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重构仓库目录为统一的 runtime 分层并清理历史 openclaw 残留。
本次迁移将网关/通道/工具/技能/脚本与协议资源集中到新结构，统一路径常量与脚本转发机制，减少顶层噪音并保证运行与测试行为一致。 Made-with: Cursor
This commit is contained in:
parent
ba3836f00f
commit
4a23b715a2
498 changed files with 2760 additions and 2200 deletions
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@ -1,617 +0,0 @@
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from __future__ import annotations
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import json
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import re
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import time
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import uuid
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from dataclasses import dataclass
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from types import SimpleNamespace
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from typing import Any, Callable, Optional
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from oclaw.chat.agent_messages import build_llm_messages
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from oclaw.chat.tool_runtime import ToolExecutionConfig
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from oclaw.chat.turn_types import TurnRunOutcome
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from oclaw.openclaw_runtime.skill_executor import SkillExecutionContext, SkillExecutor
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from oclaw.openclaw_runtime.skills import build_skill_manifest
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from oclaw.platform.llm.chat_models import ChatModel
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from oclaw.openclaw_runtime.system_prompt import build_openclaw_executor_system_prompt
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from oclaw.openclaw_runtime.types import OpenClawMemoryContext
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from oclaw.orchestration.trace import new_span_id
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from oclaw.tools.base import ToolRegistry
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_OPENCLAW_TOOL_RESULT_HARD_CAP_CHARS = 24_000
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_DIRECT_LOOP_OC_STAGE: dict[str, str] = {
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"tool_wire_filter": "wire_filter",
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"tool_result_context_guard": "tool_context_guard",
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}
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_THINK_BLOCK_RE = re.compile(r"<(think|redacted_thinking)>\s*(.*?)\s*</\1>\s*", flags=re.IGNORECASE | re.DOTALL)
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def _emit_direct_loop_trace(
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*,
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store: Any,
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session_id: str,
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trace_id: str | None,
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parent_span_id: str | None,
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event_type: str,
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payload: dict[str, Any],
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run_id: str | None,
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attempt_no: int | None,
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lang: str,
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) -> None:
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if not trace_id:
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return
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merged: dict[str, Any] = dict(payload or {})
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merged.setdefault("pipeline", "openclaw_direct_loop")
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merged.setdefault("trace_id", str(trace_id))
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merged.setdefault("lang", str(lang or ""))
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merged["oc_stage"] = _DIRECT_LOOP_OC_STAGE.get(event_type, event_type)
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rid = str(run_id or "").strip()
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if rid:
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merged.setdefault("run_id", rid)
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if attempt_no is not None:
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merged.setdefault("attempt_no", int(attempt_no))
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try:
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store.add_trace_event(
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session_id=session_id,
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trace_id=str(trace_id),
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span_id=new_span_id(),
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parent_span_id=parent_span_id,
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event_type=event_type,
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payload=merged,
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)
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except Exception:
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pass
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@dataclass(frozen=True)
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class _LoopStepResult:
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assistant_text: str
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llm_tool_calls: list[Any]
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assistant_msg_id: int
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def _json_dumps_safe(obj: Any) -> str:
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try:
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return json.dumps(obj, ensure_ascii=False, default=str)
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except Exception:
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return json.dumps({"ok": False, "error": "not_json_serializable"}, ensure_ascii=False)
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def _split_reasoning_and_body(text: str, *, explicit_reasoning: str | None = None) -> tuple[list[str], str]:
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explicit = str(explicit_reasoning or "").strip()
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raw = str(text or "")
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if not raw:
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return ([explicit] if explicit else []), ""
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if explicit:
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body = _THINK_BLOCK_RE.sub("", raw).strip()
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return [explicit], body
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chunks: list[str] = []
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for m in _THINK_BLOCK_RE.finditer(raw):
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t = str(m.group(2) or "").strip()
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if t:
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chunks.append(t)
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body = _THINK_BLOCK_RE.sub("", raw).strip()
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return chunks, body
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def _guard_tool_results_for_llm_context(
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*,
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store: Any,
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session_id: str,
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store_messages: list[Any],
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trace_id: str | None,
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parent_span_id: str | None,
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hard_cap_chars: int,
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run_id: str | None = None,
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attempt_no: int | None = None,
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lang: str = "",
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) -> list[Any]:
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"""Hard-guard overlarge `role=tool` message contents before sending to model.
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This does NOT rewrite DB history (tool_log / chat_message). It only guards the
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in-flight LLM context to prevent provider context overflow spirals.
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"""
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cap = max(4096, min(int(hard_cap_chars or _OPENCLAW_TOOL_RESULT_HARD_CAP_CHARS), 500_000))
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out: list[Any] = []
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for m in store_messages or []:
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role = str(getattr(m, "role", "") or "")
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if role != "tool":
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out.append(m)
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continue
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raw = str(getattr(m, "content", "") or "")
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if len(raw) <= cap:
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out.append(m)
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continue
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# Best-effort parse tool JSON for a minimal summary.
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ok = None
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error_code = ""
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error = ""
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try:
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obj = json.loads(raw)
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if isinstance(obj, dict):
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ok = obj.get("ok")
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error_code = str(obj.get("error_code") or "").strip()
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error = str(obj.get("error") or "").strip()
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except Exception:
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obj = None
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preview = raw[: max(1, min(4000, cap - 400))] + "\n...<tool_result_guard_truncated>"
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guarded_obj = {
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"ok": bool(ok) if ok is not None else None,
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"error_code": error_code,
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"error": error,
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"_tool_result_guarded": True,
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"original_chars": len(raw),
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"guard_cap_chars": cap,
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"preview": preview,
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"hint": (
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"Tool output was too large for safe context replay; it was truncated for the model context. "
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"Use narrower queries (e.g., smaller glob/max_results) or adjust AIA_TOOL_LLM_MESSAGE_MAX_CHARS. / "
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"工具输出过大，已在发给模型的上下文中强制截断；请缩小范围或配置 AIA_TOOL_LLM_MESSAGE_MAX_CHARS。"
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),
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}
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guarded = _json_dumps_safe(guarded_obj)
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out.append(
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SimpleNamespace(
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id=getattr(m, "id", 0),
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session_id=getattr(m, "session_id", session_id),
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role="tool",
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content=guarded,
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tool_calls=getattr(m, "tool_calls", None),
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timestamp=getattr(m, "timestamp", ""),
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attachments=getattr(m, "attachments", None),
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)
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)
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if trace_id:
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_emit_direct_loop_trace(
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store=store,
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session_id=session_id,
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trace_id=trace_id,
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parent_span_id=parent_span_id,
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event_type="tool_result_context_guard",
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payload={
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"message_id": int(getattr(m, "id", 0) or 0),
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"original_chars": int(len(raw)),
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"guarded_chars": int(len(guarded)),
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"guard_cap_chars": int(cap),
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},
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run_id=run_id,
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attempt_no=attempt_no,
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lang=lang,
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)
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return out
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def _check_stop(should_stop: Optional[Callable[[], bool]]) -> None:
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if should_stop and should_stop():
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raise RuntimeError("generation interrupted by user")
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def _build_model_context(
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*,
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store: Any,
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session_id: str,
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max_messages: int,
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system_prompt: str,
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model: ChatModel,
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lang: str,
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memory_context: OpenClawMemoryContext | None,
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trace_id: str | None,
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parent_span_id: str | None,
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tools: ToolRegistry | None = None,
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base_url: str = "",
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run_id: str | None = None,
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attempt_no: int | None = None,
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workspace_dir: str | None = None,
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skill_binding_role: str | None = None,
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) -> list[dict[str, Any]]:
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rows = store.get_messages(session_id=session_id, limit=int(max_messages))
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rows = _guard_tool_results_for_llm_context(
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store=store,
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session_id=session_id,
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store_messages=rows,
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trace_id=trace_id,
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parent_span_id=parent_span_id,
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hard_cap_chars=_OPENCLAW_TOOL_RESULT_HARD_CAP_CHARS,
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run_id=run_id,
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attempt_no=attempt_no,
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lang=lang,
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)
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final_system = build_openclaw_executor_system_prompt(
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store=store,
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tools=tools,
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base_url=str(base_url or ""),
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base_system=str(system_prompt or ""),
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memory_context=memory_context,
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lang=lang,
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workspace_dir=workspace_dir,
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skill_binding_role=skill_binding_role,
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)
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return build_llm_messages(store_messages=rows, system_prompt=final_system, model=model, lang=lang)
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def _prepare_llm_tools(
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*,
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store: Any,
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tools: ToolRegistry,
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base_url: str,
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session_id: str,
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trace_id: str | None,
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parent_span_id: str | None,
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run_id: str | None = None,
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attempt_no: int | None = None,
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lang: str = "",
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wire_policy_role: str | None = None,
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) -> list[dict[str, Any]]:
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runtime_enabled = True
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try:
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raw_flag = str(store.get_setting("AIA_SKILL_RUNTIME_ENABLED") or "").strip().lower()
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if raw_flag:
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runtime_enabled = raw_flag in {"1", "true", "yes", "on"}
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except Exception:
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runtime_enabled = True
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if runtime_enabled:
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skill_specs, _ = build_skill_manifest(registry=tools, store=store, base_url=base_url)
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raw_llm_tools = [s.as_openai_tool() for s in skill_specs]
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else:
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raw_llm_tools = tools.as_openai_tools()
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from oclaw.tools.exposure_plan import build_llm_tools_plan
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plan = build_llm_tools_plan(
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store=store,
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role=str(wire_policy_role or "").strip().lower() or "generalist",
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base_url=base_url or None,
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max_json_bytes=None,
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include_mcp=False,
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preview_internal=False,
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raw_openai_tools_override=raw_llm_tools,
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)
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llm_tools = plan.tools_wired
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if trace_id:
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try:
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import os
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raw_names = {
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str(((t.get("function") or {}) if isinstance(t, dict) else {}).get("name") or "")
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for t in (raw_llm_tools or [])
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if isinstance(t, dict)
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}
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raw_names.discard("")
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hidden = list(plan.removed_names)
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hidden_mcp = list(plan.removed_mcp_names)
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_emit_direct_loop_trace(
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store=store,
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session_id=session_id,
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trace_id=trace_id,
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parent_span_id=parent_span_id,
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event_type="tool_wire_filter",
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payload={
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"runner": "openclaw_direct",
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"base_url": base_url,
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"wire_policy_role": str(wire_policy_role or ""),
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"tools_before": len(raw_names),
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"tools_after": int(len(_tool_names_for_trace(llm_tools))),
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"hidden_total": int(len(hidden)),
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"hidden_mcp_total": int(len(hidden_mcp)),
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"hidden_mcp_preview": list(hidden_mcp)[:20],
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"role_mode": str(plan.role_mode or ""),
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"wire_policy_effective": bool(plan.wire_policy_effective),
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"max_json_bytes": plan.max_json_bytes,
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"changed_total": int(len(plan.changed_names)),
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},
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run_id=run_id,
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attempt_no=attempt_no,
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lang=lang,
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)
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# Optional richer snapshot for debugging (may be large).
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trace_plan_enabled = False
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try:
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raw = str(store.get_setting("AIA_TRACE_TOOL_EXPOSURE_PLAN") or "").strip()
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if raw:
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trace_plan_enabled = raw.lower() in {"1", "true", "yes", "on"}
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else:
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trace_plan_enabled = str(os.getenv("AIA_TRACE_TOOL_EXPOSURE_PLAN") or "").strip().lower() in {
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"1",
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"true",
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"yes",
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"on",
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}
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except Exception:
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trace_plan_enabled = str(os.getenv("AIA_TRACE_TOOL_EXPOSURE_PLAN") or "").strip().lower() in {
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"1",
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"true",
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"yes",
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"on",
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}
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if trace_plan_enabled:
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_emit_direct_loop_trace(
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store=store,
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session_id=session_id,
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trace_id=trace_id,
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parent_span_id=parent_span_id,
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event_type="tool_exposure_plan",
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payload={
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"runner": "openclaw_direct",
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"base_url": base_url,
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"wire_policy_role": str(wire_policy_role or ""),
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"role_mode": str(plan.role_mode or ""),
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"wire_policy_effective": bool(plan.wire_policy_effective),
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"max_json_bytes": plan.max_json_bytes,
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"raw_names": sorted(list(raw_names))[:300],
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"wired_names": sorted(list(set(_tool_names_for_trace(llm_tools))))[:300],
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"removed_names": list(plan.removed_names)[:300],
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"removed_mcp_names": list(plan.removed_mcp_names)[:300],
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"added_names": list(plan.added_names)[:300],
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"changed_names": list(plan.changed_names)[:300],
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},
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run_id=run_id,
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attempt_no=attempt_no,
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lang=lang,
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)
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except Exception:
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pass
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return llm_tools
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def _tool_names_for_trace(tools: list[dict[str, Any]]) -> list[str]:
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out: list[str] = []
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for t in tools or []:
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if not isinstance(t, dict):
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continue
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fn = t.get("function")
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if not isinstance(fn, dict):
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continue
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nm = str(fn.get("name") or "").strip()
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if nm:
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out.append(nm)
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return out
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def _persist_assistant_step(
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*,
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store: Any,
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session_id: str,
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turn_uuid: str,
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assistant_text: str,
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reasoning_text: str,
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llm_tool_calls: list[Any],
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) -> _LoopStepResult:
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stored_tool_calls = []
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for tc in llm_tool_calls:
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stored_tool_calls.append(
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{
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"id": str(getattr(tc, "id", "") or ""),
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"name": str(getattr(tc, "name", "") or ""),
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"arguments": dict(getattr(tc, "arguments", {}) or {}),
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"thought_signature": getattr(tc, "thought_signature", None),
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}
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)
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reasoning_chunks, assistant_body = _split_reasoning_and_body(
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assistant_text,
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explicit_reasoning=reasoning_text,
|
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)
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for idx, chunk in enumerate(reasoning_chunks):
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store.add_message(
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session_id=session_id,
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role="assistant",
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content=chunk,
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turn_uuid=turn_uuid,
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event_type="reasoning",
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event_payload={"chunk_index": int(idx), "chunk_count": len(reasoning_chunks)},
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)
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assistant_row = store.add_message(
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session_id=session_id,
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role="assistant",
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content=assistant_body,
|
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tool_calls=stored_tool_calls or None,
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turn_uuid=turn_uuid,
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event_type="tool_call" if stored_tool_calls else "assistant_text",
|
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)
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return _LoopStepResult(
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assistant_text=assistant_body,
|
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llm_tool_calls=llm_tool_calls,
|
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assistant_msg_id=int(getattr(assistant_row, "id", 0) or 0),
|
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)
|
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|
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|
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def _execute_tool_step(
|
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*,
|
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skill_exec: SkillExecutor,
|
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store: Any,
|
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tools: ToolRegistry,
|
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session_id: str,
|
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lang: str,
|
||||
user_text: str,
|
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trace_id: str | None,
|
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parent_span_id: str | None,
|
||||
workspace_owner_session_id: str | None,
|
||||
path_policy_tenant_id: str | None,
|
||||
path_policy_user_id: str | None,
|
||||
assistant_msg_id: int,
|
||||
llm_tool_calls: list[Any],
|
||||
on_tool_ui: Optional[Callable[[str, dict[str, Any]], None]],
|
||||
should_stop: Optional[Callable[[], bool]],
|
||||
signature_budget: int,
|
||||
run_id: str | None = None,
|
||||
attempt_no: int | None = None,
|
||||
turn_uuid: str | None = None,
|
||||
) -> tuple[int, dict[str, tuple[dict[str, Any], int]]]:
|
||||
t0 = time.perf_counter()
|
||||
_tool_messages, results_by_id = skill_exec.execute_skill_uses(
|
||||
ctx=SkillExecutionContext(
|
||||
store=store,
|
||||
tools=tools,
|
||||
session_id=session_id,
|
||||
lang=lang,
|
||||
user_text=user_text,
|
||||
specialist="openclaw",
|
||||
trace_id=trace_id,
|
||||
parent_span_id=parent_span_id,
|
||||
workspace_owner_session_id=workspace_owner_session_id,
|
||||
path_policy_tenant_id=path_policy_tenant_id,
|
||||
path_policy_user_id=path_policy_user_id,
|
||||
run_id=run_id,
|
||||
attempt_no=attempt_no,
|
||||
turn_uuid=turn_uuid,
|
||||
),
|
||||
assistant_msg_id=assistant_msg_id,
|
||||
skill_uses=llm_tool_calls,
|
||||
on_tool_ui=None,
|
||||
on_skill_ui=on_tool_ui,
|
||||
should_stop=should_stop,
|
||||
signature_budget=signature_budget,
|
||||
)
|
||||
return int((time.perf_counter() - t0) * 1000), results_by_id
|
||||
|
||||
|
||||
def run_openclaw_direct_loop(
|
||||
*,
|
||||
store: Any,
|
||||
session_id: str,
|
||||
lang: str,
|
||||
system_prompt: str,
|
||||
model: ChatModel,
|
||||
tools: ToolRegistry,
|
||||
user_text: str,
|
||||
attachments: list[dict[str, Any]] | None = None,
|
||||
trace_id: str | None = None,
|
||||
parent_span_id: str | None = None,
|
||||
run_id: str | None = None,
|
||||
attempt_no: int | None = None,
|
||||
max_messages: int = 80,
|
||||
max_tool_rounds: int = 8,
|
||||
max_tool_workers: int = 8,
|
||||
on_token: Optional[Callable[[str], None]] = None,
|
||||
on_progress: Optional[Callable[[str], None]] = None,
|
||||
on_tool_ui: Optional[Callable[[str, dict[str, Any]], None]] = None,
|
||||
should_stop: Optional[Callable[[], bool]] = None,
|
||||
workspace_owner_session_id: str | None = None,
|
||||
path_policy_tenant_id: str | None = None,
|
||||
path_policy_user_id: str | None = None,
|
||||
workspace_dir: str | None = None,
|
||||
memory_context: OpenClawMemoryContext | None = None,
|
||||
persist_user_message: bool = True,
|
||||
tool_signature_budget: int = 2,
|
||||
skill_binding_role: str | None = None,
|
||||
wire_policy_role: str | None = None,
|
||||
) -> TurnRunOutcome:
|
||||
"""A minimal oclaw-style loop: model -> tool_uses -> execute -> tool_results -> continue."""
|
||||
_check_stop(should_stop)
|
||||
turn_uuid = str(uuid.uuid4())
|
||||
if persist_user_message:
|
||||
store.add_message(
|
||||
session_id=session_id,
|
||||
role="user",
|
||||
content=str(user_text or ""),
|
||||
attachments=attachments,
|
||||
turn_uuid=turn_uuid,
|
||||
event_type="user_text",
|
||||
)
|
||||
|
||||
skill_exec = SkillExecutor(config=ToolExecutionConfig(max_workers=max(1, min(int(max_tool_workers or 8), 32))))
|
||||
tool_traces: list[dict[str, Any]] = []
|
||||
final_text = ""
|
||||
|
||||
base_url = str(getattr(model, "base_url", "") or "")
|
||||
|
||||
for round_idx in range(max(1, int(max_tool_rounds or 1))):
|
||||
_check_stop(should_stop)
|
||||
if on_progress:
|
||||
on_progress(f"oclaw: think ({round_idx + 1})…")
|
||||
|
||||
msgs = _build_model_context(
|
||||
store=store,
|
||||
session_id=session_id,
|
||||
max_messages=max_messages,
|
||||
system_prompt=system_prompt,
|
||||
model=model,
|
||||
lang=lang,
|
||||
memory_context=memory_context,
|
||||
trace_id=trace_id,
|
||||
parent_span_id=parent_span_id,
|
||||
tools=tools,
|
||||
base_url=base_url,
|
||||
run_id=run_id,
|
||||
attempt_no=attempt_no,
|
||||
workspace_dir=workspace_dir,
|
||||
skill_binding_role=skill_binding_role,
|
||||
)
|
||||
llm_tools = _prepare_llm_tools(
|
||||
store=store,
|
||||
tools=tools,
|
||||
base_url=base_url,
|
||||
session_id=session_id,
|
||||
trace_id=trace_id,
|
||||
parent_span_id=parent_span_id,
|
||||
run_id=run_id,
|
||||
attempt_no=attempt_no,
|
||||
lang=lang,
|
||||
wire_policy_role=wire_policy_role,
|
||||
)
|
||||
resp = model.chat(msgs, llm_tools, on_token=on_token)
|
||||
assistant_text = str(getattr(resp, "content", "") or "")
|
||||
reasoning_text = str(getattr(resp, "reasoning_content", "") or "")
|
||||
llm_tool_calls = list(getattr(resp, "tool_calls", []) or [])
|
||||
|
||||
step = _persist_assistant_step(
|
||||
store=store,
|
||||
session_id=session_id,
|
||||
turn_uuid=turn_uuid,
|
||||
assistant_text=assistant_text,
|
||||
reasoning_text=reasoning_text,
|
||||
llm_tool_calls=llm_tool_calls,
|
||||
)
|
||||
final_text = step.assistant_text
|
||||
if not step.llm_tool_calls:
|
||||
break
|
||||
|
||||
elapsed_ms, results_by_id = _execute_tool_step(
|
||||
skill_exec=skill_exec,
|
||||
store=store,
|
||||
tools=tools,
|
||||
session_id=session_id,
|
||||
lang=lang,
|
||||
user_text=str(user_text or ""),
|
||||
trace_id=trace_id,
|
||||
parent_span_id=parent_span_id,
|
||||
workspace_owner_session_id=workspace_owner_session_id,
|
||||
path_policy_tenant_id=path_policy_tenant_id,
|
||||
path_policy_user_id=path_policy_user_id,
|
||||
assistant_msg_id=step.assistant_msg_id,
|
||||
llm_tool_calls=step.llm_tool_calls,
|
||||
on_tool_ui=on_tool_ui,
|
||||
should_stop=should_stop,
|
||||
signature_budget=tool_signature_budget,
|
||||
run_id=run_id,
|
||||
attempt_no=attempt_no,
|
||||
turn_uuid=turn_uuid,
|
||||
)
|
||||
|
||||
for tc in step.llm_tool_calls:
|
||||
result, dur = results_by_id.get(str(getattr(tc, "id", "") or ""), ({}, 0))
|
||||
tool_traces.append(
|
||||
{
|
||||
"name": str(getattr(tc, "name", "") or ""),
|
||||
"tool_call_id": str(getattr(tc, "id", "") or ""),
|
||||
"ok": bool((result or {}).get("ok")) if isinstance(result, dict) else None,
|
||||
"duration_ms": int(dur),
|
||||
"round": int(round_idx + 1),
|
||||
}
|
||||
)
|
||||
|
||||
if on_progress:
|
||||
on_progress(f"oclaw: tools done ({elapsed_ms}ms)")
|
||||
|
||||
return TurnRunOutcome(
|
||||
final_text=str(final_text or ""),
|
||||
tool_traces=tuple(tool_traces),
|
||||
handoff_note="",
|
||||
turn_uuid=turn_uuid,
|
||||
)
|
||||
|
||||
|
||||
__all__ = ["run_openclaw_direct_loop"]
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue