Fix Anthropic Messages API compatibility (#25876)

Co-authored-by: Jairo David Campaña Rosero <jairocampana10001@gmail.com>
Co-authored-by: Karan Bansal <3264937+karanb192@users.noreply.github.com>
Co-authored-by: eason <85663565+mango766@users.noreply.github.com>
Co-authored-by: Yufeng He <40085740+he-yufeng@users.noreply.github.com>
Co-authored-by: qingchanghan <17794466+qingchanghan@users.noreply.github.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: Ajay Anubolu <124525760+AjAnubolu@users.noreply.github.com>
Co-authored-by: Ravitez Dondeti <13931987+dondetir@users.noreply.github.com>
Co-authored-by: Ratish P <114130421+Ratish1@users.noreply.github.com>
Co-authored-by: Xiaoshuai Zhang <15795935+jetd1@users.noreply.github.com>
Co-authored-by: Ricardo-M-L <69202550+Ricardo-M-L@users.noreply.github.com>
Co-authored-by: Xinyuan Tong <xinyuan.tong@radixark.ai>

python/sglang/srt/entrypoints/anthropic/protocol.py

@@ -1,71 +1,140 @@
-"""Pydantic models for Anthropic Messages API protocol"""
+"""Pydantic models for Anthropic Messages API protocol.
+
+Mirrors the shape of the official Anthropic Python SDK
+(``anthropic-sdk-python``): ``ContentBlock``, ``Tool``, ``MessageStreamEvent``
+and ``ContentBlockDelta`` are discriminated unions over the ``type`` field,
+so each variant carries only the fields it actually uses.
+"""
 
 import uuid
-from typing import Any, Literal, Optional
+from typing import Annotated, Any, Literal, Optional, Union
 
-from pydantic import BaseModel, Field, field_validator
+from pydantic import (
+    BaseModel,
+    Discriminator,
+    Field,
+    NonNegativeInt,
+    Tag,
+    field_validator,
+    model_validator,
+)
 
 
 class AnthropicError(BaseModel):
-    """Error structure for Anthropic API"""
+    """Error structure for Anthropic API."""
 
     type: str
     message: str
 
 
 class AnthropicErrorResponse(BaseModel):
-    """Error response structure for Anthropic API"""
+    """Error response structure for Anthropic API."""
 
     type: Literal["error"] = "error"
     error: AnthropicError
 
 
 class AnthropicUsage(BaseModel):
-    """Token usage information"""
+    """Token usage information.
 
-    input_tokens: int
+    ``input_tokens``/``output_tokens`` are ``Optional`` because Anthropic's
-    output_tokens: int
+    streaming ``message_delta`` event omits ``input_tokens`` (the spec
-    cache_creation_input_tokens: Optional[int] = None
+    requires it only on ``message_start``). Non-streaming responses set both.
-    cache_read_input_tokens: Optional[int] = None
+    """
+
+    input_tokens: Optional[NonNegativeInt] = None
+    output_tokens: Optional[NonNegativeInt] = None
+    cache_creation_input_tokens: Optional[NonNegativeInt] = None
+    cache_read_input_tokens: Optional[NonNegativeInt] = None
 
 
-class AnthropicContentBlock(BaseModel):
+# ---------- Content blocks (discriminated by ``type``) ----------
-    """Content block in message"""
 
-    type: Literal[
+
-        "text",
+class TextBlock(BaseModel):
-        "image",
+    type: Literal["text"] = "text"
-        "tool_use",
+    text: str
-        "tool_result",
+
-        "tool_reference",
+
-        "thinking",
+class ImageBlock(BaseModel):
-        "redacted_thinking",
+    type: Literal["image"] = "image"
-    ]
+    # Kept loosely typed for compat with both base64 and URL sources; the
-    text: Optional[str] = None
+    # serving layer normalises to OpenAI ``image_url`` parts.
-    # For image content
+    source: Optional[Union[dict[str, Any], str]] = None
-    source: Optional[dict[str, Any]] = None
+
-    # For tool use/result
+
-    id: Optional[str] = None
+class ToolUseBlock(BaseModel):
+    type: Literal["tool_use"] = "tool_use"
+    id: str
+    name: str
+    input: dict[str, Any] = Field(default_factory=dict)
+
+
+class ToolResultBlock(BaseModel):
+    type: Literal["tool_result"] = "tool_result"
     tool_use_id: Optional[str] = None
-    name: Optional[str] = None
+    # Some legacy payloads use ``id`` instead of ``tool_use_id``.
-    input: Optional[dict[str, Any]] = None
+    id: Optional[str] = None
-    content: Optional[str | list[dict[str, Any]]] = None
+    content: Optional[Union[str, list["AnthropicContentBlock"]]] = None
     is_error: Optional[bool] = None
-    # For thinking content
+
-    thinking: Optional[str] = None
+
+class ToolReferenceBlock(BaseModel):
+    """sglang extension: references a deferred-loaded tool by name."""
+
+    type: Literal["tool_reference"] = "tool_reference"
+    name: Optional[str] = None
+    # Anthropic-style payloads sometimes use ``tool_name``; accept both.
+    tool_name: Optional[str] = None
+    id: Optional[str] = None
+
+
+class SearchResultBlock(BaseModel):
+    type: Literal["search_result"] = "search_result"
+    # ``source`` here is a URL/identifier string (unlike ImageBlock.source).
+    source: Optional[Union[str, dict[str, Any]]] = None
+    title: Optional[str] = None
+    content: Optional[list[dict[str, Any]]] = None
+
+
+class ThinkingBlock(BaseModel):
+    type: Literal["thinking"] = "thinking"
+    thinking: str
     signature: Optional[str] = None
 
 
+class RedactedThinkingBlock(BaseModel):
+    type: Literal["redacted_thinking"] = "redacted_thinking"
+    data: Optional[str] = None
+
+
+AnthropicContentBlock = Annotated[
+    Union[
+        TextBlock,
+        ImageBlock,
+        ToolUseBlock,
+        ToolResultBlock,
+        ToolReferenceBlock,
+        SearchResultBlock,
+        ThinkingBlock,
+        RedactedThinkingBlock,
+    ],
+    Field(discriminator="type"),
+]
+
+
 class AnthropicMessage(BaseModel):
-    """Message structure"""
-
     role: Literal["user", "assistant"]
-    content: str | list[AnthropicContentBlock]
+    content: Union[str, list[AnthropicContentBlock]]
 
 
-class AnthropicTool(BaseModel):
+# ---------- Tools (discriminated by ``type`` family) ----------
-    """Tool definition"""
 
+
+class AnthropicCustomTool(BaseModel):
+    """Custom tool defined by the API user — requires ``input_schema``."""
+
+    type: Optional[Literal["custom"]] = None  # absent or explicit "custom"
     name: str
     description: Optional[str] = None
     input_schema: dict[str, Any]
@@ -73,7 +142,7 @@ class AnthropicTool(BaseModel):
 
     @field_validator("input_schema")
     @classmethod
-    def validate_input_schema(cls, v):
+    def _ensure_object_schema(cls, v):
         if not isinstance(v, dict):
             raise ValueError("input_schema must be a dictionary")
         if "type" not in v:
@@ -81,31 +150,216 @@ class AnthropicTool(BaseModel):
         return v
 
 
+class AnthropicWebSearchTool(BaseModel):
+    """Anthropic ``web_search_*`` server tool family.
+
+    No client-side ``input_schema`` — Anthropic provides the backing
+    search implementation. Tag format is ``web_search_YYYYMMDD``.
+    """
+
+    type: str = Field(pattern=r"^web_search_\d{8}$")
+    name: Literal["web_search"] = "web_search"
+    description: Optional[str] = None
+    defer_loading: Optional[bool] = None
+    max_uses: Optional[int] = None
+    allowed_domains: Optional[list[str]] = None
+    blocked_domains: Optional[list[str]] = None
+
+
+class AnthropicComputerTool(BaseModel):
+    """Anthropic ``computer_*`` server tool family."""
+
+    type: str = Field(pattern=r"^computer_\d{8}$")
+    name: Literal["computer"] = "computer"
+    description: Optional[str] = None
+    defer_loading: Optional[bool] = None
+    display_width_px: Optional[int] = None
+    display_height_px: Optional[int] = None
+    display_number: Optional[int] = None
+
+
+class AnthropicBashTool(BaseModel):
+    """Anthropic ``bash_*`` server tool family."""
+
+    type: str = Field(pattern=r"^bash_\d{8}$")
+    name: Literal["bash"] = "bash"
+    description: Optional[str] = None
+    defer_loading: Optional[bool] = None
+
+
+class AnthropicTextEditorTool(BaseModel):
+    """Anthropic ``text_editor_*`` server tool family."""
+
+    type: str = Field(pattern=r"^text_editor_\d{8}$")
+    name: Literal["str_replace_editor", "str_replace_based_edit_tool"]
+    description: Optional[str] = None
+    defer_loading: Optional[bool] = None
+
+
+def _tool_discriminator(v) -> str:
+    """Pick the right tool variant from a dict or model instance.
+
+    Pydantic discriminators don't accept ``None`` as a tag, and custom
+    tools allow ``type`` to be absent. Map missing/``custom`` to
+    ``"custom"`` and prefix-match server-tool families.
+    """
+    if isinstance(v, dict):
+        t = v.get("type")
+    else:
+        t = getattr(v, "type", None)
+    if not t or t == "custom":
+        return "custom"
+    if t.startswith("web_search_"):
+        return "web_search"
+    if t.startswith("computer_"):
+        return "computer"
+    if t.startswith("bash_"):
+        return "bash"
+    if t.startswith("text_editor_"):
+        return "text_editor"
+    return "custom"
+
+
+AnthropicTool = Annotated[
+    Union[
+        Annotated[AnthropicCustomTool, Tag("custom")],
+        Annotated[AnthropicWebSearchTool, Tag("web_search")],
+        Annotated[AnthropicComputerTool, Tag("computer")],
+        Annotated[AnthropicBashTool, Tag("bash")],
+        Annotated[AnthropicTextEditorTool, Tag("text_editor")],
+    ],
+    Discriminator(_tool_discriminator),
+]
+
+
+def is_server_tool(tool) -> bool:
+    """Return True for Anthropic built-in server-side tools."""
+    return isinstance(
+        tool,
+        (
+            AnthropicWebSearchTool,
+            AnthropicComputerTool,
+            AnthropicBashTool,
+            AnthropicTextEditorTool,
+        ),
+    )
+
+
 class AnthropicToolChoice(BaseModel):
-    """Tool Choice definition"""
+    """Tool choice strategy."""
 
     type: Literal["auto", "any", "tool", "none"]
     name: Optional[str] = None
 
 
+class AnthropicThinkingParam(BaseModel):
+    """Anthropic extended-thinking control on the request.
+
+    Mirrors the Anthropic SDK's ``ThinkingConfigParam`` discriminated
+    union of three variants — see ``anthropic-sdk-python``'s
+    ``thinking_config_{enabled,disabled,adaptive}_param.py``:
+
+    * ``enabled`` requires ``budget_tokens`` (≥1024) and accepts
+      ``display``.
+    * ``disabled`` accepts no other fields.
+    * ``adaptive`` (Claude 4.7) accepts ``display`` but not
+      ``budget_tokens``.
+
+    The serving layer treats ``adaptive`` identically to ``enabled``
+    because the local OpenAI-compatible backend has no auto-throttle
+    equivalent. ``budget_tokens`` is accepted on ``enabled`` for SDK
+    compatibility but the backend has no hard-cap knob to honor it; the
+    serving layer logs a WARNING so operators see that the requested
+    budget is not enforced. ``display="omitted"`` is accepted but
+    similarly cannot suppress reasoning mid-stream and is logged.
+    """
+
+    type: Literal["enabled", "disabled", "adaptive"]
+    budget_tokens: Optional[int] = None
+    display: Optional[Literal["summarized", "omitted"]] = None
+
+    @model_validator(mode="after")
+    def _validate_thinking_shape(self):
+        # Cross-field rules mirror the SDK's three discriminated variants.
+        if self.type == "enabled":
+            if self.budget_tokens is None:
+                raise ValueError(
+                    "thinking.budget_tokens is required when "
+                    "thinking.type is 'enabled'"
+                )
+            if self.budget_tokens < 1024:
+                raise ValueError(
+                    "thinking.budget_tokens must be >= 1024 "
+                    "(got {})".format(self.budget_tokens)
+                )
+        elif self.type == "disabled":
+            if self.budget_tokens is not None:
+                raise ValueError(
+                    "thinking.budget_tokens is not allowed when "
+                    "thinking.type is 'disabled'"
+                )
+            if self.display is not None:
+                raise ValueError(
+                    "thinking.display is not allowed when "
+                    "thinking.type is 'disabled'"
+                )
+        elif self.type == "adaptive":
+            if self.budget_tokens is not None:
+                raise ValueError(
+                    "thinking.budget_tokens is not allowed when "
+                    "thinking.type is 'adaptive'"
+                )
+        return self
+
+
+class AnthropicTaskBudget(BaseModel):
+    """Claude 4.7 ``output_config.task_budget`` — soft hint, not a hard cap.
+
+    Mirrors ``BetaTokenTaskBudgetParam`` in the Anthropic SDK: ``total``
+    and ``type`` are required; ``remaining`` is the client-tracked
+    countdown used for compaction. The hard cap on generation is still
+    ``max_tokens``; we never enforce ``task_budget`` ourselves.
+    """
+
+    type: Literal["tokens"]
+    total: int = Field(gt=0)
+    remaining: Optional[int] = Field(default=None, ge=0)
+
+
+class AnthropicOutputConfig(BaseModel):
+    """Claude 4.7 ``output_config`` block.
+
+    ``effort`` maps to the OpenAI ``reasoning_effort`` knob (``xhigh`` →
+    ``max`` because the OpenAI Literal does not include ``xhigh``).
+    ``task_budget`` is propagated as a custom-param hint.
+    """
+
+    effort: Optional[Literal["low", "medium", "high", "xhigh", "max"]] = None
+    task_budget: Optional[AnthropicTaskBudget] = None
+
+
 class AnthropicCountTokensRequest(BaseModel):
-    """Anthropic Count Tokens API request"""
+    """Anthropic count_tokens API request."""
 
     model: str
     messages: list[AnthropicMessage]
-    system: Optional[str | list[AnthropicContentBlock]] = None
+    system: Optional[Union[str, list[AnthropicContentBlock]]] = None
+    thinking: Optional[AnthropicThinkingParam] = None
     tool_choice: Optional[AnthropicToolChoice] = None
     tools: Optional[list[AnthropicTool]] = None
+    # Claude 4.7 / SDK-compatibility fields. Accepted but no-op on count.
+    output_config: Optional[AnthropicOutputConfig] = None
+    betas: Optional[list[str]] = None
 
 
 class AnthropicCountTokensResponse(BaseModel):
-    """Anthropic Count Tokens API response"""
+    """Anthropic count_tokens API response."""
 
     input_tokens: int
 
 
 class AnthropicMessagesRequest(BaseModel):
-    """Anthropic Messages API request"""
+    """Anthropic Messages API request."""
 
     model: str
     messages: list[AnthropicMessage]
@@ -113,65 +367,139 @@ class AnthropicMessagesRequest(BaseModel):
     metadata: Optional[dict[str, Any]] = None
     stop_sequences: Optional[list[str]] = None
     stream: Optional[bool] = False
-    system: Optional[str | list[AnthropicContentBlock]] = None
+    system: Optional[Union[str, list[AnthropicContentBlock]]] = None
     temperature: Optional[float] = None
+    thinking: Optional[AnthropicThinkingParam] = None
     tool_choice: Optional[AnthropicToolChoice] = None
     tools: Optional[list[AnthropicTool]] = None
     top_k: Optional[int] = None
     top_p: Optional[float] = None
+    # Claude 4.7 fields. The Anthropic SDK / Claude Code attach these even
+    # when targeting non-Anthropic backends, so the schema must accept them.
+    output_config: Optional[AnthropicOutputConfig] = None
+    betas: Optional[list[str]] = None
 
     @field_validator("model")
     @classmethod
-    def validate_model(cls, v):
+    def _validate_model(cls, v):
         if not v:
             raise ValueError("Model is required")
         return v
 
     @field_validator("max_tokens")
     @classmethod
-    def validate_max_tokens(cls, v):
+    def _validate_max_tokens(cls, v):
         if v <= 0:
             raise ValueError("max_tokens must be positive")
         return v
 
 
-class AnthropicDelta(BaseModel):
+# ---------- Stream deltas ----------
-    """Delta for streaming responses"""
+# Content-block deltas (discriminated by ``type``) vs message-end delta
+# (separate model; the wire format does not put ``type`` inside its payload).
 
-    type: Optional[Literal["text_delta", "input_json_delta"]] = None
-    text: Optional[str] = None
-    partial_json: Optional[str] = None
 
-    # Message delta fields
+class TextDelta(BaseModel):
+    type: Literal["text_delta"] = "text_delta"
+    text: str
+
+
+class InputJsonDelta(BaseModel):
+    type: Literal["input_json_delta"] = "input_json_delta"
+    partial_json: str
+
+
+class ThinkingDelta(BaseModel):
+    type: Literal["thinking_delta"] = "thinking_delta"
+    thinking: str
+
+
+class SignatureDelta(BaseModel):
+    type: Literal["signature_delta"] = "signature_delta"
+    signature: str
+
+
+AnthropicContentDelta = Annotated[
+    Union[TextDelta, InputJsonDelta, ThinkingDelta, SignatureDelta],
+    Field(discriminator="type"),
+]
+
+
+class AnthropicMessageEndDelta(BaseModel):
+    """Delta carried on ``message_delta`` events.
+
+    Anthropic's protocol does NOT put a ``type`` field inside this delta
+    payload — the SSE ``event:`` header already says ``message_delta``.
+    Stop reason and stop sequence are the only fields.
+    """
+
     stop_reason: Optional[
         Literal["end_turn", "max_tokens", "stop_sequence", "tool_use"]
     ] = None
     stop_sequence: Optional[str] = None
 
 
-class AnthropicStreamEvent(BaseModel):
+# ---------- Stream events (discriminated by ``type``) ----------
-    """Streaming event"""
 
-    type: Literal[
+
-        "message_start",
+class MessageStartEvent(BaseModel):
-        "message_delta",
+    type: Literal["message_start"] = "message_start"
-        "message_stop",
+    message: "AnthropicMessagesResponse"
-        "content_block_start",
+
-        "content_block_delta",
+
-        "content_block_stop",
+class MessageDeltaEvent(BaseModel):
-        "ping",
+    type: Literal["message_delta"] = "message_delta"
-        "error",
+    delta: AnthropicMessageEndDelta
+    usage: AnthropicUsage
+
+
+class MessageStopEvent(BaseModel):
+    type: Literal["message_stop"] = "message_stop"
+
+
+class ContentBlockStartEvent(BaseModel):
+    type: Literal["content_block_start"] = "content_block_start"
+    index: int
+    content_block: AnthropicContentBlock
+
+
+class ContentBlockDeltaEvent(BaseModel):
+    type: Literal["content_block_delta"] = "content_block_delta"
+    index: int
+    delta: AnthropicContentDelta
+
+
+class ContentBlockStopEvent(BaseModel):
+    type: Literal["content_block_stop"] = "content_block_stop"
+    index: int
+
+
+class PingEvent(BaseModel):
+    type: Literal["ping"] = "ping"
+
+
+class ErrorEvent(BaseModel):
+    type: Literal["error"] = "error"
+    error: AnthropicError
+
+
+AnthropicStreamEvent = Annotated[
+    Union[
+        MessageStartEvent,
+        MessageDeltaEvent,
+        MessageStopEvent,
+        ContentBlockStartEvent,
+        ContentBlockDeltaEvent,
+        ContentBlockStopEvent,
+        PingEvent,
+        ErrorEvent,
+    ],
+    Field(discriminator="type"),
 ]
-    message: Optional["AnthropicMessagesResponse"] = None
-    delta: Optional[AnthropicDelta] = None
-    content_block: Optional[AnthropicContentBlock] = None
-    index: Optional[int] = None
-    error: Optional[AnthropicError] = None
-    usage: Optional[AnthropicUsage] = None
 
 
 class AnthropicMessagesResponse(BaseModel):
-    """Anthropic Messages API response"""
+    """Anthropic Messages API response."""
 
     id: str = Field(default_factory=lambda: f"msg_{uuid.uuid4().hex}")
     type: Literal["message"] = "message"
@@ -183,3 +511,8 @@ class AnthropicMessagesResponse(BaseModel):
     ] = None
     stop_sequence: Optional[str] = None
     usage: Optional[AnthropicUsage] = None
+
+
+# Resolve forward references for nested types.
+ToolResultBlock.model_rebuild()
+MessageStartEvent.model_rebuild()

python/sglang/srt/entrypoints/http_server.py

@@ -425,13 +425,77 @@ from sglang.srt.entrypoints.v1_loads import router as v1_loads_router
 app.include_router(v1_loads_router)
 
 
+def _anthropic_validation_message(raw_errors) -> str:
+    """Render Pydantic-style errors for an Anthropic /v1/messages route.
+
+    Builds a short ``loc: msg`` digest that names the offending fields without
+    leaking file paths or Python internals (the default ``str(exc)`` includes
+    the dispatcher's ``File "/.../http_server.py"`` line).
+    """
+    parts: list[str] = []
+    for err in raw_errors or []:
+        loc = err.get("loc") or ()
+        if loc:
+            loc_str = ".".join(str(p) for p in loc if p not in ("body",))
+        else:
+            loc_str = ""
+        msg = (err.get("msg") or "").strip()
+        if loc_str and msg:
+            parts.append(f"{loc_str}: {msg}")
+        elif msg:
+            parts.append(msg)
+    text = "; ".join(parts) or "Invalid request"
+    if len(text) > 500:
+        text = text[:500] + "…"
+    return text
+
+
+def _anthropic_error_response(*, status_code: int, error_type: str, message: str):
+    """Anthropic-format error envelope: {"type":"error","error":{"type":...,"message":...}}."""
+    return ORJSONResponse(
+        status_code=status_code,
+        content={
+            "type": "error",
+            "error": {"type": error_type, "message": message},
+        },
+    )
+
+
 @app.exception_handler(HTTPException)
 async def validation_exception_handler(request: Request, exc: HTTPException):
     """Enrich HTTP exception with status code and other details.
 
     For /v1/responses, emit OpenAI-style nested error envelope:
     {"error": {"message": "...", "type": "...", "param": null, "code": <status>}}
+    For /v1/messages, emit Anthropic-style envelope so SDK clients can parse it.
     """
+    if request.url.path.startswith("/v1/messages"):
+        # Map HTTP status to Anthropic error.type; fall back to api_error.
+        anthropic_type = {
+            400: "invalid_request_error",
+            401: "authentication_error",
+            403: "permission_error",
+            404: "not_found_error",
+            413: "request_too_large",
+            422: "invalid_request_error",
+            429: "rate_limit_error",
+            500: "api_error",
+            502: "api_error",
+            503: "overloaded_error",
+            504: "api_error",
+        }.get(exc.status_code, "api_error")
+        # 5xx must never echo upstream detail (may contain stack/PII).
+        message = (
+            "Internal server error"
+            if exc.status_code >= 500
+            else (str(exc.detail) if exc.detail else "Request failed")
+        )
+        return _anthropic_error_response(
+            status_code=exc.status_code,
+            error_type=anthropic_type,
+            message=message,
+        )
+
     # adjust fmt for responses api
     if request.url.path.startswith("/v1/responses"):
         nested_error = {
@@ -458,8 +522,18 @@ async def validation_exception_handler(request: Request, exc: HTTPException):
 async def validation_exception_handler(request: Request, exc: RequestValidationError):
     """Override FastAPI's default 422 validation error with 400.
 
-    For /v1/responses, emit OpenAI-style nested error envelope; for other endpoints keep legacy format.
+    For /v1/messages, emit Anthropic-style envelope and scrub the message so
+    file paths or Python internals from the default ``str(exc)`` representation
+    never reach the client. For /v1/responses, keep OpenAI-style. Otherwise
+    use the legacy ErrorResponse shape.
     """
+    if request.url.path.startswith("/v1/messages"):
+        return _anthropic_error_response(
+            status_code=HTTPStatus.BAD_REQUEST.value,
+            error_type="invalid_request_error",
+            message=_anthropic_validation_message(exc.errors()),
+        )
+
     exc_str = str(exc)
     errors_str = str(exc.errors())
 
@@ -1067,9 +1141,11 @@ async def dump_expert_distribution_record_async():
 @auth_level(AuthLevel.ADMIN_OPTIONAL)
 async def update_weights_from_disk(obj: UpdateWeightFromDiskReqInput, request: Request):
     """Update the weights from disk inplace without re-launching the server."""
-    success, message, num_paused_requests = (
+    (
-        await _global_state.tokenizer_manager.update_weights_from_disk(obj, request)
+        success,
-    )
+        message,
+        num_paused_requests,
+    ) = await _global_state.tokenizer_manager.update_weights_from_disk(obj, request)
 
     content = {
         "success": success,
@@ -1093,11 +1169,12 @@ async def update_weights_from_disk(obj: UpdateWeightFromDiskReqInput, request: R
 async def init_weights_send_group_for_remote_instance(
     obj: InitWeightsSendGroupForRemoteInstanceReqInput, request: Request
 ):
-    success, message = (
+    (
-        await _global_state.tokenizer_manager.init_weights_send_group_for_remote_instance(
+        success,
+        message,
+    ) = await _global_state.tokenizer_manager.init_weights_send_group_for_remote_instance(
         obj, request
     )
-    )
     content = {"success": success, "message": message}
     if success:
         return ORJSONResponse(content, status_code=200)
@@ -1110,11 +1187,12 @@ async def init_weights_send_group_for_remote_instance(
 async def send_weights_to_remote_instance(
     obj: SendWeightsToRemoteInstanceReqInput, request: Request
 ):
-    success, message = (
+    (
-        await _global_state.tokenizer_manager.send_weights_to_remote_instance(
+        success,
+        message,
+    ) = await _global_state.tokenizer_manager.send_weights_to_remote_instance(
         obj, request
     )
-    )
     content = {"success": success, "message": message}
     if success:
         return ORJSONResponse(content, status_code=200)
@@ -1182,9 +1260,10 @@ async def destroy_weights_update_group(
     obj: DestroyWeightsUpdateGroupReqInput, request: Request
 ):
     """Destroy the parameter update group."""
-    success, message = (
+    (
-        await _global_state.tokenizer_manager.destroy_weights_update_group(obj, request)
+        success,
-    )
+        message,
+    ) = await _global_state.tokenizer_manager.destroy_weights_update_group(obj, request)
     content = {"success": success, "message": message}
     return ORJSONResponse(
         content, status_code=200 if success else HTTPStatus.BAD_REQUEST
@@ -1219,11 +1298,12 @@ async def update_weights_from_distributed(
     obj: UpdateWeightsFromDistributedReqInput, request: Request
 ):
     """Update model parameter from distributed online."""
-    success, message = (
+    (
-        await _global_state.tokenizer_manager.update_weights_from_distributed(
+        success,
+        message,
+    ) = await _global_state.tokenizer_manager.update_weights_from_distributed(
         obj, request
     )
-    )
 
     content = {"success": success, "message": message}
     if success:

python/sglang/srt/entrypoints/openai/serving_chat.py

@@ -1564,6 +1564,111 @@ class OpenAIServingChat(OpenAIServingBase):
         ):
             request.skip_special_tokens = False
 
+    def wrap_reasoning_history(self, reasoning_text: str) -> str:
+        """Wrap prior-turn reasoning in the detector's own start/end tokens.
+
+        Pulling the delimiters from the detector keeps adapters in lockstep
+        with any future parser that ships non-``<think>`` markers — Mistral's
+        ``[THINK]``, Gemma4's ``think_start_self_label = "thought\\n"``, etc.
+        Falling back to a plain string is unsafe: it would let prior
+        thinking text reach a non-reasoning model as ordinary assistant
+        content, so the caller must surface this state, not paper over it.
+        """
+        if self._reasoning_detector is None:
+            raise ValueError(
+                "Cannot rewrap thinking history: no reasoning detector is "
+                "configured for this model"
+            )
+        d = self._reasoning_detector
+        return (
+            f"{d.think_start_token}{d.think_start_self_label}"
+            f"{reasoning_text}\n{d.think_end_token}"
+        )
+
+    def _reasoning_default_mode(self) -> Optional[str]:
+        if self._reasoning_detector is None:
+            return None
+        return self._reasoning_detector.reasoning_default
+
+    def _get_reasoning_toggle_param(self) -> Optional[str]:
+        """Resolve the chat-template kwarg that toggles reasoning, if any."""
+        config = self.template_manager.reasoning_config
+        if config is not None:
+            return config.toggle_param
+
+        mode = self._reasoning_default_mode()
+        if mode in ("thinking", "enable_thinking"):
+            return mode
+        if mode in ("explicit_thinking", "explicit_enable_thinking"):
+            return mode.replace("explicit_", "")
+        return None
+
+    def apply_reasoning_enabled(
+        self, request: ChatCompletionRequest, enabled: bool
+    ) -> None:
+        """Force the request into the requested reasoning-on/off mode.
+
+        Mirrors the read-side logic in ``_get_reasoning_from_request``;
+        the two must stay in sync. Always-on models cannot be disabled,
+        so explicit ``enabled=False`` raises rather than silently leaving
+        reasoning on.
+        """
+        if not self.reasoning_parser:
+            if enabled:
+                raise ValueError(
+                    "Anthropic thinking is not supported for models without "
+                    "a reasoning parser"
+                )
+            return
+
+        if self.reasoning_parser == "hunyuan":
+            request.reasoning_effort = "medium" if enabled else "no_think"
+            return
+
+        config = self.template_manager.reasoning_config
+        is_mistral = (config is not None and config.special_case == "mistral") or (
+            config is None and self._reasoning_default_mode() == "mistral"
+        )
+        if is_mistral:
+            request.reasoning_effort = "medium" if enabled else "none"
+            return
+
+        is_always_on = (config is not None and config.special_case == "always") or (
+            config is None and self._reasoning_default_mode() == "always"
+        )
+        if is_always_on:
+            if not enabled:
+                raise ValueError(
+                    f"Reasoning parser '{self.reasoning_parser}' is always-on "
+                    f"and cannot be disabled via Anthropic thinking"
+                )
+            return
+
+        toggle_param = self._get_reasoning_toggle_param()
+        # The read side (``_get_reasoning_from_request``) returns False
+        # whenever ``config.toggle_param is None`` OR
+        # ``config.default_enabled is None``. The write side must mirror
+        # both conditions: if ``default_enabled`` is unset we cannot
+        # actually honor an ``enabled=True`` request even when the toggle
+        # name itself is resolvable, so writing the kwarg would set up the
+        # template to emit reasoning tokens while the parser ignores them
+        # (literal ``<think>`` markers leak into the assistant text).
+        config = self.template_manager.reasoning_config
+        read_side_supported = toggle_param is not None and (
+            config is None or config.default_enabled is not None
+        )
+        if not read_side_supported:
+            if not enabled:
+                return
+            raise ValueError(
+                f"Anthropic thinking is not supported for reasoning parser "
+                f"'{self.reasoning_parser}'"
+            )
+
+        chat_template_kwargs = dict(request.chat_template_kwargs or {})
+        chat_template_kwargs[toggle_param] = enabled
+        request.chat_template_kwargs = chat_template_kwargs
+
     def _get_reasoning_from_request(self, request: ChatCompletionRequest) -> bool:
         """Determine whether reasoning mode should be enabled for this request.