LLM completion truncated

A text completion stopped before the model finished it: it hit its output limit, or its context window.

A provider answers such a completion as a success, with whatever text the model had written, empty or partial, and a stop value saying why it ended. The error tells the two limits apart, since they are lifted differently, and carries the one hit as its truncation_limit:

• The output limit (max_tokens): length on OpenAI and Mistral, max_tokens on Anthropic and Bedrock, MAX_TOKENS on Gemini, and an incomplete Responses API answer whose reason is max_output_tokens, missing or unknown (the status alone says the text is unfinished; the error names the reason as its stop value when the answer gives one, and incomplete when it gives none).
• The context window (context_window), which the input and the output filled together: model_context_window_exceeded on Anthropic and Bedrock, and model_length on Mistral.

The worker raises this error instead of handing the text back, so the pipe fails rather than passing a truncated contract, summary or JSON for a result.

The message names the model by its deck handle, the pipe, the stop value, the output tokens used and the max_tokens the request sent, or that it sent none, then the next step, which depends on the limit hit:

The model 'claude-5-sonnet' was cut off before it finished the text of pipe 'ui_designer' (stop reason 'max_tokens', 4096 output tokens used, max_tokens set to 4096), so the text is incomplete. Raise the pipe's max_tokens, or shorten its input.

The model 'gpt-5' was cut off before it finished the text of pipe 'ui_designer' (stop reason 'length', 16384 output tokens used, no max_tokens sent), so the text is incomplete. Set the pipe's max_tokens up to the model's limit, lower its reasoning effort, or shorten its input.

The model 'claude-5-sonnet' filled its context window before it finished the text of pipe 'ui_designer' (stop reason 'model_context_window_exceeded', 812 output tokens used, max_tokens set to 4096), so the text is incomplete. Shorten the pipe's input, lower its reasoning effort, or choose a model with a larger context window.

A run reports it located at the failing pipe, Pipe 'ui_designer' failed (flow → ui_designer): The model …. The partial text and the provider's body never appear in it.

What to do. At the output limit, raise the pipe's max_tokens, for instance model = { model = "claude-5-sonnet", max_tokens = 16384 }, or shorten its input. When the request sent no max_tokens, the pipe's setting being left to the provider, the provider's own default cut the text, so there is no limit of the pipe's to raise: set the pipe's max_tokens, up to the model's limit, lower its reasoning effort, or shorten its input. When the model thinks before it writes, its thinking counts against the same limit, so a lower reasoning effort also leaves more room for the text. At the context window, a higher max_tokens cannot help, since the input and the output already filled the window: shorten the pipe's input, lower its reasoning effort, or choose a model with a larger context window.

For an agent. Branch on the report's fields: error_type is LLMCompletionTruncatedError, error_category is content, error_domain is input (an HTTP surface answers 422), retryable is false, since the same request stops at the same limit, and user_action.kind is change_input, its detail the next step for the limit hit. The message is caller-facing, so it survives STRICT disclosure.

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