How to Fix 'cold start latency' in AutoGen (TypeScript)

Cold start latency in AutoGen usually means your agent is taking too long to initialize before the first model call. In TypeScript projects, it typically shows up when you create clients, load tools, or build agent graphs inside the hot path instead of once at startup.

The result is not always a hard crash. More often, you get slow first responses, timeouts, or logs that look like the agent is “stuck” before it ever reaches AssistantAgent or OpenAIChatCompletionClient.

The Most Common Cause

The #1 cause is recreating the model client and agent on every request. In AutoGen TypeScript, that means you instantiate OpenAIChatCompletionClient, AssistantAgent, or tool wrappers inside your request handler instead of reusing them.

Here’s the broken pattern:

// broken.ts
import { AssistantAgent } from "@autogen/agent";
import { OpenAIChatCompletionClient } from "@autogen/openai";

export async function handleRequest(userMessage: string) {
  const modelClient = new OpenAIChatCompletionClient({
    model: "gpt-4o-mini",
    apiKey: process.env.OPENAI_API_KEY!,
  });

  const agent = new AssistantAgent({
    name: "support-agent",
    modelClient,
  });

  return await agent.run(userMessage);
}

And here’s the fixed version:

// fixed.ts
import { AssistantAgent } from "@autogen/agent";
import { OpenAIChatCompletionClient } from "@autogen/openai";

const modelClient = new OpenAIChatCompletionClient({
  model: "gpt-4o-mini",
  apiKey: process.env.OPENAI_API_KEY!,
});

const agent = new AssistantAgent({
  name: "support-agent",
  modelClient,
});

export async function handleRequest(userMessage: string) {
  return await agent.run(userMessage);
}

The difference is simple: initialize once, reuse many times.

If you’re seeing logs like:

• •Error: cold start latency exceeded threshold
• •TimeoutError: Agent initialization took too long
• •long pauses before AssistantAgent.run() starts

this pattern is usually the reason.

Other Possible Causes

1. Tool initialization is doing network work

If your tool constructor hits a database, reads secrets from a remote vault, or fetches schema metadata, that delay lands on startup.

// bad
const customerTool = new CustomerLookupTool(await fetchSchemaFromApi());

Move I/O out of constructors:

// better
const schema = await fetchSchemaFromApi();
const customerTool = new CustomerLookupTool(schema);

2. You are loading large prompts or documents synchronously

A giant system prompt or embedding index loaded from disk during request handling will feel like cold start latency.

// bad
export async function buildAgent() {
  const policyText = await fs.readFile("./policies/full-policy.txt", "utf8");
  return new AssistantAgent({ name: "policy-agent", systemMessage: policyText });
}

Preload at boot:

const policyTextPromise = fs.readFile("./policies/full-policy.txt", "utf8");

export async function initAgent() {
  const policyText = await policyTextPromise;
  return new AssistantAgent({ name: "policy-agent", systemMessage: policyText });
}

3. You are using dynamic imports in the request path

Dynamic imports can be fine for code splitting, but not when they happen on every user request.

// bad
export async function handleRequest(input: string) {
  const { createSearchTool } = await import("./tools/search");
  const tool = createSearchTool();
  // ...
}

Import at module scope when possible:

import { createSearchTool } from "./tools/search";
const tool = createSearchTool();

4. Your runtime is actually cold starting

If this runs in serverless functions, the issue may be platform-level. AutoGen is just exposing it because the first request pays for Node startup plus dependency load plus client init.

Typical examples:

• •AWS Lambda without provisioned concurrency
• •Vercel/Netlify serverless functions under low traffic
• •Docker containers scaling from zero

In those cases, your code may be fine but your deployment needs warm instances.

How to Debug It

1. •

   Time each initialization step Add timestamps around client creation, tool setup, and agent construction.

   const t0 = performance.now();
   const modelClient = new OpenAIChatCompletionClient({...});
   console.log("model client:", performance.now() - t0);
   
   const t1 = performance.now();
   const agent = new AssistantAgent({...});
   console.log("agent:", performance.now() - t1);
   

2. •

   Compare first request vs second request If request one is slow and request two is fast, you’re dealing with cold initialization or cache warming.

3. •

   Remove tools one by one Start with a bare AssistantAgent and no tools. If latency disappears, the problem is in a tool constructor or tool registration path.

4. •

   Check where objects are created Search for new OpenAIChatCompletionClient, new AssistantAgent, and any custom tool constructors inside handlers, route functions, or per-message loops.

Prevention

• •Initialize OpenAIChatCompletionClient, agents, and tools at module scope or during app bootstrap.
• •Keep constructors pure; do not do network calls, file reads, or secret fetches inside them.
• •In serverless deployments, use warmup strategies or provisioned concurrency if first-request latency matters.
• •Measure startup time separately from inference time so you know whether the bottleneck is AutoGen or your runtime.

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.