how to push message from behalf of customer - on_enter function

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

but in the case of the realtime model like openai - while using this function triggers problem - like the agent starts with something else.

Thanks — I’m following up on your note about the realtime OpenAI model behaving unexpectedly when you use

on_enter

. I can help, but I need a bit more detail so I can give a targeted fix. A few quick questions that will narrow this down: 1. Which SDK and language are you using for the agent (Python, Node/TypeScript, browser JS)? 2. Are you using OpenAI’s realtime model as the agent’s LLM inside LiveKit Agents, or are you driving messages directly to OpenAI from your app? 3. What exactly happens when the agent starts? Examples that help: ◦ Does the agent send a different first message than the

instructions

you passed to

generate_reply()

? ◦ Does it send multiple messages, or a truncated/garbled message? ◦ Any error logs printed on the server or client when this occurs? 1. Can you paste the minimal

on_enter

code you’re using (redact any keys/tokens)? 2. Do you want the agent to: ◦ Send a proactive agent message on enter (agent identity), or ◦ Cause the customer participant to send a message (user identity)? What I can suggest immediately • If the model is producing unexpected content, try keeping

instructions

concise and explicit (e.g., “Send exactly: Hello — I’m your agent.”) so the model’s first output is constrained. • If timing/race conditions seem likely (realtime stream starting before prompt is set), we may need to ensure the session/context is fully initialized before calling

generate_reply()

in

on_enter

. Let me know your SDK and I’ll show how to await readiness. • If you’re trying to emulate a customer message, remember

on_enter

runs as the agent; pushing a user-originating message must happen from the participant/client side. I can show a pattern where the agent signals the frontend and the frontend publishes the user message. Send the answers to the questions above or paste the

on_enter

snippet and any logs you have, and I’ll walk through a specific fix for your realtime setup. , with interactive elements

python yes it starts spekaing somthing else itself I want to trigger Hello on behalf of customer itself - so that agent starts the speeach as generate_reply has some problem

Thanks — I’m responding to your point that with the OpenAI realtime model in Python, calling

generate_reply()

in

on_enter

makes the agent speak something unexpected, and you want the session to start with the customer saying “Hello” instead. Short answer •

on_enter

runs as the agent.

generate_reply()

always makes the agent speak, so it cannot produce a message that originates from the customer. • To make the conversation start with a customer-originated “Hello” you must send that message from the participant side (frontend) or simulate a participant message programmatically after the agent session is initialized. Two practical ways to do this in Python (pick one): 1) Preferred — Frontend sends “Hello” after agent readiness • Flow: a. Start the room and agent session. b. Have the frontend wait until the agent is ready (e.g., after connecting or after receiving a readiness event). c. Frontend sends the customer message via the LiveKit SDK:

localParticipant.sendText("Hello", { topic: 'lk.chat' })

. • Why: This guarantees the message truly originates from the participant and avoids any realtime-model prompt/race issues that make the agent speak unexpectedly. 2) Backend simulation — simulate a participant message in

on_enter

(useful for testing/automation) • Implement a short delay to ensure session is fully initialized, then call the session input API to simulate an incoming user message. Example pattern:

import asyncio

async def on_enter(self) -> None:
    # Wait for session readiness (adjust delay as needed)
    await asyncio.sleep(0.5)

    # Simulate a user text message arriving (appears as customer-originated)
    self.session.input.text_received(
        text="Hello",
        participant=self.session.room.local_participant
    )

• Notes: ◦ This uses the agent session input system to simulate an

lk.chat

message. It makes the agent handle “Hello” as if a participant sent it. ◦ Use this only when you cannot or do not want to rely on a real frontend participant. For production, real participant-sent messages are recommended. Additional guidance for realtime OpenAI model behavior • If the agent’s LLM speaks unexpected content, it’s often due to: ◦ The agent being asked to speak (agent identity) instead of receiving a user input. ◦ Race conditions where the model starts streaming before the intended initial input is processed. • Ensuring the customer message originates from the participant side (frontend or simulated via

text_received

) avoids those problems because the agent receives explicit user input and will respond to it rather than produce its own unsolicited first utterance. Quick checklist to implement • Decide frontend vs. backend simulation. • If frontend: ◦ Add a readiness signal (room connected / agent-ready event). ◦ Call

localParticipant.sendText("Hello", { topic: 'lk.chat' })

. • If backend simulation: ◦ Add a short delay in

on_enter

. ◦ Call

self.session.input.text_received(text="Hello", participant=self.session.room.local_participant)

. Would you like a concrete frontend JS snippet for sending the text, or do you want me to help tune the delay/placement of

text_received()

in your current Python

on_enter

code? If you paste your current

on_enter

snippet, I’ll suggest the minimal change. Sources: Text and transcriptions | LiveKit Documentation, with interactive elements

await self.session.generate_reply(user_input="Hello?")

will this work ?

Short answer: No — calling

await self.session.generate_reply(user_input="Hello?")

inside

on_enter

will not make the message originate from the customer. It tells the agent to generate a reply as if it had already received "Hello?", so the agent itself will speak/respond — not a customer participant. Why: •

generate_reply(...)

always produces an agent-originated response. Passing

user_input

only seeds the agent’s reply; it does not create a participant/chat event that looks like a customer message. • That’s why you saw the agent “start speaking something else” when using

generate_reply()

— the agent is the active speaker and will generate content from its model. What will work instead (pick one): 1) Frontend (recommended for production) • Have the frontend participant send the actual user message after agent readiness: ◦ JS:

localParticipant.sendText("Hello?", { topic: "lk.chat" })

• The agent will then receive that as a genuine participant message and reply naturally. 2) Backend simulation (for testing / automation) • Simulate an incoming participant message after session init:

import asyncio

async def on_enter(self) -> None:
    await asyncio.sleep(0.5)  # ensure session is ready
    self.session.input.text_received(
        text="Hello?",
        participant=self.session.room.local_participant
    )

• This makes the agent handle "Hello?" as if a customer sent it. If you absolutely must use

generate_reply

in `on_enter`: • It will make the agent speak directly. To tightly control output, make

instructions

explicit (e.g.,

instructions="Reply exactly: Hello."

) but this still originates from the agent, not the user. Would you like: • A short frontend snippet showing where to place

sendText

after agent readiness, or • Help inserting

text_received(...)

into your current

on_enter

with the correct timing? Paste your

on_enter

if you want me to tweak it., with interactive elements