refactor: remove Any from core/ + experts/ type signatures (185 sites)

- core/ 105 sites: react.py(34), rewoo.py(30), config_driven.py(14), middleware.py(10), base.py(7), plan_executor.py(8), fallback.py(2)
- experts/ 80 sites: plan.py(15), _phase_executor.py(11), orchestrator.py(11), _debate_runner.py(9), config.py(9), board_orchestrator.py(6), _synthesizer.py(5), board.py(5), _review_gate.py(3), _rollback_handler.py(2), _divergence_detector.py(2), team.py(2)
- Strategy: object > TYPE_CHECKING Protocol > TYPE_CHECKING import
- No recursive TypeAlias (Pydantic v2 RecursionError)

Tests: 1139 passed, 0 regressions
ruff: 0 new errors

src/agentkit/core/base.py

@@ -13,7 +13,7 @@ import logging
 import time
 from abc import ABC, abstractmethod
 from datetime import datetime, timezone
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING
 
 import redis.asyncio as aioredis
 
@@ -71,7 +71,7 @@ class BaseAgent(ABC):
         # 可插拔能力（由子类或配置注入）
         self._tools: list["Tool"] = []
         self._memory: "Memory | None" = None
-        self._memory_retriever: Any | None = None
+        self._memory_retriever: object | None = None
 
         # 外部依赖注入（由 start() 时设置）
         self._registry = None
@@ -192,7 +192,7 @@ class BaseAgent(ABC):
             lines.append(f"  - {msg}")
         return "\n".join(lines)
 
-    def _build_skill_context(self) -> dict[str, Any] | None:
+    def _build_skill_context(self) -> dict[str, object] | None:
         """从当前技能配置构建 skill_context，用于 QualityGate skill_match 校验"""
         if not self._skill:
             return None
@@ -216,17 +216,17 @@ class BaseAgent(ABC):
         self._memory = memory
         return self
 
-    def use_memory_retriever(self, retriever: Any) -> "BaseAgent":
+    def use_memory_retriever(self, retriever: object) -> "BaseAgent":
         """设置记忆检索器，用于上下文注入"""
         self._memory_retriever = retriever
         return self
 
-    def set_registry(self, registry: Any) -> "BaseAgent":
+    def set_registry(self, registry: object) -> "BaseAgent":
         """注入注册中心"""
         self._registry = registry
         return self
 
-    def set_dispatcher(self, dispatcher: Any) -> "BaseAgent":
+    def set_dispatcher(self, dispatcher: object) -> "BaseAgent":
         """注入任务分发器"""
         self._dispatcher = dispatcher
         return self
@@ -489,7 +489,7 @@ class BaseAgent(ABC):
         target_agent: str,
         task: TaskMessage,
         reason: str,
-        context: dict[str, Any] | None = None,
+        context: dict[str, object] | None = None,
     ):
         """将当前任务转交给另一个 Agent"""
         if self._redis is None:

src/agentkit/core/config_driven.py

@@ -10,7 +10,7 @@
 import json
 import logging
 import os
-from typing import Any, Callable, Coroutine
+from typing import Callable, Coroutine
 
 import yaml
 
@@ -39,12 +39,12 @@ class AgentConfig:
         task_mode: str = "llm_generate",
         supported_tasks: list[str] | None = None,
         max_concurrency: int = 1,
-        input_schema: dict[str, Any] | None = None,
+        input_schema: dict[str, object] | None = None,
-        output_schema: dict[str, Any] | 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,
     ):
         self.name = name
@@ -96,7 +96,7 @@ class AgentConfig:
             )
 
     @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> "AgentConfig":
+    def from_dict(cls, data: dict[str, object]) -> "AgentConfig":
         """从字典创建配置"""
         return cls(
             name=data["name"],
@@ -128,7 +128,7 @@ class AgentConfig:
             )
         return cls.from_dict(data)
 
-    def to_dict(self) -> dict[str, Any]:
+    def to_dict(self) -> dict[str, object]:
         """序列化为字典"""
         d = {
             "name": self.name,
@@ -197,11 +197,11 @@ class ConfigDrivenAgent(BaseAgent, EvolutionMixin):
         self,
         config: AgentConfig,
         tool_registry: ToolRegistry | None = None,
-        llm_client: Any = None,
+        llm_client: object | None = None,
         custom_handlers: dict[str, Callable[..., Coroutine]] | None = None,
-        llm_gateway: Any = None,  # NEW v2 param: LLMGateway
+        llm_gateway: object | None = None,  # NEW v2 param: LLMGateway
         mcp_servers: dict[str, str] | None = None,  # NEW v2 param: MCP server URLs
-        compressor: Any = None,  # CompressionStrategy | None
+        compressor: object | None = None,  # CompressionStrategy | None
     ):
         # v2: If SkillConfig, extract skill info
         from agentkit.skills.base import SkillConfig, Skill
@@ -310,12 +310,12 @@ class ConfigDrivenAgent(BaseAgent, EvolutionMixin):
                     logger.info(f"Merged skill tool '{tool.name}' into agent '{self.name}'")
 
         # v2: Register MCP tools if mcp_servers provided
-        self._mcp_clients: list[Any] = []
+        self._mcp_clients: list[object] = []
         self._mcp_servers: dict[str, str] = mcp_servers or {}
         self._mcp_tools_registered = False
 
         # Memory integration: 从 config.memory 自动实例化 MemoryRetriever
-        self._memory_retriever: Any | None = None
+        self._memory_retriever: object | None = None
         if config.memory:
             try:
                 from agentkit.memory.retriever import MemoryRetriever
@@ -903,7 +903,7 @@ class ConfigDrivenAgent(BaseAgent, EvolutionMixin):
         )
         return await self.handle_task(enhanced_task)
 
-    def _wrap_llm_client(self, llm_client: Any):
+    def _wrap_llm_client(self, llm_client: object):
         """Wrap legacy llm_client into LLMGateway"""
         from agentkit.llm.gateway import LLMGateway
         from agentkit.llm.protocol import LLMProvider, LLMRequest, LLMResponse, TokenUsage
@@ -911,7 +911,7 @@ class ConfigDrivenAgent(BaseAgent, EvolutionMixin):
         class ClientProvider(LLMProvider):
             """Adapter: wraps legacy llm_client as an LLMProvider"""
 
-            def __init__(self, raw_client: Any):
+            def __init__(self, raw_client: object):
                 self._raw_client = raw_client
 
             async def chat(self, request: LLMRequest) -> LLMResponse:

src/agentkit/core/fallback.py

@@ -9,7 +9,6 @@ no LLM). See ``docs/plans/2026-06-29-003-feat-agent-wave2-medium-coupling-plan.m
 """
 
 from dataclasses import dataclass
-from typing import Any
 
 from agentkit.core.exceptions import (
     LLMProviderError,
@@ -54,7 +53,7 @@ class EmergencyError:
     retryable: bool  # whether a user retry might succeed
     original_error: str  # str(exc) for traceability
 
-    def to_dict(self) -> dict[str, Any]:
+    def to_dict(self) -> dict[str, object]:
         return {
             "error_code": self.error_code,
             "message": self.message,

src/agentkit/core/middleware.py

@@ -24,7 +24,7 @@ from __future__ import annotations
 import json
 import logging
 from dataclasses import dataclass, field
-from typing import Any, Awaitable, Callable, Protocol, runtime_checkable
+from typing import Awaitable, Callable, Protocol, runtime_checkable
 
 logger = logging.getLogger(__name__)
 
@@ -37,14 +37,14 @@ class RequestContext:
     """
 
     messages: list[dict[str, str]]
-    tools: list[Any] = field(default_factory=list)
+    tools: list[object] = field(default_factory=list)
     system_prompt: str | None = None
     model: str = "default"
     agent_name: str = ""
     task_type: str = ""
     task_id: str | None = None
     # 中间件间共享状态（压缩结果、token 用量、循环检测状态等）
-    metadata: dict[str, Any] = field(default_factory=dict)
+    metadata: dict[str, object] = field(default_factory=dict)
 
 
 @runtime_checkable
@@ -57,7 +57,7 @@ class Middleware(Protocol):
 
     async def before(self, ctx: RequestContext) -> RequestContext: ...
 
-    async def after(self, ctx: RequestContext, result: Any) -> Any: ...
+    async def after(self, ctx: RequestContext, result: object) -> object: ...
 
 
 class MiddlewareChain:
@@ -81,8 +81,8 @@ class MiddlewareChain:
     async def execute(
         self,
         ctx: RequestContext,
-        handler: Callable[[RequestContext], Awaitable[Any]],
+        handler: Callable[[RequestContext], Awaitable[object]],
-    ) -> Any:
+    ) -> object:
         """执行中间件链 + handler。
 
         洋葱模型：before 顺序执行 → handler → after 逆序执行。
@@ -125,7 +125,7 @@ class SummarizationMiddleware:
     after: 无操作（压缩在 before 完成）
     """
 
-    def __init__(self, compressor: Any = None) -> None:
+    def __init__(self, compressor: object | None = None) -> None:
         self._compressor = compressor
 
     async def before(self, ctx: RequestContext) -> RequestContext:
@@ -143,7 +143,7 @@ class SummarizationMiddleware:
                 logger.warning(f"SummarizationMiddleware: compression failed: {e}")
         return ctx
 
-    async def after(self, ctx: RequestContext, result: Any) -> Any:
+    async def after(self, ctx: RequestContext, result: object) -> object:
         return result
 
 
@@ -157,7 +157,7 @@ class TokenUsageMiddleware:
     async def before(self, ctx: RequestContext) -> RequestContext:
         return ctx
 
-    async def after(self, ctx: RequestContext, result: Any) -> Any:
+    async def after(self, ctx: RequestContext, result: object) -> object:
         # 从 ReActResult 或类似结构提取 token 用量
         # ReActResult 有 total_tokens 属性（非 token_usage）
         usage = getattr(result, "total_tokens", None)
@@ -184,7 +184,7 @@ class LoopDetectionMiddleware:
         ctx.metadata["loop_detection_window"] = []
         return ctx
 
-    async def after(self, ctx: RequestContext, result: Any) -> Any:
+    async def after(self, ctx: RequestContext, result: object) -> object:
         trajectory = getattr(result, "trajectory", None) or []
         if len(trajectory) < self._threshold:
             return result

src/agentkit/core/plan_executor.py

@@ -16,9 +16,9 @@ import asyncio
 import logging
 import random
 import time
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Callable, Awaitable
+from typing import Callable, Awaitable
 
 from agentkit.core.plan_schema import ExecutionPlan, PlanStep, PlanStepStatus
 from agentkit.core.protocol import TaskMessage, TaskResult, TaskStatus
@@ -42,7 +42,7 @@ class StepExecutionResult:
 
     step_id: str
     status: PlanStepStatus
-    result: dict[str, Any] | None = None
+    result: dict[str, object] | None = None
     error: str | None = None
     retry_count: int = 0
     duration_ms: float = 0.0
@@ -91,7 +91,7 @@ class PlanExecutor:
 
     def __init__(
         self,
-        agent_pool: Any,
+        agent_pool: object,
         max_retries: int = 2,
         step_timeout: float = 300.0,
         max_parallel: int = 5,
@@ -207,7 +207,7 @@ class PlanExecutor:
     async def _execute_step_with_retry(
         self,
         step: PlanStep,
-        input_data: dict[str, Any],
+        input_data: dict[str, object],
         original_task: TaskMessage,
     ) -> StepExecutionResult:
         """执行单个步骤，支持重试
@@ -281,9 +281,9 @@ class PlanExecutor:
     async def _execute_step_once(
         self,
         step: PlanStep,
-        input_data: dict[str, Any],
+        input_data: dict[str, object],
         original_task: TaskMessage,
-    ) -> dict[str, Any]:
+    ) -> dict[str, object]:
         """执行单个步骤一次
 
         通过 AgentPool 创建 Agent 执行步骤。
@@ -463,7 +463,7 @@ class PlanExecutor:
         self,
         step: PlanStep,
         step_results: dict[str, StepExecutionResult],
-    ) -> dict[str, Any]:
+    ) -> dict[str, object]:
         """将依赖步骤的结果注入到当前步骤的输入中
 
         兼容 Orchestrator 的 subtask_results 累积模式。
@@ -471,7 +471,7 @@ class PlanExecutor:
         enriched = dict(step.input_data)
 
         if step.dependencies:
-            dep_results: dict[str, dict[str, Any]] = {}
+            dep_results: dict[str, dict[str, object]] = {}
             for dep_id in step.dependencies:
                 if dep_id in step_results:
                     dep_result = step_results[dep_id]

src/agentkit/core/react.py

@@ -13,7 +13,7 @@ from collections import Counter, deque
 from collections.abc import AsyncGenerator
 from dataclasses import dataclass, field
 from datetime import datetime, timezone
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Awaitable, Callable
 
 from agentkit.core.exceptions import LLMProviderError, LoopDetectedError, TaskCancelledError, TaskTimeoutError
 from agentkit.core.protocol import CancellationToken
@@ -43,13 +43,13 @@ class ReActStep:
     step: int
     action: str  # "tool_call" or "final_answer"
     tool_name: str | None = None
-    arguments: dict[str, Any] | None = None
+    arguments: dict[str, object] | None = None
-    result: Any = None
+    result: object = None
     content: str | None = None
     tokens: int = 0
 
 
-async def _ensure_async_iterable(obj: Any, label: str = "<obj>"):
+async def _ensure_async_iterable(obj: object, label: str = "<obj>"):
     """Defensive helper: ensure the given object is an async iterable.
 
     Guards against the recurring ``'async for' requires an object with
@@ -133,7 +133,7 @@ class ReActEvent:
 
     event_type: str  # "thinking","token","tool_call","tool_result","confirmation_request","confirmation_result","phase_violation","step","final_answer","final_result","error"
     step: int
-    data: dict[str, Any] = field(default_factory=dict)
+    data: dict[str, object] = field(default_factory=dict)
     timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
 
 
@@ -230,7 +230,7 @@ class ReActEngine:
         # step and yields phase_violation ReActEvents. Non-streaming execute()
         # simply ignores the accumulator (the error dict returned to the LLM is
         # the only signal there).
-        self._phase_violations: list[dict[str, Any]] = []
+        self._phase_violations: list[dict[str, object]] = []
 
     def reset(self) -> None:
         """Reset internal state for reuse across conversations.
@@ -299,8 +299,8 @@ class ReActEngine:
         return False
 
     def _check_phase_permission(
-        self, tool_name: str, arguments: dict[str, Any]
+        self, tool_name: str, arguments: dict[str, object]
-    ) -> dict[str, Any] | None:
+    ) -> dict[str, object] | None:
         """Return None if tool is allowed; return a structured error dict if blocked.
 
         The error dict replaces what `_execute_tool` would have returned —
@@ -351,7 +351,7 @@ class ReActEngine:
                 return violation
         return None
 
-    def _check_tool_loop(self, tool_calls: list[Any]) -> str | None:
+    def _check_tool_loop(self, tool_calls: list[object]) -> str | None:
         """检测重复工具调用模式。
 
         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
@@ -406,10 +406,10 @@ 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,
     ) -> ReActResult:
         """执行 ReAct 循环
 
@@ -536,7 +536,7 @@ class ReActEngine:
 
     async def _run_loop_and_extract(
         self,
-        **kwargs: Any,
+        **kwargs: object,
     ) -> ReActResult:
         """Collect all events from _execute_loop and extract the final ReActResult.
 
@@ -564,9 +564,9 @@ 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,
-        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],
+        tool_calls: list[object],
-        usage: Any,
+        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,
+        confirmation_handler: Callable[..., Awaitable[object]] | None,
-    ) -> tuple[Any, list[ReActEvent]]:
+    ) -> 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)

src/agentkit/core/rewoo.py

@@ -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)

src/agentkit/experts/_debate_runner.py

@@ -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]]
+        self, lead: Expert, topic: str, history: list[dict[str, object]]
-    ) -> dict[str, Any]:
+    ) -> 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 ""

src/agentkit/experts/_divergence_detector.py

@@ -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:

src/agentkit/experts/_phase_executor.py

@@ -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,

src/agentkit/experts/_review_gate.py

@@ -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):

src/agentkit/experts/_rollback_handler.py

@@ -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:

src/agentkit/experts/_synthesizer.py

@@ -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]],
+        phase_results: dict[str, dict[str, object]],
-    ) -> dict[str, Any]:
+    ) -> 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(

src/agentkit/experts/board.py

@@ -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()

src/agentkit/experts/board_orchestrator.py

@@ -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.

src/agentkit/experts/config.py

@@ -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,
+        input_schema: dict[str, object] | None = None,
-        output_schema: dict[str, Any] | 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)

src/agentkit/experts/orchestrator.py

@@ -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:

src/agentkit/experts/plan.py

@@ -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)

src/agentkit/experts/team.py

@@ -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.