Threads

Create thread

beta.threads.create(ThreadCreateParams**kwargs) -> Thread

post /threads

Create a thread.

Parameters

• messages: Optional[Iterable[Message]]

  A list of messages to start the thread with.

  • content: Union[str, Iterable[MessageContentPartParam]]

    The text contents of the message.

    • str

      The text contents of the message.

    • Iterable[MessageContentPartParam]

      An array of content parts with a defined type, each can be of type text or images can be passed with image_url or image_file. Image types are only supported on Vision-compatible models.

      • class ImageFileContentBlock: …

        References an image File in the content of a message.

        • image_file: ImageFile

          • file_id: str

            The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.

          • detail: Optional[Literal["auto", "low", "high"]]

            Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.

            • "auto"

            • "low"

            • "high"

        • type: Literal["image_file"]

          Always image_file.

          • "image_file"

      • class ImageURLContentBlock: …

        References an image URL in the content of a message.

        • image_url: ImageURL

          • url: str

            The external URL of the image, must be a supported image types: jpeg, jpg, png, gif, webp.

          • detail: Optional[Literal["auto", "low", "high"]]

            Specifies the detail level of the image. low uses fewer tokens, you can opt in to high resolution using high. Default value is auto

            • "auto"

            • "low"

            • "high"

        • type: Literal["image_url"]

          The type of the content part.

          • "image_url"

      • class TextContentBlockParam: …

        The text content that is part of a message.

        • text: str

          Text content to be sent to the model

        • type: Literal["text"]

          Always text.

          • "text"

  • role: Literal["user", "assistant"]

    The role of the entity that is creating the message. Allowed values include:

    • user: Indicates the message is sent by an actual user and should be used in most cases to represent user-generated messages.

    • assistant: Indicates the message is generated by the assistant. Use this value to insert messages from the assistant into the conversation.

    • "user"

    • "assistant"

  • attachments: Optional[Iterable[MessageAttachment]]

    A list of files attached to the message, and the tools they should be added to.

    • file_id: Optional[str]

      The ID of the file to attach to the message.

    • tools: Optional[Iterable[MessageAttachmentTool]]

      The tools to add this file to.

      • class CodeInterpreterTool: …

        • type: Literal["code_interpreter"]

          The type of tool being defined: code_interpreter

          • "code_interpreter"

      • class MessageAttachmentToolFileSearch: …

        • type: Literal["file_search"]

          The type of tool being defined: file_search

          • "file_search"

  • 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.

• 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.

• tool_resources: Optional[ToolResources]

  A set of resources that are made available to the assistant's tools in this thread. The resources are specific to the type of tool. For example, the code_interpreter tool requires a list of file IDs, while the file_search tool requires a list of vector store IDs.

  • code_interpreter: Optional[ToolResourcesCodeInterpreter]

    • file_ids: Optional[Sequence[str]]

      A list of file IDs made available to the code_interpreter tool. There can be a maximum of 20 files associated with the tool.

  • file_search: Optional[ToolResourcesFileSearch]

    • vector_store_ids: Optional[Sequence[str]]

      The vector store attached to this thread. There can be a maximum of 1 vector store attached to the thread.

    • vector_stores: Optional[Iterable[ToolResourcesFileSearchVectorStore]]

      A helper to create a vector store with file_ids and attach it to this thread. There can be a maximum of 1 vector store attached to the thread.

      • chunking_strategy: Optional[ToolResourcesFileSearchVectorStoreChunkingStrategy]

        The chunking strategy used to chunk the file(s). If not set, will use the auto strategy.

        • class ToolResourcesFileSearchVectorStoreChunkingStrategyAuto: …

          The default strategy. This strategy currently uses a max_chunk_size_tokens of 800 and chunk_overlap_tokens of 400.

          • type: Literal["auto"]

            Always auto.

            • "auto"

        • class ToolResourcesFileSearchVectorStoreChunkingStrategyStatic: …

          • static: ToolResourcesFileSearchVectorStoreChunkingStrategyStaticStatic

            • chunk_overlap_tokens: int

              The number of tokens that overlap between chunks. The default value is 400.

              Note that the overlap must not exceed half of max_chunk_size_tokens.

            • max_chunk_size_tokens: int

              The maximum number of tokens in each chunk. The default value is 800. The minimum value is 100 and the maximum value is 4096.

          • type: Literal["static"]

            Always static.

            • "static"

      • file_ids: Optional[Sequence[str]]

        A list of file IDs to add to the vector store. For vector stores created before Nov 2025, there can be a maximum of 10,000 files in a vector store. For vector stores created starting in Nov 2025, the limit is 100,000,000 files.

      • 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.

Returns

• class Thread: …

  Represents a thread that contains messages.

  • id: str

    The identifier, which can be referenced in API endpoints.

  • created_at: int

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

  • 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.

  • object: Literal["thread"]

    The object type, which is always thread.

    • "thread"

  • tool_resources: Optional[ToolResources]

    A set of resources that are made available to the assistant's tools in this thread. The resources are specific to the type of tool. For example, the code_interpreter tool requires a list of file IDs, while the file_search tool requires a list of vector store IDs.

    • code_interpreter: Optional[ToolResourcesCodeInterpreter]

      • file_ids: Optional[List[str]]

        A list of file IDs made available to the code_interpreter tool. There can be a maximum of 20 files associated with the tool.

    • file_search: Optional[ToolResourcesFileSearch]

      • vector_store_ids: Optional[List[str]]

        The vector store attached to this thread. There can be a maximum of 1 vector store attached to the thread.

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
thread = client.beta.threads.create()
print(thread.id)

Response

{
  "id": "id",
  "created_at": 0,
  "metadata": {
    "foo": "string"
  },
  "object": "thread",
  "tool_resources": {
    "code_interpreter": {
      "file_ids": [
        "string"
      ]
    },
    "file_search": {
      "vector_store_ids": [
        "string"
      ]
    }
  }
}

Empty

from openai import OpenAI
client = OpenAI()

empty_thread = client.beta.threads.create()
print(empty_thread)

Response

{
  "id": "thread_abc123",
  "object": "thread",
  "created_at": 1699012949,
  "metadata": {},
  "tool_resources": {}
}

Messages

from openai import OpenAI
client = OpenAI()

message_thread = client.beta.threads.create(
  messages=[
    {
      "role": "user",
      "content": "Hello, what is AI?"
    },
    {
      "role": "user",
      "content": "How does AI work? Explain it in simple terms."
    },
  ]
)

print(message_thread)

Response

{
  "id": "thread_abc123",
  "object": "thread",
  "created_at": 1699014083,
  "metadata": {},
  "tool_resources": {}
}

Create thread and run

beta.threads.create_and_run(ThreadCreateAndRunParams**kwargs) -> Run

post /threads/runs

Create a thread and run it in one request.

Parameters

• assistant_id: str

  The ID of the assistant to use to execute this run.

• instructions: Optional[str]

  Override the default system message of the assistant. This is useful for modifying the behavior on a per-run basis.

• max_completion_tokens: Optional[int]

  The maximum number of completion tokens that may be used over the course of the run. The run will make a best effort to use only the number of completion tokens specified, across multiple turns of the run. If the run exceeds the number of completion tokens specified, the run will end with status incomplete. See incomplete_details for more info.

• max_prompt_tokens: Optional[int]

  The maximum number of prompt tokens that may be used over the course of the run. The run will make a best effort to use only the number of prompt tokens specified, across multiple turns of the run. If the run exceeds the number of prompt tokens specified, the run will end with status incomplete. See incomplete_details for more info.

• 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: Optional[Union[str, ChatModel, null]]

  The ID of the Model to be used to execute this run. If a value is provided here, it will override the model associated with the assistant. If not, the model associated with the assistant will be used.

  • str

  • Literal["gpt-5.6-sol", "gpt-5.6-terra", "gpt-5.6-luna", 78 more]

    • "gpt-5.6-sol"

    • "gpt-5.6-terra"

    • "gpt-5.6-luna"

    • "gpt-5.4"

    • "gpt-5.4-mini"

    • "gpt-5.4-nano"

    • "gpt-5.4-mini-2026-03-17"

    • "gpt-5.4-nano-2026-03-17"

    • "gpt-5.3-chat-latest"

    • "gpt-5.2"

    • "gpt-5.2-2025-12-11"

    • "gpt-5.2-chat-latest"

    • "gpt-5.2-pro"

    • "gpt-5.2-pro-2025-12-11"

    • "gpt-5.1"

    • "gpt-5.1-2025-11-13"

    • "gpt-5.1-codex"

    • "gpt-5.1-mini"

    • "gpt-5.1-chat-latest"

    • "gpt-5"

    • "gpt-5-mini"

    • "gpt-5-nano"

    • "gpt-5-2025-08-07"

    • "gpt-5-mini-2025-08-07"

    • "gpt-5-nano-2025-08-07"

    • "gpt-5-chat-latest"

    • "gpt-4.1"

    • "gpt-4.1-mini"

    • "gpt-4.1-nano"

    • "gpt-4.1-2025-04-14"

    • "gpt-4.1-mini-2025-04-14"

    • "gpt-4.1-nano-2025-04-14"

    • "o4-mini"

    • "o4-mini-2025-04-16"

    • "o3"

    • "o3-2025-04-16"

    • "o3-mini"

    • "o3-mini-2025-01-31"

    • "o1"

    • "o1-2024-12-17"

    • "o1-preview"

    • "o1-preview-2024-09-12"

    • "o1-mini"

    • "o1-mini-2024-09-12"

    • "gpt-4o"

    • "gpt-4o-2024-11-20"

    • "gpt-4o-2024-08-06"

    • "gpt-4o-2024-05-13"

    • "gpt-4o-audio-preview"

    • "gpt-4o-audio-preview-2024-10-01"

    • "gpt-4o-audio-preview-2024-12-17"

    • "gpt-4o-audio-preview-2025-06-03"

    • "gpt-4o-mini-audio-preview"

    • "gpt-4o-mini-audio-preview-2024-12-17"

    • "gpt-4o-search-preview"

    • "gpt-4o-mini-search-preview"

    • "gpt-4o-search-preview-2025-03-11"

    • "gpt-4o-mini-search-preview-2025-03-11"

    • "chatgpt-4o-latest"

    • "codex-mini-latest"

    • "gpt-4o-mini"

    • "gpt-4o-mini-2024-07-18"

    • "gpt-4-turbo"

    • "gpt-4-turbo-2024-04-09"

    • "gpt-4-0125-preview"

    • "gpt-4-turbo-preview"

    • "gpt-4-1106-preview"

    • "gpt-4-vision-preview"

    • "gpt-4"

    • "gpt-4-0314"

    • "gpt-4-0613"

    • "gpt-4-32k"

    • "gpt-4-32k-0314"

    • "gpt-4-32k-0613"

    • "gpt-3.5-turbo"

    • "gpt-3.5-turbo-16k"

    • "gpt-3.5-turbo-0301"

    • "gpt-3.5-turbo-0613"

    • "gpt-3.5-turbo-1106"

    • "gpt-3.5-turbo-0125"

    • "gpt-3.5-turbo-16k-0613"

• parallel_tool_calls: Optional[bool]

  Whether to enable parallel function calling during tool use.

• response_format: Optional[AssistantResponseFormatOptionParam]

  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.

  • Literal["auto"]

    auto is the default value

    • "auto"

  • class ResponseFormatText: …

    Default response format. Used to generate text responses.

    • type: Literal["text"]

      The type of response format being defined. Always text.

      • "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.

      • "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.

      • "json_schema"

• stream: Optional[Literal[false]]

  If true, returns a stream of events that happen during the Run as server-sent events, terminating when the Run enters a terminal state with a data: [DONE] message.

  • false

• temperature: Optional[float]

  What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

• thread: Optional[Thread]

  Options to create a new thread. If no thread is provided when running a request, an empty thread will be created.

  • messages: Optional[Iterable[ThreadMessage]]

    A list of messages to start the thread with.

    • content: Union[str, Iterable[MessageContentPartParam]]

      The text contents of the message.

      • str

        The text contents of the message.

      • Iterable[MessageContentPartParam]

        An array of content parts with a defined type, each can be of type text or images can be passed with image_url or image_file. Image types are only supported on Vision-compatible models.

        • class ImageFileContentBlock: …

          References an image File in the content of a message.

          • image_file: ImageFile

            • file_id: str

              The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.

            • detail: Optional[Literal["auto", "low", "high"]]

              Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.

              • "auto"

              • "low"

              • "high"

          • type: Literal["image_file"]

            Always image_file.

            • "image_file"

        • class ImageURLContentBlock: …

          References an image URL in the content of a message.

          • image_url: ImageURL

            • url: str

              The external URL of the image, must be a supported image types: jpeg, jpg, png, gif, webp.

            • detail: Optional[Literal["auto", "low", "high"]]

              Specifies the detail level of the image. low uses fewer tokens, you can opt in to high resolution using high. Default value is auto

              • "auto"

              • "low"

              • "high"

          • type: Literal["image_url"]

            The type of the content part.

            • "image_url"

        • class TextContentBlockParam: …

          The text content that is part of a message.

          • text: str

            Text content to be sent to the model

          • type: Literal["text"]

            Always text.

            • "text"

    • role: Literal["user", "assistant"]

      The role of the entity that is creating the message. Allowed values include:

      • user: Indicates the message is sent by an actual user and should be used in most cases to represent user-generated messages.

      • assistant: Indicates the message is generated by the assistant. Use this value to insert messages from the assistant into the conversation.

      • "user"

      • "assistant"

    • attachments: Optional[Iterable[ThreadMessageAttachment]]

      A list of files attached to the message, and the tools they should be added to.

      • file_id: Optional[str]

        The ID of the file to attach to the message.

      • tools: Optional[Iterable[ThreadMessageAttachmentTool]]

        The tools to add this file to.

        • class CodeInterpreterTool: …

          • type: Literal["code_interpreter"]

            The type of tool being defined: code_interpreter

            • "code_interpreter"

        • class ThreadMessageAttachmentToolFileSearch: …

          • type: Literal["file_search"]

            The type of tool being defined: file_search

            • "file_search"

    • 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.

  • 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.

  • tool_resources: Optional[ThreadToolResources]

    A set of resources that are made available to the assistant's tools in this thread. The resources are specific to the type of tool. For example, the code_interpreter tool requires a list of file IDs, while the file_search tool requires a list of vector store IDs.

    • code_interpreter: Optional[ThreadToolResourcesCodeInterpreter]

      • file_ids: Optional[Sequence[str]]

        A list of file IDs made available to the code_interpreter tool. There can be a maximum of 20 files associated with the tool.

    • file_search: Optional[ThreadToolResourcesFileSearch]

      • vector_store_ids: Optional[Sequence[str]]

        The vector store attached to this thread. There can be a maximum of 1 vector store attached to the thread.

      • vector_stores: Optional[Iterable[ThreadToolResourcesFileSearchVectorStore]]

        A helper to create a vector store with file_ids and attach it to this thread. There can be a maximum of 1 vector store attached to the thread.

        • chunking_strategy: Optional[ThreadToolResourcesFileSearchVectorStoreChunkingStrategy]

          The chunking strategy used to chunk the file(s). If not set, will use the auto strategy.

          • class ThreadToolResourcesFileSearchVectorStoreChunkingStrategyAuto: …

            The default strategy. This strategy currently uses a max_chunk_size_tokens of 800 and chunk_overlap_tokens of 400.

            • type: Literal["auto"]

              Always auto.

              • "auto"

          • class ThreadToolResourcesFileSearchVectorStoreChunkingStrategyStatic: …

            • static: ThreadToolResourcesFileSearchVectorStoreChunkingStrategyStaticStatic

              • chunk_overlap_tokens: int

                The number of tokens that overlap between chunks. The default value is 400.

                Note that the overlap must not exceed half of max_chunk_size_tokens.

              • max_chunk_size_tokens: int

                The maximum number of tokens in each chunk. The default value is 800. The minimum value is 100 and the maximum value is 4096.

            • type: Literal["static"]

              Always static.

              • "static"

        • file_ids: Optional[Sequence[str]]

          A list of file IDs to add to the vector store. For vector stores created before Nov 2025, there can be a maximum of 10,000 files in a vector store. For vector stores created starting in Nov 2025, the limit is 100,000,000 files.

        • 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.

• tool_choice: Optional[AssistantToolChoiceOptionParam]

  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.

  • 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.

    • "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

      • "function"

      • "code_interpreter"

      • "file_search"

    • function: Optional[AssistantToolChoiceFunction]

      • name: str

        The name of the function to call.

• tool_resources: Optional[ToolResources]

  A set of resources that are used by the assistant's tools. The resources are specific to the type of tool. For example, the code_interpreter tool requires a list of file IDs, while the file_search tool requires a list of vector store IDs.

  • code_interpreter: Optional[ToolResourcesCodeInterpreter]

    • file_ids: Optional[Sequence[str]]

      A list of file IDs made available to the code_interpreter tool. There can be a maximum of 20 files associated with the tool.

  • file_search: Optional[ToolResourcesFileSearch]

    • vector_store_ids: Optional[Sequence[str]]

      The ID of the vector store attached to this assistant. There can be a maximum of 1 vector store attached to the assistant.

• tools: Optional[Iterable[AssistantToolParam]]

  Override the tools the assistant can use for this run. This is useful for modifying the behavior on a per-run basis.

  • class CodeInterpreterTool: …

  • class FileSearchTool: …

    • type: Literal["file_search"]

      The type of tool being defined: file_search

      • "file_search"

    • file_search: Optional[FileSearch]

      Overrides for the file search tool.

      • max_num_results: Optional[int]

        The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.

        Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.

      • ranking_options: Optional[FileSearchRankingOptions]

        The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.

        See the file search tool documentation for more information.

        • score_threshold: float

          The score threshold for the file search. All values must be a floating point number between 0 and 1.

        • ranker: Optional[Literal["auto", "default_2024_08_21"]]

          The ranker to use for the file search. If not specified will use the auto ranker.

          • "auto"

          • "default_2024_08_21"

  • class FunctionTool: …

    • function: FunctionDefinition

      • 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

      • "function"

• top_p: Optional[float]

  An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

  We generally recommend altering this or temperature but not both.

• 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.

    • "auto"

    • "last_messages"

  • last_messages: Optional[int]

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

Returns

• 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.

      • "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.

      • "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.

  • max_prompt_tokens: Optional[int]

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

  • 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.

    • "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.

          • "function"

    • type: Literal["submit_tool_outputs"]

      For now, this is always submit_tool_outputs.

      • "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.

    • Literal["auto"]

      auto is the default value

      • "auto"

    • class ResponseFormatText: …

      Default response format. Used to generate text responses.

      • type: Literal["text"]

        The type of response format being defined. Always text.

        • "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.

        • "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.

        • "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.

    • "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.

    • 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.

      • "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

        • "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.

    • class CodeInterpreterTool: …

      • type: Literal["code_interpreter"]

        The type of tool being defined: code_interpreter

        • "code_interpreter"

    • class FileSearchTool: …

      • type: Literal["file_search"]

        The type of tool being defined: file_search

        • "file_search"

      • file_search: Optional[FileSearch]

        Overrides for the file search tool.

        • max_num_results: Optional[int]

          The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.

          Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.

        • ranking_options: Optional[FileSearchRankingOptions]

          The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.

          See the file search tool documentation for more information.

          • score_threshold: float

            The score threshold for the file search. All values must be a floating point number between 0 and 1.

          • ranker: Optional[Literal["auto", "default_2024_08_21"]]

            The ranker to use for the file search. If not specified will use the auto ranker.

            • "auto"

            • "default_2024_08_21"

    • class FunctionTool: …

      • function: FunctionDefinition

        • 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

        • "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.

      • "auto"

      • "last_messages"

    • last_messages: Optional[int]

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

  • 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.

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
for thread in client.beta.threads.create_and_run(
    assistant_id="assistant_id",
):
  print(thread)

Response

{
  "id": "id",
  "assistant_id": "assistant_id",
  "cancelled_at": 0,
  "completed_at": 0,
  "created_at": 0,
  "expires_at": 0,
  "failed_at": 0,
  "incomplete_details": {
    "reason": "max_completion_tokens"
  },
  "instructions": "instructions",
  "last_error": {
    "code": "server_error",
    "message": "message"
  },
  "max_completion_tokens": 256,
  "max_prompt_tokens": 256,
  "metadata": {
    "foo": "string"
  },
  "model": "model",
  "object": "thread.run",
  "parallel_tool_calls": true,
  "required_action": {
    "submit_tool_outputs": {
      "tool_calls": [
        {
          "id": "id",
          "function": {
            "arguments": "arguments",
            "name": "name"
          },
          "type": "function"
        }
      ]
    },
    "type": "submit_tool_outputs"
  },
  "response_format": "auto",
  "started_at": 0,
  "status": "queued",
  "thread_id": "thread_id",
  "tool_choice": "none",
  "tools": [
    {
      "type": "code_interpreter"
    }
  ],
  "truncation_strategy": {
    "type": "auto",
    "last_messages": 1
  },
  "usage": {
    "completion_tokens": 0,
    "prompt_tokens": 0,
    "total_tokens": 0
  },
  "temperature": 0,
  "top_p": 0
}

Example

from openai import OpenAI
client = OpenAI()

run = client.beta.threads.create_and_run(
  assistant_id="asst_abc123",
  thread={
    "messages": [
      {"role": "user", "content": "Explain deep learning to a 5 year old."}
    ]
  }
)

print(run)

Response

{
  "id": "run_abc123",
  "object": "thread.run",
  "created_at": 1699076792,
  "assistant_id": "asst_abc123",
  "thread_id": "thread_abc123",
  "status": "queued",
  "started_at": null,
  "expires_at": 1699077392,
  "cancelled_at": null,
  "failed_at": null,
  "completed_at": null,
  "required_action": null,
  "last_error": null,
  "model": "gpt-4o",
  "instructions": "You are a helpful assistant.",
  "tools": [],
  "tool_resources": {},
  "metadata": {},
  "temperature": 1.0,
  "top_p": 1.0,
  "max_completion_tokens": null,
  "max_prompt_tokens": null,
  "truncation_strategy": {
    "type": "auto",
    "last_messages": null
  },
  "incomplete_details": null,
  "usage": null,
  "response_format": "auto",
  "tool_choice": "auto",
  "parallel_tool_calls": true
}

Streaming

from openai import OpenAI
client = OpenAI()

stream = client.beta.threads.create_and_run(
  assistant_id="asst_123",
  thread={
    "messages": [
      {"role": "user", "content": "Hello"}
    ]
  },
  stream=True
)

for event in stream:
  print(event)

Response

event: thread.created
data: {"id":"thread_123","object":"thread","created_at":1710348075,"metadata":{}}

event: thread.run.created
data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710348675,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[],"tool_resources":{},"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}

event: thread.run.queued
data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710348675,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[],"tool_resources":{},"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}

event: thread.run.in_progress
data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"in_progress","started_at":null,"expires_at":1710348675,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[],"tool_resources":{},"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}

event: thread.run.step.created
data: {"id":"step_001","object":"thread.run.step","created_at":1710348076,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710348675,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":null}

event: thread.run.step.in_progress
data: {"id":"step_001","object":"thread.run.step","created_at":1710348076,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710348675,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":null}

event: thread.message.created
data: {"id":"msg_001","object":"thread.message","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[], "metadata":{}}

event: thread.message.in_progress
data: {"id":"msg_001","object":"thread.message","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[], "metadata":{}}

event: thread.message.delta
data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"Hello","annotations":[]}}]}}

...

event: thread.message.delta
data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":" today"}}]}}

event: thread.message.delta
data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"?"}}]}}

event: thread.message.completed
data: {"id":"msg_001","object":"thread.message","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"completed","incomplete_details":null,"incomplete_at":null,"completed_at":1710348077,"role":"assistant","content":[{"type":"text","text":{"value":"Hello! How can I assist you today?","annotations":[]}}], "metadata":{}}

event: thread.run.step.completed
data: {"id":"step_001","object":"thread.run.step","created_at":1710348076,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"completed","cancelled_at":null,"completed_at":1710348077,"expires_at":1710348675,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":{"prompt_tokens":20,"completion_tokens":11,"total_tokens":31}}

event: thread.run.completed
{"id":"run_123","object":"thread.run","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","status":"completed","started_at":1713226836,"expires_at":null,"cancelled_at":null,"failed_at":null,"completed_at":1713226837,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":{"prompt_tokens":345,"completion_tokens":11,"total_tokens":356},"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}

event: done
data: [DONE]

Streaming with Functions

from openai import OpenAI
client = OpenAI()

tools = [
  {
    "type": "function",
    "function": {
      "name": "get_current_weather",
      "description": "Get the current weather in a given location",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA",
          },
          "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
        },
        "required": ["location"],
      },
    }
  }
]

stream = client.beta.threads.create_and_run(
  thread={
      "messages": [
        {"role": "user", "content": "What is the weather like in San Francisco?"}
      ]
  },
  assistant_id="asst_abc123",
  tools=tools,
  stream=True
)

for event in stream:
  print(event)

Response

event: thread.created
data: {"id":"thread_123","object":"thread","created_at":1710351818,"metadata":{}}

event: thread.run.created
data: {"id":"run_123","object":"thread.run","created_at":1710351818,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710352418,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather in a given location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location"]}}}],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}}

event: thread.run.queued
data: {"id":"run_123","object":"thread.run","created_at":1710351818,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710352418,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather in a given location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location"]}}}],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}}

event: thread.run.in_progress
data: {"id":"run_123","object":"thread.run","created_at":1710351818,"assistant_id":"asst_123","thread_id":"thread_123","status":"in_progress","started_at":1710351818,"expires_at":1710352418,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather in a given location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location"]}}}],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}}

event: thread.run.step.created
data: {"id":"step_001","object":"thread.run.step","created_at":1710351819,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"tool_calls","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710352418,"failed_at":null,"last_error":null,"step_details":{"type":"tool_calls","tool_calls":[]},"usage":null}

event: thread.run.step.in_progress
data: {"id":"step_001","object":"thread.run.step","created_at":1710351819,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"tool_calls","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710352418,"failed_at":null,"last_error":null,"step_details":{"type":"tool_calls","tool_calls":[]},"usage":null}

event: thread.run.step.delta
data: {"id":"step_001","object":"thread.run.step.delta","delta":{"step_details":{"type":"tool_calls","tool_calls":[{"index":0,"id":"call_XXNp8YGaFrjrSjgqxtC8JJ1B","type":"function","function":{"name":"get_current_weather","arguments":"","output":null}}]}}}

event: thread.run.step.delta
data: {"id":"step_001","object":"thread.run.step.delta","delta":{"step_details":{"type":"tool_calls","tool_calls":[{"index":0,"type":"function","function":{"arguments":"{\""}}]}}}

event: thread.run.step.delta
data: {"id":"step_001","object":"thread.run.step.delta","delta":{"step_details":{"type":"tool_calls","tool_calls":[{"index":0,"type":"function","function":{"arguments":"location"}}]}}}

...

event: thread.run.step.delta
data: {"id":"step_001","object":"thread.run.step.delta","delta":{"step_details":{"type":"tool_calls","tool_calls":[{"index":0,"type":"function","function":{"arguments":"ahrenheit"}}]}}}

event: thread.run.step.delta
data: {"id":"step_001","object":"thread.run.step.delta","delta":{"step_details":{"type":"tool_calls","tool_calls":[{"index":0,"type":"function","function":{"arguments":"\"}"}}]}}}

event: thread.run.requires_action
data: {"id":"run_123","object":"thread.run","created_at":1710351818,"assistant_id":"asst_123","thread_id":"thread_123","status":"requires_action","started_at":1710351818,"expires_at":1710352418,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":{"type":"submit_tool_outputs","submit_tool_outputs":{"tool_calls":[{"id":"call_XXNp8YGaFrjrSjgqxtC8JJ1B","type":"function","function":{"name":"get_current_weather","arguments":"{\"location\":\"San Francisco, CA\",\"unit\":\"fahrenheit\"}"}}]}},"last_error":null,"model":"gpt-4o","instructions":null,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather in a given location","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location"]}}}],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":{"prompt_tokens":345,"completion_tokens":11,"total_tokens":356},"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}}

event: done
data: [DONE]

Retrieve thread

beta.threads.retrieve(strthread_id) -> Thread

get /threads/{thread_id}

Retrieves a thread.

Parameters

• thread_id: str

Returns

• class Thread: …

  Represents a thread that contains messages.

  • id: str

    The identifier, which can be referenced in API endpoints.

  • created_at: int

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

  • 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.

  • object: Literal["thread"]

    The object type, which is always thread.

    • "thread"

  • tool_resources: Optional[ToolResources]

    A set of resources that are made available to the assistant's tools in this thread. The resources are specific to the type of tool. For example, the code_interpreter tool requires a list of file IDs, while the file_search tool requires a list of vector store IDs.

    • code_interpreter: Optional[ToolResourcesCodeInterpreter]

      • file_ids: Optional[List[str]]

        A list of file IDs made available to the code_interpreter tool. There can be a maximum of 20 files associated with the tool.

    • file_search: Optional[ToolResourcesFileSearch]

      • vector_store_ids: Optional[List[str]]

        The vector store attached to this thread. There can be a maximum of 1 vector store attached to the thread.

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
thread = client.beta.threads.retrieve(
    "thread_id",
)
print(thread.id)

Response

{
  "id": "id",
  "created_at": 0,
  "metadata": {
    "foo": "string"
  },
  "object": "thread",
  "tool_resources": {
    "code_interpreter": {
      "file_ids": [
        "string"
      ]
    },
    "file_search": {
      "vector_store_ids": [
        "string"
      ]
    }
  }
}

Example

from openai import OpenAI
client = OpenAI()

my_thread = client.beta.threads.retrieve("thread_abc123")
print(my_thread)

Response

{
  "id": "thread_abc123",
  "object": "thread",
  "created_at": 1699014083,
  "metadata": {},
  "tool_resources": {
    "code_interpreter": {
      "file_ids": []
    }
  }
}

Modify thread

beta.threads.update(strthread_id, ThreadUpdateParams**kwargs) -> Thread

post /threads/{thread_id}

Modifies a thread.

Parameters

• thread_id: str

• 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.

• tool_resources: Optional[ToolResources]

  A set of resources that are made available to the assistant's tools in this thread. The resources are specific to the type of tool. For example, the code_interpreter tool requires a list of file IDs, while the file_search tool requires a list of vector store IDs.

  • code_interpreter: Optional[ToolResourcesCodeInterpreter]

    • file_ids: Optional[Sequence[str]]

      A list of file IDs made available to the code_interpreter tool. There can be a maximum of 20 files associated with the tool.

  • file_search: Optional[ToolResourcesFileSearch]

    • vector_store_ids: Optional[Sequence[str]]

      The vector store attached to this thread. There can be a maximum of 1 vector store attached to the thread.

Returns

• class Thread: …

  Represents a thread that contains messages.

  • id: str

    The identifier, which can be referenced in API endpoints.

  • created_at: int

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

  • 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.

  • object: Literal["thread"]

    The object type, which is always thread.

    • "thread"

  • tool_resources: Optional[ToolResources]

    A set of resources that are made available to the assistant's tools in this thread. The resources are specific to the type of tool. For example, the code_interpreter tool requires a list of file IDs, while the file_search tool requires a list of vector store IDs.

    • code_interpreter: Optional[ToolResourcesCodeInterpreter]

      • file_ids: Optional[List[str]]

        A list of file IDs made available to the code_interpreter tool. There can be a maximum of 20 files associated with the tool.

    • file_search: Optional[ToolResourcesFileSearch]

      • vector_store_ids: Optional[List[str]]

        The vector store attached to this thread. There can be a maximum of 1 vector store attached to the thread.

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
thread = client.beta.threads.update(
    thread_id="thread_id",
)
print(thread.id)

Response

{
  "id": "id",
  "created_at": 0,
  "metadata": {
    "foo": "string"
  },
  "object": "thread",
  "tool_resources": {
    "code_interpreter": {
      "file_ids": [
        "string"
      ]
    },
    "file_search": {
      "vector_store_ids": [
        "string"
      ]
    }
  }
}

Example

from openai import OpenAI
client = OpenAI()

my_updated_thread = client.beta.threads.update(
  "thread_abc123",
  metadata={
    "modified": "true",
    "user": "abc123"
  }
)
print(my_updated_thread)

Response

{
  "id": "thread_abc123",
  "object": "thread",
  "created_at": 1699014083,
  "metadata": {
    "modified": "true",
    "user": "abc123"
  },
  "tool_resources": {}
}

Delete thread

beta.threads.delete(strthread_id) -> ThreadDeleted

delete /threads/{thread_id}

Delete a thread.

Parameters

• thread_id: str

Returns

• class ThreadDeleted: …

  • id: str

  • deleted: bool

  • object: Literal["thread.deleted"]

    • "thread.deleted"

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
thread_deleted = client.beta.threads.delete(
    "thread_id",
)
print(thread_deleted.id)

Response

{
  "id": "id",
  "deleted": true,
  "object": "thread.deleted"
}

Example

from openai import OpenAI
client = OpenAI()

response = client.beta.threads.delete("thread_abc123")
print(response)

Response

{
  "id": "thread_abc123",
  "object": "thread.deleted",
  "deleted": true
}

Domain Types

Assistant Response Format Option

• 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.

  • Literal["auto"]

    auto is the default value

    • "auto"

  • class ResponseFormatText: …

    Default response format. Used to generate text responses.

    • type: Literal["text"]

      The type of response format being defined. Always text.

      • "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.

      • "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.

      • "json_schema"

Assistant Tool Choice

• 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

    • "function"

    • "code_interpreter"

    • "file_search"

  • function: Optional[AssistantToolChoiceFunction]

    • name: str

      The name of the function to call.

Assistant Tool Choice Function

• class AssistantToolChoiceFunction: …

  • name: str

    The name of the function to call.

Assistant Tool Choice Option

• 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.

  • 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.

    • "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

      • "function"

      • "code_interpreter"

      • "file_search"

    • function: Optional[AssistantToolChoiceFunction]

      • name: str

        The name of the function to call.

Thread

• class Thread: …

  Represents a thread that contains messages.

  • id: str

    The identifier, which can be referenced in API endpoints.

  • created_at: int

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

  • 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.

  • object: Literal["thread"]

    The object type, which is always thread.

    • "thread"

  • tool_resources: Optional[ToolResources]

    A set of resources that are made available to the assistant's tools in this thread. The resources are specific to the type of tool. For example, the code_interpreter tool requires a list of file IDs, while the file_search tool requires a list of vector store IDs.

    • code_interpreter: Optional[ToolResourcesCodeInterpreter]

      • file_ids: Optional[List[str]]

        A list of file IDs made available to the code_interpreter tool. There can be a maximum of 20 files associated with the tool.

    • file_search: Optional[ToolResourcesFileSearch]

      • vector_store_ids: Optional[List[str]]

        The vector store attached to this thread. There can be a maximum of 1 vector store attached to the thread.

Thread Deleted

• class ThreadDeleted: …

  • id: str

  • deleted: bool

  • object: Literal["thread.deleted"]

    • "thread.deleted"

Runs

List runs

beta.threads.runs.list(strthread_id, RunListParams**kwargs) -> SyncCursorPage[Run]

get /threads/{thread_id}/runs

Returns a list of runs belonging to a thread.

Parameters

• thread_id: str

• after: Optional[str]

  A cursor for use in pagination. after is an object ID that defines your place in the list. For instance, if you make a list request and receive 100 objects, ending with obj_foo, your subsequent call can include after=obj_foo in order to fetch the next page of the list.

• before: Optional[str]

  A cursor for use in pagination. before is an object ID that defines your place in the list. For instance, if you make a list request and receive 100 objects, starting with obj_foo, your subsequent call can include before=obj_foo in order to fetch the previous page of the list.

• limit: Optional[int]

  A limit on the number of objects to be returned. Limit can range between 1 and 100, and the default is 20.

• order: Optional[Literal["asc", "desc"]]

  Sort order by the created_at timestamp of the objects. asc for ascending order and desc for descending order.

  • "asc"

  • "desc"

Returns

• 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.

      • "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.

      • "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.

  • max_prompt_tokens: Optional[int]

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

  • 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.

    • "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.

          • "function"

    • type: Literal["submit_tool_outputs"]

      For now, this is always submit_tool_outputs.

      • "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.

    • Literal["auto"]

      auto is the default value

      • "auto"

    • class ResponseFormatText: …

      Default response format. Used to generate text responses.

      • type: Literal["text"]

        The type of response format being defined. Always text.

        • "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.

        • "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.

        • "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.

    • "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.

    • 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.

      • "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

        • "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.

    • class CodeInterpreterTool: …

      • type: Literal["code_interpreter"]

        The type of tool being defined: code_interpreter

        • "code_interpreter"

    • class FileSearchTool: …

      • type: Literal["file_search"]

        The type of tool being defined: file_search

        • "file_search"

      • file_search: Optional[FileSearch]

        Overrides for the file search tool.

        • max_num_results: Optional[int]

          The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.

          Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.

        • ranking_options: Optional[FileSearchRankingOptions]

          The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.

          See the file search tool documentation for more information.

          • score_threshold: float

            The score threshold for the file search. All values must be a floating point number between 0 and 1.

          • ranker: Optional[Literal["auto", "default_2024_08_21"]]

            The ranker to use for the file search. If not specified will use the auto ranker.

            • "auto"

            • "default_2024_08_21"

    • class FunctionTool: …

      • function: FunctionDefinition

        • 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

        • "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.

      • "auto"

      • "last_messages"

    • last_messages: Optional[int]

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

  • 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.

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
page = client.beta.threads.runs.list(
    thread_id="thread_id",
)
page = page.data[0]
print(page.id)

Response

{
  "data": [
    {
      "id": "id",
      "assistant_id": "assistant_id",
      "cancelled_at": 0,
      "completed_at": 0,
      "created_at": 0,
      "expires_at": 0,
      "failed_at": 0,
      "incomplete_details": {
        "reason": "max_completion_tokens"
      },
      "instructions": "instructions",
      "last_error": {
        "code": "server_error",
        "message": "message"
      },
      "max_completion_tokens": 256,
      "max_prompt_tokens": 256,
      "metadata": {
        "foo": "string"
      },
      "model": "model",
      "object": "thread.run",
      "parallel_tool_calls": true,
      "required_action": {
        "submit_tool_outputs": {
          "tool_calls": [
            {
              "id": "id",
              "function": {
                "arguments": "arguments",
                "name": "name"
              },
              "type": "function"
            }
          ]
        },
        "type": "submit_tool_outputs"
      },
      "response_format": "auto",
      "started_at": 0,
      "status": "queued",
      "thread_id": "thread_id",
      "tool_choice": "none",
      "tools": [
        {
          "type": "code_interpreter"
        }
      ],
      "truncation_strategy": {
        "type": "auto",
        "last_messages": 1
      },
      "usage": {
        "completion_tokens": 0,
        "prompt_tokens": 0,
        "total_tokens": 0
      },
      "temperature": 0,
      "top_p": 0
    }
  ],
  "first_id": "run_abc123",
  "has_more": false,
  "last_id": "run_abc456",
  "object": "list"
}

Example

from openai import OpenAI
client = OpenAI()

runs = client.beta.threads.runs.list(
  "thread_abc123"
)

print(runs)

Response

{
  "object": "list",
  "data": [
    {
      "id": "run_abc123",
      "object": "thread.run",
      "created_at": 1699075072,
      "assistant_id": "asst_abc123",
      "thread_id": "thread_abc123",
      "status": "completed",
      "started_at": 1699075072,
      "expires_at": null,
      "cancelled_at": null,
      "failed_at": null,
      "completed_at": 1699075073,
      "last_error": null,
      "model": "gpt-4o",
      "instructions": null,
      "incomplete_details": null,
      "tools": [
        {
          "type": "code_interpreter"
        }
      ],
      "tool_resources": {
        "code_interpreter": {
          "file_ids": [
            "file-abc123",
            "file-abc456"
          ]
        }
      },
      "metadata": {},
      "usage": {
        "prompt_tokens": 123,
        "completion_tokens": 456,
        "total_tokens": 579
      },
      "temperature": 1.0,
      "top_p": 1.0,
      "max_prompt_tokens": 1000,
      "max_completion_tokens": 1000,
      "truncation_strategy": {
        "type": "auto",
        "last_messages": null
      },
      "response_format": "auto",
      "tool_choice": "auto",
      "parallel_tool_calls": true
    },
    {
      "id": "run_abc456",
      "object": "thread.run",
      "created_at": 1699063290,
      "assistant_id": "asst_abc123",
      "thread_id": "thread_abc123",
      "status": "completed",
      "started_at": 1699063290,
      "expires_at": null,
      "cancelled_at": null,
      "failed_at": null,
      "completed_at": 1699063291,
      "last_error": null,
      "model": "gpt-4o",
      "instructions": null,
      "incomplete_details": null,
      "tools": [
        {
          "type": "code_interpreter"
        }
      ],
      "tool_resources": {
        "code_interpreter": {
          "file_ids": [
            "file-abc123",
            "file-abc456"
          ]
        }
      },
      "metadata": {},
      "usage": {
        "prompt_tokens": 123,
        "completion_tokens": 456,
        "total_tokens": 579
      },
      "temperature": 1.0,
      "top_p": 1.0,
      "max_prompt_tokens": 1000,
      "max_completion_tokens": 1000,
      "truncation_strategy": {
        "type": "auto",
        "last_messages": null
      },
      "response_format": "auto",
      "tool_choice": "auto",
      "parallel_tool_calls": true
    }
  ],
  "first_id": "run_abc123",
  "last_id": "run_abc456",
  "has_more": false
}

Create run

beta.threads.runs.create(strthread_id, RunCreateParams**kwargs) -> Run

post /threads/{thread_id}/runs

Create a run.

Parameters

• thread_id: str

• assistant_id: str

  The ID of the assistant to use to execute this run.

• include: Optional[List[RunStepInclude]]

  A list of additional fields to include in the response. Currently the only supported value is step_details.tool_calls[*].file_search.results[*].content to fetch the file search result content.

  See the file search tool documentation for more information.

  • "step_details.tool_calls[*].file_search.results[*].content"

• additional_instructions: Optional[str]

  Appends additional instructions at the end of the instructions for the run. This is useful for modifying the behavior on a per-run basis without overriding other instructions.

• additional_messages: Optional[Iterable[AdditionalMessage]]

  Adds additional messages to the thread before creating the run.

  • content: Union[str, Iterable[MessageContentPartParam]]

    The text contents of the message.

    • str

      The text contents of the message.

    • Iterable[MessageContentPartParam]

      An array of content parts with a defined type, each can be of type text or images can be passed with image_url or image_file. Image types are only supported on Vision-compatible models.

      • class ImageFileContentBlock: …

        References an image File in the content of a message.

        • image_file: ImageFile

          • file_id: str

            The File ID of the image in the message content. Set purpose="vision" when uploading the File if you need to later display the file content.

          • detail: Optional[Literal["auto", "low", "high"]]

            Specifies the detail level of the image if specified by the user. low uses fewer tokens, you can opt in to high resolution using high.

            • "auto"

            • "low"

            • "high"

        • type: Literal["image_file"]

          Always image_file.

          • "image_file"

      • class ImageURLContentBlock: …

        References an image URL in the content of a message.

        • image_url: ImageURL

          • url: str

            The external URL of the image, must be a supported image types: jpeg, jpg, png, gif, webp.

          • detail: Optional[Literal["auto", "low", "high"]]

            Specifies the detail level of the image. low uses fewer tokens, you can opt in to high resolution using high. Default value is auto

            • "auto"

            • "low"

            • "high"

        • type: Literal["image_url"]

          The type of the content part.

          • "image_url"

      • class TextContentBlockParam: …

        The text content that is part of a message.

        • text: str

          Text content to be sent to the model

        • type: Literal["text"]

          Always text.

          • "text"

  • role: Literal["user", "assistant"]

    The role of the entity that is creating the message. Allowed values include:

    • user: Indicates the message is sent by an actual user and should be used in most cases to represent user-generated messages.

    • assistant: Indicates the message is generated by the assistant. Use this value to insert messages from the assistant into the conversation.

    • "user"

    • "assistant"

  • attachments: Optional[Iterable[AdditionalMessageAttachment]]

    A list of files attached to the message, and the tools they should be added to.

    • file_id: Optional[str]

      The ID of the file to attach to the message.

    • tools: Optional[Iterable[AdditionalMessageAttachmentTool]]

      The tools to add this file to.

      • class CodeInterpreterTool: …

        • type: Literal["code_interpreter"]

          The type of tool being defined: code_interpreter

          • "code_interpreter"

      • class AdditionalMessageAttachmentToolFileSearch: …

        • type: Literal["file_search"]

          The type of tool being defined: file_search

          • "file_search"

  • 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.

• instructions: Optional[str]

  Overrides the instructions of the assistant. This is useful for modifying the behavior on a per-run basis.

• max_completion_tokens: Optional[int]

  The maximum number of completion tokens that may be used over the course of the run. The run will make a best effort to use only the number of completion tokens specified, across multiple turns of the run. If the run exceeds the number of completion tokens specified, the run will end with status incomplete. See incomplete_details for more info.

• max_prompt_tokens: Optional[int]

  The maximum number of prompt tokens that may be used over the course of the run. The run will make a best effort to use only the number of prompt tokens specified, across multiple turns of the run. If the run exceeds the number of prompt tokens specified, the run will end with status incomplete. See incomplete_details for more info.

• 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: Optional[Union[str, ChatModel, null]]

  The ID of the Model to be used to execute this run. If a value is provided here, it will override the model associated with the assistant. If not, the model associated with the assistant will be used.

  • str

  • Literal["gpt-5.6-sol", "gpt-5.6-terra", "gpt-5.6-luna", 78 more]

    • "gpt-5.6-sol"

    • "gpt-5.6-terra"

    • "gpt-5.6-luna"

    • "gpt-5.4"

    • "gpt-5.4-mini"

    • "gpt-5.4-nano"

    • "gpt-5.4-mini-2026-03-17"

    • "gpt-5.4-nano-2026-03-17"

    • "gpt-5.3-chat-latest"

    • "gpt-5.2"

    • "gpt-5.2-2025-12-11"

    • "gpt-5.2-chat-latest"

    • "gpt-5.2-pro"

    • "gpt-5.2-pro-2025-12-11"

    • "gpt-5.1"

    • "gpt-5.1-2025-11-13"

    • "gpt-5.1-codex"

    • "gpt-5.1-mini"

    • "gpt-5.1-chat-latest"

    • "gpt-5"

    • "gpt-5-mini"

    • "gpt-5-nano"

    • "gpt-5-2025-08-07"

    • "gpt-5-mini-2025-08-07"

    • "gpt-5-nano-2025-08-07"

    • "gpt-5-chat-latest"

    • "gpt-4.1"

    • "gpt-4.1-mini"

    • "gpt-4.1-nano"

    • "gpt-4.1-2025-04-14"

    • "gpt-4.1-mini-2025-04-14"

    • "gpt-4.1-nano-2025-04-14"

    • "o4-mini"

    • "o4-mini-2025-04-16"

    • "o3"

    • "o3-2025-04-16"

    • "o3-mini"

    • "o3-mini-2025-01-31"

    • "o1"

    • "o1-2024-12-17"

    • "o1-preview"

    • "o1-preview-2024-09-12"

    • "o1-mini"

    • "o1-mini-2024-09-12"

    • "gpt-4o"

    • "gpt-4o-2024-11-20"

    • "gpt-4o-2024-08-06"

    • "gpt-4o-2024-05-13"

    • "gpt-4o-audio-preview"

    • "gpt-4o-audio-preview-2024-10-01"

    • "gpt-4o-audio-preview-2024-12-17"

    • "gpt-4o-audio-preview-2025-06-03"

    • "gpt-4o-mini-audio-preview"

    • "gpt-4o-mini-audio-preview-2024-12-17"

    • "gpt-4o-search-preview"

    • "gpt-4o-mini-search-preview"

    • "gpt-4o-search-preview-2025-03-11"

    • "gpt-4o-mini-search-preview-2025-03-11"

    • "chatgpt-4o-latest"

    • "codex-mini-latest"

    • "gpt-4o-mini"

    • "gpt-4o-mini-2024-07-18"

    • "gpt-4-turbo"

    • "gpt-4-turbo-2024-04-09"

    • "gpt-4-0125-preview"

    • "gpt-4-turbo-preview"

    • "gpt-4-1106-preview"

    • "gpt-4-vision-preview"

    • "gpt-4"

    • "gpt-4-0314"

    • "gpt-4-0613"

    • "gpt-4-32k"

    • "gpt-4-32k-0314"

    • "gpt-4-32k-0613"

    • "gpt-3.5-turbo"

    • "gpt-3.5-turbo-16k"

    • "gpt-3.5-turbo-0301"

    • "gpt-3.5-turbo-0613"

    • "gpt-3.5-turbo-1106"

    • "gpt-3.5-turbo-0125"

    • "gpt-3.5-turbo-16k-0613"

• parallel_tool_calls: Optional[bool]

  Whether to enable parallel function calling during tool use.

• reasoning_effort: Optional[ReasoningEffort]

  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.

  • "none"

  • "minimal"

  • "low"

  • "medium"

  • "high"

  • "xhigh"

  • "max"

• response_format: Optional[AssistantResponseFormatOptionParam]

  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.

  • Literal["auto"]

    auto is the default value

    • "auto"

  • class ResponseFormatText: …

    Default response format. Used to generate text responses.

    • type: Literal["text"]

      The type of response format being defined. Always text.

      • "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.

      • "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.

      • "json_schema"

• stream: Optional[Literal[false]]

  If true, returns a stream of events that happen during the Run as server-sent events, terminating when the Run enters a terminal state with a data: [DONE] message.

  • false

• temperature: Optional[float]

  What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

• tool_choice: Optional[AssistantToolChoiceOptionParam]

  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.

  • 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.

    • "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

      • "function"

      • "code_interpreter"

      • "file_search"

    • function: Optional[AssistantToolChoiceFunction]

      • name: str

        The name of the function to call.

• tools: Optional[Iterable[AssistantToolParam]]

  Override the tools the assistant can use for this run. This is useful for modifying the behavior on a per-run basis.

  • class CodeInterpreterTool: …

  • class FileSearchTool: …

    • type: Literal["file_search"]

      The type of tool being defined: file_search

      • "file_search"

    • file_search: Optional[FileSearch]

      Overrides for the file search tool.

      • max_num_results: Optional[int]

        The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.

        Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.

      • ranking_options: Optional[FileSearchRankingOptions]

        The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.

        See the file search tool documentation for more information.

        • score_threshold: float

          The score threshold for the file search. All values must be a floating point number between 0 and 1.

        • ranker: Optional[Literal["auto", "default_2024_08_21"]]

          The ranker to use for the file search. If not specified will use the auto ranker.

          • "auto"

          • "default_2024_08_21"

  • class FunctionTool: …

    • function: FunctionDefinition

      • 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

      • "function"

• top_p: Optional[float]

  An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

  We generally recommend altering this or temperature but not both.

• 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.

    • "auto"

    • "last_messages"

  • last_messages: Optional[int]

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

Returns

• 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.

      • "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.

      • "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.

  • max_prompt_tokens: Optional[int]

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

  • 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.

    • "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.

          • "function"

    • type: Literal["submit_tool_outputs"]

      For now, this is always submit_tool_outputs.

      • "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.

    • Literal["auto"]

      auto is the default value

      • "auto"

    • class ResponseFormatText: …

      Default response format. Used to generate text responses.

      • type: Literal["text"]

        The type of response format being defined. Always text.

        • "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.

        • "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.

        • "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.

    • "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.

    • 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.

      • "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

        • "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.

    • class CodeInterpreterTool: …

      • type: Literal["code_interpreter"]

        The type of tool being defined: code_interpreter

        • "code_interpreter"

    • class FileSearchTool: …

      • type: Literal["file_search"]

        The type of tool being defined: file_search

        • "file_search"

      • file_search: Optional[FileSearch]

        Overrides for the file search tool.

        • max_num_results: Optional[int]

          The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.

          Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.

        • ranking_options: Optional[FileSearchRankingOptions]

          The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.

          See the file search tool documentation for more information.

          • score_threshold: float

            The score threshold for the file search. All values must be a floating point number between 0 and 1.

          • ranker: Optional[Literal["auto", "default_2024_08_21"]]

            The ranker to use for the file search. If not specified will use the auto ranker.

            • "auto"

            • "default_2024_08_21"

    • class FunctionTool: …

      • function: FunctionDefinition

        • 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

        • "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.

      • "auto"

      • "last_messages"

    • last_messages: Optional[int]

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

  • 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.

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
for run in client.beta.threads.runs.create(
    thread_id="thread_id",
    assistant_id="assistant_id",
):
  print(run)

Response

{
  "id": "id",
  "assistant_id": "assistant_id",
  "cancelled_at": 0,
  "completed_at": 0,
  "created_at": 0,
  "expires_at": 0,
  "failed_at": 0,
  "incomplete_details": {
    "reason": "max_completion_tokens"
  },
  "instructions": "instructions",
  "last_error": {
    "code": "server_error",
    "message": "message"
  },
  "max_completion_tokens": 256,
  "max_prompt_tokens": 256,
  "metadata": {
    "foo": "string"
  },
  "model": "model",
  "object": "thread.run",
  "parallel_tool_calls": true,
  "required_action": {
    "submit_tool_outputs": {
      "tool_calls": [
        {
          "id": "id",
          "function": {
            "arguments": "arguments",
            "name": "name"
          },
          "type": "function"
        }
      ]
    },
    "type": "submit_tool_outputs"
  },
  "response_format": "auto",
  "started_at": 0,
  "status": "queued",
  "thread_id": "thread_id",
  "tool_choice": "none",
  "tools": [
    {
      "type": "code_interpreter"
    }
  ],
  "truncation_strategy": {
    "type": "auto",
    "last_messages": 1
  },
  "usage": {
    "completion_tokens": 0,
    "prompt_tokens": 0,
    "total_tokens": 0
  },
  "temperature": 0,
  "top_p": 0
}

Example

from openai import OpenAI
client = OpenAI()

run = client.beta.threads.runs.create(
  thread_id="thread_abc123",
  assistant_id="asst_abc123"
)

print(run)

Response

{
  "id": "run_abc123",
  "object": "thread.run",
  "created_at": 1699063290,
  "assistant_id": "asst_abc123",
  "thread_id": "thread_abc123",
  "status": "queued",
  "started_at": 1699063290,
  "expires_at": null,
  "cancelled_at": null,
  "failed_at": null,
  "completed_at": 1699063291,
  "last_error": null,
  "model": "gpt-4o",
  "instructions": null,
  "incomplete_details": null,
  "tools": [
    {
      "type": "code_interpreter"
    }
  ],
  "metadata": {},
  "usage": null,
  "temperature": 1.0,
  "top_p": 1.0,
  "max_prompt_tokens": 1000,
  "max_completion_tokens": 1000,
  "truncation_strategy": {
    "type": "auto",
    "last_messages": null
  },
  "response_format": "auto",
  "tool_choice": "auto",
  "parallel_tool_calls": true
}

Streaming

from openai import OpenAI
client = OpenAI()

stream = client.beta.threads.runs.create(
  thread_id="thread_123",
  assistant_id="asst_123",
  stream=True
)

for event in stream:
  print(event)

Response

event: thread.run.created
data: {"id":"run_123","object":"thread.run","created_at":1710330640,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710331240,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}}

event: thread.run.queued
data: {"id":"run_123","object":"thread.run","created_at":1710330640,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710331240,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}}

event: thread.run.in_progress
data: {"id":"run_123","object":"thread.run","created_at":1710330640,"assistant_id":"asst_123","thread_id":"thread_123","status":"in_progress","started_at":1710330641,"expires_at":1710331240,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}}

event: thread.run.step.created
data: {"id":"step_001","object":"thread.run.step","created_at":1710330641,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710331240,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":null}

event: thread.run.step.in_progress
data: {"id":"step_001","object":"thread.run.step","created_at":1710330641,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710331240,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":null}

event: thread.message.created
data: {"id":"msg_001","object":"thread.message","created_at":1710330641,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[],"metadata":{}}

event: thread.message.in_progress
data: {"id":"msg_001","object":"thread.message","created_at":1710330641,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[],"metadata":{}}

event: thread.message.delta
data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"Hello","annotations":[]}}]}}

...

event: thread.message.delta
data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":" today"}}]}}

event: thread.message.delta
data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"?"}}]}}

event: thread.message.completed
data: {"id":"msg_001","object":"thread.message","created_at":1710330641,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"completed","incomplete_details":null,"incomplete_at":null,"completed_at":1710330642,"role":"assistant","content":[{"type":"text","text":{"value":"Hello! How can I assist you today?","annotations":[]}}],"metadata":{}}

event: thread.run.step.completed
data: {"id":"step_001","object":"thread.run.step","created_at":1710330641,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"completed","cancelled_at":null,"completed_at":1710330642,"expires_at":1710331240,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":{"prompt_tokens":20,"completion_tokens":11,"total_tokens":31}}

event: thread.run.completed
data: {"id":"run_123","object":"thread.run","created_at":1710330640,"assistant_id":"asst_123","thread_id":"thread_123","status":"completed","started_at":1710330641,"expires_at":null,"cancelled_at":null,"failed_at":null,"completed_at":1710330642,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":{"prompt_tokens":20,"completion_tokens":11,"total_tokens":31},"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}}

event: done
data: [DONE]

Streaming with Functions

from openai import OpenAI
client = OpenAI()

tools = [
  {
    "type": "function",
    "function": {
      "name": "get_current_weather",
      "description": "Get the current weather in a given location",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA",
          },
          "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
        },
        "required": ["location"],
      },
    }
  }
]

stream = client.beta.threads.runs.create(
  thread_id="thread_abc123",
  assistant_id="asst_abc123",
  tools=tools,
  stream=True
)

for event in stream:
  print(event)

Response

event: thread.run.created
data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710348675,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}}

event: thread.run.queued
data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"queued","started_at":null,"expires_at":1710348675,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}}

event: thread.run.in_progress
data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"in_progress","started_at":1710348075,"expires_at":1710348675,"cancelled_at":null,"failed_at":null,"completed_at":null,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":null,"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}}

event: thread.run.step.created
data: {"id":"step_001","object":"thread.run.step","created_at":1710348076,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710348675,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":null}

event: thread.run.step.in_progress
data: {"id":"step_001","object":"thread.run.step","created_at":1710348076,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"in_progress","cancelled_at":null,"completed_at":null,"expires_at":1710348675,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":null}

event: thread.message.created
data: {"id":"msg_001","object":"thread.message","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[],"metadata":{}}

event: thread.message.in_progress
data: {"id":"msg_001","object":"thread.message","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"in_progress","incomplete_details":null,"incomplete_at":null,"completed_at":null,"role":"assistant","content":[],"metadata":{}}

event: thread.message.delta
data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"Hello","annotations":[]}}]}}

...

event: thread.message.delta
data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":" today"}}]}}

event: thread.message.delta
data: {"id":"msg_001","object":"thread.message.delta","delta":{"content":[{"index":0,"type":"text","text":{"value":"?"}}]}}

event: thread.message.completed
data: {"id":"msg_001","object":"thread.message","created_at":1710348076,"assistant_id":"asst_123","thread_id":"thread_123","run_id":"run_123","status":"completed","incomplete_details":null,"incomplete_at":null,"completed_at":1710348077,"role":"assistant","content":[{"type":"text","text":{"value":"Hello! How can I assist you today?","annotations":[]}}],"metadata":{}}

event: thread.run.step.completed
data: {"id":"step_001","object":"thread.run.step","created_at":1710348076,"run_id":"run_123","assistant_id":"asst_123","thread_id":"thread_123","type":"message_creation","status":"completed","cancelled_at":null,"completed_at":1710348077,"expires_at":1710348675,"failed_at":null,"last_error":null,"step_details":{"type":"message_creation","message_creation":{"message_id":"msg_001"}},"usage":{"prompt_tokens":20,"completion_tokens":11,"total_tokens":31}}

event: thread.run.completed
data: {"id":"run_123","object":"thread.run","created_at":1710348075,"assistant_id":"asst_123","thread_id":"thread_123","status":"completed","started_at":1710348075,"expires_at":null,"cancelled_at":null,"failed_at":null,"completed_at":1710348077,"required_action":null,"last_error":null,"model":"gpt-4o","instructions":null,"tools":[],"metadata":{},"temperature":1.0,"top_p":1.0,"max_completion_tokens":null,"max_prompt_tokens":null,"truncation_strategy":{"type":"auto","last_messages":null},"incomplete_details":null,"usage":{"prompt_tokens":20,"completion_tokens":11,"total_tokens":31},"response_format":"auto","tool_choice":"auto","parallel_tool_calls":true}}

event: done
data: [DONE]

Retrieve run

beta.threads.runs.retrieve(strrun_id, RunRetrieveParams**kwargs) -> Run

get /threads/{thread_id}/runs/{run_id}

Retrieves a run.

Parameters

• thread_id: str

• run_id: str

Returns

• 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.

      • "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.

      • "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.

  • max_prompt_tokens: Optional[int]

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

  • 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.

    • "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.

          • "function"

    • type: Literal["submit_tool_outputs"]

      For now, this is always submit_tool_outputs.

      • "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.

    • Literal["auto"]

      auto is the default value

      • "auto"

    • class ResponseFormatText: …

      Default response format. Used to generate text responses.

      • type: Literal["text"]

        The type of response format being defined. Always text.

        • "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.

        • "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.

        • "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.

    • "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.

    • 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.

      • "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

        • "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.

    • class CodeInterpreterTool: …

      • type: Literal["code_interpreter"]

        The type of tool being defined: code_interpreter

        • "code_interpreter"

    • class FileSearchTool: …

      • type: Literal["file_search"]

        The type of tool being defined: file_search

        • "file_search"

      • file_search: Optional[FileSearch]

        Overrides for the file search tool.

        • max_num_results: Optional[int]

          The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.

          Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.

        • ranking_options: Optional[FileSearchRankingOptions]

          The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.

          See the file search tool documentation for more information.

          • score_threshold: float

            The score threshold for the file search. All values must be a floating point number between 0 and 1.

          • ranker: Optional[Literal["auto", "default_2024_08_21"]]

            The ranker to use for the file search. If not specified will use the auto ranker.

            • "auto"

            • "default_2024_08_21"

    • class FunctionTool: …

      • function: FunctionDefinition

        • 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

        • "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.

      • "auto"

      • "last_messages"

    • last_messages: Optional[int]

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

  • 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.

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
run = client.beta.threads.runs.retrieve(
    run_id="run_id",
    thread_id="thread_id",
)
print(run.id)

Response

{
  "id": "id",
  "assistant_id": "assistant_id",
  "cancelled_at": 0,
  "completed_at": 0,
  "created_at": 0,
  "expires_at": 0,
  "failed_at": 0,
  "incomplete_details": {
    "reason": "max_completion_tokens"
  },
  "instructions": "instructions",
  "last_error": {
    "code": "server_error",
    "message": "message"
  },
  "max_completion_tokens": 256,
  "max_prompt_tokens": 256,
  "metadata": {
    "foo": "string"
  },
  "model": "model",
  "object": "thread.run",
  "parallel_tool_calls": true,
  "required_action": {
    "submit_tool_outputs": {
      "tool_calls": [
        {
          "id": "id",
          "function": {
            "arguments": "arguments",
            "name": "name"
          },
          "type": "function"
        }
      ]
    },
    "type": "submit_tool_outputs"
  },
  "response_format": "auto",
  "started_at": 0,
  "status": "queued",
  "thread_id": "thread_id",
  "tool_choice": "none",
  "tools": [
    {
      "type": "code_interpreter"
    }
  ],
  "truncation_strategy": {
    "type": "auto",
    "last_messages": 1
  },
  "usage": {
    "completion_tokens": 0,
    "prompt_tokens": 0,
    "total_tokens": 0
  },
  "temperature": 0,
  "top_p": 0
}

Example

from openai import OpenAI
client = OpenAI()

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

print(run)

Response

{
  "id": "run_abc123",
  "object": "thread.run",
  "created_at": 1699075072,
  "assistant_id": "asst_abc123",
  "thread_id": "thread_abc123",
  "status": "completed",
  "started_at": 1699075072,
  "expires_at": null,
  "cancelled_at": null,
  "failed_at": null,
  "completed_at": 1699075073,
  "last_error": null,
  "model": "gpt-4o",
  "instructions": null,
  "incomplete_details": null,
  "tools": [
    {
      "type": "code_interpreter"
    }
  ],
  "metadata": {},
  "usage": {
    "prompt_tokens": 123,
    "completion_tokens": 456,
    "total_tokens": 579
  },
  "temperature": 1.0,
  "top_p": 1.0,
  "max_prompt_tokens": 1000,
  "max_completion_tokens": 1000,
  "truncation_strategy": {
    "type": "auto",
    "last_messages": null
  },
  "response_format": "auto",
  "tool_choice": "auto",
  "parallel_tool_calls": true
}

Modify run

beta.threads.runs.update(strrun_id, RunUpdateParams**kwargs) -> Run

post /threads/{thread_id}/runs/{run_id}

Modifies a run.

Parameters

• thread_id: str

• run_id: str

• 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.

Returns

• 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.

      • "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.

      • "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.

  • max_prompt_tokens: Optional[int]

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

  • 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.

    • "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.

          • "function"

    • type: Literal["submit_tool_outputs"]

      For now, this is always submit_tool_outputs.

      • "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.

    • Literal["auto"]

      auto is the default value

      • "auto"

    • class ResponseFormatText: …

      Default response format. Used to generate text responses.

      • type: Literal["text"]

        The type of response format being defined. Always text.

        • "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.

        • "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.

        • "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.

    • "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.

    • 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.

      • "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

        • "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.

    • class CodeInterpreterTool: …

      • type: Literal["code_interpreter"]

        The type of tool being defined: code_interpreter

        • "code_interpreter"

    • class FileSearchTool: …

      • type: Literal["file_search"]

        The type of tool being defined: file_search

        • "file_search"

      • file_search: Optional[FileSearch]

        Overrides for the file search tool.

        • max_num_results: Optional[int]

          The maximum number of results the file search tool should output. The default is 20 for gpt-4* models and 5 for gpt-3.5-turbo. This number should be between 1 and 50 inclusive.

          Note that the file search tool may output fewer than max_num_results results. See the file search tool documentation for more information.

        • ranking_options: Optional[FileSearchRankingOptions]

          The ranking options for the file search. If not specified, the file search tool will use the auto ranker and a score_threshold of 0.

          See the file search tool documentation for more information.

          • score_threshold: float

            The score threshold for the file search. All values must be a floating point number between 0 and 1.

          • ranker: Optional[Literal["auto", "default_2024_08_21"]]

            The ranker to use for the file search. If not specified will use the auto ranker.

            • "auto"

            • "default_2024_08_21"

    • class FunctionTool: …

      • function: FunctionDefinition

        • 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

        • "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.

      • "auto"

      • "last_messages"

    • last_messages: Optional[int]

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

  • 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.