Support Anthropic API /v1/messages Endpoint (#22627)

Signed-off-by: liuli <ll407707@alibaba-inc.com>
Co-authored-by: liuli <ll407707@alibaba-inc.com>
Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>
Co-authored-by: Michael Goin <mgoin64@gmail.com>

requirements/common.txt

@@ -48,3 +48,4 @@ pybase64 # fast base64 implementation
 cbor2 # Required for cross-language serialization of hashable objects
 setproctitle # Used to set process names for better debugging and monitoring
 openai-harmony >= 0.0.3  # Required for gpt-oss
+anthropic == 0.71.0

tests/entrypoints/anthropic/test_messages.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import anthropic
+import pytest
+import pytest_asyncio
+
+from ...utils import RemoteAnthropicServer
+
+MODEL_NAME = "Qwen/Qwen3-0.6B"
+
+
+@pytest.fixture(scope="module")
+def server():  # noqa: F811
+    args = [
+        "--max-model-len",
+        "2048",
+        "--enforce-eager",
+        "--enable-auto-tool-choice",
+        "--tool-call-parser",
+        "hermes",
+        "--served-model-name",
+        "claude-3-7-sonnet-latest",
+    ]
+
+    with RemoteAnthropicServer(MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client(server):
+    async with server.get_async_client() as async_client:
+        yield async_client
+
+
+@pytest.mark.asyncio
+async def test_simple_messages(client: anthropic.AsyncAnthropic):
+    resp = await client.messages.create(
+        model="claude-3-7-sonnet-latest",
+        max_tokens=1024,
+        messages=[{"role": "user", "content": "how are you!"}],
+    )
+    assert resp.stop_reason == "end_turn"
+    assert resp.role == "assistant"
+
+    print(f"Anthropic response: {resp.model_dump_json()}")
+
+
+@pytest.mark.asyncio
+async def test_system_message(client: anthropic.AsyncAnthropic):
+    resp = await client.messages.create(
+        model="claude-3-7-sonnet-latest",
+        max_tokens=1024,
+        system="you are a helpful assistant",
+        messages=[{"role": "user", "content": "how are you!"}],
+    )
+    assert resp.stop_reason == "end_turn"
+    assert resp.role == "assistant"
+
+    print(f"Anthropic response: {resp.model_dump_json()}")
+
+
+@pytest.mark.asyncio
+async def test_anthropic_streaming(client: anthropic.AsyncAnthropic):
+    resp = await client.messages.create(
+        model="claude-3-7-sonnet-latest",
+        max_tokens=1024,
+        messages=[{"role": "user", "content": "how are you!"}],
+        stream=True,
+    )
+
+    async for chunk in resp:
+        print(chunk.model_dump_json())
+
+
+@pytest.mark.asyncio
+async def test_anthropic_tool_call(client: anthropic.AsyncAnthropic):
+    resp = await client.messages.create(
+        model="claude-3-7-sonnet-latest",
+        max_tokens=1024,
+        messages=[
+            {"role": "user", "content": "What's the weather like in New York today?"}
+        ],
+        tools=[
+            {
+                "name": "get_current_weather",
+                "description": "Useful for querying the weather in a specified city.",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "City or region, for example: "
+                            "New York, London, Tokyo, etc.",
+                        }
+                    },
+                    "required": ["location"],
+                },
+            }
+        ],
+        stream=False,
+    )
+    assert resp.stop_reason == "tool_use"
+    assert resp.role == "assistant"
+
+    print(f"Anthropic response: {resp.model_dump_json()}")
+
+    @pytest.mark.asyncio
+    async def test_anthropic_tool_call_streaming(client: anthropic.AsyncAnthropic):
+        resp = await client.messages.create(
+            model="claude-3-7-sonnet-latest",
+            max_tokens=1024,
+            messages=[
+                {
+                    "role": "user",
+                    "content": "What's the weather like in New York today?",
+                }
+            ],
+            tools=[
+                {
+                    "name": "get_current_weather",
+                    "description": "Useful for querying the weather "
+                    "in a specified city.",
+                    "input_schema": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "City or region, for example: "
+                                "New York, London, Tokyo, etc.",
+                            }
+                        },
+                        "required": ["location"],
+                    },
+                }
+            ],
+            stream=True,
+        )
+
+        async for chunk in resp:
+            print(chunk.model_dump_json())

tests/utils.py

@@ -23,6 +23,7 @@ from pathlib import Path
 from typing import Any, Literal
 from unittest.mock import patch
 
+import anthropic
 import cloudpickle
 import httpx
 import openai
@@ -294,6 +295,131 @@ class RemoteOpenAIServerCustom(RemoteOpenAIServer):
             self.proc.kill()
 
 
+class RemoteAnthropicServer:
+    DUMMY_API_KEY = "token-abc123"  # vLLM's Anthropic server does not need API key
+
+    def __init__(
+        self,
+        model: str,
+        vllm_serve_args: list[str],
+        *,
+        env_dict: dict[str, str] | None = None,
+        seed: int | None = 0,
+        auto_port: bool = True,
+        max_wait_seconds: float | None = None,
+    ) -> None:
+        if auto_port:
+            if "-p" in vllm_serve_args or "--port" in vllm_serve_args:
+                raise ValueError(
+                    "You have manually specified the port when `auto_port=True`."
+                )
+
+            # Don't mutate the input args
+            vllm_serve_args = vllm_serve_args + ["--port", str(get_open_port())]
+        if seed is not None:
+            if "--seed" in vllm_serve_args:
+                raise ValueError(
+                    f"You have manually specified the seed when `seed={seed}`."
+                )
+
+            vllm_serve_args = vllm_serve_args + ["--seed", str(seed)]
+
+        parser = FlexibleArgumentParser(description="vLLM's remote Anthropic server.")
+        subparsers = parser.add_subparsers(required=False, dest="subparser")
+        parser = ServeSubcommand().subparser_init(subparsers)
+        args = parser.parse_args(["--model", model, *vllm_serve_args])
+        self.host = str(args.host or "localhost")
+        self.port = int(args.port)
+
+        self.show_hidden_metrics = args.show_hidden_metrics_for_version is not None
+
+        # download the model before starting the server to avoid timeout
+        is_local = os.path.isdir(model)
+        if not is_local:
+            engine_args = AsyncEngineArgs.from_cli_args(args)
+            model_config = engine_args.create_model_config()
+            load_config = engine_args.create_load_config()
+
+            model_loader = get_model_loader(load_config)
+            model_loader.download_model(model_config)
+
+        env = os.environ.copy()
+        # the current process might initialize cuda,
+        # to be safe, we should use spawn method
+        env["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+        if env_dict is not None:
+            env.update(env_dict)
+        self.proc = subprocess.Popen(
+            [
+                sys.executable,
+                "-m",
+                "vllm.entrypoints.anthropic.api_server",
+                model,
+                *vllm_serve_args,
+            ],
+            env=env,
+            stdout=sys.stdout,
+            stderr=sys.stderr,
+        )
+        max_wait_seconds = max_wait_seconds or 240
+        self._wait_for_server(url=self.url_for("health"), timeout=max_wait_seconds)
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.proc.terminate()
+        try:
+            self.proc.wait(8)
+        except subprocess.TimeoutExpired:
+            # force kill if needed
+            self.proc.kill()
+
+    def _wait_for_server(self, *, url: str, timeout: float):
+        # run health check
+        start = time.time()
+        while True:
+            try:
+                if requests.get(url).status_code == 200:
+                    break
+            except Exception:
+                # this exception can only be raised by requests.get,
+                # which means the server is not ready yet.
+                # the stack trace is not useful, so we suppress it
+                # by using `raise from None`.
+                result = self.proc.poll()
+                if result is not None and result != 0:
+                    raise RuntimeError("Server exited unexpectedly.") from None
+
+                time.sleep(0.5)
+                if time.time() - start > timeout:
+                    raise RuntimeError("Server failed to start in time.") from None
+
+    @property
+    def url_root(self) -> str:
+        return f"http://{self.host}:{self.port}"
+
+    def url_for(self, *parts: str) -> str:
+        return self.url_root + "/" + "/".join(parts)
+
+    def get_client(self, **kwargs):
+        if "timeout" not in kwargs:
+            kwargs["timeout"] = 600
+        return anthropic.Anthropic(
+            base_url=self.url_for(),
+            api_key=self.DUMMY_API_KEY,
+            max_retries=0,
+            **kwargs,
+        )
+
+    def get_async_client(self, **kwargs):
+        if "timeout" not in kwargs:
+            kwargs["timeout"] = 600
+        return anthropic.AsyncAnthropic(
+            base_url=self.url_for(), api_key=self.DUMMY_API_KEY, max_retries=0, **kwargs
+        )
+
+
 def _test_completion(
     client: openai.OpenAI,
     model: str,

vllm/entrypoints/anthropic/api_server.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Adapted from:
+# https://github.com/vllm/vllm/entrypoints/openai/api_server.py
+
+import asyncio
+import signal
+import tempfile
+from argparse import Namespace
+from http import HTTPStatus
+
+import uvloop
+from fastapi import APIRouter, Depends, FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, Response, StreamingResponse
+from starlette.datastructures import State
+
+import vllm.envs as envs
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.anthropic.protocol import (
+    AnthropicErrorResponse,
+    AnthropicMessagesRequest,
+    AnthropicMessagesResponse,
+)
+from vllm.entrypoints.anthropic.serving_messages import AnthropicServingMessages
+from vllm.entrypoints.launcher import serve_http
+from vllm.entrypoints.logger import RequestLogger
+from vllm.entrypoints.openai.api_server import (
+    build_async_engine_client,
+    create_server_socket,
+    lifespan,
+    load_log_config,
+    validate_api_server_args,
+    validate_json_request,
+)
+from vllm.entrypoints.openai.cli_args import make_arg_parser, validate_parsed_serve_args
+from vllm.entrypoints.openai.protocol import ErrorResponse
+from vllm.entrypoints.openai.serving_models import (
+    BaseModelPath,
+    OpenAIServingModels,
+)
+
+#
+# yapf: enable
+from vllm.entrypoints.openai.tool_parsers import ToolParserManager
+from vllm.entrypoints.utils import (
+    cli_env_setup,
+    load_aware_call,
+    process_chat_template,
+    process_lora_modules,
+    with_cancellation,
+)
+from vllm.logger import init_logger
+from vllm.utils import FlexibleArgumentParser, set_ulimit
+from vllm.utils.network_utils import is_valid_ipv6_address
+from vllm.version import __version__ as VLLM_VERSION
+
+prometheus_multiproc_dir: tempfile.TemporaryDirectory
+
+# Cannot use __name__ (https://github.com/vllm-project/vllm/pull/4765)
+logger = init_logger("vllm.entrypoints.anthropic.api_server")
+
+_running_tasks: set[asyncio.Task] = set()
+
+router = APIRouter()
+
+
+def messages(request: Request) -> AnthropicServingMessages:
+    return request.app.state.anthropic_serving_messages
+
+
+def engine_client(request: Request) -> EngineClient:
+    return request.app.state.engine_client
+
+
+@router.get("/health", response_class=Response)
+async def health(raw_request: Request) -> Response:
+    """Health check."""
+    await engine_client(raw_request).check_health()
+    return Response(status_code=200)
+
+
+@router.get("/ping", response_class=Response)
+@router.post("/ping", response_class=Response)
+async def ping(raw_request: Request) -> Response:
+    """Ping check. Endpoint required for SageMaker"""
+    return await health(raw_request)
+
+
+@router.post(
+    "/v1/messages",
+    dependencies=[Depends(validate_json_request)],
+    responses={
+        HTTPStatus.OK.value: {"content": {"text/event-stream": {}}},
+        HTTPStatus.BAD_REQUEST.value: {"model": AnthropicErrorResponse},
+        HTTPStatus.NOT_FOUND.value: {"model": AnthropicErrorResponse},
+        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": AnthropicErrorResponse},
+    },
+)
+@with_cancellation
+@load_aware_call
+async def create_messages(request: AnthropicMessagesRequest, raw_request: Request):
+    handler = messages(raw_request)
+    if handler is None:
+        return messages(raw_request).create_error_response(
+            message="The model does not support Messages API"
+        )
+
+    generator = await handler.create_messages(request, raw_request)
+
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    elif isinstance(generator, AnthropicMessagesResponse):
+        logger.debug(
+            "Anthropic Messages Response: %s", generator.model_dump(exclude_none=True)
+        )
+        return JSONResponse(content=generator.model_dump(exclude_none=True))
+
+    return StreamingResponse(content=generator, media_type="text/event-stream")
+
+
+async def init_app_state(
+    engine_client: EngineClient,
+    state: State,
+    args: Namespace,
+) -> None:
+    vllm_config = engine_client.vllm_config
+
+    if args.served_model_name is not None:
+        served_model_names = args.served_model_name
+    else:
+        served_model_names = [args.model]
+
+    if args.disable_log_requests:
+        request_logger = None
+    else:
+        request_logger = RequestLogger(max_log_len=args.max_log_len)
+
+    base_model_paths = [
+        BaseModelPath(name=name, model_path=args.model) for name in served_model_names
+    ]
+
+    state.engine_client = engine_client
+    state.log_stats = not args.disable_log_stats
+    state.vllm_config = vllm_config
+    model_config = vllm_config.model_config
+
+    default_mm_loras = (
+        vllm_config.lora_config.default_mm_loras
+        if vllm_config.lora_config is not None
+        else {}
+    )
+    lora_modules = process_lora_modules(args.lora_modules, default_mm_loras)
+
+    resolved_chat_template = await process_chat_template(
+        args.chat_template, engine_client, model_config
+    )
+
+    state.openai_serving_models = OpenAIServingModels(
+        engine_client=engine_client,
+        base_model_paths=base_model_paths,
+        lora_modules=lora_modules,
+    )
+    await state.openai_serving_models.init_static_loras()
+    state.anthropic_serving_messages = AnthropicServingMessages(
+        engine_client,
+        state.openai_serving_models,
+        args.response_role,
+        request_logger=request_logger,
+        chat_template=resolved_chat_template,
+        chat_template_content_format=args.chat_template_content_format,
+        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
+        enable_auto_tools=args.enable_auto_tool_choice,
+        tool_parser=args.tool_call_parser,
+        reasoning_parser=args.reasoning_parser,
+        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
+        enable_force_include_usage=args.enable_force_include_usage,
+    )
+
+
+def setup_server(args):
+    """Validate API server args, set up signal handler, create socket
+    ready to serve."""
+
+    logger.info("vLLM API server version %s", VLLM_VERSION)
+
+    if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
+        ToolParserManager.import_tool_parser(args.tool_parser_plugin)
+
+    validate_api_server_args(args)
+
+    # workaround to make sure that we bind the port before the engine is set up.
+    # This avoids race conditions with ray.
+    # see https://github.com/vllm-project/vllm/issues/8204
+    sock_addr = (args.host or "", args.port)
+    sock = create_server_socket(sock_addr)
+
+    # workaround to avoid footguns where uvicorn drops requests with too
+    # many concurrent requests active
+    set_ulimit()
+
+    def signal_handler(*_) -> None:
+        # Interrupt server on sigterm while initializing
+        raise KeyboardInterrupt("terminated")
+
+    signal.signal(signal.SIGTERM, signal_handler)
+
+    addr, port = sock_addr
+    is_ssl = args.ssl_keyfile and args.ssl_certfile
+    host_part = f"[{addr}]" if is_valid_ipv6_address(addr) else addr or "0.0.0.0"
+    listen_address = f"http{'s' if is_ssl else ''}://{host_part}:{port}"
+
+    return listen_address, sock
+
+
+async def run_server(args, **uvicorn_kwargs) -> None:
+    """Run a single-worker API server."""
+    listen_address, sock = setup_server(args)
+    await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)
+
+
+def build_app(args: Namespace) -> FastAPI:
+    app = FastAPI(lifespan=lifespan)
+    app.include_router(router)
+    app.root_path = args.root_path
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=args.allowed_origins,
+        allow_credentials=args.allow_credentials,
+        allow_methods=args.allowed_methods,
+        allow_headers=args.allowed_headers,
+    )
+
+    return app
+
+
+async def run_server_worker(
+    listen_address, sock, args, client_config=None, **uvicorn_kwargs
+) -> None:
+    """Run a single API server worker."""
+
+    if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
+        ToolParserManager.import_tool_parser(args.tool_parser_plugin)
+
+    server_index = client_config.get("client_index", 0) if client_config else 0
+
+    # Load logging config for uvicorn if specified
+    log_config = load_log_config(args.log_config_file)
+    if log_config is not None:
+        uvicorn_kwargs["log_config"] = log_config
+
+    async with build_async_engine_client(
+        args,
+        client_config=client_config,
+    ) as engine_client:
+        app = build_app(args)
+
+        await init_app_state(engine_client, app.state, args)
+
+        logger.info("Starting vLLM API server %d on %s", server_index, listen_address)
+        shutdown_task = await serve_http(
+            app,
+            sock=sock,
+            enable_ssl_refresh=args.enable_ssl_refresh,
+            host=args.host,
+            port=args.port,
+            log_level=args.uvicorn_log_level,
+            # NOTE: When the 'disable_uvicorn_access_log' value is True,
+            # no access log will be output.
+            access_log=not args.disable_uvicorn_access_log,
+            timeout_keep_alive=envs.VLLM_HTTP_TIMEOUT_KEEP_ALIVE,
+            ssl_keyfile=args.ssl_keyfile,
+            ssl_certfile=args.ssl_certfile,
+            ssl_ca_certs=args.ssl_ca_certs,
+            ssl_cert_reqs=args.ssl_cert_reqs,
+            **uvicorn_kwargs,
+        )
+
+    # NB: Await server shutdown only after the backend context is exited
+    try:
+        await shutdown_task
+    finally:
+        sock.close()
+
+
+if __name__ == "__main__":
+    # NOTE(simon):
+    # This section should be in sync with vllm/entrypoints/cli/main.py for CLI
+    # entrypoints.
+    cli_env_setup()
+    parser = FlexibleArgumentParser(
+        description="vLLM Anthropic-Compatible RESTful API server."
+    )
+    parser = make_arg_parser(parser)
+    args = parser.parse_args()
+    validate_parsed_serve_args(args)
+
+    uvloop.run(run_server(args))

vllm/entrypoints/anthropic/protocol.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Pydantic models for Anthropic API protocol"""
+
+import time
+from typing import Any, Literal, Optional
+
+from pydantic import BaseModel, field_validator
+
+
+class AnthropicError(BaseModel):
+    """Error structure for Anthropic API"""
+
+    type: str
+    message: str
+
+
+class AnthropicErrorResponse(BaseModel):
+    """Error response structure for Anthropic API"""
+
+    type: Literal["error"] = "error"
+    error: AnthropicError
+
+
+class AnthropicUsage(BaseModel):
+    """Token usage information"""
+
+    input_tokens: int
+    output_tokens: int
+    cache_creation_input_tokens: int | None = None
+    cache_read_input_tokens: int | None = None
+
+
+class AnthropicContentBlock(BaseModel):
+    """Content block in message"""
+
+    type: Literal["text", "image", "tool_use", "tool_result"]
+    text: str | None = None
+    # For image content
+    source: dict[str, Any] | None = None
+    # For tool use/result
+    id: str | None = None
+    name: str | None = None
+    input: dict[str, Any] | None = None
+    content: str | list[dict[str, Any]] | None = None
+    is_error: bool | None = None
+
+
+class AnthropicMessage(BaseModel):
+    """Message structure"""
+
+    role: Literal["user", "assistant"]
+    content: str | list[AnthropicContentBlock]
+
+
+class AnthropicTool(BaseModel):
+    """Tool definition"""
+
+    name: str
+    description: str | None = None
+    input_schema: dict[str, Any]
+
+    @field_validator("input_schema")
+    @classmethod
+    def validate_input_schema(cls, v):
+        if not isinstance(v, dict):
+            raise ValueError("input_schema must be a dictionary")
+        if "type" not in v:
+            v["type"] = "object"  # Default to object type
+        return v
+
+
+class AnthropicToolChoice(BaseModel):
+    """Tool Choice definition"""
+
+    type: Literal["auto", "any", "tool"]
+    name: str | None = None
+
+
+class AnthropicMessagesRequest(BaseModel):
+    """Anthropic Messages API request"""
+
+    model: str
+    messages: list[AnthropicMessage]
+    max_tokens: int
+    metadata: dict[str, Any] | None = None
+    stop_sequences: list[str] | None = None
+    stream: bool | None = False
+    system: str | list[AnthropicContentBlock] | None = None
+    temperature: float | None = None
+    tool_choice: AnthropicToolChoice | None = None
+    tools: list[AnthropicTool] | None = None
+    top_k: int | None = None
+    top_p: float | None = None
+
+    @field_validator("model")
+    @classmethod
+    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):
+        if v <= 0:
+            raise ValueError("max_tokens must be positive")
+        return v
+
+
+class AnthropicDelta(BaseModel):
+    """Delta for streaming responses"""
+
+    type: Literal["text_delta", "input_json_delta"] | None = None
+    text: str | None = None
+    partial_json: str | None = None
+
+    # Message delta
+    stop_reason: (
+        Literal["end_turn", "max_tokens", "stop_sequence", "tool_use"] | None
+    ) = None
+    stop_sequence: str | None = None
+
+
+class AnthropicStreamEvent(BaseModel):
+    """Streaming event"""
+
+    type: Literal[
+        "message_start",
+        "message_delta",
+        "message_stop",
+        "content_block_start",
+        "content_block_delta",
+        "content_block_stop",
+        "ping",
+        "error",
+    ]
+    message: Optional["AnthropicMessagesResponse"] = None
+    delta: AnthropicDelta | None = None
+    content_block: AnthropicContentBlock | None = None
+    index: int | None = None
+    error: AnthropicError | None = None
+    usage: AnthropicUsage | None = None
+
+
+class AnthropicMessagesResponse(BaseModel):
+    """Anthropic Messages API response"""
+
+    id: str
+    type: Literal["message"] = "message"
+    role: Literal["assistant"] = "assistant"
+    content: list[AnthropicContentBlock]
+    model: str
+    stop_reason: (
+        Literal["end_turn", "max_tokens", "stop_sequence", "tool_use"] | None
+    ) = None
+    stop_sequence: str | None = None
+    usage: AnthropicUsage | None = None
+
+    def model_post_init(self, __context):
+        if not self.id:
+            self.id = f"msg_{int(time.time() * 1000)}"

vllm/entrypoints/anthropic/serving_messages.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Adapted from
+# https://github.com/vllm/vllm/entrypoints/openai/serving_chat.py
+
+"""Anthropic Messages API serving handler"""
+
+import json
+import logging
+import time
+from collections.abc import AsyncGenerator
+from typing import Any
+
+from fastapi import Request
+
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.anthropic.protocol import (
+    AnthropicContentBlock,
+    AnthropicDelta,
+    AnthropicError,
+    AnthropicMessagesRequest,
+    AnthropicMessagesResponse,
+    AnthropicStreamEvent,
+    AnthropicUsage,
+)
+from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption
+from vllm.entrypoints.logger import RequestLogger
+from vllm.entrypoints.openai.protocol import (
+    ChatCompletionNamedToolChoiceParam,
+    ChatCompletionRequest,
+    ChatCompletionResponse,
+    ChatCompletionStreamResponse,
+    ChatCompletionToolsParam,
+    ErrorResponse,
+    StreamOptions,
+)
+from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
+
+logger = logging.getLogger(__name__)
+
+
+def wrap_data_with_event(data: str, event: str):
+    return f"event: {event}\ndata: {data}\n\n"
+
+
+class AnthropicServingMessages(OpenAIServingChat):
+    """Handler for Anthropic Messages API requests"""
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        models: OpenAIServingModels,
+        response_role: str,
+        *,
+        request_logger: RequestLogger | None,
+        chat_template: str | None,
+        chat_template_content_format: ChatTemplateContentFormatOption,
+        return_tokens_as_token_ids: bool = False,
+        reasoning_parser: str = "",
+        enable_auto_tools: bool = False,
+        tool_parser: str | None = None,
+        enable_prompt_tokens_details: bool = False,
+        enable_force_include_usage: bool = False,
+    ):
+        super().__init__(
+            engine_client=engine_client,
+            models=models,
+            response_role=response_role,
+            request_logger=request_logger,
+            chat_template=chat_template,
+            chat_template_content_format=chat_template_content_format,
+            return_tokens_as_token_ids=return_tokens_as_token_ids,
+            reasoning_parser=reasoning_parser,
+            enable_auto_tools=enable_auto_tools,
+            tool_parser=tool_parser,
+            enable_prompt_tokens_details=enable_prompt_tokens_details,
+            enable_force_include_usage=enable_force_include_usage,
+        )
+        self.stop_reason_map = {
+            "stop": "end_turn",
+            "length": "max_tokens",
+            "tool_calls": "tool_use",
+        }
+
+    def _convert_anthropic_to_openai_request(
+        self, anthropic_request: AnthropicMessagesRequest
+    ) -> ChatCompletionRequest:
+        """Convert Anthropic message format to OpenAI format"""
+        openai_messages = []
+
+        # Add system message if provided
+        if anthropic_request.system:
+            if isinstance(anthropic_request.system, str):
+                openai_messages.append(
+                    {"role": "system", "content": anthropic_request.system}
+                )
+            else:
+                system_prompt = ""
+                for block in anthropic_request.system:
+                    if block.type == "text" and block.text:
+                        system_prompt += block.text
+                openai_messages.append({"role": "system", "content": system_prompt})
+
+        for msg in anthropic_request.messages:
+            openai_msg: dict[str, Any] = {"role": msg.role}  # type: ignore
+            if isinstance(msg.content, str):
+                openai_msg["content"] = msg.content
+            else:
+                # Handle complex content blocks
+                content_parts: list[dict[str, Any]] = []
+                tool_calls: list[dict[str, Any]] = []
+
+                for block in msg.content:
+                    if block.type == "text" and block.text:
+                        content_parts.append({"type": "text", "text": block.text})
+                    elif block.type == "image" and block.source:
+                        content_parts.append(
+                            {
+                                "type": "image_url",
+                                "image_url": {"url": block.source.get("data", "")},
+                            }
+                        )
+                    elif block.type == "tool_use":
+                        # Convert tool use to function call format
+                        tool_call = {
+                            "id": block.id or f"call_{int(time.time())}",
+                            "type": "function",
+                            "function": {
+                                "name": block.name or "",
+                                "arguments": json.dumps(block.input or {}),
+                            },
+                        }
+                        tool_calls.append(tool_call)
+                    elif block.type == "tool_result":
+                        if msg.role == "user":
+                            openai_messages.append(
+                                {
+                                    "role": "tool",
+                                    "tool_call_id": block.id or "",
+                                    "content": str(block.content)
+                                    if block.content
+                                    else "",
+                                }
+                            )
+                        else:
+                            # Assistant tool result becomes regular text
+                            tool_result_text = (
+                                str(block.content) if block.content else ""
+                            )
+                            content_parts.append(
+                                {
+                                    "type": "text",
+                                    "text": f"Tool result: {tool_result_text}",
+                                }
+                            )
+
+                # Add tool calls to the message if any
+                if tool_calls:
+                    openai_msg["tool_calls"] = tool_calls  # type: ignore
+
+                # Add content parts if any
+                if content_parts:
+                    if len(content_parts) == 1 and content_parts[0]["type"] == "text":
+                        openai_msg["content"] = content_parts[0]["text"]
+                    else:
+                        openai_msg["content"] = content_parts  # type: ignore
+                elif not tool_calls:
+                    continue
+
+            openai_messages.append(openai_msg)
+
+        req = ChatCompletionRequest(
+            model=anthropic_request.model,
+            messages=openai_messages,
+            max_tokens=anthropic_request.max_tokens,
+            max_completion_tokens=anthropic_request.max_tokens,
+            stop=anthropic_request.stop_sequences,
+            temperature=anthropic_request.temperature,
+            top_p=anthropic_request.top_p,
+            top_k=anthropic_request.top_k,
+        )
+
+        if anthropic_request.stream:
+            req.stream = anthropic_request.stream
+            req.stream_options = StreamOptions.validate({"include_usage": True})
+
+        if anthropic_request.tool_choice is None:
+            req.tool_choice = None
+        elif anthropic_request.tool_choice.type == "auto":
+            req.tool_choice = "auto"
+        elif anthropic_request.tool_choice.type == "any":
+            req.tool_choice = "required"
+        elif anthropic_request.tool_choice.type == "tool":
+            req.tool_choice = ChatCompletionNamedToolChoiceParam.model_validate(
+                {
+                    "type": "function",
+                    "function": {"name": anthropic_request.tool_choice.name},
+                }
+            )
+
+        tools = []
+        if anthropic_request.tools is None:
+            return req
+        for tool in anthropic_request.tools:
+            tools.append(
+                ChatCompletionToolsParam.model_validate(
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": tool.name,
+                            "description": tool.description,
+                            "parameters": tool.input_schema,
+                        },
+                    }
+                )
+            )
+        if req.tool_choice is None:
+            req.tool_choice = "auto"
+        req.tools = tools
+        return req
+
+    async def create_messages(
+        self,
+        request: AnthropicMessagesRequest,
+        raw_request: Request | None = None,
+    ) -> AsyncGenerator[str, None] | AnthropicMessagesResponse | ErrorResponse:
+        """
+        Messages API similar to Anthropic's API.
+
+        See https://docs.anthropic.com/en/api/messages
+        for the API specification. This API mimics the Anthropic messages API.
+        """
+        logger.debug("Received messages request %s", request.model_dump_json())
+        chat_req = self._convert_anthropic_to_openai_request(request)
+        logger.debug("Convert to OpenAI request %s", request.model_dump_json())
+        generator = await self.create_chat_completion(chat_req, raw_request)
+
+        if isinstance(generator, ErrorResponse):
+            return generator
+
+        elif isinstance(generator, ChatCompletionResponse):
+            return self.messages_full_converter(generator)
+
+        return self.message_stream_converter(generator)
+
+    def messages_full_converter(
+        self,
+        generator: ChatCompletionResponse,
+    ) -> AnthropicMessagesResponse:
+        result = AnthropicMessagesResponse(
+            id=generator.id,
+            content=[],
+            model=generator.model,
+            usage=AnthropicUsage(
+                input_tokens=generator.usage.prompt_tokens,
+                output_tokens=generator.usage.completion_tokens,
+            ),
+        )
+        if generator.choices[0].finish_reason == "stop":
+            result.stop_reason = "end_turn"
+        elif generator.choices[0].finish_reason == "length":
+            result.stop_reason = "max_tokens"
+        elif generator.choices[0].finish_reason == "tool_calls":
+            result.stop_reason = "tool_use"
+
+        content: list[AnthropicContentBlock] = [
+            AnthropicContentBlock(
+                type="text",
+                text=generator.choices[0].message.content
+                if generator.choices[0].message.content
+                else "",
+            )
+        ]
+
+        for tool_call in generator.choices[0].message.tool_calls:
+            anthropic_tool_call = AnthropicContentBlock(
+                type="tool_use",
+                id=tool_call.id,
+                name=tool_call.function.name,
+                input=json.loads(tool_call.function.arguments),
+            )
+            content += [anthropic_tool_call]
+
+        result.content = content
+
+        return result
+
+    async def message_stream_converter(
+        self,
+        generator: AsyncGenerator[str, None],
+    ) -> AsyncGenerator[str, None]:
+        try:
+            first_item = True
+            finish_reason = None
+            content_block_index = 0
+            content_block_started = False
+
+            async for item in generator:
+                if item.startswith("data:"):
+                    data_str = item[5:].strip().rstrip("\n")
+                    if data_str == "[DONE]":
+                        stop_message = AnthropicStreamEvent(
+                            type="message_stop",
+                        )
+                        data = stop_message.model_dump_json(
+                            exclude_unset=True, exclude_none=True
+                        )
+                        yield wrap_data_with_event(data, "message_stop")
+                        yield "data: [DONE]\n\n"
+                    else:
+                        origin_chunk = ChatCompletionStreamResponse.model_validate_json(
+                            data_str
+                        )
+
+                        if first_item:
+                            chunk = AnthropicStreamEvent(
+                                type="message_start",
+                                message=AnthropicMessagesResponse(
+                                    id=origin_chunk.id,
+                                    content=[],
+                                    model=origin_chunk.model,
+                                ),
+                            )
+                            first_item = False
+                            data = chunk.model_dump_json(exclude_unset=True)
+                            yield wrap_data_with_event(data, "message_start")
+                            continue
+
+                        # last chunk including usage info
+                        if len(origin_chunk.choices) == 0:
+                            if content_block_started:
+                                stop_chunk = AnthropicStreamEvent(
+                                    index=content_block_index,
+                                    type="content_block_stop",
+                                )
+                                data = stop_chunk.model_dump_json(exclude_unset=True)
+                                yield wrap_data_with_event(data, "content_block_stop")
+                            stop_reason = self.stop_reason_map.get(
+                                finish_reason or "stop"
+                            )
+                            chunk = AnthropicStreamEvent(
+                                type="message_delta",
+                                delta=AnthropicDelta(stop_reason=stop_reason),
+                                usage=AnthropicUsage(
+                                    input_tokens=origin_chunk.usage.prompt_tokens
+                                    if origin_chunk.usage
+                                    else 0,
+                                    output_tokens=origin_chunk.usage.completion_tokens
+                                    if origin_chunk.usage
+                                    else 0,
+                                ),
+                            )
+                            data = chunk.model_dump_json(exclude_unset=True)
+                            yield wrap_data_with_event(data, "message_delta")
+                            continue
+
+                        if origin_chunk.choices[0].finish_reason is not None:
+                            finish_reason = origin_chunk.choices[0].finish_reason
+                            continue
+
+                        # content
+                        if origin_chunk.choices[0].delta.content is not None:
+                            if not content_block_started:
+                                chunk = AnthropicStreamEvent(
+                                    index=content_block_index,
+                                    type="content_block_start",
+                                    content_block=AnthropicContentBlock(
+                                        type="text", text=""
+                                    ),
+                                )
+                                data = chunk.model_dump_json(exclude_unset=True)
+                                yield wrap_data_with_event(data, "content_block_start")
+                                content_block_started = True
+
+                            if origin_chunk.choices[0].delta.content == "":
+                                continue
+                            chunk = AnthropicStreamEvent(
+                                index=content_block_index,
+                                type="content_block_delta",
+                                delta=AnthropicDelta(
+                                    type="text_delta",
+                                    text=origin_chunk.choices[0].delta.content,
+                                ),
+                            )
+                            data = chunk.model_dump_json(exclude_unset=True)
+                            yield wrap_data_with_event(data, "content_block_delta")
+                            continue
+
+                        # tool calls
+                        elif len(origin_chunk.choices[0].delta.tool_calls) > 0:
+                            tool_call = origin_chunk.choices[0].delta.tool_calls[0]
+                            if tool_call.id is not None:
+                                if content_block_started:
+                                    stop_chunk = AnthropicStreamEvent(
+                                        index=content_block_index,
+                                        type="content_block_stop",
+                                    )
+                                    data = stop_chunk.model_dump_json(
+                                        exclude_unset=True
+                                    )
+                                    yield wrap_data_with_event(
+                                        data, "content_block_stop"
+                                    )
+                                    content_block_started = False
+                                    content_block_index += 1
+
+                                chunk = AnthropicStreamEvent(
+                                    index=content_block_index,
+                                    type="content_block_start",
+                                    content_block=AnthropicContentBlock(
+                                        type="tool_use",
+                                        id=tool_call.id,
+                                        name=tool_call.function.name
+                                        if tool_call.function
+                                        else None,
+                                        input={},
+                                    ),
+                                )
+                                data = chunk.model_dump_json(exclude_unset=True)
+                                yield wrap_data_with_event(data, "content_block_start")
+                                content_block_started = True
+
+                            else:
+                                chunk = AnthropicStreamEvent(
+                                    index=content_block_index,
+                                    type="content_block_delta",
+                                    delta=AnthropicDelta(
+                                        type="input_json_delta",
+                                        partial_json=tool_call.function.arguments
+                                        if tool_call.function
+                                        else None,
+                                    ),
+                                )
+                                data = chunk.model_dump_json(exclude_unset=True)
+                                yield wrap_data_with_event(data, "content_block_delta")
+                            continue
+                else:
+                    error_response = AnthropicStreamEvent(
+                        type="error",
+                        error=AnthropicError(
+                            type="internal_error",
+                            message="Invalid data format received",
+                        ),
+                    )
+                    data = error_response.model_dump_json(exclude_unset=True)
+                    yield wrap_data_with_event(data, "error")
+                    yield "data: [DONE]\n\n"
+
+        except Exception as e:
+            logger.exception("Error in message stream converter.")
+            error_response = AnthropicStreamEvent(
+                type="error",
+                error=AnthropicError(type="internal_error", message=str(e)),
+            )
+            data = error_response.model_dump_json(exclude_unset=True)
+            yield wrap_data_with_event(data, "error")
+            yield "data: [DONE]\n\n"

vllm/entrypoints/openai/api_server.py

@@ -41,11 +41,6 @@ import vllm.envs as envs
 from vllm.config import VllmConfig
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.protocol import EngineClient
-from vllm.entrypoints.chat_utils import (
-    load_chat_template,
-    resolve_hf_chat_template,
-    resolve_mistral_chat_template,
-)
 from vllm.entrypoints.launcher import serve_http
 from vllm.entrypoints.logger import RequestLogger
 from vllm.entrypoints.openai.cli_args import make_arg_parser, validate_parsed_serve_args
@@ -90,7 +85,6 @@ from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
 from vllm.entrypoints.openai.serving_engine import OpenAIServing
 from vllm.entrypoints.openai.serving_models import (
     BaseModelPath,
-    LoRAModulePath,
     OpenAIServingModels,
 )
 from vllm.entrypoints.openai.serving_pooling import OpenAIServingPooling
@@ -107,11 +101,12 @@ from vllm.entrypoints.utils import (
     cli_env_setup,
     load_aware_call,
     log_non_default_args,
+    process_chat_template,
+    process_lora_modules,
     with_cancellation,
 )
 from vllm.logger import init_logger
 from vllm.reasoning import ReasoningParserManager
-from vllm.transformers_utils.tokenizer import MistralTokenizer
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils import (
     Device,
@@ -1655,32 +1650,9 @@ async def init_app_state(
     supported_tasks = await engine_client.get_supported_tasks()
     logger.info("Supported tasks: %s", supported_tasks)
 
-    resolved_chat_template = load_chat_template(args.chat_template)
-    if resolved_chat_template is not None:
-        # Get the tokenizer to check official template
-        tokenizer = await engine_client.get_tokenizer()
-
-        if isinstance(tokenizer, MistralTokenizer):
-            # The warning is logged in resolve_mistral_chat_template.
-            resolved_chat_template = resolve_mistral_chat_template(
-                chat_template=resolved_chat_template
-            )
-        else:
-            hf_chat_template = resolve_hf_chat_template(
-                tokenizer=tokenizer,
-                chat_template=None,
-                tools=None,
-                model_config=vllm_config.model_config,
-            )
-
-            if hf_chat_template != resolved_chat_template:
-                logger.warning(
-                    "Using supplied chat template: %s\n"
-                    "It is different from official chat template '%s'. "
-                    "This discrepancy may lead to performance degradation.",
-                    resolved_chat_template,
-                    args.model,
-                )
+    resolved_chat_template = await process_chat_template(
+        args.chat_template, engine_client, vllm_config.model_config
+    )
 
     if args.tool_server == "demo":
         tool_server: ToolServer | None = DemoToolServer()
@@ -1699,19 +1671,12 @@ async def init_app_state(
         else {}
     )
 
-    lora_modules = args.lora_modules
-    if default_mm_loras:
-        default_mm_lora_paths = [
-            LoRAModulePath(
-                name=modality,
-                path=lora_path,
-            )
-            for modality, lora_path in default_mm_loras.items()
-        ]
-        if args.lora_modules is None:
-            lora_modules = default_mm_lora_paths
-        else:
-            lora_modules += default_mm_lora_paths
+    default_mm_loras = (
+        vllm_config.lora_config.default_mm_loras
+        if vllm_config.lora_config is not None
+        else {}
+    )
+    lora_modules = process_lora_modules(args.lora_modules, default_mm_loras)
 
     state.openai_serving_models = OpenAIServingModels(
         engine_client=engine_client,

vllm/entrypoints/utils.py

@@ -6,21 +6,31 @@ import dataclasses
 import functools
 import os
 from argparse import Namespace
+from pathlib import Path
 from typing import Any
 
 from fastapi import Request
 from fastapi.responses import JSONResponse, StreamingResponse
 from starlette.background import BackgroundTask, BackgroundTasks
 
+from vllm.config import ModelConfig
 from vllm.engine.arg_utils import EngineArgs
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.chat_utils import (
+    load_chat_template,
+    resolve_hf_chat_template,
+    resolve_mistral_chat_template,
+)
 from vllm.entrypoints.openai.cli_args import make_arg_parser
 from vllm.entrypoints.openai.protocol import (
     ChatCompletionRequest,
     CompletionRequest,
     StreamOptions,
 )
+from vllm.entrypoints.openai.serving_models import LoRAModulePath
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
+from vllm.transformers_utils.tokenizers import MistralTokenizer
 from vllm.utils import FlexibleArgumentParser
 
 logger = init_logger(__name__)
@@ -254,3 +264,56 @@ def should_include_usage(
     else:
         include_usage, include_continuous_usage = enable_force_include_usage, False
     return include_usage, include_continuous_usage
+
+
+def process_lora_modules(
+    args_lora_modules: list[LoRAModulePath], default_mm_loras: dict[str, str] | None
+) -> list[LoRAModulePath]:
+    lora_modules = args_lora_modules
+    if default_mm_loras:
+        default_mm_lora_paths = [
+            LoRAModulePath(
+                name=modality,
+                path=lora_path,
+            )
+            for modality, lora_path in default_mm_loras.items()
+        ]
+        if args_lora_modules is None:
+            lora_modules = default_mm_lora_paths
+        else:
+            lora_modules += default_mm_lora_paths
+    return lora_modules
+
+
+async def process_chat_template(
+    args_chat_template: Path | str | None,
+    engine_client: EngineClient,
+    model_config: ModelConfig,
+) -> str | None:
+    resolved_chat_template = load_chat_template(args_chat_template)
+    if resolved_chat_template is not None:
+        # Get the tokenizer to check official template
+        tokenizer = await engine_client.get_tokenizer()
+
+        if isinstance(tokenizer, MistralTokenizer):
+            # The warning is logged in resolve_mistral_chat_template.
+            resolved_chat_template = resolve_mistral_chat_template(
+                chat_template=resolved_chat_template
+            )
+        else:
+            hf_chat_template = resolve_hf_chat_template(
+                tokenizer=tokenizer,
+                chat_template=None,
+                tools=None,
+                model_config=model_config,
+            )
+
+            if hf_chat_template != resolved_chat_template:
+                logger.warning(
+                    "Using supplied chat template: %s\n"
+                    "It is different from official chat template '%s'. "
+                    "This discrepancy may lead to performance degradation.",
+                    resolved_chat_template,
+                    model_config.model,
+                )
+    return resolved_chat_template