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feat(core): Instrument langgraph createReactAgent (#20344)
This PR adds instrumentation for LangGraph's `createReactAgent` API. ## createReactAgent wrapping - 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) - Injects LangChain callback handler + `lc_agent_name` + `__sentry_langgraph__` 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 ## LangChain callback handler improvements - Reads `gen_ai.agent.name` from `metadata.lc_agent_name` (convention from newer LangGraph `createAgent`, adopted for our supported versions) - Suppresses chain and tool callback spans inside agent context (based on `metadata.__sentry_langgraph__` presence) to avoid duplicates with our direct instrumentation - Extracts tool definitions from `extraParams` in `handleChatModelStart` and sets `gen_ai.request.available_tools` on chat spans - Uses `runName` for tool name in `handleToolStart` (set by LangChain's `StructuredTool.call()`) — fixes `unknown_tool` issue - Adds `gen_ai.operation.name` to tool spans - Extracts `.content` from ToolMessage objects in `handleToolEnd` instead of serializing the full wrapper - `addToolCallsAttributes` now prefers `message.tool_calls` (LangChain's normalized format) over scanning `message.content` for Anthropic-style `tool_use` items, fixing duplicate tool calls on Anthropic chat spans. Falls back to `message.content` scanning for older LangChain versions. ## OTel module patching - Patches `@langchain/langgraph/prebuilt` for `createReactAgent` (ESM + CJS file patches for `dist/prebuilt/index.cjs`) ## Exports - `instrumentCreateReactAgent` from core, browser, cloudflare Closes: #19372
1 parent 225751a commit a84b2f1

16 files changed

Lines changed: 933 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://[email protected]/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/scenario-stategraph-chat.mjs‎

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import { ChatAnthropic } from '@langchain/anthropic';
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import { END, MessagesAnnotation, START, StateGraph } from '@langchain/langgraph';
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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_stategraph_chat_1',
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type: 'message',
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role: 'assistant',
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content: [{ type: 'text', text: 'Hello from mock.' }],
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model: req.body.model,
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stop_reason: 'end_turn',
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usage: { input_tokens: 5, output_tokens: 3 },
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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 callLlm = async state => {
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const response = await llm.invoke(state.messages);
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return { messages: [response] };
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};
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const graph = new StateGraph(MessagesAnnotation)
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.addNode('agent', callLlm)
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.addEdge(START, 'agent')
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.addEdge('agent', END)
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.compile({ name: 'plain_assistant' });
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await graph.invoke({ messages: [{ role: 'user', content: 'Hi.' }] });
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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/test.ts‎

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GEN_AI_RESPONSE_TEXT_ATTRIBUTE,
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GEN_AI_RESPONSE_TOOL_CALLS_ATTRIBUTE,
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GEN_AI_SYSTEM_INSTRUCTIONS_ATTRIBUTE,
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GEN_AI_TOOL_NAME_ATTRIBUTE,
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GEN_AI_USAGE_INPUT_TOKENS_ATTRIBUTE,
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GEN_AI_USAGE_OUTPUT_TOKENS_ATTRIBUTE,
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GEN_AI_USAGE_TOTAL_TOKENS_ATTRIBUTE,
@@ -445,4 +446,111 @@ describe('LangGraph integration', () => {
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});
446447
},
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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: [
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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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expect.objectContaining({ op: 'http.client' }),
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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: [
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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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expect.objectContaining({ op: 'http.client' }),
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expect.objectContaining({ op: 'gen_ai.chat' }),
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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_ATTRIBUTE]: 'add',
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'gen_ai.tool.type': 'function',
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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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expect.objectContaining({ op: 'http.client' }),
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expect.objectContaining({ op: 'gen_ai.chat' }),
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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_ATTRIBUTE]: 'multiply',
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'gen_ai.tool.type': 'function',
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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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expect.objectContaining({ op: 'http.client' }),
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expect.objectContaining({ op: 'gen_ai.chat' }),
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],
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};
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createEsmAndCjsTests(__dirname, 'agent-tools-scenario.mjs', 'instrument-agent.mjs', (createRunner, test) => {
529+
test('should create tool execution spans for createReactAgent with tools', { timeout: 30000 }, async () => {
530+
await createRunner()
531+
.ignore('event')
532+
.expect({ transaction: EXPECTED_TRANSACTION_REACT_AGENT_TOOLS })
533+
.start()
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.completed();
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});
536+
});
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createEsmAndCjsTests(__dirname, 'scenario-stategraph-chat.mjs', 'instrument-agent.mjs', (createRunner, test) => {
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test('auto-injects langchain handler for plain StateGraph and emits chat spans', { timeout: 30000 }, async () => {
540+
await createRunner()
541+
.ignore('event')
542+
.expect({
543+
transaction: event => {
544+
const spans = event.spans ?? [];
545+
const chatSpans = spans.filter(s => s.op === 'gen_ai.chat');
546+
expect(chatSpans).toHaveLength(1);
547+
expect(chatSpans[0]?.data).toMatchObject({
548+
[GEN_AI_AGENT_NAME_ATTRIBUTE]: 'plain_assistant',
549+
});
550+
},
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})
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.start()
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.completed();
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});
555+
});
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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';

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