openagentic/dist/index.mjs.map

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

{"version":3,"sources":["../src/types.ts","../src/tools/utils.ts","../src/providers/manager.ts","../src/orchestrators/registry.ts","../src/orchestrator.ts","../src/streaming-orchestrator.ts","../src/tools/openai.ts","../src/utils/s3.ts","../src/tools/openai-image.ts","../src/tools/openai-vector-store.ts","../src/tools/gemini-image.ts","../src/tools/anthropic.ts","../src/tools/cohere.ts","../src/tools/gemini.ts","../src/tools/github.ts","../src/tools/grok.ts","../src/tools/llama.ts","../src/tools/mistral.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/html-composer.ts","../src/tools/unsplash.ts","../src/tools/slack-poster.ts","../src/tools/groq.ts","../src/tools/luma-image.ts","../src/tools/index.ts","../src/orchestrators/custom-logic.ts","../src/orchestrators/multi-ai.ts","../src/orchestrators/library/code-assessment.ts","../src/orchestrators/prompt-based.ts","../src/orchestrators/library/enhanced-image-generation.ts","../src/orchestrators/library/flash-headlines.ts","../src/orchestrators/library/news-specialist.ts","../src/orchestrators/library/video-creator.ts","../src/orchestrators/index.ts","../src/index.ts"],"sourcesContent":["import { z } from 'zod';\nimport { type Tool } from 'ai';\n\n// =============================================================================\n// CORE TYPES FOR OPENAGENTIC FRAMEWORK\n// =============================================================================\n\nexport const AIModelSchema = z.object({\n  provider: z.enum(['openai', 'anthropic', 'google', 'google-vertex', 'perplexity', 'xai', 'custom']),\n  model: z.string(),\n  apiKey: z.string().optional(),\n  baseURL: z.string().optional(),\n  temperature: z.number().min(0).max(2).optional().default(0.7),\n  maxTokens: z.number().positive().optional(),\n  topP: z.number().min(0).max(1).optional(),\n  project: z.string().optional(),\n  location: z.string().optional(),\n});\n\nexport type AIModel = z.infer<typeof AIModelSchema>;\n\nexport const MessageSchema = z.object({\n  role: z.enum(['system', 'user', 'assistant', 'tool']),\n  content: z.string(),\n  toolCallId: z.string().optional(),\n  toolCalls: z.array(z.object({\n    toolCallId: z.string(),\n    toolName: z.string(),\n    args: z.record(z.any()),\n  })).optional(),\n});\n\nexport type Message = z.infer<typeof MessageSchema>;\n\n// =============================================================================\n// API KEY MAP TYPE\n// =============================================================================\n\nexport type ApiKeyMap = Record<string, string> & {\n  // AWS credentials for S3 and other services\n  awsAccessKeyId?: string;\n  awsSecretAccessKey?: string;\n  awsRegion?: string;\n  awsS3Bucket?: string;\n  // AWS Bedrock credentials (for Anthropic via Bedrock)\n  bedrockAccessKeyId?: string;\n  bedrockSecretAccessKey?: string;\n  bedrockRegion?: string;\n};\n\n// =============================================================================\n// CORE MESSAGE TYPES (AI SDK COMPATIBLE)\n// =============================================================================\n\nexport interface CoreMessage {\n  role: 'system' | 'user' | 'assistant' | 'tool';\n  content: string | Array<any>;\n  toolCallId?: string;\n  toolCalls?: Array<{\n    toolCallId: string;\n    toolName: string;\n    args: Record<string, any>;\n  }>;\n}\n\n// =============================================================================\n// LOGGING AND DEBUG TYPES\n// =============================================================================\n\nexport type LogLevel = 'none' | 'basic' | 'detailed';\n\nexport interface LoggingConfig {\n  enableDebugLogging?: boolean;\n  logLevel?: LogLevel;\n  enableStepLogging?: boolean;\n  enableToolLogging?: boolean;\n  enableTimingLogging?: boolean;\n  enableStatisticsLogging?: boolean;\n}\n\nexport interface ExecutionStats {\n  totalDuration: number;\n  stepsExecuted: number;\n  toolCallsExecuted: number;\n  tokensUsed?: number;\n  averageStepDuration: number;\n  averageToolCallDuration: number;\n}\n\nexport interface StepInfo {\n  stepIndex: number;\n  stepType: string;\n  duration: number;\n  toolCalls: string[];\n  errors: string[];\n  startTime: number;\n  endTime: number;\n}\n\n// =============================================================================\n// OPENAGENTIC TOOL TYPES\n// =============================================================================\n\nexport type ToolDetails = {\n  toolId: string;\n  name: string;\n  useCases: string[];\n  logo: string;\n  internal?: boolean;\n}\n\nexport type OpenAgenticTool = Tool & ToolDetails;\n\n// =============================================================================\n// EXECUTION RESULT TYPES\n// =============================================================================\n\nexport const ExecutionResultSchema = z.object({\n  success: z.boolean(),\n  result: z.any().optional(),\n  error: z.string().optional(),\n  messages: z.array(MessageSchema),\n  iterations: z.number(),\n  toolCallsUsed: z.array(z.string()),\n  executionStats: z.object({\n    totalDuration: z.number(),\n    stepsExecuted: z.number(),\n    toolCallsExecuted: z.number(),\n    tokensUsed: z.number().optional(),\n    averageStepDuration: z.number(),\n    averageToolCallDuration: z.number(),\n  }).optional(),\n  usage: z.object({\n    totalTokens: z.number(),\n    promptTokens: z.number(),\n    completionTokens: z.number(),\n  }).optional(),\n});\n\nexport type ExecutionResult = z.infer<typeof ExecutionResultSchema>;\n\n// =============================================================================\n// ORCHESTRATOR TYPES\n// =============================================================================\n\n/**\n * Supported orchestrator types\n */\nexport type OrchestratorType = 'prompt-based' | 'custom-logic';\n\n/**\n * Context object passed to orchestrator execute methods\n */\nexport interface OrchestratorContext {\n  model: AIModel;\n  tools: OpenAgenticTool[];\n  messages: Message[];\n  iterations: number;\n  maxIterations: number;\n  loggingConfig: LoggingConfig;\n  orchestratorParams?: Record<string, any>;\n}\n\n/**\n * Base interface for all orchestrators\n */\nexport interface BaseOrchestrator {\n  /** Unique identifier for the orchestrator */\n  id: string;\n  \n  /** Human-readable name */\n  name: string;\n  \n  /** Description of what this orchestrator does */\n  description: string;\n  \n  /** Type of orchestrator */\n  type: OrchestratorType;\n  \n  /** Execute the orchestration logic */\n  execute(input: string | CoreMessage[], context: OrchestratorContext): Promise<ExecutionResult>;\n  \n  /** Get the orchestrator name */\n  getName(): string;\n  \n  /** Get the orchestrator description */\n  getDescription(): string;\n  \n  /** Get the orchestrator type */\n  getType(): OrchestratorType;\n  \n  /** Optional validation method for inputs */\n  validate?(input: string | CoreMessage[], context: OrchestratorContext): Promise<boolean>;\n  \n  /** Optional initialization method */\n  initialize?(context: OrchestratorContext): Promise<void>;\n  \n  /** Optional cleanup method */\n  cleanup?(context: OrchestratorContext): Promise<void>;\n}\n\n/**\n * Orchestrator that uses system prompts and standard LLM interaction\n */\nexport interface PromptBasedOrchestrator extends BaseOrchestrator {\n  type: 'prompt-based';\n  \n  /** System prompt used for orchestration */\n  systemPrompt: string;\n  \n  /** Get the system prompt */\n  getSystemPrompt(): string;\n  \n  /** Optional method to modify system prompt based on context */\n  buildSystemPrompt?(context: OrchestratorContext): string;\n}\n\n/**\n * Orchestrator that uses custom logic instead of standard LLM flow\n */\nexport interface CustomLogicOrchestrator extends BaseOrchestrator {\n  type: 'custom-logic';\n  \n  /** Custom logic function for orchestration */\n  customLogic(input: string | CoreMessage[], context: OrchestratorContext): Promise<any>;\n  \n  /** Optional method to determine if custom logic should be used */\n  shouldUseCustomLogic?(input: string | CoreMessage[], context: OrchestratorContext): boolean;\n}\n\n/**\n * Options for creating orchestrator-enabled agents\n */\nexport interface OrchestratorOptions {\n  /** Orchestrator instance or ID to use */\n  orchestrator?: string | BaseOrchestrator;\n  \n  /** Alternative parameter name for orchestrator ID */\n  orchestratorId?: string;\n  \n  /** Parameters to pass to the orchestrator */\n  orchestratorParams?: Record<string, any>;\n  \n  /** Whether to allow orchestrator to override system prompt */\n  allowOrchestratorPromptOverride?: boolean;\n  \n  /** Whether to allow orchestrator to modify tool execution */\n  allowOrchestratorToolControl?: boolean;\n}","import { type Tool } from 'ai';\nimport { tool } from 'ai';\nimport { z } from 'zod';\nimport { type OpenAgenticTool, type ToolDetails } from '../types';\nimport { createAnthropic } from '@ai-sdk/anthropic';\nimport { createAmazonBedrock } from '@ai-sdk/amazon-bedrock';\nimport { ProviderManager } from '../providers/manager';\n\n// =============================================================================\n// LANGCHAIN TOOL COMPATIBILITY\n// =============================================================================\n\n/**\n * Check if an object is a LangChain Tool or StructuredTool\n */\nfunction isLangChainTool(tool: any): boolean {\n  // Check for typical LangChain tool properties\n  return (\n    tool &&\n    typeof tool === 'object' &&\n    typeof tool.name === 'string' &&\n    typeof tool.description === 'string' &&\n    (typeof tool.call === 'function' || typeof tool.invoke === 'function')\n  );\n}\n\n/**\n * Convert LangChain's zod schema to AI SDK compatible zod schema\n */\nfunction convertLangChainSchema(schema: any): z.ZodType<any> {\n  if (!schema) {\n    // If no schema, return empty object schema\n    return z.object({});\n  }\n  \n  // If it's already a Zod schema, return as-is\n  if (schema._def) {\n    return schema;\n  }\n  \n  // If it's a JSON schema, try to convert to Zod\n  if (typeof schema === 'object' && schema.type) {\n    return convertJsonSchemaToZod(schema);\n  }\n  \n  // Fallback to any schema\n  return z.any();\n}\n\n/**\n * Convert JSON Schema to Zod schema (basic implementation)\n */\nfunction convertJsonSchemaToZod(jsonSchema: any): z.ZodType<any> {\n  if (jsonSchema.type === 'object') {\n    const shape: Record<string, z.ZodType<any>> = {};\n    const properties = jsonSchema.properties || {};\n    \n    for (const [key, prop] of Object.entries(properties)) {\n      shape[key] = convertJsonSchemaToZod(prop as any);\n    }\n    \n    return z.object(shape);\n  }\n  \n  if (jsonSchema.type === 'string') {\n    let schema = z.string();\n    if (jsonSchema.description) {\n      schema = schema.describe(jsonSchema.description);\n    }\n    return schema;\n  }\n  \n  if (jsonSchema.type === 'number') {\n    return z.number();\n  }\n  \n  if (jsonSchema.type === 'boolean') {\n    return z.boolean();\n  }\n  \n  if (jsonSchema.type === 'array') {\n    const itemSchema = jsonSchema.items ? convertJsonSchemaToZod(jsonSchema.items) : z.any();\n    return z.array(itemSchema);\n  }\n  \n  return z.any();\n}\n\n/**\n * Convert a LangChain Tool to OpenAgentic format\n * @param lcTool - LangChain Tool or StructuredTool instance\n * @param opts - Optional configuration\n * @returns OpenAgentic compatible tool\n */\nexport async function convertLangchainTool(\n  lcTool: any,\n  opts?: {\n    toolId?: string;\n    useCases?: string[];\n    logo?: string;\n    paramsSchema?: z.ZodType<any>;\n  }\n): Promise<OpenAgenticTool> {\n  if (!isLangChainTool(lcTool)) {\n    throw new Error('Provided tool is not a valid LangChain Tool or StructuredTool');\n  }\n\n  // Extract schema from LangChain tool\n  let schema: z.ZodType<any>;\n  \n  if (opts?.paramsSchema) {\n    schema = opts.paramsSchema;\n  } else if (lcTool.schema) {\n    schema = convertLangChainSchema(lcTool.schema);\n  } else {\n    // Default to empty object schema for basic tools\n    schema = z.object({\n      input: z.string().describe('Input text for the tool')\n    });\n  }\n\n  // Create AI SDK tool\n  const aiTool = tool({\n    description: lcTool.description,\n    parameters: schema,\n    execute: async (args: any) => {\n      try {\n        let result: any;\n        \n        // Try invoke first (for newer LangChain tools), then fall back to call\n        if (typeof lcTool.invoke === 'function') {\n          // For StructuredTool, pass the args object directly\n          result = await lcTool.invoke(args);\n        } else if (typeof lcTool.call === 'function') {\n          // For basic Tool, pass the input string or first argument\n          const input = typeof args === 'object' && args.input !== undefined \n            ? args.input \n            : Object.keys(args).length === 1 \n              ? Object.values(args)[0] \n              : JSON.stringify(args);\n          result = await lcTool.call(input);\n        } else {\n          throw new Error('LangChain tool has no callable invoke or call method');\n        }\n\n        // Ensure result is a string (LangChain tools typically return strings)\n        if (typeof result === 'object') {\n          return JSON.stringify(result);\n        }\n        return String(result);\n        \n      } catch (error) {\n        const errorMessage = error instanceof Error ? error.message : String(error);\n        console.error(`LangChain tool \"${lcTool.name}\" execution failed:`, errorMessage);\n        throw new Error(`Tool execution failed: ${errorMessage}`);\n      }\n    }\n  });\n\n  // Create tool details\n  const toolDetails: ToolDetails = {\n    toolId: opts?.toolId || `langchain_${lcTool.name.toLowerCase().replace(/[^a-z0-9]/g, '_')}`,\n    name: lcTool.name,\n    useCases: opts?.useCases || [\n      `LangChain ${lcTool.name} integration`,\n      lcTool.description || 'Imported from LangChain'\n    ],\n    logo: opts?.logo || '🔗' // LangChain emoji\n  };\n\n  return {\n    ...aiTool,\n    ...toolDetails\n  };\n}\n\n/**\n * Auto-detect and convert LangChain tools in a tools array\n * @param tools - Array of tools that may include LangChain tools\n * @returns Promise resolving to array with LangChain tools converted\n */\nexport async function autoConvertLangChainTools(tools: any[]): Promise<any[]> {\n  if (!Array.isArray(tools)) {\n    return tools;\n  }\n\n  const convertedTools = await Promise.all(\n    tools.map(async (tool) => {\n      if (isLangChainTool(tool)) {\n        console.log(`🔗 Auto-converting LangChain tool: ${tool.name}`);\n        return await convertLangchainTool(tool);\n      }\n      return tool;\n    })\n  );\n\n  return convertedTools;\n}\n\n/**\n * Check if a tools array contains any LangChain tools\n * @param tools - Array of tools to check\n * @returns Boolean indicating if LangChain tools are present\n */\nexport function hasLangChainTools(tools: any[]): boolean {\n  if (!Array.isArray(tools)) {\n    return false;\n  }\n  return tools.some(tool => isLangChainTool(tool));\n}\n\n/**\n * Synchronous version of convertLangchainTool for constructor use\n * @param lcTool - LangChain Tool or StructuredTool instance\n * @param opts - Optional configuration\n * @returns OpenAgentic compatible tool (synchronously)\n */\nexport function convertLangchainToolSync(\n  lcTool: any,\n  opts?: {\n    toolId?: string;\n    useCases?: string[];\n    logo?: string;\n    paramsSchema?: z.ZodType<any>;\n  }\n): OpenAgenticTool {\n  if (!isLangChainTool(lcTool)) {\n    throw new Error('Provided tool is not a valid LangChain Tool or StructuredTool');\n  }\n\n  // Extract schema from LangChain tool\n  let schema: z.ZodType<any>;\n  \n  if (opts?.paramsSchema) {\n    schema = opts.paramsSchema;\n  } else if (lcTool.schema) {\n    schema = convertLangChainSchema(lcTool.schema);\n  } else {\n    // Default to empty object schema for basic tools\n    schema = z.object({\n      input: z.string().describe('Input text for the tool')\n    });\n  }\n\n  // Create AI SDK tool\n  const aiTool = tool({\n    description: lcTool.description,\n    parameters: schema,\n    execute: async (args: any) => {\n      try {\n        let result: any;\n        \n        // Try invoke first (for newer LangChain tools), then fall back to call\n        if (typeof lcTool.invoke === 'function') {\n          // For StructuredTool, pass the args object directly\n          result = await lcTool.invoke(args);\n        } else if (typeof lcTool.call === 'function') {\n          // For basic Tool, pass the input string or first argument\n          const input = typeof args === 'object' && args.input !== undefined \n            ? args.input \n            : Object.keys(args).length === 1 \n              ? Object.values(args)[0] \n              : JSON.stringify(args);\n          result = await lcTool.call(input);\n        } else {\n          throw new Error('LangChain tool has no callable invoke or call method');\n        }\n\n        // Ensure result is a string (LangChain tools typically return strings)\n        if (typeof result === 'object') {\n          return JSON.stringify(result);\n        }\n        return String(result);\n        \n      } catch (error) {\n        const errorMessage = error instanceof Error ? error.message : String(error);\n        console.error(`LangChain tool \"${lcTool.name}\" execution failed:`, errorMessage);\n        throw new Error(`Tool execution failed: ${errorMessage}`);\n      }\n    }\n  });\n\n  // Create tool details\n  const toolDetails: ToolDetails = {\n    toolId: opts?.toolId || `langchain_${lcTool.name.toLowerCase().replace(/[^a-z0-9]/g, '_')}`,\n    name: lcTool.name,\n    useCases: opts?.useCases || [\n      `LangChain ${lcTool.name} integration`,\n      lcTool.description || 'Imported from LangChain'\n    ],\n    logo: opts?.logo || '🔗' // LangChain emoji\n  };\n\n  return {\n    ...aiTool,\n    ...toolDetails\n  };\n}\n\n// =============================================================================\n// OPENAGENTIC UTILITY FUNCTIONS\n// =============================================================================\n\n/**\n * Helper function to add a custom toolId to an AI SDK tool\n * This provides better identification and debugging capabilities\n * \n * @param tool - AI SDK tool created with tool() function, see ai's ToolParameters type for more details\n * @param toolId - Unique identifier for the tool\n * @returns Tool with toolId property for better identification\n */\nexport function toOpenAgenticTool(tool: Tool, details: ToolDetails): OpenAgenticTool {\n return {\n  ...tool,\n  ...details,\n }\n}\n\nexport function getModel(model: string): string {\n  const bedrockCredentials = ProviderManager.getBedrockCredentials();\n  if (bedrockCredentials.accessKeyId && bedrockCredentials.secretAccessKey) {\n    if(model.includes('sonnet')) {\n      return 'us.anthropic.claude-sonnet-4-20250514-v1:0';\n    } else if(model.includes('opus')) {\n      return 'us.anthropic.claude-4-opus-20250514-v1:0';\n    } else {\n      return model;\n    }\n  } else {\n    return model;\n  }\n}\n\nexport function getAnthropicModelInstance(model: string): any {\n    const bedrockCredentials = ProviderManager.getBedrockCredentials();\n    let modelInstance: any;\n    let provider: any;\n    if (bedrockCredentials.accessKeyId && bedrockCredentials.secretAccessKey) {\n        console.log('Using Bedrock');\n        provider = createAmazonBedrock({\n        region: bedrockCredentials.region,\n        accessKeyId: bedrockCredentials.accessKeyId,\n        secretAccessKey: bedrockCredentials.secretAccessKey,\n        });\n        // TODO: Add support for other Bedrock model versions\n        if(model.includes('sonnet')) {\n        modelInstance = provider('us.anthropic.claude-sonnet-4-20250514-v1:0');\n        console.log('Model: Claude Sonnet 4');\n        } else if(model.includes('opus')) {\n        modelInstance = provider('us.anthropic.claude-4-opus-20250514-v1:0');\n        console.log('Model: Claude Opus 4');\n        } else {\n        throw new Error(`Model \"${model}\" not supported`);\n        }\n    } else {\n        console.log('Using Anthropic');\n        // Validate API key\n        const apiKey = process.env.ANTHROPIC_API_KEY;\n        if (!apiKey) {\n        throw new Error('ANTHROPIC_API_KEY environment variable is required');\n        }\n        provider = createAnthropic({\n        apiKey,\n        });\n        modelInstance = provider(model);\n        console.log('Model:', model);\n    }\n    return {provider, modelInstance};\n}\n","import { getAnthropicModelInstance } from '../tools/utils';\nimport 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      'claude-3-7-sonnet-latest': { \n        contextWindow: 200000, \n        cost: { input: 0.003, output: 0.015 },\n        description: 'Latest Claude 3.7 Sonnet model'\n      },\n      'claude-3-5-sonnet-latest': { \n        contextWindow: 200000, \n        cost: { input: 0.003, output: 0.015 },\n        description: 'Latest Claude 3.5 Sonnet model'\n      },\n    }\n  },\n  google: {\n    baseURL: 'https://generativelanguage.googleapis.com/v1beta',\n    models: {\n      'gemini-2.5-pro': { \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': { \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      'gemini-2.5-flash-lite-preview-06-17': { \n        contextWindow: 1000000, \n        cost: { input: 0.000075, output: 0.0003 },\n        description: 'Fast and efficient Gemini 2.5 Flash Lite preview model'\n      },\n    }\n  },\n  'google-vertex': {\n    baseURL: 'https://us-central1-aiplatform.googleapis.com',\n    models: {\n      'gemini-2.5-pro': { \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': { \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   * Get AWS credentials from user API keys or environment variables\n   */\n  static getAwsCredentials(): {\n    accessKeyId?: string;\n    secretAccessKey?: string;\n    region?: string;\n    bucketName?: string;\n  } {\n    const userKeys = this.userApiKeys;\n    \n    return {\n      accessKeyId: userKeys?.awsAccessKeyId || process.env.MY_AWS_ACCESS_KEY_ID || process.env.AWS_ACCESS_KEY_ID,\n      secretAccessKey: userKeys?.awsSecretAccessKey || process.env.MY_AWS_SECRET_ACCESS_KEY || process.env.AWS_SECRET_ACCESS_KEY,\n      region: userKeys?.awsRegion || process.env.MY_AWS_REGION || process.env.AWS_REGION,\n      bucketName: userKeys?.awsS3Bucket || process.env.MY_S3_BUCKET_NAME || process.env.S3_BUCKET_NAME,\n    };\n  }\n\n  /**\n   * Get AWS Bedrock credentials from user API keys or environment variables\n   */\n  static getBedrockCredentials(): {\n    accessKeyId?: string;\n    secretAccessKey?: string;\n    region?: string;\n  } {\n    const userKeys = this.userApiKeys;\n    \n    return {\n      accessKeyId: userKeys?.bedrockAccessKeyId || process.env.BEDROCK_ACCESS_KEY_ID,\n      secretAccessKey: userKeys?.bedrockSecretAccessKey || process.env.BEDROCK_SECRET_ACCESS_KEY,\n      region: userKeys?.bedrockRegion || process.env.BEDROCK_REGION,\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 { provider } = getAnthropicModelInstance(model.model);\n        return provider;\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';\nimport { getModel } from './tools/utils';\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 and LangChain auto-conversion\n    if (options.tools) {\n      // Auto-convert LangChain tools if present (tree-shaking friendly)\n      let processedTools = options.tools;\n      \n      try {\n        // Dynamic import for tree-shaking - only loads if tools are present\n        const { hasLangChainTools, autoConvertLangChainTools } = require('./tools/utils');\n        \n        if (hasLangChainTools(options.tools)) {\n          // Convert synchronously since we're in constructor\n          // Note: This requires the conversion to be sync or we need to make constructor async\n          console.log('🔗 Detected LangChain tools, converting...');\n          \n          // Convert each tool individually to avoid async issues\n          processedTools = options.tools.map((tool) => {\n            if (tool && typeof tool === 'object' && \n                typeof tool.name === 'string' && \n                typeof tool.description === 'string' &&\n                (typeof tool.call === 'function' || typeof tool.invoke === 'function')) {\n              \n              // This is a LangChain tool - convert it synchronously\n              const { convertLangchainToolSync } = require('./tools/utils');\n              return convertLangchainToolSync(tool);\n            }\n            return tool;\n          });\n        }\n      } catch (error) {\n        // If utils can't be loaded or conversion fails, continue with original tools\n        console.warn('⚠️ LangChain tool conversion failed, using original tools:', error);\n        processedTools = options.tools;\n      }\n      \n      processedTools.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