fix: judge stream completion by finish reason and trailing usage

Both transport paths now share one verdict: a stream is complete on
[DONE] or on finish_reason followed by measured usage and a clean EOF
(both modes; missing_done only governs real truncation). Outcomes follow
the design's first-match table: upstream_error finishes stay transient,
fidelity mode delivers reasoning-only/length results, text mode raises
ResultInvalid for content-less length/filter endings, and non-stream
fidelity judges before building the snapshot so truncated tools reach
tool_truncated. Projections honour the reasoning alias, dialect warnings
are bounded, and responses carry finish_reason and stream_end.

Red: 39 of 46 tests in tests/unit/test_completion_transport.py failed
before this change (e.g. eof_after_finish raised 保真响应缺 [DONE];
reasoning-only length raised empty_completion; reasoning-only stream hit
the TTFT watchdog, re-confirmed by disabling alias liveness).

src/polygateway/middleware/retry.py

@@ -741,6 +741,8 @@ class RetryMW:
         return LLMResponse(
             content=result.content,
             completion=result.completion,
+            finish_reason=result.finish_reason,
+            stream_end=result.stream_end,
             thinking=result.thinking,
             model=source.model,
             provider=source.provider,

src/polygateway/transports/openai_compat.py

@@ -12,7 +12,7 @@ import json
 import math
 import re
 import time
-from dataclasses import replace
+from dataclasses import dataclass, field, replace
 from typing import TYPE_CHECKING, Any
 
 import httpx
@@ -41,16 +41,21 @@ from polygateway.transports._http_errors import compose_message, summarize_body
 from polygateway.transports._openai_completion import (
     ChatCompletionAccumulator,
     ChatDelta,
-    parse_chat_completion,
+    build_completion,
+    check_completion_body,
 )
 from polygateway.types import (
     Effort,
     EmbeddingTransportResult,
-    OpenAIChatCompletion,
     ResponseMode,
     SourceConfig,
+    StreamEnd,
     TransportResult,
-    _has_completion_output,
+    _classify_finish,
+    _judge_outcome,
+    _stream_is_complete,
+    _summarize_message,
+    _summarize_text,
 )
 
 if TYPE_CHECKING:
@@ -58,6 +63,9 @@ if TYPE_CHECKING:
     from collections.abc import AsyncIterator, Callable, Mapping
 
 _THINK_PATTERN = re.compile(r"<think>(.*?)</think>", re.DOTALL)
+# 方言告警节流上限与 finish 值截断(设计 §5.4): 键由上游决定,必须有界
+_WARN_CAP = 1024
+_FINISH_WARN_CHARS = 64
 
 # —— SSE 纯函数(VT llm.py 同款)——
 
@@ -135,6 +143,125 @@ async def _iter_sse_deltas(
             yield delta
 
 
+@dataclass
+class _StreamTiming:
+    """一次流式尝试的计时与文本片段(每尝试新建,不跨重试/对冲共享)。"""
+
+    content_parts: list[str] = field(default_factory=list)
+    reasoning_parts: list[str] = field(default_factory=list)
+    alias_parts: list[str] = field(default_factory=list)
+    chunk_times: list[float] = field(default_factory=list)
+    ttft_ms: float | None = None
+    max_gap: float = 0.0
+
+    @property
+    def has_reasoning(self) -> bool:
+        """出现过非空 reasoning_content: 别名 `reasoning` 让位(设计 §4.4)。"""
+        return bool(self.reasoning_parts)
+
+
+async def _consume_deltas(
+    guarded: AsyncIterator[ChatDelta],
+    started: float,
+    first_token_event: asyncio.Event | None,
+    content_event: asyncio.Event | None,
+) -> _StreamTiming:
+    """消费进展事件: 计时、置位对冲事件、收集文本片段。"""
+    timing = _StreamTiming()
+    last = started
+    async for delta in guarded:
+        now = time.monotonic()
+        # 增量 chunk 时间戳(1.3.8 沉默计量): 循环体只会被真实增量触发——
+        # 空 chunk/usage 帧/SSE keepalive 已在聚合器被过滤
+        timing.chunk_times.append(now)
+        if content_event is not None:
+            # 每个增量 chunk 置位一次: 中段沉默对冲的活性复位信号
+            content_event.set()
+        if timing.ttft_ms is None:
+            timing.ttft_ms = (now - started) * 1000
+            # 首个增量即对冲语义上的"首 token"(思考增量同样是存活证据,
+            # 与看门狗活性口径一致);None = 调用方未启用对冲,零分支成本
+            if first_token_event is not None:
+                first_token_event.set()
+        else:
+            timing.max_gap = max(timing.max_gap, (now - last) * 1000)
+        last = now
+        for parts, text in (
+            (timing.content_parts, delta.content),
+            (timing.reasoning_parts, delta.reasoning_content),
+            (timing.alias_parts, delta.reasoning),
+        ):
+            if text:
+                parts.append(text)
+    return timing
+
+
+def _raise_unless_deliverable(
+    mode: ResponseMode,
+    message: dict[str, Any] | None,
+    content: str,
+    thinking: str,
+    finish_reason: str | None,
+    source: SourceConfig,
+) -> None:
+    """结局判定(设计 §4.3)的唯一翻译点: 判定值 → 领域错误。
+
+    原因码以固定格式进 message(遥测 error 列可检索);不回显推理/工具内容。
+    """
+    if mode == "openai" and message is not None:
+        summary = _summarize_message(message, content)
+    else:
+        summary = _summarize_text(content, thinking)
+    verdict = _judge_outcome(mode=mode, summary=summary, finish=_classify_finish(finish_reason))
+    if verdict.deliver:
+        return
+    text = (
+        f"{source.name} {verdict.code}: finish_reason={finish_reason!r}, "
+        f"thinking_len={len(thinking)}"
+    )
+    if verdict.error_kind == "result_invalid":
+        raise ResultInvalidError(text, raw_text="", source_name=source.name, operation="chat")
+    raise TransientError(text, source_name=source.name, operation="chat")
+
+
+def _text_choice(body: dict[str, Any], source: SourceConfig) -> tuple[Any, str | None]:
+    """文本模式取首个 choice 的 message 与 finish(非字符串 finish 记 None)。"""
+    choices = body.get("choices") or []
+    if not choices:
+        raise TransientError(
+            f"{source.name} 响应缺 choices", source_name=source.name, operation="chat"
+        )
+    if not isinstance(choices, list) or not isinstance(choices[0], dict):
+        raise TransientError("chat choices 形态非法", operation="chat")
+    finish = choices[0].get("finish_reason")
+    return choices[0].get("message", {}), finish if isinstance(finish, str) else None
+
+
+def _nonstream_message(
+    body: dict[str, Any], mode: ResponseMode, source: SourceConfig
+) -> tuple[dict[str, Any], str | None]:
+    """非流式取单 choice 的原始 message 与 finish;只校验形态,不构造快照。"""
+    if mode == "openai":
+        message, finish = check_completion_body(body)
+    else:
+        message, finish = _text_choice(body, source)
+    if not isinstance(message, dict) or any(
+        message.get(key) is not None and not isinstance(message[key], str)
+        for key in ("content", "reasoning_content")
+    ):
+        raise TransientError("chat message 形态非法", operation="chat")
+    return message, finish
+
+
+def _reasoning_of(message: dict[str, Any]) -> str:
+    """非流式思考投影: 非空 reasoning_content 优先,否则 `reasoning` 别名。"""
+    primary = message.get("reasoning_content")
+    if isinstance(primary, str) and primary:
+        return primary
+    alias = message.get("reasoning")
+    return alias if isinstance(alias, str) else ""
+
+
 # —— 错误翻译(CHS invokers.py 同款)——
 
 
@@ -392,6 +519,9 @@ class OpenAICompatTransport:
         # 都会连带改掉另一侧的行为。(键空间恰好不相交,故当下**不会**互相压制;
         # 分开维护的理由是语义,不是碰撞)
         self._warned_mismatches: set[tuple[str, str, Effort | None]] = set()
+        # 方言告警(1.3.10)另立集合: 冲突删键/未知 finish 与推理对账语义无关
+        self._warned_fields: set[tuple[str, str]] = set()
+        self._warn_saturated = False
         self._client_factory = client_factory or _default_client_factory
         self._clients: dict[str, httpx.AsyncClient] = {}
 
@@ -636,54 +766,48 @@ class OpenAICompatTransport:
                 inter_token_s=source.inter_token_timeout_s or source.timeout_s,
                 total_s=source.timeout_s,
             )
-            content_parts: list[str] = []
+            timing = await _consume_deltas(guarded, started, first_token_event, content_event)
-            thinking_parts: list[str] = []
+        return self._stream_result(sink, accumulator, timing, source, profile, response_mode)
-            ttft_ms: float | None = None
+
-            last = started
+    def _stream_result(
-            max_gap = 0.0
+        self,
-            chunk_times: list[float] = []
+        sink: dict[str, Any],
-            async for delta in guarded:
+        accumulator: ChatCompletionAccumulator,
-                now = time.monotonic()
+        timing: _StreamTiming,
-                # 增量 chunk 时间戳(1.3.8 沉默计量): 循环体只会被真实增量触发——
+        source: SourceConfig,
-                # 空 chunk/usage 帧/SSE keepalive 已在 _sse_data_payload/_sse_delta
+        profile: ProviderProfile,
-                # 被过滤,这里逐字追加即可;阈值判定不在热路径,派生收敛在
+        response_mode: ResponseMode,
-                # _stall_from_chunk_times(设计 §3 派生原则)
+    ) -> TransportResult:
-                chunk_times.append(now)
+        """流结束后的统一判定(设计 §4.2-§4.3): 完整性 → 结局 → 组装。"""
-                if content_event is not None:
+        self._warn_dialect(source, accumulator.conflicts, accumulator.finish_reason)
-                    # 每个增量 chunk 置位一次: 中段沉默对冲的活性复位信号
+        thinking_parts = timing.reasoning_parts if timing.has_reasoning else timing.alias_parts
-                    content_event.set()
+        content, thinking = self._finalize_text(timing.content_parts, thinking_parts, profile)
-                if ttft_ms is None:
+        stream_end = _stream_is_complete(
-                    ttft_ms = (now - started) * 1000
+            done=bool(sink.get("done")),
-                    # 首个增量即对冲语义上的"首 token"(思考增量同样是存活证据,
+            finish_reason=accumulator.finish_reason,
-                    # 与看门狗活性口径一致);None = 调用方未启用对冲,零分支成本
+            usage_after_finish=accumulator.usage_after_finish,
-                    if first_token_event is not None:
+            usage_measured=_resolve_usage(sink.get("usage") or {})[2] == "measured",
-                        first_token_event.set()
+        )
-                else:
+        if stream_end is None:
-                    max_gap = max(max_gap, (now - last) * 1000)
+            stream_end = self._truncated(timing, thinking_parts, source, response_mode)
-                last = now
+        message = accumulator.raw_message() if response_mode == "openai" else None
-                if delta.content:
+        _raise_unless_deliverable(
-                    content_parts.append(delta.content)
+            response_mode, message, content, thinking, accumulator.finish_reason, source
-                if delta.reasoning_content:
-                    thinking_parts.append(delta.reasoning_content)
-        if response_mode == "openai" and not sink.get("done"):
-            raise TransientError(
-                "保真响应缺 [DONE]，禁止打捞", source_name=source.name, operation="chat"
         )
         completion_result = accumulator.finish() if response_mode == "openai" else None
-        salvaged = self._check_done(sink, content_parts, thinking_parts, source)
+        prompt, completion, usage_source = _resolve_stream_usage(sink, stream_end == "salvaged")
-        content, thinking = self._finalize_text(content_parts, thinking_parts, profile)
-        self._reject_empty_completion(content, completion_result, source)
-        prompt, completion, usage_source = _resolve_stream_usage(sink, salvaged)
         reasoning_tokens = _coerce_reasoning_tokens(sink.get("usage"))
-        stall_ms, stall_count = _stall_from_chunk_times(chunk_times, source.stall_threshold_ms)
+        stall_ms, stall_count = _stall_from_chunk_times(
+            timing.chunk_times, source.stall_threshold_ms
+        )
         return TransportResult(
             content=content,
             thinking=thinking,
             prompt_tokens=prompt,
             completion_tokens=completion,
             usage_source=usage_source,
-            ttft_ms=ttft_ms,
+            ttft_ms=timing.ttft_ms,
-            max_inter_token_ms=(max_gap if ttft_ms is not None else None),
+            max_inter_token_ms=(timing.max_gap if timing.ttft_ms is not None else None),
             stall_ms=stall_ms,
             stall_count=stall_count,
             raw={"usage": sink.get("usage")},
@@ -696,40 +820,55 @@ class OpenAICompatTransport:
             thinking_observation=observe_thinking(
                 thinking=thinking, reasoning_tokens=reasoning_tokens
             ),
+            finish_reason=accumulator.finish_reason,
+            stream_end=stream_end,
         )
 
-    def _check_done(
+    def _truncated(
         self,
-        sink: dict[str, Any],
+        timing: _StreamTiming,
-        content_parts: list[str],
         thinking_parts: list[str],
         source: SourceConfig,
-    ) -> bool:
+        response_mode: ResponseMode,
-        """缺 [DONE] 语义(设计 §6): 零内容恒 retry;有内容按 missing_done 策略。"""
+    ) -> StreamEnd:
-        if sink.get("done"):
+        """截断流(设计 §4.2): 保真不打捞;文本按 missing_done,零产物恒 retry。"""
-            return False
         ctx: dict[str, Any] = {"source_name": source.name, "operation": "chat"}
-        if not content_parts and not thinking_parts:
+        if response_mode == "openai":
+            raise TransientError(f"{source.name} missing_done: 保真响应截断,禁止打捞", **ctx)
+        if not any(timing.content_parts) and not any(thinking_parts):
             raise TransientError(f"{source.name} SSE early_eof: 零内容断流", **ctx)
         if source.missing_done == "retry":
             raise TransientError(f"{source.name} SSE missing_done: 截断且无 [DONE]", **ctx)
-        return True
+        return "salvaged"
 
-    def _reject_empty_completion(
+    def _warn_dialect(
-        self, content: str, completion: OpenAIChatCompletion | None, source: SourceConfig
+        self, source: SourceConfig, conflicts: frozenset[str], finish_reason: str | None
     ) -> None:
-        """空补全 → 瞬时错误(2026-07-20 人类裁决,M1 验证发现)。
+        """方言告警: 冲突删键与未登记 finish 各按 (源, 路径/值) 只喊一次。"""
-
+        for path in sorted(conflicts):
-        服务 200 且流程完整([DONE]/usage 正常)但 content 为空——MiniMax 等
+            self._warn_once(
-        网关的间歇异常形态。视为服务抖动: 退避重试/换源,**绝不缓存空响应**;
+                source.name, f"field:{path}", f"字段 {path} 合并冲突已删除,保真结果缺该字段"
-        承 CHS "VLM 零 content"归瞬时的先例(invokers.py:309)。
-        """
-        if not _has_completion_output(content, completion):
-            raise TransientError(
-                f"{source.name} 空补全(empty_completion): 流程完整但零内容",
-                source_name=source.name,
-                operation="chat",
             )
+        if finish_reason is not None and _classify_finish(finish_reason) == "unknown":
+            value = finish_reason[:_FINISH_WARN_CHARS]
+            self._warn_once(
+                source.name,
+                f"finish:{value}",
+                f"未登记的 finish_reason {value!r},按正常结束交付,该值的结果不缓存",
+            )
+
+    def _warn_once(self, source_name: str, key: str, text: str) -> None:
+        """有界节流: 键由上游决定,长跑 worker 不能让集合无限增长(设计 §5.4)。"""
+        if (source_name, key) in self._warned_fields:
+            return
+        if len(self._warned_fields) >= _WARN_CAP:
+            if not self._warn_saturated:
+                self._warn_saturated = True
+                logger.warning("方言告警节流集合已饱和({}),后续同类告警省略", _WARN_CAP)
+            return
+        self._warned_fields.add((source_name, key))
+        # 单参数传入 loguru: 文案可能含上游给的花括号(同 `_warn_on_thinking_mismatch`)
+        logger.warning("源 {} —— {}", source_name, text)
 
     def _finalize_text(
         self, content_parts: list[str], thinking_parts: list[str], profile: ProviderProfile
@@ -759,6 +898,9 @@ class OpenAICompatTransport:
         为纯时间阈值(对冲只能靠 `hedge_after_s` 触发)。`content_event`(1.3.8)
         同款: 接收但**永不置位**——非流式无"中段"概念,stall 两值走
         TransportResult 默认 None(排除条款)。
+
+        无流完整性可言,直接以 body 的 finish_reason 走结局判定;保真模式**先判定
+        后构造快照**,否则半截工具在构造时就抛 Transient,到不了 `tool_truncated`。
         """
         resp = await client.post(url, json=payload)
         if resp.status_code != 200:
@@ -772,24 +914,16 @@ class OpenAICompatTransport:
                 f"{source.name} 非流式响应非法 JSON", source_name=source.name, operation="chat"
             ) from exc
         _check_chat_error(body)
-        completion_result = parse_chat_completion(body) if response_mode == "openai" else None
+        message, finish = _nonstream_message(body, response_mode, source)
-        choices = body.get("choices") or []
-        if not choices:
-            raise TransientError(
-                f"{source.name} 响应缺 choices", source_name=source.name, operation="chat"
-            )
-        if not isinstance(choices, list) or not isinstance(choices[0], dict):
-            raise TransientError("chat choices 形态非法", operation="chat")
-        message = choices[0].get("message", {})
-        if not isinstance(message, dict) or any(
-            message.get(key) is not None and not isinstance(message[key], str)
-            for key in ("content", "reasoning_content")
-        ):
-            raise TransientError("chat message 形态非法", operation="chat")
         content, thinking = self._finalize_text(
-            [message.get("content") or ""], [message.get("reasoning_content") or ""], profile
+            [message.get("content") or ""], [_reasoning_of(message)], profile
+        )
+        self._warn_dialect(source, frozenset(), finish)
+        raw_message = message if response_mode == "openai" else None
+        _raise_unless_deliverable(response_mode, raw_message, content, thinking, finish, source)
+        completion_result = (
+            build_completion(message, body, finish) if response_mode == "openai" else None
         )
-        self._reject_empty_completion(content, completion_result, source)
         prompt, completion, usage_source = _resolve_usage(body.get("usage") or {})
         reasoning_tokens = _coerce_reasoning_tokens(body.get("usage"))
         return TransportResult(
@@ -810,6 +944,7 @@ class OpenAICompatTransport:
             thinking_observation=observe_thinking(
                 thinking=thinking, reasoning_tokens=reasoning_tokens
             ),
+            finish_reason=finish,
         )
 
     async def aclose(self) -> None:

tests/unit/test_completion_transport.py

@@ -0,0 +1,418 @@
+"""统一完成判定在 transport 两条路径上的落点(#30/#31,设计 §4.2-§4.4、§5.4)。
+
+假 SSE 按 magpie/glm 实测形态二次构造: 末块 finish_reason + usage 后干净关闭、
+不发 [DONE];以及只有思考的 length 截断、null/未知字段等方言。
+"""
+
+import asyncio
+import json
+
+import httpx
+import pytest
+from loguru import logger
+
+from polygateway.errors import AllSourcesExhausted, ResultInvalidError, TransientError
+from polygateway.transports.openai_compat import OpenAICompatTransport
+from tests.unit.test_openai_compat import _complete, _source, _sse_stream, _transport_for
+
+_USAGE = {"prompt_tokens": 11, "completion_tokens": 7}
+_TOOL = {
+    "index": 0,
+    "id": "call_1",
+    "type": "function",
+    "function": {"name": "lookup", "arguments": '{"q": 1}'},
+}
+
+
+def _f(delta=None, finish=None, usage=None, choices=True):
+    """单 choice 帧;`choices=False` 构造 usage-only 帧。"""
+    body = {"id": "up-1"}
+    if choices:
+        choice = {"index": 0, "delta": delta or {}}
+        if finish is not None:
+            choice["finish_reason"] = finish
+        body["choices"] = [choice]
+    else:
+        body["choices"] = []
+    if usage is not None:
+        body["usage"] = usage
+    return f"data: {json.dumps(body)}\n\n"
+
+
+def _assistant(**delta):
+    return _f({"role": "assistant", **delta})
+
+
+async def _run(frames, *, done, mode="text", **source_kw):
+    transport = _transport_for(lambda _: _sse_stream(*frames, done=done))
+    try:
+        return await _complete(transport, _source(**source_kw), response_mode=mode)
+    finally:
+        await transport.aclose()
+
+
+async def _run_once(body, *, mode="text"):
+    transport = _transport_for(lambda _: httpx.Response(200, json=body))
+    try:
+        return await _complete(transport, _source(), stream=False, response_mode=mode)
+    finally:
+        await transport.aclose()
+
+
+# —— #31: 缺 [DONE] 但 finish + 其后实测 usage 完整 ——
+
+
+@pytest.mark.parametrize("mode", ["text", "openai"])
+@pytest.mark.parametrize("finish", ["stop", "length"])
+async def test_eof_after_finish_with_usage_succeeds(mode, finish):
+    """验收 1: 末块 finish + usage 后干净关闭 → 成功、measured、eof_after_finish。"""
+    frames = [_assistant(content="hel"), _f({"content": "lo"}, finish=finish, usage=_USAGE)]
+    result = await _run(frames, done=False, mode=mode)
+    assert result.content == "hello" and result.stream_end == "eof_after_finish"
+    assert result.usage_source == "measured" and result.finish_reason == finish
+    assert (result.prompt_tokens, result.completion_tokens) == (11, 7)
+    if mode == "openai":
+        assert result.completion.finish_reason == finish
+
+
+async def test_eof_after_finish_usage_only_frame_and_tools():
+    """验收 2: tool_calls 结束、usage 在其后的 usage-only 帧 → 工具完整交回。"""
+    frames = [
+        _assistant(content=None, tool_calls=[_TOOL]),
+        _f({}, finish="tool_calls"),
+        _f(choices=False, usage=_USAGE),
+    ]
+    result = await _run(frames, done=False, mode="openai")
+    tool = result.completion.message["tool_calls"][0]
+    assert tool["function"] == {"name": "lookup", "arguments": '{"q": 1}'}
+    assert result.stream_end == "eof_after_finish"
+
+
+@pytest.mark.parametrize(
+    "frames",
+    [
+        [_assistant(content="partial"), _f(choices=False, usage=_USAGE)],  # 验收 3: 无 finish
+        [_assistant(content="partial"), _f({}, finish="stop")],  # 验收 4: 无 usage
+        [_assistant(content="a", **{}), _f({}, usage=_USAGE), _f({}, finish="stop")],  # usage 早
+        [_assistant(content="a"), _f({}, finish="stop", usage={})],  # usage 不可用
+    ],
+)
+async def test_incomplete_eof_still_fails(frames):
+    """截断不放宽: 保真仍不打捞,文本 retry 档仍抛。"""
+    with pytest.raises(TransientError, match="missing_done"):
+        await _run(frames, done=False, mode="openai", missing_done="salvage")
+    with pytest.raises(TransientError, match="missing_done"):
+        await _run(frames, done=False)
+
+
+async def test_text_salvage_marks_stream_end():
+    """文本 salvage 打捞 → stream_end=salvaged,usage 降为 estimated。"""
+    frames = [_assistant(content="partial"), _f(choices=False, usage=_USAGE)]
+    result = await _run(frames, done=False, missing_done="salvage")
+    assert result.stream_end == "salvaged" and result.usage_source == "estimated"
+
+
+async def test_read_error_mid_stream_fails():
+    """验收 5: 读取中途 RemoteProtocolError → 失败,不当干净 EOF。"""
+
+    class Broken(httpx.AsyncByteStream):
+        async def __aiter__(self):
+            yield _f({"role": "assistant", "content": "a"}, finish="stop", usage=_USAGE).encode()
+            raise httpx.RemoteProtocolError("peer closed")
+
+        async def aclose(self):
+            return None
+
+    transport = _transport_for(lambda _: httpx.Response(200, stream=Broken()))
+    try:
+        with pytest.raises(TransientError, match="网络错误"):
+            await _complete(transport, _source(), response_mode="openai")
+    finally:
+        await transport.aclose()
+
+
+@pytest.mark.parametrize("mode", ["text", "openai"])
+async def test_done_stream_marks_done(mode):
+    """验收 6: 带 [DONE] 的正常流行为不变,stream_end=done。"""
+    result = await _run([_assistant(content="ok"), _f({}, "stop", _USAGE)], done=True, mode=mode)
+    assert result.stream_end == "done" and result.finish_reason == "stop"
+
+
+@pytest.mark.parametrize("done", [True, False])
+@pytest.mark.parametrize("mode", ["text", "openai"])
+async def test_upstream_error_finish_is_transient(done, mode):
+    """GLM network_error: 上游自报失败,有正文也不交付。"""
+    frames = [_assistant(content="half"), _f({}, finish="network_error", usage=_USAGE)]
+    with pytest.raises(TransientError, match="upstream_finish_error"):
+        await _run(frames, done=done, mode=mode)
+
+
+# —— #30 与结局表(设计 §4.3)——
+
+_THINK = [_assistant(reasoning_content="long thought")]
+
+
+@pytest.mark.parametrize("done", [True, False])
+async def test_openai_reasoning_only_length_delivered(done):
+    """#30 验收: 只有思考 + length → 交回,content 空,思考完整。"""
+    result = await _run([*_THINK, _f({}, "length", _USAGE)], done=done, mode="openai")
+    message = result.completion.message
+    assert message.get("content") in (None, "") and message["reasoning_content"] == "long thought"
+    assert result.finish_reason == "length" and result.thinking == "long thought"
+
+
+@pytest.mark.parametrize(
+    ("frames", "finish", "openai_ok", "text_error"),
+    [
+        (_THINK, "length", True, (ResultInvalidError, "length_without_content")),
+        ([_assistant()], "length", True, (ResultInvalidError, "length_without_content")),
+        (_THINK, "content_filter", True, (ResultInvalidError, "filtered_without_content")),
+        ([_assistant()], "sensitive", True, (ResultInvalidError, "filtered_without_content")),
+        (_THINK, "stop", True, (TransientError, "empty_completion")),
+        ([_assistant(refusal="no")], "stop", True, (TransientError, "empty_completion")),
+        ([_assistant()], "stop", False, (TransientError, "empty_completion")),
+    ],
+)
+async def test_outcome_rows_stream(frames, finish, openai_ok, text_error):
+    """行 3/4/6/7/8: 两模式的分叉。"""
+    full = [*frames, _f({}, finish, _USAGE)]
+    if openai_ok:
+        assert (await _run(full, done=True, mode="openai")).finish_reason == finish
+    else:
+        with pytest.raises(TransientError, match="empty_completion"):
+            await _run(full, done=True, mode="openai")
+    error, code = text_error
+    with pytest.raises(error, match=code) as caught:
+        await _run(full, done=True)
+    assert caught.type is error  # ResultInvalid 不得被 Transient 子类掩盖
+
+
+@pytest.mark.parametrize("mode", ["text", "openai"])
+async def test_reasoning_only_without_finish_is_transient(mode):
+    """行 9: 只靠 DONE 收尾、无 finish → 无法区分抖动与结局,维持瞬时。"""
+    with pytest.raises(TransientError, match="empty_completion"):
+        await _run([*_THINK, _f(choices=False, usage=_USAGE)], done=True, mode=mode)
+
+
+async def test_openai_whitespace_content_stop_delivered():
+    """正文只有空白 + stop: 保真交回(#30 一-2)。"""
+    result = await _run(
+        [_assistant(content="  "), _f({}, "stop", _USAGE)], done=True, mode="openai"
+    )
+    assert result.completion.message["content"] == "  "
+
+
+async def test_truncated_tool_is_result_invalid():
+    """行 2: length 截断的半截工具(缺 name)→ ResultInvalid tool_truncated。"""
+    broken = {"index": 0, "id": "c", "type": "function", "function": {"arguments": '{"q'}}
+    frames = [_assistant(tool_calls=[broken]), _f({}, "length", _USAGE)]
+    with pytest.raises(ResultInvalidError, match="tool_truncated"):
+        await _run(frames, done=True, mode="openai")
+
+
+async def test_content_cannot_mask_broken_tool():
+    """行 2: 正文非空 + 坏工具 + stop → Transient tool_incomplete。"""
+    broken = {"index": 0, "id": "c", "type": "function", "function": {"arguments": "{}"}}
+    frames = [_assistant(content="text", tool_calls=[broken]), _f({}, "stop", _USAGE)]
+    with pytest.raises(TransientError, match="tool_incomplete"):
+        await _run(frames, done=True, mode="openai")
+
+
+async def test_null_refusal_and_tool_calls_stream():
+    """#30 验收二: delta 带 refusal:null 与 tool_calls:null → 正常交回。"""
+    frames = [_assistant(content="ok", refusal=None, tool_calls=None), _f({}, "stop", _USAGE)]
+    result = await _run(frames, done=True, mode="openai")
+    assert result.completion.message["content"] == "ok"
+
+
+# —— 非流式镜像 ——
+
+
+def _body(message, finish):
+    choice = {"index": 0, "message": {"role": "assistant", **message}}
+    if finish is not None:
+        choice["finish_reason"] = finish
+    return {"id": "up", "choices": [choice], "usage": _USAGE}
+
+
+async def test_nonstream_null_tool_calls_and_refusal():
+    """非流式 message 带 tool_calls:null + refusal:null → 成功。"""
+    body = _body({"content": "hi", "tool_calls": None, "refusal": None}, "stop")
+    result = await _run_once(body, mode="openai")
+    assert result.completion.message["tool_calls"] is None and result.stream_end is None
+
+
+@pytest.mark.parametrize(
+    ("message", "finish", "openai_expect", "text_expect"),
+    [
+        (
+            {"reasoning_content": "t"},
+            "length",
+            None,
+            (ResultInvalidError, "length_without_content"),
+        ),
+        ({"content": ""}, "content_filter", None, (ResultInvalidError, "filtered_without_content")),
+        ({"reasoning_content": "t"}, "stop", None, (TransientError, "empty_completion")),
+        ({"reasoning_content": "t"}, None, (TransientError, "empty_completion"), None),
+        ({"content": "a"}, "network_error", (TransientError, "upstream_finish_error"), None),
+        ({"content": "a"}, "length", None, None),
+    ],
+)
+async def test_outcome_rows_nonstream(message, finish, openai_expect, text_expect):
+    """非流式跳过流完整性,直接以 body finish 走结局表。"""
+    body = _body(message, finish)
+    for mode, expect in (("openai", openai_expect), ("text", text_expect or openai_expect)):
+        if expect is None:
+            result = await _run_once(body, mode=mode)
+            assert result.finish_reason == finish and result.stream_end is None
+        else:
+            with pytest.raises(expect[0], match=expect[1]) as caught:
+                await _run_once(body, mode=mode)
+            assert caught.type is expect[0]
+
+
+async def test_nonstream_truncated_tool_is_result_invalid():
+    """非流式先判定后构造快照: 缺 name + length → tool_truncated 而非构造期 Transient。"""
+    tool = {"id": "c", "type": "function", "function": {"arguments": "{"}}
+    with pytest.raises(ResultInvalidError, match="tool_truncated"):
+        await _run_once(_body({"tool_calls": [tool]}, "length"), mode="openai")
+
+
+# —— reasoning 别名(设计 §4.4)——
+
+
+async def test_reasoning_alias_projects_thinking():
+    """只有 reasoning 字段: 文本 thinking 取别名;非空 reasoning_content 优先。"""
+    alias = await _run(
+        [_assistant(reasoning="r1"), _f({"content": "a"}, "stop", _USAGE)], done=True
+    )
+    assert alias.thinking == "r1"
+    both = [
+        _assistant(reasoning="r1", reasoning_content="rc"),
+        _f({"content": "a"}, "stop", _USAGE),
+    ]
+    assert (await _run(both, done=True)).thinking == "rc"
+    empty_rc = [_assistant(reasoning="r1", reasoning_content=""), _f({"content": "a"}, "stop")]
+    assert (await _run(empty_rc, done=True)).thinking == "r1"
+    nonstream = await _run_once(_body({"content": "a", "reasoning": "nr"}, "stop"))
+    assert nonstream.thinking == "nr"
+
+
+async def test_reasoning_alias_keeps_ttft_alive():
+    """只有 reasoning 的思考期刷新活性,不被 TTFT 看门狗误杀。"""
+
+    class Slow(httpx.AsyncByteStream):
+        async def __aiter__(self):
+            yield _assistant().encode()
+            for _ in range(5):  # 0.5s 只有 reasoning,远超 ttft,但每帧间隔 < inter_token
+                yield _f({"reasoning": "t"}).encode()
+                await asyncio.sleep(0.1)
+            yield _f({"content": "answer"}, "stop", _USAGE).encode()
+            yield b"data: [DONE]\n\n"
+
+        async def aclose(self):
+            return None
+
+    transport = _transport_for(lambda _: httpx.Response(200, stream=Slow()))
+    try:
+        source = _source(ttft_timeout_s=0.3, inter_token_timeout_s=0.25)
+        result = await _complete(transport, source)
+        assert result.content == "answer" and result.thinking == "ttttt"
+    finally:
+        await transport.aclose()
+
+
+# —— 有界告警节流(设计 §5.4)——
+
+
+def _sink():
+    records = []
+    handle = logger.add(lambda m: records.append(m.record["message"]), level="WARNING")
+    return records, handle
+
+
+async def test_conflict_and_unknown_finish_warn_once():
+    """同源同路径冲突、同一未知 finish 值各只告警一次,不回显字段值。"""
+    frames = [
+        _assistant(content="a", ext=1),
+        _f({"ext": 2}),
+        _f({}, "end_turn", _USAGE),
+    ]
+    transport = _transport_for(lambda _: _sse_stream(*frames))
+    records, handle = _sink()
+    try:
+        for _ in range(2):
+            result = await _complete(transport, _source(), response_mode="openai")
+            assert "ext" not in result.completion.message
+    finally:
+        logger.remove(handle)
+        await transport.aclose()
+    conflict = [r for r in records if "delta.ext" in r]
+    unknown = [r for r in records if "end_turn" in r]
+    assert len(conflict) == 1 and len(unknown) == 1 and "不缓存" in unknown[0]
+
+
+async def test_warning_set_is_bounded():
+    """节流集合饱和后只记一次饱和告警,不再增长。"""
+    transport = OpenAICompatTransport()
+    transport._warned_fields.update((f"s{i}", "k") for i in range(1024))
+    records, handle = _sink()
+    try:
+        transport._warn_once("qwen_1", "field:x", "a")
+        transport._warn_once("qwen_1", "field:y", "b")
+    finally:
+        logger.remove(handle)
+        await transport.aclose()
+    assert len(transport._warned_fields) == 1024
+    assert sum("饱和" in r for r in records) == 1
+
+
+# —— 治理链: 确定性结局不重试、不计熔断 ——
+
+
+async def test_text_length_without_content_not_retried_nor_breaker_failure():
+    """文本 length 无正文 → ResultInvalid 直抛,attempts=1,熔断不记失败。"""
+    from tests.unit.test_client import _client
+
+    calls = []
+
+    def handler(_):
+        calls.append(1)
+        return _sse_stream(*_THINK, _f({}, "length", _USAGE))
+
+    client = _client(handler=handler)
+    failures = []
+    original = client._breaker_backend.record_failure
+
+    async def spy(*args, **kwargs):
+        failures.append(args)
+        return await original(*args, **kwargs)
+
+    client._breaker_backend.record_failure = spy
+    try:
+        with pytest.raises(ResultInvalidError, match="length_without_content"):
+            await client.chat([{"role": "user", "content": "hi"}])
+    finally:
+        await client._transport.aclose()
+    assert len(calls) == 1 and failures == []
+
+
+async def test_openai_reasoning_length_single_attempt_via_client():
+    """#30 验收经治理链: attempts=1;#31 形态经治理链不再重试到耗尽。"""
+    from tests.unit.test_client import _client
+
+    calls = []
+
+    def handler(_):
+        calls.append(1)
+        return _sse_stream(*_THINK, _f({}, "length", _USAGE), done=False)
+
+    client = _client(handler=handler)
+    try:
+        response = await client.chat([{"role": "user", "content": "hi"}], response_mode="openai")
+    except AllSourcesExhausted:  # pragma: no cover - 失败即回归
+        pytest.fail("reasoning-only length 不应重试到耗尽")
+    finally:
+        await client._transport.aclose()
+    assert len(calls) == 1 and response.call_stats.attempts == 1
+    assert response.stream_end == "eof_after_finish" and response.finish_reason == "length"

tests/unit/test_openai_fidelity.py

@@ -146,10 +146,10 @@ async def test_invalid_mode_combinations_fail_before_http(kwargs):
 
 
 async def test_openai_never_salvages():
-    """已收完整工具也必须有 DONE。"""
+    """无 finish 后 usage 的截断流即使收全工具也不打捞(1.3.10: 判据由 DONE 改为完成判定)。"""
     transport = _transport_for(lambda _: _sse_stream(*tool_frames(), done=False))
     try:
-        with pytest.raises(TransientError, match="DONE"):
+        with pytest.raises(TransientError, match="missing_done"):
             await transport.complete(
                 messages=[],
                 source=_source(missing_done="salvage"),
@@ -165,6 +165,28 @@ async def test_openai_never_salvages():
         await transport.aclose()
 
 
+async def test_openai_finish_with_trailing_usage_is_complete_without_done():
+    """对照: 同一工具流补上 finish 之后的 usage 帧,即使无 DONE 也完整交回(#31)。"""
+    usage = 'data: {"choices": [], "usage": {"prompt_tokens": 3, "completion_tokens": 4}}\n\n'
+    transport = _transport_for(lambda _: _sse_stream(*tool_frames(), usage, done=False))
+    try:
+        result = await transport.complete(
+            messages=[],
+            source=_source(),
+            stream=True,
+            overlay={},
+            call_id="c",
+            reasoning_effort=None,
+            first_token_event=None,
+            content_event=None,
+            response_mode="openai",
+        )
+    finally:
+        await transport.aclose()
+    assert result.stream_end == "eof_after_finish"
+    assert [t["id"] for t in result.completion.message["tool_calls"]] == ["a", "b"]
+
+
 async def test_tool_progress_sets_events():
     """工具首进展驱动两个事件与时间观测。"""
     import asyncio