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type { DebugImage } from '../types-hoist/debugMeta';\nimport type { StackParser } from '../types-hoist/stacktrace';\nimport { GLOBAL_OBJ } from './worldwide';\n\ntype StackString = string;\ntype CachedResult = [string, string];\n\nlet parsedStackResults: Record | undefined;\nlet lastKeysCount: number | undefined;\nlet cachedFilenameDebugIds: Record | undefined;\n\n/**\n * Returns a map of filenames to debug identifiers.\n */\nexport function getFilenameToDebugIdMap(stackParser: StackParser): Record {\n const debugIdMap = GLOBAL_OBJ._sentryDebugIds;\n if (!debugIdMap) {\n return {};\n }\n\n const debugIdKeys = Object.keys(debugIdMap);\n\n // If the count of registered globals hasn't changed since the last call, we\n // can just return the cached result.\n if (cachedFilenameDebugIds && debugIdKeys.length === lastKeysCount) {\n return cachedFilenameDebugIds;\n }\n\n lastKeysCount = debugIdKeys.length;\n\n // Build a map of filename -> debug_id.\n cachedFilenameDebugIds = 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resource_paths) {\n if (path && filenameDebugIdMap[path]) {\n images.push({\n type: 'sourcemap',\n code_file: path,\n debug_id: filenameDebugIdMap[path] as string,\n });\n }\n }\n\n return images;\n}\n","import { getClient } from '../../currentScopes';\nimport { captureException } from '../../exports';\nimport { SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN } from '../../semanticAttributes';\nimport { SPAN_STATUS_ERROR } from '../../tracing';\nimport { startSpan, startSpanManual } from '../../tracing/trace';\nimport type { Span, SpanAttributeValue } from '../../types-hoist/span';\nimport {\n ANTHROPIC_AI_RESPONSE_TIMESTAMP_ATTRIBUTE,\n GEN_AI_OPERATION_NAME_ATTRIBUTE,\n GEN_AI_PROMPT_ATTRIBUTE,\n GEN_AI_REQUEST_AVAILABLE_TOOLS_ATTRIBUTE,\n GEN_AI_REQUEST_FREQUENCY_PENALTY_ATTRIBUTE,\n GEN_AI_REQUEST_MAX_TOKENS_ATTRIBUTE,\n GEN_AI_REQUEST_MESSAGES_ATTRIBUTE,\n GEN_AI_REQUEST_MODEL_ATTRIBUTE,\n GEN_AI_REQUEST_STREAM_ATTRIBUTE,\n GEN_AI_REQUEST_TEMPERATURE_ATTRIBUTE,\n GEN_AI_REQUEST_TOP_K_ATTRIBUTE,\n GEN_AI_REQUEST_TOP_P_ATTRIBUTE,\n GEN_AI_RESPONSE_ID_ATTRIBUTE,\n GEN_AI_RESPONSE_MODEL_ATTRIBUTE,\n GEN_AI_RESPONSE_TEXT_ATTRIBUTE,\n GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE,\n GEN_AI_SYSTEM_ATTRIBUTE,\n} from '../ai/gen-ai-attributes';\nimport { buildMethodPath, getFinalOperationName, getSpanOperation, setTokenUsageAttributes } from '../ai/utils';\nimport { handleCallbackErrors } from '../handleCallbackErrors';\nimport { instrumentAsyncIterableStream, instrumentMessageStream } from './streaming';\nimport type {\n AnthropicAiInstrumentedMethod,\n AnthropicAiOptions,\n AnthropicAiResponse,\n AnthropicAiStreamingEvent,\n ContentBlock,\n} from './types';\nimport { shouldInstrument } from './utils';\n\n/**\n * Extract request attributes from method arguments\n */\nfunction extractRequestAttributes(args: unknown[], methodPath: string): Record {\n const attributes: Record = {\n [GEN_AI_SYSTEM_ATTRIBUTE]: 'anthropic',\n [GEN_AI_OPERATION_NAME_ATTRIBUTE]: getFinalOperationName(methodPath),\n [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: 'auto.ai.anthropic',\n };\n\n if (args.length > 0 && typeof args[0] === 'object' && args[0] !== null) {\n const params = args[0] as Record;\n if (params.tools && Array.isArray(params.tools)) {\n attributes[GEN_AI_REQUEST_AVAILABLE_TOOLS_ATTRIBUTE] = JSON.stringify(params.tools);\n }\n\n attributes[GEN_AI_REQUEST_MODEL_ATTRIBUTE] = params.model ?? 'unknown';\n if ('temperature' in params) attributes[GEN_AI_REQUEST_TEMPERATURE_ATTRIBUTE] = params.temperature;\n if ('top_p' in params) attributes[GEN_AI_REQUEST_TOP_P_ATTRIBUTE] = params.top_p;\n if ('stream' in params) attributes[GEN_AI_REQUEST_STREAM_ATTRIBUTE] = params.stream;\n if ('top_k' in params) attributes[GEN_AI_REQUEST_TOP_K_ATTRIBUTE] = params.top_k;\n if ('frequency_penalty' in params)\n attributes[GEN_AI_REQUEST_FREQUENCY_PENALTY_ATTRIBUTE] = params.frequency_penalty;\n if ('max_tokens' in params) attributes[GEN_AI_REQUEST_MAX_TOKENS_ATTRIBUTE] = params.max_tokens;\n } else {\n if (methodPath === 'models.retrieve' || methodPath === 'models.get') {\n // models.retrieve(model-id) and models.get(model-id)\n attributes[GEN_AI_REQUEST_MODEL_ATTRIBUTE] = args[0];\n } else {\n attributes[GEN_AI_REQUEST_MODEL_ATTRIBUTE] = 'unknown';\n }\n }\n\n return attributes;\n}\n\n/**\n * Add private request attributes to spans.\n * This is only recorded if recordInputs is true.\n 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addContentAttributes(span: Span, response: AnthropicAiResponse): void {\n // Messages.create\n if ('content' in response) {\n if (Array.isArray(response.content)) {\n span.setAttributes({\n [GEN_AI_RESPONSE_TEXT_ATTRIBUTE]: response.content\n .map((item: ContentBlock) => item.text)\n .filter(text => !!text)\n .join(''),\n });\n\n const toolCalls: Array = [];\n\n for (const item of response.content) {\n if (item.type === 'tool_use' || item.type === 'server_tool_use') {\n toolCalls.push(item);\n }\n }\n if (toolCalls.length > 0) {\n span.setAttributes({ [GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE]: JSON.stringify(toolCalls) });\n }\n }\n }\n // Completions.create\n if ('completion' in response) {\n span.setAttributes({ [GEN_AI_RESPONSE_TEXT_ATTRIBUTE]: response.completion });\n }\n // Models.countTokens\n if ('input_tokens' in response) {\n span.setAttributes({ [GEN_AI_RESPONSE_TEXT_ATTRIBUTE]: JSON.stringify(response.input_tokens) });\n }\n}\n\n/**\n * Add basic metadata attributes from the 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addResponseAttributes(span: Span, response: AnthropicAiResponse, recordOutputs?: boolean): void {\n if (!response || typeof response !== 'object') return;\n\n // capture error, do not add attributes if error (they shouldn't exist)\n if ('type' in response && response.type === 'error') {\n handleResponseError(span, response);\n return;\n }\n\n // Private response attributes that are only recorded if recordOutputs is true.\n if (recordOutputs) {\n addContentAttributes(span, response);\n }\n\n // Add basic metadata attributes\n addMetadataAttributes(span, response);\n}\n\n/**\n * Handle common error catching and reporting for streaming requests\n */\nfunction handleStreamingError(error: unknown, span: Span, methodPath: string): never {\n captureException(error, {\n mechanism: { handled: false, type: 'auto.ai.anthropic', data: { function: methodPath } },\n });\n\n if (span.isRecording()) {\n span.setStatus({ code: SPAN_STATUS_ERROR, message: 'internal_error' });\n span.end();\n }\n throw error;\n}\n\n/**\n * Handle streaming cases with common logic\n */\nfunction handleStreamingRequest(\n originalMethod: (...args: T) => Promise,\n target: (...args: T) => Promise,\n context: unknown,\n args: T,\n requestAttributes: Record,\n operationName: string,\n methodPath: string,\n params: Record | undefined,\n options: AnthropicAiOptions,\n isStreamRequested: boolean,\n): Promise {\n const model = requestAttributes[GEN_AI_REQUEST_MODEL_ATTRIBUTE] ?? 'unknown';\n const spanConfig = {\n name: `${operationName} ${model} stream-response`,\n op: getSpanOperation(methodPath),\n attributes: requestAttributes as Record,\n };\n\n if (isStreamRequested) {\n return startSpanManual(spanConfig, async span => {\n try {\n if (options.recordInputs && params) {\n addPrivateRequestAttributes(span, params);\n }\n const result = await originalMethod.apply(context, args);\n return instrumentAsyncIterableStream(\n result as AsyncIterable,\n span,\n options.recordOutputs ?? false,\n ) as unknown as R;\n } catch (error) {\n return handleStreamingError(error, span, methodPath);\n }\n });\n } else {\n return startSpanManual(spanConfig, span => {\n try {\n if (options.recordInputs && params) {\n addPrivateRequestAttributes(span, params);\n }\n const messageStream = target.apply(context, args);\n return instrumentMessageStream(messageStream, span, options.recordOutputs ?? false);\n } catch (error) {\n return handleStreamingError(error, span, methodPath);\n }\n });\n }\n}\n\n/**\n * Instrument a method with Sentry spans\n * Following Sentry AI Agents Manual Instrumentation conventions\n * @see https://docs.sentry.io/platforms/javascript/guides/node/tracing/instrumentation/ai-agents-module/#manual-instrumentation\n */\nfunction instrumentMethod(\n originalMethod: (...args: T) => Promise,\n methodPath: AnthropicAiInstrumentedMethod,\n context: unknown,\n options: AnthropicAiOptions,\n): (...args: T) => Promise {\n return new Proxy(originalMethod, {\n apply(target, thisArg, args: T): Promise {\n const requestAttributes = extractRequestAttributes(args, methodPath);\n const model = requestAttributes[GEN_AI_REQUEST_MODEL_ATTRIBUTE] ?? 'unknown';\n const operationName = getFinalOperationName(methodPath);\n\n const params = typeof args[0] === 'object' ? (args[0] as Record) : undefined;\n const isStreamRequested = Boolean(params?.stream);\n const isStreamingMethod = methodPath === 'messages.stream';\n\n if (isStreamRequested || isStreamingMethod) {\n return handleStreamingRequest(\n originalMethod,\n target,\n context,\n args,\n requestAttributes,\n operationName,\n methodPath,\n params,\n options,\n isStreamRequested,\n );\n }\n\n return startSpan(\n {\n name: `${operationName} ${model}`,\n op: getSpanOperation(methodPath),\n attributes: requestAttributes as Record,\n },\n span => {\n if (options.recordInputs && params) {\n addPrivateRequestAttributes(span, params);\n }\n\n return handleCallbackErrors(\n () => target.apply(context, args),\n error => {\n captureException(error, {\n mechanism: {\n handled: false,\n type: 'auto.ai.anthropic',\n data: {\n function: methodPath,\n },\n },\n });\n },\n () => {},\n result => addResponseAttributes(span, result as AnthropicAiResponse, options.recordOutputs),\n );\n },\n );\n },\n }) as (...args: T) => Promise;\n}\n\n/**\n * Create a deep proxy for Anthropic AI client instrumentation\n */\nfunction createDeepProxy(target: T, currentPath = '', options: AnthropicAiOptions): T {\n return new Proxy(target, {\n get(obj: object, prop: string): unknown {\n const value = (obj as Record)[prop];\n const methodPath = buildMethodPath(currentPath, String(prop));\n\n if (typeof value === 'function' && shouldInstrument(methodPath)) {\n return instrumentMethod(value as (...args: unknown[]) => Promise, methodPath, obj, options);\n }\n\n if (typeof value === 'function') {\n // Bind non-instrumented functions to preserve the original `this` context,\n return value.bind(obj);\n }\n\n if (value && typeof value === 'object') {\n return createDeepProxy(value, methodPath, options);\n }\n\n return value;\n },\n }) as T;\n}\n\n/**\n * Instrument an Anthropic AI client with Sentry tracing\n * Can be used across Node.js, Cloudflare Workers, and Vercel Edge\n *\n * @template T - The type of the client that extends object\n * @param client - The Anthropic AI client to instrument\n * @param options - Optional configuration for recording inputs and outputs\n * @returns The instrumented client with the same type as the input\n */\nexport function instrumentAnthropicAiClient(anthropicAiClient: T, options?: AnthropicAiOptions): T {\n const sendDefaultPii = Boolean(getClient()?.getOptions().sendDefaultPii);\n\n const _options = {\n recordInputs: sendDefaultPii,\n recordOutputs: sendDefaultPii,\n ...options,\n };\n return createDeepProxy(anthropicAiClient, '', _options);\n}\n","/**\n * This code was originally copied from the 'cookie` module at v0.5.0 and was simplified for our use case.\n * https://github.com/jshttp/cookie/blob/a0c84147aab6266bdb3996cf4062e93907c0b0fc/index.js\n * It had the following license:\n *\n * (The MIT License)\n *\n * Copyright (c) 2012-2014 Roman Shtylman \n * Copyright (c) 2015 Douglas Christopher Wilson \n *\n * Permission is hereby granted, free of charge, to any person obtaining\n * a copy of this software and associated documentation files (the\n * 'Software'), to deal in the Software without restriction, including\n * without limitation the rights to use, copy, modify, merge, publish,\n * distribute, sublicense, and/or sell copies of the Software, and to\n * permit persons to whom the Software is furnished to do so, subject to\n * the following conditions:\n *\n * The above copyright notice and this permission notice shall be\n * included in all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,\n * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\n * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\n * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\n * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\n * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\n * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n */\n\n/**\n * Parses a cookie string\n */\nexport function parseCookie(str: string): Record {\n const obj: Record = {};\n let index = 0;\n\n while (index < str.length) {\n const eqIdx = str.indexOf('=', index);\n\n // no more cookie pairs\n if (eqIdx === -1) {\n break;\n }\n\n let endIdx = str.indexOf(';', index);\n\n if (endIdx === -1) {\n endIdx = str.length;\n } else if (endIdx < eqIdx) {\n // backtrack on prior semicolon\n index = str.lastIndexOf(';', eqIdx - 1) + 1;\n continue;\n }\n\n const key = str.slice(index, eqIdx).trim();\n\n // only assign once\n if (undefined === obj[key]) {\n let val = str.slice(eqIdx + 1, endIdx).trim();\n\n // quoted values\n if (val.charCodeAt(0) === 0x22) {\n val = val.slice(1, -1);\n }\n\n try {\n obj[key] = val.indexOf('%') !== -1 ? decodeURIComponent(val) : val;\n } catch {\n obj[key] = val;\n }\n }\n\n index = endIdx + 1;\n }\n\n return obj;\n}\n","import type { ClientReport } from '../types-hoist/clientreport';\nimport type { ClientReportEnvelope, ClientReportItem } from '../types-hoist/envelope';\nimport { createEnvelope } from './envelope';\nimport { dateTimestampInSeconds } from './time';\n\n/**\n * Creates client report envelope\n * @param discarded_events An array of discard events\n * @param dsn A DSN that can be set on the header. Optional.\n */\nexport function createClientReportEnvelope(\n discarded_events: ClientReport['discarded_events'],\n dsn?: string,\n timestamp?: number,\n): ClientReportEnvelope {\n const clientReportItem: ClientReportItem = [\n { type: 'client_report' },\n {\n timestamp: timestamp || dateTimestampInSeconds(),\n discarded_events,\n },\n ];\n return createEnvelope(dsn ? { dsn } : {}, [clientReportItem]);\n}\n","import type { ScopeData } from '../scope';\nimport { getDynamicSamplingContextFromSpan } from '../tracing/dynamicSamplingContext';\nimport type { Breadcrumb } from '../types-hoist/breadcrumb';\nimport type { Event } from '../types-hoist/event';\nimport type { Span } from '../types-hoist/span';\nimport { merge } from './merge';\nimport { getRootSpan, spanToJSON, spanToTraceContext } from './spanUtils';\n\n/**\n * Applies data from the scope to the event and runs all event processors on it.\n */\nexport function applyScopeDataToEvent(event: Event, data: ScopeData): void {\n const { fingerprint, span, breadcrumbs, sdkProcessingMetadata } = data;\n\n // Apply general data\n applyDataToEvent(event, data);\n\n // We want to set the trace context for normal events only if there isn't already\n // a trace context on the event. There is a product feature in place where we link\n // errors with transaction and it relies on that.\n if (span) {\n applySpanToEvent(event, span);\n }\n\n applyFingerprintToEvent(event, fingerprint);\n applyBreadcrumbsToEvent(event, breadcrumbs);\n applySdkMetadataToEvent(event, sdkProcessingMetadata);\n}\n\n/** Merge data of two scopes together. */\nexport function mergeScopeData(data: ScopeData, mergeData: ScopeData): void {\n const {\n extra,\n tags,\n user,\n contexts,\n level,\n sdkProcessingMetadata,\n breadcrumbs,\n fingerprint,\n eventProcessors,\n attachments,\n propagationContext,\n transactionName,\n span,\n } = mergeData;\n\n mergeAndOverwriteScopeData(data, 'extra', extra);\n mergeAndOverwriteScopeData(data, 'tags', tags);\n mergeAndOverwriteScopeData(data, 'user', user);\n mergeAndOverwriteScopeData(data, 'contexts', contexts);\n\n data.sdkProcessingMetadata = merge(data.sdkProcessingMetadata, sdkProcessingMetadata, 2);\n\n if (level) {\n data.level = level;\n }\n\n if (transactionName) {\n data.transactionName = transactionName;\n }\n\n if (span) {\n data.span = span;\n }\n\n if (breadcrumbs.length) {\n data.breadcrumbs = [...data.breadcrumbs, ...breadcrumbs];\n }\n\n if (fingerprint.length) {\n data.fingerprint = [...data.fingerprint, ...fingerprint];\n }\n\n if (eventProcessors.length) {\n data.eventProcessors = [...data.eventProcessors, ...eventProcessors];\n }\n\n if (attachments.length) {\n data.attachments = [...data.attachments, ...attachments];\n }\n\n data.propagationContext = { ...data.propagationContext, ...propagationContext };\n}\n\n/**\n * Merges certain scope data. Undefined values will overwrite any existing values.\n * Exported only for tests.\n */\nexport function mergeAndOverwriteScopeData<\n Prop extends 'extra' | 'tags' | 'user' | 'contexts' | 'sdkProcessingMetadata',\n Data extends ScopeData,\n>(data: Data, prop: Prop, mergeVal: Data[Prop]): void {\n data[prop] = merge(data[prop], mergeVal, 1);\n}\n\n/** Exported only for tests */\nexport function mergeArray(\n event: Event,\n prop: Prop,\n mergeVal: ScopeData[Prop],\n): void {\n const prevVal = event[prop];\n // If we are not merging any new values,\n // we only need to proceed if there was an empty array before (as we want to replace it with undefined)\n if (!mergeVal.length && (!prevVal || prevVal.length)) {\n return;\n }\n\n const merged = [...(prevVal || []), ...mergeVal] as ScopeData[Prop];\n event[prop] = merged.length ? merged : undefined;\n}\n\nfunction applyDataToEvent(event: Event, data: ScopeData): void {\n const { extra, tags, user, contexts, level, transactionName } = data;\n\n if (Object.keys(extra).length) {\n event.extra = { ...extra, ...event.extra };\n }\n\n if (Object.keys(tags).length) {\n event.tags = { ...tags, ...event.tags };\n }\n\n if (Object.keys(user).length) {\n event.user = { ...user, ...event.user };\n }\n\n if (Object.keys(contexts).length) {\n event.contexts = { ...contexts, ...event.contexts };\n }\n\n if (level) {\n event.level = level;\n }\n\n // transaction events get their `transaction` from the root span name\n if (transactionName && event.type !== 'transaction') {\n event.transaction = transactionName;\n }\n}\n\nfunction applyBreadcrumbsToEvent(event: Event, breadcrumbs: Breadcrumb[]): void {\n const mergedBreadcrumbs = [...(event.breadcrumbs || []), ...breadcrumbs];\n event.breadcrumbs = mergedBreadcrumbs.length ? mergedBreadcrumbs : undefined;\n}\n\nfunction applySdkMetadataToEvent(event: Event, sdkProcessingMetadata: ScopeData['sdkProcessingMetadata']): void {\n event.sdkProcessingMetadata = {\n ...event.sdkProcessingMetadata,\n ...sdkProcessingMetadata,\n };\n}\n\nfunction applySpanToEvent(event: Event, span: Span): void {\n event.contexts = {\n trace: spanToTraceContext(span),\n ...event.contexts,\n };\n\n event.sdkProcessingMetadata = {\n dynamicSamplingContext: getDynamicSamplingContextFromSpan(span),\n ...event.sdkProcessingMetadata,\n };\n\n const rootSpan = getRootSpan(span);\n const transactionName = spanToJSON(rootSpan).description;\n if (transactionName && !event.transaction && event.type === 'transaction') {\n event.transaction = transactionName;\n }\n}\n\n/**\n * Applies fingerprint from the scope to the event if there's one,\n * uses message if there's one instead or get rid of empty fingerprint\n */\nfunction applyFingerprintToEvent(event: Event, fingerprint: ScopeData['fingerprint'] | undefined): void {\n // Make sure it's an array first and we actually have something in place\n event.fingerprint = event.fingerprint\n ? Array.isArray(event.fingerprint)\n ? event.fingerprint\n : [event.fingerprint]\n : [];\n\n // If we have something on the scope, then merge it with event\n if (fingerprint) {\n event.fingerprint = event.fingerprint.concat(fingerprint);\n }\n\n // If we have no data at all, remove empty array default\n if (!event.fingerprint.length) {\n delete event.fingerprint;\n }\n}\n","import { DEBUG_BUILD } from '../debug-build';\nimport type { DynamicSamplingContext } from '../types-hoist/envelope';\nimport { debug } from './debug-logger';\nimport { isString } from './is';\n\nexport const SENTRY_BAGGAGE_KEY_PREFIX = 'sentry-';\n\nexport const SENTRY_BAGGAGE_KEY_PREFIX_REGEX = /^sentry-/;\n\n/**\n * Max length of a serialized baggage string\n *\n * https://www.w3.org/TR/baggage/#limits\n */\nexport const MAX_BAGGAGE_STRING_LENGTH = 8192;\n\n/**\n * Takes a baggage header and turns it into Dynamic Sampling Context, by extracting all the \"sentry-\" prefixed values\n * from it.\n *\n * @param baggageHeader A very bread definition of a baggage header as it might appear in various frameworks.\n * @returns The Dynamic Sampling Context that was found on `baggageHeader`, if there was any, `undefined` otherwise.\n */\nexport function baggageHeaderToDynamicSamplingContext(\n // Very liberal definition of what any incoming header might look like\n baggageHeader: string | string[] | number | null | undefined | boolean,\n): Partial | undefined {\n const baggageObject = parseBaggageHeader(baggageHeader);\n\n if (!baggageObject) {\n return undefined;\n }\n\n // Read all \"sentry-\" prefixed values out of the baggage object and put it onto a dynamic sampling context object.\n const dynamicSamplingContext = Object.entries(baggageObject).reduce>((acc, [key, value]) => {\n if (key.match(SENTRY_BAGGAGE_KEY_PREFIX_REGEX)) {\n const nonPrefixedKey = key.slice(SENTRY_BAGGAGE_KEY_PREFIX.length);\n acc[nonPrefixedKey] = value;\n }\n return acc;\n }, {});\n\n // Only return a dynamic sampling context object if there are keys in it.\n // A keyless object means there were no sentry values on the header, which means that there is no DSC.\n if (Object.keys(dynamicSamplingContext).length > 0) {\n return dynamicSamplingContext as Partial;\n } else {\n return undefined;\n }\n}\n\n/**\n * Turns a Dynamic Sampling Object into a baggage header by prefixing all the keys on the object with \"sentry-\".\n *\n * @param dynamicSamplingContext The Dynamic Sampling Context to turn into a header. For convenience and compatibility\n * with the `getDynamicSamplingContext` method on the Transaction class ,this argument can also be `undefined`. If it is\n * `undefined` the function will return `undefined`.\n * @returns a baggage header, created from `dynamicSamplingContext`, or `undefined` either if `dynamicSamplingContext`\n * was `undefined`, or if `dynamicSamplingContext` didn't contain any values.\n */\nexport function dynamicSamplingContextToSentryBaggageHeader(\n // this also takes undefined for convenience and bundle size in other places\n dynamicSamplingContext?: Partial,\n): string | undefined {\n if (!dynamicSamplingContext) {\n return undefined;\n }\n\n // Prefix all DSC keys with \"sentry-\" and put them into a new object\n const sentryPrefixedDSC = Object.entries(dynamicSamplingContext).reduce>(\n (acc, [dscKey, dscValue]) => {\n if (dscValue) {\n acc[`${SENTRY_BAGGAGE_KEY_PREFIX}${dscKey}`] = dscValue;\n }\n return acc;\n },\n {},\n );\n\n return objectToBaggageHeader(sentryPrefixedDSC);\n}\n\n/**\n * Take a baggage header and parse it into an object.\n */\nexport function parseBaggageHeader(\n baggageHeader: string | string[] | number | null | undefined | boolean,\n): Record | undefined {\n if (!baggageHeader || (!isString(baggageHeader) && !Array.isArray(baggageHeader))) {\n return undefined;\n }\n\n if (Array.isArray(baggageHeader)) {\n // Combine all baggage headers into one object containing the baggage values so we can later read the Sentry-DSC-values from it\n return baggageHeader.reduce>((acc, curr) => {\n const currBaggageObject = baggageHeaderToObject(curr);\n Object.entries(currBaggageObject).forEach(([key, value]) => {\n acc[key] = value;\n });\n return acc;\n }, {});\n }\n\n return baggageHeaderToObject(baggageHeader);\n}\n\n/**\n * Will parse a baggage header, which is a simple key-value map, into a flat object.\n *\n * @param baggageHeader The baggage header to parse.\n * @returns a flat object containing all the key-value pairs from `baggageHeader`.\n */\nfunction baggageHeaderToObject(baggageHeader: string): Record {\n return baggageHeader\n .split(',')\n .map(baggageEntry => {\n const eqIdx = baggageEntry.indexOf('=');\n if (eqIdx === -1) {\n // Likely an invalid entry\n return [];\n }\n const key = baggageEntry.slice(0, eqIdx);\n const value = baggageEntry.slice(eqIdx + 1);\n return [key, value].map(keyOrValue => {\n try {\n return decodeURIComponent(keyOrValue.trim());\n } catch {\n // We ignore errors here, e.g. if the value cannot be URL decoded.\n // This will then be skipped in the next step\n return;\n }\n });\n })\n .reduce>((acc, [key, value]) => {\n if (key && value) {\n acc[key] = value;\n }\n return acc;\n }, {});\n}\n\n/**\n * Turns a flat object (key-value pairs) into a baggage header, which is also just key-value pairs.\n *\n * @param object The object to turn into a baggage header.\n * @returns a baggage header string, or `undefined` if the object didn't have any values, since an empty baggage header\n * is not spec compliant.\n */\nexport function objectToBaggageHeader(object: Record): string | undefined {\n if (Object.keys(object).length === 0) {\n // An empty baggage header is not spec compliant: We return undefined.\n return undefined;\n }\n\n return Object.entries(object).reduce((baggageHeader, [objectKey, objectValue], currentIndex) => {\n const baggageEntry = `${encodeURIComponent(objectKey)}=${encodeURIComponent(objectValue)}`;\n const newBaggageHeader = currentIndex === 0 ? baggageEntry : `${baggageHeader},${baggageEntry}`;\n if (newBaggageHeader.length > MAX_BAGGAGE_STRING_LENGTH) {\n DEBUG_BUILD &&\n debug.warn(\n `Not adding key: ${objectKey} with val: ${objectValue} to baggage header due to exceeding baggage size limits.`,\n );\n return baggageHeader;\n } else {\n return newBaggageHeader;\n }\n }, '');\n}\n","import type { ExtendedError } from '../types-hoist/error';\nimport type { Event, EventHint } from '../types-hoist/event';\nimport type { Exception } from '../types-hoist/exception';\nimport type { StackParser } from '../types-hoist/stacktrace';\nimport { isInstanceOf } from './is';\n\n/**\n * Creates exceptions inside `event.exception.values` for errors that are nested on properties based on the `key` parameter.\n */\nexport function applyAggregateErrorsToEvent(\n exceptionFromErrorImplementation: (stackParser: StackParser, ex: Error) => Exception,\n parser: StackParser,\n key: string,\n limit: number,\n event: Event,\n hint?: EventHint,\n): void {\n if (!event.exception?.values || !hint || !isInstanceOf(hint.originalException, Error)) {\n return;\n }\n\n // Generally speaking the last item in `event.exception.values` is the exception originating from the original Error\n const originalException: Exception | undefined =\n event.exception.values.length > 0 ? event.exception.values[event.exception.values.length - 1] : undefined;\n\n // We only create exception grouping if there is an exception in the event.\n if (originalException) {\n event.exception.values = aggregateExceptionsFromError(\n exceptionFromErrorImplementation,\n parser,\n limit,\n hint.originalException as ExtendedError,\n key,\n event.exception.values,\n originalException,\n 0,\n );\n }\n}\n\nfunction aggregateExceptionsFromError(\n exceptionFromErrorImplementation: (stackParser: StackParser, ex: Error) => Exception,\n parser: StackParser,\n limit: number,\n error: ExtendedError,\n key: string,\n prevExceptions: Exception[],\n exception: Exception,\n exceptionId: number,\n): Exception[] {\n if (prevExceptions.length >= limit + 1) {\n return prevExceptions;\n }\n\n let newExceptions = [...prevExceptions];\n\n // Recursively call this function in order to walk down a chain of errors\n if (isInstanceOf(error[key], Error)) {\n applyExceptionGroupFieldsForParentException(exception, exceptionId);\n const newException = exceptionFromErrorImplementation(parser, error[key]);\n const newExceptionId = newExceptions.length;\n applyExceptionGroupFieldsForChildException(newException, key, newExceptionId, exceptionId);\n newExceptions = aggregateExceptionsFromError(\n exceptionFromErrorImplementation,\n parser,\n limit,\n error[key],\n key,\n [newException, ...newExceptions],\n newException,\n newExceptionId,\n );\n }\n\n // This will create exception grouping for AggregateErrors\n // https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/AggregateError\n if (Array.isArray(error.errors)) {\n error.errors.forEach((childError, i) => {\n if (isInstanceOf(childError, Error)) {\n applyExceptionGroupFieldsForParentException(exception, exceptionId);\n const newException = exceptionFromErrorImplementation(parser, childError);\n const newExceptionId = newExceptions.length;\n applyExceptionGroupFieldsForChildException(newException, `errors[${i}]`, newExceptionId, exceptionId);\n newExceptions = aggregateExceptionsFromError(\n exceptionFromErrorImplementation,\n parser,\n limit,\n childError,\n key,\n [newException, ...newExceptions],\n newException,\n newExceptionId,\n );\n }\n });\n }\n\n return newExceptions;\n}\n\nfunction applyExceptionGroupFieldsForParentException(exception: Exception, exceptionId: number): void {\n exception.mechanism = {\n handled: true,\n type: 'auto.core.linked_errors',\n ...exception.mechanism,\n ...(exception.type === 'AggregateError' && { is_exception_group: true }),\n exception_id: exceptionId,\n };\n}\n\nfunction applyExceptionGroupFieldsForChildException(\n exception: Exception,\n source: string,\n exceptionId: number,\n parentId: number | undefined,\n): void {\n exception.mechanism = {\n handled: true,\n ...exception.mechanism,\n type: 'chained',\n source,\n exception_id: exceptionId,\n parent_id: parentId,\n };\n}\n","import { ANTHROPIC_AI_INSTRUMENTED_METHODS } from './constants';\nimport type { AnthropicAiInstrumentedMethod } from './types';\n\n/**\n * Check if a method path should be instrumented\n */\nexport function shouldInstrument(methodPath: string): methodPath is AnthropicAiInstrumentedMethod {\n return ANTHROPIC_AI_INSTRUMENTED_METHODS.includes(methodPath as AnthropicAiInstrumentedMethod);\n}\n","import { getClient, withIsolationScope } from './currentScopes';\nimport { captureException } from './exports';\nimport { SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN, SEMANTIC_ATTRIBUTE_SENTRY_SOURCE } from './semanticAttributes';\nimport { startSpanManual } from './tracing';\nimport { normalize } from './utils/normalize';\nimport { addNonEnumerableProperty } from './utils/object';\n\ninterface SentryTrpcMiddlewareOptions {\n /** Whether to include procedure inputs in reported events. Defaults to `false`. */\n attachRpcInput?: boolean;\n forceTransaction?: boolean;\n}\n\nexport interface SentryTrpcMiddlewareArguments {\n path?: unknown;\n type?: unknown;\n next: () => T;\n rawInput?: unknown;\n getRawInput?: () => Promise;\n}\n\nconst trpcCaptureContext = { mechanism: { handled: false, type: 'auto.rpc.trpc.middleware' } };\n\nfunction captureIfError(nextResult: unknown): void {\n // TODO: Set span status based on what TRPCError was encountered\n if (\n typeof nextResult === 'object' &&\n nextResult !== null &&\n 'ok' in nextResult &&\n !nextResult.ok &&\n 'error' in nextResult\n ) {\n captureException(nextResult.error, trpcCaptureContext);\n }\n}\n\ntype SentryTrpcMiddleware = T extends Promise ? T : Promise;\n\n/**\n * Sentry tRPC middleware that captures errors and creates spans for tRPC procedures.\n */\nexport function trpcMiddleware(options: SentryTrpcMiddlewareOptions = {}) {\n // eslint-disable-next-line @typescript-eslint/ban-ts-comment\n // @ts-ignore\n return async function (opts: SentryTrpcMiddlewareArguments): SentryTrpcMiddleware {\n const { path, type, next, rawInput, getRawInput } = opts;\n\n const client = getClient();\n const clientOptions = client?.getOptions();\n\n const trpcContext: Record = {\n procedure_path: path,\n procedure_type: type,\n };\n\n addNonEnumerableProperty(\n trpcContext,\n '__sentry_override_normalization_depth__',\n 1 + // 1 for context.input + the normal normalization depth\n (clientOptions?.normalizeDepth ?? 5), // 5 is a sane depth\n );\n\n if (options.attachRpcInput !== undefined ? options.attachRpcInput : clientOptions?.sendDefaultPii) {\n if (rawInput !== undefined) {\n trpcContext.input = normalize(rawInput);\n }\n\n if (getRawInput !== undefined && typeof getRawInput === 'function') {\n try {\n const rawRes = await getRawInput();\n\n trpcContext.input = normalize(rawRes);\n } catch {\n // noop\n }\n }\n }\n\n return withIsolationScope(scope => {\n scope.setContext('trpc', trpcContext);\n return startSpanManual(\n {\n name: `trpc/${path}`,\n op: 'rpc.server',\n attributes: {\n [SEMANTIC_ATTRIBUTE_SENTRY_SOURCE]: 'route',\n [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: 'auto.rpc.trpc',\n },\n forceTransaction: !!options.forceTransaction,\n },\n async span => {\n try {\n const nextResult = await next();\n captureIfError(nextResult);\n span.end();\n return nextResult;\n } catch (e) {\n captureException(e, trpcCaptureContext);\n span.end();\n throw e;\n }\n },\n ) as SentryTrpcMiddleware;\n });\n };\n}\n","/**\n * Shared utils for AI integrations (OpenAI, Anthropic, Verce.AI, etc.)\n */\nimport type { Span } from '../../types-hoist/span';\nimport {\n GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE,\n GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE,\n GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE,\n} from './gen-ai-attributes';\n/**\n * Maps AI method paths to Sentry operation name\n */\nexport function getFinalOperationName(methodPath: string): string {\n if (methodPath.includes('messages')) {\n return 'messages';\n }\n if (methodPath.includes('completions')) {\n return 'completions';\n }\n if (methodPath.includes('models')) {\n return 'models';\n }\n if (methodPath.includes('chat')) {\n return 'chat';\n }\n return methodPath.split('.').pop() || 'unknown';\n}\n\n/**\n * Get the span operation for AI methods\n * Following Sentry's convention: \"gen_ai.{operation_name}\"\n */\nexport function getSpanOperation(methodPath: string): string {\n return `gen_ai.${getFinalOperationName(methodPath)}`;\n}\n\n/**\n * Build method path from current traversal\n */\nexport function buildMethodPath(currentPath: string, prop: string): string {\n return currentPath ? `${currentPath}.${prop}` : prop;\n}\n\n/**\n * Set token usage attributes\n * @param span - The span to add attributes to\n * @param promptTokens - The number of prompt tokens\n * @param completionTokens - The number of completion tokens\n * @param cachedInputTokens - The number of cached input tokens\n * @param cachedOutputTokens - The number of cached output tokens\n */\nexport function setTokenUsageAttributes(\n span: Span,\n promptTokens?: number,\n completionTokens?: number,\n cachedInputTokens?: number,\n cachedOutputTokens?: number,\n): void {\n if (promptTokens !== undefined) {\n span.setAttributes({\n [GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE]: promptTokens,\n });\n }\n if (completionTokens !== undefined) {\n span.setAttributes({\n [GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE]: completionTokens,\n });\n }\n if (\n promptTokens !== undefined ||\n completionTokens !== undefined ||\n cachedInputTokens !== undefined ||\n cachedOutputTokens !== undefined\n ) {\n /**\n * Total input tokens in a request is the summation of `input_tokens`,\n * `cache_creation_input_tokens`, and `cache_read_input_tokens`.\n */\n const totalTokens =\n (promptTokens ?? 0) + (completionTokens ?? 0) + (cachedInputTokens ?? 0) + (cachedOutputTokens ?? 0);\n\n span.setAttributes({\n [GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE]: totalTokens,\n });\n }\n}\n","export const ANTHROPIC_AI_INTEGRATION_NAME = 'Anthropic_AI';\n\n// https://docs.anthropic.com/en/api/messages\n// https://docs.anthropic.com/en/api/models-list\nexport const ANTHROPIC_AI_INSTRUMENTED_METHODS = [\n 'messages.create',\n 'messages.stream',\n 'messages.countTokens',\n 'models.get',\n 'completions.create',\n 'models.retrieve',\n 'beta.messages.create',\n] as const;\n","import type { SeverityLevel } from '../types-hoist/severity';\n\n/**\n * Determine a breadcrumb's log level (only `warning` or `error`) based on an HTTP status code.\n */\nexport function getBreadcrumbLogLevelFromHttpStatusCode(statusCode: number | undefined): SeverityLevel | undefined {\n // NOTE: undefined defaults to 'info' in Sentry\n if (statusCode === undefined) {\n return undefined;\n } else if (statusCode >= 400 && statusCode < 500) {\n return 'warning';\n } else if (statusCode >= 500) {\n return 'error';\n } else {\n return undefined;\n }\n}\n","/**\n * OpenAI Integration Telemetry Attributes\n * Based on OpenTelemetry Semantic Conventions for Generative AI\n * @see https://opentelemetry.io/docs/specs/semconv/gen-ai/\n */\n\n// =============================================================================\n// OPENTELEMETRY SEMANTIC CONVENTIONS FOR GENAI\n// =============================================================================\n\n/**\n * The input messages sent to the model\n */\nexport const GEN_AI_PROMPT_ATTRIBUTE = 'gen_ai.prompt';\n\n/**\n * The Generative AI system being used\n * For OpenAI, this should always be \"openai\"\n */\nexport const GEN_AI_SYSTEM_ATTRIBUTE = 'gen_ai.system';\n\n/**\n * The name of the model as requested\n * Examples: \"gpt-4\", \"gpt-3.5-turbo\"\n */\nexport const GEN_AI_REQUEST_MODEL_ATTRIBUTE = 'gen_ai.request.model';\n\n/**\n * Whether streaming was enabled for the request\n */\nexport const GEN_AI_REQUEST_STREAM_ATTRIBUTE = 'gen_ai.request.stream';\n\n/**\n * The temperature setting for the model request\n */\nexport const GEN_AI_REQUEST_TEMPERATURE_ATTRIBUTE = 'gen_ai.request.temperature';\n\n/**\n * The maximum number of tokens requested\n */\nexport const GEN_AI_REQUEST_MAX_TOKENS_ATTRIBUTE = 'gen_ai.request.max_tokens';\n\n/**\n * The frequency penalty setting for the model request\n */\nexport const GEN_AI_REQUEST_FREQUENCY_PENALTY_ATTRIBUTE = 'gen_ai.request.frequency_penalty';\n\n/**\n * The presence penalty setting for the model request\n */\nexport const GEN_AI_REQUEST_PRESENCE_PENALTY_ATTRIBUTE = 'gen_ai.request.presence_penalty';\n\n/**\n * The top_p (nucleus sampling) setting for the model request\n */\nexport const GEN_AI_REQUEST_TOP_P_ATTRIBUTE = 'gen_ai.request.top_p';\n\n/**\n * The top_k setting for the model request\n */\nexport const GEN_AI_REQUEST_TOP_K_ATTRIBUTE = 'gen_ai.request.top_k';\n\n/**\n * Stop sequences for the model request\n */\nexport const GEN_AI_REQUEST_STOP_SEQUENCES_ATTRIBUTE = 'gen_ai.request.stop_sequences';\n\n/**\n * Array of reasons why the model stopped generating tokens\n */\nexport const GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE = 'gen_ai.response.finish_reasons';\n\n/**\n * The name of the model that generated the response\n */\nexport const GEN_AI_RESPONSE_MODEL_ATTRIBUTE = 'gen_ai.response.model';\n\n/**\n * The unique identifier for the response\n */\nexport const GEN_AI_RESPONSE_ID_ATTRIBUTE = 'gen_ai.response.id';\n\n/**\n * The number of tokens used in the prompt\n */\nexport const GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE = 'gen_ai.usage.input_tokens';\n\n/**\n * The number of tokens used in the response\n */\nexport const GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE = 'gen_ai.usage.output_tokens';\n\n/**\n * The total number of tokens used (input + output)\n */\nexport const GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE = 'gen_ai.usage.total_tokens';\n\n/**\n * The operation name\n */\nexport const GEN_AI_OPERATION_NAME_ATTRIBUTE = 'gen_ai.operation.name';\n\n/**\n * The prompt messages\n * Only recorded when recordInputs is enabled\n */\nexport const GEN_AI_REQUEST_MESSAGES_ATTRIBUTE = 'gen_ai.request.messages';\n\n/**\n * The response text\n * Only recorded when recordOutputs is enabled\n */\nexport const GEN_AI_RESPONSE_TEXT_ATTRIBUTE = 'gen_ai.response.text';\n\n/**\n * The available tools from incoming request\n * Only recorded when recordInputs is enabled\n */\nexport const GEN_AI_REQUEST_AVAILABLE_TOOLS_ATTRIBUTE = 'gen_ai.request.available_tools';\n\n/**\n * Whether the response is a streaming response\n */\nexport const GEN_AI_RESPONSE_STREAMING_ATTRIBUTE = 'gen_ai.response.streaming';\n\n/**\n * The tool calls from the response\n * Only recorded when recordOutputs is enabled\n */\nexport const GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE = 'gen_ai.response.tool_calls';\n\n// =============================================================================\n// OPENAI-SPECIFIC ATTRIBUTES\n// =============================================================================\n\n/**\n * The response ID from OpenAI\n */\nexport const OPENAI_RESPONSE_ID_ATTRIBUTE = 'openai.response.id';\n\n/**\n * The response model from OpenAI\n */\nexport const OPENAI_RESPONSE_MODEL_ATTRIBUTE = 'openai.response.model';\n\n/**\n * The response timestamp from OpenAI (ISO string)\n */\nexport const OPENAI_RESPONSE_TIMESTAMP_ATTRIBUTE = 'openai.response.timestamp';\n\n/**\n * The number of completion tokens used\n */\nexport const OPENAI_USAGE_COMPLETION_TOKENS_ATTRIBUTE = 'openai.usage.completion_tokens';\n\n/**\n * The number of prompt tokens used\n */\nexport const OPENAI_USAGE_PROMPT_TOKENS_ATTRIBUTE = 'openai.usage.prompt_tokens';\n\n// =============================================================================\n// OPENAI OPERATIONS\n// =============================================================================\n\n/**\n * OpenAI API operations\n */\nexport const OPENAI_OPERATIONS = {\n CHAT: 'chat',\n RESPONSES: 'responses',\n} as const;\n\n// =============================================================================\n// ANTHROPIC AI OPERATIONS\n// =============================================================================\n\n/**\n * The response timestamp from Anthropic AI (ISO string)\n */\nexport const ANTHROPIC_AI_RESPONSE_TIMESTAMP_ATTRIBUTE = 'anthropic.response.timestamp';\n","import { getGlobalSingleton } from '../carrier';\nimport { DEBUG_BUILD } from '../debug-build';\nimport type { ConsoleLevel } from '../types-hoist/instrument';\nimport { GLOBAL_OBJ } from './worldwide';\n\nexport interface SentryDebugLogger {\n disable(): void;\n enable(): void;\n isEnabled(): boolean;\n log(...args: Parameters): void;\n warn(...args: Parameters): void;\n error(...args: Parameters): void;\n}\n\nexport const CONSOLE_LEVELS: readonly ConsoleLevel[] = [\n 'debug',\n 'info',\n 'warn',\n 'error',\n 'log',\n 'assert',\n 'trace',\n] as const;\n\n/** Prefix for logging strings */\nconst PREFIX = 'Sentry Logger ';\n\n/** This may be mutated by the console instrumentation. */\nexport const originalConsoleMethods: Partial<{\n log(...args: Parameters): void;\n info(...args: Parameters): void;\n warn(...args: Parameters): void;\n error(...args: Parameters): void;\n debug(...args: Parameters): void;\n assert(...args: Parameters): void;\n trace(...args: Parameters): void;\n}> = {};\n\n/**\n * Temporarily disable sentry console instrumentations.\n *\n * @param callback The function to run against the original `console` messages\n * @returns The results of the callback\n */\nexport function consoleSandbox(callback: () => T): T {\n if (!('console' in GLOBAL_OBJ)) {\n return callback();\n }\n\n const console = GLOBAL_OBJ.console;\n const wrappedFuncs: Partial void>> = {};\n\n const wrappedLevels = Object.keys(originalConsoleMethods) as ConsoleLevel[];\n\n // Restore all wrapped console methods\n wrappedLevels.forEach(level => {\n const originalConsoleMethod = originalConsoleMethods[level];\n wrappedFuncs[level] = console[level] as (...args: unknown[]) => void;\n console[level] = originalConsoleMethod as (...args: unknown[]) => void;\n });\n\n try {\n return callback();\n } finally {\n // Revert restoration to wrapped state\n wrappedLevels.forEach(level => {\n console[level] = wrappedFuncs[level] as (...args: unknown[]) => void;\n });\n }\n}\n\nfunction enable(): void {\n _getLoggerSettings().enabled = true;\n}\n\nfunction disable(): void {\n _getLoggerSettings().enabled = false;\n}\n\nfunction isEnabled(): boolean {\n return _getLoggerSettings().enabled;\n}\n\nfunction log(...args: Parameters): void {\n _maybeLog('log', ...args);\n}\n\nfunction info(...args: Parameters): void {\n _maybeLog('info', ...args);\n}\n\nfunction warn(...args: Parameters): void {\n _maybeLog('warn', ...args);\n}\n\nfunction error(...args: Parameters): void {\n _maybeLog('error', ...args);\n}\n\nfunction _maybeLog(level: ConsoleLevel, ...args: Parameters<(typeof console)[typeof level]>): void {\n if (!DEBUG_BUILD) {\n return;\n }\n\n if (isEnabled()) {\n consoleSandbox(() => {\n GLOBAL_OBJ.console[level](`${PREFIX}[${level}]:`, ...args);\n });\n }\n}\n\nfunction _getLoggerSettings(): { enabled: boolean } {\n if (!DEBUG_BUILD) {\n return { enabled: false };\n }\n\n return getGlobalSingleton('loggerSettings', () => ({ enabled: false }));\n}\n\n/**\n * This is a logger singleton which either logs things or no-ops if logging is not enabled.\n */\nexport const debug = {\n /** Enable logging. */\n enable,\n /** Disable logging. */\n disable,\n /** Check if logging is enabled. */\n isEnabled,\n /** Log a message. */\n log,\n /** Log a warning. */\n warn,\n /** Log an error. */\n error,\n} satisfies SentryDebugLogger;\n","import { isString } from './is';\nimport { GLOBAL_OBJ } from './worldwide';\n\nconst WINDOW = GLOBAL_OBJ as unknown as Window;\n\nconst DEFAULT_MAX_STRING_LENGTH = 80;\n\ntype SimpleNode = {\n parentNode: SimpleNode;\n} | null;\n\n/**\n * Given a child DOM element, returns a query-selector statement describing that\n * and its ancestors\n * e.g. [HTMLElement] => body > div > input#foo.btn[name=baz]\n * @returns generated DOM path\n */\nexport function htmlTreeAsString(\n elem: unknown,\n options: string[] | { keyAttrs?: string[]; maxStringLength?: number } = {},\n): string {\n if (!elem) {\n return '';\n }\n\n // try/catch both:\n // - accessing event.target (see getsentry/raven-js#838, #768)\n // - `htmlTreeAsString` because it's complex, and just accessing the DOM incorrectly\n // - can throw an exception in some circumstances.\n try {\n let currentElem = elem as SimpleNode;\n const MAX_TRAVERSE_HEIGHT = 5;\n const out = [];\n let height = 0;\n let len = 0;\n const separator = ' > ';\n const sepLength = separator.length;\n let nextStr;\n const keyAttrs = Array.isArray(options) ? options : options.keyAttrs;\n const maxStringLength = (!Array.isArray(options) && options.maxStringLength) || DEFAULT_MAX_STRING_LENGTH;\n\n while (currentElem && height++ < MAX_TRAVERSE_HEIGHT) {\n nextStr = _htmlElementAsString(currentElem, keyAttrs);\n // bail out if\n // - nextStr is the 'html' element\n // - the length of the string that would be created exceeds maxStringLength\n // (ignore this limit if we are on the first iteration)\n if (nextStr === 'html' || (height > 1 && len + out.length * sepLength + nextStr.length >= maxStringLength)) {\n break;\n }\n\n out.push(nextStr);\n\n len += nextStr.length;\n currentElem = currentElem.parentNode;\n }\n\n return out.reverse().join(separator);\n } catch {\n return '';\n }\n}\n\n/**\n * Returns a simple, query-selector representation of a DOM element\n * e.g. [HTMLElement] => input#foo.btn[name=baz]\n * @returns generated DOM path\n */\nfunction _htmlElementAsString(el: unknown, keyAttrs?: string[]): string {\n const elem = el as {\n tagName?: string;\n id?: string;\n className?: string;\n getAttribute(key: string): string;\n };\n\n const out = [];\n\n if (!elem?.tagName) {\n return '';\n }\n\n // @ts-expect-error WINDOW has HTMLElement\n if (WINDOW.HTMLElement) {\n // If using the component name annotation plugin, this value may be available on the DOM node\n if (elem instanceof HTMLElement && elem.dataset) {\n if (elem.dataset['sentryComponent']) {\n return elem.dataset['sentryComponent'];\n }\n if (elem.dataset['sentryElement']) {\n return elem.dataset['sentryElement'];\n }\n }\n }\n\n out.push(elem.tagName.toLowerCase());\n\n // Pairs of attribute keys defined in `serializeAttribute` and their values on element.\n const keyAttrPairs = keyAttrs?.length\n ? keyAttrs.filter(keyAttr => elem.getAttribute(keyAttr)).map(keyAttr => [keyAttr, elem.getAttribute(keyAttr)])\n : null;\n\n if (keyAttrPairs?.length) {\n keyAttrPairs.forEach(keyAttrPair => {\n out.push(`[${keyAttrPair[0]}=\"${keyAttrPair[1]}\"]`);\n });\n } else {\n if (elem.id) {\n out.push(`#${elem.id}`);\n }\n\n const className = elem.className;\n if (className && isString(className)) {\n const classes = className.split(/\\s+/);\n for (const c of classes) {\n out.push(`.${c}`);\n }\n }\n }\n const allowedAttrs = ['aria-label', 'type', 'name', 'title', 'alt'];\n for (const k of allowedAttrs) {\n const attr = elem.getAttribute(k);\n if (attr) {\n out.push(`[${k}=\"${attr}\"]`);\n }\n }\n\n return out.join('');\n}\n\n/**\n * A safe form of location.href\n */\nexport function getLocationHref(): string {\n try {\n return WINDOW.document.location.href;\n } catch {\n return '';\n }\n}\n\n/**\n * Given a DOM element, traverses up the tree until it finds the first ancestor node\n * that has the `data-sentry-component` or `data-sentry-element` attribute with `data-sentry-component` taking\n * precedence. This attribute is added at build-time by projects that have the component name annotation plugin installed.\n *\n * @returns a string representation of the component for the provided DOM element, or `null` if not found\n */\nexport function getComponentName(elem: unknown): string | null {\n // @ts-expect-error WINDOW has HTMLElement\n if (!WINDOW.HTMLElement) {\n return null;\n }\n\n let currentElem = elem as SimpleNode;\n const MAX_TRAVERSE_HEIGHT = 5;\n for (let i = 0; i < MAX_TRAVERSE_HEIGHT; i++) {\n if (!currentElem) {\n return null;\n }\n\n if (currentElem instanceof HTMLElement) {\n if (currentElem.dataset['sentryComponent']) {\n return currentElem.dataset['sentryComponent'];\n }\n if (currentElem.dataset['sentryElement']) {\n return currentElem.dataset['sentryElement'];\n }\n }\n\n currentElem = currentElem.parentNode;\n }\n\n return null;\n}\n","type DebouncedCallback = {\n (): void | unknown;\n flush: () => void | unknown;\n cancel: () => void;\n};\ntype CallbackFunction = () => unknown;\ntype DebounceOptions = {\n /** The max. time in ms to wait for the callback to be invoked. */\n maxWait?: number;\n /** This can be overwritten to use a different setTimeout implementation, e.g. to avoid triggering change detection in Angular */\n setTimeoutImpl?: typeof setTimeout;\n};\n\n/**\n * Heavily simplified debounce function based on lodash.debounce.\n *\n * This function takes a callback function (@param fun) and delays its invocation\n * by @param wait milliseconds. Optionally, a maxWait can be specified in @param options,\n * which ensures that the callback is invoked at least once after the specified max. wait time.\n *\n * @param func the function whose invocation is to be debounced\n * @param wait the minimum time until the function is invoked after it was called once\n * @param options the options object, which can contain the `maxWait` property\n *\n * @returns the debounced version of the function, which needs to be called at least once to start the\n * debouncing process. Subsequent calls will reset the debouncing timer and, in case @paramfunc\n * was already invoked in the meantime, return @param func's return value.\n * The debounced function has two additional properties:\n * - `flush`: Invokes the debounced function immediately and returns its return value\n * - `cancel`: Cancels the debouncing process and resets the debouncing timer\n */\nexport function debounce(func: CallbackFunction, wait: number, options?: DebounceOptions): DebouncedCallback {\n let callbackReturnValue: unknown;\n\n let timerId: ReturnType | undefined;\n let maxTimerId: ReturnType | undefined;\n\n const maxWait = options?.maxWait ? Math.max(options.maxWait, wait) : 0;\n const setTimeoutImpl = options?.setTimeoutImpl || setTimeout;\n\n function invokeFunc(): unknown {\n cancelTimers();\n callbackReturnValue = func();\n return callbackReturnValue;\n }\n\n function cancelTimers(): void {\n timerId !== undefined && clearTimeout(timerId);\n maxTimerId !== undefined && clearTimeout(maxTimerId);\n timerId = maxTimerId = undefined;\n }\n\n function flush(): unknown {\n if (timerId !== undefined || maxTimerId !== undefined) {\n return invokeFunc();\n }\n return callbackReturnValue;\n }\n\n function debounced(): unknown {\n if (timerId) {\n clearTimeout(timerId);\n }\n timerId = setTimeoutImpl(invokeFunc, wait);\n\n if (maxWait && maxTimerId === undefined) {\n maxTimerId = setTimeoutImpl(invokeFunc, maxWait);\n }\n\n return callbackReturnValue;\n }\n\n debounced.cancel = cancelTimers;\n debounced.flush = flush;\n return debounced;\n}\n","import { captureException } from '../../exports';\nimport { SPAN_STATUS_ERROR } from '../../tracing';\nimport type { Span } from '../../types-hoist/span';\nimport {\n GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE,\n GEN_AI_RESPONSE_ID_ATTRIBUTE,\n GEN_AI_RESPONSE_MODEL_ATTRIBUTE,\n GEN_AI_RESPONSE_STREAMING_ATTRIBUTE,\n GEN_AI_RESPONSE_TEXT_ATTRIBUTE,\n GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE,\n} from '../ai/gen-ai-attributes';\nimport { setTokenUsageAttributes } from '../ai/utils';\nimport type { AnthropicAiStreamingEvent } from './types';\n\n/**\n * State object used to accumulate information from a stream of Anthropic AI events.\n */\ninterface StreamingState {\n /** Collected response text fragments (for output recording). */\n responseTexts: string[];\n /** Reasons for finishing the response, as reported by the API. */\n finishReasons: string[];\n /** The response ID. */\n responseId: string;\n /** The model name. */\n responseModel: string;\n /** Number of prompt/input tokens used. */\n promptTokens: number | undefined;\n /** Number of completion/output tokens used. */\n completionTokens: number | undefined;\n /** Number of cache creation input tokens used. */\n cacheCreationInputTokens: number | undefined;\n /** Number of cache read input tokens used. */\n cacheReadInputTokens: number | undefined;\n /** Accumulated tool calls (finalized) */\n toolCalls: Array>;\n /** In-progress tool call blocks keyed by index */\n activeToolBlocks: Record<\n number,\n {\n id?: string;\n name?: string;\n inputJsonParts: string[];\n }\n >;\n}\n\n/**\n * Checks if an event is an error event\n * @param event - The event to process\n * @param state - The state of the streaming process\n * @param recordOutputs - Whether to record outputs\n * @param span - The span to update\n * @returns Whether an error occurred\n */\n\nfunction isErrorEvent(event: AnthropicAiStreamingEvent, span: Span): boolean {\n if ('type' in event && typeof event.type === 'string') {\n // If the event is an error, set the span status and capture the error\n // These error events are not rejected by the API by default, but are sent as metadata of the response\n if (event.type === 'error') {\n span.setStatus({ code: SPAN_STATUS_ERROR, message: event.error?.type ?? 'unknown_error' });\n captureException(event.error, {\n mechanism: {\n handled: false,\n type: 'auto.ai.anthropic.anthropic_error',\n },\n });\n return true;\n }\n }\n return false;\n}\n\n/**\n * Processes the message metadata of an event\n * @param event - The event to process\n * @param state - The state of the streaming process\n */\n\nfunction handleMessageMetadata(event: AnthropicAiStreamingEvent, state: StreamingState): void {\n // The token counts shown in the usage field of the message_delta event are cumulative.\n // @see https://docs.anthropic.com/en/docs/build-with-claude/streaming#event-types\n if (event.type === 'message_delta' && event.usage) {\n if ('output_tokens' in event.usage && typeof event.usage.output_tokens === 'number') {\n state.completionTokens = event.usage.output_tokens;\n }\n }\n\n if (event.message) {\n const message = event.message;\n\n if (message.id) state.responseId = message.id;\n if (message.model) state.responseModel = message.model;\n if (message.stop_reason) state.finishReasons.push(message.stop_reason);\n\n if (message.usage) {\n if (typeof message.usage.input_tokens === 'number') state.promptTokens = message.usage.input_tokens;\n if (typeof message.usage.cache_creation_input_tokens === 'number')\n state.cacheCreationInputTokens = message.usage.cache_creation_input_tokens;\n if (typeof message.usage.cache_read_input_tokens === 'number')\n state.cacheReadInputTokens = message.usage.cache_read_input_tokens;\n }\n }\n}\n\n/**\n * Handle start of a content block (e.g., tool_use)\n */\nfunction handleContentBlockStart(event: AnthropicAiStreamingEvent, state: StreamingState): void {\n if (event.type !== 'content_block_start' || typeof event.index !== 'number' || !event.content_block) return;\n if (event.content_block.type === 'tool_use' || event.content_block.type === 'server_tool_use') {\n state.activeToolBlocks[event.index] = {\n id: event.content_block.id,\n name: event.content_block.name,\n inputJsonParts: [],\n };\n }\n}\n\n/**\n * Handle deltas of a content block, including input_json_delta for tool_use\n */\nfunction handleContentBlockDelta(\n event: AnthropicAiStreamingEvent,\n state: StreamingState,\n recordOutputs: boolean,\n): void {\n if (event.type !== 'content_block_delta' || !event.delta) return;\n\n // Accumulate tool_use input JSON deltas only when we have an index and an active tool block\n if (\n typeof event.index === 'number' &&\n 'partial_json' in event.delta &&\n typeof event.delta.partial_json === 'string'\n ) {\n const active = state.activeToolBlocks[event.index];\n if (active) {\n active.inputJsonParts.push(event.delta.partial_json);\n }\n }\n\n // Accumulate streamed response text regardless of index\n if (recordOutputs && typeof event.delta.text === 'string') {\n state.responseTexts.push(event.delta.text);\n }\n}\n\n/**\n * Handle stop of a content block; finalize tool_use entries\n */\nfunction handleContentBlockStop(event: AnthropicAiStreamingEvent, state: StreamingState): void {\n if (event.type !== 'content_block_stop' || typeof event.index !== 'number') return;\n\n const active = state.activeToolBlocks[event.index];\n if (!active) return;\n\n const raw = active.inputJsonParts.join('');\n let parsedInput: unknown;\n\n try {\n parsedInput = raw ? JSON.parse(raw) : {};\n } catch {\n parsedInput = { __unparsed: raw };\n }\n\n state.toolCalls.push({\n type: 'tool_use',\n id: active.id,\n name: active.name,\n input: parsedInput,\n });\n\n // eslint-disable-next-line @typescript-eslint/no-dynamic-delete\n delete state.activeToolBlocks[event.index];\n}\n\n/**\n * Processes an event\n * @param event - The event to process\n * @param state - The state of the streaming process\n * @param recordOutputs - Whether to record outputs\n * @param span - The span to update\n */\nfunction processEvent(\n event: AnthropicAiStreamingEvent,\n state: StreamingState,\n recordOutputs: boolean,\n span: Span,\n): void {\n if (!(event && typeof event === 'object')) {\n return;\n }\n\n const isError = isErrorEvent(event, span);\n if (isError) return;\n\n handleMessageMetadata(event, state);\n\n // Tool call events are sent via 3 separate events:\n // - content_block_start (start of the tool call)\n // - content_block_delta (delta aka input of the tool call)\n // - content_block_stop (end of the tool call)\n // We need to handle them all to capture the full tool call.\n handleContentBlockStart(event, state);\n handleContentBlockDelta(event, state, recordOutputs);\n handleContentBlockStop(event, state);\n}\n\n/**\n * Finalizes span attributes when stream processing completes\n */\nfunction finalizeStreamSpan(state: StreamingState, span: Span, recordOutputs: boolean): void {\n if (!span.isRecording()) {\n return;\n }\n\n // Set common response attributes if available\n if (state.responseId) {\n span.setAttributes({\n [GEN_AI_RESPONSE_ID_ATTRIBUTE]: state.responseId,\n });\n }\n if (state.responseModel) {\n span.setAttributes({\n [GEN_AI_RESPONSE_MODEL_ATTRIBUTE]: state.responseModel,\n });\n }\n\n setTokenUsageAttributes(\n span,\n state.promptTokens,\n state.completionTokens,\n state.cacheCreationInputTokens,\n state.cacheReadInputTokens,\n );\n\n span.setAttributes({\n [GEN_AI_RESPONSE_STREAMING_ATTRIBUTE]: true,\n });\n\n if (state.finishReasons.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE]: JSON.stringify(state.finishReasons),\n });\n }\n\n if (recordOutputs && state.responseTexts.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_TEXT_ATTRIBUTE]: state.responseTexts.join(''),\n });\n }\n\n // Set tool calls if any were captured\n if (recordOutputs && state.toolCalls.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE]: JSON.stringify(state.toolCalls),\n });\n }\n\n span.end();\n}\n\n/**\n * Instruments an async iterable stream of Anthropic events, updates the span with\n * streaming attributes and (optionally) the aggregated output text, and yields\n * each event from the input stream unchanged.\n */\nexport async function* instrumentAsyncIterableStream(\n stream: AsyncIterable,\n span: Span,\n recordOutputs: boolean,\n): AsyncGenerator {\n const state: StreamingState = {\n responseTexts: [],\n finishReasons: [],\n responseId: '',\n responseModel: '',\n promptTokens: undefined,\n completionTokens: undefined,\n cacheCreationInputTokens: undefined,\n cacheReadInputTokens: undefined,\n toolCalls: [],\n activeToolBlocks: {},\n };\n\n try {\n for await (const event of stream) {\n processEvent(event, state, recordOutputs, span);\n yield event;\n }\n } finally {\n // Set common response attributes if available\n if (state.responseId) {\n span.setAttributes({\n [GEN_AI_RESPONSE_ID_ATTRIBUTE]: state.responseId,\n });\n }\n if (state.responseModel) {\n span.setAttributes({\n [GEN_AI_RESPONSE_MODEL_ATTRIBUTE]: state.responseModel,\n });\n }\n\n setTokenUsageAttributes(\n span,\n state.promptTokens,\n state.completionTokens,\n state.cacheCreationInputTokens,\n state.cacheReadInputTokens,\n );\n\n span.setAttributes({\n [GEN_AI_RESPONSE_STREAMING_ATTRIBUTE]: true,\n });\n\n if (state.finishReasons.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_FINISH_REASONS_ATTRIBUTE]: JSON.stringify(state.finishReasons),\n });\n }\n\n if (recordOutputs && state.responseTexts.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_TEXT_ATTRIBUTE]: state.responseTexts.join(''),\n });\n }\n\n // Set tool calls if any were captured\n if (recordOutputs && state.toolCalls.length > 0) {\n span.setAttributes({\n [GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE]: JSON.stringify(state.toolCalls),\n });\n }\n\n span.end();\n }\n}\n\n/**\n * Instruments a MessageStream by registering event handlers and preserving the original stream API.\n */\nexport function instrumentMessageStream void }>(\n stream: R,\n span: Span,\n recordOutputs: boolean,\n): R {\n const state: StreamingState = {\n responseTexts: [],\n finishReasons: [],\n responseId: '',\n responseModel: '',\n promptTokens: undefined,\n completionTokens: undefined,\n cacheCreationInputTokens: undefined,\n cacheReadInputTokens: undefined,\n toolCalls: [],\n activeToolBlocks: {},\n };\n\n stream.on('streamEvent', (event: unknown) => {\n processEvent(event as AnthropicAiStreamingEvent, state, recordOutputs, span);\n });\n\n // The event fired when a message is done being streamed by the API. Corresponds to the message_stop SSE event.\n // @see https://github.com/anthropics/anthropic-sdk-typescript/blob/d3be31f5a4e6ebb4c0a2f65dbb8f381ae73a9166/helpers.md?plain=1#L42-L44\n stream.on('message', () => {\n finalizeStreamSpan(state, span, recordOutputs);\n });\n\n stream.on('error', (error: unknown) => {\n captureException(error, {\n mechanism: {\n handled: false,\n type: 'auto.ai.anthropic.stream_error',\n },\n });\n\n if (span.isRecording()) {\n span.setStatus({ code: SPAN_STATUS_ERROR, message: 'stream_error' });\n span.end();\n }\n });\n\n return stream;\n}\n","import type { StackFrame } from '../types-hoist/stackframe';\nimport { filenameIsInApp } from './node-stack-trace';\nimport { UNKNOWN_FUNCTION } from './stacktrace';\n\ntype WatchdogReturn = {\n /** Resets the watchdog timer */\n poll: () => void;\n /** Enables or disables the watchdog timer */\n enabled: (state: boolean) => void;\n};\n\ntype CreateTimerImpl = () => { getTimeMs: () => number; reset: () => void };\n\n/**\n * A node.js watchdog timer\n * @param pollInterval The interval that we expect to get polled at\n * @param anrThreshold The threshold for when we consider ANR\n * @param callback The callback to call for ANR\n * @returns An object with `poll` and `enabled` functions {@link WatchdogReturn}\n */\nexport function watchdogTimer(\n createTimer: CreateTimerImpl,\n pollInterval: number,\n anrThreshold: number,\n callback: () => void,\n): WatchdogReturn {\n const timer = createTimer();\n let triggered = false;\n let enabled = true;\n\n setInterval(() => {\n const diffMs = timer.getTimeMs();\n\n if (triggered === false && diffMs > pollInterval + anrThreshold) {\n triggered = true;\n if (enabled) {\n callback();\n }\n }\n\n if (diffMs < pollInterval + anrThreshold) {\n triggered = false;\n }\n }, 20);\n\n return {\n poll: () => {\n timer.reset();\n },\n enabled: (state: boolean) => {\n enabled = state;\n },\n };\n}\n\n// types copied from inspector.d.ts\ninterface Location {\n scriptId: string;\n lineNumber: number;\n columnNumber?: number;\n}\n\ninterface CallFrame {\n functionName: string;\n location: Location;\n url: string;\n}\n\n/**\n * Converts Debugger.CallFrame to Sentry StackFrame\n */\nexport function callFrameToStackFrame(\n frame: CallFrame,\n url: string | undefined,\n getModuleFromFilename: (filename: string | undefined) => string | undefined,\n): StackFrame {\n const filename = url ? url.replace(/^file:\\/\\//, '') : undefined;\n\n // CallFrame row/col are 0 based, whereas StackFrame are 1 based\n const colno = frame.location.columnNumber ? frame.location.columnNumber + 1 : undefined;\n const lineno = frame.location.lineNumber ? frame.location.lineNumber + 1 : undefined;\n\n return {\n filename,\n module: getModuleFromFilename(filename),\n function: frame.functionName || UNKNOWN_FUNCTION,\n colno,\n lineno,\n in_app: filename ? filenameIsInApp(filename) : undefined,\n };\n}\n"],"sourceRoot":""}