SpyBara
Go Premium

resources/realtime/client-events/index.md 2026-09-16 22:58 UTC to 2026-09-18 22:59 UTC

This page contains 3 additions and 2 deletions.

2026
Fri 18 22:59

Realtime client events

For the complete documentation index, see llms.txt. Markdown versions of documentation pages are available by appending .md to the page URL.

These are events that the OpenAI Realtime WebSocket server will accept from the client.

session.update

Send this event to update the session’s configuration. The client may send this event at any time to update any field except for voice and model. voice can be updated only if there have been no other audio outputs yet.

When the server receives a session.update, it will respond with a session.updated event showing the full, effective configuration. Only the fields that are present in the session.update are updated. To clear a field like instructions, pass an empty string. To clear a field like tools, pass an empty array. To clear a field like turn_detection, pass null.

Schema

Schema name: RealtimeClientEventSessionUpdate

  • session: RealtimeSessionCreateRequest or RealtimeTranscriptionSessionCreateRequest

    Update the Realtime session. Choose either a realtime session or a transcription session.

    • RealtimeSessionCreateRequest object { type, audio, include, 11 more }

      Realtime session object configuration.

      • type: "realtime"

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

        • "realtime"

      • audio: optional RealtimeAudioConfig

        Configuration for input and output audio.

        • input: optional RealtimeAudioConfigInput

          • format: optional RealtimeAudioFormats

            The format of the input audio.

            • PCMAudio object { rate, type }

              The PCM audio format. Only a 24kHz sample rate is supported.

              • rate: optional 24000

                The sample rate of the audio. Always 24000.

                • 24000

              • type: optional "audio/pcm"

                The audio format. Always audio/pcm.

                • "audio/pcm"

            • PCMUAudio object { type }

              The G.711 μ-law format.

              • type: optional "audio/pcmu"

                The audio format. Always audio/pcmu.

                • "audio/pcmu"

            • PCMAAudio object { type }

              The G.711 A-law format.

              • type: optional "audio/pcma"

                The audio format. Always audio/pcma.

                • "audio/pcma"

          • 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.

            • type: optional NoiseReductionType

              Type of noise reduction. near_field is for close-talking microphones such as headphones, far_field is for far-field microphones such as laptop or conference room microphones.

              • "near_field"

              • "far_field"

          • transcription: optional AudioTranscription

            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.

            • delay: optional "minimal" or "low" or "medium" or 2 more

              Controls how long the model waits before emitting transcription text. Higher values can improve transcription accuracy at the cost of latency. Only supported with gpt-realtime-whisper in GA Realtime sessions.

              • "minimal"

              • "low"

              • "medium"

              • "high"

              • "xhigh"

            • keywords: optional array of string

              Words or phrases to guide transcription of the input audio. Supported by gpt-transcribe and gpt-live-transcribe.

            • language: optional string

              The language of the input audio. Supplying the input language in ISO-639-1 (e.g. en) format will improve accuracy and latency.

            • languages: optional array of string

              Possible languages of the input audio, in ISO-639-1 format. Supported by gpt-transcribe and gpt-live-transcribe.

            • model: optional string or "whisper-1" or "gpt-transcribe" or "gpt-live-transcribe" or 5 more

              The model to use for transcription. Current options are whisper-1, gpt-transcribe, gpt-live-transcribe, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, gpt-4o-transcribe-diarize, and gpt-realtime-whisper. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.

              • string

              • "whisper-1" or "gpt-transcribe" or "gpt-live-transcribe" or 5 more

                The model to use for transcription. Current options are whisper-1, gpt-transcribe, gpt-live-transcribe, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, gpt-4o-transcribe-diarize, and gpt-realtime-whisper. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.

                • "whisper-1"

                • "gpt-transcribe"

                • "gpt-live-transcribe"

                • "gpt-4o-mini-transcribe"

                • "gpt-4o-mini-transcribe-2025-12-15"

                • "gpt-4o-transcribe"

                • "gpt-4o-transcribe-diarize"

                • "gpt-realtime-whisper"

            • prompt: optional string

              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". Prompt is not supported with gpt-realtime-whisper in GA Realtime sessions.

          • turn_detection: optional RealtimeAudioInputTurnDetection or null

            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.

            For gpt-realtime-whisper transcription sessions, turn detection must be set to null; VAD is not supported.

            • 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.

              • type: "server_vad"

                Type of turn detection, server_vad to turn on simple Server VAD.

                • "server_vad"

              • create_response: optional boolean

                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.

              • idle_timeout_ms: optional number or null

                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.

              • interrupt_response: optional boolean

                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.

              • prefix_padding_ms: optional number

                Used only for server_vad mode. Amount of audio to include before the VAD detected speech (in milliseconds). Defaults to 300ms.

              • silence_duration_ms: optional number

                Used only for server_vad mode. Duration of silence to detect speech stop (in milliseconds). Defaults to 500ms. With shorter values the model will respond more quickly, but may jump in on short pauses from the user.

              • threshold: optional number

                Used only for server_vad mode. Activation threshold for VAD (0.0 to 1.0), this defaults to 0.5. A higher threshold will require louder audio to activate the model, and thus might perform better in noisy environments.

            • 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.

              • type: "semantic_vad"

                Type of turn detection, semantic_vad to turn on Semantic VAD.

                • "semantic_vad"

              • create_response: optional boolean

                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.

                • "low"

                • "medium"

                • "high"

                • "auto"

              • interrupt_response: optional boolean

                Whether or not to automatically interrupt any ongoing response with output to the default conversation (i.e. conversation of auto) when a VAD start event occurs.

        • output: optional RealtimeAudioConfigOutput

          • format: optional RealtimeAudioFormats

            The format of the output audio.

          • speed: optional number

            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.

            • string

            • "alloy" or "ash" or "ballad" or 7 more

              • "alloy"

              • "ash"

              • "ballad"

              • "coral"

              • "echo"

              • "sage"

              • "shimmer"

              • "verse"

              • "marin"

              • "cedar"

            • ID object { id }

              Custom voice reference.

              • id: string

                The custom voice ID, e.g. voice_1234.

      • include: optional array of "item.input_audio_transcription.logprobs"

        Additional fields to include in server outputs.

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

        • "item.input_audio_transcription.logprobs"

      • instructions: optional string

        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.

        • number

        • "inf"

          • "inf"

      • model: optional string or "gpt-realtime" or "gpt-realtime-1.5" or "gpt-realtime-2" or 16 more

        The Realtime model used for this session.

        • string

        • "gpt-realtime" or "gpt-realtime-1.5" or "gpt-realtime-2" or 16 more

          The Realtime model used for this session.

          • "gpt-realtime"

          • "gpt-realtime-1.5"

          • "gpt-realtime-2"

          • "gpt-realtime-2.1"

          • "gpt-realtime-2.1-mini"

          • "gpt-realtime-2025-08-28"

          • "gpt-4o-realtime-preview"

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

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

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

          • "gpt-4o-mini-realtime-preview"

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

          • "gpt-realtime-mini"

          • "gpt-realtime-mini-2025-10-06"

          • "gpt-realtime-mini-2025-12-15"

          • "gpt-audio-1.5"

          • "gpt-audio-mini"

          • "gpt-audio-mini-2025-10-06"

          • "gpt-audio-mini-2025-12-15"

      • 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.

        • "text"

        • "audio"

      • parallel_tool_calls: optional boolean

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

      • prompt: optional ResponsePrompt or null

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

        • id: string

          The unique identifier of the prompt template to use.

        • variables: optional map[string or ResponseInputText or ResponseInputImage or ResponseInputFile] or null

          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.

          • string

          • ResponseInputText object { text, type, prompt_cache_breakpoint }

            A text input to the model.

            • text: string

              The text input to the model.

            • type: "input_text"

              The type of the input item. Always input_text.

              • "input_text"

            • prompt_cache_breakpoint: optional object { mode }

              Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

              • mode: "explicit"

                The breakpoint mode. Always explicit.

                • "explicit"

          • ResponseInputImage object { detail, type, file_id, 2 more }

            An image input to the model. Learn about image inputs.

            • detail: ImageDetail

              The detail level of the image to be sent to the model. One of high, low, auto, or original. Defaults to auto.

              • "low"

              • "high"

              • "auto"

              • "original"

            • type: "input_image"

              The type of the input item. Always input_image.

              • "input_image"

            • file_id: optional string or null

              The ID of the file to be sent to the model.

            • image_url: optional string or null

              The URL of the image to be sent to the model. A fully qualified URL or base64 encoded image in a data URL.

            • prompt_cache_breakpoint: optional object { mode }

              Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

              • mode: "explicit"

                The breakpoint mode. Always explicit.

                • "explicit"

          • ResponseInputFile object { type, detail, file_data, 4 more }

            A file input to the model.

            • type: "input_file"

              The type of the input item. Always input_file.

              • "input_file"

            • detail: optional "auto" or "low" or "high"

              The detail level of the file to be sent to the model. Use auto to let the system select the detail level; for GPT-5.6 and later models, auto uses high-quality rendering, which may increase input token usage. Use low for lower-cost rendering, or high to render the file at higher quality. Defaults to auto.

              • "auto"

              • "low"

              • "high"

            • file_data: optional string

              The content of the file to be sent to the model.

            • file_id: optional string or null

              The ID of the file to be sent to the model.

            • file_url: optional string

              The URL of the file to be sent to the model.

            • filename: optional string

              The name of the file to be sent to the model.

            • prompt_cache_breakpoint: optional object { mode }

              Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

              • mode: "explicit"

                The breakpoint mode. Always explicit.

                • "explicit"

        • version: optional string or null

          Optional version of the prompt template.

      • reasoning: optional RealtimeReasoning

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

        • effort: optional RealtimeReasoningEffort

          Constrains effort on reasoning for reasoning-capable Realtime models such as gpt-realtime-2.

          • "minimal"

          • "low"

          • "medium"

          • "high"

          • "xhigh"

      • tool_choice: optional RealtimeToolChoiceConfig

        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.

          • "none"

          • "auto"

          • "required"

        • ToolChoiceFunction object { name, type }

          Use this option to force the model to call a specific function.

          • name: string

            The name of the function to call.

          • type: "function"

            For function calling, the type is always function.

            • "function"

        • ToolChoiceMcp object { server_label, type, name }

          Use this option to force the model to call a specific tool on a remote MCP server.

          • server_label: string

            The label of the MCP server to use.

          • type: "mcp"

            For MCP tools, the type is always mcp.

            • "mcp"

          • name: optional string or null

            The name of the tool to call on the server.

      • tools: optional RealtimeToolsConfig

        Tools available to the model.

        • RealtimeFunctionTool object { description, name, parameters, type }

          • description: optional string

            The description of the function, including guidance on when and how to call it, and guidance about what to tell the user when calling (if anything).

          • name: optional string

            The name of the function.

          • parameters: optional unknown

            Parameters of the function in JSON Schema.

          • type: optional "function"

            The type of the tool, i.e. function.

            • "function"

        • McpTool object { server_label, type, allowed_callers, 9 more }

          Give the model access to additional tools via remote Model Context Protocol (MCP) servers. Learn more about MCP.

          • server_label: string

            A label for this MCP server, used to identify it in tool calls.

          • type: "mcp"

            The type of the MCP tool. Always mcp.

            • "mcp"

          • allowed_callers: optional array of "direct" or "programmatic" or null

            The tool invocation context(s).

            • "direct"

            • "programmatic"

          • allowed_tools: optional array of string or object { read_only, tool_names } or null

            List of allowed tool names or a filter object.

            • McpAllowedTools = array of string

              A string array of allowed tool names

            • McpToolFilter object { read_only, tool_names }

              A filter object to specify which tools are allowed.

              • read_only: optional boolean

                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.

              • tool_names: optional array of string

                List of allowed tool names.

          • authorization: optional string

            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 more

            Identifier for service connectors, like those available in ChatGPT. One of server_url, connector_id, or tunnel_id must be provided. Learn more about service connectors here.

            This field is deprecated for models released after September 1, 2026. Use server_url to connect to a remote MCP server, or tunnel_id to connect through a Secure MCP Tunnel.

            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

            • "connector_dropbox"

            • "connector_gmail"

            • "connector_googlecalendar"

            • "connector_googledrive"

            • "connector_microsoftteams"

            • "connector_outlookcalendar"

            • "connector_outlookemail"

            • "connector_sharepoint"

          • defer_loading: optional boolean

            Whether this MCP tool is deferred and discovered via tool search.

          • headers: optional map[string] or null

            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" or null

            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.

              • always: optional object { read_only, tool_names }

                A filter object to specify which tools are allowed.

                • read_only: optional boolean

                  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.

                • tool_names: optional array of string

                  List of allowed tool names.

              • never: optional object { read_only, tool_names }

                A filter object to specify which tools are allowed.

                • read_only: optional boolean

                  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.

                • tool_names: optional array of string

                  List of allowed tool names.

            • McpToolApprovalSetting = "always" or "never"

              Specify a single approval policy for all tools. One of always or never. When set to always, all tools will require approval. When set to never, all tools will not require approval.

              • "always"

              • "never"

          • server_description: optional string

            Optional description of the MCP server, used to provide more context.

          • server_url: optional string

            The URL for the MCP server. One of server_url, connector_id, or tunnel_id must be provided.

          • tunnel_id: optional string

            The Secure MCP Tunnel ID to use instead of a direct server URL. One of server_url, connector_id, or tunnel_id must be provided.

      • tracing: optional RealtimeTracingConfig or null

        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.

        • Auto = "auto"

          Enables tracing and sets default values for tracing configuration options. Always auto.

          • "auto"

        • TracingConfiguration object { group_id, metadata, workflow_name }

          Granular configuration for tracing.

          • group_id: optional string

            The group id to attach to this trace to enable filtering and grouping in the Traces Dashboard.

          • metadata: optional unknown

            The arbitrary metadata to attach to this trace to enable filtering in the Traces Dashboard.

          • workflow_name: optional string

            The name of the workflow to attach to this trace. This is used to name the trace in the Traces Dashboard.

      • truncation: optional RealtimeTruncation

        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.

          • "auto"

          • "disabled"

        • 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.

          • retention_ratio: number

            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.

          • type: "retention_ratio"

            Use retention ratio truncation.

            • "retention_ratio"

          • 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.

            • post_instructions: optional number

              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.

      • type: "transcription"

        The type of session to create. Always transcription for transcription sessions.

        • "transcription"

      • audio: optional RealtimeTranscriptionSessionAudio

        Configuration for input and output audio.

        • input: optional RealtimeTranscriptionSessionAudioInput

          • format: optional RealtimeAudioFormats

            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.

            • type: optional NoiseReductionType

              Type of noise reduction. near_field is for close-talking microphones such as headphones, far_field is for far-field microphones such as laptop or conference room microphones.

          • transcription: optional AudioTranscription

            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.

          • turn_detection: optional RealtimeTranscriptionSessionAudioInputTurnDetection or null

            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.

            For gpt-realtime-whisper transcription sessions, turn detection must be set to null; VAD is not supported.

            • 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.

              • type: "server_vad"

                Type of turn detection, server_vad to turn on simple Server VAD.

                • "server_vad"

              • create_response: optional boolean

                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.

              • idle_timeout_ms: optional number or null

                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.

              • interrupt_response: optional boolean

                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.

              • prefix_padding_ms: optional number

                Used only for server_vad mode. Amount of audio to include before the VAD detected speech (in milliseconds). Defaults to 300ms.

              • silence_duration_ms: optional number

                Used only for server_vad mode. Duration of silence to detect speech stop (in milliseconds). Defaults to 500ms. With shorter values the model will respond more quickly, but may jump in on short pauses from the user.

              • threshold: optional number

                Used only for server_vad mode. Activation threshold for VAD (0.0 to 1.0), this defaults to 0.5. A higher threshold will require louder audio to activate the model, and thus might perform better in noisy environments.

            • 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.

              • type: "semantic_vad"

                Type of turn detection, semantic_vad to turn on Semantic VAD.

                • "semantic_vad"

              • create_response: optional boolean

                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.

                • "low"

                • "medium"

                • "high"

                • "auto"

              • interrupt_response: optional boolean

                Whether or not to automatically interrupt any ongoing response with output to the default conversation (i.e. conversation of auto) when a VAD start event occurs.

      • include: optional array of "item.input_audio_transcription.logprobs"

        Additional fields to include in server outputs.

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

        • "item.input_audio_transcription.logprobs"

  • type: "session.update"

    The event type, must be session.update.

    • "session.update"

  • event_id: optional string

    Optional client-generated ID used to identify this event. This is an arbitrary string that a client may assign. It will be passed back if there is an error with the event, but the corresponding session.updated event will not include it.

Example

{
  "type": "session.update",
  "session": {
    "type": "realtime",
    "instructions": "You are a creative assistant that helps with design tasks.",
    "tools": [
      {
        "type": "function",
        "name": "display_color_palette",
        "description": "Call this function when a user asks for a color palette.",
        "parameters": {
          "type": "object",
          "properties": {
            "theme": {
              "type": "string",
              "description": "Description of the theme for the color scheme."
            },
            "colors": {
              "type": "array",
              "description": "Array of five hex color codes based on the theme.",
              "items": {
                "type": "string",
                "description": "Hex color code"
              }
            }
          },
          "required": [
            "theme",
            "colors"
          ]
        }
      }
    ],
    "tool_choice": "auto"
  }
}

input_audio_buffer.append

Send this event to append audio bytes to the input audio buffer. The audio buffer is temporary storage you can write to and later commit. A "commit" will create a new user message item in the conversation history from the buffer content and clear the buffer. Input audio transcription (if enabled) will be generated when the buffer is committed.

If VAD is enabled the audio buffer is used to detect speech and the server will decide when to commit. When Server VAD is disabled, you must commit the audio buffer manually. Input audio noise reduction operates on writes to the audio buffer.

The client may choose how much audio to place in each event up to a maximum of 15 MiB, for example streaming smaller chunks from the client may allow the VAD to be more responsive. Unlike most other client events, the server will not send a confirmation response to this event.

Schema

Schema name: RealtimeClientEventInputAudioBufferAppend

  • audio: string

    Base64-encoded audio bytes. This must be in the format specified by the input_audio_format field in the session configuration.

  • type: "input_audio_buffer.append"

    The event type, must be input_audio_buffer.append.

    • "input_audio_buffer.append"

  • event_id: optional string

    Optional client-generated ID used to identify this event.

Example

{
    "event_id": "event_456",
    "type": "input_audio_buffer.append",
    "audio": "Base64EncodedAudioData"
}

input_audio_buffer.commit

Send this event to commit the user input audio buffer, which will create a new user message item in the conversation. This event will produce an error if the input audio buffer is empty. When in Server VAD mode, the client does not need to send this event, the server will commit the audio buffer automatically.

Committing the input audio buffer will trigger input audio transcription (if enabled in session configuration), but it will not create a response from the model. The server will respond with an input_audio_buffer.committed event.

Schema

Schema name: RealtimeClientEventInputAudioBufferCommit

  • type: "input_audio_buffer.commit"

    The event type, must be input_audio_buffer.commit.

    • "input_audio_buffer.commit"

  • event_id: optional string

    Optional client-generated ID used to identify this event.

Example

{
    "event_id": "event_789",
    "type": "input_audio_buffer.commit"
}

input_audio_buffer.clear

Send this event to clear the audio bytes in the buffer. The server will respond with an input_audio_buffer.cleared event.

Schema

Schema name: RealtimeClientEventInputAudioBufferClear

  • type: "input_audio_buffer.clear"

    The event type, must be input_audio_buffer.clear.

    • "input_audio_buffer.clear"

  • event_id: optional string

    Optional client-generated ID used to identify this event.

Example

{
    "event_id": "event_012",
    "type": "input_audio_buffer.clear"
}

conversation.item.create

Add a new Item to the Conversation's context, including messages, function calls, and function call responses. This event can be used both to populate a "history" of the conversation and to add new items mid-stream, but has the current limitation that it cannot populate assistant audio messages.

If successful, the server will emit a conversation.item.added event and, when the item is finalized, a conversation.item.done event. Otherwise, an error event will be sent.

Schema

Schema name: RealtimeClientEventConversationItemCreate

  • item: ConversationItem

    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.

      • content: array of object { text, type }

        The content of the message.

        • text: optional string

          The text content.

        • type: optional "input_text"

          The content type. Always input_text for system messages.

          • "input_text"

      • role: "system"

        The role of the message sender. Always system.

        • "system"

      • type: "message"

        The type of the item. Always message.

        • "message"

      • id: optional string

        The unique ID of the item. This may be provided by the client or generated by the server.

      • object: optional "realtime.item"

        Identifier for the API object being returned - always realtime.item. Optional when creating a new item.

        • "realtime.item"

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

        The status of the item. Has no effect on the conversation.

        • "completed"

        • "incomplete"

        • "in_progress"

    • RealtimeConversationItemUserMessage object { content, role, type, 3 more }

      A user message item in a Realtime conversation.

      • content: array of object { audio, detail, image_url, 3 more }

        The content of the message.

        • audio: optional string

          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.

          • "auto"

          • "low"

          • "high"

        • image_url: optional string

          Base64-encoded image bytes (for input_image) as a data URI. For example data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA.... Supported formats are PNG and JPEG.

        • text: optional string

          The text content (for input_text).

        • transcript: optional string

          Transcript of the audio (for input_audio). This is not sent to the model, but will be attached to the message item for reference.

        • type: optional "input_text" or "input_audio" or "input_image"

          The content type (input_text, input_audio, or input_image).

          • "input_text"

          • "input_audio"

          • "input_image"

      • role: "user"

        The role of the message sender. Always user.

        • "user"

      • type: "message"

        The type of the item. Always message.

        • "message"

      • id: optional string

        The unique ID of the item. This may be provided by the client or generated by the server.

      • object: optional "realtime.item"

        Identifier for the API object being returned - always realtime.item. Optional when creating a new item.

        • "realtime.item"

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

        The status of the item. Has no effect on the conversation.

        • "completed"

        • "incomplete"

        • "in_progress"

    • RealtimeConversationItemAssistantMessage object { content, role, type, 3 more }

      An assistant message item in a Realtime conversation.

      • content: array of object { audio, text, transcript, type }

        The content of the message.

        • audio: optional string

          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.

        • text: optional string

          The text content.

        • transcript: optional string

          The transcript of the audio content, this will always be present if the output type is audio.

        • type: optional "output_text" or "output_audio"

          The content type, output_text or output_audio depending on the session output_modalities configuration.

          • "output_text"

          • "output_audio"

      • role: "assistant"

        The role of the message sender. Always assistant.

        • "assistant"

      • type: "message"

        The type of the item. Always message.

        • "message"

      • id: optional string

        The unique ID of the item. This may be provided by the client or generated by the server.

      • object: optional "realtime.item"

        Identifier for the API object being returned - always realtime.item. Optional when creating a new item.

        • "realtime.item"

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

        The status of the item. Has no effect on the conversation.

        • "completed"

        • "incomplete"

        • "in_progress"

    • RealtimeConversationItemFunctionCall object { arguments, name, type, 4 more }

      A function call item in a Realtime conversation.

      • arguments: string

        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}.

      • name: string

        The name of the function being called.

      • type: "function_call"

        The type of the item. Always function_call.

        • "function_call"

      • id: optional string

        The unique ID of the item. This may be provided by the client or generated by the server.

      • call_id: optional string

        The ID of the function call.

      • object: optional "realtime.item"

        Identifier for the API object being returned - always realtime.item. Optional when creating a new item.

        • "realtime.item"

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

        The status of the item. Has no effect on the conversation.

        • "completed"

        • "incomplete"

        • "in_progress"

    • RealtimeConversationItemFunctionCallOutput object { call_id, output, type, 3 more }

      A function call output item in a Realtime conversation.

      • call_id: string

        The ID of the function call this output is for.

      • output: string

        The output of the function call, this is free text and can contain any information or simply be empty.

      • type: "function_call_output"

        The type of the item. Always function_call_output.

        • "function_call_output"

      • id: optional string

        The unique ID of the item. This may be provided by the client or generated by the server.

      • object: optional "realtime.item"

        Identifier for the API object being returned - always realtime.item. Optional when creating a new item.

        • "realtime.item"

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

        The status of the item. Has no effect on the conversation.

        • "completed"

        • "incomplete"

        • "in_progress"

    • RealtimeMcpApprovalResponse object { id, approval_request_id, approve, 2 more }

      A Realtime item responding to an MCP approval request.

      • id: string

        The unique ID of the approval response.

      • approval_request_id: string

        The ID of the approval request being answered.

      • approve: boolean

        Whether the request was approved.

      • type: "mcp_approval_response"

        The type of the item. Always mcp_approval_response.

        • "mcp_approval_response"

      • reason: optional string or null

        Optional reason for the decision.

    • RealtimeMcpListTools object { server_label, tools, type, id }

      A Realtime item listing tools available on an MCP server.

      • server_label: string

        The label of the MCP server.

      • tools: array of object { input_schema, name, annotations, description }

        The tools available on the server.

        • input_schema: unknown

          The JSON schema describing the tool's input.

        • name: string

          The name of the tool.

        • annotations: optional unknown or null

          Additional annotations about the tool.

        • description: optional string or null

          The description of the tool.

      • type: "mcp_list_tools"

        The type of the item. Always mcp_list_tools.

        • "mcp_list_tools"

      • id: optional string

        The unique ID of the list.

    • RealtimeMcpToolCall object { id, arguments, name, 5 more }

      A Realtime item representing an invocation of a tool on an MCP server.

      • id: string

        The unique ID of the tool call.

      • arguments: string

        A JSON string of the arguments passed to the tool.

      • name: string

        The name of the tool that was run.

      • server_label: string

        The label of the MCP server running the tool.

      • type: "mcp_call"

        The type of the item. Always mcp_call.

        • "mcp_call"

      • approval_request_id: optional string or null

        The ID of an associated approval request, if any.

      • error: optional RealtimeMcpProtocolError or RealtimeMcpToolExecutionError or RealtimeMcphttpError or null

        The error from the tool call, if any.

        • RealtimeMcpProtocolError object { code, message, type }

          • code: number

          • message: string

          • type: "protocol_error"

            • "protocol_error"

        • RealtimeMcpToolExecutionError object { message, type }

          • message: string

          • type: "tool_execution_error"

            • "tool_execution_error"

        • RealtimeMcphttpError object { code, message, type }

          • code: number

          • message: string

          • type: "http_error"

            • "http_error"

      • output: optional string or null

        The output from the tool call.

    • RealtimeMcpApprovalRequest object { id, arguments, name, 2 more }

      A Realtime item requesting human approval of a tool invocation.

      • id: string

        The unique ID of the approval request.

      • arguments: string

        A JSON string of arguments for the tool.

      • name: string

        The name of the tool to run.

      • server_label: string

        The label of the MCP server making the request.

      • type: "mcp_approval_request"

        The type of the item. Always mcp_approval_request.

        • "mcp_approval_request"

  • type: "conversation.item.create"

    The event type, must be conversation.item.create.

    • "conversation.item.create"

  • event_id: optional string

    Optional client-generated ID used to identify this event.

  • previous_item_id: optional string

    The ID of the preceding item after which the new item will be inserted. If not set, the new item will be appended to the end of the conversation.

    If set to root, the new item will be added to the beginning of the conversation.

    If set to an existing ID, it allows an item to be inserted mid-conversation. If the ID cannot be found, an error will be returned and the item will not be added.

Example

{
  "type": "conversation.item.create",
  "item": {
    "type": "message",
    "role": "user",
    "content": [
      {
        "type": "input_text",
        "text": "hi"
      }
    ]
  }
}

conversation.item.retrieve

Send this event when you want to retrieve the server's representation of a specific item in the conversation history. This is useful, for example, to inspect user audio after noise cancellation and VAD. The server will respond with a conversation.item.retrieved event, unless the item does not exist in the conversation history, in which case the server will respond with an error.

Schema

Schema name: RealtimeClientEventConversationItemRetrieve

  • item_id: string

    The ID of the item to retrieve.

  • type: "conversation.item.retrieve"

    The event type, must be conversation.item.retrieve.

    • "conversation.item.retrieve"

  • event_id: optional string

    Optional client-generated ID used to identify this event.

Example

{
    "event_id": "event_901",
    "type": "conversation.item.retrieve",
    "item_id": "item_003"
}

conversation.item.truncate

Send this event to truncate a previous assistant message’s audio. The server will produce audio faster than realtime, so this event is useful when the user interrupts to truncate audio that has already been sent to the client but not yet played. This will synchronize the server's understanding of the audio with the client's playback.

Truncating audio will delete the server-side text transcript to ensure there is not text in the context that hasn't been heard by the user.

If successful, the server will respond with a conversation.item.truncated event.

Schema

Schema name: RealtimeClientEventConversationItemTruncate

  • audio_end_ms: number

    Inclusive duration up to which audio is truncated, in milliseconds. If the audio_end_ms is greater than the actual audio duration, the server will respond with an error.

  • content_index: number

    The index of the content part to truncate. Set this to 0.

  • item_id: string

    The ID of the assistant message item to truncate. Only assistant message items can be truncated.

  • type: "conversation.item.truncate"

    The event type, must be conversation.item.truncate.

    • "conversation.item.truncate"

  • event_id: optional string

    Optional client-generated ID used to identify this event.

Example

{
    "event_id": "event_678",
    "type": "conversation.item.truncate",
    "item_id": "item_002",
    "content_index": 0,
    "audio_end_ms": 1500
}

conversation.item.delete

Send this event when you want to remove any item from the conversation history. The server will respond with a conversation.item.deleted event, unless the item does not exist in the conversation history, in which case the server will respond with an error.

Schema

Schema name: RealtimeClientEventConversationItemDelete

  • item_id: string

    The ID of the item to delete.

  • type: "conversation.item.delete"

    The event type, must be conversation.item.delete.

    • "conversation.item.delete"

  • event_id: optional string

    Optional client-generated ID used to identify this event.

Example

{
    "event_id": "event_901",
    "type": "conversation.item.delete",
    "item_id": "item_003"
}

response.create

This event instructs the server to create a Response, which means triggering model inference. When in Server VAD mode, the server will create Responses automatically.

A Response will include at least one Item, and may have two, in which case the second will be a function call. These Items will be appended to the conversation history by default.

The server will respond with a response.created event, events for Items and content created, and finally a response.done event to indicate the Response is complete.

The response.create event includes inference configuration like instructions and tools. If these are set, they will override the Session's configuration for this Response only.

Responses can be created out-of-band of the default Conversation, meaning that they can have arbitrary input, and it's possible to disable writing the output to the Conversation. Only one Response can write to the default Conversation at a time, but otherwise multiple Responses can be created in parallel. The metadata field is a good way to disambiguate multiple simultaneous Responses.

Clients can set conversation to none to create a Response that does not write to the default Conversation. Arbitrary input can be provided with the input field, which is an array accepting raw Items and references to existing Items.

Schema

Schema name: RealtimeClientEventResponseCreate

  • type: "response.create"

    The event type, must be response.create.

    • "response.create"

  • event_id: optional string

    Optional client-generated ID used to identify this event.

  • response: optional RealtimeResponseCreateParams

    Create a new Realtime response with these parameters

    • audio: optional RealtimeResponseCreateAudioOutput

      Configuration for audio input and output.

      • output: optional object { format, voice }

        • format: optional RealtimeAudioFormats

          The format of the output audio.

          • PCMAudio object { rate, type }

            The PCM audio format. Only a 24kHz sample rate is supported.

            • rate: optional 24000

              The sample rate of the audio. Always 24000.

              • 24000

            • type: optional "audio/pcm"

              The audio format. Always audio/pcm.

              • "audio/pcm"

          • PCMUAudio object { type }

            The G.711 μ-law format.

            • type: optional "audio/pcmu"

              The audio format. Always audio/pcmu.

              • "audio/pcmu"

          • PCMAAudio object { type }

            The G.711 A-law format.

            • type: optional "audio/pcma"

              The audio format. Always audio/pcma.

              • "audio/pcma"

        • 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.

          • string

          • "alloy" or "ash" or "ballad" or 7 more

            • "alloy"

            • "ash"

            • "ballad"

            • "coral"

            • "echo"

            • "sage"

            • "shimmer"

            • "verse"

            • "marin"

            • "cedar"

          • ID object { id }

            Custom voice reference.

            • id: string

              The custom voice ID, e.g. voice_1234.

    • conversation: optional string or "auto" or "none"

      Controls which conversation the response is added to. Currently supports auto and none, with auto as the default value. The auto value means that the contents of the response will be added to the default conversation. Set this to none to create an out-of-band response which will not add items to default conversation.

      • string

      • "auto" or "none"

        Controls which conversation the response is added to. Currently supports auto and none, with auto as the default value. The auto value means that the contents of the response will be added to the default conversation. Set this to none to create an out-of-band response which will not add items to default conversation.

        • "auto"

        • "none"

    • input: optional array of ConversationItem

      Input items to include in the prompt for the model. Using this field creates a new context for this Response instead of using the default conversation. An empty array [] will clear the context for this Response. Note that this can include references to items that previously appeared in the session using their id.

      • 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.

        • content: array of object { text, type }

          The content of the message.

          • text: optional string

            The text content.

          • type: optional "input_text"

            The content type. Always input_text for system messages.

            • "input_text"

        • role: "system"

          The role of the message sender. Always system.

          • "system"

        • type: "message"

          The type of the item. Always message.

          • "message"

        • id: optional string

          The unique ID of the item. This may be provided by the client or generated by the server.

        • object: optional "realtime.item"

          Identifier for the API object being returned - always realtime.item. Optional when creating a new item.

          • "realtime.item"

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

          The status of the item. Has no effect on the conversation.

          • "completed"

          • "incomplete"

          • "in_progress"

      • RealtimeConversationItemUserMessage object { content, role, type, 3 more }

        A user message item in a Realtime conversation.

        • content: array of object { audio, detail, image_url, 3 more }

          The content of the message.

          • audio: optional string

            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.

            • "auto"

            • "low"

            • "high"

          • image_url: optional string

            Base64-encoded image bytes (for input_image) as a data URI. For example data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA.... Supported formats are PNG and JPEG.

          • text: optional string

            The text content (for input_text).

          • transcript: optional string

            Transcript of the audio (for input_audio). This is not sent to the model, but will be attached to the message item for reference.

          • type: optional "input_text" or "input_audio" or "input_image"

            The content type (input_text, input_audio, or input_image).

            • "input_text"

            • "input_audio"

            • "input_image"

        • role: "user"

          The role of the message sender. Always user.

          • "user"

        • type: "message"

          The type of the item. Always message.

          • "message"

        • id: optional string

          The unique ID of the item. This may be provided by the client or generated by the server.

        • object: optional "realtime.item"

          Identifier for the API object being returned - always realtime.item. Optional when creating a new item.

          • "realtime.item"

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

          The status of the item. Has no effect on the conversation.

          • "completed"

          • "incomplete"

          • "in_progress"

      • RealtimeConversationItemAssistantMessage object { content, role, type, 3 more }

        An assistant message item in a Realtime conversation.

        • content: array of object { audio, text, transcript, type }

          The content of the message.

          • audio: optional string

            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.

          • text: optional string

            The text content.

          • transcript: optional string

            The transcript of the audio content, this will always be present if the output type is audio.

          • type: optional "output_text" or "output_audio"

            The content type, output_text or output_audio depending on the session output_modalities configuration.

            • "output_text"

            • "output_audio"

        • role: "assistant"

          The role of the message sender. Always assistant.

          • "assistant"

        • type: "message"

          The type of the item. Always message.

          • "message"

        • id: optional string

          The unique ID of the item. This may be provided by the client or generated by the server.

        • object: optional "realtime.item"

          Identifier for the API object being returned - always realtime.item. Optional when creating a new item.

          • "realtime.item"

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

          The status of the item. Has no effect on the conversation.

          • "completed"

          • "incomplete"

          • "in_progress"

      • RealtimeConversationItemFunctionCall object { arguments, name, type, 4 more }

        A function call item in a Realtime conversation.

        • arguments: string

          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}.

        • name: string

          The name of the function being called.

        • type: "function_call"

          The type of the item. Always function_call.

          • "function_call"

        • id: optional string

          The unique ID of the item. This may be provided by the client or generated by the server.

        • call_id: optional string

          The ID of the function call.

        • object: optional "realtime.item"

          Identifier for the API object being returned - always realtime.item. Optional when creating a new item.

          • "realtime.item"

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

          The status of the item. Has no effect on the conversation.

          • "completed"

          • "incomplete"

          • "in_progress"

      • RealtimeConversationItemFunctionCallOutput object { call_id, output, type, 3 more }

        A function call output item in a Realtime conversation.

        • call_id: string

          The ID of the function call this output is for.

        • output: string

          The output of the function call, this is free text and can contain any information or simply be empty.

        • type: "function_call_output"

          The type of the item. Always function_call_output.

          • "function_call_output"

        • id: optional string

          The unique ID of the item. This may be provided by the client or generated by the server.

        • object: optional "realtime.item"

          Identifier for the API object being returned - always realtime.item. Optional when creating a new item.

          • "realtime.item"

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

          The status of the item. Has no effect on the conversation.

          • "completed"

          • "incomplete"

          • "in_progress"

      • RealtimeMcpApprovalResponse object { id, approval_request_id, approve, 2 more }

        A Realtime item responding to an MCP approval request.

        • id: string

          The unique ID of the approval response.

        • approval_request_id: string

          The ID of the approval request being answered.

        • approve: boolean

          Whether the request was approved.

        • type: "mcp_approval_response"

          The type of the item. Always mcp_approval_response.

          • "mcp_approval_response"

        • reason: optional string or null

          Optional reason for the decision.

      • RealtimeMcpListTools object { server_label, tools, type, id }

        A Realtime item listing tools available on an MCP server.

        • server_label: string

          The label of the MCP server.

        • tools: array of object { input_schema, name, annotations, description }

          The tools available on the server.

          • input_schema: unknown

            The JSON schema describing the tool's input.

          • name: string

            The name of the tool.

          • annotations: optional unknown or null

            Additional annotations about the tool.

          • description: optional string or null

            The description of the tool.

        • type: "mcp_list_tools"

          The type of the item. Always mcp_list_tools.

          • "mcp_list_tools"

        • id: optional string

          The unique ID of the list.

      • RealtimeMcpToolCall object { id, arguments, name, 5 more }

        A Realtime item representing an invocation of a tool on an MCP server.

        • id: string

          The unique ID of the tool call.

        • arguments: string

          A JSON string of the arguments passed to the tool.

        • name: string

          The name of the tool that was run.

        • server_label: string

          The label of the MCP server running the tool.

        • type: "mcp_call"

          The type of the item. Always mcp_call.

          • "mcp_call"

        • approval_request_id: optional string or null

          The ID of an associated approval request, if any.

        • error: optional RealtimeMcpProtocolError or RealtimeMcpToolExecutionError or RealtimeMcphttpError or null

          The error from the tool call, if any.

          • RealtimeMcpProtocolError object { code, message, type }

            • code: number

            • message: string

            • type: "protocol_error"

              • "protocol_error"

          • RealtimeMcpToolExecutionError object { message, type }

            • message: string

            • type: "tool_execution_error"

              • "tool_execution_error"

          • RealtimeMcphttpError object { code, message, type }

            • code: number

            • message: string

            • type: "http_error"

              • "http_error"

        • output: optional string or null

          The output from the tool call.

      • RealtimeMcpApprovalRequest object { id, arguments, name, 2 more }

        A Realtime item requesting human approval of a tool invocation.

        • id: string

          The unique ID of the approval request.

        • arguments: string

          A JSON string of arguments for the tool.

        • name: string

          The name of the tool to run.

        • server_label: string

          The label of the MCP server making the request.

        • type: "mcp_approval_request"

          The type of the item. Always mcp_approval_request.

          • "mcp_approval_request"

    • instructions: optional string

      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.

      • number

      • "inf"

        • "inf"

    • metadata: optional Metadata or null

      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.

    • 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.

      • "text"

      • "audio"

    • parallel_tool_calls: optional boolean

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

    • prompt: optional ResponsePrompt or null

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

      • id: string

        The unique identifier of the prompt template to use.

      • variables: optional map[string or ResponseInputText or ResponseInputImage or ResponseInputFile] or null

        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.

        • string

        • ResponseInputText object { text, type, prompt_cache_breakpoint }

          A text input to the model.

          • text: string

            The text input to the model.

          • type: "input_text"

            The type of the input item. Always input_text.

            • "input_text"

          • prompt_cache_breakpoint: optional object { mode }

            Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

            • mode: "explicit"

              The breakpoint mode. Always explicit.

              • "explicit"

        • ResponseInputImage object { detail, type, file_id, 2 more }

          An image input to the model. Learn about image inputs.

          • detail: ImageDetail

            The detail level of the image to be sent to the model. One of high, low, auto, or original. Defaults to auto.

            • "low"

            • "high"

            • "auto"

            • "original"

          • type: "input_image"

            The type of the input item. Always input_image.

            • "input_image"

          • file_id: optional string or null

            The ID of the file to be sent to the model.

          • image_url: optional string or null

            The URL of the image to be sent to the model. A fully qualified URL or base64 encoded image in a data URL.

          • prompt_cache_breakpoint: optional object { mode }

            Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

            • mode: "explicit"

              The breakpoint mode. Always explicit.

              • "explicit"

        • ResponseInputFile object { type, detail, file_data, 4 more }

          A file input to the model.

          • type: "input_file"

            The type of the input item. Always input_file.

            • "input_file"

          • detail: optional "auto" or "low" or "high"

            The detail level of the file to be sent to the model. Use auto to let the system select the detail level; for GPT-5.6 and later models, auto uses high-quality rendering, which may increase input token usage. Use low for lower-cost rendering, or high to render the file at higher quality. Defaults to auto.

            • "auto"

            • "low"

            • "high"

          • file_data: optional string

            The content of the file to be sent to the model.

          • file_id: optional string or null

            The ID of the file to be sent to the model.

          • file_url: optional string

            The URL of the file to be sent to the model.

          • filename: optional string

            The name of the file to be sent to the model.

          • prompt_cache_breakpoint: optional object { mode }

            Marks the exact end of a reusable prompt prefix. The breakpoint inherits its TTL from the request's prompt_cache_options.ttl; the boundary is not rounded to a token block.

            • mode: "explicit"

              The breakpoint mode. Always explicit.

              • "explicit"

      • version: optional string or null

        Optional version of the prompt template.

    • reasoning: optional RealtimeReasoning

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

      • effort: optional RealtimeReasoningEffort

        Constrains effort on reasoning for reasoning-capable Realtime models such as gpt-realtime-2.

        • "minimal"

        • "low"

        • "medium"

        • "high"

        • "xhigh"

    • tool_choice: optional ToolChoiceOptions or ToolChoiceFunction or ToolChoiceMcp

      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.

        • "none"

        • "auto"

        • "required"

      • ToolChoiceFunction object { name, type }

        Use this option to force the model to call a specific function.

        • name: string

          The name of the function to call.

        • type: "function"

          For function calling, the type is always function.

          • "function"

      • ToolChoiceMcp object { server_label, type, name }

        Use this option to force the model to call a specific tool on a remote MCP server.

        • server_label: string

          The label of the MCP server to use.

        • type: "mcp"

          For MCP tools, the type is always mcp.

          • "mcp"

        • name: optional string or null

          The name of the tool to call on the server.

    • tools: optional array of RealtimeFunctionTool or object { server_label, type, allowed_callers, 9 more }

      Tools available to the model.

      • RealtimeFunctionTool object { description, name, parameters, type }

        • description: optional string

          The description of the function, including guidance on when and how to call it, and guidance about what to tell the user when calling (if anything).

        • name: optional string

          The name of the function.

        • parameters: optional unknown

          Parameters of the function in JSON Schema.

        • type: optional "function"

          The type of the tool, i.e. function.

          • "function"

      • McpTool object { server_label, type, allowed_callers, 9 more }

        Give the model access to additional tools via remote Model Context Protocol (MCP) servers. Learn more about MCP.

        • server_label: string

          A label for this MCP server, used to identify it in tool calls.

        • type: "mcp"

          The type of the MCP tool. Always mcp.

          • "mcp"

        • allowed_callers: optional array of "direct" or "programmatic" or null

          The tool invocation context(s).

          • "direct"

          • "programmatic"

        • allowed_tools: optional array of string or object { read_only, tool_names } or null

          List of allowed tool names or a filter object.

          • McpAllowedTools = array of string

            A string array of allowed tool names

          • McpToolFilter object { read_only, tool_names }

            A filter object to specify which tools are allowed.

            • read_only: optional boolean

              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.

            • tool_names: optional array of string

              List of allowed tool names.

        • authorization: optional string

          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 more

          Identifier for service connectors, like those available in ChatGPT. One of server_url, connector_id, or tunnel_id must be provided. Learn more about service connectors here.

          This field is deprecated for models released after September 1, 2026. Use server_url to connect to a remote MCP server, or tunnel_id to connect through a Secure MCP Tunnel.

          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

          • "connector_dropbox"

          • "connector_gmail"

          • "connector_googlecalendar"

          • "connector_googledrive"

          • "connector_microsoftteams"

          • "connector_outlookcalendar"

          • "connector_outlookemail"

          • "connector_sharepoint"

        • defer_loading: optional boolean

          Whether this MCP tool is deferred and discovered via tool search.

        • headers: optional map[string] or null

          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" or null

          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.

            • always: optional object { read_only, tool_names }

              A filter object to specify which tools are allowed.

              • read_only: optional boolean

                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.

              • tool_names: optional array of string

                List of allowed tool names.

            • never: optional object { read_only, tool_names }

              A filter object to specify which tools are allowed.

              • read_only: optional boolean

                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.

              • tool_names: optional array of string

                List of allowed tool names.

          • McpToolApprovalSetting = "always" or "never"

            Specify a single approval policy for all tools. One of always or never. When set to always, all tools will require approval. When set to never, all tools will not require approval.

            • "always"

            • "never"

        • server_description: optional string

          Optional description of the MCP server, used to provide more context.

        • server_url: optional string

          The URL for the MCP server. One of server_url, connector_id, or tunnel_id must be provided.

        • tunnel_id: optional string

          The Secure MCP Tunnel ID to use instead of a direct server URL. One of server_url, connector_id, or tunnel_id must be provided.

Example

// Trigger a response with the default Conversation and no special parameters
{
  "type": "response.create",
}

// Trigger an out-of-band response that does not write to the default Conversation
{
  "type": "response.create",
  "response": {
    "instructions": "Provide a concise answer.",
    "tools": [], // clear any session tools
    "conversation": "none",
    "output_modalities": ["text"],
    "metadata": {
      "response_purpose": "summarization"
    },
    "input": [
      {
        "type": "item_reference",
        "id": "item_12345"
      },
      {
        "type": "message",
        "role": "user",
        "content": [
          {
            "type": "input_text",
            "text": "Summarize the above message in one sentence."
          }
        ]
      }
    ]
  }
}

response.cancel

Send this event to cancel an in-progress response. The server will respond with a response.done event with a status of response.status=cancelled. If there is no response to cancel, the server will respond with an error. It's safe to call response.cancel even if no response is in progress, an error will be returned the session will remain unaffected.

Schema

Schema name: RealtimeClientEventResponseCancel

  • type: "response.cancel"

    The event type, must be response.cancel.

    • "response.cancel"

  • event_id: optional string

    Optional client-generated ID used to identify this event.

  • response_id: optional string

    A specific response ID to cancel - if not provided, will cancel an in-progress response in the default conversation.

Example

{
    "type": "response.cancel",
    "response_id": "resp_12345"
}

output_audio_buffer.clear

WebRTC/SIP Only: Emit to cut off the current audio response. This will trigger the server to stop generating audio and emit a output_audio_buffer.cleared event. This event should be preceded by a response.cancel client event to stop the generation of the current response. Learn more.

Schema

Schema name: RealtimeClientEventOutputAudioBufferClear

  • type: "output_audio_buffer.clear"

    The event type, must be output_audio_buffer.clear.

    • "output_audio_buffer.clear"

  • event_id: optional string

    The unique ID of the client event used for error handling.

Example

{
    "event_id": "optional_client_event_id",
    "type": "output_audio_buffer.clear"
}