Cancels a model response with the given ID. Only responses created with the background parameter set to true can be cancelled.

The ID of the response to cancel.

Opaque per-user identity string used to scope endpoint-scoped data to a specific end user. The caller must have the agents/endpoints/UserIdentityImpersonation/action RBAC permission.

Session ID for this request. Only present for hosted agent responses. Returns the provided session ID or an auto-generated one if not provided in the request.

Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

This field is being replaced by safety_identifier and prompt_cache_key. Use prompt_cache_key instead to maintain caching optimizations. A stable identifier for your end-users. Used to boost cache hit rates by better bucketing similar requests and to help OpenAI detect and prevent abuse. Learn more.

The model deployment to use for the creation of this response.

Configuration options for a text response from the model. Can be plain text or structured JSON data. Learn more: - Text inputs and outputs - Structured Outputs

An object specifying the format that the model must output. Configuring { "type": "json_schema" } enables Structured Outputs, which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide. The default format is { "type": "text" } with no additional options. Not recommended for gpt-4o and newer models:* Setting to { "type": "json_object" } enables the older JSON mode, which ensures the message the model generates is valid JSON. Using json_schema is preferred for models that support it.

JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.

The type of response format being defined. Always json_schema.

A description of what the response format is for, used by the model to determine how to respond in the format.

The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.

Default response format. Used to generate text responses.

The type of response format being defined. Always text.

JSON object response format. An older method of generating JSON responses. Using json_schema is recommended for models that support it. Note that the model will not generate JSON without a system or user message instructing it to do so.

The type of response format being defined. Always json_object.

Constrains the verbosity of the model's response. Lower values will result in more concise responses, while higher values will result in more verbose responses. Currently supported values are low, medium, and high. The default is medium.

An array of tools the model may call while generating a response. You can specify which tool to use by setting the tool_choice parameter. We support the following categories of tools: - Built-in tools: Tools that are provided by OpenAI that extend the model's capabilities, like web search or file search. Learn more about built-in tools. - MCP Tools: Integrations with third-party systems via custom MCP servers or predefined connectors such as Google Drive and SharePoint. Learn more about MCP Tools. - Function calls (custom tools): Functions that are defined by you, enabling the model to call your own code with strongly typed arguments and outputs. Learn more about function calling. You can also use custom tools to call your own code.

Controls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools.

Use this option to force the model to call a specific function.

For function calling, the type is always function.

The name of the function to call.

Use this option to force the model to call a specific tool on a remote MCP server.

For MCP tools, the type is always mcp.

The label of the MCP server to use.

Constrains the tools available to the model to a pre-defined set.

Allowed tool configuration type. Always allowed_tools.

Constrains the tools available to the model to a pre-defined set. auto allows the model to pick from among the allowed tools and generate a message. required requires the model to call one or more of the allowed tools.

A list of tool definitions that the model should be allowed to call. For the Responses API, the list of tool definitions might look like: json [ { "type": "function", "name": "get_weather" }, { "type": "mcp", "server_label": "deepwiki" }, { "type": "image_generation" } ]

Use this option to force the model to call a specific custom tool.

For custom tool calling, the type is always custom.

The name of the custom tool to call.

The tool to call. Always programmatic_tool_calling.

Forces the model to call the apply_patch tool when executing a tool call.

The tool to call. Always apply_patch.

Forces the model to call the shell tool when a tool call is required.

The tool to call. Always shell.

Indicates that the model should use a built-in tool to generate a response. Learn more about built-in tools.

Indicates that the model should use a built-in tool to generate a response. Learn more about built-in tools.

Indicates that the model should use a built-in tool to generate a response. Learn more about built-in tools.

Indicates that the model should use a built-in tool to generate a response. Learn more about built-in tools.

Indicates that the model should use a built-in tool to generate a response. Learn more about built-in tools.

Indicates that the model should use a built-in tool to generate a response. Learn more about built-in tools.

Indicates that the model should use a built-in tool to generate a response. Learn more about built-in tools.

Indicates that the model should use a built-in tool to generate a response. Learn more about built-in tools.

Reference to a prompt template and its variables. Learn more.

The unique identifier of the prompt template to use.

Optional map of values to substitute in for variables in your prompt. The substitution values can either be strings, or other Response input types like images or files.

The type of the input item. Always input_text.

The text input to the model.

Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

The breakpoint mode. Always explicit.

The type of the input item. Always input_image.

Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

The breakpoint mode. Always explicit.

The type of the input item. Always input_file.

The name of the file to be sent to the model.

The content of the file to be sent to the model.

Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

The breakpoint mode. Always explicit.

The URL of the file to be sent to the model.

Specifies the processing type used for serving the request. - If set to 'auto', then the request will be processed with the service tier configured in the Project settings. Unless otherwise configured, the Project will use 'default'. - If set to 'default', then the request will be processed with the standard pricing and performance for the selected model. - If set to 'flex', then the request will be processed with the Flex Processing service tier. - To opt-in to Fast mode at the request level, include the service_tier=fast or service_tier=priority parameter for Responses or Chat Completions. The response will show service_tier=priority regardless of if you specify service_tier=fast or priority in your request. - If set to 'ultrafast', then the request will be processed with the access-controlled Ultrafast Processing service tier. This tier is currently available for gpt-5.6-sol; a response served through it will show service_tier=ultrafast. - When not set, the default behavior is 'auto'. When the service_tier parameter is set, the response body will include the service_tier value based on the processing mode actually used to serve the request. This response value may be different from the value set in the parameter.

Unique identifier for this Response.

The object type of this resource - always set to response.

The status of the response generation. One of completed, failed, in_progress, cancelled, queued, or incomplete.

Unix timestamp (in seconds) of when this Response was created.

An error object returned when the model fails to generate a Response.

The original error type, or null if it was not recorded.

The request parameter associated with the error, or null if none was recorded.

The error code for the response.

A human-readable description of the error.

The public explanation for this block.

The public continuation instruction.

An array of content items generated by the model. - The length and order of items in the output array is dependent on the model's response. - Rather than accessing the first item in the output array and assuming it's an assistant message with the content generated by the model, you might consider using the output_text property where supported in SDKs.

Configuration options for reasoning models.

Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, xhigh, and max. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response. Not all reasoning models support every value. See the reasoning guide for model-specific support.

Represents token usage details including input tokens, output tokens, a breakdown of output tokens, and the total tokens used.

The prompt-caching options that were applied to the response. Supported for gpt-5.6 and later models.

Prompt cache diagnostics requested for this response.

Moderation results or errors for the response input and output.

Moderation results or an error for a response moderation check.

An error produced while attempting moderation for the response input or output.

The object type, which was always error for moderation failures.

The error code.

The error message.

A moderation result produced for the response input or output.

The object type, which was always moderation_result for successful moderation results.

The moderation model that produced this result.

A boolean indicating whether the content was flagged by any category.

A dictionary of moderation categories to booleans, True if the input is flagged under this category.

A dictionary of moderation categories to scores.

Which modalities of input are reflected by the score for each category.

Moderation results or an error for a response moderation check.

An error produced while attempting moderation for the response input or output.

The object type, which was always error for moderation failures.

The error code.

The error message.

A moderation result produced for the response input or output.

The object type, which was always moderation_result for successful moderation results.

The moderation model that produced this result.

A boolean indicating whether the content was flagged by any category.

A dictionary of moderation categories to booleans, True if the input is flagged under this category.

A dictionary of moderation categories to scores.

Which modalities of input are reflected by the score for each category.

Whether to allow the model to run tool calls in parallel.

The conversation that this response belonged to. Input items and output items from this response were automatically added to this conversation.

The unique ID of the conversation that this response was associated with.

The agent used for this response

The name of the agent.

The version identifier of the agent.

Model selection metadata providing visibility into routing decisions.

Routing decision metadata from Model Router.

The routing mode used by Model Router for the request.

Effective Model Router session affinity metadata for a request.

Effective modes reported for Model Router session affinity.

Conversation identifier sources supported by Model Router session affinity.

Final outcomes reported for Model Router session affinity.

Ordered trace of routing decisions. Contains one entry for Chat Completions, and may contain multiple entries for Responses API or agent scenarios where multiple inference turns occur.

The content filter evaluation results.

The public explanation for this block.

The public continuation instruction.

The public explanation for this block.

The public continuation instruction.