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# ask-ai
b
i see that user message don't appears in chat context after user answer to agent greetings and agent call tool based on user input
t
Thanks for reaching out, Aleh. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Thanks — I’m looking into why your user message isn’t appearing in chat context after the user replies and the agent calls a tool. I have a few quick clarifying questions so I can diagnose this precisely: 1. How is the user message submitted? (web text input, speech→transcription, SIP participant, etc.) 2. Are you using 
session.generate_reply(user_input=...)
, 
lk.chat
events, or another API path when the agent handles the reply? 3. Do you see a 
FunctionCall
and 
FunctionCallOutput
entry in the chat history when the agent invokes the tool? 4. Are you explicitly calling 
agent.update_chat_ctx()
anywhere after tool calls or replies? 5. Can you share a minimal code snippet or sequence of events around the greeting, the user reply, and the tool call (redact any secrets)? With those details I can point to the exact places to check (transcription event handling, 
user_input
passing to 
generate_reply
, and chat-context update after function/tool calls) and give concrete next steps. Would you like me to suggest likely fixes for each of those areas while you gather the snippet? Sources: Interrupt User | LiveKit docs | SIP participant | LiveKit docs | Agent speech and audio | LiveKit docs | Agents v0.x migration guide - Node.js | LiveKit Docs
b
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class GreetingsAgent(BaseAgent):
    def __init__(
        self,
        intro_message: str,
        company_name: str,
        customer_full_name: str,
        chat_ctx: ChatContext | None = None,
    ) -> None:
        instructions = greetings_agent_instructions.invoke(
            {
                "customer_full_name": customer_full_name,
                "company_name": company_name,
            }
        ).text
        super().__init__(instructions=instructions, chat_ctx=chat_ctx)
        self.participant: rtc.RemoteParticipant | None = None
        self.intro_message: str = intro_message

    def set_participant(self, participant: rtc.RemoteParticipant) -> None:
        self.participant = participant

    async def on_enter(self) -> None:
        await self.session.say(text=self.intro_message)

    @function_tool()
    async def correct_customer(self, context: RunContext[SessionState]) -> "IdentityVerifyAgent":
        """Transition to identity verification when speaking with the correct customer.

        Use this tool when you receive clear and explicit confirmation that you are
        speaking with the intended customer.
        """
        verification_message = greetings_agent_correct_customer_instructions.invoke({}).text
        return IdentityVerifyAgent(
            customer_data=context.userdata.customer_data,
            company_name=context.userdata.company_name,
            intro_message=verification_message,
            chat_ctx=self.chat_ctx,
        )
dubug correct_customer i see that sometimes there is no user messages in chat_context when tool already called
t
Thanks — this is helpful. I’m responding about why the 
correct_customer
tool sometimes runs without the user’s message appearing in 
chat_ctx
. A few focused checks and one quick debug I recommend: 1) Confirm how the user reply reaches the agent • Is the reply coming in as a 
user_input_transcribed
event or as a text input via 
lk.chat
? If it’s transcription, ensure your transcription handler forwards that event into the same chat flow the agent reads. 2) Ensure the agent receives the user_input when calling generate_reply • If any code calls 
session.generate_reply(user_input=...)
make sure the exact user text is passed there before the tool runs. If you rely on implicit handling, race conditions can occur where the tool executes before the user_input is attached. 3) Check for race conditions around tool invocation • Tools decorated with 
@function_tool()
may be invoked as soon as the agent’s policy decides to call them. If the user reply and the agent’s decision to call 
correct_customer
happen nearly simultaneously, the chat context might not have been updated yet. • Add logging for the timestamps of: receiving user message, updating chat context, agent decision to call tool, and the tool invocation. That will show ordering. 4) Verify chat context updates after external events • If you modify chat context outside the agent’s normal flow, call 
agent.update_chat_ctx()
(or the equivalent in your runtime) before continuing. Confirm the chat history includes the user message at that point. 5) Quick instrumentation you can add now • In the code path that handles incoming user messages, immediately log the event and append it to 
self.chat_ctx
(or call the helper that does). Then, in 
correct_customer
, log 
self.chat_ctx.history
(or 
context.chat_ctx
) to see whether the message is present at tool-entry time. If you want, paste: • the code that receives/transcribes user replies, and • any place you call 
session.generate_reply(...)
or otherwise trigger the agent decision loop I can point to the exact spots to add logging or a small code change to ensure the user message is included before 
correct_customer
runs. Would you like that?
b
so here
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@function_tool()
async def correct_customer(self, context: RunContext[SessionState]) -> "IdentityVerifyAgent":
    """Transition to identity verification when speaking with the correct customer.

    Use this tool when you receive clear and explicit confirmation that you are
    speaking with the intended customer.
    """
    verification_message = greetings_agent_correct_customer_instructions.invoke({}).text
    return IdentityVerifyAgent(
        customer_data=context.userdata.customer_data,
        company_name=context.userdata.company_name,
        intro_message=verification_message,
        chat_ctx=self.chat_ctx,
    )
i want to ensure there is user messanges in chat context before processing
t
You're asking how to guarantee the user message is present in the chat context before 
correct_customer
runs. Good — here's a concise checklist and a small pattern you can apply immediately. 1) Ensure incoming messages are added to chat context synchronously • In your message handler (transcription or text input), append the user message to the same 
ChatContext
instance the agent uses, then persist/update it immediately (call the runtime helper you use to save/update chat context). 2) Gate tool execution on chat history state • In the 
correct_customer
tool, check the chat history for the expected latest user message before returning the next agent. If missing, return a short fallback (ask the user to repeat) or re-run the agent decision after waiting briefly. Example pattern (pseudo-Python to drop into your tool):
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@function_tool()
async def correct_customer(self, context: RunContext[SessionState]) -> "IdentityVerifyAgent":
    # quick check: is the last entry a user message containing confirmation?
    last = self.chat_ctx.history[-1] if self.chat_ctx.history else None
    if not last or last.role != "user" or "yes" not in last.content.lower():
        # either re-check after ensuring chat_ctx updated, or ask for confirmation
        await self.session.say(text="Sorry — I didn't catch that. Are you the account owner?")
        return None  # let the agent continue after new user input arrives

    verification_message = greetings_agent_correct_customer_instructions.invoke({}).text
    return IdentityVerifyAgent(
        customer_data=context.userdata.customer_data,
        company_name=context.userdata.company_name,
        intro_message=verification_message,
        chat_ctx=self.chat_ctx,
    )
3) Add lightweight instrumentation to surface race conditions • Log timestamps for: message received, chat_ctx append/update, agent decision, tool invocation. If the tool timestamp precedes the chat_ctx update, you have a race. 4) If you use 
session.generate_reply(user_input=...)
• Make sure you call it with the exact user text immediately after appending the message to 
chat_ctx
. That call attaches the input into the agent’s decision flow. 5) Short-term fixes • Introduce a tiny debounce/wait (e.g., await asyncio.sleep(0.1)) before the chat-history check if you detect frequent races — use only as a diagnostic or temporary mitigation, not a long-term fix. If you want, paste your incoming-message handler (where transcription / lk.chat events are processed) and I’ll show exactly where to append/update the 
chat_ctx
and add the check above. Would you like that?
b
Delayed transcription Realtime models don't provide interim transcription results, and in general the user input transcriptions can be considerably delayed and often arrive after the agent's response. If you need realtime transcriptions, you should consider an STT-LLM-TTS pipeline or add a separate STT plugin for realtime transcription. is there any work around? i want to use realtime model
t
You're right — realtime models here don't provide interim transcripts, so transcriptions can arrive after the agent already decides to call a tool. Below are practical workarounds to preserve chat correctness while still using realtime models. 1) Add a quick chat-history gate in the tool (recommended) • Before returning IdentityVerifyAgent, check that the latest chat history contains the expected user confirmation. If not, prompt the user for a short verbal/text confirmation and return None so the agent waits for the new message. • This avoids race conditions and keeps behavior deterministic. 2) Use a separate realtime STT stream alongside the realtime model • Run a dedicated STT plugin or service that emits interim transcripts (partial results) to your agent pipeline or to the ChatContext as soon as they arrive. • Append partial or final transcripts immediately to 
chat_ctx
(mark partial vs final) so the agent has the latest user text before tooling decisions. 3) Make the agent decision wait for finalized transcription • When you detect a transcription event is in-flight, delay the agent’s decision for that turn: ◦ Option A: If you control the event loop, set a short await (e.g., up to a few hundred ms) with retries checking for 
chat_ctx
update. ◦ Option B: If the agent runs immediately on incoming audio, have the agent return a non-terminal response like “I’m listening — please confirm” when no finalized transcript is present. 4) Instrument timestamps and logs to detect races • Log: message received, transcript finalization time, chat_ctx append time, agent decision time, tool invocation time. • Use logs to tune debounce/wait durations or to decide between gating vs adding STT. 5) Short-term pragmatic fixes • Small debounce (await asyncio.sleep(0.05–0.2)) before the chat-history check can reduce races during testing. Treat this as temporary; prefer explicit event gating or separate STT for production. Example pattern for your 
correct_customer
tool (concise):
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@function_tool()
async def correct_customer(...):
    last = self.chat_ctx.history[-1] if self.chat_ctx.history else None
    if not last or last.role != "user" or "yes" not in last.content.lower():
        await self.session.say(text="Sorry — I didn't catch that. Are you the account owner?")
        return None
    ...
Which option would you like to try first — I can: • show how to append partial transcripts to 
chat_ctx
, or • add the minimal gating logic into your agent flow, or • review your transcription handler to place the append/update in the right spot.
b
using events When you detect a transcription event is in-flight, delay the agent’s decision for that turn: ◦ Option A: If you control the event loop, set a short await (e.g., up to a few hundred ms) with retries checking for 
chat_ctx
update. how i can stop agent from creating response while chat_ctx is updating
t
You're asking how to stop the agent from creating a response while 
chat_ctx
is still being updated. Good question — use event-driven gating so the agent only acts after the turn is committed. Below are concise, practical options with small code patterns you can apply. 1) Best: run decision logic in on_user_turn_completed • Override `on_user_turn_completed`; it runs only after the user's turn is finalized and the message is in 
chat_ctx
. Trigger tool calls or 
generate_reply
from here. Example:
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async def on_user_turn_completed(self, turn_ctx: ChatContext, new_message: ChatMessage):
    if "yes" in new_message.text_content().lower():
        await self.session.generate_reply(instructions="Call correct_customer tool")
2) If you must react to transcription events: wait for chat_ctx via an Event • Use an async Event that 
on_user_turn_completed
sets when a new message arrives; other handlers wait for it with a timeout before generating replies. Pattern:
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from asyncio import Event, wait_for, TimeoutError

self._msg_event = Event()

async def on_user_turn_completed(self, turn_ctx, new_message):
    self._last_user_message = new_message
    self._msg_event.set()

async def wait_for_message(self, timeout=2.0):
    try:
        await wait_for(self._msg_event.wait(), timeout)
        return True
    except TimeoutError:
        return False
    finally:
        self._msg_event.clear()
Then before generating a reply:
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if await agent.wait_for_message(timeout=0.5):
    await session.generate_reply(...)
else:
    # fallback: ask for confirmation or skip tool
3) Disable preemptive generation (if available) • Set 
enable_preemptive_generation=False
when creating the session so the agent won't start generation before turn completion. 4) Cancel premature generation as a last resort • If a reply starts too early, keep the generator handle and cancel it when you detect the chat_ctx mismatch:
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handle = session.generate_reply(user_input="...")
# later, if chat_ctx not updated:
handle.cancel()
5) Small practical mitigations • Add a quick history check inside your 
@function_tool()
(safeguard):
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last = self.chat_ctx.history[-1] if self.chat_ctx.history else None
if not last or last.role != "user":
    await self.session.say("Sorry—can you confirm?")
    return None
• Instrument timestamps for: transcript received, chat_ctx append, decision time, tool invocation — this reveals exact races. Which approach would you like to implement first? I can provide the exact snippet to insert into your message handler, 
on_user_turn_completed
, or your 
correct_customer
tool. Sources: Pipeline nodes and hooks | LiveKit docs | Text and transcriptions | LiveKit docs | Agent speech and audio | LiveKit docs
b
Copy code
on_user_turn_completed
t
Great—since you picked 
on_user_turn_completed
, use it as the single synchronization point so the agent only acts after the user turn is finalized and added to 
chat_ctx
. Below is a compact, ready-to-drop pattern and a couple of small safeguards to ensure 
correct_customer
only runs when the user message is present. 1) Implement on_user_turn_completed (preprocess then trigger)
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async def on_user_turn_completed(self, turn_ctx: ChatContext, new_message: ChatMessage) -> None:
    # new_message is available here and will be added to chat_ctx after this hook
    text = new_message.text_content().lower()

    # optional preprocessing (RAG, validation, augment context)
    if "correct" in text or "yes" in text:
        # safe to call generate_reply or call tool now
        await self.session.generate_reply(instructions="Call correct_customer tool")
2) Guard inside the tool (defensive)
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@function_tool()
async def correct_customer(self, context: RunContext[SessionState]) -> "IdentityVerifyAgent":
    last = self.chat_ctx.history[-1] if self.chat_ctx.history else None
    if not last or last.role != "user":
        await self.session.say("Sorry — I didn’t catch that. Can you confirm?")
        return None
    # proceed with transition
    ...
3) Recommended session settings & instrumentation • Disable preemptive generation: set 
preemptive_generation=False
(or 
enable_preemptive_generation=False
) on session to avoid speculative replies. • Log timestamps for: transcript finalization, on_user_turn_completed entry, chat_ctx append, generate_reply start, and tool invocation to confirm ordering. 4) If you must react to transcribed events • Treat 
user_input_transcribed
only for UI/interim updates. Do NOT call 
generate_reply
there; instead wait for 
on_user_turn_completed
to trigger any decision. If you want, paste your AgentSession creation and I’ll show exactly where to set 
preemptive_generation=False
and add logging. Would you like that? Sources: Pipeline nodes and hooks | LiveKit docs | Agent speech and audio | LiveKit docs
b
text_content' is not callable
t
You're seeing that error because 
text_content
is a property, not a method — remove the parentheses. Replace:
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text = new_message.text_content().lower()
with:
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text = new_message.text_content.lower()
Quick checklist of where to fix this: • In 
on_user_turn_completed
, access 
new_message.text_content
(no 
()
). • In any other code reading a ChatMessage, use the property form. • If you need normalized string helpers, do 
content = (new_message.text_content or "").lower()
to avoid None. If you'd like, paste the exact ChatMessage usage you have elsewhere and I’ll show the corrected lines.
b
i dont see my code reach on_user_turn_complete maybe because of
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turn_detection=TurnDetection(
    type="semantic_vad",
    eagerness="auto",
    create_response=True,
    interrupt_response=True,
),
in my llm?
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llm = RealtimeModel.with_azure(
    azure_deployment=agent_settings.LLM_MODEL,
    voice=agent_settings.LLM_VOICE,
    modalities=["text"],
    turn_detection=TurnDetection(
        type="semantic_vad",
        eagerness="auto",
        create_response=True,
        interrupt_response=True,
    ),
)
t
You're likely correct — those 
turn_detection
settings can cause the model to auto-generate responses (and interrupt) before the SDK treats the user turn as "completed," which prevents 
on_user_turn_completed
from firing where you expect it. Here are focused steps to diagnose and fix it. 1) Short explanation • 
create_response=True
lets the realtime model begin generating an assistant response as soon as it thinks the user finished speaking. That can preempt the normal turn-finalization lifecycle and skip your 
on_user_turn_completed
hook. • 
interrupt_response=True
lets the model interrupt ongoing responses, which can further complicate lifecycle ordering. 2) Quick changes to test • Disable auto-response so turn finalization happens normally:
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turn_detection=TurnDetection(
    type="semantic_vad",
    eagerness="auto",
    create_response=False,
    interrupt_response=False,
)
• Restart your agent and confirm 
on_user_turn_completed
is invoked. 3) If you need auto-response but still want the hook • Keep 
create_response=False
and implement your own short wait + 
on_user_turn_completed
handling (recommended). • Or use 
create_response=True
but also: ◦ Disable preemptive generation on the session (if available) and/or ◦ Add a guard in your generated-response flow to check 
chat_ctx
(Event or history check) and cancel or delay if the message isn’t present. 4) Debugging tips • Add logs in these places with timestamps: transcription event, model decision to create a response, entry to 
on_user_turn_completed
, and any `generate_reply`/tool invocation. • Temporarily set 
create_response=False
to confirm the hook behavior — if it fires then, the setting was the cause. • Ensure your agent actually implements the hook with the correct signature and is registered before the session starts. 5) Minimal checklist to report back if it still fails • Current 
TurnDetection
settings you use after changes. • AgentSession creation snippet (any 
preemptive_generation
flag). • A short log showing events/timestamps around one failing turn. Want me to suggest the exact logging placements or show how to add the Event-based wait if you must keep 
create_response=True
?
b
with create_responce=False i don't get response from agent at all
t
You're right: setting 
create_response=False
stops the realtime model from auto-generating a reply — it prevents premature responses but also means nothing will reply unless you trigger it yourself after the turn is finalized. Here's how to keep responsiveness while ensuring the message is in 
chat_ctx
. 1) Trigger replies from on_user_turn_completed • Ensure your agent implements the hook and calls 
session.generate_reply()
or 
session.say()
there. Example:
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async def on_user_turn_completed(self, turn_ctx: ChatContext, new_message: ChatMessage) -> None:
    text = (new_message.text_content or "").lower()
    if "yes" in text or "correct" in text:
        await self.session.generate_reply(instructions="Call correct_customer tool")
    else:
        await self.session.generate_reply(instructions="Handle general reply")
2) If you need quicker responses, keep create_response=True but gate actions • Keep 
create_response=True
to preserve low-latency replies, but in the generated-response flow check 
chat_ctx
(or use the Event pattern) and delay/cancel if the user message isn't present yet. This avoids losing responsiveness while preventing race conditions. 3) Use an Event wait as a middle ground • Let transcription events arrive, then in the handler wait briefly for 
on_user_turn_completed
to set an Event before calling `generate_reply`: Pattern:
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# set by on_user_turn_completed
self._msg_event.set()

# caller:
if await wait_for(self._msg_event.wait(), timeout=0.5):
    await session.generate_reply(...)
else:
    await session.say("Sorry — can you repeat that?")
4) Practical checklist • With `create_response=False`: you must call `generate_reply`/`say` from 
on_user_turn_completed
. • With `create_response=True`: keep it, but add the chat_ctx guard or cancellation logic to avoid acting on incomplete context. • Add logging for transcript final, on_user_turn_completed entry, and generate_reply start to verify ordering. Tell me which approach you prefer (fully manual via on_user_turn_completed, keep create_response with guards, or Event wait) and I’ll give the exact snippet to plug into your agent/session code.
b
can i handle my task using llm_node?
t
You're right to focus on 
on_user_turn_completed
— it's the correct place to guarantee the user message is committed to 
chat_ctx
before any decision or tool runs. Summary and next steps you can apply now: 1) Use on_user_turn_completed as your sync point • Implement:
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async def on_user_turn_completed(self, turn_ctx: ChatContext, new_message: ChatMessage) -> None:
    text = (new_message.text_content or "").lower()
    if "yes" in text or "correct" in text:
        await self.session.generate_reply(instructions="Call correct_customer tool")
    else:
        await self.session.generate_reply(instructions="Handle general reply")
• This hook runs after the user turn finalizes and before the agent proceeds, eliminating the race. 2) If you disabled model auto-response (create_response=False) • You must trigger replies from this hook (use 
generate_reply
or 
say
) because the model won't auto-reply. 3) If you keep create_response=True for low latency • Add a guard (Event or chat history check) so generated responses verify 
chat_ctx
contains the latest user message; cancel or delay if not. • Defensive check inside your tool:
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last = self.chat_ctx.history[-1] if self.chat_ctx.history else None
if not last or last.role != "user":
    await self.session.say("Sorry — I didn’t catch that. Can you confirm?")
    return None
4) If on_user_turn_completed still doesn't fire • Confirm turn detection settings: 
create_response=True
and 
interrupt_response=True
can cause the model to preempt turn finalization. Temporarily set 
create_response=False
to verify the hook fires, then reintroduce guarded generation. • Ensure your agent registers the hook before session start. 5) Instrumentation to diagnose • Log timestamps for: transcription final, on_user_turn_completed entry, chat_ctx append, generate_reply start, tool invocation. This shows ordering and confirms the fix. If you want, paste your AgentSession creation and turn_detection block and I’ll mark the exact lines to change and add the logging calls. Which snippet should I look at? Sources: Pipeline nodes and hooks | LiveKit docs | Turn detection and interruptions | LiveKit Docs | LiveKit turn detector plugin | LiveKit docs
b
i want to handle it using llm_node
t
You can do it in 
llm_node
, but it’s fragile and I’ll warn you about the tradeoffs and give a safe example. Short summary • Risk: 
llm_node
runs during LLM inference. Waiting or mutating context there can cause latency, race conditions, or inconsistent state because the LLM may already be streaming based on the prior context. • Safer alternative: 
on_user_turn_completed
— it runs after transcription finalization and before the LLM runs, so changes there are reliably seen by the LLM and tools. If you still want 
llm_node
, follow this guarded pattern: 1) Block until the final message is present (use a short timeout) 2) Inject/persist any extra context via 
update_chat_ctx()
before delegating to the default LLM node 3) Yield the default LLM chunks Example (concise):
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async def llm_node(self, chat_ctx: ChatContext, tools, model_settings=None):
    # Wait for a short window for on_user_turn_completed to finish/persist
    try:
        await asyncio.wait_for(self._turn_finalized_event.wait(), timeout=1.0)
    except asyncio.TimeoutError:
        # fallback: if no final turn, either proceed or ask for confirmation
        pass

    # Optionally inject context and persist so tools/LLM see it
    extra = await my_rag_lookup(chat_ctx.messages[-1].text_content or "")
    chat_ctx.add_message(role="system", content=extra)
    await self.update_chat_ctx(chat_ctx)

    # Delegate to default LLM processing and stream chunks through
    async for chunk in Agent.default.llm_node(self, chat_ctx, tools, model_settings):
        yield chunk
Notes and safeguards • You must maintain 
self._turn_finalized_event
(set it inside 
on_user_turn_completed
) so 
llm_node
can wait reliably. • Keep the wait short (e.g., <=1s) to limit latency. • Always call 
update_chat_ctx()
after mutating 
chat_ctx
so changes persist. • Add logging/timestamps around transcript finalization, 
on_user_turn_completed
, 
llm_node
entry, and tool calls to verify ordering. • Prefer 
on_user_turn_completed
for correctness; use this 
llm_node
pattern only if you accept higher complexity and carefully manage the event and timeouts. If you want, I can: • Provide the exact 
on_user_turn_completed
snippet to set 
_turn_finalized_event
, or • Convert your current 
llm_node
into the guarded pattern above with proper event wiring. Which do you want me to generate? Sources: Pipeline nodes and hooks | LiveKit docs
b
in my case i have delayed transcription in chat_ctx will usage of chat history help me?
t
Yes — checking chat history can help, but it’s a mitigation rather than a full cure for delayed transcription. Practical guidance: • Use on_user_turn_completed as the primary place to rely on chat history — at that point the user message will be finalized and available in the history. Inspect 
turn_ctx
/ 
new_message
there instead of reacting to transcription events. • Defensive checks in tools/llm flow: ◦ Before acting, verify the last chat history entry is a user message you expect:
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last = self.chat_ctx.history[-1] if self.chat_ctx.history else None
    if not last or last.role != "user":
        await self.session.say("Sorry—I didn't catch that. Can you confirm?")
        return None
• If the transcript is delayed, ask for a brief confirmation rather than proceeding. • Combine history checks with an Event wait if you need to trigger outside on_user_turn_completed: ◦ Set an Event in 
on_user_turn_completed
and wait (with a short timeout) where you would otherwise generate a reply. This avoids acting on incomplete history. • If you override 
llm_node
(not recommended), persist any injected context with 
update_chat_ctx()
and guard with the same Event wait; keep waits short to limit latency. • For best UX: consider running a separate realtime STT (interim results) to populate chat history faster, or keep 
create_response=True
but add the history guard/cancellation so you don’t act on missing data. If you want, paste the handler where you append transcripts to chat_ctx (or your on_user_turn_completed implementation) and I’ll mark exactly where to add the history check or Event wiring. Which snippet shall I look at?
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