import type { AssistantMessage } from "../types.js";
/**
 * Check if an assistant message represents a context overflow error.
 *
 * This handles two cases:
 * 1. Error-based overflow: Most providers return stopReason "error" with a
 *    specific error message pattern.
 * 2. Silent overflow: Some providers accept overflow requests and return
 *    successfully. For these, we check if usage.input exceeds the context window.
 *
 * ## Reliability by Provider
 *
 * **Reliable detection (returns error with detectable message):**
 * - Anthropic: "prompt is too long: X tokens > Y maximum" or "request_too_large"
 * - OpenAI (Completions & Responses): "exceeds the context window"
 * - Google Gemini: "input token count exceeds the maximum"
 * - xAI (Grok): "maximum prompt length is X but request contains Y"
 * - Groq: "reduce the length of the messages"
 * - Cerebras: 400/413 status code (no body)
 * - Mistral: "Prompt contains X tokens ... too large for model with Y maximum context length"
 * - OpenRouter (all backends): "maximum context length is X tokens"
 * - llama.cpp: "exceeds the available context size"
 * - LM Studio: "greater than the context length"
 * - Kimi For Coding: "exceeded model token limit: X (requested: Y)"
 *
 * **Unreliable detection:**
 * - z.ai: Sometimes accepts overflow silently (detectable via usage.input > contextWindow),
 *   sometimes returns rate limit errors. Pass contextWindow param to detect silent overflow.
 * - Xiaomi MiMo: Truncates input to fit contextWindow then returns stopReason "length" with
 *   output=0. Pass contextWindow param to detect via the "filled context + zero output" signal.
 * - Ollama: May truncate input silently for some setups, but may also return explicit
 *   overflow errors that match the patterns above. Silent truncation still cannot be
 *   detected here because we do not know the expected token count.
 *
 * ## Custom Providers
 *
 * If you've added custom models via settings.json, this function may not detect
 * overflow errors from those providers. To add support:
 *
 * 1. Send a request that exceeds the model's context window
 * 2. Check the errorMessage in the response
 * 3. Create a regex pattern that matches the error
 * 4. The pattern should be added to OVERFLOW_PATTERNS in this file, or
 *    check the errorMessage yourself before calling this function
 *
 * @param message - The assistant message to check
 * @param contextWindow - Optional context window size for detecting silent overflow (z.ai)
 * @returns true if the message indicates a context overflow
 */
export declare function isContextOverflow(message: AssistantMessage, contextWindow?: number): boolean;
/**
 * Get the overflow patterns for testing purposes.
 */
export declare function getOverflowPatterns(): RegExp[];
//# sourceMappingURL=overflow.d.ts.map