@posthog/ai/dist/langchain/index.mjs.map

PostHog Node.js AI integrations

{"version":3,"file":"index.mjs","sources":["../../src/sanitization/base64_recognizer.ts","../../src/sanitization/media_type_context.ts","../../src/sanitization/binary_content_redactor.ts","../../src/sanitization.ts","../../src/utils.ts","../../src/serializeError.ts","../../src/gatewayWarning.ts","../../src/langchain/callbacks.ts"],"sourcesContent":["const DATA_URL_PREFIX_RE = /^data:([^;,\\s]+)(?:;[^;,\\s]+)*;base64,/i\nconst BASE64_ALPHABET_RE = /^[A-Za-z0-9+/_=-]+$/\n\nexport type Base64Recognition = { kind: 'data-url'; mediaType: string } | { kind: 'raw' } | { kind: 'none' }\n\nexport class Base64Recognizer {\n  recognize(value: string, minLength: number): Base64Recognition {\n    const dataUrl = DATA_URL_PREFIX_RE.exec(value)\n    if (dataUrl) return { kind: 'data-url', mediaType: dataUrl[1] }\n\n    if (value.length < minLength) return { kind: 'none' }\n\n    const confidencePrefix = value.slice(0, minLength)\n    if (BASE64_ALPHABET_RE.test(confidencePrefix)) {\n      return { kind: 'raw' }\n    } else {\n      return { kind: 'none' }\n    }\n  }\n}\n","const MIME_HINT_KEYS = ['mediaType', 'media_type', 'mimeType', 'mime_type'] as const\n\nconst STRONG_CONTEXT_KEYS = new Set([\n  'data',\n  'file_data',\n  'fileData',\n  'image_url',\n  'imageUrl',\n  'video_url',\n  'videoUrl',\n  'audio',\n  'audio_data',\n  'audioData',\n  'inline_data',\n  'inlineData',\n  'source',\n  'result',\n])\n\nconst STRONG_CONTEXT_TYPES = new Set([\n  'image',\n  'image_url',\n  'input_image',\n  'audio',\n  'input_audio',\n  'video',\n  'video_url',\n  'file',\n  'input_file',\n  'document',\n  'media',\n  'file-data',\n])\n\nconst FILE_FAMILY_TYPES = new Set(['file', 'input_file', 'document', 'media', 'file-data'])\n\nconst KNOWN_AUDIO_FORMATS = new Set(['wav', 'mp3', 'ogg', 'flac', 'm4a', 'aac', 'webm'])\n\nexport class MediaTypeContext {\n  static readonly EMPTY = new MediaTypeContext(undefined, undefined)\n\n  constructor(\n    private readonly parent: Record<string, unknown> | undefined,\n    private readonly key: string | undefined\n  ) {}\n\n  inferMediaType(): string | undefined {\n    return (\n      this.inferFromSiblingMime() ?? this.inferFromSiblingFormat() ?? this.inferFromParentType() ?? this.inferFromKey()\n    )\n  }\n\n  inferFromSiblingMime(): string | undefined {\n    if (!this.parent) return undefined\n    for (const hint of MIME_HINT_KEYS) {\n      const v = this.parent[hint]\n      if (typeof v === 'string') return v\n    }\n    return undefined\n  }\n\n  inferFromSiblingFormat(): string | undefined {\n    if (!this.parent) return undefined\n    const fmt = this.parent.format\n    if (typeof fmt === 'string' && KNOWN_AUDIO_FORMATS.has(fmt.toLowerCase())) {\n      return `audio/${fmt.toLowerCase()}`\n    }\n    return undefined\n  }\n\n  inferFromParentType(): string | undefined {\n    if (!this.parent) return undefined\n    const t = this.parent.type\n    if (typeof t !== 'string') return undefined\n    if (t === 'image' || t === 'image_url' || t === 'input_image') return 'image'\n    if (t === 'audio' || t === 'input_audio') return 'audio'\n    if (t === 'video' || t === 'video_url') return 'video'\n    if (FILE_FAMILY_TYPES.has(t)) return 'application/octet-stream'\n    return undefined\n  }\n\n  inferFromKey(): string | undefined {\n    if (!this.key) return undefined\n    const key = this.key.toLowerCase()\n    if (key.includes('audio')) return 'audio'\n    if (key.includes('video')) return 'video'\n    if (key.includes('image')) return 'image'\n    if (key.includes('file') || key.includes('document')) return 'application/octet-stream'\n    return undefined\n  }\n\n  signalsBinary(): boolean {\n    if (this.parent) {\n      for (const hint of MIME_HINT_KEYS) {\n        if (typeof this.parent[hint] === 'string') return true\n      }\n      const fmt = this.parent.format\n      if (typeof fmt === 'string' && KNOWN_AUDIO_FORMATS.has(fmt.toLowerCase())) return true\n      const t = this.parent.type\n      if (typeof t === 'string' && STRONG_CONTEXT_TYPES.has(t)) return true\n    }\n    if (this.key && STRONG_CONTEXT_KEYS.has(this.key)) return true\n    return false\n  }\n}\n","import { Base64Recognizer } from './base64_recognizer'\nimport { MediaTypeContext } from './media_type_context'\n\nconst STRONG_CONTEXT_MIN_LENGTH = 64\nconst WEAK_CONTEXT_MIN_LENGTH = 1024\n\nexport class BinaryContentRedactor {\n  private visited: WeakSet<object> = new WeakSet()\n\n  constructor(private readonly recognizer: Base64Recognizer = new Base64Recognizer()) {}\n\n  redact<T>(value: T): T\n  redact(value: unknown): unknown {\n    if (this.isMultimodalEnabled()) return value\n    this.visited = new WeakSet()\n    return this.walk(value, MediaTypeContext.EMPTY)\n  }\n\n  private walk(value: unknown, ctx: MediaTypeContext): unknown {\n    if (value === null || value === undefined) return value\n    if (typeof value === 'string') return this.redactString(value, ctx)\n    if (typeof value !== 'object') return value\n\n    // Buffer extends Uint8Array, so this branch catches both.\n    if (typeof Uint8Array !== 'undefined' && value instanceof Uint8Array) {\n      return this.placeholderFor(ctx.inferMediaType())\n    }\n\n    if (this.visited.has(value)) return null\n    this.visited.add(value)\n\n    if (Array.isArray(value)) {\n      return value.map((item) => this.walk(item, ctx))\n    }\n\n    const obj = value as Record<string, unknown>\n    const out: Record<string, unknown> = {}\n    for (const k of Object.keys(obj)) {\n      out[k] = this.walk(obj[k], new MediaTypeContext(obj, k))\n    }\n    return out\n  }\n\n  private redactString(value: string, ctx: MediaTypeContext): string {\n    const minLength = ctx.signalsBinary() ? STRONG_CONTEXT_MIN_LENGTH : WEAK_CONTEXT_MIN_LENGTH\n    const recognition = this.recognizer.recognize(value, minLength)\n    switch (recognition.kind) {\n      case 'data-url':\n        return this.placeholderFor(recognition.mediaType)\n      case 'raw':\n        return this.placeholderFor(ctx.inferMediaType())\n      case 'none':\n        return value\n    }\n  }\n\n  private placeholderFor(mediaType?: string): string {\n    if (!mediaType) return '[base64 redacted]'\n    if (mediaType === 'application/octet-stream') return '[base64 file redacted]'\n    return `[base64 ${mediaType} redacted]`\n  }\n\n  private isMultimodalEnabled(): boolean {\n    const val = process.env._INTERNAL_LLMA_MULTIMODAL || ''\n    return val.toLowerCase() === 'true' || val === '1' || val.toLowerCase() === 'yes'\n  }\n}\n","import { BinaryContentRedactor } from './sanitization/binary_content_redactor'\n\nconst redactor = new BinaryContentRedactor()\n\nexport function redactBase64DataUrl(str: string): string\nexport function redactBase64DataUrl(str: unknown): unknown\nexport function redactBase64DataUrl(str: unknown): unknown {\n  return redactor.redact(str)\n}\n\nexport const sanitizeOpenAI = (data: unknown): unknown => redactor.redact(data)\nexport const sanitizeOpenAIResponse = (data: unknown): unknown => redactor.redact(data)\nexport const sanitizeAnthropic = (data: unknown): unknown => redactor.redact(data)\nexport const sanitizeGemini = (data: unknown): unknown => redactor.redact(data)\nexport const sanitizeLangChain = (data: unknown): unknown => redactor.redact(data)\n","import { PostHog } from 'posthog-node'\nimport type OpenAIOrignal from 'openai'\nimport type AnthropicOriginal from '@anthropic-ai/sdk'\nimport type { ResponseCreateParamsWithTools } from 'openai/lib/ResponsesParser'\nimport type {\n  FormattedMessage,\n  FormattedContent,\n  FormattedAudioContent,\n  FormattedImageContent,\n  FormattedDocumentContent,\n} from './types'\nimport { v4 as uuidv4 } from 'uuid'\nimport { isString } from './typeGuards'\nimport { redactBase64DataUrl } from './sanitization'\n\ntype ChatCompletionCreateParamsBase = OpenAIOrignal.Chat.Completions.ChatCompletionCreateParams\ntype MessageCreateParams = AnthropicOriginal.Messages.MessageCreateParams\ntype ResponseCreateParams = OpenAIOrignal.Responses.ResponseCreateParams\ntype EmbeddingCreateParams = OpenAIOrignal.EmbeddingCreateParams\ntype TranscriptionCreateParams = OpenAIOrignal.Audio.Transcriptions.TranscriptionCreateParams\n\nconst TOKEN_PROPERTY_KEYS = new Set([\n  '$ai_input_tokens',\n  '$ai_output_tokens',\n  '$ai_cache_read_input_tokens',\n  '$ai_cache_creation_input_tokens',\n  '$ai_total_tokens',\n  '$ai_reasoning_tokens',\n])\n\nexport function getTokensSource(posthogProperties?: Record<string, unknown>): string {\n  if (posthogProperties && Object.keys(posthogProperties).some((key) => TOKEN_PROPERTY_KEYS.has(key))) {\n    return 'passthrough'\n  }\n  return 'sdk'\n}\n\n// limit large outputs by truncating to 200kb (approx 200k bytes)\nexport const MAX_OUTPUT_SIZE = 200000\nconst STRING_FORMAT = 'utf8'\n\n// Reused across calls to avoid per-invocation allocation; truncate() runs\n// hundreds of times for prompts with many parts.\nconst sharedTextEncoder = new TextEncoder()\nconst sharedTextDecoder = new TextDecoder(STRING_FORMAT, { fatal: false })\n\nexport const utf8ByteLength = (str: string): number => sharedTextEncoder.encode(str).byteLength\n\n/**\n * Safely converts content to a string, preserving structure for objects/arrays.\n * - If content is already a string, returns it as-is\n * - If content is an object or array, stringifies it with JSON.stringify to preserve structure\n * - Otherwise, converts to string with String()\n *\n * This prevents the \"[object Object]\" bug when objects are naively converted to strings.\n *\n * @param content - The content to convert to a string\n * @returns A string representation that preserves structure for complex types\n */\nexport function toContentString(content: unknown): string {\n  if (typeof content === 'string') {\n    return content\n  }\n  if (content !== undefined && content !== null && typeof content === 'object') {\n    try {\n      return JSON.stringify(content)\n    } catch {\n      // Fallback for circular refs, BigInt, or objects with throwing toJSON\n      return String(content)\n    }\n  }\n  return String(content)\n}\n\nexport interface MonitoringEventPropertiesWithDefaults {\n  distinctId?: string\n  traceId: string\n  properties?: Record<string, any>\n  privacyMode: boolean\n  groups?: Record<string, any>\n  modelOverride?: string\n  providerOverride?: string\n  costOverride?: CostOverride\n  captureImmediate?: boolean\n}\n\nexport type MonitoringEventProperties = Partial<MonitoringEventPropertiesWithDefaults>\n\nexport type MonitoringParams = {\n  [K in keyof MonitoringEventProperties as `posthog${Capitalize<string & K>}`]: MonitoringEventProperties[K]\n}\n\nexport interface CostOverride {\n  inputCost: number\n  outputCost: number\n}\n\nexport const getModelParams = (\n  params:\n    | ((\n        | ChatCompletionCreateParamsBase\n        | MessageCreateParams\n        | ResponseCreateParams\n        | ResponseCreateParamsWithTools\n        | EmbeddingCreateParams\n        | TranscriptionCreateParams\n      ) &\n        MonitoringParams)\n    | null\n): Record<string, any> => {\n  if (!params) {\n    return {}\n  }\n  const modelParams: Record<string, any> = {}\n  const paramKeys = [\n    'temperature',\n    'max_tokens',\n    'max_completion_tokens',\n    'top_p',\n    'frequency_penalty',\n    'presence_penalty',\n    'n',\n    'stop',\n    'stream',\n    'streaming',\n    'language',\n    'response_format',\n    'timestamp_granularities',\n  ] as const\n\n  for (const key of paramKeys) {\n    if (key in params && (params as any)[key] !== undefined) {\n      modelParams[key] = (params as any)[key]\n    }\n  }\n  return modelParams\n}\n\n/**\n * Helper to format responses (non-streaming) for consumption\n */\nexport const formatResponse = (response: any, provider: string): FormattedMessage[] => {\n  if (!response) {\n    return []\n  }\n  if (provider === 'anthropic') {\n    return formatResponseAnthropic(response)\n  } else if (provider === 'openai') {\n    return formatResponseOpenAI(response)\n  } else if (provider === 'gemini') {\n    return formatResponseGemini(response)\n  }\n  return []\n}\n\nexport const formatResponseAnthropic = (response: any): FormattedMessage[] => {\n  const output: FormattedMessage[] = []\n  const content: FormattedContent = []\n\n  for (const choice of response.content ?? []) {\n    if (choice?.type === 'text' && choice?.text) {\n      content.push({ type: 'text', text: choice.text })\n    } else if (choice?.type === 'tool_use' && choice?.name && choice?.id) {\n      content.push({\n        type: 'function',\n        id: choice.id,\n        function: {\n          name: choice.name,\n          arguments: choice.input || {},\n        },\n      })\n    }\n  }\n\n  if (content.length > 0) {\n    output.push({\n      role: 'assistant',\n      content,\n    })\n  }\n\n  return output\n}\n\nexport const formatResponseOpenAI = (response: any): FormattedMessage[] => {\n  const output: FormattedMessage[] = []\n\n  if (response.choices) {\n    for (const choice of response.choices) {\n      const content: FormattedContent = []\n      let role = 'assistant'\n\n      if (choice.message) {\n        if (choice.message.role) {\n          role = choice.message.role\n        }\n\n        if (choice.message.content) {\n          content.push({ type: 'text', text: choice.message.content })\n        }\n\n        if (choice.message.tool_calls) {\n          for (const toolCall of choice.message.tool_calls) {\n            content.push({\n              type: 'function',\n              id: toolCall.id,\n              function: {\n                name: toolCall.function.name,\n                arguments: toolCall.function.arguments,\n              },\n            })\n          }\n        }\n\n        // Handle audio output (gpt-4o-audio-preview)\n        if (choice.message.audio) {\n          content.push({\n            type: 'audio',\n            ...choice.message.audio,\n          })\n        }\n      }\n\n      if (content.length > 0) {\n        output.push({\n          role,\n          content,\n        })\n      }\n    }\n  }\n\n  // Handle Responses API format\n  if (response.output) {\n    const content: FormattedContent = []\n    let role = 'assistant'\n\n    for (const item of response.output) {\n      if (item.type === 'message') {\n        role = item.role\n\n        if (item.content && Array.isArray(item.content)) {\n          for (const contentItem of item.content) {\n            if (contentItem.type === 'output_text' && contentItem.text) {\n              content.push({ type: 'text', text: contentItem.text })\n            } else if (contentItem.text) {\n              content.push({ type: 'text', text: contentItem.text })\n            } else if (contentItem.type === 'input_image' && contentItem.image_url) {\n              content.push({\n                type: 'image',\n                image: contentItem.image_url,\n              })\n            }\n          }\n        } else if (item.content) {\n          content.push({ type: 'text', text: String(item.content) })\n        }\n      } else if (item.type === 'function_call') {\n        content.push({\n          type: 'function',\n          id: item.call_id || item.id || '',\n          function: {\n            name: item.name,\n            arguments: item.arguments || {},\n          },\n        })\n      }\n    }\n\n    if (content.length > 0) {\n      output.push({\n        role,\n        content,\n      })\n    }\n  }\n\n  return output\n}\n\nexport const buildInlineDataBlock = (\n  mimeType: string,\n  data: string\n): FormattedAudioContent | FormattedImageContent | FormattedDocumentContent => {\n  if (mimeType.startsWith('audio/')) {\n    return { type: 'audio', mime_type: mimeType, data }\n  }\n  if (mimeType.startsWith('image/')) {\n    return { type: 'image', inline_data: { mime_type: mimeType, data } }\n  }\n  return { type: 'document', inline_data: { mime_type: mimeType, data } }\n}\n\nexport const formatResponseGemini = (response: any): FormattedMessage[] => {\n  const output: FormattedMessage[] = []\n\n  if (response.candidates && Array.isArray(response.candidates)) {\n    for (const candidate of response.candidates) {\n      if (candidate.content && candidate.content.parts) {\n        const content: FormattedContent = []\n\n        for (const part of candidate.content.parts) {\n          if (part.text) {\n            content.push({ type: 'text', text: part.text })\n          } else if (part.functionCall) {\n            content.push({\n              type: 'function',\n              function: {\n                name: part.functionCall.name,\n                arguments: part.functionCall.args,\n              },\n            })\n          } else if (part.inlineData) {\n            // Handle inline data (images, audio, documents)\n            const mimeType = part.inlineData.mimeType || part.inlineData.mime_type || 'application/octet-stream'\n            let data = part.inlineData.data\n\n            // Handle binary data (Uint8Array/Buffer -> base64)\n            if (data instanceof Uint8Array) {\n              if (typeof Buffer !== 'undefined') {\n                data = Buffer.from(data).toString('base64')\n              } else {\n                let binary = ''\n                for (let i = 0; i < data.length; i++) {\n                  binary += String.fromCharCode(data[i])\n                }\n                data = btoa(binary)\n              }\n            }\n\n            // Sanitize base64 data for images and other large inline data\n            data = redactBase64DataUrl(data)\n\n            content.push(buildInlineDataBlock(mimeType, data))\n          }\n        }\n\n        if (content.length > 0) {\n          output.push({\n            role: 'assistant',\n            content,\n          })\n        }\n      } else if (candidate.text) {\n        output.push({\n          role: 'assistant',\n          content: [{ type: 'text', text: candidate.text }],\n        })\n      }\n    }\n  } else if (response.text) {\n    output.push({\n      role: 'assistant',\n      content: [{ type: 'text', text: response.text }],\n    })\n  }\n\n  return output\n}\n\nexport const mergeSystemPrompt = (params: MessageCreateParams & MonitoringParams, provider: string): any => {\n  if (provider == 'anthropic') {\n    const messages = params.messages || []\n    if (!(params as any).system) {\n      return messages\n    }\n    const systemMessage = (params as any).system\n    return [{ role: 'system', content: systemMessage }, ...messages]\n  }\n  return params.messages\n}\n\nexport const withPrivacyMode = (client: PostHog, privacyMode: boolean, input: any): any => {\n  return (client as any).privacy_mode || privacyMode ? null : input\n}\n\nfunction toSafeString(input: unknown): string {\n  if (input === undefined || input === null) {\n    return ''\n  }\n  if (typeof input === 'string') {\n    return input\n  }\n  try {\n    return JSON.stringify(input)\n  } catch {\n    console.warn('Failed to stringify input', input)\n    return ''\n  }\n}\n\nexport const truncate = (input: unknown): string => {\n  const str = toSafeString(input)\n  if (str === '') {\n    return ''\n  }\n\n  // Check if we need to truncate and ensure STRING_FORMAT is respected\n  const buffer = sharedTextEncoder.encode(str)\n  if (buffer.length <= MAX_OUTPUT_SIZE) {\n    // Ensure STRING_FORMAT is respected\n    return sharedTextDecoder.decode(buffer)\n  }\n\n  // Truncate the buffer and ensure a valid string is returned.\n  // fatal: false means we get U+FFFD at the end if truncation broke the encoding.\n  const truncatedBuffer = buffer.slice(0, MAX_OUTPUT_SIZE)\n  let truncatedStr = sharedTextDecoder.decode(truncatedBuffer)\n  if (truncatedStr.endsWith('\\uFFFD')) {\n    truncatedStr = truncatedStr.slice(0, -1)\n  }\n  return `${truncatedStr}... [truncated]`\n}\n\n/**\n * Calculate web search count from raw API response.\n *\n * Uses a two-tier detection strategy:\n * Priority 1 (Exact Count): Count actual web search calls when available\n * Priority 2 (Binary Detection): Return 1 if web search indicators are present, 0 otherwise\n *\n * @param result - Raw API response from any provider (OpenAI, Perplexity, OpenRouter, Gemini, etc.)\n * @returns Number of web searches performed (exact count or binary 1/0)\n */\nexport function calculateWebSearchCount(result: unknown): number {\n  if (!result || typeof result !== 'object') {\n    return 0\n  }\n\n  // Priority 1: Exact Count\n  // Check for OpenAI Responses API web_search_call items\n  if ('output' in result && Array.isArray(result.output)) {\n    let count = 0\n\n    for (const item of result.output) {\n      if (typeof item === 'object' && item !== null && 'type' in item && item.type === 'web_search_call') {\n        count++\n      }\n    }\n\n    if (count > 0) {\n      return count\n    }\n  }\n\n  // Priority 2: Binary Detection (1 or 0)\n\n  // Check for citations at root level (Perplexity)\n  if ('citations' in result && Array.isArray(result.citations) && result.citations.length > 0) {\n    return 1\n  }\n\n  // Check for search_results at root level (Perplexity via OpenRouter)\n  if ('search_results' in result && Array.isArray(result.search_results) && result.search_results.length > 0) {\n    return 1\n  }\n\n  // Check for usage.search_context_size (Perplexity via OpenRouter)\n  if ('usage' in result && typeof result.usage === 'object' && result.usage !== null) {\n    if ('search_context_size' in result.usage && result.usage.search_context_size) {\n      return 1\n    }\n  }\n\n  // Check for annotations with url_citation in choices[].message or choices[].delta (OpenAI/Perplexity)\n  if ('choices' in result && Array.isArray(result.choices)) {\n    for (const choice of result.choices) {\n      if (typeof choice === 'object' && choice !== null) {\n        // Check both message (non-streaming) and delta (streaming) for annotations\n        const content = ('message' in choice ? choice.message : null) || ('delta' in choice ? choice.delta : null)\n\n        if (typeof content === 'object' && content !== null && 'annotations' in content) {\n          const annotations = content.annotations\n\n          if (Array.isArray(annotations)) {\n            const hasUrlCitation = annotations.some((ann: unknown) => {\n              return typeof ann === 'object' && ann !== null && 'type' in ann && ann.type === 'url_citation'\n            })\n\n            if (hasUrlCitation) {\n              return 1\n            }\n          }\n        }\n      }\n    }\n  }\n\n  // Check for annotations in output[].content[] (OpenAI Responses API)\n  if ('output' in result && Array.isArray(result.output)) {\n    for (const item of result.output) {\n      if (typeof item === 'object' && item !== null && 'content' in item) {\n        const content = item.content\n\n        if (Array.isArray(content)) {\n          for (const contentItem of content) {\n            if (typeof contentItem === 'object' && contentItem !== null && 'annotations' in contentItem) {\n              const annotations = contentItem.annotations\n\n              if (Array.isArray(annotations)) {\n                const hasUrlCitation = annotations.some((ann: unknown) => {\n                  return typeof ann === 'object' && ann !== null && 'type' in ann && ann.type === 'url_citation'\n                })\n\n                if (hasUrlCitation) {\n                  return 1\n                }\n              }\n            }\n          }\n        }\n      }\n    }\n  }\n\n  // Check for grounding_metadata (Gemini)\n  if ('candidates' in result && Array.isArray(result.candidates)) {\n    for (const candidate of result.candidates) {\n      if (\n        typeof candidate === 'object' &&\n        candidate !== null &&\n        'grounding_metadata' in candidate &&\n        candidate.grounding_metadata\n      ) {\n        return 1\n      }\n    }\n  }\n\n  return 0\n}\n\n/**\n * Extract available tool calls from the request parameters.\n * These are the tools provided to the LLM, not the tool calls in the response.\n */\nexport const extractAvailableToolCalls = (provider: string, params: any): unknown[] | null => {\n  if (provider === 'anthropic') {\n    if (params.tools) {\n      return params.tools\n    }\n\n    return null\n  } else if (provider === 'gemini') {\n    if (params.config && params.config.tools) {\n      return params.config.tools\n    }\n\n    return null\n  } else if (provider === 'openai') {\n    if (params.tools) {\n      return params.tools\n    }\n\n    return null\n  } else if (provider === 'vercel') {\n    if (params.tools) {\n      return params.tools\n    }\n\n    return null\n  }\n\n  return null\n}\n\nexport enum AIEvent {\n  Generation = '$ai_generation',\n  Embedding = '$ai_embedding',\n}\n\nexport function sanitizeValues(obj: any): any {\n  if (obj === undefined || obj === null) {\n    return obj\n  }\n  const jsonSafe = JSON.parse(JSON.stringify(obj))\n  if (typeof jsonSafe === 'string') {\n    // Sanitize lone surrogates by round-tripping through UTF-8\n    return new TextDecoder().decode(new TextEncoder().encode(jsonSafe))\n  } else if (Array.isArray(jsonSafe)) {\n    return jsonSafe.map(sanitizeValues)\n  } else if (jsonSafe && typeof jsonSafe === 'object') {\n    return Object.fromEntries(Object.entries(jsonSafe).map(([k, v]) => [k, sanitizeValues(v)]))\n  }\n  return jsonSafe\n}\n\nconst POSTHOG_PARAMS_MAP: Record<keyof MonitoringParams, string> = {\n  posthogDistinctId: 'distinctId',\n  posthogTraceId: 'traceId',\n  posthogProperties: 'properties',\n  posthogPrivacyMode: 'privacyMode',\n  posthogGroups: 'groups',\n  posthogModelOverride: 'modelOverride',\n  posthogProviderOverride: 'providerOverride',\n  posthogCostOverride: 'costOverride',\n  posthogCaptureImmediate: 'captureImmediate',\n}\n\nexport function extractPosthogParams<T>(body: T & MonitoringParams): {\n  providerParams: T\n  posthogParams: MonitoringEventPropertiesWithDefaults\n} {\n  const providerParams: Record<string, unknown> = {}\n  const posthogParams: Record<string, unknown> = {}\n\n  for (const [key, value] of Object.entries(body)) {\n    if (POSTHOG_PARAMS_MAP[key as keyof MonitoringParams]) {\n      posthogParams[POSTHOG_PARAMS_MAP[key as keyof MonitoringParams]] = value\n    } else if (key.startsWith('posthog')) {\n      console.warn(`Unknown Posthog parameter ${key}`)\n    } else {\n      providerParams[key] = value\n    }\n  }\n\n  return {\n    providerParams: providerParams as T,\n    posthogParams: addDefaults(posthogParams),\n  }\n}\n\nfunction addDefaults(params: MonitoringEventProperties): MonitoringEventPropertiesWithDefaults {\n  return {\n    ...params,\n    privacyMode: params.privacyMode ?? false,\n    traceId: params.traceId ?? uuidv4(),\n  }\n}\n\nexport function formatOpenAIResponsesInput(input: unknown, instructions?: string | null): FormattedMessage[] {\n  const messages: FormattedMessage[] = []\n\n  if (instructions) {\n    messages.push({\n      role: 'system',\n      content: instructions,\n    })\n  }\n\n  if (Array.isArray(input)) {\n    for (const item of input) {\n      if (typeof item === 'string') {\n        messages.push({ role: 'user', content: item })\n      } else if (item && typeof item === 'object') {\n        const obj = item as Record<string, unknown>\n        const role = isString(obj.role) ? obj.role : 'user'\n\n        // Handle content properly - preserve structure for objects/arrays\n        const content = obj.content ?? obj.text ?? item\n        messages.push({ role, content: toContentString(content) })\n      } else {\n        messages.push({ role: 'user', content: toContentString(item) })\n      }\n    }\n  } else if (typeof input === 'string') {\n    messages.push({ role: 'user', content: input })\n  } else if (input) {\n    messages.push({ role: 'user', content: toContentString(input) })\n  }\n\n  return messages\n}\n","import { sanitizeValues } from './utils'\n\nconst DEFAULT_MAX_DEPTH = 3\nconst MAX_STACK_LINES = 20\n\nexport function serializeError(value: unknown, depth = DEFAULT_MAX_DEPTH): unknown {\n  if (depth < 0 || value === null || typeof value !== 'object') {\n    return value\n  }\n  if (value instanceof Error) {\n    const out: Record<string, unknown> = {\n      name: value.name,\n      message: value.message,\n      stack: truncateStack(value.stack),\n    }\n    for (const key of Object.keys(value)) {\n      out[key] = serializeError((value as unknown as Record<string, unknown>)[key], depth - 1)\n    }\n    if (value.cause !== undefined) {\n      out.cause = serializeError(value.cause, depth - 1)\n    }\n    return out\n  }\n  if (Array.isArray(value)) {\n    return value.map((item) => serializeError(item, depth - 1))\n  }\n  return value\n}\n\nexport function stringifyError(error: unknown): string {\n  try {\n    return JSON.stringify(sanitizeValues(serializeError(error)))\n  } catch {\n    if (error instanceof Error) {\n      return JSON.stringify({ name: error.name, message: error.message })\n    }\n    return JSON.stringify({ message: String(error) })\n  }\n}\n\nfunction truncateStack(stack: string | undefined): string | undefined {\n  if (!stack) {\n    return stack\n  }\n  const lines = stack.split('\\n')\n  if (lines.length <= MAX_STACK_LINES) {\n    return stack\n  }\n  return [...lines.slice(0, MAX_STACK_LINES), '... (truncated)'].join('\\n')\n}\n","// Warn when a wrapper's base_url points at the PostHog AI Gateway: the gateway\n// emits its own $ai_generation, so each call would be captured (and, for billable\n// products, billed) twice. We only warn — the wrapper's event carries data the\n// gateway never sees (groups, custom properties, trace hierarchy).\n\n// Keep in sync with the gateway's deployed hosts (see services/llm-gateway in the\n// main repo). gateway.us.posthog.com is live today; the rest are listed ahead of\n// any traffic moving to them.\nexport const POSTHOG_AI_GATEWAY_HOSTS: readonly string[] = [\n  'gateway.posthog.com',\n  'gateway.us.posthog.com',\n  'gateway.eu.posthog.com',\n  'ai-gateway.us.posthog.com',\n  'ai-gateway.eu.posthog.com',\n]\n\n// Swap for the dedicated AI Gateway page once it ships.\nconst GATEWAY_DOCS_URL = 'https://posthog.com/docs/ai-observability'\n\nconst extractHost = (baseURL: string): string | undefined => {\n  try {\n    // Tolerate bare hosts that omit a scheme, e.g. \"gateway.us.posthog.com/v1\".\n    const hasScheme = /^[a-z][a-z0-9+.-]*:\\/\\//i.test(baseURL)\n    return new URL(hasScheme ? baseURL : `https://${baseURL}`).hostname.toLowerCase()\n  } catch {\n    return undefined\n  }\n}\n\nexport const isPostHogAiGatewayUrl = (baseURL: string | undefined | null): boolean => {\n  if (!baseURL) {\n    return false\n  }\n  const host = extractHost(baseURL)\n  return host !== undefined && POSTHOG_AI_GATEWAY_HOSTS.includes(host)\n}\n\n// Warns on every gateway call by design: the misconfiguration is impossible to\n// miss that way, and a doubled bill is worse than noisy logs.\nexport const warnIfPostHogAiGateway = (baseURL: string | undefined | null): void => {\n  if (!isPostHogAiGatewayUrl(baseURL)) {\n    return\n  }\n  console.warn(\n    '[PostHog] The PostHog AI wrapper is pointed at the PostHog AI Gateway. ' +\n      'Both capture $ai_generation, so every call is double-counted and double-billed. ' +\n      `Use one or the other — see ${GATEWAY_DOCS_URL}.`\n  )\n}\n\n// OTel spans don't pass through captureAiGeneration, so detect the gateway from\n// the span's host/URL attributes instead. These follow the GenAI / HTTP semantic\n// conventions: `server.address` is a bare host, `url.full` a full URL, both of\n// which isPostHogAiGatewayUrl accepts.\nconst OTEL_GATEWAY_URL_ATTRIBUTES = ['server.address', 'url.full'] as const\n\nexport const warnIfPostHogAiGatewayOtelAttributes = (attributes: Record<string, unknown> | undefined): void => {\n  if (!attributes) {\n    return\n  }\n  for (const key of OTEL_GATEWAY_URL_ATTRIBUTES) {\n    const value = attributes[key]\n    if (typeof value === 'string' && isPostHogAiGatewayUrl(value)) {\n      warnIfPostHogAiGateway(value)\n      return\n    }\n  }\n}\n","import { PostHog } from 'posthog-node'\nimport { withPrivacyMode, getModelParams, toContentString } from '../utils'\nimport { BaseCallbackHandler } from '@langchain/core/callbacks/base'\nimport { version } from '../../package.json'\nimport type { Serialized } from '@langchain/core/load/serializable'\nimport type { ChainValues } from '@langchain/core/utils/types'\nimport type { LLMResult } from '@langchain/core/outputs'\nimport type { AgentAction, AgentFinish } from '@langchain/core/agents'\nimport type { DocumentInterface } from '@langchain/core/documents'\nimport { ToolCall } from '@langchain/core/messages/tool'\nimport { BaseMessage } from '@langchain/core/messages'\nimport { sanitizeLangChain } from '../sanitization'\nimport { stringifyError } from '../serializeError'\nimport { warnIfPostHogAiGateway } from '../gatewayWarning'\n\ninterface SpanMetadata {\n  /** Name of the trace/span (e.g. chain name) */\n  name: string\n  /** Timestamp (in ms) when the run started */\n  startTime: number\n  /** Timestamp (in ms) when the run ended (if already finished) */\n  endTime?: number\n  /** The input state */\n  input?: any\n}\n\ninterface GenerationMetadata extends SpanMetadata {\n  /** Provider used (e.g. openai, anthropic) */\n  provider?: string\n  /** Model name used in the generation */\n  model?: string\n  /** The model parameters (temperature, max_tokens, etc.) */\n  modelParams?: Record<string, any>\n  /** The base URL—for example, the API base used */\n  baseUrl?: string\n  /** The tools used in the generation */\n  tools?: Record<string, any>\n}\n\n/** A run may either be a Span or a Generation */\ntype RunMetadata = SpanMetadata | GenerationMetadata\n\n/** Storage for run metadata */\ntype RunMetadataStorage = { [runId: string]: RunMetadata }\n\nexport class LangChainCallbackHandler extends BaseCallbackHandler {\n  public name = 'PosthogCallbackHandler'\n  private client: PostHog\n  private distinctId?: string | number\n  private traceId?: string | number\n  private properties: Record<string, any>\n  private privacyMode: boolean\n  private groups: Record<string, any>\n  private debug: boolean\n\n  private runs: RunMetadataStorage = {}\n  private parentTree: { [runId: string]: string } = {}\n\n  constructor(options: {\n    client: PostHog\n    distinctId?: string | number\n    traceId?: string | number\n    properties?: Record<string, any>\n    privacyMode?: boolean\n    groups?: Record<string, any>\n    debug?: boolean\n  }) {\n    if (!options.client) {\n      throw new Error('PostHog client is required')\n    }\n    super()\n    this.client = options.client\n    this.distinctId = options.distinctId\n    this.traceId = options.traceId\n    this.properties = options.properties || {}\n    this.privacyMode = options.privacyMode || false\n    this.groups = options.groups || {}\n    this.debug = options.debug || false\n  }\n\n  // ===== CALLBACK METHODS =====\n\n  public handleChainStart(\n    chain: Serialized,\n    inputs: ChainValues,\n    runId: string,\n    parentRunId?: string,\n    tags?: string[],\n    metadata?: Record<string, unknown>,\n    _runType?: string,\n    runName?: string\n  ): void {\n    this._logDebugEvent('on_chain_start', runId, parentRunId, { inputs, tags })\n    this._setParentOfRun(runId, parentRunId)\n    this._setTraceOrSpanMetadata(chain, inputs, runId, parentRunId, metadata, tags, runName)\n  }\n\n  public handleChainEnd(\n    outputs: ChainValues,\n    runId: string,\n    parentRunId?: string,\n    tags?: string[],\n    _kwargs?: { inputs?: Record<string, unknown> }\n  ): void {\n    this._logAndPopTraceOrSpan('on_chain_end', runId, parentRunId, { outputs, tags }, outputs)\n  }\n\n  public handleChainError(\n    error: Error,\n    runId: string,\n    parentRunId?: string,\n    tags?: string[],\n    _kwargs?: { inputs?: Record<string, unknown> }\n  ): void {\n    this._logAndPopTraceOrSpan('on_chain_error', runId, parentRunId, { error, tags }, error)\n  }\n\n  public handleChatModelStart(\n    serialized: Serialized,\n    messages: BaseMessage[][],\n    runId: string,\n    parentRunId?: string,\n    extraParams?: Record<string, unknown>,\n    tags?: string[],\n    metadata?: Record<string, unknown>,\n    runName?: string\n  ): void {\n    this._logDebugEvent('on_chat_model_start', runId, parentRunId, { messages, tags })\n    this._setParentOfRun(runId, parentRunId)\n    // Flatten the two-dimensional messages and convert each message to a plain object\n    const input = messages.flat().map((m) => this._convertMessageToDict(m))\n    this._setLLMMetadata(serialized, runId, input, metadata, extraParams, runName)\n  }\n\n  public handleLLMStart(\n    serialized: Serialized,\n    prompts: string[],\n    runId: string,\n    parentRunId?: string,\n    extraParams?: Record<string, unknown>,\n    tags?: string[],\n    metadata?: Record<string, unknown>,\n    runName?: string\n  ): void {\n    this._logDebugEvent('on_llm_start', runId, parentRunId, { prompts, tags })\n    this._setParentOfRun(runId, parentRunId)\n    this._setLLMMetadata(serialized, runId, prompts, metadata, extraParams, runName)\n  }\n\n  public handleLLMEnd(\n    output: LLMResult,\n    runId: string,\n    parentRunId?: string,\n    tags?: string[],\n    _extraParams?: Record<string, unknown>\n  ): void {\n    this._logAndPopGeneration('on_llm_end', runId, parentRunId, { output, tags }, output)\n  }\n\n  public handleLLMError(\n    err: Error,\n    runId: string,\n    parentRunId?: string,\n    tags?: string[],\n    _extraParams?: Record<string, unknown>\n  ): void {\n    this._logAndPopGeneration('on_llm_error', runId, parentRunId, { err, tags }, err)\n  }\n\n  public handleToolStart(\n    tool: Serialized,\n    input: string,\n    runId: string,\n    parentRunId?: string,\n    tags?: string[],\n    metadata?: Record<string, unknown>,\n    runName?: string\n  ): void {\n    this._logAndSetTraceOrSpan(\n      'on_tool_start',\n      tool,\n      input,\n      runId,\n      parentRunId,\n      { input, tags },\n      tags,\n      metadata,\n      runName\n    )\n  }\n\n  public handleToolEnd(output: any, runId: string, parentRunId?: string, tags?: string[]): void {\n    this._logAndPopTraceOrSpan('on_tool_end', runId, parentRunId, { output, tags }, output)\n  }\n\n  public handleToolError(err: Error, runId: string, parentRunId?: string, tags?: string[]): void {\n    this._logAndPopTraceOrSpan('on_tool_error', runId, parentRunId, { err, tags }, err)\n  }\n\n  public handleRetrieverStart(\n    retriever: Serialized,\n    query: string,\n    runId: string,\n    parentRunId?: string,\n    tags?: string[],\n    metadata?: Record<string, unknown>,\n    name?: string\n  ): void {\n    this._logAndSetTraceOrSpan(\n      'on_retriever_start',\n      retriever,\n      query,\n      runId,\n      parentRunId,\n      { query, tags },\n      tags,\n      metadata,\n      name\n    )\n  }\n\n  public handleRetrieverEnd(\n    documents: DocumentInterface[],\n    runId: string,\n    parentRunId?: string,\n    tags?: string[]\n  ): void {\n    this._logAndPopTraceOrSpan('on_retriever_end', runId, parentRunId, { documents, tags }, documents)\n  }\n\n  public handleRetrieverError(err: Error, runId: string, parentRunId?: string, tags?: string[]): void {\n    this._logAndPopTraceOrSpan('on_retriever_error', runId, parentRunId, { err, tags }, err)\n  }\n\n  public handleAgentAction(action: AgentAction, runId: string, parentRunId?: string, tags?: string[]): void {\n    this._logDebugEvent('on_agent_action', runId, parentRunId, { action, tags })\n    this._setParentOfRun(runId, parentRunId)\n    this._setTraceOrSpanMetadata(null, action, runId, parentRunId)\n  }\n\n  public handleAgentEnd(action: AgentFinish, runId: string, parentRunId?: string, tags?: string[]): void {\n    this._logDebugEvent('on_agent_finish', runId, parentRunId, { action, tags })\n    this._popRunAndCaptureTraceOrSpan(runId, parentRunId, action)\n  }\n\n  // ===== PRIVATE HELPERS =====\n\n  private _logAndSetTraceOrSpan(\n    eventName: string,\n    serialized: Serialized,\n    input: any,\n    runId: string,\n    parentRunId: string | undefined,\n    debugPayload: Record<string, unknown>,\n    tags?: string[],\n    metadata?: Record<string, unknown>,\n    runName?: string\n  ): void {\n    this._logDebugEvent(eventName, runId, parentRunId, debugPayload)\n    this._setParentOfRun(runId, parentRunId)\n    this._setTraceOrSpanMetadata(serialized, input, runId, parentRunId, metadata, tags, runName)\n  }\n\n  private _logAndPopTraceOrSpan(\n    eventName: string,\n    runId: string,\n    parentRunId: string | undefined,\n    debugPayload: Record<string, unknown>,\n    result: any\n  ): void {\n    this._logDebugEvent(eventName, runId, parentRunId, debugPayload)\n    this._popRunAndCaptureTraceOrSpan(runId, parentRunId, result)\n  }\n\n  private _logAndPopGeneration(\n    eventName: string,\n    runId: string,\n    parentRunId: string | undefined,\n    debugPayload: Record<string, unknown>,\n    result: LLMResult | Error\n  ): void {\n    this._logDebugEvent(eventName, runId, parentRunId, debugPayload)\n    this._popRunAndCaptureGeneration(runId, parentRunId, result)\n  }\n\n  private _setParentOfRun(runId: string, parentRunId?: string): void {\n    if (parentRunId) {\n      this.parentTree[runId] = parentRunId\n    }\n  }\n\n  private _popParentOfRun(runId: string): void {\n    delete this.parentTree[runId]\n  }\n\n  private _findRootRun(runId: string): string {\n    let id = runId\n    while (this.parentTree[id]) {\n      id = this.parentTree[id]\n    }\n    return id\n  }\n\n  private _setTraceOrSpanMetadata(\n    serialized: any,\n    input: any,\n    runId: string,\n    parentRunId?: string,\n    ...args: any[]\n  ): void {\n    // Use default names if not provided: if this is a top-level run, we mark it as a trace, otherwise as a span.\n    const defaultName = parentRunId ? 'span' : 'trace'\n    const runName = this._getLangchainRunName(serialized, ...args) || defaultName\n    this.runs[runId] = {\n      name: runName,\n      input,\n      startTime: Date.now(),\n    } as SpanMetadata\n  }\n\n  private _setLLMMetadata(\n    serialized: Serialized | null,\n    runId: string,\n    messages: any,\n    metadata?: any,\n    extraParams?: any,\n    runName?: string\n  ): void {\n    const runNameFound = this._getLangchainRunName(serialized, { extraParams, runName }) || 'generation'\n    const generation: GenerationMetadata = {\n      name: runNameFound,\n      input: sanitizeLangChain(messages),\n      startTime: Date.now(),\n    }\n    if (extraParams) {\n      generation.modelParams = getModelParams(extraParams.invocation_params)\n\n      if (extraParams.invocation_params && extraParams.invocation_params.tools) {\n        generation.tools = extraParams.invocation_params.tools\n      }\n    }\n    if (metadata) {\n      if (metadata.ls_model_name) {\n        generation.model = metadata.ls_model_name\n      }\n      if (metadata.ls_provider) {\n        generation.provider = metadata.ls_provider\n      }\n    }\n    if (serialized && 'kwargs' in serialized && serialized.kwargs.openai_api_base) {\n      generation.baseUrl = serialized.kwargs.openai_api_base\n    }\n    this.runs[runId] = generation\n  }\n\n  private _popRunMetadata(runId: string): RunMetadata | undefined {\n    const endTime = Date.now()\n    const run = this.runs[runId]\n    if (!run) {\n      console.warn(`No run metadata found for run ${runId}`)\n      return undefined\n    }\n    run.endTime = endTime\n    delete this.runs[runId]\n    return run\n  }\n\n  private _getTraceId(runId: string): string {\n    return this.traceId ? String(this.traceId) : this._findRootRun(runId)\n  }\n\n  private _getParentRunId(traceId: string, _runId: string, parentRunId?: string): string | undefined {\n    // Replace the parent-run if not found in our stored parent tree.\n    if (parentRunId && !this.parentTree[parentRunId]) {\n      return traceId\n    }\n    return parentRunId\n  }\n\n  private _popRunAndCaptureTraceOrSpan(\n    runId: string,\n    parentRunId: string | undefined,\n    outputs: ChainValues | DocumentInterface[] | AgentFinish | Error | any\n  ): void {\n    const traceId = this._getTraceId(runId)\n    this._popParentOfRun(runId)\n    const run = this._popRunMetadata(runId)\n    if (!run) {\n      return\n    }\n    if ('modelParams' in run) {\n      console.warn(`Run ${runId} is a generation, but attempted to be captured as a trace/span.`)\n      return\n    }\n    const actualParentRunId = this._getParentRunId(traceId, runId, parentRunId)\n    this._captureTraceOrSpan(traceId, runId, run as SpanMetadata, outputs, actualParentRunId)\n  }\n\n  private _captureTraceOrSpan(\n    traceId: string,\n    runId: string,\n    run: SpanMetadata,\n    outputs: ChainValues | DocumentInterface[] | AgentFinish | Error | any,\n    parentRunId?: string\n  ): void {\n    const eventName = parentRunId ? '$ai_span' : '$ai_trace'\n    const latency = run.endTime ? (run.endTime - run.startTime) / 1000 : 0\n    const eventProperties: Record<string, any> = {\n      $ai_lib: 'posthog-ai',\n      $ai_lib_version: version,\n      $ai_trace_id: traceId,\n      $ai_input_state: withPrivacyMode(this.client, this.privacyMode, run.input),\n      $ai_latency: latency,\n      $ai_span_name: run.name,\n      $ai_span_id: runId,\n      $ai_framework: 'langchain',\n    }\n    if (parentRunId) {\n      eventProperties['$ai_parent_id'] = parentRunId\n    }\n\n    Object.assign(eventProperties, this.properties)\n    if (!this.distinctId) {\n      eventProperties['$process_person_profile'] = false\n    }\n    if (outputs instanceof Error) {\n      eventProperties['$ai_error'] = stringifyError(outputs)\n      eventProperties['$ai_is_error'] = true\n    } else if (outputs !== undefined) {\n      eventProperties['$ai_output_state'] = withPrivacyMode(this.client, this.privacyMode, outputs)\n    }\n    this.client.capture({\n      distinctId: this.distinctId ? this.distinctId.toString() : runId,\n      event: eventName,\n      properties: eventProperties,\n      groups: this.groups,\n    })\n  }\n\n  private _popRunAndCaptureGeneration(\n    runId: string,\n    parentRunId: string | undefined,\n    response: LLMResult | Error\n  ): void {\n    const traceId = this._getTraceId(runId)\n    this._popParentOfRun(runId)\n    const run = this._popRunMetadata(runId)\n    if (!run || typeof run !== 'object' || !('modelParams' in run)) {\n      console.warn(`Run ${runId} is not a generation, but attempted to be captured as such.`)\n      return\n    }\n    const actualParentRunId = this._getParentRunId(traceId, runId, parentRunId)\n    this._captureGeneration(traceId, runId, run as GenerationMetadata, response, actualParentRunId)\n  }\n\n  private _captureGeneration(\n    traceId: string,\n    runId: string,\n    run: GenerationMetadata,\n    output: LLMResult | Error,\n    parentRunId?: string\n  ): void {\n    const latency = run.endTime ? (run.endTime - run.startTime) / 1000 : 0\n    warnIfPostHogAiGateway(run.baseUrl)\n    const eventProperties: Record<string, any> = {\n      $ai_lib: 'posthog-ai',\n      $ai_lib_version: version,\n      $ai_trace_id: traceId,\n      $ai_span_id: runId,\n      $ai_span_name: run.name,\n      $ai_parent_id: parentRunId,\n      $ai_provider: run.provider,\n      $ai_model: run.model,\n      $ai_model_parameters: run.modelParams,\n      $ai_input: withPrivacyMode(this.client, this.privacyMode, run.input),\n      $ai_http_status: 200,\n      $ai_latency: latency,\n      $ai_base_url: run.baseUrl,\n      $ai_framework: 'langchain',\n    }\n\n    if (run.tools) {\n      eventProperties['$ai_tools'] = run.tools\n    }\n\n    if (output instanceof Error) {\n      eventProperties['$ai_http_status'] = (output as any).status || 500\n      eventProperties['$ai_error'] = stringifyError(output)\n      eventProperties['$ai_is_error'] = true\n    } else {\n      // Handle token usage\n      const [inputTokens, outputTokens, additionalTokenData] = this.parseUsage(output, run.provider, run.model)\n      eventProperties['$ai_input_tokens'] = inputTokens\n      eventProperties['$ai_output_tokens'] = outputTokens\n\n      // Add additional token data to properties\n      if (additionalTokenData.cacheReadInputTokens) {\n        eventProperties['$ai_cache_read_input_tokens'] = additionalTokenData.cacheReadInputTokens\n      }\n      if (additionalTokenData.cacheWriteInputTokens) {\n        eventProperties['$ai_cache_creation_input_tokens'] = additionalTokenData.cacheWriteInputTokens\n      }\n      if (additionalTokenData.reasoningTokens) {\n        eventProperties['$ai_reasoning_tokens'] = additionalTokenData.reasoningTokens\n      }\n      if (additionalTokenData.webSearchCount !== undefined) {\n        eventProperties['$ai_web_search_count'] = additionalTokenData.webSearchCount\n      }\n\n      // Extract stop reason from generation info\n      const stopReason = this._extractStopReason(output)\n      if (stopReason) {\n        eventProperties['$ai_stop_reason'] = stopReason\n      }\n\n      // Handle generations/completions\n      let completions\n      if (output.generations && Array.isArray(output.generations)) {\n        const lastGeneration = output.generations[output.generations.length - 1]\n        if (Array.isArray(lastGeneration) && lastGeneration.length > 0) {\n          // Check if this is a ChatGeneration by looking at the first item\n          const isChatGeneration = 'message' in lastGeneration[0] && lastGeneration[0].message\n\n          if (isChatGeneration) {\n            // For ChatGeneration, convert messages to dict format\n            completions = lastGeneration.map((gen: any) => {\n              return this._convertMessageToDict(gen.message)\n            })\n          } else {\n            // For n