diff --git a/adapters/llm.py b/adapters/llm.py
new file mode 100644
index 0000000..db49991
--- /dev/null
+++ b/adapters/llm.py
@@ -0,0 +1,574 @@
+"""GovernedLLMClient — 四层治理栈统一 LLM 调用网关。
+
+组合熔断器、Redis 缓存、指数退避重试、流式 SSE 消费 + 三层活性看门狗，
+并在每次调用后自动写入遥测。provider 差异处理覆盖 DeepSeek（reasoning_content）
+和 Qwen（<think> 标签剥离）。
+
+治理栈五层（ARCHITECTURE.md §5）:
+  1. 硬超时 — asyncio.wait_for + StreamLivenessTimeout
+  2. 指数退避重试 — max_retries + base_delay + max_delay
+  3. 熔断器 — CircuitBreaker（连续 N 失败开路，冷却后半开探针）
+  4. Redis 响应缓存 — content-addressed hash(model + messages)
+  5. 遥测 — TelemetryRecorder（每次调用必录）
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import re
+import time
+from typing import TYPE_CHECKING, Any
+from uuid import uuid4
+
+import httpx
+from loguru import logger
+
+from adapters.streaming import StreamLivenessTimeout, stream_with_liveness_timeouts
+from core.types import LLMResponse
+
+if TYPE_CHECKING:
+    from collections.abc import AsyncIterator
+
+    from adapters.breaker import CircuitBreaker
+
+
+class CircuitOpenError(Exception):
+    """熔断器已开启，拒绝调用。"""
+
+
+class _SseAnomaly(Exception):  # noqa: N818 — 参考 CHSAnalyzer2 命名
+    """SSE 协议异常（malformed_json 等）。"""
+
+    def __init__(self, kind: str) -> None:
+        self.kind = kind
+        super().__init__(f"SSE 协议异常: {kind}")
+
+
+# ── SSE 解析（模块级纯函数） ────────────────────────────────────────
+
+
+def _sse_data_payload(raw: str) -> str | None:
+    """提取一行 SSE 的 data 段；ping/空行/非 data 行返回 None。
+
+    参数:
+        raw: 原始 SSE 行文本。
+
+    返回:
+        data 段 payload 字符串，或 None（应被跳过的行）。
+    """
+    line = raw.strip()
+    if not line or line.startswith(":") or not line.startswith("data:"):
+        return None
+    return line[len("data:"):].strip()
+
+
+def _sse_delta(chunk: dict, usage_sink: dict) -> tuple[bool, str] | None:
+    """旁路收集 usage 并提取一帧增量 token。
+
+    返回 (True, content) 或 (False, reasoning_content)，无增量则 None。
+    优先 content（正式输出）；否则取 reasoning_content（thinking 模型的思考 token）。
+
+    参数:
+        chunk: 一帧解析后的 SSE JSON。
+        usage_sink: 旁路字典；含 usage 的帧写入 usage_sink["usage"]。
+
+    返回:
+        (is_content, text) 或 None。
+    """
+    if chunk.get("usage"):
+        usage_sink["usage"] = chunk["usage"]
+    choices = chunk.get("choices") or []
+    if not choices:
+        return None
+    delta = choices[0].get("delta") or {}
+    content = delta.get("content")
+    if content:
+        return (True, content)
+    reasoning = delta.get("reasoning_content")
+    if reasoning:
+        return (False, reasoning)
+    return None
+
+
+async def _iter_sse_deltas(
+    lines: AsyncIterator[str], usage_sink: dict
+) -> AsyncIterator[tuple[bool, str]]:
+    """OpenAI 兼容 SSE 行流 → 逐帧产出 (是否 content, 文本)。
+
+    usage 旁路写入 usage_sink。内部消化 ping/空行/[DONE]/usage-only 帧。
+
+    参数:
+        lines: 底层 SSE 文本行异步迭代器。
+        usage_sink: 旁路字典。
+
+    产出:
+        (is_content, text)。
+
+    异常:
+        _SseAnomaly: data 段 JSON 解析失败。
+    """
+    async for raw in lines:
+        data = _sse_data_payload(raw)
+        if data is None:
+            continue
+        if data == "[DONE]":
+            usage_sink["done"] = True
+            return
+        try:
+            chunk = json.loads(data)
+        except json.JSONDecodeError as exc:
+            raise _SseAnomaly("malformed_json") from exc
+        delta = _sse_delta(chunk, usage_sink)
+        if delta is not None:
+            yield delta
+
+
+# ── Provider 差异处理 ────────────────────────────────────────────────
+
+
+def _build_thinking_body(provider: str) -> dict:
+    """构造 thinking/reasoning 启用参数（provider 差异处理）。
+
+    参数:
+        provider: provider 标识（如 "deepseek"、"qwen" 等）。
+
+    返回:
+        合并进请求体的字典片段。
+    """
+    provider_lower = provider.lower()
+    if "deepseek" in provider_lower:
+        return {"thinking": {"type": "enabled"}}
+    if "qwen" in provider_lower:
+        return {"enable_thinking": True}
+    return {}
+
+
+_THINK_PATTERN = re.compile(r"<think>(.*?)</think>", re.DOTALL)
+
+
+def _strip_qwen_thinking(content: str) -> tuple[str, str]:
+    """剥离 Qwen 模型 <think>...</think> 标签，拆分为 (正文, 思考内容)。
+
+    参数:
+        content: 含有可能的 <think> 标签的模型原始输出。
+
+    返回:
+        (stripped_content, thinking_text)。无 think 标签时 thinking 为空字符串。
+    """
+    match = _THINK_PATTERN.search(content)
+    if match is None:
+        return content, ""
+    thinking = match.group(1).strip()
+    stripped = _THINK_PATTERN.sub("", content).strip()
+    return stripped, thinking
+
+
+# ── 瞬时错误判定 ─────────────────────────────────────────────────────
+
+_TRANSIENT_STATUS_CODES = frozenset({429, 500, 502, 503, 504})
+_FATAL_STATUS_CODES = frozenset({401, 403})
+
+
+def _is_transient_error(exc: Exception) -> bool:
+    """判断异常是否为瞬时可重试错误。
+
+    参数:
+        exc: 捕获的异常。
+
+    返回:
+        True 表示可重试，False 表示不可重试。
+    """
+    if isinstance(exc, (httpx.ConnectError, httpx.ReadTimeout, httpx.WriteTimeout)):
+        return True
+    if isinstance(exc, httpx.HTTPStatusError):
+        return exc.response.status_code in _TRANSIENT_STATUS_CODES
+    return isinstance(exc, (StreamLivenessTimeout, _SseAnomaly))
+
+
+def _is_fatal_auth_error(exc: Exception) -> bool:
+    """判断异常是否为不可恢复的认证/授权错误。
+
+    参数:
+        exc: 捕获的异常。
+
+    返回:
+        True 表示应立即 force_open 熔断器。
+    """
+    if isinstance(exc, httpx.HTTPStatusError):
+        return exc.response.status_code in _FATAL_STATUS_CODES
+    return False
+
+
+# ── GovernedLLMClient ────────────────────────────────────────────────
+
+
+class GovernedLLMClient:
+    """四层治理栈统一 LLM 调用网关，实现 LLMProvider Protocol。
+
+    构造函数接收所有组件（依赖注入），不从全局状态获取任何配置。
+
+    参数:
+        model: 模型名称。
+        base_url: API 基础 URL（如 http://localhost:8000/v1）。
+        api_key: API 密钥。
+        provider: provider 标识（如 "deepseek"、"qwen"）。
+        thinking: 是否启用 thinking/reasoning 模式。
+        breaker: 熔断器实例。
+        cache: Redis 响应缓存实例。
+        telemetry: 遥测记录器实例。
+        timeout_s: 总超时秒数。
+        ttft_timeout_s: 首 token 超时秒数。
+        inter_token_timeout_s: token 间超时秒数。
+        max_retries: 最大重试次数。
+        retry_base_delay_s: 重试基础延迟秒数。
+        retry_max_delay_s: 重试最大延迟秒数。
+    """
+
+    def __init__(
+        self,
+        *,
+        model: str,
+        base_url: str,
+        api_key: str,
+        provider: str,
+        thinking: bool,
+        breaker: CircuitBreaker,
+        cache: Any,
+        telemetry: Any,
+        timeout_s: float,
+        ttft_timeout_s: float,
+        inter_token_timeout_s: float,
+        max_retries: int,
+        retry_base_delay_s: float,
+        retry_max_delay_s: float,
+    ) -> None:
+        self._model = model
+        self._base_url = base_url.rstrip("/")
+        self._api_key = api_key
+        self._provider = provider
+        self._thinking = thinking
+        self._breaker = breaker
+        self._cache = cache
+        self._telemetry = telemetry
+        self._timeout_s = timeout_s
+        self._ttft_timeout_s = ttft_timeout_s
+        self._inter_token_timeout_s = inter_token_timeout_s
+        self._max_retries = max_retries
+        self._retry_base_delay_s = retry_base_delay_s
+        self._retry_max_delay_s = retry_max_delay_s
+        self._http = httpx.AsyncClient(
+            base_url=self._base_url,
+            headers={
+                "Authorization": f"Bearer {self._api_key}",
+                "Content-Type": "application/json",
+            },
+            timeout=httpx.Timeout(timeout_s),
+        )
+
+    async def chat(
+        self,
+        messages: list[dict[str, Any]],
+        *,
+        session_id: str | None = None,
+        parent_call_id: str | None = None,
+    ) -> LLMResponse:
+        """发起 LLM 调用，经四层治理栈：熔断 → 缓存 → 重试+流式 → 遥测。
+
+        参数:
+            messages: OpenAI 格式消息列表。
+            session_id: 会话 ID（传递到遥测）。
+            parent_call_id: 父调用 ID（传递到遥测）。
+
+        返回:
+            LLMResponse 统一响应。
+
+        异常:
+            CircuitOpenError: 熔断器开路。
+            httpx.HTTPStatusError: 不可恢复的 HTTP 错误（401/403）。
+        """
+        started = time.monotonic()
+        call_id = str(uuid4())
+
+        # ① 熔断器检查
+        if self._breaker.is_open(self._provider, time.monotonic()):
+            raise CircuitOpenError(
+                f"熔断器已开启，拒绝调用 provider={self._provider}"
+            )
+
+        # ② 缓存查询
+        cached = await self._cache.get(self._model, messages)
+        if cached is not None:
+            response = LLMResponse(
+                content=cached.content,
+                thinking=cached.thinking,
+                model=cached.model,
+                provider=cached.provider,
+                prompt_tokens=cached.prompt_tokens,
+                completion_tokens=cached.completion_tokens,
+                latency_ms=0,
+                ttft_ms=None,
+                max_inter_token_ms=None,
+                cache_hit=True,
+                call_id=call_id,
+            )
+            await self._telemetry.record_llm_call(
+                call_id=call_id,
+                parent_call_id=parent_call_id,
+                session_id=session_id,
+                model_name=self._model,
+                provider=self._provider,
+                messages=json.dumps(messages, ensure_ascii=False),
+                response=response.content,
+                thinking=response.thinking,
+                prompt_tokens=response.prompt_tokens,
+                completion_tokens=response.completion_tokens,
+                latency_ms=0,
+                ttft_ms=None,
+                max_inter_token_ms=None,
+                cache_hit=True,
+                error=None,
+            )
+            return response
+
+        # ③ 重试循环 + 流式消费
+        last_exc: Exception | None = None
+        for attempt in range(self._max_retries):
+            attempt_start = time.monotonic()
+            try:
+                sse_lines = await self._stream_request(messages)
+                content, thinking_text, ttft_ms, max_itoken_ms, usage = (
+                    await self._consume_stream(sse_lines)
+                )
+
+                # 熔断器记成功
+                self._breaker.record_success(self._provider)
+
+                # provider 差异：Qwen think 标签剥离
+                if "qwen" in self._provider.lower() and "<think>" in content:
+                    content, qwen_thinking = _strip_qwen_thinking(content)
+                    if qwen_thinking:
+                        thinking_text = qwen_thinking
+
+                latency_ms = int((time.monotonic() - started) * 1000)
+                prompt_tokens = usage.get("prompt_tokens", 0)
+                completion_tokens = usage.get("completion_tokens", 0)
+
+                response = LLMResponse(
+                    content=content,
+                    thinking=thinking_text,
+                    model=self._model,
+                    provider=self._provider,
+                    prompt_tokens=prompt_tokens,
+                    completion_tokens=completion_tokens,
+                    latency_ms=latency_ms,
+                    ttft_ms=ttft_ms,
+                    max_inter_token_ms=max_itoken_ms,
+                    cache_hit=False,
+                    call_id=call_id,
+                )
+
+                # ④ 写缓存
+                await self._cache.set(self._model, messages, response)
+
+                # ⑤ 遥测
+                await self._telemetry.record_llm_call(
+                    call_id=call_id,
+                    parent_call_id=parent_call_id,
+                    session_id=session_id,
+                    model_name=self._model,
+                    provider=self._provider,
+                    messages=json.dumps(messages, ensure_ascii=False),
+                    response=content,
+                    thinking=thinking_text,
+                    prompt_tokens=prompt_tokens,
+                    completion_tokens=completion_tokens,
+                    latency_ms=latency_ms,
+                    ttft_ms=ttft_ms,
+                    max_inter_token_ms=max_itoken_ms,
+                    cache_hit=False,
+                    error=None,
+                )
+
+                return response
+
+            except Exception as exc:
+                last_exc = exc
+                attempt_latency = int((time.monotonic() - attempt_start) * 1000)
+
+                # 致命错误（401/403）→ force_open + 遥测 + 立即抛出
+                if _is_fatal_auth_error(exc):
+                    self._breaker.force_open(self._provider, time.monotonic())
+                    await self._telemetry.record_llm_call(
+                        call_id=call_id,
+                        parent_call_id=parent_call_id,
+                        session_id=session_id,
+                        model_name=self._model,
+                        provider=self._provider,
+                        messages=json.dumps(messages, ensure_ascii=False),
+                        response="",
+                        thinking="",
+                        prompt_tokens=0,
+                        completion_tokens=0,
+                        latency_ms=attempt_latency,
+                        ttft_ms=None,
+                        max_inter_token_ms=None,
+                        cache_hit=False,
+                        error=str(exc),
+                    )
+                    raise
+
+                # 瞬时错误 → 记失败 + 遥测 + 退避重试
+                if _is_transient_error(exc):
+                    self._breaker.record_failure(self._provider, time.monotonic())
+                    await self._telemetry.record_llm_call(
+                        call_id=str(uuid4()),  # 每次重试用新 call_id
+                        parent_call_id=parent_call_id,
+                        session_id=session_id,
+                        model_name=self._model,
+                        provider=self._provider,
+                        messages=json.dumps(messages, ensure_ascii=False),
+                        response="",
+                        thinking="",
+                        prompt_tokens=0,
+                        completion_tokens=0,
+                        latency_ms=attempt_latency,
+                        ttft_ms=None,
+                        max_inter_token_ms=None,
+                        cache_hit=False,
+                        error=str(exc),
+                    )
+                    logger.warning(
+                        "llm.call.transient_error",
+                        extra={
+                            "provider": self._provider,
+                            "attempt": attempt + 1,
+                            "max_retries": self._max_retries,
+                            "error": str(exc),
+                        },
+                    )
+                    if attempt < self._max_retries - 1:
+                        delay = min(
+                            self._retry_base_delay_s * (2 ** attempt),
+                            self._retry_max_delay_s,
+                        )
+                        if delay > 0:
+                            await asyncio.sleep(delay)
+                    continue
+
+                # 非瞬时、非致命 → 记遥测后直接抛出
+                await self._telemetry.record_llm_call(
+                    call_id=call_id,
+                    parent_call_id=parent_call_id,
+                    session_id=session_id,
+                    model_name=self._model,
+                    provider=self._provider,
+                    messages=json.dumps(messages, ensure_ascii=False),
+                    response="",
+                    thinking="",
+                    prompt_tokens=0,
+                    completion_tokens=0,
+                    latency_ms=attempt_latency,
+                    ttft_ms=None,
+                    max_inter_token_ms=None,
+                    cache_hit=False,
+                    error=str(exc),
+                )
+                raise
+
+        # 所有重试耗尽
+        assert last_exc is not None
+        raise last_exc
+
+    async def _stream_request(
+        self, messages: list[dict[str, Any]]
+    ) -> list[str]:
+        """发起流式 HTTP 请求并收集全部 SSE 行。
+
+        此方法为主要的 HTTP 交互点，测试通过 patch 替换以注入假 SSE 行。
+
+        参数:
+            messages: OpenAI 格式消息列表。
+
+        返回:
+            SSE 文本行列表。
+
+        异常:
+            httpx.HTTPStatusError: HTTP 状态码错误。
+            httpx.ConnectError: 连接错误。
+        """
+        payload: dict[str, Any] = {
+            "model": self._model,
+            "messages": messages,
+            "stream": True,
+            "stream_options": {"include_usage": True},
+        }
+        if self._thinking:
+            payload.update(_build_thinking_body(self._provider))
+
+        collected_lines: list[str] = []
+        async with self._http.stream(
+            "POST", "/chat/completions", json=payload
+        ) as resp:
+            if resp.status_code >= 400:
+                await resp.aread()
+                resp.raise_for_status()
+            async for line in resp.aiter_lines():
+                collected_lines.append(line)
+        return collected_lines
+
+    async def _consume_stream(
+        self, sse_lines: list[str]
+    ) -> tuple[str, str, float | None, float | None, dict]:
+        """消费 SSE 行序列，累积 content/thinking，测量 TTFT 和 max_inter_token_ms。
+
+        参数:
+            sse_lines: SSE 文本行列表。
+
+        返回:
+            (content, thinking, ttft_ms, max_inter_token_ms, usage_dict)。
+        """
+        usage_sink: dict = {}
+
+        async def _lines_iter() -> AsyncIterator[str]:
+            for line in sse_lines:
+                yield line
+
+        raw_deltas = _iter_sse_deltas(_lines_iter(), usage_sink)
+        guarded = stream_with_liveness_timeouts(
+            raw_deltas,
+            ttft_s=self._ttft_timeout_s,
+            inter_token_s=self._inter_token_timeout_s,
+            total_s=self._timeout_s,
+        )
+
+        content_parts: list[str] = []
+        thinking_parts: list[str] = []
+        ttft_ms: float | None = None
+        max_inter_token_ms: float | None = None
+        stream_start = time.monotonic()
+        last_token_time = stream_start
+
+        async for is_content, text in guarded:
+            now = time.monotonic()
+            if ttft_ms is None:
+                ttft_ms = (now - stream_start) * 1000
+            else:
+                gap_ms = (now - last_token_time) * 1000
+                if max_inter_token_ms is None or gap_ms > max_inter_token_ms:
+                    max_inter_token_ms = gap_ms
+            last_token_time = now
+
+            if is_content:
+                content_parts.append(text)
+            else:
+                thinking_parts.append(text)
+
+        content = "".join(content_parts)
+        thinking = "".join(thinking_parts)
+        usage = usage_sink.get("usage", {})
+        return content, thinking, ttft_ms, max_inter_token_ms, usage
+
+    async def close(self) -> None:
+        """关闭底层 HTTP 客户端。"""
+        await self._http.aclose()
diff --git a/tests/unit/test_governed_llm.py b/tests/unit/test_governed_llm.py
new file mode 100644
index 0000000..2970654
--- /dev/null
+++ b/tests/unit/test_governed_llm.py
@@ -0,0 +1,306 @@
+"""GovernedLLMClient 单元测试 — 四层治理栈核心路径验证。"""
+
+from __future__ import annotations
+
+import json
+import time
+from typing import Any
+from unittest.mock import patch
+
+import httpx
+import pytest
+
+from adapters.breaker import CircuitBreaker
+from adapters.llm import CircuitOpenError, GovernedLLMClient
+from core.types import LLMResponse
+
+# ── 辅助类 ──────────────────────────────────────────────────────────
+
+
+class _FakeRedisCache:
+    """内存字典替身，模拟 RedisResponseCache 的 get/set 接口。"""
+
+    def __init__(self) -> None:
+        self._store: dict[str, LLMResponse] = {}
+
+    async def get(
+        self, model: str, messages: list[dict[str, str]]
+    ) -> LLMResponse | None:
+        key = f"{model}:{json.dumps(messages, sort_keys=True)}"
+        return self._store.get(key)
+
+    async def set(
+        self,
+        model: str,
+        messages: list[dict[str, str]],
+        response: LLMResponse,
+    ) -> None:
+        key = f"{model}:{json.dumps(messages, sort_keys=True)}"
+        self._store[key] = response
+
+
+class _FakeTelemetry:
+    """记录所有 record_llm_call 调用的替身。"""
+
+    def __init__(self) -> None:
+        self.calls: list[dict[str, Any]] = []
+
+    async def record_llm_call(self, **kwargs: Any) -> None:
+        self.calls.append(kwargs)
+
+
+# ── 工具函数 ─────────────────────────────────────────────────────────
+
+
+async def _async_line_iter(lines: list[str]):
+    """将字符串列表转为异步行迭代器。"""
+    for line in lines:
+        yield line
+
+
+def _make_sse_lines(
+    content_chunks: list[str],
+    *,
+    reasoning_chunks: list[str] | None = None,
+    model: str = "test-model",
+    usage: dict[str, int] | None = None,
+) -> list[str]:
+    """构造 SSE 文本行序列，模拟 OpenAI 兼容流式响应。"""
+    lines: list[str] = []
+
+    # 先产出 reasoning 帧
+    for chunk in reasoning_chunks or []:
+        frame = {
+            "choices": [{"delta": {"reasoning_content": chunk}}],
+            "model": model,
+        }
+        lines.append(f"data: {json.dumps(frame)}\n")
+
+    # 再产出 content 帧
+    for chunk in content_chunks:
+        frame = {
+            "choices": [{"delta": {"content": chunk}}],
+            "model": model,
+        }
+        lines.append(f"data: {json.dumps(frame)}\n")
+
+    # usage 帧
+    if usage is None:
+        usage = {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}
+    frame_usage = {"choices": [], "usage": usage, "model": model}
+    lines.append(f"data: {json.dumps(frame_usage)}\n")
+
+    # 终止标记
+    lines.append("data: [DONE]\n")
+    return lines
+
+
+def _build_client(
+    *,
+    breaker: CircuitBreaker | None = None,
+    cache: _FakeRedisCache | None = None,
+    telemetry: _FakeTelemetry | None = None,
+    provider: str = "test-provider",
+    model: str = "test-model",
+    thinking: bool = False,
+) -> GovernedLLMClient:
+    """构造标准测试客户端。"""
+    return GovernedLLMClient(
+        model=model,
+        base_url="http://localhost:8000/v1",
+        api_key="test-key",
+        provider=provider,
+        thinking=thinking,
+        breaker=breaker or CircuitBreaker(fail_threshold=3, cooldown_s=60),
+        cache=cache or _FakeRedisCache(),
+        telemetry=telemetry or _FakeTelemetry(),
+        timeout_s=30.0,
+        ttft_timeout_s=10.0,
+        inter_token_timeout_s=5.0,
+        max_retries=3,
+        retry_base_delay_s=0.0,  # 测试不等待
+        retry_max_delay_s=0.0,
+    )
+
+
+# ── 测试用例 ─────────────────────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_circuit_open_raises():
+    """熔断器开启 → 直接抛 CircuitOpenError，不发请求。"""
+    breaker = CircuitBreaker(fail_threshold=1, cooldown_s=600)
+    breaker.force_open("test-provider", time.monotonic())
+    client = _build_client(breaker=breaker)
+
+    with pytest.raises(CircuitOpenError):
+        await client.chat([{"role": "user", "content": "hello"}])
+
+
+@pytest.mark.asyncio
+async def test_cache_hit_returns_cached_and_records_telemetry():
+    """缓存命中 → 返回缓存响应 + 新 call_id + 遥测记录 cache_hit=True。"""
+    cache = _FakeRedisCache()
+    telemetry = _FakeTelemetry()
+    client = _build_client(cache=cache, telemetry=telemetry)
+
+    # 预填缓存
+    cached_resp = LLMResponse(
+        content="cached answer",
+        thinking="",
+        model="test-model",
+        provider="test-provider",
+        prompt_tokens=10,
+        completion_tokens=5,
+        latency_ms=0,
+        ttft_ms=None,
+        max_inter_token_ms=None,
+        cache_hit=True,
+        call_id="old-id",
+    )
+    messages = [{"role": "user", "content": "hello"}]
+    await cache.set("test-model", messages, cached_resp)
+
+    result = await client.chat(messages)
+
+    assert result.content == "cached answer"
+    assert result.cache_hit is True
+    # 每次调用都生成新 call_id
+    assert result.call_id != "old-id"
+
+    # 遥测已记录
+    assert len(telemetry.calls) == 1
+    assert telemetry.calls[0]["cache_hit"] is True
+
+
+@pytest.mark.asyncio
+async def test_successful_streaming_call():
+    """正常流式调用 → 正确累积 content + thinking + 采集 ttft_ms + tokens。"""
+    telemetry = _FakeTelemetry()
+    client = _build_client(telemetry=telemetry)
+    messages = [{"role": "user", "content": "hello"}]
+
+    sse_lines = _make_sse_lines(
+        ["Hello", " world"],
+        reasoning_chunks=["Let me", " think"],
+        usage={"prompt_tokens": 20, "completion_tokens": 10, "total_tokens": 30},
+    )
+
+    async def fake_stream_request(_messages: Any) -> list[str]:
+        return sse_lines
+
+    with patch.object(client, "_stream_request", side_effect=fake_stream_request):
+        result = await client.chat(messages)
+
+    assert result.content == "Hello world"
+    assert result.thinking == "Let me think"
+    assert result.prompt_tokens == 20
+    assert result.completion_tokens == 10
+    assert result.ttft_ms is not None
+    assert result.ttft_ms >= 0
+    assert result.cache_hit is False
+
+    # 遥测已记录
+    assert len(telemetry.calls) == 1
+    assert telemetry.calls[0]["cache_hit"] is False
+    assert telemetry.calls[0]["error"] is None
+
+
+@pytest.mark.asyncio
+async def test_transient_error_retries_and_records_telemetry():
+    """ConnectError 重试 2 次后成功 → 结果正确 + 遥测记录成功。"""
+    telemetry = _FakeTelemetry()
+    client = _build_client(telemetry=telemetry)
+    messages = [{"role": "user", "content": "hello"}]
+
+    sse_lines = _make_sse_lines(["OK"])
+    call_count = 0
+
+    async def flaky_stream(_messages: Any) -> list[str]:
+        nonlocal call_count
+        call_count += 1
+        if call_count <= 2:
+            raise httpx.ConnectError("connection refused")
+        return sse_lines
+
+    with patch.object(client, "_stream_request", side_effect=flaky_stream):
+        result = await client.chat(messages)
+
+    assert result.content == "OK"
+    assert call_count == 3
+
+    # 前 2 次失败也应记遥测
+    error_calls = [c for c in telemetry.calls if c.get("error") is not None]
+    assert len(error_calls) == 2
+    # 最后 1 次成功也记了遥测
+    success_calls = [c for c in telemetry.calls if c.get("error") is None]
+    assert len(success_calls) == 1
+
+
+@pytest.mark.asyncio
+async def test_fatal_error_force_opens_breaker():
+    """401 → force_open 熔断器 + 遥测记录 error。"""
+    breaker = CircuitBreaker(fail_threshold=3, cooldown_s=60)
+    telemetry = _FakeTelemetry()
+    client = _build_client(breaker=breaker, telemetry=telemetry)
+    messages = [{"role": "user", "content": "hello"}]
+
+    mock_response = httpx.Response(
+        status_code=401,
+        request=httpx.Request("POST", "http://localhost:8000/v1/chat/completions"),
+    )
+
+    async def auth_fail(_messages: Any) -> list[str]:
+        raise httpx.HTTPStatusError(
+            "Unauthorized", request=mock_response.request, response=mock_response
+        )
+
+    with patch.object(client, "_stream_request", side_effect=auth_fail), \
+         pytest.raises(httpx.HTTPStatusError):
+        await client.chat(messages)
+
+    # 熔断器已被 force_open
+    assert breaker.is_open("test-provider", time.monotonic())
+
+    # 遥测已记录 error
+    assert len(telemetry.calls) == 1
+    assert telemetry.calls[0]["error"] is not None
+
+
+@pytest.mark.asyncio
+async def test_qwen_thinking_stripped():
+    """Qwen 模型 <think> 标签被正确剥离到 thinking 字段。"""
+    from adapters.llm import _strip_qwen_thinking
+
+    content_with_think = "<think>这是思考过程</think>这是正式输出"
+    content, thinking = _strip_qwen_thinking(content_with_think)
+    assert content == "这是正式输出"
+    assert thinking == "这是思考过程"
+
+    # 无 think 标签时原样返回
+    plain = "纯文本输出"
+    content2, thinking2 = _strip_qwen_thinking(plain)
+    assert content2 == "纯文本输出"
+    assert thinking2 == ""
+
+
+@pytest.mark.asyncio
+async def test_parent_call_id_forwarded_to_telemetry():
+    """parent_call_id 和 session_id 正确传递到遥测记录。"""
+    telemetry = _FakeTelemetry()
+    client = _build_client(telemetry=telemetry)
+    messages = [{"role": "user", "content": "hello"}]
+
+    sse_lines = _make_sse_lines(["result"])
+
+    async def fake_stream(_messages: Any) -> list[str]:
+        return sse_lines
+
+    with patch.object(client, "_stream_request", side_effect=fake_stream):
+        await client.chat(
+            messages, session_id="sess-42", parent_call_id="parent-99"
+        )
+
+    assert len(telemetry.calls) == 1
+    assert telemetry.calls[0]["session_id"] == "sess-42"
+    assert telemetry.calls[0]["parent_call_id"] == "parent-99"