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Responses

OpenResponses Types

Data models and types for the OpenResponses API specification.

For the full OpenResponses type definitions, see the openresponses-types package documentation.

any_llm.types.responses

ResponsesParams

Bases: BaseModel

Normalized parameters for responses API.

This model is used internally to pass structured parameters from the public API layer to provider implementations, avoiding very long function signatures while keeping type safety.

Source code in src/any_llm/types/responses.py 
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class ResponsesParams(BaseModel):
    """Normalized parameters for responses API.

    This model is used internally to pass structured parameters from the public
    API layer to provider implementations, avoiding very long function
    signatures while keeping type safety.
    """

    model_config = ConfigDict(extra="forbid")

    model: str
    """Model identifier (e.g., 'mistral-small-latest')"""

    input: str | ResponseInputParam
    """The input payload accepted by provider's Responses API.
        For OpenAI-compatible providers, this is typically a list mixing
        text, images, and tool instructions, or a dict per OpenAI spec.
    """

    instructions: str | None = None

    max_tool_calls: int | None = None

    text: Any | None = None

    tools: list[dict[str, Any]] | None = None
    """List of tools for tool calling. Should be converted to OpenAI tool format dicts"""

    tool_choice: str | dict[str, Any] | None = None
    """Controls which tools the model can call"""

    temperature: float | None = None
    """Controls randomness in the response (0.0 to 2.0)"""

    top_p: float | None = None
    """Controls diversity via nucleus sampling (0.0 to 1.0)"""

    max_output_tokens: int | None = None
    """Maximum number of tokens to generate"""

    response_format: dict[str, Any] | type[BaseModel] | None = None
    """Format specification for the response"""

    stream: bool | None = None
    """Whether to stream the response"""

    parallel_tool_calls: bool | None = None
    """Whether to allow parallel tool calls"""

    top_logprobs: int | None = None
    """Number of top alternatives to return when logprobs are requested"""

    stream_options: dict[str, Any] | None = None
    """Additional options controlling streaming behavior"""

    reasoning: dict[str, Any] | None = None
    """Configuration options for reasoning models."""

    presence_penalty: float | None = None
    """Penalizes new tokens based on whether they appear in the text so far."""

    frequency_penalty: float | None = None
    """Penalizes new tokens based on their frequency in the text so far."""

    truncation: str | None = None
    """Controls how the service truncates the input when it exceeds the model context window."""

    store: bool | None = None
    """Whether to store the response so it can be retrieved later."""

    service_tier: str | None = None
    """The service tier to use for this request."""

    user: str | None = None
    """A unique identifier representing your end user."""

    metadata: dict[str, str] | None = None
    """Key-value pairs for custom metadata (up to 16 pairs)."""

    previous_response_id: str | None = None
    """The ID of the response to use as the prior turn for this request."""

    include: list[str] | None = None
    """Items to include in the response (e.g., 'reasoning.encrypted_content')."""

    background: bool | None = None
    """Whether to run the request in the background and return immediately."""

    safety_identifier: str | None = None
    """A stable identifier used for safety monitoring and abuse detection."""

    prompt_cache_key: str | None = None
    """A key to use when reading from or writing to the prompt cache."""

    prompt_cache_retention: str | None = None
    """How long to retain a prompt cache entry created by this request."""

    conversation: str | dict[str, Any] | None = None
    """The conversation to associate this response with (ID string or ConversationParam object)."""
background = None class-attribute instance-attribute

Whether to run the request in the background and return immediately.

conversation = None class-attribute instance-attribute

The conversation to associate this response with (ID string or ConversationParam object).

frequency_penalty = None class-attribute instance-attribute

Penalizes new tokens based on their frequency in the text so far.

include = None class-attribute instance-attribute

Items to include in the response (e.g., 'reasoning.encrypted_content').

input instance-attribute

The input payload accepted by provider's Responses API. For OpenAI-compatible providers, this is typically a list mixing text, images, and tool instructions, or a dict per OpenAI spec.

max_output_tokens = None class-attribute instance-attribute

Maximum number of tokens to generate

metadata = None class-attribute instance-attribute

Key-value pairs for custom metadata (up to 16 pairs).

model instance-attribute

Model identifier (e.g., 'mistral-small-latest')

parallel_tool_calls = None class-attribute instance-attribute

Whether to allow parallel tool calls

presence_penalty = None class-attribute instance-attribute

Penalizes new tokens based on whether they appear in the text so far.

previous_response_id = None class-attribute instance-attribute

The ID of the response to use as the prior turn for this request.

prompt_cache_key = None class-attribute instance-attribute

A key to use when reading from or writing to the prompt cache.

prompt_cache_retention = None class-attribute instance-attribute

How long to retain a prompt cache entry created by this request.

reasoning = None class-attribute instance-attribute

Configuration options for reasoning models.

response_format = None class-attribute instance-attribute

Format specification for the response

safety_identifier = None class-attribute instance-attribute

A stable identifier used for safety monitoring and abuse detection.

service_tier = None class-attribute instance-attribute

The service tier to use for this request.

store = None class-attribute instance-attribute

Whether to store the response so it can be retrieved later.

stream = None class-attribute instance-attribute

Whether to stream the response

stream_options = None class-attribute instance-attribute

Additional options controlling streaming behavior

temperature = None class-attribute instance-attribute

Controls randomness in the response (0.0 to 2.0)

tool_choice = None class-attribute instance-attribute

Controls which tools the model can call

tools = None class-attribute instance-attribute

List of tools for tool calling. Should be converted to OpenAI tool format dicts

top_logprobs = None class-attribute instance-attribute

Number of top alternatives to return when logprobs are requested

top_p = None class-attribute instance-attribute

Controls diversity via nucleus sampling (0.0 to 1.0)

truncation = None class-attribute instance-attribute

Controls how the service truncates the input when it exceeds the model context window.

user = None class-attribute instance-attribute

A unique identifier representing your end user.