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

When you see cold start latency in production in AutoGen TypeScript, it usually means your agent is doing too much work on the first request: loading models, initializing tools, creating clients, or spinning up long-lived state lazily. In production this shows up as slow first-token time, request timeouts, or an upstream gateway timing out before the agent responds.

In practice, this is rarely a single “AutoGen bug.” It’s usually an initialization pattern that works locally and falls apart under real traffic.

The Most Common Cause

The #1 cause is creating your AutoGen runtime, model client, or agent graph inside the request handler instead of reusing warmed instances.

That means every request pays the startup cost again:

• •OpenAI/Azure client construction
• •tool registration
• •memory store setup
• •model warmup
• •agent graph assembly

Broken vs fixed

// ❌ Broken: everything is created inside the handler
import { AssistantAgent } from "@autogen/agentchat";
import { OpenAIChatCompletionClient } from "@autogen/openai";

export async function POST(req: Request) {
  const body = await req.json();

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

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

  const result = await agent.run([{ role: "user", content: body.message }]);
  return Response.json({ output: result });
}

// ✅ Fixed: initialize once and reuse across requests
import { AssistantAgent } from "@autogen/agentchat";
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 POST(req: Request) {
  const body = await req.json();
  const result = await agent.run([{ role: "user", content: body.message }]);
  return Response.json({ output: result });
}

If you’re running serverless, this matters even more. A fresh container means a fresh cold start, but recreating objects on every invocation makes it worse.

Other Possible Causes

1) Lazy tool initialization

If your tools connect to databases, vector stores, or internal APIs on first use, the first AutoGen turn blocks on those calls.

// Bad: tool opens DB connection during first call
const searchTool = async (query: string) => {
  const db = await connectToVectorDb(); // expensive cold path
  return db.search(query);
};

Fix by initializing the dependency at startup and injecting it into the tool.

const dbPromise = connectToVectorDb();

const searchTool = async (query: string) => {
  const db = await dbPromise;
  return db.search(query);
};

2) Rebuilding prompts and schemas on every request

Large prompt templates and JSON schemas can add measurable latency if you generate them repeatedly.

// Bad
export async function buildSystemPrompt() {
  return `
    You are a support agent.
    ${await loadPolicyText()}
    ${JSON.stringify(await loadToolSchema())}
  `;
}

Cache static prompt fragments and schema definitions outside the handler.

const policyTextPromise = loadPolicyText();
const toolSchemaPromise = loadToolSchema();

3) No warmup traffic after deploy

A new pod or lambda may be healthy but still cold. If your first user request hits it directly, they absorb the startup cost.

Use a warmup route or synthetic ping:

// Example warmup endpoint
export async function GET() {
  await agent.run([{ role: "user", content: "ping" }]);
  return Response.json({ ok: true });
}

4) Excessive logging or tracing on the hot path

Verbose tracing can add overhead if you serialize large message histories or tool payloads synchronously.

// Bad: logging full transcript on every turn
console.log(JSON.stringify(messages));

Log only metadata:

• •request id
• •agent name
• •token count
• •elapsed ms

How to Debug It

1. •

   Measure each phase separately

   • •Time client creation.
   • •Time agent construction.
   • •Time tool setup.
   • •Time agent.run().

   If construction is slow, you have an initialization problem. If run() is slow only on the first call, you likely have lazy loading somewhere downstream.

2. •

   Add a cold/warm marker Track whether the process has already initialized:

   let warmed = false;
   
   export async function POST(req: Request) {
     const startedAt = Date.now();
     if (!warmed) console.log("cold start detected");
     warmed = true;
     // ...
   }
   

3. •

   Inspect where the time goes Look for:

   • •OpenAIChatCompletionClient creation inside handlers
   • •database connections created in tools
   • •dynamic imports in request flow
   • •schema generation from filesystem reads
4. •

   Check infra timeouts Sometimes AutoGen is fine and your platform is not. Compare:

   • •app logs vs gateway logs
   • •container startup time vs request timeout
   • •first-token latency vs total response latency

Prevention

• •Initialize OpenAIChatCompletionClient, agents like AssistantAgent, and shared tools at module scope or app startup.
• •Pre-warm containers after deploy with a synthetic request so the first user doesn’t pay the cold path.
• •Keep expensive I/O out of the first turn; inject already-open connections into tools instead of opening them lazily.

If you’re still seeing cold start latency in production, assume one of two things:

• •something expensive is being built per request,
• •or something expensive is being hidden behind a lazy boundary.

In AutoGen TypeScript, that boundary is usually your code, not the framework.

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.