Absolutely! You can add timeouts to your LLM calls and set up retries if they take too long. This is all handled by the

FallbackAdapter

class, which lets you easily customize timeout and retry settings to fit your needs. Here’s what you can do: • Timeout Configuration: Use the

attempt_timeout

parameter to set how long each LLM attempt should wait before timing out. • Retry Configuration: You can tweak retries with options like: ◦ `max_retry_per_llm`: How many times to retry per LLM. ◦ `retry_interval`: How long to wait between retry attempts. ◦ `retry_on_chunk_sent`: Whether to retry even after some chunks have been sent. Here’s a quick example to get you started:

fallback_llm = llm.FallbackAdapter(
    llm=[OpenAILLM(model="gpt-4")],
    attempt_timeout=5.0,
    max_retry_per_llm=1,
    retry_interval=0.5,
    retry_on_chunk_sent=False
)

Plus, the

FallbackAdapter

comes with handy built-in retry logic using exponential backoff for errors that can be recovered from. It can also switch to other LLMs in your list if the main one runs into trouble or times out. Pretty neat, right? Sources: livekit-plugins/livekit-plugins-anthropic/livekit/plugins/anthropic/llm.py | LLM Integrations | livekit/livekit_composite | DeepWiki | Agents Core Architecture | livekit/livekit_composite | DeepWiki