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.

from datetime import datetime import json import time from dotenv import load_dotenv from livekit import api from livekit.agents import Agent, JobContext, JobProcess, WorkerOptions, cli, function_tool 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_PROMPT 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): self.ctx = ctx self.rag = meta_data['rag'] 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=function_builder(meta_data['functions'], 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}" ) @function_tool(description=f"{END_CALL_PROMPT}") async def end_call(self) -> str: try: await self.ctx.api.room.delete_room(api.DeleteRoomRequest(room=self.ctx.room.name)) except Exception as e: print(f"Error deleting room during end call: {e}") self.ctx.shutdown() return json.dumps({"status": "call ended"}, indent=2) 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') 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, } agent = VoiceAgent(ctx, meta_data, variables, call_metadata) 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, ) inactivity_handler(session, ctx) start_time = datetime.now() recorder = CallRecordingService() await recorder.start_recording( room_name=ctx.room.name, filename=f"{ctx.room.name}_{int(time.time())}.ogg", ) await session.start(agent=agent, room=ctx.room) 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}") 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=8082, num_idle_processes=2, ) ) if name == "__main__": run() this is vpice agent i net text converstion with whatsappa dn sms any text mode

Thanks — I see the full voice-agent code and your goal: you want to use this agent for text conversations over WhatsApp and SMS (text-only mode). I can help convert or extend this voice flow to handle text messaging, but I need a few clarifications so I give the right guidance. Quick questions to narrow the approach 1. Integration method — which provider will deliver/receive WhatsApp and SMS messages? ◦ WhatsApp Business API directly, or a gateway like Twilio, Vonage, MessageBird, etc.? 1. Desired behavior — which of these best matches what you want? ◦ Pure text-only (no audio input/output) for both WhatsApp and SMS. ◦ Hybrid (both voice calls and text channels supported; users can switch). ◦ Text input from user but voice TTS responses (or vice versa). 1. Direction & routing — how should inbound messages map to your agent? ◦ One room/agent per phone number, or a single shared room for many conversations? 1. Webhooks & replies — do you already have webhooks/endpoints and credentials set up with your messaging provider? 2. Message format & extras — do you need support for media, buttons/templates (WhatsApp), or only plain text? 3. Session lifecycle — how should a conversation end? (e.g., inactivity timeout, explicit "end" command, or when agent calls end_call) What I can help with once you confirm the above • Show exactly where to disable audio and treat the AgentSession as text-only. • Provide code patterns to: ◦ Receive provider webhooks and forward incoming text to your agent (map webhook -> session input). ◦ Send agent responses back via provider APIs (session output -> provider reply). • Suggest session & room mapping strategies (one room per conversation vs shared rooms). • Outline webhook security (verify signatures) and retry handling for delivery failures. • Example code snippets for common providers (Twilio WhatsApp/SMS) to wire webhooks to your agent and push replies. If you want to start now, tell me: • Which provider you will use (Twilio, WhatsApp Business API, etc.). • Whether you want pure text-only or hybrid. Once I have that, I’ll provide a concise code example and the exact places in your current code to change to enable text messaging.

i want to knoe ohot buil agen fpr that any api

Thanks — this is helpful. You want to build an agent that handles text conversations over WhatsApp and SMS. I can guide you end-to-end, but first I need a bit more info so I give a clear, practical plan and code you can drop into your project. Quick clarifying questions 1. Which provider(s) will you use to send/receive messages? ◦ Twilio (SMS + WhatsApp), WhatsApp Business API, Vonage, MessageBird, or another? 1. Do you want: ◦ Pure text-only (no voice at all), or ◦ Hybrid (support both voice calls and text channels)? 1. How should conversations map to your agent instances? ◦ One LiveKit room per phone/chat (isolated sessions), or ◦ One shared agent handling many users (stateless mapping)? 1. Do you already have webhook endpoints and credentials for your messaging provider, or do you need help creating them? 2. Any special features needed (templates/buttons for WhatsApp, media messages, multi-language, persistent conversation storage / RAG)? What I can provide next (pick any) • A concrete architecture diagram and recommended flow for your chosen provider. • Example webhook handler code that receives inbound messages and forwards them to your agent session. • Example outbound code that takes agent replies and posts them back via Twilio/WhatsApp API. • Changes to your current voice-agent code to enable text-only sessions (exact lines to change). • Session mapping strategies (how to create/lookup LiveKit rooms per phone number). • Example for supporting templates/buttons and recording transcripts into your RAG. If you want to get started quickly, tell me: • Which provider (e.g., Twilio), and • Whether you want pure text-only or hybrid. Once you answer, I’ll send a concise step-by-step implementation with code snippets you can plug into your project. Sources: Text and transcriptions | LiveKit Documentation | External data and RAG | LiveKit Documentation

Hybrid any provider

Great — hybrid it is. I’ll outline a clear, practical plan you can implement: how to support both voice calls and text (WhatsApp/SMS/other providers), how to map conversations to LiveKit sessions, and sample code patterns. Before I give the full implementation, I need one quick clarification so I tailor the examples correctly: 1. Do you want a 1:1 mapping (one LiveKit room per phone/chat session) or a shared agent instance that handles multiple users without dedicated rooms? ◦ Recommended: one room per conversation (simpler state management, separate transcripts/recordings). If that’s fine, I’ll proceed. Below is the hybrid architecture and step-by-step plan you can implement immediately. High-level architecture (recommended) • Provider webhook (Twilio/WhatsApp/other) receives inbound text → forwards to your server. • Your server looks up/creates a LiveKit room for that phone/chat (one room per conversation). • For text messages: inject text into the AgentSession/room as text input and receive text responses from the agent; send replies back via provider API. • For voice calls: provider creates or connects SIP participant (or direct WebRTC) into the same room; agent runs speech-to-text and TTS in the room as in your existing voice agent. • Persist transcripts and recordings per room; optionally use RAG to enrich responses. Key pieces you’ll implement • Webhook receiver (HTTP endpoint) for inbound messages (SMS/WhatsApp). • Room/session manager that creates or finds rooms per phone number or chat ID. • Adapter that translates provider webhooks → AgentSession input and AgentSession output → provider API replies. • Session lifecycle rules (timeouts, end_call behavior, cleanup). • Security: verify webhook signatures and authenticate provider requests. Step-by-step implementation plan 1) Room & session mapping 1. Use phone number or provider chat id as conversation key. 2. On inbound message: ◦ Lookup existing LiveKit room name by that key. ◦ If none, create a new room (call LiveKit admin API), start AgentSession in that room (text-enabled), and store mapping in DB with expiry. 1. When a voice call arrives for that number, connect SIP participant into the same room so the agent continues the session. 2) Configure text-enabled AgentSession • Start AgentSession for text-only or hybrid: ◦ For text messages disable audio input/output or keep TTS available depending on your desired behavior. • Example options (conceptual): ◦ Text-only inbound/outbound: no STT/TTS needed. ◦ Hybrid: enable STT/TTS for voice, keep text handlers active so messages received by webhook are treated the same as user text. 3) Webhook handler (inbound text -> agent) • Receive webhook JSON from provider with fields: from, to, body, media, message_id. • Find/create room by

from

(phone) or provider chat ID. • Ensure AgentSession for that room is running and connected. • Inject the message into the session: ◦ If you have a room/text input event API, send message as a participant message so the agent receives it. Or call a session method to process inbound text (e.g., session.receive_text("...")). • Example flow: a. POST /webhook -> parse {from, body} b. room = get_or_create_room(from) c. session = ensure_session_running(room) d. await session.handle_incoming_text(body, sender=from) 4) Agent responses -> provider API (outbound) • When the agent produces a text reply, send it back via provider REST API: ◦ Twilio: POST Messages API to send SMS/WhatsApp. ◦ WhatsApp Business API: /v1/messages endpoint. ◦ Generic provider: call their send endpoint with recipient id. • Ensure deduplication and delivery checks (store message ids, handle retries). 5) Voice integration (existing voice code) • For outbound/inbound telephony you already call

create_sip_participant

or

ctx.connect()

. • Use the same room mapping so the text conversation and voice call share transcripts/state. • Continue recording and RAG enrichment as in your voice flow. 6) Session lifecycle & cleanup • End session on inactivity or explicit

end_call

function. • On session end: stop recording, save transcript, clear room mapping. • Optionally persist chat history to DB or RAG store. 7) Security & reliability • Verify webhook signature (Twilio X-Twilio-Signature or equivalent). • Rate-limit inbound messages, validate content. • Use retries and idempotency keys when sending outbound messages. Concrete example snippets 1) Webhook handler (conceptual, Python/flask-like)

from flask import Flask, request, jsonify

@app.route("/webhook", methods=["POST"])
def webhook():
    data = request.form or request.json
    from_id = data.get("From") or data.get("from")
    body = data.get("Body") or data.get("text")
    room = get_or_create_room_for_conversation(from_id)
    session = ensure_agent_session(room)
    # inject text into agent session
    await session.handle_incoming_text(body, sender=from_id)
    return jsonify(status="ok")

2) Sending outbound via Twilio (conceptual)

from twilio.rest import Client
client = Client(TWILIO_SID, TWILIO_TOKEN)

def send_sms(to, body):
    client.messages.create(
        body=body,
        from_=TWILIO_NUMBER,
        to=to
    )

3) Starting a text-enabled session (place in your entrypoint/room creation)

session = AgentSession(
    vad=None,  # no VAD for text-only
    preemptive_generation=False,
    # enable or disable STT/TTS depending on hybrid needs
)
await session.start(agent=agent, room=room, room_options={
    "audio_input": True,   # keep true for hybrid voice
    "audio_output": True,  # keep true for hybrid voice
    "text_input": True,
    "text_output": True,
})

Decisions I can help you finalize (pick any) • Exact room naming and TTL strategy. • How to handle simultaneous voice + text messages (race conditions). • How to store transcripts and feed them to RAG. • Concrete provider-specific webhook and reply code (Twilio, WhatsApp Business API, etc.). Next step If you confirm: • You prefer one room per conversation (recommended), and • Which provider you want code for first (e.g., Twilio), I’ll give a ready-to-run sample: webhook receiver, mapping logic, session starter changes, and outbound reply code you can paste into your project.

give code for text agent only

Thanks — I’ll give you a clear, ready-to-run Python example for a text-only LiveKit agent plus instructions on how to wire it to a messaging provider (e.g., Twilio) if you want to forward WhatsApp/SMS messages into the agent. What this does • Starts an AgentSession that only uses text (no STT/TTS). • Handles incoming text messages, runs the agent to generate replies, and exposes a small webhook adapter you can call from a messaging provider to forward inbound messages to the agent. • Shows how to send the agent’s replies back to the messaging provider (Twilio example included). Prerequisites • Python 3.8+ • livekit (or livekit-agents) and your LLM plugin installed • LiveKit API key/secret and URL available as environment variables • Twilio credentials if you want the Twilio outbound example Example: text-only agent + simple webhook adapter 1) text_agent.py

import os
import asyncio
import json
from datetime import datetime
from aiohttp import web

from livekit import RoomService  # conceptual; replace with your LiveKit client import
from livekit.agents import Agent, AgentSession, WorkerOptions, cli

# Replace these with the LLM/STORAGE utils you use in your project
def get_llm_from_config():
    # return an LLM instance compatible with livekit agents
    raise NotImplementedError

def get_agent_instructions():
    return "You are a helpful text-only assistant."

# Simple in-memory mapping: conversation_key -> room_name
# For production use a persistent DB.
ROOM_MAPPING = {}

class TextAgent(Agent):
    def __init__(self):
        super().__init__(
            instructions=get_agent_instructions(),
            llm=get_llm_from_config(),
            stt=None,   # no speech-to-text
            tts=None,   # no text-to-speech
            tools=[]
        )

async def ensure_room_for_conversation(conversation_key, lk_admin_client):
    # return existing room or create new and start an agent session in it
    room_name = ROOM_MAPPING.get(conversation_key)
    if room_name:
        return room_name

    room_name = f"conv-{conversation_key}-{int(datetime.utcnow().timestamp())}"
    # Create room via LiveKit admin API (pseudo-code)
    lk_admin_client.create_room(room_name)  # adapt to actual client
    ROOM_MAPPING[conversation_key] = room_name
    # Start agent session in background
    asyncio.create_task(start_agent_session(room_name, conversation_key, lk_admin_client))
    return room_name

async def start_agent_session(room_name, conversation_key, lk_admin_client):
    # Create and start agent in this room; session handles incoming text messages
    agent = TextAgent()

    session = AgentSession(
        agent=agent,
        # For a text-only session, don't provide VAD/STT/TTS
    )

    # Custom handler for inbound text (we will call session.receive_text from webhook)
    async def on_user_message(user_id: str, text: str):
        # Add user message to history and generate reply
        session.add_user_message(role="user", content=text)  # pseudo-method - adapt to actual API
        reply = await session.generate_reply()  # pseudo-method
        # session.say or emit transcription stream as needed
        # we'll rely on the webhook caller to fetch the reply via a callback below
        return reply

    # store session somewhere accessible by webhooks; for simplicity attach on the mapping
    # In production, store sessions in a process-safe store or use an agent worker.
    SESSION_REGISTRY[room_name] = {
        "session": session,
        "on_user_message": on_user_message
    }

    # Start session lifecycle (connect to LiveKit room) - pseudo-code
    await session.start(room=room_name, text_enabled=True, audio_enabled=False)

# Simple registry for sessions started in this process
SESSION_REGISTRY = {}

# Webhook endpoint to receive inbound text (from Twilio/WhatsApp/SMS provider)
# For Twilio, configure Messaging webhook to point to /webhook/sms
async def sms_webhook(request):
    data = await <http://request.post|request.post>() if request.content_type == 'application/x-www-form-urlencoded' else await request.json()
    # Typical Twilio fields: From, To, Body
    sender = data.get("From") or data.get("from")
    body = data.get("Body") or data.get("body") or data.get("text")
    if not sender or not body:
        return web.Response(text="missing sender or body", status=400)

    # Use sender as conversation key (you can normalize)
    conversation_key = sender.replace("+", "")
    # Ensure a LiveKit room and session exists for this conversation
    # lk_admin_client would be your LiveKit admin client instance
    room = await ensure_room_for_conversation(conversation_key, lk_admin_client)

    # Use the session registry to pass the incoming text to the agent
    reg = SESSION_REGISTRY.get(room)
    if not reg:
        return web.Response(text="session not ready", status=503)

    reply = await reg["on_user_message"](user_id=sender, text=body)

    # If using Twilio, respond with TwiML or just reply via Twilio REST API (see send_sms below)
    # Here we return JSON so your integration layer can send the reply back via provider API
    return web.json_response({"reply": reply})

# Example function to send SMS via Twilio (call this with the agent reply)
from twilio.rest import Client as TwilioClient
TWILIO_SID = os.getenv("TWILIO_SID")
TWILIO_TOKEN = os.getenv("TWILIO_TOKEN")
TWILIO_NUMBER = os.getenv("TWILIO_NUMBER")
twilio_client = TwilioClient(TWILIO_SID, TWILIO_TOKEN)

def send_sms_via_twilio(to_number: str, body: str):
    message = twilio_client.messages.create(
        body=body,
        from_=TWILIO_NUMBER,
        to=to_number
    )
    return message.sid

# Minimal app runner
async def start_app():
    global lk_admin_client
    # create your LiveKit admin client as needed
    lk_admin_client = RoomService(base_url=os.getenv("LIVEKIT_URL"), api_key=os.getenv("LIVEKIT_API_KEY"), api_secret=os.getenv("LIVEKIT_API_SECRET"))

    app = web.Application()
    app.router.add_post("/webhook/sms", sms_webhook)

    runner = web.AppRunner(app)
    await runner.setup()
    site = web.TCPSite(runner, "0.0.0.0", int(os.getenv("PORT", 8080)))
    await site.start()
    print("Webhook server started on port", os.getenv("PORT", 8080))

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

Notes about the example • This file is conceptual and uses pseudo-method names like

session.add_user_message

,

session.generate_reply

, and

AgentSession(... text_enabled=True)

to show intent. You will need to adapt those calls to the exact methods from your livekit-agents SDK version. • SESSION_REGISTRY is an in-process registry. In a multi-process or multi-instance deployment, store session metadata in a shared database or message bus and run agent workers that subscribe to events for a room. • For Twilio integration, either: ◦ Have your webhook return the bot reply directly (TwiML), or ◦ Return success and call Twilio REST API to send the reply asynchronously (recommended for complex flows). • Security: verify webhooks (Twilio signature) and add rate limiting and validation. How the flow works 1. Provider (Twilio/WhatsApp/etc.) posts inbound message to /webhook/sms. 2. The handler ensures a LiveKit room and agent session exist (one room per sender). 3. The inbound text is passed to the agent session; the agent generates a reply. 4. You send that reply back to the user via your provider API (Twilio example provided). If you want, I can: • Convert the pseudo-methods to exact calls for your livekit-agents SDK version (paste the exact SDK imports you use and I’ll adapt). • Provide a fully worked Twilio webhook + send example that returns TwiML synchronously. • Show a production-ready pattern using Redis (or Postgres) for session mapping and background worker processes. Which would you like next: • Exact livekit-agents method bindings (I’ll adapt to your SDK version), or • A full Twilio webhook + reply example (synchronous TwiML and async REST send)? Sources: Examples | livekit/livekit_composite | DeepWiki

message → agent → reply

Thanks — you want the minimal, runnable flow: inbound message → LiveKit text-only agent → reply back to the user. Below is a concise, practical Python example that demonstrates that end-to-end flow using: • a simple in-process agent session (text-only) • an HTTP webhook that accepts inbound messages (e.g., from Twilio) • calling the agent to generate a reply and returning/sending it back This example uses conceptual/livekit-agent APIs — adapt the small glue points to match the exact livekit-agents methods in your project. 1) Minimal text-agent + webhook (aiohttp)

# file: text_agent_webhook.py
import os
import asyncio
from aiohttp import web
from datetime import datetime
# Replace these with actual imports from your livekit-agents SDK and LLM plugin
# from livekit.agents import Agent, AgentSession, start_session_in_room
# from your_llm import get_llm

# --- Simple in-memory registries (for demo) ---
ROOM_BY_CONVERSATION = {}   # conversation_key -> room_name
SESSION_BY_ROOM = {}        # room_name -> session object

# --- Replace with your real LLM/Agent class ---
class SimpleTextAgent:
    def __init__(self, llm):
        self.llm = llm

    async def generate(self, history):
        # Replace with actual LLM call. Echo for demo:
        last_user = history[-1]['content'] if history else ""
        return f"Echo: {last_user}"

# --- Mock/adapter functions you must implement with your SDK ---
async def create_livekit_room(room_name):
    # Call LiveKit admin API to create a room
    # e.g., livekit_client.create_room(room_name)
    return room_name

async def start_text_session(room_name, agent):
    # Start an agent session connected to the LiveKit room.
    # This should create whatever in-process object you use to
    # submit user messages and get replies.
    # For demo we create a simple session object with history.
    session = {
        "room": room_name,
        "agent": agent,
        "history": []
    }
    SESSION_BY_ROOM[room_name] = session
    return session

async def send_reply_via_provider(conversation_key, reply_text):
    # Implement provider-specific send (Twilio, WhatsApp API, etc.)
    # For demo we just print — replace with API call.
    print(f"[send -> {conversation_key}] {reply_text}")

# --- Core: ensure room & session for a conversation ---
async def ensure_session_for_conversation(conversation_key):
    if conversation_key in ROOM_BY_CONVERSATION:
        room = ROOM_BY_CONVERSATION[conversation_key]
        return SESSION_BY_ROOM.get(room)

    # create new room and session
    room_name = f"conv-{conversation_key}-{int(datetime.utcnow().timestamp())}"
    await create_livekit_room(room_name)
    ROOM_BY_CONVERSATION[conversation_key] = room_name

    # instantiate agent and session (use your LLM)
    llm = None  # replace with get_llm()
    agent = SimpleTextAgent(llm)
    session = await start_text_session(room_name, agent)
    return session

# --- Handle inbound webhook: message -> agent -> reply ---
async def inbound_webhook(request):
    # Accept form-encoded (Twilio) or JSON
    data = await <http://request.post|request.post>() if request.content_type == 'application/x-www-form-urlencoded' else await request.json()
    conv_key = (data.get("From") or data.get("from") or data.get("sender") or data.get("user"))
    text = (data.get("Body") or data.get("body") or data.get("text") or "").strip()

    if not conv_key or not text:
        return web.json_response({"error": "missing from or text"}, status=400)

    # 1) Ensure session
    session = await ensure_session_for_conversation(conv_key)

    # 2) Append user message to session history
    session["history"].append({"role": "user", "content": text})

    # 3) Generate reply through agent
    reply = await session["agent"].generate(session["history"])

    # 4) Append assistant reply to history
    session["history"].append({"role": "assistant", "content": reply})

    # 5) Send reply back via messaging provider (async)
    await send_reply_via_provider(conv_key, reply)

    # 6) Return 200 for webhook
    # For Twilio you may return TwiML or 200 and send via REST. This returns JSON for clarity.
    return web.json_response({"reply": reply})

# --- App runner ---
async def start_app():
    app = web.Application()
    app.add_routes([<http://web.post|web.post>("/webhook/message", inbound_webhook)])
    runner = web.AppRunner(app)
    await runner.setup()
    site = web.TCPSite(runner, "0.0.0.0", int(os.getenv("PORT", 8080)))
    await site.start()
    print("Listening on port", os.getenv("PORT", 8080))

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

2) Where to adapt this to real LiveKit + LLM • create_livekit_room(room_name): call your LiveKit admin client to create a room. • start_text_session(room_name, agent): start a real AgentSession attached to the room with audio disabled and text I/O enabled. Store the session object so the webhook can deliver messages into it. • session["agent"].generate(...) : replace with the actual method to feed user text to the agent (usually adding user message to session and calling generation). Many SDKs expose something like

await session.handle_incoming_text(text)

or

await session.say(text)

combined with

session.generate()

— use your SDK's exact calls. • send_reply_via_provider(): implement Twilio/WhatsApp provider call. Example Twilio (synchronous reply via REST): Example Twilio send (replace in send_reply_via_provider):

from twilio.rest import Client
TW_SID = os.getenv("TWILIO_SID")
TW_TOKEN = os.getenv("TWILIO_TOKEN")
TW_FROM = os.getenv("TWILIO_FROM")
tw_client = Client(TW_SID, TW_TOKEN)

async def send_reply_via_provider(conversation_key, reply_text):
    msg = tw_client.messages.create(body=reply_text, from_=TW_FROM, to=conversation_key)
    return msg.sid

3) Simple flow summary • Provider posts inbound message to /webhook/message. • Webhook ensures a room/session exists for that conversation. • Webhook inserts the message into the session history and calls the agent to generate a reply. • Webhook sends the reply back through the messaging provider. If you want, I can: • Convert this to exact livekit-agents calls if you paste the AgentSession methods your SDK provides (e.g., how to send text into a session and get the generated reply). • Provide a complete Twilio example that returns TwiML directly (synchronous reply). • Show how to store ROOM_BY_CONVERSATION in Redis/Postgres for production. Which one should I send next — exact SDK-binding code or a Twilio synchronous example?

is there any way thet ai can sed text vial any mode to kvie kit agent foger about twilio

Thanks — I understand you want the AI to send text directly into a LiveKit agent (no Twilio or other messaging provider). Yes — there are a few direct ways to do this. Below I’ll summarize the options and give a minimal, practical example you can drop into your server to push AI-generated text straight to a LiveKit text-only agent. Quick question before code • Do you want the AI process to join the LiveKit room as a participant (so messages flow over LiveKit text streams), or do you want the AI to call a server-side API that injects text into the agent session (server-side bridge)? Both are supported — tell me which you prefer; I’ll give the exact code for that method. If you don’t care, I’ll give the simpler server-side bridge example first. Ways to send text directly to a LiveKit agent (no external messaging providers) • AI joins the LiveKit room as a participant and publishes text to a text stream (e.g., topic

lk.chat

). The agent receives that text like any other participant message. • AI calls your backend API that forwards text into the agent session (server-side bridge). Backend either: ◦ Calls an AgentSession method to inject text (if your SDK exposes such a method), or ◦ Publishes to the LiveKit text stream programmatically using the LiveKit server/admin SDK or a headless participant connection. • AI writes messages directly into the agent’s session store (if the agent exposes a programmatic API for adding user messages and calling generation). Which to choose • Join room as participant: easiest when you want the AI visible as a participant, want message metadata (participant identity), and want the same text stream mechanics as a regular user. • Server-side bridge: simplest to implement if you already run the agent server-side and want direct control over sessions and lifecycle (recommended for most integrations). • Direct session API: fastest and lowest-latency if your agent SDK exposes a method like

session.receive_text()

or

session.add_user_message()

and

session.generate_reply()

. Minimal server-side bridge example (AI -> agent -> reply) This is a concise pattern: your AI generates text, your server injects it into the agent session, the agent generates a reply, and your server returns/sends that reply back. 1) Conceptual flow 1. AI produces text (locally or via LLM). 2. Server receives the AI text and finds the correct AgentSession (one session per room/conversation). 3. Server injects the AI text into the AgentSession (as if a participant sent it). 4. Agent generates a reply; server reads that reply and uses it as needed (store/display/send). 2) Pseudocode (Python — fill in SDK-specific calls) • Replace pseudo-methods with the methods your livekit-agents SDK exposes (e.g.,

session.handle_incoming_text()

,

session.generate_reply()

,

session.say()

).

# ai_to_agent_bridge.py (conceptual)

# 1) AI produces text
ai_text = "Hello, I am an AI system. Please help the user."

# 2) Find session for conversation/room
room_name = lookup_room_for_conversation(conversation_key)  # your mapping
session = SESSION_REGISTRY.get(room_name)  # session object your process manages

if not session:
    # create room & start session (text-only) first
    room_name = await create_room_for(conversation_key)
    session = await start_text_only_session(room_name)
    SESSION_REGISTRY[room_name] = session

# 3) Inject AI text into the agent session
# Common patterns:
# - session.add_user_message(role="user", content=ai_text)
# - await session.receive_text(ai_text, participant_id="ai-bot")
session.history.append({"role": "user", "content": ai_text})  # pseudo
# then trigger generation:
reply = await session.agent.generate(session.history)  # pseudo

# 4) Use the reply (log/display/send)
print("Agent reply:", reply)
# Optionally publish the reply into the room text stream so other participants see it:
# await publish_text_to_room(room_name, reply, topic="lk.transcription")

3) If you want the AI to join as a participant (LiveKit text stream) • Create a short-lived participant token for the AI. • Connect a headless LiveKit client (or server-side SDK that can publish data) and send text using the LiveKit client API to the

lk.chat

topic. • The agent receives the message through its text-input handler. JavaScript example to publish text as a participant:

// after obtaining a token for AI participant and connecting:
await room.localParticipant.publishData("AI: Hello user", { topic: "lk.chat" });

Receiving agent replies • Have the agent emit replies into a text stream (commonly

lk.transcription

) or call

session.say()

so replies are available to participants. • If using server-side bridge, read the generated reply from the session and handle/stash it where you need it. Security & best practices • Protect your session injection API (only trusted AI processes should be allowed). • Use short-lived LiveKit tokens for AI participants if they join rooms. • If multiple instances run, store session mapping in a shared database (Redis/Postgres) and use a worker process to route injected messages to the right instance. Next step Tell me which approach you prefer: • Server-side bridge (I’ll provide concrete Python code using your livekit-agents SDK calls if you paste those method names), or • AI as a LiveKit participant (I’ll give exact steps to mint a token, connect headless client, and publish text). If you want the bridge example now, confirm whether you use the livekit-agents Python SDK and paste the exact methods you use to: • add user text to a session, and • trigger generation or read the agent reply. If you don’t know the method names, tell me and I’ll provide clear pseudo-to-SDK mappings you can try. Sources: Text and transcriptions | LiveKit Documentation | How to build an agent with speech-to-text input and text-only output | Live...