from __future__ import annotations

import asyncio
import unittest
from types import SimpleNamespace
from unittest.mock import AsyncMock, patch

from models import AssistantConfig, RuntimeTool
from pipecat.frames.frames import (
    LLMContextFrame,
    LLMFullResponseEndFrame,
    LLMFullResponseStartFrame,
    LLMMessagesAppendFrame,
    LLMMessagesUpdateFrame,
    LLMRunFrame,
    LLMTextFrame,
    LLMUpdateSettingsFrame,
    OutputTransportMessageUrgentFrame,
    TTSSpeakFrame,
)
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.base import GREETING_CONTEXT_MARKER
from services.brains.dify_llm import (
    DifyLLMService,
    last_user_text,
    normalize_api_base,
)
from services.brains.workflow_brain import WorkflowBrain
from services.runtime_variables import prepare_dynamic_config
from services.action_runtime import ActionOutcome, ActionStatus
from services.workflow.models import (
    LLMRouteResult,
    RouteStatus,
    WorkflowStatus,
)


class FakeLLM:
    def __init__(self):
        self.functions = {}
        self.function_options = {}

    def register_function(self, name, handler, **options):
        self.functions[name] = handler
        self.function_options[name] = options


class FakeCallEnd:
    def __init__(self):
        self.ending = False
        self.reason = ""
        self.armed = False
        self.finished = False
        self.response_started = False
        self.waited_for_text: bool | None = None
        self.tracked_speeches = 0

    def begin(self, reason: str) -> None:
        self.ending = True
        self.reason = reason

    def begin_response(self) -> None:
        self.response_started = True

    def arm_after_speech(self) -> None:
        self.armed = True

    def track_speech(self) -> None:
        self.tracked_speeches += 1

    async def arm_after_tracked_speech(self) -> None:
        self.armed = True
        if self.tracked_speeches == 0:
            await self.finish()

    async def finish_after_current_speech(self, *, has_text: bool) -> None:
        self.waited_for_text = has_text
        if has_text:
            self.armed = True
        else:
            await self.finish()

    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


async def noop_queue_frame(_frame):
    return None


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",
            )

    def test_prompt_realtime_keeps_dynamic_variable_definitions(self):
        assistant = AssistantUpsert(
            name="realtime prompt",
            type="prompt",
            runtimeMode="realtime",
            dynamicVariableDefinitions={
                "user_name": {
                    "type": "string",
                    "required": True,
                    "default": None,
                }
            },
        )
        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):
        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_startup_only_tool_is_not_registered_with_llm(self):
        startup_tool = RuntimeTool(
            id="opening_message",
            name="开场确认",
            function_name="show_message",
            type="client",
        )
        conversation_tool = RuntimeTool(
            id="lookup_order",
            name="查询订单",
            function_name="lookup_order",
            type="http",
        )
        cfg = AssistantConfig(
            type="prompt",
            tools=[startup_tool, conversation_tool],
            llm_tool_ids=[conversation_tool.id],
            startup={
                "actions": [
                    {
                        "id": "opening_message",
                        "phase": "opening",
                        "tool_id": startup_tool.id,
                        "required": True,
                    }
                ]
            },
        )
        brain = build_brain(cfg)
        llm = FakeLLM()
        visible_schemas = []

        await brain.setup(
            cfg,
            BrainRuntime(
                context=LLMContext(messages=[]),
                llm=llm,
                queue_frame=noop_queue_frame,
                set_system_prompt=lambda _prompt: None,
                set_tools=visible_schemas.extend,
                call_end=FakeCallEnd(),
            ),
        )

        self.assertIn(startup_tool.id, brain._tool_by_id)
        self.assertNotIn(startup_tool.function_name, llm.functions)
        self.assertIn(conversation_tool.function_name, llm.functions)
        self.assertEqual(len(visible_schemas), 1)

    async def test_preflight_runs_multiple_server_tools_in_order(self):
        tools = [
            RuntimeTool(
                id=f"preflight_{index}",
                name=f"预检动作 {index}",
                function_name=f"preflight_{index}",
                type="http",
            )
            for index in (1, 2)
        ]
        cfg = AssistantConfig(
            type="prompt",
            tools=tools,
            startup={
                "actions": [
                    {
                        "id": tool.id,
                        "phase": "preflight",
                        "tool_id": tool.id,
                        "required": True,
                    }
                    for tool in tools
                ]
            },
        )
        brain = build_brain(cfg)
        await brain.setup(
            cfg,
            BrainRuntime(
                context=LLMContext(messages=[]),
                llm=FakeLLM(),
                queue_frame=noop_queue_frame,
                set_system_prompt=lambda _prompt: None,
                set_tools=lambda _tools: None,
                call_end=FakeCallEnd(),
            ),
        )
        brain._actions.execute = AsyncMock(
            side_effect=[
                ActionOutcome(
                    invocation_id=f"act_{index}",
                    status=ActionStatus.SUCCESS,
                    duration_ms=index,
                )
                for index in (1, 2)
            ]
        )

        await brain.run_preflight()

        self.assertEqual(
            [call.args[0].id for call in brain._actions.execute.await_args_list],
            ["preflight_1", "preflight_2"],
        )

    async def test_opening_stage_starts_speech_and_releases_on_confirmation(self):
        tool = RuntimeTool(
            id="opening_data",
            name="加载开场数据",
            function_name="load_opening_data",
            type="http",
        )
        cfg = AssistantConfig(
            type="prompt",
            greeting="请阅读并确认重要信息",
            tools=[tool],
            startup={
                "execution_mode": "sequential",
                "opening_message": {
                    "title": "重要提示",
                    "message": "请确认已阅读。",
                    "confirm_label": "确认",
                },
                "actions": [
                    {
                        "id": "opening_data",
                        "phase": "opening",
                        "tool_id": "opening_data",
                        "arguments": {},
                        "required": True,
                    }
                ],
            },
        )
        brain = build_brain(cfg)
        input_states = []
        queued = []

        async def queue_frame(frame):
            queued.append(frame)

        confirmation_started = asyncio.Event()
        user_confirmed = asyncio.Event()
        client_calls = []

        class FakeClientTools:
            async def call(self, function_name, arguments, **options):
                client_calls.append((function_name, arguments, options))
                confirmation_started.set()
                await user_confirmed.wait()
                return {"status": "ok", "data": {"action": "confirmed"}}

        await brain.setup(
            cfg,
            BrainRuntime(
                context=LLMContext(messages=[]),
                llm=FakeLLM(),
                queue_frame=queue_frame,
                set_system_prompt=lambda _prompt: None,
                set_tools=lambda _tools: None,
                call_end=FakeCallEnd(),
                client_tools=FakeClientTools(),
                set_input_enabled=input_states.append,
            ),
        )
        called_tool_ids = []

        async def execute(tool, *_args, **_kwargs):
            called_tool_ids.append(tool.id)
            return ActionOutcome(
                invocation_id=f"act_{len(called_tool_ids)}",
                status=ActionStatus.SUCCESS,
                duration_ms=len(called_tool_ids),
            )

        brain._actions.execute = AsyncMock(side_effect=execute)

        self.assertEqual(await brain.greeting(cfg), "")
        await brain.on_connected(greeting_pending=False)
        opening_task = asyncio.create_task(brain.on_client_ready())
        await confirmation_started.wait()

        self.assertEqual(input_states, [False])
        self.assertEqual(called_tool_ids, [])
        self.assertEqual(client_calls[0][0], "show_message")
        self.assertFalse(client_calls[0][1]["dismissible"])
        self.assertTrue(
            any(
                isinstance(frame, TTSSpeakFrame)
                and frame.text == "请阅读并确认重要信息"
                for frame in queued
            )
        )
        self.assertTrue(
            any(
                isinstance(frame, OutputTransportMessageUrgentFrame)
                and frame.message.get("type") == "transcript"
                and frame.message.get("content") == "请阅读并确认重要信息"
                for frame in queued
            )
        )

        user_confirmed.set()
        await opening_task
        self.assertEqual(input_states, [False, True])
        self.assertEqual(called_tool_ids, ["opening_data"])
        self.assertEqual(
            len(
                [
                    frame
                    for frame in queued
                    if isinstance(frame, OutputTransportMessageUrgentFrame)
                    and frame.message.get("type") == "startup-action-result"
                ]
            ),
            1,
        )

        # Replayed client-ready must not execute startup actions twice.
        await brain.on_client_ready()
        self.assertEqual(brain._actions.execute.await_count, 1)

    async def test_required_opening_failure_keeps_input_blocked_and_ends_call(self):
        cfg = AssistantConfig(
            type="prompt",
            startup={
                "opening_message": {
                    "title": "重要提示",
                    "message": "请确认已阅读。",
                    "confirm_label": "确认",
                }
            },
        )
        brain = build_brain(cfg)
        call_end = FakeCallEnd()
        input_states = []

        async def queue_frame(_frame):
            pass

        class FailingClientTools:
            async def call(self, *_args, **_kwargs):
                return {"status": "error", "message": "客户端未显示消息"}

        await brain.setup(
            cfg,
            BrainRuntime(
                context=LLMContext(messages=[]),
                llm=FakeLLM(),
                queue_frame=queue_frame,
                set_system_prompt=lambda _prompt: None,
                set_tools=lambda _tools: None,
                call_end=call_end,
                client_tools=FailingClientTools(),
                set_input_enabled=input_states.append,
            ),
        )

        await brain.on_connected(greeting_pending=False)
        await brain.on_client_ready()

        self.assertEqual(input_states, [False])
        self.assertTrue(call_end.ending)
        self.assertTrue(call_end.finished)
        self.assertEqual(call_end.reason, "startup_action_failed")

    async def test_realtime_prompt_brain_renders_dynamic_variables(self):
        cfg = prepare_dynamic_config(
            AssistantConfig(
                type="prompt",
                runtimeMode="realtime",
                prompt="服务用户 {{user_name}}",
                greeting="您好，{{user_name}}",
                dynamic_variable_definitions={
                    "user_name": {"type": "string", "required": True}
                },
            ),
            {"user_name": "王先生"},
            assistant_id="asst_realtime",
        )
        brain = build_brain(cfg)
        self.assertEqual(brain.system_prompt(cfg), "服务用户 王先生")
        self.assertEqual(await brain.greeting(cfg), "您好，王先生")
        context = LLMContext(
            messages=[{"role": "system", "content": brain.system_prompt(cfg)}]
        )
        brain.prepare_greeting_context("您好，王先生", context)
        self.assertEqual(
            [message["role"] for message in context.get_messages()],
            ["system", "system"],
        )
        self.assertNotEqual(context.get_messages()[0]["role"], "assistant")
        self.assertEqual(
            context.get_messages()[1]["content"],
            f"{GREETING_CONTEXT_MARKER}\n您好，王先生",
        )

    async def test_session_update_refreshes_prompt_without_running_llm(self):
        cfg = prepare_dynamic_config(
            AssistantConfig(
                type="prompt",
                runtimeMode="pipeline",
                prompt="面板状态：{{panel_open}}",
                dynamic_variable_definitions={
                    "panel_open": {"type": "boolean", "default": False}
                },
            ),
            {},
            assistant_id="asst_session_update",
        )
        brain = build_brain(cfg)
        prompts = []
        queued_frames = []

        async def queue_frame(frame):
            queued_frames.append(frame)

        await brain.setup(
            cfg,
            BrainRuntime(
                context=LLMContext(
                    messages=[{"role": "system", "content": brain.system_prompt(cfg)}]
                ),
                llm=FakeLLM(),
                queue_frame=queue_frame,
                set_system_prompt=prompts.append,
                set_tools=lambda _tools: None,
                call_end=FakeCallEnd(),
            ),
        )

        result = await brain.on_session_update({"panel_open": True})

        self.assertEqual(result.changed, ["panel_open"])
        self.assertEqual(result.dynamic_variables, {"panel_open": True})
        self.assertEqual(prompts, ["面板状态：true"])
        self.assertEqual(queued_frames, [])

    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 brain.on_assistant_text_start("closing-turn")
        await llm.functions["end_call"](params)
        self.assertEqual(call_end.reason, "用户已完成咨询")
        self.assertFalse(call_end.finished)
        await brain.on_assistant_text_end("closing-turn", "", False)
        self.assertTrue(call_end.finished)
        self.assertFalse(call_end.waited_for_text)
        self.assertTrue(call_end.response_started)
        self.assertEqual(params.result["action"], "ending_call")

    async def test_end_call_waits_for_prompt_generated_closing_speech(self):
        tool = RuntimeTool(
            id="end-call",
            name="结束通话",
            function_name="end_call",
            type="end_call",
            definition={"config": {"message_type": "none"}},
        )
        cfg = AssistantConfig(type="prompt", tools=[tool])
        brain = build_brain(cfg)
        llm = FakeLLM()
        call_end = FakeCallEnd()

        await brain.setup(
            cfg,
            BrainRuntime(
                context=LLMContext(messages=[]),
                llm=llm,
                queue_frame=lambda _frame: None,
                set_system_prompt=lambda _prompt: None,
                set_tools=lambda _tools: None,
                call_end=call_end,
            ),
        )
        await brain.on_assistant_text_start("closing-turn")
        await llm.functions["end_call"](FakeFunctionParams({}))
        await brain.on_assistant_text_end(
            "closing-turn",
            "感谢您的来电。祝您生活愉快，再见。",
            False,
        )

        self.assertFalse(call_end.finished)
        self.assertTrue(call_end.armed)
        self.assertTrue(call_end.waited_for_text)

    async def test_http_tool_renders_secrets_and_updates_prompt_variable(self):
        requests = []

        class FakeResponse:
            status_code = 200
            content = b'{"order":{"status":"paid"}}'

            def raise_for_status(self):
                return None

            def json(self):
                return {"order": {"status": "paid"}}

        class FakeClient:
            def __init__(self, **_kwargs):
                pass

            async def __aenter__(self):
                return self

            async def __aexit__(self, *_args):
                return None

            async def request(self, method, url, **kwargs):
                requests.append((method, url, kwargs))
                return FakeResponse()

        cfg = prepare_dynamic_config(
            AssistantConfig(
                type="prompt",
                runtimeMode="pipeline",
                prompt="订单状态：{{order_status}}",
                dynamic_variable_definitions={
                    "order_status": {"type": "string", "default": "unknown"}
                },
                tools=[
                    RuntimeTool(
                        id="lookup",
                        name="查询订单",
                        function_name="lookup_order",
                        type="http",
                        description="查询订单状态",
                        definition={
                            "config": {
                                "method": "GET",
                                "url": "https://example.test/orders/{order_id}",
                                "headers": {"Authorization": "Bearer {{secret__token}}"},
                                "parameters": [
                                    {
                                        "name": "order_id",
                                        "type": "string",
                                        "location": "path",
                                        "required": True,
                                    },
                                    {
                                        "name": "Authorization",
                                        "type": "string",
                                        "location": "header",
                                        "required": False,
                                    },
                                ],
                                "dynamic_variable_assignments": {
                                    "order_status": "response.order.status"
                                },
                            }
                        },
                        secrets={"dynamic_variables": {"secret__token": "server-token"}},
                    )
                ],
            ),
            {},
            assistant_id="asst_1",
        )
        brain = build_brain(cfg)
        llm = FakeLLM()
        prompts = []
        visible_tools = []

        async def queue_frame(_frame):
            pass

        await brain.setup(
            cfg,
            BrainRuntime(
                context=LLMContext(messages=[]),
                llm=llm,
                queue_frame=queue_frame,
                set_system_prompt=prompts.append,
                set_tools=lambda tools: visible_tools.extend(tools or []),
                call_end=FakeCallEnd(),
            ),
        )
        params = FakeFunctionParams(
            {"order_id": "A/1", "Authorization": "attacker-value"}
        )
        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")
        self.assertEqual(
            requests[0][2]["headers"]["Authorization"], "Bearer server-token"
        )
        self.assertEqual(params.result["updated_variables"], ["order_status"])
        self.assertEqual(prompts[-1], "订单状态：paid")


class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
    def test_client_tool_session_wait_disables_flow_timeout(self):
        brain = WorkflowBrain(
            {
                "specVersion": 3,
                "settings": {},
                "nodes": [{"id": "start", "type": "start", "data": {}}],
                "edges": [],
            }
        )
        timeout_tool = RuntimeTool(
            id="client_timeout",
            name="限时等待",
            function_name="wait_with_timeout",
            type="client",
            definition={
                "type": "client",
                "config": {"timeout_seconds": 7},
            },
        )
        session_tool = RuntimeTool(
            id="client_session",
            name="会话内等待",
            function_name="wait_for_session",
            type="client",
            definition={
                "type": "client",
                "config": {
                    "timeout_seconds": 7,
                    "response_wait_mode": "session",
                },
            },
        )

        self.assertEqual(brain._flow_tool(timeout_tool, "start").timeout_secs, 7.0)
        self.assertIsNone(brain._flow_tool(session_tool, "start").timeout_secs)

    async def test_session_update_refreshes_current_agent_without_routing(self):
        cfg = prepare_dynamic_config(
            AssistantConfig(
                type="workflow",
                graph={
                    "specVersion": 3,
                    "settings": {"globalPrompt": "面板 {{panel_open}}"},
                    "nodes": [
                        {"id": "start", "type": "start", "data": {}},
                        {
                            "id": "agent",
                            "type": "agent",
                            "data": {"prompt": "等待用户操作"},
                        },
                    ],
                    "edges": [
                        {
                            "id": "begin",
                            "source": "start",
                            "target": "agent",
                            "data": {"mode": "always", "priority": 0},
                        }
                    ],
                },
                dynamic_variable_definitions={
                    "panel_open": {"type": "boolean", "default": False}
                },
            ),
            {},
            assistant_id="asst_workflow_session_update",
        )
        brain = WorkflowBrain(cfg)
        queued = []

        async def queue_frame(frame):
            queued.append(frame)

        brain._runtime = BrainRuntime(
            context=LLMContext(messages=[]),
            llm=FakeLLM(),
            queue_frame=queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=FakeCallEnd(),
        )
        brain._state.enter("agent", WorkflowStatus.WAITING_USER)

        result = await brain.on_session_update({"panel_open": True})

        self.assertEqual(result.changed, ["panel_open"])
        prompt_updates = [
            frame for frame in queued if isinstance(frame, LLMUpdateSettingsFrame)
        ]
        self.assertEqual(len(prompt_updates), 1)
        self.assertIn("面板 true", prompt_updates[0].delta.system_instruction)
        self.assertFalse(any(isinstance(frame, LLMRunFrame) for frame in queued))
        variable_event = next(
            frame.message
            for frame in queued
            if isinstance(frame, OutputTransportMessageUrgentFrame)
            and frame.message.get("type") == "workflow-variables"
        )
        self.assertEqual(variable_event["reason"], "session_update")
        self.assertEqual(variable_event["changed"], ["panel_open"])

    async def test_agent_vision_tool_is_scoped_to_effective_stage(self):
        brain = WorkflowBrain(
            {
                "specVersion": 3,
                "settings": {
                    "globalPrompt": "全局规则",
                    "defaultLlmResourceId": "llm_global",
                    "visionEnabled": True,
                    "visionModelResourceId": "vision_global",
                },
                "nodes": [
                    {"id": "start", "type": "start", "data": {}},
                    {
                        "id": "agent",
                        "type": "agent",
                        "data": {
                            "prompt": "观察用户需要展示的物品",
                            "inheritGlobalConfig": True,
                        },
                    },
                ],
                "edges": [
                    {
                        "id": "begin",
                        "source": "start",
                        "target": "agent",
                        "data": {"mode": "always", "priority": 0},
                    }
                ],
            }
        )
        scopes = []
        vision_function = object()

        async def queue_frame(_frame):
            pass

        brain._runtime = BrainRuntime(
            context=LLMContext(messages=[]),
            llm=FakeLLM(),
            queue_frame=queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=FakeCallEnd(),
            set_vision_scope=scopes.append,
            vision_function=vision_function,
        )

        await brain._apply_agent_stage("agent")
        inherited_config = brain._agent_config("agent")
        self.assertIn(vision_function, inherited_config["functions"])
        self.assertIn("fetch_user_image", inherited_config["role_message"])
        self.assertEqual(
            scopes[-1],
            {
                "enabled": True,
                "vision_model_resource_id": "vision_global",
                "llm_resource_id": "llm_global",
            },
        )

        brain._engine.data("agent").update(
            {
                "inheritGlobalConfig": False,
                "llmResourceId": "llm_agent",
                "visionEnabled": False,
                "visionModelResourceId": "",
            }
        )
        await brain._apply_agent_stage("agent")
        custom_config = brain._agent_config("agent")
        self.assertNotIn(vision_function, custom_config["functions"])
        self.assertNotIn("fetch_user_image", custom_config["role_message"])
        self.assertFalse(scopes[-1]["enabled"])

    async def test_initial_message_starts_without_workflow_greeting(self):
        brain = WorkflowBrain(
            {
                "specVersion": 3,
                "settings": {"globalPrompt": "全局规则"},
                "nodes": [
                    {
                        "id": "start",
                        "type": "start",
                        "data": {"greeting": "欢迎使用"},
                    },
                    {
                        "id": "message",
                        "type": "message",
                        "data": {
                            "speech": "请问您怎么称呼？",
                            "showMessage": False,
                        },
                    },
                    {
                        "id": "agent",
                        "type": "agent",
                        "data": {"prompt": "收集用户信息"},
                    },
                ],
                "edges": [
                    {
                        "id": "begin",
                        "source": "start",
                        "target": "message",
                        "data": {"mode": "always", "priority": 0},
                    },
                    {
                        "id": "after_message",
                        "source": "message",
                        "target": "agent",
                        "data": {"mode": "always", "priority": 0},
                    }
                ],
            }
        )
        queued = []

        async def queue_frame(frame):
            queued.append(frame)

        class FakeManager:
            def __init__(self):
                self.current_node = None

            async def initialize(self, config):
                self.current_node = config["name"]

            async def set_node_from_config(self, config):
                self.current_node = config["name"]
                for action in config.get("pre_actions", []):
                    await action["handler"](action, self)

        brain._runtime = BrainRuntime(
            context=LLMContext(messages=[]),
            llm=FakeLLM(),
            queue_frame=queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=FakeCallEnd(),
        )
        brain._manager = FakeManager()

        self.assertNotIn("greeting", brain._engine.data("start"))
        self.assertEqual(
            await brain.greeting(
                AssistantConfig(type="workflow", greeting="旧助手级开场白")
            ),
            "",
        )
        await brain.on_connected()

        self.assertEqual(brain._manager.current_node, "message")
        for _ in range(3):
            await asyncio.sleep(0)
        self.assertEqual(brain._manager.current_node, "agent")
        message_speech_frames = [
            frame for frame in queued if isinstance(frame, TTSSpeakFrame)
        ]
        self.assertEqual(len(message_speech_frames), 1)
        self.assertEqual(message_speech_frames[0].text, "请问您怎么称呼？")

        # Stray generic greeting notifications must not replay the Message.
        await brain.on_greeting_finished()
        self.assertEqual(
            len([frame for frame in queued if isinstance(frame, TTSSpeakFrame)]),
            1,
        )

    async def test_action_publishes_updated_session_variables(self):
        tool = RuntimeTool(
            id="lookup",
            name="查询订单",
            function_name="lookup_order",
            type="http",
        )
        cfg = prepare_dynamic_config(
            AssistantConfig(
                type="workflow",
                graph={
                    "specVersion": 3,
                    "settings": {},
                    "nodes": [
                        {"id": "start", "type": "start", "data": {}},
                        {
                            "id": "lookup_action",
                            "type": "action",
                            "data": {
                                "toolId": "lookup",
                                "resultAssignments": {
                                    "order_status": "order.status"
                                },
                            },
                        },
                    ],
                    "edges": [],
                },
                dynamic_variable_definitions={
                    "order_status": {"type": "string", "default": "pending"}
                },
                tools=[tool],
            ),
            {},
            assistant_id="asst_workflow_action",
        )
        brain = WorkflowBrain(cfg)
        queued = []

        async def queue_frame(frame):
            queued.append(frame)

        async def execute(_tool, _arguments, *, result_assignments=None):
            self.assertEqual(result_assignments, {"order_status": "order.status"})
            brain._store.assign("order_status", "paid")
            return {
                "status": "ok",
                "updated_variables": ["order_status"],
            }

        brain._runtime = BrainRuntime(
            context=LLMContext(messages=[]),
            llm=FakeLLM(),
            queue_frame=queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=FakeCallEnd(),
            session_id="conv_action",
        )
        brain._tools.execute = execute

        outcome = await brain._enter_action("lookup_action")

        self.assertEqual(outcome.status, ActionStatus.SUCCESS)
        self.assertEqual(outcome.updated_variables, ("order_status",))
        self.assertEqual(
            brain._store.values["system__last_action_invocation_id"],
            outcome.invocation_id,
        )
        variable_events = [
            frame.message
            for frame in queued
            if isinstance(frame, OutputTransportMessageUrgentFrame)
            and frame.message.get("type") == "workflow-variables"
        ]
        self.assertEqual(variable_events[-1]["reason"], "action")
        self.assertEqual(variable_events[-1]["changed"], ["order_status"])
        self.assertEqual(variable_events[-1]["variables"], {"order_status": "paid"})
        completed_event = next(
            frame.message
            for frame in queued
            if isinstance(frame, OutputTransportMessageUrgentFrame)
            and frame.message.get("event") == "action_completed"
        )
        self.assertEqual(completed_event["outcome"]["status"], "success")
        self.assertEqual(completed_event["outcome"]["updatedVariables"], ["order_status"])
        self.assertEqual(completed_event["sessionId"], "conv_action")
        self.assertEqual(completed_event["workflowRevision"], brain._engine.revision)
        self.assertNotIn("result", completed_event["outcome"])

    async def test_action_result_assignment_modes_reach_tool_executor(self):
        tool = RuntimeTool(
            id="client_action",
            name="客户端操作",
            function_name="show_message",
            type="client",
        )
        brain = WorkflowBrain(
            AssistantConfig(
                type="workflow",
                graph={
                    "specVersion": 3,
                    "settings": {},
                    "nodes": [
                        {"id": "start", "type": "start", "data": {}},
                        {
                            "id": "inherit_action",
                            "type": "action",
                            "data": {
                                "toolId": "client_action",
                                "resultAssignmentMode": "inherit",
                            },
                        },
                        {
                            "id": "override_action",
                            "type": "action",
                            "data": {
                                "toolId": "client_action",
                                "resultAssignmentMode": "override",
                                "resultAssignments": {"choice": "action"},
                            },
                        },
                        {
                            "id": "none_action",
                            "type": "action",
                            "data": {
                                "toolId": "client_action",
                                "resultAssignmentMode": "none",
                            },
                        },
                    ],
                    "edges": [],
                },
                tools=[tool],
            )
        )
        captured = []

        async def execute(_tool, _arguments, *, result_assignments=None):
            captured.append(result_assignments)
            return {"status": "ok", "updated_variables": []}

        brain._runtime = BrainRuntime(
            context=LLMContext(messages=[]),
            llm=FakeLLM(),
            queue_frame=noop_queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=FakeCallEnd(),
        )
        brain._tools.execute = execute

        await brain._enter_action("inherit_action")
        await brain._enter_action("override_action")
        await brain._enter_action("none_action")

        self.assertEqual(captured, [None, {"choice": "action"}, {}])

    async def test_action_client_error_sets_error_status(self):
        tool = RuntimeTool(
            id="client_action",
            name="客户端操作",
            function_name="show_message",
            type="client",
        )
        brain = WorkflowBrain(
            AssistantConfig(
                type="workflow",
                graph={
                    "specVersion": 3,
                    "settings": {},
                    "nodes": [
                        {"id": "start", "type": "start", "data": {}},
                        {
                            "id": "action",
                            "type": "action",
                            "data": {"toolId": "client_action"},
                        },
                    ],
                    "edges": [],
                },
                tools=[tool],
            )
        )

        async def execute(_tool, _arguments, *, result_assignments=None):
            return {
                "status": "error",
                "message": "用户关闭了确认弹窗",
                "updated_variables": [],
            }

        brain._runtime = BrainRuntime(
            context=LLMContext(messages=[]),
            llm=FakeLLM(),
            queue_frame=noop_queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=FakeCallEnd(),
        )
        brain._tools.execute = execute

        outcome = await brain._enter_action("action")

        self.assertEqual(outcome.status, ActionStatus.FAILURE)
        self.assertEqual(outcome.error.code, "tool_error")
        self.assertEqual(brain._store.values["system__last_action_status"], "error")
        self.assertEqual(
            brain._store.values["system__last_action_error"],
            "用户关闭了确认弹窗",
        )

    async def test_cancelled_action_does_not_follow_failure_or_default_edge(self):
        tool = RuntimeTool(
            id="client_action",
            name="客户端操作",
            function_name="show_message",
            type="client",
        )
        brain = WorkflowBrain(
            AssistantConfig(
                type="workflow",
                graph={
                    "specVersion": 3,
                    "settings": {},
                    "nodes": [
                        {"id": "start", "type": "start", "data": {}},
                        {
                            "id": "action",
                            "type": "action",
                            "data": {"toolId": "client_action"},
                        },
                        {"id": "end", "type": "end", "data": {}},
                    ],
                    "edges": [
                        {
                            "id": "after_action",
                            "source": "action",
                            "target": "end",
                            "data": {"mode": "always"},
                        }
                    ],
                },
                tools=[tool],
            )
        )
        queued = []

        async def queue_frame(frame):
            queued.append(frame)

        async def execute(_tool, _arguments, *, result_assignments=None):
            return {
                "status": "error",
                "message": "会话已结束",
                "updated_variables": [],
            }

        brain._runtime = BrainRuntime(
            context=LLMContext(messages=[]),
            llm=FakeLLM(),
            queue_frame=queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=FakeCallEnd(),
        )
        brain._tools.execute = execute

        config = await brain._resolve_path("action")

        self.assertEqual(config["name"], "action")
        self.assertEqual(
            brain._store.values["system__last_action_status"],
            "cancelled",
        )
        self.assertFalse(
            any(
                isinstance(frame, OutputTransportMessageUrgentFrame)
                and frame.message.get("event") == "edge_selected"
                for frame in queued
            )
        )

    async def test_action_block_policy_only_suppresses_input_while_running(self):
        tool = RuntimeTool(
            id="client_action",
            name="客户端操作",
            function_name="show_message",
            type="client",
        )
        brain = WorkflowBrain(
            AssistantConfig(
                type="workflow",
                graph={
                    "specVersion": 3,
                    "settings": {},
                    "nodes": [
                        {"id": "start", "type": "start", "data": {}},
                        {
                            "id": "block_action",
                            "type": "action",
                            "data": {
                                "toolId": "client_action",
                                "userInputPolicy": "block",
                            },
                        },
                        {
                            "id": "queue_action",
                            "type": "action",
                            "data": {
                                "toolId": "client_action",
                                "userInputPolicy": "queue",
                            },
                        },
                    ],
                    "edges": [],
                },
                tools=[tool],
            )
        )
        input_states = []

        async def execute(_tool, _arguments, *, result_assignments=None):
            input_states.append("executing")
            return {"status": "ok", "updated_variables": []}

        brain._runtime = BrainRuntime(
            context=LLMContext(messages=[]),
            llm=FakeLLM(),
            queue_frame=noop_queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=FakeCallEnd(),
            set_input_enabled=input_states.append,
        )
        brain._tools.execute = execute

        await brain._enter_action("block_action")
        self.assertEqual(input_states, [False, "executing", True])

        input_states.clear()
        await brain._enter_action("queue_action")
        self.assertEqual(input_states, ["executing"])

    async def test_message_starts_speech_and_releases_on_confirmation(self):
        brain = WorkflowBrain(
            AssistantConfig(
                type="workflow",
                graph={
                    "specVersion": 3,
                    "settings": {},
                    "nodes": [
                        {"id": "start", "type": "start", "data": {}},
                        {
                            "id": "message",
                            "type": "message",
                            "data": {
                                "speech": "请先确认 {{customer}} 的重要信息。",
                                "showMessage": True,
                                "title": "重要提示",
                                "message": "请核对客户信息。",
                                "confirmLabel": "确认",
                                "requireConfirmation": True,
                            },
                        },
                    ],
                    "edges": [],
                },
            )
        )
        brain._store.values["customer"] = "王先生"
        events = []

        async def queue_frame(frame):
            if isinstance(frame, TTSSpeakFrame):
                events.append(("speech", frame.text))

        class OrderedCallEnd(FakeCallEnd):
            def __init__(self):
                super().__init__()
                self.playback_completion = None

            def track_speech(self):
                self.tracked_speeches += 1
                events.append("tracked")
                self.playback_completion = asyncio.get_running_loop().create_future()
                return self.playback_completion

        message_started = asyncio.Event()
        user_confirmed = asyncio.Event()

        class FakeClientTools:
            async def call(self, function_name, arguments, **options):
                self.function_name = function_name
                self.arguments = arguments
                self.options = options
                events.append("message_displayed")
                message_started.set()
                await user_confirmed.wait()
                return {"status": "ok", "data": {"action": "confirmed"}}

        input_states = []
        call_end = OrderedCallEnd()
        client_tools = FakeClientTools()
        brain._runtime = BrainRuntime(
            context=LLMContext(messages=[]),
            llm=FakeLLM(),
            queue_frame=queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=call_end,
            client_tools=client_tools,
            set_input_enabled=input_states.append,
        )
        brain._message_stages.set_client_tools(client_tools)

        message_task = asyncio.create_task(brain._enter_message("message"))
        await message_started.wait()

        self.assertEqual(
            events,
            [
                "tracked",
                ("speech", "请先确认 王先生 的重要信息。"),
                "message_displayed",
            ],
        )
        self.assertEqual(input_states, [False])
        self.assertEqual(client_tools.function_name, "show_message")
        self.assertEqual(client_tools.options["response_wait_mode"], "session")

        user_confirmed.set()
        result = await message_task
        self.assertTrue(result.succeeded)
        self.assertEqual(result.action, "confirmed")
        self.assertFalse(call_end.playback_completion.done())
        self.assertEqual(input_states, [False, True])

    async def test_speech_only_message_waits_for_transport_playback(self):
        brain = WorkflowBrain(
            {
                "specVersion": 3,
                "settings": {},
                "nodes": [
                    {"id": "start", "type": "start", "data": {}},
                    {
                        "id": "message",
                        "type": "message",
                        "data": {"speech": "正在为您准备服务。"},
                    },
                ],
                "edges": [],
            }
        )

        class PlaybackCallEnd(FakeCallEnd):
            def __init__(self):
                super().__init__()
                self.completion = None

            def track_speech(self):
                self.completion = asyncio.get_running_loop().create_future()
                return self.completion

        call_end = PlaybackCallEnd()
        input_states = []
        brain._runtime = BrainRuntime(
            context=LLMContext(messages=[]),
            llm=FakeLLM(),
            queue_frame=noop_queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=call_end,
            set_input_enabled=input_states.append,
        )

        message_task = asyncio.create_task(brain._enter_message("message"))
        await asyncio.sleep(0)
        self.assertFalse(message_task.done())
        self.assertEqual(input_states, [False])

        call_end.completion.set_result(None)
        result = await message_task
        self.assertTrue(result.succeeded)
        self.assertEqual(input_states, [False, True])

    async def test_message_between_agents_resumes_after_playback(self):
        graph = {
            "specVersion": 3,
            "settings": {},
            "nodes": [
                {"id": "start", "type": "start", "data": {}},
                {
                    "id": "opening",
                    "type": "message",
                    "data": {"speech": "欢迎使用。"},
                },
                {
                    "id": "agent1",
                    "type": "agent",
                    "data": {"prompt": "收集基本信息"},
                },
                {
                    "id": "middle",
                    "type": "message",
                    "data": {"speech": "现在进入信息确认。"},
                },
                {
                    "id": "agent2",
                    "type": "agent",
                    "data": {"prompt": "确认信息", "contextPolicy": "fresh"},
                },
                {
                    "id": "end",
                    "type": "end",
                    "data": {"scope": "session"},
                },
            ],
            "edges": [
                {
                    "id": "start-opening",
                    "source": "start",
                    "target": "opening",
                    "data": {"mode": "always"},
                },
                {
                    "id": "opening-agent1",
                    "source": "opening",
                    "target": "agent1",
                    "data": {"mode": "always"},
                },
                {
                    "id": "agent1-middle",
                    "source": "agent1",
                    "target": "middle",
                    "data": {
                        "mode": "llm",
                        "priority": 10,
                        "condition": "基本信息已经收集完成",
                    },
                },
                {
                    "id": "middle-agent2",
                    "source": "middle",
                    "target": "agent2",
                    "data": {"mode": "always"},
                },
                {
                    "id": "agent2-end",
                    "source": "agent2",
                    "target": "end",
                    "data": {
                        "mode": "llm",
                        "priority": 10,
                        "condition": "用户确认可以结束通话",
                    },
                },
            ],
        }
        brain = WorkflowBrain(graph)
        queued = []
        input_states = []

        class PlaybackCallEnd(FakeCallEnd):
            def __init__(self):
                super().__init__()
                self.completions = []

            def track_speech(self):
                completion = asyncio.get_running_loop().create_future()
                self.completions.append(completion)
                return completion

        class FakeManager:
            def __init__(self):
                self.current_node = None
                self.configs = []

            async def initialize(self, config):
                self.current_node = config["name"]
                self.configs.append(config)

            async def set_node_from_config(self, config):
                self.current_node = config["name"]
                self.configs.append(config)

        async def queue_frame(frame):
            queued.append(frame)

        call_end = PlaybackCallEnd()
        manager = FakeManager()

        class MatchingRouter:
            async def select_edge(self, **kwargs):
                edge = kwargs["edges"][0]
                return LLMRouteResult(
                    status=RouteStatus.MATCHED,
                    function_name=kwargs["edge_name"](edge),
                )

        brain._router = MatchingRouter()
        brain._runtime = BrainRuntime(
            context=LLMContext(messages=[]),
            llm=FakeLLM(),
            queue_frame=queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=call_end,
            set_input_enabled=input_states.append,
        )
        brain._manager = manager

        await brain.on_connected()
        await asyncio.sleep(0)
        self.assertEqual(manager.current_node, "opening")
        self.assertEqual(len(call_end.completions), 1)

        call_end.completions[0].set_result(None)
        for _ in range(5):
            await asyncio.sleep(0)
            if manager.current_node == "agent1":
                break
        self.assertEqual(manager.current_node, "agent1")

        # The user-turn processor must return while the second Message is
        # still waiting for its transport playback boundary.
        await asyncio.wait_for(
            brain.on_user_turn_end("基本信息已经收集完成"),
            timeout=0.1,
        )
        await asyncio.sleep(0)
        self.assertEqual(manager.current_node, "middle")
        self.assertEqual(len(call_end.completions), 2)
        self.assertFalse(call_end.completions[1].done())

        call_end.completions[1].set_result(None)
        for _ in range(5):
            await asyncio.sleep(0)
            if manager.current_node == "agent2":
                break
        self.assertEqual(manager.current_node, "agent2")
        self.assertTrue(any(isinstance(frame, LLMRunFrame) for frame in queued))
        self.assertEqual(
            manager.configs[-1]["task_messages"],
            [
                {"role": "user", "content": "基本信息已经收集完成"},
                {"role": "assistant", "content": "现在进入信息确认。"},
            ],
        )

        await brain.on_assistant_text_end("agent2-turn", "信息确认完成", False)
        await brain.on_user_turn_end("结束通话")
        self.assertEqual(manager.current_node, "end")
        self.assertTrue(call_end.finished)

    async def test_nodes_without_outgoing_edges_remain_active(self):
        queued = []

        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")
        queued.clear()
        await start_brain.on_client_ready()
        ready_messages = [
            frame.message
            for frame in queued
            if isinstance(frame, OutputTransportMessageUrgentFrame)
        ]
        self.assertIn(
            {"type": "node-active", "nodeId": "start"},
            ready_messages,
        )
        self.assertTrue(
            any(
                message.get("type") == "workflow-variables"
                and message.get("reason") == "client_ready"
                and message.get("nodeId") == "start"
                for message in ready_messages
            )
        )

        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()
        await agent_brain.on_client_ready()
        self.assertTrue(
            any(
                isinstance(frame, OutputTransportMessageUrgentFrame)
                and frame.message
                == {"type": "node-active", "nodeId": "agent"}
                for frame in queued
            )
        )
        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_start_llm_conditions_wait_for_and_route_first_user_turn(self):
        brain = WorkflowBrain(
            {
                "specVersion": 3,
                "settings": {"globalPrompt": "全局规则"},
                "nodes": [
                    {
                        "id": "start",
                        "type": "start",
                        "data": {"name": "Start"},
                    },
                    {
                        "id": "eat",
                        "type": "agent",
                        "data": {
                            "name": "点饭",
                            "prompt": "帮助用户点饭",
                            "contextPolicy": "fresh",
                            "entryMode": "wait_user",
                        },
                    },
                    {"id": "drink", "type": "agent", "data": {}},
                    {"id": "run", "type": "agent", "data": {}},
                ],
                "edges": [
                    {
                        "id": "eat",
                        "source": "start",
                        "target": "eat",
                        "data": {
                            "mode": "llm",
                            "priority": 10,
                            "condition": "用户想吃饭",
                        },
                    },
                    {
                        "id": "drink",
                        "source": "start",
                        "target": "drink",
                        "data": {
                            "mode": "llm",
                            "priority": 20,
                            "condition": "用户想喝水",
                        },
                    },
                    {
                        "id": "run",
                        "source": "start",
                        "target": "run",
                        "data": {
                            "mode": "llm",
                            "priority": 30,
                            "condition": "用户想跑步",
                        },
                    },
                ],
            }
        )
        queued = []

        async def queue_frame(frame):
            queued.append(frame)

        brain._runtime = BrainRuntime(
            context=LLMContext(messages=[]),
            llm=FakeLLM(),
            queue_frame=queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=FakeCallEnd(),
        )

        class FakeManager:
            def __init__(self):
                self.current_node = None
                self.config = None

            async def initialize(self, config):
                self.current_node = config["name"]
                self.config = config

            async def set_node_from_config(self, config):
                self.current_node = config["name"]
                self.config = config

        class FakeRouter:
            def __init__(self):
                self.calls = 0

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

        manager = FakeManager()
        router = FakeRouter()
        brain._manager = manager
        brain._router = router

        await brain.on_connected()
        self.assertEqual(manager.current_node, "start")
        self.assertEqual(router.calls, 0)

        handled = await brain.on_user_turn_end("我想吃饭")

        self.assertTrue(handled)
        self.assertEqual(router.calls, 1)
        self.assertEqual(manager.current_node, "eat")
        self.assertIn(
            {"role": "user", "content": "我想吃饭"},
            manager.config["task_messages"],
        )
        self.assertTrue(any(isinstance(frame, LLMRunFrame) for frame in queued))
        self.assertIn("我想吃饭", brain._store.values["system__conversation_history"])

    async def test_start_expression_condition_also_waits_for_user_turn(self):
        brain = WorkflowBrain(
            {
                "specVersion": 3,
                "settings": {},
                "nodes": [
                    {"id": "start", "type": "start", "data": {}},
                    {"id": "agent", "type": "agent", "data": {}},
                ],
                "edges": [
                    {
                        "id": "route",
                        "source": "start",
                        "target": "agent",
                        "data": {
                            "mode": "expression",
                            "priority": 10,
                            "expression": {
                                "combinator": "and",
                                "rules": [
                                    {
                                        "variable": "route",
                                        "operator": "eq",
                                        "value": "agent",
                                    }
                                ],
                            },
                        },
                    }
                ],
            }
        )
        brain._store.values["route"] = "agent"

        config = await brain._initial_node_config()

        self.assertEqual(config["name"], "start")
        self.assertEqual(brain._state.status.value, "waiting_user")

    async def test_automatic_node_can_follow_llm_condition(self):
        brain = WorkflowBrain(
            {
                "specVersion": 3,
                "settings": {"globalPrompt": "全局规则"},
                "nodes": [
                    {"id": "start", "type": "start", "data": {}},
                    {
                        "id": "handoff",
                        "type": "handoff",
                        "data": {"name": "人工转接", "targetType": "human"},
                    },
                    {
                        "id": "agent",
                        "type": "agent",
                        "data": {"name": "继续服务", "prompt": "继续处理"},
                    },
                ],
                "edges": [
                    {
                        "id": "to-agent",
                        "source": "handoff",
                        "target": "agent",
                        "data": {
                            "mode": "llm",
                            "priority": 10,
                            "condition": "转接后仍需 AI 继续服务",
                        },
                    }
                ],
            }
        )
        queued = []

        async def queue_frame(frame):
            queued.append(frame)

        brain._runtime = BrainRuntime(
            context=LLMContext(messages=[]),
            llm=FakeLLM(),
            queue_frame=queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=FakeCallEnd(),
        )

        class FakeRouter:
            async def select_edge(self, **kwargs):
                self.node_name = kwargs["node_name"]
                return LLMRouteResult(
                    status=RouteStatus.MATCHED,
                    function_name="goto_to_agent",
                )

        router = FakeRouter()
        brain._router = router

        config = await brain._resolve_path("handoff")

        self.assertEqual(config["name"], "agent")
        self.assertEqual(router.node_name, "人工转接")

    async def test_mixed_edge_conditions_follow_priority(self):
        brain = WorkflowBrain(
            {
                "specVersion": 3,
                "settings": {},
                "nodes": [
                    {"id": "start", "type": "start", "data": {}},
                    {"id": "agent", "type": "agent", "data": {}},
                    {"id": "llm-target", "type": "end", "data": {}},
                    {"id": "expression-target", "type": "end", "data": {}},
                    {"id": "default-target", "type": "end", "data": {}},
                ],
                "edges": [
                    {
                        "id": "llm",
                        "source": "agent",
                        "target": "llm-target",
                        "data": {
                            "mode": "llm",
                            "priority": 10,
                            "condition": "大模型条件成立",
                        },
                    },
                    {
                        "id": "expression",
                        "source": "agent",
                        "target": "expression-target",
                        "data": {
                            "mode": "expression",
                            "priority": 20,
                            "expression": {
                                "combinator": "and",
                                "rules": [
                                    {
                                        "variable": "route",
                                        "operator": "eq",
                                        "value": "expression",
                                    }
                                ],
                            },
                        },
                    },
                    {
                        "id": "default",
                        "source": "agent",
                        "target": "default-target",
                        "data": {"mode": "always", "priority": 30},
                    },
                ],
            }
        )
        brain._store.values["route"] = "expression"

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

            async def select_edge(self, **kwargs):
                self.edge_ids = [edge["id"] for edge in kwargs["edges"]]
                return self.result

        router = FakeRouter()
        brain._router = router

        selected = await brain._select_edge("agent")
        self.assertEqual(router.edge_ids, ["llm"])
        self.assertEqual(selected["id"], "expression")

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

        expression_edge = next(
            edge for edge in brain._engine.edges if edge["id"] == "expression"
        )
        expression_edge["data"]["priority"] = 5
        router.edge_ids = []
        selected = await brain._select_edge("agent")
        self.assertEqual(selected["id"], "expression")
        self.assertEqual(router.edge_ids, [])

    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",
                "enableInterrupt": False,
                "turnConfig": {
                    "bargeIn": {"strategy": "transcription"},
                    "vad": {"confidence": 0.55},
                },
            },
            "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 = []
        turn_configs = []
        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))

        async def apply_turn_config(enable_interrupt, turn_config):
            turn_configs.append((enable_interrupt, turn_config))

        runtime = BrainRuntime(
            context=context,
            llm=llm,
            queue_frame=queue_frame,
            set_system_prompt=lambda _prompt: None,
            set_tools=lambda _tools: None,
            call_end=call_end,
            worker=worker,
            context_aggregator=pair,
            switch_services=switch_services,
            set_knowledge_scope=knowledge_scopes.append,
            apply_turn_config=apply_turn_config,
        )
        await brain.setup(cfg, runtime)
        greeting = await brain.greeting(cfg)
        self.assertEqual(greeting, "")
        self.assertEqual(context.get_messages(), [])
        await brain.on_connected()
        self.assertEqual(brain._manager.current_node, "agent")
        await brain.on_client_ready()
        variable_events = [
            frame.message
            for frame in queued
            if isinstance(frame, OutputTransportMessageUrgentFrame)
            and frame.message.get("type") == "workflow-variables"
        ]
        self.assertEqual(variable_events[0]["reason"], "initialized")
        self.assertEqual(variable_events[0]["variables"], {"user_name": "王先生"})
        self.assertNotIn("system__conversation_id", variable_events[0]["variables"])
        self.assertEqual(
            service_switches,
            [("llm_global", "asr_global", "tts_global")],
        )
        self.assertEqual(knowledge_scopes[-1]["knowledge_base_id"], "kb_global")
        self.assertEqual(turn_configs[-1][0], False)
        self.assertEqual(turn_configs[-1][1]["vad"]["confidence"], 0.55)

        brain._engine.data("agent").update(
            {
                "inheritGlobalConfig": False,
                "llmResourceId": "llm_agent",
                "asrResourceId": "asr_agent",
                "ttsResourceId": "tts_agent",
                "knowledgeBaseId": "kb_agent",
                "knowledgeMode": "on_demand",
                "enableInterrupt": True,
                "turnConfig": {
                    "bargeIn": {"strategy": "vad"},
                    "turnDetection": {"strategy": "smart_turn"},
                },
            }
        )
        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")
        self.assertEqual(turn_configs[-1][0], True)
        self.assertEqual(
            turn_configs[-1][1]["turnDetection"]["strategy"],
            "smart_turn",
        )
        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.assertFalse(generate_config["respond_immediately"])
        worker.frames.clear()
        await brain._manager.set_node_from_config(generate_config)
        self.assertFalse(any(isinstance(frame, LLMRunFrame) for frame in worker.frames))
        await brain._after_node_activated(generate_config)
        self.assertTrue(any(isinstance(frame, LLMRunFrame) for frame in queued))

        brain._engine.data("agent")["entryMode"] = "wait_user"
        self.assertEqual(
            brain._agent_config(
                "agent",
                [{"role": "assistant", "content": "正在进入下一阶段"}],
            )["task_messages"],
            [{"role": "assistant", "content": "正在进入下一阶段"}],
        )

        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 LLMRouteResult(
                    status=RouteStatus.MATCHED,
                    function_name="goto_finish",
                )

        brain._router = FakeRouter()
        handled = await brain.on_user_turn_end("我的需求已经说完了")
        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))
        transition_context_frames = [
            frame
            for frame in worker.frames
            if isinstance(frame, LLMMessagesAppendFrame)
            and frame.messages
            == [{"role": "assistant", "content": "正在为你结束流程"}]
        ]
        self.assertTrue(transition_context_frames)
        transition_events = [
            frame.message
            for frame in queued
            if isinstance(frame, OutputTransportMessageUrgentFrame)
            and frame.message.get("source") == "workflow-edge-transition"
        ]
        self.assertEqual(transition_events[0]["content"], "正在为你结束流程")
        self.assertEqual(transition_events[0]["nodeId"], "end")
        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,
            ["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__":
    unittest.main()