Add DeepSeek DSML-to-tool_calls conversion at transport and runtime layers.

Promote DSML markup from assistant text into native tool calls during streaming and completion, filter DSML from UI tokens, and document AIA_DSML_TEXT_TOOLS for local vLLM proxies.

Co-authored-by: Cursor <cursoragent@cursor.com>

docs/ENVIRONMENT_VARIABLES.md

@@ -153,6 +153,12 @@
   - 示例：`_local/system.env.example`
   - 生效：`svc/llm/transports/openai_chat_completions.py`
 
+- `AIA_DSML_TEXT_TOOLS`
+  - 默认：未设置时，若 LLM **`base_url`** 或 **`model`** 含 `deepseek` 则 **开启**；否则关闭
+  - 作用：将 assistant 正文/reasoning 中的 **DeepSeek DSML** 标记（如 `<｜DSML｜tool_calls>…`）自动转换为原生 **`tool_calls`** 并执行；streaming 时过滤 DSML，避免泄漏到 UI
+  - 取值：`1` / `true` 强制开启（本地 vLLM/Foundry 等代理 **建议设置**）；`0` / `false` 强制关闭
+  - 生效：`runtime/dsml_tool_parse.py`、`svc/llm/transports/openai_chat_completions.py`、`runtime/direct_loop.py`
+
 ## 工具执行与安全
 
 - `AIA_DISABLE_TOOL_CONFIRM`

runtime/direct_loop.py

@@ -24,7 +24,10 @@ from runtime.types import OclawMemoryContext
 from runtime.orchestration.trace import new_span_id
 from runtime.tools.base import ToolRegistry
 from runtime.hooks_runtime import trigger_hook_event
-from runtime.dsml_tool_parse import strip_first_dsml_tool_calls_block, try_parse_deepseek_v4_dsml_tool_calls
+from runtime.dsml_tool_parse import (
+    dsml_text_tools_enabled,
+    try_parse_dsml_tool_calls_from_fields,
+)
 from runtime.tools.experts.network_ops.netx_tools import ops_netx_system_context_extension
 
 _OCLAW_TOOL_RESULT_HARD_CAP_CHARS = 24_000
@@ -611,6 +614,7 @@ def _build_model_context(
         skill_binding_role=skill_binding_role,
         workspace_owner_session_id=workspace_owner_session_id,
         session_id=session_id,
+        model_id=str(getattr(model, "model", "") or ""),
     )
     # Hook integration: wiki-auto-inject can prepend retrieval snippets
     # before prompt build when query/topic hints indicate supplemental lookup.
@@ -838,22 +842,23 @@ def _prepare_llm_tools(
     return llm_tools
 
 
-def _dsml_text_tools_enabled(*, base_url: str, model_id: str = "") -> bool:
-    """When true, treat DeepSeek-V4-style DSML in assistant ``content`` as native tool calls.
-
-    Opt-in via ``AIA_DSML_TEXT_TOOLS=1``, opt-out via ``=0``. When unset, defaults on if
-    ``base_url`` or ``model_id`` looks like DeepSeek (per upstream DSML tool spec).
-    """
-    env = str(os.getenv("AIA_DSML_TEXT_TOOLS") or "").strip().lower()
-    if env in {"0", "false", "no", "off"}:
-        return False
-    if env in {"1", "true", "yes", "on"}:
-        return True
-    bu = str(base_url or "").strip().lower()
-    mid = str(model_id or "").strip().lower()
-    if "deepseek" in bu or "deepseek" in mid:
-        return True
-    return False
+def _promote_dsml_tool_calls(
+    *,
+    allow: bool,
+    assistant_text: str,
+    reasoning_text: str,
+    llm_tool_calls: list[Any],
+) -> tuple[str, str, list[Any]]:
+    """Runtime fallback: promote DSML in content/reasoning to native tool calls."""
+    if not allow or llm_tool_calls:
+        return assistant_text, reasoning_text, llm_tool_calls
+    parsed, clean_content, clean_reasoning = try_parse_dsml_tool_calls_from_fields(
+        content=assistant_text,
+        reasoning_content=reasoning_text,
+    )
+    if parsed is not None:
+        return clean_content, clean_reasoning, parsed
+    return assistant_text, reasoning_text, llm_tool_calls
 
 
 def _tool_names_for_trace(tools: list[dict[str, Any]]) -> list[str]:
@@ -890,10 +895,15 @@ def _chat_with_empty_body_retry(
     resp = model.chat(msgs, llm_tools, on_token=on_token)
     while True:
         content = str(getattr(resp, "content", "") or "")
+        reasoning = str(getattr(resp, "reasoning_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 allow_dsml_text_tools and try_parse_deepseek_v4_dsml_tool_calls(content) is not None:
-            textual_tool_intent = False
+        textual_tool_intent = (not tool_calls) and bool(
+            _extract_textual_tool_intent_names(f"{content}\n{reasoning}")
+        )
+        if allow_dsml_text_tools:
+            parsed, _, _ = try_parse_dsml_tool_calls_from_fields(content=content, reasoning_content=reasoning)
+            if parsed is not None:
+                textual_tool_intent = False
         if (content.strip() or tool_calls) and not textual_tool_intent:
             return resp
         elapsed_ms = int((time.perf_counter() - started) * 1000.0)
@@ -1409,7 +1419,11 @@ def run_oclaw_direct_loop(
 
     base_url = str(getattr(model, "base_url", "") or "")
     model_id = str(getattr(model, "model", "") or "")
-    allow_dsml_text_tools = _dsml_text_tools_enabled(base_url=base_url, model_id=model_id)
+    allow_dsml_text_tools = dsml_text_tools_enabled(base_url=base_url, model_id=model_id)
+    if not allow_dsml_text_tools and bool(getattr(model, "thinking_mode_enabled", False)):
+        mid = model_id.lower()
+        if mid.startswith("deepseek-") or "deepseek" in mid:
+            allow_dsml_text_tools = True
 
     max_rounds = max(1, int(max_tool_rounds or 1))
     for round_idx in range(max_rounds):
@@ -1462,14 +1476,16 @@ def run_oclaw_direct_loop(
         assistant_text = str(getattr(resp, "content", "") or "")
         reasoning_text = str(getattr(resp, "reasoning_content", "") or "")
         llm_tool_calls = list(getattr(resp, "tool_calls", []) or [])
-        if allow_dsml_text_tools and not llm_tool_calls:
-            dsml_parsed = try_parse_deepseek_v4_dsml_tool_calls(assistant_text)
-            if dsml_parsed is not None:
-                llm_tool_calls = dsml_parsed
-                stripped = strip_first_dsml_tool_calls_block(assistant_text)
-                if stripped is not None:
-                    assistant_text = stripped
-        textual_tool_intent_names = _extract_textual_tool_intent_names(assistant_text) if not llm_tool_calls else []
+        assistant_text, reasoning_text, llm_tool_calls = _promote_dsml_tool_calls(
+            allow=allow_dsml_text_tools,
+            assistant_text=assistant_text,
+            reasoning_text=reasoning_text,
+            llm_tool_calls=llm_tool_calls,
+        )
+        combined_for_intent = f"{assistant_text}\n{reasoning_text}".strip()
+        textual_tool_intent_names = (
+            _extract_textual_tool_intent_names(combined_for_intent) if not llm_tool_calls else []
+        )
 
         if textual_tool_intent_names:
             step = _persist_dsml_protocol_mismatch_step(

runtime/dsml_tool_parse.py

@@ -1,4 +1,4 @@
-"""Parse DeepSeek-V4 DSML ``tool_calls`` blocks from assistant text.
+"""Parse DeepSeek DSML tool-call blocks from assistant text.
 
 Reference: `encoding/README.md` and `encoding/encoding_dsv4.py` in the upstream
 `deepseek-ai/DeepSeek-V4-Pro` repository on Hugging Face (DSML grammar for
@@ -11,41 +11,54 @@ separator (U+FF5C ``｜``); we normalize those before parsing.
 from __future__ import annotations
 
 import json
+import os
 import re
 import uuid
 from typing import Any
 
 from svc.llm.transports.base import LLMToolCall
 
-# Official DeepSeek-V4 DSML token uses FULLWIDTH VERTICAL LINE (U+FF5C).
+# Official DeepSeek DSML token uses FULLWIDTH VERTICAL LINE (U+FF5C).
 _DSML_PIPE = "\uFF5C"
+_DSML_BARS = ("|", _DSML_PIPE)
+_DSML_WRAPPER_KINDS = ("tool_calls", "function_calls", "tool_call", "function_call")
 
-_RE_TOOL_CALLS_OPEN = re.compile(
-    rf"<\s*{_DSML_PIPE}\s*DSML\s*{_DSML_PIPE}\s*tool_calls\s*>",
-    flags=re.IGNORECASE,
-)
-_RE_TOOL_CALLS_CLOSE = re.compile(
-    rf"</\s*{_DSML_PIPE}\s*DSML\s*{_DSML_PIPE}\s*tool_calls\s*>",
-    flags=re.IGNORECASE,
-)
 _RE_INVOKE_OPEN = re.compile(
-    rf"<\s*{_DSML_PIPE}\s*DSML\s*{_DSML_PIPE}\s*invoke\s+name\s*=\s*\"([^\"]+)\"\s*>",
+    rf"<\s*[{''.join(re.escape(b) for b in _DSML_BARS)}]\s*DSML\s*[{''.join(re.escape(b) for b in _DSML_BARS)}]\s*invoke\s+name\s*=\s*\"([^\"]+)\"\s*>",
     flags=re.IGNORECASE,
 )
 _RE_INVOKE_CLOSE = re.compile(
-    rf"</\s*{_DSML_PIPE}\s*DSML\s*{_DSML_PIPE}\s*invoke\s*>",
+    rf"</\s*[{''.join(re.escape(b) for b in _DSML_BARS)}]\s*DSML\s*[{''.join(re.escape(b) for b in _DSML_BARS)}]\s*invoke\s*>",
     flags=re.IGNORECASE,
 )
 _RE_PARAM_OPEN = re.compile(
-    rf"<\s*{_DSML_PIPE}\s*DSML\s*{_DSML_PIPE}\s*parameter\s+name\s*=\s*\"([^\"]+)\"\s+string\s*=\s*\"(true|false)\"\s*>",
+    rf"<\s*[{''.join(re.escape(b) for b in _DSML_BARS)}]\s*DSML\s*[{''.join(re.escape(b) for b in _DSML_BARS)}]\s*parameter\s+name\s*=\s*\"([^\"]+)\"\s+string\s*=\s*\"(true|false)\"\s*>",
     flags=re.IGNORECASE,
 )
 _RE_PARAM_CLOSE = re.compile(
-    rf"</\s*{_DSML_PIPE}\s*DSML\s*{_DSML_PIPE}\s*parameter\s*>",
+    rf"</\s*[{''.join(re.escape(b) for b in _DSML_BARS)}]\s*DSML\s*[{''.join(re.escape(b) for b in _DSML_BARS)}]\s*parameter\s*>",
     flags=re.IGNORECASE,
 )
 
 
+def _dsml_open_tokens() -> list[str]:
+    tokens = [f"<{bar}DSML{bar}{kind}>" for bar in _DSML_BARS for kind in _DSML_WRAPPER_KINDS]
+    tokens.extend(f"<||DSML||{kind}>" for kind in _DSML_WRAPPER_KINDS)
+    return tokens
+
+
+def _dsml_close_tokens() -> list[str]:
+    tokens = [f"</{bar}DSML{bar}{kind}>" for bar in _DSML_BARS for kind in _DSML_WRAPPER_KINDS]
+    tokens.extend(f"</||DSML||{kind}>" for kind in _DSML_WRAPPER_KINDS)
+    return tokens
+
+
+_DSML_OPEN_TOKENS = _dsml_open_tokens()
+_DSML_CLOSE_TOKENS = _dsml_close_tokens()
+_MAX_OPEN_TOKEN_LEN = max(len(t) for t in _DSML_OPEN_TOKENS)
+_MAX_CLOSE_TOKEN_LEN = max(len(t) for t in _DSML_CLOSE_TOKENS)
+
+
 def normalize_dsml_markup(text: str) -> str:
     """Map common gateway variants to the canonical DSML delimiter sequence."""
     s = str(text or "")
@@ -54,21 +67,51 @@ def normalize_dsml_markup(text: str) -> str:
     return s
 
 
+def _find_earliest_token(text: str, tokens: list[str]) -> tuple[int, str] | None:
+    best: tuple[int, str] | None = None
+    for token in tokens:
+        idx = text.find(token)
+        if idx != -1 and (best is None or idx < best[0]):
+            best = (idx, token)
+    return best
+
+
+def _longest_dsml_open_prefix_suffix_length(text: str) -> int:
+    max_len = min(len(text), _MAX_OPEN_TOKEN_LEN - 1)
+    for length in range(max_len, 0, -1):
+        suffix = text[-length:]
+        if any(token.startswith(suffix) for token in _DSML_OPEN_TOKENS):
+            return length
+    return 0
+
+
+def _wrapper_kind_regex(kind: str) -> tuple[re.Pattern[str], re.Pattern[str]]:
+    bars = rf"[{''.join(re.escape(b) for b in _DSML_BARS)}]"
+    open_re = re.compile(rf"<\s*{bars}\s*DSML\s*{bars}\s*{re.escape(kind)}\s*>", flags=re.IGNORECASE)
+    close_re = re.compile(rf"</\s*{bars}\s*DSML\s*{bars}\s*{re.escape(kind)}\s*>", flags=re.IGNORECASE)
+    return open_re, close_re
+
+
 def _find_tool_calls_block_span(normalized: str) -> tuple[int, int] | None:
-    m_open = _RE_TOOL_CALLS_OPEN.search(normalized)
-    if not m_open:
-        return None
-    start = int(m_open.start())
-    from_pos = int(m_open.end())
-    m_close = _RE_TOOL_CALLS_CLOSE.search(normalized, from_pos)
-    if not m_close:
-        return None
-    end = int(m_close.end())
-    return (start, end)
+    best: tuple[int, int] | None = None
+    for kind in _DSML_WRAPPER_KINDS:
+        open_re, close_re = _wrapper_kind_regex(kind)
+        m_open = open_re.search(normalized)
+        if not m_open:
+            continue
+        start = int(m_open.start())
+        from_pos = int(m_open.end())
+        m_close = close_re.search(normalized, from_pos)
+        if not m_close:
+            continue
+        end = int(m_close.end())
+        if best is None or start < best[0]:
+            best = (start, end)
+    return best
 
 
 def strip_first_dsml_tool_calls_block(text: str) -> str | None:
-    """Remove the first well-formed ``tool_calls`` DSML block; return None if none found."""
+    """Remove the first well-formed DSML wrapper block; return None if none found."""
     raw = str(text or "")
     if not raw:
         return None
@@ -94,6 +137,19 @@ def _decode_param_value(raw_value: str, *, string_flag: str) -> Any:
         return v_strip
 
 
+def _parse_invoke_body_json(body: str) -> dict[str, Any] | None:
+    stripped = str(body or "").strip()
+    if not stripped or not stripped.startswith("{"):
+        return None
+    try:
+        parsed = json.loads(stripped)
+    except Exception:
+        return None
+    if not isinstance(parsed, dict):
+        return None
+    return dict(parsed)
+
+
 def _parse_invoke_body(body: str) -> dict[str, Any] | None:
     args: dict[str, Any] = {}
     pos = 0
@@ -113,6 +169,11 @@ def _parse_invoke_body(body: str) -> dict[str, Any] | None:
             return None
         args[pname] = _decode_param_value(raw_val, string_flag=sflag)
         pos = int(cm.end())
+    if args:
+        return args
+    json_args = _parse_invoke_body_json(b)
+    if json_args is not None:
+        return json_args
     return args
 
 
@@ -137,25 +198,7 @@ def _parse_invokes(inner: str) -> list[tuple[str, dict[str, Any]]] | None:
     return out
 
 
-def try_parse_deepseek_v4_dsml_tool_calls(text: str) -> list[LLMToolCall] | None:
-    """
-    If ``text`` contains a complete first ``tool_calls`` DSML block, return
-    ``LLMToolCall`` rows (may be empty if the block has no ``invoke`` tags).
-
-    Returns ``None`` when no block is found or the block is malformed.
-    """
-    raw = str(text or "")
-    if not raw.strip():
-        return None
-    norm = normalize_dsml_markup(raw)
-    span = _find_tool_calls_block_span(norm)
-    if span is None:
-        return None
-    a, b = span
-    inner = norm[a:b]
-    invokes = _parse_invokes(inner)
-    if invokes is None:
-        return None
+def _invokes_to_llm_tool_calls(invokes: list[tuple[str, dict[str, Any]]]) -> list[LLMToolCall]:
     out: list[LLMToolCall] = []
     for name, args in invokes:
         out.append(
@@ -169,8 +212,158 @@ def try_parse_deepseek_v4_dsml_tool_calls(text: str) -> list[LLMToolCall] | None
     return out
 
 
+def _parse_dsml_block_inner(inner: str) -> list[LLMToolCall] | None:
+    invokes = _parse_invokes(normalize_dsml_markup(inner))
+    if invokes is None:
+        return None
+    return _invokes_to_llm_tool_calls(invokes)
+
+
+def try_parse_deepseek_v4_dsml_tool_calls(text: str) -> list[LLMToolCall] | None:
+    """
+    If ``text`` contains a complete first DSML wrapper block, return
+    ``LLMToolCall`` rows (may be empty if the block has no ``invoke`` tags).
+
+    Returns ``None`` when no block is found or the block is malformed.
+    """
+    raw = str(text or "")
+    if not raw.strip():
+        return None
+    norm = normalize_dsml_markup(raw)
+    span = _find_tool_calls_block_span(norm)
+    if span is None:
+        return None
+    a, b = span
+    inner = norm[a:b]
+    return _parse_dsml_block_inner(inner)
+
+
+def try_parse_dsml_tool_calls_from_fields(
+    *,
+    content: str = "",
+    reasoning_content: str = "",
+) -> tuple[list[LLMToolCall] | None, str, str]:
+    """Search ``content`` then ``reasoning_content`` for DSML tool calls.
+
+    Returns ``(calls, cleaned_content, cleaned_reasoning)``. When parsing succeeds,
+    the field that contained the block is stripped; the other field is unchanged.
+    """
+    for field_name, text in (("content", str(content or "")), ("reasoning_content", str(reasoning_content or ""))):
+        parsed = try_parse_deepseek_v4_dsml_tool_calls(text)
+        if parsed is None:
+            continue
+        stripped = strip_first_dsml_tool_calls_block(text)
+        clean = stripped if stripped is not None else ""
+        if field_name == "content":
+            return parsed, clean, str(reasoning_content or "")
+        return parsed, str(content or ""), clean
+    return None, str(content or ""), str(reasoning_content or "")
+
+
+def dsml_text_tools_enabled(*, base_url: str = "", model_id: str = "") -> bool:
+    """Whether DSML-in-text should be promoted to native tool calls."""
+    env = str(os.getenv("AIA_DSML_TEXT_TOOLS") or "").strip().lower()
+    if env in {"0", "false", "no", "off"}:
+        return False
+    if env in {"1", "true", "yes", "on"}:
+        return True
+    bu = str(base_url or "").strip().lower()
+    mid = str(model_id or "").strip().lower()
+    if "deepseek" in bu or "deepseek" in mid:
+        return True
+    if mid.startswith("deepseek-"):
+        return True
+    return False
+
+
+class DeepSeekTextFilter:
+    """Stream filter: hide DSML from visible text and capture blocks for recovery."""
+
+    def __init__(self) -> None:
+        self._buffer = ""
+        self._inside_dsml = False
+        self._dsml_capture = ""
+        self._captured_blocks: list[str] = []
+        self._visible_parts: list[str] = []
+
+    def push(self, chunk: str) -> list[str]:
+        self._buffer += str(chunk or "")
+        return self._consume(final=False)
+
+    def flush(self) -> list[str]:
+        return self._consume(final=True)
+
+    @property
+    def visible_text(self) -> str:
+        return "".join(self._visible_parts)
+
+    def recovered_tool_calls(self) -> list[LLMToolCall]:
+        out: list[LLMToolCall] = []
+        for block in self._captured_blocks:
+            parsed = _parse_dsml_block_inner(block)
+            if parsed:
+                out.extend(parsed)
+        return out
+
+    def _consume(self, *, final: bool) -> list[str]:
+        output: list[str] = []
+
+        def emit(text: str) -> None:
+            if text:
+                output.append(text)
+                self._visible_parts.append(text)
+
+        while self._buffer:
+            if self._inside_dsml:
+                close = _find_earliest_token(self._buffer, _DSML_CLOSE_TOKENS)
+                if close:
+                    idx, token = close
+                    self._dsml_capture += self._buffer[:idx]
+                    self._captured_blocks.append(self._dsml_capture)
+                    self._dsml_capture = ""
+                    self._buffer = self._buffer[idx + len(token) :]
+                    self._inside_dsml = False
+                    continue
+                keep = 0 if final else min(len(self._buffer), _MAX_CLOSE_TOKEN_LEN - 1)
+                self._dsml_capture += self._buffer[: len(self._buffer) - keep]
+                self._buffer = self._buffer[len(self._buffer) - keep :]
+                if final:
+                    if self._dsml_capture:
+                        self._captured_blocks.append(self._dsml_capture)
+                        self._dsml_capture = ""
+                    self._inside_dsml = False
+                return output
+
+            open_match = _find_earliest_token(self._buffer, _DSML_OPEN_TOKENS)
+            if open_match:
+                idx, token = open_match
+                emit(self._buffer[:idx])
+                self._buffer = self._buffer[idx + len(token) :]
+                self._inside_dsml = True
+                self._dsml_capture = ""
+                continue
+
+            if final:
+                emit(self._buffer)
+                self._buffer = ""
+                return output
+
+            keep = _longest_dsml_open_prefix_suffix_length(self._buffer)
+            emit_len = len(self._buffer) - keep
+            if emit_len <= 0:
+                return output
+            emit(self._buffer[:emit_len])
+            self._buffer = self._buffer[emit_len:]
+            return output
+
+        return output
+
+
 __all__ = [
+    "DeepSeekTextFilter",
+    "dsml_text_tools_enabled",
     "normalize_dsml_markup",
     "strip_first_dsml_tool_calls_block",
     "try_parse_deepseek_v4_dsml_tool_calls",
+    "try_parse_dsml_tool_calls_from_fields",
 ]

runtime/system_prompt.py

@@ -7,6 +7,7 @@ from typing import Any
 
 from svc.config.paths import PROJECT_ROOT
 from runtime.memory_stage import render_memory_context_block
+from runtime.dsml_tool_parse import dsml_text_tools_enabled
 from runtime.project_context_prompt import build_project_context_block
 from runtime.skill_role_binding import SKILL_ROLE_BINDING_KEY
 from runtime.skills import workspace_skills_layout_signature
@@ -99,8 +100,19 @@ def _executor_prompt_settings_signature(store: Any) -> tuple[str, ...]:
     return tuple(parts)
 
 
-def _unified_skill_policy_guidance() -> str:
+def _unified_skill_policy_guidance(*, dsml_recovery_enabled: bool = False) -> str:
     # Global policy for all agents (including dynamic/ephemeral) — appended to base_system in build_executor_system_prompt.
+    if dsml_recovery_enabled:
+        tool_call_lines = (
+            "- 工具调用优先使用模型的原生 `tool_calls` 协议；若模型输出 DeepSeek DSML 标记，"
+            "运行时会自动转换，请勿在可见回复中重复或解释 DSML/XML 语法。\n"
+        )
+    else:
+        tool_call_lines = (
+            "- 工具调用必须通过模型的原生 `tool_calls` 协议返回；不要在正文输出 DSML/XML/工具协议 JSON 模板。\n"
+            "- 若当前模型链路不支持原生 `tool_calls`，请用自然语言明确说明“当前无法发起工具调用”，"
+            "并请求切换到支持该协议的链路；不要伪造或模拟工具调用。\n"
+        )
     return (
         "## 技能使用政策（Skill Usage Policy）：\n"
         "- 每次会话启动后，在进行文件/路径相关操作前，必须先读取 `skills/_workspace/public/path-convention/SKILL.md` 了解当前路径规范。\n"
@@ -119,8 +131,7 @@ def _unified_skill_policy_guidance() -> str:
         "- 如果脚本依赖相对路径（例如 `.learnings/`），请将工作目录设置为用户工作区。\n"
         "- 在没有显式工具调用成功结果前，不要假设脚本已经执行。\n"
         "- 在 Windows 上，`.sh` 可能需要 Git Bash、WSL 或等效环境。\n"
-        "- 工具调用必须通过模型的原生 `tool_calls` 协议返回；不要在正文输出 DSML/XML/工具协议 JSON 模板。\n"
-        "- 若当前模型链路不支持原生 `tool_calls`，请用自然语言明确说明“当前无法发起工具调用”，并请求切换到支持该协议的链路；不要伪造或模拟工具调用。\n"
+        f"{tool_call_lines}"
         "- 当用户目标是“安装 skill/技能”时，必须遵循 `oclaw-skill-manager` 的安装策略，并以其为唯一规范来源。\n"
         "- 安装路径强约束：仅允许 `skill_auto_install`（`_workspace` lane）；不得改用任何非 auto 路径或脚本绕过。\n"
         "- 严禁臆测前置条件：不要把未在规范中声明的环境变量、端口、服务启动状态当作必需前提。\n"
@@ -157,6 +168,7 @@ def build_executor_system_prompt(
     skill_binding_role: str | None = None,
     workspace_owner_session_id: str | None = None,
     session_id: str | None = None,
+    model_id: str = "",
 ) -> str:
     """Build the final system string for the oclaw executor (memory block + skills catalog).
 
@@ -172,6 +184,7 @@ def build_executor_system_prompt(
         skill_binding_role=skill_binding_role,
         workspace_owner_session_id=workspace_owner_session_id,
         session_id=session_id,
+        model_id=model_id,
     )
     mem_block = render_memory_context_block(memory_context or OclawMemoryContext())
     if not mem_block:
@@ -193,6 +206,7 @@ def get_executor_prompt_static(
     skill_binding_role: str | None = None,
     workspace_owner_session_id: str | None = None,
     session_id: str | None = None,
+    model_id: str = "",
 ) -> str:
     excl, lane_seg = _skill_catalog_lane_flags(
         skill_binding_role=skill_binding_role,
@@ -201,6 +215,7 @@ def get_executor_prompt_static(
     )
     cache_key = (
         str(base_url or "").strip(),
+        str(model_id or "").strip().lower(),
         str(base_system or "").strip(),
         str(workspace_dir or "").strip(),
         str(skill_binding_role or "").strip().lower(),
@@ -217,7 +232,8 @@ def get_executor_prompt_static(
     if isinstance(cached, str):
         return cached
     final_system = str(base_system or "").strip()
-    guide = _unified_skill_policy_guidance().strip()
+    dsml_recovery = dsml_text_tools_enabled(base_url=str(base_url or ""), model_id=str(model_id or ""))
+    guide = _unified_skill_policy_guidance(dsml_recovery_enabled=dsml_recovery).strip()
     if guide and guide not in final_system:
         final_system = f"{final_system}\n\n{guide}".strip()
     project_block = build_project_context_block(store=store, workspace_dir=workspace_dir)

svc/llm/transports/openai_chat_completions.py

@@ -16,6 +16,11 @@ from svc.llm.transports.base import (
     coerce_thought_signature_for_storage,
     normalize_image_b64_payload,
 )
+from runtime.dsml_tool_parse import (
+    DeepSeekTextFilter,
+    dsml_text_tools_enabled,
+    try_parse_dsml_tool_calls_from_fields,
+)
 from svc.persistence.assistant_store import get_assistant_store
 
 logger = logging.getLogger(__name__)
@@ -450,6 +455,37 @@ def _extract_thought_signature_from_tool_delta(tc: Any) -> str | None:
     return None
 
 
+def _should_recover_dsml_tool_calls(
+    model: str | None,
+    base_url: str | None,
+    *,
+    thinking_mode_enabled: bool = False,
+) -> bool:
+    if dsml_text_tools_enabled(base_url=str(base_url or ""), model_id=str(model or "")):
+        return True
+    if thinking_mode_enabled:
+        mid = str(model or "").strip().lower()
+        if mid.startswith("deepseek-") or "deepseek" in mid:
+            return True
+    return False
+
+
+def _promote_dsml_in_llm_response(
+    content: str,
+    reasoning: str,
+    tool_calls: list[LLMToolCall],
+) -> tuple[str, str, list[LLMToolCall]]:
+    if tool_calls:
+        return content, reasoning, tool_calls
+    parsed, clean_content, clean_reasoning = try_parse_dsml_tool_calls_from_fields(
+        content=content,
+        reasoning_content=reasoning,
+    )
+    if parsed is not None:
+        return clean_content, clean_reasoning, parsed
+    return content, reasoning, tool_calls
+
+
 class OpenAIChatModel(ChatModel):
     def __init__(
         self,
@@ -598,12 +634,12 @@ class OpenAIChatModel(ChatModel):
         msg = completion.choices[0].message
         reasoning_parts = getattr(msg, "reasoning_content", None) or ""
         reasoning_text = str(reasoning_parts).strip() if reasoning_parts else ""
-        content = msg.content or ""
-        if on_token:
-            if reasoning_text:
-                on_token(reasoning_text)
-            if content:
-                on_token(content)
+        content = str(msg.content or "")
+        recover_dsml = _should_recover_dsml_tool_calls(
+            self.model,
+            self.base_url,
+            thinking_mode_enabled=bool(getattr(self, "thinking_mode_enabled", False)),
+        )
 
         tool_calls: list[LLMToolCall] = []
         if msg.tool_calls:
@@ -624,6 +660,18 @@ class OpenAIChatModel(ChatModel):
                 if sig is None and fn is not None:
                     sig = _extract_thought_signature_from_tool_delta(fn)
                 tool_calls.append(LLMToolCall(id=tid, name=name, arguments=args, thought_signature=sig))
+
+        if recover_dsml:
+            content, reasoning_text, tool_calls = _promote_dsml_in_llm_response(
+                content, reasoning_text, tool_calls
+            )
+
+        if on_token:
+            if reasoning_text:
+                on_token(reasoning_text)
+            if content:
+                on_token(content)
+
         return LLMResponse(content=content, tool_calls=tool_calls, reasoning_content=reasoning_text)
 
     def chat(
@@ -658,12 +706,24 @@ class OpenAIChatModel(ChatModel):
             completion = self._create_chat_completion(norm_msgs, tools, stream=False)
             return self._llm_response_from_completion(completion, on_token=on_token)
 
-        content_parts: list[str] = []
         tool_acc: dict[int, dict[str, Any]] = {}
         reasoning_parts: list[str] = []
+        recover_dsml = _should_recover_dsml_tool_calls(
+            self.model,
+            self.base_url,
+            thinking_mode_enabled=bool(getattr(self, "thinking_mode_enabled", False)),
+        )
+        dsml_filter = DeepSeekTextFilter() if recover_dsml else None
+
+        def _emit_visible_text(parts: list[str]) -> None:
+            if not on_token:
+                return
+            for part in parts:
+                if part:
+                    on_token(part)
 
         def _consume_stream(stream_obj: Any) -> None:
-            nonlocal content_parts, tool_acc, reasoning_parts
+            nonlocal tool_acc, reasoning_parts
             for chunk in stream_obj:
                 if not chunk.choices:
                     continue
@@ -692,16 +752,23 @@ class OpenAIChatModel(ChatModel):
                         if sig is not None:
                             slot["thought_signature"] = sig
                 if delta.content:
-                    content_parts.append(delta.content)
-                    if on_token:
-                        on_token(delta.content)
+                    if dsml_filter is not None:
+                        _emit_visible_text(dsml_filter.push(delta.content))
+                    else:
+                        if on_token:
+                            on_token(delta.content)
 
         try:
             _consume_stream(stream)
         except Exception:
             raise
 
-        content = "".join(content_parts)
+        if dsml_filter is not None:
+            _emit_visible_text(dsml_filter.flush())
+            content = dsml_filter.visible_text
+        else:
+            content = ""
+
         reasoning_text = "".join(reasoning_parts).strip()
 
         tool_calls: list[LLMToolCall] = []
@@ -722,6 +789,15 @@ class OpenAIChatModel(ChatModel):
             tsig = coerce_thought_signature_for_storage(ts_raw) if ts_raw is not None else None
             tool_calls.append(LLMToolCall(id=str(tid), name=str(name), arguments=args, thought_signature=tsig))
 
+        if recover_dsml and not tool_calls:
+            recovered = dsml_filter.recovered_tool_calls() if dsml_filter is not None else []
+            if recovered:
+                tool_calls = recovered
+            else:
+                content, reasoning_text, tool_calls = _promote_dsml_in_llm_response(
+                    content, reasoning_text, tool_calls
+                )
+
         return LLMResponse(content=content, tool_calls=tool_calls, reasoning_content=reasoning_text)
 
 

tests/test_dsml_tool_parse.py

@@ -1,9 +1,11 @@
 from __future__ import annotations
 
 from runtime.dsml_tool_parse import (
+    DeepSeekTextFilter,
     normalize_dsml_markup,
     strip_first_dsml_tool_calls_block,
     try_parse_deepseek_v4_dsml_tool_calls,
+    try_parse_dsml_tool_calls_from_fields,
 )
 
 
@@ -63,3 +65,72 @@ def test_strip_removes_block_keeps_prefix() -> None:
 
 def test_malformed_returns_none() -> None:
     assert try_parse_deepseek_v4_dsml_tool_calls("<||DSML||tool_calls>broken") is None
+
+
+def test_parse_function_calls_wrapper_v32() -> None:
+    text = (
+        "<||DSML||function_calls>\n"
+        "<||DSML||invoke name=\"get_weather\">\n"
+        "<||DSML||parameter name=\"location\" string=\"true\">Tokyo</||DSML||parameter>\n"
+        "</||DSML||invoke>\n"
+        "</||DSML||function_calls>"
+    )
+    calls = try_parse_deepseek_v4_dsml_tool_calls(text)
+    assert calls is not None and len(calls) == 1
+    assert calls[0].name == "get_weather"
+    assert calls[0].arguments == {"location": "Tokyo"}
+
+
+def test_parse_json_invoke_body() -> None:
+    text = (
+        "<||DSML||tool_calls>\n"
+        "<||DSML||invoke name=\"get_weather\">\n"
+        '{"location": "Tokyo", "count": 5}\n'
+        "</||DSML||invoke>\n"
+        "</||DSML||tool_calls>"
+    )
+    calls = try_parse_deepseek_v4_dsml_tool_calls(text)
+    assert calls is not None and len(calls) == 1
+    assert calls[0].arguments == {"location": "Tokyo", "count": 5}
+
+
+def test_parse_from_reasoning_content() -> None:
+    dsml = (
+        "<||DSML||tool_calls>\n"
+        "<||DSML||invoke name=\"run_command\"></||DSML||invoke>\n"
+        "</||DSML||tool_calls>"
+    )
+    parsed, clean_content, clean_reasoning = try_parse_dsml_tool_calls_from_fields(
+        content="summary text",
+        reasoning_content=f"thinking\n{dsml}",
+    )
+    assert parsed is not None and len(parsed) == 1
+    assert parsed[0].name == "run_command"
+    assert clean_content == "summary text"
+    assert "tool_calls" not in clean_reasoning
+
+
+def test_stream_filter_hides_dsml_and_recovers() -> None:
+    p = "\uFF5c"
+    full = (
+        f"visible prefix\n"
+        f"<{p}DSML{p}tool_calls>\n"
+        f"<{p}DSML{p}invoke name=\"run_command\">\n"
+        f"<{p}DSML{p}parameter name=\"command\" string=\"true\">echo hi</{p}DSML{p}parameter>\n"
+        f"</{p}DSML{p}invoke>\n"
+        f"</{p}DSML{p}tool_calls>"
+    )
+    filt = DeepSeekTextFilter()
+    visible = ""
+    chunk_size = 7
+    for i in range(0, len(full), chunk_size):
+        for part in filt.push(full[i : i + chunk_size]):
+            visible += part
+    for part in filt.flush():
+        visible += part
+    assert "visible prefix" in visible
+    assert "DSML" not in visible
+    recovered = filt.recovered_tool_calls()
+    assert len(recovered) == 1
+    assert recovered[0].name == "run_command"
+    assert recovered[0].arguments == {"command": "echo hi"}

tests/test_openai_chat_dsml_stream.py

@@ -0,0 +1,122 @@
+"""OpenAI Chat Completions transport: DeepSeek DSML stream filter and recovery."""
+
+from __future__ import annotations
+
+import os
+from types import SimpleNamespace
+
+import pytest
+
+from svc.llm.transports.openai_chat_completions import (
+    OpenAIChatModel,
+    _promote_dsml_in_llm_response,
+    _should_recover_dsml_tool_calls,
+)
+from svc.llm.transports.base import LLMToolCall
+
+
+def test_should_recover_dsml_for_deepseek_url() -> None:
+    assert _should_recover_dsml_tool_calls("deepseek-chat", "https://api.deepseek.com/v1")
+
+
+def test_should_not_recover_for_unrelated_gateway() -> None:
+    assert not _should_recover_dsml_tool_calls("gpt-4o-mini", "https://api.openai.com/v1")
+
+
+def test_should_recover_when_env_forced(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("AIA_DSML_TEXT_TOOLS", "1")
+    assert _should_recover_dsml_tool_calls("local-model", "http://127.0.0.1:8000/v1")
+
+
+def test_promote_dsml_in_llm_response() -> None:
+    dsml = (
+        "prefix\n"
+        "<||DSML||tool_calls>\n"
+        "<||DSML||invoke name=\"run_command\">\n"
+        "<||DSML||parameter name=\"command\" string=\"true\">echo hi</||DSML||parameter>\n"
+        "</||DSML||invoke>\n"
+        "</||DSML||tool_calls>"
+    )
+    content, reasoning, calls = _promote_dsml_in_llm_response(dsml, "", [])
+    assert len(calls) == 1
+    assert calls[0].name == "run_command"
+    assert calls[0].arguments == {"command": "echo hi"}
+    assert "DSML" not in content
+    assert "prefix" in content
+    assert reasoning == ""
+
+
+def test_promote_skips_when_native_tool_calls_present() -> None:
+    dsml = '<||DSML||tool_calls><||DSML||invoke name="x"></||DSML||invoke></||DSML||tool_calls>'
+    native = [LLMToolCall(id="call_1", name="native_tool", arguments={})]
+    content, _, calls = _promote_dsml_in_llm_response(dsml, "", native)
+    assert calls is native
+    assert "DSML" in content
+
+
+def test_chat_stream_filters_dsml_and_promotes_tool_calls(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("OPENAI_API_KEY", "sk-test")
+    model = OpenAIChatModel(
+        model="deepseek-chat",
+        api_key="sk-test",
+        base_url="https://api.deepseek.com/v1",
+    )
+    dsml_text = (
+        "hello "
+        "<||DSML||tool_calls>"
+        "<||DSML||invoke name=\"run_command\">"
+        "<||DSML||parameter name=\"command\" string=\"true\">echo</||DSML||parameter>"
+        "</||DSML||invoke>"
+        "</||DSML||tool_calls>"
+    )
+
+    def fake_stream(_norm_msgs, _tools, *, stream: bool):  # noqa: ANN001, ARG001
+        assert stream is True
+        chunks = []
+        for i in range(0, len(dsml_text), 4):
+            delta = SimpleNamespace(
+                content=dsml_text[i : i + 4],
+                tool_calls=None,
+                reasoning_content="",
+            )
+            chunks.append(SimpleNamespace(choices=[SimpleNamespace(delta=delta)]))
+        return iter(chunks)
+
+    model._create_chat_completion = fake_stream  # type: ignore[method-assign]
+    seen: list[str] = []
+    resp = model.chat([], [], on_token=seen.append)
+    assert "hello" in resp.content
+    assert "DSML" not in resp.content
+    assert len(resp.tool_calls) == 1
+    assert resp.tool_calls[0].name == "run_command"
+    assert resp.tool_calls[0].arguments == {"command": "echo"}
+    joined = "".join(seen)
+    assert "DSML" not in joined
+    assert "hello" in joined
+
+
+def test_llm_response_from_completion_promotes_dsml(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("OPENAI_API_KEY", "sk-test")
+    model = OpenAIChatModel(
+        model="deepseek-chat",
+        api_key="sk-test",
+        base_url="https://api.deepseek.com/v1",
+    )
+    dsml = (
+        "<||DSML||tool_calls>"
+        "<||DSML||invoke name=\"read_file\"></||DSML||invoke>"
+        "</||DSML||tool_calls>"
+    )
+    completion = SimpleNamespace(
+        choices=[
+            SimpleNamespace(
+                message=SimpleNamespace(content=dsml, reasoning_content="", tool_calls=None),
+            )
+        ]
+    )
+    seen: list[str] = []
+    resp = model._llm_response_from_completion(completion, on_token=seen.append)
+    assert len(resp.tool_calls) == 1
+    assert resp.tool_calls[0].name == "read_file"
+    assert "DSML" not in resp.content
+    assert "DSML" not in "".join(seen)