Assistants

List assistants

$ openai beta:assistants list

get /assistants

List assistants

Parameters

• --after: optional string

  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 string

  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 number

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

• --order: optional "asc" or "desc"

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

Returns

• ListAssistantsResponse: object { data, first_id, has_more, 2 more }

  • data: array of Assistant

    • id: string

      The identifier, which can be referenced in API endpoints.

    • created_at: number

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

    • description: string

      The description of the assistant. The maximum length is 512 characters.

    • instructions: string

      The system instructions that the assistant uses. The maximum length is 256,000 characters.

    • metadata: map[string]

      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: string

      ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

    • name: string

      The name of the assistant. The maximum length is 256 characters.

    • object: "assistant"

      The object type, which is always assistant.

    • tools: array of unknown

      A list of tool enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types code_interpreter, file_search, or function.

    • response_format: optional "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

      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.

      • union_member_0: "auto"

        auto is the default value

      • response_format_text: object { type }

        Default response format. Used to generate text responses.

        • type: "text"

          The type of response format being defined. Always text.

      • response_format_json_object: object { type }

        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: "json_object"

          The type of response format being defined. Always json_object.

      • response_format_json_schema: object { json_schema, type }

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

        • json_schema: object { name, description, schema, strict }

          Structured Outputs configuration options, including a JSON Schema.

          • name: string

            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 string

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

          • schema: optional map[unknown]

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

          • strict: optional boolean

            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: "json_schema"

          The type of response format being defined. Always json_schema.

    • temperature: optional number

      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_resources: optional object { code_interpreter, file_search }

      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 object { file_ids }

        • file_ids: optional array of string

          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 object { vector_store_ids }

        • vector_store_ids: optional array of string

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

    • top_p: optional number

      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.

  • first_id: string

  • has_more: boolean

  • last_id: string

  • object: string

Example

openai beta:assistants list \
  --api-key 'My API Key'

Response

{
  "data": [
    {
      "id": "id",
      "created_at": 0,
      "description": "description",
      "instructions": "instructions",
      "metadata": {
        "foo": "string"
      },
      "model": "model",
      "name": "name",
      "object": "assistant",
      "tools": [
        {}
      ],
      "response_format": "auto",
      "temperature": 1,
      "tool_resources": {
        "code_interpreter": {
          "file_ids": [
            "string"
          ]
        },
        "file_search": {
          "vector_store_ids": [
            "string"
          ]
        }
      },
      "top_p": 1
    }
  ],
  "first_id": "asst_abc123",
  "has_more": false,
  "last_id": "asst_abc456",
  "object": "list"
}

Create assistant

$ openai beta:assistants create

post /assistants

Create assistant

Parameters

• --model: string or ChatModel

  ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

• --description: optional string

  The description of the assistant. The maximum length is 512 characters.

• --instructions: optional string

  The system instructions that the assistant uses. The maximum length is 256,000 characters.

• --metadata: optional map[string]

  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.

• --name: optional string

  The name of the assistant. The maximum length is 256 characters.

• --reasoning-effort: optional "none" or "minimal" or "low" or 4 more

  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.

• --response-format: optional "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

  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.

• --temperature: optional number

  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-resources: optional object { code_interpreter, file_search }

  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.

• --tool: optional array of unknown

  A list of tool enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types code_interpreter, file_search, or function.

• --top-p: optional number

  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.

Returns

• assistant: object { id, created_at, description, 10 more }

  Represents an assistant that can call the model and use tools.

  • id: string

    The identifier, which can be referenced in API endpoints.

  • created_at: number

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

  • description: string

    The description of the assistant. The maximum length is 512 characters.

  • instructions: string

    The system instructions that the assistant uses. The maximum length is 256,000 characters.

  • metadata: map[string]

    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: string

    ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

  • name: string

    The name of the assistant. The maximum length is 256 characters.

  • object: "assistant"

    The object type, which is always assistant.

  • tools: array of unknown

    A list of tool enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types code_interpreter, file_search, or function.

  • response_format: optional "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

    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.

    • union_member_0: "auto"

      auto is the default value

    • response_format_text: object { type }

      Default response format. Used to generate text responses.

      • type: "text"

        The type of response format being defined. Always text.

    • response_format_json_object: object { type }

      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: "json_object"

        The type of response format being defined. Always json_object.

    • response_format_json_schema: object { json_schema, type }

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

      • json_schema: object { name, description, schema, strict }

        Structured Outputs configuration options, including a JSON Schema.

        • name: string

          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 string

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

        • schema: optional map[unknown]

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

        • strict: optional boolean

          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: "json_schema"

        The type of response format being defined. Always json_schema.

  • temperature: optional number

    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_resources: optional object { code_interpreter, file_search }

    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 object { file_ids }

      • file_ids: optional array of string

        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 object { vector_store_ids }

      • vector_store_ids: optional array of string

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

  • top_p: optional number

    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.

Example

openai beta:assistants create \
  --api-key 'My API Key' \
  --model gpt-4o

Response

{
  "id": "id",
  "created_at": 0,
  "description": "description",
  "instructions": "instructions",
  "metadata": {
    "foo": "string"
  },
  "model": "model",
  "name": "name",
  "object": "assistant",
  "tools": [
    {}
  ],
  "response_format": "auto",
  "temperature": 1,
  "tool_resources": {
    "code_interpreter": {
      "file_ids": [
        "string"
      ]
    },
    "file_search": {
      "vector_store_ids": [
        "string"
      ]
    }
  },
  "top_p": 1
}

Retrieve assistant

$ openai beta:assistants retrieve

get /assistants/{assistant_id}

Retrieve assistant

Parameters

• --assistant-id: string

  The ID of the assistant to retrieve.

Returns

• assistant: object { id, created_at, description, 10 more }

  Represents an assistant that can call the model and use tools.

  • id: string

    The identifier, which can be referenced in API endpoints.

  • created_at: number

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

  • description: string

    The description of the assistant. The maximum length is 512 characters.

  • instructions: string

    The system instructions that the assistant uses. The maximum length is 256,000 characters.

  • metadata: map[string]

    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: string

    ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

  • name: string

    The name of the assistant. The maximum length is 256 characters.

  • object: "assistant"

    The object type, which is always assistant.

  • tools: array of unknown

    A list of tool enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types code_interpreter, file_search, or function.

  • response_format: optional "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

    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.

    • union_member_0: "auto"

      auto is the default value

    • response_format_text: object { type }

      Default response format. Used to generate text responses.

      • type: "text"

        The type of response format being defined. Always text.

    • response_format_json_object: object { type }

      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: "json_object"

        The type of response format being defined. Always json_object.

    • response_format_json_schema: object { json_schema, type }

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

      • json_schema: object { name, description, schema, strict }

        Structured Outputs configuration options, including a JSON Schema.

        • name: string

          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 string

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

        • schema: optional map[unknown]

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

        • strict: optional boolean

          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: "json_schema"

        The type of response format being defined. Always json_schema.

  • temperature: optional number

    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_resources: optional object { code_interpreter, file_search }

    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 object { file_ids }

      • file_ids: optional array of string

        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 object { vector_store_ids }

      • vector_store_ids: optional array of string

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

  • top_p: optional number

    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.

Example

openai beta:assistants retrieve \
  --api-key 'My API Key' \
  --assistant-id assistant_id

Response

{
  "id": "id",
  "created_at": 0,
  "description": "description",
  "instructions": "instructions",
  "metadata": {
    "foo": "string"
  },
  "model": "model",
  "name": "name",
  "object": "assistant",
  "tools": [
    {}
  ],
  "response_format": "auto",
  "temperature": 1,
  "tool_resources": {
    "code_interpreter": {
      "file_ids": [
        "string"
      ]
    },
    "file_search": {
      "vector_store_ids": [
        "string"
      ]
    }
  },
  "top_p": 1
}

Modify assistant

$ openai beta:assistants update

post /assistants/{assistant_id}

Modify assistant

Parameters

• --assistant-id: string

  The ID of the assistant to modify.

• --description: optional string

  The description of the assistant. The maximum length is 512 characters.

• --instructions: optional string

  The system instructions that the assistant uses. The maximum length is 256,000 characters.

• --metadata: optional map[string]

  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 string or "gpt-5" or "gpt-5-mini" or "gpt-5-nano" or 39 more

  ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

• --name: optional string

  The name of the assistant. The maximum length is 256 characters.

• --reasoning-effort: optional "none" or "minimal" or "low" or 4 more

  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.

• --response-format: optional "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

  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.

• --temperature: optional number

  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-resources: optional object { code_interpreter, file_search }

  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.

• --tool: optional array of unknown

  A list of tool enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types code_interpreter, file_search, or function.

• --top-p: optional number

  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.

Returns

• assistant: object { id, created_at, description, 10 more }

  Represents an assistant that can call the model and use tools.

  • id: string

    The identifier, which can be referenced in API endpoints.

  • created_at: number

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

  • description: string

    The description of the assistant. The maximum length is 512 characters.

  • instructions: string

    The system instructions that the assistant uses. The maximum length is 256,000 characters.

  • metadata: map[string]

    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: string

    ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

  • name: string

    The name of the assistant. The maximum length is 256 characters.

  • object: "assistant"

    The object type, which is always assistant.

  • tools: array of unknown

    A list of tool enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types code_interpreter, file_search, or function.

  • response_format: optional "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

    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.

    • union_member_0: "auto"

      auto is the default value

    • response_format_text: object { type }

      Default response format. Used to generate text responses.

      • type: "text"

        The type of response format being defined. Always text.

    • response_format_json_object: object { type }

      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: "json_object"

        The type of response format being defined. Always json_object.

    • response_format_json_schema: object { json_schema, type }

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

      • json_schema: object { name, description, schema, strict }

        Structured Outputs configuration options, including a JSON Schema.

        • name: string

          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 string

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

        • schema: optional map[unknown]

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

        • strict: optional boolean

          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: "json_schema"

        The type of response format being defined. Always json_schema.

  • temperature: optional number

    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_resources: optional object { code_interpreter, file_search }

    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 object { file_ids }

      • file_ids: optional array of string

        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 object { vector_store_ids }

      • vector_store_ids: optional array of string

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

  • top_p: optional number

    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.

Example

openai beta:assistants update \
  --api-key 'My API Key' \
  --assistant-id assistant_id

Response

{
  "id": "id",
  "created_at": 0,
  "description": "description",
  "instructions": "instructions",
  "metadata": {
    "foo": "string"
  },
  "model": "model",
  "name": "name",
  "object": "assistant",
  "tools": [
    {}
  ],
  "response_format": "auto",
  "temperature": 1,
  "tool_resources": {
    "code_interpreter": {
      "file_ids": [
        "string"
      ]
    },
    "file_search": {
      "vector_store_ids": [
        "string"
      ]
    }
  },
  "top_p": 1
}

Delete assistant

$ openai beta:assistants delete

delete /assistants/{assistant_id}

Delete assistant

Parameters

• --assistant-id: string

  The ID of the assistant to delete.

Returns

• assistant_deleted: object { id, deleted, object }

  • id: string

  • deleted: boolean

  • object: "assistant.deleted"

Example

openai beta:assistants delete \
  --api-key 'My API Key' \
  --assistant-id assistant_id

Response

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

Domain Types

Assistant

• assistant: object { id, created_at, description, 10 more }

  Represents an assistant that can call the model and use tools.

  • id: string

    The identifier, which can be referenced in API endpoints.

  • created_at: number

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

  • description: string

    The description of the assistant. The maximum length is 512 characters.

  • instructions: string

    The system instructions that the assistant uses. The maximum length is 256,000 characters.

  • metadata: map[string]

    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: string

    ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

  • name: string

    The name of the assistant. The maximum length is 256 characters.

  • object: "assistant"

    The object type, which is always assistant.

  • tools: array of unknown

    A list of tool enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types code_interpreter, file_search, or function.

  • response_format: optional "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

    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.

    • union_member_0: "auto"

      auto is the default value

    • response_format_text: object { type }

      Default response format. Used to generate text responses.

      • type: "text"

        The type of response format being defined. Always text.

    • response_format_json_object: object { type }

      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: "json_object"

        The type of response format being defined. Always json_object.

    • response_format_json_schema: object { json_schema, type }

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

      • json_schema: object { name, description, schema, strict }

        Structured Outputs configuration options, including a JSON Schema.

        • name: string

          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 string

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

        • schema: optional map[unknown]

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

        • strict: optional boolean

          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: "json_schema"

        The type of response format being defined. Always json_schema.

  • temperature: optional number

    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_resources: optional object { code_interpreter, file_search }

    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 object { file_ids }

      • file_ids: optional array of string

        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 object { vector_store_ids }

      • vector_store_ids: optional array of string

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

  • top_p: optional number

    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.

Assistant Deleted

• assistant_deleted: object { id, deleted, object }

  • id: string

  • deleted: boolean

  • object: "assistant.deleted"

Assistant Stream Event

• assistant_stream_event: object { data, event, enabled } or object { data, event } or object { data, event } or 21 more

  Represents an event emitted when streaming a Run.

  Each event in a server-sent events stream has an event and data property:

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

  We emit events whenever a new object is created, transitions to a new state, or is being streamed in parts (deltas). For example, we emit thread.run.created when a new run is created, thread.run.completed when a run completes, and so on. When an Assistant chooses to create a message during a run, we emit a thread.message.created event, a thread.message.in_progress event, many thread.message.delta events, and finally a thread.message.completed event.

  We may add additional events over time, so we recommend handling unknown events gracefully in your code. See the Assistants API quickstart to learn how to integrate the Assistants API with streaming.

  • thread.created: object { data, event, enabled }

    Occurs when a new thread is created.

    • data: object { id, created_at, metadata, 2 more }

      Represents a thread that contains messages.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • created_at: number

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

      • metadata: map[string]

        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: "thread"

        The object type, which is always thread.

      • tool_resources: object { code_interpreter, file_search }

        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 object { file_ids }

          • file_ids: optional array of string

            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 object { vector_store_ids }

          • vector_store_ids: optional array of string

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

    • event: "thread.created"

    • enabled: optional boolean

      Whether to enable input audio transcription.

  • thread.run.created: object { data, event }

    Occurs when a new run is created.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

        • reason: optional "max_completion_tokens" or "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: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

        • code: "server_error" or "rate_limit_exceeded" or "invalid_prompt"

          One of server_error, rate_limit_exceeded, or invalid_prompt.

          • "server_error"

          • "rate_limit_exceeded"

          • "invalid_prompt"

        • message: string

          A human-readable description of the error.

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

        • submit_tool_outputs: object { tool_calls }

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

          • tool_calls: array of RequiredActionFunctionToolCall

            A list of the relevant tool calls.

            • id: string

              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: object { arguments, name }

              The function definition.

              • arguments: string

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

              • name: string

                The name of the function.

            • type: "function"

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

        • type: "submit_tool_outputs"

          For now, this is always submit_tool_outputs.

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

        • union_member_0: "auto"

          auto is the default value

        • response_format_text: object { type }

          Default response format. Used to generate text responses.

          • type: "text"

            The type of response format being defined. Always text.

        • response_format_json_object: object { type }

          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: "json_object"

            The type of response format being defined. Always json_object.

        • response_format_json_schema: object { json_schema, type }

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

          • json_schema: object { name, description, schema, strict }

            Structured Outputs configuration options, including a JSON Schema.

            • name: string

              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 string

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

            • schema: optional map[unknown]

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

            • strict: optional boolean

              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: "json_schema"

            The type of response format being defined. Always json_schema.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

        • Auto: "none" or "auto" or "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"

        • assistant_tool_choice: object { type, function }

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

          • type: "function" or "code_interpreter" or "file_search"

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

            • "function"

            • "code_interpreter"

            • "file_search"

          • function: optional object { name }

            • name: string

              The name of the function to call.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

        • type: "auto" or "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 number

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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: number

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

        • prompt_tokens: number

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

        • total_tokens: number

          Total number of tokens used (prompt + completion).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.created"

  • thread.run.queued: object { data, event }

    Occurs when a run moves to a queued status.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.queued"

  • thread.run.in_progress: object { data, event }

    Occurs when a run moves to an in_progress status.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.in_progress"

  • thread.run.requires_action: object { data, event }

    Occurs when a run moves to a requires_action status.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.requires_action"

  • thread.run.completed: object { data, event }

    Occurs when a run is completed.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.completed"

  • thread.run.incomplete: object { data, event }

    Occurs when a run ends with status incomplete.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.incomplete"

  • thread.run.failed: object { data, event }

    Occurs when a run fails.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.failed"

  • thread.run.cancelling: object { data, event }

    Occurs when a run moves to a cancelling status.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.cancelling"

  • thread.run.cancelled: object { data, event }

    Occurs when a run is cancelled.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.cancelled"

  • thread.run.expired: object { data, event }

    Occurs when a run expires.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.expired"

  • thread.run.step.created: object { data, event }

    Occurs when a run step is created.

    • data: object { id, assistant_id, cancelled_at, 13 more }

      Represents a step in execution of a run.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • assistant_id: string

        The ID of the assistant associated with the run step.

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expired_at: number

        The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.

      • failed_at: number

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

      • last_error: object { code, message }

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

        • code: "server_error" or "rate_limit_exceeded"

          One of server_error or rate_limit_exceeded.

          • "server_error"

          • "rate_limit_exceeded"

        • message: string

          A human-readable description of the error.

      • metadata: map[string]

        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: "thread.run.step"

        The object type, which is always thread.run.step.

      • run_id: string

        The ID of the run that this run step is a part of.

      • status: "in_progress" or "cancelled" or "failed" or 2 more

        The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.

        • "in_progress"

        • "cancelled"

        • "failed"

        • "completed"

        • "expired"

      • step_details: MessageCreationStepDetails or ToolCallsStepDetails

        The details of the run step.

        • message_creation_step_details: object { message_creation, type }

          Details of the message creation by the run step.

          • message_creation: object { message_id }

            • message_id: string

              The ID of the message that was created by this run step.

          • type: "message_creation"

            Always message_creation.

        • tool_calls_step_details: object { tool_calls, type }

          Details of the tool call.

          • tool_calls: array of unknown

            An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.

          • type: "tool_calls"

            Always tool_calls.

      • thread_id: string

        The ID of the thread that was run.

      • type: "message_creation" or "tool_calls"

        The type of run step, which can be either message_creation or tool_calls.

        • "message_creation"

        • "tool_calls"

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        Usage statistics related to the run step. This value will be null while the run step's status is in_progress.

        • completion_tokens: number

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

        • prompt_tokens: number

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

        • total_tokens: number

          Total number of tokens used (prompt + completion).

    • event: "thread.run.step.created"

  • thread.run.step.in_progress: object { data, event }

    Occurs when a run step moves to an in_progress state.

    • data: object { id, assistant_id, cancelled_at, 13 more }

      Represents a step in execution of a run.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • assistant_id: string

        The ID of the assistant associated with the run step.

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expired_at: number

        The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.

      • failed_at: number

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

      • last_error: object { code, message }

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

      • metadata: map[string]

        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: "thread.run.step"

        The object type, which is always thread.run.step.

      • run_id: string

        The ID of the run that this run step is a part of.

      • status: "in_progress" or "cancelled" or "failed" or 2 more

        The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.

      • step_details: MessageCreationStepDetails or ToolCallsStepDetails

        The details of the run step.

      • thread_id: string

        The ID of the thread that was run.

      • type: "message_creation" or "tool_calls"

        The type of run step, which can be either message_creation or tool_calls.

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        Usage statistics related to the run step. This value will be null while the run step's status is in_progress.

    • event: "thread.run.step.in_progress"

  • thread.run.step.delta: object { data, event }

    Occurs when parts of a run step are being streamed.

    • data: object { id, delta, object }

      Represents a run step delta i.e. any changed fields on a run step during streaming.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • delta: unknown

      • object: "thread.run.step.delta"

        The object type, which is always thread.run.step.delta.

    • event: "thread.run.step.delta"

  • thread.run.step.completed: object { data, event }

    Occurs when a run step is completed.

    • data: object { id, assistant_id, cancelled_at, 13 more }

      Represents a step in execution of a run.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • assistant_id: string

        The ID of the assistant associated with the run step.

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expired_at: number

        The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.

      • failed_at: number

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

      • last_error: object { code, message }

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

      • metadata: map[string]

        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: "thread.run.step"

        The object type, which is always thread.run.step.

      • run_id: string

        The ID of the run that this run step is a part of.

      • status: "in_progress" or "cancelled" or "failed" or 2 more

        The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.

      • step_details: MessageCreationStepDetails or ToolCallsStepDetails

        The details of the run step.

      • thread_id: string

        The ID of the thread that was run.

      • type: "message_creation" or "tool_calls"

        The type of run step, which can be either message_creation or tool_calls.

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        Usage statistics related to the run step. This value will be null while the run step's status is in_progress.

    • event: "thread.run.step.completed"

  • thread.run.step.failed: object { data, event }

    Occurs when a run step fails.

    • data: object { id, assistant_id, cancelled_at, 13 more }

      Represents a step in execution of a run.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • assistant_id: string

        The ID of the assistant associated with the run step.

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expired_at: number

        The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.

      • failed_at: number

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

      • last_error: object { code, message }

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

      • metadata: map[string]

        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: "thread.run.step"

        The object type, which is always thread.run.step.

      • run_id: string

        The ID of the run that this run step is a part of.

      • status: "in_progress" or "cancelled" or "failed" or 2 more

        The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.

      • step_details: MessageCreationStepDetails or ToolCallsStepDetails

        The details of the run step.

      • thread_id: string

        The ID of the thread that was run.

      • type: "message_creation" or "tool_calls"

        The type of run step, which can be either message_creation or tool_calls.

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        Usage statistics related to the run step. This value will be null while the run step's status is in_progress.

    • event: "thread.run.step.failed"

  • thread.run.step.cancelled: object { data, event }

    Occurs when a run step is cancelled.

    • data: object { id, assistant_id, cancelled_at, 13 more }

      Represents a step in execution of a run.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • assistant_id: string

        The ID of the assistant associated with the run step.

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expired_at: number

        The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.

      • failed_at: number

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

      • last_error: object { code, message }

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

      • metadata: map[string]

        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: "thread.run.step"

        The object type, which is always thread.run.step.

      • run_id: string

        The ID of the run that this run step is a part of.

      • status: "in_progress" or "cancelled" or "failed" or 2 more

        The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.

      • step_details: MessageCreationStepDetails or ToolCallsStepDetails

        The details of the run step.

      • thread_id: string

        The ID of the thread that was run.

      • type: "message_creation" or "tool_calls"

        The type of run step, which can be either message_creation or tool_calls.

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        Usage statistics related to the run step. This value will be null while the run step's status is in_progress.

    • event: "thread.run.step.cancelled"

  • thread.run.step.expired: object { data, event }

    Occurs when a run step expires.

    • data: object { id, assistant_id, cancelled_at, 13 more }

      Represents a step in execution of a run.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • assistant_id: string

        The ID of the assistant associated with the run step.

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expired_at: number

        The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.

      • failed_at: number

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

      • last_error: object { code, message }

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

      • metadata: map[string]

        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: "thread.run.step"

        The object type, which is always thread.run.step.

      • run_id: string

        The ID of the run that this run step is a part of.

      • status: "in_progress" or "cancelled" or "failed" or 2 more

        The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.

      • step_details: MessageCreationStepDetails or ToolCallsStepDetails

        The details of the run step.

      • thread_id: string

        The ID of the thread that was run.

      • type: "message_creation" or "tool_calls"

        The type of run step, which can be either message_creation or tool_calls.

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        Usage statistics related to the run step. This value will be null while the run step's status is in_progress.

    • event: "thread.run.step.expired"

  • thread.message.created: object { data, event }

    Occurs when a message is created.

    • data: object { id, assistant_id, attachments, 11 more }

      Represents a message within a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

        If applicable, the ID of the assistant that authored this message.

      • attachments: array of object { file_id, tools }

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

        • file_id: optional string

          The ID of the file to attach to the message.

        • tools: optional array of CodeInterpreterTool or object { type }

          The tools to add this file to.

          • code_interpreter_tool: object { type }

            • type: "code_interpreter"

              The type of tool being defined: code_interpreter

          • AssistantToolsFileSearchTypeOnly: object { type }

      • completed_at: number

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

      • content: array of unknown

        The content of the message in array of text and/or images.

      • created_at: number

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

      • incomplete_at: number

        The Unix timestamp (in seconds) for when the message was marked as incomplete.

      • incomplete_details: object { reason }

        On an incomplete message, details about why the message is incomplete.

        • reason: "content_filter" or "max_tokens" or "run_cancelled" or 2 more

          The reason the message is incomplete.

          • "content_filter"

          • "max_tokens"

          • "run_cancelled"

          • "run_expired"

          • "run_failed"

      • metadata: map[string]

        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: "thread.message"

        The object type, which is always thread.message.

      • role: "user" or "assistant"

        The entity that produced the message. One of user or assistant.

        • "user"

        • "assistant"

      • run_id: string

        The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.

      • status: "in_progress" or "incomplete" or "completed"

        The status of the message, which can be either in_progress, incomplete, or completed.

        • "in_progress"

        • "incomplete"

        • "completed"

      • thread_id: string

        The thread ID that this message belongs to.

    • event: "thread.message.created"

  • thread.message.in_progress: object { data, event }

    Occurs when a message moves to an in_progress state.

    • data: object { id, assistant_id, attachments, 11 more }

      Represents a message within a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

        If applicable, the ID of the assistant that authored this message.

      • attachments: array of object { file_id, tools }

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

      • completed_at: number

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

      • content: array of unknown

        The content of the message in array of text and/or images.

      • created_at: number

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

      • incomplete_at: number

        The Unix timestamp (in seconds) for when the message was marked as incomplete.

      • incomplete_details: object { reason }

        On an incomplete message, details about why the message is incomplete.

      • metadata: map[string]

        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: "thread.message"

        The object type, which is always thread.message.

      • role: "user" or "assistant"

        The entity that produced the message. One of user or assistant.

      • run_id: string

        The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.

      • status: "in_progress" or "incomplete" or "completed"

        The status of the message, which can be either in_progress, incomplete, or completed.

      • thread_id: string

        The thread ID that this message belongs to.

    • event: "thread.message.in_progress"

  • thread.message.delta: object { data, event }

    Occurs when parts of a Message are being streamed.

    • data: object { id, delta, object }

      Represents a message delta i.e. any changed fields on a message during streaming.

      • id: string

        The identifier of the message, which can be referenced in API endpoints.

      • delta: object { content, role }

        The delta containing the fields that have changed on the Message.

        • content: optional array of unknown

          The content of the message in array of text and/or images.

        • role: optional "user" or "assistant"

          The entity that produced the message. One of user or assistant.

          • "user"

          • "assistant"

      • object: "thread.message.delta"

        The object type, which is always thread.message.delta.

    • event: "thread.message.delta"

  • thread.message.completed: object { data, event }

    Occurs when a message is completed.

    • data: object { id, assistant_id, attachments, 11 more }

      Represents a message within a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

        If applicable, the ID of the assistant that authored this message.

      • attachments: array of object { file_id, tools }

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

      • completed_at: number

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

      • content: array of unknown

        The content of the message in array of text and/or images.

      • created_at: number

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

      • incomplete_at: number

        The Unix timestamp (in seconds) for when the message was marked as incomplete.

      • incomplete_details: object { reason }

        On an incomplete message, details about why the message is incomplete.

      • metadata: map[string]

        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: "thread.message"

        The object type, which is always thread.message.

      • role: "user" or "assistant"

        The entity that produced the message. One of user or assistant.

      • run_id: string

        The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.

      • status: "in_progress" or "incomplete" or "completed"

        The status of the message, which can be either in_progress, incomplete, or completed.

      • thread_id: string

        The thread ID that this message belongs to.

    • event: "thread.message.completed"

  • thread.message.incomplete: object { data, event }

    Occurs when a message ends before it is completed.

    • data: object { id, assistant_id, attachments, 11 more }

      Represents a message within a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

        If applicable, the ID of the assistant that authored this message.

      • attachments: array of object { file_id, tools }

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

      • completed_at: number

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

      • content: array of unknown

        The content of the message in array of text and/or images.

      • created_at: number

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

      • incomplete_at: number

        The Unix timestamp (in seconds) for when the message was marked as incomplete.

      • incomplete_details: object { reason }

        On an incomplete message, details about why the message is incomplete.

      • metadata: map[string]

        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: "thread.message"

        The object type, which is always thread.message.

      • role: "user" or "assistant"

        The entity that produced the message. One of user or assistant.

      • run_id: string

        The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.

      • status: "in_progress" or "incomplete" or "completed"

        The status of the message, which can be either in_progress, incomplete, or completed.

      • thread_id: string

        The thread ID that this message belongs to.

    • event: "thread.message.incomplete"

  • error_event: object { data, event }

    Occurs when an error occurs. This can happen due to an internal server error or a timeout.

    • data: object { code, message, param, type }

      • code: string

      • message: string

      • param: string

      • type: string

    • event: "error"

Assistant Tool

• assistant_tool: unknown

Code Interpreter Tool

• code_interpreter_tool: object { type }

  • type: "code_interpreter"

    The type of tool being defined: code_interpreter

File Search Tool

• file_search_tool: object { type, file_search }

  • type: "file_search"

    The type of tool being defined: file_search

  • file_search: optional object { max_num_results, ranking_options }

    Overrides for the file search tool.

    • max_num_results: optional number

      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 object { score_threshold, ranker }

      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: number

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

      • ranker: optional "auto" or "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"

Function Tool

• function_tool: object { function, type }

  • function: object { name, description, parameters, strict }

    • name: string

      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 string

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

    • parameters: optional map[unknown]

      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 boolean

      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: "function"

    The type of tool being defined: function

Message Stream Event

• message_stream_event: object { data, event } or object { data, event } or object { data, event } or 2 more

  Occurs when a message is created.

  • thread.message.created: object { data, event }

    Occurs when a message is created.

    • data: object { id, assistant_id, attachments, 11 more }

      Represents a message within a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

        If applicable, the ID of the assistant that authored this message.

      • attachments: array of object { file_id, tools }

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

        • file_id: optional string

          The ID of the file to attach to the message.

        • tools: optional array of CodeInterpreterTool or object { type }

          The tools to add this file to.

          • code_interpreter_tool: object { type }

            • type: "code_interpreter"

              The type of tool being defined: code_interpreter

          • AssistantToolsFileSearchTypeOnly: object { type }

      • completed_at: number

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

      • content: array of unknown

        The content of the message in array of text and/or images.

      • created_at: number

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

      • incomplete_at: number

        The Unix timestamp (in seconds) for when the message was marked as incomplete.

      • incomplete_details: object { reason }

        On an incomplete message, details about why the message is incomplete.

        • reason: "content_filter" or "max_tokens" or "run_cancelled" or 2 more

          The reason the message is incomplete.

          • "content_filter"

          • "max_tokens"

          • "run_cancelled"

          • "run_expired"

          • "run_failed"

      • metadata: map[string]

        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: "thread.message"

        The object type, which is always thread.message.

      • role: "user" or "assistant"

        The entity that produced the message. One of user or assistant.

        • "user"

        • "assistant"

      • run_id: string

        The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.

      • status: "in_progress" or "incomplete" or "completed"

        The status of the message, which can be either in_progress, incomplete, or completed.

        • "in_progress"

        • "incomplete"

        • "completed"

      • thread_id: string

        The thread ID that this message belongs to.

    • event: "thread.message.created"

  • thread.message.in_progress: object { data, event }

    Occurs when a message moves to an in_progress state.

    • data: object { id, assistant_id, attachments, 11 more }

      Represents a message within a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

        If applicable, the ID of the assistant that authored this message.

      • attachments: array of object { file_id, tools }

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

      • completed_at: number

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

      • content: array of unknown

        The content of the message in array of text and/or images.

      • created_at: number

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

      • incomplete_at: number

        The Unix timestamp (in seconds) for when the message was marked as incomplete.

      • incomplete_details: object { reason }

        On an incomplete message, details about why the message is incomplete.

      • metadata: map[string]

        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: "thread.message"

        The object type, which is always thread.message.

      • role: "user" or "assistant"

        The entity that produced the message. One of user or assistant.

      • run_id: string

        The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.

      • status: "in_progress" or "incomplete" or "completed"

        The status of the message, which can be either in_progress, incomplete, or completed.

      • thread_id: string

        The thread ID that this message belongs to.

    • event: "thread.message.in_progress"

  • thread.message.delta: object { data, event }

    Occurs when parts of a Message are being streamed.

    • data: object { id, delta, object }

      Represents a message delta i.e. any changed fields on a message during streaming.

      • id: string

        The identifier of the message, which can be referenced in API endpoints.

      • delta: object { content, role }

        The delta containing the fields that have changed on the Message.

        • content: optional array of unknown

          The content of the message in array of text and/or images.

        • role: optional "user" or "assistant"

          The entity that produced the message. One of user or assistant.

          • "user"

          • "assistant"

      • object: "thread.message.delta"

        The object type, which is always thread.message.delta.

    • event: "thread.message.delta"

  • thread.message.completed: object { data, event }

    Occurs when a message is completed.

    • data: object { id, assistant_id, attachments, 11 more }

      Represents a message within a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

        If applicable, the ID of the assistant that authored this message.

      • attachments: array of object { file_id, tools }

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

      • completed_at: number

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

      • content: array of unknown

        The content of the message in array of text and/or images.

      • created_at: number

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

      • incomplete_at: number

        The Unix timestamp (in seconds) for when the message was marked as incomplete.

      • incomplete_details: object { reason }

        On an incomplete message, details about why the message is incomplete.

      • metadata: map[string]

        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: "thread.message"

        The object type, which is always thread.message.

      • role: "user" or "assistant"

        The entity that produced the message. One of user or assistant.

      • run_id: string

        The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.

      • status: "in_progress" or "incomplete" or "completed"

        The status of the message, which can be either in_progress, incomplete, or completed.

      • thread_id: string

        The thread ID that this message belongs to.

    • event: "thread.message.completed"

  • thread.message.incomplete: object { data, event }

    Occurs when a message ends before it is completed.

    • data: object { id, assistant_id, attachments, 11 more }

      Represents a message within a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

        If applicable, the ID of the assistant that authored this message.

      • attachments: array of object { file_id, tools }

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

      • completed_at: number

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

      • content: array of unknown

        The content of the message in array of text and/or images.

      • created_at: number

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

      • incomplete_at: number

        The Unix timestamp (in seconds) for when the message was marked as incomplete.

      • incomplete_details: object { reason }

        On an incomplete message, details about why the message is incomplete.

      • metadata: map[string]

        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: "thread.message"

        The object type, which is always thread.message.

      • role: "user" or "assistant"

        The entity that produced the message. One of user or assistant.

      • run_id: string

        The ID of the run associated with the creation of this message. Value is null when messages are created manually using the create message or create thread endpoints.

      • status: "in_progress" or "incomplete" or "completed"

        The status of the message, which can be either in_progress, incomplete, or completed.

      • thread_id: string

        The thread ID that this message belongs to.

    • event: "thread.message.incomplete"

Run Step Stream Event

• run_step_stream_event: object { data, event } or object { data, event } or object { data, event } or 4 more

  Occurs when a run step is created.

  • thread.run.step.created: object { data, event }

    Occurs when a run step is created.

    • data: object { id, assistant_id, cancelled_at, 13 more }

      Represents a step in execution of a run.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • assistant_id: string

        The ID of the assistant associated with the run step.

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expired_at: number

        The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.

      • failed_at: number

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

      • last_error: object { code, message }

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

        • code: "server_error" or "rate_limit_exceeded"

          One of server_error or rate_limit_exceeded.

          • "server_error"

          • "rate_limit_exceeded"

        • message: string

          A human-readable description of the error.

      • metadata: map[string]

        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: "thread.run.step"

        The object type, which is always thread.run.step.

      • run_id: string

        The ID of the run that this run step is a part of.

      • status: "in_progress" or "cancelled" or "failed" or 2 more

        The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.

        • "in_progress"

        • "cancelled"

        • "failed"

        • "completed"

        • "expired"

      • step_details: MessageCreationStepDetails or ToolCallsStepDetails

        The details of the run step.

        • message_creation_step_details: object { message_creation, type }

          Details of the message creation by the run step.

          • message_creation: object { message_id }

            • message_id: string

              The ID of the message that was created by this run step.

          • type: "message_creation"

            Always message_creation.

        • tool_calls_step_details: object { tool_calls, type }

          Details of the tool call.

          • tool_calls: array of unknown

            An array of tool calls the run step was involved in. These can be associated with one of three types of tools: code_interpreter, file_search, or function.

          • type: "tool_calls"

            Always tool_calls.

      • thread_id: string

        The ID of the thread that was run.

      • type: "message_creation" or "tool_calls"

        The type of run step, which can be either message_creation or tool_calls.

        • "message_creation"

        • "tool_calls"

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        Usage statistics related to the run step. This value will be null while the run step's status is in_progress.

        • completion_tokens: number

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

        • prompt_tokens: number

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

        • total_tokens: number

          Total number of tokens used (prompt + completion).

    • event: "thread.run.step.created"

  • thread.run.step.in_progress: object { data, event }

    Occurs when a run step moves to an in_progress state.

    • data: object { id, assistant_id, cancelled_at, 13 more }

      Represents a step in execution of a run.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • assistant_id: string

        The ID of the assistant associated with the run step.

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expired_at: number

        The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.

      • failed_at: number

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

      • last_error: object { code, message }

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

      • metadata: map[string]

        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: "thread.run.step"

        The object type, which is always thread.run.step.

      • run_id: string

        The ID of the run that this run step is a part of.

      • status: "in_progress" or "cancelled" or "failed" or 2 more

        The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.

      • step_details: MessageCreationStepDetails or ToolCallsStepDetails

        The details of the run step.

      • thread_id: string

        The ID of the thread that was run.

      • type: "message_creation" or "tool_calls"

        The type of run step, which can be either message_creation or tool_calls.

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        Usage statistics related to the run step. This value will be null while the run step's status is in_progress.

    • event: "thread.run.step.in_progress"

  • thread.run.step.delta: object { data, event }

    Occurs when parts of a run step are being streamed.

    • data: object { id, delta, object }

      Represents a run step delta i.e. any changed fields on a run step during streaming.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • delta: unknown

      • object: "thread.run.step.delta"

        The object type, which is always thread.run.step.delta.

    • event: "thread.run.step.delta"

  • thread.run.step.completed: object { data, event }

    Occurs when a run step is completed.

    • data: object { id, assistant_id, cancelled_at, 13 more }

      Represents a step in execution of a run.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • assistant_id: string

        The ID of the assistant associated with the run step.

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expired_at: number

        The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.

      • failed_at: number

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

      • last_error: object { code, message }

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

      • metadata: map[string]

        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: "thread.run.step"

        The object type, which is always thread.run.step.

      • run_id: string

        The ID of the run that this run step is a part of.

      • status: "in_progress" or "cancelled" or "failed" or 2 more

        The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.

      • step_details: MessageCreationStepDetails or ToolCallsStepDetails

        The details of the run step.

      • thread_id: string

        The ID of the thread that was run.

      • type: "message_creation" or "tool_calls"

        The type of run step, which can be either message_creation or tool_calls.

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        Usage statistics related to the run step. This value will be null while the run step's status is in_progress.

    • event: "thread.run.step.completed"

  • thread.run.step.failed: object { data, event }

    Occurs when a run step fails.

    • data: object { id, assistant_id, cancelled_at, 13 more }

      Represents a step in execution of a run.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • assistant_id: string

        The ID of the assistant associated with the run step.

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expired_at: number

        The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.

      • failed_at: number

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

      • last_error: object { code, message }

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

      • metadata: map[string]

        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: "thread.run.step"

        The object type, which is always thread.run.step.

      • run_id: string

        The ID of the run that this run step is a part of.

      • status: "in_progress" or "cancelled" or "failed" or 2 more

        The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.

      • step_details: MessageCreationStepDetails or ToolCallsStepDetails

        The details of the run step.

      • thread_id: string

        The ID of the thread that was run.

      • type: "message_creation" or "tool_calls"

        The type of run step, which can be either message_creation or tool_calls.

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        Usage statistics related to the run step. This value will be null while the run step's status is in_progress.

    • event: "thread.run.step.failed"

  • thread.run.step.cancelled: object { data, event }

    Occurs when a run step is cancelled.

    • data: object { id, assistant_id, cancelled_at, 13 more }

      Represents a step in execution of a run.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • assistant_id: string

        The ID of the assistant associated with the run step.

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expired_at: number

        The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.

      • failed_at: number

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

      • last_error: object { code, message }

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

      • metadata: map[string]

        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: "thread.run.step"

        The object type, which is always thread.run.step.

      • run_id: string

        The ID of the run that this run step is a part of.

      • status: "in_progress" or "cancelled" or "failed" or 2 more

        The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.

      • step_details: MessageCreationStepDetails or ToolCallsStepDetails

        The details of the run step.

      • thread_id: string

        The ID of the thread that was run.

      • type: "message_creation" or "tool_calls"

        The type of run step, which can be either message_creation or tool_calls.

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        Usage statistics related to the run step. This value will be null while the run step's status is in_progress.

    • event: "thread.run.step.cancelled"

  • thread.run.step.expired: object { data, event }

    Occurs when a run step expires.

    • data: object { id, assistant_id, cancelled_at, 13 more }

      Represents a step in execution of a run.

      • id: string

        The identifier of the run step, which can be referenced in API endpoints.

      • assistant_id: string

        The ID of the assistant associated with the run step.

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expired_at: number

        The Unix timestamp (in seconds) for when the run step expired. A step is considered expired if the parent run is expired.

      • failed_at: number

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

      • last_error: object { code, message }

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

      • metadata: map[string]

        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: "thread.run.step"

        The object type, which is always thread.run.step.

      • run_id: string

        The ID of the run that this run step is a part of.

      • status: "in_progress" or "cancelled" or "failed" or 2 more

        The status of the run step, which can be either in_progress, cancelled, failed, completed, or expired.

      • step_details: MessageCreationStepDetails or ToolCallsStepDetails

        The details of the run step.

      • thread_id: string

        The ID of the thread that was run.

      • type: "message_creation" or "tool_calls"

        The type of run step, which can be either message_creation or tool_calls.

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        Usage statistics related to the run step. This value will be null while the run step's status is in_progress.

    • event: "thread.run.step.expired"

Run Stream Event

• run_stream_event: object { data, event } or object { data, event } or object { data, event } or 7 more

  Occurs when a new run is created.

  • thread.run.created: object { data, event }

    Occurs when a new run is created.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

        • reason: optional "max_completion_tokens" or "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: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

        • code: "server_error" or "rate_limit_exceeded" or "invalid_prompt"

          One of server_error, rate_limit_exceeded, or invalid_prompt.

          • "server_error"

          • "rate_limit_exceeded"

          • "invalid_prompt"

        • message: string

          A human-readable description of the error.

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

        • submit_tool_outputs: object { tool_calls }

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

          • tool_calls: array of RequiredActionFunctionToolCall

            A list of the relevant tool calls.

            • id: string

              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: object { arguments, name }

              The function definition.

              • arguments: string

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

              • name: string

                The name of the function.

            • type: "function"

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

        • type: "submit_tool_outputs"

          For now, this is always submit_tool_outputs.

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

        • union_member_0: "auto"

          auto is the default value

        • response_format_text: object { type }

          Default response format. Used to generate text responses.

          • type: "text"

            The type of response format being defined. Always text.

        • response_format_json_object: object { type }

          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: "json_object"

            The type of response format being defined. Always json_object.

        • response_format_json_schema: object { json_schema, type }

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

          • json_schema: object { name, description, schema, strict }

            Structured Outputs configuration options, including a JSON Schema.

            • name: string

              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 string

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

            • schema: optional map[unknown]

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

            • strict: optional boolean

              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: "json_schema"

            The type of response format being defined. Always json_schema.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

        • Auto: "none" or "auto" or "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"

        • assistant_tool_choice: object { type, function }

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

          • type: "function" or "code_interpreter" or "file_search"

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

            • "function"

            • "code_interpreter"

            • "file_search"

          • function: optional object { name }

            • name: string

              The name of the function to call.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

        • type: "auto" or "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 number

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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: number

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

        • prompt_tokens: number

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

        • total_tokens: number

          Total number of tokens used (prompt + completion).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.created"

  • thread.run.queued: object { data, event }

    Occurs when a run moves to a queued status.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.queued"

  • thread.run.in_progress: object { data, event }

    Occurs when a run moves to an in_progress status.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.in_progress"

  • thread.run.requires_action: object { data, event }

    Occurs when a run moves to a requires_action status.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.requires_action"

  • thread.run.completed: object { data, event }

    Occurs when a run is completed.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.completed"

  • thread.run.incomplete: object { data, event }

    Occurs when a run ends with status incomplete.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.incomplete"

  • thread.run.failed: object { data, event }

    Occurs when a run fails.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.failed"

  • thread.run.cancelling: object { data, event }

    Occurs when a run moves to a cancelling status.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.cancelling"

  • thread.run.cancelled: object { data, event }

    Occurs when a run is cancelled.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.cancelled"

  • thread.run.expired: object { data, event }

    Occurs when a run expires.

    • data: object { id, assistant_id, cancelled_at, 24 more }

      Represents an execution run on a thread.

      • id: string

        The identifier, which can be referenced in API endpoints.

      • assistant_id: string

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

      • cancelled_at: number

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

      • completed_at: number

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

      • created_at: number

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

      • expires_at: number

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

      • failed_at: number

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

      • incomplete_details: object { reason }

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

      • instructions: string

        The instructions that the assistant used for this run.

      • last_error: object { code, message }

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

      • max_completion_tokens: number

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

      • max_prompt_tokens: number

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

      • metadata: map[string]

        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: string

        The model that the assistant used for this run.

      • object: "thread.run"

        The object type, which is always thread.run.

      • parallel_tool_calls: boolean

        Whether to enable parallel function calling during tool use.

      • required_action: object { submit_tool_outputs, type }

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

      • response_format: "auto" or ResponseFormatText or ResponseFormatJSONObject or ResponseFormatJSONSchema

        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.

      • started_at: number

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

      • status: unknown

      • thread_id: string

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

      • tool_choice: "none" or "auto" or "required" or AssistantToolChoice

        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.

      • tools: array of unknown

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

      • truncation_strategy: object { type, last_messages }

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

      • usage: object { completion_tokens, prompt_tokens, total_tokens }

        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.).

      • temperature: optional number

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

      • top_p: optional number

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

    • event: "thread.run.expired"

Thread Stream Event

• thread_stream_event: object { data, event, enabled }

  Occurs when a new thread is created.

  • data: object { id, created_at, metadata, 2 more }

    Represents a thread that contains messages.

    • id: string

      The identifier, which can be referenced in API endpoints.

    • created_at: number

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

    • metadata: map[string]

      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: "thread"

      The object type, which is always thread.

    • tool_resources: object { code_interpreter, file_search }

      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 object { file_ids }

        • file_ids: optional array of string

          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 object { vector_store_ids }

        • vector_store_ids: optional array of string

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

  • event: "thread.created"

  • enabled: optional boolean

    Whether to enable input audio transcription.