How to Fix 'async event loop error when scaling' in LangGraph (TypeScript)

If you’re seeing async event loop error when scaling in LangGraph TypeScript, you’re usually hitting a runtime mismatch: async work is being scheduled in a place where the graph execution model expects a clean, single event loop flow. It tends to show up when you move from local testing to concurrent requests, background workers, or serverless deployments.

In practice, this usually means one of three things: you’re reusing a compiled graph incorrectly, mixing sync and async APIs, or letting multiple invocations share mutable state. The stack trace often points at GraphRecursionError, InvalidUpdateError, or a lower-level Node runtime complaint like Error: async callback was not invoked or TypeError: Cannot read properties of undefined during concurrent execution.

The Most Common Cause

The #1 cause is calling a LangGraph runnable inside another async path without awaiting it correctly, then reusing the same shared state or stream across requests.

This shows up a lot with Express handlers, queue consumers, or Next.js route handlers where people fire-and-forget graph execution.

// ❌ Broken
import { StateGraph } from "@langchain/langgraph";

const graph = new StateGraph()
  .addNode("step", async (state) => {
    return { count: state.count + 1 };
  })
  .addEdge("__start__", "step")
  .addEdge("step", "__end__")
  .compile();

let sharedState = { count: 0 };

app.post("/run", (req, res) => {
  // Fire-and-forget. Multiple requests now race on sharedState.
  graph.invoke(sharedState);

  res.json({ ok: true });
});

// ✅ Fixed
import { StateGraph } from "@langchain/langgraph";

const graph = new StateGraph()
  .addNode("step", async (state) => {
    return { count: state.count + 1 };
  })
  .addEdge("__start__", "step")
  .addEdge("step", "__end__")
  .compile();

app.post("/run", async (req, res) => {
  const input = { count: Number(req.body.count ?? 0) };

  const result = await graph.invoke(input);

  res.json(result);
});

The important part is that every invocation gets its own input object and the handler waits for completion before returning. If you share objects between requests, you’ll eventually hit race conditions that look like event loop problems but are really state isolation bugs.

Other Possible Causes

1) Mixing invoke() and stream() incorrectly

If you start streaming and also try to read the final result from the same execution path, you can end up with overlapping async consumption.

// ❌ Broken
const stream = await graph.stream(input);

const result = await graph.invoke(input); // second execution overlaps

// ✅ Fixed
for await (const chunk of await graph.stream(input)) {
  console.log(chunk);
}

Use one execution mode per request. Don’t start a stream and then run a second invocation for the same payload.

2) Running nested event-loop work inside a node

A node should return data, not manage its own lifecycle of parallel tasks unless those tasks are awaited explicitly.

// ❌ Broken
.addNode("fetch", async () => {
  fetch("https://api.example.com/data"); // not awaited
  return { done: true };
})

// ✅ Fixed
.addNode("fetch", async () => {
  const response = await fetch("https://api.example.com/data");
  const data = await response.json();
  return { data };
})

Unawaited promises are one of the fastest ways to get flaky behavior under load.

3) Recompiling graphs per request

Compiling inside the request path increases pressure on the runtime and can expose subtle concurrency bugs.

// ❌ Broken
app.post("/run", async (req, res) => {
  const graph = new StateGraph()
    .addNode("step", step)
    .addEdge("__start__", "step")
    .addEdge("step", "__end__")
    .compile();

  const result = await graph.invoke(req.body);
  res.json(result);
});

// ✅ Fixed
const graph = new StateGraph()
  .addNode("step", step)
  .addEdge("__start__", "step")
  .addEdge("step", "__end__")
  .compile();

Compile once at startup. Invoke many times. That’s the stable pattern.

4) Using non-thread-safe shared memory in reducers or checkpoints

If your reducer mutates arrays or objects in place, concurrent runs can corrupt state and surface as runtime errors during scaling.

// ❌ Broken reducer mutates in place
function reducer(state: any, update: any) {
  state.messages.push(update.message);
  return state;
}

// ✅ Fixed reducer returns new objects
function reducer(state: any, update: any) {
  return {
    ...state,
    messages: [...state.messages, update.message],
  };
}

This matters more when you use checkpointing or multiple workers against the same process.

How to Debug It

1. •

   Check whether the error only happens under concurrency

   • •Run one request at a time.
   • •Then run five parallel requests.
   • •If it only breaks under load, suspect shared state or unawaited promises.
2. •

   Inspect where the graph is compiled

   • •Search for .compile().
   • •If it’s inside a handler or job function, move it to module scope.
   • •You want one compiled StateGraph instance reused safely across requests.
3. •

   Verify every node returns a resolved value

   • •Look for missing await.
   • •Look for nodes that start background work and immediately return.
   • •Add logging before and after each node to see which one never settles.
4. •

   Turn on stack traces around invocation

   • •Wrap execution in a try/catch and log the full error object.
   • •Pay attention to InvalidUpdateError, GraphRecursionError, and any Node runtime messages about pending callbacks or rejected promises.

try {
  const result = await graph.invoke(input);
  console.log(result);
} catch (error) {
    console.error("LangGraph failure:", error);
    throw error;
}

If your stack trace points into a specific node function, that’s usually where the bad async pattern lives. If it points into request handling code instead, the bug is probably around lifecycle management outside LangGraph itself.

Prevention

• •Compile graphs once at startup, not per request.
• •Keep node functions pure-ish: no shared mutable objects, no fire-and-forget promises.
• •Use one execution mode per request: invoke, stream, or background job handling — not all three mixed together.
• •Treat reducers and checkpoint stores as concurrency-sensitive code paths; return new objects instead of mutating in place.

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By Cyprian Aarons, AI Consultant at Topiax.