fix(core): Langgraph state graph invoke accepts null to resume

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

@@ -0,0 +1,43 @@
+import { END, MemorySaver, MessagesAnnotation, START, StateGraph } from '@langchain/langgraph';
+import * as Sentry from '@sentry/node';
+
+async function run() {
+  await Sentry.startSpan({ op: 'function', name: 'langgraph-resume-test' }, async () => {
+    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,
+              },
+            },
+          },
+        ],
+      };
+    };
+
+    // Test: invoke with null input (resume after human-in-the-loop interrupt)
+    // See: https://docs.langchain.com/oss/javascript/langgraph/use-functional-api#resuming-after-an-error
+    const checkpointer = new MemorySaver();
+    const graph = new StateGraph(MessagesAnnotation)
+      .addNode('agent', mockLlm)
+      .addEdge(START, 'agent')
+      .addEdge('agent', END)
+      .compile({ name: 'resume_agent', checkpointer });
+
+    const config = { configurable: { thread_id: 'resume-thread-1' } };
+    await graph.invoke({ messages: [{ role: 'user', content: 'Hello' }] }, config);
+    await graph.invoke(null, config);
+  });
+
+  await Sentry.flush(2000);
+}
+
+run();

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

@@ -318,4 +318,50 @@ describe('LangGraph integration', () => {
       });
     },
   );
+
+  // Test for null input resume scenario (https://github.com/getsentry/sentry-javascript/issues/19353)
+  const EXPECTED_TRANSACTION_RESUME = {
+    transaction: 'langgraph-resume-test',
+    contexts: {
+      trace: expect.objectContaining({
+        status: 'ok',
+      }),
+    },
+    spans: expect.arrayContaining([
+      // create_agent span
+      expect.objectContaining({
+        data: {
+          [GEN_AI_OPERATION_NAME_ATTRIBUTE]: 'create_agent',
+          [SEMANTIC_ATTRIBUTE_SENTRY_OP]: 'gen_ai.create_agent',
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: 'auto.ai.langgraph',
+          [GEN_AI_AGENT_NAME_ATTRIBUTE]: 'resume_agent',
+        },
+        description: 'create_agent resume_agent',
+        op: 'gen_ai.create_agent',
+        origin: 'auto.ai.langgraph',
+        status: 'ok',
+      }),
+      // invoke_agent span with null input (resume)
+      expect.objectContaining({
+        data: expect.objectContaining({
+          [GEN_AI_OPERATION_NAME_ATTRIBUTE]: 'invoke_agent',
+          [SEMANTIC_ATTRIBUTE_SENTRY_OP]: 'gen_ai.invoke_agent',
+          [SEMANTIC_ATTRIBUTE_SENTRY_ORIGIN]: 'auto.ai.langgraph',
+          [GEN_AI_AGENT_NAME_ATTRIBUTE]: 'resume_agent',
+          [GEN_AI_PIPELINE_NAME_ATTRIBUTE]: 'resume_agent',
+          [GEN_AI_CONVERSATION_ID_ATTRIBUTE]: 'resume-thread-1',
+        }),
+        description: 'invoke_agent resume_agent',
+        op: 'gen_ai.invoke_agent',
+        origin: 'auto.ai.langgraph',
+        status: 'ok',
+      }),
+    ]),
+  };
+
+  createEsmAndCjsTests(__dirname, 'scenario-resume.mjs', 'instrument.mjs', (createRunner, test) => {
+    test('should not throw when invoke is called with null input (resume scenario)', async () => {
+      await createRunner().ignore('event').expect({ transaction: EXPECTED_TRANSACTION_RESUME }).start().completed();
+    });
+  });
 });

packages/core/src/tracing/langgraph/index.ts

@@ -136,7 +136,7 @@ function instrumentCompiledGraphInvoke(
             const recordInputs = options.recordInputs;
             const recordOutputs = options.recordOutputs;
             const inputMessages =
-              args.length > 0 ? ((args[0] as { messages?: LangChainMessage[] }).messages ?? []) : [];
+              args.length > 0 ? ((args[0] as { messages?: LangChainMessage[] } | null)?.messages ?? []) : [];
 
             if (inputMessages && recordInputs) {
               const normalizedMessages = normalizeLangChainMessages(inputMessages);