hi

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

how to detect ivr or agent spakein other side

Thanks — I understand you want to detect whether the far end is an IVR/automated system or a live agent. Below are practical approaches you can use with LiveKit, plus a few clarifying questions so I can tailor the recommendation to your setup. Short summary of options you can combine • Enable built‑in IVR detection at the session level: set

ivr_detection=True

on your AgentSession so LiveKit's agent logic will try to identify IVR systems automatically. • DTMF detection/handling: listen for DTMF events (menu-driven prompts often expect DTMF). Use

dtmf_received

events to detect and respond to menu prompts, and

send_dtmf_events

to navigate IVR menus when needed. • Silence / timing heuristics: detect long silence, structured pause patterns, or consistent prompt lengths (voicemail greetings and IVRs often follow predictable timing). • LLM / content-based detection: run an LLM classifier on the captured audio transcript or use function-calling tools to classify greetings/menus vs. natural conversation (note: this is probabilistic — typical accuracy is in the 70–80% range). • Combine signals for best results: built‑in

ivr_detection

+ DTMF presence + silence/timing heuristics + transcript-based classification gives the most reliable outcome. Concrete starting points 1. Enable session IVR detection ◦ Example: set

AgentSession(ivr_detection=True)

when creating the session so IVR detection is active for that call. 1. Handle DTMF events ◦ Subscribe to

dtmf_received

events and inspect the digit(s). Presence of structured digit prompts strongly indicates an IVR. ◦ Use the

send_dtmf_events

tool to respond to IVR prompts when you want to navigate a menu. 1. Use silence and timing checks ◦ Track when the remote side last spoke. Long consistent silences after a greeting or long fixed-length prompts can indicate voicemail/automated systems. ◦ Implement a configurable silence threshold (e.g., N seconds) to mark likely voicemail. 1. Use transcript-based/LLM approaches ◦ After capturing the greeting or initial few seconds, analyze the transcript for phrases like “press 1,” “to continue,” “please leave a message,” which are strong IVR signals. ◦ Optionally call an LLM function to classify the audio/transcript; treat results as probabilistic and combine with DTMF/silence signals. Questions to tailor a recommended implementation • Which SDK/platform are you using (Web, Node agent, Python agent, iOS, Android)? • Do you want automatic detection only, or also automatic actions (e.g., leave voicemail, route to different flow, send DTMF)? • Do you already capture remote audio transcripts or only raw audio/events? • Is minimizing false-positives (mistaking agents for IVR) or minimizing false-negatives more important for your use case? If you tell me your platform and whether you want detection only or detection + automated handling, I’ll give a short code example and recommended thresholds for that environment. Sources: Agent session | LiveKit Documentation | examples/dtmf | livekit/livekit_composite | DeepWiki | How to detect and handle silence during calls | send_dtmf_events | LiveKit Documentation

we need to end call if that case

Got it — you want the call to automatically end when the far side is detected as an IVR/automated system. I can walk you through a reliable approach and give a short code pattern once I know your platform. Recommended approach (combine signals for reliability) • Enable session IVR detection: set

ivr_detection=True

on the AgentSession so LiveKit will attempt IVR identification automatically. • Listen for DTMF events: IVRs commonly emit or ask for DTMF input. If you receive structured DTMF (or detect a prompt asking for digits), treat that as a strong IVR signal. • Use transcript/keyword detection: look for phrases like “press 1,” “please leave a message,” or “recording” in the initial transcript — these are strong indicators. • Use silence/timing heuristics as a fallback: long, consistent greetings or a fixed-length prompt pattern often indicate voicemail/IVR. • Combine signals and apply a decision rule: require 1–2 strong signals (e.g., built‑in ivr_detection OR DTMF OR explicit IVR keywords) before ending the call to reduce false positives. Minimal decision flow example 1. Wait for initial greeting (first 3–8 seconds) or for

ivr_detection

result. 2. If

ivr_detection == true

, end call immediately. 3. Otherwise, if DTMF is observed or transcript contains IVR keywords, end call. 4. Otherwise keep the call open. Questions so I can give a concrete code example • Which SDK/platform are you using? (Web, Node/Python agent, iOS, Android) • Do you already capture transcripts in real time, or only audio/events? • How aggressive should detection be? (e.g., end on a single signal, or require two signals to avoid false positives) If you tell me the platform and your preference for aggressiveness, I’ll provide a short code snippet showing: 1) enabling

ivr_detection

, 2) subscribing to

dtmf_received

and transcript events, and 3) the exact call-end logic (calling the room/session disconnect).

voice mail detectsipn

Thanks — I understand you want reliable voicemail detection and to automatically end the call when voicemail is detected. Below I’ll outline practical detection signals, a safe decision rule to avoid false positives, and a short example flow. I also need two quick details so I can give a concrete code snippet for your platform. Key signals to detect voicemail (use a combination) • Built‑in IVR/voicemail detection: enable

ivr_detection=True

on your AgentSession. If this flag returns true, treat it as a strong signal. • Transcript keyword detection: listen for phrases like “please leave a message,” “after the tone,” “press 1,” “record your message,” or “leave a message.” These are strong indicators of voicemail. • Silence/greeting pattern: voicemails often have a longer fixed greeting followed by silence and then a beep. Detect a long continuous monologue followed by extended silence or a sudden tone. • Beep detection: detecting the beep tone after a greeting is a very strong voicemail signal. • Absence of conversational interactivity: no turn-taking or no responses to prompts when agent speaks can be an indicator. Recommended decision rule (safe, low false-positive) • Treat

ivr_detection == true

or beep detection as immediate voicemail → end call. • Otherwise, require two of the other signals within the first N seconds (e.g., 8–12s): keyword in transcript + long greeting pattern, OR keyword + silence after greeting, OR DTMF presence + structured prompts → end call. • If signals are ambiguous, keep call open or escalate to human workflow. High-level flow to implement 1. Start session with voicemail/IVR detection enabled: ◦ Set

AgentSession(ivr_detection=True)

. 1. Capture these event streams in parallel: ◦

ivr_detection

result. ◦ Real-time transcript (if available) to match keywords. ◦ Audio events for beep/silence detection and DTMF events. 1. Run decision rule within an initial window (e.g., first 8–12 seconds). 2. If voicemail is detected per rule, call the room/session disconnect to end the call. Questions so I can give exact code • Which platform/SDK are you using? (Web, Node/Python agent, iOS, Android) • Do you have real‑time transcripts available, or only audio/events? If you tell me those two details, I’ll provide a concise code snippet tailored to your SDK showing: 1) enabling

ivr_detection

, 2) subscribing to

dtmf_received

, transcript events, and audio/beep/silence detection, and 3) the precise call-end logic (calling disconnect).

how to add pii

Thanks — I want to make sure I give you the right guidance. When you say “how to add PII,” do you mean one of these? • Pass user information (name, phone, email) into the agent/session so the agent can use it during the call (personalization). • Store or log PII in your system (transcripts, job records, analytics). • Allow the live agent or bot to capture PII from the call (e.g., address, card info) and keep it in call state. • Or do you mean protecting/redacting PII (masking card numbers, removing names from logs, preventing sensitive data from being recorded)? A few quick notes so you can pick the right option: • You can provide user-specific data to an agent via job metadata, room metadata, or participant attributes for personalization. • For security and compliance, do not store card numbers or other high-risk PII in transcripts; use PCI‑compliant payment flows instead. • LiveKit supports redaction/filtering: you can filter transcripts in real time, override LLM outputs to remove PII, disable recording for sensitive sessions, and redact transcripts before export. Tell me which of the options above matches your intent (or describe your exact use case), and which SDK/platform you’re using (Web, Node/Python agent, iOS, Android). I’ll then give a concise, platform-specific example and recommended safeguards. Sources: Job lifecycle | LiveKit Documentation | Redacting PII from agent logs and transcripts | LiveKit | Handling payments with PCI compliance | LiveKit

i want when recordin need to make tone on PII daat and maskl in transxtrioption

Thanks — this is clear and a good requirement. I can help you implement: 1) detect PII in the live audio/transcript, 2) play a masking tone (so the recording replaces the sensitive audio), and 3) redact/mask the PII in the transcript before storage or display. A few quick questions so I give a concrete, runnable example and safe defaults: • Which SDK/platform are you using? (Web, Node/Python agent, iOS, Android) • Do you have real‑time transcription enabled right now, or only post‑call transcripts? • Do you want the tone to be inserted into the recorded audio (so recordings contain the tone) or only played live to participants (recording stays altered via server-side mixing)? • Any specific PII types you must detect (credit card numbers, SSNs, emails, phone numbers, names)? PCI rules prohibit storing card numbers — do you want to block those entirely? Recommended high‑level approach (I’ll give code once you tell me the platform) 1. Real‑time transcription + pattern matching ◦ Stream audio through your transcription pipeline and run regex / pattern detectors on the transcript for PII types (credit cards, SSN, phone, email). ◦ Treat detections as events and include context (timestamp, participant ID). 1. Play a masking tone into the call recording ◦ When PII is detected, inject a short tone (beep or low-pass masked audio) into the remote track so the recorded file contains the tone instead of the raw sensitive audio. ◦ Implementation choices: ▪︎ Client-side: instruct the participant SDK to play a local tone and mute the microphone momentarily. ▪︎ Server-side mixing: mix a tone into the recorded composite track at the detection timestamp (preferred for guaranteed redaction in stored recordings). 1. Mask/redact transcript ◦ Replace detected PII substrings in the transcript with a placeholder like “[REDACTED]” or “[MASKED_PHONE]”. ◦ Store only the redacted transcript and log detection metadata (type, timestamp) separately if needed for audit. 1. Safety and compliance ◦ For card numbers: never store full numbers. Use tokenized payment flows or redirect to PCI-compliant IVR/payment gateway. ◦ Keep raw audio access restricted and delete raw unredacted recordings as soon as redaction is complete. ◦ Log detections for audits but avoid including the raw sensitive value in logs. If you confirm platform and transcription setup, I’ll provide: • A short code snippet showing how to detect PII from transcript events. • How to trigger tone insertion (client and server options). • How to redact the transcript before saving.

from datetime import datetime import json from dotenv import load_dotenv from livekit import api from livekit.agents import Agent, JobContext, JobProcess, WorkerOptions, cli, room_io from livekit.agents.beta.tools import EndCallTool from livekit.agents.voice import AgentSession from livekit.plugins import silero from agents.functions.function_builder import function_builder from agents.utils import get_llm_from_config, get_stt_from_config, get_tts_from_config, inactivity_handler from agents.utils import WELCOME_MESSAGE, OUTBOUND_MESSAGE, END_CALL_DESCRIPTION from agents.utils import get_all_metadata, get_all_instructions, substitute_variables, get_interruption_params from agents.services.call_recording import CallRecordingService from agents.api import end_call from agents.rag import get_embed_model load_dotenv() def prewarm(proc: JobProcess): proc.userdata["vad"] = silero.VAD.load() proc.userdata["embedding_model"] = get_embed_model() class VoiceAgent(Agent): def __init__(self, ctx, meta_data, variables=None, call_metadata=None, transfer_context=None): self.ctx = ctx self.rag = meta_data['rag'] self.transfer_context = transfer_context self.call_metadata = call_metadata self._transfer_config = self._extract_transfer_config(meta_data.get('functions', [])) self._pause_inactivity = None self._resume_inactivity = None tools = function_builder(meta_data['functions'], call_metadata) if self._transfer_config is not None and transfer_context: transfer_tool = self._build_transfer_tool() tools.append(transfer_tool) end_call_description = self._extract_end_call_description(meta_data.get('functions', [])) end_call_tool = EndCallTool( extra_description=end_call_description, delete_room=True, end_instructions=None ) tools.append(end_call_tool) super().__init__( instructions=get_all_instructions(meta_data['agent'], variables), stt=get_stt_from_config(meta_data['stt']), llm=get_llm_from_config(meta_data['llm']), tts=get_tts_from_config(meta_data['tts']), tools=tools ) @staticmethod def _extract_end_call_description(functions: list) -> str: for fn in functions or []: if fn.get('name') == 'end_call' and fn.get('endpoint') == 'internal://end_call': return fn.get('description') or END_CALL_DESCRIPTION return END_CALL_DESCRIPTION def set_inactivity_controls(self, pause_fn, resume_fn): self._pause_inactivity = pause_fn self._resume_inactivity = resume_fn def _extract_transfer_config(self, functions): for func in functions or []: if func.get('name') == 'transfer_call' and func.get('endpoint') == 'internal://transfer': return func.get('transfer_config', {}) return None def _build_transfer_tool(self): return build_cold_transfer_tool( agent_ref=self, transfer_config=self._transfer_config, transfer_context=self.transfer_context, call_metadata=self.call_metadata, ) async def on_user_turn_completed(self, turn_ctx, new_message) -> None: if not self.rag: return query = new_message.text_content if not query or len(query) < 3: return rag_content = await self.rag.query(query) if rag_content: turn_ctx.add_message( role="assistant", content=f"[Knowledge Base Context]: {rag_content}" ) async def entrypoint(ctx: JobContext): meta_data = await get_all_metadata(ctx) dial_info = meta_data.get('dial_info', {}) to_number = dial_info.get('to_number') from_number = dial_info.get('from_number') sip_trunk_id = dial_info.get('sip_trunk_id') variables = dial_info.get('variables', {}) is_outbound = bool(to_number) if is_outbound: if not sip_trunk_id: ctx.shutdown() return try: await ctx.api.sip.create_sip_participant( api.CreateSIPParticipantRequest( room_name=ctx.room.name, sip_trunk_id=sip_trunk_id, sip_call_to=to_number, participant_identity=to_number, participant_name=f"Call to {to_number}", krisp_enabled=True, wait_until_answered=True, ) ) except api.TwirpError as e: ctx.shutdown() return is_telephony = True channel = "PHONE" else: await ctx.connect() is_telephony = False for participant in ctx.room.remote_participants.values(): if hasattr(participant, 'attributes') and participant.attributes: attrs = participant.attributes sip_phone = attrs.get('sip.phoneNumber') if sip_phone: from_number = sip_phone to_number = attrs.get('sip.trunkPhoneNumber') sip_trunk_id = attrs.get('sip.trunkID', '') is_telephony = True break channel = "PHONE" if is_telephony else "WEB" call_metadata = { "from_number": from_number if is_telephony else None, "to_number": to_number if is_telephony else None, "call_type": channel, "direction": "OUTBOUND" if is_outbound else "INBOUND" if is_telephony else None, } participant_identity = "" if is_telephony: for participant in ctx.room.remote_participants.values(): if hasattr(participant, 'kind') and str(participant.kind) == 'ParticipantKind.SIP': participant_identity = participant.identity break if hasattr(participant, 'attributes') and participant.attributes: if participant.attributes.get('sip.phoneNumber'): participant_identity = participant.identity break transfer_trunk_id = dial_info.get('sip_trunk_id') or "" transfer_context = { "ctx": ctx, "room_name": ctx.room.name, "participant_identity": participant_identity, "sip_trunk_id": transfer_trunk_id, } if is_telephony else None agent = VoiceAgent(ctx, meta_data, variables, call_metadata, transfer_context) agent_data = meta_data['agent'] interruption_sensitivity = agent_data.get('interruption_sensitivity', 0.5) min_interruption_duration, false_interruption_timeout = get_interruption_params(interruption_sensitivity) session = AgentSession( vad=ctx.proc.userdata.get("vad"), preemptive_generation=False, min_interruption_duration=min_interruption_duration, false_interruption_timeout=false_interruption_timeout, ) pause_inactivity, resume_inactivity = inactivity_handler(session, ctx) if agent._transfer_config is not None and is_telephony: agent.set_inactivity_controls(pause_inactivity, resume_inactivity) start_time = datetime.now() recorder = CallRecordingService() await recorder.start_recording( room_name=ctx.room.name, filename=f"{ctx.room.name}_{int(time.time())}.ogg", ) is_transfer_agent = agent._transfer_config is not None and is_telephony room_options = room_io.RoomOptions(delete_room_on_close=not is_transfer_agent) await session.start(agent=agent, room=ctx.room, room_options=room_options) if is_telephony and is_outbound: greeting = agent_data.get('greeting_message', OUTBOUND_MESSAGE) else: greeting = agent_data.get('greeting_message', WELCOME_MESSAGE) greeting = substitute_variables(greeting, variables) await session.say(greeting) async def on_session_end(): recording_url = await recorder.stop_and_wait() end_call_data = { "session_id": ctx.job.id, "started_at": start_time.isoformat(), "ended_at": datetime.now().isoformat(), "transcript": json.dumps(session.history.to_dict() if hasattr(session, "history") else {"items": []}, indent=2), "agent_id": agent_data.get('id'), "client_id": agent_data.get('client_id'), "recording_url": "recording_url", "direction": "OUTBOUND" if is_outbound else "INBOUND" if is_telephony else None, "channel": channel, "from_number": from_number if is_telephony else None, "to_number": to_number if is_telephony else None, } try: await end_call(end_call_data) except Exception as e: print(f"Error logging call: {e}") if not is_transfer_agent: try: await ctx.api.room.delete_room(api.DeleteRoomRequest(room=ctx.room.name)) except Exception as e: print(f"Error deleting room: {e}") ctx.add_shutdown_callback(on_session_end) def run(): cli.run_app( WorkerOptions( entrypoint_fnc=entrypoint, prewarm_fnc=prewarm, agent_name="voice-agent", port=8081, ) ) if name == "__main__": run(in this

Thanks — this is clear and you’ve already got the right place to integrate the logic. Below I’ll give a focused, runnable plan you can drop into your existing Python agent code plus short code snippets that match your setup. I’ll cover: • real‑time PII detection (transcript-level), • playing/mixing a masking tone (practical options), • redacting transcripts before storage, • recommended safe behavior for recordings (PCI guidance), • exactly where to put the pieces in your posted code. Quick note about a technical limitation • You cannot reliably insert a masking tone directly into a participant’s live audio stream on the server so that only specific spoken words are replaced in the recorded composite. Because of that, the practical approaches are: 1) temporarily stop recording while PII is spoken, 2) play a tone live and resume recording (client-side or via server-side mixer if you control mixing), or 3) redact transcript and delete raw unredacted audio before saving. I’ll show how to implement options 1 and 2, and how to always redact transcripts. 1) Real‑time PII detection (transcript-based) • Override your agent’s STT node (or hook into your STT event handler) to scan final transcripts with regex patterns. When a PII match is found, emit a PII event (type, timestamp, participant id). Add this PII patterns block (near top of your file):

import re

PII_PATTERNS = {
    'credit_card': re.compile(r'\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b'),
    'ssn': re.compile(r'\b\d{3}[-]?\d{2}[-]?\d{4}\b'),
    'phone': re.compile(r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b'),
    'email': re.compile(r'\b[\w.+-]+@[\w.-]+\.\w{2,}\b'),
}

Example STT override (place inside your VoiceAgent class):

from livekit.agents import stt, ModelSettings
from typing import AsyncIterable, Optional

async def stt_node(self, audio: AsyncIterable["rtc.AudioFrame"], model_settings: ModelSettings) -> Optional[AsyncIterable[stt.SpeechEvent]]:
    async for event in super().stt_node(audio, model_settings):
        # Only inspect final transcript events
        if isinstance(event, stt.SpeechEvent) and event.type == stt.SpeechEventType.FINAL_TRANSCRIPT:
            for alt in event.alternatives:
                for name, pattern in PII_PATTERNS.items():
                    if pattern.search(alt.text):
                        # redact transcript text immediately for agent/LLM
                        alt.text = pattern.sub(f'[{name.upper()}]', alt.text)
                        # notify your flow about detection so you can act (stop recording, play tone, end call...)
                        await self.on_pii_detected({
                            "type": name,
                            "timestamp": alt.start_time if hasattr(alt, 'start_time') else None,
                            "text_snippet": "[REDACTED]"
                        })
        yield event

2) What to do when PII is detected — options Option A — Stop recording temporarily (recommended for guaranteed non‑recording of sensitive data) • You already have CallRecordingService in entrypoint. Expose it to the agent or provide a callable the agent can use. Example on PII detection handler:

async def on_pii_detected(self, pii_info):
    # stop recording quickly to avoid sensitive audio capture
    try:
        await self.ctx.proc.userdata['recorder'].stop_and_wait()
    except Exception:
        pass
    # optionally tell participant/agent
    await self.session.say("I cannot accept sensitive data over voice. Please use the secure link.")
    # resume recording after a short safe window if desired
    await asyncio.sleep(1.0)
    await self.ctx.proc.userdata['recorder'].start_recording(room_name=self.ctx.room.name, filename=...)

• Pros: raw sensitive audio never reaches stored recording. • Cons: recording will have gaps. Option B — Play a masking tone live (client or mixer) • If you control the client or have a server mixer that can inject audio into the composite track, play a short beep over the live audio when PII is detected. • Client-side approach: send a message to the client to play a local beep and mute mic momentarily; this ensures the beep appears in the local recording but requires client cooperation. • Server-side mixing: only possible if you have server mixing control to insert a tone into the composite. If not available, use Option A. Example command to tell client to play tone (pseudo):

await self.session.send_control_event({"type":"play_tone", "tone":"beep", "duration_ms":300})

Then client SDK plays tone and optionally mutes mic for that duration. 3) Redact before storage / export (mandatory) • Always run a final redaction pass on conversation history and transcript before saving or sending to downstream systems. Add helper and use in your on_session_end:

def redact_pii_in_text(text: str) -> str:
    for name, pattern in PII_PATTERNS.items():
        text = pattern.sub(f'[{name.upper()}]', text)
    return text

# in your on_session_end
safe_transcript = []
for item in session.history.to_dict().get('items', []):
    if 'content' in item:
        item['content'] = redact_pii_in_text(item['content'])
    safe_transcript.append(item)

end_call_data['transcript'] = json.dumps(safe_transcript, indent=2)

• Also redact anything you store in logs or analytics. 4) PCI & compliance guidance (musts) • Do not store or log card numbers. Redirect payments to PCI‑compliant payment pages/IVR. • Prefer stopping recording when payment/card data is at risk. • Limit access to raw unredacted recordings and delete them once redaction is complete. 5) Where to wire this into your posted code • Put PII patterns and stt_node override into VoiceAgent. • Make recorder available to the agent — you already create recorder in entrypoint. Store it in ctx.proc.userdata so agent can reference:

ctx.proc.userdata['recorder'] = recorder

• Implement on_pii_detected in VoiceAgent to call recorder stop/start or send control events via session. • Ensure on_session_end does transcript redaction before calling end_call. 6) Small checklist before deploying • Test regex false positives/negatives using recorded transcripts. • Decide your sensitivity: immediate stop vs require two signals. • Audit logs to ensure no raw PII is accidentally written. • For payments, implement non‑recorded payment handoff. If you want, I can: • Provide a complete patch with the functions inserted into your posted file, or • Produce a minimal client play-tone snippet if you control the web/mobile client. Which would you prefer (server changes patch, or server+client tone approach)? Also confirm whether your STT is real‑time (yes/no) so I tailor the exact code. Sources: Redacting PII from agent logs and transcripts | LiveKit | Handling payments with PCI compliance | LiveKit | Prebuilt tools | LiveKit Documentation