openagentic/dist/index.mjs.map

A TypeScript framework for building AI agents with self-contained tool orchestration capabilities

{"version":3,"sources":["../src/providers/manager.ts","../src/orchestrators/registry.ts","../src/orchestrator.ts","../src/types.ts","../src/tools/utils.ts","../src/tools/openai.ts","../src/utils/s3.ts","../src/tools/openai-image.ts","../src/tools/anthropic.ts","../src/tools/gemini.ts","../src/tools/github.ts","../src/tools/grok.ts","../src/tools/llama.ts","../src/tools/newsdata.ts","../src/tools/perplexity.ts","../src/tools/qrcode.ts","../src/tools/websearch.ts","../src/tools/elevenlabs.ts","../src/tools/video-generation.ts","../src/tools/gemini-tts.ts","../src/tools/inception-labs.ts","../src/tools/index.ts","../src/orchestrators/prompt-based.ts","../src/orchestrators/custom-logic.ts","../src/orchestrators/multi-ai.ts","../src/orchestrators/library/video-creator.ts","../src/orchestrators/library/code-assessment.ts","../src/index.ts","../src/streaming-orchestrator.ts","../src/orchestrators/index.ts"],"sourcesContent":["import type { AIModel, ApiKeyMap } from '../types';\n\n// Provider configuration with model metadata\nexport const providerConfigs = {\n  openai: {\n    baseURL: 'https://api.openai.com/v1',\n    models: {\n      'gpt-4': { \n        contextWindow: 8192, \n        cost: { input: 0.03, output: 0.06 },\n        description: 'Most capable GPT-4 model'\n      },\n      'gpt-4-turbo': { \n        contextWindow: 128000, \n        cost: { input: 0.01, output: 0.03 },\n        description: 'GPT-4 Turbo with larger context window'\n      },\n      'gpt-4o': { \n        contextWindow: 128000, \n        cost: { input: 0.005, output: 0.015 },\n        description: 'GPT-4 Omni - fastest and most cost-effective'\n      },\n      'gpt-4o-mini': { \n        contextWindow: 128000, \n        cost: { input: 0.00015, output: 0.0006 },\n        description: 'Smaller, faster GPT-4o variant'\n      },\n      'o3': { \n        contextWindow: 200000, \n        cost: { input: 0.06, output: 0.24 },\n        description: 'Latest reasoning model'\n      },\n      'o3-mini': { \n        contextWindow: 200000, \n        cost: { input: 0.015, output: 0.06 },\n        description: 'Smaller o3 variant with faster inference'\n      },\n    }\n  },\n  anthropic: {\n    baseURL: 'https://api.anthropic.com',\n    models: {\n      'claude-opus-4-20250514': { \n        contextWindow: 200000, \n        cost: { input: 0.015, output: 0.075 },\n        description: 'Most capable Claude 4 model'\n      },\n      'claude-sonnet-4-20250514': { \n        contextWindow: 200000, \n        cost: { input: 0.003, output: 0.015 },\n        description: 'Balanced Claude 4 model for most use cases'\n      },\n    }\n  },\n  google: {\n    baseURL: 'https://generativelanguage.googleapis.com/v1beta',\n    models: {\n      'gemini-2.5-pro-preview-06-05': { \n        contextWindow: 2000000, \n        cost: { input: 0.001, output: 0.002 },\n        description: 'Latest Gemini 2.5 Pro preview model'\n      },\n      'gemini-2.5-flash-preview-05-20': { \n        contextWindow: 1000000, \n        cost: { input: 0.0005, output: 0.001 },\n        description: 'Fast Gemini 2.5 Flash preview model'\n      },\n      'gemini-1.5-pro': { \n        contextWindow: 2000000, \n        cost: { input: 0.00125, output: 0.005 },\n        description: 'Gemini 1.5 Pro with large context window'\n      },\n      'gemini-1.5-flash': { \n        contextWindow: 1000000, \n        cost: { input: 0.000075, output: 0.0003 },\n        description: 'Fast and efficient Gemini 1.5 model'\n      },\n    }\n  },\n  'google-vertex': {\n    baseURL: 'https://us-central1-aiplatform.googleapis.com',\n    models: {\n      'gemini-2.5-pro-preview-06-05': { \n        contextWindow: 2000000, \n        cost: { input: 0.001, output: 0.002 },\n        description: 'Latest Gemini 2.5 Pro preview model via Vertex AI'\n      },\n      'gemini-2.5-flash-preview-05-20': { \n        contextWindow: 1000000, \n        cost: { input: 0.0005, output: 0.001 },\n        description: 'Fast Gemini 2.5 Flash preview model via Vertex AI'\n      },\n      'gemini-1.5-pro': { \n        contextWindow: 2000000, \n        cost: { input: 0.00125, output: 0.005 },\n        description: 'Gemini 1.5 Pro via Vertex AI'\n      },\n      'gemini-1.5-flash': { \n        contextWindow: 1000000, \n        cost: { input: 0.000075, output: 0.0003 },\n        description: 'Fast Gemini 1.5 model via Vertex AI'\n      },\n    }\n  },\n  perplexity: {\n    baseURL: 'https://api.perplexity.ai',\n    models: {\n      'llama-3.1-sonar-small-128k-online': { \n        contextWindow: 127072, \n        cost: { input: 0.0002, output: 0.0002 },\n        description: 'Small Llama 3.1 Sonar with online search'\n      },\n      'llama-3.1-sonar-large-128k-online': { \n        contextWindow: 127072, \n        cost: { input: 0.001, output: 0.001 },\n        description: 'Large Llama 3.1 Sonar with online search'\n      },\n      'llama-3.1-sonar-huge-128k-online': { \n        contextWindow: 127072, \n        cost: { input: 0.005, output: 0.005 },\n        description: 'Huge Llama 3.1 Sonar with online search'\n      },\n    }\n  },\n  xai: {\n    baseURL: 'https://api.x.ai/v1',\n    models: {\n      'grok-beta': { \n        contextWindow: 131072, \n        cost: { input: 0.005, output: 0.015 },\n        description: 'Grok conversational AI model'\n      },\n    }\n  },\n};\n\n// =============================================================================\n// CENTRALIZED PROVIDER MANAGER\n// =============================================================================\n\nexport class ProviderManager {\n  private static userApiKeys: ApiKeyMap | undefined;\n\n  /**\n   * Set user-provided API keys that take precedence over environment variables\n   */\n  static setUserApiKeys(apiKeys: ApiKeyMap | undefined): void {\n    this.userApiKeys = apiKeys;\n    \n    if (apiKeys && Object.keys(apiKeys).length > 0) {\n      console.log('🔑 User API keys configured for providers:', Object.keys(apiKeys).length);\n    }\n  }\n\n  /**\n   * Create a model configuration from a string or AIModel object\n   * Automatically detects provider from model name if string is provided\n   */\n  static createModel(input: string | AIModel): AIModel {\n    if (typeof input === 'string') {\n      return this.autoDetectProvider(input);\n    }\n    return this.validateAndNormalizeModel(input);\n  }\n\n  /**\n   * Create an AI SDK provider instance for the given model\n   */\n  static async createProvider(model: AIModel): Promise<any> {\n    const apiKey = model.apiKey ?? this.getDefaultApiKey(model.provider); // Fix: Use nullish coalescing\n    \n    switch (model.provider) {\n      case 'openai': {\n        const { createOpenAI } = await import('@ai-sdk/openai');\n        const config: any = {};\n        if (apiKey !== undefined) config.apiKey = apiKey; // Fix: Explicit undefined check\n        if (model.baseURL !== undefined) config.baseURL = model.baseURL;\n        return createOpenAI(config);\n      }\n      case 'anthropic': {\n        const { createAnthropic } = await import('@ai-sdk/anthropic');\n        const config: any = {};\n        if (apiKey !== undefined) config.apiKey = apiKey;\n        return createAnthropic(config);\n      }\n      case 'google': {\n        const { createGoogleGenerativeAI } = await import('@ai-sdk/google');\n        const config: any = {};\n        if (apiKey !== undefined) config.apiKey = apiKey;\n        return createGoogleGenerativeAI(config);\n      }\n      case 'google-vertex': {\n        const { createVertex } = await import('@ai-sdk/google-vertex');\n        const config: any = {};\n        if (model.project !== undefined) config.project = model.project;\n        if (model.location !== undefined) config.location = model.location;\n        return createVertex(config);\n      }\n      case 'perplexity': {\n        const { createPerplexity } = await import('@ai-sdk/perplexity');\n        const config: any = {};\n        if (apiKey !== undefined) config.apiKey = apiKey;\n        return createPerplexity(config);\n      }\n      case 'xai': {\n        const { createXai } = await import('@ai-sdk/xai');\n        const config: any = {};\n        if (apiKey !== undefined) config.apiKey = apiKey;\n        return createXai(config);\n      }\n      case 'custom': {\n        if (!model.baseURL) {\n          throw new Error('Custom provider requires baseURL');\n        }\n        const { createOpenAI } = await import('@ai-sdk/openai');\n        const config: any = {\n          baseURL: model.baseURL,\n        };\n        if (apiKey !== undefined) config.apiKey = apiKey;\n        return createOpenAI(config);\n      }\n      default:\n        throw new Error(`Unsupported provider: ${model.provider}`);\n    }\n  }\n\n  /**\n   * Create a provider for a specific provider name (for tool context)\n   */\n  static async createProviderByName(providerName: string, apiKey?: string): Promise<any> {\n    const key = apiKey ?? this.getDefaultApiKey(providerName as AIModel['provider']); // Fix: Use nullish coalescing\n    \n    switch (providerName) {\n      case 'openai': {\n        const { createOpenAI } = await import('@ai-sdk/openai');\n        if (!key) throw new Error('OpenAI API key not found');\n        return createOpenAI({ apiKey: key });\n      }\n      case 'anthropic': {\n        const { createAnthropic } = await import('@ai-sdk/anthropic');\n        if (!key) throw new Error('Anthropic API key not found');\n        return createAnthropic({ apiKey: key });\n      }\n      case 'google': {\n        const { createGoogleGenerativeAI } = await import('@ai-sdk/google');\n        if (!key) throw new Error('Google API key not found');\n        return createGoogleGenerativeAI({ apiKey: key });\n      }\n      case 'perplexity': {\n        const { createPerplexity } = await import('@ai-sdk/perplexity');\n        if (!key) throw new Error('Perplexity API key not found');\n        return createPerplexity({ apiKey: key });\n      }\n      case 'xai': {\n        const { createXai } = await import('@ai-sdk/xai');\n        if (!key) throw new Error('xAI API key not found');\n        return createXai({ apiKey: key });\n      }\n      default:\n        throw new Error(`Unsupported provider: ${providerName}`);\n    }\n  }\n\n  /**\n   * Get all available providers and their models\n   */\n  static getAllProviders(): Array<{ provider: string; models: string[] }> {\n    return Object.entries(providerConfigs).map(([provider, config]) => ({\n      provider,\n      models: Object.keys(config.models),\n    }));\n  }\n\n  /**\n   * Get supported models for a provider\n   */\n  static getProviderModels(provider: string): string[] {\n    const config = providerConfigs[provider as keyof typeof providerConfigs];\n    return config ? Object.keys(config.models) : [];\n  }\n\n  /**\n   * Check if a model is supported by a provider\n   */\n  static isModelSupported(provider: string, model: string): boolean {\n    const models = this.getProviderModels(provider);\n    return models.includes(model);\n  }\n\n  /**\n   * Get model information (context window, cost, description)\n   */\n  static getModelInfo(provider: string, model: string) {\n    const config = providerConfigs[provider as keyof typeof providerConfigs];\n    if (!config) {\n      throw new Error(`Unknown provider: ${provider}`);\n    }\n    \n    const modelInfo = config.models[model as keyof typeof config.models];\n    if (!modelInfo) {\n      throw new Error(`Unknown model: ${model} for provider: ${provider}`);\n    }\n    \n    return modelInfo;\n  }\n\n  // Private methods\n  private static autoDetectProvider(modelName: string): AIModel {\n    let provider: AIModel['provider'];\n    let apiKey: string | undefined;\n\n    // OpenAI models\n    if (modelName.includes('gpt') || modelName.includes('o3')) {\n      provider = 'openai';\n      apiKey = this.getDefaultApiKey('openai');\n    }\n    // Anthropic models\n    else if (modelName.includes('claude')) {\n      provider = 'anthropic';\n      apiKey = this.getDefaultApiKey('anthropic');\n    }\n    // Google models\n    else if (modelName.includes('gemini')) {\n      provider = 'google';\n      apiKey = this.getDefaultApiKey('google');\n    }\n    // xAI models\n    else if (modelName.includes('grok')) {\n      provider = 'xai';\n      apiKey = this.getDefaultApiKey('xai');\n    }\n    // Perplexity models\n    else if (modelName.includes('llama') && modelName.includes('sonar')) {\n      provider = 'perplexity';\n      apiKey = this.getDefaultApiKey('perplexity');\n    }\n    // Default to OpenAI for unknown models\n    else {\n      provider = 'openai';\n      apiKey = this.getDefaultApiKey('openai');\n      console.warn(`Unknown model \"${modelName}\", defaulting to OpenAI provider`);\n    }\n\n    // Validate model is supported by detected provider\n    if (!this.isModelSupported(provider, modelName)) {\n      throw new Error(`Model \"${modelName}\" not found in ${provider} configuration`);\n    }\n\n    return {\n      provider,\n      model: modelName,\n      apiKey,\n      temperature: 0.7,\n    };\n  }\n\n  private static validateAndNormalizeModel(model: AIModel): AIModel {\n    // Ensure required fields are present\n    if (!model.provider || !model.model) {\n      throw new Error('AIModel must have provider and model fields');\n    }\n\n    // Create a normalized copy to avoid mutating the original\n    const normalizedModel: AIModel = { ...model };\n\n    // Add default API key if not provided\n    if (normalizedModel.apiKey === undefined) {\n      normalizedModel.apiKey = this.getDefaultApiKey(normalizedModel.provider);\n    }\n\n    // Add default temperature if not provided\n    if (normalizedModel.temperature === undefined) {\n      normalizedModel.temperature = 0.7;\n    }\n\n    return normalizedModel;\n  }\n\n  private static getDefaultApiKey(provider: AIModel['provider']): string | undefined {\n    // First check user-provided API keys\n    if (this.userApiKeys && provider in this.userApiKeys) {\n      return this.userApiKeys[provider];\n    }\n    \n    // Then fall back to environment variables\n    switch (provider) {\n      case 'openai':\n        return process.env.OPENAI_API_KEY;\n      case 'anthropic':\n        return process.env.ANTHROPIC_API_KEY;\n      case 'google':\n        return process.env.GOOGLE_API_KEY;\n      case 'google-vertex':\n        return undefined; // Vertex uses service account auth\n      case 'perplexity':\n        return process.env.PERPLEXITY_API_KEY;\n      case 'xai':\n        return process.env.XAI_API_KEY;\n      case 'custom':\n        return undefined; // Custom providers handle their own auth\n      default:\n        return undefined;\n    }\n  }\n}","import type { BaseOrchestrator, OrchestratorType } from '../types';\n\n// =============================================================================\n// ORCHESTRATOR REGISTRY\n// =============================================================================\n\n/**\n * Global registry for storing orchestrator instances\n */\nconst orchestratorRegistry = new Map<string, BaseOrchestrator>();\n\n/**\n * Register an orchestrator in the global registry\n * @param orchestrator - The orchestrator to register\n * @throws {Error} If an orchestrator with the same ID already exists\n */\nexport function registerOrchestrator(orchestrator: BaseOrchestrator): void {\n  if (!orchestrator || !orchestrator.id) {\n    throw new Error('Orchestrator must have a valid ID');\n  }\n\n  if (orchestratorRegistry.has(orchestrator.id)) {\n    throw new Error(`Orchestrator with ID \"${orchestrator.id}\" already exists`);\n  }\n\n  // Validate orchestrator structure\n  validateOrchestrator(orchestrator);\n\n  orchestratorRegistry.set(orchestrator.id, orchestrator);\n  \n  console.log(`🎭 Orchestrator registered: ${orchestrator.id} (${orchestrator.type})`);\n}\n\n/**\n * Get an orchestrator by ID\n * @param id - The orchestrator ID\n * @returns The orchestrator instance or undefined if not found\n */\nexport function getOrchestrator(id: string): BaseOrchestrator | undefined {\n  if (!id || typeof id !== 'string') {\n    return undefined;\n  }\n\n  return orchestratorRegistry.get(id);\n}\n\n/**\n * List all registered orchestrators\n * @returns Array of all registered orchestrators\n */\nexport function listOrchestrators(): BaseOrchestrator[] {\n  return Array.from(orchestratorRegistry.values());\n}\n\n/**\n * Get orchestrators by type\n * @param type - The orchestrator type to filter by\n * @returns Array of orchestrators matching the type\n */\nexport function getOrchestratorsByType(type: OrchestratorType): BaseOrchestrator[] {\n  return Array.from(orchestratorRegistry.values()).filter(o => o.type === type);\n}\n\n/**\n * Check if an orchestrator is registered\n * @param id - The orchestrator ID\n * @returns True if the orchestrator is registered\n */\nexport function hasOrchestrator(id: string): boolean {\n  return orchestratorRegistry.has(id);\n}\n\n/**\n * Unregister an orchestrator\n * @param id - The orchestrator ID to remove\n * @returns True if the orchestrator was removed, false if it didn't exist\n */\nexport function unregisterOrchestrator(id: string): boolean {\n  const removed = orchestratorRegistry.delete(id);\n  \n  if (removed) {\n    console.log(`🎭 Orchestrator unregistered: ${id}`);\n  }\n  \n  return removed;\n}\n\n/**\n * Clear all registered orchestrators\n */\nexport function clearOrchestratorRegistry(): void {\n  const count = orchestratorRegistry.size;\n  orchestratorRegistry.clear();\n  \n  console.log(`🎭 Orchestrator registry cleared: ${count} orchestrators removed`);\n}\n\n/**\n * Get registry statistics\n * @returns Object with registry statistics\n */\nexport function getRegistryStats(): {\n  total: number;\n  byType: Record<OrchestratorType, number>;\n  orchestratorIds: string[];\n} {\n  const orchestrators = Array.from(orchestratorRegistry.values());\n  \n  const byType: Record<OrchestratorType, number> = {\n    'prompt-based': 0,\n    'custom-logic': 0,\n  };\n\n  orchestrators.forEach(o => {\n    byType[o.type]++;\n  });\n\n  return {\n    total: orchestrators.length,\n    byType,\n    orchestratorIds: orchestrators.map(o => o.id),\n  };\n}\n\n// =============================================================================\n// VALIDATION HELPERS\n// =============================================================================\n\n/**\n * Validate orchestrator structure and required methods\n * @param orchestrator - The orchestrator to validate\n * @throws {Error} If the orchestrator is invalid\n */\nfunction validateOrchestrator(orchestrator: BaseOrchestrator): void {\n  // Required properties\n  if (!orchestrator.id || typeof orchestrator.id !== 'string') {\n    throw new Error('Orchestrator must have a valid string ID');\n  }\n\n  if (!orchestrator.name || typeof orchestrator.name !== 'string') {\n    throw new Error('Orchestrator must have a valid string name');\n  }\n\n  if (!orchestrator.description || typeof orchestrator.description !== 'string') {\n    throw new Error('Orchestrator must have a valid string description');\n  }\n\n  if (!orchestrator.type || !['prompt-based', 'custom-logic'].includes(orchestrator.type)) {\n    throw new Error('Orchestrator must have a valid type (prompt-based or custom-logic)');\n  }\n\n  // Required methods\n  if (typeof orchestrator.execute !== 'function') {\n    throw new Error('Orchestrator must implement execute method');\n  }\n\n  if (typeof orchestrator.getName !== 'function') {\n    throw new Error('Orchestrator must implement getName method');\n  }\n\n  if (typeof orchestrator.getDescription !== 'function') {\n    throw new Error('Orchestrator must implement getDescription method');\n  }\n\n  if (typeof orchestrator.getType !== 'function') {\n    throw new Error('Orchestrator must implement getType method');\n  }\n\n  // Type-specific validation\n  if (orchestrator.type === 'prompt-based') {\n    const promptOrchestrator = orchestrator as any;\n    if (!promptOrchestrator.systemPrompt || typeof promptOrchestrator.systemPrompt !== 'string') {\n      throw new Error('Prompt-based orchestrator must have a valid systemPrompt property');\n    }\n\n    if (typeof promptOrchestrator.getSystemPrompt !== 'function') {\n      throw new Error('Prompt-based orchestrator must implement getSystemPrompt method');\n    }\n  }\n\n  if (orchestrator.type === 'custom-logic') {\n    const customOrchestrator = orchestrator as any;\n    if (typeof customOrchestrator.customLogic !== 'function') {\n      throw new Error('Custom-logic orchestrator must implement customLogic method');\n    }\n  }\n\n  // Optional method validation\n  if (orchestrator.validate && typeof orchestrator.validate !== 'function') {\n    throw new Error('Orchestrator validate property must be a function if provided');\n  }\n\n  if (orchestrator.initialize && typeof orchestrator.initialize !== 'function') {\n    throw new Error('Orchestrator initialize property must be a function if provided');\n  }\n\n  if (orchestrator.cleanup && typeof orchestrator.cleanup !== 'function') {\n    throw new Error('Orchestrator cleanup property must be a function if provided');\n  }\n}\n\n/**\n * Resolve orchestrator from string ID or instance\n * @param orchestratorInput - String ID or orchestrator instance\n * @returns The orchestrator instance or undefined if not found\n */\nexport function resolveOrchestrator(orchestratorInput: string | BaseOrchestrator | undefined): BaseOrchestrator | undefined {\n  if (!orchestratorInput) {\n    return undefined;\n  }\n\n  if (typeof orchestratorInput === 'string') {\n    return getOrchestrator(orchestratorInput);\n  }\n\n  if (typeof orchestratorInput === 'object' && orchestratorInput.id) {\n    // Validate the orchestrator object\n    try {\n      validateOrchestrator(orchestratorInput);\n      return orchestratorInput;\n    } catch (error) {\n      console.warn(`🎭 Invalid orchestrator object: ${error instanceof Error ? error.message : String(error)}`);\n      return undefined;\n    }\n  }\n\n  return undefined;\n}","import { generateText } from 'ai';\nimport type { AIModel, Message, CoreMessage, ExecutionResult, OpenAgenticTool, LoggingConfig, LogLevel, ExecutionStats, StepInfo, BaseOrchestrator, OrchestratorContext, OrchestratorOptions, PromptBasedOrchestrator, CustomLogicOrchestrator } from './types';\nimport { ProviderManager } from './providers/manager';\nimport { resolveOrchestrator } from './orchestrators/registry';\n\nexport class Orchestrator {\n  private model: AIModel;\n  private tools: Record<string, OpenAgenticTool> = {};\n  private messages: Message[] = [];\n  private iterations = 0;\n  private maxIterations: number;\n  private customLogic?: (input: string, context: any) => Promise<any>;\n  \n  // Orchestrator support\n  private orchestrator?: BaseOrchestrator;\n  private orchestratorOptions: OrchestratorOptions;\n  \n  // Logging configuration\n  private loggingConfig: LoggingConfig;\n  private executionStartTime = 0;\n  private stepTimings: number[] = [];\n  private toolCallTimings: number[] = [];\n  private stepsExecuted = 0;\n  private toolCallsExecuted = 0;\n\n  constructor(options: {\n    model: string | AIModel;\n    tools?: any[];\n    systemPrompt?: string;\n    maxIterations?: number;\n    customLogic?: (input: string, context: any) => Promise<any>;\n    enableDebugLogging?: boolean;\n    logLevel?: LogLevel;\n    enableStepLogging?: boolean;\n    enableToolLogging?: boolean;\n    enableTimingLogging?: boolean;\n    enableStatisticsLogging?: boolean;\n  } & OrchestratorOptions) {\n    // Use ProviderManager for centralized model creation\n    this.model = ProviderManager.createModel(options.model);\n    this.maxIterations = options.maxIterations || 10;\n    this.customLogic = options.customLogic;\n    \n    // Store orchestrator options\n    this.orchestratorOptions = {\n      orchestrator: options.orchestrator,\n      orchestratorId: options.orchestratorId,\n      orchestratorParams: options.orchestratorParams,\n      allowOrchestratorPromptOverride: options.allowOrchestratorPromptOverride ?? true,\n      allowOrchestratorToolControl: options.allowOrchestratorToolControl ?? true,\n    };\n    \n    // Resolve orchestrator if provided\n    this.orchestrator = resolveOrchestrator(\n      options.orchestrator || options.orchestratorId\n    );\n    \n    // Configure logging\n    this.loggingConfig = {\n      enableDebugLogging: options.enableDebugLogging ?? false,\n      logLevel: options.logLevel ?? 'basic',\n      enableStepLogging: options.enableStepLogging ?? false,\n      enableToolLogging: options.enableToolLogging ?? false,\n      enableTimingLogging: options.enableTimingLogging ?? false,\n      enableStatisticsLogging: options.enableStatisticsLogging ?? false,\n    };\n    \n    // Register tools with validation\n    if (options.tools) {\n      options.tools.forEach((tool, index) => {\n        const toolName = tool.toolId;\n      \n        // Ensure toolId uniqueness\n        if (this.tools[toolName]) {\n          throw new Error(`Tool with name ${toolName} already exists`);\n        }\n        \n        this.tools[toolName] = tool;\n      });\n    }\n    \n    // Add system prompt if provided (orchestrator may override this)\n    if (options.systemPrompt) {\n      this.messages.push({\n        role: 'system',\n        content: options.systemPrompt,\n      });\n    }\n\n    this.log('🔧', 'Orchestrator initialized', {\n      model: `${this.model.provider}/${this.model.model}`,\n      toolsCount: Object.keys(this.tools).length,\n      maxIterations: this.maxIterations,\n      loggingLevel: this.loggingConfig.logLevel,\n      hasCustomLogic: !!this.customLogic,\n      hasOrchestrator: !!this.orchestrator,\n      orchestratorId: this.orchestrator?.id,\n      orchestratorType: this.orchestrator?.type,\n    });\n  }\n\n  // Core execution method - supports both string and message array inputs\n  public async execute(input: string): Promise<ExecutionResult>;\n  public async execute(messages: CoreMessage[]): Promise<ExecutionResult>;\n  public async execute(input: string | CoreMessage[]): Promise<ExecutionResult> {\n    this.executionStartTime = Date.now();\n    this.resetExecutionStats();\n    \n    try {\n      // Validate input before logging to prevent errors\n      const inputType = typeof input === 'string' ? 'string' : \n                       Array.isArray(input) ? 'message_array' : \n                       'invalid';\n      const inputLength = typeof input === 'string' ? input.length : \n                         Array.isArray(input) ? input.length : \n                         'unknown';\n      \n      this.log('🚀', 'Execution starting', {\n        inputType,\n        inputLength,\n        modelInfo: `${this.model.provider}/${this.model.model}`,\n        toolsAvailable: Object.keys(this.tools).length,\n        maxSteps: this.maxIterations,\n        hasCustomLogic: !!this.customLogic,\n        hasOrchestrator: !!this.orchestrator,\n        orchestratorType: this.orchestrator?.type,\n      });\n\n      // If custom logic is provided, use it (takes precedence over orchestrator)\n      if (this.customLogic) {\n        return await this.executeWithCustomLogic(input);\n      }\n\n      // If orchestrator is available, delegate to it\n      if (this.orchestrator) {\n        return await this.executeWithOrchestrator(input);\n      }\n\n      // Handle different input types with standard execution\n      let result: ExecutionResult;\n      if (typeof input === 'string') {\n        result = await this.executeWithString(input);\n      } else if (Array.isArray(input)) {\n        result = await this.executeWithMessages(input);\n      } else {\n        throw new Error('Input must be either a string or an array of messages');\n      }\n\n      // Add execution statistics to result\n      const executionStats = this.calculateExecutionStats();\n      result.executionStats = executionStats;\n\n      this.log('✅', 'Execution completed successfully', {\n        totalDuration: executionStats.totalDuration,\n        stepsExecuted: executionStats.stepsExecuted,\n        toolCallsExecuted: executionStats.toolCallsExecuted,\n        averageStepDuration: executionStats.averageStepDuration,\n        resultLength: result.result?.length || 0,\n      });\n\n      return result;\n    } catch (error) {\n      const executionStats = this.calculateExecutionStats();\n      const errorMessage = error instanceof Error ? error.message : JSON.stringify(error);\n      \n      this.log('❌', 'Execution failed', {\n        error: errorMessage,\n        totalDuration: executionStats.totalDuration,\n        stepsExecuted: executionStats.stepsExecuted,\n        toolCallsExecuted: executionStats.toolCallsExecuted,\n        stackTrace: error instanceof Error ? error.stack : undefined,\n      });\n      \n      const errorResult: ExecutionResult = {\n        success: false,\n        error: errorMessage,\n        messages: this.messages,\n        iterations: this.iterations,\n        toolCallsUsed: [],\n        executionStats,\n      };\n\n      return errorResult;\n    }\n  }\n\n  // Execute with orchestrator delegation\n  private async executeWithOrchestrator(input: string | CoreMessage[]): Promise<ExecutionResult> {\n    if (!this.orchestrator) {\n      throw new Error('Orchestrator not available');\n    }\n\n    this.log('🎭', 'Delegating to orchestrator', {\n      orchestratorId: this.orchestrator.id,\n      orchestratorType: this.orchestrator.type,\n      orchestratorName: this.orchestrator.name,\n    });\n\n    try {\n      // For prompt-based orchestrators, we can handle them specially\n      if (this.orchestrator.type === 'prompt-based') {\n        return await this.executeWithPromptBasedOrchestrator(input, this.orchestrator as PromptBasedOrchestrator);\n      }\n\n      // For custom-logic orchestrators, delegate completely\n      if (this.orchestrator.type === 'custom-logic') {\n        return await this.executeWithCustomLogicOrchestrator(input, this.orchestrator as CustomLogicOrchestrator);\n      }\n\n      throw new Error(`Unknown orchestrator type: ${this.orchestrator.type}`);\n\n    } catch (error) {\n      this.log('❌', 'Orchestrator execution failed', {\n        orchestratorId: this.orchestrator.id,\n        error: error instanceof Error ? error.message : String(error),\n        stackTrace: error instanceof Error ? error.stack : undefined,\n      });\n\n      throw error;\n    }\n  }\n\n  // Execute with custom logic orchestrator\n  private async executeWithCustomLogicOrchestrator(\n    input: string | CoreMessage[],\n    orchestrator: CustomLogicOrchestrator\n  ): Promise<ExecutionResult> {\n    this.log('🎭', 'Executing with custom logic orchestrator', {\n      orchestratorId: orchestrator.id,\n      orchestratorName: orchestrator.name,\n    });\n\n    try {\n      // Build orchestrator context\n      const context: OrchestratorContext = {\n        model: this.model,\n        tools: Object.values(this.tools),\n        messages: this.messages,\n        iterations: this.iterations,\n        maxIterations: this.maxIterations,\n        loggingConfig: this.loggingConfig,\n        orchestratorParams: this.orchestratorOptions.orchestratorParams,\n      };\n\n      // Initialize orchestrator if it has an initialize method\n      if (orchestrator.initialize) {\n        await orchestrator.initialize(context);\n      }\n\n      // Validate input if orchestrator has a validate method\n      if (orchestrator.validate) {\n        const isValid = await orchestrator.validate(input, context);\n        if (!isValid) {\n          throw new Error('Orchestrator validation failed');\n        }\n      }\n\n      // Execute with orchestrator\n      const result = await orchestrator.execute(input, context);\n\n      // Update our internal state based on orchestrator result\n      this.iterations = result.iterations || 0;\n      this.stepsExecuted = result.iterations || 0;\n\n      // Cleanup orchestrator if it has a cleanup method\n      if (orchestrator.cleanup) {\n        await orchestrator.cleanup(context);\n      }\n\n      this.log('✅', 'Custom logic orchestrator execution completed', {\n        orchestratorId: orchestrator.id,\n        success: result.success,\n        iterations: result.iterations,\n        toolCallsUsed: result.toolCallsUsed?.length || 0,\n      });\n\n      return result;\n    } catch (error) {\n      this.log('❌', 'Custom logic orchestrator execution failed', {\n        orchestratorId: orchestrator.id,\n        error: error instanceof Error ? error.message : String(error),\n        stackTrace: error instanceof Error ? error.stack : undefined,\n      });\n\n      throw error;\n    }\n  }\n\n  // Execute with prompt-based orchestrator (optimized path)\n  private async executeWithPromptBasedOrchestrator(\n    input: string | CoreMessage[],\n    orchestrator: PromptBasedOrchestrator\n  ): Promise<ExecutionResult> {\n    this.log('🎭', 'Executing with prompt-based orchestrator', {\n      orchestratorId: orchestrator.id,\n      orchestratorName: orchestrator.name,\n      allowPromptOverride: this.orchestratorOptions.allowOrchestratorPromptOverride,\n      allowToolControl: this.orchestratorOptions.allowOrchestratorToolControl,\n    });\n\n    // Build context for orchestrator\n    const context: OrchestratorContext = {\n      model: this.model,\n      tools: Object.values(this.tools),\n      messages: this.messages,\n      iterations: this.iterations,\n      maxIterations: this.maxIterations,\n      loggingConfig: this.loggingConfig,\n      orchestratorParams: this.orchestratorOptions.orchestratorParams,\n    };\n\n    // Use the orchestrator's execute method which handles tool filtering and prompt override\n    return await orchestrator.execute(input, context);\n  }\n\n  // Execute with string input (original behavior)\n  private async executeWithString(input: string): Promise<ExecutionResult> {\n    const provider = await ProviderManager.createProvider(this.model);\n\n    const generateConfig: any = {\n      model: provider(this.model.model),\n      prompt: input,\n      maxSteps: this.maxIterations,\n      onStepFinish: this.createStepFinishCallback(),\n    };\n\n    // Add system message if it exists\n    const systemMessage = this.messages.find(m => m.role === 'system');\n    if (systemMessage) {\n      generateConfig.system = systemMessage.content;\n    }\n\n    // Add tools if available\n    if (Object.keys(this.tools).length > 0) {\n      generateConfig.tools = this.convertToAISDKTools();\n    }\n\n    // Add model parameters conditionally\n    if (this.model.temperature !== undefined) {\n      generateConfig.temperature = this.model.temperature;\n    }\n\n    if (this.model.maxTokens !== undefined) {\n      generateConfig.maxTokens = this.model.maxTokens;\n    }\n\n    if (this.model.topP !== undefined) {\n      generateConfig.topP = this.model.topP;\n    }\n    \n    this.log('📝', 'Starting text generation', {\n      prompt: this.sanitizeForLogging(input),\n      systemMessage: systemMessage ? 'present' : 'none',\n      toolsEnabled: Object.keys(this.tools).length > 0,\n    });\n\n    const result = await generateText(generateConfig);\n\n    // Update our internal state\n    this.iterations = result.steps?.length || 1;\n    this.stepsExecuted = this.iterations;\n    \n    // Store the conversation in our messages for future reference\n    this.messages = [\n      ...this.messages.filter(m => m.role === 'system'),\n      { role: 'user', content: input },\n      { role: 'assistant', content: result.text || '' }\n    ];\n\n    // Extract tool calls from steps if available\n    const toolCallsUsed: string[] = [];\n    if (result.steps) {\n      result.steps.forEach(step => {\n        if (step.toolCalls) {\n          step.toolCalls.forEach(toolCall => {\n            toolCallsUsed.push(toolCall.toolName || toolCall.toolCallId || 'unknown');\n          });\n        }\n      });\n    }\n\n    this.log('📊', 'Text generation completed', {\n      resultLength: result.text?.length || 0,\n      stepsExecuted: this.iterations,\n      toolCallsUsed: toolCallsUsed.length,\n      tokensUsed: result.usage?.totalTokens,\n      finishReason: result.finishReason,\n    });\n\n    const executionResult: ExecutionResult = {\n      success: true,\n      result: result.text,\n      messages: this.messages,\n      iterations: this.iterations,\n      toolCallsUsed,\n    };\n\n    return executionResult;\n  }\n\n  // Execute with message array (new behavior)\n  private async executeWithMessages(inputMessages: CoreMessage[]): Promise<ExecutionResult> {\n    // Handle empty message arrays\n    if (inputMessages.length === 0) {\n      const errorResult: ExecutionResult = {\n        success: false,\n        error: 'Empty message array provided. At least one message is required.',\n        messages: this.messages,\n        iterations: this.iterations,\n        toolCallsUsed: [],\n      };\n      return errorResult;\n    }\n    \n    const provider = await ProviderManager.createProvider(this.model);\n\n    // Convert CoreMessage[] to AI SDK compatible format\n    const convertedMessages = this.convertCoresToAISDK(inputMessages);\n\n    this.log('📝', 'Processing message array', {\n      messageCount: inputMessages.length,\n      messageTypes: inputMessages.map(m => m.role),\n      hasSystemMessage: inputMessages.some(m => m.role === 'system'),\n      lastMessageRole: inputMessages[inputMessages.length - 1]?.role,\n    });\n\n    const generateConfig: any = {\n      model: provider(this.model.model),\n      messages: convertedMessages,\n      maxSteps: this.maxIterations,\n      onStepFinish: this.createStepFinishCallback(),\n    };\n\n    // Extract system message from input if present, otherwise use existing one\n    const systemMessage = inputMessages.find(m => m.role === 'system');\n    if (systemMessage) {\n      generateConfig.system = typeof systemMessage.content === 'string' \n        ? systemMessage.content \n        : JSON.stringify(systemMessage.content);\n    } else {\n      const existingSystemMessage = this.messages.find(m => m.role === 'system');\n      if (existingSystemMessage) {\n        generateConfig.system = existingSystemMessage.content;\n      }\n    }\n\n    // Add tools if available\n    if (Object.keys(this.tools).length > 0) {\n      generateConfig.tools = this.convertToAISDKTools();\n    }\n\n    // Add model parameters conditionally\n    if (this.model.temperature !== undefined) {\n      generateConfig.temperature = this.model.temperature;\n    }\n\n    if (this.model.maxTokens !== undefined) {\n      generateConfig.maxTokens = this.model.maxTokens;\n    }\n\n    if (this.model.topP !== undefined) {\n      generateConfig.topP = this.model.topP;\n    }\n    \n    const result = await generateText(generateConfig);\n\n    // Update our internal state\n    this.iterations = result.steps?.length || 1;\n    this.stepsExecuted = this.iterations;\n    \n    // Update internal messages with the full conversation context\n    this.messages = [\n      ...this.messages.filter(m => m.role === 'system'),\n      ...this.convertCoreToInternal(inputMessages.filter(m => m.role !== 'system')),\n      { role: 'assistant', content: result.text || '' }\n    ];\n\n    // Extract tool calls from steps if available\n    const toolCallsUsed: string[] = [];\n    if (result.steps) {\n      result.steps.forEach(step => {\n        if (step.toolCalls) {\n          step.toolCalls.forEach(toolCall => {\n            toolCallsUsed.push(toolCall.toolName || toolCall.toolCallId || 'unknown');\n          });\n        }\n      });\n    }\n\n    this.log('📊', 'Message array processing completed', {\n      resultLength: result.text?.length || 0,\n      stepsExecuted: this.iterations,\n      toolCallsUsed: toolCallsUsed.length,\n      messagesInHistory: this.messages.length,\n      tokensUsed: result.usage?.totalTokens,\n      finishReason: result.finishReason,\n    });\n\n    const executionResult: ExecutionResult = {\n      success: true,\n      result: result.text,\n      messages: this.messages,\n      iterations: this.iterations,\n      toolCallsUsed,\n    };\n\n    return executionResult;\n  }\n\n  // Tool management methods\n  public addTool(tool: OpenAgenticTool): void {\n    const toolName = tool.toolId;\n    \n    // Ensure uniqueness\n    if (this.tools[toolName]) {\n      throw new Error(`Tool with name ${toolName} already exists`);\n    }\n    \n    this.tools[toolName] = tool;\n    this.log('🔧', 'Tool added', { toolId: tool.toolId, toolName: tool.name });\n  }\n\n  public removeTool(toolName: string): void {\n    if (this.tools[toolName]) {\n      delete this.tools[toolName];\n      this.log('🗑️', 'Tool removed', { toolId: toolName });\n    }\n  }\n\n  public getTool(toolName: string): any {\n    return this.tools[toolName];\n  }\n\n  public getAllTools(): OpenAgenticTool[] {\n    return Object.values(this.tools);\n  }\n\n  // Model switching using ProviderManager\n  public switchModel(model: string | AIModel): void {\n    const oldModel = `${this.model.provider}/${this.model.model}`;\n    this.model = ProviderManager.createModel(model);\n    const newModel = `${this.model.provider}/${this.model.model}`;\n    \n    this.log('🔄', 'Model switched', { \n      from: oldModel, \n      to: newModel,\n      temperature: this.model.temperature,\n      maxTokens: this.model.maxTokens,\n    });\n  }\n\n  // Get model information\n  public getModelInfo(): any {\n    try {\n      const modelInfo = ProviderManager.getModelInfo(this.model.provider, this.model.model) as any;\n      return {\n        provider: this.model.provider,\n        model: this.model.model,\n        contextWindow: modelInfo?.contextWindow,\n        cost: modelInfo?.cost,\n        description: modelInfo?.description,\n      };\n    } catch (error) {\n      return {\n        provider: this.model.provider,\n        model: this.model.model,\n        error: 'Model info not available',\n      };\n    }\n  }\n\n  // Orchestrator management methods\n  public getOrchestrator(): BaseOrchestrator | undefined {\n    return this.orchestrator;\n  }\n\n  public setOrchestrator(orchestrator: string | BaseOrchestrator | undefined): void {\n    const resolvedOrchestrator = resolveOrchestrator(orchestrator);\n    \n    if (orchestrator && !resolvedOrchestrator) {\n      throw new Error(`Failed to resolve orchestrator: ${typeof orchestrator === 'string' ? orchestrator : 'invalid orchestrator object'}`);\n    }\n\n    const oldId = this.orchestrator?.id;\n    this.orchestrator = resolvedOrchestrator;\n    \n    this.log('🎭', 'Orchestrator changed', {\n      from: oldId || 'none',\n      to: this.orchestrator?.id || 'none',\n      type: this.orchestrator?.type,\n    });\n  }\n\n  public hasOrchestrator(): boolean {\n    return !!this.orchestrator;\n  }\n\n  // Utility methods\n  public getMessages(): Message[] {\n    return [...this.messages];\n  }\n\n  public addMessage(message: Message): void {\n    this.messages.push(message);\n    this.log('💬', 'Message added', { role: message.role, contentLength: message.content.length });\n  }\n\n  public reset(): void {\n    this.messages = this.messages.filter(m => m.role === 'system');\n    this.iterations = 0;\n    this.resetExecutionStats();\n    this.log('🔄', 'Orchestrator reset', { systemMessagesRetained: this.messages.length });\n  }\n\n  public clear(): void {\n    this.messages = [];\n    this.iterations = 0;\n    this.resetExecutionStats();\n    this.log('🧹', 'Orchestrator cleared', { allMessagesRemoved: true });\n  }\n\n  // Create step finish callback for AI SDK\n  private createStepFinishCallback() {\n    return (result: any) => {\n      const stepDuration = Date.now() - this.executionStartTime;\n      this.stepTimings.push(stepDuration);\n      \n      this.stepsExecuted++;\n      \n      const toolCallsInStep = result.toolCalls?.map((tc: any) => tc.toolName || tc.toolCallId || 'unknown') || [];\n      \n      this.log('📝', `Step ${this.stepsExecuted} completed`, {\n        stepType: result.stepType || 'unknown',\n        finishReason: result.finishReason || 'unknown',\n        duration: `${stepDuration}ms`,\n        toolCalls: toolCallsInStep,\n        text: result.text ? `${result.text.length} chars` : 'no_result',\n        reasoning: result.reasoning ? 'has_reasoning' : 'no_reasoning',\n        tokensUsed: result.usage?.totalTokens || 'unknown',\n      });\n    };\n  }\n\n  // Helper methods for message conversion\n  private convertCoresToAISDK(coreMessages: CoreMessage[]): any[] {\n    return coreMessages\n      .filter(m => m.role !== 'system') // System messages handled separately\n      .map(m => {\n        if (m.role === 'user') {\n          return { \n            role: 'user' as const, \n            content: typeof m.content === 'string' ? m.content : JSON.stringify(m.content)\n          };\n        } else if (m.role === 'assistant') {\n          return { \n            role: 'assistant' as const, \n            content: typeof m.content === 'string' ? m.content : JSON.stringify(m.content)\n          };\n        } else if (m.role === 'tool') {\n          return { \n            role: 'tool' as const, \n            content: [{ \n              type: 'tool-result' as const, \n              toolCallId: m.toolCallId || '',\n              toolName: 'unknown',\n              result: typeof m.content === 'string' ? m.content : JSON.stringify(m.content)\n            }] \n          };\n        }\n        return { \n          role: 'user' as const, \n          content: typeof m.content === 'string' ? m.content : JSON.stringify(m.content)\n        };\n      });\n  }\n\n  private convertCoreToInternal(coreMessages: CoreMessage[]): Message[] {\n    return coreMessages.map(m => ({\n      role: m.role,\n      content: typeof m.content === 'string' ? m.content : JSON.stringify(m.content),\n      toolCallId: m.toolCallId,\n      toolCalls: m.toolCalls,\n    }));\n  }\n\n  private convertToAISDKTools(): Record<string, any> {\n    const tools: Record<string, any> = {};\n    \n    Object.entries(this.tools).forEach(([key, tool]) => {\n      tools[key] = {\n        description: tool.description,\n        parameters: tool.parameters,\n        execute: async (args: any, context?: any) => {\n          const toolCallStartTime = Date.now();\n          \n          this.log('🔧', `Tool execution starting: ${tool.toolId}`, {\n            toolName: tool.name,\n            parameters: this.sanitizeForLogging(args),\n            context: context ? 'present' : 'none',\n          });\n          \n          try {\n            if (!tool.execute) {\n              throw new Error(`Tool ${tool.toolId} has no execute function`);\n            }\n            \n            const result = await tool.execute(args, context);\n            const toolCallDuration = Date.now() - toolCallStartTime;\n            this.toolCallTimings.push(toolCallDuration);\n            this.toolCallsExecuted++;\n            \n            this.log('✅', `Tool execution completed: ${tool.toolId}`, {\n              duration: `${toolCallDuration}ms`,\n              resultType: typeof result,\n              resultSize: typeof result === 'string' ? result.length : JSON.stringify(result).length,\n              success: true,\n            });\n            \n            return result;\n          } catch (error) {\n            const toolCallDuration = Date.now() - toolCallStartTime;\n            this.toolCallTimings.push(toolCallDuration);\n            \n            this.log('❌', `Tool execution failed: ${tool.toolId}`, {\n              duration: `${toolCallDuration}ms`,\n              error: error instanceof Error ? error.message : JSON.stringify(error),\n              stackTrace: error instanceof Error ? error.stack : undefined,\n              parameters: this.sanitizeForLogging(args),\n            });\n            \n            throw error;\n          }\n        },\n      };\n    });\n    \n    return tools;\n  }\n\n  // Logging and statistics methods\n  private log(emoji: string, message: string, data?: any): void {\n    if (!this.loggingConfig.enableDebugLogging || this.loggingConfig.logLevel === 'none') {\n      return;\n    }\n\n    const timestamp = new Date().toISOString();\n    const logLevel = this.loggingConfig.logLevel;\n    \n    if (logLevel === 'basic') {\n      console.log(`${emoji} [${timestamp}] ${message}`);\n    } else if (logLevel === 'detailed' && data) {\n      console.log(`${emoji} [${timestamp}] ${message}`, data);\n    }\n  }\n\n  private sanitizeForLogging(data: any): any {\n    if (typeof data === 'string') {\n      // Limit string length and remove potential sensitive data patterns\n      const sanitized = data.length > 200 ? `${data.substring(0, 200)}...` : data;\n      return sanitized.replace(/\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b/g, '[EMAIL]')\n                    .replace(/\\b\\d{4}[\\s-]?\\d{4}[\\s-]?\\d{4}[\\s-]?\\d{4}\\b/g, '[CARD]')\n                    .replace(/\\b\\d{3}-\\d{2}-\\d{4}\\b/g, '[SSN]');\n    }\n    \n    if (typeof data === 'object' && data !== null) {\n      const sanitized = { ...data };\n      // Sanitize common sensitive field names\n      const sensitiveFields = ['password', 'apiKey', 'token', 'secret', 'key', 'auth'];\n      sensitiveFields.forEach(field => {\n        if (field in sanitized) {\n          sanitized[field] = '[REDACTED]';\n        }\n      });\n      return sanitized;\n    }\n    \n    return data;\n  }\n\n  private resetExecutionStats(): void {\n    this.stepTimings = [];\n    this.toolCallTimings = [];\n    this.stepsExecuted = 0;\n    this.toolCallsExecuted = 0;\n  }\n\n  private calculateExecutionStats(): ExecutionStats {\n    const totalDuration = Date.now() - this.executionStartTime;\n    const averageStepDuration = this.stepTimings.length > 0 \n      ? this.