refactor: tech debt Wave 1+2 (except Exception 收尾 + core/experts Any 治理) #10
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@ -13,7 +13,7 @@ import logging
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import time
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from abc import ABC, abstractmethod
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from datetime import datetime, timezone
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from typing import TYPE_CHECKING, Any
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from typing import TYPE_CHECKING
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import redis.asyncio as aioredis
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@ -71,7 +71,7 @@ class BaseAgent(ABC):
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# 可插拔能力（由子类或配置注入）
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self._tools: list["Tool"] = []
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self._memory: "Memory | None" = None
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self._memory_retriever: Any | None = None
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self._memory_retriever: object | None = None
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# 外部依赖注入（由 start() 时设置）
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self._registry = None
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@ -192,7 +192,7 @@ class BaseAgent(ABC):
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lines.append(f" - {msg}")
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return "\n".join(lines)
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def _build_skill_context(self) -> dict[str, Any] | None:
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def _build_skill_context(self) -> dict[str, object] | None:
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"""从当前技能配置构建 skill_context，用于 QualityGate skill_match 校验"""
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if not self._skill:
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return None
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@ -216,17 +216,17 @@ class BaseAgent(ABC):
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self._memory = memory
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return self
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def use_memory_retriever(self, retriever: Any) -> "BaseAgent":
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def use_memory_retriever(self, retriever: object) -> "BaseAgent":
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"""设置记忆检索器，用于上下文注入"""
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self._memory_retriever = retriever
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return self
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def set_registry(self, registry: Any) -> "BaseAgent":
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def set_registry(self, registry: object) -> "BaseAgent":
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"""注入注册中心"""
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self._registry = registry
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return self
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def set_dispatcher(self, dispatcher: Any) -> "BaseAgent":
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def set_dispatcher(self, dispatcher: object) -> "BaseAgent":
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"""注入任务分发器"""
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self._dispatcher = dispatcher
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return self
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@ -489,7 +489,7 @@ class BaseAgent(ABC):
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target_agent: str,
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task: TaskMessage,
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reason: str,
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context: dict[str, Any] | None = None,
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context: dict[str, object] | None = None,
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):
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"""将当前任务转交给另一个 Agent"""
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if self._redis is None:
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@ -10,7 +10,7 @@
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import json
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import logging
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import os
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from typing import Any, Callable, Coroutine
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from typing import Callable, Coroutine
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import yaml
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@ -39,12 +39,12 @@ class AgentConfig:
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task_mode: str = "llm_generate",
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supported_tasks: list[str] | None = None,
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max_concurrency: int = 1,
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input_schema: dict[str, Any] | None = None,
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output_schema: dict[str, Any] | None = None,
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input_schema: dict[str, object] | None = None,
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output_schema: dict[str, object] | None = None,
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prompt: dict[str, str] | None = None,
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llm: dict[str, Any] | None = None,
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llm: dict[str, object] | None = None,
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tools: list[str] | None = None,
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memory: dict[str, Any] | None = None,
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memory: dict[str, object] | None = None,
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custom_handler: str | None = None,
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):
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self.name = name
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@ -96,7 +96,7 @@ class AgentConfig:
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)
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@classmethod
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def from_dict(cls, data: dict[str, Any]) -> "AgentConfig":
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def from_dict(cls, data: dict[str, object]) -> "AgentConfig":
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"""从字典创建配置"""
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return cls(
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name=data["name"],
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@ -128,7 +128,7 @@ class AgentConfig:
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)
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return cls.from_dict(data)
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def to_dict(self) -> dict[str, Any]:
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def to_dict(self) -> dict[str, object]:
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"""序列化为字典"""
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d = {
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"name": self.name,
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@ -197,11 +197,11 @@ class ConfigDrivenAgent(BaseAgent, EvolutionMixin):
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self,
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config: AgentConfig,
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tool_registry: ToolRegistry | None = None,
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llm_client: Any = None,
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llm_client: object | None = None,
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custom_handlers: dict[str, Callable[..., Coroutine]] | None = None,
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llm_gateway: Any = None, # NEW v2 param: LLMGateway
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llm_gateway: object | None = None, # NEW v2 param: LLMGateway
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mcp_servers: dict[str, str] | None = None, # NEW v2 param: MCP server URLs
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compressor: Any = None, # CompressionStrategy | None
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compressor: object | None = None, # CompressionStrategy | None
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):
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# v2: If SkillConfig, extract skill info
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from agentkit.skills.base import SkillConfig, Skill
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@ -310,12 +310,12 @@ class ConfigDrivenAgent(BaseAgent, EvolutionMixin):
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logger.info(f"Merged skill tool '{tool.name}' into agent '{self.name}'")
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# v2: Register MCP tools if mcp_servers provided
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self._mcp_clients: list[Any] = []
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self._mcp_clients: list[object] = []
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self._mcp_servers: dict[str, str] = mcp_servers or {}
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self._mcp_tools_registered = False
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# Memory integration: 从 config.memory 自动实例化 MemoryRetriever
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self._memory_retriever: Any | None = None
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self._memory_retriever: object | None = None
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if config.memory:
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try:
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from agentkit.memory.retriever import MemoryRetriever
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@ -903,7 +903,7 @@ class ConfigDrivenAgent(BaseAgent, EvolutionMixin):
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)
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return await self.handle_task(enhanced_task)
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def _wrap_llm_client(self, llm_client: Any):
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def _wrap_llm_client(self, llm_client: object):
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"""Wrap legacy llm_client into LLMGateway"""
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from agentkit.llm.gateway import LLMGateway
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from agentkit.llm.protocol import LLMProvider, LLMRequest, LLMResponse, TokenUsage
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@ -911,7 +911,7 @@ class ConfigDrivenAgent(BaseAgent, EvolutionMixin):
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class ClientProvider(LLMProvider):
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"""Adapter: wraps legacy llm_client as an LLMProvider"""
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def __init__(self, raw_client: Any):
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def __init__(self, raw_client: object):
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self._raw_client = raw_client
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async def chat(self, request: LLMRequest) -> LLMResponse:
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@ -9,7 +9,6 @@ no LLM). See ``docs/plans/2026-06-29-003-feat-agent-wave2-medium-coupling-plan.m
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"""
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from dataclasses import dataclass
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from typing import Any
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from agentkit.core.exceptions import (
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LLMProviderError,
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@ -54,7 +53,7 @@ class EmergencyError:
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retryable: bool # whether a user retry might succeed
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original_error: str # str(exc) for traceability
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def to_dict(self) -> dict[str, Any]:
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def to_dict(self) -> dict[str, object]:
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return {
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"error_code": self.error_code,
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"message": self.message,
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@ -24,7 +24,7 @@ from __future__ import annotations
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import json
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import logging
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from dataclasses import dataclass, field
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from typing import Any, Awaitable, Callable, Protocol, runtime_checkable
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from typing import Awaitable, Callable, Protocol, runtime_checkable
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logger = logging.getLogger(__name__)
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@ -37,14 +37,14 @@ class RequestContext:
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"""
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messages: list[dict[str, str]]
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tools: list[Any] = field(default_factory=list)
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tools: list[object] = field(default_factory=list)
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system_prompt: str | None = None
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model: str = "default"
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agent_name: str = ""
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task_type: str = ""
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task_id: str | None = None
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# 中间件间共享状态（压缩结果、token 用量、循环检测状态等）
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metadata: dict[str, Any] = field(default_factory=dict)
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metadata: dict[str, object] = field(default_factory=dict)
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@runtime_checkable
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@ -57,7 +57,7 @@ class Middleware(Protocol):
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async def before(self, ctx: RequestContext) -> RequestContext: ...
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async def after(self, ctx: RequestContext, result: Any) -> Any: ...
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async def after(self, ctx: RequestContext, result: object) -> object: ...
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class MiddlewareChain:
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@ -81,8 +81,8 @@ class MiddlewareChain:
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async def execute(
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self,
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ctx: RequestContext,
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handler: Callable[[RequestContext], Awaitable[Any]],
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) -> Any:
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handler: Callable[[RequestContext], Awaitable[object]],
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) -> object:
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"""执行中间件链 + handler。
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洋葱模型：before 顺序执行 → handler → after 逆序执行。
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@ -125,7 +125,7 @@ class SummarizationMiddleware:
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after: 无操作（压缩在 before 完成）
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"""
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def __init__(self, compressor: Any = None) -> None:
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def __init__(self, compressor: object | None = None) -> None:
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self._compressor = compressor
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async def before(self, ctx: RequestContext) -> RequestContext:
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@ -143,7 +143,7 @@ class SummarizationMiddleware:
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logger.warning(f"SummarizationMiddleware: compression failed: {e}")
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return ctx
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async def after(self, ctx: RequestContext, result: Any) -> Any:
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async def after(self, ctx: RequestContext, result: object) -> object:
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return result
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@ -157,7 +157,7 @@ class TokenUsageMiddleware:
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async def before(self, ctx: RequestContext) -> RequestContext:
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return ctx
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async def after(self, ctx: RequestContext, result: Any) -> Any:
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async def after(self, ctx: RequestContext, result: object) -> object:
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# 从 ReActResult 或类似结构提取 token 用量
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# ReActResult 有 total_tokens 属性（非 token_usage）
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usage = getattr(result, "total_tokens", None)
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@ -184,7 +184,7 @@ class LoopDetectionMiddleware:
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ctx.metadata["loop_detection_window"] = []
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return ctx
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async def after(self, ctx: RequestContext, result: Any) -> Any:
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async def after(self, ctx: RequestContext, result: object) -> object:
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trajectory = getattr(result, "trajectory", None) or []
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if len(trajectory) < self._threshold:
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return result
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@ -16,9 +16,9 @@ import asyncio
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import logging
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import random
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import time
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from dataclasses import dataclass, field
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from dataclasses import dataclass
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from enum import Enum
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from typing import Any, Callable, Awaitable
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from typing import Callable, Awaitable
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from agentkit.core.plan_schema import ExecutionPlan, PlanStep, PlanStepStatus
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from agentkit.core.protocol import TaskMessage, TaskResult, TaskStatus
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@ -42,7 +42,7 @@ class StepExecutionResult:
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step_id: str
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status: PlanStepStatus
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result: dict[str, Any] | None = None
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result: dict[str, object] | None = None
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error: str | None = None
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retry_count: int = 0
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duration_ms: float = 0.0
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@ -91,7 +91,7 @@ class PlanExecutor:
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def __init__(
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self,
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agent_pool: Any,
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agent_pool: object,
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max_retries: int = 2,
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step_timeout: float = 300.0,
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max_parallel: int = 5,
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@ -207,7 +207,7 @@ class PlanExecutor:
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async def _execute_step_with_retry(
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self,
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step: PlanStep,
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input_data: dict[str, Any],
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input_data: dict[str, object],
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original_task: TaskMessage,
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) -> StepExecutionResult:
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"""执行单个步骤，支持重试
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@ -281,9 +281,9 @@ class PlanExecutor:
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async def _execute_step_once(
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self,
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step: PlanStep,
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input_data: dict[str, Any],
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input_data: dict[str, object],
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original_task: TaskMessage,
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) -> dict[str, Any]:
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) -> dict[str, object]:
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"""执行单个步骤一次
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通过 AgentPool 创建 Agent 执行步骤。
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@ -463,7 +463,7 @@ class PlanExecutor:
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self,
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step: PlanStep,
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step_results: dict[str, StepExecutionResult],
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) -> dict[str, Any]:
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) -> dict[str, object]:
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"""将依赖步骤的结果注入到当前步骤的输入中
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兼容 Orchestrator 的 subtask_results 累积模式。
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@ -471,7 +471,7 @@ class PlanExecutor:
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enriched = dict(step.input_data)
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if step.dependencies:
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dep_results: dict[str, dict[str, Any]] = {}
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dep_results: dict[str, dict[str, object]] = {}
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for dep_id in step.dependencies:
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if dep_id in step_results:
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dep_result = step_results[dep_id]
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|
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@ -13,7 +13,7 @@ from collections import Counter, deque
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from collections.abc import AsyncGenerator
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from dataclasses import dataclass, field
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from datetime import datetime, timezone
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from typing import TYPE_CHECKING, Any
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from typing import TYPE_CHECKING, Awaitable, Callable
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from agentkit.core.exceptions import LLMProviderError, LoopDetectedError, TaskCancelledError, TaskTimeoutError
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from agentkit.core.protocol import CancellationToken
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@ -43,13 +43,13 @@ class ReActStep:
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step: int
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action: str # "tool_call" or "final_answer"
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tool_name: str | None = None
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arguments: dict[str, Any] | None = None
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result: Any = None
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arguments: dict[str, object] | None = None
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result: object = None
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content: str | None = None
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tokens: int = 0
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async def _ensure_async_iterable(obj: Any, label: str = "<obj>"):
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async def _ensure_async_iterable(obj: object, label: str = "<obj>"):
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"""Defensive helper: ensure the given object is an async iterable.
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Guards against the recurring ``'async for' requires an object with
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@ -133,7 +133,7 @@ class ReActEvent:
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event_type: str # "thinking","token","tool_call","tool_result","confirmation_request","confirmation_result","phase_violation","step","final_answer","final_result","error"
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step: int
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data: dict[str, Any] = field(default_factory=dict)
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data: dict[str, object] = field(default_factory=dict)
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timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
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@ -230,7 +230,7 @@ class ReActEngine:
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# step and yields phase_violation ReActEvents. Non-streaming execute()
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# simply ignores the accumulator (the error dict returned to the LLM is
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# the only signal there).
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self._phase_violations: list[dict[str, Any]] = []
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self._phase_violations: list[dict[str, object]] = []
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def reset(self) -> None:
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"""Reset internal state for reuse across conversations.
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@ -299,8 +299,8 @@ class ReActEngine:
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return False
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def _check_phase_permission(
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self, tool_name: str, arguments: dict[str, Any]
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) -> dict[str, Any] | None:
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self, tool_name: str, arguments: dict[str, object]
|
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) -> dict[str, object] | None:
|
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"""Return None if tool is allowed; return a structured error dict if blocked.
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The error dict replaces what `_execute_tool` would have returned —
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@ -351,7 +351,7 @@ class ReActEngine:
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return violation
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return None
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def _check_tool_loop(self, tool_calls: list[Any]) -> str | None:
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def _check_tool_loop(self, tool_calls: list[object]) -> str | None:
|
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"""检测重复工具调用模式。
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|
||||
将当前步的工具调用 hash 加入滑动窗口，若同一 hash 在窗口内出现
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@ -406,10 +406,10 @@ class ReActEngine:
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memory_retriever: "MemoryRetriever | None" = None,
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task_id: str | None = None,
|
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compressor: "CompressionStrategy | None" = None,
|
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retrieval_config: dict[str, Any] | None = None,
|
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retrieval_config: dict[str, object] | None = None,
|
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cancellation_token: CancellationToken | None = None,
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timeout_seconds: float | None = None,
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confirmation_handler: Any | None = None,
|
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confirmation_handler: Callable[..., Awaitable[object]] | None = None,
|
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) -> ReActResult:
|
||||
"""执行 ReAct 循环
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|
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|
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@ -536,7 +536,7 @@ class ReActEngine:
|
|||
|
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async def _run_loop_and_extract(
|
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self,
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||||
**kwargs: Any,
|
||||
**kwargs: object,
|
||||
) -> ReActResult:
|
||||
"""Collect all events from _execute_loop and extract the final ReActResult.
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|
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|
|
@ -564,9 +564,9 @@ class ReActEngine:
|
|||
memory_retriever: "MemoryRetriever | None" = None,
|
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task_id: str | None = None,
|
||||
compressor: "CompressionStrategy | None" = None,
|
||||
retrieval_config: dict[str, Any] | None = None,
|
||||
retrieval_config: dict[str, object] | None = None,
|
||||
cancellation_token: CancellationToken | None = None,
|
||||
confirmation_handler: Any | None = None,
|
||||
confirmation_handler: Callable[..., Awaitable[object]] | None = None,
|
||||
stream: bool = False,
|
||||
effective_timeout: float = 0.0,
|
||||
) -> AsyncGenerator[ReActEvent, None]:
|
||||
|
|
@ -666,7 +666,7 @@ class ReActEngine:
|
|||
)
|
||||
|
||||
# 构建初始消息
|
||||
conversation: list[dict[str, Any]] = []
|
||||
conversation: list[dict[str, object]] = []
|
||||
system_content = self._build_system_message(
|
||||
stable=system_prompt or "",
|
||||
volatile=memory_context,
|
||||
|
|
@ -726,7 +726,7 @@ class ReActEngine:
|
|||
# 流式模式：用 chat_stream，yield token events
|
||||
stream_content_chunks: list[str] = []
|
||||
stream_usage = None
|
||||
stream_tool_calls: list[Any] = []
|
||||
stream_tool_calls: list[object] = []
|
||||
stream_model = model
|
||||
# U3/G8: delta_flush 节流 buffer
|
||||
_flush_buffer: list[str] = []
|
||||
|
|
@ -840,7 +840,7 @@ class ReActEngine:
|
|||
)
|
||||
|
||||
# Act: 记录 assistant 消息（含 tool_calls）到对话历史
|
||||
assistant_msg: dict[str, Any] = {
|
||||
assistant_msg: dict[str, object] = {
|
||||
"role": "assistant",
|
||||
"content": response.content or "",
|
||||
"tool_calls": [
|
||||
|
|
@ -874,7 +874,7 @@ class ReActEngine:
|
|||
if i in parallelizable_set
|
||||
]
|
||||
|
||||
all_results: list[Any] = [None] * len(response.tool_calls)
|
||||
all_results: list[object] = [None] * len(response.tool_calls)
|
||||
|
||||
# Execute serial tools first (handles confirmation flow)
|
||||
for i, tc in serial_calls:
|
||||
|
|
@ -1452,10 +1452,10 @@ class ReActEngine:
|
|||
memory_retriever: "MemoryRetriever | None" = None,
|
||||
task_id: str | None = None,
|
||||
compressor: "CompressionStrategy | None" = None,
|
||||
retrieval_config: dict[str, Any] | None = None,
|
||||
retrieval_config: dict[str, object] | None = None,
|
||||
cancellation_token: CancellationToken | None = None,
|
||||
timeout_seconds: float | None = None,
|
||||
confirmation_handler: Any | None = None,
|
||||
confirmation_handler: Callable[..., Awaitable[object]] | None = None,
|
||||
) -> AsyncGenerator[ReActEvent, None]:
|
||||
"""Execute ReAct loop, yielding ReActEvent objects.
|
||||
|
||||
|
|
@ -1514,7 +1514,7 @@ class ReActEngine:
|
|||
volatile: str,
|
||||
*,
|
||||
model: str,
|
||||
) -> str | list[dict[str, Any]] | None:
|
||||
) -> str | list[dict[str, object]] | None:
|
||||
"""构建双块结构 system message(stable + volatile)。
|
||||
|
||||
- prompt_cache_enable=False 或无 stable+volatile → 返回 str(或 None)
|
||||
|
|
@ -1535,7 +1535,7 @@ class ReActEngine:
|
|||
|
||||
provider_name = self._get_provider_name(model)
|
||||
if provider_name == "anthropic":
|
||||
blocks: list[dict[str, Any]] = []
|
||||
blocks: list[dict[str, object]] = []
|
||||
if stable:
|
||||
blocks.append(
|
||||
{
|
||||
|
|
@ -1675,8 +1675,8 @@ class ReActEngine:
|
|||
@staticmethod
|
||||
def _build_response_from_stream(
|
||||
content: str,
|
||||
tool_calls: list[Any],
|
||||
usage: Any,
|
||||
tool_calls: list[object],
|
||||
usage: object,
|
||||
model: str,
|
||||
) -> LLMResponse:
|
||||
"""Build an LLMResponse from accumulated stream chunks."""
|
||||
|
|
@ -1723,7 +1723,7 @@ class ReActEngine:
|
|||
async def _build_tool_result_message(
|
||||
self,
|
||||
tool_call_id: str,
|
||||
result: Any,
|
||||
result: object,
|
||||
compressor: "CompressionStrategy | None" = None,
|
||||
tool_name: str | None = None,
|
||||
) -> dict:
|
||||
|
|
@ -1742,7 +1742,7 @@ class ReActEngine:
|
|||
}
|
||||
|
||||
async def _execute_tool(
|
||||
self, tool_name: str, arguments: dict[str, Any], tools: list[Tool]
|
||||
self, tool_name: str, arguments: dict[str, object], tools: list[Tool]
|
||||
) -> dict:
|
||||
"""执行工具调用，处理成功和失败情况"""
|
||||
# U3/G6: phase enforcement — check before dispatch. If the tool is
|
||||
|
|
@ -1788,11 +1788,11 @@ class ReActEngine:
|
|||
|
||||
async def _execute_tool_with_confirmation(
|
||||
self,
|
||||
tc: Any,
|
||||
tc: object,
|
||||
tools: list[Tool],
|
||||
step: int,
|
||||
confirmation_handler: Any,
|
||||
) -> tuple[Any, list[ReActEvent]]:
|
||||
confirmation_handler: Callable[..., Awaitable[object]] | None,
|
||||
) -> tuple[object, list[ReActEvent]]:
|
||||
"""Execute a tool call with confirmation flow support.
|
||||
|
||||
Used in the parallel execution path for serial (non-parallelizable) tools
|
||||
|
|
@ -1886,7 +1886,7 @@ class ReActEngine:
|
|||
|
||||
return tool_result, events
|
||||
|
||||
def _should_execute_parallel(self, tool_calls: list[Any]) -> bool:
|
||||
def _should_execute_parallel(self, tool_calls: list[object]) -> bool:
|
||||
"""Determine if tool calls should be executed in parallel.
|
||||
|
||||
- parallel_tools=True: always parallel (if >1 tool)
|
||||
|
|
@ -1905,7 +1905,7 @@ class ReActEngine:
|
|||
return len(parallelizable_indices) > 1
|
||||
return False
|
||||
|
||||
def _get_parallelizable_indices(self, tool_calls: list[Any]) -> list[int]:
|
||||
def _get_parallelizable_indices(self, tool_calls: list[object]) -> list[int]:
|
||||
"""Get indices of tool_calls that have _parallelizable=true in arguments.
|
||||
|
||||
LLM marks parallelizable tools by including _parallelizable: true
|
||||
|
|
@ -1918,7 +1918,7 @@ class ReActEngine:
|
|||
indices.append(i)
|
||||
return indices
|
||||
|
||||
def _parse_text_tool_calls(self, content: str) -> list[dict[str, Any]]:
|
||||
def _parse_text_tool_calls(self, content: str) -> list[dict[str, object]]:
|
||||
"""从文本中解析工具调用模式
|
||||
|
||||
支持格式：
|
||||
|
|
@ -1926,7 +1926,7 @@ class ReActEngine:
|
|||
2. ```tool\n{"name": "...", "arguments": {...}}\n```
|
||||
3. <tool_use>\n{"name": "...", "arguments": {...}}\n</tool_use>
|
||||
"""
|
||||
calls: list[dict[str, Any]] = []
|
||||
calls: list[dict[str, object]] = []
|
||||
|
||||
# 格式 1: Action: tool_name(args)
|
||||
action_pattern = re.compile(r"Action:\s*(\w+)\((.+?)\)", re.DOTALL)
|
||||
|
|
@ -1999,7 +1999,7 @@ class ReActEngine:
|
|||
return calls
|
||||
|
||||
@staticmethod
|
||||
def _extract_tool_call_from_malformed(text: str) -> dict[str, Any] | None:
|
||||
def _extract_tool_call_from_malformed(text: str) -> dict[str, object] | None:
|
||||
"""从畸形文本中尝试提取工具调用。
|
||||
|
||||
处理场景：
|
||||
|
|
@ -2066,7 +2066,7 @@ class ReActEngine:
|
|||
return None
|
||||
name = name_match.group(1)
|
||||
|
||||
arguments: dict[str, Any] = {}
|
||||
arguments: dict[str, object] = {}
|
||||
# 提取 "key": "value" 模式
|
||||
for kv_match in re.finditer(r'"(\w+)"\s*:\s*"([^"]*)"', text):
|
||||
key = kv_match.group(1)
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ import logging
|
|||
import re
|
||||
import time
|
||||
from dataclasses import dataclass, field
|
||||
from typing import TYPE_CHECKING, Any
|
||||
from typing import TYPE_CHECKING, Awaitable, Callable
|
||||
|
||||
from agentkit.core.exceptions import LLMProviderError, TaskCancelledError, TaskTimeoutError
|
||||
from agentkit.core.protocol import CancellationToken
|
||||
|
|
@ -52,7 +52,7 @@ class ReWOOPlanStep:
|
|||
|
||||
step_id: int
|
||||
tool_name: str
|
||||
arguments: dict[str, Any]
|
||||
arguments: dict[str, object]
|
||||
reasoning: str = ""
|
||||
|
||||
|
||||
|
|
@ -164,10 +164,10 @@ class ReWOOEngine:
|
|||
memory_retriever: "MemoryRetriever | None" = None,
|
||||
task_id: str | None = None,
|
||||
compressor: "CompressionStrategy | None" = None,
|
||||
retrieval_config: dict[str, Any] | None = None,
|
||||
retrieval_config: dict[str, object] | None = None,
|
||||
cancellation_token: CancellationToken | None = None,
|
||||
timeout_seconds: float | None = None,
|
||||
confirmation_handler: Any | None = None,
|
||||
confirmation_handler: Callable[..., Awaitable[object]] | None = None,
|
||||
) -> ReActResult:
|
||||
"""执行 ReWOO 三阶段流程
|
||||
|
||||
|
|
@ -241,9 +241,9 @@ class ReWOOEngine:
|
|||
memory_retriever: "MemoryRetriever | None" = None,
|
||||
task_id: str | None = None,
|
||||
compressor: "CompressionStrategy | None" = None,
|
||||
retrieval_config: dict[str, Any] | None = None,
|
||||
retrieval_config: dict[str, object] | None = None,
|
||||
cancellation_token: CancellationToken | None = None,
|
||||
confirmation_handler: Any | None = None,
|
||||
confirmation_handler: Callable[..., Awaitable[object]] | None = None,
|
||||
) -> ReActResult:
|
||||
tools = tools or []
|
||||
tool_schemas = self._build_tool_schemas(tools) if tools else None
|
||||
|
|
@ -350,7 +350,7 @@ class ReWOOEngine:
|
|||
|
||||
# 如果计划为空（无需工具），直接让 LLM 回答
|
||||
if not plan.steps:
|
||||
llm_messages: list[dict[str, Any]] = []
|
||||
llm_messages: list[dict[str, object]] = []
|
||||
if effective_system_prompt:
|
||||
llm_messages.append({"role": "system", "content": effective_system_prompt})
|
||||
llm_messages.extend(messages)
|
||||
|
|
@ -399,7 +399,7 @@ class ReWOOEngine:
|
|||
)
|
||||
|
||||
# ── Phase 2: Execution ──
|
||||
tool_results: list[dict[str, Any]] = []
|
||||
tool_results: list[dict[str, object]] = []
|
||||
for plan_step in plan.steps:
|
||||
# 协作式取消检查
|
||||
if cancellation_token is not None:
|
||||
|
|
@ -524,10 +524,10 @@ class ReWOOEngine:
|
|||
memory_retriever: "MemoryRetriever | None" = None,
|
||||
task_id: str | None = None,
|
||||
compressor: "CompressionStrategy | None" = None,
|
||||
retrieval_config: dict[str, Any] | None = None,
|
||||
retrieval_config: dict[str, object] | None = None,
|
||||
cancellation_token: CancellationToken | None = None,
|
||||
timeout_seconds: float | None = None,
|
||||
confirmation_handler: Any | None = None,
|
||||
confirmation_handler: Callable[..., Awaitable[object]] | None = None,
|
||||
):
|
||||
"""Execute ReWOO flow, yielding ReActEvent objects.
|
||||
|
||||
|
|
@ -637,7 +637,7 @@ class ReWOOEngine:
|
|||
|
||||
# Empty plan: direct answer
|
||||
if not plan.steps:
|
||||
llm_messages: list[dict[str, Any]] = []
|
||||
llm_messages: list[dict[str, object]] = []
|
||||
if effective_system_prompt:
|
||||
llm_messages.append({"role": "system", "content": effective_system_prompt})
|
||||
llm_messages.extend(messages)
|
||||
|
|
@ -676,7 +676,7 @@ class ReWOOEngine:
|
|||
return
|
||||
|
||||
# ── Phase 2: Execution ──
|
||||
tool_results: list[dict[str, Any]] = []
|
||||
tool_results: list[dict[str, object]] = []
|
||||
for plan_step in plan.steps:
|
||||
if cancellation_token is not None:
|
||||
cancellation_token.check()
|
||||
|
|
@ -806,10 +806,10 @@ class ReWOOEngine:
|
|||
memory_retriever: "MemoryRetriever | None" = None,
|
||||
task_id: str | None = None,
|
||||
compressor: "CompressionStrategy | None" = None,
|
||||
retrieval_config: dict[str, Any] | None = None,
|
||||
retrieval_config: dict[str, object] | None = None,
|
||||
cancellation_token: CancellationToken | None = None,
|
||||
total_tokens: int = 0,
|
||||
confirmation_handler: Any | None = None,
|
||||
confirmation_handler: Callable[..., Awaitable[object]] | None = None,
|
||||
):
|
||||
"""Stream version: try fallback strategies in configured order, yielding events from the first successful one.
|
||||
|
||||
|
|
@ -902,7 +902,7 @@ class ReWOOEngine:
|
|||
"reasoning": plan.reasoning,
|
||||
"steps": [{"step_id": s.step_id, "tool_name": s.tool_name, "arguments": s.arguments, "reasoning": s.reasoning} for s in plan.steps],
|
||||
})
|
||||
tool_results: list[dict[str, Any]] = []
|
||||
tool_results: list[dict[str, object]] = []
|
||||
for plan_step in plan.steps:
|
||||
if cancellation_token is not None:
|
||||
cancellation_token.check()
|
||||
|
|
@ -934,9 +934,9 @@ class ReWOOEngine:
|
|||
memory_retriever: "MemoryRetriever | None" = None,
|
||||
task_id: str | None = None,
|
||||
compressor: "CompressionStrategy | None" = None,
|
||||
retrieval_config: dict[str, Any] | None = None,
|
||||
retrieval_config: dict[str, object] | None = None,
|
||||
cancellation_token: CancellationToken | None = None,
|
||||
confirmation_handler: Any | None = None,
|
||||
confirmation_handler: Callable[..., Awaitable[object]] | None = None,
|
||||
):
|
||||
"""Stream: ReAct fallback"""
|
||||
logger.warning("ReWOO planning failed in stream mode, falling back to ReActEngine")
|
||||
|
|
@ -969,7 +969,7 @@ class ReWOOEngine:
|
|||
"""Stream: Direct LLM fallback"""
|
||||
logger.warning("Falling back to direct LLM call in stream mode")
|
||||
try:
|
||||
direct_messages: list[dict[str, Any]] = []
|
||||
direct_messages: list[dict[str, object]] = []
|
||||
if effective_system_prompt:
|
||||
direct_messages.append({"role": "system", "content": effective_system_prompt})
|
||||
direct_messages.extend(messages)
|
||||
|
|
@ -1012,7 +1012,7 @@ class ReWOOEngine:
|
|||
"reasoning": plan.reasoning,
|
||||
"steps": [{"step_id": s.step_id, "tool_name": s.tool_name, "arguments": s.arguments, "reasoning": s.reasoning} for s in plan.steps],
|
||||
})
|
||||
tool_results: list[dict[str, Any]] = []
|
||||
tool_results: list[dict[str, object]] = []
|
||||
for plan_step in plan.steps:
|
||||
if cancellation_token is not None:
|
||||
cancellation_token.check()
|
||||
|
|
@ -1043,11 +1043,11 @@ class ReWOOEngine:
|
|||
memory_retriever: "MemoryRetriever | None" = None,
|
||||
task_id: str | None = None,
|
||||
compressor: "CompressionStrategy | None" = None,
|
||||
retrieval_config: dict[str, Any] | None = None,
|
||||
retrieval_config: dict[str, object] | None = None,
|
||||
cancellation_token: CancellationToken | None = None,
|
||||
trajectory: list[ReActStep] | None = None,
|
||||
total_tokens: int = 0,
|
||||
confirmation_handler: Any | None = None,
|
||||
confirmation_handler: Callable[..., Awaitable[object]] | None = None,
|
||||
) -> ReActResult | None:
|
||||
"""按配置的 fallback 策略顺序尝试回退，返回第一个成功的结果
|
||||
|
||||
|
|
@ -1136,7 +1136,7 @@ class ReWOOEngine:
|
|||
# Execute the simplified plan
|
||||
trajectory: list[ReActStep] = []
|
||||
total_tokens = simplified_tokens
|
||||
tool_results: list[dict[str, Any]] = []
|
||||
tool_results: list[dict[str, object]] = []
|
||||
for plan_step in plan.steps:
|
||||
if cancellation_token is not None:
|
||||
cancellation_token.check()
|
||||
|
|
@ -1195,9 +1195,9 @@ class ReWOOEngine:
|
|||
memory_retriever: "MemoryRetriever | None" = None,
|
||||
task_id: str | None = None,
|
||||
compressor: "CompressionStrategy | None" = None,
|
||||
retrieval_config: dict[str, Any] | None = None,
|
||||
retrieval_config: dict[str, object] | None = None,
|
||||
cancellation_token: CancellationToken | None = None,
|
||||
confirmation_handler: Any | None = None,
|
||||
confirmation_handler: Callable[..., Awaitable[object]] | None = None,
|
||||
) -> ReActResult | None:
|
||||
"""ReAct fallback: delegate to ReActEngine"""
|
||||
logger.warning("ReWOO planning failed, falling back to ReActEngine")
|
||||
|
|
@ -1240,7 +1240,7 @@ class ReWOOEngine:
|
|||
"""Direct fallback: simple LLM call without tools"""
|
||||
logger.warning("Falling back to direct LLM call")
|
||||
try:
|
||||
direct_messages: list[dict[str, Any]] = []
|
||||
direct_messages: list[dict[str, object]] = []
|
||||
if effective_system_prompt:
|
||||
direct_messages.append({"role": "system", "content": effective_system_prompt})
|
||||
direct_messages.extend(messages)
|
||||
|
|
@ -1319,7 +1319,7 @@ class ReWOOEngine:
|
|||
if plan is not None and plan.steps:
|
||||
trajectory: list[ReActStep] = []
|
||||
total_tokens = plan_tokens
|
||||
tool_results: list[dict[str, Any]] = []
|
||||
tool_results: list[dict[str, object]] = []
|
||||
for plan_step in plan.steps:
|
||||
if cancellation_token is not None:
|
||||
cancellation_token.check()
|
||||
|
|
@ -1396,7 +1396,7 @@ class ReWOOEngine:
|
|||
tool_descriptions = self._build_tool_descriptions(tools)
|
||||
|
||||
# 构建规划消息
|
||||
planning_messages: list[dict[str, Any]] = [
|
||||
planning_messages: list[dict[str, object]] = [
|
||||
{"role": "system", "content": _PLANNING_SYSTEM_PROMPT},
|
||||
]
|
||||
|
||||
|
|
@ -1451,7 +1451,7 @@ class ReWOOEngine:
|
|||
async def _synthesis_phase(
|
||||
self,
|
||||
messages: list[dict[str, str]],
|
||||
tool_results: list[dict[str, Any]],
|
||||
tool_results: list[dict[str, object]],
|
||||
model: str,
|
||||
agent_name: str,
|
||||
task_type: str,
|
||||
|
|
@ -1468,7 +1468,7 @@ class ReWOOEngine:
|
|||
cancellation_token.check()
|
||||
|
||||
# 构建综合消息
|
||||
synthesis_messages: list[dict[str, Any]] = [
|
||||
synthesis_messages: list[dict[str, object]] = [
|
||||
{"role": "system", "content": _SYNTHESIS_SYSTEM_PROMPT},
|
||||
]
|
||||
|
||||
|
|
@ -1600,7 +1600,7 @@ class ReWOOEngine:
|
|||
return None
|
||||
|
||||
async def _execute_tool(
|
||||
self, tool_name: str, arguments: dict[str, Any], tools: list[Tool]
|
||||
self, tool_name: str, arguments: dict[str, object], tools: list[Tool]
|
||||
) -> dict:
|
||||
"""执行工具调用，处理成功和失败情况"""
|
||||
tool = self._find_tool(tool_name, tools)
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ import asyncio
|
|||
import json
|
||||
import logging
|
||||
import re
|
||||
from typing import TYPE_CHECKING, Any
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from .expert import Expert
|
||||
from .plan import PhaseStatus, PlanPhase, TeamPlan
|
||||
|
|
@ -28,7 +28,7 @@ class DebateRunnerMixin:
|
|||
_phase_semaphore: asyncio.Semaphore
|
||||
MAX_DEBATE_ROUNDS: int
|
||||
|
||||
async def _execute_debate_phase(self, phase: PlanPhase, plan: TeamPlan) -> dict[str, Any]:
|
||||
async def _execute_debate_phase(self, phase: PlanPhase, plan: TeamPlan) -> dict[str, object]:
|
||||
"""Execute a DEBATE phase: Lead-facilitated structured debate (5 stages).
|
||||
Parse config → Lead opens → experts argue in parallel rounds → Lead
|
||||
summarizes → Lead adjudicates → write conclusion to workspace."""
|
||||
|
|
@ -86,7 +86,7 @@ class DebateRunnerMixin:
|
|||
)
|
||||
|
||||
# Debate history for context (Lead opening + expert arguments + Lead summaries)
|
||||
history: list[dict[str, Any]] = [
|
||||
history: list[dict[str, object]] = [
|
||||
{"expert": lead.config.name, "content": opening, "round": 0, "role": "moderator"}
|
||||
]
|
||||
|
||||
|
|
@ -103,7 +103,7 @@ class DebateRunnerMixin:
|
|||
break
|
||||
|
||||
# Experts argue in parallel (with concurrency limit)
|
||||
async def _bounded_debate(e: Any) -> str:
|
||||
async def _bounded_debate(e: object) -> str:
|
||||
async with self._phase_semaphore:
|
||||
return await self._generate_debate_argument(e, topic, history, round_num)
|
||||
|
||||
|
|
@ -234,7 +234,7 @@ class DebateRunnerMixin:
|
|||
return f"辩论主题：{topic}。请各位专家发表看法。"
|
||||
|
||||
async def _generate_debate_argument(
|
||||
self, expert: Expert, topic: str, history: list[dict[str, Any]], round_num: int
|
||||
self, expert: Expert, topic: str, history: list[dict[str, object]], round_num: int
|
||||
) -> str:
|
||||
"""Generate an expert's debate argument for the current round."""
|
||||
gateway = self._get_llm_gateway(expert)
|
||||
|
|
@ -269,7 +269,7 @@ class DebateRunnerMixin:
|
|||
return response.content.strip()
|
||||
|
||||
async def _generate_debate_summary(
|
||||
self, lead: Expert, topic: str, history: list[dict[str, Any]], round_num: int
|
||||
self, lead: Expert, topic: str, history: list[dict[str, object]], round_num: int
|
||||
) -> str:
|
||||
"""Generate Lead's summary of the current debate round."""
|
||||
gateway = self._get_llm_gateway(lead)
|
||||
|
|
@ -307,8 +307,8 @@ class DebateRunnerMixin:
|
|||
return f"[第 {round_num} 轮辩论完成，小结生成失败]"
|
||||
|
||||
async def _generate_debate_verdict(
|
||||
self, lead: Expert, topic: str, history: list[dict[str, Any]]
|
||||
) -> dict[str, Any]:
|
||||
self, lead: Expert, topic: str, history: list[dict[str, object]]
|
||||
) -> dict[str, object]:
|
||||
"""Generate Lead's final verdict for the debate."""
|
||||
gateway = self._get_llm_gateway(lead)
|
||||
if not gateway:
|
||||
|
|
@ -371,7 +371,7 @@ class DebateRunnerMixin:
|
|||
"conclusion": f"辩论主题：{topic}。裁决生成失败，建议参考辩论历史自行判断。",
|
||||
}
|
||||
|
||||
def _format_debate_history(self, history: list[dict[str, Any]]) -> str:
|
||||
def _format_debate_history(self, history: list[dict[str, object]]) -> str:
|
||||
"""Format debate history as readable text for LLM prompts."""
|
||||
if not history:
|
||||
return ""
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import TYPE_CHECKING, Any
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from .expert import Expert
|
||||
from .plan import PhaseStatus, PhaseType, PlanPhase, TeamPlan
|
||||
|
|
@ -23,7 +23,7 @@ class DivergenceDetectorMixin:
|
|||
# Shared state provided by TeamOrchestrator (annotations only)
|
||||
_team: ExpertTeam
|
||||
_debate_count: int
|
||||
_checkpoint: Any
|
||||
_checkpoint: object
|
||||
MAX_DEBATES: int
|
||||
|
||||
async def _maybe_add_plan_review_debate(self, lead: Expert, plan: TeamPlan, task: str) -> None:
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ import asyncio
|
|||
import copy
|
||||
import logging
|
||||
from datetime import datetime, timezone
|
||||
from typing import TYPE_CHECKING, Any
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from agentkit.core.config_driven import ConfigDrivenAgent
|
||||
from agentkit.core.protocol import TaskMessage, TaskResult, TaskStatus
|
||||
|
|
@ -37,7 +37,7 @@ class PhaseExecutorMixin:
|
|||
# U4: State offloading helpers — keep memory lean for long-horizon runs.
|
||||
_OFFLOAD_SUMMARY_LIMIT = 500
|
||||
|
||||
def _offload_result(self, content: str, ref_key: str) -> dict[str, Any]:
|
||||
def _offload_result(self, content: str, ref_key: str) -> dict[str, object]:
|
||||
"""Create an offloaded result: summary in memory, full content in workspace."""
|
||||
if not isinstance(content, str):
|
||||
content = str(content) if content is not None else ""
|
||||
|
|
@ -64,17 +64,17 @@ class PhaseExecutorMixin:
|
|||
logger.warning(f"Failed to read offloaded output '{ref_key}': {e}")
|
||||
return content
|
||||
|
||||
async def _execute_phase(self, phase: PlanPhase, plan: TeamPlan) -> dict[str, Any]:
|
||||
async def _execute_phase(self, phase: PlanPhase, plan: TeamPlan) -> dict[str, object]:
|
||||
"""Execute a single phase, dispatching by phase_type."""
|
||||
if phase.phase_type == PhaseType.DEBATE:
|
||||
return await self._execute_debate_phase(phase, plan)
|
||||
return await self._execute_execution_phase(phase, plan)
|
||||
|
||||
async def _execute_execution_phase(self, phase: PlanPhase, plan: TeamPlan) -> dict[str, Any]:
|
||||
async def _execute_execution_phase(self, phase: PlanPhase, plan: TeamPlan) -> dict[str, object]:
|
||||
"""Execute a standard EXECUTION phase. Split into 3 sub-methods (U2, KTD3 isolation)."""
|
||||
expert, agent, lead = await self._prepare_phase_context(phase, plan)
|
||||
last_error: str | None = None
|
||||
result: dict[str, Any] | None = None
|
||||
result: dict[str, object] | None = None
|
||||
|
||||
try:
|
||||
# U3: 返工循环 — 最多 MAX_REWORKS + 1 次（1 次初始 + MAX_REWORKS 次返工）
|
||||
|
|
@ -135,11 +135,11 @@ class PhaseExecutorMixin:
|
|||
self,
|
||||
expert: Expert,
|
||||
phase: PlanPhase,
|
||||
dependency_outputs: dict[str, Any],
|
||||
dependency_outputs: dict[str, object],
|
||||
collaboration_outputs: dict[str, str],
|
||||
) -> TaskMessage:
|
||||
"""Build TaskMessage for execution with context isolation."""
|
||||
input_data: dict[str, Any] = {
|
||||
input_data: dict[str, object] = {
|
||||
"task": phase.task_description,
|
||||
"team_id": self._team.team_id,
|
||||
"phase_id": phase.id,
|
||||
|
|
@ -180,12 +180,12 @@ class PhaseExecutorMixin:
|
|||
lead: Expert,
|
||||
phase: PlanPhase,
|
||||
plan: TeamPlan,
|
||||
) -> tuple[dict[str, Any], str | None, bool, str, bool]:
|
||||
) -> tuple[dict[str, object], str | None, bool, str, bool]:
|
||||
"""Run one rework iteration: read deps, build input, execute, review. Returns
|
||||
(result, last_error, passed, feedback, degraded). Raises RuntimeError on retry
|
||||
exhaustion."""
|
||||
# 每次迭代重新读取依赖输出（前置阶段可能在返工期间完成）
|
||||
dependency_outputs: dict[str, Any] = {}
|
||||
dependency_outputs: dict[str, object] = {}
|
||||
for dep_id in phase.depends_on:
|
||||
dep_phase = plan.get_phase(dep_id)
|
||||
if dep_phase and dep_phase.status == PhaseStatus.COMPLETED and dep_phase.result:
|
||||
|
|
@ -216,7 +216,7 @@ class PhaseExecutorMixin:
|
|||
|
||||
# 执行专家任务（带重试，MAX_RETRIES 处理瞬时失败）
|
||||
last_error: str | None = None
|
||||
result: dict[str, Any] | None = None
|
||||
result: dict[str, object] | None = None
|
||||
for attempt in range(self.MAX_RETRIES + 1):
|
||||
try:
|
||||
task_result: TaskResult = await agent.execute(task_msg)
|
||||
|
|
@ -265,7 +265,7 @@ class PhaseExecutorMixin:
|
|||
lead: Expert,
|
||||
phase: PlanPhase,
|
||||
plan: TeamPlan,
|
||||
result: dict[str, Any],
|
||||
result: dict[str, object],
|
||||
passed: bool,
|
||||
feedback: str,
|
||||
degraded: bool = False,
|
||||
|
|
|
|||
|
|
@ -10,7 +10,6 @@ import json
|
|||
import logging
|
||||
import re
|
||||
from dataclasses import dataclass
|
||||
from typing import Any
|
||||
|
||||
from agentkit.core.exceptions import LLMProviderError
|
||||
|
||||
|
|
@ -45,7 +44,7 @@ class ReviewGateMixin:
|
|||
"""Mixin: Lead 验收阶段输出质量 + 解析风险标记。由 TeamOrchestrator 组合。"""
|
||||
|
||||
async def _review_phase_output(
|
||||
self, lead: Expert, phase: PlanPhase, result: dict[str, Any]
|
||||
self, lead: Expert, phase: PlanPhase, result: dict[str, object]
|
||||
) -> ReviewResult:
|
||||
"""Lead 验收阶段输出质量。
|
||||
|
||||
|
|
@ -93,7 +92,7 @@ class ReviewGateMixin:
|
|||
)
|
||||
|
||||
# P2: 优先尝试直接解析整个响应为 JSON，避免贪婪正则匹配过多
|
||||
review: dict[str, Any] | None = None
|
||||
review: dict[str, object] | None = None
|
||||
try:
|
||||
review = json.loads(response.content)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import TYPE_CHECKING, Any
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from agentkit.orchestrator.rollback import RollbackExecutor
|
||||
|
||||
|
|
@ -27,7 +27,7 @@ class RollbackHandlerMixin:
|
|||
_rollback_timeout: float
|
||||
|
||||
async def _mark_dependents_failed(
|
||||
self, failed_phase_id: str, plan: TeamPlan, phase_results: dict[str, dict[str, Any]]
|
||||
self, failed_phase_id: str, plan: TeamPlan, phase_results: dict[str, dict[str, object]]
|
||||
) -> None:
|
||||
"""Mark all phases that depend on the failed phase as FAILED."""
|
||||
for ph in plan.phases:
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ from __future__ import annotations
|
|||
|
||||
import logging
|
||||
from datetime import datetime, timezone
|
||||
from typing import TYPE_CHECKING, Any
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from agentkit.core.protocol import TaskMessage, TaskResult
|
||||
|
||||
|
|
@ -29,7 +29,7 @@ class SynthesizerMixin:
|
|||
|
||||
async def _synthesize_results(
|
||||
self, lead: Expert, task: str, completed_phases: list[PlanPhase]
|
||||
) -> dict[str, Any]:
|
||||
) -> dict[str, object]:
|
||||
"""Lead Expert synthesizes results using BEST strategy.
|
||||
|
||||
The Lead Expert evaluates all completed phase results and produces
|
||||
|
|
@ -114,8 +114,8 @@ class SynthesizerMixin:
|
|||
self,
|
||||
task: str,
|
||||
plan: TeamPlan,
|
||||
phase_results: dict[str, dict[str, Any]],
|
||||
) -> dict[str, Any]:
|
||||
phase_results: dict[str, dict[str, object]],
|
||||
) -> dict[str, object]:
|
||||
"""Fallback to single agent mode when pipeline execution fails.
|
||||
|
||||
Uses the lead expert (or first active expert) to complete the original task.
|
||||
|
|
@ -128,7 +128,7 @@ class SynthesizerMixin:
|
|||
active = self._team.active_experts
|
||||
expert = active[0] if active else None
|
||||
|
||||
fallback_result: dict[str, Any] | None = None
|
||||
fallback_result: dict[str, object] | None = None
|
||||
if expert:
|
||||
try:
|
||||
task_msg = TaskMessage(
|
||||
|
|
|
|||
|
|
@ -16,7 +16,6 @@ import enum
|
|||
import logging
|
||||
import time
|
||||
import uuid
|
||||
from typing import Any
|
||||
|
||||
from .config import ExpertConfig
|
||||
from .expert import Expert
|
||||
|
|
@ -72,7 +71,7 @@ class BoardTeam:
|
|||
|
||||
# Discussion state
|
||||
self._topic: str = ""
|
||||
self._history: list[dict[str, Any]] = []
|
||||
self._history: list[dict[str, object]] = []
|
||||
self._current_round: int = 0
|
||||
self._max_rounds: int = max_rounds
|
||||
self._user_interventions: list[str] = [] # Pending user messages
|
||||
|
|
@ -125,7 +124,7 @@ class BoardTeam:
|
|||
return self._max_rounds
|
||||
|
||||
@property
|
||||
def history(self) -> list[dict[str, Any]]:
|
||||
def history(self) -> list[dict[str, object]]:
|
||||
return self._history.copy()
|
||||
|
||||
def get_expert(self, name: str) -> Expert | None:
|
||||
|
|
@ -254,7 +253,7 @@ class BoardTeam:
|
|||
|
||||
return "\n\n---\n\n".join(lines)
|
||||
|
||||
async def compress_history(self, moderator: Expert, llm_gateway: Any) -> None:
|
||||
async def compress_history(self, moderator: Expert, llm_gateway: object) -> None:
|
||||
"""Compress discussion history when it exceeds token threshold.
|
||||
|
||||
The moderator summarizes each round's key points, replacing
|
||||
|
|
@ -291,7 +290,7 @@ class BoardTeam:
|
|||
|
||||
# Parse compressed history back to entries
|
||||
# This is a best-effort compression; if parsing fails, keep original
|
||||
new_history: list[dict[str, Any]] = []
|
||||
new_history: list[dict[str, object]] = []
|
||||
current_round = 0
|
||||
for line in compressed.split("\n"):
|
||||
line = line.strip()
|
||||
|
|
|
|||
|
|
@ -19,7 +19,6 @@ from __future__ import annotations
|
|||
|
||||
import asyncio
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from .expert import Expert
|
||||
from .board import BoardTeam, BoardStatus
|
||||
|
|
@ -43,7 +42,7 @@ class BoardOrchestrator:
|
|||
def __init__(self, team: BoardTeam) -> None:
|
||||
self._team = team
|
||||
|
||||
async def execute(self, topic: str) -> dict[str, Any]:
|
||||
async def execute(self, topic: str) -> dict[str, object]:
|
||||
"""Execute a board meeting discussion.
|
||||
|
||||
Flow:
|
||||
|
|
@ -351,7 +350,7 @@ class BoardOrchestrator:
|
|||
logger.warning(f"Moderator summary generation failed: {e}")
|
||||
return f"[第 {round} 轮讨论完成，主持人小结生成失败]"
|
||||
|
||||
async def _generate_final_conclusion(self, moderator: Expert, topic: str) -> dict[str, Any]:
|
||||
async def _generate_final_conclusion(self, moderator: Expert, topic: str) -> dict[str, object]:
|
||||
"""Generate moderator's final conclusion.
|
||||
|
||||
The moderator gives:
|
||||
|
|
@ -428,7 +427,7 @@ class BoardOrchestrator:
|
|||
"dissent_points": [],
|
||||
}
|
||||
|
||||
async def _generate_fallback_conclusion(self, moderator: Expert, topic: str) -> dict[str, Any]:
|
||||
async def _generate_fallback_conclusion(self, moderator: Expert, topic: str) -> dict[str, object]:
|
||||
"""Generate a fallback conclusion when execution fails.
|
||||
|
||||
Uses existing discussion history to provide a basic summary.
|
||||
|
|
@ -491,7 +490,7 @@ class BoardOrchestrator:
|
|||
return True
|
||||
return False
|
||||
|
||||
def _get_llm_gateway(self, expert: Expert | None = None) -> Any:
|
||||
def _get_llm_gateway(self, expert: Expert | None = None) -> object:
|
||||
"""Get LLM gateway from the given expert or the moderator's agent.
|
||||
|
||||
Falls back to other active experts if the primary target has no gateway.
|
||||
|
|
@ -509,7 +508,7 @@ class BoardOrchestrator:
|
|||
return gateway
|
||||
return None
|
||||
|
||||
async def _broadcast_event(self, event_type: str, data: dict[str, Any]) -> None:
|
||||
async def _broadcast_event(self, event_type: str, data: dict[str, object]) -> None:
|
||||
"""Broadcast a board event to the team channel.
|
||||
|
||||
Events are emitted via handoff_transport for WebSocket relay.
|
||||
|
|
|
|||
|
|
@ -3,7 +3,6 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Any
|
||||
|
||||
from agentkit.core.config_driven import AgentConfig
|
||||
|
||||
|
|
@ -24,12 +23,12 @@ class ExpertConfig(AgentConfig):
|
|||
task_mode: str = "llm_generate",
|
||||
supported_tasks: list[str] | None = None,
|
||||
max_concurrency: int = 1,
|
||||
input_schema: dict[str, Any] | None = None,
|
||||
output_schema: dict[str, Any] | None = None,
|
||||
input_schema: dict[str, object] | None = None,
|
||||
output_schema: dict[str, object] | None = None,
|
||||
prompt: dict[str, str] | None = None,
|
||||
llm: dict[str, Any] | None = None,
|
||||
llm: dict[str, object] | None = None,
|
||||
tools: list[str] | None = None,
|
||||
memory: dict[str, Any] | None = None,
|
||||
memory: dict[str, object] | None = None,
|
||||
custom_handler: str | None = None,
|
||||
# Expert 专属字段
|
||||
persona: str = "",
|
||||
|
|
@ -70,7 +69,7 @@ class ExpertConfig(AgentConfig):
|
|||
self.decision_framework = decision_framework
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, Any]) -> ExpertConfig:
|
||||
def from_dict(cls, data: dict[str, object]) -> ExpertConfig:
|
||||
"""从字典创建配置"""
|
||||
return cls(
|
||||
name=data["name"],
|
||||
|
|
@ -98,7 +97,7 @@ class ExpertConfig(AgentConfig):
|
|||
decision_framework=data.get("decision_framework", ""),
|
||||
)
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
def to_dict(self) -> dict[str, object]:
|
||||
"""序列化为字典，包含 Expert 专属字段"""
|
||||
d = super().to_dict()
|
||||
d["persona"] = self.persona
|
||||
|
|
@ -125,7 +124,7 @@ class ExpertTemplate:
|
|||
is_builtin: bool = False
|
||||
description: str = ""
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
def to_dict(self) -> dict[str, object]:
|
||||
"""序列化为字典"""
|
||||
return {
|
||||
"name": self.name,
|
||||
|
|
@ -135,7 +134,7 @@ class ExpertTemplate:
|
|||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, Any]) -> ExpertTemplate:
|
||||
def from_dict(cls, data: dict[str, object]) -> ExpertTemplate:
|
||||
"""从字典创建模板"""
|
||||
config_data = data["config"]
|
||||
config = ExpertConfig.from_dict(config_data)
|
||||
|
|
|
|||
|
|
@ -14,7 +14,6 @@ import asyncio
|
|||
import json
|
||||
import logging
|
||||
import re
|
||||
from typing import Any
|
||||
|
||||
from agentkit.core.exceptions import LLMProviderError
|
||||
from agentkit.llm.gateway import LLMGateway
|
||||
|
|
@ -72,7 +71,7 @@ class TeamOrchestrator(
|
|||
self,
|
||||
team: ExpertTeam,
|
||||
max_concurrent_phases: int | None = None,
|
||||
checkpoint: Any = None,
|
||||
checkpoint: object | None = None,
|
||||
workspace_root: str | None = None,
|
||||
rollback_timeout: float | None = None,
|
||||
) -> None:
|
||||
|
|
@ -95,7 +94,7 @@ class TeamOrchestrator(
|
|||
self._workspace_root = workspace_root
|
||||
self._rollback_timeout = rollback_timeout or self.DEFAULT_ROLLBACK_TIMEOUT
|
||||
|
||||
async def execute(self, task: str) -> dict[str, Any]:
|
||||
async def execute(self, task: str) -> dict[str, object]:
|
||||
"""Execute a task in pipeline mode. Lead decomposes → topological sort →
|
||||
execute layers (parallel within layer) → synthesize. Returns dict with
|
||||
status/result/phase_results/plan."""
|
||||
|
|
@ -175,7 +174,7 @@ class TeamOrchestrator(
|
|||
|
||||
# 4. Set EXECUTING status, execute phases
|
||||
self._team.set_status(TeamStatus.EXECUTING)
|
||||
phase_results: dict[str, dict[str, Any]] = {}
|
||||
phase_results: dict[str, dict[str, object]] = {}
|
||||
|
||||
return await self._run_pipeline(lead, plan, phase_results, task)
|
||||
|
||||
|
|
@ -183,9 +182,9 @@ class TeamOrchestrator(
|
|||
self,
|
||||
lead: Expert,
|
||||
plan: TeamPlan,
|
||||
phase_results: dict[str, dict[str, Any]],
|
||||
phase_results: dict[str, dict[str, object]],
|
||||
task: str,
|
||||
) -> dict[str, Any]:
|
||||
) -> dict[str, object]:
|
||||
"""Execute the pipeline loop: run pending phases, synthesize, return result.
|
||||
|
||||
Shared by execute() and resume(). phase_results may be pre-populated
|
||||
|
|
@ -220,7 +219,7 @@ class TeamOrchestrator(
|
|||
break
|
||||
|
||||
# Execute all phases in this layer in parallel (with concurrency limit)
|
||||
async def _bounded_phase(ph: PlanPhase) -> dict[str, Any]:
|
||||
async def _bounded_phase(ph: PlanPhase) -> dict[str, object]:
|
||||
async with self._phase_semaphore:
|
||||
return await self._execute_phase(ph, plan)
|
||||
|
||||
|
|
@ -335,7 +334,7 @@ class TeamOrchestrator(
|
|||
await self._broadcast_event("team_dissolved", {"team_id": self._team.team_id})
|
||||
return await self._fallback_to_single_agent(task, plan, phase_results)
|
||||
|
||||
async def resume(self, plan_id: str) -> dict[str, Any]:
|
||||
async def resume(self, plan_id: str) -> dict[str, object]:
|
||||
"""Resume from last checkpoint: load plan, restore completed/failed phases,
|
||||
continue via _run_pipeline. Returns same dict shape as execute()."""
|
||||
if self._checkpoint is None:
|
||||
|
|
@ -362,7 +361,7 @@ class TeamOrchestrator(
|
|||
|
||||
# 3. Load checkpoints, mark completed phases
|
||||
checkpoints = await self._checkpoint.list_checkpoints(plan_id)
|
||||
phase_results: dict[str, dict[str, Any]] = {}
|
||||
phase_results: dict[str, dict[str, object]] = {}
|
||||
completed_phase_ids: set[str] = set()
|
||||
failed_phase_ids: set[str] = set()
|
||||
|
||||
|
|
@ -492,7 +491,7 @@ class TeamOrchestrator(
|
|||
|
||||
# First pass: create phases with IDs, build name->id mapping
|
||||
name_to_id: dict[str, str] = {}
|
||||
raw_phases: list[dict[str, Any]] = []
|
||||
raw_phases: list[dict[str, object]] = []
|
||||
|
||||
for item in items:
|
||||
if not isinstance(item, dict):
|
||||
|
|
@ -584,7 +583,7 @@ class TeamOrchestrator(
|
|||
return gateway
|
||||
return None
|
||||
|
||||
async def _broadcast_event(self, event_type: str, data: dict[str, Any]) -> None:
|
||||
async def _broadcast_event(self, event_type: str, data: dict[str, object]) -> None:
|
||||
"""Broadcast an orchestration event to the team channel via handoff_transport."""
|
||||
if self._team.handoff_transport:
|
||||
try:
|
||||
|
|
|
|||
|
|
@ -15,7 +15,6 @@ from __future__ import annotations
|
|||
import enum
|
||||
import uuid
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any
|
||||
|
||||
|
||||
class MergeStrategy(str, enum.Enum):
|
||||
|
|
@ -82,9 +81,9 @@ class SubTask:
|
|||
description: str = ""
|
||||
assigned_expert: str = ""
|
||||
status: SubTaskStatus = SubTaskStatus.PENDING
|
||||
result: dict[str, Any] | None = None
|
||||
result: dict[str, object] | None = None
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
def to_dict(self) -> dict[str, object]:
|
||||
"""序列化为字典"""
|
||||
return {
|
||||
"id": self.id,
|
||||
|
|
@ -95,7 +94,7 @@ class SubTask:
|
|||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, Any]) -> SubTask:
|
||||
def from_dict(cls, data: dict[str, object]) -> SubTask:
|
||||
"""从字典创建 SubTask"""
|
||||
return cls(
|
||||
id=data.get("id", str(uuid.uuid4())),
|
||||
|
|
@ -124,7 +123,7 @@ class CollaborationContract:
|
|||
content_description: str = ""
|
||||
status: str = "pending"
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
def to_dict(self) -> dict[str, object]:
|
||||
"""序列化为字典"""
|
||||
return {
|
||||
"from_expert": self.from_expert,
|
||||
|
|
@ -134,7 +133,7 @@ class CollaborationContract:
|
|||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, Any]) -> CollaborationContract:
|
||||
def from_dict(cls, data: dict[str, object]) -> CollaborationContract:
|
||||
"""从字典创建 CollaborationContract"""
|
||||
return cls(
|
||||
from_expert=data.get("from_expert", ""),
|
||||
|
|
@ -176,9 +175,9 @@ class PlanPhase:
|
|||
task_description: str = ""
|
||||
depends_on: list[str] = field(default_factory=list)
|
||||
status: PhaseStatus = PhaseStatus.PENDING
|
||||
result: dict[str, Any] | None = None
|
||||
result: dict[str, object] | None = None
|
||||
phase_type: PhaseType = PhaseType.EXECUTION
|
||||
debate_config: dict[str, Any] | None = None
|
||||
debate_config: dict[str, object] | None = None
|
||||
collaboration_contracts: list[CollaborationContract] = field(default_factory=list)
|
||||
rework_count: int = 0
|
||||
review_feedback: str | None = None
|
||||
|
|
@ -188,7 +187,7 @@ class PlanPhase:
|
|||
validation_command: str | None = None
|
||||
rollback_command: str | None = None
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
def to_dict(self) -> dict[str, object]:
|
||||
"""序列化为字典"""
|
||||
# Serialize result to string to match frontend ITeamPlanPhase.result type
|
||||
result_str: str | None = None
|
||||
|
|
@ -197,7 +196,7 @@ class PlanPhase:
|
|||
result_str = self.result.get("content", str(self.result))
|
||||
else:
|
||||
result_str = str(self.result)
|
||||
out: dict[str, Any] = {
|
||||
out: dict[str, object] = {
|
||||
"id": self.id,
|
||||
"name": self.name,
|
||||
"assigned_expert": self.assigned_expert,
|
||||
|
|
@ -219,7 +218,7 @@ class PlanPhase:
|
|||
return out
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, Any]) -> PlanPhase:
|
||||
def from_dict(cls, data: dict[str, object]) -> PlanPhase:
|
||||
"""从字典创建 PlanPhase"""
|
||||
contracts_data = data.get("collaboration_contracts", [])
|
||||
if not isinstance(contracts_data, list):
|
||||
|
|
@ -272,7 +271,7 @@ class TeamPlan:
|
|||
status: PlanStatus = PlanStatus.DRAFT
|
||||
lead_expert: str = ""
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
def to_dict(self) -> dict[str, object]:
|
||||
"""序列化为字典"""
|
||||
return {
|
||||
"id": self.id,
|
||||
|
|
@ -284,7 +283,7 @@ class TeamPlan:
|
|||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, Any]) -> TeamPlan:
|
||||
def from_dict(cls, data: dict[str, object]) -> TeamPlan:
|
||||
"""从字典创建 TeamPlan"""
|
||||
subtasks = [SubTask.from_dict(st) for st in data.get("subtasks", [])]
|
||||
phases = [PlanPhase.from_dict(ph) for ph in data.get("phases", [])]
|
||||
|
|
@ -307,7 +306,7 @@ class TeamPlan:
|
|||
return None
|
||||
|
||||
def update_subtask_status(
|
||||
self, subtask_id: str, status: SubTaskStatus, result: dict[str, Any] | None = None
|
||||
self, subtask_id: str, status: SubTaskStatus, result: dict[str, object] | None = None
|
||||
) -> None:
|
||||
"""更新子任务状态和可选的结果"""
|
||||
st = self.get_subtask(subtask_id)
|
||||
|
|
@ -343,7 +342,7 @@ class TeamPlan:
|
|||
return None
|
||||
|
||||
def update_phase_status(
|
||||
self, phase_id: str, status: PhaseStatus, result: dict[str, Any] | None = None
|
||||
self, phase_id: str, status: PhaseStatus, result: dict[str, object] | None = None
|
||||
) -> None:
|
||||
"""更新阶段状态和可选的结果"""
|
||||
ph = self.get_phase(phase_id)
|
||||
|
|
|
|||
|
|
@ -17,7 +17,6 @@ import enum
|
|||
import logging
|
||||
import time
|
||||
import uuid
|
||||
from typing import Any
|
||||
|
||||
from .config import ExpertConfig
|
||||
from .expert import Expert
|
||||
|
|
@ -63,7 +62,7 @@ class ExpertTeam:
|
|||
workspace: SharedWorkspace | None = None,
|
||||
pool: AgentPool | None = None,
|
||||
template_registry: ExpertTemplateRegistry | None = None,
|
||||
redis_client: Any = None,
|
||||
redis_client: object | None = None,
|
||||
):
|
||||
self.team_id = team_id or str(uuid.uuid4())
|
||||
# U4: Accept redis_client for SharedWorkspace state offloading.
|
||||
|
|
|
|||
Loading…
Reference in New Issue