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ModelTimeoutError — the OpenAI Agents SDK’s own deadline on one model call

agents.exceptions.ModelTimeoutError: Model call timed out after <timeout> seconds.

(Our rendering of the message the class builds, with the number left as a placeholder.)

This error is not the network giving up and not the provider timing out. It is the SDK’s own clock: you, or a setting you inherited, gave each model call a number of seconds, and one attempt ran past it. This page is about the class: what it carries, what it is a kind of, the setting that arms it, the line that raises it, and whether the runner tries again first. Every claim about the library below is read off the Python source of openai-agents-python at release tag v0.23.1, fetched on 10 October 2026, with the address of each quoted line given; where we draw a conclusion from those lines rather than quote them, we say so.

The class

Defined in src/agents/exceptions.py (exceptions.py, lines 477–484):

class ModelTimeoutError(AgentsException): """Exception raised when a model-call attempt exceeds its configured timeout.""" timeout_seconds: float def __init__(self, timeout_seconds: float): self.timeout_seconds = timeout_seconds super().__init__(f"Model call timed out after {timeout_seconds:g} seconds.")

That is all of it: one field, one constructor argument, and a message built from the number. The class defines no __str__ of its own, so its string form is that message. The :g format drops a trailing .0, so a timeout of 30.0 reads Model call timed out after 30 seconds. (our reading). The number is the timeout you configured, not a measured duration (our reading of the raise site below).

Its parent is AgentsException(Exception), “Base class for all exceptions in the Agents SDK.”, which adds a run_data attribute set to None in its constructor (exceptions.py, lines 434–441). So except AgentsException catches it, and except TimeoutError does not: neither class derives from Python’s TimeoutError (our reading of the two class lines). The same file defines a sibling for function tools, ToolTimeoutError (line 507), which is a different deadline and not covered here.

The setting that arms it

The deadline is a field on ModelSettings (model_settings.py, lines 215–221):

timeout: Annotated[FiniteFloat, Field(gt=0)] | None = None """Maximum duration in seconds for each model-call attempt. The timeout is enforced cooperatively through normal asyncio cancellation. It bounds the complete model attempt, including transport waits, but does not replace provider-specific phase timeout configuration or bound the full run, tool calls, or retry backoff. """

Three things follow from that docstring. The default is None, and with None the helper below simply awaits the call (model_retry.py, lines 295–296), so the error only appears if something set a number. The number must be greater than zero. And it is per attempt: a run that makes ten model calls gets ten separate deadlines, and time spent in tools or waiting between retries does not count against any of them (our reading).

The runner hands that field to the model call as it is (run_internal/run_loop.py, lines 2698–2702, the non-streamed call):

retry_settings=model_settings.retry, get_retry_advice=model.get_retry_advice, previous_response_id=previous_response_id, conversation_id=conversation_id, timeout=model_settings.timeout,

The streamed call passes timeout=model_settings.timeout the same way (line 2308). Both the agent and the run config carry model_settings; elsewhere in the same file the runner calls current_agent.model_settings.resolve( with run_config.model_settings (lines 1256–1257), and the parameter of resolve is named override (model_settings.py, line 254). The call quoted above takes its settings from get_model_settings(execution_agent, run_config) (line 2617), which we did not read. So a timeout on the run config is the first place to look when you never set one on the agent (our inference).

Where it is raised

Every timed model attempt goes through one helper, _await_model_attempt, which waits on the call with asyncio.wait({task}, timeout=timeout) (line 300) and, if the call has not finished, builds the error (run_internal/model_retry.py, lines 305–310):

if task in done: return await task timeout_error = ModelTimeoutError( timeout_error_seconds if timeout_error_seconds is not None else timeout )

It then cancels and drains the unfinished call (line 311) and ends with raise timeout_error from None (line 315). The from None means the traceback does not chain the cancelled provider call underneath it, so there is no lower-level error to read: the class and the number are what you get (our reading).

For a non-streamed call the helper wraps the whole request (model_retry.py, line 612):

response = await _await_model_attempt(get_response(), timeout)

For a streamed call the deadline is set once when the attempt starts, deadline = asyncio.get_running_loop().time() + timeout (line 713), and each wait for the next event is given only what remains of it, while the error still reports the configured timeout (lines 765–770). So a stream that is producing events steadily can still hit the deadline if the whole response takes longer than the setting (our reading).

Does it retry first?

The _await_model_attempt call at line 612 sits inside the retry loop, and an error from it goes to the same retry decision as any other. The ceiling for that decision comes from ModelSettings.retry (model_retry.py, lines 654–656):

max_retries=( max(retry_settings.max_retries or 0, 0) if retry_settings is not None else 0 ),

and the decision begins if attempt > max_retries: return RetryDecision(retry=False) (lines 403–404); when the decision is no, the loop re-raises the error (lines 666–667). The retry field is documented as “Opt-in runner-managed retry settings for model calls.” (model_settings.py, line 189). So with retry unset, the first timed-out attempt is the last one, and the error reaches your code (our reading). A timeout is also explicitly not treated as an abort by the retry module (line 75). Whether a timeout is retried once you do set max_retries depends on the retry policy you give it, which we did not read in this run.

What to do

from agents.agent import Agent from agents.exceptions import ModelTimeoutError from agents.model_settings import ModelSettings from agents.run import Runner agent = Agent( name='worker', model_settings=ModelSettings(timeout=120), # seconds, per model-call attempt ) try: result = await Runner.run(agent, prompt) except ModelTimeoutError as e: log.warning('model call exceeded %ss', e.timeout_seconds) raise

(Our sketch, built from the class, field and module names above. run.py imports Agent from .agent and defines class Runner, but we did not read the Agent constructor or Runner.run’s signature in this run.)

• Find the number first. e.timeout_seconds is the setting that fired; search your agent and run-config ModelSettings for it before you touch the model (our reading).
• Compare it with how long a single call needs. The deadline covers the whole attempt, streamed or not, including a long answer or a long think. If the call is simply slow, raise timeout or ask for less per call (our inference).
• If you want a second try, ask for it. Retries are opt-in through ModelSettings.retry; without them the runner does not try again.
• Do not catch it as TimeoutError. Catch ModelTimeoutError, or AgentsException if you handle the SDK’s errors together.
• Do not confuse it with the provider’s timeout. The docstring says this setting “does not replace provider-specific” phase timeout configuration; an error raised by the client library is a different exception (our reading).

The thing worth keeping even if you never see this class again: this timeout is per attempt, is off unless someone set it, and is not retried unless someone asked (our reading).

What is behind this site

The short version: ModelTimeoutError is the OpenAI Agents SDK’s AgentsException for a single model-call attempt that ran past ModelSettings.timeout. It carries timeout_seconds, the configured number, and its message is Model call timed out after that many seconds. It is raised in run_internal/model_retry.py, and unless ModelSettings.retry is set, the runner does not try the call again.

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