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API Reference
Beta
Assistants

Retrieve assistant

Deprecated
beta.assistants.retrieve(strassistant_id) -> Assistant
GET/assistants/{assistant_id}

Retrieves an assistant.

ParametersExpand Collapse
assistant_id: str
ReturnsExpand Collapse
class Assistant: …

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

id: str

The identifier, which can be referenced in API endpoints.

created_at: int

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

description: Optional[str]

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

maxLength512
instructions: Optional[str]

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

maxLength256000
metadata: Optional[Metadata]

Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

model: str

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[str]

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

maxLength256
object: Literal["assistant"]

The object type, which is always assistant.

tools: List[AssistantTool]

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.

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

The type of tool being defined: code_interpreter

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

The type of tool being defined: file_search

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

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

description: Optional[str]

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

parameters: Optional[FunctionParameters]

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

Omitting parameters defines a function with an empty parameter list.

strict: Optional[bool]

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

type: Literal["function"]

The type of tool being defined: function

response_format: Optional[AssistantResponseFormatOption]

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

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

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

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

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

auto is the default value

class ResponseFormatText: …

Default response format. Used to generate text responses.

type: Literal["text"]

The type of response format being defined. Always text.

class ResponseFormatJSONObject: …

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

type: Literal["json_object"]

The type of response format being defined. Always json_object.

class ResponseFormatJSONSchema: …

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

json_schema: JSONSchema

Structured Outputs configuration options, including a JSON Schema.

name: str

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

description: Optional[str]

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

schema: Optional[Dict[str, object]]

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

strict: Optional[bool]

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

type: Literal["json_schema"]

The type of response format being defined. Always json_schema.

temperature: Optional[float]

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

minimum0
maximum2
tool_resources: Optional[ToolResources]

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

code_interpreter: Optional[ToolResourcesCodeInterpreter]
file_ids: Optional[List[str]]

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

top_p: Optional[float]

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

We generally recommend altering this or temperature but not both.

minimum0
maximum1

Retrieve assistant

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
assistant = client.beta.assistants.retrieve(
    "assistant_id",
)
print(assistant.id)
{
  "id": "id",
  "created_at": 0,
  "description": "description",
  "instructions": "instructions",
  "metadata": {
    "foo": "string"
  },
  "model": "model",
  "name": "name",
  "object": "assistant",
  "tools": [
    {
      "type": "code_interpreter"
    }
  ],
  "response_format": "auto",
  "temperature": 1,
  "tool_resources": {
    "code_interpreter": {
      "file_ids": [
        "string"
      ]
    },
    "file_search": {
      "vector_store_ids": [
        "string"
      ]
    }
  },
  "top_p": 1
}
Returns Examples
{
  "id": "id",
  "created_at": 0,
  "description": "description",
  "instructions": "instructions",
  "metadata": {
    "foo": "string"
  },
  "model": "model",
  "name": "name",
  "object": "assistant",
  "tools": [
    {
      "type": "code_interpreter"
    }
  ],
  "response_format": "auto",
  "temperature": 1,
  "tool_resources": {
    "code_interpreter": {
      "file_ids": [
        "string"
      ]
    },
    "file_search": {
      "vector_store_ids": [
        "string"
      ]
    }
  },
  "top_p": 1
}