Marc Mintel
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{"version":3,"file":"index.js","sources":["../../../../src/tracing/anthropic-ai/index.ts"],"sourcesContent":["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 { handleCallbackErrors } from '../../utils/handleCallbackErrors';\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_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 { instrumentAsyncIterableStream, instrumentMessageStream } from './streaming';\nimport type {\n AnthropicAiInstrumentedMethod,\n AnthropicAiOptions,\n AnthropicAiResponse,\n AnthropicAiStreamingEvent,\n ContentBlock,\n} from './types';\nimport { handleResponseError, messagesFromParams, setMessagesAttribute, shouldInstrument } from './utils';\n\n/**\n * Extract request attributes from method arguments\n */\nfunction extractRequestAttributes(args: unknown[], methodPath: string): Record<string, unknown> {\n const attributes: Record<string, unknown> = {\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<string, unknown>;\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 */\nfunction addPrivateRequestAttributes(span: Span, params: Record<string, unknown>): void {\n const messages = messagesFromParams(params);\n setMessagesAttribute(span, messages);\n\n if ('prompt' in params) {\n span.setAttributes({ [GEN_AI_PROMPT_ATTRIBUTE]: JSON.stringify(params.prompt) });\n }\n}\n\n/**\n * Add content attributes when recordOutputs is enabled\n */\nfunction 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<ContentBlock> = [];\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 response\n */\nfunction addMetadataAttributes(span: Span, response: AnthropicAiResponse): void {\n if ('id' in response && 'model' in response) {\n span.setAttributes({\n [GEN_AI_RESPONSE_ID_ATTRIBUTE]: response.id,\n [GEN_AI_RESPONSE_MODEL_ATTRIBUTE]: response.model,\n });\n\n if ('created' in response && typeof response.created === 'number') {\n span.setAttributes({\n [ANTHROPIC_AI_RESPONSE_TIMESTAMP_ATTRIBUTE]: new Date(response.created * 1000).toISOString(),\n });\n }\n if ('created_at' in response && typeof response.created_at === 'number') {\n span.setAttributes({\n [ANTHROPIC_AI_RESPONSE_TIMESTAMP_ATTRIBUTE]: new Date(response.created_at * 1000).toISOString(),\n });\n }\n\n if ('usage' in response && response.usage) {\n setTokenUsageAttributes(\n span,\n response.usage.input_tokens,\n response.usage.output_tokens,\n response.usage.cache_creation_input_tokens,\n response.usage.cache_read_input_tokens,\n );\n }\n }\n}\n\n/**\n * Add response attributes to spans\n */\nfunction 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<T extends unknown[], R>(\n originalMethod: (...args: T) => R | Promise<R>,\n target: (...args: T) => R | Promise<R>,\n context: unknown,\n args: T,\n requestAttributes: Record<string, unknown>,\n operationName: string,\n methodPath: string,\n params: Record<string, unknown> | undefined,\n options: AnthropicAiOptions,\n isStreamRequested: boolean,\n isStreamingMethod: boolean,\n): R | Promise<R> {\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<string, SpanAttributeValue>,\n };\n\n // messages.stream() always returns a sync MessageStream, even with stream: true param\n if (isStreamRequested && !isStreamingMethod) {\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<AnthropicAiStreamingEvent>,\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<T extends unknown[], R>(\n originalMethod: (...args: T) => R | Promise<R>,\n methodPath: AnthropicAiInstrumentedMethod,\n context: unknown,\n options: AnthropicAiOptions,\n): (...args: T) => R | Promise<R> {\n return new Proxy(originalMethod, {\n apply(target, thisArg, args: T): R | Promise<R> {\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' ? 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AnthropicAiResponse, options.recordOutputs),\n );\n },\n );\n },\n }) as (...args: T) => R | Promise<R>;\n}\n\n/**\n * Create a deep proxy for Anthropic AI client instrumentation\n */\nfunction createDeepProxy<T extends object>(target: T, currentPath = '', options: AnthropicAiOptions): T {\n return new Proxy(target, {\n get(obj: object, prop: string): unknown {\n const value = (obj as Record<string, unknown>)[prop];\n const methodPath = buildMethodPath(currentPath, String(prop));\n\n if (typeof value === 'function' && shouldInstrument(methodPath)) {\n return instrumentMethod(value as (...args: unknown[]) => unknown | Promise<unknown>, 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<T extends object>(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, '', 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