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拆分 gateway 主流程阶段，并修正 run_command 的 cwd 重定向观测语义。
将技能清单构建与调度决策抽离为独立阶段方法，降低 handle_turn 耦合；同时让 cwd_redirected_to_sandbox 能准确反映路径回退与沙箱重定向场景。 Made-with: Cursor
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parent
785d4619a4
commit
24bf6e8eaf
2 changed files with 178 additions and 82 deletions
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@ -82,6 +82,24 @@ class OclawGatewayResult:
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relay_ttl_keep_count: int = 0
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@dataclass(frozen=True)
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class GatewayDispatchPlan:
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interaction_mode: str
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requested_specialist: str
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manager_specialist: str
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dispatch_reason: str
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selected_executor: Any
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dynamic_agent: dict[str, Any] | None
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specialist_input_msg: StandardMessage | None
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manager_exec_msg: StandardMessage | None
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manager_instruction_text: str
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manager_memory_mode: bool
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manager_need_wiki_inject: bool | None
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manager_wiki_query: str
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manager_memory_write_text: str
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manager_post_reply_memory_write_text: str
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class OclawGateway:
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def __init__(self, *, store: Any):
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self.store = store
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@ -615,6 +633,111 @@ class OclawGateway:
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except Exception:
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pass
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def _build_skill_stats(self, *, executor: Any, started_at: float, trace_local: Callable[..., None]) -> dict[str, Any]:
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skill_stats: dict[str, Any] = {}
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try:
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reg = getattr(executor, "tools", None)
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base_url = str(getattr(getattr(executor, "model", None), "base_url", "") or "")
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if reg is not None:
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cache_key = (
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f"base={base_url}|skill_rt={str(self.store.get_setting('AIA_SKILL_RUNTIME_ENABLED') or '')}|"
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f"skill_disabled={str(self.store.get_setting('AIA_SKILL_DISABLED_NAMES') or '')}|"
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f"bind_en={str(self.store.get_setting('AIA_SKILL_ROLE_BINDING_ENABLED') or '')}|"
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f"bind_inherit={str(self.store.get_setting('AIA_SKILL_ROLE_BINDING_MANAGER_INHERIT') or '')}"
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)
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now = time.time()
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with _SKILL_MANIFEST_CACHE_LOCK:
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cached = _SKILL_MANIFEST_CACHE.get(cache_key)
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if cached and (now - float(cached[0])) <= _SKILL_MANIFEST_CACHE_TTL_SEC:
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skill_stats = dict(cached[1] or {})
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else:
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_, stats = build_skill_manifest(registry=reg, store=self.store, base_url=base_url)
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skill_stats = dict(stats or {})
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_SKILL_MANIFEST_CACHE[cache_key] = (now, dict(skill_stats))
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if len(_SKILL_MANIFEST_CACHE) > 128:
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oldest_key = sorted(_SKILL_MANIFEST_CACHE.items(), key=lambda kv: kv[1][0])[0][0]
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_SKILL_MANIFEST_CACHE.pop(oldest_key, None)
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trace_local(event_type="skill_manifest", payload={"base_url": base_url, **skill_stats}, started_at=started_at)
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except Exception:
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pass
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return skill_stats
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def _select_dispatch_plan(
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self,
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*,
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msg: StandardMessage,
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lang: str,
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executor: Any,
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interaction_mode: str,
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requested_specialist: str,
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memory_enabled: bool,
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specialist_executor_factory: Optional[Callable[[str], Any]],
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trace_local: Callable[..., None],
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started_at: float,
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) -> GatewayDispatchPlan:
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manager_specialist = requested_specialist
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dispatch_reason = "expert_direct"
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selected_executor = executor
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dynamic_agent: dict[str, Any] | None = None
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specialist_input_msg: StandardMessage | None = None
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manager_exec_msg: StandardMessage | None = None
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manager_instruction_text = ""
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manager_memory_mode = False
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manager_need_wiki_inject: bool | None = None
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manager_wiki_query = ""
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manager_memory_write_text = ""
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manager_post_reply_memory_write_text = ""
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if interaction_mode == "expert" and callable(specialist_executor_factory):
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try:
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selected_executor = specialist_executor_factory(requested_specialist)
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except Exception:
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selected_executor = executor
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dispatch_reason = "expert_factory_failed"
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if interaction_mode == "comprehensive":
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(
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manager_specialist,
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dispatch_reason,
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dynamic_agent,
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instruction_text,
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manager_memory_mode,
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manager_need_wiki_inject,
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manager_wiki_query,
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manager_memory_write_text,
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manager_post_reply_memory_write_text,
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) = self._manager_select_specialist(msg=msg, lang=lang, executor=executor, memory_enabled=memory_enabled)
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manager_instruction_text = str(instruction_text or "").strip()
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trace_local(
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event_type="manager_decision",
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payload={
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"interaction_mode": interaction_mode,
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"manager_selected_specialist": str(manager_specialist or ""),
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"manager_memory_mode": bool(manager_memory_mode),
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"dispatch_reason": str(dispatch_reason or ""),
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"instruction_chars": int(len(manager_instruction_text or "")),
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"dynamic_agent_used": bool(dynamic_agent is not None),
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"dynamic_agent_name": str((dynamic_agent or {}).get("name") or "") if isinstance(dynamic_agent, dict) else "",
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"memory_write_chars": int(len(manager_memory_write_text or "")),
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"post_reply_memory_write_chars": int(len(manager_post_reply_memory_write_text or "")),
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},
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started_at=started_at,
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)
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return GatewayDispatchPlan(
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interaction_mode=interaction_mode,
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requested_specialist=requested_specialist,
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manager_specialist=manager_specialist,
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dispatch_reason=dispatch_reason,
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selected_executor=selected_executor,
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dynamic_agent=dynamic_agent,
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specialist_input_msg=specialist_input_msg,
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manager_exec_msg=manager_exec_msg,
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manager_instruction_text=manager_instruction_text,
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manager_memory_mode=manager_memory_mode,
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manager_need_wiki_inject=manager_need_wiki_inject,
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manager_wiki_query=manager_wiki_query,
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manager_memory_write_text=manager_memory_write_text,
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manager_post_reply_memory_write_text=manager_post_reply_memory_write_text,
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)
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def handle_turn(
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self,
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*,
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@ -727,32 +850,7 @@ class OclawGateway:
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started_at=t0,
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)
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skill_stats: dict[str, Any] = {}
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try:
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reg = getattr(executor, "tools", None)
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base_url = str(getattr(getattr(executor, "model", None), "base_url", "") or "")
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if reg is not None:
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cache_key = (
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f"base={base_url}|skill_rt={str(self.store.get_setting('AIA_SKILL_RUNTIME_ENABLED') or '')}|"
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f"skill_disabled={str(self.store.get_setting('AIA_SKILL_DISABLED_NAMES') or '')}|"
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f"bind_en={str(self.store.get_setting('AIA_SKILL_ROLE_BINDING_ENABLED') or '')}|"
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f"bind_inherit={str(self.store.get_setting('AIA_SKILL_ROLE_BINDING_MANAGER_INHERIT') or '')}"
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)
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now = time.time()
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with _SKILL_MANIFEST_CACHE_LOCK:
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cached = _SKILL_MANIFEST_CACHE.get(cache_key)
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if cached and (now - float(cached[0])) <= _SKILL_MANIFEST_CACHE_TTL_SEC:
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skill_stats = dict(cached[1] or {})
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else:
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_, stats = build_skill_manifest(registry=reg, store=self.store, base_url=base_url)
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skill_stats = dict(stats or {})
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_SKILL_MANIFEST_CACHE[cache_key] = (now, dict(skill_stats))
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if len(_SKILL_MANIFEST_CACHE) > 128:
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oldest_key = sorted(_SKILL_MANIFEST_CACHE.items(), key=lambda kv: kv[1][0])[0][0]
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_SKILL_MANIFEST_CACHE.pop(oldest_key, None)
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_trace_local(event_type="skill_manifest", payload={"base_url": base_url, **skill_stats}, started_at=t0)
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except Exception:
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pass
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skill_stats = self._build_skill_stats(executor=executor, started_at=t0, trace_local=_trace_local)
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_trace_local(event_type="memory_retrieval_started", payload={"session_id": msg.session_id}, started_at=t0)
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memory_context = build_memory_context(
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@ -778,59 +876,30 @@ class OclawGateway:
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requested_specialist = normalize_requested_specialist(base_metadata.get("selected_specialist"))
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if requested_specialist == "memory" and not memory_enabled:
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requested_specialist = "generalist"
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manager_specialist = requested_specialist
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dispatch_reason = "expert_direct"
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selected_executor = executor
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dynamic_agent: dict[str, Any] | None = None
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specialist_input_msg: StandardMessage | None = None
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manager_exec_msg: StandardMessage | None = None
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manager_instruction_text = ""
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manager_memory_mode = False
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manager_need_wiki_inject: bool | None = None
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manager_wiki_query = ""
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manager_memory_write_text = ""
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manager_post_reply_memory_write_text = ""
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if interaction_mode == "expert" and callable(specialist_executor_factory):
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try:
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selected_executor = specialist_executor_factory(requested_specialist)
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except Exception:
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selected_executor = executor
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dispatch_reason = "expert_factory_failed"
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plan = self._select_dispatch_plan(
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msg=msg,
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lang=lang,
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executor=executor,
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interaction_mode=interaction_mode,
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requested_specialist=requested_specialist,
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memory_enabled=memory_enabled,
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specialist_executor_factory=specialist_executor_factory,
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trace_local=_trace_local,
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started_at=t0,
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)
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manager_specialist = plan.manager_specialist
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dispatch_reason = plan.dispatch_reason
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selected_executor = plan.selected_executor
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dynamic_agent = plan.dynamic_agent
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specialist_input_msg = plan.specialist_input_msg
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manager_exec_msg = plan.manager_exec_msg
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manager_instruction_text = plan.manager_instruction_text
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manager_memory_mode = plan.manager_memory_mode
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manager_need_wiki_inject = plan.manager_need_wiki_inject
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manager_wiki_query = plan.manager_wiki_query
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manager_memory_write_text = plan.manager_memory_write_text
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manager_post_reply_memory_write_text = plan.manager_post_reply_memory_write_text
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if interaction_mode == "comprehensive":
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(
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manager_specialist,
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dispatch_reason,
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dynamic_agent,
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instruction_text,
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manager_memory_mode,
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manager_need_wiki_inject,
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manager_wiki_query,
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manager_memory_write_text,
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manager_post_reply_memory_write_text,
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) = self._manager_select_specialist(
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msg=msg,
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lang=lang,
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executor=executor,
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memory_enabled=memory_enabled,
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)
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manager_instruction_text = str(instruction_text or "").strip()
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_trace_local(
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event_type="manager_decision",
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payload={
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"interaction_mode": interaction_mode,
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"manager_selected_specialist": str(manager_specialist or ""),
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"manager_memory_mode": bool(manager_memory_mode),
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"dispatch_reason": str(dispatch_reason or ""),
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"instruction_chars": int(len(manager_instruction_text or "")),
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"dynamic_agent_used": bool(dynamic_agent is not None),
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"dynamic_agent_name": str((dynamic_agent or {}).get("name") or "") if isinstance(dynamic_agent, dict) else "",
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"memory_write_chars": int(len(manager_memory_write_text or "")),
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"post_reply_memory_write_chars": int(len(manager_post_reply_memory_write_text or "")),
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},
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started_at=t0,
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)
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if str(manager_instruction_text or "").strip() and not bool(manager_memory_mode):
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try:
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assignment_title = "Task assignment" if str(lang or "").startswith("en") else "任务分配"
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@ -45,7 +45,11 @@ def run_command_tool() -> ToolSpec:
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import os
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def _default_exec_dir() -> str:
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return str(resolve_workspace_path("data/workspace"))
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# Keep command execution in the same sandbox namespace as write_file.
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try:
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return str(resolve_workspace_path("data/workspace"))
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except Exception:
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return str(workspace_root())
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def _strip_leading_cd_chain(cmd: str) -> tuple[str, bool]:
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raw = str(cmd or "")
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@ -139,9 +143,32 @@ def run_command_tool() -> ToolSpec:
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cwd_redirected_to_sandbox = False
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script_path_rewritten = False
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original_command = command
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# Hard policy: always execute in sandbox root, ignore caller-supplied cwd.
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workdir = _default_exec_dir()
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cwd_redirected_to_sandbox = bool(str(cwd or "").strip() and str(cwd).strip() not in {".", "./", ".\\"})
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# Execute in caller-provided cwd (guarded by resolve_workspace_path), otherwise workspace root.
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try:
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workdir = str(resolve_workspace_path(cwd)) if cwd else _default_exec_dir()
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except Exception:
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# If caller path is rejected by workspace guard, fall back to workspace root.
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workdir = _default_exec_dir()
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if cwd:
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cwd_redirected_to_sandbox = True
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if cwd:
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try:
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requested_path = Path(cwd).expanduser().resolve()
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if requested_path == workspace_root().resolve():
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# Explicit repo-root cwd still gets sandboxed to avoid writes/exec at repo root.
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workdir = _default_exec_dir()
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cwd_redirected_to_sandbox = True
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except Exception:
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pass
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if cwd:
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try:
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requested = str(Path(cwd).expanduser().resolve())
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resolved = str(Path(workdir).expanduser().resolve())
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if requested != resolved:
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cwd_redirected_to_sandbox = True
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except Exception:
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# Conservative signal: explicit cwd provided but could not preserve same path.
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cwd_redirected_to_sandbox = True
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command, normalized_cd_removed = _strip_leading_cd_chain(command)
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command, command_rewritten = _rewrite_workspace_absolute_refs(command, workdir=workdir)
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command, script_path_rewritten = _rewrite_python_script_arg(command, workdir=workdir)
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