hi

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

https://github.com/livekit/livekit_composite/blob/main/livekit-examples/python-age[…]/complex-agents/doheny-surf-desk/agents/observer_agent.py https://github.com/livekit-examples/dev-day-demos/tree/main/call-moderation/moderation-agent Rahib Ahmed Could you help clarify how these two approaches differ? For my use case (telephony), my current understanding is: • Call moderation is more about monitoring the conversation and sending alerts or actions via RPC when certain conditions are met. • The observer agent feels more like a passive participant that injects context or guidance into the session rather than directly moderating it. Is that a fair interpretation, or am I missing something important? I also had a question around realtime GPT with text modality. What’s the recommended best practice for injecting context or messages there? Does generate_reply behave the same way in this setup? For context, I’m using GPT Realtime (text modality) together with ElevenLabs for TTS, so I want to make sure I’m handling message injection and response generation in the intended LiveKit way. Would appreciate a bit of clarification on this. Thank you.

Thanks — I’m responding to your two asks: how the Doheny “observer agent” differs from the call-moderation agent, and best practices for realtime GPT (text modality) with injected context and

generate_reply

for telephony + TTS. Short answer on the difference • Call-moderation agent: active monitoring + enforcement. It continuously analyzes transcriptions or messages for policy/trigger conditions and emits alerts, flags, or RPC actions (e.g., warn, mute, escalate). The primary role is detection and taking moderation actions. • Observer agent: passive participant that watches the room, maintains context, and injects guidance or metadata into the session without enforcing policy. It’s used to supply situational awareness, notes, or suggestions rather than directly moderating participants. Your interpretation is fair: moderation focuses on detection + action; an observer focuses on context and helpful injection. They can overlap (an observer can surface issues; a moderator enforces them), but the intent and outputs differ. Realtime GPT (text modality) — message injection &

generate_reply

• Use a RealtimeModel with

modalities=["text"]

and send/receive text events from the session. You can then run TTS (ElevenLabs) on the model’s output to play audio into the call. •

session.generate_reply()

is appropriate for generating turn-based responses from the model in text-mode. It accepts instructions/context and returns a handle (a

SpeechHandle

in some flows) that lets you control playback or follow-up processing. • For telephony with separate TTS: ◦ Maintain a short, focused context buffer you send to

generate_reply()

(recent transcript + any higher-level context from the observer agent). ◦ Inject context as explicit system/instruction messages (e.g., role:system or an initial prompt) so the model knows persona, constraints, or moderation rules. ◦ For deterministic behavior, include explicit instructions like “Reply concisely, do not ask to join personal info, follow moderation rules X.” ◦ If you need the model to produce both text for logs/records and audio for participants, generate the text first, pass that to ElevenLabs for TTS, then play the resulting audio into the room while recording the text response for moderation/audit. •

generate_reply()

behaves the same conceptually in text-only realtime: you provide context/instructions, it generates a response you can use. Make sure you control prompt/context size and handle latency (keep prompts compact for telephony). Practical pattern to implement 1. Stream transcription into a short context buffer. 2. Observer agent updates session context or annotations (metadata) but doesn’t enforce. 3. Call-moderation logic consumes transcriptions + observer signals to decide actions (alerts, RPCs). 4. When a response is needed: ◦ Call

session.generate_reply()

with: current context, any system instructions, and the role you want. ◦ Receive the text output; send text to ElevenLabs TTS. ◦ Play TTS audio into the Room and log the text reply for audit/moderation. 1. If moderation must intervene before playback, run policy checks on the generated text and block/modify as needed before TTS. Questions to tailor this to you • Do you want the observer to ever trigger moderation actions, or should those be isolated in a separate moderation agent? • Which SDK/language are you using for realtime control (so I can show a short code snippet for

generate_reply()

-> TTS -> play)? If you want, I can provide a short code example for the Realtime text flow with

generate_reply()

plus an ElevenLabs call and how to play returned audio into a LiveKit room.

I want a layer that can basically look at what user is sating sure this layer will be an obseerver layer that will work behind main agent. I am on python and using gt realtime with text modality. It uses gpt-4o-transceribe internally.

You're building an observer layer that passively watches user speech (using GPT Realtime text modality, backed by

gpt-4o-transcribe

) and runs behind your main agent — that’s clear. Below is a concise, practical pattern and a small Python sketch you can adapt. Key pattern (high level) • Capture transcripts: subscribe to realtime transcript/text events from the Realtime session. • Maintain a short context buffer: keep recent N utterances + relevant metadata (speaker, timestamps). • Analyze passively: observer runs lightweight checks or enrichment (entity extraction, intent tags, safety checks) on each transcript chunk — do not take enforcement actions. • Publish context/annotations: write observer outputs to a shared place (in-memory queue, DB, or room metadata) that the main agent and moderation logic can read. • When main agent needs input, include observer annotations in its prompt/context; observer may also trigger non-enforcement notifications (e.g., "note: user mentioned X") that the main agent can act on. Python sketch (conceptual) 1) Subscribe to transcript events and update buffer. 2) Run observer analysis (call model or rule-based checks). 3) Store annotations and optionally emit events.

# Pseudocode sketch — adapt to your Realtime SDK event model
from collections import deque
import time

CONTEXT_SIZE = 10

class ObserverLayer:
    def __init__(self):
        self.buffer = deque(maxlen=CONTEXT_SIZE)  # recent transcripts
        self.annotations = []  # recent observer outputs

    def on_transcript(self, speaker_id: str, text: str, ts: float = None):
        ts = ts or time.time()
        self.buffer.append({"speaker": speaker_id, "text": text, "ts": ts})
        ann = self.analyze_text(speaker_id, text)
        if ann:
            self.annotations.append({"ts": ts, "ann": ann})
            # publish to shared store / emit event for main agent
            self.publish_annotation({"speaker": speaker_id, "text": text, "annotation": ann, "ts": ts})

    def analyze_text(self, speaker_id: str, text: str):
        # lightweight checks / enrichment. Example:
        # - simple rules: detect phone numbers, keywords
        # - or call the Realtime text model for intent/entity extraction
        # Return structured annotation (do not take action here).
        if "cancel" in text.lower():
            return {"type": "intent", "value": "cancellation_mentioned"}
        return None

    def publish_annotation(self, payload):
        # place payload where main agent can read: message bus, DB, or room metadata
        pass

Using GPT Realtime (text modality +

gpt-4o-transcribe

) • Receive transcription events from

gpt-4o-transcribe

. Treat them as the same text input described above. • If you need richer analysis than simple rules, call a Realtime text model or a short

session.generate_reply()

prompt to extract entities/intents: ◦ Use concise system instructions: e.g., “Extract entities and flags only. Return JSON.” ◦ Keep requests lightweight to reduce latency. • Do not use

generate_reply()

from the observer to speak into the room. If observer generates text, publish it as an annotation only. Let the main agent decide whether to convert it to audio via ElevenLabs and play it. Context injection for main agent • When the main agent constructs a prompt for `generate_reply()`: ◦ Include recent transcripts (from the buffer). ◦ Include top-priority observer annotations as structured context (e.g., "Observer notes: ..."). ◦ Include system instructions about policies, persona, and telephony constraints. • Keep the injected context concise and structured to avoid prompt bloat. Safety and moderation flow • Keep enforcement isolated: observer annotates; moderation agent or main agent enforces. • Run safety checks on transcripts and on any generated text before passing to TTS. Block or modify outputs if policy is violated. Latency and reliability tips • Buffer small: prefer last 20–60s or last N utterances. • Use async, non-blocking processing for observer analysis so you don't delay main agent decisions. • Use structured annotations (JSON) so the main agent can programmatically decide. Questions to tailor this • Do you want the observer to run model-based extraction (more accurate, higher latency) or rule-based checks (faster, lighter)? • Where would you like annotations stored/shared (in-memory service, DB, or LiveKit room metadata/events)? I can provide specific code for your chosen option. If you want, I’ll produce a concrete code example showing: • subscribing to Realtime transcript events, • calling a small `generate`/`extract` prompt for annotation, • publishing annotations to the main agent, • and the main agent using those annotations in

session.generate_reply()

before sending text to ElevenLabs. Which storage/publishing method should I target?