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hi
t
Thanks for reaching out, Arun. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Hi — thanks for reaching out! How can I help you today? If you have an issue, please share a brief description (error messages, project/room IDs, SDK/platform, and steps to reproduce) and I’ll help you troubleshoot.
c
def get_llm_from_config(llm_config: dict): provider = llm_config.get("provider", "") model = llm_config.get("model", "") if provider == "OPENAI": return openai.LLMStream( model=model ) else: return openai.LLMStream( model="gpt-4.1-mini" ) now tokeing Using proactor: IocpProactor 151700.363 INFO livekit.agents job runner initialized {"tid": 29732, "elapsed_time": 0.15} 151700.368 DEBUG livekit.agents http_session(): creating a new httpclient ctx 151700.372 ERROR livekit.agents unhandled exception while running the job task Traceback (most recent call last): File "C:\Users\work\work\agents\.venv\Lib\site-packages\opentelemetry\util\_decorator.py", line 71, in async_wrapper return await func(*args, **kwargs) # type: ignore ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\work\work\agents\.venv\Lib\site-packages\livekit\agents\ipc\job_proc_lazy_main.py", line 286, in _traceable_entrypoint await self._job_entrypoint_fnc(job_ctx) File "C:\Users\work\work\agents\agents\testagents\web\v1_test_agent.py", line 53, in entrypoint agent = V1TestAgent(ctx, meta_data) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\work\work\agents\agents\testagents\web\v1_test_agent.py", line 25, in init llm=get_llm_from_config(meta_data['llm']), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\work\work\agents\agents\utils\config.py", line 52, in get_llm_from_config return openai.LLMStream( ^^^^^^^^^^^^^^^^^ TypeError: LLMStream.__init__() missing 1 required positional argument: 'llm' 151701.092 DEBUG livekit.….deepgram Established new Deepgram STT WebSocket connection: {"headers": {"dg-project-id": "e5a9dda5-e2d7-4e5f-81e8-6f788e4c0070", "dg-request-id": "b660cdeb-7c9f-4800-b22c-486841a3578f", "Date": "Wed, 31 Dec 2025 094701 GMT"}}
t
Thanks — I looked at the traceback and the code you pasted. You're hitting this error because 
LLMStream
expects an existing 
LLM
instance (argument 
llm
), not a model name string. What to change • Return an 
openai.LLM
instance from 
get_llm_from_config
, not 
LLMStream
. Example fix:
Copy code
def get_llm_from_config(llm_config: dict):
    provider = llm_config.get("provider", "")
    model = llm_config.get("model", "")

    if provider == "OPENAI":
        return openai.LLM(model=model or "gpt-4.1-mini")
    else:
        return openai.LLM(model="gpt-4.1-mini")
If you need streaming later • Create the 
LLM
first, then call 
.chat()
to get a stream and iterate chunks:
Copy code
llm = openai.LLM(model="gpt-4.1-mini")
async with llm.chat(chat_ctx=chat_ctx) as stream:
    async for chunk in stream:
        # handle chunk.delta.content
If you want, tell me whether you need streaming output now or later and share the surrounding code where you consume the LLM — I’ll help adapt it. Sources: LLM Output Replacement | LiveKit docs
c
LLMStream i need o use this
t
Got it — you need to use 
LLMStream
. I can help—quick clarifying questions so I give the exact code you need: 1. Do you want to stream chat responses (i.e., call 
.chat()
and handle incremental deltas) or stream completion tokens from a non-chat API? 2. Where will 
LLMStream
be created/consumed in your code? (show the function or file that will iterate the stream — e.g., job entrypoint or agent handler) 3. Are you using async context (async/await) already where you plan to consume the stream? Short answer summary while you reply: • You must pass an 
LLM
instance into 
LLMStream
(not a model string). • Typical flow: create 
openai.LLM(...)
→ call 
.chat()
to get a stream → wrap or consume that stream with 
LLMStream
if you need its helpers. Share the small snippet where you plan to consume the stream and I’ll provide the exact corrected code.
c
i want sue it for viois angt strinmin only
t
Thanks — I want to make sure I give a working snippet for your voice agent streaming. A few quick clarifying questions: 1. Do you want to stream the LLM output to a TTS engine (so you send chunks to speech) or stream audio from the user into the LLM (speech→text→LLM)? 2. Are you using the LLM chat stream (
.chat()
) and consuming incremental deltas, or a completion-style stream? 3. Share the spot in your code where you plan to consume the stream (the function that will iterate chunks). If you can paste 6–12 lines around that consumer, I’ll adapt the exact 
LLMStream
usage. 4. Confirm you’re using async/await and an async event loop (yes/no). Answer those and I’ll provide a concise, copy-pasteable example that creates an 
openai.LLM
, gets the chat stream, wraps/uses 
LLMStream
, and shows sending chunks to TTS for voice output.
c
how to use 
LLMStream giev code
t
Thanks — below is a concise, copy-pasteable async example that shows how to create an 
openai.LLM
, get a chat stream, wrap/consume it with 
LLMStream
, and forward incremental text chunks to a hypothetical TTS function for voice output. Notes: • 
LLMStream
requires an 
LLM
instance (passed via 
.chat()
), not a model string. • Replace 
send_to_tts(chunk_text)
with your TTS sending function (sync/async as needed). • This example consumes 
chunk.delta.content
incremental deltas from the chat stream.
Copy code
import asyncio
from openai import LLM, LLMStream

# Replace with your actual TTS handler
async def send_to_tts(text: str):
    # send chunk to your TTS engine (websocket, API, etc.)
    print("TTS:", text)

async def stream_llm_to_tts(model: str = "gpt-4.1-mini"):
    # 1) create the LLM
    llm = LLM(model=model)

    # 2) open a chat stream (chat_ctx may include system/user messages)
    chat_ctx = {
        "messages": [
            {"role": "system", "content": "You are a concise voice assistant."},
            {"role": "user", "content": "Read this aloud progressively."}
        ]
    }

    # 3) use the LLM.chat() async context and consume with LLMStream utilities
    async with llm.chat(chat_ctx=chat_ctx) as raw_stream:
        stream = LLMStream(llm=llm, stream=raw_stream)  # wrap if you want helpers
        async for chunk in stream:  # chunk is a stream delta object
            # incremental text usually at chunk.delta.content
            content = getattr(chunk.delta, "content", None)
            if content:
                # send each incremental piece to TTS
                await send_to_tts(content)

# Run example
if __name__ == "__main__":
    asyncio.run(stream_llm_to_tts())
If you want: • A sync-style wrapper for your specific TTS API, paste a small snippet of that API and I’ll adapt this. • Handling of partial tokens (buffering) or final assembled text before TTS, tell me whether your TTS accepts short fragments or needs full sentences.
c
def get_llm_from_config(llm_config: dict): provider = llm_config.get("provider", "") model = llm_config.get("model", "") if provider == "OPENAI": return openai.LLMStream( model=model ) else: return openai.LLMStream( model="gpt-4.1-mini" ) in thisnonly i nded llm strem
t
Thanks — you must still create an 
LLM
first; 
LLMStream
cannot be instantiated with a 
model
string. If you need your 
get_llm_from_config
to return something you can later call to stream, return the 
LLM
and create the 
LLMStream
where you actually consume it. Minimal change to your function (returns an 
LLM
):
Copy code
def get_llm_from_config(llm_config: dict):
    provider = llm_config.get("provider", "")
    model = llm_config.get("model", "")

    if provider == "OPENAI":
        return openai.LLM(model=model or "gpt-4.1-mini")
    else:
        return openai.LLM(model="gpt-4.1-mini")
Example of creating an 
LLMStream
when consuming the LLM (voice-agent streaming):
Copy code
async def consume_and_tts(llm, chat_ctx, send_to_tts):
    async with llm.chat(chat_ctx=chat_ctx) as raw_stream:
        stream = openai.LLMStream(llm=llm, stream=raw_stream)
        async for chunk in stream:
            content = getattr(chunk.delta, "content", None)
            if content:
                await send_to_tts(content)
If you truly must have 
get_llm_from_config
return an object named 
LLMStream
, return a thin wrapper object that creates the underlying 
LLM
and exposes an async generator method to stream. I can provide that wrapper if you want — do you want the wrapper returned or keep the 
LLM
return and create 
LLMStream
at consume time?
c
give alln in one ,mehd
t
Got it — one self-contained example that returns an LLM from config, then creates and consumes an 
LLMStream
to forward incremental text to a TTS function. Drop this into your project (replace auth and TTS send logic).
Copy code
import asyncio
from openai import LLM, LLMStream

# --- Replace this with your real TTS sender (websocket/API) ---
async def send_to_tts(text: str):
    # Example: push incremental text to your TTS engine
    print("TTS chunk:", text)

# --- Return an LLM from config (must return LLM, not LLMStream) ---
def get_llm_from_config(llm_config: dict) -> LLM:
    provider = llm_config.get("provider", "")
    model = llm_config.get("model", "")

    # Only OPENAI supported in this example; adjust if you have other providers
    if provider == "OPENAI":
        return LLM(model=model or "gpt-4.1-mini")
    else:
        return LLM(model="gpt-4.1-mini")

# --- Helper that creates LLMStream and forwards increments to TTS ---
async def stream_llm_to_tts(llm: LLM, chat_ctx: dict, send_to_tts_fn):
    # open a chat stream (chat_ctx contains initial messages)
    async with llm.chat(chat_ctx=chat_ctx) as raw_stream:
        # Create LLMStream wrapper with the LLM and the raw_stream
        stream = LLMStream(llm=llm, stream=raw_stream)

        # Iterate incremental chunks
        async for chunk in stream:
            # incremental content is usually at chunk.delta.content
            partial = getattr(chunk.delta, "content", None)
            if partial:
                # send each partial piece to TTS
                await send_to_tts_fn(partial)

# --- Example usage: create LLM from config and start streaming ---
async def main():
    llm_config = {"provider": "OPENAI", "model": "gpt-4.1-mini"}  # or from your meta_data
    llm = get_llm_from_config(llm_config)

    chat_ctx = {
        "messages": [
            {"role": "system", "content": "You are a concise voice assistant."},
            {"role": "user", "content": "Read this aloud progressively for TTS."}
        ]
    }

    await stream_llm_to_tts(llm, chat_ctx, send_to_tts)

if __name__ == "__main__":
    asyncio.run(main())
If you want a wrapper so 
get_llm_from_config
directly returns an object that exposes an async generator (so callers don't need to call 
.chat()
), I can provide that version next. Which do you prefer?
c
agent_function = { "name": "get_user_details", "description": "Fetch user details from external service", "endpoint": "https://api.example.com/users/{user_id}", "method": "POST", "headers": { "Authorization": "Bearer {{API_KEY}}", "Content-Type": "application/json" }, "body_template": { "user_id": "{{user_id}}", "include_profile": True }, "query_params": { "verbose": "true" }, "params": { "user_id": { "type": "string", "required": True, "description": "User identifier" } }, "timeout_seconds": 30 } i want creat tool call from this mehtd
t
Thanks — below is a concise, copy-pasteable async LiveKit agent tool that implements your 
agent_function
spec using 
aiohttp
. It uses 
@function_tool()
so the agent can call it by name. Replace env/loading of 
API_KEY
and adjust error handling as needed.
Copy code
import os
import asyncio
import aiohttp
from dotenv import load_dotenv

from livekit.agents import function_tool, RunContext

load_dotenv()
API_KEY = os.getenv("API_KEY")  # set in env or .env

@function_tool()
async def get_user_details(context: RunContext, user_id: str):
    """
    Fetch user details from external service.
    Matches the provided agent_function configuration.
    """
    url = f"<https://api.example.com/users/{user_id}>"
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json",
    }
    body = {"user_id": user_id, "include_profile": True}
    params = {"verbose": "true"}
    timeout = aiohttp.ClientTimeout(total=30)  # timeout_seconds

    try:
        async with aiohttp.ClientSession(timeout=timeout) as session:
            async with <http://session.post|session.post>(url, headers=headers, json=body, params=params) as resp:
                text = await resp.text()
                if resp.status >= 200 and resp.status < 300:
                    try:
                        data = await resp.json()
                    except Exception:
                        data = text
                    return {"user_data": data}
                else:
                    return {"error": f"HTTP {resp.status}: {text}"}
    except asyncio.TimeoutError:
        return {"error": "Request timed out after 30 seconds"}
    except Exception as e:
        return {"error": f"Request failed: {str(e)}"}
If you want this generated from the 
agent_function
dict dynamically (factory that creates 
@function_tool
wrappers), tell me and I’ll provide that pattern. Sources: Tool definition and use | LiveKit docs
c
i have jso i need crete amehtioda on run time
t
Good — below is a concise, copy-pasteable pattern that takes a JSON-style function spec at runtime and registers a LiveKit async tool for it. It safely injects secrets from env vars, validates required params, makes the HTTP call with aiohttp, and returns JSON-friendly results. Notes: • Replace URL templating, header templating, and body templating placeholders of the form {{VAR}} with values from the call args or environment. • This creates a callable decorated with 
@function_tool()
so LiveKit will register it.
Copy code
import os
import re
import asyncio
import aiohttp
from typing import Dict, Any
from livekit.agents import function_tool, RunContext

TEMPLATE_RE = re.compile(r"\{\{([^}]+)\}\}")

def render_template(obj, params: Dict[str, Any], env=os.environ):
    """Recursively replace {{var}} in strings using params then env."""
    if isinstance(obj, str):
        def repl(m):
            key = m.group(1).strip()
            return str(params.get(key, env.get(key, "")))
        return TEMPLATE_RE.sub(repl, obj)
    if isinstance(obj, dict):
        return {k: render_template(v, params, env) for k, v in obj.items()}
    if isinstance(obj, list):
        return [render_template(x, params, env) for x in obj]
    return obj

def create_tool_from_spec(spec: Dict[str, Any]):
    """
    spec: dict like your agent_function JSON.
    Returns: a function decorated with @function_tool that performs the request.
    """
    name = spec.get("name")
    method = spec.get("method", "GET").upper()
    endpoint_tmpl = spec.get("endpoint", "")
    headers_tmpl = spec.get("headers", {})
    body_tmpl = spec.get("body_template")
    params_tmpl = spec.get("query_params", {})
    timeout_seconds = spec.get("timeout_seconds", 30)
    param_defs = spec.get("params", {})

    # Build required param list for runtime validation
    required_params = [p for p, d in param_defs.items() if d.get("required")]

    @function_tool(name=name)
    async def tool_fn(context: RunContext, **call_args):
        # Validate required params
        for p in required_params:
            if p not in call_args or call_args.get(p) in (None, ""):
                return {"error": f"Missing required parameter: {p}"}

        # Render endpoint, headers, body, query params with incoming args and env
        rendered_endpoint = render_template(endpoint_tmpl, call_args)
        rendered_headers = render_template(headers_tmpl, call_args)
        rendered_body = render_template(body_tmpl, call_args) if body_tmpl is not None else None
        rendered_query = render_template(params_tmpl, call_args)

        timeout = aiohttp.ClientTimeout(total=timeout_seconds)

        try:
            async with aiohttp.ClientSession(timeout=timeout) as session:
                http_method = getattr(session, method.lower(), None)
                if http_method is None:
                    return {"error": f"Unsupported HTTP method: {method}"}

                async with http_method(
                    rendered_endpoint,
                    headers=rendered_headers or None,
                    json=rendered_body if rendered_body is not None else None,
                    params=rendered_query or None,
                ) as resp:
                    text = await resp.text()
                    if 200 <= resp.status < 300:
                        try:
                            data = await resp.json()
                        except Exception:
                            data = text
                        return {"result": data}
                    else:
                        return {"error": f"HTTP {resp.status}: {text}"}
        except asyncio.TimeoutError:
            return {"error": f"Request timed out after {timeout_seconds} seconds"}
        except Exception as e:
            return {"error": f"Request failed: {str(e)}"}

    # Attach metadata for introspection if needed
    tool_fn.__name__ = name
    tool_fn.__doc__ = spec.get("description", "")
    return tool_fn
Usage example (register at runtime):
Copy code
# your JSON spec
agent_function = {
  "name": "get_user_details",
  "description": "Fetch user details from external service",
  "endpoint": "<https://api.example.com/users/{{user_id}}>",
  "method": "POST",
  "headers": {
    "Authorization": "Bearer {{API_KEY}}",
    "Content-Type": "application/json"
  },
  "body_template": {
    "user_id": "{{user_id}}",
    "include_profile": True
  },
  "query_params": {"verbose": "true"},
  "params": {
    "user_id": {"type": "string", "required": True, "description": "User identifier"}
  },
  "timeout_seconds": 30
}

# create and register the tool (decorator already registers on import)
dynamic_tool = create_tool_from_spec(agent_function)
# If your framework needs the function object to be imported/loaded, ensure this module is imported
# at agent startup so LiveKit picks up the @function_tool-decorated tool.

# Example local call (for testing) - simulate LiveKit passing RunContext
# await dynamic_tool(RunContext(...), user_id="abc123")
If you want: • automatic type coercion/validation for params, • retries/backoff, • logging or metrics, I can add those. Which one do you want next?
c
how i wil create reuired data for fcuntion tool i need greate funaiton toool call from that json
t
Great — here’s a compact, copy-pasteable solution that (1) creates a LiveKit 
@function_tool
at runtime from your JSON spec, (2) validates/constructs the required call data from the spec’s 
params
, and (3) shows how to call the generated tool with the required data. Paste this module into your agent code and import it at startup so LiveKit registers the dynamic tool.
Copy code
import os
import re
import asyncio
import aiohttp
from typing import Dict, Any
from livekit.agents import function_tool, RunContext

TEMPLATE_RE = re.compile(r"\{\{([^}]+)\}\}")

def render_template(obj, params: Dict[str, Any], env=os.environ):
    if isinstance(obj, str):
        def repl(m):
            key = m.group(1).strip()
            return str(params.get(key, env.get(key, "")))
        return TEMPLATE_RE.sub(repl, obj)
    if isinstance(obj, dict):
        return {k: render_template(v, params, env) for k, v in obj.items()}
    if isinstance(obj, list):
        return [render_template(x, params, env) for x in obj]
    return obj

def build_call_data_from_spec(spec: Dict[str, Any], call_args: Dict[str, Any]):
    """
    Validate required params and build a concrete payload dict for the HTTP call.
    Returns (ok: bool, payload_or_error: dict or str).
    """
    param_defs = spec.get("params", {})
    # validate required
    for name, defn in param_defs.items():
        if defn.get("required") and (name not in call_args or call_args.get(name) in (None, "")):
            return False, f"Missing required parameter: {name}"

    # All params passed are available to template rendering (and env vars)
    return True, call_args

def create_tool_from_spec(spec: Dict[str, Any]):
    name = spec.get("name")
    method = spec.get("method", "GET").upper()
    endpoint_tmpl = spec.get("endpoint", "")
    headers_tmpl = spec.get("headers", {})
    body_tmpl = spec.get("body_template")
    params_tmpl = spec.get("query_params", {})
    timeout_seconds = spec.get("timeout_seconds", 30)

    @function_tool(name=name)
    async def tool_fn(context: RunContext, **call_args):
        ok, payload_or_err = build_call_data_from_spec(spec, call_args)
        if not ok:
            return {"error": payload_or_err}

        rendered_endpoint = render_template(endpoint_tmpl, payload_or_err)
        rendered_headers = render_template(headers_tmpl, payload_or_err)
        rendered_body = render_template(body_tmpl, payload_or_err) if body_tmpl is not None else None
        rendered_query = render_template(params_tmpl, payload_or_err)

        timeout = aiohttp.ClientTimeout(total=timeout_seconds)
        try:
            async with aiohttp.ClientSession(timeout=timeout) as session:
                http_method = getattr(session, method.lower(), None)
                if http_method is None:
                    return {"error": f"Unsupported HTTP method: {method}"}

                async with http_method(
                    rendered_endpoint,
                    headers=rendered_headers or None,
                    json=rendered_body if rendered_body is not None else None,
                    params=rendered_query or None,
                ) as resp:
                    text = await resp.text()
                    if 200 <= resp.status < 300:
                        try:
                            data = await resp.json()
                        except Exception:
                            data = text
                        return {"result": data}
                    else:
                        return {"error": f"HTTP {resp.status}: {text}"}
        except asyncio.TimeoutError:
            return {"error": f"Request timed out after {timeout_seconds} seconds"}
        except Exception as e:
            return {"error": f"Request failed: {str(e)}"}

    tool_fn.__name__ = name
    tool_fn.__doc__ = spec.get("description", "")
    return tool_fn

# -------------------------
# Example: create + call
# -------------------------
# Your JSON spec
agent_function = {
  "name": "get_user_details",
  "description": "Fetch user details from external service",
  "endpoint": "<https://api.example.com/users/{{user_id}}>",
  "method": "POST",
  "headers": {
    "Authorization": "Bearer {{API_KEY}}",
    "Content-Type": "application/json"
  },
  "body_template": {
    "user_id": "{{user_id}}",
    "include_profile": True
  },
  "query_params": {"verbose": "true"},
  "params": {
    "user_id": {"type": "string", "required": True, "description": "User identifier"}
  },
  "timeout_seconds": 30
}

# Create and register the tool (decorator registers at import time)
dynamic_tool = create_tool_from_spec(agent_function)

# Example local test call (simulate RunContext)
async def test_call():
    # Build the required call data from spec (user supplies these)
    call_data = {"user_id": "abc123"}
    # Call the function directly for testing (no real RunContext needed for direct call)
    res = await dynamic_tool(None, **call_data)
    print("tool response:", res)

# For manual testing
if __name__ == "__main__":
    asyncio.run(test_call())
How to create the required data for the tool call • Look at 
spec["params"]
. Required params are those with 
"required": True
. • Build a dict containing those keys before calling the tool — e.g., 
{"user_id": "abc123"}
for your spec. • The 
render_template
function will substitute 
{{user_id}}
into endpoint/body/headers and 
{{API_KEY}}
from environment variables. Quick checklist before calling: • Ensure required params present in the call dict. • Ensure env vars used in headers/body (e.g., 
API_KEY
) are set. • Call the function as 
await tool_fn(context, **call_data)
or let the agent call it via tool invocation. If you want, I can: • add type coercion/validation, • add retry/backoff, • produce OpenAPI-style param validation errors. Which enhancement do you want next?
c
will this like i have teili inbound call agent will it habndlw muplie cuncrent call at a time?
t
Yes — inbound call agents can handle multiple concurrent calls. Each incoming call is dispatched to its own room and runs as an isolated agent session, so multiple calls run in parallel without interfering with each other. What that means for your dynamic function-tool approach • Each agent session will call its own tool instance concurrently; the dynamic tool created with 
@function_tool
is safe to be invoked from many sessions at once because the function is stateless (per-call data comes in as arguments). • Ensure any shared resources used by the tool (file writes, in-memory caches, DB connections, or synchronous blocking calls) are concurrency-safe or use per-session instances. Practical recommendations • Use async HTTP (aiohttp) like in the provided code so calls don’t block the event loop. • Keep tool functions stateless; pass per-call data via arguments. • If you use environment secrets (API_KEY), read them from env once at startup or via safe secrets storage. • Add retries/backoff for transient errors and reasonable timeouts (your spec uses 30s). • Monitor and scale agent servers if load grows (LiveKit scales agent servers and job distribution automatically). Sizing hints • Start with recommended baseline resources (for example, ~4 CPU cores and 8GB RAM per agent server) and scale horizontally as concurrency increases. • If many sessions use heavy STT/LLM/TTS inference simultaneously, ensure your inference quota/plan and agent servers are scaled accordingly. If you want, I can: • Add retry/backoff to the dynamic tool, • Show a concurrency-safe pattern for shared DB or cache access, • Recommend autoscaling settings based on expected concurrent calls. Which of these would you like next? Sources: Server lifecycle | LiveKit docs | examples/frontdesk | livekit/livekit_composite | DeepWiki | Agents telephony integration | LiveKit docs | LiveKit Pricing | Agent server | LiveKit docs
c
what is minum reuie ment for 5 contenr in boudl calls
t
For 5 concurrent inbound calls, a reasonable minimum baseline is: • CPU: 4 vCPUs • RAM: 8 GB • Network: 100 Mbps stable uplink • Process model: one agent server instance able to run multiple jobs (each call runs as an isolated session) • Concurrency considerations: ◦ Use async I/O (aiohttp) to avoid blocking the event loop. ◦ Make tools stateless or ensure shared resources (DB, files) are concurrency-safe. ◦ Set per-request timeouts (e.g., 30s) and add retries/backoff for transient failures. • Media / inference: ◦ Ensure your STT/TTS/LLM inference capacity (quota or external services) can handle 5 simultaneous streams. • Monitoring & scaling: ◦ Monitor CPU, memory, request latency, and inference usage; scale horizontally if any resource is saturated. If you want, I can: • propose a two-node deployment plan for redundancy, • add autoscaling rules based on CPU or concurrent sessions, • estimate cost for a cloud provider if you share preferred provider (AWS/GCP/Azure). Which would you like?
c
t3.small aws not enoeu i am suing 2 one for agents and one for livkie server?
t
Thanks — quick answer: a single t3.small per role is likely underprovisioned for 5 concurrent inbound voice calls. t3.small (2 vCPU, 2 GiB RAM) will struggle with STT/LLM/TTS I/O and media processing. Recommended minimum for 5 concurrent calls: • Agent servers (worker role): at least 4 vCPU / 8 GiB RAM each (example: t3.xlarge or t3a.xlarge). You can start with one such instance and add another for redundancy. • LiveKit server (SFU/room signaling): at least 2 vCPU / 4–8 GiB RAM (example: t3.medium / t3.large), depending on media relay load. • Network: stable 100 Mbps+ uplink. • Use async I/O for tools and nonblocking clients. Options if you want to save cost: • Horizontal scale smaller instances (two or more t3.medium/t3.large) for agents behind a load balancer instead of one large machine. • Use managed inference (cloud STT/LLM/TTS) to reduce CPU load on agent hosts. • Offload heavy audio processing (transcoding/recording) to separate services. Operational notes: • Monitor CPU, memory, and inference latency under load; autoscale agents when saturation nears 70–80%. • Keep timeouts and retries configured to avoid resource lockups. If you tell me: • current instance types and whether you use external STT/LLM/TTS services, I’ll give a concrete two-node configuration (instance types + counts) you can start with. Which details can you share?
c
only audio no veido
t
Thanks — since it's audio-only, here’s a concise, practical AWS sizing recommendation and deployment notes for handling 5 concurrent inbound voice calls. Recommended baseline (single-region, production-ready): • Agent servers (workers handling STT/LLM/TTS + call logic): 1–2 instances, each with 4 vCPU / 8 GiB RAM (example: t3.xlarge or t3a.xlarge). Start with one; add second for redundancy/scale. • LiveKit server (signaling + SFU for audio relay): 1 instance with 2 vCPU / 4–8 GiB RAM (example: t3.medium or t3.large). • Network: 100 Mbps stable uplink per server. • Storage: small SSD for logs (EBS gp3), monitoring volumes as needed. If you want lower-cost alternatives: • Use two t3.medium/t3.large agent instances behind a load balancer instead of one xlarge (horizontal scale). • Offload STT/TTS/LLM to managed cloud services (reduces agent CPU needs). Operational recommendations: • Use async I/O (aiohttp) and non-blocking code for tools. • Keep tools stateless; use pooled DB connections if needed. • Set per-request timeouts (e.g., 30s) and add retries/backoff for tool HTTP calls. • Monitor CPU/memory, network, and inference latencies; autoscale agents when CPU >70–80% or call latency increases. • For high availability, run at least 2 agent instances across AZs. Quick checklist to validate capacity: • Confirm whether STT/TTS/LLM are run locally or via cloud APIs (local inference needs more CPU). • Run a small load test with 5 concurrent audio sessions and watch CPU/RAM and network. • If latencies spike or CPU saturates, add another agent instance or move inference to managed services. Want a concrete two-node example (instance types, counts, and autoscale rules) I can paste as a ready plan?
c
t3.medium cost per month?
t
I can’t fetch live AWS pricing here. Typical on‑demand cost for a t3.medium (2 vCPU, 4 GiB) is roughly $0.041–$0.05 per hour depending on region — that’s about $30–$37/month if run 24/7. Notes: • Region, savings plans, reserved instances, and spot instances change price significantly. • Add EBS (storage), data transfer, and OS/license costs to estimate total monthly bill. Would you like a more precise estimate for a specific AWS region or with Savings Plan / reserved instance assumptions?
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