import asyncio
import contextvars
from functools import partial
from typing import (
    TYPE_CHECKING,
    Any,
    Coroutine,
    Dict,
    Iterable,
    List,
    Literal,
    Optional,
    Type,
    Union,
    cast,
)

import httpx
from pydantic import BaseModel

import litellm
from litellm._logging import verbose_logger
from litellm.completion_extras.litellm_responses_transformation.transformation import (
    LiteLLMResponsesTransformationHandler,
)
from litellm.constants import request_timeout
from litellm.litellm_core_utils.asyncify import run_async_function
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.litellm_core_utils.prompt_templates.common_utils import (
    update_responses_input_with_model_file_ids,
    update_responses_tools_with_model_file_ids,
)
from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig
from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler
from litellm.responses.litellm_completion_transformation.handler import (
    LiteLLMCompletionTransformationHandler,
)
from litellm.responses.utils import ResponsesAPIRequestUtils
from litellm.types.llms.openai import (
    AllMessageValues,
    PromptObject,
    Reasoning,
    ResponseIncludable,
    ResponseInputParam,
    ResponsesAPIOptionalRequestParams,
    ResponsesAPIResponse,
    ToolChoice,
    ToolParam,
)

# Handle ResponseText import with fallback
if TYPE_CHECKING:
    from litellm.types.llms.openai import ResponseText  # type: ignore
else:
    ResponseText = str  # Fallback for ResponseText import
from litellm.litellm_core_utils.get_litellm_params import get_litellm_params
from litellm.llms.openai.data_residency import infer_openai_data_residency
from litellm.secret_managers.main import get_secret_str
from litellm.types.responses.main import *
from litellm.types.router import GenericLiteLLMParams
from litellm.utils import ProviderConfigManager, client

if TYPE_CHECKING:
    from mcp.types import Tool as MCPTool
else:
    MCPTool = Any

from .streaming_iterator import BaseResponsesAPIStreamingIterator

####### ENVIRONMENT VARIABLES ###################
# Initialize any necessary instances or variables here
base_llm_http_handler = BaseLLMHTTPHandler()
litellm_completion_transformation_handler = LiteLLMCompletionTransformationHandler()
#################################################


def _has_file_search_tool(tools: Optional[Any]) -> bool:
    """Return True if any tool in the list has type 'file_search'."""
    if not tools:
        return False
    return any(isinstance(t, dict) and t.get("type") == "file_search" for t in tools)


def mock_responses_api_response(
    mock_response: str = "In a peaceful grove beneath a silver moon, a unicorn named Lumina discovered a hidden pool that reflected the stars. As she dipped her horn into the water, the pool began to shimmer, revealing a pathway to a magical realm of endless night skies. Filled with wonder, Lumina whispered a wish for all who dream to find their own hidden magic, and as she glanced back, her hoofprints sparkled like stardust.",
):
    return ResponsesAPIResponse(
        **{  # type: ignore
            "id": "resp_67ccd2bed1ec8190b14f964abc0542670bb6a6b452d3795b",
            "object": "response",
            "created_at": 1741476542,
            "status": "completed",
            "error": None,
            "incomplete_details": None,
            "instructions": None,
            "max_output_tokens": None,
            "model": "gpt-4.1-2025-04-14",
            "output": [
                {
                    "type": "message",
                    "id": "msg_67ccd2bf17f0819081ff3bb2cf6508e60bb6a6b452d3795b",
                    "status": "completed",
                    "role": "assistant",
                    "content": [
                        {
                            "type": "output_text",
                            "text": mock_response,
                            "annotations": [],
                        }
                    ],
                }
            ],
            "parallel_tool_calls": True,
            "previous_response_id": None,
            "reasoning": {"effort": None, "summary": None},
            "store": True,
            "temperature": 1.0,
            "text": {"format": {"type": "text"}},
            "tool_choice": "auto",
            "tools": [],
            "top_p": 1.0,
            "truncation": "disabled",
            "usage": {
                "input_tokens": 36,
                "input_tokens_details": {"cached_tokens": 0},
                "output_tokens": 87,
                "output_tokens_details": {"reasoning_tokens": 0},
                "total_tokens": 123,
            },
            "user": None,
            "metadata": {},
        }
    )


async def aresponses_api_with_mcp(
    input: Union[str, ResponseInputParam],
    model: str,
    include: Optional[List[ResponseIncludable]] = None,
    instructions: Optional[str] = None,
    max_output_tokens: Optional[int] = None,
    prompt: Optional[PromptObject] = None,
    metadata: Optional[Dict[str, Any]] = None,
    parallel_tool_calls: Optional[bool] = None,
    previous_response_id: Optional[str] = None,
    reasoning: Optional[Reasoning] = None,
    store: Optional[bool] = None,
    background: Optional[bool] = None,
    stream: Optional[bool] = None,
    temperature: Optional[float] = None,
    text: Optional["ResponseText"] = None,
    tool_choice: Optional[ToolChoice] = None,
    tools: Optional[Iterable[ToolParam]] = None,
    top_p: Optional[float] = None,
    truncation: Optional[Literal["auto", "disabled"]] = None,
    user: Optional[str] = None,
    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
    # The extra values given here take precedence over values defined on the client or passed to this method.
    extra_headers: Optional[Dict[str, Any]] = None,
    extra_query: Optional[Dict[str, Any]] = None,
    extra_body: Optional[Dict[str, Any]] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
    # LiteLLM specific params,
    custom_llm_provider: Optional[str] = None,
    **kwargs,
) -> Union[ResponsesAPIResponse, BaseResponsesAPIStreamingIterator]:
    """
    Async version of responses API with MCP integration.

    When MCP tools with server_url="litellm_proxy" are provided, this function will:
    1. Get available tools from the MCP server manager
    2. Insert the tools into the messages/input
    3. Call the standard responses API
    4. If require_approval="never" and tool calls are returned, automatically execute them
    """
    from litellm.responses.mcp.litellm_proxy_mcp_handler import (
        LiteLLM_Proxy_MCP_Handler,
    )

    # Parse MCP tools and separate from other tools
    (
        mcp_tools_with_litellm_proxy,
        other_tools,
    ) = LiteLLM_Proxy_MCP_Handler._parse_mcp_tools(tools)

    # Process MCP tools through the complete pipeline (fetch + filter + deduplicate + transform)
    # Extract user_api_key_auth from litellm_metadata (where it's added by add_user_api_key_auth_to_request_metadata)
    user_api_key_auth = kwargs.get("user_api_key_auth") or kwargs.get(
        "litellm_metadata", {}
    ).get("user_api_key_auth")

    # Extract MCP auth headers from request (for dynamic auth when fetching tools)
    mcp_auth_header: Optional[str] = None
    mcp_server_auth_headers: Optional[Dict[str, Dict[str, str]]] = None
    secret_fields = kwargs.get("secret_fields")
    if secret_fields and isinstance(secret_fields, dict):
        (
            mcp_auth_header,
            mcp_server_auth_headers,
            _,
            _,
        ) = ResponsesAPIRequestUtils.extract_mcp_headers_from_request(
            secret_fields=secret_fields, tools=tools
        )

    # Get original MCP tools (for events) and OpenAI tools (for LLM) by reusing existing methods
    (
        original_mcp_tools,
        tool_server_map,
    ) = await LiteLLM_Proxy_MCP_Handler._process_mcp_tools_without_openai_transform(
        user_api_key_auth=user_api_key_auth,
        mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy,
        litellm_trace_id=kwargs.get("litellm_trace_id"),
        mcp_auth_header=mcp_auth_header,
        mcp_server_auth_headers=mcp_server_auth_headers,
    )
    openai_tools = LiteLLM_Proxy_MCP_Handler._transform_mcp_tools_to_openai(
        original_mcp_tools
    )

    # Combine with other tools
    all_tools = openai_tools + other_tools if (openai_tools or other_tools) else None

    # Prepare call parameters for reuse
    call_params = {
        "include": include,
        "instructions": instructions,
        "max_output_tokens": max_output_tokens,
        "prompt": prompt,
        "metadata": metadata,
        "parallel_tool_calls": parallel_tool_calls,
        "reasoning": reasoning,
        "store": store,
        "background": background,
        "stream": stream,
        "temperature": temperature,
        "text": text,
        "tool_choice": tool_choice,
        "top_p": top_p,
        "truncation": truncation,
        "user": user,
        "extra_headers": extra_headers,
        "extra_query": extra_query,
        "extra_body": extra_body,
        "timeout": timeout,
        "custom_llm_provider": custom_llm_provider,
        **kwargs,
    }

    # Handle MCP streaming if requested
    if stream and mcp_tools_with_litellm_proxy:
        # Generate MCP discovery events using the already processed tools
        from litellm._uuid import uuid
        from litellm.responses.mcp.mcp_streaming_iterator import (
            create_mcp_list_tools_events,
        )

        base_item_id = f"mcp_{uuid.uuid4().hex[:8]}"
        mcp_discovery_events = await create_mcp_list_tools_events(
            mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy,
            user_api_key_auth=user_api_key_auth,
            base_item_id=base_item_id,
            pre_processed_mcp_tools=original_mcp_tools,
        )

        return LiteLLM_Proxy_MCP_Handler._create_mcp_streaming_response(
            input=input,
            model=model,
            all_tools=all_tools,
            mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy,
            mcp_discovery_events=mcp_discovery_events,
            call_params=call_params,
            previous_response_id=previous_response_id,
            tool_server_map=tool_server_map,
            **kwargs,
        )

    # Determine if we should auto-execute tools
    should_auto_execute = bool(
        mcp_tools_with_litellm_proxy
    ) and LiteLLM_Proxy_MCP_Handler._should_auto_execute_tools(
        mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy
    )

    # Prepare parameters for the initial call
    initial_call_params = LiteLLM_Proxy_MCP_Handler._prepare_initial_call_params(
        call_params=call_params, should_auto_execute=should_auto_execute
    )

    #########################################################
    # Make initial response API call
    #########################################################
    response = await aresponses(
        input=input,
        model=model,
        tools=all_tools,
        previous_response_id=previous_response_id,
        **initial_call_params,
    )

    verbose_logger.debug("Initial response %s", response)

    #########################################################
    # Auto-Execute Tools Handling
    # If auto-execute tools is True, then we need to execute the tool calls
    #########################################################
    if should_auto_execute and isinstance(
        response, ResponsesAPIResponse
    ):  # type: ignore
        tool_calls = LiteLLM_Proxy_MCP_Handler._extract_tool_calls_from_response(
            response=response
        )

        if tool_calls:
            user_api_key_auth = kwargs.get("litellm_metadata", {}).get(
                "user_api_key_auth"
            )

            # Extract MCP auth headers from the request to pass to MCP server
            secret_fields = kwargs.get("secret_fields")
            (
                mcp_auth_header,
                mcp_server_auth_headers,
                oauth2_headers,
                raw_headers_from_request,
            ) = ResponsesAPIRequestUtils.extract_mcp_headers_from_request(
                secret_fields=secret_fields,
                tools=tools,
            )

            tool_results = await LiteLLM_Proxy_MCP_Handler._execute_tool_calls(
                tool_server_map=tool_server_map,
                tool_calls=tool_calls,
                user_api_key_auth=user_api_key_auth,
                mcp_auth_header=mcp_auth_header,
                mcp_server_auth_headers=mcp_server_auth_headers,
                oauth2_headers=oauth2_headers,
                raw_headers=raw_headers_from_request,
                litellm_call_id=kwargs.get("litellm_call_id"),
                litellm_trace_id=kwargs.get("litellm_trace_id"),
            )

            if tool_results:
                follow_up_input = LiteLLM_Proxy_MCP_Handler._create_follow_up_input(
                    response=response, tool_results=tool_results, original_input=input
                )

                # Prepare parameters for follow-up call (restores original stream setting)
                follow_up_call_params = (
                    LiteLLM_Proxy_MCP_Handler._prepare_follow_up_call_params(
                        call_params=call_params, original_stream_setting=stream or False
                    )
                )

                # Create tool execution events for streaming if needed
                tool_execution_events = []
                if stream:
                    tool_execution_events = (
                        LiteLLM_Proxy_MCP_Handler._create_tool_execution_events(
                            tool_calls=tool_calls, tool_results=tool_results
                        )
                    )

                final_response = await LiteLLM_Proxy_MCP_Handler._make_follow_up_call(
                    follow_up_input=follow_up_input,
                    model=model,
                    all_tools=all_tools,
                    response_id=response.id,
                    **follow_up_call_params,
                )

                # If streaming and we have tool execution events, wrap the response
                if (
                    stream
                    and tool_execution_events
                    and (
                        hasattr(final_response, "__aiter__")
                        or hasattr(final_response, "__iter__")
                    )
                ):
                    from litellm.responses.mcp.mcp_streaming_iterator import (
                        MCPEnhancedStreamingIterator,
                    )

                    final_response = MCPEnhancedStreamingIterator(
                        tool_server_map=tool_server_map,
                        base_iterator=final_response,
                        mcp_events=tool_execution_events,
                        user_api_key_auth=user_api_key_auth,
                    )

                # Add custom output elements to the final response (for non-streaming)
                elif isinstance(final_response, ResponsesAPIResponse):
                    # Fetch MCP tools again for output elements (without OpenAI transformation)
                    (
                        mcp_tools_for_output,
                        _,
                    ) = await LiteLLM_Proxy_MCP_Handler._process_mcp_tools_without_openai_transform(
                        user_api_key_auth=user_api_key_auth,
                        mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy,
                        mcp_auth_header=mcp_auth_header,
                        mcp_server_auth_headers=mcp_server_auth_headers,
                    )
                    final_response = (
                        LiteLLM_Proxy_MCP_Handler._add_mcp_output_elements_to_response(
                            response=final_response,
                            mcp_tools_fetched=mcp_tools_for_output,
                            tool_results=tool_results,
                        )
                    )
                return final_response

    return response


@client
async def aresponses(
    input: Union[str, ResponseInputParam],
    model: str,
    include: Optional[List[ResponseIncludable]] = None,
    instructions: Optional[str] = None,
    max_output_tokens: Optional[int] = None,
    prompt: Optional[PromptObject] = None,
    metadata: Optional[Dict[str, Any]] = None,
    parallel_tool_calls: Optional[bool] = None,
    previous_response_id: Optional[str] = None,
    reasoning: Optional[Reasoning] = None,
    store: Optional[bool] = None,
    background: Optional[bool] = None,
    stream: Optional[bool] = None,
    temperature: Optional[float] = None,
    text: Optional["ResponseText"] = None,
    text_format: Optional[Union[Type["BaseModel"], dict]] = None,
    tool_choice: Optional[ToolChoice] = None,
    tools: Optional[Iterable[ToolParam]] = None,
    top_p: Optional[float] = None,
    truncation: Optional[Literal["auto", "disabled"]] = None,
    user: Optional[str] = None,
    service_tier: Optional[str] = None,
    safety_identifier: Optional[str] = None,
    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
    # The extra values given here take precedence over values defined on the client or passed to this method.
    extra_headers: Optional[Dict[str, Any]] = None,
    extra_query: Optional[Dict[str, Any]] = None,
    extra_body: Optional[Dict[str, Any]] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
    # LiteLLM specific params,
    custom_llm_provider: Optional[str] = None,
    **kwargs,
) -> Union[ResponsesAPIResponse, BaseResponsesAPIStreamingIterator]:
    """
    Async: Handles responses API requests by reusing the synchronous function
    """
    local_vars = locals()
    try:
        loop = asyncio.get_event_loop()
        kwargs["aresponses"] = True

        # Convert text_format to text parameter if provided
        text = ResponsesAPIRequestUtils.convert_text_format_to_text_param(
            text_format=text_format, text=text
        )
        if text is not None:
            # Update local_vars to include the converted text parameter
            local_vars["text"] = text

        # get custom llm provider so we can use this for mapping exceptions
        if custom_llm_provider is None:
            _, custom_llm_provider, _, _ = litellm.get_llm_provider(
                model=model, api_base=local_vars.get("base_url", None)
            )
            # Update local_vars with detected provider (fixes #19782)
            local_vars["custom_llm_provider"] = custom_llm_provider

        #########################################################
        # ASYNC PROMPT MANAGEMENT
        # Run the async hook here so async-only prompt loggers are honoured.
        # Then pop prompt_id from kwargs so the sync responses() path does NOT
        # re-run the hook (which would double-prepend template messages).
        # Pass merged_optional_params via an internal kwarg so responses()
        # can apply them to local_vars without re-invoking the hook.
        #########################################################
        litellm_logging_obj = kwargs.get("litellm_logging_obj", None)
        prompt_id = cast(Optional[str], kwargs.get("prompt_id", None))
        prompt_variables = cast(Optional[dict], kwargs.get("prompt_variables", None))
        original_model = model

        if isinstance(
            litellm_logging_obj, LiteLLMLoggingObj
        ) and litellm_logging_obj.should_run_prompt_management_hooks(
            prompt_id=prompt_id, non_default_params=kwargs
        ):
            if isinstance(input, str):
                client_input: List[AllMessageValues] = [
                    {"role": "user", "content": input}
                ]
            else:
                client_input = [
                    item  # type: ignore[misc]
                    for item in input
                    if isinstance(item, dict) and "role" in item
                ]
            (
                model,
                merged_input,
                merged_optional_params,
            ) = await litellm_logging_obj.async_get_chat_completion_prompt(
                model=model,
                messages=client_input,
                non_default_params=kwargs,
                prompt_id=prompt_id,
                prompt_variables=prompt_variables,
                prompt_label=kwargs.get("prompt_label", None),
                prompt_version=kwargs.get("prompt_version", None),
            )
            input = cast(Union[str, ResponseInputParam], merged_input)
            if model != original_model:
                _, custom_llm_provider, _, _ = litellm.get_llm_provider(model=model)
            kwargs.pop("prompt_id", None)
            kwargs["_async_prompt_merged_params"] = merged_optional_params

        func = partial(
            responses,
            input=input,
            model=model,
            include=include,
            instructions=instructions,
            max_output_tokens=max_output_tokens,
            prompt=prompt,
            metadata=metadata,
            parallel_tool_calls=parallel_tool_calls,
            previous_response_id=previous_response_id,
            reasoning=reasoning,
            store=store,
            background=background,
            stream=stream,
            temperature=temperature,
            text=text,
            tool_choice=tool_choice,
            tools=tools,
            top_p=top_p,
            truncation=truncation,
            user=user,
            extra_headers=extra_headers,
            extra_query=extra_query,
            extra_body=extra_body,
            timeout=timeout,
            custom_llm_provider=custom_llm_provider,
            service_tier=service_tier,
            safety_identifier=safety_identifier,
            **kwargs,
        )

        ctx = contextvars.copy_context()
        func_with_context = partial(ctx.run, func)
        init_response = await loop.run_in_executor(None, func_with_context)

        if asyncio.iscoroutine(init_response):
            response = await init_response
        else:
            response = init_response

        # Update the responses_api_response_id with the model_id
        if isinstance(response, ResponsesAPIResponse):
            response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
                responses_api_response=response,
                litellm_metadata=kwargs.get("litellm_metadata", {}),
                custom_llm_provider=custom_llm_provider,
            )
            # Stamp custom_llm_provider so callbacks can identify the provider
            # (mirrors litellm/main.py:1371 for chat completions)
            response._hidden_params["custom_llm_provider"] = custom_llm_provider

        if response is None:
            raise ValueError(
                f"Got an unexpected None response from the Responses API: {response}"
            )

        return response
    except Exception as e:
        raise litellm.exception_type(
            model=model,
            custom_llm_provider=custom_llm_provider,
            original_exception=e,
            completion_kwargs=local_vars,
            extra_kwargs=kwargs,
        )


def _apply_prompt_management_to_responses_call(
    input: Union[str, ResponseInputParam],
    model: str,
    custom_llm_provider: Optional[str],
    litellm_logging_obj: Optional[LiteLLMLoggingObj],
    kwargs: Dict[str, Any],
    local_vars: Dict[str, Any],
) -> tuple[Union[str, ResponseInputParam], str, Optional[str]]:
    async_merged = kwargs.pop("_async_prompt_merged_params", None)
    if async_merged is not None:
        for key, value in async_merged.items():
            local_vars[key] = value
        return input, model, custom_llm_provider

    prompt_id = cast(Optional[str], kwargs.get("prompt_id", None))
    prompt_variables = cast(Optional[dict], kwargs.get("prompt_variables", None))
    original_model = model

    if isinstance(input, str):
        client_input: List[AllMessageValues] = [{"role": "user", "content": input}]
    else:
        client_input = [
            item  # type: ignore[misc]
            for item in input
            if isinstance(item, dict) and "role" in item
        ]

    if isinstance(
        litellm_logging_obj, LiteLLMLoggingObj
    ) and litellm_logging_obj.should_run_prompt_management_hooks(
        prompt_id=prompt_id, non_default_params=kwargs
    ):
        (
            model,
            merged_input,
            merged_optional_params,
        ) = litellm_logging_obj.get_chat_completion_prompt(
            model=model,
            messages=client_input,
            non_default_params=kwargs,
            prompt_id=prompt_id,
            prompt_variables=prompt_variables,
            prompt_label=kwargs.get("prompt_label", None),
            prompt_version=kwargs.get("prompt_version", None),
        )
        input = cast(Union[str, ResponseInputParam], merged_input)
        local_vars["input"] = input
        local_vars["model"] = model
        if model != original_model:
            _, custom_llm_provider, _, _ = litellm.get_llm_provider(model=model)
            local_vars["custom_llm_provider"] = custom_llm_provider
        for key, value in merged_optional_params.items():
            local_vars[key] = value

    return input, model, custom_llm_provider


# Opt-in via model id (mirrors the `responses/` prefix pattern on chat completions).
_OPENAI_CHAT_COMPLETIONS_RESPONSES_MODEL_PREFIX = "openai/chat_completions/"


def _normalize_openai_chat_completions_responses_model(model: str) -> tuple[str, bool]:
    """
    Strip `openai/chat_completions/<name>` → `openai/<name>` and return True when the
    prefix was applied (same effect as use_chat_completions_api=True).
    """
    if not model.startswith(_OPENAI_CHAT_COMPLETIONS_RESPONSES_MODEL_PREFIX):
        return model, False
    remainder = model[len(_OPENAI_CHAT_COMPLETIONS_RESPONSES_MODEL_PREFIX) :]
    if not remainder:
        return model, False
    return f"openai/{remainder}", True


def _pop_use_chat_completions_api_kw(kwargs: Dict[str, Any]) -> bool:
    """Pop use_chat_completions_api; True when the chat-completions bridge is requested."""
    use_cc = kwargs.pop("use_chat_completions_api", None)
    return bool(use_cc)


def _resolve_model_provider_for_responses(
    model: str,
    custom_llm_provider: Optional[str],
    litellm_params: GenericLiteLLMParams,
    local_vars: Dict[str, Any],
) -> tuple[str, Optional[str]]:
    if custom_llm_provider is not None and not litellm_params.custom_llm_provider:
        litellm_params.custom_llm_provider = custom_llm_provider
    (
        model,
        custom_llm_provider,
        dynamic_api_key,
        dynamic_api_base,
    ) = litellm.get_llm_provider(
        model=model,
        litellm_params=litellm_params,
    )
    local_vars["custom_llm_provider"] = custom_llm_provider
    if dynamic_api_key is not None:
        litellm_params.api_key = dynamic_api_key
    if dynamic_api_base is not None:
        litellm_params.api_base = dynamic_api_base
    return model, custom_llm_provider


def _apply_managed_file_id_mapping(
    input: Union[str, ResponseInputParam],
    tools: Optional[Iterable[ToolParam]],
    kwargs: Dict[str, Any],
    local_vars: Dict[str, Any],
) -> tuple[Union[str, ResponseInputParam], Optional[Iterable[ToolParam]]]:
    model_file_id_mapping = kwargs.get("model_file_id_mapping")
    model_info_id = (
        kwargs.get("model_info", {}).get("id")
        if isinstance(kwargs.get("model_info"), dict)
        else None
    )

    input = cast(
        Union[str, ResponseInputParam],
        update_responses_input_with_model_file_ids(
            input=input,
            model_id=model_info_id,
            model_file_id_mapping=model_file_id_mapping,
        ),
    )
    local_vars["input"] = input

    if tools:
        tools = cast(
            Optional[Iterable[ToolParam]],
            update_responses_tools_with_model_file_ids(
                tools=cast(Optional[List[Dict[str, Any]]], tools),
                model_id=model_info_id,
                model_file_id_mapping=model_file_id_mapping,
            ),
        )
        local_vars["tools"] = tools

    return input, tools


def _responses_try_dispatch_mcp_gateway(
    *,
    tools: Optional[Iterable[ToolParam]],
    input: Union[str, ResponseInputParam],
    model: str,
    include: Optional[List[ResponseIncludable]],
    instructions: Optional[str],
    max_output_tokens: Optional[int],
    prompt: Optional[PromptObject],
    metadata: Optional[Dict[str, Any]],
    parallel_tool_calls: Optional[bool],
    previous_response_id: Optional[str],
    reasoning: Optional[Reasoning],
    store: Optional[bool],
    background: Optional[bool],
    stream: Optional[bool],
    temperature: Optional[float],
    text: Any,
    tool_choice: Optional[ToolChoice],
    top_p: Optional[float],
    truncation: Optional[Literal["auto", "disabled"]],
    user: Optional[str],
    extra_headers: Optional[Dict[str, Any]],
    extra_query: Optional[Dict[str, Any]],
    extra_body: Optional[Dict[str, Any]],
    timeout: Optional[Union[float, httpx.Timeout]],
    custom_llm_provider: Optional[str],
    kwargs: Dict[str, Any],
    _is_async: bool,
) -> Optional[Any]:
    """Return a response when MCP gateway handles the call; otherwise None."""
    from litellm.responses.mcp.litellm_proxy_mcp_handler import (
        LiteLLM_Proxy_MCP_Handler,
    )

    if not LiteLLM_Proxy_MCP_Handler._should_use_litellm_mcp_gateway(tools=tools):
        return None
    mcp_call_kwargs = {
        "input": input,
        "model": model,
        "include": include,
        "instructions": instructions,
        "max_output_tokens": max_output_tokens,
        "prompt": prompt,
        "metadata": metadata,
        "parallel_tool_calls": parallel_tool_calls,
        "previous_response_id": previous_response_id,
        "reasoning": reasoning,
        "store": store,
        "background": background,
        "stream": stream,
        "temperature": temperature,
        "text": text,
        "tool_choice": tool_choice,
        "tools": tools,
        "top_p": top_p,
        "truncation": truncation,
        "user": user,
        "extra_headers": extra_headers,
        "extra_query": extra_query,
        "extra_body": extra_body,
        "timeout": timeout,
        "custom_llm_provider": custom_llm_provider,
        **kwargs,
    }
    if _is_async:
        return aresponses_api_with_mcp(**mcp_call_kwargs)
    return run_async_function(aresponses_api_with_mcp, **mcp_call_kwargs)


def _responses_try_dispatch_emulated_file_search(
    *,
    tools: Optional[Iterable[ToolParam]],
    input: Union[str, ResponseInputParam],
    model: str,
    responses_api_provider_config: Optional[BaseResponsesAPIConfig],
    use_chat_completions_api: bool,
    include: Optional[List[ResponseIncludable]],
    instructions: Optional[str],
    max_output_tokens: Optional[int],
    prompt: Optional[PromptObject],
    metadata: Optional[Dict[str, Any]],
    parallel_tool_calls: Optional[bool],
    previous_response_id: Optional[str],
    reasoning: Optional[Reasoning],
    store: Optional[bool],
    background: Optional[bool],
    stream: Optional[bool],
    temperature: Optional[float],
    text: Any,
    tool_choice: Optional[ToolChoice],
    top_p: Optional[float],
    truncation: Optional[Literal["auto", "disabled"]],
    user: Optional[str],
    service_tier: Optional[str],
    safety_identifier: Optional[str],
    text_format: Optional[Union[Type[BaseModel], dict]],
    allowed_openai_params: Optional[List[str]],
    extra_headers: Optional[Dict[str, Any]],
    extra_query: Optional[Dict[str, Any]],
    extra_body: Optional[Dict[str, Any]],
    timeout: Optional[Union[float, httpx.Timeout]],
    custom_llm_provider: Optional[str],
    kwargs: Dict[str, Any],
    _is_async: bool,
) -> Optional[Any]:
    """Return a response when emulated file_search handles the call; otherwise None."""
    if not _has_file_search_tool(tools) or not (
        responses_api_provider_config is None
        or use_chat_completions_api is True
        or not responses_api_provider_config.supports_native_file_search()
    ):
        return None
    from litellm.responses.file_search.emulated_handler import (
        aresponses_with_emulated_file_search,
    )

    _internal_skip = {"litellm_call_id", "aresponses"}
    emulated_kwargs = {
        "include": include,
        "instructions": instructions,
        "max_output_tokens": max_output_tokens,
        "prompt": prompt,
        "metadata": metadata,
        "parallel_tool_calls": parallel_tool_calls,
        "previous_response_id": previous_response_id,
        "reasoning": reasoning,
        "store": store,
        "background": background,
        "stream": stream,
        "temperature": temperature,
        "text": text,
        "tool_choice": tool_choice,
        "top_p": top_p,
        "truncation": truncation,
        "user": user,
        "service_tier": service_tier,
        "safety_identifier": safety_identifier,
        "text_format": text_format,
        "allowed_openai_params": allowed_openai_params,
        "extra_headers": extra_headers,
        "extra_query": extra_query,
        "extra_body": extra_body,
        "timeout": timeout,
        "custom_llm_provider": custom_llm_provider,
        **(
            {
                **(
                    {"use_chat_completions_api": True}
                    if use_chat_completions_api
                    else {}
                ),
                **{k: v for k, v in kwargs.items() if k not in _internal_skip},
            }
        ),
    }
    if _is_async:
        return aresponses_with_emulated_file_search(
            input=input, model=model, tools=tools, **emulated_kwargs
        )
    return run_async_function(
        aresponses_with_emulated_file_search,
        input=input,
        model=model,
        tools=tools,
        **emulated_kwargs,
    )


@client
def responses(
    input: Union[str, ResponseInputParam],
    model: str,
    include: Optional[List[ResponseIncludable]] = None,
    instructions: Optional[str] = None,
    max_output_tokens: Optional[int] = None,
    prompt: Optional[PromptObject] = None,
    metadata: Optional[Dict[str, Any]] = None,
    parallel_tool_calls: Optional[bool] = None,
    previous_response_id: Optional[str] = None,
    reasoning: Optional[Reasoning] = None,
    store: Optional[bool] = None,
    background: Optional[bool] = None,
    stream: Optional[bool] = None,
    temperature: Optional[float] = None,
    text: Optional["ResponseText"] = None,
    text_format: Optional[Union[Type["BaseModel"], dict]] = None,
    tool_choice: Optional[ToolChoice] = None,
    tools: Optional[Iterable[ToolParam]] = None,
    top_p: Optional[float] = None,
    truncation: Optional[Literal["auto", "disabled"]] = None,
    user: Optional[str] = None,
    service_tier: Optional[str] = None,
    safety_identifier: Optional[str] = None,
    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
    # The extra values given here take precedence over values defined on the client or passed to this method.
    extra_headers: Optional[Dict[str, Any]] = None,
    extra_query: Optional[Dict[str, Any]] = None,
    extra_body: Optional[Dict[str, Any]] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
    # LiteLLM specific params,
    allowed_openai_params: Optional[List[str]] = None,
    custom_llm_provider: Optional[str] = None,
    **kwargs,
):
    """
    Synchronous version of the Responses API.
    Uses the synchronous HTTP handler to make requests.
    """
    local_vars = locals()

    try:
        litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj")  # type: ignore
        litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
        _is_async = kwargs.pop("aresponses", False) is True
        use_chat_completions_api = _pop_use_chat_completions_api_kw(kwargs)

        # Convert text_format to text parameter if provided
        text = ResponsesAPIRequestUtils.convert_text_format_to_text_param(
            text_format=text_format, text=text
        )
        if text is not None:
            # Update local_vars to include the converted text parameter
            local_vars["text"] = text

        # get llm provider logic
        litellm_params = GenericLiteLLMParams(**kwargs)

        #########################################################
        # MOCK RESPONSE LOGIC
        #########################################################
        if litellm_params.mock_response and isinstance(
            litellm_params.mock_response, str
        ):
            return mock_responses_api_response(
                mock_response=litellm_params.mock_response
            )

        _stripped_model, _from_chat_completions_prefix = (
            _normalize_openai_chat_completions_responses_model(model)
        )
        model = _stripped_model
        local_vars["model"] = model
        use_chat_completions_api = (
            use_chat_completions_api or _from_chat_completions_prefix
        )

        model, custom_llm_provider = _resolve_model_provider_for_responses(
            model=model,
            custom_llm_provider=custom_llm_provider,
            litellm_params=litellm_params,
            local_vars=local_vars,
        )

        #########################################################
        # PROMPT MANAGEMENT
        # If aresponses() already ran the async hook, it pops prompt_id and
        # passes the result via _async_prompt_merged_params — apply those
        # directly and skip the sync hook to avoid double-merging.
        #########################################################
        input, model, custom_llm_provider = _apply_prompt_management_to_responses_call(
            input=input,
            model=model,
            custom_llm_provider=custom_llm_provider,
            litellm_logging_obj=litellm_logging_obj,
            kwargs=kwargs,
            local_vars=local_vars,
        )

        #########################################################
        # Update input and tools with provider-specific file IDs if managed files are used
        #########################################################
        input, tools = _apply_managed_file_id_mapping(
            input=input, tools=tools, kwargs=kwargs, local_vars=local_vars
        )

        #########################################################
        # Native MCP Responses API
        #########################################################
        _mcp_dispatch = _responses_try_dispatch_mcp_gateway(
            tools=tools,
            input=input,
            model=model,
            include=include,
            instructions=instructions,
            max_output_tokens=max_output_tokens,
            prompt=prompt,
            metadata=metadata,
            parallel_tool_calls=parallel_tool_calls,
            previous_response_id=previous_response_id,
            reasoning=reasoning,
            store=store,
            background=background,
            stream=stream,
            temperature=temperature,
            text=text,
            tool_choice=tool_choice,
            top_p=top_p,
            truncation=truncation,
            user=user,
            extra_headers=extra_headers,
            extra_query=extra_query,
            extra_body=extra_body,
            timeout=timeout,
            custom_llm_provider=custom_llm_provider,
            kwargs=kwargs,
            _is_async=_is_async,
        )
        if _mcp_dispatch is not None:
            return _mcp_dispatch

        # get provider config
        responses_api_provider_config: Optional[BaseResponsesAPIConfig]
        if custom_llm_provider is None:
            responses_api_provider_config = None
        else:
            responses_api_provider_config = (
                ProviderConfigManager.get_provider_responses_api_config(
                    model=model,
                    provider=custom_llm_provider,
                )
            )

        local_vars.update(kwargs)
        # Map reasoning_effort (from litellm_params/proxy config) to reasoning when not set
        if reasoning is None and "reasoning_effort" in local_vars:
            _mapped = LiteLLMResponsesTransformationHandler()._map_reasoning_effort(
                local_vars.pop("reasoning_effort")
            )
            if _mapped is not None:
                reasoning = _mapped
                local_vars["reasoning"] = _mapped
        # Get ResponsesAPIOptionalRequestParams with only valid parameters
        response_api_optional_params: ResponsesAPIOptionalRequestParams = (
            ResponsesAPIRequestUtils.get_requested_response_api_optional_param(
                local_vars
            )
        )

        _file_search_dispatch = _responses_try_dispatch_emulated_file_search(
            tools=tools,
            input=input,
            model=model,
            responses_api_provider_config=responses_api_provider_config,
            use_chat_completions_api=use_chat_completions_api,
            include=include,
            instructions=instructions,
            max_output_tokens=max_output_tokens,
            prompt=prompt,
            metadata=metadata,
            parallel_tool_calls=parallel_tool_calls,
            previous_response_id=previous_response_id,
            reasoning=reasoning,
            store=store,
            background=background,
            stream=stream,
            temperature=temperature,
            text=text,
            tool_choice=tool_choice,
            top_p=top_p,
            truncation=truncation,
            user=user,
            service_tier=service_tier,
            safety_identifier=safety_identifier,
            text_format=text_format,
            allowed_openai_params=allowed_openai_params,
            extra_headers=extra_headers,
            extra_query=extra_query,
            extra_body=extra_body,
            timeout=timeout,
            custom_llm_provider=custom_llm_provider,
            kwargs=kwargs,
            _is_async=_is_async,
        )
        if _file_search_dispatch is not None:
            return _file_search_dispatch

        if responses_api_provider_config is None or use_chat_completions_api is True:
            return litellm_completion_transformation_handler.response_api_handler(
                model=model,
                input=input,
                responses_api_request=response_api_optional_params,
                custom_llm_provider=custom_llm_provider,
                _is_async=_is_async,
                stream=stream,
                extra_headers=extra_headers,
                extra_body=extra_body,
                timeout=timeout if timeout is not None else request_timeout,
                **kwargs,
            )

        # Get optional parameters for the responses API
        responses_api_request_params: Dict = (
            ResponsesAPIRequestUtils.get_optional_params_responses_api(
                model=model,
                responses_api_provider_config=responses_api_provider_config,
                response_api_optional_params=response_api_optional_params,
                allowed_openai_params=allowed_openai_params,
            )
        )

        litellm_logging_obj.update_from_kwargs(
            kwargs=kwargs,
            model=model,
            user=user,
            optional_params=dict(responses_api_request_params),
            litellm_params={
                **responses_api_request_params,
                "aresponses": _is_async,
                "litellm_call_id": litellm_call_id,
                "model_info": kwargs.get("model_info"),
                "data_residency": infer_openai_data_residency(
                    custom_llm_provider, litellm_params.api_base
                ),
                "metadata": (
                    kwargs["litellm_metadata"]
                    if "litellm_metadata" in kwargs
                    else kwargs.get("metadata")
                ),
            },
            custom_llm_provider=custom_llm_provider,
        )

        # Decode any litellm-encoded encrypted-content item IDs back to their original IDs
        input = ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input(
            input
        )

        # Call the handler with _is_async flag instead of directly calling the async handler
        if custom_llm_provider is None:
            raise ValueError("custom_llm_provider is required but passed as None")

        response = base_llm_http_handler.response_api_handler(
            model=model,
            input=input,
            responses_api_provider_config=responses_api_provider_config,
            response_api_optional_request_params=responses_api_request_params,
            custom_llm_provider=custom_llm_provider,
            litellm_params=litellm_params,
            logging_obj=litellm_logging_obj,
            extra_headers=extra_headers,
            extra_body=extra_body,
            timeout=timeout or request_timeout,
            _is_async=_is_async,
            client=kwargs.get("client"),
            fake_stream=responses_api_provider_config.should_fake_stream(
                model=model, stream=stream, custom_llm_provider=custom_llm_provider
            ),
            litellm_metadata=kwargs.get("litellm_metadata", {}),
            shared_session=kwargs.get("shared_session"),
        )

        # Update the responses_api_response_id with the model_id
        if isinstance(response, ResponsesAPIResponse):
            response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
                responses_api_response=response,
                litellm_metadata=kwargs.get("litellm_metadata", {}),
                custom_llm_provider=custom_llm_provider,
            )
            # Stamp custom_llm_provider so callbacks can identify the provider
            # (mirrors litellm/main.py:1371 for chat completions)
            response._hidden_params["custom_llm_provider"] = custom_llm_provider

        return response
    except Exception as e:
        raise litellm.exception_type(
            model=model,
            custom_llm_provider=custom_llm_provider,
            original_exception=e,
            completion_kwargs=local_vars,
            extra_kwargs=kwargs,
        )


@client
async def adelete_responses(
    response_id: str,
    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
    # The extra values given here take precedence over values defined on the client or passed to this method.
    extra_headers: Optional[Dict[str, Any]] = None,
    extra_query: Optional[Dict[str, Any]] = None,
    extra_body: Optional[Dict[str, Any]] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
    # LiteLLM specific params,
    custom_llm_provider: Optional[str] = None,
    **kwargs,
) -> DeleteResponseResult:
    """
    Async version of the DELETE Responses API

    DELETE /v1/responses/{response_id} endpoint in the responses API

    """
    local_vars = locals()
    try:
        loop = asyncio.get_event_loop()
        kwargs["adelete_responses"] = True

        # get custom llm provider from response_id
        decoded_response_id: DecodedResponseId = (
            ResponsesAPIRequestUtils._decode_responses_api_response_id(
                response_id=response_id,
            )
        )
        response_id = decoded_response_id.get("response_id") or response_id
        custom_llm_provider = (
            decoded_response_id.get("custom_llm_provider") or custom_llm_provider
        )

        func = partial(
            delete_responses,
            response_id=response_id,
            custom_llm_provider=custom_llm_provider,
            extra_headers=extra_headers,
            extra_query=extra_query,
            extra_body=extra_body,
            timeout=timeout,
            **kwargs,
        )

        ctx = contextvars.copy_context()
        func_with_context = partial(ctx.run, func)
        init_response = await loop.run_in_executor(None, func_with_context)

        if asyncio.iscoroutine(init_response):
            response = await init_response
        else:
            response = init_response
        return response
    except Exception as e:
        raise litellm.exception_type(
            model=None,
            custom_llm_provider=custom_llm_provider,
            original_exception=e,
            completion_kwargs=local_vars,
            extra_kwargs=kwargs,
        )


@client
def delete_responses(
    response_id: str,
    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
    # The extra values given here take precedence over values defined on the client or passed to this method.
    extra_headers: Optional[Dict[str, Any]] = None,
    extra_query: Optional[Dict[str, Any]] = None,
    extra_body: Optional[Dict[str, Any]] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
    # LiteLLM specific params,
    custom_llm_provider: Optional[str] = None,
    **kwargs,
) -> Union[DeleteResponseResult, Coroutine[Any, Any, DeleteResponseResult]]:
    """
    Synchronous version of the DELETE Responses API

    DELETE /v1/responses/{response_id} endpoint in the responses API

    """
    local_vars = locals()
    try:
        litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj")  # type: ignore
        litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
        _is_async = kwargs.pop("adelete_responses", False) is True

        # get llm provider logic
        litellm_params = GenericLiteLLMParams(**kwargs)

        # get custom llm provider from response_id
        decoded_response_id: DecodedResponseId = (
            ResponsesAPIRequestUtils._decode_responses_api_response_id(
                response_id=response_id,
            )
        )
        response_id = decoded_response_id.get("response_id") or response_id
        custom_llm_provider = (
            decoded_response_id.get("custom_llm_provider") or custom_llm_provider
        )

        if custom_llm_provider is None:
            raise ValueError("custom_llm_provider is required but passed as None")

        # get provider config
        responses_api_provider_config: Optional[BaseResponsesAPIConfig] = (
            ProviderConfigManager.get_provider_responses_api_config(
                model=None,
                provider=custom_llm_provider,
            )
        )

        if responses_api_provider_config is None:
            raise ValueError(
                f"DELETE responses is not supported for {custom_llm_provider}"
            )

        local_vars.update(kwargs)

        # Pre Call logging
        litellm_logging_obj.update_from_kwargs(
            kwargs=local_vars,
            model=None,
            optional_params={
                "response_id": response_id,
            },
            litellm_params={
                "litellm_call_id": litellm_call_id,
            },
            custom_llm_provider=custom_llm_provider,
        )

        # Call the handler with _is_async flag instead of directly calling the async handler
        response = base_llm_http_handler.delete_response_api_handler(
            response_id=response_id,
            custom_llm_provider=custom_llm_provider,
            responses_api_provider_config=responses_api_provider_config,
            litellm_params=litellm_params,
            logging_obj=litellm_logging_obj,
            extra_headers=extra_headers,
            extra_body=extra_body,
            timeout=timeout or request_timeout,
            _is_async=_is_async,
            client=kwargs.get("client"),
            shared_session=kwargs.get("shared_session"),
        )

        return response
    except Exception as e:
        raise litellm.exception_type(
            model=None,
            custom_llm_provider=custom_llm_provider,
            original_exception=e,
            completion_kwargs=local_vars,
            extra_kwargs=kwargs,
        )


@client
async def aget_responses(
    response_id: str,
    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
    # The extra values given here take precedence over values defined on the client or passed to this method.
    extra_headers: Optional[Dict[str, Any]] = None,
    extra_query: Optional[Dict[str, Any]] = None,
    extra_body: Optional[Dict[str, Any]] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
    # LiteLLM specific params,
    custom_llm_provider: Optional[str] = None,
    **kwargs,
) -> ResponsesAPIResponse:
    """
    Async: Fetch a response by its ID.

    GET /v1/responses/{response_id} endpoint in the responses API

    Args:
        response_id: The ID of the response to fetch.
        custom_llm_provider: Optional provider name. If not specified, will be decoded from response_id.

    Returns:
        The response object with complete information about the stored response.
    """
    local_vars = locals()
    try:
        loop = asyncio.get_event_loop()
        kwargs["aget_responses"] = True

        # get custom llm provider from response_id
        decoded_response_id: DecodedResponseId = (
            ResponsesAPIRequestUtils._decode_responses_api_response_id(
                response_id=response_id,
            )
        )
        response_id = decoded_response_id.get("response_id") or response_id
        custom_llm_provider = (
            decoded_response_id.get("custom_llm_provider") or custom_llm_provider
        )

        func = partial(
            get_responses,
            response_id=response_id,
            custom_llm_provider=custom_llm_provider,
            extra_headers=extra_headers,
            extra_query=extra_query,
            extra_body=extra_body,
            timeout=timeout,
            **kwargs,
        )

        ctx = contextvars.copy_context()
        func_with_context = partial(ctx.run, func)
        init_response = await loop.run_in_executor(None, func_with_context)

        if asyncio.iscoroutine(init_response):
            response = await init_response
        else:
            response = init_response

        # Update the responses_api_response_id with the model_id
        if isinstance(response, ResponsesAPIResponse):
            response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
                responses_api_response=response,
                litellm_metadata=kwargs.get("litellm_metadata", {}),
                custom_llm_provider=custom_llm_provider,
            )
        return response
    except Exception as e:
        raise litellm.exception_type(
            model=None,
            custom_llm_provider=custom_llm_provider,
            original_exception=e,
            completion_kwargs=local_vars,
            extra_kwargs=kwargs,
        )


@client
def get_responses(
    response_id: str,
    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
    # The extra values given here take precedence over values defined on the client or passed to this method.
    extra_headers: Optional[Dict[str, Any]] = None,
    extra_query: Optional[Dict[str, Any]] = None,
    extra_body: Optional[Dict[str, Any]] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
    # LiteLLM specific params,
    custom_llm_provider: Optional[str] = None,
    **kwargs,
) -> Union[ResponsesAPIResponse, Coroutine[Any, Any, ResponsesAPIResponse]]:
    """
    Fetch a response by its ID.

    GET /v1/responses/{response_id} endpoint in the responses API

    Args:
        response_id: The ID of the response to fetch.
        custom_llm_provider: Optional provider name. If not specified, will be decoded from response_id.

    Returns:
        The response object with complete information about the stored response.
    """
    local_vars = locals()
    try:
        litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj")  # type: ignore
        litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
        _is_async = kwargs.pop("aget_responses", False) is True

        # get llm provider logic
        litellm_params = GenericLiteLLMParams(**kwargs)

        # get custom llm provider from response_id
        decoded_response_id: DecodedResponseId = (
            ResponsesAPIRequestUtils._decode_responses_api_response_id(
                response_id=response_id,
            )
        )
        response_id = decoded_response_id.get("response_id") or response_id
        custom_llm_provider = (
            decoded_response_id.get("custom_llm_provider") or custom_llm_provider
        )

        if custom_llm_provider is None:
            raise ValueError("custom_llm_provider is required but passed as None")

        # get provider config
        responses_api_provider_config: Optional[BaseResponsesAPIConfig] = (
            ProviderConfigManager.get_provider_responses_api_config(
                model=None,
                provider=custom_llm_provider,
            )
        )

        if responses_api_provider_config is None:
            raise ValueError(
                f"GET responses is not supported for {custom_llm_provider}"
            )

        local_vars.update(kwargs)

        # Pre Call logging
        litellm_logging_obj.update_from_kwargs(
            kwargs=local_vars,
            model=None,
            optional_params={
                "response_id": response_id,
            },
            litellm_params={
                "litellm_call_id": litellm_call_id,
            },
            custom_llm_provider=custom_llm_provider,
        )

        # Call the handler with _is_async flag instead of directly calling the async handler
        response = base_llm_http_handler.get_responses(
            response_id=response_id,
            custom_llm_provider=custom_llm_provider,
            responses_api_provider_config=responses_api_provider_config,
            litellm_params=litellm_params,
            logging_obj=litellm_logging_obj,
            extra_headers=extra_headers,
            extra_body=extra_body,
            timeout=timeout or request_timeout,
            _is_async=_is_async,
            client=kwargs.get("client"),
            shared_session=kwargs.get("shared_session"),
        )

        # Update the responses_api_response_id with the model_id
        if isinstance(response, ResponsesAPIResponse):
            response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
                responses_api_response=response,
                litellm_metadata=kwargs.get("litellm_metadata", {}),
                custom_llm_provider=custom_llm_provider,
            )

        return response
    except Exception as e:
        raise litellm.exception_type(
            model=None,
            custom_llm_provider=custom_llm_provider,
            original_exception=e,
            completion_kwargs=local_vars,
            extra_kwargs=kwargs,
        )


@client
async def alist_input_items(
    response_id: str,
    after: Optional[str] = None,
    before: Optional[str] = None,
    include: Optional[List[str]] = None,
    limit: int = 20,
    order: Literal["asc", "desc"] = "desc",
    extra_headers: Optional[Dict[str, Any]] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
    custom_llm_provider: Optional[str] = None,
    **kwargs,
) -> Dict:
    """Async: List input items for a response"""
    local_vars = locals()
    try:
        loop = asyncio.get_event_loop()
        kwargs["alist_input_items"] = True

        decoded_response_id = (
            ResponsesAPIRequestUtils._decode_responses_api_response_id(
                response_id=response_id
            )
        )
        response_id = decoded_response_id.get("response_id") or response_id
        custom_llm_provider = (
            decoded_response_id.get("custom_llm_provider") or custom_llm_provider
        )

        func = partial(
            list_input_items,
            response_id=response_id,
            after=after,
            before=before,
            include=include,
            limit=limit,
            order=order,
            extra_headers=extra_headers,
            timeout=timeout,
            custom_llm_provider=custom_llm_provider,
            **kwargs,
        )

        ctx = contextvars.copy_context()
        func_with_context = partial(ctx.run, func)
        init_response = await loop.run_in_executor(None, func_with_context)

        if asyncio.iscoroutine(init_response):
            response = await init_response
        else:
            response = init_response
        return response
    except Exception as e:
        raise litellm.exception_type(
            model=None,
            custom_llm_provider=custom_llm_provider,
            original_exception=e,
            completion_kwargs=local_vars,
            extra_kwargs=kwargs,
        )


@client
def list_input_items(
    response_id: str,
    after: Optional[str] = None,
    before: Optional[str] = None,
    include: Optional[List[str]] = None,
    limit: int = 20,
    order: Literal["asc", "desc"] = "desc",
    extra_headers: Optional[Dict[str, Any]] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
    custom_llm_provider: Optional[str] = None,
    **kwargs,
) -> Union[Dict, Coroutine[Any, Any, Dict]]:
    """List input items for a response"""
    local_vars = locals()
    try:
        litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj")  # type: ignore
        litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
        _is_async = kwargs.pop("alist_input_items", False) is True

        litellm_params = GenericLiteLLMParams(**kwargs)

        decoded_response_id = (
            ResponsesAPIRequestUtils._decode_responses_api_response_id(
                response_id=response_id
            )
        )
        response_id = decoded_response_id.get("response_id") or response_id
        custom_llm_provider = (
            decoded_response_id.get("custom_llm_provider") or custom_llm_provider
        )

        if custom_llm_provider is None:
            raise ValueError("custom_llm_provider is required but passed as None")

        responses_api_provider_config: Optional[BaseResponsesAPIConfig] = (
            ProviderConfigManager.get_provider_responses_api_config(
                model=None,
                provider=custom_llm_provider,
            )
        )

        if responses_api_provider_config is None:
            raise ValueError(
                f"list_input_items is not supported for {custom_llm_provider}"
            )

        local_vars.update(kwargs)

        litellm_logging_obj.update_from_kwargs(
            kwargs=local_vars,
            model=None,
            optional_params={"response_id": response_id},
            litellm_params={"litellm_call_id": litellm_call_id},
            custom_llm_provider=custom_llm_provider,
        )

        response = base_llm_http_handler.list_responses_input_items(
            response_id=response_id,
            custom_llm_provider=custom_llm_provider,
            responses_api_provider_config=responses_api_provider_config,
            litellm_params=litellm_params,
            logging_obj=litellm_logging_obj,
            after=after,
            before=before,
            include=include,
            limit=limit,
            order=order,
            extra_headers=extra_headers,
            timeout=timeout or request_timeout,
            _is_async=_is_async,
            client=kwargs.get("client"),
            shared_session=kwargs.get("shared_session"),
        )

        return response
    except Exception as e:
        raise litellm.exception_type(
            model=None,
            custom_llm_provider=custom_llm_provider,
            original_exception=e,
            completion_kwargs=local_vars,
            extra_kwargs=kwargs,
        )


@client
async def acancel_responses(
    response_id: str,
    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
    # The extra values given here take precedence over values defined on the client or passed to this method.
    extra_headers: Optional[Dict[str, Any]] = None,
    extra_query: Optional[Dict[str, Any]] = None,
    extra_body: Optional[Dict[str, Any]] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
    # LiteLLM specific params,
    custom_llm_provider: Optional[str] = None,
    **kwargs,
) -> ResponsesAPIResponse:
    """
    Async version of the POST Cancel Responses API

    POST /v1/responses/{response_id}/cancel endpoint in the responses API

    """
    local_vars = locals()
    try:
        loop = asyncio.get_event_loop()
        kwargs["acancel_responses"] = True

        # get custom llm provider from response_id
        decoded_response_id: DecodedResponseId = (
            ResponsesAPIRequestUtils._decode_responses_api_response_id(
                response_id=response_id,
            )
        )
        response_id = decoded_response_id.get("response_id") or response_id
        custom_llm_provider = (
            decoded_response_id.get("custom_llm_provider") or custom_llm_provider
        )

        func = partial(
            cancel_responses,
            response_id=response_id,
            custom_llm_provider=custom_llm_provider,
            extra_headers=extra_headers,
            extra_query=extra_query,
            extra_body=extra_body,
            timeout=timeout,
            **kwargs,
        )

        ctx = contextvars.copy_context()
        func_with_context = partial(ctx.run, func)
        init_response = await loop.run_in_executor(None, func_with_context)

        if asyncio.iscoroutine(init_response):
            response = await init_response
        else:
            response = init_response
        return response
    except Exception as e:
        raise litellm.exception_type(
            model=None,
            custom_llm_provider=custom_llm_provider,
            original_exception=e,
            completion_kwargs=local_vars,
            extra_kwargs=kwargs,
        )


@client
def cancel_responses(
    response_id: str,
    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
    # The extra values given here take precedence over values defined on the client or passed to this method.
    extra_headers: Optional[Dict[str, Any]] = None,
    extra_query: Optional[Dict[str, Any]] = None,
    extra_body: Optional[Dict[str, Any]] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
    # LiteLLM specific params,
    custom_llm_provider: Optional[str] = None,
    **kwargs,
) -> Union[ResponsesAPIResponse, Coroutine[Any, Any, ResponsesAPIResponse]]:
    """
    Synchronous version of the POST Responses API

    POST /v1/responses/{response_id}/cancel endpoint in the responses API

    """
    local_vars = locals()
    try:
        litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj")  # type: ignore
        litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
        _is_async = kwargs.pop("acancel_responses", False) is True

        # get llm provider logic
        litellm_params = GenericLiteLLMParams(**kwargs)

        # get custom llm provider from response_id
        decoded_response_id: DecodedResponseId = (
            ResponsesAPIRequestUtils._decode_responses_api_response_id(
                response_id=response_id,
            )
        )
        response_id = decoded_response_id.get("response_id") or response_id
        custom_llm_provider = (
            decoded_response_id.get("custom_llm_provider") or custom_llm_provider
        )

        if custom_llm_provider is None:
            raise ValueError("custom_llm_provider is required but passed as None")

        # get provider config
        responses_api_provider_config: Optional[BaseResponsesAPIConfig] = (
            ProviderConfigManager.get_provider_responses_api_config(
                model=None,
                provider=custom_llm_provider,
            )
        )

        if responses_api_provider_config is None:
            raise ValueError(
                f"CANCEL responses is not supported for {custom_llm_provider}"
            )

        local_vars.update(kwargs)

        # Pre Call logging
        litellm_logging_obj.update_from_kwargs(
            kwargs=local_vars,
            model=None,
            optional_params={
                "response_id": response_id,
            },
            litellm_params={
                "litellm_call_id": litellm_call_id,
            },
            custom_llm_provider=custom_llm_provider,
        )

        # Call the handler with _is_async flag instead of directly calling the async handler
        response = base_llm_http_handler.cancel_response_api_handler(
            response_id=response_id,
            custom_llm_provider=custom_llm_provider,
            responses_api_provider_config=responses_api_provider_config,
            litellm_params=litellm_params,
            logging_obj=litellm_logging_obj,
            extra_headers=extra_headers,
            extra_body=extra_body,
            timeout=timeout or request_timeout,
            _is_async=_is_async,
            client=kwargs.get("client"),
            shared_session=kwargs.get("shared_session"),
        )

        return response
    except Exception as e:
        raise litellm.exception_type(
            model=None,
            custom_llm_provider=custom_llm_provider,
            original_exception=e,
            completion_kwargs=local_vars,
            extra_kwargs=kwargs,
        )


@client
async def acompact_responses(
    input: Union[str, ResponseInputParam],
    model: str,
    instructions: Optional[str] = None,
    previous_response_id: Optional[str] = None,
    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
    # The extra values given here take precedence over values defined on the client or passed to this method.
    extra_headers: Optional[Dict[str, Any]] = None,
    extra_query: Optional[Dict[str, Any]] = None,
    extra_body: Optional[Dict[str, Any]] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
    # LiteLLM specific params,
    custom_llm_provider: Optional[str] = None,
    **kwargs,
) -> ResponsesAPIResponse:
    """
    Async version of the POST Compact Responses API

    POST /v1/responses/compact endpoint in the responses API

    Runs a compaction pass over a conversation, returning encrypted, opaque items.
    """
    local_vars = locals()
    try:
        loop = asyncio.get_event_loop()
        kwargs["acompact_responses"] = True

        # get custom llm provider so we can use this for mapping exceptions
        if custom_llm_provider is None:
            _, custom_llm_provider, _, _ = litellm.get_llm_provider(
                model=model, api_base=local_vars.get("base_url", None)
            )
            # Update local_vars with detected provider (fixes #19782)
            local_vars["custom_llm_provider"] = custom_llm_provider

        func = partial(
            compact_responses,
            input=input,
            model=model,
            instructions=instructions,
            previous_response_id=previous_response_id,
            extra_headers=extra_headers,
            extra_query=extra_query,
            extra_body=extra_body,
            timeout=timeout,
            custom_llm_provider=custom_llm_provider,
            **kwargs,
        )

        ctx = contextvars.copy_context()
        func_with_context = partial(ctx.run, func)
        init_response = await loop.run_in_executor(None, func_with_context)

        if asyncio.iscoroutine(init_response):
            response = await init_response
        else:
            response = init_response

        # Update the responses_api_response_id with the model_id
        if isinstance(response, ResponsesAPIResponse):
            response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
                responses_api_response=response,
                litellm_metadata=kwargs.get("litellm_metadata", {}),
                custom_llm_provider=custom_llm_provider,
            )

        return response
    except Exception as e:
        raise litellm.exception_type(
            model=model,
            custom_llm_provider=custom_llm_provider,
            original_exception=e,
            completion_kwargs=local_vars,
            extra_kwargs=kwargs,
        )


@client
def compact_responses(
    input: Union[str, ResponseInputParam],
    model: str,
    instructions: Optional[str] = None,
    previous_response_id: Optional[str] = None,
    # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
    # The extra values given here take precedence over values defined on the client or passed to this method.
    extra_headers: Optional[Dict[str, Any]] = None,
    extra_query: Optional[Dict[str, Any]] = None,
    extra_body: Optional[Dict[str, Any]] = None,
    timeout: Optional[Union[float, httpx.Timeout]] = None,
    # LiteLLM specific params,
    custom_llm_provider: Optional[str] = None,
    **kwargs,
) -> Union[ResponsesAPIResponse, Coroutine[Any, Any, ResponsesAPIResponse]]:
    """
    Synchronous version of the POST Compact Responses API

    POST /v1/responses/compact endpoint in the responses API

    Runs a compaction pass over a conversation, returning encrypted, opaque items.
    """
    local_vars = locals()
    try:
        litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj")  # type: ignore
        litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
        _is_async = kwargs.pop("acompact_responses", False) is True

        # get llm provider logic
        litellm_params = GenericLiteLLMParams(**kwargs)

        model, custom_llm_provider = _resolve_model_provider_for_responses(
            model=model,
            custom_llm_provider=custom_llm_provider,
            litellm_params=litellm_params,
            local_vars=local_vars,
        )

        if custom_llm_provider is None:
            raise ValueError("custom_llm_provider is required but passed as None")

        # get provider config
        responses_api_provider_config: Optional[BaseResponsesAPIConfig] = (
            ProviderConfigManager.get_provider_responses_api_config(
                model=model,
                provider=custom_llm_provider,
            )
        )

        if responses_api_provider_config is None:
            raise ValueError(
                f"COMPACT responses is not supported for {custom_llm_provider}"
            )

        local_vars.update(kwargs)

        # Build optional params for compact endpoint
        response_api_optional_params: ResponsesAPIOptionalRequestParams = (
            ResponsesAPIRequestUtils.get_requested_response_api_optional_param(
                local_vars
            )
        )

        # Get optional parameters for the responses API
        responses_api_request_params: Dict = (
            ResponsesAPIRequestUtils.get_optional_params_responses_api(
                model=model,
                responses_api_provider_config=responses_api_provider_config,
                response_api_optional_params=response_api_optional_params,
                allowed_openai_params=None,
            )
        )

        # Pre Call logging
        litellm_logging_obj.update_from_kwargs(
            kwargs=local_vars,
            model=model,
            optional_params=dict(responses_api_request_params),
            litellm_params={
                **responses_api_request_params,
                "litellm_call_id": litellm_call_id,
                "data_residency": infer_openai_data_residency(
                    custom_llm_provider, litellm_params.api_base
                ),
            },
            custom_llm_provider=custom_llm_provider,
        )

        # Decode any litellm-encoded encrypted-content item IDs back to their original IDs
        # before forwarding to the upstream provider.
        input = ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input(
            input
        )

        # Call the handler with _is_async flag instead of directly calling the async handler
        response = base_llm_http_handler.compact_response_api_handler(
            model=model,
            input=input,
            responses_api_provider_config=responses_api_provider_config,
            response_api_optional_request_params=responses_api_request_params,
            litellm_params=litellm_params,
            logging_obj=litellm_logging_obj,
            custom_llm_provider=custom_llm_provider,
            extra_headers=extra_headers,
            extra_body=extra_body,
            timeout=timeout or request_timeout,
            _is_async=_is_async,
            client=kwargs.get("client"),
            shared_session=kwargs.get("shared_session"),
        )

        # Update the responses_api_response_id with the model_id
        if isinstance(response, ResponsesAPIResponse):
            response = ResponsesAPIRequestUtils._update_responses_api_response_id_with_model_id(
                responses_api_response=response,
                litellm_metadata=kwargs.get("litellm_metadata", {}),
                custom_llm_provider=custom_llm_provider,
            )

        return response
    except Exception as e:
        raise litellm.exception_type(
            model=model,
            custom_llm_provider=custom_llm_provider,
            original_exception=e,
            completion_kwargs=local_vars,
            extra_kwargs=kwargs,
        )


# ---------------------------------------------------------------------------
# Responses API WebSocket mode
# ---------------------------------------------------------------------------


def _build_litellm_metadata_for_ws(kwargs: dict) -> dict:
    metadata: dict = {**(kwargs.get("litellm_metadata") or {})}
    guardrails = (
        (kwargs.get("metadata") or {}).get("guardrails")
        or kwargs.get("guardrails")
        or []
    )
    if guardrails:
        metadata["guardrails"] = guardrails
    return metadata


@client
async def _aresponses_websocket(
    model: str,
    websocket: Any,
    api_base: Optional[str] = None,
    api_key: Optional[str] = None,
    timeout: Optional[float] = None,
    **kwargs,
):
    """
    Private function to handle the Responses API WebSocket mode.

    For PROXY use only.

    Resolves the LLM provider from ``model``, looks up the matching
    ``BaseResponsesAPIConfig``, and hands off to
    ``BaseLLMHTTPHandler.async_responses_websocket``.
    """
    litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj")  # type: ignore
    user = kwargs.get("user", None)
    litellm_params = GenericLiteLLMParams(**kwargs)
    litellm_params_dict = get_litellm_params(**kwargs)

    (
        model,
        _custom_llm_provider,
        dynamic_api_key,
        dynamic_api_base,
    ) = litellm.get_llm_provider(
        model=model,
        api_base=api_base,
        api_key=api_key,
    )

    litellm_params_dict["data_residency"] = infer_openai_data_residency(
        _custom_llm_provider,
        dynamic_api_base or litellm_params.api_base or litellm.api_base,
    )

    litellm_logging_obj.update_from_kwargs(
        kwargs=kwargs,
        model=model,
        user=user,
        optional_params={},
        litellm_params=litellm_params_dict,
        custom_llm_provider=_custom_llm_provider,
    )

    responses_api_provider_config: Optional[BaseResponsesAPIConfig] = None
    if _custom_llm_provider is not None:
        responses_api_provider_config = (
            ProviderConfigManager.get_provider_responses_api_config(
                model=model,
                provider=litellm.LlmProviders(_custom_llm_provider),
            )
        )

    resolved_api_base = (
        dynamic_api_base or litellm_params.api_base or litellm.api_base or None
    )
    resolved_api_key = (
        dynamic_api_key
        or litellm_params.api_key
        or litellm.api_key
        or litellm.openai_key
        or get_secret_str("OPENAI_API_KEY")
    )

    # Extract params that we're passing explicitly to avoid duplicates in **kwargs
    _explicit_keys = {
        "user_api_key_dict",
        "litellm_metadata",
        "custom_llm_provider",
        "model",
        "websocket",
        "litellm_logging_obj",
        "api_base",
        "api_key",
        "timeout",
    }
    remaining_kwargs = {k: v for k, v in kwargs.items() if k not in _explicit_keys}

    await base_llm_http_handler.async_responses_websocket(
        model=model,
        websocket=websocket,
        logging_obj=litellm_logging_obj,
        responses_api_provider_config=responses_api_provider_config,
        api_base=resolved_api_base,
        api_key=resolved_api_key,
        timeout=timeout,
        user_api_key_dict=kwargs.get("user_api_key_dict"),
        litellm_metadata=_build_litellm_metadata_for_ws(kwargs),
        custom_llm_provider=_custom_llm_provider,
        **remaining_kwargs,
    )
