CreateResponseRequestBody

Request schema for creating a new agent response. Configures the agent's model, input, tools, output format, and behavior. Supports both foreground (blocking) and background (asynchronous) execution with optional streaming.

Example Usage

import { CreateResponseRequestBody } from "@meetkai/mka1/models/operations";

let value: CreateResponseRequestBody = {
  model: "Beetle",
};

Fields

Field	Type	Required	Description
background	boolean	➖	Whether to run the model response in the background asynchronously. When true, the request returns immediately with a response ID while the agent processes in the background. Use GET /responses/{id} to retrieve results later. When false, the request blocks until completion. Defaults to false.
conversation	operations.Conversation	➖	The conversation that this response belongs to. Can be a conversation ID string or a conversation object. Used to maintain context and history across multiple agent interactions. Optional - omit for one-off interactions.
include	operations.Include[]	➖	Additional fields to include in the response output. Allows requesting specific nested data like web search sources, code interpreter outputs, computer screenshots, file search results, input images, output logprobs, or reasoning content. These fields may have performance or cost implications.
input	operations.CreateResponseInput	➖	The user input to send to the agent. Can be a simple text string for basic queries, or an array of item objects containing text, images, files, or audio for multimodal interactions. This is the main content the agent will respond to.
instructions	string	➖	System or developer instructions inserted into the model's context before user input. Use this to guide the agent's behavior, set personality, define constraints, or provide domain-specific knowledge. Acts as a persistent system message for this response.
maxOutputTokens	number	➖	The maximum number of tokens the model can generate in its output. Sets an upper bound to control costs and response length. The actual output may be shorter if the model finishes naturally. Must be a positive integer.
maxToolCalls	number	➖	The maximum total number of tool calls the agent can make during this response. Useful for controlling execution time and preventing infinite loops. Applies to all built-in tools like web search, file search, code interpreter, etc. Must be a positive integer.
metadata	Record<string, string>	➖	Custom metadata to attach to this response as key-value pairs. Keys must be strings up to 64 characters, values must be strings up to 512 characters. Maximum 16 pairs. Useful for tracking, categorization, or passing custom identifiers through your system.
model	string	✔️	The ID of the model to use for generating the response. Can be an OpenAI model (e.g., 'gpt-4', 'gpt-4-turbo', 'o1'), Anthropic model (e.g., 'claude-3-5-sonnet'), Google model, or a custom registered model. Required field.
parallelToolCalls	boolean	➖	Whether to allow the agent to execute multiple tool calls in parallel. When true, the agent can make concurrent tool calls for efficiency. When false, tools are called sequentially. Defaults to true for better performance.
previousResponseId	string	➖	The unique ID of a previous response to continue from. Used for multi-turn conversations to maintain context and history. The agent will have access to all previous interactions in the chain.
prompt	operations.Prompt	➖	Reference to a prompt template to use for this response, along with variables to substitute. Allows using predefined, versioned prompt templates instead of inline instructions. The template system supports variable interpolation.
promptCacheKey	string	➖	A stable identifier used for caching prompts and context. Helps reduce costs and latency by reusing cached prompt processing. Useful for prompts that are used repeatedly with minor variations.
reasoning	operations.Reasoning	➖	Configuration options for reasoning models like GPT-5 and O-series (o1, o3). Controls reasoning effort level (minimal, low, medium, high) and summary verbosity (auto, concise, detailed). Only applicable to reasoning-capable models.
safetyIdentifier	string	➖	A stable identifier used to help detect and prevent policy violations across multiple requests. Helps the system identify patterns of abuse or misuse. Useful for compliance tracking and safety monitoring.
serviceTier	operations.ServiceTier	➖	Specifies the processing tier for serving the request. 'auto' lets the system choose, 'default' uses standard processing, 'flex' allows flexible scheduling for lower priority, 'priority' provides faster processing. Defaults to 'auto'.
store	boolean	➖	Whether to store the generated response for later retrieval. When true, the response is saved and can be retrieved via GET /responses/{id}. When false, the response is not persisted after generation. Defaults to true.
stream	boolean	➖	If set to true, the response data will be streamed using Server-Sent Events (SSE) for real-time updates as the agent generates the response. When false, the response is returned as a single complete object. Defaults to false.
streamOptions	operations.CreateResponseStreamOptions	➖	Additional options for configuring streaming behavior when stream is enabled.
temperature	number	➖	Controls randomness in the model's output. Higher values (e.g., 1.5-2.0) make output more random and creative, lower values (e.g., 0.0-0.5) make it more focused and deterministic. Must be between 0 and 2. Defaults to 1 for balanced behavior.
text	operations.Text	➖	Configuration options for text output from the model. Includes response format (text, JSON object, JSON schema) and verbosity level (low, medium, high). Use this to control output structure and detail level.
toolChoice	operations.ToolChoice	➖	Controls how the agent selects which tools to use. Can be 'none' (no tools), 'auto' (model decides), 'required' (must use tools), or a specific tool selection object. Use this to force or prevent tool usage.
tools	operations.CreateResponseTools[]	➖	Array of tool definitions that the agent can use during response generation. Supports built-in tools (web search, file search, code interpreter, computer use, image generation) and custom tools (functions, MCP, local shell). Each tool definition specifies its type and configuration.
topLogprobs	number	➖	The number of most likely tokens to return at each position along with their log probabilities. Must be between 0 and 20. Useful for understanding model confidence and exploring alternative outputs.
topP	number	➖	Nucleus sampling parameter. The model considers only the tokens with top_p cumulative probability. Lower values (e.g., 0.1) make output more focused, higher values (e.g., 0.9) allow more diversity. Must be between 0 and 1. Defaults to 1. Alternative to temperature.
truncation	operations.Truncation	➖	The truncation strategy for handling inputs that exceed the model's context window. 'auto' automatically truncates old messages to fit, 'disabled' returns an error if context is too long. Defaults to 'disabled'.
user	string	➖	DEPRECATED: A unique identifier for the end-user. Use safety_identifier for policy violation detection and prompt_cache_key for caching instead. This field is maintained for backwards compatibility.