diff --git a/examples/vllm_openai_example/README.md b/examples/vllm_openai_example/README.md
new file mode 100644
index 000000000..b812aa415
--- /dev/null
+++ b/examples/vllm_openai_example/README.md
@@ -0,0 +1,29 @@
+# vllm_openai_example
+
+This is an example of OpenAI compatible server based on vLLM and FastAPI showcasing integration with Langfuse for for event tracing and response generation.
+
+1. **Shutdown Behavior**: The application defines shutdown logic using FastAPI's lifespan feature. On shutdown, it flushes all events to Langfuse, ensuring data integrity and completeness.
+
+2. **Endpoints**:
+   - `/health`: Returns healthcheck status
+   - `/v1/models`: Shows available models
+   - `/version`: Shows current version of vLLM
+   - `/v1/chat/completions`: Generates chat completion and tracks sync and async responses to Langfuse
+   - `/v1/completions`: Generates completion and tracks sync and async responses to Langfuse
+   - More info can be found [here](https://github.com/vllm-project/vllm/tree/main/vllm/entrypoints/openai)
+
+3. **Integration**:
+   - Langfuse: Utilized for event tracing with `@observe` decorator. It uses `transform_to_string` parameter to correctly process async results.
+
+4. **Dependencies**:
+   - Langfuse: Library for event tracing and management.
+   - vLLM: vLLM is a fast and easy-to-use library for LLM inference and serving
+
+5. **Usage**:
+   - Preparation: Ensure that you meet requirements specified in [vllm repo](https://github.com/vllm-project/vllm) to setup running environment
+   - Starting the Server: Navigate to the root directory of the project `langfuse-python/examples/vllm_openai_example`. Run the application using `python3 api_server.py --model <MODEL_PATH> --langfuse-public-key <PBK> --langfuse-secret-key <SCK> --langfuse-host <URL>`.
+   - Access endpoints at `http://localhost:8000`.
+
+Consider checking `api_server.py`, `cli_args.py`, `serving_chat.py`, `serving_competion.py` as they are modified version of vLLM code.
+Also check `langfuse_utils.py` as it contains main logic for correctly processing `stream=True` requests from API.
+For more details on vLLM and Langfuse refer to their respective documentation.
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new file mode 100644
index 000000000..b2dd4bb09
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+]
+
+[[package]]
+name = "xformers"
+version = "0.0.26.post1"
+description = "XFormers: A collection of composable Transformer building blocks."
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.dependencies]
+numpy = "*"
+torch = "2.3.0"
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.10"
+content-hash = "1e6d61f5e5ed6a14d3fecd885f9ccef14ca3547073a5249ad9733bf584d53e37"
diff --git a/examples/vllm_openai_example/pyproject.toml b/examples/vllm_openai_example/pyproject.toml
new file mode 100644
index 000000000..31b3431e1
--- /dev/null
+++ b/examples/vllm_openai_example/pyproject.toml
@@ -0,0 +1,16 @@
+[tool.poetry]
+name = "vllm-openai-example"
+version = "0.1.0"
+description = "This example explains how to basically integrate LangFuse inside vllm openai-compatible server"
+authors = ["dimaioksha <dmitrii.ioksha@gmail.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+vllm = "^0.4.0"
+langfuse = "^2.13.3"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
diff --git a/examples/vllm_openai_example/vllm_openai_example/__init__.py b/examples/vllm_openai_example/vllm_openai_example/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/vllm_openai_example/vllm_openai_example/api_server.py b/examples/vllm_openai_example/vllm_openai_example/api_server.py
new file mode 100644
index 000000000..48a90a289
--- /dev/null
+++ b/examples/vllm_openai_example/vllm_openai_example/api_server.py
@@ -0,0 +1,192 @@
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/api_server.py
+import asyncio
+import importlib
+import inspect
+import os
+from contextlib import asynccontextmanager
+from http import HTTPStatus
+
+import uvicorn
+import vllm
+from fastapi import Request, FastAPI
+from fastapi.exceptions import RequestValidationError
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, Response, StreamingResponse
+from prometheus_client import make_asgi_app
+from vllm.engine.arg_utils import AsyncEngineArgs
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.logger import init_logger
+from vllm.usage.usage_lib import UsageContext
+from langfuse.utils.langfuse_singleton import LangfuseSingleton
+
+from cli_args import make_arg_parser
+from protocol import (
+    ChatCompletionRequest,
+    ChatCompletionResponse,
+    CompletionRequest,
+    ErrorResponse,
+)
+from serving_chat import OpenAIServingChat
+from serving_completion import OpenAIServingCompletion
+
+TIMEOUT_KEEP_ALIVE = 5  # seconds
+
+openai_serving_chat: OpenAIServingChat
+openai_serving_completion: OpenAIServingCompletion
+logger = init_logger(__name__)
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    async def _force_log():
+        while True:
+            await asyncio.sleep(10)
+            await engine.do_log_stats()
+
+    if not engine_args.disable_log_stats:
+        asyncio.create_task(_force_log())
+
+    yield
+
+    # Flush all events to be sent to Langfuse on shutdown and terminate all Threads gracefully.
+    # This operation is blocking.
+    LangfuseSingleton().get().flush()  # IMPORTANT if you do not want to lose logs
+
+
+app = FastAPI(lifespan=lifespan)
+
+
+def parse_args():
+    parser = make_arg_parser()
+    return parser.parse_args()
+
+
+# Add prometheus asgi middleware to route /metrics requests
+metrics_app = make_asgi_app()
+app.mount("/metrics", metrics_app)
+
+
+@app.exception_handler(RequestValidationError)
+async def validation_exception_handler(_, exc):
+    err = openai_serving_chat.create_error_response(message=str(exc))
+    return JSONResponse(err.model_dump(), status_code=HTTPStatus.BAD_REQUEST)
+
+
+@app.get("/health")
+async def health() -> Response:
+    """Health check."""
+    await openai_serving_chat.engine.check_health()
+    return Response(status_code=200)
+
+
+@app.get("/v1/models")
+async def show_available_models():
+    models = await openai_serving_chat.show_available_models()
+    return JSONResponse(content=models.model_dump())
+
+
+@app.get("/version")
+async def show_version():
+    ver = {"version": vllm.__version__}
+    return JSONResponse(content=ver)
+
+
+@app.post("/v1/chat/completions")
+async def create_chat_completion(request: ChatCompletionRequest, raw_request: Request):
+    generator = await openai_serving_chat.create_chat_completion(request, raw_request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(), status_code=generator.code)
+    if request.stream:
+        return StreamingResponse(content=generator, media_type="text/event-stream")
+    else:
+        assert isinstance(generator, ChatCompletionResponse)
+        return JSONResponse(content=generator.model_dump())
+
+
+@app.post("/v1/completions")
+async def create_completion(request: CompletionRequest, raw_request: Request):
+    generator = await openai_serving_completion.create_completion(request, raw_request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(), status_code=generator.code)
+    if request.stream:
+        return StreamingResponse(content=generator, media_type="text/event-stream")
+    else:
+        return JSONResponse(content=generator.model_dump())
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=args.allowed_origins,
+        allow_credentials=args.allow_credentials,
+        allow_methods=args.allowed_methods,
+        allow_headers=args.allowed_headers,
+    )
+
+    # We initialize langfuse singleton when starting OpenAI server
+    langfuse_client = LangfuseSingleton().get(
+        secret_key=args.langfuse_secret_key,
+        public_key=args.langfuse_public_key,
+        host=args.langfuse_host,
+    )
+
+    if token := os.environ.get("VLLM_API_KEY") or args.api_key:
+
+        @app.middleware("http")
+        async def authentication(request: Request, call_next):
+            root_path = "" if args.root_path is None else args.root_path
+            if not request.url.path.startswith(f"{root_path}/v1"):
+                return await call_next(request)
+            if request.headers.get("Authorization") != "Bearer " + token:
+                return JSONResponse(content={"error": "Unauthorized"}, status_code=401)
+            return await call_next(request)
+
+    for middleware in args.middleware:
+        module_path, object_name = middleware.rsplit(".", 1)
+        imported = getattr(importlib.import_module(module_path), object_name)
+        if inspect.isclass(imported):
+            app.add_middleware(imported)
+        elif inspect.iscoroutinefunction(imported):
+            app.middleware("http")(imported)
+        else:
+            raise ValueError(
+                f"Invalid middleware {middleware}. " f"Must be a function or a class."
+            )
+
+    logger.info(f"vLLM API server version {vllm.__version__}")
+    logger.info(f"args: {args}")
+
+    if args.served_model_name is not None:
+        served_model_names = args.served_model_name
+    else:
+        served_model_names = [args.model]
+    engine_args = AsyncEngineArgs.from_cli_args(args)
+    engine = AsyncLLMEngine.from_engine_args(
+        engine_args, usage_context=UsageContext.OPENAI_API_SERVER
+    )
+    openai_serving_chat = OpenAIServingChat(
+        engine,
+        served_model_names,
+        args.response_role,
+        args.lora_modules,
+        args.chat_template,
+    )
+    openai_serving_completion = OpenAIServingCompletion(
+        engine, served_model_names, args.lora_modules
+    )
+
+    app.root_path = args.root_path
+    uvicorn.run(
+        app,
+        host=args.host,
+        port=args.port,
+        log_level=args.uvicorn_log_level,
+        timeout_keep_alive=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,
+    )
diff --git a/examples/vllm_openai_example/vllm_openai_example/cli_args.py b/examples/vllm_openai_example/vllm_openai_example/cli_args.py
new file mode 100644
index 000000000..fbacab279
--- /dev/null
+++ b/examples/vllm_openai_example/vllm_openai_example/cli_args.py
@@ -0,0 +1,152 @@
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/cli_args.py
+"""
+This file contains the command line arguments for the vLLM's
+OpenAI-compatible server. It is kept in a separate file for documentation
+purposes.
+"""
+
+import argparse
+import json
+import ssl
+
+from vllm.engine.arg_utils import AsyncEngineArgs
+from serving_engine import LoRA
+
+
+class LoRAParserAction(argparse.Action):
+    def __call__(self, parser, namespace, values, option_string=None):
+        lora_list = []
+        for item in values:
+            name, path = item.split("=")
+            lora_list.append(LoRA(name, path))
+        setattr(namespace, self.dest, lora_list)
+
+
+def make_arg_parser():
+    parser = argparse.ArgumentParser(
+        description="vLLM OpenAI-Compatible RESTful API server."
+    )
+    parser.add_argument("--host", type=str, default=None, help="host name")
+    parser.add_argument("--port", type=int, default=8000, help="port number")
+    parser.add_argument(
+        "--uvicorn-log-level",
+        type=str,
+        default="info",
+        choices=["debug", "info", "warning", "error", "critical", "trace"],
+        help="log level for uvicorn",
+    )
+    parser.add_argument(
+        "--allow-credentials", action="store_true", help="allow credentials"
+    )
+    parser.add_argument(
+        "--allowed-origins", type=json.loads, default=["*"], help="allowed origins"
+    )
+    parser.add_argument(
+        "--allowed-methods", type=json.loads, default=["*"], help="allowed methods"
+    )
+    parser.add_argument(
+        "--allowed-headers", type=json.loads, default=["*"], help="allowed headers"
+    )
+    parser.add_argument(
+        "--api-key",
+        type=str,
+        default=None,
+        help="If provided, the server will require this key "
+        "to be presented in the header.",
+    )
+    parser.add_argument(
+        "--served-model-name",
+        nargs="+",
+        type=str,
+        default=None,
+        help="The model name(s) used in the API. If multiple "
+        "names are provided, the server will respond to any "
+        "of the provided names. The model name in the model "
+        "field of a response will be the first name in this "
+        "list. If not specified, the model name will be the "
+        "same as the `--model` argument.",
+    )
+    parser.add_argument(
+        "--lora-modules",
+        type=str,
+        default=None,
+        nargs="+",
+        action=LoRAParserAction,
+        help="LoRA module configurations in the format name=path. "
+        "Multiple modules can be specified.",
+    )
+    parser.add_argument(
+        "--chat-template",
+        type=str,
+        default=None,
+        help="The file path to the chat template, "
+        "or the template in single-line form "
+        "for the specified model",
+    )
+    parser.add_argument(
+        "--response-role",
+        type=str,
+        default="assistant",
+        help="The role name to return if " "`request.add_generation_prompt=true`.",
+    )
+    parser.add_argument(
+        "--ssl-keyfile",
+        type=str,
+        default=None,
+        help="The file path to the SSL key file",
+    )
+    parser.add_argument(
+        "--ssl-certfile",
+        type=str,
+        default=None,
+        help="The file path to the SSL cert file",
+    )
+    parser.add_argument(
+        "--ssl-ca-certs", type=str, default=None, help="The CA certificates file"
+    )
+    parser.add_argument(
+        "--ssl-cert-reqs",
+        type=int,
+        default=int(ssl.CERT_NONE),
+        help="Whether client certificate is required (see stdlib ssl module's)",
+    )
+    parser.add_argument(
+        "--root-path",
+        type=str,
+        default=None,
+        help="FastAPI root_path when app is behind a path based routing proxy",
+    )
+    parser.add_argument(
+        "--middleware",
+        type=str,
+        action="append",
+        default=[],
+        help="Additional ASGI middleware to apply to the app. "
+        "We accept multiple --middleware arguments. "
+        "The value should be an import path. "
+        "If a function is provided, vLLM will add it to the server "
+        "using @app.middleware('http'). "
+        "If a class is provided, vLLM will add it to the server "
+        "using app.add_middleware(). ",
+    )
+
+    # We add langufuse parameters to use them when initializing
+    parser.add_argument(
+        "--langfuse-secret-key",
+        type=str,
+        help="Langfuse private key for tracking experiments",
+    )
+    parser.add_argument(
+        "--langfuse-public-key",
+        type=str,
+        help="Langfuse public key for tracking experiments",
+    )
+    parser.add_argument(
+        "--langfuse-host",
+        type=str,
+        help="Langfuse host in format 'http://{SOME_DOMAIN}:{SOME_PORT}'",
+    )
+
+    parser = AsyncEngineArgs.add_cli_args(parser)
+    return parser
diff --git a/examples/vllm_openai_example/vllm_openai_example/langfuse_utils.py b/examples/vllm_openai_example/vllm_openai_example/langfuse_utils.py
new file mode 100644
index 000000000..f4ff29883
--- /dev/null
+++ b/examples/vllm_openai_example/vllm_openai_example/langfuse_utils.py
@@ -0,0 +1,32 @@
+import pydantic
+from typing import Iterable, Any, Dict
+from functools import partial
+from protocol import ChatCompletionStreamResponse, CompletionStreamResponse
+
+
+def base_langfuse_stream_processor(
+    generator: Iterable[str], _streaming_model: pydantic.BaseModel = None
+) -> Dict[str, Any]:
+    result_content = ""
+    result_responses = []
+    # Saving whole response takes more time than only saving `result_content`
+    # Consider remove saving whole response if this time is significant for you
+    for item in generator:
+        result_responses.append(item)
+        raw_object = item.rsplit("data: ", 1)[1]
+        try:
+            single_response = _streaming_model.model_validate_json(raw_object)
+            content = single_response.choices[0].delta.content
+        except pydantic.ValidationError:  # receive 'data: [DONE]\n\n'
+            content = ""
+        result_content += content if content is not None else ""
+
+    return {"content": result_content, "data": result_responses}
+
+
+langfuse_chat_transformer = partial(
+    base_langfuse_stream_processor, _streaming_model=ChatCompletionStreamResponse
+)
+langfuse_completion_transformer = partial(
+    base_langfuse_stream_processor, _streaming_model=CompletionStreamResponse
+)
diff --git a/examples/vllm_openai_example/vllm_openai_example/protocol.py b/examples/vllm_openai_example/vllm_openai_example/protocol.py
new file mode 100644
index 000000000..e5e282de7
--- /dev/null
+++ b/examples/vllm_openai_example/vllm_openai_example/protocol.py
@@ -0,0 +1,450 @@
+# Original source
+# https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/protocol.py
+import time
+from typing import Dict, List, Literal, Optional, Union
+
+import torch
+from pydantic import BaseModel, Field, model_validator
+from typing_extensions import Annotated
+from vllm.sampling_params import SamplingParams
+from vllm.utils import random_uuid
+
+
+class ErrorResponse(BaseModel):
+    object: str = "error"
+    message: str
+    type: str
+    param: Optional[str] = None
+    code: int
+
+
+class ModelPermission(BaseModel):
+    id: str = Field(default_factory=lambda: f"modelperm-{random_uuid()}")
+    object: str = "model_permission"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    allow_create_engine: bool = False
+    allow_sampling: bool = True
+    allow_logprobs: bool = True
+    allow_search_indices: bool = False
+    allow_view: bool = True
+    allow_fine_tuning: bool = False
+    organization: str = "*"
+    group: Optional[str] = None
+    is_blocking: bool = False
+
+
+class ModelCard(BaseModel):
+    id: str
+    object: str = "model"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    owned_by: str = "vllm"
+    root: Optional[str] = None
+    parent: Optional[str] = None
+    permission: List[ModelPermission] = Field(default_factory=list)
+
+
+class ModelList(BaseModel):
+    object: str = "list"
+    data: List[ModelCard] = Field(default_factory=list)
+
+
+class UsageInfo(BaseModel):
+    prompt_tokens: int = 0
+    total_tokens: int = 0
+    completion_tokens: Optional[int] = 0
+
+
+class ResponseFormat(BaseModel):
+    # type must be "json_object" or "text"
+    type: Literal["text", "json_object"]
+
+
+class ChatCompletionRequest(BaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/chat/create
+    messages: List[Dict[str, str]]
+    model: str
+    frequency_penalty: Optional[float] = 0.0
+    logit_bias: Optional[Dict[str, float]] = None
+    logprobs: Optional[bool] = False
+    top_logprobs: Optional[int] = None
+    max_tokens: Optional[int] = None
+    n: Optional[int] = 1
+    presence_penalty: Optional[float] = 0.0
+    response_format: Optional[ResponseFormat] = None
+    seed: Optional[int] = None
+    stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
+    stream: Optional[bool] = False
+    temperature: Optional[float] = 0.7
+    top_p: Optional[float] = 1.0
+    user: Optional[str] = None
+
+    # doc: begin-chat-completion-sampling-params
+    best_of: Optional[int] = None
+    use_beam_search: Optional[bool] = False
+    top_k: Optional[int] = -1
+    min_p: Optional[float] = 0.0
+    repetition_penalty: Optional[float] = 1.0
+    length_penalty: Optional[float] = 1.0
+    early_stopping: Optional[bool] = False
+    ignore_eos: Optional[bool] = False
+    min_tokens: Optional[int] = 0
+    stop_token_ids: Optional[List[int]] = Field(default_factory=list)
+    skip_special_tokens: Optional[bool] = True
+    spaces_between_special_tokens: Optional[bool] = True
+    # doc: end-chat-completion-sampling-params
+
+    # doc: begin-chat-completion-extra-params
+    echo: Optional[bool] = Field(
+        default=False,
+        description=(
+            "If true, the new message will be prepended with the last message "
+            "if they belong to the same role."
+        ),
+    )
+    add_generation_prompt: Optional[bool] = Field(
+        default=True,
+        description=(
+            "If true, the generation prompt will be added to the chat template. "
+            "This is a parameter used by chat template in tokenizer config of the "
+            "model."
+        ),
+    )
+    include_stop_str_in_output: Optional[bool] = Field(
+        default=False,
+        description=(
+            "Whether to include the stop string in the output. "
+            "This is only applied when the stop or stop_token_ids is set."
+        ),
+    )
+    guided_json: Optional[Union[str, dict, BaseModel]] = Field(
+        default=None,
+        description=("If specified, the output will follow the JSON schema."),
+    )
+    guided_regex: Optional[str] = Field(
+        default=None,
+        description=("If specified, the output will follow the regex pattern."),
+    )
+    guided_choice: Optional[List[str]] = Field(
+        default=None,
+        description=("If specified, the output will be exactly one of the choices."),
+    )
+    guided_grammar: Optional[str] = Field(
+        default=None,
+        description=("If specified, the output will follow the context free grammar."),
+    )
+    guided_decoding_backend: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, will override the default guided decoding backend "
+            "of the server for this specific request. If set, must be either "
+            "'outlines' / 'lm-format-enforcer'"
+        ),
+    )
+
+    # doc: end-chat-completion-extra-params
+
+    def to_sampling_params(self) -> SamplingParams:
+        if self.logprobs and not self.top_logprobs:
+            raise ValueError("Top logprobs must be set when logprobs is.")
+
+        logits_processors = None
+        if self.logit_bias:
+
+            def logit_bias_logits_processor(
+                token_ids: List[int], logits: torch.Tensor
+            ) -> torch.Tensor:
+                assert self.logit_bias is not None
+                for token_id, bias in self.logit_bias.items():
+                    # Clamp the bias between -100 and 100 per OpenAI API spec
+                    bias = min(100, max(-100, bias))
+                    logits[int(token_id)] += bias
+                return logits
+
+            logits_processors = [logit_bias_logits_processor]
+
+        return SamplingParams(
+            n=self.n,
+            presence_penalty=self.presence_penalty,
+            frequency_penalty=self.frequency_penalty,
+            repetition_penalty=self.repetition_penalty,
+            temperature=self.temperature,
+            top_p=self.top_p,
+            min_p=self.min_p,
+            seed=self.seed,
+            stop=self.stop,
+            stop_token_ids=self.stop_token_ids,
+            max_tokens=self.max_tokens,
+            min_tokens=self.min_tokens,
+            logprobs=self.top_logprobs if self.logprobs else None,
+            prompt_logprobs=self.top_logprobs if self.echo else None,
+            best_of=self.best_of,
+            top_k=self.top_k,
+            ignore_eos=self.ignore_eos,
+            use_beam_search=self.use_beam_search,
+            early_stopping=self.early_stopping,
+            skip_special_tokens=self.skip_special_tokens,
+            spaces_between_special_tokens=self.spaces_between_special_tokens,
+            include_stop_str_in_output=self.include_stop_str_in_output,
+            length_penalty=self.length_penalty,
+            logits_processors=logits_processors,
+        )
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_guided_decoding_count(cls, data):
+        guide_count = sum(
+            [
+                "guided_json" in data and data["guided_json"] is not None,
+                "guided_regex" in data and data["guided_regex"] is not None,
+                "guided_choice" in data and data["guided_choice"] is not None,
+            ]
+        )
+        if guide_count > 1:
+            raise ValueError(
+                "You can only use one kind of guided decoding "
+                "('guided_json', 'guided_regex' or 'guided_choice')."
+            )
+        return data
+
+
+class CompletionRequest(BaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/completions/create
+    model: str
+    prompt: Union[List[int], List[List[int]], str, List[str]]
+    best_of: Optional[int] = None
+    echo: Optional[bool] = False
+    frequency_penalty: Optional[float] = 0.0
+    logit_bias: Optional[Dict[str, float]] = None
+    logprobs: Optional[int] = None
+    max_tokens: Optional[int] = 16
+    n: int = 1
+    presence_penalty: Optional[float] = 0.0
+    seed: Optional[int] = None
+    stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
+    stream: Optional[bool] = False
+    suffix: Optional[str] = None
+    temperature: Optional[float] = 1.0
+    top_p: Optional[float] = 1.0
+    user: Optional[str] = None
+
+    # doc: begin-completion-sampling-params
+    use_beam_search: Optional[bool] = False
+    top_k: Optional[int] = -1
+    min_p: Optional[float] = 0.0
+    repetition_penalty: Optional[float] = 1.0
+    length_penalty: Optional[float] = 1.0
+    early_stopping: Optional[bool] = False
+    stop_token_ids: Optional[List[int]] = Field(default_factory=list)
+    ignore_eos: Optional[bool] = False
+    min_tokens: Optional[int] = 0
+    skip_special_tokens: Optional[bool] = True
+    spaces_between_special_tokens: Optional[bool] = True
+    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
+    # doc: end-completion-sampling-params
+
+    # doc: begin-completion-extra-params
+    include_stop_str_in_output: Optional[bool] = Field(
+        default=False,
+        description=(
+            "Whether to include the stop string in the output. "
+            "This is only applied when the stop or stop_token_ids is set."
+        ),
+    )
+    response_format: Optional[ResponseFormat] = Field(
+        default=None,
+        description=(
+            "Similar to chat completion, this parameter specifies the format of "
+            "output. Only {'type': 'json_object'} or {'type': 'text' } is "
+            "supported."
+        ),
+    )
+    guided_json: Optional[Union[str, dict, BaseModel]] = Field(
+        default=None,
+        description=("If specified, the output will follow the JSON schema."),
+    )
+    guided_regex: Optional[str] = Field(
+        default=None,
+        description=("If specified, the output will follow the regex pattern."),
+    )
+    guided_choice: Optional[List[str]] = Field(
+        default=None,
+        description=("If specified, the output will be exactly one of the choices."),
+    )
+    guided_grammar: Optional[str] = Field(
+        default=None,
+        description=("If specified, the output will follow the context free grammar."),
+    )
+    guided_decoding_backend: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, will override the default guided decoding backend "
+            "of the server for this specific request. If set, must be one of "
+            "'outlines' / 'lm-format-enforcer'"
+        ),
+    )
+
+    # doc: end-completion-extra-params
+
+    def to_sampling_params(self):
+        echo_without_generation = self.echo and self.max_tokens == 0
+
+        logits_processors = None
+        if self.logit_bias:
+
+            def logit_bias_logits_processor(
+                token_ids: List[int], logits: torch.Tensor
+            ) -> torch.Tensor:
+                assert self.logit_bias is not None
+                for token_id, bias in self.logit_bias.items():
+                    # Clamp the bias between -100 and 100 per OpenAI API spec
+                    bias = min(100, max(-100, bias))
+                    logits[int(token_id)] += bias
+                return logits
+
+            logits_processors = [logit_bias_logits_processor]
+
+        return SamplingParams(
+            n=self.n,
+            best_of=self.best_of,
+            presence_penalty=self.presence_penalty,
+            frequency_penalty=self.frequency_penalty,
+            repetition_penalty=self.repetition_penalty,
+            temperature=self.temperature,
+            top_p=self.top_p,
+            top_k=self.top_k,
+            min_p=self.min_p,
+            seed=self.seed,
+            stop=self.stop,
+            stop_token_ids=self.stop_token_ids,
+            ignore_eos=self.ignore_eos,
+            max_tokens=self.max_tokens if not echo_without_generation else 1,
+            min_tokens=self.min_tokens,
+            logprobs=self.logprobs,
+            use_beam_search=self.use_beam_search,
+            early_stopping=self.early_stopping,
+            prompt_logprobs=self.logprobs if self.echo else None,
+            skip_special_tokens=self.skip_special_tokens,
+            spaces_between_special_tokens=(self.spaces_between_special_tokens),
+            include_stop_str_in_output=self.include_stop_str_in_output,
+            length_penalty=self.length_penalty,
+            logits_processors=logits_processors,
+            truncate_prompt_tokens=self.truncate_prompt_tokens,
+        )
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_guided_decoding_count(cls, data):
+        guide_count = sum(
+            [
+                "guided_json" in data and data["guided_json"] is not None,
+                "guided_regex" in data and data["guided_regex"] is not None,
+                "guided_choice" in data and data["guided_choice"] is not None,
+            ]
+        )
+        if guide_count > 1:
+            raise ValueError(
+                "You can only use one kind of guided decoding "
+                "('guided_json', 'guided_regex' or 'guided_choice')."
+            )
+        return data
+
+
+class LogProbs(BaseModel):
+    text_offset: List[int] = Field(default_factory=list)
+    token_logprobs: List[Optional[float]] = Field(default_factory=list)
+    tokens: List[str] = Field(default_factory=list)
+    top_logprobs: Optional[List[Optional[Dict[str, float]]]] = None
+
+
+class CompletionResponseChoice(BaseModel):
+    index: int
+    text: str
+    logprobs: Optional[LogProbs] = None
+    finish_reason: Optional[Literal["stop", "length"]] = None
+    stop_reason: Union[None, int, str] = Field(
+        default=None,
+        description=(
+            "The stop string or token id that caused the completion "
+            "to stop, None if the completion finished for some other reason "
+            "including encountering the EOS token"
+        ),
+    )
+
+
+class CompletionResponse(BaseModel):
+    id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
+    object: str = "text_completion"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[CompletionResponseChoice]
+    usage: UsageInfo
+
+
+class CompletionResponseStreamChoice(BaseModel):
+    index: int
+    text: str
+    logprobs: Optional[LogProbs] = None
+    finish_reason: Optional[Literal["stop", "length"]] = None
+    stop_reason: Union[None, int, str] = Field(
+        default=None,
+        description=(
+            "The stop string or token id that caused the completion "
+            "to stop, None if the completion finished for some other reason "
+            "including encountering the EOS token"
+        ),
+    )
+
+
+class CompletionStreamResponse(BaseModel):
+    id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
+    object: str = "text_completion"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[CompletionResponseStreamChoice]
+    usage: Optional[UsageInfo] = Field(default=None)
+
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+
+class ChatCompletionResponseChoice(BaseModel):
+    index: int
+    message: ChatMessage
+    logprobs: Optional[LogProbs] = None
+    finish_reason: Optional[Literal["stop", "length"]] = None
+    stop_reason: Union[None, int, str] = None
+
+
+class ChatCompletionResponse(BaseModel):
+    id: str = Field(default_factory=lambda: f"chatcmpl-{random_uuid()}")
+    object: str = "chat.completion"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[ChatCompletionResponseChoice]
+    usage: UsageInfo
+
+
+class DeltaMessage(BaseModel):
+    role: Optional[str] = None
+    content: Optional[str] = None
+
+
+class ChatCompletionResponseStreamChoice(BaseModel):
+    index: int
+    delta: DeltaMessage
+    logprobs: Optional[LogProbs] = None
+    finish_reason: Optional[Literal["stop", "length"]] = None
+    stop_reason: Union[None, int, str] = None
+
+
+class ChatCompletionStreamResponse(BaseModel):
+    id: str = Field(default_factory=lambda: f"chatcmpl-{random_uuid()}")
+    object: str = "chat.completion.chunk"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[ChatCompletionResponseStreamChoice]
+    usage: Optional[UsageInfo] = Field(default=None)
diff --git a/examples/vllm_openai_example/vllm_openai_example/serving_chat.py b/examples/vllm_openai_example/vllm_openai_example/serving_chat.py
new file mode 100644
index 000000000..7e7b0ea4a
--- /dev/null
+++ b/examples/vllm_openai_example/vllm_openai_example/serving_chat.py
@@ -0,0 +1,402 @@
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/serving_chat.py
+import codecs
+import time
+from typing import AsyncGenerator, AsyncIterator, List, Optional, Union
+
+from fastapi import Request
+import vllm
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.logger import init_logger
+from vllm.model_executor.guided_decoding import get_guided_decoding_logits_processor
+from vllm.outputs import RequestOutput
+from vllm.utils import random_uuid
+from langfuse.decorators import observe, langfuse_context
+from langfuse_utils import langfuse_chat_transformer
+
+
+from protocol import (
+    ChatCompletionRequest,
+    ChatCompletionResponse,
+    ChatCompletionResponseChoice,
+    ChatCompletionResponseStreamChoice,
+    ChatCompletionStreamResponse,
+    ChatMessage,
+    DeltaMessage,
+    ErrorResponse,
+    UsageInfo,
+)
+from serving_engine import LoRA, OpenAIServing
+
+logger = init_logger(__name__)
+
+
+class OpenAIServingChat(OpenAIServing):
+    def __init__(
+        self,
+        engine: AsyncLLMEngine,
+        served_model_names: List[str],
+        response_role: str,
+        lora_modules: Optional[List[LoRA]] = None,
+        chat_template=None,
+    ):
+        super().__init__(
+            engine=engine,
+            served_model_names=served_model_names,
+            lora_modules=lora_modules,
+        )
+        self.response_role = response_role
+        self._load_chat_template(chat_template)
+
+    @observe(
+        capture_input=False,
+        capture_output=True,
+        as_type="generation",
+        transform_to_string=langfuse_chat_transformer,
+    )
+    async def create_chat_completion(
+        self, request: ChatCompletionRequest, raw_request: Request
+    ) -> Union[ErrorResponse, AsyncGenerator[str, None], ChatCompletionResponse]:
+        """Completion API similar to OpenAI's API.
+
+        See https://platform.openai.com/docs/api-reference/chat/create
+        for the API specification. This API mimics the OpenAI
+        ChatCompletion API.
+
+        NOTE: Currently we do not support the following feature:
+            - function_call (Users should implement this by themselves)
+        """
+
+        # Some Langfuse updates to make request look better in UI
+        langfuse_context.update_current_trace(
+            name="create_chat_completion", version="vLLM", release=vllm.__version__
+        )
+        langfuse_context.update_current_observation(
+            input=request, model=request.model, version="vLLM", release=vllm.__version__
+        )
+
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        try:
+            prompt = self.tokenizer.apply_chat_template(
+                conversation=request.messages,
+                tokenize=False,
+                add_generation_prompt=request.add_generation_prompt,
+            )
+        except Exception as e:
+            logger.error(f"Error in applying chat template from request: {str(e)}")
+            return self.create_error_response(str(e))
+
+        request_id = f"cmpl-{random_uuid()}"
+        try:
+            # Tokenize/detokenize depending on prompt format (string/token list)
+            prompt_ids, prompt_text = self._validate_prompt_and_tokenize(
+                request, prompt=prompt
+            )
+            sampling_params = request.to_sampling_params()
+            lora_request = self._maybe_get_lora(request)
+            decoding_config = self.engine.engine.decoding_config
+            guided_decoding_backend = (
+                request.guided_decoding_backend
+                or decoding_config.guided_decoding_backend
+            )
+            guided_decode_logits_processor = await get_guided_decoding_logits_processor(
+                guided_decoding_backend, request, await self.engine.get_tokenizer()
+            )
+            if guided_decode_logits_processor:
+                if sampling_params.logits_processors is None:
+                    sampling_params.logits_processors = []
+                sampling_params.logits_processors.append(guided_decode_logits_processor)
+        except ValueError as e:
+            return self.create_error_response(str(e))
+
+        result_generator = self.engine.generate(
+            prompt_text, sampling_params, request_id, prompt_ids, lora_request
+        )
+        # Streaming response
+        if request.stream:
+            # If we are using streaming Langfuse will handle it because of capture_output=True and we passed
+            # transform_to_string=langfuse_chat_transformer
+            return self.chat_completion_stream_generator(
+                request, result_generator, request_id
+            )
+        else:
+            try:
+                full_generator = await self.chat_completion_full_generator(
+                    request, raw_request, result_generator, request_id
+                )
+                langfuse_context.update_current_observation(
+                    output=full_generator.model_dump()
+                )
+                return full_generator
+            except ValueError as e:
+                # TODO: Use a vllm-specific Validation Error
+                langfuse_context.update_current_observation(
+                    output=None,
+                    status_message="Failed to process message. Something wrong with vLLM instance",
+                )
+                return self.create_error_response(str(e))
+
+    def get_chat_request_role(self, request: ChatCompletionRequest) -> str:
+        if request.add_generation_prompt:
+            return self.response_role
+        else:
+            return request.messages[-1]["role"]
+
+    async def chat_completion_stream_generator(
+        self,
+        request: ChatCompletionRequest,
+        result_generator: AsyncIterator[RequestOutput],
+        request_id: str,
+    ) -> Union[ErrorResponse, AsyncGenerator[str, None]]:
+        model_name = self.served_model_names[0]
+        created_time = int(time.time())
+        chunk_object_type = "chat.completion.chunk"
+        first_iteration = True
+
+        # Send response for each token for each request.n (index)
+        assert request.n is not None
+        previous_texts = [""] * request.n
+        previous_num_tokens = [0] * request.n
+        finish_reason_sent = [False] * request.n
+        try:
+            async for res in result_generator:
+                # We need to do it here, because if there are exceptions in
+                # the result_generator, it needs to be sent as the FIRST
+                # response (by the try...catch).
+                if first_iteration:
+                    # Send first response for each request.n (index) with
+                    # the role
+                    role = self.get_chat_request_role(request)
+                    for i in range(request.n):
+                        choice_data = ChatCompletionResponseStreamChoice(
+                            index=i,
+                            delta=DeltaMessage(role=role),
+                            logprobs=None,
+                            finish_reason=None,
+                        )
+                        chunk = ChatCompletionStreamResponse(
+                            id=request_id,
+                            object=chunk_object_type,
+                            created=created_time,
+                            choices=[choice_data],
+                            model=model_name,
+                        )
+                        data = chunk.model_dump_json(exclude_unset=True)
+                        yield f"data: {data}\n\n"
+
+                    # Send response to echo the input portion of the
+                    # last message
+                    if request.echo:
+                        last_msg_content = ""
+                        if (
+                            request.messages
+                            and isinstance(request.messages, list)
+                            and request.messages[-1].get("content")
+                            and request.messages[-1].get("role") == role
+                        ):
+                            last_msg_content = request.messages[-1]["content"]
+
+                        if last_msg_content:
+                            for i in range(request.n):
+                                choice_data = ChatCompletionResponseStreamChoice(
+                                    index=i,
+                                    delta=DeltaMessage(content=last_msg_content),
+                                    finish_reason=None,
+                                )
+                                chunk = ChatCompletionStreamResponse(
+                                    id=request_id,
+                                    object=chunk_object_type,
+                                    created=created_time,
+                                    choices=[choice_data],
+                                    logprobs=None,
+                                    model=model_name,
+                                )
+                                data = chunk.model_dump_json(exclude_unset=True)
+                                yield f"data: {data}\n\n"
+                    first_iteration = False
+
+                for output in res.outputs:
+                    i = output.index
+
+                    if finish_reason_sent[i]:
+                        continue
+
+                    delta_token_ids = output.token_ids[previous_num_tokens[i] :]
+                    top_logprobs = (
+                        output.logprobs[previous_num_tokens[i] :]
+                        if output.logprobs
+                        else None
+                    )
+
+                    if request.logprobs:
+                        logprobs = self._create_logprobs(
+                            token_ids=delta_token_ids,
+                            top_logprobs=top_logprobs,
+                            num_output_top_logprobs=request.logprobs,
+                            initial_text_offset=len(previous_texts[i]),
+                        )
+                    else:
+                        logprobs = None
+
+                    delta_text = output.text[len(previous_texts[i]) :]
+                    previous_texts[i] = output.text
+                    previous_num_tokens[i] = len(output.token_ids)
+                    if output.finish_reason is None:
+                        # Send token-by-token response for each request.n
+                        choice_data = ChatCompletionResponseStreamChoice(
+                            index=i,
+                            delta=DeltaMessage(content=delta_text),
+                            logprobs=logprobs,
+                            finish_reason=None,
+                        )
+                        chunk = ChatCompletionStreamResponse(
+                            id=request_id,
+                            object=chunk_object_type,
+                            created=created_time,
+                            choices=[choice_data],
+                            model=model_name,
+                        )
+                        data = chunk.model_dump_json(exclude_unset=True)
+                        yield f"data: {data}\n\n"
+                    else:
+                        # Send the finish response for each request.n only once
+                        prompt_tokens = len(res.prompt_token_ids)
+                        final_usage = UsageInfo(
+                            prompt_tokens=prompt_tokens,
+                            completion_tokens=previous_num_tokens[i],
+                            total_tokens=prompt_tokens + previous_num_tokens[i],
+                        )
+                        choice_data = ChatCompletionResponseStreamChoice(
+                            index=i,
+                            delta=DeltaMessage(content=delta_text),
+                            logprobs=logprobs,
+                            finish_reason=output.finish_reason,
+                            stop_reason=output.stop_reason,
+                        )
+                        chunk = ChatCompletionStreamResponse(
+                            id=request_id,
+                            object=chunk_object_type,
+                            created=created_time,
+                            choices=[choice_data],
+                            model=model_name,
+                        )
+                        if final_usage is not None:
+                            chunk.usage = final_usage
+                        data = chunk.model_dump_json(
+                            exclude_unset=True, exclude_none=True
+                        )
+                        yield f"data: {data}\n\n"
+                        finish_reason_sent[i] = True
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            data = self.create_streaming_error_response(str(e))
+            yield f"data: {data}\n\n"
+        # Send the final done message after all response.n are finished
+        yield "data: [DONE]\n\n"
+
+    async def chat_completion_full_generator(
+        self,
+        request: ChatCompletionRequest,
+        raw_request: Request,
+        result_generator: AsyncIterator[RequestOutput],
+        request_id: str,
+    ) -> Union[ErrorResponse, ChatCompletionResponse]:
+        model_name = self.served_model_names[0]
+        created_time = int(time.time())
+        final_res: RequestOutput = None
+
+        async for res in result_generator:
+            if await raw_request.is_disconnected():
+                # Abort the request if the client disconnects.
+                await self.engine.abort(request_id)
+                return self.create_error_response("Client disconnected")
+            final_res = res
+        assert final_res is not None
+
+        choices = []
+
+        role = self.get_chat_request_role(request)
+        for output in final_res.outputs:
+            token_ids = output.token_ids
+            top_logprobs = output.logprobs
+
+            if request.logprobs:
+                logprobs = self._create_logprobs(
+                    token_ids=token_ids,
+                    top_logprobs=top_logprobs,
+                    num_output_top_logprobs=request.logprobs,
+                )
+            else:
+                logprobs = None
+
+            choice_data = ChatCompletionResponseChoice(
+                index=output.index,
+                message=ChatMessage(role=role, content=output.text),
+                logprobs=logprobs,
+                finish_reason=output.finish_reason,
+                stop_reason=output.stop_reason,
+            )
+            choices.append(choice_data)
+
+        if request.echo:
+            last_msg_content = ""
+            if (
+                request.messages
+                and isinstance(request.messages, list)
+                and request.messages[-1].get("content")
+                and request.messages[-1].get("role") == role
+            ):
+                last_msg_content = request.messages[-1]["content"]
+
+            for choice in choices:
+                full_message = last_msg_content + choice.message.content
+                choice.message.content = full_message
+
+        num_prompt_tokens = len(final_res.prompt_token_ids)
+        num_generated_tokens = sum(
+            len(output.token_ids) for output in final_res.outputs
+        )
+        usage = UsageInfo(
+            prompt_tokens=num_prompt_tokens,
+            completion_tokens=num_generated_tokens,
+            total_tokens=num_prompt_tokens + num_generated_tokens,
+        )
+        response = ChatCompletionResponse(
+            id=request_id,
+            created=created_time,
+            model=model_name,
+            choices=choices,
+            usage=usage,
+        )
+
+        return response
+
+    def _load_chat_template(self, chat_template):
+        tokenizer = self.tokenizer
+
+        if chat_template is not None:
+            try:
+                with open(chat_template, "r") as f:
+                    tokenizer.chat_template = f.read()
+            except OSError as e:
+                JINJA_CHARS = "{}\n"
+                if not any(c in chat_template for c in JINJA_CHARS):
+                    msg = (
+                        f"The supplied chat template ({chat_template}) "
+                        f"looks like a file path, but it failed to be "
+                        f"opened. Reason: {e}"
+                    )
+                    raise ValueError(msg) from e
+
+                # If opening a file fails, set chat template to be args to
+                # ensure we decode so our escape are interpreted correctly
+                tokenizer.chat_template = codecs.decode(chat_template, "unicode_escape")
+
+            logger.info(f"Using supplied chat template:\n{tokenizer.chat_template}")
+        elif tokenizer.chat_template is not None:
+            logger.info(f"Using default chat template:\n{tokenizer.chat_template}")
+        else:
+            logger.warning("No chat template provided. Chat API will not work.")
diff --git a/examples/vllm_openai_example/vllm_openai_example/serving_completion.py b/examples/vllm_openai_example/vllm_openai_example/serving_completion.py
new file mode 100644
index 000000000..d1e7159c2
--- /dev/null
+++ b/examples/vllm_openai_example/vllm_openai_example/serving_completion.py
@@ -0,0 +1,394 @@
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/serving_completion.py
+import time
+from typing import (
+    AsyncGenerator,
+    AsyncIterator,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Tuple,
+)
+
+from fastapi import Request
+import vllm
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.logger import init_logger
+from vllm.model_executor.guided_decoding import get_guided_decoding_logits_processor
+from vllm.outputs import RequestOutput
+from vllm.utils import merge_async_iterators, random_uuid
+from langfuse.decorators import observe, langfuse_context
+from langfuse_utils import langfuse_completion_transformer
+
+from protocol import (
+    CompletionRequest,
+    CompletionResponse,
+    CompletionResponseChoice,
+    CompletionResponseStreamChoice,
+    CompletionStreamResponse,
+    LogProbs,
+    UsageInfo,
+)
+from serving_engine import LoRA, OpenAIServing
+
+logger = init_logger(__name__)
+
+TypeTokenIDs = List[int]
+TypeTopLogProbs = List[Optional[Dict[int, float]]]
+TypeCreateLogProbsFn = Callable[
+    [TypeTokenIDs, TypeTopLogProbs, Optional[int], int], LogProbs
+]
+
+
+def parse_prompt_format(prompt) -> Tuple[bool, list]:
+    # get the prompt, openai supports the following
+    # "a string, array of strings, array of tokens, or array of token arrays."
+    prompt_is_tokens = False
+    prompts = [prompt]  # case 1: a string
+    if isinstance(prompt, list):
+        if len(prompt) == 0:
+            raise ValueError("please provide at least one prompt")
+        elif isinstance(prompt[0], str):
+            prompt_is_tokens = False
+            prompts = prompt  # case 2: array of strings
+        elif isinstance(prompt[0], int):
+            prompt_is_tokens = True
+            prompts = [prompt]  # case 3: array of tokens
+        elif isinstance(prompt[0], list) and isinstance(prompt[0][0], int):
+            prompt_is_tokens = True
+            prompts = prompt  # case 4: array of token arrays
+        else:
+            raise ValueError(
+                "prompt must be a string, array of strings, "
+                "array of tokens, or array of token arrays"
+            )
+    return prompt_is_tokens, prompts
+
+
+class OpenAIServingCompletion(OpenAIServing):
+    def __init__(
+        self,
+        engine: AsyncLLMEngine,
+        served_model_names: List[str],
+        lora_modules: Optional[List[LoRA]] = None,
+    ):
+        super().__init__(
+            engine=engine,
+            served_model_names=served_model_names,
+            lora_modules=lora_modules,
+        )
+
+    @observe(
+        capture_input=False,
+        capture_output=True,
+        as_type="generation",
+        transform_to_string=langfuse_completion_transformer,
+    )
+    async def create_completion(self, request: CompletionRequest, raw_request: Request):
+        """Completion API similar to OpenAI's API.
+
+        See https://platform.openai.com/docs/api-reference/completions/create
+        for the API specification. This API mimics the OpenAI Completion API.
+
+        NOTE: Currently we do not support the following feature:
+            - suffix (the language models we currently support do not support
+            suffix)
+        """
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        # Return error for unsupported features.
+        if request.suffix is not None:
+            return self.create_error_response("suffix is not currently supported")
+
+        model_name = self.served_model_names[0]
+        request_id = f"cmpl-{random_uuid()}"
+        created_time = int(time.time())
+
+        # Some Langfuse updates to make request look better in UI
+        langfuse_context.update_current_trace(
+            name="create_completion", version="vLLM", release=vllm.__version__
+        )
+        langfuse_context.update_current_observation(
+            input=request, model=request.model, version="vLLM", release=vllm.__version__
+        )
+
+        # Schedule the request and get the result generator.
+        generators = []
+        try:
+            sampling_params = request.to_sampling_params()
+            lora_request = self._maybe_get_lora(request)
+            decoding_config = self.engine.engine.decoding_config
+            guided_decoding_backend = (
+                request.guided_decoding_backend
+                or decoding_config.guided_decoding_backend
+            )
+            guided_decode_logit_processor = await get_guided_decoding_logits_processor(
+                guided_decoding_backend, request, await self.engine.get_tokenizer()
+            )
+            if guided_decode_logit_processor is not None:
+                if sampling_params.logits_processors is None:
+                    sampling_params.logits_processors = []
+                sampling_params.logits_processors.append(guided_decode_logit_processor)
+            prompt_is_tokens, prompts = parse_prompt_format(request.prompt)
+
+            for i, prompt in enumerate(prompts):
+                if prompt_is_tokens:
+                    prompt_formats = self._validate_prompt_and_tokenize(
+                        request,
+                        prompt_ids=prompt,
+                        truncate_prompt_tokens=sampling_params.truncate_prompt_tokens,
+                    )
+                else:
+                    prompt_formats = self._validate_prompt_and_tokenize(
+                        request,
+                        prompt=prompt,
+                        truncate_prompt_tokens=sampling_params.truncate_prompt_tokens,
+                    )
+                prompt_ids, prompt_text = prompt_formats
+
+                generators.append(
+                    self.engine.generate(
+                        prompt_text,
+                        sampling_params,
+                        f"{request_id}-{i}",
+                        prompt_token_ids=prompt_ids,
+                        lora_request=lora_request,
+                    )
+                )
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            return self.create_error_response(str(e))
+
+        result_generator: AsyncIterator[Tuple[int, RequestOutput]] = (
+            merge_async_iterators(*generators)
+        )
+
+        # Similar to the OpenAI API, when n != best_of, we do not stream the
+        # results. In addition, we do not stream the results when use
+        # beam search.
+        stream = (
+            request.stream
+            and (request.best_of is None or request.n == request.best_of)
+            and not request.use_beam_search
+        )
+
+        # Streaming response
+        if stream:
+            # If we are using streaming Langfuse will handle it because of capture_output=True and we passed
+            # transform_to_string=langfuse_completion_transformer
+            return self.completion_stream_generator(
+                request,
+                raw_request,
+                result_generator,
+                request_id,
+                created_time,
+                model_name,
+                num_prompts=len(prompts),
+            )
+
+        # Non-streaming response
+        final_res_batch: RequestOutput = [None] * len(prompts)
+        try:
+            async for i, res in result_generator:
+                if await raw_request.is_disconnected():
+                    # Abort the request if the client disconnects.
+                    await self.engine.abort(f"{request_id}-{i}")
+                    return self.create_error_response("Client disconnected")
+                final_res_batch[i] = res
+            response = self.request_output_to_completion_response(
+                final_res_batch, request, request_id, created_time, model_name
+            )
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            langfuse_context.update_current_observation(
+                output=None,
+                status_message="Failed to process message. Something wrong with vLLM instance",
+            )
+            return self.create_error_response(str(e))
+
+        # When user requests streaming but we don't stream, we still need to
+        # return a streaming response with a single event.
+        if request.stream:
+            response_json = response.model_dump_json()
+
+            async def fake_stream_generator() -> AsyncGenerator[str, None]:
+                yield f"data: {response_json}\n\n"
+                yield "data: [DONE]\n\n"
+
+            return fake_stream_generator()
+        # Track final output with Langfuse
+        langfuse_context.update_current_observation(output=response)
+        return response
+
+    async def completion_stream_generator(
+        self,
+        request: CompletionRequest,
+        raw_request: Request,
+        result_generator: AsyncIterator[Tuple[int, RequestOutput]],
+        request_id: str,
+        created_time: int,
+        model_name: str,
+        num_prompts: int,
+    ) -> AsyncGenerator[str, None]:
+        assert request.n is not None
+        previous_texts = [""] * request.n * num_prompts
+        previous_num_tokens = [0] * request.n * num_prompts
+        has_echoed = [False] * request.n * num_prompts
+
+        try:
+            async for prompt_idx, res in result_generator:
+                # Abort the request if the client disconnects.
+                if await raw_request.is_disconnected():
+                    await self.engine.abort(f"{request_id}-{prompt_idx}")
+                    raise StopAsyncIteration()
+
+                for output in res.outputs:
+                    i = output.index + prompt_idx * request.n
+                    # TODO(simon): optimize the performance by avoiding full
+                    # text O(n^2) sending.
+
+                    assert request.max_tokens is not None
+                    if request.echo and request.max_tokens == 0:
+                        # only return the prompt
+                        delta_text = res.prompt
+                        delta_token_ids = res.prompt_token_ids
+                        top_logprobs = res.prompt_logprobs
+                        has_echoed[i] = True
+                    elif request.echo and request.max_tokens > 0 and not has_echoed[i]:
+                        # echo the prompt and first token
+                        delta_text = res.prompt + output.text
+                        delta_token_ids = res.prompt_token_ids + output.token_ids
+                        top_logprobs = res.prompt_logprobs + (output.logprobs or [])
+                        has_echoed[i] = True
+                    else:
+                        # return just the delta
+                        delta_text = output.text[len(previous_texts[i]) :]
+                        delta_token_ids = output.token_ids[previous_num_tokens[i] :]
+                        top_logprobs = (
+                            output.logprobs[previous_num_tokens[i] :]
+                            if output.logprobs
+                            else None
+                        )
+
+                    if request.logprobs is not None:
+                        logprobs = self._create_logprobs(
+                            token_ids=delta_token_ids,
+                            top_logprobs=top_logprobs,
+                            num_output_top_logprobs=request.logprobs,
+                            initial_text_offset=len(previous_texts[i]),
+                        )
+                    else:
+                        logprobs = None
+
+                    previous_texts[i] = output.text
+                    previous_num_tokens[i] = len(output.token_ids)
+                    finish_reason = output.finish_reason
+                    stop_reason = output.stop_reason
+                    if output.finish_reason is not None:  # return final usage
+                        prompt_tokens = len(res.prompt_token_ids)
+                        completion_tokens = len(output.token_ids)
+                        final_usage = UsageInfo(
+                            prompt_tokens=prompt_tokens,
+                            completion_tokens=completion_tokens,
+                            total_tokens=prompt_tokens + completion_tokens,
+                        )
+                    else:
+                        final_usage = None
+                    response_json = CompletionStreamResponse(
+                        id=request_id,
+                        created=created_time,
+                        model=model_name,
+                        choices=[
+                            CompletionResponseStreamChoice(
+                                index=i,
+                                text=delta_text,
+                                logprobs=logprobs,
+                                finish_reason=finish_reason,
+                                stop_reason=stop_reason,
+                            )
+                        ],
+                        usage=final_usage,
+                    ).model_dump_json(exclude_unset=True)
+                    yield f"data: {response_json}\n\n"
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            data = self.create_streaming_error_response(str(e))
+            yield f"data: {data}\n\n"
+        yield "data: [DONE]\n\n"
+
+    def request_output_to_completion_response(
+        self,
+        final_res_batch: List[RequestOutput],
+        request: CompletionRequest,
+        request_id: str,
+        created_time: int,
+        model_name: str,
+    ) -> CompletionResponse:
+        choices: List[CompletionResponseChoice] = []
+        num_prompt_tokens = 0
+        num_generated_tokens = 0
+        for final_res in final_res_batch:
+            assert final_res is not None
+            prompt_token_ids = final_res.prompt_token_ids
+            prompt_logprobs = final_res.prompt_logprobs
+            prompt_text = final_res.prompt
+
+            for output in final_res.outputs:
+                assert request.max_tokens is not None
+                if request.echo and request.max_tokens == 0:
+                    token_ids = prompt_token_ids
+                    top_logprobs = prompt_logprobs
+                    output_text = prompt_text
+                elif request.echo and request.max_tokens > 0:
+                    token_ids = prompt_token_ids + output.token_ids
+                    top_logprobs = (
+                        prompt_logprobs + output.logprobs if request.logprobs else None
+                    )
+                    output_text = prompt_text + output.text
+                else:
+                    token_ids = output.token_ids
+                    top_logprobs = output.logprobs
+                    output_text = output.text
+
+                if request.logprobs is not None:
+                    assert top_logprobs is not None, (
+                        "top_logprobs must be provided when logprobs " "is requested"
+                    )
+                    logprobs = self._create_logprobs(
+                        token_ids=token_ids,
+                        top_logprobs=top_logprobs,
+                        num_output_top_logprobs=request.logprobs,
+                    )
+                else:
+                    logprobs = None
+
+                choice_data = CompletionResponseChoice(
+                    index=len(choices),
+                    text=output_text,
+                    logprobs=logprobs,
+                    finish_reason=output.finish_reason,
+                    stop_reason=output.stop_reason,
+                )
+                choices.append(choice_data)
+
+            num_prompt_tokens += len(prompt_token_ids)
+            num_generated_tokens += sum(
+                len(output.token_ids) for output in final_res.outputs
+            )
+
+        usage = UsageInfo(
+            prompt_tokens=num_prompt_tokens,
+            completion_tokens=num_generated_tokens,
+            total_tokens=num_prompt_tokens + num_generated_tokens,
+        )
+
+        return CompletionResponse(
+            id=request_id,
+            created=created_time,
+            model=model_name,
+            choices=choices,
+            usage=usage,
+        )
diff --git a/examples/vllm_openai_example/vllm_openai_example/serving_engine.py b/examples/vllm_openai_example/vllm_openai_example/serving_engine.py
new file mode 100644
index 000000000..bf3d6197d
--- /dev/null
+++ b/examples/vllm_openai_example/vllm_openai_example/serving_engine.py
@@ -0,0 +1,249 @@
+# Original source
+# https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/serving_engine.py
+import asyncio
+import json
+from dataclasses import dataclass
+from http import HTTPStatus
+from typing import Dict, List, Optional, Tuple, Union
+
+from pydantic import Field
+from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
+from typing_extensions import Annotated
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.sequence import Logprob
+from vllm.transformers_utils.tokenizer import get_tokenizer
+
+from protocol import (
+    ChatCompletionRequest,
+    CompletionRequest,
+    ErrorResponse,
+    LogProbs,
+    ModelCard,
+    ModelList,
+    ModelPermission,
+)
+
+logger = init_logger(__name__)
+
+
+@dataclass
+class LoRA:
+    name: str
+    local_path: str
+
+
+class OpenAIServing:
+    def __init__(
+        self,
+        engine: AsyncLLMEngine,
+        served_model_names: List[str],
+        lora_modules=Optional[List[LoRA]],
+    ):
+        self.engine = engine
+        self.served_model_names = served_model_names
+        if lora_modules is None:
+            self.lora_requests = []
+        else:
+            self.lora_requests = [
+                LoRARequest(
+                    lora_name=lora.name,
+                    lora_int_id=i,
+                    lora_local_path=lora.local_path,
+                )
+                for i, lora in enumerate(lora_modules, start=1)
+            ]
+
+        self.max_model_len = 0
+        # Lazy initialized
+        self.tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
+
+        try:
+            event_loop = asyncio.get_running_loop()
+        except RuntimeError:
+            event_loop = None
+
+        if event_loop is not None and event_loop.is_running():
+            # If the current is instanced by Ray Serve,
+            # there is already a running event loop
+            event_loop.create_task(self._post_init())
+        else:
+            # When using single vLLM without engine_use_ray
+            asyncio.run(self._post_init())
+
+    async def _post_init(self):
+        engine_model_config = await self.engine.get_model_config()
+        self.max_model_len = engine_model_config.max_model_len
+
+        # A separate tokenizer to map token IDs to strings.
+        self.tokenizer = get_tokenizer(
+            engine_model_config.tokenizer,
+            tokenizer_mode=engine_model_config.tokenizer_mode,
+            tokenizer_revision=engine_model_config.tokenizer_revision,
+            trust_remote_code=engine_model_config.trust_remote_code,
+            truncation_side="left",
+        )
+
+    async def show_available_models(self) -> ModelList:
+        """Show available models. Right now we only have one model."""
+        model_cards = [
+            ModelCard(
+                id=served_model_name,
+                root=self.served_model_names[0],
+                permission=[ModelPermission()],
+            )
+            for served_model_name in self.served_model_names
+        ]
+        lora_cards = [
+            ModelCard(
+                id=lora.lora_name,
+                root=self.served_model_names[0],
+                permission=[ModelPermission()],
+            )
+            for lora in self.lora_requests
+        ]
+        model_cards.extend(lora_cards)
+        return ModelList(data=model_cards)
+
+    def _create_logprobs(
+        self,
+        token_ids: List[int],
+        top_logprobs: List[Optional[Dict[int, Logprob]]],
+        num_output_top_logprobs: Optional[int] = None,
+        initial_text_offset: int = 0,
+    ) -> LogProbs:
+        """Create OpenAI-style logprobs."""
+        logprobs = LogProbs()
+        last_token_len = 0
+        if num_output_top_logprobs:
+            logprobs.top_logprobs = []
+
+        for i, token_id in enumerate(token_ids):
+            step_top_logprobs = top_logprobs[i]
+            if step_top_logprobs is None:
+                token = self.tokenizer.decode(token_id)
+                logprobs.tokens.append(token)
+                logprobs.token_logprobs.append(None)
+                assert logprobs.top_logprobs is not None
+                logprobs.top_logprobs.append(None)
+            else:
+                token_logprob = step_top_logprobs[token_id].logprob
+                token = step_top_logprobs[token_id].decoded_token
+                logprobs.tokens.append(token)
+                logprobs.token_logprobs.append(token_logprob)
+
+                if num_output_top_logprobs:
+                    assert logprobs.top_logprobs is not None
+                    logprobs.top_logprobs.append(
+                        {
+                            # Convert float("-inf") to the
+                            # JSON-serializable float that OpenAI uses
+                            p.decoded_token: max(p.logprob, -9999.0)
+                            for i, p in step_top_logprobs.items()
+                        }
+                        if step_top_logprobs
+                        else None
+                    )
+
+            if len(logprobs.text_offset) == 0:
+                logprobs.text_offset.append(initial_text_offset)
+            else:
+                logprobs.text_offset.append(logprobs.text_offset[-1] + last_token_len)
+            last_token_len = len(token)
+        return logprobs
+
+    def create_error_response(
+        self,
+        message: str,
+        err_type: str = "BadRequestError",
+        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
+    ) -> ErrorResponse:
+        return ErrorResponse(message=message, type=err_type, code=status_code.value)
+
+    def create_streaming_error_response(
+        self,
+        message: str,
+        err_type: str = "BadRequestError",
+        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
+    ) -> str:
+        json_str = json.dumps(
+            {
+                "error": self.create_error_response(
+                    message=message, err_type=err_type, status_code=status_code
+                ).model_dump()
+            }
+        )
+        return json_str
+
+    async def _check_model(self, request) -> Optional[ErrorResponse]:
+        if request.model in self.served_model_names:
+            return None
+        if request.model in [lora.lora_name for lora in self.lora_requests]:
+            return None
+        return self.create_error_response(
+            message=f"The model `{request.model}` does not exist.",
+            err_type="NotFoundError",
+            status_code=HTTPStatus.NOT_FOUND,
+        )
+
+    def _maybe_get_lora(self, request) -> Optional[LoRARequest]:
+        if request.model in self.served_model_names:
+            return None
+        for lora in self.lora_requests:
+            if request.model == lora.lora_name:
+                return lora
+        # if _check_model has been called earlier, this will be unreachable
+        raise ValueError("The model `{request.model}` does not exist.")
+
+    def _validate_prompt_and_tokenize(
+        self,
+        request: Union[ChatCompletionRequest, CompletionRequest],
+        prompt: Optional[str] = None,
+        prompt_ids: Optional[List[int]] = None,
+        truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None,
+    ) -> Tuple[List[int], str]:
+        if not (prompt or prompt_ids):
+            raise ValueError("Either prompt or prompt_ids should be provided.")
+        if prompt and prompt_ids:
+            raise ValueError("Only one of prompt or prompt_ids should be provided.")
+
+        if prompt_ids is None:
+            tokenizer_kwargs = (
+                {}
+                if truncate_prompt_tokens is None
+                else {
+                    "truncation": True,
+                    "max_length": truncate_prompt_tokens,
+                }
+            )
+            input_ids = self.tokenizer(prompt, **tokenizer_kwargs).input_ids
+        elif truncate_prompt_tokens is not None:
+            input_ids = prompt_ids[-truncate_prompt_tokens:]
+        else:
+            input_ids = prompt_ids
+
+        input_text = prompt if prompt is not None else self.tokenizer.decode(prompt_ids)
+        token_num = len(input_ids)
+
+        if request.max_tokens is None:
+            if token_num >= self.max_model_len:
+                raise ValueError(
+                    f"This model's maximum context length is "
+                    f"{self.max_model_len} tokens. However, you requested "
+                    f"{token_num} tokens in the messages, "
+                    f"Please reduce the length of the messages.",
+                )
+            request.max_tokens = self.max_model_len - token_num
+
+        if token_num + request.max_tokens > self.max_model_len:
+            raise ValueError(
+                f"This model's maximum context length is "
+                f"{self.max_model_len} tokens. However, you requested "
+                f"{request.max_tokens + token_num} tokens "
+                f"({token_num} in the messages, "
+                f"{request.max_tokens} in the completion). "
+                f"Please reduce the length of the messages or completion.",
+            )
+        else:
+            return input_ids, input_text