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Cancel a run

Deprecated
beta.threads.runs.cancel(strrun_id, RunCancelParams**kwargs) -> Run
POST/threads/{thread_id}/runs/{run_id}/cancel

Cancels a run that is in_progress.

ParametersExpand Collapse
thread_id: str
run_id: str
ReturnsExpand Collapse
class Run: …

Represents an execution run on a thread.

id: str

The identifier, which can be referenced in API endpoints.

assistant_id: str

The ID of the assistant used for execution of this run.

cancelled_at: Optional[int]

The Unix timestamp (in seconds) for when the run was cancelled.

completed_at: Optional[int]

The Unix timestamp (in seconds) for when the run was completed.

created_at: int

The Unix timestamp (in seconds) for when the run was created.

expires_at: Optional[int]

The Unix timestamp (in seconds) for when the run will expire.

failed_at: Optional[int]

The Unix timestamp (in seconds) for when the run failed.

incomplete_details: Optional[IncompleteDetails]

Details on why the run is incomplete. Will be null if the run is not incomplete.

reason: Optional[Literal["max_completion_tokens", "max_prompt_tokens"]]

The reason why the run is incomplete. This will point to which specific token limit was reached over the course of the run.

Accepts one of the following:
"max_completion_tokens"
"max_prompt_tokens"
instructions: str

The instructions that the assistant used for this run.

last_error: Optional[LastError]

The last error associated with this run. Will be null if there are no errors.

code: Literal["server_error", "rate_limit_exceeded", "invalid_prompt"]

One of server_error, rate_limit_exceeded, or invalid_prompt.

Accepts one of the following:
"server_error"
"rate_limit_exceeded"
"invalid_prompt"
message: str

A human-readable description of the error.

max_completion_tokens: Optional[int]

The maximum number of completion tokens specified to have been used over the course of the run.

minimum256
max_prompt_tokens: Optional[int]

The maximum number of prompt tokens specified to have been used over the course of the run.

minimum256
metadata: Optional[Metadata]

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.

model: str

The model that the assistant used for this run.

object: Literal["thread.run"]

The object type, which is always thread.run.

parallel_tool_calls: bool

Whether to enable parallel function calling during tool use.

required_action: Optional[RequiredAction]

Details on the action required to continue the run. Will be null if no action is required.

submit_tool_outputs: RequiredActionSubmitToolOutputs

Details on the tool outputs needed for this run to continue.

tool_calls: List[RequiredActionFunctionToolCall]

A list of the relevant tool calls.

id: str

The ID of the tool call. This ID must be referenced when you submit the tool outputs in using the Submit tool outputs to run endpoint.

function: Function

The function definition.

arguments: str

The arguments that the model expects you to pass to the function.

name: str

The name of the function.

type: Literal["function"]

The type of tool call the output is required for. For now, this is always function.

type: Literal["submit_tool_outputs"]

For now, this is always submit_tool_outputs.

response_format: Optional[AssistantResponseFormatOption]

Specifies the format that the model must output. Compatible with GPT-4o, GPT-4 Turbo, and all GPT-3.5 Turbo models since gpt-3.5-turbo-1106.

Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.

Setting to { "type": "json_object" } enables JSON mode, which ensures the message the model generates is valid JSON.

Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly "stuck" request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.

Accepts one of the following:
Literal["auto"]

auto is the default value

class ResponseFormatText: …

Default response format. Used to generate text responses.

type: Literal["text"]

The type of response format being defined. Always text.

class ResponseFormatJSONObject: …

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.

type: Literal["json_object"]

The type of response format being defined. Always json_object.

class ResponseFormatJSONSchema: …

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

json_schema: JSONSchema

Structured Outputs configuration options, including a JSON Schema.

name: str

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.

description: Optional[str]

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

schema: Optional[Dict[str, object]]

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

strict: Optional[bool]

Whether to enable strict schema adherence when generating the output. If set to true, the model will always follow the exact schema defined in the schema field. Only a subset of JSON Schema is supported when strict is true. To learn more, read the Structured Outputs guide.

type: Literal["json_schema"]

The type of response format being defined. Always json_schema.

started_at: Optional[int]

The Unix timestamp (in seconds) for when the run was started.

status: RunStatus

The status of the run, which can be either queued, in_progress, requires_action, cancelling, cancelled, failed, completed, incomplete, or expired.

Accepts one of the following:
"queued"
"in_progress"
"requires_action"
"cancelling"
"cancelled"
"failed"
"completed"
"incomplete"
"expired"
thread_id: str

The ID of the thread that was executed on as a part of this run.

tool_choice: Optional[AssistantToolChoiceOption]

Controls which (if any) tool is called by the model. none means the model will not call any tools and instead generates a message. auto is the default value and 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 before responding to the user. Specifying a particular tool like {"type": "file_search"} or {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool.

Accepts one of the following:
Literal["none", "auto", "required"]

none means the model will not call any tools 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 before responding to the user.

Accepts one of the following:
"none"
"auto"
"required"
class AssistantToolChoice: …

Specifies a tool the model should use. Use to force the model to call a specific tool.

type: Literal["function", "code_interpreter", "file_search"]

The type of the tool. If type is function, the function name must be set

Accepts one of the following:
"function"
"code_interpreter"
"file_search"
function: Optional[AssistantToolChoiceFunction]
name: str

The name of the function to call.

tools: List[AssistantTool]

The list of tools that the assistant used for this run.

Accepts one of the following:
class CodeInterpreterTool: …
type: Literal["code_interpreter"]

The type of tool being defined: code_interpreter

class FileSearchTool: …
type: Literal["file_search"]

The type of tool being defined: file_search

Accepts one of the following:
class FunctionTool: …
name: str

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

description: Optional[str]

A description of what the function does, used by the model to choose when and how to call the function.

parameters: Optional[FunctionParameters]

The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.

Omitting parameters defines a function with an empty parameter list.

strict: Optional[bool]

Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.

type: Literal["function"]

The type of tool being defined: function

truncation_strategy: Optional[TruncationStrategy]

Controls for how a thread will be truncated prior to the run. Use this to control the initial context window of the run.

type: Literal["auto", "last_messages"]

The truncation strategy to use for the thread. The default is auto. If set to last_messages, the thread will be truncated to the n most recent messages in the thread. When set to auto, messages in the middle of the thread will be dropped to fit the context length of the model, max_prompt_tokens.

Accepts one of the following:
"auto"
"last_messages"
last_messages: Optional[int]

The number of most recent messages from the thread when constructing the context for the run.

minimum1
usage: Optional[Usage]

Usage statistics related to the run. This value will be null if the run is not in a terminal state (i.e. in_progress, queued, etc.).

completion_tokens: int

Number of completion tokens used over the course of the run.

prompt_tokens: int

Number of prompt tokens used over the course of the run.

total_tokens: int

Total number of tokens used (prompt + completion).

temperature: Optional[float]

The sampling temperature used for this run. If not set, defaults to 1.

top_p: Optional[float]

The nucleus sampling value used for this run. If not set, defaults to 1.

Cancel a run

from openai import OpenAI
client = OpenAI()

run = client.beta.threads.runs.cancel(
  thread_id="thread_abc123",
  run_id="run_abc123"
)

print(run)

{
  "id": "run_abc123",
  "object": "thread.run",
  "created_at": 1699076126,
  "assistant_id": "asst_abc123",
  "thread_id": "thread_abc123",
  "status": "cancelling",
  "started_at": 1699076126,
  "expires_at": 1699076726,
  "cancelled_at": null,
  "failed_at": null,
  "completed_at": null,
  "last_error": null,
  "model": "gpt-4o",
  "instructions": "You summarize books.",
  "tools": [
    {
      "type": "file_search"
    }
  ],
  "tool_resources": {
    "file_search": {
      "vector_store_ids": ["vs_123"]
    }
  },
  "metadata": {},
  "usage": null,
  "temperature": 1.0,
  "top_p": 1.0,
  "response_format": "auto",
  "tool_choice": "auto",
  "parallel_tool_calls": true
}
Returns Examples
{
  "id": "run_abc123",
  "object": "thread.run",
  "created_at": 1699076126,
  "assistant_id": "asst_abc123",
  "thread_id": "thread_abc123",
  "status": "cancelling",
  "started_at": 1699076126,
  "expires_at": 1699076726,
  "cancelled_at": null,
  "failed_at": null,
  "completed_at": null,
  "last_error": null,
  "model": "gpt-4o",
  "instructions": "You summarize books.",
  "tools": [
    {
      "type": "file_search"
    }
  ],
  "tool_resources": {
    "file_search": {
      "vector_store_ids": ["vs_123"]
    }
  },
  "metadata": {},
  "usage": null,
  "temperature": 1.0,
  "top_p": 1.0,
  "response_format": "auto",
  "tool_choice": "auto",
  "parallel_tool_calls": true
}