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fork of prime agent with changes i want but are not upstreamable probqbly (and some fixes)
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7.5 kB · 144 lines
TypeScript
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145import 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;}