xuhd
be757b1f2f
feat(skills): skill_list 可见性过滤 + skill 只读工具常驻 + 进化面板对照展示 - skill_list:默认 lead agent 改为只暴露未归档、且内置或属于当前用户的技能; 自定义 agent 仍严格按白名单。SkillStore 不可用时降级放行全部自定义技能。 - prompt:注入系统提示词的技能列表剔除已归档(state=archived)技能。 - tools:skill_list/skill_view 只读工具改为常驻提供;skill_manage 写工具仍按 生效配置(含 per-user 覆盖)的 skill_evolution.enabled 门控。 - curator:should_run_now 解析异常时改为不运行;get_curator_status 返回上次 LLM 建议与实际工具调用两份记录,供前端对照。 - 前端:技能进化面板新增建议 vs 实际动作对照展示 + 配套 i18n。 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> @
194 lines
8.6 KiB
Python
194 lines
8.6 KiB
Python
import logging
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from langchain.tools import BaseTool
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from deerflow.config import get_app_config
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from deerflow.config.app_config import AppConfig
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from deerflow.reflection import resolve_variable
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from deerflow.sandbox.security import is_host_bash_allowed
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from deerflow.tools.builtins import ask_clarification_tool, present_file_tool, scheduled_task_tool, task_tool, view_image_tool
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from deerflow.tools.builtins.tool_search import reset_deferred_registry
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logger = logging.getLogger(__name__)
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BUILTIN_TOOLS = [
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present_file_tool,
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ask_clarification_tool,
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]
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SUBAGENT_TOOLS = [
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task_tool,
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# task_status_tool is no longer exposed to LLM (backend handles polling internally)
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]
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def _is_host_bash_tool(tool: object) -> bool:
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"""Return True if the tool config represents a host-bash execution surface."""
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group = getattr(tool, "group", None)
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use = getattr(tool, "use", None)
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if group == "bash":
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return True
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if use == "deerflow.sandbox.tools:bash_tool":
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return True
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return False
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def get_available_tools(
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groups: list[str] | None = None,
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include_mcp: bool = True,
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model_name: str | None = None,
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subagent_enabled: bool = False,
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is_scheduled_run: bool = False,
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*,
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app_config: AppConfig | None = None,
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) -> list[BaseTool]:
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"""Get all available tools from config.
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Note: MCP tools should be initialized at application startup using
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`initialize_mcp_tools()` from deerflow.mcp module.
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Args:
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groups: Optional list of tool groups to filter by.
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include_mcp: Whether to include tools from MCP servers (default: True).
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model_name: Optional model name to determine if vision tools should be included.
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subagent_enabled: Whether to include subagent tools (task, task_status).
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Returns:
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List of available tools.
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"""
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config = app_config or get_app_config()
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tool_configs = [tool for tool in config.tools if groups is None or tool.group in groups]
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# Do not expose host bash by default when LocalSandboxProvider is active.
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if not is_host_bash_allowed(config):
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tool_configs = [tool for tool in tool_configs if not _is_host_bash_tool(tool)]
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loaded_tools_raw = [(cfg, resolve_variable(cfg.use, BaseTool)) for cfg in tool_configs]
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# Warn when the config ``name`` field and the tool object's ``.name``
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# attribute diverge — this mismatch is the root cause of issue #1803 where
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# the LLM receives one name in its tool schema but the runtime router
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# recognises a different name, producing "not a valid tool" errors.
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for cfg, loaded in loaded_tools_raw:
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if cfg.name != loaded.name:
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logger.warning(
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"Tool name mismatch: config name %r does not match tool .name %r (use: %s). The tool's own .name will be used for binding.",
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cfg.name,
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loaded.name,
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cfg.use,
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)
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loaded_tools = [t for _, t in loaded_tools_raw]
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# Conditionally add tools based on config
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builtin_tools = BUILTIN_TOOLS.copy()
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if not is_scheduled_run:
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builtin_tools.append(scheduled_task_tool)
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# skill_list / skill_view 是只读工具(发现、查看技能),与"自我进化"写能力无关,
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# 始终提供给 agent。
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from deerflow.tools.builtins.skill_tools import skill_list_tool, skill_view_tool
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builtin_tools.extend([skill_list_tool, skill_view_tool])
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# skill_manage 能创建/修改/删除技能,属于"自我进化"写能力,按 skill_evolution.enabled
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# 门控。该开关用户可在 per-user 配置里覆盖,所以按"生效配置(含 per-user 覆盖)"判断 ——
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# Gateway 每次 run 重建 agent,用户改了配置下次 run 即生效;解析失败退回全局 config.yaml。
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try:
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from deerflow.config.skill_evolution_runtime import load_skill_evolution_config_for_user
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from deerflow.runtime.user_context import get_effective_user_id
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skill_evolution_config = load_skill_evolution_config_for_user(get_effective_user_id())
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except Exception: # noqa: BLE001 - 解析失败不能阻断 agent 构建
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skill_evolution_config = getattr(config, "skill_evolution", None)
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if getattr(skill_evolution_config, "enabled", False):
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from deerflow.tools.skill_manage_tool import skill_manage_tool
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builtin_tools.append(skill_manage_tool)
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# Add subagent tools only if enabled via runtime parameter
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if subagent_enabled:
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builtin_tools.extend(SUBAGENT_TOOLS)
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logger.info("Including subagent tools (task)")
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# If no model_name specified, use the first model (default)
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if model_name is None and config.models:
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model_name = config.models[0].name
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# Add view_image_tool only if the model supports vision
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model_config = config.get_model_config(model_name) if model_name else None
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if model_config is not None and model_config.supports_vision:
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builtin_tools.append(view_image_tool)
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logger.info(f"Including view_image_tool for model '{model_name}' (supports_vision=True)")
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# Get cached MCP tools if enabled
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# NOTE: We use ExtensionsConfig.from_file() instead of config.extensions
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# to always read the latest configuration from disk. This ensures that changes
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# made through the Gateway API (which runs in a separate process) are immediately
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# reflected when loading MCP tools.
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mcp_tools = []
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# Reset deferred registry upfront to prevent stale state from previous calls
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reset_deferred_registry()
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if include_mcp:
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try:
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from deerflow.config.extensions_config import ExtensionsConfig
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from deerflow.mcp.cache import get_cached_mcp_tools
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extensions_config = ExtensionsConfig.from_file()
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if extensions_config.get_enabled_mcp_servers():
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mcp_tools = get_cached_mcp_tools()
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if mcp_tools:
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logger.info(f"Using {len(mcp_tools)} cached MCP tool(s)")
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# When tool_search is enabled, register MCP tools in the
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# deferred registry and add tool_search to builtin tools.
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if config.tool_search.enabled:
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from deerflow.tools.builtins.tool_search import DeferredToolRegistry, set_deferred_registry
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from deerflow.tools.builtins.tool_search import tool_search as tool_search_tool
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registry = DeferredToolRegistry()
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for t in mcp_tools:
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registry.register(t)
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set_deferred_registry(registry)
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builtin_tools.append(tool_search_tool)
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logger.info(f"Tool search active: {len(mcp_tools)} tools deferred")
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except ImportError:
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logger.warning("MCP module not available. Install 'langchain-mcp-adapters' package to enable MCP tools.")
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except Exception as e:
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logger.error(f"Failed to get cached MCP tools: {e}")
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# Add invoke_acp_agent tool if any ACP agents are configured
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acp_tools: list[BaseTool] = []
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try:
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from deerflow.tools.builtins.invoke_acp_agent_tool import build_invoke_acp_agent_tool
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if app_config is None:
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from deerflow.config.acp_config import get_acp_agents
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acp_agents = get_acp_agents()
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else:
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acp_agents = getattr(config, "acp_agents", {}) or {}
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if acp_agents:
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acp_tools.append(build_invoke_acp_agent_tool(acp_agents))
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logger.info(f"Including invoke_acp_agent tool ({len(acp_agents)} agent(s): {list(acp_agents.keys())})")
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except Exception as e:
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logger.warning(f"Failed to load ACP tool: {e}")
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logger.info(f"Total tools loaded: {len(loaded_tools)}, built-in tools: {len(builtin_tools)}, MCP tools: {len(mcp_tools)}, ACP tools: {len(acp_tools)}")
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# Deduplicate by tool name — config-loaded tools take priority, followed by
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# built-ins, MCP tools, and ACP tools. Duplicate names cause the LLM to
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# receive ambiguous or concatenated function schemas (issue #1803).
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all_tools = loaded_tools + builtin_tools + mcp_tools + acp_tools
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seen_names: set[str] = set()
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unique_tools: list[BaseTool] = []
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for t in all_tools:
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if t.name not in seen_names:
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unique_tools.append(t)
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seen_names.add(t.name)
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else:
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logger.warning(
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"Duplicate tool name %r detected and skipped — check your config.yaml and MCP server registrations (issue #1803).",
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t.name,
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)
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return unique_tools
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