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* Context truncation types and interfaces\n */\n\nimport type { TokenCount } from '../types/common.d';\n\n/**\n * Supported AI models for token counting\n */\nexport type ModelType =\n | 'gpt-4'\n | 'gpt-4-32k'\n | 'gpt-4-turbo'\n | 'gpt-4o'\n | 'gpt-3.5-turbo'\n | 'gpt-3.5-turbo-16k'\n | 'claude-3-opus'\n | 'claude-3-sonnet'\n | 'claude-3-haiku'\n | 'claude-3-5-sonnet'\n | 'claude-3-5-haiku';\n\n/**\n * Message importance levels\n */\nexport enum MessageImportance {\n SYSTEM = 'system',\n CRITICAL = 'critical',\n HIGH = 'high',\n NORMAL = 'normal',\n LOW = 'low'\n}\n\n/**\n * Truncation strategy types\n */\nexport type TruncationStrategyType =\n | 'oldest-first'\n | 'least-relevant'\n | 'sliding-window'\n | 'importance-based'\n | 'custom';\n\n/**\n * Message structure for context management\n */\nexport interface ContextMessage {\n role: 'system' | 'user' | 'assistant' | 'function' | 'tool';\n content: string | null;\n name?: string;\n function_call?: {\n name: string;\n arguments: string;\n };\n tool_calls?: Array<{\n id: string;\n type: 'function';\n function: {\n name: string;\n arguments: string;\n };\n }>;\n tool_call_id?: string;\n importance?: MessageImportance;\n metadata?: Record;\n embedding?: number[];\n}\n\n/**\n * Token usage information\n */\nexport interface TokenUsage {\n totalTokens: number;\n messageTokens: number;\n overheadTokens: number;\n remainingTokens: number;\n}\n\n/**\n * Context configuration\n */\nexport interface ContextConfig {\n model: ModelType;\n maxTokens: number;\n reservedTokens?: number;\n truncationStrategy?: TruncationStrategyType;\n preserveSystemMessages?: boolean;\n preserveRecentCount?: number;\n slidingWindowConfig?: {\n keepFirst: number;\n keepLast: number;\n };\n warningThreshold?: number;\n onWarning?: (usage: TokenUsage) => void;\n onTruncate?: (removed: ContextMessage[], remaining: ContextMessage[]) => void;\n customTruncationFn?: (messages: ContextMessage[], maxTokens: number) => ContextMessage[];\n}\n\n/**\n * Truncation strategy interface\n */\nexport interface TruncationStrategy {\n name: TruncationStrategyType;\n truncate(\n messages: ContextMessage[],\n maxTokens: number,\n currentTokens: number,\n config: ContextConfig\n ): ContextMessage[];\n}\n\n/**\n * Token counter result\n * Re-export from common types for backwards compatibility\n */\nexport type { TokenCount };\n\n/**\n * Model token limits mapping\n */\nexport const MODEL_TOKEN_LIMITS: Record = {\n 'gpt-4': 8192,\n 'gpt-4-32k': 32768,\n 'gpt-4-turbo': 128000,\n 'gpt-4o': 128000,\n 'gpt-3.5-turbo': 4096,\n 'gpt-3.5-turbo-16k': 16384,\n 'claude-3-opus': 200000,\n 'claude-3-sonnet': 200000,\n 'claude-3-haiku': 200000,\n 'claude-3-5-sonnet': 200000,\n 'claude-3-5-haiku': 200000,\n};\n\n/**\n * Model encoding mapping for tiktoken\n */\nexport const MODEL_ENCODING_MAP: Record = {\n 'gpt-4': 'cl100k_base',\n 'gpt-4-32k': 'cl100k_base',\n 'gpt-4-turbo': 'cl100k_base',\n 'gpt-4o': 'o200k_base',\n 'gpt-3.5-turbo': 'cl100k_base',\n 'gpt-3.5-turbo-16k': 'cl100k_base',\n 'claude-3-opus': 'cl100k_base',\n 'claude-3-sonnet': 'cl100k_base',\n 'claude-3-haiku': 'cl100k_base',\n 'claude-3-5-sonnet': 'cl100k_base',\n 'claude-3-5-haiku': 'cl100k_base',\n};\n","/**\n * Token counting utilities using tiktoken (Node.js) or fallback approximation (browser)\n */\n\nimport {\n ContextMessage,\n ModelType,\n TokenCount,\n MODEL_ENCODING_MAP,\n} from './types';\n\n// Lazy-loaded tiktoken (only in Node.js environments)\nlet tiktokenModule: typeof import('tiktoken') | null = null;\nlet tiktokenLoadAttempted = false;\n\n/**\n * Attempt to load tiktoken (Node.js only)\n * Returns null in browser environments\n */\nasync function loadTiktoken(): Promise {\n if (tiktokenLoadAttempted) {\n return tiktokenModule;\n }\n\n tiktokenLoadAttempted = true;\n\n try {\n // Check if we're in a Node.js environment\n if (typeof process !== 'undefined' && process.versions && process.versions.node) {\n tiktokenModule = await import('tiktoken');\n return tiktokenModule;\n }\n } catch (error) {\n // Silently fail in browser environments or if tiktoken is not installed\n console.debug('tiktoken not available, using fallback token counter');\n }\n\n return null;\n}\n\n/**\n * Browser-compatible approximate token counter\n * Uses character-based estimation (approximately 4 chars per token)\n */\nclass FallbackTokenCounter {\n private readonly CHARS_PER_TOKEN = 4;\n\n /**\n * Count tokens in a string (approximate)\n */\n countStringTokens(text: string): number {\n if (!text) return 0;\n return Math.ceil(text.length / this.CHARS_PER_TOKEN);\n }\n\n /**\n * Count tokens in a message with estimated breakdown\n */\n countMessageTokens(message: ContextMessage): TokenCount {\n const breakdown = {\n role: 0,\n name: 0,\n content: 0,\n functionCall: 0,\n toolCalls: 0,\n };\n\n // Every message has overhead (start, role, end tokens)\n let tokens = 3;\n\n // Role tokens\n breakdown.role = Math.ceil(message.role.length / this.CHARS_PER_TOKEN);\n tokens += breakdown.role;\n\n // Name tokens\n if (message.name) {\n breakdown.name = Math.ceil(message.name.length / this.CHARS_PER_TOKEN) + 1;\n tokens += breakdown.name;\n }\n\n // Content tokens\n if (message.content) {\n breakdown.content = this.countStringTokens(message.content);\n tokens += breakdown.content;\n }\n\n // Function call tokens\n if (message.function_call) {\n const fnName = this.countStringTokens(message.function_call.name);\n const fnArgs = this.countStringTokens(message.function_call.arguments);\n breakdown.functionCall = fnName + fnArgs + 3;\n tokens += breakdown.functionCall;\n }\n\n // Tool calls tokens\n if (message.tool_calls && message.tool_calls.length > 0) {\n for (const toolCall of message.tool_calls) {\n const toolId = this.countStringTokens(toolCall.id);\n const toolType = this.countStringTokens(toolCall.type);\n const fnName = this.countStringTokens(toolCall.function.name);\n const fnArgs = this.countStringTokens(toolCall.function.arguments);\n breakdown.toolCalls += toolId + toolType + fnName + fnArgs + 5;\n }\n tokens += breakdown.toolCalls;\n }\n\n // Tool call response overhead\n if (message.tool_call_id) {\n tokens += this.countStringTokens(message.tool_call_id) + 2;\n }\n\n return {\n tokens,\n characters: 0,\n breakdown,\n };\n }\n}\n\n/**\n * TokenCounter class for accurate token counting\n * Automatically uses tiktoken in Node.js or fallback in browsers\n */\nexport class TokenCounter {\n private encoders: Map = new Map();\n private tiktokenReady = false;\n private fallbackCounter = new FallbackTokenCounter();\n private initPromise: Promise | null = null;\n\n /**\n * Initialize tiktoken (lazy loading)\n */\n private async init(): Promise {\n if (this.initPromise) {\n return this.initPromise;\n }\n\n this.initPromise = (async () => {\n const tiktoken = await loadTiktoken();\n this.tiktokenReady = tiktoken !== null;\n })();\n\n return this.initPromise;\n }\n\n /**\n * Get or create encoder for a model (sync - uses fallback if tiktoken not ready)\n */\n private getEncoderSync(model: ModelType): any {\n if (!this.tiktokenReady || !tiktokenModule) {\n return null;\n }\n\n const encodingName = MODEL_ENCODING_MAP[model] || 'cl100k_base';\n\n if (!this.encoders.has(encodingName)) {\n try {\n const encoder = tiktokenModule.encoding_for_model(model as any);\n this.encoders.set(encodingName, encoder);\n } catch (error) {\n // Fallback to cl100k_base if model-specific encoding fails\n try {\n const encoder = tiktokenModule.encoding_for_model('gpt-4' as any);\n this.encoders.set(encodingName, encoder);\n } catch (e) {\n return null;\n }\n }\n }\n\n return this.encoders.get(encodingName) || null;\n }\n\n /**\n * Count tokens in a string\n */\n countStringTokens(text: string, model: ModelType): number {\n if (!text) return 0;\n\n const encoder = this.getEncoderSync(model);\n if (!encoder) {\n // Use fallback\n return this.fallbackCounter.countStringTokens(text);\n }\n\n return encoder.encode(text).length;\n }\n\n /**\n * Count tokens in a message with detailed breakdown\n */\n countMessageTokens(message: ContextMessage, model: ModelType): TokenCount {\n const encoder = this.getEncoderSync(model);\n\n if (!encoder) {\n // Use fallback counter\n return this.fallbackCounter.countMessageTokens(message);\n }\n\n const breakdown = {\n role: 0,\n name: 0,\n content: 0,\n functionCall: 0,\n toolCalls: 0,\n };\n\n // Every message follows <|start|>{role/name}\\n{content}<|end|>\\n\n let tokens = 3; // start, role, end tokens\n\n // Role tokens\n breakdown.role = encoder.encode(message.role).length;\n tokens += breakdown.role;\n\n // Name tokens\n if (message.name) {\n breakdown.name = encoder.encode(message.name).length + 1; // +1 for name prefix\n tokens += breakdown.name;\n }\n\n // Content tokens\n if (message.content) {\n breakdown.content = encoder.encode(message.content).length;\n tokens += breakdown.content;\n }\n\n // Function call tokens (legacy format)\n if (message.function_call) {\n const fnName = encoder.encode(message.function_call.name).length;\n const fnArgs = encoder.encode(message.function_call.arguments).length;\n breakdown.functionCall = fnName + fnArgs + 3; // +3 for structure tokens\n tokens += breakdown.functionCall;\n }\n\n // Tool calls tokens\n if (message.tool_calls && message.tool_calls.length > 0) {\n for (const toolCall of message.tool_calls) {\n const toolId = encoder.encode(toolCall.id).length;\n const toolType = encoder.encode(toolCall.type).length;\n const fnName = encoder.encode(toolCall.function.name).length;\n const fnArgs = encoder.encode(toolCall.function.arguments).length;\n breakdown.toolCalls += toolId + toolType + fnName + fnArgs + 5; // +5 for structure tokens\n }\n tokens += breakdown.toolCalls;\n }\n\n // Tool call response overhead\n if (message.tool_call_id) {\n tokens += encoder.encode(message.tool_call_id).length + 2;\n }\n\n return {\n tokens,\n characters: 0, // Not tracked in this implementation\n breakdown,\n };\n }\n\n /**\n * Count tokens for an array of messages\n */\n countMessagesTokens(messages: ContextMessage[], model: ModelType): number {\n let total = 0;\n for (const message of messages) {\n total += this.countMessageTokens(message, model).tokens;\n }\n // Add overhead for message list\n total += 3; // every reply is primed with <|start|>assistant<|message|>\n return total;\n }\n\n /**\n * Estimate tokens remaining after adding a message\n */\n estimateRemainingTokens(\n messages: ContextMessage[],\n maxTokens: number,\n model: ModelType\n ): number {\n const used = this.countMessagesTokens(messages, model);\n return Math.max(0, maxTokens - used);\n }\n\n /**\n * Check if adding a message would exceed token limit\n */\n wouldExceedLimit(\n messages: ContextMessage[],\n newMessage: ContextMessage,\n maxTokens: number,\n model: ModelType\n ): boolean {\n const currentTokens = this.countMessagesTokens(messages, model);\n const newTokens = this.countMessageTokens(newMessage, model).tokens;\n return currentTokens + newTokens > maxTokens;\n }\n\n /**\n * Find the index where messages would exceed token limit\n */\n findTokenLimitIndex(\n messages: ContextMessage[],\n maxTokens: number,\n model: ModelType,\n fromEnd: boolean = true\n ): number {\n let totalTokens = 3; // base overhead\n const messagesToCheck = fromEnd ? [...messages].reverse() : messages;\n\n for (let i = 0; i < messagesToCheck.length; i++) {\n const message = messagesToCheck[i];\n if (!message) continue;\n const messageTokens = this.countMessageTokens(message, model).tokens;\n totalTokens += messageTokens;\n\n if (totalTokens > maxTokens) {\n return fromEnd ? messages.length - i : i;\n }\n }\n\n return fromEnd ? 0 : messages.length;\n }\n\n /**\n * Free encoder resources\n */\n dispose(): void {\n if (this.tiktokenReady) {\n for (const encoder of this.encoders.values()) {\n if (encoder && typeof encoder.free === 'function') {\n encoder.free();\n }\n }\n }\n this.encoders.clear();\n }\n\n /**\n * Check if tiktoken is available\n */\n isTiktokenAvailable(): boolean {\n return this.tiktokenReady;\n }\n\n /**\n * Preload tiktoken (optional, for better performance)\n * Call this early in Node.js environments to ensure tiktoken is ready\n */\n async preload(): Promise {\n await this.init();\n return this.tiktokenReady;\n }\n}\n\n/**\n * Singleton instance for easy access\n */\nexport const tokenCounter = new TokenCounter();\n","/**\n * Context Manager with automatic truncation\n */\n\nimport { TokenCounter } from './TokenCounter';\nimport {\n ContextMessage,\n ContextConfig,\n TokenUsage,\n TruncationStrategy,\n MessageImportance,\n MODEL_TOKEN_LIMITS,\n} from './types';\n\n/**\n * Oldest-first truncation strategy\n * Removes oldest messages first while preserving system messages\n */\nclass OldestFirstStrategy implements TruncationStrategy {\n name = 'oldest-first' as const;\n\n truncate(\n messages: ContextMessage[],\n maxTokens: number,\n _currentTokens: number,\n config: ContextConfig\n ): ContextMessage[] {\n const counter = new TokenCounter();\n const result: ContextMessage[] = [];\n const preserveSystemMessages = config.preserveSystemMessages ?? true;\n const preserveRecentCount = config.preserveRecentCount ?? 1;\n\n // Separate system messages and regular messages\n const systemMessages = preserveSystemMessages\n ? messages.filter((m) => m.role === 'system')\n : [];\n const otherMessages = messages.filter((m) => m.role !== 'system');\n\n // Always include system messages\n result.push(...systemMessages);\n\n // Calculate tokens used by system messages\n let tokensUsed = counter.countMessagesTokens(systemMessages, config.model);\n\n // Preserve recent messages\n const recentMessages = otherMessages.slice(-preserveRecentCount);\n const recentTokens = counter.countMessagesTokens(recentMessages, config.model);\n\n // Add messages from end, skipping those we'll add as recent\n const middleMessages = otherMessages.slice(0, -preserveRecentCount);\n\n // Add middle messages from newest to oldest until we hit limit\n for (let i = middleMessages.length - 1; i >= 0; i--) {\n const message = middleMessages[i];\n if (!message) continue;\n const messageTokens = counter.countMessageTokens(message, config.model).tokens;\n\n if (tokensUsed + messageTokens + recentTokens <= maxTokens) {\n result.push(message);\n tokensUsed += messageTokens;\n }\n }\n\n // Add recent messages at the end\n result.push(...recentMessages);\n\n counter.dispose();\n\n // Restore original order (system messages are already at start)\n const nonSystemResult = result.filter((m) => m.role !== 'system');\n return [...systemMessages, ...nonSystemResult.sort((a, b) => {\n return messages.indexOf(a) - messages.indexOf(b);\n })];\n }\n}\n\n/**\n * Sliding window truncation strategy\n * Keeps first N and last M messages\n */\nclass SlidingWindowStrategy implements TruncationStrategy {\n name = 'sliding-window' as const;\n\n truncate(\n messages: ContextMessage[],\n maxTokens: number,\n _currentTokens: number,\n config: ContextConfig\n ): ContextMessage[] {\n const counter = new TokenCounter();\n const keepFirst = config.slidingWindowConfig?.keepFirst ?? 1;\n const keepLast = config.slidingWindowConfig?.keepLast ?? 5;\n\n // Separate system messages\n const systemMessages = (config.preserveSystemMessages ?? true)\n ? messages.filter((m) => m.role === 'system')\n : [];\n const otherMessages = messages.filter((m) => m.role !== 'system');\n\n // Get first and last messages\n const firstMessages = otherMessages.slice(0, keepFirst);\n const lastMessages = otherMessages.slice(-keepLast);\n\n // Combine and deduplicate\n const combined = [...systemMessages, ...firstMessages];\n\n for (const msg of lastMessages) {\n if (!combined.includes(msg)) {\n combined.push(msg);\n }\n }\n\n // If still over limit, trim from the middle\n let result = combined;\n let tokensUsed = counter.countMessagesTokens(result, config.model);\n\n while (tokensUsed > maxTokens && result.length > systemMessages.length + 2) {\n // Remove from middle (after first, before last)\n const middleIndex = systemMessages.length + Math.floor(\n (result.length - systemMessages.length) / 2\n );\n result.splice(middleIndex, 1);\n tokensUsed = counter.countMessagesTokens(result, config.model);\n }\n\n counter.dispose();\n return result;\n }\n}\n\n/**\n * Importance-based truncation strategy\n * Preserves messages based on importance level\n */\nclass ImportanceBasedStrategy implements TruncationStrategy {\n name = 'importance-based' as const;\n\n truncate(\n messages: ContextMessage[],\n maxTokens: number,\n _currentTokens: number,\n config: ContextConfig\n ): ContextMessage[] {\n const counter = new TokenCounter();\n const importanceOrder = [\n MessageImportance.SYSTEM,\n MessageImportance.CRITICAL,\n MessageImportance.HIGH,\n MessageImportance.NORMAL,\n MessageImportance.LOW,\n ];\n\n // Group messages by importance\n const grouped = new Map();\n for (const importance of importanceOrder) {\n grouped.set(importance, []);\n }\n\n // Classify messages\n for (const message of messages) {\n const importance = message.importance ?? (\n message.role === 'system'\n ? MessageImportance.SYSTEM\n : MessageImportance.NORMAL\n );\n grouped.get(importance)?.push(message);\n }\n\n // Add messages by importance until we hit the limit\n const result: ContextMessage[] = [];\n let tokensUsed = 0;\n\n for (const importance of importanceOrder) {\n const messagesOfImportance = grouped.get(importance) ?? [];\n\n for (const message of messagesOfImportance) {\n const messageTokens = counter.countMessageTokens(message, config.model).tokens;\n\n if (tokensUsed + messageTokens <= maxTokens) {\n result.push(message);\n tokensUsed += messageTokens;\n } else if (importance === MessageImportance.SYSTEM || importance === MessageImportance.CRITICAL) {\n // Always include system and critical messages, even if over limit\n result.push(message);\n tokensUsed += messageTokens;\n }\n }\n }\n\n counter.dispose();\n\n // Restore original order\n return result.sort((a, b) => messages.indexOf(a) - messages.indexOf(b));\n }\n}\n\n/**\n * Least relevant truncation strategy (using embeddings if available)\n * Falls back to oldest-first if no embeddings\n */\nclass LeastRelevantStrategy implements TruncationStrategy {\n name = 'least-relevant' as const;\n\n /**\n * Calculate cosine similarity between two vectors\n */\n private cosineSimilarity(a: number[], b: number[]): number {\n if (a.length !== b.length) return 0;\n\n let dotProduct = 0;\n let normA = 0;\n let normB = 0;\n\n for (let i = 0; i < a.length; i++) {\n const aVal = a[i];\n const bVal = b[i];\n if (aVal === undefined || bVal === undefined) continue;\n dotProduct += aVal * bVal;\n normA += aVal * aVal;\n normB += bVal * bVal;\n }\n\n const denominator = Math.sqrt(normA) * Math.sqrt(normB);\n return denominator === 0 ? 0 : dotProduct / denominator;\n }\n\n truncate(\n messages: ContextMessage[],\n maxTokens: number,\n _currentTokens: number,\n config: ContextConfig\n ): ContextMessage[] {\n const counter = new TokenCounter();\n const preserveSystemMessages = config.preserveSystemMessages ?? true;\n const preserveRecentCount = config.preserveRecentCount ?? 2;\n\n // Check if we have embeddings\n const hasEmbeddings = messages.some((m) => m.embedding && m.embedding.length > 0);\n\n if (!hasEmbeddings) {\n // Fallback to oldest-first strategy\n const strategy = new OldestFirstStrategy();\n return strategy.truncate(messages, maxTokens, _currentTokens, config);\n }\n\n // Separate messages\n const systemMessages = preserveSystemMessages\n ? messages.filter((m) => m.role === 'system')\n : [];\n const otherMessages = messages.filter((m) => m.role !== 'system');\n\n // Get recent messages (to preserve and use as context for relevance)\n const recentMessages = otherMessages.slice(-preserveRecentCount);\n const middleMessages = otherMessages.slice(0, -preserveRecentCount);\n\n // Calculate average embedding of recent messages\n const recentEmbeddings = recentMessages\n .filter((m) => m.embedding)\n .map((m) => m.embedding!);\n\n let avgEmbedding: number[] | null = null;\n if (recentEmbeddings.length > 0 && recentEmbeddings[0]) {\n const embeddingLength = recentEmbeddings[0].length;\n avgEmbedding = new Array(embeddingLength).fill(0);\n\n for (const embedding of recentEmbeddings) {\n if (!embedding) continue;\n for (let i = 0; i < embeddingLength; i++) {\n const val = embedding[i];\n const avgVal = avgEmbedding[i];\n if (val !== undefined && avgVal !== undefined) {\n avgEmbedding[i] = avgVal + val;\n }\n }\n }\n\n for (let i = 0; i < embeddingLength; i++) {\n const avgVal = avgEmbedding[i];\n if (avgVal !== undefined) {\n avgEmbedding[i] = avgVal / recentEmbeddings.length;\n }\n }\n }\n\n // Score messages by relevance to recent context\n const scoredMessages = middleMessages.map((message) => {\n let relevanceScore = 0;\n\n if (avgEmbedding && message.embedding) {\n relevanceScore = this.cosineSimilarity(message.embedding, avgEmbedding);\n }\n\n return { message, score: relevanceScore };\n });\n\n // Sort by relevance (highest first)\n scoredMessages.sort((a, b) => b.score - a.score);\n\n // Build result with most relevant messages\n const result: ContextMessage[] = [...systemMessages];\n let tokensUsed = counter.countMessagesTokens(systemMessages, config.model);\n const recentTokens = counter.countMessagesTokens(recentMessages, config.model);\n\n // Add most relevant messages until we hit limit\n for (const { message } of scoredMessages) {\n const messageTokens = counter.countMessageTokens(message, config.model).tokens;\n\n if (tokensUsed + messageTokens + recentTokens <= maxTokens) {\n result.push(message);\n tokensUsed += messageTokens;\n }\n }\n\n // Add recent messages\n result.push(...recentMessages);\n\n counter.dispose();\n\n // Restore original order\n return result.sort((a, b) => messages.indexOf(a) - messages.indexOf(b));\n }\n}\n\n/**\n * Context Manager for automatic context truncation\n */\nexport class ContextManager {\n private config: ContextConfig;\n private counter: TokenCounter;\n private strategies: Map;\n\n constructor(config: ContextConfig) {\n this.config = {\n reservedTokens: 1000,\n preserveSystemMessages: true,\n preserveRecentCount: 1,\n warningThreshold: 0.8,\n truncationStrategy: 'oldest-first',\n ...config,\n };\n\n this.counter = new TokenCounter();\n\n // Initialize strategies\n this.strategies = new Map();\n this.strategies.set('oldest-first', new OldestFirstStrategy());\n this.strategies.set('sliding-window', new SlidingWindowStrategy());\n this.strategies.set('importance-based', new ImportanceBasedStrategy());\n this.strategies.set('least-relevant', new LeastRelevantStrategy());\n }\n\n /**\n * Get current token usage\n */\n getTokenUsage(messages: ContextMessage[]): TokenUsage {\n const messageTokens = this.counter.countMessagesTokens(messages, this.config.model);\n const reservedTokens = this.config.reservedTokens ?? 1000;\n const maxTokens = this.config.maxTokens;\n\n return {\n totalTokens: messageTokens,\n messageTokens: messageTokens,\n overheadTokens: 3, // base overhead\n remainingTokens: Math.max(0, maxTokens - messageTokens - reservedTokens),\n };\n }\n\n /**\n * Check if messages need truncation\n */\n needsTruncation(messages: ContextMessage[]): boolean {\n const usage = this.getTokenUsage(messages);\n const maxTokens = this.config.maxTokens - (this.config.reservedTokens ?? 1000);\n return usage.totalTokens > maxTokens;\n }\n\n /**\n * Check if approaching token limit\n */\n isApproachingLimit(messages: ContextMessage[]): boolean {\n const usage = this.getTokenUsage(messages);\n const maxTokens = this.config.maxTokens - (this.config.reservedTokens ?? 1000);\n const threshold = this.config.warningThreshold ?? 0.8;\n return usage.totalTokens >= maxTokens * threshold;\n }\n\n /**\n * Truncate messages using configured strategy\n */\n truncate(messages: ContextMessage[]): ContextMessage[] {\n const maxTokens = this.config.maxTokens - (this.config.reservedTokens ?? 1000);\n const currentTokens = this.counter.countMessagesTokens(messages, this.config.model);\n\n // Check if truncation is needed\n if (currentTokens <= maxTokens) {\n return messages;\n }\n\n // Get strategy\n let strategy: TruncationStrategy;\n\n if (this.config.truncationStrategy === 'custom' && this.config.customTruncationFn) {\n // Use custom function\n const result = this.config.customTruncationFn(messages, maxTokens);\n if (this.config.onTruncate) {\n const removed = messages.filter((m) => !result.includes(m));\n this.config.onTruncate(removed, result);\n }\n return result;\n } else {\n strategy = this.strategies.get(this.config.truncationStrategy ?? 'oldest-first')!;\n }\n\n // Execute truncation\n const result = strategy.truncate(messages, maxTokens, currentTokens, this.config);\n\n // Callback\n if (this.config.onTruncate) {\n const removed = messages.filter((m) => !result.includes(m));\n this.config.onTruncate(removed, result);\n }\n\n return result;\n }\n\n /**\n * Add message with automatic truncation\n */\n addMessage(\n messages: ContextMessage[],\n newMessage: ContextMessage\n ): ContextMessage[] {\n const updatedMessages = [...messages, newMessage];\n\n // Check for warning threshold\n if (this.isApproachingLimit(updatedMessages) && this.config.onWarning) {\n const usage = this.getTokenUsage(updatedMessages);\n this.config.onWarning(usage);\n }\n\n // Truncate if needed\n if (this.needsTruncation(updatedMessages)) {\n return this.truncate(updatedMessages);\n }\n\n return updatedMessages;\n }\n\n /**\n * Add multiple messages with automatic truncation\n */\n addMessages(\n messages: ContextMessage[],\n newMessages: ContextMessage[]\n ): ContextMessage[] {\n let result = messages;\n\n for (const message of newMessages) {\n result = this.addMessage(result, message);\n }\n\n return result;\n }\n\n /**\n * Get maximum token limit for current model\n */\n getModelMaxTokens(): number {\n return MODEL_TOKEN_LIMITS[this.config.model] ?? 8192;\n }\n\n /**\n * Update configuration\n */\n updateConfig(config: Partial): void {\n this.config = { ...this.config, ...config };\n }\n\n /**\n * Register a custom truncation strategy\n */\n registerStrategy(strategy: TruncationStrategy): void {\n this.strategies.set(strategy.name, strategy);\n }\n\n /**\n * Clean up resources\n */\n dispose(): void {\n this.counter.dispose();\n }\n}\n"]}