feat: Anthropic Messages API compat (/v1/messages)

Add a wire-compatible Anthropic endpoint alongside the existing OpenAI one
so Claude Code / anthropic-sdk / Cursor Agent can hit Lingma directly.

- app/anthropic_schema.py (new): request model + content-block flattener
  + internal-messages adapter + affinity key helper. Handles text / image /
  tool_use / tool_result blocks; unknown types degrade gracefully.
- app/auth.py: add require_anthropic_key (x-api-key, Bearer fallback)
  and AnthropicAuthError so auth failures render in Anthropic's error
  envelope instead of FastAPI's {detail:...} wrapper.
- app/main.py: POST /v1/messages. Shares LingmaPool / SessionCache /
  InFlightGuard / StatsCollector with the OpenAI path — same api_key +
  same conversation prefix hits the same upstream sessionId across both
  protocols (KV cache carries over). Streaming emits the named Anthropic
  event sequence (message_start / content_block_start / content_block_delta
  / content_block_stop / message_delta / message_stop). No claude-*
  model mapping table: resolve_model's default fallback handles it.
- README.md / DESIGN.md: document the new endpoint, add decision 5.12,
  iteration history M5, and a 4.3b streaming flow diagram.
- Bump FastAPI app version to 0.4.0.

Made-with: Cursor

DESIGN.md

@@ -37,6 +37,7 @@
 ### 目标
 
 1. **OpenAI 协议兼容**：任何支持 OpenAI 的客户端（curl、Cursor、Dify、LangChain、LiteLLM…）不改代码就能接入 Lingma。
+1b. **Anthropic Messages 协议兼容**：Claude Code / anthropic-sdk-python / Cursor Agent 等只会说 Anthropic 的客户端也能直接接入，和 OpenAI 共享同一 session cache 与池。
 2. **单节点生产可用**：自用场景下能长期跑 7×24，包含合理的观测、鉴权、背压、错误恢复。
 3. **最大化利用单账号 / 多账号的配额**：通过多实例池 + 会话复用把后端吞吐做到接近原始 VSCode 插件水平。
 4. **降低运维成本**：首次登录成功后，可以导出一份 bundle 永久复用，彻底摆脱浏览器自动化的不稳定性。
@@ -101,6 +102,7 @@
 | `config.py` | 178 | env → `Settings` dataclass；`LINGMA_ACCOUNTS` 多格式解析；bundle 字段归一化 | `main.py` | — |
 | `model_map.py` | 84 | Lingma 模型 `key ↔ displayName` 双向映射；请求 `model` 解析（`id` 或 `name` 都认） | `main.py` | — |
 | `openai_schema.py` | 91 | OpenAI 请求/响应 Pydantic；多模态内容 `flatten_content` 降级 | `main.py`, `session_cache.py` | — |
+| `anthropic_schema.py` | ~140 | Anthropic Messages 请求 Pydantic；content blocks `flatten_anthropic_content`；`anthropic_to_internal_messages` 归一化到内部消息；`affinity_key_for_anthropic` 选池键 | `main.py` | — |
 | `stats.py` | 85 | 请求次数 / token 估算 / Prometheus 文本 | `main.py` | — |
 | `logging_config.py` | 56 | 结构化 JSON logger；`request_id` 通过 `ContextVar` 注入每行 | 所有模块 | — |
 | `bootstrap_lingma.py` | 199 | 启动时从 Marketplace / VSIX 提取 Lingma 二进制到 `data/bin/` | 容器启动脚本 | — |
@@ -274,6 +276,58 @@ async def event_stream():
 1. `ticket_transferred=True` 一旦设成 true，外层 `finally` 就不会 release ticket；责任转交给 `event_stream()` 的 finally。否则会 release 两次（虽然幂等，但会把 in_flight 计成 -1）。
 2. `chat_stream` 走的是 JSON-RPC **notify** 而非 request。早期版本用 request 会等 30s 才下第一个字节（见决策 5.1）。
 
+### 4.3b 流式 Anthropic Messages（/v1/messages）
+
+输入输出协议都不同于 OpenAI，但中间层完全复用：
+
+```
+client ──► POST /v1/messages (x-api-key / Bearer)
+   │
+   ▼
+require_anthropic_key                      # x-api-key 优先；缺了 → AnthropicAuthError
+   │
+anthropic_to_internal_messages(req)        # system → role="system"；content blocks flatten
+   │                                        # 结果与 OpenAI 路径完全同构 (role/content dict)
+   ▼
+session_cache lookup / affinity pick       # 与 OpenAI 共享同一 SessionCache 实例
+   │                                        # → 同一用户切协议不丢 KV cache
+   ▼
+pool.pick(affinity) + ensure_logged_in
+   │
+   ▼
+resolve_model("claude-3-5-sonnet-*")       # 兜底到 default_model
+   │
+   ▼
+chat_guard.try_acquire()                   # 与 OpenAI 路径同一 in-flight 池
+   │
+   ▼ stream=true
+StreamingResponse(event_stream())
+   │
+   ├─ event: message_start           ← 一次性：id / model / usage.input_tokens
+   ├─ event: content_block_start     ← index=0, type=text
+   ├─ event: content_block_delta     ← 每片 chunk 包一次
+   │  ...
+   ├─ event: content_block_stop
+   ├─ event: message_delta           ← stop_reason (+ output_tokens)
+   └─ event: message_stop            ← 终止，无 [DONE]
+   │
+   ▼ finally
+session_cache.put(write_key, upstream_sessionId, inst.name)   # 仅 success
+ticket.release() + inst.in_flight--
+```
+
+与 OpenAI 路径的差异点：
+
+| 环节 | OpenAI | Anthropic |
+|---|---|---|
+| 鉴权 | `Authorization: Bearer` | `x-api-key`（fallback Bearer）|
+| 系统消息 | messages 数组里的 `role:"system"` | 顶层 `system` 字段 |
+| 内容结构 | `str` 或 `[{type:"text"|"image_url"...}]` | `str` 或 `[{type:"text"|"image"|"tool_use"|"tool_result"...}]` |
+| 流式帧 | `data: {delta:{content:"..."}}` + `[DONE]` | 命名事件序列 `message_start / content_block_* / message_delta / message_stop` |
+| usage 语义 | `prompt_tokens / completion_tokens` | `input_tokens / output_tokens` |
+| 错误 envelope | `{"error":{...}}` | `{"type":"error","error":{...}}` |
+| finish 语义 | `finish_reason: "stop"\|"length"` | `stop_reason: "end_turn"\|"max_tokens"` |
+
 ### 4.4 Lingma 子进程与 LSP 通信
 
 ```
@@ -520,6 +574,16 @@ FastAPI `lifespan` 退出 → `pool.close()` → 每个 `client.close()` → 进
 - **方案**：`client._proc` + `client._terminate_proc()`。pool 只负责 `client.start()` / `client.close()` 的调度，进程操作封装在 client 内部。
 - **权衡**：client 文件变长，但边界清晰——pool 只看状态和在途数，具体进程是 client 的事。
 
+### 5.12 Anthropic Messages 端点独立编排而非内部转发
+
+- **问题**：既要兼容 Anthropic API，又不能把 `v1_chat_completions` 的编排路径搞成大杂烩。
+- **方案**：单独写一个 `v1_messages` 端点，前半段（auth / 归一化到内部 messages / affinity / session cache lookup / instance pick / prompt 构造 / ticket 获取）与 OpenAI 端点结构对齐但各自实现；后半段（SSE 事件生成 / 响应包装）按 Anthropic 格式输出。
+- **共享的下沉层**：`LingmaPool` / `SessionCache` / `InFlightGuard` / `StatsCollector` / `LingmaGatewayClient.chat_stream|chat_complete` / `resolve_model`。
+- **为何不用一层统一抽象**：两端的输入/输出对象形状差异足够大（system 位置、content 类型、SSE 事件名、错误 envelope），抽象出来的中间类型反而掩盖差异、增加维护成本。当前重复代码约 150 行，但每条分支读起来直接对应 wire 协议，调试、改协议时都是线性阅读。
+- **会话复用跨协议**：`session_cache.build_key(api_key, messages)` 在两端都接收归一化后的 `{role, content}` 列表——同一用户从 OpenAI 切 Anthropic（只要对话前缀一致）可直接命中同一上游 `sessionId`，等于白送 KV cache。
+- **错误路径**：`AnthropicAuthError` 专用异常 + `@app.exception_handler` 渲染 Anthropic envelope；端点内部其他错误（HTTPException、backpressure）用 `_anthropic_error()` helper 直接返 `JSONResponse`，绕过 FastAPI 默认 `{"detail":...}` 包装。
+- **模型名**：不维护 `claude-* → dashscope_*` 映射表。`resolve_model` 的末位兜底（default_model / first available）会把所有陌生 id 退回到实际可用的 Lingma key，Anthropic 客户端继续传 `claude-3-5-sonnet-*` 即可工作。
+
 ---
 
 ## 6. 扩展指引（要做 X 改哪里）
@@ -527,6 +591,7 @@ FastAPI `lifespan` 退出 → `pool.close()` → 每个 `client.close()` → 进
 | 需求 | 改哪些文件 | 关键入口 |
 |---|---|---|
 | 加一个新的 OpenAI 端点（如 embeddings） | `main.py`, `openai_schema.py` | 仿照 `v1_models` 加 `@app.post("/v1/embeddings", dependencies=[Depends(auth_guard)])` |
+| 扩展 Anthropic 端点（如 count_tokens / tool_use 贯通） | `main.py::v1_messages`, `anthropic_schema.py` | count_tokens 只读：复用 `estimate_tokens`；tool_use 需要 Lingma 上游支持，payload 转发点在 `chat_stream` / `chat_complete` |
 | 加一种新的实例调度策略（如加权轮询） | `lingma_pool.py::pick()` | 当前是 affinity → least-in-flight → round-robin |
 | 改认证为 JWT / OAuth | `auth.py` | 三个 `require_*` 函数是全部入口；`main.py` 里只有 `*_guard` 代理 |
 | 增加限流（按 api_key 配额） | `concurrency.py` 加 `PerKeyGuard`；`main.py` 在 `chat_guard.try_acquire()` 后再来一层 | 注意 ticket 释放顺序（内层先释放） |
@@ -604,6 +669,17 @@ uvicorn app.main:app --reload --port 8317
 
 收益：单轮没有显著改变（推理仍然花最多时间），但第 2 轮起 TTFB 降 40%~60%，视 prompt 长度。
 
+### M5 — Anthropic Messages 兼容
+
+- **场景**：Claude Code / Cursor Agent / anthropic-sdk-python / 各种 agent 框架只会说 Anthropic 协议。
+- **改动**：
+  - 新增 `anthropic_schema.py`：`AnthropicMessagesRequest` + `anthropic_to_internal_messages` + `flatten_anthropic_content` + `affinity_key_for_anthropic`。
+  - `auth.py` 新增 `require_anthropic_key`（`x-api-key` 优先，Bearer 回退）+ `AnthropicAuthError`。
+  - `main.py` 新增 `/v1/messages` 端点：复用 `LingmaPool` / `SessionCache` / `InFlightGuard`；流式按 `message_start / content_block_start|delta|stop / message_delta / message_stop` Anthropic SSE 协议输出；错误 envelope 改写成 `{"type":"error","error":{...}}`。
+  - `@app.exception_handler(AnthropicAuthError)` 渲染 Anthropic 错误 wire 格式。
+- **关键设计**：两端共享同一 `SessionCache`，同一 api_key 下的会话前缀哈希一致 → 跨协议命中同一上游 `sessionId`。详见 §5.12。
+- **模型名**：不维护 `claude-* → dashscope_*` 映射表，靠 `resolve_model` 末位兜底。
+
 ### M4 — 生产硬化包（commit `2febc37`）
 
 用户代号"选项 A"。

README.md

@@ -2,7 +2,10 @@
 
 把本地 Lingma 插件封装成 OpenAI 兼容接口。任何能调 OpenAI 的客户端（Cursor、Dify、LangChain、curl…）都能直接接入。
 
-**支持：** `GET /v1/models` / `POST /v1/chat/completions`（含 SSE 流式） / Bearer 鉴权 / Prometheus / 多账号实例池 / 会话复用 / 免浏览器登录态注入。
+**支持：**
+- OpenAI 兼容：`GET /v1/models` / `POST /v1/chat/completions`（含 SSE 流式） / Bearer 鉴权
+- **Anthropic 兼容**：`POST /v1/messages`（含 Anthropic SSE 事件流） / `x-api-key` 鉴权
+- Prometheus / 多账号实例池 / 会话复用（跨两种协议共享） / 免浏览器登录态注入
 
 > 想看架构、模块划分、设计决策、二开路线图 → 直接读 [`DESIGN.md`](./DESIGN.md)。
 
@@ -160,6 +163,7 @@ curl -s http://127.0.0.1:8317/v1/chat/completions \
 | GET | `/healthz` | 免鉴权；返回 `ok` / `pool_size` / `pool_ready` / 每实例状态 |
 | GET | `/v1/models` | OpenAI 兼容；`id` 是 Lingma 原 key，`name` 是可读名 |
 | POST | `/v1/chat/completions` | OpenAI 兼容；`stream=true` 走 SSE；`model: "agent"` 切 agent 模式 |
+| POST | `/v1/messages` | **Anthropic Messages 兼容**；`x-api-key` 或 `Authorization: Bearer`；`stream=true` 走 Anthropic 命名事件 SSE |
 
 **chat 请求示例（非流式）**
 
@@ -182,6 +186,44 @@ curl -N http://127.0.0.1:8317/v1/chat/completions \
   }'
 ```
 
+**Anthropic Messages 示例（非流式）**
+
+```bash
+curl -s http://127.0.0.1:8317/v1/messages \
+  -H "x-api-key: $API_KEY" \
+  -H "anthropic-version: 2023-06-01" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model":"claude-3-5-sonnet-20241022",
+    "max_tokens":256,
+    "system":"你是一个简洁的助手",
+    "messages":[{"role":"user","content":"你好"}]
+  }'
+```
+
+**Anthropic Messages 示例（流式）**
+
+```bash
+curl -N http://127.0.0.1:8317/v1/messages \
+  -H "x-api-key: $API_KEY" \
+  -H "anthropic-version: 2023-06-01" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model":"claude-3-5-sonnet-20241022",
+    "max_tokens":256,
+    "stream":true,
+    "messages":[{"role":"user","content":"写一首四行诗"}]
+  }'
+# 返回 message_start / content_block_start / content_block_delta* /
+#      content_block_stop / message_delta / message_stop
+```
+
+说明：
+- **模型名兼容**：客户端可以继续传 `claude-3-*` 等名字；未识别的 model 会回退到 `DEFAULT_MODEL` 对应的 Lingma key，后端实际仍由 Lingma 提供（Qwen 系列）。如需显式选模型，直接传 Lingma key（`dashscope_qmodel` 等）。
+- **会话复用共享**：Anthropic 与 OpenAI 两个端点共用同一 `SessionCache`，只要 API key 相同、对话前缀相同，就会命中同一上游 `sessionId`。
+- **多模态**：`image` 块会被降级为 `[image]` 占位符（Lingma 不支持 vision）；`tool_use` / `tool_result` 会以纯文本形式保留语义。
+- **鉴权**：优先 `x-api-key` 头（Anthropic 官方 SDK 默认），回退 `Authorization: Bearer`（方便 curl / OpenAI 风格客户端）。
+
 ### 3.2 观测（`METRICS_TOKEN` 或 `API_KEYS`）
 
 | 方法 | 路径 | 说明 |

app/anthropic_schema.py

@@ -0,0 +1,165 @@
+from __future__ import annotations
+
+"""Anthropic Messages API schema + content adapters.
+
+Why this exists
+---------------
+The Anthropic Messages API (`POST /v1/messages`) is wire-incompatible with
+OpenAI chat completions even though it covers the same ground:
+
+* auth:     `x-api-key` header (not `Authorization: Bearer`)
+* system:   separate top-level field, never a message role
+* content:  `str` or array of typed blocks (`text`, `image`, `tool_use`, ...)
+* streaming: a named-event SSE protocol (`message_start`, `content_block_delta`,
+             `message_delta`, `message_stop`) rather than OpenAI's `delta.content`
+* errors:   `{"type":"error","error":{"type":"...","message":"..."}}`
+
+We keep a separate schema module rather than squeezing everything into
+`openai_schema.py` so both adapters stay small and auditable. Both eventually
+collapse to the same Lingma prompt shape inside `main.py`.
+"""
+
+import json
+from typing import Any, Literal
+
+from pydantic import BaseModel
+
+
+# Anthropic accepts either a raw string or a list of typed content blocks.
+# We keep the list loosely typed (plain dicts) so future block kinds
+# (e.g. `thinking`, `document`) don't break the gateway — they simply fall
+# into the generic flattener below.
+AnthropicContent = str | list[dict[str, Any]] | None
+
+
+class AnthropicMessage(BaseModel):
+    # Anthropic: system is a top-level field, messages only carry user/assistant.
+    role: Literal["user", "assistant"]
+    content: AnthropicContent = None
+
+
+class AnthropicMessagesRequest(BaseModel):
+    model: str
+    # max_tokens is REQUIRED by Anthropic. We default to a sane value so callers
+    # that forget it don't 422 — easier migration from OpenAI clients.
+    max_tokens: int = 1024
+    messages: list[AnthropicMessage]
+    system: AnthropicContent = None
+    stream: bool = False
+    temperature: float | None = None
+    top_p: float | None = None
+    top_k: int | None = None
+    stop_sequences: list[str] | None = None
+    # metadata.user_id is the official hint for per-user routing / abuse tracking.
+    metadata: dict[str, Any] | None = None
+    # Tools / tool_choice are accepted but we can't forward them to Lingma yet —
+    # they're preserved here so the request doesn't 422, and the flattener
+    # surfaces any tool_use blocks as `[tool_use] {...}` text so the assistant
+    # still sees the context.
+    tools: list[dict[str, Any]] | None = None
+    tool_choice: dict[str, Any] | None = None
+
+
+def flatten_anthropic_content(content: AnthropicContent) -> str:
+    """Reduce Anthropic block arrays to a plain-string prompt for Lingma.
+
+    Handled block types:
+      * text          -> verbatim text
+      * image         -> `[image]` placeholder (Lingma has no vision)
+      * tool_use      -> `[tool_use] {json}` so the assistant can reference it
+      * tool_result   -> `[tool_result] ...` (string or nested blocks)
+      * unknown       -> fall back to `.text` / `.content` if present, else drop
+
+    Returning an empty string here means the caller (prompt builder) will skip
+    the whole message rather than emit a bare `[role] ` line.
+    """
+    if content is None:
+        return ""
+    if isinstance(content, str):
+        return content
+    if not isinstance(content, list):
+        return str(content)
+
+    parts: list[str] = []
+    for item in content:
+        if not isinstance(item, dict):
+            parts.append(str(item))
+            continue
+        t = item.get("type")
+        if t == "text":
+            text = item.get("text") or ""
+            if text:
+                parts.append(text)
+        elif t == "image":
+            parts.append("[image]")
+        elif t == "tool_use":
+            # Compact one-line JSON keeps prompt_tokens estimate stable.
+            try:
+                payload = json.dumps(
+                    {"name": item.get("name"), "input": item.get("input")},
+                    ensure_ascii=False,
+                )
+            except Exception:
+                payload = str(item)
+            parts.append(f"[tool_use] {payload}")
+        elif t == "tool_result":
+            inner = item.get("content")
+            if isinstance(inner, str):
+                parts.append(f"[tool_result] {inner}")
+            elif isinstance(inner, list):
+                parts.append(f"[tool_result] {flatten_anthropic_content(inner)}")
+        else:
+            fallback = item.get("text") or item.get("content")
+            if isinstance(fallback, str) and fallback:
+                parts.append(fallback)
+    return "\n".join(p for p in parts if p)
+
+
+def anthropic_to_internal_messages(req: AnthropicMessagesRequest) -> list[dict]:
+    """Project an Anthropic request into the gateway's internal message list.
+
+    Internal shape matches what `_messages_to_prompt` already expects:
+    `[{"role": "system"|"user"|"assistant", "content": "..."}]`. This means
+    session-cache hashing is identical across OpenAI and Anthropic callers —
+    a user who migrates between the two endpoints keeps their session affinity
+    as long as they send the same conversation prefix.
+    """
+    out: list[dict] = []
+    if req.system:
+        sys_text = flatten_anthropic_content(req.system)
+        if sys_text:
+            out.append({"role": "system", "content": sys_text})
+    for m in req.messages:
+        text = flatten_anthropic_content(m.content)
+        out.append({"role": m.role, "content": text})
+    return out
+
+
+def affinity_key_for_anthropic(req: AnthropicMessagesRequest) -> str | None:
+    """Best-effort stable routing key for an Anthropic request.
+
+    Priority mirrors the OpenAI side:
+      1. metadata.user_id (the official per-user hint)
+      2. hash of the system prompt
+      3. hash of the first message
+
+    Kept here rather than in `main.py` because it needs the flatten helper and
+    the request type — `main.py` stays endpoint-shaped, not schema-shaped.
+    """
+    import hashlib
+
+    if req.metadata:
+        user_id = req.metadata.get("user_id")
+        if isinstance(user_id, str) and user_id.strip():
+            return user_id.strip()
+
+    if req.system:
+        text = flatten_anthropic_content(req.system)
+        if text:
+            return "sys:" + hashlib.sha1(text.encode("utf-8")).hexdigest()[:16]
+
+    if req.messages:
+        text = flatten_anthropic_content(req.messages[0].content)
+        if text:
+            return "first:" + hashlib.sha1(text.encode("utf-8")).hexdigest()[:16]
+    return None

app/auth.py

@@ -98,6 +98,58 @@ def require_metrics_access(
         )
 
 
+class AnthropicAuthError(Exception):
+    """Raised when an Anthropic Messages request fails authentication.
+
+    Carries enough context for the endpoint to render the Anthropic-shaped
+    error body (`{"type":"error","error":{"type":..., "message":...}}`) — we
+    don't use `HTTPException` here because FastAPI would wrap the detail in
+    `{"detail": ...}`, which is not the Anthropic wire format.
+    """
+
+    def __init__(self, status_code: int, error_type: str, message: str) -> None:
+        super().__init__(message)
+        self.status_code = status_code
+        self.error_type = error_type
+        self.message = message
+
+
+def require_anthropic_key(request: Request, api_keys: list[str]) -> None:
+    """Authenticate a `POST /v1/messages` request the Anthropic way.
+
+    Accept order:
+      1. `x-api-key` header (official Anthropic SDK / CLI / Claude Code)
+      2. `Authorization: Bearer <token>` (OpenAI-shaped clients / curl)
+
+    Empty `api_keys` means auth is disabled — the startup auth-posture warning
+    already covers that case loudly, same as `require_bearer`.
+
+    Note: we keep `anthropic-version` header permissive (don't parse/validate)
+    so clients on any official version work without gateway churn.
+    """
+    if not api_keys:
+        return
+
+    token = request.headers.get("x-api-key", "").strip()
+    if not token:
+        auth = request.headers.get("authorization", "")
+        if auth.startswith("Bearer "):
+            token = auth[len("Bearer ") :].strip()
+
+    if not token:
+        raise AnthropicAuthError(
+            status.HTTP_401_UNAUTHORIZED,
+            "authentication_error",
+            "missing x-api-key header (or Authorization: Bearer ...)",
+        )
+    if not _match_any(token, api_keys):
+        raise AnthropicAuthError(
+            status.HTTP_401_UNAUTHORIZED,
+            "authentication_error",
+            "invalid x-api-key",
+        )
+
+
 def require_admin_access(
     request: Request,
     api_keys: list[str],

app/main.py

@@ -10,7 +10,18 @@ from contextlib import asynccontextmanager
 from fastapi import Depends, FastAPI, HTTPException, Request
 from fastapi.responses import JSONResponse, StreamingResponse
 
-from .auth import require_admin_access, require_bearer, require_metrics_access
+from .anthropic_schema import (
+    AnthropicMessagesRequest,
+    affinity_key_for_anthropic,
+    anthropic_to_internal_messages,
+)
+from .auth import (
+    AnthropicAuthError,
+    require_admin_access,
+    require_anthropic_key,
+    require_bearer,
+    require_metrics_access,
+)
 from .concurrency import BackpressureRejected, InFlightGuard
 from .config import Settings, load_settings
 from .lingma_pool import LingmaPool, PoolInstance
@@ -85,7 +96,24 @@ async def lifespan(_app: FastAPI):
             await pool.close()
 
 
-app = FastAPI(title="Lingma OpenAI Gateway", version="0.3.0", lifespan=lifespan)
+app = FastAPI(title="Lingma OpenAI Gateway", version="0.4.0", lifespan=lifespan)
+
+
+@app.exception_handler(AnthropicAuthError)
+async def _anthropic_auth_error_handler(_request: Request, exc: AnthropicAuthError):
+    """Render auth failures on /v1/messages in the Anthropic wire format.
+
+    FastAPI's default handler wraps everything in `{"detail": ...}`, which
+    Anthropic SDKs don't parse. We emit the canonical
+    `{"type":"error","error":{"type":"...","message":"..."}}` instead.
+    """
+    return JSONResponse(
+        status_code=exc.status_code,
+        content={
+            "type": "error",
+            "error": {"type": exc.error_type, "message": exc.message},
+        },
+    )
 
 
 @app.middleware("http")
@@ -594,6 +622,356 @@ async def v1_chat_completions(req: ChatCompletionsRequest, request: Request):
             ticket.release()
 
 
+def _anthropic_error(status_code: int, error_type: str, message: str) -> JSONResponse:
+    """Build an Anthropic-shaped error response (`type:error` envelope)."""
+    return JSONResponse(
+        status_code=status_code,
+        content={"type": "error", "error": {"type": error_type, "message": message}},
+    )
+
+
+def _anthropic_stop_reason(completion_tokens: int, max_tokens: int) -> str:
+    """Approximate Anthropic `stop_reason`.
+
+    Lingma doesn't expose a `max_tokens` knob, so we can't truly enforce it;
+    we report `max_tokens` only when the generated length meets or exceeds
+    the caller's stated ceiling. Everything else is `end_turn`.
+    """
+    if max_tokens and completion_tokens >= max_tokens:
+        return "max_tokens"
+    return "end_turn"
+
+
+@app.post("/v1/messages")
+async def v1_messages(req: AnthropicMessagesRequest, request: Request):
+    """Anthropic Messages API compatible endpoint.
+
+    Wire contract:
+      * auth:   `x-api-key` header (fallback Authorization: Bearer)
+      * body:   Anthropic Messages spec (system top-level, content blocks, ...)
+      * stream: named-event SSE (message_start / content_block_delta / ...)
+
+    Internally we:
+      1. Normalise to the gateway's internal message list (`role/content` dicts)
+      2. Reuse the same pool pick + session cache + backpressure guard as
+         `/v1/chat/completions`. Session-cache keys include the API key, so
+         Anthropic and OpenAI callers on the same key share KV-cache warmth.
+      3. Re-wrap outputs in Anthropic's response / SSE format.
+    """
+    # ------------------------------------------------------------- auth
+    try:
+        require_anthropic_key(request, settings.api_keys)
+    except AnthropicAuthError as exc:
+        return _anthropic_error(exc.status_code, exc.error_type, exc.message)
+
+    # ------------------------------------------------------------- plumbing
+    try:
+        p = _require_pool()
+    except HTTPException as exc:
+        return _anthropic_error(exc.status_code, "overloaded_error", "gateway not ready")
+
+    messages_dump = anthropic_to_internal_messages(req)
+    # Prefer the auth token actually accepted so session-cache bucketing is
+    # consistent regardless of which auth header style the caller used.
+    api_key = (
+        request.headers.get("x-api-key", "").strip()
+        or _extract_api_key(request)
+        or "-"
+    )
+
+    # ------------------------------------------------------------- session reuse
+    # Anthropic clients don't expose an ask_mode, so we always run in "chat".
+    ask_mode = "chat"
+
+    reuse_eligible = (
+        session_cache.enabled and ask_mode == "chat" and len(messages_dump) >= 2
+    )
+    lookup_key: str | None = None
+    write_key: str | None = None
+    cached_session_id: str | None = None
+    cached_instance_name: str | None = None
+    if reuse_eligible:
+        lookup_key = session_cache.build_key(api_key, messages_dump[:-1])
+        write_key = session_cache.build_key(api_key, messages_dump)
+        entry = await session_cache.get(lookup_key)
+        if entry is not None:
+            cached_session_id = entry.session_id
+            cached_instance_name = entry.instance_name or None
+
+    affinity = cached_instance_name or affinity_key_for_anthropic(req)
+    inst = p.pick(affinity_key=affinity)
+
+    if cached_instance_name and inst.name != cached_instance_name:
+        logger.info(
+            "anthropic session cache instance %s unhealthy, falling back to %s",
+            cached_instance_name,
+            inst.name,
+        )
+        cached_session_id = None
+        if lookup_key:
+            await session_cache.invalidate(lookup_key)
+
+    try:
+        await _ensure_instance_logged_in(inst)
+    except HTTPException as exc:
+        # 503/401/502 from login: map to closest Anthropic kind.
+        err_type = "authentication_error" if exc.status_code == 401 else "overloaded_error"
+        detail = exc.detail if isinstance(exc.detail, dict) else {}
+        msg = (detail.get("error") or {}).get("message") or str(detail) or "upstream error"
+        return _anthropic_error(exc.status_code, err_type, msg)
+
+    # ------------------------------------------------------------- prompt & model
+    models = await inst.client.query_models()
+    available = flatten_model_keys(models)
+    name_map = build_model_name_map(models)
+    # Anthropic callers send `claude-*` model names. resolve_model's
+    # final fallback (default_model / first available) handles that cleanly
+    # without us having to hard-code a mapping table.
+    model = resolve_model(req.model, available, settings.default_model, name_map)
+
+    if cached_session_id:
+        prompt = _last_user_text(messages_dump)
+        is_reply = True
+    else:
+        prompt = _messages_to_prompt(messages_dump)
+        is_reply = False
+
+    if not prompt:
+        return _anthropic_error(400, "invalid_request_error", "messages is empty")
+
+    prompt_tokens = estimate_tokens(prompt)
+
+    # ------------------------------------------------------------- backpressure
+    try:
+        ticket = await chat_guard.try_acquire()
+    except BackpressureRejected as exc:
+        retry_after = max(1, int(exc.retry_after))
+        logger.warning("anthropic rejected by backpressure, retry_after=%ds", retry_after)
+        resp = _anthropic_error(
+            429,
+            "overloaded_error",
+            "too many in-flight requests, please retry later",
+        )
+        resp.headers["Retry-After"] = str(retry_after)
+        return resp
+
+    inst.in_flight += 1
+    message_id = f"msg_{uuid.uuid4().hex}"
+    logger.info(
+        "anthropic.start inst=%s model=%s stream=%s prompt_tokens~%d reuse=%s",
+        inst.name,
+        model,
+        req.stream,
+        prompt_tokens,
+        bool(cached_session_id),
+        extra={
+            "ctx_instance": inst.name,
+            "ctx_model": model,
+            "ctx_ask_mode": ask_mode,
+            "ctx_stream": req.stream,
+            "ctx_prompt_tokens": prompt_tokens,
+            "ctx_in_flight": chat_guard.in_flight,
+            "ctx_affinity": affinity,
+            "ctx_session_reuse": bool(cached_session_id),
+            "ctx_api": "anthropic",
+        },
+    )
+
+    ticket_transferred = False
+
+    def _sse(event: str, data: dict) -> str:
+        return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
+
+    try:
+        if req.stream:
+            completion_tokens_holder = {"n": 0}
+            stream_meta: dict = {}
+            max_tokens = req.max_tokens
+
+            async def event_stream(_ticket=ticket, _inst=inst, _meta=stream_meta):
+                success = False
+                try:
+                    # 1) message_start — Anthropic SDKs read this first to get
+                    #    the message envelope (id/model/initial usage).
+                    start_payload = {
+                        "type": "message_start",
+                        "message": {
+                            "id": message_id,
+                            "type": "message",
+                            "role": "assistant",
+                            "model": model,
+                            "content": [],
+                            "stop_reason": None,
+                            "stop_sequence": None,
+                            # input_tokens is authoritative here; output_tokens
+                            # is seeded to 0 and updated in message_delta.
+                            "usage": {
+                                "input_tokens": prompt_tokens,
+                                "output_tokens": 0,
+                            },
+                        },
+                    }
+                    yield _sse("message_start", start_payload)
+
+                    # 2) content_block_start for a single text block (index 0).
+                    yield _sse(
+                        "content_block_start",
+                        {
+                            "type": "content_block_start",
+                            "index": 0,
+                            "content_block": {"type": "text", "text": ""},
+                        },
+                    )
+
+                    # 3) content_block_delta stream of text tokens.
+                    async for chunk in _inst.client.chat_stream(
+                        prompt,
+                        model,
+                        ask_mode,
+                        session_id=cached_session_id,
+                        is_reply=is_reply,
+                        out_meta=_meta,
+                    ):
+                        if not chunk:
+                            continue
+                        completion_tokens_holder["n"] += estimate_tokens(chunk)
+                        yield _sse(
+                            "content_block_delta",
+                            {
+                                "type": "content_block_delta",
+                                "index": 0,
+                                "delta": {"type": "text_delta", "text": chunk},
+                            },
+                        )
+
+                    # 4) content_block_stop closes the single text block.
+                    yield _sse(
+                        "content_block_stop",
+                        {"type": "content_block_stop", "index": 0},
+                    )
+
+                    # 5) message_delta carries the terminal stop_reason and
+                    #    the final cumulative output_tokens count.
+                    stop_reason = _anthropic_stop_reason(
+                        completion_tokens_holder["n"], max_tokens
+                    )
+                    yield _sse(
+                        "message_delta",
+                        {
+                            "type": "message_delta",
+                            "delta": {
+                                "stop_reason": stop_reason,
+                                "stop_sequence": None,
+                            },
+                            "usage": {"output_tokens": completion_tokens_holder["n"]},
+                        },
+                    )
+
+                    # 6) message_stop — terminal event, no [DONE] sentinel.
+                    yield _sse("message_stop", {"type": "message_stop"})
+                    success = True
+                except asyncio.CancelledError:
+                    logger.info("anthropic.stream cancelled (inst=%s)", _inst.name)
+                    raise
+                except Exception as exc:
+                    logger.warning("anthropic.stream error (inst=%s): %s", _inst.name, exc)
+                    # Best-effort error frame. Anthropic clients treat any
+                    # unexpected event gracefully; we prefer visibility over
+                    # silent truncation.
+                    try:
+                        yield _sse(
+                            "error",
+                            {
+                                "type": "error",
+                                "error": {
+                                    "type": "api_error",
+                                    "message": str(exc) or "upstream error",
+                                },
+                            },
+                        )
+                    except Exception:
+                        pass
+                finally:
+                    # Session write-back only on clean finish — partial streams
+                    # leave Lingma's session in an indeterminate state.
+                    if success and write_key:
+                        sid = _meta.get("session_id")
+                        if sid:
+                            await session_cache.put(write_key, sid, _inst.name)
+                    await stats_collector.record_chat(
+                        stream=True,
+                        success=success,
+                        prompt_tokens=prompt_tokens,
+                        completion_tokens=completion_tokens_holder["n"],
+                    )
+                    _inst.in_flight = max(0, _inst.in_flight - 1)
+                    _ticket.release()
+
+            ticket_transferred = True
+            return StreamingResponse(
+                event_stream(),
+                media_type="text/event-stream",
+                headers={
+                    "Cache-Control": "no-cache, no-transform",
+                    "X-Accel-Buffering": "no",
+                    "Connection": "keep-alive",
+                },
+            )
+
+        # ------------------------------------------------------------- non-stream
+        try:
+            result = await inst.client.chat_complete(
+                prompt,
+                model,
+                ask_mode,
+                session_id=cached_session_id,
+                is_reply=is_reply,
+            )
+        except Exception as exc:
+            logger.warning("anthropic.complete error (inst=%s): %s", inst.name, exc)
+            await stats_collector.record_chat(
+                stream=False,
+                success=False,
+                prompt_tokens=prompt_tokens,
+                completion_tokens=0,
+            )
+            if cached_session_id and lookup_key:
+                await session_cache.invalidate(lookup_key)
+            return _anthropic_error(502, "api_error", "upstream lingma error")
+
+        text = result.get("text") or ""
+        completion_tokens = estimate_tokens(text)
+        await stats_collector.record_chat(
+            stream=False,
+            success=True,
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+        )
+        if write_key:
+            sid = result.get("sessionId")
+            if sid:
+                await session_cache.put(write_key, sid, inst.name)
+
+        response_body: dict = {
+            "id": message_id,
+            "type": "message",
+            "role": "assistant",
+            "model": model,
+            "content": [{"type": "text", "text": text}],
+            "stop_reason": _anthropic_stop_reason(completion_tokens, req.max_tokens),
+            "stop_sequence": None,
+            "usage": {
+                "input_tokens": prompt_tokens,
+                "output_tokens": completion_tokens,
+            },
+        }
+        return JSONResponse(content=response_body)
+    finally:
+        if not ticket_transferred:
+            inst.in_flight = max(0, inst.in_flight - 1)
+            ticket.release()
+
+
 @app.post("/internal/auto-login/start", dependencies=[Depends(admin_auth_guard)])
 async def internal_auto_login_start(instance: str | None = None):
     p = _require_pool()