OpenAI Realtime Transcription

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Event Logs

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\n\n \n \n",[],[192,284],["D",1756839724543],[228],[192],"39f932d0-8935-11f0-98af-0224a6c84d84","realtime.md","39f93366-8935-11f0-98af-0224a6c84d84","38edc7a2-8935-11f0-98af-0224a6c84d84",28,["D",1756952214731],"Realtime API\n============\n\nBuild low-latency, multimodal LLM applications with the Realtime API.\n\nThe OpenAI Realtime API enables low-latency communication with [models](/docs/models) that natively support speech-to-speech interactions as well as multimodal inputs (audio, images, and text) and outputs (audio and text). These APIs can also be used for [realtime audio transcription](/docs/guides/realtime-transcription).\n\nVoice agents\n------------\n\nOne of the most common use cases for the Realtime API is building voice agents for speech-to-speech model interactions in the browser. Our recommended starting point for these types of applications is the [Agents SDK for TypeScript](https://openai.github.io/openai-agents-js/guides/voice-agents/), which uses a [WebRTC connection](/docs/guides/realtime-webrtc) to the Realtime model in the browser, and [WebSocket](/docs/guides/realtime-websocket) when used on the server.\n\n```js\nimport { RealtimeAgent, RealtimeSession } from \"@openai/agents/realtime\";\n\nconst agent = new RealtimeAgent({\n name: \"Assistant\",\n instructions: \"You are a helpful assistant.\",\n});\n\nconst session = new RealtimeSession(agent);\n\n// Automatically connects your microphone and audio output\nawait session.connect({\n apiKey: \"\",\n});\n```\n\n[\n\nVoice Agent Quickstart\n\nFollow the voice agent quickstart to build Realtime agents in the browser.\n\n](https://openai.github.io/openai-agents-js/guides/voice-agents/quickstart/)\n\nTo use the Realtime API directly outside the context of voice agents, check out the other connection options below.\n\nConnection methods\n------------------\n\nWhile building [voice agents with the Agents SDK](https://openai.github.io/openai-agents-js/guides/voice-agents/) is the fastest path to one specific type of application, the Realtime API provides an entire suite of flexible tools for a variety of use cases.\n\nThere are three primary supported interfaces for the Realtime API:\n\n[\n\nWebRTC connection\n\nIdeal for browser and client-side interactions with a Realtime model.\n\n](/docs/guides/realtime-webrtc)[\n\nWebSocket connection\n\nIdeal for middle tier server-side applications with consistent low-latency network connections.\n\n](/docs/guides/realtime-websocket)[\n\nSIP connection\n\nIdeal for VoIP telephony connections.\n\n](/docs/guides/realtime-sip)\n\nDepending on how you'd like to connect to a Realtime model, check out one of the connection guides above to get started. You'll learn how to initialize a Realtime session, and how to interact with a Realtime model using client and server events.\n\nAPI Usage\n---------\n\nOnce connected to a realtime model using one of the methods above, learn how to interact with the model in these usage guides.\n\n* **[Prompting guide](/docs/guides/realtime-models-prompting):** learn tips and best practices for prompting and steering Realtime models.\n* **[Inputs and outputs](/docs/guides/realtime-inputs-outputs):** Learn how to pass audio, text, and image inputs to the model, and how to receive audio and text back.\n* **[Managing conversations](/docs/guides/realtime-conversations):** Learn about the Realtime session lifecycle and the key events that happen during a conversation.\n* **[Webhooks and server-side controls](/docs/guides/realtime-server-controls):** Learn how you can control a Realtime session on the server to call tools and implement guardrails.\n* **[Function calling](/docs/guides/realtime-function-calling):** Give the realtime model access to call custom code in your own application when appropriate.\n* **[MCP servers](/docs/guides/realtime-mcp):** Give realtime models access to new capabilities via Model Context Protocol (MCP) servers.\n* **[Realtime audio transcription](/docs/guides/realtime-transcription):** Transcribe audio streams in real time over a WebSocket connection.\n\nWas this page useful?",[],[197,304,296],"guides","39b0a236-8935-11f0-8c91-0224a6c84d84","realtime-websocket.md","39b0a2ea-8935-11f0-8c91-0224a6c84d84",27,["D",1756952213879],"Realtime API with WebSocket\n===========================\n\nConnect to the Realtime API using WebSockets on a server.\n\n[WebSockets](https://developer.mozilla.org/en-US/docs/Web/API/WebSockets_API) are a broadly supported API for realtime data transfer, and a great choice for connecting to the OpenAI Realtime API in server-to-server applications. For browser and mobile clients, we recommend connecting via [WebRTC](/docs/guides/realtime-webrtc).\n\nIn a server-to-server integration with Realtime, your backend system will connect via WebSocket directly to the Realtime API. You can use a [standard API key](/settings/organization/api-keys) to authenticate this connection, since the token will only be available on your secure backend server.\n\n![connect directly to realtime API](https://openaidevs.retool.com/api/file/464d4334-c467-4862-901b-d0c6847f003a)\n\nConnect via WebSocket\n---------------------\n\nBelow are several examples of connecting via WebSocket to the Realtime API. In addition to using the WebSocket URL below, you will also need to pass an authentication header using your OpenAI API key.\n\nIt is possible to use WebSocket in browsers with an ephemeral API token as shown in the [WebRTC connection guide](/docs/guides/realtime-webrtc), but if you are connecting from a client like a browser or mobile app, WebRTC will be a more robust solution in most cases.\n\nws module (Node.js)\n\nConnect using the ws module (Node.js)\n\n```javascript\nimport WebSocket from \"ws\";\n\nconst url = \"wss://api.openai.com/v1/realtime?model=gpt-realtime\";\nconst ws = new WebSocket(url, {\n headers: {\n Authorization: \"Bearer \" + process.env.OPENAI_API_KEY,\n },\n});\n\nws.on(\"open\", function open() {\n console.log(\"Connected to server.\");\n});\n\nws.on(\"message\", function incoming(message) {\n console.log(JSON.parse(message.toString()));\n});\n```\n\nwebsocket-client (Python)\n\nConnect with websocket-client (Python)\n\n```python\n# example requires websocket-client library:\n# pip install websocket-client\n\nimport os\nimport json\nimport websocket\n\nOPENAI_API_KEY = os.environ.get(\"OPENAI_API_KEY\")\n\nurl = \"wss://api.openai.com/v1/realtime?model=gpt-realtime\"\nheaders = [\"Authorization: Bearer \" + OPENAI_API_KEY]\n\ndef on_open(ws):\n print(\"Connected to server.\")\n\ndef on_message(ws, message):\n data = json.loads(message)\n print(\"Received event:\", json.dumps(data, indent=2))\n\nws = websocket.WebSocketApp(\n url,\n header=headers,\n on_open=on_open,\n on_message=on_message,\n)\n\nws.run_forever()\n```\n\nWebSocket (browsers)\n\nConnect with standard WebSocket (browsers)\n\n```javascript\n/*\nNote that in client-side environments like web browsers, we recommend\nusing WebRTC instead. It is possible, however, to use the standard \nWebSocket interface in browser-like environments like Deno and \nCloudflare Workers.\n*/\n\nconst ws = new WebSocket(\n \"wss://api.openai.com/v1/realtime?model=gpt-realtime\",\n [\n \"realtime\",\n // Auth\n \"openai-insecure-api-key.\" + OPENAI_API_KEY, \n // Optional\n \"openai-organization.\" + OPENAI_ORG_ID,\n \"openai-project.\" + OPENAI_PROJECT_ID,\n ]\n);\n\nws.on(\"open\", function open() {\n console.log(\"Connected to server.\");\n});\n\nws.on(\"message\", function incoming(message) {\n console.log(message.data);\n});\n```\n\nSending and receiving events\n----------------------------\n\nRealtime API sessions are managed using a combination of [client-sent events](/docs/api-reference/realtime_client_events/session) emitted by you as the developer, and [server-sent events](/docs/api-reference/realtime_server_events/error) created by the Realtime API to indicate session lifecycle events.\n\nOver a WebSocket, you will both send and receive JSON-serialized events as strings of text, as in this Node.js example below (the same principles apply for other WebSocket libraries):\n\n```javascript\nimport WebSocket from \"ws\";\n\nconst url = \"wss://api.openai.com/v1/realtime?model=gpt-realtime\";\nconst ws = new WebSocket(url, {\n headers: {\n Authorization: \"Bearer \" + process.env.OPENAI_API_KEY,\n },\n});\n\nws.on(\"open\", function open() {\n console.log(\"Connected to server.\");\n\n // Send client events over the WebSocket once connected\n ws.send(\n JSON.stringify({\n type: \"session.update\",\n session: {\n type: \"realtime\",\n instructions: \"Be extra nice today!\",\n },\n })\n );\n});\n\n// Listen for and parse server events\nws.on(\"message\", function incoming(message) {\n console.log(JSON.parse(message.toString()));\n});\n```\n\nThe WebSocket interface is perhaps the lowest-level interface available to interact with a Realtime model, where you will be responsible for both sending and processing Base64-encoded audio chunks over the socket connection.\n\nTo learn how to send and receive audio over Websockets, refer to the [Realtime conversations guide](/docs/guides/realtime-conversations#handling-audio-with-websockets).\n\nWas this page useful?",[],[197,304,306],"3907d908-8935-11f0-9ad7-0224a6c84d84","realtime-webrtc.md","3907d9a8-8935-11f0-9ad7-0224a6c84d84",25,["D",1756952213152],"Realtime API with WebRTC\n========================\n\nConnect to the Realtime API using WebRTC.\n\n[WebRTC](https://webrtc.org/) is a powerful set of standard interfaces for building real-time applications. The OpenAI Realtime API supports connecting to realtime models through a WebRTC peer connection.\n\nFor browser-based speech-to-speech voice applications, we recommend starting with the [Agents SDK for TypeScript](https://openai.github.io/openai-agents-js/guides/voice-agents/quickstart/), which provides higher-level helpers and APIs for managing Realtime sessions. The WebRTC interface is powerful and flexible, but lower level than the Agents SDK.\n\nWhen connecting to a Realtime model from the client (like a web browser or mobile device), we recommend using WebRTC rather than WebSocket for more consistent performance.\n\nOverview\n--------\n\nConnecting to the Realtime API via WebRTC requires a backend server that can generate an ephemeral API key that can be safely used in a client-side environment (like a browser).\n\nThe process for initializing a WebRTC connection using an ephemeral API key is as follows:\n\n1. A browser makes a request to a developer-controlled server to mint an ephemeral API key.\n2. The developer's server uses a [standard API key](/settings/organization/api-keys) to request an ephemeral key from the [OpenAI REST API](/docs/api-reference/realtime-sessions), and returns that new key to the browser.\n3. The browser uses the ephemeral key to authenticate a session directly with the OpenAI Realtime API as a [WebRTC peer connection](https://developer.mozilla.org/en-US/docs/Web/API/RTCPeerConnection).\n\n![connect to realtime via WebRTC](https://openaidevs.retool.com/api/file/55b47800-9aaf-48b9-90d5-793ab227ddd3)\n\nInitializing a peer connection\n------------------------------\n\nIn the browser, you can use standard WebRTC APIs to initialize a peer connection object. The code below requires a server-side endpoint that generates an ephemeral API token (which we'll see code for in a moment).\n\n```javascript\n// Get a session token for OpenAI Realtime API\nconst tokenResponse = await fetch(\"/token\");\nconst data = await tokenResponse.json();\nconst EPHEMERAL_KEY = data.value;\n\n// Create a peer connection\nconst pc = new RTCPeerConnection();\n\n// Set up to play remote audio from the model\naudioElement.current = document.createElement(\"audio\");\naudioElement.current.autoplay = true;\npc.ontrack = (e) => (audioElement.current.srcObject = e.streams[0]);\n\n// Add local audio track for microphone input in the browser\nconst ms = await navigator.mediaDevices.getUserMedia({\n audio: true,\n});\npc.addTrack(ms.getTracks()[0]);\n\n// Set up data channel for sending and receiving events\nconst dc = pc.createDataChannel(\"oai-events\");\n\n// Start the session using the Session Description Protocol (SDP)\nconst offer = await pc.createOffer();\nawait pc.setLocalDescription(offer);\n\nconst baseUrl = \"https://api.openai.com/v1/realtime/calls\";\nconst model = \"gpt-realtime\";\nconst sdpResponse = await fetch(`${baseUrl}?model=${model}`, {\n method: \"POST\",\n body: offer.sdp,\n headers: {\n Authorization: `Bearer ${EPHEMERAL_KEY}`,\n \"Content-Type\": \"application/sdp\",\n },\n});\n\nconst answer = {\n type: \"answer\",\n sdp: await sdpResponse.text(),\n};\nawait pc.setRemoteDescription(answer);\n```\n\nThe WebRTC APIs provide rich controls for handling media streams and input devices. For more guidance on building user interfaces on top of WebRTC, [refer to the docs on MDN](https://developer.mozilla.org/en-US/docs/Web/API/WebRTC_API).\n\nSee the next section for details on implementing the server-side component that powers the `/token` endpoint used in the code above.\n\nCreating an ephemeral token\n---------------------------\n\nTo create an ephemeral token to use on the client-side, you will need to build a small server-side application (or integrate with an existing one) to make an [OpenAI REST API](/docs/api-reference/realtime-sessions) request for an ephemeral key. You will use a [standard API key](/settings/organization/api-keys) to authenticate this request on your backend server.\n\nBelow is an example of a simple Node.js [express](https://expressjs.com/) server which mints an ephemeral API key using the REST API:\n\n```javascript\nimport express from \"express\";\n\nconst app = express();\n\nconst sessionConfig = JSON.stringify({\n session: {\n type: \"realtime\",\n model: \"gpt-realtime\",\n audio: {\n output: {\n voice: \"marin\",\n },\n },\n },\n});\n\n// An endpoint which would work with the client code above - it returns\n// the contents of a REST API request to this protected endpoint\napp.get(\"/token\", async (req, res) => {\n try {\n const response = await fetch(\n \"https://api.openai.com/v1/realtime/client_secrets\",\n {\n method: \"POST\",\n headers: {\n Authorization: `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: sessionConfig,\n }\n );\n\n const data = await response.json();\n res.json(data);\n } catch (error) {\n console.error(\"Token generation error:\", error);\n res.status(500).json({ error: \"Failed to generate token\" });\n }\n});\n\napp.listen(3000);\n```\n\nYou can create a server endpoint like this one on any platform that can send and receive HTTP requests. Just ensure that **you only use standard OpenAI API keys on the server, not in the browser.**\n\nSending and receiving events\n----------------------------\n\nRealtime API sessions are managed using a combination of [client-sent events](/docs/api-reference/realtime_client_events/session) emitted by you as the developer, and [server-sent events](/docs/api-reference/realtime_server_events/error) created by the Realtime API to indicate session lifecycle events.\n\nWhen connecting to a Realtime model via WebRTC, you will not have to handle audio events to hear model responses in the same granular way you must with [WebSockets](/docs/guides/realtime-websocket). The WebRTC peer connection object, if configured as above, will do much of that work for you.\n\nTo send and receive other client and server events, you will use the WebRTC peer connection's [data channel](https://developer.mozilla.org/en-US/docs/Web/API/WebRTC_API/Using_data_channels).\n\n```javascript\n// This is the data channel set up in the browser code above...\nconst dc = pc.createDataChannel(\"oai-events\");\n\n// Listen for server events\ndc.addEventListener(\"message\", (e) => {\n const event = JSON.parse(e.data);\n console.log(event);\n});\n\n// Send client events\nconst event = {\n type: \"conversation.item.create\",\n item: {\n type: \"message\",\n role: \"user\",\n content: [\n {\n type: \"input_text\",\n text: \"hello there!\",\n },\n ],\n },\n};\ndc.send(JSON.stringify(event));\n```\n\nTo learn more about managing Realtime conversations, refer to the [Realtime conversations guide](/docs/guides/realtime-conversations).\n\n[\n\nRealtime Console\n\nCheck out the WebRTC Realtime API in this light weight example app.\n\n/Users/emcho/devel/openai-realtime-console\n\nWas this page useful?",[],[197,304,314],"397729b6-8935-11f0-9096-0224a6c84d84","realtime-transcription.md","39772a88-8935-11f0-9096-0224a6c84d84",26,["D",1756952213233],"Realtime transcription\n======================\n\nLearn how to transcribe audio in real-time with the Realtime API.\n\nYou can use the Realtime API for transcription-only use cases, either with input from a microphone or from a file. For example, you can use it to generate subtitles or transcripts in real-time. With the transcription-only mode, the model will not generate responses.\n\nIf you want the model to produce responses, you can use the Realtime API in [speech-to-speech conversation mode](/docs/guides/realtime-conversations).\n\nRealtime transcription sessions\n-------------------------------\n\nTo use the Realtime API for transcription, you need to create a transcription session, connecting via [WebSockets](/docs/guides/realtime?use-case=transcription#connect-with-websockets) or [WebRTC](/docs/guides/realtime?use-case=transcription#connect-with-webrtc).\n\nUnlike the regular Realtime API sessions for conversations, the transcription sessions typically don't contain responses from the model.\n\nThe transcription session object is also different from regular Realtime API sessions:\n\n```json\n{\n object: \"realtime.transcription_session\",\n id: string,\n input_audio_format: string,\n input_audio_transcription: [{\n model: string,\n prompt: string,\n language: string\n }],\n turn_detection: {\n type: \"server_vad\",\n threshold: float,\n prefix_padding_ms: integer,\n silence_duration_ms: integer,\n } | null,\n input_audio_noise_reduction: {\n type: \"near_field\" | \"far_field\"\n },\n include: list[string] | null\n}\n```\n\nSome of the additional properties transcription sessions support are:\n\n* `input_audio_transcription.model`: The transcription model to use, currently `gpt-4o-transcribe`, `gpt-4o-mini-transcribe`, and `whisper-1` are supported\n* `input_audio_transcription.prompt`: The prompt to use for the transcription, to guide the model (e.g. \"Expect words related to technology\")\n* `input_audio_transcription.language`: The language to use for the transcription, ideally in ISO-639-1 format (e.g. \"en\", \"fr\"...) to improve accuracy and latency\n* `input_audio_noise_reduction`: The noise reduction configuration to use for the transcription\n* `include`: The list of properties to include in the transcription events\n\nPossible values for the input audio format are: `pcm16` (default), `g711_ulaw` and `g711_alaw`.\n\nYou can find more information about the transcription session object in the [API reference](/docs/api-reference/realtime-sessions/transcription_session_object).\n\nHandling transcriptions\n-----------------------\n\nWhen using the Realtime API for transcription, you can listen for the `conversation.item.input_audio_transcription.delta` and `conversation.item.input_audio_transcription.completed` events.\n\nFor `whisper-1` the `delta` event will contain full turn transcript, same as `completed` event. For `gpt-4o-transcribe` and `gpt-4o-mini-transcribe` the `delta` event will contain incremental transcripts as they are streamed out from the model.\n\nHere is an example transcription delta event:\n\n```json\n{\n \"event_id\": \"event_2122\",\n \"type\": \"conversation.item.input_audio_transcription.delta\",\n \"item_id\": \"item_003\",\n \"content_index\": 0,\n \"delta\": \"Hello,\"\n}\n```\n\nHere is an example transcription completion event:\n\n```json\n{\n \"event_id\": \"event_2122\",\n \"type\": \"conversation.item.input_audio_transcription.completed\",\n \"item_id\": \"item_003\",\n \"content_index\": 0,\n \"transcript\": \"Hello, how are you?\"\n}\n```\n\nNote that ordering between completion events from different speech turns is not guaranteed. You should use `item_id` to match these events to the `input_audio_buffer.committed` events and use `input_audio_buffer.committed.previous_item_id` to handle the ordering.\n\nTo send audio data to the transcription session, you can use the `input_audio_buffer.append` event.\n\nYou have 2 options:\n\n* Use a streaming microphone input\n* Stream data from a wav file\n\nVoice activity detection\n------------------------\n\nThe Realtime API supports automatic voice activity detection (VAD). Enabled by default, VAD will control when the input audio buffer is committed, therefore when transcription begins.\n\nRead more about configuring VAD in our [Voice Activity Detection](/docs/guides/realtime-vad) guide.\n\nYou can also disable VAD by setting the `turn_detection` property to `null`, and control when to commit the input audio on your end.\n\nAdditional configurations\n-------------------------\n\n### Noise reduction\n\nYou can use the `input_audio_noise_reduction` property to configure how to handle noise reduction in the audio stream.\n\nThe possible values are:\n\n* `near_field`: Use near-field noise reduction.\n* `far_field`: Use far-field noise reduction.\n* `null`: Disable noise reduction.\n\nThe default value is `near_field`, and you can disable noise reduction by setting the property to `null`.\n\n### Using logprobs\n\nYou can use the `include` property to include logprobs in the transcription events, using `item.input_audio_transcription.logprobs`.\n\nThose logprobs can be used to calculate the confidence score of the transcription.\n\n```json\n{\n \"type\": \"transcription_session.update\",\n \"input_audio_format\": \"pcm16\",\n \"input_audio_transcription\": {\n \"model\": \"gpt-4o-transcribe\",\n \"prompt\": \"\",\n \"language\": \"\"\n },\n \"turn_detection\": {\n \"type\": \"server_vad\",\n \"threshold\": 0.5,\n \"prefix_padding_ms\": 300,\n \"silence_duration_ms\": 500,\n },\n \"input_audio_noise_reduction\": {\n \"type\": \"near_field\"\n },\n \"include\": [ \n \"item.input_audio_transcription.logprobs\",\n ],\n}\n```\n\nWas this page useful?",[],[197,304,322],"3a3db9b4-8935-11f0-bb34-0224a6c84d84",["D",1756952215183],[],[197,304,32],"38edc6ee-8935-11f0-98af-0224a6c84d84",["D",1756952212978],[222,223,224,225,226],[197,304],["D",1756952212884],[221],[197],"390439f6-8935-11f0-939c-0224a6c84d84","settings.local.json","39043ac8-8935-11f0-939c-0224a6c84d84",24,["D",1756952213071],"{\n \"permissions\": {\n \"allow\": [\n \"Read(//Users/emcho/devel/juberti-demos/realtime/**)\"\n ],\n \"deny\": [],\n \"ask\": []\n }\n}",[],[203,341],["D",1756952212789],[219],[203],["D",1756839724543],{"_97":96,"_104":100},{"_97":96,"_98":-5,"_99":100,"_18":52,"_101":102,"_103":24},{"_13":83,"_31":84,"_41":40,"_85":42,"_86":87,"_88":89,"_90":48,"_91":92,"_93":62,"_94":43,"_95":96},"ctx",{"_76":-5,"_126":388,"_389":390,"_391":392,"_393":24,"_394":24,"_395":24,"_396":56,"_397":-5,"_398":-7,"_399":-7,"_400":60,"_401":24,"_402":24,"_403":-5,"_404":24,"_405":24,"_406":24,"_407":-5},"ENV",{"_359":360,"_361":362,"_363":84,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379,"_380":381,"_382":383,"_384":385,"_386":387},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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