feat(cloudflare/vercel-edge): Add manual instrumentation for LangGraph

.size-limit.js

@@ -240,7 +240,7 @@ module.exports = [
     import: createImport('init'),
     ignore: [...builtinModules, ...nodePrefixedBuiltinModules],
     gzip: true,
-    limit: '158 KB',
+    limit: '159 KB',
   },
   {
     name: '@sentry/node - without tracing',

dev-packages/cloudflare-integration-tests/package.json

@@ -13,6 +13,7 @@
     "test:watch": "yarn test --watch"
   },
   "dependencies": {
+    "@langchain/langgraph": "^1.0.1",
     "@sentry/cloudflare": "10.23.0"
   },
   "devDependencies": {

dev-packages/cloudflare-integration-tests/suites/tracing/langgraph/index.ts

@@ -0,0 +1,66 @@
+import { END, MessagesAnnotation, START, StateGraph } from '@langchain/langgraph';
+import * as Sentry from '@sentry/cloudflare';
+
+interface Env {
+  SENTRY_DSN: string;
+}
+
+export default Sentry.withSentry(
+  (env: Env) => ({
+    dsn: env.SENTRY_DSN,
+    tracesSampleRate: 1.0,
+    sendDefaultPii: true,
+  }),
+  {
+    async fetch(_request, _env, _ctx) {
+      // Define simple mock LLM function
+      const mockLlm = (): {
+        messages: {
+          role: string;
+          content: string;
+          response_metadata: {
+            model_name: string;
+            finish_reason: string;
+            tokenUsage: { promptTokens: number; completionTokens: number; totalTokens: number };
+          };
+          tool_calls: never[];
+        }[];
+      } => {
+        return {
+          messages: [
+            {
+              role: 'assistant',
+              content: 'Mock response from LangGraph agent',
+              response_metadata: {
+                model_name: 'mock-model',
+                finish_reason: 'stop',
+                tokenUsage: {
+                  promptTokens: 20,
+                  completionTokens: 10,
+                  totalTokens: 30,
+                },
+              },
+              tool_calls: [],
+            },
+          ],
+        };
+      };
+
+      // Create and instrument the graph
+      const graph = new StateGraph(MessagesAnnotation)
+        .addNode('agent', mockLlm)
+        .addEdge(START, 'agent')
+        .addEdge('agent', END);
+
+      Sentry.instrumentLangGraph(graph, { recordInputs: true, recordOutputs: true });
+
+      const compiled = graph.compile({ name: 'weather_assistant' });
+
+      await compiled.invoke({
+        messages: [{ role: 'user', content: 'What is the weather in SF?' }],
+      });
+
+      return new Response(JSON.stringify({ success: true }));
+    },
+  },
+);

dev-packages/cloudflare-integration-tests/suites/tracing/langgraph/test.ts

@@ -0,0 +1,59 @@
+import { expect, it } from 'vitest';
+import { createRunner } from '../../../runner';
+
+// These tests are not exhaustive because the instrumentation is
+// already tested in the node integration tests and we merely
+// want to test that the instrumentation does not break in our
+// cloudflare SDK.
+
+it('traces langgraph compile and invoke operations', async ({ signal }) => {
+  const runner = createRunner(__dirname)
+    .ignore('event')
+    .expect(envelope => {
+      const transactionEvent = envelope[1]?.[0]?.[1] as any;
+
+      expect(transactionEvent.transaction).toBe('GET /');
+
+      // Check create_agent span
+      const createAgentSpan = transactionEvent.spans.find((span: any) => span.op === 'gen_ai.create_agent');
+      expect(createAgentSpan).toMatchObject({
+        data: {
+          'gen_ai.operation.name': 'create_agent',
+          'sentry.op': 'gen_ai.create_agent',
+          'sentry.origin': 'auto.ai.langgraph',
+          'gen_ai.agent.name': 'weather_assistant',
+        },
+        description: 'create_agent weather_assistant',
+        op: 'gen_ai.create_agent',
+        origin: 'auto.ai.langgraph',
+      });
+
+      // Check invoke_agent span
+      const invokeAgentSpan = transactionEvent.spans.find((span: any) => span.op === 'gen_ai.invoke_agent');
+      expect(invokeAgentSpan).toMatchObject({
+        data: expect.objectContaining({
+          'gen_ai.operation.name': 'invoke_agent',
+          'sentry.op': 'gen_ai.invoke_agent',
+          'sentry.origin': 'auto.ai.langgraph',
+          'gen_ai.agent.name': 'weather_assistant',
+          'gen_ai.pipeline.name': 'weather_assistant',
+          'gen_ai.request.messages': '[{"role":"user","content":"What is the weather in SF?"}]',
+          'gen_ai.response.model': 'mock-model',
+          'gen_ai.usage.input_tokens': 20,
+          'gen_ai.usage.output_tokens': 10,
+          'gen_ai.usage.total_tokens': 30,
+        }),
+        description: 'invoke_agent weather_assistant',
+        op: 'gen_ai.invoke_agent',
+        origin: 'auto.ai.langgraph',
+      });
+
+      // Verify tools are captured
+      if (invokeAgentSpan.data['gen_ai.request.available_tools']) {
+        expect(invokeAgentSpan.data['gen_ai.request.available_tools']).toMatch(/get_weather/);
+      }
+    })
+    .start(signal);
+  await runner.makeRequest('get', '/');
+  await runner.completed();
+});

dev-packages/cloudflare-integration-tests/suites/tracing/langgraph/wrangler.jsonc

@@ -0,0 +1,7 @@
+{
+  "name": "worker-name",
+  "compatibility_date": "2025-06-17",
+  "main": "index.ts",
+  "compatibility_flags": ["nodejs_compat"]
+}
+

dev-packages/node-integration-tests/package.json

@@ -31,6 +31,7 @@
     "@hono/node-server": "^1.19.4",
     "@langchain/anthropic": "^0.3.10",
     "@langchain/core": "^0.3.28",
+    "@langchain/langgraph": "^0.2.32",
     "@nestjs/common": "^11",
     "@nestjs/core": "^11",
     "@nestjs/platform-express": "^11",

dev-packages/node-integration-tests/suites/tracing/langgraph/instrument-with-pii.mjs

@@ -0,0 +1,10 @@
+import * as Sentry from '@sentry/node';
+import { loggingTransport } from '@sentry-internal/node-integration-tests';
+
+Sentry.init({
+  dsn: 'https://public@dsn.ingest.sentry.io/1337',
+  release: '1.0',
+  tracesSampleRate: 1.0,
+  sendDefaultPii: true,
+  transport: loggingTransport,
+});

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

@@ -0,0 +1,10 @@
+import * as Sentry from '@sentry/node';
+import { loggingTransport } from '@sentry-internal/node-integration-tests';
+
+Sentry.init({
+  dsn: 'https://public@dsn.ingest.sentry.io/1337',
+  release: '1.0',
+  tracesSampleRate: 1.0,
+  sendDefaultPii: false,
+  transport: loggingTransport,
+});

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

@@ -0,0 +1,92 @@
+import { tool } from '@langchain/core/tools';
+import { END, MessagesAnnotation, START, StateGraph } from '@langchain/langgraph';
+import { ToolNode } from '@langchain/langgraph/prebuilt';
+import * as Sentry from '@sentry/node';
+import { z } from 'zod';
+
+async function run() {
+  await Sentry.startSpan({ op: 'function', name: 'langgraph-tools-test' }, async () => {
+    // Define tools
+    const getWeatherTool = tool(
+      async ({ city }) => {
+        return JSON.stringify({ city, temperature: 72, condition: 'sunny' });
+      },
+      {
+        name: 'get_weather',
+        description: 'Get the current weather for a given city',
+        schema: z.object({
+          city: z.string().describe('The city to get weather for'),
+        }),
+      },
+    );
+
+    const getTimeTool = tool(
+      async () => {
+        return new Date().toISOString();
+      },
+      {
+        name: 'get_time',
+        description: 'Get the current time',
+        schema: z.object({}),
+      },
+    );
+
+    const tools = [getWeatherTool, getTimeTool];
+    const toolNode = new ToolNode(tools);
+
+    // Define mock LLM function that returns without tool calls
+    const mockLlm = () => {
+      return {
+        messages: [
+          {
+            role: 'assistant',
+            content: 'Response without calling tools',
+            response_metadata: {
+              model_name: 'gpt-4-0613',
+              finish_reason: 'stop',
+              tokenUsage: {
+                promptTokens: 25,
+                completionTokens: 15,
+                totalTokens: 40,
+              },
+            },
+            tool_calls: [],
+          },
+        ],
+      };
+    };
+
+    // Routing function - check if there are tool calls
+    const shouldContinue = state => {
+      const messages = state.messages;
+      const lastMessage = messages[messages.length - 1];
+
+      // If the last message has tool_calls, route to tools, otherwise end
+      if (lastMessage.tool_calls && lastMessage.tool_calls.length > 0) {
+        return 'tools';
+      }
+      return END;
+    };
+
+    // Create graph with conditional edge to tools
+    const graph = new StateGraph(MessagesAnnotation)
+      .addNode('agent', mockLlm)
+      .addNode('tools', toolNode)
+      .addEdge(START, 'agent')
+      .addConditionalEdges('agent', shouldContinue, {
+        tools: 'tools',
+        [END]: END,
+      })
+      .addEdge('tools', 'agent')
+      .compile({ name: 'tool_agent' });
+
+    // Simple invocation - won't call tools since mockLlm returns empty tool_calls
+    await graph.invoke({
+      messages: [{ role: 'user', content: 'What is the weather?' }],
+    });
+  });
+
+  await Sentry.flush(2000);
+}
+
+run();

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

@@ -0,0 +1,52 @@
+import { END, MessagesAnnotation, START, StateGraph } from '@langchain/langgraph';
+import * as Sentry from '@sentry/node';
+
+async function run() {
+  await Sentry.startSpan({ op: 'function', name: 'langgraph-test' }, async () => {
+    // Define a simple mock LLM function
+    const mockLlm = () => {
+      return {
+        messages: [
+          {
+            role: 'assistant',
+            content: 'Mock LLM response',
+            response_metadata: {
+              model_name: 'mock-model',
+              finish_reason: 'stop',
+              tokenUsage: {
+                promptTokens: 20,
+                completionTokens: 10,
+                totalTokens: 30,
+              },
+            },
+          },
+        ],
+      };
+    };
+
+    // Create and compile the graph
+    const graph = new StateGraph(MessagesAnnotation)
+      .addNode('agent', mockLlm)
+      .addEdge(START, 'agent')
+      .addEdge('agent', END)
+      .compile({ name: 'weather_assistant' });
+
+    // Test: basic invocation
+    await graph.invoke({
+      messages: [{ role: 'user', content: 'What is the weather today?' }],
+    });
+
+    // Test: invocation with multiple messages
+    await graph.invoke({
+      messages: [
+        { role: 'user', content: 'Hello' },
+        { role: 'assistant', content: 'Hi there!' },
+        { role: 'user', content: 'Tell me about the weather' },
+      ],
+    });
+  });
+
+  await Sentry.flush(2000);
+}
+
+run();