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Responses

Response Types

Data models and types for API responses.

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."""
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

model instance-attribute

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

parallel_tool_calls = None class-attribute instance-attribute

Whether to allow parallel tool calls

reasoning = None class-attribute instance-attribute

Configuration options for reasoning models.

response_format = None class-attribute instance-attribute

Format specification for the response

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)