Add Dify integration and enhance workflow node specifications

- Introduce new fields `dify_api_url` and `dify_api_key` in `AssistantConfig` for Dify API integration. - Update `requirements.txt` to include `dify-client-python` for Dify SDK support. - Modify `config_resolver` to handle Dify connection information. - Add a new `globalNode` type in workflow specifications to provide unified settings across workflows. - Enhance node specifications with additional constraints and default values for better configuration management. - Update frontend components to support the new `globalNode` type and its properties, improving workflow editor functionality.
2026-07-11 22:26:31 +08:00
parent dfb9c5bd11
commit 00270a5c01
23 changed files with 1270 additions and 414 deletions
--- a/backend/models.py
+++ b/backend/models.py
@@ -79,6 +79,8 @@ class AssistantConfig(BaseModel):
    graph: 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 = ""
+    dify_api_key: str = ""
    fastgpt_api_url: str = ""
    fastgpt_api_key: str = ""
    fastgpt_app_id: str = ""
--- a/backend/requirements.txt
+++ b/backend/requirements.txt
@@ -8,6 +8,9 @@ Pillow>=11.1.0,<13
 # FastGPT 类型助手:本地 SDK(包 /api/v1/chat/completions 流式 + chatId 会话)
 fastgpt-client @ file:///Users/wangx/Code/AI-VideoAssistant-Project/fastgpt-python-sdk

+# Dify 类型助手:异步流式 Runtime API SDK
+dify-client-python==1.0.3
+
 fastapi
 httpx
 uvicorn[standard]
--- a/backend/schemas.py
+++ b/backend/schemas.py
@@ -24,10 +24,10 @@ ToolParameterLocation = Literal["path", "query", "body", "header"]
 # 外部应用类型:其 config.apiKey 是该助手私有密钥,读时打码 / 写时哨兵
 EXTERNAL_TYPES = {"dify", "fastgpt", "opencode"}

-# 支持 realtime(语音到语音)的类型;外部托管大脑只能走 cascade。
+# MVP 仅 PromptBrain 支持 realtime;Workflow 和外部托管大脑只走 pipeline。
 # 与 services.brains 各 BrainSpec.supported_runtime_modes 对齐(此处独立声明,
 # 避免 HTTP schema 层为做校验而引入 pipecat 重依赖)。
-REALTIME_CAPABLE_TYPES = {"prompt", "workflow"}
+REALTIME_CAPABLE_TYPES = {"prompt"}


 class CamelModel(BaseModel):
--- a/backend/services/brains/init.py
+++ b/backend/services/brains/init.py
@@ -1,6 +1,6 @@
 """可插拔的「大脑」:把不同助手类型在运行时的差异收口到各自的 Brain 实现。"""

-from services.brains.base import Brain, BrainSpec
+from services.brains.base import Brain, BrainRuntime, BrainSpec
 from services.brains.registry import SPECS, build_brain

-__all__ = ["Brain", "BrainSpec", "SPECS", "build_brain"]
+__all__ = ["Brain", "BrainRuntime", "BrainSpec", "SPECS", "build_brain"]
--- a/backend/services/brains/base.py
+++ b/backend/services/brains/base.py
@@ -1,46 +1,117 @@
-"""「大脑」抽象:把不同助手类型(prompt/workflow/fastgpt/…)在运行时的差异收口。
+"""Conversation-brain contracts shared by every assistant type.

-cascade 管线骨架对所有类型一致(STT → LLM 槽 → TTS),变化的只有:
-  - 谁产出助手文本(LLM 槽里塞什么)——build_llm
-  - 开场白来源(静态 / 外部异步拉取)——greeting
-  - 对话上下文归谁维护——spec.owns_context
-  - 是否支持 realtime——spec.supported_runtime_modes
-
-阶段 1 只抽到「够 fastgpt 用」的程度;workflow 编排仍内联在 pipeline.py,
-待阶段 2 再搬进 WorkflowBrain 收口。
+Brain selects who owns reasoning and conversation state.  The Pipecat pipeline
+still owns media transport, STT/TTS, transcript delivery, and interruption
+semantics.  This keeps assistant-specific orchestration out of pipeline.py
+without coupling brains to Pipecat internals more than necessary.
 """

 from __future__ import annotations

+from collections.abc import Awaitable, Callable
 from dataclasses import dataclass
-from typing import Protocol, runtime_checkable
+from typing import Any, Protocol, runtime_checkable

 from models import AssistantConfig
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.frames.frames import Frame
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.frame_processor import FrameProcessor


@dataclass(frozen=True)
 class BrainSpec:
-    """类型元数据。单一来源,供运行时门控与上下文归属决策复用。"""
+    """Static capabilities used by validation and runtime dispatch."""

    type: str
    supported_runtime_modes: frozenset[str]
-    # True:由本服务维护 LLMContext(prompt/workflow);
-    # False:上下文/知识库/工具由外部服务端接管(fastgpt/dify),本地不写 context。
+    # False means context, knowledge bases, and tools live on an external agent.
    owns_context: bool


-@runtime_checkable
-class Brain(Protocol):
-    """每通电话 new 一个实例(可持有 chatId / 当前节点等会话状态)。"""
+class CallEndPort(Protocol):
+    """Small call-lifecycle surface available to a brain."""
+
+    @property
+    def ending(self) -> bool: ...
+
+    def begin(self, reason: str) -> None: ...
+
+    def arm_after_speech(self) -> None: ...
+
+    async def finish(self) -> None: ...
+
+
+@dataclass(frozen=True)
+class BrainRuntime:
+    """Pipeline-owned capabilities injected into one brain session."""
+
+    context: LLMContext
+    llm: Any
+    queue_frame: Callable[[Frame], Awaitable[None]]
+    set_system_prompt: Callable[[str], None]
+    set_tools: Callable[[list[FunctionSchema] | None], None]
+    call_end: CallEndPort
+
+
+class BaseBrain:
+    """No-op lifecycle defaults for brains without local orchestration."""

    spec: BrainSpec

    async def greeting(self, cfg: AssistantConfig) -> str:
-        """开场白。内部类型通常直接用 cfg.greeting;外部类型异步拉取后端配置。"""
-        ...
+        return cfg.greeting
+
+    def system_prompt(self, cfg: AssistantConfig) -> str:
+        return cfg.prompt if self.spec.owns_context else ""

    def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor:
-        """返回丢进管线 LLM 槽位的帧处理器(标准 LLMService 或外部托管的伪 LLM)。"""
-        ...
+        raise NotImplementedError
+
+    async def setup(self, cfg: AssistantConfig, runtime: BrainRuntime) -> None:
+        """Register tools and initialize per-call orchestration."""
+
+    async def on_connected(self) -> None:
+        """Handle a connected client after the common greeting is queued."""
+
+    def record_user_message(self, content: str) -> None:
+        """Observe a committed user message for brain-owned routing state."""
+
+    async def on_assistant_text_start(self, turn_id: str) -> None:
+        """Observe the start of a generated assistant turn."""
+
+    async def on_assistant_text_end(
+        self,
+        turn_id: str,
+        content: str,
+        interrupted: bool,
+    ) -> None:
+        """Observe the completion of a generated assistant turn."""
+
+
+@runtime_checkable
+class Brain(Protocol):
+    """One instance per call; implementations may keep conversation state."""
+
+    spec: BrainSpec
+
+    async def greeting(self, cfg: AssistantConfig) -> str: ...
+
+    def system_prompt(self, cfg: AssistantConfig) -> str: ...
+
+    def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor: ...
+
+    async def setup(self, cfg: AssistantConfig, runtime: BrainRuntime) -> None: ...
+
+    async def on_connected(self) -> None: ...
+
+    def record_user_message(self, content: str) -> None: ...
+
+    async def on_assistant_text_start(self, turn_id: str) -> None: ...
+
+    async def on_assistant_text_end(
+        self,
+        turn_id: str,
+        content: str,
+        interrupted: bool,
+    ) -> None: ...
--- a/backend/services/brains/dify_brain.py
+++ b/backend/services/brains/dify_brain.py
@@ -0,0 +1,61 @@
+"""Dify-hosted brain: prompt, workflow, tools, and context live in Dify."""
+
+from __future__ import annotations
+
+from uuid import uuid4
+
+from dify_client import AsyncClient
+from loguru import logger
+from models import AssistantConfig
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.frame_processor import FrameProcessor
+
+from services.brains.base import BaseBrain, BrainSpec
+from services.brains.dify_llm import DifyLLMService, normalize_api_base
+
+
+class DifyBrain(BaseBrain):
+    spec = BrainSpec(
+        type="dify",
+        supported_runtime_modes=frozenset({"pipeline"}),
+        owns_context=False,
+    )
+
+    def __init__(self):
+        self._user_id = f"ai-video-{uuid4().hex}"
+        self._client: AsyncClient | None = None
+
+    def _get_client(self, cfg: AssistantConfig) -> AsyncClient:
+        if self._client is None:
+            if not cfg.dify_api_key:
+                raise ValueError("缺少 Dify Agent apiKey")
+            self._client = AsyncClient(
+                api_key=cfg.dify_api_key,
+                api_base=normalize_api_base(cfg.dify_api_url),
+            )
+        return self._client
+
+    async def greeting(self, cfg: AssistantConfig) -> str:
+        """Use Dify's opening statement, with the local greeting as fallback."""
+        try:
+            api_base = normalize_api_base(cfg.dify_api_url)
+            response = await self._get_client(cfg).arequest(
+                f"{api_base}/parameters",
+                "GET",
+                params={"user": self._user_id},
+                timeout=15.0,
+            )
+            opening = str(response.json().get("opening_statement") or "").strip()
+            return opening or cfg.greeting
+        except ValueError:
+            raise
+        except Exception as exc:  # noqa: BLE001 - greeting failure should not block a call
+            logger.warning(f"Dify 获取开场白失败,回退 cfg.greeting: {exc}")
+            return cfg.greeting
+
+    def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor:
+        return DifyLLMService(
+            cfg,
+            client=self._get_client(cfg),
+            user_id=self._user_id,
+        )
--- a/backend/services/brains/dify_llm.py
+++ b/backend/services/brains/dify_llm.py
@@ -0,0 +1,127 @@
+"""Dify chat applications exposed as a Pipecat LLM processor."""
+
+from __future__ import annotations
+
+from uuid import uuid4
+
+from dify_client import AsyncClient, models
+from loguru import logger
+from models import AssistantConfig
+from pipecat.frames.frames import (
+    Frame,
+    LLMContextFrame,
+    LLMFullResponseEndFrame,
+    LLMFullResponseStartFrame,
+    LLMTextFrame,
+)
+from pipecat.processors.frame_processor import FrameDirection
+from pipecat.services.llm_service import LLMService
+from pipecat.services.settings import LLMSettings
+
+
+def normalize_api_base(url: str) -> str:
+    """Accept a Dify host, /v1 base URL, or full chat endpoint."""
+    base = (url or "https://api.dify.ai").strip().rstrip("/")
+    if base.endswith("/chat-messages"):
+        base = base[: -len("/chat-messages")]
+    if not base.endswith("/v1"):
+        base = f"{base}/v1"
+    return base
+
+
+def last_user_text(messages: list[dict]) -> str:
+    for message in reversed(messages or []):
+        if message.get("role") != "user":
+            continue
+        content = message.get("content")
+        if isinstance(content, str):
+            return content
+        if isinstance(content, list):
+            return "".join(
+                str(part.get("text") or "")
+                for part in content
+                if isinstance(part, dict)
+            )
+    return ""
+
+
+class DifyLLMService(LLMService):
+    """Stream Dify answer events into Pipecat's standard text frames."""
+
+    def __init__(
+        self,
+        cfg: AssistantConfig,
+        *,
+        client: AsyncClient | None = None,
+        user_id: str | None = None,
+    ):
+        super().__init__(
+            settings=LLMSettings(
+                model=None,
+                system_instruction=None,
+                temperature=None,
+                max_tokens=None,
+                top_p=None,
+                top_k=None,
+                frequency_penalty=None,
+                presence_penalty=None,
+                seed=None,
+                filter_incomplete_user_turns=None,
+                user_turn_completion_config=None,
+            )
+        )
+        self._client = client or AsyncClient(
+            api_key=cfg.dify_api_key,
+            api_base=normalize_api_base(cfg.dify_api_url),
+        )
+        self._user_id = user_id or f"ai-video-{uuid4().hex}"
+        self._conversation_id = ""
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if not isinstance(frame, LLMContextFrame):
+            await self.push_frame(frame, direction)
+            return
+
+        user_text = last_user_text(frame.context.get_messages())
+        if not user_text:
+            return
+
+        await self.push_frame(LLMFullResponseStartFrame())
+        try:
+            request = models.ChatRequest(
+                query=user_text,
+                inputs={},
+                user=self._user_id,
+                response_mode=models.ResponseMode.STREAMING,
+                conversation_id=self._conversation_id,
+                auto_generate_name=False,
+            )
+            events = await self._client.achat_messages(request, timeout=120.0)
+            async for event in events:
+                conversation_id = getattr(event, "conversation_id", "")
+                if conversation_id:
+                    self._conversation_id = conversation_id
+
+                event_name = str(getattr(event, "event", ""))
+                if event_name == "error":
+                    logger.error(
+                        "Dify 流式错误: "
+                        f"code={getattr(event, 'code', '')} "
+                        f"message={getattr(event, 'message', '')}"
+                    )
+                    continue
+                text = (
+                    getattr(event, "answer", "")
+                    if event_name in {"message", "agent_message"}
+                    else ""
+                )
+                if event_name == "text_chunk":
+                    text = getattr(getattr(event, "data", None), "text", "")
+                if text:
+                    await self.push_frame(LLMTextFrame(text))
+        except Exception as exc:  # noqa: BLE001 - one failed turn must not kill the call
+            logger.error(f"Dify 调用失败: {exc}")
+        finally:
+            await self.push_frame(LLMFullResponseEndFrame())
--- a/backend/services/brains/fastgpt_brain.py
+++ b/backend/services/brains/fastgpt_brain.py
@@ -15,7 +15,7 @@ from models import AssistantConfig
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.frame_processor import FrameProcessor

-from services.brains.base import BrainSpec
+from services.brains.base import BaseBrain, BrainSpec
 from services.brains.fastgpt_llm import FastGPTLLMService, normalize_base_url


@@ -38,15 +38,16 @@ def _extract_welcome(payload: Any) -> str:
    return ""


-class FastGPTBrain:
+class FastGPTBrain(BaseBrain):
    def __init__(self):
-        self.spec = BrainSpec(
-            type="fastgpt",
-            supported_runtime_modes=frozenset({"pipeline"}),
-            owns_context=False,
-        )
        self._chat_id = uuid4().hex

+    spec = BrainSpec(
+        type="fastgpt",
+        supported_runtime_modes=frozenset({"pipeline"}),
+        owns_context=False,
+    )
+
    async def greeting(self, cfg: AssistantConfig) -> str:
        """优先用 FastGPT 后台配置的开场白;无 app_id 或取不到时回退 cfg.greeting。"""
        if not cfg.fastgpt_app_id:
--- a/backend/services/brains/internal_brain.py
+++ b/backend/services/brains/internal_brain.py
@@ -1,37 +0,0 @@
-"""内部 LLM 大脑:prompt 与 workflow。
-
-二者都用本地维护的 LLMContext + OpenAI 兼容 LLM,支持 cascade 与 realtime。
-workflow 的图编排(切提示/转移工具/node-active)阶段 1 仍内联在 pipeline.py,
-这里只负责提供 LLM 槽位与元数据,行为与改造前完全一致。
-"""
-
-from __future__ import annotations
-
-from models import AssistantConfig
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.frame_processor import FrameProcessor
-
-from services.brains.base import BrainSpec
-
-_CASCADE_AND_REALTIME = frozenset({"pipeline", "realtime"})
-
-
-class InternalBrain:
-    """prompt / workflow 共用。"""
-
-    def __init__(self, brain_type: str):
-        self.spec = BrainSpec(
-            type=brain_type,
-            supported_runtime_modes=_CASCADE_AND_REALTIME,
-            owns_context=True,
-        )
-
-    async def greeting(self, cfg: AssistantConfig) -> str:
-        # 内部类型的开场白由 pipeline.py 现有逻辑(workflow 起始节点 / cfg.greeting)决定,
-        # 该方法仅为满足 Brain 协议,实际不在内部路径上被调用。
-        return cfg.greeting
-
-    def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor:
-        from services.pipecat.service_factory import create_llm
-
-        return create_llm(cfg)
--- a/backend/services/brains/prompt_brain.py
+++ b/backend/services/brains/prompt_brain.py
@@ -0,0 +1,106 @@
+"""Local prompt assistant, including prompt-only reusable tools."""
+
+from __future__ import annotations
+
+from uuid import uuid4
+
+from models import AssistantConfig
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.frames.frames import OutputTransportMessageUrgentFrame, TTSSpeakFrame
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.frame_processor import FrameProcessor
+from pipecat.services.llm_service import (
+    FunctionCallParams,
+    FunctionCallResultProperties,
+)
+from pipecat.utils.time import time_now_iso8601
+
+from services.brains.base import BaseBrain, BrainRuntime, BrainSpec
+
+
+class PromptBrain(BaseBrain):
+    spec = BrainSpec(
+        type="prompt",
+        supported_runtime_modes=frozenset({"pipeline", "realtime"}),
+        owns_context=True,
+    )
+
+    def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor:
+        from services.pipecat.service_factory import create_llm
+
+        return create_llm(cfg)
+
+    async def setup(self, cfg: AssistantConfig, runtime: BrainRuntime) -> None:
+        schemas: list[FunctionSchema] = []
+        for tool in cfg.tools:
+            if tool.type != "end_call":
+                continue
+            schema, handler = self._make_end_call_tool(tool, runtime)
+            schemas.append(schema)
+            runtime.llm.register_function(tool.function_name, handler)
+        runtime.set_tools(schemas)
+
+    @staticmethod
+    def _make_end_call_tool(tool, runtime: BrainRuntime):
+        config = (tool.definition or {}).get("config") or {}
+        message_type = str(config.get("message_type") or "none")
+        custom_message = str(config.get("custom_message") or "").strip()
+        capture_reason = bool(config.get("capture_reason", True))
+
+        async def end_call(params: FunctionCallParams) -> None:
+            reason = str(params.arguments.get("reason") or "end_call_tool").strip()
+            runtime.call_end.begin(reason)
+            await params.result_callback(
+                {"status": "success", "action": "ending_call"},
+                properties=FunctionCallResultProperties(run_llm=False),
+            )
+
+            if message_type != "custom" or not custom_message:
+                await runtime.call_end.finish()
+                return
+
+            turn_id = uuid4().hex
+            timestamp = time_now_iso8601()
+            for message in (
+                {
+                    "type": "assistant-text-start",
+                    "turn_id": turn_id,
+                    "timestamp": timestamp,
+                },
+                {
+                    "type": "assistant-text-delta",
+                    "turn_id": turn_id,
+                    "delta": custom_message,
+                },
+                {
+                    "type": "assistant-text-end",
+                    "turn_id": turn_id,
+                    "content": custom_message,
+                    "interrupted": False,
+                },
+            ):
+                await runtime.queue_frame(
+                    OutputTransportMessageUrgentFrame(message=message)
+                )
+            runtime.call_end.arm_after_speech()
+            await runtime.queue_frame(
+                TTSSpeakFrame(custom_message, append_to_context=False)
+            )
+
+        properties = (
+            {
+                "reason": {
+                    "type": "string",
+                    "description": "结束本次通话的简短原因。",
+                }
+            }
+            if capture_reason
+            else {}
+        )
+        schema = FunctionSchema(
+            name=tool.function_name,
+            description=tool.description or "结束当前通话。",
+            properties=properties,
+            required=["reason"] if capture_reason else [],
+        )
+        return schema, end_call
--- a/backend/services/brains/registry.py
+++ b/backend/services/brains/registry.py
@@ -1,25 +1,40 @@
-"""类型 → Brain 工厂。新增一种大脑 = 加一个 brain 文件 + 在此注册一行。"""
+"""Explicit assistant-type registry. Unsupported types never silently degrade."""

 from __future__ import annotations

+from collections.abc import Callable
+
 from models import AssistantConfig

 from services.brains.base import Brain, BrainSpec
+from services.brains.dify_brain import DifyBrain
 from services.brains.fastgpt_brain import FastGPTBrain
-from services.brains.internal_brain import InternalBrain
+from services.brains.prompt_brain import PromptBrain
+from services.brains.workflow_brain import WorkflowBrain
+
+
+def _workflow(cfg: AssistantConfig) -> Brain:
+    return WorkflowBrain(cfg.graph)
+
+
+BRAIN_FACTORIES: dict[str, Callable[[AssistantConfig], Brain]] = {
+    "prompt": lambda _cfg: PromptBrain(),
+    "workflow": _workflow,
+    "dify": lambda _cfg: DifyBrain(),
+    "fastgpt": lambda _cfg: FastGPTBrain(),
+}

-# 各类型的元数据(供 schema 校验 / realtime 门控复用,无需实例化 Brain)。
 SPECS: dict[str, BrainSpec] = {
-    "prompt": InternalBrain("prompt").spec,
-    "workflow": InternalBrain("workflow").spec,
-    "fastgpt": FastGPTBrain().spec,
+    "prompt": PromptBrain.spec,
+    "workflow": WorkflowBrain.spec,
+    "dify": DifyBrain.spec,
+    "fastgpt": FastGPTBrain.spec,
 }


 def build_brain(cfg: AssistantConfig) -> Brain:
-    """按 cfg.type 构造每通电话的 Brain 实例(未知类型回退 prompt)。"""
-    if cfg.type == "fastgpt":
-        return FastGPTBrain()
-    if cfg.type in ("prompt", "workflow"):
-        return InternalBrain(cfg.type)
-    return InternalBrain("prompt")
+    try:
+        factory = BRAIN_FACTORIES[cfg.type]
+    except KeyError as exc:
+        raise ValueError(f"尚未实现的助手类型: {cfg.type}") from exc
+    return factory(cfg)
--- a/backend/services/brains/workflow_brain.py
+++ b/backend/services/brains/workflow_brain.py
@@ -0,0 +1,188 @@
+"""Local graph-driven workflow assistant and its per-call state."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+from loguru import logger
+from models import AssistantConfig
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.frames.frames import OutputTransportMessageUrgentFrame, TTSSpeakFrame
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.frame_processor import FrameProcessor
+
+from services.brains.base import BaseBrain, BrainRuntime, BrainSpec
+from services.workflow_engine import WorkflowEngine
+
+
+@dataclass
+class WorkflowState:
+    current: str
+    ended: bool = False
+    turns_in_node: int = 0
+    end_turn_id: str | None = None
+
+
+class WorkflowBrain(BaseBrain):
+    spec = BrainSpec(
+        type="workflow",
+        supported_runtime_modes=frozenset({"pipeline"}),
+        owns_context=True,
+    )
+    _FALLBACK_AFTER_TURNS = 2
+
+    def __init__(self, graph: dict[str, Any]):
+        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")
+        self._state = WorkflowState(current=self._engine.start_id)
+        self._history: list[dict[str, str]] = []
+        self._cfg: AssistantConfig | None = None
+        self._runtime: BrainRuntime | None = None
+
+    async def greeting(self, cfg: AssistantConfig) -> str:
+        return self._engine.greeting() or cfg.greeting
+
+    def system_prompt(self, cfg: AssistantConfig) -> str:
+        return self._engine.system_prompt_for(self._state.current)
+
+    def build_llm(self, cfg: AssistantConfig, context: LLMContext) -> FrameProcessor:
+        from services.pipecat.service_factory import create_llm
+
+        return create_llm(cfg)
+
+    async def setup(self, cfg: AssistantConfig, runtime: BrainRuntime) -> None:
+        self._cfg = cfg
+        self._runtime = runtime
+        for edge in self._engine.edges:
+            if edge.get("target"):
+                runtime.llm.register_function(
+                    self._engine.edge_fn_name(edge),
+                    self._make_transition_handler(edge),
+                )
+        self._apply_node(self._state.current)
+        logger.info(
+            f"工作流模式启用: 起始节点={self._engine.name(self._state.current)}"
+        )
+
+    async def on_connected(self) -> None:
+        await self._emit_node_active(self._state.current)
+
+    def record_user_message(self, content: str) -> None:
+        if content:
+            self._history.append({"role": "user", "content": content})
+
+    async def on_assistant_text_start(self, turn_id: str) -> None:
+        if self._state.ended and self._state.end_turn_id is None:
+            self._state.end_turn_id = turn_id
+
+    async def on_assistant_text_end(
+        self,
+        turn_id: str,
+        content: str,
+        interrupted: bool,
+    ) -> None:
+        if not content or interrupted:
+            return
+        self._history.append({"role": "assistant", "content": content})
+        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:
+        runtime = self._require_runtime()
+        runtime.set_system_prompt(self._engine.system_prompt_for(node_id))
+        if self._engine.is_end(node_id):
+            runtime.set_tools([])
+            return
+        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:
+        self._state.current = target
+        self._state.turns_in_node = 0
+        if self._engine.is_end(target):
+            self._state.ended = True
+        await self._emit_node_active(target)
+        self._apply_node(target)
+
+    async def _emit_node_active(self, node_id: str | None) -> None:
+        if node_id:
+            await self._require_runtime().queue_frame(
+                OutputTransportMessageUrgentFrame(
+                    message={"type": "node-active", "nodeId": node_id}
+                )
+            )
+
+    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:
+        if self._cfg is None:
+            raise RuntimeError("WorkflowBrain 尚未初始化配置")
+        return self._cfg
+
+    @staticmethod
+    def _require(value: str, label: str) -> str:
+        if value:
+            return value
+        raise ValueError(f"缺少模型资源配置: {label}")
--- a/backend/services/config_resolver.py
+++ b/backend/services/config_resolver.py
@@ -139,6 +139,8 @@ async def resolve_runtime_config(
        # 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 {},
        # 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),
        fastgpt_api_url=str(_value(agent_resource, "apiUrl", assistant.api_url)),
        fastgpt_api_key=_secret(agent_resource, "apiKey", assistant.api_key),
        fastgpt_app_id=str(_value(agent_resource, "appId", assistant.app_id)),
--- a/backend/services/node_specs.py
+++ b/backend/services/node_specs.py
@@ -1,6 +1,6 @@
 """工作流节点规格 + 图校验(对齐 dograh 的 node-spec / GraphConstraints 思路)。

-当前实现 3 个核心节点:开始(startCall)/智能体(agentNode)/结束(endCall)。
+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。
@@ -13,7 +13,7 @@ from __future__ import annotations
 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 = "1"
+SPEC_VERSION = "2"

 # 每个节点的图约束。None 表示不限制。
 #   min_incoming / max_incoming:入边数量
@@ -27,11 +27,28 @@ NODE_SPECS: list[dict[str, Any]] = [
        "icon": "Play",
        "accent": "mint",
        "addable": False,
-        "constraints": {"minIncoming": 0, "maxIncoming": 0},
+        "constraints": {
+            "minIncoming": 0,
+            "maxIncoming": 0,
+            "minInstances": 1,
+            "maxInstances": 1,
+        },
        "fields": [
-            {"key": "name", "label": "节点名称", "type": "text"},
-            {"key": "greeting", "label": "开场白", "type": "textarea"},
-            {"key": "prompt", "label": "节点提示词", "type": "textarea"},
+            {"key": "name", "label": "节点名称", "type": "text", "default": "开始"},
+            {"key": "greeting", "label": "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,
+            },
        ],
    },
    {
@@ -44,9 +61,26 @@ NODE_SPECS: list[dict[str, Any]] = [
        "addable": True,
        "constraints": {"minIncoming": 1},
        "fields": [
-            {"key": "name", "label": "节点名称", "type": "text"},
-            {"key": "prompt", "label": "节点提示词", "type": "textarea", "required": True},
-            {"key": "allowInterrupt", "label": "允许用户打断", "type": "switch"},
+            {"key": "name", "label": "节点名称", "type": "text", "default": "智能体节点"},
+            {
+                "key": "prompt",
+                "label": "节点提示词",
+                "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,
+            },
        ],
    },
    {
@@ -59,8 +93,40 @@ NODE_SPECS: list[dict[str, Any]] = [
        "addable": True,
        "constraints": {"minIncoming": 1, "minOutgoing": 0, "maxOutgoing": 0},
        "fields": [
-            {"key": "name", "label": "节点名称", "type": "text"},
-            {"key": "prompt", "label": "结束语提示词", "type": "textarea"},
+            {"key": "name", "label": "节点名称", "type": "text", "default": "结束"},
+            {"key": "prompt", "label": "结束语提示词", "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。",
+            },
        ],
    },
 ]
@@ -79,7 +145,7 @@ def validate_graph(graph: dict[str, Any]) -> list[str]:
    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;
+      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。

@@ -120,6 +186,21 @@ def validate_graph(graph: dict[str, Any]) -> list[str]:
    if type_counts.get("endCall", 0) == 0:
        errors.append("工作流至少需要一个「结束」节点")

+    for node_type, spec in _SPEC_BY_NAME.items():
+        # 开始节点上方已有更明确的中文错误提示,避免重复报错。
+        if node_type == "startCall":
+            continue
+        constraints = spec["constraints"]
+        count = type_counts.get(node_type, 0)
+        _check_count(
+            errors,
+            count,
+            constraints,
+            "Instances",
+            node_type,
+            "实例",
+        )
+
    # 统计入边/出边
    incoming: dict[str, int] = {nid: 0 for nid in node_ids}
    outgoing: dict[str, int] = {nid: 0 for nid in node_ids}
--- a/backend/services/pipecat/call_lifecycle.py
+++ b/backend/services/pipecat/call_lifecycle.py
@@ -0,0 +1,60 @@
+"""Shared call termination timing for prompt tools and workflow end nodes."""
+
+from __future__ import annotations
+
+from collections.abc import Awaitable, Callable
+
+from loguru import logger
+from pipecat.frames.frames import BotStartedSpeakingFrame, BotStoppedSpeakingFrame
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+
+
+class CallEndCoordinator:
+    """End immediately or after the currently armed closing speech finishes."""
+
+    def __init__(self, queue_end: Callable[[str], Awaitable[None]]):
+        self._queue_end = queue_end
+        self._ending = False
+        self._armed = False
+        self._speaking = False
+        self._finished = False
+        self._reason = "completed"
+
+    @property
+    def ending(self) -> bool:
+        return self._ending
+
+    def begin(self, reason: str) -> None:
+        self._ending = True
+        self._reason = reason or "completed"
+
+    def arm_after_speech(self) -> None:
+        self._armed = True
+
+    async def finish(self) -> None:
+        if self._finished:
+            return
+        self._finished = True
+        await self._queue_end(self._reason)
+
+    async def observe(self, frame) -> None:
+        if isinstance(frame, BotStartedSpeakingFrame) and self._armed:
+            self._speaking = True
+        elif (
+            isinstance(frame, BotStoppedSpeakingFrame)
+            and self._armed
+            and self._speaking
+        ):
+            logger.info("结束语播报完毕,挂断通话")
+            await self.finish()
+
+
+class EndCallAfterSpeechProcessor(FrameProcessor):
+    def __init__(self, coordinator: CallEndCoordinator):
+        super().__init__()
+        self._coordinator = coordinator
+
+    async def process_frame(self, frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+        await self.push_frame(frame, direction)
+        await self._coordinator.observe(frame)
--- a/backend/services/pipecat/pipeline.py
+++ b/backend/services/pipecat/pipeline.py
@@ -3,7 +3,7 @@
 关键设计:**transport 由调用方传入**,管线本身不关心是 WebRTC 还是 WS。
 这就是"同时支持多种输出"的落点——加输出方式不用动这里。

-对应 dograh 的 pipeline_builder.py + run_pipeline.py(已砍掉 workflow 引擎/DB/录音/指标)。
+对话编排交给 Brain;本文件只保留共享媒体管线、输入输出和通话生命周期。
 """

 import asyncio
@@ -16,21 +16,22 @@ from loguru import logger
 from models import AssistantConfig
 from openai import AsyncOpenAI
 from PIL import Image
-from services.brains import build_brain
+from services.brains import Brain, BrainRuntime, build_brain
 from services.conversation_history import ConversationRecorder
+from services.pipecat.call_lifecycle import (
+    CallEndCoordinator,
+    EndCallAfterSpeechProcessor,
+)
 from services.pipecat.service_factory import (
    create_realtime_service,
    create_stt,
    create_tts,
 )
-from services.workflow_engine import WorkflowEngine

 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import (
-    BotStartedSpeakingFrame,
-    BotStoppedSpeakingFrame,
    EndFrame,
    InputTransportMessageFrame,
    InterruptionFrame,
@@ -57,10 +58,7 @@ from pipecat.runner.utils import (
    get_transport_client_id,
    maybe_capture_participant_camera,
 )
-from pipecat.services.llm_service import (
-    FunctionCallParams,
-    FunctionCallResultProperties,
-)
+from pipecat.services.llm_service import FunctionCallParams
 from pipecat.turns.user_start import (
    TranscriptionUserTurnStartStrategy,
    VADUserTurnStartStrategy,
@@ -399,8 +397,7 @@ async def run_pipeline(
        cfg.runtimeMode == "realtime"
        and "realtime" not in brain.spec.supported_runtime_modes
    ):
-        logger.warning(f"类型 {cfg.type} 不支持 realtime,回退 cascade")
-        cfg.runtimeMode = "pipeline"
+        raise ValueError(f"类型 {cfg.type} 不支持 realtime 运行模式")

    if cfg.runtimeMode == "realtime":
        if vision_enabled:
@@ -408,6 +405,7 @@ async def run_pipeline(
        await run_realtime_pipeline(
            transport,
            cfg,
+            brain=brain,
            assistant_id=assistant_id,
            channel=channel,
        )
@@ -416,45 +414,24 @@ async def run_pipeline(
    stt = create_stt(cfg)
    tts = create_tts(cfg)

-    # ---- workflow 图引擎(可选)----
-    # 有节点图时按图驱动:开场白/系统提示来自起始节点,每轮回复后按条件路由。
-    engine = WorkflowEngine(cfg.graph or {})
-    workflow_active = engine.has_graph()
-    wf_state = {
-        # 开始节点本身就是会话节点(有自己的 prompt,可多轮),从它开始
-        "current": engine.start_id if workflow_active else None,
-        "ended": False,
-        "turns_in_node": 0,
-        # 结束流程的精确计时:只在「结束节点自己的结束语」真正说完时挂断。
-        "end_turn_id": None,   # 结束节点回复的 turn_id(其 text_start 在 ended 之后)
-        "end_armed": False,    # 结束语文本已生成完(已下发 data channel)
-        "end_speaking": False,  # 结束语音频已开始播报
-        "end_frame_queued": False,
-    }
-    call_end_state = {
-        "ending": False,
-        "armed": False,
-        "speaking": False,
-        "frame_queued": False,
-        "reason": "completed",
-    }
-    history: list[dict] = []
-    # 当前节点没有可调用转移工具(全是空条件)时,才启用文本兜底路由
-    FALLBACK_AFTER_TURNS = 2
+    greeting = await brain.greeting(cfg)
+    system_content = brain.system_prompt(cfg)

-    if workflow_active:
-        greeting = engine.greeting() or cfg.greeting
-        system_content = engine.system_prompt_for(wf_state["current"])
-        logger.info(
-            f"工作流模式启用: 起始节点={engine.name(wf_state['current'])}"
+    worker_holder: dict = {}
+
+    async def queue_call_end(reason: str) -> None:
+        worker = worker_holder.get("worker")
+        if worker is None:
+            return
+        logger.info(f"结束通话: reason={reason}")
+        await worker.queue_frame(
+            OutputTransportMessageUrgentFrame(
+                message={"type": "call-ended", "reason": reason}
+            )
        )
-    elif brain.spec.owns_context:
-        greeting = cfg.greeting
-        system_content = cfg.prompt
-    else:
-        # 外部托管(fastgpt 等):开场白来自对方后台,系统提示/上下文不归我们维护
-        greeting = await brain.greeting(cfg)
-        system_content = ""
+        await worker.queue_frame(EndFrame())
+
+    call_end = CallEndCoordinator(queue_call_end)

    def with_vision_hint(text: str) -> str:
        if not vision_enabled:
@@ -466,7 +443,7 @@ async def run_pipeline(
    context = LLMContext(
        messages=[{"role": "system", "content": with_vision_hint(system_content)}]
    )
-    # LLM 槽由大脑提供:内部类型=OpenAI 兼容服务;fastgpt=包 SDK 的伪 LLM。
+    # 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)
    user_aggregator = LLMUserAggregator(
        context,
@@ -474,9 +451,7 @@ async def run_pipeline(
            vad_analyzer=SileroVADAnalyzer(),
            user_mute_strategies=[
                FunctionCallUserMuteStrategy(),
-                CallEndingUserMuteStrategy(
-                    lambda: bool(call_end_state["ending"])
-                ),
+                CallEndingUserMuteStrategy(lambda: call_end.ending),
            ],
            user_turn_strategies=UserTurnStrategies(
                start=[
@@ -489,9 +464,7 @@ async def run_pipeline(
        ),
    )
    assistant_aggregator = PassthroughLLMAssistantAggregator(context)
-    text_input = TextInputProcessor(
-        should_ignore_input=lambda: bool(call_end_state["ending"])
-    )
+    text_input = TextInputProcessor(should_ignore_input=lambda: call_end.ending)
    vision_capture = VisionCaptureProcessor()
    vision_native_mode = vision_enabled and _vision_uses_main_llm(cfg)
    vision_state: dict[str, str | None] = {"client_id": None}
@@ -572,99 +545,6 @@ async def run_pipeline(
    if vision_enabled:
        llm.register_function(VISION_TOOL_NAME, fetch_user_image)

-    end_call_tools = [
-        tool
-        for tool in cfg.tools
-        if cfg.type == "prompt" and tool.type == "end_call"
-    ]
-    end_call_schemas: list[FunctionSchema] = []
-    worker_holder: dict = {}
-
-    async def queue_call_end(reason: str) -> None:
-        if call_end_state["frame_queued"] or worker_holder.get("worker") is None:
-            return
-        call_end_state["frame_queued"] = True
-        logger.info(f"结束通话: reason={reason}")
-        await worker_holder["worker"].queue_frame(
-            OutputTransportMessageUrgentFrame(
-                message={"type": "call-ended", "reason": reason}
-            )
-        )
-        await worker_holder["worker"].queue_frame(EndFrame())
-
-    def make_end_call_handler(tool):
-        config = (tool.definition or {}).get("config") or {}
-        message_type = str(config.get("message_type") or "none")
-        custom_message = str(config.get("custom_message") or "").strip()
-        capture_reason = bool(config.get("capture_reason", True))
-
-        async def end_call(params: FunctionCallParams) -> None:
-            reason = str(params.arguments.get("reason") or "end_call_tool").strip()
-            call_end_state["ending"] = True
-            logger.info(
-                f"End Call Tool EXECUTED: {tool.function_name}, reason={reason}"
-            )
-            await params.result_callback(
-                {"status": "success", "action": "ending_call"},
-                properties=FunctionCallResultProperties(run_llm=False),
-            )
-
-            if message_type != "custom" or not custom_message:
-                await queue_call_end(reason)
-                return
-
-            call_end_state["reason"] = reason
-            call_end_state["armed"] = True
-            turn_id = uuid4().hex
-            timestamp = time_now_iso8601()
-            for message in (
-                {
-                    "type": "assistant-text-start",
-                    "turn_id": turn_id,
-                    "timestamp": timestamp,
-                },
-                {
-                    "type": "assistant-text-delta",
-                    "turn_id": turn_id,
-                    "delta": custom_message,
-                },
-                {
-                    "type": "assistant-text-end",
-                    "turn_id": turn_id,
-                    "content": custom_message,
-                    "interrupted": False,
-                },
-            ):
-                await worker_holder["worker"].queue_frame(
-                    OutputTransportMessageUrgentFrame(message=message)
-                )
-            await worker_holder["worker"].queue_frame(
-                TTSSpeakFrame(custom_message, append_to_context=False)
-            )
-
-        properties = (
-            {
-                "reason": {
-                    "type": "string",
-                    "description": "结束本次通话的简短原因。",
-                }
-            }
-            if capture_reason
-            else {}
-        )
-        schema = FunctionSchema(
-            name=tool.function_name,
-            description=tool.description or "结束当前通话。",
-            properties=properties,
-            required=["reason"] if capture_reason else [],
-        )
-        return schema, end_call
-
-    for end_call_tool in end_call_tools:
-        schema, handler = make_end_call_handler(end_call_tool)
-        end_call_schemas.append(schema)
-        llm.register_function(end_call_tool.function_name, handler)
-
    def set_visible_tools(schemas: list[FunctionSchema] | None = None) -> None:
        tools = list(schemas or [])
        if vision_enabled:
@@ -674,31 +554,6 @@ async def run_pipeline(
        else:
            context.set_tools()

-    # Workflow 结束节点和 end_call 固定结束语都等到 BotStoppedSpeakingFrame
-    # 再挂断,确保文字(data channel)与音频完整送达。
-    class EndCallAfterSpeech(FrameProcessor):
-        async def process_frame(self, frame, direction: FrameDirection):
-            await super().process_frame(frame, direction)
-            await self.push_frame(frame, direction)
-            armed = wf_state["end_armed"] or call_end_state["armed"]
-            # 结束语文本生成完(end_armed)→ 其音频开始(end_speaking)→ 音频说完才挂断。
-            # 配对 started/stopped,避免被结束节点之前的话(如先答一句再转移)的
-            # stopped 事件提前触发,导致结束语被截断。
-            if isinstance(frame, BotStartedSpeakingFrame) and armed:
-                if wf_state["end_armed"]:
-                    wf_state["end_speaking"] = True
-                if call_end_state["armed"]:
-                    call_end_state["speaking"] = True
-            elif (
-                isinstance(frame, BotStoppedSpeakingFrame)
-                and (wf_state["end_speaking"] or call_end_state["speaking"])
-                and worker_holder.get("worker") is not None
-            ):
-                logger.info("结束语播报完毕,挂断通话")
-                wf_state["end_frame_queued"] = True
-                reason = str(call_end_state["reason"] or "completed")
-                await queue_call_end(reason)
-
    recorder = await ConversationRecorder.start(
        assistant_id=assistant_id,
        assistant_name=cfg.name,
@@ -718,7 +573,7 @@ async def run_pipeline(
            # waiting for a TTS provider to emit spoken-text/timestamp frames.
            assistant_aggregator,
            tts,
-            EndCallAfterSpeech(),
+            EndCallAfterSpeechProcessor(call_end),
            ConversationHistoryProcessor(recorder),
            transport.output(),
        ]
@@ -749,15 +604,6 @@ async def run_pipeline(
    greeting_transcript_sent = False
    pending_text_inputs: list[str] = []

-    async def emit_node_active(node_id: str | None) -> None:
-        """通知前端当前激活的节点,画布据此高亮。"""
-        if node_id:
-            await worker.queue_frame(
-                OutputTransportMessageUrgentFrame(
-                    message={"type": "node-active", "nodeId": node_id}
-                )
-            )
-
    def set_system_prompt(text: str) -> None:
        """替换上下文里的系统提示(节点切换时整体替换,而非追加)。"""
        messages = context.get_messages()
@@ -767,89 +613,18 @@ async def run_pipeline(
        else:
            messages.insert(0, {"role": "system", "content": content})

-    def apply_node(node_id: str | None) -> None:
-        """进入节点:设置系统提示 + 把出边注册为可调用的转移工具。"""
-        set_system_prompt(engine.system_prompt_for(node_id))
-        if engine.is_end(node_id):
-            set_visible_tools([])  # 终止节点不展示转移工具,但保留视觉工具
-            return
-        schemas = [
-            FunctionSchema(
-                name=engine.edge_fn_name(edge),
-                description=engine.edge_description(edge),
-                properties={},
-                required=[],
-            )
-            for edge in engine.outgoing(node_id)
-        ]
-        set_visible_tools(schemas)
-
-    async def go_to_node(target: str) -> None:
-        """执行转移:切当前节点、重置计数、点亮画布、设置提示/工具。
-
-        结束节点:设 ended 标记,apply_node 会清空工具,模型据结束语提示说完后,
-        on_assistant_text_end 里排入 EndFrame 挂断,不再多轮。
-        """
-        wf_state["current"] = target
-        wf_state["turns_in_node"] = 0
-        if engine.is_end(target):
-            wf_state["ended"] = True
-        await emit_node_active(target)
-        apply_node(target)
-
-    async def speak_transition(edge: dict | None) -> None:
-        """切换瞬间播报过渡语(可选),掩盖切节点/新一轮生成的延迟。不写入上下文。"""
-        speech = engine.edge_transition_speech(edge)
-        if speech:
-            await worker.queue_frame(TTSSpeakFrame(speech, append_to_context=False))
-
-    def make_transition_handler(edge: dict):
-        target = edge.get("target")
-
-        async def handler(params):
-            logger.info(f"LLM 触发转移 → {engine.name(target)}")
-            # 进结束节点不播过渡语(结束语本身就是收尾,避免打断挂断时序)
-            if not engine.is_end(target):
-                await speak_transition(edge)
-            await go_to_node(target)
-            # 返回工具结果,pipecat 随即在新节点的提示/工具下继续生成
-            await params.result_callback({"status": "ok"})
-
-        return handler
-
-    async def fallback_route() -> None:
-        """文本兜底:模型迟迟不调用转移工具时,用一次轻量分类器判断是否转移。"""
-        if not workflow_active or wf_state["ended"]:
-            return
-        if wf_state["turns_in_node"] < FALLBACK_AFTER_TURNS:
-            return
-        if not engine.outgoing(wf_state["current"]):
-            return
-        target = await engine.route(
-            wf_state["current"],
-            history,
-            api_key=_require(cfg.llm_api_key, "LLM apiKey"),
-            base_url=_require(cfg.llm_base_url, "LLM apiUrl"),
-            model=_require(cfg.model, "LLM modelId"),
-        )
-        if target and target != wf_state["current"]:
-            logger.info(f"文本兜底触发转移 → {engine.name(target)}")
-            if not engine.is_end(target):
-                await speak_transition(engine.find_edge(wf_state["current"], target))
-            # 仅切换节点提示/工具,下一轮用户输入即在新节点处理
-            await go_to_node(target)
-
-    # 把每条边注册成 LLM 可调用的转移函数(按边唯一命名,处理器全局注册一次,
-    # 由各节点的 context.tools 控制当前可见哪些)。
-    if workflow_active:
-        for edge in engine.edges:
-            if edge.get("target"):
-                llm.register_function(
-                    engine.edge_fn_name(edge), make_transition_handler(edge)
-                )
-        apply_node(wf_state["current"])  # 设初始节点的提示与工具
-    else:
-        set_visible_tools(end_call_schemas)
+    set_visible_tools([])
+    await brain.setup(
+        cfg,
+        BrainRuntime(
+            context=context,
+            llm=llm,
+            queue_frame=worker.queue_frame,
+            set_system_prompt=set_system_prompt,
+            set_tools=set_visible_tools,
+            call_end=call_end,
+        ),
+    )

    async def append_user_text_to_context(text: str, *, run_llm: bool) -> None:
        await worker.queue_frame(
@@ -862,19 +637,12 @@ async def run_pipeline(
    @user_aggregator.event_handler("on_user_turn_stopped")
    async def on_user_turn_stopped(_aggregator, _strategy, message):
        if message.content:
-            history.append({"role": "user", "content": message.content})
+            brain.record_user_message(message.content)
        await queue_transcript("user", message.content, message.timestamp)

    @assistant_aggregator.event_handler("on_assistant_text_start")
    async def on_assistant_text_start(_aggregator, turn_id, timestamp):
-        # 进入结束节点后,第一条「开始生成」的回复就是结束节点自己的结束语
-        # (其 text_start 发生在 ended 置位之后,不会误认转移前的那句)。
-        if (
-            workflow_active
-            and wf_state["ended"]
-            and wf_state["end_turn_id"] is None
-        ):
-            wf_state["end_turn_id"] = turn_id
+        await brain.on_assistant_text_start(turn_id)
        await worker.queue_frame(
            OutputTransportMessageUrgentFrame(
                message={
@@ -909,24 +677,12 @@ async def run_pipeline(
                }
            )
        )
-        # 助手把话说完(未被打断)后:累加本节点轮次,必要时走文本兜底路由。
-        # 正常情况下转移由 LLM 直接调用转移工具完成(go_to_node),无需这里处理。
-        if content and not interrupted and workflow_active:
-            history.append({"role": "assistant", "content": content})
-            if turn_id == wf_state["end_turn_id"]:
-                # 结束节点的结束语文本已生成完(也已下发 data channel),武装挂断;
-                # 真正的 EndFrame 由 EndCallAfterSpeech 在结束语「说完」时排入。
-                wf_state["end_armed"] = True
-            elif not wf_state["ended"]:
-                wf_state["turns_in_node"] += 1
-                await fallback_route()
-        elif content and not interrupted:
-            history.append({"role": "assistant", "content": content})
+        await brain.on_assistant_text_end(turn_id, content, interrupted)

    @text_input.event_handler("on_text_input")
    async def on_text_input(_processor, text):
        pending_text_inputs.append(text)
-        history.append({"role": "user", "content": 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())

@@ -941,7 +697,7 @@ async def run_pipeline(
    @text_input.event_handler("on_text_append")
    async def on_text_append(_processor, text):
        # 静默追加:写进上下文但不打断、不触发推理;transcript 照常上报
-        history.append({"role": "user", "content": text})
+        brain.record_user_message(text)
        await queue_transcript("user", text, time_now_iso8601())
        await append_user_text_to_context(text, run_llm=False)

@@ -969,9 +725,7 @@ async def run_pipeline(
            if brain.spec.owns_context:
                context.add_message({"role": "assistant", "content": greeting})
            await worker.queue_frame(TTSSpeakFrame(greeting, append_to_context=False))
-        # 工作流:点亮当前(开始)节点。开始节点即首个会话节点。
-        if workflow_active:
-            await emit_node_active(wf_state["current"])
+        await brain.on_connected()

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(_transport, _client):
@@ -996,12 +750,17 @@ async def run_realtime_pipeline(
    transport,
    cfg: AssistantConfig,
    *,
+    brain: Brain,
    assistant_id: str | None = None,
    channel: str = "webrtc",
 ) -> None:
    """Run a speech-to-speech model that owns ASR, reasoning, and synthesis."""
-    realtime = create_realtime_service(cfg)
+    realtime = create_realtime_service(
+        cfg,
+        instructions=brain.system_prompt(cfg),
+    )
    text_input = RealtimeTextInputProcessor()
+    greeting = await brain.greeting(cfg)

    recorder = await ConversationRecorder.start(
        assistant_id=assistant_id,
@@ -1058,8 +817,8 @@ async def run_realtime_pipeline(

    @transport.event_handler("on_client_connected")
    async def on_client_connected(_transport, _client):
-        if cfg.greeting:
-            await realtime.speak(cfg.greeting)
+        if greeting:
+            await realtime.speak(greeting)

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(_transport, _client):
--- a/backend/services/pipecat/service_factory.py
+++ b/backend/services/pipecat/service_factory.py
@@ -142,7 +142,7 @@ def create_tts(cfg: AssistantConfig):
    )


-def create_realtime_service(cfg: AssistantConfig):
+def create_realtime_service(cfg: AssistantConfig, *, instructions: str):
    """Create a speech-to-speech service that owns STT, LLM, and TTS."""
    if cfg.realtime_interface_type == "stepfun-realtime":
        from services.pipecat.stepfun_realtime import StepFunRealtimeService
@@ -151,7 +151,7 @@ def create_realtime_service(cfg: AssistantConfig):
            api_key=_require(cfg.realtime_api_key, "Realtime apiKey"),
            model=_require(cfg.realtimeModel, "Realtime modelId"),
            base_url=_require(cfg.realtime_base_url, "Realtime apiUrl"),
-            instructions=cfg.prompt,
+            instructions=instructions,
            voice=str(cfg.realtime_values.get("voice") or "linjiajiejie"),
            input_sample_rate=int(
                cfg.realtime_values.get("inputSampleRate") or 24000
--- a/backend/services/workflow_engine.py
+++ b/backend/services/workflow_engine.py
@@ -3,6 +3,7 @@
 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。
@@ -27,6 +28,10 @@ class WorkflowEngine:
            (nid for nid, n in self.nodes.items() if n.get("type") == "startCall"),
            None,
        )
+        self.global_id: str | None = next(
+            (nid for nid, n in self.nodes.items() if n.get("type") == "globalNode"),
+            None,
+        )

    # ---- 结构查询 ----
    def node_type(self, nid: str | None) -> str | None:
@@ -80,10 +85,25 @@ class WorkflowEngine:
        return self.data(self.start_id).get("greeting") or ""

    def system_prompt_for(self, nid: str | None) -> str:
-        """节点系统提示:仅用该节点自己的 prompt(开始节点也是会话节点)。"""
+        """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)}]"
-        prompt = str(self.data(nid).get("prompt") or "").strip()
-        return f"{header}\n{prompt}" if prompt else header
+        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]
+        if global_prompt:
+            sections.append(f"[全局规则]\n{global_prompt}")
+        if prompt:
+            sections.append(f"[当前节点任务]\n{prompt}")
+        return "\n\n".join(sections)

    # ---- 路由:决定下一节点 ----
    async def route(
--- a/backend/tests/test_brains.py
+++ b/backend/tests/test_brains.py
@@ -0,0 +1,303 @@
+from __future__ import annotations
+
+import unittest
+from types import SimpleNamespace
+
+from models import AssistantConfig, RuntimeTool
+from pipecat.frames.frames import (
+    LLMContextFrame,
+    LLMFullResponseEndFrame,
+    LLMFullResponseStartFrame,
+    LLMTextFrame,
+)
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.frame_processor import FrameDirection
+from schemas import AssistantUpsert, REALTIME_CAPABLE_TYPES
+from services.brains import BrainRuntime, SPECS, build_brain
+from services.brains.dify_llm import (
+    DifyLLMService,
+    last_user_text,
+    normalize_api_base,
+)
+from services.brains.workflow_brain import WorkflowBrain
+
+
+class FakeLLM:
+    def __init__(self):
+        self.functions = {}
+
+    def register_function(self, name, handler):
+        self.functions[name] = handler
+
+
+class FakeCallEnd:
+    def __init__(self):
+        self.ending = False
+        self.reason = ""
+        self.armed = False
+        self.finished = False
+
+    def begin(self, reason: str) -> None:
+        self.ending = True
+        self.reason = reason
+
+    def arm_after_speech(self) -> None:
+        self.armed = True
+
+    async def finish(self) -> None:
+        self.finished = True
+
+
+class FakeFunctionParams:
+    def __init__(self, arguments=None):
+        self.arguments = arguments or {}
+        self.result = None
+        self.properties = None
+
+    async def result_callback(self, result, properties=None):
+        self.result = result
+        self.properties = properties
+
+
+class BrainRegistryTests(unittest.TestCase):
+    def test_capability_matrix(self):
+        self.assertEqual(
+            {
+                name: spec.supported_runtime_modes
+                for name, spec in SPECS.items()
+            },
+            {
+                "prompt": frozenset({"pipeline", "realtime"}),
+                "workflow": frozenset({"pipeline"}),
+                "dify": frozenset({"pipeline"}),
+                "fastgpt": frozenset({"pipeline"}),
+            },
+        )
+        self.assertEqual(
+            REALTIME_CAPABLE_TYPES,
+            {
+                name
+                for name, spec in SPECS.items()
+                if "realtime" in spec.supported_runtime_modes
+            },
+        )
+
+    def test_unknown_brain_does_not_fallback_to_prompt(self):
+        with self.assertRaisesRegex(ValueError, "尚未实现"):
+            build_brain(AssistantConfig(type="opencode"))
+
+    def test_workflow_realtime_is_rejected_at_schema_boundary(self):
+        with self.assertRaises(ValueError):
+            AssistantUpsert(
+                name="workflow",
+                type="workflow",
+                runtimeMode="realtime",
+            )
+
+
+class DifyHelpersTests(unittest.TestCase):
+    def test_normalize_api_base(self):
+        self.assertEqual(
+            normalize_api_base("https://api.dify.ai"),
+            "https://api.dify.ai/v1",
+        )
+        self.assertEqual(
+            normalize_api_base("https://example.test/v1/chat-messages"),
+            "https://example.test/v1",
+        )
+
+    def test_last_user_text(self):
+        self.assertEqual(
+            last_user_text(
+                [
+                    {"role": "user", "content": "first"},
+                    {"role": "assistant", "content": "answer"},
+                    {
+                        "role": "user",
+                        "content": [{"type": "text", "text": "latest"}],
+                    },
+                ]
+            ),
+            "latest",
+        )
+
+
+class DifyLLMServiceTests(unittest.IsolatedAsyncioTestCase):
+    async def test_streams_sdk_events_and_keeps_conversation_id(self):
+        class FakeDifyClient:
+            requests = []
+
+            async def achat_messages(self, request, **_kwargs):
+                self.requests.append(request)
+
+                async def events():
+                    yield SimpleNamespace(
+                        event="message",
+                        answer="你好",
+                        conversation_id="conversation-1",
+                    )
+                    yield SimpleNamespace(
+                        event="message_end",
+                        conversation_id="conversation-1",
+                    )
+
+                return events()
+
+        client = FakeDifyClient()
+        service = DifyLLMService(
+            AssistantConfig(type="dify"),
+            client=client,
+            user_id="test-user",
+        )
+        frames = []
+
+        async def push_frame(frame, *_args, **_kwargs):
+            frames.append(frame)
+
+        service.push_frame = push_frame
+        context = LLMContext(messages=[{"role": "user", "content": "问题"}])
+        await service.process_frame(
+            LLMContextFrame(context),
+            FrameDirection.DOWNSTREAM,
+        )
+
+        self.assertIsInstance(frames[0], LLMFullResponseStartFrame)
+        self.assertIsInstance(frames[1], LLMTextFrame)
+        self.assertEqual(frames[1].text, "你好")
+        self.assertIsInstance(frames[-1], LLMFullResponseEndFrame)
+        self.assertEqual(service._conversation_id, "conversation-1")
+
+        context.add_message({"role": "user", "content": "追问"})
+        await service.process_frame(
+            LLMContextFrame(context),
+            FrameDirection.DOWNSTREAM,
+        )
+        self.assertEqual(client.requests[-1].conversation_id, "conversation-1")
+
+
+class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
+    async def test_end_call_tool_is_owned_by_prompt_brain(self):
+        brain = build_brain(
+            AssistantConfig(
+                type="prompt",
+                tools=[
+                    RuntimeTool(
+                        id="end-call",
+                        name="结束通话",
+                        function_name="end_call",
+                        type="end_call",
+                        definition={
+                            "config": {
+                                "message_type": "none",
+                                "capture_reason": True,
+                            }
+                        },
+                    )
+                ],
+            )
+        )
+        llm = FakeLLM()
+        call_end = FakeCallEnd()
+        visible_tools = []
+
+        async def queue_frame(_frame):
+            pass
+
+        await brain.setup(
+            AssistantConfig(
+                type="prompt",
+                tools=[
+                    RuntimeTool(
+                        id="end-call",
+                        name="结束通话",
+                        function_name="end_call",
+                        type="end_call",
+                        definition={"config": {"capture_reason": True}},
+                    )
+                ],
+            ),
+            BrainRuntime(
+                context=LLMContext(messages=[]),
+                llm=llm,
+                queue_frame=queue_frame,
+                set_system_prompt=lambda _prompt: None,
+                set_tools=lambda tools: visible_tools.extend(tools or []),
+                call_end=call_end,
+            ),
+        )
+
+        self.assertEqual(visible_tools[0].name, "end_call")
+        params = FakeFunctionParams({"reason": "用户已完成咨询"})
+        await llm.functions["end_call"](params)
+        self.assertEqual(call_end.reason, "用户已完成咨询")
+        self.assertTrue(call_end.finished)
+        self.assertEqual(params.result["action"], "ending_call")
+
+
+class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
+    async def test_transition_and_end_are_owned_by_workflow_brain(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=context,
+            llm=llm,
+            queue_frame=queue_frame,
+            set_system_prompt=prompts.append,
+            set_tools=lambda tools: visible_tools.append(tools or []),
+            call_end=call_end,
+        )
+        await brain.setup(AssistantConfig(type="workflow", graph=graph), runtime)
+
+        self.assertIn("goto_finish", llm.functions)
+        self.assertIn("收集需求", prompts[-1])
+        self.assertEqual(visible_tools[-1][0].name, "goto_finish")
+
+        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.assertTrue(call_end.ending)
+        self.assertTrue(call_end.armed)
+
+
+if __name__ == "__main__":
+    unittest.main()
--- a/frontend/src/components/workflow/GenericNode.tsx
+++ b/frontend/src/components/workflow/GenericNode.tsx
@@ -144,4 +144,5 @@ export const nodeTypes = {
  startCall: GenericNode,
  agentNode: GenericNode,
  endCall: GenericNode,
+  globalNode: GenericNode,
 };
--- a/frontend/src/components/workflow/WorkflowEditor.tsx
+++ b/frontend/src/components/workflow/WorkflowEditor.tsx
@@ -90,6 +90,14 @@ export type WorkflowEditorProps = {
 let nodeSeq = 0;
 const NONE = "__none__";

+function defaultNodeData(spec: RuntimeNodeSpec): WorkflowNodeData {
+  const data: WorkflowNodeData = { name: spec.displayName };
+  for (const field of spec.fields) {
+    if (field.default !== undefined) data[field.key] = field.default;
+  }
+  return data;
+}
+
 function toFlow(graph: WorkflowGraph): { nodes: Node[]; edges: Edge[] } {
  return {
    nodes: graph.nodes.map((n) => ({
@@ -179,12 +187,34 @@ function Canvas({
  const isValidConnection = useCallback(
    (c: Connection | Edge) => {
      if (c.source === c.target) return false;
+      const source = nodes.find((n) => n.id === c.source);
      const target = nodes.find((n) => n.id === c.target);
-      if (!target) return false;
-      const spec = specsByType[target.type as string];
-      return !!spec?.hasTarget;
+      if (!source || !target) return false;
+
+      const sourceSpec = specsByType[source.type as string];
+      const targetSpec = specsByType[target.type as string];
+      if (!sourceSpec?.hasSource || !targetSpec?.hasTarget) return false;
+      if (edges.some((e) => e.source === c.source && e.target === c.target)) {
+        return false;
+      }
+
+      const sourceLimit = sourceSpec.constraints.maxOutgoing;
+      if (
+        sourceLimit !== undefined &&
+        edges.filter((e) => e.source === c.source).length >= sourceLimit
+      ) {
+        return false;
+      }
+      const targetLimit = targetSpec.constraints.maxIncoming;
+      if (
+        targetLimit !== undefined &&
+        edges.filter((e) => e.target === c.target).length >= targetLimit
+      ) {
+        return false;
+      }
+      return true;
    },
-    [nodes, specsByType],
+    [edges, nodes, specsByType],
  );

  const addNode = useCallback(
@@ -195,11 +225,7 @@ function Canvas({
        x: window.innerWidth / 2,
        y: window.innerHeight / 2,
      });
-      const data: WorkflowNodeData = { name: spec.displayName };
-      if (spec.type === "agentNode") {
-        data.allowInterrupt = true;
-        data.prompt = "";
-      }
+      const data = defaultNodeData(spec);
      setNodes((ns) => [...ns, { id, type: spec.type, position, data }]);
      setAddOpen(false);
      setEditingId(id);
@@ -281,6 +307,14 @@ function Canvas({
  const editingSpec = editingNode ? specsByType[editingNode.type as string] : null;
  const editingEdge = edges.find((e) => e.id === editingEdgeId);
  const addableSpecs = Object.values(specsByType).filter((s) => s.addable);
+  const canAddSpec = useCallback(
+    (spec: RuntimeNodeSpec) => {
+      const limit = spec.constraints.maxInstances;
+      if (limit === undefined) return true;
+      return nodes.filter((node) => node.type === spec.type).length < limit;
+    },
+    [nodes],
+  );

  return (
    <NodeSpecsContext.Provider value={specsByType}>
@@ -398,11 +432,13 @@ function Canvas({
                  ) : null}
                  {addableSpecs.map((spec) => {
                    const Icon = spec.icon;
+                    const canAdd = canAddSpec(spec);
                    return (
                      <button
                        key={spec.type}
                        type="button"
-                        className="group relative flex items-start gap-4 overflow-hidden rounded-2xl border border-hairline bg-card p-4 text-left shadow-sm transition-[border-color,box-shadow,transform] hover:-translate-y-0.5 hover:border-hairline-strong hover:shadow-md"
+                        disabled={!canAdd}
+                        className="group relative flex items-start gap-4 overflow-hidden rounded-2xl border border-hairline bg-card p-4 text-left shadow-sm transition-[border-color,box-shadow,transform] hover:-translate-y-0.5 hover:border-hairline-strong hover:shadow-md disabled:cursor-not-allowed disabled:opacity-55 disabled:hover:translate-y-0 disabled:hover:border-hairline disabled:hover:shadow-sm"
                        onClick={() => addNode(spec)}
                      >
                        <span
@@ -421,8 +457,13 @@ function Canvas({
                          <Icon size={17} />
                        </div>
                        <div className="min-w-0 flex-1">
-                          <div className="text-sm font-medium text-foreground">
-                            {spec.displayName}
+                          <div className="flex items-center gap-2 text-sm font-medium text-foreground">
+                            <span>{spec.displayName}</span>
+                            {!canAdd && (
+                              <span className="rounded-full bg-surface-strong px-2 py-0.5 text-[10px] font-normal text-muted-foreground">
+                                已添加
+                              </span>
+                            )}
                          </div>
                          <div className="mt-1 text-xs leading-5 text-muted-foreground">
                            {spec.description}
@@ -610,6 +651,11 @@ function NodeForm({
  const [draft, setDraft] = useState<WorkflowNodeData>({ ...data });
  const set = (key: string, val: unknown) =>
    setDraft((d) => ({ ...d, [key]: val }));
+  const missingRequired = spec.fields.some((field) => {
+    if (!field.required) return false;
+    const value = draft[field.key];
+    return typeof value === "string" ? !value.trim() : value == null;
+  });

  return (
    <>
@@ -677,7 +723,9 @@ function NodeForm({
        ) : (
          <span />
        )}
-        <Button onClick={() => onSave(draft)}>保存</Button>
+        <Button disabled={missingRequired} onClick={() => onSave(draft)}>
+          保存
+        </Button>
      </DialogFooter>
    </>
  );
--- a/frontend/src/components/workflow/specs.ts
+++ b/frontend/src/components/workflow/specs.ts
@@ -11,7 +11,11 @@ import { Circle, type LucideIcon } from "lucide-react";

 import type { NodeSpecDto } from "@/lib/api";

-export type WorkflowNodeType = "startCall" | "agentNode" | "endCall";
+export type WorkflowNodeType =
+  | "startCall"
+  | "agentNode"
+  | "endCall"
+  | "globalNode";

 export type WorkflowNodeData = {
  /** 节点显示名 */
@@ -22,6 +26,8 @@ export type WorkflowNodeData = {
  prompt?: string;
  /** 允许打断(仅 agentNode) */
  allowInterrupt?: boolean;
+  /** 是否合并全局节点提示词(start/agent 默认开启,end 默认关闭) */
+  addGlobalPrompt?: boolean;
  [key: string]: unknown;
 };

@@ -30,6 +36,7 @@ export type FieldSpec = {
  label: string;
  type: "text" | "textarea" | "switch";
  required?: boolean;
+  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) */
@@ -44,6 +51,14 @@ export type RuntimeNodeSpec = {
  hasTarget: boolean;
  /** 出边句柄(结束节点没有) */
  hasSource: boolean;
+  constraints: {
+    minIncoming?: number;
+    maxIncoming?: number;
+    minOutgoing?: number;
+    maxOutgoing?: number;
+    minInstances?: number;
+    maxInstances?: number;
+  };
  fields: FieldSpec[];
 };

@@ -77,11 +92,13 @@ export function toRuntimeSpec(dto: NodeSpecDto): RuntimeNodeSpec {
    addable: dto.addable,
    hasTarget: dto.constraints.maxIncoming !== 0,
    hasSource: dto.constraints.maxOutgoing !== 0,
+    constraints: dto.constraints,
    fields: dto.fields.map((f) => ({
      key: f.key,
      label: f.label,
      type: f.type,
      required: f.required,
+      default: f.default,
    })),
  };
 }
@@ -104,27 +121,52 @@ export type WorkflowGraph = {
  viewport?: { x: number; y: number; zoom: number };
 };

-/** 新建工作流的默认图:开始 → 智能体 → 结束 */
+/** 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 {
    nodes: [
      {
        id: "start",
        type: "startCall",
-        position: { x: 80, y: 160 },
-        data: { name: "开始", greeting: "你好,我是 AI 视频助手,有什么可以帮你?" },
+        position: { x: 100, y: 120 },
+        data: {
+          name: "开始",
+          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",
-        position: { x: 400, y: 160 },
-        data: { name: "智能体节点", prompt: "", allowInterrupt: true },
+        position: { x: 420, y: 120 },
+        data: {
+          name: "智能体节点",
+          prompt: "根据用户需求提供清晰、准确的帮助。",
+          allowInterrupt: true,
+          addGlobalPrompt: true,
+        },
      },
      {
        id: "end",
        type: "endCall",
-        position: { x: 720, y: 160 },
-        data: { name: "结束", prompt: "感谢你的来电,再见!" },
+        position: { x: 740, y: 120 },
+        data: {
+          name: "结束",
+          prompt: "总结已经完成的事项,礼貌道别并结束通话。",
+          addGlobalPrompt: false,
+        },
+      },
+      {
+        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: [
--- a/frontend/src/lib/api.ts
+++ b/frontend/src/lib/api.ts
@@ -338,6 +338,7 @@ export type NodeFieldSpec = {
  label: string;
  type: "text" | "textarea" | "switch";
  required?: boolean;
+  default?: unknown;
 };

 export type NodeConstraints = {
@@ -345,6 +346,8 @@ export type NodeConstraints = {
  maxIncoming?: number;
  minOutgoing?: number;
  maxOutgoing?: number;
+  minInstances?: number;
+  maxInstances?: number;
 };

 export type NodeSpecDto = {