steven_li
33a9845566
feat(engine): 添加技能查看工具并优化异步任务管理 - 添加SkillViewTool到引擎加载器中，增强技能管理功能 - 在AgentLoop中引入_active_direct_task来跟踪活跃任务 - 实现直接任务执行时的同步处理逻辑 - 更新工具实例化方式以支持依赖注入 feat(config): 增加智能体运行时参数配置支持 - 扩展AgentDefaultsConfig添加max_tokens和temperature字段 - 实现配置解析函数_first_config_value处理多个配置源 - 支持通过Web API动态更新智能体运行时参数 - 添加前端页面配置表单和验证逻辑 refactor(provider): 统一最大令牌数参数类型为可选整型 - 将所有LLM提供者的max_tokens参数改为int | None类型 - 为AnthropicProvider实现模型特定的最大令牌数默认值 - 调整参数传递逻辑，优先级：调用参数 > 配置文件 > 模型默认值 - 移除硬编码的默认值，改用条件判断 feat(process): 增强事件投影功能 - 添加工具调用开始/结束事件的映射逻辑 - 实现技能激活事件的识别和展示 - 添加辅助函数处理工具调用名称和参数提取 - 优化运行记录关联逻辑，提升事件匹配准确性 fix(web): 更新网络请求客户端信任环境设置 - 将WebFetchTool和WebSearchTool的trust_env参数设为True - 确保HTTP客户端能够正确使用系统代理配置 - 修复可能的网络连接问题 test: 添加配置加载和事件投影相关测试 - 新增智能体默认参数配置测试用例 - 实现API配置持久化和重载测试 - 添加技能卡片和工具事件的投影测试 ```
327 lines
14 KiB
Python
327 lines
14 KiB
Python
"""Centralized runtime loading for Beaver agents."""
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from __future__ import annotations
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import asyncio
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import os
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Callable
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from beaver.coordinator.registry import AgentRegistry
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from beaver.engine.context import ContextBuilder
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from beaver.engine.session import SessionManager
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from beaver.foundation.config import BeaverConfig, load_config
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from beaver.integrations.mcp import MCPConnectionManager
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from beaver.memory.curated.store import MemoryStore
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from beaver.memory.runs import RunMemoryStore
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from beaver.memory.skills import SkillLearningStore
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from beaver.services.memory_service import MemoryService
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from beaver.skills.drafts import DraftService
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from beaver.skills.learning import EvidenceSelector, SkillDraftSynthesizer, SkillLearningPipelineService, SkillLearningService
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from beaver.skills.learning.safety import SkillDraftSafetyChecker
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from beaver.skills.learning.eval import SkillDraftEvaluator
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from beaver.skills.publisher import SkillPublisher
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from beaver.skills.reviews import ReviewService
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from beaver.skills.specs import SkillSpecStore
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from beaver.tasks import TaskExecutionPlanner, TaskService
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from beaver.tasks.skill_resolver import TaskSkillResolver
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from beaver.skills import SkillAssembler, SkillsLoader
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from beaver.tools import ObjectBackedTool, ToolAssembler, ToolExecutor, ToolRegistry
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from beaver.tools.builtins import (
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ClarifyTool,
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CronTool,
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DelegateTool,
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EchoTool,
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ExecuteCodeTool,
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ListDirectoryTool,
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MemoryTool,
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PatchFileTool,
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ProcessTool,
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ReadFileTool,
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SearchFilesTool,
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SendMessageTool,
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SpawnTool,
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SessionSearchTool,
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SkillManageTool,
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SkillViewTool,
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SkillsListTool,
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TerminalTool,
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TodoTool,
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WebFetchTool,
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WebSearchTool,
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WriteFileTool,
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)
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@dataclass(slots=True)
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class EngineLoadResult:
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"""描述当前 agent runtime 已经装好的依赖。
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这里同时保留两类字段：
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1. `tools/skills/memory_stores/permissions`
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- 便于做状态展示、调试、轻量测试
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2. `session_manager/tool_registry/...`
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- 供真正的运行时主链直接使用
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"""
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workspace: Path
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config: BeaverConfig = field(default_factory=BeaverConfig)
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tools: list[str] = field(default_factory=list)
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skills: list[str] = field(default_factory=list)
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memory_stores: list[str] = field(default_factory=list)
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permissions: list[str] = field(default_factory=list)
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session_manager: SessionManager | None = None
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curated_memory_store: MemoryStore | None = None
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memory_service: MemoryService | None = None
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run_memory_store: RunMemoryStore | None = None
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skill_learning_store: SkillLearningStore | None = None
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tool_registry: ToolRegistry | None = None
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tool_assembler: ToolAssembler | None = None
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tool_executor: ToolExecutor | None = None
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context_builder: ContextBuilder | None = None
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skills_loader: SkillsLoader | None = None
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skill_assembler: SkillAssembler | None = None
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skill_spec_store: SkillSpecStore | None = None
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draft_service: DraftService | None = None
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review_service: ReviewService | None = None
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skill_publisher: SkillPublisher | None = None
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skill_learning_service: SkillLearningService | None = None
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skill_learning_pipeline: SkillLearningPipelineService | None = None
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agent_registry: AgentRegistry | None = None
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task_skill_resolver: TaskSkillResolver | None = None
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task_service: TaskService | None = None
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task_execution_planner: TaskExecutionPlanner | None = None
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mcp_manager: MCPConnectionManager | None = None
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mcp_report: dict[str, dict] = field(default_factory=dict)
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closeables: list[tuple[str, Callable[[], None]]] = field(default_factory=list, repr=False)
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closed: bool = False
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def register_closeable(self, name: str, close_fn: Callable[[], None]) -> None:
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"""登记一个由 runtime 统一关闭的资源。"""
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self.closeables.append((name, close_fn))
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def close(self) -> None:
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"""按后进先出顺序关闭 runtime 资源。
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这一步先保持同步、最小、可组合：
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1. 只管理已经明确需要关闭的资源
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2. 暂不引入 async shutdown 协议
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3. 为后续 Web/Gateway lifespan 留统一入口
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"""
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if self.closed:
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return
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errors: list[tuple[str, BaseException]] = []
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for name, close_fn in reversed(self.closeables):
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try:
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close_fn()
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except BaseException as exc: # pragma: no cover - defensive cleanup path
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errors.append((name, exc))
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self.closed = True
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if errors:
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parts = ", ".join(f"{name}: {exc}" for name, exc in errors)
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raise RuntimeError(f"Runtime shutdown failed for {parts}")
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class EngineLoader:
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"""为任意 Beaver agent 装载共享 runtime 能力。
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当前先做“最小可运行主链”需要的装配：
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- session manager
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- curated memory store
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- context builder
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- built-in tools
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- tool executor
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等主链跑稳后，再把 skills、权限、MCP、delegation 逐步加进来。
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"""
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def __init__(
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self,
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*,
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workspace: str | Path | None = None,
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config_path: str | Path | None = None,
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config: BeaverConfig | None = None,
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session_manager: SessionManager | None = None,
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curated_memory_store: MemoryStore | None = None,
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memory_service: MemoryService | None = None,
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run_memory_store: RunMemoryStore | None = None,
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skill_learning_store: SkillLearningStore | None = None,
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tool_registry: ToolRegistry | None = None,
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tool_assembler: ToolAssembler | None = None,
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context_builder: ContextBuilder | None = None,
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skills_loader: SkillsLoader | None = None,
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skill_assembler: SkillAssembler | None = None,
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skill_spec_store: SkillSpecStore | None = None,
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draft_service: DraftService | None = None,
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review_service: ReviewService | None = None,
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skill_publisher: SkillPublisher | None = None,
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skill_learning_service: SkillLearningService | None = None,
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skill_learning_pipeline: SkillLearningPipelineService | None = None,
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agent_registry: AgentRegistry | None = None,
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task_skill_resolver: TaskSkillResolver | None = None,
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task_service: TaskService | None = None,
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task_execution_planner: TaskExecutionPlanner | None = None,
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) -> None:
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self.config = config or load_config(workspace=workspace, config_path=config_path)
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configured_workspace = self.config.agents_defaults.workspace
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env_workspace = os.getenv("BEAVER_WORKSPACE")
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self.workspace = Path(workspace or configured_workspace or env_workspace or Path.cwd())
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self._session_manager = session_manager
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self._curated_memory_store = curated_memory_store
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self._memory_service = memory_service
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self._run_memory_store = run_memory_store
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self._skill_learning_store = skill_learning_store
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self._tool_registry = tool_registry
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self._tool_assembler = tool_assembler
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self._context_builder = context_builder
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self._skills_loader = skills_loader
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self._skill_assembler = skill_assembler
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self._skill_spec_store = skill_spec_store
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self._draft_service = draft_service
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self._review_service = review_service
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self._skill_publisher = skill_publisher
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self._skill_learning_service = skill_learning_service
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self._skill_learning_pipeline = skill_learning_pipeline
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self._agent_registry = agent_registry
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self._task_skill_resolver = task_skill_resolver
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self._task_service = task_service
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self._task_execution_planner = task_execution_planner
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def load(self) -> EngineLoadResult:
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"""装配当前主链需要的最小 runtime 对象。"""
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workspace = self.workspace
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session_manager = self._session_manager or SessionManager(workspace)
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curated_root = workspace / "memory" / "curated"
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curated_memory_store = self._curated_memory_store or MemoryStore(curated_root)
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memory_service = self._memory_service or MemoryService(curated_root, store=curated_memory_store)
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memory_service.initialize()
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run_memory_store = self._run_memory_store or RunMemoryStore(workspace / "memory" / "runs")
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skill_learning_store = self._skill_learning_store or SkillLearningStore(workspace / "memory" / "skills")
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tool_registry = self._tool_registry or ToolRegistry()
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skill_spec_store = self._skill_spec_store or SkillSpecStore(workspace)
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skills_loader = self._skills_loader or SkillsLoader(workspace, skill_store=skill_spec_store)
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if self._tool_registry is None:
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# 这里先注册最小工具集，满足主链的 tool loop。
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tool_registry.register_many(
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[
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ObjectBackedTool(EchoTool()),
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ObjectBackedTool(MemoryTool(store=memory_service.get_store())),
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ObjectBackedTool(SessionSearchTool(db=session_manager)),
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ObjectBackedTool(ListDirectoryTool()),
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ObjectBackedTool(ReadFileTool()),
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ObjectBackedTool(SearchFilesTool()),
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ObjectBackedTool(WriteFileTool()),
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ObjectBackedTool(PatchFileTool()),
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ObjectBackedTool(WebFetchTool()),
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ObjectBackedTool(WebSearchTool()),
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ObjectBackedTool(TerminalTool()),
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ObjectBackedTool(ProcessTool()),
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ObjectBackedTool(ExecuteCodeTool()),
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ObjectBackedTool(TodoTool()),
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ObjectBackedTool(ClarifyTool()),
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ObjectBackedTool(SendMessageTool()),
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ObjectBackedTool(DelegateTool()),
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ObjectBackedTool(SpawnTool()),
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SkillsListTool(),
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ObjectBackedTool(SkillViewTool(loader=skills_loader)),
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SkillManageTool(),
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CronTool(),
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]
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)
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context_builder = self._context_builder or ContextBuilder()
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tool_assembler = self._tool_assembler or ToolAssembler()
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tool_executor = ToolExecutor(tool_registry)
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skill_assembler = self._skill_assembler or SkillAssembler(skills_loader)
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draft_service = self._draft_service or DraftService(skill_spec_store)
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review_service = self._review_service or ReviewService(skill_spec_store)
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skill_publisher = self._skill_publisher or SkillPublisher(skill_spec_store)
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evidence_selector = EvidenceSelector(run_memory_store, session_manager=session_manager)
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skill_learning_service = self._skill_learning_service or SkillLearningService(
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run_store=run_memory_store,
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learning_store=skill_learning_store,
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draft_service=draft_service,
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evidence_selector=evidence_selector,
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synthesizer=SkillDraftSynthesizer(),
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)
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skill_learning_pipeline = self._skill_learning_pipeline or SkillLearningPipelineService(
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learning_store=skill_learning_store,
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learning_service=skill_learning_service,
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draft_service=draft_service,
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review_service=review_service,
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publisher=skill_publisher,
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safety_checker=SkillDraftSafetyChecker(
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allowed_tool_names={spec.name for spec in tool_registry.list_specs()},
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allowed_tool_prefixes={
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f"mcp_{server_id}_"
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for server_id in self.config.tools.mcp_servers
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if str(server_id).strip()
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},
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),
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evaluator=SkillDraftEvaluator(run_memory_store),
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)
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agent_registry = self._agent_registry or AgentRegistry(workspace)
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task_skill_resolver = self._task_skill_resolver or TaskSkillResolver(
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skills_loader=skills_loader,
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draft_service=draft_service,
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)
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task_service = self._task_service or TaskService(workspace / "tasks")
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task_execution_planner = self._task_execution_planner or TaskExecutionPlanner(task_skill_resolver=task_skill_resolver)
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mcp_manager = MCPConnectionManager(
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self.config.tools.mcp_servers,
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authz_config=self.config.authz,
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backend_identity=self.config.backend_identity,
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)
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result = EngineLoadResult(
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workspace=workspace,
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config=self.config,
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tools=[spec.name for spec in tool_registry.list_specs()],
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skills=[record.name for record in skills_loader.list_skills(filter_unavailable=False)],
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memory_stores=["curated"],
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permissions=[],
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session_manager=session_manager,
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curated_memory_store=memory_service.get_store(),
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memory_service=memory_service,
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run_memory_store=run_memory_store,
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skill_learning_store=skill_learning_store,
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tool_registry=tool_registry,
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tool_assembler=tool_assembler,
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tool_executor=tool_executor,
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context_builder=context_builder,
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skills_loader=skills_loader,
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skill_assembler=skill_assembler,
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skill_spec_store=skill_spec_store,
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draft_service=draft_service,
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review_service=review_service,
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skill_publisher=skill_publisher,
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skill_learning_service=skill_learning_service,
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skill_learning_pipeline=skill_learning_pipeline,
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agent_registry=agent_registry,
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task_skill_resolver=task_skill_resolver,
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task_service=task_service,
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task_execution_planner=task_execution_planner,
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mcp_manager=mcp_manager,
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)
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if self._session_manager is None:
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result.register_closeable("session_manager", session_manager.close)
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result.register_closeable("mcp_manager", lambda: _close_mcp_manager(mcp_manager))
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return result
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def _close_mcp_manager(manager: MCPConnectionManager) -> None:
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try:
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loop = asyncio.get_running_loop()
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except RuntimeError:
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asyncio.run(manager.close())
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return
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loop.create_task(manager.close())
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