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Realtime
Calls

Accept call

realtime.calls.accept(strcall_id, CallAcceptParams**kwargs)
POST/realtime/calls/{call_id}/accept

Accept an incoming SIP call and configure the realtime session that will handle it.

ParametersExpand Collapse
call_id: str
type: Literal["realtime"]

The type of session to create. Always realtime for the Realtime API.

audio: Optional[RealtimeAudioConfigParam]

Configuration for input and output audio.

include: Optional[List[Literal["item.input_audio_transcription.logprobs"]]]

Additional fields to include in server outputs.

item.input_audio_transcription.logprobs: Include logprobs for input audio transcription.

instructions: Optional[str]

The default system instructions (i.e. system message) prepended to model calls. This field allows the client to guide the model on desired responses. The model can be instructed on response content and format, (e.g. “be extremely succinct”, “act friendly”, “here are examples of good responses”) and on audio behavior (e.g. “talk quickly”, “inject emotion into your voice”, “laugh frequently”). The instructions are not guaranteed to be followed by the model, but they provide guidance to the model on the desired behavior.

Note that the server sets default instructions which will be used if this field is not set and are visible in the session.created event at the start of the session.

max_output_tokens: Optional[Union[int, Literal["inf"]]]

Maximum number of output tokens for a single assistant response, inclusive of tool calls. Provide an integer between 1 and 4096 to limit output tokens, or inf for the maximum available tokens for a given model. Defaults to inf.

model: Optional[Union[str, Literal["gpt-realtime", "gpt-realtime-1.5", "gpt-realtime-2", 16 more]]]

The Realtime model used for this session.

output_modalities: Optional[List[Literal["text", "audio"]]]

The set of modalities the model can respond with. It defaults to ["audio"], indicating that the model will respond with audio plus a transcript. ["text"] can be used to make the model respond with text only. It is not possible to request both text and audio at the same time.

parallel_tool_calls: Optional[bool]

Whether the model may call multiple tools in parallel. Only supported by reasoning Realtime models such as gpt-realtime-2.

prompt: Optional[ResponsePromptParam]

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

reasoning: Optional[RealtimeReasoningParam]

Configuration for reasoning-capable Realtime models such as gpt-realtime-2.

tool_choice: Optional[RealtimeToolChoiceConfigParam]

How the model chooses tools. Provide one of the string modes or force a specific function/MCP tool.

tools: Optional[RealtimeToolsConfigParam]

Tools available to the model.

tracing: Optional[RealtimeTracingConfigParam]

Realtime API can write session traces to the Traces Dashboard. Set to null to disable tracing. Once tracing is enabled for a session, the configuration cannot be modified.

auto will create a trace for the session with default values for the workflow name, group id, and metadata.

truncation: Optional[RealtimeTruncationParam]

When the number of tokens in a conversation exceeds the model’s input token limit, the conversation be truncated, meaning messages (starting from the oldest) will not be included in the model’s context. A 32k context model with 4,096 max output tokens can only include 28,224 tokens in the context before truncation occurs.

Clients can configure truncation behavior to truncate with a lower max token limit, which is an effective way to control token usage and cost.

Truncation will reduce the number of cached tokens on the next turn (busting the cache), since messages are dropped from the beginning of the context. However, clients can also configure truncation to retain messages up to a fraction of the maximum context size, which will reduce the need for future truncations and thus improve the cache rate.

Truncation can be disabled entirely, which means the server will never truncate but would instead return an error if the conversation exceeds the model’s input token limit.

Accept call

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
client.realtime.calls.accept(
    call_id="call_id",
    type="realtime",
)
Returns Examples