generate_reply(instructions="") reads out the function name out loud...why?

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

It reads out the entire function name, does not matter what instructions I place.

Thanks — I hear your frustration that the function name is always being spoken no matter what you put in

instructions

. I can help troubleshoot, but I need a bit more detail to pinpoint the cause. Please share: • The exact code snippet where you call

generate_reply(...)

and how you pass its result to TTS (copy/paste is best). • An example

instructions

string you used that still produced the function name. • Which SDK, agent, or runtime you’re using (browser JS, Node, Python, or a specific agent wrapper) and whether you’re using a separate TTS call or an automatic speech pipeline. Quick checks you can try right away: • Make sure you are not accidentally including the function call or debug text in the output path (for example, logging the call into the response text). • Try a simple explicit instruction like

instructions="Say: Hello world"

and show me both the generated text and the spoken result. • If you have a separate TTS step, try sending a hardcoded string to TTS (bypass the LLM) to confirm the TTS layer isn’t echoing the call. Send the code and one failing example and I’ll analyze it and give an exact fix.

I just need to know why... on_enter it works fine... but when called from a @function_tool it reads out the entre function name

Thanks — that extra detail helps. You're seeing the function name spoken when called from a

@function_tool

because the tool's return value is fed back into the LLM and can become part of the generated/speech output. In

on_enter

no tool return value is injected, so you don't see the echo. What to do: • If you call

generate_reply(...)

inside the tool, make the tool return

None

(or

None, "log message"

) so nothing extra is sent back to the LLM. Example (Python):

@function_tool
async def my_tool(self, context: RunContext):
    await context.session.generate_reply(instructions="Say hello to the user")
    return None  # prevents the tool return value from being interpreted/spoken

If that doesn't fix it, please paste the exact tool function (how you call

generate_reply

) and the tool's return statement and I’ll pinpoint the change. Sources: Tool definition and use | LiveKit docs | Pipeline nodes and hooks | LiveKit docs | SIP Lifecycle Management Agent | LiveKit docs

what is the diff between @function_tool and a regular function

Good question — here’s a concise comparison so you can see when to use each. What

@function_tool

does: • Exposes the function to the LLM as a callable tool the agent can invoke at runtime. • Includes metadata (name, description, arg types) so the LLM knows when/how to call it. • Is selected and called asynchronously by the LLM based on user input. • Sends the function’s return value back to the LLM (so returning a string can become part of the conversation/speech). • Is intended for actions the model may decide to run (lookups, external APIs, agent handoffs). What a regular function does: • Is only callable from your code (not visible or callable by the LLM). • Has no automatic metadata exposed to the LLM. • Is used for internal logic, helpers, or synchronous tasks you control. • Its return value stays in local code unless you explicitly pass it to the LLM or another component. When to use each: • Use

@function_tool

for capabilities you want the model to autonomously call (e.g., fetch data, perform actions). • Use regular functions for internal helpers and logic you don’t want the LLM to invoke. Quick tip related to your earlier issue: • If you call

generate_reply()

inside a

@function_tool

, return

None

(or

None, "log"

) so the tool doesn’t send an unintended string back to the LLM and get spoken. Sources: Tool definition and use | LiveKit docs

@function_tool async def finalize_call(self, speak_with_rep: bool = None) -> str: """ Ask caller if they want to speak with someone. Args: speak_with_rep: True if user wants to speak with rep, False if they decline, None to ask the question """ logger.info("✅ finalize_call started") if speak_with_rep is None: # First call - ask the question await self.session.say("Would you like to speak with someone in regards to your project?", allow_interruptions=False, ) return "" elif speak_with_rep is True: # User said yes logger.info("✅ User wants transfer") return await self.speak_to_representative() else: # _speak_with_rep is False_ # User said no logger.info("⛔ User declined transfer") await end_call(session=self.session) # ✅ Pass session too return None whenever user says no it does not collect it and log user declined but when user says yes in logs and calls the transfer....what am i doing wrong?

Thanks — I see the

finalize_call

code and that the "yes" path logs and transfers correctly but the "no" path neither logs nor records the user's "no". Likely cause • Your tool is declared to return

str

. Returning

None

from a

@function_tool

can cause the agent runtime to treat that as "no return value" and not send it back to the LLM in a way that continues the flow or triggers logging the way you expect. That can make the LLM not register the "no" outcome. • The first branch returns an empty string

""

(which becomes a string return), the yes branch returns whatever

speak_to_representative()

returns (a string), but the no branch returns

None

— this mismatch is the probable root. What to do • Return a string consistently from every branch. Return

""

or a short status message when you want silence but still a string sent back. • Alternatively, explicitly log before returning and ensure

end_call

doesn't swallow or asynchronously hide the log. Suggested fix (minimal change)

@function_tool
async def finalize_call(self, speak_with_rep: bool = None) -> str:
    <http://logger.info|logger.info>("✅ finalize_call started")

    if speak_with_rep is None:
        await self.session.say(
            "Would you like to speak with someone in regards to your project?",
            allow_interruptions=False,
        )
        return ""

    elif speak_with_rep is True:
        <http://logger.info|logger.info>("✅ User wants transfer")
        return await self.speak_to_representative()

    else:  # speak_with_rep is False
        <http://logger.info|logger.info>("⛔ User declined transfer")
        await end_call(session=self.session)
        return ""  # return a string instead of None

If you still don’t see the "User declined transfer" log after this, tell me: • How the tool is being invoked (what triggers the False path). • The implementation of

end_call

(it may raise/exit early or mute logs).

JUST TELL ME HOW TO COLLEC THE RESPONSE CORRECTLY

Understood — here’s the exact fix to collect the response reliably. Problem: the tool returns mixed types (""/str vs None). The runtime converts tool returns to strings and sends them back to the LLM; returning None can cause the "no" outcome to be ignored. Fix: always return a string status, and ensure you parse the boolean argument passed into the tool. Use explicit logging before returning. Example:

@function_tool
async def finalize_call(self, speak_with_rep: bool = None) -> str:
    <http://logger.info|logger.info>("✅ finalize_call started")

    if speak_with_rep is None:
        await self.session.say(
            "Would you like to speak with someone in regards to your project?",
            allow_interruptions=False,
        )
        return ""  # ask-question phase; return empty string

    if speak_with_rep is True:
        <http://logger.info|logger.info>("✅ User wants transfer")
        result = await self.speak_to_representative()
        return result or "transfer_initiated"

    # speak_with_rep is False
    <http://logger.info|logger.info>("⛔ User declined transfer")
    await end_call(session=self.session)
    return "user_declined"  # return a string so the tool result is recorded

Also confirm: • The caller/LLM is passing the boolean

speak_with_rep

(True/False) to this tool rather than a string. • Your intent parsing maps user "no" -> False. If you paste the actual tool invocation (how the LLM or caller calls

finalize_call

) I’ll tell you exactly how to map the user's utterance to the boolean.

async def on_enter(self) -> None: # _if self.userdata.has_existing_booking:_ # _logger.info("_🔮 Bookings Exists") return await self.finalize_call()

You're calling

finalize_call()

from

on_enter

which correctly asks the question, but you must ensure the user's answer is passed back into

finalize_call

as the

speak_with_rep

boolean in a follow-up call. Steps to collect the response correctly: 1. On enter: only ask the question and return (no decision).

async def on_enter(self) -> None:
    await self.finalize_call()  # speak_with_rep is None -> asks question
    return None

1. Ensure the agent/LLM maps the user's reply (yes/no) into a second call to the tool with the boolean argument. Example flow the LLM should perform: • LLM sees user answer -> calls tool:

finalize_call(speak_with_rep=True)

or

finalize_call(speak_with_rep=False)

1. Make

finalize_call

handle three states and always return a string (never None). Example:

@function_tool
async def finalize_call(self, speak_with_rep: bool = None) -> str:
    <http://logger.info|logger.info>("✅ finalize_call started")

    if speak_with_rep is None:
        await self.session.say(
            "Would you like to speak with someone in regards to your project?",
            allow_interruptions=False,
        )
        return ""  # asked question

    if speak_with_rep is True:
        <http://logger.info|logger.info>("✅ User wants transfer")
        result = await self.speak_to_representative()
        return result or "transfer_initiated"

    # speak_with_rep is False
    <http://logger.info|logger.info>("⛔ User declined transfer")
    await end_call(session=self.session)
    return "user_declined"

1. Verify the LLM/tool invocation: • Confirm the model/tool-calling layer is configured to call

finalize_call

with the boolean, not pass a raw string. • If you rely on intent parsing, ensure "no"/"nah"/"not now" map to False and "yes"/"yeah" map to True. Quick checklist if it still fails: • Do not return None from the tool (use a string). • Confirm the second invocation occurs (inspect logs for the tool being called with True/False). • Confirm

end_call

does not terminate the process before logging. If you paste the actual follow-up tool invocation (how the LLM calls

finalize_call

after the user replies), I’ll tell you exactly how to map the utterances to True/False and where to adjust.

async def end_call(session: AgentSession, params_filler: bool = False): print("☎️ Ending call") await session.generate_reply(instructions="Thank you for calling Hue-Line. Someone will contact you soon.") await hangup_call() return None still reads it out loud

Thanks — I know this is urgent. You’ve shown

end_call

returns None but the function name (or tool return) is still being spoken. Short answer and exact steps to fix it: Why it still speaks • When a function decorated with

@function_tool

is called, its return value (or the tool call itself) is sent back into the LLM context. The LLM can then generate speech that echoes that tool/text. Even if

end_call

returns None, the tool caller (your

finalize_call

or the runtime) may still produce a message/state that the LLM speaks. Do this exact, minimal change to stop it: 1) Don’t let the LLM generate the final TTS text for end-of-call messages. Use

session.say(...)

(or

session.generate_reply(...)

) to speak the exact phrase, then ensure the tool returns a sentinel that won’t be spoken (preferably

None

) — and make the tool itself NOT be a

@function_tool

. 2) Convert

end_call

to a plain helper (no

@function_tool

) and ensure the tool that calls it returns a string consistently or returns None correctly. Example changes: • Make

end_call

a plain helper:

# plain helper, not a @function_tool
async def end_call(session: AgentSession, params_filler: bool = False):
    print("☎️ Ending call")
    await session.say("Thank you for calling Hue-Line. Someone will contact you soon.")
    await hangup_call()
    return None

• Ensure the calling tool returns a string or None consistently. For

finalize_call

(which is the @function_tool), do this:

@function_tool
async def finalize_call(self, speak_with_rep: bool = None) -> str:
    <http://logger.info|logger.info>("✅ finalize_call started")

    if speak_with_rep is None:
        await self.session.say(
            "Would you like to speak with someone in regards to your project?",
            allow_interruptions=False,
        )
        return ""   # asked question

    if speak_with_rep is True:
        <http://logger.info|logger.info>("✅ User wants transfer")
        await self.speak_to_representative()
        return "transfer_initiated"

    # speak_with_rep is False
    <http://logger.info|logger.info>("⛔ User declined transfer")
    await end_call(session=self.session)   # plain helper call
    return ""   # return empty string (not None) so runtime doesn't inject unexpected text

3) If you must keep

end_call

as a

@function_tool

, then: • Have it call

session.say(...)

for the spoken phrase and return

None, ""

(two-tuple) or

None

explicitly — but note tool returns still get stringified and can trigger LLM output; making it a plain helper is safer. Verification steps • Confirm

finalize_call

is the only

@function_tool

exposed to the model for this flow. • Watch logs: you should see the

session.say

text being sent, then

finalize_call

returning

""

(or

transfer_initiated

) — no additional LLM-generated speech should occur. • If you still hear a function name, paste the tool call logs (the runtime’s tool-call entry and the LLM response) and I’ll point out the exact spot producing the spoken text. If you want, paste the runtime tool-call log for the "no" path now and I’ll mark the single line you need to change.

end_call is not @functon_tool its only finalaize_call, oyu have the code

You're almost there — here's the exact, minimal pattern that collects the yes/no correctly and prevents the function name or return value from being spoken. 1) Ask once (on_enter)

async def on_enter(self) -> None:
    await self.session.say("Would you like to speak with someone in regards to your project?", allow_interruptions=False)
    return None

2) Expose only the decision handler as a tool that accepts a bool

@function_tool
async def finalize_call(self, speak_with_rep: bool = None) -> None:
    <http://logger.info|logger.info>("✅ finalize_call started")

    if speak_with_rep is None:
        # defensive: if called without value, re-ask
        await self.session.say("Would you like to speak with someone in regards to your project?", allow_interruptions=False)
        return None

    if speak_with_rep is True:
        <http://logger.info|logger.info>("✅ User wants transfer")
        await self.speak_to_representative()
        return None

    # speak_with_rep is False
    <http://logger.info|logger.info>("⛔ User declined transfer")
    await self.session.say("Okay, I won't transfer you. Thank you for calling.")
    await end_call(session=self.session)  # end_call can be a plain helper
    return None

Key points to ensure: • The LLM will map user "yes"/"no" -> True/False and call

finalize_call(speak_with_rep=True/False)

automatically if the tool signature and docstring are clear. • Always return

None

from the

@function_tool

to suppress any extra LLM reply being converted into speech. • Use

session.say(...)

(or

session.generate_reply(...)

) to speak exact phrases; do not rely on returning strings from the tool for speech. • Keep

end_call

as a plain helper (not a

@function_tool

) that calls

session.say(...)

then hangs up and returns None. If you still hear the function name after this, paste the runtime tool-call log showing the LLM's tool invocation and response and I’ll point to the exact offending line.

why cant i just use generate_reply??? the static say is not ideal

You're right — you can use

generate_reply()

, but here's exactly why it causes the function name (or other unexpected text) to be spoken and how to use it safely. Why generate_reply can cause the function name to be spoken •

generate_reply()

asks the LLM to produce text based on context. When used inside a

@function_tool

, the tool's return value (or any string placed into the conversation context) is sent back to the LLM. The LLM may then echo or narrate that tool call/return in its generated reply, which the TTS will speak. •

session.say()

emits an explicit spoken utterance and does not rely on the LLM to produce the exact wording, so it avoids the LLM reinterpreting tool returns and speaking them. How to keep using

generate_reply()

safely • Use

generate_reply()

only to produce content you want the LLM to generate and speak. • If you call

generate_reply()

inside a

@function_tool

, make sure the tool returns

None

(not a string). Returning a string causes that string to be injected back into the LLM context and can be echoed. • If you want the LLM to generate a reply but avoid the LLM narrating the tool call itself, do this pattern: 1) Ask question via LLM (on_enter)

await self.session.generate_reply(instructions="Ask: 'Would you like to speak with a representative?'")

2) Let the LLM call your tool with a boolean:

@function_tool
async def finalize_call(self, speak_with_rep: bool = None) -> None:
    if speak_with_rep is None:
        # defensive: re-ask using generate_reply if you prefer LLM phrasing
        await self.session.generate_reply(instructions="Please ask: 'Would you like to speak with a representative?'")
        return None

    if speak_with_rep:
        # If you want LLM phrasing for the transfer message, generate it and send to TTS,
        # but ensure the tool returns None so the LLM doesn't echo the tool
        reply = await self.session.generate_reply(instructions="Tell user we're transferring them, briefly.")
        # If generate_reply returns text, send it explicitly to TTS via session.say or session.play_tts
        await self.session.say(reply)
        await self.speak_to_representative()
        return None

    # user declined
    decline_reply = await self.session.generate_reply(instructions="Acknowledge decline succinctly.")
    await self.session.say(decline_reply)
    await end_call(session=self.session)
    return None

Key rules • Always return

None

from

@function_tool

functions when you do not want the LLM to generate an additional reply from the tool return. • If

generate_reply()

is used to create the phrase, explicitly send that phrase to TTS using

session.say()

(or session.play_tts). Do not rely on returning the generated string from the tool. • Keep tool return types consistent (prefer None) to avoid the runtime stringifying returns and feeding them back into the LLM. If you want, I can convert the snippet above into your exact flow (keeping LLM-generated phrasing but preventing echoes). Paste where you call the tool and I’ll produce the one-line change.