"})
-
-        return handler
-
-    async def _fallback_route(self) -> None:
-        if self._state.ended:
-            return
-        if self._state.turns_in_node < self._FALLBACK_AFTER_TURNS:
-            return
-        if not self._engine.outgoing(self._state.current):
-            return
-
-        cfg = self._require_config()
-        target = await self._engine.route(
-            self._state.current,
-            self._history,
-            api_key=self._require(cfg.llm_api_key, "LLM apiKey"),
-            base_url=self._require(cfg.llm_base_url, "LLM apiUrl"),
-            model=self._require(cfg.model, "LLM modelId"),
-        )
-        if target and target != self._state.current:
-            logger.info(f"文本兜底触发转移 → {self._engine.name(target)}")
-            if not self._engine.is_end(target):
-                await self._speak_transition(
-                    self._engine.find_edge(self._state.current, target)
-                )
-            await self._go_to_node(target)
-
     def _require_runtime(self) -> BrainRuntime:
         if self._runtime is None:
             raise RuntimeError("WorkflowBrain 尚未绑定 pipeline runtime")
         return self._runtime
 
-    def _require_config(self) -> AssistantConfig:
+    def _require_manager(self) -> FlowManager:
-        if self._cfg is None:
+        if self._manager is None:
-            raise RuntimeError("WorkflowBrain 尚未初始化配置")
+            raise RuntimeError("Workflow FlowManager 尚未初始化")
-        return self._cfg
+        return self._manager
-
-    @staticmethod
-    def _require(value: str, label: str) -> str:
-        if value:
-            return value
-        raise ValueError(f"缺少模型资源配置: {label}")

backend/services/config_resolver.py

@@ -12,7 +12,13 @@ from db.models import (
     ModelResource,
     Tool,
 )
-from models import AssistantConfig, RuntimeTool
+from models import (
+    AssistantConfig,
+    RuntimeKnowledgeBase,
+    RuntimeModelResource,
+    RuntimeTool,
+)
+from services.node_specs import graph_references, normalize_graph
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
@@ -83,7 +89,7 @@ def _secret(resource: ModelResource | None, key: str, default: str = "") -> str:
 
 
 async def _tools_for(session: AsyncSession, assistant: Assistant) -> list[RuntimeTool]:
-    if assistant.type != "prompt":
+    if assistant.type not in {"prompt", "workflow"}:
         return []
     tools = (
         await session.execute(
@@ -134,6 +140,43 @@ async def resolve_runtime_config(
         else None
     )
 
+    graph = normalize_graph(assistant.graph or {}) if assistant.type == "workflow" else {}
+    refs = graph_references(graph) if graph else {
+        "model_resources": set(),
+        "knowledge_bases": set(),
+    }
+    workflow_resources: dict[str, RuntimeModelResource] = {}
+    if refs["model_resources"]:
+        resources = (
+            await session.execute(
+                select(ModelResource).where(ModelResource.id.in_(refs["model_resources"]))
+            )
+        ).scalars().all()
+        workflow_resources = {
+            resource.id: RuntimeModelResource(
+                id=resource.id,
+                name=resource.name,
+                capability=resource.capability,
+                interface_type=resource.interface_type,
+                values=resource.values or {},
+                secrets=resource.secrets or {},
+            )
+            for resource in resources
+            if resource.enabled
+        }
+    workflow_knowledge: dict[str, RuntimeKnowledgeBase] = {}
+    if refs["knowledge_bases"]:
+        knowledge_rows = (
+            await session.execute(
+                select(KnowledgeBase).where(KnowledgeBase.id.in_(refs["knowledge_bases"]))
+            )
+        ).scalars().all()
+        workflow_knowledge = {
+            kb.id: RuntimeKnowledgeBase(id=kb.id, name=kb.name, description=kb.description)
+            for kb in knowledge_rows
+            if kb.status == "active"
+        }
+
     return AssistantConfig(
         name=assistant.name,
         type=assistant.type,
@@ -150,7 +193,9 @@ async def resolve_runtime_config(
         knowledge_base_description=knowledge_base.description if knowledge_base else "",
         knowledge_retrieval_config=assistant.knowledge_retrieval_config or {},
         # workflow 图:仅 workflow 类型非空,引擎据此启用图驱动对话
-        graph=(assistant.graph or {}) if assistant.type == "workflow" else {},
+        graph=graph,
+        workflow_model_resources=workflow_resources,
+        workflow_knowledge_bases=workflow_knowledge,
         # 外部托管类型连接信息(DB 存真 key,直接注入)
         dify_api_url=str(_value(agent_resource, "apiUrl", assistant.api_url)),
         dify_api_key=_secret(agent_resource, "apiKey", assistant.api_key),

backend/services/node_specs.py

@@ -1,132 +1,110 @@
-"""工作流节点规格 + 图校验(对齐 dograh 的 node-spec / GraphConstraints 思路)。
+"""Workflow v3 node catalog, v2 compatibility normalization, and validation."""
-
-当前实现 4 个核心节点:开始(startCall)/智能体(agentNode)/结束(endCall)/全局(globalNode)。
-本模块是「节点类型」的唯一事实源:
-  - /api/node-types 接口直接吐这里的规格;
-  - 助手保存时用这里的约束校验 workflow 图。
-
-新增节点类型只需在 NODE_SPECS 里加一条并补充约束。前端 specs.ts 与此保持一致。
-"""
 
 from __future__ import annotations
 
+from collections import defaultdict, deque
+from copy import deepcopy
 from typing import Any
 
-# 规格版本号:节点定义有破坏性变更时 +1,前端可据此判断是否需要刷新缓存。
-SPEC_VERSION = "2"
 
-# 每个节点的图约束。None 表示不限制。
+SPEC_VERSION = "3"
-#   min_incoming / max_incoming:入边数量
+NODE_TYPES = {"start", "agent", "action", "handoff", "end"}
-#   min_outgoing / max_outgoing:出边数量
+EDGE_MODES = {"llm", "expression", "always"}
+AGENT_ENTRY_MODES = {"wait_user", "generate", "fixed_speech"}
+AUTOMATIC_NODE_TYPES = {"start", "action", "handoff"}
+EXPRESSION_OPERATORS = {
+    "eq",
+    "neq",
+    "gt",
+    "gte",
+    "lt",
+    "lte",
+    "contains",
+    "in",
+    "exists",
+}
+
 NODE_SPECS: list[dict[str, Any]] = [
     {
-        "name": "startCall",
+        "name": "start",
-        "displayName": "开始",
+        "displayName": "Start",
-        "category": "call_node",
+        "category": "control_node",
-        "description": "工作流入口,每个流程有且仅有一个。播放开场白,并用自己的提示词进行多轮对话,满足出边条件后流转。",
+        "description": "初始化会话、动态变量和全局观察器，可播放固定开场白。",
         "icon": "Play",
         "accent": "mint",
         "addable": False,
         "constraints": {
             "minIncoming": 0,
             "maxIncoming": 0,
+            "minOutgoing": 0,
             "minInstances": 1,
             "maxInstances": 1,
         },
         "fields": [
-            {"key": "name", "label": "节点名称", "type": "text", "default": "开始"},
+            {"key": "name", "label": "节点名称", "type": "text", "default": "Start"},
-            {"key": "greeting", "label": "开场白", "type": "textarea", "default": ""},
+            {"key": "greeting", "label": "固定开场白", "type": "textarea", "default": ""},
-            {"key": "prompt", "label": "节点提示词", "type": "textarea", "default": ""},
-            {
-                "key": "allowInterrupt",
-                "label": "允许用户打断",
-                "type": "switch",
-                "default": True,
-            },
-            {
-                "key": "addGlobalPrompt",
-                "label": "应用全局提示词",
-                "type": "switch",
-                "default": True,
-            },
         ],
     },
     {
-        "name": "agentNode",
+        "name": "agent",
-        "displayName": "智能体节点",
+        "displayName": "Agent",
-        "category": "call_node",
+        "category": "conversation_node",
-        "description": "对话处理单元。按提示词与用户多轮交互,可有多个并通过条件边流转。",
+        "description": "阶段智能体：绑定上下文、工具、知识库及 ASR/TTS 资源。",
         "icon": "Bot",
         "accent": "sky",
         "addable": True,
-        "constraints": {"minIncoming": 1},
+        "constraints": {"minIncoming": 1, "minOutgoing": 0},
         "fields": [
-            {"key": "name", "label": "节点名称", "type": "text", "default": "智能体节点"},
+            {"key": "name", "label": "节点名称", "type": "text", "default": "Agent"},
             {
                 "key": "prompt",
-                "label": "节点提示词",
+                "label": "阶段提示词",
                 "type": "textarea",
                 "required": True,
                 "default": "",
             },
-            {
-                "key": "allowInterrupt",
-                "label": "允许用户打断",
-                "type": "switch",
-                "default": True,
-            },
-            {
-                "key": "addGlobalPrompt",
-                "label": "应用全局提示词",
-                "type": "switch",
-                "default": True,
-            },
         ],
     },
     {
-        "name": "endCall",
+        "name": "action",
-        "displayName": "结束",
+        "displayName": "Action",
-        "category": "call_node",
+        "category": "execution_node",
-        "description": "终止节点,礼貌结束对话。可有多个,均无出边。",
+        "description": "确定性执行指定工具，并将结果字段写入会话动态变量。",
+        "icon": "Zap",
+        "accent": "peach",
+        "addable": True,
+        "constraints": {"minIncoming": 1, "minOutgoing": 1},
+        "fields": [
+            {"key": "name", "label": "节点名称", "type": "text", "default": "Action"},
+        ],
+    },
+    {
+        "name": "handoff",
+        "displayName": "Handoff",
+        "category": "execution_node",
+        "description": "转交其他 AI、人工、队列或电话；MVP 发送转交事件后继续路由。",
+        "icon": "PhoneForwarded",
+        "accent": "lavender",
+        "addable": True,
+        "constraints": {"minIncoming": 1, "minOutgoing": 0},
+        "fields": [
+            {"key": "name", "label": "节点名称", "type": "text", "default": "Handoff"},
+            {"key": "target", "label": "转交目标", "type": "text", "default": ""},
+            {"key": "message", "label": "转交提示", "type": "textarea", "default": ""},
+        ],
+    },
+    {
+        "name": "end",
+        "displayName": "End",
+        "category": "control_node",
+        "description": "结束 AI 流程或整个音视频会话。",
         "icon": "Flag",
         "accent": "rose",
         "addable": True,
         "constraints": {"minIncoming": 1, "minOutgoing": 0, "maxOutgoing": 0},
         "fields": [
-            {"key": "name", "label": "节点名称", "type": "text", "default": "结束"},
+            {"key": "name", "label": "节点名称", "type": "text", "default": "End"},
-            {"key": "prompt", "label": "结束语提示词", "type": "textarea", "default": ""},
+            {"key": "message", "label": "固定结束语", "type": "textarea", "default": ""},
-            {
-                "key": "addGlobalPrompt",
-                "label": "应用全局提示词",
-                "type": "switch",
-                "default": False,
-            },
-        ],
-    },
-    {
-        "name": "globalNode",
-        "displayName": "全局节点",
-        "category": "global_node",
-        "description": "为整个工作流提供统一的人设、语气和公共规则。无需连线,每个流程最多一个。",
-        "icon": "Globe2",
-        "accent": "lavender",
-        "addable": True,
-        "constraints": {
-            "minIncoming": 0,
-            "maxIncoming": 0,
-            "minOutgoing": 0,
-            "maxOutgoing": 0,
-            "maxInstances": 1,
-        },
-        "fields": [
-            {"key": "name", "label": "节点名称", "type": "text", "default": "全局设定"},
-            {
-                "key": "prompt",
-                "label": "全局提示词",
-                "type": "textarea",
-                "required": True,
-                "default": "你是一个友好、专业的语音助手。请使用简短、自然、适合口语表达的句子。",
-            },
         ],
     },
 ]
@@ -135,108 +113,372 @@ _SPEC_BY_NAME = {spec["name"]: spec for spec in NODE_SPECS}
 
 
 def node_types_response() -> dict[str, Any]:
-    """/api/node-types 的响应体(camelCase,直接喂前端)。"""
     return {"specVersion": SPEC_VERSION, "nodeTypes": NODE_SPECS}
 
 
-def validate_graph(graph: dict[str, Any]) -> list[str]:
+def _edge_data_v3(edge: dict, source_type: str) -> dict:
-    """校验 workflow 图,返回错误信息列表(空列表 = 通过)。
+    data = deepcopy(edge.get("data") or {})
+    if data.get("mode") in EDGE_MODES:
+        data.setdefault("priority", 10)
+        return data
+    condition = str(data.pop("condition", "") or "").strip()
+    transition = data.pop("transition_speech", data.get("transitionSpeech", ""))
+    data.update(
+        {
+            "mode": "llm" if condition and source_type == "agent" else "always",
+            "priority": 10,
+            "condition": condition,
+            "transitionSpeech": transition,
+        }
+    )
+    return data
 
-    基础规则(对齐 dograh 的核心不变量):
-      1. 节点类型必须是已注册类型;
-      2. 有且仅有一个 startCall;
-      3. 至少有一个 endCall,全局节点最多一个;
-      4. 边的 source/target 必须指向存在的节点;
-      5. 入边/出边数量满足各节点类型的约束。
 
-    空图(无节点)视为草稿,直接放行,方便先存后编排。
+def _normalize_agent_data(data: dict[str, Any]) -> None:
-    """
+    """Add v3 Agent defaults without changing existing node-level behavior."""
-    errors: list[str] = []
+    data.setdefault("contextPolicy", "inherit")
-    nodes = graph.get("nodes") or []
+    data.setdefault("entryMode", "wait_user")
-    edges = graph.get("edges") or []
+    data.setdefault("entrySpeech", "")
+    if "inheritGlobalConfig" not in data:
+        has_node_overrides = any(
+            (
+                data.get("llmResourceId"),
+                data.get("asrResourceId"),
+                data.get("ttsResourceId"),
+                data.get("knowledgeBaseId"),
+                data.get("toolIds"),
+            )
+        )
+        data["inheritGlobalConfig"] = not has_node_overrides
 
-    if not nodes:
-        return errors  # 草稿:放行
 
-    node_ids: set[str] = set()
+def _normalize_settings(settings: dict[str, Any], *, global_prompt: str = "") -> None:
-    type_counts: dict[str, int] = {}
+    settings.setdefault("globalPrompt", global_prompt)
-    node_type_by_id: dict[str, str] = {}
+    settings.setdefault("defaultLlmResourceId", "")
+    settings.setdefault("defaultAsrResourceId", "")
+    settings.setdefault("defaultTtsResourceId", "")
+    settings.setdefault("toolIds", [])
+    settings.setdefault("knowledgeBaseId", "")
+    settings.setdefault("knowledgeMode", "automatic")
+    settings.setdefault("knowledgeTopN", 5)
+    settings.setdefault("knowledgeScoreThreshold", 0.0)
 
+
+def normalize_graph(graph: dict[str, Any] | None) -> dict[str, Any]:
+    """Return a deep-copied v3 graph; preserve v3 IDs and migrate v2 semantics."""
+    source = deepcopy(graph or {})
+    if str(source.get("specVersion") or "") == SPEC_VERSION:
+        settings = source.setdefault("settings", {})
+        _normalize_settings(settings)
+        source.setdefault("nodes", [])
+        source.setdefault("edges", [])
+        for node in source["nodes"]:
+            if node.get("type") != "agent":
+                continue
+            data = node.setdefault("data", {})
+            _normalize_agent_data(data)
+        return source
+
+    nodes = source.get("nodes") or []
+    edges = source.get("edges") or []
+    global_prompt = ""
+    mapped_nodes: list[dict] = []
+    type_by_id: dict[str, str] = {}
+    start_prompt_nodes: dict[str, str] = {}
+
+    type_map = {
+        "startCall": "start",
+        "agentNode": "agent",
+        "endCall": "end",
+        "start": "start",
+        "agent": "agent",
+        "action": "action",
+        "handoff": "handoff",
+        "end": "end",
+    }
     for node in nodes:
-        node_id = node.get("id")
+        old_type = str(node.get("type") or "")
-        node_type = node.get("type")
+        data = deepcopy(node.get("data") or {})
-        if not node_id:
+        if old_type == "globalNode":
-            errors.append("存在缺少 id 的节点")
+            global_prompt = str(data.get("prompt") or "")
             continue
-        if node_id in node_ids:
+        new_type = type_map.get(old_type, old_type)
-            errors.append(f"节点 id 重复:{node_id}")
+        migrated = deepcopy(node)
-        node_ids.add(node_id)
+        migrated["type"] = new_type
-        if node_type not in _SPEC_BY_NAME:
+        if new_type == "end":
-            errors.append(f"未知节点类型:{node_type}(节点 {node_id})")
+            data["message"] = data.pop("message", data.pop("prompt", ""))
-            continue
+            data.setdefault("scope", "session")
-        node_type_by_id[node_id] = node_type
+        elif new_type == "agent":
-        type_counts[node_type] = type_counts.get(node_type, 0) + 1
+            _normalize_agent_data(data)
+        elif new_type == "start":
+            prompt = str(data.pop("prompt", "") or "").strip()
+            if prompt:
+                start_prompt_nodes[str(node.get("id"))] = prompt
+            for key in ("allowInterrupt", "addGlobalPrompt"):
+                data.pop(key, None)
+        migrated["data"] = data
+        mapped_nodes.append(migrated)
+        if migrated.get("id"):
+            type_by_id[str(migrated["id"])] = new_type
 
-    start_count = type_counts.get("startCall", 0)
+    mapped_edges: list[dict] = []
-    if start_count == 0:
+    for edge in edges:
-        errors.append("工作流必须有一个「开始」节点")
+        migrated = deepcopy(edge)
-    elif start_count > 1:
+        migrated["data"] = _edge_data_v3(
-        errors.append("工作流只能有一个「开始」节点")
+            migrated, type_by_id.get(str(migrated.get("source")), "")
+        )
+        mapped_edges.append(migrated)
 
-    if type_counts.get("endCall", 0) == 0:
+    # A v2 Start was conversational. Insert a synthetic Agent so its prompt remains active.
-        errors.append("工作流至少需要一个「结束」节点")
+    for start_id, prompt in start_prompt_nodes.items():
-
+        synthetic_id = f"{start_id}-migrated-agent"
-    for node_type, spec in _SPEC_BY_NAME.items():
+        start_node = next((n for n in mapped_nodes if n.get("id") == start_id), None)
-        # 开始节点上方已有更明确的中文错误提示,避免重复报错。
+        position = (start_node or {}).get("position") or {"x": 100, "y": 120}
-        if node_type == "startCall":
+        mapped_nodes.append(
-            continue
+            {
-        constraints = spec["constraints"]
+                "id": synthetic_id,
-        count = type_counts.get(node_type, 0)
+                "type": "agent",
-        _check_count(
+                "position": {"x": position.get("x", 100) + 300, "y": position.get("y", 120)},
-            errors,
+                "data": {
-            count,
+                    "name": "迁移的开场 Agent",
-            constraints,
+                    "prompt": prompt,
-            "Instances",
+                    "contextPolicy": "inherit",
-            node_type,
+                    "inheritGlobalConfig": True,
-            "实例",
+                    "entryMode": "wait_user",
+                    "entrySpeech": "",
+                },
+            }
+        )
+        for edge in mapped_edges:
+            if edge.get("source") == start_id:
+                edge["source"] = synthetic_id
+                edge["data"] = _edge_data_v3(edge, "agent")
+                if str(edge["data"].get("condition") or "").strip():
+                    edge["data"]["mode"] = "llm"
+        mapped_edges.append(
+            {
+                "id": f"e-{start_id}-{synthetic_id}",
+                "source": start_id,
+                "target": synthetic_id,
+                "data": {"mode": "always", "priority": 0, "transitionSpeech": ""},
+            }
         )
 
-    # 统计入边/出边
+    settings = deepcopy(source.get("settings") or {})
-    incoming: dict[str, int] = {nid: 0 for nid in node_ids}
+    _normalize_settings(settings, global_prompt=global_prompt)
-    outgoing: dict[str, int] = {nid: 0 for nid in node_ids}
+    return {
-    for edge in edges:
+        "specVersion": 3,
-        source = edge.get("source")
+        "settings": settings,
-        target = edge.get("target")
+        "nodes": mapped_nodes,
-        if source not in node_ids:
+        "edges": mapped_edges,
-            errors.append(f"连线指向了不存在的源节点:{source}")
+        **({"viewport": deepcopy(source["viewport"])} if source.get("viewport") else {}),
-            continue
+    }
-        if target not in node_ids:
-            errors.append(f"连线指向了不存在的目标节点:{target}")
-            continue
-        outgoing[source] += 1
-        incoming[target] += 1
 
-    for node_id, node_type in node_type_by_id.items():
-        constraints = _SPEC_BY_NAME[node_type]["constraints"]
-        name = node_type
-        _check_count(errors, incoming[node_id], constraints, "Incoming", name, "入边")
-        _check_count(errors, outgoing[node_id], constraints, "Outgoing", name, "出边")
 
+def _validate_expression(expression: Any) -> list[str]:
+    if not isinstance(expression, dict):
+        return ["表达式条件不能为空"]
+    combinator = expression.get("combinator", "and")
+    if combinator not in {"and", "or"}:
+        return ["表达式组合方式必须是 and 或 or"]
+    rules = expression.get("rules")
+    if not isinstance(rules, list) or not rules:
+        return ["表达式至少需要一条规则"]
+    errors = []
+    for rule in rules:
+        if not isinstance(rule, dict) or not rule.get("variable"):
+            errors.append("表达式规则缺少变量")
+        elif rule.get("operator") not in EXPRESSION_OPERATORS:
+            errors.append(f"不支持的表达式运算符:{rule.get('operator')}")
     return errors
 
 
-def _check_count(
+def validate_graph(graph: dict[str, Any]) -> list[str]:
-    errors: list[str],
+    graph = normalize_graph(graph)
-    actual: int,
+    nodes = graph.get("nodes") or []
-    constraints: dict[str, int],
+    edges = graph.get("edges") or []
-    suffix: str,
+    if not nodes:
-    node_type: str,
+        return []
-    label: str,
+
-) -> None:
+    errors: list[str] = []
-    lo = constraints.get(f"min{suffix}")
+    node_by_id: dict[str, dict] = {}
-    hi = constraints.get(f"max{suffix}")
+    counts: dict[str, int] = defaultdict(int)
-    display = _SPEC_BY_NAME[node_type]["displayName"]
+    for node in nodes:
-    if lo is not None and actual < lo:
+        node_id = str(node.get("id") or "")
-        errors.append(f"「{display}」节点{label}数量不能少于 {lo}(当前 {actual})")
+        node_type = str(node.get("type") or "")
-    if hi is not None and actual > hi:
+        if not node_id:
-        errors.append(f"「{display}」节点{label}数量不能多于 {hi}(当前 {actual})")
+            errors.append("存在缺少 id 的节点")
+            continue
+        if node_id in node_by_id:
+            errors.append(f"节点 id 重复:{node_id}")
+        if node_type not in NODE_TYPES:
+            errors.append(f"未知节点类型:{node_type}(节点 {node_id})")
+        node_by_id[node_id] = node
+        counts[node_type] += 1
+
+        if node_type == "agent":
+            data = node.get("data") or {}
+            entry_mode = data.get("entryMode", "wait_user")
+            if entry_mode not in AGENT_ENTRY_MODES:
+                errors.append(f"Agent 节点 {node_id} 的进入模式无效:{entry_mode}")
+            elif entry_mode == "fixed_speech" and not str(
+                data.get("entrySpeech") or ""
+            ).strip():
+                errors.append(f"Agent 节点 {node_id} 的固定进入语不能为空")
+
+    if counts["start"] != 1:
+        errors.append("工作流必须有且仅有一个 Start 节点")
+    incoming: dict[str, int] = defaultdict(int)
+    outgoing: dict[str, int] = defaultdict(int)
+    adj: dict[str, list[str]] = defaultdict(list)
+    auto_adj: dict[str, list[str]] = defaultdict(list)
+    priorities: dict[str, set[int]] = defaultdict(set)
+    always_counts: dict[str, int] = defaultdict(int)
+    for edge in edges:
+        edge_id = str(edge.get("id") or "")
+        source_id = str(edge.get("source") or "")
+        target_id = str(edge.get("target") or "")
+        if source_id not in node_by_id:
+            errors.append(f"边 {edge_id} 指向不存在的源节点:{source_id}")
+            continue
+        if target_id not in node_by_id:
+            errors.append(f"边 {edge_id} 指向不存在的目标节点:{target_id}")
+            continue
+        if source_id == target_id and node_by_id[source_id].get("type") != "agent":
+            errors.append(f"自动节点不能自连:{source_id}")
+        data = edge.get("data") or {}
+        mode = data.get("mode")
+        if mode not in EDGE_MODES:
+            errors.append(f"边 {edge_id} 的判断模式无效:{mode}")
+        if mode == "llm" and node_by_id[source_id].get("type") != "agent":
+            errors.append(f"LLM 判断边只能从 Agent 发出:{edge_id}")
+        if mode == "llm" and not str(data.get("condition") or "").strip():
+            errors.append(f"LLM 判断边缺少自然语言条件:{edge_id}")
+        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边")
+        incoming[target_id] += 1
+        outgoing[source_id] += 1
+        adj[source_id].append(target_id)
+        source_is_automatic = node_by_id[source_id].get("type") != "agent"
+        target_is_automatic = node_by_id[target_id].get("type") != "agent"
+        if source_is_automatic and target_is_automatic:
+            auto_adj[source_id].append(target_id)
+
+    for node_id, node in node_by_id.items():
+        spec = _SPEC_BY_NAME.get(str(node.get("type")))
+        if not spec:
+            continue
+        constraints = spec["constraints"]
+        for actual, suffix, label in (
+            (incoming[node_id], "Incoming", "入边"),
+            (outgoing[node_id], "Outgoing", "出边"),
+        ):
+            lo = constraints.get(f"min{suffix}")
+            hi = constraints.get(f"max{suffix}")
+            if lo is not None and actual < lo:
+                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}")
+        node_type = node.get("type")
+        if (
+            node_type in AUTOMATIC_NODE_TYPES
+            and outgoing[node_id] > 0
+            and always_counts[node_id] != 1
+        ):
+            errors.append(f"自动节点 {node_id} 存在出边时必须有且仅有一条默认边")
+
+    start_id = next(
+        (node_id for node_id, node in node_by_id.items() if node.get("type") == "start"),
+        None,
+    )
+    if start_id:
+        reached = {start_id}
+        queue = deque([start_id])
+        while queue:
+            current = queue.popleft()
+            for target in adj[current]:
+                if target not in reached:
+                    reached.add(target)
+                    queue.append(target)
+        for node_id in node_by_id.keys() - reached:
+            errors.append(f"节点不可从 Start 到达:{node_id}")
+
+    # Reject cycles made only of instantaneous nodes; Agent cycles are valid waits.
+    visiting: set[str] = set()
+    visited: set[str] = set()
+
+    def visit(node_id: str) -> bool:
+        if node_id in visiting:
+            return True
+        if node_id in visited:
+            return False
+        visiting.add(node_id)
+        for target in auto_adj[node_id]:
+            if visit(target):
+                return True
+        visiting.remove(node_id)
+        visited.add(node_id)
+        return False
+
+    automatic_node_ids = (
+        node_id
+        for node_id, node in node_by_id.items()
+        if node.get("type") != "agent"
+    )
+    if any(visit(node_id) for node_id in automatic_node_ids):
+        errors.append("Start/Action/Handoff/End 之间不能形成无等待循环")
+    return list(dict.fromkeys(errors))
+
+
+def graph_references(graph: dict[str, Any]) -> dict[str, set[str]]:
+    """Collect externally referenced IDs for save/runtime validation."""
+    normalized = normalize_graph(graph)
+    settings = normalized.get("settings") or {}
+    resources = {
+        str(value)
+        for value in (
+            settings.get("defaultLlmResourceId"),
+            settings.get("defaultAsrResourceId"),
+            settings.get("defaultTtsResourceId"),
+        )
+        if value
+    }
+    tools: set[str] = {str(tool_id) for tool_id in settings.get("toolIds") or []}
+    knowledge: set[str] = (
+        {str(settings["knowledgeBaseId"])}
+        if settings.get("knowledgeBaseId")
+        else set()
+    )
+    for node in normalized.get("nodes") or []:
+        data = node.get("data") or {}
+        inherits_global = (
+            node.get("type") == "agent" and data.get("inheritGlobalConfig", True)
+        )
+        if not inherits_global:
+            for resource_id in (
+                data.get("llmResourceId"),
+                data.get("asrResourceId"),
+                data.get("ttsResourceId"),
+            ):
+                if resource_id:
+                    resources.add(str(resource_id))
+            for tool_id in data.get("toolIds") or []:
+                tools.add(str(tool_id))
+            if data.get("knowledgeBaseId"):
+                knowledge.add(str(data["knowledgeBaseId"]))
+        if data.get("toolId"):
+            tools.add(str(data["toolId"]))
+    return {"model_resources": resources, "tools": tools, "knowledge_bases": knowledge}

backend/services/pipecat/pipeline.py

@@ -23,6 +23,8 @@ from services.pipecat.call_lifecycle import (
     EndCallAfterSpeechProcessor,
 )
 from services.pipecat.service_factory import (
+    config_with_resource,
+    create_llm,
     create_realtime_service,
     create_stt,
     create_tts,
@@ -32,6 +34,7 @@ from services.knowledge import search as search_knowledge
 
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.flows import FlowsFunctionSchema
 from pipecat.frames.frames import (
     EndFrame,
     InputTransportMessageFrame,
@@ -40,6 +43,7 @@ from pipecat.frames.frames import (
     LLMFullResponseStartFrame,
     LLMContextFrame,
     LLMTextFrame,
+    ManuallySwitchServiceFrame,
     LLMMessagesAppendFrame,
     OutputTransportMessageUrgentFrame,
     TextFrame,
@@ -48,6 +52,8 @@ from pipecat.frames.frames import (
     UserImageRequestFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.llm_switcher import LLMSwitcher
+from pipecat.pipeline.service_switcher import ServiceSwitcher
 from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
@@ -397,15 +403,59 @@ class KnowledgeRetrievalProcessor(FrameProcessor):
         self._knowledge_base_id = knowledge_base_id
         self._top_n = top_n
         self._score_threshold = score_threshold
+        self._mode = "automatic" if knowledge_base_id else "disabled"
         self._last_signature = ""
 
+    def set_scope(self, scope: dict) -> None:
+        self._knowledge_base_id = scope.get("knowledge_base_id") or None
+        self._mode = str(scope.get("mode") or "disabled")
+        self._top_n = int(scope.get("top_n") or 5)
+        self._score_threshold = float(scope.get("score_threshold") or 0.0)
+        self._last_signature = ""
+
+    def _clear_context(self, messages: list[dict]) -> None:
+        # Remove the legacy Workflow knowledge message so an in-flight context
+        # created before this compatibility fix cannot keep sending that role.
+        messages[:] = [
+            message
+            for message in messages
+            if not (
+                message.get("role") == "developer"
+                and KNOWLEDGE_CONTEXT_MARKER in str(message.get("content") or "")
+            )
+        ]
+        system_message = next(
+            (message for message in messages if message.get("role") == "system"),
+            None,
+        )
+        if system_message is not None:
+            content = str(system_message.get("content") or "")
+            system_message["content"] = content.split(KNOWLEDGE_CONTEXT_MARKER, 1)[0].rstrip()
+
+    def _set_context(self, messages: list[dict], block: str) -> None:
+        """Store retrieved knowledge in a provider-compatible system message."""
+        self._clear_context(messages)
+        system_message = next(
+            (message for message in messages if message.get("role") == "system"),
+            None,
+        )
+        if system_message is None:
+            messages.insert(0, {"role": "system", "content": block})
+            return
+        content = str(system_message.get("content") or "").rstrip()
+        system_message["content"] = f"{content}\n\n{block}" if content else block
+
     async def process_frame(self, frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
-        if not self._knowledge_base_id or not isinstance(frame, LLMContextFrame):
+        if not isinstance(frame, LLMContextFrame):
             await self.push_frame(frame, direction)
             return
 
         messages = frame.context.get_messages()
+        if self._mode != "automatic" or not self._knowledge_base_id:
+            self._clear_context(messages)
+            await self.push_frame(frame, direction)
+            return
         user_messages = [message for message in messages if message.get("role") == "user"]
         if not user_messages:
             await self.push_frame(frame, direction)
@@ -435,16 +485,53 @@ class KnowledgeRetrievalProcessor(FrameProcessor):
             for index, item in enumerate(results)
         ) or "未检索到相关资料。"
         block = f"{KNOWLEDGE_CONTEXT_MARKER}\n当前问题的知识库检索结果：\n{sources}"
-        system_message = next((message for message in messages if message.get("role") == "system"), None)
+        self._set_context(messages, block)
-        if system_message is None:
-            messages.insert(0, {"role": "system", "content": block})
-        else:
-            content = str(system_message.get("content") or "")
-            base = content.split(KNOWLEDGE_CONTEXT_MARKER, 1)[0].rstrip()
-            system_message["content"] = f"{base}\n\n{block}" if base else block
         await self.push_frame(frame, direction)
 
 
+class UserTurnRoutingProcessor(FrameProcessor):
+    """Give a brain first right of refusal before a new user turn reaches the LLM."""
+
+    def __init__(self, brain: Brain):
+        super().__init__()
+        self._brain = brain
+        self._last_user_message: dict | None = None
+
+    async def process_frame(self, frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+        if direction != FrameDirection.DOWNSTREAM or not isinstance(
+            frame, LLMContextFrame
+        ):
+            await self.push_frame(frame, direction)
+            return
+
+        user_message = next(
+            (
+                message
+                for message in reversed(frame.context.get_messages())
+                if message.get("role") == "user"
+                and isinstance(message.get("content"), str)
+                and str(message.get("content") or "").strip()
+            ),
+            None,
+        )
+        if user_message is None:
+            await self.push_frame(frame, direction)
+            return
+
+        if user_message is self._last_user_message:
+            # Programmatic LLMRunFrame after a node transition reuses the same
+            # user message. It is a response run, not another routing event.
+            await self.push_frame(frame, direction)
+            return
+        self._last_user_message = user_message
+
+        content = str(user_message.get("content") or "").strip()
+        handled = await self._brain.on_user_turn_end(content)
+        if not handled:
+            await self.push_frame(frame, direction)
+
+
 class PassthroughLLMAssistantAggregator(LLMAssistantAggregator):
     """聚合 LLM 回复进上下文,同时继续把回复帧交给下游 TTS。"""
 
@@ -457,7 +544,6 @@ class PassthroughLLMAssistantAggregator(LLMAssistantAggregator):
         self._stream_turn_id: str | None = None
         self._stream_timestamp = ""
         self._stream_text = ""
-
     async def process_frame(self, frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
@@ -505,6 +591,72 @@ class PassthroughLLMAssistantAggregator(LLMAssistantAggregator):
         self._stream_text = ""
 
 
+class WorkflowAggregatorPair:
+    """Small public-shape adapter required by Pipecat FlowManager."""
+
+    def __init__(self, user_aggregator, assistant_aggregator):
+        self._user = user_aggregator
+        self._assistant = assistant_aggregator
+
+    def user(self):
+        return self._user
+
+    def assistant(self):
+        return self._assistant
+
+
+def _workflow_service_switcher(
+    cfg: AssistantConfig, capability: str, base_service: FrameProcessor
+):
+    """Build one switcher and an ID lookup for every referenced voice resource."""
+    create = create_stt if capability == "ASR" else create_tts
+    settings = cfg.graph.get("settings") or {}
+    default_key = (
+        "defaultAsrResourceId" if capability == "ASR" else "defaultTtsResourceId"
+    )
+    default_id = str(settings.get(default_key) or "")
+    services_by_id = {}
+    for resource_id, resource in cfg.workflow_model_resources.items():
+        if resource.capability != capability:
+            continue
+        services_by_id[resource_id] = (
+            base_service
+            if resource_id == default_id
+            else create(config_with_resource(cfg, resource))
+        )
+    primary = services_by_id.get(default_id, base_service)
+    services = [primary]
+    services.extend(
+        service for service in services_by_id.values() if service is not primary
+    )
+    if base_service is not primary:
+        services.append(base_service)
+    return ServiceSwitcher(services=services), services_by_id, primary
+
+
+def _workflow_llm_switcher(cfg: AssistantConfig, base_service):
+    """Build an LLM switcher for the global model and Agent overrides."""
+    settings = cfg.graph.get("settings") or {}
+    default_id = str(settings.get("defaultLlmResourceId") or "")
+    services_by_id = {}
+    for resource_id, resource in cfg.workflow_model_resources.items():
+        if resource.capability != "LLM":
+            continue
+        services_by_id[resource_id] = (
+            base_service
+            if resource_id == default_id
+            else create_llm(config_with_resource(cfg, resource))
+        )
+    primary = services_by_id.get(default_id, base_service)
+    services = [primary]
+    services.extend(
+        service for service in services_by_id.values() if service is not primary
+    )
+    if base_service is not primary:
+        services.append(base_service)
+    return LLMSwitcher(llms=services), services_by_id, primary
+
+
 async def run_pipeline(
     transport,
     cfg: AssistantConfig,
@@ -545,8 +697,38 @@ async def run_pipeline(
         )
         return
 
-    stt = create_stt(cfg)
+    graph_settings = cfg.graph.get("settings") or {}
-    tts = create_tts(cfg)
+    default_llm_resource = cfg.workflow_model_resources.get(
+        str(graph_settings.get("defaultLlmResourceId") or "")
+    )
+    default_asr_resource = cfg.workflow_model_resources.get(
+        str(graph_settings.get("defaultAsrResourceId") or "")
+    )
+    default_tts_resource = cfg.workflow_model_resources.get(
+        str(graph_settings.get("defaultTtsResourceId") or "")
+    )
+    stt = create_stt(
+        config_with_resource(cfg, default_asr_resource)
+        if cfg.type == "workflow" and default_asr_resource
+        else cfg
+    )
+    tts = create_tts(
+        config_with_resource(cfg, default_tts_resource)
+        if cfg.type == "workflow" and default_tts_resource
+        else cfg
+    )
+    stt_processor = stt
+    tts_processor = tts
+    stt_services: dict[str, FrameProcessor] = {}
+    tts_services: dict[str, FrameProcessor] = {}
+    current_voice_services: dict[str, FrameProcessor] = {"asr": stt, "tts": tts}
+    if cfg.type == "workflow":
+        stt_processor, stt_services, current_voice_services["asr"] = (
+            _workflow_service_switcher(cfg, "ASR", stt)
+        )
+        tts_processor, tts_services, current_voice_services["tts"] = (
+            _workflow_service_switcher(cfg, "TTS", tts)
+        )
 
     greeting = await brain.greeting(cfg)
     system_content = brain.system_prompt(cfg)
@@ -594,17 +776,33 @@ async def run_pipeline(
         return "\n\n".join(part for part in [text, *hints] if part)
 
     context = LLMContext(
-        messages=[{"role": "system", "content": with_vision_hint(system_content)}]
+        messages=(
+            []
+            if cfg.type == "workflow"
+            else [{"role": "system", "content": with_vision_hint(system_content)}]
+        )
     )
+    input_state = {"enabled": True}
     # LLM 槽由大脑提供:本地模型或 Dify/FastGPT 外部托管适配器。
-    llm = brain.build_llm(cfg, context)
+    llm = brain.build_llm(
+        config_with_resource(cfg, default_llm_resource)
+        if cfg.type == "workflow" and default_llm_resource
+        else cfg,
+        context,
+    )
+    llm_services: dict[str, FrameProcessor] = {}
+    current_llm_service = llm
+    if cfg.type == "workflow":
+        llm, llm_services, current_llm_service = _workflow_llm_switcher(cfg, llm)
     user_aggregator = LLMUserAggregator(
         context,
         params=LLMUserAggregatorParams(
             vad_analyzer=create_vad_analyzer(cfg.turnConfig),
             user_mute_strategies=[
                 FunctionCallUserMuteStrategy(),
-                CallEndingUserMuteStrategy(lambda: call_end.ending),
+                CallEndingUserMuteStrategy(
+                    lambda: call_end.ending or not input_state["enabled"]
+                ),
             ],
             user_turn_strategies=create_user_turn_strategies(
                 cfg.turnConfig,
@@ -612,8 +810,11 @@ async def run_pipeline(
             ),
         ),
     )
+    user_turn_router = UserTurnRoutingProcessor(brain)
     assistant_aggregator = PassthroughLLMAssistantAggregator(context)
-    text_input = TextInputProcessor(should_ignore_input=lambda: call_end.ending)
+    text_input = TextInputProcessor(
+        should_ignore_input=lambda: call_end.ending or not input_state["enabled"]
+    )
     vision_capture = VisionCaptureProcessor()
     knowledge_retrieval = KnowledgeRetrievalProcessor(
         automatic_knowledge_id,
@@ -723,14 +924,55 @@ async def run_pipeline(
             }
         )
 
-    if vision_enabled:
+    flow_global_functions = []
+    if cfg.type == "workflow" and vision_enabled:
+        async def flow_fetch_user_image(args, _flow_manager):
+            question = str((args or {}).get("question") or "请描述当前画面。")
+            user_id = vision_state.get("client_id")
+            if not user_id:
+                return {
+                    "status": "no_video_client",
+                    "message": "当前还没有可用的摄像头视频流。",
+                }
+            request = UserImageRequestFrame(
+                user_id=user_id,
+                text=question,
+                append_to_context=False,
+                function_name=VISION_TOOL_NAME,
+            )
+            try:
+                frame = await vision_capture.request_image(llm, request)
+                observation = await _analyze_image_with_vision_model(cfg, frame, question)
+                return {
+                    "status": "ok",
+                    "question": question,
+                    "observation": observation or "视觉模型没有返回有效观察结果。",
+                }
+            except asyncio.TimeoutError:
+                return {"status": "timeout", "message": "等待摄像头视频帧超时。"}
+            except Exception as exc:  # noqa: BLE001 - return tool errors to the LLM
+                logger.warning(f"Workflow 视觉理解失败:{exc}")
+                return {"status": "error", "message": "视觉理解暂时不可用。"}
+
+        flow_global_functions.append(
+            FlowsFunctionSchema(
+                name=VISION_TOOL_NAME,
+                description=vision_schema.description,
+                properties=vision_schema.properties,
+                required=vision_schema.required,
+                handler=flow_fetch_user_image,
+                cancel_on_interruption=True,
+            )
+        )
+
+    if vision_enabled and cfg.type != "workflow":
         llm.register_function(VISION_TOOL_NAME, fetch_user_image)
     if cfg.knowledge_base_id and knowledge_mode == "on_demand":
         llm.register_function(KNOWLEDGE_TOOL_NAME, search_bound_knowledge)
 
     def set_visible_tools(schemas: list[FunctionSchema] | None = None) -> None:
         tools = list(schemas or [])
-        if vision_enabled:
+        if vision_enabled and cfg.type != "workflow":
             tools.append(vision_schema)
         if cfg.knowledge_base_id and knowledge_mode == "on_demand":
             tools.append(knowledge_schema)
@@ -751,15 +993,16 @@ async def run_pipeline(
             transport.input(),
             vision_capture,
             text_input,
-            stt,
+            stt_processor,
             user_aggregator,
+            user_turn_router,
             knowledge_retrieval,
             llm,
             # Aggregate the streamed LLM text before TTS. On interruption,
             # Pipecat commits the generated prefix immediately instead of
             # waiting for a TTS provider to emit spoken-text/timestamp frames.
             assistant_aggregator,
-            tts,
+            tts_processor,
             EndCallAfterSpeechProcessor(call_end),
             ConversationHistoryProcessor(recorder),
             transport.output(),
@@ -774,6 +1017,51 @@ async def run_pipeline(
         enable_rtvi=False,
     )
     worker_holder["worker"] = worker
+    default_workflow_services = {
+        "llm": current_llm_service,
+        **current_voice_services,
+    }
+
+    async def switch_workflow_services(
+        llm_resource_id: str | None,
+        asr_resource_id: str | None,
+        tts_resource_id: str | None,
+    ) -> None:
+        nonlocal current_llm_service
+        requested = (
+            ("llm", llm_services, llm_resource_id),
+            ("asr", stt_services, asr_resource_id),
+            ("tts", tts_services, tts_resource_id),
+        )
+        for kind, services, resource_id in requested:
+            target = (
+                services.get(resource_id)
+                if resource_id
+                else default_workflow_services[kind]
+            )
+            if target is None:
+                raise ValueError(f"Workflow {kind.upper()} 资源未加载:{resource_id}")
+            current = (
+                current_llm_service
+                if kind == "llm"
+                else current_voice_services[kind]
+            )
+            if current is target:
+                continue
+            await worker.queue_frame(ManuallySwitchServiceFrame(service=target))
+            if kind == "llm":
+                current_llm_service = target
+            else:
+                current_voice_services[kind] = target
+            await worker.queue_frame(
+                OutputTransportMessageUrgentFrame(
+                    message={
+                        "type": "service-switched",
+                        "capability": kind.upper(),
+                        "resourceId": resource_id,
+                    }
+                )
+            )
 
     async def queue_transcript(role: str, content: str, timestamp: str) -> None:
         if content:
@@ -810,6 +1098,16 @@ async def run_pipeline(
             set_system_prompt=set_system_prompt,
             set_tools=set_visible_tools,
             call_end=call_end,
+            worker=worker,
+            context_aggregator=WorkflowAggregatorPair(
+                user_aggregator,
+                assistant_aggregator,
+            ),
+            transport=transport,
+            switch_services=switch_workflow_services,
+            set_knowledge_scope=knowledge_retrieval.set_scope,
+            set_input_enabled=lambda enabled: input_state.__setitem__("enabled", enabled),
+            flow_global_functions=flow_global_functions,
         ),
     )
 
@@ -823,8 +1121,6 @@ async def run_pipeline(
 
     @user_aggregator.event_handler("on_user_turn_stopped")
     async def on_user_turn_stopped(_aggregator, _strategy, message):
-        if message.content:
-            brain.record_user_message(message.content)
         await queue_transcript("user", message.content, message.timestamp)
 
     @assistant_aggregator.event_handler("on_assistant_text_start")
@@ -869,7 +1165,6 @@ async def run_pipeline(
     @text_input.event_handler("on_text_input")
     async def on_text_input(_processor, text):
         pending_text_inputs.append(text)
-        brain.record_user_message(text)
         # 前端显示不依赖 interruption 后续事件,必须在打断前先排入发送队列。
         await queue_transcript("user", text, time_now_iso8601())
 

backend/services/pipecat/service_factory.py

@@ -5,7 +5,7 @@
 """
 
 from loguru import logger
-from models import AssistantConfig
+from models import AssistantConfig, RuntimeModelResource
 
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.openai.stt import OpenAISTTService
@@ -26,6 +26,42 @@ from services.pipecat.xfyun_tts import DEFAULT_XFYUN_TTS_URL, XfyunTTSService
 TTS_STOP_FRAME_TIMEOUT_S = 1.0
 
 
+def config_with_resource(
+    cfg: AssistantConfig, resource: RuntimeModelResource
+) -> AssistantConfig:
+    """Return a call-local config view for one workflow model resource."""
+    result = cfg.model_copy(deep=True)
+    values = resource.values or {}
+    secrets = resource.secrets or {}
+    if resource.capability == "LLM":
+        result.model = str(values.get("modelId") or "")
+        result.llm_interface_type = resource.interface_type
+        result.llm_values = values
+        result.llm_secrets = secrets
+        result.llm_api_key = str(secrets.get("apiKey") or "")
+        result.llm_base_url = str(values.get("apiUrl") or "")
+    elif resource.capability == "ASR":
+        result.asr = str(values.get("modelId") or "")
+        result.stt_language = str(values.get("language") or "")
+        result.stt_interface_type = resource.interface_type
+        result.stt_values = values
+        result.stt_secrets = secrets
+        result.stt_api_key = str(secrets.get("apiKey") or "")
+        result.stt_base_url = str(values.get("apiUrl") or "")
+    elif resource.capability == "TTS":
+        result.tts_model = str(values.get("modelId") or "")
+        result.voice = str(values.get("voice") or "")
+        result.tts_speed = float(values.get("speed") or 1.0)
+        result.tts_interface_type = resource.interface_type
+        result.tts_values = values
+        result.tts_secrets = secrets
+        result.tts_api_key = str(secrets.get("apiKey") or "")
+        result.tts_base_url = str(values.get("apiUrl") or "")
+    else:
+        raise ValueError(f"工作流模型资源能力无效:{resource.capability}")
+    return result
+
+
 def _require(value: str, label: str) -> str:
     if value:
         return value

backend/services/runtime_variables.py

@@ -175,9 +175,9 @@ def prepare_dynamic_config(
     trusted_values: dict[str, Any] | None = None,
 ) -> AssistantConfig:
     """Validate and merge one call's values without mutating stored config."""
-    if cfg.type != "prompt":
+    if cfg.type not in {"prompt", "workflow"}:
         if client_values:
-            raise DynamicVariableError("动态变量目前仅支持 prompt 助手")
+            raise DynamicVariableError("动态变量仅支持 prompt 和 workflow 助手")
         return cfg
 
     supplied = _validate_public(client_values)
@@ -214,9 +214,13 @@ def prepare_dynamic_config(
     prepared = cfg.model_copy(deep=True)
     prepared.dynamic_variables = merged
     prepared.conversation_id = conversation_id
-    # Validate prompt and greeting before media resources are allocated.
+    # Validate top-level prompt/greeting before media resources are allocated.
+    # Workflow node templates are rendered lazily as their nodes become active.
     store = DynamicVariableStore.from_config(prepared)
-    store.render(prepared.prompt)
+    if prepared.type == "prompt":
+        store.render(prepared.prompt)
+    elif prepared.type == "workflow":
+        store.render_data(prepared.graph)
     store.render(prepared.greeting)
     return prepared
 

backend/services/tool_executor.py

@@ -0,0 +1,154 @@
+"""Reusable deterministic tool execution shared by Prompt, Agent, and Action."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+from typing import Any
+from urllib.parse import quote
+
+import httpx
+from models import RuntimeTool
+
+from services.runtime_variables import (
+    DynamicVariableError,
+    DynamicVariableStore,
+    value_at_path,
+)
+
+
+class ToolExecutionError(RuntimeError):
+    pass
+
+
+class ToolExecutor:
+    def __init__(self, store: DynamicVariableStore):
+        self.store = store
+
+    def register_secrets(self, tool: RuntimeTool) -> None:
+        dynamic = (tool.secrets or {}).get("dynamic_variables") or {}
+        for name, value in dynamic.items():
+            if not str(name).startswith("secret__"):
+                raise DynamicVariableError(f"工具密钥变量必须以 secret__ 开头: {name}")
+            self.store.secrets[str(name)] = str(value)
+
+    @staticmethod
+    def schema_parts(tool: RuntimeTool) -> tuple[dict[str, Any], list[str]]:
+        config = (tool.definition or {}).get("config") or {}
+        parameters = list(config.get("parameters") or [])
+        properties = {
+            str(parameter.get("name")): {
+                "type": str(parameter.get("type") or "string"),
+                "description": str(parameter.get("description") or ""),
+            }
+            for parameter in parameters
+            if parameter.get("name")
+        }
+        required = [
+            str(parameter["name"])
+            for parameter in parameters
+            if parameter.get("name") and parameter.get("required", True)
+        ]
+        return properties, required
+
+    async def execute(
+        self,
+        tool: RuntimeTool,
+        arguments: dict[str, Any] | None = None,
+        *,
+        result_assignments: dict[str, str] | None = None,
+    ) -> dict[str, Any]:
+        self.register_secrets(tool)
+        if tool.type != "http":
+            raise ToolExecutionError(f"Action 暂不支持工具类型: {tool.type}")
+        return await self._execute_http(
+            tool,
+            dict(arguments or {}),
+            result_assignments=result_assignments,
+        )
+
+    async def _execute_http(
+        self,
+        tool: RuntimeTool,
+        arguments: dict[str, Any],
+        *,
+        result_assignments: dict[str, str] | None,
+    ) -> dict[str, Any]:
+        config = (tool.definition or {}).get("config") or {}
+        parameters = list(config.get("parameters") or [])
+        url = self.store.render(str(config.get("url") or ""))
+        configured_headers = self.store.render_data(
+            deepcopy(config.get("headers") or {}), allow_secrets=True
+        )
+        secret_headers = self.store.render_data(
+            deepcopy((tool.secrets or {}).get("headers") or {}), allow_secrets=True
+        )
+        headers: dict[str, str] = {}
+        query: dict[str, object] = {}
+        body = self.store.render_data(deepcopy(config.get("body") or {}))
+
+        for parameter in parameters:
+            name = str(parameter.get("name") or "")
+            if not name or name not in arguments:
+                continue
+            value = arguments[name]
+            location = str(parameter.get("location") or "body")
+            if location == "path":
+                url = url.replace(f"{{{name}}}", quote(str(value), safe=""))
+            elif location == "query":
+                query[name] = value
+            elif location == "header":
+                headers[name] = str(value)
+            else:
+                body[name] = value
+
+        headers.update({str(key): str(value) for key, value in configured_headers.items()})
+        headers.update({str(key): str(value) for key, value in secret_headers.items()})
+        try:
+            async with httpx.AsyncClient(
+                timeout=float(config.get("timeout_seconds") or 15),
+                follow_redirects=False,
+            ) as client:
+                response = await client.request(
+                    str(config.get("method") or "GET"),
+                    url,
+                    headers=headers,
+                    params=query,
+                    json=body if body else None,
+                )
+            response.raise_for_status()
+        except httpx.TimeoutException as exc:
+            raise ToolExecutionError("HTTP 工具调用超时") from exc
+        except httpx.HTTPStatusError as exc:
+            raise ToolExecutionError(f"HTTP 工具返回错误状态:{exc.response.status_code}") from exc
+        except httpx.RequestError as exc:
+            raise ToolExecutionError(f"HTTP 工具调用失败:{exc}") from exc
+
+        if len(response.content) > 1_000_000:
+            raise ToolExecutionError("HTTP 工具响应超过 1 MB 限制")
+        try:
+            payload: Any = response.json()
+        except ValueError:
+            payload = {"text": response.text[:8000]}
+
+        assignments = (
+            result_assignments
+            if result_assignments is not None
+            else config.get("dynamic_variable_assignments") or {}
+        )
+        updated: list[str] = []
+        for variable_name, path in assignments.items():
+            try:
+                value = value_at_path(payload, str(path))
+            except KeyError:
+                try:
+                    value = value_at_path({"response": payload}, str(path))
+                except KeyError:
+                    continue
+            self.store.assign(str(variable_name), value)
+            updated.append(str(variable_name))
+        return {
+            "status": "ok",
+            "status_code": response.status_code,
+            "data": payload,
+            "updated_variables": updated,
+        }

backend/services/workflow_engine.py

@@ -1,170 +1,204 @@
-"""工作流图引擎(第一版)。
+"""Pure Workflow v3 graph queries and deterministic edge evaluation."""
-
-对应 dograh 的 pipecat_engine.py,极简实现:
-  - 单个 startCall 入口,开场白来自该节点;
-  - agentNode 用各自的 prompt 驱动多轮对话;
-  - globalNode 不参与连线,按节点开关向会话节点注入统一提示词;
-  - 每轮助手回复后,用一次轻量 LLM「路由」判断是否满足某条出边的 condition,
-    满足则切换当前节点(linear = 单边;branching = 多边按条件分流);
-  - 到达 endCall 播放结束语并停止路由。
-
-只读图结构,不持有对话状态(当前节点由 pipeline 维护),便于单测。
-"""
 
 from __future__ import annotations
 
 import re
+from dataclasses import dataclass
 from typing import Any
 
-from loguru import logger
+from services.node_specs import normalize_graph
+from services.runtime_variables import DynamicVariableStore
+
+
+@dataclass(frozen=True)
+class AgentStageConfig:
+    """The complete assistant configuration active inside one Agent node."""
+
+    inherits_global: bool
+    llm_resource_id: str
+    asr_resource_id: str
+    tts_resource_id: str
+    tool_ids: tuple[str, ...]
+    knowledge_base_id: str | None
+    knowledge_mode: str
+    knowledge_top_n: int
+    knowledge_score_threshold: float
 
 
 class WorkflowEngine:
     def __init__(self, graph: dict[str, Any]):
-        nodes = graph.get("nodes") or []
+        self.graph = normalize_graph(graph)
-        self.nodes: dict[str, dict] = {n["id"]: n for n in nodes if n.get("id")}
+        self.settings = self.graph.get("settings") or {}
-        self.edges: list[dict] = graph.get("edges") or []
+        self.nodes: dict[str, dict] = {
-        self.start_id: str | None = next(
+            str(node["id"]): node
-            (nid for nid, n in self.nodes.items() if n.get("type") == "startCall"),
+            for node in self.graph.get("nodes") or []
-            None,
+            if node.get("id")
-        )
+        }
-        self.global_id: str | None = next(
+        self.edges: list[dict] = list(self.graph.get("edges") or [])
-            (nid for nid, n in self.nodes.items() if n.get("type") == "globalNode"),
+        self.start_id = next(
+            (
+                node_id
+                for node_id, node in self.nodes.items()
+                if node.get("type") == "start"
+            ),
             None,
         )
 
-    # ---- 结构查询 ----
+    def has_graph(self) -> bool:
-    def node_type(self, nid: str | None) -> str | None:
+        return bool(self.start_id)
-        return self.nodes.get(nid or "", {}).get("type")
 
-    def data(self, nid: str | None) -> dict:
+    def node_type(self, node_id: str | None) -> str | None:
-        return self.nodes.get(nid or "", {}).get("data") or {}
+        return self.nodes.get(node_id or "", {}).get("type")
 
-    def name(self, nid: str | None) -> str:
+    def data(self, node_id: str | None) -> dict:
-        return self.data(nid).get("name") or (self.node_type(nid) or "")
+        return self.nodes.get(node_id or "", {}).get("data") or {}
 
-    def outgoing(self, nid: str | None) -> list[dict]:
+    def name(self, node_id: str | None) -> str:
-        return [e for e in self.edges if e.get("source") == nid]
+        return str(self.data(node_id).get("name") or self.node_type(node_id) or "")
+
+    def outgoing(self, node_id: str | None) -> list[dict]:
+        result = [edge for edge in self.edges if edge.get("source") == node_id]
+        return sorted(
+            result,
+            key=lambda edge: int((edge.get("data") or {}).get("priority", 10)),
+        )
+
+    def has_outgoing(self, node_id: str | None) -> bool:
+        return any(edge.get("source") == node_id for edge in self.edges)
+
+    def edge_mode(self, edge: dict) -> str:
+        return str((edge.get("data") or {}).get("mode") or "always")
 
     def edge_fn_name(self, edge: dict) -> str:
-        """每条边对应一个 LLM 函数名(稳定、合法标识符)。"""
         raw = edge.get("id") or f"{edge.get('source')}_{edge.get('target')}"
         slug = re.sub(r"[^a-z0-9]+", "_", str(raw).lower()).strip("_")
         return f"goto_{slug or 'next'}"
 
-    def edge_condition(self, edge: dict) -> str:
+    def edge_description(self, edge: dict) -> str:
-        return (edge.get("data") or {}).get("condition") or ""
+        data = edge.get("data") or {}
+        target = self.name(str(edge.get("target") or ""))
+        condition = str(data.get("condition") or "").strip()
+        if condition:
+            return f"当满足以下条件时转到「{target}」：{condition}"
+        return f"当当前阶段任务完成时转到「{target}」。"
 
     def edge_transition_speech(self, edge: dict | None) -> str:
-        """命中该边、切换节点瞬间播报的过渡语(可选,掩盖延迟,不写入上下文)。"""
         if not edge:
             return ""
-        return (edge.get("data") or {}).get("transition_speech") or ""
+        data = edge.get("data") or {}
-
+        return str(
-    def find_edge(self, source: str | None, target: str | None) -> dict | None:
+            data.get("transitionSpeech") or data.get("transition_speech") or ""
-        for edge in self.edges:
-            if edge.get("source") == source and edge.get("target") == target:
-                return edge
-        return None
-
-    def edge_description(self, edge: dict) -> str:
-        """作为转移函数的 description 交给 LLM:满足该条件时模型应调用此函数。"""
-        cond = self.edge_condition(edge)
-        target = self.name(edge.get("target"))
-        if cond:
-            return f"当满足以下条件时调用以转到节点「{target}」:{cond}"
-        return f"当当前节点任务完成、应继续推进对话时调用以转到节点「{target}」。"
-
-    def is_end(self, nid: str | None) -> bool:
-        return self.node_type(nid) == "endCall"
-
-    def has_graph(self) -> bool:
-        return self.start_id is not None
-
-    def greeting(self) -> str:
-        return self.data(self.start_id).get("greeting") or ""
-
-    def system_prompt_for(self, nid: str | None) -> str:
-        """组合当前节点提示与可选的全局提示(开始节点也是会话节点)。"""
-        header = f"[当前节点:{self.name(nid)}]"
-        node_data = self.data(nid)
-        prompt = str(node_data.get("prompt") or "").strip()
-        node_type = self.node_type(nid)
-        default_add_global = node_type in {"startCall", "agentNode"}
-        add_global = bool(node_data.get("addGlobalPrompt", default_add_global))
-        global_prompt = (
-            str(self.data(self.global_id).get("prompt") or "").strip()
-            if add_global and self.global_id
-            else ""
         )
 
-        sections = [header]
+    def global_prompt(self) -> str:
-        if global_prompt:
+        return str(self.settings.get("globalPrompt") or "").strip()
-            sections.append(f"[全局规则]\n{global_prompt}")
+
+    def inherits_global_config(self, node_id: str) -> bool:
+        """Return the Agent's explicit configuration scope, defaulting to global."""
+        return bool(self.data(node_id).get("inheritGlobalConfig", True))
+
+    def agent_stage_config(self, node_id: str) -> AgentStageConfig:
+        """Resolve either Workflow defaults or one Agent's complete override."""
+        data = self.data(node_id)
+        inherits_global = self.inherits_global_config(node_id)
+        source = self.settings if inherits_global else data
+        llm_key = "defaultLlmResourceId" if inherits_global else "llmResourceId"
+        asr_key = "defaultAsrResourceId" if inherits_global else "asrResourceId"
+        tts_key = "defaultTtsResourceId" if inherits_global else "ttsResourceId"
+        knowledge_base_id = str(source.get("knowledgeBaseId") or "")
+        return AgentStageConfig(
+            inherits_global=inherits_global,
+            llm_resource_id=str(source.get(llm_key) or ""),
+            asr_resource_id=str(source.get(asr_key) or ""),
+            tts_resource_id=str(source.get(tts_key) or ""),
+            tool_ids=tuple(str(tool_id) for tool_id in source.get("toolIds") or []),
+            knowledge_base_id=knowledge_base_id or None,
+            knowledge_mode=(
+                str(source.get("knowledgeMode") or "automatic")
+                if knowledge_base_id
+                else "disabled"
+            ),
+            knowledge_top_n=int(source.get("knowledgeTopN") or 5),
+            knowledge_score_threshold=float(
+                source.get("knowledgeScoreThreshold") or 0.0
+            ),
+        )
+
+    def prompt_for(self, node_id: str, store: DynamicVariableStore) -> str:
+        """Build the Agent system prompt according to its inheritance setting."""
+        prompt = store.render(str(self.data(node_id).get("prompt") or "").strip())
+        sections = [f"[当前阶段：{self.name(node_id)}]"]
+        if self.inherits_global_config(node_id) and self.global_prompt():
+            sections.append(f"[全局规则]\n{store.render(self.global_prompt())}")
         if prompt:
-            sections.append(f"[当前节点任务]\n{prompt}")
+            sections.append(f"[当前阶段任务]\n{prompt}")
         return "\n\n".join(sections)
 
-    # ---- 路由:决定下一节点 ----
+    def greeting(self, store: DynamicVariableStore) -> str:
-    async def route(
+        return store.render(str(self.data(self.start_id).get("greeting") or ""))
+
+    def expression_matches(self, expression: dict, values: dict[str, Any]) -> bool:
+        results = []
+        for rule in expression.get("rules") or []:
+            name = str(rule.get("variable") or "")
+            operator = str(rule.get("operator") or "")
+            expected = rule.get("value")
+            exists = name in values
+            actual = values.get(name)
+            try:
+                if operator == "exists":
+                    matched = exists if expected is not False else not exists
+                elif operator == "eq":
+                    matched = actual == expected
+                elif operator == "neq":
+                    matched = actual != expected
+                elif operator == "gt":
+                    matched = actual > expected
+                elif operator == "gte":
+                    matched = actual >= expected
+                elif operator == "lt":
+                    matched = actual < expected
+                elif operator == "lte":
+                    matched = actual <= expected
+                elif operator == "contains":
+                    matched = expected in actual
+                elif operator == "in":
+                    matched = actual in expected
+                else:
+                    matched = False
+            except (TypeError, ValueError):
+                matched = False
+            results.append(matched)
+        if not results:
+            return False
+        return (
+            all(results)
+            if expression.get("combinator", "and") == "and"
+            else any(results)
+        )
+
+    def deterministic_edge(
         self,
-        nid: str | None,
+        node_id: str,
-        history: list[dict],
+        store: DynamicVariableStore,
         *,
-        api_key: str,
+        include_default: bool,
-        base_url: str,
+    ) -> dict | None:
-        model: str,
+        default = None
-    ) -> str | None:
+        for edge in self.outgoing(node_id):
-        """根据对话历史判断当前节点是否应转移。返回目标节点 id,或 None 表示停留。"""
+            data = edge.get("data") or {}
-        outs = self.outgoing(nid)
+            mode = data.get("mode")
-        if not outs:
+            if mode == "expression" and self.expression_matches(
-            return None
+                data.get("expression") or {}, store.values
+            ):
+                return edge
+            if mode == "always":
+                default = edge
+        return default if include_default else None
 
-        options = []
+    def llm_edges(self, node_id: str) -> list[dict]:
-        for i, edge in enumerate(outs, 1):
+        return [
-            edata = edge.get("data") or {}
+            edge
-            cond = edata.get("condition") or "(无明确条件,作为默认后继)"
+            for edge in self.outgoing(node_id)
-            tgt_name = self.name(edge.get("target"))
+            if self.edge_mode(edge) in {"llm", "always"}
-            options.append(f"{i}. 条件:{cond} → 目标节点:{tgt_name}")
+        ]
-
-        convo = "\n".join(
-            f"{m['role']}: {m['content']}" for m in history[-8:] if m.get("content")
-        )
-        system = (
-            "你是语音对话的流程路由器。根据最近的对话,判断是否已满足某条转移条件。\n"
-            "规则:仅当某条件被明确满足时,返回其编号;若都不满足或不确定,返回 0 "
-            "(停留在当前节点继续对话)。只输出一个数字,不要任何解释。"
-        )
-        user = (
-            "可选转移:\n"
-            + "\n".join(options)
-            + f"\n\n对话记录:\n{convo}\n\n请只返回编号(0 表示停留):"
-        )
-
-        try:
-            from openai import AsyncOpenAI
-
-            client = AsyncOpenAI(api_key=api_key, base_url=base_url or None)
-            resp = await client.chat.completions.create(
-                model=model,
-                messages=[
-                    {"role": "system", "content": system},
-                    {"role": "user", "content": user},
-                ],
-                temperature=0,
-                max_tokens=5,
-            )
-            text = (resp.choices[0].message.content or "").strip()
-        except Exception as exc:  # noqa: BLE001 - 路由失败不应中断通话
-            logger.warning(f"工作流路由调用失败,停留当前节点: {exc}")
-            return None
-
-        match = re.search(r"\d+", text)
-        if not match:
-            return None
-        idx = int(match.group())
-        if idx < 1 or idx > len(outs):
-            return None
-        target = outs[idx - 1].get("target")
-        logger.info(f"工作流路由: {self.name(nid)} → {self.name(target)} (edge {idx})")
-        return target

backend/services/workflow_router.py

@@ -0,0 +1,129 @@
+"""Pre-response LLM routing for Workflow Agent edges.
+
+The router deliberately uses a separate, short completion.  Its only output is
+a required function choice, so the current Agent cannot speak before the graph
+has decided whether the user turn belongs to another node.
+"""
+
+from __future__ import annotations
+
+import json
+from collections.abc import Callable
+from typing import Any
+
+from loguru import logger
+from models import AssistantConfig
+from openai import AsyncOpenAI
+
+
+STAY_ON_CURRENT_AGENT = "workflow_stay_on_current_agent"
+MAX_ROUTING_HISTORY_ENTRIES = 20
+
+
+class WorkflowLLMRouter:
+    """Select one LLM edge before the conversational LLM is allowed to reply."""
+
+    def __init__(self, cfg: AssistantConfig):
+        self._cfg = cfg
+
+    async def select_edge(
+        self,
+        *,
+        node_name: str,
+        node_prompt: str,
+        edges: list[dict[str, Any]],
+        history: list[dict[str, str]],
+        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."""
+        if not edges:
+            return STAY_ON_CURRENT_AGENT
+
+        names = {edge_name(edge) for edge in edges}
+        stay_name = STAY_ON_CURRENT_AGENT
+        while stay_name in names:
+            stay_name = f"_{stay_name}"
+
+        tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": edge_name(edge),
+                    "description": edge_description(edge),
+                    "parameters": {"type": "object", "properties": {}},
+                },
+            }
+            for edge in edges
+        ]
+        tools.append(
+            {
+                "type": "function",
+                "function": {
+                    "name": stay_name,
+                    "description": "所有转移条件都不满足，继续由当前 Agent 处理用户消息。",
+                    "parameters": {"type": "object", "properties": {}},
+                },
+            }
+        )
+
+        ordered_conditions = "\n".join(
+            f"{index + 1}. {edge_description(edge)}"
+            for index, edge in enumerate(edges)
+        )
+        router_prompt = (
+            "你是工作流路由器，不是对话助手。收到一轮完整用户输入后，"
+            "必须且只能调用一个提供的函数，禁止输出任何口头回复。\n"
+            "按给出的顺序判断转移条件；选择第一个明确满足的转移函数。"
+            "如果没有条件满足，调用留在当前 Agent 的函数。\n\n"
+            f"当前节点：{node_name}\n"
+            f"当前节点任务：{node_prompt or '未配置'}\n"
+            f"转移条件：\n{ordered_conditions}"
+        )
+        recent_history = history[-MAX_ROUTING_HISTORY_ENTRIES:]
+        routing_input = json.dumps(
+            {
+                "conversation": recent_history,
+                "session_variables": variables,
+            },
+            ensure_ascii=False,
+            separators=(",", ":"),
+        )
+        extra_body = self._cfg.llm_values.get("extraBody")
+        request_extra = (
+            {"extra_body": extra_body} if isinstance(extra_body, dict) else {}
+        )
+        client = AsyncOpenAI(
+            api_key=self._cfg.llm_api_key,
+            base_url=self._cfg.llm_base_url,
+            timeout=15.0,
+        )
+        try:
+            response = await client.chat.completions.create(
+                model=self._cfg.model,
+                messages=[
+                    {"role": "system", "content": router_prompt},
+                    {"role": "user", "content": routing_input},
+                ],
+                tools=tools,
+                tool_choice="required",
+                temperature=0,
+                **request_extra,
+            )
+            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 Agent")
+                return STAY_ON_CURRENT_AGENT
+            selected = str(tool_calls[0].function.name or "")
+            if selected == stay_name:
+                return STAY_ON_CURRENT_AGENT
+            if selected not in names:
+                logger.warning(f"Workflow 路由 LLM 返回未知函数:{selected}")
+                return STAY_ON_CURRENT_AGENT
+            return 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 Agent:{exc}")
+            return None
+        finally:
+            await client.close()

backend/tests/test_brains.py

@@ -9,7 +9,10 @@ from pipecat.frames.frames import (
     LLMContextFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
+    LLMRunFrame,
     LLMTextFrame,
+    OutputTransportMessageUrgentFrame,
+    TTSSpeakFrame,
 )
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.frame_processor import FrameDirection
@@ -111,6 +114,19 @@ class BrainRegistryTests(unittest.TestCase):
         )
         self.assertIn("user_name", assistant.dynamic_variable_definitions)
 
+    def test_workflow_keeps_dynamic_variables_and_tool_bindings(self):
+        assistant = AssistantUpsert(
+            name="workflow",
+            type="workflow",
+            toolIds=["tool_a"],
+            dynamicVariableDefinitions={
+                "customer": {"type": "string", "required": False, "default": "王先生"}
+            },
+            graph={},
+        )
+        self.assertEqual(assistant.tool_ids, ["tool_a"])
+        self.assertIn("customer", assistant.dynamic_variable_definitions)
+
 
 class DifyHelpersTests(unittest.TestCase):
     def test_normalize_api_base(self):
@@ -363,7 +379,7 @@ class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
         params = FakeFunctionParams(
             {"order_id": "A/1", "Authorization": "attacker-value"}
         )
-        with patch("services.brains.prompt_brain.httpx.AsyncClient", FakeClient):
+        with patch("services.tool_executor.httpx.AsyncClient", FakeClient):
             await llm.functions["lookup_order"](params)
 
         self.assertEqual(requests[0][1], "https://example.test/orders/A%2F1")
@@ -375,68 +391,315 @@ class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
 
 
 class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
-    async def test_transition_and_end_are_owned_by_workflow_brain(self):
+    async def test_nodes_without_outgoing_edges_remain_active(self):
-        graph = {
-            "nodes": [
-                {
-                    "id": "start",
-                    "type": "startCall",
-                    "data": {"name": "开始", "prompt": "收集需求"},
-                },
-                {
-                    "id": "end",
-                    "type": "endCall",
-                    "data": {"name": "结束", "prompt": "礼貌结束"},
-                },
-            ],
-            "edges": [
-                {
-                    "id": "finish",
-                    "source": "start",
-                    "target": "end",
-                    "data": {"condition": "需求已收集"},
-                }
-            ],
-        }
-        brain = WorkflowBrain(graph)
-        llm = FakeLLM()
-        context = LLMContext(messages=[])
         queued = []
-        prompts = []
-        visible_tools = []
-        call_end = FakeCallEnd()
 
         async def queue_frame(frame):
             queued.append(frame)
 
+        runtime = BrainRuntime(
+            context=LLMContext(messages=[]),
+            llm=FakeLLM(),
+            queue_frame=queue_frame,
+            set_system_prompt=lambda _prompt: None,
+            set_tools=lambda _tools: None,
+            call_end=FakeCallEnd(),
+        )
+
+        class FakeManager:
+            def __init__(self, current_node=None):
+                self.current_node = current_node
+
+            async def initialize(self, config):
+                self.current_node = config["name"]
+
+        start_brain = WorkflowBrain(
+            {
+                "specVersion": 3,
+                "settings": {},
+                "nodes": [{"id": "start", "type": "start", "data": {}}],
+                "edges": [],
+            }
+        )
+        start_brain._runtime = runtime
+        start_brain._manager = FakeManager()
+        await start_brain.on_connected()
+        self.assertEqual(start_brain._manager.current_node, "start")
+
+        agent_brain = WorkflowBrain(
+            {
+                "specVersion": 3,
+                "settings": {"globalPrompt": "全局规则"},
+                "nodes": [
+                    {"id": "start", "type": "start", "data": {}},
+                    {
+                        "id": "agent",
+                        "type": "agent",
+                        "data": {"prompt": "持续回答"},
+                    },
+                ],
+                "edges": [
+                    {
+                        "id": "begin",
+                        "source": "start",
+                        "target": "agent",
+                        "data": {"mode": "always", "priority": 0},
+                    }
+                ],
+            }
+        )
+        agent_brain._runtime = runtime
+        agent_brain._manager = FakeManager("agent")
+        queued.clear()
+        handled = await agent_brain.on_user_turn_end("请继续回答")
+        self.assertTrue(handled)
+        self.assertEqual(agent_brain._manager.current_node, "agent")
+        self.assertTrue(any(isinstance(frame, LLMRunFrame) for frame in queued))
+
+        handoff_brain = WorkflowBrain(
+            {
+                "specVersion": 3,
+                "settings": {},
+                "nodes": [
+                    {"id": "start", "type": "start", "data": {}},
+                    {
+                        "id": "handoff",
+                        "type": "handoff",
+                        "data": {"targetType": "human"},
+                    },
+                ],
+                "edges": [],
+            }
+        )
+        handoff_brain._runtime = runtime
+        handoff_config = await handoff_brain._resolve_path("handoff")
+        self.assertEqual(handoff_config["name"], "handoff")
+        self.assertTrue(
+            any(
+                isinstance(frame, OutputTransportMessageUrgentFrame)
+                and frame.message.get("type") == "handoff-requested"
+                for frame in queued
+            )
+        )
+
+    async def test_transition_and_end_are_owned_by_workflow_brain(self):
+        graph = {
+            "specVersion": 3,
+            "settings": {
+                "globalPrompt": "全局规则",
+                "defaultLlmResourceId": "llm_global",
+                "defaultAsrResourceId": "asr_global",
+                "defaultTtsResourceId": "tts_global",
+                "knowledgeBaseId": "kb_global",
+                "knowledgeMode": "automatic",
+            },
+            "nodes": [
+                {
+                    "id": "start",
+                    "type": "start",
+                    "data": {"name": "Start"},
+                },
+                {
+                    "id": "agent",
+                    "type": "agent",
+                    "data": {
+                        "name": "收集需求",
+                        "prompt": "服务 {{user_name}}",
+                        "contextPolicy": "fresh",
+                    },
+                },
+                {
+                    "id": "end",
+                    "type": "end",
+                    "data": {"name": "End", "message": "感谢来电", "scope": "session"},
+                },
+            ],
+            "edges": [
+                {
+                    "id": "begin",
+                    "source": "start",
+                    "target": "agent",
+                    "data": {"mode": "always", "priority": 0},
+                },
+                {
+                    "id": "finish",
+                    "source": "agent",
+                    "target": "end",
+                    "data": {
+                        "mode": "llm",
+                        "priority": 10,
+                        "condition": "需求已收集",
+                        "transitionSpeech": "正在为你结束流程",
+                    },
+                }
+            ],
+        }
+        cfg = prepare_dynamic_config(
+            AssistantConfig(
+                type="workflow",
+                graph=graph,
+                dynamic_variable_definitions={
+                    "user_name": {"type": "string", "required": True}
+                },
+            ),
+            {"user_name": "王先生"},
+            assistant_id="asst_workflow",
+        )
+        brain = WorkflowBrain(cfg)
+        llm = FakeLLM()
+        context = LLMContext(messages=[])
+        queued = []
+        service_switches = []
+        knowledge_scopes = []
+        call_end = FakeCallEnd()
+
+        class FakeWorker:
+            def __init__(self):
+                self.frames = []
+                self.handlers = {}
+
+            def set_reached_downstream_filter(self, *_args):
+                pass
+
+            def event_handler(self, name):
+                def decorator(fn):
+                    self.handlers[name] = fn
+                    return fn
+                return decorator
+
+            async def queue_frame(self, frame):
+                self.frames.append(frame)
+
+            async def queue_frames(self, frames):
+                self.frames.extend(frames)
+
+        worker = FakeWorker()
+        pair = SimpleNamespace(
+            user=lambda: SimpleNamespace(_context=context),
+            assistant=lambda: SimpleNamespace(has_function_calls_in_progress=False),
+        )
+
+        async def queue_frame(frame):
+            queued.append(frame)
+
+        async def switch_services(llm_id, asr_id, tts_id):
+            service_switches.append((llm_id, asr_id, tts_id))
+
         runtime = BrainRuntime(
             context=context,
             llm=llm,
             queue_frame=queue_frame,
-            set_system_prompt=prompts.append,
+            set_system_prompt=lambda _prompt: None,
-            set_tools=lambda tools: visible_tools.append(tools or []),
+            set_tools=lambda _tools: None,
             call_end=call_end,
+            worker=worker,
+            context_aggregator=pair,
+            switch_services=switch_services,
+            set_knowledge_scope=knowledge_scopes.append,
         )
-        await brain.setup(AssistantConfig(type="workflow", graph=graph), runtime)
+        await brain.setup(cfg, runtime)
-
+        await brain.on_connected()
-        self.assertIn("goto_finish", llm.functions)
+        self.assertEqual(brain._manager.current_node, "agent")
-        self.assertIn("收集需求", prompts[-1])
+        self.assertEqual(
-        self.assertEqual(visible_tools[-1][0].name, "goto_finish")
+            service_switches,
-
+            [("llm_global", "asr_global", "tts_global")],
-        params = FakeFunctionParams()
-        await llm.functions["goto_finish"](params)
-        self.assertEqual(params.result, {"status": "ok"})
-        self.assertIn("礼貌结束", prompts[-1])
-        self.assertEqual(visible_tools[-1], [])
-
-        await brain.on_assistant_text_start("closing-turn")
-        await brain.on_assistant_text_end(
-            "closing-turn",
-            "感谢来电，再见。",
-            False,
         )
+        self.assertEqual(knowledge_scopes[-1]["knowledge_base_id"], "kb_global")
+
+        brain._engine.data("agent").update(
+            {
+                "inheritGlobalConfig": False,
+                "llmResourceId": "llm_agent",
+                "asrResourceId": "asr_agent",
+                "ttsResourceId": "tts_agent",
+                "knowledgeBaseId": "kb_agent",
+                "knowledgeMode": "on_demand",
+            }
+        )
+        await brain._apply_agent_stage("agent")
+        self.assertEqual(
+            service_switches[-1],
+            ("llm_agent", "asr_agent", "tts_agent"),
+        )
+        self.assertEqual(knowledge_scopes[-1]["knowledge_base_id"], "kb_agent")
+        agent_config = brain._agent_config("agent")
+        self.assertIn("王先生", agent_config["role_message"])
+        self.assertIn("工作流路由已在用户一轮输入结束时完成", agent_config["role_message"])
+        self.assertEqual(agent_config["task_messages"], [])
+        self.assertFalse(agent_config["respond_immediately"])
+        self.assertFalse(any(isinstance(frame, LLMRunFrame) for frame in worker.frames))
+        self.assertEqual(
+            agent_config["context_strategy"].strategy.value,
+            "reset",
+        )
+
+        brain._engine.data("agent")["entryMode"] = "generate"
+        generate_config = brain._agent_config("agent")
+        self.assertTrue(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))
+
+        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.assertEqual(
+            fixed_config["task_messages"],
+            [{"role": "assistant", "content": "您好，王先生"}],
+        )
+        worker.frames.clear()
+        queued.clear()
+        await brain._manager.set_node_from_config(fixed_config)
+        self.assertTrue(any(isinstance(frame, TTSSpeakFrame) for frame in queued))
+        self.assertFalse(any(isinstance(frame, LLMRunFrame) for frame in worker.frames))
+
+        self.assertFalse(
+            any(
+                function.name == "goto_finish"
+                for function in brain._agent_config("agent")["functions"]
+            )
+        )
+        await brain.on_assistant_text_end("old-turn", "需求已收集", False)
+        self.assertEqual(brain._manager.current_node, "agent")
+
+        class FakeRouter:
+            async def select_edge(self, **_kwargs):
+                return "goto_finish"
+
+        brain._router = FakeRouter()
+        handled = await brain.on_user_turn_end("我的需求已经说完了")
+        self.assertTrue(handled)
+        self.assertEqual(brain._manager.current_node, "end")
+        self.assertIn("我的需求已经说完了", brain._store.values["system__conversation_history"])
         self.assertTrue(call_end.ending)
         self.assertTrue(call_end.armed)
+        self.assertTrue(any(getattr(frame, "text", "") == "感谢来电" for frame in queued))
+        assistant_transcripts = [
+            frame.message.get("content")
+            for frame in queued
+            if isinstance(frame, OutputTransportMessageUrgentFrame)
+            and frame.message.get("type") == "transcript"
+            and frame.message.get("role") == "assistant"
+        ]
+        self.assertEqual(
+            assistant_transcripts,
+            ["您好，王先生", "正在为你结束流程", "感谢来电"],
+        )
+        self.assertIn(
+            "正在为你结束流程",
+            brain._store.values["system__conversation_history"],
+        )
+        self.assertIn(
+            "感谢来电",
+            brain._store.values["system__conversation_history"],
+        )
 
 
 if __name__ == "__main__":

backend/tests/test_pipeline_knowledge.py

@@ -1,7 +1,15 @@
 import unittest
 
 from models import AssistantConfig
-from services.pipecat.pipeline import _knowledge_tool_description
+from pipecat.frames.frames import LLMContextFrame
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.frame_processor import FrameDirection
+from services.pipecat.pipeline import (
+    KNOWLEDGE_CONTEXT_MARKER,
+    KnowledgeRetrievalProcessor,
+    UserTurnRoutingProcessor,
+    _knowledge_tool_description,
+)
 
 
 class KnowledgeToolDescriptionTest(unittest.TestCase):
@@ -33,6 +41,57 @@ class KnowledgeToolDescriptionTest(unittest.TestCase):
         self.assertNotIn("\n  ", description)
         self.assertLess(len(description), 1000)
 
+    def test_workflow_knowledge_uses_system_role(self):
+        processor = KnowledgeRetrievalProcessor(None)
+        messages = [
+            {"role": "assistant", "content": "你好"},
+            {
+                "role": "developer",
+                "content": f"{KNOWLEDGE_CONTEXT_MARKER}\n旧检索结果",
+            },
+        ]
+
+        processor._set_context(
+            messages,
+            f"{KNOWLEDGE_CONTEXT_MARKER}\n新检索结果",
+        )
+
+        self.assertEqual(messages[0]["role"], "system")
+        self.assertIn("新检索结果", messages[0]["content"])
+        self.assertFalse(any(message["role"] == "developer" for message in messages))
+
+
+class UserTurnRoutingProcessorTest(unittest.IsolatedAsyncioTestCase):
+    async def test_routes_each_user_message_once_before_response_run(self):
+        class FakeBrain:
+            def __init__(self):
+                self.turns = []
+
+            async def on_user_turn_end(self, content):
+                self.turns.append(content)
+                return True
+
+        brain = FakeBrain()
+        processor = UserTurnRoutingProcessor(brain)
+        forwarded = []
+
+        async def push_frame(frame, direction):
+            forwarded.append((frame, direction))
+
+        processor.push_frame = push_frame
+        context = LLMContext(messages=[{"role": "user", "content": "我叫李白"}])
+        frame = LLMContextFrame(context)
+
+        await processor.process_frame(frame, FrameDirection.DOWNSTREAM)
+        self.assertEqual(brain.turns, ["我叫李白"])
+        self.assertEqual(forwarded, [])
+
+        # A queued LLMRunFrame after the transition uses the same context. It
+        # must reach the target Agent without invoking routing a second time.
+        await processor.process_frame(frame, FrameDirection.DOWNSTREAM)
+        self.assertEqual(brain.turns, ["我叫李白"])
+        self.assertEqual(forwarded, [(frame, FrameDirection.DOWNSTREAM)])
+
 
 if __name__ == "__main__":
     unittest.main()

backend/tests/test_workflow_router.py

@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+import unittest
+from types import SimpleNamespace
+from unittest.mock import patch
+
+from models import AssistantConfig
+from services.workflow_router import WorkflowLLMRouter
+
+
+class WorkflowLLMRouterTest(unittest.IsolatedAsyncioTestCase):
+    async def test_uses_required_tool_choice_without_developer_messages(self):
+        requests = []
+
+        class FakeCompletions:
+            async def create(self, **kwargs):
+                requests.append(kwargs)
+                return SimpleNamespace(
+                    choices=[
+                        SimpleNamespace(
+                            message=SimpleNamespace(
+                                tool_calls=[
+                                    SimpleNamespace(
+                                        function=SimpleNamespace(name="goto_age", arguments="{}")
+                                    )
+                                ]
+                            )
+                        )
+                    ]
+                )
+
+        class FakeClient:
+            def __init__(self, **_kwargs):
+                self.chat = SimpleNamespace(completions=FakeCompletions())
+                self.closed = False
+
+            async def close(self):
+                self.closed = True
+
+        cfg = AssistantConfig(
+            type="workflow",
+            model="deepseek-chat",
+            llm_api_key="secret",
+            llm_base_url="https://llm.test/v1",
+        )
+        router = WorkflowLLMRouter(cfg)
+        edges = [
+            {
+                "id": "age",
+                "data": {"condition": "用户已经回答姓名", "priority": 10},
+            }
+        ]
+
+        with patch("services.workflow_router.AsyncOpenAI", FakeClient):
+            selected = await router.select_edge(
+                node_name="询问姓名",
+                node_prompt="询问用户姓名",
+                edges=edges,
+                history=[{"role": "user", "message": "我叫李白"}],
+                variables={"customer_type": "new"},
+                edge_name=lambda _edge: "goto_age",
+                edge_description=lambda _edge: "用户已经回答姓名",
+            )
+
+        self.assertEqual(selected, "goto_age")
+        self.assertEqual(requests[0]["tool_choice"], "required")
+        self.assertEqual(
+            [message["role"] for message in requests[0]["messages"]],
+            ["system", "user"],
+        )
+        self.assertNotIn("developer", str(requests[0]["messages"]))
+
+
+if __name__ == "__main__":
+    unittest.main()

backend/tests/test_workflow_v3.py

@@ -0,0 +1,328 @@
+from __future__ import annotations
+
+import unittest
+
+from models import AssistantConfig, RuntimeModelResource
+from services.pipecat.service_factory import config_with_resource
+from services.node_specs import graph_references, normalize_graph, validate_graph
+from services.runtime_variables import DynamicVariableStore, prepare_dynamic_config
+from services.workflow_engine import WorkflowEngine
+
+
+def valid_graph():
+    return {
+        "specVersion": 3,
+        "settings": {"globalPrompt": "服务 {{customer}}"},
+        "nodes": [
+            {"id": "start", "type": "start", "data": {"name": "Start"}},
+            {
+                "id": "agent",
+                "type": "agent",
+                "data": {"name": "Agent", "prompt": "处理订单", "contextPolicy": "fresh"},
+            },
+            {"id": "end", "type": "end", "data": {"name": "End", "scope": "flow"}},
+        ],
+        "edges": [
+            {
+                "id": "begin",
+                "source": "start",
+                "target": "agent",
+                "data": {"mode": "always", "priority": 0},
+            },
+            {
+                "id": "paid",
+                "source": "agent",
+                "target": "end",
+                "data": {
+                    "mode": "expression",
+                    "priority": 10,
+                    "expression": {
+                        "combinator": "and",
+                        "rules": [
+                            {"variable": "order_status", "operator": "eq", "value": "paid"}
+                        ],
+                    },
+                },
+            },
+        ],
+    }
+
+
+class WorkflowGraphTests(unittest.TestCase):
+    def test_agent_entry_mode_defaults_and_validation(self):
+        graph = valid_graph()
+        normalized = normalize_graph(graph)
+        agent = next(node for node in normalized["nodes"] if node["type"] == "agent")
+        self.assertEqual(agent["data"]["entryMode"], "wait_user")
+        self.assertEqual(agent["data"]["entrySpeech"], "")
+        self.assertTrue(agent["data"]["inheritGlobalConfig"])
+        self.assertEqual(agent["data"]["contextPolicy"], "fresh")
+
+        agent["data"]["entryMode"] = "fixed_speech"
+        self.assertTrue(
+            any("固定进入语不能为空" in error for error in validate_graph(normalized))
+        )
+        agent["data"]["entrySpeech"] = "您好，{{customer}}"
+        self.assertEqual(validate_graph(normalized), [])
+
+    def test_voice_resource_creates_isolated_runtime_config(self):
+        base = AssistantConfig(type="workflow", asr="default", voice="default")
+        asr = RuntimeModelResource(
+            id="asr_1",
+            capability="ASR",
+            interface_type="openai-asr",
+            values={"modelId": "sensevoice", "language": "zh", "apiUrl": "https://asr.test"},
+            secrets={"apiKey": "secret"},
+        )
+        resolved = config_with_resource(base, asr)
+        self.assertEqual(resolved.asr, "sensevoice")
+        self.assertEqual(resolved.stt_api_key, "secret")
+        self.assertEqual(base.asr, "default")
+
+        llm = RuntimeModelResource(
+            id="llm_1",
+            capability="LLM",
+            interface_type="openai-llm",
+            values={"modelId": "deepseek-chat", "apiUrl": "https://llm.test/v1"},
+            secrets={"apiKey": "llm-secret"},
+        )
+        llm_resolved = config_with_resource(base, llm)
+        self.assertEqual(llm_resolved.model, "deepseek-chat")
+        self.assertEqual(llm_resolved.llm_api_key, "llm-secret")
+
+    def test_global_and_custom_agent_references_are_preserved(self):
+        graph = valid_graph()
+        graph["settings"].update(
+            {
+                "defaultLlmResourceId": "llm_global",
+                "defaultAsrResourceId": "asr_global",
+                "defaultTtsResourceId": "tts_global",
+                "toolIds": ["tool_global"],
+                "knowledgeBaseId": "kb_global",
+            }
+        )
+        agent = next(node for node in graph["nodes"] if node["type"] == "agent")
+        agent["data"].update(
+            {
+                "inheritGlobalConfig": False,
+                "llmResourceId": "llm_agent",
+                "asrResourceId": "asr_agent",
+                "ttsResourceId": "tts_agent",
+                "toolIds": ["tool_agent"],
+                "knowledgeBaseId": "kb_agent",
+            }
+        )
+
+        refs = graph_references(graph)
+        self.assertEqual(
+            refs["model_resources"],
+            {
+                "llm_global",
+                "asr_global",
+                "tts_global",
+                "llm_agent",
+                "asr_agent",
+                "tts_agent",
+            },
+        )
+        self.assertEqual(refs["tools"], {"tool_global", "tool_agent"})
+        self.assertEqual(refs["knowledge_bases"], {"kb_global", "kb_agent"})
+
+    def test_existing_agent_override_disables_implicit_inheritance(self):
+        graph = valid_graph()
+        agent = next(node for node in graph["nodes"] if node["type"] == "agent")
+        agent["data"]["toolIds"] = ["legacy_tool"]
+        normalized = normalize_graph(graph)
+        normalized_agent = next(
+            node for node in normalized["nodes"] if node["type"] == "agent"
+        )
+        self.assertFalse(normalized_agent["data"]["inheritGlobalConfig"])
+
+    def test_inherited_agent_ignores_stale_custom_references(self):
+        graph = valid_graph()
+        agent = next(node for node in graph["nodes"] if node["type"] == "agent")
+        agent["data"].update(
+            {
+                "inheritGlobalConfig": True,
+                "llmResourceId": "stale_llm",
+                "asrResourceId": "stale_asr",
+                "ttsResourceId": "stale_tts",
+                "toolIds": ["stale_tool"],
+                "knowledgeBaseId": "stale_kb",
+            }
+        )
+
+        refs = graph_references(graph)
+
+        self.assertNotIn("stale_llm", refs["model_resources"])
+        self.assertNotIn("stale_tool", refs["tools"])
+        self.assertNotIn("stale_kb", refs["knowledge_bases"])
+
+    def test_agent_effective_config_inherits_then_switches_to_override(self):
+        graph = valid_graph()
+        graph["settings"].update(
+            {
+                "defaultLlmResourceId": "llm_global",
+                "defaultAsrResourceId": "asr_global",
+                "defaultTtsResourceId": "tts_global",
+                "toolIds": ["tool_global"],
+                "knowledgeBaseId": "kb_global",
+                "knowledgeMode": "on_demand",
+                "knowledgeTopN": 8,
+                "knowledgeScoreThreshold": 0.4,
+            }
+        )
+        engine = WorkflowEngine(graph)
+        inherited = engine.agent_stage_config("agent")
+        self.assertEqual(inherited.llm_resource_id, "llm_global")
+        self.assertEqual(inherited.tool_ids, ("tool_global",))
+        self.assertEqual(inherited.knowledge_mode, "on_demand")
+
+        engine.data("agent").update(
+            {
+                "inheritGlobalConfig": False,
+                "llmResourceId": "llm_agent",
+                "toolIds": ["tool_agent"],
+                "knowledgeBaseId": "",
+            }
+        )
+        custom = engine.agent_stage_config("agent")
+        self.assertEqual(custom.llm_resource_id, "llm_agent")
+        self.assertEqual(custom.tool_ids, ("tool_agent",))
+        self.assertEqual(custom.knowledge_mode, "disabled")
+
+    def test_start_agent_and_handoff_may_have_no_outgoing_edge(self):
+        terminal_graphs = [
+            {
+                "specVersion": 3,
+                "settings": {},
+                "nodes": [{"id": "start", "type": "start", "data": {}}],
+                "edges": [],
+            },
+            {
+                "specVersion": 3,
+                "settings": {},
+                "nodes": [
+                    {"id": "start", "type": "start", "data": {}},
+                    {
+                        "id": "agent",
+                        "type": "agent",
+                        "data": {"prompt": "持续处理用户问题"},
+                    },
+                ],
+                "edges": [
+                    {
+                        "id": "begin",
+                        "source": "start",
+                        "target": "agent",
+                        "data": {"mode": "always", "priority": 0},
+                    }
+                ],
+            },
+            {
+                "specVersion": 3,
+                "settings": {},
+                "nodes": [
+                    {"id": "start", "type": "start", "data": {}},
+                    {"id": "handoff", "type": "handoff", "data": {}},
+                ],
+                "edges": [
+                    {
+                        "id": "begin",
+                        "source": "start",
+                        "target": "handoff",
+                        "data": {"mode": "always", "priority": 0},
+                    }
+                ],
+            },
+        ]
+
+        for graph in terminal_graphs:
+            with self.subTest(node=graph["nodes"][-1]["type"]):
+                self.assertEqual(validate_graph(graph), [])
+
+        action_without_exit = {
+            "specVersion": 3,
+            "settings": {},
+            "nodes": [
+                {"id": "start", "type": "start", "data": {}},
+                {"id": "action", "type": "action", "data": {}},
+            ],
+            "edges": [
+                {
+                    "id": "begin",
+                    "source": "start",
+                    "target": "action",
+                    "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)
+            )
+        )
+
+    def test_v2_start_prompt_is_preserved_in_synthetic_agent(self):
+        graph = normalize_graph(
+            {
+                "nodes": [
+                    {"id": "s", "type": "startCall", "data": {"prompt": "询问需求"}},
+                    {"id": "e", "type": "endCall", "data": {"prompt": "再见"}},
+                ],
+                "edges": [
+                    {"id": "done", "source": "s", "target": "e", "data": {"condition": "完成"}}
+                ],
+            }
+        )
+        migrated = next(node for node in graph["nodes"] if node["type"] == "agent")
+        self.assertEqual(migrated["data"]["prompt"], "询问需求")
+        self.assertEqual(validate_graph(graph), [])
+
+    def test_rejects_automatic_cycle_and_duplicate_priorities(self):
+        graph = valid_graph()
+        graph["nodes"].insert(1, {"id": "action", "type": "action", "data": {}})
+        graph["edges"] = [
+            {"id": "a", "source": "start", "target": "action", "data": {"mode": "always", "priority": 0}},
+            {"id": "b", "source": "action", "target": "start", "data": {"mode": "always", "priority": 0}},
+            {"id": "c", "source": "agent", "target": "end", "data": {"mode": "always", "priority": 10}},
+        ]
+        errors = validate_graph(graph)
+        self.assertTrue(any("无等待循环" in error for error in errors))
+
+    def test_expression_routes_using_session_variables(self):
+        cfg = prepare_dynamic_config(
+            AssistantConfig(
+                type="workflow",
+                graph=valid_graph(),
+                dynamic_variable_definitions={
+                    "customer": {"type": "string", "default": "王先生"},
+                    "order_status": {"type": "string", "default": "pending"},
+                },
+            ),
+            {},
+            assistant_id="asst_test",
+        )
+        store = DynamicVariableStore.from_config(cfg)
+        engine = WorkflowEngine(cfg.graph)
+        self.assertIsNone(engine.deterministic_edge("agent", store, include_default=False))
+        store.assign("order_status", "paid")
+        self.assertEqual(
+            engine.deterministic_edge("agent", store, include_default=False)["target"],
+            "end",
+        )
+        self.assertIn("王先生", engine.prompt_for("agent", store))
+
+        inherited_prompt = engine.prompt_for("agent", store)
+        self.assertIn("服务 王先生", inherited_prompt)
+        self.assertIn("处理订单", inherited_prompt)
+
+        engine.data("agent")["inheritGlobalConfig"] = False
+        custom_prompt = engine.prompt_for("agent", store)
+        self.assertNotIn("服务 王先生", custom_prompt)
+        self.assertIn("处理订单", custom_prompt)
+
+
+if __name__ == "__main__":
+    unittest.main()

frontend/src/components/editor/knowledge-retrieval-config-dialog.tsx

@@ -0,0 +1,168 @@
+"use client";
+
+import { Settings2 } from "lucide-react";
+import { useState } from "react";
+
+import { Button } from "@/components/ui/button";
+import {
+  Dialog,
+  DialogContent,
+  DialogDescription,
+  DialogFooter,
+  DialogHeader,
+  DialogTitle,
+} from "@/components/ui/dialog";
+import { Input } from "@/components/ui/input";
+import {
+  Select,
+  SelectContent,
+  SelectItem,
+  SelectTrigger,
+  SelectValue,
+} from "@/components/ui/select";
+import type { KnowledgeRetrievalConfig } from "@/lib/api";
+
+export const DEFAULT_KNOWLEDGE_RETRIEVAL_CONFIG: KnowledgeRetrievalConfig = {
+  mode: "automatic",
+  topN: 5,
+  scoreThreshold: 0,
+};
+
+export function KnowledgeRetrievalConfigDialog({
+  disabled,
+  value,
+  onChange,
+}: {
+  disabled: boolean;
+  value: KnowledgeRetrievalConfig;
+  onChange: (config: KnowledgeRetrievalConfig) => void;
+}) {
+  const [open, setOpen] = useState(false);
+  const [draft, setDraft] = useState(value);
+  const [error, setError] = useState<string | null>(null);
+
+  function openDialog() {
+    setDraft(value);
+    setError(null);
+    setOpen(true);
+  }
+
+  function saveDraft() {
+    if (draft.topN === 0 || draft.topN < -1 || !Number.isInteger(draft.topN)) {
+      setError("Top N 必须为 -1 或大于 0 的整数");
+      return;
+    }
+    if (draft.scoreThreshold < 0 || draft.scoreThreshold > 1) {
+      setError("最低相关度必须在 0 到 1 之间");
+      return;
+    }
+    onChange(draft);
+    setOpen(false);
+  }
+
+  return (
+    <>
+      <button
+        type="button"
+        disabled={disabled}
+        onClick={openDialog}
+        aria-label="打开知识库高级配置"
+        title={
+          disabled
+            ? "请先选择知识库"
+            : `${value.mode === "automatic" ? "自动检索" : "模型主动检索"} · Top N ${value.topN === -1 ? "不限" : value.topN} · 最低相关度 ${value.scoreThreshold}`
+        }
+        className="flex h-5 w-5 items-center justify-center rounded-full text-muted-soft transition-colors hover:bg-surface-strong hover:text-foreground disabled:cursor-not-allowed disabled:opacity-40"
+      >
+        <Settings2 size={14} />
+      </button>
+
+      <Dialog open={open} onOpenChange={setOpen}>
+        <DialogContent className="sm:max-w-lg">
+          <DialogHeader>
+            <DialogTitle>知识库高级配置</DialogTitle>
+            <DialogDescription>
+              设置检索触发方式、返回数量和相关度过滤条件。
+            </DialogDescription>
+          </DialogHeader>
+
+          <div className="space-y-5 py-2">
+            <div className="space-y-2">
+              <div className="text-sm font-medium text-foreground">检索方式</div>
+              <Select
+                value={draft.mode}
+                onValueChange={(mode: "automatic" | "on_demand") =>
+                  setDraft({ ...draft, mode })
+                }
+              >
+                <SelectTrigger className="w-full border-hairline-strong bg-background">
+                  <SelectValue />
+                </SelectTrigger>
+                <SelectContent>
+                  <SelectItem value="automatic">自动检索</SelectItem>
+                  <SelectItem value="on_demand">模型主动检索</SelectItem>
+                </SelectContent>
+              </Select>
+              <p className="text-xs text-muted-foreground">
+                {draft.mode === "automatic"
+                  ? "每轮用户提问后自动检索，响应行为更稳定。"
+                  : "由大模型判断是否调用知识库，依赖模型的工具调用能力。"}
+              </p>
+            </div>
+
+            <label className="block">
+              <span className="mb-2 block text-sm font-medium text-foreground">
+                最多返回片段数
+              </span>
+              <Input
+                type="number"
+                step="1"
+                min="-1"
+                value={draft.topN}
+                onChange={(event) =>
+                  setDraft({ ...draft, topN: Number(event.target.value) })
+                }
+              />
+              <span className="mt-1.5 block text-xs text-muted-foreground">
+                填写 -1 时保留所有达到阈值的结果。
+              </span>
+            </label>
+
+            <label className="block">
+              <span className="mb-2 block text-sm font-medium text-foreground">
+                最低相关度
+              </span>
+              <Input
+                type="number"
+                step="0.01"
+                min="0"
+                max="1"
+                value={draft.scoreThreshold}
+                onChange={(event) =>
+                  setDraft({
+                    ...draft,
+                    scoreThreshold: Number(event.target.value),
+                  })
+                }
+              />
+              <span className="mt-1.5 block text-xs text-muted-foreground">
+                仅保留相关度达到该值的片段，范围 0–1。
+              </span>
+            </label>
+
+            {error && <p className="text-sm text-destructive">{error}</p>}
+          </div>
+
+          <DialogFooter>
+            <Button type="button" variant="outline" onClick={() => setOpen(false)}>
+              取消
+            </Button>
+            <Button type="button" onClick={saveDraft}>
+              保存配置
+            </Button>
+          </DialogFooter>
+        </DialogContent>
+      </Dialog>
+    </>
+  );
+}

frontend/src/components/editor/section-card.tsx

@@ -0,0 +1,92 @@
+"use client";
+
+/**
+ * Compact section chrome shared by assistant editors and workflow node panels.
+ * Density matches the debug preview drawer (text-sm titles, tight padding).
+ */
+
+import { HelpCircle } from "lucide-react";
+import type { ReactNode } from "react";
+
+import {
+  Card,
+  CardContent,
+  CardHeader,
+  CardTitle,
+} from "@/components/ui/card";
+import {
+  Popover,
+  PopoverContent,
+  PopoverTrigger,
+} from "@/components/ui/popover";
+import { cn } from "@/lib/utils";
+
+export function SectionCard({
+  icon,
+  title,
+  description,
+  children,
+  className,
+}: {
+  icon?: ReactNode;
+  title?: string;
+  description?: string;
+  children: ReactNode;
+  className?: string;
+}) {
+  const hasHeader = Boolean(title);
+
+  return (
+    <Card
+      size="sm"
+      className={cn(
+        "gap-3 rounded-2xl border border-hairline bg-card py-3.5 text-card-foreground shadow-sm ring-0",
+        className,
+      )}
+    >
+      {hasHeader && (
+        <CardHeader className="gap-0 px-4">
+          <div className="flex items-center gap-2.5">
+            {icon && (
+              <div className="flex h-8 w-8 shrink-0 items-center justify-center rounded-full bg-surface-strong text-foreground">
+                {icon}
+              </div>
+            )}
+            <div className="flex min-w-0 items-center gap-1.5">
+              <CardTitle className="text-sm font-medium leading-none">
+                {title}
+              </CardTitle>
+              {description && <HelpHint text={description} />}
+            </div>
+          </div>
+        </CardHeader>
+      )}
+      <CardContent className={cn("px-4", hasHeader && "space-y-3")}>
+        {children}
+      </CardContent>
+    </Card>
+  );
+}
+
+export function HelpHint({ text }: { text: string }) {
+  return (
+    <Popover>
+      <PopoverTrigger asChild>
+        <button
+          type="button"
+          aria-label="查看说明"
+          onClick={(event) => event.stopPropagation()}
+          className="flex h-5 w-5 shrink-0 items-center justify-center rounded-full text-muted-soft transition-colors hover:bg-surface-strong hover:text-foreground"
+        >
+          <HelpCircle size={13} />
+        </button>
+      </PopoverTrigger>
+      <PopoverContent
+        align="start"
+        className="w-72 text-sm leading-6 text-muted-foreground"
+      >
+        {text}
+      </PopoverContent>
+    </Popover>
+  );
+}

frontend/src/components/pages/AssistantPage.tsx

@@ -21,7 +21,6 @@ import {
   Save,
   Mic,
   Send,
-  HelpCircle,
   Waypoints,
   AudioLines,
   Terminal,
@@ -53,13 +52,6 @@ import {
   DialogHeader,
   DialogTitle,
 } from "@/components/ui/dialog";
-import {
-  Sheet,
-  SheetContent,
-  SheetDescription,
-  SheetHeader,
-  SheetTitle,
-} from "@/components/ui/sheet";
 import {
   DropdownMenu,
   DropdownMenuContent,
@@ -94,12 +86,6 @@ import { PageHeader } from "@/components/ui/page-header";
 import { FilterPills } from "@/components/ui/filter-pills";
 import { SearchInput } from "@/components/ui/search-input";
 import { ListToolbar } from "@/components/ui/list-toolbar";
-import {
-  Card,
-  CardContent,
-  CardHeader,
-  CardTitle,
-} from "@/components/ui/card";
 import { useCallback, useEffect, useRef, useState } from "react";
 import { useRouter } from "next/navigation";
 import {
@@ -131,6 +117,7 @@ import {
   WorkflowEditor,
   type WorkflowSettings,
 } from "@/components/workflow/WorkflowEditor";
+import { HelpHint, SectionCard } from "@/components/editor/section-card";
 import {
   defaultGraph,
   type WorkflowGraph,
@@ -369,7 +356,7 @@ type AssistantTypeOption = {
   label: string;
   description: string;
   icon: React.ReactNode;
-  /** 提示词、Dify、FastGPT 类型已落地，工作流暂时显示占位页 */
+  /** 提示词、工作流、Dify、FastGPT 已落地；OpenCode 暂时显示即将上线 */
   available: boolean;
 };
 
@@ -386,7 +373,7 @@ const assistantTypeOptions: AssistantTypeOption[] = [
     label: "使用工作流构建",
     description: "用可视化编排串联多个节点，适合多步骤、带分支的复杂流程。",
     icon: <Workflow size={20} />,
-    available: false,
+    available: true,
   },
   {
     type: "Dify",
@@ -407,7 +394,7 @@ const assistantTypeOptions: AssistantTypeOption[] = [
     label: "使用 OpenCode 构建",
     description: "对接 OpenCode 服务，通过提示词驱动代码助手并支持实时语音对话。",
     icon: <Terminal size={20} />,
-    available: true,
+    available: false,
   },
 ];
 
@@ -478,10 +465,26 @@ export function AssistantPage(props: AssistantPageProps) {
     defaultGraph(),
   );
   const [workflowSettings, setWorkflowSettings] = useState<WorkflowSettings>({
+    globalPrompt: defaultGraph().settings.globalPrompt,
+    llm: defaultGraph().settings.defaultLlmResourceId,
+    asr: defaultGraph().settings.defaultAsrResourceId,
+    tts: defaultGraph().settings.defaultTtsResourceId,
+    toolIds: defaultGraph().settings.toolIds,
+    knowledgeBaseId: defaultGraph().settings.knowledgeBaseId,
+    knowledgeRetrievalConfig: {
+      mode: defaultGraph().settings.knowledgeMode,
+      topN: defaultGraph().settings.knowledgeTopN,
+      scoreThreshold: defaultGraph().settings.knowledgeScoreThreshold,
+    },
     allowInterrupt: true,
     turnConfig: defaultTurnConfig(),
   });
-  const [debugOpen, setDebugOpen] = useState(false);
+  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 [activeNodeId, setActiveNodeId] = useState<string | null>(null);
   const [dynamicVariablesOpen, setDynamicVariablesOpen] = useState(false);
 
@@ -708,6 +711,7 @@ export function AssistantPage(props: AssistantPageProps) {
             name: workflowName,
             graph: workflowGraph,
             settings: workflowSettings,
+            dynamicVariableDefinitions: workflowDynamicVariableDefinitions,
           }),
         );
       }
@@ -848,20 +852,41 @@ export function AssistantPage(props: AssistantPageProps) {
               ? (assistant.graph as WorkflowGraph)
               : defaultGraph();
           const wfSettings: WorkflowSettings = {
-            llm: assistant.modelResourceIds.LLM,
+            llm:
-            asr: assistant.modelResourceIds.ASR,
+              graph.settings?.defaultLlmResourceId ||
-            tts: assistant.modelResourceIds.TTS,
+              assistant.modelResourceIds.LLM,
+            asr: graph.settings?.defaultAsrResourceId || assistant.modelResourceIds.ASR,
+            tts: graph.settings?.defaultTtsResourceId || assistant.modelResourceIds.TTS,
+            toolIds: graph.settings?.toolIds ?? [],
+            knowledgeBaseId:
+              graph.settings?.knowledgeBaseId || assistant.knowledgeBaseId || "",
+            knowledgeRetrievalConfig: {
+              mode:
+                graph.settings?.knowledgeMode ||
+                assistant.knowledgeRetrievalConfig.mode,
+              topN:
+                graph.settings?.knowledgeTopN ??
+                assistant.knowledgeRetrievalConfig.topN,
+              scoreThreshold:
+                graph.settings?.knowledgeScoreThreshold ??
+                assistant.knowledgeRetrievalConfig.scoreThreshold,
+            },
+            globalPrompt: graph.settings?.globalPrompt ?? "",
             allowInterrupt: assistant.enableInterrupt,
             turnConfig: assistant.turnConfig,
           };
           setWorkflowName(assistant.name);
           setWorkflowGraph(graph);
           setWorkflowSettings(wfSettings);
+          setWorkflowDynamicVariableDefinitions(
+            assistant.dynamicVariableDefinitions ?? {},
+          );
           setSavedSnapshot(
             JSON.stringify({
               name: assistant.name,
               graph,
               settings: wfSettings,
+              dynamicVariableDefinitions: assistant.dynamicVariableDefinitions ?? {},
             }),
           );
         }
@@ -912,7 +937,11 @@ export function AssistantPage(props: AssistantPageProps) {
           ...(workflowSettings.asr ? { ASR: workflowSettings.asr } : {}),
           ...(workflowSettings.tts ? { TTS: workflowSettings.tts } : {}),
         },
+        knowledgeBaseId: workflowSettings.knowledgeBaseId || null,
+        knowledgeRetrievalConfig: workflowSettings.knowledgeRetrievalConfig,
+        toolIds: workflowSettings.toolIds,
         graph: workflowGraph as unknown as Record<string, unknown>,
+        dynamicVariableDefinitions: workflowDynamicVariableDefinitions,
       }),
     );
   }
@@ -932,6 +961,7 @@ export function AssistantPage(props: AssistantPageProps) {
                   name: workflowName,
                   graph: workflowGraph,
                   settings: workflowSettings,
+                  dynamicVariableDefinitions: workflowDynamicVariableDefinitions,
                 })
               : null;
   const dirty =
@@ -1377,7 +1407,11 @@ export function AssistantPage(props: AssistantPageProps) {
 
           <div className="flex shrink-0 gap-2">
             {saveError && (
-              <span className="self-center text-xs text-destructive">
+              <span
+                role="alert"
+                title={saveError}
+                className="line-clamp-1 max-w-[min(42vw,560px)] self-center text-right text-sm leading-5 text-destructive"
+              >
                 {saveError}
               </span>
             )}
@@ -1385,7 +1419,12 @@ export function AssistantPage(props: AssistantPageProps) {
               variant="outline"
               className="gap-2 border-hairline-strong text-foreground hover:bg-surface-strong"
               disabled={!editingId}
-              onClick={() => setDebugOpen(true)}
+              onClick={() => {
+                setWorkflowSettingsOpen(false);
+                setWorkflowEditingNodeId(null);
+                setWorkflowEditingEdgeId(null);
+                setWorkflowDebugOpen(true);
+              }}
             >
               <Bug size={16} />
               调试
@@ -1411,43 +1450,51 @@ export function AssistantPage(props: AssistantPageProps) {
             onChange={setWorkflowGraph}
             settings={workflowSettings}
             onSettingsChange={setWorkflowSettings}
+            onOpenDynamicVariables={() => setDynamicVariablesOpen(true)}
+            editingNodeId={workflowEditingNodeId}
+            onEditingNodeIdChange={setWorkflowEditingNodeId}
+            editingEdgeId={workflowEditingEdgeId}
+            onEditingEdgeIdChange={setWorkflowEditingEdgeId}
+            settingsOpen={workflowSettingsOpen}
+            onSettingsOpenChange={setWorkflowSettingsOpen}
+            debugOpen={workflowDebugOpen}
+            onDebugOpenChange={(open) => {
+              setWorkflowDebugOpen(open);
+              if (!open) setActiveNodeId(null);
+            }}
+            debugPanel={
+              <DebugDrawer
+                overlay
+                assistantId={editingId}
+                onClose={() => {
+                  setWorkflowDebugOpen(false);
+                  setActiveNodeId(null);
+                }}
+                hasUnsavedChanges={dirty}
+                onNodeActive={setActiveNodeId}
+                dynamicVariablesEnabled
+                dynamicVariableDefinitions={workflowDynamicVariableDefinitions}
+              />
+            }
             activeNodeId={activeNodeId}
             modelOptions={{
               llm: credOptions("LLM"),
               asr: credOptions("ASR"),
               tts: credOptions("TTS"),
             }}
+            toolOptions={tools
+              .filter((tool) => tool.status === "active")
+              .map((tool) => ({ value: tool.id, label: tool.name }))}
+            knowledgeOptions={kbOptions}
           />
         </div>
 
-        <Sheet
+        <DynamicVariablesDialog
-          open={debugOpen}
+          open={dynamicVariablesOpen}
-          onOpenChange={(open) => {
+          onOpenChange={setDynamicVariablesOpen}
-            setDebugOpen(open);
+          definitions={workflowDynamicVariableDefinitions}
-            if (!open) setActiveNodeId(null);
+          onChange={setWorkflowDynamicVariableDefinitions}
-          }}
+        />
-          modal={false}
-        >
-          <SheetContent
-            side="right"
-            showOverlay={false}
-            onInteractOutside={(e) => e.preventDefault()}
-            className="w-[440px] gap-0 border-l border-hairline bg-card p-0 sm:max-w-[440px]"
-          >
-            <SheetHeader className="sr-only">
-              <SheetTitle>语音调试</SheetTitle>
-              <SheetDescription>
-                与当前助手进行语音对话调试,画布会高亮正在激活的节点。
-              </SheetDescription>
-            </SheetHeader>
-            <DebugDrawer
-              assistantId={editingId}
-              asSheet
-              hasUnsavedChanges={dirty}
-              onNodeActive={setActiveNodeId}
-            />
-          </SheetContent>
-        </Sheet>
       </div>
     );
   }
@@ -1488,10 +1535,10 @@ export function AssistantPage(props: AssistantPageProps) {
           </div>
         </div>
 
-        <div className="flex min-h-0 flex-1 gap-6">
+        <div className="flex min-h-0 flex-1 gap-4">
-          <div className="scrollbar-subtle min-w-0 flex-1 space-y-5 overflow-y-auto pr-1">
+          <div className="scrollbar-subtle min-w-0 flex-1 space-y-3 overflow-y-auto pr-1">
             <SectionCard
-              icon={<Boxes size={18} />}
+              icon={<Boxes size={15} />}
               title="Dify 应用配置"
               description="从「模型资源」中选择 Dify 应用。开场白、知识库、提示词等对话编排请在 Dify 平台配置，本页不重复设置。"
             >
@@ -1505,7 +1552,7 @@ export function AssistantPage(props: AssistantPageProps) {
             </SectionCard>
 
             <SectionCard
-              icon={<Brain size={18} />}
+              icon={<Brain size={15} />}
               title="语音配置"
               description="从「模型资源」中选择语音识别与语音合成。大模型、知识库与开场白由 Dify 应用提供，请前往 Dify 平台配置。"
             >
@@ -1526,7 +1573,7 @@ export function AssistantPage(props: AssistantPageProps) {
             </SectionCard>
 
             <SectionCard
-              icon={<Sparkles size={18} />}
+              icon={<Sparkles size={15} />}
               title="交互策略"
               description="设置实时视频对话时的交互体验"
             >
@@ -1584,10 +1631,10 @@ export function AssistantPage(props: AssistantPageProps) {
           </div>
         </div>
 
-        <div className="flex min-h-0 flex-1 gap-6">
+        <div className="flex min-h-0 flex-1 gap-4">
-          <div className="scrollbar-subtle min-w-0 flex-1 space-y-5 overflow-y-auto pr-1">
+          <div className="scrollbar-subtle min-w-0 flex-1 space-y-3 overflow-y-auto pr-1">
             <SectionCard
-              icon={<Database size={18} />}
+              icon={<Database size={15} />}
               title="FastGPT 应用配置"
               description="从「模型资源」中选择 FastGPT 应用。开场白、知识库、提示词等对话编排请在 FastGPT 平台配置，本页不重复设置。"
             >
@@ -1601,7 +1648,7 @@ export function AssistantPage(props: AssistantPageProps) {
             </SectionCard>
 
             <SectionCard
-              icon={<Brain size={18} />}
+              icon={<Brain size={15} />}
               title="语音配置"
               description="从「模型资源」中选择语音识别与语音合成。大模型、知识库与开场白由 FastGPT 应用提供，请前往 FastGPT 平台配置。"
             >
@@ -1622,7 +1669,7 @@ export function AssistantPage(props: AssistantPageProps) {
             </SectionCard>
 
             <SectionCard
-              icon={<Sparkles size={18} />}
+              icon={<Sparkles size={15} />}
               title="交互策略"
               description="设置实时视频对话时的交互体验"
             >
@@ -1684,10 +1731,10 @@ export function AssistantPage(props: AssistantPageProps) {
           </div>
         </div>
 
-        <div className="flex min-h-0 flex-1 gap-6">
+        <div className="flex min-h-0 flex-1 gap-4">
-          <div className="scrollbar-subtle min-w-0 flex-1 space-y-5 overflow-y-auto pr-1">
+          <div className="scrollbar-subtle min-w-0 flex-1 space-y-3 overflow-y-auto pr-1">
             <SectionCard
-              icon={<Terminal size={18} />}
+              icon={<Terminal size={15} />}
               title="OpenCode 服务配置"
               description="从「模型资源」中选择 OpenCode 服务资源。"
             >
@@ -1701,7 +1748,7 @@ export function AssistantPage(props: AssistantPageProps) {
             </SectionCard>
 
             <SectionCard
-              icon={<MessageSquareText size={18} />}
+              icon={<MessageSquareText size={15} />}
               title="提示词"
               description="描述助手的角色、能力和回答要求"
             >
@@ -1714,7 +1761,7 @@ export function AssistantPage(props: AssistantPageProps) {
             </SectionCard>
 
             <SectionCard
-              icon={<Brain size={18} />}
+              icon={<Brain size={15} />}
               title="模型与语音配置"
               description="配置 OpenCode 使用的大语言模型、语音识别与语音合成资源。"
             >
@@ -1759,7 +1806,7 @@ export function AssistantPage(props: AssistantPageProps) {
             </SectionCard>
 
             <SectionCard
-              icon={<Sparkles size={18} />}
+              icon={<Sparkles size={15} />}
               title="交互策略"
               description="设置实时视频对话时的交互体验"
             >
@@ -1823,10 +1870,10 @@ export function AssistantPage(props: AssistantPageProps) {
         }
       />
 
-      <div className="flex min-h-0 flex-1 gap-6">
+      <div className="flex min-h-0 flex-1 gap-4">
-        <div className="scrollbar-subtle min-w-0 flex-1 space-y-5 overflow-y-auto pr-1">
+        <div className="scrollbar-subtle min-w-0 flex-1 space-y-3 overflow-y-auto pr-1">
           <SectionCard>
-            <div className="grid grid-cols-1 gap-4 md:grid-cols-2">
+            <div className="grid grid-cols-1 gap-3 md:grid-cols-2">
               <div
                 role="button"
                 tabIndex={0}
@@ -1838,25 +1885,25 @@ export function AssistantPage(props: AssistantPageProps) {
                   }
                 }}
                 className={[
-                  "cursor-pointer rounded-2xl border p-5 text-left transition-colors",
+                  "cursor-pointer rounded-xl border p-3.5 text-left transition-colors",
                   form.runtimeMode === "pipeline"
                     ? "border-primary bg-primary/5 ring-1 ring-primary"
                     : "border-hairline bg-canvas-soft hover:border-hairline-strong",
                 ].join(" ")}
               >
                 <div className="flex items-center justify-between gap-3">
-                  <div className="flex items-center gap-3">
+                  <div className="flex items-center gap-2.5">
-                    <div className="flex h-10 w-10 shrink-0 items-center justify-center rounded-full bg-surface-strong text-foreground">
+                    <div className="flex h-8 w-8 shrink-0 items-center justify-center rounded-full bg-surface-strong text-foreground">
-                      <Waypoints size={18} />
+                      <Waypoints size={15} />
                     </div>
                     <div className="flex items-center gap-1.5">
-                      <span className="font-medium text-foreground">Pipeline 模式</span>
+                      <span className="text-sm font-medium text-foreground">Pipeline 模式</span>
                       <HelpHint text="通过 ASR、LLM 和 TTS 级联组成语音管线，灵活选配各模块。" />
                     </div>
                   </div>
                   {form.runtimeMode === "pipeline" && (
-                    <span className="flex h-6 w-6 shrink-0 items-center justify-center rounded-full bg-primary text-primary-foreground">
+                    <span className="flex h-5 w-5 shrink-0 items-center justify-center rounded-full bg-primary text-primary-foreground">
-                      <Check size={14} />
+                      <Check size={12} />
                     </span>
                   )}
                 </div>
@@ -1873,25 +1920,25 @@ export function AssistantPage(props: AssistantPageProps) {
                   }
                 }}
                 className={[
-                  "cursor-pointer rounded-2xl border p-5 text-left transition-colors",
+                  "cursor-pointer rounded-xl border p-3.5 text-left transition-colors",
                   form.runtimeMode === "realtime"
                     ? "border-primary bg-primary/5 ring-1 ring-primary"
                     : "border-hairline bg-canvas-soft hover:border-hairline-strong",
                 ].join(" ")}
               >
                 <div className="flex items-center justify-between gap-3">
-                  <div className="flex items-center gap-3">
+                  <div className="flex items-center gap-2.5">
-                    <div className="flex h-10 w-10 shrink-0 items-center justify-center rounded-full bg-surface-strong text-foreground">
+                    <div className="flex h-8 w-8 shrink-0 items-center justify-center rounded-full bg-surface-strong text-foreground">
-                      <AudioLines size={18} />
+                      <AudioLines size={15} />
                     </div>
                     <div className="flex items-center gap-1.5">
-                      <span className="font-medium text-foreground">Realtime 模式</span>
+                      <span className="text-sm font-medium text-foreground">Realtime 模式</span>
                       <HelpHint text="使用原生实时语音模型，模型直接处理音频输入并生成语音回复。" />
                     </div>
                   </div>
                   {form.runtimeMode === "realtime" && (
-                    <span className="flex h-6 w-6 shrink-0 items-center justify-center rounded-full bg-primary text-primary-foreground">
+                    <span className="flex h-5 w-5 shrink-0 items-center justify-center rounded-full bg-primary text-primary-foreground">
-                      <Check size={14} />
+                      <Check size={12} />
                     </span>
                   )}
                 </div>
@@ -1900,7 +1947,7 @@ export function AssistantPage(props: AssistantPageProps) {
           </SectionCard>
 
           <SectionCard
-            icon={<MessageSquareText size={18} />}
+            icon={<MessageSquareText size={15} />}
             title="提示词"
             description="描述助手的角色、能力和回答要求"
           >
@@ -1918,7 +1965,7 @@ export function AssistantPage(props: AssistantPageProps) {
 
           {form.runtimeMode === "pipeline" ? (
             <SectionCard
-              icon={<Brain size={18} />}
+              icon={<Brain size={15} />}
               title="模型配置"
               description="从「模型资源」中选择大语言模型、语音识别与语音合成"
             >
@@ -1963,7 +2010,7 @@ export function AssistantPage(props: AssistantPageProps) {
             </SectionCard>
           ) : (
             <SectionCard
-              icon={<Brain size={18} />}
+              icon={<Brain size={15} />}
               title="模型配置"
               description="当前模式下 ASR 与 TTS 由 Realtime 模型内置完成"
             >
@@ -1978,7 +2025,7 @@ export function AssistantPage(props: AssistantPageProps) {
           )}
 
           <SectionCard
-            icon={<Bot size={18} />}
+            icon={<Bot size={15} />}
             title="开场白"
             description="助手与用户首次对话时的开场语"
           >
@@ -1995,7 +2042,7 @@ export function AssistantPage(props: AssistantPageProps) {
 
           {form.runtimeMode === "pipeline" && (
             <SectionCard
-              icon={<Database size={18} />}
+              icon={<Database size={15} />}
               title="知识库配置"
               description="选择助手回答时可检索的业务知识来源"
             >
@@ -2021,7 +2068,7 @@ export function AssistantPage(props: AssistantPageProps) {
           )}
 
           <SectionCard
-            icon={<Wrench size={18} />}
+            icon={<Wrench size={15} />}
             title="工具"
             description="配置该提示词助手可以调用的工具"
           >
@@ -2033,7 +2080,7 @@ export function AssistantPage(props: AssistantPageProps) {
           </SectionCard>
 
           <SectionCard
-            icon={<Sparkles size={18} />}
+            icon={<Sparkles size={15} />}
             title="交互策略"
             description="设置实时视频对话时的交互体验"
           >
@@ -2132,7 +2179,8 @@ function SegmentedIconButton({
 
 function DebugDrawer({
   assistantId,
-  asSheet = false,
+  overlay = false,
+  onClose,
   hasUnsavedChanges = false,
   onNodeActive,
   vision = false,
@@ -2140,7 +2188,8 @@ function DebugDrawer({
   dynamicVariableDefinitions = {},
 }: {
   assistantId: string | null;
-  asSheet?: boolean;
+  overlay?: boolean;
+  onClose?: () => void;
   hasUnsavedChanges?: boolean;
   onNodeActive?: (nodeId: string | null) => void;
   vision?: boolean;
@@ -2179,14 +2228,25 @@ function DebugDrawer({
     [camera, preview],
   );
 
-  const containerClass = asSheet
-    ? "flex h-full min-w-0 flex-1 flex-col overflow-hidden"
-    : "hidden min-w-0 flex-1 flex-col overflow-hidden rounded-2xl border border-hairline bg-card shadow-sm lg:flex";
-
   return (
-    <aside className={containerClass}>
+    <aside
-      <div className="flex shrink-0 items-center justify-between gap-3 border-b border-hairline px-5 py-3">
+      className={overlay
+        ? "flex h-full min-w-0 flex-1 flex-col overflow-hidden bg-card"
+        : "hidden min-w-0 flex-1 flex-col overflow-hidden rounded-2xl border border-hairline bg-card shadow-sm lg:flex"}
+    >
+      <div className={`flex min-h-14 shrink-0 items-center justify-between gap-3 border-b border-hairline py-3 ${overlay ? "px-4" : "px-5"}`}>
         <div className="flex min-w-0 items-center gap-2.5">
+          {overlay && onClose && (
+            <button
+              type="button"
+              aria-label="关闭调试预览"
+              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>
+          )}
           <div className="shrink-0 text-sm font-medium text-foreground">
             调试与预览
           </div>
@@ -3344,29 +3404,6 @@ function EditableTitle({
   );
 }
 
-function HelpHint({ text }: { text: string }) {
-  return (
-    <Popover>
-      <PopoverTrigger asChild>
-        <button
-          type="button"
-          aria-label="查看说明"
-          onClick={(event) => event.stopPropagation()}
-          className="flex h-5 w-5 items-center justify-center rounded-full text-muted-soft transition-colors hover:bg-surface-strong hover:text-foreground"
-        >
-          <HelpCircle size={14} />
-        </button>
-      </PopoverTrigger>
-      <PopoverContent
-        align="start"
-        className="w-72 text-sm leading-6 text-muted-foreground"
-      >
-        {text}
-      </PopoverContent>
-    </Popover>
-  );
-}
-
 function DynamicVariableEditorHint({
   count,
   onOpen,
@@ -3624,45 +3661,6 @@ function DynamicVariablesDialog({
   );
 }
 
-function SectionCard({
-  icon,
-  title,
-  description,
-  children,
-}: {
-  icon?: React.ReactNode;
-  title?: string;
-  description?: string;
-  children: React.ReactNode;
-}) {
-  const hasHeader = Boolean(title);
-
-  return (
-    <Card className="rounded-2xl border-hairline bg-card text-card-foreground shadow-sm">
-      {hasHeader && (
-        <CardHeader>
-          <div className="flex items-center gap-3">
-            {icon && (
-              <div className="flex h-10 w-10 items-center justify-center rounded-full bg-surface-strong text-foreground">
-                {icon}
-              </div>
-            )}
-
-            <div className="flex items-center gap-1.5">
-              <CardTitle className="text-base font-medium">{title}</CardTitle>
-              {description && <HelpHint text={description} />}
-            </div>
-          </div>
-        </CardHeader>
-      )}
-
-      <CardContent className={hasHeader ? "space-y-4" : undefined}>
-        {children}
-      </CardContent>
-    </Card>
-  );
-}
-
 function TextAreaField({
   label,
   value,
@@ -3679,7 +3677,7 @@ function TextAreaField({
   return (
     <label className="block">
       {label && (
-        <div className="mb-2 text-sm font-medium text-foreground">{label}</div>
+        <div className="mb-1.5 text-sm font-medium text-foreground">{label}</div>
       )}
       <Textarea
         value={value}
@@ -3688,7 +3686,7 @@ function TextAreaField({
         rows={rows}
         // Override ui/textarea's field-sizing-content so `rows` sets a real height
         // instead of collapsing to min-h-16 when the value is short.
-        className="field-sizing-fixed min-h-32 resize-y border-hairline-strong bg-background text-foreground placeholder:text-muted-soft"
+        className="field-sizing-fixed min-h-28 resize-y border-hairline-strong bg-background text-sm text-foreground placeholder:text-muted-soft"
       />
     </label>
   );
@@ -3714,7 +3712,7 @@ function ResourceSelectField({
   return (
     <div className="block">
       {label && (
-        <div className="mb-2 text-sm font-medium text-foreground">{label}</div>
+        <div className="mb-1.5 text-sm font-medium text-foreground">{label}</div>
       )}
 
       <Select
@@ -4023,18 +4021,18 @@ function ToggleRow({
     <div
       className={[
         "flex items-center justify-between border border-hairline bg-canvas-soft",
-        hasIcon ? "rounded-2xl p-5" : "rounded-xl p-4",
+        hasIcon ? "rounded-xl p-3.5" : "rounded-xl px-3.5 py-3",
       ].join(" ")}
     >
       <div>
         <div
           className={[
-            "flex items-center font-medium text-foreground",
+            "flex items-center text-sm font-medium text-foreground",
-            hasIcon ? "gap-3" : "gap-1.5",
+            hasIcon ? "gap-2.5" : "gap-1.5",
           ].join(" ")}
         >
           {icon && (
-            <span className="flex h-10 w-10 shrink-0 items-center justify-center rounded-full bg-surface-strong text-foreground">
+            <span className="flex h-8 w-8 shrink-0 items-center justify-center rounded-full bg-surface-strong text-foreground">
               {icon}
             </span>
           )}
@@ -4044,7 +4042,7 @@ function ToggleRow({
           </span>
         </div>
         {description && (
-          <div className="mt-1 text-sm text-muted-foreground">
+          <div className="mt-1 text-xs text-muted-foreground">
             {description}
           </div>
         )}

frontend/src/components/turn-config-editor.tsx

@@ -1,8 +1,9 @@
 "use client";
 
 import type { ReactNode } from "react";
-import { HelpCircle, Settings2 } from "lucide-react";
+import { Settings2 } from "lucide-react";
 
+import { HelpHint } from "@/components/editor/section-card";
 import { Input } from "@/components/ui/input";
 import { Switch } from "@/components/ui/switch";
 import {
@@ -13,11 +14,6 @@ import {
   DialogTitle,
   DialogTrigger,
 } from "@/components/ui/dialog";
-import {
-  Popover,
-  PopoverContent,
-  PopoverTrigger,
-} from "@/components/ui/popover";
 import {
   Select,
   SelectContent,
@@ -59,7 +55,7 @@ export function TurnConfigEditor({
     });
 
   return (
-    <div className="flex items-center justify-between gap-4 rounded-2xl border border-hairline bg-card p-4 shadow-sm">
+    <div className="flex items-center justify-between gap-3 rounded-xl border border-hairline bg-canvas-soft px-3.5 py-3">
       <div className="flex items-center gap-1.5">
         <span className="text-sm font-medium text-foreground">
           允许用户打断
@@ -72,7 +68,7 @@ export function TurnConfigEditor({
               aria-label="打开允许用户打断高级配置"
               className="flex h-5 w-5 items-center justify-center rounded-full text-muted-soft transition-colors hover:bg-surface-strong hover:text-foreground"
             >
-              <Settings2 size={14} />
+              <Settings2 size={13} />
             </button>
           </DialogTrigger>
           <DialogContent className="max-h-[calc(100vh-3rem)] overflow-y-auto sm:max-w-6xl lg:max-w-[88rem] lg:overflow-hidden">
@@ -161,29 +157,6 @@ function ConfigSection({ title, children }: { title: string; children: ReactNode
   );
 }
 
-function HelpHint({ text }: { text: string }) {
-  return (
-    <Popover>
-      <PopoverTrigger asChild>
-        <button
-          type="button"
-          aria-label="查看允许用户打断说明"
-          onClick={(event) => event.stopPropagation()}
-          className="flex h-5 w-5 items-center justify-center rounded-full text-muted-soft transition-colors hover:bg-surface-strong hover:text-foreground"
-        >
-          <HelpCircle size={14} />
-        </button>
-      </PopoverTrigger>
-      <PopoverContent
-        align="start"
-        className="w-72 text-sm leading-6 text-muted-foreground"
-      >
-        {text}
-      </PopoverContent>
-    </Popover>
-  );
-}
-
 function NumberField({ label, value, min, max, step, onChange }: { label: string; value: number; min: number; max: number; step: number; onChange: (value: number) => void }) {
   return (
     <label className="block space-y-2">

frontend/src/components/workflow/ConditionEdge.tsx

@@ -1,14 +1,13 @@
 "use client";
 
 /**
- * 条件边。边携带 condition(自然语言条件,LLM 据此决定是否走这条路径)与
+ * Workflow v3 edge: LLM judgement, variable expression, or deterministic default.
- * label(日志里识别该路径的短标签)。悬停/选中时在标签旁显示「编辑 / 删除」按钮。
  */
 
 import {
   BaseEdge,
   EdgeLabelRenderer,
-  getSmoothStepPath,
+  getBezierPath,
   type EdgeProps,
   useReactFlow,
 } from "@xyflow/react";
@@ -36,20 +35,22 @@ export function ConditionEdge({
   // 点击标签:只选中这条边(露出编辑/删除按钮),不直接进入编辑。
   const selectThisEdge = () =>
     setEdges((eds) => eds.map((e) => ({ ...e, selected: e.id === id })));
-  const [path, labelX, labelY] = getSmoothStepPath({
+  const [path, labelX, labelY] = getBezierPath({
     sourceX,
     sourceY,
     sourcePosition,
     targetX,
     targetY,
     targetPosition,
-    borderRadius: 8,
+    curvature: 0.28,
-    offset: 20,
   });
 
-  const label = ((data?.label as string) || (data?.condition as string) || "")
+  const mode = (data?.mode as string) || "always";
-    .toString()
+  const label = (
-    .trim();
+    (data?.label as string) ||
+    (mode === "llm" ? (data?.condition as string) : "") ||
+    (mode === "expression" ? "变量表达式" : "默认路径")
+  ).toString().trim();
   const expanded = hovered || selected;
 
   return (

frontend/src/components/workflow/GenericNode.tsx

@@ -27,9 +27,30 @@ export function GenericNode({ id, type, data, selected }: NodeProps) {
 
   const nodeData = data as WorkflowNodeData;
   const Icon = spec.icon;
-  const preview = (nodeData.greeting || nodeData.prompt || "")
+  const preview = (nodeData.greeting || nodeData.prompt || nodeData.message || "")
     .toString()
     .trim();
+  const entryModeLabel = {
+    wait_user: "等待用户",
+    generate: "立即回复",
+    fixed_speech: "固定进入语",
+  }[nodeData.entryMode ?? "wait_user"];
+  const inheritsGlobal = nodeData.inheritGlobalConfig !== false;
+  const meta = type === "agent"
+    ? inheritsGlobal
+      ? [entryModeLabel, "继承全局配置"]
+      : [
+          entryModeLabel,
+          "自定义配置",
+          nodeData.llmResourceId ? "独立 LLM" : null,
+          `${nodeData.toolIds?.length ?? 0} 工具`,
+          nodeData.knowledgeBaseId ? "知识库" : null,
+          nodeData.asrResourceId ? "独立 ASR" : null,
+          nodeData.ttsResourceId ? "独立 TTS" : null,
+        ].filter(Boolean)
+    : type === "action" && nodeData.toolId
+      ? ["确定性工具"]
+      : [];
 
   return (
     <div
@@ -46,7 +67,7 @@ export function GenericNode({ id, type, data, selected }: NodeProps) {
       {spec.hasTarget && (
         <Handle
           type="target"
-          position={Position.Left}
+          position={Position.Top}
           className="!h-3 !w-3 !border-[3px] !border-card !bg-muted-soft"
         />
       )}
@@ -86,7 +107,7 @@ export function GenericNode({ id, type, data, selected }: NodeProps) {
         >
           <Pencil size={13} />
         </button>
-        {type !== "startCall" && (
+        {type !== "start" && (
           <button
             type="button"
             title="删除节点"
@@ -129,11 +150,22 @@ export function GenericNode({ id, type, data, selected }: NodeProps) {
         </p>
       )}
 
+      {meta.length > 0 && (
+        <div className="mt-3 flex flex-wrap gap-1.5">
+          {meta.map((item) => (
+            <span key={item as string} className="rounded-full bg-surface-strong px-2 py-0.5 text-[10px] text-muted-foreground">
+              {item}
+            </span>
+          ))}
+        </div>
+      )}
+
       {spec.hasSource && (
         <Handle
           type="source"
-          position={Position.Right}
+          position={Position.Bottom}
-          className="!h-3 !w-3 !border-[3px] !border-card !bg-primary"
+          title="拖到节点或画布空白处"
+          className="!h-3 !w-3 !border-[3px] !border-card !bg-primary transition-transform hover:!scale-125"
         />
       )}
     </div>
@@ -141,8 +173,9 @@ export function GenericNode({ id, type, data, selected }: NodeProps) {
 }
 
 export const nodeTypes = {
-  startCall: GenericNode,
+  start: GenericNode,
-  agentNode: GenericNode,
+  agent: GenericNode,
-  endCall: GenericNode,
+  action: GenericNode,
-  globalNode: GenericNode,
+  handoff: GenericNode,
+  end: GenericNode,
 };

frontend/src/components/workflow/specs.ts

@@ -1,36 +1,67 @@
-/**
+/** Workflow v3 public graph types and defaults. */
- * 工作流节点的前端类型与运行期规格。
- *
- * 节点「目录」(有哪些类型、各自的字段与约束)由后端 /api/node-types 提供,
- * 通过 useNodeSpecs 拉取后用 toRuntimeSpec 转成带 React 组件(图标)的运行期规格。
- * 本文件只保留:类型定义、默认图、图标/配色解析。新增节点类型改后端即可。
- */
 
 import * as LucideIcons from "lucide-react";
 import { Circle, type LucideIcon } from "lucide-react";
 
 import type { NodeSpecDto } from "@/lib/api";
 
-export type WorkflowNodeType =
+export type WorkflowNodeType = "start" | "agent" | "action" | "handoff" | "end";
-  | "startCall"
+export type ContextPolicy = "inherit" | "fresh";
-  | "agentNode"
+export type KnowledgeMode = "automatic" | "on_demand" | "disabled";
-  | "endCall"
+export type AgentEntryMode = "wait_user" | "generate" | "fixed_speech";
-  | "globalNode";
+export type EdgeMode = "llm" | "expression" | "always";
+export type ExpressionOperator =
+  | "eq"
+  | "neq"
+  | "gt"
+  | "gte"
+  | "lt"
+  | "lte"
+  | "contains"
+  | "in"
+  | "exists";
 
 export type WorkflowNodeData = {
-  /** 节点显示名 */
   name: string;
-  /** 开场白(仅 startCall) */
   greeting?: string;
-  /** 节点提示词 */
   prompt?: string;
-  /** 允许打断(仅 agentNode) */
+  contextPolicy?: ContextPolicy;
-  allowInterrupt?: boolean;
+  inheritGlobalConfig?: boolean;
-  /** 是否合并全局节点提示词(start/agent 默认开启,end 默认关闭) */
+  entryMode?: AgentEntryMode;
-  addGlobalPrompt?: boolean;
+  entrySpeech?: string;
+  toolIds?: string[];
+  knowledgeBaseId?: string;
+  knowledgeMode?: KnowledgeMode;
+  knowledgeTopN?: number;
+  knowledgeScoreThreshold?: number;
+  llmResourceId?: string;
+  asrResourceId?: string;
+  ttsResourceId?: string;
+  toolId?: string;
+  arguments?: Record<string, unknown>;
+  resultAssignments?: Record<string, string>;
+  targetType?: "ai" | "human" | "queue" | "phone";
+  target?: string;
+  message?: string;
+  scope?: "flow" | "session";
   [key: string]: unknown;
 };
 
+export type ExpressionRule = {
+  variable: string;
+  operator: ExpressionOperator;
+  value?: unknown;
+};
+
+export type WorkflowEdgeData = {
+  mode: EdgeMode;
+  priority: number;
+  condition?: string;
+  expression?: { combinator: "and" | "or"; rules: ExpressionRule[] };
+  label?: string;
+  transitionSpeech?: string;
+};
+
 export type FieldSpec = {
   key: string;
   label: string;
@@ -39,7 +70,6 @@ export type FieldSpec = {
   default?: unknown;
 };
 
-/** 解析后的运行期节点规格(DTO + 解析出的 React 图标 + 派生句柄) */
 export type RuntimeNodeSpec = {
   type: string;
   displayName: string;
@@ -47,9 +77,7 @@ export type RuntimeNodeSpec = {
   icon: LucideIcon;
   accent: string;
   addable: boolean;
-  /** 入边句柄(开始节点没有) */
   hasTarget: boolean;
-  /** 出边句柄(结束节点没有) */
   hasSource: boolean;
   constraints: {
     minIncoming?: number;
@@ -62,7 +90,6 @@ export type RuntimeNodeSpec = {
   fields: FieldSpec[];
 };
 
-/** 渐变 token → CSS 变量名(图标底色用),未知配色回落到 sky */
 export const ACCENT_VAR: Record<string, string> = {
   mint: "--gradient-mint",
   sky: "--gradient-sky",
@@ -75,13 +102,11 @@ export function accentVar(accent: string): string {
   return ACCENT_VAR[accent] ?? ACCENT_VAR.sky;
 }
 
-/** 按名字解析 Lucide 图标,找不到回落到 Circle(对齐 dograh resolveIcon)。 */
 export function resolveIcon(name: string): LucideIcon {
   const icons = LucideIcons as unknown as Record<string, LucideIcon>;
   return icons[name] ?? Circle;
 }
 
-/** 后端 DTO → 运行期规格。hasTarget/hasSource 由入/出边上限派生。 */
 export function toRuntimeSpec(dto: NodeSpecDto): RuntimeNodeSpec {
   return {
     type: dto.name,
@@ -93,12 +118,12 @@ export function toRuntimeSpec(dto: NodeSpecDto): RuntimeNodeSpec {
     hasTarget: dto.constraints.maxIncoming !== 0,
     hasSource: dto.constraints.maxOutgoing !== 0,
     constraints: dto.constraints,
-    fields: dto.fields.map((f) => ({
+    fields: dto.fields.map((field) => ({
-      key: f.key,
+      key: field.key,
-      label: f.label,
+      label: field.label,
-      type: f.type,
+      type: field.type,
-      required: f.required,
+      required: field.required,
-      default: f.default,
+      default: field.default,
     })),
   };
 }
@@ -106,6 +131,18 @@ export function toRuntimeSpec(dto: NodeSpecDto): RuntimeNodeSpec {
 export type NodeSpecMap = Record<string, RuntimeNodeSpec>;
 
 export type WorkflowGraph = {
+  specVersion: 3;
+  settings: {
+    globalPrompt: string;
+    defaultLlmResourceId: string;
+    defaultAsrResourceId: string;
+    defaultTtsResourceId: string;
+    toolIds: string[];
+    knowledgeBaseId: string;
+    knowledgeMode: "automatic" | "on_demand";
+    knowledgeTopN: number;
+    knowledgeScoreThreshold: number;
+  };
   nodes: Array<{
     id: string;
     type: WorkflowNodeType;
@@ -116,62 +153,77 @@ export type WorkflowGraph = {
     id: string;
     source: string;
     target: string;
-    data?: { condition?: string; label?: string; transition_speech?: string };
+    data: WorkflowEdgeData;
   }>;
   viewport?: { x: number; y: number; zoom: number };
 };
 
-/** 新建工作流的默认图:全局规则 + 开始 → 智能体 → 结束 */
 export function defaultGraph(): WorkflowGraph {
   return {
+    specVersion: 3,
+    settings: {
+      globalPrompt:
+        "你是一个友好、专业的语音助手。请使用简短、自然、适合口语表达的句子。",
+      defaultLlmResourceId: "",
+      defaultAsrResourceId: "",
+      defaultTtsResourceId: "",
+      toolIds: [],
+      knowledgeBaseId: "",
+      knowledgeMode: "automatic",
+      knowledgeTopN: 5,
+      knowledgeScoreThreshold: 0,
+    },
     nodes: [
       {
         id: "start",
-        type: "startCall",
+        type: "start",
-        position: { x: 100, y: 120 },
+        position: { x: 360, y: 60 },
         data: {
-          name: "开始",
+          name: "Start",
-          greeting: "你好,我是 AI 视频助手,有什么可以帮你?",
+          greeting: "你好，我是 AI 视频助手，有什么可以帮你？",
-          prompt: "了解用户的需求,并在信息明确后进入下一节点。",
-          allowInterrupt: true,
-          addGlobalPrompt: true,
         },
       },
       {
         id: "agent-1",
-        type: "agentNode",
+        type: "agent",
-        position: { x: 420, y: 120 },
+        position: { x: 360, y: 300 },
         data: {
-          name: "智能体节点",
+          name: "Agent",
-          prompt: "根据用户需求提供清晰、准确的帮助。",
+          prompt: "了解用户需求并提供清晰、准确的帮助。",
-          allowInterrupt: true,
+          contextPolicy: "inherit",
-          addGlobalPrompt: true,
+          inheritGlobalConfig: true,
+          entryMode: "wait_user",
+          entrySpeech: "",
         },
       },
       {
         id: "end",
-        type: "endCall",
+        type: "end",
-        position: { x: 740, y: 120 },
+        position: { x: 360, y: 540 },
         data: {
-          name: "结束",
+          name: "End",
-          prompt: "总结已经完成的事项,礼貌道别并结束通话。",
+          message: "感谢你的来电，再见。",
-          addGlobalPrompt: false,
+          scope: "session",
-        },
-      },
-      {
-        id: "global",
-        type: "globalNode",
-        position: { x: 100, y: 400 },
-        data: {
-          name: "全局设定",
-          prompt:
-            "你是一个友好、专业的语音助手。请使用简短、自然、适合口语表达的句子。",
         },
       },
     ],
     edges: [
-      { id: "e-start-agent", source: "start", target: "agent-1", data: {} },
+      {
-      { id: "e-agent-end", source: "agent-1", target: "end", data: {} },
+        id: "e-start-agent",
+        source: "start",
+        target: "agent-1",
+        data: { mode: "always", priority: 0 },
+      },
+      {
+        id: "e-agent-end",
+        source: "agent-1",
+        target: "end",
+        data: {
+          mode: "llm",
+          priority: 10,
+          condition: "当前阶段任务已经完成，适合结束会话",
+        },
+      },
     ],
   };
 }

frontend/src/lib/api.ts

@@ -11,6 +11,35 @@ export const API_BASE =
   process.env.NEXT_PUBLIC_API_BASE_URL ?? "http://localhost:8000";
 
 export type ModelType = "LLM" | "ASR" | "TTS" | "Realtime" | "Embedding" | "Agent";
+
+function formatErrorDetail(detail: unknown): string | null {
+  if (typeof detail === "string") return detail;
+  if (Array.isArray(detail)) {
+    const messages = detail
+      .map((item) => {
+        if (!item || typeof item !== "object") return null;
+        const record = item as { loc?: unknown; msg?: unknown };
+        const message = typeof record.msg === "string" ? record.msg : null;
+        if (!message) return null;
+        const path = Array.isArray(record.loc)
+          ? record.loc
+              .filter((part) => part !== "body")
+              .map(String)
+              .join(".")
+          : "";
+        return path ? `${path}：${message}` : message;
+      })
+      .filter((message): message is string => Boolean(message));
+    return messages.length ? messages.join("；") : null;
+  }
+  if (detail && typeof detail === "object") {
+    const record = detail as { message?: unknown; msg?: unknown };
+    if (typeof record.message === "string") return record.message;
+    if (typeof record.msg === "string") return record.msg;
+  }
+  return null;
+}
+
 async function request<T>(path: string, init?: RequestInit): Promise<T> {
   const isFormData = init?.body instanceof FormData;
   const res = await fetch(`${API_BASE}${path}`, {
@@ -30,8 +59,8 @@ async function request<T>(path: string, init?: RequestInit): Promise<T> {
   if (!res.ok) {
     let detail = `请求失败 (${res.status})`;
     try {
-      const body = (await res.json()) as { detail?: string };
+      const body = (await res.json()) as { detail?: unknown };
-      if (body?.detail) detail = body.detail;
+      detail = formatErrorDetail(body?.detail) ?? detail;
     } catch {
       // 响应体不是 JSON,沿用默认错误信息
     }