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feat(node): Add LangChain v1 support
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11 files changed

+1021
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‎dev-packages/node-integration-tests/suites/tracing/langchain/v1/instrument-with-pii.mjs‎

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import * as Sentry from '@sentry/node';
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import { loggingTransport } from '@sentry-internal/node-integration-tests';
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Sentry.init({
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dsn: 'https://public@dsn.ingest.sentry.io/1337',
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release: '1.0',
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tracesSampleRate: 1.0,
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sendDefaultPii: true,
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transport: loggingTransport,
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beforeSendTransaction: event => {
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// Filter out mock express server transactions
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if (event.transaction.includes('/v1/messages') || event.transaction.includes('/v1/chat/completions')) {
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return null;
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}
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return event;
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},
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});

‎dev-packages/node-integration-tests/suites/tracing/langchain/v1/instrument.mjs‎

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import * as Sentry from '@sentry/node';
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import { loggingTransport } from '@sentry-internal/node-integration-tests';
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Sentry.init({
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dsn: 'https://public@dsn.ingest.sentry.io/1337',
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release: '1.0',
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tracesSampleRate: 1.0,
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sendDefaultPii: false,
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transport: loggingTransport,
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beforeSendTransaction: event => {
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// Filter out mock express server transactions
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if (event.transaction.includes('/v1/messages') || event.transaction.includes('/v1/chat/completions')) {
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return null;
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}
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return event;
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},
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});

‎dev-packages/node-integration-tests/suites/tracing/langchain/v1/scenario-init-chat-model.mjs‎

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import * as Sentry from '@sentry/node';
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import express from 'express';
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import { initChatModel } from 'langchain';
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function startMockOpenAIServer() {
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const app = express();
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app.use(express.json());
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app.post('/v1/chat/completions', (req, res) => {
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const model = req.body.model;
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if (model === 'error-model') {
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res.status(404).json({
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error: {
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message: 'Model not found',
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type: 'invalid_request_error',
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param: null,
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code: 'model_not_found',
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},
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});
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return;
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}
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// Simulate OpenAI response
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res.json({
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id: 'chatcmpl-init-test-123',
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object: 'chat.completion',
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created: 1677652288,
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model: model,
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system_fingerprint: 'fp_44709d6fcb',
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choices: [
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{
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index: 0,
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message: {
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role: 'assistant',
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content: 'Hello from initChatModel!',
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},
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finish_reason: 'stop',
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},
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],
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usage: {
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prompt_tokens: 8,
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completion_tokens: 12,
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total_tokens: 20,
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},
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});
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});
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return new Promise(resolve => {
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const server = app.listen(0, () => {
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resolve(server);
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});
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});
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}
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async function run() {
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const server = await startMockOpenAIServer();
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const baseUrl = `http://localhost:${server.address().port}/v1`;
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await Sentry.startSpan({ op: 'function', name: 'main' }, async () => {
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// Set OpenAI API key in environment
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process.env.OPENAI_API_KEY = 'mock-api-key';
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// Test 1: Initialize chat model using unified API with model string
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const model1 = await initChatModel('gpt-4o', {
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temperature: 0.7,
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maxTokens: 100,
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modelProvider: 'openai',
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configurableFields: ['model'],
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configuration: {
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baseURL: baseUrl,
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},
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});
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await model1.invoke('Tell me about LangChain');
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// Test 2: Initialize with different model
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const model2 = await initChatModel('gpt-3.5-turbo', {
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temperature: 0.5,
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modelProvider: 'openai',
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configuration: {
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baseURL: baseUrl,
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},
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});
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await model2.invoke([
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{ role: 'system', content: 'You are a helpful assistant' },
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{ role: 'user', content: 'What is AI?' },
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]);
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// Test 3: Error handling
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try {
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const errorModel = await initChatModel('error-model', {
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modelProvider: 'openai',
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configuration: {
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baseURL: baseUrl,
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},
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});
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await errorModel.invoke('This will fail');
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} catch {
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// Expected error
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}
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});
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await Sentry.flush(2000);
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server.close();
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}
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run();

‎dev-packages/node-integration-tests/suites/tracing/langchain/v1/scenario-message-truncation.mjs‎

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import { ChatAnthropic } from '@langchain/anthropic';
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import * as Sentry from '@sentry/node';
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import express from 'express';
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function startMockAnthropicServer() {
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const app = express();
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app.use(express.json());
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app.post('/v1/messages', (req, res) => {
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const model = req.body.model;
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res.json({
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id: 'msg_truncation_test',
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type: 'message',
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role: 'assistant',
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content: [
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{
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type: 'text',
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text: 'Response to truncated messages',
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},
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],
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model: model,
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stop_reason: 'end_turn',
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stop_sequence: null,
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usage: {
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input_tokens: 10,
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output_tokens: 15,
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},
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});
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});
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return new Promise(resolve => {
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const server = app.listen(0, () => {
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resolve(server);
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});
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});
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}
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async function run() {
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const server = await startMockAnthropicServer();
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const baseUrl = `http://localhost:${server.address().port}`;
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await Sentry.startSpan({ op: 'function', name: 'main' }, async () => {
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const model = new ChatAnthropic({
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model: 'claude-3-5-sonnet-20241022',
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apiKey: 'mock-api-key',
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clientOptions: {
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baseURL: baseUrl,
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},
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});
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const largeContent1 = 'A'.repeat(15000); // ~15KB
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const largeContent2 = 'B'.repeat(15000); // ~15KB
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const largeContent3 = 'C'.repeat(25000); // ~25KB (will be truncated)
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// Create one very large string that gets truncated to only include Cs
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await model.invoke(largeContent3 + largeContent2);
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// Create an array of messages that gets truncated to only include the last message (result should again contain only Cs)
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await model.invoke([
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{ role: 'system', content: largeContent1 },
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{ role: 'user', content: largeContent2 },
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{ role: 'user', content: largeContent3 },
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]);
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});
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await Sentry.flush(2000);
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server.close();
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}
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run();

‎dev-packages/node-integration-tests/suites/tracing/langchain/v1/scenario-openai-before-langchain.mjs‎

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import * as Sentry from '@sentry/node';
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import express from 'express';
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function startMockAnthropicServer() {
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const app = express();
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app.use(express.json());
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app.post('/v1/messages', (req, res) => {
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res.json({
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id: 'msg_test123',
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type: 'message',
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role: 'assistant',
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content: [
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{
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type: 'text',
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text: 'Mock response from Anthropic!',
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},
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],
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model: req.body.model,
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stop_reason: 'end_turn',
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stop_sequence: null,
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usage: {
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input_tokens: 10,
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output_tokens: 15,
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},
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});
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});
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return new Promise(resolve => {
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const server = app.listen(0, () => {
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resolve(server);
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});
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});
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}
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async function run() {
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const server = await startMockAnthropicServer();
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const baseURL = `http://localhost:${server.address().port}`;
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await Sentry.startSpan({ op: 'function', name: 'main' }, async () => {
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// EDGE CASE: Import and instantiate Anthropic client BEFORE LangChain is imported
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// This simulates the timing issue where a user creates an Anthropic client in one file
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// before importing LangChain in another file
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const { default: Anthropic } = await import('@anthropic-ai/sdk');
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const anthropicClient = new Anthropic({
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apiKey: 'mock-api-key',
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baseURL,
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});
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// Use the Anthropic client directly - this will be instrumented by the Anthropic integration
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await anthropicClient.messages.create({
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model: 'claude-3-5-sonnet-20241022',
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messages: [{ role: 'user', content: 'Direct Anthropic call' }],
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temperature: 0.7,
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max_tokens: 100,
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});
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// NOW import LangChain - at this point it will mark Anthropic to be skipped
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// But the client created above is already instrumented
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const { ChatAnthropic } = await import('@langchain/anthropic');
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// Create a LangChain model - this uses Anthropic under the hood
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const langchainModel = new ChatAnthropic({
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model: 'claude-3-5-sonnet-20241022',
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temperature: 0.7,
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maxTokens: 100,
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apiKey: 'mock-api-key',
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clientOptions: {
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baseURL,
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},
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});
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// Use LangChain - this will be instrumented by LangChain integration
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await langchainModel.invoke('LangChain Anthropic call');
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// Create ANOTHER Anthropic client after LangChain was imported
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// This one should NOT be instrumented (skip mechanism works correctly)
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const anthropicClient2 = new Anthropic({
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apiKey: 'mock-api-key',
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baseURL,
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});
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await anthropicClient2.messages.create({
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model: 'claude-3-5-sonnet-20241022',
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messages: [{ role: 'user', content: 'Second direct Anthropic call' }],
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temperature: 0.7,
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max_tokens: 100,
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});
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});
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await Sentry.flush(2000);
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server.close();
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}
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run();

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