Thanks — I’m happy to help. You’re asking how to access the transcript of a conversation. Below are the practical ways you can get transcripts in LiveKit and quick pointers for each method so you can pick what fits your setup. Real-time transcripts (recommended for live UIs) • Use an AgentSession with STT enabled. Transcriptions are published in real time to the frontend on the

lk.transcription

text stream topic. • Listen for the

user_input_transcribed

event inside your AgentSession to receive transcribed text. Final transcripts are indicated by

transcript.is_final

so you can decide when to persist or display them. • In client SDKs, register a text stream handler (e.g.,

registerTextStreamHandler

) and check the

lk.transcription_final

flag to tell interim vs final results. Frontend/react convenience • If you use React, use the

useTranscriptions

hook to receive and render transcriptions in the UI as they arrive. Saving transcripts for later • Log final transcripts when you receive

transcript.is_final

to a file, database, or any storage you use. • If a user disconnects mid-turn, call

await session.aclose()

to flush incomplete turns so you don’t lose partial transcripts. TTS-aligned transcripts with timing (word/sentence timing) • Enable word/sentence timing by setting

use_tts_aligned_transcript=True

in AgentSession. • Implement the

transcription_node

method to receive

TimedString

objects that include

start_time

and

end_time

. • Note: word-level timing is supported by specific TTS providers (e.g., Cartesia, ElevenLabs); other providers provide sentence-level alignment. Observability dashboard (session review and downloads) • Enable Agent Observability in the LiveKit Cloud Dashboard for sessions you want recorded. • The dashboard provides synchronized audio playback, full transcripts, turn-by-turn traces, and logs. • You can download audio, transcripts, and logs directly from a session in the dashboard. Quick implementation checklist 1. Decide if you need real-time display or post-call storage. 2. Enable STT in your AgentSession and register

user_input_transcribed

or a text stream handler. 3. Persist transcripts on

transcript.is_final

to your storage. 4. If you need timing alignment, set

use_tts_aligned_transcript=True

and use

transcription_node

. 5. For recorded searchable sessions, enable Agent Observability in the dashboard. If you want, tell me: • Which SDK or framework you’re using (browser JS, React, Python agent, etc.), and • Whether you want realtime display, long-term storage, or time-aligned transcripts. I’ll give a short code example tailored to your stack. Sources: Text and transcriptions | LiveKit Documentation | Transcriber | LiveKit Documentation | Pi Zero Transcriber | LiveKit Documentation | How to capture real-time agent transcriptions during conversations | Repeater | LiveKit Documentation, with interactive elements

Thanks — this is helpful context. You’re asking how to capture/access the conversation transcript from the AgentSession in your shown Python agent code. I can walk you through concrete options and give a short code example for each, but first a quick clarification so I focus the answer correctly: Which of these do you want to do? • Capture real-time transcripts as they arrive (for live UI display). • Persist final transcripts to storage (file, DB) for later review. • Get time-aligned transcripts (word/sentence timings) for syncing with TTS. • Use the LiveKit Cloud Dashboard observability to download session transcripts. If you tell me which one (or more) you want, I’ll provide a concise code snippet tailored to your AgentSession setup. Below are recommended approaches and a basic example you can adapt immediately. Recommended approaches • Real-time / frontend: Listen for

user_input_transcribed

events in the AgentSession or register a text stream handler in the client to receive

lk.transcription

and

lk.transcription_final

flags. Use interim results for live display and finalize on

is_final

. • Persisting transcripts: In the agent, on receiving

user_input_transcribed

with

transcript.is_final

, append the final transcript to your

conversation_log

and write to a file or DB. • Time-aligned transcripts: Enable

use_tts_aligned_transcript=True

in your AgentSession and implement

transcription_node

to receive

TimedString

objects with `start_time`/`end_time`. • Flushing incomplete turns: If users disconnect mid-turn, call

await session.aclose()

to flush partial transcripts before the session ends. • Dashboard review: Enable Agent Observability/recording in your project to get full session transcripts and downloads from the LiveKit Cloud Dashboard. Quick Python example (capture + persist final transcripts) • Add an event handler for transcriptions and save final transcripts into your conversation_log.

# inside your Agent class or entrypoint scope

async def on_user_input_transcribed(session, transcript):
    # transcript.is_final indicates final vs interim
    text = transcript.transcript
    is_final = getattr(transcript, "is_final", False)

    # Store interim or final as desired
    if is_final:
        conversation_log.append({"role": "user", "text": text, "timestamp": time.time()})
        # persist to disk (append)
        with open("/var/logs/session_transcripts.txt", "a") as f:
            f.write(f"{time.time()} USER: {text}\n")
    else:
        # optional: update live UI/state with interim text
        pass

# Register handler after session created
session.register_event_handler("user_input_transcribed", on_user_input_transcribed)

Notes for your current code • You already create

conversation_log = []

. Use that list to accumulate final transcript entries and then persist as needed. • If you need time alignment, set

use_tts_aligned_transcript=True

when creating

AgentSession

and add a

transcription_node

method to receive timed words/sentences. • Make sure

session.start(...)

and

session.generate_reply(...)

are awaited appropriately and the handlers are registered before starting the session so you don’t miss early transcriptions. Next step Tell me which option you want (real-time display, persistent storage, time-aligned transcripts, or dashboard downloads) and which SDK/framework you’re using for the client (plain Python agent, browser JS, React). I’ll give a short, tailored snippet you can drop into your code. , with interactive elements

Great — thanks for clarifying. Below is a concise, server-side Python approach you can drop into your agent code to capture final transcripts from the AgentSession and persist them to a JSON file. This stores one JSON file per session (safe for concurrent sessions if you use unique filenames) and appends transcript items into an array with timestamps and roles. What this does • Listens for final transcription events (

user_input_transcribed

/

transcript.is_final

) from the AgentSession. • Appends final transcript entries to an in-memory list (

conversation_log

). • Writes the full conversation to a JSON file when the session ends (or periodically if you prefer). Example (adapt to your agent/session code)

import json
import time
import os
from pathlib import Path

# where to save transcripts
TRANSCRIPT_DIR = Path("/var/logs/livekit_transcripts")
TRANSCRIPT_DIR.mkdir(parents=True, exist_ok=True)

def make_session_filename(session_id: str) -> Path:
    # create a unique file per session; change naming if you prefer
    timestamp = int(time.time())
    return TRANSCRIPT_DIR / f"transcript_{session_id}_{timestamp}.json"

# in your agent/session scope:
conversation_log = []  # accumulate final transcript entries

async def on_user_input_transcribed(session, transcript):
    """
    Handler for user transcription events.
    Called by the AgentSession when user speech is transcribed.
    """
    try:
        text = transcript.transcript
        is_final = getattr(transcript, "is_final", False)

        if not text:
            return

        if is_final:
            conversation_log.append({
                "role": "user",
                "text": text,
                "timestamp": time.time()
            })
            # optional: immediately persist incremental updates to disk
            # persist_conversation(session.session_id, conversation_log)
    except Exception:
        # keep handler robust; don't let exceptions break the session
        return

async def on_conversation_item_added(session, item):
    """
    Optional: capture when conversation items are committed to history,
    including agent replies. Use this if agent outputs are not produced via transcript events.
    """
    try:
        # example shape — adjust depending on item attributes in your setup
        text = getattr(item, "text", None) or getattr(item, "transcript", None)
        role = getattr(item, "role", "agent")
        if text:
            conversation_log.append({
                "role": role,
                "text": text,
                "timestamp": time.time()
            })
    except Exception:
        return

def persist_conversation(session_id: str, log: list):
    """
    Write conversation log to JSON file atomically.
    """
    filepath = make_session_filename(session_id)
    # write to a temp file then rename for atomicity
    tmp_path = filepath.with_suffix(".json.tmp")
    with open(tmp_path, "w", encoding="utf-8") as f:
        json.dump({"session_id": session_id, "items": log}, f, ensure_ascii=False, indent=2)
    os.replace(tmp_path, filepath)  # atomic on most OSes

# When session ends or periodically:
async def on_session_end(session):
    # flush any partial turns before closing
    await session.aclose()
    # persist the collected final transcripts
    persist_conversation(session.session_id, conversation_log)

Integration notes & tips • Register handlers before starting the session so you don’t miss early events: ◦

session.register_event_handler("user_input_transcribed", on_user_input_transcribed)

◦

session.register_event_handler("conversation_item_added", on_conversation_item_added)

(optional) • Use

transcript.is_final

to store only finalized text. Interim transcripts are useful for live UI but usually not persisted. • Call

await session.aclose()

before persisting to ensure any in-flight speech is flushed to transcription events. • If multiple sessions run concurrently, use unique filenames (session id + timestamp) as shown. For very high throughput, consider writing to a database or S3 instead of local disk. • If you want to include time-aligned words/sentences, enable

use_tts_aligned_transcript=True

and capture

TimedString

objects in `transcription_node`; you can serialize those into JSON the same way. If you want, I can: • Provide a shorter copy-paste-ready snippet tuned to your exact agent framework (show me how you create the AgentSession and I’ll match the handler registration). • Show an example that flushes and persists to the file after each final transcript (for lower memory usage). • Show how to store multiple sessions in one rolling JSON file or upload to S3. Which would you like next? , with interactive elements

Thanks — I’ll provide a complete, self-contained Python example you can run on your server to capture final transcripts from an AgentSession and write them to a JSON file. This example follows the approach we discussed: it registers handlers for transcription and conversation items, accumulates final entries in memory, flushes any in-flight audio at session end, and writes the session transcript to a uniquely named JSON file. Notes before running • Replace the placeholders for creating your

AgentSession

with the actual constructor and arguments your project uses. • Make sure the transcription and conversation events names match how your AgentSession emits them; this example uses

user_input_transcribed

and

conversation_item_added

as described. • Ensure the process has write permission to the

TRANSCRIPT_DIR

path. Complete example

import asyncio
import json
import os
import time
from pathlib import Path
from typing import Any

# === CONFIG ===
TRANSCRIPT_DIR = Path("./livekit_transcripts")
TRANSCRIPT_DIR.mkdir(parents=True, exist_ok=True)

# === Utilities ===
def make_session_filename(session_id: str) -> Path:
    timestamp = int(time.time())
    safe_id = session_id.replace("/", "_")
    return TRANSCRIPT_DIR / f"transcript_{safe_id}_{timestamp}.json"

def persist_conversation(session_id: str, log: list[dict[str, Any]]):
    filepath = make_session_filename(session_id)
    tmp_path = filepath.with_suffix(".json.tmp")
    with open(tmp_path, "w", encoding="utf-8") as f:
        json.dump({"session_id": session_id, "items": log}, f, ensure_ascii=False, indent=2)
    os.replace(tmp_path, filepath)
    print(f"Transcript saved to {filepath}")

# === Transcript capture logic ===
class TranscriptCollector:
    def __init__(self, session_id: str):
        self.session_id = session_id
        self.log: list[dict[str, Any]] = []

    async def handle_user_input_transcribed(self, session, transcript):
        """
        Expected: transcript has .transcript and .is_final attributes.
        Adjust attribute names if your transcript object differs.
        """
        try:
            text = getattr(transcript, "transcript", None)
            is_final = getattr(transcript, "is_final", False)

            if not text:
                return

            if is_final:
                entry = {
                    "role": "user",
                    "text": text,
                    "timestamp": time.time()
                }
                self.log.append(entry)
                print(f"[final user] {text}")
            else:
                # Optional: handle interim text (not persisted here)
                interim = text
                print(f"[interim user] {interim}")

        except Exception as e:
            # Keep handler robust; log and continue
            print(f"Error in user_input_transcribed handler: {e}")

    async def handle_conversation_item_added(self, session, item):
        """
        Expected: item may contain agent reply text and role.
        Adjust attribute access if your item object structure differs.
        """
        try:
            # Try common attribute names
            text = getattr(item, "text", None) or getattr(item, "transcript", None)
            role = getattr(item, "role", None) or "agent"
            if text:
                entry = {
                    "role": role,
                    "text": text,
                    "timestamp": time.time()
                }
                self.log.append(entry)
                print(f"[{role}] {text}")
        except Exception as e:
            print(f"Error in conversation_item_added handler: {e}")

    async def flush_and_persist(self, session):
        # Attempt to flush incomplete turns before persisting
        try:
            await session.aclose()
        except AttributeError:
            # If session.aclose not present in your API, skip
            pass
        except Exception as e:
            print(f"Warning: error while closing session: {e}")

        persist_conversation(self.session_id, self.log)

# === Example main flow ===
async def main():
    # --- Placeholder: create your AgentSession here ---
    # Replace the block below with how you create and start your AgentSession.
    # The example assumes:
    #  - `session` is your AgentSession instance
    #  - it supports register_event_handler(event_name, coroutine_fn)
    #  - it exposes session.session_id (or similar) for naming files
    #
    # Example (pseudocode):
    # session = AgentSession(agent=MyAgent(), use_tts_aligned_transcript=False, ...)
    # await session.start()
    #
    # For this runnable template, we'll simulate a session object.
    class FakeTranscript:
        def __init__(self, transcript, is_final):
            self.transcript = transcript
            self.is_final = is_final

    class FakeItem:
        def __init__(self, text, role="agent"):
            self.text = text
            self.role = role

    class FakeSession:
        def __init__(self, session_id):
            self.session_id = session_id
            self._handlers = {}
        def register_event_handler(self, name, handler):
            self._handlers[name] = handler
        async def emit(self, name, *args):
            h = self._handlers.get(name)
            if h:
                await h(self, *args)
        async def aclose(self):
            # simulate flushing
            await asyncio.sleep(0.01)

    # Create a fake session for demonstration; replace with real session creation
    session = FakeSession(session_id="demo-session-123")

    # Create collector and register handlers
    collector = TranscriptCollector(session.session_id)
    session.register_event_handler("user_input_transcribed", collector.handle_user_input_transcribed)
    session.register_event_handler("conversation_item_added", collector.handle_conversation_item_added)

    # --- In real code you'd start the session and let it run while events come in ---
    # For demo: emit some fake events to show how it works
    await session.emit("user_input_transcribed", FakeTranscript("hello", is_final=False))
    await asyncio.sleep(0.05)
    await session.emit("user_input_transcribed", FakeTranscript("hello world", is_final=True))
    await session.emit("conversation_item_added", FakeItem("Hi there, how can I help?", role="agent"))
    await session.emit("user_input_transcribed", FakeTranscript("I need help with billing", is_final=True))
    await session.emit("conversation_item_added", FakeItem("I can help with billing questions.", role="agent"))

    # End of session: flush and persist to JSON
    await collector.flush_and_persist(session)

if __name__ == "__main__":
    asyncio.run(main())

How to adapt this to your real AgentSession • Replace the FakeSession with the actual AgentSession creation and ensure you register handlers before calling

session.start()

so you don’t miss early transcriptions. • If your transcript object uses different field names, adjust

getattr(transcript, "...")

accordingly. • This example persists one file per session. If you prefer a single rolling file or uploading to S3/DB, I can provide that variant. Would you like a version that: • Immediately writes each final transcript entry to disk (append) to minimize memory use? • Uses

use_tts_aligned_transcript=True

and includes timing details in the JSON? • Uploads transcripts to S3 instead of local disk? Tell me which and I’ll give that exact variant. , with interactive elements