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A timeout or disconnection tells you that one part of the request path stopped waiting; it does not, by itself, tell you whether the model provider received the request, whether the agent finished, or whether a tool action took effect. Match client logs with provider records, identify which timer or connection failed, inspect the run and its side effects, and only then adjust settings or retry.
First, locate where the request failed
Correlate your client’s failure with the provider’s request and error data using the timestamp, model, and project. Filter provider dashboards to one project and one model at a time so unrelated traffic does not obscure the result. OpenAI’s troubleshooting guidance says a client-side error with no corresponding Service Health data likely did not reach OpenAI; timeouts, proxies, and network issues are common explanations for that mismatch. This is a clue about the boundary, not proof of a specific cause.
Record the exact time and timezone, any client or provider request ID, the HTTP status or error code, the model and project, and the configured client-side timeout. Compare latency percentiles such as P50, P90, P95, and P99 with the baseline, along with the error percentage; raw error counts alone can hide whether a small traffic spike or a broad slowdown is involved. Include these details when escalating an issue.
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“Timeout” can refer to separate limits at the client, proxy, model-call, tool, or overall workflow layer. A model call can exceed one timer while the rest of the agent run is still active, or a tool can be waiting after the model call has returned. Compare each configured limit with the duration and expected behavior of that specific operation.
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Model-call timeout is not the run deadline
The OpenAI Agents Python SDK documents ModelSettings.timeout as the limit for a single model-call attempt, including transport waits. It does not bound the entire agent run, function-tool execution, or retry backoff. If that attempt exceeds its limit, the SDK cancels it after cleanup and raises ModelTimeoutError. When SDK-managed retry policies are enabled, timeout failures are classified and replay-safety rules apply. Confirm the installed package version and local configuration before assuming another runtime behaves the same way. See the Agents SDK configuration documentation.
Also compare the client or proxy limit with the model call’s usual duration. If an upstream limit is shorter than the response time, the client can fail before the provider records a corresponding failure. A longer model-call timeout will not fix a tool-level or overall-run deadline, so change only the setting that is actually expiring.
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Check streaming and idle connections
For a long request, establish whether the client is streaming, what its read timeout measures, and whether a proxy or other network intermediary closes idle connections. Some transport timers measure time between bytes; others measure time between application-level content chunks. Those are not interchangeable.
Streaming, keep-alive, and SDK behavior
Anthropic’s Python SDK documentation identifies idle network connections as a possible source of failed or timed-out requests without a response, discusses TCP keep-alive, and recommends streaming for long requests. Its documented timeout retry default is two retries, but that is a vendor SDK default that can change; verify the behavior for the version you use. The same documentation says a non-streaming request that takes approximately 10 minutes is expected to trigger a ValueError unless streaming or a timeout override is used. That threshold is specific to the documented SDK, not a general limit for AI requests. See the Anthropic Python SDK documentation.
For the OpenAI Agents Python SDK’s optional Responses WebSocket transport, the documentation advises increasing ping_timeout for long reasoning turns that encounter keepalive timeouts. The documented connection processes one response at a time and has a maximum duration of 60 minutes. Fully consume streamed results before the session context exits: leaving while a request is in flight may close the connection. The SDK recommends HTTP/SSE when reliability matters more than WebSocket latency. These are specific SDK constraints, not universal rules for WebSockets or a general performance ranking. See the Agents SDK WebSocket documentation.
Distinguish an idle read from a quiet content stream
The LangChain OpenAI reference distinguishes an inter-byte HTTP read timeout from an application-level timeout between parsed content chunks. Server-sent event (SSE) keepalive comments may reset the HTTP read timer without counting as content chunks. If a stream appears connected but your application reports a timeout, determine whether the timer is waiting for bytes or for parsed content. Check the behavior of the installed integration version in the LangChain OpenAI reference.
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Find out whether the agent run or a tool action completed
OpenAI’s Agents API recovery documentation states: “An error event or a disconnected stream doesn’t confirm the turn’s final state.” Retrieve the session or turn status and inspect saved output and tool results before deciding the run failed. A turn that appears unsuccessful may already have changed files or called an external tool. If it remains active, continue following it; if it completed, use its result rather than resubmitting the work. See OpenAI’s Errors and recovery — Agents API.
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For managed environments, connection events indicate environment state; they do not restart a killed command or guarantee that a tool succeeded. A disconnect during a turn can cause a tool to fail even if the overall turn completes. Pending input may also not be recovered after a process crash. Check the tool results and final agent response, then verify durable state—for example, whether a file or external record actually changed—before sending the action again. See the Agents API reference.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Retry only when the outcome and replay safety are clear
Before retrying, establish whether the previous attempt reached the provider, whether a response started, whether the run is still active, and whether any state-changing tool could already have taken effect. A timeout can leave an outcome uncertain, so blindly replaying an external action risks duplicating it. Where needed, use application-specific deduplication or reconcile the external system’s state before repeating the operation.
For rate limits, overload, timeouts, and temporary service failures, OpenAI advises checking the outcome first, limiting retries, waiting, and honoring Retry-After when present. Bound both the number of attempts and the total deadline. Stop automatic retries if the error changes or the retry limit is reached. Invalid input, credentials, permissions, and billing limits need correction rather than another identical attempt. The Agents SDK retry policy can use normalized timeout and network classifications, whether a response started, and replay-safety information; do not assume the same policy is active in every application. See OpenAI’s recovery guidance and the Agents SDK configuration documentation.
Prepare an incident record that can be investigated
Capture enough information to match the client event to the provider and reconstruct what the agent did:
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- Exact start and failure timestamps, including timezone, and elapsed time until failure.
- Provider or client request ID, plus session and turn IDs where applicable.
- Model, project, endpoint, transport, and SDK or package version.
- Timeout values for the client, proxy, model call, tool, and overall run.
- HTTP status and error code; stream events or chunks received; and whether the provider recorded a matching request.
- Affected latency percentiles (P50, P90, P95, P99), baseline, and error percentage.
- Tool actions or other side effects that might have completed before the disconnect.
OpenAI’s API troubleshooting guidance recommends filtering by model and project, examining request errors and latency, and including request IDs and timezone-qualified timestamps when escalating. The Agents API observability documentation covers session and turn failure or cancellation, as well as environment and error events.
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