feat: 增强日历功能并集成 AgentScope 代理服务
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qzl
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from __future__ import annotations
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from types import SimpleNamespace
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from typing import Any, cast
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from uuid import uuid4
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import pytest
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from sqlalchemy.ext.asyncio import AsyncSession
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from core.agent.domain.system_agent_config import SystemAgentLLMConfig
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from core.agent.domain.user_context import UserAgentContext, parse_profile_settings
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from core.agentscope.runtime.config_loader import RuntimeStageConfig
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from core.agentscope.runtime.orchestrator import AgentScopeRuntimeOrchestrator
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def _ctx() -> UserAgentContext:
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return UserAgentContext(
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user_id=uuid4(),
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username="alice",
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bio=None,
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settings=parse_profile_settings(
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{
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"version": 1,
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"preferences": {
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"interface_language": "zh-CN",
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"ai_language": "zh-CN",
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"timezone": "Asia/Shanghai",
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"country": "CN",
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},
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}
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),
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)
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def _stage_config() -> dict[str, RuntimeStageConfig]:
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llm = SystemAgentLLMConfig(temperature=0.1, max_tokens=256, timeout_seconds=30)
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return {
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"intent": RuntimeStageConfig("intent", "qwen3.5-flash", "dashscope", llm),
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"execution": RuntimeStageConfig("execution", "deepseek-chat", "deepseek", llm),
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"report": RuntimeStageConfig("report", "deepseek-chat", "deepseek", llm),
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}
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class _FakeRunner:
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def __init__(self) -> None:
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self.intent_calls = 0
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self.execution_calls = 0
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self.report_calls = 0
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async def run_json_stage(
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self,
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*,
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stage_config: RuntimeStageConfig,
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agent_name: str,
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system_prompt: str,
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user_prompt: str,
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toolkit: Any | None,
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) -> dict[str, Any]:
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del agent_name, system_prompt, user_prompt, toolkit
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if stage_config.stage == "intent":
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self.intent_calls += 1
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return {
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"route": "DIRECT_RESPONSE",
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"intent_summary": "直接问候",
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"direct_response": "你好",
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"tasks": [],
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"complexity": "simple",
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}
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self.report_calls += 1
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return {
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"assistant_text": "已完成",
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"response_metadata": {"source": "report-agent"},
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}
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class _ComplexRunner(_FakeRunner):
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async def run_json_stage(
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self,
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*,
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stage_config: RuntimeStageConfig,
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agent_name: str,
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system_prompt: str,
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user_prompt: str,
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toolkit: Any | None,
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) -> dict[str, Any]:
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del agent_name, system_prompt, user_prompt, toolkit
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if stage_config.stage == "intent":
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self.intent_calls += 1
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return {
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"route": "TASK_EXECUTION",
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"intent_summary": "需要写入日历",
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"direct_response": None,
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"tasks": [
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{"task_id": "t1", "title": "创建事件", "objective": "写入明天会议"}
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],
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"complexity": "complex",
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}
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if stage_config.stage == "execution":
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self.execution_calls += 1
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return {
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"task_id": "t1",
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"status": "SUCCESS",
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"execution_summary": "done",
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"execution_data": {},
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"user_feedback_needs": [],
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}
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self.report_calls += 1
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return {
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"assistant_text": "任务执行完成",
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"response_metadata": {"source": "report-agent"},
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}
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@pytest.mark.asyncio
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async def test_runtime_direct_response_skips_execution(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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fake_runner = _FakeRunner()
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async def _fake_config_loader(
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_session: AsyncSession,
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) -> dict[str, RuntimeStageConfig]:
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return _stage_config()
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class _FakeToolkit:
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def get_json_schemas(self) -> list[dict[str, Any]]:
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return [
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{
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"type": "function",
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"function": {
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"name": "calendar.read",
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"description": "read",
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"parameters": {"type": "object", "properties": {}},
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},
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}
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]
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async def call_tool_function(self, tool_call: dict[str, Any]):
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del tool_call
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if False:
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yield None
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monkeypatch.setattr(
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"core.agentscope.runtime.orchestrator.build_stage_toolkit",
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lambda **_: _FakeToolkit(),
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)
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orchestrator = AgentScopeRuntimeOrchestrator(
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runner=fake_runner,
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config_loader=_fake_config_loader,
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)
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result = await orchestrator.run(
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session=cast(AsyncSession, SimpleNamespace()),
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owner_id=uuid4(),
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user_token="token",
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user_context=_ctx(),
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user_input="你好",
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)
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assert result.intent.route == "DIRECT_RESPONSE"
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assert result.execution is None
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assert result.report.assistant_text == "已完成"
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assert fake_runner.execution_calls == 0
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@pytest.mark.asyncio
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async def test_runtime_complex_route_runs_execution(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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fake_runner = _ComplexRunner()
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async def _fake_config_loader(
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_session: AsyncSession,
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) -> dict[str, RuntimeStageConfig]:
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return _stage_config()
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class _FakeToolkit:
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def get_json_schemas(self) -> list[dict[str, Any]]:
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return [
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{
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"type": "function",
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"function": {
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"name": "calendar.read",
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"description": "read",
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"parameters": {"type": "object", "properties": {}},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "calendar.write",
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"description": "write",
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"parameters": {"type": "object", "properties": {}},
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},
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},
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]
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async def call_tool_function(self, tool_call: dict[str, Any]):
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del tool_call
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if False:
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yield None
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monkeypatch.setattr(
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"core.agentscope.runtime.orchestrator.build_stage_toolkit",
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lambda **_: _FakeToolkit(),
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)
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orchestrator = AgentScopeRuntimeOrchestrator(
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runner=fake_runner,
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config_loader=_fake_config_loader,
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)
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result = await orchestrator.run(
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session=cast(AsyncSession, SimpleNamespace()),
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owner_id=uuid4(),
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user_token="token",
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user_context=_ctx(),
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user_input="帮我安排明天会议",
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)
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assert result.intent.route == "TASK_EXECUTION"
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assert result.execution is not None
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assert result.execution.overall_status == "SUCCESS"
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assert fake_runner.execution_calls == 1
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@@ -0,0 +1,115 @@
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from __future__ import annotations
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import json
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from types import SimpleNamespace
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import pytest
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from core.agent.domain.system_agent_config import SystemAgentLLMConfig
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from core.agentscope.runtime.config_loader import RuntimeStageConfig
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from core.agentscope.runtime.react_runner import (
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AgentScopeReActRunner,
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_parse_json_text,
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_to_litellm_model,
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)
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def _stage_config() -> RuntimeStageConfig:
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return RuntimeStageConfig(
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stage="intent",
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model_code="qwen3.5-flash",
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provider_name="dashscope",
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llm_config=SystemAgentLLMConfig(
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temperature=0.1, max_tokens=128, timeout_seconds=30
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),
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)
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def test_to_litellm_model_keeps_prefixed_model() -> None:
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assert (
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_to_litellm_model(provider_name="dashscope", model_code="openai/gpt-4o")
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== "openai/gpt-4o"
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)
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def test_to_litellm_model_builds_prefixed_model() -> None:
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assert (
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_to_litellm_model(provider_name="dashscope", model_code="qwen3.5-flash")
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== "dashscope/qwen3.5-flash"
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)
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def test_parse_json_text_supports_fenced_json() -> None:
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parsed = _parse_json_text('```json\n{"route":"DIRECT_RESPONSE"}\n```')
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assert parsed["route"] == "DIRECT_RESPONSE"
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def test_parse_json_text_rejects_non_json() -> None:
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with pytest.raises(json.JSONDecodeError):
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_parse_json_text("not-json")
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@pytest.mark.asyncio
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async def test_run_json_stage_wraps_json_decode_error(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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pytest.importorskip("agentscope")
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import agentscope.agent as agent_module
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import agentscope.formatter as formatter_module
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import agentscope.memory as memory_module
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class _FakeAgent:
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def __init__(self, **kwargs: object) -> None:
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del kwargs
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async def __call__(self, _msg: object) -> object:
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return SimpleNamespace(get_text_content=lambda: "not-json")
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monkeypatch.setattr(agent_module, "ReActAgent", _FakeAgent)
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monkeypatch.setattr(formatter_module, "OpenAIChatFormatter", lambda: object())
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monkeypatch.setattr(memory_module, "InMemoryMemory", lambda: object())
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runner = AgentScopeReActRunner()
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monkeypatch.setattr(runner, "_build_model", lambda **_: object())
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with pytest.raises(RuntimeError, match="agent output format invalid"):
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await runner.run_json_stage(
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stage_config=_stage_config(),
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agent_name="intent-agent",
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system_prompt="sys",
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user_prompt="user",
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toolkit=None,
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)
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@pytest.mark.asyncio
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async def test_run_json_stage_wraps_runtime_error(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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pytest.importorskip("agentscope")
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import agentscope.agent as agent_module
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import agentscope.formatter as formatter_module
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import agentscope.memory as memory_module
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class _FakeAgent:
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def __init__(self, **kwargs: object) -> None:
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del kwargs
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async def __call__(self, _msg: object) -> object:
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raise ValueError("boom")
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monkeypatch.setattr(agent_module, "ReActAgent", _FakeAgent)
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monkeypatch.setattr(formatter_module, "OpenAIChatFormatter", lambda: object())
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monkeypatch.setattr(memory_module, "InMemoryMemory", lambda: object())
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runner = AgentScopeReActRunner()
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monkeypatch.setattr(runner, "_build_model", lambda **_: object())
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with pytest.raises(RuntimeError, match="agent execution failed"):
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await runner.run_json_stage(
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stage_config=_stage_config(),
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agent_name="intent-agent",
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system_prompt="sys",
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user_prompt="user",
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toolkit=None,
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)
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