Server events
These are events emitted from the OpenAI Realtime WebSocket server to the client.
Returned when a Session is created. Emitted automatically when a new connection is established as the first server event. This event will contain the default Session configuration.
session: RealtimeSessionCreateRequest { type, audio, include, 9 more } or RealtimeTranscriptionSessionCreateRequest { type, audio, include } The session configuration.
The session configuration.
RealtimeSessionCreateRequest = object { type, audio, include, 9 more } Realtime session object configuration.
Realtime session object configuration.
Configuration for input and output audio.
Configuration for input and output audio.
The format of the input audio.
The format of the input audio.
noise_reduction: optional object { type } Configuration for input audio noise reduction. This can be set to null to turn off.
Noise reduction filters audio added to the input audio buffer before it is sent to VAD and the model.
Filtering the audio can improve VAD and turn detection accuracy (reducing false positives) and model performance by improving perception of the input audio.
Configuration for input audio noise reduction. This can be set to null to turn off.
Noise reduction filters audio added to the input audio buffer before it is sent to VAD and the model.
Filtering the audio can improve VAD and turn detection accuracy (reducing false positives) and model performance by improving perception of the input audio.
Configuration for input audio transcription, defaults to off and can be set to null to turn off once on. Input audio transcription is not native to the model, since the model consumes audio directly. Transcription runs asynchronously through the /audio/transcriptions endpoint and should be treated as guidance of input audio content rather than precisely what the model heard. The client can optionally set the language and prompt for transcription, these offer additional guidance to the transcription service.
Configuration for input audio transcription, defaults to off and can be set to null to turn off once on. Input audio transcription is not native to the model, since the model consumes audio directly. Transcription runs asynchronously through the /audio/transcriptions endpoint and should be treated as guidance of input audio content rather than precisely what the model heard. The client can optionally set the language and prompt for transcription, these offer additional guidance to the transcription service.
The language of the input audio. Supplying the input language in
ISO-639-1 (e.g. en) format
will improve accuracy and latency.
model: optional string or "whisper-1" or "gpt-4o-mini-transcribe" or "gpt-4o-mini-transcribe-2025-12-15" or 2 moreThe model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
The model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
"whisper-1" or "gpt-4o-mini-transcribe" or "gpt-4o-mini-transcribe-2025-12-15" or 2 moreThe model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
The model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
An optional text to guide the model's style or continue a previous audio
segment.
For whisper-1, the prompt is a list of keywords.
For gpt-4o-transcribe models (excluding gpt-4o-transcribe-diarize), the prompt is a free text string, for example "expect words related to technology".
Configuration for turn detection, ether Server VAD or Semantic VAD. This can be set to null to turn off, in which case the client must manually trigger model response.
Server VAD means that the model will detect the start and end of speech based on audio volume and respond at the end of user speech.
Semantic VAD is more advanced and uses a turn detection model (in conjunction with VAD) to semantically estimate whether the user has finished speaking, then dynamically sets a timeout based on this probability. For example, if user audio trails off with "uhhm", the model will score a low probability of turn end and wait longer for the user to continue speaking. This can be useful for more natural conversations, but may have a higher latency.
Configuration for turn detection, ether Server VAD or Semantic VAD. This can be set to null to turn off, in which case the client must manually trigger model response.
Server VAD means that the model will detect the start and end of speech based on audio volume and respond at the end of user speech.
Semantic VAD is more advanced and uses a turn detection model (in conjunction with VAD) to semantically estimate whether the user has finished speaking, then dynamically sets a timeout based on this probability. For example, if user audio trails off with "uhhm", the model will score a low probability of turn end and wait longer for the user to continue speaking. This can be useful for more natural conversations, but may have a higher latency.
ServerVad = object { type, create_response, idle_timeout_ms, 4 more } Server-side voice activity detection (VAD) which flips on when user speech is detected and off after a period of silence.
Server-side voice activity detection (VAD) which flips on when user speech is detected and off after a period of silence.
Whether or not to automatically generate a response when a VAD stop event occurs. If interrupt_response is set to false this may fail to create a response if the model is already responding.
If both create_response and interrupt_response are set to false, the model will never respond automatically but VAD events will still be emitted.
Optional timeout after which a model response will be triggered automatically. This is useful for situations in which a long pause from the user is unexpected, such as a phone call. The model will effectively prompt the user to continue the conversation based on the current context.
The timeout value will be applied after the last model response's audio has finished playing,
i.e. it's set to the response.done time plus audio playback duration.
An input_audio_buffer.timeout_triggered event (plus events
associated with the Response) will be emitted when the timeout is reached.
Idle timeout is currently only supported for server_vad mode.
Whether or not to automatically interrupt (cancel) any ongoing response with output to the default
conversation (i.e. conversation of auto) when a VAD start event occurs. If true then the response will be cancelled, otherwise it will continue until complete.
If both create_response and interrupt_response are set to false, the model will never respond automatically but VAD events will still be emitted.
Used only for server_vad mode. Amount of audio to include before the VAD detected speech (in
milliseconds). Defaults to 300ms.
SemanticVad = object { type, create_response, eagerness, interrupt_response } Server-side semantic turn detection which uses a model to determine when the user has finished speaking.
Server-side semantic turn detection which uses a model to determine when the user has finished speaking.
Whether or not to automatically generate a response when a VAD stop event occurs.
eagerness: optional "low" or "medium" or "high" or "auto"Used only for semantic_vad mode. The eagerness of the model to respond. low will wait longer for the user to continue speaking, high will respond more quickly. auto is the default and is equivalent to medium. low, medium, and high have max timeouts of 8s, 4s, and 2s respectively.
Used only for semantic_vad mode. The eagerness of the model to respond. low will wait longer for the user to continue speaking, high will respond more quickly. auto is the default and is equivalent to medium. low, medium, and high have max timeouts of 8s, 4s, and 2s respectively.
The format of the output audio.
The format of the output audio.
The speed of the model's spoken response as a multiple of the original speed. 1.0 is the default speed. 0.25 is the minimum speed. 1.5 is the maximum speed. This value can only be changed in between model turns, not while a response is in progress.
This parameter is a post-processing adjustment to the audio after it is generated, it's also possible to prompt the model to speak faster or slower.
voice: optional string or "alloy" or "ash" or "ballad" or 7 more or object { id } The voice the model uses to respond. Supported built-in voices are
alloy, ash, ballad, coral, echo, sage, shimmer, verse,
marin, and cedar. You may also provide a custom voice object with
an id, for example { "id": "voice_1234" }. Voice cannot be changed
during the session once the model has responded with audio at least once.
We recommend marin and cedar for best quality.
The voice the model uses to respond. Supported built-in voices are
alloy, ash, ballad, coral, echo, sage, shimmer, verse,
marin, and cedar. You may also provide a custom voice object with
an id, for example { "id": "voice_1234" }. Voice cannot be changed
during the session once the model has responded with audio at least once.
We recommend marin and cedar for best quality.
Additional fields to include in server outputs.
item.input_audio_transcription.logprobs: Include logprobs for input audio transcription.
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 number or "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.
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 string or "gpt-realtime" or "gpt-realtime-1.5" or "gpt-realtime-2025-08-28" or 13 moreThe Realtime model used for this session.
The Realtime model used for this session.
output_modalities: optional array of "text" or "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.
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.
Reference to a prompt template and its variables.
Learn more.
Reference to a prompt template and its variables. Learn more.
variables: optional map[string or ResponseInputText { text, type } or ResponseInputImage { detail, type, file_id, image_url } or ResponseInputFile { type, file_data, file_id, 2 more } ]Optional map of values to substitute in for variables in your
prompt. The substitution values can either be strings, or other
Response input types like images or files.
Optional map of values to substitute in for variables in your prompt. The substitution values can either be strings, or other Response input types like images or files.
ResponseInputImage = object { detail, type, file_id, image_url } An image input to the model. Learn about image inputs.
An image input to the model. Learn about image inputs.
How the model chooses tools. Provide one of the string modes or force a specific
function/MCP tool.
How the model chooses tools. Provide one of the string modes or force a specific function/MCP tool.
ToolChoiceOptions = "none" or "auto" or "required"Controls which (if any) tool is called by the model.
none means the model will not call any tool 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.
Controls which (if any) tool is called by the model.
none means the model will not call any tool 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.
ToolChoiceFunction = object { name, type } Use this option to force the model to call a specific function.
Use this option to force the model to call a specific function.
Tools available to the model.
Tools available to the model.
RealtimeFunctionTool = object { description, name, parameters, type }
McpTool = object { server_label, type, allowed_tools, 7 more } Give the model access to additional tools via remote Model Context Protocol
(MCP) servers. Learn more about MCP.
Give the model access to additional tools via remote Model Context Protocol (MCP) servers. Learn more about MCP.
allowed_tools: optional array of string or object { read_only, tool_names } List of allowed tool names or a filter object.
List of allowed tool names or a filter object.
McpToolFilter = object { read_only, tool_names } A filter object to specify which tools are allowed.
A filter object to specify which tools are allowed.
Indicates whether or not a tool modifies data or is read-only. If an
MCP server is annotated with readOnlyHint,
it will match this filter.
An OAuth access token that can be used with a remote MCP server, either with a custom MCP server URL or a service connector. Your application must handle the OAuth authorization flow and provide the token here.
connector_id: optional "connector_dropbox" or "connector_gmail" or "connector_googlecalendar" or 5 moreIdentifier for service connectors, like those available in ChatGPT. One of
server_url or connector_id must be provided. Learn more about service
connectors here.
Currently supported connector_id values are:
- Dropbox:
connector_dropbox
- Gmail:
connector_gmail
- Google Calendar:
connector_googlecalendar
- Google Drive:
connector_googledrive
- Microsoft Teams:
connector_microsoftteams
- Outlook Calendar:
connector_outlookcalendar
- Outlook Email:
connector_outlookemail
- SharePoint:
connector_sharepoint
Identifier for service connectors, like those available in ChatGPT. One of
server_url or connector_id must be provided. Learn more about service
connectors here.
Currently supported connector_id values are:
- Dropbox:
connector_dropbox - Gmail:
connector_gmail - Google Calendar:
connector_googlecalendar - Google Drive:
connector_googledrive - Microsoft Teams:
connector_microsoftteams - Outlook Calendar:
connector_outlookcalendar - Outlook Email:
connector_outlookemail - SharePoint:
connector_sharepoint
Optional HTTP headers to send to the MCP server. Use for authentication or other purposes.
require_approval: optional object { always, never } or "always" or "never"Specify which of the MCP server's tools require approval.
Specify which of the MCP server's tools require approval.
McpToolApprovalFilter = object { always, never } Specify which of the MCP server's tools require approval. Can be
always, never, or a filter object associated with tools
that require approval.
Specify which of the MCP server's tools require approval. Can be
always, never, or a filter object associated with tools
that require approval.
always: optional object { read_only, tool_names } A filter object to specify which tools are allowed.
A filter object to specify which tools are allowed.
Indicates whether or not a tool modifies data or is read-only. If an
MCP server is annotated with readOnlyHint,
it will match this filter.
never: optional object { read_only, tool_names } A filter object to specify which tools are allowed.
A filter object to specify which tools are allowed.
Indicates whether or not a tool modifies data or is read-only. If an
MCP server is annotated with readOnlyHint,
it will match this filter.
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.
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.
Enables tracing and sets default values for tracing configuration options. Always auto.
TracingConfiguration = object { group_id, metadata, workflow_name } Granular configuration for tracing.
Granular configuration for tracing.
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.
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.
"auto" or "disabled"The truncation strategy to use for the session. auto is the default truncation strategy. disabled will disable truncation and emit errors when the conversation exceeds the input token limit.
The truncation strategy to use for the session. auto is the default truncation strategy. disabled will disable truncation and emit errors when the conversation exceeds the input token limit.
RetentionRatioTruncation = object { retention_ratio, type, token_limits } Retain a fraction of the conversation tokens when the conversation exceeds the input token limit. This allows you to amortize truncations across multiple turns, which can help improve cached token usage.
Retain a fraction of the conversation tokens when the conversation exceeds the input token limit. This allows you to amortize truncations across multiple turns, which can help improve cached token usage.
Fraction of post-instruction conversation tokens to retain (0.0 - 1.0) when the conversation exceeds the input token limit. Setting this to 0.8 means that messages will be dropped until 80% of the maximum allowed tokens are used. This helps reduce the frequency of truncations and improve cache rates.
token_limits: optional object { post_instructions } Optional custom token limits for this truncation strategy. If not provided, the model's default token limits will be used.
Optional custom token limits for this truncation strategy. If not provided, the model's default token limits will be used.
Maximum tokens allowed in the conversation after instructions (which including tool definitions). For example, setting this to 5,000 would mean that truncation would occur when the conversation exceeds 5,000 tokens after instructions. This cannot be higher than the model's context window size minus the maximum output tokens.
RealtimeTranscriptionSessionCreateRequest = object { type, audio, include } Realtime transcription session object configuration.
Realtime transcription session object configuration.
The type of session to create. Always transcription for transcription sessions.
Configuration for input and output audio.
Configuration for input and output audio.
input: optional RealtimeTranscriptionSessionAudioInput { format, noise_reduction, transcription, turn_detection }
The PCM audio format. Only a 24kHz sample rate is supported.
The PCM audio format. Only a 24kHz sample rate is supported.
noise_reduction: optional object { type } Configuration for input audio noise reduction. This can be set to null to turn off.
Noise reduction filters audio added to the input audio buffer before it is sent to VAD and the model.
Filtering the audio can improve VAD and turn detection accuracy (reducing false positives) and model performance by improving perception of the input audio.
Configuration for input audio noise reduction. This can be set to null to turn off.
Noise reduction filters audio added to the input audio buffer before it is sent to VAD and the model.
Filtering the audio can improve VAD and turn detection accuracy (reducing false positives) and model performance by improving perception of the input audio.
Configuration for input audio transcription, defaults to off and can be set to null to turn off once on. Input audio transcription is not native to the model, since the model consumes audio directly. Transcription runs asynchronously through the /audio/transcriptions endpoint and should be treated as guidance of input audio content rather than precisely what the model heard. The client can optionally set the language and prompt for transcription, these offer additional guidance to the transcription service.
Configuration for input audio transcription, defaults to off and can be set to null to turn off once on. Input audio transcription is not native to the model, since the model consumes audio directly. Transcription runs asynchronously through the /audio/transcriptions endpoint and should be treated as guidance of input audio content rather than precisely what the model heard. The client can optionally set the language and prompt for transcription, these offer additional guidance to the transcription service.
The language of the input audio. Supplying the input language in
ISO-639-1 (e.g. en) format
will improve accuracy and latency.
model: optional string or "whisper-1" or "gpt-4o-mini-transcribe" or "gpt-4o-mini-transcribe-2025-12-15" or 2 moreThe model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
The model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
"whisper-1" or "gpt-4o-mini-transcribe" or "gpt-4o-mini-transcribe-2025-12-15" or 2 moreThe model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
The model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
An optional text to guide the model's style or continue a previous audio
segment.
For whisper-1, the prompt is a list of keywords.
For gpt-4o-transcribe models (excluding gpt-4o-transcribe-diarize), the prompt is a free text string, for example "expect words related to technology".
Configuration for turn detection, ether Server VAD or Semantic VAD. This can be set to null to turn off, in which case the client must manually trigger model response.
Server VAD means that the model will detect the start and end of speech based on audio volume and respond at the end of user speech.
Semantic VAD is more advanced and uses a turn detection model (in conjunction with VAD) to semantically estimate whether the user has finished speaking, then dynamically sets a timeout based on this probability. For example, if user audio trails off with "uhhm", the model will score a low probability of turn end and wait longer for the user to continue speaking. This can be useful for more natural conversations, but may have a higher latency.
Configuration for turn detection, ether Server VAD or Semantic VAD. This can be set to null to turn off, in which case the client must manually trigger model response.
Server VAD means that the model will detect the start and end of speech based on audio volume and respond at the end of user speech.
Semantic VAD is more advanced and uses a turn detection model (in conjunction with VAD) to semantically estimate whether the user has finished speaking, then dynamically sets a timeout based on this probability. For example, if user audio trails off with "uhhm", the model will score a low probability of turn end and wait longer for the user to continue speaking. This can be useful for more natural conversations, but may have a higher latency.
ServerVad = object { type, create_response, idle_timeout_ms, 4 more } Server-side voice activity detection (VAD) which flips on when user speech is detected and off after a period of silence.
Server-side voice activity detection (VAD) which flips on when user speech is detected and off after a period of silence.
Whether or not to automatically generate a response when a VAD stop event occurs. If interrupt_response is set to false this may fail to create a response if the model is already responding.
If both create_response and interrupt_response are set to false, the model will never respond automatically but VAD events will still be emitted.
Optional timeout after which a model response will be triggered automatically. This is useful for situations in which a long pause from the user is unexpected, such as a phone call. The model will effectively prompt the user to continue the conversation based on the current context.
The timeout value will be applied after the last model response's audio has finished playing,
i.e. it's set to the response.done time plus audio playback duration.
An input_audio_buffer.timeout_triggered event (plus events
associated with the Response) will be emitted when the timeout is reached.
Idle timeout is currently only supported for server_vad mode.
Whether or not to automatically interrupt (cancel) any ongoing response with output to the default
conversation (i.e. conversation of auto) when a VAD start event occurs. If true then the response will be cancelled, otherwise it will continue until complete.
If both create_response and interrupt_response are set to false, the model will never respond automatically but VAD events will still be emitted.
Used only for server_vad mode. Amount of audio to include before the VAD detected speech (in
milliseconds). Defaults to 300ms.
SemanticVad = object { type, create_response, eagerness, interrupt_response } Server-side semantic turn detection which uses a model to determine when the user has finished speaking.
Server-side semantic turn detection which uses a model to determine when the user has finished speaking.
Whether or not to automatically generate a response when a VAD stop event occurs.
eagerness: optional "low" or "medium" or "high" or "auto"Used only for semantic_vad mode. The eagerness of the model to respond. low will wait longer for the user to continue speaking, high will respond more quickly. auto is the default and is equivalent to medium. low, medium, and high have max timeouts of 8s, 4s, and 2s respectively.
Used only for semantic_vad mode. The eagerness of the model to respond. low will wait longer for the user to continue speaking, high will respond more quickly. auto is the default and is equivalent to medium. low, medium, and high have max timeouts of 8s, 4s, and 2s respectively.
Returned when a session is updated with a session.update event, unless
there is an error.
session: RealtimeSessionCreateRequest { type, audio, include, 9 more } or RealtimeTranscriptionSessionCreateRequest { type, audio, include } The session configuration.
The session configuration.
RealtimeSessionCreateRequest = object { type, audio, include, 9 more } Realtime session object configuration.
Realtime session object configuration.
Configuration for input and output audio.
Configuration for input and output audio.
The format of the input audio.
The format of the input audio.
noise_reduction: optional object { type } Configuration for input audio noise reduction. This can be set to null to turn off.
Noise reduction filters audio added to the input audio buffer before it is sent to VAD and the model.
Filtering the audio can improve VAD and turn detection accuracy (reducing false positives) and model performance by improving perception of the input audio.
Configuration for input audio noise reduction. This can be set to null to turn off.
Noise reduction filters audio added to the input audio buffer before it is sent to VAD and the model.
Filtering the audio can improve VAD and turn detection accuracy (reducing false positives) and model performance by improving perception of the input audio.
Configuration for input audio transcription, defaults to off and can be set to null to turn off once on. Input audio transcription is not native to the model, since the model consumes audio directly. Transcription runs asynchronously through the /audio/transcriptions endpoint and should be treated as guidance of input audio content rather than precisely what the model heard. The client can optionally set the language and prompt for transcription, these offer additional guidance to the transcription service.
Configuration for input audio transcription, defaults to off and can be set to null to turn off once on. Input audio transcription is not native to the model, since the model consumes audio directly. Transcription runs asynchronously through the /audio/transcriptions endpoint and should be treated as guidance of input audio content rather than precisely what the model heard. The client can optionally set the language and prompt for transcription, these offer additional guidance to the transcription service.
The language of the input audio. Supplying the input language in
ISO-639-1 (e.g. en) format
will improve accuracy and latency.
model: optional string or "whisper-1" or "gpt-4o-mini-transcribe" or "gpt-4o-mini-transcribe-2025-12-15" or 2 moreThe model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
The model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
"whisper-1" or "gpt-4o-mini-transcribe" or "gpt-4o-mini-transcribe-2025-12-15" or 2 moreThe model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
The model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
An optional text to guide the model's style or continue a previous audio
segment.
For whisper-1, the prompt is a list of keywords.
For gpt-4o-transcribe models (excluding gpt-4o-transcribe-diarize), the prompt is a free text string, for example "expect words related to technology".
Configuration for turn detection, ether Server VAD or Semantic VAD. This can be set to null to turn off, in which case the client must manually trigger model response.
Server VAD means that the model will detect the start and end of speech based on audio volume and respond at the end of user speech.
Semantic VAD is more advanced and uses a turn detection model (in conjunction with VAD) to semantically estimate whether the user has finished speaking, then dynamically sets a timeout based on this probability. For example, if user audio trails off with "uhhm", the model will score a low probability of turn end and wait longer for the user to continue speaking. This can be useful for more natural conversations, but may have a higher latency.
Configuration for turn detection, ether Server VAD or Semantic VAD. This can be set to null to turn off, in which case the client must manually trigger model response.
Server VAD means that the model will detect the start and end of speech based on audio volume and respond at the end of user speech.
Semantic VAD is more advanced and uses a turn detection model (in conjunction with VAD) to semantically estimate whether the user has finished speaking, then dynamically sets a timeout based on this probability. For example, if user audio trails off with "uhhm", the model will score a low probability of turn end and wait longer for the user to continue speaking. This can be useful for more natural conversations, but may have a higher latency.
ServerVad = object { type, create_response, idle_timeout_ms, 4 more } Server-side voice activity detection (VAD) which flips on when user speech is detected and off after a period of silence.
Server-side voice activity detection (VAD) which flips on when user speech is detected and off after a period of silence.
Whether or not to automatically generate a response when a VAD stop event occurs. If interrupt_response is set to false this may fail to create a response if the model is already responding.
If both create_response and interrupt_response are set to false, the model will never respond automatically but VAD events will still be emitted.
Optional timeout after which a model response will be triggered automatically. This is useful for situations in which a long pause from the user is unexpected, such as a phone call. The model will effectively prompt the user to continue the conversation based on the current context.
The timeout value will be applied after the last model response's audio has finished playing,
i.e. it's set to the response.done time plus audio playback duration.
An input_audio_buffer.timeout_triggered event (plus events
associated with the Response) will be emitted when the timeout is reached.
Idle timeout is currently only supported for server_vad mode.
Whether or not to automatically interrupt (cancel) any ongoing response with output to the default
conversation (i.e. conversation of auto) when a VAD start event occurs. If true then the response will be cancelled, otherwise it will continue until complete.
If both create_response and interrupt_response are set to false, the model will never respond automatically but VAD events will still be emitted.
Used only for server_vad mode. Amount of audio to include before the VAD detected speech (in
milliseconds). Defaults to 300ms.
SemanticVad = object { type, create_response, eagerness, interrupt_response } Server-side semantic turn detection which uses a model to determine when the user has finished speaking.
Server-side semantic turn detection which uses a model to determine when the user has finished speaking.
Whether or not to automatically generate a response when a VAD stop event occurs.
eagerness: optional "low" or "medium" or "high" or "auto"Used only for semantic_vad mode. The eagerness of the model to respond. low will wait longer for the user to continue speaking, high will respond more quickly. auto is the default and is equivalent to medium. low, medium, and high have max timeouts of 8s, 4s, and 2s respectively.
Used only for semantic_vad mode. The eagerness of the model to respond. low will wait longer for the user to continue speaking, high will respond more quickly. auto is the default and is equivalent to medium. low, medium, and high have max timeouts of 8s, 4s, and 2s respectively.
The format of the output audio.
The format of the output audio.
The speed of the model's spoken response as a multiple of the original speed. 1.0 is the default speed. 0.25 is the minimum speed. 1.5 is the maximum speed. This value can only be changed in between model turns, not while a response is in progress.
This parameter is a post-processing adjustment to the audio after it is generated, it's also possible to prompt the model to speak faster or slower.
voice: optional string or "alloy" or "ash" or "ballad" or 7 more or object { id } The voice the model uses to respond. Supported built-in voices are
alloy, ash, ballad, coral, echo, sage, shimmer, verse,
marin, and cedar. You may also provide a custom voice object with
an id, for example { "id": "voice_1234" }. Voice cannot be changed
during the session once the model has responded with audio at least once.
We recommend marin and cedar for best quality.
The voice the model uses to respond. Supported built-in voices are
alloy, ash, ballad, coral, echo, sage, shimmer, verse,
marin, and cedar. You may also provide a custom voice object with
an id, for example { "id": "voice_1234" }. Voice cannot be changed
during the session once the model has responded with audio at least once.
We recommend marin and cedar for best quality.
Additional fields to include in server outputs.
item.input_audio_transcription.logprobs: Include logprobs for input audio transcription.
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 number or "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.
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 string or "gpt-realtime" or "gpt-realtime-1.5" or "gpt-realtime-2025-08-28" or 13 moreThe Realtime model used for this session.
The Realtime model used for this session.
output_modalities: optional array of "text" or "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.
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.
Reference to a prompt template and its variables.
Learn more.
Reference to a prompt template and its variables. Learn more.
variables: optional map[string or ResponseInputText { text, type } or ResponseInputImage { detail, type, file_id, image_url } or ResponseInputFile { type, file_data, file_id, 2 more } ]Optional map of values to substitute in for variables in your
prompt. The substitution values can either be strings, or other
Response input types like images or files.
Optional map of values to substitute in for variables in your prompt. The substitution values can either be strings, or other Response input types like images or files.
ResponseInputImage = object { detail, type, file_id, image_url } An image input to the model. Learn about image inputs.
An image input to the model. Learn about image inputs.
How the model chooses tools. Provide one of the string modes or force a specific
function/MCP tool.
How the model chooses tools. Provide one of the string modes or force a specific function/MCP tool.
ToolChoiceOptions = "none" or "auto" or "required"Controls which (if any) tool is called by the model.
none means the model will not call any tool 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.
Controls which (if any) tool is called by the model.
none means the model will not call any tool 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.
ToolChoiceFunction = object { name, type } Use this option to force the model to call a specific function.
Use this option to force the model to call a specific function.
Tools available to the model.
Tools available to the model.
RealtimeFunctionTool = object { description, name, parameters, type }
McpTool = object { server_label, type, allowed_tools, 7 more } Give the model access to additional tools via remote Model Context Protocol
(MCP) servers. Learn more about MCP.
Give the model access to additional tools via remote Model Context Protocol (MCP) servers. Learn more about MCP.
allowed_tools: optional array of string or object { read_only, tool_names } List of allowed tool names or a filter object.
List of allowed tool names or a filter object.
McpToolFilter = object { read_only, tool_names } A filter object to specify which tools are allowed.
A filter object to specify which tools are allowed.
Indicates whether or not a tool modifies data or is read-only. If an
MCP server is annotated with readOnlyHint,
it will match this filter.
An OAuth access token that can be used with a remote MCP server, either with a custom MCP server URL or a service connector. Your application must handle the OAuth authorization flow and provide the token here.
connector_id: optional "connector_dropbox" or "connector_gmail" or "connector_googlecalendar" or 5 moreIdentifier for service connectors, like those available in ChatGPT. One of
server_url or connector_id must be provided. Learn more about service
connectors here.
Currently supported connector_id values are:
- Dropbox:
connector_dropbox
- Gmail:
connector_gmail
- Google Calendar:
connector_googlecalendar
- Google Drive:
connector_googledrive
- Microsoft Teams:
connector_microsoftteams
- Outlook Calendar:
connector_outlookcalendar
- Outlook Email:
connector_outlookemail
- SharePoint:
connector_sharepoint
Identifier for service connectors, like those available in ChatGPT. One of
server_url or connector_id must be provided. Learn more about service
connectors here.
Currently supported connector_id values are:
- Dropbox:
connector_dropbox - Gmail:
connector_gmail - Google Calendar:
connector_googlecalendar - Google Drive:
connector_googledrive - Microsoft Teams:
connector_microsoftteams - Outlook Calendar:
connector_outlookcalendar - Outlook Email:
connector_outlookemail - SharePoint:
connector_sharepoint
Optional HTTP headers to send to the MCP server. Use for authentication or other purposes.
require_approval: optional object { always, never } or "always" or "never"Specify which of the MCP server's tools require approval.
Specify which of the MCP server's tools require approval.
McpToolApprovalFilter = object { always, never } Specify which of the MCP server's tools require approval. Can be
always, never, or a filter object associated with tools
that require approval.
Specify which of the MCP server's tools require approval. Can be
always, never, or a filter object associated with tools
that require approval.
always: optional object { read_only, tool_names } A filter object to specify which tools are allowed.
A filter object to specify which tools are allowed.
Indicates whether or not a tool modifies data or is read-only. If an
MCP server is annotated with readOnlyHint,
it will match this filter.
never: optional object { read_only, tool_names } A filter object to specify which tools are allowed.
A filter object to specify which tools are allowed.
Indicates whether or not a tool modifies data or is read-only. If an
MCP server is annotated with readOnlyHint,
it will match this filter.
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.
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.
Enables tracing and sets default values for tracing configuration options. Always auto.
TracingConfiguration = object { group_id, metadata, workflow_name } Granular configuration for tracing.
Granular configuration for tracing.
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.
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.
"auto" or "disabled"The truncation strategy to use for the session. auto is the default truncation strategy. disabled will disable truncation and emit errors when the conversation exceeds the input token limit.
The truncation strategy to use for the session. auto is the default truncation strategy. disabled will disable truncation and emit errors when the conversation exceeds the input token limit.
RetentionRatioTruncation = object { retention_ratio, type, token_limits } Retain a fraction of the conversation tokens when the conversation exceeds the input token limit. This allows you to amortize truncations across multiple turns, which can help improve cached token usage.
Retain a fraction of the conversation tokens when the conversation exceeds the input token limit. This allows you to amortize truncations across multiple turns, which can help improve cached token usage.
Fraction of post-instruction conversation tokens to retain (0.0 - 1.0) when the conversation exceeds the input token limit. Setting this to 0.8 means that messages will be dropped until 80% of the maximum allowed tokens are used. This helps reduce the frequency of truncations and improve cache rates.
token_limits: optional object { post_instructions } Optional custom token limits for this truncation strategy. If not provided, the model's default token limits will be used.
Optional custom token limits for this truncation strategy. If not provided, the model's default token limits will be used.
Maximum tokens allowed in the conversation after instructions (which including tool definitions). For example, setting this to 5,000 would mean that truncation would occur when the conversation exceeds 5,000 tokens after instructions. This cannot be higher than the model's context window size minus the maximum output tokens.
RealtimeTranscriptionSessionCreateRequest = object { type, audio, include } Realtime transcription session object configuration.
Realtime transcription session object configuration.
The type of session to create. Always transcription for transcription sessions.
Configuration for input and output audio.
Configuration for input and output audio.
input: optional RealtimeTranscriptionSessionAudioInput { format, noise_reduction, transcription, turn_detection }
The PCM audio format. Only a 24kHz sample rate is supported.
The PCM audio format. Only a 24kHz sample rate is supported.
noise_reduction: optional object { type } Configuration for input audio noise reduction. This can be set to null to turn off.
Noise reduction filters audio added to the input audio buffer before it is sent to VAD and the model.
Filtering the audio can improve VAD and turn detection accuracy (reducing false positives) and model performance by improving perception of the input audio.
Configuration for input audio noise reduction. This can be set to null to turn off.
Noise reduction filters audio added to the input audio buffer before it is sent to VAD and the model.
Filtering the audio can improve VAD and turn detection accuracy (reducing false positives) and model performance by improving perception of the input audio.
Configuration for input audio transcription, defaults to off and can be set to null to turn off once on. Input audio transcription is not native to the model, since the model consumes audio directly. Transcription runs asynchronously through the /audio/transcriptions endpoint and should be treated as guidance of input audio content rather than precisely what the model heard. The client can optionally set the language and prompt for transcription, these offer additional guidance to the transcription service.
Configuration for input audio transcription, defaults to off and can be set to null to turn off once on. Input audio transcription is not native to the model, since the model consumes audio directly. Transcription runs asynchronously through the /audio/transcriptions endpoint and should be treated as guidance of input audio content rather than precisely what the model heard. The client can optionally set the language and prompt for transcription, these offer additional guidance to the transcription service.
The language of the input audio. Supplying the input language in
ISO-639-1 (e.g. en) format
will improve accuracy and latency.
model: optional string or "whisper-1" or "gpt-4o-mini-transcribe" or "gpt-4o-mini-transcribe-2025-12-15" or 2 moreThe model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
The model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
"whisper-1" or "gpt-4o-mini-transcribe" or "gpt-4o-mini-transcribe-2025-12-15" or 2 moreThe model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
The model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, and gpt-4o-transcribe-diarize. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.
An optional text to guide the model's style or continue a previous audio
segment.
For whisper-1, the prompt is a list of keywords.
For gpt-4o-transcribe models (excluding gpt-4o-transcribe-diarize), the prompt is a free text string, for example "expect words related to technology".
Configuration for turn detection, ether Server VAD or Semantic VAD. This can be set to null to turn off, in which case the client must manually trigger model response.
Server VAD means that the model will detect the start and end of speech based on audio volume and respond at the end of user speech.
Semantic VAD is more advanced and uses a turn detection model (in conjunction with VAD) to semantically estimate whether the user has finished speaking, then dynamically sets a timeout based on this probability. For example, if user audio trails off with "uhhm", the model will score a low probability of turn end and wait longer for the user to continue speaking. This can be useful for more natural conversations, but may have a higher latency.
Configuration for turn detection, ether Server VAD or Semantic VAD. This can be set to null to turn off, in which case the client must manually trigger model response.
Server VAD means that the model will detect the start and end of speech based on audio volume and respond at the end of user speech.
Semantic VAD is more advanced and uses a turn detection model (in conjunction with VAD) to semantically estimate whether the user has finished speaking, then dynamically sets a timeout based on this probability. For example, if user audio trails off with "uhhm", the model will score a low probability of turn end and wait longer for the user to continue speaking. This can be useful for more natural conversations, but may have a higher latency.
ServerVad = object { type, create_response, idle_timeout_ms, 4 more } Server-side voice activity detection (VAD) which flips on when user speech is detected and off after a period of silence.
Server-side voice activity detection (VAD) which flips on when user speech is detected and off after a period of silence.
Whether or not to automatically generate a response when a VAD stop event occurs. If interrupt_response is set to false this may fail to create a response if the model is already responding.
If both create_response and interrupt_response are set to false, the model will never respond automatically but VAD events will still be emitted.
Optional timeout after which a model response will be triggered automatically. This is useful for situations in which a long pause from the user is unexpected, such as a phone call. The model will effectively prompt the user to continue the conversation based on the current context.
The timeout value will be applied after the last model response's audio has finished playing,
i.e. it's set to the response.done time plus audio playback duration.
An input_audio_buffer.timeout_triggered event (plus events
associated with the Response) will be emitted when the timeout is reached.
Idle timeout is currently only supported for server_vad mode.
Whether or not to automatically interrupt (cancel) any ongoing response with output to the default
conversation (i.e. conversation of auto) when a VAD start event occurs. If true then the response will be cancelled, otherwise it will continue until complete.
If both create_response and interrupt_response are set to false, the model will never respond automatically but VAD events will still be emitted.
Used only for server_vad mode. Amount of audio to include before the VAD detected speech (in
milliseconds). Defaults to 300ms.
SemanticVad = object { type, create_response, eagerness, interrupt_response } Server-side semantic turn detection which uses a model to determine when the user has finished speaking.
Server-side semantic turn detection which uses a model to determine when the user has finished speaking.
Whether or not to automatically generate a response when a VAD stop event occurs.
eagerness: optional "low" or "medium" or "high" or "auto"Used only for semantic_vad mode. The eagerness of the model to respond. low will wait longer for the user to continue speaking, high will respond more quickly. auto is the default and is equivalent to medium. low, medium, and high have max timeouts of 8s, 4s, and 2s respectively.
Used only for semantic_vad mode. The eagerness of the model to respond. low will wait longer for the user to continue speaking, high will respond more quickly. auto is the default and is equivalent to medium. low, medium, and high have max timeouts of 8s, 4s, and 2s respectively.
Sent by the server when an Item is added to the default Conversation. This can happen in several cases:
- When the client sends a
conversation.item.createevent. - When the input audio buffer is committed. In this case the item will be a user message containing the audio from the buffer.
- When the model is generating a Response. In this case the
conversation.item.addedevent will be sent when the model starts generating a specific Item, and thus it will not yet have any content (andstatuswill bein_progress).
The event will include the full content of the Item (except when model is generating a Response) except for audio data, which can be retrieved separately with a conversation.item.retrieve event if necessary.
A single item within a Realtime conversation.
A single item within a Realtime conversation.
RealtimeConversationItemSystemMessage = object { content, role, type, 3 more } A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemUserMessage = object { content, role, type, 3 more } A user message item in a Realtime conversation.
A user message item in a Realtime conversation.
content: array of object { audio, detail, image_url, 3 more } The content of the message.
The content of the message.
Base64-encoded audio bytes (for input_audio), these will be parsed as the format specified in the session input audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
detail: optional "auto" or "low" or "high"The detail level of the image (for input_image). auto will default to high.
The detail level of the image (for input_image). auto will default to high.
Base64-encoded image bytes (for input_image) as a data URI. For example data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA.... Supported formats are PNG and JPEG.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemAssistantMessage = object { content, role, type, 3 more } An assistant message item in a Realtime conversation.
An assistant message item in a Realtime conversation.
content: array of object { audio, text, transcript, type } The content of the message.
The content of the message.
Base64-encoded audio bytes, these will be parsed as the format specified in the session output audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCall = object { arguments, name, type, 4 more } A function call item in a Realtime conversation.
A function call item in a Realtime conversation.
The arguments of the function call. This is a JSON-encoded string representing the arguments passed to the function, for example {"arg1": "value1", "arg2": 42}.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCallOutput = object { call_id, output, type, 3 more } A function call output item in a Realtime conversation.
A function call output item in a Realtime conversation.
The output of the function call, this is free text and can contain any information or simply be empty.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeMcpApprovalResponse = object { id, approval_request_id, approve, 2 more } A Realtime item responding to an MCP approval request.
A Realtime item responding to an MCP approval request.
RealtimeMcpListTools = object { server_label, tools, type, id } A Realtime item listing tools available on an MCP server.
A Realtime item listing tools available on an MCP server.
RealtimeMcpToolCall = object { id, arguments, name, 5 more } A Realtime item representing an invocation of a tool on an MCP server.
A Realtime item representing an invocation of a tool on an MCP server.
error: optional RealtimeMcpProtocolError { code, message, type } or RealtimeMcpToolExecutionError { message, type } or RealtimeMcphttpError { code, message, type } The error from the tool call, if any.
The error from the tool call, if any.
Returned when a conversation item is finalized.
The event will include the full content of the Item except for audio data, which can be retrieved separately with a conversation.item.retrieve event if needed.
A single item within a Realtime conversation.
A single item within a Realtime conversation.
RealtimeConversationItemSystemMessage = object { content, role, type, 3 more } A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemUserMessage = object { content, role, type, 3 more } A user message item in a Realtime conversation.
A user message item in a Realtime conversation.
content: array of object { audio, detail, image_url, 3 more } The content of the message.
The content of the message.
Base64-encoded audio bytes (for input_audio), these will be parsed as the format specified in the session input audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
detail: optional "auto" or "low" or "high"The detail level of the image (for input_image). auto will default to high.
The detail level of the image (for input_image). auto will default to high.
Base64-encoded image bytes (for input_image) as a data URI. For example data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA.... Supported formats are PNG and JPEG.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemAssistantMessage = object { content, role, type, 3 more } An assistant message item in a Realtime conversation.
An assistant message item in a Realtime conversation.
content: array of object { audio, text, transcript, type } The content of the message.
The content of the message.
Base64-encoded audio bytes, these will be parsed as the format specified in the session output audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCall = object { arguments, name, type, 4 more } A function call item in a Realtime conversation.
A function call item in a Realtime conversation.
The arguments of the function call. This is a JSON-encoded string representing the arguments passed to the function, for example {"arg1": "value1", "arg2": 42}.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCallOutput = object { call_id, output, type, 3 more } A function call output item in a Realtime conversation.
A function call output item in a Realtime conversation.
The output of the function call, this is free text and can contain any information or simply be empty.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeMcpApprovalResponse = object { id, approval_request_id, approve, 2 more } A Realtime item responding to an MCP approval request.
A Realtime item responding to an MCP approval request.
RealtimeMcpListTools = object { server_label, tools, type, id } A Realtime item listing tools available on an MCP server.
A Realtime item listing tools available on an MCP server.
RealtimeMcpToolCall = object { id, arguments, name, 5 more } A Realtime item representing an invocation of a tool on an MCP server.
A Realtime item representing an invocation of a tool on an MCP server.
error: optional RealtimeMcpProtocolError { code, message, type } or RealtimeMcpToolExecutionError { message, type } or RealtimeMcphttpError { code, message, type } The error from the tool call, if any.
The error from the tool call, if any.
This event is the output of audio transcription for user audio written to the user audio buffer. Transcription begins when the input audio buffer is committed by the client or server (when VAD is enabled). Transcription runs asynchronously with Response creation, so this event may come before or after the Response events.
Realtime API models accept audio natively, and thus input transcription is a separate process run on a separate ASR (Automatic Speech Recognition) model. The transcript may diverge somewhat from the model's interpretation, and should be treated as a rough guide.
The event type, must be
conversation.item.input_audio_transcription.completed.
usage: object { input_tokens, output_tokens, total_tokens, 2 more } or object { seconds, type } Usage statistics for the transcription, this is billed according to the ASR model's pricing rather than the realtime model's pricing.
Usage statistics for the transcription, this is billed according to the ASR model's pricing rather than the realtime model's pricing.
Returned when the text value of an input audio transcription content part is updated with incremental transcription results.
The event type, must be conversation.item.input_audio_transcription.delta.
The log probabilities of the transcription. These can be enabled by configurating the session with "include": ["item.input_audio_transcription.logprobs"]. Each entry in the array corresponds a log probability of which token would be selected for this chunk of transcription. This can help to identify if it was possible there were multiple valid options for a given chunk of transcription.
The log probabilities of the transcription. These can be enabled by configurating the session with "include": ["item.input_audio_transcription.logprobs"]. Each entry in the array corresponds a log probability of which token would be selected for this chunk of transcription. This can help to identify if it was possible there were multiple valid options for a given chunk of transcription.
Returned when an input audio transcription segment is identified for an item.
Returned when an earlier assistant audio message item is truncated by the
client with a conversation.item.truncate event. This event is used to
synchronize the server's understanding of the audio with the client's playback.
This action will truncate the audio and remove the server-side text transcript to ensure there is no text in the context that hasn't been heard by the user.
Returned when an item in the conversation is deleted by the client with a
conversation.item.delete event. This event is used to synchronize the
server's understanding of the conversation history with the client's view.
Returned when an input audio buffer is committed, either by the client or
automatically in server VAD mode. The item_id property is the ID of the user
message item that will be created, thus a conversation.item.created event
will also be sent to the client.
SIP Only: Returned when an DTMF event is received. A DTMF event is a message that
represents a telephone keypad press (0–9, *, #, A–D). The event property
is the keypad that the user press. The received_at is the UTC Unix Timestamp
that the server received the event.
Sent by the server when in server_vad mode to indicate that speech has been
detected in the audio buffer. This can happen any time audio is added to the
buffer (unless speech is already detected). The client may want to use this
event to interrupt audio playback or provide visual feedback to the user.
The client should expect to receive a input_audio_buffer.speech_stopped event
when speech stops. The item_id property is the ID of the user message item
that will be created when speech stops and will also be included in the
input_audio_buffer.speech_stopped event (unless the client manually commits
the audio buffer during VAD activation).
Returned in server_vad mode when the server detects the end of speech in
the audio buffer. The server will also send an conversation.item.created
event with the user message item that is created from the audio buffer.
Returned when the Server VAD timeout is triggered for the input audio buffer. This is configured
with idle_timeout_ms in the turn_detection settings of the session, and it indicates that
there hasn't been any speech detected for the configured duration.
The audio_start_ms and audio_end_ms fields indicate the segment of audio after the last
model response up to the triggering time, as an offset from the beginning of audio written
to the input audio buffer. This means it demarcates the segment of audio that was silent and
the difference between the start and end values will roughly match the configured timeout.
The empty audio will be committed to the conversation as an input_audio item (there will be a
input_audio_buffer.committed event) and a model response will be generated. There may be speech
that didn't trigger VAD but is still detected by the model, so the model may respond with
something relevant to the conversation or a prompt to continue speaking.
Returned when a new Response is created. The first event of response creation,
where the response is in an initial state of in_progress.
The response resource.
The response resource.
audio: optional object { output } Configuration for audio output.
Configuration for audio output.
output: optional object { format, voice }
The format of the output audio.
The format of the output audio.
voice: optional string or "alloy" or "ash" or "ballad" or 7 moreThe voice the model uses to respond. Voice cannot be changed during the
session once the model has responded with audio at least once. Current
voice options are alloy, ash, ballad, coral, echo, sage,
shimmer, verse, marin, and cedar. We recommend marin and cedar for
best quality.
The voice the model uses to respond. Voice cannot be changed during the
session once the model has responded with audio at least once. Current
voice options are alloy, ash, ballad, coral, echo, sage,
shimmer, verse, marin, and cedar. We recommend marin and cedar for
best quality.
"alloy" or "ash" or "ballad" or 7 moreThe voice the model uses to respond. Voice cannot be changed during the
session once the model has responded with audio at least once. Current
voice options are alloy, ash, ballad, coral, echo, sage,
shimmer, verse, marin, and cedar. We recommend marin and cedar for
best quality.
The voice the model uses to respond. Voice cannot be changed during the
session once the model has responded with audio at least once. Current
voice options are alloy, ash, ballad, coral, echo, sage,
shimmer, verse, marin, and cedar. We recommend marin and cedar for
best quality.
Which conversation the response is added to, determined by the conversation
field in the response.create event. If auto, the response will be added to
the default conversation and the value of conversation_id will be an id like
conv_1234. If none, the response will not be added to any conversation and
the value of conversation_id will be null. If responses are being triggered
automatically by VAD the response will be added to the default conversation
max_output_tokens: optional number or "inf"Maximum number of output tokens for a single assistant response,
inclusive of tool calls, that was used in this response.
Maximum number of output tokens for a single assistant response, inclusive of tool calls, that was used in this response.
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.
The list of output items generated by the response.
The list of output items generated by the response.
RealtimeConversationItemSystemMessage = object { content, role, type, 3 more } A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemUserMessage = object { content, role, type, 3 more } A user message item in a Realtime conversation.
A user message item in a Realtime conversation.
content: array of object { audio, detail, image_url, 3 more } The content of the message.
The content of the message.
Base64-encoded audio bytes (for input_audio), these will be parsed as the format specified in the session input audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
detail: optional "auto" or "low" or "high"The detail level of the image (for input_image). auto will default to high.
The detail level of the image (for input_image). auto will default to high.
Base64-encoded image bytes (for input_image) as a data URI. For example data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA.... Supported formats are PNG and JPEG.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemAssistantMessage = object { content, role, type, 3 more } An assistant message item in a Realtime conversation.
An assistant message item in a Realtime conversation.
content: array of object { audio, text, transcript, type } The content of the message.
The content of the message.
Base64-encoded audio bytes, these will be parsed as the format specified in the session output audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCall = object { arguments, name, type, 4 more } A function call item in a Realtime conversation.
A function call item in a Realtime conversation.
The arguments of the function call. This is a JSON-encoded string representing the arguments passed to the function, for example {"arg1": "value1", "arg2": 42}.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCallOutput = object { call_id, output, type, 3 more } A function call output item in a Realtime conversation.
A function call output item in a Realtime conversation.
The output of the function call, this is free text and can contain any information or simply be empty.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeMcpApprovalResponse = object { id, approval_request_id, approve, 2 more } A Realtime item responding to an MCP approval request.
A Realtime item responding to an MCP approval request.
RealtimeMcpListTools = object { server_label, tools, type, id } A Realtime item listing tools available on an MCP server.
A Realtime item listing tools available on an MCP server.
RealtimeMcpToolCall = object { id, arguments, name, 5 more } A Realtime item representing an invocation of a tool on an MCP server.
A Realtime item representing an invocation of a tool on an MCP server.
error: optional RealtimeMcpProtocolError { code, message, type } or RealtimeMcpToolExecutionError { message, type } or RealtimeMcphttpError { code, message, type } The error from the tool call, if any.
The error from the tool call, if any.
output_modalities: optional array of "text" or "audio"The set of modalities the model used to respond, currently the only possible values are
[\"audio\"], [\"text\"]. Audio output always include a text transcript. Setting the
output to mode text will disable audio output from the model.
The set of modalities the model used to respond, currently the only possible values are
[\"audio\"], [\"text\"]. Audio output always include a text transcript. Setting the
output to mode text will disable audio output from the model.
status: optional "completed" or "cancelled" or "failed" or 2 moreThe final status of the response (completed, cancelled, failed, or
incomplete, in_progress).
The final status of the response (completed, cancelled, failed, or
incomplete, in_progress).
Additional details about the status.
Additional details about the status.
error: optional object { code, type } A description of the error that caused the response to fail,
populated when the status is failed.
A description of the error that caused the response to fail,
populated when the status is failed.
reason: optional "turn_detected" or "client_cancelled" or "max_output_tokens" or "content_filter"The reason the Response did not complete. For a cancelled Response, one of turn_detected (the server VAD detected a new start of speech) or client_cancelled (the client sent a cancel event). For an incomplete Response, one of max_output_tokens or content_filter (the server-side safety filter activated and cut off the response).
The reason the Response did not complete. For a cancelled Response, one of turn_detected (the server VAD detected a new start of speech) or client_cancelled (the client sent a cancel event). For an incomplete Response, one of max_output_tokens or content_filter (the server-side safety filter activated and cut off the response).
usage: optional RealtimeResponseUsage { input_token_details, input_tokens, output_token_details, 2 more } Usage statistics for the Response, this will correspond to billing. A
Realtime API session will maintain a conversation context and append new
Items to the Conversation, thus output from previous turns (text and
audio tokens) will become the input for later turns.
Usage statistics for the Response, this will correspond to billing. A Realtime API session will maintain a conversation context and append new Items to the Conversation, thus output from previous turns (text and audio tokens) will become the input for later turns.
input_token_details: optional RealtimeResponseUsageInputTokenDetails { audio_tokens, cached_tokens, cached_tokens_details, 2 more } Details about the input tokens used in the Response. Cached tokens are tokens from previous turns in the conversation that are included as context for the current response. Cached tokens here are counted as a subset of input tokens, meaning input tokens will include cached and uncached tokens.
Details about the input tokens used in the Response. Cached tokens are tokens from previous turns in the conversation that are included as context for the current response. Cached tokens here are counted as a subset of input tokens, meaning input tokens will include cached and uncached tokens.
The number of input tokens used in the Response, including text and audio tokens.
output_token_details: optional RealtimeResponseUsageOutputTokenDetails { audio_tokens, text_tokens } Details about the output tokens used in the Response.
Details about the output tokens used in the Response.
Returned when a Response is done streaming. Always emitted, no matter the
final state. The Response object included in the response.done event will
include all output Items in the Response but will omit the raw audio data.
Clients should check the status field of the Response to determine if it was successful
(completed) or if there was another outcome: cancelled, failed, or incomplete.
A response will contain all output items that were generated during the response, excluding any audio content.
The response resource.
The response resource.
audio: optional object { output } Configuration for audio output.
Configuration for audio output.
output: optional object { format, voice }
The format of the output audio.
The format of the output audio.
voice: optional string or "alloy" or "ash" or "ballad" or 7 moreThe voice the model uses to respond. Voice cannot be changed during the
session once the model has responded with audio at least once. Current
voice options are alloy, ash, ballad, coral, echo, sage,
shimmer, verse, marin, and cedar. We recommend marin and cedar for
best quality.
The voice the model uses to respond. Voice cannot be changed during the
session once the model has responded with audio at least once. Current
voice options are alloy, ash, ballad, coral, echo, sage,
shimmer, verse, marin, and cedar. We recommend marin and cedar for
best quality.
"alloy" or "ash" or "ballad" or 7 moreThe voice the model uses to respond. Voice cannot be changed during the
session once the model has responded with audio at least once. Current
voice options are alloy, ash, ballad, coral, echo, sage,
shimmer, verse, marin, and cedar. We recommend marin and cedar for
best quality.
The voice the model uses to respond. Voice cannot be changed during the
session once the model has responded with audio at least once. Current
voice options are alloy, ash, ballad, coral, echo, sage,
shimmer, verse, marin, and cedar. We recommend marin and cedar for
best quality.
Which conversation the response is added to, determined by the conversation
field in the response.create event. If auto, the response will be added to
the default conversation and the value of conversation_id will be an id like
conv_1234. If none, the response will not be added to any conversation and
the value of conversation_id will be null. If responses are being triggered
automatically by VAD the response will be added to the default conversation
max_output_tokens: optional number or "inf"Maximum number of output tokens for a single assistant response,
inclusive of tool calls, that was used in this response.
Maximum number of output tokens for a single assistant response, inclusive of tool calls, that was used in this response.
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.
The list of output items generated by the response.
The list of output items generated by the response.
RealtimeConversationItemSystemMessage = object { content, role, type, 3 more } A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemUserMessage = object { content, role, type, 3 more } A user message item in a Realtime conversation.
A user message item in a Realtime conversation.
content: array of object { audio, detail, image_url, 3 more } The content of the message.
The content of the message.
Base64-encoded audio bytes (for input_audio), these will be parsed as the format specified in the session input audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
detail: optional "auto" or "low" or "high"The detail level of the image (for input_image). auto will default to high.
The detail level of the image (for input_image). auto will default to high.
Base64-encoded image bytes (for input_image) as a data URI. For example data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA.... Supported formats are PNG and JPEG.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemAssistantMessage = object { content, role, type, 3 more } An assistant message item in a Realtime conversation.
An assistant message item in a Realtime conversation.
content: array of object { audio, text, transcript, type } The content of the message.
The content of the message.
Base64-encoded audio bytes, these will be parsed as the format specified in the session output audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCall = object { arguments, name, type, 4 more } A function call item in a Realtime conversation.
A function call item in a Realtime conversation.
The arguments of the function call. This is a JSON-encoded string representing the arguments passed to the function, for example {"arg1": "value1", "arg2": 42}.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCallOutput = object { call_id, output, type, 3 more } A function call output item in a Realtime conversation.
A function call output item in a Realtime conversation.
The output of the function call, this is free text and can contain any information or simply be empty.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeMcpApprovalResponse = object { id, approval_request_id, approve, 2 more } A Realtime item responding to an MCP approval request.
A Realtime item responding to an MCP approval request.
RealtimeMcpListTools = object { server_label, tools, type, id } A Realtime item listing tools available on an MCP server.
A Realtime item listing tools available on an MCP server.
RealtimeMcpToolCall = object { id, arguments, name, 5 more } A Realtime item representing an invocation of a tool on an MCP server.
A Realtime item representing an invocation of a tool on an MCP server.
error: optional RealtimeMcpProtocolError { code, message, type } or RealtimeMcpToolExecutionError { message, type } or RealtimeMcphttpError { code, message, type } The error from the tool call, if any.
The error from the tool call, if any.
output_modalities: optional array of "text" or "audio"The set of modalities the model used to respond, currently the only possible values are
[\"audio\"], [\"text\"]. Audio output always include a text transcript. Setting the
output to mode text will disable audio output from the model.
The set of modalities the model used to respond, currently the only possible values are
[\"audio\"], [\"text\"]. Audio output always include a text transcript. Setting the
output to mode text will disable audio output from the model.
status: optional "completed" or "cancelled" or "failed" or 2 moreThe final status of the response (completed, cancelled, failed, or
incomplete, in_progress).
The final status of the response (completed, cancelled, failed, or
incomplete, in_progress).
Additional details about the status.
Additional details about the status.
error: optional object { code, type } A description of the error that caused the response to fail,
populated when the status is failed.
A description of the error that caused the response to fail,
populated when the status is failed.
reason: optional "turn_detected" or "client_cancelled" or "max_output_tokens" or "content_filter"The reason the Response did not complete. For a cancelled Response, one of turn_detected (the server VAD detected a new start of speech) or client_cancelled (the client sent a cancel event). For an incomplete Response, one of max_output_tokens or content_filter (the server-side safety filter activated and cut off the response).
The reason the Response did not complete. For a cancelled Response, one of turn_detected (the server VAD detected a new start of speech) or client_cancelled (the client sent a cancel event). For an incomplete Response, one of max_output_tokens or content_filter (the server-side safety filter activated and cut off the response).
usage: optional RealtimeResponseUsage { input_token_details, input_tokens, output_token_details, 2 more } Usage statistics for the Response, this will correspond to billing. A
Realtime API session will maintain a conversation context and append new
Items to the Conversation, thus output from previous turns (text and
audio tokens) will become the input for later turns.
Usage statistics for the Response, this will correspond to billing. A Realtime API session will maintain a conversation context and append new Items to the Conversation, thus output from previous turns (text and audio tokens) will become the input for later turns.
input_token_details: optional RealtimeResponseUsageInputTokenDetails { audio_tokens, cached_tokens, cached_tokens_details, 2 more } Details about the input tokens used in the Response. Cached tokens are tokens from previous turns in the conversation that are included as context for the current response. Cached tokens here are counted as a subset of input tokens, meaning input tokens will include cached and uncached tokens.
Details about the input tokens used in the Response. Cached tokens are tokens from previous turns in the conversation that are included as context for the current response. Cached tokens here are counted as a subset of input tokens, meaning input tokens will include cached and uncached tokens.
The number of input tokens used in the Response, including text and audio tokens.
output_token_details: optional RealtimeResponseUsageOutputTokenDetails { audio_tokens, text_tokens } Details about the output tokens used in the Response.
Details about the output tokens used in the Response.
Returned when a new Item is created during Response generation.
A single item within a Realtime conversation.
A single item within a Realtime conversation.
RealtimeConversationItemSystemMessage = object { content, role, type, 3 more } A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemUserMessage = object { content, role, type, 3 more } A user message item in a Realtime conversation.
A user message item in a Realtime conversation.
content: array of object { audio, detail, image_url, 3 more } The content of the message.
The content of the message.
Base64-encoded audio bytes (for input_audio), these will be parsed as the format specified in the session input audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
detail: optional "auto" or "low" or "high"The detail level of the image (for input_image). auto will default to high.
The detail level of the image (for input_image). auto will default to high.
Base64-encoded image bytes (for input_image) as a data URI. For example data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA.... Supported formats are PNG and JPEG.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemAssistantMessage = object { content, role, type, 3 more } An assistant message item in a Realtime conversation.
An assistant message item in a Realtime conversation.
content: array of object { audio, text, transcript, type } The content of the message.
The content of the message.
Base64-encoded audio bytes, these will be parsed as the format specified in the session output audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCall = object { arguments, name, type, 4 more } A function call item in a Realtime conversation.
A function call item in a Realtime conversation.
The arguments of the function call. This is a JSON-encoded string representing the arguments passed to the function, for example {"arg1": "value1", "arg2": 42}.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCallOutput = object { call_id, output, type, 3 more } A function call output item in a Realtime conversation.
A function call output item in a Realtime conversation.
The output of the function call, this is free text and can contain any information or simply be empty.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeMcpApprovalResponse = object { id, approval_request_id, approve, 2 more } A Realtime item responding to an MCP approval request.
A Realtime item responding to an MCP approval request.
RealtimeMcpListTools = object { server_label, tools, type, id } A Realtime item listing tools available on an MCP server.
A Realtime item listing tools available on an MCP server.
RealtimeMcpToolCall = object { id, arguments, name, 5 more } A Realtime item representing an invocation of a tool on an MCP server.
A Realtime item representing an invocation of a tool on an MCP server.
error: optional RealtimeMcpProtocolError { code, message, type } or RealtimeMcpToolExecutionError { message, type } or RealtimeMcphttpError { code, message, type } The error from the tool call, if any.
The error from the tool call, if any.
Returned when an Item is done streaming. Also emitted when a Response is interrupted, incomplete, or cancelled.
A single item within a Realtime conversation.
A single item within a Realtime conversation.
RealtimeConversationItemSystemMessage = object { content, role, type, 3 more } A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemUserMessage = object { content, role, type, 3 more } A user message item in a Realtime conversation.
A user message item in a Realtime conversation.
content: array of object { audio, detail, image_url, 3 more } The content of the message.
The content of the message.
Base64-encoded audio bytes (for input_audio), these will be parsed as the format specified in the session input audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
detail: optional "auto" or "low" or "high"The detail level of the image (for input_image). auto will default to high.
The detail level of the image (for input_image). auto will default to high.
Base64-encoded image bytes (for input_image) as a data URI. For example data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA.... Supported formats are PNG and JPEG.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemAssistantMessage = object { content, role, type, 3 more } An assistant message item in a Realtime conversation.
An assistant message item in a Realtime conversation.
content: array of object { audio, text, transcript, type } The content of the message.
The content of the message.
Base64-encoded audio bytes, these will be parsed as the format specified in the session output audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCall = object { arguments, name, type, 4 more } A function call item in a Realtime conversation.
A function call item in a Realtime conversation.
The arguments of the function call. This is a JSON-encoded string representing the arguments passed to the function, for example {"arg1": "value1", "arg2": 42}.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCallOutput = object { call_id, output, type, 3 more } A function call output item in a Realtime conversation.
A function call output item in a Realtime conversation.
The output of the function call, this is free text and can contain any information or simply be empty.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeMcpApprovalResponse = object { id, approval_request_id, approve, 2 more } A Realtime item responding to an MCP approval request.
A Realtime item responding to an MCP approval request.
RealtimeMcpListTools = object { server_label, tools, type, id } A Realtime item listing tools available on an MCP server.
A Realtime item listing tools available on an MCP server.
RealtimeMcpToolCall = object { id, arguments, name, 5 more } A Realtime item representing an invocation of a tool on an MCP server.
A Realtime item representing an invocation of a tool on an MCP server.
error: optional RealtimeMcpProtocolError { code, message, type } or RealtimeMcpToolExecutionError { message, type } or RealtimeMcphttpError { code, message, type } The error from the tool call, if any.
The error from the tool call, if any.
Returned when the text value of an "output_text" content part is updated.
Returned when the text value of an "output_text" content part is done streaming. Also emitted when a Response is interrupted, incomplete, or cancelled.
Returned when the model-generated transcription of audio output is updated.
Returned when the model-generated transcription of audio output is done streaming. Also emitted when a Response is interrupted, incomplete, or cancelled.
Returned when the model-generated audio is updated.
Returned when the model-generated audio is done. Also emitted when a Response is interrupted, incomplete, or cancelled.
Returned when the model-generated function call arguments are updated.
Returned when the model-generated function call arguments are done streaming. Also emitted when a Response is interrupted, incomplete, or cancelled.
Returned when MCP tool call arguments are updated during response generation.
Returned when MCP tool call arguments are finalized during response generation.
Returned when an MCP tool call has started and is in progress.
Returned when an MCP tool call has completed successfully.
Emitted at the beginning of a Response to indicate the updated rate limits. When a Response is created some tokens will be "reserved" for the output tokens, the rate limits shown here reflect that reservation, which is then adjusted accordingly once the Response is completed.
Returned when a conversation item is created. There are several scenarios that produce this event:
- The server is generating a Response, which if successful will produce
either one or two Items, which will be of type
message(roleassistant) or typefunction_call. - The input audio buffer has been committed, either by the client or the
server (in
server_vadmode). The server will take the content of the input audio buffer and add it to a new user message Item. - The client has sent a
conversation.item.createevent to add a new Item to the Conversation.
A single item within a Realtime conversation.
A single item within a Realtime conversation.
RealtimeConversationItemSystemMessage = object { content, role, type, 3 more } A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
A system message in a Realtime conversation can be used to provide additional context or instructions to the model. This is similar but distinct from the instruction prompt provided at the start of a conversation, as system messages can be added at any point in the conversation. For major changes to the conversation's behavior, use instructions, but for smaller updates (e.g. "the user is now asking about a different topic"), use system messages.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemUserMessage = object { content, role, type, 3 more } A user message item in a Realtime conversation.
A user message item in a Realtime conversation.
content: array of object { audio, detail, image_url, 3 more } The content of the message.
The content of the message.
Base64-encoded audio bytes (for input_audio), these will be parsed as the format specified in the session input audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
detail: optional "auto" or "low" or "high"The detail level of the image (for input_image). auto will default to high.
The detail level of the image (for input_image). auto will default to high.
Base64-encoded image bytes (for input_image) as a data URI. For example data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA.... Supported formats are PNG and JPEG.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemAssistantMessage = object { content, role, type, 3 more } An assistant message item in a Realtime conversation.
An assistant message item in a Realtime conversation.
content: array of object { audio, text, transcript, type } The content of the message.
The content of the message.
Base64-encoded audio bytes, these will be parsed as the format specified in the session output audio type configuration. This defaults to PCM 16-bit 24kHz mono if not specified.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCall = object { arguments, name, type, 4 more } A function call item in a Realtime conversation.
A function call item in a Realtime conversation.
The arguments of the function call. This is a JSON-encoded string representing the arguments passed to the function, for example {"arg1": "value1", "arg2": 42}.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeConversationItemFunctionCallOutput = object { call_id, output, type, 3 more } A function call output item in a Realtime conversation.
A function call output item in a Realtime conversation.
The output of the function call, this is free text and can contain any information or simply be empty.
The unique ID of the item. This may be provided by the client or generated by the server.
RealtimeMcpApprovalResponse = object { id, approval_request_id, approve, 2 more } A Realtime item responding to an MCP approval request.
A Realtime item responding to an MCP approval request.
RealtimeMcpListTools = object { server_label, tools, type, id } A Realtime item listing tools available on an MCP server.
A Realtime item listing tools available on an MCP server.
RealtimeMcpToolCall = object { id, arguments, name, 5 more } A Realtime item representing an invocation of a tool on an MCP server.
A Realtime item representing an invocation of a tool on an MCP server.
error: optional RealtimeMcpProtocolError { code, message, type } or RealtimeMcpToolExecutionError { message, type } or RealtimeMcphttpError { code, message, type } The error from the tool call, if any.
The error from the tool call, if any.