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Apply LLM Gateway to Streaming

Learn how to analyze streaming audio transcripts using LLM Gateway.

For the complete documentation index, see llms.txt

Overview

A Large Language Model (LLM) is a machine learning model that uses natural language processing (NLP) to generate text. LLM Gateway is a unified API that provides access to 25+ models from Claude, GPT, Gemini, and more through a single interface. You can use LLM Gateway to analyze streaming audio transcripts in real time, for example to summarize a live conversation or extract action items as they happen.

By the end of this tutorial, you'll be able to use LLM Gateway to analyze a streaming audio transcript from your microphone.

Here's the full sample code for what you'll build in this tutorial:

python

import pyaudio
import websocket
import json
import threading
import time
import wave
from urllib.parse import urlencode
from datetime import datetime

# --- Configuration ---
YOUR_API_KEY = "<YOUR_API_KEY>"  # Replace with your actual API key

# LLM Gateway Configuration
PROMPT = "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}"
LLM_GATEWAY_CONFIG = {
    "model": "claude-sonnet-4-6",
    "messages": [
        {"role": "user", "content": PROMPT}
    ],
    "max_tokens": 4000
}

CONNECTION_PARAMS = {
    "sample_rate": 16000,
    "format_turns": True,  # Request formatted final transcripts
    "speech_model": "u3-rt-pro",  # USM 3 Pro model
    "llm_gateway": json.dumps(LLM_GATEWAY_CONFIG)  # LLM Gateway configuration
}
API_ENDPOINT_BASE_URL = "wss://streaming.assemblyai.com/v3/ws"
API_ENDPOINT = f"{API_ENDPOINT_BASE_URL}?{urlencode(CONNECTION_PARAMS)}"

# Audio Configuration
FRAMES_PER_BUFFER = 800  # 50ms of audio (0.05s * 16000Hz)
SAMPLE_RATE = CONNECTION_PARAMS["sample_rate"]
CHANNELS = 1
FORMAT = pyaudio.paInt16

# Global variables for audio stream and websocket
audio = None
stream = None
ws_app = None
audio_thread = None
stop_event = threading.Event()  # To signal the audio thread to stop

# WAV recording variables
recorded_frames = []  # Store audio frames for WAV file
recording_lock = threading.Lock()  # Thread-safe access to recorded_frames

def save_wav_file():
    """Save recorded audio frames to a WAV file."""
    if not recorded_frames:
        print("No audio data recorded.")
        return

    # Generate filename with timestamp
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"recorded_audio_{timestamp}.wav"

    try:
        with wave.open(filename, 'wb') as wf:
            wf.setnchannels(CHANNELS)
            wf.setsampwidth(2)  # 16-bit = 2 bytes
            wf.setframerate(SAMPLE_RATE)

            # Write all recorded frames
            with recording_lock:
                wf.writeframes(b''.join(recorded_frames))

        print(f"Audio saved to: {filename}")
        print(f"Duration: {len(recorded_frames) * FRAMES_PER_BUFFER / SAMPLE_RATE:.2f} seconds")

    except Exception as e:
        print(f"Error saving WAV file: {e}")

# --- WebSocket Event Handlers ---

def on_open(ws):
    """Called when the WebSocket connection is established."""
    print("WebSocket connection opened.")
    print(f"Connected to: {API_ENDPOINT_BASE_URL}")

    # Start sending audio data in a separate thread
    def stream_audio():
        global stream
        print("Starting audio streaming...")
        while not stop_event.is_set():
            try:
                audio_data = stream.read(FRAMES_PER_BUFFER, exception_on_overflow=False)

                # Store audio data for WAV recording
                with recording_lock:
                    recorded_frames.append(audio_data)

                # Send audio data as binary message
                ws.send(audio_data, websocket.ABNF.OPCODE_BINARY)
            except Exception as e:
                print(f"Error streaming audio: {e}")
                # If stream read fails, likely means it's closed, stop the loop
                break
        print("Audio streaming stopped.")

    global audio_thread
    audio_thread = threading.Thread(target=stream_audio)
    audio_thread.daemon = (
        True  # Allow main thread to exit even if this thread is running
    )
    audio_thread.start()

def on_message(ws, message):
    try:
        data = json.loads(message)
        msg_type = data.get('type')

        if msg_type == "Begin":
            session_id = data.get('id')
            expires_at = data.get('expires_at')
            print(f"Session started: {session_id}")
        elif msg_type == "Turn":
            end_of_turn = data.get('end_of_turn', False)

            if end_of_turn:
                transcript = data.get('transcript', '')
                print(f"\nTranscript:\n{transcript}\n")
        elif msg_type == "LLMGatewayResponse":
            # Extract the LLM response content
            llm_data = data.get('data', {})
            llm_content = llm_data.get("choices", [{}])[0].get("message", {}).get("content", "")
            print(f"LLM Response:\n{llm_content}\n")
        elif msg_type == "Termination":
            audio_duration = data.get('audio_duration_seconds', 0)
            session_duration = data.get('session_duration_seconds', 0)
            print(f"Session terminated: {audio_duration} seconds of audio processed")
    except json.JSONDecodeError as e:
        print(f"Error decoding message: {e}")
    except Exception as e:
        print(f"Error handling message: {e}")

def on_error(ws, error):
    """Called when a WebSocket error occurs."""
    print(f"\nWebSocket Error: {error}")
    # Attempt to signal stop on error
    stop_event.set()


def on_close(ws, close_status_code, close_msg):
    """Called when the WebSocket connection is closed."""
    print(f"\nWebSocket Disconnected: Status={close_status_code}, Msg={close_msg}")

    # Save recorded audio to WAV file
    save_wav_file()

    # Ensure audio resources are released
    global stream, audio
    stop_event.set()  # Signal audio thread just in case it's still running

    if stream:
        if stream.is_active():
            stream.stop_stream()
        stream.close()
        stream = None
    if audio:
        audio.terminate()
        audio = None
    # Try to join the audio thread to ensure clean exit
    if audio_thread and audio_thread.is_alive():
        audio_thread.join(timeout=1.0)

# --- Main Execution ---
def run():
    global audio, stream, ws_app

    # Initialize PyAudio
    audio = pyaudio.PyAudio()

    # Open microphone stream
    try:
        stream = audio.open(
            input=True,
            frames_per_buffer=FRAMES_PER_BUFFER,
            channels=CHANNELS,
            format=FORMAT,
            rate=SAMPLE_RATE,
        )
        print("Microphone stream opened successfully.")
        print("Speak into your microphone. Press Ctrl+C to stop.")
        print("Audio will be saved to a WAV file when the session ends.")
    except Exception as e:
        print(f"Error opening microphone stream: {e}")
        if audio:
            audio.terminate()
        return  # Exit if microphone cannot be opened

    # Create WebSocketApp
    ws_app = websocket.WebSocketApp(
        API_ENDPOINT,
        header={"Authorization": YOUR_API_KEY},
        on_open=on_open,
        on_message=on_message,
        on_error=on_error,
        on_close=on_close,
    )

    # Run WebSocketApp in a separate thread to allow main thread to catch KeyboardInterrupt
    ws_thread = threading.Thread(target=ws_app.run_forever)
    ws_thread.daemon = True
    ws_thread.start()

    try:
        # Keep main thread alive until interrupted
        while ws_thread.is_alive():
            time.sleep(0.1)
    except KeyboardInterrupt:
        print("\nCtrl+C received. Stopping...")
        stop_event.set()  # Signal audio thread to stop

        # Send termination message to the server
        if ws_app and ws_app.sock and ws_app.sock.connected:
            try:
                terminate_message = {"type": "Terminate"}
                print(f"Sending termination message: {json.dumps(terminate_message)}")
                ws_app.send(json.dumps(terminate_message))
                # Give a moment for messages to process before forceful close
                time.sleep(5)
            except Exception as e:
                print(f"Error sending termination message: {e}")

        # Close the WebSocket connection (will trigger on_close)
        if ws_app:
            ws_app.close()

        # Wait for WebSocket thread to finish
        ws_thread.join(timeout=2.0)

    except Exception as e:
        print(f"\nAn unexpected error occurred: {e}")
        stop_event.set()
        if ws_app:
            ws_app.close()
        ws_thread.join(timeout=2.0)

    finally:
        # Final cleanup (already handled in on_close, but good as a fallback)
        if stream and stream.is_active():
            stream.stop_stream()
        if stream:
            stream.close()
        if audio:
            audio.terminate()
        print("Cleanup complete. Exiting.")


if __name__ == "__main__":
    run()

python

import logging
from typing import Type

import assemblyai as aai
from assemblyai.streaming.v3 import (
    BeginEvent,
    LLMGatewayResponseEvent,
    StreamingClient,
    StreamingClientOptions,
    StreamingError,
    StreamingEvents,
    StreamingParameters,
    TurnEvent,
    TerminationEvent,
)
from assemblyai.streaming.v3.models import LLMGatewayConfig, LLMGatewayMessage

api_key = "<YOUR_API_KEY>"
prompt = "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}"

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def on_begin(self: Type[StreamingClient], event: BeginEvent):
    print(f"Session started: {event.id}")

def on_turn(self: Type[StreamingClient], event: TurnEvent):
    if event.end_of_turn:
        print(f"\nTranscript:\n{event.transcript}\n")

def on_llm_response(self: Type[StreamingClient], event: LLMGatewayResponseEvent):
    # Extract the actual LLM response content from the data
    llm_content = event.data.get("choices", [{}])[0].get("message", {}).get("content", "")
    print(f"LLM Response:\n{llm_content}\n")

def on_terminated(self: Type[StreamingClient], event: TerminationEvent):
    print(
        f"Session terminated: {event.audio_duration_seconds} seconds of audio processed"
    )

def on_error(self: Type[StreamingClient], error: StreamingError):
    print(f"Error occurred: {error}")

def main():
    client = StreamingClient(
        StreamingClientOptions(
            api_key=api_key,
            api_host="streaming.assemblyai.com",
        )
    )

    client.on(StreamingEvents.Begin, on_begin)
    client.on(StreamingEvents.Turn, on_turn)
    client.on(StreamingEvents.LLMGatewayResponse, on_llm_response)
    client.on(StreamingEvents.Termination, on_terminated)
    client.on(StreamingEvents.Error, on_error)

    client.connect(
        StreamingParameters(
            sample_rate=16000,
            speech_model="u3-rt-pro",
            format_turns=True,
            llm_gateway=LLMGatewayConfig(
                model="claude-sonnet-4-6",
                messages=[
                    LLMGatewayMessage(role="user", content=prompt)
                ],
                max_tokens=4000
            )
        )
    )

    try:
        client.stream(
            aai.extras.MicrophoneStream(sample_rate=16000)
        )
    finally:
        client.disconnect(terminate=True)

if __name__ == "__main__":
    main()

javascript

const WebSocket = require("ws");
const mic = require("mic");
const querystring = require("querystring");
const fs = require("fs");

// --- Configuration ---
const YOUR_API_KEY = "<YOUR_API_KEY>"; // Replace with your actual API key

// LLM Gateway Configuration
const PROMPT =
  "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}";
const LLM_GATEWAY_CONFIG = {
  model: "claude-sonnet-4-6",
  messages: [{ role: "user", content: PROMPT }],
  max_tokens: 4000,
};

const CONNECTION_PARAMS = {
  sample_rate: 16000,
  format_turns: true, // Request formatted final transcripts
  speech_model: "u3-rt-pro", // USM 3 Pro model
  llm_gateway: JSON.stringify(LLM_GATEWAY_CONFIG), // LLM Gateway configuration
};
const API_ENDPOINT_BASE_URL = "wss://streaming.assemblyai.com/v3/ws";
const API_ENDPOINT = `${API_ENDPOINT_BASE_URL}?${querystring.stringify(CONNECTION_PARAMS)}`;

// Audio Configuration
const SAMPLE_RATE = CONNECTION_PARAMS.sample_rate;
const CHANNELS = 1;

// Global variables
let micInstance = null;
let micInputStream = null;
let ws = null;
let stopRequested = false;

// WAV recording variables
let recordedFrames = []; // Store audio frames for WAV file

// --- Helper functions ---
function formatTimestamp(timestamp) {
  return new Date(timestamp * 1000).toISOString();
}

function createWavHeader(sampleRate, channels, dataLength) {
  const buffer = Buffer.alloc(44);

  // RIFF header
  buffer.write("RIFF", 0);
  buffer.writeUInt32LE(36 + dataLength, 4);
  buffer.write("WAVE", 8);

  // fmt chunk
  buffer.write("fmt ", 12);
  buffer.writeUInt32LE(16, 16); // fmt chunk size
  buffer.writeUInt16LE(1, 20); // PCM format
  buffer.writeUInt16LE(channels, 22);
  buffer.writeUInt32LE(sampleRate, 24);
  buffer.writeUInt32LE(sampleRate * channels * 2, 28); // byte rate
  buffer.writeUInt16LE(channels * 2, 32); // block align
  buffer.writeUInt16LE(16, 34); // bits per sample

  // data chunk
  buffer.write("data", 36);
  buffer.writeUInt32LE(dataLength, 40);

  return buffer;
}

function saveWavFile() {
  if (recordedFrames.length === 0) {
    console.log("No audio data recorded.");
    return;
  }

  // Generate filename with timestamp
  const timestamp = new Date().toISOString().replace(/[:.]/g, "-").slice(0, 19);
  const filename = `recorded_audio_${timestamp}.wav`;

  try {
    // Combine all recorded frames
    const audioData = Buffer.concat(recordedFrames);
    const dataLength = audioData.length;

    // Create WAV header
    const wavHeader = createWavHeader(SAMPLE_RATE, CHANNELS, dataLength);

    // Write WAV file
    const wavFile = Buffer.concat([wavHeader, audioData]);
    fs.writeFileSync(filename, wavFile);

    console.log(`Audio saved to: ${filename}`);
    console.log(
      `Duration: ${(dataLength / (SAMPLE_RATE * CHANNELS * 2)).toFixed(2)} seconds`
    );
  } catch (error) {
    console.error(`Error saving WAV file: ${error}`);
  }
}

// --- Main function ---
async function run() {
  console.log(
    "Starting AssemblyAI streaming transcription with LLM Gateway..."
  );
  console.log("Audio will be saved to a WAV file when the session ends.");

  // Initialize WebSocket connection
  ws = new WebSocket(API_ENDPOINT, {
    headers: {
      Authorization: YOUR_API_KEY,
    },
  });

  // Setup WebSocket event handlers
  ws.on("open", () => {
    console.log("WebSocket connection opened.");
    console.log(`Connected to: ${API_ENDPOINT_BASE_URL}`);
    // Start the microphone
    startMicrophone();
  });

  ws.on("message", (message) => {
    try {
      const data = JSON.parse(message);
      const msgType = data.type;

      if (msgType === "Begin") {
        const sessionId = data.id;
        console.log(`Session started: ${sessionId}`);
      } else if (msgType === "Turn") {
        const endOfTurn = data.end_of_turn;

        if (endOfTurn) {
          const transcript = data.transcript || "";
          console.log(`\nTranscript:\n${transcript}\n`);
        }
      } else if (msgType === "LLMGatewayResponse") {
        // Extract the LLM response content
        const llmData = data.data || {};
        const llmContent = llmData.choices?.[0]?.message?.content || "";
        console.log(`LLM Response:\n${llmContent}\n`);
      } else if (msgType === "Termination") {
        const audioDuration = data.audio_duration_seconds;
        console.log(
          `Session terminated: ${audioDuration} seconds of audio processed`
        );
      }
    } catch (error) {
      console.error(`\nError handling message: ${error}`);
      console.error(`Message data: ${message}`);
    }
  });

  ws.on("error", (error) => {
    console.error(`\nWebSocket Error: ${error}`);
    cleanup();
  });

  ws.on("close", (code, reason) => {
    console.log(`\nWebSocket Disconnected: Status=${code}, Msg=${reason}`);
    cleanup();
  });

  // Handle process termination
  setupTerminationHandlers();
}

function startMicrophone() {
  try {
    micInstance = mic({
      rate: SAMPLE_RATE.toString(),
      channels: CHANNELS.toString(),
      debug: false,
      exitOnSilence: 6, // This won't actually exit, just a parameter for mic
    });

    micInputStream = micInstance.getAudioStream();

    micInputStream.on("data", (data) => {
      if (ws && ws.readyState === WebSocket.OPEN && !stopRequested) {
        // Store audio data for WAV recording
        recordedFrames.push(Buffer.from(data));

        // Send audio data to WebSocket
        ws.send(data);
      }
    });

    micInputStream.on("error", (err) => {
      console.error(`Microphone Error: ${err}`);
      cleanup();
    });

    micInstance.start();
    console.log("Microphone stream opened successfully.");
    console.log("Speak into your microphone. Press Ctrl+C to stop.");
  } catch (error) {
    console.error(`Error opening microphone stream: ${error}`);
    cleanup();
  }
}

function cleanup() {
  stopRequested = true;

  // Save recorded audio to WAV file
  saveWavFile();

  // Stop microphone if it's running
  if (micInstance) {
    try {
      micInstance.stop();
    } catch (error) {
      console.error(`Error stopping microphone: ${error}`);
    }
    micInstance = null;
  }

  // Close WebSocket connection if it's open
  if (ws && [WebSocket.OPEN, WebSocket.CONNECTING].includes(ws.readyState)) {
    try {
      // Send termination message if possible
      if (ws.readyState === WebSocket.OPEN) {
        const terminateMessage = { type: "Terminate" };
        console.log(
          `Sending termination message: ${JSON.stringify(terminateMessage)}`
        );
        ws.send(JSON.stringify(terminateMessage));
      }
      ws.close();
    } catch (error) {
      console.error(`Error closing WebSocket: ${error}`);
    }
    ws = null;
  }

  console.log("Cleanup complete.");
}

function setupTerminationHandlers() {
  // Handle Ctrl+C and other termination signals
  process.on("SIGINT", () => {
    console.log("\nCtrl+C received. Stopping...");
    cleanup();
    // Give time for cleanup before exiting
    setTimeout(() => process.exit(0), 1000);
  });

  process.on("SIGTERM", () => {
    console.log("\nTermination signal received. Stopping...");
    cleanup();
    // Give time for cleanup before exiting
    setTimeout(() => process.exit(0), 1000);
  });

  // Handle uncaught exceptions
  process.on("uncaughtException", (error) => {
    console.error(`\nUncaught exception: ${error}`);
    cleanup();
    // Give time for cleanup before exiting
    setTimeout(() => process.exit(1), 1000);
  });
}

// Start the application
run();

javascript

import { AssemblyAI } from "assemblyai";
import recorder from "node-record-lpcm16";
import { Readable } from "stream";

// --- Configuration ---
const API_KEY = "<YOUR_API_KEY>";

// LLM Gateway Configuration
const PROMPT =
  "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}";
const LLM_GATEWAY_CONFIG = {
  model: "claude-sonnet-4-6",
  messages: [{ role: "user", content: PROMPT }],
  max_tokens: 4000,
};

const run = async () => {
  const client = new AssemblyAI({
    apiKey: API_KEY,
  });

  const transcriber = client.streaming.transcriber({
    sampleRate: 16000,
    formatTurns: true,
    speechModel: "u3-rt-pro",
    llmGateway: LLM_GATEWAY_CONFIG,
  });

  // Session started event
  transcriber.on("open", ({ id }) => {
    console.log(`Session started: ${id}`);
  });

  // Error event
  transcriber.on("error", (error) => {
    console.error(`\nError: ${error}`);
  });

  // Close event
  transcriber.on("close", (code, reason) => {
    console.log("Session closed:", code, reason || "");
  });

  // Turn event - displays transcript when turn ends
  transcriber.on("turn", (turn) => {
    if (turn.end_of_turn) {
      console.log(`\nTranscript:\n${turn.transcript}\n`);
    }
  });

  // LLM Gateway Response event - displays LLM summary
  transcriber.on("llmGatewayResponse", (response) => {
    const llmContent = response.data?.choices?.[0]?.message?.content || "";
    console.log(`\nLLM Response:\n${llmContent}\n`);
  });

  // Termination event
  transcriber.on("termination", (event) => {
    console.log(
      `Session terminated: ${event.audio_duration_seconds} seconds of audio processed`
    );
  });

  try {
    await transcriber.connect();

    console.log("Connecting to streaming transcript service");
    console.log("Starting recording");

    const recording = recorder.record({
      channels: 1,
      sampleRate: 16000,
      audioType: "wav", // Linear PCM
    });

    Readable.toWeb(recording.stream()).pipeTo(transcriber.stream());

    // Stop recording and close connection using Ctrl-C
    process.on("SIGINT", async function () {
      console.log();
      console.log("Stopping recording");
      recording.stop();

      console.log("Closing streaming transcript connection");
      await transcriber.close();

      process.exit();
    });
  } catch (error) {
    console.error(`Error: ${error}`);
  }
};

run();

Before you begin

To complete this tutorial, you need:

Python or Node installed.
An AssemblyAI account with a credit card set up.
A microphone connected to your computer.
Basic understanding of how to Transcribe streaming audio.

Step 1: Install prerequisites

Install the required packages via pip:

bash

pip install pyaudio websocket-client

Install the AssemblyAI Python SDK via pip:

bash

pip install "assemblyai[extras]"

Install the required packages via NPM:

bash

npm install ws mic

Install the AssemblyAI JavaScript SDK and a recording package via NPM:

bash

npm install assemblyai node-record-lpcm16

Step 2: Connect to Universal Streaming

In this step, you'll set up a connection to the Universal Streaming API with the llm_gateway parameter. This parameter configures LLM Gateway to process your streaming transcripts.

For more information about streaming transcription, see Transcribe streaming audio.

python

import pyaudio
import websocket
import json
import threading
import time
import wave
from urllib.parse import urlencode
from datetime import datetime

# --- Configuration ---
YOUR_API_KEY = "<YOUR_API_KEY>"  # Replace with your actual API key

# LLM Gateway Configuration
PROMPT = "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}"
LLM_GATEWAY_CONFIG = {
    "model": "claude-sonnet-4-6",
    "messages": [
        {"role": "user", "content": PROMPT}
    ],
    "max_tokens": 4000
}

CONNECTION_PARAMS = {
    "sample_rate": 16000,
    "format_turns": True,  # Request formatted final transcripts
    "speech_model": "u3-rt-pro",  # USM 3 Pro model
    "llm_gateway": json.dumps(LLM_GATEWAY_CONFIG)  # LLM Gateway configuration
}
API_ENDPOINT_BASE_URL = "wss://streaming.assemblyai.com/v3/ws"
API_ENDPOINT = f"{API_ENDPOINT_BASE_URL}?{urlencode(CONNECTION_PARAMS)}"

# Audio Configuration
FRAMES_PER_BUFFER = 800  # 50ms of audio (0.05s * 16000Hz)
SAMPLE_RATE = CONNECTION_PARAMS["sample_rate"]
CHANNELS = 1
FORMAT = pyaudio.paInt16

# Global variables for audio stream and websocket
audio = None
stream = None
ws_app = None
audio_thread = None
stop_event = threading.Event()  # To signal the audio thread to stop

# WAV recording variables
recorded_frames = []  # Store audio frames for WAV file
recording_lock = threading.Lock()  # Thread-safe access to recorded_frames

python

import logging
from typing import Type

import assemblyai as aai
from assemblyai.streaming.v3 import (
    BeginEvent,
    LLMGatewayResponseEvent,
    StreamingClient,
    StreamingClientOptions,
    StreamingError,
    StreamingEvents,
    StreamingParameters,
    TurnEvent,
    TerminationEvent,
)
from assemblyai.streaming.v3.models import LLMGatewayConfig, LLMGatewayMessage

api_key = "<YOUR_API_KEY>"
prompt = "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}"

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

javascript

const WebSocket = require("ws");
const mic = require("mic");
const querystring = require("querystring");
const fs = require("fs");

// --- Configuration ---
const YOUR_API_KEY = "<YOUR_API_KEY>"; // Replace with your actual API key

// LLM Gateway Configuration
const PROMPT =
  "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}";
const LLM_GATEWAY_CONFIG = {
  model: "claude-sonnet-4-6",
  messages: [{ role: "user", content: PROMPT }],
  max_tokens: 4000,
};

const CONNECTION_PARAMS = {
  sample_rate: 16000,
  format_turns: true, // Request formatted final transcripts
  speech_model: "u3-rt-pro", // USM 3 Pro model
  llm_gateway: JSON.stringify(LLM_GATEWAY_CONFIG), // LLM Gateway configuration
};
const API_ENDPOINT_BASE_URL = "wss://streaming.assemblyai.com/v3/ws";
const API_ENDPOINT = `${API_ENDPOINT_BASE_URL}?${querystring.stringify(CONNECTION_PARAMS)}`;

// Audio Configuration
const SAMPLE_RATE = CONNECTION_PARAMS.sample_rate;
const CHANNELS = 1;

// Global variables
let micInstance = null;
let micInputStream = null;
let ws = null;
let stopRequested = false;

// WAV recording variables
let recordedFrames = []; // Store audio frames for WAV file

javascript

import { AssemblyAI } from "assemblyai";
import recorder from "node-record-lpcm16";
import { Readable } from "stream";

// --- Configuration ---
const API_KEY = "<YOUR_API_KEY>";

// LLM Gateway Configuration
const PROMPT =
  "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}";
const LLM_GATEWAY_CONFIG = {
  model: "claude-sonnet-4-6",
  messages: [{ role: "user", content: PROMPT }],
  max_tokens: 4000,
};

The llm_gateway parameterisa JSON-stringified object that follows the same interface as the LLM Gateway chat completions API. It accepts the following fields:

Key	Type	Description
`model`	string	The model to use. See Available models.
`messages`	array	An array of message objects. The `content` field contains your prompt.
`max_tokens`	number	The maximum number of tokens to generate.

Step 3: Stream audio and analyze with LLM Gateway

In this step, you'll stream audio from your microphone, collect the transcribed text from completed turns, and then send the accumulated transcript to LLM Gateway for analysis when the session ends.

Set up the event handlers to stream audio and collect transcripts from completed turns.

python

def save_wav_file():
    """Save recorded audio frames to a WAV file."""
    if not recorded_frames:
        print("No audio data recorded.")
        return

    # Generate filename with timestamp
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"recorded_audio_{timestamp}.wav"

    try:
        with wave.open(filename, 'wb') as wf:
            wf.setnchannels(CHANNELS)
            wf.setsampwidth(2)  # 16-bit = 2 bytes
            wf.setframerate(SAMPLE_RATE)

            # Write all recorded frames
            with recording_lock:
                wf.writeframes(b''.join(recorded_frames))

        print(f"Audio saved to: {filename}")
        print(f"Duration: {len(recorded_frames) * FRAMES_PER_BUFFER / SAMPLE_RATE:.2f} seconds")

    except Exception as e:
        print(f"Error saving WAV file: {e}")

# --- WebSocket Event Handlers ---

def on_open(ws):
    """Called when the WebSocket connection is established."""
    print("WebSocket connection opened.")
    print(f"Connected to: {API_ENDPOINT_BASE_URL}")

    # Start sending audio data in a separate thread
    def stream_audio():
        global stream
        print("Starting audio streaming...")
        while not stop_event.is_set():
            try:
                audio_data = stream.read(FRAMES_PER_BUFFER, exception_on_overflow=False)

                # Store audio data for WAV recording
                with recording_lock:
                    recorded_frames.append(audio_data)

                # Send audio data as binary message
                ws.send(audio_data, websocket.ABNF.OPCODE_BINARY)
            except Exception as e:
                print(f"Error streaming audio: {e}")
                # If stream read fails, likely means it's closed, stop the loop
                break
        print("Audio streaming stopped.")

    global audio_thread
    audio_thread = threading.Thread(target=stream_audio)
    audio_thread.daemon = (
        True  # Allow main thread to exit even if this thread is running
    )
    audio_thread.start()

def on_message(ws, message):
    try:
        data = json.loads(message)
        msg_type = data.get('type')

        if msg_type == "Begin":
            session_id = data.get('id')
            expires_at = data.get('expires_at')
            print(f"Session started: {session_id}")
        elif msg_type == "Turn":
            end_of_turn = data.get('end_of_turn', False)

            if end_of_turn:
                transcript = data.get('transcript', '')
                print(f"\nTranscript:\n{transcript}\n")
        elif msg_type == "LLMGatewayResponse":
            # Extract the LLM response content
            llm_data = data.get('data', {})
            llm_content = llm_data.get("choices", [{}])[0].get("message", {}).get("content", "")
            print(f"LLM Response:\n{llm_content}\n")
        elif msg_type == "Termination":
            audio_duration = data.get('audio_duration_seconds', 0)
            session_duration = data.get('session_duration_seconds', 0)
            print(f"Session terminated: {audio_duration} seconds of audio processed")
    except json.JSONDecodeError as e:
        print(f"Error decoding message: {e}")
    except Exception as e:
        print(f"Error handling message: {e}")

def on_error(ws, error):
    """Called when a WebSocket error occurs."""
    print(f"\nWebSocket Error: {error}")
    # Attempt to signal stop on error
    stop_event.set()


def on_close(ws, close_status_code, close_msg):
    """Called when the WebSocket connection is closed."""
    print(f"\nWebSocket Disconnected: Status={close_status_code}, Msg={close_msg}")

    # Save recorded audio to WAV file
    save_wav_file()

    # Ensure audio resources are released
    global stream, audio
    stop_event.set()  # Signal audio thread just in case it's still running

    if stream:
        if stream.is_active():
            stream.stop_stream()
        stream.close()
        stream = None
    if audio:
        audio.terminate()
        audio = None
    # Try to join the audio thread to ensure clean exit
    if audio_thread and audio_thread.is_alive():
        audio_thread.join(timeout=1.0)

python

def on_begin(self: Type[StreamingClient], event: BeginEvent):
    print(f"Session started: {event.id}")

def on_turn(self: Type[StreamingClient], event: TurnEvent):
    if event.end_of_turn:
        print(f"\nTranscript:\n{event.transcript}\n")

def on_llm_response(self: Type[StreamingClient], event: LLMGatewayResponseEvent):
    # Extract the actual LLM response content from the data
    llm_content = event.data.get("choices", [{}])[0].get("message", {}).get("content", "")
    print(f"LLM Response:\n{llm_content}\n")

def on_terminated(self: Type[StreamingClient], event: TerminationEvent):
    print(
        f"Session terminated: {event.audio_duration_seconds} seconds of audio processed"
    )

def on_error(self: Type[StreamingClient], error: StreamingError):
    print(f"Error occurred: {error}")

javascript

// --- Helper functions ---
function formatTimestamp(timestamp) {
  return new Date(timestamp * 1000).toISOString();
}

function createWavHeader(sampleRate, channels, dataLength) {
  const buffer = Buffer.alloc(44);

  // RIFF header
  buffer.write("RIFF", 0);
  buffer.writeUInt32LE(36 + dataLength, 4);
  buffer.write("WAVE", 8);

  // fmt chunk
  buffer.write("fmt ", 12);
  buffer.writeUInt32LE(16, 16); // fmt chunk size
  buffer.writeUInt16LE(1, 20); // PCM format
  buffer.writeUInt16LE(channels, 22);
  buffer.writeUInt32LE(sampleRate, 24);
  buffer.writeUInt32LE(sampleRate * channels * 2, 28); // byte rate
  buffer.writeUInt16LE(channels * 2, 32); // block align
  buffer.writeUInt16LE(16, 34); // bits per sample

  // data chunk
  buffer.write("data", 36);
  buffer.writeUInt32LE(dataLength, 40);

  return buffer;
}

function saveWavFile() {
  if (recordedFrames.length === 0) {
    console.log("No audio data recorded.");
    return;
  }

  // Generate filename with timestamp
  const timestamp = new Date().toISOString().replace(/[:.]/g, "-").slice(0, 19);
  const filename = `recorded_audio_${timestamp}.wav`;

  try {
    // Combine all recorded frames
    const audioData = Buffer.concat(recordedFrames);
    const dataLength = audioData.length;

    // Create WAV header
    const wavHeader = createWavHeader(SAMPLE_RATE, CHANNELS, dataLength);

    // Write WAV file
    const wavFile = Buffer.concat([wavHeader, audioData]);
    fs.writeFileSync(filename, wavFile);

    console.log(`Audio saved to: ${filename}`);
    console.log(
      `Duration: ${(dataLength / (SAMPLE_RATE * CHANNELS * 2)).toFixed(2)} seconds`
    );
  } catch (error) {
    console.error(`Error saving WAV file: ${error}`);
  }
}

function startMicrophone() {
  try {
    micInstance = mic({
      rate: SAMPLE_RATE.toString(),
      channels: CHANNELS.toString(),
      debug: false,
      exitOnSilence: 6, // This won't actually exit, just a parameter for mic
    });

    micInputStream = micInstance.getAudioStream();

    micInputStream.on("data", (data) => {
      if (ws && ws.readyState === WebSocket.OPEN && !stopRequested) {
        // Store audio data for WAV recording
        recordedFrames.push(Buffer.from(data));

        // Send audio data to WebSocket
        ws.send(data);
      }
    });

    micInputStream.on("error", (err) => {
      console.error(`Microphone Error: ${err}`);
      cleanup();
    });

    micInstance.start();
    console.log("Microphone stream opened successfully.");
    console.log("Speak into your microphone. Press Ctrl+C to stop.");
  } catch (error) {
    console.error(`Error opening microphone stream: ${error}`);
    cleanup();
  }
}

javascript

// Session started event
transcriber.on("open", ({ id }) => {
  console.log(`Session started: ${id}`);
});

// Error event
transcriber.on("error", (error) => {
  console.error(`\nError: ${error}`);
});

// Close event
transcriber.on("close", (code, reason) => {
  console.log("Session closed:", code, reason || "");
});

// Turn event - displays transcript when turn ends
transcriber.on("turn", (turn) => {
  if (turn.end_of_turn) {
    console.log(`\nTranscript:\n${turn.transcript}\n`);
  }
});

// LLM Gateway Response event - displays LLM summary
transcriber.on("llmGatewayResponse", (response) => {
  const llmContent = response.data?.choices?.[0]?.message?.content || "";
  console.log(`\nLLM Response:\n${llmContent}\n`);
});

// Termination event
transcriber.on("termination", (event) => {
  console.log(
    `Session terminated: ${event.audio_duration_seconds} seconds of audio processed`
  );
});

Define a function to send the accumulated transcript to LLM Gateway for analysis. This function uses the LLM Gateway chat completions API to process the transcript with your prompt.

When using the raw WebSocket approach with llm_gateway in the connection parameters, LLM Gateway responses are received as LLMGatewayResponse messages through the WebSocket, handled by the on_message callback registered in the previous step. No separate API call is needed.

When using the Python SDK with LLMGatewayConfig, analysis responses are received automatically through the LLMGatewayResponseEvent event handler registered in the previous step. No separate API call is needed.

When using the raw WebSocket approach with llm_gateway in the connection parameters, LLM Gateway responses are received as LLMGatewayResponse messages through the WebSocket, handled by the on_message callback in the run() function. No separate API call is needed.

When using the JavaScript SDK with llmGateway in the transcriber options, LLM Gateway responses are received automatically through the llmGatewayResponse event handler registered in the previous step. No separate API call is needed.

Run the streaming session and analyze the transcript with LLM Gateway when the session ends.

python

# --- Main Execution ---
def run():
    global audio, stream, ws_app

    # Initialize PyAudio
    audio = pyaudio.PyAudio()

    # Open microphone stream
    try:
        stream = audio.open(
            input=True,
            frames_per_buffer=FRAMES_PER_BUFFER,
            channels=CHANNELS,
            format=FORMAT,
            rate=SAMPLE_RATE,
        )
        print("Microphone stream opened successfully.")
        print("Speak into your microphone. Press Ctrl+C to stop.")
        print("Audio will be saved to a WAV file when the session ends.")
    except Exception as e:
        print(f"Error opening microphone stream: {e}")
        if audio:
            audio.terminate()
        return  # Exit if microphone cannot be opened

    # Create WebSocketApp
    ws_app = websocket.WebSocketApp(
        API_ENDPOINT,
        header={"Authorization": YOUR_API_KEY},
        on_open=on_open,
        on_message=on_message,
        on_error=on_error,
        on_close=on_close,
    )

    # Run WebSocketApp in a separate thread to allow main thread to catch KeyboardInterrupt
    ws_thread = threading.Thread(target=ws_app.run_forever)
    ws_thread.daemon = True
    ws_thread.start()

    try:
        # Keep main thread alive until interrupted
        while ws_thread.is_alive():
            time.sleep(0.1)
    except KeyboardInterrupt:
        print("\nCtrl+C received. Stopping...")
        stop_event.set()  # Signal audio thread to stop

        # Send termination message to the server
        if ws_app and ws_app.sock and ws_app.sock.connected:
            try:
                terminate_message = {"type": "Terminate"}
                print(f"Sending termination message: {json.dumps(terminate_message)}")
                ws_app.send(json.dumps(terminate_message))
                # Give a moment for messages to process before forceful close
                time.sleep(5)
            except Exception as e:
                print(f"Error sending termination message: {e}")

        # Close the WebSocket connection (will trigger on_close)
        if ws_app:
            ws_app.close()

        # Wait for WebSocket thread to finish
        ws_thread.join(timeout=2.0)

    except Exception as e:
        print(f"\nAn unexpected error occurred: {e}")
        stop_event.set()
        if ws_app:
            ws_app.close()
        ws_thread.join(timeout=2.0)

    finally:
        # Final cleanup (already handled in on_close, but good as a fallback)
        if stream and stream.is_active():
            stream.stop_stream()
        if stream:
            stream.close()
        if audio:
            audio.terminate()
        print("Cleanup complete. Exiting.")


if __name__ == "__main__":
    run()

python

def main():
    client = StreamingClient(
        StreamingClientOptions(
            api_key=api_key,
            api_host="streaming.assemblyai.com",
        )
    )

    client.on(StreamingEvents.Begin, on_begin)
    client.on(StreamingEvents.Turn, on_turn)
    client.on(StreamingEvents.LLMGatewayResponse, on_llm_response)
    client.on(StreamingEvents.Termination, on_terminated)
    client.on(StreamingEvents.Error, on_error)

    client.connect(
        StreamingParameters(
            sample_rate=16000,
            speech_model="u3-rt-pro",
            format_turns=True,
            llm_gateway=LLMGatewayConfig(
                model="claude-sonnet-4-6",
                messages=[
                    LLMGatewayMessage(role="user", content=prompt)
                ],
                max_tokens=4000
            )
        )
    )

    try:
        client.stream(
            aai.extras.MicrophoneStream(sample_rate=16000)
        )
    finally:
        client.disconnect(terminate=True)

if __name__ == "__main__":
    main()

javascript

// --- Main function ---
async function run() {
  console.log(
    "Starting AssemblyAI streaming transcription with LLM Gateway..."
  );
  console.log("Audio will be saved to a WAV file when the session ends.");

  // Initialize WebSocket connection
  ws = new WebSocket(API_ENDPOINT, {
    headers: {
      Authorization: YOUR_API_KEY,
    },
  });

  // Setup WebSocket event handlers
  ws.on("open", () => {
    console.log("WebSocket connection opened.");
    console.log(`Connected to: ${API_ENDPOINT_BASE_URL}`);
    // Start the microphone
    startMicrophone();
  });

  ws.on("message", (message) => {
    try {
      const data = JSON.parse(message);
      const msgType = data.type;

      if (msgType === "Begin") {
        const sessionId = data.id;
        console.log(`Session started: ${sessionId}`);
      } else if (msgType === "Turn") {
        const endOfTurn = data.end_of_turn;

        if (endOfTurn) {
          const transcript = data.transcript || "";
          console.log(`\nTranscript:\n${transcript}\n`);
        }
      } else if (msgType === "LLMGatewayResponse") {
        // Extract the LLM response content
        const llmData = data.data || {};
        const llmContent = llmData.choices?.[0]?.message?.content || "";
        console.log(`LLM Response:\n${llmContent}\n`);
      } else if (msgType === "Termination") {
        const audioDuration = data.audio_duration_seconds;
        console.log(
          `Session terminated: ${audioDuration} seconds of audio processed`
        );
      }
    } catch (error) {
      console.error(`\nError handling message: ${error}`);
      console.error(`Message data: ${message}`);
    }
  });

  ws.on("error", (error) => {
    console.error(`\nWebSocket Error: ${error}`);
    cleanup();
  });

  ws.on("close", (code, reason) => {
    console.log(`\nWebSocket Disconnected: Status=${code}, Msg=${reason}`);
    cleanup();
  });

  // Handle process termination
  setupTerminationHandlers();
}

function cleanup() {
  stopRequested = true;

  // Save recorded audio to WAV file
  saveWavFile();

  // Stop microphone if it's running
  if (micInstance) {
    try {
      micInstance.stop();
    } catch (error) {
      console.error(`Error stopping microphone: ${error}`);
    }
    micInstance = null;
  }

  // Close WebSocket connection if it's open
  if (ws && [WebSocket.OPEN, WebSocket.CONNECTING].includes(ws.readyState)) {
    try {
      // Send termination message if possible
      if (ws.readyState === WebSocket.OPEN) {
        const terminateMessage = { type: "Terminate" };
        console.log(
          `Sending termination message: ${JSON.stringify(terminateMessage)}`
        );
        ws.send(JSON.stringify(terminateMessage));
      }
      ws.close();
    } catch (error) {
      console.error(`Error closing WebSocket: ${error}`);
    }
    ws = null;
  }

  console.log("Cleanup complete.");
}

function setupTerminationHandlers() {
  // Handle Ctrl+C and other termination signals
  process.on("SIGINT", () => {
    console.log("\nCtrl+C received. Stopping...");
    cleanup();
    // Give time for cleanup before exiting
    setTimeout(() => process.exit(0), 1000);
  });

  process.on("SIGTERM", () => {
    console.log("\nTermination signal received. Stopping...");
    cleanup();
    // Give time for cleanup before exiting
    setTimeout(() => process.exit(0), 1000);
  });

  // Handle uncaught exceptions
  process.on("uncaughtException", (error) => {
    console.error(`\nUncaught exception: ${error}`);
    cleanup();
    // Give time for cleanup before exiting
    setTimeout(() => process.exit(1), 1000);
  });
}

// Start the application
run();

javascript

const run = async () => {
  const client = new AssemblyAI({
    apiKey: API_KEY,
  });

  const transcriber = client.streaming.transcriber({
    sampleRate: 16000,
    formatTurns: true,
    speechModel: "u3-rt-pro",
    llmGateway: LLM_GATEWAY_CONFIG,
  });

  // ... (event handlers from Step 1)

  try {
    await transcriber.connect();

    console.log("Connecting to streaming transcript service");
    console.log("Starting recording");

    const recording = recorder.record({
      channels: 1,
      sampleRate: 16000,
      audioType: "wav", // Linear PCM
    });

    Readable.toWeb(recording.stream()).pipeTo(transcriber.stream());

    // Stop recording and close connection using Ctrl-C
    process.on("SIGINT", async function () {
      console.log();
      console.log("Stopping recording");
      recording.stop();

      console.log("Closing streaming transcript connection");
      await transcriber.close();

      process.exit();
    });
  } catch (error) {
    console.error(`Error: ${error}`);
  }
};

run();

The output will look something like this:

Session started: de5d9927-73a6-4be8-b52d-b4c07be37e6b

Transcript: Hi, my name is Sonny.

Transcript: I am a voice agent.

Stopping...

Session terminated: 12s of audio processed

Analyzing conversation with LLM Gateway...
The speaker introduces themselves as Sonny and identifies as a voice agent.

Want to make your LLM requests more resilient? Use fallback models to automatically switch to a backup model if your primary model is unavailable.

Next steps

In this tutorial, you've learned how to analyze streaming audio transcripts using LLM Gateway. The type of output depends on your prompt, so try exploring different prompts to see how they affect the output. Here are a few more prompts to try:

"Provide an analysis of the transcript and offer areas to improve with exact quotes."
"What's the main take-away from the transcript?"
"Generate a set of action items from this transcript."

To learn more about LLM Gateway and streaming, see the following resources:

Need some help?

If you get stuck, or have any other questions, we'd love to help you out. Contact our support team at support@assemblyai.com or create a support ticket.

Apply LLM Gateway to Streaming ​

Overview ​

Before you begin ​

Step 1: Install prerequisites ​

Step 2: Connect to Universal Streaming ​

Step 3: Stream audio and analyze with LLM Gateway ​

Next steps ​

Need some help? ​

Apply LLM Gateway to Streaming

Overview

Before you begin

Step 1: Install prerequisites

Step 2: Connect to Universal Streaming

Step 3: Stream audio and analyze with LLM Gateway

Next steps

Need some help?