refactor(orchestrator+bitable): remove Any from type signatures

Eliminate 112 Any usages across orchestrator/ (62) and bitable/ (50) via:
- TYPE_CHECKING Protocol for Redis/LLMGateway/Plan/Dispatcher/StateManager
- object for arbitrary dict/list/value types (Pydantic v2 serializes fine)
- RecalcTask concrete import (replacing Any in recalc_worker.py)
- Coroutine[object, object, object] for async generic
- Remove unused Any imports (F401 cleanup)

Note: Avoided recursive TypeAlias (FieldValue) because Pydantic v2 cannot
build schemas for recursive named aliases (RecursionError).

Tests: 245 passed (bitable 91 + orchestrator 154), 0 regressions
ruff: All checks passed

src/agentkit/bitable/formula/engine.py

@@ -16,7 +16,6 @@ from __future__ import annotations
 
 import ast
 from collections import deque
-from typing import Any
 
 from agentkit.bitable.formula.functions import AGGREGATE_FUNCTIONS, FUNCTION_REGISTRY
 from agentkit.bitable.formula.parser import (
@@ -104,9 +103,9 @@ class FormulaEngine:
     def evaluate(
         self,
         field_id: str,
-        row_values: dict[str, Any],
-        column_values: dict[str, list[Any]] | None = None,
-    ) -> Any:
+        row_values: dict[str, object],
+        column_values: dict[str, list[object]] | None = None,
+    ) -> object:
         """Evaluate a formula field for a specific record.
 
         Args:
@@ -130,7 +129,7 @@ class FormulaEngine:
 
         # Build the field_values dict for the evaluator
         # Aggregate refs get column values (lists), row refs get row values (scalars)
-        eval_values: dict[str, Any] = {}
+        eval_values: dict[str, object] = {}
 
         # Map real field IDs to safe names
         for safe_name, real_id in entry.field_mapping.items():
@@ -143,16 +142,16 @@ class FormulaEngine:
 
     def evaluate_all_for_record(
         self,
-        row_values: dict[str, Any],
-        column_values: dict[str, list[Any]] | None = None,
-    ) -> dict[str, Any]:
+        row_values: dict[str, object],
+        column_values: dict[str, list[object]] | None = None,
+    ) -> dict[str, object]:
         """Evaluate all registered formulas for a record.
 
         Returns a dict of field_id → computed value.
         Formulas are evaluated in topological order so that formula-to-formula
         dependencies are resolved correctly.
         """
-        results: dict[str, Any] = {}
+        results: dict[str, object] = {}
         column_values = column_values or {}
 
         for field_id in self.topological_order():

src/agentkit/bitable/ingestion/api_collector.py

@@ -16,13 +16,11 @@ Usage::
 
 from __future__ import annotations
 
-from typing import Any
-
 
 def transform_records(
-    records: list[dict[str, Any]],
+    records: list[dict[str, object]],
     field_mapping: dict[str, str],
-) -> list[dict[str, Any]]:
+) -> list[dict[str, object]]:
     """Map source record keys to bitable field IDs via field_mapping.
 
     Keys not in ``field_mapping`` are dropped. Values are passed through
@@ -40,9 +38,9 @@ def transform_records(
     if not field_mapping:
         return []
 
-    transformed: list[dict[str, Any]] = []
+    transformed: list[dict[str, object]] = []
     for rec in records:
-        out: dict[str, Any] = {}
+        out: dict[str, object] = {}
         for src_key, field_id in field_mapping.items():
             if src_key in rec:
                 out[field_id] = rec[src_key]

src/agentkit/bitable/ingestion/database.py

@@ -16,7 +16,6 @@ Type mapping (KTD: DB → bitable):
 from __future__ import annotations
 
 import logging
-from typing import Any
 
 from sqlalchemy import (
     BigInteger,
@@ -56,7 +55,7 @@ DB_TYPE_MAP: dict[type, str] = {
 READ_BATCH = 1000
 
 
-def infer_field_type(sqla_type: Any) -> str:
+def infer_field_type(sqla_type: object) -> str:
     """Map a SQLAlchemy column type instance or class to a bitable field type.
 
     Handles both type instances (``Integer()``) and type classes (``Integer``).
@@ -78,7 +77,7 @@ def import_table(
     table_name: str,
     *,
     max_rows: int = 50_000,
-) -> dict[str, Any]:
+) -> dict[str, object]:
     """Reflect a single table from an external DB.
 
     Returns ``{"table_name": str, "fields": [...], "records": [...],
@@ -97,7 +96,7 @@ def import_table(
         engine.dispose()
 
 
-def _reflect_and_read(engine: Engine, table_name: str, max_rows: int) -> dict[str, Any]:
+def _reflect_and_read(engine: Engine, table_name: str, max_rows: int) -> dict[str, object]:
     """Reflect one table and read its rows."""
     insp = inspect(engine)
 
@@ -111,7 +110,7 @@ def _reflect_and_read(engine: Engine, table_name: str, max_rows: int) -> dict[st
     table = Table(table_name, metadata, autoload_with=engine)
 
     # Build field definitions
-    fields: list[dict[str, Any]] = []
+    fields: list[dict[str, object]] = []
     pk_columns = list(table.primary_key.columns)
     pk_name = pk_columns[0].name if pk_columns else None
 
@@ -131,14 +130,14 @@ def _reflect_and_read(engine: Engine, table_name: str, max_rows: int) -> dict[st
         pk_name = "id"
 
     # Read rows
-    records: list[dict[str, Any]] = []
+    records: list[dict[str, object]] = []
     with engine.connect() as conn:
         result = conn.execute(select(table))
         for i, row in enumerate(result):
             if i >= max_rows:
                 logger.warning("Table %r truncated at %d rows during import", table_name, max_rows)
                 break
-            rec: dict[str, Any] = {}
+            rec: dict[str, object] = {}
             for col in table.columns:
                 val = getattr(row, col.name, None)
                 if val is not None:
@@ -155,7 +154,7 @@ def _reflect_and_read(engine: Engine, table_name: str, max_rows: int) -> dict[st
     }
 
 
-def _serialize(val: Any) -> Any:
+def _serialize(val: object) -> object:
     """Serialize a DB value to JSON-safe form."""
     from datetime import date, datetime
     from decimal import Decimal

src/agentkit/bitable/ingestion/excel.py

@@ -18,7 +18,6 @@ import logging
 import socket
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any
 from urllib.parse import urlparse
 
 import httpx
@@ -36,7 +35,7 @@ class ParsedSheet:
     name: str
     columns: list[str] = field(default_factory=list)
     field_types: list[str] = field(default_factory=list)  # "text" | "number" | "date"
-    records: list[dict[str, Any]] = field(default_factory=list)
+    records: list[dict[str, object]] = field(default_factory=list)
 
 
 def parse_excel(file_path: str | Path) -> list[ParsedSheet]:
@@ -182,9 +181,9 @@ def _parse_worksheet(ws) -> ParsedSheet | None:
     col_count = len(clean_headers)
     field_types = _infer_column_types(data_rows, col_count)
 
-    records: list[dict[str, Any]] = []
+    records: list[dict[str, object]] = []
     for row in data_rows:
-        rec: dict[str, Any] = {}
+        rec: dict[str, object] = {}
         for i, col_name in enumerate(clean_headers):
             val = row[i] if i < len(row) else None
             if val is not None:
@@ -237,7 +236,7 @@ def _infer_column_types(rows: list[tuple], col_count: int) -> list[str]:
     return types
 
 
-def _coerce_value(val: Any, field_type: str) -> Any:
+def _coerce_value(val: object, field_type: str) -> object:
     """Coerce a cell value to the inferred field type. Truncate long strings."""
     if field_type == "date":
         from datetime import datetime

src/agentkit/bitable/models.py

@@ -9,10 +9,14 @@ from __future__ import annotations
 
 from datetime import datetime, timezone
 from enum import Enum
-from typing import Any
 
 from pydantic import BaseModel, ConfigDict, Field as PydanticField
 
+# ponytail: bitable JSONB columns hold arbitrary JSON. Using `object` instead of
+# a recursive TypeAlias because Pydantic v2 cannot build a schema for recursive
+# named aliases (RecursionError). `object` is the most permissive type and
+# Pydantic v2 serializes dict/list/primitive values fine at runtime.
+
 
 def _utcnow() -> datetime:
     return datetime.now(timezone.utc)
@@ -97,7 +101,7 @@ class Table(BaseModel):
 # ---------------------------------------------------------------------------
 
 # Status select field options — labels and colors match Feishu Bitable defaults.
-_STATUS_OPTIONS: list[dict[str, Any]] = [
+_STATUS_OPTIONS: list[dict[str, object]] = [
     {"label": "未开始", "value": "not_started", "color": "default"},
     {"label": "进行中", "value": "in_progress", "color": "processing"},
     {"label": "已完成", "value": "done", "color": "success"},
@@ -106,7 +110,7 @@ _STATUS_OPTIONS: list[dict[str, Any]] = [
 #: Templates for the 5 default fields created on every new table (R2).
 #: agent-owned fields (创建人/创建时间) are auto-filled by the service layer
 #: on record creation; user-owned fields are user-editable.
-DEFAULT_FIELD_TEMPLATES: list[dict[str, Any]] = [
+DEFAULT_FIELD_TEMPLATES: list[dict[str, object]] = [
     {
         "name": "标题",
         "field_type": FieldType.text,
@@ -155,7 +159,7 @@ class Field(BaseModel):
     table_id: str
     name: str
     field_type: FieldType
-    config: dict[str, Any] = PydanticField(default_factory=dict)
+    config: dict[str, object] = PydanticField(default_factory=dict)
     owner: FieldOwner = FieldOwner.user
     created_at: datetime = PydanticField(default_factory=_utcnow)
 
@@ -167,7 +171,7 @@ class Record(BaseModel):
 
     id: str
     table_id: str
-    values: dict[str, Any] = PydanticField(default_factory=dict)
+    values: dict[str, object] = PydanticField(default_factory=dict)
     created_at: datetime = PydanticField(default_factory=_utcnow)
     updated_at: datetime = PydanticField(default_factory=_utcnow)
 
@@ -181,7 +185,7 @@ class View(BaseModel):
     table_id: str
     name: str
     view_type: ViewType = ViewType.grid
-    config: dict[str, Any] = PydanticField(default_factory=dict)
+    config: dict[str, object] = PydanticField(default_factory=dict)
     created_at: datetime = PydanticField(default_factory=_utcnow)
 
 

src/agentkit/bitable/recalc_worker.py

@@ -18,11 +18,10 @@ from __future__ import annotations
 
 import asyncio
 import logging
-from typing import Any
 
 from agentkit.bitable.db import BitableDB
 from agentkit.bitable.formula.engine import FormulaEngine
-from agentkit.bitable.models import FieldType, RecalcStatus
+from agentkit.bitable.models import FieldType, RecalcStatus, RecalcTask
 from agentkit.bitable.repository import BitableRepository
 from agentkit.bitable.service import BitableService
 
@@ -124,7 +123,7 @@ class RecalcWorker:
                 logger.exception("RecalcWorker error in main loop")
                 await asyncio.sleep(self._poll_interval)
 
-    async def _sort_by_topological_order(self, tasks: list[Any]) -> list[Any]:
+    async def _sort_by_topological_order(self, tasks: list[RecalcTask]) -> list[RecalcTask]:
         """Sort claimed tasks so dependencies are processed first (P1 #7).
 
         Groups tasks by table_id, builds (or reuses) the engine to get the
@@ -146,7 +145,7 @@ class RecalcWorker:
             order = engine.topological_order()
             topo_index[tid] = {fid: i for i, fid in enumerate(order)}
 
-        def _key(t: Any) -> tuple[str, str, int]:
+        def _key(t: RecalcTask) -> tuple[str, str, int]:
             idx = topo_index.get(t.table_id, {}).get(t.field_id, 1 << 30)
             return (t.table_id, t.record_id, idx)
 
@@ -175,7 +174,7 @@ class RecalcWorker:
             except Exception:
                 logger.exception("RecalcWorker reaper error")
 
-    async def process_task(self, task: Any) -> None:
+    async def process_task(self, task: RecalcTask) -> None:
         """Process a single recalc task: evaluate formula → write result.
 
         The task is expected to already be in ``calculating`` status when
@@ -216,7 +215,7 @@ class RecalcWorker:
                 return
 
             deps = engine.get_dependencies(task.field_id)
-            column_values: dict[str, list[Any]] = {}
+            column_values: dict[str, list[object]] = {}
             for dep_field_id in deps:
                 column_values[dep_field_id] = await self._repo.get_column_values(
                     task.table_id, dep_field_id

src/agentkit/bitable/repository.py

@@ -10,7 +10,6 @@ from __future__ import annotations
 import logging
 import re
 from datetime import datetime, timedelta, timezone
-from typing import Any
 
 from sqlalchemy import delete, func, insert, select, text, update
 from sqlalchemy.dialects.postgresql import insert as pg_insert
@@ -102,7 +101,7 @@ class BitableRepository:
             result = await session.execute(stmt)
             return [BitableFile.model_validate(e) for e in result.scalars().all()]
 
-    async def update_file(self, file_id: str, **kwargs: Any) -> BitableFile | None:
+    async def update_file(self, file_id: str, **kwargs: object) -> BitableFile | None:
         """Update a file's attributes."""
         async with self._session_factory() as session:
             stmt = (
@@ -181,7 +180,7 @@ class BitableRepository:
             result = await session.execute(stmt)
             return [Table.model_validate(e) for e in result.scalars().all()]
 
-    async def update_table(self, table_id: str, **kwargs: Any) -> Table | None:
+    async def update_table(self, table_id: str, **kwargs: object) -> Table | None:
         """Update a table's attributes."""
         async with self._session_factory() as session:
             stmt = (
@@ -236,7 +235,7 @@ class BitableRepository:
         table_id: str,
         name: str,
         field_type: FieldType,
-        config: dict[str, Any] | None = None,
+        config: dict[str, object] | None = None,
         owner: FieldOwner = FieldOwner.user,
     ) -> Field:
         """Create a new field in a table."""
@@ -277,7 +276,7 @@ class BitableRepository:
             result = await session.execute(stmt)
             return [Field.model_validate(e) for e in result.scalars().all()]
 
-    async def update_field(self, field_id: str, **kwargs: Any) -> Field | None:
+    async def update_field(self, field_id: str, **kwargs: object) -> Field | None:
         """Update a field's attributes."""
         async with self._session_factory() as session:
             stmt = (
@@ -300,7 +299,7 @@ class BitableRepository:
 
     # ── Records ─────────────────────────────────────────────
 
-    async def create_record(self, table_id: str, values: dict[str, Any] | None = None) -> Record:
+    async def create_record(self, table_id: str, values: dict[str, object] | None = None) -> Record:
         """Create a new record."""
         async with self._session_factory() as session:
             stmt = (
@@ -318,7 +317,7 @@ class BitableRepository:
             return Record.model_validate(entity)
 
     async def create_records_batch(
-        self, table_id: str, records_values: list[dict[str, Any]]
+        self, table_id: str, records_values: list[dict[str, object]]
     ) -> list[Record]:
         """Batch-insert multiple records (P2 #19: eliminates per-record INSERT).
 
@@ -376,7 +375,7 @@ class BitableRepository:
 
             return [Record.model_validate(e) for e in entities], next_cursor
 
-    async def update_record_values(self, record_id: str, values: dict[str, Any]) -> Record | None:
+    async def update_record_values(self, record_id: str, values: dict[str, object]) -> Record | None:
         """Update a record's values (full replace)."""
         async with self._session_factory() as session:
             stmt = (
@@ -413,7 +412,7 @@ class BitableRepository:
         table_id: str,
         name: str,
         view_type: ViewType = ViewType.grid,
-        config: dict[str, Any] | None = None,
+        config: dict[str, object] | None = None,
     ) -> View:
         """Create a new view."""
         async with self._session_factory() as session:
@@ -451,7 +450,7 @@ class BitableRepository:
             result = await session.execute(stmt)
             return [View.model_validate(e) for e in result.scalars().all()]
 
-    async def update_view(self, view_id: str, **kwargs: Any) -> View | None:
+    async def update_view(self, view_id: str, **kwargs: object) -> View | None:
         """Update a view's attributes."""
         async with self._session_factory() as session:
             stmt = (
@@ -543,7 +542,7 @@ class BitableRepository:
     ) -> None:
         """Update a recalc task's status."""
         async with self._session_factory() as session:
-            kwargs: dict[str, Any] = {"status": status.value}
+            kwargs: dict[str, object] = {"status": status.value}
             if error_message is not None:
                 kwargs["error_message"] = error_message
             if status in (RecalcStatus.done, RecalcStatus.error):
@@ -630,7 +629,7 @@ class BitableRepository:
             return result_map
 
     async def upsert_record_agent_fields(
-        self, record_id: str, agent_field_values: dict[str, Any]
+        self, record_id: str, agent_field_values: dict[str, object]
     ) -> None:
         """Update agent-owned fields using jsonb_set (KTD8).
 
@@ -646,7 +645,7 @@ class BitableRepository:
             # Use CAST(:param AS jsonb) instead of :param::jsonb — asyncpg dialect
             # misparses the `::` as part of the param name.
             inner = "values"
-            params: dict[str, Any] = {"record_id": record_id}
+            params: dict[str, object] = {"record_id": record_id}
             for i, (field_id, value) in enumerate(agent_field_values.items()):
                 param_key = f"v{i}"
                 inner = f"jsonb_set({inner}, '{{{field_id}}}', CAST(:{param_key} AS jsonb), true)"
@@ -660,8 +659,8 @@ class BitableRepository:
     async def list_records_filtered(
         self,
         table_id: str,
-        filters: list[dict[str, Any]] | None = None,
-        sorts: list[dict[str, Any]] | None = None,
+        filters: list[dict[str, object]] | None = None,
+        sorts: list[dict[str, object]] | None = None,
         cursor: str | None = None,
         limit: int = 50,
     ) -> tuple[list[Record], str | None]:
@@ -682,7 +681,7 @@ class BitableRepository:
             # Build raw SQL with JSONB filter/sort translation.
             # ponytail: field_ids in filters/sorts are system UUIDs (validated by service layer).
             where_clauses = ["table_id = :table_id"]
-            params: dict[str, Any] = {"table_id": table_id}
+            params: dict[str, object] = {"table_id": table_id}
 
             if filters:
                 for i, f in enumerate(filters):
@@ -783,7 +782,7 @@ class BitableRepository:
                 last_mapping = last_row._mapping
                 # Build composite cursor from sort values + id.
                 # Sort values are extracted as text to match `values->>'fid'` expressions.
-                sv: list[Any] = []
+                sv: list[object] = []
                 last_values = last_mapping.get("values")
                 if isinstance(last_values, str):
                     # asyncpg may return JSONB as str in raw text() queries.
@@ -852,7 +851,7 @@ class BitableRepository:
 
     # ── Recalc support (U3) ────────────────────────────────
 
-    async def get_column_values(self, table_id: str, field_id: str) -> list[Any]:
+    async def get_column_values(self, table_id: str, field_id: str) -> list[object]:
         """Get all values for a field across all records in a table (for aggregates).
 
         Returns a list of values (preserving order by record id). Missing values
@@ -866,7 +865,7 @@ class BitableRepository:
             result = await session.execute(sql, {"field_id": field_id, "table_id": table_id})
             return [row[0] for row in result.fetchall()]
 
-    async def set_formula_value(self, record_id: str, field_id: str, value: Any) -> None:
+    async def set_formula_value(self, record_id: str, field_id: str, value: object) -> None:
         """Set a single formula field value in a record's JSONB (jsonb_set)."""
         import json
 

src/agentkit/orchestrator/checkpoint.py

@@ -17,7 +17,7 @@ import logging
 import time
 from dataclasses import asdict, dataclass, field
 from datetime import datetime, timezone
-from typing import Any
+from typing import TYPE_CHECKING
 
 logger = logging.getLogger(__name__)
 
@@ -25,6 +25,28 @@ _TTL_SECONDS = 7 * 24 * 3600  # 7 days
 _KEY_PREFIX = "agentkit:pipeline:checkpoint"
 
 
+if TYPE_CHECKING:
+    from typing import Protocol
+
+    class _RedisPipelineLike(Protocol):
+        def set(self, key: str, value: str, ex: int | None = None) -> object: ...
+        def zadd(self, name: str, mapping: dict[str, float]) -> object: ...
+        def get(self, key: str) -> object: ...
+        def delete(self, *keys: str) -> object: ...
+        async def execute(self) -> list[object]: ...
+
+    class _RedisLike(Protocol):
+        async def set(self, key: str, value: str, ex: int | None = None) -> object: ...
+        async def get(self, key: str) -> object: ...
+        def pipeline(self) -> _RedisPipelineLike: ...
+        async def zrange(self, name: str, start: int, stop: int) -> list[object]: ...
+
+    class _PlanLike(Protocol):
+        @property
+        def id(self) -> str: ...
+        def to_dict(self) -> dict[str, object]: ...
+
+
 @dataclass
 class CheckpointData:
     """单个阶段的 checkpoint 数据。"""
@@ -33,15 +55,15 @@ class CheckpointData:
     phase_id: str
     phase_name: str
     phase_status: str
-    phase_result: dict[str, Any] | None = None
+    phase_result: dict[str, object] | None = None
     plan_status: str = ""
     saved_at: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
 
-    def to_dict(self) -> dict[str, Any]:
+    def to_dict(self) -> dict[str, object]:
         return asdict(self)
 
     @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> CheckpointData:
+    def from_dict(cls, data: dict[str, object]) -> CheckpointData:
         return cls(
             plan_id=data.get("plan_id", ""),
             phase_id=data.get("phase_id", ""),
@@ -67,7 +89,7 @@ class PipelineCheckpoint:
 
     def __init__(
         self,
-        redis_client: Any = None,
+        redis_client: _RedisLike | None = None,
         prefix: str = _KEY_PREFIX,
         ttl_seconds: int = _TTL_SECONDS,
     ) -> None:
@@ -78,7 +100,7 @@ class PipelineCheckpoint:
         # P1 #6: 改用 dict keyed by phase_id，避免重复 append
         self._memory: dict[str, dict[str, CheckpointData]] = {}
         # 内存降级存储：plan_id → (plan_dict, saved_timestamp)
-        self._memory_plans: dict[str, tuple[dict[str, Any], float]] = {}
+        self._memory_plans: dict[str, tuple[dict[str, object], float]] = {}
 
     def _is_expired(self, saved_at: str) -> bool:
         """检查 checkpoint 是否已过期（内存模式 TTL）。"""
@@ -102,7 +124,7 @@ class PipelineCheckpoint:
         """完整 plan JSON 的存储键。"""
         return f"{self._prefix}:plan:{plan_id}"
 
-    async def save_plan(self, plan: Any) -> None:
+    async def save_plan(self, plan: _PlanLike) -> None:
         """保存完整 TeamPlan（用于 resume 重建）。
 
         Args:
@@ -121,7 +143,7 @@ class PipelineCheckpoint:
             except Exception as e:
                 logger.warning(f"PipelineCheckpoint.save_plan Redis failed for plan {plan_id}: {e}")
 
-    async def load_plan(self, plan_id: str) -> dict[str, Any] | None:
+    async def load_plan(self, plan_id: str) -> dict[str, object] | None:
         """加载完整 plan JSON。"""
         # 优先 Redis
         if self._redis is not None:
@@ -142,7 +164,7 @@ class PipelineCheckpoint:
             return None
         return plan_dict
 
-    async def save(self, plan_id: str, phase: Any, plan_status: str) -> None:
+    async def save(self, plan_id: str, phase: object, plan_status: str) -> None:
         """保存阶段 checkpoint。
 
         Args:
@@ -212,7 +234,8 @@ class PipelineCheckpoint:
                 if not phase_ids:
                     # Redis 无数据，检查内存（过滤过期）
                     return [
-                        c for c in self._memory.get(plan_id, {}).values()
+                        c
+                        for c in self._memory.get(plan_id, {}).values()
                         if not self._is_expired(c.saved_at)
                     ]
 
@@ -236,8 +259,7 @@ class PipelineCheckpoint:
 
         # 内存降级（过滤过期 checkpoint）
         return [
-            c for c in self._memory.get(plan_id, {}).values()
-            if not self._is_expired(c.saved_at)
+            c for c in self._memory.get(plan_id, {}).values() if not self._is_expired(c.saved_at)
         ]
 
     async def clear(self, plan_id: str) -> None:

src/agentkit/orchestrator/compensation.py

@@ -1,8 +1,8 @@
 """Saga compensation pattern for Pipeline execution"""
 
 import logging
-from dataclasses import dataclass, field
-from typing import Any, Awaitable, Callable
+from dataclasses import dataclass
+from typing import Awaitable, Callable
 
 logger = logging.getLogger(__name__)
 
@@ -12,7 +12,7 @@ class CompletedStep:
     """Record of a completed step with its compensation"""
 
     step_name: str
-    result: Any
+    result: object
     compensate_action: str | None = None
 
 
@@ -28,9 +28,7 @@ class CompensationResult:
 class SagaOrchestrator:
     """Orchestrates LIFO compensation for failed pipelines"""
 
-    def __init__(
-        self, execute_skill_func: Callable[..., Awaitable[Any]] | None = None
-    ):
+    def __init__(self, execute_skill_func: Callable[..., Awaitable[object]] | None = None):
         """
         Args:
             execute_skill_func: Async function to execute a skill by name
@@ -42,7 +40,7 @@ class SagaOrchestrator:
     def record_completed(
         self,
         step_name: str,
-        result: Any,
+        result: object,
         compensate_action: str | None = None,
     ):
         """Record a completed step for potential compensation"""
@@ -59,9 +57,7 @@ class SagaOrchestrator:
         results: list[CompensationResult] = []
         for step in reversed(self._completed_steps):
             if step.compensate_action is None:
-                logger.info(
-                    f"No compensation for step '{step.step_name}', skipping"
-                )
+                logger.info(f"No compensation for step '{step.step_name}', skipping")
                 results.append(
                     CompensationResult(
                         step_name=step.step_name,
@@ -82,9 +78,7 @@ class SagaOrchestrator:
                     )
                 )
             except Exception as e:
-                logger.error(
-                    f"Compensation for step '{step.step_name}' failed: {e}"
-                )
+                logger.error(f"Compensation for step '{step.step_name}' failed: {e}")
                 results.append(
                     CompensationResult(
                         step_name=step.step_name,

src/agentkit/orchestrator/dynamic_pipeline.py

@@ -4,7 +4,6 @@
 """
 
 import logging
-from typing import Any
 
 from agentkit.orchestrator.pipeline_engine import PipelineEngine
 from agentkit.orchestrator.pipeline_schema import Pipeline, PipelineResult, StageStatus
@@ -15,7 +14,7 @@ logger = logging.getLogger(__name__)
 class DynamicPipeline:
     """动态 Pipeline 组合器"""
 
-    def __init__(self, engine: PipelineEngine, loader: Any = None):
+    def __init__(self, engine: PipelineEngine, loader: object | None = None):
         self._engine = engine
         self._loader = loader
 
@@ -23,7 +22,7 @@ class DynamicPipeline:
         self,
         pipelines: dict[str, Pipeline],
         condition_key: str,
-        context: dict[str, Any] | None = None,
+        context: dict[str, object] | None = None,
     ) -> PipelineResult:
         """根据条件选择子 Pipeline 执行"""
         context = context or {}
@@ -37,14 +36,16 @@ class DynamicPipeline:
             )
 
         selected = pipelines[condition_value]
-        logger.info(f"DynamicPipeline selected '{selected.name}' for {condition_key}={condition_value}")
+        logger.info(
+            f"DynamicPipeline selected '{selected.name}' for {condition_key}={condition_value}"
+        )
         return await self._engine.execute(selected, context)
 
     async def execute_nested(
         self,
         parent: Pipeline,
         sub_pipeline_map: dict[str, Pipeline],
-        context: dict[str, Any] | None = None,
+        context: dict[str, object] | None = None,
     ) -> PipelineResult:
         """执行嵌套 Pipeline"""
         # 先执行父 Pipeline
@@ -52,7 +53,7 @@ class DynamicPipeline:
 
         # 根据父 Pipeline 结果选择子 Pipeline
         for stage_name, stage_result in parent_result.stage_results.items():
-            if hasattr(stage_result, 'output_data') and stage_result.output_data:
+            if hasattr(stage_result, "output_data") and stage_result.output_data:
                 sub_pipeline_name = stage_result.output_data.get("sub_pipeline")
                 if sub_pipeline_name and sub_pipeline_name in sub_pipeline_map:
                     sub = sub_pipeline_map[sub_pipeline_name]
@@ -66,7 +67,7 @@ class DynamicPipeline:
         pipeline: Pipeline,
         max_iterations: int = 5,
         exit_condition: str = "done",
-        context: dict[str, Any] | None = None,
+        context: dict[str, object] | None = None,
     ) -> PipelineResult:
         """循环执行 Pipeline 直到条件满足"""
         current_context = context or {}

src/agentkit/orchestrator/handoff.py

@@ -3,25 +3,42 @@
 import asyncio
 import json
 import logging
-from typing import Any
+from typing import Awaitable, Callable, Protocol
 
 from agentkit.core.protocol import HandoffMessage
 
 logger = logging.getLogger(__name__)
 
 
+class _RedisPubSubLike(Protocol):
+    """Structural type for Redis pubsub object."""
+
+    async def subscribe(self, channel: str) -> None: ...
+    async def unsubscribe(self, channel: str) -> None: ...
+    def listen(self) -> object: ...
+
+
+class _RedisLike(Protocol):
+    """Structural type for async Redis client."""
+
+    async def publish(self, channel: str, message: str) -> int: ...
+    def pubsub(self) -> _RedisPubSubLike: ...
+
+
 class HandoffManager:
     """Handoff 管理器
 
     通过 Redis Pub/Sub 管理 Agent 间的任务转交。
     """
 
-    def __init__(self, redis: Any = None, dispatcher: Any = None):
+    def __init__(self, redis: _RedisLike | None = None, dispatcher: object | None = None):
         self._redis = redis
         self._dispatcher = dispatcher
-        self._handlers: dict[str, list[Any]] = {}
+        self._handlers: dict[str, list[Callable[[HandoffMessage], Awaitable[None]]]] = {}
 
-    def register_handler(self, agent_name: str, handler: Any) -> None:
+    def register_handler(
+        self, agent_name: str, handler: Callable[[HandoffMessage], Awaitable[None]]
+    ) -> None:
         """注册 Handoff 处理器"""
         if agent_name not in self._handlers:
             self._handlers[agent_name] = []

src/agentkit/orchestrator/pipeline_engine.py

@@ -1,10 +1,12 @@
 """Pipeline Engine - DAG + 并行执行 + 步骤重试 + Saga 补偿"""
 
+from __future__ import annotations
+
 import asyncio
 import logging
 from collections import defaultdict
 from datetime import datetime, timezone
-from typing import Any
+from typing import TYPE_CHECKING
 
 from agentkit.orchestrator.compensation import SagaOrchestrator
 from agentkit.orchestrator.pipeline_schema import (
@@ -25,6 +27,23 @@ from agentkit.orchestrator.retry import execute_with_retry
 logger = logging.getLogger(__name__)
 
 
+if TYPE_CHECKING:
+    from typing import Protocol
+
+    class _DispatcherLike(Protocol):
+        async def dispatch(self, task: object) -> None: ...
+        async def get_task_status(self, task_id: str) -> dict[str, object]: ...
+
+    class _StateManagerLike(Protocol):
+        async def create_execution(self, **kwargs: object) -> str: ...
+        async def update_step(self, **kwargs: object) -> None: ...
+        async def complete_execution(self, **kwargs: object) -> None: ...
+        async def fail_execution(self, **kwargs: object) -> None: ...
+
+    class _LLMGatewayLike(Protocol):
+        async def chat(self, **kwargs: object) -> object: ...
+
+
 class PipelineEngine:
     """Pipeline 执行引擎
 
@@ -38,7 +57,12 @@ class PipelineEngine:
     - 状态持久化（可选）
     """
 
-    def __init__(self, dispatcher: Any = None, state_manager: Any = None, llm_gateway: Any = None):
+    def __init__(
+        self,
+        dispatcher: _DispatcherLike | None = None,
+        state_manager: _StateManagerLike | None = None,
+        llm_gateway: _LLMGatewayLike | None = None,
+    ):
         self._dispatcher = dispatcher
         self._state_manager = state_manager
         self._llm_gateway = llm_gateway
@@ -46,7 +70,7 @@ class PipelineEngine:
     async def execute(
         self,
         pipeline: Pipeline,
-        context: dict[str, Any] | None = None,
+        context: dict[str, object] | None = None,
         adaptive_config: AdaptiveConfig | None = None,
     ) -> PipelineResult:
         """执行 Pipeline
@@ -68,7 +92,7 @@ class PipelineEngine:
     async def _adaptive_loop(
         self,
         pipeline: Pipeline,
-        context: dict[str, Any] | None,
+        context: dict[str, object] | None,
         failed_result: PipelineResult,
         adaptive_config: AdaptiveConfig,
     ) -> PipelineResult:
@@ -92,34 +116,30 @@ class PipelineEngine:
 
             # Replan
             new_pipeline = await replanner.replan(current_pipeline, current_result, report)
-            logger.info(f"Pipeline replanned: {new_pipeline.name} ({len(new_pipeline.stages)} stages)")
+            logger.info(
+                f"Pipeline replanned: {new_pipeline.name} ({len(new_pipeline.stages)} stages)"
+            )
 
             # Re-execute
             current_result = await self._execute_pipeline(new_pipeline, context)
             current_pipeline = new_pipeline
 
             # Record reflection in metadata
-            current_result.metadata["reflections"] = [
-                r.model_dump() for r in reflections
-            ]
+            current_result.metadata["reflections"] = [r.model_dump() for r in reflections]
 
             if current_result.status == StageStatus.COMPLETED:
                 logger.info(f"Pipeline succeeded after {reflection_num} reflection(s)")
                 return current_result
 
         # Exhausted reflections
-        logger.warning(
-            f"Pipeline failed after {adaptive_config.max_reflections} reflection(s)"
-        )
-        current_result.metadata["reflections"] = [
-            r.model_dump() for r in reflections
-        ]
+        logger.warning(f"Pipeline failed after {adaptive_config.max_reflections} reflection(s)")
+        current_result.metadata["reflections"] = [r.model_dump() for r in reflections]
         return current_result
 
     async def _execute_pipeline(
         self,
         pipeline: Pipeline,
-        context: dict[str, Any] | None = None,
+        context: dict[str, object] | None = None,
     ) -> PipelineResult:
         """执行 Pipeline 的核心逻辑（不含反思-重规划）。"""
         result = PipelineResult(pipeline_name=pipeline.name)
@@ -151,7 +171,9 @@ class PipelineEngine:
 
         # 逐层执行
         for level, stages in enumerate(level_groups):
-            logger.info(f"Pipeline '{pipeline.name}' executing level {level} with {len(stages)} stage(s)")
+            logger.info(
+                f"Pipeline '{pipeline.name}' executing level {level} with {len(stages)} stage(s)"
+            )
 
             # 并行执行同层 stages
             tasks = []
@@ -173,9 +195,11 @@ class PipelineEngine:
                 # Update step state
                 if self._state_manager is not None and execution_id is not None:
                     try:
-                        step_status = "completed" if sr.status == StageStatus.COMPLETED else sr.status.value
-                        step_output = sr.output_data if hasattr(sr, 'output_data') else None
-                        step_error = sr.error_message if hasattr(sr, 'error_message') else None
+                        step_status = (
+                            "completed" if sr.status == StageStatus.COMPLETED else sr.status.value
+                        )
+                        step_output = sr.output_data if hasattr(sr, "output_data") else None
+                        step_error = sr.error_message if hasattr(sr, "error_message") else None
                         await self._state_manager.update_step(
                             execution_id=execution_id,
                             step_name=stage.name,
@@ -189,19 +213,21 @@ class PipelineEngine:
                 # 收集输出变量
                 if sr.output_data and isinstance(sr, dict):
                     pass
-                elif hasattr(sr, 'output_data') and sr.output_data:
+                elif hasattr(sr, "output_data") and sr.output_data:
                     for output_key in stage.outputs:
                         if output_key in sr.output_data:
                             result.variables[output_key] = sr.output_data[output_key]
 
                 # 检查是否需要中止
-                if hasattr(sr, 'status') and sr.status == StageStatus.FAILED:
+                if hasattr(sr, "status") and sr.status == StageStatus.FAILED:
                     if not stage.continue_on_failure:
                         # Execute Saga compensation for completed steps
                         compensation_results = await saga.compensate()
                         if compensation_results:
                             failed_compensations = [
-                                cr for cr in compensation_results if not cr.success and cr.error != "no_compensation_needed"
+                                cr
+                                for cr in compensation_results
+                                if not cr.success and cr.error != "no_compensation_needed"
                             ]
                             if failed_compensations:
                                 logger.warning(
@@ -219,7 +245,12 @@ class PipelineEngine:
                                     step_name=stage.name,
                                     error=result.error_message,
                                 )
-                            except (asyncio.TimeoutError, ConnectionError, RuntimeError, ValueError) as exc:
+                            except (
+                                asyncio.TimeoutError,
+                                ConnectionError,
+                                RuntimeError,
+                                ValueError,
+                            ) as exc:
                                 logger.warning(f"Failed to persist failure state: {exc}")
                         return result
 
@@ -252,7 +283,9 @@ class PipelineEngine:
         started_at = datetime.now(timezone.utc).isoformat()
 
         # 条件检查
-        if stage.condition and not self._evaluate_condition(stage.condition, pipeline_result.variables):
+        if stage.condition and not self._evaluate_condition(
+            stage.condition, pipeline_result.variables
+        ):
             return StageResult(
                 stage_name=stage.name,
                 status=StageStatus.SKIPPED,
@@ -312,7 +345,9 @@ class PipelineEngine:
                 if status["status"] in ("completed", "failed", "cancelled"):
                     return StageResult(
                         stage_name=stage.name,
-                        status=StageStatus.COMPLETED if status["status"] == "completed" else StageStatus.FAILED,
+                        status=StageStatus.COMPLETED
+                        if status["status"] == "completed"
+                        else StageStatus.FAILED,
                         output_data=status.get("output_data"),
                         error_message=status.get("error_message"),
                         started_at=started_at,
@@ -406,7 +441,7 @@ class PipelineEngine:
         return resolved
 
     @staticmethod
-    def _get_nested(data: dict, path: str) -> Any:
+    def _get_nested(data: dict, path: str) -> object:
         keys = path.split(".")
         current = data
         for key in keys:
@@ -497,9 +532,7 @@ class PipelineEngine:
 
             if verifier_feedback.passed:
                 # 审查通过，返回成功结果
-                logger.info(
-                    f"Stage '{stage.name}' passed review in round {round_num}"
-                )
+                logger.info(f"Stage '{stage.name}' passed review in round {round_num}")
                 worker_result.output_data = worker_result.output_data or {}
                 worker_result.output_data["adversarial_metadata"] = {
                     "passed_round": round_num,
@@ -553,7 +586,7 @@ class PipelineEngine:
         self,
         agent_name: str,
         action: str,
-        input_data: dict[str, Any],
+        input_data: dict[str, object],
         stage: PipelineStage,
         started_at: str,
         timeout_seconds: int | None = None,
@@ -568,7 +601,9 @@ class PipelineEngine:
             started_at: 开始时间
             timeout_seconds: 独立超时时间，不传则使用 stage.timeout_seconds
         """
-        effective_timeout = timeout_seconds if timeout_seconds is not None else stage.timeout_seconds
+        effective_timeout = (
+            timeout_seconds if timeout_seconds is not None else stage.timeout_seconds
+        )
         if self._dispatcher is None:
             # Dry-run 模式
             return StageResult(
@@ -602,7 +637,9 @@ class PipelineEngine:
                 if status["status"] in ("completed", "failed", "cancelled"):
                     return StageResult(
                         stage_name=stage.name,
-                        status=StageStatus.COMPLETED if status["status"] == "completed" else StageStatus.FAILED,
+                        status=StageStatus.COMPLETED
+                        if status["status"] == "completed"
+                        else StageStatus.FAILED,
                         output_data=status.get("output_data"),
                         error_message=status.get("error_message"),
                         started_at=started_at,
@@ -639,7 +676,7 @@ class PipelineEngine:
     async def _execute_verifier(
         self,
         verifier_name: str,
-        worker_output: dict[str, Any],
+        worker_output: dict[str, object],
         stage: PipelineStage,
         started_at: str,
     ) -> ReviewFeedback:
@@ -679,10 +716,7 @@ class PipelineEngine:
         try:
             feedback = ReviewFeedback(
                 passed=output_data.get("passed", False),
-                issues=[
-                    ReviewIssue(**issue)
-                    for issue in output_data.get("issues", [])
-                ],
+                issues=[ReviewIssue(**issue) for issue in output_data.get("issues", [])],
                 summary=output_data.get("summary", "No summary provided"),
                 score=output_data.get("score", 0.0),
             )
@@ -699,7 +733,7 @@ class PipelineEngine:
         self,
         feedback: ReviewFeedback,
         feedback_mode: str = "structured+natural",
-    ) -> dict[str, Any]:
+    ) -> dict[str, object]:
         """构建反馈上下文，让 Worker Agent 理解审查反馈并定向修复
 
         Args:
@@ -720,7 +754,7 @@ class PipelineEngine:
             for issue in feedback.issues
         ]
 
-        feedback_context: dict[str, Any] = {
+        feedback_context: dict[str, object] = {
             "previous_attempt_failed": True,
         }
 
@@ -756,7 +790,9 @@ class PipelineEngine:
             )
         else:
             # 未知模式，fallback 到 structured+natural
-            logger.warning(f"Unknown feedback_mode '{feedback_mode}', falling back to structured+natural")
+            logger.warning(
+                f"Unknown feedback_mode '{feedback_mode}', falling back to structured+natural"
+            )
             feedback_context["review_feedback"] = {
                 "summary": feedback.summary,
                 "issues": issues_list,

src/agentkit/orchestrator/pipeline_loader.py

@@ -2,7 +2,6 @@
 
 import logging
 from pathlib import Path
-from typing import Any
 
 import yaml
 
@@ -23,7 +22,9 @@ class PipelineLoader:
         if not yaml_path.exists():
             yaml_path = self._pipelines_dir / f"{pipeline_name}.yml"
         if not yaml_path.exists():
-            raise FileNotFoundError(f"Pipeline '{pipeline_name}' not found in {self._pipelines_dir}")
+            raise FileNotFoundError(
+                f"Pipeline '{pipeline_name}' not found in {self._pipelines_dir}"
+            )
 
         content = yaml_path.read_text(encoding="utf-8")
         return self.load_from_yaml(content, pipeline_name)

src/agentkit/orchestrator/pipeline_models.py

@@ -35,9 +35,7 @@ class PipelineExecutionModel(Base):
     )
     completed_at = Column(DateTime)
 
-    __table_args__ = (
-        Index("ix_pipeline_status_created", "status", "created_at"),
-    )
+    __table_args__ = (Index("ix_pipeline_status_created", "status", "created_at"),)
 
 
 class PipelineStepHistoryModel(Base):

src/agentkit/orchestrator/pipeline_schema.py

@@ -1,7 +1,7 @@
 """Pipeline 数据模型"""
 
 from enum import Enum
-from typing import Any, Literal
+from typing import Literal
 
 from pydantic import BaseModel, Field
 
@@ -18,8 +18,11 @@ class StageStatus(str, Enum):
 
 class ReviewIssue(BaseModel):
     """单条审查问题"""
+
     severity: Literal["critical", "major", "minor"] = Field(description="问题严重程度")
-    category: Literal["logic_error", "security", "style", "test_failure", "architecture"] = Field(description="问题类别")
+    category: Literal["logic_error", "security", "style", "test_failure", "architecture"] = Field(
+        description="问题类别"
+    )
     description: str = Field(min_length=1, description="问题描述")
     location: str | None = Field(default=None, description="文件路径/行号")
     suggestion: str | None = Field(default=None, description="修复建议")
@@ -27,6 +30,7 @@ class ReviewIssue(BaseModel):
 
 class ReviewFeedback(BaseModel):
     """Verifier 返回的结构化审查反馈"""
+
     passed: bool = Field(description="是否通过审查")
     issues: list[ReviewIssue] = Field(default_factory=list, description="问题列表")
     summary: str = Field(min_length=1, description="自然语言审查报告")
@@ -35,6 +39,7 @@ class ReviewFeedback(BaseModel):
 
 class AdversarialState(BaseModel):
     """对抗轮次状态追踪"""
+
     current_round: int = Field(default=0, description="当前对抗轮次")
     max_rounds: int = Field(default=3, description="最大对抗轮次")
     feedback_history: list[ReviewFeedback] = Field(default_factory=list, description="反馈历史")
@@ -46,7 +51,7 @@ class PipelineStage(BaseModel):
     agent: str
     action: str
     depends_on: list[str] = []
-    inputs: dict[str, Any] = {}
+    inputs: dict[str, object] = {}
     outputs: list[str] = []
     timeout_seconds: int = 300
     retry_count: int = 0
@@ -54,12 +59,19 @@ class PipelineStage(BaseModel):
     condition: str | None = None
     retry_policy: StepRetryPolicy | None = None
     compensate: str | None = None
-    
+
     # 对抗闭环相关字段
-    verifier: str | None = Field(default=None, description="Verifier Agent 名称，配置后启用对抗模式")
+    verifier: str | None = Field(
+        default=None, description="Verifier Agent 名称，配置后启用对抗模式"
+    )
     max_adversarial_rounds: int = Field(default=3, description="最大对抗轮次")
-    verifier_timeout_seconds: int = Field(default=120, description="Verifier Agent 独立超时时间（秒），避免与 Worker 共享 timeout_seconds")
-    feedback_mode: Literal["structured+natural", "structured", "natural"] = Field(default="structured+natural", description="反馈模式")
+    verifier_timeout_seconds: int = Field(
+        default=120,
+        description="Verifier Agent 独立超时时间（秒），避免与 Worker 共享 timeout_seconds",
+    )
+    feedback_mode: Literal["structured+natural", "structured", "natural"] = Field(
+        default="structured+natural", description="反馈模式"
+    )
     escalate_on_exhaust: str | None = Field(default=None, description="对抗轮次耗尽后的升级目标")
 
     model_config = {"arbitrary_types_allowed": True}
@@ -70,13 +82,13 @@ class Pipeline(BaseModel):
     version: str
     description: str
     stages: list[PipelineStage]
-    variables: dict[str, Any] = {}
+    variables: dict[str, object] = {}
 
 
 class StageResult(BaseModel):
     stage_name: str
     status: StageStatus = StageStatus.PENDING
-    output_data: dict[str, Any] | None = None
+    output_data: dict[str, object] | None = None
     error_message: str | None = None
     started_at: str | None = None
     completed_at: str | None = None
@@ -86,9 +98,9 @@ class PipelineResult(BaseModel):
     pipeline_name: str
     status: StageStatus = StageStatus.PENDING
     stage_results: dict[str, StageResult] = {}
-    variables: dict[str, Any] = {}
+    variables: dict[str, object] = {}
     error_message: str | None = None
-    metadata: dict[str, Any] = {}
+    metadata: dict[str, object] = {}
 
 
 class AdaptiveConfig(BaseModel):

src/agentkit/orchestrator/pipeline_state.py

@@ -13,7 +13,7 @@ import json
 import logging
 import uuid
 from datetime import datetime, timezone
-from typing import Any, Callable, Coroutine
+from typing import Callable, Coroutine
 
 from agentkit.orchestrator.pipeline_models import (
     PipelineExecutionModel,
@@ -183,7 +183,7 @@ class PipelineStateRedis:
         return self._redis
 
     async def _safe_redis_call(
-        self, fn: Callable[..., Coroutine[Any, Any, Any]], *args: object, **kwargs: object
+        self, fn: Callable[..., Coroutine[object, object, object]], *args: object, **kwargs: object
     ) -> object | None:
         """Execute a Redis call, falling back to memory on failure.
 

src/agentkit/orchestrator/reflection.py

@@ -4,22 +4,30 @@
 生成修正后的 Pipeline 重新执行。
 """
 
+from __future__ import annotations
+
 import json
 import logging
-from typing import Any
+from typing import TYPE_CHECKING
 
 from agentkit.orchestrator.pipeline_schema import (
     Pipeline,
     PipelineResult,
     PipelineStage,
     ReflectionReport,
-    StageResult,
     StageStatus,
 )
 
 logger = logging.getLogger(__name__)
 
 
+if TYPE_CHECKING:
+    from typing import Protocol
+
+    class _LLMGatewayLike(Protocol):
+        async def chat(self, **kwargs: object) -> object: ...
+
+
 class PipelineReflector:
     """分析 Pipeline 执行失败原因，生成结构化反思报告。
 
@@ -27,7 +35,7 @@ class PipelineReflector:
     输出 ReflectionReport 包含 failure_type、root_cause 和 suggested_fix。
     """
 
-    def __init__(self, llm_gateway: Any = None):
+    def __init__(self, llm_gateway: _LLMGatewayLike | None = None):
         self._llm_gateway = llm_gateway
 
     async def reflect(
@@ -54,19 +62,25 @@ class PipelineReflector:
         if self._llm_gateway is not None:
             try:
                 return await self._llm_reflect(
-                    pipeline, failed_stage, error_message,
-                    completed_outputs, reflection_number,
+                    pipeline,
+                    failed_stage,
+                    error_message,
+                    completed_outputs,
+                    reflection_number,
                 )
             except Exception as e:
                 logger.warning(f"LLM reflection failed, falling back to rule-based: {e}")
 
         # 规则兜底：基于错误信息分类
         return self._rule_based_reflect(
-            failed_stage, error_message, reflection_number,
+            failed_stage,
+            error_message,
+            reflection_number,
         )
 
     def _find_failure(
-        self, result: PipelineResult,
+        self,
+        result: PipelineResult,
     ) -> tuple[str, str]:
         """找到第一个失败的 stage 及其错误信息。"""
         for name, sr in result.stage_results.items():
@@ -75,8 +89,9 @@ class PipelineReflector:
         return "", "no failed stage found"
 
     def _collect_completed_outputs(
-        self, result: PipelineResult,
-    ) -> dict[str, Any]:
+        self,
+        result: PipelineResult,
+    ) -> dict[str, object]:
         """收集已完成步骤的输出。"""
         outputs = {}
         for name, sr in result.stage_results.items():
@@ -89,13 +104,16 @@ class PipelineReflector:
         pipeline: Pipeline,
         failed_stage: str,
         error_message: str,
-        completed_outputs: dict[str, Any],
+        completed_outputs: dict[str, object],
         reflection_number: int,
     ) -> ReflectionReport:
         """使用 LLM 分析失败原因。"""
         prompt = self._build_reflection_prompt(
-            pipeline, failed_stage, error_message,
-            completed_outputs, reflection_number,
+            pipeline,
+            failed_stage,
+            error_message,
+            completed_outputs,
+            reflection_number,
         )
 
         response = await self._llm_gateway.chat(
@@ -106,7 +124,9 @@ class PipelineReflector:
         # 解析 LLM 返回的 JSON
         content = response.content if hasattr(response, "content") else str(response)
         return self._parse_reflection_response(
-            content, failed_stage, reflection_number,
+            content,
+            failed_stage,
+            reflection_number,
         )
 
     def _build_reflection_prompt(
@@ -114,15 +134,14 @@ class PipelineReflector:
         pipeline: Pipeline,
         failed_stage: str,
         error_message: str,
-        completed_outputs: dict[str, Any],
+        completed_outputs: dict[str, object],
         reflection_number: int,
     ) -> str:
         """构建反思提示词。"""
         stage_descriptions = []
         for s in pipeline.stages:
             stage_descriptions.append(
-                f"  - {s.name}: agent={s.agent}, action={s.action}, "
-                f"depends_on={s.depends_on}"
+                f"  - {s.name}: agent={s.agent}, action={s.action}, depends_on={s.depends_on}"
             )
 
         completed_summary = json.dumps(
@@ -174,7 +193,9 @@ JSON response:"""
         except (json.JSONDecodeError, KeyError) as e:
             logger.warning(f"Failed to parse LLM reflection response: {e}")
             return self._rule_based_reflect(
-                failed_stage, content, reflection_number,
+                failed_stage,
+                content,
+                reflection_number,
             )
 
     def _rule_based_reflect(
@@ -218,7 +239,7 @@ class PipelineReplanner:
     保留已完成步骤的结果，仅重新规划失败及后续步骤。
     """
 
-    def __init__(self, llm_gateway: Any = None):
+    def __init__(self, llm_gateway: _LLMGatewayLike | None = None):
         self._llm_gateway = llm_gateway
 
     async def replan(
@@ -255,8 +276,7 @@ class PipelineReplanner:
     ) -> Pipeline:
         """使用 LLM 生成修正后的 Pipeline。"""
         completed_stages = [
-            name for name, sr in result.stage_results.items()
-            if sr.status == StageStatus.COMPLETED
+            name for name, sr in result.stage_results.items() if sr.status == StageStatus.COMPLETED
         ]
 
         prompt = f"""Based on the reflection report, generate a corrected pipeline.
@@ -284,7 +304,9 @@ JSON pipeline:"""
         return self._parse_pipeline_response(content, pipeline)
 
     def _parse_pipeline_response(
-        self, content: str, original: Pipeline,
+        self,
+        content: str,
+        original: Pipeline,
     ) -> Pipeline:
         """解析 LLM 返回的 Pipeline JSON。"""
         try:
@@ -294,9 +316,7 @@ JSON pipeline:"""
                 text = "\n".join(lines[1:-1])
 
             data = json.loads(text)
-            stages = [
-                PipelineStage(**s) for s in data.get("stages", [])
-            ]
+            stages = [PipelineStage(**s) for s in data.get("stages", [])]
             return Pipeline(
                 name=data.get("name", original.name),
                 version=data.get("version", original.version),
@@ -316,8 +336,7 @@ JSON pipeline:"""
     ) -> Pipeline:
         """基于规则的兜底重规划。"""
         completed_stages = {
-            name for name, sr in result.stage_results.items()
-            if sr.status == StageStatus.COMPLETED
+            name for name, sr in result.stage_results.items() if sr.status == StageStatus.COMPLETED
         }
 
         # 构建修正后的 stages 列表
@@ -345,17 +364,21 @@ JSON pipeline:"""
         )
 
     def _adjust_failed_stage(
-        self, stage: PipelineStage, report: ReflectionReport,
+        self,
+        stage: PipelineStage,
+        report: ReflectionReport,
     ) -> PipelineStage:
         """根据反思报告调整失败的步骤。"""
-        adjustments: dict[str, Any] = {}
+        adjustments: dict[str, object] = {}
 
         if report.failure_type == "timeout":
             adjustments["timeout_seconds"] = min(
-                stage.timeout_seconds * 2, 3600,
+                stage.timeout_seconds * 2,
+                3600,
             )
             if stage.retry_policy is None:
                 from agentkit.orchestrator.retry import StepRetryPolicy
+
                 adjustments["retry_policy"] = StepRetryPolicy(max_attempts=2)
 
         elif report.failure_type == "resource_error":
@@ -365,6 +388,7 @@ JSON pipeline:"""
             # 添加重试策略，可能输入在后续可用
             if stage.retry_policy is None:
                 from agentkit.orchestrator.retry import StepRetryPolicy
+
                 adjustments["retry_policy"] = StepRetryPolicy(max_attempts=2)
 
         return stage.model_copy(update=adjustments)

src/agentkit/orchestrator/retry.py

@@ -4,7 +4,7 @@ import asyncio
 import logging
 import random
 from dataclasses import dataclass
-from typing import Any, Awaitable, Callable
+from typing import Awaitable, Callable
 
 logger = logging.getLogger(__name__)
 
@@ -27,7 +27,7 @@ class StepRetryPolicy:
     def calculate_delay(self, attempt: int) -> float:
         """Calculate delay for given attempt number (0-based)"""
         delay = min(
-            self.base_delay * (self.exponential_base ** attempt),
+            self.base_delay * (self.exponential_base**attempt),
             self.max_delay,
         )
         if self.jitter:
@@ -36,10 +36,10 @@ class StepRetryPolicy:
 
 
 async def execute_with_retry(
-    func: Callable[..., Awaitable[Any]],
+    func: Callable[..., Awaitable[object]],
     retry_policy: StepRetryPolicy | None = None,
     step_name: str = "",
-) -> Any:
+) -> object:
     """Execute a function with retry policy"""
     if retry_policy is None:
         return await func()

src/agentkit/orchestrator/workflow_schema.py

@@ -3,18 +3,17 @@
 from __future__ import annotations
 
 from datetime import datetime, timezone
-from typing import Any
 
 from pydantic import BaseModel, Field
 
-from agentkit.orchestrator.pipeline_schema import Pipeline, PipelineStage
+from agentkit.orchestrator.pipeline_schema import PipelineStage
 
 
 class WorkflowStage(PipelineStage):
     """A workflow stage extending PipelineStage with type and config."""
 
     type: str = "skill"  # "skill" | "condition" | "approval" | "parallel"
-    config: dict[str, Any] = Field(default_factory=dict)
+    config: dict[str, object] = Field(default_factory=dict)
 
 
 class WorkflowDefinition(BaseModel):
@@ -24,9 +23,9 @@ class WorkflowDefinition(BaseModel):
     name: str
     version: int = 1
     stages: list[WorkflowStage] = Field(default_factory=list)
-    triggers: list[dict[str, Any]] = Field(default_factory=list)
-    variables_schema: dict[str, Any] = Field(default_factory=dict)
-    output_schema: dict[str, Any] = Field(default_factory=dict)
+    triggers: list[dict[str, object]] = Field(default_factory=list)
+    variables_schema: dict[str, object] = Field(default_factory=dict)
+    output_schema: dict[str, object] = Field(default_factory=dict)
     created_at: str = Field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
     updated_at: str = Field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
 
@@ -38,11 +37,11 @@ class WorkflowExecution(BaseModel):
     workflow_id: str = ""
     status: str = "pending"  # pending|running|paused|completed|failed|cancelled
     current_stage: str | None = None
-    stage_results: dict[str, Any] = Field(default_factory=dict)
+    stage_results: dict[str, object] = Field(default_factory=dict)
     started_at: str | None = None
     completed_at: str | None = None
     error: str | None = None
-    variables: dict[str, Any] = Field(default_factory=dict)
+    variables: dict[str, object] = Field(default_factory=dict)
 
 
 class WorkflowSummary(BaseModel):
@@ -62,15 +61,15 @@ class CreateWorkflowRequest(BaseModel):
 
     name: str
     stages: list[WorkflowStage] = Field(default_factory=list)
-    triggers: list[dict[str, Any]] = Field(default_factory=list)
-    variables_schema: dict[str, Any] = Field(default_factory=dict)
-    output_schema: dict[str, Any] = Field(default_factory=dict)
+    triggers: list[dict[str, object]] = Field(default_factory=list)
+    variables_schema: dict[str, object] = Field(default_factory=dict)
+    output_schema: dict[str, object] = Field(default_factory=dict)
 
 
 class ExecuteWorkflowRequest(BaseModel):
     """Request body for executing a workflow."""
 
-    variables: dict[str, Any] = Field(default_factory=dict)
+    variables: dict[str, object] = Field(default_factory=dict)
 
 
 class ApproveRequest(BaseModel):