First, identify which limit actually fired. A context-window overflow, an output cap, a session budget, a rate limit, and an account spending or credit limit are different problems—and each calls for a different fix. Read the exact error, inspect usage data, then change the request or account setting that matches the evidence.
Start with the exact error
Do not assume a generic “token budget exceeded” message means the prompt was too long. Check the returned error code and message, along with the provider or framework that produced it. In the OpenAI API, context_length_exceeded means the input exceeds the model’s context window; session_budget_exceeded means the session has reached its usage budget. Those errors point to different limits and different remedies. See OpenAI’s error and recovery guidance.
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If the message instead mentions request rate, token rate, spend, credits, or a session allowance, follow that specific error’s instructions and account guidance. Do not try to fix an account or rate limit by trimming conversation history unless the provider identifies context size as the cause.
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Inspect the usage information returned for the failed request, where available. Token counts are more useful than estimating from character length: endpoint usage fields differ, and some APIs report input and output separately. OpenAI’s token guidance describes how to inspect usage for its endpoints.
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For an agent workflow, look beyond a single aggregate total if request-level details are available. The OpenAI Agents SDK can expose aggregate run usage and individual request entries, with input, output, total, cached, cache-write, and reasoning-token details. Those fields can help locate whether a particular model call, tool result, or repeated step is driving usage. See the Agents SDK usage documentation.
Determine whether the context window or output cap is the constraint
A context window is the request’s overall token capacity, not a limit on only the latest user message. The request can include system and developer instructions, conversation history, tool definitions and results, retrieved documents, and the output the model is asked to produce. Input and output draw on the available request capacity; some models also account for reasoning tokens. The context window and maximum output are separate model limits, and both vary by model. OpenAI explains these distinctions in its conversation-state documentation and token guidance.
If the request fits the context window but the model stops before completing the answer, check whether the output allowance is too small for the task. Conversely, raising an output allowance will not solve an input that already exceeds the context window. Some models can truncate output when the applicable limit is reached, so check the model’s current context and maximum-output limits rather than relying on a remembered figure.
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Find avoidable context before changing models
Review the complete request that was sent—not just the visible user prompt. Agent runs often accumulate material across turns and tool calls, and long results or documents may be included in full. Look for:
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- Conversation turns that no longer affect the current task.
- Repeated instructions, examples, or duplicated document passages.
- Large tool outputs that can be filtered to the relevant fields or excerpted.
- Documents sent wholesale when only a few sections are needed.
- Instructions that can be stated once instead of repeated in every agent step.
OpenAI recommends removing unnecessary or repeated material, summarizing or preprocessing large inputs, and splitting large inputs into smaller parts. See its token-counting and usage guidance.
Choose a fix that preserves the task
| Evidence | What to check | Appropriate response |
|---|---|---|
context_length_exceeded or usage at the context limit |
All instructions, history, tool definitions and results, retrieved content, and requested output | Remove irrelevant or repeated context, summarize or preprocess large material, split the task into smaller requests, or select a model with suitable limits. |
| Output ends early or an output-limit error | The selected model’s maximum output and the requested response size | Allow enough output for the task if supported, or ask for a staged response. Increasing output allowance does not make oversized input fit. |
session_budget_exceeded |
The session’s configured usage budget and provider guidance | Adjust or manage the session budget as appropriate; do not treat it as a context-window error. |
| Rate, spend, or credit error | The exact provider error and account or project limits | Follow the relevant rate-limit or billing guidance. Prompt trimming is not a substitute for resolving an account limit. |
When summarizing or splitting, preserve the information needed for the answer: definitions, constraints, relevant evidence, and any necessary references. A shorter prompt is not automatically a better prompt if it removes context the agent needs. For very large inputs, process bounded sections and carry forward a focused summary rather than repeatedly resending the full source.
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Verify behavior for the exact model and framework
Context handling and overflow behavior can differ across providers, model versions, APIs, and agent frameworks. Anthropic documents model-dependent context-window behavior and recommends counting tokens for the specific request; consult its Claude context-window documentation. LangChain’s ContextOverflowError reference describes an overflow involving the combined prompt, history, and instructions.
Before changing production settings, confirm the model identifier actually used by the agent, its current context and output limits, and whether the framework retries, truncates, or transforms the request. Provider documentation is the authority for the current behavior of that provider’s API.
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