what are the most likely causes of intermittent request timeouts from open ai llm?

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

I have no resource constraints, the timeout settings are 5 seconds, open ai says it has no issues and i have great connection!

Thanks for checking those resource constraints, timeout settings, and network issues already! To help figure this out together, could you share a bit more? • Are you working with just one LLM instance, or do you have a fallback adapter that uses multiple providers? • Have you noticed if the timeouts happen at certain times of day or with specific kinds of requests? • Do you have any metrics like Time To First Token (TTFT) or token usage that we could look at to dig deeper? Getting this info will really help us pinpoint what’s going on and find the best way to fix it. Sources: LLM Integrations | livekit/livekit_composite | DeepWiki

i have fallback adapters but they aren't getting triggered. No the timeouts are spread evenly. How do i get TTFT metrics?

Hey there! If you want to collect Time To First Token (TTFT) metrics for your OpenAI LLM requests, here’s a simple way to do it: For Python: 1. First, initialize the OpenAI LLM with the model you want. 2. Then, set up an event listener to catch the

metrics_collected

event on your LLM instance. 3. Finally, create a callback function to handle the metrics, including TTFT.

from livekit.agents.metrics import LLMMetrics
from livekit.plugins.openai import LLM

# Initialize the OpenAI LLM
llm = LLM(model="gpt-4")

# Define a callback function to handle metrics
def on_metrics_collected(metrics):
    ttft = metrics.ttft  # Access the Time To First Token
    print(f"TTFT: {ttft:.4f} seconds")

# Attach the event listener
llm.on("metrics_collected", on_metrics_collected)

# Use the LLM to generate completions and trigger metrics collection
async def generate_completion(prompt):
    async with llm.chat(chat_ctx=prompt) as response:
        pass

# Start generating completions to collect metrics
generate_completion("Hello, how are you?")

For JavaScript: 1. Initialize the OpenAI LLM using the JavaScript client. 2. Subscribe to the

metrics_collected

event on your LLM instance. 3. Handle the metrics inside the event callback.

const { OpenAILLM } = require("livekit-plugins");

// Initialize the OpenAI LLM
const llm = new OpenAILLM({
  model: "gpt-4",
});

// Define an event listener for metrics
llm.on("metrics_collected", (metrics) => {
  const ttft = metrics.ttft; // Access the Time To First Token
  console.log(`TTFT: ${ttft.toFixed(4)} seconds`);
});

// Use the LLM to generate completions and trigger metrics collection
async function generateCompletion(prompt) {
  const response = await llm.chat(prompt);
  // Process the response
}

// Start generating completions to collect metrics
generateCompletion("Hello, how are you?");

And that’s it! These steps will help you keep an eye on TTFT and other useful metrics. If you have any questions or want a hand with anything else, just let me know—I’m here to help!

P95 for TTFT about 2100ms

Hey there! A P95 TTFT (Time To First Token) of 2100ms means that 95% of your requests are getting their first token in 2.1 seconds or less. That’s pretty good and within the usual limits, but it might also hint at some spots where things could be a bit quicker. Here are a few common reasons and some friendly tips to help out: • Network Latency: Try hosting your app closer to the OpenAI LLM service region to cut down on any delays. • Model Size and Complexity: Bigger models can take a bit longer to get going. If speed is super important, maybe a smaller model could do the trick. • Server Load: If either the LLM service or your own server is really busy, that can slow things down. Adding more resources or spreading out the load might help smooth things out. • API Gateway Overhead: It’s worth double-checking your API gateway setup to see if there’s any extra latency sneaking in. If you want to dive deeper into any of these or just chat about it, I’m here to help! Sources: Capturing metrics | LiveKit Docs

how can i specify open ai llm service region?

To specify the OpenAI LLM service region, it depends a bit on how you're integrating it. Here’s a quick rundown: • For AWS Bedrock: Just use the

region

parameter when you initialize the LLM class. Here’s an example:

from livekit.plugins.aws import LLM

llm = LLM(
    model="anthropic.claude-3-5-sonnet-20240620-v1:0",
    region="us-west-2"  # This sets your AWS region
)

• For Azure OpenAI: Use the

azure_endpoint

parameter inside the

with_azure

method. Here’s how:

I'm using openai

Hey there! For OpenAI, you can set a region-specific endpoint by using the

base_url

parameter in the LLM configuration. Here’s a quick example to help you out:

from livekit.plugins.openai import LLM

openai_llm = LLM(
    model="gpt-4",
    base_url="<YOUR_REGION_SPECIFIC_ENDPOINT>"  # Just swap this with your region-specific endpoint
)

Right now, OpenAI doesn’t have detailed docs on region-specific endpoints, so if you’re unsure, it’s a good idea to check in with OpenAI support. And of course, if you have any questions or need a hand, just give me a shout!

If i'm getting timeouts "{"message": "livekit.plugins.openai.llm.LLM recovery failed\nTraceback (most recent call last):\n File \"/home/appuser/.local/lib/python3.13/site-packages/livekit/agents/llm/fallback_adapter.py\", line 140, in _try_generate\n async for chunk in stream:\n ...<3 lines>...\n yield chunk\n File \"/home/appuser/.local/lib/python3.13/site-packages/livekit/agents/llm/llm.py\", line 325, in __anext__\n raise exc # noqa: B904\n ^^^^^^^^^\n File \"/home/appuser/.local/lib/python3.13/site-packages/opentelemetry/util/_decorator.py\", line 71, in async_wrapper\n return await func(*args, **kwargs) # type: ignore\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/appuser/.local/lib/python3.13/site-packages/livekit/agents/llm/llm.py\", line 174, in _main_task\n return await self._run()\n ^^^^^^^^^^^^^^^^^\n File \"/home/appuser/.local/lib/python3.13/site-packages/livekit/plugins/openai/llm.py\", line 683, in _run\n raise APITimeoutError(retryable=retryable) from None\nlivekit.agents._exceptions.APITimeoutError: Request timed out. (body=None, retryable=True)", "level": "WARNING", "name": "livekit.agents", "pid": 2954, "job_id": "AJ_LgnjeJx5zof6", "timestamp": "2025-08-01T150634.818835+00:00"}" like this. what would be the best httpx.timeout config to tweak?

Hey there! To help with that

APITimeoutError

you're seeing in your OpenAI LLM integration, you can tweak the

httpx.Timeout

settings a bit. Here's a setup that tends to work well:

from httpx import Timeout

# Recommended timeout configuration
timeout = Timeout(connect=30.0, read=10.0, write=10.0, pool=10.0)

# Pass this timeout to your LLM initialization
llm = OpenAILLM(
    model="gpt-4",
    timeout=timeout
)

What’s going on here: • Connect Timeout: Bumped up to 30 seconds so there’s plenty of time to establish a connection. • Read/Write Timeout: Set to 10 seconds to give enough time for data to flow smoothly. • Pool Timeout: Also 10 seconds, allowing extra time for connection pooling tasks. This setup gives you a bit more breathing room with timeouts, but still keeps things running efficiently. If you want to chat more about this or need a hand with anything else, just let me know—I’m here to help!