feat: bridge Lingma tool events to OpenAI/Anthropic responses

Add structured tool event propagation from Lingma stream/finish metadata and map it to OpenAI tool_calls and Anthropic tool_use/tool_result in both streaming and non-streaming responses. Add focused bridge tests and update docs/design notes to match current behavior.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

CLAUDE.md

@@ -0,0 +1,95 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Primary docs to read first
+- `README.md` (runtime commands, env model, API examples)
+- `DESIGN.md` (architecture decisions, module boundaries, request lifecycle)
+- `.env.example` (authoritative env var reference)
+
+No Cursor/Copilot rule files were found in this repo (`.cursorrules`, `.cursor/rules/`, `.github/copilot-instructions.md`).
+
+## Common development commands
+
+### Start locally
+```bash
+pip install -r requirements.txt
+uvicorn app.main:app --reload --port 8317
+```
+
+### Start with Docker Compose
+```bash
+cp .env.example .env
+mkdir -p data secrets
+docker compose up -d --build
+docker compose logs -f
+```
+
+### Run tests
+```bash
+# current focused suite
+python3 -m unittest tests/test_tool_call_bridge.py
+
+# discover all unittest tests under tests/
+python3 -m unittest discover -s tests -p "test_*.py"
+
+# run a single test method
+python3 -m unittest tests.test_tool_call_bridge.ToolCallBridgeTests.test_openai_non_stream_bridges_tool_calls
+```
+
+### Smoke-check running gateway
+```bash
+API_KEY=$(grep '^API_KEYS=' .env | cut -d= -f2 | cut -d, -f1)
+curl -s http://127.0.0.1:8317/healthz
+curl -s http://127.0.0.1:8317/v1/models -H "Authorization: Bearer $API_KEY"
+```
+
+### Linting/type-checking status
+- There is currently no repo-configured lint/type command (no `ruff`/`flake8`/`mypy` config found).
+- Do not invent tooling commands; if linting is needed, add tooling in a dedicated change first.
+
+## Architecture (big picture)
+
+### What this service is
+A FastAPI gateway that fronts Lingma and exposes:
+- OpenAI-compatible API (`/v1/models`, `/v1/chat/completions`)
+- Anthropic Messages-compatible API (`/v1/messages`, `/v1/messages/count_tokens`)
+
+Both protocols share the same backend pool, backpressure guard, stats, and session reuse logic.
+
+### Request lifecycle (important for most changes)
+1. Authenticate request (`app/auth.py`)
+2. Normalize inbound protocol payload to internal message shape (`openai_schema.py` / `anthropic_schema.py`)
+3. Session-cache lookup (`app/session_cache.py`) for prefix-based reuse
+4. Pick backend instance (`app/lingma_pool.py`) with affinity + least-in-flight
+5. Acquire concurrency ticket (`app/concurrency.py`)
+6. Call Lingma via websocket/LSP client (`app/lingma_client.py`)
+7. Map upstream result/stream back to wire protocol in `app/main.py`
+8. Record stats and release ticket (including stream-finally paths)
+
+### Core module boundaries
+- `app/main.py`: API entrypoint + orchestration + wire-format adapters
+- `app/lingma_pool.py`: multi-instance lifecycle, selection, health-aware fallback
+- `app/lingma_client.py`: subprocess + LSP-over-WebSocket transport to Lingma
+- `app/session_cache.py`: LRU+TTL cache of conversation-prefix -> upstream session id (+ instance binding)
+- `app/concurrency.py`: in-flight guard and queue timeout/backpressure behavior
+- `app/stats.py`: usage counters and Prometheus text
+
+### Protocol-specific notes
+- Anthropic and OpenAI endpoints are separate adapters over shared internals.
+- Response-side tool bridge is implemented: upstream Lingma tool events are surfaced as:
+  - OpenAI: `tool_calls` (stream + non-stream)
+  - Anthropic: `tool_use` / `tool_result` blocks (stream + non-stream)
+- Request-side `tools` / `tool_choice` are accepted by schemas but not forwarded to Lingma.
+
+### Operational invariants to preserve
+- One request must stay on one Lingma instance for session continuity.
+- Session cache entries include instance identity; invalidate on unhealthy instance mismatch.
+- Streaming paths must always release in-flight tickets in `finally`.
+- Multi-instance mode must use isolated workdirs per instance.
+
+### Deployment/runtime model
+- Container startup runs `python /app/app/bootstrap_lingma.py` before uvicorn.
+- Compose mounts:
+  - `./data -> /app/data` (persistent Lingma binary/cache/workdirs)
+  - `./secrets -> /secrets:ro` (session bundles, secrets)

DESIGN.md

@@ -47,7 +47,8 @@
 
 - **逆向 Lingma 后端协议**：之前评估过（曾经的"B1 终极方案"），需要反编译二进制，维护成本高、政策风险大，放弃。
 - **多租户 / 水平扩缩**：单容器即可；真要大规模部署 → 套层反代 + N 个网关副本就够，不在进程内解决。
-- **完整 function calling / tools**：OpenAI schema 里保留了字段，但目前不透传给 Lingma（Lingma 侧没有等价能力）。
+- **请求侧完整 function calling / tools 透传**：OpenAI schema 里保留了字段，但目前不会把 `tools`/`tool_choice` 透传给 Lingma（上游无等价输入协议）。
+- **响应侧工具事件桥接**：若 Lingma 上游产出 tool 事件，网关会向 OpenAI 输出 `tool_calls`，向 Anthropic 输出 `tool_use` / `tool_result`（stream + non-stream）。
 - **多模态**：请求里的 image/audio 会被降级成占位符 `[image]` / `[audio]`，因为 Lingma chat 不支持。
 
 ---
@@ -591,7 +592,7 @@ 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` |
+| 扩展 Anthropic 端点（如 count_tokens / tool_use 相关能力） | `main.py::v1_messages`, `anthropic_schema.py` | count_tokens 只读：复用 `estimate_tokens`；响应侧 `tool_use/tool_result` 桥接已支持，若要请求侧 tools 透传仍需改 `lingma_client.py` payload |
 | 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） |
@@ -627,7 +628,7 @@ uvicorn app.main:app --reload --port 8317
 | 标签 | 描述 | 影响 | 计划 |
 |---|---|---|---|
 | D1 | `config.py` 还是纯 `dataclass` + `os.getenv`，未迁 `pydantic-settings` | 类型校验靠自己 cast | 低优，收益有限，有精力再做 |
-| D3 | 无单元测试骨架 | 重构要靠 deploy 验证 | 想加 CI 时优先补 |
+| D3 | 已有基础单测覆盖 tool-call bridge（OpenAI/Anthropic，stream + non-stream），但整体测试矩阵仍不完整 | 回归仍依赖手工验证与定向测试 | 后续补充会话复用、背压、鉴权和异常路径用例 |
 | Docker non-root | 容器还是 root 跑 | 容器逃逸时影响宿主 | 需要加 `gosu` + chown entrypoint，涉及数据迁移，谨慎推进 |
 | ADMIN_TOKEN 轮换 | 没有过期机制，只能重启 | 自用场景不影响 | 接 Vault / sops 时一并做 |
 | Lingma 版本漂移 | 新版 Lingma 改 LSP 方法或新增必需 cache 文件时会无声崩 | 注入失败会 fallback，但 chat 不回话题型的错误不易定位 | 加一个 `/internal/smoke` 端点做端到端自检 |

README.md

@@ -221,7 +221,8 @@ curl -N http://127.0.0.1:8317/v1/messages \
 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。
+- **多模态**：`image` 块会被降级为 `[image]` 占位符（Lingma 不支持 vision）。
+- **工具事件桥接**：当 Lingma 上游返回 `tool` 事件时，网关会输出为 OpenAI `tool_calls`（含 stream/non-stream）和 Anthropic `tool_use`/`tool_result` blocks（含 stream/non-stream）；但请求侧 `tools`/`tool_choice` 仍不会透传到 Lingma。
 - **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`）

app/lingma_client.py

@@ -9,7 +9,7 @@ import subprocess
 import time
 import uuid
 from pathlib import Path
-from typing import AsyncIterator, Callable, Optional
+from typing import Any, AsyncIterator, Callable, Optional
 
 import websockets
 
@@ -103,6 +103,58 @@ class LspWsRpcClient:
         self._on_disconnect = on_disconnect
         self._closed = False
 
+    @staticmethod
+    def _extract_tool_event(params: dict[str, Any]) -> dict[str, Any] | None:
+        candidates: list[dict[str, Any]] = []
+        if isinstance(params.get("toolCall"), dict):
+            candidates.append(params["toolCall"])
+        if isinstance(params.get("tool_call"), dict):
+            candidates.append(params["tool_call"])
+        if isinstance(params.get("tool"), dict):
+            candidates.append(params["tool"])
+        data = params.get("data")
+        if isinstance(data, dict):
+            if isinstance(data.get("toolCall"), dict):
+                candidates.append(data["toolCall"])
+            if isinstance(data.get("tool_call"), dict):
+                candidates.append(data["tool_call"])
+            if isinstance(data.get("tool"), dict):
+                candidates.append(data["tool"])
+
+        if not candidates:
+            return None
+
+        raw = candidates[0]
+        tool_id = (
+            raw.get("toolCallId")
+            or raw.get("tool_call_id")
+            or raw.get("id")
+            or params.get("toolCallId")
+            or params.get("tool_call_id")
+        )
+        name = raw.get("name") or raw.get("toolName") or raw.get("tool_name")
+        call_input = raw.get("input")
+        if call_input is None:
+            call_input = raw.get("arguments")
+        if call_input is None:
+            call_input = raw.get("args")
+
+        result_payload = raw.get("result")
+        if result_payload is None:
+            result_payload = params.get("result")
+        if result_payload is None and isinstance(data, dict):
+            result_payload = data.get("result")
+
+        if not tool_id:
+            return None
+
+        return {
+            "id": str(tool_id),
+            "name": str(name or "tool"),
+            "input": call_input if call_input is not None else {},
+            "result": result_payload,
+        }
+
     async def start(self):
         self._reader_task = asyncio.create_task(self._reader_loop())
 
@@ -185,7 +237,16 @@ class LspWsRpcClient:
                     stream["parts"].append(text)
                     if stream["first_chunk_at"] is None:
                         stream["first_chunk_at"] = time.monotonic()
-                    stream["chunks"].put_nowait(text)
+                    stream["chunks"].put_nowait({"type": "text", "text": text})
+
+        if method in {"tool/call/sync", "tool/invoke", "tool/call/approve"}:
+            req_id = params.get("requestId")
+            stream = self._chat_streams.get(req_id)
+            if stream is not None:
+                tool_event = self._extract_tool_event(params)
+                if tool_event is not None:
+                    stream["tool_events"].append(tool_event)
+                    stream["chunks"].put_nowait({"type": "tool", "tool": tool_event})
 
         if method == "chat/finish":
             req_id = params.get("requestId")
@@ -224,6 +285,7 @@ class LspWsRpcClient:
             "chunks": asyncio.Queue(),
             "done": asyncio.Event(),
             "finish": None,
+            "tool_events": [],
             "started_at": time.monotonic(),
             "first_chunk_at": None,
             "finish_at": None,
@@ -239,7 +301,7 @@ class LspWsRpcClient:
             with contextlib.suppress(Exception):
                 stream["chunks"].put_nowait(None)
 
-    async def consume_stream(self, request_id: str, timeout: float) -> AsyncIterator[str]:
+    async def consume_stream(self, request_id: str, timeout: float) -> AsyncIterator[dict[str, Any]]:
         stream = self._chat_streams.get(request_id)
         if stream is None:
             return
@@ -266,6 +328,7 @@ class LspWsRpcClient:
             "finish": stream.get("finish") or {},
             "firstTokenLatencyMs": first_ms,
             "totalLatencyMs": total_ms,
+            "toolEvents": stream.get("tool_events") or [],
         }
 
 
@@ -722,8 +785,12 @@ class LingmaGatewayClient:
         session_id: str | None = None,
         is_reply: bool = False,
         out_meta: dict | None = None,
-    ) -> AsyncIterator[str]:
-        """Stream `chat/answer` chunks.
+    ) -> AsyncIterator[dict[str, Any]]:
+        """Stream chat events.
+
+        Yields structured events:
+          * {"type": "text", "text": "..."}
+          * {"type": "tool", "tool": {...}}
 
         If `out_meta` is provided, the final `chat/finish` payload's sessionId
         (and the raw finish dict) is written into it when the stream ends or is
@@ -739,10 +806,10 @@ class LingmaGatewayClient:
         self.rpc.create_stream(request_id)
         try:
             await self._kick_chat_ask(payload)
-            async for chunk in self.rpc.consume_stream(
+            async for event in self.rpc.consume_stream(
                 request_id, timeout=max(60.0, self.rpc_timeout + 60.0)
             ):
-                yield chunk
+                yield event
         finally:
             # Runs on normal completion, exception, or consumer GeneratorExit (client disconnect).
             if out_meta is not None:
@@ -753,6 +820,7 @@ class LingmaGatewayClient:
                     out_meta["finish"] = finish
                     out_meta["request_id"] = request_id
                     out_meta["chars"] = len(stream_result.get("text") or "")
+                    out_meta["tool_events"] = stream_result.get("toolEvents") or []
                 except Exception:
                     pass
             self.rpc.pop_stream(request_id)

app/main.py

@@ -6,6 +6,7 @@ import json
 import time
 import uuid
 from contextlib import asynccontextmanager
+from typing import Any
 
 from fastapi import Depends, FastAPI, HTTPException, Request
 from fastapi.responses import JSONResponse, StreamingResponse
@@ -350,6 +351,78 @@ def _include_usage(stream_options: dict | None) -> bool:
     return bool(stream_options.get("include_usage"))
 
 
+def _stream_event_type(event: Any) -> str:
+    if isinstance(event, dict):
+        t = event.get("type")
+        if t in {"text", "tool"}:
+            return t
+    return "text"
+
+
+def _stream_text(event: Any) -> str:
+    if isinstance(event, dict):
+        if event.get("type") == "text":
+            text = event.get("text")
+            if isinstance(text, str):
+                return text
+        return ""
+    if isinstance(event, str):
+        return event
+    return ""
+
+
+def _stream_tool_event(event: Any) -> dict[str, Any] | None:
+    if isinstance(event, dict) and event.get("type") == "tool":
+        tool = event.get("tool")
+        if isinstance(tool, dict):
+            return tool
+    return None
+
+
+def _json_string(value: Any) -> str:
+    if isinstance(value, str):
+        return value
+    try:
+        return json.dumps(value if value is not None else {}, ensure_ascii=False)
+    except Exception:
+        return "{}"
+
+
+def _openai_tool_call(tool: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "id": str(tool.get("id") or f"call_{uuid.uuid4().hex}"),
+        "type": "function",
+        "function": {
+            "name": str(tool.get("name") or "tool"),
+            "arguments": _json_string(tool.get("input")),
+        },
+    }
+
+
+def _anthropic_tool_use_block(tool: dict[str, Any]) -> dict[str, Any]:
+    return {
+        "type": "tool_use",
+        "id": str(tool.get("id") or f"toolu_{uuid.uuid4().hex}"),
+        "name": str(tool.get("name") or "tool"),
+        "input": tool.get("input") if tool.get("input") is not None else {},
+    }
+
+
+def _anthropic_tool_result_block(tool: dict[str, Any]) -> dict[str, Any] | None:
+    if "result" not in tool:
+        return None
+    result = tool.get("result")
+    if isinstance(result, str):
+        content: Any = result
+    else:
+        content = _json_string(result)
+    return {
+        "type": "tool_result",
+        "tool_use_id": str(tool.get("id") or ""),
+        "content": content,
+    }
+
+
 @app.post("/v1/chat/completions", dependencies=[Depends(auth_guard)])
 async def v1_chat_completions(req: ChatCompletionsRequest, request: Request):
     p = _require_pool()
@@ -485,7 +558,10 @@ async def v1_chat_completions(req: ChatCompletionsRequest, request: Request):
                         is_reply=is_reply,
                         out_meta=_meta,
                     ):
-                        completion_tokens_holder["n"] += estimate_tokens(chunk)
+                        if _stream_event_type(chunk) == "tool":
+                            tool = _stream_tool_event(chunk)
+                            if not tool:
+                                continue
                             payload = {
                                 "id": completion_id,
                                 "object": "chat.completion.chunk",
@@ -494,7 +570,34 @@ async def v1_chat_completions(req: ChatCompletionsRequest, request: Request):
                                 "choices": [
                                     {
                                         "index": 0,
-                                    "delta": {"content": chunk},
+                                        "delta": {
+                                            "tool_calls": [
+                                                {
+                                                    "index": 0,
+                                                    **_openai_tool_call(tool),
+                                                }
+                                            ]
+                                        },
+                                        "finish_reason": None,
+                                    }
+                                ],
+                            }
+                            yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
+                            continue
+
+                        text = _stream_text(chunk)
+                        if not text:
+                            continue
+                        completion_tokens_holder["n"] += estimate_tokens(text)
+                        payload = {
+                            "id": completion_id,
+                            "object": "chat.completion.chunk",
+                            "created": created,
+                            "model": model,
+                            "choices": [
+                                {
+                                    "index": 0,
+                                    "delta": {"content": text},
                                     "finish_reason": None,
                                 }
                             ],
@@ -596,6 +699,13 @@ async def v1_chat_completions(req: ChatCompletionsRequest, request: Request):
             sid = result.get("sessionId")
             if sid:
                 await session_cache.put(write_key, sid, inst.name)
+        tool_events = result.get("toolEvents") or []
+        message_content = result.get("text") or ""
+        tool_calls: list[dict[str, Any]] = []
+        if isinstance(tool_events, list):
+            for item in tool_events:
+                if isinstance(item, dict):
+                    tool_calls.append(_openai_tool_call(item))
         response = ChatCompletionResponse(
             id=f"chatcmpl-{uuid.uuid4().hex}",
             created=int(time.time()),
@@ -604,10 +714,15 @@ async def v1_chat_completions(req: ChatCompletionsRequest, request: Request):
                 ChatCompletionChoice(
                     index=0,
                     finish_reason="stop",
-                    message={"role": "assistant", "content": result.get("text") or ""},
+                    message={
+                        "role": "assistant",
+                        "content": message_content,
+                        "tool_calls": tool_calls or None,
+                    },
                 )
             ],
         )
+
         data = response.model_dump()
         data["latency"] = {
             "first_token_ms": result.get("firstTokenLatencyMs"),
@@ -810,6 +925,8 @@ async def v1_messages(req: AnthropicMessagesRequest, request: Request):
 
             async def event_stream(_ticket=ticket, _inst=inst, _meta=stream_meta):
                 success = False
+                block_index = 0
+                text_block_open = False
                 try:
                     # 1) message_start — Anthropic SDKs read this first to get
                     #    the message envelope (id/model/initial usage).
@@ -833,17 +950,6 @@ async def v1_messages(req: AnthropicMessagesRequest, request: Request):
                     }
                     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,
@@ -852,22 +958,79 @@ async def v1_messages(req: AnthropicMessagesRequest, request: Request):
                         is_reply=is_reply,
                         out_meta=_meta,
                     ):
-                        if not chunk:
+                        if _stream_event_type(chunk) == "tool":
+                            if text_block_open:
+                                yield _sse(
+                                    "content_block_stop",
+                                    {"type": "content_block_stop", "index": block_index},
+                                )
+                                block_index += 1
+                                text_block_open = False
+
+                            tool = _stream_tool_event(chunk)
+                            if not tool:
                                 continue
-                        completion_tokens_holder["n"] += estimate_tokens(chunk)
+
+                            tool_use_block = _anthropic_tool_use_block(tool)
+                            yield _sse(
+                                "content_block_start",
+                                {
+                                    "type": "content_block_start",
+                                    "index": block_index,
+                                    "content_block": tool_use_block,
+                                },
+                            )
+                            yield _sse(
+                                "content_block_stop",
+                                {"type": "content_block_stop", "index": block_index},
+                            )
+                            block_index += 1
+
+                            tool_result_block = _anthropic_tool_result_block(tool)
+                            if tool_result_block is not None:
+                                yield _sse(
+                                    "content_block_start",
+                                    {
+                                        "type": "content_block_start",
+                                        "index": block_index,
+                                        "content_block": tool_result_block,
+                                    },
+                                )
+                                yield _sse(
+                                    "content_block_stop",
+                                    {"type": "content_block_stop", "index": block_index},
+                                )
+                                block_index += 1
+                            continue
+
+                        text = _stream_text(chunk)
+                        if not text:
+                            continue
+                        completion_tokens_holder["n"] += estimate_tokens(text)
+                        if not text_block_open:
+                            yield _sse(
+                                "content_block_start",
+                                {
+                                    "type": "content_block_start",
+                                    "index": block_index,
+                                    "content_block": {"type": "text", "text": ""},
+                                },
+                            )
+                            text_block_open = True
+
                         yield _sse(
                             "content_block_delta",
                             {
                                 "type": "content_block_delta",
-                                "index": 0,
-                                "delta": {"type": "text_delta", "text": chunk},
+                                "index": block_index,
+                                "delta": {"type": "text_delta", "text": text},
                             },
                         )
 
-                    # 4) content_block_stop closes the single text block.
+                    if text_block_open:
                         yield _sse(
                             "content_block_stop",
-                        {"type": "content_block_stop", "index": 0},
+                            {"type": "content_block_stop", "index": block_index},
                         )
 
                     # 5) message_delta carries the terminal stop_reason and
@@ -972,12 +1135,25 @@ async def v1_messages(req: AnthropicMessagesRequest, request: Request):
             if sid:
                 await session_cache.put(write_key, sid, inst.name)
 
+        content_blocks: list[dict[str, Any]] = []
+        if text:
+            content_blocks.append({"type": "text", "text": text})
+        tool_events = result.get("toolEvents") or []
+        if isinstance(tool_events, list):
+            for item in tool_events:
+                if not isinstance(item, dict):
+                    continue
+                content_blocks.append(_anthropic_tool_use_block(item))
+                tool_result = _anthropic_tool_result_block(item)
+                if tool_result is not None:
+                    content_blocks.append(tool_result)
+
         response_body: dict = {
             "id": message_id,
             "type": "message",
             "role": "assistant",
             "model": model,
-            "content": [{"type": "text", "text": text}],
+            "content": content_blocks,
             "stop_reason": _anthropic_stop_reason(completion_tokens, req.max_tokens),
             "stop_sequence": None,
             "usage": {

tests/test_tool_call_bridge.py

@@ -0,0 +1,292 @@
+from __future__ import annotations
+
+import json
+import sys
+import types
+import unittest
+from unittest.mock import AsyncMock, patch
+
+# app.main imports playwright via auto_login; tests don't exercise that path.
+# Inject a lightweight stub so unit tests run without installing playwright.
+_playwright = types.ModuleType("playwright")
+_playwright_async = types.ModuleType("playwright.async_api")
+
+
+class _StubPlaywrightTimeoutError(Exception):
+    pass
+
+
+async def _stub_async_playwright():
+    raise RuntimeError("playwright is stubbed in unit tests")
+
+
+_playwright_async.TimeoutError = _StubPlaywrightTimeoutError
+_playwright_async.async_playwright = _stub_async_playwright
+sys.modules.setdefault("playwright", _playwright)
+sys.modules.setdefault("playwright.async_api", _playwright_async)
+
+from starlette.requests import Request
+
+from app.anthropic_schema import AnthropicMessagesRequest
+from app.openai_schema import ChatCompletionsRequest
+import app.main as main
+
+
+class _FakeTicket:
+    def __init__(self) -> None:
+        self.released = False
+
+    def release(self) -> None:
+        self.released = True
+
+
+class _FakeGuard:
+    def __init__(self) -> None:
+        self.in_flight = 0
+
+    async def try_acquire(self) -> _FakeTicket:
+        return _FakeTicket()
+
+
+class _FakeClient:
+    def __init__(self, *, stream_events: list[dict], complete_result: dict) -> None:
+        self._stream_events = stream_events
+        self._complete_result = complete_result
+
+    async def query_models(self) -> dict:
+        return {
+            "chat": [
+                {
+                    "key": "org_auto",
+                    "displayName": "Auto",
+                }
+            ]
+        }
+
+    async def chat_complete(self, *args, **kwargs) -> dict:
+        return self._complete_result
+
+    async def chat_stream(self, *args, **kwargs):
+        out_meta = kwargs.get("out_meta")
+        if isinstance(out_meta, dict):
+            out_meta["session_id"] = "sess-stream"
+        for event in self._stream_events:
+            yield event
+
+
+class _FakeInstance:
+    def __init__(self, client: _FakeClient) -> None:
+        self.name = "inst-test"
+        self.client = client
+        self.in_flight = 0
+
+
+class _FakePool:
+    def __init__(self, inst: _FakeInstance) -> None:
+        self._inst = inst
+
+    def pick(self, affinity_key: str | None = None) -> _FakeInstance:
+        return self._inst
+
+
+def _make_request(path: str, headers: dict[str, str] | None = None) -> Request:
+    header_pairs = []
+    for k, v in (headers or {}).items():
+        header_pairs.append((k.lower().encode("latin-1"), v.encode("latin-1")))
+    scope = {
+        "type": "http",
+        "http_version": "1.1",
+        "method": "POST",
+        "scheme": "http",
+        "path": path,
+        "raw_path": path.encode("latin-1"),
+        "query_string": b"",
+        "headers": header_pairs,
+        "client": ("testclient", 12345),
+        "server": ("testserver", 80),
+        "root_path": "",
+    }
+    return Request(scope)
+
+
+async def _collect_stream(response) -> str:
+    chunks: list[str] = []
+    async for part in response.body_iterator:
+        if isinstance(part, bytes):
+            chunks.append(part.decode("utf-8"))
+        else:
+            chunks.append(str(part))
+    return "".join(chunks)
+
+
+class ToolCallBridgeTests(unittest.IsolatedAsyncioTestCase):
+    async def test_openai_non_stream_bridges_tool_calls(self) -> None:
+        fake_client = _FakeClient(
+            stream_events=[],
+            complete_result={
+                "text": "done",
+                "toolEvents": [
+                    {
+                        "id": "call_123",
+                        "name": "search_docs",
+                        "input": {"query": "gateway"},
+                        "result": {"ok": True},
+                    }
+                ],
+                "sessionId": "sess-1",
+                "firstTokenLatencyMs": 12,
+                "totalLatencyMs": 34,
+            },
+        )
+        req = ChatCompletionsRequest(
+            model="org_auto",
+            messages=[{"role": "user", "content": "hi"}],
+            stream=False,
+        )
+
+        with (
+            patch.object(main, "pool", _FakePool(_FakeInstance(fake_client))),
+            patch.object(main, "chat_guard", _FakeGuard()),
+            patch.object(main, "_ensure_instance_logged_in", AsyncMock(return_value={"id": "u"})),
+            patch.object(main.stats_collector, "record_chat", AsyncMock(return_value=None)),
+        ):
+            response = await main.v1_chat_completions(req, _make_request("/v1/chat/completions"))
+
+        payload = json.loads(response.body)
+        message = payload["choices"][0]["message"]
+        self.assertEqual(message["content"], "done")
+        self.assertIsInstance(message["tool_calls"], list)
+        self.assertEqual(message["tool_calls"][0]["function"]["name"], "search_docs")
+        self.assertEqual(
+            json.loads(message["tool_calls"][0]["function"]["arguments"]),
+            {"query": "gateway"},
+        )
+
+    async def test_openai_stream_bridges_tool_and_text_events(self) -> None:
+        fake_client = _FakeClient(
+            stream_events=[
+                {
+                    "type": "tool",
+                    "tool": {
+                        "id": "call_stream_1",
+                        "name": "read_file",
+                        "input": {"path": "README.md"},
+                    },
+                },
+                {"type": "text", "text": "hello"},
+            ],
+            complete_result={},
+        )
+        req = ChatCompletionsRequest(
+            model="org_auto",
+            messages=[{"role": "user", "content": "hi"}],
+            stream=True,
+            stream_options={"include_usage": True},
+        )
+
+        with (
+            patch.object(main, "pool", _FakePool(_FakeInstance(fake_client))),
+            patch.object(main, "chat_guard", _FakeGuard()),
+            patch.object(main, "_ensure_instance_logged_in", AsyncMock(return_value={"id": "u"})),
+            patch.object(main.stats_collector, "record_chat", AsyncMock(return_value=None)),
+        ):
+            response = await main.v1_chat_completions(req, _make_request("/v1/chat/completions"))
+            body = await _collect_stream(response)
+
+        self.assertIn('"tool_calls"', body)
+        self.assertIn('"content": "hello"', body)
+        self.assertIn('"usage"', body)
+        self.assertIn("data: [DONE]", body)
+
+    async def test_anthropic_non_stream_bridges_tool_blocks(self) -> None:
+        fake_client = _FakeClient(
+            stream_events=[],
+            complete_result={
+                "text": "ok",
+                "toolEvents": [
+                    {
+                        "id": "toolu_1",
+                        "name": "lookup",
+                        "input": {"k": "v"},
+                        "result": {"value": 1},
+                    }
+                ],
+                "sessionId": "sess-2",
+            },
+        )
+        req = AnthropicMessagesRequest(
+            model="claude-3-5-sonnet-20241022",
+            max_tokens=256,
+            messages=[{"role": "user", "content": "hi"}],
+            stream=False,
+        )
+
+        with (
+            patch.object(main, "pool", _FakePool(_FakeInstance(fake_client))),
+            patch.object(main, "chat_guard", _FakeGuard()),
+            patch.object(main, "_ensure_instance_logged_in", AsyncMock(return_value={"id": "u"})),
+            patch.object(main.stats_collector, "record_chat", AsyncMock(return_value=None)),
+            patch.object(main.settings, "api_keys", ["test-key"]),
+        ):
+            response = await main.v1_messages(
+                req,
+                _make_request(
+                    "/v1/messages",
+                    headers={"x-api-key": "test-key", "anthropic-version": "2023-06-01"},
+                ),
+            )
+
+        payload = json.loads(response.body)
+        types = [item["type"] for item in payload["content"]]
+        self.assertEqual(types, ["text", "tool_use", "tool_result"])
+        self.assertEqual(payload["content"][1]["name"], "lookup")
+        self.assertEqual(payload["content"][2]["tool_use_id"], "toolu_1")
+
+    async def test_anthropic_stream_bridges_tool_and_text_events(self) -> None:
+        fake_client = _FakeClient(
+            stream_events=[
+                {
+                    "type": "tool",
+                    "tool": {
+                        "id": "toolu_stream_1",
+                        "name": "read",
+                        "input": {"file": "a.txt"},
+                        "result": "done",
+                    },
+                },
+                {"type": "text", "text": "world"},
+            ],
+            complete_result={},
+        )
+        req = AnthropicMessagesRequest(
+            model="claude-3-5-sonnet-20241022",
+            max_tokens=256,
+            messages=[{"role": "user", "content": "hi"}],
+            stream=True,
+        )
+
+        with (
+            patch.object(main, "pool", _FakePool(_FakeInstance(fake_client))),
+            patch.object(main, "chat_guard", _FakeGuard()),
+            patch.object(main, "_ensure_instance_logged_in", AsyncMock(return_value={"id": "u"})),
+            patch.object(main.stats_collector, "record_chat", AsyncMock(return_value=None)),
+            patch.object(main.settings, "api_keys", ["test-key"]),
+        ):
+            response = await main.v1_messages(
+                req,
+                _make_request(
+                    "/v1/messages",
+                    headers={"x-api-key": "test-key", "anthropic-version": "2023-06-01"},
+                ),
+            )
+            body = await _collect_stream(response)
+
+        self.assertIn("event: message_start", body)
+        self.assertIn('"type": "tool_use"', body)
+        self.assertIn('"type": "tool_result"', body)
+        self.assertIn('"type": "text_delta"', body)
+        self.assertIn("event: message_stop", body)
+
+
+if __name__ == "__main__":
+    unittest.main()