wehub-resource-sync
115 lines
3.7 KiB
Python
115 lines
3.7 KiB
Python
import asyncio
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import inspect
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from agents import (
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Agent,
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ModelRetrySettings,
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ModelSettings,
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RetryDecision,
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RunConfig,
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Runner,
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retry_policies,
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)
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def format_error(error: object) -> str:
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if not isinstance(error, BaseException):
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return "Unknown error"
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return str(error) or error.__class__.__name__
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async def main() -> None:
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apply_policies = retry_policies.any(
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# On OpenAI-backed models, provider_suggested() follows provider retry advice,
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# including fallback retryable statuses when x-should-retry is absent
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# (for example 408/409/429/5xx).
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retry_policies.provider_suggested(),
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retry_policies.retry_after(),
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retry_policies.network_error(),
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retry_policies.http_status([408, 409, 429, 500, 502, 503, 504]),
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)
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async def policy(context) -> bool | RetryDecision:
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raw_decision = apply_policies(context)
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decision: bool | RetryDecision
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if inspect.isawaitable(raw_decision):
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decision = await raw_decision
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else:
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decision = raw_decision
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if isinstance(decision, RetryDecision):
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if not decision.retry:
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print(
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f"[retry] stop after attempt {context.attempt}/{context.max_retries + 1}: "
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f"{format_error(context.error)}"
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)
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return False
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print(
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" | ".join(
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part
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for part in [
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f"[retry] retry attempt {context.attempt}/{context.max_retries + 1}",
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(
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f"waiting {decision.delay:.2f}s"
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if decision.delay is not None
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else "using default backoff"
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),
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f"reason: {decision.reason}" if decision.reason else None,
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f"error: {format_error(context.error)}",
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]
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if part is not None
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)
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)
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return decision
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if not decision:
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print(
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f"[retry] stop after attempt {context.attempt}/{context.max_retries + 1}: "
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f"{format_error(context.error)}"
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)
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return decision
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retry = ModelRetrySettings(
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max_retries=4,
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backoff={
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"initial_delay": 0.5,
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"max_delay": 5.0,
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"multiplier": 2.0,
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"jitter": True,
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},
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policy=policy,
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)
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# RunConfig-level model_settings are shared defaults for the run.
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# If an Agent also defines model_settings, the Agent wins for overlapping
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# keys, while nested objects like retry/backoff are merged.
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run_config = RunConfig(model_settings=ModelSettings(retry=retry))
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agent = Agent(
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name="Assistant",
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instructions="You are a concise assistant. Answer in 3 short bullet points at most.",
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# Prefix with litellm/ to route this request through the LiteLLM adapter.
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model="litellm/openai/gpt-4o-mini",
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# This Agent repeats the same retry config for clarity. In real code you
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# can keep shared defaults in RunConfig and only put per-agent overrides
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# here when you need different retry behavior.
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model_settings=ModelSettings(retry=retry),
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)
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print(
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"Retry support is configured. You will only see [retry] logs if a transient failure happens."
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)
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result = await Runner.run(
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agent,
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"Explain exponential backoff for API retries in plain English.",
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run_config=run_config,
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)
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print("\nFinal output:\n")
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print(result.final_output)
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if __name__ == "__main__":
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asyncio.run(main())
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