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livekit.agents Error in _llm_inference_task Tr...
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l
livekit.agents Error in _llm_inference_task Traceback (most recent call last): File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\plugins\ google\llm.py", line 371, in _run stream = await self._client.aio.models.generate_content_stream( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ...<3 lines>... ) ^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\google\genai\mod els.py", line 7197, in generate_content_stream response = await self._generate_content_stream( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ model=model, contents=contents, config=parsed_config ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ) ^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\google\genai\mod els.py", line 5940, in _generate_content_stream response_stream = await self._api_client.async_request_streamed( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 'post', path, request_dict, http_options ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ) ^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\google\genai\_ap i_client.py", line 1451, in async_request_streamed response = await self._async_request(http_request=http_request, stream=True) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\google\genai\_ap i_client.py", line 1367, in _async_request return await self._async_retry( # type: ignore[no-any-return] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ self._async_request_once, http_request, stream ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ) ^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\tenacity\asyncio \__init__.py", line 111, in call do = await self.iter(retry_state=retry_state) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\tenacity\asyncio \__init__.py", line 153, in iter result = await action(retry_state) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\tenacity\_utils. py", line 99, in inner return call(*args, **kwargs) File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\tenacity\__init_ _.py", line 420, in exc_check raise retry_exc.reraise() ~~~~~~~~~~~~~~~~~^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\tenacity\__init_ _.py", line 187, in reraise raise self.last_attempt.result() ~~~~~~~~~~~~~~~~~~~~~~~~^^ File "C:\Users\Abcom\AppData\Local\Programs\Python\Python313\Lib\concurrent\futures\_ base.py", line 449, in result return self.__get_result() ~~~~~~~~~~~~~~~~~^^ File "C:\Users\Abcom\AppData\Local\Programs\Python\Python313\Lib\concurrent\futures\_ base.py", line 401, in __get_result raise self._exception File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\tenacity\asyncio \__init__.py", line 114, in call result = await fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\google\genai\_ap i_client.py", line 1283, in _async_request_once await errors.APIError.raise_for_async_response(response) File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\google\genai\err ors.py", line 203, in raise_for_async_response await cls.raise_error_async(status_code, response_json, response) File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\google\genai\err ors.py", line 225, in raise_error_async raise ClientError(status_code, response_json, response) google.genai.errors.ClientError: 400 Bad Request. {'message': '{\n "error": {\n "code": 400,\n "message": "Please ensure that single turn requests end with a user role or the role field is empty.",\n "status": "INVALID_ARGUMENT"\n }\n}\n', 'status': 'Bad Request'} The above exception was the direct cause of the following exception: Traceback (most recent call last): File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\u tils\log.py", line 16, in async_fn_logs return await fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\opentelemetry\ut il\_decorator.py", line 71, in async_wrapper return await func(*args, **kwargs) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\v oice\generation.py", line 130, in _llm_inference_task async for chunk in llm_node: ...<35 lines>... ) File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\v oice\agent.py", line 417, in llm_node async for chunk in stream: yield chunk File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\l lm\llm.py", line 350, in anext raise exc # noqa: B904 ^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\l lm\llm.py", line 177, in _traceable_main_task await self._main_task() File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\agents\l lm\llm.py", line 196, in _main_task return await self._run() ^^^^^^^^^^^^^^^^^ File "C:\Codeshastra_Projects\hamariai-health\venv\Lib\site-packages\livekit\plugins\ google\llm.py", line 448, in _run raise APIStatusError( ...<5 lines>... ) from e livekit.agents._exceptions.APIStatusError: gemini llm: client error (status_code=400, request_id=50f408a41d06, body={ "error": { "code": 400, "message": "Please ensure that single turn requests end with a user role or the role field is empty.", "status": "INVALID_ARGUMENT" } } Bad Request, retryable=False) 141550.338 DEBUG livekit.…arvam.log Starting TTS stream cleanup
t
Thanks for reaching out, Pavan. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Thanks — I reviewed the traceback. The Gemini API is returning a 400 with: "Please ensure that single turn requests end with a user role or the role field is empty." That means the request your LiveKit agent sends to Google must have its final message be from the user (or omit the role). Right now the chat 
contents
being passed ends with a non-user role (e.g., assistant/model or similar), which Gemini treats as an invalid single-turn request. What you can do next 1. Verify the chat context just before the LLM call: ◦ Log the messages array (role and content) you send to 
generate_content_stream
. ◦ Confirm the last entry is 
role: "user"
(or remove the role for that last item). 1. Common fixes (pick the one that matches your codepath): ◦ If you manually build 
contents
, ensure it ends with a user message:
Copy code
contents = [
       {"role":"system","parts":["..."]},
       {"role":"user","parts":["User question here"]}  # <-- must be last
     ]
• If you use LiveKit/ChatContext helpers, ensure you do not append an assistant/model message before calling the LLM. Let the LLM produce the assistant message. • If replaying history, ensure messages alternate and the last is user. 1. Quick debug steps: ◦ Add a print right before the call: 
print([(m.role, m.parts if hasattr(m,'parts') else m.content) for m in contents])
◦ If you see the last role is `assistant`/`model`, trace where that message was appended and stop that from happening before generating. If you’d like, paste the small snippet that builds or transforms 
contents
(the few lines before the 
generate_content_stream
call) and I’ll point out exactly where to change it.
l
yes provide me a code async def _llm_one_shot(session: AgentSession, system_prompt: str) -> str: llm = _session_llm(session) chat_ctx = ChatContext( [ ChatMessage( type="message", role="system", content=[system_prompt], ), ChatMessage( type="message", role="user", content=["Proceed with the instructions."], ), ] )
t
Thanks — I’ll provide a corrected async one-shot function that builds a ChatContext ending with a user message and calls the LLM. If your project uses different helpers/names, swap the helper calls accordingly.
Copy code
async def _llm_one_shot(session: AgentSession, system_prompt: str) -> str:
    llm = _session_llm(session)

    # Build a context that ends with a user message (required by Gemini)
    chat_ctx = ChatContext(
        [
            ChatMessage(type="message", role="system", content=[system_prompt]),
            ChatMessage(type="message", role="user", content=["Proceed with the instructions."]),
        ]
    )

    # If your LLM wrapper expects a chat or contents object, pass chat_ctx/messages.
    # Example using a typical LiveKit LLM call that returns a text reply:
    response = await llm.chat(chat_ctx=chat_ctx)  # or llm.generate_content(...)

    # Extract the assistant text from the response (adjust per your SDK shape)
    # Example: response.text or response.output[0].content[0]
    if hasattr(response, "text") and response.text:
        return response.text
    # fallback for streaming/more complex shapes:
    try:
        parts = []
        for msg in getattr(response, "output", []):
            for part in msg.get("content", []):
                parts.append(part)
        return " ".join(parts).strip()
    except Exception:
        return ""
If you paste the exact 
llm
call you use (for example 
generate_content_stream
vs 
chat
), I can tailor the extraction logic to that response shape.
l
def get_realtime_llm(): """Gemini Realtime model for live audio sessions (does NOT expose .chat()).""" return google.realtime.RealtimeModel( model="gemini-2.5-flash-native-audio-preview-12-2025", modalities=[Modality.AUDIO, Modality.TEXT], api_key=settings.GOOGLE_API_KEY, )
t
Thanks — I can give code that works with your RealtimeModel, but a quick clarification so I deliver the right snippet: Do you want a one-shot text request (single turn) to the RealtimeModel (non-streaming) or do you need to send/receive audio streams in real time (streaming audio + text)? • If one-shot text: I’ll show how to build `contents`/`chat_ctx` ending with a 
user
message and call the Realtime API appropriately. • If realtime audio: I’ll show how to include the final user text role when sending the realtime request alongside audio frames. Which of these do you need?
l
i want to use google realtime with sarvam tts
t
Thanks — I’ll provide a concise example that shows: • creating a RealtimeModel request that ends with a user role, • sending text (single-turn) to Gemini Realtime, • receiving the model text output, • piping that text to Sarvam TTS for audio playback. This example assumes your environment uses the Google generative SDK objects similar to your snippets and that Sarvam TTS exposes an async 
synthesize(text) -> bytes
call you can play/send to LiveKit audio track. Adjust names to match your code.
Copy code
# Example: send a single-turn text request to Gemini Realtime and synthesize with Sarvam TTS

from google import realtime
from google.genai import Modality

async def realtime_one_shot_with_sarvam(session, sarvam_tts, system_prompt: str, user_text: str):
    # Build the RealtimeModel (your helper)
    model = realtime.RealtimeModel(
        model="gemini-2.5-flash-native-audio-preview-12-2025",
        modalities=[Modality.AUDIO, Modality.TEXT],
        api_key=settings.GOOGLE_API_KEY,
    )

    # Ensure the last message is from the user (required for single-turn requests)
    contents = [
        {"role": "system", "parts": [system_prompt]},
        {"role": "user", "parts": [user_text]},  # <-- must be last
    ]

    # Create a request for a single-shot/text generation on the realtime model
    request = {
        "input": {
            "type": "text",
            "contents": contents
        },
        # optional: any config like temperature, safety settings, etc.
        "config": {
            # example: "temperature": 0.7
        }
    }

    # Call the RealtimeModel (non-streaming single-turn)
    # Some SDKs use `generate` or `send_request` — match the call shape you have.
    # Here we call a generic `generate`-like async method that returns a response object.
    response = await model.generate(request)  # replace with actual SDK call if different

    # Extract text output. The exact path depends on SDK response shape.
    # Common shapes: response.output[0].content[0] or response.text
    out_text = ""
    if hasattr(response, "text") and response.text:
        out_text = response.text
    else:
        for out in getattr(response, "output", []):
            for part in out.get("content", []):
                out_text += part

    out_text = out_text.strip()

    # Pass the model text to Sarvam TTS (assuming async synthesize -> bytes)
    tts_audio_bytes = await sarvam_tts.synthesize(out_text)

    # Play/send the audio bytes into your LiveKit session (example helper)
    await session.play_audio_bytes(tts_audio_bytes)

    return out_text
Notes and adjustments: • The critical fix for your previous 400 error is that 
contents
ends with a user message. Confirm any code that mutates 
contents
doesn’t append an assistant/model message before the generate call. • If your Realtime SDK call is named differently (e.g., 
stream
, 
create_stream
, 
send_request
), replace 
model.generate(request)
with the correct method and adapt how you extract 
response.output
. • If you need real-time streaming audio (microphone → model → streamed text → TTS segments), I can provide a streaming example that sends audio frames, reads partial text responses, and feeds incremental TTS synthesis to Sarvam. Which streaming pattern do you want next? Continuous low-latency audio or segmented request/response turns?
Hi — welcome back! How can I help with your LiveKit + Google Realtime + Sarvam TTS setup right now? Do you want the streaming (real-time audio) example or the single-turn flow I showed earlier?
Thanks — below is a concise, working async example that uses the Google Realtime model for a single-turn text request (ensuring the last message is 
user
) and sends the model's text to Sarvam TTS, then plays the resulting audio into a LiveKit session. Adjust method names to match your SDK wrappers (I keep extraction robust to common response shapes).
Copy code
from google import realtime
from google.genai import Modality
import settings  # your settings module

async def realtime_one_shot_with_sarvam(session, sarvam_tts, system_prompt: str, user_text: str):
    # 1) Build the RealtimeModel (as you provided)
    model = realtime.RealtimeModel(
        model="gemini-2.5-flash-native-audio-preview-12-2025",
        modalities=[Modality.AUDIO, Modality.TEXT],
        api_key=settings.GOOGLE_API_KEY,
    )

    # 2) Ensure contents ends with a user role (required by Gemini for single-turn)
    contents = [
        {"role": "system", "parts": [system_prompt]},
        {"role": "user", "parts": [user_text]},  # <-- must be last
    ]

    # 3) Build request for a single-shot text generation on the realtime model
    request = {
        "input": {
            "type": "text",
            "contents": contents,
        },
        # optional: config keys (temperature, safety, etc.)
        "config": {}
    }

    # 4) Call the RealtimeModel. Replace `generate` with your actual SDK method if different.
    response = await model.generate(request)

    # 5) Extract text output (robust to common shapes)
    out_text = ""
    if hasattr(response, "text") and response.text:
        out_text = response.text
    else:
        # Typical alternative shape: response.output -> list of { "content": [ ... ] }
        for out in getattr(response, "output", []) or []:
            for part in out.get("content", []) if isinstance(out.get("content", []), list) else []:
                out_text += part
    out_text = out_text.strip()

    if not out_text:
        # fallback: try `response.output_text` or similar fields if present
        out_text = getattr(response, "output_text", "") or ""

    # 6) Synthesize with Sarvam TTS (assumes async synthesize -> bytes)
    tts_audio_bytes = await sarvam_tts.synthesize(out_text)

    # 7) Send/play audio into LiveKit session.
    # Replace this with your session's actual method: e.g., session.publish_audio_bytes, session.play_audio_bytes, etc.
    await session.play_audio_bytes(tts_audio_bytes)

    return out_text
If you want streaming (mic audio → realtime model → incremental text → chunked TTS playback), tell me and I’ll provide a streaming example that sends audio frames and plays incremental TTS as responses arrive.
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