generate reply is getting timeout on initial greeting for gemini-2.5-flash-native-audio-preview-12-2025 sometimes error "failed to generate a reply: generate_reply timed out waiting for generation_created event."

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

while using below code, if incase the agent is already speaking and the call limit is reached the generate_reply fails to generate the end_call text

if session.llm is not None:
    try:
        await session.interrupt()
        if agent_info.model_type == "Multimodal":

            handle = await session.generate_reply(instructions=f"<you must say this entire message only>: {end_message}")
            await handle

{"message": "failed to generate a reply: generate_reply timed out waiting for generation_created event.", "level": "ERROR", "name": "livekit.agents", "pid": 14035, "job_id": "AJ_vjHB5sTmMR6n", "room_id": "RM_YRv6hRKHQpx9", "timestamp": "2026-05-18T063047.321276+00:00"} and it does not speak hangup message what should be done

Thanks — I understand the problem: when the agent is already speaking and you hit the call limit,

generate_reply()

times out and the hangup message isn’t produced. What to do (quick, actionable steps): 1. Ensure the interruption fully completes before calling

generate_reply()

◦ Await interruption and any active speech handle:

await session.interrupt()
   if session.current_speech:
       await session.current_speech

1. Use a non-interruptible final message for hangups ◦ Prevent further interruptions so the hangup always plays:

handle = await session.generate_reply(
       instructions=f"<you must say this entire message only>: {end_message}",
       allow_interruptions=False
   )
   await handle

1. Add retry/backoff around

generate_reply()

and a fallback to

session.say()

◦ Retry transient failures a few times, then fallback to

session.say()

(bypasses LLM generation) to guarantee delivery:

try:
       await safe_generate_reply(...)  # retry wrapper
   except Exception:
       await session.say(end_message, allow_interruptions=False)

1. Consider pre-cached TTS for hangup audio ◦ Pre-synthesize or cache the hangup audio and play it directly with

session.say(audio=...)

to avoid generation delays. 1. Check infra and timeouts if failures persist ◦ Avoid burstable AWS instances (t3/t4g) that can get CPU-throttled. ◦ Increase

generate_reply_timeout

if your model/backend can be slower:

generate_reply_timeout=15.0

Questions to narrow this down (please share any that apply): • Does this only happen during the very first greeting or any time the agent is speaking? • Are you running LiveKit/agents on burstable instances (t3/t4g) or seeing CPU credit exhaustion? • Do you have retry logic now, and what is your current

generate_reply_timeout

value? • Can you share a failure log timestamp and whether

session.current_speech

was active at that time? If you share those details I’ll suggest the exact code changes and timeout/retry values. Sources: Turns overview | LiveKit Documentation | Agent speech and audio | LiveKit Documentation | EndCallTool | LiveKit Documentation | Amazon Nova Sonic integration guide | LiveKit Documentation | Troubleshooting Latency and Timeout Errors with Turn Detection on AWS, with interactive elements

getting this error when trying to use realtime gemini model , server rejected WebSocket connection: HTTP 404, for gemini-live-2.5-flash-native-audio we have set vertexai = true

Thanks — I’m responding to your WebSocket 404 when using the realtime Gemini model with

vertexai = true

. Root cause: Realtime Gemini (Gemini Live API /

gemini-live-...

or

gemini-2.5-flash-native-audio

) does not support Vertex AI mode. Setting

vertexai = true

causes the plugin to attempt the wrong endpoint and leads to a 404. What to do: 1. Use the Live API configuration (Vertex AI off) and provide an API key:

llm = google.realtime.RealtimeModel(
    model="gemini-2.5-flash-native-audio-preview-12-2025",
    api_key="YOUR_GOOGLE_API_KEY",   # or set GOOGLE_API_KEY env var
    vertexai=False,
    voice="Puck"
)

1. If you need programmatic replies (like guaranteed hangup speech), prefer

session.say()

or pre-cached TTS when using realtime models, since

generate_reply()

is not supported/reliable for RealtimeModel types. Quick checks to confirm: • Are you currently setting

vertexai=True

? If so change to

vertexai=False

and provide

GOOGLE_API_KEY

. • Are you passing

GOOGLE_APPLICATION_CREDENTIALS

instead of an API key? For the Live API you must use an API key (or set

GOOGLE_API_KEY

). If you confirm your current config (value of

vertexai

, whether you set

GOOGLE_API_KEY

or

GOOGLE_APPLICATION_CREDENTIALS

, and the exact model string), I’ll give the exact minimal config snippet to fix the 404 and a recommended fallback for hangup messages. Sources: Agent speech and audio | LiveKit Documentation, with interactive elements

gemini-2.5-flash-native-audio-preview-12-2025 api model for this generate_reply get's timeout but if used gemini-live-2.5-flash-native-audio (vertexai) model the generate_reply works as expected

Thanks — I understand:

generate_reply()

times out with

gemini-2.5-flash-native-audio-preview-12-2025

, but

gemini-live-2.5-flash-native-audio

(Vertex AI) works. Short explanation and next steps: • Reason: The preview non‑realtime model (

gemini-2.5-flash-native-audio-preview-12-2025

) can experience generation_created event delays or incompatibilities with realtime workflows, causing

generate_reply()

to time out. The realtime Live API model (

gemini-live-2.5-flash-native-audio

) uses a different endpoint/flow and is working for you, which explains the difference. • Immediate mitigations: ◦ Use the realtime Live model (

gemini-live-...

) if you need reliable mid-call generation and realtime speech. ◦ For the preview API model, wrap

generate_reply()

in retry/backoff and increase

generate_reply_timeout

to allow slower starts. ◦ For guaranteed hangup/playout, prefer

session.say()

or pre-cached TTS audio as a fallback (bypass LLM generation). • Infra checks (if you keep using the preview model): ◦ Ensure stable network connectivity to the model provider and no packet loss. ◦ Avoid burstable instances (t3/t4g) that may throttle CPU and increase inference latency. Questions to recommend a precise fix: 1. Do you need LLM-driven dynamic replies mid-call, or are static/final hangup messages sufficient? 2. Which hosting instances are you using for agents (instance types)? 3. What is your current

generate_reply_timeout

? 4. Would switching fully to

gemini-live-2.5-flash-native-audio

be acceptable for your flow? Tell me answers to 1–4 and I’ll provide the exact code snippets (retry/backoff, timeout config, or reliable `session.say()`/cached audio example). , with interactive elements

truncate is not supported by Ultravox so how to interrupt using session.interrupt() and using generate replay() ?

Thanks — I’m responding to your question about interrupting Ultravox (which doesn’t support

truncate

) and then calling

generate_reply()

. What to do (concise steps): 1. Call and await interruption, then ensure any active speech handle is finished/cancelled:

await session.interrupt()
if session.current_speech:
    await session.current_speech

1. Immediately call

generate_reply()

(Ultravox keeps context internally so no

truncate

needed):

handle = await session.generate_reply(user_input="your prompt or updated context")
await handle

1. For deterministic control, consider manual turn handling: • Set

turn_detection="manual"

and explicitly start/stop turns when you want the model to respond. 1. If you see timeouts after interrupt: • Wait for the interruption to fully complete (step 1), add retry/backoff around

generate_reply()

, or fall back to

session.say()

/ pre-cached TTS for critical messages. If you want, share a short code snippet of your current flow or a failing log and I’ll suggest the exact minimal changes. Sources: Ultravox plugin guide | LiveKit Documentation | Turns overview | LiveKit Documentation, with interactive elements

how to update XAI model for realtime as there is no option to provide model

Thanks — I’m responding to your question about updating the realtime XAI model when there’s no explicit model option. Short answer: the realtime model is set in the LLM configuration when you create the AgentSession; you can’t change the model after the session is created. To update it you must instantiate the session (or LLM) with the desired realtime model. What to do next (examples and checks): • Python (RealtimeModel / Gemini Live API example)

from livekit.agents import AgentSession, google

llm = google.realtime.RealtimeModel(
    model="gemini-live-2.5-flash-native-audio",  # choose desired realtime model
    api_key="YOUR_GOOGLE_API_KEY",               # or set GOOGLE_API_KEY env var
    vertexai=False,
    voice="Puck"
)
session = AgentSession(llm=llm, ...)

• Node / TypeScript example

const llm = new google.realtime.RealtimeModel({
  model: "gemini-live-2.5-flash-native-audio",
  apiKey: process.env.GOOGLE_API_KEY,
  vertexai: false,
  voice: "Puck",
});
const session = new AgentSession({ llm, ... });

Important notes: • You cannot swap the realtime model on an existing session; create a new session with the new model. • For Vertex AI vs Live API: realtime Live models often require

vertexai=False

with an API key; Vertex mode is for non-realtime models. • If you need programmatic fallback (e.g., for hangups), use

session.say()

or pre-cached TTS when realtime LLM generation is unsuitable. To give precise steps for your environment, please tell me: • Which SDK/agent code you’re using (Python or Node)? • Which realtime model string you want to use? • Are you using Vertex AI or an API key?, with interactive elements

XAI realtime

Got it — you mean the XAI realtime model. Short answer: set the realtime XAI model when you create the AgentSession’s LLM; you cannot change it on an existing session. Create a new session with the desired realtime XAI model. Which SDK are you using (Python or Node)? Also tell me the exact realtime model string you want to use and whether you’re using Vertex AI or a Google API key so I can give the precise code snippet. , with interactive elements

xAI Realtime API returned an error: RealtimeError(message=\"Model 'grok-4-1-fast-non-reasoning' is not supported for the Realtime API also not able to use model attribute in the xai session = AgentSession(llm=model) ``` Params: voice – Voice used for audio responses. Defaults to "marin". api_key – OpenAI API key. If None and not using Azure, read from OPENAI_API_KEY. base_url – HTTP base URL of the OpenAI/Azure API. If not provided, uses OPENAI_BASE_URL for OpenAI; for Azure, constructed from AZURE_OPENAI_ENDPOINT. turn_detection – Server-side turn-detection options. http_session – Optional shared HTTP session. max_session_duration – Seconds before recycling the connection. conn_options – Retry/backoff and connection settings. Raises: ValueError – If OPENAI_API_KEY is missing in non-Azure mode, or if Azure endpoint cannot be determined when in Azure mode. Copied from: RealtimeModel.init

Thanks — I’m responding to the XAI realtime errors and confusion about where to set the model. Short answers and fixes: 1) Unsupported model error • The error means that particular model (e.g.,

grok-4-1-fast-non-reasoning

) is not available on the XAI Realtime API. Use a Realtime-supported model name (example:

grok-4-1-fast

) — check your provider’s list of realtime-compatible XAI models. 2) Where to set the model • Pass the model string when constructing the RealtimeModel (not directly on AgentSession). Example pattern: Python

from livekit.agents import AgentSession, openai  # or xai provider class

llm = openai.RealtimeModel(
    model="grok-4-1-fast",    # realtime-supported model
    api_key="YOUR_OPENAI_KEY",
    voice="marin"
)
session = AgentSession(llm=llm, ...)

Node/TS (conceptual)

const llm = new openai.RealtimeModel({
  model: "grok-4-1-fast",
  apiKey: process.env.OPENAI_API_KEY,
  voice: "marin",
});
const session = new AgentSession({ llm, ... });

3) If model attribute isn’t accepted • Ensure you’re using the RealtimeModel class from the provider plugin (not a generic LLM class). The realtime model must be specified to the RealtimeModel constructor; AgentSession only accepts the constructed LLM instance. 4) Next steps I can help with • Tell me which SDK you’re using (Python or Node), the exact model string you tried, and whether you’re using OpenAI or Azure-style endpoints. I’ll give the exact minimal snippet and a supported model name to use., with interactive elements

as the generate reply is not much reliable, some llm model can prioritse system prompt and bypass the generate_reply text. is there any other way to force the message in the llm for that turn?

You’re asking how to guarantee a specific message is delivered for a turn when

generate_reply()

is unreliable or the model ignores/inherits system prompt priority. Short answer — reliable options (ranked by reliability): • Use

session.say()

(most reliable) ◦ Bypasses LLM generation and plays TTS (or cached audio) directly. Use

allow_interruptions=False

for guaranteed playback. ◦ Example:

await session.interrupt()
    await session.say(end_message, allow_interruptions=False)

• Play pre‑synthesized (cached) audio for the message ◦ Pre-generate hangup/fallback audio and play it with

session.say(audio=cached_audio, text=end_message, add_to_chat_ctx=False)

to avoid runtime generation delays. • Use

generate_reply()

with strict controls (if you must use LLM) ◦ Ensure interruption fully completed, set

allow_interruptions=False

, increase

generate_reply_timeout

, and add retry/backoff:

await session.interrupt()
    if session.current_speech:
        await session.current_speech
    handle = await session.generate_reply(
        instructions=f"<you must say this entire message only>: {end_message}",
        allow_interruptions=False
    )
    await handle

• Note: some realtime models or model/system prompt combos may still override or ignore injected instructions. • Encode the message as a deterministic tool call or action the agent must perform ◦ If your agent supports tools, use a tool invocation whose output is controlled and then play that output via

session.say()

. When to choose which: • For guaranteed delivery of one-off texts (hangups, legal notices): use

session.say()

or cached audio. • For dynamic LLM-driven wording but high reliability: try the generate_reply approach with retries +

allow_interruptions=False

, but also prepare a

session.say()

fallback if generate fails. • For full determinism: pre-generate audio or use a tool that returns fixed text/audio. Questions to recommend exact code: • Which SDK are you using (Python or Node)? • Are you using a realtime model (Gemini Live, Ultravox, xAI Realtime) or a non-realtime API model? • Do you require the text to come from the LLM (dynamic) or is static/tightly controlled text acceptable? Tell me those and I’ll give the minimal snippet and fallback strategy tailored to your flow. , with interactive elements

can't use pre‑synthesized audio as we are using only realtime model (llm_model) can't use session.say()

Thanks — I understand: you must force a specific message during a turn while using only a realtime LLM (no pre-synth audio, no

session.say()

), and

generate_reply()

is unreliable or ignored. Short reality check • Pure realtime speech-to-speech models cannot be guaranteed to read a script verbatim. There is no documented way to force deterministic verbatim output from a realtime model in all cases. Practical options (ordered by practicality): • Strong system prompt + generation controls ◦ Put the message into a high-priority system instruction and set generation parameters for determinism (temperature=0, low top_p). This increases likelihood but isn’t guaranteed. • Use a deterministic tool call the model must invoke ◦ Expose a tool (e.g.,

play_fixed_message()

) that returns the exact string for that turn. Prompt the model to call the tool for that action — tool output is deterministic and can be used as the turn’s content. • Disable interruptions + strict generate settings + retries ◦ Ensure you fully interrupt current speech, call

generate_reply()

with

allow_interruptions=False

, increase

generate_reply_timeout

, and implement retry/backoff. Include the exact message in

instructions

with explicit markers. This reduces failures but still isn’t 100% guaranteed. • Half-cascade architecture (if feasible) ◦ Use the realtime model for STT/understanding and a separate TTS instance for output (modalities=[“text”] + TTS). This restores deterministic scripted speech (similar to

session.say()

), but requires adding a TTS component. Which I recommend now • If you can add a deterministic tool, do that — it’s the most reliable within a pure realtime LLM flow without separate TTS. If adding a tool is impossible, use the strong system-prompt +

allow_interruptions=False

+ retries approach as a fallback. Quick questions to give exact code: 1. Which realtime model are you using (Gemini Live, OpenAI Realtime/xAI, Ultravox, etc.)? 2. Can you add a simple tool that returns a fixed string, or is modifying the agent/toolset impossible? 3. Do you control session settings like

allow_interruptions

,

generate_reply_timeout

, and temperature? Tell me 1–3 and I’ll provide the minimal snippet for either: (A) tool-based deterministic flow, or (B) strongest

generate_reply()

invocation with retries and settings. Sources: Realtime models overview | LiveKit Documentation | How to Build a Background Observer for Voice AI Guardrails | LiveKit, with interactive elements

**Fix 1 — Prevent `generate_reply()` conflict with activity-based flow (`realtime_api.py`)**

When Gemini has already received audio via the `activity_start/end` path and is processing it, `generate_reply()` should not inject a redundant `ActivityEnd` and `"."` placeholder turn. Options:
- Track whether an active audio turn is being processed; if so, skip the placeholder and wait for the natural `generation_created` event from the audio path
- Or skip `generate_reply()` entirely when `_in_user_activity` was recently `True`

how to use this fix to solve the "*generate_reply timed out waiting for generation_created event" error*

Thanks — I’m responding to your request on applying the suggested fix so

generate_reply()

no longer times out waiting for a

generation_created

event when the realtime audio path is already producing a generation. Goal: avoid injecting a redundant placeholder generation when an audio-based activity is in flight. Main idea — track when a user audio turn is being processed and skip the

generate_reply()

placeholder / skip calling

generate_reply()

while that flag is true. Minimal implementation outline (Python, place in your realtime handling code such as realtime_api.py): 1) Add a flag on the session (or handler) to indicate an active audio turn • e.g.

session._in_user_activity = False

2) Set the flag when you receive activity_start and clear it on activity_end / generation_created from the audio path

# on audio activity start
session._in_user_activity = True

# on audio activity end OR when audio path emits its generation_created/completed event
session._in_user_activity = False

3) Guard generate_reply usage • Before creating the placeholder

ActivityEnd

or calling

generate_reply()

, check the flag and skip if True:

# Pseudocode where you currently inject placeholder/generate_reply
if getattr(session, "_in_user_activity", False):
    # skip placeholder/skip calling generate_reply() and wait for audio path events
    logger.debug("Skipping generate_reply() because an audio activity is in flight")
else:
    # safe to call generate_reply() as before
    handle = await session.generate_reply(instructions=..., allow_interruptions=False)
    await handle

4) Optional: short-delay re-check instead of outright skip • If you want to attempt generation still but avoid racing, add a short backoff loop:

for _ in range(5):
    if not getattr(session, "_in_user_activity", False):
        break
    await asyncio.sleep(0.2)
else:
    logger.debug("Still in activity; skipping generate_reply()")
    return
# then call generate_reply()

Why this prevents the timeout • The timeout occurs because

generate_reply()

expects a

generation_created

event but the audio path is already initiating generation events; injecting an extra placeholder or generating concurrently can confuse the event flow. Skipping the placeholder or generate call while

_in_user_activity

is True prevents the duplicate/competing generation and avoids waiting for an event that the placeholder won’t produce. Where to wire events • Set

_in_user_activity = True

on receiving the audio path’s start event (e.g.,

activity_start

, STT start). • Clear it when you receive

activity_end

, the audio path’s

generation_created

/

generation_completed

event, or when the model signals it finished processing that user turn. Validation and testing • Add logging around state changes and around where you skip

generate_reply()

to verify behavior. • Reproduce the race (start speaking, call generate_reply()) and ensure you no longer see

generate_reply timed out waiting for generation_created event

. Questions to tailor the snippet • Which SDK / runtime file are you editing (Python SDK, repo path to realtime_api.py)? • What events does your audio path emit for start/end and generation_created? (I can give exact placement lines if you paste the small handler functions.) If you want, paste the current portion of realtime_api.py where you inject the placeholder or call

generate_reply()

and I’ll produce the exact patch. , with interactive elements

in realtime model , how to force a message to llm without using session.say/ session.generate, function-tools reply?

Short answer: in a pure realtime speech-to-speech model there is no guaranteed way to force the model to verbatim speak a message outside of the model’s normal generation APIs. Alternatives below (ranked by reliability) and what I need to give you exact code. Why: realtime models control generation internally; you can’t inject a guaranteed-played string unless you either bypass generation (TTS/pre‑synth) or provide a deterministic artifact the agent must use. Alternatives you can use now: • Deterministic tool call (recommended if you can add a tool) ◦ Expose a simple tool (e.g.,

play_fixed_message()

) that returns the exact string. Prompt the model to call that tool for the turn. Tool output is deterministic and can be treated as the turn content for playout. • Strong system prompt + strict generation settings (best-effort) ◦ Put the message in a high-priority system instruction, set temperature=0/top_p low, use

allow_interruptions=False

and retries. This increases chance but is not 100% reliable. • Half‑cascade (realtime STT + separate TTS) ◦ Use the realtime model only for input (modalities=["text"]), send the desired message to a TTS instance you control and play it. This restores deterministic scripted output but requires adding a TTS component. • Tool-backed fallback + retries ◦ Attempt

generate_reply()

with retries; if it fails, call a tool that returns the fixed message and force the model to use it. What I need to give you exact code/snippets: 1. Which realtime model are you using (Gemini Live, xAI Realtime, Ultravox, OpenAI Realtime)? 2. Which SDK (Python or Node)? 3. Can you add a simple deterministic tool to your agent/toolset? 4. Can you change session settings (allow_interruptions, timeouts, temperature)? Tell me 1–4 and I’ll return the minimal implementation for the deterministic tool flow or the strongest generate_retry + system-prompt approach tailored to your realtime model and SDK. , with interactive elements

in realtime model , how to force a message to llm without using session.say/ session.generate, function-tools reply?

Short answer: you can’t reliably force a verbatim message into a pure realtime speech‑to‑speech model outside of the standard generation paths. Realtime models control generation internally, so there’s no guaranteed “inject-and-play” mechanism that bypasses the model’s generation behavior. What you can do instead (ranked by reliability): • Deterministic tool call (recommended if possible) ◦ Add a simple tool (e.g.,

play_fixed_message()

) that returns the exact string. Prompt the model to invoke that tool for the turn; tool output is deterministic and can be used as the played content. • Half‑cascade (realtime STT + separate TTS) ◦ Use the realtime model for input only (modalities=["text"]) and a separate TTS component to play your scripted message. This gives you deterministic playout without relying on the realtime model to read verbatim. • Strong system prompt + strict generation controls (best-effort) ◦ Put the message into a high-priority system instruction, set temperature=0/top_p low, use

allow_interruptions=False

, increase timeout, and add retries. This raises the chance the model will produce the exact text but is not 100% guaranteed. • Retry + fallback flow ◦ Try

generate_reply()

with retries and, if it fails, fall back to a tool response or a TTS path (if you can add one). If none of these are possible (no tools, no TTS, must stay pure realtime), there is no documented or reliable method to force verbatim message delivery. If you want a concrete implementation, tell me: 1) Which realtime model (Gemini Live, xAI Realtime, Ultravox, OpenAI Realtime, etc.)? 2) Which SDK (Python or Node)? 3) Can you add a simple tool or a TTS component? Tell me 1–3 and I’ll provide the minimal code for the best viable option for your environment. Sources: Agent speech and audio | LiveKit Documentation | The ReAct Pattern for Voice Agents and How AI Agents Think, Act, and Respon... | Realtime models overview | LiveKit Documentation | Turns overview | LiveKit Documentation | Text and transcriptions | LiveKit Documentation, with interactive elements

how to force conversation_item_added event

Thanks — I’m responding to your question about forcing a

conversation_item_added

event. Short answer: you cannot directly emit/force the

conversation_item_added

event via a public API. It’s emitted by the framework when a conversation item (user message, assistant reply, tool call, handoff) is actually added to the chat context. How to cause the event (practical options) • Add a real conversation item: ◦ User speech → STT result added to chat (triggers event). ◦ Agent reply via

generate_reply()

(creates assistant item). ◦ Tool/function call or agent handoff (adds items and triggers the event). • For testing: populate a ChatContext and call

update_chat_ctx()

to simulate prior items (this builds history but does not retroactively emit past events). • If you need to observe behavior in tests, simulate a user message or agent reply so the framework emits the event naturally. If you tell me: 1) SDK (Python or Node) and version, and 2) Whether this is for testing or runtime logic (and a short example of what you want to achieve), I’ll provide the exact minimal code snippet to add an item so the

conversation_item_added

event fires or to simulate it in tests. Sources: Agent session | LiveKit Documentation | Pipeline nodes and hooks | LiveKit Documentation | Logic and structure overview | LiveKit Documentation | Test framework | LiveKit Documentation | How to Build a Background Observer for Voice AI Guardrails | LiveKit, with interactive elements

how to change the agent state forcefully

You can’t directly force-set an agent’s internal state with a single public API call. The agent state is driven by session lifecycle and actions (start, user turns, generation, speech, end). Below are the supported, practical ways to move the agent into a desired state—pick the one that matches your use case. How to cause specific state transitions (actions you can call) • Move to listening (start session)

await session.start(agent=MyAgent(), room=room)

• Move to speaking (generate a reply)

await session.generate_reply(instructions="...")  # transitions to thinking → speaking

• Stop speaking / interrupt current speech

await session.interrupt()      # stops current speech/output
# wait for any speech handle to finish/cancel
if session.current_speech:
    await session.current_speech

• Force end / close session

session.end()  # transitions toward closing/terminated

• Influence behavior via instructions (change how agent thinks/acts)

await agent.update_instructions("New high-priority system instructions")

Indirect controls and patterns • Commit user turn / manual turn control ◦ Use turn-management RPCs or

commit_user_turn()

to force processing of the latest input and drive the session into thinking/speaking. • Use tools / function calls ◦ Trigger deterministic tool outputs or handoffs; tool outputs are added to conversation history and cause state changes naturally. • Manual turn handling for deterministic flow ◦ Set

turn_detection="manual"

and explicitly start/stop turns to control state transitions. Best practices and limits • Realtime models: many interruption/state controls are limited because the model does server-side turn detection. Some interruption flags may be ignored by the realtime model. • No direct API to set enum state: do not attempt to mutate internal state variables; use the actions above to keep the session consistent. • Add logging around

agent_state_changed

events to verify state transitions during testing. If you tell me: 1) Which exact state you want to force (listening, thinking, speaking, closing), 2) SDK (Python or Node), and 3) Whether you’re using a realtime model, I’ll give the exact minimal snippet and any extra steps (waits, interrupts, or turn-mode changes) to achieve that transition reliably. Sources: Agents and handoffs | LiveKit Documentation | Turns overview | LiveKit Documentation | Build a salary negotiation coach with a LemonSlice avatar | LiveKit, with interactive elements

how to set agent state

Thanks — I’m responding to your question about how to set an agent’s state. Short answer: you cannot directly set the agent’s internal state enum. Agent state is driven by session lifecycle/actions. To move the agent into a desired state, perform the action that causes that transition. Quick actions to cause common states (Python examples): • To enter listening (start the session)

await session.start(agent=MyAgent(), room=room)

• To enter thinking → speaking (generate a reply)

await session.generate_reply(instructions="...")  # goes to thinking then speaking

• To stop speaking / return to listening

await session.interrupt()
if session.current_speech:
    await session.current_speech
# session will move back to listening when done

• To close the session

session.end()

Other controls: • Commit user turn / manual turns: use turn-management RPCs or

commit_user_turn()

to force processing of input. • Update behavior:

await agent.update_instructions("new system instructions")

to change how the agent behaves. • For deterministic control, set

turn_detection="manual"

and explicitly start/stop turns. Notes and limits: • Realtime models handle turn-detection server-side; some interruption controls may be ignored. • Do not mutate internal state variables—use the session APIs above to keep session behavior consistent. • You can listen for state changes via the

agent_state_changed

event to observe transitions. Want a concrete snippet? Tell me: 1) Which state you want to reach (listening, thinking, speaking, closing), 2) SDK (Python or Node), and 3) Whether you’re using a realtime model. , with interactive elements

i want to forcefully change the agent state to speaking how do i do that

I hear you — you want the agent to be in the "speaking" state immediately. Short answer: you cannot directly set the internal state enum. To force "speaking", perform the action that causes that transition — typically start a generation or play audio. Below are the reliable ways and minimal code snippets. If using LLM generation (standard approach) • Python:

# ensure not currently speaking
await session.interrupt()
if session.current_speech:
    await session.current_speech

# trigger generation → thinking → speaking
handle = await session.generate_reply(instructions="Speak this message now", allow_interruptions=False)
await handle

• Node:

await session.interrupt();
if (session.currentSpeech) await session.currentSpeech;

const handle = await session.generateReply({ instructions: "Speak this message now", allowInterruptions: false });
await handle;

If using a realtime model that controls turn detection • Wait for interrupt to fully complete (avoid racing with server-side audio processing). • Use

allow_interruptions=False

, increase

generate_reply_timeout

, and add a short retry/backoff if the first call times out. If you can’t/shouldn’t use generation • Play deterministic audio via TTS (if available) or call a tool that returns fixed text and then generate/play it — both cause speaking state. Notes • Realtime models may ignore some interruption flags; behavior depends on the model. • For testing, call

generate_reply()

with deterministic params (temperature=0) to increase repeatability. Tell me which SDK (Python or Node) and whether you’re on a realtime model; I’ll give the exact minimal snippet tailored to your environment and include retries/timeouts if you want. , with interactive elements

in generate reply what is input_modality – The input mode to use for generating the reply and what if i gave audio? i want to initialize the generate_reply but it should not invoke any agent speech generation

Thanks — I’m responding to your two questions: what

input_modality

means for

generate_reply()

, and how to call/initialize

generate_reply()

without causing the agent to speak. Short answers •

input_modality

(modalities) determines whether the session expects text, audio, or both as input. Setting audio means the model will process spoken input (VAD/turn detection) rather than text typed input. • You cannot make

generate_reply()

silently produce an internal reply while also keeping the session’s TTS active —

generate_reply()

is intended to produce a spoken reply when a TTS plugin or realtime audio output is configured. There’s no built-in flag on

generate_reply()

to prevent speech output. What you can do instead (pick one): • Use text-only modality so generation does not route through the realtime audio pipeline ◦ Initialize the LLM/session with text-only modalities (e.g.,

modalities=["text"]

or

[ "text" ]

depending on provider). Then call

generate_reply()

— the session will produce a reply as text and you can handle playback yourself. ◦ This is the simplest way to avoid automatic speech output while still using

generate_reply()

. • Move generation outside the realtime session (standalone LLM call) ◦ Call your LLM directly (standalone client) to get the text response, update chat context with the result, and only play audio when you want. This fully separates generation from playout. • Disable or remove TTS from the session (if feasible) ◦ If no TTS plugin is configured,

generate_reply()

won’t synthesize/play audio. Use this if you control session construction. • Use ChatContext manipulation for background or non-speaking updates ◦ Update the conversation context (

update_chat_ctx

) or create messages in the chat context to register content without triggering speech. Practical notes • For realtime models that handle audio directly, you must set

modalities

at session/LLM creation — you can’t change

input_modality

per

generate_reply()

call on all providers. • If you need the model to process audio input but not speak for a particular turn, consider streaming STT->text to a standalone LLM call, then decide whether to play audio. If you want a concrete snippet, tell me: 1) SDK: Python or Node? 2) Which realtime model/provider? 3) Do you need the model to accept audio input but avoid speaking this specific turn? Give those and I’ll return the exact minimal code for the best option. Sources: Amazon Nova Sonic integration guide | LiveKit Documentation | Chat context | LiveKit Documentation, with interactive elements