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Use two separate limits for every LLM request: a per-attempt timeout and an overall operation deadline. The attempt timeout stops one stalled connection or generation; the deadline covers all attempts, backoff sleeps, response parsing, tool calls, and orchestration. Retry only failures that may clear on their own, use exponential backoff with jitter, honor a valid Retry-After as a minimum wait, and cap both attempts and elapsed time. If that bounded budget is exhausted, fail over only to an approved model or endpoint that still meets the task’s quality, latency, privacy, and cost requirements.
This policy prevents three common production failures: a worker waiting forever, a retry storm that worsens rate limiting, and a fallback that silently changes the answer or violates data-handling requirements.
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Start with two time budgets
A request timeout and an operation deadline answer different questions. The timeout asks, “How long may this individual model-call attempt run?” The deadline asks, “How long may the caller wait for the entire operation?”
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Set a timeout on each network/model attempt so a broken connection, stalled read, or unusually slow generation cannot occupy a worker indefinitely. If an attempt times out, close or cancel it before deciding whether another attempt is allowed. Keep connect, write, and read phases in mind when your client exposes them separately.
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Overall operation deadline
Start a monotonic clock when the operation begins. The remaining budget must include every retry attempt, backoff delay, response decoding, tool or function execution, moderation step, and any orchestration around the model call. A model-call timeout does not automatically limit an entire agent run. For example, the OpenAI Agents SDK documents ModelSettings.timeout as a per-model-call limit; it does not cover the full agent run, function-tool execution, or retry backoff.
Let D be the caller-facing deadline, A the number of attempts, Ti each attempt timeout, and Si each sleep. A valid policy satisfies:
elapsed_time = Σ(Ti + Si) + tool_and_parsing_time <= DIn practice, calculate a fresh remaining budget before every attempt and pass the smaller of the configured attempt timeout and that remaining time to the client. Never start a retry when the remaining budget cannot cover a useful attempt and its cleanup.
Choose budgets by workload
- Interactive chat: use a tight deadline and few retries. A user is more likely to prefer a clear failure or a fast approved fallback than a minute of silence.
- Background batch generation: allow a larger deadline, provided the queue and worker limits can absorb it.
- Long-context or deliberately slower tiers: increase the attempt timeout instead of blindly adding retries. Google Cloud’s Gemini guidance specifically distinguishes real-time chat, which should fail fast with limited retries, from Flex-style workloads where a longer timeout may be preferable.
- Agent workflows: reserve part of the deadline for tools and orchestration; do not give the model every millisecond in the caller’s budget.
There is no universal safe timeout. Record the workload, region, model, streaming mode, and SDK version alongside the value so a later change is reviewable.
Classify the error before retrying
A retry is useful only when the same request could plausibly succeed without changing it. Inspect the provider’s error code and response body, not only the HTTP status: a 5xx response can contain a non-retryable explanation, while a transport disconnect may have no HTTP response at all.
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| Failure class | Typical treatment | Reason |
|---|---|---|
| Connection reset, DNS/TCP failure, socket timeout | Retry within the deadline | The request may not have reached the service or the connection may recover. |
| 408 request timeout | Usually retry | The service or intermediary timed out the request; use backoff and a cap. |
| 429 rate limit or resource exhaustion | Retry after the required delay | Immediate repetition increases contention. Honor Retry-After when valid. |
| Selected 5xx errors, such as 500 or 503 | Usually retry | These can indicate temporary service or capacity problems, subject to provider guidance. |
| 400 malformed request or unsupported parameter | Do not retry unchanged | Fix the payload, model, schema, or API version. |
| 401/403 authentication or permission failure | Do not retry blindly | Refresh credentials or correct access configuration. |
| 402 billing failure, quota exhaustion, or account action required | Do not retry until the account issue is fixed | More requests cannot repair billing or quota state. |
Google’s Gemini guidance explicitly lists 408, 429, 5xx, socket timeouts, and TCP disconnects as transient candidates and advises against retrying 400, 402, or 403 errors. OpenAI likewise warns that quota, billing, and other action-required failures should not be retried. Treat these as provider examples, not a substitute for reading the current error contract for your endpoint.
Build bounded exponential backoff
For retry number n, increase the delay exponentially and add randomness so many workers do not wake at once. A common capped formula is:
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base = min(max_delay, initial_delay * 2**n)
sleep = random_uniform(0, base) # full jitterUse a monotonic clock and recompute the remaining operation budget after every sleep. If the server returns a valid Retry-After, treat that value as a minimum: wait at least that long, then add bounded jitter if doing so still fits the deadline. If the requested delay is longer than the remaining budget or your product’s maximum wait, defer the work or fail instead of retrying early.
Cap attempts and elapsed time
Set both an attempt limit and an absolute deadline. Either one can terminate the loop. A count alone is unsafe because a few long attempts may consume minutes; a time limit alone is unsafe because very short failures can create a large request burst. Remember that unsuccessful requests can count toward per-minute limits, so immediate repetition can make throttling worse.
Keep cancellation authoritative
Propagate caller cancellation through sleeps, HTTP requests, streaming readers, and tool calls. A retry loop that ignores cancellation can continue spending tokens and capacity after the user has left or an upstream deadline has expired.
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Account for retries already performed by an SDK
Hosted-model clients often retry internally. If your application adds another loop without understanding that policy, a nominal “three retries” setting can produce many more network requests. Choose one owner for the retry budget, or count SDK attempts inside the same deadline and observability data.
| Provider and documented context | Documented behavior | What to verify before deployment |
|---|---|---|
| OpenAI official SDKs | Eligible 429 and 503 responses are retried according to SDK settings. Retry-After handling varies by SDK and version. |
Installed language package, retry setting, status-code list, delay calculation, and cancellation behavior. |
| OpenAI Agents SDK | Per-model-call timeout is separate from the full agent run. Runner-managed retries are opt-in and apply replay-safety checks. | Whether a tool or model call is replay-safe before enabling runner retries. |
| Anthropic Python SDK | Documentation lists two default retries for connection errors, 408, 409, 429, and 5xx responses. Its documented default request timeout is 10 minutes and is configurable. | Python SDK release, timeout overrides, and whether your proxy has a shorter idle limit. |
| Gemini API Python SDK | Documentation describes automatic retries up to four times for transient errors, with an approximately one-second initial delay and a maximum delay of 60 seconds. | Whether you are using the Gemini API SDK rather than a Google Cloud Vertex AI client, and the release-specific retry configuration. |
| Google Cloud Gemini Enterprise Agent Platform | Retry options expose delay, attempt, status-code, and jitter controls. | Endpoint, payment tier, regional capacity, idempotency behavior, and the SDK version in use. |
These are documented examples, not cross-provider guarantees. SDK defaults change. Pin and inspect the version you deploy, expose effective retry settings in configuration, and include the SDK name and version in startup diagnostics.
Implement one retry controller
A central controller makes deadlines, logging, and policy consistent across model clients. The following pseudocode is intentionally provider-neutral:
function invoke_with_policy(request, policy, cancel_signal):
start = monotonic_now()
last_error = null
for attempt in 0 .. policy.max_attempts - 1:
remaining = policy.deadline - (monotonic_now() - start)
if remaining <= 0:
break
attempt_timeout = min(policy.attempt_timeout, remaining)
result = call_provider(
request,
timeout=attempt_timeout,
cancel_signal=cancel_signal,
sdk_retries=policy.sdk_retry_setting
)
if result.success:
return result
last_error = result.error
if not is_transient(result.error):
break
if attempt == policy.max_attempts - 1:
break
delay = exponential_full_jitter(attempt, policy.initial_delay,
policy.max_delay)
retry_after = parse_valid_retry_after(result.headers)
if retry_after exists:
delay = max(delay, retry_after)
remaining = policy.deadline - (monotonic_now() - start)
if delay >= remaining:
break
sleep(delay, cancel_signal)
return failure(last_error, elapsed=monotonic_now() - start)When the SDK has its own retries enabled, make max_attempts represent the combined budget, or disable the SDK loop and let this controller own it. Do not assume a timeout exception proves that the server did not process the request; a response can be lost after the server accepts work.
Protect against duplicate side effects
Retries are safest for read-like, idempotent operations. A generative request can be stochastic even when it does not modify server-side state, so two successful attempts may produce different text. A request that triggers an external action, writes a record, sends a message, or mutates a conversation needs stronger protection.
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- Use a provider-supported idempotency key when the operation supports one.
- Store an application request ID and deduplicate downstream effects.
- Separate generation from execution: generate a proposed tool call, validate it, then execute it once under your own transaction or idempotency guard.
- Log whether an attempt may have reached the provider before retrying after a timeout or disconnect.
Google Cloud documentation distinguishes idempotent operations and notes that generative requests may remain stochastic. “Idempotent” therefore means duplicate execution does not create an unwanted side effect; it does not mean duplicate generations will be byte-for-byte identical.
Handle streaming as a separate failure mode
Before the first token is delivered, a failed stream may be restartable under the same rules as a non-streaming request. After output reaches the caller, transparent replay is unsafe: the caller may already have rendered text, and a second generation can duplicate or contradict it.
OpenAI guidance explicitly warns against automatically replaying a request after consuming output when an error occurs after streaming begins. The Agents SDK also checks replay safety before retries.
Choose a stream recovery contract
- Partial-response contract: return the text received, mark it incomplete, and let the application decide what to do.
- Fresh-generation contract: ask the application layer whether a new generation is acceptable, clearly labeling it as a new attempt rather than a continuation.
- Buffered delivery: hold output until a safe checkpoint, trading latency for the ability to discard an incomplete response. This is suitable only when the product can tolerate the extra delay.
Never promise seamless continuation unless you can identify what the user has already received and make the continuation semantics explicit.
Design fallbacks as routing decisions
A fallback is not a generic “try another model” button. It can target a second model, another endpoint or region, or another provider. Route only after the retry policy has classified the original error and exhausted the permitted transient-recovery budget.
When a fallback is appropriate
- A regional endpoint is overloaded or unavailable while an approved alternative has capacity.
- The primary model’s latency budget is exhausted and a lower-latency model can meet the task’s minimum quality.
- A service-specific outage affects one endpoint but not another.
When not to fail over automatically
- The error is a malformed request, invalid credential, billing problem, or disallowed operation.
- The alternative lacks a required capability, context window, tool interface, safety behavior, or output schema.
- Data residency, contractual privacy controls, or geography would change.
- The task has already produced visible stream output or triggered a side effect without a safe replay design.
- The alternative’s cost or latency would violate the caller’s budget.
Compare alternatives against the task
| Decision axis | Questions to answer |
|---|---|
| Capability and quality | Can the alternative handle the required context, tools, structured output, language, and safety constraints? |
| Latency and deadline | Can it complete within the remaining operation budget, including connection setup and parsing? |
| Privacy and geography | Will the same data handling, retention, residency, and contractual controls apply? |
| Cost and capacity | Does the route fit the per-request budget, quota, payment tier, and current capacity? |
| Streaming and replay | Does it expose compatible streaming behavior, cancellation, and state semantics? |
| Observability | Can you distinguish primary failure, fallback attempt, fallback success, and final failure in metrics and logs? |
Google Cloud recommends dynamic global endpoint routing for availability and notes that regional overload may justify failover. It also warns that frequent errors under reserved capacity can indicate that purchased capacity has been exceeded; adding retries may not solve that underlying condition. For real-time chat, keep retry and fallback limits especially strict so failover does not turn a temporary outage into an unusable wait.
Provider-specific configuration notes
OpenAI API clients
Official SDKs retry eligible 429 and 503 responses subject to their settings. The handling of Retry-After differs by SDK and release, so test the exact package you ship. Treat timeout and cancellation separately from retryable status codes, and record the SDK’s effective attempt count. Do not replay a stream after output has been consumed unless your application explicitly handles partial output.
OpenAI Agents SDK
Use the model-call timeout to bound one attempt, then enforce a separate runner or caller deadline for tools, agent steps, and backoff. Runner-managed retries are opt-in and incorporate replay-safety rules. A tool that sends an email, charges a customer, or mutates state should not be retried merely because the surrounding model call is retryable.
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Anthropic Python SDK
The Python SDK documentation lists a 10-minute default request timeout and two default retries for connection errors, 408, 409, 429, and 5xx responses. Both timeout and retry behavior are configurable. Long non-streaming requests can exceed network idle limits; the documentation recommends streaming for longer requests. Treat the 10-minute figure as this Python SDK’s documented default, not a recommendation for every workload or language.
Gemini API Python SDK
Gemini API troubleshooting documentation describes automatic retries up to four times for transient errors, with an approximately one-second initial delay and a maximum delay of 60 seconds. Direct REST clients and custom loops must implement their own error filtering, exponential backoff, jitter, and cap. Do not transfer these values automatically to Google Cloud Vertex AI clients; the documentation contexts are different.
Google Cloud Gemini platform
The Google Cloud Gemini Enterprise Agent Platform exposes configurable delay, attempt, status-code, and jitter controls. Endpoint choice, payment tier, latency tolerance, and idempotency affect the correct policy. Regional overload may call for endpoint failover, while a capacity limit under a reserved tier may require capacity or purchasing changes rather than more retries.
Make failures diagnosable
Every attempt should be observable without logging sensitive prompts or generated content by default. At minimum, emit:
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- provider, endpoint or region, model, SDK name, and SDK version;
- attempt number and whether the attempt was initiated by the application or SDK;
- start time, attempt timeout, elapsed time, and remaining deadline;
- HTTP status, provider error code, transport error type, and parsed
Retry-After; - sleep duration, cancellation reason, token usage when available, and whether output had started;
- fallback route, final disposition, and whether a side effect may have occurred.
Useful metrics include success rate by attempt number, timeout rate, 429 rate, deadline exhaustion, fallback rate, stream failures after first token, and latency percentiles for primary versus fallback routes. Alert on rising retry volume before user-visible failures; retry storms often appear in metrics first.
Quick Recap
Test the policy before production
- Inject connection resets, DNS failures, socket timeouts, 408, 429 with and without
Retry-After, and representative 5xx responses. - Verify that 400, authentication, billing, and quota-action errors stop without another request.
- Advance a fake monotonic clock to prove the total deadline includes backoff, parsing, and tools.
- Run many workers together and confirm jitter prevents synchronized retries.
- Measure actual network calls with SDK retries enabled and disabled; confirm the combined count never exceeds policy.
- Terminate a stream before the first token and after partial output. Ensure only the first case follows automatic replay rules.
- Simulate a primary outage and verify that fallback preserves privacy, capability, schema, cost, and latency requirements.
- Repeat timeout tests where the provider may have accepted the request, then verify idempotency and deduplication.
Operational checklist
- Define an attempt timeout and a separate caller-facing deadline.
- Include retry sleeps, tools, parsing, and orchestration in the deadline.
- Retry only documented transient transport, timeout, rate-limit, and selected server failures.
- Use exponential backoff with jitter and honor valid
Retry-Aftervalues as a minimum. - Cap both attempts and total elapsed time.
- Inspect and configure SDK-managed retries so application and SDK loops do not multiply requests.
- Protect state-changing operations with idempotency or deduplication.
- Do not automatically replay a stream after output has reached the caller.
- Fail over only to an approved alternative that meets capability, privacy, latency, cost, and replay requirements.
- Log the original error and fallback outcome, and recheck defaults whenever the provider or SDK version changes.
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