The Tool Desk
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Start with the response, not the status alone
For a failed request, capture the HTTP status and the response’s structured error details, including any error type or code and message. A status narrows the possibilities, but it does not always tell you whether the problem is a bad request, temporary throttling, an account limit, or service overload. OpenAI’s error-code guide describes common cases.
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Also record request and response timing, relevant rate-limit headers, and the request ID when available. Do not log the API key or other secrets. The specific error details determine whether to correct the request, change account settings, wait and retry, or investigate connectivity.
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| 401 authentication error | Whether the key is valid and active; whether the request uses the intended organization or project; whether the key was revoked; and whether an IP allowlist applies. | Use the correct active credential and project context, or update the applicable allowlist. Keep the key out of browser and app-side code. |
| 400 request error | The error body, required fields, parameter names, and parameter values. | Correct the request shape or invalid value. Do not resend an unchanged request expecting a different result. |
| 429 temporary rate limit | Error code or message, Retry-After, rate-limit headers, concurrent callers, and recent traffic increases. |
Reduce or smooth traffic, then retry within a bounded policy that respects the server delay. |
| 429 credits, spend, or usage limit | The specific error details and the billing, project, or organization context. | Take the indicated account action, such as adding credits or adjusting the applicable limit. Retrying alone will not restore access. |
| 500 or 503 | Error details, any Retry-After value, and OpenAI’s current service status. |
For a potentially transient issue, wait and retry appropriately. If it persists, check service status and contact support with the request ID. |
| Timeout or connection failure | Client deadline and cancellation behavior; network, proxy, SSL, or firewall settings; and whether a response or request ID arrived. | Keep retries within an end-to-end time budget. Use a unique X-Client-Request-Id to help investigate requests whose response was not received. |
Fix authentication and request errors before retrying
401: verify credentials and request context
Check that the request is using the intended active API key and the correct organization or project context. A revoked key or a configured IP allowlist can also explain an authentication failure. Correct the configuration before making another request. Treat API keys as secrets: do not place them in browser code or an app distributed to users.
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400: correct the request
Read the error body and compare the request with the endpoint’s expected fields, parameter names, and value constraints. A 400 generally calls for changing the request, not repeating it unchanged. If the error identifies a particular field or value, fix that issue and send a new request.
Tell temporary 429 throttling apart from account limits
An OpenAI API 429 can mean temporary rate limiting, but it can also indicate exhausted credits or a spend or usage limit. Use the response’s error code and message to distinguish these cases. A temporary throttle may be recoverable by reducing traffic and retrying later; an account limit requires the relevant billing or limit action. Waiting and retrying does not add credits or change an account limit.
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For a temporary rate limit, respect the server’s delay
- Inspect
Retry-After. If present and valid, do not retry before that delay has elapsed. - Add a small random delay to reduce the chance that many callers retry together.
- If the header is absent or unusable, use bounded exponential backoff with jitter.
- Set both a maximum number of attempts and a total retry-time budget. If a valid server delay is longer than your configured maximum, defer the operation rather than retrying early.
Check the installed OpenAI SDK’s retry settings and behavior, including how it handles long delays. If your application also retries, account for both layers so their attempts do not multiply into an unbounded loop.
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Requests and tokens both affect rate limits, and limits vary by model, project, account, and platform conditions; there is no single request-per-minute or token-per-minute value that applies universally. Check the relevant response headers for the applicable limits, remaining capacity, and reset values.
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OpenAI’s rate-limit guide also notes that a slow_down response can occur when traffic rises too quickly, even when ordinary requests-per-minute and tokens-per-minute limits have not been exhausted. Treat it as a signal to pace requests and ramp traffic gradually, not as a cue to resume the same burst after a short fixed pause. Avoid redundant calls as well: failed requests can still contribute toward per-minute limits.
Handle streaming retries carefully
An HTTP error returned before a stream starts is different from an error event received after streaming has begun. If output has already been consumed, consider whether replaying the request would create duplicate work or application-side effects before automatically retrying it.
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Investigate 500 and 503 errors as possible service issues
A 500 points to a server-side issue; a 503 can indicate model overload. Check the error details and OpenAI’s current service status. If the condition appears transient, retry with an appropriate delay and a bounded attempt and time budget; honor Retry-After if supplied. If the errors persist, contact support with the request ID and relevant non-secret diagnostic details.
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A timeout does not prove the request was never processed
A client timeout means the client stopped waiting; by itself, it does not establish whether the service received or processed the request. Record the attempt’s timing and the overall deadline behavior. Before replaying, consider whether the first request might have completed and whether duplicate work would matter to your application.
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Generate a unique client-side trace ID for each request and, where supported, send it in X-Client-Request-Id. OpenAI’s API reference debugging guidance says this can help support determine whether a request arrived and when, including when a timeout or network problem prevented the client from receiving x-request-id. Follow the reference’s ASCII and length constraints for the client ID.
For connection errors, check the path to the API
Review network settings, proxy configuration, SSL certificates, and firewall rules. For slow but successful responses, compare client-observed timing with openai-processing-ms when available; the difference can help distinguish service processing time from time spent in the caller’s network and application.
Log enough to diagnose failures without logging secrets
OpenAI recommends logging server-generated x-request-id values in production so support can investigate a particular request. The API reference also documents diagnostic headers. Where present and relevant, retain:
x-request-idfor the server-side request identifier.openai-processing-msalongside your own timing measurements.- Rate-limit limit, remaining, and reset headers for requests and tokens, plus project-token headers where applicable.
- The HTTP status, structured error details, client trace ID, retry count, and timing for each attempt.
Exclude API keys and other secrets from logs. These records help distinguish a request problem from a rate or account limit, a slow response, and a connection failure.
Use streaming to improve perceived latency, not to repair errors
Streaming can show users output sooner, and chunking can help reduce the time they wait for a large result. OpenAI’s latency optimization guide covers latency techniques. They do not fix invalid credentials, malformed requests, exhausted credits, throttling, network faults, or server errors; diagnose those conditions separately.
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