import type { AssistantMessage } from "../types.js";
/**
* Regex patterns to detect context overflow errors from different providers.
*
* These patterns match error messages returned when the input exceeds
* the model's context window.
*
* Provider-specific patterns (with example error messages):
*
* - Anthropic: "prompt is too long: 213462 tokens > 200000 maximum"
* - Anthropic: "413 {\"error\":{\"type\":\"request_too_large\",\"message\":\"Request exceeds the maximum size\"}}"
* - OpenAI: "Your input exceeds the context window of this model"
* - Google: "The input token count (1196265) exceeds the maximum number of tokens allowed (1048575)"
* - xAI: "This model's maximum prompt length is 131072 but the request contains 537812 tokens"
* - Groq: "Please reduce the length of the messages or completion"
* - OpenRouter: "This endpoint's maximum context length is X tokens. However, you requested about Y tokens"
* - llama.cpp: "the request exceeds the available context size, try increasing it"
* - LM Studio: "tokens to keep from the initial prompt is greater than the context length"
* - GitHub Copilot: "prompt token count of X exceeds the limit of Y"
* - MiniMax: "invalid params, context window exceeds limit"
* - Kimi For Coding: "Your request exceeded model token limit: X (requested: Y)"
* - Cerebras: "400/413 status code (no body)"
* - Mistral: "Prompt contains X tokens ... too large for model with Y maximum context length"
* - z.ai: Does NOT error, accepts overflow silently - handled via usage.input > contextWindow
* - Xiaomi MiMo: Truncates input to fill contextWindow exactly, then returns finish_reason "length"
* with output=0 (no room left to generate). Detected via stopReason "length" + zero output +
* input filling the context window.
* - Ollama: Some deployments truncate silently, others return errors like "prompt too long; exceeded max context length by X tokens"
*/
const OVERFLOW_PATTERNS = [
/prompt is too long/i, // Anthropic token overflow
/request_too_large/i, // Anthropic request byte-size overflow (HTTP 413)
/input is too long for requested model/i, // Amazon Bedrock
/exceeds the context window/i, // OpenAI (Completions & Responses API)
/input token count.*exceeds the maximum/i, // Google (Gemini)
/maximum prompt length is \d+/i, // xAI (Grok)
/reduce the length of the messages/i, // Groq
/maximum context length is \d+ tokens/i, // OpenRouter (all backends)
/exceeds the limit of \d+/i, // GitHub Copilot
/exceeds the available context size/i, // llama.cpp server
/greater than the context length/i, // LM Studio
/context window exceeds limit/i, // MiniMax
/exceeded model token limit/i, // Kimi For Coding
/too large for model with \d+ maximum context length/i, // Mistral
/model_context_window_exceeded/i, // z.ai non-standard finish_reason surfaced as error text
/prompt too long; exceeded (?:max )?context length/i, // Ollama explicit overflow error
/context[_ ]length[_ ]exceeded/i, // Generic fallback
/too many tokens/i, // Generic fallback
/token limit exceeded/i, // Generic fallback
/^4(?:00|13)\s*(?:status code)?\s*\(no body\)/i, // Cerebras: 400/413 with no body
];
/**
* Patterns that indicate non-overflow errors (e.g. rate limiting, server errors).
* Error messages matching any of these are excluded from overflow detection
* even if they also match an OVERFLOW_PATTERN.
*
* Example: Bedrock formats throttling errors as "ThrottlingException: Too many tokens,
* please wait before trying again." which would match the /too many tokens/i overflow
* pattern without this exclusion.
*/
const NON_OVERFLOW_PATTERNS = [
/^(Throttling error|Service unavailable):/i, // AWS Bedrock non-overflow errors (human-readable prefixes from formatBedrockError)
/rate limit/i, // Generic rate limiting
/too many requests/i, // Generic HTTP 429 style
];
/**
* 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 function isContextOverflow(message: AssistantMessage, contextWindow?: number): boolean {
if (message.stopReason === "error" && message.errorMessage) {
// Skip messages matching known non-overflow patterns (e.g. throttling / rate-limit)
const isNonOverflow = NON_OVERFLOW_PATTERNS.some((p) => p.test(message.errorMessage!));
if (!isNonOverflow && OVERFLOW_PATTERNS.some((p) => p.test(message.errorMessage!))) {
return true;
}
}
if (contextWindow && message.stopReason === "stop") {
const inputTokens = message.usage.input + message.usage.cacheRead;
if (inputTokens > contextWindow) {
return true;
}
}
// Case 3: Length-stop overflow (Xiaomi MiMo style) - server truncates oversized input
// to fit the context window, leaving no room for output. Returns stopReason "length"
// with output=0 and input+cacheRead filling the context window.
if (contextWindow && message.stopReason === "length" && message.usage.output === 0) {
const inputTokens = message.usage.input + message.usage.cacheRead;
if (inputTokens >= contextWindow * 0.99) {
return true;
}
}
return false;
}