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feat(core): Instrument langgraph createReactAgent
This PR adds instrumentation for LangGraph's `createReactAgent` API. It: - extracts agent name, LLM model, and tools from params - wraps compiled graph's `invoke()` with invoke_agent span - wraps tool `invoke()` with execute_tool spans (name, type, description, arguments, result per our new conventions) - injects LangChain callback handler + `lc_agent_name` metadata at invoke level for chat span creation and agent name propagation to all child spans - suppresses `StateGraph.compile` instrumentation inside `createReactAgent` to avoid duplicate spans The LangChain callback handler was improved to: - read gen_ai.agent.name from `metadata.lc_agent_name` (this is a convention in newer versions of LangGraph createAgent so we took this over for our supported version) - suppresses chain and tool callback spans inside agent context to avoid duplicates with our direct instrumentation (based on `metadata.loc_agent_name` presence) - extracts tool definitions from `extraParams` in `handleChatModelStart` - supports OpenAI tool calls via `message.tool_calls` (not just Anthropic content scanning) - normalizes tool call format and use our new convention attributes - uses `runName` for tool name (set by LangChain's StructuredTool) It exports: - `instrumentCreateReactAgent` from core, browser, cloudflare - New constants: - GEN_AI_TOOL_CALL_ARGUMENTS_ATTRIBUTE, - GEN_AI_TOOL_CALL_RESULT_ATTRIBUTE Closes: #19372
1 parent 50438f9 commit 7bcbcae

14 files changed

Lines changed: 733 additions & 54 deletions

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‎dev-packages/node-integration-tests/suites/tracing/langgraph/agent-scenario.mjs‎

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import { ChatAnthropic } from '@langchain/anthropic';
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import { HumanMessage, SystemMessage } from '@langchain/core/messages';
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import { createReactAgent } from '@langchain/langgraph/prebuilt';
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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_react_agent_123',
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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: 'Paris is the capital of France.',
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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: 20,
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output_tokens: 10,
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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 llm = 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 agent = createReactAgent({ llm, tools: [], name: 'helpful_assistant' });
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await agent.invoke({
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messages: [new SystemMessage('You are a helpful assistant.'), new HumanMessage('What is the capital of France?')],
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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/langgraph/agent-tools-scenario.mjs‎

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import { tool } from '@langchain/core/tools';
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import { ChatAnthropic } from '@langchain/anthropic';
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import { createReactAgent } from '@langchain/langgraph/prebuilt';
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import { HumanMessage } from '@langchain/core/messages';
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import * as Sentry from '@sentry/node';
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import express from 'express';
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import { z } from 'zod';
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let callCount = 0;
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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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callCount++;
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const model = req.body.model;
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if (callCount === 1) {
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// First call: model decides to call the "add" tool
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res.json({
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id: 'msg_1',
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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: 'tool_use',
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id: 'toolu_add_1',
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name: 'add',
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input: { a: 3, b: 5 },
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},
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],
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model: model,
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stop_reason: 'tool_use',
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usage: { input_tokens: 20, output_tokens: 10 },
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});
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} else if (callCount === 2) {
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// Second call: model sees add result=8, calls "multiply"
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res.json({
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id: 'msg_2',
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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: 'tool_use',
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id: 'toolu_mul_1',
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name: 'multiply',
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input: { a: 8, b: 4 },
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},
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],
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model: model,
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stop_reason: 'tool_use',
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usage: { input_tokens: 30, output_tokens: 10 },
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});
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} else {
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// Third call: model returns final answer
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res.json({
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id: 'msg_3',
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type: 'message',
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role: 'assistant',
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content: [{ type: 'text', text: 'The result is 32.' }],
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model: model,
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stop_reason: 'end_turn',
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usage: { input_tokens: 40, output_tokens: 10 },
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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, () => resolve(server));
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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 llm = 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: { baseURL: baseUrl },
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});
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const addTool = tool(
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async ({ a, b }) => {
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return String(a + b);
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},
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{
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name: 'add',
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description: 'Add two numbers',
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schema: z.object({ a: z.number(), b: z.number() }),
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},
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);
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const multiplyTool = tool(
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async ({ a, b }) => {
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return String(a * b);
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},
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{
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name: 'multiply',
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description: 'Multiply two numbers',
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schema: z.object({ a: z.number(), b: z.number() }),
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},
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);
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const agent = createReactAgent({
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llm,
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tools: [addTool, multiplyTool],
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name: 'math_assistant',
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});
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await agent.invoke({
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messages: [new HumanMessage('Calculate (3 + 5) * 4')],
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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/langgraph/instrument-agent.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 && event.transaction.includes('/v1/messages')) {
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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/langgraph/test.ts‎

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});
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},
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);
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// createReactAgent tests
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const EXPECTED_TRANSACTION_REACT_AGENT = {
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transaction: 'main',
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spans: expect.arrayContaining([
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// invoke_agent span (no create_agent span expected)
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expect.objectContaining({
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data: expect.objectContaining({
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[GEN_AI_OPERATION_NAME_ATTRIBUTE]: 'invoke_agent',
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[SEMANTIC_ATTRIBUTE_SENTRY_OP]: 'gen_ai.invoke_agent',
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[SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: 'auto.ai.langgraph',
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[GEN_AI_AGENT_NAME_ATTRIBUTE]: 'helpful_assistant',
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[GEN_AI_PIPELINE_NAME_ATTRIBUTE]: 'helpful_assistant',
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}),
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description: 'invoke_agent helpful_assistant',
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op: 'gen_ai.invoke_agent',
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origin: 'auto.ai.langgraph',
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status: 'ok',
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}),
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// chat span (from Anthropic integration) should be a child with inherited agent name
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expect.objectContaining({
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data: expect.objectContaining({
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[GEN_AI_AGENT_NAME_ATTRIBUTE]: 'helpful_assistant',
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}),
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op: 'gen_ai.chat',
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}),
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]),
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};
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createEsmAndCjsTests(__dirname, 'agent-scenario.mjs', 'instrument-agent.mjs', (createRunner, test) => {
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test('should instrument createReactAgent with agent and chat spans', { timeout: 30000 }, async () => {
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await createRunner()
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.ignore('event')
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.expect({ transaction: EXPECTED_TRANSACTION_REACT_AGENT })
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.start()
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.completed();
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});
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});
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// createReactAgent with tools - verifies tool execution spans
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const EXPECTED_TRANSACTION_REACT_AGENT_TOOLS = {
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transaction: 'main',
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spans: expect.arrayContaining([
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// invoke_agent span
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expect.objectContaining({
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data: expect.objectContaining({
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[GEN_AI_OPERATION_NAME_ATTRIBUTE]: 'invoke_agent',
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[GEN_AI_AGENT_NAME_ATTRIBUTE]: 'math_assistant',
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}),
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op: 'gen_ai.invoke_agent',
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status: 'ok',
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}),
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// execute_tool span for "add"
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expect.objectContaining({
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data: expect.objectContaining({
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[GEN_AI_OPERATION_NAME_ATTRIBUTE]: 'execute_tool',
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'gen_ai.tool.name': 'add',
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}),
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description: 'execute_tool add',
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op: 'gen_ai.execute_tool',
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status: 'ok',
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}),
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// execute_tool span for "multiply"
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expect.objectContaining({
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data: expect.objectContaining({
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[GEN_AI_OPERATION_NAME_ATTRIBUTE]: 'execute_tool',
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'gen_ai.tool.name': 'multiply',
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}),
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description: 'execute_tool multiply',
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op: 'gen_ai.execute_tool',
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status: 'ok',
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}),
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]),
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};
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createEsmAndCjsTests(__dirname, 'agent-tools-scenario.mjs', 'instrument-agent.mjs', (createRunner, test) => {
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test('should create tool execution spans for createReactAgent with tools', { timeout: 30000 }, async () => {
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await createRunner()
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.ignore('event')
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.expect({ transaction: EXPECTED_TRANSACTION_REACT_AGENT_TOOLS })
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.start()
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.completed();
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});
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});
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});

‎packages/browser/src/index.ts‎

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instrumentOpenAiClient,
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instrumentGoogleGenAIClient,
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instrumentLangGraph,
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instrumentCreateReactAgent,
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createLangChainCallbackHandler,
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instrumentLangChainEmbeddings,
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logger,

‎packages/cloudflare/src/index.ts‎

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withStreamedSpan,
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spanStreamingIntegration,
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instrumentLangGraph,
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instrumentCreateReactAgent,
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} from '@sentry/core';
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export { withSentry } from './withSentry';

‎packages/core/src/index.ts‎

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export { createLangChainCallbackHandler, instrumentLangChainEmbeddings } from './tracing/langchain';
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export { LANGCHAIN_INTEGRATION_NAME } from './tracing/langchain/constants';
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export type { LangChainOptions, LangChainIntegration } from './tracing/langchain/types';
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export { instrumentStateGraphCompile, instrumentLangGraph } from './tracing/langgraph';
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export { instrumentStateGraphCompile, instrumentCreateReactAgent, instrumentLangGraph } from './tracing/langgraph';
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export { LANGGRAPH_INTEGRATION_NAME } from './tracing/langgraph/constants';
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export type { LangGraphOptions, LangGraphIntegration, CompiledGraph } from './tracing/langgraph/types';
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export type { OpenAiClient, OpenAiOptions, InstrumentedMethod } from './tracing/openai/types';

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