Help me stub out an agent written in node that will transcribe a call and call an endpoint with the notes at the end to store the transcription.

Thanks for reaching out, Andres. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

actually I'm gonna do it in Python instead can we set that up

Thanks — I’ll help you set up a Python LiveKit agent that transcribes a call and POSTs the final notes to your endpoint. I’ll respond to your request to: create a Python agent that listens for

user_input_transcribed

events, collects final transcript segments, and sends them (once the call ends or on demand) to a storage endpoint. Below is a compact, runnable stub you can adapt. 1) Prereqs • Python 3.8+ • Install required packages (example uses requests and dotenv; replace imports with your environment’s packages if needed): ◦ pip install requests python-dotenv • Make sure your LiveKit Agent Python package is installed and available (the stub uses typical names from LiveKit agent examples — adjust imports to match your installed package). 2) Example agent stub

# transcriber_agent.py
import os
import datetime
import requests
from dotenv import load_dotenv

# Adjust these imports to match your LiveKit agent package
from livekit.agents import AgentServer, JobContext, AgentSession, Agent, cli, inference

load_dotenv()

# Configuration - set these in your .env or export as env vars
TRANSCRIPTION_ENDPOINT = os.getenv("TRANSCRIPTION_ENDPOINT", "https://example.com/store-transcript")
AGENT_NAME = os.getenv("AGENT_NAME", "transcriber-agent")

# In-memory buffer for final transcript segments
transcript_buffer = []

def post_notes(payload):
    """POST JSON payload to your storage endpoint. Add auth headers if needed."""
    try:
        resp = requests.post(TRANSCRIPTION_ENDPOINT, json=payload, timeout=10)
        resp.raise_for_status()
        print("Notes posted successfully")
        return True
    except Exception as e:
        print("Failed to post notes:", e)
        return False

server = AgentServer()

@server.rtc_session(agent_name=AGENT_NAME)
async def entrypoint(ctx: JobContext):
    # Create an STT-only session (no LLM or TTS required)
    session = AgentSession(
        stt=inference.STT(model="deepgram/nova-3-general"),  # change model/provider as needed
    )

    @session.on("user_input_transcribed")
    def handle_transcript(event):
        # event.transcript should contain .transcript and .is_final
        t = getattr(event, "transcript", None)
        if not t:
            return

        if getattr(t, "is_final", False):
            text = getattr(t, "transcript", "") or ""
            ts = getattr(t, "timestamp", None) or datetime.datetime.utcnow().isoformat()
            participant = None
            if getattr(event, "participant", None):
                participant = getattr(event.participant, "identity", None) or getattr(event.participant, "sid", None)

            segment = {
                "text": text,
                "timestamp": ts,
                "participant": participant or "unknown"
            }
            transcript_buffer.append(segment)
            print("Final segment captured:", text)
        else:
            # optional: handle interim results if you want
            interim_text = getattr(t, "transcript", "")
            if interim_text:
                print("Interim:", interim_text)

    # Start the session and connect to the room
    await session.start(
        agent=Agent(instructions="You are a transcription agent that records user speech."),
        room=ctx.room
    )
    await ctx.connect()
    print("Agent session started and connected")

    # Optionally flush on room disconnect - event name may vary; adapt if needed
    @ctx.on("room_disconnected")
    async def on_room_disconnected(info):
        print("Room disconnected — flushing transcript notes")
        await flush_transcript_notes()

async def flush_transcript_notes():
    """Combine segments and post once (or stream segments if preferred)."""
    global transcript_buffer
    if not transcript_buffer:
        print("No transcript to send")
        return

    combined = "\n".join([s["text"] for s in transcript_buffer])
    payload = {
        "generated_at": datetime.datetime.utcnow().isoformat(),
        "notes": combined,
        "segments": transcript_buffer,
    }

    success = post_notes(payload)
    if success:
        transcript_buffer = []  # clear after successful send
    else:
        print("Keep buffer for retry")

if __name__ == "__main__":
    # Run the agent server CLI (adjust to how your package launches agents)
    cli.run_app(server)

3) Customization suggestions and production notes • Replace

inference.STT(model="...")

with the STT provider and model you prefer or have credentials for. • The exact class names and event shapes (

AgentServer

,

JobContext

,

AgentSession

,

session.on("user_input_transcribed")

) are consistent with LiveKit agent examples — if your package uses different names, let me know which package/version and I’ll adapt. • Authentication: add headers or tokens in

post_notes()

if your endpoint requires auth. • Retry/backoff: implement retries with exponential backoff for

post_notes()

if transient failures are possible. • Memory: for long calls, consider streaming each final segment to your endpoint instead of buffering everything in memory. • Speaker diarization: the example tries to capture

event.participant.identity

— modify if your event shape differs. • Flushing policy: I show flushing on room disconnect; you can also flush on explicit signal (e.g., an API call or a participant leaving). Next steps I can take for you • Convert this to use async HTTP (aiohttp) if your agent is async and you prefer non-blocking calls. • Add retry/backoff logic to

post_notes()

. • Stream each final segment immediately instead of buffering everything. • Update the stub to match the exact LiveKit Python package and version you have installed — tell me the package name/version and I’ll adapt the imports and event handlers precisely. Which of these would you like next, and can you confirm the LiveKit Python package/version you’re using or paste the import names you have available? Sources: Transcriber | LiveKit Documentation | Pi Zero Transcriber | LiveKit Documentation | TTS Translator with Gladia STT | LiveKit Documentation | Agent session | LiveKit Documentation, with interactive elements

My project says I am almost at 80% of the free inference credits

Build: STT deepgram - nova_3_multi incl. Inference credits ($0.552 per hour)

Can I change to OPENAI and have it use my personal account to avoid running out?

Thanks — that makes sense. You can switch your agent’s STT from Deepgram to OpenAI and have transcription usage billed to your personal OpenAI account so it won’t consume LiveKit inference credits. Quick summary of what to do: • Use the OpenAI STT plugin for LiveKit and configure your agent to use

openai.STT(...)

instead of the Deepgram model. • Set your personal OpenAI API key in the environment (e.g.,

OPENAI_API_KEY

) so requests go through your OpenAI account. • Be aware that costs will then follow OpenAI’s pricing for the chosen model instead of LiveKit Inference credits. Example changes (Python) • Replace the Deepgram STT in your

AgentSession

with the OpenAI STT plugin. For example:

from livekit.plugins import openai

session = AgentSession(
    stt=openai.STT(model="gpt-4o-transcribe"),  # choose the OpenAI model you prefer
    # other components...
)

• Ensure your OpenAI key is set in your environment or .env: ◦ export OPENAI_API_KEY="sk-..." ◦ or add to your .env file Operational notes and recommendations • Authentication: Put your OpenAI API key in

OPENAI_API_KEY

. The plugin will use that key so billing goes to your OpenAI account. • Model choice: Pick the OpenAI transcription model you prefer (e.g.,

gpt-4o-transcribe

); pricing varies by model and usage. • Testing: After switching, test with a short call to confirm transcripts arrive and your endpoint receives expected data. • Monitoring: Keep an eye on your OpenAI usage/billing dashboard since transcription can consume tokens/credits quickly for long calls. • Fallbacks: If you want to conserve your OpenAI usage, consider: ◦ lowering sample rate or chunking, ◦ sending only final segments, ◦ or switching back to Deepgram for lower-cost tiers if needed. If you’d like, I can: • Update your Python agent stub to use

openai.STT(...)

and show where to read

OPENAI_API_KEY

. • Add a safe toggle so you can run either Deepgram or OpenAI via an environment flag (e.g.,

STT_PROVIDER=deepgram|openai

). • Show an async version using

aiohttp

for non-blocking POSTs. Which would you prefer, and do you want me to produce the updated Python stub now? Sources: OpenAI STT plugin guide | LiveKit Documentation | OpenAI and LiveKit | LiveKit Documentation, with interactive elements

what package do I need to install to have that openai plugin?

Yes — to use the OpenAI plugin with LiveKit Agents in Python, install the extras-enabled package: • With pip:

pip install "livekit-agents[openai]~=1.4"

• Or with uv (if you use uv tooling):

uv add "livekit-agents[openai]~=1.4"

After installing: • Set your OpenAI API key in the environment (e.g.,

OPENAI_API_KEY="sk-..."

or in your .env). • Import the plugin in your agent code:

from livekit.plugins import openai
  session = AgentSession(stt=openai.STT(model="gpt-4o-transcribe"))

If you want, I can update your Python agent stub to use

openai.STT(...)

and show where to read

OPENAI_API_KEY

, or add a provider toggle so you can switch between Deepgram and OpenAI with an env var. Which would you prefer? Sources: OpenAI TTS plugin guide | LiveKit Documentation | OpenAI LLM models | LiveKit Documentation | Models overview | LiveKit Documentation, with interactive elements

It will work like this right?

async def entrypoint(ctx: JobContext):
    ctx.log_context_fields = {"room": ctx.room.name}

    stt_engine = openai.STT(model="gpt-4o-transcribe")
    segments: list[dict] = []
    started_at = datetime.now(timezone.utc)
    tasks: dict[str, asyncio.Task] = {}

Yes — that snippet is a good start. It shows the right idea: set context logging, create an OpenAI STT engine, prepare a segments buffer, capture the start time, and track async tasks. A few quick confirmations and suggestions to make it work end-to-end: • Imports and env ◦ Ensure you import required names: e.g.,

datetime

,

timezone

,

asyncio

, and the LiveKit types (

JobContext

,

AgentSession

,

Agent

,

cli

,

inference

/

openai

plugin) at top of the file. ◦ Make sure

OPENAI_API_KEY

is set in env or .env before running. • STT creation ◦

stt_engine = openai.STT(model="gpt-4o-transcribe")

is correct for selecting OpenAI transcribe. That will route billing to your OpenAI key. • Session wiring ◦ Use the

stt_engine

when creating the session, e.g.:

session = AgentSession(stt=stt_engine)

• Start the session and connect to the room:

await session.start(agent=Agent(instructions="..."), room=ctx.room)
    await ctx.connect()

• Handling transcription events ◦ Listen for

user_input_transcribed

and collect only final segments:

@session.on("user_input_transcribed")
    def on_transcript(event):
        t = getattr(event, "transcript", None)
        if not t: return
        if getattr(t, "is_final", False):
            segments.append({
                "text": t.transcript,
                "timestamp": getattr(t, "timestamp", None) or datetime.now(timezone.utc).isoformat(),
                "participant": getattr(event.participant, "identity", "unknown")
            })

• If you want to send each final segment immediately, start an async task to POST it and keep track in

tasks

for retries/cleanup. • Flushing / lifecycle ◦ On room end (e.g.,

room_disconnected

) or when you want to finalize notes, combine

segments

into one

notes

string and POST to your storage endpoint. ◦ Clear

segments

after a successful POST or implement retry/backoff on failure. • Async HTTP ◦ If your agent is async, prefer non-blocking HTTP (e.g.,

aiohttp

) to avoid blocking the event loop when posting notes. Your

tasks

dict is a good place to track those POST tasks. If you like, I can: • Produce a full, runnable example that plugs your snippet into a complete entrypoint with imports, session creation, event handlers, and an async POST using

aiohttp

. • Add provider-toggle code so you can switch between Deepgram and OpenAI via an env var. Which would you prefer? , with interactive elements