from __future__ import annotations import json import os import re import time import uuid import copy import threading from dataclasses import dataclass from pathlib import Path from types import SimpleNamespace from typing import Any, Callable, Optional from oclaw.runtime.chat.agent_messages import build_llm_messages, get_last_build_llm_messages_stats from oclaw.runtime.chat.media_redact import redact_embedded_image_blobs from oclaw.runtime.chat.tool_runtime import ToolExecutionConfig from oclaw.runtime.chat.turn_types import TurnRunOutcome from oclaw.runtime.skill_executor import SkillExecutionContext, SkillExecutor from oclaw.runtime.skills import build_skill_manifest from oclaw.platform.llm.chat_models import ChatModel from oclaw.runtime.system_prompt import build_oclaw_executor_system_prompt from oclaw.runtime.types import OclawMemoryContext from oclaw.runtime.orchestration.trace import new_span_id from oclaw.runtime.tools.base import ToolRegistry from oclaw.runtime.hooks_runtime import trigger_hook_event from oclaw.runtime.tools.experts.network_ops.netx_tools import ops_netx_system_context_extension _OCLAW_TOOL_RESULT_HARD_CAP_CHARS = 24_000 _OCLAW_ATTACHMENT_TEXT_REPLAY_CAP_CHARS = 4_000 _OCLAW_IMAGE_TOOL_RESULT_REPLAY_CAP_CHARS = 4_000 _DIRECT_LOOP_OC_STAGE: dict[str, str] = { "tool_wire_filter": "wire_filter", "tool_result_context_guard": "tool_context_guard", "tool_pairing_guard": "tool_pairing_guard", } _THINK_BLOCK_RE = re.compile( r"<\s*(?:antml:)?(think|thinking|thought|redacted_thinking)\s*>\s*(.*?)\s*", flags=re.IGNORECASE | re.DOTALL, ) _DSML_INVOKE_NAME_RE = re.compile(r"invoke\s+name\s*=\s*['\"]([^'\"\s>]+)['\"]", flags=re.IGNORECASE) _JSON_TOOL_NAME_RE = re.compile(r"['\"]name['\"]\s*:\s*['\"]([^'\"\s]{1,120})['\"]", flags=re.IGNORECASE) _TOOL_WIRE_CACHE_LOCK = threading.Lock() _TOOL_WIRE_CACHE: dict[str, tuple[float, list[dict[str, Any]]]] = {} _TOOL_WIRE_CACHE_TTL_SEC = 300.0 _TOOL_WIRE_FROZEN_SIGNATURE: str | None = None _TOOL_WIRE_LAST_WARM_TS_MS: int = 0 _TOOL_WIRE_LAST_WARM_ROLES: tuple[str, ...] = () _TOOL_WIRE_LAST_WARM_COUNT: int = 0 def _safe_int(raw: Any, default: int, *, min_value: int = 1, max_value: int = 2_000_000) -> int: try: value = int(raw) except Exception: return default if value < min_value: return default return min(value, max_value) def _safe_nonneg_int(raw: Any, default: int, *, max_value: int = 2_000_000) -> int: try: value = int(raw) except Exception: return max(0, int(default)) if value < 0: return max(0, int(default)) return min(value, max_value) def _oclaw_config_path() -> Path: raw = str(os.getenv("AIA_OCLAW_CONFIG_PATH") or "").strip() if raw: p = Path(raw) return p if p.is_absolute() else p.resolve() return Path(__file__).resolve().parents[1] / "oclaw.json" def _image_tool_result_replay_cap_chars(store: Any) -> int: default = _OCLAW_IMAGE_TOOL_RESULT_REPLAY_CAP_CHARS raw_setting = "" try: raw_setting = str(store.get_setting("AIA_IMAGE_TOOL_RESULT_REPLAY_CAP_CHARS") or "").strip() except Exception: raw_setting = "" if raw_setting: return _safe_int(raw_setting, default, min_value=600, max_value=30_000) raw_env = str(os.getenv("AIA_IMAGE_TOOL_RESULT_REPLAY_CAP_CHARS") or "").strip() if raw_env: return _safe_int(raw_env, default, min_value=600, max_value=30_000) try: cfg_path = _oclaw_config_path() if cfg_path.exists() and cfg_path.is_file(): obj = json.loads(cfg_path.read_text(encoding="utf-8")) tab = ( (((obj.get("plugins") or {}).get("entries") or {}).get("memory-wiki") or {}) .get("auto", {}) .get("attachments", {}) .get("tabular", {}) ) if isinstance(tab, dict): return _safe_int(tab.get("image_result_replay_cap_chars"), default, min_value=600, max_value=30_000) except Exception: pass return default def _video_tool_result_replay_cap_chars(store: Any) -> int: default = 4_000 raw_setting = "" try: raw_setting = str(store.get_setting("AIA_VIDEO_TOOL_RESULT_REPLAY_CAP_CHARS") or "").strip() except Exception: raw_setting = "" if raw_setting: return _safe_int(raw_setting, default, min_value=600, max_value=30_000) raw_env = str(os.getenv("AIA_VIDEO_TOOL_RESULT_REPLAY_CAP_CHARS") or "").strip() if raw_env: return _safe_int(raw_env, default, min_value=600, max_value=30_000) try: cfg_path = _oclaw_config_path() if cfg_path.exists() and cfg_path.is_file(): obj = json.loads(cfg_path.read_text(encoding="utf-8")) tab = ( (((obj.get("plugins") or {}).get("entries") or {}).get("memory-wiki") or {}) .get("auto", {}) .get("attachments", {}) .get("tabular", {}) ) if isinstance(tab, dict): return _safe_int(tab.get("video_result_replay_cap_chars"), default, min_value=600, max_value=30_000) except Exception: pass return default def _tool_wire_freeze_enabled(store: Any) -> bool: raw = "" try: raw = str(store.get_setting("AIA_TOOL_WIRE_FROZEN_ON_STARTUP") or "").strip().lower() except Exception: raw = "" if not raw: raw = str(os.getenv("AIA_TOOL_WIRE_FROZEN_ON_STARTUP") or "").strip().lower() if not raw: return True return raw in {"1", "true", "yes", "on"} def _tool_wire_settings_signature(store: Any) -> tuple[bool, str]: runtime_enabled = True try: raw_flag = str(store.get_setting("AIA_SKILL_RUNTIME_ENABLED") or "").strip().lower() if raw_flag: runtime_enabled = raw_flag in {"1", "true", "yes", "on"} except Exception: runtime_enabled = True sig = "|".join( [ f"rt={int(bool(runtime_enabled))}", f"mcp={str(store.get_setting('AIA_ENABLE_MCP_TOOLS') or '')}", f"plugin={str(store.get_setting('AIA_ENABLE_PLUGIN_TOOLS') or '')}", f"skill_rt={str(store.get_setting('AIA_SKILL_RUNTIME_ENABLED') or '')}", f"skill_disabled={str(store.get_setting('AIA_SKILL_DISABLED_NAMES') or '')}", f"bind_en={str(store.get_setting('AIA_SKILL_ROLE_BINDING_ENABLED') or '')}", f"bind_inherit={str(store.get_setting('AIA_SKILL_ROLE_BINDING_MANAGER_INHERIT') or '')}", ] ) return runtime_enabled, sig def _tool_wire_cache_key( *, store: Any, base_url: str, wire_policy_role: str | None, runtime_enabled: bool, settings_sig: str | None = None, ) -> str: _, sig = _tool_wire_settings_signature(store) effective_sig = str(settings_sig or sig) return ( f"base={base_url}|role={str(wire_policy_role or '').strip().lower()}|" f"rt={int(bool(runtime_enabled))}|{effective_sig}" ) def warm_tool_wire_cache( *, store: Any, tools: ToolRegistry, base_url: str, roles: list[str] | tuple[str, ...], ) -> dict[str, int]: global _TOOL_WIRE_FROZEN_SIGNATURE, _TOOL_WIRE_LAST_WARM_TS_MS, _TOOL_WIRE_LAST_WARM_ROLES, _TOOL_WIRE_LAST_WARM_COUNT freeze_enabled = _tool_wire_freeze_enabled(store) runtime_enabled, sig = _tool_wire_settings_signature(store) warmed = 0 for role in roles or []: _ = _prepare_llm_tools( store=store, tools=tools, base_url=base_url, session_id="startup-prewarm", trace_id=None, parent_span_id=None, run_id="startup-prewarm", attempt_no=0, lang="", wire_policy_role=str(role or "").strip().lower() or None, ) warmed += 1 with _TOOL_WIRE_CACHE_LOCK: _TOOL_WIRE_FROZEN_SIGNATURE = f"rt={int(bool(runtime_enabled))}|{sig}" if freeze_enabled else None _TOOL_WIRE_LAST_WARM_TS_MS = int(time.time() * 1000) _TOOL_WIRE_LAST_WARM_ROLES = tuple(str(x or "").strip().lower() for x in roles or []) _TOOL_WIRE_LAST_WARM_COUNT = int(warmed) return {"roles_warmed": int(warmed), "frozen": int(bool(freeze_enabled))} def tool_wire_freeze_status(*, store: Any | None = None) -> dict[str, Any]: enabled = True if store is not None: enabled = _tool_wire_freeze_enabled(store) with _TOOL_WIRE_CACHE_LOCK: return { "enabled": bool(enabled), "frozen": bool(isinstance(_TOOL_WIRE_FROZEN_SIGNATURE, str) and _TOOL_WIRE_FROZEN_SIGNATURE.strip()), "frozen_signature": str(_TOOL_WIRE_FROZEN_SIGNATURE or ""), "last_warm_ts_ms": int(_TOOL_WIRE_LAST_WARM_TS_MS), "last_warm_roles": list(_TOOL_WIRE_LAST_WARM_ROLES), "last_warm_count": int(_TOOL_WIRE_LAST_WARM_COUNT), "cache_entries": int(len(_TOOL_WIRE_CACHE)), } def _emit_direct_loop_trace( *, store: Any, session_id: str, trace_id: str | None, parent_span_id: str | None, event_type: str, payload: dict[str, Any], run_id: str | None, attempt_no: int | None, lang: str, ) -> None: if not trace_id: return merged: dict[str, Any] = dict(payload or {}) merged.setdefault("pipeline", "oclaw_direct_loop") merged.setdefault("trace_id", str(trace_id)) merged.setdefault("lang", str(lang or "")) merged["oc_stage"] = _DIRECT_LOOP_OC_STAGE.get(event_type, event_type) rid = str(run_id or "").strip() if rid: merged.setdefault("run_id", rid) if attempt_no is not None: merged.setdefault("attempt_no", int(attempt_no)) try: store.add_trace_event( session_id=session_id, trace_id=str(trace_id), span_id=new_span_id(), parent_span_id=parent_span_id, event_type=event_type, payload=merged, ) except Exception: pass @dataclass(frozen=True) class _LoopStepResult: assistant_text: str llm_tool_calls: list[Any] assistant_msg_id: int def _json_dumps_safe(obj: Any) -> str: try: return json.dumps(obj, ensure_ascii=False, default=str) except Exception: return json.dumps({"ok": False, "error": "not_json_serializable"}, ensure_ascii=False) def _tool_message_with_content(m: Any, content: str, *, sid: str = "") -> SimpleNamespace: return SimpleNamespace( id=getattr(m, "id", 0), session_id=str(getattr(m, "session_id", None) or sid or ""), role="tool", content=content, tool_calls=getattr(m, "tool_calls", None), timestamp=getattr(m, "timestamp", ""), attachments=getattr(m, "attachments", None), turn_uuid=getattr(m, "turn_uuid", None), event_type=getattr(m, "event_type", None), event_payload=getattr(m, "event_payload", None), ) def _split_reasoning_and_body(text: str, *, explicit_reasoning: str | None = None) -> tuple[list[str], str]: explicit = str(explicit_reasoning or "").strip() raw = str(text or "") if not raw: return ([explicit] if explicit else []), "" if explicit: body = _THINK_BLOCK_RE.sub("", raw).strip() return [explicit], body chunks: list[str] = [] for m in _THINK_BLOCK_RE.finditer(raw): t = str(m.group(2) or "").strip() if t: chunks.append(t) body = _THINK_BLOCK_RE.sub("", raw).strip() return chunks, body def _guard_tool_results_for_llm_context( *, store: Any, session_id: str, store_messages: list[Any], trace_id: str | None, parent_span_id: str | None, hard_cap_chars: int, run_id: str | None = None, attempt_no: int | None = None, lang: str = "", active_turn_uuid: str | None = None, ) -> list[Any]: """Hard-guard overlarge `role=tool` message contents before sending to model. This does NOT rewrite DB history (tool_log / chat_message). It only guards the in-flight LLM context to prevent provider context overflow spirals. """ cap = max(4096, min(int(hard_cap_chars or _OCLAW_TOOL_RESULT_HARD_CAP_CHARS), 500_000)) image_cap = _image_tool_result_replay_cap_chars(store) video_cap = _video_tool_result_replay_cap_chars(store) out: list[Any] = [] for m in store_messages or []: role = str(getattr(m, "role", "") or "") if role != "tool": out.append(m) continue if str(getattr(m, "turn_uuid", "") or "") == str(active_turn_uuid or "") and str(active_turn_uuid or "").strip(): out.append(m) continue raw = str(getattr(m, "content", "") or "") try: _parsed0 = json.loads(raw) _parsed1 = redact_embedded_image_blobs(_parsed0) raw = _json_dumps_safe(_parsed1) except Exception: pass # Best-effort parse tool JSON for image-query specific guard and overflow metadata. ok = None error_code = "" error = "" obj: dict[str, Any] | None = None try: parsed = json.loads(raw) if isinstance(parsed, dict): obj = parsed ok = obj.get("ok") error_code = str(obj.get("error_code") or "").strip() error = str(obj.get("error") or "").strip() except Exception: obj = None if isinstance(obj, dict): task = str(obj.get("task") or "").strip().lower() text = str(obj.get("text") or "") has_attachment_id = bool(str(obj.get("attachment_id") or "").strip()) # Guard image describe/OCR result replay aggressively to avoid long visual transcripts # occupying context across future rounds. if task in {"describe", "ocr"} and has_attachment_id and len(text) > image_cap: preview = text[:image_cap] + "\n..." guarded_obj = dict(obj) guarded_obj["text"] = preview guarded_obj["_image_tool_result_guarded"] = True guarded_obj["image_result_original_chars"] = len(text) guarded_obj["image_result_replay_cap_chars"] = image_cap guarded_obj["image_result_hint"] = ( "Image analysis result was truncated for context replay. " "Refine query_image_attachment(question=...) for narrower evidence. / " "图片分析结果在上下文回放中已截断，请缩小 query_image_attachment 的问题范围。" ) guarded = _json_dumps_safe(guarded_obj) out.append(_tool_message_with_content(m, guarded, sid=session_id)) continue # Guard video transcript replay similarly (usually long). if str(obj.get("task") or "").strip().lower() == "transcript" and has_attachment_id and len(text) > video_cap: preview = text[:video_cap] + "\n..." guarded_obj = dict(obj) guarded_obj["text"] = preview guarded_obj["_video_tool_result_guarded"] = True guarded_obj["video_result_original_chars"] = len(text) guarded_obj["video_result_replay_cap_chars"] = video_cap guarded = _json_dumps_safe(guarded_obj) out.append(_tool_message_with_content(m, guarded, sid=session_id)) continue if len(raw) <= cap: out.append(_tool_message_with_content(m, raw, sid=session_id)) continue preview = raw[: max(1, min(4000, cap - 400))] + "\n..." guarded_obj = { "ok": bool(ok) if ok is not None else None, "error_code": error_code, "error": error, "_tool_result_guarded": True, "original_chars": len(raw), "guard_cap_chars": cap, "preview": preview, "hint": ( "Tool output was too large for safe context replay; it was truncated for the model context. " "Use narrower queries (e.g., smaller glob/max_results) or adjust AIA_TOOL_LLM_MESSAGE_MAX_CHARS. / " "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。" ), } guarded = _json_dumps_safe(guarded_obj) out.append(_tool_message_with_content(m, guarded, sid=session_id)) if trace_id: _emit_direct_loop_trace( store=store, session_id=session_id, trace_id=trace_id, parent_span_id=parent_span_id, event_type="tool_result_context_guard", payload={ "message_id": int(getattr(m, "id", 0) or 0), "original_chars": int(len(raw)), "guarded_chars": int(len(guarded)), "guard_cap_chars": int(cap), }, run_id=run_id, attempt_no=attempt_no, lang=lang, ) return out def _guard_text_attachments_for_llm_context( *, store_messages: list[Any], cap_chars: int, active_turn_uuid: str | None = None, ) -> list[Any]: """Guard overlarge user text attachments for model context replay. This does NOT rewrite DB history. It only guards the in-flight LLM context to prevent large attachments from overwhelming context windows. """ cap = max(800, min(int(cap_chars or _OCLAW_ATTACHMENT_TEXT_REPLAY_CAP_CHARS), 80_000)) out: list[Any] = [] for m in store_messages or []: role = str(getattr(m, "role", "") or "") if role != "user": out.append(m) continue # Never guard the active user turn. if str(getattr(m, "turn_uuid", "") or "") == str(active_turn_uuid or "") and str(active_turn_uuid or "").strip(): out.append(m) continue raw_att = getattr(m, "attachments", None) if not raw_att: out.append(m) continue try: att_obj = json.loads(raw_att) if isinstance(raw_att, str) else raw_att except Exception: out.append(m) continue if isinstance(att_obj, dict): atts = [att_obj] elif isinstance(att_obj, list): atts = att_obj else: out.append(m) continue has_text_ref = any( isinstance(a, dict) and str(a.get("type") or "").strip().lower() == "text_ref" for a in atts ) changed = False next_atts: list[dict[str, Any]] = [] for a in atts: if not isinstance(a, dict): continue if str(a.get("type") or "").strip().lower() != "text": next_atts.append(a) continue content = str(a.get("content") or "") # If this user message already has a text_ref, keep inline text very small in replay context. # The model can retrieve evidence via query_text_attachment(text_id=...). if has_text_ref and content: changed = True name = str(a.get("name") or "attachment") next_atts.append( { **a, "content": ( "# Attachment (collapsed; text_ref available)\n" f"- name: {name}\n" "- note: use `query_text_attachment` with `text_id` from `text_ref` for details.\n" "..." ), "_attachment_context_guarded": True, "_attachment_context_collapsed": True, } ) continue if len(content) <= cap: next_atts.append(a) continue changed = True name = str(a.get("name") or "attachment") hint_lines = [ "# Attachment (summarized for context replay)", f"- name: {name}", f"- original_chars: {len(content)}", f"- replay_cap_chars: {cap}", ] if has_text_ref: hint_lines.append("- note: use `query_text_attachment` with `text_id` from `text_ref` for details.") else: hint_lines.append("- note: attachment was large; re-upload or provide a smaller excerpt if needed.") preview = content[: min(1200, cap)] next_atts.append( { **a, "content": "\n".join(hint_lines) + "\n\n## Preview\n" + preview + "\n\n...", "_attachment_context_guarded": True, } ) if not changed: out.append(m) continue out.append( SimpleNamespace( id=getattr(m, "id", 0), session_id=getattr(m, "session_id", ""), role="user", content=getattr(m, "content", ""), tool_calls=getattr(m, "tool_calls", None), timestamp=getattr(m, "timestamp", ""), attachments=next_atts, turn_uuid=getattr(m, "turn_uuid", ""), event_type=getattr(m, "event_type", ""), event_payload=getattr(m, "event_payload", None), ) ) return out def _check_stop(should_stop: Optional[Callable[[], bool]]) -> None: if should_stop and should_stop(): raise RuntimeError("generation interrupted by user") def _build_model_context( *, store: Any, session_id: str, max_messages: int, system_prompt: str, model: ChatModel, lang: str, memory_context: OclawMemoryContext | None, trace_id: str | None, parent_span_id: str | None, tools: ToolRegistry | None = None, base_url: str = "", run_id: str | None = None, attempt_no: int | None = None, workspace_dir: str | None = None, skill_binding_role: str | None = None, workspace_owner_session_id: str | None = None, user_text: str = "", prompt_build_context: dict[str, Any] | None = None, active_turn_uuid: str | None = None, ) -> list[dict[str, Any]]: rows = store.get_messages(session_id=session_id, limit=int(max_messages)) rows = _guard_tool_results_for_llm_context( store=store, session_id=session_id, store_messages=rows, trace_id=trace_id, parent_span_id=parent_span_id, hard_cap_chars=_OCLAW_TOOL_RESULT_HARD_CAP_CHARS, run_id=run_id, attempt_no=attempt_no, lang=lang, active_turn_uuid=active_turn_uuid, ) rows = _guard_text_attachments_for_llm_context( store_messages=rows, cap_chars=_OCLAW_ATTACHMENT_TEXT_REPLAY_CAP_CHARS, active_turn_uuid=active_turn_uuid, ) final_system = build_oclaw_executor_system_prompt( store=store, tools=tools, base_url=str(base_url or ""), base_system=str(system_prompt or ""), memory_context=memory_context, lang=lang, workspace_dir=workspace_dir, skill_binding_role=skill_binding_role, workspace_owner_session_id=workspace_owner_session_id, session_id=session_id, ) # Hook integration: wiki-auto-inject can prepend retrieval snippets # before prompt build when query/topic hints indicate supplemental lookup. try: pb_ctx = prompt_build_context if isinstance(prompt_build_context, dict) else {} user_text_final = str(user_text or "").strip() wiki_query = str(pb_ctx.get("wiki_query") or "").strip() hook_ctx = { "userText": (wiki_query or user_text_final), "prepend_system_context": "", "need_wiki_inject": pb_ctx.get("need_wiki_inject"), "memory_mode": str(pb_ctx.get("memory_mode") or ""), "wiki_query": wiki_query, } hook_out = trigger_hook_event( event_type="llm", action="before_prompt_build", session_key=str(session_id or "system"), context=hook_ctx, ) prepend = str((hook_out or {}).get("prepend_system_context") or "").strip() if prepend: final_system = f"{prepend}\n\n{final_system}".strip() except Exception: pass try: if str(skill_binding_role or "").strip().lower() == "ops": ext = ops_netx_system_context_extension(lang=lang or "zh") if str(ext or "").strip(): final_system = f"{final_system}\n\n{ext.strip()}".strip() except Exception: pass trunc_raw = str(store.get_setting("AIA_TOOL_CONTEXT_TRUNCATE_ENABLED") or "").strip().lower() tool_context_truncate_enabled = trunc_raw not in ("0", "false", "no", "off") llm_messages = build_llm_messages( store_messages=rows, system_prompt=final_system, model=model, lang=lang, tool_context_truncate_enabled=tool_context_truncate_enabled, active_turn_uuid=active_turn_uuid, ) try: stats = get_last_build_llm_messages_stats() dropped_unpaired = int(stats.get("dropped_unpaired_tool_rows") or 0) dropped_no_id = int(stats.get("dropped_no_id_tool_rows") or 0) dropped_total = dropped_unpaired + dropped_no_id if dropped_total > 0 and trace_id: _emit_direct_loop_trace( store=store, session_id=session_id, trace_id=trace_id, parent_span_id=parent_span_id, event_type="tool_pairing_guard", payload={ "dropped_total": int(dropped_total), "dropped_unpaired_tool_rows": int(dropped_unpaired), "dropped_no_id_tool_rows": int(dropped_no_id), }, run_id=run_id, attempt_no=attempt_no, lang=lang, ) except Exception: pass return llm_messages def _prepare_llm_tools( *, store: Any, tools: ToolRegistry, base_url: str, session_id: str, trace_id: str | None, parent_span_id: str | None, run_id: str | None = None, attempt_no: int | None = None, lang: str = "", wire_policy_role: str | None = None, ) -> list[dict[str, Any]]: global _TOOL_WIRE_FROZEN_SIGNATURE now = time.time() runtime_enabled, sig = _tool_wire_settings_signature(store) freeze_enabled = _tool_wire_freeze_enabled(store) frozen_sig = _TOOL_WIRE_FROZEN_SIGNATURE if freeze_enabled else None if isinstance(frozen_sig, str) and frozen_sig.strip(): # Startup-prewarmed frozen mode: execution path reuses precomputed tool wiring # and does not perform per-turn policy revalidation. sig = frozen_sig try: rt_head = str(frozen_sig).split("|", 1)[0].strip().lower() runtime_enabled = rt_head == "rt=1" except Exception: pass cache_key = _tool_wire_cache_key( store=store, base_url=base_url, wire_policy_role=wire_policy_role, runtime_enabled=runtime_enabled, settings_sig=sig, ) with _TOOL_WIRE_CACHE_LOCK: cached = _TOOL_WIRE_CACHE.get(cache_key) if cached and ( (isinstance(frozen_sig, str) and frozen_sig.strip()) or (now - float(cached[0])) <= _TOOL_WIRE_CACHE_TTL_SEC ): return copy.deepcopy(cached[1]) if runtime_enabled: skill_specs, _ = build_skill_manifest(registry=tools, store=store, base_url=base_url) raw_llm_tools = [s.as_openai_tool() for s in skill_specs] else: raw_llm_tools = tools.as_openai_tools() from oclaw.runtime.tools.exposure_plan import build_llm_tools_plan plan = build_llm_tools_plan( store=store, role=str(wire_policy_role or "").strip().lower() or "generalist", base_url=base_url or None, max_json_bytes=None, include_mcp=False, preview_internal=False, raw_openai_tools_override=raw_llm_tools, ) llm_tools = plan.tools_wired if trace_id: try: import os raw_names = { str(((t.get("function") or {}) if isinstance(t, dict) else {}).get("name") or "") for t in (raw_llm_tools or []) if isinstance(t, dict) } raw_names.discard("") hidden = list(plan.removed_names) hidden_mcp = list(plan.removed_mcp_names) _emit_direct_loop_trace( store=store, session_id=session_id, trace_id=trace_id, parent_span_id=parent_span_id, event_type="tool_wire_filter", payload={ "runner": "oclaw_direct", "base_url": base_url, "wire_policy_role": str(wire_policy_role or ""), "tools_before": len(raw_names), "tools_after": int(len(_tool_names_for_trace(llm_tools))), "hidden_total": int(len(hidden)), "hidden_mcp_total": int(len(hidden_mcp)), "hidden_mcp_preview": list(hidden_mcp)[:20], "role_mode": str(plan.role_mode or ""), "wire_policy_effective": bool(plan.wire_policy_effective), "max_json_bytes": plan.max_json_bytes, "changed_total": int(len(plan.changed_names)), }, run_id=run_id, attempt_no=attempt_no, lang=lang, ) # Optional richer snapshot for debugging (may be large). trace_plan_enabled = False try: raw = str(store.get_setting("AIA_TRACE_TOOL_EXPOSURE_PLAN") or "").strip() if raw: trace_plan_enabled = raw.lower() in {"1", "true", "yes", "on"} else: trace_plan_enabled = str(os.getenv("AIA_TRACE_TOOL_EXPOSURE_PLAN") or "").strip().lower() in { "1", "true", "yes", "on", } except Exception: trace_plan_enabled = str(os.getenv("AIA_TRACE_TOOL_EXPOSURE_PLAN") or "").strip().lower() in { "1", "true", "yes", "on", } if trace_plan_enabled: _emit_direct_loop_trace( store=store, session_id=session_id, trace_id=trace_id, parent_span_id=parent_span_id, event_type="tool_exposure_plan", payload={ "runner": "oclaw_direct", "base_url": base_url, "wire_policy_role": str(wire_policy_role or ""), "role_mode": str(plan.role_mode or ""), "wire_policy_effective": bool(plan.wire_policy_effective), "max_json_bytes": plan.max_json_bytes, "raw_names": sorted(list(raw_names))[:300], "wired_names": sorted(list(set(_tool_names_for_trace(llm_tools))))[:300], "removed_names": list(plan.removed_names)[:300], "removed_mcp_names": list(plan.removed_mcp_names)[:300], "added_names": list(plan.added_names)[:300], "changed_names": list(plan.changed_names)[:300], }, run_id=run_id, attempt_no=attempt_no, lang=lang, ) except Exception: pass with _TOOL_WIRE_CACHE_LOCK: _TOOL_WIRE_CACHE[cache_key] = (now, copy.deepcopy(llm_tools)) if len(_TOOL_WIRE_CACHE) > 256: oldest_key = sorted(_TOOL_WIRE_CACHE.items(), key=lambda kv: kv[1][0])[0][0] _TOOL_WIRE_CACHE.pop(oldest_key, None) return llm_tools def _tool_names_for_trace(tools: list[dict[str, Any]]) -> list[str]: out: list[str] = [] for t in tools or []: if not isinstance(t, dict): continue fn = t.get("function") if not isinstance(fn, dict): continue nm = str(fn.get("name") or "").strip() if nm: out.append(nm) return out def _chat_with_empty_body_retry( *, model: Any, msgs: list[dict[str, Any]], llm_tools: list[dict[str, Any]], on_token: Optional[Callable[[str], None]], on_progress: Optional[Callable[[str], None]], progress_label: str = "oclaw: think", ) -> Any: # Empty assistant body can occur transiently at upstream gateways. # Retry until non-empty (bounded by retry count and total timeout). retry_max = _safe_nonneg_int(os.getenv("AIA_EMPTY_ASSISTANT_RETRY_MAX"), 1, max_value=3) retry_delay_ms = _safe_nonneg_int(os.getenv("AIA_EMPTY_ASSISTANT_RETRY_DELAY_MS"), 1200, max_value=15_000) retry_total_timeout_ms = _safe_nonneg_int(os.getenv("AIA_EMPTY_ASSISTANT_RETRY_TOTAL_TIMEOUT_MS"), 30_000, max_value=300_000) started = time.perf_counter() retries_done = 0 resp = model.chat(msgs, llm_tools, on_token=on_token) while True: content = str(getattr(resp, "content", "") or "") tool_calls = list(getattr(resp, "tool_calls", []) or []) textual_tool_intent = (not tool_calls) and bool(_extract_textual_tool_intent_names(content)) if (content.strip() or tool_calls) and not textual_tool_intent: return resp elapsed_ms = int((time.perf_counter() - started) * 1000.0) if retries_done >= retry_max or elapsed_ms >= retry_total_timeout_ms: return resp if textual_tool_intent: if on_progress: on_progress(f"{progress_label} retry-native-tool-calls ({retries_done + 1}/{retry_max})…") repair_msgs = list(msgs) + [ { "role": "system", "content": ( "Do not output textual tool intent/templates (DSML/XML/JSON). " "If a tool is needed, return native tool_calls only." ), } ] retries_done += 1 resp = model.chat(repair_msgs, llm_tools, on_token=on_token) continue if on_progress: on_progress(f"{progress_label} retry-empty ({retries_done + 1}/{retry_max})…") if retry_delay_ms > 0: time.sleep(float(retry_delay_ms) / 1000.0) retries_done += 1 resp = model.chat(msgs, llm_tools, on_token=on_token) def _extract_dsml_invoke_names(text: str) -> list[str]: raw = str(text or "") if not raw: return [] out: list[str] = [] seen: set[str] = set() for m in _DSML_INVOKE_NAME_RE.finditer(raw): nm = str(m.group(1) or "").strip() if not nm or nm in seen: continue seen.add(nm) out.append(nm) if len(out) >= 8: break return out def _extract_textual_tool_intent_names(text: str) -> list[str]: raw = str(text or "") if not raw: return [] lower = raw.lower() marker_hit = ("tool_calls" in lower) or ("invoke name" in lower) or ("parameter name" in lower) if not marker_hit: return [] out: list[str] = [] seen: set[str] = set() for nm in _extract_dsml_invoke_names(raw): key = str(nm or "").strip() if key and key not in seen: seen.add(key) out.append(key) if len(out) < 8: for m in _JSON_TOOL_NAME_RE.finditer(raw): nm = str(m.group(1) or "").strip() if not nm or nm in seen: continue seen.add(nm) out.append(nm) if len(out) >= 8: break if out: return out return ["unknown_tool"] def _persist_dsml_protocol_mismatch_step( *, store: Any, session_id: str, turn_uuid: str, assistant_text: str, invoke_names: list[str], ) -> _LoopStepResult: names = [str(x or "").strip() for x in (invoke_names or []) if str(x or "").strip()] if not names: names = ["unknown_tool"] stored_tool_calls: list[dict[str, Any]] = [] for nm in names: stored_tool_calls.append( { "id": f"call_dsml_{uuid.uuid4().hex}", "name": nm, "arguments": {}, "thought_signature": None, } ) assistant_row = store.add_message( session_id=session_id, role="assistant", content="", tool_calls=stored_tool_calls, turn_uuid=turn_uuid, event_type="tool_call", event_payload={ "protocol_mismatch": "textual_tool_intent", "raw_excerpt": str(assistant_text or "")[:2000], }, ) for tc in stored_tool_calls: tcid = str(tc.get("id") or "").strip() tname = str(tc.get("name") or "").strip() or "unknown_tool" tool_result = { "ok": False, "error_code": "model_protocol_mismatch_dsml", "error": "model_returned_textual_tool_intent_instead_of_native_tool_calls", "detail": {"tool_name": tname}, } store.add_message( session_id=session_id, role="tool", content=_json_dumps_safe(tool_result), tool_calls={ "tool_call_id": tcid, "name": tname, "assistant_message_id": int(getattr(assistant_row, "id", 0) or 0), }, turn_uuid=turn_uuid, event_type="tool_result", event_payload={"tool_name": tname, "protocol_mismatch": "textual_tool_intent"}, ) return _LoopStepResult( assistant_text="", llm_tool_calls=[], assistant_msg_id=int(getattr(assistant_row, "id", 0) or 0), ) def _persist_assistant_step( *, store: Any, session_id: str, turn_uuid: str, assistant_text: str, reasoning_text: str, llm_tool_calls: list[Any], ) -> _LoopStepResult: stored_tool_calls = [] for tc in llm_tool_calls: stored_tool_calls.append( { "id": str(getattr(tc, "id", "") or ""), "name": str(getattr(tc, "name", "") or ""), "arguments": dict(getattr(tc, "arguments", {}) or {}), "thought_signature": getattr(tc, "thought_signature", None), } ) reasoning_chunks, assistant_body = _split_reasoning_and_body(assistant_text, explicit_reasoning=reasoning_text) reasoning_full = "\n".join([str(x or "").strip() for x in reasoning_chunks if str(x or "").strip()]).strip() # Keep empty body as-is when model returns nothing and there are no tool calls. # The UI should treat this as an invisible intermediate/final empty response. # # Persist full reasoning on the primary assistant row's ``event_payload.reasoning_content`` for # both thinking and non-thinking profiles. Previously non-thinking used separate # ``event_type=reasoning`` rows with only chunk_index in payload, so JSON inspection looked # like "no reasoning_content anywhere" while tools carried rich ``event_payload``. assistant_row = store.add_message( session_id=session_id, role="assistant", content=assistant_body, tool_calls=stored_tool_calls or None, turn_uuid=turn_uuid, event_type="tool_call" if stored_tool_calls else "assistant_text", event_payload=({"reasoning_content": reasoning_full} if reasoning_full else None), ) return _LoopStepResult( assistant_text=assistant_body, llm_tool_calls=llm_tool_calls, assistant_msg_id=int(getattr(assistant_row, "id", 0) or 0), ) def _execute_tool_step( *, skill_exec: SkillExecutor, store: Any, tools: ToolRegistry, session_id: str, lang: str, user_text: str, trace_id: str | None, parent_span_id: str | None, workspace_dir: 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, workspace_lane_role: str | None = None, 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="oclaw", trace_id=trace_id, parent_span_id=parent_span_id, workspace_dir=workspace_dir, 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, workspace_lane_role=workspace_lane_role, ), 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 _maybe_image_specialist_legacy_gateway_turn( *, store: Any, session_id: str, turn_uuid: str, lang: str, model: ChatModel, user_text: str, attachments: list[dict[str, Any]] | None, skill_binding_role: str | None, on_token: Optional[Callable[[str], None]], on_progress: Optional[Callable[[str], None]], ) -> TurnRunOutcome | None: """When the UI selects **image** specialist, skip Responses/chat-model transports. Vision/gen HTTP goes through :func:`oclaw.platform.llm.image_legacy_client.send_legacy_image_messages` (``/chat/completions`` lane). Disable with ``AIA_IMAGE_SPECIALIST_DISABLE_LEGACY_GATEWAY_LANE=1``. End-to-end notes and safe edit boundaries: ``docs/IMAGE_SPECIALIST_LANE.md``. """ if str(os.getenv("AIA_IMAGE_SPECIALIST_DISABLE_LEGACY_GATEWAY_LANE") or "").strip().lower() in ( "1", "true", "yes", "on", ): return None if str(skill_binding_role or "").strip().lower() != "image": return None from oclaw.platform.llm.image_legacy_client import ( IMAGE_SPECIALIST_DEFAULT_PROMPT_ZH, collect_legacy_lane_images_with_session_fallback, legacy_image_assistant_body_with_placeholder, legacy_image_turn_bundle, send_legacy_image_messages, ) imgs, legacy_img_src = collect_legacy_lane_images_with_session_fallback( store=store, session_id=session_id, attachments=attachments, ) if not imgs: hint_en = "Image specialist received no image input. Attach an image and try again." hint_zh = "图片专家未收到可用的图片输入；请先上传或附上图片后再试。" hint = hint_en if str(lang or "").startswith("en") else hint_zh store.add_message( session_id=session_id, role="assistant", content=hint, turn_uuid=turn_uuid, event_type="assistant_text", ) return TurnRunOutcome( final_text=hint, tool_traces=tuple(), handoff_note="image_specialist_legacy_missing_attachment", turn_uuid=turn_uuid, ) if on_progress: if legacy_img_src.endswith("_history"): if str(lang or "").startswith("en"): on_progress("oclaw: reusing earlier session images (no new upload this turn)…") else: on_progress("oclaw: 本轮未上传新图，使用会话中较早的图片作为输入…") on_progress("oclaw: image specialist (legacy multimodal HTTP)…") prompt_plain = str(user_text or "").strip() if not prompt_plain: prompt_plain = IMAGE_SPECIALIST_DEFAULT_PROMPT_ZH resp = send_legacy_image_messages( images=imgs, prompt=prompt_plain, model=str(getattr(model, "model", "") or "").strip() or None, api_key=str(getattr(model, "api_key", "") or "").strip() or None, base_url=str(getattr(model, "base_url", "") or "").strip() or None, ) ok, body_text, produced = legacy_image_turn_bundle(resp) body_text = legacy_image_assistant_body_with_placeholder( lang=lang, body_text=body_text, produced=produced if ok else None, ) store.add_message( session_id=session_id, role="assistant", content=body_text, turn_uuid=turn_uuid, event_type="assistant_text", attachments=(produced or None) if ok else None, ) if ok and on_token and body_text: on_token(body_text) return TurnRunOutcome( final_text=body_text, tool_traces=tuple(), handoff_note="image_specialist_legacy_http" if ok else "image_specialist_legacy_upstream_failed", turn_uuid=turn_uuid, ) def _maybe_video_specialist_legacy_gateway_turn( *, store: Any, session_id: str, turn_uuid: str, lang: str, model: ChatModel, user_text: str, attachments: list[dict[str, Any]] | None, skill_binding_role: str | None, on_token: Optional[Callable[[str], None]], on_progress: Optional[Callable[[str], None]], should_stop: Optional[Callable[[], bool]] = None, ) -> TurnRunOutcome | None: """When the UI selects **video** specialist, skip Responses/chat-model transports. Uses DashScope async ``video-synthesis`` (see :mod:`oclaw.platform.llm.video_generation_client`). With a user image (or session image fallback), sends ``input.img_url`` for **image-to-video**; otherwise **text-to-video**. Disable with ``AIA_VIDEO_SPECIALIST_DISABLE_LEGACY_GATEWAY_LANE=1``. """ if str(os.getenv("AIA_VIDEO_SPECIALIST_DISABLE_LEGACY_GATEWAY_LANE") or "").strip().lower() in ( "1", "true", "yes", "on", ): return None if str(skill_binding_role or "").strip().lower() != "video": return None from oclaw.platform.llm.image_legacy_client import collect_legacy_lane_images_with_session_fallback from oclaw.platform.llm.video_generation_client import ( VIDEO_SPECIALIST_DEFAULT_PROMPT_ZH, legacy_video_assistant_body_with_placeholder, legacy_video_turn_bundle, send_video_generation_request, ) frames, frame_src = collect_legacy_lane_images_with_session_fallback( store=store, session_id=session_id, attachments=attachments, max_images=1, ) frame_url = str(frames[0]).strip() if frames else None if on_progress: if frame_url: if frame_src.endswith("_history"): if str(lang or "").startswith("en"): on_progress("oclaw: reusing an earlier session image as first frame…") else: on_progress("oclaw: 使用会话中较早的图片作为图生视频首帧…") on_progress("oclaw: video specialist (DashScope image-to-video)…") else: on_progress("oclaw: video specialist (DashScope text-to-video)…") prompt_plain = str(user_text or "").strip() or VIDEO_SPECIALIST_DEFAULT_PROMPT_ZH resp = send_video_generation_request( prompt=prompt_plain, model=str(getattr(model, "model", "") or "").strip() or None, api_key=str(getattr(model, "api_key", "") or "").strip() or None, base_url=str(getattr(model, "base_url", "") or "").strip() or None, img_url=frame_url, on_progress=on_progress, should_stop=should_stop, ) ok, body_text, produced = legacy_video_turn_bundle(resp) body_text = legacy_video_assistant_body_with_placeholder( lang=lang, body_text=body_text, produced=produced if ok else None, ) store.add_message( session_id=session_id, role="assistant", content=body_text, turn_uuid=turn_uuid, event_type="assistant_text", attachments=(produced or None) if ok else None, ) if ok and on_token and body_text: on_token(body_text) return TurnRunOutcome( final_text=body_text, tool_traces=tuple(), handoff_note="video_specialist_legacy_http" if ok else "video_specialist_legacy_upstream_failed", turn_uuid=turn_uuid, ) def run_oclaw_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: OclawMemoryContext | None = None, persist_user_message: bool = True, persisted_user_text: str | None = None, tool_signature_budget: int = 2, skill_binding_role: str | None = None, wire_policy_role: str | None = None, prompt_build_context: dict[str, Any] | None = None, turn_uuid: str | None = None, ) -> TurnRunOutcome: """A minimal oclaw-style loop: model -> tool_uses -> execute -> tool_results -> continue.""" _check_stop(should_stop) turn_uuid = str(turn_uuid or "").strip() or str(uuid.uuid4()) persisted_text = str(user_text if persisted_user_text is None else persisted_user_text or "") if persist_user_message: store.add_message( session_id=session_id, role="user", content=persisted_text, attachments=attachments, turn_uuid=turn_uuid, event_type="user_text", ) legacy_early = _maybe_image_specialist_legacy_gateway_turn( store=store, session_id=session_id, turn_uuid=turn_uuid, lang=lang, model=model, user_text=str(user_text or ""), attachments=attachments, skill_binding_role=skill_binding_role, on_token=on_token, on_progress=on_progress, ) if legacy_early is not None: return legacy_early video_early = _maybe_video_specialist_legacy_gateway_turn( store=store, session_id=session_id, turn_uuid=turn_uuid, lang=lang, model=model, user_text=str(user_text or ""), attachments=attachments, skill_binding_role=skill_binding_role, on_token=on_token, on_progress=on_progress, should_stop=should_stop, ) if video_early is not None: return video_early 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 = "" hit_tool_round_limit = False workspace_lane_role = str(skill_binding_role or wire_policy_role or "generalist").strip().lower() or "generalist" base_url = str(getattr(model, "base_url", "") or "") max_rounds = max(1, int(max_tool_rounds or 1)) for round_idx in range(max_rounds): _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, workspace_owner_session_id=workspace_owner_session_id, user_text=str(user_text or ""), prompt_build_context=prompt_build_context, active_turn_uuid=turn_uuid, ) 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 = _chat_with_empty_body_retry( model=model, msgs=msgs, llm_tools=llm_tools, on_token=on_token, on_progress=on_progress, progress_label="oclaw: think", ) assistant_text = str(getattr(resp, "content", "") or "") reasoning_text = str(getattr(resp, "reasoning_content", "") or "") llm_tool_calls = list(getattr(resp, "tool_calls", []) or []) textual_tool_intent_names = _extract_textual_tool_intent_names(assistant_text) if not llm_tool_calls else [] if textual_tool_intent_names: step = _persist_dsml_protocol_mismatch_step( store=store, session_id=session_id, turn_uuid=turn_uuid, assistant_text=assistant_text, invoke_names=textual_tool_intent_names, ) else: 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 if round_idx == (max_rounds - 1): # Reached tool-round cap with pending tool calls. Execute this batch, then # force one no-tool synthesis pass to guarantee a visible assistant body. hit_tool_round_limit = True # Stream UI: emit one session.tool per pending call before execution (tool_use_result fires after). if callable(on_tool_ui): for tc in step.llm_tool_calls: try: on_tool_ui( "tool_use_call", { "phase": "call", "tool_name": str(getattr(tc, "name", "") or ""), "tool_call_id": str(getattr(tc, "id", "") or ""), "arguments": dict(getattr(tc, "arguments", {}) or {}), }, ) except Exception: pass 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_dir=workspace_dir, 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, workspace_lane_role=workspace_lane_role, 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)") if hit_tool_round_limit: _check_stop(should_stop) if on_progress: on_progress("oclaw: finalize…") 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, workspace_owner_session_id=workspace_owner_session_id, user_text=str(user_text or ""), prompt_build_context=prompt_build_context, active_turn_uuid=turn_uuid, ) # Final pass forbids extra tool calls; model must synthesize answer. resp = _chat_with_empty_body_retry( model=model, msgs=msgs, llm_tools=[], on_token=on_token, on_progress=on_progress, progress_label="oclaw: finalize", ) step = _persist_assistant_step( store=store, session_id=session_id, turn_uuid=turn_uuid, assistant_text=str(getattr(resp, "content", "") or ""), reasoning_text=str(getattr(resp, "reasoning_content", "") or ""), llm_tool_calls=[], ) final_text = step.assistant_text return TurnRunOutcome( final_text=str(final_text or ""), tool_traces=tuple(tool_traces), handoff_note="", turn_uuid=turn_uuid, ) def run_direct_loop(**kwargs: Any) -> TurnRunOutcome: return run_oclaw_direct_loop(**kwargs) __all__ = ["run_oclaw_direct_loop", "run_direct_loop", "warm_tool_wire_cache", "tool_wire_freeze_status"]