steven_li
cdfc222c9f
- 引入AgentTeamOrchestrator支持多agent协同任务执行 - 增加第三方swarms库依赖并配置git协议替换以改善包管理 - 扩展DelegationManager支持团队任务调度和进度跟踪 - 实现中文bigram分词算法提升中文任务检索准确性 - 调整A2AClient和DelegationManager超时时间从30秒增至600秒 - 优化AgentRunResult状态判断逻辑增加有意义摘要检测 - 修改Dockerfile配置npm仓库镜像地址和git协议映射 - 更新CLI命令行接口支持网关端口配置传递 - 调整提供者超时配置机制增强请求稳定性 - 移除过时的support_group字段简化agent描述符结构 - 增强错误处理和进度事件报告机制改进用户体验
420 lines
17 KiB
Python
420 lines
17 KiB
Python
"""统一 agent 注册表。
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这个模块把当前工作区里“可被委派”的执行体统一抽象成 `AgentDescriptor`：
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1. workspace 手工登记的远端 A2A agent；
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2. plugin 提供的本地 prompt agent；
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3. skill 元数据里声明的 agent cards；
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4. 内置 local fallback agent。
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上层委派逻辑只和 `AgentDescriptor` 打交道，不需要关心来源细节。
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"""
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from __future__ import annotations
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import json
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import re
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from dataclasses import asdict, dataclass, field
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from pathlib import Path
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from typing import Any
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from nanobot.agent.plugins import PluginLoader
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from nanobot.agent.skills import SkillsLoader
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_TOKEN_RE = re.compile(r"[a-z0-9_-]+")
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_CJK_RE = re.compile(r"[\u4e00-\u9fff]+")
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@dataclass
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class AgentDescriptor:
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"""委派层使用的统一 agent 描述对象。"""
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# 稳定 ID，供路由、持久化和精确匹配使用。
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id: str
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# 面向 UI/日志的展示名。
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name: str
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# 简短说明，主要供模型和前端展示。
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description: str
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# 来源类型：builtin / plugin / skill / workspace。
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source: str
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# 运行方式：local_prompt / local_fallback / a2a_remote 等。
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kind: str
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# 底层协议，目前主要是 a2a 或 None。
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protocol: str | None = None
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plugin_name: str | None = None
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skill_name: str | None = None
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model: str | None = None
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system_prompt: str | None = None
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endpoint: str | None = None
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base_url: str | None = None
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card_url: str | None = None
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auth_env: str | None = None
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auth_mode: str = "none"
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auth_audience: str | None = None
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auth_scopes: list[str] = field(default_factory=list)
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enabled: bool = True
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tags: list[str] = field(default_factory=list)
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aliases: list[str] = field(default_factory=list)
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capabilities: dict[str, Any] = field(default_factory=dict)
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metadata: dict[str, Any] = field(default_factory=dict)
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support_streaming: bool = False
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def matches(self, target: str) -> bool:
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"""判断给定目标字符串是否命中当前 agent。"""
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probe = (target or "").strip().lower()
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if not probe:
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return False
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# 同时支持按 id / name / alias 命中，方便模型用自然语言近似引用。
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candidates = {self.id.lower(), self.name.lower()}
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candidates.update(alias.lower() for alias in self.aliases if alias)
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return probe in candidates
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def searchable_text(self) -> str:
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"""构造一段用于简单相关性匹配的可搜索文本。"""
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fields = [
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self.id,
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self.name,
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self.description,
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" ".join(self.tags),
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" ".join(self.aliases),
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self.plugin_name or "",
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self.skill_name or "",
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]
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return " ".join(part for part in fields if part).lower()
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def public_dict(self) -> dict[str, Any]:
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"""导出给前端使用的安全字典。"""
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data = asdict(self)
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# system_prompt 属于内部实现细节，不应默认暴露给前端。
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data.pop("system_prompt", None)
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return data
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class WorkspaceAgentStore:
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"""workspace 级 agent 存储。
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这里保存的是用户在 Web UI 或本地配置里手工登记的 agent，
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文件位置固定为 `<workspace>/agents/registry.json`。
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"""
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def __init__(self, workspace: Path):
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self.workspace = workspace
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# 单独放到 `agents/` 目录，便于和 skills / memory / files 等目录职责分离。
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self.directory = workspace / "agents"
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self.path = self.directory / "registry.json"
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def list_agents(self) -> list[dict[str, Any]]:
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"""读取并返回所有手工登记 agent。"""
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if not self.path.exists():
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return []
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try:
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raw = json.loads(self.path.read_text(encoding="utf-8"))
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except (OSError, json.JSONDecodeError, ValueError):
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# 存储损坏时不抛异常拖垮主流程，直接视为空。
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return []
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if not isinstance(raw, list):
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return []
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result: list[dict[str, Any]] = []
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for item in raw:
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# 仅接受带 id 的对象，保证后续 registry 至少有稳定主键。
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if isinstance(item, dict) and item.get("id"):
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result.append(item)
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return result
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def save_agents(self, agents: list[dict[str, Any]]) -> None:
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"""将 agent 列表完整覆写到 registry 文件。"""
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self.directory.mkdir(parents=True, exist_ok=True)
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self.path.write_text(
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json.dumps(agents, indent=2, ensure_ascii=False),
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encoding="utf-8",
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)
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def upsert_agent(self, agent: dict[str, Any]) -> dict[str, Any]:
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"""按 id 新增或更新一个 agent 记录。"""
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record = dict(agent)
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agent_id = str(record.get("id", "")).strip()
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if not agent_id:
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raise ValueError("Agent id is required")
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record["id"] = agent_id
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# 对基础展示字段做最小兜底，避免后续 UI 或提示词出现空值。
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record.setdefault("name", agent_id)
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record.setdefault("description", record["name"])
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record.setdefault("protocol", "a2a")
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record.setdefault("enabled", True)
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record.setdefault("tags", [])
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# 先剔除旧记录再 append，最后统一排序，保持存储文件稳定可读。
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agents = [a for a in self.list_agents() if a.get("id") != agent_id]
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agents.append(record)
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agents.sort(key=lambda item: item.get("id", "").lower())
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self.save_agents(agents)
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return record
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def delete_agent(self, agent_id: str) -> bool:
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"""按 id 删除一个 agent，删除成功返回 True。"""
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target = agent_id.strip()
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if not target:
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return False
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agents = self.list_agents()
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filtered = [a for a in agents if a.get("id") != target]
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if len(filtered) == len(agents):
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return False
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self.save_agents(filtered)
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return True
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class AgentRegistry:
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"""构建并查询当前可委派 agent 集合。"""
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def __init__(
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self,
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workspace: Path,
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plugins: PluginLoader | None = None,
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skills: SkillsLoader | None = None,
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allow_skill_cards: bool = True,
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allow_workspace_agents: bool = True,
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include_local_fallback: bool = True,
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include_plugin_agents: bool = True,
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):
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self.workspace = workspace
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# 插件和技能加载器允许外部复用同一个实例，避免重复扫描磁盘。
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self.plugins = plugins or PluginLoader(workspace)
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self.skills = skills or SkillsLoader(workspace, extra_dirs=self.plugins.get_skill_dirs())
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self.allow_skill_cards = allow_skill_cards
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self.allow_workspace_agents = allow_workspace_agents
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self.include_local_fallback = include_local_fallback
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self.include_plugin_agents = include_plugin_agents
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self.workspace_store = WorkspaceAgentStore(workspace)
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def list_agents(self, include_local_fallback: bool | None = None) -> list[AgentDescriptor]:
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"""按统一格式列出当前可见 agent。"""
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if include_local_fallback is None:
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include_local_fallback = self.include_local_fallback
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agents: list[AgentDescriptor] = []
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if self.allow_workspace_agents:
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for record in self.workspace_store.list_agents():
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if not record.get("enabled", True):
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continue
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agent = self._workspace_record_to_descriptor(record)
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if agent:
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agents.append(agent)
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# plugin agents 本质上是“带独立系统提示词的本地执行器”。
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if self.include_plugin_agents:
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for plugin in self.plugins.plugins.values():
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for agent in plugin.agents.values():
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agents.append(
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AgentDescriptor(
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id=f"plugin:{agent.name}",
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name=agent.name,
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description=agent.description or agent.name,
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source="plugin",
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kind="local_prompt",
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protocol=None,
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plugin_name=agent.plugin_name,
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model=agent.model,
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system_prompt=agent.system_prompt,
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aliases=[agent.name],
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metadata={"plugin_name": agent.plugin_name},
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)
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)
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if self.allow_skill_cards:
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# skill 里声明的 card 视为远端 A2A agent 的静态入口。
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for card in self.skills.list_skill_agent_cards():
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agent = self._skill_card_to_descriptor(card)
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if agent:
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agents.append(agent)
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if include_local_fallback:
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# 永远保留一个本地兜底执行器，确保自动路由时至少有可执行目标。
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agents.append(
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AgentDescriptor(
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id="local-subagent",
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name="Local Subagent",
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description="Local fallback agent that can use files, shell, and web tools.",
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source="builtin",
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kind="local_fallback",
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protocol=None,
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aliases=["subagent", "local"],
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)
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)
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seen: set[str] = set()
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result: list[AgentDescriptor] = []
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for agent in agents:
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# 去重规则按 id 小写匹配，优先保留先出现的来源。
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key = agent.id.lower()
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if key in seen:
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continue
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seen.add(key)
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result.append(agent)
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return result
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def get_agent(self, target: str) -> AgentDescriptor | None:
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"""按 id / name / alias 获取单个 agent。"""
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probe = (target or "").strip()
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if not probe:
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return None
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for agent in self.list_agents():
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if agent.matches(probe):
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return agent
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return None
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def suggest_agents(self, query: str, limit: int = 5) -> list[AgentDescriptor]:
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"""基于简单词项打分为一段任务文本推荐 agent。"""
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query_text = query or ""
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query_lower = query_text.lower()
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tokens = {token for token in _TOKEN_RE.findall(query_lower) if len(token) > 2}
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query_cjk_bigrams = self._cjk_bigrams(query_text)
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scored: list[tuple[int, AgentDescriptor]] = []
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for agent in self.list_agents(include_local_fallback=False):
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haystack = agent.searchable_text()
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haystack_cjk_bigrams = self._cjk_bigrams(haystack)
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score = 0
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for token in tokens:
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# token 命中一次给基础分。
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if token in haystack:
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score += 2
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# 如果查询里直接出现了 agent 名或 id，再给更高权重。
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if agent.name.lower() in query_lower or agent.id.lower() in query_lower:
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score += 5
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for phrase in [agent.name, agent.id, *agent.tags, *agent.aliases]:
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phrase_text = str(phrase or "").strip()
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if not phrase_text:
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continue
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if phrase_text.lower() in query_lower or phrase_text in query_text:
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score += 3
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if query_cjk_bigrams and haystack_cjk_bigrams:
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# 中文任务没有空格分词，先用 bigram overlap 做粗粒度召回。
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score += min(6, len(query_cjk_bigrams & haystack_cjk_bigrams))
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if score > 0:
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scored.append((score, agent))
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scored.sort(key=lambda item: (-item[0], item[1].name.lower()))
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return [agent for _, agent in scored[:limit]]
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@staticmethod
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def _cjk_bigrams(text: str) -> set[str]:
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"""提取中文 bigram，用于中文任务的轻量召回。"""
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chunks = _CJK_RE.findall(str(text or ""))
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result: set[str] = set()
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for chunk in chunks:
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if len(chunk) == 1:
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result.add(chunk)
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continue
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for index in range(len(chunk) - 1):
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result.add(chunk[index:index + 2])
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return result
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def build_agents_summary(self) -> str:
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"""把 agent 列表格式化成 prompt 可直接嵌入的 XML 片段。"""
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agents = self.list_agents()
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if not agents:
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return ""
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def esc(value: str) -> str:
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# 这里手工转义最基础的 XML 特殊字符，避免描述文本破坏结构。
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return (
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value.replace("&", "&")
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.replace("<", "<")
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.replace(">", ">")
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)
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lines = ["<agents>"]
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for agent in agents:
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lines.append(" <agent>")
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lines.append(f" <id>{esc(agent.id)}</id>")
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lines.append(f" <name>{esc(agent.name)}</name>")
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lines.append(f" <source>{esc(agent.source)}</source>")
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lines.append(f" <kind>{esc(agent.kind)}</kind>")
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lines.append(f" <description>{esc(agent.description)}</description>")
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if agent.protocol:
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lines.append(f" <protocol>{esc(agent.protocol)}</protocol>")
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if agent.tags:
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lines.append(f" <tags>{esc(', '.join(agent.tags))}</tags>")
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lines.append(" </agent>")
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lines.append("</agents>")
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return "\n".join(lines)
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def list_public_agents(self) -> list[dict[str, Any]]:
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"""列出脱敏后的 agent 结构，供 Web API 使用。"""
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return [agent.public_dict() for agent in self.list_agents()]
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def _workspace_record_to_descriptor(self, record: dict[str, Any]) -> AgentDescriptor | None:
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"""把 workspace registry 里的原始记录转成统一描述对象。"""
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protocol = str(record.get("protocol") or "a2a").lower()
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if protocol != "a2a":
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# 当前仅支持把 workspace 记录解释成 A2A agent。
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return None
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agent_id = str(record.get("id", "")).strip()
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if not agent_id:
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return None
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name = str(record.get("name") or agent_id)
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return AgentDescriptor(
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id=agent_id,
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name=name,
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description=str(record.get("description") or name),
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source="workspace",
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kind="a2a_remote",
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protocol="a2a",
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endpoint=record.get("endpoint") or record.get("base_url"),
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base_url=record.get("base_url") or record.get("endpoint"),
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card_url=record.get("card_url"),
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auth_env=record.get("auth_env"),
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auth_mode=str(record.get("auth_mode") or "none").strip().lower() or "none",
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auth_audience=(str(record.get("auth_audience") or "").strip() or None),
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auth_scopes=[
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str(scope).strip()
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for scope in record.get("auth_scopes", [])
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if str(scope).strip()
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],
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enabled=bool(record.get("enabled", True)),
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tags=[str(tag) for tag in record.get("tags", []) if str(tag).strip()],
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aliases=[
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alias
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for alias in [record.get("name"), *record.get("aliases", [])]
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if isinstance(alias, str) and alias.strip()
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],
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capabilities=record.get("capabilities", {}) if isinstance(record.get("capabilities"), dict) else {},
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metadata=record.get("metadata", {}) if isinstance(record.get("metadata"), dict) else {},
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support_streaming=bool(record.get("support_streaming", False)),
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)
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def _skill_card_to_descriptor(self, card: dict[str, Any]) -> AgentDescriptor | None:
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"""把 skill frontmatter 中的 agent card 转成统一描述对象。"""
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card_id = str(card.get("id") or "").strip()
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skill_name = str(card.get("skill_name") or "").strip()
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if not card_id:
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return None
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name = str(card.get("name") or card_id)
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return AgentDescriptor(
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id=card_id,
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name=name,
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description=str(card.get("description") or name),
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source="skill",
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kind="a2a_remote",
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protocol="a2a",
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skill_name=skill_name or None,
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endpoint=card.get("endpoint") or card.get("base_url"),
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base_url=card.get("base_url") or card.get("endpoint"),
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card_url=card.get("url") or card.get("card_url"),
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auth_env=card.get("auth_env"),
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auth_mode=str(card.get("auth_mode") or "none").strip().lower() or "none",
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auth_audience=(str(card.get("auth_audience") or "").strip() or None),
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auth_scopes=[
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str(scope).strip()
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for scope in card.get("auth_scopes", [])
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if str(scope).strip()
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],
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tags=[str(tag) for tag in card.get("tags", []) if str(tag).strip()],
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aliases=[
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alias
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for alias in [card.get("name"), *card.get("aliases", [])]
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if isinstance(alias, str) and alias.strip()
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],
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capabilities=card.get("capabilities", {}) if isinstance(card.get("capabilities"), dict) else {},
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metadata=card.get("metadata", {}) if isinstance(card.get("metadata"), dict) else {},
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support_streaming=bool(card.get("support_streaming", False)),
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)
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