补充并收敛管理台与运行时改动，完成本轮代码提交。

包含模型配置/thinking 透传、MCP 运行时与安装体验增强、以及会话渲染与图片显示链路修复，确保流式与历史展示行为一致。

Made-with: Cursor

interfaces/admin/models_api.py

@@ -276,6 +276,17 @@ def include_model_mgmt_routes(
             mode_save = mode_raw
         model = payload.get("model")
         base_url = payload.get("base_url")
+        thinking_mode_enabled = payload.get("thinking_mode_enabled")
+        if thinking_mode_enabled is None:
+            think_enabled: bool | None = None
+        else:
+            think_enabled = bool(thinking_mode_enabled)
+        reasoning_effort_raw = payload.get("reasoning_effort")
+        reasoning_effort: str | None = None
+        if reasoning_effort_raw is not None:
+            reasoning_effort = str(reasoning_effort_raw or "").strip().lower()
+            if reasoning_effort not in ("", "low", "medium", "high"):
+                raise HTTPException(status_code=400, detail="invalid_reasoning_effort")
         model_s = str(model).strip() if model is not None else str(prof.get("model") or "").strip()
         bu_s = str(base_url).strip() if base_url is not None else str(prof.get("base_url") or "").strip()
         store.update_llm_profile(
@@ -284,6 +295,8 @@ def include_model_mgmt_routes(
             mode=mode_save,
             model=model_s or None,
             base_url=bu_s or None,
+            thinking_mode_enabled=think_enabled,
+            reasoning_effort=reasoning_effort,
         )
         store.set_setting(_active_key(ctx), pid)
         return {"ok": True, "profile": store.get_llm_profile(pid)}

interfaces/admin/static/theme-deepseek.css

@@ -471,6 +471,7 @@ body.theme-ds-body .card {
 
 /* 与 .chat-avatar-slot 48px + .chat-row gap 10px 一致 */
 .chat-msg-col--assistant {
+  width: min(max(0px, calc(50% + 2cm - 58px)), 100%);
   max-width: min(max(0px, calc(50% + 2cm - 58px)), 100%);
 }
 
@@ -570,6 +571,7 @@ body.theme-ds-body .card {
 
 .chat-msg--assistant {
   align-self: flex-start;
+  width: 100%;
   background: rgba(255, 255, 255, 0.04);
   border: 1px solid var(--ds-border, rgba(255, 255, 255, 0.08));
 }

platform/llm/transports/openai_chat_completions.py

@@ -30,6 +30,47 @@ def _is_minimax_compat(model: str | None, base_url: str | None) -> bool:
     return ("minimax" in m) or ("minimax" in b)
 
 
+def _truthy_env(name: str, default: str = "") -> bool:
+    v = os.getenv(name)
+    if v is None:
+        v = default
+    return str(v or "").strip().lower() in ("1", "true", "yes", "on")
+
+
+def _should_disable_thinking(base_url: str | None) -> bool:
+    # Some OpenAI-compatible gateways enable "thinking" mode and require replaying
+    # reasoning_content in subsequent turns. When the client does not preserve it,
+    # the gateway returns HTTP 400. Allow disabling thinking at request level.
+    #
+    # Safety: do NOT send unknown fields to official OpenAI endpoints by default.
+    if _truthy_env("AIA_LLM_THINKING_FORCE_DISABLED", "0"):
+        return True
+    if _truthy_env("AIA_LLM_THINKING_FORCE_ENABLED", "0"):
+        return False
+    b = str(base_url or "").strip().lower()
+    if not b:
+        return False
+    if "api.openai.com" in b:
+        return False
+    # Default-on for non-official OpenAI-compatible gateways.
+    return _truthy_env("AIA_LLM_THINKING_DISABLED", "1")
+
+
+def _should_enable_thinking(base_url: str | None, *, thinking_mode_enabled: bool = False) -> bool:
+    if _truthy_env("AIA_LLM_THINKING_FORCE_ENABLED", "0"):
+        return True
+    if _truthy_env("AIA_LLM_THINKING_FORCE_DISABLED", "0"):
+        return False
+    if not bool(thinking_mode_enabled):
+        return False
+    b = str(base_url or "").strip().lower()
+    if not b:
+        return False
+    if "api.openai.com" in b:
+        return False
+    return True
+
+
 def _find_thought_signature_in_obj(o: Any) -> str | None:
     if isinstance(o, dict):
         for k in ("thought_signature", "thoughtSignature"):
@@ -102,10 +143,15 @@ class OpenAIChatModel(ChatModel):
         model: str | None = None,
         api_key: str | None = None,
         base_url: str | None = None,
+        thinking_mode_enabled: bool = False,
+        reasoning_effort: str | None = None,
     ):
         self.model = model or os.getenv("OPENAI_MODEL") or "gpt-4o-mini"
         self.api_key = api_key or os.getenv("OPENAI_API_KEY")
         self.base_url = base_url or os.getenv("OPENAI_BASE_URL")
+        self.thinking_mode_enabled = bool(thinking_mode_enabled)
+        eff = str(reasoning_effort or "").strip().lower()
+        self.reasoning_effort = eff if eff in ("low", "medium", "high") else ""
 
         if not self.api_key:
             raise RuntimeError("未设置 OPENAI_API_KEY，无法使用 OpenAI 模型")
@@ -177,6 +223,19 @@ class OpenAIChatModel(ChatModel):
                 cleaned_msgs.append(m)
 
         kwargs: dict[str, Any] = {"model": self.model, "messages": cleaned_msgs, "stream": stream}
+        if _should_enable_thinking(self.base_url, thinking_mode_enabled=bool(getattr(self, "thinking_mode_enabled", False))):
+            extra_body = kwargs.get("extra_body") if isinstance(kwargs.get("extra_body"), dict) else {}
+            extra_body = dict(extra_body)
+            extra_body["thinking"] = {"type": "enabled"}
+            kwargs["extra_body"] = extra_body
+            eff = str(getattr(self, "reasoning_effort", "") or "").strip().lower()
+            if eff in ("low", "medium", "high"):
+                kwargs["reasoning_effort"] = eff
+        elif _should_disable_thinking(self.base_url):
+            extra_body = kwargs.get("extra_body") if isinstance(kwargs.get("extra_body"), dict) else {}
+            extra_body = dict(extra_body)
+            extra_body["thinking"] = {"type": "disabled"}
+            kwargs["extra_body"] = extra_body
         if use_tools:
             try:
                 from oclaw.platform.config.paths import db_path
@@ -195,7 +254,20 @@ class OpenAIChatModel(ChatModel):
                 kwargs["tools"] = plan.tools_wired
             except Exception:
                 kwargs["tools"] = tools
-        return self._client.chat.completions.create(**kwargs)
+        try:
+            return self._client.chat.completions.create(**kwargs)
+        except Exception as exc:
+            msg = str(exc)
+            if "reasoning_content" in msg and "thinking mode" in msg and "must be passed back" in msg:
+                # Provider requires replaying assistant.reasoning_content in thinking mode.
+                # As a safety fallback, force-disable thinking and retry once.
+                extra_body = kwargs.get("extra_body") if isinstance(kwargs.get("extra_body"), dict) else {}
+                extra_body = dict(extra_body)
+                extra_body["thinking"] = {"type": "disabled"}
+                kwargs["extra_body"] = extra_body
+                kwargs.pop("reasoning_effort", None)
+                return self._client.chat.completions.create(**kwargs)
+            raise
 
     def _llm_response_from_completion(self, completion: Any, *, on_token: Optional[Callable[[str], None]]) -> LLMResponse:
         msg = completion.choices[0].message

platform/llm/transports/openai_responses.py

@@ -104,10 +104,21 @@ def parse_openai_responses_stream_events(
 class OpenAIResponsesModel(ChatModel):
     """OpenAI Responses API transport (OpenAI-compatible gateways may implement this surface)."""
 
-    def __init__(self, *, model: str | None = None, api_key: str | None = None, base_url: str | None = None):
+    def __init__(
+        self,
+        *,
+        model: str | None = None,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        thinking_mode_enabled: bool = False,
+        reasoning_effort: str | None = None,
+    ):
         self.model = (model or os.getenv("OPENAI_MODEL") or "gpt-4o-mini").strip()
         self.api_key = (api_key or os.getenv("OPENAI_API_KEY") or "").strip()
         self.base_url = (base_url or os.getenv("OPENAI_BASE_URL") or "").strip() or None
+        self.thinking_mode_enabled = bool(thinking_mode_enabled)
+        eff = str(reasoning_effort or "").strip().lower()
+        self.reasoning_effort = eff if eff in ("low", "medium", "high") else ""
         if not self.api_key:
             raise RuntimeError("未设置 OPENAI_API_KEY，无法使用 OpenAI Responses")
         try:
@@ -188,9 +199,41 @@ class OpenAIResponsesModel(ChatModel):
         norm = self._normalize_messages(messages)
         # OpenAI-compatible gateways differ: some require input={"messages":[...]} with role=user only.
         stream_errors: list[str] = []
+        b = str(self.base_url or "").strip().lower()
+        force_disable = str(os.getenv("AIA_LLM_THINKING_FORCE_DISABLED") or "").strip().lower() in ("1", "true", "yes", "on")
+        force_enable = str(os.getenv("AIA_LLM_THINKING_FORCE_ENABLED") or "").strip().lower() in ("1", "true", "yes", "on")
+        mode_enabled = bool(getattr(self, "thinking_mode_enabled", False))
+        extra_body: dict[str, Any] = {}
+        if b and ("api.openai.com" not in b):
+            if force_disable:
+                extra_body["thinking"] = {"type": "disabled"}
+            elif force_enable or mode_enabled:
+                extra_body["thinking"] = {"type": "enabled"}
+            else:
+                # Default: disable thinking for non-official gateways unless explicitly enabled.
+                if str(os.getenv("AIA_LLM_THINKING_DISABLED") or "1").strip().lower() in ("1", "true", "yes", "on"):
+                    extra_body["thinking"] = {"type": "disabled"}
+        thinking = {"extra_body": extra_body} if extra_body else {}
+        reasoning_effort = str(getattr(self, "reasoning_effort", "") or "").strip().lower()
+        if reasoning_effort not in ("low", "medium", "high"):
+            reasoning_effort = ""
         stream_variants: list[dict[str, Any]] = [
-            {"model": self.model, "input": {"messages": norm}, "tools": tools or None, "stream": True},
-            {"model": self.model, "input": norm, "tools": tools or None, "stream": True},
+            {
+                **thinking,
+                **({"reasoning_effort": reasoning_effort} if reasoning_effort else {}),
+                "model": self.model,
+                "input": {"messages": norm},
+                "tools": tools or None,
+                "stream": True,
+            },
+            {
+                **thinking,
+                **({"reasoning_effort": reasoning_effort} if reasoning_effort else {}),
+                "model": self.model,
+                "input": norm,
+                "tools": tools or None,
+                "stream": True,
+            },
         ]
         try:
             for payload in stream_variants:
@@ -205,15 +248,48 @@ class OpenAIResponsesModel(ChatModel):
                                 on_token(text)
                     return LLMResponse(content=text, tool_calls=tool_calls)
                 except Exception as exc:
-                    stream_errors.append(str(exc))
+                    emsg = str(exc)
+                    # Fallback: provider thinking-mode replay contract.
+                    if "reasoning_content" in emsg and "thinking mode" in emsg and "must be passed back" in emsg:
+                        try:
+                            forced = dict(payload)
+                            eb = forced.get("extra_body") if isinstance(forced.get("extra_body"), dict) else {}
+                            eb = dict(eb)
+                            eb["thinking"] = {"type": "disabled"}
+                            forced["extra_body"] = eb
+                            forced.pop("reasoning_effort", None)
+                            stream = self._client.responses.create(**forced)
+                            text, tool_calls, final_resp = parse_openai_responses_stream_events(stream, on_token=on_token)
+                            if (not text.strip()) and final_resp:
+                                ot = final_resp.get("output_text")
+                                if isinstance(ot, str) and ot.strip():
+                                    text = ot
+                                    if on_token:
+                                        on_token(text)
+                            return LLMResponse(content=text, tool_calls=tool_calls)
+                        except Exception:
+                            pass
+                    stream_errors.append(emsg)
                     continue
             raise RuntimeError("; ".join(stream_errors) or "responses_stream_all_variants_failed")
         except Exception as exc:
             logger.info("responses stream failed; fallback to non-stream (%s)", exc)
             nonstream_errors: list[str] = []
             for payload in (
-                {"model": self.model, "input": {"messages": norm}, "tools": tools or None},
-                {"model": self.model, "input": norm, "tools": tools or None},
+                {
+                    **thinking,
+                    **({"reasoning_effort": reasoning_effort} if reasoning_effort else {}),
+                    "model": self.model,
+                    "input": {"messages": norm},
+                    "tools": tools or None,
+                },
+                {
+                    **thinking,
+                    **({"reasoning_effort": reasoning_effort} if reasoning_effort else {}),
+                    "model": self.model,
+                    "input": norm,
+                    "tools": tools or None,
+                },
             ):
                 try:
                     resp = self._client.responses.create(**payload)
@@ -224,7 +300,25 @@ class OpenAIResponsesModel(ChatModel):
                         on_token(text)
                     return LLMResponse(content=text, tool_calls=tool_calls)
                 except Exception as e2:
-                    nonstream_errors.append(str(e2))
+                    emsg2 = str(e2)
+                    if "reasoning_content" in emsg2 and "thinking mode" in emsg2 and "must be passed back" in emsg2:
+                        try:
+                            forced = dict(payload)
+                            eb = forced.get("extra_body") if isinstance(forced.get("extra_body"), dict) else {}
+                            eb = dict(eb)
+                            eb["thinking"] = {"type": "disabled"}
+                            forced["extra_body"] = eb
+                            forced.pop("reasoning_effort", None)
+                            resp = self._client.responses.create(**forced)
+                            d = _as_dict(resp) or {}
+                            text = str(d.get("output_text") or "")
+                            tool_calls = _collect_tool_calls_from_response_dict(d)
+                            if on_token and text:
+                                on_token(text)
+                            return LLMResponse(content=text, tool_calls=tool_calls)
+                        except Exception:
+                            pass
+                    nonstream_errors.append(emsg2)
                     continue
             raise RuntimeError(
                 "openai_responses_request_failed: "

platform/persistence/sqlite_store.py

@@ -912,6 +912,10 @@ class SqliteStore:
                 conn.execute("ALTER TABLE llm_profile ADD COLUMN hide_in_ui INTEGER NOT NULL DEFAULT 0")
             if "owner_user_id" not in prof_cols:
                 conn.execute("ALTER TABLE llm_profile ADD COLUMN owner_user_id TEXT")
+            if "thinking_mode_enabled" not in prof_cols:
+                conn.execute("ALTER TABLE llm_profile ADD COLUMN thinking_mode_enabled INTEGER NOT NULL DEFAULT 0")
+            if "reasoning_effort" not in prof_cols:
+                conn.execute("ALTER TABLE llm_profile ADD COLUMN reasoning_effort TEXT")
             conn.execute(
                 """
                 CREATE TABLE IF NOT EXISTS llm_profile_user_grant (
@@ -2349,8 +2353,8 @@ class SqliteStore:
             conn.execute(
                 """
                 INSERT INTO llm_profile
-                    (id, name, mode, model, base_url, api_key, updated_at, is_builtin, hide_in_ui, owner_user_id)
-                VALUES (?, ?, ?, ?, ?, NULL, ?, 0, 0, ?)
+                    (id, name, mode, model, base_url, api_key, updated_at, is_builtin, hide_in_ui, owner_user_id, thinking_mode_enabled, reasoning_effort)
+                VALUES (?, ?, ?, ?, ?, NULL, ?, 0, 0, ?, 0, '')
                 """,
                 (profile_id, name, mode, model, base_url, ts, own),
             )
@@ -2393,7 +2397,9 @@ class SqliteStore:
                 SELECT id, name, mode, model, base_url, api_key, updated_at,
                        COALESCE(is_builtin, 0) AS is_builtin,
                        COALESCE(hide_in_ui, 0) AS hide_in_ui,
-                       owner_user_id
+                       owner_user_id,
+                       COALESCE(thinking_mode_enabled, 0) AS thinking_mode_enabled,
+                       COALESCE(reasoning_effort, '') AS reasoning_effort
                 FROM llm_profile
                 {where_sql}
                 ORDER BY COALESCE(is_builtin, 0) DESC, COALESCE(hide_in_ui, 0) ASC, updated_at DESC
@@ -2465,6 +2471,8 @@ class SqliteStore:
                     "is_builtin": is_builtin,
                     "hide_in_ui": bool(int(r["hide_in_ui"] or 0)),
                     "owner_user_id": own,
+                    "thinking_mode_enabled": bool(int(r["thinking_mode_enabled"] or 0)),
+                    "reasoning_effort": str(r["reasoning_effort"] or "").strip().lower(),
                     "mutable": mutable,
                     "visibility_reason": vis,
                 }
@@ -2478,7 +2486,9 @@ class SqliteStore:
                 SELECT id, name, mode, model, base_url, api_key, updated_at,
                        COALESCE(is_builtin, 0) AS is_builtin,
                        COALESCE(hide_in_ui, 0) AS hide_in_ui,
-                       owner_user_id
+                       owner_user_id,
+                       COALESCE(thinking_mode_enabled, 0) AS thinking_mode_enabled,
+                       COALESCE(reasoning_effort, '') AS reasoning_effort
                 FROM llm_profile
                 WHERE id = ?
                 """,
@@ -2497,6 +2507,8 @@ class SqliteStore:
             "is_builtin": bool(int(r["is_builtin"] or 0)),
             "hide_in_ui": bool(int(r["hide_in_ui"] or 0)),
             "owner_user_id": str(r["owner_user_id"] or "").strip() if r["owner_user_id"] is not None else "",
+            "thinking_mode_enabled": bool(int(r["thinking_mode_enabled"] or 0)),
+            "reasoning_effort": str(r["reasoning_effort"] or "").strip().lower(),
         }
 
     def update_llm_profile(
@@ -2506,16 +2518,26 @@ class SqliteStore:
         mode: str,
         model: str | None,
         base_url: str | None,
+        *,
+        thinking_mode_enabled: bool | None = None,
+        reasoning_effort: str | None = None,
     ) -> None:
         ts = utc_now_iso()
+        think_val = None if thinking_mode_enabled is None else (1 if bool(thinking_mode_enabled) else 0)
+        eff = None if reasoning_effort is None else str(reasoning_effort or "").strip().lower()
+        if eff is not None and eff not in ("", "low", "medium", "high"):
+            eff = ""
         with self._connect() as conn:
             conn.execute(
                 """
                 UPDATE llm_profile
-                SET name = ?, mode = ?, model = ?, base_url = ?, updated_at = ?
+                SET name = ?, mode = ?, model = ?, base_url = ?,
+                    thinking_mode_enabled = COALESCE(?, thinking_mode_enabled),
+                    reasoning_effort = COALESCE(?, reasoning_effort),
+                    updated_at = ?
                 WHERE id = ?
                 """,
-                (name, mode, model, base_url, ts, profile_id),
+                (name, mode, model, base_url, think_val, eff, ts, profile_id),
             )
 
     def delete_llm_profile(self, profile_id: str) -> None:

runtime/agents/factory.py

@@ -90,6 +90,24 @@ def _build_executor_components(
         m = (raw or "").strip().lower()
         return m if m in ("openai", "openai_responses", "anthropic", "ollama", "rule", "google") else "rule"
 
+    def _profile_thinking_config(profile: dict[str, Any] | None) -> tuple[bool, str]:
+        if not isinstance(profile, dict):
+            return False, ""
+        think = bool(profile.get("thinking_mode_enabled"))
+        eff = str(profile.get("reasoning_effort") or "").strip().lower()
+        if eff not in ("", "low", "medium", "high"):
+            eff = ""
+        return think, eff
+
+    def _apply_profile_thinking(model_obj: object, profile: dict[str, Any] | None) -> object:
+        think, eff = _profile_thinking_config(profile)
+        try:
+            setattr(model_obj, "thinking_mode_enabled", think)
+            setattr(model_obj, "reasoning_effort", eff)
+        except Exception:
+            pass
+        return model_obj
+
     def _build_chat_model_for_profile(
         target_profile_id: str | None,
         *,
@@ -123,7 +141,7 @@ def _build_executor_components(
         if mode == "openai_responses":
             if not api_key:
                 return StaticTextChatModel(_openai_missing_key_user_message(lang)), mode
-            return OpenAIResponsesModel(model=model_name, api_key=api_key, base_url=bu or None), mode
+            return _apply_profile_thinking(OpenAIResponsesModel(model=model_name, api_key=api_key, base_url=bu or None), profile), mode
         if mode == "anthropic":
             akey = (
                 (openai_api_key if allow_runtime_overrides else None)
@@ -155,10 +173,10 @@ def _build_executor_components(
         if mode == "ollama":
             ollama_base = (bu or DEFAULT_OLLAMA_BASE_URL).strip() or DEFAULT_OLLAMA_BASE_URL
             ollama_key = api_key or _OLLAMA_DUMMY_KEY
-            return OpenAIChatModel(model=model_name, api_key=ollama_key, base_url=ollama_base), mode
+            return _apply_profile_thinking(OpenAIChatModel(model=model_name, api_key=ollama_key, base_url=ollama_base), profile), mode
         if not api_key:
             return StaticTextChatModel(_openai_missing_key_user_message(lang)), mode
-        return OpenAIChatModel(model=model_name, api_key=api_key, base_url=bu or None), mode
+        return _apply_profile_thinking(OpenAIChatModel(model=model_name, api_key=api_key, base_url=bu or None), profile), mode
 
     valid_profile_ids = {p["id"] for p in store.list_llm_profiles(visible_only=True, **list_kw)}
     if active_pid and active_pid not in valid_profile_ids:

runtime/chat/agent_messages.py

@@ -199,7 +199,28 @@ def build_llm_messages(
 ) -> list[dict[str, Any]]:
     """把 DB 中的消息序列转换为 LLM messages。"""
     out: list[dict[str, Any]] = [{"role": "system", "content": (system_prompt or "").strip()}]
+    thinking_mode_enabled = bool(getattr(model, "thinking_mode_enabled", False))
     allow_signature_replay = _allow_reasoning_signature_replay(model)
+    reasoning_by_turn: dict[str, list[tuple[int, str]]] = {}
+    if thinking_mode_enabled:
+        for m in store_messages or []:
+            if str(getattr(m, "role", "") or "") != "assistant":
+                continue
+            if str(getattr(m, "event_type", "") or "").strip().lower() != "reasoning":
+                continue
+            tid = str(getattr(m, "turn_uuid", "") or "").strip()
+            if not tid:
+                continue
+            try:
+                idx = 0
+                ep = getattr(m, "event_payload", None)
+                if isinstance(ep, str) and ep.strip():
+                    payload = json.loads(ep)
+                    if isinstance(payload, dict):
+                        idx = int(payload.get("chunk_index") or 0)
+            except Exception:
+                idx = 0
+            reasoning_by_turn.setdefault(tid, []).append((idx, str(getattr(m, "content", "") or "")))
     historical_tool_ids = _collect_historical_tool_call_ids(
         store_messages=store_messages, full_rounds=_replay_recent_tool_rounds()
     )
@@ -207,7 +228,46 @@ def build_llm_messages(
     # that is not present in the assistant tool_calls within the same request context.
     # This can happen when context windows are trimmed and the assistant tool_calls row is dropped.
     valid_tool_call_ids: set[str] = set()
-    for m in store_messages:
+    # Precompute tool_call_id suffix sets to detect broken tool_calls -> tool pairing.
+    tool_ids_after: list[set[str]] = []
+    seen_tool_ids: set[str] = set()
+    for m in reversed(store_messages or []):
+        role = str(getattr(m, "role", "") or "")
+        if role == "tool":
+            tcid = _tool_call_id_from_tool_row(getattr(m, "tool_calls", None))
+            if tcid:
+                seen_tool_ids.add(tcid)
+        tool_ids_after.append(set(seen_tool_ids))
+    tool_ids_after.reverse()
+    def _attach_reasoning_content(row: dict[str, Any], m: Any) -> dict[str, Any]:
+        if not thinking_mode_enabled:
+            return row
+        rc = ""
+        ep = getattr(m, "event_payload", None)
+        if isinstance(ep, str) and ep.strip():
+            try:
+                payload = json.loads(ep)
+                if isinstance(payload, dict):
+                    rc = str(payload.get("reasoning_content") or "").strip()
+            except Exception:
+                rc = ""
+        if not rc:
+            tid = str(getattr(m, "turn_uuid", "") or "").strip()
+            chunks = reasoning_by_turn.get(tid) or []
+            if chunks:
+                rc = "\n".join(
+                    [
+                        str(x[1] or "").strip()
+                        for x in sorted(chunks, key=lambda t: int(t[0] or 0))
+                        if str(x[1] or "").strip()
+                    ]
+                ).strip()
+        # Provider contract: in thinking mode, the field must be present for every assistant message,
+        # even if empty (some gateways error on missing key).
+        row["reasoning_content"] = rc
+        return row
+
+    for i, m in enumerate(store_messages):
         role = str(getattr(m, "role", "") or "")
         event_type = str(getattr(m, "event_type", "") or "").strip().lower()
         if event_type == "reasoning":
@@ -408,6 +468,19 @@ def build_llm_messages(
                     tool_calls = None
 
             if tool_calls and isinstance(tool_calls, list):
+                # Guard: only include tool_calls if tool results exist later in this trimmed window.
+                want_ids = [str(tc.get("id") or "").strip() for tc in tool_calls if isinstance(tc, dict) and str(tc.get("id") or "").strip()]
+                suffix = tool_ids_after[i] if (i >= 0 and i < len(tool_ids_after)) else set()
+                if want_ids and any(tid not in suffix for tid in want_ids):
+                    tool_calls = None
+                if tool_calls is None:
+                    out.append(
+                        _attach_reasoning_content(
+                            {"role": "assistant", "content": _strip_reasoning_blocks(getattr(m, "content", "") or "")},
+                            m,
+                        )
+                    )
+                    continue
                 api_tool_calls = []
                 gemini_fc = gemini_openai_compat_client(model)
                 for idx, tc in enumerate(tool_calls):
@@ -439,16 +512,29 @@ def build_llm_messages(
                     api_tool_calls.append(entry)
                 if api_tool_calls:
                     out.append(
-                        {
-                            "role": "assistant",
-                            "content": _strip_reasoning_blocks(getattr(m, "content", "") or ""),
-                            "tool_calls": api_tool_calls,
-                        }
+                        _attach_reasoning_content(
+                            {
+                                "role": "assistant",
+                                "content": _strip_reasoning_blocks(getattr(m, "content", "") or ""),
+                                "tool_calls": api_tool_calls,
+                            },
+                            m,
+                        )
                     )
                 else:
-                    out.append({"role": "assistant", "content": _strip_reasoning_blocks(getattr(m, "content", "") or "")})
+                    out.append(
+                        _attach_reasoning_content(
+                            {"role": "assistant", "content": _strip_reasoning_blocks(getattr(m, "content", "") or "")},
+                            m,
+                        )
+                    )
             else:
-                out.append({"role": "assistant", "content": _strip_reasoning_blocks(getattr(m, "content", "") or "")})
+                out.append(
+                    _attach_reasoning_content(
+                        {"role": "assistant", "content": _strip_reasoning_blocks(getattr(m, "content", "") or "")},
+                        m,
+                    )
+                )
             continue
 
         if role == "tool":

runtime/direct_loop.py

@@ -807,6 +807,7 @@ def _persist_assistant_step(
     assistant_text: str,
     reasoning_text: str,
     llm_tool_calls: list[Any],
+    thinking_mode_enabled: bool = False,
 ) -> _LoopStepResult:
     stored_tool_calls = []
     for tc in llm_tool_calls:
@@ -819,19 +820,18 @@ def _persist_assistant_step(
             }
         )
 
-    reasoning_chunks, assistant_body = _split_reasoning_and_body(
-        assistant_text,
-        explicit_reasoning=reasoning_text,
-    )
-    for idx, chunk in enumerate(reasoning_chunks):
-        store.add_message(
-            session_id=session_id,
-            role="assistant",
-            content=chunk,
-            turn_uuid=turn_uuid,
-            event_type="reasoning",
-            event_payload={"chunk_index": int(idx), "chunk_count": len(reasoning_chunks)},
-        )
+    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()
+    if not thinking_mode_enabled:
+        for idx, chunk in enumerate(reasoning_chunks):
+            store.add_message(
+                session_id=session_id,
+                role="assistant",
+                content=chunk,
+                turn_uuid=turn_uuid,
+                event_type="reasoning",
+                event_payload={"chunk_index": int(idx), "chunk_count": len(reasoning_chunks)},
+            )
     assistant_row = store.add_message(
         session_id=session_id,
         role="assistant",
@@ -839,6 +839,7 @@ def _persist_assistant_step(
         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 (thinking_mode_enabled and reasoning_full) else None),
     )
     return _LoopStepResult(
         assistant_text=assistant_body,
@@ -1002,6 +1003,7 @@ def run_oclaw_direct_loop(
             assistant_text=assistant_text,
             reasoning_text=reasoning_text,
             llm_tool_calls=llm_tool_calls,
+            thinking_mode_enabled=bool(getattr(model, "thinking_mode_enabled", False)),
         )
         final_text = step.assistant_text
         if not step.llm_tool_calls:
@@ -1082,6 +1084,7 @@ def run_oclaw_direct_loop(
             assistant_text=str(getattr(resp, "content", "") or ""),
             reasoning_text=str(getattr(resp, "reasoning_content", "") or ""),
             llm_tool_calls=[],
+            thinking_mode_enabled=bool(getattr(model, "thinking_mode_enabled", False)),
         )
         final_text = step.assistant_text
 

runtime/tools/mcp/runtime.py

@@ -196,14 +196,57 @@ class McpProcessRuntime:
         req = {"jsonrpc": "2.0", "id": rid, "method": str(method), "params": params or {}}
         p.stdin.write(json.dumps(req, ensure_ascii=False) + "\n")
         p.stdin.flush()
+        skipped: list[str] = []
+        max_skip = 60
         while True:
             line = p.stdout.readline()
             if not line:
-                return {"ok": False, "error_code": "mcp_runtime_empty_response", "error": "empty_response"}
+                # Process may have exited early (common when runtime deps are missing).
+                rc = None
+                try:
+                    rc = p.poll()
+                except Exception:
+                    rc = None
+                err_tail = ""
+                if rc is not None and p.stderr is not None:
+                    try:
+                        err_tail = (p.stderr.read() or "")[-2000:]
+                    except Exception:
+                        err_tail = ""
+                out: dict[str, Any] = {"ok": False, "error_code": "mcp_runtime_empty_response", "error": "empty_response"}
+                if rc is not None:
+                    out["exit_code"] = int(rc)
+                if err_tail.strip():
+                    out["stderr_tail"] = err_tail.strip()
+                return out
+            s = str(line).strip()
+            if not s:
+                # Some servers emit blank lines; ignore.
+                continue
+            # Some MCP servers print logs/banner to stdout. Skip non-JSON lines until a JSON-RPC response arrives.
+            if not (s.startswith("{") or s.startswith("[")):
+                skipped.append(s[:200])
+                if len(skipped) > max_skip:
+                    return {
+                        "ok": False,
+                        "error_code": "mcp_runtime_protocol_mismatch",
+                        "error": "non_jsonrpc_response",
+                        "skipped": skipped[-12:],
+                    }
+                continue
             try:
-                obj = json.loads(line)
+                obj = json.loads(s)
             except Exception as exc:
-                return {"ok": False, "error_code": "mcp_runtime_bad_json", "error": str(exc)}
+                # If a server mixes JSON with log fragments, keep skipping until we see a clean JSON object.
+                skipped.append(s[:200])
+                if len(skipped) > max_skip:
+                    return {
+                        "ok": False,
+                        "error_code": "mcp_runtime_bad_json",
+                        "error": str(exc),
+                        "skipped": skipped[-12:],
+                    }
+                continue
             if not isinstance(obj, dict):
                 return {"ok": False, "error_code": "mcp_runtime_invalid_payload", "error": "response_not_object"}
             if "jsonrpc" not in obj and "id" not in obj:

runtime/workspaces/generalist/ROLE_SYSTEM.md

@@ -16,4 +16,5 @@
 
 ## 主要事项：
 - 工具失败时先报告 `error_code` 与原因，再给下一步。
+- Windows（PowerShell/CMD）如遇部分命令“空输出/乱码”，优先尝试 `chcp 65001 > nul && <command>`。
 - 禁止伪造工具结果。