How to Fix 'async event loop error in production' in LangChain (TypeScript)

When LangChain throws an async event loop error in production, it usually means you’re mixing async work with a runtime that already owns the event loop, or you’re blocking that loop with sync code. In TypeScript, this shows up most often in serverless handlers, Next.js route handlers, queue workers, or Express middleware that call LangChain in the wrong execution path.

The symptom is usually one of these:

• •Error: Event loop is closed
• •Error: This operation was aborted
• •TypeError: Cannot read properties of undefined
• •Hanging requests after calling invoke(), stream(), or batch() inside an already-running request lifecycle

The Most Common Cause

The #1 cause is using the wrong LangChain method for the runtime shape.

In TypeScript, people often call sync-style wrappers or fire-and-forget promises inside a request handler, then the process exits before the async work finishes. The result is an event loop error in production even though it “works locally.”

Broken vs fixed

// BROKEN
import { ChatOpenAI } from "@langchain/openai";
import { PromptTemplate } from "@langchain/core/prompts";
import { RunnableSequence } from "@langchain/core/runnables";

const model = new ChatOpenAI({ model: "gpt-4o-mini" });

const prompt = PromptTemplate.fromTemplate(
  "Summarize this claim note: {note}"
);

const chain = RunnableSequence.from([prompt, model]);

export function handler(req: Request) {
  const body = req.json(); // not awaited
  chain.invoke({ note: body.note }); // fire-and-forget
  return new Response("ok");
}

// FIXED
import { ChatOpenAI } from "@langchain/openai";
import { PromptTemplate } from "@langchain/core/prompts";
import { RunnableSequence } from "@langchain/core/runnables";

const model = new ChatOpenAI({ model: "gpt-4o-mini" });

const prompt = PromptTemplate.fromTemplate(
  "Summarize this claim note: {note}"
);

const chain = RunnableSequence.from([prompt, model]);

export async function handler(req: Request) {
  const body = await req.json();
  const result = await chain.invoke({ note: body.note });
  return Response.json({ result });
}

If you are using AgentExecutor, RunnableLambda, or any chain built on top of RunnableSequence, the rule is the same: do not let async work escape the request lifecycle.

Other Possible Causes

1) Using sync APIs around async LangChain components

A lot of LangChain components are async under the hood. If you wrap them in sync code, you can trigger weird runtime behavior.

// BROKEN
const output = chain.invoke({ input }); // if not awaited, promise leaks
console.log(output);

// FIXED
const output = await chain.invoke({ input });
console.log(output);

2) Mixing Node and Edge runtimes incorrectly

LangChain models and tools that depend on Node APIs can fail in Edge runtimes.

// Next.js route running on Edge by accident
export const runtime = "edge";

If your stack uses filesystem access, native fetch polyfills, or Node-only SDKs, force Node:

export const runtime = "nodejs";

3) Reusing a closed client or stream

If you keep a singleton ChatOpenAI or vector store client across hot reloads or serverless invocations, you can end up with a dead connection.

// BROKEN
let model = new ChatOpenAI({ model: "gpt-4o-mini" });

export async function handler() {
  return await model.invoke("hello");
}

In serverless environments, initialize per invocation when needed:

// FIXED
export async function handler() {
  const model = new ChatOpenAI({ model: "gpt-4o-mini" });
  return await model.invoke("hello");
}

4) Unhandled promise rejection inside tools

A failing tool can bubble up as an event loop issue if you don’t catch it properly.

// BROKEN
const toolResult = myTool.invoke({ id }); // no await, no catch

// FIXED
try {
  const toolResult = await myTool.invoke({ id });
} catch (err) {
  console.error("Tool failed:", err);
}

This matters a lot with AgentExecutor, where one bad tool call can terminate the whole run:

• •AgentExecutor
• •StructuredTool
• •DynamicStructuredTool
• •custom tool wrappers around HTTP clients

How to Debug It

1. •

   Find the first real stack frame

   • •Don’t stop at Event loop is closed.
   • •Scroll to the first file in your app code where LangChain is called.
   • •Look for .invoke(), .stream(), .batch(), or .call() used without await.
2. •

   Check your runtime

   • •In Next.js, verify whether the route is running on Edge.
   • •In AWS Lambda or Vercel Functions, confirm you are not returning before async work completes.
   • •In Express/Fastify, make sure your handler is marked async.
3. •

   Turn on verbose logs

   • •Add logging around every LangChain boundary.
   • •Log before and after each call:

     console.log("before invoke");
     const res = await chain.invoke(input);
     console.log("after invoke", res);
     

   • •If “after invoke” never prints, you have a lifecycle problem.
4. •

   Isolate the component

   • •Replace your agent with a plain ChatOpenAI call.
   • •Then add prompt formatting.
   • •Then add tools.
   • •Then add memory.
   • •The first layer that breaks tells you where the event loop issue starts.

Prevention

• •Always use async/await end-to-end for LangChain calls in TypeScript.
• •Avoid Edge runtime unless every dependency is Edge-safe.
• •Treat tools and external API calls as failure points; wrap them in try/catch and log context.
• •In serverless code, never fire-and-forget LangChain work after returning a response.

If you want a simple rule: if your code calls LangChain and returns before that promise settles, production will eventually punish you for it.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.