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First identify which limit the agent hit: a model’s context window, an output cap, a session budget, a rate limit, or an account spending or credit limit. Read the exact error code and message, then inspect request usage. “Token budget exceeded” alone does not tell you which remedy to use.
1. Read the exact error code and message
Start with the returned error rather than assuming that every budget warning means the prompt is too large. For example, OpenAI documents context_length_exceeded as input exceeding the model’s context window, while session_budget_exceeded indicates that a session reached its usage budget. Those errors point to different problems and require different fixes. See OpenAI’s API error guidance.
If the message is vague, check the provider’s request logs or API response for a more specific code and account guidance. Distinguish context or output limits from session budgets, request-rate limits, token-rate limits, and spend or credit limits before changing prompts.
2. Inspect usage for the request that failed
Use the usage information returned for the relevant endpoint or agent run, where available. Check input, output, and total token counts; some systems also report cached input, cache-write, or reasoning-token usage. A run-level total can show overall consumption, while request-level entries help pinpoint which model call or agent step used the tokens.
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OpenAI’s documentation describes endpoint usage fields and ways to inspect token counts in Understanding and counting tokens. For the OpenAI Agents SDK, see its usage documentation for aggregate run and per-request details. Field names and availability depend on the API or SDK in use.
3. Check the limit that applies to the selected model
Context window
A context window is the token capacity available to a request. It is not just the latest user message: the request may include system and developer instructions, conversation history, tool definitions and results, retrieved documents, and the requested response. Input and output use the available capacity, and some models also count reasoning tokens. The applicable context window varies by model.
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Check the exact model and API documentation rather than relying on a general limit quoted for a product or model family. OpenAI explains context and conversation-state considerations in its conversation state guide.
Maximum output
A model’s maximum output is distinct from its context-window size. A request may fit its input but still have too little output allowance for the desired answer; reaching the applicable limit can truncate a response. Check both limits and leave enough output capacity for the task.
Provider and framework behavior
Overflow behavior is not universal. Anthropic documents model-dependent context handling and recommends counting tokens for the specific request; consult its current context windows documentation. If an agent runs through a framework, check that framework’s error definition too: LangChain, for example, describes a ContextOverflowError for prompts, history, and instructions that exceed a model’s token or context limit.
4. Find what is making the request too large
When the evidence indicates a context overflow, inspect the entire request assembled for the failing call. Common sources of unnecessary size include:
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- Accumulated conversation turns that are no longer needed.
- Repeated system or developer instructions, examples, or copied context.
- Large tool results or retrieved documents sent in full.
- A requested response that needs more output capacity than the request allows.
Use usage telemetry to identify the oversized request or step when possible, instead of guessing from the visible chat alone. Agent calls can contain context that is not shown as the latest user message.
5. Match the fix to the diagnosed limit
If the context window is full
- Remove repeated or irrelevant instructions, examples, and history.
- Summarize earlier conversation turns while retaining facts the agent needs.
- Preprocess large documents or tool results to keep only relevant material.
- Split a large task or document into smaller requests, then combine the results if appropriate.
- Keep enough output allowance for the response you expect.
These approaches reduce or restructure input rather than simply retrying the same oversized request. OpenAI’s token guidance also discusses removing unnecessary material, summarizing or preprocessing large inputs, and dividing them into smaller parts: Understanding and counting tokens.
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If a session, rate, or account limit fired
Follow the specific error and provider or account guidance for that limit. Shortening the prompt may not resolve a session budget, request-rate limit, token-rate limit, or spend or credit restriction. The OpenAI error guide distinguishes session-budget errors from context-length errors: Errors and recovery.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Verify the change with a new usage check
After changing the request or account settings, retry with the same model and inspect the resulting error and usage data. If the failure persists, confirm that the retry used the intended model, that the assembled request actually changed, and that no other limit is being reported. Recheck current provider documentation for the exact model version, API, and framework, since limits and overflow handling differ.
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