438 lines
16 KiB
Python
438 lines
16 KiB
Python
"""
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pipeline-core: Agent 能力定义层
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对照 Hermes Agent 的 config.yaml + system prompt builder + tool registry：
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在此定义每个产线的 Agent 配置——模型、工具、技能、记忆、上下文压缩。
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每个产线（pipeline）可以有不同的 AgentConfig，通过 sd_org_settings 或
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pipelines 表的 agent_config 字段存储。pipeline-service 执行时读取此配置。
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"""
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import json
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import os
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from dataclasses import dataclass, field
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from typing import Dict, List, Optional
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# ═══════════════════════════════════════════════════════════
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# 数据模型
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# ═══════════════════════════════════════════════════════════
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@dataclass
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class ToolDefinition:
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"""工具定义 — 等价于 HA 的 tool schema"""
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name: str
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description: str
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parameters: dict = field(default_factory=dict) # JSON Schema for params
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enabled: bool = True
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category: str = "general" # project / task / repo / shell / agent
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requires_confirmation: bool = False # 是否需要用户确认
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@dataclass
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class CompressionConfig:
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"""上下文压缩配置"""
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enabled: bool = True
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threshold: float = 0.50 # 达到上下文窗口的 50% 时触发压缩
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target_ratio: float = 0.20 # 压缩到 20%
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keep_recent: int = 8 # 保留最近 N 轮
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@dataclass
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class MemoryConfig:
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"""记忆系统配置"""
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enabled: bool = True
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user_profile_enabled: bool = True # 用户偏好
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cross_session_enabled: bool = True # 跨会话事实
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max_entries: int = 50 # 最多保留条数
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@dataclass
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class SkillConfig:
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"""技能配置 — 三级隔离：全局 + 组织 + 用户"""
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enabled: bool = True
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base_dir: str = "skills" # 技能根目录，其下为 global/orgs/users/
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max_skills_per_turn: int = 5
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enable_global: bool = True # 启用全局技能
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enable_org: bool = True # 启用组织技能
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enable_user: bool = True # 启用用户技能
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@dataclass
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class AgentConfig:
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"""Agent 完整配置 — 一个产线一个配置"""
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# ── 模型 ──
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model_name: str = ""
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temperature: float = 0.4
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max_turns: int = 30 # 最大 tool-calling 轮次（替代硬编码 10）
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# ── 系统提示词 ──
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system_prompt: str = "" # 产线专属系统提示词
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personality: str = "" # 人设描述
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# ── 上下文 ──
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compression: CompressionConfig = field(default_factory=CompressionConfig)
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context_limit: int = 64000 # token 上限估计
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# ── 记忆 ──
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memory: MemoryConfig = field(default_factory=MemoryConfig)
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# ── 技能 ──
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skills: SkillConfig = field(default_factory=SkillConfig)
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# ── 工具 ──
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tools: List[ToolDefinition] = field(default_factory=list)
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# ── 会话 ──
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session_isolation: str = "project" # "project" | "user" | "none"
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history_limit: int = 50 # 加载历史消息数
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# ── 安全 ──
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require_approval: bool = False # 危险命令需确认
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allowed_workdirs: List[str] = field(default_factory=list)
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def to_dict(self) -> dict:
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"""序列化为 JSON"""
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return {
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"model_name": self.model_name,
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"temperature": self.temperature,
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"max_turns": self.max_turns,
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"system_prompt": self.system_prompt,
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"personality": self.personality,
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"compression": {
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"enabled": self.compression.enabled,
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"threshold": self.compression.threshold,
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"target_ratio": self.compression.target_ratio,
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"keep_recent": self.compression.keep_recent,
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},
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"memory": {
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"enabled": self.memory.enabled,
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"user_profile_enabled": self.memory.user_profile_enabled,
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"cross_session_enabled": self.memory.cross_session_enabled,
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"max_entries": self.memory.max_entries,
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},
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"skills": {
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"enabled": self.skills.enabled,
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"max_skills_per_turn": self.skills.max_skills_per_turn,
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"base_dir": self.skills.base_dir,
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"enable_global": self.skills.enable_global,
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"enable_org": self.skills.enable_org,
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"enable_user": self.skills.enable_user,
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},
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"tools": [
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{
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"name": t.name,
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"description": t.description,
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"parameters": t.parameters,
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"enabled": t.enabled,
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"category": t.category,
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}
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for t in self.tools
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],
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"session_isolation": self.session_isolation,
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"history_limit": self.history_limit,
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"context_limit": self.context_limit,
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}
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@classmethod
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def from_dict(cls, data: dict) -> "AgentConfig":
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"""从 JSON 反序列化"""
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comp = data.get("compression", {})
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mem = data.get("memory", {})
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sk = data.get("skills", {})
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return cls(
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model_name=data.get("model_name", ""),
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temperature=data.get("temperature", 0.4),
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max_turns=data.get("max_turns", 30),
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system_prompt=data.get("system_prompt", ""),
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personality=data.get("personality", ""),
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compression=CompressionConfig(
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enabled=comp.get("enabled", True),
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threshold=comp.get("threshold", 0.50),
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target_ratio=comp.get("target_ratio", 0.20),
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keep_recent=comp.get("keep_recent", 8),
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),
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memory=MemoryConfig(
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enabled=mem.get("enabled", True),
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user_profile_enabled=mem.get("user_profile_enabled", True),
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cross_session_enabled=mem.get("cross_session_enabled", True),
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max_entries=mem.get("max_entries", 50),
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),
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skills=SkillConfig(
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enabled=sk.get("enabled", True),
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base_dir=sk.get("base_dir", "skills"),
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max_skills_per_turn=sk.get("max_skills_per_turn", 5),
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enable_global=sk.get("enable_global", True),
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enable_org=sk.get("enable_org", True),
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enable_user=sk.get("enable_user", True),
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),
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tools=[ToolDefinition(**t) if isinstance(t, dict) else t for t in data.get("tools", [])],
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session_isolation=data.get("session_isolation", "project"),
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history_limit=data.get("history_limit", 50),
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context_limit=data.get("context_limit", 64000),
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)
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# ═══════════════════════════════════════════════════════════
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# SDLC 默认配置
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# ═══════════════════════════════════════════════════════════
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SDLC_DEFAULT_TOOLS = [
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ToolDefinition(
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name="switch_project",
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description="切换到指定项目。用户说「切换到XXX」时调用。",
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parameters={"project_name": "项目名称"},
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category="project",
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),
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ToolDefinition(
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name="create_project",
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description="创建新的软件项目",
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parameters={"name": "项目名称", "description": "项目描述"},
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category="project",
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),
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ToolDefinition(
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name="create_task",
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description="创建开发任务，分配给角色agent执行",
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parameters={
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"title": "任务标题",
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"role": "目标角色(requirement/design/develop/test/deploy)",
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"description": "任务详细描述",
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},
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category="task",
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),
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ToolDefinition(
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name="list_tasks",
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description="查看当前项目的任务列表",
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parameters={"role": "按角色筛选(可选)", "state": "按状态筛选(可选)"},
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category="task",
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),
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ToolDefinition(
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name="task_detail",
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description="查看任务详情",
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parameters={"task_id": "任务ID"},
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category="task",
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),
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ToolDefinition(
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name="start_agents",
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description="启动角色agent执行已提交的任务",
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parameters={},
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category="agent",
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),
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ToolDefinition(
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name="diagnose_project",
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description="诊断项目：汇总卡点/失败/问题/运行中任务",
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parameters={},
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category="agent",
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),
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ToolDefinition(
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name="list_deliverables",
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description="查看交付件列表",
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parameters={"role": "按角色筛选(可选)"},
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category="task",
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),
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ToolDefinition(
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name="view_deliverable",
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description="查看交付件详细内容",
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parameters={"deliverable_id": "交付件ID"},
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category="task",
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),
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ToolDefinition(
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name="list_questions",
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description="查看待回答的问题",
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parameters={},
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category="agent",
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),
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ToolDefinition(
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name="answer_question",
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description="回答agent提出的问题",
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parameters={"question_id": "问题ID", "answer": "回答内容"},
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category="agent",
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),
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ToolDefinition(
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name="add_repo",
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description="添加项目关联的Git仓库",
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parameters={"repo_url": "仓库URL", "repo_name": "仓库名称", "default_branch": "默认分支(默认main)"},
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category="repo",
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),
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ToolDefinition(
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name="list_repos",
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description="查看项目关联的仓库列表",
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parameters={},
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category="repo",
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),
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ToolDefinition(
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name="run_command",
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description="在工作空间中执行shell命令",
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parameters={"command": "命令"},
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category="shell",
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requires_confirmation=True,
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),
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ToolDefinition(
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name="check_progress",
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description="查看项目整体进展",
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parameters={},
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category="agent",
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),
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ToolDefinition(
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name="add_bug",
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description="提交Bug",
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parameters={"title": "Bug标题", "description": "描述", "severity": "严重程度"},
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category="task",
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),
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# ── v2 新增 ──
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ToolDefinition(
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name="ask_user",
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description="向用户提问澄清意图（不确定时使用）",
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parameters={"question": "问题"},
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category="agent",
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),
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ToolDefinition(
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name="delegate_subtask",
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description="派生子agent调查子任务（并行执行）",
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parameters={"goal": "子任务目标", "context": "背景信息"},
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category="agent",
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),
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]
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SDLC_DEFAULT_CONFIG = AgentConfig(
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model_name="deepseek-v4-pro",
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temperature=0.4,
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max_turns=30,
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system_prompt="""你是 SDLC 开发产线的 AI 驾驶舱助理。
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## 核心原则
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- 说做就做：承诺调用工具时立刻调，不要只描述计划
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- 每次只输出一个 JSON
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- 工具调用后等待结果，再决定下一步
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- 不确定用户意图时，使用 ask_user 工具澄清
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## 项目上下文
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当前项目：{current_project}
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{project_context}
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## 可用工具
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{tools_description}
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## 输出格式
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每次只输出一个 JSON 对象：
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- 回复用户：{"action":"reply","message":"回复内容"}
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- 调用工具：{"action":"tool_call","tool":"工具名","params":{}}
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- 提问澄清：{"action":"tool_call","tool":"ask_user","params":{"question":"问题"}}
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""",
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tools=SDLC_DEFAULT_TOOLS,
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session_isolation="project",
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history_limit=50,
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compression=CompressionConfig(enabled=True, threshold=0.50, target_ratio=0.20, keep_recent=6),
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memory=MemoryConfig(enabled=True),
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skills=SkillConfig(enabled=True, base_dir="skills"),
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)
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# ═══════════════════════════════════════════════════════════
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# 配置加载器
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# ═══════════════════════════════════════════════════════════
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async def load_agent_config(pipeline_id: str = None, project_id: str = None) -> AgentConfig:
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"""加载产线的 Agent 配置。
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优先级：
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1. 项目级 sd_org_settings.agent_config
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2. 产线级 pipelines.agent_config
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3. SDLC_DEFAULT_CONFIG
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"""
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try:
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from sqlor.dbpools import DBPools
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db = DBPools()
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async with db.sqlorContext("pipeline") as sor:
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# 项目级配置
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if project_id:
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recs = await sor.sqlExe(
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"SELECT agent_config FROM sd_org_settings WHERE project_id=${pid}$",
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{"pid": project_id},
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)
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if recs:
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raw = getattr(recs[0], "agent_config", "")
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if raw:
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try:
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data = json.loads(raw) if isinstance(raw, str) else raw
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data["tools"] = _merge_tools(data.get("tools", []), SDLC_DEFAULT_TOOLS)
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return AgentConfig.from_dict(data)
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except (json.JSONDecodeError, TypeError):
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pass
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# 产线级配置
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if pipeline_id:
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recs = await sor.sqlExe(
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"SELECT agent_config FROM pipelines WHERE id=${pid}$",
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{"pid": pipeline_id},
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)
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if recs:
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raw = getattr(recs[0], "agent_config", "")
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if raw:
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try:
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data = json.loads(raw) if isinstance(raw, str) else raw
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data["tools"] = _merge_tools(data.get("tools", []), SDLC_DEFAULT_TOOLS)
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return AgentConfig.from_dict(data)
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except (json.JSONDecodeError, TypeError):
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pass
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except Exception:
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pass
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return SDLC_DEFAULT_CONFIG
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def _merge_tools(custom_tools: list, default_tools: list) -> list:
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"""合并自定义工具和默认工具。自定义覆盖同名工具。"""
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merged = {t["name"] if isinstance(t, dict) else t.name: t for t in default_tools}
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for t in custom_tools:
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name = t["name"] if isinstance(t, dict) else t.name
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merged[name] = t
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return list(merged.values())
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async def save_agent_config(project_id: str, config: AgentConfig):
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"""保存项目级 Agent 配置到 sd_org_settings"""
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from sqlor.dbpools import DBPools
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db = DBPools()
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async with db.sqlorContext("pipeline") as sor:
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config_json = json.dumps(config.to_dict(), ensure_ascii=False)
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# UPDATE-first 防止竞态
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await sor.sqlExe(
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"UPDATE sd_org_settings SET agent_config=${cfg}$ WHERE project_id=${pid}$",
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{"cfg": config_json, "pid": project_id},
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)
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existing = await sor.sqlExe(
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"SELECT id FROM sd_org_settings WHERE project_id=${pid}$",
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{"pid": project_id},
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)
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if not existing:
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from appPublic.uniqueID import getID
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await sor.C("sd_org_settings", {
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"id": getID(),
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"project_id": project_id,
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"agent_config": config_json,
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})
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# ═══════════════════════════════════════════════════════════
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# 模块加载
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# ═══════════════════════════════════════════════════════════
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def load_pipeline_core():
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"""注册 Agent 配置管理函数到 ServerEnv"""
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from ahserver.serverenv import ServerEnv
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env = ServerEnv()
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env.load_agent_config = load_agent_config
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env.save_agent_config = save_agent_config
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env.AgentConfig = AgentConfig
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env.SDLC_DEFAULT_CONFIG = SDLC_DEFAULT_CONFIG
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MODULE_NAME = "pipeline_core"
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